#Step 1: Find path of data file and copy path (if using windows)
#Step 2: In the console below, type readClipboard()
#Step 3: Copy and paste path to the line below in quotes
#CHANGE BETWEEN QUOTES IN THIS LINE TO REFLECT DIRECTORY OF DATA:
myDataLocation = "/Users/jonathan/Dropbox/!EPSY 905/Lectures/13 PCA, EFA, and CFA/mv16epsy905_lecture13"
#SET WORKING DIRECTORY TO LOCATION OF DATA FILE
setwd(myDataLocation)
#IMPORT DATA AND PUT INTO DATASET
data01 = read.csv(file="mv16epsy905_lecture13a.csv",header=TRUE)
data02 = read.csv(file="mv16epsy905_lecture13b.csv",header=TRUE)
#AUTOMATING PACKAGES NEEDED FOR ANALYSES--------------------------------------------------------------------
haspackage = require("lavaan")
## Loading required package: lavaan
## This is lavaan 0.5-20
## lavaan is BETA software! Please report any bugs.
if (haspackage==FALSE){
install.packages("lavaan")
}
library(lavaan)
#Advanced Matrix Operations --------------------------------------------------------------------------------
#correlation matrix of SAT data
sat_corrmat = cor(data01)
#eigenvalues and eigenvectors of correlation matrix:
sat_eigen = eigen(x = sat_corrmat, symmetric = TRUE)
sat_eigen$values
## [1] 1.7752238 0.2247762
sat_eigen$vectors
## [,1] [,2]
## [1,] 0.7071068 -0.7071068
## [2,] 0.7071068 0.7071068
#demonstration of spectral decomposition
corr_rank1 = sat_eigen$values[1] * sat_eigen$vectors[,1] %*% t(sat_eigen$vectors[,1])
corr_rank1
## [,1] [,2]
## [1,] 0.8876119 0.8876119
## [2,] 0.8876119 0.8876119
corr_rank2 = corr_rank1 + sat_eigen$values[2] * sat_eigen$vectors[,2] %*% t(sat_eigen$vectors[,2])
corr_rank2
## [,1] [,2]
## [1,] 1.0000000 0.7752238
## [2,] 0.7752238 1.0000000
#Principal Components Analysis of SAT data ---------------------------------------------------------------
#PCA of Correlation matrix (scale. = TRUE)
sat_pca_corr = prcomp(x = data01, scale. = TRUE)
#show the results (compare to eigenvalues/eigenvectors)
sat_pca_corr
## Standard deviations:
## [1] 1.3323753 0.4741057
##
## Rotation:
## PC1 PC2
## SATV -0.7071068 0.7071068
## SATM -0.7071068 -0.7071068
#show the summary statistics
summary(sat_pca_corr)
## Importance of components:
## PC1 PC2
## Standard deviation 1.3324 0.4741
## Proportion of Variance 0.8876 0.1124
## Cumulative Proportion 0.8876 1.0000
#create same analysis but with covariance matrix (for visual) scale.=FALSE (covariance matrix)
sat_pca_cov = prcomp(x = data01, scale. = FALSE)
#create augmented data matrix for plot
data01a = data01
data01a$type = "Raw"
data01b = data.frame(SATV = sat_pca_cov$x[,1], SATM = sat_pca_cov$x[,2], type="PC")
data01c = rbind(data01a, data01b)
plot(x = data01c$SATV, y = data01c$SATM, ylab = "SATM/PC2", xlab = "SATV/PC1", cex.main=1.5, frame.plot=FALSE, col=ifelse(data01c$type=="Raw", "red", "blue"))
legend(0, 400, pch=1, col=c("red", "blue"), c("Data", "PCs"), bty="o", box.col="darkgreen", cex=1.5)
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JExSecIn/DTVK+UHteccvWGI1OOUHRrjsX9/xtn/FDjyg+6ljpzbdK/obrvaGi886WW3yxksfZOW0IEACdNp4zo0QAAQQQQAABBBBAAAEEEEAAgYwL5KtmsinqBbkr/yt/wskWqPxamsGyIddbR26lFaWZZpL/6GP5Bx+qCXi6nXdIMic17kNpte5SVU6ytT712++pVgfL6uxlQcQD9pVbZmn5l15WYdMtpM+/sCJKVXLrrCVn2aSaZRb5j+3aFpDUe5YRas1t1VfRjdfJWTX6v6MVjjle/uzz00vPY5mtIXN14c5277/Jv/Gm/JCHpR8siDtTa0U3W1bsZpv+HbfBNVuAAAHQFvCQuEUEEEAAAQQQQAABBBBAAAEEEECgMYH87HNJ332fVnf//XcLXG6gyIKhDaeBe6sS72+6RfEBh6QBQpsKHyrHR1dcmlSNT1ZLtOntIWDqZrSs0urmx45Vodt/rNDSt3Jr91R09eVyCy1UPJx8hnP9HYMU73NAci9um62Uu9UyRadyi08/K8lCDUHY6KzT5Q7cT2666er9ig+ZqocfLX/jLUmwNnr0QUVr9qjXhy/ThgAB0GnjOTNKBBBAAAEEEEAAAQQQQAABBBDIuEC+92ZSyO605rbrp+im65N1PssNOyls1N0Cgr/nky7Rr5MU2dT5ci3fYx1p6LNyfTZWNOg2uaqqcl3lR71la4uuKU2arOi2mxT1swJKU6n5199QYXnLOrXAbXTfXYosQ7VSKxx9nPw5F0g2NT/33qh6Qd1K53EsOwIW4qchgAACCCCAAAIIIIAAAggggAACCLR4gY02qB2CreEZihxVan6hUMk9LPhpbeaZKgY/4yeeTIKfaj9nGlitEPwMl3NdllJ0cTo9PT7p1LBrqrX45NNsrVP7jYMPaDT4GX40ZIiqqy0B8Ol4+Wuun2r3wYVajkDlfxNazji4UwQQQAABBBBAAAEEEEAAAQQQQGCaFnDPDa8Zv7/0chUOO6rme8ON2KazxwsvZdmf1Wt92lqZflydQkgNTvCD7k72RPvtLWfrfTaluZ12kGxtzrAmaFiTc2o0b4WZQuV55Sz788jDmnRJ55yiow5P+saD7mrSOXTKlgAB0Gw9T0aDAAIIIIAAAggggAACCCCAAALTqIB/5dV05Isuknz6Cy9Rfq75FYoFxS+9pNgCnLGtz1lYZ0PFi3SRvv02mUauVVZO+7/yWlm54rVDQaWmtpCBGtYKDa14flPPLdvvrdHSL79Kyy0r17592W4ND9Tc96vlx9jwHL5nR4AAaHaeJSNBAAEEEEAAAQQQQAABBBBAAIFpWWDCV8noc88Pldty81TC9oVK6XG31RUvtITiflb1/YmnLCJpc8hnn13RC8PkVliuuu+E8nrV104yOsv3+uORkAEaWvH89Nuf/uurr+OsOv2UtCRr1arB66ef5X/4YUpOpW8GBAiAZuAhMgQEEEAAAQQQQAABBBBAAAEEEEBAbdqkCN99p9ydtyp65w25sC7obLOlmZ42FVyhyJFliLpzz1TVN58rWsnWxgyZoKEVz0+/1f/bxq4R2rffpZ9N/Ruq0odWPD/99qf/uurr+OJ1m3glH6b6//RTUg0+VLenTVsC5ct1TVsOjBYBBBBAAAEEEEAAAQQQQAABBBBo0QJu8UVtHc8P5Z8bIdepk6LFFrWq8Pc2OiY//Pmkj1t8sbJ9wzH/xii79nC5ZZcp26/hgdA/tErXbnhOxe9hen+o22RT2f3PPze9ovvwEUnhpCT4GwLBtGlKgAzQaepxM1gEEEAAAQQQQAABBBBAAAEEEMiqgNukdzK0+PL+TR5i/L8h0kcfS/PPZ1Phly97ntt04/TaV14tH8dl+9U94J8ZKo2yNTvbziGt1r3uoT+9naz7GdYsDVPZB9zY5OvEl12Z9C2Oo8kn0jETAgRAM/EYGQQCCCCAAAIIIIAAAggggAACCEzrAm7H7aWwNubzLyq+6NJGOfxXXyne/+CkX3TkoRX7uy37Sgt1SgKa/vSzKvYNB/3336uw135Jv+iwg+Wqpt4k5JqK7sefLD9mTKP3Eg++W37wPZKtARrtv0+j/emQPQECoNl7powIAQQQQAABBBBAAAEEEEAAAQSmQQHX2gJ8V1jg02Z4x4cfpfisc+ULhZIS/p13VehhFd1D9mf3VeT22qNkv+JON910ivr/V8pFik86TYUTLPgY1tUs0fwHH6jQcz3p3fel5a1a+8EHlOj153dFlo3q+vZJ1iMtrLme/Asvlr1YfN0AxdvvnByPzj5drkN1UaayZ3AgiwLOW8viwBgTAggggAACCCCAAAIIIIAAAgggkDWB+LY7FF96uWQBzKSoT9V00pzt5HptKHfWaYpmnTU5Hh9yuEVBLeSzxGKKdtlJWmkFOSv+4y3g6R94UN6uo7wFR5fpotxjQ5RMLW8CVhJQ3Hv/9NyFOyva1a69cleFKuv+k0/lH3xIfuBt0q+/SYstkl57/vnl331P/vY75Yc9J//ZZ9KMM8p16ii34fpyW28pN/PMNb/u83n5e+5L79POk2WTaq655LquqMgyUd3K3eR//FFx783kn7Zp9hbwdX02kdu8j5zdk377zdYrfVPxTQOll15JrusswzV3zpk1v8HGtCVAAHTaet6MFgEEEEAAAQQQQAABBBBAAAEEWqBAbIHAeG2r6D7egoflWi4nd+xRyp16ouKHH1F8gE1rHzO2dO8q67v7rorOPzsJjJbuVHpv/PQzivc7SBr9TukOkVOYjh9dfL5kU9/jgw9P1+sslFk7tP2cis47S5GdEz/xpOJ9LGP0/TL3bb/oem2g6OorLPA7p+KTT5O/8BLpl19L38s8HdJrb9uv9HH2ThMCBECnicfMIBFAAAEEEEAAAQQQQAABBBBAoKUKJAHHdTdKsy7DIJZbJs2aXGZpaeJEeStkFP7Rz78kQ3Sb9lbu3sHJFPUkI9OyMjX2gyRr0s1tmZTdV5XbcnO5hRb60yRhar1/6JH0d98fIz95spJrW3ZmWC/UWbV2b/dWCEFbqx6v6aeT22kHhSJELqwlahXc/WsjFd9ws/Ts8PQ+1l9XevwJKQRKQ+bqnrvbva4izTab/Pjx8g8/Kn/1dZYROilZ6zTJXF1qSfnPP7c1Pm2dT8sG9Z99br81vVzHBeU2WC/9PVsagDZtCxAAnbafP6NHAAEEEEAAAQQQQAABBBBAAIFmLBBPmKB4gYXTKeU2TTx65jFFJaq1x1aZ3W/SN5mCHobjjjlSuTNP/ddGFgKkhbXWl4Y+mwQzQ0A2BEVLtfjGmxXvsa9UvaaoO+k4RSccK2cZrQ1bCKrG/XaQf+IpqVNH5V4ZITf77A278R2BegIEQOtx8AUBBBBAAAEEEEAAAQQQQAABBBBoPgKhmFCyzuUMMyj68F1Fc89d8eYKfbaQv+9/SbGiaMKniuaYo2L/v+tgslbo7lZxfb55lXt5uJyt4VmuhTU/CyHI+/kXSfZm7tOxcja9vVzzv/6aFnB64SW5Qw5Q7sLzynVlPwKJQIQDAggggAACCCCAAAIIIIAAAggggEDzE4h/+UX+mWHJjUWXnN9o8DN0dHffKYUp3zaN3B9+9L82qPi8C9P7vvDcisHP0CkUZUqCn1akKSlgdNW1Fe/bWTA4d21/ydYa9VdeLf/DDxX7cxABAqC8AwgggAACCCCAAAIIIIAAAggggEAzFPD9r7booFVyb2XZn3vt0aQ7jKJIbovNkr5hzcx/o3lbE1Tvvp9Wp++b3kul+/D3W8aqNbfNVslnHAKijTTXZSlpte5J8SP/+JON9ObwtC5AAHRafwMYPwIIIIAAAggggAACCCCAAAIITDWBsPZlPPA2FTbtq/w8HZWvaq387HMpv8rqis84OykMVPfH/LhxKhxzvPLLrqT8rO2Un35m5W06eGG7nWwqe3UgsGPH5JSwzmfh0COVn7eT8jm7rmuV/jPTHApT5eM33kj6uc37pD/xzTfp5z/8179nwU9rrttKchaQDS22gkmF7XdWfsFF0jHO0jYZc+Ho4+TfeDPp44rB0urzk50V/rhVV06PNrF/hUtxKOMCVRkfH8NDAAEEEEAAAQQQQAABBBBAAAEE/hEB//obKmy7ozT6nfq/9933kq1XGds/OvcCReedJbf7ropPPk3+nPNt2vfv9ft/8qn8rXfU7rOq5vGQhxVv0a+m0nvtQdv66ae0Avqy3eT7WJX1o49ID+cL9br9Y18mWZX20GadNaneXthuZ6l6Kn+yP/z5PZ9Uh/ehQrxL9/q52qfbVlHeW+arc9UHak6qv+HC9W2XL/5e/cN8Q6BGgAzQGgo2EEAAAQQQQAABBBBAAAEEEEAAgT8n4J8ZqsJqa6bBz0U6K7r6cuU+fl+5/I/KffO5ogfvleu9oTRpsuK99ldhyeXkTztLsgJAbucdlLPq7rlJXyn322Tl3huVBEnVqlV6M2+OUtzLsjp//sWKG+Xktt5C0bDHFf06SdFH78udeYo07zxJX3/vA4o3t0BpaLZW5r/SQiDTmh8zVoWVbJp6CH62n1PRuWcq9+6b6RgnT1Ru6ONyu1jAOEQxQ/89rGhS2G7fvtHgZ9L/0/Hhw9YYTX8v+cIfBEoIUAW+BAq7EEAAAQQQQAABBBBAAAEEEEAAgaYK+E8/VWG5btLX3yTBzOiqy+Usa7NUi2+7Q/H2u9ic8FiaqbVyQ+6TW2P1Ul1VuHOw/Nbb1x6zIGf0xstlK7sXjj1R/qxza/svtaSqRr1a+/0f2vLffqvCnPNZIaY0A9Wt3VPRoFvlZp+95B0UdthF/pbbao65TXopd99dNd9LbYQM0ULnJaRxHyo34hm5Vaqnw5fqzL5pXoAM0Gn+FQAAAQQQQAABBBBAAAEEEEAAAQT+ikB8/Mlp8NMyPKPrry4b/Ay/kaxbWZzaHTI0V1yh7E/nttrCTqgzDdymzUdzzFG+/5mnSrvtXHt8801rt//BrSTQ2Xmh9BfbtVV03+Cywc/QKTrs4Pp317NH/e8lvvlbb0+Cn1pwAWllCz7TEKggQAC0Ag6HEEAAAQQQQAABBBBAAAEEEEAAgUoCIdvRW9EjTVel6LKLG526HV99XZoZOWc76Rs7d1D5TMc4FDEKVeCL7eJLk7Uxi18bfsY//CDd90DNbjf67Zrtf3zju+/SnwxjbLj+Z8ObWWZpaYH5a/b6jz6u2S614Ue9pXi/g5JD0aknNmpe6hrsm7YECIBOW8+b0SKAAAIIIIAAAggggAACCCCAwJ8UiH/7TfHI1xU/+6zi6sI7/rEnbB3PgtxaPeUWXLDRK3srZhSa23G75NNbdfRyzV/WPz1UzAL9fpIKvTZVuI+GLb7nPsVWdV4Tv6451GjgsaZn/Y0QeI2HDlM8ytYetTVKp7QlVd0nfCXNNmsy1T/etK/is8+T//VX+V9+kf/wQ/kvvpC3ZQD8uHGKN95M+viT2p/pf638K3+cuh+mvcc3D1ThPz0ls0jWQt2xzhIBtVdgC4F6AlSBr8fBFwQQQAABBBBAAAEEEEAAAQQQQKC+QOGMs+X/e4X05YR6B+KZZ5YW6pTuW37ZesfKfrE1K0NzvXvJX3CJ/Afjku8l/4wene5efHHpE8uK/OFH6aFHFc/YRnH43VD4aLJlfY4ZkxRXSjpbkSQddbh05jlJkLDkdUvsjCdOlN9pd/mnnrFiSz/X6xHPP6+i445WtNce9faX++Krx6geq8t1WUr+zHMVH3OCdMIpSdGnmvMiy8sLGa7hH5sq784+Q373vSULkha6WvGkbl2TJQNcmzbyVvAoCTZXZ4e6flsqGnBNzaXYQKCSABmglXQ4hgACCCCAAAIIIIAAAggggAAC06xAbEG3/NwLyB9/cm3wMwTtqizIGFqYcv7Gm+l2MeiXfiv/14J7SQvZkaEVv6ff6v/98af0e+sZ5TbpnW6HAGcooGQV1pPq6q++Vhv8tCBs9O4bikK1+dBCvya0+KprFM9l4wzZqcXgZ5XlzEUuPfuT8Yr3PkD5RZZSXJzaXum61ddwrVsr6mvZnXPPlfZumE0a7i8EP0OzIkZuC+sbWsh4nbG19MJL8hdfpvjk0+WvHSCF4KcFfqObr1futpvlWrVK+/MXgUYEyABtBIjDCCCAAAIIIIAAAggggAACCCAw7QnEn32ueJElLUD5axKQc702lDvrVEVduiQYYZq4P+Mc+UsvS6bA+zsGK950Y0XbbF0Zq0OHNJA34oWkn5vHsjjLtY5W4Ce0zz6TtzVGQ4seHyLZ9HIf1vq0e9Qss0hLd5E7aD9F7dsnfQoPPJh8asbGA4SF8y6UP/LYtP9MM8kdcYicFSWKQnartfiFF+WPPl7+6aFJ0DXutJg0fpwiC26Wa24eG6M1//qbKqy+lvSTZZQut4ycFXGSBZCdZZsqBC/bzCb/yadmeLn0vyGK11g7veR88yr33ij5J5+W3n1P/vvv5eZqL9d1paRolCsuCZD25i8CjQo4Wz+hOtTeaF86IIAAAggggAACCCCAAAIIIIAAAtOEQH6BzjbtfLw0/fSKnn5U0aqrlBx34UXLUlx59fSYBfei8R8omnvukn3DzsIOu8jfYkWTVrLq7y+/qlDEJzqhOgDZ4KwQfIxXWaN2b6sZlJv4mZwFKiu1/AorS6+9LnVdUVUvPle2a7Ke6Qo2rhAasqnq0SsjFNl4S7XYAq5xXwvuFixr04KZVa+9WKpbss//9JMKbS0IGoLH1tzOOyi6+gq56aZLvjf8420Kf2GDTaSxHySH3LZbKzfwxobd+I7AnxaI/vSZnIgAAggggAACCCCAAAIIIIAAAghkUCC+8uo0+Gljix57sGzwMww9Z+tUapk0KzRMOY/77VBRxG3XLz1uwc8kG7LfVmX7Ryt3SwsJVfdwlmHaWPAzHvF8Gvy0cyLL5KzU4m12TIOf7edU9PpLZYOf4RpRyG692aahhzbyDcWPPJZul/gbpr6rY8f0iE1/j67tXzb4GTq5hRdWdGudgKdNh6chMDUFCIBOTU2uhQACCCCAAAIIIIAAAggggAACLV4gvvCSdAxr/EfRGtXZnRVGFZ13Vu3RoVYhvsLam245K5Zka3omLayNWT1dvPYCDbY23KB2RyOZn/H77yteu3r9zwUXULT1lrXnNthK1vJ8591kbwg+RmFt00ZaMr1/8UWTXvEpp5ft7QsFyaq8J+2LL+X7W0C5QgsZo/7YE2t72LR3GgJTU6Dxt3tq/hrXQgABBBBAAAEEEEAAAQQQQAABBJq7wAfjkjuMjj6i3p36jz6SHzpM8WOPy49+W7460Bmtt67cUYelfW06edxv+2Tdyronh77xnYNVWKl7uiZmKDJka3gWVlld8ZNPWZVzWwtzmAVPH31MftRbij/5RIVQEf32O2su46+/UXk7PwQ667YQcC1YADFecvm0iJFNNY+eqc3QTKa6X9FfoZp9PPhuxSHgeM316SUsqBqtvVbdy1Xcdnvtnh5//Y3y/UJhpu++lzqkSwHE+x+iwm57yY+3JQUatPippxMD/8RTUts5kqPefP/u5sMars+/oPjhR+TtfkMQlpZdAdYAze6zZWQIIIAAAggggAACCCCAAAIIIDCFArFVZY9nbJOcFf3+gyxwIh+qpF9xlfR2mjFZc0mbOh7Wq4yOPUpq106FmSyAV6yiPr2td2nrhrp555GsiI9/8WXpKyv+E1qP1RWde6binXaT3mkk29GKH0VXXS4/6G75hx5Jzw9/Z7XiR7PPnlaRD9etDsYqBDSffTJZ09Pve2C63mjxnopnW5F1zWJV6CdNkhburKr33yoeafQzfmu04i4rJIWhquKfS/aP77pH8RbbyPXtI9d7I6sgv7/06282j95+eKUV5aySu8zZh3VKQ2X30CyzNBp0m+KlV7QCSTOo6mcLoP4NLX7kUcVnnSc9a2ujhvVMi80KRrmNNlB04nFyyyxd3MtnRgTSEmIZGQzDQAABBBBAAAEEEEAAAQQQQAABBP6SQDGQaBfxH3+ieDObRv7GqPSSIUNxqSXlZphBPhTssUxRf/FlKtxws6Lbb1YIPiYB0M5WQOlDyyJ9Zph83ZtZbBFFRx4mt5OtE/rll1J1pfW6Xf6wHQoHWaAzN+Q+xXcMUnyQZZp+OcGCl5PTf4onWEZpEnC84Vpp3IeK24XAqwU4iy0ETKezAkch6Pn77+lnOPbVV8UeTfsMmauNtaJhKAq1845yq1nW6kmnyt9zn2SB4CQYXLyGBYij/faWO/gAC5BWT1S2oPPUbj4EtkMW6q13pJeewSxWWlbObP3nn0tvWtX5u+5Vwe4xOvUkRccdPbVvgev9iwJkgP6L+Pw0AggggAACCCCAAAIIIIAAAgg0P4F8ztbojC0IFwKeX38jLWpFes47W67XhnK5XM0Ne5sGHh9zvGVmPmrVkCx4F+J2FvyLBt8u13MNy3AcmQYrZ51VbrFF5RZZODnXT5yoQrf/JIFKdeqo6OzTk0xMWcA1ZEY6mzruLZjqzzzHgob3W7alxQbvvFXRFpsn58fWzw8abAHYD6U5ZpezTNPIshdDiz8dr7jzEtJvIeMyUpiy7izbNKoTbI2feNIqum+TZKaGc9xuOytnhYqa0kKBqNgyS2UZk1U/fVfylDC1vLBqD2nppVT1xis1fZJp5i+/Im9T/9XKMi5DJujSXeScDdCaf+NNFZbtKnVeSFVjRtec91c3wpqkca8+8qFwkwWCo1NOlNt9F7k6Jt4C0vG5F8hfclmSGRqWNMidfcZf/WnObyYCBECbyYPgNhBAAAEEEEAAAQQQQAABBBBAoHkI5DtaoZ/i1Gybrp67/y45C2KWa4Wjj5M/54L0cAhW/vaDogqZkoXeFox78GGp20rKPXS/3BwWaC3T4rBu5/EnJ4WTcqNHyi24YJme6e6ae7dp5NFLzynq0qVk/3jCBMVzLVBzLHrgbkU2Xb2xlg8BSgtUasUVVPXy8JLdvWWYFuacN8lATe55icVL9mu4s3D8SfJnnCO3x67KXX1Fw8N/+nt86hmWgXqaNFd75Z56VK7C/cRDHlbcZwvLks0r+t89iizoTWv5AtW5xS1/IIwAAQQQQAABBBBAAAEEEEAAAQQQmCoCa65Rcxl3ygkVg5+hozv9FKkY8GzfvmLw0z8zNA1+tplNufsGVwx+hmuHqdiun03D/+nnNIgXdpZp8S231gRuo8ceLBv8DKdHdp/q1DFsJi3ea7/iZtnPOKxBGoKf1qJjjizbz9m0fbf9tsnx+PCj5JswpT0pMBWyL61Fu+yYfE6NP96m+IfMztBydw6sGPwMfUImbXTOmWFT8ZHHNOnek878adYCBECb9ePh5hBAAAEEEEAAAQQQQAABBBBA4J8WcD/+WPOTfqNNFb/9Ts33hhuxTTVPCvfk8+khK/YTplyXa/FNA5ND0SEHys09d7lu9fYnU+Qts9QPuku+YUGjOj3jCy5Ov1l2ZvQfm2LfSItusvVCi82mpcdW5b5ci5+1CvWbWmZkaIvaWqZW4KhSi044xtYubSM/5BHFhx4hX1wXtMRJ/rPPVOi9mfTDj3Jbbp5M6S/R7U/t8nffK/34ky1fsIHcGqs36Rpu/32k+SyDdbQ991debdI5dGreAgRAm/fz4e4QQAABBBBAAAEEEEAAAQQQQKCJAmGNyfjRxxRfc53iK69SfP//5L/9toln13bzTw9Nv1TZep8h89Kqnhd23VNh2nixxRbwLFxwkeK2VmzonXfT3VYVXt/ZuphWUKdcK17b9bWAXxNbMu3dgprhXvTSy+XPqq4oH+25W00fb/fphw5TPOBGxZddoVCh3Y8fnxwPQVJ39OE1feMlllN87fW2jGlcu8/WGy1s0U/xGuumxZNaz6jouadqjpfbcHPNZeuWWrDXqtgnhaLWWl9+2LP1Mir95MmKr7pGhRVWkUbZmp9LLaGoiWuRlvvdhvtrvDevHLCte16SwbpJ72RX8fy6x9lueQJNKN3V8gbFHSOAAAIIIIAAAggggAACCCCAwLQjEKp4xyefLn+zBdx+/qX+wC2I6TbupejM0+QWX6z+sRLffCgeNPFrafrpFL36guLuayYV0/2AmxT+iUP18MgCo1asyKJ56RVCtfOrLpO/9/5kersf/5nccsuWuLrtqg4+auHOpY+X2eusv3/5VQte2rXL9NGvv6ZHbN3SJLgYivpc3l/61oKydVu4gGVD5s44RbmzTlf+2gHpmC27NN5jX2nP/RTPaIWgQlZr8Cg2W0MzenmEonbtinsqfkbrrC332BAVttpOemaYCmuskxaW6mjrmIZM1jFj7fq/J9dw66yl6I5bGl1uoOIPljgYvEIrFqAq0aXkrtA/PN1isLhkJ3a2GAECoC3mUXGjCCCAAAIIIIAAAggggAACCCDQUCB++hnFW2yTVmuPLLK36spyXZZK1+R8f0yS/RgqqResuE1khXWiHbdveIn630OV93Adm8YeiuVE334hf9Ch8mHq+qTJkk1xr2lhrcs1LIvylgGKbDp7YdDd6SHLeizb7JzkGlYoSDPMULbbHw6E/qFNbwHYci1UU7egrB/1luIwpTwEGEOzauxupRUlqywvm+bun3omDUiubkHHk46XZpstDYDOaYHNbyxjNkzht2zamhYqze+3j3Knnlizq6kbrscayr1v92PT8/3A26WxH6TPKlwgZxOTLVgbHbCvTalvekZsU3879AvZnEkg0/xMp+mtGPit5N30q9HzXxao8G/kv3xn/DwCCCCAAAIIIIAAAggggAACCCBQQcDbdPB4w00sG/NXuQ3XU3TphXILL1zvDB+qnR9/svw1NrV7p92ToGO0tRUVKtNcCIDON59kU781+m0rJGTB1P9enPwTfzo+nYIegoPLLl2vyFAo9OOrp767jh3LXN12h+zHMN07FBPqvmr5fg2O+OriQ27BBRocqfO1dWtbR/MH+Z33SD61wnLKXflfuW5d63SyGKmtcRqfd6H86WclmbNhmnpo7ugjlDv0IMUvv5KMXaE6vVWqTwom1bvClH1xs86q3CkWPLV/QraubL1RhQzT+eeTm2WWKbvYlPYO3qG9YcsSrNUz3W7C35pnWcm7CdehS/MQcPYvaAiE0xBAAAEEEEAAAQQQQAABBBBAAIEWI+BtundhCZtmPu5DuT13U9T/MrmQAVmmxRddasV4jpRsDcvce6Pk5p23TE9LgLSK6P7q6+SOOES5c88q26/ugfixxxWvZ+tGduqoqg/eqXuo3nYhVEW/4BK5vfdIgpP1Dpb54l94UYVV1pAsQzP3xcdyNuW+VCuEdTZDdqe1ZEr5/XfJhUBjmRb/b0ha2Kh6zc/o+wmKLFiZpRbfOVjx1pb1u+Lyyr00vOI7Uhy3t3VcCx0Xlb6fpNzY0XILLVQ8xGcLFSj9b0wLHQy3jQACCCCAAAIIIIAAAggggAAC04aAt2I9IfiZZGJebgHFCsHPIJJUXbcK40lRozPOqYgU7WWZohZL9ZddKW9ZoI21pPjSYUcl3SILbFZq0R672fT8nPx1A2xNT8u0bKSFNUkLBx2W9AqB3nLBz6RDH8uGLbZ1elYMfibd1lsnWes02bbp8VkLfoZxhfVfNa8VqnrlNSXvTDLYyn/io21ZAAt+unVtDVOCn5WxWshRAqAt5EFxmwgggAACCCCAAAIIIIAAAgggUCsQ33Zn8iU6/hi5qnQKd+3R0lvRySckB/ygu+TDOpdlmlthebldd04KKhU22rRiEDQpNtS3n1V+f0tadGG5A/crc9V0t1tsUbn997GK6lZFfpO+8q+NLNvf/2xFibbZQXrhJWmB+RUdmQZCy55Qp/q8P+ZExZbFWq7FNlU+XnyZZPmApI9Ni0+mp5c7oYXuDxmw0XlpFm+8/8GKB99dcSSFk0+Tv+paqdUMis4/u2JfDrYcAabAt5xnxZ0igAACCCCAAAIIIIAAAggggIAJeJuyXZjB1o60z9zkr+XC2pdNbPmFFk8yR3Nvv16xKry3Ku+FdTaUnhuRTJvXZptaNXUrEBQqxIcCOW3b2jT6edIp5598mk5PH/akQoAzZG16K86kF1+Wn/CVNOsscosuIrfRBnJWQd1bdfU4BFYfe8LWJLWiRlZIKaw3qu8mpQWIwng627RrW79UH30stZlNuacfk1vWApYVWn6p5ew6Nv2+Xdv0PkNfW8M0Ov5oacu+imzqfByKIJ15rvwNN9VUYA99ZIWTosG3y83ZVn7482kw1O7D2X0k9z2PZVG24FY46lj5cy9MRuD6bCwts3QSBHeW6anZ29h+J/+4PY8RLyTFmaKbrbDVNlu34BFz63UFCIDW1WAbAQQQQAABBBBAAAEEEEAAAQSavUAobFSYy4oBtZ9TVV9asaIpaIWe61lwcqiipx5RtGaPimcmQdBeVp38yacq9lOHuZUb/rRlaS6QTJuPrbiQvpr4x3Os6rnbZSdFp5+sUGCosPralt354h/71d1jleJzr7+UBFbr7i61nW/bIani7j7/UH5jm+7/8qulutXuswrp0a03yj/xlHz/aywQOHsa5K3tkW7ZcgAuBFDPPl2uU6eGR1vM98I558sff7JkAeiybeaZFd1+s6JeFvymZUaAAGhmHiUDQQABBBBAAAEEEEAAAQQQQGDaEAhrbhZmmiPJzKz60bIyp6Dlu3ZPAoO5l56TW2nFimcWbF1Pf+EltX0sgKlCnH4PhYiqiwclO0J25tztpUceT49blXi3wXpJlqgmTZYfYVmVj9oxm/qerEk591zJupS1F6+wZYFKffO5qiw4V6nlF1wkqV6f++g9OQvGJkWOjjleevvdNLO0eLIFOt0mveRC4Sirel9YbOl0PdVwPEzj772R3IJWPd2cwzqlfsjDyXIAIVMyGnSrorXXKl6pxXz6t99RobcFsz8YF5I9rQq9ZdnaEgMKtcHDswzFomwZgKRZRm7unjvlLEhNy4YAAdBsPEdGgQACCCCAAAIIIIAAAggggMA0JZDvYAG6L75U7q3X5JZcokljTwKn7Wwqt1WQz034VM6msZdrhQsukj/8mPSwTV+PhtyrqHNnherzYQq8m2UWxTZN3u+wi/zge2ovY1mp0XVXKbIgYsPmx45VvPs+SQZqzTELQOqu21W16cbJ1HnZNZ1VYs/bp7qsII39IO3aqpWqfv6u5rRSG8Uq8NHg2xT1tWBfnRZbMFPffKtovnnr7LW46B52P9cOSPa5E49VWCe1YUGpsDZoWD/T332fNFNr5Z57utHp+PV+5F/+4r/8UoWuq0lhqYKVuyo34Bq5JRZP14GdPFmabbZkzLFl+sY7WxGr0C8EQR9/SC4En2ktXsBC3DQEEEAAAQSOGeaSAABAAElEQVQQQAABBBBAAAEEEECgZQmELMXQ4mvKF/ppOCJ/6+1pJuMqK1cMfsbffCN/5HHp6auurKp330yCn2GHsynpIfgZWmRBydyg26Q61dfdEYeWDH6G/i4EUG+7KWymLXKqyv+YBD/DDjf99EnwM2xXhYDnmNFSv63CVytW9Ivym22Zbpf566qnbRcDmnW7RbaeZ8PgZ/zgQzXBz7DOaHTicX8IfoZruA4d0vVBd93JsiQt+zYEfetmv9b9oWa4HR9wSG1QM6ylasHP0EL2q2vTpmbM0Vo9lXt+qDT/fNLQZ+UvurQZjoZb+jMCZID+GTXOQQABBBBAAAEEEEAAAQQQQACBf1XAW8XzwnJdLVJYpVwoPtTNtis0/+mnKqy4qhUW+iqdxr3F5mV7F6yqu7/73iTbMZo0MSkeVK5zUpBpgYWl8Z+lXRpZlzS/WBfpvTE1l8s995Rcd7uvCi3f1rJWLSgbWpW3zNAyzVufQmfLhv3ue0UWaI2KwdMy/fPLd5NGvpEcjU49UdEJx5bpme4OxZ0KSyybTCOP7hyoyNYFbe7NvzVahZBJ23pG5d5/S64JxZziJ55UvI4F2EPxqc8/krNgNK1lC5AB2rKfH3ePAAIIIIAAAggggAACCCCAwDQp4JbuInfgfkkl87C2ox/2bFkH//6YtKK7BT/d+usqqhD8DBfxjz+ZXMvttUfF4GfSydb2TIKfC86ffA0B1vjT8el2qb/vj033WuZhaPHgu9Pvlf4OfbzmaD4EZsu0sGZldM4ZydF4lz0U33ZHmZ72u6++VhP81GJWof7Qg8r2LR4IGarRwfsnX31T7rt44r/46e9KlydwO27fpOBnuNVkjdOVLGhqgeRQIIrW8gWqWv4QGAECCCCAAAIIIIAAAggggAACCEyLAtF5Zyu2ojb+/gdV6LGurdu4uq3naNPTw/qZhYJl/c2UrNepp55JP5dbJqnw3ajVpElJF3dQGuyr1N+HQKI112sj+RtvSQrpxIceoTgUPLLiR0mRHWdVd8JaklYUKfkeTthjF+k8W2f0ldcU1p5MCg2N+zAtzGNZiu4/3a1QUe+kEE/VUksqH64RCvace6EKFnz0L71iATpbE9Sm5GuB+RXttL2iELDdc3f5d9+z4k2XKt52J/kbbpbbfRcr+GQBvZnM46OPFT/wYHI83IbCNP7/3SMXjjWhufXM2Zp/daTiq69TfINN6Q9rZgbzsJZmt5Xkjj5c0TJWFKpO899+a8/pfxaofk76zLJlQ9GhTh3lNlxfrueacqEQ0d/Qap7PumvXXN2PfF3+vgcUCiOFIKfmbCe3wvJym20i17Fj0s9Zf//yq1J4vlSEr7FrqRtMgW+pT477RgABBBBAAAEEEEAAAQQQQACBZC3KQr8dpEF3VdZYuZtyT1hRm0YCfWH9zzhMObdWabp58ccKJ5wsf/rZik47SfFF/62Zql48XvbTpr5rtZ5pADMUVirVZp5J0ZGHyR17lAozzFq/knu5/lf+V9H22yZro8ZH2Tqm31qQtFLbYVtV3XR9pR71jvnvv1ehjVWwb6xZQDF67EE5C4r6s89TfM750uQfSp/VZUlFl1ygsAbn1G757j2kES8oN/xpqV07hfVA/SOPlf6ZXCS3846KzjtL/pZbFR9o9gfsq9ylF5buz94WI0AGaIt5VNwoAggggAACCCCAAAIIIIAAAgg0FIhPPq02+GmZfElmYah4XojTjMfYMkE/+1x64UWrwL63opsHyNm6oeVaZNPI7cykxZZhGVVPVS/X380+uywvU/HjFtC04Gm9Fu4nVF0PQciQJRmyUott/Y3TrRD87GSZkNv1k1t+ufT+x1lW632WLWlZpPGJp0oPPVr/3Lnnklt3LWnRRZNr+6HDLFNxpPTDj4p32DXJzsxdeK6cTfUPWak+FDsK1eR//FHqMHe65mj79vKnnC5XLvhavM8Gn/FhR9XuCVmbds/OKqZrjtltbVPLPA0On3+RZE7G83WWLOs2BCBDC1mVbtPe0kILJZmu/rWR8gNvl0aNVrxeLykEQffbp/b6U2HLzd4mfT5PD5M/14KwIePT1vZ0222TZNnKjof79Y89IW9BdH/dDSqYp9tow/TXrS+t5QuQAdrynyEjQAABBBBAAAEEEEAAAQQQQGCaFIivvErxvgfZ9PIqRRecI2fBs1JTqeN771e88+7S95Pk9t9Huf9eVNErP6MFxWxKtzv3TOWOOLRi3/ihRxRvtGn9PltspqpQHb5By4fp32vUTsVODi+3rHIvWMDN1tds2EJgs7D19tIXX9Ye2rafqgbeUPu9eiueOFFxz/UtmPhWsie64lJF++z5h37FHf6NN1VY1gpHzTuPcp+MramEXjxe6jO+6hrFex+QHpp5ZkVjRyuyQGrDFt8xKAnE6vff00NWGCq64xZFa1o2ZoPmrY8/8xzFFowNLbrrDkWbNfBscM6UfC0cfpT8BZdYpq35/vqb3JabK7rqcoXAdcPmx45NvW1ZAs3UOql4H916o6Jttm7Yle8tTIAA6L/8wD6zdS++sf9C9JP916nwTytbe2M2Sw+fddZZ1bZt2+T7v3yL/DwCCCCAAAIIIIAAAggggAACzU7Af/65CgsvKf30c5MqnvsXX7J1QtexwOavaqzyej70G/qsZJmWVVYFvFLzFigthIBpsbWaQVU/W5ZhmZZvP5/01cTao3fdrqrN+9R+b7CV38AyJh+x9USrW2PT8vNdu0th7crpp1M0+WtFJQKrxWvlOy0mffiRorsbDzrGVgE+nsUyWu0ztOhKC7DuXT7Amr/ElgM4+Ij0pyzzs+rRB9PtMn/ji/+r+BDrb8HS3JjRcrPMUqbnlO0OxbEKa9jztOb69lFkgWkX1lMt07xlyRZWXFWydVStApZyX38m10gWcJlLsbsZCViuMu2fFJg8ebIuv/xyde/ePQlyzjvvvFp66aW18sorq2fPnlp11VW15JJLar755tMs9i/7CiusoH322UcPPmgLFIfFjmkIIIAAAggggAACCCCAAAIIIGDrbV6aBD/dVn0V9duqURHXrauio9OAXGwZh5VayJ5MmmVeFvY9sFJXFSZMqHfc7bdvve9/+GJT3eu1U8+o97Xulzifrxf8TAoe1e1QYjsa9mRacOk3y6w86tgSPWp3RVasKLT4wEPlv7Bp6xWaP+aEmuBnCAyHquoV200Daw8/8ZSSsdTu+cNWdLBllq6+mjThK/lrB/zh+J/eEYpPVTdvSWeVgp9Jt1zOFn+tSs8IgdK/qThT8Z74/GcECID+M8768ssvtd9++ykEPPfff3+NGDFCIRhaqeXtf+hee+019e/fX71799YyVkEtBEJpCCCAAAIIIIAAAggggAACCEzrAt6mtYcWNaFSe9EqTH+XFbrxjz8p/8MPxd1/+Iys6rrbYZtkv7/yahX6ba84Lq4M2qD7f9aqt8M//bS8reFZqoVsUQ26u/6h199UPuwv0eJ1Nqi/19br9B9+WH9fg29RCPJtbOtpWvP3pEYNutR8dbvtIvVYXfp0vGVJWtXzN0fVHKu74S0+4a+/sWZXMoW8tU0RL9P8xx+na5LOalmcIQAZ7K5rPKiZBEHtmmHJgqnV4nvuSy9lyyTI1hst7LqnQpZnqeZtlm4hrEX61mjZlNxk3VX/8KOlurKvhQkwBf4feGDffvutevTooTfffLPm18J/cejQoYMWWGABzTnnnLZG84z2H3JmUAh6/mL/wzdp0iR98skn+uijj/RrnQWJI/svDxdccIEOPvjgmmuxgQACCCCAAAIIIIAAAggggEAWBeJHH5O/6175ka+n08at0I6z4KR6bSS/9XbJNO+cTTcP637GFrTyx58k/+wI2f+ptuifzaK0iu9uBSvSc9xRKlYYzy+7kvTGKEVXX5Fc179i08Ut6zAUw3GLL2ZFejaWsynpoVBSPqzXGdbtbGrrtKA07iMLsloWoS1tp59/VrIOZvhuU+OTIkg/WoGm0EJmYd2ganFadrjvsB0+67ZVuknPvyh38P7yQ+2e3n/frm+B05Ct2HYOuQ3XlzvnDCVFnO4crDisHdrIdPxwef/11yqsb9Psw7qXoQq6FU4KAVTXccE0UGjT6eMbb7YCR2PSu9l5e1UNuFYFyxj14TdefNnW1rRCTuF+w5jatZV69pDuGCy3/roWsDUPm04eAsq5mwak1yjz19s6poU550vGUzXxszK9pmx3oXcfKwL1sNxJx8uff2Gyrqfmai+3y45WBGk1WwvUiiQViyDdPDDJKtYC8ycG/vL+ciceq9wpJ07Zj9K72QkQAP2bH8mP9l8V1l133STjM/xU165ddeihh2rttddOAp+N/fzvthjwiy++qJtuukkDBgyw/91MFxAOmaAbbbRRY6dzHAEEEEAAAQQQQAABBBBAAIEWJ+DffifJ1AsBv4rNCtlEE8fLb2zVzoc8XLGrll1a0dAnFIdgX2PXXXThpFBOKNqTn8UCelZdvdFma226By1YawG3UGyn0bb37lL/axvtlnRYYnGpcyfpfw9V7m8BSHf4IRbE7KO4m2V2WkCzKl8dcK1wprdAbXyMBQivuMoCtvkKPS3GOfxpxedfJN1dnVlZqfdaa6aV5194KQmG5h5+oFJveQsIF6oss9QCwDm770anq1e8Wnow392CsVaFPjf8aWnmmVXYx6baP2dB8lLN4s5uS1tS4dIL5UMQ+cDDmlQ0q9Sl2Ne8BAiA/s3PIwQtd9111+RX+vXrp4EDB9p/ELH/IvIn2pAhQ9SnT58kCBrWDR05cuSfvtaf+HlOQQABBBBAAAEEEEAAAQQQQOBvF0gqn1tAU5MmSx3mVrTvXnJr90wKEunrbyz78VnFlpmnD8al92IFhGVZjEkLmX3bbCXXc02r+j2D/Ijn5W3as8aMTY/b7MskWzFkXoaMzwP3U2QFesLv6LvvLXt0uOL+V0tvv2uZlZa16e20QiE9N/y1wkKWbmob4YBFy0JBoLqZmnUzNy2LUG1mS8/PWZZmyAa1WZ6ytTmTVjcDtBgnKGaEhuuErFGbJVqydV1Rbtut5ayCfLJm5v3/S6e7W3HlpFmwVza1XjO2UtVP35W8RKmd3maiJhm3zw1PsiKdZdCq80JyvTZUvNW2aXZksLKMyaRFdp8r2b303Uyab94k0OgH2xT/ulXrrahRuEe30/bK3VA54BvWIS106CjN2U5VEz4tdYtTvK+waV/5+x9UdM+divpskpwfCiPF91kw1gLt3p67s99zKyyfZr8uuUTSp3DCyfKnn63olBMUnXjcFP8uJzQvAQKgf/Pz2GOPPXTttdcm63e+/PLLtvSF/Y/lX2iXXnqpDjrooOQKY8aMUefOnf/C1TgVAQQQQAABBBBAAAEEEEAAgeYjENa3LKzU3QKa3ySBzOiaK5UE4RrcorfZkYUOCyb9iofcaScpd/wxxa/1PuOQzbftjhaMtMBnaJYZGX32oaL27dPvdf56C3jGJ50qf0adQklWQKnqhWF1etVu5hdYRLaGXe2OOeZQ9OIwRSX+/3ooBBTPvUC9+w4nlqvsnj/xFOm0s2qvbVvuiYeUW6tnvX3hS1ij1G+5rfzd99YeW7izqt5/q/b7X9jKL9rFpt2Pqb1Cl6XkRr5ocVoL1DZo+XstO3SzrevtjW68VtGO29fb1/BLPPA2xdvvImdB6VwjVeMbnlvue8EMvRmG9V9z/7XM1Sa2YuZodO8gRbYsAq1lC/y5VMSWPeZ/9O6fe+655Pc23njjvxz8DBfq27dvzf2/9957NdtsIIAAAggggAACCCCAAAIIINDSBeIjj02Dn5v0UjTwxpLBzzBGF5KL1l+vdri2XmW54GfoFG21hbTeurX9F7Yp7iWCn6GDs4Cet3odNW2m1sqNeKbm6x823rQ1MOs2yyYsFfwMXSJbr9MdsG/d3tKqK9f/Xudb1akn1flmmzblv1TwM3QKs01zd1m268Z1lsubmoG76sJKyQ0tubiq3nylZPAzHK/qs6m04vJJ15o/21sGaYXmLZM2vuDipIfra8sITKUW2XquoXlbx9R/ZWu9NqF5y4AN0+Y1y8xy663ThDPo0twFCID+zU/o00/TlO3557fU96nQ2lpqfzGL9OeQPk9DAAEEEEAAAQQQQAABBBBAIAMC3qaH+1AhvfWMivpf1vj6j6F4UbFZoR0/vMy6jtYnFNfRI48Ve0vjx9dul9o69MjavVa0yFsxpnItqlO4OOljBX8qNf/4U/UOu1XKB0Dj4pT24hnffVfcKvmZVKof/U7tsQkTarf/6tbzFhAsNiu+lPxW8XupT1uKoG7zjd37cSdKr72upADRzjvWPfUvbYdlApwF1DX5hyS7NFS0r9SSQkw77pZ0iY44VC4sm0Br8QIEQP/mR1icoj5iRPn/IZ6SWxg+fHhNIaTll2/wX1Om5EL0RQABBBBAAAEEEEAAAQQQQOBfFAgBtDAlvNj8gw8lm26T3nIdOhR3l/+sOx3behUOP1qhmE9oBZvGXgjrc1oLhXViO1av4roVNYrHjUuOJ33qrvMZdlhB46TZNO/Q/ANDks/in7j62smxiy8r7q75TKaLV3+r2zd/+RXSs+lM0WJnf+0A+e+/L35V3f7xXvvV7E82LEsyv+OuNfvyP/xQsx2/NVrxAgtLYz+o2afHnqjdtq3CX0mkenVk7bXGfah4/s6KR42q3Vdnq3CWLR9wZP11M/0W29TpUbvpJ01SYW+rbH/Wecm6q9EN18jZ+q11W1iW4K+06PJLkjVk/aOPK16vl1Wm/7Dk5fyLL6mwao90fdluK8lZAJSWDQHWAP2bn+Puu++u6667Tq1atdLDDz+sHj3sX6Q/2b6z/1rSs2fPpPjRHLamyNfFRZ7/5PU4DQEEEEAAAQQQQAABBBBAAIF/UiAecKPicy9MixIVg59hDclOHaV555GesbUzLzpP0cFWqbtCi7/5RnFb62/N7b6LQhAx/eIsWunT7eLfuoWJwnTzkZZlaBmMbhfLMvzkU/lXX5O++dYqhM8khWrrvW0K+UmnpWcPGihtuZ207DL23a4bsitt/dGkhcJF88+XFFvSe+9Ltt5mTbGltEflv1awSV9WZ2gGg3Df5YogWfVy1Ql2VrywESistXnjLWkhpVCEKBQlqusSAoxrryl3/91lp7E3/I18ZJmQ4RoWoNYDD9Zez5YfcGG6e5s2kgVGQxBRFmBOmsUuZAHOmmJOS9u6oTYt33XqKP3yi7xlfCZrlgb/VjMouvG6ZLkCb5mr/sqrFVtxJ4WMWsvCVds55LpaUHKrvnI7bCdnywlMSfOvjVQhFNca/1lSzMrZlH73n9WSYlihqJO3YLF/8un0klZkKvfA3XJzzTUlP0HfZixAAPRvfjhjx47VEksskWRtzjbbbDrnnHO0yy67aPrpp5+iXw4V3/fcc0+99JL9D4m1Y489VmecccYUXYPOCCCAAAIIIIAAAggggAACCPwbArEFtOKVV5dsqnqj7dwzVdVI5l2S7dhlBYt+WrTvthulfk2cMn3gftL1N9QG6Io3Y0WRagokFffZp3vqEfme69fZU2HTgmZ66ZUKHRocuu0maZsm3vdii6aBu6YEQWebVRpyn7RazwY/WOKr+TkrNJXbbecSB+vvyrtWyQ43+FY5W54v3nan2mrw9bsmz8VttIHc3Xco7mj3HqrGhyBvmUxO17NHEvh2FmiOr+iv+Kjj6j+jhs9nkc7K3XqTnFWgn5IWqszHx5wgf5MFh2ML5jZstvyCO/QgRccdLWeJbLTsCBAA/Qee5fnnn68jjjii5pdmmWWWJBN0ueWWU6dOnTSX/ReFGW1NiZAlmrf/AvaL/VeQSfZfSD6xKnKh0vvQoUM1qk5a+XrrraeHHnooWeC45qJsIIAAAggggAACCCCAAAIIINAMBeLPPle88JKWdZlOT9cq3RQdfqi04Xqhco/0+JOKz78oyf5Mbj9UaH/7dUWLLFJ2NEk19eksKzK0uhmeViRIXS0wGqbQhwBXWHcyZHiGrL+GzTIXo9NPTiqOOyuI5CdPln92uPxlV8gPeSTtXTdot3QXRYcdZNXNN7V1SltLQ4fJX3ipilP3610+3FPIKP3JxhyyOsP3kAg1nWUt2lqUSat73506SitYFmUIXoYgYThn7Dip3LqmlmAVMiKTjMxQEGryJMsm/So9L7167V/LHnW77SwdcYhNA59benCI/KlnSXXWUHVnnqrcMUfWnlNiK5+zMdt9uUMOVO7Cc5MecTC49HL598faWC1Ls11buTXXkDvsYEXt2iV98jPZfYZj/+mu6HhbpmDYc2lA1GIg7v/sXQe81NTTPcmuXcSGFRAUREFAQEWxgYgFRcCKFeVv772jgKIoIth7owgiiBUriNiQoih2+QQVKwoqYiPJ/c7cvOxLdjf79qkoZeYnu7fMvbk5ycMf552ZqV8PlihtvKX19c+/CKb/QNt2uu4H56Tj4eywPRzeg5CXhnlcg2sHhErcVVaG+/houLu3t/7V+TBffcW0Bk/BfPgRMJ/vyHq14BB/Z+894Qi2asscAkqA/kuP9L777sMpp5zCv+8r/sL/i9fda6+9MGzYMEgIvJoioAgoAoqAIqAIKAKKgCKgCCgCioAisKQj4G1ULyS8SNS5Tz8Gt/1uRY/sk9gyovwTI5GZnfd12E759FZfpzJXp/gc2BXZh4cX9fauvwE454LEnLNwHjJCZBaxSO0YTUnYdeahYVE38e0JYUfiLmFvTkK2xdaJoajjrc5/z0tId2T16yH7KUPri5g3jPdzxDHJmZfHISuh20XMa0tS+aWJlTMMS8/Op/qyiPmffwHTqKkNRZdp561JyFColWbeuhuDufhsqoLsnE/T3BLjPhWrpkZIhDosbJU54djEfLwTDBmGQIoPrbQi3MH32lD4+HzUliJGwalnwtxxN8PuayIzfTKcTTaJpvVbESiKAH/VovZvICBh75+xop2Erm8gv3Gphq3E3BydO3fGE088YZWfSn5WAzx1VQQUAUVAEVAEFAFFQBFQBBQBRUAR+M8Q8Pv1D8lPqh3d8YxkTCE/5YDuaScDNSpUnfPnw88jLAtuYsPYv62pJM0Muq7AJRrISOi7qC8jo/owjfy0LpKfM2bOZRXEbGwsamZFWZlnaeSndRPla9zOPyfeS7Szhx+a6ItiNo38FEfnkiTJi8MOTq6P9TJ168D55rMwNJ3jZu/OsdnCpnP0keEg1bS+5Pksw8zOFWQ3n38p8tOQKA3OC0lk97abUslPuaTk/hQfUYjix58QXHxZGSdRl+UdAVWA/kdvwOzZszFp0iR88sknNtz9p59+wgLK7Vfgb8RWp7R7jTXWgFSQb9y4MZo3b27H/qOj6mUVAUVAEVAEFAFFQBFQBBQBRUARUAQUgb+EgLchlXkswCNhzpmnHq1yDwmFj4gwMH1c9meGdaeYt9paYYi5zNdaF9nv5qR4ktxjijm/biyknoRcNkiP0PQ2bwJIWHdkw4cg2+2gqJf4Dlj7I2hA/5hlze+xXrLptWgdFmKKhrsfgez9VDMWMU8KAG0hBZgqzfV/TU2J5226hS1ElPMmSZz9anauW6zhX3MdzIWX2innx2+RKREC7q1YIywCRRLS+WImMiUEXr7k2hQCXIwK2myKglamgweGIDj6OIBFqrKvvSRDVZqZMwe+pFagIjTzzedwKkLuq1yoDsslAkqALpePXW9aEVAEFAFFQBFQBBQBRUARUAQUAUVgWUDASG5F5jOEVMRm3ktHqnv/BxZIzkoh6/6PodGbbQpI0R4JVV6JOS1p7oypcLfaKneyQHJzvjMjrA6+dXO4FWneDPNf+u2okJQ8kTTnzluROa4HzPffA98z/HqtNZmvkfk6xzFvaId9cvvZRu2N4Y4ZCbNJXWACSTRWekfbXeFwMti5HfB5HkHKnJeZvr2ZA3I+8N1cG04te+PV1+Dvsntyb+ndzFB3Vg4HQ7Vt3shDqK5caQVgG4ajL1qU9O+wG9wnSfh++SXwAfNMbrQhsFUTuzbocULSV9SxTz8O7Mx9ZrxrVY1gvlGpih5sxtyYVDkmbLtt4b70PHN+srK7VKUn+YtmDGUf8xiCbhUqzWhBBdErYeNgDk0bek9S1KHoKm5RhXfn1JOQuWkgjFRul8JFqzFFAEnOqOK6/+xzMHtReSlGNapzZS+4omCVs0huUypnA76LZhfm5WSFd2t8Ztl5xcPwQwemPe12BMxDo+DecTPc44+NhmHkvZK9f+J5uLcjOV5j5u/T2eZrdYfdD/ewbrEZbSoCSQSUAE3i8a/3vuL/qObNm8d8wL/aP1IISarFiwJ0HVZVk76aIqAIKAKKgCKgCCgCioAioAgoAoqAIhAhIGSWuftequaGAm9MpryxYoYh3s6uLEBzwrFwD9w/cl+s38ErryA48XSSfCTihKyKjOQYJC/jrFmWKMtStSgkqTntLJgHRxSSemvUgLN/Fzh33ALnjz/g1yQRaSpuLF6ISPbP7597JnDdoOjKpb+bUDH43vuVPvl75fdbNK8k8ipXFW9JpXI5chyH4p7h6K47VxZ+KuUncyQy7X0LkVmOsRARJky0nhIqbp57IZl3tMmWcA89BM4Zp9oCQ94aJFGFwGRhKFu86d0YRlIZfc8OcMWX75d/250wJ/OZl2MkP53PqRRdffWS3l7rnYDJU5F542U4JHjNW9NtYSxbYErIz8hIrjsH7c8iWmfZSvT+JZfBXHWtLWYlldvVFIE0BPjTqfZvIiBh7rfccgvatGljSc6NN94YTZs2RevWrdGuXTvssMMONuy9du3aVPvXQMuWLXHSSSfhqadYnSz6y//fPLBeSxFQBBQBRUARUAQUAUVAEVAEFAFFYIlBwFBl6TdrheAkElCTSH5KZfEGm4XEFf/NaF6gMvKgw+C17QDz3XeL9dx+14OorKRSUghFIf2EpFuRikj5lr6Qn2I8VzB1GoKatWBuvaOS/BTVqvwR+5kV2O9nGDSJTyG/kM2E4/IpVdHjltd3ulCRKORjObZlI0vI5lzz9iq41iHFw95z6+MNipioYoqPlG7Lucs1wWm11cr1BrYk0VthZszjYRV2KmTRqGGo6nzvAwSX9rIh5EaKJlVwzZj9GSDkpyg/xVfWsJiz7OGzwJJ/eHc4hx1iC05F+6d+S9GrBx+okvy060l6ixnep3/WufBbbU+i/KFQ+Sm5XregorjmGlZhbPpdB5/K2OCRR+FU4G0q1ttN9EMRKIKAKkCLgLI4hr6lZLtPnz4YMmSIzfX5V66xFcMF+vXrh332oeReTRFQBBQBRUARUAQUAUVAEVAEFAFFYLlCwLz/Afwd24YEIgkht8/lcPbtCGeVVSwOhmHl5qGHEfTuG4Yvb1ofmUkT4dSq9Y/j5Mk5XpsU7ksC1r32KrhdK4voBBKOfS4VeZ9WkKDRCaiwdI46nKHTveFKWDgtIFFrel0Jc9e9YUh85CvfLBgkSkUh4TCXYfBrMwSaasJg4ivA3ffFPcN2U4aOt2gGZz7DxkUpuy5JyVmzgVdeK/RtubVVNZrf/4AjIfasKA4qUYPJU4Gbbiv0rwgHxy8LScpSiSlh5NIuRjTzjBDydNGfcCSUnOSdEYKURDCmvVW49+7tgPr1eQ6G+TNC1EhuTZdEMknhnBI2WiXP+9CD7f05zK8qhaPsfc54r/KZRL78dq/sBacHiUumSBCzCmKmCLDvieAi5LFPwlpsnbXhDh8Mp13bXNi7Yei8ufcBBJLPU1SirCwPSWFAZShOOJapBb4AZjJfqmAhxaNatYTDd9WMe9Hu7Y4YWqUi2e9IlerTz9kcoHj9DVsJXtSp7knHw6lXT04WisJen4Sgbz8b9g7CgzY7MGXB6yyKdCPcE4+3fvqhCBRDQAnQYqj8w2PzmU9k1113xYwZM3I7O/yN2Ib8y6du3bqoxf8ZrcK/wKTau8e/oH///XdbGOkLJmmWyvF/xH6T4TKMYMCAATjzzDNze2lDEVAEFAFFQBFQBBQBRUARUAQUAUVg2UbA8N+JftNWlmhyunSiso4kVQXxmX/nZu5c+Pt0AaZMg9O+HTIvPJ3v8rf6/sUMO776WruHw/DzTP9+qft5WaoWI5Ul1aruh2/DJdFXzIKvvkawZTOrBrXz/PevhM6nWWJv/hvbmfA8MrswlLqIeU+OZf7OZFoAx1vIqPKY0jS2zltnIzBfXeUIMc+OebiyH2t5QlIec1xshKTjwnlwV6WKsoh569UOydxormULZKe9HvUS395dJHmPPykx5n77OVzJVVrEvBokWYWIjIzvSPbX+VEv8S35Nf0TTwWEeI7s9YnIbr9d1Et8W/XxdsRX8oNy38zrL8FpzueVYn6vK2CEjCdRmpk+BU7DBimeJMH5PuWquZPkzox9DE7r4ueQTRLFstjPvPcWnMZbpu6vE4qAEqCL+R1YuHAhOnTogNdfD/8y23bbbXH22Wejffv2lvis6vKLFi3C5MmTMXjwYNx3333Mq7zILpGQ+I4dO1a1XOcVAUVAEVAEFAFFQBFQBBQBRUARUASWAQRyhE+zrZgn8ZVc6G/arRlGIfrNtrHFfdzHR8OV4j3/gAUkYgPJF7nIszk7M6NHlNzV26wxVaCfhj4kG91ffoCbEiYuad/8Ji3CfKKyYr1ayH5LdWGKJYhEqjczX81OJ4VlbynIVPFvasmnmSUBmmZenc2AOV/mpt0PSNxuwfD5FPOclRMzJavAH8pCRSNiZOo9tyPb4+jE+ngnsTfDyrN/LohPJ9reCacAd95TOUYVanYECdoU8489Eeae+8PZKghnQ5GWrbr+J3kJ+lrSsQQmsql/FAtYDXkQzoFdkXl4eHidIp/BlKkIhFyl5RdCKuJuh7w2u8KqRamAzf5MBa+aIlACgTKTZJTYQadKIjBy5Mgc+dmtWzdMmjQJ8i2qz3JsBf7ltuOOO+KOO+7Ao48+CumLXXjhhTaBdDl7qI8ioAgoAoqAIqAIKAKKgCKgCCgCisDSjUBwTxju7V7Tt0ryU+7UWX99uBdfYG/aVKz9JxAwN91qyU/+4xTO8MFVbxlXUVIJGgy4IXVNIDlNpZhSZBJuXcri1dHZ9keOTveWCvUR+SlekSo1bcWXXyVmghfGJfrxjuQ3zTcvfq38SSpzEzZ6TKIb7wRRHtVokPsKCZ1qcbzFadbsVFcjuT0Z2p4zhtwbht+nmRlGslvIz802tTleg/urfv6SGgErrcgcoo/B/MDw/jSbPCU3E/RnFfo5c3L9Yo1g7DMsmlSxhu+JkLNqikApBFQBWgqdf2DuuOOOw913341mzZph6tSpOQLzr25944034owzzrDLZ86cic0242+l1BQBRUARUAQUAUVAEVAEFAFFQBFQBJZZBIQM8uswfJj5GTNURDopYdv5AFgV6Aab2II22V9i4dz5jtXoezvsEhZf2nEHZF95seTKgGRasNraSR8qB9377oSz7TYwn39u81s6LAIckPg03ZlPUnJ9xsz99IPUkPmEMrJijXPOmTbs37z9jiXrnMZbMMfoXOD0swtJz4MPROZOVp2vWTN3RUOC0QwZhuB/J+bGbIPndvpfDXe/fWCkUBCJQIc5TAOmGzBdD05WWJcF61IlO/ZRuO++B/PpLDjsBwzpdgYPhWEV9XxzrrgchkpGPEei9RcSv1KtnvldcVh3FgJiPtO4ZbMMW78VDslN8+FHQC1ea+edgWsHAG++FfcM2wfuD5x6IhzJPUqi2GmwKQLJUXrU/3juChWs5Ddd+CvcZ58I82o++jjMxzNtXlSn9bZwd2wDv/1eMOMnwOnbG+aSywHmUM1OY07OKVNgmIcTP8zj3uQoqDZ216587v5enWCefR7uqOFwD+haeD6OSEEt8yivXZ/v6yziuwEJfOKNXXcO75OV4B0qgs1GG8HcchvMdQO5iHlLRYFKDNx774B7DLFSUwRSEFACNAWYf2q4cePG+OCDD3DJJZfgyiuv/Nvbfvnll5AK8WJjx47F3nvv/bf3rGoDOXv//v3/EcVpwBwjEtZwyy234OSTT67q0jqvCCgCioAioAgoAoqAIqAIKAKKwHKPgGHhF79NW1sgJvvaS9XCw1uHhW/mzUfmh6/gxEipam0Sc/bqbQ589jmck09A5pZ0NacsCXjuQM4thC2JLJA8K8ecg/aHefwpsCAGSbMRJM2YzzTP/HdmwDTfNhxlgR/ElYx5vokuSTu89bYlXu34ioyy3G5bFgjaAJjPIlKiKmRF+pz1vBi4girGcmxj5g3NU46WXPa/HsA9sfybpZzl2UnhIf6buizbn0TjI2PKcgWLM0leWRsKL2Tq3CLh5FRxgjlcpQiSy8JawfYkwiWVgcNL/PZ74XU2qcvCRDfB3XtP+GefBzOQ7euvhXvW6YW+HPFY9R1vTof74rMwV10L83y64jbawO3DXLRS6KoPC3D17gn3skuiKf1WBAoQ4K8N1BYnAnMqZNt16tT5Ry6zDv9ikjB4yQX6W95vxv6RCxTZ5M8//7TXKzL1l4fef//9v7xWFyoCioAioAgoAoqAIqAIKAKKgCKwXCFAEYk1FvqptkVroj2qvcE/tIDqvbKtHlWA0a2aFMIv8HPbOaIKzPWqaEgl9LdnhGpQknSgyEgqxCfWb0X15bsV/2ZtWI2oS1GSVocA/XF+FYeNTf/G0HQhksslQOtuHFtcRXOdtWCIgbWI/GSRZqlcb0PeRYH6x5/hHzoFovYUi4fiMwcrKzuHBZKEECVJHnTsDEMyG7WrcRY3A7NCGVSVvB9yvSiVwX/9fltA9GNJRkAVoIv56bRo0QLTp09H9+7dcf/99//tq40fP94WUJKNPmUi6fop1fP+9oXyNoiUm3nD1e5KASgJ4x84cKBWsq82erpAEVAEFAFFQBFQBBQBRUARUASWRwRs8Zm6DRlWvU4YAs9w7HLMVoNfj2IcVuHOLqwG2VZic681C9VMnsqQ6x2RnVhapRcwlDuoQUWhmBCxQlKtthqcJ8fA+fprGCEZSWQ6DPc2m9aHOfxoQMLL5f4qiD535ntwU1K/FYTAC0F4VW9ei+slL6eEqbNolBGi87Szgbz8ls6oB+Hu1g4QNamEyZPEcxhS7VDAlNtbFKKS93KNNeCymJChKhXTqSDlXk6jRjDNmsIc2A34/At7m7mPtUgIDh8K9+OPYSSku9Y6MOQHnBEjYR4YmnPLNXZoDVAd6bz/gQ2Bdxo2RCBkqqhP88k9VmDHNYwwfYVE5Mz/A9ZaE2hHReb4l4qqbJ0zT4XhWfEqSU4JgW+0Ocz2VM+efg7w9Te5I9iGhJ4/PAzuTmFBIhmTnKPmYqotB96Y9GXPOf5/cAb2T1S8l5yogYTXR/lchfwmvu5I7nvQAQV7yIDf+YBQ9Ssh9HJPfNfdq68AdtmZ78RspgFgCPz668FQqWtuvQPmxlt4ML5PTVj5/T2mSWAhKbfH0bKVmiJQFAElQIvC8s8NHnvssbjnnnuoDF8ZzzzzDHbddde/vPmPlLu3a9fOEqprU/7+Q6kEwn/5Kot34VlnnYVBgwYpAbp4YdbdFQFFQBFQBBQBRUARUAQUAUVgGUPAa7QVwJyM7nNPwu2we1l3F9x8KwISf06njsg8/khZa6py8vv1h7moJ8OhV4BLUtWVfJQlzFtrfUu65VwYqpxNCVUOSNAFO+2Wc8UqKyP7K8O+U8xbmQpFUSZWmHP/Xch0PzLqJr59ycXZtFVirGSldpckY4x4dG4ZhMzJeXlBK3YL3pgchoTHd2el9iyjN4uZV3+LkNSLJkuQycEnnyDYvGnkab/dhfMShGN80jvkcCBeDIp5R7MMWS9mUvDIj+dodSgyZTV1R/KDFjHvTBKmN5B4jOyow5B94N6oV/Dtn3QazO13heMkwDPfMX+t5EYtYsFNtyAQQlasfj1kGArvbLKJ7Rb7CFhUKRDiWUhQWmb2RyX9i+2hY8sXAvy1iNriROCiiy6yIeu/8zcmnTt3ttXcJaS8uiYq0j322MOSn7L2xBOL/8Vb3X3VXxFQBBQBRUARUAQUAUVAEVAEFAFFYMlHIFK3BRdeClPGvynN998j6HuNvTHnH1TGOaefAgjpSVWkOfKYqoHbqkmlD0kw99yzKvt5LafNDsDmVLpGttmmUav490YMZ4+M6s00daG4ZOQcLZj/MzIJny5l8XMw/6V7GMm2FHNJMiaM1dTTyE/rJ6HlcTvmqHgv0XapBM03d1UWLEoxp3mz5ExrKj1TzJF9RE0a2Uorp5Kf4uI0TRKxToMG0cqi3xnmALUFmmSW70wa+WkX77B9bg/3ovOqJDPdrp2B1lTNitWoUaV/6KifyzMCSoAu5qcvVdqvuoqSddpPzJshxOW6/I1Hp06d0LNnT9x777146qmnIKHtr732GiZOnIjnnnsOo0aNsirJU045BU35l4yE0k+ZMsXuI0ToFVdQCq6mCCgCioAioAgoAoqAIqAIKAKKgCKwXCBgicf69WyhmIDEow3FTrlzM28eQ4oPBL75FlJ8yO2yX4pn9YeFfLNn4VIz4mH4l/Upvcn/zaqclyI6LMiUZkaiHL/7rnL6+3mV7WKt+DxDpAOqPNPMZ7V2SHh5ZBLKXsoYCp4zqkz9h0fluvkNb2T+nGOL8+T75fr554xUkjmHyob31NjKTkXLkxDxFDN35Sky+YzSzJfQ8nhtEQq3fKbaSzNz2x2JKTP4wUQ/vxNcfW1lQaVFiyCh8ak2fkJuKpBw+2LV7HMeFH5S3QwW2bK2YAEMK8GrKQKlECitVS+1UufKRuDcc89lUbV1IGSmFC5awB/OJ5980v4pe5MKx7322gvDhjFvRpk5X6q7v/orAoqAIqAIKAKKgCKgCCgCioAioAgseQg4VOplHiXhuFM7GIY4+8yv6F7ZG85ee8ARYpFmFi6EGfUIgp69gS/mAHXrIPPQ0L90M0KimpdeBqSwL5WbYF5Mp+0ucFjoJzPgGngTOTf1TRjmqPRGj4E76DqG5rfPXStgFe/gDIY0M9dnzkg6BvUbITj0EOZ4XBuOnJEhzKYOi+T88iswhGelsjRn33wDr9sRyFx2CZzGW+aGDYk7f9DNrEi+IDcm4eqm9c7wtmkJrE/y8quvWFrcBzamSlT2fPGlREi7LPR229NWgAdD2PEtiVfJpdmUqQZqMARc7j1ux58K74JLgc6d4EjxH8mLKdeRfT/+JO7Jewrgb8jw7ZbNWUzpg7Aw0MpUWvJ5gGrSXOGeaBVzqnr7sViQ5B+d86XNL2rq1A5zod6SJB3tkiZb039f5tgk6UelL0TJKXlOJW+q5FCNGwnl4LqBcKgydchLiBkJ7X/lVRjJ05lnZre9YIaQRN2xDVOphpo5IabN/UOAaW+F3pJr1Se2M2dSZdwPznE94Ky3np2ze0+egqBPX5ixz/Ld4bAUh2LuUfMAc6hKDk8puPQV3wumOED9enB23YXv2sRw721a2fyt8p47VAu7Jx4HKXIVmRCjck3zyGPh0PbbAZMm2/WSv1VNEUhDQHOApiGzGMbn8jdOkv9SVJ/f8C/ycm0lSvOF+JR8ovvuy7/klmLTHKBL8cPToysCioAioAgoAoqAIqAIKAKKwH+OgKF60O96cFgoRk6zGskvqZouhNSs2ZU5MVlUJzN6BBypel4NMyTzgot7wjz2REggxteyOrdz4P5w+5J4rV8f/t77wTzzXKWHEGZC8EleTiHjIiPR5T4/FkH7vSrPF83lfzNU2mWRpOB/JyQrqpM4A4vg4AeqQoU4jduzTwJ7dSogOOMuUdsZcC3MpZdR+UgSsyqT+xlyH3B496o8w/l11rbh2AUkZNrqXjxHrz5ps8lxyctJgjuelzTpkNfr1ZN7X1E5mOG91KsXvi+C3/xYbtXaJKCF5I7jKmSwkLC//sb3ahbfr9jzvJXFkE4+vXJvhvzbvYU4FgJXnpEYQ/3dB+5BcOXVliwHa5lgPudMOJ37ZJEuS+J+/wPcKa/B3HNfLHcovSQXqKQNENJ0LglfMb737l232Z+DgCpkp+dFyPS5PJzTT0WgCALZImM6tJgQqFWrFvr27Wv/zJ49G5MmTcInn3yCn3/+2YbHizJ0BSZJXp1/sa3BCnMSPt+4cWM0b97cji2mY+m2ioAioAgoAoqAIqAIKAKKgCKgCCgCSwkCDnNZZt6l8vL2uxBQUYe3WJGcVbCtCRG1845wpTL34YeS02K/GhY89DCCY44LyUEWOXI6tLWqREu6MXzcvPwKzPCR8J94Cu7Q+5F5+nFYpaeQYf/3aUh6RsSiXFtUiaJIJPFlidJYwaLUY3kezJNPhYSXVEE/+ABgwsSwWnlUsZz5PrFbO+CRR8NtJDw9VrAodW9OmOEjyiM/ZRMhcV99NSQHy9mfZy+b/JT9R6aHp8t0wqSCfTlniBZNeClqwem4J8y4F8NnFI1utikr29cI3x9REFeoPbF1M6pxSbRKmH1EkpLUdvZoC/P8eJLivMeIDOUzlgJbhmpffDor2plk6CZwDjkQ7nkswEXVaXDiqeEcVcVYfTU47fnsNq1vn4N5azpAxaglWsXrvfcguUPN0Uci6H99+N7EVa0ka52DSMKffw6cDTaAf2lIejpV5XStPJ22llMEVAG6nD74/+q2VQH6XyGv11UEFAFFQBFQBBQBRUARUAQUgWURAfMjlXxCFEpYspBDohT8CxaQ1Ay6MG9oYOCQfHL7XQlHQrxjZr78EsG5F9rcn8hm4JIAdXdvbz0CKcwkZJaQVaJIZcEhCaH2azDsWojPiLyjqtAdPZyh6VQXSp5HIRm3bs4w8Z8QdD2kUtkq90OpYGY+85iutloYSi/qv7XXAhgS7VAp6uVVahf1qSPh2y1bAOMnMEz9D5sD1YarU61akH+0eVPgtZeQEQy/mxuGwHNv/+TTgLvvt/eV+6C60x32AAJRoT4wNCQHu3SCy2sF2+xQqUyMFoga8lOmKZBQ/A8/BiSnqPjuy1D3KNw78iWR6I4ZaclrG2b+8wJgq8aWBA6kcj2VkQlbmaHjn7wLV879/ofAerXCvaWKupDicaNqN/vnLzASts/nh4UkUnkWCVnPVV6X/YSwZso+Z+C1yJx5OgwjWCGEs6gza9eGQx+vFpWichaqi/H6G5bMzC5geLzkU5W9F/wS7h17b4IBg+w7Y4/UZntkqNbNf0cNyXW/bYcQQ6qFM29NhtNoc7vELGL6AknDIJjwPvMVzX6nriTMnyYhfx9ckv5qikAaAkqApiGj44sFASVAFwusuqkioAgoAoqAIqAIKAKKgCKgCCgCfxkByfHob7YlK/f+zLyiveBecmHJvXwJkb+6v63wnZn5PhxGMKaZ16Ej8AKVg2Ktt0V20sthO+XT33NfmOdeCGdbtUR26mspnhQj1tksDLkWDxKjzndzyAMLcVrcvNUYgi1KSjGSs1m/oh2OJD69zgcAj1OJKiYE5Z8L4JJ0LWbBRx8j2ILKycioqMz+8XPUK/j2VqwBCLEX2WGHIEtytZj5s2bDbLpFcuq3H5EV0rKIea13oqJyauVMk8bIUjFczAJiEQgmMXMXfA83hUT39z8EZgxzb4palMS15ITNvPhcbHWyad5+B34LkqUV5Lf75Sy4G22YdKro+XffC3PcyWGvRXNkpk2qUsFsvv4avmBD8j3z9We5PKRFL6CDyz0CfGvVljQEfOZu+fjjjzF27Fi8/vrr1coXuqTdi55HEVAEFAFFQBFQBBQBRUARUAQUAUVgyUYguHaAJT+dvfeokvyUO3H79gmVlVRkmhtYjKiURepFkojOQ8NKedo5VxScUTi25IssZVSr5uy+u0qSnz7VjTnyUxaVIErtntEZpEMCzxRUerdeoWuFWjE3IkWJSpkU7ombKFVTLFO/XsFMGvkpju7NA5P+kjs1xVw5Z8utK2cZ/p5GfoqTc/UVoa+odqVPlXAp86UYV6T83bxhKvkpe7iHHMTCWOuE2zGtgxTzqsqCM8+1Kl/JSxsVYapqjc4vvwgoAfovPvvx48fj9NNPt8WMil12xowZaN++PdX9q6FRo0bYZ5990KZNG2zIpNWtW7fGXXcxx0vFXzTF1uuYIqAIKAKKgCKgCCgCioAioAgoAoqAIlBdBMxDo+wS9/JLy1oquUVdFp0RC0aMLL2G1eqtCYnYkUWTWA8jzayir2PnMCxenGZ/nuYajseLCx/QDcGzz6f6m+NOSs5RgWlJ0eRoZe/lpPLUXHxZ5Vxey6MyMmEMTfdYfT3VpjNva9xEVZli3jnnF8x477xbMBYNBHfeEzXDb8mfmmKBhNBHeVXF55tvYcdS/DGuQslbMW/Gv5jmCV/2ZVqFyNzhg6Nm0W+nRg241/TNzQXX35Br5zcMRWP+GWeTlB5t85i6ETGb76h9RSCGQHH9dsxBm38fgd+Za0OITyEwxaSoUb717NkT/fr1Yz5hL3/K9idPngz5M3jwYAwfPpwpOGoX9dNBRUARUAQUAUVAEVAEFAFFQBFQBBSBZR8BK465bzDMw6NhJAcoc3I6derA+d/RcDt3KgDAnz4d5hyGtjNc21YTlzDn5gzbvpAqus9INIr6brttc+vMNBZaGvcijFQGZ3Elu3eH9nBkDc1pu2tYUZw5KL277wPO4j5SPCcyFrtBPxJakh9SjKHYeO99+M23hXNYN0tcGVFlSs5RyfH560KYBx8K83Q2YGi7FOGhAMh7lOTgPQwPF9JQ9peclFs0Aq6kujASCEk4OP/dHbASfLD2mswX+QsL9fih+lCUnpKP88efwnPEPs3AG+HxPjFlWpi/kvknwWtbpSPTAliT8H4WLhaMPIbnY9Zs5v+cH16bpB3q12NOz5eta+Jjw03gbbppqDqVSuorrwSswrNLJXfJlRk3ksPeCsRACvnIPYmyVcLt5R4kd2e+Nd8GXtMmTCmwnc2FaqSIkeQZfZWkrewfN3IMXoPGcHbcAWYtpgmQ58G9zcefME8qCUyP14hMCNBatRHsxmfboCGcwIdhXlWbO1VyfsozERNMeTZz8eXw+GykIBGOOhwun0PAQs/mukEw9/KZVZg76DqbKzXqp327PY5GwEJbuH8oMGky/EOZi/ak45lzdHs4LBgt+W6lmFbQrz/w9gzitSLc4UPgCM7VNPM91ctPPQ0jBL28G7XWhcMcrc6eHeBUpeCt5rXUfclAQHOA/gvP4YADDsAjjzySu1L9+vXx6aef5vp33303jjvuuFx/3XXXxdZbb40GDRrw79X5tlL8dP7PKlJ/NmvWDK+99ppViuYWLSUNzQG6lDwoPaYioAgoAoqAIqAIKAKKgCKgCCyxCPi9rghzcErhoWImarobSDod0x3+OzNg2u9VWEgnf12TLZkr8i0EL4wLi9YIwVTMtmmJzPXXwtl5J3gb1AW+LaF0jNaTMMvMY77GzgeGFd2j8WLf222DzNjH4NcmCVqM/Cuyxp07B8HGJMHS8IivEXJVKtaTdCzLzj0LuC4vrDxtoZCFK/DPL3kEZ5p/N+IxIlTfprnkxkkA2mJQotqsylhoChdTOXrS6VV5WuLcHU3imSH/QdeDwyrvpVZJlfg3XoGzTxeYF8aX8gznWDAp+9pLVftVeFh1Z5ZkcNwcdoQ8FiI5Mhbaygy7H06bHaKRsr6F+Awu7QVzz31J8jdavQZ/dqR6PUldR4hltWUGAf5KQW1xIjBu3Lgc+bkRq8ldc8016Nq1a+6SM2fOxJlnnmn7kqz5oosusn9WzfuNgxCgp512Gl555RW88847uPzyy3Hdddfl9tGGIqAIKAKKgCKgCCgCioAioAgoAorAso+At8MuVh1n71SUeKwW7jC/oij5pJo2RNm3YAGCHicguOseVuueXAmKkGh164SV1Jm/E6LuFJWh2HsfwDvm2LDCojrhVgAAQABJREFUuXCDG7PSepf94GyxuVUkilLOFsCZ+ib8dntQLbpNIfkpeTNFuSiRjZE6U/bmNaS4EaZMlR7VkFRaUlWaI7SE3BITRSh9/HMuSJKfcp9C6lGlh/kkAEWxyhD2yILaZZKfsiBSMUaL5cyyt1RSFyWg7B0nUm8PIzkjdymIlCNP421xkGr38qdce+Txcj3D+81Xd6atlkJPox+tnBWs5ZnGSV/BVJ69vDeDh8EIyRhFpEZz0Q5yn2KyngpY98UJcJ8fi4D8RHAWiVYhy2PPQwpSYWtGvo6fACfK6xnuUPVndI9UKDunngjz+JOseD8zfFeoKna2aQXn4APg9Dgajihmq2Hm3ffg70s+Rp6xKKb32QvOTjsCa60Jw5B98/w44LVJCCR36dPPIvPYKJ6f75zasoGAUVusCOy///72rxkSmuaDDz4ouNZVV11l5/k2mV69ehXMxwd+++03Q2Wo9V9zzTXNwoUL49NLRZtkrz3/wIEDl4rz6iEVAUVAEVAEFAFFQBFQBBQBRUARWFIQYAi2WYSV7B9WKTfMYVlwNP/bb82iVjvk/Ky/u7LxbrylwFcGFl1wSdKX+/t9+5ngjz8K/Fk13Hg9exX4L9pu5wJfu/f2Oxf4epdebmQfseDPP3PXkev5V11T4L/ozHOtb/6Hd9Othb7OymbRuHHWVbDxf/rJthd9/bVZJHMV2OW+Dzk8f1vb9x4YUujLtd6M9+x84HkmqMDe47kXrVSj0H+fLrm9/blzKWz0w71HjS70lXM997ydX7RggVk0a1bYXrTILFptrQJ/7+Kedl4+vDlzjJxBLPjyS+N16lrgH3zzjZ33uZ//ww+2LR/+yFFm0ZrrVfrXrGX8ESMr5+kra8QCvlce8bLYrVLTBFOn5fykIfcn9xlZ8MGHoe8GdQ2jWaPhKr994mCv0Wr7nK+sD/4m/xF89ZVZtHH9cO8d25rg409y+8cbwcSXzaJNGlb6Ffk5iPtre+lBgAS+2uJEoHHjxpbwYw7QopdheLydZ9i7Yf7Poj7xwYkTJ1p/IUxZIT4+tVS0lQBdKh6THlIRUAQUAUVAEVAEFAFFQBFQBJYwBPzHnghJGSHiSCJWZYsyq+b8F736Wkn3Ra1aV/qutUFJX5nMEYhC3G3TpqT/orU3SPiXdJa94/411i3pnk88lnL2Xnk1cQ65h1K2aMvmSf/Duqe6L5r4StKXZGspW9Q6jxiu2yDV3ZK3gnPsT6ozJ3zyCotqxkjN+o1KuRvvhptye/sDBpX0lUnv+JND/yZbGyGCS9mizba0vvLulmveQYfaNd5lvctdUpaft//B4b7t9siR7mkLhTCOSFAh5dWWDQSo9VZbnAh89tlndvsWLVoUvcysWbPseKtWrZhHmBL0Kmy77bbDChK2QIv2rmKJTisCioAioAgoAoqAIqAIKAKKgCKgCCzlCASnnx3eQbOmyFzRq+Td+A+OqAxtp6dzyeUl/bHBhpXzrEPhv/t+ZT+v5fXpmxhxGm6W6Bd0tmWofMw8Fs5JM18K0sxjgaHItmwUtYp/L2Kofcz8ocNivbympAmojv3xe9K78RbJfqzntN421iuj+dVXSafd2yf7sV52gw1ivTKajzD0/SeG8kcmBYpKmLl/SG42GDw0105ruDcPsoWiJGWCGVVZ66SYv3t2mIM0OPNcmHnzirkkxgJWjTcPc08WunJPPC4x93c6Zsa7MI88Zot2uQ8+UGVuT2f99eHeH6Y+CK65DqbMXLR/54y6dvEjoAToYsZYChmJzZhRPIH0Ntsk/2dQ1XG+Z8LeRRW5NdZbb72q3HVeEVAEFAFFQBFQBBQBRUARUAQUAUVgKUcgqKhCLrfh3nlLlXdjruwX+lQUcTFSwTvFGJ8MTJ6SmDXHnpDoJzpXXp3oGuZMLGlSdT5u16bXsjDdjox7Ap/Ogj1fcrSyF88zylFzWgVJXOmRa5mOXXLtqOE993zUTHz7kgP009mJMVx7fbIf65lDj4r12GSuTO+ue5NjFT1P/j0vuVfjNpyEdYr5w4YXzDANQcFYNBAcnSQOnSjvauQQ+zZSnPmtt4E1azIP5lo2l6dhnZJSJtXY3TNPtS5VEaDOcf8Lc8XOmg2/Q0eYL75I3TogcRuw6ruYe0UvOBvGSPnUVeVNBKPHWEen+5FwyiSU3ba7hmf/6ecwN2h5l1KvJRgBZidWW5wICMH59ttvY8yYMRgwYEDBpXbZZRfceeedePNNJpJmAuKqVKBPPskEwDSHSYhbtmxZsJ8OKAKKgCKgCCgCioAioAgoAoqAIqAILGMIvPhSeENCPrXezrYNVX5GFHPjJwBfUlHIoi6oWxfOnh2Az1nkRUyUiS+/ysI8f4B5N+HmFdu1Pl9+CUhBpA2pNJQCQ6J2e2MKvI3rh0WBooJAUuG85posdhNTXa6xhi0oE7z6OgvevAhR2uH7H4B11obTdCtAlI2zGRXJ4jX4ZaG9nBTe8eptDudoklG7tQX/EQwzYSKsEnHm/4U+UnSHKjx8840lzsD5RFEl8Wqxdegb/2QRI89ZOT5Sur1nJ8TuprhvVOiIJHS19j7+ZHgnkigU3MD74X/4k+RnVGgofrXffoe3ErGUYkUsSmSLDUmEqBSUkoJG+UaC27vqGu5JTZsUJoqKFBHbnK22KrDwV5g3pyMYNx7mqaeB/yPhyTFssD6rp28PrLiSdXfa7sJru1YlafiuSTEgS2x//Y1VTWLT+iwYtDefVzte0oXTYXe7zgh5WsKELM08+jD8tnwneQ5/i2ZwTjgWLotryZ7gfZu3psPcNxjmmefsTg6Vn+7ZZ5TY9S9M8Rpizu67VWux+JvJU4HpvM9O+1RrrToveQgoAbqYn4mErN9zzz2QUPf+/fvjvPPOS1yxffv2qFmzJubOnYt+/frhkksuSczHO5/zf2IsHmSHRFkq69QUAUVAEVAEFAFFQBFQBBQBRUARUASWbQRMReo0kNRiNj6YG25GIKHoQljmmYlXLW+wKSDkpCglP/4krMyd52/JTxkTAlRIKfEX++rr8Dv6FOJOyLPIhPwk0QkSg8FO7aLR3LcZRdXd5VeE/XXWCck6Vqe39tnnML372j+5BfGGELdCnIqNn2C/Cj4qSC07fsxRAEm0sqxeXe79eVmu4uRcdD6MkI3lmKgWSdpaUlL8BXeSfKl2DZ/hBRUcQEQ0R85CZsbHjjwcGDIsmuXeJD4RIzwrZ2zLnfQygqatgPfeQ7B7x7xZHnEYVadCmovVqmUJUGkGJ5N8LELSmkE3A02bICMh8FLhXYw8RlUmSs7MG6wWTzLYPDQKZuBN8PmnwGquAbdvb7innFQw9XcHjBD8NIfEb3VM/AXlaH111qrvkoeAEqCL+Zl0794dd999N6ZMmYLzzz/f5u3s3bs31pH/AdA2oPxaVJ177LEHZHyVVVbBGWecUaAEfeONN9C5c2d8++23dt0FF1xgv/VDEVAEFAFFQBFQBBQBRUARUAQUAUVg2UbAqV3bEjFCpgUHH8bciyQXxXbdGe6BXQHJbynKwXdJdkn+z7dnhPOiHI3CxOttEo7lf661Zjgiys14aLaoCvnv05y6UPYRdagoDsUkLD8iNEnMOiTonHa7kkxb1ypKrZJw6IOh8vPzL0I1Y7jSKvHMlGkkVKkKlf1ErSiKTjn3jyR136GSNF/5KCrSdfnv6Hmcl2vHrVzyU9ZUg/wU97LJT3H+Oo80lrFSdmmv5CzVlSG+xETUndGzE684+ZlcVbQX7E2VpZgodvnsnSMPg7NNqxBrIaBFPfzYE9bFPDw6vJ70SH46nfeFI4pHeWdERTrtTZghfJYz3oPffi84fS6367A2CfAyzFlzTWRGDIVhPlJ5P42okoVgX2VlOPWpLt17z/B8FTxJGVtWy8XhO05EmYd0PvhWl2/0txb9jJS/Uj2XQAQcqeW0BJ5rmTrSF8xzIeHqkr9TTJSbnTp1wm677Yatt97akqGs6I4jjjiCf9d4aNSoEXbccUfU518EH374IWTuU8nNUWFCqt5///1Rd6n6PuusszBo0CCrZGVF+KXq7HpYRUARUAQUAUVAEVAEFAFFQBFQBP4LBFiVGsGG9SovTULGHXIfXIYl55v8E9+vtTHww7zKKYZSZxf9UtmPtQxzUvrrULm4IDbPnJDZ+aH4JuZqmwVh4CswLP+LmXAlZD3PzHffwa/TIKlkZGh31iPxWcS8HdsC+TlFzzwF2YEDCry92+4ETj69YBwL5yFbJNTfc0nm5tMf27REdsprBXt4t93BvamEzLf53yBLMi/fvJVr2jQDifH+VyN77lmJIekExCRYnyrUuJFsdn6ei8zqq8dHbds7vDvw4EPJ8S6dkB3zcHKMPZ/vidlk8yTeLCSVmT6laOGf4KGHEXQ7MrGPFAlyDz0kMSYdQzWqqI5N32tIlpJGJJbO3nsgM/bxAt8lbcA/70KY6wbBueAcZPr1Lft4/u57w4x7Ee6IIXAPOajsdeq4ZCLAXymoLW4E6tSpY1WeDRvyt3K0n5irZejQoejRo4clRjfZZBN069bNkp8y/9FHH+Hee+9Fz549MWzYsAT5KTlDb731VnFTUwQUAUVAEVAEFAFFQBFQBBQBRUARWA4QcKVwixSqEaNKMPPcU0XJT5mWehEStp2wLUiKpZjkabSh77F5Z/TwWC+vefihyYHttilKfoqTw8K9ogxN2EEHJLrxjtO3d7wL7NmhKPkpTtmTjk/6suf0u7Io+SmOzntvFfgXIz/FKXvSCYCE+MfMOePUouSnuDgkgPOtGPkpPq4UMxYla9wOPrAo+SkuGRLd8szjVoz8tL58T5xB/eOuwHnnFCU/xcmSemuvVekvxHoR8lMcHBbUylzZG/YZRURyEQK+crMlp2VzjvI4NtfoggVlHcxQTW1enGBzuDp77VHWGnVashFI/hQt2Wddqk/XunVrWwleCiE1b9682vey5ZZb4rbbbsOECROwapHfZlV7Q12gCCgCioAioAgoAoqAIqAIKAKKgCKw9CCwMVWdYgyLNvmV1cOZ3GfmHEbbxUmzxlvm5oo23v+wcni11ZBhtGKaZa64LDnF4jalzPQ4KjGdufLyRD/eKagof+G58ekq2xLWnWbmtdfTpoqP54XZm6HppLAjqQKqY3nkKhqFYqliWwSSjzMeCl/MKT4m6sy4jRgZ7xVp5/kX8UgMJfav5trERv9ex9mxDbDzjsB3cxGcckaVFza//Qb/6GOJO1WuJ58AR+uvVInZ0uCgBOi/+JRWWmklnH322Zg+fbpVeT744IO4+OKLrfqzY8eOEHXn9ttvDymM1KVLF5x66qm44447rP/777+PE0880f427188sl5KEVAEFAFFQBFQBBQBRUARUAQUAUXgP0bASK7MmZUqw+DIHvD7XFX0VAHJMr/7/5Kk2cjR8Pnv0GLmjXmMeSIXVU5xfTB6TGU/rxX07JMcIVnk9ctTHVZ4eNcydH3Hdgn/4LK89fHZqGhSNNahI/xnw+rg0VD0XfR+WMDJixcOipzl+9iT4j3b9k44pWBMBnwpGJVvP/wAP2XvoP3e+d7wOhdXuvqSD/XTWUl/qeouuU+LmGmxXcGod931BWMyYP74A6Z/3tz4CTZ8vdiCYNQjzKk6r3KKRbUkLL6Y2VQJl/WGuYQEeMR7jn26mOsSOZa57Sagxuo2l6l/5DEweQR3dGjD4tOS5xTTqBhuuBncyy6JpvR7KUdAc4Au5Q9waTu+5gBd2p6YnlcRUAQUAUVAEVAEFAFFQBFQBP5rBMw7M+A33xZoQiXnjz8BX34VHon5KJ09dwdabh0Wu3n9DRgSXiApaW29Wlb1FnbK+GzVIiR+xLVObYBhz7byuxBeEolIgg1zvgw3olLUFjEKe1V/xv1FAbnoT+aqFOLVACvwOivxz095xY2q3nXJ9hC1pPyRkPFIORlXc4pKN96vzt3IsxHiOgpHZ25VVlOuzP/ZdCsWLWIxKbH69eAcxSJV+UWQxjB/p6yP8poKCctzOswxmiuC9CvfpanTEAweFhK3GRdO754wl/aGFEnKzvrIXmJp+Aieex7BgUzhIPlumYbAYrLzTrBFkr5m/tTnXoAZ/hCLffE9J2aZZ56AIwXG1JYJBJQAXSYe49JzE0qALj3PSk+qCCgCioAioAgoAoqAIqAIKAJLBgLBhJcQtGOl7La7wBn3DEyXg2CeZLg3uauiJiQVc2RmbhoEb6UaFURjUc/EYObPBfD37gSMm5AYL9ZxpKp3tyOKTRWOtWAauAvOA8r1X3lloEd34NY7CvcqNrLRhmFV8WJzf3dsY+795dfl7dKkMfDe++X5itdIkooHH16ev1QiFzJSSOhy7MHBwGHJ9AMFy1gcS6q+49hjQvL0jruZAJUkqucXuNqBrRrDvWkgnJYt4NdkLtM1aiD709zivkvoqHn/AxsGbyZMLH5Chxww89y6A/vDWXfd4j46ulQiwLddTRFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBJZUBJxaVHLSDKt8Z0Q1+PhoBJ99ZityW8Xn/PlkbThea10q9zrCufgCuFT1eXvvlyQ/hfASE9VhlCNUCLAK8+s2AL75lnNkgdrsAEhuy19/tapA25ZQ/Ioq7eb4k6JlVX+/9TYg1cwjkyJAtTcm6cbz8FLwSbiJqpU5Gq39/nuS/FyBfqJUZE7GYufGV2USlNH1q/NdLvkpe+aTn4Kf4Cv3J3hHhGOE+SFHJE8SqUVlVNqCjdyzGMPTE1aTKtoNNwj3lIl5nP+aOESK0BNPCd25j/vsEzBPMVz9/z4NFb0brA+nzfYwTNNnjj8Fzvff2/PJlZxbboBDZanNx0pVJFblPWy2KRwWPHJ2b8/XzIX5+JNw74r3MuwsHZ8O8+FmXnwO5o3JCB57AvjgQxhi61AtLcSus38XVX0uHY+y2qes+Nuv2ut0gSKgCCgCioAioAgoAoqAIqAIKAKKgCKgCPwbCDTYDFidIecsfmTmzIFTuzbcTTYB7rw19ep+7yuBZyryZ1LV58z/tmilcY85D7HJ5uE+Qn5SaZh5fiycVi2L7u1fcDHMtdcDPy/IzTs/fIXM2mvn+lHDZ35J07AJyTkStEICih16MLKiTixi3so1kwrH1VeH8+HbyEQFoGJr/F9+galRqNDLGpKnRczLMIQ/P9ycRZayVxOnPLP5ONciuZhnqXu3aA1MJ8kbt6v6IHvR+fER25YcrUGWz1KIyoisrEEl5c9zC3xlwGO+Vwx9MDnHSu/ZM05LjlX0vLV5biFLo+dDDN0OuwPyJ8/Mp59CnoqZ8HJIerPttmsLp2ED4JR0gtu8MM7u5Iiydyk1p/V2yPCP2vKDAH8FoaYIKAKKgCKgCCgCioAioAgoAoqAIqAIKAJLKgIOlXrOvh3JVJHDyy9yk3JoEyuS5FxyYVHyU5Zm69YN83tG+yz8NZX8FBf3rNMjz/BbCNMi5KdMyrh7/bUJ/8yAaxL9ROeeOxJdXHJBUfJTnDIk9grs4AMKhnIDzz2Za0aNYuSnzGWjnJiRo3zXI04p5r5BAjHPipGf4uKKEnTLRknvyy5O9mO97JB7Y72wmUZ+yqx7+81J/3a7JvuxnrPppsDWzZhXloSpkNTNtgrJz5hPflOKIQUDb7LDzgFd86e1rwgssQgoAbrEPho9mCKgCCgCioAioAgoAoqAIqAIKAKKgCIQIuD2JEnG3J7m5tsQPDm2JCz+uRdWqh1Z+do949SS/mhUoQAVL1Y69x8ckeofPDgyOceQ4lIW9OydmA6G5KkZY7POitlYj80rr072q+g5RZSiuSUvvZJr/qXG/J/Sl8Urqad7Vc5E6syKEeeLOZVzeS1R0VbHgq6dGccusfMVJmHsJcw9pntu1ul+ZK6d1ghOPxuY+X9A4y3gHFSCcE7bQMcVgf8IASVA/yPg9bKKgCKgCCgCioAioAgoAoqAIqAIKAJLFgKGBWZMfpj0EnJEyV3oXtHL5oQM9j8YwQ03w1TkkgyYpzMgcSlm2Da332Xb8uHeeyerXK+V6+c3jOTb/OCDxLA55nhExJv/00+QcHMxnzlIzfkXJXzByvPBW9NzY/6XFVXixf/Nt4CI3Ksg5cyd9+R8JdTck/yTFWauSqpFpQK916xVNA1v3PjK9r6F6kMz8EZ4DwyxPh6JQxvez579vuKq3Nqo4a0R5laVvsWhYsJjwakCIw7e/YPtsEfMPGIhJvcQbFTftuMfXptdcl1v9uxcO3h9EjCnEiOZMDfdCn/aNOvjCwE9NwyH95k2wDQtTEXgtWR+1goL5PoV76z/PEPTJS1AFFovPtPehPnuu8g98R2MHoPg8j65MUOMgodHV/bl56FiL8Mz+YceGb5bq6yMzAP3wJGq82qKwFKCgFaBX0oe1LJyTK0Cv6w8Sb0PRUARUAQUAUVAEVAEFAFFYOlHQMgdQxLIDBsO8/KrwA9U20kVbObXdPbeE+7JJ8DZcosl6kZzOTjlVEJARbk1pS8koxQw8gPpAVs1QXZGSKyFA4WfZuZM+A23AiTPqOQD/XNRoVOxkZVXCgv8pFUMz1+zLYnMKTyL4CvVfaJCQJGf3IuEhzPEGlIk6fsfopll93v99YBvi5OTf/um+ezx7nvhNswf6+y3L5xt+AxWWxXms89hnngKmFEx33W/sLjR6EdDf6qG7XP4/Y/wmXANpC3PhpXf3YeGwt2rCEn8tw+tGygCiw8B/u2ipggoAoqAIqAIKAKKgCKgCCgCioAioAgsXwiYWbPgt94JwUGHwTz6REh+rrRiGDrOatkSau5TfeeffV5OabkkIOQcxcrhUf7LOPkphxO1XkR+Sr9JY/ksbQtCdSeE9Gpchn+0mxSQWZEkaDkmxOzKK4eeQpjmk58yI/ciBJuYEIPVMcmPurisTu3yd96iUfm+4nll7/L911iDePP9LMeE/Lzi8krPXxbCPPgQgrPPR3DCqbBKWyE/WQ3evf0mZEY/BOeKXsDGG4Vr5J0QwlNM1KXSl2cjz/GwQ+Ds0SGc009FYClCQBWgS9HDWhaOqgrQZeEp6j0oAoqAIqAIKAKKgCKgCCgCSzcC5v0P4O/SPiQ9SXC5rAbudO4EySFpw8rffgfBfYNh7rybxI8HpyPVoI8/8p+H/AZTpyHYnqHVQhYKGbU9q483YQ5OUU9mmT+TRBfeYtj5O++GD4g+7ndfwF133dQHZr7+Gr6EcAvB9vPPlX5SCEgIt0USWs9rrbhCSIoxFLzARLm53TYsotMMTsCwbTmLVEWfPLUyF2l8kVzr7NOBC84Nzz3wBqDfdWEhnrjf4m6z+jqYMqBARVu/HvDprMKrEwYpRJUweQ4VYeKJcemIknX99bm/F5LFUmhIUgQU8xeVpRDbQhDL8xW8hXxMU8JKASM5+++/EUP6rroK8Aef1bffhsdYsyaLG/FZ8dlk/u99mKeehiGxL2kFsOEGcNpsD6ftrnD4jA3D5P32ewE/8fnXr2fVolapK8SnENc8i/mC5x41mmcLmPtzf6pAh/EVFEDUFIGlAwElQJeO57TMnFIJ0GXmUeqNKAKKgCKgCCgCioAioAgoAkslAmbhQvjNtwVIBjn77AV3xFA4kaIy747MlKnwO+1vw5Sdc89Epn+/PI9/rys5PoO1NwgJLJ7XnfwK3JTwfG/zrYBPZoaH26QusrM/LnlQb5OGDH//otKnzQ7IvvpiZT/W8iQn5bvvV45QNZv9PUacVs7YlueSmIsTfg0aIPvJu3leYdfbfmfgjSmJuaxhjtIU85wKVWk0z4r02XnfRL3Et7c6yUg++4TN+xrZlPyoBXtzYdpZvMv6UHGZl2P02SeQTVFKFmBC5WV2DsnJIub1HwCcf0ly5rWXkN2B5HcR86++BubimPpznXWQ/Z7kZYqZ+fPhNyN5zbykltiU3J6r8JkVMfPyK/C7HGSJard3T7iX5Z2ryBodUgSWFAQ0BH5JeRJ6DkVAEVAEFAFFQBFQBBQBRUARUAQUgcWOgBkwyJKfaLk13FEjUslPOYiz7TbIPDaKzFcGZtBNMB9/stjPl3YBc/zJIfnJs7jvTEklP+25b7+xchvmewxuvb2yX6yVV8zGGTm0mFc4NuTe5Nzmmyf7+b01qfaM24gH4r1E2xk9ItEHi+2UtHwF4oBr0t1nvlcwl0Z+WkdRtcatRM7LbJ/L4p62nUZ+2slmTZP+p56U7Md62fPOifXCZhr5KbOZiy4Ajj6yck2n0ukBgquImRRl2qkN3GEPpJKfsqGz8078mRluBcHB1ddSFRojzSuvqC1FYIlEIO8neok8ox5KEVAEFAFFQBFQBBQBRUARUAQUAUVAEfjbCEjRo+C2O+0+mRsGwInyUpbY2WGuS6fH0TY0ObjrnhKei3fKjGT4Mc05+US49euXvFhmt90SeTSDXn1L+mP2Z5Xz69VChqkA0swZ/nByiukEStr8ZMh8wfrYYrNPl1iPzd/S1Z/WMa4slYFTz7LDxT4yVNBWyyoqq+fWHHJArpnf8J55Nn+odF/C4ON2Z/p7FRSp4C6V4tPMl72HkqSsMIcKzzQzzOtp7goJ7cxNA+GssEKaa27cbdcWTreDbToEwzQRaorA0oKAEqBLy5PScyoCioAioAgoAoqAIqAIKAKKgCKgCPw9BCQv5TffApvWh7PTjmXv5XY/wvqap6tJdJV9hUpHyUEqyjrzzgyYb75h9DhJ2/cYcv4bcz3SnL69K51LtJxpr9PZCT3mzoV32FEwzC1p5swJ92buT4/X8nZqlwxR/24u/P+dAEPizXtzOrzeV8JjeLf3zjswX31li0MlLss9vX06w/vxR3jHnwKv1fbht/TzCU0uFCVt8OZb8EaNgtd+T3htO8B7cASCDz4E3p6R2Fo6EhbvMXTdO/2ccG+ezSMu3uFHF/hKTkyv79XwXnwJ3oHd4HXkuYaNgPfUU/DrNSrwlzB3uxcLXdlzH/0/eLNnw1u/ToEvjjke3iGHw2MeVu/4k7j/ofAefgTeSCqE9+5c4O813wbB77/bZxewunrw6msIeD5LwOfn9Zw1G/7NLLrF+/KHDoN/063wp02Dz/GgIdMZ5JmpvSn8efPyRpme8/I+MHUaEDTmHK0w88prUbPw+/VJYd7P5k3hbN28cD5lxKn4eQj+jZ8HIWk/+wxmxrswUX7TlHPpsCJQCgHNAVoKHZ37xxHQHKD/OKS6oSKgCCgCioAioAgoAoqAIqAIlIlAMOYxBPuzinXX/ZB5ZGSZq0jaSd5QySHJQjXZXwqJp7I3KuFopPDSNdfBjH0mJKUi3402BCTX5zjm5FxtNV7/h2imym+fpJvZiWrQcu36a4ELLwVKKAxzW5FELlooKOeQ19hgA5LP3+QN/gddKexUzv1FRxOVMInMxWKbbAKQ3CvLRJ0pldjjJrk616gRFj+S4lRxRezppwI33gxwXfbPBfFVuXYweCiC7sfCOepwZJj7s1wTYt7fsJ6tIp/9uszzl7t5hZ/k3w36Xw/7Swcp7hVZ3TphrtLzz4Gz3nrRqH4rAlUioArQKiFSB0VAEVAEFAFFQBFQBBQBRUARUAQUgWUCgYj4EhKsOhb5R+urs7YKX8NQa/+8C+G32A5mOElZqcTNyvRoRuVfrXWBr74OyU/ZR6qDV8NcFjOqlnkk2Mq9x5RCOanXy8+pmer4FyYy1aA26pF0rI4de0z53tU5h+y6DQtKlWtSrGnVVZPeogr+9jtWe/+xkvysvTGcj2bAPXj/0Dc/lD++Q/Sso/c7PleqHflH60v5VnNOFND+KWfA324nGCpsbeEqkp5o2gRYZ21brMsMuAH+ZlsieCgvHUM1r6XuyxcC2eXrdvVuFQFFQBFQBBQBRUARUAQUAUVAEVAEllcEHFFT0gwrwFfLIv+NNqrWsnKcg6N6wDBMGyuuAOf0U+CyII4jysAKMwzb988+H2BYtygRA4aQu2eeFk2X/PaP7FE5f+JxIUnGUGJI+PS6JFdbkYD7+GPg6edCv3i1cSGcTjoBrpBcDKUPhMBkmDY+or+YhOXHjdXmQfINP1ChysrjtrAOCzDljOHzORM1Y+dOcIScCxiWv+JKwONPpistWSVd0haAofxYmySY5MWcGXuGPveJbAXSHOvWCokz2b8mFZKSh5Th59Y+/iTyDL9X5rVlvYSNyz3yOSRyj8o9RybE30r0/53EY2DYZl8KSC34JfSIn0MKOB1PdaVc90veOyvUG1EsvjEZeG1S6D96TLQz0GhzYLNNwxQNQmzKuyopDF4YH/rEcoE6UjRp21YwD7BYlRDkNVYHGMLu9LsSGcGH5j9BPMVWpUo0zaL3OXq/0/zyxyP/ip+n/Om/2pdfBgRMXWAe49n5XJxzzoR70vFwYjlpDVMQBH37wTz6BIJuRxL7BXCP7fFXL6nrliMElABdjh623qoioAgoAoqAIqAIKAKKgCKgCCgCyzUC27QKCaFpb8J8/jmcunXLgsMwdF7M2XXnsvzLdQpuuiUkPxnGnBn7GJwd2xQsldyM2fHPwnNJZEk+UJKhDpWDVeUwDe4fDAwbHu63+urI3nZTwd7RgOTCzJmQbjOmIdukcW5IGqQGWWSIuS/b7QFMmCi9nGVNeoh4Ym9ZwXyT2elTcmvjDW81kncRUVkxUa29efbsnxVkZHxjtiUXKI44Ojn6x8/IRmrG5AwKzt1pX2QfH5XnFXY9Knhx3aDk3FezkV1zzeRYRc9bpSZJ1D8q5+T5fPhOZT/W8oQw3X6X2Aib552NjKgijzoiOR7rmftJjorlPcdwMPx02mwPZDMwr7wKQ3LZqUXiuAyTVBJizq555ypjbSkX069/SH5S6Zl59kk4QtDnmcOf4cyYhxHceAuCM85BcPLp1s9psXWep3YVgSQC9u+w5JD2FAFFQBFQBBQBRUARUAQUAUVAEVAEFIFlDwGHYdvOAV1JJFLAd/FlZd2g+f57BANCcss58rCy1pTjZJizMeh1pXV1B99blPxM7NN627BLEtQ/+4LEVH7HUEEYSC7PCnMOOTBqFv8WFWNkm9RBpvGWUa/wu2+v5FgKgZh0ivWu7x/r5DXfSBKrebNVd3tQEZhimcMOKZhJIz+to+T+jNsOreO9RDvbv1+iL5008tM6Nk6Syzj1hIL10UC29XZWPRr15duSn/GBvHYw/kXg3ffsqMtcmWnmkKB19u3I3KIegsv6pLklxg2rzJsKVax7xKGJub/TkaJbwdXX2i3ch4YWJT/j+7tUSztnnBKe/byL4lPaVgSKIqAEaFFYdFARUAQUAUVAEVAEFAFFQBFQBBQBRWBZRMDt3RNgeLKEnUuRlVJmfv4ZfteDGdY9D06H9nB3b1/KvVpzVlU6bz6ctrvAZTh4VeY++EAYEi2OLBDjSyh7ivkSWi25IcUYsu3cemPYTvuM5xad/Tn8FyekeQKdDkjOVTcP5AGFRGRuw/2qIGpzjimNIVR5ppjfpm3KTMpwfuGjSy9PcSSZLirNPPNK4fLhh0nvydOS/fxeHhnrS8GjFAveYTGtjp3DWYbUu10r2in+bl8Sn0wbYG6/C8Htd6Z4hcOG6Q38znxGDPl39u8MZ/t0UrjkRkUmzUMPAyx25HTqCLf9bkU8Cofc3vwlBtXThgXCpFK8miJQCgENgS+Fjs4pAoqAIqAIKAKKgCKgCCgCioAioAgsUwg49evDvfdOBIceheD8i2EYDu/27Q1ns81y9ym5CKUae3DO+cyROdMWJXKH3Jubr07DLGJhIebxNJIHkmpJh0V4HCoshbQRc7odZL+jD6uw+/AjFrb5iVWuGZLcdCuIUs/luc11zH14zgXW1eywC/x77oDbYDOYOV9Scphl7tC6CF4YD5zLkOwKcx8bBbdCpemPHw8z6lHmpPwyDP+notX8yfPl2+4d4Ume0RfGAbNJLElYvOTg3Ikh+iRt881jQRr07QW88hrwxZywiJOE88dUqLk1vC+P2EsYv5lC4o/kq9Ny6zAv66zZObeoYUPR99kbeHM6C0T9yHyXNZi7lKkMxj4duVR+k3T0GrdgPs2G9H8rzOu5RSOLDSa9UelX0fJaksA79WRWS7+VuTeZS3PNtYAD94dbe8MCX/Cd8Dp0BN7/gOTyt2EOUMm9ecD+wH0PFPqvsia8K3sBz/N5fP5ZmLt0dxJ7o1jY59ffkv7jJ8A75HCg58Vw5xC/336H5Kv1Jc/qaWcXpAUwxNu77y44L78CIzlNJecqw9EdErFCZMpZhfh2X+G1qzB5F10S5MFxJyM46XSYN6bA7XVpMg8tn5F59HEE8l7J+8B3zr379ip2Lj5t/vgj/HmQ3KX8RYT8PDqNNo/9PPAXDmWaU7MmnL32gBk52q53ehxd5kp1Wx4RcAxtebxxvef/BoGzzjoLgwYNwsCBA3HmmWf+N4fQqyoCioAioAgoAoqAIqAIKAKKwHKPQDByFIJjjqsko7ZqTDKmns3NaN5+h4V25oYYMWdl5vHRZecLDRcxyp4kY9DnKlhlm1R2j5vkb5Sw81mzkZnwnM2lGFC1GTAHIqa+GfckeZeBQ+WpJaUYDu3t2wV46pmkT1rv4vORpcLPO+wo4CHmrxRiLN+E3IxogT2ocH2OpGc5JgWDiu2XtjZ+nTSfJXFcKq/n5SVNPaaQsyzKU7bttw+LPz1VnjuJPjz3QnmYr7EG3MmvwCWxWK5JztjgxFOBP/4k4c1VQryTrMfCXyGFuEQFbY2pGDJjRsLZsAhJHHoU/RSFpqR8MEIAU+mZMCHX5br8mclMe52EOEnsMs3vdQVM777258O9vDLtQ5nL1W05QkAVoMvRw9ZbVQQUAUVAEVAEFAFFQBFQBBQBRUARCBFwDz4QDvM6BiRPzMOjmTPxfRj+yVlDqtxOOxnOiaxCLVXLq2HBY6xQfcTRlUSPkKtUzYHqNzPjPSoCv8jtFojCdNBNtqq1HaSq0Nl2m7Bq+Nff2HB388xz8PnHueg8oDrEExV2BYWFSKgiy/sRZaqEvkfkp1x8UmEod+6g+Y3qkJ+y1iWr5i8m/ZXsLVXZF4eVS37KtatDfor/6jXls2rj++dSrRs8+3zVvuLxywJI7lpbWb68FXCPpip3l51CklKKHL3zLgz/5GzLRnCpCnb+dwyceM7YnEN6Ixj+EIL/Mc8pla2WXOUvFRwhPdm3v2z4dFZusfzioDoEaO79FYJdTREogYAqQEuAo1P/PAKqAP3nMdUdFQFFQBFQBBQBRUARUAQUAUXg7yFgGDqNd2bACOG4MnNmkpyJh8RXZ/fg8ScRdD3IEnKSJ9Ht1xdOwwaJLaRITXBANxvmjkhJufZa9L0STvcj4VSErMsi8+OPCK6/AVIhW4rV5KzxFnCGPQDno49hPvjQhnlLxXhnzZrwZe9IwRotENLpmSeQ2WCDaAQ+w6eNVHWXcOR8a9oEztHdbYoAIS+d7baFueFm4P8+zfcEJH3AdVfDYXV488UXcOrUgdmtLXDmuVblWrDggnNIiJKIlbB0IS5bUfHHcGZcznyUxexQhkWPm8AQ+J+A1VYD2u0KCElXjIRt3hzYgsrHqVNDvIgThMB+YmyxnYH770aGZLchXs5aa8IX7PfYlyTiL4X+so+oDCdODMm8rZoAJKZFyVvUrr0akFytM0lyS4X13doC11wHyHsWNxKKLlXGAXNsghjKtW2aBOaGdViAywwdHvfmO7oyMPx+uD/8GKYOWGctmJYt+QyuZ2qAZ0NfEoIOK8tnNm+YXFtGz4apRz8Pq7JwGHOJOvVJWP4FCx5krt3Dj7YrHRaicq/sldhLgpKF4A8OPiz8hQGfQea9t8pWmPpcZx5+hGktmA7imO5/4YS6ZHlBQAnQ5eVJLyH3qQToEvIg9BiKgCKgCCgCioAioAgoAoqAIvCPI2C+/hp+o6a2SIyErJcKyfXvvheGeRetrUiF3/tvwyXRlGb+Y4/DdKnMj+i+y9ylTRoXdfclJ6LklIzspoHInnpS1Cv4tnk246PPPI7sniRGi5i3NgnU+czFGbOs+T3WSzYL9ibRm/2hCOHKZV4D3k8ewVqtvUleZ38jSVrEvJ13C3OUxuaqtTcJxWyQl7uzYq9gyhQE2+0c25nNP35GWpV5b9U1QwI1WtFpH2RJgKaZtwdD5Z+vTE3g/PgtMkIYFzF/IvOCtu0QKntXXw3ZBSRV/yMzs2bBb7y1TSvhDrgG7tlnpJ7EF6K3Inets1tbZMY9k+obTcgvB/y6/OXCgl+Q+ezjaqepiPbR7+UDAXf5uE29S0VAEVAEFAFFQBFQBBQBRUARUAQUAUVg8SIQXEHFH8kYp0unkuSnnMLZNUaYSSGit6aXPtzrsSI+K7GYEovXpJmpVzcx5a62aqJfZYe5RlONRZX+lj36cPryJ5kf8u/YMcx1mmZlEGppS+14F6pCU8zddtuCmTTy0zoeQoVw3KTYVSl7cULlLJ99GvkpThmGsTtD7wv9mWvTv/7GyrX/ciu4rI8lPx2G75ciP+VY7lEk7CveUzN+AsP9qaytwoKevcOftw7tlfysAiud5jumICgCioAioAgoAoqAIqAIKAKKgCKgCCgCisDfQ8B4HsyIkXYT96orqt5MCtrELBh0U6xX2DR33FM5KAVjJk+p7Oe3WN0+bsENt8S7Vbd7HJ/uc+nl6XPlzJycrgLEtjuVs0O6z+13p89tV7i3J6kPyrXHnkr19Lr3KJjzppcgtCXva9wGD4v3Em1PQv09v3LMUNxZRV7SzGHdwsrwXGUkLP4/MPPbbzCPPEqmn8RTXxKVVZjDFAHupRflvIJBTLdQwmxaiJtvA0Q9fe1VJTx1ShEIEdAiSPomKAKKgCKgCCgCioAioAgoAoqAIqAIKAJ/F4F33wtDwxs1hLMl805WYYahyta2bcVCR9OAV1+Hf/Z5cBmq7my6aW61mfYmfCEdGe5rbWcSeS8zzFnWs5q8eellYM6XNgeo9CV8GG+9Hfqus3ZYvZtV7b3jGAI/g2f85JMw/FpyaTahivSbb0Pf+OeYx+FtVC/0i8g28SfZhG+/i3vatg1zt+o9sl0s9ISVVuI4mTpWEC8w4uS5q4Qh2vFJKWITL8hUMWf3lurqsufChQALO9m8oREe8T243lthdaBO7fDsUuRpDfa/Ys5NEnIFttIa8JgzFeuuGz47uQchl78tggnzjXor8hzMiYnfeY+Sf1TOxGOL6rfAWu4A74RjQwUkUyOAuS3N2nweU6aGzzu+YNEieA2bAKLcnft9uL+oQgWT6D0R//XWY25X4s89zPrrw0gOVXkeDHVHwwZUFe8Cx2JP3wO7AnfcXfR5xS+92NpT+U7/Ssy3aWlzwpZzHeeCcwGqP224P/OC+hcxjcRJLEJWN1Q0S75QvDEZQd9+ME8+HZKrt98MyX2rpghUhYDmAK0KIZ3/RxHQHKD/KJy6mSKgCCgCioAioAgoAoqAIqAILCEIBGOfQbBPFzgd90TmKar2qrAoJ6X70vMIdtsrrMgerdlwA0uYWXJy3vxoFGBBGyGEgrPOAzYhKfT555ZnrHRgS6q8S2EhIeiOOBQYPSYkAxNOKR3Z8zPuWY6xsj3efb8cz9Bn442AL78q3786nnLPcZVkddZW5Vt745BgrspP5lNI3NSlnfZmcSYSeeXYqqvC6bwvzPCRlvjG518UriIR6pxyItyLzkfwwjiYAw+zPqVynRZu8s+MBA89jKAbC3odciAyI4aWvWlAojpYda2kv7w7NdcIi3X9+FM4t0YNuPew8NGB+yd9tacIpCCQTRnXYUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAESgXAeZmtCYKwjJMlHqkKUPFobQo9nMOOxRmLAkxqRIeVQoXNeNObVj1m8QXSSAz8eVwdyEqpUL37rtZYhQMwRdC0rw0keRnRcj0a5PKJz9l13LJT/GtDvkp/ouL/JS9Fxf5KXuLurZcK6JgLbl0aokw+fyFElL+xpRwVMjPWuvCYe5Lq3Zlrk9DZSSmvglzzQD4o0h6dz8yf4d/tx8pUUURXA1zhLgXY6V7yaVr+IsF++5E70/9enAO2h/uuWdBwubVFIFyEVACtFyk1E8RUAQUAUVAEVAEFAFFQBFQBBQBRUARSEHAqV/PzpgZ78KQxHFc1/ZTP+ptEvoz1Bc+SR+qLzMsXiNr8Q0JUFG6CclFkscw7NkXAlT2lnB5sXa7IvPIQ3DWXDPsV3yaOXPgSzV1IWI/nVU516I5MlNfD8O7pYr7uutw/1rw16fqc+7cSr+odeetAAvqQHJQZngvXbsAj7J93sWRR+W3EFGfM7T+st5hpXUhbPtcDmy2Zajaq/QMW0cwR+XR3YGBzHu6iOc88zRg3HhgwA35nsDGGwIfvANcfS0g4f6tmDKgT0+gPVWzbxYhEO++DWi2FXADzy8h88fyOjXXAqQKfL5JmPlnPPe1A4FnnwWaNwOu6gP0OCG873z/kzlOhSXuuIsh81TmHn4YsEKGZ+mY7wnUWB3u91S8fvYZz/8RsBHvo1lTYPBQBMefEob7S2h8hTl33gIc0BV4cQLw/TxgR2K4Vk2YRjyT3IeQqxXP0yH55/KczgpMSRAz8+Zb8E/g3iRC0ffqcCbPJ+a+WJtO/Xp2fzOdz646Nv3t0LvBpsgMHwIjaQzk5+Gnn/H/7F0JvFXTF1773EsizdIgjaaIDKGoNMlQhoRMSYaEaDCPoSJKGUKGMhSZImkmKSoZQoU0oiKRlAbeOWf/v7XPPfeec++5993nT+Nav9+7Zw9r77PPd6783ve+tRbtXYFUOXxvxQSBf4CAhMD/A9BkyT9HQELg/zl2slIQEAQEAUFAEBAEBAFBQBAQBLZtBOz9D0aOzcVkvfMmWacivDmHuQhNd9udZ0hOzvuokC8yhnyG2cw+FMTf3PnJ6diyBaSqVUv2gw37UhB1Q59PDR1Tn+KzQB5mMZNnMzCnBj5IMSYlI8y55Q7S9z8YmskVYp2x9409KdavT2i937GPO4GIVasBK8reVLY0xX8DWRZh9m0gTfv+83MzYRxfHa0Gtd8YhZybIEMDlvPcV11L9MRTKW+QevFfo/d2li4jXfPAlC9yoMY2/I5oe0iGI0xv3kxu6zNJv/e+N3vIwRSfmyDNI/z/qyHO1+nsU9MQ4DGkeVCNG+V1K+eKq0g/PZTU9d0o9uD9ea0RJ0EgHwTwZxwxQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQ+H8RsK7uYrZwe95Iev36nNup1lANcq5PLnoDLotze+Y0LqDj24nNs5KfxqUeFJABU/WPCvQKb+oTGmd10lCS/j+mDwNJnM2GQAVZFIunBbV2vTr76ht6Zp/LZ+bCMMEZXBI/q22wW3j7m2/DPih8lc1iNap7ClzfAcrebOQnuyiEjuvixX1vomuuTLW3YIvP6H+nnet6EleFL8y4qJMeBuIeOV2tKy4rzF3mBYEiISAEaJHgEmdBQBAQBAQBQUAQEAQEAUFAEBAEBAFBIBoBxSRmXRB8CxaS06Yt6TVroh15lEmwZH5ERRqV2rOZiyrhNGt2avrnX0hHVSr3PW6/12+Zqw6qQUMzXHsJIcbp1rhF+kiqf0HHVPuftC7LQVKe1CZjRxuKxqzGeU+D1rdfsBduH98s3EfPRrqAvO3hR7O62idmhsDbk9/N6k9Tp4XnWEGaxVyEtZsUCf48UiTYUxLqTn8scLUvuoTonXHJEVXUXK3Jlf9/Q/W4DtXpaxEhDN5tey7pdQhjz2Kcw9Q5vZ3J58rrFKraiwkC/yYC8X9zM9lLEBAEBAFBQBAQBAQBQUAQEAQEAUFAENhZEVC77kqxt98g57imJl+lc9BhZCHkW7U9HTk+Ea7OZCPyeLrPI7fhk8gj+TeIzRrViZYuI/fiy8h9fAhyHf5B9BuI081/mSJHVLEi0Z9/psjSChWIvppLzoFQYh58ENGyH7x5SxGVLOlVh08nmjZuJDu2O1FZ5MLkCvFMqO6K/JEcRv3rb5mvC+pVE7qO5yHOIQk3c9a//870xYjxZTUmPx/nquR9Y8iNmU5Q8mooAe3dSnmFmjj3KRv7cs7UJCHsDZvP4qVxdsxXqoTCOMVwD5CePyJcPGrvAts7izkzzuCfJcu5qWptsnfBuVkxiWObXKesVIwqZIV57zlxlqpVvQMyCb0xi7LxxNZkM34oVEWc15Wfgc+xYWPg4RJNkJTOldeQdUkHosPreZj/+CPp0WPIvetez4nzsPp4NT+Z7IbHkup1B8VQCMnhHJnIkaqffd7LF8or9sD7xr1YVbm1TKFyfeztUeQ0bk56wiRy6tTz/ntAcSODIX8PkfPTHfYC6WeGAmCHVOuTkd808cxb6+By3x0SAckBukO+1m33oSQH6Lb7buRkgoAgIAgIAoKAICAICAKCgCDw7yCgV64k5/yLvaI9/pYI6/UIwsQACEvVpTNZDz1Azu13ET040PfMfq1zEHE+Refgw4l+WZ3dz5/hojtffxNNFvo+wWuJPUCmouBOPoYiP7QexGy+xsVrfosgW6PWlymNIkNro2aixw5Cjsz0sPJoT5DEexKty52eILS0VQsUSMqh5gw6F/XcL4Gw5O9J0MDbeuRxWJmrLu9E+vnhHoka9I9qQz0Ze288OfvuZ6rEx39YFOW1xcY0CkE57S8Kq5jT/3tAXyHvrHV/H1JMFosJAv8yAvgTgpggIAgIAoKAICAICAKCgCAgCAgCgoAgIAj8WwioypUpPnWyKYakzkVYb8W9PRUgqyRr1SSFnI+xLz+l2GODyB34SJj8ZPUk/7CxKjJoIDOd/ZHfM0h+Bn2CbV43/+v8yU/2z5f8ZN+ikJ/sny/5yb5FIT/Zf8F3/JmfFYX85B0/nJnfvuwVpe7MtfqRJ7xZKFBVt2uIDgBhyepVVtKiQj0rh7k4ljF+t4mvBZUvDwUv1KVB4+8MKs2r0a9R/Lt5qffDRPVWNi7WFZ85jaxRr5A66wzzbOYZ+Vn3r22ePTZvjil6JOTnVn5ZO/Dt0/413YGfVB5NEBAEBAFBQBAQBAQBQUAQEAQEAUFAENiCCHAl+NjI4RT/6XuK/bXe/MQXfU2xRweSQnVu552xpG++3TsRVG9q5AsUdzeZn9jf6ynubCRTTbx719SpfXIQxYjUxt+NT8zeQDH2xY9avjjly0RawtQzT5q9eD+1dlWyTVehYny6gWzzfeinZSAkfzb+6pcf0z29Pgo68b4Ze5/WJtp/0XzP96913jNgLa1eHu2LauD+3vTNF8m2NWFMyp9DzH379ivjQ9ibflvptdf/isI6IJ/Trf3Zyf3o+++SbfXq8JTnhpQi1vppWWq/ObNM2wLmJu8rrwiG2h95eHI/Wrk02VZj3kjtPWuW1wZBHhvYn+LfzqXY5nUU+/tPiq/6kWJvgDA85yzjozkHbCI0X91zJ8XxfCG8+XuzYinFEphzTk22bSmXpnXm6RR7faT3bHhGftb4gnnm2dUB+5vzyocg8F8hICHw/xWysm8kAhICHwmLDAoCgoAgIAgIAoKAICAICAKCwE6IgMmFyXkvQc6pX1dQrBRyY2Yxe58aRCt+8mb32IPif/6WxROiz1YgHidNTs2f3Iri40an+mktk9syOHbXrRTvdWdwJNm2zzyH6K23k31uMBGXzTL2Ro7H+Jg3I93tPvcR3X53aC7n3pzXNEh+Nm9G8XfHhdb7HXvCRKKTT/e75ppzb7VbyJeaNqH4FOwRYS5ycLqVqodmcu5dAyH7y5Yl/Vn9yQRolGnkyXT2qoLcsKkCQrn29vewGzQ2IefW04+TdVknf1iugsBOi4AoQHfaVy8PLggIAoKAICAICAKCgCAgCAgCgoAgsLUQcB4YkCz6o96bkJP8NGdcHSA8oUp0VqzIenR1UfvwHArN5DQ/5D7hpM44Lbv7MUeH56KUlWGPcJ3U7qAAAEAASURBVO/YtPWBWevC8wO9PJpcwChg1k09A71wU9U7LDxQWK8mCOegNW4U7IXaOgdxHXJMdNTzz4SGdY5K94rDxNsibNy3PJ7DfexxL99mpYqkzk/7Lvj7yFUQ2MkQEAJ0J3vh8riCgCAgCAgCgoAgIAgIAoKAICAICAJbHwH9WCL/Y7myFGt8fM4DOatR8CgYXg1v3fGKrGv0LSiqFLSffib7p4R6NDiOtj3nC69aemBcn3BioJfWvPWO8ACqsdtcaCnCIsfTFJ7BZS5Xtk8ze9r0tBGva/+KsPa0au3ueRdF+vJg1DPZY7KoRf9EcaclS8N73ds33A/09BHHBnpe0/7ww4wxHnBZsYqK7SF79rlQN9hxXnudCFXSk4bq7vqXX5Ld9AaTn263682w9chDxJXYxQQBQQBKdQFBEBAEBAFBQBAQBAQBQUAQEAQEAUFAENhZEdAIX9aj3iI9/SPSK38ihYI0VKM6qVNOInXSiWQUeAFw3KkfkB4z1hTe0QhLVntXIFX/KFPcRdWuHfAE6QbiUr85mvQH00mvWEmKC9dUr2b2pZ9Xeb5NUspC/dEMckcjtyUqmuu1f5DiQjhHHE5648bUvrtCEfh3AdG775HT6QpS7dqSQiEZsh3S8+aTHvEy0fLlnj8rO7X22pVrkJ3aJXcLz5URup5rBarS57039inS3k1a5r/3b2u8vYsBZ5XQe/HzM3ns4xB8jtPaku0XjmJiMtgO+nEb88lzBxWzUfuyf6MWZB/fkCxWYNatQ7QS3zOkDuAf2rSZPbz78X0LQCLvUoIIRLhCnkwqW4b01GlEo5C2IFg8qnw5oo9mklP9ACImNtevB5hAns+9WzH84LvLxDBvfX9vsvDdEBMEBAEPAckBKt+ELYqA5ADdonDLzQQBQUAQEAQEAUFAEBAEBAFBIAsCGqSY2+te0lyFfTPycEZZ7VpkDepPXMxIf/Y5OVddSzT70yhPME6KVIcLyXroAaKSJUn3uZ9cDnMvpDK4urEnWRedT06Xrqg4PiN6b38UhZKozkFEc1HlOx/reCHRc8Pz8fR8jq6P5/skP3+uRJ4g2/JasNdeKHQEJWs+1uZUIiaZ87V7oXi9I5w7NOvS/UAWL1yUdTp9Qt16E+m+/dKHo/sgwwk5O2nN79Hz/igXvLrjFlRDr0Cav1OFGXK+qlkg0f9EHtDGLb175FoD/OJvv5HLQ+YEgZ0OASFAd7pXvnUfWAjQrYu/3F0QEAQEAUFAEBAEBAFBQBAQBCAGXLeOnNZnEkH1SRBJqtPbeD/I+6g3bSKa8yW5L4A4/GaBN4/clPo1EEpMlFapTOriC0k1bECqFIjO5StIT5xM+qWRnjKzVg2iSpWSZKY6pRUp5HBUIFM1Fzz6ah65L44wV/Muqu3rqfz+3ABCDIrPjheROv44UmVKk0boup78HulhzxuFp/GvjL2hVCVcrc6XkTtlKoojrUR8J0i1ffcl1aolubeDEGSVIYhYwrPu8MbEcKDifcbzBpWwGZOFDLC6klWa+RpC1A3pzerLBd95ofR/4V1wrlSQxkZZ/OB9ZOHdOD1u9Ah4Ph+UvobY9lMd8DOxurjHdRTr2Y0c/h60ap1SsvJ+rAJlBSqfkdfxd9c3KIvjUwOFsPxxuQoCOykCQoDupC9+az22EKBbC3m5ryAgCAgCgoAgIAgIAoKAICAIMAIahJHb5kzSYycQ7VuVYq+9RIqVj2mmQajphx4m9+bbkwSYuqozWf3v98Lk0/2XLiXnbBTx+WyONwN1X+y1EaQiiufwGZxSUAtyCHPCmPi0Hh1IqgRCodPMnjmTqGHT8ChUqfF33gyPJXp2TVQZX7osPDdpLMVbNg+PoWc//iTR1d0yxrNVGk+GgQdX9OlF8VtvDo6Ytt0Patib78wYz7q3hRDu9JDyJx+leOfLM/awZ34MTJqExyvuTfGfvg+PJXr2QYcSfQtCMmiPDKB416uDI6btLF5MuvbB4XEmmDevoxgTk2lm16ln0hYEh62vvyDrILyHHGbXrkO0eAlR6VIU/31VVk/OAasrVvO+hyBL1ROPUgzkd7o5a9aQPvgIIqR1MAaf+JOPpbtJXxDYKRHAnwnEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBIGdAwE98lWP/NyrPMU+mBxJfjISisOUobwjVNI2hhyLVt97IslP41+jBlk3ecVnjP9ZUH1GkJ/Gl0msKwMEFu5l9esTSX6yf7xBAyLOHxowBRVhNlPXppF6eNYo8pPXx6+6MmMb9f7EjLHkQDeE6qdZFPnJLvGbbkzzRBckcjaz5n6WMRVFfrJTvMExRMWKhfyZQM5manRmSHgU+cnrY7VqQZEJUjNg6rqukeQnu6gPpwQ80YQ6szDy02GSkslPttaneNcsnxp5UH0Vqpo1LZL85KWxsmVBAC8jqroPd4mGPEPOH394bfkUBHZyBIQA3cm/APL4goAgIAgIAoKAICAICAKCgCAgCOxMCLj3P2ge1wKBqKpXz/noejyIQA4v3203E/6unx6a0999OKC2Q5i73rw5q7/FeS59g9rU9avC+2Pp10s6hEYsDoXPYjo9j+czT2TxjB7W36QpJYNuCxcHe0VvL12WdY37zddZ5yInOOdmwFwUm8pq6SHyHEKey/wiVb4Ppy/IYkw8crh60rgwUSGmWTXKxmT4c8947YhPh0PbURTLWKeLKRahVk5fphbMNfvyuG53Xvq09AWBnRIBIUB3ytcuDy0ICAKCgCAgCAgCgoAgIAgIAoLAzoeARpg65+Ak5NdUFxRODGmuyA5TZyFfKMzlCt5ZjCu+08xZRMVBlh6JfI7I6amnTM3ijb0Se/sOuvf95HwF4irCHM4vmka+OicGCNTAGoeJvpGvBUbQPOcCcmYhZDzC7AcezBy9rnvmmD8ydpzfSl7t3tFqVLtPv6RPsgFS2eZCQVHW/uKMUfupZzPGeMBduJDohx/Dc4OfJDsL6azTw+VBUtqDo4lh8x44z2rANM7h+vk5A+PcdJhUX/VLaNRGfs8o4/B6m9Mf/L7WTKt77syqLDUOt96Z3Cb+7JBkO1cjVrw40eEJghVV48UEAUEAwmwBQRAQBAQBQUAQEAQEAUFAEBAEBAFBQBDYlhHQH80g92WErs8AmYPCQMRVsWvVJIU8mKrDBaRKl87r+JqLGsHUsUeTKkwBCD+dUN6p9u1Ij3g5pcQzu6R9cMEbV4P8PILUMfVRNX4OuSNGknvXPd46LkqESvGEHJ/q2GNI+/k/UbSIUESJc1/qw+qTvcsuCHfHj+Mmitv8lSqAxLf0i/KgGr3Jx8kFdPy8mcF28Hh//U26QRP4w5eN/YNtbzT1WQByUIHIzddQgd3Otwo777nrnlS4RjJx885Xk42fvIyfq3jp/Pe+pjvZXXt4W2fDpBhSDwA/LjLkFitJeCvZrVQpIj/kfOAjZA96NPfe55xFsdtvyb4fZsx3nj1QcKsoxgS//hz5aLMQwkXZS3wFgR0BAVGA7ghvUZ5BEBAEBAFBQBAQBAQBQUAQEAQEgR0QAb1qFaq1n0HO8c1IQ93H1dmJQ5ORO1FPepfc63qSg4I/7tPRKsEMSH7/3RvikOV8zPevVs3zXuup9qKWal/Rx9Xb/UI5rNz89HOvKjhXErehzlz7B+kJk4gSyjx1BwiwIBnL6sgNGz3iaiOuvIaNic/XQcL2vsvr+59M3PkWbPPY4YcRXXS+P+sRn74PX/2274FiPHkbE7VFMU4jkK9VqZKvp+fXvGn+/tXxLoNnCeIQbPs7RuQ89adCV+aWx0MhHHyXwf2CbV7IVdpfGRHaIrLjfwdZ1VkU2x95TNn4vmKCgCAgClD5DggCgoAgIAgIAoKAICAICAKCgCAgCGx7COglS8g54USiH5cb9Zu6pgtZXCyGCawNCC8HsaiffY705PfIveJq0lBgxvpHhFwHH63CXl6PVaR5mEIldz3/G/x87XnvlVgfsVah0BCbXrKMiCvM+wYS0uL8ncc1JNqIc4+bRPr5F4kSIda6VRuQnAk9ZMWKUBpC8ckEKIeyM5lWEso/G6ToGpC3HS8jYiWpb1DCkoO1TJIyAWfINzQ2bfI8mDDmHzYmZVHIiaDuJBf+FvpMYgLLpIGczduyhbFn26AoSsQVKzJ34SJQvvqV7x0k9t57P9PfH0lXxS773p/xVLC77w5MsB+/A8aI1bes+PTfSb8BKX9u7Q4ikv0MoYk+Pxe/K+YZ08PsS+D9MN78jvjscezN9+If5Ct12rSl2JhRvGt24+8cV69f/2d2n6iZTxIFpXylb5SPjAkCOxECogDdiV62PKogIAgIAoKAICAICAKCgCAgCAgC2wMCGspHJocM+dnoOIp9N49ive82oeMKJKFClW7r3LMpNmksWSOeM8SeHvAwuUOezvl46rBDQUQp0sjVqYPEX7ZVrKCEaRQ0YlN+XkXTS/s4uI5HQDJZympPmHpkAMU//5isrleTdcThZB1/PMX63kPxFUuJmp3ALikCct+qFFu5lOK//UTxzX9QvOBPim9aS/FVP1Bs+RJS559r8ooaso3X3XUbxf/8DT4J37/hvxH+G0GUcth20BoeSxbG43+uofhf67D3Bu+K9bQQOVHTDWRdXG+O/El3Nf2flkX7YjzKsu4dVGb6C98b7+3N52Y8+OqC4P0jnHPTuIMAztgbvjxG++/n75i6Lvraw5D3dDZS/O/1BiML/QxDxXmz9wbguO5Xiq8H9oy/DQL5sYEZ7jR1sudj3iXjjb03AH/cQ7U9w/jrd8aRc9tdmWsDI+p0EORs+L6agkher9BP/dIrns+eexbqKw6CwM6AgBCgO8NblmcUBAQBQUAQEAQEAUFAEBAEBAFBYDtCQHPuxK+/JTqkDsXGjSZWYmYz6/z2ZA3ziE/35ttJr1mTzdXbp9HxRkWpHxmc1c+fsNqBhGWbMNlcVLszzTXqQzHRVLliaqp8ObKuujLVT2tZDz0QHmmNfKZZ1HoK4c86WPU9HqPYzTeE1wd7N/YM9sjq0Y0sVlBGWLx27czRCWMyx/yRG7r7reQ1DlI6yiLHu3eNcvXGpr2bMRdv1jRjjAfirIxNv2+fuyN9eVB9OCU8B6zjNWuExxI9i9WalSqF5+6+I9wP9OJXdyGquk9gBOdDiHs2i70xklSbU8207tef3HURhGticaxnt+Q2uuUpyXauhrN0GdGixZ7LGQkCNdcCmRMEdgIEhADdCV6yPKIgIAgIAoKAICAICAKCgCAgCAgC2wsCGupJ9+HHzHFjgx8mhaJBhZnV/hxSJyFcnvNrDn0+p7vV63Yz797dO1VgJssK1eBYIigzjaKTczCCbM1pP61KTqvzcCY/F2hyNNBYsTLQQXP6R+F+eu/JZ1IjXCDJL7aTGk22rIp7J9vccLuFCdHQZETHiuWgCuYl0gFErMtrKFFYKtL34cJJ6dC6n9NSGQx5NjQd7KhfVge74RD68IzXSyPSFaclyGIupzPgVA1FMPXWayjYhLyoCJ/Xt92Ze2WLZt78tA/JeeKpnL6sEtUHH+75sJL3+eyY5NxIJgWBHQyBHP+q7WBPKo8jCAgCgoAgIAgIAoKAICAICAKCgCCwzSGgkWtR/xnIb/jJp0RMVu1fm1TjRnmfV13eyfjqseNzrrFOaELquqtNnkcHijp36HPEpCsbh8Vrzs/I7V9/JeeiS4h++NH0Oa+mPvUM5Phc4vXTPm1U/Ta5HRPjGmScO/gJ0pwfEmYjNN5OEHYaxZTcG25OeCYuIBY5jymbC0zc5SsSE1j7Eoof+RihyBLnn9TjJiTnOWWA/gu5QxPmvuCF7Pt9wl52x8tN1wZBZiP02jf7CJC8aeY2P5nst98xo/a8+WQvWOC1nwKZNn5imjfOV+tAbx7ntvH+bFzZ7AMOMdfQB9S09kiQfzBn9WpyEvk+7SnI4zkCz5lmdtNWyREH5Knj43nTbcnxZAPvxpCRGLB/+IHsL740Uy7wcU8+LenmN+wsikqHi2oF8GR/3ed+cr74wl+avDr4XriHH5Ps+w2bscphrDJVp55sPPSY1PuIWqJYkZsogqSvupbsM9pFuZHz9hjSe5ZL5oBVj+M7KSYICAIGAaVhgoUgsKUQ6N69Ow0aNIgGDhxI3bqlpPxb6v5yH0FAEBAEBAFBQBAQBAQBQUAQ2PoI6G8XgBx8kgxZ+f33YPzwa+keu5PiQkEocqRBHqkL2lNs+HN5H1YvX05OVYRz71OF4j8uzrmOSUm3S1fSTw/1/PxCOAny0xS6+RtEKBN5KGRj3XsXuf0HEbFqc5c4qVYtSTVsYIozaZCLeuJkos8T5BjyUDIZqwtR6iUPyAWShr2Q7CIO3hCcyQEOx04QtFxIR91yA8i4fkRntyXF6sH3PyBKVKAnKD/5bAbXX38jqn8kkV8MJ7nhDthIxyzXI3LhqGD+V8a3SmUiLjbEqlomvP3vAe9TDoTib8DSN17P4e6cZ5W/D4yzb0hNYApScR8Fu+KFFDhyn32O3MuQJgF7cj7RXMZEsa6FPLM+McvPvDeUvpXw8+cG79z+HDZSCJ2P9b8/15YyJwjsVAjEd6qnlYcVBAQBQUAQEAQEAUFAEBAEBAFBQBDYaggY4vHWO0gPAJnIYdxsTBrtjlBgkDh60rveGD41lIpFMj9U3ldK5ljMoenWDT3I4ft9/wOqs6eFN2/clFp9WhtSyPEYO+9cclGwRj/3Aul3xpuflBNaHM7MFdrLliFdzyueFJrP1mEVYJAATdco+eQnr+eq7bsW83Z6bZQpPG46XJmclZE/r0KF+eHePH9yWoCiEKAnNCaaOi21PlerFHJw/pE9d2XGUuBiKtlnTEQM8HMGScgIl9BQubJhIjI0mdapXZPoy7mpQcaXw9f5J8Ks++8l9/KrUjNMnoLAjzKF/LKa8eN3uBbFqAoz5Ik1Znuq41zusSpVyEERJX1sI6JPP/fuwYri9DQAIFPVS89RDN9bMUFAEEghIARoCgtpCQKCgCAgCAgCgoAgIAgIAoKAICAI/EcImNyeZ59H+s23PRVl58vJurQjEchCBRUeFy/SY8aSe+udRJxTcdRo0h/PJnXM0fmdiIlMtrT8l95g+FPPnUdOkxaechKKUwU1Je0CRR8Xo+FK5Kj4rX/CGTgk+6WR5CIc3hr7FsWegWr1vntN+LkJVwcBqPauAKXlUaRxZs1qvt/WEHVGiL1vFaAsbNGUVLnypJns5RyYH3yYDFOmduf5niiiVIlU86akK6HSPav6oEzVS5cSgXA1yj8o/HTv+zx/o0ztZc6uQI6x6YWLyB3+Eul74cMkHF+DxjlJIRw0iltWPrJPIqTcuOVLfrJzOvlpYWNW8vqW3l8TIASDSknfn8/mnyWd/GTylMdYlcvkKFS4SdUrrw+pMDFXbV+oc0thPyh4mUhf+j3RZpDTbEHys2MHkN/AeTbSLvC75zDzA/Yn9UAf0kcdZ9zdt8eaK/E74fGHHvG+nzYIZxCvXNFdL4bimAlpVoT6BPYBB3jrcnxq/yy7Q1Wah8UYo09mkAOlqr7+JqIpH5i8t7RbMaOcVr3uoFjL5nnsJC6CwM6HgITA73zvfKs+sYTAb1X45eaCgCAgCAgCgoAgIAgIAoLAVkPA6QXi8O4+hjSKvf2GF0IecRoXIfFu9QR5hOrvsa/ngDxMKOUi/P0ht28/o9BUCCmPDX3KH8646vXryTn0KKJl35M6vTVZI54nxWHNEabnfEEO8n7STz+T6nYNxQb2j/DyhriSt1sKZKhvHKL88XSKgxyNMrv5SSCwpqamQGLFNyEEO8I4bycVg+IyYNbo18k6rXVgJNU0OSJHezk8/dG4ThCA/kDgaiuQvkE7pRXFx44OjiTb9u4gI5H3MmhF2hupDuJ/giSOMLs08EsjVou0N/bM5m8zOf3sc+G7rl1FcSZKI8y2QIYymYmUBpwKQZ3cimLjojFxX3mN3PYXhXdZ+i3Fq1cPj6X17LpHEHFRqeMbUnz6lLRZ6QoCgsC/iQD+5CMmCAgCgoAgIAgIAoKAICAICAKCgCAgCPx3COgffyTdD+QhOMHYGyOzkp98AqtaNSIOxWb75Rdy7+3rtXN8mqJCgx41Hlx9PZe5/Qca8pPzY1qvjMhKfvIe6vB6FOM8jlAc6kcfJ85dms2skiAoWVXp29zPspKf7KIG9PM9vSs/dxaL7wp1KtShSQO56hfQSY4FG10DClQeR2h2UYxJ4az2wtCsU3lNPDMku9vkhNoyu0d4hhWRQbuhR7AXaltcECj4fjCbjfw0C/er7a1PFHRSN18f2i/Ysc49G6kJoEz1DecqjPx0Z87yyE+ssXpc56+UqyAgCPxHCAT+df6P7iDbCgKCgCAgCAgCgoAgIAgIAoKAICAI7NQIaM5xufkvUsijqZokyM0ciMT63pOc5YJIufKBaoQ2G/Xd6l+JmjQiq2WL5Nr0Bofh6yHPmOHYwwNIIdS9MFNHHkHq0ksQTu2Sy9XBc1kgX6d68KFcnqSfHRaeX7I03E/vcei3b1Am6o9m+L3M6+VdwmO7pBGF4dmMnnv+xRljyYE33kw2/1HjzWgVJe+lps8s2pZ+yLy/yk+D4PcDV4tJ5DQCNDCd2XxleGjMPeLwUD+jw4W0fDsjd/5NF4WW3BaneN4I17fOPN1fKVdBQBD4jxAQAvQ/Ala2FQQEAUFAEBAEBAFBQBAQBAQBQUAQ8BBwJ0wyDdXhgrwgUQ2OJavX7Z4vCgu5V1xFOr1QEWb1p5+R07i5V4UduThjLxRCUH7xJdGqX4hq1SS+R9D0KhQQQsg7qzx1sEo4nKzEuXXiOUy4+3tTTL5N95NPUKTdJfvBAcHtTDEiu159cjmHZ5q54yYQPZ4Wpo8cl+74iWR/8RXZrduSXace2R0vJRsVyJ3+IFPTyD0XPi7yidrPPU/2KaeTjVyi9jvjyJ36AXJeLgvfEbkinVsSeAZmXKgb7dPaBUYSTT7HeR3Ifuttsjt0IrtTZ7LHTyL71NOIRr6a4W+XqUg2pwD48EMPE1y5b5cLqFb9Va++TvZFl5Dz1VxyoNp1UBDL+ewzsq/tSbrnjb5X8mrvVpJs5GB1QfhyflN+PhffBbtslr3xnPb7H5CNwkX2BR3JRni6+YmBoEyoOf3N7aOPIwd7GYXyZ5+bXJ5MtjucZ7W+lwPU96WSe5F9d2/iFAoauGt8l/Tq1WRPm0b27qW9XJy+8xtvkdMWRbMiCnI5Dz1Mbu06XuEtkNrW+xP9VXIVBASB/xAByQH6H4IrW2ciIDlAMzGREUFAEBAEBAFBQBAQBAQBQWBHR8CuXN3k0YytXEqqUgRxFQGAhsrROfwYFK35ypvdswSppk2IULSINoC0AvmZLGhTs4YJVVd1DorYKTXkQn3ogphSZ55GsVGvemQWE1IjXiZaiEI2vqFIj2rciNQ1XYw6jwlRpwTykLKKkAvyoNp6yFhZyApAJrw4PP2cs0i/8nrKpXx54iI6ppAPVxsPEqytWhJNnJzyzdXiXKisWl25MpdXao5zmwbvxSHj+1YlKl3Kq8jOZwmoVlMLt7MWF67yixwVdvS98C5YLZyPodAU4XtATFjnY1UqA1uQofOR15ON88By6gK+Jxe1YhIbRLex4ruR9d4EstKIeG9SPgUBQeDfRkAUoP82orKfICAICAKCgCAgCAgCgoAgIAgIAoJAGAGf9AmGcYc9MnqKicRj6nvjtWsRrf+TNCpy60eQi/PZ5zzys1RJUrfcQLE5H1Nh5KfZCOo+YyAy9bTp5Ox3MLm9envkZxkQV4cfRnTg/iZUWk+ZashSp1VrcnzCk9f7ba5EvntxL6yaSURf7QfiNjZyOPKLDveIL74hFIyEyvPEOUR9QtIPx26cpjT0Thj9CTUo2QkCLdojPAoil/wCUkx+csj40mVEc6CEZTIunfyM45nyNa5uXxTLkeM0YxsmuYtihRDfoa1ypEgI+XHnwvPIeuYJonz2BwHO+W3j8z4nxeplriiP7wJxZXioemkRCHb+74C/1yedSNbKZUJ+ZgAuA4LAf4eAKED/O2xl5wgERAEaAYoMCQKCgCAgCAgCgoAgIAgIAjs4AvZhRxF9NY9iMz8gdSxUnXma06wVaYQzW++8SeqwuqRnfmyUpIRK4iaM/biGpIpAquoPPyKnUXOi/WqjENIyEFI2CgQdR7F77zKFglSClNQI39ZQhRpy9JfVKZIT52YVqnpkAFmHHJJ8Chdh3e6lnT2VH/ugEFPspRfMPIep62HPe8QjqzcPPohU58vJrXc0ESsw0+2s08lq09oLqYZy0BRtYsIyyipDcQg1q0KxKAIOes89iV5+lQjnDxkK9KiNa0m9/AqI3w+hgMQzQYmrWjYnl0nd9ArmvLgMlK4I906GjTPhWwL7//57aGvTQUoBQ7R+9x0qxG8mYkXm/sAYpLUhfdNXHFoXeCw1Sl4zBTUkv0/+jmRY1X2IykP5umAh0V9/ecWGmExlNe5XczPczTlYpctkMRO8ZcumlLfp3vwurrnKI6d/+hnPAN8DDyDNawc+YvLWEqtAWb3JSs5r4TvpPaIlODuTmazqbX0q0fgJRB9/gucuRjGoOlXDBuZO7pT3Sb81hmj5CqO6NYQ+8slaRSGa088sfUFAEPhHCAgB+o9gk0X/FAEhQP8pcrJOEBAEBAFBQBAQBAQBQUAQ2H4RcK7rYZSb6obuFHvgvrweRIPUc/YBKcaKytUrSHFo8f9pXDDJ4byUGzeZndR1V5P10IPkE5/p2+sVK8jZH2Qd8kQaQzh0/IPJ6W6mb7MCdM/yyTnrxaFkXXh+sp/ecM7vQJrJyoDFNcjDLGYrkIRB69eb4jdeHxxJtu09QOT5Z8aoatGMYpPHJefTGxl716pB8UXfpLuZvl33SFQvnx+aK9K56x1K8TmzQ+v9jn3xpUQvjPC75lqkveseQvGvPg2t9zv2Dz8QVYO6N2C59tazPyHn+KYeSY7Q9tjns0hVqBBYnWpqKGvdLl1JPz2UiHPRfvvVv/J9Td1BWoKAIPD/ImD9vxvIekFAEBAEBAFBQBAQBAQBQUAQEAQEAUEgFwLWRReYaf34ENJMROVh7h13G/JJtTn1XyOTFCsT993XuzvUe9bA/lnJT3YySsAgkXhO26wnj5co4SkfEx5u1x5ZfXlCI29pyBoUooz1Q+YTi7KRn2Y6rYK5nh1NCobuH+x8+H6wF26/OjzcL6zHCsqgPfdMsBduPw7VZdBY5ZnLOKw/aI8OCvZC7Ti/90PqhMZydbjYkVEIsxMranMojRXOYT3xKFET5AtFkS33/gdzbS1zgoAgsBUQEAJ0K4AutxQEBAFBQBAQBAQBQUAQEAQEAUFgZ0JAHXUkqXPbmZBn5zQUCOKQ7RzmPoY8n089a8KdrT735PAs2pTmsGWoOo0h5Fnf90DODdxb7wrPzw0rH8OT6I18MTW0dq2pip4aSGvd3z80YN1+S6if0UnL12kf1TDDxR+INYdyEbkmk5YeEp+cyNLIFaI9LPCMWZaHhjl8PGivvB7shdvDXgj3/Zyt4dFUj3OaBu1ZKDCzmI2q7TTv6yyzmcPaPwuH5nP+2VFvZToFRpgEjfW/34zo514knfa+Aq7SFAQEga2AAJJ4iAkCgoAgIAgIAoKAICAICAKCgCAgCAgC/y0C1pDB5HBF9y/nkgPyzurXh1S7tqEcnnrxYnLvuhf5N0eaw1hPPkbqoAP/0cHciZORM3SWV+28ZnXc6yxSP/7o5aXkSuhou7fdRfqzz4lJVoXcj75xSLMejdyNEyZ6Q1zhHUpQ/e4U07cHDCQai7yPv63xqtL3vJbijRtT/PQ2ZDP5+J6noHQbtSD3qs7IF3ow6YWLjEJU18HzMLmbRt65p55Bui1+UCCHkKuUlYSmYnuTxkQ9bvSPlrri3PbESUQjXvGqjiPPJx1ej+iUVkR4LlOAJ+VN9q57EnW8iOjTz7yiTBWRv/LgOkTPRyg699qH7GYnIJflsaQ5zybIVIW99cR3iRjTNLNL7UV0eSeiqdOJfllFVGFvouNB0IIIzDCQzjbIYTr6KFJfI8weKQ7UgQeSy+2HHs5wt/fGuzq/vVeJnYtJcbGq5i2Ipn2Q4UsvvkT2fJDUTzyGIkM/mfylCuHrzgMPEb31doa/3ec+iiEfq+bv5WrsXbqU+R6o6tVJJ96h6nQxaeDJfd2po8k7qpd9TwRyVnFe0COPIKMsxu5M9FON6l6+V64EX/cQjIoJAoLAtoCA5ADdFt7CTnQGyQG6E71seVRBQBAQBAQBQUAQEAQEAUEgDQGT1/MskFkfzvBmUMWdDjuUFIoaGVLpmwXeOIrJWE8/kTOHZtrWpusix6dGMSL92iivSE26E9/vj3Wkzgbxet655HYAaecrFFG0R9WsSXrTJq8ozppAsZ+BUIp2BwnJqkoQdpHGcyDL4s88SXYZEIBr/4h0yxgsUYKIz8XVwvOxXGcIrt8HxYOeeZzopNOCo7nbrVoSgTjOyziEf8nSvFyNEwhPmv1pfv5MeqN4VN7GxO+cL/Jzr7Yv0fc/eL6lQaauR3i744bX1j0Y3wEQqXGEtr83gdwmwIXXsYKYidWg4burzj6LLFR+VyjO5JxyGunxk8gaN5qsk0FGiwkCgsA2gYC1TZxCDiEICAKCgCAgCAgCgoAgIAgIAoKAILDDI8BFZGIfvEvWsKdAfKK4EMhIQlVyJoyIyU8Qgay44yIyuQoIRQHlzptHLpSL+iUoIpmoYqKwApSJrPbcEyQjG98Ppqd/RNaZp1NswVxSXa5ApXBUDf9uEekJOMcHUDEy+clKTT/vo09OBslP3j+Yg5Lnnn2O7NIViFgxmrdhHasP87XgGXKtWb4cBOhzuTwy5yqCuM3XcoXJ57tHNj/Gtii2T+X8vU9onPJlJSo/R5NGpNqfTYrVt6ww9VMdcHEjn4hl0pTJz+rVSJ3e2pDodEQ9TxkMpatTpx65r72BL1di+yI+QupQ0hIEBIH/AgFRgP4XqMqeWREQBWhWaGRCEBAEBAFBQBAQBAQBQUAQ2OkQ4CrrtHgJ6Q0bSaEoEYdkK590LAIa7vIV5NY6yIQlMympelxLqu+9ZAVIOnfhQnLbnEW04Duzs2rZnGKTxpq2tm0ihGDrlStJFS9uwpgViubYlasTIVdoyFgdOOMDMkWPEhM2K1pPhtKSK8EHbQ8UAHoThOy77xN9PscjRls0JUtZ5HbtHvT02o2OIxr4IFkIU9c/ryIFss3dfz+EtZ8erSi9txfF2p5OmslOPKuCStG5936EtUeEnjPJC5UivfSq90z7VCE67xyi628i2vxX+Cz8DlBlnhYvJZo12yN6EQ6vQJDqm7FHuvFzPvsE0fjJRBwejnPTyVBNXn41FJbrw95cMOjFoaQ3biL9MfYGyaiOrk96r/JE7RGiz+R10PgdjhhGCqSkXrKMFM6tj6lPxOpdfG9ChncXe38iORzuPnWa9304EmHp3a7Bs2Lvr+aF3Im/A2+MJLUn0gMkjL8LesTL5HbqTMR5PLn4FF/LlKHYW6+SatzIdzVXvWwZubfeSfpl4MqkZ7lySDHwG8XmzyFVB99JMUFAENgmEBACdJt4DTvPIYQA3XnetTypICAICAKCgCAgCAgCgoAgsKUQsGtCrbl0GXJsInT+i0/IOmD/yFszueXsCYIqQfhZqDpusQI0izntLyT9yuup2ZbNKD5pXKqf1rLrg8DkHJsJs2ZPJ6s+yLoIs3eBKpWJV99QXT1+dju/l3G11W7hMagW41NBOEaYzbkyoYYNWlxvDnZD7fS9lb0BPHIs5ON37L4PIMfonX7XXIuyN/W9h+K33Bha73dszhl6yeV+t8h7q+u7UexBEMARpkF8OwceGprJee6zziUaNTrpr5CSIXbZJcl+eoMrv7u33OENgyiOrQRZW1Qla/qm0hcEBIF/DQH8KUNMEBAEBAFBQBAQBAQBQUAQEAQEAUFAENg+EXBfH+WRnzi+NXlsVvKTn06xShJFiXxzb7rNb0ZeNXI6Bi3Wr2+wm9nesCE0lo38NE49uoZ81YxZoX5hHYXclFnt44/DU7FCfvW3WLqYMs7VmtVAAocsoLINjfsdTkMQtMbHB3vh9glhdSUFlJlhx0SvSpXQsOaK7dmMlbRBdXEh57bqHRbaSXFRpxymenZDKoWynkelSkJ+5sBKpgSBrYFAIf8Kbo0jyT0FAUFAEBAEBAFBQBAQBAQBQUAQEAQEgfwQcPsP9BwPrUvW8TnItcR21q0I+S4PFSgbwrOd6R967ajPIU+HRvXsT0L9YIcLMKUX7rERGp3VuKJ6wPTDgwO9cNMOKkUTUzpNKRla0f7iUNeEcIdHwj1Xh/tNWoT7wR6nEQhaxNmC07RmTahL514Y7gd7TU8M9jJTCoRnQWgiPD5o3W4I9kJtZ/hL4fB6kKE6Rz5Vd0JYXatfeS20X7Cj//qL3IsvRe7YxLMuXkw6PZQ/uEDagoAgsMURSPvXYovfX24oCAgCgoAgIAgIAoKAICAICAKCgCCwgyCg584jPQvqQ+SvNOq92rVINTuBVERRII18mXrKVJMDlFg5ibBh1bBB1ryJGsVo9EczvL15P6j9VPOmJncnw6c6nM+XQk0hR2Ns9OvkHIe1MN32XLLPR7jzJwhd/2U10a67onDSPiYfqV80yRRUAlnmXtmV3CdAirKqkZ+Rc36Wg+pv77294kn+3Tn0mck15JLkkO9IQ9GlkMHfhKLvlgh1tws8xSJXKP/775Cr6bz4EtmvvmEqldNfmOd78tlBxoVC69kZR7FR8EcNfZ5oJpSmv4Go27sCKRQEMjlG03dfuJjsknhGVkwysctWHOfivf/Eu0ozu3J1FLAqBUx+9vz5GaCCpHXrcWMn7I2CUvahRxEdgnyvv6MIEZ5bc+GhhcBj2Q9hX8Zk733JerAvWR1SxKk7egwZ9S7nGw0azmeKUF16CVn4PnE+UZfvz0R2evX2TZvIadYKxbbOI/13AanVePdcFb5ECXI5jcGMmd7O/NybNpPu248cVhBXrEj0++8GC83fgwLkDH1iCNG8r7F2D6QewNjSZSikhHyjRxwePJ20BQFBYCsiIDlAtyL4O+OtJQfozvjW5ZkFAUFAEBAEBAFBQBAQBHZ0BNw3R5PLeSG5knu67V6c1JWXk3X7LaRQSEaDaHLv7kP62WHJXJyhJagOb3EBo1NOMsPu+InIrXg70ZdzQ26mUwyEH8grJtGs6agun4cC1N/EroMQ56jz+g7BK85P0z8img+SqzDD89KuxVC0COReyT0p/geItQiz4yDLUACIunYhevSJCI8sQ0zS8d75WJXKRH4F+8L8mUDlAlAbNxbm6c3jXRoiMD9vENwVQJDmCK0P7lMM+LGylPEJGhOyPI73nTQeY0yYwMzHOASfie5vI76r6esti9S40aRPapM+k9kH1rHxb6Mg0h2k3xlP1phRZLU+JdNPRgQBQWCrICAK0K0Cu9xUEBAEBAFBQBAQBAQBQUAQEAQEge0fAS4q5F51Lemnh3oPg0ruqlVLoqr7mPBy/fEnqCL+MemHHiEHBWWs+3uT2/Mmj5TjvJONjiN11BFEXEX8+x9IT5xsiE731DPIve5qiBot0oMe9faG2s7sjUrnTNLpTz8n+hCkZIIM0599QVQEApSgcszb3n4nU0GYbTGqmxs1Js/nyjPJhCPb1OneNd/PfMlP3i+9gj0Ti/vVRq5KkJerUShp0SKjYDS3ZhzzJT95Aasgi2Jr8iRteU8oNw12TIByXk9WenKbx9n4u7PffkQobERMghcWhu+t8j6Z5F21KjXC7yi4nos/+cQrqr/r96emfPmd8WsLpgzw/UE063fGpUh9FOQSEwQEgW0HASFAt513IScRBAQBQUAQEAQEAUFAEBAEBAFBYLtCwL2mm0d+QvVoDehH6vJLSaVVD9dfzSWn89UgQmeTe14Hj7BEBfPYk4+ROvCA0PNy3kQ9+Elyb76NCDkxjc4PBJd1X29S13QhxWq/gGmo+BwOp+Z1N91K+tSTSNUGwVeImarxPukFJaP6eg5ZJUt6Idwcvo2QdveV10hf0NHbyQ+fBgGm7rqN1B23evk+OeT7gAOIQ6F1/YZEXyHsmW1DQkW5D4jgbLYHwvj/WOeFSvs+R4IM/nAK0QMDiT4CuduuLdHFFyJnKZSc6//0vVJXqAypZg0iJqB3x7k7X0700qtErJhlA4Hnm+p/H8V6dve7yavdpSvRk08n+8nGOwivr1uX6MURRDFQB5wmYDrOdOElSZdk4+A6FJ/3ObnIfUlLluFM1cmqVYvsw/BufEwCYfzW11+QrlyJaMpUnBHEZtMTSP3xB7k1D/K25HMn/C3gYSHs3P3+eyJOG1AdIfFMfsLsvaoQ/fobWqmw/NjKpYQdiTjnJ6dWOO9cilWuTM7Z5xFNfBc/k3nWmHriUbI6X4ZQfbwHVoWWLgWsy5s0Ac4JJ3qEa78BnvO+VSk2/T0i7GUIVITbmzB/EMduv/6ke98H9eedXhg8Vqga1fEpJggIAtsKAhICv628iZ3kHBICv5O8aHlMQUAQEAQEAUFAEBAEBIEdHgEXqkj39HYmN2Ts/Umkjjk66zNzkRgHuRwJJBftVZ5iK5eZiuzZFjgDHybdA0pRmOp/P4i7btlcyT4LxBzUpcaOPZriM6dl9eUJ5+7epHv1TvpYq5eTxaRXhNn7HQyVJEg93xbOpzjymmazZFi77zDpHYq3bOH3Qlf7zHOI3no7NYa8o/HvQfBlMZMfNDCnRr9GsdPaBEZSTfvo47ycpv4QiNu4C2VqFrMtqCKDYeUgsuNPDY70ttuAlGWlo28IE487CcLXHwtc7RLlPCLSHzupFcXHJ96XP5a42u0vIgLxnDRUgY+vAzGZxZzzO5B+GYSvb1C3xr+b5/dCV718OTlVa6fGcviyk31pZyLOmZow67WXyGJCOou5r71B7jkXeLP77ot3+V0WTxkWBASBrYGAtTVuKvcUBAQBQUAQEAQEAUFAEBAEBAFBQBDYvhFw7+hlHsB6oG9O8pOd9LtTPPKTQ4g59NovMGN2yPzQb7yVHNSjUu3kYKBhgSBNGqtMg+RcciLV0H36pTooxJON/DRO6Tkcr742tTaqdUaYkFTNmkZ5eWOcKzRg1qCE0jAwlqup6yGHaTbDOymSBclPLLR635V9+ZWXZp+LmuFCQUHrdHGwF2rHR74Y6hOrcXNZOnE9Kwf5zcWLuEiUb1wkKYcpLoQVtDS1cnCK2yY9AytI2cqW9q7yKQgIAtsMAkKAbjOvQg4iCAgCgoAgIAgIAoKAICAICAKCwPaBAIeem9BmqDnVlVcUemj96uueT7MTzNXl6uVZTK9Y4RGknBe0BH5mzCJW72Uzq0YNUu3PTk67t9yRbKc37GnTU3kkMWmNHJ7uEu5D1ReyySBys5j9K4jdt8aEZnXX7MpVGhlQOmKV2x9h71nMXro0c+aYRplj/kjnrn7Lu6YRnMFJG+kD0s29/Kr0oVT/gktSbW4FwuzDE4keV0QPWufse7sfzQh6msJGtl+FPjxjevqxJ1KjSL0Qy1HwSaNQlx9WbxZ9DLKcw9izmH4l/O7140OyeILgx7t3Wp+J4lR/eD7470MXJZ9q1p1lQhAQBP4tBOL/1kayjyAgCAgCgoAgIAgIAoKAICAICAKCwM6BgJ79iXlQ1aJZzlB2Hw3f3+p4EbnvvU8a5FM20598BkYJiroTQPBBMcoVtXlM5cinGXv5RbK5KBKHq8+bT3ZFhNvXP5KoHMKvOdcnE4A//Ij8mimCzXr6cbKQu9JF/kc98BHSEyZ5eSBRKEih0JK6rBNyPf7iHZOVq7wHfkwo+l5QNe5ZAvH0Ls4IFy4IxPk80+2Jp8lZsozUddcgz2UTr8gQFK3uoMcyiUMQvfYu2PPQQ4g4jynnUmWC7ptvoqvV/7yK7Nju3h35bGxcHIgL9Ph9b9R8mnOXwP7lynpFfviZ1qxJ5SsN+BLSG9jFSnp+PsHJ5+E1EYSptzfIasab781+v/1G9OeG4K5e+/e1eD/VvL35+difCwZxftf0ok28onhpslm5WR578xk0MP8V5w4So3w/xyEHeVjVlZeTdeH5RIcc7PnjO6FfH0Vu34Tyd3dgxuTk3wXkHngouSDQicnr9euRRxVz5cpgDt+Zb771zluhAr4Xv5BGnlQHZ7U4J+thh3rnXbKUWKHsDhjk5SLdpwpRKahA53/t5XbNkRbC21w+BQFBYEshIATolkJa7iMICAKCgCAgCAgCgoAgIAgIAoLAjoKATwwy4ZOP+f5MHLH5fa8X/uTCQmxMeDKxxeaPeb3Iz9gnH5FTBmHObLw/iNOsBpLTAsHpXNgROSRf8UjDgLMG+WUIUX/s/j5EKLKUtNWrjTox2Q82rgBxOm4i0XIoWWFc2d5Utw/6BNtHHYnQbPxqPuNjj6z9/Asi/slmTBQWJFSbPjnp+zogE4N2Q3eiBwPK0j//BCmJn2zW9gzkU33Lmw0ULTIDftEof21VvJ8fl/s9j+yMIjx9j2ogPbmQEVuwCjv3g2cqvhtZ380nt2otnvGMz+IXovLHAtfYprXkdrue9FPPkEbxLAc/xvjrkwaJuvVG0pxndO58r7o8V5jPZvEYWW+MJLdRM+yjPRL0yac977S9+Y8B1otDyUXBLw0CVOM7yC5igoAgsG0gIATotvEe5BSCgCAgCAgCgoAgIAgIAoKAICAIbD8IlNzTO+s6qObyMa6wDuVfksj010etZV82rsztE6D+mDcT+anHBQhPVgqyQRVojPeJ49dfnzgEoWbvU5PID5kG6UZH1ye1/36mYI+e8yURwpiTSsoBARLR2zH758sg10CwGmPVIgr5GPWpfxZWMyL3aJLk/OmnlBqUz8mKyKClj/nPEPTJ1n7okWwz0eM++Rk9Gx4Nkp/hmejeDz9Ej6ePbtpM7uFHp4/m7OtxEyiGiu76qs7kPj2U9KTJIFtxP8YcVdsV1Lea2y++5BGprND0bRd8LxhjKEKNCtW0Qbiy2Q65PibHNUCu2/qkx4PcZtKU30OlSqSaNCJ10flkndjSW+P/N5HrO+55yqcgIAhsQQSsLXgvuZUgIAgIAoKAICAICAKCgCAgCAgCgsCOgAAThTA9C6rFPEyh4jabfv1NczVEo2llfvhzetZs7D+7UH920F/NJbfTld5mKLoT+/l7itsbKK43k4UK5VwBPf43yNoe13k+TDImyE+FsfjGtRSfOpliTz1OsRHPU/zrL8havgQh5YlfmX+B4jNh1pyPzb68NxX8mWxbj4NsZOKVQ6k5pJqt8fEUX/KtOYs5B9bE/1pH8c9mpchdPgdCv1lBGEPlcN6Xf1Ti/KZ6+wP3efulf25YY3yD56BFUDb65pOu6FvvT0zuTcDCvw89FkHu8nOsXRU+B85EK5f6O4evUEkm9wvuPTahJmVvn9gFwagSe/MaHxe18XeE/9f19v0V4fO+DXsqtTdjl8CHn8cnyN1255H71Vek6h5CsUceovi3cym+6Q+K/YXnXPYdxYY9DYKyhdmRQ9lNmgD01BOP4HuBdwi/WOI7wu/H4mJK/rtHegQ2haJTsQEP4Lvxpfm+xHjdD4so9uKwJPmpQazrz+d4/onvvOnIhyAgCGx1BIQA3cqvYOXKlTRv3jyaPXs2TZ06lWbNmoUUL9/QCiT+3hzMabKVzym3FwQEAUFAEBAEBAFBQBAQBAQBQcBHQB1/HBFXvP5yLumZIPMKMdXmVONhws3R8vuRy448Aqq9SkRLlxEtAeFWCWHtHCaexTSINeeSy718mfCxbr2JVKA6uOUTWZiLD0jkgfT3urwTSK20scScxWdofbLv6V33rUoWiDDf4nGoBxNmdbmCrE9nJEk5HlaBauzBc5gltQMh3o0akjX+bVJVqyZ2Yy41oWLFSJxD2dMNZG2cc1bCgueI16pFnN803awTmiSH4hxGn7D41V38ZvJqfTCZ4pzLEhY6BxSPdPmlST+/EefQ+YSF9j7lJCIUygpZxw4US+zN4z4useLFKf7lJ0RlSqfcUQgrDn/f4qyeTZh5Hr+iPFIBuG3O8qeSV8VqzoSpk1t578ZPG1D/SIoFCngFfS3O3dm9q7/UXFWbU8L9wN7+hPl+swL0iHqkqlTxh+UqCAgC2wAC/woB+jf/lcP/a04RHqoAkvHlqObHP+s4vGEnsPX4a+DgwYOpYcOGVBJhHFXwj2LdunXpmGOOoaZNm1KDBg2oTp06SHezDyIl9qQjjjiCunTpQmPHjv1HGO8EkMojCgKCgCAgCAgCgoAgIAgIAoLAFkZAgUAzhX1wX+fKa0hviCh2EziTQvEj2gNk3XrknwRxqtqfE5gNN5mIUiATfeN2kJzyx/2rHovQdz9nJgr8qEtShJnvE7oGCFEaPSb371nHNQwtpf085Wt4MNXTVSqnlI4YzkmCIV9k0j6cmVcxqaQ/GrEL2ge74Tbn2wxaBFkXnM5oV9w7Y8gfsK68zG/md938V8jPuir1bkMT6Lhc7Z5TJfiWnnfUH09cYzf2TCk1UeTKnTg5zSPV1YxBgLdQt92cmoxoxbhwVeC7wjk9c5kGr+HedJtxsa6PIKxzLZY5QUAQ+M8R+McE6K+Q9F999dVUr149KoFKcmXKlKEWLVpQ7969adOmTXkd/CtI1KviL1z8M3DgwLzWbK9Oq5DkmfFiwvOaa66hmTNnIjICfxnKYTb+sZ8zZw49+eST1Lp1azr00EMNEZpjiUwJAoKAICAICAKCgCAgCAgCgoAgsEUQMCTPQQcQfTWPnFatSXMuywhjsYx+Zhgqbyd+T8TvQfq5FyI8vSGNXJFcWds3/ebbxGPZzO1zf3LKGtSfVI58oe4XyO3pKwB5FULbnbt7J9dnNO7pGx56fyo5XJAnwpzZn5CuXCM0owc/EeoHO2rM2FQXGHFFdztLsSd78JMp30TLObZxxpg/4J6WpobE/m6w0JDviKu94LtAz2u6Tbxw8YwJDLgnemre4Jw9LxB2H5gw42m/97qXXRnwCDfdS9LIUVSJd3LwC5wugYsa+ebeeoffzLjqG24Jjz2DXKFrA2RrYFbjd3H38SGh74ruE60U5mX662/IaQaFKRc+Qqh9LoI/cBtpCgKCwBZEQOF/Rrqo95s8eTJ16NAB+at/jly6H/4qNmzYMDruOIRF5LDPPvuMjjrqKOPRq1cvuuuuu3J4b79Tv//+OzVp0oTmzp2bfAj+C2YlhA/su+++tNdee1FxyP2LIVE2k54c+s6K2B9//BFF8r6nv/CPvm8cHjBgwADq1q2bP7RdXbt3706DBg0yhPf2+gzbFeByWEFAEBAEBAFBQBAQBAQBQeA/REAvXkxOc4SJc8GZEnuQQki5OvVkL5SbiU6QglyUhrioEAR4CoVikhXRUVDGurQjKQ5vR6izXraMNCq3axBThEI4VL2ad3IuOMNFhTg0nkt6s9qU+/zz8yqihYuMn7riUooNGUwaBCdXcNfvjDNzGudQUDSqY48hvfoX0g89irOWyKg8TuXLIZYcoeGsFORq5pzH0ydL09SDtBvuraAn4nlWCXKuzSAxyuHzXLW8YQM8y0aibxZ48xb2Lg4l7HEYZ3Xju1MMbuZ+3tP+t5+MKbAmu8B7ViYnoZz8V4wxMmH7eEf8shmTbHQDfv81RakYAzZOJeCg7ZPk3Pfn2rcjgkKWyWozxvcoW4bU+e0p9tADRsHrHIDcof734OQTSV14vskHyvvq7xYi9+wo0sNf9u61O+7N9/9tDRGH4h96iLfvRryn3TC+K74DS5cRLV+Bokg4B6t6l3kEvGrelNTFF5LiXKX4/ulFi0m/OZr0iyOQDxbnP/Jwir07nlTp0t695FMQEAS2GQSKTIAyKXfQQQf3MVtCAABAAElEQVTh/zn4H0LCqkFev2bNmpCikYm6u+++m26//XbfLeO6MxCgjFPLli2N4pMBqF+/PvXo0YOaN29uiM8MUNIGOE0A5wd94YUXDKnMfTYOiT/llHAOkrSl22RXCNBt8rXIoQQBQUAQEAQEAUFAEBAEBIF/jIBevZpcDoMfNTr7Hpw789GBZJ3WmtxXXye3+w0eQRi1AtwZK+iswQ+T5orgJ7RMkltR7v5Y7IeFJmeoc/V1RPO/8YejrzgPtWhONPS56Pn00ddBnqHQTl721GCil14lmvpBXu6xGVCVcv7K3wKFf3KtvBnY3f9gLo/knOp7N+lb70r2C220a0sEsjAv69qF6NHsCteMPXrivQx4OGM4cuBCYD1iZHYC1V8EMlRB9UsbN5C+KTv34Lub6+mtyUL4u9v6TI9UDU0GOrvtZr6zTIjnUq2aFTGL1BWXkTWgHykmV8UEAUFgm0OgyARou3bt6I033jAPcsEFFxglHysYWUj66aef0s0330xTpuCvWAm744476J577vG7oevOQICyErZTp07mudu3b08jRoxIJnkOgZFHZ9y4cXTGGWcQk6CcN/SLL1CZMJCTJI8ttrqLEKBb/RXIAQQBQUAQEAQEAUFAEBAEBIH/BAH92efkvvKaV7kdKcBYZan2q02q9Smkzj6LFCs2E6ahttMgQjl/p168xFN1IkJONTyWLJCfrLBjYpVD6416FAQTHXgAFJdQF3Iod7FdPbUeqzRZBcrWCkTpe/hd1IbysFZNsjgXKFSmCrUVNFSOetwE0i9AqWdUm2BZkcbNqAB5LZNWHHnHc76SkQsF+eHXvqKTfdn49zD2ZcNWZEGV6FdcZ4Wi3+Z5fu5jjyZ19FHEz03TPgJBO997Fp5P9+exf8uCe/PzlID6k9W1rK5klSUXUVr7e+os/9Z9o/ZhHPzoRr43Y+jjxOdktWhC8JOBCatzUcRJHbg/lMLfE02ZGibQIdJCNWGj1rRuvt6ojDWrkvGcCvU1VNMm5L49lmjWx0aladSkPy7HdwhnQgSruS+LvPh7wOdEPk+Ti5TVoiDtaeRrRMgva/Xu5amLWSEKxS/neFWNjyd13rnmux712DImCAgC2wgCHAKfr6FiOWvZzQ+I0KzLHn30UQ1iLunbr1+/SF8QpkmfXr16Rfps74OXXXaZeUbk79QoFvV/P87DDz+cxGzRokX/935begOEvZvzI+frlr613E8QEAQEAUFAEBAEBAFBQBAQBLYTBNyCAl3QqJkuoGK64IBDtDv/68iTu7atCw6r7/mxL37sW+/QbpbfvezX3gj5sr8z7IXovR1HF1TcN+xvFdfOH39E+jszZuqCPcqG/Rs1j/RF6jNdUKla2HeXErrg2wWR/gX39Qv78rOeeEqkLw8WlKmY4e+uWxfpX/DNNxm+BWUqRPqavXcvk+HvLFsW6c/j5h3yef2f2nUifc3ehx+T8vP9J70b6Y/cpRl4281aRfryoH3Dzd7eajfv2ri5dsFxRJm7YYO2r7jK88M7N2ev3zDKVcYEAUFgO0EAf3LJ377hv6gk7IEHHvCbGVcu8jNy5EjadVf8VQ7GqtCXX345w29nGPjoI/x1D9amTRvahf/i9n/aWWchNCJh332Xmazan5OrICAICAKCgCAgCAgCgoAgIAgIAtsrAvrpZ4mm43epqvtQbNp7pOpA4RdhCspB6xaEhPuG/JYc3sxV6qMsxiHewTlUplcdLohyRYpP/Lp8zVXhuZNPIitLkSWrwbFEUBoGzRqXKuYUHI/h3LFRrwSHSE0YTfED9g+N+Z34zTf6zeQ19jpCxLOYNeyp8AyehZWwURY/8MCMYWv6+xlj/oA16R2/mbxa6VXnEzNmHBgHzRo2JNgNta2XXwj1+V3FWyJVQYTFUAAp/ifSBiBU3Td1TRe/mXFVPRCCz8ZK05o1KDbxHVJQHUeZgjKW88kS8oz6Sl/rsk5RrjImCAgC2wkCRSJAFyxYYB6LK77XqFEj5yOeffbZhvTkEG2QwdSxY0eaOnVqzjU74uRyls7DuNL9v2HlypVLEqmb/HCMf2Nj2UMQEAQEAUFAEBAEBAFBQBAQBASBbQQBt98AcxLOG6oqVMh5Ku2HTbMXwpj18y/m9Kc9S6TmORScCbFsxiHrAVNHHBboRTTfGRceDBZGCs+Q88BDoRHd4fJQv7CO8+XcrC4KxHHIONS7CKbK5Cji4xcqync/LjgVtA2bgr1Qm1MVhKx4itwMjSc6UAB7YeyJvp43P8rNjJncnJzegA3h73oNQv9zmMspDj6YlvLYr1aqLS1BQBDY7hAoEgGKEG7zgLtznpA8rG3btvTQQ94/6ryW+z6JmsfyHcKlVi3vH8mZM2f+K88zY8YMkwOUNzv88MP/lT1lE0FAEBAEBAFBQBAQBAQBQUAQEAS2FQT0F196VeVB4lmntyn8WGPGej6s2IS59z1ImvOERpg7egxRkPj6629yr+kW4Yl9eI+HHg7NaZCWbjpJl/Cwe98f8uWOHvRYxlhyYMKkZNM0VqwgnaUQkj1xctiXe81aZY5hxAZp6zQIK1G5uro7b160/2uvZ4w7depljPGADbLZPfWMjDl7xqyMMeM/+xOiVb+E5txzL0DqT+RpjTDdDorLoK1bT/aEicGRZBtpEcitXB05TP9Ijumnhybb6Q09Hvsw2c0kKD9H7YPIjcIVC93Fi8ndtzbRipXJbfTb7yTb0hAEBIHtD4F4UY68HycHhq3AP8x//PEHlSpVqtDl1113HX3//femWNLvv/9uKpfPmjUrrwrohW6+HTgceeSRpljRK6+8Qpdccgk1aZL2P6IiPMPatWupZ8+eZkXZsmULVeEWYWtxFQQEAUFAEBAEBAFBQBAQBAQBQWCbQEB/60UeckEk31xUVNcvv0r6oxle0SOIclStmqROPZl81Z+69UbSTEJC3efUOojU+ShMc8zRRBz6/SOKIHHBpXfGe1uWxBgX5AEBqp98muyhz3tKQhBjFMOvyUySMQHqq0P3hgqViTyscavtRy5C2MkFicfiUfZl8jVI6h10AIryLCAmzey3QbouWOjdj30Rps8FkWjzZu8shxxClCAnnfJV/Ecu/Iqz2iq3QtKcK1Gsya17FCXKNqX25vP4z5gaJfpjXeF7B/2PO4Fsgwnu4JOM6Zhw6gHGF3vr+B5kJ1LmmW1wDH4XSQue6+TTyWYRVhzvhUVZu2IfB/fhokVs7Fv3YKKvQPD++ps3FvHpf6+o40VEI142xaDck9qQWxWFjJqeYMLi+R3rD6YTfZ1I/4e91RWXkh7yDOk5X5Lb+z5ymbjmQkz8/alSmVSTRmRdeD6p+ninYoKAILDNIlAkBahPgPLToNBR3g/Vv39/8nNXLlmyhE466SRDoOa9wXbseMstt5iQ9c34n9vpp59OQ4YMwb/ZgX/Y83w2rvh+4oknGjKVl1x55ZV5rhQ3QUAQEAQEAUFAEBAEBAFBQBAQBLYjBCD8MIbUa/qnn0wleLdpK9JPPYvq6SCmflvjhTBPnUbuDbcYopH91blne+uYiPtltVFfuud1ILf1meR2udYjP3fxNEDqZKgnO3X0/PmTf0eD2tBUSGficz3aAWJQPZH2+y+TnUx+srFfkPxk4g+py4x9+RURh6oz2cl+TEZib/3e+6n9DwMBWhTbq3z+3iOH5/YNPKNxHDwot39wtutVwZ6Hgb8fX0OYgKTsmpafkzH3f4LkJ+/a4BgQnbum9udUBOvWeTjye/LJzxhymw58kOioIz1fJlizWeJ7ZR12KFkLvyby88r+COXtCyNI9+pN+omnUuQncLYmjyXFhCnbtA/JveNuoo9mespQJlvxbvUjj5Nz9PHknHM+af+7662QT0FAENiGEPD+9c/zQLVr16ajjz6aZs+eTb179yYmRM8999xCV3Me0OHDhxOqyBOHgn/++efUoEEDuvXWWwtdu707cAh837596YYbbjCkLxOX3GYlaL169YyKc++996bixYsjd/NuhGqA+H/jZvzbvg5/pPyRUOmdpk2bhj8I4q9ZCWMi9N577/W7chUEBAFBQBAQBAQBQUAQEAQEAUFgx0EgkfNTL1xIzlENiVb+RFS2DFnXXk2qzalE1fYlAiGmP/2c9DNDSY+baJ7dvf5mD4Oa1Sk2/DlyOR/nwkUemVmxItSg9QlhjOSeeyGZXJO+GpSJtoPrECFi0RBrrDQsAYXobhifO9/sqS/qlMKXi+7gPIbgK0AOSlYksgrxp589HyY5P4RS1bf6R5IFFSE1Ot4jP3FePjcrVY2NGOl7etdixYj2rUq0e3GcCeHdiMAMkYmrfw375+qBlAtZaURx8vNx7ky+FvwNRSYIRd+u6e63vGJRwRyp/IxmTYJkfPTxlC+TvhX3TmCCeVZ7Johocy8XhOjjQ1L+XFNkDQhEQwxjmHOUMmm8dJnnM/tT74zcK49xLlbE5CITofjdmUqVRHuT8dc9byQCV2GMsctinEsWpzCkegz4WvPnkPvJJ6SRb1YzSY0oV2Jlce1apLp0JuusM0jjXTqNmns7gtRV57ZD0awLSR1Wl4jvtQih8m+OJj34SdKvjSIHhLcp2oXf8cUEAUFgG0OgqNXqp0+fbv7NwGOY6/HHH69B6GkQdIVutXr1an3ooYcm14MYTbZ79epV6Prt2WHo0KEaJGfyeX38inqFehZpYX7bbqHo1q2bwWDgwIHb7TPIwQUBQUAQEAQEAUFAEBAEBAFB4L9DwF26VBdQMV1gFTdX+4SW2v3116w3LGhxsufPa/Bjt78wq6/7yy+6QO3m/ST8nfemZPUvqF0ntHdBpWrZfT/+OOzL+/e8Mbv/bqUy/LM5F8yYmeFbULVWNnddcEDdDH/7r78i/Qvwe7rBO4GHafe8KdKXBwsaNcvwd7LszeMZe596Rva9G7fI8EdO00h/FCnSTq97w/516kX68qAzfoLne/jRWX3SJ+zb7kzub3folD6d7Ls//KALsK951gaNNYozJeekIQgIAtsGAkUKgWfuFoQn3XffffhjToy79OGHH9KDDz5I33zzjenn+ihfvjy9//77RkXKfqaqWmIB4Mi1dLuf4/yfnAuVVa8V8dfHolgx/GWJw+fHjBlD48ePJ87/KSYICAKCgCAgCAgCgoAgIAgIAoLAjoiAql6dqDIUf6ykhPLPGvsWKT+kPOKBrZt6hkdPbBHuB3pqr72gFqyZCj+vVZOsZk0DHmnN664JDajrAwrJ0AzEkYiWNDk3A+PW/X0CvbRmZ6hCg1ajerAXascbHBvqm86MqZlj/si4t/yWd4WyMRYMKQ/MxvF7ulGwBsZid2aP1rSGDA54ogn1p5VlbzN+8EEhf6tfdkzU0wFVKa+CUjPGis8IU1CkWnfdTnTqSanZu25LtdNait9zOfwuzbk8J05Km83sakRjsjrUN+uGHn4z46qq4pyTxpJRJ8/8mPSw5zN8ZEAQEAS2LgLQvBfdbr75ZkOEcjj3/PleSEDlypXz2ojJOyZNBw0aRHfffTciDDbktW5HcNoL/7Pt06eP+Vm2bBlxMaiFCOvgcHcuKrUeuWB2QahAiRIlqGTJksTh83Xq1KHDDjvMjO0IGMgzCAKCgCAgCAgCgoAgIAgIAoKAIJALAc25I/0q7hwmzQVp/ByPEQtV82ZEFfYyeT/NdFrV8YwlgSrwXCgpl6kFKGQUcNBI65bT0oU9HL7P4exRNmp0eNRS4X5hveEInb/5+kgv9fBjoXPTpk2RfslBJjC5qE/C3FFvUaxjB78bvn7xZbi/BwoU5bKyCGEPGq/nlAMRZiGnJ95+yorn3ttBtXZKpEDgRRZ/D7KYwjNat95Ebk/8dOpM6uPppPbZJ4s3+PeHByfD8NXZbUkdgkJLOUyBSLYeRJGkcy4gd9CjZF3WKYe3TAkCgsAWR+D/FaIir6d++eWX/1FYNnJc6g4dOmgQffqee+75f48i67cDBCQEfjt4SXJEQUAQEAQEAUFAEBAEBAFBYCsi4Prh3qUqeCHFJcpq57kXkI7RzTiVu3y5tk9rmwxTNiHIZfbW7pIlxtdZv147ixaBU3VM3+4/MOy7W0ntPDEkGbLsrlmj3UTItfvzz7pg9zJhf4TlOxMmmb1M6PikybqgoMDrX3xp2JdDystW0vbatd789I90wbcLvHN8PDvTl/1vvNWbxxobPn7YesFhR2X6I5TfRkg/m/P9D9pZtcpbO3d+KMTfD0F3P/vcmwcmwb3tMe9k7s1nmZuZ5q5gZkQoPvsCQzYO/XYRUu+HgNtPPZu5957ltPPTT8Y/+OEg1VtBzQMz/TtfbdxcvENOheAm8Lb79st4Tvv67KH7vAmvtZu18u5RuTre5cTw3n//7fXnAcNiJT2/ClU1fxfyMd6/oGxFs85dtiyfJeIjCAgCWwgBxffZ4qyr3DCJABeGWrNmDXI5bzQ/XAipFBJzswK0HMI8uL8jWffu3Y36FzlACWTojvRo8iyCgCAgCAgCgoAgIAgIAoKAIPAvIOAOf4lcFB0y6kxEyOnnh3u7IlxdIdzZhMgjklB/+hnpiZNRSAfKRRQlUj26kb79Ls+XC/ak/6rLhXo4rJ4NBXvUaa29yvLc59+7uIK4X7mcC9xwASC/3xIq08lT2DM/45Rx/trCVsTha4d0j4WtyH++dGmveFA+KxgztiBuPMbFmJg1YBVpcG4XBJQWACPfgvjyWHq/CqJGVyQUtLxvjeqk9t/P+GkuVrUIak5//+rViRA1mbf1uJbooUdInXkaxUa9mnMZV2p3z2pPespUz49D7E0xpgQ1wv2AYtaa+xlZhag/gzd0mp9k9rbeG0850ysEF0lbEBAE/nME8H8AsS2JAIe5Dx48mBo2bGhIzipVqlDdunXpmGOOoaZNm1KDBg1M2Ps+kOLvueeedMQRR1CXLl1o7Nix+H+BcNVb8l3JvQQBQUAQEAQEAUFAEBAEBAFBYCsg4KdJw+9DseeeIWv4MKLq1YgWLyH9yOPk9riR3DvuJj36HaK//yZ13jkUm/Mx0RWBkOOo35188pMfCb9vWd1BmlVNhEAzARYkLDkcPNjPFg4eBQ9XSq9VI2omeuzAA6PHs41yhfV8rXnTfD29PKDHNQj7M44bUHmdq6+nYaquuSqcOzSIL+8S7JcvhwrqZ3t7M/nJey1ZSnrCJNLjJhAxAcpjjB3Muv0mompVPf/CPu++naxWLf/H3pXAy1S+4ec7cwvRQhvaLG20aCW0SBJSUtrLUpRSolIkS5YohVZKWhEqRVRKQkkqkeXfIu2bNu3bnXO+//N+Z2bumXtn5t4byvK+v9/cOed87/nOd5657phnnvd9wixZazFhSAqbK68AKpQPMwsTuxHyU9ZTGvLTTZhsCVCCtRSzVB1WBBSBdYhA+NdlLSecO3cujjnmmGJnufDCC9G8eXNn6CO9LjenWL16NVjmj0ceecT1+izJvcf5jePixYvdY/To0dh///0xdOhQnHjiiSU5XXMUAUVAEVAEFAFFQBFQBBQBRUAR2PgQoPGRi48/cU/euWeT5GSvzgWvwb7Kx1fsq1m+PIwoQps1haHJrBjs2l1rFNwrlYWmCVWbYnpDEQpNFWDfWwk8NS1UelI96h9cN1SPyjzSC1T6YK5ZE6pBRQXI/p121L3hnOe2L5hb1J2iXCSp53qVci349LPwIVmiHH2fhF4yRIW5zdZhj00h+IQA/P6HApXh8hXJzHANrAgUYhd/k4QtQ2WqrOubbwpyRKmajDoHkJykMpO+EnQqBlU0JBH5vOKdMOOJJ5OZ4fGmTYGDD4Rhj1W7Fdf9+pvAnLkh2SuE5cI3wnxRa7ZoDkx6jOsUUpFrFmJPiOCX5gBvLyNx+WwBKXpUw5BM/mo1MeS9bVeRBlY7A9KPVZSW330P++zMcG6qKaVXph37AOwnn7o5xETItD0X9o03YQffhOBJvk6fEFOJeocD8noIxj/La8l1i0nW1hWAmbOAfoMQtGwR5lapHD7n+Bk8OokK4w68Z94vCV/TtEmYLa+9YC+v0dLlsI894V7LeOcuyBvNfqAlDHdPzDVUGWsoAorAhoPAPy6BFzXiQw89hFtvvRXLly+HlHJXSb5RZbi/L774gl+y7epGxAVdyp+vueYa/u3nH9JNPNbwD6kQxMuWLUvdqdy34LX77rtDzJHK8Q+6uL0L6fknv30UYyT2SHXO8X9FmlF7LCMQzDfW8nEtgU/9CuiGIqAIKAKKgCKgCCgCioAioAhkQMCS7POr7EHyrwxiX38Kw/ZgxYXf/GSnJnR5R9RF3oJ5GU9hn0nYHXYpGDv2GMTEZV4ItgwRP6wBsOitghGa7OStThBzBUfdFnt9AhULEXA3DkBer2sKZYa78TwSeVGVKYnOvL9IZGaIuCgwy1dKH9l3H+S9U8iQKJER3+/g0Dwqckae/TOyl74ZN4Var1FVmzfh4fSkxJ5/xVVU4kYIQRKGefm/ZsyVg/HdagKff5EaN5d1RuyOkan96EZANWhw4ikFhypWRN4PJLyzRDBhIoJ2F6ZaCHj33Anvoo5Zssm1Ll0Gv96Rjvj2bugDr2/vrLnxQ+sDby12497DY+Gdf27W3OSA/eQT+NX3cQR97LsvYaSVgoYioAhsEAh4/2QVQtKdf/756NChgyM/ZQ5xds8Vc+bMSQ1//fXXECf5c845h0Zzf6WOb4ob4nIvis0k+Xn44YeDplEQRaiQwgsWLMC0adMwadIkPPzww5gwYQKmTJmCWbNm4T06DkrJvGB70UUXOYd4+WZTSMRnnnlmU4RL70kRUAQUAUVAEVAEFAFFQBFQBDZzBMxOO8E0PpbKwz8R3HRLsWgEFORIKXUyvEH9k5tFnmP0WcDeexYcpwIxG/npkuj+HQ1zfa/obtp2nig9KViJRjby0+Vc0C6ayjL/aun7kb28rai+LBzzXyp8pGD/1psKtmWrOG+JBkek5ZvhN6ftp+1c1S1t1zRulLZfeMe77JL0Qz2uSt+P7Jnj+LqLAjMR5mKSmznCO+csmCsuCzNEXCWq1RwR9ODrx56x5uKOOclPmcLrfnlqpuCa62CFhC4mgv6DyLJS/dm6lZKfxWClw4rAv41A+l/nElxdyM82bdpg/PjxqewKLCeQ47mibt26jrgT9WcyJk6ciKaU4NM1L3lok3uePHmyIznlxs466yy89tpr7llUnyUJaRXQsGFD3HPPPXjqqaccCSrnCYEsZKiGIqAIKAKKgCKgCCgCioAioAgoApsaAo7EJJ9lhw5DIGXrOSK49vrUqBBo3nGNU/uFN2gl78qxU8fZhzKIqBNTx5Mbo+9Lbrlnm1AEph2M7hT6jBaf8lR0NH37o4/T999/P32/mD0jJejZ4oGH0kfKbJm+X3iPrQTS4qFxabvRHe/TT6O7sMWQlPZAlulHwnz0UWSv0Oa774UtBOQwCU1zUstCCem7lqIi+3iizF96iL69ND0hsmd5Xfv8LNeOwBtKorKYcG0X9q8dZn29GkGu15JZwT1jYB98hMrlLeH1L/idLOYyOqwIKAL/EgKlJkAffPBBTJ061S1Pyrj79OmDT/kH8Oyzz8655L322gvDhw/H559/jn79+qVK3+fNm4d7770357kb8+Crr77qln/ggQc6haeUsP/TaNGiBW65JfwGVBSlH+V64/inF9HzFAFFQBFQBBQBRUARUAQUAUVAEfiHCEirNPvxx7CvLYRd8T9Y9prMFZYVb5Y9MO3C12EjxJqpVxfeoBvIKlk6dp8Jv3dfSG7hsOwLipkvhIfZo9J7qICwlGu7NchaZE1CkC1eAvzAXo877pCaKjj1DIgzeOFwZCfPSwsSXP7gofDZj9Jnf1C/Vx/4kyYj/t13iNeqk5bqds5ui+C99+GIOulvSYLOskVa8CQ/U8+aXSQ/zj6mliY8lkSg3JtdtQpxfu6LlynaBsCefyHiY+5DsORtBA+PQ8B+ncHKlYh37wFMZv/KaPz0M+IntkKcJdrxgTcifl4HxIfdijjbrsUbHAP0HRDNhu3dDwFfE1HXBuMmIBj/KIJ33kUw72X4zU5Oy0Wbc+Bf3AU+FZLi5u7W/f5Kt+9fcjlstKSdZwadLoX98MP0ObhnyRX4Us6eDL5eQfOTEIwZCxttFSC5MsYenb60KJA+oruHhklRJXBymuSzTfyeGPYLFSOk4sKwp2psEoVfZcMydnt1T9hly4ucZvna+12uQNA5VIx6o+6AqVGjSJ4eUAQUgf8WgVL1ABWl5t57742P+SaQR1m6GPqIqvGfxOOPP47TTz/dnbrzzjtjFf+wl5fm0ZtY1K5dG++88w569+6NQYOK/5apuNuP9lKVMngxlVrfMWzYMIwcOXKdKE6lt+nvfGO88sorXS/T9b12nV8RUAQUgY0Bga9++BInD2yK73/9fmNYrq5REdhgEdh3l1p4us8L9AGJbbBr1IUpApsqApZtzoIhN9M4Zgrw1dcFt7lFHkyjY+i63RVesxNSx4Np02FH3gH78iup/o1ucNddYM46Hd61V8PssAMCko1Bn/5kvDi6VTmYY44KS8XZaszSyAj/ezc1pxkzCrGOHRDMpLP4rbfBirlPfjw1DjGlOYS9Mdln0pzaiiTt687oyCWQ5DJHc+4a1Z05kRWilEY4qZDrzn05tVvsBj8vOzOkYhOZUJ3XLI24RUq9hcwtSWxL4pTkZ4lCyuzF6Ojb70qUjhP5WVT6o1IdWWzsvBNwWmvg7nvCVI/3QJLb1K7l2gYI4YtXF4TGRJLRoS0MvTHso5PD/B3oIi+vAbkDSB/XVyg0+uJLN2aOP87l23PaUTHaArFp/B3MEH6v66kovgXekIHwepIkLmH4o++FvaRrQfYhB8GIqlX6e36wKvwd/ptVrfxd9+66DV6nCIlbcJZuKQKKwH+MAP8qlzykBFvIT4l27dr9Y/JTzpcyeiFPpQxe+mFKSb30udzUQhSvErvR1W5dxPbsWSNl8UJG/8FvBv+N+OCDD5zJ1bq81q/FfBO8Lq+lcykCioAisKEjsPTjJXjn8/9t6MvU9SkCGzwC3/y4Gt/9/C12LmxCssGvXBeoCGzcCIgSL+jQCfgt0SNRHLpFkSeKzfdXwr7wYvho0xrm9uGwnS6BnfFceNNbbgHsv48zjXFKPpaj21tGwh9zP7xxD8Lr3dO5uQe9+8HOfolO4gW9Pt0E4vIuik4hBOlc7p9xTkjCymAevwzZjwSbOKN/xs9lQpiR/JSwQtKKiS+d3p0a9IcfwvJoN5r4sd22wI8/hTs1qegrDQEqqsFfI+RrdN7C2zRiKjEBKqQbycAk+Vd4qiL7hx4SurAXGchwgAQzKhHPkhKgQgIKoUxFabHR8QKYA/aDFQK0ejWSpsR/AZW5fKSCpeOovitJxQ/h1a/niMTg1FMQ3DAIWE418RNPpVLdBslq7zoaK9OZXkrbHSWcqzUfP0O74Ofp0oR3wP7w5QT5vZbPsW8tgeUjFfw9MyefCG/wABg63GsoAorAholAqQhQcXtPxrXXXpvc/MfPgwcPdgSoTBCd+x9PuAGeWLNmTSxZssT1Ab344ovXeoVSUp/smXrwwXyz+Rdi9OjRGDBgQFgyspbX69u3L8aMGYNatfgfEQ1FQBFQBBSBNAQa7d8YY7uOSzumO4qAIlAyBI64ug6+JfnJ4tuSnaBZioAisE4QCB56BEF7kp8M0/pkeP2uh6lzYGpuKS23DzyMYMBg16vREZhUbwqJJ30STdvzYISgTISoOoN+/OzxzEwErU4DWILstTkVsVnPwlI44wizr0hasnrQCCl5RD34lUhMsdLMstzakagkLsXd24iKMFLqLOXn4mDuiEwh3rYJryu9Hr0+vWBfZTn9lyRJ6QhvqlcD6h8BX0rPhVy9/6HECvkk1z3vbJhvviX5+gPVLrvC9Ra97a4CV/dfeY8SJCzNg1QQrngXRsq+uW/32RumUkX2zqR5j8wd6ddp+vWmcnUeIKXWNIHC1hWAJo1DIlbI27/+KiA/RQnKe8Trb4SqW1G/cy3Yd1+AJesuZs8Jn+VnBVZcntKKBCfXLSRnZaoyt+H9PTktnPe779P7o151BVW3FWA++dj147TV9+D9/gjcOYp17AFAxa+LmAfTtQuJ7P3Yg3MZqHAKlZp1DuAHfbY3uONugEpeW/dwl25OOcm1N7CvzA+JaZmL6zZHNqRyl5WH/Qe5UnpJltdeHlaIdJb6g+XmoDO82a92qMJ0MxJGlt672GWXxJGiT4Zj8g6Ryi2akvFIMt8c2QDeIw/w92QB8PEnIWac09BAyghxrKEIKAIbNgLsnVHioGu7+3tB06MSn1Nc4g477ODmPOGEE4pL3SjHL7zwQnd/ZcuWtXPmzFmre1izZo096KCD3HyVKlVaq7n+q5O7devm1j9ixIj/agl6XUVAEVAENjgEnls0w5ZvY2zrwS02uLXpghSBjQWBGh2ruH9HbCmxsSxZ16kIbPQIBMtX2PwyW9t8lLH+rSNz3g97U9r88pVcbv52O9ngk09y5vs3DApzt9rOBqtW5czNP6pxmMt15O+xlw3eX5k1P/jzT5u/ZbhmWbdb++IlWfPzd9y1YG7Jr9swe+4ff6TnSv7godnzD66bnu+VzZorA/mmbHp+n/5Z89MwkXXklc+a6+aud1T63B0vzpqf/8j4tNz4jGez5sqA/8Ism19u29Q5/rTpWfODufPCvD1r28D3s+YVHsg/+jh3nj9uQuGh1H7w1uJw7qrVbPDXX6njxW2wd2o496h7ikvVcUVAEdiAEfBKQ8+K2ZHE7rvvXprTcubus88+bvzDDE2Qc564kQz26tXLlaz/yf4lrVq1cm7uf//9d6lXLyrSpk2bOjWpnNy5c+dSz6EnKAKKgCKgCCgCioAioAgoAoqAIrCuEAjYUxF//Q1zcUd4V1ItmCtEzfh7QhX5C8uIk+XIWc5xCs5zz+I5f7AH6A1ZssLDpv35qXHT7XKYvfZM7RfeMKLIPP+cgsMsh/YOqlOwX3jrfK4hErGbs5d755UtG5bdR/LzrstROclelGlRi8rNXCFqzUjkDegX2Su0yR6qaSGtAHKEYfuAtLh5SNpudMdrdZLr25k6xhYIucJrchwxPzdMEVNg6eWZLRo2CBW20lvz3vuyZaUdD2Y8C8x7hcrQ7dgD9MS0seiOOfgg4MD9XdsDe9ud0aGs25amT65dA3ujGulhqqEIKAIbLQKlIkCl/6TE92w6vK4inujRse22266rKTeoeaQE/sYbwzfJn376yRGXVL3ipJNOQp8+fXD//fdjxowZmD17NqS8fd68eXj++echJlFUSaJLly444IADIOXub7zxhrs3IUIHDiz0ZrlB3bUuRhFQBBQBRUARUAQUAUVAEVAENmUELMuorRBP7HXpDepf7K1KqbyrPxaSj47edlyiRDvHmd5NgwGWV9snnszoAJ881bBvaDLsNb0Q0D09V9g1LFtPBolY+803yb2izzNmph3zpdw8S7gyeLrWRyM+fkJ0N317HkvAo7Flmehe0W2W+UfDZ7uArHFdn/Sh9xMl4ulHU3v2KZbBRyPhaB49lNqe/2pYAp84YCfnJkClZNwmS/Kl3F0MjLKEOK97Qwe50aDb1QhemJUlMzxs31qM4PwObseR5oVI4sInewkCOyA+wdSnCw+n7dv/vcOesiFx611zFcyOO6aN644ioAhsXAjklWa5e+yxh0sX0yJx8t5KXOLWMsT9XWJdmQSt5XLWy+lXX301hDwWMlOMi35hM/Dp06e7R2kv2KxZM2cY5ck3ZxqKgCKgCCgCioAioAgoAoqAIqAIrEcEWM0IsPei/fAj544uRjDm8MPCPpUk+0zjRs6tvbgl2EQvSu+Ctgh6XIeA+9IvNFdIz0Y0qA+8PB+WpJutXRt24iQaBrH/IntjmsMOhXdGGzeXm0cMgtgjM2h4LOx5Z8FWooBn4eth/8w9WMV4fGO6kN8LyL1Ewm/RCkaIMXF+pwGP65Up/SyFkHzv/UgmN6/pjbj0+rywPbDinbAnZTV+Tt6Z5NjwO9KIQXfieRcg/tI8eOwZ6oyLuMaA/T/RkwTlG2+6lNQPXj9ei2pUUaiyXylWk5iVdQtpPGRY2C80lczdo45DvM91NOV5K1ynmPvsx3XvStyWsodoNPg5NF6TKlAxlZL+lWJCRNWnFWOf3v2Krvv5F6Jnp20nX0uwDydW0MSR3ECcxHPs2qthEqIpOcFSOBXcfhfsTbc4pTBEaSu/SzSzQotmaXNGd1zPzyu6wBLngK+N7d4V3lXdYMQBPhGutywNlQL2FhWVsDntFJgrLnOjlgQ7SA7bTz4FKLgyggf7j5qyZeGd0BT2+p6wg4YiOPUM2C6XwLuW5Gakd6jl53V779jQfOmXX2GaNYUhbhqKgCKwcSNQKgI0Wa4utywqxVNOOWWt7l4Uj99JE2PGpkyAyv116NABLVu2xMiRI53q82t5wylhlOGbpBCfHTt2dHOU8DRNUwQUAUVAEVAEFAFFQBFQBBQBReAfIWBJatkRtyO4a3RosBOdRdy6xWhHgoY+JYqkSvOYo8P05H4xJxvOb0mABmee74yOoumWO8E5bWksVNYdNi/MgG11OrBmDewjhRSm774HzCwg9cQgyTIPT1EFuGgx7HHNo1MX3e50ATD2wZAoFDf5ASRMk1HYHV6IWDEoYhs0FzwvkHOzBVsI4J5EubesUwjJbCHzntoKEEd0mb933/TMxTQKikZtvk7/ezc8IsTvhRfntoqrVo0GPx8Dv/0enSV9O/HamV49YM8nLpavxLAR8IffBtAxXZSSohB2Rk5+4M413S8HDidxfk47oASvvTfiFgSsErV0mLcyN82RxGRJSFAhVt3c+fFwbmnBcMcIgBWXPo2ZrGD9fUTlK1lSwn5GG4jxVmxgfwQ03hIVqBg0+XfSpKl2LZiqJITld2LpMuDv/HBuEtfemFEQZaqGIqAIbNwIlIoAPf3009G9e3dID0sp615bAlRKvJNx/PHHJzc32ecd+UYgzvfy+JhvKq+99hpWrlxJw8Kf+bf6J6cM3YLf2tFkimZ820DK52vzW846deq4Y5ssMHpjioAioAgoAoqAIqAIKAKKgCKwwSAgjtv+Sex3mCybrlEd5uA6zh3dKUFFVSkKRYYV1++SRJJASrRAQwkr2uwLL4azJ8u/pcR5+0qhA7coJEXtR7JWwk583JGf4Qm5f9qpJD4jisLc2Rz9mGpCIR9LGuVIyiYJ0OLO2Xmn4jIKxoVsXPhmwX5xW7nK+zOdK3hK5LrXxGsnaFjZlnPqHxEqUZcsLSBYSZSb5sfBu74XTL26CCZMDMeSvwvuQpl/GF4/dkNf2NatEAgJ+uxMp2q1SChbt8iDOeF4eNddA3P0UbBUfPqt2rj+nm7GWvvAkIxFXp5zkMeit2AfeBg++5WKk7sraW/ZAsGAwbDTn3FqXiuKXom8WKhs7nk1vOObhMf0pyKgCGz0CJSKABUCT4x8HnvsMdePctCgQbj++txlC9kQuueee1yfSxmvXr06WrRokS11kzxejd+syUNDEVAEFAFFQBFQBBQBRUARUAQUgQ0FAfv55/CPPi4sv65zAGK3D3cEU3R9ltVsfjuqFp+fBcyeg+C+++F1pBIwV+y+G8uuVyNJaBrZLyb81lRzShmzBElY75mp8AopTv2hw2CvowpSiMG7R4e58vOohsCoO+C99joVg9/RGGkvqv4qAKecQXXjb8APVPrJQ4Kl4Gb6FHhUktqvvnKl0qheDfbxKQi6XuVSEO1FefKJwKWdgUmPhaXktVhaTqIOZ54bEnB//RUStHLmTiyNX/gK8Mh4lmWzVF3ayDU/AbF99oJ/IklmUTNG1aRNGgOD+8M8+zzsp5/BKWAbN6Lq80zgs89lRiooE88kCXHbLfCoxrUrVsDksQT+0INJNLJNwJ2jwtzvCpSQYhZlu17Ge6VSlmXwZu+9YE9sBlzdk+pYvpYSn30WPlcoHz5n+pl47YJXF4TkJ3O856eDqyFpvtIpNF0pPOc3FPckw0q5vEQJXvswkTwsy+ZjT0yCKJLd3N9+C1OJBPhee8Ik+n3a5SvgH9sU+JWv65ENELvtVphDDk5O4Z7tRx8huLY37GN8TU8jllNJhJIAjU0cBytENVsdSC9Ys9124dzyrKEIKAKbFgKldahfunSpLVeuHN9d3Jc3tnfv3pZ9LUs8zV9//WWHDRtmY7FYao477rijxOdr4saNQLdu3dzrTvXvxn0junpFQBFQBNYhAs8tmmHLtzG29eAW63BWnUoR2LwQqNGxivt39NUPX25eN653qwisYwTyjz7O5qOMjTdpboPffss6e7Bmjc2PlXO5+XnlbfDue1lzZSDeu2+YW21v90ziMme+P2FimM+15Me2ssHvv2fNT80tufJ4cXbWXBnIr12nYG7my71ki/w6h6Xlxl+aky3VHc/fqmJ6/nMzs+bHH3w4LTf/2KZZc2Ug35RNy8+FeX7dI9Ny87fbOffcZbZJz+90SdZ8nxg4nCtsHz5Xqpw1NzkQxOM2f8/aLt8v5vVJnlOS5yA/3+bvs7+bN372+Vb2c0W834BwzdvuaIPVq3Ol6pgioAhsYghQr166EEfyO++8M3WSlHNLqfZNN92EWbNm4csv2Q8lQyxbtgzDhw/H/vvvjx49elAlH0rr27dvj8suuyzDGZv3IcFnzpw57vERv63SUAQUAUVAEVAEFAFFQBFQBBQBRWB9IhA88xwwj2pFlmR7j02AyWF6K0o5cyYVmhIsaw/63hBuZ/npUX0IKX0WAx4pjWZvxVyRUl4yyVzYHqZcuazpokiNhpdUGkYPRrdpbBMNp/qLHohui9ozGnPmRfeKbu+7T9qxGE13soVlH8pomKOPjO4W3RbFZySC6tUie4U2hw0udEB0KNnDnENVZDQSbunRQ8ltcxTXWb0aFZchjqYdX9tiwt5GDuGDVcDeVG4m+8AWc05Jhu0DD1G9uZI9afeG98AYqmDzcp4W698HpiX7vf70MwL2C9VQBBSBzQeBUhOgAs0FF1yAvn37sm1LeLqQnj179oT08dyF7mnb8c2wfv36OPLII9kbe19UrFgRBx54IK666irX8zIJ72mnnYYxY8Ykd/U5gsCvfDM59thj3WPs2LGREd1UBBQBRUARUAQUAUVAEVAEFAFFYN0jYMc/6iZ1jtslKAH2BvV35jJykqUpT0D37Kyx5ke6qm8ZDm/B58ImNZET/f+9E7qryzGWOYtxTc6YyFL0SATDb4/spW/6q1cXlJInhuKj70lPiu6NZzl5JOytNPrJEjY/n30wF6eNxm8enraftiOl+5GwLHvPFnHpnRqEhkLJHNstUZ6fPBB9vrUQBj/+FB0tsm3pVJ8W7LuZLawQmWwVkIqDD0ptZtoIHp3E8nO61ZO/9YYPW6eGQlbK/Rlen+tgypTJdPkix7yB/d0xy98bCtyKjOsBRUAR2DQRyP31SI57vuGGG9C4cWO0bdsWn376aVqmGPqIwU+2kN6XogZt3Zo9TzQUAUVAEVAEFAFFQBFQBBQBRUARUAT+FQQc4fP6G7BvvAlLEyFDp21Ir0b2nrSvvOrWYFqdVKK1GHo5eOyhGJx8qusFGdQ/GrhlKMxxjWFo7iphP/gAwb1jYW+/i30x/wZ22MGRm77kXnIxvBrVACoyrfTllDE5Z2iBMs97chJMlSrhcRovWfadtF99DVOePSpr1oA5/rgCx3LpCSlmSVSZ+j16wru4I8yee4bnkpy0L86GvfQKt+9+iKJSCLA7RyOougvsJBJidCh3atN99obpemkBWSriHyEgKVSJb1+V/T7pKM6+pJaffa1gKNixt2aRoDI2ToMeEHP8SBJYVLA78j7FNIh9J12IalEITubELyQm7OOJP/6Elfzttw9VjYJf4Rg1Bn7ch+nbG96uu7jRgOpXe30/YNr0wtmI5xGzFs3gHXsM7A8/wFKoBDEXevGlEIfoGSSR4+IeT5Wq+e47MowebCXmC5Eq+YKFR/wCC9v2AvgznoEQ5zjsUPonmZBY5H0HdIYXolHCu6EPvBOpvlxHIb/Ldj5x5zLMSSeWeFbpK+r6kLLHqpXXgH1YLYlr+w37iwrxTxWvOe7YsBdsiWfVREVAEdjQETBS0r82i/ydzYinTZuG8ePHY+bMmciXb70yhLiaN2/eHCeffLIjPtlHNEPWpnfoA77hu2/rSnlrv/Db07p167qzOnfujCuuiLxRJ+YSde3GFt27d8fIkSPBHqBgP9CNbfm6XkVAEVAE1gsCM996BqcNaYmmBzfHlOsyfHhaL1fVSRWBTQuBmp2qYvWPX+ODe79A5YohWbJp3aHejSKw9ggED5Os7DugwFgoOmW5siFBSWIr9vcvKQIzmpJtO35cM2eGlBpnibtzWJcS6aTRkJBUl10KM3QQgk4kFukIXmzssTvyPn4fAR3bg+v6AELIFQ66gSOf5KFEn17AwCHhdvKnkHZSai5l8kLAJkPMiYR0/Y3mOiWJQf2B6/koSVTl3yAhV7/4siTZQJfOwGhWRibaxBV7UvVqwEcfp6e56kx+tCchmRbdLwdG3JF2KOsOiXAxF8KMZ7OmpAb4ed57/WVg/gIE3Xs4wtaNVSDJmiC5nSmRHOTvljf8ZnidL0qdvi427Lffwt9pN0co533zeammjB/TJGz3sMP2JOS/L3ou78NccRm8nj3SjJyKJuoRRUAR2FgQ4LvF2sVW7Atz1llnuYeUbX9G1zgpiZfHFvzWr3Llyu5Ro0YNVjzwjXAzi6OOOorvtXyzXYsYPXo05FE41pK7Ljyd7isCioAioAgoAoqAIqAIKAKKwCaIAF1rEbTvCDv5ifDuqleDOeF4GLqfOwXjgoUAlZXO5pYZlkpLQyVfScNss7U71Zx/Duyy5cCSpQDVdS5IQBq6bXvduzpHb7vwdZKlVBBKCEkYo7KSKsZUiHI0KaqhSjF+aVe6ud8bDpNYNM2awlTbI1SM0lXdzp2XOhVvLwu3Oa9bixB5UmqfJGEPOhDs1wYk+3j+/kfBubIWKjrFnV7wwkqWeYtDezKmTE1uFf8sas5vqZosaUx6PJ38jGIgcxTel7VKSI9W+YwtStFoeTx7uDpiVwhoKklLGoYqUnntSxTEyN4xCrF77nLqy+DWkbCCkfR4FTd2Cb5OpvXJThlq2CpvnUdSVCWvVynCCi5LE78rQn7uVZNK4iZUGleGXbOGqlL+W1hIlfTgm+A/ORWxp6fAkM/QUAQUgY0bgbUmQKO3X6FCBdSqVcs9osd1WxFQBBQBRUARUAQUAUVAEVAEFAFF4L9BIEV+brctvDtHwpxzlitTjq5GiC+//jEkzEiWNm8Fs+xNGIpZShJ28dsuzfUOrXMg7F9/AdJrk58PTaVKqSns+yvhNzvJlVFLibE3+g5Xom6FaBQ1plyPSsbgvPYsRycpyNJ4R36WLQNPSusv7lTE5EZK7P299g+vkSz73roCYg+Ndcek1BusrhNFqilbFsHjUxAIAcpy51SQpPU+WQmP641GwPLq4MjG4aFkb0+Sj7G3XnNkri8k2sqVwBH14FWtiuD2O2G7UQ0ZnVvK21cuR0wIQMFECMsdd4T/KBWw53YI55YScwkSnbFXZsPUPdwR047YZK6YUcX79AMG3RTmffRx+Hzgfshb8DKCP0lyLl8BECeacMDjNeOHNQAWvQWMfTDM5c/YvFkQA6M42x+4kvxGxyBvv9oQ86ug0yWws+ekcl3v1RWLAVHKytyiMN1/P6qHP2HLgzZU474Dy9YGPkv0YzcOQOzWmwE+HLko90MVqCmEZ8Hk62bLzS/tBL79DnblBzCiXi0mLIli//RzwlJ+5npj74F3QbsiZ0mLCJ/tCLBsBfymLRF7/ZW03+UiJ+gBRUAR2OARWOsS+A3+Dv/jBd59993O/OlPeVNibM3yixYtWhT5D0fhZf7999+YMmWKO1y7dm1nIlU459FHHy18aIPf1xL4Df4l0gUqAorAf4CAlsD/B6DrJTc5BLQEfpN7SfWG1hECruy9XUcqH7dFbP4cmNq1ss7sX9MLdtgIN25akfR56vGsucmBgGrO4LjmANWksc9W5fycE693JIk3EqttWrveoUb6YWYIIfSCclRqJsIbfhMVpEVbgiXH47vWZLn5F8ldgH1B856fUbBfaCvulU2pXd3Qu0uRx56fmSJVKp0cPOwQ5L3xanKvyHO84s4pck0GzZvzETv00CJ5ciB+2plAVFlKJ/a88Q9lzHX5huuOhHn6CcRaZu596d86AvZqtgRIBrHOi/+W3Cvy7L8wC5ZEXzK8GU/Ba9EsuVvk2W9+Muxzz/MGSSKuXAGvJl+D/yB8UTY/NA6m59WIDRlU7AqCu0YhuKx7mHfA/shbSjI4SwiZ60t7B/mdPe9sxB55IEumHlYEFIGNAQF+HaWxPhG49NJL0ahRI5x77rlYsmQJv3z8hV/k/Yj7778fVfktYbYQI6kkASpmUYMGFf/HPNtcelwRUAQUAUVAEVAEFAFFQBFQBDY/BKRlluv5yVt3ys8c5Keg43XpDP+2uwCKMezU6bBUEJpDD8kKnBgXBVdc5ca9y9nfM1maneGM4GkSkiSShCj1qM7MRn7KqZ4oNanidApQ7gcTJuckQDHxEeCohFKT+eakFjJN9jBUM1qa+EjQOTwb+enGr+H9zXvFbbofLBPPGT/9nDbsHZIdP1x3bToB+tLctHOL7OxH8nrFO6nD3glNU9tFNk47FYgSoO3PL5ISPWCvuS66C3M0yeocEXt2GuI7EAuqdwMS7N4rL+XIXn9D3mWXwGdvW3sb1beibCapmS0sWysE/Qs+V3s9EkRolhNEYRqbPB7+vlQ1j38UtndPGBokaSgCisDGiQD/8v+zEGOfiRMnomvXrs6sZxdK+o8//niIwu+99977Z5NuomeJgnPhwoXo0aMHKwc8ZxZ1wAEHYPLkyZvoHettKQKKgCKgCCgCioAioAgoAorAf44AXcXxyadA9Wqu7L249Zg92LOxV49Umj9seGq78IaUlgcnkWRb/j+gFl2zu3YpnJK2byc/7va9bpe7ku60wUI7lr4SSfLTDb25CD57MmYLEy3dZpLtV0ByFT4nTqVjWr9MKiODVez3mS3oYp4WM55L243uxF0fVRs9BPts9nwz5v60XNDdPlv40rfy8y/Thv1r00nL6KAV1W8kzNerI3vpm3ES3q5va+SwnflCZC/zptf/+nBAiO3/KKRXrel4get16lSpi5dkXYll+4OU4dFRDWHOPTtrbnLA/Zs47xynGHbnJwf0WRFQBDY6BPL+yYp/4JtdmzZt8NJL6d/yiPHRrFmzMGrUKFx77bXo3bv3Zml8lAlTMYC6+eabXfl727ZtnVnUmWeeiWnTpuGuu+7Ctttum+k0PaYIKAKKgCKgCCgCioAioAgoAorAP0JA+hhKmKY0eMmhzoxO7vW7Hr70yJz7MjDpcfj8HGM6tIU5+CC6eZcDPvyI6lC6sgsx+O13AI2JYtOegEka0kQni2yn1kITo2RY9tC0M56FXfVh2AOUJjSmQX0qUPPDFJabg0ZHErb+0fBPbE4n+UuA+vWc8lD6hNq7RwOfRcrfJZlGNnGPa62+ByDO5luyNyY/w2IFydo1NAyKxu+/I9hzPwTVqoWGTEIGSml+Hj8q//gTCTPeYzSoIoxvUQEQNS1Nflzun38A71IE9NEnBZkJ46KgDckzlmebU08B9qzp1LVgv9Xg4fGw9z0Q5kdMjtzcJzaDubIbUOcAGkbNgb2FbQlefa1g7uQW3d3j9z0ISA9LweXnXwB5XYTokz6skRCc49uwL+eF7d3riT33JB4r2MeTJCzXkoojif8rC+Ber9Napw5n3OjcCbi8uzOt8vm7DInNeQAAQABJREFUZqgYtuyZ6TBjb1BzwH4wJ7ek2VYxqtmMk5f8oHfHCAQfrIKlgtZnmwUhRD2WrOOA/cPX8f2VrvervXl4OGnF7RB7/FEYipNKEmK8Ja9V8ne4JOdojiKgCGx4CJSaAP3mm2/QoEEDrMrxLdlf/GM7YMAASMmFPGsUICDl8EuXLoWUxksPz/Hjx2PevHl48MEH0bhx44JE3VIEFAFFQBFQBBQBRUARUAQUAUVgLRCwq79xZ4u7d0lDiFIplw8OYN9KbttHJrhHpvPNscfAG/cATI7WXqnzksZA4jxP4jPodrUjrFLjiQ1LUg/lyro9c8jBsLL2p54mA2phpz/jHoXPkX3T/AR4Ex6CX7FyOMx8fPhx+AiPpP/cZhvgxWeBBo1C1/mPmZsr3pgPHN4wzBCXd95Dykk8w3nm6m6wQ4aFplL9BgLyKByGB7hMb/ZMBMkSfplb2g/wkSlM3+tghZi9na0KJH75BWD5t3uER9J/EhNDla4dyrX88ivsSJaK85ExtsiDd2EHBCRAkfjdyZiXOChmSwHPQX4ctt5R7vN/NJ+35ghS06EdvKGDYEiKro8wbGPgzZyO4KprHSFuR90Ln49sYXpcBbPTTtmGixw3/J2VsMnf4SIZekARUAQ2BgRK9pVH5E4eeOCBFPm5Bb+pOvHEEyFGP/Pnz3d9LRs2TLwp8Jxhw4bh4+LeSCJzby6b2223HSZMmIBx48Y55ednLPFo0qQJrrzySiTNkjYXLPQ+FQFFQBFQBBQBRUARUAQUAUVg/SBgElVmlv4CpQkjpJrEwXVgel/rnsVECWW2DMvpzz8H3nPTECNxVyLyU+YSwpERULXpH0GyTPpdbl8JhipC775R8B592JFkaEgF4h9/ulzLvqGGClQXp5wM7LF7qLiUA6JopTs6jjkK3qIFiD0zFUHSBT457k7M8uOP3wEp4aei04XMJ49kRLflWOfLkiNhXuHxQvv2559dvrm4I4woNGvWCF3at9kaOHB/mGuuBI4MPzsHUQOnffYuuMfkFQUD3qf55TvEbugLb4cEkSg9QZP4JHPlWdS4tfZ1RzyqWWODb4D31ccwp1GFSvUjvMR9igJyhx0AKoRdCGOZ7GG6bfh6hQOZf9r33nfkpxvlXDK/d+cIeJPGuWd3PR4X9aRP8tiKSnY9hSE3Ebt9OGLL2Lf2Khpm7V+bDsRU6gqZvldNmIsuDJWvvL7J/7tUq0i9lonf4VKdrMmKgCKw4SBAlWapYt9995U/i+5x++23FzmXvUFtu3btUjkdOnQokqMHChD45JNP7NFHH53Ca7/99rNvvfWWpVFS6hhbCRScsJFvdevWzd3XiBEjNvI70eUrAoqAIrDuEHhu0Qxbvo2xrQe3WHeT6kyKwGaGQI2OVdy/o69++HIzu3O9XUUgOwL+1KdtPsrY/IaNsidlGPFvGeHOi7fvmGH0nx2KNz0xXEtsq3DuTpfY4KefMk4Wv3NUmCtr36qi2w7eeDNjbvJg8NZim79lhfC8yrvb4Pvvk0Npz/Fbw3tzuMj8pqyN9+qTlpPc8VevtvmVqhSsRfK5/oDHM0X+wCEFueUruW3/qWmZUt2xeNfuYX6tOqnzsiZHBuLNTwrnfmR85Gj6Zvz6fi4n3u2q9IEMez5fhxQeJ4Svk3/XqAyZBYeC776z+VX2SJ0Xf+fdgsHIVvDBBzb/iKPCvOr72ODbbyOj/+6mP25CiMkJLUt14Xi/AeF5l3cv1XmarAgoAhsWAqVSgC5fvhzvvvuuY2/F8OiyyyLfgiU43Ri/YRoyZAjK0rlP4u23306M6FMmBHbffXfXS1UwE0XtCvZhqVevHoYOHZopXY8pAoqAIqAIKAKKgCKgCCgCioAiUCIETJPGoQKO5jyWfSdLEpYl2MGYsS7VnHRiSU4pWU6LZmGe77O/ZVfE7r0bJouizrv0YmDnRIkye3SCFXTI5UbPcnf/gotSvUO9666BqVQp47piV7K3pigfk9HpAsRuHJDcS3v2WCYt6tS0OIrmOVnKp/Ou71mQ+ttvrv+kew0KjqZtSX9MF++En7FRvnzaeKYd+8EHsLNmc+6YK/vPlCPHvMRrZ8dPhE0qerMke/I6sP+qixc4t4hhW7bIkh0eDnrSAClp2lRtD8SyuKObmjURe+l54Ii67JH6Mdx5OWdef4PSJkFwsy/Ohl35QYkuZNnezz7wsMs1J+XGpEQTapIioAj8ZwhE/vIXv4YPP/wwlXTaaaexQiBSIpAa4d/OKlVQvz5LFxhaAh8BJsumOMP37NkTr732GqiwZRVGvhKgWbDSw4qAIqAIKAKKgCKgCCgCioAiUDIEDEvETZfOrn7P79gZ9s8/iz0xGDAYeG8lsM9ezsCm2BNKmrAVy7KTQVIsV8jnTHN5l4IU9pnM9tlTkqRUPuVizvJw075twbmZtqKfY6c+XaR3ZdopUkYdjUR/0uihtO2DDkzbzViinsgwjY9FGunJ8vFcYfk50e9IsyP23DQXtM/ZU9PUPdyVzYtRVXDFVbmmdWOmT68wJwhIVh4BQ6FOtrCffw479sHUsPT3zBWG4qjY+AdZrr8F7IMPw7IF3H8RQoob9jhF3CeO/PeQbH+QYzFBz97Ap1zvwWwH0eS4HJk6pAgoAhs6AqUiQFmWnbqf7YtpYLzrrru6XHGM//XXX1Pn6UZ2BA455BCw/B1dukTe7LOn64gioAgoAoqAIqAIKAKKgCKgCCgCORHwriexJe7jdFP3m50Eu3p1xnxRfvp9b4AdOMSp5LxRd8CIE/q6imdnpmayV1yJgCY+LI5MHYtu2I/oND/psYJDQuK98GLBfmRL5ggGF1TPiSO42Zp9NrNE8OYigCrUVNDsJxh5R2q38EZwOXt1RuOleRCsMoWlYbARtWoyBNOzzkvuFXn26xwGiFI0EWbpMlhxoc8Qlp+rg1anAXNfBnbdBd7Afhmy0g/FaGaF8ls5BaN/cZesBLjrcSmq0mQseA2BGCtlCb8Le2wmX7tDDoJ35ulZMgsOmxo1YKSPqx/Q4OnpgoF/ecvhthu5inmvIDj5NAiumcKRzVddExpGsfdtTP49RInzTCfpMUVAEdigESjVO5q4uyejTJkyyc2Mz1UjToBi8lOrVq2MeXowHYFybFh95513OnOpqVOnusG6dVkuoKEIKAKKgCKgCCgCioAioAgoAopAKREQI6TY01PgH8/yXZJnfo19gQP2D0nAX0m+iaJRPtt99jnwxZch+cnydO/YRqW8Eg2OnnkO9pYRsMtXADK3GPSwlN2c2ool+MvcfOYquqMPp8u8qBKFeNxpR+BPfs78mw8pATfU6Egbtb9pTlSzBvA1CVuShEHzkxFImfYvvwDyudSLOXLPGRcl3LlF7eqddYYjKO2MZ50y1H6wyp1jKu8MU68ugh/WhPclZd9CPpIMtVdeg7iQqBUr8iaogGRbNwjJ+eVX4bXkjB1pFkQilq618KvtDQiJJusQw6YKXLecs2w58PsfNBbaHvju+/A6U6YiHtuKJlAkZbeUz9AkfaWsX/BJhjipk1yzI+6AP3U6PDHsObKBW4/96ivY52fBjrkfWENBEvGKTXvCleHbr7+GfewJZyhlP/+Crx2VsjSKMk2b0JCoNcz++9GQaDyCM86BvXcs/Cf5+VIwFWxlDSRH2YcNeOc9mh/9xFYD27p1yHWCblcjuOkWmDanwogxFXGxL8+HfeJJIOmGznxvwbzkXRT7bI49BnbyEwhIxuO++2FJaltRV3JuQzzNcceG19uRvxPrKQznFvzclwHPPQ+/Zq3QIOn440JDrzVrYF95FQHxwocfOdWq99BY97uznpak0yoCisC/hECpCNBA3gwSUdy3H9LPMhlR4jR5TJ9zI9C8eXPIQ0MRUAQUAUVAEVAEFAFFQBFQBBSBtUHAsD+j9+w0BCew56SQegvfyDwdCUsz7EZ4HdplHs9yNPjkEwSNm4WEUTTnD5KBJNbs0FtSRz32yQxIuFqSa9IT0j1So5GNvfaEx96RQY+ewKOTQ8JWSL5oCIkXCe/aqxCwv2MgCkUp448EaUfYZ2YWHBGy79wzgVFjwmPfUwkojyxhXngG9qCEMEWIYnlkiqpV4M2cjuCBh4Dht4cZ8jn6RxKMmYLEat43LCmn6lJUmli2ImufTHPC8fDuvQvYZRf4/QfCDhseEq6Ree2ChbATHwOuuQ7eoP7wLu4EPDAGwQUXhwSukLiZguSvN2Wic6a3TU90pKr0+LR33O0emU4x99wFL5MLfaZkOZbsnTphIoKHxqVl2dffJLn6VLju666FufZqcuEkw9dDmIPqIPbGfAQXURVLEtTePNw9ilyKTvKuV239I4oM6QFFQBHY+BAoFQG68d2erlgRUAQUAUVAEVAEFAFFQBFQBBSBzRsBMXwJWrYOyc+yVCHW2jdUOP5GApH9GSHHhNBjObi9mgQleyV6551TItCCha8jOOo49qWkqlBi371hWp8Csx8rAKmCdOpFKV9PjPsN2ffynXecEFJKuZ0CVFSUUvotikRRgNIYFrLmRscDqz4M55WfotAUJaWUsCdVmlyzU2ty2BcSluXzUmYtPUw96ZNZ73CAJfH2M5KMolAd+2B4Pvcx8XGZNVQ9CjknilBRdca4BsElxo/L34dKTisEYjLEoGmXqqFyVfqqyrrzmCtmRiSYgxatCpSjcg57XzrVZfJ8KaUW4lCuRULSv/paxG65CbElb8ApV6eyL+m771HxuSY0XJIyc1FikoiTPq4yv+DqzIpatYQ5nf4ce+/lcBD1bTDu0bDEu/PlCKZNd9vu3iqyPL96tRBree1FuSrCpfc/cNcKTj8HsWemwps9E8HSpbD9B8POfzVU3crFtt3GKVOtEJNUcpos5ePJ24w+W5LAoih1IT1MRe15zpkwtfl7wvnkfqXtgZDUwXV9Yfh75T32KExEWBWdb223zW67IcYvBexrCxE8PgVYtBjSwkB+x+QLA9PqJJgTm683EnZt16/nKwKKQOkRUAK09JjpGYqAIqAIKAKKgCKgCCgCioAioAhsFAhIf0f/JJKfn3wKNDgCscnjYaggLBzS8zC4vp9TwgV0VHel1EcdWTgtbT+QvpTHNg3JTbby8p5/Gt6Rhc654jIEJDeDqtVDMvF/JD8Z3s03QsrhM6n87Psr4Z9yekgoJq5oqHyMdbowsVfwFLBUPZCSdCFwpdyd4fW/HqZ3z7QepuaQgwGSWn6LE2BPOYMELDWhJBhd/iP3wzv3bLcd/SH9Rf1jSMK+/Arw1uJwiOrV2KcfwGRQPtovv4R/xrnA/AWpabw7R8DrcklqP7oR0BAo6HwZ7K23IaDpkNe1C4y4tycc3KO5ye3goktD8pMl/bHHJpCQbJgccs9CknrEKZj8OIL2HYGE6tW0Px8ee4KaDE7zljgE7S5ky4Bn4LMvZmzRAngHHghMmZQ2d3InuPNuzs/S+znzgM4XJQ/nfA569wWo8pQwHTsgNmZUWr4zbWp7HoJZLyI463z2CaWKlm0SYncnVLRp2etuxxxRDzE+NBQBRWDTR4Bf3WgoAoqAIqAIKAKKgCKgCCgCioAioAhsiggEQ24Oy8HpTh5jGXcm8lPuW5R2sZtISrKMXFzGfSHmRGmZIyyJKtcDkwpH792lRcnPxLlSJm2GDCiYSRSEF3fMSH5KklMz7lmjIL9SxYzkpyR4VF6a4cMKcmVL5hZFZoaItTo5VGQmx0gImnPOSu6lPUvbNynZT4um7BWZgfyUHEMfDHNZhOyU+76IJGSW8OhW70142I0Gva6H9PvMFVLebx+ZQEVrBcRefK4I+Rk91zujDbA3iWEJuY8+vTKSn25Yyt+ffAyGfTCl52pwbW93WrYfoo6Ex56l7ClqP/44W1rquF3xP7Y8uDXcp5C0CKapTI7RaT32/AzXm9aOutcpNCPDuqkIKAKKwD9GgO88GoqAIqAIKAKKgCKgCCgCioAioAgoApsaApY9OKWHo1Qvx8beA7MVS7WLCW9gf2CvmsD/3nUmQtnSA5Zi21mz3bB38xB4u++WLTU8LqXiyWA5tO3RK7mX+XnGcwXHKdbM5hjvkqJz84DtWsi5vWCmcKt8hYIjUpqfi+iVPqaRMHWojMwRri9nctyyFF/mzxHeqWwXcMZprpdnIK9VjkiSiF6f68LS8Vy5i96imdRSGlxt6dSuAQ2WcoVhSwFv7GjXDsGOfxT288+zpkv5uGlH8ptmSv55HWCllD9HBEJ+JlzjDVWeZo89cmTz15VqXdMjfA2DmxPEac4zdFARUAQUgeIRyPy1WPHnYcKECXjzzVDCnil93rx5qcOjR49G5cqVU/uZNho1agR5aCgCioAioAgoAoqAIqAIKAKKgCKgCKw9AvbFl9jXkr0e69dzpFJJZhQlqCuhpomOlf6Rp1AxmSnuuS8ktUiweSxzLzaihCaT7fiJdIO/OaMq0e97Q+jGLpOytN6Vqi9eAkgZe6aYTsWghPTWJNEm65bSfyNO74UieOLJVOm7G2J5vn/xpcgbe2+hTC7hR7qhd+ycdtypErt3hWGf1MIhCk28xXVKSH9NOr3La+DK2sOjGX96l14MX0rKBe8bB2bMkfuxc+ayZycVr3SJLy4C9hF10Zr9SGmK5Oa+Y0TO0xyxyfJ7+9gU2OlUC+cob/duGgx/9hxX7u83aY7YuAcyEpsBFaL20UQpfZXKrvVBzkUkBr1LLoI/8EbYmS84gtWw9YCGIqAIKAJrg8A/JkAfffTREl/3nnvuKTZXyguUAC0WJk1QBBQBRUARUAQUAUVAEVAEFAFFoGQIiJEOw7D3Z2nCNKzv0u1772c9zb6REMNUr541JzqQmuuC9sD9DzrDIb9mLZizToc57FAaCZEw/PgTR7xZIdYkSHqJEY2lSY2Y5Lg+nuFI2k9nGCRH2EMTt91JcyGSmlWoMtyOpj9CAItCMWn4Q2dzF9tuGx4X9ej9DyP++JMw0vO09r4hcSn9KoV0pVrVxcEHhft0ivd3qQGwLB+/Ux36Jx8VKoSmRsm55V7OP8c5zAuB5z87E3YuBULSp1TK56vtAdOsKbwL24ekYX2+PoZXId7+naMAIXDFUIkELHbckffNax9+mGtNgDoHwMjaGfbNRQgeHk/H9jnA51+ERkxU4opbvF1C9SfDIwHqyNBPP4Mogo0QyjnCNKgfEqCJ351sqYbris14ij1VSbC+8ir8fbiu01rDNDo6dHv/9lvnJC+vnTOp8rywBUPSCT7bxInjRgRUNalE/mAV8NlnwJ57FnOGDisCioAikBuBf0yA5p5WRxUBRUARUAQUAUVAEVAEFAFFQBFQBP5LBOxPP7nLGyECSxPJfCHgssWaxBj7UZYoEnOZs0+HFQJUQlznb7sLrHBPD+nfSXMj1CC5uuMO4VjiXtITE3s//ew2zJltOB8JUAkhJ+WRjEJl8jiyAVBrH+CWkWHGz784B3bMeDZ5RvicUJV6D45BUOfw8JjMRbf3VPxJgjUaJCwN+2rKfdm7RkdHwu1vv4N9YxF8lndLqbd3Q9+QGCVxay/vnp4vKtKPPgaeeCo8Tmd6+8svCC7uQmXl5PRc2fvue1hRocq6GVaIXyFM/+CaBcNiCFBsF5KrSGDqJsnyw+xXG7E35iMQtfAjJGInTHKPtPRwGYAQs8wvVdB13kUJ1lKqeTVZEVAENksESkWAHnTQQejZs+d6AerIwm6B6+UqOqkioAgoAoqAIqAIKAKKgCKgCCgCmwcCZqcdQxIuStaV4NbtF1QTMszOO2fPrlolHCOZV6LYeScqGknALVgYpm9VDrE5LyAQwnHVh04RiipVnFo1WPgGIP0w6TIPOqu7yKUc5H1CFI69+hQshapXcbKXPpXSo9L1PyUZZ2fOCpWVVGYaKZeXM6QUX9SnL80NnerZBgC77QrvvLMRsHwcdDsPWI6dCqoxTU2qQEnSWpKhpjx7q5JwdC0HBA8qIh0JKSeIivWqKyC9PrEnFY0subfszWlFuSm9NgcNhT9lqlOtuvm5bo9GSkYIWpKooKrUvjALwdBbqIT83KlQ/QOomP3kU4AYmi6d4bU5lYZHe4XrWb4Cdhznvf8hN529uhcga6JpEXZIkMluJMuP5O+KYFqCMHxdYg/eB9ufJk5TqVxdtpwkLK/Ha5kD9gcaN0Jw4GHumKWa1lAJWuIo5VpKPK8mKgKKwGaJQKkI0Hr16kEeGoqAIqAIKAKKgCKgCCgCioAioAgoAhs2Aq60nEu0z89yJkLSdqwkIfkuDj04a7pp0Qz2vgcc8RiQ1BOn91xhDj0E9r2VsA+NC9NohGNIJMb4KBIsEXeGQKIQFVKSYQ47pEha8oDcp33zLWDuy+Gh7bdH3vw5yeG054BEZ1Cdyk8hL1maLhFr0jgk69Iywx1LctaSAAVL5F0cuD/yXn8lQyar5Un8BTvRDIrEKn77zeV4kx6BJ87zkTB0OgcfttMF8Fu2puHUO+FozepF173XnjB8gKZDQcXKoamSkJ9774nYzOkw1apFZiZOjY4B+PCpnrTDbweklF7i4DowecV//A9IDEskf3fcTgl+yDrMFZdlzAzEVGslS9lfXQAc2TBjTuGDjkiVlgJCnO+6a+Fh3VcEFAFFoNQIlOLrF+CCCy5Ar169MHfuXJrZ5Zf6YnqCIqAIKAKKgCKgCCgCioAioAgoAorAv4RAvbpOySgKSzv58RJd1JLEs2Pud7ne6adlPUd6S7p+luLo3umSrHnJAZOcS9SeDNOhbXKoyLPXkOpH6aMpwdJ0HH5oRoOdMIFzkYyNhrmwfXQ3bdsjuYrGjcJjQlRW3C4r+SlJ5qSWYW7ip3dVt7T96I5H1SOiLvEsNy98fjQf0vtz60SZNwfM2WemDUd3PMHjCL6eiTB7743C5GdyTJ6dMVWs4OO+9PYsLuz8V4F5JHdZNi89StdVJF/74MabSjxlMORml2uobi0pcV/iyTVREVAENksECv4iluD2ly1bhqFDh6JRo0bYnt+qnXLKKRCH948++qgEZ2uKIqAIKAKKgCKgCCgCioAioAgoAorAv4WAlBt7LE2WCC7tCvv+ypyXllLx4GwSk+y5aE5sBlM30fMyy1lSfi0h5dxBMQSrpTEPkgpEOpnnIhLdpCw/T0ZxJk6BKCKTISXWA/sl9zI+e+3OKzjOfppiDpQt7CefFAyx5Bw0+skZNElKhfQKpXt7tnAK1ES7AZcTPTfTSVxrMsSl3SbbAyQPRp7N7rsDbGGXDEs1ba6wX38N/9z2LsXrcSXM1lvnSi/VmHflFexDSkXqs88juHVksecG990f9jctVxZez6uLzdcERUARUARKgkCpCNDLLrsMJ5xwAsqWLYtf+Md36tSpuOSSS1CjRg3ss88+uOKKK/DMM8/g999/L8m1NUcRUAQUAUVAEVAEFAFFQBFQBBQBRWA9ImA6tINpzRLsH9bAr380AnHlzhCWZdj+sU1hZ80GqlSGd89dGbLSD8XYOxPVq7mDwZnnwb/kcpqmB+lJ3AtenA1btXpobCSj+XEELU9BGrmYOEv6RAoBBprqJEOMkvy2HRCIMVKhCES1eNU1BUfl+ie0hF35gTtmWYou5J71fdcGIBg3AUHUaCjuI7jyGtgMcwvBmNZXlKZKQRO60idK1i0/97q5E+f6o+8NHcuTq6HCNOjSDZYtAgqH/eYbd113PGlYlCg/L5wr+/b77wH2DoX0J01EQLwzkbeWvVP9iy4FFr2VTAWenAr7OnurZohg1ovw6x4Z9hWV3qk9e6RlybVlzn8ahuIp735iY/i7cHVP+F2vRNKgKzqnvFZ+r+sRyNoZ3t23w2j5exQi3VYEFIG1QIB9n0X3X7r4g9+QSRn8c8895x7vvfde2gRl2Oj5qKOOQrNmzdxjv/32SxvXnc0Xge7du2PkyJEYMWIEunXLXj6y+SKkd64IKAKbIwIz33oGpw1piaYHN8eU62ZsjhD8a/f8BZU2L730UomvJ2V38sVvOZYxymPHHXdEzZo13XaJJ9HEfwWBmp2qYvWPX+ODe79A5YpV/pVr6kUUgY0FASHqgnPbwT71dLjkffaCoSJTyCVHRL36Guw89s/0SR6yRDz29BSY/Uv2GS6gwtGZ3CRVmKLyrLUv597FuZW7HpRJZSPVmeY6ko333Bca82y5BUyTxgB7eBqWeFsxInrmuZCIk5W2b0vTJPaNZO9QF7EYsO8+MDQokntyc4vBTzIuaAc893zo0C6kopCFUfKRn1NBlauLs04HVrD3ppj2SOxSlarX5jB0nnek4uIlrncqEg7vhkpEK4ZIXKMQea78n67tqYjOvcP2oYP962+Gw5V3DucWEyRe3y5dBivrTLrU9+sNDBricmN//QIj91ko7Jt0jT+8IbE6BKZSpXBtkrMjzYZk3VETJMGQ7vEoSwOmYUNgr6CKMklM168Hc/SREFLSrl4dGjctIbEqcVRDxJ6c7MYCIUzvfzAcFwd5CZo9yetlOl4Ar2WL8FgpfgacT0hbMaYSRai0LjC1a5Hp9GDffQ92xrOOqAdL973hw+B17VKK2TVVEVAEFIFiEBACdG2DJfCWpfCWJfF26623FkI17bHrrrvaCy+80E6ePJlfHP2wtpfT8zdiBEh6ut8NEqAb8V3o0hUBRUARWLcIPLdohi3fxtjWg1us24l1tiIITJ8+Pe3/KIX/z1KSfZKido899rCXX365ffXVV4tcY0M/sHDhQop5vt/Ql1nq9dXoWMX9O/rqhy9Lfa6eoAhsDghQmWn9e++z+VWr2XyUKfrYsoKNX97dBv/w70P8okttfmyrovMmr7XvgdZftsxBHXz+uY23u5D55TLn71nb+pMeS70s8e49bP4W5TPnyvx54Zi/apWNX9rV5puy2XMlv2JlG7z8io336Bnm7bRb5nyP82y7oxvz315q49f0Kn5umb9uQ+sPGBzOuXOWuQ3zKlV2OcEr821+uW3D7d9/T913dCOYOy+c7+jjrD/yjtxzcw3x5ifZYNlyN0X+TruG+VtVDJ9ljdEH1+HffKulmZUNvvrK5h/VOH18+yqptSbPix/fwgbffhtdYom2g7cW2/hxzdLnj66lYSMbLHitRHNpkiKgCCgCpUHgHylAc3GqYo60gN/SJdWhS5bwm7OIyDTGb7PESV5K6UUhethhh/ELn1JV4ue6vI5t4AioAnQDf4F0eYqAIvCfIKAK0H8P9hkzZqBly3RDi7W9ep06dTB27FgceuihazvVej2fX0I7M8v77rsPK1eudC2M1usF/+XJVQH6LwOul9toEZBScLBs3L6xyCkAzbbb0lE8oQjdZpu1ui9Xoj72Aeeubung7fpIUuFnul8Or3r1InO70mpRQopJE8ufTZUqcP0+6Qxf2PjGldazLN4+NQ1u7q22CtWgnDtoeyHw2usAlY3OxIc9Rs0F7YGaNWBEcSm9M6l4NFJe/8RTANWUoPoU9Q4HXn4V3iP3w+xXG/bl+a6vpqHiH9WrOZWszG2fnwUcdyzw4ku0jKeKte15gNyXzP3TT+HcvLvg0clhmbrcaeNGwOw5LOO+DYbO53buPNgvvnTKUUOVrShwAykF53rMXbfBdgn7ZOb9+I2cXSSkpN/fe39g991gTm0FO/JOeEMHwZzMcv+X5sB+/oXrsWr22B2maROqZOlGz3Bl5dvQnImfuT2Zm2vCiv+5knYjpk0HHeic482WW3J9X7BNwjEs4/88bIPQ+1qYM9rAsPrBzSWtBKiCdWZGorwlvrEFc1PjLqmEPyx9RKTdgv30M6dOFVWvOY7q0r32LOEMmqYIKAKKQOkQWOcEaOHLr6asfubMme7x/PPP4zu6CkZDzJSOP/54tGrVCmeddVZ0SLc3QQSUAN0EX1S9JUVAEVhrBJQAXWsISzzB+iBA5eLyBW+vXr0wcODAEq/l30qUL6KFoO3Zsyeo/HSXXbVqlRKg/9YLoNdRBBSB9Y6Af/W1sLfeFl6nUsWwhD+L67nrMXplD0hf0WTEPnwHJgNBK+PBgMEI+iX+tm9dAR5LxD0SdZkiICEabEOyMFJ2H1vyOkzUGT5yYjDi9rAP6OH8Ao2EtGlxAmIzpkYyCjZl3X7VaoCYGZEsFJIy9vKLjlwtyCq6JeZU0p8VRzZA3suziyYkjrj5G5D8XPgG0LA+YlMfd6XwmU5whkknnUoi+S2YY49BbPbMTGl6TBFQBBSBDQoBNmhZv7Hzzjujbdu27iHf2i1atMipQ4UUfe2119x/xCdOnIiXX35ZCdD1+1Lo7IqAIqAIKAKKgCKQAYEGDRpgB1HBZAj5v8uvv/7qHmIA+QndgP8UV99C4VNRNWjQIFf1Is8bUvzM/nydOnVKW1JhZVXaoO4oAoqAIrCRIWCOb5IiQJ3iMgv5KbdlqISMjbwV8RdIBoqZ0Q5UhmYhPx0MzU4AEgSod9PgrOSn5HrsA2rPPQv2gYfdqSBhmo38lATTho7y117nyE+3f/aZ4XkZfsq6DfuWOuJWFJpUeiLHfcoUltWZwcAb3WzeOdnndrk0h3LkJ+d1PWArVnTnZfphKlcmUfsU/DqHU306F8GUp+CdekqmVD2mCCgCisAGg8B6J0Cjdyql7ocffrh79OnTBz/++CNmzZrlCNEVK1ZEU3VbEVAEFAFFQBFQBDZABOTDFN/ARfIIbLed+yC5AS6zVEu68cYbccwxVL2UIKTVj7T3mTp1Km655RZ6WSTMNBLnDh48GPLlL/uDlmA2TVEEFAFFQBFYJwgkjYw4WUAlqCFp6Ur7s0wePEvFYtLId82Prow9a/7ixalZgrvvgTn9NLjS8dTR9A0pbU8RoL/8ynL9r1xpf3pWYo9EophOYeUqVxoPqilzhdxXUrlqDmebAH6+zhbS6sC5qS//X9gK4MIO2VLd8eCe+9yzN7AfTA7yMzmJ2WkneH2vQ3DpFaGplRKgSWj0WRFQBDZQBLL/xfwXFrwdPzi1adMG0otK+oZqKAKKgCKgCCgCisCGh4B9ZT58urbG9z0Afrlt4e+0G/ztq8LfajvEDz0Cfs/esKKi2QxiCzoKy5e5ovJctmwZjj2WPeEKBQ3/XJVLocO6qwgoAorARouA69W56C3I+4H9kP06Ix4PhW9KiDfpV2lffgV2ydthj8zCSZF9u2YN7FuLw/wPeB6V96UNO/OF8BR+ASWl5H7dhghemOWu7dYga+GaAvbr9PsNQHAyy7fF8V5UlLJeqhjlnty9yT3KvSZahrj+nzJ71SoAyURxYg+mP1NkieIcHwy5mf1IL0gb889uCykZLxyWLvP+sU1D8pN9RaVsPqh3FILHpyBgxYFzip/3snNHD1iJ4MrlTz0jNY1lnn9ee/b+pBq0UFj2+AxOYG/QBx8BtiqH2GMTID0+Jezvv8MuXwErc/O920ofU1YK4LWFoWv8aVSlljDMWVwP127nED/5glRDEVAEFIENGIF/VQG6AeOgS1MEFAFFQBFQBBSBQgjYN96ET4MGZyyRHJMPaTuyXJwfGMEPrXiLZod80D0W5pST4Q2/GaZatWT2Jv281157uR7nQoLOnz8/da9SNt+7d2+8+OKLqWO6oQgoAorAxoaAIwQnTERwx93A6+wLaSN3sEtVmLPPgNezR6pPpBjoBINvgn3sCeC7sN+wO0PeN2gC5F3VDd5JJ6YmcUTfyDuABa9RthmZfOedYM5sA+86GvAIoVmCsJ9+6rK8SY8g6N4DWPw2gqa5De8M1Yv48SfY2+/iPbIf6GXdADEpSobhRt3Dw56b3PTuvxdBf/YCpdlSwP6XAftwmiPqAmIixXu3814BfvvdnW2uoiryvZWAEKVzX4a/W01Xrm5q1nBEp5CbWJaogNx1F3gPjYUdNCQsJz/9nOQKMj6bzmxpQrMn27Ez7PiJ8CfSeKleXRgaWSEed+QmliwNz91pR8TYs9QcfBDsQq77Rr4+Yuj0Z6R6oUJ5mKOODF+DGtVhttoq43UzHXRK0V12AcTIiEpX7E5CWUMRUAQUgQ0UgVIRoF/zm6sP+K3c+ojd+cdSHhqKgCKgCCgCioAi8N8jENw1CkG3q/lhikQnDSXMhe3hkeDEYYcWqEjo2Csf+Cx7f1lx5n1yGny6y3qTxsE7gaqWzSBEEfrEE0/gsMMOw+cRFc7s2bMhj8aNG28GKOgtKgKKwKaGgP32W/htzg4d1eXmypMU23cfQNzRP/rYEYX2lpHwxz4I79GHge9/QEBCDn/8GUIhZd0kCEElI95515GAAYlA26olzO0jYDtdEhJxkl2uLFBrXyoVeY1PPnXmPvb2u+Gzj6YQg17rVuGcuX7Ke5VEpUrArrwuCdCcQTWkEIbSu9LF7Lnhs6g8SQKCak68+17YEzM5EZ3lY/NeZLn3GATDRjjSz0ovzmQIYUqjIW9AX3jHNkJww6BQKVqb9/bBKoelI0mT+RXZRqZjB0f0yr0H1asBVKLmDJa8y7o99gq1dLAP+g6gi/yTwKuvwfKRim22hml/Prw+JHlZzu53uypVOg+PC92vlnOuBw2L8f5KWGkJIMHXsdSRl6AUSL5qKAKKgCKwISNQKgJU+l117tx5vdxP//790a9fv/Uyt06qCCgCioAioAgoAiVHwJW033SrO8F0vxxev+sz9lIz5akaaU5zCD5sn16OMBUSNDiRRggP3gfvvNwqlpKvaMPOlJ6f0s6nWbNmaQuVMvnSEqCiHl2+fDk+++wz/PDDDxTZrkHZsmX5+bWiM2oSonXrrbdOu86/ubOhr+/fxEKvpQhsqghY9nn2jzyWxBiFL1R6ejcOgDmjDQz/FiXDvrkIwfX9IaXnQXMSlImSeKfc7NsbpjYJtkRYKed+iMpMGgnZqdNhX6A6/ncSjFQneoP6w5x7dprq0L69lLkk9pgbnHYmMIFEqJRa5wqqKPHhRwjO4PvOu++HX9z17wNz0IHAl1QmChEr/Ta33Qb2rtGwk+iMfl6HghkPPACxsaNh+CVfMiwN76w4qF/UBWz4zPz2iK1YAu+yS91D1mnf47WkfJz9MM1hh8BUrZo8HZbEooTXvavDz4rSVdYi5KsQxKLaJHnoXOlbnw5Mm+GIZlG+otHRYa5UWnBuMWqy4x4Nydcrr3HXlPfm2LgHeewukp8LHCkt/bmNlPUfUS/1ZaV/bjvYCZOAMrzuNVfBu/xSmB13dGuTH1JCHwwaGvbxpMO8TwVtbMSw1HiuDSmn57d/vEmSqpF7z3WOjikCioAi8J8hwNKGEsfo0aMtF7peHiRAS7wOTdx4EWBfNPf7M2LEiI33JnTlioAioAisYwSeWzTDlm9jbOvBLdbxzKWfzh/7gM1HGZtfZmvrT3qs1BP4NwxKnR+8uqDU56/vE6ZPn17k/zFz5sxZ68uSGLSsZEmbOy8vj23Vfi7R3PyS2bZq1cqS6Eybo/D/u2KxmGUPUjtmzBj7999/Z52bFTt2l112cY+qVasWmbNy5cqpccmbPHly1rlkYF2vL+fF/uFgjY5V3L+jr3748h/OoKcpAoqAIBBvfXr4d/zgujZYvTonKPkdO4e5fN+ID7wxZ27w4Yc2v8L2Yf52O9ngs89y5vtDbg5zy21rg5Urc+bG+w1IrSO/+j42WLUqd/6gIQX5XHvw9ddZ8+P9BqZy46e0yZoXHWDPTptfcWd3XsC/x7kidZ87VLXB4iW5Uq3/2BM2P1bO5psy1n9hVs5cGfTvGhWufZsdbLDgtZz5+dX2Tt2nP+WpnLnJQf/RSeE5DY5JHtJnRUARUAQ2WARKpQAtzNLusMMOqF+/Pr9oihUeKvX+vvvuW+pz9ARFQBFQBBQBRUARWHcIOIOIS7u6CT0qSjwqfkobHpU/oh6yI+6Af8a5iL3zNkyFCqWdZqPLN8agXbt2GDhwYGrtcZYDzp07Fy1bZu9Dt2jRInTq1AmLIy7DqQkybPjsvfrGG2+4x7Bhw0BCF9KLtHDItb9gT7psIW2NovG7qHgyxPpaX4ZL6SFFQBHYABCw81917UzAEurYtCcgTt+5wrDvo3zD4kL6QOaKn6nCpJmPi1/4nCyXz3KO9Be1LJ+3D493atPYxHFZMgHD0vPkOrxhQ2Fq1Mia6waia2Vf61y9Rr1OHeAPvZkq0L9hn3raGUEZ9jTNFdJrE3SXl5J4U7Nm1lQxWnK5zJBWAuagOllzZcBrcyow6AYEvfoguLonvCXszZolrBgnUXUr4bEqw1AVmiu8rl0QiLqUEfToBXMy2xXk+Jwv6k9R6kp4LLfXUAQUAUVgQ0egVARoOen5EonvvvsO8h/jM888E2eddRbq1q0bGdVNRUARUAQUAUVAEdiYEAh6Xe8+4Jnzz4HX7p9/mJEPn/6cea4Hmx1xO4z0INsMojABKrc8a9asrASomCSdcsop5AMShEApMXr//ffRqFEjrFq1ypXJl/L0YtM39PUVewOaoAgoAqVGIBDXcIa54jIY6aWZI+w334S9I8uUcSXi9tFJsHeMgJH9DBE8nCAwpf/kincQjJuA2A19M2QWHPKGDoJPIyb75FTnVG622aZgMLJlX3k1tRdcQ/LukDow1aunjkU3KJ9nSX6ETOVnWstemNlIUEOTH3MlTY2GhGXh/sg7kJeDAJX7tHSDl7Lw2M03Ri9dZFt6aINksDnheHhNjisynumArAU0bsLbyyAO99lIUzvj2dCMqsERJeqjai69GLhzlGslgFUf0tl9Hsxxx2ZaApzj/dn8f4K0SeDraTq0y5inBxUBRUAR2JAQ8EqzmLZt2+Ldd98Fy9WRVGx++eWXYDkz6tWrhz333BPXX389VqxYUZppNVcRUAQUAUVAEVAE/mMExEHXfRBjjzBv8A1rtRpRjHg3DXZzBNJrbTMxRqhJlU959kWNxsKFC6O7qe1vaTDSpk2bIuTn8ccfj0mTJrkvmOX/WF9RXbVgwQJMmDABTZo0SZ2f3JCchx9+OLmbemaJu+tLKr1Jb7/99tTx5MaQIUNS45LToEGD5JB7Xt/rS7uY7igCisAGg4BNmPB4p59W7JqEIBP3dtOEJNnBVC7++hvA3qDZws6e44a8C9q75+S13E6WH/9n707gbCr/P4B/zrkUqZQl2bKv2XdFlMquUiRlSQtSUfEjCqVF2rTZ/klIlpRQpEUoWUqESIREopU2lXvO//t9zpxx587cmXvH7PN5Xr8x957znOec855+M/d+7/N8v1ZxKUp0QVOpnP4f3NWS5zJCix+ramUTwAs2vBBBqV4fnDZdcloORrD3zQhKBffg4re9PNWS09O0mjVMgjdXCjQl1+wHR3kV4bWTBGOdp56BpHmBqWQ/dZopdiRLzBHscyucXjebMc1M1KZNkhvWVH3XDlYXmdkZZbM0h6jMztTmmyZ1qL/PzBpNqkPYNg1cB958DZDiftqcHjfCeV+KPkluar+5sgrBWfIOgo2lCr3mLJVCToE3pMp8njx+F36nAAUokGUFYv5NVaVKFVOsSAsWbdq0CXPmzDEv1Pfu3WtmIDz88MPQr5o1a+K6664zM0PLRfj0Lcuq8MIoQAEKUIACuUzAfWOhecOmRY2s0qVP+u7tyy71Ktru2Qv3o49hXdzypMfMDgNo4FFnZPrtsMyQSqqNGDECv0mqAL/pEnp9TdW1a1d/U/x3HbNJkybmddV7770HyRUqBYr/jt//5JNP4uabb4Ztn/hcu2DBgrjppptMnyNHjuDOO73UBv5Bunonuddn6X19/nXwOwUokMUE/NQZlSqmfGEHvvf6SFVyTXXiSuV1d/8BWJGO9Ptf1MzrIX2jaVaVyjDV07XYTqQWN5Y9V5bLy/JwSJDOvfPuSL297Y0aytL5pnC3bJVCPslfiwb47GWL4ZwthZQk6OvcMzTy2Nr3+adh970lcp+4PeqlTSu7x9K0vy75d/2fV1IH+/tiGNuSgLB1r6QeeFBmrh78Ac5l7U0BJugYWuhKiz79IoWZtFWuiMBCSZMQw/jegfyXAhSgQOYInHilnIrz16lTB2PHjsWePXvM7ISBAweiuH5KJ23Lli0YPnw4ykv+Fc0T+swzzyA831QqTslDKEABClCAAhRIBwH349VmVH9WSVqcwurYzhsmZGliWoyblcfQivChTdMFhTfNtzlz5swEm4cNG5Zk8DNBJ3miM0Tvv1/e3Ic0XQq/c6dXbThkc6ofZvXrS/WN8UAKUCBlAT/no8z0S7GF9vVn+vvbkjrY3ydL0E3LE0iqV+Jt/tjJzTKMG8tUZY+bxZp4oLAtusz7eNx9+tcW1iXBU83pGU2T63V3fZNg5mSkw/yZkzGvlPB/Psldt7/P7xvpIsK2W8UlyKutcUOgUgVvGf0nUsF+jaxo0OBntSqwn3wMgc0bYFWt4vXlvxSgAAWygcBJBUBD709nJowfPx775ZO5Dz/8EH379kXhwoVNl7Vr10Kqf0OqjKJVq1ZmydWvv8Z9chQ6CB9TgAIUoAAFKJApAloASVukXGKpuSirbh1zmD92asbIbsfobM3QJlXgIdXaQzdh2bJlOHbsWPw2qRYP/RA52pbULFFdKp9WLatfX1rdJ8ehAAWSEDivtLdxaxQpzcqcF9d3G9wvt5nHlr8tiaERt899932v73lxxyfVN2Sb61+Lf20h+/yHVtxYbrceUlxJZsjLMm7rodEI/PoDAhvWILDyPQS+2gxrn/yt8wsNSQEiTH/FGyK565Ye7u+/w7m0rdc3r8zwXDgf9m+HENi4DoEV75qCf/aRw14KmVPywn1iPBxZep9i8+/Jv8cUD/A6+CbJe5fxOsc8tveztK/oiDxff4nAvp0IrP4QgU9WILD/G+TZ9gVsyUMaKddrlLfAbhSgAAUyXCDmJfApXaEuv2rZsqX5ev75503y/9mzZ+PNN9+EvglYvny5+RowYABat25tlnN16tQpUc6slM7D/RSgAAUoQAEKpKHAkaPeYIXOTrtBCxXyxpIl2LmlFSlSJNGt6lL3c0IqKdetWxezZs0yS+V3794NPSZ85miiQUI2lClTBgGZ2aMV4f0WuiTe35ba71n9+lJ7XzyOAhRIWcC6/FKpvL4Dzqw5CDROvsCt1fIiCTTmgcmfqb+Pisjkl3p1I57EjL3uU7jTZpg+WvgnpWZmdGpe0QKnwbowYa7i0GN1LPf95bI83TF97S83wpbflaaFXJPOOXU/+wTBM+R3tQZK9e+TbUualhahwyV67Nw1xCsOJHus3j1hx+XgjA+m6nY9argsjZeiQ06bjnCfnSBB01awO8oy8gjNXLcEYdXbvmNAhF4JN7vyntqVXKbarMsjGxpvyYPqSBEpa9gQWHKfKTVXPpwz+cB17Lifj0mLkwapcVI6N/dTgAIUSG+BNA+Ahl6wzmho06aN+fpHEk0vWbIE8+bNw+LFi/Hnn3+a7/r4tNNOw4QJE6DVU9koQAEKUIACFMgEAZmxYtqxuMIQaXEJ/izHCBWB0+IUWW0MXT4e3vLly5dgU9myZaFfqW36+upMqYQcupomfJZpasfW47L69Z3MvfFYClAgeQH75j6meJA7+f/g3tIHmhMyUrMk17DV9Rq4ErzTZt3UG1oEL1Kze/dAUKuj7/sOkL8L1g3XRepqtmvxHWfgPRKxlLF73gAr7Hdp6MFuyD5LivDFBz9DO8U91mu0Bg6AO/YJb4tUa9d7idQ0COu+9LK327IkUHlbpK5mu91SgqmPPwrnznskH+l9yQdANZB6rqROkcCwM3MW7B7XJzu27nTuHw3IcnwN2lqVKkbsrwFQlC0DfLkd7qQpsG7rF7Gvv8N56FGT+xMN6sEKCRz7+/mdAhSgQHYWSPljoDS6u1Plj9xVV10FnQ166NAh3HPPPWb2gg6vbxY0jygbBShAAQpQgAKZI+AXPnIlJ1qaNX+sUiXTbMisPlBoYSO9Vl0Zc8YZZ6TJZWuezylTppjVM7qqJrSFzgYN3Z6Rj7P69WWkBc9FgewqYNU4H5YW75Gq68EOV8Hdtj3irbgywcX9448T+2U2ZWjF8BM74h4dkqJweeI+bJOZo/jhUKIu/gbNien0ux3usvfMzFJ71Ah/V5Lf3ekz47e7Ix+EKxXZIzVzjb+FrEyQPKBBqXYeqTnPTzBBWN1v9b052aCwP4bVvy+guTQ1+Lh5i7850XerQAHYj44x252+A+C8vTRRn9ANzrgnzcxSyIeW9hNjQ3cleqzV4v0+zqDBcF57PVGf0A3OhElwH3kMCNgIPP146C4+pgAFKJAjBNJ1BmiokCOf4K1cuRLz58/HG2+8wYJIoTh8TAEKUIACFMhsgXqSr3P5CrjvypvNdm3S5Gri87zVr5cm42WHQcIDoFqNXSu8R9s0sLlr1y5T1EiLG+nXjh07zHet5p7ZLatfX2b78PwUSCsBDQC6H30MfL4J7uHDsM4+G5CCM9ZlraBBs/RstgS/gpo38qPVCDa6EJbke9TZm5YUt9WmQU/3rSVwxkil8G1fAflOBSSI6E56EcENsvT8vntlafal8TM23e1fwdEZpROnmMAqip0DSDA0eIHMYLzlJjNL0ZIJMa6sEEShwrCcIJzZ84AvNgP58yEwfzassAJzie7/qx3eployY3XzVgRbXgar/62wb5WAZfVqZp8J2Er+UTPLcf1nEoyVt8JxBZbcLt3hTHwOVod2pqK9HuDKBB1Hluub69YNMo49Pm7WqD5PpmlxI6tta5k5Oh3u6k9g1aoZsbctS+pdcXOfnwinU2e4vXrAvr1//AxM89/CipVwH5Pgpy7zlz8p9uQX4vdHHFh22FdLEHv4/ySwOQ7OtdfDfe0NoImkNtDc1FqTQ2t2SMDT/WAFsHSZGUrv0Wp2YXLDch8FKECBbCmQrgFQDXquWrXKLHvXoKfO/Axt+oagefPmuPbaa6OqfBp6LB9TgAIUoAAFKJB2Alb7tqZog/vGQvn+GPzKtKk9g/v9996bd6nMa5bhpXagbHbcvn37Elxx8eLFEzxP6sk333xjCkkuXbrU5AVNqk9mbsvq15eZNjw3BdJawJVcmrpc2RkjS5F1xmRck1XgXpOAoHXHbbBHDIMlqTDSo2lxm8C7b8Ppfwfcl2fClWsJ6vUUlPPlzw9IQBZO3BVVrigByjkml2ZQCxB9ugHOFddIcDEAFC0KSPEg/CGBTW261PweKZ7z0ANwdXbn9FfgSo5KbfH3F/q4aBEEliyE1aC+6ZPsP397heWs554GJD+m+9QzcJ95AUH5wukSMNaZ+D/+aAK1ZpySJRCYMxNBuUds3QbIjFDnup7mGiVpc1x+0IQz7e3pL8ZU+MeS5ed6X+7BH5K9dN0ZkOt2ZLWEM1JsJOga1Dypp4n1WWd51/3fcW8MydNtvzQFWqAo2hZ4+EEEJdDpDh0hAdDXgUgzQeXnbv3fRFmG3z3aodmPAhSgQLYSSPMAqAY9P/roIxP0fP3115MMemrFeA16dunSBSVKlMhWYLxYClCAAhSgQE4UsFpcBFSqAOz8Bu4UeZMXRa6w5BycBx4Ggg6sq6+EpW+Cc0H7XoK+4QHQBg0aRLxzXTI+fPhwszJGXz9F004//XRTRFJzqKdl3s+kzp3Vry+pa+Y2CmRnAS1u43SVWXq67Ftb9arejE95v+TKbD33408AmU3ojnsKwYWLEXhrAayKFb2+afyv5tsMTJNZm31vhiMzE12dHfiLzBjUgnka3GzWFPb13WDd2AtWXm9Ze2Dnl2a2pDNHZm9u+NzLJanXJTM+dWalPegO6BJ795M1cOJmG8oUeW8m5n//eXegz3U8naH4409wpkyFLVXbU/xQTnOPSvDYkkCm/fhYuDKr0hn/nJmpapbaaxBWArBoWB92t65mdqilwVydTqmtfl0pniSBUrm2+KX5Z58FS1ZEuBu/kJmu22FpoacYmhuXE9qSehfRNHvoYFjXXgPnqWfhys/X5Er960bY6pcAAEAASURBVG/vEmueD/uazrDuHABLg6IxNPe77+DOeCV+tqvmX4WkL4hv/nNNafDiS3DbtYalM0PZKEABCuQwgTQJgPpBz9deew0a9Pzhh8SfcukbAD/oqdVL2ShAAQpQgAIUyDoCuirDHn0/nOt7ywyUB83SPatcuVRdoKNL9aZOM8vq7JEjUjVGdjxozRp54xzWGjWSpYZJtP379+OSSy6Bfo/U9GdSXpac1q5dG7Vq1YJ+gNyyZUt573oqdGl9aAA0lmX2kc4Xuj2rX1/otfIxBXKCgM78dGQZtkkdIgFDW2fiJVE93N24CcGb+gISlAte3gGB9R/DKiIVzdOpWU0aIyBf2lxNw/G3BORkhmRSFcU1oGiWy8uSeV22bWZcysxLSz648Zsuhw+27QQc/R1Wm8thT3wWVtmy0OCvFIbwZo3quV56GZq30v2/l+BIQDTwwjP+EEl/P0uKGB3+0QvUduoA6/zqCIihNpOn9HeZiSofxiUKpO7da/pYnToiMHK4l8NUZ7jqvcQVRgp2lyXqEgB1JahrNU76d7oZJPwfWdZuWvno/5aqReDZpwD5MgHU336THKhFoPk8U9PUNdi6A7B9B1CtCgIye1R/pq7+HHUJfNzYznuSGuCmfsCqjxGUGbyB5ctSfc7UXCePoQAFKJARAqkOgGrQ8+OPP4YGPTWvZ1JBT33B7i9vr1BBZpWwUYACFKAABSiQZQXs7t3gzn0N7qK3Eex0NQIr3ot5FojJ9XbNdd7sz/tkiWYyec+yLEQqL+ytt95KdOQFF1yQaJtWb2/Tpk2i4GehQoXQvn17NGvWzAQ8a9SoAZ3xmVQ7dsxb7unvc93QBaT+1tR9z+rXl7q74lEUyNoCmv/RD34G1q6CBsKSalbdOgh8/CGCl7YF1qwzVdIDs6Yn1TXNt5mAYDLV0kNPaAKNYSlA9PdUsMeNXvCzWxfYct1+INUs5w9Z0q+5QbUCffDiy+FOmCy5MdvDbn156CkSPLYuaQl3jvz90tmnkssztJkAbBK/Sx2dZRm3PN+6XYJ/0sz1nHtu6OEwKWIkJ6nmA9XiRtF84KT5Q135MFBny6Y2DYyZORrl7NEEFxzyxJFl7yb4KblRAx8tj0+bYGa/mhmwXmf7MsnZuu4jBJu2kFnGayTfqOQBvX94yEh8SAEKUCD7C8QUANWg5+rVq+OXtx88eDCRQPXq1U3QUwOfVapUSbSfGyhAAQpQgAIUyLoC9isvyxsgWQ4vOdGCjZohsGBe1EFMZ8k7cGSmjC6RtK7oAPvBUVn3RtP4yjTP+ezZsxOMWrVqVdStK8sqw9qIESPw5ZdfJtiqs0H1Q2UNgqbU/pRCIaGzP7V/WgZAs/r1peTD/RTIbgKuLPc2hXnkwjW/Y6Tgp39fGhgLzH0FwSo14c6eC1eLDlWr6u/Ost/dRfIhkc6KLF3Ku0/bTvZadaaiLfkrnXuGmpUJyQZAddn73Pkml2dQqqkHpEhQcs2R2aZOz5u8LnVrw07md6/V+Uqg1H3AZ59LMHYSrAH9kxvazCJ1+t8JSN5Oq4+kCIhxyXqyg8ew0yx9lyXtGoQNzHs1PvgZaQhLAtaBV6Yh2LwVnMefgqUpCzR3KhsFKECBHCKQ/F+dsJt88cUXcdFFF+H5559HaPCzUqVKuO+++7Blyxbzgn7kyJEMfobZ8SkFKEABClAgOwjom53A+0uBxg2B3XsQrN8EQS1WsXNXxMt3138qM0Y7w2kvbxI1+Ck5zOzZM6OaJRNx0Gy249lnn5WUaiE51eT6e/funegutM+sWbMSbNc0QcuWLYsq+KkHhr4G8weKNoeo3z/S96x+fZGum9spkJ0FzEzBn34GateELTkno2lW6dKwelwvn37I/+a/Ec0hmd7HFOCRqzC5QENmHyZ3YdYAmZmpxZfWfwb3228jdrWlgJAlleq1uZI3NHjHoIh9HUnX5lSsLjNRZdm9VEC3JZicXNPZkvbTj5suuizf0RQvEZors/MdmeVq8rieUxT2mMz7INBdsMgUfbKkErxVpXKEK0642VR/v6iZFK/6A+477ybcyWcUoAAFsrlATDNAw2cXnH/++Wa2p85u0KUA38ofJf1KTatcuTI0kMpGAQpQgAIUoEDmCliy/C+wUvKB3TUE7mQpgjH5RQTlS/OHWU2bmIIW0KI93+2HK/nCsP+Ad8FSndh+YCTsIXdn7g1k8Nk1JdC4ceMSnFWXricVAN0r+eaO6pvukNa5c2fkyRP9S7Lly5eHHO09TKsAaFa/vkQ3zg0UyAkCMrNQm9X6spjuRvtrsM/9bENMx2VWZ/86Y7lPrUhvtWhuUrO44mQlU0tC81se3yqz6z/dAPf5STguM0Ktm/vA6nYNoMvaNYgqf8tcWa1g/oZp7uuZ02BH8R5UCxDhwZFmJqpzc3+ZeSurI/rfKn8TG0uAVvKPHvjeBAwdqT6Pb/dJ1fnTEXhjLiwpYJVZLTXeeq22/HflyN92c3yXqzPr8nleClCAAmkuEP2r7SROrcu3dLZnWrTRo0dj1KjM+4QsLe6BY1CAAhSgAAVyioC+6QxMkEq0/W6B8+R4uK8vMHnEXC2kEN6KSoGGG66DLYUvrFKlwvfm6OebNm1Ct27dcFwLfoQ0re5erFixkC3ew19++SXRtpo1aybaFmmDBjpfekmWNIa18CXxobvz5csX+tQ8jtQ/M64v0cVxAwVymYArKTS0xRoss0oUN8e5UvwnW7RDUlxIW8kYg4IlS3rHaXGiFFqe9asRvKE33FlzTBV599Fx0K9ETSq+24vmw77k4kS7Im2wNSdmubJecaYPPoQrX0k2WVIfeOVlWNWrJbk7wzb6/13E/XcS9Xn9/v7xUR/IjhSgAAWytkBMS+Cz9q3w6ihAAQpQgAIUSGsBLWIUmD4VgZ8PSgGFD2BPfh72Iw/CHvsQ7GlTENiwBoEf9iHw1OO5Kvj5448/4tZbb0X9+vVx4EDcDNg4fM39effdSc+CrVYt8RviL774Iuof22OPPYZ169Yl6h9eFCm0g1aNt8Ny7f2mlYWTaJlxfUlcBjdRIHcJ+Dkitcp6DM2N+/+xX608hkMzp6t/nxF+/0S8KL9/lAWYNPhof70FVtvWgBZW8n//6Uz7MufBkgJ9ef74Oabgp39t9g3dEfhmO+ynxsFqeRFQXGaWFjgNkErvVterYc+fLX8X12Z+8FMvWFMHaJPUNDG13+L+O/SPj+lgdqYABSiQdQVimgGqRY2SWs6VFrdXp06dtBiGY1CAAhSgAAUokA4COiMUzS6EyQ+WDuNn5pBaVCh8Wbp/PZr+R/f//vvv0KDh5s2b8emnn5qCkEeSCFYULlwYixcvhgYdk2pnSQCgfPny2L17d/zu8ePHo1evXiiVzOxZnfmpq2Ueeuih+ONCH+j1JdcKyGyn0D7Tp09H48aydDOsZdb1hV0Gn1IgVwlofkZX7tiV6tuxNPeTtV73qtHld4xl7PToa+5z77fQ606p0FPo+f37tKpWCd2c7GOzrH3JwmT7pHanBpytu+4E9Csrt7i8n+7qT4CukgYgyuZ+4v13GIt3lEOzGwUoQIFMFYgpANqyZUvoFxsFKEABClCAAhTIKQLt27dPk1spUqQIFi1ahIoVKyY7XpcuXaAzOf32008/oUWLFnj00Ueh+zSvut80+Dpv3jxMmDABn332mb/Z9AnNzX44haWhmmt9w4YTeQInTpyIVatWoXbt2iYw2qNHD3NuPUFmXF/8jfEBBXKhgJmpmDcP3OWyrHrH11EVrHH//hvutBlGy+rUIVuoWZ3am+JAzoTJsLt3i+qaHc3XqTk1pcgR6nLCTFRocZ1s+e8iOOZRkw7AfXAUopkpbCrHL35b8jHI/zq0i+V07EsBClAgywvYWf4KeYEUoAAFKEABClAgiwtceOGFWL9+PZo2bZrilT744IOoV69egn46I/Taa6/FaaedhurVq5txSkuV54Iy06hPnz4Jgp+69P7hhx9OcPzq1asTPA9/ogHW8Ka53F999VUzY3XFihXxuzPj+uJPzgcUyIUC1tlnw7rlJlOxO3hTX7j//puigjPkXq8AXYN6sC5umWL/rNDB6iVV2oudA8hMV+e5F1K8JPfnn+Hc7lVz1+J6oR8OpXgwO8BqUB/WJS2Bn3+Bc8ddKYq4wSC0wBOO/QNLAtS5Lad3ikDsQAEKZHsBBkCz/Y+QN0ABClCAAhSgQGYIaHEhTQ2kS+K1Eny5cuWiuoxTTjkFs2fPRqUkKg9rLs/t27dj7dq12L9/P4LyhtRvxYsXx9y5czF58mRceeWV/mbzXQOge/fuTbAt9Ikunb/oIslXF6Ft27Ytfk9mXF/8yfmAArlUwH7gfpOf0gQH218JV/IMJ9Xcf/5BUIJZ7guTgHxSrG7ic9kmMGhp4aHnx5vZhc5dQ+A88TRcSe+RVHN37kKw5WXAnr1Ak0am4npS/bgteQH7uadNRXp35qsI9rkV7l9/JXmA++uvcDp3hfvu+yZIbT+W8EO2JA/iRgpQgALZTCCmAOiMGTNw7rnnmq/335dfjmwUoAAFKEABClAghwto/kwNVl588cW44YYbMHToUEyaNMkEKKdNm4YGDRrELKBL0nUGpub/LFSoULLHa1/tp7NEu3btavpqsSKdIeo3XQ6vszkjtfz585uZnp07d0bevHkTdduxY0eCbRl9fQlOzicUyIUClqTQCCx+wxTVcd9fjmDF6gjeNRjO0mVwN30B58MVcMY8gmDlGnCfn2iCn/as6WaWX3pyacDMmTkLwet64HiT5jheuwGCbTrCeXgs3F27Yj61fU1n2I+PBSTwqbNYg3Uawhn/HNyPV8PduAnOgoXQWbDBGnWBrfLBTI3qCCyYB0t+b7lf7YDzwEMItu6A47Xq43jTi0zFd2f2XLjy4RFbYgGtRG+/Jn8bpFCTpkww/12NehDOB8u9/66WvYvg0OEIVqgGd5EsfS9cCIFFr8MqKSkH2ChAAQrkMAFLXjBrzu2oms446Nevn+mrCf47dMge+Waiujl2yhCBu+66y7yJe/rppzFokLekJUNOzJNQgAIUyMICyz5fgqsf7YDL67bFG8PlDQhbrhL4W3L57dy5E7skmPDNN9/gZ1n2qflEdcZns2bNUKZMmTT1+FVm+uj5NG/omVIh+bzzzkNJebObVGBUT5zR15fam61wSwkc+u0H7JpyAOeeXTy1w/A4CmSqgHvgAJz+d8BdvCTyddStjcDkF2A1jP3Dl8iDJt6jgUXnnqHAwR8S79QtAdss3beffAyWpO+IpTlvLYFz593eDM+kDtSxb+xlqq1D3q46A++BO+MVCZxGeOtauhTsZ56EfdUVSY2W67e5W7YieOttwNr1ES2sy1rBnvw8rChXM0QciDsoQAEKZFGBmIogZdF74GVRgAIUoAAFKECBbCugszNr1aplvjLiJs6WfIONGjWK+lQZfX1RXxg7UiAHCujMu8CiN+Bu+BzOfJkRumGjWQ5vnXUWUK0qtJCQ1frydF/2HrxvFNyH44q1NW4Iu3cPWFqESH5fubIs3X1zkRTXmQ130v8huHYdAu8vhVW4cNQ/EVsK7FiXXwp34WK4by+F+81uQIq+WcXPhXVBU1hdr4FVqSLcgwcRbNUG2C6z1E89BVbPG2B1FIOy8sGQ9Hc/2wDnpenARpkle/W1wCNjYA8bEvV15JaOVs0ayLNmFRwttLVgEfDlNuiyd515jDq1YF99FawmjXMLB++TAhTIpQIMgObSHzxvmwIUoAAFKEABClCAAhTImgJW/XoIyFdmNGfqNC/4eUpek7PT1gJNIc2qVRO4oiPcoYMRvPIaYNNmONdcB1uDoIFASM/kH1qSD9nqcjWgX0k0LQYVvLKLF/ysVcNbCl++fIKeGrSzBvSH++wLMlv1f3DulVyq5crCvlaOY0skYF9yMaBfbBSgAAVyoUBMOUBzoQ9vmQIUoAAFKEABClCAAhSgQK4QMJXXddm7NHvqZIQHP0MRrKpVEFjxHlCyBNwVq+BOnxm6+6QfuxOnAOs/A8qXQ+DDd2GFBT/9E2h1eHvg7V6BJdmoFc/d33/3d/M7BShAAQpQwAgwAMr/EChAAQpQgAIUoAAFKEABClAA7pSpwJGjsNq1hn1D9xRFLCmQaz81zvRzxj2ZYv9oO2iZCq0Sr00rx1spFIsz/frdClzUDPjxpzQPxpoL4T8UoAAFKJCtBRgAzdY/Pl48BShAAQpQgAIUoAAFKECBtBFwFr1lBrL69416QEsqu6Oo5JLcsRPu1zujPi7ZjlIRHvsPAJLr027bOtmuoTvt/hIElWYqmofu4GMKUIACFMj1AqnOAbp69WocO3YszQCrV68O/WKjAAUoQAEKUIACFKAABShAgUwQiAtgWhc0ifrkli0V26VQkvuWFDPa8TWsypWiPjZSRx1Hm9X0RGGe4OgxMrNTKsHv3w8cD8pOCzgtP6yGDWBJJXq7Xt34/v7xkcbndgpQgAIUyH0CqQ6Ajh07Nk21Ro8ejVGjRqXpmByMAhSgAAUoQAEKUIACFKAABaIUOHLE61iwYJQHxHXz+x89GttxkXqHXIez+hM4ba8AwvN6yjJ5/PmXl3+0flO47drAnvWyN2JaXUek6+N2ClCAAhTIdgJcAp/tfmS8YApQgAIUoAAFKEABClCAAukgUKyYN+gBWX4eSzvwvdf7nKKxHBW5b9x1uBs+h9O8lRf81Jmm7dvCXrIQ9q8/wP56C6xHHgBKlTTjuEveQbBOo7S9jshXyD0UoAAFKJDNBFI9A7Rhw4Yo5v+BTIObrly5chqMwiEoQAEKUIACFKAABShAAQpQIDUCVoN6Jn+m+45UXb/15qiGcGW2pbtmLRCQAKUsQ0+Lptdh2qcbvO9FisDeuA52XLDTbDzrLOBeqVgvX8EHHoI7+iHg231mly6LZ6MABShAAQqECqQ6ADpy5Eh06NAhdCw+pgAFKEABClCAAhSgAAUoQIFsKmB1vcYEQJ3Hn4bVuyesU05J8U5MtfZ//oV1WStYhQun2D+aDlbp0sAZp8vMzz+AvHlh79wKWwOeEVpg1H04fuwfYOzjXg/JScpGAQpQgAIUCBXgEvhQDT6mAAUoQAEKUIACFKAABSiQSwWsbl2B6lWBXd/A6Xc7XM2zmUxz3nsf7qPjTA/7wZHJ9Ixtl6PFdjX4qc1xYG390nsc4V83GIS1e3f8XnfxkvjHfEABClCAAhRQAQZA+d8BBShAAQpQgAIUoAAFKEABCsAKBBCYNR0ocBrcaTPgXHE13H3esvJQHve//+A89QycDleZiuzWiKGwmpyo2B7aN5rHzjcScF23Hs4fcUFPObdpsqweEtwMXtYOzoRJcI8fTzScq8e27QR33uuAP2NVxmKjAAUoQAEKhAqkegl86CB8TAEKUIACFKAABShAAQpQgALZX8CqUxv2WwvgXHMddCZlcNl7sC65GKhXB1a+fHD37IUWHMKhw+ZmrcGDYI8ZHfONO9NnwvHzdobMNHUk+IoiRbzxzjsPVtvWcCdMhjNgEPDIOFhtLodVtgzcP/8EPvtcqsCvNEFYFDsH1tiH4d54C/CH7GOjAAUoQAEKhAgwABqCwYcUoAAFKEABClCAAhSgAAVyu4DdsgUsKTrkjBgF99U50KJIkK8EC+Jr1YAtAUdbApSxNJ3l6TS8EPhqx4nDLEvWJnqzPfHnX8CfcbNO/z6GwAsy01TyizrD7we274A79eWE15E3D6ybb5QgrFzr3397+0ICqidOwkcUoAAFKJCbBRgAzc0/fd47BShAAQpQgAIUoAAFKECBJAS0EFFgxktwxz8B9933gd174EpuTqv4ubCaN4NV4/wkjkp+k+b2dMpUBn75xet4QRPYj46BfVFz89zRivKPPQl33JMyq1OWu//wA4Ky1D5w90DYV8oy901fwP1kDdyDP8AqUACoUN4rvhRXIMmdNdsb95S8yV8I91KAAhSgQK4TYAA01/3IecMUoAAFKEABClCAAhSgAAWiE7AKFYIpjhRd92R7Oc0v8YKfMtvTnvsK7Gs6J+hvn3km8PADOH5dF6BmA7PPvWconEtawpal+bo8X78iNWfKVG9XuXKRunA7BShAAQrkUoGYAqCdOnVC1apSFVBazZo104zsr7/+wvfff4+KFSum2ZgciAIUoAAFKEABClCAAhSgAAWyhoCz/EOTs1Ovxn5pcqLgZ+hV5qlRA8eLFAZ++tlsdrr3gr1tU2iXRI+d7V8BH31sttu33ZpoPzdQgAIUoEDuFpBEK9G34sWLo0WLFuarkHwSeLJt27ZtuPPOO1GiRAm88sorJzscj6cABShAAQpQgAIUoAAFKECBLCjgjBrjXVXFCrB79UjxCu1nnzrRR4Kbjr9s/sTW+EfO9wfhNJFl9JqkVAoo2XcMiN/HBxSgAAUoQAEViCkAmhZk//77L+bMmWOCqOeffz6ee+45HDlyJC2G5hgUoAAFKEABClCAAhSgAAUokIkCJs/nwsUIPv4UnGnT4ezc6V3Nxi/Md+vWm6K6Ovu6a2Fd0SG+r1Oz/omx4rfCO0+5KsDR34FAAPaHUqGejQIUoAAFKBAmENMS+LBjY3q6d+9eTJkyBVOnTsXhw4djOpadKUABClCAAhSgAAUoQAEKUCDrCjirP4HTpy+wc5fMxPTqxftV450zTpfK7n+ai7daXxr1TQTenI/jp8tSeD1WZ3lWrgmnoOQJLSzb/vlHiiQdkgho0Bvv1FNhv/sWbFk+z0YBClCAAhQIF0jXAKjjOFiyZAkmTpyId955B/o8vOks0D59+qBnz57hu/icAhSgAAUoQAEKUIACFKAABbK4QPCW/nBfnHbiKvNKFfbTJegpq/9M8PL3P+L3OZLXM6ZliKVKAju+BvLlA6SKPI4c9b78EaWgktX6MlivTocdVw3e38XvFKAABShAAV8gXQKghw4dMjM9dcbnt99+658r/vsZZ5yBbt264aabbkLjxo3jt/MBBShAAQpQgAIUoAAFKEABCmQfgWD3nnBnz/MuuFxZ2P83AXYrqfYe15yffoI7aDDcWXO8LW06wfnhW9jR1pQ4+IM5zhoxFFaP7t65du8BZCao1aA+0OVq2BIEZaMABShAAQokJ5CmAdAVK1aY2Z4LFizAf//9l+i8VapUwb333osuXbrgtNNOS7SfGyhAAQpQgAIUoEB6Chw4cAAffvhhTKewLAt5ZTbTqbK8Mp/MQDr33HNRunRppEVByEgX8s0332DhwoXYs2cP9u3bZz5QPnjwIIoVK4YKFSqgYsWK5rt+kFy3bt1Iw3A7BShAgXQVcOa+Fh/8tLpfi8Cs6YnOZ0tRIrzyMo5/9rk3k1PeJzrNW8H+cmOivuEbnM82SG5PmfEpzerTG3aJ4sCwIeHd+JwCFKAABSiQosBJB0B/++03zJgxA5MmTcL27dsTnVDfIGjOz38kR0ubNm3Qq1evRH24gQIUoAAFKEABCmSEwKZNm9CjR8rVh6O5lnLlyqFVq1a49tprcemll0ZzSLJ99LXSG2+8gRdffNEEad24HHqhB+lrqi1btoRuMq+vRo8ezVU1CVT4hAIUyAgB5/ZB3mlq10wy+Bl6DfZDo+F06e5t2rYdztJlsNu2Du2S6HF8/zLnecHPRD24gQIUoAAFKBCdQKrXCnz22WdmCXvJkiUxcODABMFPnR1x3XXX4d1334UWPzr77LOjuxr2ogAFKEABClCAAtlEQGdnarDysssuQ8uWLfHVV1+l+sqXLl2K8847D927d8fy5culfogb9ViaZ71JkyZo167dSV1D1CdkRwpQgAIi4GzeDEg+T232wvnme3L/2Nd0BipWiO/i3Htf/OOkHgQvbQvs9dKp2S//X1JduI0CFKAABSgQtUBMM0D/+usvzJkzxyxz1wBoeNMX37179zb5PQsWLBi+m88pQAEKUIACFKBAjhRYuXIlmjVrZoo/NmrUKOp7DEr14lGjRuGRRx5JMeipy/CTSjHkn0yDqJ9//jk++ugjVKpUyd/M7xSgAAVOSsA9fhxYuw6u5t38+2+gRAlYTRvDnTbDG/ecorDLlIk/h/v993DXy3vFHyR3p9R+sDTo2bABLMnTaa9ZCadEWeA/GfOLLQhe1wPWk+MSzO50XnkVzj1DgcM/mjGtuwfCbtnCPHZlFry7Zp03tqRUsyqUBxo3ghUImP38hwIUoAAFKBBJIKYA6MyZM9GvX78EY5UtWxY33HCD+dIcn8k1zaHFRgEKUIACFKAABbKSwAUXXIAimqMumXZcAgC6RP1vefOvOTk1l2j4LM2ff/4Z7du3N6tiUhpPT3XkyBFcddVVSeYkPeWUU0zaIF1ar0vt9UvH/EmKiezevdvM9HzhhRewfv36BFethSj1GA2C6oxSNgpQgAKpFXDld5Tz2BNwJ8nsy19/SzhMQBYSFi7sbStVynx3P/oYwftGA/Id4ZPYi50De9AdsAbeLvlApwPXXu8dM0dyiMqXk+9UII+8Nf1LAqyO440r/1r3DUNgzGgJqH4KZ8QouMs/lP1hgxcpDPv2/rDuGQRLK8+zUYACFKAABZIQiCkAGnp8hw4dcP/996Nhw4ZgYDNUho8pQAEKUIACOU/g2DFIsA3Ytg2QOJ9U3IUUAwJq1oQU4QGy8+QbnX3ZooU3uyjan5wGQZ944gloENIJebOuAcq7777b5EdPaaz+/fsnCn5qGiHdPnjwYJlkVSLREBoE1S+dZdqzZ08z43TQoEHYuXNnfF+9Ng2Crl69GkWLFo3fzgcUoAAFohVwt2xFsNPV8UvQUfN8WHXrQCrBAXv2wl31UfwMTZ3pGRwxEu4j47zhC8jMzBbNIZ/CAL//boKX2PkNnHvvB16dA2vI3V58VP+QyAxR+TQIOPaPHKtf0nS7nMue+RLsalXhPDpOgp8jvaBq/nwy9kWA5ASFrE50tUjS9h1wRj9kxg4sXgCrMmfAe5D8lwIUoAAFQgVSHQB96623oIUEtLCRfmn+qzPPPDN0bD6mAAUoQAEKUCCbC3zxBSTQBynOY95rJnk3OnlSFoNI4A9SHT3JLjluo86ufPbZZ1GvXj306dMnwWzQWbNm4dFHH4XmSY/UtM/s2bMT7D5NlnNq5fdYCipp3s+aEoXWWaz79++PH08Dog8//DDGjx8fv40PKEABCkQj4Ep+4+DFl8unXb8AjRog8MIzsBrUT3Co++uvCLbpKJ+MyVL37w96wc88AdgjR0CXrFsFCiTo73y4As6AgcCWL+Hefpe376yCyPPzQTj7D5gl9pDiuqhaGfILTWKgEgSVZoKfwyX4aVuwhg2GLRXgrbD3nO4naxDUsTdtRrDlZQh8uhpWMr9/vZPzXwpQgAIUyG0C3l+WKO9ag5x9+/aND3TqC21N/n/NNdegWLFiJvenJuLXfFZsFKAABShAAQpkXwGd8SkTEc3szlde8YKf558PKYAIjBgB3HuvF/TUgKdMepRAG1BZ3reOi5sAlH3vPLYr19zn+hXadEbo3LlzQzclePztt99iwIABCbYVkGDB22+/HVPw0x+gtPwQ9PXXWWed5W8y36dOnSrxBAkosFGAAhSIQSDYvZcJflod2iKw6oNEwU8dypIit/brCX/P2S88C/v+4YmCn9rfvrglAus+BiR3KI4e1U2SR7SJ+W6XKgktkGTf3Ae25FL2g5/esncv+GnPn4PAI2MSBT/NOBc0RWD1CliXtAQO/gCn181mXP5DAQpQgAIUCBWIKQBavnx5TJo0CQcPHjRLu1q2bBm//P2YvFPSF/tt27Y1OaeGDRuWoDK8njQ8V1bohfAxBShAAQpQgAJZQ0D+zMuScMjffG9puwZCdYX11q2QDz6Bhx6CFO0BJDW45MMEPv0UuOIKWcEoQdOhUrfi2mu9OhlZ427S/yr0NU9400rukdpQQdL8n6FNl9Pr66rUtvMlOj1kyJAEh//xxx/mdVuCjXxCAQpQIBkBZ+FimY0p+U5KFIf96gxYp0puzghNA5cI2e8sWRqhp7fZ0oJIccWMzJbr5I9FMk1zfupaeWuozPy8Sv7IJNMsmUFvv/aq5CUtBPeDD+ForlA2ClCAAhSgQIhATAFQ/zhdotWjRw+Tt0qXWI2QqSCl4pJfa5/vpfLfY489hurVq0Mrwx+N+5TPP57fKUABClCAAhTImgKSrk1mIXr5PitUADZuBCZMACpWjHy9DRoAb74JyUcJmYUIzJsHWRWSoI5F5INzwJ7KMvW1sF8MJO5+Qpejh96ivibSZe6hTQtK3qRTa0+yaaFKnUka2nSZ/r///hu6KeJjDcpqFfl58gN87rnnJMA9E1pZXosusVGAArlDwJ3tzeq0h9wNDVgm13SpvFSHO9Fl4VsI6pKACM15fiLcsY+f2LtB8ndGaKbauwYxJeenLnuPplmFCsHWIkvS3FcTzk6N5nj2oQAFKECBnC2QqgBoKEkFeXf0kEwF0eVcS+Sdjy6H18qlflu3bp3kp/7LPH3//fdNviutoMpGAQpQgAIUoEDWE+je3St0pMWNtOhRjRrRX6MsAsGaNZAiPcCiRcBIWbmYW5oGMUPbjz/+GPo0/vGCBQtkpqxMlQ1pWlQyb968IVtS97CQvPm/8cYbTTC2U6dOko5gnORufUMKVAUiDqjXMmXKFFSrVs0soa9fv77M4L0Wd955pymypDlG9bWergLSbd99913EsbiDAhTI/gLuJ2vNTejy95Sau1p+4WuTAkl+c0uWR3DIMDiHD/ub4Mybj+M168G5Q3J/ujKl8xyvOJt/rviOIQ9cnYUq1d6ti5onuew9pGuCh1bH9ua55gVlowAFKEABCoQKnHQA1B9Mc7Xo8vfXXnsNBw4cwNNPP22S8vv79fuXX36J7vLOSvOF6gt0XR4WWjk1tC8fU4ACFKAABSiQsQJSlwdS41ACaF4AU+JpMbeqVYH584E8eYCxYwEtopQbmqYHCm3hAVF/nxY/Cm0a+Oym02XTqOlSeg2+6ixTXRKvK3EiBUD1Q2q9Ts3v/tVXXyV7BXtkppfOCq1UqRIefPDBZPtyJwUokI0FpKK7aWXKpHwTPxwyfazWl5liSeaJzDh3nxgPp9h5OG7nx3ErH5xrpUre1m3eeE0awVqz0nss+TojNv93atkoriN0EK0Ory3u2rwn/JcCFKAABSgg+ajTA6GITP0YNGgQNm/eLHnBPoUuySpYsGD8qX6X9XUvv/wyWrVqhTLyx1VzZ+kLazYKUIACFKAABTJHQCflPPCAd+4xYyCBsdRfh+YP1RXdWhMxN8TKfvnlF5P+J1SsRhJTZ3+VqsnhuUEbNWoETS2UVu1UycdnWVaKw+3atQsdO3bEoUNeACPFA+I6/CPLXUeNGoXBgwdHewj7UYAC2UnA/30Ut4Iv2UsvEPe768+/YBU/1+uqRY78pfP6h0WbzkI/vzrst99EnjWrYP8Tl5bDP97rlfBfP53Hn38m3J7SM7+/fx8p9ed+ClCAAhTINQLpEgAN1WsgicEmTpxoCie9ImVkL7744gQvzDVHluYLnT59euhhfEwBClCAAhSgQAYKrFjhFTo6TybP3HLLyZ9YYmTmPa8uhQ9ZCXnyA2fBEbQCe3hLKgCqH/YGNSoc0lpotDiDm6Ym0lU7ocv0z5aKzjpjdPXq1eZDaS2gpOmNVsh/GLpqJ49O6Q1pTz31lMkXGrKJDylAgZwgUKG8uQv3s8j5OeNvM6Svu0ESRksLTJmAPEd/hB38C/ZXm2EfOYw8x/9Enq2fw27XxvTxx7bijjcbw/7x9/l9w3ZHfOp+9rm3L5mxIx7MHRSgAAUokKMF0j0A6uvlz58f119/vZn58M0330DzXZUuXdrfnWu/a8GorVJWd70kWtM3GWvXrsX27dtNGoHwHGG5Fok3TgEKUIAC6S6gBYy0de7sLV/3nqX+3+LFgebNgePHgXffTf04Wf1IXfquuTHDW8OGDcM3JZolqh3q1auXqF96b9Agp84A9du5555rgpmaM/SCCy6Q2b9lTTGl8yQargHal156yRS+1NmlfnNlZtdYzXHARgEK5CgBq72X+9N9cVqK96X5OXF6AeBTCZbuPwCcVxpWDS8fqKZHs6tUhn3mmYnGceLG9s+VqINuaCS/Q4tIPpavvoa7+pMkuyS10Z3qXbcddx9J9eE2ClCAAhTInQIZFgAN5S1XrpzJH7V3717orImuXbsiX758oV1y7GNd/v/CCy+YNxhnyguCkiVLmlypjRs3NrNjmzZtiurVq6NUqVKyeuQM88aof//+ePvttyVnuJtjXXhjFKAABSiQuQJS/Nu0yy9Pu+to3dobyx877UbOGiOtkYpPV1xxBX7++ecEF6TbNPdmeNMPPcPbOeecE74p3Z+vXLkywTn+97//maBngo1hT5o1a5Yo96cWt2Qu9zAoPqVANhewb5X8JVJ53Z37Gpx3liV7N5a8f7Nu7BXfx7q9f/zjSA+cl2cAqz4GihaBdf11kbrBkmXz9h23mf3BAQPhRrEk33l9Ady3lgJnngGrz4nringS7qAABShAgVwlkCkBUF9YPxlsLe+O5s6da/KA+ttz4nfNsTVgwAAT8Lz99tulSu4aaDA0uXZcps1s3LgRkyZNQocOHVCrVi0TCE3uGO6jAAUoQAEKpEbAL+4tBb/TrEnhcNMk202Wbv/99x/+lcIdyX399ttvppjjsmXL8OKLL+JyiRTrbEnNdR7aTj/9dFMsKHSb/zipAKjmTc/o9vHHEnwIaYW16lUUTVfy6Gu34jK9VwOiWmle85+yUYACOUfAkkkY9sgRUq1dirB3vR7OksQpPvy7deX9jbt2nf8U7vIVcI8ejX8e/sCZOQtO3wFms/3EWFh+rtDwjnHPrXsGAVUqSTW9LXA6XAU3md83Gvx0evbxxn7kQViZ8Ls1wm1wMwUoQAEKZBGBhAmd0umiNPinVU937NhhZjFWqVLFVDzV2Y+5oWnRg8suuwxbtmyJv10tUKBvIHR5WdGiRaEpAnRpmQY9den7UXnx8J28G9X8W1pwQJsuldc3G08++aQpMhU/GB9QgAIUoAAFTlJA4n+mhaxyPskRIas7vCHi/oyd9HjpNYD+jU6r9oBUkoqU4ic056Z/vrPOOst/mGHf9TVHaJsxY4ZJUxSpWrzfV1+3/SkFRnLLqh3/vvmdArlNwB42BO4OWXr+8kw47a+E2/kKM9PTqltbZofK74/de+AsXAz3uQnAEQl4agEk/UX/zrsIVq4Be+DtMMvbS5cCJJ+wu+5TuFOmwn3vA0NpDf8f7J5SGT6FZkkhpMCiNxC8+HK4H670xr5zAKyO7aVSXxnILyRozk9d9m5mfsp4OgvVHpDyTNQUTs3dFKAABSiQAwVSHQDVQN2cOXOghY00MKczFTWQF940Sf6IESNMUC903/Dhw82MyMcff1yKJEhlwBza9I1C+/bt44OfmhPs7rvvRqtWrZL0CmfQWSmaH1TfnEybNg36/K677kLlypXRrl278O58TgEKUIACFEiVQMGC3mE6waaMvK9Mi+ZP1vHHTosxs+oY+sHmyJEjzd/oSNeYVLBTP/DUD0QzstWsWdOkIPLP+cEHH5gZnTqz9fzzvfx9/r7w7wx+hovwOQVypkBg2v/BkcrtzugxcN9YaL6SulOrQ1vYL00xwchgT1k+/9FqOMNHAvoV3gqdDZ35aYcsmw/vEv7cqlwJgU9lzF43w31/OZxRYwD9Cm+y7N2WmZ8MfobD8DkFKEABCvgCqVoCv2/fPtSpUwc9evSALgU7cOBAovxXegJNpn/PPfckCn7qPl1m9vTTT+PKK69MVBFV9+eUNm/ePLPcXe+nW7dupsiRfk8qWJzUPefNmxcXXnghJk+ejDfffBP6XNuwYcOYdyspMG6jAAUoQIFUCVSSVYba5PPMNGv+WP7YaTZwFhvo0ksvxXvvvYfRo0dDA6GRWlKBzp9++ilS93TbfssttyS6Ti3CqJXr9UurwWt+T38FSrpdCAemAAWytIA9+C4Edm0zgUU0uwAoWQKQICZqVIfV7xYEPvoAgcULYMkkGEuKp+VZ9QHsdxbB6t0DqFoZOFtmuMssUOuSlrCfGofAN9tjCn76OFaJEgi8twT28ndg3XwjUL2qN3YpWU3YojnscY94Y3Pmp0/G7xSgAAUokIRAzDNA9YW6BuT2hyX00txYoW3nzp2m0ru/rWLFirjuuutQrVo1LFq0CAsXLsTff/+Nt956C88++2yyMyb8MbLj908+8aoWav5OncWpubNS23TG5xNPPIGBAweaGaV79uxBhbRM1pbaC+NxFKAABSiQ7QUkpSNefx3yNxro46VRO+l70rG0aTX47N40sFlAlmMWlOmsmrezbt26aNCgATT4qal9omlabT28hRdQCt+fHs8rSURaV+I8/PDDiYb/8ssvTa5Tfb2h93vJJZegbdu25qusBDjYKECB3CVgye8t615Zsi5f0TS7tVTS0690aPbFLQH9YqMABShAAQqkQiDmAKi+YPaDn1ql/KGHHjJ5KcNfFOssCJ3lqU1ni2rCfX0hrU0Doa/Lu6xrrrnGPNclYzfddBO0KnpOa6tXrza31LFjx/jZmydzj1dffbUJgOoYX3/9NQOgJ4PJYylAAQpQIF5A/rzIqg1J4Sb1LmShh+Sojt+Vqgfvvgvs3QsUK5b1A6DvysVedNFFyd6nrsA4mQ8xdfCkAqCaJz0z2pgxY8wH0ePHj4+4okTT+CxevNh86TXWr1/f5Aq94YYbol7Jkhn3xnNSgAIUoAAFKEABClAgXCCm6Yj6In3q1KlmjLPPPtvkprzzzjsRHvzUF8y6XNtv/iwC/7l+10Ben7gpJn9IcmydCZoTmx8sjlQQIdZ71kqt/jJ4nUHLRgEKUIACFEgLgdKlgc6dvToWkrr7pFowCAwd6g1x++2QXN8nNVy6H3zKKaeYQoRajDDS18kGP/UmdDVM+BJ5/4PStLxJ15XyzSk0vQ4tqqjn7yw/eDVIqW3YsMHkMddl8itXrkypO/dTgAIUoAAFKEABClAgywjEFABdunRp/CwBLcRTtarkX0miaR6sv/76y+zR4KgW/Emq3Xij5HCJa0uWLPEf5qjv/hL1NWvWpMl96ZJ6LYSkTZffsVGAAhSgAAXSSuCRRyABQEiBQ0jhvdSPqjNJN22S1G8SVJWXC2xxAjoDVGdRhjZ9zZSW7dixY9Bq7a1bt8bYsWOxbt06aOHKSK1JkyZmVc7333+PSZMmmdzsKa3IOXz4sFn6rymM2ChAAQpQgAIUoAAFKJAdBGIKgOoydr/16tXLf5jo+4cffhi/TZd+R2rVq1eP37VX18nlwOa/0Zk7d+5Jz5bQPKtaVEpboUKFUK5cuRwoxluiAAUoQIHMEtBiRRIDM61fP2DOnNiuRCceSgYcPPOMF0iVOoCS/ia2MXJ6706dOiW4RQ08at7NtGqaZ/3gwYPQZf333nsvNMA5aNCgFIfXFSZ9+/bFggULTGFLneGpaY/q1auXaNaqDqZBVc1J/tprr6U4NjtQgAIUoAAFKEABClAgswViCoD6eary5MljZhdEuvjQAKgmz4/UNIjnV0T1x47UN7tu1zcfumRdZ2RcccUVppq7nxs1lnvaJFNpLr/8cplRI1NqpPXTd6ZsFKAABShAgTQW6N0bGDYMksdbc3ZDgmdAWJ3DJM/47bfAVVcBDzzgLXnXjDkSe2MLE9DXAuFNiySmVXtFp++GNT/lUNjmiE/1dZ7mRNUiSbrsXQOqz0hUW5fwhzdNc8RGAQpQgAIUoAAFKECBrC6QJ5YL/PXXX013rX4aiJDQ68cff8TWrVtNP82X1aJFi2RP4eexjCb3VLIDZdGdugT+EVlTOGTIEBw5csQELvWxumhxKJ3FWUwqROTPnx/58uUzMyo0WHr06FF899132LVrF1atWhVvqrepgVAtXsBGAQpQgAIUSA+BRx/1iiBJmm8zm1Pjc5q2+8orgYYNvdmdel5J4S2rG4A33gBmzfLyh551FiCLHuRvVXpcWfYfs1atWuZv/549e+JvRoOL+sHmya7s0KXp72gVq5Cmlep1Fmekph/KpvQaTF+naM53TV2kH2x/9tln8cN9/vnnCErS10ivC+M78gEFKEABClCAAhSgAAUyUSCmAGj58uVNsvxffvkFmmA/PJG/3ofmsvKT7+sLbi2WFKnpi39d1q1Ng6o5tQ0ePBi6tGzAgAGm4urvv/9uij6lpvBTmzZt5E3mrJOuRJtTrXlfFKAABSiQNgL9+3vBTlnlDEk/LQVzvC/5bFP+tkOCXglnhkpNHSlw6PUpUyZtriGnjvKATJPt2bNn/O39888/5oPS+fPnx2+L9YEGIbt37x6fJ9w/PnzFiOYEffnll/HVV1+Zr6JFi2Lz5s1+92S/n3HGGdDXNN26dYvvp0vhdYZoqVKl4rfxAQUoQAEKUIACFKAABbKagLyNib7pjEVtOltAZ3om1ZYtWxa/WWcqJtc2btwYv1sLA+TkprMmvpX1gZpPK9Z71Yq0umRu8eLF0EJUmjqAjQIUoAAFKJDeAjJ5UD74BDQF+G23AdWqQT6Ag+SI9IKfsnBBivpAck1C8lgCGr9j8DPln8oNN9yACy64IEHH119/HaNGjUqwLZYnGpj84IMPEhxSsGBBSWMgeQxCmn54rcWOVqxYgR9++AFbtmxBLHnYz9IpviFNV/vk5A+xQ26VDylAAQpQgAIUoAAFsrFATDNAa9euHX+rOkvhNn03FNK08ntoNXetQJpc0xffftNcUzm96SwLzaelX/pmY+3atdi5c6dZ7q7L43VmqOYLPf3006EVWHX5vBaKUnfdxkYBClCAAhTIDIELLwT0S5sWFJc/WSYQKvE1893bw3+jFdAg5PPPPw9dnu44TvxhDz74IHbv3o2pkkA1pWXp/kH//fcfHn/8cYwfP97fFP/9ueeew2mnnRb/XB80atQIZcuWTRD0fOihh/Diiy8m6BfpiRZXCm06lqbwYaMABShAAQpQgAIUoEBWFogpAKozQPVFuy5x1xfbV8taN80L5bdnn30WP/30k3mqeayaN2/u70r0XQN/kydPjt/erl27+Me54YG+YdAvNgpQgAIUoEB2EpD6OJLWJTtdcda81rp16+Kee+4xr6dCr1CLGH366acm52avXr1QoECB0N0JHi9cuBD/+9//8PXXXyfYrk80mNqjR49E23VDly5dEpxXA66am1TzfCbXNPip+UpD28033xz6lI8pQAEKUIACFKAABSiQJQViCoBqHkvNWTV9+nQzc6Bx48bmxbvmBtVlV6EvigdJ2dikcoSqgibM79y5s1lKr8+vlKoKJ5v4X8dhowAFKEABClCAAtlFYNy4cdA0NzoDM7Tt2LHD5A0fMWKEKXyoK0L0tZbOtNR9mr9TC07q96SaVn2///77k9pltt19992YM2eOKbbodxooyV51m6bq0ZUnJUuWNPnGNe+7FmTU2aSzZ882BY/8Y8455xxznf5zfqcABShAAQpQgAIUoEBWFYgpAKo3MXbsWCxfvty8aNaclknNFtC8VrfffnuCe9YE/49KWdnXXnsN27Zti9+nMxuSWrYV3yGHP/j++++hby40fYB+6ZsbzdmlS+A14MxlZTn8PwDeHgUoQAEK5GqBMWPGmNzg+noqdDm8omihyHnz5sXko7M7Q1fYJHWw5iLX2aPNmjUzrz38PmvWrEHHjh3NU12Cr2l5/vzzT393gu+amuftt982r1cS7OATClCAAhSgAAUoQAEKZEGBmIog6fXri+ZPpBxsw4YNk7ydVq1amRfEmhQ/tGl1Uq16Ghr81GI+WjSpTC6qmKB5Pl944QVT/ECDnDrDombNmtDZtBdffDGaNm1q8n5qNVWttlqvXj30l1K8+iZDUw+wUYACFKAABSiQswQGDBiAd955By1btkz1jenrh5UrV5qAaR7NU5BC0yX4GlwtUaJEkj214GWk4GfVqlWhy+E1hykbBShAAQpQgAIUoAAFsoNAwihllFeswbn169ebF+uae0orjN51113mxfD777+P8AqhOqwm4fcL+WhwtGvXrqYI0IV+VYUoz51dux06dMgsE9OAp86O1VkWGgxNrh2XShMbN2401Vo7dOhg8nNpIJSNAhSgAAUoQIGcJXDZZZfhww8/NFXZ+/btm2zuT//OdYZm/fr1sWDBAvPhdKwFJdu3b2+KLk2cODGqvOSaC/6pp57C5s2bzQe2/nXwOwUoQAEKUIACFKAABbK6QMpTBJK5A63ynlKl99DD/WVeTZo0ieqFduix2fnxr7/+Cn1js2XLlvjb0PyoxYsXx3nnnQetDp8/f36TB0yDnseOHTOV4b/77jtomgFNH6BN83116tQJTz75JDTHKhsFKEABClCAArEJaNAvK6+oqFGjhvngUwtLHjhwwKQc0tcD+/fvx+HDh6EfQleuXNl8af70aGZ7JiekOUj79euHW265xbzm2LdvH/RLX3/8/fffptilrv7R1265acVOcmbcRwEKUIACFKAABSiQ/QROKgAa6+3mxqCdLh/TN1t+8FNTB2jxAU0VoIHPlNp///1nZtvOmDED06ZNgz7X2bb65qddu3YpHc79FKAABShAAQpkQwHNwakBzowqEhkIBEyhJS22xEYBClCAAhSgAAUoQIGcJpCqJfA5DSE970fza+lyd23dunUzy/71ezTBTz1Gl7dpmgAtaPDmm2+a57p92LBhiYol6HY2ClCAAhSgAAUoQAEKUIACFKAABShAAQpQ4IRAhs4APXHa3PNIC0Zpq1WrFnQWZ3hxqFgkdMbnE088gYEDB5oZpXv27EGFChViGSJVfadMmYIJEyakScBVq95rO3jwYKquhQdRgAIUoAAFKEABClCAAhSgAAUoQAEKUCAWAQZAY9FKRd/Vq1ebozp27Bg/ezMVw8QfcvXVV5sAqG74+uuvMyQA+tFHH+GLL76Iv4a0ePDjjz+mxTAcgwIUoAAFKEABClCAAhSgAAUoQAEKUIACyQowAJosz8nv1KIF2kqXLn3yg8kIhQsXNoFUzQWqxQkyok2dOhVDhw5Nk6IR48aNwyuvvGJmxGbEtfMcFKAABShAAQpQgAIUoAAFKEABClCAArlbgAHQdP756xL1TZs2mTygffv2Pemz6ZJ6DX5qq1u37kmPF80AWohBq9KmRStSpEhaDMMxKEABClCAAhSgAAUoQAEKUIACFKAABSgQlQCLIEXFlPpO9evXNwfPnTsXK1euTP1AcuRvv/2Ge+65x4xRqFChDKsMe1IXzYMpQAEKUIACFKAABShAAQpQgAIUoAAFKJCJAgyApjP+vffea5asHzt2DFdccYWp5v7vv//GfFadRXr55Zeb2aR6cL9+/WIegwdQgAIUoAAFKEABClCAAhSgAAUoQAEKUCC3CXAJfDr/xHUJ/COPPIIhQ4bgyJEjJnCpj1u0aIE6deqYWZzFihVD/vz5kS9fPhw/fhwaLD169Ci+++477Nq1C6tWrcLWrVvjr1QDoWPGjIl/zgcUoAAFKEABClCAAhSgAAUoQAEKUIACFKBA0gIMgCbtkqZbBw8ebIoXDRgwwBQu+v333/HWW2+Zr1hP1KZNG8yaNQu2zcm7sdqxPwUoQAEKUIACFKAABShAAQpQgAIUoEDuE2AULYN+5jfeeCO+/fZbDB8+HOeee25MZz311FPN8vnFixdj6dKl0PyfbBSgAAUoQAEKUIACFKAABShAAQpQgAIUoEDKApwBmrJRmvUoWrQoHn74YfO1d+9erF27Fjt37jTL3XV5vM4MzZs3L04//XSceeaZ0OXz1atXR+3atc22NLsQDkQBClCAAhSgAAUoQAEKUIACFKAABShAgVwiwABoJv2gy5YtC/2Jkq9sAABAAElEQVRiowAFKEABClCAAhSgAAUoQAEKUIACFKAABdJPgAHQ9LNNduR//vkHurQ91vbzzz+bPKJ6XKlSpWI9nP0pQAEKUIACFKAABShAAQpQgAIUoAAFKJCrBJgDNAN/3PPnz0fbtm3hV32vVq0aevbsidWrV0d9Fb1790bp0qXNV9QHsSMFKEABClCAAhSgAAUoQAEKUIACFKAABXKpAAOgGfCD//PPP9GrVy906dIF77zzDg4fPgzXdfHVV19h5syZuOiii3D33XfHz+zMgEviKShAAQpQgAIUoAAFKEABClCAAhSgAAUokCsEGADNgB+zVn6fMWNG/JkKFCiAcuXKwbIss81xHDz99NOoU6cO9uzZE9+PDyhAAQpQgAIUoAAFKEABClCAAhSgAAUoQIGTE2AA9OT8Ujx606ZNeOGFF0w/Xfq+cOFCU/V99+7d+PXXXzFu3DgULFjQ7P/666/RsmVLBkFTVGUHClCAAhSgAAUoQAEKUIACFKAABShAAQpEJ8AAaHROqe41ceJEBINB5MmTB8uWLUOnTp1g2x67Bj6HDBmC7du3o3bt2uYc+/btQ6tWrXDo0KFUn5MHUoACFKAABShAAQpQgAIUoAAFKEABClCAAp4AA6Dp/F+CBje1de/ePT7IGX7K4sWLY9WqVWjRooXZpcvg27dvD80dykYBClCAAhSgAAUoQAEKUIACFKAABShAAQqkXoAB0NTbRXXkjh07TL/69esn2//MM8/E0qVL0bRpU9Nvw4YN6Nq1q5k9muyB3EkBClCAAhSgAAUoQAEKUIACFKAABShAAQpEFGAANCJN2uz4999/zUCnnXZaigPmz58fixYtQsWKFU3fJUuW4I477kjxOHagAAUoQAEKUIACFKAABShAAQpQgAIUoAAFkhZgADRplzTbWqlSJTPWtm3bohqzSJEieOedd1C0aFHTX3OIPvXUU1Edy04UoAAFKEABClCAAhSgAAUoQAEKUIACFKBAQgEGQBN6pPkzPwA6a9Ys/PLLL1GNX6FCBTMTVGeEahs8eDBmzJgR1bHsRAEKUIACFKAABShAAQpQgAIUoAAFKEABCpwQYAD0hEW6PNLiR9oOHz5sCiFFW929SZMm0KCpVox3XRc33ngjHnjgATiOky7XyUEpQAEKUIACFKAABShAAQpQgAIUoAAFKJATBRgATeefqlZzv/TSS81Zli1bhmrVqplg5vPPP5/ima+66ipMmDABlmWZwOfo0aPN8vgUD2QHClCAAhSgAAUoQAEKUIACFKAABShAAQpQwAgwAJoB/yFMnjwZNWvWNGf69ddf8fLLL2PSpElRnblv37546aWXkCdPHtOfM0CjYmMnClCAAhSgAAUoQAEKUIACFKAABShAAQoYAQZAM+A/hPLly2P9+vUYMGAAChQoYM5YokSJqM/cu3dvbNy4Ec2bN4/6GHakAAUoQAEKUIACFKAABShAAQpQgAIUoAAFAAZAM+i/gnz58kGXvf/2229Ys2aNKWwUy6lr1KiBVatWYfr06dD8oGeeeWYsh7MvBShAAQpQgAIUoAAFKEABClCAAhSgAAVypYC3rjpX3nrm3LQuZdcAZmpbz549oV9sFKAABShAAQpQgAIUoAAFKEABClCAAhSgQMoCnAGashF7UIACFKAABShAAQpQgAIUoAAFKEABClCAAtlUgAHQbPqD42VTgAIUoAAFKEABClCAAhSgAAUoQAEKUIACKQswAJqyEXtQgAIUoAAFKEABClCAAhSgAAUoQAEKUIAC2VSAAdBs+oPjZVOAAhSgAAUoQAEKUIACFKAABShAAQpQgAIpCzAAmrIRe1CAAhSgAAUoQAEKUIACFKAABShAAQpQgALZVIAB0Gz6g+NlU4ACFKAABShAAQpQgAIUoAAFKEABClCAAikLMACashF7UIACFKAABShAAQpQgAIUoAAFKEABClCAAtlUIE82vW5eNgUoQAEKUCDHCfz73z84+Mv3Oe6+eEMUyAgBxwlmxGl4DgpQgAIUoAAFKECBbCjAAGg2/KHxkilAAQpQIGcKrNi6HJX6lsqZN8e7okAGCViwMuhMPA0FKEABClCAAhSgQHYRYAA0u/ykeJ0UoAAFKJBjBWqVrYPzz6uBn3//OcfeI2+MAhkhULVkNRQ5s2hGnIrnoAAFKEABClCAAhTIRgIMgGajHxYvlQIUoAAFcqZA8UIlsO7JzTnz5nhXFKAABShAAQpQgAIUoAAFMlmARZAy+QfA01OAAhSgAAUoQAEKUIACFKAABShAAQpQgALpJ8AAaPrZcmQKUIACFKAABShAAQpQgAIUoAAFKEABClAgkwUYAM3kHwBPTwEKUIACFKAABShAAQpQgAIUoAAFKEABCqSfAAOg6WfLkSlAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFMFmAANJN/ADw9BShAAQpQgAIUoAAFKEABClCAAhSgAAUokH4CDICmny1HpgAFKEABClCAAhSgAAUoQAEKUIACFKAABTJZgAHQTP4B8PQUoAAFKEABClCAAhSgAAUoQAEKUIACFKBA+gkwAJp+thyZAhSgAAUoQAEKUIACFKAABShAAQpQgAIUyGQBBkAz+QfA01OAAhSgAAUoQAEKUIACFKAABShAAQpQgALpJ8AAaPrZcmQKUIACFKAABShAAQpQgAIUoAAFKEABClAgkwUYAM3kHwBPTwEKUIACFKAABShAAQpQgAIUoAAFKEABCqSfAAOg6WfLkSlAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFMFmAANJN/ADw9BShAAQpQgAIUoAAFKEABClCAAhSgAAUokH4CDICmny1HpgAFKEABClCAAhSgAAUoQAEKUIACFKAABTJZgAHQTP4B8PQUoAAFKEABClCAAhSgAAUoQAEKUIACFKBA+gkwAJp+thyZAhSgAAUoQAEKUIACFKAABShAAQpQgAIUyGQBBkAz+QfA01OAAhSgAAUoQAEKUIACFKAABShAAQpQgALpJ8AAaPrZcmQKUIACFKAABShAAQpQgAIUoAAFKEABClAgkwUYAM3kHwBPTwEKUIACFKAABShAAQpQgAIUoAAFKEABCqSfAAOg6WfLkSlAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFMFmAANJN/ADw9BShAAQpQgAIUoAAFKEABClCAAhSgAAUokH4CDICmny1HpgAFKEABClCAAhSgAAUoQAEKUIACFKAABTJZgAHQTP4B8PQUoAAFKEABClCAAhSgAAUoQAEKUIACFKBA+gkwAJp+thyZAhSgAAUoQAEKUIACFKAABShAAQpQgAIUyGQBBkAz+QfA01OAAhSgAAUoQAEKUIACFKAABShAAQpQgALpJ8AAaPrZcmQKUIACFKAABShAAQpQgAIUoAAFKEABClAgkwUYAM3kHwBPTwEKUIACFKAABShAAQpQgAIUoAAFKEABCqSfAAOg6WfLkSlAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFMFsiTyefn6SlAAQpQIJMEdu8GvvwS+OUXoGhRoFYtoFSpTLoYnpYCFKAABShAAQpQgAIUoAAFKJBOAgyAphMsh6UABXKewNGjwOrVwP79QB757XneecCFFwL58mWfe3VdYN484JFHgM2bE19348bAyJFAu3aJ93ELBShAAQpQgAIUoAAFKEABClAgOwowAJodf2q8ZgpQIEMFdu0C7rsPWLAA+PffhKcuUADo2RMYNQooVizhvqz27I8/gBtuABYu9K6sUCHggguAwoWBQ4e84O66dUD79kCfPsDEicApp2S1u+D1UCBrCvz5J3DgAKAfMpQsCZx+eta8Tl4VBShAAQpQgAIUoAAFcqMAA6C58afOe6YABaIWmDPHCwb+/bc367NFC6ByZeD4cWDrVuCzz7xA4WuvAfPnA7o/Kza93iuuAJYv9wKejz8O9Ojh3ZN/vf/8A0yaBIwYAbz0EqABHb3/pJounV+2DNi3z7PQpfOXXAI0bAhYVlJHcBsFcp6A4wAzZgAvvgisWQPoc236/wGdTa0fJOhXIOBt578UoAAFKEABClCAAhSgQOYIMACaOe48KwUokA0EdMZn9+7ejC6d5Tl2LFC8eMIL37EDGDjQCwa2bQusWgU0aJCwT1Z4pkveNfhZooR3jRUqJL6qU0/17kWDuC1bAnPnekHNW2890VdniN59N/DJJye2hT6qXh3Q4CqX0Ieq8HFOFNDg/1VXAZ9/7t2d/v+nTBkv+Pntt8Datd7Xc88Bb74JlC+fExV4TxSgAAUoQAEKUIACFMgeAnb2uExeJQUoQIGMFdAl4b16ecHPRx8Fpk9PHPzUK6pSBViyBLjlFkBniXbpAuhMyqzUtMiRBiW16YzOpIKf3l7v3zp1gP/7P++xLu33l/1PmQI0a+YFP4sUAW6+GRg/Hnj+eeCOO7ycqNu2eUvodRYpGwVyqoAGP3WGpwY/y5UDXnnFKyamH4h89ZX3WP+/VqkSsGWL1/ebb3KqBu+LAhSgAAUoQAEKUIACWV+AAdCs/zPiFVKAApkgoDMmf//dWzY+bFjyF2DLb9IJEwANHO7dC2igMCs1zfmp+T9btwaaN4/uyjSQW7s28MMP3szR118H+vXzlrsPHuzdpwZJdfbrgAHAs88CGuB58kkgb16vyNK4cdGdi70okJ0EdJl7587e/zc07cOmTcD11wOnnXbiLvLnB6691guQtmkD/PSTN1tUU1GwUYACFKAABShAAQpQgAIZL8AAaMab84wUoEAWF9AAhy7/1vbAA973lP7VqvBaKEnb7Nne96zy78cfe1fSqVNsV+T3f+89b7anFnd5+mlvNqkWfwpvaqDL4zUfqgaFhw/3ZsOF9+NzCmRnAZ3tuWEDULYs8MYbwJlnRr4bLYSk/3/QvME6E3Tq1Mh9uYcCFKAABShAAQpQgAIUSD8BBkDTz5YjU4AC2VRg926vKnrp0t4syGhvQ/NearGT9euBYDDao9K/38GD3jl0qW4sze+vuUN/+82bQTpoUMojaLGl/v09g4ceSrk/e1AgOwn4QUz9cKRgwZSvXIOg/v8PtLgYGwUoQAEKUIACFKAABSiQ8QIMgGa8Oc9IAQpkcQE/YKgFTWJpuuy1aFEv8Hf4cCxHpm9fvS5tmqM0lvbXX15vzXeobehQ73s0//7vf16vRYtO5BCN5jj2ocDRo4DmktVcmpqGIiu1Y8eA1au9NA+6DD7a1rGjt0T+00+BI0eiPYr9KEABClCAAhSgAAUoQIG0EmAANK0kOQ4FKJBjBPzl3X4AMJYb84/xx4jl2PTq61ef9qtVR3sev78WUTrlFOCii6I90iuIpMt+NYDF4i/Ru+XWnv/9B0ycCDRqBJx1FnD++UC1at7jCy4Apk3LGrOq9cMRnd1dqhSgMzujbfnyeUvmNY3EgQPRHsV+FKAABShAAQpQgAIUoEBaCTAAmlaSHIcCFMgxArr0W3NY6iw0P6AZzc3t3Ano7DWtkJ5cXsBoxkrLPro0X9usWdHPxtQl7wsWeMfpv3pPurw/lnbuuV7vH3+M5Sj2zW0CX3/tFRC77TZAZ0jqjOXq1YGqVb3A+5o1QJ8+XnDUn42c24x4vxSgAAUoQAEKUIACFKDAyQkwAHpyfjyaAhTIgQJnnw00bQroclcNGkbbXnzR69m+fbRHZEy/Fi2AWrW8yu1jxkR3ziH/z969wO3a1XMCvw2mGcaMYYzK8Bo5zDhMMqiQnVMyKjpIEVtKURIpUjN2oUR0UCmlXkpnhQ7SQbvQiZRKJcqbFEk0iJHBNfe36/Ob+/+sva77vp+9n/28+33f9f98nuc6/dfh+l/rWve1fuv3/6+7rVbvec9q9UVfNOvbx147jGCOEvYcMizQswDw07tmsgHg+fM/v1ppN69//Wr1xjfO+95BLGaM5Gtda7X64z/u5TSfE7/3aU+b2aRPetJq9drXLuuezRVt+QM+YH6XMFTFCf7sz16tvvVbVyuxcpdEX3LJJXNa7NEhwwLDAsMCwwLDAsMCwwLDAsMCwwLHa4EBgB6vvUdpwwLDApcRC2SxHyu7v/3tuyv96levVj/xEzPAcec779Y/Tg1sVnXD4LzPfeZV3JfK/+d/Xq3E7wTmfsiHrFYPfvDsuit+qJWv95W/+IsZwLrSlWbwat90Q++KYwFu71/91TPIeYMbzO3rpjddrbSZCDbo133dDH4C47mg09FOq4g1+1mftVpd7Wqr1c1utlphk97ylvMiZhjdj3zkubvQP+tZ80SCiQB/YpTqG7wXP/VTq9WXfMlqdd3rzkBnrZt99fMOfc7nXFjs8Lae43hYYFhgWGBYYFhgWGBYYFhgWODyaoEBgF5en+y4r2GBYYFzsgAQ5XrXW60sZvRlX7ZavfnNy9m94hWrFTdzLC+rn1/jGsu6l9YVLNCHPWwGaAGc4io++ckzoPQP/zADOT/7s7Mr8v3vPy/y8tjHrlaf8RmrlVXdCRB1X1GWWIlf+qWr1YUUD3Xf+g+9828BoCSWJyaltghwXxKrrQvJYGEybvJPeMKsqe1yj9dGTUJgaAJVvYdf+7Wr1cd8zMzWvP3t57YImD8beehDV6sb3Whmn2ZxtKtcZWaqvvzlq9W97jUvgPbiF8+u+uoSee97VysTKeQ2t5m35/s/cPl5z1utfuAHZjDYO8/e+0zmnO+6jfyHBYYFhgWGBYYFhgWGBYYFhgUuDQt8wLSWS6PgUeYV0wLf9V3ftXrQgx60euADH7j6zlDsrpimGHd9GbCAOJjXve5q9ZrXrFYWMeHmChi1uA9w73d/d3aRf9zj5uPrX39men3wB1+4N/fMZ65WwCBMuiXBmAOGXuc6s4a4i+4Z2PT0p88A01Ja5zHipMV4+43fWK0+//O3aY9rV1QLYGwCCrWpG994Pyv8zM+sVre+9Ryagcs5dugTnziD7D/yI6vV7W43g/fJzRcO4PQ7vmNefAgD89d+bX6fo7Nr+yu/slolrMX97rda3eUusyv+K18510P+AFqru9/qVqsVpig3dy77GKz6jOc+d55McO6DPmhXiWd/3f1iowJk/+zPzsyH+/7XfM1q9aM/OoPJZ2qMM8MCwwLDAsMCwwLDAsMCwwLDApdPCwwA9PL5XC/YuxoA6AX7aEbFFizwt3+7Wt3xjqsVNuTSdJEV0uH5973v4RcKWij2vJ52T1bV/sVfnBlsYi5a5OjqV5/di7/hG+bFZ2olHvCA1eq7v3t2T8buxLoDprTyy788g0BihmLh/eRPthrjeFhgdnv/yI+cV1LHyvQO7SMWGZOOaFsATwuOYV5+5mfO53v/Af5f+IUzk3s9D7fSnvcRTEoM1be8ZX6/v+/75lTikF7zmvNEwsd//My0vMlN5vvAegayYo6L+4k9/lEftVphiopler7EhMPXf/1m8TJxfzHTP+7jViss1N/8zdXq2c+eJyY+4iNm4Bkz/PIugGBxYbWRP/mT+Rl5ZiasbnjDw4Hhl3dbjfsbFhgWGBYYFhgWGBYYFrg8W2AAoJfnp3sB3tsAQC/AhzKqtJcFsECBoFhn73jHDHRyheUm/03fdH6Bjb0qeAxKd73ravXjPz4XhL2Hfffpnz7bwmI2FrA5fXq+jvWGmXc+2W5HfcuYrtiItp/2aTNzD7NvyNFbwOJEAHcgnXfrMOK984w++qNnliN3ePE+d4lytFtt0mJJ3ON3yVOfulrd/OZze1Bn8XQjQFCgJyYoAeICG4Gmf/RH8zn/3SeWKGb1+ZRb3GIOJQDcFMO3x6oFAIqP+ku/tFp92IetVi972Xxv57Nel1be//iPMzCtz/q7v+vXwiJWP/Zj8zPua4yzwwLDAsMCwwLDAsMCwwLDApcXC5xHR6zLi4nGfQwLDAsMC8wgRsC/K6o9AAUAJPEEufL6a+XDP3y1+v7vnxmxPYZoq38hHHNpxmrl4l/ltredQSKxT4HcQ47OAmkbS6zqfUrC7MPOBPztI2E4AzUxArnF7xLu7ERbqOCnc8Az8X+FwHj0o1erl7zkzFjBWNAPecj5Z4Zb8V4cVYC9sBPs0pOrXnUGY7/xG1ern/u51erkyTmmap5HL81l8RzAU8zWX/3VmaluHyD8iZ84v+fAcM9NCAaxYoHbP/RDl8U7HXUeFhgWGBYYFhgWGBYYFhgW2NcCH7Sv4tAbFhgWGBYYFhgWwPoEJHB1t8gKppt4qNh0X/zF82I0lxXWpJiNXGHFeo0AggBd7on8zd/MMSe5TANJrihihXMg0bvfvVphFGLEYmselVisiLzhDXN8WWEZrnzl2a0cILXkmu2ZYTF6RlaCt3r8YcA7+gDQl750PwA0i5997ufO9W3/qwcQ0R/QDTucPOIRs5s9RugHfuB87nz+v9e95tzX4bUXwc+Uz14WROISLl4vkJcr+PkQkwrKO+64yCYsgJ8AX8/bom9V9FXCloiXCgi/z33muK3iPA8ZFhgWGBYYFhgWGBYYFhgWuHxaYP3pPmRYYFhgWOCKbQHMLXEBgS6f+qnzoj0YXxYMArIMOWgBC7vc9KYzeGCBmOc/f7X6mZ9ZrbDKLivgJ4CTC3XAT+7J3IE9b66zmImPf/xqhdFKXve6y3+YA/eMRQjsDLPy2799DnWAPXm1q61Wj3nMub8TT3nKamUxIuI5/MEfzKAmRvHDHz4vPCZ+Zm/FcmEWPB/AFsmK7PPR7v8f//GzzrZFwGouAHDCXXyXWMX+kz5p/gMaE7E3z7dYjO1Nb5oBZEDsPuIdvtOdZk02PUp5wQvmNuP9snjcla40T5AAJbFTz7fot4Ge3l0hOVrwM+UDZgGeQpuQu92tv3DUfHX8HxYYFhgWGBYYFhgWGBYYFrisW2AAoJf1JzjqPyxwKVjgne+c46aJf/kpnzK7FV73uqvVve99pgvopVC9vYt861vnhUq+4AtWqwc9aF645I1vnNlhXFq5TVpYxeIlV0QRy/DyKkC+971vvjuLV4kJea1rHbxbbFeLOWUFcAvafMu3HNS5vBxhL2JecifHyrRoj9XCLQDmHMCRjW5zm9XqK75iAxwf9v5/+IfncvQhWRAIKCmGrPdM+ATgIRANSPr6129K8Cz0MeQa15i3mKOHkeh/6IfulworlYg5ehgRH5QAAc+3/M7vzCV80Red6aa/rWyLNZGkn4/O/r/FrCy6BLwW//dd75qZn2KuYu3+7M+uVte5zhxvM8Dy2Ze2nFIbI1zaP/mT5/1t/7VvrHZg9UMfuk1zXBsWGBYYFhgWGBYYFhgWGBa4LFtgAKCX5ac36j4scMwWwBADFomjhi2D+Qe4sEIyd8p73WtmjnEpDLh0zFXcuziunwAWQAvAxerOXCaBPxih97vfDNBg/gF3MYp2CcDQKtWXVcF+xHrkDmtVeIu6YIpZ6Ojudz+4sMtl9R7V27PHmCO3vvX87Oej/n8uwto8AYxflsUzxn4E9HNvJ84BgcKaw/Kkg6kJEAJmYWNihwL0hD7QRtqYqXNus96Xf/ncFwA5r33tGbjEtLvHPeZFiKzg7hloW2996+yGDJTOJIrJFSCpcrB0gZ9f9VWrFWBRfgGlswBRyt61jT426z4SUNzq6fsKe2JGk6Sfj87Pf0AjucpV5u2+/wPuJv2+6Xp68rjmNVer5zxn7k+BkH4X/v7v598Cz/rUqZlJq52ZdBLOYEmkE3JC/+x93bdfVQ9AOjbuN3/zUu5nng8b9hnPOPPaODMsMCwwLDAsMCwwLDAsMCxwObHANGRY4Bgt8J3f+Z1rCG01PfCBDzzGUkdRR2GBf/zHabrZzUCg89+NbzxNT3vaNP3e703Tm988Tb/8y9N061tP0wd90Hz92teepr/5m6Mo+ezz+N//e5qe+MRp+p//c5rucIdpOnVqmp7+9Gl6y1um6apXnet5gxtM03ve0y/jfe+bpu/6rlnvSleapt/8zTP1Xve6afrWb52mj/3YafqAD5h1P/RDp+l615umn/mZaWK3y4K8/vXT9BmfMdc/z/iDP/jgMRv80A9N0z//82Xhjpbr+HmfN9+X+9tX3vWujS3ud799U104etrpN37jNP37f7+5D8/54z5umq573fncf/gP8/u8rdZve9ucRlrvVRXt/cM+7GD+aUt1+9CHblLpOz7qo+Y0n/iJ0/T4x0/T3/7tNP3930/T537ufP7619+U+Z/+0zS94x3T9Md/PF/7kA+Zpj/9001+2/b+4R+mSRnq8pznbNPcXGM37/W//tfT9Ed/tDm/be/Rj57LuNrVjuddufjiubyv/uppeu5z5774ta+dpn/6p221nCbvPFt86qdu19t1VX9wnevMeX32Z0/Tn/zJcopLLpnLU+4Nb3imHnvf/ObT5LnWNuNd/bIvm6YXvvDMNPXMr/3anO4LvqCe3b2vvXnO//JfHs8z212joTEsMCwwLDAsMCwwLDAsMCxw1BZYf4IOGRY4PgsMAPT4bH02JQHrXvziabrXvabptredgb0f/uFp+u3fnqa73nUeWH7ER0zTC16wnPtrXjNN//k/z7oG5JeGAD7vcpdpAtjVQXT2A9KeODFN//f/7q7hne4052NwH/BPujvfeZr+xb/YlGGQ3gJAn/Zp0/SGN+wu49LUePnLN/UG2jzykRsQ4//8n2kCKnzDN2zu9eTJS7O20wTIArh/x3dM01d91TR9xVfM7fXnfm6a/uqvdtct4AqQ/jDy4R8+P+tzBYwOU+a56gLBvu/7Ns/OO3DlK0/Tf/2vZ4Kh3pl95GUvm8EidvzzP59TfMu3bN4DQNKnf/rcZpz3nuWdU/7Hf/w0aVcRExJXv/omPRBKO/yYj9mck+6a15ymt789qTYTMje96ea93Fw9c88EiHw8P33dO985Tc9//jQ94QkzcAhU7cnXfd2c7nM+Z3f70lf+m38z65t8Od/y3vdO0+1ud9BO7tHff/yPc7/9F3/Rr8UjHjHrsd+5CNBaeZ6XiYJdAkj2OyLNr/zKRtvkSvpTbUjf+aVfOk2f9VnTVCdjvvmbp8nkVE+e8Yw53x642tOv59IvAOCHDAsMCwwLDAsMCwwLDAsMC1z+LLD+/BwyLHB8FhgA6PHZ+rAlYUaGHZUBdLv9wA+cppe8ZHfOAI0McJ/5zN36R6mBUfYpnzIPgg2mv+RLpukHfmCaHvawafr+79+wytwbxuM+7DFgDeaZNBhIwJP/8T/mY2ANIBTwG3AU4HDxxdP0SZ806/zbfztNr3zlUd7l0eUFsLjKVeZ63uIWB4GptpTnPW+a3As7/NiPtVeP51h72tZOMRx//Mc3z6JXK+3YPQBQDyMAGekAS5cVwfpUZwDSmoD/frZ2rbvn6Hr+fuIn6tWD+1hyL3rRND3ucdP03/7bnObhD5+me997k/4a15imP/uzg+kc3eQmGx1ltSCydwpz8vM/fwOCpU62d7vbmc9UPxNQ+ja3WQbFvJc/8iMzaOvZ28cQBLLVMuwDOX/xFw/W3/ucNgc4NiHQigkRtsD+ls9xTBJgeWayKffB/iYEAMg5h9nruVUBjIfx7Xmei7Clsh7zmP1zSbu70Y3mNN/zPXMe+mztFMu3Cpa+ybjYV7oewxU4ry4mqw4jJk6kA4IOGRYYFhgWGBYYFhgWGBYYFrh8WmD9uTdkWOD4LDAA0OOz9WFKwvzKYNnAGdjwqEfNA/o73nF2/3QdYPCQh+yXMz1puNeeD7nkkmkCyP38z0/Tb/zG7G4PqMjAHxAAIGiFm24GurYGyn/3d63WmcfcfekbnH/v9877H/3Rfbf4pAaccueUDjtqydU++pfGNuzWL/qi/dz1n/Ws+X6w3HpA1/m8BwyxgFb/5b/MwDawChv0wQ+epi/8wrlu7C1EwxJLLGzEb/u2w9VWmfLGoNxXnvrUOfwCAFAYBu31KAXb2bv25V8+g3Tqpu0rK+EbsJIxu3uStiwUBNsCCF/60oOaJgmEeQizkQ3yFzDZscmGJQnY5hkm7RKI7n00kQHgVC79pX4HgzPMvU/4hGkC4L7xjdPELn/4h9P0sz87g5rycH8B6xxL98VfPE23vOXsXl3Z21/7tQf7hbe+dQP6SgsMx27FjJceKO68v20MxSX7HPa80CP/7t/N5f33/z5NeY8vumjDwnzVq+Znok4man791zelmAxyns2W3pON9vIe4BBo+a/+1TRho+4r+g7PQ2gB77C6AOn1L9vkd35nmgC69AHZrfz1X8/Mf+0y7ORWp3fsvZTnYV3ne3mNc8MCwwLDAsMCwwLDAsMCwwIXpgXWn3tDhgWOzwIDAD0+W+9bEnakgZ8BMpYkJlYrgD46+dvHtdNAVJ4GosCIoxCMn4svnt1rU5dsubsb/DvG4lpyY7zHPWade95zw9C0v0ue/ew53YkT80DdfWEb7RLu2gFdsJwuJAHQYlQBIsQD3Fe4nbPzj/7ovin21wNmY9diCAKZML2wZxNXkd0xPHvtVCmeU9jHWIE9CdDF7fowknSf+ZnbU2EDKjtAa9potoAe4FmPwbY954NXMe4+8iPnZ5G8e9uf/umD6eoRpqI0Jga4ytuvoQGEu4g9tRNg261uNQOHbVnb4mqGqQiUxFCUFoC3S+5+91kX+29JXv3qDZuxrVOOTUB4dx0DPu9//zP7CAzXn/zJDbConvUZAWYBuLFH8s6W2z8m/fkWbT9sZMC6eutnsGfVBQNeLE2i/iaxnNePY3yHbal9bgtnMuew/f/v/u6ct/ocVsI8D7v2QQ/aLwcTX+4HG/0v//LMNPoM14H7+wiGsN8MaUykDBkWGBYYFhgWGBYYFhgWGBa4fFpg/bk3ZFjg+CwwANDjs/U+JRkkY+8AlbDoemIhIwND7C+Ll9gHXLz73T3tg+cARfQBFOcqystiLfIEQlgUQ/w6i6W4D+f9AXKWJGCAe3nFK2bwDyCyC6TFnpJ3XOGxvPYVTCxpuWfvE3N033zpYW9Z+MS6Yv/rf80DeGy/JYCw5i2derHfYQTIIx323FEJht9SzFZl5Q9Qt0u0t7jK/uqvnqltgSr5aTP7ypvetKnDNkBRHMks6pMyAPNcxi04VNspQKp19d23PgEGleE5iGP5B38wh3QQ0zUgkOvAv9///X7OmKJ0AD9YhQH3AOLAaJMYrgOl2SDylKfM5/UdrvsDqC0tUgNUpcMVG/svaXbFx038zYsvTsn9LaDPBI6wEy3w7J0VE1KZ+4SjwCANo7MHvHqH3cdP/dQMpGKZ7rqPfq3P7qz2514++ZMPslTZFfvXNc8F+1zdvAMBTMMaBcLv8y7tqqGYp8oTp/OwIhastP68G4fpG8UGla7ndq9OwHo2qDFGl+oX4N97so83wFI+4/ywwLDAsMCwwLDAsMCwwLDAhW2B9efjkGGB47PAAECPz9b7lMTN0yCSW/eSWNGXDrYO+cqvnI/bFaDnqwf/Z2XgXsy8g5rbj7hWJuagQSqwpx0sW7hJPfN3n/v08wzjFYBEgEfS7GK1PulJs14AhNOn3598738BIPaJobpPpmGjJQZi7jtb4BpQsbVTzRuAQ98iKocRLD7psPqOQiqwLV+gGwDKH6Am92S778Ja3GPp99yyU3/X9wVxA2oCVZYEozbPAwDDLbkyCKVzDHR0XfkAx8O6IIcNCzAGcPUES1P+iUPLfb91UdY2bnazg/aVxh8GbsDQb//2M+NvCpMRXVuMV1t26i1ElTAb0pG8R9i8S4LVGLsDJbcJRmcF0O17B1owdF8WNmak+wGYXmihK06cmOtm0a9WMN/Ftm3fm/qsMJ+B20chFpKSt8mdtq1vy1//FXBd+sPG480CThZn68mpU3O9lAHc7/WDQqaYyFK+dtKbLOnlPc4NCwwLDAsMCwwLDAsMCwwLXDYtsP7sGzIscHwWGADo+bE1AAXzjNv5vsJtEqsTEFNXVm7TGzhirRkgSoP5ZMBoMZBdEoZPZY7tStO7HnBFmUuLFgXwwfBUX/fVG+Rz01X/uDFjeDkOMNMr37lv+qZZT8w6+gbPhxFxAqUD3p6reF5ckeXnD+vLQkzAXfefWJWuYUotATjiJdIB1B1GsBalu+pVD5Oqrxt3evkBD3thBdKO6PhbYt9yf8dEA1pVcKUHsiUsgfywDLdJtScbL0nAdO2vxlvs6QPQwwbFSN1XPMsAk4997HKqgGQYcFlh3SRBBMOz3hc7mFyIm39sDdhq2w/wim50bLkRp0zgUyvY1vTkDzQLk1qc0iXhEi3NrlAFwDN63nmLPv3mb24AWyDqD/7gpq5svs1utS7i4sr3XBcJqnme6z5mt74YEL+tv3/b26aJ/SxsZoLBxFX6rksuOddaHEyfRecO404fBrGYtWy8FOP1YEmbI2CldEsTGNrjd3/35rl/7MfOfeMDHjBN97vfHLc1bZ0HgFi9Q4YFhgWGBYYFhgWGBYYFhgUu3xZYfz4OGRY4PgsMAPTobI1lZWCfxUUMBv1hat7+9gfdVXulAkDoA0F2SeKjPeMZs2s1cAnYsI25xiVU/has2Mcde6kO8gFaKHPJjVfauDVz684CH70FLdQ5i2hYcOPii+d6AjiXxGIsylePuFZvAx96+WQ17iXGXi+N+HbiXwLL2AEYLSSBWINsi923xK61Wnpi7AFAAEGtZOEP7s2HEWUq/7ArLbdlcNWXj7+ldohVBvBh+wq6VZdjoRwAdcmrt8UIBLpUCZBIH2gKGIkA+SySVJl0YiwuiUVyUu62ldRresBs0pjA2EcCCvaYrTV9wEgTFljHynG/3kULybhf50wqJFQFNl1W5wYKpW7Y17W9Z3Gl9Av0tGuxdO0vMYMDdnsvwwBdWggJgGxhHfkBIAH56qQd6Hu0CaDW13/9rENX/9STuDgH0NQW9gnLgTmo/MMypHt1OKpzLSP/MPle61rz/fQmGQ6TT6ubha3kv09fbyINw5htM2kAmDyMJHzHrskDbUIbV1bvT5xXcUyHDAsMCwwLDAsMCwwLDAsMC1z+LbD+JBwyLHB8FhgA6NHYGtMtIJ5BHXAOoyqMFucABIBATJiehMkJKNklAUXEigRIBVjbFr8Q80g9rOJ8LiKupXyAutsk7vxc2bmBBmDpsZ0CInGxDmizxEQDNGMVqsOtbz2zHu0DkzBo5cEuj3rUttpN0+d93pzHPiwpg3YrmmN5KSt/ysxgnkt9bwGQWgugWph2971vvTLvY+IBlDDDdsVArakDgHFrPhdhO/cGkFoS9aLjeQJXYpOAbGGSxUaAMM+Da3cPFG1doOMinvRL212u98BSaZW5S8TaBDqL4QjMk26J1drmJe4t/Sc/ub1y8Niq8PTYCYOWjR17doBD+1/zNdPEbZ97eVZ4T/tyXbsIS5Sud1/oC9f0ORbB0n4ct3/emXZFbzEqxXqsuq95zcF6m6AADKe+V7vaQf2atu6bDFqSkyfnPMS8TAzgXQCyvEwiKOMGN1jK+fjPi4WsTha/IkuLvc1XD/5PPyZG5j6if/F+ec7sK/SCOLOtCK2ADa5ewGLtZElMxIhNSlf7zySAvvUwkt8ki4ntEr+BWMHCJGj/Fkd6+MOnqffbsCuvcX1YYFhgWGBYYFhgWGBYYFjgsmuB9SfokGGB47PAAEDP3dZcqAM6YDRZEbfGN3vta2fQMUDR0sCSnkEoNuEuMcgO+0680DCzgCc9yWJJAJR9mW29fJwDBKrnrsUsEgMUwEEANtL1GJeAtOtff74OyKH3yEfO6ep/TKkwlYBGAezo9/6AX73FpLisex5AHQzOJXEN0Ja86XN1x+BsgaClGKdt3qdPz/kBENsYkHTDnOUuuo9gOmLiAe4s7nQukvvc5lYOvAgLE8gcFpu0QisEQLRtn2EAH/EC85yle/zjD9bacZ08SL1s5Xvy5EH93lFYuViOSyLmqomJmn/dV8ddrrgATWnEMe0JkLEugFTzr/vaU2VwAysDOkYPcOoZh/X8CZ8wl60tZ1Gj6LovbMwAqTmP+YzxF3nzmw/aGoP9tredJmC6d7aC1omnKi/PQagHACyGrbAFqVfKAlj2xOQJHe3Au5jJEXXZJp6FdMD0fUS/4n0DzgHavaNPe9r2d36ffKsO8Dx9e+6bbdhNmI1e/FXp9dXsRXfXAnbc5z23pbZ6zWvOoVBqvfSVaT/6q9/6rXp13sfqNTmh3t43zEvgu2OTO5VlfGbqzRl9QljLWOxDhgWGBYYFhgWGBYYFhgWGBYYF9rHA+rNzyLDA8VlgAKDnZmuuvgEft4FGShEjLaAOQLIVoKa8gKnveld79cxjbs8BogxYMblawRiyYEoG5kcRO+8TP3HOr8c8quVzaVUu0ER8zqyS3VvFWTqD7bjESgdYwuJjVyAv9+7cB/ATcJBjW2AVl2kMVbr1uhXZq8T9HUCxJECigErAAM+sBUvDvks9fvInl3KbVxcH7FkMKDE0e2AwNhhARf13xUb88z/fxJPEuGVj7SBAibYEXBJvcAmgS42xmHMfOdduAfvusba7pLHNRIC6t2AWkKeCzsC+ADTyqxIwpebd7kvbiymbfMKAW4olW2OOJm92q+0m57cxDrMoUO+d9Y6EYendz/0ChhODtoJnXJermCxJHZa22lLaKTd6eeQebnzjacr76r0IGHqTm2yY6L/4i5sy0pe1ZSmjutcDl3uu1YDpmlY9WrsAy7KSfNuO3Adm4JLc4x5z/kvPNOkwHrXTxLOsdbLvPgG8bSzVpN93295vW45jz/dhDzszRwsmuc6u28Tvhv6HLnthHLt/fQ875hpb3/veB3PyftRJIn0qF3Ns5LCO5auN1hAEmeQSx3gfyeJt2O2AXW3DxAzQVb9TJwT3yW/oDAsMCwwLDAsMCwwLDAsMC1wxLLD+FB0yLHB8FhgA6LnZ2mDSAHLfmHRPf/qsjx3UG3zf8Ibzda7y+wjQqoJd4vIZtGIPyitxAw2cWzbePvn3dMKsA/7ukrA6MbbUi61aMDJ5cMXMwDv1pl//nMceC9DG7VTsSUAPPQxDoCD5oz/aDPKBA3F15z5KFwjSgnRzyvl/FnIBGog52hMxGuUVQNWzaN1ZMfaAFgGl6v04BwhrwcmEBKArZIE4g1UADNhwF100l48NnAVVav51X1nb4qoCmumzbU8AmJhkNU+regOn6zn72kjrChzwSruMiKeatJlAqIxDoDYGJ7ff06enSR5hPifdEuMs7Ej2fdKTZma2OKIkLFt5uN86IZHFtQBL9ZlhU/YkMX9bhh12b9zXga3CGySGJVBa/NLcg0mCvMfqSrg3K98fFnWtS9Kpe94xITjEESZ3vesmb7ref8AUlnkAM6zNCpYCcivDM2W0W6zPJUn7+Omf3pRfY8kCxcI4TL5YkBUEdp+A35bNDjDPxMELX7hUg3mSAuCf/D0DfY+JB7FHT5zY9B9Yt7tWs18qST4pI3YDcusrsELvcIeD76S2GzEhlHf34otz9sytviTvtfeG/VrRpr07sWGNm0vX7wwX8wqEpt4Ace29ZXp6L8PQbvNry9cPRVff7tmljaUc7R1Yu89vRpv/OB4WGBYYFhgWGBYYFhgWGBa4/FpgPVQZMixwfBYYAOjZ25pbogGeQeRhViDPIhO9GJXcFuUJnHv5y3fXLfEWDX4DoGTQaQtMANJaZOWoBBtO3hhMuwQDLuBAmHK/8AtnpgIOAW/li7kHsMjAv96P/Qz05RuQBNM1sSOxmYBIAFVMsABm9OOGzy7bVn8HjLKnvzYmYq19gDQLgCQOJ7AzgoWW5wLo4U7PFTfpAmph7rXxGbHGkhbQhdWKuSdtjTdbXfHZRhnc/rleY3UpD1AcOy4tHFRBK+1KLECLIhF2DnDFnmyXOgRQTv7ZAmzYnwCsgHDuVxu3wjRQEEjp3qRhA3FUk36b6zpWXNLJ80//dC4n/7EOW3fs5BubOvbutpIQC54j5lpYm/R7rGeTH67VFd3lmRAQFiyqoQ7Cfkx9bD1TMRDtA49uetONHVyrdWbHHrOxZY+adEgZbIRdCayu53O9vmt0PRcgoomFALzR1Q6WJKxartXc6JMGeGnBtPQB2IdhpgJ3Ez8SGzKTH9pp3m/lZSE19dGuxKLFFtXWbYF4zgf8VFavr5EXIDiMVnXR/xxGKsCcya/YVRzNTKxoPwn7wBZ5LxM2wjbvSFu+tPKSji0xZ7eJe/VO+Kv9vX4QW7Q+4zyXbIHaLZsaMJt3DDNfyBPPkajzS186r2ivvcgH+F3bKbsKFxLQmo78MJF33ctcyvg/LDAsMCwwLDAsMCwwLDAscHm3wPoTcciwwPFZYACgZ29rDC2DOqDaYWRXujAPsWYwRntiAIq1BgxRB4ASpg997EGAAhCwBYZ6eR32HGBOmQDLfcTiTnH9lw7gwe3WgBt7D1MqrrlAizD3DKzFtsNmBe5w5Y87sXwAOpW55F6dc80f+13nOvNfztkCWAB82yRsyG2MSekDegA6MUwDMliMygIvKRfwVBc1wpRy7Ta32YCynmW7gjx2mgVK8pyTn61zQMaAFNxPw37t3VuAOmmFFmjF4iU1/7ofMNGzCRvVMwz4UXUBpAFH3SegP3Y5ceIgoFjT1X0A4C7B/E2ayjK0OEzO79pi6lURHzf3lNifwh4kH+2zFeCu60DmTIQAr8JkBRRVwd6t7E9pTXgASutzZrPoAbYz8aBdAxPDukvd6KTOyrvkko3do9PbBmRzDQCNOd1KrRe9pQWLwuD2vgfIpA80TTncr8XFxIwOuByQ0MrjWKwBzbIQlfOei3vEXJVHWyf2CIDuOS2xtnNvwNXUd5/2lnT6WfVwX7Xv105yH/ozoKP3F9iXOLHO591wj3mXknfdPuYxcxkYrEDMfYRbv3plgTB9jj7QOfaz0Beg/TnPmftejNi4wrOnMqv80i9t6isP98221fbaaSav9EUY+u1EAeaqPjAA6a4F9Godxv6wwLDAsMCwwLDAsMCwwLDA5dcC60/MIcMCx2eBAYCeva1PnZoHli3za1eOmJ0Gk8C9ngBIEh+QntXKAQAYggakFvLIQN/16lrZy++oz2FbBtzZBSQqG1gboEF9l/4wtwJ8YHn1YgHGLgGpuP+yV8Q+gLnap5YHyNtnoaDEIm1ZmSnHlkt4mFy1DPtYmQEJxMdrJasuA05J2KPAw7hpO48tWcHjtpwcu6+ws6RbkpMnZ/sDKgKOY0v2yoiNU4Yt9nKVyhqNHpA6cWcDzroWoMU+IAQoBnQF/CVttlzol5hxtXwAWdJ49tiuOVb/AC5AJII5muu26lcBv4B02jfBgPWeX+Uqm3Tvv9D8CwAEGPT8gP7ydx+tYNKlXkvtVNrEvPVsEr5BnFfXgN22gFNb5doCHYFugN0wooHkgGj29gyELYg7NDZqnpE2vyTy9oeBnP0KtrJhGJ253tsqu4ZGEBM35dMXHxhz2WI8ubcaDxaAnny9X2ykD8A0DCjpunuu/cLSfQEgA+5X1uSSvvMBGQF/bRvF9G0Zvp51nnfqjl3cxkZty0xoAxMpuwSjFXgJdNXu2Ub/lBAtnru+pCdiHXORVzc2bBe3A1b7zcESjY2VoX1hwmZCT5kmRbaJdhxgfJ/72pbXuHb0FgCY+30fMiwwLDAsMCwwLDAsMCxwXBZYf4IOGRY4PgsMAPTsbW2wadCIcXYYEStQOoP3bYLhGbZQBs51Cyxcin+4Ld+la4BZABKWELDE1sD4Fa84M0XcdQ1mn/GMM6/nDFAuMRXdC0DRQj0ACu6ymEoAZAP4xNPkmtouNpT8rn712XbcgoEp7CGuYhUx6CqoUm1m3+AdSLFNAh5h2vWEK2tc++WpPM+zfV6AszbWq1WvgSfqIYwCAaSwuby4nhMrZNe6A2owAoHAAOUAN9FJDM059fL/gJ3AUHau4EyYuPJUPhAJ2y5l2Fbm7z5sS8zIClx5dhUE4+pe888+5lpPMAABi9peABlpEm/Tvns0mMeCTn5Asosv3hznPF1swAq0afsB4KKXrXJbd3PPNCApFmDinXJdjgBG2Sv2znNIvktb4FOE3SrLOW3AYlqZHMDSTd21ycqSTj5PfOJsh9RZ+IFtkroB0djRMbCe6M8qG1W7dh27MGBX0tsCcatgQdfr9pNHzgNsM4ni3rT19r0yOVTT6geWBHsYSK8tug/plmK8tnmkb9gWogHr13vTu38TBEv9Si0rcTS3uedr35/yKWfaz/2kDWBsVpC/llH38y4DS2vogaqjPwDchpHqOPZo++Garu5b2E39hDpZKqfqj/3zawHhW3gy6AM8F3/aLcb5UX5fnN+7GLkPCwwLDAsMCwwLDAtcVi2w/vQYMixwfBYYAOjZ2zoAFeDuMIL1ZJARN8VtaQFUBrnclzEkb3SjabrTnabpmc/cj/G3Le9c466IkZTBT28LKGhdSrlR0wVW3OIW8yI1YS9iF2IkZXAOqEk8yZRbt2960wwicontLfQR3bjHYw4CU5UPdAS0AhETv9F5ZQIpDOzDkKv3hrHGVbknAdMAJa0k1p68gBwBzrARww4L+5MO0DYxINURuOY8m6k30BmwGYYkgKQuDgRc7TFtAUz1fuxrF60873kzO6+ChXQ9twBojgO81nPA6SzwBEhJeZirWdTKubgy5/rSFtjVSoCepLHoU/YryxBA3+pGr27dV9qhsmq8xgqsJcyBtBUwr/dPH/gWF+ZaDrf7CuRiXrYsSAxPdvKuV4Chlpc8teNMAuRctlh2Ee/HPvbWHmv9kt427tUBY8Vl3CaphzrWZ1AZfUD906dntiB97aUCo3X/1Km5tKc9bQMIA0a596esbD2DgK5s2JuQkdv97z+nxa4OkN+6dGMAhz2b/OsWKB1W9lzDM/9n4kOYkZ5gsLaTBrUM++yuXS71PwBGevqRngAOAbbJF5Ct7Ziwatuq/mQfAJSrflz4e/Gpe/UI0xnjHRi6r2CTqvsuxui++Q29w1vABELCaqQdef/i3ZFzfpuEdBkyLDAsMCwwLDAsMCwwLHA+LLD+JBwyLHB8FhgA6NnbGmhnkAAQWAIaerkDQ6TblzHTy+OozonDmQGP+xBvE7AIZACmAfTq9TaeIRAA8JHBkm0F/xwDiizGs02w3Oj2ALKaDqONHhf5+953w7rDVInbuutAtCrYYM4DUONu7pgLe0/ifvrkJx+8CnwKuBLQyCJJAc0quAJ4cO/KAU5wJc3iNliRYU65vvQHKIur+sGazOCudADW2Bzrq0pcupfyz3ls0Ag2Y4Ax1wOcAmz2Ad6SZ2/bullrf62e8gN4AYWJ2LYBDQFi2KFCCwBFw3Sr+WAYVwFqxUZVr+6757RlZWgzlaEW3dTNMRZ2FWzLe96zz/yj7x2rQChAERARl/T0DT0gUHxXf1m8B2M4zyZ1q1uTFktiEqXqJnZpq28Cpk4q1DR1n+0ycQHkz0JEVQdzOO+VegNo2ZlO3pOq39vvxa5Nne92tzkvi3JlkgnLkIs38b7WPNkcmzZgXL3GdXxJwtw1AdYKRrf3Xl6eNaawSRQTHXFH9+7n/XJuCQRNW6xgfsozeaIMbQk4zuaRhGNwPe1DP9AyZqOvr7fAFEA1AKg+ZR9X6NjUJM5hRKgD9dMOhxy/BTzbTPJppz/yI9Ok341g+Yr17f3xnLCk9wHRk35shwWGBYYFhgWGBYYFhgX2tcD6U2PIsMDxWWAAoOdma/E5DRCAHvuIRW4M+rmYLQFb++RzFDqXXLJZHVxsv6UBssFSgJmeO6WBk0F3BtvskT+AWQXXluqd/HtMx6QBvsZlN/n3toDRVi66aK6TVa0JN+qkrSzD+eocM9H1FkgLGxLLrIowAC0QhxlqsZEw38IcA5YG+EkdgCoBwXLOVtqldhKgA0OnxraM63pAWfmoG4YdN2FMz1pG9mvsP+7jCTGQ671tfebuKfdIN3EF23TcLQlgJ6DkNmAV6FV+dQAAQABJREFUKB/ACHj0h384p8//XGvLaWPjAjPD1m11Hef5AVpPn07u87a6zsvncY/b2LBdKCsM314Z9Zy2HAEEVxsEAM3EgHT6jADs9dnWPHv77YI0Jmvqs/XcemLSomdbbL+2/SqXTSJAx9oWXNfGT5w4mFb7iQs6HfenfaYvSvuo99WGH0iZztNLX5xwGc9+9rxIXPIAUNYJmbw73qMsGER3aWGkMKHdD/DIcwRGW3wu7cvvAiZoFWEb5It1B7jPM7A4UE9Sf/WrkraojQgdUgWYmgkrZWHAB9TUd1XBMs/vV2xTtyZ67n3vZYBWXplYapm2tZzePha9svT7Q47fAgHjxV+28NuS+O33znpW2tFhWL5LeY7zwwLDAsMCwwLDAsMCwwLVAuvPjCHDAsdngQGAnputARcAE4CAOHzb5HWv2wx6T53apnk814CBBjZAvsog6pVeWV0VGMAcAwLUgXNvH5DSgkq1nAzEX/KSenazj6ESBmDyxzKrrE/n6Tz/+Zt09rjpJo3nFQmTDzjaCvAiTM8Ag4AjrCxl9NgwPTf7lJtt4nzmuI1T2IsZqMyeZLEV9anpADNhoSmnBaABxCm/buXTxl61yFEPBJOOftIrn7BLDxyLXrZYa0v5RqfdAoOxaKsAsqoeEFN7dg64FiAN2BrAqer39sWabCVgnnuOhEXpXBjgANo2T8Ampl61Fx33U0UsvtZ2WFoB0JNvBbicSxosPCELole3tb0C3FyzCFt0sL30SYAs7q4A8Apua0Opv/sJWJz02QLVSNiYzmO55nrdys+iWjknTRVsStdSVn1+v/7rVXPexwKnf93rzsfAO8eeU+4FiNNKYl+GXZoFvKTNuy+Nvi5gomtLfyYy2K8KBmjqkMV/LPLm2Tpv5ftWEmO6rjSvH867jeXays/93FyvMPestn7JJTO4zt7CNBBhVapdtQlpn/CEOX3q6h7Zs33v5lxmFi+dGqIh17ZthT6QzsTbvgKoM4HDPd9EWQv+7pvPFV1P/Gq2915W1ueSXbTlhJ7ZNzTCUl7j/LDAsMCwwLDAsMCwwLBAa4H1Z8mQYYHjs8AAQM/d1tzADSj8AQraBS4MnO93vw2gJj7cpc2kiPs+kK9lKi1ZBBMxzDOsMvdVQSzgDwCG+xzACVOpdfM2gO1JXGMNzloJW9MAHrsrK69jh33Hd2xsn2dgYJ84geocV1JutgAN7Bdx8qorcm+1d6CrPIFfmLuAVcfcZqsAJeJWD3wMMJb6ZMtWYfkBrVLH5GVATzeA1pWvvLk3AHGVuNIn76WtWKmthImWNAFaHAOFI9zGA3pFtz7vnLPVFiIYd7mHqgNIqcfb9gM+tzpA3yoBxVKeZ+955JkLheBdC9gjv3oPiRvZllMX6ombLx3MXyuHi8Ur3mfKxT6ssReV8ZCHzPfrmSf+bVuO+yQYk4nDC2xIO2n1WzARK9pfmJJhDT7ykRuXa3mopxiPAQUBb3e84/LzqACY50lM8LT1cSwMQNqJ/qGChJ5DtXcvvXPtRIDyMCpdC0MUwJl9IGArwg+wm7qYbIobfGLrAulbd3N2V3/lYGtH4h6cEAe/93sHJxlyH95DwHMFk10DolbBenbec9I+IwFbPYtWtLNMbGBMkoSM0PY8zyqA6zDk0weFYakfUL64t9pi2otyE59YXvp1etqgPi/2xmqu9U65AdyxtA8jAZ3Fx90lYhjrc9Wr/fPOYby3z3VXnlfk6/oudnzAA/a3QhaTq78P+6cemsMCwwLDAsMCwwLDAsMCyxZYf5YMGRY4PgsMAPRobC0eYWVqGZhxqQQ2VQDg5MlNTLqjKfnschEnzyCoBzzUHA2qMaMMuDE9c49WcK8AIoB3ScKSVR4gpmUZSpcFdRJXM3lhLLGfdNhJBNgWYCAAVPLOAjoG8FxyA3wBdAIO0O39XXTRNInnGQEwJNaeOoStFpdnrtAW8UhMRqBCdaFVBsDKQjhteYDbVgJIpb0ADAMMS59YgAElap7A54CB9bxzVTDn6nX7wDJMrJynn/bhHFArjDJAb68cbaGyxPZxA29BPq7BAdK0s4A0qVe2WaQmA3nnK+hW24TnHpdleolVWHWSr22tk0F/2qVr6pb61TTtPltEwraUL8AhurVOQN0wg7WX2Dq6S9vq2o6BV+9JfGETGwmTII+wWOm14RZyLu9VyqRXwaUAXrluq73W+8m1tGPHsZtF0e5+940dogsITgxRtuN6mwmAvFPef6zJpAFKthJ7s6d2TTf1aBmm0kYfcOx913/oO2rfxs451ia1i0zCaF+nT88LxNWy7ANwSX2XLFxW5VWvmuvo96InD3zgfB2QDNDNZBuAs4o4o4n3i4luEk4d9NmeX1zOTf5koS4hKlrJwn4JFXLJJZtnG+ZqTYOxrxx9p4Wb9hF2ziRMb+Ipeah3AGJlmFzSr9z2tvOq5bUf1EZMvA3ZbgG215a87+9853bdetVEQSal3v3uemXsDwsMCwwLDAsMCwwLDAucmwXWn3lDhgWOzwIDAD06WxssWsSndQkH5gCyXvjCoyvrXHOyAIlBJRfnJeF6WUEV+r2/pdWQa76AxaTtsUgSh08MwLrwTADIuKcmTwyvsPySr7oauLcgDpCi3gcAACMKG861pLcF1GCaRbCegBZVzz4QsJ4DWmVQLzYfkFR+2kJWdA8I5Ly0FvaJYFsBapxP+wG6BFyVxjNLGY7Fbzt1alP/MNlcq39YuOTNb97YrLIVq679lG/fYlOACDELA37X+6ATO6ibgbJ3wHmAhe0+f3E1DtOoTRNwJ+cr+BGQO9eWttpSpAJpVV/dc5/1/GH2LXgVwT6tIGryiY1ybAugjy0dp83W1eqrfjt5gZ1ar/f2lVFd6AMu0gVuk8QHTHrPNQLwz/ltW3FCc11+3JcDwAYcz/1lC8TX1jA5E9pC/UwkySt6yRc4e/p0ajZvAacpO/FSo1+Z7lzSA35678ISjW5vq3z9DhHyIHFanQ97F/gYcFS+Ce0hP/FBW9E+vE+ee49hSb+y3HNP6g70tFCdfiGTBRZ0Chs7bGCgZvqg9BEmZZRdhe0B1OrKHT4St3j9ZivqnAkeCxvtIwFZL7poXhxqKU1Yq95vTO36uyANMA+wm2fHvglFsZTnFf28d8Dz9e4cVrJYGNB+yLDAsMCwwLDAsMCwwLDAUVlg/WkyZFjg+CwwANCjt7WBJQYO5qMBc3UxPPrSzi7HMP7EYmsFOFABMkAcF04u4be85QbYCEhgALyPZKEj6VoxmM0K72E3YTwCMwEMbawybKMAI6lHb6tuAewAP7/92wdLzjXspgA0wIgAHdHGRAUG9soAdAQ4ADAkBELuN0y2ADgYZvLBrLSgD/AvZWPsRa9XVs5hP0bCHsy1bC2Akn366uk495xrS1v6FZQBilSALumA6NkPszBl5fyubWUz9hZpUm4P6OzdC9073GFTp5TNrolRyXZYkrl2mG11V14KRdAyfIV2CIDQK0tbCfCKafVbvzVNFnGiu40RmskH7unuzx8gdAl8Tl70AvSb6EidrASeGJNpk64B3LARpXPsOSccRtL2tjV+JRfz2n6SlxXI8xwBiXmXAKJhnfXyzjkgI/AuAhisAHn0ADcmobhfBwjUTlOP6Dm3VG6Nuym8RsuGBkT2mObqWN+l1NU27boF+KqO2Itt+IPU15ZdxfusAKAJFtf032K72k8ePBZaids8+wNRw4AWDiKTTXHFr2kTu1Yd6kJYVSf7APS0820L3rmmvvp/IR62CSaj8Cb024mybemuiNd4dLCTfuCwksk4k0dDhgWGBYYFhgWGBYYFhgWOygLrT5MhwwLHZ4EBgB6frS+kkuIq/tCHnlmrLA5koBSX4WgtgUbcw3cJQFOe/ir7MekMrAIGYM0Z+NLFZKpicB7XcEBHZUgl/2zDjAJ+tgCDeKT0ACAAlMrqAlJGpIsLbPJd2laAhKt/ZYAlDfZfWHAVqAH4RCdbwEPAoZyzFbczAlgJAFB1MLZacKcCUFWXPXvX2liLAUJq2nYfs6s9l2MARfbbbe4nsR/b6+1xwBznr3e9ObZhdFoWcM4DtwD8QOfYpnff0a+215ZaiTtx9Ou2LoTDPTfMxKrT7nOVD1NRm1xaWMtCTdK6H4zT5NMyZXO+3dYYp+4prMWqp23WviDX8o7m2HbJdb9d5CZswpoWk7m+B64BKPN8bANIAksz8VHrAXSsIGhi8tZy2n3M1pThWtjOecYps01Xw2TQvctdNqBeq+u4dXtP/rbifNLRP+0SISa4f9MHcItTqn1gsmfSpc0DoE0/7236MTGgI/pTK8TTA076bYiec+2fdw3IXOU+99noYVrXiQZ64htjqsbevXAEyc9kWN4rYQz2EfXxLvgzqXRZFYuVWZCMfUxumAg5SjE5y0b6vBqyZFcZfmPiGQBEHTIsMCwwLDAsMCwwLDAscFQWWH9uDhkWOD4LDAD0+Gx9IZWU+HiVSah+WUXYoDexFmu9xWmsA+Lqnhs35qrf7gdsahcJid5Tn7qJwxh2lQUwMDIthmFgGGYawLBdZAkr7a53PRPMszI44KtKAClAQiSLjLhHACwQs4JgGcADbSwOA3wKyBq7cEuNcB2v6aNTt+pcGbfAkNyj8uLiWdPYtzgUAWDErq1OPf7UT908O3XChq15595qGvsW7SK1jqlfq9senzjx/qSTOJfttd5xgHnX2KGn056rIFvcZluduDs737Ij97GddFyzq4hZ2dpMO03Z2i85fbrfBtL+op9tnRwwadCWQU8M3ujbAq7CvnUMwPf+AvWBKlU3+4lR+f5Krv+ZSNhli7CX5eHdCyDneImJnJi53r8wUNt3JnWyxZjOdaALO1uATJv1F+BbmwIWxT6AUWLxKefcS2VB1zLafXE6q/z5nx+0WWV1AqmrJExHm2c9dh+92McPf/hcTtsP1/zrPhf/AH2VNV116r7JmzDR1Sd9kb6DnTybuPLrhyrL3bvLLk984iYkQYBU4HAWx0p5WKW5riz5aiP1PdZOw1pOunZrcTjpTaQssWbbNI4zEcBT4bImGNz1/a1tx2ROwlMcxX0lvIQy95UXvGB+JiZYhgwLDAsMCwwLDAsMCwwLHKUF1p99Q4YFjs8CAwA9PltfSCVxGzQYNSCu7t4BxKxq3ArmE30D8AzQ6ARYw0LcJQE22viFNR3QEMCYMnpbzCcDZNcAmNUN26A+A33XA5CcPLkppS6uwT35Vrea3VkN4KOPhVaZhMIAYOcFgLnd7WbWWQ/4ATz87u9OU2L2hfHZu5d6LvFRMZrq+bqfOqkn19YKWsW1t+r39t/zntkWALY8k6oH6KrARS2j6m3bl6ZKFpGqabTBepz9XnmeTX2u0QUuVemFacgzTRrbmpc2nWOLTwV8r/oW+qkgetiWlS0H1KxlcZNOvvKqrvlVr5ZjPwy7ysCs+VR956ueFemrYHtW/TAbld+GhAgruur39gNsKydsvVYPqJdzmOZhkDm37d6Thg4gMgI4yzVb9+l5AEFzPmE0HOc+c822Z0PtqpWAmmmH7FsXgAJckh6IDfjr9V/upy7cBBBNuIEnP3nOb5//+h33AsiqrNeltBaWo597qfbIvkmOxIMFUr/oRQdzS5gE/WyARnZrmZ5ClQgRUZ+1MvQxwpEAU3dJFgvzrhxG4ja/L5h8mLzPly6AN2zuPAvt1jOo/a9r+qSjYF8K9yI/fXwPlG/vVRvLbxymbwSbtI0jm2tjOywwLDAsMCwwLDAsMCywrwXWnyVDhgWOzwIDAD0+W19oJYUVZ2AKEDMYyiCsdb3DPAI0us7lM3oYkwFNDPB3SdKJJ7hNDAwz6Eqaug0ICPCIKx9QtepkwP+VX7kBQy6+eHaXjl4P6Mq1ujXojwAH4n5r9fEMVBNnTzoAQGIk5nrNb2nfYjGRDOhb3QAV7fkeuBMX2KpLz6JTEQzSev1s9k+d6udRGUMA5H3yrgxNYFIAH2lj9zafuOoDqJO+Z4+kA95kH7NV268AWwvQJS+u4gCBXUBb8q7bl71stvgSS9UEQsoFgqT9yCPna369/R5TrE5YSMNlOqu21+eThXx6+bbn7nnPtJ7ZdgH6q578sCXruaV9TMG2nnQBjFUqQ9F1z6VlI3vfarmZsKAfcFifV+ui/pHTpzf2zkJC2iEgOc9BnkDg5OFZWRE9/VJAeO21subVl1gsDCtdeuCqtIBD5Xk22rP8sRkTEuH9Cdf/TF6FSQvsS/+X69nqQzH01Vlf6B2tbSp1t017sMW4rSKf9JOvf/18JUxtz8P1CPZ7QN2af903EYKVvCRY/vRbVu6Sfs5nYTOxKi9EYSe/o9VetR/h6WChuir6JRN3aXcmW5ZAS+C652IChw2wnwHWtTx5O7ZgFBubmKtxY2vZ9vV1yqdrchQT2wSj90Vb9mffOQv5DRkWGBYYFhgWGBYYFhgWOKwF1p8ZQ4YFjs8CAwCdbf22t80f8NysMffaQcO5PhEDHwAZpiHmDnBALEggFxbepSEGUgEULHyBpWWgA4ioArgJ280A3aJOYe4BhEgGaEux6Ohwu5S/v233jJGa/KNvmzLqOQBflQo2RM+91YFmzlfARd7Yk2JhXnTRpp7RNfirwk2z1QNKGBAmjW2AhXrOvvwqsJfr6hF2JnZbrWN02i2wLGAvgAMIEVstAYYYWQE6lgDVtpx6zE4WoqrnlvaBTonZuqTTOw8Ui/RinEpDJ2m9W/V+Y4Nc723Zl50jYbq1utyFc67XNnNt2xboFVkCG/Mct+WTaz1AWfoKooRNJ03apvixWM/JB6usrU/qYbvkRl5X/a4s2OTbbsMuz/kAfo49h6U8gDQki0LRB/AsPYeaT2JN1vsTIiOgae5TnlzqxSjNOZMYS27JuQdbehH9eW13AebZKmn0pemP1MPCZ7nW23q/LQxWxQRVYgbrY7BT9dOARUx94Q/St3vuwLWatzwB8dp7de8XF9VvVRWu0tJWhr93JiExErJB/557NzHEJV5frk7CiYjfmjqz6+/8Ti1lsw+oVZ4t8BcLWFzQXWJiSroTJ3ZpHt91dXffnkXalbYOgARU5pkEmF+q2YtfvEnfAryA3xqaInlmq7wsLJj83/KWzbMwGfCsZx1kc7I3eyY2sAmjlsHvfnJPKcuicDX2ccob22GBYYFhgWGBYYFhgWGBJQusP9+GDAscnwUuVADUBzgw0iDZh7cBFRDFINhqvkch2A1cGVs2kI95g0ox8bBtzkXcByZLHZRnsJAt4EzszUtDMA7b1amBY+IDYnnVRXw8g7jgWQgj9bfoRNyoxQpbkrCyDIKXRF4ZRMs/IKMBM5Cay13LDLWAR8RK06nX0lZdA4DQcY/VlW8JBGsXY+LmDpjIvbfl1YVdAA7aW3W9jz3adEBMoEFlKQbMaHUdx17SYXOSxHht9dUjLDWD5sqOM1Bu9XvH2kHEqtA9nYBtvWv7nqsL9bB1D4wCBrT5LZUN6O1dY9sqFURt885xO/DPeduEhKjnsp9yalzGXOtt86xc857e/e6b+8295PknvXZFHvvYja5rS+20ppcnAKPGwk2+vS2wFTsy15badK5nC3AhwP4eKxEoV21c+yhtOPls27bvaw9E1xf12Ks9cBWzNOziWi7WXZUajoOeNsqlvj5L5wHC6SP8PlgsSfgPC2Z5/62YXsFniwhVAWIB+2pd2n0gZXTYzSJ2mcyy0Bzhxi5d2pPwJwHrxRmNO7sF3oRmwJTmnp5+2IQeLwDp/fm9WwIsPe+v+Iq5PODwu9891yH/pUuYiNRH3diOLdJuol+3+mK628KrVP3zve/9q7/77kcbqvelvsDwfSRAtDTx0KgxcJ3XR3reJg2A2bVPN1mkH41YCKu+E56z30L9ZBi/8vQuBmB1P6dOTdMb3zj/Zpootu9c7lX/14LoKXNshwWGBYYFhgWGBYYFhgVaC6w/N4YMCxyfBS5EANSALAwZH+C9P65/AePOxlrANEBO8vbx78MfU6jGdfNRv23Qta1sDLkKdGD6POIR0/TCF87gLnZWjd3HtfzSEC5w4sQtgRcGn1gslbX5vvdtgLcKrBi49yRAJntX9+tWt4IbmEUGxOLTSWcAxwUzbK4KYlqA6WlP27i63/zmBwGUPOd2e/JkW4ODjCj6Kd9+2E5SAShaUKPN3zGAqcq1r71pd9EH9iyxJKt9k18v7iJgA8ga+cEfPLMc6XuAU+pRt/R6ACMdrKMIwKOmO8p9kxBkXzf9+u6yu2dUQd6lumH/kbMB2rCeyFJbqICHugAmluqxdB5bLBL351b3u797k28PwGv1e8c15ib3656Od6Z3vnduqR7iEEb0Gb023gO8a372vefYcZjnvfK5gFepbuw9fecqUxX4ExBSe+pNlkmTvhsglDbonV7qUwFPiRMKcLQa/JJgdAd47S3woz8FwvrNBCrKl11Mqvm9UT8g9atfPZeAjZl6AVUTQxWjOEAXlqwJyABhJiFbBmC1X8Dqygpeuh+/HRaskj7vN12TcWEcJm8Adfv+mnxrgVN2zyTRz//8UsnHd772ibwSPCPfA8T9A7tzj7a8L/aRgPW+KxJ6RnoTtiYtWvF7wD09ZQEnqwC6fYeEzRs9W+Cp5xSmqn4rXgM1j+x7j/MMsnBero3tsMCwwLDAsMCwwLDAsMCSBdafHUOGBY7PAhcaAAqwCPPNxz7XPqxCi3UAIrFWMhg0OH3taw9vK6zOxCkzWAWcVeBIjvJNjDYAxjOesVwOl3nufupq4GMAJi5bYv0ZxD33ucvpH/3ozYC0dXVcTnW0V6xwXWN71oGQfWyqurq50h/3uM3Aik4L9NHBWqmMOgPzJamAXV2hFmsogDigJDE1DQADiGQArh4VbM19cG/3HHKcLZfXKoDUXMvW9QCz8iAYsrmufbh3W/nlfN22rKReHau+ffmFweP+6qIy4pq2+o65VkYSh7CnV8/V52N1cSy0XF8CS/Osf/3XN+BVzSfpe1v3RJZYqm2aCia41oJlrX6OE08VeFrbR64b9FfQMm3JdbaP3rZtdX92T71y0na25ZNrdHs2x7arEmAq6Q67xcLrMVU91wA1ytt3Ua1avomKPCOAYcC2qmM/i6896lEbW2+zewX9TRZUad9b95HyEns1+vr23nOKftLmXQKuLYHOFTDX74fRKa8wJ5Nvb+s5JuRF6tfbClmgXn4bLTS0j/hNS72f9KSDKTC30/bzewtYtMiNcmIDdWaHvNv6XqFbgKZhXNZnpn8H8O0SE1nSaRtCsQDP/J4rz+9+2KV+g02C+c0WNzohRbSxCtYnfq93WvkAcX20mLj6j3axr131O5frfuvZz/1p2z258Y3ne/X86WqPgPxdEg8Ctspz81vSfr+0+dT41GIARzBC9wmBsm+70zbzjHzLDRkWGBYYFhgWGBYYFhgW2GWB9effkGGB47PAhQSAcm3PR/23fdtBd61qEXHBrAJusOTjvw6Eqt7SfhiE3L12DT6zgi7QtWWcAmTDeGgHtwGu3E+YN0v1cd5gwUDIgHCJRbkt/blc4woXt8jcB+br93zPNAHRMqBxjXtiHWzVuJ5JC0gyYK6DaNfClFuqawBKg8JWsE+1iW3AhWdksJ56YNeG0QRUwjbKtWzVEdOJaEcBBSrTy7Os94KFlWMDbgCbgbY8w9BJ/nUrjqTBPHaV88CuymKtuvYDyGkTWRW81qvqVzYzN+nK3KwARU3T7gPFIqdOnWkr+jXGKlZZ7NDmte3YyuiRXhxLaQ8D8F35yv26ctmNCK2wrU77XAubrtU9eTKlzCE72uu949qOtfsadmGpTwFQEYBR3pVe3jnXPhtAoPifuZ62nuNs9VmkhrloWXjRbbfAJpKVptvrtU7eq7pwmXdJvN42TXvM5boVLr9Vj8t5bKzukac+dTOpEP3o5TjbCpr5zekBmp7bEsCbfIDB3tulSYvb3ja1277FXpWn36R9xMQhfWX3BACd39HUtW714Zil+S0zoVfjO4rhST99WEDS1lW/V7ZzWf0ccBwgGdvQb7LfwLRx71dc8vUd6et4VBDsSc9Q21Lf3nNST78B+sbzKUKpZLHAbWzY9M/abd5J9+X3YZsEyMwEg/tO2JNt6VwL6Oq9J37vMpnIPvpc76PfKb9x+vr623HixPuT7fyXeMm+s4YMCwwLDAsMCwwLDAsMC+yywPozZMiwwPFZ4EICQLkC+hC//e133391owOM7SvYGcowKBZrbR/JwMGiNQQjKCuoZ+BgkAY0sugGV8EM9IFcLQNJHj2x0rj85H1cYkAZ0M4gtII8WTgBIwzQWcHp1K+3yrh7qH+Asmc/OymWt0nD3X5JLr54Y9vo97ZpE2IUZgBfB3PShHFkQIhNmvujlzS9vHPOQFtbIGwEmMm1bIEjces0aI3rpzICGER3adsu9FRZZkmDqddzGc71dpv2Wc8bTFepjNPo9ezSyyv6S9sai25JZ9t5cV9zPbHncly32NmkhqKo19v9tAHngbMAn1andyz2LCCrd23buQpOVdfrmiaAinP3ve+GAc/ueXerfm+/TqosgV41HMcuUK9XRtt+KqM7+urca791kkU+9AHbYeEnva1zAcQ82xo3OO+4kAYBYqTBTMOCTD51gkad4o6e67baetqPcvZ5X8PoSz7ArSpL7wp2YyvKBgq3aRxrm7sE0089vvd7lzW5krfgcerOlmEvmwxrJSz4AJVY+2EvY6zukqz2bmJMmbwx6or2FuFJ+ezgeZqs06enzMpibtuVfsG5CvIpx7nDTpruupdcF95GGSbe6kRhrmcb1qVvBW05dce+3yaZcEybEJ5hXzFxknSY+ynTc/a734p6eQeSxn3VeNvRtyiitgAg9Vyib+t8DZ2TNGM7LDAsMCwwLDAsMCwwLBALrD8xhgwLHJ8FLhQA9PTpeeDAZU9Myn3k935v/kAHLO3Lgghgd7/77S4BqIV5J5aaj38MQ+cycAMSGLg6V8VAQ50yEAAAWihgl4irqByDveOSMPoMXmL3ysrDAnM/ROy4AERsEjBPnYHD2C9cEblBAs+wbfZ9LvKXjz+D3J70YmcmTbutA1wMzx5wF8C5ps0zq+fsc7lszzkW+iAC1Oulr6670gDfA9Q4rmBT2lZbFoZpJGzTVmfbsfLChqW3dD+eLzFA3pZfrrWgK0C93lv02i077bvQTptWOyTAwPaa47TR7Fc3bu9iL017rrpYW1G7ve645tu7vs85K9dHvCutPeWhLm27YuMAhbUc72HvvMmZKvsyOmveYZ05F/CrXref9qOsGqOw1avHibHrHsXsrAuZBeiKft5j7y7BzAzA5dkutb16XvgTfdrFF/efq7LCjsVuxEgMsOdaXRgu9co29XPcxjr2bkTPVh8ZgE/9qoQRWfXbffaqLOea3j42pDTab09e8pIzmdb69BYgV7c2BIzfvPRrAeEBtmkXJjN3iWetfpmIat30pbeifQ+crrbwzsTV33mgYsJfpA6AVd4MeY9MXtVJmOid6xZQrA4WENwmicsJhCSZUBVGZ5sELM79H9bNPLZOiAH2iAdEW25Y875H0paUG28E+tpFy8CPjVNH7WOpjLbMcTwsMCwwLDAsMCwwLHDFs8D682LIsMDxWeBCAUAD6uwaOLSWyeB531XUw3qwuu2SuHby5MHVjfMxn1VTgQHYHj2JayBX1rgtYlftcm9zPSyj8zE4a+uaWI+YMoCEKnUQbABsAQSDL7FAY4tsb3KTmvLs95NfYgPWnKrrtwFWAAkgWMCKpLelExsarMflsOq0+y0Dry4C0wOVkj6usnUQXsGi6GVbAU/MGwyZCtBEzzZu8PYxiTFbc31bGdGx9XwjS/dR82rDIexjO+XE3sqq5df9bWzNqre03wJFd7zjmWXV9lHzqbbMIB0QGFfcqgsgqeB92HRV5zD77Lv0jMO0NoFQ28ZS/p5PgDZtX+zAPDP3mHtr0z/5yXMryCrbrm97HrW++jGyDfxLeXS066QHkNT2Fb1sU9+6EMwSm/Ye99gwYLla72OvlJNtJnTcD6Zoztdt3JLrOfs9ewGd8zyi73emshmVld+e6CgbMzfHiRPcunB7ttze29Xlk27JJT6LYglLYJElHg/5DdJnpd/EXM/9AsgSvqDWQ79R41gnnAgGZpjvgGuswcTobkFINqhSn7H+vLJ6qx6WvYkm4RF67aj2T2KTbhNAXNplXVhsW5rDXMvvfQ/MrflYxT3PD3Ac1/a0gapb9/PMkhZIeRjJ72h+B4SKWBKTtsrxzUMSliR280xiS++88BLamOeV/igLvilPzPAhwwLDAsMCwwLDAsMCwwKtBdafG0OGBY7PAhcKACr+oI/tfVznqnXCImldDatO9n2Y+xD30Q5s6Ak3uzrIwpQ7ceJMMBQQWAGfmtfp0/O9GCgAtwKsWXhil4RNgS10viWsk557o7IBgAFkM+CqW2yvXQPOfe+Bu2Dy9nyAIsAcoEFlQ2GEEYNtehmAJW19dthbQIiwXAGUCbMQ/d5W2WGQJl5nq1fZqOpZy211l44rcJE20+piBLX32OrsOk7sSHbTrjL4renagXWuhaFW768Xy1AdLSZWJXkcZlvLEeOvl1b9IwlP0dOr57CYcgx4SXxE5xLDMNezxRQkAPm8B4d5FvoHoR+S37Yt1nN9BgGRtqXR7jJxYZs61jTacuJquh4AhE6ebdXv7WMOR7yn1ZbRZ8MWBHStVyf17gGXleWsvJ5rNlCutU0A1NQlWyBM7x5r+1FOD9RMHnWbvtm91oW5lNG7H78b8Q6o/UhAoYsumpnzqSMQsE4wpf2pI3nLW+a2FEZdvW/eA1X81iVMRNXTxjBPA2ZhIvot9Jf3LSzptMdMMrC9VcADXLpnk2hAaXYKkI+x7Hhb2BP1y+8iXfe9JBisqQtd7axlBjuP+bmP1NAIvEiOUkwGqkvbF/bKCBtTO8iCgpUR3qaJ94o+KH34Pgsn1XyUpX752xYDPeE/tEvtWExT6fzu/9qvbd533wlV6KZN/+VfzqGBpNNeAL9DhgWGBYYFhgWGBYYFhgWqBdafCUOGBY7PAhcKABpmEbe8w0hiYmXwtSutwbePcTFEW8kHvusGeZVdERDNtfwBlnpAKkYHHcAD4SbveBvbgh5wAQBkwJqBs/PnSwLi7RpEGZwBqLkiGqiG6Yi1chQi/x6AEDtn2wIXdSAbHYPKgOnOZSBmUN+WYSAZN86ktwWcGKBHllzl9wXDPM+wpGo59itwoX201/c5DhsWMFPZUDVtFibBftsH8JFn4jJq+wbKNb/evoFx2LA9MKyXpp5LrMIAMPWa/WpvcTGzGIprAQTaNO1xXQSlMiGrXtjozmFu13Kr3rb9Ojkipm1Pd6lN9HRb0C861c1abMQKdkUnzzHHthVorud7cTtdrzEq92XDVrBFuIDa7pbu59Sp+a0TjqPWa2m/gmh0LKgTIM/xUrvQxkh9X5aAcPlo27ZsGdCsFyOXjnziFu5+0s+mPnE/drz0575aiXu5hZfIJZds0uvnItpBXQxOGdzE1b22D8+gxsEGVuV3uNbrKU/ZAOexA0AyE3rxeHBOrNUAr2KCLknYrHl+3uWehJmqPu5baI785raMWjqPfnQvlzPPJQREQimcqXF2Z3yHqEcLCvZyw0aNnQOupw9s9bP6O31lpP5c0w8j9X3IO7AtfQB5Cz5ixKe+mZQEfrdiEpue74VIGMNCLAwZFhgWGBYYFhgWGBYYFqgWWH82DBkWOD4LXCgAaBYp4UZ1GAFo+Ng2cN9HEvvwla88qA0EBHT4e8ITDl5zVAfzX/AFm7hXvQV7gKsZ4L/tbXP8T3W0yMM2ef7zzxw4bNM/12thclWgd588Y/Ntq9zukw+dCqbUwbmB1TOfeeaAvALd7FwHdBmctVuAQwA5wE9dWCWMnTYN0CISRlOrs+1YmZUlFuC9l0Y5mJ65ts890dVWyatfvQHpqg2TX7bA1oCL2/Sib/vZn71ZzKO6bVYd+3HNT/7t9V3HAJqIiYCevSqAtis/99e7R8y3Kr1nC/zd9xkoY0m3lrOrvr3rJ05s2kQvNmjShC1ule6c27YN+48Ol13u0dv0cw0Ttr6vOb9ra8EXEkZZTz+LwrDn933fwfoEdOulq+e8B5GevbQffXDSVGB76RlGN1vAT5UARLluK6/6e5Fr7i39UNo31iK35+hke/Lk5r1TnomLgKpPf/qmBjU2o3aASRx2JOZuJgkSwoAOZmnK4cJeV3YXgsa13ruTNNql2JxVxH12HcM+YWn03z3x25uJqcTXZQ/gK9DMxKLf1Uwc6rOf+MSDOf31X29sqdyAoXTrpNLBVJujTGr5DTxKwfxUH/3/PlIBXun8Nv3+788peSH4jQ046jobEfFMHVfge76y/P/UqYPP1mThLhHTWznakUlZ+/kDXrdhC/TdWUzN719E+807+apX5ezYDgsMCwwLDAsMCwwLDAusvy2GEYYFjtMCFwoAGuaRQfxhJEyEfV3nw6S4850PlhJWjkFCK8BRH/1hCGGHWsDFINHgPABETZfYctwHffxLv2uwEuYSt/7jkCwuIZbpa187x6MzgNklcYnEwNxH2I+rMtaeQZNyb3ObOa5bBlMG4uwUGzuPJRR30ejZilEntEAWP3Gup5c0AVAwlLhwkgqERM82gzT797nPNNVVhitwVNO0+9XlPABEq5NFMJzfF3xp86gsZjHneqBFwIg2be+4l54eQCoC1OilPcw5oE9PP4N7ZdUYtD3dnKsgFvAjbqWuL4GxGL5ErN/ks23bunHrawLgbEvnGoCg6uz7rLO4SWWj1nxq+zXZUhl/Admqfm/fpFMkwFerh7FVbVyvt+2lVy6dumhKVsmu+dgPaNee7x3rq3v6bZ+uPfTSt+fq/dXnGrfkVh/AE8lvQ6vTHqtv7CX0yCteMb/3zrWu/tHzW0W851b7lifGKqDXJBTQOBN6rumrcixP/ak2nokDsUoxa+XvWaVvw2QVG9TCgK5po4Du9JvtvWhrLbhpNfksshZ7thNrflse/vBN/OYv+7I+A78tr8emxC6lF9d47Pm73GU+tw/LMJNTfkOOUtg3jGts5H3k5Mn92qlvioiQF7Gz39ZdkgUj2SzfTCZ6dgkme8D8ujiifNrQOZi5PA1cA7K3q7+Hce93dciwwLDAsMCwwLDAsMCwQCyw/nQYcmla4B3veMc65tvr1gOUV0ynT59eLzzysvUA7g1r1663r2M+rr8GL2dyoQCgAu4bfBl0YU3uI7/yK/PHtsFlBYO2pQX0GThwhw6LJcH+uZW1H+0eeUCssFzighjW6oMffGaJBv0GksrilmdQwHV8STIgE2fNYAXwAdQD6AANDfp//Mf3v8+lcnIeaNtz/wamYAu1DNmks43bKPtvE0ycuDi6/6U/zKaIemVgRz/7BmthWzmX83QyCF7KP+dbxmoGqrluqy1VYDXXemAKQFe9opMtdk6VbeBs0tgG+Gj3PY9eOXUAC1yo4HHNt+63OjUmZBsioKbjekoST7Je27Xv/amLvWzTt5jZEkC6LZ1nSbyvtW3UNLWd9EJaVN2lffcRWdLptZUl3d554FSVduVnadxLgK1eHu25sAfb89ifkSU2dJumHrftMszGqsMe3LIj9dq++5hoAeQ83wr41jxMmpAw5FzbBjrX+nLTJRavk66+j44ruI6hjokZ0NfESV00qNZJ+an7D/3QXIb/cWmvuva5kAd0f9SjNi703l3xd9t6tek9a0BcRD+d8oUioG8y5/Wv37ShvNfy5uFQ89TO9EFtewPI1U8igGeYmNKb6Lr97WegjG6dXMKwz33kvIkJvzvA1crMBvZn4so9AXYDtl500VxXALHzcQ3vLaQXe9iGcZ9+o17r7QOIX/rSafJb79kDiz1/ZbYSVit7Abq3iX4179C1rnXmb4/nduLENAmr00pWnGdr9lqavDRhk74dwO/7Is+3vpdt/jn2TNJ+pMv7VFm5vAOymBzdXqxPbVl6E8hDhgWGBYYFhgWGBYYFhgVigfXnwZDjtMBfr32pHrpefvba6y/yD1ujP6v1F9rS3wetRzvXWE+ff+va7+xZ66BI/5wlVY+zwkdc1oUCgLqtsCYNjuoArnfLgIgMSAGDh5G46xooAkQzYDl58mAu733vxl3agMsAyAc8UI88/vHzcWVSzVfm/5gr9APIGMi0wpUvixHRNQjKwNBx+2cg02PEtPluO1bvDLrkb0ADpKtgpTpgVCXeWvIT41EaA6nW/S06tuKD1vsw8AZKABnYqw6m3VNiR0rr2VawqrVBPc5gTFlhaAJc8oyrrv0aly6s41anPc4A3XlAQGJ1KrPeR02HjUQA4QFZqj2qrv0KnnB5ZPcwmlrdeozFJT5ngI1tZdR09sOajjtoe70FImubaXWXjrOCMFtkVepWN+zr9vxhjj3vCLCgZ4dq4115mxzIe1t1gf9V6rV992u+3rFeOu9EJGBNT6+eSztzzvuZRVMcL71P6kJqPMKa57Z9oBkAbIk9Km2AE++Q97ree8DDbWW4Ftd2256uZ51y5L8E9vbS5pz+L+Ke6juftsRdudeGgJUt29jvhf4++dvqq9i5Si8+LCApk25Jb/Is/bNnqR8VMoW7dO49uranTtVSZmCxTnZU3ewDLwNiKQNTL+7XJujiMYB9mn7PRELiJQPTMnm01FewCwDRc2JXgDAQMf2X3wiTmem/8yyV7ZvAb3LAUSB4FkGTL0kYCID5NsE+dd/1uff0feJdfPHBsAGxl61n7/db/NQqAeBd9zvTLjaEMWsRvLyzPCKq9IDVej37mYRVF32G5wFY9ztn4a3KFHc9wHDad2XdJ8/eVmz2et/2MaGVV8vQXnwn9cTklnT6pSHDAsMCwwLDAsMCwwLDArHA+vNgyHFY4J3vfOd0h7VPzi7QcwkMdf7T11/PgNDLslxIAKiYV4lLyU0rA9/WvlaXzcCM2/gS86FNl2MDLCu0+xg3eAQ62o9rlnpgK2RQZtAbBkbip3nsGI7StaBIyrGt4IBBMvdxLBKgrdhvGVADAyo7D4CB7WOwaYBloFIBA7HCzka4IKqzP6yZsCANmgj2rTINluhwewvODxgQE9L5pcUa5IHVEtAAI6eCm66TGodTfgbcFlmoknAFrrd/BtCpO8CTK6oBcoCeCrRIm4G584AYAEHyDOMrx0vbxJk14K9Mp6qfAbn7B24FoDXQ9Uyrbm+fXpWlhXIqW6+9116+vXO1HG77rc5hmYxLAAs33Eje77asfY/da9pWm4bbLtFOl3RqmoAPObeeB/v/z8u5+r5FxzYLj6QvqNd27QPQSdruNn39UgAdem19l9LqGyNp962uSaac6wFoubZtqx+N6J96ujXUQ+96PZd3pZ6zX2M66vt7z3YJcGvz0n4ygdFew1aMbIt3W9P5jch7or+uYFDiO9ZnkL4q5SRWdM2z3ddPp4/S/7YeEtVu7JB2Wyd7lGdSZVsMV5MVyvY7lT77Gc/YgNbKYXsxQ7FHw7a8//3n/jcTksJX1IXo2vtJ/bAoIxZSSv8IWMukiJjgcdU3QYp1Lz9lv+UtG+BevbBE91kUUbvNu+R3YEl4g/D2SP09W27cvhP8/gGL0/fq1/KNID/fJAkVI717dl/AW+B2fWaAyoDIS3XZdt4zjU1T13br2dZQPcLIRKfG6lwqJ99I7Jxvg6S3NfnIJi3QW/MT2ofu3e5Wz479YYFhgWGBYYFhgWGBK7oF1p8HQ863Bf5yPV3/GesRRQU3P2D9ZXfVNc3iWusvvRuu0Zmbr/2dv2FNjbrleir/xmtU40vWX/WfvPbRutL666+m+xfrL+AH1iVyz3fljzj/CwkAdWsYLdUdzyADY8QgCyMuA0Ef0lgcdQGHw5gGexG44IM+H/IGJWG25BwgtrqJZYBlwBMXfIODJYn7ex3wJO9s3eNFF831UB/32xMAX+KESpsYcVUX+IM9A6Az2JafrWODudTjYQ+bU3Hjo+N8deV/+cs3oKEBElA4jEDsuG12z8DYoKiCJLWemDfuAWgQAOHEiY2GdEugSuyWbZiMUgNLeoNBgHbA0ZQnfQbdycsWOFV1nGOjuIErx0CvbSv0DG4zKE6evQGja9WFMbq2GH9V8syqTrvf1rdeDzBRz2U/5YQRlPP7bKUJUEGfjZbSKefkyeXrS+nkSYCTSzr1vFXRW/vX60v7ecfD8u7p1fvrtbFemnpOeIUIcCVuzlVnCVivOtmv9ck528q6VV6dWKl6++57n3t11eaq7Jtf1cPwYxfnsAGX7Jr+Rr+0ra0n7/rOAPRMriTv3r0kneePJZe+AkCZdNGp27zbFmczUaSetWy6AdvCatQvmrBJGXQyEVbzbve1jRZgAmpVPfEvse+dY9fYzXNqY43S6bWhgPR5to94xEbP/ZrUE98yDPpqH7/PuS99q7zue995wg94WO/TxF4m15RlwjO/g7knbTmgZ84BROukSvL03ZAJvkxS5B7q1m+OvLSjbZ4mCXnj/eE1Ueua/LQVCz/JD0t3HUHpgPjNAHq2bVabUA/t+SjEfQAWxYDV3j0D7UwMaH1iT7SV2NS3Qe+3Wr+YuLJ0TdyGyWnCS7gC4PU+AC4AWB5A9SHDAsMCwwLDAsMCwwLDArHA+vNgyPm0wHvXPlTc3QNifs6avvfEdTCjd73rXXsV+w/rpTB/Y70Czu3WKwp88PqrNvk8GzXlMigXGgDKhAZtPuaXmEkAKmBd6559NubHdGljnhm4YbAY9LQf9lZCzYAnYEU7YEw9uNZn4IONg1UETOS+CMzFuhR/NCutGhz0VqBPftnGvc7gtS40AdRMeRnY9LaA4yrqlIEwQEscPQM7dZbeYC1gn0FeXdSk5mM/aeQXUKnVccxlUd5A6MQGcwxo5f4XUKTW30C7566NYVWBgbhI1rS9/bCIXQPIxga2PTYjYCMuhDWOWi/vnKuDTC6sAIsABNHpbS2Co32zd65XoCHneluLnZDetfYccHQbINTq5zgLCSlnqV5xmU2as9nKP7LE3GsBkrMpp8aHZfuzyaOmWaqTCYxI3I1rut5+tS9wN4uY2F8CtzMpAxjpvUttOQHpct5iRZnIyLneFghOKgNzXwA68TBNYPTS1PdE/vrjTFhUpmWvXjkHvIosPdewtqXx7qcu6Q+S17ZtjXlogbzkkTTysthQ+tGct+2V4/3KvVZdYGIVIFe9rk2RTFZlbra+i+lT2Fdf3qtTnVwD6gVkrGXVfcCoBXDSVsUx1Ze3EltHD0u2ivbqXezVSXmA5haoM4mXuuR9SNuqeduvvx+nTrVXN8ePfeycp/aD7bpNTKZm0TagZk/+6q/mybnnPGf+DhD65tIW3zY1lq7fN+3EM/L904LRmKrEvWRS5bnPnc/t+v+0p8321F+YpB0yLDAsMCwwLDAsMCwwLBALrD/xhpxPCzxmTY8IaHmL9Yj6n1qE6xCFAz0DgmKUnktehyj2SFUvRAA0NwgI8+FsMOPjG1D3ghdMExDyKIVLoQGUQfYll+zO2cIhFeDAXIR/GygZCGPMVGZcXHOXco6LpEHHvhJQw8CL1HiRwAwgqTph1NqG9ZKBIjf3KuwcQDc67Raw2DJcah7241oLpNkmWUiJC6mBc4Bb+3HFBCL81E8dZEq2QE3qmBhwwOWco9sDF1zHkokeEJYoK+fqVj4ZkAOFgAr1eo3DVs/bT/mAinQ1b3rT5nzVB8gG2FFmQAI6db+mafexoCIGqu31wx6H4dam4/5dJc+v1dv3OAPqnn4tp3d91zksvgBS7WRHTWsyglj1vp7fZ9/AXt+0jy7ALwDVPvrRAYgRAMKSvSr7EGs27dz9Y5vF5ZpeD+RXlhARkdgtdbD1DgRoqucPs6+9VOGSm3cl+SgjYUhyznZpYqwH2HqPKuDSi7nZu0fpapkBI7n0x0OhXrefRfXcl4Ve2uvtsXe63rO+qx4n3Eib7pd+abZcXZU7uu7F71jeR+0k8S7lI+4kAVA6Tp8f/WpvLE+/h2Hrnzgxx2+sYHfqZhIlfWSAMuW0krz0tbn/GuIg+plIw6i3kNCtbjXX129rTywamLrYso3fPuI3C9iZ30zX/ZYtCaZn7PC4xy1pHTxv/jzv2q6Fjw6mvPSPfAvk+VcbZt97ZfKgim8x101Gb5sQlQazN/2VSeEhwwLDAsMCwwLDAsMCwwLVAutPiiHn0wK3XY9MAKD/bT3Vjc15rvLgNRUxgOqbt1HezrWgkv5JT3rS2hX6S9eD6C8+57//tPZBVP978mu7AkvcA/ddobTG0MpAod1i2jz5yduNqskk3S5wseYEFJYOGIA1mjwMhFuGjHQGjnQCtto3wK3CLZfLPgaIgSqQKzEbxUzdRwBB8m6ZPTUtF7oMllPv3rbaLqBoq/eUp2zuvWUyVjBBuh7QAdgIMKmOFqNoy5BP3PfaaxnY1fMJk1DPGcjXGKfbFnSp6bbt1/iQVe8hD9lYO4uI1Ou79tlRPDd6lSXbpgvIDWgNK7HV2XYMcAwgt00PWMd29Xm2z3opvbhzJAuStXptm6ixC7eBAjWfsCCVAySq17Lv+Wf/bLfAuwjmd2VIJs+47ebYlv3WPxnvl7oQ1enTZ9bJxEkVP5E1r33367PSz/TS6QMiQL2eTj3nWWUhIOe1nRoSJCBlTWMfYEewt9trveMKfrLdJZfM8R0D/NV20erqSxL707UAk71y6rksjiTmZT2ffYBeylWnyvx07C+6S1sxNCNA4ToZkHsDTAbg9Y7lvN+B2hcACIGIbbsGar7sZSnlzG2en0lHoV7UtceaxOhP34o1mXAh2xYADLt06f6d1y7rgmln1nAGXOm6R2DovgL4lc47dpRiASKsfqxgzwPQiqnJS8Zk2lGIdsvzQ/+HFWoCF+NeqIae0M+kqvrwPmm/O7Qxk4VpQ9zsD2PPXrnj3LDAsMCwwLDAsMCwwOXPAuvPpyHn0wL/dU3dO0rA7+1vf/v785PnL9dghOfxJm62Hg0FdD2q7R1F7L+cCVd6YJvBg0V3fIAbSBn4+4CvYgGDMIu2ucZJI/ZnPuqBEv4wMcUmM+DlEo/Bsi2+WMoOMBl2V87v2hogGmz5C5io/CWBb9MFnBpI2TfA3CVYY3S5/e8jASG4tbfC5j3QDoOyuuIpz0D14osP5hAw1vVtfxXUim3oAzcrcJA8asxEz2wfcM1AP4wzsUorcOFa8rYNOME9NXMuYQtVvd5+2qRrBpP7sgyx+M4G/DToJ9zvK3Os1q2yQj3nLFKSZ191l/YrK6+CZVW/llPP77sPIKwi5mCb1rOq7aW93h5XwKteq+B2nWSoOvvuay9LdRKDL7KkU8vxrtfFWaTNPfTSJ+9s9w0nUcu8znXm1GIFO7/0fF2zirS+MnUx6RIX7Zpnu08nUt+Rqhc2tXNtnvu21bpwDJC2fbcxzxMLUzl5r91z236d64HWgM3afjK5Ve+Ffdr8XI/d7Kdu7jtxQGsennsWN2I7ngy9+tQ09i2QFhsL6VEBTsA6nYCV9uVZy8lzsg0gLA/3rM7uv8YqjX5Cw3C/z8SEicee+E2OfYB3vT5cPhYwaoG6Nj9gnvtIKJH2+tJx+maTIEchVpbfBeqyH/brYReCPIr6mTBNOB72MiGhrQDaTUzkG8k1nh277H4UdRp5DAsMCwwLDAsMCwwLXPYssP5UGHI+LZBV3x+x5Et1yML/z3okHzf4p/EjPgb563UAqReug7T96jq6/bn+PW9NPbm4RZqO4R7OZxGANivMZjDqA7z9w6B40YsO1sJgroJVmCf1o/1Vr5pdG6Nz2AHSwdLmo7CFAHOHlXpPBtd1sN7mlXIM1v/4jzegBOBumxwWAA0D1CAcsGgwGpfIsM8MkDU54Id7UPe4RsjIHlYAAEAASURBVNZ7Mri3yAIRWiB2p1/16n5dmTvhASroWgGDOkg2kPuzPzvo2hugqObf7lulPW2kxvusesD3MJ+40OY+6NT9mqbdBwZEAga0Ogkr0J4/zHFlQGN25nnWPHoATdhpVc/+EsDCHbVKm26f4wpiAd16abS/yL4xN+szMYivrrO9MpzTJonFV5Z0tp0XL5F78jadXBN/NmCXc3U/OnXLTbWKGJX1eva9G5FXvvLgs99VRvIAuke0n17syCVgnq0raCJPoUYCvqUM27bvDvhVdbb1E1XP/lJIgtZVmi3btO2xPqjay29NAEJhCRL/uKZzjyYdCKbq0ntT06RPcQ5QBkQM0JuwHHkv0++xr9AydPM+mHwK67Pmb19+CXfAbb4F2rhP00tfmTi9F110MPTA+29s/S/vRxZ6y+ri2mQr2IK5j9Srgq/0/eb5fck7a9KmTnpJl2vJw0Saxa6WJGESdoWuadNjtCujTqi1Ovsev/Odm8WH3I/fb14D6xD27392Flfy/ZF25r4zubZvGUelZ2Kg99vj/RMSx3fUkGGBYYFhgWGBYYFhgWGBJQusP5+GnE8LfOb6ax9r8uTJk0dSDAAyLMw/5Jc45FK1AFZCBYgAVBYbsvIod2nx/QzOMjBqY1JhklTQxwCD618dhBtUAhKPQhJ30sD0MAIwyYDO1iB7m4RpGjfIsPYwNrYJoFL+XDB3SVY5rvVq9w1Gs5q6wXQPUDBYt9iHtJ6F2HgZwAMt67No889xBWLUOwPtXLfVDgwic+4wgEnSANmr1Niw0ZFv6p9zS1vARw/Mc9+Ai8jZuJwvldmej9t4BvTt9fZY26vnajzaer7dD0C9r21q+rh07/PMuM0uhVCoebb73vtIey3HS2Beru+zrUxxNuml2Zf9LO2STepCPUtu6UDlFjDq1QfgpS/pXeNKGwkrrqdXz1lpOvXW11RwrzeR5X1IrEXAYZ3MqPnWffeVMoBKgMalOKg1nfZMML1zvq6MnXPZ1vaccBRhSio/daBfQXxhNvz+BHAHUNbryb/dAh0jbFL7xzxL4HrAMizMgLAm3lr2vfS1jspTp9r/KM/Ejz409QFCA+HiFl8nbVI/i2vRx54HwobhaCGjniijuur77WUngJ8V52Nr2xMnNnUxuQZ4y2JDJrfE85RG+er6rGf1SpxXOqeDWXkYOexk4VLeJnAzmcaWayejRfFOB6De5dq/mMkRXTCxqv1arMziSBjGQ4YFhgWGBYYFhgWGBYYFdllg/dk15Hxa4DZrP1iA5b9aj5he1NJIDlnwe9Z+yAFUPwLVZcilbgHu5wYvgLPnP79fHYMqA8IMRn/hFw7qGTQB4IBMGWDJE7PH6rhvectB/XM5eutbN4O2uvjIrjxPnZrTZaC6i42qzu4BE8n9YSI6NpDfJgBSeoDabRJ3cLr5M/iu9st5oGrEwLQHcBiUt6yuCnLkvuW5DsP7/wf3KcPWQg0Rg8qwlKpOu9+WWQHa6upa01UQtOdmXXWzX+tigY+wjlzv2dL5sCazQEjy2nfLFktMtzYP8eXquX2AGPp/8AezxQE/Nf1R7gOGIgbZvby1uwA+vev7nMvCWilrnzTbdLbZMGXYbstj6Vp1u6/l1HdGWn0fqWDeUp7OVzANy0uc4oBqtQ23eWDmAYSi03vHa5r0EwlFLX3OVT37WcBHf1xdn12rdmjT5Vid9DsEeFrvMTr6F0CsY/vVjrXviX5va4GhKkJYtGkB6MKLtOmzCE89L5xBD8jHsqvis6a1HaAyQFnyNAkWhq56BBS9852nKTGx6zvkvfv7v9+UFMby1a++sY+QGFlp3WRQT8STVgcgZViogNGe8LpIezbREkZr7sGzu+lNp+n+95/zdN0K7kvieWehPC7bWSip6gNG5W/S7DDygAfM6bTPc5HEFzcJYHGlXYIZqn1qW/aHDAsMCwwLDAsMCwwLDAtcliyw/uwacj4tYKGiuKz/u/WojCv8+zIqPETBr15/aX7OekQY9uc9RPQfcqlawEqlBi4G21nReVuFxNOjDyxt2S1Jh6VoMI5Zer4kjB/g1L4Sl8oADACrXZKBJ0ZTBoEGWUsipG3sWWPUtfoZRNPFZM2AFQAAeMT0ca3+rYnT7xeskdxDvd7uVxc7QIAYqBh69Co44TgAjQEhBppnGBd75yqoWctpwYkA40kb3eRfQR0u9GLMRgcQf6MbbY7ZJa6ZAZXpikkbASQkfd1WMLKyk6vOrv2UYbuURwv+bsszYEnVYb+6DtzZrKZe87PfA3ycr4zkJZ02r3qsri2YUq9nX7sj3M5z7jBbEwf72tXPUAWu2na9VC7wh9SYl9HFyMukkHPyXALYk6a3BZhGxFXu6VQgzzvdvk+9NO256ppsYaD2umOLr+zjIk438Y7bfCxUFtG3t9cd76p/wEF98VL7qCEYOIikb+yV55w2WfsHDFWLweXal395v65h8nvecRvXt6eObXlAythGnsIVYBOmL65tBMgYRm4m2oB97KMvNOmTPrEtR18rHmmVSy7ZtMG093YBQL8bF1+8WSTNhB3xW6Ke2gawz7MTTzj1E79zH0nsSgzUVuSpv3Z/S7FM2zSA1fRD7eKCre6u4zBoMSn3lSzAJM71kGGBYYFhgWGBYYFhgWGBy5IF1p/AQ863Be6/HjEGuLQVF/QGazrC/1wHWnr0ehnsZ60pAFzbX7JefvPF62Uwn7v253nqOljiA9Zf/Xe4wx3WYMqnH0h/vfXo4598sQ+5VC2QBRZat/ZtlYq7sQHdpSXqm4HjLqalOtbYmRnEftVX7a69gWNAn7hqY1r2BBARkLXnypg01Q02YBHQMeABkDKr1KdM92rgK3ZYwCED6KSJLXrbCthiz+Z+qi434rgmAxwwfXJ9H4YY3TvdKXd4cICdfGzFFu3VmfsiMaiPPkakUAyOA0pUF+s5xQaUSDrbJXCh6mQfgBFQIedsnYsAs+u1ffbdJ6ZVdHMPOc5W+ZlPSvvJtX22XL0N/PfRBYC07rv7pEu4CWDtkn59rmECRjdtNsdL27Bh2X1JZ+l87xn2dNmrSiZToisfExT1fnJtaQvYb8uXPs9VeYnluJRHzi+1Xfm3ZSTNa16zuaN96h3QLukT7zjHS9tMwvTA/F6aCl7qg0neidbutd5i5gJIsygUgDIuzsoRIzh9lHySNiEilJNV0Nt6hQ3rPFA3jErPXPtOTE79HjAv/WJCgnAJryEYsCfb56Ler33tpq8OQKfMllXqedd7SH39NtT4oZidtX8w+WfChOeFtlUZsMDa2vbYo0oAYh4b+64yjgEMRGfrnot5YpvKs9qnllv3w4b1e1zvs+rss2+ylc20h2333Ob1hjfM6TyPIcMCwwLDAsMCwwLDAsMClyULrD99hhyHBR6zpiX86zW1oQKhZ7N//fUqCX+xjR53HDczynj/wj5nM3BILLzqVntpmLMCOUuxvDSzyrIySEvcTQPcfSQxRzMwNUCug0agosF2QIWb3/zg9baM5IPdWAUTJwP56HBZDXibc7atXq4tgZXYPxHAQpveccuec66CpWFjGgT33LXpByBRlgUxUq9t25aJG5dzwEBdjEkeYtJW4Y4Zu7cgxP9j70zAZCvKNB0KKtCyuDAoDN5GGqVBUbTd2vGWjQuIItM60y6gF3ABYUDbHVGuu92gjYiIonJt0RFHEcEV6aHgikurICIIonARBVERBUUU0Jx8M+ar/CtunMyTt7K4VZXf/zxV5+Q5EXEi3rNkxnf++CMeM3qeUk+8l1nGNLV1RJFvfWt4ujIvdZFnpwSUMk38jAgwqP4xbVzHg1EW74eYpq3wFvOU64gFsiZvxsi4zF9+LmOgaj9ijgwxQ9uHLeO5LEW3Wl68uWUS5GrpmrZxX0QPa8UTLtMjemOrV7dri65llUOIj1JM1r4yLTGOo3iK16fScm3p/mVbvCaZvAljQIbSxyUhP1Qu7W4S8mOecp0YlDLuPbw8yzSImjoO+/Q84iUdIUjwBlUeQmso9qvycP1F4/0qE5Qpj5Y8/0uPdjzv5bl48cX9PAikinkqD9pamJgocOo45f2MF3k8LnE6ebFAeu4peSPDSmUw0ZKM54kE0KZzgDDdxgtS4Vx4wTTIeJ6TlnJ5iaHjMtGVnm/Kj0esXi7StqZ4loidEtwpr2kov8odtuTFI7zKsAbD8rFfoRzmc7RKm3o4jQmYgAmYgAmYgAmMQqD708d2RxH4ZTfAEkPX79N1xRpF/Lxb99fz3l2Xu89piuo7qsI+TiMBYsHRcWByhFHs+utzPsSh9WkIJPIQoh100BBlGXK4YkWnw/Do2AndZ59c2xhDNA7rHNSW6LXJsRAa6RjTceYzf4gwTALB0L4mW7Wqn56Odmld5+mZYeoqt1zGTjT7GE4pAUiTX+hzzMtQSIzzre2Rj7axZLs8tBA98FDFO1MdYIkTyqPtiDIIHJqQif2IHeq4K72WpGeijSgo4z0UxRqlZRmNcAw6LnWUEBLT19ZjGbX9bIsiWlOaQdvhE61JHKydp6Zya2lpd/SeajpOU5lsp4y4Pw7l1XaOrXi75WRiSlMu47XFOuJ1mab2mecSpnNbSxO3SZjiGJoYpsaKPLFOHGdqql8n9mlYcCy/XI8vLqIIGdejl1/5UqKpbuVx4jvC6N2ndAhqut+1TUsJZ7qPaBu2bFm/vaQto9CsixisY2pZi/vLs7mNiK4ytDzggNmhVDRjOwzLSc00S3qvof//n7xEVV5tyQsrruloTARIWrjFZxfHj88q8sRwG4jO8fzq+FwDka2GuJ94Yj4O1zrHYsnw9PiCie8zYsNKtMYbFrGxO8Cm95zFC5SYzkzmVNYttimu67pvCumOt2dT6AAx5LnxmtfM/r7jhRQv7UiDuMuLBsLC4A3LRH7EmJZISlvFIdZt1HVEWo43SjgcHUP35g03aIuXJmACJmACJmACJrDwCXR/+tjWB4E1a9Z0PvGJT3Te3J0C+ZXdgEov6rprPbvrHve87virl3TdLF7T/XV8YvcX/nnnndcVRbqqiG1BEdAwa2J7jWJ0sujk8de2wzVK+aOmJYYXnUd1zMolHTFmdI8m7yHyaYb1uD+uT0/3y0eIKAVIhDeE2BNOiLnq64pZyXGbjEkzEGvLdtQ+q+Neig4StRTigLx0xuNMzIgStTLLbYifMmLGxg6+0jLkX8eUVw37ENOiMESnt9axRnBAWJfh7SXxQMdgSbo4gQjbKDOm0bomFtFnLfE+iqbt67qMInwsA2FO1uTxGNOzHq9jBHaG/Jdpys8S5Epv2TJd7TNiBSbRopZG2zgfigGsbW2WtIlhqpi8r2v5dB7bCtllGUxEI2Oof7mfzwgeTSKxrl+OT5gPxLdaXWLMTeJHxutbx4znUdualrxEqaWXV6na1JS/3I53noz3jdrPvR+fXbSttP3376dXvtoyMmRIuUQo0uo8lvm4TnlpxUzjcV+NH/tJf+GFs2v4wx/OzhvLQRSUkS6+xIjrMY/W2R/DCFDP2osAnl0yRMkY65h40ZjikfJyjZdhnFt9X3K80uNdQ8HZxz1GaBm8ImM8ZNVzaqo59nbv4C3/aVTEN76xdgZedOh5wEtOvlsuuSRP5qTvGcIA6N7Acze+9EM8jWKv6h6XxP7ke3UcpqHseKmOYoqzXb6sGqUMpzUBEzABEzABEzCB9UGg+7PRZgImMCoBTZgR46a1KYMODp0ZOogLxRjKV3olqsNFh5zJdmJcMrxs5NlD55SZ6suQtHjWMXxYgh8dcmYBVrm1JXHqGGLeZBK0EAlrtmZNp0PnMpZNHNAoZGof3qbRmKhJ+7TES6xJYFAaLfFaq7WPji68ZJoMSvmalnihaR8itYZ2IrJEpvLqot16T8KQxNLDS2XFZRQWOf+IGBJg1EGP6bUuR3R5VWl7m6W4qw2D8iBiRK9CiWyD8rAvCi1Nx1EsXtLXBLRhx2B/NHEr80Uv53Jf+bmprggosqc8pX9dlPnbfq6J4+TFo04Wxf5B5cZ2I+bFyWVq9w4vVEphLnpV144VuXANINDG49bysE0vp+LLilqdlF9e3mLAUh53SqNlGQs1DumnbvGYylMuEbRlTaIXIUN0L5bnjTrEMjUkXNu4rg85pNNBaJadcsrsPEp74IGd7iiT5gnLlI6lvNvjNtbjiAAmjYsvc9hPfRDRy3Og+KbUURML8uwlVmZMS/tKY6g43y1lXeJn+OGtGzmU5YzymYmMKL8cLk/5CqXBd1k5o7rCaSDS8iJAgjoTCpXGNU6dn/jE/J1GebxUYKK8KJiW+Ub9zPe26vH977fPTexWGIz6+6f9EZzSBEzABEzABEzABOaHQPcnjM0ETGBUAnhl0THHU22UGFhdp98F03Gg8xMnx6BDg9cKXnkIa1EYKr18mE2dTqo6mqTF8wUhDtEq5sXjSR1ZPE2YRZohh8RB6zo49+JdStwkbZNX6bJl+XiUVdp11/XFMjxk3va2nJZ6I7qpnnHZnX9sxj74wZwmii0xLeuILxIj+ExHmE6/0ql++qylOu4MMRaXKOwpnZaEItBxVEeGQZYCBzNlIwBIeF2xotO5+upOh+2Uhcdt9LJS+eUSsUad6unpfntiOuojARKBR+eTNNoe09fW4zDLplmsmzxCa+VRh5pwj6dltFreUbbJK63Mw7USrdzf5nMUkzQcvZZPx1kX0RkxnOG2tXLLbYiAUcwr98fPnHddB//6r6phfxnTsq6ZsBFXoxjVdhIqypCohNhflq/P8eWBtrFEQMQ0RJu4wYqByP4mi+dI5XGumNhH4pG2154fPNtKsZL08X7g2LV4xSq3XOpa4XmqfYjtEkm5pyXCIjCPGuqhbAdCnJ4/HEf3HZ6bOj5LwpBgcaI9XSMxHet81yCklab7TbOc61mI+B+NZ51e4uDdiVis9sdjUVeGueOZOg475pjcZl4MRtN3Add3KabDX898xY3Fg5R7iOfppZfGku7YdV5gwovwAG2MF3p6MaDvpzb5nMYETMAETMAETMAEFgKB7s8emwmYwLoQkHg4aNbyWC5D3uWNSOf7jjQ8TxEWo4dWnEyDDmTcR90QSBnyLG8rOrIInzI6dUyU0SSAsR3PJnWWGWYbhQ+VwxJBg3hydMTwjlXMxJgmDv+O21lXpxnPPib9wDRZhTrQ6hRLqKQ9eNch3igNHXJ5Y7EfryiJAUpDOXHIMMNmVXZcxglYolCBx9JWW83OI3Eh5i/jyxIPLtYBr60zzsiz3uscSGxAnI5pKZd2UCaiqNrEdnjHmImnnz67bqpTWZ7K1P64lDARt7H+hS/kc8P/YZ5byqs28Vn3nPbF4cTaxpLYqJg8leK+Nut4YGlilUHpEdgRVkohbFAe7Vu1qlfFnlectsUlZesz95AELbbF86c0tSUinUwx+8p0eJqV20b5zLWLx180iUGxHDhJrGdyNAzRKqbReu1aYx9CnIxnhNIPW3I/yPCiIz3nTLGc+dxkPCP04mLQceL5QkTGs0/pm+6HZzwjHxV+CIKkH3RuSSMhjbx45tde8PAigZnPFU+VNLzkiPFQm2Kgwl7PE+ojT0dYlXUjJjSe42on3xXPelb/M8LvoPuI6xpxPoqTPIvw3I9lss5keXyHEiuzGzloJhwDbYqe1pxXXgLFF3Tkp0zyztXwTOVe5JqQmEmZ8prmmVya7gfqEE1hLeLEYnH/HbHOy1wJx2996+Aj8p2ve5a2RGF9cE7vNQETMAETMAETMIGFQaD7s9BmAiZQEsCrEw/FH/1o9vDvmO7MM3MnDW8czYIb95frRx6Z0+PFxcQBDPcjptm+++bJhxDiapP7lOW0/UzHlI64BEx1KOngyuOSbXH26FrZdBolYjbFCmN2ezxJEP1Y0jaM2WU5Rhzumfes/R/BVV5QdKJLQ3xVG+KwQeJssh1xQHE9yUs8UARM5dESsVTDbiVa0LGXF1v0TkRIK4dyMvy/NAnbOgbLmsBHp1wiQhQjGDpZE6iYDV7WdgIdPKV0DMQpxZ6jTnjoYuqsq74ICPDHNNxe+2pLriGxYz/s2ngZIhpwb9W86mrHkacb+w49NNcPT9paWrbJawwBpxRexKQpr7YrZAGCUXnuScN5bVuWyiyXiLrRoriptG1EN6VlWYrqbEMAizZqmZShe5918iu8QmSAEK0QDByPtOUfzzp5X3K/rFo1O02cDXxQPf/lX3KLYvryWPEzw8BLUyiBeH2VafjMcH0JjrzoaKpXZCFRk/xlSA7Vi3ABWuclk16AUD4ir/Y1LRGfomgYX2aRB6EZD0NibXKvUr9Yx/Ja4XqECec2psPTL8aq/tCHZteNZxYie/k8oQ7xGHq2kXb16k7nIx/JHvQ6Fi82otfkmjWzBX/KI20UZtmGB6rOId91xFPVc0wvwGJ4El72XHEFZ2Zupsmbli3rdBCB+Z1AfXi+6/g6At9Pur+JHc4QeO4DnrmqO89ShPnIWvnviCXH1rnAU7v2ApLfOBLNEUzH+Vvljmijj2ECJmACJmACJmACEOj+ZLOZgAlAgI7Lv/97nsSDTiMdGv7olNJBY9ZVDRUWMU2eggDATM01wyNRw8woCzFwkABEXC2GPM/FahNylEIobaOT2samp/s8JG4Oy8fwdo6B2Pr73w9LnffjhYroRGeMIY6lRUFKYqfaitBXmsQancvaEm8pvCujaaILmJWiR9n+GJMwikXlsRC6tX/lytzZVQe4TMtkRTpfdE6JzxbT0PGOn+O6OrIItfLM23PPfno8pHRcJgtSejxfo2jBdSihiPLx0JUYpHawPQ45L72uVC9EKK2XS84j8e20nWHOWtcSr9VoMb3SsFRb4ram9aah3nhryRBrm/K33R49gWMeRA9Zk4dgTM+6rgnWuUbKa7NMz2dEYYwZsWv7y22UKY7UCy+vJlZcL3pWUh+8JWN5TcPRYxrW5WWIN325j8/ly4QovktcquXTNq7z6OWM2KZ4jUqDWMj9QjpEOmI2SnCr3W/Uietd7Vc5CPQyRMpaWAd4Rs9m8lKOPIl1XLYzNFmenDovpOW7KlpZD9WnXOoehmGMSawJhsqXOdHbnWdfWV7tsyYh4lri5QhekzqHRxzRr/W3v93piC3PGgRAvi81tJ7rT96JOg4MEFR5Hsnzk2drObpAz37ERjxl9SzjvJciZb9G7dY4r/JG55wphnT02udaiiMoGMEw6Huf9sF+UBzsdrVbt1Sf+lT/e4HzxncgXHkZKc9t6ghzzpvNBEzABEzABEzABBYjge7PGZsJmABiW+z40TnFg4zOa+xg03nCO0VGp0ux7egcsB9xi84ps6fTicbrhH2UGb2W8NpBtMPLkLSIqRoGiUAVO9I6XptlFLvorBDfMnb45L1CnfjDS7PJ6JDiSUrHV/EHB6WP5cgjUB3ruG/QuobCE0eutDhjMp00vFDk6Vd6pESvKHFVm+OS84J3YWRE51VeWTEt69He+Ma+AMI5w8OpTF9+plMp4/qRqBHTkUZiUG1/TAuHmvgRO+Mcr1Y36ixhIpaJF6hM2+FUDvdH3C4tiqPK27SUpxEvFtROiUAxDyJzNAQspY/pBq0jHsYhz4PSIt5jox6DMvEwxgaVr30MJ9X1q21tlng2y5rSx5ARZTu4N+UFjdjG9aZy9LxD3Iz3RO06QQAcRYzGg7ysCy81ojdjFOVUp6Zl7bonbU3s5Vq/8kpRyxMENZWr7ZSvlyFsg4nEV+47hg9jsR60j0nyMK7rNvcD4rHCZPB8RTCMzx+dE+ogb0qu53POycdRTEb2x3xqh5YS2vlO0ks2TepDmhUr+s8DeOm4ePgT+1LnDg6DxDyli8/wz3++L9jj6a9Jd3gG6MXJK17R6Ug05xjxGa42lEvEYbz9o/H85vyQFq9VDA9lMcYDda52442dzlOf2r9vOBbPLmKDIo5GETtOukbsV8RGzjFir9JJzOVlKmL8+jB+2+CdHF80ijffoYQhgK3NBEzABEzABEzABBYrge5PNpsJTDYBxE952DAsGfEyDufEe/FjH+t3nuisxY409JhBOQ4rV6dBSzpAilNJx1EzaZfkEXaY9Zt8CE4SYsp0TZ+POqrfIcMzsmZ0cik/ChpxSDl58JhhIiF1ytQOLekklQzKYzEUlPSnnlruGfyZIYLk04zhZWp506ouWjJUH+EBjx91ftkX15WWc6COZ9zGcEbZD34wW9hQuqmpTgePMs6PtiE6SHhAOGA7IkDNY4rrJwp6dN5VzihLhsrKq41wDeSNQgyf8cSKRuy86EVYHg9WhGqIJtGAtGXeKHCQp614VQ6ZJu9rX1vnQGc8GnH34rmjrLId5WfqrXu6ySOTPOKnZVnOsM94SsmaxNaawNtUroSkcj/eujJEuHL/oM8Io1icAEiTgMV8vGwoLXKPact11ZvrKb704b6riSuRW5uJuzieXsiwzgsMJlcr68F5J6at6o2Ig6hLrMOYtunZrXuatLxAwLh3lZf2IabpM6Id64ieiFhR/OR5KC5Kr6XuK4bMy1sez+haes694rZyLSkvZcVnksquLaMXJm2KL/+UXl6WfI7P0Ngm9vHdieAnYVb5WZahTIgBHO9XXjDyUoUh/TEfgq5eHNJG3Y98hyIaIvzG7y9ExyjWa6g21xrbESv5bpBYzzN8HMYLwlNO6U8KFNvAeaFtuta4BstJgxBBycN9wXeuXv5xPTKx3/oy6jI9nV/O0j7CKZSjX9ZX3XxcEzABEzABEzABE5gLge5PL5sJTC4BOkfq/CGcxThkJRU6XvKawmOn7BC84x31zj0dHHUgGc6HsDbMNKSODmI5tK8pL21R55Thy03GPupEJ1ETVtBZk0DEELzoUbRsWY6lqU6lOnl0Lolp1mSKZXfWWU0p6tsVZw4huMmi0Kv61JZREEC0VBrOB51mPuORo/ODeClR8eyz++mVr1ySD54SShCG6WzLSyx29sm79da5TIRlOricM64llVsTPMp9pCmFcURm0kkMiWIE3lulRVGTIbESpaKwFvOIj+qipdIw9FkMtI/rRuu1JWJR6eVcepiSj7odd1z2kG4SqmL58iaL26hbvLcRQOL+UdcRZPTSpMwr7z/YDBr6X+bTZ1iLJee6ib1iWxIyQAKRymhalveivILLY3D80ng+qlx5BupzXIo/16KGyur6iunKdZ55hG3QduokoY9tTbxJIyuHm8MF71LqoTbqHtFx2iw5D3o+cixeeNXy4Ulbu4cljCoPaXjBJGFW23nuxonmYjxipWlaxnYx+RAT7VFnvWAp61VOxHfwwX1GTceIzw3qGodrN3lrMlFeNJ57CNbxGRWPB0N9//AdgsehRETSrViRPW9Z5zmrcvTiJr7goKyml3hcKzWRP9a17TrXmO7Z9743C7TE+UZI1DO/fFlE2ZwD2qEZ2PlNoe9NvJhtJmACJmACJmACJmAC4yXQ/ellM4HJJaBZovH8jAJJExFEUHWy8ZrC6PyU8eTwXqETR+dLHW86OgiUbQRQOkKKu1XGestHXfu/vLjo9MfOeplSEw0xzJn4fhIJGc6NB6QEDIS56el+bsROtSEOmf3EJ/pp4to+++T0xE4dxagHx4nCRi0/dZeAQ/r4R6eYjre2yZtUsem0nSXiCN5aEiPwQCu9EaMYimCIeEqsSM49Airl0F4Z5cXzruPhpab4dngFq/PO/kGikvKzPPZYHaW/1GRPEsLwPJUnKqJ7aYh0KhNxMYriZVp9rrVHZZRLpY1eWtwPEmkkCCHKxJibiMdlWbXPKkf7JBrwWeKJ9mlJXWRtvQyjaISwgmCi8pqW5OHaZMiw0pT11fZyyfWAlTFfY7pYJzzKxJo0Et5j+rjOc0lDjzkOEz7VwkPwjMNoByJNTIOXYqwD11nNqxWBL1qsZ6xT03XPvYUpLfd6vJ7Yzr1Y2v3u18+jvG2W3AORTelRynNSdeKYeMUN481512RniIdcP2ovIqWeOdRPL694UcP3DMYEdcPqzrmIky2VM3nrWlJd5VlJPsKbROPlT3xpA2/F4MR7UucdMbw0PW9UX16maP1b3+qnpm3EwdazSmnKJRMrISDKFMOTdLoe8S6ens7Hobz4PI1iOnlgLZEyHkvio46zrkvFon7uc/sl8N3NsRCHS+M7WvFYY/xwJlTiuuF643loMwETMAETMAETMAETGB+B7k8zmwlMLoHly3MHpa3ICCkEPzo1GhIpoZJteLDJg1BUf/jDnF6dLryhNIGP0tSW8jKK8f5q6bRNHlpM/jDIiONFXTSbucQvRFzN2s4wWSaAiYbnDfkQdTHFqKQ9ceh43puFLdIjFo5i8iRi6GobYzgnx3ne82anVsd9amr29lIgo2PMRDGaQZyy4h+evZiECzraMjr2pGUIYymgq2Mey0LkKEUc9ktYYF3ecpzPM86YXReVde65qkGn86Uv5TQIHOrgM9Q9zv7OUMvSqLOOLe9YYtrVLA77VR1qS9omoQUvL4T8WjpEV8UURBhDkNUwfqWXEK/PWnLdSjgir14oICgpTVzGodIIpRJPSKNzGtPX1uO1hXhTS7PXXv3tUfCM57aWL26Lzw4mBor7tN4k+mt/XOpeittYZ1irrEl0LtnQDnkAco2qvXi4ffWra9eVa50XBzJCF5T1qH3mGoqmFzakLVlyDUUjLEeT0BqPFdNwvUbuKo+J0cpjci2VVnupomPpfkRQ1jMfj2ztj0s8MCXM4/0X40vifc/zWOeEoeAazh09yGsvjd70pnw8eSLyvNC9z9Dz0uRNTt14OYHnIuvypNT3RsyHkBrbwjriubzAeb5gPIf0rOEeYQQCIQTwTOY5TT6dY9rKi5xomnQwHkvptY3Pevazje+0GHJEXuQx9m75HRGP2Xad70A9uwnjgmlIOzG+o+GVq+uaESgMpY+ml2qjho+JZXjdBEzABEzABEzABExgbQLdn4c2E5hMAng/IWbhaTHIY7KkQ0eUjg4dLUQedbxqQ9zIqw4lgpa8LdUhLMuOnyUIIey0MU0ewTDsQcZM66ozwpMEUXnP0FGueZ6oU7nffv3S8XahrLhNexnKiVAFY4ZIt7Ezz8zlUZdSgG3Kv2ZNFgYQNTQpiDz1OEcSHpSf8156B4lHXJI3DmdetarPTedE4uvLXqbS+0uJDJRJWbFsreNtJc8vtiF6E9NRHnUSpJVeS5giLiIwyWML8aMmrpIH4Sa2hVoizJf1qgkopTesPMhUl9oScUxWm9UdIRKTSFvWQ0KCyqZdCD26RrW99Har1Y2yJJgqH8vymNoXBVJtw4svmsQk7W9aRiGUc6Z0cbu2saSeuu55JsV9TesS2djPPcAQXKWNbWRCtvhZ3o5RaK7Vizx4VSpcBkPREZQQbeTNXPKQkMc9wPWMt6S8fpuuadUZ/njBRWu6ruOELFynsX1xXWXXluIdj8f6u96VOfKswINceRVSgueKREHtg3/kqe2lByAvALRPS+5RZofXZy3JS6gMbMWKvJ/Jsy69NK/rhQPHLQ32etZpMificPIsUPm/+EWZq3+/wJAXQKTV9Uv4kdLidaNQBcTWVgxkysHzU/FguT6of7Toxa3vGp6NCqOgtAy7b3oJAIs4CeAHPqBcecmziLpQLkwVaob2lc+S2TnbfdIoCcrjXGnSqdiGr32tH3aH++OKK9YuWxMV1livndpbTMAETMAETMAETMAE2hLo/kyzmcBkEqDjQUcFr6ZRTZM2qOMXh72VZdEJ4zh49TD5Eev8TU+XKWd/psNIOjqDbUxD85smP4plyNsRsUGdLQlxtSHW6njTwadTjUBKRw7PFurI8Ee8WkrThEXwqnlZxfQImfLOoeOHVxD56RjjJUPH9vWv73QuvDDmyuvEf6MetGf16k5HwxERbqLBVEMp8QDCs6omjuF9VLPoPYrwJO9ZZvWV4Z0pTy3qJAGC9UF/q1aphNyGckgpeRkqqTIQGiRKIAjo/Gk/QlMpAiGcRMEHL1ulZ0l5DMtEbGSp8pWmLI/tzNxMvNboaUk+JgGSIYarjGFLtQMxU3Fnue4QERAtJA5TDucuCrvydmt7DNL9/d/3Pbf4rHu6LIOQEVgUw8o08bOGPrMNoQzBWs+NmK5cJ22coIj9TZ6c7NM5gpE8fRE7y3IZvs1LFZ1DxCLlJS3Xm4SnMq8+kxdvTxkiYPSAVzo8M+XFxj2j4zQJmcoXl3gxy/Curt2n3AOI4mpTzK91BHDu05e+dPZ9qf1NHvN6tsEEdhLlly3LoQN0X3Psqak+76ZrkPtRFidOUj3KJddhKcrhZajngp5xul4V5kPH4Hmr6wZvUjxMOYY82nUuVq5Ujv5SL+3KOvGZF0gyxMh4XphMT56rCn2ic6/J/bgeonBNWQx1V7s4BhNGqb6MTCg9JMmj2N18X11/fW4b50rXAoJ4ae9+d06H96VM3wc8syU0a9+6LJkwUc8w8YMJIQH0Hc12fndcfHH9CJwT0hAOxmYCJmACJmACJmACJjA+At2fWDYTmEwC8rBECBvVopBBB48OcpNpyCOdOkzDSBkuP8gYzkkniA53GyOmJOnx9BlmeH8hmJBenTV1VMvh7FFMIQ4i3n3yZCW//hBVoxBIHZjRWF4wcMYzs2YwkucU9UdsUrm15T/+YxZiVRYdV8Wcox3yQkJ0YB/iKp1feaohrtW8UiVQ074mUxmxXohJCCJiqH3xs3hzHRB3Tmm0LL3EFNNV+1nqXGkb4oM68GxD4JQgwczMEm04NxI88ETWxFqIJFGsVbnlknMjYQFPPoa86jP58ayS56EEGerBNYy1jcsob1bq+5Of5HMnj0zKw3tN5UvEhzuivIR86g5LpSvbEj/HlwVxe1wnHIQ+l9d9HEarNOWScxYtCj0xrcT0uI316OHJZ9qFx3WZjqHYUcCpDc3WOSvz6pphu64ZzjNeeoRh0D3MfsR2XiTIuOfL8srP1FkvN9gX7ws+634vPXxPOCEfhfNblln7XHpdl0OoS5Yqgxc8pSkWsdLEpe4l7mkJ8JogL6ZjPW7H45lydR4QxGL8zjIvL3/i8G3qCG/lJ73W9cKBoeekUUgIlgjVetmB9yXXibx0eabXTC+vyjqxnWeOGGg/IjDGixy2IYwj+stDVdcVL6hK+5d/yXk0ioF4yrxQ47uPsj7/+TJHfyIq7kF5mYsFdSvt2mv7L094zssQX3U9IkyPw/htwUtRPfPFiCX3FQLnoFEneuFYerCOo24uwwRMwARMwARMwAQmmUD355jNBCaTAJ5wdFAQNRDq2hoeMOrMqaM3KK+GKuK5RsdTw+5qQxZjOepI4j3SxjTpCmLGIEFWZdUENngwey2eKYcf3unEIcV4BKqDiaCByISQF9PAA4E0CiR0POWJxH463nj3vOENeWZvCcLsY4ikRD48Cuko4gmK9x9CDJ1vdajxlIsiJmwZkh7FHMosO6EIOAiiNSM2Ink4v4NMHVTS1v7gFD3EmMxEQjjtqwlYlCORnLiWGjJcE0t1TIbpSuhj+Dym60BpWDK0HW8tDdkl7AEipgRHyiDOpcqKeVnXduqEwIJ96EP9tsNYIhYij0Q+zqHOZ1nmoM94UckQUeP9pnzE4tQxo4jLfSVBQ2kRknTdaBt1jpPAtBXZlF9M4vUWX4woHdcCk61F075hS50fhCA8GJVe8Tf1WUu8cWUasq59TUsJYYhG3GPnndc/DiI6puHTEjERVzG8ndV+zrWYlMfSvcB2BG2M50uZDkGa4e96zrBfz0uljR7A2lZbxtnUOR7PtJgOb8F4Xem65tmp+ySmr60/5zmU3DexjGlJU/OSRezVdar2cv/rvGkffLnXomlynXgchs/zEiyy5rlLyBOMdkkUJbanhqPHcBU5Zf+/vHjjcWrrXJsyXq7I05EQMfGlQc3bFm9nPSMUdkDespoEqpxJnmMhrlIe9VHoAF1/vAyLBgNNVEj81NIk8POyY5ym8CG83OD5Txzc+JKidiz46dnf5CFay+dtJmACJmACJmACJmACwwl0fzraTGByCchrshQoBhH55Cdzp0veRHjDDDN5q3Gck0/O+RGJmowhf+qwlZ3fpjx0cCVGtJ14SMMVa51abaNzro49nXLEM4YcyuSBeNhhfTGUoctRhMWb51//tT/5hsrWEjEAQUQdYQTSONRSx2KJUKXYg3Toy6H1hDbQ8EjKR+jCIxQmp50WS1p7XZNskH6Y4Y1Yimpqj8QLPscJZ2LMWKVlydBRfUbwkXcVHXI6+hKeEMToQEswkXgchS/qjWCg8lhSDrN5M/yVvMrPPvhEwYfr7sADc50QY2IbEUmpj0yCRTwWIoSuw7i9XMcrlOHCTZMKRREUDzbdQ2U58bPuGdoHEw3ppo3yMI7pYww+hNbIJaaL65xbCbz77ptDCsT9cZ3j8hkehIzgmo77B60rziHxgvFww5oYILxKaMLTT4IQ5YvBoGOx7z/+Ix+D/1GcRLBUGd/8Zv8eRhQVL44dr3nK077yuBIaOU58MaJ0lKXjaVvTkmMwnBnBLIqZ8I7iNseSWKZrk3sphmZAGOPaLsvh2LTtM5+pnzvCb8jkQd5UX23n+SQ+PBO4N9nHc1DekNx7el6QVjOF81yVV2mTZzUiMeJbvFepo9rAPSpxnWPU7G1vy3XifCgUheqvJSx51qxZM7sEYr7GlxJKH4VSnmMf/nD/XqIeeEWSD968OJMYT5iKmuElHj3ydQ0inGJ8V/CM0ks6ht+X3xek47uBOvI9N07jBR3PANrE86WN6QUWnsF3lHFPcs74bcMkj4S54DvbZgImYAImYAImYAJLjUD3J5/NBCaXwKpVueODkFab+KckQwdNcSC1nJoqU639GTGHDhYdsZUr83ptmJ5yKg2eX6N0RDShEceijEGGF5A6pogs8p5hGx1u6oe4SSeVbXR2mRk5GkKthpvj6UonjzqTniHJ0c4/v9NpM2QYQWyYwUQCUa0DTz3UNjyM2pqG4A6K6UrHnUmPVD5LOrnUA9FN4izbEQ+YKV4GL0TEmJd1POpKwQDPMcXrxLtTeYiNKGGNbZyn8trlOpXAony1JaKghEEEolhX1Xn58nxslVfyZrinznntGBKctA/Ps2i6vmqiV0zHusQBlRWXOg71ZAZ1jE59TBPX5UWJkMcEZmofaRDPYloJnmzTkPi99+7HJmSYckyvdQRWidsSZ7RP9dVnzqmG+2sby3IYt8TwmAaPsZo4Gq8T0lMfznONNXFfoxF/MjIhPy8oynZEgZz9ygMfvVCIdcWTPHrBRcExpivX1RbuKYYGix/tprw4Gzp5KRfhHIvhEfTSi+cdJo9J7r8YtxWxDrFR517HU70iBzz7OE/ahjAZXyooD0tenOle53rGeC4ohAftI53if+qa55whCCIGsp/7VsJdLD+u891UehHqu0XpiHWqkBg8bxBx4z0fn2fkoe4IzvGep84KV9BrUPcfw9bFQ8dCuOV7h3il8hJlHyKqxFrNnM61gyc8+znHTUY4l/jdRXrOWXkvEXKmJn5SrtjjCTpuY1JC6sT12XR8HXN6un9t1Ib9K924lrzI5Fzoe4966o/rjbjl8iAe1zFdjgmYgAmYgAmYgAmsTwLdnzs2E5hcAnScNRSQYbKlkBTJICrRiaKDgHcGnQPW6YgOMzq4ElzU+aUDWBodUA35pPPI8O9RLXo/Uk86zdEQIqPn1Q475L0aYlsKkIpRhydNaV/6UmaAgCxDDECsoJ0ahkonW8IIzBCCESIQWPG6kbjBPkRS4rINMzrHMKLzXU6qQV4N90SgjYJLU7kSNalnGQc15pGgRfsQJ1asyAzgTiiF6FVGexBYmHEdu+66vsgi703SlH8I86U1Df0tY/jh9STRhnIZfhv5xmMpZiIiRxNzCTmILfLQxfu1NK5VykYQwytP/KPAhbBSGkIZ+ST86L5iGx1zmUSKWP/aOueFe1UWxeOYHnGoFE7YL/FU9WdbOTwevnioRcODMpY/aF1iGqwiH92DMS/HiiI+9Y77m9ajMImAxERCMnlDxnuScngGRuNFggS32nEYIq0yuI8RDAm3oLQS1/VZafG25HqLIQN4Dmu/0mspD/T4TInDwBGF1V6W4kuZxLxVOUykhmCmzwhO8mDXNpbxGUj4jVLII82//Vv/vorXEedToirpaBOCYdzGdnmgijc8okc+AiNe9XjO84xWWSzxekRIZj0e+53vzF6efIfoeQEPntPRotBLGTwfqE/kT36FPuAa5IUMafnDYxXjuRbDeVBXmWaAV57aknowc3o0QnXoWaAJx/iuqBnPW51fXuJpVIaORduJF12+uCvL0nXYNtxMmX/QZ35TqHye+bVnJ+cej1u1m++i+TY80jXUHl7wQ3wmjrSeD2znuiivn/mum8s3ARMwARMwARMwgfki0P15YzOBySaA2KWOAOIbHTJ53kGGYasMY1YnFPEOrzd5p9BJYGjoMCMuZvSmwUsIbxji7jHsDGFNw/noCI0yLL88toZIqiNIJzkKA9pOB1dDUvHgJB3igdrDjOukpV7yEtKxaI86dqU4Ks8kRAI6fDoe7OIs0ioLr0KlYUm5bQzBhvTwKw3RSB16RMCmIfXkIx6pjl/G9Yvlnn12Tsf5QTzBiJkqbya8fBS3DrFQoh2CM3kRNTgOHcymSW/YjyCBV1QUdhGwotih+hL3FEPYidcX+yVYKi31RkTSZy0RTppM3kGkkTcaQ81L4zqSKEOYB3k+6RgsS8PjlHNEPSXaIl5KwFKeGIsw7kPYU8zEeBzaqBAMcGsSgGMexOA4bBrvQe0vuXN9lyZPaOVpWuq8cy1wz1G/YWl5PuE9Jq9n0sNBw5j5jPCm8x3FKsXrjPWNoivXhGKpUk58YYIAWj5LanXF2y6+ZJCXbEzLeY4zpEdRUUOWYx1Zj/lZj5PXsP+oo/r3uNJyruSdr20suYZk4oT3dOmlynVYWi3UQyxb68RB1bWGyCt2iKw67/Fa4juAlyJY9AKt3aM6BvXVdxGhDjhH8qwnn65hns+6B3n2X3RRPk4MOcFLuej9z7VAmQiZK1dm/jyzNRGTXpzpXOYSc1gX2s0fcWF54SUOiH6aKIvrhGc9oTj4HojXjMpiGeML0+4DDoh78zoT6kkY597DU1EvD7m+h3lbqkTSqa4MAZ8PK+Ngc94RGzmXXBtRuMVbuYnLuOrGS0o9AxCQdc3E8i+/PI9MgD/XbAyREdN53QRMwARMwARMwAQWE4HuTxubCZjAmjWzvR7oICOu8KeOAh0BvCERP2XyEkOgaNNpkReoOrO1JR1QCZA6zrosEQtUv/I4dNRLzxuOwRBu0uK5xfDhY4/Nn+moRaPTODWV99EJZUh6NARJykF8kbBFp09ia0zLugQTdWDJy2zJw0yddJY1o6OttiMO0PnX+cPr5rjj+qIk6RADBhmTwpCuFGyIOyoxWMfjGiKeH+KDtukYGoKsjjfbFVuWdYkkCDkIHDLNdhzLa1pX2SwRQvBixDS8W+dF+WFRM4maTECFCI4gQh55tcY8zKLMPtonsUTlr1gRU+ZZ3rkm2P+a1/SHDHPNRmEvxh+EBxYFvigWqa6UifCCKIyoozqUHonazhLmeC9GwwMvptH60Uf3U+HpFkVZnhEIDEqLKE5MWz5zz3A+qDN1kzV5qb773X1xXeWxRIDi+o1ekMwCrpnL1Wa8DkvTRF8qD1EKE0dedmB4PupcIxSSnnbyXImCKds5HrFHo9VESB1TS/LRhpoh/isdS9WrTMuzsuk5Rz6EVl40RYu8JYbqWNwr0XjRAW/2037dm0qvJS8f5OWJiIWgefXV/RAT8Tikk2ctwihiJZ53lMW1z/0Q7wEdg6XuW4Zra/I+7m2dP64v7lW98JGHJul5vqksvotkfHchxFNnDPFfz6gofPH8XrasXwbXx0EH5e8SvfSK9wIvFfCAZHg7Xrocu7xOeges/Hvve/vHgT8v67jvmRQu1oHnLhNnYVHM5AVSG9MzHRESg8EvfpGfb/EFVN677v95IcP3aXxG6VzQPrxczzln3ctvm5PvankAM+mYQg805Vf4CM71OHk0Hc/bTcAETMAETMAETGA+CXR/jtpMwAQgQEeAeIQMV5R4RAdFnS/izpUCHp1vdY7xwhk0hD6KNgh9dPwZ7ktHlM4v4gvD0tQJHddZQSj5yldy3DxEkkF1hIGGYdLRV1w94k7SdrxC8A7S8EqGR0eBTnXGsw928opDhFA8PqWJSw21hCdDFslb88SKeVhHICJtnFyjTHPSSc2iBXn1x/DYQfazn+VzjaAZPYSVBxGDGKwSn1SulggDDOmUsMA5x/tY+6PQzuQZCs2AaEHZnJso4EUPOpXBko68xBPKjEIbdWWIN+kk1qgzzHUscVhtYikvtgc+MG+VUNMkXMnrLNaJdRntwENUbeEaq82SXeaHezR5f8V0hCHQ9Ru3N63rXMT98KCOMrU3poE951n3vvZJoCWvBC+OIc9hXjyQtoyjSvp99ulfCyqvtqTc6I2t64b7VUOGlQ/O0RCIdN1QL65J2sAzQkIHefFY073L8wnhA7GEfVwH8jjks6537ld5CmoCHUS5plAP5JNIF+uo9eipyHEGxeXlZY2uedKWf4QukQekyi+9pZWHZyTGy4YyDV7/iIukhTvCoa4h8eI5Ep/hPNN0r+kYy5bl8CaqswRmyorXNeIeHPBQlye2yuAFA97nMoaOqzzScF7Jo9ENyseyDJuhMrQknifp8E4sjTYT4iKWV1svJ8KToIkQyuRFg4xjPOMZ+Rh81+heisfhXuIFU/ksjiE0hr1Ei9/JeNKvWLF27FCuXwRpTUQ2qN5t93Gf8CICIR5B+I6Ms6mXmnjxx+dIU925luWBr9EGTWm93QRMwARMwARMwAQWOoHuT1ybCZhASYBhuAxP5Q/xaZBJhKNzhgiBJwyCI4If8TYRJWLHlM74QjbEH8SQ0tOpFHsYOlfGQVS7GN4ID3kl0WEeZPKMZZZi2OtYDKccZJr0CcFlkCHwIGRJLIodacQHyhkkxlA2M8iTb1icuBgDlA62vAgZJq/JRRC7ZBpWrjohUGFwUPxVvMSiZ1UphihvuUR4Kw2BhnQqA9FT63j2lRZDGHA9i3mM9xfz0KlWXL5YHzzR8AiWVyH7OJ7EDcUaRRhbvTrXMebnetRM2BwPkUQiakxXruta0vY4Q7pER8TV6JnFukzeycpfW3KMUmyJw3j1wkBCF6JYNIYMq9x4nrVNS8RwXkJEi+enbGtMh+emxE/K496SF+Kpp+aQATqOXkIQYkJiMN6GMTagXhJxjWoYOV6Gupepi4YUq9ypqTyUls+0c5DpulDe17++nprnb2wX6Tl/8kAWE55FTEYXLXpE6ji0S23TNspHZMV4rms/17TETdWhnHSIPMuW9c+vyiyXCIaqD+nPPJOcfYvXp0RnnidRxOIl1Qte0H/ulsfgOq/FoOwfJa/Jm3PVqnJP/zMiHmKt2h2PhWf3pz/dT8sa1xFhAUiHN2/0LI0peX4q1jbPJbw7ufbwjmTUAs9hvNijyBzzs851qPog2Me68NzgZaY8UknHiwudUz7zXGGb7le2sa7ruTzeYvqs+wKObY0XszDg5ZDNBEzABEzABEzABBYzge5PGpsJmMBcCRDjTGIfHYXaHx3xmufXXI89X/kRWiT+0MlFgKITjmg0bFIEefuoUxk7oLX6auguwykxeVMhHg8yeaiWYkGZhxl1VWbt3LANjyKE6yaTSHXggU0p+tslVuBloxiPivNG57oU1fGwLevF9SShI+6LwrQ66EwkxPliNvMooHFMhnPKmAyEsvA0QxhgHQ8khR7gGi29nMmr9iD2MLSUfByrZni1qt41wZm8nIuVK/vhATjvDBFFvGF/9DDks0QWRDFEWMQUzgP7EEmi1xyg/96ZAABAAElEQVTb9Mf1pzqwrlh38npVOsU0jR5hlB89U/Gk0/WsfHEJU0TPaLp/lE7tQNzGiLepeI6kofzSY4/rJR6X83vkkbMFoLe8pd9mHYsl3rhTU30vRe1DdMfkDYYQjTCk/VwHXH/lZGBcy/JiVVrOCR7csY5cl3H2+ig2SRjmOm4yrgeVL4ERr0VEM8QYvXjBY5901Pe1r+3HUGYb55QlYUx0rVBHxRHFY1LHGLTEC1MisOobvVM5tvLLS1rpYBrjthKzVPcGIp3CO3ANywOXFyXxnqUshtJzDOXlpYruSZ4dNSMGMseDiybXQoBsY4yC4Hhx8q2YDwFfLyxIx/mmPfHlBtt5ccNM4zKuH3kTsh82PPu5JoizyjmW8M11L9FZ+UdZlvce5yk+Ozk+f7r/uK+YfEjD6TkWIisMJOqTHq/NxWoMf4cD19GwF36xjXDQSxxGQthMwARMwARMwARMYLES6P6cs5mACYyDAMIRHSg6hupo0eGmA49HlWZEH8ex7qgyGJpHh4nhrZqso82xo5cdncba0OpYDl5TOg4dLAlatclmlE9iHp1uxLMmo3MtcYYON148CAyIGsTOw6uIodPUE4GqHDascklHGjxJh1mcAVkeoBoqiyBRWm0mao4V/8ivTijXF8cgDqeEVUIo0FFVW7XkesTwqpSYVHolx+PAk+GZ0RAOVZ6WNbGYfBJpENA0w3ssX+sqJ/JkOKs8lJSOJW1QHE0EKV1fiCXyEl62LPNCnGfCppifdQlftItzT3zCmAZRFNFEAkzcpzbFbcTsQ7BBYOPa1b4yhmQpxJAuplc+eIkJ7ZNXLt5qEmYRjySirlhBS7IhtClkgsqrLbmPp6eVK4sgEtIkkikfsRZLU4xXrj8EKqWNS84DQ8WjcZ/FNKxzPdcsip8wUf3K/IjTes4ihGKIOiVbhlJjGpZPzFMJoiqzFO60XUvORTmTuK5BpWGpIeM8NxE0dW+yj88Yk9/wWferXljQVup+wQU5XfxPWvIoriqTy8lLGo6DwppQjkKSUOc2JmG2NmEdor3uEYRvngMSiDUBHB6cEr25P2LsSL4n8R7Xs4h2lX+EOxjH9yUTJXGd6L7ScbhuqDvPYrYh4MJzkGmkB+eL+LiL0RRyhXt0VNP1zrVkMwETMAETMAETMIHFSqD7089mAiZgAs0ENBx2UAy+mPuDH8ydSoY5SqyJoktMG9clDND51uQ4eFrVjBhy8uiUN1stHZ11iSRMiNRkCIcrV+Z6I0TUOria2KbtMEDFzlSnG+GF9SikIJTEYcWHH57FXGLbIa4hQiG0kS8KaVF8ZPiy2qghzaQnjcQgPLoktGjGetWLJW2On1lHUIvCMuEJopCAwEH9iLnHsPooKCIY1gQOvGjx9NLEIxwHzzFN0MT5YbhrWReOW3qFIozFoeQKJYBI9Na3zi4Dsa40CejlscrPEp3Zzozz8pqkvYTIwBDUJaKTjjABGHED5WmL+FiWzWfaRqxJhicrL9ejJhLjvCFYsA+RBlFHYjqiDKKfhvJTV7xDdRzuP0Q4RKimmK3cIzWBF+9BGR7LxEHU9cSzIIb10PG0pH6IYQizMiYi0n6WXLMI1XjDInYRY7S8DktmvMDgGo/H5jzE6wcP4Xgc1hHsSBev3zJN/IyXM+eQe0BtZj/XbjTdUzFvuc4xYz6eLZRJnfBwZL+OUQsVguBEWtLoJYCur6mp3NbyhUWsI+tnnJHTDYtzrHycX9rB/RqNmKMSvgl7IOFTaXQdEkOUcyIPfV6EROO6KIehR2685OD65z4YlyH+TU/3420iyuolQ/RWHnQ8xdjlfl2MxnMKzrXn4bD26KVkGVN6WD7vNwETMAETMAETMIGFRKD7U8hmAiZgAs0EGFotcQYP1yhqlLmiByCT7cizCpFsmNE5k/CpznBNtCGGnbyLEBnLTriOQ+dZs1Uj3rQxeWsi0JVGeRr2OSwEgPJqcie1hyVefAiHDL2U8MF2BKCaSdiTGFSLIXvWWX1hQseKAjTbEMMQwrQfhhKSCFkgYYN8SsP5kOGdGYeEK025RNCUsEBdCT8Q0zAMk1nU2aYh/HgXaUhmFKlqwhz5GNZeij6ItRr2rHbpuExcEw1vUImITDCD1yZDz2EszuSN7UVUxbgO5NmKyItXpO4JTQ7DeV21qi9cIuhoWLHqVFuWAhV1UzrOH6E2uE9Ysh1hTB6StAcxE2M7++X9m7c2/6+JwdxbCPII8TpPnJt4TUt4R5w+7rh8TAn9HJ+YrzFG5Ste0W+P2lVbxvMOa5XJtQlrriGdP/JzfCZ4wrgOohhfK59tXEO61rheEE4VLoHzp+HXvGwRY/JpkqQoeJdhC0hHGeSLcWtzDfsTKek61fP1+OOVIi+5r9UWrmGdJ4RZDE9KjjUsVIi8e/EkbWNcu5RLjNFoGqaPAKhrXvsZ3q72XHFF3oqwrW2a4Rxv1ejxzossXn4h7nKtxRcOz3xmnqBLxxjnUqEYeB63NV4E6MVOHCrfNv/6TsezS9faKB62vKTg5QTPoGHexuu7jT6+CZiACZiACZiACQwi0P2JazMBEzCBwQQQjCRKIDLhnSPBAeGK2HyatZeOswRHDd2l09XGiMsnsYNypqY6HSY/wYMK8USTArEPgYZOd5MhBpGOmIUS15rSajvehxICEURKY+gpZeJFGWdgLtPpszrZ5Gn6QyCoCb0qQ8M0lb82yQpp6aRqCKrSxiUc9VnDzhFn2IaQJTGIeK14fkmcRYBi4hfF7qPtDI3F24trYbvtsqccIveVV/Y9VRG/JJLE88bxFIMTD0B5dsZYiaTR8VXn2hKBT/E0YYBYpbJj+pNOynEUiXeK+Kx9BxxArrVNghYdftKWk0MxqZYmimE/Ig48uEZ1n+gYeE5JrNO1jUCJuMXwfU0qRXoEOAlsqlX0EFaZ5RJPu3hdKKYu6eQtqPJqS92nZbnxM6KX4r9yPyPCEk5CaXT+8JLleVC+/CCWojwmaafYKj9LuBAnWUPHFd8Sr1ddD4iKmmQNQVRCJNcboTZ07riep6c7HUQ0tlHn6AWqlwFsl6hDqIdYHyY84/riOpYISt10PknL86X0XI1lsM6kYHGmb4aBy7Oe/RKsednBc4fJmiSms5+05EH41csFGPOSif2DvPPxQpaAXXum1a4HeEh4lYALAz0b8YYvjfNGXYj7GU3xjXnJgBjOM510lEVYEUS5aLzQWrWq307OgZ4jMR358I4l/i3XJRNSMUs5XGA1zHgGUg9GLIxi++6b8+HluhgNkZl2w62t4a1LHs6dzQRMwARMwARMwAQWM4HuTxqbCZiACQwngKC4bFnuCNEZ4q/s+CMmxMlx4ozJCE/DDG9TCSMS5HQsLfEQYuhvk+enjkFnmDyvfKW2tFseckjOJ4+/mAvvMgkuCFNr1sS9s9fjhEQInJp9HWGBvPCIcSln5+5/Uuw12gKbYaY4gdQTIUNeehKVyjiApWiKaHvJJTmerZhL8EFw0rDvWj3+8z8zO0SaUiCuCZMqv80Sz0x56OoaIR+iGiKPTDOSDyqT6zQOS1ZeLSVUqgxNnqT9LBFgEHDEW2njEg9OCVYILohpCvWAKMxkPhKxJLDBmhialM+LBMpjG2ItQl55XyCg14YLS6wiv+JgxvprXSEWSEdbEN9Ypz5cG1y7CMvEm+Vagj0ik0zDpcnDH/wxYlnKm5bwDRIw2aaXJ6QrX04gZFEOQmo0htCrDC0RwHkpUT6H2I/oXbPyGVYKzoitJWPaXQrb1FEiL+k13JvzyvBqziFhN+TRiEgevQa5x3Rvil255DrlHHAtyAhNQDrOD6EyWNfkcUqjJYKjZl5nIp9RDIGPsqkDYv2FF+bPvLAoTXWCURnDlOuGcjhXK1fmdUTjcoKtskzyiV15rxIKQt79lF3+cY65JwaZwoUwqdYopucl3tGL0WLsWBgPM4R7vVQYNpnhsLK83wRMwARMwARMwATWN4HuT0ebCZiACbQjgAcSHWM8muRVhPcSnX/EntpESdHDjaGPTd45xKyUkIFYhziElxmeKsxSTmefjngcUjuo1hJmEJhGMc0s3eQdyCRNiutI2xm6yZB4hEE8o/CWiRP5vOlN+eh4X9FRhxvCcBtj2Ch5JCQhMAwzeeuQb3q6z5TPCEMxXiJlIUDJA400TX8MZ49ebLV6SLDjfNUsDuluOo68BNnPtRMnNFLoA0S26BkLU0yCEHnptEsoi8diCCvnVsN0c87Z/6MAxjU5zC66KAuF1BXhW8eTtylLiViI0hpaT3vkCUh7FEMRkfqJT8zlUBdErNgWrgMN19axyLNmTb+mhCzQdUMahEvuU0It4I3HsPzYTsQ8jHwa5sv9JsPDl3JqcXmjNyxpOIcIv/Fcsp1jIKY2GdcidaatsS1Kj1gpQZnyan8cQxNjKV9cqm3kRSSuGQwIWRCZx2PBDc9StlGfs8/OQ7XlzYzYiDc5htjL85K0MKdsnhV4TrMN71sNCUfwY3g7XteEFCDcQWk8/yRa66VGKRCSh+t7aiofg+tslCHPOuYLXpDzc+2ojopTygsoni8SWGG1apVy9pd4byL0sl/Xo0I19FPV13iuwogXEnhdY3DRCxBeyPBSBC9Ojs0Qf3n2ko9ns+67Xubwj/NLmqaZ7kPSWauI2uTjfl2spu9GXkpFz/GyPdyDO++c28uIC5sJmIAJmIAJmIAJLHYC3Z9xNhMwARNYNwIIom1MQhAdRwQlBAI8uvA2I65cnJiH/TWPuzbHiWkkLDF5zygmj7xBw0oZes9QyCaBhHbSOWeIcDQNa0VMbBKClR7xQ16TeLFSJn94oQ4zxVItBShEl2h4JOoYiDrEFER8QaDDw0zDYJuEolgW6xJJiPs5yCQiwI/zLSFHwg7tREyUaVZqMWC4M4bwo23yUuUzgksplOGBF0VDRERmBi9NMVdVLuLLqBavCzzoyhcDiGIMFdYxWMIYr7aYlzZo8hHqjiAs71AmIyEf15nON+IfYSSiKS5qPFa5XsZBlEcfZV99dS5NXmClYIKgq2Mo3mNZPp8VdiHWrVxHuCQt3suDDBFMnBDVdK4REAcZ7Chfnpe1eLplfrzemDUez12JZpwrrlvuGcRPGV6d8h6+//07HV6m4AmNGKrzCGtd75SH6KuXI9xv8Bxm118/OxYrHuWEPTjxxHwPc3/o3sfb8rvfHVZifT/iIV6b4iV2y5bNvpfgf9pp9TIQQBEsdb4QTEcxhVbgOU7cX+pAWYT0KL2HKZc648Wue70p7qk8sZs8hZvquGJFrkMZr7Up/ULcjpgsD1iuY2LE4r3P9wH78OJF4NV1yrVbevQvxHa5TiZgAiZgAiZgAiYwjED3p6TNBEzABOafAB302JGmI1v+4Vk5zMuwbU01Y3LNO2pQGZpBnHiOwwwRio44Hl54HiFg0bFmYhx5LMUy8H5DPKHdCCFNw0DxSkJAIR0iCUIzgh2f4+zcsey4jpenBIDIWOIDQ3QRnun8an8tDimxQNmPuNjGNFyf2HyDDEFInnh4/kp0lGcXAktpceZ41Vmiij6zlJcZ6wpXwLpEPsQmPEDl/Rg9GhG2VQcJqnAcxRC2Y32e97zm3MRXZDKsmF7riMESWxA4S+Hx1FNzPkQ8JmeR+MwwdrwMozHUFcEVT1mEMf7gjyCPR3NptAFe1AXvQYYTs47AGY2JguTNivej6qA2lEvqOsjzlpAC5MErcpgRG5K0iNgIYazjyTzImEiIdBJqB52bWjkS3HXf1PIzzF2CMMfiD1G09LImbzxP8hIlBMAwQ+RTzOWmZyrXMR6cNS/SYeWX+7l+FDtSbWKJR+uRRw4WxyQ6S0zjxdcopucxoUn0zGDStmFGKAPY8IyoxT6VpzzhGdoazy29FCpfNLQtY6Gk457Bq1vPwXhe4zqhKHi+2EzABEzABEzABExgKRDo/oS1mYAJmMAdQwBREG9GPD4RIRCXEAQZXocQNk6TaIOQNIpJxPvsZ0fJ1T4tnXE86+hkIqQwlJY4hqtWZTGHyWbUAWXGbcW3lMcgItYw71Fqg9haEwhLwYTPhBqo2agCKAIXdWe26mEmL0M8CxEh1WaWZVxGhu8iirIvCpwxD+sITxKnFMM1CqfR44/htSqLIbRMoKO8lEV8VpXfJFTX2qgh8BIWYkzcWnomjuI4eIriTSgxGDGR84dwhtBemiYuwmsXQ0zbZZdcFh62czVEcu5LMWCJKIzgyMuBGOaB+1nCEGkQqxBomaxIMVJpH2UgBjbFXdTs9ohtw0zeoghrvDBQ/ZryEU6Cax1hUAKlRPGmPOV2CajyrpyeLlPkz3g9fvSj+Ti6DiJHQh2Unp4I3BIJ8Tht8q7nGYp4SnkIglwbeEcS85jQELzYwPO09Dqu17T9VkRXxHCOi7dnFG8HlaKwF4pxe8YZg1Kvve/kk/MxNZFavIfXTj17i0JyMBqgNLxoeZbSnvJ5U6bVZ+J+kh7vyaVihO/AAxQxm3uUZyL3LHFlv/nNpdJKt8METMAETMAETMAEMoHuTzmbCZiACSw9AnityGPozDPbtQ/Rkw4uQ2nbdvDblTw7FR6aePfVxBGOjxCCZ1UUQcij9MQJHGZxkpAo7FG+joHQxLDHJqOOpEW8aGMaqt9m5nFiCCp+YCnKxmMxBJnh69SDjrlES7WjtixFEg3fJi2etLR7ejqHYWCbvD5VlmZIlqg3Svw7hhxTjvK2iUG7fHnOw7BTBCLyS/CteSITa5ZrgbbEmJpMTqS8keG6rnOOGG4ur2Xx0RKx85//uT9JCtwl2OuYGp7PsFqFwmCo/po1StFf4jlM2XiCtzE8eUmvIfAM1a3Z6af3xS6GRBOjlnylR2stb9wmb11dL3gM1wzvwOh9LF5xCQNCgEQjdIQ8tzn/xLRFhEKcR+RF1NfLE9jj5XhHGuxoAy8V2ryE4RxTT/LoBc6wyYnK9ugljEILjNJmRGDuE5jWPPL1Eoayh3mtM6yeFxKc+1HjhpZt8mcTMAETMAETMAETMIH1Q6D7s9RmAiZgAkuTwNFH5843giZDMQcZcUclpLQZYjmorLb7GO5PB/9lL+t0GPqKcEicu9I7TOW9+925PQgKiHo1MQBhT4Ih6Ri2i3ct6/zhLYZ3XxuT6MCkOW0Mzz6OgbjT1IZYDuJrjPupOsKDGKsSz9iukACsI0IwSRDelXibKl9c4kkZTZ69MU25jlgSQwEwXFdpSrEqlq31Qw/tp9eERm1CFjCUnDYhsMCaY8pjktm3o11+eRakSYPgHA0vPWbbZh8T7YzLEELlwUe7uK++8IU89Fzt5HyUohhCIHVBDKYMPCOJT8k2Ys2WRhquHfYPE6TISzzcOOyeYfgIzgjE5F+1qj+ZFGXiOUkdeJnAZ/4YCt3GECKVR4J9LR4v51JD7BHD8e7m2cP9IAFYYQMoT2K76oCHuDxUdbxyifdtzStYZczXEsFXAiwsa+3XsYkdq3YSFgQPVdrRNEGa8pXLF74w55P4WF5jZfryszxHy/uIdNwvmswKT2vqWIZg4XuB+LU6BzyDbSZgAiZgAiZgAiZgAouTQPfnqM0ETMAEliYBxA7FCkSUQDArO+14WR57bF9wokNMx3ihGl5giAHqkOMtivDDMGQNodU+zUBPWzTcEyGwja1c2T8Gw7TbmmIZInoM44igolmbJfip7louW9bpaOgx2/ACjQIuopnSspSHKIymp2fXmok+yC8PvpivyctV9aM84tjWDFEmzoSOYKJ4lm2HWSOUxfpwPARZ2ofh0cx1Kq9mvEaZEbw0vDAphyH+4zQNZY4xEwkNQD0RjzRZUjzmihW5LgzNlhGTUl6BtZcSGmZM7Nta+1SOlpxTOEV25TrMSuFqairnQcwcFs+R6011ZtIwrkmOQWzMaAirGibO9V96HWoSJUIdEHZBQmoZgoL7hhAMeLgidnJ/Ew6DmI1th2vHeo1zHWFZz5mHPKTTwWs+eqr/6lc5jIM8oBEguXYVpoCXMyWXpvrBXS+l4D1skqtaOfI8xTu6Zty7PF+4jjkGSyaLw8te55LteJGuWlUrwdtMwARMwARMwARMwAQWC4HuzzqbCZiACSxdAgxljx48CIGIOHgYMgQ7Cm8INrEzv1CpfPvbeZgwHfPaHyJf6UHHEE6lHTYjN15xEgT22ms0CohCEumYqAVBpGbMmC3vLIQUJhhR/aamsiemJi+RJ2o5iz3lEvNQ+VjiUScvNcSWYUacSvIx03zNEGtUHukQ0hlCjxCNKMi1FEMMqBzEXQlFTTEvy+NJZIztISYsYkw8BseHV804X+QfNdZiray4jVmgdV7l1crweI5Vu56Igck+6r1mTSyp05FXXy1WKdzkbYqoPCgUBbOb69xQJh67eJYS/gGhn5cZXMu1GawR13R+ECLxxK4ZQqTSIZwj2srrVRyUD6GXNuMNK+Fa+xBHyQ8PhZ3A05z0eO0OaqfKWChLuEePbARwXsBw3qIYzSRbnE8ZIi7tJeZkG9t//5weEZPnEeep5DqsHHnTEoJhkPG85L7Suaae/CGCIpDWJgsbVJ73mYAJmIAJmIAJmIAJLDwC3Z93NhMwARNY+gTwVKrF5aNjTcecIb2LzX79607nne/MghIzPiPIsK3JmNFXHXtEFyYYiXbeebMnt0HQwIt2VGOCGnluITjjucbQcmIxIjQhjMoLc+ed+96DGr7NJDMyQgJQZ85TbTIiTUgjIZtJgZhQRu0cNrmWPNPwzmsyGCCsSRRW2XGJOIMYG23lylwPvAavvTbuWXudSVmIfUmZMCmPBS/E0GHXKZOZUIbE47WPtO5bmPxG9WK4P9ccxzr++H6ZCHnEr5UQVhMWP/KRnK8ptAJilTyWibtJ+ihiIkIy/B/hjeMjOLbxFu3XMq8xjDwKXhwTxs99bp4ASsPxOQbnV+wVdoHYqPJGRujjWiQdM8GXxj1AOYi6Mjw99Uwq70WlWahLXhThVRtjDdM+BF687sUq1h+RUUI+8USbni14ZR52WObF+eF+lif26tWxxMHrPAu5b4iVK9F5cI48QuCKK/L9g+g5zIt9WHnebwImYAImYAImYAImsHAIdH+u2kzABExgcggwVBcBA8GBYcKT5tlTzriOoIUoIUESEYM/vA7LcAGjXCVM3IIHmMorl4hFeFYxPFaGZ5nSyduTIeRsq01ww9BgpdcETIhymPYN82CVGIf31zBjwiFmR0aUY4gvAhj1ev3r6wIL/JgxmjoSD7JpWPr0dN+jbtdd8xBh6kM+zhcxLSW0DaojwiB5EJ9HjZU4qNy476STsqDEcSQ+I6wzYQ8eewxTZh8CaOkhqXI00dOgc0NbEIIpS394oEr0ZBvXLudjLtcpXBE9dYzaEk/lMgyEJq5CfMVLmMm6yIu3Ymknnpj3wQsBD1ZTU3kovYRexO/5EK3LuszHZ7yREX15tg4ToonRikgMK7gSCoC4tgjchCIgTIleBPBc4qUJptAIvDxpa4Ss4Dh77tk2h9OZgAmYgAmYgAmYgAksZQLdn4Y2EzABEzCBSSLAxB4InKXoiViwbFmOXzkuHnhvvf3teQIaJsBBoETkKCcb0fGilypxFxWHD6FLhoepYjJSZ+It4s3HOqLkLbd0Os95Tv6MR+Qgk0hLnebD8OxE7KJu/CHq4T3JUH2GAjP8X/vwRGaoNMaM4GxnsqtBXr05df6vCYYOPjhuHf86HpoSplX3uCRu5SBPPYalk15idVMNGe6M4IpQJo9S8rHO0Hc8btt69jUdQ9uJTUp5hDDgmsEjmGsOIb9mP/1p/9rcZZdO58ADc5uYyEzGLOTaTr0R76LHaWTGOoIu120cNq6yltKSGKhxCH3Jgc94fDKhlAyW8vImvMIwY9IqieVtQ1AMK9P7TcAETMAETMAETMAEFjeB7s9tfrTbTMAETMAEJpHAJZekdPnlKW29dUq77prSXe+6/ik85SkpffnLs+uxySYp3f3uKf3mNynddlt/3+6799P+3d+ldP75Kb3lLSn94hcpvfe9KS1bltJVV/XTx7Vzz03p8Y9P6a/+KqUrr0zpv/yXuHd869SXurzjHSn96ldrl3uPe6T06len9M//nNLd7tbfT92o49RUSmeckdJmm/X3lWtHHJHS29+e0/zwhynd5z5livF/PvroXO/73z+l//k/U/qv/zWl3XZLaaedBh+LtJ/+dGZyyCHNab/3vZT23Teliy9uTsM5O/74lP7H/2hOM197uG+e/vSU4C37279N6eEPT2nNmpS++c2U/vznlDbcMKWHPSylb30rp3rGM1JasSKlhzwkp/uHf0jpXvdK6ZZbUvrDH1KijHPOmb/rUXVdn8tbb03pE59I6bTT8vm94YaU7n3vlHbZJaVnPjNfT3CL9qEPpfSiF6W0wQYpHXVUSi99aV6PaVj/zGdS2n//lG66KaVDD03pPe8pU/izCZiACZiACZiACZjAJBKwADqJZ91tNgETMIExEvj971P6y18GC3SjHu6DH8zixZ/+VM+JUHjssSkdeGB///R0Sk94Qkp3vnNKD35wShdemEXdCy7op9Eaohppf/nLlN785pTe8Abtmb/l7ben9NWvpnTRRSldf30WvR70oCxw3uUuax/35z9P6RGPSOmaa1J6wANSQnB86lNniz7f/35Khx+e0he+kLcjlO6559plzceWX/86pW22yYI0jBH0hhmi4c4751SIhIimNTv77Cwu3nxzSgisL395Sk9+chbqEcG/9rUsfMITe+c7U3rFK/J67T/X0bXXpkR5971vZl9LN+o2BMtjjsmCHIJbNF4mIJButVWu6+abp/TJT6aEaC/jvD3taSk98YkpIfDtvXdKCL+PfWwWvxH7bH0Cb31rSkcemX1GuSee85x8ryOW/uhHKZ16ahaeyfHc56b07/+eBeh+CV4zARMwARMwARMwAROYVAIWQCf1zLvdJmACJjAHAv/3/6aESPmVr2SvTIradNPsAYj3FULOOGyPPVI688ws7j3wgSltt11Kr399So9+dL30t70t79fe5z0vpY9+VJ9S+u1vU/rAB1JCSEG4xdv085/Pomk/1cJZ+8lPsoiGaIrhKYhoigB8xRX5j+2Ia3jU0Z470l71qiw+4rX49a+ntMUWzUeH9/LlKX33uykddFBKJ5xQT0ub8UZG6ORaev/7mz2T8azFE5CxLJ/7XBaIY6n/8R9ZKGf5xz/29yCeIZDhdTvIs7afY/Aa5wcBmPa/+90pbbttFq/x+IUNnqBnnZUS3p7R4MD1iKj3pjdlD2E8SH/605Q+/OGUDjggpvY6BLhfEcQRPGu25ZaZ5UteUtvrbSZgAiZgAiZgAiZgApNKwALopJ55t9sETMAE1oEAAiLDd/E0lDE0Ha/L6AGH0IW321yHYjNEH8EPO+WUlJ71rLw+6D9p/s//6afYYYe+5+APfpASnpjYfvtlcS0OO+/tWGD/GEKPSMZQ78sum105xB684BgCP19D+GcfcfYnPCrxVsRrEYEasfmRj5ydhk944z7/+SnhsUq6//zPLNqunTK3h3P93/97Hs58pzvVUvW3aSj+9tundOmlKeFNS70QTz/1qZyO65NwCIQ7QFy88ca8HX4ci6H7czW8jvEsxjP5sMNyaXil/tu/ZSETQTMaYRcIF8D5JR9CKca1yzXMdQ8v29oEuIfxEublCII5n/FGRmDmJQDn2WYCJmACJmACJmACJmACkYAF0EjD6yZgAiZgAo0EED8RuxARFbcSL7r73S9nue66LDgRi5J1vODwCmwa5tx4oGIHw8C/850sbElAK5LMfERoIxYoHncIggx9lthFIsQxxK7XvGZtb7yZQhbwCmLPVVfldhG3FQENcW99GkP099ore3ZSD8RvhCj4M9SfOKaEJ8BLk/oy7Puv/7peY64b2oUo/eMfZ1GrnrK/lfALxI5ELCf+IwIY5/gb38giK+IwYjdiJ0b61atTeuMbc90YPn366XMPHYAHKkPeqTvCHLFbd9wxxwilLtFrmZiX1BHhmBcKH/kINcuGmMewedJcdVUWbrXPSxMwARMwARMwARMwARMwgXUjYAF03bg5lwmYgAlMHAHiT37xiznmHiIWAmfNiA2J99555+VhwEwGMxeRDiGIYxFvEbEKgZWh16URy5NhxAhcDMcn5qOENGJ9MjyZeJLss42XAOeG80I8TNZL22ijHNOVWKuD+CME4rnJ9cMEOW2NGKBcEy98Yb5GGDZPuARCNPzN39RLQZBFCMeDlGHweGE2XdP1EtbeynB8Jt0h/ucb35iHtiPGI8Qrziv3EJPz4AGK1yf3RzkMn5ighJlg6D6xam0mYAImYAImYAImYAImYAJzI2ABdG78nNsETMAEJoLAl76UPeSIQYmX5TCvTrxFieOIB9s4YhkiTj3mMTluJ8ARNonjyMzRxFlkch1mlsYQOvEYZUj0YjG8HRGMmfho442zeIfwRXiBxWTE+cT7Ec9GhPB73jMP5cYrsxT5au1CICU+61veMjuWay1t3IaHKR6VDL8///wcM5alwifEtOU6s45r5vCTTir3jvYZ8f3Vr07pXe/q50P8R5jFExmxEy9ejLp+9rN5Uqa8pf+fGLqEmcAzFa9SmwmYgAmYgAmYgAmYgAmYwNwIWACdGz/nNgETMIGJICBBBmGHCUjaGJPyMEQeoYeYj3M1xDViIyLG4r1XGrEi8Rz83/87JTwOF4Mh3L32tSl961tr1xaRd7/9slcrQ6InwRQzc5TrDC5cXwwxZ4Z3ROSDD84xU9swu+qqLJbDmyH7m2zSJtfgNJxXxFxmqy+NlwevfGVKTNKDp2jNHvrQLCLTrlpM1VoebzMBEzABEzABEzABEzABE2gmYAG0mY33mIAJmIAJdAng1YYnIrNoIy61FeNIj4cmQ6IRlvAGHIdRLhMCEV+Ucont+LjHDRaUxnHccZaBgMvM33g7YrB58pOz5+ctt+R4mniEMnwa3ngKxhiSOdfS+6+h7P/rf6V03HHt28eEW89+do75iaclcUeJRdrW/v7vc8xQhsw/6Ultcw1Ph1cqYigTQO25Z/ZaxjN6kDFJExM2IcRyfS8WMX9Qm7zPBEzABEzABEzABEzABNY3gQ3XdwV8fBMwARMwgYVNgCHmiHIIcW3FT1qEcMMM7AyZv/rq8QmglIunIH+L1RjijfhJTFPiluJVWwpdP/pR9mQkDuTuu+fh05opfLG2e1i9/9t/yymIMcts6m1jxzJcHGMCIWxUTqRnoiLEx3EaHrwIoJR94ok5dMOw8olli0CON3N5TQzL6/0mYAImYAImYAImYAImYAJ1At3IVDYTMAETMAETaCZw2215nyZxaU659h4EPkzCVP402f8Z7v7GN+Y4lXh2vu51daEL8fjLX86ejTfdlMMJ4I27lO1Rj8oTVa1Zk9KqVe1aSnxYPEC5PhnGvi5WC6mwLuWUefbZJ6Wdd04JMfuAA7JHb5kmfiYG6Yc+lNvCNWIzARMwARMwARMwARMwARMYDwELoOPh6FJMwARMYMkSuM99slh33XX9SYjaNBaxjpmusW22yUv/z0PfEdyYgfypTx1MZIMNshD413+dPWlPPXVw+sW+lziuzCaPHXZY9nrNn+r/uSaZxIhQAQcemBKcsEsvzcu2/5X+fvdrm6NdOs7fKafkme+JTUuYAx0rlvCb36T0spel9IIX5K2EeGiavT7m87oJmIAJmIAJmIAJmIAJmEA7AhZA23FyKhMwAROYWAJ4cTI0GS9OPBbbGkN/b7ghxz1kchpbnhmdIe14KjJbeBtjGDST5mB4Oi51Y6IrZk0nduw//ENKxAUl7ms0BGTE4Ec8IqXLL0/pYQ9L6aij+vE7mYCrreFtilfuxhunRCzQcRsz0RNblBcJZ5+dPULxdH3xi1Mi1ulTnpLS1lvnIf/ca4ifL3rRuGvh8kzABEzABEzABEzABExgsgl4EqTJPv9uvQmYgAm0IsDQXLzT8Eojpudf/dXgbIilj31sFpbe9rY8zHtwjsnYixBGPM/HPz7Hhmzbajxpt98+JWYQH3ecyrZ1uCPTcf3gEYkYiG26aZ7oCqEQb0kmwGJCLmxqKouh97pX9q588INz7NDzz0+J9WH2j/+YhX2GqH/4w8NSr/t+YukS+5V76eabZ5dDrNM99sjerw95yOx9/mQCJmACJmACJmACJmACJjB3AhZA587QJZiACZjAkifAEGNmr/7+91Paa6/siYjHXM0Qr/BuI4Yjgt1llw0XTGvlLMVtJ5+cZwR/3vNS+uhH27eQOKx3vWuODXnrre3zLfaUZ52V0sqVeRKhsi33v38OI4Awz1Bz2SGHpPS+9+Xh8GeemT2QtS8u8SJ91atSete78uzxxBLlep1vw5v1q19N6aqrUuJcIuriYb3llvN9ZJdvAiZgAiZgAiZgAiZgApNLwALo5J57t9wETMAERiLARC6PfnQe1r7LLv0hx3Gm7vPOy6IUHnoIpKtXp/R3fzfSYZZ04tNPz7N77733aOEEfv3rlO5975Twcrz++iWNqNq4n/0spQsuSOmXv0xpiy1S2nHHlBhaXrM//SmlJzwhpa99LaXNNkvp8MNT2n//lLbaKqdGzD/33DwRFUIkkycxizwemDYTMAETMAETMAETMAETMIGlScAC6NI8r26VCZiACcwLAbw5n/70PKs1B8BrDTEKDzxiMV57bT4sXm2nnZbSIx85L9VYtIX+4Ac5BiQxUa+5JiUm/WljX/hCSk97Whagv/GNNjkmOw3xQ/EMZQIiDJEe705CNyCm/u53eTuiKGke//j82f9NwARMwARMwARMwARMwASWJoFul8BmAiZgAiZgAu0IIHZedFFKRx+d0g47pERcQ7zozjkni5/Mon3kkXnYu8XPtZnutFNKDN0mfiVeh23tAx/IKQk/YBtOYJNNUmIiJCYdQrBn0qmrr84xQhE///ZvU3rzm7OQ//jHDy/PKUzABEzABEzABEzABEzABBY3AXuALu7z59qbgAmYwHol8JOfZGGJYcXbbpsn6lmvFVoEBz/hhJQOPjjHqPz2t/PQ9kHVZub3Zz87D+e+4orh6QeVNan7iKGK6MzkQ3jfMozeZgImYAImYAImYAImYAImMDkELIBOzrl2S03ABEzABBYAASaJWr48T+xDLNXPfjal7barV4yJkphQiriWCKcHHVRP560mYAImYAImYAImYAImYAImYALNBCyANrPxHhMwARMwAROYFwLXXZfSbrvlIdkbbZTSAQekxMRICKG33JIn/Fm1Kk8iRQVe+cocdmBeKuNCTcAETMAETMAETMAETMAETGCJE7AAusRPsJtnAiZgAiawMAncdFNKhx6a0sknp9Tp1OvIJFNHHZXSfvvV93urCZiACZiACZiACZiACZiACZjAcAIWQIczcgoTMAETMAETmDcCF1+cZyJnMiniVG68cfYE3XPPfuzPeTu4CzYBEzABEzABEzABEzABEzCBCSBgAXQCTrKbaAImYAImYAImYAImYAImYAImYAImYAImYAKTSuDOk9pwt9sETMAETMAETMAETMAETMAETMAETMAETMAETGDpE7AAuvTPsVtoAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAhNLwALoxJ56N9wETMAETMAETMAETMAETMAETMAETMAETMAElj4BC6BL/xy7hSZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiYwsQQsgE7sqXfDTcAETMAETMAETMAETMAETMAETMAETMAETGDpE7AAuvTPsVtoAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAhNLwALoxJ56N9wETMAETMAETMAETMAETMAETMAETMAETMAElj4BC6BL/xy7hSZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiYwsQQsgE7sqXfDTcAETMAETMAETMAETMAETMAETMAETMAETGDpE7AAuvTPsVtoAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAhNLwALoxJ56N9wETMAETMAETMAETMAETMAETMAETMAETMAElj4BC6BL/xy7hSZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiYwsQQ2nNiWu+EmMKEE9txzz7R69eq0zTbbpDvf2e9AJvQycLMXGIHbb789XXPNNWmLLbZIm2+++QKrnatjApNL4De/+U363e9+1/vO3GCDDSYXhFtuAguIwJ///Ofed+amm26a7nGPeyygmrkqJjDZBG688cZ08803p69//etp5513nmwYbv2CJHCnTtcWZM1cKRMwgXkhgLhy0003zUvZLtQETMAETMAETMAETMAETMAETGByCRx//PHp4IMPnlwAbvmCJWAP0AV7alwxE5gfAttuu2265JJL0umnn54e8IAHzM9BXKoJmMBIBM4999x00EEHpeXLl6cPfOADI+V1YhMwgfkjsM8++6QLLrggfexjH0sPf/jD5+9ALtkETKA1gfPPPz/tu+++6WEPe1j6+Mc/3jqfE5qACcwvAX7L8pt22bJl83sgl24C60jAAug6gnM2E1isBDTsfbvttks77rjjYm2G620CS4rAlVde2WvP3e9+d9+XS+rMujGLncAmm2zSawKdOX9nLvaz6fovFQLXX399ryncn74vl8pZdTuWAgF+x9pMYCETcADAhXx2XDcTMAETMAETMAETMAETMAETMAETMAETMAETMIE5EbAAOid8zmwCJmACJmACJmACJmACJmACJmACJmACJmACJrCQCVgAXchnx3UzARMwARMwARMwARMwARMwARMwARMwARMwAROYEwELoHPC58wmYAImYAImYAImYAImYAImYAImYAImYAImYAILmYAF0IV8dlw3EzABEzABEzABEzABEzABEzABEzABEzABEzCBORGwADonfM5sAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiawkAlYAF3IZ8d1MwETMAETMAETMAETMAETMAETMAETMAETMAETmBMBC6BzwufMJmACJmACJmACJmACJmACJmACJmACJmACJmACC5mABdCFfHZcNxMwARMwARMwARMwARMwARMwARMwARMwARMwgTkRsAA6J3zObAImYAImYAImYAImYAImYAImYAImYAImYAImsJAJWABdyGfHdTMBEzABEzABEzABEzABEzABEzABEzABEzABE5gTAQugc8LnzCZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAguZgAXQhXx2XDcTMAETMAETMAETMAETMAETMAETMAETMAETMIE5EbAAOid8zmwCJmACJmACJmACJmACJmACJmACJmACJmACJrCQCVgAXchnx3UzARMwARMwARMwARMwARMwARMwARMwARMwAROYEwELoHPC58wmYAImYAImYAImYAImYAImYAImYAImYAImYAILmYAF0IV8dlw3E5gHAptvvnmv1E033XQeSneRJmAC60Jgs80262XT/bkuZTiPCZjA+Ano3tRy/EdwiSZgAqMS0Hel78tRyTm9CcwvAd2Tukfn92gu3QRGJ3CnTtdGz+YcJmACi5XA1Vdfna666qq0fPnyxdoE19sEliSBr3zlK2nXXXdNW2655ZJsnxtlAouRwLXXXpsuu+yytNtuuy3G6rvOJrBkCZx99tlpxx13TFtvvfWSbaMbZgKLjcCvfvWrdMEFF6Tdd999sVXd9Z0QAhZAJ+REu5kmYAImYAImYAImYAImYAImYAImYAImYAImMIkEPAR+Es+622wCJmACJmACJmACJmACJmACJmACJmACJmACE0LAAuiEnGg30wRMwARMwARMwARMwARMwARMwARMwARMwAQmkYAF0Ek8626zCZiACZiACZiACZiACZiACZiACZiACZiACUwIAQugE3Ki3UwTMAETMAETMAETMAETMAETMAETMAETMAETmEQCFkAn8ay7zSZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiYwIQQsgE7IiXYzTcAETMAETMAETMAETMAETMAETMAETMAETGASCVgAncSz7jabgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYwIQQsAA6ISfazTQBEzABEzABEzABEzABEzABEzABEzABEzCBSSRgAXQSz7rbbAImYAImYAImYAImYAImYAImYAImYAImYAITQsAC6IScaDfTBEzABEzABEzABEzABEzABEzABEzABEzABCaRgAXQSTzrbrMJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJTAgBC6ATcqLdTBMwARMwARMwARMwARMwARMwARMwARMwAROYRAIWQCfxrLvNJmACJmACJmACJmACJmACJmACJmACJmACJjAhBCyATsiJdjNNwARMwARMwARMwARMwARMwARMwARMwARMYBIJWACdxLPuNpuACZiACZiACZiACZiACZiACZiACZiACZjAhBCwADohJ9rNNAETMAETMAETMAETMAETMAETMAETMAETMIFJJGABdBLPuttsAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAhNCwALohJxoN9METMAETMAETMAETMAETMAETMAETMAETMAEJpGABdBJPOtuswmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAlMCAELoBNyot1MEzABEzABEzABEzABEzABEzABEzABEzABE5hEAhZAJ/Gsu80mYAImYAImYAImYAImYAImYAImYAImYAImMCEELIBOyIl2M03ABEzABEzABEzABEzABEzABEzABEzABExgEglYAJ3Es+42m4AJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmMCEELAAOiEn2s00ARMwARMwARMwARMwARMwARMwARMwARMwgUkkYAF0Es+622wCJmACJmACJmACJmACJmACJmACJmACJmACE0LAAuiEnGg3c+kQ+PWvf52OOOKItMcee6Ttt98+bbLJJmnnnXdOz3jGM9IxxxyTbr311laN/fOf/5w+/OEPpyc+8Ylp2bJl6R73uEfafffd05vf/Ob0ne98p1UZJLriiivSC1/4wrTrrrumzTbbLO200069z6tWrUo333xz63Kc0AQmjcC47sFJ4+b2mkATgXF9P47re21c5TS119tNYDETeN/73pfuc5/79P5+/OMfD23KuL4zx1XO0Ao7gQksAgLnn39+OvLII9Oee+6Zttpqq97f1NRUr6954403tmrBuL7rxlVOq0o70eQS6NhMwAQWDYFjjz22s8UWW3S6T6zGvwc+8IGd6enpgW362c9+1nnQgx7UWMaGG27YOfnkkweWwc6jjz66c5e73KWxnMc85jGdG264YWg5TmACk0ZgXPfgpHFze02gicC4vh/H9b02rnKa2uvtJrCYCVxyySWdjTbaaOb346WXXjqwOeP6zhxXOQMr650msEgIvOc97xnYj9tyyy07n//85we2ZlzfdeMqZ2BlvdMEugTuBIXJlX/dchNYPAQ+/vGPp3333Xemwk95ylPS4x73uLT11lsn3px/5jOfST/4wQ96+zfeeOOeFyfemKXddNNNvXwXXXRRb9dDH/rQtNdee6VtttkmrV69Op122mnplltuSXe6053Scccdlw455JCyiN5nPDwPOOCA3nr3R2x61rOelbqCZ/rpT3+aul+W6Xvf+15v34Mf/OB01lln9d4oVgvyRhOYMALjugcnDJubawKNBMb1/Tiu77VxldPYYO8wgUVMgJFKj3rUo9KFF14404quAJp23HHHmc9xZVzfmeMqJ9bN6yawGAkg/6xYsSJ1nV161b/rXe/aGxFIf/B3v/tdry/4jW98o7fv7ne/e69P2XWwWaup4/quG1c5a1XQG0ygRgAB1GYCJrCwCVx55ZWdTTfdtPemHI/Lrki5VoW7Pyg7hx566Mzb9K6w2WFbaa94xStm0vzTP/1T509/+tOsJF/96lc7m2++eS8NnqDXXHPNrP18+OUvf9npiqy9NKQ955xzZqXhuM9+9rNnjnPwwQfP2u8PJjDJBMZxD04yP7fdBCKBcX0/jut7bVzlxDZ63QSWEoFXvepVM78Pu33T3vogD9BxfWeOq5yldC7clskk0A2BNnMPPuABD+jgGR2tGyaiE+/TrsNN3N1bH9d33bjKWauC3mACDQRSw3ZvNgETWEAE3v72t898Ub3uda9rrNntt9/eecQjHjGT9pvf/OastN34aJ3um7ze/vvd735riZ9K/LnPfW6mjJUrV2rzzJI66Efr+9///pntcYUvT9WFY3bfvMfdXjeBiSQwrntwIuG50SZQITCu78dxfa+Nq5xKU73JBBY9AUI03fnOd+79htSLfX5PNgmg4/rOHFc5i/4EuAETT+CPf/xj5973vnfvHuzOI9H5yU9+UmVCnxJxlPuTexahMtq4vuvGVU6sm9dNYBABT4LUvattJrDQCZx77rkzVXzxi188s16ubLDBBumZz3zmzOYLLrhgZp2VT33qU+n3v/99b9tBBx2UGPJQs6c97WlJQx1OPPHEdNttt81KdtJJJ/U+d3+8pv3333/WPn3oflmml7/85b2PHPOjH/2odnlpAhNLYFz34MQCdMNNoCAwru/HcX2vjaucopn+aAKLnsBvf/vb9PznPz/95S9/6U26yaQrMsIu1Wxc35njKqdWR28zgcVE4Itf/GK6/vrre1Wmn9Z1iKlWnz7lfvvtl3bYYYdeyIrLL798VrpxfdeNq5xZlfMHExhAwALoADjeZQILhQCzZD760Y/uiZJNX1Sq633ve1+tpu6Qhpl1VhTPhfUnP/nJLBqN2eGxn//856n7xn4m3VVXXZWuu+663mdmCWwSUUnwhCc8oRdLlHVitNlMYNIJjOMenHSGbr8JRALj+H4c1/fauMqJ7fO6CSwVAi95yUt6ceLvec97JkSPJtEztndc35njKifWzesmsBgJ8DJAhsA5yA4//PCE8Pn1r389Pfaxj51JOq7vunGVM1Mxr5hACwIWQFtAchITWN8EPvKRj/TEy8suu2zoD0ZNPkSdd9lll1lV7w6J733GO7PcNyth98NDHvKQmU0XX3zxzLrKYAMTKA2y7uyBSYJsLGNQHu8zgaVMQPfPXO7BpczHbTOBUQmM4/tR9yXHnsv32rjKGZWB05vAQifAS/BTTjmlV80TTjihN4Fnmzrrnprrd+a4ymlTZ6cxgYVM4Pzzz+9VD4ea7bfffp2qqvuJzP7OXCeEzrQeCVgAXY/wfWgTGDcBZu6LnpaPfOQjZx2C2eIxZnzvTqY0a1/5IXqaIrzKfvSjH2k1bbfddjPrTSsqh7pde+21Tcm83QQmgsA47sGJAOVGmsCYCQz6fhzX99q4yhlz012cCaxXAt0Yg+mQQw7p1WGfffZJ3Qk4W9dnXN+Z4yqndcWd0AQWIIFbbrkl6V6QcElIivPOOy9142mnPfbYo+fpedhhh/VeWLCvZuP6rhtXObU6epsJNBGwANpExttNYBESeMtb3pJ+8Ytf9Gq+++67zxIob7755tSdmKi3b6utthraOrw3ZTfccINWU3cyo5n1uZQzU4hXTGBCCIzrHpwQXG6mCYyVwKDvx3F9r42rnLE23IWZwHokgIBC3M8bb7wxbbvttum9731v69qM6ztzXOW0rrgTmsACJcDLCIma97rXvXpzPPBSojvLezriiCPSmWee2Rvuftxxx6XnPOc56UlPetJa4dRo2ri+68ZVzgLF7WotUAIWQBfoiXG1TGBUAgwDPProo3vZNttss/TBD35wVhH8+JRtvPHGWm1cxjR/+MMfZtKNq5yZAr1iAhNCwPfOhJxoN3PBEfD344I7Ja7QhBDgd+nq1at74Zu4D7fYYovWLR/Xd+a4ymldcSc0gQVKHeBlWQAAJgFJREFUgJEQso022ig9/elP73l6brjhhr3QaEykS4g0PmNnn3122nXXXWfmflDecd1T4ypH9fLSBNoQsADahpLTmMACJ/C5z30uvehFL5qp5THHHNN70z6zobtSfunFfbX1u93tbjObowA6rnJmCveKCUwIAd87E3Ki3cwFRcDfjwvqdLgyE0TgggsuSG94wxt6LX7pS1+adtttt5FaP67vzHGVM1LlndgEFiCBeC/gKPPlL3+5J3AyfwR/n/70p3vL7373u+lBD3pQrwXMGP+yl71sVmtiOQipw8x9ymGEvP+OJGAB9I6k7WOZwDwQ4I06b+xuv/32Xun82DzggAPWOlKM+am0ayUKG2Ka+OU2rnLCobxqAhNBwPfORJxmN3IBEfD34wI6Ga7KRBEg1iBDa2+77ba00047pXe84x0jt39c35njKmfkBjiDCSwwArfeeutMjejnMSfEueee27tHZ3Z0VxA/p6enZzy2P/nJT/Y8uZVmXPfUuMpRvbw0gTYEsn9zm5ROYwImMFYCe++990xMzlrBy5cvT69+9atru/5fe/cBLFdVPw78QCDUEDoBEkIJIIj0KvyGIr0XRUY6KEUQGToICg4gwjhIRySghCKigEg3IErvIASkhiZICUWkS/a/3ztzL/ve27dvX7jJP2/v58y87C3nnr3nc3bnvvfNKcWx4447Lh177LHF/k9+8pMu+8WJ+sass85a7H788cfFdm8bjXmGDh1aZCurnKJAGwQqIuC7U5GGVs2pQsDzcapoBjdRUYFDDz00xQKaEeC4+OKLU+N/pLdLUtYzs6xy2r1v+QhMrQKN34W4x5///OdpyJAhTW937rnnTkceeWQ6/PDDs/MxlUX8bRqpsZzGvxezk03+aczjb8omQA5NUQEB0CnK7c0IfCFw/fXXF702vzj6xVZvD6TIEf9rt9dee6ULL7wwu2DQoEHprLPOSnvvvfcXBXTbaiyvcdLpbtmK3cY8Madonhq3G/Pk57u/NuZpvLZ7PvsEOl2grO9gpzupH4EvIzApz8fGZ1PjM6u3+2jM03ht43Zjnv6W01t+xwkMBIEbbrgh+5007vXggw/OFuR89913e9x6Y2+0+L7keSK4EnMQlvXMLKucHhVwgMAAE2j8LsSt5wHN3qqx5pprFqeeeOKJYrusZ11Z5RQ3ZoNAGwKGwLeBJAuBqUkgfkncdNNNi+DnLLPMkq6++uqWwc+4/1jUaIEFFsiq8vLLL/dZpcY8w4YNK/IvtthixXZjnuJgt408T/wyGysOSgSqKlDWd7CqfupNoC+BSX0+lvVcK6ucvurpPIGpWeCaa64pbu+kk05Kc8wxR9OfK6+8ssi36qqrFnnuvPPO7HhZz8yyyilu1gaBASowfPjw4s6jd3bjfnGiYWPUqFHF3rPPPltsl/WsK6uc4sZsEGhDQA/QNpBkITA5BBofJM3Kj8Bm9xQTUa+//vrZBNVxbv7550+xwMNKK63UPWvT/ZiH6dVXX03xR+Kbb76Z5plnnqb54uAzzzxTnFtllVWK7SgjT33VIeZ+evHFF7PsX/va1yZpCFT+Xl4JdIJAGd/BTnBQBwJlC3yZ52NZz7WyyinbRnkEBqpAWc/MssoZqI7um0AIREeU+Nvxtddey+bnfe6551JjkLO70jvvvFMciv/IyFNZz7qyysnvyyuBdgQEQNtRkofAZBAYOXJkv0qNoUEbbrhhEfyMCapjGP2IESPaLme11VZLY8eOzfLHXC6xeFJv6fbbby9OxXV5Wm655VKs5vfJJ590mRA7P9/4et9992X54lhjGY15bBOokkAZ38EqeakrgXYEvuzzsaznWlnltFNneQhMrQKbbLJJivkD+0rRAzQfVrvPPvsU1zT+flzWM7Oscvqqk/MEpnaB+C7EyMFId999d8sAaGNnmEUWWaSoWlnPurLKKW7MBoF2BGoSAQIDQmCHHXao1b/T2U+9R2ZtwoQJ/b7vhx9+uCijvghTr9fXe23W6kMjsrwrr7xyj3xxbX4vDz30UI/z+YH6PKVFvmuvvTY/7JVAZQXK+g5WFlDFCTQRKOP5WNZzraxymlTTIQIdJdD4va0vmNS0bmU9M8sqp+lNOkhgAAnUV3Qv/jarT6lWmzhxYq93v8022xR5651uuuQr61lXVjldbs4OgRYCqcU5pwgQmEoE6r02iwdQfehCrT50YZLvrD5cPitrmmmmqY0ZM6ZHOR9++GFtgw02KN7viiuu6JGnPuy+OB/B2PoQiR55rrvuuiKIWu+t2vIB2+NiBwh0sEAZ38EO5lE1Av0SKOv5WNZzraxy+oUgM4EBKNBOADSqVdYzs6xyBiC1WyZQCHz00Ue1ei/r4u+4I444ojjXuNH4LFtooYVq9QUGG0/XGs9/mb8Fyyqny83ZIdBCQAC0BY5TBKYGgfpQ89qSSy5ZPKiWWGKJ2mabbdbWz+mnn96jCvXhDrUIfkYPzmmnnbZ2/PHH155//vlafTXOWn3Ye62+ImDxXquvvnrt888/71FGHNh4442LfMsvv3ztlltuqcVD9ZVXXqmddtppRfAz3kPvz6aEDlZUoKzvYEX5VJtAIVD287Gs51pZ5RQVtUGgAwXaDYCW9cwsq5wObApVqpjATTfdVBs0aFDxd9y+++5b++tf/1qrr91Qe+mll2onnHBCl/ORv1kq61lXVjnN7tExAt0FBEC7i9gnMJUJ3HjjjcUDKh923u7rnnvu2bQ2MfyhvshSl3LzIe952fVJsWv1hZKaXh8HYwh+fU7SlmVEWaeeemqvZThBoKoCZXwHq2qn3gRygbKfj2U918oqJ6+nVwKdKNBuADTqXtYzs6xyOrE91KlaArfeemttvvnm6/J33ODBg7vsRyeWVn/HlfWsK6ucarWg2k6qwLT1AIVEgMBULDBu3LjS72777bdP99xzT6rP75nq/wOYlR8rtkeqP/zSgQcemJ1vNYn9nHPOmW644YZ01FFHpdiOlJcR27Hqe30YfFZW7EsECHwhUMZ38IvSbBGopkDZz8eynmtllVPNVlVrAj0FynpmllVOzzt0hMDAElh33XVTfW7cVJ+DMw0ZMiS7+fpowKISa621Vrr55ptb/h1X1rOurHKKm7dBoIXANBE5bXHeKQIEOlygPudneuSRR1J9yENadNFFU324fRo6dGi/az1+/PjsQTrTTDOl+jD9FKsF1v/nsN/luIBA1QTK+g5WzU19CUxugbKea2WVM7nrq3wCA0GgrGdmWeUMBDP3SKCVQISDnn766fToo4+m2WabLfs7Lv4m7G8q61lXVjn9vX/5qyEgAFqNdlZLAgQIECBAgAABAgQIECBAgAABApUU0D2rks2u0gQIECBAgAABAgQIECBAgAABAgSqISAAWo12VksCBAgQIECAAAECBAgQIECAAAEClRQQAK1ks6s0AQIECBAgQIAAAQIECBAgQIAAgWoICIBWo53VkgABAgQIECBAgAABAgQIECBAgEAlBQRAK9nsKk2AAAECBAgQIECAAAECBAgQIECgGgICoNVoZ7UkQIAAAQIECBAgQIAAAQIECBAgUEkBAdBKNrtKEyBAgAABAgQIECBAgAABAgQIEKiGgABoNdpZLQkQIECAAAECBAgQIECAAAECBAhUUkAAtJLNrtIECBAgQIAAAQIECBAgQIAAAQIEqiEgAFqNdlZLAgQIECBAgAABAgQIECBAgAABApUUEACtZLOrNAECBAgQIECAAAECBAgQIECAAIFqCAiAVqOd1ZIAAQIECBAgQIAAAQIECBAgQIBAJQUEQCvZ7CpNgAABAgQIECBAgAABAgQIECBAoBoCAqDVaGe1JECAAAECBAgQIECAAAECBAgQIFBJAQHQSja7ShMgQIAAAQIECBAgQIAAAQIECBCohoAAaDXaWS0JECBAgAABAgQIECBAgAABAgQIVFJAALSSza7SBAgQIECAAAECBAgQIECAAAECBKohIABajXZWSwIECBAgQIAAAQIECBAgQIAAAQKVFBAArWSzqzQBAgQIECBAgAABAgQIECBAgACBaggIgFajndWSAAECBAgQIECAAAECBAgQIECAQCUFBEAr2ewqTYAAAQIECBAgQIAAAQIECBAgQKAaAgKg1WhntSRAgAABAgQIECBAgAABAgQIECBQSQEB0Eo2u0oTIECAAAECBAgQIECAAAECBAgQqIaAAGg12lktCRAgQIAAAQIECBAgQIAAAQIECFRSQAC0ks2u0gQIECBAgAABAgQIECBAgAABAgSqISAAWo12VksCBAgQIECAAAECBAgQIECAAAEClRQQAK1ks6s0AQIECBAgQIAAAQIECBAgQIAAgWoICIBWo53VkgABAgQIECBAgAABAgQIECBAgEAlBQRAK9nsKk2AAAECBAgQIECAAAECBAgQIECgGgICoNVoZ7UkQIAAAQIECBAgQIAAAQIECBAgUEkBAdBKNrtKEyBAgAABAgQIECBAgAABAgQIEKiGgABoNdpZLQkQIECAAAECBAgQIECAAAECBAhUUkAAtJLNrtIECBAgQIAAAQIECBAgQIAAAQIEqiEgAFqNdlZLAgQIECBAgAABAgQIECBAgAABApUUEACtZLOrNAECBAgQIECAAAECBAgQIECAAIFqCAiAVqOd1ZIAAQIECBAgQIAAAQIECBAgQIBAJQUEQCvZ7CpNgAABAgQIECBAgAABAgQIECBAoBoCAqDVaGe1JECAAAECBAgQIECAAAECBAgQIFBJAQHQSja7ShMgQIAAAQIECBAgQIAAAQIECBCohoAAaDXaWS0JECBAgAABAgQIECBAgAABAgQIVFJAALSSza7SBAgQIECAAAECBAgQIECAAAECBKohIABajXZWSwIECBAgQIAAAQIECBAgQIAAAQKVFBAArWSzqzQBAgQIECBAgAABAgQIECBAgACBaggIgFajndWSAAECBAgQIECAAAECBAgQIECAQCUFBEAr2ewqTYAAAQIECBAgQIAAAQIECBAgQKAaAgKg1WhntSRAgAABAgQIECBAgAABAgQIECBQSQEB0Eo2u0oTIECAAAECBAgQIECAAAECBAgQqIaAAGg12lktCRAgQIAAAQIECBAgQIAAAQIECFRSQAC0ks2u0gQIECBAgAABAgQIECBAgAABAgSqISAAWo12VksCBAgQIECAAAECBAgQIECAAAEClRQQAK1ks6s0AQIECBAgQIAAAQIECBAgQIAAgWoITFeNaqolAQIECBAgQIBAo0CtVkvjx49Pzz//fJpjjjnSfPPNl4YPH96YxXYbAmPHjk0ff/xxWnHFFdMCCyzQxhXlZ/nss8/SK6+8kl566aU000wzpcUWWyzNNddc5b+REgkQIECAAAECA1Rgmvovv7UBeu9umwABAgQIECBAoB8CTz/9dDrzzDPTww8/nP7xj3+k//znP12uHjlyZNp8883TQQcdlBZddNEu59rZueCCC9JRRx1VZL355pvTsssuW+z3trHtttumu+66q7fTbR8/+eST02233Zauv/767Jrdd989/exnP2v7+jzjU089ldZee+1sNwLD4TXttD0HTr3++utp/vnnT/Hr9Lhx49LSSy+dPvnkkxSOrdI000yTBg8enGacccbs+tVXXz3ttttu6Stf+Uqry7qc++ijj9If/vCHdP7556c77rgjTZw4scv5oUOHZm15zDHHpCWXXLLLuf7sbLLJJunGG29MZ5xxRtp///37c6m8BAgQIECAAIGpRkAAdKppCjdCgAABAgQIEJh8AieccEI67rjjUvQW7CvNMMMM6dhjj01HHHFEX1m7nF911VXT/fffXxzbZ5990jnnnFPs97ax7rrrZoHL3s63e/zcc89Nw4YNS1tvvXV2SQQv//Wvf6VBgwa1W0SW70c/+lE68cQTs+1DDz00RWC1WRozZkzaZZdd0ogRI7Lel5EnAqAR2JyUtMYaa6TLL788K6/V9RFY3nnnndMbb7zRKlt2Luq+6667prCZfvrp+8zfmOHss89O++23X3ZIALRRxjYBAgQIECAw0AQMgR9oLeZ+CRAgQIAAAQL9FDj11FPT0UcfXVy14IILpi233DLF69xzz52iJ+MLL7yQrr766vTOO+9kQbwjjzwy6/V42GGHFde12njssceK4GcMv54wYUK65JJLsuDhkCFDWl2a9VD873//2zTPBx98kJ588snsXAQWl1lmmab54uA888yTNttss2w4f9Qpfv7yl7+kjTfeuNdrup+InpQR2MzTnnvumW/2eL3pppuyYxtttFGPc/mBlVdeOd8sXqPH6P/+97/04YcfppdffjkbQh8n77777rTVVltlPTpnnnnmIn/jRgSxf/rTnxY9PmPI+ze/+c2s9+nCCy+clfncc8+lP/7xjyl6sn7++ecpeua+//776bLLLms7GHzppZemH/zgB41vbZsAAQIECBAgMHAFYgi8RIAAAQIECBAg0JkC9V6CtXqQLKY8yn5OOumkWr2XYtPKvvfee7Xvf//7Rd5678Has88+2zRv94MHHnhgdl29l2Gt3luwKKPei7B71n7tP/DAA0VZ9eBnW9fWg7bFNTvuuGNb1+SZ6gHT4tr/+7//yw/3eK0HSmv1gGuWtz4UvThfnw+0uD7M+0r1oey1eqCxyzX1XqVNLxs9enSXfPUh6bV6kLdp3nqAtXbeeefVog3zto/36SvVg7K1eq/XLtfF9dGmEgECBAgQIEBgoAr0nMyo/huORIAAAQIECBAg0BkC0Zsx5ouMFPNMHn744dn8k81qN9tss2VzhMa8j5Gi92D0Hu0rffrpp+niiy/Osq255prpO9/5TjHcOoZeT+m0xx57FG8ZvVqjF2m76be//W2R9bvf/W6x3X3jwQcfTG+++Waabrrp0je+8Y3up9vej16tp59+ejbHZn7RVVddVfTwzI9Fr84f/vCH+W42RD+Gpc8777zFscaNGPr+ve99L/3qV78qDsd0BLFQUm/p73//e1puueXSKaeckrV9b/kcJ0CAAAECBAgMNAEB0IHWYu6XAAECBAgQINAPgSeeeKLIHcPe+0qxQM/BBx9cZLvzzjuL7d42/vznP6e33norOx3Dzeecc85i2HkstlTGAke9vXez47Hoz1prrZWdiuBnBEHbSTFM/Morr8yyzj777Olb3/pWr5fFwkCRVltttRR5v2yKoHEELSPFfTz++ONdiowpCfJpAmKO09hvJ8UQ/phjNVIMu//lL3/Z47J6T45srs911lknPfPMM9n5WJDJEPgeVA4QIECAAAECA1RAAHSANpzbJkCAAAECBAi0IxC9OPPUfdX3/Hj31wgexryW3/72t9vq3Vgfml0UkfcejUV68tTOQkh53rJeG+fujLlI20mxqnrMyxkpApIxv2ZvqZ35P3u7ttnxCBpHMDVPjYtJRbtFkDlSBKhjHtD+pEMOOSRF794ITsc8od1TzHsaCx5FIDRS2EUP1+gNmqd4X4kAAQIECBAgMFAFLII0UFvOfRMgQIAAAQIE2hBoXDQoVjPfdtttU1+LEsUq8HkPx77eIlZZj1XJI0WvwWWXXTbb3mKLLbKeke+++2664oorsp6HsTjSlErRe/OAAw7IelPGQkixYnpvw8Xze/rNb36Tb6ZWw9/rc6Wme+65J8vbnwWWisJ72fjss8+KMxEQzVP0Sq3PLZrtLr744oVxfr6v10033TRb3GraaVv3fdhwww3Tj3/84xTTGEgECBAgQIAAgU4SaP1bUCfVVF0IECBAgAABAhUU2HXXXYuejDEcfrHFFstWZh8/fnwpGjFnZt7LNN4rTzG35Q477JDt1hddShdeeGF+aoq8zjLLLMX7x9Dvyy+/vOX7hsftt9+e5VlxxRXTCius0Gv+sWPHZsPJ55577rTSSiv1mq8/J6KX57hx44pLGgPXd9xxR3F86aWXLrb7s9Eq+BnnIqAbvVoFP/ujKi8BAgQIECAwUAQEQAdKS7lPAgQIECBAgMAkCESQLoKP+RDmWLgnFkJadNFF0xJLLJHqK4ln815OmDCh36XHkOk8sBlBtMZh71HY7rvvXpQZi/HkQ6yLg5N5o7EXZ75IU29vedFFFxX313hds/x579gNNtggtQosNru22bFwiXbIh98PHz48C1TneaOXbZ4mNQCaX9/sNT4bjcPvm+VxjAABAgQIECAwkAUEQAdy67l3AgQIECBAgEAbAjGX53XXXZcWXHDBLrljwZuzzjorbbfddmmeeebJ5nw89thj01NPPdUlX287sWr4s88+m52OYGD38lddddW01FJLZecjXwxFn5Ip3j/vSXnfffcVC/x0v4cIQEYANNLMM8+czf/ZPU/jfj7/ZzvD36P3abOfWDTq0UcfTTE/afQ4HTNmTPEWsbp7Y2D11VdfLc5NjgBoUbgNAgQIECBAgECHCgiAdmjDqhYBAgQIECBAoFEgFieKgGfMc7neeuulwYMHN57Oej/Giu2xwE4ELffYY48UvUVbpQsuuKA4vdtuuxXbjRuNx6fWxZBiiPnzzz+f3XbMHTp06NDGKnTZjmkEXn755axHbcyZ2VeafvrpU7OfCDgvv/zyaaeddkqPPPJIUUz0oo1V3htTzKOapxEjRuSbXgkQIECAAAECBNoUEABtE0o2AgQIECBAgMBAF4hVzWOezltuuSW9/fbb2criBx54YNbzMx8iH3XMh7avssoqWbCvWb1jzspYNT1SBAy7B+3yayKgN2jQoGw3VjJvHM6d55mcrxFgzIO9va0GH/OY5qnd4e+xQvqwYcPyy77066hRo7L2yHuiNhYYwdI8NQZD82NeCRAgQIAAAQIEWgtYBb61j7MECBAgQIAAgY4UiEWCNt988+wnKhhDsmOY/Pnnn5/yRXdefPHFLLD5wAMPFHOI5hixqFA+Z2XMJxo9S3tL0WvxhRdeyBZL+vWvf51imP2USjEH6lZbbZWtRB/D8O+9994u811GHX7/+99ntxOr2K+11lotby2f/3OjjTZqmS8/2VtgeK655koLL7xwGjlyZPYac3Dmgdr82vy1MdAa7SQRIECAAAECBAj0T0AAtH9echMgQIAAAQIEOlIgAoXROzR+oqdkLGD02WefpYceeihF0C+G0Dem0aNHF7sPP/xw2nfffYv9VhsRYD366KPTdNNNuV9D99xzzywAGvcViyE1Lvhz1VVXpffffz+75cjXKkWwNOY9jdTO/J+RL8r/smmhhRYqivjnP/9ZbNsgQIAAAQIECBBoT8AQ+Pac5CJAgAABAgQIDDiB6Ln5i1/8Ih122GHp9ttvb/v+d9xxx7TlllsW+fOgX35g3LhxWU/KfL8/rzEE/pprrunPJV86byzQlAcRo+dqLEqUp3z4e/S+jOBvq/S3v/0tffLJJ2nIkCFpzTXXbJW11HONc41OykJSMaXBV7/61RQOJ554Ynr99ddLvT+FESBAgAABAgSmdgEB0Km9hdwfAQIECBAgQGASBe6+++50yCGHpFNOOSWdfvrp/Splm222KfJ3n7fzwgsvLM4dfPDB6Z133unzZ7/99iuuOffcc4vtKbERK6rnizHFwk4333xz9rZRr5gPNVIEfBvn2swOdvsnH/6+7rrrZgsbdTs92XYjADrjjDNm5cfK8ePHj+/Xe915550pFm8aO3ZsOv7443sdat+vQmUmQIAAAQIECAwgAQHQAdRYbpUAAQIECBAg0B+BpZdeush+/fXXpw8++KDY72ujMci24IILFtljWPyYMWOK/VgtfvbZZ+/zZ6+99iquiUBczMc5JVMM6c8XesoXb7riiivSxIkTs9voa/GjyJQHQNsd/l5W/Waeeea0xRZbZMVFb84jjjiiX0WfffbZRf7tt98+zTHHHMW+DQIECBAgQIBAFQQEQKvQyupIgAABAgQIVFJg7bXXzhbZicrH/JXR+6+d9Omnn6bf/e53Rdavf/3rxfa1116b3njjjWx/xRVXTI1B1iJTk41ll102rbzyytmZCOJN6V6gseDQ+uuvn71/DMGPYfB5IDQWIorh4a1SBISffvrpLEu7CyC1Kq+/52Lo+gwzzJBdFos2XXrppW0VEc6XXXZZkXfvvfcutm0QIECAAAECBKoiIABalZZWTwIECBAgQKByArHQ0OGHH17U+6STTsoWN2o1B+RTTz2V1lhjjRTzfEaKldFjtfg8XXDBBflm2nnnnYvtdjait2ieYtX4jz/+ON+dIq/5IkcTJkzIgoJ33XVX9r7ROzSGybdKN910U3Z68cUXT7Hq/ZROo0aNSgcddFDxtjvttFMW0I45SZul6Kl72mmnpQMOOKA4HcHPaFuJAAECBAgQIFA1gWnq/wNfq1ql1ZcAAQIECBAgUBWB+FVvl112yVY/z+scgdGllloqrbDCCil6ccb8kjFHZAQ9Iyj40UcfZVlnmWWWdNtttxU9N1977bU0YsSI9Pnnn6dBgwalmENzvvnmy4vt8/W9995L888/f1H+RRdd1GcQ9cEHHyzef5lllkmPPfZYn+/TW4YIFi6wwALp7bffTnPNNVeKQGgEPl944YWsXr1dF8e33nrr9Kc//Sntv//+6Ywzzug1a7xHPl9nZCrzV+0oO4LOMXQ/T/POO282v+nyyy+fhg8fnl599dUUK8Vfcskl6ZlnnsmzpU033TRbfCrard00evTolE8NcOaZZ6bGeVzbLUM+AgQIECBAgMDUIDDd1HAT7oEAAQIECBAgQGDyCMS8l9FrM+bpjLkgY87LGP4dgcT4iSBksxTzfsa5fNh65In9CH5GiiHj/Ql+xjVDhw5N2223XRGMPeecc/oMgMZ1ZaUYQh49J2NBqAh+Rorh7BHUbZWiN+Wtt96aZZnS83823lfcf0xNMGzYsCIIG9MRnHzyyY3ZumxH+++www7pvPPOy4LWXU7aIUCAAAECBAhURKD1WJ+KIKgmAQIECBAgQKCTBaaffvosYHb//fdnvUGj12BvKYZ4H3PMMenJJ59M6623XpdsX2b4e15Q4zD4WKU+VjWfkikfBp+/Z97DMd9v9hq9Yt9///1sDs511lmnWZYpdix6rEYAN3rr7rvvvil66TZL0ct3k002SdGDNuYLnXXWWZtlc4wAAQIECBAgUAkBQ+Ar0cwqSYAAAQIECBD4QiCGZccw6X//+9/prbfeSrHoUfTmjMWAIgAqdRV4/PHH07333putnr7tttt2Pfn/eS96p8ZUBK+88kr2M2TIkLTEEkukRRZZJEUQVCJAgAABAgQIEEhJANSngAABAgQIECBAgAABAgQIECBAgACBjhUwBL5jm1bFCBAgQIAAAQIECBAgQIAAAQIECBAQAPUZIECAAAECBAgQIECAAAECBAgQIECgYwUEQDu2aVWMAAECBAgQIECAAAECBAgQIECAAAEBUJ8BAgQIECBAgAABAgQIECBAgAABAgQ6VkAAtGObVsUIECBAgAABAgQIECBAgAABAgQIEBAA9RkgQIAAAQIECBAgQIAAAQIECBAgQKBjBQRAO7ZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQFQnwECBAgQIECAAAECBAgQIECAAAECBDpWQAC0Y5tWxQgQIECAAAECBAgQIECAAAECBAgQEAD1GSBAgAABAgQIECBAgAABAgQIECBAoGMFBEA7tmlVjAABAgQIECBAgAABAgQIECBAgAABAVCfAQIECBAgQIAAAQIECBAgQIAAAQIEOlZAALRjm1bFCBAgQIAAAQIECBAgQIAAAQIECBAQAPUZIECAAAECBAgQIECAAAECBAgQIECgYwUEQDu2aVWMAAECBAgQIECAAAECBAgQIECAAAEBUJ8BAgQIECBAgAABAgQIECBAgAABAgQ6VkAAtGObVsUIECBAgAABAgQIECBAgAABAgQIEBAA9RkgQIAAAQIECBAgQIAAAQIECBAgQKBjBQRAO7ZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQFQnwECBAgQIECAAAECBAgQIECAAAECBDpWQAC0Y5tWxQgQIECAAAECBAgQIECAAAECBAgQEAD1GSBAgAABAgQIECBAgAABAgQIECBAoGMFBEA7tmlVjAABAgQIECBAgAABAgQIECBAgAABAVCfAQIECBAgQIAAAQIECBAgQIAAAQIEOlZAALRjm1bFCBAgQIAAAQIECBAgQIAAAQIECBAQAPUZIECAAAECBAgQIECAAAECBAgQIECgYwUEQDu2aVWMAAECBAgQIECAAAECBAgQIECAAAEBUJ8BAgQIECBAgAABAgQIECBAgAABAgQ6VkAAtGObVsUIECBAgAABAgQIECBAgAABAgQIEBAA9RkgQIAAAQIECBAgQIAAAQIECBAgQKBjBQRAO7ZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQFQnwECBAgQIECAAAECBAgQIECAAAECBDpWQAC0Y5tWxQgQIECAAAECBAgQIECAAAECBAgQEAD1GSBAgAABAgQIECBAgAABAgQIECBAoGMFBEA7tmlVjAABAgQIECBAgAABAgQIECBAgAABAVCfAQIECBAgQIAAAQIECBAgQIAAAQIEOlZAALRjm1bFCBAgQIAAAQIECBAgQIAAAQIECBAQAPUZIECAAAECBAgQIECAAAECBAgQIECgYwUEQDu2aVWMAAECBAgQIECAAAECBAgQIECAAAEBUJ8BAgQIECBAgAABAgQIECBAgAABAgQ6VkAAtGObVsUIECBAgAABAgQIECBAgAABAgQIEBAA9RkgQIAAAQIECBAgQIAAAQIECBAgQKBjBQRAO7ZpVYwAAQIECBAgQIAAAQIECBAgQIAAAQFQnwECBAgQIECAAAECBAgQIECAAAECBDpWQAC0Y5tWxQgQIECAAAECBAgQIECAAAECBAgQEAD1GSBAgAABAgQIECBAgAABAgQIECBAoGMFBEA7tmlVjAABAgQIECBAgAABAgQIECBAgACB/wdUI+mV/VlOOgAAAABJRU5ErkJggg==)
#Principal Components analysis of Gambling Data------------------------------------------------------------
#listwise removal of missing data (common in PCA -- but still a problem)
data02a = data02[which(is.na(data02$X1)==FALSE & is.na(data02$X3)==FALSE & is.na(data02$X5)==FALSE & is.na(data02$X9)==FALSE & is.na(data02$X10)==FALSE &
is.na(data02$X13)==FALSE & is.na(data02$X14)==FALSE & is.na(data02$X18)==FALSE & is.na(data02$X21)==FALSE & is.na(data02$X23)==FALSE),]
#analysis of covariance matrix of gambling data items
gambling_pca_cov = prcomp(x = data02a, scale. = FALSE)
gambling_pca_cov
## Standard deviations:
## [1] 2.0484706 1.3228618 1.0883066 0.8360793 0.7509565 0.7336463 0.6861620
## [8] 0.6483578 0.5823026 0.4643768
##
## Rotation:
## PC1 PC2 PC3 PC4 PC5
## X1 -0.3126472 0.10459331 0.1662869 -0.843735151 0.364943452
## X3 -0.2262923 0.07828499 0.1775587 -0.133596441 -0.524752411
## X5 -0.3199396 0.09403404 0.2294114 0.025555994 -0.492411902
## X9 -0.2433589 0.08687880 0.1596160 0.030715826 -0.117324662
## X10 -0.2798829 0.08714575 0.2352173 0.084941435 -0.194587548
## X13 -0.3293561 0.15569572 0.1863762 0.220349288 0.326257817
## X14 -0.4493615 -0.86166945 -0.2311989 0.005504759 -0.004148522
## X18 -0.3471876 0.40458523 -0.8343757 -0.028338155 -0.100291367
## X21 -0.2720582 0.13735301 0.0617232 0.198445605 0.179410916
## X23 -0.3258344 0.09836941 0.1385856 0.415552100 0.385544166
## PC6 PC7 PC8 PC9 PC10
## X1 -0.08957759 0.090472261 0.002045810 0.028416754 0.039583080
## X3 0.68009146 0.343237952 -0.191565009 -0.015252456 0.025623885
## X5 -0.62592643 -0.090211626 -0.395234597 -0.043942452 0.187031376
## X9 -0.06172329 -0.152582899 0.152138070 0.044974999 -0.916942010
## X10 -0.02579154 -0.093484479 0.842208707 -0.027303911 0.306918670
## X13 0.29451341 -0.504704067 -0.225373117 -0.524435934 0.101286325
## X14 0.02615629 -0.037268705 -0.001356776 -0.004497823 0.002084634
## X18 -0.01866511 0.009093169 0.052785485 -0.074203041 0.001980035
## X21 0.15471581 -0.249558686 -0.142870079 0.841805796 0.128352736
## X23 -0.14657740 0.717890018 -0.025173918 -0.071163812 -0.032749511
summary(gambling_pca_cov)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 2.0485 1.3229 1.0883 0.83608 0.75096 0.73365
## Proportion of Variance 0.4043 0.1686 0.1141 0.06736 0.05434 0.05186
## Cumulative Proportion 0.4043 0.5730 0.6871 0.75447 0.80881 0.86067
## PC7 PC8 PC9 PC10
## Standard deviation 0.68616 0.64836 0.58230 0.46438
## Proportion of Variance 0.04537 0.04051 0.03267 0.02078
## Cumulative Proportion 0.90604 0.94655 0.97922 1.00000
prop_var = t(summary(gambling_pca_cov)$importance[2:3,])
#creating a scree plot and a proportion of variance plot
par(mfrow = c(1,2))
plot(gambling_pca_cov, type="l", main = "Scree Plot of PCA Eigenvalues", lwd = 5)
matplot(prop_var, type="l", main = "Proportion of Variance Explained by Component", lwd = 5)
legend(x=5, y=.5, legend = c("Component Variance", "Cumulative Variance"), lty = 1:2, lwd=5, col=1:2)
![](data:image/png;base64,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)
#new variables from PCA:
View(gambling_pca_cov$x)
#EFA of Gambling Data with crazy constraints ------------------------------------------------------------------------------------
#step 1: determine number of factors in data
#one-factor model
EFA_1factor = factanal(x = data02a, factors = 1, rotation = "none")
EFA_1factor
##
## Call:
## factanal(x = data02a, factors = 1, rotation = "none")
##
## Uniquenesses:
## X1 X3 X5 X9 X10 X13 X14 X18 X21 X23
## 0.677 0.728 0.550 0.417 0.527 0.488 0.857 0.816 0.538 0.573
##
## Loadings:
## Factor1
## X1 0.569
## X3 0.521
## X5 0.670
## X9 0.764
## X10 0.688
## X13 0.715
## X14 0.378
## X18 0.429
## X21 0.680
## X23 0.653
##
## Factor1
## SS loadings 3.828
## Proportion Var 0.383
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 161.14 on 35 degrees of freedom.
## The p-value is 4.23e-18
#two-factor model
EFA_2factor = factanal(x = data02a, factors = 2, rotation = "none")
EFA_2factor
##
## Call:
## factanal(x = data02a, factors = 2, rotation = "none")
##
## Uniquenesses:
## X1 X3 X5 X9 X10 X13 X14 X18 X21 X23
## 0.680 0.727 0.525 0.375 0.488 0.456 0.858 0.774 0.424 0.564
##
## Loadings:
## Factor1 Factor2
## X1 0.563
## X3 0.517
## X5 0.669 -0.166
## X9 0.770 -0.181
## X10 0.690 -0.190
## X13 0.720 0.160
## X14 0.374
## X18 0.432 0.200
## X21 0.700 0.293
## X23 0.652 0.104
##
## Factor1 Factor2
## SS loadings 3.861 0.269
## Proportion Var 0.386 0.027
## Cumulative Var 0.386 0.413
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 56.43 on 26 degrees of freedom.
## The p-value is 0.000497
#constraint demonstration (lambda^T psi-1 lambda = diag)
Lambda = matrix(EFA_2factor$loadings, ncol=2)
Psi = diag(EFA_2factor$uniquenesses)
t(Lambda) %*% solve(Psi) %*% Lambda
## [,1] [,2]
## [1,] 7.698164e+00 -1.94289e-16
## [2,] 4.163336e-17 5.57827e-01
#three-factor model
EFA_3factor = factanal(x = data02a, factors = 3, rotation = "none")
EFA_3factor
##
## Call:
## factanal(x = data02a, factors = 3, rotation = "none")
##
## Uniquenesses:
## X1 X3 X5 X9 X10 X13 X14 X18 X21 X23
## 0.661 0.714 0.534 0.005 0.523 0.464 0.831 0.782 0.388 0.548
##
## Loadings:
## Factor1 Factor2 Factor3
## X1 0.438 0.347 0.164
## X3 0.402 0.315 0.159
## X5 0.563 0.331 0.198
## X9 0.997
## X10 0.586 0.322 0.171
## X13 0.541 0.487
## X14 0.275 0.257 0.164
## X18 0.299 0.344 -0.101
## X21 0.508 0.537 -0.256
## X23 0.463 0.485
##
## Factor1 Factor2 Factor3
## SS loadings 2.940 1.378 0.231
## Proportion Var 0.294 0.138 0.023
## Cumulative Var 0.294 0.432 0.455
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 31.24 on 18 degrees of freedom.
## The p-value is 0.027
#constraint demonstration (lambda^T psi-1 lambda = diag)
Lambda = matrix(EFA_3factor$loadings, ncol=3)
Psi = diag(EFA_3factor$uniquenesses)
t(Lambda) %*% solve(Psi) %*% Lambda
## [,1] [,2] [,3]
## [1,] 2.026141e+02 2.220446e-16 -3.122502e-16
## [2,] -2.220446e-16 2.683701e+00 -1.873501e-16
## [3,] -2.636780e-16 -1.942890e-16 4.352294e-01
#four-factor model
EFA_4factor = factanal(x = data02a, factors = 4, rotation = "none")
EFA_4factor
##
## Call:
## factanal(x = data02a, factors = 4, rotation = "none")
##
## Uniquenesses:
## X1 X3 X5 X9 X10 X13 X14 X18 X21 X23
## 0.529 0.718 0.535 0.324 0.495 0.489 0.848 0.778 0.292 0.005
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## X1 0.356 0.457 0.127 0.346
## X3 0.322 0.403
## X5 0.452 0.480 0.174
## X9 0.468 0.630 0.196 -0.144
## X10 0.458 0.502 0.186
## X13 0.509 0.494
## X14 0.292 0.227
## X18 0.316 0.294 -0.141 0.128
## X21 0.491 0.548 -0.408
## X23 0.997
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 2.542 1.933 0.327 0.183
## Proportion Var 0.254 0.193 0.033 0.018
## Cumulative Var 0.254 0.447 0.480 0.499
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 10.2 on 11 degrees of freedom.
## The p-value is 0.512
#constraint demonstration (lambda^T psi-1 lambda = diag)
Lambda = matrix(EFA_4factor$loadings, ncol=4)
Psi = diag(EFA_4factor$uniquenesses)
t(Lambda) %*% solve(Psi) %*% Lambda
## [,1] [,2] [,3] [,4]
## [1,] 2.023692e+02 0.000000e+00 -2.081668e-16 1.318390e-16
## [2,] 0.000000e+00 4.558736e+00 7.524363e-17 -1.139496e-16
## [3,] -1.942890e-16 1.860491e-16 9.039206e-01 -2.110721e-16
## [4,] 6.245005e-17 -1.070108e-16 -2.041332e-16 3.539411e-01
#step 2: interpret the factors for final solution (4-factor model)
#varimax rotation
EFA_4factor_varimax = factanal(x = data02a, factors = 4, rotation = "varimax")
EFA_4factor_varimax
##
## Call:
## factanal(x = data02a, factors = 4, rotation = "varimax")
##
## Uniquenesses:
## X1 X3 X5 X9 X10 X13 X14 X18 X21 X23
## 0.529 0.718 0.535 0.324 0.495 0.489 0.848 0.778 0.292 0.005
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## X1 0.301 0.209 0.114 0.569
## X3 0.360 0.213 0.113 0.307
## X5 0.532 0.224 0.202 0.301
## X9 0.714 0.291 0.157 0.239
## X10 0.597 0.231 0.202 0.234
## X13 0.414 0.461 0.236 0.268
## X14 0.224 0.129 0.162 0.242
## X18 0.147 0.346 0.144 0.246
## X21 0.300 0.746 0.184 0.166
## X23 0.266 0.270 0.904 0.188
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 1.772 1.255 1.086 0.873
## Proportion Var 0.177 0.125 0.109 0.087
## Cumulative Var 0.177 0.303 0.411 0.499
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 10.2 on 11 degrees of freedom.
## The p-value is 0.512
#promax rotation
EFA_4factor_varimax = factanal(x = data02a, factors = 4, rotation = "promax")
EFA_4factor_varimax
##
## Call:
## factanal(x = data02a, factors = 4, rotation = "promax")
##
## Uniquenesses:
## X1 X3 X5 X9 X10 X13 X14 X18 X21 X23
## 0.529 0.718 0.535 0.324 0.495 0.489 0.848 0.778 0.292 0.005
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## X1 -0.104 0.861
## X3 0.249 0.308
## X5 0.530 0.171
## X9 0.885
## X10 0.699
## X13 0.254 0.368 0.101
## X14 0.274
## X18 -0.126 0.314 0.269
## X21 0.906 -0.106
## X23 1.037
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 1.712 1.102 1.072 1.040
## Proportion Var 0.171 0.110 0.107 0.104
## Cumulative Var 0.171 0.281 0.389 0.493
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4
## Factor1 1.000 0.635 -0.628 -0.631
## Factor2 0.635 1.000 -0.736 -0.852
## Factor3 -0.628 -0.736 1.000 0.768
## Factor4 -0.631 -0.852 0.768 1.000
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 10.2 on 11 degrees of freedom.
## The p-value is 0.512
#CFA version of EFA using lavaan ---------------------------------------------------------------------------------------
#NOTE: THIS VERSION USES DATA WHERE ANY INCOMPLETE OBSERVATIONS ARE REMOVED TO MATCH NUMBERS (EXAMPLE PURPOSES ONLY)
#IN PRACTICE: DO NOT USE LISTWISE DELETION AS ML WILL STILL WORK!
#step #1: determining number of factors
#one factor CFA
CFA_1factor.syntax = "
factor1 =~ X1 + X3 + X5 + X9 + X10 + X13 + X14 + X18 + X21 + X23
"
#for comparison with EFA we are using standardized factors (var = 1; mean = 0)
CFA_1factor.model = cfa(model = CFA_1factor.syntax, data = data02a, estimator = "MLR", std.lv = TRUE)
summary(CFA_1factor.model, fit.measures = TRUE, standardized = TRUE)
## lavaan (0.5-20) converged normally after 26 iterations
##
## Number of observations 1333
##
## Estimator ML Robust
## Minimum Function Test Statistic 161.846 104.001
## Degrees of freedom 35 35
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.556
## for the Yuan-Bentler correction
##
## Model test baseline model:
##
## Minimum Function Test Statistic 4148.081 2238.585
## Degrees of freedom 45 45
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.969 0.969
## Tucker-Lewis Index (TLI) 0.960 0.960
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -16575.733 -16575.733
## Scaling correction factor 2.354
## for the MLR correction
## Loglikelihood unrestricted model (H1) -16494.810 -16494.810
## Scaling correction factor 1.924
## for the MLR correction
##
## Number of free parameters 30 30
## Akaike (AIC) 33211.466 33211.466
## Bayesian (BIC) 33367.322 33367.322
## Sample-size adjusted Bayesian (BIC) 33272.025 33272.025
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.052 0.038
## 90 Percent Confidence Interval 0.044 0.060 0.032 0.045
## P-value RMSEA <= 0.05 0.318 0.997
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.026 0.026
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## factor1 =~
## X1 0.581 0.039 15.002 0.000 0.581 0.569
## X3 0.451 0.038 11.896 0.000 0.451 0.521
## X5 0.647 0.041 15.684 0.000 0.647 0.670
## X9 0.542 0.034 16.182 0.000 0.542 0.764
## X10 0.598 0.034 17.355 0.000 0.598 0.688
## X13 0.684 0.037 18.334 0.000 0.684 0.715
## X14 0.562 0.038 14.601 0.000 0.562 0.378
## X18 0.547 0.038 14.438 0.000 0.547 0.429
## X21 0.564 0.036 15.774 0.000 0.564 0.680
## X23 0.635 0.033 19.346 0.000 0.635 0.653
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## X1 1.818 0.028 65.015 0.000 1.818 1.781
## X3 1.549 0.024 65.300 0.000 1.549 1.789
## X5 1.588 0.026 60.049 0.000 1.588 1.645
## X9 1.420 0.019 72.984 0.000 1.420 1.999
## X10 1.560 0.024 65.466 0.000 1.560 1.793
## X13 1.547 0.026 59.027 0.000 1.547 1.617
## X14 2.340 0.041 57.474 0.000 2.340 1.574
## X18 1.794 0.035 51.372 0.000 1.794 1.407
## X21 1.431 0.023 63.009 0.000 1.431 1.726
## X23 1.561 0.027 58.619 0.000 1.561 1.606
## factor1 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## X1 0.706 0.063 11.130 0.000 0.706 0.677
## X3 0.546 0.043 12.732 0.000 0.546 0.728
## X5 0.513 0.047 10.888 0.000 0.513 0.550
## X9 0.210 0.016 12.932 0.000 0.210 0.417
## X10 0.399 0.043 9.297 0.000 0.399 0.527
## X13 0.447 0.047 9.541 0.000 0.447 0.488
## X14 1.894 0.078 24.226 0.000 1.894 0.857
## X18 1.326 0.096 13.842 0.000 1.326 0.816
## X21 0.370 0.036 10.303 0.000 0.370 0.538
## X23 0.542 0.047 11.570 0.000 0.542 0.573
## factor1 1.000 1.000 1.000
EFA_1factor
##
## Call:
## factanal(x = data02a, factors = 1, rotation = "none")
##
## Uniquenesses:
## X1 X3 X5 X9 X10 X13 X14 X18 X21 X23
## 0.677 0.728 0.550 0.417 0.527 0.488 0.857 0.816 0.538 0.573
##
## Loadings:
## Factor1
## X1 0.569
## X3 0.521
## X5 0.670
## X9 0.764
## X10 0.688
## X13 0.715
## X14 0.378
## X18 0.429
## X21 0.680
## X23 0.653
##
## Factor1
## SS loadings 3.828
## Proportion Var 0.383
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 161.14 on 35 degrees of freedom.
## The p-value is 4.23e-18
#two factor CFA: one item removed from factor 2 and zero covariance between factors
CFA_2factor.syntax = "
factor1 =~ X1 + X3 + X5 + X9 + X10 + X13 + X14 + X18 + X21 + X23
factor2 =~ X3 + X5 + X9 + X10 + X13 + X14 + X18 + X21 + X23
factor1 ~ 0*factor2
"
#for comparison with EFA we are using standardized factors (var = 1; mean = 0)
CFA_2factor.model = cfa(model = CFA_2factor.syntax, data = data02a, estimator = "MLR", std.lv = TRUE)
summary(CFA_2factor.model, fit.measures = TRUE, standardized = TRUE)
## lavaan (0.5-20) converged normally after 64 iterations
##
## Number of observations 1333
##
## Estimator ML Robust
## Minimum Function Test Statistic 56.704 39.189
## Degrees of freedom 26 26
## P-value (Chi-square) 0.000 0.047
## Scaling correction factor 1.447
## for the Yuan-Bentler correction
##
## Model test baseline model:
##
## Minimum Function Test Statistic 4148.081 2238.585
## Degrees of freedom 45 45
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.993 0.994
## Tucker-Lewis Index (TLI) 0.987 0.990
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -16523.162 -16523.162
## Scaling correction factor 2.243
## for the MLR correction
## Loglikelihood unrestricted model (H1) -16494.810 -16494.810
## Scaling correction factor 1.924
## for the MLR correction
##
## Number of free parameters 39 39
## Akaike (AIC) 33124.324 33124.324
## Bayesian (BIC) 33326.937 33326.937
## Sample-size adjusted Bayesian (BIC) 33203.051 33203.051
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.030 0.020
## 90 Percent Confidence Interval 0.019 0.040 0.007 0.029
## P-value RMSEA <= 0.05 0.999 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.015 0.015
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## factor1 =~
## X1 0.578 0.039 14.876 0.000 0.578 0.566
## X3 0.452 0.038 11.780 0.000 0.452 0.522
## X5 0.659 0.041 16.022 0.000 0.659 0.682
## X9 0.557 0.032 17.200 0.000 0.557 0.784
## X10 0.613 0.035 17.707 0.000 0.613 0.705
## X13 0.671 0.042 16.164 0.000 0.671 0.702
## X14 0.559 0.039 14.279 0.000 0.559 0.376
## X18 0.524 0.045 11.627 0.000 0.524 0.411
## X21 0.555 0.043 13.021 0.000 0.555 0.669
## X23 0.621 0.034 18.196 0.000 0.621 0.639
## factor2 =~
## X3 -0.023 0.049 -0.466 0.641 -0.023 -0.027
## X5 -0.098 0.069 -1.425 0.154 -0.098 -0.101
## X9 -0.076 0.069 -1.093 0.274 -0.076 -0.107
## X10 -0.108 0.075 -1.430 0.153 -0.108 -0.124
## X13 0.218 0.072 3.034 0.002 0.218 0.228
## X14 -0.011 0.082 -0.140 0.888 -0.011 -0.008
## X18 0.306 0.073 4.205 0.000 0.306 0.240
## X21 0.297 0.089 3.344 0.001 0.297 0.358
## X23 0.161 0.069 2.322 0.020 0.161 0.166
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## factor1 ~
## factor2 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## X1 1.818 0.028 65.015 0.000 1.818 1.781
## X3 1.549 0.024 65.300 0.000 1.549 1.789
## X5 1.588 0.026 60.049 0.000 1.588 1.645
## X9 1.420 0.019 72.984 0.000 1.420 1.999
## X10 1.560 0.024 65.466 0.000 1.560 1.793
## X13 1.547 0.026 59.027 0.000 1.547 1.617
## X14 2.340 0.041 57.474 0.000 2.340 1.574
## X18 1.794 0.035 51.372 0.000 1.794 1.407
## X21 1.431 0.023 63.009 0.000 1.431 1.726
## X23 1.561 0.027 58.619 0.000 1.561 1.606
## factor1 0.000 0.000 0.000
## factor2 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## X1 0.709 0.065 10.958 0.000 0.709 0.680
## X3 0.545 0.043 12.668 0.000 0.545 0.727
## X5 0.489 0.053 9.306 0.000 0.489 0.525
## X9 0.189 0.020 9.463 0.000 0.189 0.375
## X10 0.369 0.043 8.665 0.000 0.369 0.488
## X13 0.417 0.048 8.621 0.000 0.417 0.456
## X14 1.896 0.078 24.415 0.000 1.896 0.858
## X18 1.258 0.101 12.423 0.000 1.258 0.774
## X21 0.291 0.041 7.040 0.000 0.291 0.424
## X23 0.534 0.051 10.465 0.000 0.534 0.564
## factor1 1.000 1.000 1.000
## factor2 1.000 1.000 1.000
EFA_2factor
##
## Call:
## factanal(x = data02a, factors = 2, rotation = "none")
##
## Uniquenesses:
## X1 X3 X5 X9 X10 X13 X14 X18 X21 X23
## 0.680 0.727 0.525 0.375 0.488 0.456 0.858 0.774 0.424 0.564
##
## Loadings:
## Factor1 Factor2
## X1 0.563
## X3 0.517
## X5 0.669 -0.166
## X9 0.770 -0.181
## X10 0.690 -0.190
## X13 0.720 0.160
## X14 0.374
## X18 0.432 0.200
## X21 0.700 0.293
## X23 0.652 0.104
##
## Factor1 Factor2
## SS loadings 3.861 0.269
## Proportion Var 0.386 0.027
## Cumulative Var 0.386 0.413
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 56.43 on 26 degrees of freedom.
## The p-value is 0.000497
#three factor CFA:
CFA_3factor.syntax = "
factor1 =~ X1 + X3 + X5 + X9 + X10 + X13 + X14 + X18 + X21 + X23
factor2 =~ X3 + X5 + X9 + X10 + X13 + X14 + X18 + X21 + X23
factor3 =~ X5 + X9 + X10 + X13 + X14 + X18 + X21 + X23
factor1 ~ 0*factor2 + 0*factor3
factor2 ~ 0*factor3
"
#for comparison with EFA we are using standardized factors (var = 1; mean = 0)
CFA_3factor.model = cfa(model = CFA_3factor.syntax, data = data02a, estimator = "MLR", std.lv = TRUE)
## Warning in lav_object_post_check(lavobject): lavaan WARNING: some estimated
## variances are negative
## Warning in lav_object_post_check(lavobject): lavaan WARNING: observed
## variable error term matrix (theta) is not positive definite; use
## inspect(fit,"theta") to investigate.
summary(CFA_3factor.model, fit.measures = TRUE, standardized = TRUE)
## lavaan (0.5-20) converged normally after 245 iterations
##
## Number of observations 1333
##
## Estimator ML Robust
## Minimum Function Test Statistic 31.402 32.372
## Degrees of freedom 18 18
## P-value (Chi-square) 0.026 0.020
## Scaling correction factor 0.970
## for the Yuan-Bentler correction
##
## Model test baseline model:
##
## Minimum Function Test Statistic 4148.081 2238.585
## Degrees of freedom 45 45
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.997 0.993
## Tucker-Lewis Index (TLI) 0.992 0.984
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -16510.511 -16510.511
## Scaling correction factor 2.290
## for the MLR correction
## Loglikelihood unrestricted model (H1) -16494.810 -16494.810
## Scaling correction factor 1.924
## for the MLR correction
##
## Number of free parameters 47 47
## Akaike (AIC) 33115.022 33115.022
## Bayesian (BIC) 33359.195 33359.195
## Sample-size adjusted Bayesian (BIC) 33209.897 33209.897
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.024 0.024
## 90 Percent Confidence Interval 0.008 0.037 0.009 0.038
## P-value RMSEA <= 0.05 1.000 0.999
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.012 0.012
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## factor1 =~
## X1 0.595 0.043 13.915 0.000 0.595 0.582
## X3 0.464 0.043 10.770 0.000 0.464 0.535
## X5 0.654 0.041 15.870 0.000 0.654 0.677
## X9 0.527 0.043 12.127 0.000 0.527 0.742
## X10 0.593 0.044 13.580 0.000 0.593 0.681
## X13 0.648 0.048 13.617 0.000 0.648 0.677
## X14 0.603 0.084 7.199 0.000 0.603 0.406
## X18 0.512 0.053 9.603 0.000 0.512 0.401
## X21 0.522 0.051 10.283 0.000 0.522 0.630
## X23 0.631 0.046 13.755 0.000 0.631 0.649
## factor2 =~
## X3 -0.009 0.091 -0.095 0.924 -0.009 -0.010
## X5 -0.016 0.604 -0.027 0.978 -0.016 -0.017
## X9 0.120 4.094 0.029 0.977 0.120 0.169
## X10 0.009 0.646 0.014 0.989 0.009 0.010
## X13 0.265 0.233 1.136 0.256 0.265 0.277
## X14 -0.071 0.275 -0.257 0.797 -0.071 -0.047
## X18 0.294 0.567 0.518 0.604 0.294 0.230
## X21 0.381 0.496 0.768 0.443 0.381 0.459
## X23 0.152 0.567 0.268 0.789 0.152 0.156
## factor3 =~
## X5 -0.081 0.154 -0.526 0.599 -0.081 -0.084
## X9 -0.484 0.936 -0.517 0.605 -0.484 -0.681
## X10 -0.093 0.404 -0.230 0.818 -0.093 -0.107
## X13 0.022 2.195 0.010 0.992 0.022 0.023
## X14 0.048 0.829 0.058 0.954 0.048 0.032
## X18 0.079 2.207 0.036 0.971 0.079 0.062
## X21 0.056 3.082 0.018 0.985 0.056 0.068
## X23 0.075 1.095 0.069 0.945 0.075 0.077
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## factor1 ~
## factor2 0.000 0.000 0.000
## factor3 0.000 0.000 0.000
## factor2 ~
## factor3 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## X1 1.818 0.028 65.015 0.000 1.818 1.781
## X3 1.549 0.024 65.300 0.000 1.549 1.789
## X5 1.588 0.026 60.049 0.000 1.588 1.645
## X9 1.420 0.019 72.984 0.000 1.420 1.999
## X10 1.560 0.024 65.466 0.000 1.560 1.793
## X13 1.547 0.026 59.027 0.000 1.547 1.617
## X14 2.340 0.041 57.474 0.000 2.340 1.574
## X18 1.794 0.035 51.372 0.000 1.794 1.407
## X21 1.431 0.023 63.009 0.000 1.431 1.726
## X23 1.561 0.027 58.619 0.000 1.561 1.606
## factor1 0.000 0.000 0.000
## factor2 0.000 0.000 0.000
## factor3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## X1 0.689 0.069 9.938 0.000 0.689 0.661
## X3 0.535 0.053 10.050 0.000 0.535 0.714
## X5 0.498 0.057 8.756 0.000 0.498 0.535
## X9 -0.022 1.675 -0.013 0.990 -0.022 -0.043
## X10 0.397 0.059 6.734 0.000 0.397 0.524
## X13 0.425 0.049 8.640 0.000 0.425 0.464
## X14 1.839 0.162 11.315 0.000 1.839 0.832
## X18 1.271 0.099 12.827 0.000 1.271 0.782
## X21 0.267 0.048 5.611 0.000 0.267 0.388
## X23 0.519 0.054 9.538 0.000 0.519 0.549
## factor1 1.000 1.000 1.000
## factor2 1.000 1.000 1.000
## factor3 1.000 1.000 1.000
EFA_3factor
##
## Call:
## factanal(x = data02a, factors = 3, rotation = "none")
##
## Uniquenesses:
## X1 X3 X5 X9 X10 X13 X14 X18 X21 X23
## 0.661 0.714 0.534 0.005 0.523 0.464 0.831 0.782 0.388 0.548
##
## Loadings:
## Factor1 Factor2 Factor3
## X1 0.438 0.347 0.164
## X3 0.402 0.315 0.159
## X5 0.563 0.331 0.198
## X9 0.997
## X10 0.586 0.322 0.171
## X13 0.541 0.487
## X14 0.275 0.257 0.164
## X18 0.299 0.344 -0.101
## X21 0.508 0.537 -0.256
## X23 0.463 0.485
##
## Factor1 Factor2 Factor3
## SS loadings 2.940 1.378 0.231
## Proportion Var 0.294 0.138 0.023
## Cumulative Var 0.294 0.432 0.455
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 31.24 on 18 degrees of freedom.
## The p-value is 0.027
#fit is not significantly worse...can stop here!
#re-examining three factor estimates
summary(CFA_3factor.model, fit.measures = TRUE, standardized = TRUE)
## lavaan (0.5-20) converged normally after 245 iterations
##
## Number of observations 1333
##
## Estimator ML Robust
## Minimum Function Test Statistic 31.402 32.372
## Degrees of freedom 18 18
## P-value (Chi-square) 0.026 0.020
## Scaling correction factor 0.970
## for the Yuan-Bentler correction
##
## Model test baseline model:
##
## Minimum Function Test Statistic 4148.081 2238.585
## Degrees of freedom 45 45
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.997 0.993
## Tucker-Lewis Index (TLI) 0.992 0.984
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -16510.511 -16510.511
## Scaling correction factor 2.290
## for the MLR correction
## Loglikelihood unrestricted model (H1) -16494.810 -16494.810
## Scaling correction factor 1.924
## for the MLR correction
##
## Number of free parameters 47 47
## Akaike (AIC) 33115.022 33115.022
## Bayesian (BIC) 33359.195 33359.195
## Sample-size adjusted Bayesian (BIC) 33209.897 33209.897
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.024 0.024
## 90 Percent Confidence Interval 0.008 0.037 0.009 0.038
## P-value RMSEA <= 0.05 1.000 0.999
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.012 0.012
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## factor1 =~
## X1 0.595 0.043 13.915 0.000 0.595 0.582
## X3 0.464 0.043 10.770 0.000 0.464 0.535
## X5 0.654 0.041 15.870 0.000 0.654 0.677
## X9 0.527 0.043 12.127 0.000 0.527 0.742
## X10 0.593 0.044 13.580 0.000 0.593 0.681
## X13 0.648 0.048 13.617 0.000 0.648 0.677
## X14 0.603 0.084 7.199 0.000 0.603 0.406
## X18 0.512 0.053 9.603 0.000 0.512 0.401
## X21 0.522 0.051 10.283 0.000 0.522 0.630
## X23 0.631 0.046 13.755 0.000 0.631 0.649
## factor2 =~
## X3 -0.009 0.091 -0.095 0.924 -0.009 -0.010
## X5 -0.016 0.604 -0.027 0.978 -0.016 -0.017
## X9 0.120 4.094 0.029 0.977 0.120 0.169
## X10 0.009 0.646 0.014 0.989 0.009 0.010
## X13 0.265 0.233 1.136 0.256 0.265 0.277
## X14 -0.071 0.275 -0.257 0.797 -0.071 -0.047
## X18 0.294 0.567 0.518 0.604 0.294 0.230
## X21 0.381 0.496 0.768 0.443 0.381 0.459
## X23 0.152 0.567 0.268 0.789 0.152 0.156
## factor3 =~
## X5 -0.081 0.154 -0.526 0.599 -0.081 -0.084
## X9 -0.484 0.936 -0.517 0.605 -0.484 -0.681
## X10 -0.093 0.404 -0.230 0.818 -0.093 -0.107
## X13 0.022 2.195 0.010 0.992 0.022 0.023
## X14 0.048 0.829 0.058 0.954 0.048 0.032
## X18 0.079 2.207 0.036 0.971 0.079 0.062
## X21 0.056 3.082 0.018 0.985 0.056 0.068
## X23 0.075 1.095 0.069 0.945 0.075 0.077
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## factor1 ~
## factor2 0.000 0.000 0.000
## factor3 0.000 0.000 0.000
## factor2 ~
## factor3 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## X1 1.818 0.028 65.015 0.000 1.818 1.781
## X3 1.549 0.024 65.300 0.000 1.549 1.789
## X5 1.588 0.026 60.049 0.000 1.588 1.645
## X9 1.420 0.019 72.984 0.000 1.420 1.999
## X10 1.560 0.024 65.466 0.000 1.560 1.793
## X13 1.547 0.026 59.027 0.000 1.547 1.617
## X14 2.340 0.041 57.474 0.000 2.340 1.574
## X18 1.794 0.035 51.372 0.000 1.794 1.407
## X21 1.431 0.023 63.009 0.000 1.431 1.726
## X23 1.561 0.027 58.619 0.000 1.561 1.606
## factor1 0.000 0.000 0.000
## factor2 0.000 0.000 0.000
## factor3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## X1 0.689 0.069 9.938 0.000 0.689 0.661
## X3 0.535 0.053 10.050 0.000 0.535 0.714
## X5 0.498 0.057 8.756 0.000 0.498 0.535
## X9 -0.022 1.675 -0.013 0.990 -0.022 -0.043
## X10 0.397 0.059 6.734 0.000 0.397 0.524
## X13 0.425 0.049 8.640 0.000 0.425 0.464
## X14 1.839 0.162 11.315 0.000 1.839 0.832
## X18 1.271 0.099 12.827 0.000 1.271 0.782
## X21 0.267 0.048 5.611 0.000 0.267 0.388
## X23 0.519 0.054 9.538 0.000 0.519 0.549
## factor1 1.000 1.000 1.000
## factor2 1.000 1.000 1.000
## factor3 1.000 1.000 1.000
#NO FACTOR LOADINGS ON FACTOR 2 OR FACTOR 3 ARE SIGNIFICANTLY DIFFERENT FROM ZERO -- SO WE DON'T HAVE FACTORS -- THE ONE FACTOR MODEL IS BEST!