sampling/0000755000176200001440000000000015033773102012065 5ustar liggesuserssampling/MD50000644000176200001440000001644015033773102012402 0ustar liggesusersa0a1174a68f0a0a20169db0817b93cbc *DESCRIPTION ae72356fe82d0ef390267faddc7628af *NAMESPACE 9da2c0433ff0f277f4dca4edb4e4cc38 *R/HTestimator.R 08c4d7114334d10a0caec1fc6bf71968 *R/HTstrata.r c9b1ae0be6fe3df9883ede499bb10339 *R/Hajekestimator.r d1fe76f0337a7a83a6e902ef57541ff1 *R/Hajekstrata.r fe4f3c4849c77f39e23f698eca641df9 *R/UPMEpik2frompikw.R e46e4d37db086ced1aa7be52a49cd99c *R/UPMEpikfromq.R 23780f67aa119e06e017e57affdd5076 *R/UPMEpiktildefrompik.R 0dccea40f8c13c0b820558079c6049ec *R/UPMEqfromw.R 545df0ce43ca06cfb0c7fbc9442a15a1 *R/UPMEsfromq.R 102b1a3871bb37821dc7ad8ef53f22df *R/UPbrewer.R 18d63d5af3691ef430eb0129db69e2c2 *R/UPmaxentropy.R 593f5cc0fc0acf8705657d50c172cac8 *R/UPmaxentropypi2.R 7fa457ee403a0b911d8d189811d2fcf8 *R/UPmidzuno.R 0002b2eb0657eac34889d4bb8f75bde7 *R/UPmidzunopi2.R c6af0290109363c3d31363b94144cb4e *R/UPminimalsupport.R 78b55dad22fdeed69d97cb9ee11f237b *R/UPmultinomial.R de6f26f391766cf54b1b0e9234bd7d94 *R/UPopips.r 695bcb24ea0da460420aec129d36955b *R/UPpivotal.r 367109233af0ba4fb12deb6ed85680ec *R/UPpoisson.R 3e614b0d70ad5ea13ffa93131d421f31 *R/UPrandompivotal.R 1c9ace9f09024f5a9a79dbe0e8ee0df9 *R/UPrandomsystematic.R 524efbe9b151122e0e390002d6c8e6e5 *R/UPsampford.R b687f9fee230d9dc101f05a2c63a1fa9 *R/UPsampfordpi2.r 25806b316c79131009c208b7827d7301 *R/UPsystematic.R 925deff4870d5ce2203e418ae99c655d *R/UPsystematicpi2.R c0eb4ec2628625425f6210cdfbbb6fca *R/UPtille.R d002737a0f285f107e31f5a12aece8de *R/UPtillepi2.R dc87e66c2501fe1aead27dfe1b5ef170 *R/as_int.r 9ac79cb69b3ffd8f96984f49ede89d84 *R/balancedcluster.R ff2082de62b9741eece459369ccd1fed *R/balancedstratification.R 59065fbcb53f0015bd7990735caec82b *R/balancedtwostage.R 53e62760fea98e7c30cdc7eeedc42818 *R/calib.r 28d3d2ac6fc3a6008683e313472c6b90 *R/calibev.r 999511be362810b6970f43f50c9b2196 *R/checkcalibration.R 7937c838f969518d281942465939efdb *R/cleanstrata.R f31a31792ede02a3c86e2a492acb5380 *R/cluster.r 39c9d86bca5f4e38e3130c3a9325a74b *R/disjunctive.R 9c2972ba701bd6a2cc3988d34c45554f *R/fastflightcube.R 4685754109fa3b574f94774ed4b62ff7 *R/gencalib.R f8f217acc40d29baab54a330c10bcd74 *R/getdata.r d25c378c17416caa65b913522b929a06 *R/inclusionprobabilities.R 2fe76fd3a0dfce5268a28997cbdaf052 *R/inclusionprobastrata.R 6ee3f3f14a53db1df0a458d266b55980 *R/landingcube.R 0a103aad8de9560a72eb9cc1851c17bf *R/mstage.r c8d2f7e09c4074d3dd323b3248ee6e6b *R/postest.r 1aa9e564d16d64b1e616ea09838534e3 *R/poststrata.r 75769b91b99bcc971e5bf3808fb2f013 *R/ratioest.r fd2544a8cb4d5217ee747cc35aeb9bea *R/ratioest_strata.r b22724653d0e7ba80c4df99f242b793f *R/regest.r b122c6f4e381461121006625138345fa *R/regest_strata.r 6852035727c0ec3c9ab6d2d85f6aeae3 *R/rhg.r cc40a03d7b7fddf52b7d2790d0513797 *R/rhg_strata.r f5a7f5e7fb324169076bed95f2ed2397 *R/rmodel.r bf4b40ee7262d8f14af763eba89a76f1 *R/samplecube.R 3d3548f11701d8a50858b15db0fa2682 *R/srswor.R 968691ea093c11bc866c59c6920f25d6 *R/srswor1.R 2a3f22e5754f8fcaa0a7cf656fba9e06 *R/srswr.R c14b989cae91ceb752f857f044bda7b6 *R/strata.r bddf8f11e93733f4393e28b8a2462d1b *R/varHT.r 65c054c0a234bddb75b4684cf19751b2 *R/varest.r 6820788410303d82df42fd8260c7c324 *R/vartaylor_ratio.r 55e2d5b9dfeba5d0fcf76e3768b2e6c9 *R/writesample.R f1f6bf2b1c948d4986b6694fbf1d37a3 *build/vignette.rds 8daf23fb7558b0dcdf8578a71b509e2e *data/MU284.rda 4134ceaeb1231bf2a3496f9afd74ccee *data/belgianmunicipalities.rda fe8d882c7a82998ed38cc6caf36f8529 *data/rec99.rda 04160e33b7934beedf9e42b0e58752d2 *data/swissmunicipalities.rda c2e9ab2471691383ba3811fc3600d3b5 *inst/doc/HT_Hajek_estimators.R f688a2aa443123bae71648bdb2785eee *inst/doc/HT_Hajek_estimators.Snw 83ae2d004d34235feb1af5bf5e9f568c *inst/doc/HT_Hajek_estimators.pdf 2bfeb089af2a94b23dc9af746e1d1345 *inst/doc/UPexamples.R 649d0f521a31da148c2176204591d1bb *inst/doc/UPexamples.Snw 8cdea729b783a99d9c5fe4ba0ce2f598 *inst/doc/UPexamples.pdf c4a5a449c9aa4be510841155de373d23 *inst/doc/calibration.R 3d5dba939e56a1709461977ca78ca7e3 *inst/doc/calibration.Snw 05d8a76594ecc47fde91a2dae0f2f101 *inst/doc/calibration.pdf a9f4afacb53b413b625317c65fff7e59 *man/HTestimator.Rd fa3365516ad50d03ab08668c96d43d58 *man/HTstrata.Rd 92849da4772776f01284ca5f57cda6c3 *man/Hajekestimator.Rd b26f2e44dd4a8d473a6cb7f7ea8485cd *man/Hajekstrata.Rd 73adb6d709be2360841e69e9fb0df6d9 *man/MU284.Rd 3641cf1b43781204de09f83d8e367d48 *man/UPbrewer.Rd 73d3ded94d47639b57ef3e9c652eece8 *man/UPmaxentropy.Rd 3b4afa1865e1741769f6d167c8179d0d *man/UPmidzuno.Rd 9807420064358ad70c2ff3eb0c1a93fb *man/UPmidzunopi2.Rd 2d0e82def37e4a20b6a64862b91b4160 *man/UPminimalsupport.Rd c5d69b1290ab4e1bfbce2c967426d67a *man/UPmultinomial.Rd 0634b370f4eb5a75200b9654a1855c87 *man/UPopips.Rd 4799044c34747914df4251c7692e68c1 *man/UPpivotal.Rd 1775831b942ee2bb275d93dd8a905c27 *man/UPpoisson.Rd 542864978594b0b7d297fe5eb2c40111 *man/UPrandompivotal.Rd 35eeab044e5d9a837d968dfb1bd184cb *man/UPrandomsystematic.Rd 7592102dfaf8bd6b89d7543739c8ba61 *man/UPsampford.Rd 0a645ca4a87fbe72aa9e72d38ac8d003 *man/UPsampfordpi2.Rd d5697462a287ec318a70155c7d3b713c *man/UPsystematic.Rd 530aea369cb0bb04137d6a39d0c5ae8b *man/UPsystematicpi2.Rd 80280edba5011cd94a16646fb54e9a55 *man/UPtille.Rd 51b840a16c82f22638dcfd322116591b *man/UPtillepi2.Rd 759ba49012f43aa328f0edafa81a5e17 *man/balancedcluster.Rd cf2ac1050213108ef29e7c942e6b8619 *man/balancedstratification.Rd 9f7b77e60478b5dd4a0827d5149feb4d *man/balancedtwostage.Rd 659f97a964a181ff80e117289b4c8316 *man/belgianmunicipalities.Rd db2e7a7606c87b9c11c49c471be92707 *man/calib.Rd bb19b54fece01ad049e7f0ed05b0a0b5 *man/calibev.Rd 0a0c32cfc773232ae1b2b6f078a7ade3 *man/checkcalibration.Rd ac12eccf1b6a8c59cc1485db8d551ded *man/cleanstrata.Rd 71ef37cd1ea18672adc2581fc994a58f *man/cluster.Rd 2331d0998cdce42acd317e15d8baef44 *man/disjunctive.Rd db506e56428e3209d387179631a08ca3 *man/fastflightcube.Rd 809f2c59e9b0e6825affac5f10d04acb *man/gencalib.Rd c190847b141b7ee2b66368fd266613ea *man/getdata.Rd 00131269b40d6fff6ad0f98f4aa0458b *man/inclusionprobabilities.Rd d7f340dc2245098aacbf318b64c6b119 *man/inclusionprobastrata.Rd c61107ac45db6a82ae6578ddcdba0581 *man/landingcube.Rd cb828bc7cbac179e094d8594491a3a74 *man/mstage.Rd dbc0e2484618c4b8586c1e80f8a69d22 *man/postest.Rd e1a87ef127f6295212c8df268715fe36 *man/poststrata.Rd 9f118952e508180c8320e85fbb2cabcf *man/ratioest.Rd 71152b76a8757252fb0781eb04379510 *man/ratioest_strata.Rd e748f5f8f4e8cb0d2987ce1b9e639a19 *man/rec99.Rd 117beb0b919ac2ba83e0acc5c0d9d1dd *man/regest.Rd 05fbbdf72e0a7eb6bff84dfb0344a6d1 *man/regest_strata.Rd 6832776fdbec1cb66a6715aaee4c64bb *man/rhg.Rd 0a458898c00c06f0922cb0729ca07905 *man/rhg_strata.Rd 88b8759c878eef5037bfa85445af204e *man/rmodel.Rd 8e0dd4e88f832cf16d7add7293bd9660 *man/samplecube.Rd 059d7d91746599d85fb051bab729e69c *man/sampling-internal.Rd cd9b2ff7df69b71be7d17db78c2867fa *man/srswor.Rd ab2ca117f4d61837c634e980bcdcbf82 *man/srswor1.Rd c7aecb7cf42beab574c052cb4ce9d397 *man/srswr.Rd 9cade8207cfb07a9dc47c889a938a295 *man/strata.Rd a364efc8434566bc73a71218ca5ad9cc *man/swissmunicipalities.Rd 9df67c2b59e5fc09a09980c04cec4c04 *man/varHT.Rd 4ca3d99e19c0821af777ca01339fe5a8 *man/varest.Rd bb08fc7ba29b9b3668d6ec4876d8c8b8 *man/vartaylor_ratio.Rd 3dc52cb41e07b47cc8ee0adcc2dbbcc4 *man/writesample.Rd 1a2fa79cf90ad39905a943ccdaa1b9af *src/init.c a5fccd459d68d965fb6f694875cc5804 *src/str.c f688a2aa443123bae71648bdb2785eee *vignettes/HT_Hajek_estimators.Snw 649d0f521a31da148c2176204591d1bb *vignettes/UPexamples.Snw 3d5dba939e56a1709461977ca78ca7e3 *vignettes/calibration.Snw sampling/R/0000755000176200001440000000000015011321164012257 5ustar liggesuserssampling/R/poststrata.r0000644000176200001440000000160114520143727014657 0ustar liggesuserspoststrata<-function(data, postnames = NULL) { if (missing(data) | missing(postnames)) stop("incomplete input") data = data.frame(data) if(is.null(colnames(data))) stop("the column names in data are missing") index = 1:nrow(data) m = match(postnames, colnames(data)) if (any(is.na(m))) stop("the names of the poststrata are wrong") data2 = cbind.data.frame(data[, m]) x1 = data.frame(unique(data[, m])) colnames(x1) = postnames nr_post=0 post=numeric(nrow(data)) nh=numeric(nrow(x1)) for(i in 1:nrow(x1)) { expr=rep(FALSE, nrow(data2)) for(j in 1:nrow(data2)) expr[j]=all(data2[j, ]==x1[i, ]) y=index[expr] if(is.matrix(y)) nh[i]=nrow(y) else nh[i]=length(y) post[expr]=i } result=cbind.data.frame(data,post) names(result)=c(names(data),"poststratum") list(data=result, npost=nrow(x1)) } sampling/R/srswor1.R0000644000176200001440000000016614520143727014040 0ustar liggesusers"srswor1" <- function(n,N) {j=0 s=numeric(N) for(k in 1:N) if(runif(1)<(n-j)/(N-k+1)) {j=j+1;s[k]=1;} s } sampling/R/cleanstrata.R0000644000176200001440000000020714520143727014715 0ustar liggesusers"cleanstrata" <- function(strata) { a=sort(unique(strata)) b=1:length(a) names(b)=a as.vector(b[as.character(strata)]) } sampling/R/UPMEpik2frompikw.R0000644000176200001440000000072114520143730015522 0ustar liggesusers"UPMEpik2frompikw" <-function(pik,w) { n=sum(pik) n=.as_int(n) N=length(pik) M=array(0,c(N,N)) for(k in 1:N) for(l in 1:N) if(pik[k]!=pik[l] & k!=l) M[k,l]= (pik[k]*w[l]-pik[l]*w[k])/(w[l]-w[k]) else M[k,l]=-1 for(i in 1:N) M[i,i]=pik[i] for(k in 1:N) { tt=0 comp=0 for(l in 1:N) {if(M[k,l]!=-1) tt=tt+M[k,l] else comp=comp+1 } cc=(n*pik[k]-tt)/comp for(l in 1:N) if(M[k,l]==-1) M[k,l]=cc } M } sampling/R/UPsampford.R0000644000176200001440000000117014520143730014466 0ustar liggesusersUPsampford<-function(pik,eps=1e-6,max_iter=500) { if(any(is.na(pik))) stop("there are missing values in the pik vector") n=sum(pik) n=.as_int(n) list= pik>eps & pik < 1-eps pikb=pik[list] n=sum(pikb) N=length(pikb) s=pik if(N<1) stop("the pik vector has all elements outside of the range [eps,1-eps]") else { sb=rep(2,N) y=pikb/(1-pikb)/sum(pikb/(1-pikb)) step=0 while(sum(sb<=1)!=N & step<=max_iter) { sb=as.vector(rmultinom(1,1,pikb/sum(pikb))+rmultinom(1,.as_int(n-1),y)) step=step+1 } if(sum(sb<=1)==N) s[list]=sb else stop("Too many iterations. The algorithm was stopped.") } s } sampling/R/UPmaxentropypi2.R0000644000176200001440000000053314520143730015476 0ustar liggesusers"UPmaxentropypi2" <-function(pik) { n=sum(pik) n=.as_int(n) N=length(pik) M=array(0,c(N,N)) if(n>=2) { pik2=pik[pik>0 & pik<1] pikt=UPMEpiktildefrompik(pik2) w=pikt/(1-pikt) M[pik>0 & pik<1,pik>0 & pik<1]=UPMEpik2frompikw(pik2,w) M[,pik==1]=pik for(k in 1:N) if(pik[k]==1) M[k,]=pik } if(n==1) for(k in 1:N) M[k,k]=pik[k] M } sampling/R/UPmaxentropy.R0000644000176200001440000000122214520143730015057 0ustar liggesusers"UPmaxentropy" <-function(pik) { if(is.data.frame(pik)) if(ncol(pik)>1) stop("pik is not a vector") else pik=unlist(pik) else if(is.matrix(pik)) if(ncol(pik)>1) stop("pik is not a vector") else pik=pik[,1] else if(is.list(pik)) if(length(pik)>1) stop("pik is not a vector") else pik=unlist(pik) n=sum(pik) n=.as_int(n) if(n>=2) { pik2=pik[pik!=1] n=sum(pik2) n=.as_int(n) piktilde=UPMEpiktildefrompik(pik2) w=piktilde/(1-piktilde) q=UPMEqfromw(w,n) s2=UPMEsfromq(q) s=rep(0,times=length(pik)) s[pik==1]=1 s[pik!=1][s2==1]=1 } if(n==0) s=rep(0,times=length(pik)) if(n==1) s=as.vector(rmultinom(1, 1,pik)) s } sampling/R/UPsampfordpi2.r0000644000176200001440000000151614520143730015145 0ustar liggesusersUPsampfordpi2<-function(pik) { n=sum(pik) n=.as_int(n) if(n<2) stop("the sample size<2") N=length(pik) p=pik/n pikl=matrix(0,N,N) Lm=rep(0, n) lambda=p/(1-n*p) Lm[1]=1 if(n>=2) for (i in 2:n) { for (r in 1:(i-1)) Lm[i]=Lm[i]+((-1)^(r-1))*sum(lambda^r)*Lm[i-r] Lm[i]=Lm[i]/(i - 1) } if(any(Lm<0)) stop("it is not possible to compute pik2 for this example") t1=(n + 1) - (1:n) Kn=1/sum(t1*Lm/n^t1) Lm2=rep(0, n - 1) t2=(1:(n - 1)) t3=n - t2 for (i in 2:N) { for (j in 1:(i - 1)) { Lm2[1]=1 Lm2[2]=Lm[2] - (lambda[i] + lambda[j]) if(n>3) for (m in 3:(n - 1)) { Lm2[m]=Lm[m] - (lambda[i] + lambda[j]) * Lm2[m -1] - lambda[i] * lambda[j] * Lm2[m - 2] } pikl[i, j]=Kn * lambda[i] * lambda[j] * sum((t2+1-n*(p[i] + p[j]))*Lm2[t3]/n^(t2 - 1)) pikl[j, i]=pikl[i, j] } pikl[i, i]=pik[i] } pikl[1, 1]=pik[1] pikl } sampling/R/HTstrata.r0000644000176200001440000000151614520143727014212 0ustar liggesusersHTstrata<-function (y, pik, strata, description=FALSE) { str <- function(st, h, n) .C("str", as.double(st), as.integer(h), as.integer(n), s = double(n), PACKAGE = "sampling")$s if(any(is.na(pik))) stop("there are missing values in pik") if(any(is.na(y))) stop("there are missing values in y") if(length(y)!=length(pik)) stop("y and pik have different sizes") if (is.matrix(y)) sample.size <- nrow(y) else sample.size <- length(y) h <- unique(strata) s1 <- 0 for (i in 1:length(h)) { s <- str(strata, h[i], sample.size) est<-HTestimator(y[s == 1], pik[s == 1]) s1 <- s1 + est if(description) cat("For stratum",i,",the Horvitz-Thompson estimator is:",est,"\n") } if(description) cat("The Horvitz-Thompson estimator is:\n") s1 } sampling/R/rhg_strata.r0000644000176200001440000000141214520143727014611 0ustar liggesusersrhg_strata<-function(X,selection) { if(is.matrix(X)) X=as.data.frame(X) m=match(selection,names(X),nomatch=0) if(sum(m)==0) stop("the 'selection' should be the name of one the X columns") if(!("Stratum" %in% names(X))) stop("the column 'Stratum' is missing") result=NULL u=unique(X$Stratum) for(i in 1:length(u)) {si=X[X$Stratum==u[i],] x=cbind.data.frame(si$ID_unit,si$status,si[,m]) names(x)=c("ID_unit","status",names(X)[m]) result=rbind.data.frame(result,rhg(x,selection)) } res = NULL mm = match(names(X), names(result), nomatch = 0) index = (1:ncol(X))[mm == 0] if (length(index) > 0) { res = cbind.data.frame(X[X$ID_unit==result$ID_unit, index], result) names(res)[1:length(index)] = names(X)[index] } res } sampling/R/varest.r0000644000176200001440000000141714520143730013756 0ustar liggesusersvarest<-function(Ys,Xs=NULL,pik,w=NULL) { if (any(is.na(pik))) stop("there are missing values in pik") if (any(is.na(Ys))) stop("there are missing values in y") if (length(Ys) != length(pik)) stop("y and pik have different sizes") if(!is.null(Xs)) {if(is.data.frame(Xs)) Xs=as.matrix(Xs) if(is.vector(Xs) & (length(Ys)!= length(Xs))) stop("x and y have different sizes") if(is.matrix(Xs) & (length(Ys) != nrow(Xs))) stop("x and y have different sizes") } a=(1-pik)/sum(1-pik) if(is.null(Xs)) {A=sum(a*Ys/pik) var=sum((1-pik)*(Ys/pik-A)^2)/(1-sum(a^2)) } else {B=t(Xs*w) beta=ginv(B%*%Xs)%*%B%*%Ys e=Ys-Xs%*%beta A=sum(a*e/pik) var=sum((1-pik)*(e/pik-A)^2)/(1-sum(a^2)) } var }sampling/R/UPtille.R0000644000176200001440000000105214520143730013763 0ustar liggesusers"UPtille" <- function(pik,eps=1e-6) { if(any(is.na(pik))) stop("there are missing values in the pik vector") n=sum(pik) n=.as_int(n) list = pik > eps & pik < 1 - eps pikb = pik[list] N = length(pikb) s=pik if(N<1) stop("the pik vector has all elements outside of the range [eps,1-eps]") else { n=sum(pikb) sb=rep(1,N) b=rep(1,N) for(i in 1:(N-n)) {a=inclusionprobabilities(pikb,N-i) v=1-a/b b=a p=v*sb p=cumsum(p) u=runif(1) for(j in 1:length(p)) if(u=3) for(i in 3:ncol(X1)) x=list(x,unique(X1[,i])) x=expand.grid(x) ng=1 prob=rhgroup=numeric(nrow(X1)) for (i in 1:nrow(x)) { expr=rep(FALSE, nrow(X1)) for(j in 1:nrow(X1)) { expr[j] = all(X1[j,2:ncol(X1)] == x[i, ]) if(expr[j]) rhgroup[j]=ng } if(any(expr)) ng=ng+1 } gr=unique(rhgroup) if(is.data.frame(X1)) X1=cbind.data.frame(X1,rhgroup) else X1=cbind(X1,rhgroup) for(i in 1:length(gr)) {l=nrow(X1[X1[,ncol(X1)]==gr[i],]) lr=nrow(X1[X1[,ncol(X1)]==gr[i] & X1[,1]==1,]) for(j in 1:length(prob)) if(rhgroup[j]==gr[i] & X1[j,1]==1) prob[j]=lr/l } result=cbind.data.frame(X$ID_unit,X1,prob) names(result)=c("ID_unit",names(X1),"prob_resp") res = NULL mm = match(names(X), names(result), nomatch = 0) if(0 %in% mm) {index = (1:ncol(X))[mm == 0] res = cbind.data.frame(X[X$ID_unit==result$ID_unit, index], result) names(res)[1:length(index)] = names(X)[index] } else res=result res } sampling/R/UPsystematic.R0000644000176200001440000000045314520143730015043 0ustar liggesusers"UPsystematic"<-function(pik,eps=1e-6) { if(any(is.na(pik))) stop("there are missing values in the pik vector") list=pik > eps & pik < 1-eps pik1 = pik[list] N = length(pik1) a = (c(0, cumsum(pik1)) - runif(1, 0, 1))%%1 s1 = as.integer(a[1:N] > a[2:(N + 1)]) s = pik s[list] = s1 s } sampling/R/UPrandompivotal.R0000644000176200001440000000031514520143730015532 0ustar liggesusers"UPrandompivotal" <- function(pik,eps=1e-6) { if(any(is.na(pik))) stop("there are missing values in the pik vector") N=length(pik) v=sample.int(N,N) s=numeric(N) s[v]=UPpivotal(pik[v],eps) s } sampling/R/checkcalibration.R0000644000176200001440000000130414520143727015700 0ustar liggesuserscheckcalibration<-function (Xs, d, total, g, EPS = 1e-06) { if (is.null(g)) stop("the g-weight vector is null") if (!is.matrix(Xs)) Xs <- as.matrix(Xs) tr <- crossprod(Xs, g * d) expression<- max(abs(tr - total)/total) if(any(total<=sqrt(.Machine$double.eps))) expression<- max(abs(tr - total)) if (expression < EPS) { result <- TRUE message <- "the calibration is done" value <- EPS } else { message <- cat("the calibration cannot be done. The max EPS value is given by 'value'.\n") value <- expression result <- FALSE } list(message = message, result = result, value = value) } sampling/R/HTestimator.R0000644000176200001440000000036214520143727014661 0ustar liggesusersHTestimator<-function(y,pik) { if(any(is.na(pik))) stop("there are missing values in pik") if(any(is.na(y))) stop("there are missing values in y") if(length(y)!=length(pik)) stop("y and pik have different sizes") crossprod(y,1/pik) } sampling/R/UPpoisson.R0000644000176200001440000000022014520143730014340 0ustar liggesusers"UPpoisson" <- function(pik) {if(any(is.na(pik))) stop("there are missing values in the pik vector") as.numeric(runif(length(pik)) EPS & pik < (1 - EPS)]) > 0) pikstar = fastflightcube(X, pik, order, comment) else { if (comment) cat("\nNO FLIGHT PHASE") pikstar = pik } if (length(pikstar[pikstar > EPS & pikstar < (1 - EPS)]) > 0) pikfin = landingcube(X, pikstar, pik, comment) else { if (comment) cat("\nNO LANDING PHASE") pikfin = pikstar } } else { p=length(X)/length(pik) pikstar=pik for(i in 0:(p-1)) { if (length(pikstar[pikstar > EPS & pikstar < (1 - EPS)]) > 0) pikstar = fastflightcube(X[,1:(p-i)]/pik*pikstar, pikstar, order, comment) } pikfin = pikstar for(i in 1:N) if(runif(1) EPS, ]/pik[pik > EPS] TOT = t(A) %*% pik[pik > EPS] EST = t(A) %*% pikfin[pik > EPS] DEV = 100 * (EST - TOT)/TOT cat("\n\nQUALITY OF BALANCING\n") if(is.null(colnames(X))) Vn = as.character(1:length(TOT)) else Vn=colnames(X) for(i in 1:length(TOT)) if(Vn[i]=="") Vn[i]=as.character(i) d = data.frame(TOTALS = c(TOT), HorvitzThompson_estimators = c(EST), Relative_deviation = c(DEV)) rownames(d)<-Vn print(d) } round(pikfin) } sampling/R/ratioest.r0000644000176200001440000000064714520143727014316 0ustar liggesusersratioest<-function(y,x,Tx,pik) {if (any(is.na(pik))) stop("there are missing values in pik") if (any(is.na(y))) stop("there are missing values in y") if (any(is.na(x))) stop("there are missing values in x") if (length(y) != length(pik) | length(x)!=length(pik) | length(x)!=length(y)) stop("y, x and pik have different lengths") sum(y/pik)*Tx/sum(x/pik) } sampling/R/balancedcluster.R0000644000176200001440000000115614520143726015552 0ustar liggesusers"balancedcluster" <- function(X,m,cluster,selection=1,comment=TRUE,method=1) { cluster=cleanstrata(cluster) if(comment==TRUE) cat("\nSELECTION OF A SAMPLE OF CLUSTERS\n") p=dim(X)[2] N=dim(X)[1] H=max(cluster) XC=array(0,c(H,p)) Ni=rep(0,times=H) for(h in 1:H) { Ni[h]=sum(as.integer(cluster==h)) for(j in 1:p) XC[h,j]=sum(X[cluster==h,j]) } if(selection==1) pik=inclusionprobabilities(Ni,m) else pik=rep(m/H,times=H) s=samplecube(cbind(pik,XC),pik,1,comment,method) res=array(0,c(N,2)) for(h in 1:H) { res[cluster==h,1]=s[h] res[cluster==h,2]=pik[h] } res } sampling/R/UPtillepi2.R0000644000176200001440000000111614520143730014377 0ustar liggesusers"UPtillepi2" <- function(pik,eps=1e-6) { if(any(is.na(pik))) warning("there are missing values in the pik vector") n=sum(pik) n=.as_int(n) list = pik > eps & pik < 1 - eps pikb = pik[list] N = length(pikb) #ppf=pik%*%t(pik) ppf=matrix(0,length(pik),length(pik)) if(N<1) stop("the pik vector has all elements outside of the range [eps,1-eps]") else { n=sum(pikb) if(N>n) { UN=rep(1,N) b=rep(1,N) pp=1 for(i in 1:(N-n)) { a=inclusionprobabilities(pikb,N-i) vv=1-a/b b=a d=vv %*% t(UN) pp=pp*(1-d-t(d)) } diag(pp)=pikb ppf[list,list]=pp } } ppf } sampling/R/rmodel.r0000644000176200001440000000104114520143727013733 0ustar liggesusersrmodel<-function(formula,weights,X) { cl <- match.call() mf <- match.call(expand.dots = FALSE) m <- match(c("formula", "weights"), names(mf), 0) mf <- mf[c(1, m)] mf$drop.unused.levels <- TRUE mf[[1]] <- as.name("model.frame") mf <- eval(mf, parent.frame()) mt <- attr(mf, "terms") y <- model.response(mf, "numeric") w <- as.vector(model.weights(mf)) x <- model.matrix(mt, mf, contrasts) prob<-glm(y~x,family="binomial",weights=w)$fitted.values result<-cbind.data.frame(X,prob) names(result)<-c(names(X),"prob_resp") result } sampling/R/UPsystematicpi2.R0000644000176200001440000000111714520143730015454 0ustar liggesusersUPsystematicpi2<-function(pik) { n=sum(pik) n=.as_int(n) pik1 = pik[pik > 0 & pik < 1] N = length(pik1) Vk = cumsum(pik1) Vk1=Vk%%1 if(Vk1[N]!=0) Vk1[N]=0 r = c(sort(Vk1), 1) cent = (r[1:N] + r[2:(N + 1)])/2 p = r[2:(N + 1)] - r[1:N] A = matrix(c(0, Vk), nrow = N + 1, ncol = N) - t(matrix(cent,nrow = N, ncol = N + 1)) A = A%%1 M = matrix(as.integer(A[1:N, ] > A[2:(N + 1), ]), N, N) pi21 = M %*% diag(p) %*% t(M) pi2 = pik %*% t(pik) pi2[pik > 0 & pik < 1, pik > 0 & pik < 1] = pi21 pi2 } sampling/R/UPrandomsystematic.R0000644000176200001440000000032314520143730016240 0ustar liggesusers"UPrandomsystematic" <- function(pik,eps=1e-6) { if(any(is.na(pik))) stop("there are missing values in the pik vector") N=length(pik) v=sample.int(N,N) s=numeric(N) s[v]=UPsystematic(pik[v],eps) s } sampling/R/UPMEsfromq.R0000644000176200001440000000022114520143730014400 0ustar liggesusers"UPMEsfromq" <- function(q) { n=ncol(q) N=nrow(q) s=rep(0,times=N) for(k in 1:N) if(n!=0) if(runif(1)1+EPS)) warning("in a stratum the sample size is larger than the population size\n") pik=nh[strata]/Nh[strata] pik } sampling/R/Hajekstrata.r0000644000176200001440000000231414520143727014716 0ustar liggesusersHajekstrata<-function(y,pik,strata,N=NULL,type=c("total","mean"),description=FALSE) { str <- function(st, h, n) .C("str", as.double(st), as.integer(h), as.integer(n), s = double(n), PACKAGE = "sampling")$s if(any(is.na(pik))) stop("there are missing values in pik") if(any(is.na(y))) stop("there are missing values in y") if(length(y)!=length(pik)) stop("y and pik have different sizes") if (is.matrix(y)) sample.size <- nrow(y) else sample.size <- length(y) if(!is.vector(N)) N <- as.vector(N) h <- unique(strata) if(length(N)!=length(h)) stop("N should be a vector with the length equal to the number of strata") options(warn=-1) s1 <- 0 for (i in 1:length(h)) { s <- str(strata, h[i], sample.size) est <- Hajekestimator(y[s == 1], pik[s == 1], type="mean") s1 <- s1 + est*N[i] if(description) if(type=="mean") cat("For stratum ",i,", the Hajek estimator is:",est,"\n") else cat("For stratum ",i,", the Hajek estimator is:",est*N[i],"\n") } if(description) cat("The Hajek estimator is:\n") if(type=="mean") return(s1/sum(N)) else return(s1) } sampling/R/UPopips.r0000644000176200001440000000105115033727302014046 0ustar liggesusersUPopips<-function(lambda, type=c("pareto","uniform","exponential"),eps = 1e-6) { if(anyNA(lambda)) stop("there are missing values in the lambda vector") type <- match.arg(type) list<- (lambda > eps & lambda < 1 - eps) lambda1 <- lambda[list] N <- length(lambda1) n <-.as_int(sum(lambda1)) omega <- runif(N) s<-lambda s1<-switch(type,pareto=(omega*(1-lambda1)/((1-omega)*lambda1)), uniform=omega/lambda1, exponential=log(1-omega)/log(1-lambda1)) s1<-order(s1) s1<-s1[seq_len(n)] s[list][s1]<-1 s } sampling/R/fastflightcube.R0000644000176200001440000000601014520143727015404 0ustar liggesusers"fastflightcube" <- function(X,pik,order=1,comment=TRUE) { EPS = 1e-11 "algofastflightcube" <- function(X,pik) { "jump" <- function(X,pik){ N = length(pik) p = round(length(X)/length(pik)) X<-array(X,c(N,p)) X1=cbind(X,rep(0,times=N)) kern<-svd(X1)$u[,p+1] listek=abs(kern)>EPS buff1<-(1-pik[listek])/kern[listek] buff2<- -pik[listek]/kern[listek] la1<-min( c(buff1[(buff1>0)] , buff2[(buff2>0)]) ) pik1<- pik+la1*kern buff1<- -(1-pik[listek])/kern[listek] buff2<- pik[listek]/kern[listek] la2<-min(c(buff1[(buff1>0)] , buff2[(buff2>0)])) pik2<- pik-la2*kern q<-la2/(la1+la2) if (runif(1)(1-EPS) | psikEPS & pik<(1-EPS))])==(p+1)) psik <- jump(B,psik) pik[ind]=psik pik } "reduc" <- function(X) { EPS=1e-11 N=dim(X)[1] Re=svd(X) array(Re$u[,(Re$d>EPS)] , c(N,sum(as.integer(Re$d>EPS)))) } N = length(pik); p = round(length(X)/length(pik)) X<-array(X,c(N,p)) if (order==1) o<-sample.int(N,N) else { if(order==2) o<-seq(1,N,1) else o<-order(pik,decreasing=TRUE) } liste<-o[(pik[o]>EPS & pik[o]<(1-EPS))] if(comment==TRUE){ cat("\nBEGINNING OF THE FLIGHT PHASE\n") cat("The matrix of balanced variable has",p," variables and ",N," units\n") cat("The size of the inclusion probability vector is ",length(pik),"\n") cat("The sum of the inclusion probability vector is ",sum(pik),"\n") cat("The inclusion probability vector has ",length(liste)," non-integer elements\n") } pikbon<-pik[liste]; Nbon=length(pikbon); Xbon<-array(X[liste,] ,c(Nbon,p)) pikstar<-pik flag=0 if(Nbon>p){if(comment==TRUE) cat("Step 1 ") pikstarbon<-algofastflightcube(Xbon,pikbon) pikstar[liste]=pikstarbon flag=1 } liste<-o[(pikstar[o]>EPS & pikstar[o]<(1-EPS))] pikbon<-pikstar[liste] Nbon=length(pikbon) Xbon<-array(X[liste,] ,c(Nbon,p)) pbon=dim(Xbon)[2] if(Nbon>0){ Xbon=reduc(Xbon) pbon=dim(Xbon)[2] } k=2 while(Nbon>pbon & Nbon>0){ if(comment==TRUE) cat("Step ",k,", ") k=k+1 pikstarbon<-algofastflightcube(Xbon/pik[liste]*pikbon,pikbon) pikstar[liste]=pikstarbon liste<-o[(pikstar[o]>EPS & pikstar[o]<(1-EPS))] pikbon<-pikstar[liste] Nbon=length(pikbon) Xbon<-array(X[liste,] ,c(Nbon,p)) if(Nbon>0) { Xbon=reduc(Xbon) pbon=dim(Xbon)[2] } flag=1 } if(comment==TRUE) if(flag==0) cat("NO FLIGHT PHASE") if(comment==TRUE) cat("\n") pikstar } sampling/R/cluster.r0000644000176200001440000001365114520143727014144 0ustar liggesuserscluster<-function (data, clustername, size, method = c("srswor", "srswr", "poisson", "systematic"), pik, description = FALSE) { if (size == 0) stop("the size is zero") if (missing(method)) { warning("the method is not specified; by default, the method is srswor") method = "srswor" } if (!(method %in% c("srswor", "srswr", "poisson", "systematic"))) stop("the name of the method is wrong") if (method %in% c("poisson", "systematic") & missing(pik)) stop("the vector of probabilities is missing") if (method %in% c("poisson", "systematic") & !missing(pik)) if(!is.vector(pik)) pik=as.vector(pik) data = data.frame(data) index = 1:nrow(data) if (missing(clustername)) { if (method == "srswor") result = data.frame(index[srswor(size, nrow(data)) == 1], rep(size/nrow(data), size)) if (method == "srswr") { s = srswr(size, nrow(data)) st = s[s != 0] l = length(st) result = data.frame(index[s != 0]) result = cbind.data.frame(result, st, prob = rep(1-(1-1/nrow(data))^size,l)) colnames(result) = c("ID_unit", "Replicates", "Prob") } if (method == "poisson") { pikk = inclusionprobabilities(pik, size) s = (UPpoisson(pikk) == 1) if (length(s) > 0) result = data.frame(index[s], pikk[s]) if (description) cat("\nNumber of units in the population and number of selected units:", nrow(data), length(s), "\n") } if (method == "systematic") { pikk = inclusionprobabilities(pik, size) s = (UPsystematic(pikk) == 1) result = data.frame(index[s], pikk[s]) } if (method != "srswr") colnames(result) = c("ID_unit", "Prob") if (description) cat("\nNumber of units in the population and number of selected units:", nrow(data), sum(size), "\n") } else { data = data.frame(data) m = match(clustername, colnames(data)) if (length(m) > 1) stop("there are too many specified variables as clusters") if (is.na(m)) stop("the cluster name is wrong") x1 = factor(data[, m]) result = NULL if (nlevels(x1) == 0) stop("the cluster variable has 0 categories") else { nr_cluster = nlevels(x1) if (method == "srswor") { s = as.data.frame(levels(x1)[srswor(size, nr_cluster) == 1]) names(s) = c("cluster") r = cbind.data.frame(index, data[, m]) names(r) = c("index", "cluster") r = merge(r, s, by.x = "cluster", by.y = "cluster", sort = TRUE) result = cbind.data.frame(r, rep(size/nr_cluster, nrow(r))) } if (method == "srswr") { s = srswr(size, nr_cluster) st = cbind.data.frame(levels(x1)[s != 0], s[s != 0]) names(st) = c("cluster", "repl") r = cbind.data.frame(index, data[, m]) names(r) = c("index", "cluster") r = merge(r, st, by.x = "cluster", by.y = "cluster") result = cbind.data.frame(r, rep(1-(1-1/nr_cluster)^size, nrow(r))) } if (method == "systematic") { pikk = inclusionprobabilities(pik, size) s = (UPsystematic(pikk) == 1) st = cbind.data.frame(levels(x1)[s], pikk[s]) names(st) = c("cluster", "prob") r = cbind.data.frame(index, data[, m]) names(r) = c("index", "cluster") result = merge(r, st, by.x = "cluster", by.y = "cluster") } if (method == "poisson") { pikk = inclusionprobabilities(pik, size) s = (UPpoisson(pikk) == 1) if (any(s)) { st = cbind.data.frame(levels(x1)[s], pikk[s]) names(st) = c("cluster", "prob") r = cbind.data.frame(index, data[, m]) names(r) = c("index", "cluster") result = merge(r, st, by.x = "cluster", by.y = "cluster") if (description) { cat("Number of selected clusters:", sum(s), "\n") cat("\nNumber of units in the population and number of selected units:", nrow(data), nrow(result), "\n") } } else { if (description) { cat("Number of selected clusters: 0\n") cat("Population total and number of selected units:", nrow(data), 0, "\n") } result = NULL } } if (method == "srswr") { colnames(result) = c(clustername, "ID_unit", "Replicates", "Prob") if (description) { cat("Number of selected clusters:", length(s[s != 0]), "\n") cat("Number of units in the population and number of selected units:", nrow(data), nrow(result), "\n") } } else if (!is.null(result)) colnames(result) = c(clustername, "ID_unit", "Prob") if (description & !(method %in% c("poisson", "srswr"))) { cat("Number of selected clusters:", size, "\n") cat("Number of units in the population and number of selected units:", nrow(data), nrow(result), "\n") } } } result } sampling/R/UPpivotal.r0000644000176200001440000000165014520143730014374 0ustar liggesusers"UPpivotal" <- function(pik,eps=1e-6) { if(any(is.na(pik))) stop("there are missing values in the pik vector") N<-length(pik) s<-rep(0,times=N) a<-pik[1] b<-pik[2] i<-1 j<-2 k<-3 while(k<=N) { u<-runif(1) if(a>=eps & a<= 1-eps & b>=eps & b<= 1-eps) if(a+b>1) { if(u<(1-b)/(2-a-b)) {b<-a+b-1;a<-1} else {a<-a+b-1;b<-1} } else{ if(u< b/(a+b)) {b<- a+b;a<-0} else {a<- a+b;b<-0} } if( (a 1-eps)& (k<=N)) {s[i]=a;a=pik[k];i=k;k=k+1;} if( (b 1-eps)& (k<=N) ) {s[j]=b;b=pik[k];j=k;k=k+1;} } u<-runif(1) if(a>=eps & a<= 1-eps & b>=eps & b<= 1-eps) if(a+b>1) { if(u<(1-b)/(2-a-b)) {b<-a+b-1;a<-1} else {a<-a+b-1;b<-1} } else{ if(u< b/(a+b)) {b<-a+b;a<-0} else {a<- a+b;b<-0} } s[i]=a; s[j]=b; s } sampling/R/mstage.r0000644000176200001440000003300314520143727013734 0ustar liggesusersmstage<-function (data, stage = c("stratified", "cluster", ""), varnames, size, method = c("srswor", "srswr", "poisson", "systematic"), pik, description = FALSE) { if (missing(size)) stop("the size argument is missing") if (!missing(stage) & missing(varnames)) stop("indicate the stage argument") if (!missing(stage)) { number = length(stage) for (i in 1:length(stage)) if (!(stage[i] %in% c("stratified", "cluster", ""))) stop("the stage argument is wrong") } else number = length(size) if (number > 1) { if (!missing(varnames)) { if (!is.list(size)) stop("the size must be a list") size = as.list(size) varnames = as.list(varnames) size1 = size[[1]] varnames1 = varnames[[1]] if (method[[1]] %in% c("systematic", "poisson")) pik1 = pik[[1]] } else { size1 = size[[1]] varnames1 = NULL if (method[[1]] %in% c("systematic", "poisson")) pik1 = pik[[1]] } } else { size1 = size if(missing(method)) method="srswor" else if (method %in% c("systematic", "poisson")) pik1 = pik } if (description) cat("STAGE 1", "\n") if (missing(stage)) { if (missing(varnames)) if (missing(method)) s = strata(data, stratanames = NULL, size = size1, description) else if (method[[1]] %in% c("systematic", "poisson")) s = strata(data, stratanames = NULL, size = size1, method[[1]], pik = pik1, description) else s = strata(data, stratanames = NULL, size = size1, method[[1]], description) else s = strata(data, stratanames = NULL, size1, method[[1]], pik = pik1, description) } else if (stage[1] == "stratified") { s = strata(data, varnames1, size1, method="srswor",description) dimension_st = table(s$Stratum) if(description) cat("Number of strata:",length(dimension_st),"\n") } else { s = cluster(data, varnames1, size1, method[[1]], pik1, description) if (is.null(s)) stop("0 selected units in the first stage") m = match(varnames1, names(s)) nl = nlevels(as.factor(s[, m])) lev = levels(as.factor(s[, m])) if (nl >= 1) { dimension_cl = NULL for (i in 1:nl) if(nrow(subset(s,s[, m] == unique(s[,m])[i]))>0) dimension_cl = c(dimension_cl,nrow(subset(s,s[, m] == unique(s[,m])[i]))) } dimension = dimension_cl } if (is.null(s)) stop("0 selected units in the first stage") if (number > 1) if ((is.element("cluster", stage) & is.element("stratified", stage))) result = getdata(data, s) else result = s res = list() res[[1]] = s if (number >= 2) for (j in 2:number) { if (description) cat("STAGE ", j, "\n") if (!missing(varnames)) { if (stage[[j]] == "cluster") { if (stage[[j - 1]] == "stratified") { k = length(dimension_st) s1 = NULL limit = 0 dimension = list() if (k >= 1) for (ii in 1:k) { r = res[[j - 1]][(limit + 1):(limit + dimension_st[ii]), ] r = getdata(data, r) m = match(varnames[[j]], names(r)) if (method[[j]] %in% c("systematic", "poisson")) { index = res[[j - 1]][(limit + 1):(limit + dimension_st[ii]), ]$ID_unit pikk = pik[[j]][index] if (!is.null(r)) s3 = cluster(r, clustername = varnames[[j]], size = size[[j]][ii], method = method[[j]], pik = pikk, description) else s3 = NULL } else { s3 = cluster(r, clustername = varnames[[j]], size = size[[j]][ii], method = method[[j]], description = description) } limit = limit + dimension_st[ii] if (method[[j]] == "srswr") { s3 = cbind.data.frame(r[s3$ID_unit, m], r[s3$ID_unit, ]$ID_unit, s3$Replicates, s3$Prob, r[s3$ID_unit, ]$Prob * s3$Prob) colnames(s3) = c(varnames[[j]], "ID_unit", "Replicates", paste("Prob_", j, "_stage"), "Prob") } else if(!is.null(s3)) { s3 = cbind.data.frame(r[s3$ID_unit, m], r[s3$ID_unit, ]$ID_unit, s3$Prob, r[s3$ID_unit, ]$Prob * s3$Prob) colnames(s3) = c(varnames[[j]], "ID_unit", paste("Prob_", j, "_stage"), "Prob") } if (!is.null(s3)) { m = match(varnames[[j]], names(s3)) for (l in 1:nlevels(as.factor(s3[, m]))) dimension = c(dimension, table(s3[, m])[l]) s1 = rbind(s1, s3) } } } else if (stage[[j - 1]] == "cluster") { m_cl = match(varnames, names(res[[j-1]]),0) mat=res[[j-1]][, m_cl] nl = nlevels(as.factor(mat)) if (nl >= 1) { dimension_cl =NULL for (i in 1:nl) if(length(subset(mat, mat==unique(mat)[i]))>0) dimension_cl = c(dimension_cl,length(subset(mat, mat==unique(mat)[i]))) k = length(dimension_cl) } else stop("error in the previous stage") s1 = NULL limit = 0 dimension = list() if (k > length(size[[j]])) { warning("the number of selected clusters in the previous stage is larger than the size argument") warning("the size 1 is added") size1 = size[[j]] for (i in 1:(k - length(size[[j]]))) size1 = c(size1, 1) } else size1 = size[[j]] if (k >= 1) for (ii in 1:k) { r = res[[j - 1]][(limit + 1):(limit + dimension_cl[ii]), ] r = getdata(data, r) m = match(varnames[[j]], names(r)) if (method[[j]] %in% c("systematic", "poisson")) { m1 = match(varnames[[j - 1]], names(r)) m2 = match(varnames[[j - 1]], names(data)) mm = match(r[1, m1], levels(factor(data[, m2]))) pikk = as.numeric(pik[[j]][[mm]]) if (!is.null(r)) s3 = cluster(r, clustername = varnames[[j]], size = size1[[ii]], method = method[[j]], pik = pikk, description) else s3 = NULL } else s3 = cluster(r, clustername = varnames[[j]], size = size1[ii], method = method[[j]], pik, description) limit = limit + dimension_cl[ii] if (method[[j]] == "srswr") { s3 = cbind.data.frame(r[s3$ID_unit, m], r[s3$ID_unit, ]$ID_unit, s3$Replicates, s3$Prob, r[s3$ID_unit, ]$Prob * s3$Prob) colnames(s3) = c(varnames[[j]], "ID_unit", "Replicates", paste("Prob_", j, "_stage"), "Prob") } else if (!is.null(s3)) { s3 = cbind.data.frame(r[s3$ID_unit, m], r[s3$ID_unit, ]$ID_unit, s3$Prob, r[s3$ID_unit, ]$Prob * s3$Prob) colnames(s3) = c(varnames[[j]], "ID_unit", paste("Prob_", j, "_stage"), "Prob") } if (!is.null(s3)) { m = match(varnames[[j]], names(s3)) for (l in 1:nlevels(as.factor(s3[, m]))) dimension = c(dimension, table(s3[, m])[l]) s1 = rbind(s1, s3) } } } } else if (j > 1) { k = length(dimension) s1 = NULL limit = 0 count = 0 if (k > length(size[[j]])) { warning("the number of selected clusters at the previous stage is larger than the size argument") warning("the size 1 is added") size1 = size[[j]] for (i in 1:(k - length(size[[j]]))) size1 = c(size1,1) } else size1 = size[[j]] if (k >= 1) for (i in 1:k) for (ii in 1:length(dimension[[i]])) { r = res[[j - 1]][(limit + 1):(limit + dimension[[i]][ii]), ] count = count + 1 if (method[[j]] %in% c("systematic", "poisson")) { index = res[[j - 1]][(limit + 1):(limit + dimension[[i]][ii]), ]$ID_unit pikk = pik[[j]][index] if (!is.null(r)) s2 = strata(r, NULL, size = size1[count], method = method[[j]], pik = pikk, description) else s2 = NULL } else s2 = strata(r, NULL, size = size1[count], method = method[[j]], pik, description) limit = limit + dimension[[i]][ii] if (method[[j]] == "srswr") { s2 = cbind.data.frame(r[s2$ID_unit, ]$ID_unit, s2$Replicates, s2$Prob, r[s2$ID_unit, ]$Prob * s2$Prob) colnames(s2) = c("ID_unit", "Replicates", paste("Prob_", j, "_stage"), "Prob") } else if (!is.null(s2)) { s2 = cbind.data.frame(r[s2$ID_unit, ]$ID_unit, s2$Prob, r[s2$ID_unit, ]$Prob * s2$Prob) colnames(s2) = c("ID_unit", paste("Prob_", j, "_stage"), "Prob") } if (!is.null(s2)) s1 = rbind(s1, s2) } } } else { if (missing(stage)) { if (missing(method)) s1 = strata(result, stratanames = NULL, size = size[[j]], description = description) else if (method[[j]] == "poisson" | method[[j]] == "systematic") s1 = strata(result, stratanames = NULL, size = size[[j]], method = method[[j]], pik = pik[[j]], description = description) else s1 = strata(result, stratanames = NULL, size = size[[j]], method = method[[j]], description = description) if (method[[j]] == "srswr") { s1 = cbind.data.frame(result[s1$ID_unit, ]$ID_unit, s1$Replicates, s1$Prob, result[s1$ID_unit, ]$Prob * s1$Prob) colnames(s1) = c("ID_unit", "Replicates", paste("Prob_", j, "_stage"), "Prob") } else { s1 = cbind.data.frame(result[s1$ID_unit, ]$ID_unit, s1$Prob, result[s1$ID_unit, ]$Prob * s1$Prob) colnames(s1) = c("ID_unit", paste("Prob_", j, "_stage"), "Prob") } } } if (!is.null(s1)) { result = s1 res[[j]] = result } else number = number - 1 } if (!is.null(names(res[[1]]))) { m = match("Prob", names(res[[1]])) names(res[[1]])[m] = "Prob_ 1 _stage" } names(res) = c(1:number) res } sampling/R/landingcube.R0000644000176200001440000000354614520143727014700 0ustar liggesusers"landingcube" <- function(X,pikstar,pik,comment=TRUE) # landing phase of the cube method ###################################################### { # extraction of the non-integer values for the landing phase EPS=1e-11 p=dim(X)[2] N=dim(X)[1] liste=(pikstar>EPS & pikstar<(1-EPS)) pikland=pikstar[liste] Nland=length(pikland) Xland=array(X[liste,] ,c(Nland,p)) nland=sum(pikland) FLAGI=(abs(nland-round(nland))EPS,]/pik[pik>EPS] cost=rep(0,times=lll) for(i in 1:lll) cost[i]=t(Asmp[,i]) %*% ginv(t(A) %*% A) %*% Asmp[,i] # linear programming V = t(cbind(SSS,rep(1,times=lll))) b=c(pikland,1) constdir=rep("==",times=(Nland+1)) x=lp("min",cost,V,constdir,b)$solution # choice of the sample u=runif(1,0,1) i=0 ccc=0 while(ccc= C || bounds[1] > bounds[2]) stop("The conditions low bounds[2])) { g[g < bounds[1]] = bounds[1] g[g > bounds[2]] = bounds[2] list = (1:length(g))[g > bounds[1] & g < bounds[2]] if (length(list) != 0) { g1 = g[list] t2 = total - c(t(g[-list] * d[-list]) %*% Xs[-list, ]) Xs1 = Xs[list, ] Zs1 = Zs[list, ] d1 = d[list] q1 = q[list] list1 = list } } t1 = c(t(d1) %*% Xs1) lambda1 = ginv(t(Xs1 * d1 * q1) %*% Zs1, tol = EPS) %*% (t2 - t1) if (length(list1) > 1) g1 = 1 + q1 * c(Zs1 %*% lambda1) else if (length(list1) == 1) { g1 = 1 + q1 * c(as.vector(Zs1) %*% as.vector(lambda1)) } g[list1] = g1 tr = crossprod(Xs, g * d) expression = max(abs(tr - total)/total) if(any(total==0)) expression = max(abs(tr - total)) if (expression < EPS1 & all(g >= bounds[1] & g <= bounds[2])) break } if (l == max_iter) { cat("No convergence in", max_iter, "iterations with the given bounds. \n") cat("The bounds for the g-weights are:", min(g), " and ", max(g), "\n") g=NULL } } else if (method == "raking") { lambda = as.matrix(rep(0, ncol(Xs))) w1 = as.vector(d * exp(Zs %*% lambda * q)) T = t(Xs) for (l in 1:max_iter) { phi = t(Xs) %*% w1 - total T1 = t(Xs * w1) phiprim = T1 %*% Zs lambda = lambda - ginv(phiprim, tol = EPS) %*% phi w1 = as.vector(d * exp(Zs %*% lambda * q)) if (any(is.na(w1)) | any(is.infinite(w1)) | any(is.nan(w1))) { warning("No convergence") g = NULL der = g l = max_iter break } tr = crossprod(Xs, w1) expression = max(abs(tr - total)/total) if(any(total==0)) expression = max(abs(tr - total)) if (expression < EPS1) break } if (l == max_iter) { warning("No convergence") g = NULL der = g } else {g = w1/d; der=g} } else if (method == "logit") if (missing(bounds)) stop("Specify the bounds") else { if (bounds[2] <= C || bounds[1] >= C || bounds[1] > bounds[2]) stop("The conditions low EPS1 | any(g < bounds[1]) | any(g > bounds[2])) { lambda1 = rep(0, ncol(Xs)) list = 1:length(g) t2 = total Xs1 = Xs d1 = d Zs1 = Zs g1 = g q1 = q list1 = 1:length(g) for (l in 1:max_iter) { if (any(g < bounds[1]) | any(g > bounds[2])) { g[g < bounds[1]] = bounds[1] g[g > bounds[2]] = bounds[2] list = (1:length(g))[g > bounds[1] & g < bounds[2]] if (length(list) != 0) { g1 = g[list] t2 = total - c(t(g[-list] * d[-list]) %*% Xs[-list, ]) Xs1 = Xs[list, ] Zs1 = Zs[list, ] d1 = d[list] q1 = q[list] list1 = list } else break } if (is.vector(Xs1)) { warning("no convergence") g1 = g = NULL break } t1 = c(t(d1) %*% Xs1) phi = t(Xs1) %*% as.vector(d1 * g1) T = t(Xs1 * as.vector(d1 * g1)) phiprime = T %*% Zs1 lambda1 = lambda1 - ginv(phiprime, tol = EPS) %*% (as.vector(phi) - t2) u = exp(A * (Zs1 %*% lambda1 * q1)) F = g1 = (bounds[1] * (bounds[2] - C) + bounds[2] * (C - bounds[1]) * u)/(bounds[2] - C + (C - bounds[1]) * u) if (any(is.na(g1))) { warning("no convergence") g1 = g = NULL break } g[list1] = g1 der = g-1 tr = crossprod(Xs, g * d) expression = max(abs(tr - total)/total) if(any(total==0)) expression = max(abs(tr - total)) if (expression < EPS1 & all(g >= bounds[1] & g <= bounds[2])) break } if (l == max_iter) { cat("no convergence in", max_iter, "iterations with the given bounds. \n") cat("the bounds for the g-weights are:", min(g), " and ", max(g), "\n") cat(" and the g-weights are given by g\n") g = NULL der = g } } } if (description && !is.null(g)) { par(mfrow = c(3, 2), pty = "s") hist(g) boxplot(g, main = "Boxplot of g") hist(d) boxplot(d, main = "Boxplot of d") hist(g * d) boxplot(g * d, main = "Boxplot of w=g*d") if (method %in% c("truncated", "raking", "logit")) cat("number of iterations ", l, "\n") cat("summary - initial weigths d\n") print(summary(d)) cat("summary - final weigths w=g*d\n") print(summary(as.vector(g * d))) } g } sampling/R/regest_strata.r0000644000176200001440000000241214520143727015323 0ustar liggesusersregest_strata<-function(formula,weights,Tx_strata,strata,pikl,sigma=rep(1,length(weights)),description=FALSE) { cl <- match.call() mf <- match.call(expand.dots = FALSE) m <- match(c("formula", "weights"), names(mf), 0) mf <- mf[c(1, m)] mf$drop.unused.levels <- TRUE mf[[1]] <- as.name("model.frame") mf <- eval(mf, parent.frame()) mt <- attr(mf, "terms") y <- model.response(mf, "numeric") w <- as.vector(model.weights(mf)) x <- model.matrix(mt, mf, contrasts) str <- function(st, h, n) .C("str", as.double(st), as.integer(h), as.integer(n), s = double(n), PACKAGE = "sampling")$s sample.size = length(y) h = unique(strata) s1 = 0 for (i in 1:length(h)) { s=str(strata, h[i], sample.size) ys=y[s==1] xs=x[s==1,] r=regest(ys~xs-1,Tx=Tx_strata[h[i]],weights=weights[s==1],pikl=pikl[s==1,s==1],n=length(s[s==1]),sigma[s==1]) est=r$regest s1 = s1 + est if(description) {cat("Stratum ",h[i],", the regression estimator is:",est,"\n") cat("Number of units:",sum(s),"\n") cat("Beta coefficient(s):", r$coefficients,"\n") cat("Std. error:", r$std_error,"\n") cat("t-value:", r$t_value, "\n") cat("p_value:",r$p_value,"\n") cat("cov_matrix:\n") print(r$cov_matrix) } } if(description) cat("The regression estimator is:\n") s1 } sampling/R/calib.r0000644000176200001440000001762014520143726013534 0ustar liggesuserscalib<-function (Xs, d, total, q = rep(1, length(d)), method = c("linear", "raking", "truncated", "logit"), bounds = c(low = 0, upp = 10), description = FALSE, max_iter = 500) { if (any(is.na(Xs)) | any(is.na(d)) | any(is.na(total)) | any(is.na(q))) stop("the input should not contain NAs") if (!(is.matrix(Xs) | is.array(Xs))) Xs = as.matrix(Xs) if (is.matrix(Xs)) if (length(total) != ncol(Xs)) stop("Xs and total have different dimensions") if (is.vector(Xs) & length(total) != 1) stop("Xs and total have different dimensions") if (any(is.infinite(q))) stop("there are Inf values in the q vector") if (missing(method)) stop("specify a method") if (!(method %in% c("linear", "raking", "logit", "truncated"))) stop("the specified method is not in the list") if (method %in% c("linear", "raking") & !missing(bounds)) stop("for the linear and raking the bounds are not allowed") EPS = .Machine$double.eps EPS1 = 1e-06 n = length(d) lambda = as.matrix(rep(0, n)) lambda1 = ginv(t(Xs * d * q) %*% Xs, tol = EPS) %*% (total - as.vector(t(d) %*% Xs)) if (method == "linear" | max(abs(lambda1)) < EPS) g = 1 + q * as.vector(Xs %*% lambda1) else if (method == "truncated") { if (!missing(bounds)) { if (bounds[2] <= 1 || bounds[1] >= 1 || bounds[1] > bounds[2]) warning("The conditions low<1 bounds[2])) { g[g < bounds[1]] = bounds[1] g[g > bounds[2]] = bounds[2] list = (1:length(g))[g > bounds[1] & g < bounds[2]] if (length(list) != 0) { g1 = g[list] t2 = total - as.vector(t(g[-list] * d[-list]) %*% Xs[-list, ]) Xs1 = Xs[list, ] d1 = d[list] q1 = q[list] list1 = list } } t1 = as.vector(t(d1) %*% Xs1) lambda1 = ginv(t(Xs1 * d1 * q1) %*% Xs1, tol = EPS) %*% (t2 - t1) if (length(list1) > 1) g1 = 1 + q1 * as.vector(Xs1 %*% lambda1) else if (length(list1) == 1) { g1 = 1 + q1 * as.vector(as.vector(Xs1) %*% as.vector(lambda1)) } g[list1] = g1 tr = crossprod(Xs, g * d) expression = max(abs(tr - total)/total) if(any(total==0)) expression = max(abs(tr - total)) if (expression < EPS1 & all(g >= bounds[1] & g <= bounds[2])) break } if (l == max_iter) { cat("No convergence in", max_iter, "iterations with the given bounds. \n") cat("The bounds for the g-weights are:", min(g), " and ", max(g), "\n") cat(" and the g-weights are given by g\n") } } else if (method == "raking") { lambda = as.matrix(rep(0, ncol(Xs))) w1 = as.vector(d * exp(Xs %*% lambda * q)) for (l in 1:max_iter) { phi = t(Xs) %*% w1 - total T1 = t(Xs * w1) phiprim = T1 %*% Xs lambda = lambda - ginv(phiprim, tol = EPS) %*% phi w1 = as.vector(d * exp(Xs %*% lambda * q)) if (any(is.na(w1)) | any(is.infinite(w1))) { warning("No convergence") g = NULL break } tr = crossprod(Xs, w1) expression = max(abs(tr - total)/total) if(any(total==0)) expression = max(abs(tr - total)) if (expression < EPS1) break } if (l == max_iter) { warning("No convergence") g = NULL } else g = w1/d } else if (method == "logit") { if (bounds[2] <= 1 || bounds[1] >= 1 || bounds[1] > bounds[2]) stop("The conditions low<1 EPS1 | any(g < bounds[1]) | any(g > bounds[2])) { lambda1 = rep(0, ncol(Xs)) list = 1:length(g) t2 = total Xs1 = Xs d1 = d g1 = g q1 = q list1 = 1:length(g) for (l in 1:max_iter) { if (any(g < bounds[1]) | any(g > bounds[2])) { g[g < bounds[1]] = bounds[1] g[g > bounds[2]] = bounds[2] list = (1:length(g))[g > bounds[1] & g < bounds[2]] if (length(list) != 0) { g1 = g[list] t2 = total - as.vector(t(g[-list] * d[-list]) %*% Xs[-list, ]) Xs1 = Xs[list, ] d1 = d[list] q1 = q[list] list1 = list } else break } if (is.vector(Xs1)) { warning("no convergence") g1 = g = NULL break } t1 = as.vector(t(d1) %*% Xs1) phi = t(Xs1) %*% as.vector(d1 * g1) - t1 T = t(Xs1 * as.vector(d1 * g1)) phiprime = T %*% Xs1 lambda1 = lambda1 - ginv(phiprime, tol = EPS) %*% (as.vector(phi) - t2 + t1) u = exp(A * (Xs1 %*% lambda1 * q1)) F = g1 = (bounds[1] * (bounds[2] - 1) + bounds[2] * (1 - bounds[1]) * u)/(bounds[2] - 1 + (1 - bounds[1]) * u) if (any(is.na(g1))) { warning("no convergence") g1 = g = NULL break } g[list1] = g1 tr = crossprod(Xs, g * d) expression = max(abs(tr - total)/total) if(any(total==0)) expression = max(abs(tr - total)) if (expression < EPS1 & all(g >= bounds[1] & g <= bounds[2])) break } if (l == max_iter) { cat("no convergence in", max_iter, "iterations with the given bounds. \n") cat("the bounds for the g-weights are:", min(g), " and ", max(g), "\n") cat(" and the g-weights are given by g\n") g = NULL } } } if (description && !is.null(g)) { par(mfrow = c(3, 2), pty = "s") hist(g) boxplot(g, main = "Boxplot of g") hist(d) boxplot(d, main = "Boxplot of d") hist(g * d) boxplot(g * d, main = "Boxplot of w=g*d") if (method %in% c("truncated", "raking", "logit")) cat("number of iterations ", l, "\n") cat("summary - initial weigths d\n") print(summary(d)) cat("summary - final weigths w=g*d\n") print(summary(as.vector(g * d))) } g } sampling/R/balancedtwostage.R0000644000176200001440000000157414520143726015732 0ustar liggesusers"balancedtwostage" <- function(X,selection,m,n,PU,comment=TRUE,method=1) { N=dim(X)[1] p=dim(X)[2] str=cleanstrata(PU) M=max(PU) res1=balancedcluster(X,m,PU,method,comment) if(selection==2) { pik2=rep(n/N*M/m,times=N); if(n/N*M/m>1) stop("at the second stage, inclusion probabilities larger than 1"); } if(selection==1) { pik2=inclusionprobastrata(str,rep(n/m ,times=max(str))); if(max(pik2)>1) stop("at the second stage, inclusion probabilities larger than 1"); } liste=(res1[,1]==1) sf=rep(0,times=N) sf[liste]=balancedstratification(array(X[liste,]/res1[,2][liste],c(sum(as.integer(liste)),p)),cleanstrata(str[liste]),pik2[liste],comment,method) x=cbind(sf,res1[,2]*pik2,res1[,1],res1[,2],pik2) colnames(x)=c("second_stage","final_pik", "primary","pik_first_stage", "pik_second_stage") x } sampling/R/strata.r0000644000176200001440000001034314520143727013754 0ustar liggesusersstrata<-function(data, stratanames=NULL, size, method=c("srswor","srswr","poisson","systematic"),pik,description=FALSE) { if(missing(method)) {warning("the method is not specified; by default, the method is srswor") method="srswor" } if(!(method %in% c("srswor","srswr","poisson","systematic"))) stop("the method name is not in the list") if(method %in% c("poisson","systematic") & missing(pik)) stop("the vector of probabilities is missing") if(missing(stratanames)|is.null(stratanames)) { if(length(size)>1) stop("the argument giving stratification variable is missing. The argument size should be a value.") if(method=="srswor") result=data.frame((1:nrow(data))[srswor(size,nrow(data))==1],rep(size/nrow(data),size)) if(method=="srswr") { s=srswr(size,nrow(data)) st=s[s!=0] l=length(st) result=data.frame((1:nrow(data))[s!=0]) result=cbind.data.frame(result,st,prob=rep(1-(1-1/nrow(data))^size,l)) colnames(result)=c("ID_unit","Replicates","Prob") } if(method=="poisson") { pikk=inclusionprobabilities(pik,size) s=(UPpoisson(pikk)==1) if(length(s)>0) result=data.frame((1:nrow(data))[s],pikk[s]) if(description) cat("\nPopulation total and number of selected units:",nrow(data),sum(s),"\n") } if(method=="systematic") { pikk=inclusionprobabilities(pik,size) s=(UPsystematic(pikk)==1) result=data.frame((1:nrow(data))[s],pikk[s]) } if(method!="srswr") colnames(result)=c("ID_unit","Prob") if(description & method!="poisson") cat("\nPopulation total and number of selected units:",nrow(data),sum(size),"\n") } else { data=data.frame(data) index=1:nrow(data) m=match(gsub(" ",".",stratanames),colnames(data)) if(any(is.na(m))) stop("the names of the strata are wrong") data2=cbind.data.frame(data[,m],index) colnames(data2)=c(stratanames,"index") x1=data.frame(unique(data[,m])) colnames(x1)=stratanames result=NULL for(i in 1:nrow(x1)) { if(is.vector(x1[i,])) data3=data2[data2[,1]==x1[i,],] else {as=data.frame(x1[i,]) names(as)=names(x1) data3=merge(data2, as, by = intersect(names(data2), names(as))) } y=sort(data3$index) if(description & method!="poisson") {cat("Stratum" ,i,"\n") cat("\nPopulation total and number of selected units:",length(y),size[i],"\n") } if(method!="srswr" & length(y)0),m[m>0]),] } else if (is.data.frame(m)) { res = NULL if (!is.null(names(m))) { mm = match(names(data), names(m), nomatch = 0) index = (1:ncol(data))[mm == 0] if (length(index) > 0) { res = cbind.data.frame(data[m$ID_unit, index], m) names(res)[1:length(index)] = names(data)[index] } else res = m } } else if (is.list(m)) { res = list() if (length(m) >= 1) for (j in 1:length(m)) { mm = match(names(data), names(m[[j]]), nomatch = 0) index = (1:ncol(data))[mm == 0] if (length(index) > 0) { res[[j]] = cbind.data.frame(data[m[[j]]$ID_unit, index], m[[j]]) names(res[[j]])[1:length(index)] = names(data)[index] } } else res = m } res } sampling/R/srswor.R0000644000176200001440000000011314520143727013747 0ustar liggesusers"srswor" <- function(n,N) {s<-rep(0,times=N);s[sample.int(N,n)]<-1;s} sampling/R/UPMEqfromw.R0000644000176200001440000000067614520143730014422 0ustar liggesusers"UPMEqfromw" <- function(w,n) { N=length(w) expa=array(0,c(N,n)) for(i in 1:N) expa[i,1]= sum(w[i:N]) for(i in (N-n+1):N) expa[i,N-i+1]=exp(sum(log(w[i:N]))) for(i in (N-2):1) for(z in 2:min(N-i,n)) { expa[i,z]=w[i]*expa[i+1,z-1]+expa[i+1,z] } q=array(0,c(N,n)) for(i in N:1) q[i,1]= w[i]/expa[i,1] for(i in N:(N-n+1)) q[i,N-i+1]=1 for(i in (N-2):1) for(z in 2:min(N-i,n)) q[i,z] = w[i]*expa[i+1,z-1]/expa[i,z] q } sampling/R/UPbrewer.R0000644000176200001440000000113614520143727014151 0ustar liggesusers"UPbrewer" <- function(pik, eps = 1e-06) { if(any(is.na(pik))) stop("there are missing values in the pik vector") n=sum(pik) n=.as_int(n) list = pik > eps & pik < 1 - eps pikb = pik[list] N = length(pikb) s=pik if(N<1) stop("the pik vector has all elements outside of the range [eps,1-eps]") else { sb=rep(0,N) n=sum(pikb) for (i in 1:n) { a = sum(pikb*sb) p = (1-sb)*pikb*((n-a)-pikb)/((n-a)-pikb*(n-i+1)) p = p/sum(p) p = cumsum(p) u=runif(1) for(j in 1:length(p)) if(u 0) warning("there are zero values in the initial vector a\n") if (nneg > 0) { warning("there are ", nneg, " negative value(s) shifted to zero\n") a[(a < 0)] = 0 } if(identical(a,rep(0,length(a)))) pik1=a else { pik1 =n * a/sum(a) pik=pik1[pik1>0] list1=pik1>0 list = pik >= 1 l = length(list[list == TRUE]) if(l>0) { l1=0 while (l != l1) { x=pik[!list] x=x/sum(x) pik[!list] = (n - l)*x pik[list] = 1 l1 = l list = (pik >= 1) l = length(list[list == TRUE]) } pik1[list1]=pik } } pik1 } sampling/R/writesample.R0000644000176200001440000000020515033700765014747 0ustar liggesuserswritesample<-function(n,N) { cc<-choose(N,n) P<-matrix(0,cc,N) R<-combn(N,n) for(i in seq_len(cc)) P[i,R[,i]]<-1 P }sampling/R/varHT.r0000644000176200001440000000157314520143730013501 0ustar liggesusersvarHT<-function(y, pikl, method=1) { if(any(is.na(pikl))) stop("there are missing values in pikl") if (any(is.na(y))) stop("there are missing values in y") if(!(is.data.frame(pikl) | is.matrix(pikl))) stop("pikl should be a matrix or a data frame") if(is.data.frame(pikl) | is.matrix(pikl)) if(nrow(pikl)!=ncol(pikl)) stop("pikl is not a square matrix") if (length(y) != nrow(pikl)) stop("y and pik have different sizes") if(!missing(method) & !(method %in% c(1,2))) stop("the method should be 1 or 2") if(is.data.frame(pikl)) pikl=as.matrix(pikl) pik=diag(pikl) pik1=outer(pik,pik,"*") delta=pikl-pik1 diag(delta)=pik*(1-pik) y1=outer(y,y,"*") if(method==1)return(sum(y1*delta/(pik1*pikl))) if(method==2) {y2=outer(y/pik,y/pik,"-")^2 return(0.5*sum(y2*(pik1-pikl)/pikl)) } } sampling/R/UPmidzunopi2.R0000644000176200001440000000017214520143730014754 0ustar liggesusers"UPmidzunopi2" <- function(pik) { N=length(pik) UN=rep(1,times=N) b=1-pik%*%t(UN) 1-b-t(b)+UPtillepi2(1-pik) } sampling/R/UPMEpiktildefrompik.R0000644000176200001440000000036714520143730016301 0ustar liggesusers"UPMEpiktildefrompik" <-function(pik,eps=1e-6) { n=sum(pik) n=.as_int(n) pikt=pik arr=1 while(arr>eps) { w=(pikt)/(1-pikt) q=UPMEqfromw(w,n) pikt1=pikt+pik-UPMEpikfromq(q) arr=sum(abs(pikt-pikt1)) pikt=pikt1 } pikt } sampling/R/UPmultinomial.R0000644000176200001440000000024114520143730015203 0ustar liggesusers"UPmultinomial" <- function(pik) {if(any(is.na(pik))) stop("there are missing values in the pik vector") as.vector(rmultinom(1,sum(pik),pik/sum(pik))) } sampling/R/as_int.r0000644000176200001440000000063314520143726013733 0ustar liggesusers#' Compare x to its round value #' @param x a double #' @noRd .as_int<-function(x) {if(!is.integer(x)) { xo = round(x) if(any(x > .Machine$integer.max)) stop("the input has entries too large to be integer") if(!identical(TRUE, (ax <- all.equal(xo, x)))) warning("the argument is not integer") else x=xo } x } sampling/R/regest.r0000644000176200001440000000470014520143727013747 0ustar liggesusersregest<-function(formula,Tx,weights,pikl,n,sigma=rep(1,length(weights))) { cl <- match.call() mf <- match.call(expand.dots = FALSE) m <- match(c("formula", "weights"), names(mf), 0) mf <- mf[c(1, m)] mf$drop.unused.levels <- TRUE mf[[1]] <- as.name("model.frame") mf <- eval(mf, parent.frame()) mt <- attr(mf, "terms") y <- model.response(mf, "numeric") w <- as.vector(model.weights(mf)) pik<-1/w if(!identical(sigma,rep(1,length(pik)))) w<-w/sigma^2 x <- model.matrix(mt, mf, contrasts) if(ncol(x)==1) x=as.vector(x) if (any(is.na(pik))) stop("there are missing values in pik") if (any(is.na(y))) stop("there are missing values in y") if (any(is.na(x))) stop("there are missing values in x") if(is.vector(x)) {if (length(y) != length(pik) | length(x)!=length(pik) | length(x)!=length(y)) stop("y, x and pik have different lengths") } else if(is.matrix(x)) {if (length(y) != length(pik) | nrow(x)!=length(pik) | nrow(x)!=length(y)) stop("y, x and pik have different sizes") if(ncol(x)>2 & length(Tx)!=ncol(x)-1) stop("x and Tx have different sizes") } model<-lm(y~x-1,weights=w) e<-model$residuals beta<-model$coefficient # variance of beta, Sarndal p. 194 delta<-matrix(0,nrow(pikl),ncol(pikl)) for(k in 1:(nrow(delta)-1)) {for(l in (k+1):ncol(delta)) delta[l,k]<-delta[k,l]<-1-pikl[k,k]*pikl[l,l]/pikl[k,l] delta[k,k]<-1-pikl[k,k] } delta[nrow(delta),ncol(delta)]<-1-pikl[nrow(delta),ncol(delta)] j_start<-1 if(is.matrix(x)) { if(all(x[,1]==rep(1,nrow(x)))) j_start<-2 xx<-as.matrix(x[,j_start:ncol(x)]) s<-0 for(i in 1:ncol(xx)) if(j_start==2) s<-s+sum(beta[i+1]*(Tx[i]-HTestimator(xx[,i],pik))) else s<-s+sum(beta[i]*(Tx[i]-HTestimator(xx[,i],pik))) est<-HTestimator(y,pik)+s } else est<-HTestimator(y,pik)+sum(beta*(Tx-HTestimator(x,pik))) V<-t(x*w*e)%*%delta%*%(x*e*w) inv<-ginv(t(x * w) %*% x) var_beta<-inv%*%V%*%inv z<-list() class(z) <- c("regest") z$call <- cl z$formula <- formula z$x <- x z$y <- y z$weights<-w z$regest<-as.numeric(est) z$coefficients<-beta z$std_error<-sqrt(diag(var_beta)) z$t_value<-beta/sqrt(diag(var_beta)) # number of degrees of freedom is number of obs-1 if intercept, and number of obs otherwise if(j_start==1) z$p_value<-2*(1-pt(z$t_value,n-1)) else z$p_value<-2*(1-pt(z$t_value,n)) z$cov_matrix<-var_beta z } sampling/R/balancedstratification.R0000644000176200001440000000107314520143726017112 0ustar liggesusers"balancedstratification" <- function(X,strata,pik,comment=TRUE,method=1) { strata=cleanstrata(strata) H=max(strata) N=dim(X)[1] pikstar=rep(0,times=N) for(h in 1:H) { if(comment==TRUE) cat("\nFLIGHT PHASE OF STRATUM",h) pikstar[strata==h]=fastflightcube(cbind(X[strata==h,],pik[strata==h]),pik[strata==h],1,comment) } if(comment==TRUE) cat("\nFINAL TREATMENT") XN=cbind(disjunctive(strata)*pik,X)/pik*pikstar if(is.null(colnames(X))==FALSE) colnames(XN)<-c(paste("Stratum", 1:H, sep = ""),colnames(X)) samplecube(XN,pikstar,1,comment,method) } sampling/R/srswr.R0000644000176200001440000000011014520143727013565 0ustar liggesusers"srswr" <- function(n,N) as.vector(rmultinom(1,n,rep(n/N,times=N))) sampling/R/Hajekestimator.r0000644000176200001440000000107614520143727015433 0ustar liggesusersHajekestimator<-function(y,pik,N=NULL,type=c("total","mean")) { if(any(is.na(pik))) stop("there are missing values in pik") if(any(is.na(y))) stop("there are missing values in y") if(length(y)!=length(pik)) stop("y and pik have different sizes") if(missing(type) | is.null(N)) { if(missing(type)) warning("the type estimator is missing") warning("by default the mean estimator is computed") est<-crossprod(y,1/pik)/sum(1/pik)} else if(type=="total") est<-N*crossprod(y,1/pik)/sum(1/pik) est } sampling/R/UPminimalsupport.R0000644000176200001440000000114214520143730015735 0ustar liggesusers"UPminimalsupport" <- function(pik) { if(any(is.na(pik))) stop("there are missing values in the pik vector") basicsplit<-function(pik) { N=length(pik) n=sum(pik) A=(1:N)[pik==0] B=(1:N)[pik==1] C=setdiff(setdiff(1:N,A),B) D=C[sample.int(length(C), round(n-length(B)))] s1v=rep(0,times=N) s1v[c(B,D)]=1 alpha=min(1-max(pik[setdiff(C,D)]),min(pik[D])) pikb= (pik-alpha*s1v)/(1-alpha) if(runif(1,0,1)= 1) if(min(pikl)==0) {ss=NULL warning("There are zero values in the 'pikl' matrix. The variance estimator can not be computed.\n") } piks=as.vector(diag(pikl)) if(!checkcalibration(Xs,d,total,g,EPS)$result) stop("The calibration is not possible. The calibration estimator is not computed.\n") if(is.data.frame(Xs)) Xs=as.matrix(Xs) if(!is.vector(Ys)) Ys=as.vector(Ys) if(is.matrix(Xs)) n=nrow(Xs) else n=length(Xs) if(ns!=length(Ys) | ns!=length(piks) | ns!=n | ns!=length(d)) stop("The parameters have different sizes.\n") w=g*d wtilde=w*q B=t(Xs*wtilde) beta=ginv(B%*%Xs)%*%B%*%Ys e=Ys-Xs%*%beta if(!with) e=e*w else e=e*d ss=0 for(k in 1:ns) {ss2=0 for(l in 1:ns) ss2=ss2+(1-piks[k]*piks[l]/pikl[k,l])*e[l] ss=ss+e[k]*ss2 } list(calest=sum(w*Ys),evar=as.numeric(ss)) } sampling/R/postest.r0000644000176200001440000000371714520143727014166 0ustar liggesuserspostest<-function(data, y, pik, NG, description=FALSE) { if (missing(data) | missing(y) | missing(pik) | missing(NG)) stop("incomplete input") str <- function(st, h, n) .C("str", as.double(st), as.integer(h), as.integer(n), s = double(n), PACKAGE = "sampling")$s data=as.data.frame(data) sample.size=nrow(data) t_post=0 if(!is.null(colnames(data))) {m = match("Stratum", colnames(data)) if(!any(is.na(m))) { m = match("poststratum", colnames(data)) if (any(is.na(m))) stop("the column 'poststratum' is missing") h=unique(data$Stratum) g=unique(data$poststratum) for(j in 1:length(g)) {p=str(data$poststratum, g[j], sample.size) Ng=sum(NG[,j]) t1=t2=0 for (i in 1:length(h)) {s = str(data$Stratum, h[i], sample.size) shg=s*p if(!all(shg==0)) { nhg=length(shg[shg==1]) t1=t1+sum(y[shg==1]/pik[shg==1]) t2=t2+sum(1/pik[shg==1]) if(description) {cat("Stratum ",j,", postratum ", i," \n") cat("the postratified estimator is:",Ng*t1/t2,"\n") } t_post=t_post+Ng*t1/t2 } else if(description) cat("Stratum ",j,", postratum ", i," empty intersection set \n") }} } else { g=unique(data$poststratum) for(j in 1:length(g)) {p=str(data$poststratum, g[j], sample.size) Ng=NG[j] t1=Ng*sum(y[p==1]/pik[p==1])/sum(1/pik[p==1]) t_post=t_post+t1 if(description) {cat("postratum ", j," \n") cat("the postratified estimator is:",t1,"\n") } } } } else stop("the column names in data are missing") t_post } sampling/R/UPMEpikfromq.R0000644000176200001440000000047014520143730014727 0ustar liggesusers"UPMEpikfromq" <-function(q) { n=ncol(q) N=nrow(q) pro=array(0,c(N,n)) pro[1,n]=1 for(i in 2:N) for(j in 2:n) { pro[i,j]=pro[i,j]+pro[i-1,j]*(1-q[i-1,j]) pro[i,j-1]=pro[i,j-1]+pro[i-1,j]*(q[i-1,j]) }; for(i in 2:N) { pro[i,1]=pro[i,1]+pro[i-1,1]*(1-q[i-1,1]) } rowSums(pro*q) } sampling/R/ratioest_strata.r0000644000176200001440000000140114520143727015661 0ustar liggesusersratioest_strata<-function(y,x,TX_strata,pik,strata,description=FALSE) { if (missing(x) | missing(y) | missing(pik) | missing(strata)) stop("incomplete input") if(!is.vector(x)) x=as.vector(x) if(length(y)!=length(x)) stop("x and y have different sizes") str <- function(st, h, n) .C("str", as.double(st), as.integer(h), as.integer(n), s = double(n), PACKAGE = "sampling")$s sample.size = length(y) h = unique(strata) s1 = 0 for (i in 1:length(h)) { s=str(strata, h[i], sample.size) ys=y[s==1] xs=x[s==1] r=ratioest(ys,xs,TX_strata[h[i]],pik[s==1]) s1 = s1 + r if(description) {cat("Stratum ",h[i],", the ratio estimator is:",r,"\n") cat("Number of units:",sum(s),"\n") } } if(description) cat("The ratio estimator is:\n") s1 } sampling/vignettes/0000755000176200001440000000000015033751703014100 5ustar liggesuserssampling/vignettes/HT_Hajek_estimators.Snw0000644000176200001440000001202514520143726020460 0ustar liggesusers\documentclass[a4paper]{article} %\VignetteIndexEntry{Horvitz-Thompson estimator and Hajek estimator} %\VignettePackage{sampling} \newcommand{\sampling}{{\tt sampling}} \newcommand{\R}{{\tt R}} \setlength{\parindent}{0in} \setlength{\parskip}{.1in} \setlength{\textwidth}{140mm} \setlength{\oddsidemargin}{10mm} \title{Comparing the Horvitz-Thompson estimator and Hajek estimator} \author{} \usepackage{Sweave} \usepackage[latin1]{inputenc} \usepackage{amsmath} \begin{document} \maketitle <>= library(sampling) ps.options(pointsize=12) options(width=60) @ Consider a finite population with labels $U=\{1, 2, \dots, N\}.$ Suppose $y_k, k\in U$ are values of the variable of interest in the population. We wish to estimate the total $\sum_{k=1}^N y_k$ using a sample $s$ selected from the population $U.$ Assume that the sample is taken according to a sampling scheme having inclusion probabilities $\pi_k= Pr(k\in s).$ When $\pi_k$ is proportional to a positive quantity $x_k$ available over $U,$ and $s$ has a predetermined sample size $n,$ then $$\pi_k=\frac{nx_k}{\sum_{i=1}^N x_i},$$ and the sampling scheme is said to be probability proportional to size ($\pi$ps). The H\'ajek estimator of the population total is defined as $$\hat{y}_{Hajek}=N\frac{\sum_{k\in s} y_k/\pi_k}{\sum_{k\in s} 1/\pi_k},$$ while the Horvitz-Thompson estimator is $$\hat{y}_{HT}=\sum_{k\in s} y_k/\pi_k.$$ S$\ddot{a}$rndal, Swenson, and Wretman (1992, p. 182) give several cases for considering the H\'ajek estimator as `usually the better estimator' compared to the Horvitz-Thompson estimator when a $\pi$ps sampling design is used: \begin{itemize} \item[a)] the $y_k-\bar{y}_U$ tend to be small, \item[b)] the sample size is not fixed, \item[c)] $\pi_k$ are weakly or negatively correlated with $y_k$. \end{itemize} Monte Carlo simulation is used here to compare the accuracy of both estimators using a sample size (or the expected value of the sample size) equal to 20. Four cases are considered: \begin{itemize} \item[Case 1.] $y_k$ is constant for $k=1, \dots, N$; this case corresponds to the case a) above; \item[Case 2.] Poisson sampling is used to draw a sample $s$; this case corresponds to the case b) above; \item[Case 3.] $y_k$ are generated using the following model: $x_k=k, \pi_k=nx_k/\sum_{i=1}^N x_i, y_k=1/\pi_k;$ this case corresponds to the case c) above; \item[Case 4.] $y_k$ are generated using the following model: $x_k=k, y_k=5(x_k+\epsilon_k),\epsilon_k\sim N(0, 1/3);$ in this case the Horvitz-Thompson estimator should perform better than the H\'ajek estimator. \end{itemize} Till\'e sampling is used in Cases 1, 3 and 4. Poisson sampling is used in Case 2. The \verb@belgianmunicipalities@ dataset is used in Cases 1 and 2 as population, with $x_k=Tot04_k.$ In Case 2, the variable of interest is TaxableIncome. The mean square error (MSE) is computed using simulations for each case and estimator. The H\'ajek estimator should perform better than the Horvitz-Thompson estimator in Cases 1, 2 and 3. <>= data(belgianmunicipalities) attach(belgianmunicipalities) # sample size n=20 pik=inclusionprobabilities(Tot04,n) N=length(pik) @ Number of runs (for an accurate result, increase this value to 10000): <>= sim=10 ss=ss1=array(0,c(sim,4)) @ Defines the variables of interest: <>= cat("Case 1\n") y1=rep(3,N) cat("Case 2\n") y2=TaxableIncome cat("Case 3\n") x=1:N pik3=inclusionprobabilities(x,n) y3=1/pik3 cat("Case 4\n") epsilon=rnorm(N,0,sqrt(1/3)) pik4=pik3 y4=5*(x+epsilon) @ Monte-Carlo simulation and computation of the Horvitz-Thompson and H\'ajek estimators: <>= ht=numeric(4) hajek=numeric(4) for(i in 1:sim) { cat("Simulation ",i,"\n") cat("Case 1\n") s=UPtille(pik) ht[1]=HTestimator(y1[s==1],pik[s==1]) hajek[1]=Hajekestimator(y1[s==1],pik[s==1],N,type="total") cat("Case 2\n") s1=UPpoisson(pik) ht[2]=HTestimator(y2[s1==1],pik[s1==1]) hajek[2]=Hajekestimator(y2[s1==1],pik[s1==1],N,type="total") cat("Case 3\n") ht[3]=HTestimator(y3[s==1],pik3[s==1]) hajek[3]=Hajekestimator(y3[s==1],pik3[s==1],N,type="total") cat("Case 4\n") ht[4]=HTestimator(y4[s==1],pik4[s==1]) hajek[4]=Hajekestimator(y4[s==1],pik4[s==1],N,type="total") ss[i,]=ht ss1[i,]=hajek } @ Estimation of the MSE and computation of the ratio $MSE_{HT}/MSE_{Hajek}:$ <>= #true values tv=c(sum(y1),sum(y2),sum(y3),sum(y4)) for(i in 1:4) { cat("Case ",i,"\n") cat("The mean of the Horvitz-Thompson estimators:",mean(ss[,i])," and the true value:",tv[i],"\n") MSE1=var(ss[,i])+(mean(ss[,i])-tv[i])^2 cat("MSE Horvitz-Thompson estimator:",MSE1,"\n") cat("The mean of the Hajek estimators:",mean(ss1[,i])," and the true value:",tv[i],"\n") MSE2=var(ss1[,i])+(mean(ss1[,i])-tv[i])^2 cat("MSE Hajek estimator:",MSE2,"\n") cat("Ratio of the two MSE:", MSE1/MSE2,"\n") } <>= <> <> <> <> <> sampling.newpage() @ \end{document} sampling/vignettes/UPexamples.Snw0000644000176200001440000001104314520143726016653 0ustar liggesusers\documentclass[a4paper]{article} %\VignetteIndexEntry{UP - unequal probability sampling designs} %\VignettePackage{sampling} \newcommand{\sampling}{{\tt sampling}} \newcommand{\R}{{\tt R}} \setlength{\parindent}{0in} \setlength{\parskip}{.1in} \setlength{\textwidth}{140mm} \setlength{\oddsidemargin}{10mm} \title{Unequal probability sampling designs} \author{} \usepackage{Sweave} \usepackage[latin1]{inputenc} \usepackage{amsmath} \begin{document} \maketitle <>= library(sampling) ps.options(pointsize=12) options(width=60) @ \section{Examples of maximum entropy sampling design and related functions} a) Example 1 @ Consider the Belgian municipalities data set as population, and a sample size n=50 <>= data(belgianmunicipalities) attach(belgianmunicipalities) n=50 @ Compute the inclusion probabilties proportional to the `averageincome' variable <>= pik=inclusionprobabilities(averageincome,n) @ Draw a random sample using the maximum entropy sampling design <>= s=UPmaxentropy(pik) @ The sample is <>= as.character(Commune[s==1]) @ Compute the joint inclusion probabilities <>= pi2=UPmaxentropypi2(pik) @ Check the result <>= rowSums(pi2)/pik/n detach(belgianmunicipalities) @ b) Example 2 @ Selection of samples from Belgian municipalities data set, sample size 50. Once the matrix q (see below) is computed, a sample is quickly selected. Monte Carlo simulation can be used to compare the true inclusion probabilities with the estimated ones. <>= data(belgianmunicipalities) attach(belgianmunicipalities) pik=inclusionprobabilities(averageincome,50) pik=pik[pik!=1] n=sum(pik) pikt=UPMEpiktildefrompik(pik) w=pikt/(1-pikt) q=UPMEqfromw(w,n) @ Draw a sample using the q matrix <>= UPMEsfromq(q) @ Monte Carlo simulation to check the sample selection; the difference between pik and the estimated inclusion prob. (object tt below) is almost 0. <>= sim=10000 N=length(pik) tt=rep(0,N) for(i in 1:sim) tt = tt+UPMEsfromq(q) tt=tt/sim max(abs(tt-pik)) detach(belgianmunicipalities) @ \section{Example of unequal probability (UP) sampling designs} Selection of samples from the Belgian municipalities data set, with equal or unequal probabilities, and study of the Horvitz-Thompson estimator accuracy using boxplots. The following sampling schemes are used: Poisson, random systematic, random pivotal, Till\'e, Midzuno, systematic, pivotal, and simple random sampling without replacement. Monte Carlo simulations are used to study the accuracy of the Horvitz-Thompson estimator of a population total. The aim of this example is to demonstrate the effect of using auxiliary information in sampling designs. We use: \begin{itemize} \item some $\pi$ps sampling designs with Horvitz-Thompson estimation, using auxiliary information in a sampling desing (size measurements of population units in 2004); \item simple random sampling without replacement with Horvitz-Thompson estimation, where no auxiliary information is used. \end{itemize} <>= b=data(belgianmunicipalities) pik=inclusionprobabilities(belgianmunicipalities$Tot04,200) N=length(pik) n=sum(pik) @ Number of simulations (for an accurate result, increase this value to 10000): <>= sim=10 ss=array(0,c(sim,8)) @ Defines the variable of interest: <>= y=belgianmunicipalities$TaxableIncome @ Simulation and computation of the Horvitz-Thompson estimators: <>= ht=numeric(8) for(i in 1:sim) { cat("Step ",i,"\n") s=UPpoisson(pik) ht[1]=HTestimator(y[s==1],pik[s==1]) s=UPrandomsystematic(pik) ht[2]=HTestimator(y[s==1],pik[s==1]) s=UPrandompivotal(pik) ht[3]=HTestimator(y[s==1],pik[s==1]) s=UPtille(pik) ht[4]=HTestimator(y[s==1],pik[s==1]) s=UPmidzuno(pik) ht[5]=HTestimator(y[s==1],pik[s==1]) s=UPsystematic(pik) ht[6]=HTestimator(y[s==1],pik[s==1]) s=UPpivotal(pik) ht[7]=HTestimator(y[s==1],pik[s==1]) s=srswor(n,N) ht[8]=HTestimator(y[s==1],rep(n/N,n)) ss[i,]=ht } @ Boxplots of the estimators: <>= colnames(ss) <- c("poisson","rsyst","rpivotal","tille","midzuno","syst","pivotal","srswor") boxplot(data.frame(ss), las=3) <>= <> <> <> <> <> sampling.newpage() @ \end{document} sampling/vignettes/calibration.Snw0000644000176200001440000003675414520143726017077 0ustar liggesusers\documentclass[a4paper]{article} \usepackage{pdfpages} %\VignetteIndexEntry{calibration and adjustment for nonresponse} %\VignettePackage{sampling} \newcommand{\sampling}{{\tt sampling}} \newcommand{\R}{{\tt R}} \setlength{\parindent}{0in} \setlength{\parskip}{.1in} \setlength{\textwidth}{140mm} \setlength{\oddsidemargin}{10mm} \title{Calibration and generalized calibration} \author{} \usepackage{Sweave} \usepackage[latin1]{inputenc} \usepackage{amsmath} \begin{document} \maketitle <>= library(sampling) ps.options(pointsize=12) options(width=60) @ \section{Example 1} Example of using the \verb@calib@ function for calibration and nonresponse adjustment (with response homogeneity groups). @ \noindent We create the following population data frame (the population size is $N=250$): \begin{itemize} \item there are four variables: \verb@state@, \verb@region@, \verb@income@ and \verb@sex@; \item the \verb@state@ variable has 2 categories: 'A' and 'B'; the \verb@region@ variable has 3 categories: 1, 2, 3 (regions within states); \item the \verb@income@ and \verb@sex@ variables are randomly generated using the uniform distribution. \end{itemize} <>= data = rbind(matrix(rep("A", 150), 150, 1, byrow = TRUE), matrix(rep("B", 100), 100, 1, byrow = TRUE)) data = cbind.data.frame(data, c(rep(1, 60), rep(2,50), rep(3, 60), rep(1, 40), rep(2, 40)), 1000 * runif(250)) sex = runif(nrow(data)) for (i in 1:length(sex)) if (sex[i] < 0.3) sex[i] = 1 else sex[i] = 2 data = cbind.data.frame(data, sex) names(data) = c("state", "region", "income", "sex") summary(data) @ \noindent We compute the population stratum sizes: <>= table(data$state) @ We select a stratified sample. The \verb@state@ variable is used as a stratification variable. The sample stratum sizes are 25 and 20, respectively. The method is 'srswor' (equal probability, without replacement). <>= s=strata(data,c("state"),size=c(25,20), method="srswor") @ We obtain the observed data: <>= s=getdata(data,s) @ The \verb@status@ variable is used in the \verb@rhg_strata@ function. The \verb@status@ column is added to $s$ (1 - sample respondent, 0 otherwise); it is randomly generated using the uniform distribution U(0,1). The response probability for all units is 0.3. <>= status=runif(nrow(s)) for(i in 1:length(status)) if(status[i]<0.3) status[i]=0 else status[i]=1 s=cbind.data.frame(s,status) @ We compute the response homeogeneity groups using the \verb@region@ variable: <>= s=rhg_strata(s,selection="region") @ We select only the sample respondents: <>= sr=s[s$status==1,] @ We create the population data frame of sex and region indicators: <>= X=cbind(disjunctive(data$sex),disjunctive(data$region)) @ We compute the population totals for each sex and region: <>= total=c(t(rep(1,nrow(data)))%*%X) @ The first method consists in calibrating with all strata. The respondent data frame of \verb@sex@ and \verb@region@ indicators is created. The initial weights using the inclusion prob. and the response probabilities are computed. <>= Xs = X[sr$ID_unit,] d = 1/(sr$Prob * sr$prob_resp) summary(d) @ We compute the g-weights using the linear method: <>= g = calib(Xs, d, total, method = "linear") summary(g) @ The final weights are: <>= w=d*g summary(w) @ We check the calibration: <>= checkcalibration(Xs, d, total, g) @ The second method consists in calibrating in each stratum. The respondent data frame of \verb@sex@ and \verb@region@ indicators is created in each stratum. The initial weights using the inclusion prob. and response probabilities are computed in each stratum. <>= cat("stratum 1\n") data1=data[data$state=='A',] X1=X[data$state=='A',] total1=c(t(rep(1, nrow(data1))) %*% X1) sr1=sr[sr$Stratum==1,] Xs1=X[sr1$ID_unit,] d1 = 1/(sr1$Prob * sr1$prob_resp) g1=calib(Xs1, d1, total1, method = "linear") checkcalibration(Xs1, d1, total1, g1) cat("stratum 2\n") data2=data[data$state=='B',] X2=X[data$state=='B',] total2=c(t(rep(1, nrow(data2))) %*% X2) sr2=sr[sr$Stratum==2,] Xs2=X[sr2$ID_unit,] d2 = 1/(sr2$Prob * sr2$prob_resp) g2=calib(Xs2, d2, total2, method = "linear") checkcalibration(Xs2, d2, total2, g2) @ <>= <> <> <> <> <> <> <> <> <> <> <> <> <> <> sampling.newpage() @ \section{Example 2} This is an example for \begin{itemize} \item variance estimation of the calibration estimator (using the \verb@calibev@ and \verb@varest@ functions), \item variance estimator of the Horvitz-Thompson estimator (using the \verb@varest@ and \verb@varHT@ functions). \end{itemize} We generate an artificial population and use Till\'e sampling. The population size is 100, and the sample size is 20. There are three auxiliary variables (two categorical and one continuous; the matrix $X$). The vector $Z=(150, 151, \dots, 249)'$ is used to compute the first-order inclusion probabilities. The variable of interest $Y$ is computed using the model $Y_j=5*Z_j*(\varepsilon_j+\sum_{i=1}^{100} X_{ij}), \varepsilon_j\sim N(0,1/3), iid, j=1,\dots, 100.$ The calibration estimator uses the linear method. Simulations are conducted to estimate the MSE of the two variance estimators of the calibration estimator. Since the linear method is used in calibration, the calibration estimator corresponds to the generalized regression estimator. For the latter an approximate variance can be computed on the population level and used in the bias estimation of the variance estimators. For the Horvitz-Thompson estimator, the variance can be computed on the population level and compared with the simulations' result. Use 10000 simulation runs to obtain accurate results (for time consuming reason, in the following program, the number of runs is only 10). <>= X=cbind(c(rep(1,50),rep(0,50)),c(rep(0,50),rep(1,50)),1:100) # vector of population totals total=apply(X,2,"sum") Z=150:249 # the variable of interest Y=5*Z*(rnorm(100,0,sqrt(1/3))+apply(X,1,"sum")) # inclusion probabilities pik=inclusionprobabilities(Z,20) # joint inclusion probabilities pikl=UPtillepi2(pik) # number of runs; let nsim=10000 for an accurate result nsim=10 c1=c2=c3=c4=c5=c6=numeric(nsim) for(i in 1:nsim) { # draws a sample s=UPtille(pik) # computes the inclusion prob. for the sample piks=pik[s==1] # the sample matrix of auxiliary information Xs=X[s==1,] # computes the g-weights g=calib(Xs,d=1/piks,total,method="linear") # computes the variable of interest in the sample Ys=Y[s==1] # computes the joint inclusion prob. for the sample pikls=pikl[s==1,s==1] # computes the calibration estimator and its variance estimation cc=calibev(Ys,Xs,total,pikls,d=1/piks,g,with=FALSE,EPS=1e-6) c1[i]=cc$calest c2[i]=cc$evar # computes the variance estimator of the calibration estimator (second method) c3[i]=varest(Ys,Xs,pik=piks,w=g/piks) # computes the variance estimator of the HT estimator using varest() c4[i]=varest(Ys,pik=piks) # computes the variance estimator of the HT estimator using varHT() c5[i]=varHT(Ys,pikls,2) # computes the Horvitz-Thompson estimator c6[i]=HTestimator(Ys,piks) } cat("the population total:",sum(Y),"\n") cat("the calibration estimator under simulations:", mean(c1),"\n") N=length(Y) delta=matrix(0,N,N) for(k in 1:(N-1)) for(l in (k+1):N) delta[k,l]=delta[l,k]=pikl[k,l]-pik[k]*pik[l] diag(delta)=pik*(1-pik) var_HT=0 var_asym=0 e=lm(Y~X)$resid for(k in 1:N) for(l in 1:N) {var_HT=var_HT+Y[k]*Y[l]*delta[k,l]/(pik[k]*pik[l]) var_asym=var_asym+e[k]*e[l]*delta[k,l]/(pik[k]*pik[l])} cat("the approximate variance of the calibration estimator:",var_asym,"\n") cat("first variance estimator of the calibration est. using calibev function:\n") cat("MSE of the first variance estimator:", var(c2)+(mean(c2)-var_asym)^2,"\n") cat("second variance estimator of the calibration est. using varest function:\n") cat("MSE of the second variance estimator:", var(c3)+(mean(c3)-var_asym)^2,"\n") cat("the Horvitz-Thompson estimator under simulations:", mean(c6),"\n") cat("the variance of the HT estimator:", var_HT, "\n") cat("the variance estimator of the HT estimator under simulations:", mean(c4),"\n") cat("MSE of the variance estimator 1 of HT estimator:", var(c4)+(mean(c4)-var_HT)^2,"\n") cat("MSE of the variance estimator 2 of HT estimator:", var(c5)+(mean(c5)-var_HT)^2,"\n") @ <>= <> sampling.newpage() @ \section{Example 3} This is an example of generalized calibration used to handle unit nonresponse with different forms of response probabilities. Consider the population $U$, the sample $s$ and the set of respondents $r$ with $r\subseteq s \subseteq U.$ The response mechanism is given by the distribution $q(r|s)$ such that for every fixed $s$ we have $$q(r|s)\geq 0, \mbox{ for all } r\in \mathcal{R}_s \mbox{ and } \sum_{s\in {\mathcal R}_s} q(r|s)=1,$$ where ${\mathcal R}_s=\{r | r \subseteq s\}.$ The variable of interest $y_k$ is known only for $k\in r.$ Under unit nonresponse we define the response indicator $R_k=1$ if unit $k\in r$ and 0 otherwise and the response probabilities $p_k=Pr(R_k=1| k\in s).$ It is assumed that $R_k$ are independent Bernoulli variables with expected value equal to $p_k.$ We assume that the units respond independently of each other and of $s$ and so $$q(r|s)=\prod_{k\in r} p_k \prod_{k \in \bar{r}} (1-p_k).$$ The nonresponse model can be rewritten as $$q(r|s, \boldsymbol{\gamma})=\prod_{k\in r} F_k^{-1}(\boldsymbol{\gamma}) \prod_{k \in \bar{r}} (1-F^{-1}_k(\boldsymbol{\gamma})).$$ In calibration method it is assumed that $$\sum_{k\in r} \mathbf{x}_kd_kF_k(\boldsymbol{\gamma})=\sum_{k\in r} \mathbf{x}_kd_kF(\boldsymbol{\gamma}^T\mathbf{x}_k)=\sum_{k\in U} \mathbf{x}_k,$$ where $F_k(\boldsymbol{\gamma})=F(\boldsymbol{\gamma}^T\mathbf{x}_k), p_k=F_k(\boldsymbol{\gamma})^{-1},$ and $d_k$ are the initial weigths. In generalized calibration a different equation is used $$\sum_{k\in r} \mathbf{x}_kd_kF(\boldsymbol{\gamma}^T\mathbf{z}_k)=\sum_{k\in U} \mathbf{x}_k,$$ where $\mathbf{z}_k$ is not necessary equal to $\mathbf{x}_k,$ but $\mathbf{z}_k$ and $\mathbf{x}_k$ have to be highly correlated. $\mathbf{z}_k$ should be known only for $k\in r.$ The components of $\mathbf{z}_k$ that are not also components of $\mathbf{x}_k$ are often known as \emph{instrumental variables}. Let $w_k$ be the final weights (obtained after applying generalized calibration). It is possible to assume different forms of response probabilities: \begin{itemize} \item Linear weight adjustment (it can be implemented by using the argument \texttt{method="linear"} in gencalib() function or \texttt{method="truncated"} if bounds are allowed): $p_k=1/(1+ {\boldsymbol\gamma}^T\mathbf{z}_k)$ and $w_k=d_k(1+\mathbf{h}^T\mathbf{z}_k),$ where $\mathbf{h}$ is a consistent estimate of ${\boldsymbol\gamma}.$ \item Raking weight adjustment (it can be implemented by using the argument \texttt{method="raking"} in gencalib()): $p_k=1/\exp(\boldsymbol{\gamma}^T\mathbf{z}_k)$ and $w_k=d_k \exp(\mathbf{h}^T\mathbf{z}_k).$ \item Logistic weight adjustment (it can be implemented by using the argument \texttt{method="raking"} in gencalib()): $p_k=1/(1+\exp(\boldsymbol{\gamma}^T\mathbf{z}_k)), w_k=d_k (1+\exp(\mathbf{h}^T\mathbf{z}_k)),$ but we calibrate on $\sum_{k\in U} \mathbf{x}_k-\sum_{k\in r} \mathbf{x}_k d_k$ instead of $\sum_{k\in U} \mathbf{x}_k.$\item Generalized exponential weight adjustment (Folsom and Singh, 2000; it can be implemented by using the argument \texttt{method="logit"} in gencalib()): $$p_k=1/F(\boldsymbol{\gamma}^T\mathbf{z}_k), w_k=d_kF(\mathbf{h}^T\mathbf{z}_k),$$ $$F(\mathbf{h}^T\mathbf{z}_k)=\frac{L(U-C)+U(C-L)\exp(A\mathbf{h}^T\mathbf{z}_k)}{(U-C)+(C-L)\exp(A\mathbf{h}^T\mathbf{z}_k)}\in (L, U),$$ where $A=(U-L)/((C-L)(U-C))$ and $L\geq 0,1C>L,$ ($C=1$ in the paper of Deville and Sarndal, 1992). The g-weights are centered around of $C.$ For $L=1, C=2$ and $U=\infty, F(\mathbf{h}^T\mathbf{z}_k)$ approaches $1+\exp(\mathbf{h}^T\mathbf{z}_k)$ and for $C=1, L=0, U=\infty,$ $\exp(\mathbf{h}^T\mathbf{z}_k).$ \end{itemize} We exemplify the last form of response probabilities (generalized exponential weight adjustment) using artificial data. We generate a population of size $N=400$ and consider the auxiliary information $X$ following a Gamma distribution with parameters 3 and 4. The instrumental variable $Z$ is generated using the model $Z=2X+\varepsilon,$ where $\varepsilon\sim U(0,1).$ The variable of interest is $Y$ generated using the model $Y=3X+\varepsilon_1,$ where $\varepsilon_1\sim N(0,1).$ We consider here that the nonresponse is not missing at random and the response probabilities $p$ depend on the variable of interest $y$ which may be missing. We draw a simple random sampling without replecement of size $n=100$ and generate the set of respondents $r$ using Poisson sampling with the probabilties $p.$ The bounds are fixed to 1 and 5, and the constant $C=1.5.$ Three estimators are computed: \begin{itemize} \item the generalized calibration estimator using $Z$ as instrumental variable, \item the generalized calibration estimator using $Y$ as instrumental variable, \item the generalized calibration estimator using $X$ as instrumental variable, which is the same with the calibration estimator, but the g-weights are centered around $C$. \end{itemize} The convergence of the method is not guaranteed due to the bounds. Thus $g1, g2, g3$ can be null. If it the case, repeat the code (considering another $s$ and $r$). <>= N=400 n=100 X=rgamma(N,3,4) total=sum(X) Z=2*X+runif(N) Y=3*X+rnorm(N) print(cor(X,Y)) print(cor(X,Z)) L=1 U=5 C=1.5 A=(U-L)/((C-L)*(U-C)) p=((U-C)+(C-L)*exp(A*Y*0.3))/(L*(U-C)+U*(C-L)*exp(A*Y*0.3)) summary(p) bounds=c(L,U) s=srswor(n,N) r=numeric(n) for(j in 1:n) if(runif(1)>= <> sampling.newpage() @ \end{document} sampling/data/0000755000176200001440000000000015033732366013005 5ustar liggesuserssampling/data/belgianmunicipalities.rda0000644000176200001440000007336314520143732020043 0ustar liggesusers‹ ě˝ xTU¶÷}N†JĄ2Ďó@IHç± a S-B%I + “˘ 8‹""Š#**J;·s·¶˘­m;µłÝ­­íĐ6mÓŠďoí˝S§č{ż{űö{ß÷ľ÷ů.Ďł8u¦˝×^ëżţkí}NUšęOęç9ÉcYV”Ë˙Ń|ډâ?۲RăŮf-ň·, řÚZ;ŰÍľ–@GŔßnYŃśLc›Ä]Sů,w¦# Čd22™\\‡ü9NŰ©÷ŰĹČdrň0ňňä-ämä Úď‹ B† Ă‘±H#çNŁ˝ČxdBÖgß^ĐŻ}ů ň>ň!÷ő“A!Ý‘J¤9ů)ň"ňää3äîÁ6×ŰŚÉ^‚‘UČUČNää äiÚĆ`QŮHRĂ1i;)E¤ßS›îł"qMÂ5v!˛y ůň&mਠ„1F äú\¤…lD@žE°™Ĺ=Ö§Č_¸żŘřÂĆNvr*˛ iEđ˝ه|„|…üŤöą'j2†ýghçR¶ĺ~´źäxBßö0AŚeőFú řʼnLD¦!ç!Ź!żD>FŽ U˛lěcc»½6ř°ç!7#ŚĹĆćQ\(h´·-~^‡üČ͢vµ¦#3Ćd…ěoćô˛]´Ń×>ą9€üŚ6ÝH ¸˘Ä˙~LŘřĆľÁ7¶ř,DM«‚ ­“ń9ţµŻŃQĹxŁůś»ůĎH B{0âEŔ´ý äKÎźĂVÚżą yAß(ěŐźó™Ü+-B6ł?6-loq˝.­?qŽ8´· żă^‰»í`S0v”íkl'‹Â6QŘ0 śD fóą—ţě|5Z“Ť…ݬµČzDĆXrŠ{lüm3~ű ¤ Y‰`s[0J˙öďi‡ë˘°Ô8Úâ_ń8ŚÂÖ,>?QŘÇú–Ďő~Ťž‚SlâĚS¶đÇŻń±dѦ…Ď,ąď$$€lEö#đŠŐŚ,Fŕ [»‘;»‘§hÇý‰wűމź3K8ó~˛°Ą .mŃŘ÷!Ķ-ńĂx-ějă[âj2ţW68´ńˇý"Üu9ţ-đoɸ±™f-i“Ř´[0cĎ10o˝ĘglnKśÂúZ׳;Âmâ*Jâ}>źńˇ-x•ÄżZđ‰uň:ňGÎÁ 6¶°;‘ó‘]Ü ·EÁ÷QřÂbkmă89ŔbĽVB,Z=±ÉR~µđŻV-8Ęúśë“üjď6ľy‘ţ8%Ř}ἵ—cŘ4Jp+IMbżZ`Í:ąg,bÂćĽÝ!.mÁ‹ŕklŃ˙Ů‚=Á2yŔÜĂaö1Ú–|WG«QpŤE˛ńµýź‰ ľµżu1‚ ­GÉgä'[ěĘľMž‰"öŁdüŘČ"_X‡äA ®µŕ*ë„ügÁŃ–p Řłń­ Řâ30†Ł$ďa ·Ř·„KiĎ#¶řě"äVŰXä śXřŢ[oBd cŘ—\MŽ´Č·Ö×죛MÜÚ`8 żFá#[ň×˝naK¸µ“ŘŇ‚»,r‹…-˘đYTź…Â)W#ŘІlâĆĆöăä†Eś“Ü"|G´i›ĽbĂ6¶´Á™ý2‚nQâ7t¶ŕI ´„ÓK›śhż˘SS”đ«\/Ř@‹X±„kŔż6-ň¶ő–,r‹Ťn¶đ+94Jô VŁ$ßĘ„ăÉ#yÄ‚Ď-r˝Mޱ±ź ŘŘĦf°Ą=±5÷Ř‚iĆfɸŕk ?Xwr‰ÂnQéWhąZü+ý-í:J¤şQQćňúZ:ü!łçöúBíÍË‚fßĂ~G»żĹçs®X˛¤%°Ôßjö“˝vżŻłvFČßŢ"u›:ëmYîosv‚íN‹-+ýˇ@›ÓBŚ·Ő·Ć|Žó¶.5Ă»m‹ýmmţđ˝ě†ZüÍËşZ‹×G:#:n[ćďş>ÚŰÖî\ر,ĐŢľ¦;‚¨6CjEś\ëw4Śó†®fÝŢżmq°my¸ĎPK°k°QŢöđ¸ÚŰť[řÜć_ě Ś}çt´·cYX‘ÜŃľ®s‘[…{ę¤Z©ăřBáFŐĐŇezăpg±ŢUľ°AÜŢ5«|––®›]c}ţ–°Ë<ěačÚúÎć®›ÇúśÓî±>OX/Ůď.msöDk°-ě&Ůůs'Č•8’c]ĘŽőűCmae]˛6PśŢëzâX˙ŇŔmx!pĆň°ĘţĺËý+AÇxcý-A ë¦¬±ţP3Ć©ťE§ţZďR_DzŤ(#´ E ^ď†c±\ëôj ´·űÂ=ËůŽđP¤íHëHcˇŔjgč…{flkÚüµýťíá°pŹ řC‹üţĺá[ÜÓć¨Ăn„r–µáćŘkk3ql‹ŻŤČ\Nśë[üKŰÂ86Hü·t„{âĄ`§ăÉ Xn?fl0Ř5Ф±býV˙ şşĆ#ÜJ{ˇöłěw‚ÂpřĐDghimŁ?¸"زŘi˘ł˝˝ÓQˇłCЦ ±-m‹8wvÝ”ÖuÔ_;n™ŻĽ™)'‚­ŽW8Ľ2Ě.v—‡çaŻbôů‡"x› î°CbŘő;·v.%´::Ë‘03±×ć_ć‹0q¨sµżĹ!SĎŘÎĄ 3 --ľČřë -8D™8VŚŘäďä˘ĹN+´˛Čצšq‘˝$Śó‡–˛bs(Ă­k°ŐZI"ŁšÓD(č˛Ú;Â@t+»3ň.߲߹řďúH•Ňhí„ŇąÚąY:ěöť<Ë^›łCsaüŽkń­ „Z¸`Ëżč1H’á@­¤-g8rś«ÚkçůlG’žq@˛9ŇTr¤CóHmCG "EąŐ)V;ŘąŇá¸q!ÉAa ÔűZ¶Ž­÷µ¶†QSďśWď/ťčó…)ÝÂcÎ>ţqčĐUďÄşĚPďשÓßąŞË{ęhuîhŹ Xöpž"а%ęţBáđŤĺH8µÇŐZ"Î%łK’¨ťŐ„ť^mľ°Íąż=lhî_ÝÚéäČú` Ýˇ.vŰšýaýbę1tx(ˇ @;K}ç’%áKŮ -ęěHBCł6‘h _ް8"ŰÇ6ř—·»zm ź®u‚ą¬ŤÄB\CŰR®uŘ 8í†"îŤo­đ×Nő‡ă#ˇ!tf§żĺDM€G8ŇÚWe‚€ŕki^pz‘ôÖ©x·®č ÷ëćȉ­vřśLß F¤:WD `ĄŁaśěD d<ý8´4ŢjVŘŹ8Řé)䑜ŢI 1¨đMYăý«—)6žÖŽk!˙8őčřk„î®ńÂ7aŔ°×ęlwv‹üÍg:÷C~pîYö#ÔMĐVFTA©ă WÔ¶0tĽî—+Ă@O”ň­¶ĹW;7âć¸ń@2蔜ăCľVŞ ‡68 ¨bSęÍz(i<,ş¸ŮĐm¸ÓÎP„q'=„|Nć™ŕ°{<źŰ—ť2Â5ÁéČ ţČü3!"ř67ź;ů;Úť}¨Ň!ešió­XŃL¸Žźŕ­8tüíN:uOęŞ%\*¸„íĂ:¸'´řE"rĺvÄNçĘ®čMžň5• 5CD˝ÉŃÖ%N đjáŹf'í{&„­'ĐY„P^#Ky ëdu7{-ŐxěDߢp ŠžčkqŽ;µ1Ç»™ÍÇÚöÎs!í4ß ±Î]ßÓqk0:aŻëÂDŮ#Óx Ś状6§dHm ÷.ű{˘č-8p=3"i˘3ţ–Ú†ĺN ťčw"9m˘ŹŐWÔRçÔRÁ. ·MŠovŇ+ű­ĺNu7ŃßÖîg˘pOää ™#ÍË˙>\Ą&v¨ö|-‘Íp ˘&BŰPX Ůqhb˘š;tDîv„ťŻwT˽Π”řŢďčÚŮ.Ou”a2ÓYlM ú—údnÖîF¦">gŽÇ\l‰,Śą¬D$..mq¦ ˛OµE‰Őć\ňG Ŕ51ŘQÇL ’Á`¸*ČäHÇ2Á®Î/Óxu®ł%Lz4Öą&\ËĹL †mä™Ř‰1Ôś"¬7Fř&zb8ýş'I?sëImÍR2ua}’ĐDkg[»ł(;©Ă)*ă&ťP»&ť0Qź,4âŚ2őU8`Ó'ű[ý¸YEä,_ë˘p›±“#Şs÷äŕâ` ‚[&w®E°rÜdމ@¸,‹źâkiŤÄˇgŠŻuĹ ŐzúSEwO¸Nq›3mű!j¸ĆĹ-Ný1EM®"H,nJ - —ŕ SZ¨N¬Ú¦´©ÂΙľ&Ni“’¬V…t¸« †‹¸ČŁDzuJpy{ŕ §@›BÂÓ ZIŻ/´Üimľĺި#mę›ÚşpO ů$Gěw"|š ű [NY2ĄłÓ }OŁŻ”Ôš:‹#ă|-ľÖ’â]yIŁTęÎ]rÉ ź“33Řo’Ú¤zC‘ëRqŤ>ßňĹaFq±Š<iN¶9¬Ő($,6c&âoY~BţŹm”rˇËPŤ~şgFו‹cťÜ×čŹÄ=§'Éçiô/m‰@oŁĚ "XĹŶpꥡp9“Účo/eöß^:÷„ĎÍńö€“Ö¸®s­2ŇDüčLkc‹Âl:;!ťŐĆ-óŻ 8뉍EDđébÇĄ4\î-wLđ; ě8«`nŮ‹(˘ŐŮPÄŤŽą´u‘¬7„Ť`Šá$v—ű#89†NłmEyL·ŰmJIm®AsżŽ@¸«ŕň¶ĺjj§5™­­uŠ4.oëpubáŐ§8Á­łŚĆ˝+ŧ‘.­\Ťťt¦ů©`uąßoÚýa$Ęn«_*‡Ą¦úţ~żĺ„§JˇăÔ9S# x9×ę_Üĺm×T‚ñ{_§Mő…L¸Ť§Ž7uÂ1»'T3î©°d§M•y/íL€;:BÁ0Vb§úťXHgg5˝’é)ĄHČÉś\•SĹĎNËĐqűż\dB…ŕ¸Ë&ń2ĐäŔňö-ľvçŽöv_[óšČý3ş¦J’ępnďl‰Ěő S‹‰äČ qM .ö­tŚ]Ăg٧»|žÉG=­7k“ý> ÁVÂÓ)ŕb¦:Sś”©Á‹e¶ÔÖÜžĄ«Ăş© ÂŢáô®nđ–vÖÎ4;KrXH@ 9["´łĹé?jŹ`(ĎÔ`gŻň·„#€#NŮĘ@;Ű›ťňĎ5•éľăËiľÖđÂAÜ4Č8ö¦{Usä´bšo- 1Ľ6ďž®V85[Â4ç’ćüîâĐ g)všI’aĆĚp–řěĚ—gřWD>5WűĄ+„:# ęb•żk/iƲ@K€’4r Â5#Đáđ†gsćE‘ë%I˛dYë«=ań6~Fp…~€ľ*\"Y$˛éŘťNťËNg8‰ÇĎěčKŰś0™ŮéOş9+k±N’q5ů"§ M”V--EÄ6ůÚśyC“ĎQŚťEOá5ŢY˘ź·v—ĄłşLţđđ2Őń)ň Bž(rje„=”…|ť! śó2ôAő®˝vbäD$]ťRĺ§}fFŔ/®°ął ŹŞ+Ckj‰ć0Í žqăgĹ”ÎĐfIµX« mçžÎVź“ş˘g­đ…O1§ŚXoqĎęđ- ţfß™‘ČYâ5ü@ň¬ż_MO¨G9şÜyŢĹMÁČ%×lÁ ČŮřŘ)×fK)‰ ÷l¨5č$®µ…´ÉŮ•ťŰ$Ě–é˘zšŃµH;{™Ăéł—Zeq1T;®E5Üá\Öé,ˇĎD$rµS;/˛~źhs¬ŕfŻ#b–?;@>Šh;nv°m©CŇěF ě’­Â…âf‡üTűÁpďˇ  ^s‡Âľkvç"gŃĆýw‰/vNssŘ”Is}ť«őG§3“ť«žÜµEîËL7\yÎeôíÔĺ]ˇb&xż$—ŃuĽŃ_«ۆÓ+M´Ŕ±Î«ÔĎÁ@ŰJgN|BA3×A‡{n‹˙„…§ąŠ«®ĺ@ű Ňć†zţ…Rq­^_ ű-A"ýÄŐ$.’Ây…°±'Żt–ßăç“8–É<şk4óáĶ7:ć“Ű#:Ť™ďŃq|^ësʬůĹk~ä‹Oqó‘/µ¸ç[#궱ߩDü|YŽ´ńü`Ç CśO™ÖvÂ2ÉüΖ°&ńó;ĄôvŞšŘů«ś wĎ_…çťĆćSÔ39Ň/9śđ"Yl3ŐyŹL˝Y«†¶Ä×Ü Ę{Ôćťýęo‘cČ÷ČČqýzu wŐÄ"nÄp}M2’‚ĐjM’…d#HRŚ” ĄHRŤôBj‘:¤7ŇékYużE>Bţ€|Š|‰üAź:ô©C—:ôěͰzG#čŃď9äyä0ňňKäeää×Č«ČkČëČ›Čo·‘w‘÷‘úěGźý>C>Gľ@ľBľFŽ"ôߏľ›°XănJ@ĆßÄř›ăobüMéH’‰`‹&lŃ”ä"yH>‚mN~ AŹ“é˙äO?!ôyňź‘ż ß ô{2ă=űĎŹA°ýü8ćÓ˙|úOűói{~w¤ Á¦ó±çüAČd(2 ŽŚ@F"ŁŃČÄkY žDžE~`ĎŘrv\€`żď čąŕcż,ŔN °Ńô]€}ü ůA×…řf!:-Äď {"g!ľ^8i@Ć#I–廹yyyAß‹ľóŃ·źů°•żřčĎ&áűEŘzv^D_‹˙"°¶>µE`mQ7¤ )GŔÝlľ¦áş5\·FŽa«5Řg vX~kĆYÖ:Ú^çB°ń:üĽŽk×ăŹőřc=Ř€6ŕ÷ ř|>߀›ŔÚ&ôݶ6«MŘixÚ„­6a«MÜ» _nĂ›°ÓEŚő˘G‘ǧ‘ź#Ď ŕô˛ŰŰ‘˝ČČ~är7rȲ¶\‚\lE®B¶!× ;ë,kńĽŤÂjŰ9ČzäbËÚIÜí쏀…ťŚw'cÝ99 ˇßIŤ´“Ňe6Ý®ö`×=ŘuxÝíöô@Ŕ×ÚŮCüŢÎXnÇ·Í˝ř|/6Ű‹Íö“ýŚy?ţÚFöc»ýÄŃ~âx?×ŕÚ`ů±{űŔ–°ăěx€~ŕ»ŕć}Çŕ…»±ëÝ´y7±q:ŹŇĆA|q~iď ńy¶‡°íˇź!Řö¸>®ź˙Ď2ögÁüłőX|>;aü‡Űađzń†—÷C"ÄŇaprëĎ@ć!§!g $Ć—°ŐK`ë%0ő÷˝4áž—µ—h˙ĄFd299Ő˛^!f^Á†Ż[ŻĂ{ŻĂuŻĂŻŹ×ŹëWßŔžo›o1†·É·Ç·ŕ±·ŔŘ[Ř÷-ěńö}z®y=ކkŢĆoď›÷ŔŮ{ŕë=đřóńó8|»˝ŹÝŢÇn_2¦/›Ůş}y:ÂŚńK0ń%ĺĆ—Lúż$k| žľĽą Ů‚€ą/ŻEŔÝpu„ńÁFGŕ™#pËl{|ˇý#ŘěČ\¤!ŤˇÝ#ç"›Ú:ŽŹ€ă#`÷ř?ţŹ€˙#űbŕ(Ľy ĹĆG±ńQ¸î(>:Š­ŹŇßQřě(~= żĄßŁě~t&ÂŘŽÎAŰQĆv”ůŐQô8ĘŹ2ĆŁĚ…Ź˘ÓQňőQô:şaĚÇŔÓ1büv=†MʎĂ1¸ăśrŚ~ŹÁ­Çç1°tŚqcśÇ-;k>r*ŇŚ,CH ҆¬@Ö"ëó‘ ‘ÍČ•ČuČőČŤČM–ť7©GšŮČädd˛ń#KĄýäŃOQR‡ôEF!Ü_4™ĐNŃČYČ%ČUČŐ–=‚űF,GĐqD'˛Ę˛ÇĚBčo ýŤˇŻ1č=¦áü•–]O»ő zŐĎCsýéČb„öę–=]&V#}‘aL§ č2ń„ë'Ň÷ÄVËnLAŇ ¤)C¸ż±72„ ¶ěfl׌횱]óv»5ß…BDž@ŢGţ|eŮ‹cgŮKńÉRl¶t#‚Ý—nAhcéČcČ“ČĎ‘WOÚXú9ňňĄe/GďĺsĆ»»,_ŹěDv9µ“ţ2Ű˙Čż¨˙‘˙¶ý˙s‰ů‘ď'ţŹüť¸ţGţS%îż™¸˙›Iü˙ŁŇU;%üIâ?!I˙%ë?Qrţ’Ü˙"Éűż(%˙ ”ţ+Ňíß‘˛˙ ”˙RńďH÷@*˙©úĄú”š˙éő@j˙R÷oH˙Gü“2đźAF˙;2äź”ˇ˙ ŚüOQ˙‡eôŚů'eÂ’Lü'dŇ?!3˙iúwdÖ? ł˙2ç”Sţ7äÔ˙MYđo©ť †[ŃGI«—ZVŃ›–•ÁŘĆżgYýî†^W’.Ǧ÷ł¬Îwk·¬ľ»,«÷¦¬€ľŢ˛¬Ô[H{-«}“ąŻ{8ĎÝćÜĹ´KÍSľśô™Ę})\{>Ď·¬Lr\×U^nYĹ!\»„ĐÁ—[?·¬ž`Łx2ňWÚŇä8ţMľť/ŕ8ü•·ť1ŇOÂÎÓOźsĐ)ůzÚĽ†6ݵ¬ýŘ-˙BúüýŽă“Ţ–5ţĎżťńQ3$Ń^56ɨ×?ť“¸ ]ŁhëîÁičYŔÜ"<ąß /îÍĆćIô“ŘŠ˝Úřü!í‡\›îEwđ5±[,>˧Źäť¤ťżq?cÉHc‹ŠŔO·+,ëđ“…Ďňc6éŽ_Ş/âŢNl˙ Řh’/Ýbę¨pëÂ~c©łĄűŻhk˛±E*xMˇ˙8ę‚tî‘vź"FĐĺ2đš;ś1˘ÇČ+#8A?µëń÷ŰŘ;ea“ü“iŹq¬źĆýř: d`ŰĚm´ţiÇč‡v2o¶¬3vscHáxÖĆŽ‰żĹ§_ëXHŁý<â#ťZ8ţgl/ĂNÄ[&ÎK[‰ÉҰ•l?F°s)ă‡MŇđaÜP>ó?AŔTuT/tĎöë¸Í¤®«!βŠh—óů€Ľ;×$|Ăńžčň"öz™6°ăŔ˝ÚÇĄ2~ěźýŤeß˙ş÷ĘĄô‡ôS~ŔCs‰,Îĺˇl—BĚ'ź)Äná lŔr}­‡{ňřśđ:N˘Ň7z%âŻ۲NâúÂ?’Jč· ý66˘×ôĂwýȉyçŇŕ"^3ŘŤý ă#nň[öŮ`v,2Űdľ„ŽđB 6Čľś€×tü\ d7™ÄSęFĆü®Á'ÝÁqâ9Ś}âŔŘ>¤ě÷ÓŞŔrw0/ř„[ÁaĄôYÉ8ł‰Ť-ěĚ5)2nt®Ăoé÷ˇ+¸NĽüŔ…é`1»ÇĄĘ›l‰‹xlQN’ű ?śS†ÍÜč‘Ěő°”oyđu qßÖĆqlä>Ť ˛Ĺޱŕ-Žułu7×u‡Ëăi?Žv=Źá:6L8_Ç}Ü=߯ą·ßcňÓ Ř˝Sŕ·,b·≕ÄyôÁX‹°A=5|ĘW–íĂî˝Ŕo5ö[¶ŠđE.Ř-ÁvUÄl.÷ÔÁŐé`3>ń OÜ/ň„Ly~ţľŔ^ÄBÉayC <ă®'9÷<ý§YqýC‚Ĺ4x+Ľ$‚×Řg°yˇe5·D|źvăxëÁI:íxΔ»a‹>±`/Vö?o!Ň/öŤĄ-ţŠÇľ‰ô‘‡^1Ĩ‹řO˙ÓáÖ2tYK¬Ą¸° 8ŘDžÉľ _#ůŘ>‘{Ňáł‰ŚŁLUSIđJúoQb:‡q`ďxgäsŘV‚ĎóÁy 5w± 6†ŃWyŞĚâúˇŻ2Nb)I~Ź »¦ĂM)Ä{xŞ› ŮÇą“Čç…ÚĎýâŮ›…Ř9 ?%aŰâ9v±…/ă…·±‘=≋ę˘ >Çâűxę7x-e.żUľ‚ .ňx Ľć!GDÉč8lű¨ÎC‰čżQű0ćtÚŰéô/~Ą^đ€ŻXâ4Ž}±čK™’ó±Yô›†$~_ÇrđŻ›ń'ĹŇý¸Ń#,çdóůQŤGü+µ ¸H$vňÉÉÄQ¶OŔ6Ůpöłp‡ăÉs± ţM$'•%Đ6­ş“óÔy`2v1:aăřˇąÔsžS;ÚVô–;čż~âx&ţžô[5ßb+úL§˝Tđ^űcűŕ8˝űbź4ü\^ é'…¨ya,ÜSÖ—sÜźĂxĘáęj–žŕ˘?€ĎLt.Ä=°} íO&^Ćo×aߞش¸”ăŘ>…ńWNÇÔ/9Oŕř°î.ˇ­Tü:–>2É+ý™k¦vhž/çú4lZĆŠč#;§ŕóŃpguL.ü¶2¨u2©§‘«’Áj1U ‹đÍ#đč8­çť–ť‹çp=±čăÚ^ŕđ:Ú>‰:¤ś|=Ľ4›śÔ ŰľClßfűSŹřáŠéđĆPüZGţŚÓ±C öMŁŤz®oŔ^‰ô•ŮW×uiâ ňQcČ%¶*Ŕ]!íäéźuźŤ^’Ź»sü{ěĆÁ!ÄAşeŤ–/ÓaZŤďrđSďHŤŔś1‡zm°ÔN5ňĄT¶`(ŤüžŤ˙’‰ď^Ř&XÍ'N*i7‘q§Q¤Ównł®ŻĘá„Bě}NΧŹLú-˘^¨Â–ŁńO>’‚ “đW5zg»ńÔV‰ëŔľJçX±śNLŕż\8Éͱj–\üźĽIľ¶°I2ua ýŐ ŇÁÝpňăđ‘„n±č]SŔýP¸+Aęb(ě€Ĺ ňňggŃ|źŹß«©7ş3ćjôŠe\őř¨Ľ§Áí)č‘Ă}.â»Řpg%ř, ˛Ąn’¸$'ąĐżŰĄĂ}ٴ׌]u›ÎMl3†ö'p~$úŐˇg \UD|dăă|©eáK‡´N]3fPKf_đy:y$Ujbôn˝˝i+g§drD"6Ч&©LĐő^řK≭L°P…łđQţŘAžčŽÄ~Ýß”RĂtc ŕ;ťëúrwb ŕBýSžshżľÍ&”a»Lj‰:ü ö ¦Ę«•ä–¸­†q$ F©A+¨)Óf&ůŞ8M-“s•e&SŔ`ĺCş®Ë˝ĆË—ĎřŚMł>Őń—Š-Rŕ˛$b0} /Đs´!řą÷'őŇŘM¤ŢJ _E»Ä­şvK§'Łs}Nóeä“KčąLü>ăůđnÜç‚ äĄd>§,ô/|޲·Q‹L$ƇĂ%ŮŚ­»”a—\¸ 3é§”kłŘĎÂwiřş ÝĘŕůjćIpb¶ŤĂ~Áë•čš„}kɇUÂď’ëŃ;€Ţe`§ľČłŻ'甩TtJ!‡ö÷’Ă$ňłŢΠöÎÓq´ăů’-<ŕÁEŚ;Ýňáçî\ź ëHĎĎôőř¨řń€ą űčyZ>ąÄEüĺcűX|ĺŁnâ4ŽrSłő€G<ř5ŽXőĐv ¶p‘+Ţ ]ËČś*˙ IÔőG&÷¤r}şUScxŔf"¶Í‡Ká§ÉpRjв۱]jŠ:x˙lěQ„ťňĐ˝ ŚôâxÜ2€š%{SŔĽű¸‰µ8ę7×x¨É °U9\Ř ü¤€‰L®s‘«=Ôăńź*ĚYqkőĎg€ů´ßéůJRŽžcĺCäŰ~‰?č‰y–뙟Ą˙ ßÍä™'2ŻŹ]ij'Ć f ĺq˝§őJBßü]\äˇdđ?ť+ Ö &±g*qq ťMp@>×%ľ¬ëÚÉđJ)ř­¤HÂ/épĐpüź‚©lü><ŐÂA%čśG(–ů(’¦†Â‘9ř¶â Ž©aÔcŘ)Q~·\¦Áł)Ä~o QLě #“ŕ‰ńGä;zN!\[„哙ÔŔ±đ`4Çâăuý*őU<Ś‘5©y°©Gę.lŐ ŽŠżBź‹‘SüLßůŤj8 :ŰÂÝńŃš'âĐóŮÚŤř0žúÜCNöťńĄ›6]cô©Ł˙ńŕ¨7±5ţĎ bě=i{đw–5ŚŹ‚W·'ëWYv-~šťFóFQÓĎââd°ŐűZ]OĹOŁ%·ˇĎÔ!UŘeń÷:fÇh·śö¨A†ă» 8ĄĚžĂ˘ľéŤ¬†3¨yŞß_Ƕ ÷ Ţ< ß+±ěâŕL°15ŰŠŞű;ÁËz°úľž±=©SöPŹÔßjŮ}‰Ż;^SďmŰ»[v…ĽGMż[W˙pö)Ä^¸Ý—–Ŕ]eŘű<ěw¬ÁS=¸®;•uč8ŰőĆŻ™ !&z›ä•Tâxö®˘BĚ]AŚáź&ň[Ő‹š?úÁźéđ|vď †Ë¨WjÉU-Ô·ÔŮÔ7ĺÔĺčÔkŻ^«9‰z5ś ‡LŢŞ×ůĘáźr⿸+‚‹Çsďµ–}*vHÝ[…/‡ÁÔ;gQSKĹä†jÁŮÄ{%zf ü~9~© >«3`uńۇ6ŇČĎ9±+…ŕ6÷ ý>z!EśÍ!&*đď™ÄŰŮĺ´…íđ× Ú(&®Î$>˛ńM0žCĚăăϲě_3Şńá@8q¶™L8|{଍ä×ÔěeŃë2˝_"yŕ"ô‹&—6ŇV=ăŞ%ż•˘S1ńťLßđyy­vńo˛láÂ>ř; »¬ă—Áťkŕ•SńIŘ–Ąëńpuýe–ť@Ü•Áőµŕ¸|T›KoŃk™ĄÔ+Uřř͓ћkJ¨/«±e|'v‡SČeečŘŹŘŻ ­ |V gÍě«}Q7̲{QŹ…·ÖP[ŚĹ–c$ŽńĹĹř¸žv‹®ąq| ýÝ.ßW€§jeĚŚł'c:[Ôđy:¸@LO!NsŔé\ć}yԽɛą¦ô`Ěc©aĎćsÝĄVôypr1±:Ý*ơ×0îĚq]5řŻÇű“ËK™Ďżˇ×*K86.,çÚŇđ?µZ>1?{sŕ’Y÷CŻ\pűąÎ)÷Ŕ Ă8żNjłě$üę%Ż,c–^ˇ±ő˘FKdĽ9ř4 }ÁZ5\;čj+:-GײíCЉx™ ˙zź×±6ő€Gé*ć Uźëµ­iđ»—{fŔ•yÔ䣔1–Ý Ö&á›<8{ń=Ť:%ĽS †^găŕÄiäĚÚřž¬Ç%’áq1\ ő‚×ňÉ9ç¤ëüÚ]¸=ˬA2† 8e(~+"f0î đE¶)"沆ëuŐL8ĚË\!ăËŽŁ&-B÷ţÔĺ‰äŃţčăySçş4ěť^ŇŔýT¸#±'ăź,üOIÄJy¬z¦¬Ńő®›\pöé¶Kóf)ö«Ű3ńM&98—Ô¬Ś—W·JOł˘^#¶N…3O˘Í/‹á`¬ď1]Χ}á›Eô[ ţ‹Đ!ż§€óň@ćÇôKm7,7pĚ˸‹‰‰Ř?‰XĚÂn?El—LśWÓóęrňt•¬7 ÄW-őÖ/e ý˱Q<ń›B|v—9ücŘ„Ľ…Ţü‘@{ŕÍA+t®”:Gj˛<ě4đ€^ÓKbĚ…ŕé&ĆÝŔśíř§•ţhŮ2童 †Ây˛^ÉŻB·1čÖH™˙.ýĂń«ŔIďőŁ×:xî,ü{*ř÷ő§Í‘Ňí‡uäéÜ5‰ĽQĂř;h»zäËÂ>ů°"MŻçťGŤ¤mߤk·Í`)Xü–ž· c<±˛ćDţ$kşp\ÜązN]¸VŻŰĄă{7X鉤 tLĹ–^ÚĎč¦k•xđ—Hí^ň®ž_Ä‚A—Ôň“őzsîzMţsĂc äžîR?ꙋôš°‡ń§’á˘$®©A·"¸.Žx̧6.e\‰ŕlč8Ź:$ ~Ď#7¤ă·´eś#áŐÁiÄ{élý,)c ±m.ąsĐ]zŤ¦\ň,ůŁö#Ëľůwz ąĚÁů?^i ľJ‰˝Yô›†ď0&üQď”p~÷e'G<ł[dE/BßlZ”/L“UzÍ»JÖüé7Ť¸I˻ᓾäőĘ_é5&‰›T°UĆ|¤PrËoő\hµD7úKĄÍllUÎrČ#•ŕż [ŕ§Ś?čçI•ř¸<6‰ąó(|´U¸›m1zăëdîŻ &kNŃkW1`˛’š „±Ąá§qpt&Xď{µž€˝ tM“5!řşXÖ2O*6Ĺx˛Đ9—<źOśd€ĂĚgČ OĆďôŐ«JÇÄÝŘ yŁüSËN7Sŕůáč:î(‡Ł·P4Ň©|ljńÎ…ËĄîű5µ×Ô‚5`˛‰X‡®Ŕx/| LÄĎ)ňÜLŚÂn^b ‘ü’~3e. ÇfS7wŁ–Ě†żĘŃ«ö˛É µÄ“{&8Žážě]ŹźŕמÔ;™p±s—›gv˝5ţňTtĚf>ü¤őúOŁ>L#ŽłůśĚ¶fˇž›çá» ě“pžĆgÚt˝ÎLž-ĂOpňÉy µý ‘Ję ™z®źBmť„O«É%č/ů L×SĄÁu™u›Ř7Gž;OdÎAÍ4G~9 {ÉĽśő„÷«ĆčçdÁĎp|š8XŻ3çsM2\?}JđóËňŁŕąC7xăćYđc>~­š©ź·TÓ,ń: )öj=R±S±é"ţĆ1?”żoÔśvăúlňD*Ü•"ĎOŇ4ľŇđaăíGť»ů Ť'׍dŰ@ĽŤßR[G3w)Dź,ÚÍ—ç4Ä׹đ^*ů(îΠďĚ˝ú»˘éŘ"îĘÄg`×,ř>EžG_Éč—ř'=‡ë>Hű$/E?ÇŚ»éਵSć$íKŕo•ľŞźo–JŽ>˘ňŞŁŇŕŕ:°Uqť~©×$š‘^`4|u#f3Ą–XŞ×Ď{Pä“ď*ęÉĂňśáÎĺY7>,ď#ŕůě/,űMaî])Xăş ú©zS?—ÍŞŐó¸<â)L¦ĚŃy0EÖ3Űőׄ^«j +‘Ü™G]—@Ě&Čş-q‘¸@ŻC%ŁĂ<©µ‰‘ ŕ ±uSĘ~?ňt_83o›®\pu¶đŕăXę“LđwԲϢť±ĚCcăLbľ ß•4éç´ŐpS:×ËÚôŮň%s˝¦PÁu%ä´îř#qµ~~Gm°Źz¨ś:!źTĹîÄA<üźĚĽeŃ•ú݇Ęý¬w 8/ýP~Noęđu:ő\—H=0‚Üś.Ď7ä9ůÖÓižŮ}­źÓK źçŐë2?Nšgv\ď!ŽJ¨“â÷ąďęçJąŘŃÇç2ů'óQ7ńéB?7y®ÇĂ=qĨ‹ůJ?K€Ës°EÜ^łE˝Ô_žKÝLp"ë´]‰ťâÁV"ăČĺŢ"ÚÇü&ĺË^€ľ˝°a5÷¬ř ç°y.(óUÔąÄIť¬»?É[ôłkőźż{°Sľ¬ĄaźôH®kR>ö<Ąź˙d8ňQńšo§É<I|_Ż;Éşďbá â Ť8‰{qH:ę+c‰=ńŢcÁ`¬<§ęĄ×9b…·xj†Dě–‡_cđŤëlť3¦»2¸r-cN>ŞsŢFtÉ& ¨9ó‰źDć:éŕv"üPB˝Q $Ń_::'ç°-¸YĎFbďZüW">ĎĹč\ íŰC9žWŹżŕ°,05tµ^Ç‘gŰę™sĄ™ŹżĹřtuZ’Ě+Ŕ[Ţ·yFŻ·kQäŇ$°–O{™ćŞxü‘„˙Üň,´\~köĺąÚ>˝Vä–ÚŚz+žXЎÝěäÂŢ)Rs˘WíD}§×ťÜ˛Ö›§źxđuL3í?éđpüĄúŮšÔŞ±•ú™° ěąŔG&cLĆßŃäţ¤é:F\ÄM8ËalnyN]Ü\ç†ăc9–Ăy7Ҫ׳dͰ–Ü–pTçżdćĺi´—Ţł©~ÎĐŤnYCôÚ[?tJ[Ý>ĐëÄU§ë<*µrěý~L x)•g‚7E<ł›bE_JnÉ¤× 3±Ű$ňA?jŹ^e\˙„~F™ w”Q«ő'N{ČZ4ą«Ż`˝ËŕĎBÁ5ńÔŽ%nË*ős3‰˝rĆ“†zÂĺŕ°Üd’ß ŃĄőN ś<_yÁĐꍞعyKńŹ•ääZl“#kä Jň@É˝ć?;fR'÷g<©ŕ(ď ÍaiOéWu‹đS:±›BŚŽ–<¶@?Ę{ÓÔ+ÄĎ4tH¦®Č &jŔw!ş>DLŚ t¶3Áćlt¬˙ sÄJ]_]C?ó‰rür-Mí\—ĽŤo’ů1ęÇ&ÓŃ{(q^7Y?ł›&k»ř0 ,Źăţzyod§YđYÚůşN‘%;V“ ‰‘đŐ[žĂrl„<ďŽŃ5Ë$ę·!2·§ÎČJŐÜ™w\sg9ř*š«źaĄ“ě0z"íK]kČúuÚÍ:g&Sô˘nOÎ 6*©!K9/1Žną}ôó´rpVHěŢĂś8‹Ćz|Ú“úY{ş¬eP×÷Â˙ 2×\­ë‡<Ć™zPż•ıԞzť1…z}}ô!ţ“c7‰l¨‰˘_ŃďÍ%>Çu5z-Ł{NÂOečx1ůż›GŻł xQ??Αç;×čąk|çş_ŻeĹX¶ü¶ĂDĆ0\ž[Ósź2¸2W~O…űłč·;dqM¶KŻŹVáßnň›.ŻË¤áC©ťˇ‡ćë÷»’Đż>¨ŠÓţJˇoůŐľrjďgéyŃőŕ˛ü¤|)L®Eź â;‡JO^Yc"f3đ“KÖ@¨ âČ…ňQ_=ź”ço݉17őtŠĽç$kÎđ¤{w“w°ß…ízž–Źž®zľ ďş©]ÝÔ–qĚ…ÝذÇÍ:Făfęu—yN×%Ŕ!y智ĹK†Ŕďi˛ÎtXóm×WźŁ×i;őó~y×n2¶O]cŮ!ňnâ¤ŰžM<Qĺa‡2p[CüçQOX¦×'¤.Š'fâľŐő“§źn·€Ľ\Î\ Ű(ť2e<ÓÍ3;°Ăů8ě–.|#ďÂŤY’»áěXęŻl®9¸Od\ië÷ąâŕşt‰[ě˸żŘú]H|+ĽÉµ±_čwżâżŇ<]@Ć<Ąźď'çÓ8V¶×őŇ~H%w] eS\Ąą'ńiý§{eÝ»t´ţ:GcK‡†cŻüU𞞏żµp_ńëÚ6EÄd>¶Ę"g Ą­l[ž˛ĺa•šó„ĺ;ilS›ő&“†““°ýxy§t”^«v›"|Ż&s]:Ĺ Ńŕ »%ŹnrR|~?&3É<ł“w„°U7đĎŘbťę–XôNĄuXŠćş8lčĆö‰Ś'žś@ŚÄČ;Ş`&ťăńśŹ—÷é/–tű.b..^żs™ŚîŃÔI`?¸ő"|•GĽ¸_Đ㍧m79ÇçĺP[ş±YŞ<óßÁO}8ž¨kŇd™ăŔc čšCžxtăëlćgÉŻęç`IŚ·śÚ'…ÜYELąńIţúy¦<M!ćĘdŽaÄ3»;-×—ÔÝuűő|ąg7ýg Óß|î•őFâ®DžĂÉşmLŁĆ)Á^§áÇnÜW–†`ÇbćŤŕeăß—»u’ŹäyŰęę·đ;ýz’'cË©č=б_ĹśćÜ—-»8™ߌŔź#ĺŮ,×LÁ¦'ŃooÉłĚa§‚ŃëĆX/€ë«»ĹŕŁ| d~3AŢi&·4Ŕ5ĂŃŁ‚q–ëŕź Ş>đÖż~罊vv«…ŘĄzĚŁ˝ˇ÷[ö2řâLâ q‹UIĽěÜĄž)ZCÓ_±ěžź[Önęăqë,»,ÜA-pňPËľk“ew‡N;äž«cÁÉ|lć®gŠĹ6Äóyäói;÷#ýĽ©š6Ęä]°2~č]¤ßŹÔ‘/Ŕ˙©Ś%¸©˘Ć6Ă­ŁőoKUQS¦ß~ä tś8ž8BŢŔÜ­Śľj/ŇÚ˛›fwęőŔrúě…_‹ŕ˘y`.€w'ß®ßÉ)Ă_ĺ§čueyvÔ°’6{[öBÉO_ęµÉˇŕy:ó–łeÍ@žł2G)Łs¨++ĺ˝ x+w‰^Ł© ~«îŐëęáúŢÜź&ďĐ/uÄXŘĘ%şT]oÍ›łoĽ»>9 ťňä˝eĆ8N+‚[WČĽLŢŻŮ«ß[QĎěćZöłř°úC˝n> ľžŚOćܬçłŃuÄóúýŢRňCoňH"9ńblM~ťB>­oŐëýĄŘ¤HŢ­$ż "G–“/zŃż˙IË.Ă×}±EcX‡—˙5čxJ«žO …#k¶ëgÄă^°ěx0SF,ÖĘ{Ś· ®[Š}+¨ßKg°>Ţ€‡Şi«„U 7’;óŔNąaJ?Ťť~ř¶BŢ™ő\Úś.«hŻn1„mÇ’łV[c©©ĆĐ®÷—úĎ‹ĘÚőiäŤÉđî ć^ńË^°w2űµ´7Ž8î‰MO>¨Çmş¬]óy2±^sÁeŢmÚ^©űZ‡ő žKLť…Nu~+úlůŽńŢ8W?/ŻÂľsáÉljÚjt­†·űS/–ÂŃĹOčwdJđ˙ňD Ç[ÁÚjĆ ůŞ~”eËł9/vňĂyKđ÷ĆŮKćýŕ/‡ş.‹ö‰Źjć,ŽYŃ2_–wSÚ©yjdíëĽô]–çüFż×9 ?V1g­†k˰×4lĺ%×ĎřązßÜŞ żä?Zö"¶“Ý<8s.cꆱ3ĐŻ\ĆőäŮ©ňť?ţ!îęeŤ‹ú˛{NgL•Ś·cÉ'ćĎ—*{égpň2‹ş%,e‚űˇÄW!véOߤxRçÂ,ę‡îRG_«ç W[v~/ ý©÷i»?\ěyÝ<ł«Ňď §2ĆF°ń^W–÷řăĺ=]tÍć|ź}ú{AŮ úýßĺ`˝µdObˇ©Ĺľ3áÉLę¬\ěßźř%ĎçÁoé+ęeÚ9ţ? Ě|A~FľëKśŽsŕľđIł<Ł,Ó\/kç)čőČLů®ř^°ÓđVLíŘă~Ç' ›dČ39x)™qU·•ř©śz JćňÎb>č!.=ňÝ„Ďő;ÁźŃőcöË`“»GŻłĆĂł‰äőB¶72«'.ţĆ8ŠéłôMË–÷ŕjÁí`r÷@rP:U [É?cŕµFY‡†»“ŻWńą7çÓOq°ÝOŹ#ÉIý9?‚Hm]Ci'Gö!ŻM"GWĂ}í{˛@}"Óą®,!‡ź&ó7tj‡‡óÁęeKeś‹©ĹRąŽ˝O׆čÓ#kEgęw˛ä;_ňÝ t©±Éą=dMżźÂ“Z?ńĘśŻŹ>/k9đEɵúűZ±Ăô»b)ňť ů~<•ţ˛ÉőîŹäGŹóDý^Ifľ^;óH}O¬%˘gĽSCŽ/â޸[ô»Ąň¬,{Ĺ}ß×ĎeÄŻňÎiú$Đoí¤É{EÄn¦| ›,@˙\ôżĘ:ţ̧jw[öŤ!ýN ɰ. LĐqŰ@ —`ÓY ô;}5ŞG 8·PŹE×NÇŹ˙Ťţžú[×ö@°«µ‚-öQßĹśÄgâU}ĎVţ~ůö‰›đwđe'ťcňÎ9É"fŐßÉ&7ŞďŢŽłô÷Źĺű ňý`ůmŚ~rŚ{ľBő7Üá őwĺ39ÎÔ‹¬E¦_y Őď”4šăňbč,łjúżę{ęCĚuŚSý&yXň;ĂŚň]OâNýÝkůM'řŘ:]ë*ďÚ"N-lý{ôąyÁÖň÷ÄŁňř ľ%!ÉßWż«s˝ľžâőřqÁ3üŞľËM^´´n¶Řj ·ŘëR¶ÇÍýŁŚ- Ě8ÉĎ–ÇśmĆI-eQĂ©—9ĐÝž¬˙~ąŐŰ\“mĆlqŰ»b'ŃÓŘ8ËŘť:C}^Ć<žsď]z˛…g¬ăű2cź ă±-¸łşă~ń•ĺ|GZt—ďÝˤŞŘŘ´ëwvd<čiµ™~E?}|nü.mô0ţ ĄśűZúc ¦Őß—sň¬y¦ńe—ť¤OżĄ±ć5Çĺů‚S¬Ů—öŰ8öň™ń}źŹ_Ľ\˙ű‰±ńLąNô0ş” ®řLÝdŐłýň2’…|bâAlÍç׍.Ôşu—•+} ţjÎg9żâ1Űó ®®1YfĆ)v»ŇŚCúřĐŇ|gÚIÔř¶EŻdŁż\#Ü”alżÚ´Ue8ńQą×Ä|Ű?» –%Ć+MýÍŘ6ĆŘ@păăó—ćÚÓM»sŤoÄKŤŤÂ5Ǥ}s~[ňŽŠgÁaĽű$KóaˇéÓ2xžv™ľ—ÝŤ]eűŤŘŰŘ\8RâTâeťńíQKÇÍÇFÇc·xŽ=eĆý Ąc\îO1zŠMÓŤ˘ĚXĹÔ ŃäC«›ŃŃel#~˝ËřXbăYÁąĆc¤ĐäňźŠŐSŤ­Ä7™FÜ–ć€|cń]_sÜet”ńü4[:¶ę Ćb͵.ă»Íř~4znsL?¶ńűbKó§č4ŢŇy„y‘âây–ż’$çĚ3¶=„S®“ţŤý%&Gšö…»űÂg’7™w¨XˇfP»ÂřW|6ĆؤČôe’řj]béÚI®s›1ä •™ľsmĆ?ŐĆŁŚ‚Á±üfĆăÓ!†… ^G›1Ć›v¦ó…Ć#ő;/Ş?ěÓ×ô!ă_§ý%qĂËÚÉgć*.Ŕ˛- <ťnl }ś…ě5űµ&Ć%¶KŚO+L§ë‹ŤŹGÇÍSLüŞy‡Ń_bVršpő{ĆOĹćśÔľńĆźk nJŚžâ{©Í_¶t -\%<ÇÜNĹśÔ,źšńťm0őµÉÁ“Ć÷ҦpÍ!}ĚţÄŕIj˙cÉoR'JŽíaĆ'şDظĐr~.ËřPx ÚŚ?Őôcě"±(9…9©âťĆ÷Âď…[,]ďľkîńDäąg…Ąç1_Ę5ČOM?… R÷H˝ńŠ˙Wćž=Ćg]5ôý™ÉK ç~kô_Çgá“Ó .ëőł3 ľÓÍx%¦Lld|Ç™\ő±›Ä‡ÄŕŻ-]űýÁč"víŞ7Ą~’şEbQb4ÉřÄel‘n®ý|Ä™ľţdě(şIĚI^ţ­ńáű[}  >$Źĺ|?ÓĆ=&–Äw3 ćnÝ?Fî6÷Č5Ý .rM|dÜĆšó ŻI.”ĄČtŁżč4Úč(|UjtŔö"ÓÖcŰ˙µÜŐ2zTh›(Ś?n©şçG©ĎjŚ[DwcgÁťŘ¦ÜÄVˇćb¶´SeÚFh?ÚҦ`ďyä^síËYOe^aKM!¶šjĆ>׌é¶W›Ü|’ŃErÄPáă'á.Y˙úÔô/q(Ľř´Ń‰šÂž§ű‰˝ŘäĐçÍ5’Óű[ ?,7ă; ľ…ď.7”ý“-=żlňF¦ąŻČô+|?ŰRgld+řyŽm×zó ;Óđ—ÔŹĽ¤ßČ:‚ŕQćj˛®#kŹĽŞś„l3~˝ä¸ž'Jl¦š¸ź_mtëaěřĚq˝~ňąÁˇÔ߲Ć&qýžń•ÄďKćŘuĆ/ÂoýĽÇőšŹÔńR«'Ëşž¬%~ex2Ů`#hrKµÉ-]uśÄýwÇťy|ٱ]…9eâŁĆŘ_ňĘ[–^—’ń¤˙‹%„ce$ő€Ô˛Ć'*ü-µ†¬É ˙HlÎ6 Ľr›î#F8©ĐŚEđ1iâş–žËHŤ)qź`®n|7ŔřCđ ő‰ä‰§&Ó~-ẀÁłč%k3RŹ 5˘ÖqéFřLę‡3 'H–z$ÉŕL0­ň—Ʋj÷KÇ_˛ÁÝ/,]oË=˛6t…ÁĽŕďă×J“›{ť“ô1eo±óŁ«ě/5yNrĘFă'áRYGś'<*\ó[yĎćWĆĎ­Ć{Ś^YÇőżżš±I<~dĆ‘ŁőTăzb­ĄëͮߏnöeŚ2çL ďHm!µÉ3ĺ+‚…?ż 'HMŘ`l`›~$~ÔsÓ]kuŇöfsnb„~=Ťď«ÍV°$1đ3K׎/ Żź ßu·śą|¶Ń_r –®)ĐË®7v—s]uŐF=¶¨3Ť_:Śís Vf™c[MŰÉfśp‹š;žoâ?Yj'ďwÚdđŢŻkĎýp'ŰÝߍ€xo«Ű.ÉĹ{KúGęÁĘăĎ_!Žő:}‰8Ěű“’€2Ţ[Ď?Y dďő?¸^¶7ßr®8ß{Çů3Ä Ţűýł…$Ľ÷ ™+ Ë{ť˙1IţŢű†ľ+ö^»îY!ďţµĺR${oţ·ß¨ă>t«ţź&†ň><`„j÷N뎪żŁ«í®Ć1őęľ÷CJŻ]wôň¨ţ&Ě ®ďýí JϢ S÷ÝÚÚ˘ú‹Šbő޵d„ÇŢÁŻJ"ö>ôçs7«í°÷Ą°ňľřbYşţµÍ‰Ę.§ćIŕÝ×ówj<ŰŢü„lďÎ˙Z%đ‹ó^”íMÁ­jś#VöQöřËúÓU?}KQî˝fě}ŻŞ~˛ű)}wÎ< Ćqh„üří®yMŮ}w}†j˙öß}ˇŽ_sWÂOU˙ó.WvżuŃ)˝;ľ’—#-ďŐŰ{Şv8°ěŐî'M·©vţě+µßôąňÇu×ôyYŮăů"´ĺ}trž–wŰ•5Ę^·Śµ©ńjýŤę§tŚŢ_ĽüĽŇűOÎVţ;v‹~ŢWžüĹ ëĺm‘=cş)˙^ű“jŐîOßź¦®{Ř%¤é}v÷Ż„,ĽŹüi˝˛ďžĚďUO~~|Ü_ů“AĄçÝy›dű•łeBç}iY 9č/ă^źĘ{ş‡Âĺ‡Rý?xÚíß*\ćnVţYőňLe·'×÷¶˛ĎswKyoüäĺŹÇÓŐőżţŚş÷Ş+5Fá÷łn Ç».oR×?ÜMůyWß6ĺĎm?ŰŞöU±Xáěůăsřg叫Őńűď:Oé1l‹ş˙Ň”8üđÇ8ĺ×8ußŢÇV}­ěĐľGáüţ;7K‚ńŢ{kÉ{Ęţ'7Şdzé ©°˙®7”~[ţř°ÂĹÁwŻU8Ý_WŁŽßöJ÷… w÷Ç©¸Ř˝§YááľSv*;]żóqeŹ-#ľWqqÓ•+żßüëj…= ‡©ýk‡Pv:xÁňť˛˝"nú uţp»z(zoéÖFeŻ =[Ůçś…*^o8şňe‡§¨öŻúˇYéq×o×V*ý˙$YáäŰÇ?#řť1ŕj·öq÷{?R¤ĺ=ř×5Ł•?îQăž|Í*ć'©óŰż˙ Š·Ó|Ęo7^5UmŻ(ÎRü°íMŹçăŰîUçěˇĆµŰ˙ú^e‡gnŃĽŐňđŐ˙CňĹEË{qűµČvkN/…»[’z¨~v,;¤Ž_=z¦ňáŠRW÷GŮăPc­Šł;łţđ[ŐţĄÍĘ<Ý~§lúráąĘël¨{Pµ·˙ŹśŁÚąg´˛ďOžhPăľâŐŰu\~×Cé±őŘW?QúźÓ[ĹŃmŚVýżîYĐű ߂٠Ď{.-;Şě˝ďµ˝cNę%j?o„ŠÓsF~˘đűÓKf)ľŘµl‰ÚßÖó=Ĺ ŰžZŻôľ)ń …»ł'žŞü÷ŔĚč}˛==±Báá¶o1jűýůĘ˙׿źŁđ}ĺ7Ł~ŻrĎR~8÷ňűÔ¸vx‡âŮk/üDŮëćŤ?hśođÉ$Ü{řôÍĎ){ź˝XůgóołTüÝ—ő‘âýŹ×*ľąŻY~$‡xKyDů7ôă8Ą˙î-ŃŠßnüđŐţΓ–©ńߺާâôŮę‹^§Aď5~ˇ®ßąý)0˝×Ť>s›şoLśň󽣷Ö*ŻŤQńyµďâ«ëFx”}źîű—ŇÎŽźŹ¸LŮkf­ňĂŤ˝ű˝Ú_»]ńŘ– ŢSú_úŇ›Ň˙üRĹÓýż: đ{Çš›UŢŰ{ďßďďŰ3[Ý÷đů2í,~˝Tá+ęţł&ĎUzďlUůîÖ†F•ôf5Iż+ůŻŞť[¦_®ř˙Ž^–⩝źţLNzţáśrµĘ|eçý_u*žŰ˝ôBµ˝Ăw®jďĘ^ťĘľVݏIŮĄő$…›[6îzK]÷łMeĘŻ'O:¬îÍPq~çŻÎWvyŕ篪qěđ©ňĎG;fÚť˝«ę“;ł/UqqÓ_7+íüă; ·÷–Ű Ç[¶Ľ˘đ´óčĂĘ7ĺŐ(;쩨ąVé1v•ŇóęĎRăÝöÚŁŞßí/d«~÷|łRńňö›R•?îËm].Ű»«fÝĄp>â…Ë=Ă?şCŹWçé­óć¨~Wz©ţ¶~´ö ę§ńRĺç퇷©ăO^řń/Ĺź·ÍËS|úІűT»ű®żFőűśkóŹrţö g¨<ďokÔ6ĺŞT˝źŠŁë‡Ĺ(nHľFámKćHĹo—ďż[ŤoĂ}?^żi’ę÷ćëš•ÝŢYqTĺ«C÷Ľ¤p——ˇâôęŐ«ßń۵ŞNصë 5®=ÝßV¸Ű7ŞLůuçąőR`{oűŐ&ŐĎMď·ëş,ďN…×ŮóÔ¸o»ŻPŤgĎ•^•źö]‘©p~ů9*ž¸îĺ7•7ľvˇĘ/ ˝¬Qƽ社*ď^Qó˘:çŇ›_ŢúÓߨ~.ë3]ńÖŐ P¸z|čĘ?»ľv©|´Ď©*.o‰¶/?łkI_eď…íşţzć-Ĺ_×dť¤ô;rŠÖ{Ô4Őîµk7ŞřşĽĎ(µﳏöSý%} Ćµ}Ä™ WgďűłÚnî˙”˛çy9kTű›ćşµÝµŞţ·ĆíVütë çźÜ:3Náíüg«ëŻüSĺ—‹Cź)žÚž®đ|É3şnĽĺű/íEç\©đýLćę?Čx®¬Ńö»ů·qj<k˝vž¶OĺżsţŇ_éżißűĘnç?uąş~Ăgű•ß.X_¬Žďťţ±âëK¦G«řÚýŁňĎ•=«•>kO>޶Ź îŰüQ¦âý[ę÷©ýóß˝KŤă’—ęÔý«Ţ—ődě¶ňEĄ÷5{ŐÄÓ»ă’ĺçíňîoóň×=?ťőŁß_ÎWú]ůu¦Ę—»ß; ěrkßż*ľşďéß]©üł©Lůýí53ŁÇ˙2äËâŚăËŢ—Ëö‰§–¨ţöÔmr)=wNVő˙mŤ©ŠŹ÷m>IĺŤ]V,ďÝţŻŢQ×]ţ Ő˙6kžŠŁM7|§»6 ꬶ_-Sqt~g®ňăEĂŢSv»éęĺ—}žžŠ?n›Ţ[Í—¶§]¨ňĹŐîĄpłnÄDeďK?ĽYůéÂç¶ďTvřšz/ÚéUí]łČĄôżú÷ůjüWíŻöŻÜCáâŠß˙FésNĺůŞť›gíSőÂcϧOQ¸ťyHŮű=żSńżjŔ8…ǵŹ\üjohťâÍKjSőĆŮłÇ+˙n3A·űô"µîđv… G”=Ö÷UxÚőIŞĂĎýLçµóŢIPílžv«Ď•éŁU»/NPí­yőL5ţóFW«v¶]ąWůž©řçŽŘŮ O?·ńľŇű°ú óŢkNýPéřą¤oď·Üń´Şw‡.PőÍî]‰jžşuÉ\…—§¨úűi÷5Ů{ćŁĘ®„Tľąęâő2!÷^_ĽPŮëĐ•kF)ű/}CńŢ®Žg5_٬ôüÉÄ •żîjĐuŢž·ľW<°óőíĘż»Ż8®xpgÍPŐőPö94äF5żŘ™—¦®żí§;VűźU÷ýdËgjţsĂIŻ(»]ą{‘Šß>ŹĆ(żíďZËřÇöQzÜą.őtŐ˙ ˛ŹŐý—~ű¤÷cO(ţż/ńíG”}Źřőş{Đ0ŐĎĆ ^Ňńs©˛ÓęPňߎ5»•ß·ľs­Ş›o{ôőŔęÂ~˛ ˙ÔłşŐ ˇŮďÔÖG»źőěÍçEťqt{|Ď~ŻĹV˙ň «ÄűzÔ®ç:ě×}uŐ­F?łř9׌noÄĆz/ł˙tĹ®žý/¶/_Ňc5Ż‹žµ®ÁŢ˙ÍŇŘ·öfĹů˝S»Î¶żşë‘¨sűť±őÓ¨=»‰˝˛fLôËËvFýíŮkěQSţČ…1c—\˝¨Ď›®Żž<7n}|ďÔ S÷EßwĐŽNą/úę«í­‰É1+hŚYąoá˙ŞęĽăŁ*Ö>>svS@D¤#*(Š23głˇ„ş›Ín˛ŮM#’M/$¤]‘ŢĽńŠ ‚˘ AŻĺUA Š(\E X°7xÇcÎăýc?™=e~3Oů>3Ůä,?±`*˛ţŰřlţU|쑢ČKú-1x2˘×żöńIwţ˘ý#ÖˇíÚÖŽ?¸wĄměÉ­Ú±ď"ůÓyř´ëŠ#+úRó­ýX[óě/üôŰÝů¶óů3)ݢ6őkäť÷j S>µ/(»VŰ>ŕj»ę“çŁâ^zÜŢăćuťŘá‰J}˘sÄ[‡ŁGf̲k«âŰ?(ŹÚŐĄ«öÄc—˘›ăĆG4=u'ßď|ż}Ů·ŽNëoů%2´°ç51®Ě¶ůżŹńřůČO%±ę†‰m׺źä‡ŐúuÉołń±aQ3“ÓyŐą×´1Ďh'\jöߣ5Ťhď•­-ö>±=}¨mrü‡ÚNϞȦ'ű˝bUä›?§}ąˇÉö¬wtÔŢ˙Ť<á\ńÂWŰ´™Ý“Ů™oËl“V,â#;=Ń0ó»ďÓdŰŽ1·i#‡+틼łŻ}ßŔ/˝>‡÷č˝Q«5E›š–mëţH?z°­}Úď?°«Ă§Ű®Ů:4b×Î^UŁĹÍîŻ%üń‰íe¶‡§,b˙ňáűl×ÜWĐíŐĚ${TK›ŢµmĂ/ŃŐwÜąb`ś­ř­Ó|ÚOźňęź=ö6ž2­çÂ/x÷çßć-ÎWÚ,Ű6@ëĎöŰÚĎ~Aëá‹¶őČÝb»áhOvŢ˙"ĎMüM«¨_ÓúŢĹźJĽ±t€‹ĎZš9pLȶéĂh~ĺúg˘ćîę5˝®ŚŹ]¶—=Ř®—˝ió*>¶w8Ňýt5żľ~ ?±ű W0S[ąu˘öúnÝo wăWŻ ťýî„Ý5°Ňöč »mW6EłŮjQÔŻ7ťŇFÄ.â˝'Ös×lçč%V˝gcÄ/É솟ÓŢ:’Ł=qÇEľ•MfW[6sµşk~T×¶%ń9§>Ń:-¸Éľ¦9ńş§źŇ’׳óÝvŘßűň«ČĎ&DLxďk{ÓŤµ,±Ă%mö6Ćż8šűĚ'Úš»ŰuŮżŹ?ŔŐb˛ŇůÎřÄŃö ź„ůLö-Ď{7‹/¨´mÉ‹mř’oóŕÎľżbO{šw{4Ä>ěŐŐ6cŢ7ü[§j·ŔçżUÍţąń#Ú>Ěľôć˙ň‡_úŚżyřŔő˝Ď˝n{ú§·"űś[a[ÜVÔánŰâśĂQ-oÎĺŻ|1ě#÷Đ–dí÷UłŁ÷y¶ĎŢí®5ś‰łÝsĄ6˙ö‡ěËn,ŐŃËmŰ?śĄÍ­l«|ń'>đŤ!¶µźëÜľűI-˝]¬mďíž—z^¶ÝrésűŚ›"ç/ę¦Íęľlŕđţmxţëěńíë´૵qW†DdĽs‘÷čą]k^zQëă=˘}đŹ_µÓÇÚńÚ=a^[ţŔüxmݲţhŰnëŢXiŰłs"ĎjÓ_[őůŢóµO¸ďŕ;,ĽuOńĽĘ ˝Î^ołtO‘öÝćnQ¬ĐkËí¸îÚ’´mťrĘ#ş7­´ÜÎřO‹†ń©ă&łŮ·¬ć|ŃBvůA/k©ŘŔ·źďĎÍźÖcfLoűđsÝlßOíÇ{ĚţŢvCîí–®ÓµÓ˘X;yüZ-Ë_a»ć‚Sk›ÓŹ/ŰÝŽďëŞ]­ż˝G;ŇËN_|žź[ى­˙HűG2vkwŤŢĚ>9˛MsTë;¬–ý牎|Ü€őQ9Sń+‹ßµę]ˇU9ŮöβEÚ©čÎüxű5ü™ßÓíŢŁŻńÝ'ń"Ď%vé—®v•t’;Î-áwľWËFýk »°Źłű‡~Ĺć|jÓÜÖ—÷OČ·ń“Gµ%›~dO<\Ă; řŤ˝2ĹĆťk’ŘŰę´Ţé-lSěĹkš37qíą üęo^6í2íÚş:öŔşx¶ţč4¶Ł|7ki©c{űGŘWĎf;g[T ĎěPÁęNľÉ¶-nbWâµ\_"Ű´;2čNVýˇ´ŽĐů*Ű<ľjľ‡‡ 寍~5"qĺ^ör»Ď¸>3G;ěKmÝŇě'O­ŕ·žş©÷îçlż˝”ˇťî8?ę %ÚÖűh“ŻÔ6ŹźkkwĂjmÎĆŰľ¬ůěnŢ?ş ďŻń ÷,g?]ťÇ¦v˙FëQţź¨]b'?Ęç“Oü›MŚb _<†¸ŇŘ×:gNf?Î;Ŕćî ˛ůłŢgËObÓ§íłE]Čîßß—­éëc8Öók1îősXŐćçŘŞď†s×ŔČ~ wŰfä^ßvŕ sóYŢ5ćşű헵• µÚ7–js÷%ň›÷ÝĹz÷DÔ¤*[˛ý1Űľ‹¸çx6?~¶ŁýŘĹ|sót-Đ1ž·řD¤6–Ű^yᬶîJ˝ÖEľŞM˝;™™˛‘ÍÉł?ÓMëŘëv˘ôŚ6hôÉ{–×ńo®ľĹŻď_Îěe·ňŐŰ»đ§FÚî{ěwmü¤Ż"Ɔó¸s»ě?ly€Ík~[»xz+~ş‘ŰźÚČn|ü [¸lŻ*”,ap>»}o?6cĘ‹ěű^}Xɢ ěţď^cÉIvî»ZĂĘŽf+nżĚš6\ЦČ{ź<Ć˙ÝóÇÎáŢťŮ*ç0­ß¤<đdíŹÎ˙Çť3ÚkźÍĺv ¶_ľéy¶ô»S|Ö¦Ł,řő|~ÇöĽë gµ Ďöa«o´uíÄŘĚëËŲ|Ţç,e¬:š±{+»›±ć Ś…c¬xcó?allĆ&`K0áĆĆ`+Uű.c…[Ń®c¬[ˇşYŚÍjËŘh,K>E_čŁâ}ô÷uk›8Íüč·f,Ž˙‡±IXć×<ë2Vú:ŽÝÇXHGű=ĆʱŭÇ8k?C˙ŘÖ”a|EXŽĆOlËkǸ°|-źĘXĄqn'c%ťđÂVuäťĐÉÄqč•bśĹ_uú ¬ńÇâUŠ~J7`Î;0l˙C“0Żţ¸ĆxëqĆé3ć[ń0úĂö*tÚŘvâÜ(,K^…&®Ď}ÚüĺŚ7±2Ĺv̇yŚĂ¶olQŹqżjw#ćŽseß2v PWŚ1ÜĚŘd‰16Â^‡L[LĹňľ1sꎟs/ĆfźÇýŘ&4aüł1˙¦•;Ć4 ÷O€ď*Ńw3ü6ulżĺc żč-řđ:Ćć@gZc q|Rî…^ř3óşJĚj*Ƈ­čT,Ű›}8w sÚŘĚ€ýÇb›3ąŻíŚ-@,”¤06[Ł*,×pÍÜ7[Ś­jĆ?ö)]_aaز~(A|ŐbI?cŹ­Â¤ßđţ Ćĺýř¨ˇcSŕł:Ä\ă"ô [ÎÇVa üZ‹­{ĂrÄÓ0Ř˙ś€±îÁµłfÄH ¶†kfŔ¦ĎGŘR” ż ±® "öJrq[Ú1°c%¶ĺđcŮ‹xŐ¦đY>ĆťŤ.Ŕ¸ uóŮ Eč7˝ÇJócŢ!Ü›uĆpŻů}0×ř5Zr©h8t1ŻÜ4ÄŘ%ôď1žXÉď‚řéű?Äâ-×)ś/‚frĽó.B~#‹%#1ţů°lZ1~Ä=EłzlÝKaó˛‡ÍXÍycGĚ„ ?@«1dü:ľsš„řbüˇc&ó Ó>ř |šů,‹<äc6rÂŹ)ĂĽ«PC˛ÁýĚŁ¶n§Eŕq~(ĂËżÇ5W?b?L-Ă|+p®®#ć?ä"öŠŔöQđ1r/XÂ|*ÁÄ‘“yŕLö-E]É]jĆĂ(Ř/„ţý?ŕ:ě?ŽCK`X5ň·&úđe5¸3 ?ˡc|Bߥ`D|ŞÂ°a|W _gďBČ…|_l´Q˙ňáËř=€|)ŰÁ řfřÚ†6ę[!¸Zx îŮŹůŔ?ˇ)hźBźGˇ›÷â}‘Qo%°cáÓź…đQ â$yZ†!gĘŕ§R0 k"VŚ| Łď ‘“ČńŃĐž€šT±ĺ˘żZ°6€ĽÉ0ü„ąem†OPŁ|`LóôÁ—i°o:ňĎşŔčLäHśM½9ȧś6č˙yGLyQűsP›ó?0ç‚ϲůČż‘W68X ććĂ&ŁÁťÜ13ú-1jŽâúôŐ€ŘČ_aĆX¬ô~DËAlf· Ôź<đ)y?Ňŕ!l9őş Z^÷bÎůČłô_ ż† ť‡÷•°_.â-Śř+Â8Şá×BŚÉ.Ő&LmDÎ7řÍ?]ĚBÍ·sÁ˙졸g7úîGk§"ÔÔ$čqM2üî7ž˙Ř÷Ây]ńă÷`L!Ř7€¸ ŕšŘ-űdb,ÄMőÇ;Ř´S6úÎÂz)÷e O|íĂ|üČŮ ^^ŚÍ[d¬@  —U…ľŕ7/jHěď3ćqnŔu}đBîúŔÖLä‡ óňbMDĽř_™ŕµő,±ŕĂ2ŔĽ4ĉ>ČF~eö6ŹĎňA'ă5´Q2á[?ňČ…tM.ÄgN4ÁҬŰ`¬W2_`GÖJ.ÄQębĽ/ĆGłi°‡ů“Śx†ř‚TđďPÔ7r&eÁą9];MÎĎĘÉňçs côĎ‘ŹC°{ýz7~#a‹QŕE.®5~Ťź‹ `~Äu!úČŰgţItÖ8!ŚŃ§€S…¨¬M˛ał¬=B¨µůXăäŽ7ź‰Dľeâ}Áqc*@ž ź˝‡}Ń÷Č#¸kćękřX»xQcаôĂ/ÁH“7Ą˝MżĎńyÜüîŁ6dC75*Űm¬#˛łLFzÁ†B#gá§L#&ÁcüčĎĂOآĽ "Ż|`ztň1Ç40ÂŹqĺ'éŰĚśsOǵ7ÎŰ›1äFlpm&|71č1âČUÔ…LÔ>_‰™ű>ă™}¸Ď›¦ –»` Ö«)đ±92ë»tÔódÔ‰ Ś#ąš Î§4ůŕEťôc®nŘ(ő&3Ăü.µ$h^ĺC^fŔgCP?2X—¸q]Z ú1žůłÖâ: ĚôźIŕ5öřĚŤzáuă»űaßDĽwˇ&daě^0Ŕ‹Zš żd 3q,ą›.xŔÇ$Ôb7Öź™gâ/ĂX# Öý8ž>xa;/ćí˙ÜüŢ_lžŤřň!ÎŇwYŕ‡9îżĆÔňăśÇxfrÇő1Î#?=—Ígän*Ćî…íҧٰ˝ëgó;˝=é8–’h®í|M¸q”€µt&â+ y’˛Ďk"ęję¤k¤xÄpÖw.Ě/y°ąÎ󀿩·ń‘—Ánji*x2¶÷"|ŕLj€űÉ4čř°óżnşÜ—\đ9 ĆĄç©` ëŞ üLArő1ąň§ˇ›Ű‡őЇXô Áéf~x MpÖŤ\÷"ć˝MüëĹ]¨MŮ`C˛Ó¬ ™ČáśOÁş:q›{§"&ÓŤýjA"b>ă°ń©böőĽi~żń'„nÄy“™cÖňĹ‹9ůŔ†,äŹ6ň`l^ŘÜőź˙ăĎţqĆâĆ\2ĐGňŃ…:•źyŕ/ř•:Ő|6]&l›‚ń¤ĂWĚ7ߨéĐIĆZ)ţ&€ŤC±¶‚Ú?ÇüNůxŚ/5:~pÂqđÝ`Ô†ÁĆ3»Ŕätěâ(ţHAĄ`žĂ±ľM'Rçi`  ýşŔĄdp! 1érźŇaL†ż/9ź˙Uc ¶ăxĐ{śÉ˘ ŃďłćżKţ7óu!ţ’Á‘dÄ…µ6{ÉTôëĆ\Óýů?ćCÝ[Ĺ#'ÓŔĽě·ÍďŤuaI¨Ż™¨ )mÍşŕEěű±nKAÜ Íý5/|Äş+ů•ŠxKBî$ωĜ’¦›ßC€űíĆGŤÜüîŃ®Ż0ţŃĂřŇřĂră`TRc}ý„†ŠżŢÚÜľŔ_Í謦Ć暆˛ÖSmF4556”׌÷×oEĂ ŐÚO^cýßŢFÇÓăBůżĘż_hťK®©¬ÄŮżż˝·±ţďo™ÁżŢ^ O —Ž®p7”ˇGë`ăřđčń83ľ¦±µźkĂÍM᪊šż_Ů®ľ˘Ľ&ÜĐzě,Ő¦©ńŢ{ţf-cůůçM<¦µ!Z­cç­łĺzkĂŃÚmm8[­Ď§ŇbY­«%¬–´ZĘjéVËaµb­–ÓjYÂŇ–†°4„Ą!, aiKCXÂŇ–†´4¤Ą!- iiHKCZŇŇ–†´4¤Ąˇ, ei(KCYĘŇP–†˛4”Ąˇ, eič–†nič–†nič–†nič–†nič–†ni8, ‡Ąá°4–†ĂŇpXKĂai8, ‡ĄkiÄZ±–F¬ĄkiÄZ±–F¬ĄkiÄZNKĂii8- §Ąá´4ś–†ÓŇpZNKĂiiÄYq–FśĄgiÄYq–FśĄgiÄYq­¶A¨CMAMIMEMťšjĆRÓIMR‹!µR‹!µR‹!µR‹!µR‹!µR¤&HMš 5Aj‚Ô© R¤&HM’š$5Ij’Ô$©IR“¤&IM’š$5EjŠÔ©)RS¤¦HM‘š"5EjŠÔtRÓIM'5ťÔtRÓIM'5ťÔtRÓIÍAjRsšÔ¤ć 5©9HÍAjR‹%µXR‹%µXR‹%µXR‹%µXR‹%µXRs’š“Ôś¤ć$5'©9IÍIjNRs’š“ÔâH-ŽÔâH-ŽÔâH-ŽÔâH-ŽÔâHŤX"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"%‚X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X"‰%’X˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘%ŠX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Ktb‰N,щ%:±D'–čÄťX˘Kt%Ćg ¶Ö§ĂňżNµ-ŹßSŮţó“ă_|Żü?ZŤR@çsampling/data/swissmunicipalities.rda0000644000176200001440000037171514520143732017614 0ustar liggesusers‹ „˝ śdm^×węÜĎ©îŞę®[w#2Á}‰ĂFHPŃDçŇÓł°Ë®űÂ.˘j¦kzŠ·şj¨ËěÎ&!ÂJTŤąAąŞÄ\LBHL ^6ĆÄ\!bąCŚúýťSOť˙9oÍňůĽývźçśó\ţ—ß˙ňüź3~řĺź—yîyžďů˙ř3ôů_ËózżűëŹÍÖëŰíbötöb2źmfÓµç}nŤř9öĽö‡ĘÇ˝s~ŽŠ®Ę{j;á'صąź3~4d‡źÓÝőůîůńîůá®-Řő•™>»ßGć=w˙x÷ٶľąďŢ;Ůý´wĎąqNvĎŹĚO{7~×ôŃŢ=·#QчżëÇÍßßý™çÜłjŹĚü˛Ýőńn~ÁîÚ˝çćďďćčúqcąľOwď_ěúČ |Ó_dć¶ëëÄôíĆ´´čěÚŽMn -ň]_®Mżă]Žîťöî:3c÷w﫯ĐđŵŮçF»v+'n-Áîąán¬ţn<÷®{ßŃŃ˝Z¸ľ†»źÁîąľWÉÜ©áQnř©ß‰ű|÷÷Ĺî:7÷ZŤąôwżÝŘn.ŽÇçŤ{ާYKŕŐ×=4ĎôÍ{nÝN–Ü»ŽnŽf®'ďăÝ|Ć»µ¸űéîů®WɬÓńĎýîy• ť™y8ř»gĎĽJ†­l9ůrrjĺÍĘŤÓŮŔĐÍÍÉ=«5„fVg-ÝďˇWÉd{÷LfhćäŇáë+Ú][ý°Ř20×NŰćŮĚĚĎßÍ;ß]źy•~:ůNĚť,/śląçĎĽşŚ;™°s°Xĺd0đ*´|éšů:~X[0đ*]psIwż“OŢôwóÓďŽá©Ó=§#án^]Ă3+?–ľmÓojÖíÖĺŢѵĂv‹)}3†“«`ן“ŰqŁ/§Ű±ˇKäŐuÔÉ“çąo±Ě÷*YvşăčęhęlŞŁ‘žu8ädh`®ÝŘNśvô™ţ˝ôŚ“˙ŻŽ•ąWé•›·“7¦í׍ĺĆv6ÉńüÜ«ă\Ë«| ‡?nnMÓ.şżföL»ÓGk#_t/1ô±şśěćâä+őęry>śď®5˙®{čŐuÇ˝ëlü©WáŤĂb‡MNöN ÜťßwF^%Î's˛ęđÝaµ{ŢÉÓWwĎúv~nMVgÜŹł'–ľŽ‡V6/vów¶ĐŮ+Źö=çĂYĽuşęĆł˛d}?ßđĂÉÜŔ«l [‡“-×—ŁípG;§®o燞›~ňźÜ3Nśżĺžłř46í§^eGÜü/Ľş,XźŇńĂúŽkÜšťÜ<ťě9Üqô9Ý˝y•ţ;»ăěFčUx?ÜŤieÂb’{Ďůś™űަC3®ó+\nßÍÉľ“›çGşG+‹ŃľW§ˇ»ďöh-&Řś„“KËKŹŚ˝şnŮß-ŻÂĆ&ŤĎO˝úśćž“9Ç_—×kÚk'ĂněSďÝó±üu4wxę0ĐÚ;'ç˸99ĚrĽrëqý:źĎa—Ő›;µúźző8Ŕa€×Úę&Ć Ţ:šş5[Ţąľ-ýF8ßĂŇŐ=ë|~w;7ý_KăˇyÎŮGC›µ~V#űŤ6Ç —Ç93}:ý±Ąw\®Çáľ][SoěZá˝ű»Ó¸ç°ĘůŽgV.ťtď8źĎ­Ď®Űb´ŁĺEă™ÓF_§fL‹ŻM;|aîą5Ů܏Ŧ<řćľÓÇ[7g‹“vŽŽ¦Çf\›{wą^'ó6Żaű±:2ňęxččäî»5YÚ;ą±şbuĘa¦ÍkŽ˝:mÝłvź9oümc;_ۧËo›qíZšcÚ<®“9;OG—±ňĺúęx&¸ľÜ»gć'w.ź64?vĽĽ1†›Źőś?éd»ÉϼѧŤ{¬/i}đŔ«óËúłV^Ý>F36tq˨ńăl´łsM&ô*č›ţŮ8ĂÎĂa¨•Kkg+ĆY;ř©°ľ©ăî·ăꓵĐ{·l7óÂ.·4jĽÜł©Ű—ěú,ÎٸĂɡíĂŇ1hĽoőÜÉŹË 9ľ:ěµy»KןëÇĘšËĹÖŹÎ}¦Ťw¬­nÚ{ßúžú‰ĽJN-}­beŇúĎ®oÚľš¶ydţv?6_fe¤Ůź]KhţnúŽw–6®´ëpĎ8ݶs·ţźťĂiăoŰć0˝)«®o+{®˙&Ć7㕦łmMŢŘöCńCSoGćžóŤśś7ieçgcť¦ÜZYµ<°řÜÄ K+W6Gjçd}'ÓVěţ±O­śŘ¸ŕÂ<ßä§ĹJ›o´8lm–»‰oV^­L¸ő8sżíşš¸auމÉjs±–勣±ťŹ•“f\eeôž5ă»¶ˇWçť[W“Vź¬Î۱š>ŁŤÓ?ק߸ŢůbEµđĂíŮů^•űťbCűyőšaׇžOĚßö·ëÓŐ¸¶ŘڏšńÜ'2Ď»×odúpďůf ß«jţěŢKË-˝ďÝ|mę‘“Uko¬ŢŘ>›úy¨Ć$2÷š˛hűjʰ{ֶÝęzłÍŇËŇşIsË›C¶ĘňŕĐĽ­ŤięŹ]«ëËŃ61ď6ý)K[Ű_S‡Üű˙a~“M±~ŹŁC“Vďšţ¶}Ćb˛őI¬˝i®ÓbcâŐçĐ” ‹eMú;Ü9Ôo“vö;÷C>ßř±ô&´rÔĦY?ÍŇĆĘh“FÖÎć˝ćřÖÖX]ł4lҬ‰;öÇúĘŽîÖ®ZŮhĆiNÎÓżťw;ZŤ>šs94?+ŹÍµÂĚfŰ!;hmĽĄµ[»ĹÇ&–‡î»÷ůɉąß´˝MĚmba›­`m“ĎÍÁÎĹŇÚĆ V7›<°ëkʰĄű›l“{ÎĆ݇hj±ó&⣕Ë&FŰţ¬<şy4}ű¦\5±¤)×vťŽo‡ôŻéÇXął¸Ö´5MÜňŁŐč»ÇXßü>4í«ĄĄő˙šsôŐ¤»•ۦκţ,âCSžů¤Mڱc5ů`×7žsshćŮÎć<›ŘŃ|Ż©3v]–V6›:Ř\—•;K‹¦śÚ8°ĺŐélń"0ĎŮś‹őŐlěŇśóˇÄ꽫[Ľ ÷ß„«ÖvÚgěÚš8déeu®É×o3&łľźĺµőséJϬX,¶ţŘ![ÖÔS7¦]ďˇÇ®˝I«&&ÂřCrř¦u6ő°É‡fÜęć~ČOoúMM{ĐśG“ĆVţĹťMl1ŘňČÚ«Mąnň ĺ˝Y6ŮG+‡0őĐA㧉K‡čcǰď4sk«›ţšĺusmv ;ʦď˙&¬ Ľ:>ĽÉź=¤ďMůŤ˝wËžĹ^űľÍY6ĺ©™mâ±]słÜ;‰÷n˝}“mµĽ·8dynu˙Pî÷SéżËú)Ö'lúÇlłýÝ´˙–.Ml±ţ~«1Ö!z4e´71Խߔ…¦~Ú54ýÜ&ďÉ^Řë.5±ĆŇűNŘüę›b 'ÖW9„‹ÍyŘë7í‹Xąlâµý»©{Í>ŃěŽŇݦť?¤çM˙ěŤhÚŽ&˙,MšăŮç¬˙ßÔ'»Oää(iôő¦8ëMvöMĎXşŤßMŰŰ”ŹO…™‡ě†“Ăć\üF˙oňYßôÓ”éȫݎ‰3‡xr¨ŹCülŇň߯ŽGľwŁš~ŤaŤkĺňMľÜˇąŰµY»)'M{ÝÄ­7a€ĄąÝŁp´°~סy5ăřCĽlňČęű±űL‡xÚôŻáÍ!˝µĽ:Ä»Cúe×ßě«+5Çh¶7mÇ!^Řx«ég5ĺŁ);‡l¨Ą—Ťi›ľŤ“›Vă٦mřTxß\˙![Ú´ŁvÝÍńţ5çń¦Ř8:0~S¦šv(8$Co’­ćś›>|S‡›t˛ţ[ÓŢYl±}Ţ»×zHGš˛mqÝŇţ®Â̦Ýx–4ió&ž6qĚá€[ă!»th̦˙ú&˝ůTsnęoS|Zb~>•Ě6}ĘCxź‡ôţ_×´Ť‡ltÓĆ5űpż?Ö7é5Ţ9$˙vM|Şy˛/‡äř—Âz‹M˙ÝîŃ5×xěűÍ\Ř/ĹĂćúált Ë—_ WŢ$łMůiâ@dú;Ôg«ńw“^MžDޱń~síÍűnüC~XS.ŁĆxMĚ:”÷nęCÔř»)çMÝkögűjĆ·MŰؤĺ‰]GÓ|*<ÄŁC7e»©Vfé}¦ąÎ&ľŮu7ç}Ďš÷É\ \ßÍ˝îf?v­‡ú>$+‡đ¬I»^Ë»&O›sJă6ĺđĐ8‡ěGsü]u‹¶ó˙ÍóňŽżżĘó†‰{ĂóŽţi\ŁĎ˘mŤx˙I~?幡祀÷~‡çu˙Q®żźźĺů˙ÖóÎ^—®Ôŕ1mťż˙ĎäţNĎý]nüĎ;ůůÍóůďăĆđ~/÷ż1˙ m˙??@Ű+úţóÜ˙$?˙çťţ&¦ËśŹ™KÎrćŘ˙ĎüMÚ&<˙1kývúbŚě˙fü_É<˙SŢgkËßâą?Ę:Ţáú«ą×ĺoÖŇ˙ąf}ăżÍ:˙]Öńś~röďăçá]ćs–A>ú÷~~č?ţ7¸ţúŽ'ż†ń~×Ě­uÍ3˙ sűi~ß/ˇáXőę—ôóßń›9ŚWüţ#üüaújóţ­ç]üZúřĺĐ÷ôń5¬zřĚĹgÍŢKúţ|Ďëý1îAÓsÖ–˙xÄüŽţî˘äńůĎńĚB˙đzÄŔmhyţ—yöĎrý[yDsü0Ďţ)ÖÍďÖŚż‘ű˙=ăţ‡Ěó‹ďWҶĺ÷?Ëł˙$óű{ü|?ýüEćůĄĽĂś2xwţóĐŕŹó¬ć÷_3îĹ\ŕkţ¬ţŹţŢăţ‘h˝ŹóóŻňëňţ14;˙ßyö\źsý›y9 %›ż9ú§ďwÓÇ÷0žčöĽË|»ČÚP˛ńłÎŹń7|;†žs;‚'ŢÉď?M[Ę{€Lź@§ŃÁűwą˙/3ořÜ}ĆřĐŘűݬť9Ĺ‚>ŕWüoňű˙dý_ĚóßĹ3‹y gżźwţžeÝľxńiĽűŤŚ]ĽA>č·÷ÍĚ ńl_2/šükž×?§Ü?˙ꏆń˙ ‡5÷ĐŹşŹ™Çđ3ů»ÇŘa¬ťu/χýłů­ő}-ďý<ű˙Ńşs† yđgxÁ3żŠqio!ßŮżM;sÍXă™kCŹŕßăy~wŕŃ{)óέÁűY·ä]jCŰŕŰ | ‘ŮcĺZăżÄ=äÓCöŹ>ťß/ +ô??âYdx -Τ»đ4@žÚĐú@ţsčĹĽŽľťyi®đÓ&ţoaÎ<7ú:®Ń+ź±˘‚ľx§‹î!ëŮOń÷żĹ< őńkcľá/Łí?ăYĆ:ú_yü ă\?ú!ú‚vŢďaÎ<ýUƇ‡=hÖâť.k@ÇŁć>çcžáąľ'ôőqZź˙ĎĚ \Č$·Čp›5Fŕ]Ž®·nhűCĚWzŽ\xßD;´?•B7ŚXËŃ_ă}=+zĐĚ:Ž„ođĽ&%´g:Ďň­ŚOFźKźČa—9‘ቾžuŁŹ#č1+{¬ł-ý@~˝˙†ţŕc!ŻçŕǨĂ5Ďxo{ĹYž!kÍulÉČ ;ů Đ :·'śŢđĂó#ťŮA>ü߼;ş(YýŹůľ{ř›µůčgëżáÁ)ú’ţC¬ şz˙>óD~bú9Fö˘˙>ŃÁŮ‹Ŕď|?gŢ=0¦ ~ô y |ŁRh}ÄóÝĎŕpĽýÚČ[oÎmá5rÔB.Źźđ<˛­ówXžaOz ú+céËŹsŚëk]¬3@żŹ~†u‚cÚ|ôy˝ý÷đýwőĂ}üóŚMźˇdö×ó–¶Ď3éóóďĐvëL˛Ž-аu¶ŻűwřűŁĽtą~ íŕQ&™G¶#ěä ¶`Ŕó=ěN:e˙Ď@O~Ś÷ŕăžźA7˙×î>/Áo;:ţ˙és ŤXă´č˘ḏ̌Ć´Đ· ś?ýr®áM=00Ç^ŤŔŢň}†Đĺ9ÇFĚ·˙^že¬.üóŔ—cä$–}sʱ=§ČĂ™ëÁ×[wŽĽ!{ňęqo(:k˝ŕK űĐA¦‡ČĘ 2‚'w‘écž•CpţżŔOţî KYďýŠ×_Wú#mlpÚÄĐŰCĎt©+ů‘ĎÂĎĐł3ŮjxěŁ#lAë_äG: °!öŐ§Ź#úőˇŰ =<~Žľ…ą€ë4ÍŁlč3N„ě›!8ÖBV<ůČg„ŚÇĐ1V=ô8ľâľh&;€ýŠ˙spi„Ť<˙Q®ˇU—ą·5°tĎ1`MG˘ĽëˇC-Ůmä÷Ţ„čC;ćŁŘŇa¬3Ů6á4k8×=äôYéaëĂGô‹máCţçvź+eNgđ˝ ŚľŚ9 O}ćÖEÎα+Ç`á™Î aö×K_őŕÇ\Ď‘ăv?ţmÜÂ>ž˘·=üµş9‡†ĚŰG–3xÚa-CéŐV=°ÄgÝ>żüł1öéX2-Ler-dŁ%ă™ĆęâřŕęöşÍú;čâ)¸t*]>Ćľtű Ă]äéÚĺđ0Ä·đ’ľá‰ŹŹÔBŽŘŘ.xî±ţ3dwďCtvŹ6Ź3a>¶˘ ޵÷9öęLrÉÚŘąťéâ#őĐ‘3|Ú3ÖçýFÖÁZ``íĚ-EŽGĐ«‡lyĚşN÷şś˙ý°Ž.ĽÍ°˝_Í{`¸ŹOŐG‡ŽÍ@z„팠ŻťStĄ‡Ýď3ď!Ď‚cđ,am=0¦,fĚů\ń™O›ö x`ó»Â~ů»Ő+ÎVĐ~€Lu™Ç úpżBćvŠ˝é3ď1zëA“2äÁ›´Ëd‹ńĺ:đ=”oĎŃ‚ćçĐ­ ľ á9ěóŢ1Ďó ŕA ±I)ôkˇŰ}|™‹ßÉ;’oŢíaß{`ĄOß)ş9D[Ř»>Řx?ĎÁŮÎżŔ3řE9ţ¨Ç}ŮÎŃ©÷Zô›ŃŢF†ŹŃÍýżĹú»˛QŘÁŢÁ·Ü + ĄĎO&śŕOd`\€|fČpĘĽ‡Ř­#ü©LhłŢě<b°ëD~•ü0ä`ĎCč~Śě̵ĹÚä9b°#…WměF„M?+ÚĐüěgţĚ/ÄřŠ3¸ĺ+1źŚgúZz7;<ćŃC>bč1-±m§Ü?ĄÝCď|ć{~x¬yČúéá‡f`ß´ ۱Ť>rÔCWBä.ÄƵ±GjWϱĄçčC Ť=°¤ ¶÷ç)2ĐĹ˙í{GČOüňŔŚ6¸ěŃ€ ťËg‡nc0°K_Cpdđ»x;Ö8Ś9„şaŻÎˇm›ľN™ű9â3ś)~á˝ňďăď´Ŕĺűs žű<€s)üIÁ…ăf<ˇç)Źô-0a=|ĚF0`Ž)4IÁ>O<„Vúš1Ç1Ţ#>ýźŁ“#ôěYČżŔ¦·łÁ÷\ôĶőĺ/-ä'Ł­…ŻB‹±â°u€Nu{®§Â-ú€Mg`T@q~䇢gĹ!čCŰĹŽ§őŠřş…_xľ\€úÖQźç\x}O„9Řď.¸<Ď%“¬#dÜ6㎱7>qJ|‚˛0`Ý­ ľ÷ą?¶θ‰=3é2ń_W±5vµ…îđĄ[ř)ţú9óĐ©6Ľ0ö1˛ŕ#ź1‡cśĽ!:™c[‡č…r]dňČsŔýSÖ7†G=Ö’źňžä}?“?‚ź6_†ÜŠą%Ă`ń đ›‡čsÄ<3°čy<†Ö)X”#7-ôµE|×ú>‚ç]ÖěźödëÁŃ6ľA—>đôŰu†ŐĆŻęŕ·u ŃLŠĎĚË—­óĚĺ^„ř]>vů ľ·Đ•:Ŕ6|0Źxßs»čľĎZşĐá yJÁ3ŹßhŰS …_ÔĹĂă1vâşöˇtĎg„Luńu»ŕµ‡­#ëmě_ś×čaF<ě1źý Đýctu€0DöFřxçđ¤ÍBÖá#C§`§‡Í ‰łBĺ]č+ď>rč›môŰ^Gř+]ůŕ€Űx§>ö Ë1ó ±%]ŧŠĂˇŃ4i˙J~©ěžGNrxËÇ~b«ô¬­xBţëëÁ·L żËšGđţ±9#|Óžôśuô±c¶°őmôşĎ\Sđ;€=ÖŇ%n ńŰŹ‘«ÔÁ—Š‘˙>ĎŽ± zŐăůLú¦XO4÷§Đ¬Ĺś3°¸ËZ"ćŇ“Ďék†śăuÁĚ€ţ<0ąÍ3t˝N´ˇĂôőčďü3ĘŽ g¬ňŮB†Š1­x—#W>}#·9wĆűcŹđ_ʆ`jý8}Ö豦XuŠntŃ™6ň×GżZňő‘‰ -•N‚ł=äú;ÓÇ?HńýFřg=p/EÎF¬·…<CÓîç—źD8WOYoGţ®üá ŘŐ#ć6€n)ńF_ @úüÝçůô˝]pÚOĐ·1ú:DżŽĐ;ţ÷d‡Đż>~Çóí#c)úź‚›=ř~†3b.t>Fř #Ĺر | !~Oş´yw$[ RĆŔş,Ŕä 0ëT6ú§Čz<௞Ń_˙2hęA—Sô"C·[čŰ= '¶8ăN‘ťˇü'ŇCżÎˇóŢS>="ĂcŃY ŃgO1*ľŚ'›Í=°čťHĺÓ#s=ôd„¬ôŔĹŚß'ř§Šł…íčČHţ>HŞ<Źüř8bĚŚM•Ëă†ňy cÚG¬1#vY×)8“âg¬Őcţm|‹ \Éá{†ďäµř ízř#ägŔ:`SЬř˝gŘ—sô1EÇrěęv¨ †ő„—Čő şŮÂWI‘ó!ó9Ć_č÷ČěŚ=GÇĎx·6ś‡žÓw€|đ!ŕů6zŕ3Ç!´Jĺźa›{ô×B÷FĘđN ]h+î‘?¦ř™wŹŕá_"‚?=ŃzÁ«|÷•KSĚnĺ𩏞t“¶xÂŘg`Ý1ýúřLmd˛ĂđŇg-Cđ+%öIŕm„.ĺĐ˝Íď‘ü9°° Ć ”ż†–=hďËźÄtí!{-ôŃC/<č6NńÇʲ®='ż…1Bô¤µ 8ß§ź!˛z‚ηĐëÚ·ŕ×Ü)6c}]ž*O&_şĘâvXË98ŇăŮ ĹMŕM l §+ßXyUžëÉg >r–*ngť>ň>DÖĎđ7zđ´Źzřrm|¸rvŽXg Ü tĎ U ?˝đ•ß–Ťö›‘ż×™ňëřźľć‹M+ď@Ľ0?:đĽÍXgŠq„cčá| “°mÉżđąoqď]‘ÓŮ2fĘZÇŠQŃĂ ü ']ůxČCG~śĂó ßďL6ż#”ď»đˇGL›ŃŢźCdÓĂ˙ô™˙|;WN~ź+O…ü¶Ŕ¦ŢE®ÇŕYMO™ăţ„Š·č#UN]íˇ/#ĹUĚ­…~DšqMOy0!‚_çđ§ ÍČI€¶dʉŰčk ýSü­T~µdy?‚Ć-Ů-° Ť®úÚŻ\cŹz’=a"ş4 ď!~[ §`N[~zÔBžÖŘc>ąě&úëŃĎ<Ę_>â >>ű…ě=˛`ç{ČQ ľ ›Ź,śaş¬Ą-‹éŔGLë!źÄčŘŢĄ˙!ýڰˇdŠyśâ‹§üęȇAŢĚ;Sľ Yěŕ_ůđ§ĂšR|™şw™k›ń|ÉŻ0?"`¬Ű0„n©âníۿщsdÉ˙KÜăw˙ÇyĽhŁëçČ{K×Ęgăke ć–bË=ÖvˇüŹl<â?¤Ú’lňn›±/°SůyůO@ Áú;B†Bd ďCřr>ŤčϧŹź.Ĺ7é»w”Ăe~C°áŮo+žÁ¦ůÂU寄oČUʆŠ˙ˇ­/ű˝:C†.Ű៍%Ę;A‡!Ř:PěG<ăßCżř6†ďçZ?p,_YŕO ŢňŠď…řć1>ĽŻ\%qx*ě@źÚźçß3ęű…˙ꄹwiKŕÁľŘßŔ݆`ő9cúĚi¤ř\ąd0U~Rľžr3ňźŕ‡’·gčęzś!ďô Ł-=fśˇl?ř;ţ KCpp XJ•ŹW‚ 9‚ Íý<†úV] yO‘gĄ`C ż!ŔŹb7SĺąYk†ě ѱy ďçĘŮ+—HżCĹYč@Š}nq?A§şč|]ko†ůž đŻOŕA‡ţZČřH1’ôŰUN]ľµč\Ťˇ×ő ˙0ý"·gĽ*ĎÇÜGĘWkśňnž ŔÇclCOq k»Ŕ7ëh? ™nCż1ׂ¶âÍüg|™[5‚ÇmÖÓERćŘWŚ‹,dđçXľ•0›ľü瀹\°Ć:•bă}äĽ-Ľ…çĐîŚŘlĎÚ“gĐ»‡ĽŕÉŢűĚŁ­ ^·ŐßÂśŔWźő§řgš8—hźĚ´'*?‘ŕ ąOńŹÎ”Äżńëşď+>âďctČW¬‹<´‘ń>şŮ׼Ĺ[p(ŕť”ţ20<ĺů”k_>>rÖűđŞŁÜ02’ü÷×"»=ŇĐúŚuŔžsÖÓSŚmBâ ™ËĎħIˇUGűťŠé±ë)|n#'cč9ÖţúužŚÁĂ@ŘŤ/—“Ęw‰ĎŘŘ|?SÎůkăWEčB‹9g˘ô<•÷Â>´äż˘C1siďÜî ;Ö"g’!ń¬%ß?Ł%_H~r+Ďúňáŕ—Ź-H°¬/eĚ?†Cěç|;˙/ÄĆíÓg‡XaO‡čĚzäCq¬‡Ţű¬i$_˙%A7GȦʬQ#ŮcđyÄű>ţĽŢŤůť`;ČSO8ö^ŕ Ćđ;]íˇ€±!xu&»ŽŤ8¶ÁŁcl\ ®ôń G¬ˇĂzü>Wľ…~/čł÷´nă7ČŰ9zŐÇżh#ŰCäŕ [s†Lyđg(™Ä?i1nŚ>ťŁ#‰ňôčÎßĺśůwÇŚqFâĄ|!ĹÂ3â_{^¬+Ô>¸rĘŘ÷ľP‡uŤˇQ€˝ccc|¤ßž2NYĽP®\é˘Omáýő°'ˇr[Čűßz„ż? ż °°G_ú7Ô>şbôg(=ÄîűčŮHůú?Ţ)kî(g€žwŔďzr.żLI°‡ĹžňmĐ•.üâ»t”gÖ^;ö)ß|ĺeÇŔ?źńRá8::cމGä<Ăw?—ż‚N÷ĺjŹY‚9<ČÉŹn)©}Ců­Č×PX¦#)wż.ŕe€NŽ‘Ť!cő°mCÖ3T-ĆáyüšˇÖ_ŰĘg`G‡ČđŮóe+CŠ.Ų Ä CńS}ŕ«űŠ‘đůÚŠQ5>xź)ç,ż~§Đ.Ĺ.úĐŘësěđ»Ś÷‘‡ ůűâ;2pżż†đ~fdČÁń'ůĚOĹôľ«} tÔG/FĘçŃm0â›ÖëĆʵÁ»±ě-4 Ŕç3ĺ%¤čę™b]ĺ‹áËPű`ŕ‚ö Ń• Ö—˘‹ú'ÜĎÁÂ:ś%#d˛ÎÚ‡fů)ز‘ĆQĘ:ŰĘ7J^ŔĚSć3@6Ďd_±5±ö…”ó+ÇÚCTNzc7‡`í{ť0®ýĚe¶u|Úä—€‘ěĹľv€ĎŇ׾,˛2&–°Ć¶üwŮvbísńµ}>'ň‰ŕ»>ôĄëČQ‡őřś©ö<Đ—ˇbMé;ó±î×ÇĚs(ż9ËŐ=BĎô:EF20«Ą~ŔÎľü0ÜGRâž1r鱾D˛~÷˙$ă %¬;Qžźd„îĄřm©ü0'ë{Ěűyöůi«ŽG{Ľ—Ňž ÷‘ʶj Ŕť±rcô•ŕ Ťdë•ĎC.cÖ1T\Ď/ĐůąNYÇ9r0–ŤFž:Ę· ‘éţúŕö€¸·˙‹ô >§Řţ¬HŔ/ţĄČřůc'Đ=C{řŰ)Řsü]´ˇëôľeřť=âŁ< y&@Ţ`j˘<°jTäcŃďĽáż¬)Oěî˛8B_íŤĘFĂ—P9dôn„N•ß@Î:Č÷ąâ!dägÄzrt¨+_;‚ #äáz÷đ•p÷LűL¬!C§şČ‡Ź]ë2îő…+Š­đÝ2ĹKČř;ěCŁ1˛(7K?çČT_'şA“ ĹGÚłR<ÝĘŻ‚çđj¬Eq!ú=d=-°`„l TĎ€ü^”źÜHO™Ă8 „–02ĄżcdŃGVşĚů™čŁGcx—ţŃű®öôŃD>¶ďLúÍś:Đ)řŢ#^⏠•O#ŢiŃđÇůŰ1V ㌴˙€;§ÇĐr&´Ŕźěnm]tnť†ŘÚŘ7ĆÇ:BĹŘ—v,ăť¶Ş^ĄŘľL±Šň‘ŕm† ^(÷ĹuúŰŕ?8|.;¬ü.4>{ôťj˙@v=éČ_B˙:ŠÁŃŮLz«¶=v”÷R~ =ÎË"×ţbGöIţ°öC´VĹřx#t®Ă|SdsLűż­…Ź4ř~®±‡Cđh,+Ţ"fČäŁg-0ÉGÇ#l^N]`“}ɡö.”Ź€¦c0´vv”ź’/ćvägA÷ 䩏 ů¬%äz„žĄô“B“ p˛?†<;ćwÂşčÍ_9eŽ]0ý|ěś1V˝î!»ýt[_{ĺĐ#ŐŢ.6˙‚çGĘwÁ«™ ń[Gü$ÄÄgÂWlY~ŤY÷ŮÉw_z€ß0„öر6ň;€n]ů,ŚŮ‘Ü ßTc‚ŤëBŹťşw[Řă>80Bf.ä/Ńç?†^lçü|„ˇ|lÜ…üeůÁÚËUŤ‡p[0„§ô?Qť:ÓA6ĆĘ€Óct~¬>đ«SŤĂu˘:ĹŚ’ipz„ÍÉÇBzŘś”µŽd7ŔË.şŃUş3RĚ­<”jCx¦% <꨺´đˇ»Ş5Ă|ŐĺI‘µqEK>˛Ţ#ćϰ%úéăźtŔż‘ü*|ŤůH•S =CźŰň1 ŁW)ţđXÓAţSb‰ľňTĘcČ>‰çŞWTÜŁőa«G`Î@őŘ1×cävO/đÝűĚĎ—ĽśA×sdçŚh.Ú ‡đěB5Ş›źË\:ČĎL Ótz¬ý&ĺźáńś;ĆÖ Á‹ árá ô…'’3ŐO`ŁĆU˝© Hµ·„üö™ă€5ôUË, äk)÷….#ł}é´r’Ţé 3cäx ţáu˝/TÓŔĎ…rEşŹN —€˝ >G,HđaGŘš éB‡Žr•Ř•!1Ő@1 ôíŔÓ¶XoŠţ´wďűĐÝG¦:˘xÓż}Ć Ă´đáŮH>ôŃ#d-–g a‡µŹ‘Ăöî[›‚˝}üߡl€bsäj˙:’°~€ţv“#cŮ1°­#=EÎűř }dĄŹťĎXKźu] Cĺ…ÁĆŽbő%|ÖÜyfČ\ŹeźŔ§14ĽkϤ“ř.Ő±0ć™đ {Đkú?UúCŞŁVŤ„l†÷íĺ~j•ËU-@„žŞî9Łďűť”5=бÄĚ3űr°%Sś­BŐ:@‹¨UÚ¬6ö/^—4 ľľôťĺkÄđ1˙Óeî»đą~ľôĹň/ë+‡Čf¨śńß-sşň5l]¬şůxŞ Ň{Řţ<‰˙^ż©^$ UĂŇ.ăŐ ź@µĆ v3QŽ“÷[čN¤ř ąŚŔŤLűwČUŚzË&i˙˝ ŕUŚíT yň{Ęú‚6Ë˙/ľˇĚÓ«ÎUőK‘rßSÖŤjOSű)řŰÖŢ>@®öo,óh±b[03}XćÂĽĎ+÷`µĄĽGććŕYô·Ęük„Žä?TÖ>gßQćBlYü+Ę<Şjڤ»1ľZĚłŢOđ›q"ńNµ.ޱ6ńw—~rŢ…`Eţ“eLžĽUĆ}8• kŞqH~Gé'‡Šá4ţ¦O?ˇęm¦ÜűQ­Mř‡ĘV5ÁždűA‡6]µi |Q˝ž÷ăeÍ›ęçTÓśAÇą‹±;ŢÜsţŇVi@uV ňŁ÷ńg—5–ĘÉ(żÖ’GÎĺxT×ń˘´ŃńŹ•qp¬Zl`¬¸™™[‚ý͡a¬łćÓË:ăÖŻ/sůO”ű™öî•ű]‘ę»˙f™Pj*büŐ©j:hÉŽtV#˙ĺeÝ{Ęúňď*ăŤP9KüąD>Ú'ĘşŐ“ĆŘŔH5ÇčD*ĹÁŹÖ7—~žňâŞďQťD­Ś°=ůź)ëÔT/é}˛ĚŹ$Ę·¨/ćنvŞΔ˙üÂ]ÝŇʚGĹîÁ§•űt:o#ÓŮ'Ë˝ýbźZ˛Äşýl—ăÁ®$ÚŁ"ŐC)?'{ ż2Ć‹ń1Tg®sXćÁ»$+÷Hă÷”ńlţéeÝŚňÂń÷—őÁ÷”ů+ĹÂÉw–çl“¤Ř€–| ćžé¬`7rá×O—ű.Eĺĺ9˛Duš?^îch8“=SÝÔוűľtę§K_WőYžl´Č™Cüse-bHťaQÍ{ďZŚUścP˝‰r±Úóűá˛6FűžÉ“rO[9NůöĘ/+wá+ű¦rŹSµµń—g<<í!KV…ˇ_ZĆŢń·–5Ţ×—y\ĹŰÚkKľşŚőµ÷ŘBCíBĂqcŐ›Ŕëř§ĘĽRüŁĽ'Ůűʲn®Ĺ<~ç+'˘ ôČđŃsÉ"69Ö<4KŻLtÂ.%’1t'ţ…]­;2’(ż¨3čşÎO%ČHŢG`BŽ}‘yPŤsڤe}^Kç$>żĚufŞ­Pmł|íÓKו‹§őÍI~gą/áýtą?¨|ŚjŁ<ů&Ň[ť Đü±'‘j‘ťÁ‰đĺsîĹÂtJuéÚçJ”{rÚ÷Ő~hÄ» 8«&Ýi}vyvI{Ů zŕĂóśĐyĆD{›ż¬Ě§ŇĺŰdËŃ7ŐIJČ{ndôź(×ýr9č­šĎô‡ĘóÚ#ÓŮ6ť1,öˇÁťuŇľ¸r–Ę©+*>Dçtž%ÄIÁyŐ)©Î5αÖ\đ˙(ëśYŻŚ‰säU{˛Ş»®ĘsÉď-ó:0˘úŃľD`«/Ű#ßş«&Ü˙x™ ?ݬ…ië §joY§öUŁ•ŁźŞŮj#‰ü·Ią˙ČďděL5çĐ;–MQM‚jń+FNTű[ś÷ÁvE`ŞÎTFż»¬#T­¸Îjf:÷¤óŘ“Dąwt^gîtŢJ{oŞ÷VŢ^g2ä(GfcŐĆŁW:“Ş´rr!üŹ$źŞŻE/Cĺlĺ÷3ďHç7UGĽ;·âŁĎ-äVçĐbáT»¬żÎ%WŞCýceî/DöU“IĎTs.>(&a]-ěb"_OĎ˙ť2źŘBľíßé<Č'Ęśw¶*st˛(fÓÜÁ®޵ŻŇ%É™ę<ľ°ĚkÇи ˝uŢGąí%?RÖkÄ:kÇí±b$…ß: — Ă4˙_WžýđÁćT±’rőĐLu:{Şú—Tűčžö,ĂŹ–őˇ|{ü“˵ϢŘ Uý|Şłmذ ß@5őŞÓÍ{ąOĄ}_Őč꼄ę´×­ýăđçĘ\„j“rhÚúdYĂĄšGíűFĽ—[¨vSyťÁÔyŘLgW©ŕ Ë|r¨}­q™ËÖž«ök|áΡwčśťFćř ™jě7€ :kýŕnďä[Ęś\Pť˘Î{đ.ţŚ2ż©ŽęO”{ÚĂÍäh\ćiZńd_ű¡Ü/Đ™˘|Ë Y„L'ŕÎ}µéŞ©Ć¨F\ő!sH>łÜC $Űür?…Ówvµ€<›?+롵Ď+ÎT ‘|éĎ&ŠłĺżĘ§Äv‡ŘŮÝ×™§˛iď_¶Uş¤ó8Ş'§ť+M…kň ±Î˙EČtKX˘ýńßUÖ´đ…µß¬3‹:›Ł=Ů\±š°YȶĚqgŞÖZçH?Uđťeî.údąW˙d™g‰ńĹ"­Qu\ÚK—Żţ‹eî9W¤öfᏯ8ąŠä‰®Ř1Źu霯ň”ŞÓĘŃéč«Ëóv!ĎęĚvň幥y ´×‚˝LXO­đńýUC¨óuęÔäkjßU9™/(sSĘ꼷΍$Řśś±už©ÍsŞűW˝]Śäčp†^¤Ú/–-WíjÝe‡U_ţ­ĺŮFťóÉ~´ÜR ‚ęqC0!×Yĺë±»z¤s ‰ö§~¶¬©ÓžuŽî¨®;BT_–¨îHőHĘżÓ‡ŻřCk@v[ň»U‹ 0ť‰QŚĘšZŚ›*Á‰ ‰ę˙´w¦oť…ŐŮ(ťëTrK{ŘĂX¶HľvɇgˇęŞY§j’u^^gB#řá“ęŞö U ŻsŘŞ_TÍ\¤xýE™÷M”WQ~ÚeŞ ‘Žëü+ţP¤[ű]Ú'†żrˇZąđűĘ˝¬ß8”đóe˝x®xÝ“:¨vWٍéŹxWűĹĘÍu(ňk± Ş…SnNµĘÓ‡ĐLµ-‘ěŻňRčŻę0užXg}[ňďţląŻ‘`“cŐŮĂ{OXöFď+÷ŰĺtĎUĘÓžŹr^’?č™Đ¦š˝űÖ·2ůćč¤ę€uĆR{ žjQXgŇ/sŐ‘ü׼¬uÓY~ĺđsʼn<§=Őż«ö/ŽŁëĹůC0-‚Ď‘|nôHµÚĎč_çöcáö©~µWśé-ţ˝<ôIgźTź®3ÉÝro6gý:§§}«H>ä—•{‚އé'WśoˇzýD:©ř+ŹFEŕ`ýcć”H~3ÓYOŐwđ^®sĚMgşcŐŰ(gˇřDgĺţ|YśA_ŘŻú#đˇ—ő±âů^Śj\Ö\Ę®#k±dC6Jů'Ĺńňu7ťŤŇ9ĚD1¬ôšëYÎU7ˇý(0EgĹ÷Ę?c;sp9V ©pć›Ë˝m}Bg¶BôGç—#ÖĄzŢ’]ťWßňßUoŞ:TđΗߥ3~Ęý(ďŞü€öĆ?ÇçŇ^·ę'UoÁŐ5ކ+CďBŐ“ˇ#±ěd<Ďuö˝M±Ř‘:+ĺ©ÖEţ t ±i9ýfĐ8ŃytĚSYçŻŕ‘Ϋ{ßĆűÂćÓU/Č÷ÔÜ壩;«3ÖŞ-ę\ń-Bl„/;¤ł&’Od<ˇt|ŤeďT˙'{Ěšsä5A÷TËĘ>*§¦šĺ¸t&śĘdSé„gôťŤ@q0¸«ĆC~ŻćĄÜl+ă«ćBgMBŮQÚťłBfBd$;µojż $÷¬[gNŐ‚‚ :÷ŻşSŐ­«>?‚N™l±Î©1žÎ—ę»:OŻýŮż§-ŮSÝÎÝâŁäř<‘0{#óŞŹO«Ą=GáŠ|2lšę’ô­ŽÝJ ťĹđ›;€=ąňČĘQÉwQIyGÉ„ň›ÚŰ×>)ľIĎtF§ĎUűĐ‚˙:Űh?”±cdOß„ĐŮŐ*«ţPgkCĹŞ©–Lłnť‘ĚÁťżĎŃ}Ő}¨6Z{Î:ˇs|:#©3ŞCoéĚ›âSŮdä˛%_›©óΡě-sI´Ż÷…EšÇ‹Áę@>«ö–?­Ü÷-ľ#[Š?—ă¨ţ(“=÷tžR߸P @L ¬ďĹč@řcĚő‹iSľőÄŞPţDűă˛#Ú'Q%» ÜĄr:Ç$Ľ•®Ľ ~ ľaŁď„Čc‹yč&y1QţS>]1™t@çĐŁ–đRĽS^QyŐ}Hď»`?ä9Rm;}DđF߂ɴ˙‰ŹfŠ#ŔÇÜJ‘ŬÉĺcË^kż@ßŔUn šŞîTµ‘l˛đR¸+ąn‡ąčK9FŐ˘źéOçMĹNŕo cÉŹbŮ}ć“á/D’Cť’ݤÜ,Ż:t}“Č“í…f‰l›ö’$C`x Ű­< Îr€ăúÖI®žěr¤o÷čĚeÎÜrĹ6ÚăŃ™ °OßZ‰”·W] ďé\ł·«‡ĎÁĂXş†Ť×÷†t>!Q@çägc'UŻŁó©ÎO˛ţ\gŽ´©@ůd:G.CtFçĄcŮťăWí:¨ďcč<—Ź=ŽUďť#čŞďVřŇŐ(!3ŞÍ‹ń·t69†ŻúŢ‚ľ‡JwtnPz®ą#W™ňFĚ=ćbcTŠw˛ŘÉ–ü ů}Ş/BVt†Ü×™)ů`ÚgzŰ+Î:¦Z«|bťCPü#_[ą-ť›@[Ř©P´@ŠÚYĺś”•â$đ"R­łňŕŘΛŞó=:Ď©łŇžü;Ů_íăę\ xk/Ąs—-ů6Âp.‘żçňç$ޱŐ^ŽpSĎ2çý‹ŔĂDueŘŇPů<ŐĘ0/ťeV­PB 1ş#“‰ö7é7‘ŻüĂeý Î Ć_K_ÚwRlţ%ÚŐŢžâô2ÖYť ąpD±›ě ďçâ-:ž|Gy6A°–‚?±äMr&{…Né»F:cŐ"–Ée§ĹÉšěú®ďÔ¤·e]Ą‡Î„ČnÄĽsá6< µ?*|QM*8#©ňÚłVó|ŚŹk˙@y2ř”A÷\5 řvŞĺÔ·Ot!Ôž¬â@é·rúüގCDżą|>íąń[ßçI “ęź=äWçěUóĄłöcĆČşÎčű<ˇj äĎë‰j™źľ±2N‹ßú¦B€OŞsѱüv솾ů{%Đ0’˙A,ščśkN„/ňĄĘŕ'ÄzýĐY }˙%×U ’+.–ct†Vő~>61’ß sm˛`©ľ»aŐim˛źŞˇ@§"ĺkŕ˙é|şjâsŐW# ˇďx¤čÎY댓Î6fŕE ĽĽâ+d^gŕž‰·éŔ>±ú&WĚ\ôíŐ ÇĘńăçéŰP-l«ę­tN"—‹?:@‰ľ÷ @_Rd+Öž™ö+Ńď\öKvşę»ú~C":íú†‚ŻłŞĂS#ťTľFz uöFç~´a©&ß9Qí6÷T/¬şóLůad>ç=ť=÷ńŃŃš„ČS®\<ô.Î0'}ď"ĆŠá,Đą ť“KD[Ö+§ó›ÚOPnߡĄ_~±bTŢŐw^ô=ŁâĽýEĚ#RM†|)ř©o˝EĐ­Ĺz#ě˝Î úňńC5OžüQř¬sř1ş§ď•řȉľů’Óg.ý„Ć:˙®ożč›[:é=p#BŽ3d:×[ĘůiŹô“<#>«~ żUßÎhŁw±|#ćĂ}ǧ8K«}V퉨.–9¨n.ŇŢ™tSľźň Čl€^FÂztH5•Ĺ7őčĆ×ţhEg}TËŤĽ·ä3€=ř$Ąj<ńŹu®Ng#"Ů Őě(—„Léűd:–i˙]Š„›ÚKĂţ†Đ'R®Mą^Ó7_x›ˇ-ĹϲíčM.Ű#ßJő(ÂyřĂ3ť—ő…uĘ“!3 ëiiďKg_x7Áż ‘ł\5Sâ±ř˘ůÂĎHľÖ,˙şĺŠ»…‡Ę—"ŁľĆS~I×ĐHß*jΠA¦üŁręß]žyĚ W¦z ěgÂ:tN$C®ő˝žâ[řX™ü6íź*ď¨ý<­Cq~F$ű¤Úx¨sšň(÷©xMţ›žENä+Ô>úć)'Ĺť·ENô˝¶@5ŞóRM—ňv<¦łŁžö´Šśµŕ“ÎÁEŞ/WŽIď w±ě$ýĹĘŮ)®ńŔgŠ•Ńç ’~¨Ö@>ŹöbĺWÁ«>´Ŕą´IXg¤˝CúĐYť{ tf ąµG-¬C6t†>Ň~ľÎ;A˙xÓ.+ćsô0ŃśrÂŚëÁKŐ'ÂŐ˘Hß‘YO2KĚI®”ţŃOŽŽ¨î?‘ ĽFWsÖŮ’˘xGő6ŇE°Qß§Ôą Őźęűyú†ĄÎ$µ$ŰŠďt. Ů }s,Ë[Ş7ĐŢ€ňÜÓąĚH{bଧ:Ő«ÖEyoětFę;žmđ(R?:몽rů…ÄĐ:ÓĄof޵ fŐw]"ÖŰV>_9]Ő#ŐąöЏďÉĎ)‡€,ćČŠĎüCĺŰ—Ü+·Łz5Ń]ËuîM{ܬOµÄ>ŞďŚäĘa뜲Łďę›-Ĺ)Ú‹ĆĂúG,<‘ź yjż•uëĽs-ő=ťŻÄ˙OĄĹ7¸ô-—řˇ×üqá§ęŻ}ô&ăT—)Ö“-U®ůUS§ [§u}» Q,®˝[Ő‰nĘjŹJň¬3 ŕŚľĹŁ3 ú>H";ُž$ÄúţW¦xě $óÚ·’ż&˘¸}ÔąŔLy`ĺ董â;DŠ×y?ĂćęĽR¬Xý3vľ™°KgftĆěHä‹@ŐQČI¬|>:«oqÄŞ×R<ˇ˝í“âŁé;#±âAĺš‘÷x“ČiżUűQĘé “:K#Ż:ť+ún§ľ÷“¨^Ç:Kź–O•çĐÜxVç[ěz ţ§Č˘ľi¨łľj3CÂO9Qíűńn&?EűhĘ3)ď…íČuŽ™M´÷ ď2lQ˘Üžň^¬EßAi©ĆůJYg®şüŻÝŤ”WVÜ!˙N{›ňq„ŁňT÷ĄłĽϱöN/ÁĆÄŞsRŤ’rŞťá~‹q}á€äBu[ňç±S1ýé|qńíGí…ăŁę۱ąr#ň#„ĎŘĘŢ%ŕ†Ź­Đw43ĺ˝´o¤XđEyF0“bb}_+‡_‘ňg`o¬xNń†ęŘ”ŹSť«0}ÖwĹô¶Xµ~:ʦşLůpĽ«ďŕ„˛ Ę…)ĆGníCÉĎvČ.¨†|L„ŞwTíz©ZyOqĄj=ä‡*Şú ĺ…7ŕZKçŇ•“ľ!-Ů2ŮppYçŔCůîŇä8‘ţ˘Ł:Ó®sŕôŹä›ĂÇH{mČL¨ÚHx)O+PątüO}ßWgżbĺćT?ٰ(ÓcŐâ[ë;Pľ‘Î]ë› ˇöűÁ#}9W°üí»(ż«}â‚⼭|2ťŃޙ֢ܠâůŚŁsß‘öÓĹĺr”ľ¤ ťÎá@N_Nçňę{%'‹R®řŢäząz¶»ŠîMV“íî"áâÉtuc/·űËŚËŹM7ÓĹľ§'›ŹÍćű«§óéb˝řz:˛ĽŢ?Üľw=źńîşz#QÓ;K÷DŞKs»soz=]ÝN‹µ™pzoú¬č¨şľ™®6űëüŢtýôůť÷Ü˙[Ť–÷™k–-O¦ł;÷g«Űim€vqsvç=oĐ=ßŰ5é…·_Ě¦Ż«±iźŰ±7ł›w–Ďěě63C˛î˝éöf˝ą3ą˝soţdć¨utďŮłé|ł˛Ô=Ąm9ßLW‹ćÓĂęÎěöÎĺ- ŘLŐ˘{7“Ő˘ş¨fŢ»Y,«żW“ýoV–îŃ˝ŮÍ|ş»8ľ7[MďÎ'w?2›ďc—s×WĆܦ«_iŘľśšËŮÂdţÄ2°}oţô9,ŘT,ŕŤůĆď'[ }Ä߬÷Ú• ĚáE=ł®z{±<}n.7Ď÷ô:ľ7ßÜy{óą_´|ޤUsÜ ů{ŃdÔŤDˇxüÖ»ÚľěĂoU3AŤ0 ÚlŚĚçş\?7ĂÜ7‹˝Fęţz3Ůlj÷?63k{9YLÍă/'b:«$ŕöÚHßmˇpwďĎÖëéMĄŘťdzď¶&í{·ëĺĽFóřŢâz:]íŕjţ¬Î”Eˇ˝›Í~Şpm˛ŢWŠ^˝U=1›®¦ëýÔvJ‹×ËĹěi%t/^LݧóŠç/^Ě÷ďĆ÷ľv;›OöWhÖ^öŁ{«'§].¦×Ë»Ůląpb–ÓĚŠkZAK%‘«˝ö¶.śčdµš“ű€Oa­¦łÉíć­ý2ÝŤĘí¸6Ă·§ÓŞÇů3\µęčrşš˝śŢyĎGVlVëËíÔ^[˙¤¸–éťŘ'n·.éúchŢîzŔµĐ˛‹÷Ň“ xűöŢov*Órë…éň…ˇ~¦ëŐäůţáöýéâťB^÷żoúŠ˝C«;aQbÁőcPŰë®áKŻ ĺi€ěďÓ{¦ëŐÍőlj=»®Ú¶‹÷Ţ_M^n±»fDóčŇşU\żcedec*]Ú§!ÚĘ>»@JMGčć˛r˘Űş„‡XĽ•i*Шš›^2Śçr=y=3"żrŻ*ŹŻhy:5:U—ÄŚ˛§Ć™=*Żj¶<)Ű̢ëŔ‘Şanq|*ťź6z°€7ÝB‹—ÓőĚĚâĺdć¨qµ'ÝżÄs\9yîď©ßÇ}Z) ťUmq]ŃŞiŻşyŃd]ŤÁý™Di=ťŢ•E¨G ÷g×ĆgŁżéün)é{ňĐd±<ş/şzúÚÄE÷ŮU„Ä`A®÷îýYaţî>FoŚ«FóĆ\ĺĹdú»Žił%(ç:ěp9źľÂe` {\r­\} f˙u5™Ą•ëŐÍl˝çrĎęVÉ]ű¦hŻĽ_˝oăć˝Z™{«ç‹]WT‚E«5ářµ%2÷ ŐŹîkjËgk€$ ŞŞ˛őâjł6.ÎěĺžJÉýy-ľă˛2Zůýůt†šUâ~®!ťŚ"KŕÎzýÄęĂ|¶y]•9jěěrűţ|{[÷v“űË'5Ĺ_>}gc,őňşňvď/kvšëgĎćVç—°íîý%ÖűUŐÉ<Ü_Vyţ~ÇČ#5łŇ¤kú\.ŻëP°$|®˝±\o+™".3seÄćŐŘ‹gËąysVëxŃha˝ˇăűVĽóÍdV…4BUĂKµ& I…fKâÖůÚwµ†YA©}ŹČřtyçýŰbüu…Ţj}ďa‡™ßÚÎ7ý—u-^ÖpeYÇŘâŇz{jXUO´Ő0»±‰Š\M·µ_-V® ÓöZp:źU!F¬ĆŐ+óČÍ«"»TQ‰ém絹oçKÇŐS®V7w߆ň„(3…Pô‹ĘŹăšŘlaĚírkĂPfüR‡ő´Ö`Â(řcýÉĺk#l«‰;]×ĺ¶ĘădĹĄq“ňű«)\5öő–Ů9śWt§qSşšŢ>1äéŞáf˘¨Á"`ß4™Në+ŔÚąq…WScc¸x=łIĚŁű"Ńdkui@[–Aárµ˛Ŕ=¤Ď&sRµ˝¶FgzńÚZ®^Ů$ ,^㫯ëŹň®qÔ ŇÝ«ů¬rźWĘŢŕÚâłČHżUÍł¸±^âAŢůŔl±5‘;÷ÖĆ“(®×“{c’qáýŐňiEÂĄţjÉKą–VA2µmíC›»§J­¬kmšOnź,«wµÝ'çŇâbnĹn»¨rwą.gk™-Ŕ`ĹîvŃB4^źČ „X,ďÜ[­g•SÍU̲Ú*÷RŰŔ›¦&8Ů?ż}RË~&4»Âý§Ď­Ô)šŢtŞVăţO=/޵ €VPĂč6 «É¬Â'®×V#\KµŕZŢ~×3_a"aµX"l­uäöÔÚ đţöyĺoń·I|lg•+YOylMr˝¸ŘTW‹×Ć’o—Ur„™nfäUe‹ä[Çó¤l™,î wîMöŹ—7^ooď<^îóÖ4ŢXÚť łŰ;ď'ä·ű ©nžkä›™µGj™k6UUµUÉs¨°˛ÜD2ÓąY« ţuۦż ĚşÂ0fŻäAEŕ‘ ´z$5ĂŇ“ýÝ2§j'±^/&‹ű âîOMč3°7>0ť?}^ˇFO÷^Ý}đ|—yUo/ás{mBnµßyĎŁjWáţvS [Ű÷·eŘ´ş6rB“!Ϧ¶ÍP\ŻQ"‡1‰ZŞ‘ť=$Ĺ&OžT đ(ąť¬ /f‹ZĂbňzźřŤhXíAôÁäff@00™?è™Ä-µÔ>×/$ö–1|0Ůgj¸y+‹>7ýß.WŐltŤ‡µżßŃő;´Í–{ă¸ký2~·˛ă{‹ÁňWÝ mg¦űĹ҇ëËą±&+‘oR-—ë^á¤ęUYý#]mo°\W{CxLŰ®Ńdjh¬IüÉz¶'KÂŐ|bY˘üúôÚL†ő´"ę‰Zßđ˘6{†7fw?:UJ°%7öYڱ2!Z\4!ŘZ–ľ$¬‘ ?«Š čűĺĚäÓt‰y5[&Ż÷Öľ˙`ŞTÖbňŢ"±(Ż"ÄtnÓâäĹn*ń8z ÷n˛±úµk{mÉť<¨ąşÜLöŮ˝čÁÔp¨ö“Ę›*.W{5×ĺ|˛źzČĺížüýÄX0]/ו˝Ôő Ą¸í/¦{çą]\ßJ0f8$űuí‰Íd}]ëőe•śŹiX<}encĂçµÓ} ŢçRú5}7rőÜ-´mµ|5}ëÝíWéľ=Űc˘ŠhŢM%/.—făąx?{Ż(Ď'›éd{÷úW}pDťŤó]€đ­w´íî”_lÖ“ůK“q(ďî;×č›™ÝčěZ– +=¶ő‹*ĎB­,mňjjćőRVg}÷zz·“ÝQě|耭ߧRkw>2}UY­Ú­őÝGËŐf_9PÝ* ᇧ&\:«nĘAĹÖCČąßd˝XůrąŞ˛ Őt¶Ôn1ő»ŠőĐŰ˝Ş´÷Í{·î¸lúĐäk·VűžŰ‰BŰéęÖĆuLmş”Úiýž1ďšŐńůřÝ/!ßKřpß| …ť7«]K ¶Áymf»®ö6ôbVŤčzVí“׫*?¦ëŹ?YY<»SĂ…ékÔf'ßh(˙lBtQŃi&ÓUˇc¬–…¦Ů‹{a‡őś÷‡»‰]ŰUŐővĄpÖXq¬Ńuµ›r]ˇý\ŠĽGśH×ű€“‹—“ą]cü`ľ˝©’µ–¦š$}°|öl5©ňllĆ]śÄĘOĚ,ç׫iĺ˝=PüăE‚Ş’Ne–nź(Uą]íĺŕ´h^Ü  nëÄôZÜ©x„CR¶ŁË[¬ţęÎ{ľdoÖmkeh˙mećú\Áűb“˝ľbˇ×‡o}¨cS­úövk«‚„µ™îâ™U—öeŞ&˛‹î™MĘ=ËĹ˙ĐJ{u7UYî=źÚŢ7KË€/ŞxĺÁrődż?śë ÎVŞś©Ĺ&F4<ťęÉ_¬îÚngžVwYó-+ě7ďPV_róî‡ŔáŐ~Ęăę®TĽ‰]ťým›ž`Ů«ëŞ~E ą™ˇÖ}ÔiiîÍŇîŢhy““SSĂlńéŢ·$XMmź‹jKOÄ]ěs–ÄęĹd# R†}zmm™2/Ťg±\V»®ś”]Ă•‚®ÜŤŁÝ 랊©›‰,޵YÚfJpľ1KŰĚžÚí=ó©YUUŽrb†*r’uĎ̵U Cß {q»÷ŃtľkŞ\´<›Ě^®ŚlĺŃN^ŮGn¦¦¤˘xdnČV …˙x]yCiŃTˇuôŔ$ĹŠ6¦r­c3˝®žßµ€fÝjąťĚĚ.~ŮöbV›Éfş™š<M/÷’rú¨pŞPsSî¬×•ĆłžâŞ*˘ŕzýÔ8ŢÇŠj‰@ĹÓXá›:^WÉ÷J¦Y^Kń°á}ČU5ßíÄfqăâÚÜ ž™ůok›X×őŕąl¨ä'z`ŠčÜźWW‹jφ®W ý&µëů¬ ¶6*VBbn€‡*c©ĚăCTUe‘ޏVŃťjž©ů›ŮâĹÔ\¨Á„ő×/¦ŐÓ·/žŻ¦U Şă©.3ł·\\.ÍŘ ›‘®ífLÎu}sń6;›îCb:8q÷m`ÚšŃĽŻˇ‰Nmź*ݵoźöp:3‰”í˘¬!«eNgőýŕ‡‡ă‡˘éŢX=śZ@ĺ¦-]˝~e®LIWk»˝Ý~¨Pňş1ôËéÜČřĂŮôĆRhŮV°ä4Ô+-őČ­M¶ixń¬¶ËŻ·^¨qoSZlÁ1×Ĺ 0˝/{î?tďJWĹĹ­}Imyµ°ąkŤ`HšgŤŮZŞÉqą0;ŞĽľ¨Cş;~¸,ęOËr‡=U˝Ĺޡ ZQŕď›ő°)SfüMťËF ŻŤä0Á×V°ąřôá¶^4ýp[/HârŁ®şŘT;• n&·«…Ůč|XŘÉI• Úµ¬–{‚ňţË*9©*˘¨ĘM˙Ňn]>úwżx˘ÝR7ăKL| Ő°¸n4¬OĚŠęňŞa._©ň”hľ¨]/7ĆS:ľ|ş-6ŰjćŻj¬Ä'§ńe żóËëRŮ+š^>«Iç—7E[Ő\V;?üYŰ”Őu­®´hĐŢĎăi™­ĽÄ\Ř(.M9Č卸˛ĺÜHPx93÷fµ-™čŇF~© ÷sľ¨WyDU›}© ęn˝R<Ľśď×Üż, śŰ§řŇ*ä .ç·űáçŐŞÂËŰ'{…ăo“öŹŠłűaą°»W—ŤS—\Ěk‹4ć äb¶_đB„4ŹŇ×ÍŇěE%—JśTŮW]nžĘŐvŮRx¤Őř4™zÓčŇěpw3źĘî^L ü؇4”¸ßG?şD˛Ż§u—zßf’ť´a*%y1űÚ-Ön _ŞŘ^ë˝űŃĺÚnnÄ—/ÖV„^Ô *ą¬ŐđpiĹ/äňőľź•Ý»4žR§¸(­[µ+7ŞZ‰XßžŐÁä—*”«UĹ\ę,B ď.‹Âˇ˝cÁ  Ă.ę›.ŮĄ `͆ô˛^ËňÖłZ˙ëŤĘv×ÝËuáÍ ©¶ kͦd˝l~]»¬ ]ZsÜľ,îµ=oqą–Wäz|©-1UL î™á®±Č÷5óÂíň¦ž´lŞ’QĺőĚĺ?lŠ GŤ{őzO:ß”ŮÄ…éLMęEęcöd^…M˝Ú˝÷OÜ6|N{‘]3 »ŕm†!ĽÜTÁĹeĂ^ÖđöëéÜ?Ä´X‰Ćć'ú˛V+r©śoŵ—Ęě]ËW&5?š<_-ö˘«Úéş–}գɬŞ\z¤˛ö}\ýHeě¦Â’g1ÄŐĹr»?Ď÷HŐľ, ĺňĄqůŁGÓ§űóGÓç«ÉÜĘŠŐ˛ĺä‘°rq÷Ł8۵ňÍ7®ź”wßŢ|î—ĚŢ™Oö{‹ÝĹ üČtks‡´Z8í?š.f tÜ}{b˘0‹•)Ě{$źüÉdoŽ•łÝôĐ‹ŮÓ*A«†•Í7Ň@ÜbB˝äŃÔîµµMC•OTFʦuÍŹ*°~4›(Ó]ŹfתÝ/b6­`±]^iiŤŹŞc{ŃŁYU@GXž˝MLą|mPŽŰ*¦ÝďŹp)wűyQĐYMŽŃĚK§ű†»WËu UŠ;µÂ‘ĽhŞ©-3‹´\Ë:V-ńŁů¤~µ®]}4·ěMąÜšäaôh>»­D{^ťŽáĹ­)Tˇ×-ř¸źQQńhV©emn7ęŞUáäńŁ]•ßí´˛ď#×XB[‘¦­Â’Îţ®MŹ´‹VâM®¨ ·y/˝G ĺŐű·¦’:/Úk!Ě®ĺýĹ™ ·öëZ®ŞcKŹ–[säŃŞ88Ż„peň&‰®* Íĺv§Ĺű9¬Tj,Z§ly§pŇ+Óůh5˝™ŐŠDsťi¨±ť–-ĹöňÝ/ťnMŐTyÇÔí«Až›Ť’iS9®­5˘ÉTöĄŹVł'f_)âz_ĎëËYY8­žßÖĘxmöYĽä‘ŔĘ îv^[L‡†ůÝVĆÚjťŐŽGŹěi©GŰęl}*˝R9VÜ­ ±ň«‰Ě˝U:µĘ”řjr]ąşáU%Rąţž®Lé!ďÎo¦Ć׹*˘ŞwfŐ™Fş›ßîť3ş«¤ćŞ(ݍşşµĽçÉ˝Z%W(Ĺj©Ś‚CV“¨LMvUG4hÂzUůŐôIýtpvŐ8jzDŻj«źR› ®¦LľšLqUqµ}ŤÖ»y«©Ú‰âjił?Wâ•×%h]UV^a›&őřďJç2ě1ÓSZV“;ďO&ĹyM§Ű˝Ýťf…#“ZUŃŁ®^[·–†°-lQ#«ŘÚç“«b°z}&úWd Ż*Ó”\Íž=«Â žUoĄ„Ń•Ůçäâ¶*çş‚ ¦śëj¶\™ťÇÎŐěĹł»ŞnfÔ–—ÖĆ:ĹWµjWłí“™ÍÓp;±{Wł—t`g˙r53“ťW™›‹ŐŢki_©Dżv$ żšOë%ŇŮŐ|YËҰ­îąZ,« ‘äj9ź“~\^›*ß«ĆkÝ7ŐđůŐňö–őWjÍ´6ĽżZšÍQyŐĐ­Ąöm {µ\]ŰÚ]/ç{ą]Ř»7vÁËÂ]0ȲTÝpíś–k2ç´ÔdéČĺ|V›ĄÍĂ]ËÉŐr{»ÔQ•Ƈ şŐŤéÝ/Ş2áEsĺ~aŻ«Î"]U µš’ĘśËgµsE «ś<ŰWO÷8—{O°p d Íű@’ łŽ‹–ĆY‚´áR”×/'óZżfďę¨l(Đ͵wUrV˝ÉGĚé§Žk˙ĐäéÓ徢۵{H T¨ÔádwOčórúj˝/áÉ˦ô°ŮĚа}ĄídÜł˝EÓËéşÚd‚hÓ*Ű”ëę™%µĚpUśb6\śî­aV\Ě–óµ˝kx<}ńÂhe‘S®]˝Şć>% ´Ćž‹Ř×ŐX3q;1ň Ł3Żş(.Ť¦ňÄŇlQ0ą o‹‹ WŇ÷µŹŠŐ´^×ňď˝˘íąŠk›4í˘˝W2áí“;ďąWí]©„Ľ†S@ź]đö•ý,ĂŐę•Á`ąp7űŃŠ+›ńŚŻ¶ÚXňśńŘ^WNŤĐuđÁMV1ęřj«­×zN=mś¶>âzs;i`ĺvńşqiqq»Â©n®n+ŻY­6‹Ú®-Ż­{µ-üčŰí¦ëí¦8 yc6ăętÔ0ÝÜ}TDzsűť¸µO°?žz\•S‚•Ú“¬ú™ÝZÝx\ë×ň3™Ú¬›0˘ rť-fÖŮf*:d, Ô6ˇ]ŻŤ8Líx˙¶ţ&POÖ¦j0Iµöc–TE¶ZÜč,S5Ž˝®Y=©}ä SKí0ác%Vf+"úĹÓýQ;n ˛łô­Š6®Uś˙¨6ČjK]™K͢±-đ¸ř~ŐÓ©yâ¶öńŤaѲK4ß}{3Ńw ě$k¬*Vłš ¬V5-ĐÍ)¬Ť3Ąë×ĆťďęzyÓÜe‡:aˇŚ%7ĚĆXŹgJćI6 §fŔ’ŮČ{<{Wl–?Vş~n˝ŞÇ…Ť|2YT°3S(¸znag¶°Ŕ5S h”šűę=sjg·2._Ű7}ڱ´{󺮹říÇď2§ĹKĆ’GŹ ş\ěż«üÓžĹűć|ĚŞĽ~żqÓl…eŹ—¸7ć@S»h¨™•„&^53P˘Bd´gwjÍŐ‰ 3·°®ëZrűńŇjňńăbű’×”źF“eyĽ4{CŹ—f{+äjfţ®„śż·ć;)'Ź‹Ă„E>RGŻ,ńW+#ĘęĺcU/ëT=ß’?Ţ>©M$}Ľ˝­Ăضř ŐľĂí˘ö Ý·›ž\nj~ŐŃăí»ľ/?®ť—{Ü8•Łë†í­ťĘÉv—•xßÓeu ~ßłg†ď«*ĘŁ÷Ëî÷ÖŢ7źWĹś'źµ7łű¬ę˙żwżłd˛5I[h›Rh %eˇÁ.H3“„™,P{“Ü$—ÎĚîĚM›€¬˛XvAńÇ* " ›TAE6QY*»¬˘˘żĎűś3÷yľ“âO˙Ż^™×Ě÷|·ç9ĎŮĎyÎWGZŁ×=:(•x´wŠzŁŚDcg:ŰÉ(őÓÖ¶ť‹7d1şť"Ę5íŚĆ –wZs€1aĘwďLAÎťpü|'ű‚PJÔICĘp»3O=6wćŽí\&Ô“ĹĽv.— €şŽűŮĹ|‡e9ŢŘ™měoí,™lc;YáěŃ:ź›×v˛'ŐŻí GËm÷X9˛Ő¸6«i^ŰÍúĂÔŻÍÂ*×vĹ0Łi3QCg/î/fa®í—IsýµýáMvĚtOĄ-%­k ¸gŻfŢĐ. %v˛ÖXZ^I›[€2 pŃĹdČréź1ŻXX0=ŃşŃ úÇ D/ącÔufÜAzϱě˘5ńľ /ÉeŢú­1ďÚ»Ę1lŹćÂbW÷ř`ą“śľ]klń]”çfRČČĺÜETn.çIG˛ť/íÎkďę•„ĎÇ e¸˛ĘžÍ]yË“©]˝yŻĐČ;_ćĐotľÜČsW/«łŃ]Ńł®ąóm@S8ǡޔcíµpĹ0Ď©tľŰM G'¤EK±a׼tRç´†‚ë ~¨Üč`Ň€e_xÂ`óĂ•d ´w!fł=™»ćű$Ć–w̱niëŁŕY+Ž]‹™:kíZćo»ú”Ąë—«ĄvőGô ôËłŃőKKą}ş«¤Ż6ďtŽč:gőËŠhl——ç'U$Ŕ°d Mět‡§JšE uőybб˛ÓÜ•s?G™ µk0„ę“‚ß5<4źńíµůŮ,ü¸n×p¶_É)qă.Ű_ T 9ŰUŘčYĺ|Ź—ňň»Nf†Ç”ąÁ*¨¦/şňС´k’cö5.d»ß¦/’¸YčÓhňÖŇć`A®>N‘ý‘îŽőĺ™Ä‰-ىĺşi8źjo7Ą“Ă3rÖF-:7ŻžŮ~ńjü*:Ć8ŐĎšSn0ŔJwzP7eç'‡ťuúáއt±6FsílGÄ—Ę7›ÉöňśÝÝŢ€&x«ëd á‰’ĂÚpo¶1†C-®UŚ 5ůdTëĚ,˛$?î§Ŕ߸ëe Ǚˆ4fÇA űúĂčß+·÷¶ŕűđĺ^źîtFű%¦ó`®^u8ďŚ×îé *mŘJ[řtw^Ł0M‹†^ʸé8ë:M",gS== ë ›×ëĚ  G–ń%MÜIqHĆę€%×j´M«pĽŠŮ‘^!ú'|SNTrI¦®§Ů»ĽŇMŢ×9I”ĂŮ€iö0§ęGVďôF§¦»Űݞúő2í :2ĘÎĐŃŐ¦¨n=ĚV|Xv—č—Iő¦őÄëgGi}czŘËß:?_Ú¸ ÉZ«L‰l¦@łŽ{i 3¶ÖÉĄŁ˝DĂĄĺ5äž‹Ž­¶sÔo~ÚZ­íx7¸ä°mÎÚ ŽuKW–w8N›Ĺ’Xi‹ĚYÚ±Ó K3Űl'-×ăUe™K«SÇł‰{yŇQ]I˝G¦­)Q)2m>÷ŃűvHŮ,–o‘áňT í§rńyr9™Rú;“´3ť’1Íńp>Ęu|Ś„đ Ç ° ăpR‡kw-$XÖ,Y°ĂÇ{)^8%@oqÇ5K^ź^5HŮŇ™Žmxd3Ę O„A©0›ăů•ü0+ź9m“*&@^·=ię¶KµgĘ­•¦VŹwĐrˇ›=|Ąžé¬mo\häXđwN”ô\Ěčď˙ŘČöw@ľ7mĆ:,Íg””ŐúŃbv”úňkŁ} [f:ŁôŇiö N®iZ*Đ©ňGLfÖdÖŕ ÇF…FZ*"ÔhňŹGčhťIÎ}{FVIFĎë9¦ŞyxxA¸ZĄ¬Í¶Ţł$łŔ~s¦{,µĂYvŔ`ů†|ˇ^Ú+»ę8kď§ŁŚZÇí0_uŢQ*ëŰ Ŕr”­–Z]Ďtł–\í5má8+Ż<;›gŁĆfÖÔ;ŐgRŤ 'Ë9™.Ą¶YgKJÝ7g¬§gé†ŇжdEˇöS×”«t,âčcýĺ¬ňM€4Ł Ž–ËĄ•+$8UĘŇĎtec'­ôe¦Ý8GVj°J„S@ĽłqIPÁ•í'ÓÜn*i?ž»s KNĄ·žLŤv·x˛ÄkäNŰC«´nI-”úśę0ﯹ~¦gµ”HĄz~Ýşe…fËŁWôŹ•·č´9ž}U¤9Ó?žşd4fú©ů“NÍw3ĺBo±ôĺ«™ţüÚ ;l´*<$‹5ëj×QżĄżŇÉ@ř+ukkŃ n~Ăź±ü%ů4”>öÎk°ĚúzqöíÍ ó/@Ń1ĄÓK&Ó°\έ«Wů“VJ÷r””ĘđÔ©,j˛›âĺt”yšµÝŁ…ţ=`w·ôťÝÝĽÉňřîîŃr«úćî’ö°TcJý鬼Ց†ÚMIgąâP r[o\^ÎJŹwwW˛ äŘnˤŻÎs?f7[ťň˛dBĎŽŹŹş`ŹŰqî+läř¨BŘś‘Łťůü}9óépńhĆŢçO۸[ fxęôž•~"âŤÝ˝#ŮÄ{GŽ QÝ=Ô[Zý„Ňú€XČ-۰ t×Ü›k#‡t×¶PXçđc«ĚŻyâ±ůäpN¨ż|l틏Sł MęÎ]S5/:>Ě?ÉăĚJ.â˛뱞plŔţčŞ¸Żź64śĺĽÄŰvŠ·­ą`MÉŇTśîϱŁY^égMmbÚąřŽńd.l<ďĆn)p+x#±ţÁi/·:ĺ$drhŇ/őÝý#‰˝űG39GZ®“Ąý›Ą¨­ÝĄb”öîţ‰N‰‘uś%ÜĆ8Î%‹î/•ě&k›Đ»‡"—Íöv°{»ÉíÝ=,Qh|7¤Zú Üî!]˛ĂĽ™j=űNćűéş¶JqmYł?NL3aÇýSyŃ«ÖpѵPşkíwyč†Á¨B+¦Şó ł0‰Şővx|mG2›B·ÔÇomËőŹYĘ««\Ţä8eŔÓZ©ŘľĚůě+ŤöĚc§ínťpp.. uą°°‡•D…AŽç;L6ät9a3K¤0‡eck‚Jbď“PˇńT7bYĐŘKK”JÇ 0Ń‘ánŢ”rZ JŢ—ö>\Č -›BVöäLTŮđäo.'gĎ5Ń”)‡m# ćčćů,ÇWOw×>e%ŕN°,ÖlDY١Í`ąsSyą4âÁ¨"ÁÖaąw¬lŮĄ¬2 Ŕîá˛âÇ ćrÔF‘[YçRŐyŇ @&dmnkD¬˝}­€ ŕüŽ<:Ŕśu7®ÂNűÄę‰ŰhL¤a•ż,¶‡Ú„|_ńű xF~Í=Ąďmíé–6PŤíé–Íă‰=ÝQCˇtŹ@ůĽ­\˛üőČ3VŢxkZmiŰs´,-{˛>¶­=Ç2+¬µ§”L×aďD¶żqOŢ&kO)¦¤sY±üžrŚłľ'+SŮcaňśF Ě!ÝłߤwoÜłt”dXČ”µméDľ±|ĎRŢNGą UĎ>şÜŘ“;Ö{˝l»ůřžÁB§d˝Ní±R5_Z—AG@˝d°XGËňÓ"T¤)|fđ6÷ –˛Osh/}şsŹőňMz{m‡wM'Ő(éY§Ň©úž´łľąg9ë)ŢŢłĽ†—×nřYg >št{VÖöqŘłr|ˇ3X.Ó÷J٧زÇ> ć-«Övܸzrm•ĆŢαT,±·“™ ă{;ÖđHÚą AŰôɽť…C˝>mâ;‹#4´]ĘKxöv$&űé5ĄÔDdogy)ą[ëöZ–eÇ:l¨Ow ʤŹv–›Vëń˛±Rżą·{8ß{»ÉśÚ+MtrmÇě±˝.“™ç€‘Ąl‡ÝĚ\·W*ěäľ}‘u0ŢË6ŹŁ˝TŐ2j±Ą×@V٧÷¦íşt9Ç«IŠ{K Z{Źvň®ćěŮ6@V—Ľ÷h·tÁ¸rżqďŃa.Kö=™žÖŁ)Ϩ–oLÇ:Jô=eßëíŰ÷QŤHyći…ć^*^:ĄŁ‘»¶··’ő<ŞďÍşśîťgĎ_/{Î|V»žş'űŚĂža'űľŹŔÄÇŤ^0ßíeE´:>Z 2˛Ŕ÷ös'iý^ú–śZŰa~]€K-âÚ{‰€gťO÷ö–:KYvO€“é ·LßkĹŤk:¬ßçĄAŢ&|ÂÁűDť4˘Îů ŰŮ“úŰĺÍľô2ľ×âµËIţ BÔre0LäHôů4“ÔÁśżwČ?Ý2¨3(ciÚž§¤‡O%úŔŚëuNôÓĹË™Hiîdí [::žµCŮKW…ě+:÷GE\0č§>ăëhŮoŤ—3ľŰhŔíO[· ˇáÎŮÝ+™QÎŰWRo‰zVę=ľ—ĎZ™úŰ‚,Ú÷¬u—&ݬöuď ˛q(#7 N[–Q2kb/ˇöRJ~rݵŻ?’ń~ÜaóýáŞĂ¶AS;ř|}ů ›|®w˘—góö2ŃŻqő3CFhNó&RśČ6 ë‚ᱼÇLkď°—5woěÍ6‚·÷—ćłYÖ÷S•ĂC‡ť,Ć×|č°·8 *NĚvÖî}©ĎvŽžJ§-ˇłťóÎ’ďŮšĺB)7‹÷°N.î$ÁÍajě0ŰI-KŰd{ ő¦(ŁNŠY˛ęß3ťbÝ%pr.ôôTÂŘívFźűžíbÇAž5T‚´ÉăÎş‚ĚK^Rý–đ:%[ËĄŻ óiç÷ŮgZK݇,ňměɉłÝăÝ•~jćl·7ęä)Óßw¨%»xbő|V˝^*Ěç‚cĺnP“łl…:\*UN°ä‹h0˝ĺ<šŻ9ö˛ÝĎëf»«xČjžg» VOżś°°¦é©žłŰlłÝE9ŮjOř'„J}´JůU˝ĄüH=c( ź5 iÉ=đYÄA–xmÍĘJIßdkYÜ5+ŘaÖ±Âòą›Uźí%‘6Ű[ÓÓpbÖ jsbZ?;Ú‘ăąÎz¸ćŕlłaNŮŤ8m†jÎćŮ˙±Yąë;řĚi¶y¦oĂě(@YÚ]ŘśÍ=ý–Ąý—fÎ6g]tt”˛ łt«_,ű§Üż’yđÂlŞ`šőv`ůZ÷ÖôxŃ ‰ ¬Ý^¨aćŰ«'g{ý…2 —SpDKrMH<ѱŹ×čBLפ˘6ÍíD¶y¶¨łÜIŐ%ă¶3‰o‡ J’iŁ™]`€ň‡aĘ<ôćl˙HÖO†ŁňŽ”Ůľ!` —ţ±rgîI käj‚íËX¸|Pćid›üä˝ WŹŹ•z`ĺegĄ{‹ –sÝqÍ≓éY:‘¤ř/®ö«,ďýŘ”ź+ĺźŰ~f1ĂŽ—ó4¨ kD{ß7Čär'@YšШ{¶ W-ľű{ĄTöbFtúÎşÎöűŮę- 2 b;^âÉ5îő#@©/»fXjŢ,ě.Ź¬Ů řÚKöĺÖŹĘ}»G°¬ĺ<0«VK’Rdgś;Ű_YUIŠŰnö±5»ęôMaëâląhKD $+ţťpŮë…„™•ăYŞS“ZYŰ”NŘ–>¬¤`éç•÷pßÎU ťZ×iíNd•´łĂC%37ś_Ň›óňݵgŇşĆh÷rsÄ1ÓÝqő }5N‹3ß9-ĄIĚ—KśÖ‘ŢŤ>«ďĎËÚ¸‹ľçłŽŽň=+:Î?SÍŐýaĆČó'FK?6g¦ÍR¸sť…î‘NĆe ‹©ŹĚVđn‚Ă雚aݤšAˇeq…vÔÍöË@>ç}"9®Â¶Ły€pµ]ët5{ZF8Ű–ź=Đ™ß^®YŇxÇŇ ÎľĽ‘UčJtĄ VsťA.ń:yB'łş@ť,}{ÎÂZéäđPfĘńцü ˙¨@nQ ë'JUô€ě>Ďłhż%ÎzٰOôGVž.ľ©›„7ő39}2 ZÉ-•ÉO¬—m Đü0ËwČ;š´ ¤9t2Ö¤0j%o麎ÂČÍ9Tě‘ěÝ<#Çńb¦`çňŤmv6ˇ˛…<|Ľ—śÔ)­ť4Ňč)4űXVË>ó˝ßK ÁŽRCČÉ€ćţQ‚%u zi“çŔAą_k ^i[ÝX€J 5/{{0LŤě”%˘ 3ĂEÇ ĄŻ·ń©‚#Ůc¦ dŰvňž˙4Y g15°dĘ‹Dň*`Ą’žuô•9zTmî™p%k˝!k?ł‡OÝF'”wiZ˝ěĆî '*ą_©™X]„şúنKJŔárůâ’÷´&5×ÇnKG‡˛ŹMĚĺßË›ëĎ÷m#Ŕh-ű Ý„äľmrăĹ:c¬ ¤ŤsýĹĂÝCýS;ćŘŇš’}şn1ŰwÁቔB›[“©ĎőGÂŁÁw^łç仢›vŰZ?dnPz¬î^É; Ěő%ěOćGý e˛ăşéI'Ź—v:Î-uó=9ŤąĄ„î1”ęZsKóìeni`íŽKjfĺR·…‹UÁ° Ý·%k<ÎľçNö˙ÂJÖŹĄm©ë–}l@|}1»y&˙ ©äHŚ4ăřißŘjSXŰM_Yť\=>•BŔŕ‡nö5Vž4·Ň]:ŢYĚž´mš[éĘtĄ“úÜ7h^ÜI8˛NĆőFË#äŽĺDŔ˛¸Ď <¬Ă[$Uł·¤|­ĐŰ'S ĽT“íLŁE^É{Ké®îáĄpłFAąHžD­ĎĄĆ’BK·WJśŤ łk!YcD‡¬nHőä]Z7ű4Ë„ć±ä1e Ę'F€nů©IÝú= iÓ5÷ ĘQ8-‡wÎ3‹#XÖĐÄ`GłDäň©s"É0ľ‚ÝKO´´ţ|ÉYɧĽ¶óE{Ž8NVl87LߥkÎ łÚ ÖÜ0˙k}n8jŞ çsF–ľ«ĂSĄĽ‰RJO€ňçUô„…˝•Ń×Ţć†Ů›ť4úÁáÎň¨`ÖŽeq%Wn8XĘ&·§XĽ._>śiéĆ\ö)†©ąáĘ©Ó2‰ň|Ťą,ÓÜ—×NµöYýű”ícŻ)ŮÝű: ˝üq‹©ŤŽťĺĄěHü¶:/Žş™CÜŘ—•<éěyźý1ŤTL™¬‚‰}|Ľ”Ăm4Şëöѵżăjj|:éŠĹT–ČAްYÇĺfËÍ}ÖŤ=]?XÎü¨±}ě=—0î¦! ˝¶<µ-s©äś6Á“ĺ\ß—•Có7®I~ă#ŕéźżmq.s+ux2×dűŽŰ:¦Ç÷Hqí8Ř9‘*Ú÷mRŞÍ@ś~đŘnJo)m­S?źÎ óÍT—÷Ţ·ö•w.ęp9V÷őřĘĽ’tŕ>ůĺ§äîÍňuűđyłr™ ű(»Řa¤»ś5xlîëÍÜż}}ÜĽĽŁňľţ ŐkLéhńäڸƾ~bÍMô ‘ťŃ9l4řÇĹóÓkv†7öe ,ŻůJÉ>ľ‚“ńŞŰŁÁRPuSvr!ˇ¤Nb' Ą{ň0;t7m¶˛şG7ęHďśĆŮ=™ŐUé¸WrĆěx%;»b’㬇y´TCëeOę—ćŰď%ÚZÇá˛rÔżMwĄ‰ Ez5Ńë`X2¸!?Yn°ľŹŰ—–˛@ ÝĄĚK[¤çR1<1ş%›óĂôćľa®ßö łŽ$í}ĂG[ĚčuĂů$wŰ×_HŤ]Ć÷ EĄT ’şáę`…VdqĽq_ô”ÚqĺüQdŘČĂÝwcşjb˙˙FHVޱżë[+Ňö ™>hěďf».öw7dá“őű»«ŞÚí®˘}?‘ÎLLí÷Ćňĺ ÍýÝlFk©dW‡Ç3‹¦µż—Żccö5ÂöţůAţ}©ćţŇÇCöSuU.&ŰĎż|#ŘArăÔĄ­_~[Ţ]fCNŰtŐvřh‹Ő:?._7ź¶ńj*ŕk>/gĐŇ.$d»ŽüƵێşfß‘ĎoÍF˘ýń­Ĺ9°›ś¬ÝÍăŻY»ťgZŢĎăčI&ÍÄţţ`™^ľY«śýůž#-$ŇÚ/ď;ł$Ě!Éům˙ űŞr]G'Óťyň´±?svZűË]ú÷—ľ±źÚüRĚbţŃ”Ćţś’tçeůňoNčĚď8rŃÎůůä…¶©*¦Ľ7;Ö*$ ’ăţü|~ĚNéŐ)nĺŘŇű¶bµˇHś=Ł|¶Táąć\ qcŘr·}@?ä|?í|©Hßĺ}ÜFŢßjG»Ă, Ů8ĐIqó1ş·–>ťxŔ­Ă^~űţ⑏ űúˇ†§˛Ë:2łGĐ1ű´lŁvęněÎJÄŘ>Đ•ôËŚéÝó]kÇ6ŞDŃńĘZŁmň@—Ż”-–vi°O¦‚üÝÁ‘Ρä;Ź „7źµĂl@’/gçe§Ž?-ڬѶžLŃ<ŽłrŮ]űtÜ©ěéËť…ě⬝¦¦Ľh#˙V n\Ś‚/ +âŹÚ‘.×U'#m°J#Ď@‡ËĄĎ˘ °’ě=ôTÚÇÂÁhçČôQŔIýÝß^n|2Ě*ĽRK Ç Söű@ďehóŮůc‹© aĘÚ0n·n’)Ő7áÍ;‡RĹ}€uSßťqu—F5g“ˇegjOě°Tɨ:í˘ůQW›őŃ’R„…”ŕOŕáŕ1Ă^y_ł”ŤoĂ*”]bý…ÖrxnúnĘŕűŘŃ3JŕŽ^k}ĆF39c|Éő­«çLҰY`)űćĺĆŇŮ™ţO9ńđlg×čßŰžOý„G'äN g2dutňhďĐ|*ÔߜΙČ:®ÎŐµŮ÷5|˘ź·ŮvXúĚ„?öTÔdíÍ˝|{¬gx˘[j|ťłždÎvŰń eÓĺ »0K«úq–ůăxˇ›}‡Éy±ä®;`ßčZ–ł(ódÄҲŐS™°Ž˛okčĺüĽ‘f·őÉbńď(Z0Ĺßxq…ücÄ‚Jô$!×[ÉÚеôiśDd>sčí(omÚ>`[•2!ĘÇl2Í!ůNb=SzjÔ>ŕ@r^Z†ěĐIv ů׍Ű@JZŚŹO rš•V›żŤĎÔnN'bgʇZmę2%?3P¦·ő Rł9ŽKŰTuśď˘;‹cűŃ űDGé5łiéYô{ŽŤÄĽć*OôTřŃôtč,ÇĆĽçÂGşő ;ÄG˛:ކŔ¬Ž÷ ­WK±ËćÁÎń,hG©Xý –QV¤©ă#ĄO—: ű¶ÍA (çMkôňëó­ü›úôňźÔ/Č %“ťaĺM®/%Ă’ŵ'€8J“=ÓA«[ČOëárFůüÁĽë/(Qň‚•ň~ăŤm3ڶďv`ŘO`ĄďAôVłkwóäŕTVÎN­YĘÍ>B©,ĽđŇT'ü”žpÝę6("]WvR׳8äóáăĎż±dumE;ĄŻĽ$:9LÓ×ď8HÝGę8ŘÉzśiRËţÍř¬|ő Édć>€Ňk†ůQ·›?Ď?đUúŔÎA+NG˝śŽÖî[™ ¸T}qo”?Ĺx°;oŚěłYŤÝ„={ń´[(‹,•şŤě–?H»Ŕr4 Čż/˛N'N™“ź ¬q0÷éttFýt–a?hßöČkY&t¤ô˝âV'›S:ۤJĂÔ‹łBŕĺć8~<·ć8Ťş®ăQ|č _Ě*3{oţH°>yĄŠÉ24\Qîg¶Č@ş»ďQ¦Uř‡ĐOó8'íó,Ź%Ź ´8Nyź ľOZŽ1®[ýd©}ÖcDU«ŔąLÎi˝˛¸ž^ĹkRŘď`©bJ/Ţúóěy” $ˇ ¶’E°öňłşŔV?  ÄöŽćYłýß!eÁ×fK{ –ľý9qÝĎĺĆsZÓ—mŇÁkc?:Ďôňn´ă€Ź—Zw)7BQh.>Ęi§?źľ8žĎšEÖĘçá˙{O:Č~ •<·}¤ŔÚ)ŻĎ'O^9Áć ‘ŰŇ8x2łąt×óôęő”ČŤňčmj›®ę¤`Bëá'úYď¦Sě|⺎EH3AŢş®s*kD?v]·üMęćuÝ^¤şnŤ`~ľ”˝s@âńÖu $…X®łY˛;®ăC˝#ęuÔAT'óĎ!4ŻłĽĹčM˝n©#ä¶ëzÇĹôGZŮ1gԚŶÖ_gĽ· ›VÇŻŁQLްFŻÉöôµŻë—?ˇ{ť}n.c•v@Ň”†ĄŤś× óxkíşá±t&˙~·˛ŕ‡ĺ§ć_VŻ_7L…×Q~<]8Č*Źt2»mâ:kŐSÚϵ Jrzüş©c\XČîłĎAÍç"P \©épŮ?S¬^¬"f12?LÓXÇ>°ŁťĂ’şúëÇúYÇfô˘¸ĂŚN\ńKFßE7\\g?E‹§Ĺiî®wéGŹßüŤ˘Řň‡ÂćoęÍçjÄ?Ô€6EőGzÎ:Öë[ßŐ0žŻţ˝ŽGkäíÇčU“lJě÷u˙€~ߪçTď}»®×;«ďěq‚ݬó ¦ńµď§g>YÇOĐoŤyüŹô÷/éš'ęř±şćÝ÷z¶®/ľ­żß¤źťşNhmhěcźÖ3Ç»“~ţüŞĆŃx§žs©žs¶žó+şî‹úŃł—éřńz®ŢŮĐ<ň•šÓ:~Źćxwáç/u˙ tď—×űż ëωgÖ‹â·čď˙ÔůżÖ=Ľënşî:§9WźŞÍ·v†”ŇçˇJ]§ńľ^ĎÜ~±`ěotď˙Ń|ţNÇZź6¸úľŢ/ďĽyµÎżLcŐśŠ˙ұđł~AĎŐXęšc]ói4t˙µ‚é\íúűĺú[ă­ýĽ~„ zWë·tŻđ[ŐőŐ÷1ŁşpÚüG˝Së´é«Eqűç .š¨=RĎݬßbś)áˇň1Áź©ßş·Ąµoü‰Îé§x«ŕďĐߢ‰úőĂű?í8ű;§‹ÖÚlJž^˘c˝§Ş±6Ek^U÷~Î}@÷ý«ŕ[tíë}Śuá˝.ë®®±×¬űţLżź^ §­]]t×Đü*ŻÓď7Ţ~)Ć‚7­ů´t˙¸ˇ¦÷Ő5ÎĎ»B瞤{Ä5ŃîŘOÖëÚź!°^—ţ©~kLŐ=şî%>ÇŞŢ;Ů^ZşďëşObľ*Ú«ţ@çÁźÖwńJČé°*:­|JĎ>ˇsšU×oŃ WDĹ÷ôűÝÂ…ćyţ=$O5·¦ć˝ţŻtí6§íę«ő­ĺ´ôÇm•HÍ˙qś?Js©ťYw˙TE;µwé:ťŻj…Ž«šĎ¶łôž?׹Ósô®‰{ioŃÄ{M­Qˇ±ŢnťÓ^Cs©Š¦’c˙˘żĹ{­UMJď|›ÎźŻkŁđSwţ@×IÂV·ëž?˘E»Ćűť—śi§[^ç˛käK 9őÇë&ťŻŠö+kS÷¶źĄc­ĺzÉÍšxo‹ć߸HrEăŻţş~„·şdu[ës{ńhĺ)Î× ÍŁ)ą˛:onŇďŞ5h˙ŞŰ+•ŹčÖňˇúN×kžW<Äią&üÖ°#$ç6CÇĎp>mK¦7ÎÁN×ß≉Đ5’5•×á&«őî–hˇ&˝Őţ]ÁĄCЏ.Eîµ ý9ÁţÝuĆßp;©Ś˝Âó™Ň‹5É®–x±ˇ5Ş g-ÉťďMŹŁ'ľŁź7„f}&ťŻjÂĂéůÚ çë¶žŃ|–żż¬¬ÉFÓOeÎ鮡9×DKgÂ+âßştƬ›ŕUńU]´0ÁZ_弇|çąŔšňŞ˛ç*Oôő©ľĹi«vđ.9µY8kŇnä_Ý>©b ˙UŃů6ŃҤđS-TĐe?rŮÝú5×uuéŰŠt}ĺ7űłŻtPhľµZč±Ëś×«˘ë‚y Utá—\¶UˇË.—«â÷ÉWą,†>‹÷¸ŰmřşäfMkZYvž€›˛*Ř,Îźř@Í]n×6°…´Ž5͡}•Ű=cg¸ëUýŰ›uŃaMăk˘+%Ű üž%Z—ş)ÎŹMţĽű,Ő—ąlzXđ˛ëź]ÖbżđĚĆ{ś7kŇô”äPýł!–śľŃ Ťˇ!yQű†ëšĘÍî?`ď°UÉă{ę§*ąXżW_Łë…ۻȧ¨üĄÓçÔŰ\ÇbËÔdCµp:Ýő2{˝x«ňl—ŁU˝·řNđ§d˙Ägçu=ł*~nŻđ6 ×Ĺ‹[Î÷ŘĐHS¶MSx© _Ĺ+ťęďőµŻi]¶^:ŠąÝŐéÓ|&ÉËŠtBýGnKŐ*.›Ç¤'gźŔĂ÷đź 6€čěŽĎěP˝§ňĐUš÷¦›]˙Ř^źöµ?W|T˝Üíż6ë/ZmÉv®ŕK<ÚďĽ\YqÚšŘęr =(ű·Îs¦\7Ż÷14Ďňu˝—tălŐÇŹď3%§&YŘx”Ëal°¦Ţsć}},v?úý‰=+YUü)ĹJÎK›겦)^®wý] W±ţĆyŞů:Ő .ÝĽĐmÝ3¤K«ö1µx·ôrE2;Ľ.ůu.ó=ź}µ?Żň+dpő[4TjnëTžá>ZíanËť‰Ś/TĄ*’]•­Ž?ô&ë9q_,е·s>®ęůÉĄ)ů?µ;_Ż[öŘ@ń^·q‰MTäď54®ón++¨ßóÄ×ŃXšżŕ4źŐĐâăń—3µű¸Ľl@óâ÷â÷Ý>© «u®Š˙ý8Á;Ęočš 6şE2ľţx·Skř’˙›¶¸N˝BďüŃXCň¨Nëţ |ÓíÄ$cš˘1|ĺŞüÔͲyĆŢçúp ˙řZ§­ŠlóŤĐ‚dĘyŹňu[Ż÷â™1­o {M>óbç=ě4ähCĎăřc/@ÎNŠ6꺯®gTz.ď‡řwŐkÜ»¶ô śđYƵćçŠOۢŐbŕ¶~[EkT˙ ×GMŃ{;›ýc§uä?1›Ibş¦Ęzč=›ó˛ ÖßÝqTťáV>î~bE¶LSňy«äwK4„ ÝŕËÔ.‰ő‘Ď|vĎŰ+¬E˝%ýSH/źs˝ë»»Qsž|ă—ŘG[kS|ËeţHE:ˇrw÷wĆ´–͵ţu÷±‘ąřcčÂŞhkLş°ţsnkT˙6ÖUř;ţ~ťÇµ°'+Orú)„Ł&2z›~䇩#«t±ÇacÄŘńwMtŢ@çOAŽiőJčÉćqŤ»¦gÖágřű+ČňßňńžŃ=’Umb)đ‹ćTE¶hmůT>輖ě3b5Ö’5<Ü#úkk-šŹ }}Đĺ¶ ö|.!&˘5­Šľ&Ąg«âŹÖ|¬MK|mqńţĄ’…ţ®ü˛Żső«î÷4ĎwߡŢ÷5"ľ‰ 44Î1Ńaă_ܶáëř«a»Kť©÷M]~Ĺq§cäVmżë—&>•ÖzÓż…ßúIýHŻWĄë“.s¦toő÷śž±˝*K®ź«Ä~Ţĺ'1ü§űˇ«‰aÉĆÇĎ$aľ%6ŁđR˝L 'fCb»a›É&Ş#—˙"|6͵"[Ż@Ožé˛ Ţ»D4_ü™Ë?|Ý*ăú·íđí*GÜ–( cŇ“Ő'»>A×T¸^şćśwąż^˙·őѡŘWííNó[ÉŻË.^ä´ŠoĎŮĐqń\§ëú'ť†-v úlÁß_p[¨"y^|Yx]¨HÔĎuz'ţ\ů‰ĎŁ‚îŃş·~ŮĺÎä;ýýřAĹ;ÝŽN±E‰9VĄ3 ­SíťľŽřÔ•oús°Ç[Ř÷s™~&ń!Ɇ†Öˇ2ícš|›ó¶fqg÷‹l˝ńŻ„űó$‡/@˝Ďe(±ţBk_ü§ë|ě¸ ~ç_;Nt^yľŰ„Ěťyž‹ß#™M,uËO|LĆŇ 5é&â'řÄ%-¶‹Ý,ú_ruJöl]s­‰–ę˛ă,,ľ«K~V~ÓiÉ|ÄË#ľ¤ó ÍkRú®Nü ˙ą÷2Ź˝Śď§ż ĆŤŤţö;şŃä>ňó{®§«Z[‹_â×ĘŞ DĎăwđ{·V‡§îć¶Pµ˛ý5+€>\ŹVoqľ©üťűŚŤ=ns›o^ďěľaăJ·q‰ŮŽďąOŰ~śëîşäDí“N#f+ž›ŕ|—agŠF¶áă˙kŘíš[8'»Y8Şęyë±ÍĎöu§Áţ|¨Óú°‚ ĄçÔ‘ń TX:?â÷ çĐ%ÍĎąďV%®¨uŻŁ«ßăó1ž vüěૠ{çÉţ>ü{‹­?Éi}Z‘m·ńÝŻÜ.ańÖ‘ůІ6Ďő:˛&YřłÂö,ńřÚ \ć×Yűs\–X,˙Kş´ú·/+öąA+u|ÉÚŠäĐ~ŢWs<Ďeaő>îB6lŁâr:©ţ~čĺ:~‘ĎŤ{{Ľ®˛ßăNřŚ+†ć^äëJ\«éŢ±Żş>ŻáOâű  EďĹ'tÝ—]>A–ËřFČiń >ĆúË‘W+Ä —>%ôůlDĐVA ›†ď=}ěĐ…ŮĎrŰ^5˝ö1×C5Ť˝ ˙ăc‘C¸ű@ Ń1…ĆĽÓ<2qů É‘Ú]B˙b?ČŽ«Č6oÉ_¨jŢř©řČ*lRr`w¦»Ům—ŠÎ5㹎cËsiŽŐŽß‹M ű;°ú×!Ąłk˘Ńć‹|ś¬áeß ;„ĽŤäúäëŽ\:Çă•ČěŘ‹5çBňgěÜőČ-ôĹŮCí÷:]ăËÖA?Éíďâa!wÎđµj`×~ĎslUńmă.˙đąŃ5•źw=Š^°5@>Ňm8ĂŃ3BźI~o/´D_äÔ°ĹálÇšlŰv˛ćNlýĚ—Äłżç2;ˇ-×Â'z¦Ď­ ­3É˝Ę aS›Ť5<îtU#~Mü@ţAC÷ă§Y4KŚPcż™‚ŢĽŹăÜl*]Sp}`|CŢŘݰ<âşßvĐřżOĽ°N6ý9ŇE›°Wžç÷żG6Éß!g±M°‘ńE$§ďřV_k‹?&Ćţ!ß°á±UńÝ>öĄžŃ$'ŹÍňgN/6ç» /ú±ŘÂ?űZ¶?8ĆN%Ď-ŁŰEUčWt}Hΰ > ą‹?B|˝ńŃ8~RĐĆ­!lj b HĆÖµ®“±žŹtŰ»@K`[}đJ§;|˛ ů?řŘĹ \Wm†˘–ăr_OĆZűëăłE»–;ŃĽňđ}·\ZoKţűżŁ®ą˛ĄÖť2Bż7żÄé™Ně·O¤ąT5ŹâénłßQňÚ3ňżŕ¤ř¤Çˇżíă˛xŕk|îř6ě`˛SÎ:ćt´áŐn—NÁ5uZÄĂ>8%.ţ{ŤŰ(Đölˇ÷×xć_[â´ç ü|áqyüDÉ”Il–/ą| ŽGüYŔ o3ö˛ĹŃuoá3.'đ=ř»Č?ŢI˝ËśÓlxűł]¶T^ď÷PKEnBbŃcŕ‰<Źh†xO‹Zí®+Ćżď<‚Ź‚źĎó‰«âkś?2W ‹ő`?_Q$ťÎšý“ó?ă0€~ Ç‹ĎMë>~Źń¬äC:a~˛×aŠŠű{Ääľ qŠŁ }<ÜKL˝q^í5+ä*>ydüéü?xţAţ!«‰ůmú‡ UńÖÔŹ|M±i©Ż+D“ă’wĹĄ.+±;‘MčĐ:ôţQç=ę#ˇÓšô~:’žĂfý5‰C+ÔŻJ&4Y ř…şrŚźuůŰ`lĎr~SŻŹń…>>čZ¨R§ńi_“ŠžGÝ\íő.'©{%ŢÔ˘îPëJŤ,1đDnăü׹żoĽ˙«N'ř‹uŮÉçö5oż<äŤhŞ î‰Sżů”ËIj´°7ńíŰ/ ąÁşCNĎú¦ă{ý·&Z- ľŐרúŰnż’ďă=Ĺa˙\Çďf˝%?[Zď _Ńó~ÁבZŕqÂ?÷÷R'Đ ďW_cĂ!t@ŚŕąÁ[Äëďé4Mmh =ŹĚ˙ˇÓČ$±ĆH­Ü'\Vo»E׍Ť>Ňĺ#6řôAľ]‹ÝFĽ—ëöů;b¶ń‡ć"ľ+şX§5˛ĽĚ}ďqĽÍ§1áűc‡ gk$ôŁŽz ů{µË–&1áj\rŁľěr‰:ü.lĚć}ÎäöŃ%5Ůe…žMŞŐk Üö/u}ĽÔ÷P„=cëřcçő»ăő.S­ĘűDĐÝEÎď9qä×_ş[ _W´F]Rqo§)Ë˙Ĺî{IÜwfĽFü]ďż8ävŔ3\¶áOçĹg0ŰfCŘ·ä>5Ä&Îđ|‰ŮŠĐ 6ˇp\'ŻĽÝ턍÷sú0{f«ă”Řr ›ÝHî ůoqťx{36¤hĄ&ťŹdťŠË|}ŕ{|+âąřÓgĹG'®Ťl|·Ű)EŘ2ŘŐäĹ rĘä|#QĄ|DĚéV×/ĹDáĹ-ř=zn[ýr—»ëÉw<Ĺ×–yY˝+6ÄKنč&—Áf—P›%úŔN]GÎZD× Ţ8Óĺ:ąŠ&xş}Đä”Ë%rtmÁĆŹű©ˇnÍô9ČE_óöt˙°¸Wó¬±'Bú|ňb?g>×_9ŢŚŽD/ăÄV>ä4ańoâ€[ťŻ¨YCá¶ú˙ĂAăŘtßw~˛ŔßúzZZ¦'Ďú]ż~ü¤ç,ÇŽ/†l/ŹçCţŤóśÍťt,t|§ĎŁ›ćÁĎođZ?tSí¨ËŹâ×}Ľř«ČNęîĚ%ŃvÚ§öG>—ř8ńžKlřŐ.‹°sđŐČááŰ7§"vÎyňdM챸]cëöçÎOŘ®Đywh ›˘®µšşźŰĆëßrz0ůBđ |őQÇŮXäÚţŮu6'± Šë›Đ&2Ť<ęRÜ‹ÝÜ××üăŻŇëMřô§c‹Kł–,ľé˛¸úÇEs%rŁšOë"—yĚk ľ"ÖőäřÁÄŹ“±ńV—¶›é;NŚ‘¸ő­cŹuÚfźďśřFČ÷›}Ţuţ¦&𖬎–€|8±R«WĽĹßcľ÷¤ËÖźš|ł‰týř+śź¶ýŔy?_ŢxNrľŔV NţFź›ŮŽs!?°Éţ1Ö ĽÂűčü·8/[ÍŐz§ă»żPŹđŚĐŰ![ŕÓ÷ů˝Ř¬F—/pľŻłľÔ•`W1žł}üříĚo\r®…ńçqłťeÍđcÇĄŮdőš…â ·A°G ä6xŰëď _@ÄÔČ· ;§‚˙źŘ?ŘđÝ×Ĺż79u}árźy‚^%'{IĐĺYˇżžęz„80µ}{şsĐăgß|ËzŁăájö‘qČIâÁŔ5÷‡ď:íNˇ‘MŘÍřzG]o/Ďů<Ŕ1mdľ„Éwř ľ(śgyĎřÁCĚY~u±°ú+‹G4]fn^ć9]j‰,^ m3Ö›}-ÉáX -zřAßÄĂŰ^óHMˇŃĎ1·Ĺ¨—7ąÝŠÍŢG~K÷Ú^ŔË]oÁ3¶G đu.ŁĚžBÝč6(1ó‰…KË—ČfEĆ[Ź ţ\ĚýötWMqť%Ď€{Ëß×"OB^rľ:îű6ŤGđm>|ŐrZ˛¸.±4lذ·ĐGMÎë\›uBçŁóy¬ËâČLdvË˝śgŔÍ1ćţ»~?Ϣž v> őř–‡Ňóš—8O˝*®Ç?|żĎ Ő&_Í2d<Şʷ»l.Đéđ¦ôÉ8vý›}^ÄÎđˇŮjţ˙‚îí8d,V3 =V#ÖL,řcÝ™˙ń_ě©ű‚ă×x}‰/Řöç[]řsÝsť· kxßbŠŇWÄxáß6ůČĂ[<ý ź‡Ů%ĎŽ5_ňu«ŠfŰÔěáŰ‘ż]7á‰Eż—Ľ±DĂË/;NÇńŤŻq߉{*čAč˝Gí4ňŮüP·ÁÉç`˙ZÝ>˛•ú!rÁźwz'~nńťë Ö~Ď‚G˙FçpgşfÜĺ[Ú%żţĐ‘÷÷¸ö}ŤM!b1×7ůś­îěüxu‡đů]‚~ďçűA‰S±ŻÚň‚ó~]±9řPó“㡆­‰MôrßŰMľ‘śJk›ÓšŮ«ÔŘĽÜc ć—°˙żßťńĎńlě×[‚ŢŃřýäćźđŻlU“ĄčÖw:M“w0ąCŢż ý YÔ:óÇÖG¦ă;ˇËżč´‚Ľ4˙»˝‹/ń˙m6ď ®÷gń.hóîNŻÄKmż(×b;’‹$· /A“‡cÍ·.ż~č÷š} ż|$Ć-¬1T«.­^|5)™Đ"î]‡,÷÷šť‚mGü}w¬'4*:ŞSçvŇéŰrąŹâť6¶Łtí${JčűżĹG°Ąî縳śďű-—ëÔĎÚřy§lvö×Ř·ďĆZ“fŽŰś®°őřµädB¶Őľă4K­ űOͧ:ÍX“xŇ»"†‚ÝL®yL^řýľ–ČUjĐÍž?ĺô\ścßZ^ÚşěMNOô  >Vó´ř:<}ŕëŚĆüŚŽ?âyęOLBż ηř^č5b˘¦ón y.śÜî9ncľ{¬Ë4ł-‘Wżcţ@ĽëkŰ˙bř•óyÓ'ÂjŔnŤx,ôvG+¶({żŞźp»Ŕt×zŕ›X€řˇ}nÄbYűG„}ô¶Đ‘ŻÔyöÚa§ügŘ9ĐöĽô]÷uđ—äUoç´mľ{ä‚ȲoĆhvżó1%ËÇ㯡7Ż/̆¨ă[˝#Îý™óŚĹ/7;>Řg‚=\‡¶–B˙Aď׺n·Úž[B®`k c‰Í3–L'6‰îÄ·XçĎ'>»¨U·äí±ÉĐiÔ<[®{kĐď.ż~ű±ŰWč j °őŘsdöÖ!/_ć¸1{=ńڰ5ĐEĚ[o›Ó ű€ĚŻÂ6€_çĽĆÝxňA>Ołëxô>×mó<6ôҢ—Ç+=>‚}b¶4öó…ËlĆt_ěr{ą)ęfČ?Äfí{ý4vbíľN3řEěAŔţ7› ˝€1ßßq…?Ó|jŚíNŽ'Ó§U{˙Îż€kbDđ!zâź|-,OuĚíb‘ÔYśęúb$+ЇřóżďëkĎ"÷>Ď»µŃ]ŘTÄ™Ż ;mĆ×Ŕâˇŕ ]/´Ďď3\·'¦MÝ&żYëU?ýůA‹÷ ^zj<‹8±ě*r&«‹óç>ě.ělí[üąŤ3´ďëľ6vťÉ äíź]=ŢńbuˇĚçkń®ç»ŢĂ®1{ý‡®Ąľ‡:ňüŕň1žűyx~`ŹČźÔ‘›îEfc_á+Őç„­B].ý,¦ŤOo”÷9î' ·Ćń/:ŤŻHŇĂÄ|OÖţą1ôďŇĽZČYč߉zü»ľŞŁŻ‰7G~a¬Ńkü˝–×€~ŕkf1Ć„Ţz…ŹĹě‡×úzÂű’Őmüyt9StˇÖpŚŘkKýäK"Ż*˝F=>ş9fq*pţ ńŽnĐësb|GoŐŤcô˘x‘Ľ4eř_Đ6ú ÍrFŢj*hôŢζפďďł:‚C!ożďüŔ¸k¬ňşMâ&zÇč€1â UF>ěk\Ážwú1^Zt>ĆŽŞ|ËÇFMű¦,'Š<˙[ŽĹŚá;ôřE&\áx·ś|p0hЏ¸ç‡~=¶Ş­ľćńXž‹l†~Č«óĚ÷űX›Ľ‹ń!·ÁŚ7±OÉ+#óÄ.Ź5:ËńŐ„ĆßČjË$‡,gMcŁă†µ0›ěŠ #tň#{YżÇ…‡}#˝=A­Óí}.ÄĘ,†­űŮki>±Ö{Úő¶%ą(“9ßuÜ‘·ŘÔ}śO‰ŻX,€˝Č üŽŰ‡Lí;>ů˛O”=›<ůkuLWů;ĚNzqĐňRĐ)u{čźjŕ›‘zj/;Čcč‚=UsÝhµjř"Z›ö,±XÖvŹË‚*ruĆÚâ1<Óť7ű8Řwdľ ksÓű[°ťmíŘgů+Aż¬çK ł%ăšÜ‹ŹďA#Ň[ísĽżŠŮlş—Ä8Ŕ띂ǠK|ČŠăÖö“ĽÁéÖüĆ—ř;Ě/DÎîŤőÇN"ĆůŞÂuŐ>—őVs÷xŻ‘6ŮÂü_ôŻjí·;ťXo/Ç<6l'bë_ôg\Ż;^jč^|3ěŢsśŽř×Č—†?đÉ âqĎŚcđűÁX×Ëś?Ółĺ?Ď=ÇyČö¨í ľ@ç\÷lp~1;óŽÎ+ô;!w†ŻcşęßśvĐ}C– L˝~=rIcüÚ:r{+lpžAť«Ĺćn5c'?[7Ô`g®’Ďf=‰óm Ü_/8üGľ» ů ą1ľĂń…Ýa=^ iưIŹĆšb3üÄ׋ú6bßĐůjyĚozVŚăăao×'Ţň{Ž[óÓYűWoH÷±ßßÓôpÇéśkč•ÖŕŮ0ć =ŃyÉhg>ćF-Á×|ÍčÓdň› ť·ŻľĆń9ön—–÷‰ý^¦k6ůsĚ%÷€ŻpµÓ*?ô”‚ĎlŻ·ě„1lhđ]±¦—¸Źe60ľ v tuç&ë°1Ř'K>ň^ľfŘůÄĄ¬víŚĐĄ3ˇákôz|ďoŘ»‚FÁ=úç>1źłśţ«ŘMŹvd˛˙°Qoő˝ŹD¦ˇk‘eř¤ě&ŽĎ†_ }~Âs MžAÝk)ż Ć¸¶n“ ÇőЦ#î Ü—do8€_Eݵ»Ç€ă+t ŤÝĎëçń»-fíÎ?ž‡-űF±ÇÍ6ĄÖśÖbČ1äđVŻÇ¶X*ňöďś–‰KŤÁŻč†ţłÇ]ĎŁ3ţ:ćŽí~DźuÖz!^#cµ$ďtÜSźdy&jNá]ĆClů Ołß Ú#ľ4ákf~ÜeN3ÔťŰ> äýŮ®ŰL^ß/čLôV»«Ď«=<ÖtúđŤńţ]łŘĺŽ'[áŞů ×¶żđˇŽG«ĄxDŕöćŕëɸ‡g!W?ĎĽ8đř_KŰĎňÄÂý›Oů;Śv6Ë~×;ćÓ=/ž üdčDp„\ć9«ăÇ®ţ‹°uďĺ±xŢcrőL§żö‹\WCŻŁm®·‹±\ĺşÔět)řFב;ĽC\łłĺí7ôw˝Ż«É©ŐŘąż§ĹĽ“ݸćcľî&Ży/ńRô5ű3ń?.ŤµwäŢ~Ö×ÄöË„ž ľktŠß°ľWmżřŞŰŐĎnóÝřľ¬ůQ§KčÚň°Đ0>?qâ/…owW§5x:łŤˇUtĆSť¶)›_Ě©ăĆçŰ ÜaS=ÝiŃrAZ7öfšmą¸ßëů4˙›Z)Ó‰Ä^Á/qOş.°q퉺â”˙îă›đąŮĽ.sĄnţb¶řŢ{ť†WC@ŕ?żŔéÓd4űĆXSpM źqÓă±f'cÍôjëÍŻEţ ‹V㞼¸âbü}ű°°ó‘LëeňřʰóáMěßĂNKłĽ«ă˝†­oµˇ÷qÜšľpĐë|Đ™ŢOM,˛´Nť7îă¦×źŐž~!h_şžŠőůK7qoôy ŁÍ«'Č[úý’?üXŕťzż7;îM6 ąÔŻ5ó®w„Ž_vZ7ýv»ŹţV<Łă¸4ůłZ×ößĂaş›»‚—ľYx˙ż›Ř+g‡‡ýOr^Đ'>˛÷~ŤéAÖݰęăÇ˙{¬żćKť Ĺ‘mŻőő$˙köŢj <,¶ő==Ţe】ٟr´|r÷©NŹř×ěë6†ŹüWťÎÍƦŰďČ*z‚Yţĺ•1dńÎ#üżÍńbĎťŚ5ąłëÓ3ĐČBĐÍĂď6ĆG~oqľ5OŤř¸.ř‡řžd*}Ϭ—'6qPd*Ľő!§ăă‡Çş˛ŽKnëXőŤN/ô2ąBýÝĹľ®&~ěkb1-äŕŠŰÁčoü?öĘN%ź‹ď}§°=XGüň?ąĽ+ě+höQîY ¨ÖÓö,Š×m?Ý]ćŮ>^d{Ăk›ÍľgOp|` YÝ ¶öŐÝ|ŻéܧÇř‘óÔK}××Ůx_š÷ág [äńnËŃďąiןtü¶±'Á-c??Ö‡ĺűť/Ů?Ţ«Q†~7?ťý’Đ|‹ďđpÇź­x†żO:,?óŢÉ#c/q›O{AĐ9ĽôŃÇŞ˝€^řv¬÷ă©q/ω™ŤL ˝(ř OŠ÷qÝKśžŤî.ńu3˝}NŕíaˇŹÖ9ŮÚ6B‡ĂcÄ 7;M[o¬ç,ďţ˛ Ł_ < Ţvůý]«}DĽwĘă!ÖOěë~-{qĚ÷ĺŮв{9QëČÚ#W…!K8Vë¨kĐ>˛E2ĄľŃçOŹá:µ0đţ’d̸ř±ÍřžsÂŻű Ż?ýéÍů"rŇě‹&ž_ý;_?ä&˝ČÚľ•g8ŇźÁtţďç˝·: XÍńNÉfö&7É/Qˇřf§!ó˘vĘö@˘#Ž»Ž6˙ůxŘß»jošó3Žoöü°÷»ŇzZŔO}Ěěé&ďŚN&çÜDvB÷ߊ}ä ‘Wň«©ažBŽ /ěÁ±wţšŰOÖsíĄÎ?f l/,îKżjfäŢŢć2Ăô^ß×}Ţ ®óŹŐÚ>·'FL|×_zOMóĹÉß˙śŹÍlüěVümdóóü:ëe#Z©Aż?rü™˙×Äôč#i=0ˇ;äĂwą\éEzëŮ^ęż÷uŔç ţlq©‹]67ÄŹµGy¬ßf|ĘeY5ę$jçůĽš5§qz3ŕ+›ďŔăĚOtbý§>áú‘}$äÔéťŮD¶OĽ‹Ëú‚™=Äý? Úwů…ĎT{žŹ§vÄyÝö(Qoö÷ľľÖO‹µí8Ψó·:7j‚~ÝĺŹŃy5t 2‘}Ëg:]YßWńGíţ>güŤÚł]~ÁOf#’_űçiŰ/_¶°ţMŘ ÍŻÖcšFäí…§Jü[#§EĚëFç}‹9!7^'Úz“Ź™zRâŐ[ ë/fűŠţ8č÷ś u|Zxő-Îoě[´ü ńň ×/ţ•_[;ĺň©†=Rs:3Ů+š 7µĺBŕ1ä휦¬N~B7Üěëcµę´Ú¸Ôu–ů™ä ‚Fju:±}ÇśgMk\ő†ŤŤ_5áx"Öh2»ěĽF2jСđ˝Â°é}i˝EŃŮ[üčýŞŚ©Fž&j·Ě^dmżč=â’ócä±ČoPűC ě3~ťŐÔŕH'Đ÷¦Žü"ţB<@}űýŻŐ¨yä]VÁ‡’ë;ĺĽG­~Uş„üęí˝ĎŚů <ľÄ–ű9§'ă­ËC?@kZwËżk˘ě@z+YüůΗ¦kŮ)zŁőfĐoęb­˙5ű¦”<§ßžŐ7ĘFh##±K˙Č÷ĘĐ×Ňö÷i]ščRę Á«\ö[Oyh®ćr„op˙o}ĚmyłŘ·‚®}ó2ű§M#7®v[ÍjßµŢôâá ͆óVö)ńZrÄÔ’R‡řĽŕ| ä0¶ăţ”Ë=˧ÁK“Î#č'zŁá1'ł°ą±Ő/vŢ€_­wóX ôÚ{śöčŻmńj$ω‹[śgy\ŹýőČĹ›o…ÍđR_‹±?Ťůť/­ö×\‡Ř¸ oäű:ç/«ß8ËeĹs‘C6őę6ĎÁ6ÄWü/˝÷ÉÎŁ¶o ës„vŕ?«;»GČę÷8ý7ŕż/{^Čężďë´iuďqüYΗ}úg†Lř’ÓŐUÝ+pLLA4iţ#5źŇ+Ô76¸Ü!żF]‹íŻŹ4ľĽĚ^|dŰU.?élőňŘRĎvĽZ]˛˙énK›Śd®? ó—\޲?ÝâÖZzXᣀ?üI» MâŻÜěű–ĚgĆަNäG!$“čsk6ńoęîád~=ńSläÇůü¬O±RdyÄ•×Ö‡é^ÎçV·@Î|b{Ěş=j˝˙^ń+|ş†ŽAfSc}~đ"ĽIśŕi1WüK®Űďýé°#čiôsČe‘ĹŚđé±ĺ¨ řyçëUD ă..g-^˙~çeËŃŢRř7+ŕ%ôĆdý[ć‚wŃa«qî3||ć[`;Ľ×yŘö‹Ýks^Ľ ý†Ż-qâ9ř'ŐŘŰkľÝÖww:ů˘ßCÍ“őŠl8?š<†W©"ĎE –šQΡ«ëű!¬ üŚN†^e;4‘‘č†"ÖđŤÎGfjĽ|·Çćńu§©ÚşÂk/ íűşĚ$'Ć~&Öľ¤÷KŤqłWڢ͎wÓźÄ/ßíóˇo2śŢĘŔ¬¶÷ýSxřQ1˙nOYţ»ŕsŽË6ťvńÁč}Ś´ýâČŕÄ\ľë:“ľ7Bć@Ź·‘ĚŢ;#řęć°aîěcd˝™ł=óaÎăVÓöI§q‹a˛Öř š†uř—Źw»ĐěܧůرéLľŕÇ>ËéÁúrŔßÝŢAÎŕÇÔą ţĹiÄöę}ŰůĚúm±w„5G/üžÓ055Ö‹‡řň„ËUŰ[,9Y§&śwMÖ˘Ż_ÄϡwěOŮPŘÖ¶K´‚~ÁćŦÁwŻ-.ô >‹Ó»Fž+ü—gěŐCÇšnă™ř¶äC^ôNŢ |Ł đ˙^ě¶şŐé2'ę4Ů3‰?Dľ?Űá ÁG8źň˝ě~ëű„OŹ]-BĽ”g#‡‘ýźóőç{ ضçő·ő=帶˝O Z-!ŕý“^“j~7úćE1.Öžš™¶ŹˇţĆ€Ż ™Ôudµqׯá/7Ľľđž|ď<ç_ úźXîšůaŹĽÍéÖjÉv]î´e±9ělj\źXxlčż‚·Ŕ×}ľź®»l4ż:řrČç§;maŇsÁěęË^íňÂtŕ]bŤă@ż“ ¦„ş„˙ă~łĹ)‰wŚ­ł· ? |łÖŰ Ź‡˘ eYťĺ˝üőŐ|öÓĂ^˙'›ůwäÁ˙ÓůżŰŇ|IlúőÎďôtÁO¶÷ü pňç‹ĺľÄiÓ|ŇYçob°ut Ž-¦……m€ ‹íí’{%N ńIcÄ~ße™ńűł¨ žňZ×S¶çw—ó¨}Ű_ş“ľ§ßąŮÓÄýĐíčBb–äkűnb\enđŇÝ‚ČqĽ7d<<#ýV×|éËn˛|€Wěf|&|ňG_ ţB÷ýĐiĘľçAľčÎÎf'Ŕ جÄjr[Űů\Ç‘}ߌzkj­î¸»źË«§»Ó•Ů­ďpYhşZůąX—CńĽ}±Ţđ ~ËYÎřO›@?(ä&4OěaŕôN®Ň|,âLŚşąlµÍŐ~~µŚX†ĺˇŞ1çGűs-7 îîëĽ?ŠŤžzźÜ=ŔĺŠŮöČ;âř7»ü1»Y´C şő˙%ŢđíXCd+1ć¨Ĺłş÷Ý.,&…\…?±ű#§°Í‰‹Ŕűŕ yěýżŮČ˝—9ßÚÜžéçéÉhyÔU˙]vÜçcűZXO˝Ł&™gßĺ"Ć‚őóŽwËĽ/xč±čÇą<°8BÔˇŘZâ[<Ň×ĂxŮőV§;óŔ?>±łř­g ž?ř¤ä qč…kÉ!âsľĹéŮ|MÉÓ;ŕđµ{ĆMżŰéÝěeôă÷C—Á_Ô꛸ÉăKÔĘđÝ űľŮ=]_ŮŢŞ÷űÚ ÷Í&üŽË‹ď}=ěÂťţEĐ1ă#O˙ZÝ==Ö[}ôU_SŰGüä1%ňŔÔ·Á«[ÂćÔRbG‘ł –ôő5ĐŃő…Ç9­¸ ?;čéN;Ć—đ!1ć3}í¬n‡±ˇ{Ĺě1ý´Ó*ß6łÜ(ű¨ÄnÁŢ˙#—Ik&ŢóůÂm势×băOń5łś ±öżtš6˙ě™NĂSűíX;p cĽÔůŃĆűÇ…Ůä?±É]bÓB×ßđąüőnO[ł7ďň[ྎoëůó}Ç)s1ÚfMÉámŤµxNĚ寝®É+X.Ś8ÖCśÖLçR´1Ö>_ďc·|ďfÝ…ó:ţ'ď#–ý“x?ú [™Žî&ďusŕçe~ŻĹG_í4mşń˘ xs§ÓźůĘđńńÎŔű\~}ůD»ĺ:Ţî<ŚĚµ6ľ5'ŘĹźŽąc—cżËa†7x,ö”Řž?ńŰ»ňAżč4i÷A#ßt4_ýťsd=yPôŹü#‹‡Ý-Ö|??žÇňťÜ‰u}•ó‡ÉpôÔ‡ť¶-7~}Ś? [™ ö)ľ6Ţúű,Ö„îÝá2Ćę­ˇKâ#čß ĽÜěúě+q.ěŰ·€ßţň°ż“Ř‘Č5d<|łşO<öZY_Öű9Ë8ż´„OqqŚń©á ’»x–ă׾U|Š~ VcnŘĂT‹÷ţť 'â-‹Nć3Ö+ťťMÇ?ě#â ůmÇvžz¬Ë)ăEüž},îG°%.ůGĚ>ÇF »Đä1křU§Ećf}jn)ĽY~^Ľ ›¦ăx˛z‘o‰őc|ř5čedËo–g{kĐő‚óz>ŻÝÍ“ÓÂvEo=Ăe—őł‡'ѕ䡑…ř÷v˝b4÷)ź·ĹýţŃiÍô[ġŤ/٧€Ś>4vKĚ3ô†ýů§Í>ÁźxŤŹŁľ(kŕťĚ ůĆ~Zt~;úüď}ýMrÝ>4V‹Ťţ“ă‰%3^{mĐĹJÜωq»g:흯„Ś}ŤÓH±ZË ]=čý"·ĎÍŘęϲľ™ÄްsĚ;~ąÎyÉâIçĹĽľçëÂ3·˘ ËÓ ‡‘÷8…Ç˙8®ż˝ăĐč’ąáŚg ç׿7pÁ3đA±×4H<ůźľoŕ®4­™ř'Á3ĚßĺUńĽŐ:â˙qď‰gŚ9ÝCďÄ`‰ź˝ËéŃrBŕ/ň$đń*4N•8ß´ŹŰ|‹]!k ?o2Ű ~ü§Sóµ¨çCö2ź+ťVŚ7‘ń_öyÝË{žx:7đěřBĐz ÚÝëN^ěš ‡n¬řĆř/§łńŻq6{ăŮN7ÖëŮÄ|ońő±Řîłc\ĵ©[@ź`ź}ľđçX¬ÍúŔˇśŢĚ>âZô÷ÇůŘwčXlŮŐ­9tŔßůłŤ>ń.qú1\“ű‡ŻŃÔý±öÄ(á;ěfěŞí.+ŚF°áyx“xÖËź#ş ů‰ÝK~ßjgĚm2č Ű úůaĽűٱíŻţŮçf¶72–şd9ţúčg.ď÷!»Y“_‹ů|Řqnz˙ö'Î+&÷ž|ôµ%»O¬Íř5¦§3!»đaŕťOĆ3‘Í;\Ćíô+›żŠů®Ú›ŘĹČ<řzčm’ŻDďbkÎ 2oäĆ›'čRř™}i\ţ†OđG9ňřřRŘŤČ ä#ňz: ţ‰| ťĽ>Ţ?|pĚy2Ö ą?c‡ŕç>!đ.ľçwř˝f÷Ŕ“_ Ľ`ŰÁ—<—Úś'ýŔďʱ‚ďgÄďţ¶ŹÇüŹWŐ=´ĐŘo_üuŕ{ŕâŰA›Řßč üŤ»lÜëŔ§müLŕŰ{çcMéc¶őfĽďř­X{bfđÄúŰĺ1ľëťľ ¶.p[Źń>&Ö[Űí˛?řî_ ś Çř>Ń ČL|ż{Çş c<Śű’Ű?ň9›¬†_Á!¸8äs4:ybáúż›š¸ěsŽ\;ş{ëi±Fđ5&ŘAä6ţ)ĆŔúđ^ř›ęsŽ7ŁŃ«Bźă#ŕ“mŤ9Äľxa#ß.ćŚţCoŠ÷ Wn)Ľž߀µ„żˇlʇĹsnŠńŻöIŔžţŰXďĄ;×=Â×ÝlÓ3ďĆß—ÇxŃB_ ĺŚíßü}¦s:\âa|"háÍŽö ™˝{~ĐxF6‰Ď¬61Wh}6ÖţCg`ËţgČ·ÄÚB˝+óS¬Ó|,f§?!ž…Üą{üý– ]éb‹­Ë}ŔłĐ쮸÷©±n\Çşăą&ŢuC1ęŤjş;[‚=ý˛Ŕ+4ţéŕY캣A/t\^6Ť0&ž-ł@w!‹řbäşîôoĂ‹Źt^t°'®oĽóÉGźÂÓČä:áDĚ—9=%Ć78žŤ±m|ěyn÷}5Ţ…‡&ř‡] ˝žř=óxgŔYĂvĽóľ±¶[c]C%ôő•sh{™‰Řcv7Ţż<,Ćňř€ß)ொu@N}7đ…Ť Eď Ó𳡷ͱžĚĄxC.®Öńí÷?5Ć}E<|· |púć•gć4ăÁ˙vŢ8źČ™çĆśˇgÇôxy<ďŠx¶ r›X<†Ý°=đ„\GĆ Óń“‘ Óń÷µŁGÄߏ‰őĺŮŘ©ČyäĺCbLë˙ŤŔ×úŔÝ/~.Ľ0ülhň¦X§‡ĹśVó÷‰ç0·»ĆřÎŹuŮsŠůňŽŮŔă÷Ů1·ăq˙9ńÓ,FuÔ˙ϛҟµĂśń·năú‰ř}YüľĐă/˙ż˙;çż9÷¬˙Ď«ü?N“ĂúŃŤőIüźüűXúsűóâŹĆďkĘ=™ŤĺóńÇ›B×ţ/ţŮ^Ł˙Á?űŐ˙ňŮ˙íżKÖ/ź~ɦǭüÇ˙ČĆ{łmŮßżń?Öč¶Ăę~UɲŤţé—ŽĎüôsS_Ž?ÎM0ł§ă_cň¶ďkČŽ¨Ż]đŢO˙¨Ł·ű˙8·Á“–WXý›ý€Żź“×řŻú‘˙ţ=Ąw=dßůŐÓáŐůÓa?íßÄ•?ýý¶ňÔyTWyć•ţ‹>¤ĄOWo{#Wű-­8·*_fąÝ˙Ĺż:a˝˙]~á<ä!ßrżÍ{ďçčż˙/qďvGQ¶Ď9gO{z/¤R!¤„$@ @ˇ…N€Đ‘^Ez't‘&ŇD¤¨ Ň•¦Š ‚‚€ŇEé Šĺżď™űÉλś7€˙÷_˙ą®ąvwvćióĚ33»÷™­4g ȇ<ţj5?˙żŇî:ď÷Ů2ó×ƹ۱źŻlöWx(=÷HÚ¬W㲹hěá»ý¶ťáşě;ů/Č”ëšG˘ëŘŹÍ?’®«×'D‡ëµ· ďÔٲůYÇÜDG‡CůĐ®ůúoDÝŞ ÎĄ0lť)t Ž˙ŠňNíšćŞ_ÔWüŢÖř5q>Óň9ę~Á÷rü"?˙ľüÜOçççš6ĎÜŕ;vŮ›˙hy®kÚÜs˘éî7lÜŽýë%˙7ăźy>/ćÚŕÉől†ÖýYÁűřśt—ÎŮĹß4CĎVŤÁśźEcÇŚm*Q× ~üËꉹfi ťc~XYšąß;s=^ř®~ÄÉ;ŕë»úů˝˝_ţ 9í7KÇX.›OîľĐ/˙Gť ÉÜíćWĂľźú1}7şćçNŇ9źžŘóĄŐÜËÎ_đóß?Š•ôÔcśô+Ż•Qźőë,üüw ˛żC":=Ł|ö/ĹďćGܧ Ú«`ôwhP^?~»Ŕ?ĺŹë‡ěX‹_··ş®ď×]1Ď©Ť‹u5Ç®łm1W)ţH+>]&'[Wç*|žňX¦LÜĆ[v-îŞß>ŃąőUÎËnŚňźŚÎc¸Vś×@>ţW•uGuÎO˛1&ň‘Oů8wę8&“żG˛ö«"6ÎútvóvéąÇüŘo™ŽűĄYÄDúőhŁßµŃ9ßő¨\˘uKÂöţkTć8ţ7|TcrĄ5şŕ3[ÇxŤÇXĹv˛g” ~µ˝ť˙.|ö—żćÓyőź§çţýß=î÷ér«hÜšÉ8¬ë˛ő“4˝)ú¶7ÁNQ!ô­ÂŔLEÍeý3{5ňÔť~ٵ ëŃçvIiř˙±Ý•Y”©ąrÓŁá´­W>ó4ä;̵˘:öž"ţýŐ}ćŻúŚžÍĆŹß[çŻ~zŔkřźÖÎżlżGŁsęk}…68ľ3͢ćUÜ'ÎăŽ3ý’ż2}”ý{ç4Żd}~W¸+Sá?{Ě!J75Ö§ŃŻ˛šąŤ—}¬tǧóăůTáO]Tž¦ŁÍ)ůś!k÷ë}bĐr?ýtţ˙üŰKtąBĽŞöd™YćáŕqüöŰ:ě·‘WěöߎÝ?˝í˙[Ę#ß'pO»÷tĂúô7‹•Ď<˙á\ŇÇľj”™™/•®ĎÔyA'\cźÝťóůďęâ´ X»ř—h\M˛ĎPbŮrÁßüłŐ.~ĺsdęy]y×Lţ 驍«.»>çóüűtţf:ď0yřËgç?v{±î7)·šg/ţ>1Ńó?˙¸Re_× 1˙ý ş”żÚőýrůBB|ß Ź2gtM#oýÓĆ)Žőśűm.K“ŁÂ›ů_Ź×ôiu?ö]î/Ŕç’3Żç9Ĺě2÷ĺ°xxä§Ix<Ëíňé{¶Ćö˙3Ë>·h´®Dßí¶8ş&Ţ”ď2v×őĚpḨlMÄwmÝ;— Âu&__Ý3>{¶¸}”·(s­_%ćÁ÷ÓWž G~‹Ŕż7nô‹×xuľ•k`ßRçŇąó˙îţŔýQKśsgűćçůa\ĘGóůUĎƸFŃ<–ßńż.žŃU1îŐâůçĐčCŤ6ßőß?¦Ě§ÂˇD±ąćpuĆSúA¬OűçdDÜ ×ĂzXë:?gĽń{»Ť¸ź—ýÎíÝ@66DóŠâ :ąáÓdsŃ{#âňŮgWŠ÷6ţ”‰ŰSyźó˝O'~çß2ůě˙ćŐÔѸś‹Ö!Ĺwć‰ßűĄ‹_’™xý­ď|ĆXžýĺŁ1ą­Yć˙_Ç™”DZƞ±ß¦Ł=wµy]4óßą`VĽőűÉŘ/öâFřß”Ktms‹GŮţgď,˛Ď…O |˛^&źýŹĎŐµVňß%°çUźď6ňńZŤĎ†ąŐ˙–şÎëZöąh>XQĚ÷˙$/ŽZĂsÝRŕ0ĚŹbą˙O¸­kőź”ěĎçcJZ.ŽNŁ÷ąeQ™Y‘NńółďDĺŃÇ+öL+łĆÉež»řoŘyôŽ6§ů˙’·Řřˇn˛_3^śˇë虝˙Ůú‹ó‚Ďý;üżUďŁő±pąď KZďů}XřĹŃ‚úQ1űěău1z¶eăŠÍw¸W®µplc[Dv*=•±ufć›ýř??Ďcgý÷ż~aź©T05¶aâý+űLśrÁo“żčâţčĆ;ťË%šoć†wÎďô¬Î5§7ZËpŻ>÷¸'Ş7W' ~éěůu47ČĹó,Ó?óL#c´^ăĎžNUň…Čd?›cÓŐZČćűQ )˝äü3’„±*ű®ÁŘňą6źCgĎ5-ćÜĂc±ńËý:k™ç”É’ !óŮx>ť)S0»pgńłQ[DkřśĆáNqDżÖFóAţ÷-nŽ éâďáŮ–ĎŹŢ1â±Á~6VvŃü37ö™Fď46IËř}büHöłg•~źhĚóϤ»XCÇs}˙ýÚ‡±ŐâÚŕLůŘŽ.üď‚­S‰őśůé"|–RÎΡí?łclçČ8Č˙3Ů»Ž“qź`ߡĎń9˘Öăůěs<ţLďşľqŇŕ÷©x˙¬ß“«)gż¸˝öŽÎŻĚäÝďŇv_ ćČăhą‡‰ýÇI?îóŕÇžěł|îËbXZ§=Žé\$˙|t18=őß9ü×Ďë_¦]ąV!ÎÔćLzÇÉ˙MTµfĘĹĎ ěÇő űu7]ó˙żĎ ÄwŇ܇’9¬m‹5_˘ą[ćÇďśş8¦h¬(˝¨ëł28×ăĽ"‹ŕó.Ś›ůFř€L|đ{8Ř/űÎcŞÍŤâµ â“߯ŠţżŹăś1ÖŤřÖľâĹ˙(dźá›ęťB~÷Čß#ţ9ŲRü~^~濯×č§±>ž/•â>ɱÜć%GH‡kz8NeßpŽţ`:_–ä…§µŠßKśĎp˘q8Ď˙śä:˙2ó˝¦ÓÄ9÷°˙:­bťŰ܇}D1Ä˙'Ľo$“Ť;këhďô_sťőôž˙ĆĂmz¦Ŕ}łřĽźýŕ0g·9@?ç7ŰËŻ›Wä]ÚđsałÍ˘y÷Eň?ëzÎĺ÷ŤâŢĚ žUúqŤć€üďd<÷š¦çě˛OňIççóüůý›2cI§˙{¤ľĆ÷(y‹źškČOďÔý·”¸g÷6"ÖŮŢźsýśHţď€6±ç錭#~ěSÇK÷‘)Ť‚=ëĺűdľ«âĹąCIySŮsnŐ\–8ę:÷uâľy˙ Ź÷VÄÜ›ß˙ŕ¦ůě‚{ř˙˘?ú·?ň]9ĺ~7<—ô˙‘ä™7ůĽă˝đŚŘŻßv ó;˙.ú^Íóß÷˝éżąţżcś_ÓĎiŽżďhřŕ;púËŤ!Ď?ý™sUbCŘ×~xűw ´;×}ěç? u=?úăźť0N2üBĽůîăÂĹâÇw¨\gż.ůř˙ľ§{É…ą mÎXţ­ «_W~$şUt>‘\—JvĆ?Î)Çźr«ľIée`yű?Ú ŐůžŽ÷H_®EťÝH|i+®ńΠΉg˝PvýX¶alŮOúÓ8Źä;ř3DgĄř0.ܢű?ĎĄ:^¤ĽeŇ[ű"ú˙ňÝ®kĘqčž.އIçEăj·ę["ŢWU[SŹť]x×»\őK^3#HÜÚŢ:_"ÝvW}¶+׎|žt¤ę_-ąř çk˛-ń–ě[ÇŠ˙ăŔ~rś űvvSózägąKÄgąčţ„{0ď)Ů)#źCotô{ Qvöám%Űn’˙Zom'yWčüpń8U‰:sÎŔ9ǵ­E‡ţĆ÷¨(.ýě˙‹$×Ń’ikѧOŘsg^sýĽžÚjąňW¨Ť–+oĺ±­ć‰6ű }csÝg<ßAöZ¤şËdËĄ’íěí±ŻdŘ9ŇŰb}…cŢDŃŘF¶ŰL¶ß]÷ÉkWŐŮH:SÇC¤Ë¦ş7CçKŢ#Ĺ›4řű˛+éÍ”ěKUÎÚ~™dßPşî'ş ¤Ăv’ńlż&šúO˛÷=Ň^¨6ÝOşŘ±‰hĐ?ć«]h7ĆĆ)Ş3]z¬ďŇ˙­MSŢÚ*ł­äe™SĄďBÉA}IôYç,Ů`±lC˝¶ťyâmůóŐf¶ďÄfşżd]"[LTţŽâ»Hö"yţU]S†ÍEo™d›©ü™˘;[<6” ÔwŽîł<ç+łÔ^$m1W¶)y6”Í—¨ÝöŃ˝ťuˇű1Şg¨ěa˘5E6Y*ţÓUoѡ}ésëJţUN˙źöí0GuN“,›ëz¶čĚŻuĄÇ޿Ţł‰d#ý7Uťi˘EÝwUĘ;Aĺx>V´iëőĹwWÉH˝'‰źńç{ł!j×ídçQ*?A4Ic'É5O¶ŕ\”q“ľM&×Ó=Ę6Ů­úv­o&Ć»E҉1ĺ2ɵťĘ/’.<ßXmłD¶Ů@´¶s©_Îriż+=J^®a¶TŢH·ę›}ŢŽŁĄ›öAô6_ :¤?_m±“Kż—ş@ÇMTnĽx“îpÉ=S:X\ěŇţ6Otg‰żůę8GȆ,3H´ť)˛yŻ©v¦źÚb}tôť-=É‹ľČ¸?MuÖ=Ţ_Kí±ŹčNŚęÍQŢ—ö»©˛3Űť>»†Úq¤hŹ˙5Tžt6“}ČsBÔ>łU‡v / ýőĹďŮ–´9ßX*ŰNU˝I:nµÁ$Ą1˘;R¶^+˛‹íů7ĚĄ1hˇňć©=,^Ť– Śß¬Čv#ٶXץľ5Y6ĄĽiĘź#ý÷˝µ¤ëdŃš(¨î¦’q†ôµÉbŐ§ÍIćjkúó–â1ŐĄűčLT*ZÓ¤ŻůÝŇq±î–|”g  wivŢČĄ{"¬é9Orß:’a´ęŚŤ%şo˙E][÷ęţĆ.őŮ ˛ ď ­Q.ýŢtŐ#ľ”§‡hŰ ÍĆɤď-• KÇ1J;K–˘?DçŰëz¦čL’,óĹořŚŐq¤ä'ú<öíůşg˛Zś¦¶/y×Ţ e·^:R÷˛ËŮĚl:]Ľ¦I–őDg°ęNźőE{˝GIßéşŢD<&¨ü8ĺŹŃőé4Nyä×G:ŮĚGGtíÝ}u˝ĄîOQ~Ů źňHgŽîŤÚq”Ę W$; íj“9.í+fÓ‘:_ [S×Y.í+kIî!â?\e¶ÔýŃĘ·óŞ?H|×P[ŮüdštéŮu´teľĹá’ż›tś¬ă•ë«ú#›Ž”†)oľě0Iô(Űfâ%üĐ*ş˝%ë —ú^—ĆÓutßäę§2]Ű{ëŢ`•[CeĚĎÍG‡Hľˇ.ýNßxŮÇF:őp©ŹŤĐµŃ¤˛ý#žăU·—řĎ%mćí©sň°ľ?PTż·ň»´őuiŰ÷Đő$ń˘z{‰˙hńí/]űI˙Ř.äó%ńěéiçäÝŞóˇ’·§KăůŚ]ú*YµxÔ#˛ń@ń"ľ}%ó—î3$Ş×Ou­Żöréţb6¶ôÖu»KżŹŮĎĄţmíŔůËDéoýśzŤSył?SłKÇ‘şg}ČĆĽÁŞg{ŞđĽĹĄýżżh ’StŢ[×푎f·Éܢ{ć_}]ęďăUÇţ—5ÔĄ1–ů3”omBŮfI‡Ňm°č™Ś=uŢ":LM.ŤĄ}toKűp/—Žw-şn‹Ú¬CĽűF¶í©ríşŻÎŠźĹŃ~’·Od›éŃîŇq´I˛uŹt1źč®ú=tݧäěŐ©Eí`ľhĺ{‰^ʍ-ęşIç]K-ţ ß6%k'ë7őH§ţJă”gňńŢ Ů×üČę÷r©ßZ{[ü`ţX•7Ű7Gímű"µéş]4Ě7›#Ű[žőiłm·Žé>\ĺ×rißŕŇ=Pĺ¬N·¨śÉnqŔ|Ş9Ňa ÚŞ[ÔÖ<ÚÜŢüŐôéٲ5âi>dńż{d«±‘mÚŐNv>ÖĄ~l}Ą·ĘXĚjÖŃú†Ĺ X‹•ÝEÇüĆüŰxťc][$OłyŻŐ‹ăaE˛´Şn»KcOE´úËVÍ‘˝Í‡¬ŤÍŽÝ#ݬZßjŠěŘ%ăaů­*;4Ň} —ĆĂž:ou©ü6 T™¦¨mlî7Iů­âa¶ę-­‘­ĚÇ{ĘŽ6ď Nˇ¦Ô_÷lŽev´qËlmýĆüŔbŹůźĹŐŞx÷Wůµ…Ĺ[k÷¦Č>±/6‰·éß[4űŠN›ä6ßěń±ŘÔµťÍ˝¬]Ě›3mh±ĐÚĐlYUY“ßth’<=#:5ń2®F´¬Z23™:˘dţۤ6¬EeLVëf;łWŹ(ßúFU©‡ęk_űNşšüĽ?4âgvé&žŐHNó«f—~÷ş®DąFüŤGSÄ7ÖˇęRźęŮ7ăę’Íělű‡XźjqéŢmµgSâ6¶˛MŃý’ř”]m~bĺLwëCfWÓ…i°K}Ĺćú-‘í¬|“hLu—úWłKăXLËÚ˘îŇľi±¬Ů–÷ŠQ›ÔD×tËúPĹuöałMSDĂÚŘúo[TÎĆëËe—Ć“¦~)˛›µcłŽ­Ş×'ŇłŃj‰Úąń7L>‹1mQąn.ŤÖżJ‘.µ^?ŮÎÚ Éh¶ń4ţÖ­ďš FßĘW%O%Ň™rZĽ+ŞŽŤ÷6&Ôtż :M®łžOŚ~ěCuŮU׹],&Ú\Áę]›X˙K\ęKÖ_lb6±xb嬿X{ćŁ6ëć:Ź?+‘}şE4ÍG­˙>ĹčXu©O[˝VŃjÓŃúŠń°ľT”މKcR%˘oe ťë?ĂÍg‹QąféÇ ¦¨žĺ—"}­ÇuL/‹5ßbűĆ>líRpťű…őűö(ĎRénń°ńm‰ědôÍÇŠQýRÄ«9˘]ŽlV‹h[›Ô˘˛vŻ=j‹8n5Gö«fę$.ő/+czĹ60ŮmL6úÖŽćőOÝuÖ§ŕRź©D¶°ŘXvťű¸Ĺh«ëb<‹’Ѱş±OV"ަWٶăyŢĄ~dmŹ ĆÓęZżO˘{,›‹Ú¶éf>eşäŁö.+?‰Ęeý¬-Şű¬É™ŹňLθMM§¸^!˛•É`t*‘Mꑬqß)Ft­}bŠc‡Éhe-^ÔdłÄĄý%¶±µłé’w©ŹÄń ě>íæŹő‹öH§Ř^qžÉkídǸŤb[˘{v?ťç2×16ßřwdÚĆ⡥8öXą|D#{ŚýŮü1ÖŃâCVľlŰUŁúIÄłѶş9—ú@.*oíš‹ĘĺuŽÚcĎSůí âŽçşđ{â9ź%NŽP>ăűP>›"^ʏ {ĎĚçĐÄÁ">Ěăłxb%ř|×Öś;»đ.2đ˝:çÓ†ŕ3š1âß.~öŚŚď×=bËNsé˙MĆ«~Ey9—ľÓć\ÚÖgö “´ů^”řŢĄŇ‘ó]b‚ř<Ś@úßNV*(źĎ 7”ľ|¶Éw öŽŢžC·ş´·ąô˙8d—ĽęŘĽĆö§›&Ůě=Ą=Oµgö„6ŕücŞĘΛˎ{—ď\ú<Ńž—2ŽĚré3x[÷Ú»¤ą.}źŔçOëČ®”ˇ$ű\úě>4R˛Ú»H{ĆÉ|{]’ŤYw®ů¬Ď°P† °y¸=żłwTS]ÚÇşKßéâoďéˢŃ&YĘŞ;F:Ő\О~3ŔĄcE‹ęwH÷.}¦ÖC6lÓ5í<_ő¶UY‹9ÉG^“]:gč¦ű¶ľł9úÝ©ăs˘kĎ©ěyŞůěäčľ˝˙´g±Öţ9—öýzTĆćâö<ŞâŇwUÍâmk˝n*»ŽKÇtłˇéa~js›Zd;›wÚ>/C]ú~ÍĆy{ćÝćŇqŮžŐŰs‹qk¸ÔOăçţÖž5Ѱµ˘ÍůŻŁ­{âő“­ű»t^>"’ÇhôqéüŮŢĂŮ·ŹÇĘö˝]şN±gß6ď¶gó˛Q»Ř|Őąt¬·fłą=7oRy›źĹk‡śKßM=ťęŰ{ż–¨NÎĄď*[\úŽÜ|ŰÖ¦ö= ›kŰ˙ţ¬ŻŘ:ŐŢźÚ\~Kçgí.í7ńł@[ßç":9—îůló)»?c´5§Ísmží$WĽfŹ×˝ć+¦OÁĄĎl­nsŕQ{gńŘći%—>3Îąt]ckEÚrąęĺD7^;–$s-Ę·ö±µůť'.}vomŻl=gö¨F÷śK} žç[7»´oŮ˙™m—î®óšĄ—KŰÍli~•‹ěŻ«âçń<Őô¶9s˙Čî¶¶ČGôěą¶ékků>çŇg;ÎĄqĎü¬ĂĄköJT'ŐéíŇuŤŐµçM¶&µw|6vYě±5 ÍËmŤbóv{Ţ`6µv.Gel>·O.˘eë-[«9ŮÄä5ľv´>jşÇëcóĹń°v¶v°i¸[ßÇ˙‡5^ö ÇŢí­oŮű•şK×6ž9ÉUŐąů…=óŹmŻSÍźÍGÚ\ęóŮçGćgIDÇä°uŇL—úB!Jö 3~žaó ›7Xű˙x]Í{˝]ş-E<­MĚ˙M¦řůĹDçŇ8a1Áć,6.šźŐŁĽzÄĎô6z&ß ťŰŃÚ¤Ő5ŮĚ>v´ř·iĽÖµ9‹ů—µA\ĆEşZ;·˙öŰű ÓŮlimVŽęYł©‹ŽÖ˙Ě>Îu–ŰŽIćÜ|Čâ†ů„éŃÇĄ18~ÖÇË7;šLńŘ?źůĹĎDr.›-¶™îŁÍVöüÍdłw(ătm~l}ÁldôĘQ]ÓŐĆ5óÇn‘\V.ŚflÇB&ßlŇá:·‹Ĺ2ó[{c×ć‡ń3ëźqX?µv·9¶sťe·yaö‹ÍkcY’čhĎXŤ§ĺŰ/~†eóŰř—íc6&ĹóŹŘßl\˛·ťĹq»gĺťô6{¸L]‹áąFü,)»]$Źů¸éŕ\ç9žéo|śëěű±ż[|Ťß'X,4ż×Wńó8ç:óČĆú8®»LůŘÇăgťYą­Žő[;=ăŘ?o3ŰNąL˛v·˛ÎĄ13ű¬Đć1V×lá˘úI”Ç_KÄ'ÎĎgęŰŹ×1.ÂlűĽńŠöYYe+‹ťYŰ9—Ćľ¸Çí÷ăvK˘ú;»EĽ]”oő›Łű±_Äeř+gň¬Ĺţë÷ˇxŽaţšmŹ8ÇrÄ>dyl®Ë¬Ííg~ŃĺY˝x ĚGzÇĽłĎ׹Ř\8ž·ÇeMîb”oím1.{-n';¦Äó›'Úx÷‰\ć:ű‹é2emn•-›-ĎĂb‰űÍWÍ6µč^¶™­b=ă±'î_Ć/žßgeŰÄĘ™eËfő‹Çó¬?geŹ}Â5(÷ż®Ú#+l “?.c2Ç~Ďű˛¶‰qÝXŹbtnőc?ČŇhdó¸˝c=˛tM·VÜ·ťű´ŤŤfĽnŹyÇý>ľÓj¤W#ăľhżlŰŮĽ.ć•Ő7ëăö´5k,c9ĘłyE–źýâzqĚŚ};ëßvĎécÚŤâuˇAŮř÷[«kó›c6jSëŐč^±AąěuĚĎ’=w°ëV׸źgĺĚćĹ6Ť×uVŢäËĆ»¸nWýľ‘1Żbćş«ń>¦ű–‹ędËdeĚŽŻŮ_6Ƭ®l¶^¶}cľ1ď®®W'O¶ŽĹxű5šCŮ/–%®_mP6É\wŐďâvÍĆž8wE«Q{eËÄxŠě:=;oěŠgWvÉňŽý#~ľc4â1);ÇlD»‘>±LąLĘĘŮhí×Hö_#YVWľQť®|2+ďęx|V™¦¨LŁ~ܞɏÎ}ZţFkÖ®tĘúmŁ_vŢ™Ą•í›6‡j$ٵˇ=űĎĘ‘ŤY]Ů;;˙hT®+Ý“.ňłôýJ]ÜëŞýşš{™|ź%›=ClôËÎßńéfÇÁFňĹ4Ś˙ęč6O?ĎŻ‘îŤć4]Ĺ–®~«“guő>oÜčŠĎębÜ祹ş_WńÄîĹí»:ü5šÇZĚĎ–m¤CWń©QŚčĘG?KĆ,ÝĎ*»ş~fżŐÍc?ĎŹ<˛}1;gnT§+?p_ ˙łâô˙âS]Őm´vČęV‰îwĹżQLŚŻżHüýżč3«‹•ź—ßgÉŃhL˙˘±±Ż®â‹sŤűó屺yÍů±ţę|•÷»ę‡˙W±2ţ5Z—ýŻľô˙¦ţęÖŤÖh]ÉđE|µ«{˙ký®úzW±ďóĐ˙˘e˙żđ‘/Jë˙Z†˙Ků˙˙üýŻzuţćç]n˝¸Ü”sť{s±sŻüŰą±N:íFçöľŰąíľîÜFÇ:·ţ`çŕ8o çf_ęÜ—ć;7ëçÖŮÍąič?“Q*üqÍŰś›8Çď97ţlçĆýĐąQď;7ćĎÎŤ~éç†}âÜđářOçF¬ëÜ×87ôWH5¤†:7ř×Î Ůçsp>éČpˇîŔHâ|ŇÓ8oEęé\˙3qś†ôs¤®pnŔ?é—HEŢŢČ«:×ď.çú˘Nż· {ßH"aśí»y?ŔńN]OBúäés?ŇEHżq®÷;H{ ­ô¬s˝îu®'dě Ţ˝ oĎ+qś‹4çŃÝ_v®děń;¤ H(Ű6ę±éܿǿ„ô5$ŘŁű“Č‚ă¤/c÷{$čÜmß út;é9¤ŮČC;uđ8 Ç÷śk?Çď í‰4 éy$زĺŔłíŘţ}¤ŹqŽ:í7!Áí ŃYÚ÷ś{'$čĐöGŕ¸Ç‘8BǶë¶GÚŇąÖk‘ŢŔ9tm…-Úú!­‡¶î‹#x´n‡?iĹş®ĺŹ8‚^+ÚşuÜňď8^Ű´Ŕľ­ ×ňc$čÓRGúę´<Śô]Ô›Žă7‘Ŕ§ĺ ¤5‘7Łí[Đ–ÍĐĄůE$Ř©ĺ‘P®™ůĐżů$ř[óëH§y¤%Hđ‡ćŃÎ5튄vkz ×đ·¦[B‚˙4}ßm: ĽšŕëMđ•úWp„ľM í‚´3R;ěTßGŚÇMˇţ.ާ!ÁŢMđۦő‘ę·#ťŹD~°a|¨߬¨rČ«Cď:|´˝ęđéúHčźuÜ«AÇúV8ţGŇď:úZý Žv¬ýiw¤gĂ·Ujk#ÁĎjđÁlU{é§H' í‡túx z×ŕÓ5řm >PýHtĐFµqΓ©†ľT›Ô/ÔŻţ G´O­ňŞUřqö®mŽÔ çµú*Î!_-îUŃŽUřSm\E[T Oő$ô‹*ú@uąčÜߩފt9ʡOV÷Aş ±¨Š¶¨|€#d«@ďj_$ř\:WŃ>UاŠv®n´)Ę oWĐŢŘ® ›VŻŞ°íUyé[H}Ř®r–ó߆«Ľ¤zč;•WĐ'+čŘ«ߨ G}ôą t¬…ů*‡;˙ť† üŁ‚ö«\…ź©@Ż údeO•AĚ© Ő5üż‚~VżT˙*9˙M˝2úMń±‚ţZŮ v,W¶©@·2lXFß­ôB‚ţeÄć2t)#¦U`ď2â\ń¨üd8/}˘ ÝĘ·é~_†o–#Ęđő2ř—ŃGËč?eŘ˝ ›”a“2l\F_)_Śß,ĂĆeô©2lUFćw)Wv,Ł-ËđŁ2ü ü(útů P¦Ä2°[±˝ĽŻx –”aŰň^Hh“|«Ü¦:čËe´%żaZh•~‹#bA q˛t3Î1î•áe´ c]ö(ÁveÄŰeŕüwĆË­%ÄůrwѦeÄŞŇkHÔ>Q˘Îđ­ňXśSÖ?„ňľ ƧúZ í_:éË:Bîúéj$řD }Ľt•ęĂďJ»«ĚW‘0~–"!6”Đ·ř-yO“r".”Đ×Jč«%´ei}Ý˙¦Ęn(Z,ŘU‚?•0^MôĂŇZH°C :”ĐJ‘¦ Ář˝ű|¦ÝŠĺ%Řą~^Ä^DĽçw{J°y¶-a®PDa—l[D|*őABĽ)˘1NŃ‹A‚ý‹čE´Eq«ţ\Dź)˘ť‹óEÄôâĄ*qŞ1Ľń»áwEŘ­xĽč ˙żŤ„řPDś("áĎü&ˇŻ8_|Rex„oá×Eřrsš"üą*bl,">ŃĎŠ°=żsčy Ń׊_Ő}ر¸ľĘ]†„6+n§˛,/˘_ç‹sUíT\„„~_¤-¨é`®U„/·EB›1&áłEřYňáçĹŤ•?CeP/ˇMK‹hb“^Y˘ť‹hÓ"üĽČ6`bŚ(".űÖčpLŠ•P>yStáď |µ±&'˝ í…9G›&hŹq,A_NţŞűl;Äí힬č&đŹä>ä#^%7čú÷˘yžĘ^Łkô—mť M’“=_}'AŰđ{é Ú=9éTĺÁßŘ;ąXĺ‚t™âY‚8’`śJ06%č Ú,9\ů— ˇ}“ăD ţ—,ŇőąŞ6M0v'kÉŢŞ‡ů-ż/›ě/91&đ±íÍoŠ%hĎdKÉĹkôu~‡ĐçÍWý9*·ŇBńE[%y Ć=~ŰÜë‹>š`śKÖQ>ü(©«úw2Cőř]WôŃţ’ ý’áâ_JĐćĚ)’18ŇţđˇdĘ@Źb^‚ř• ˙&đŤúe‚R@?M0/H0?+ Ţ07/<q˛ś§Óî{9‘_Ŕ#o'đĂd„Ęa^]€} l_8g}»đr©đ¶ô)(Á {đ›Żôďć7ü–ż!VřSŐ_c *üGtĐ× čŰ…'U1­?.ÜôőůĽ†ŢmÄ©ü°ź.|Kĺŕw…ë”wyĐĄź(üR<áO…[D óťÂâw±ô»DżťíŻ1o*¦DůQâ}ř\á"ń= ţR8I÷1†0ţŽ’,§#Í/Ę8TŔxQŔxY€ßŕoúĎRɵ­î©{čďô•âIţXŔŘ^€O°F(Ŕ× [¨ÜlŐÇXS@Ľ+L ÖŹć*o¬čo¨˛CÄ·Â ÝÇ­€ůdëŤýs_ĺc.Y€˙čsŤřeˇ¤r ˝ŚůWÄ“ţŽq®¨,ů#ňűöţŐŮżuóˇâhóżűZ÷ź§?ˇ`žţůAţi•©…üBG¨›˙PôÇó[ä? ´yN9ňôăľ:ňú@‹˛đű€ůżÝřMé<ĆĹ<ü5˙¤h˘oćŃWňčOyô]~ł+ŹľšSy{ĺ?6ÉĂçůýŘU÷^Pú‰hľŞ2ʉ&ôĘŁßć§ůýF_ćzéÉ{XÓň;»yĚ˙řÝ­üʤć1ü^hk†<úJţn$ÄŘ<újţá`7O~—ç÷ČT9ćݨzwJWĚ'óđűüąây2ż“ťÇXÇřĎďzÚç© é O濥ĽoH6ôĺüĄ’WŃ„ŻçŃ'ňč'yĚőňč'yÄ~ł;ľî‘úJţ,%ô—üŃHWDô17Čź)ąNŐ5Ö9ůËUÇdFżÎď/z¤~›?Dz`.ź?B×´Ë‘˛-óvţ[Ř^ŹóÄ÷+ňčůăU†uOSZ&ö“<Ě;JôĐ_ň—äŃßó;‰Î¶âą—hSÎCEs•ĄŚĎňëňăä—ÍxŤ‘ßTuĐ7óyŚuůí$ËFHł%+iašÇX›ßQe6S»đzÉAű¨Dý¦ŞŢö’ožĘl§„ř”ßDúm˘ň{)ź˛!–ä‡ňŚłÄs¬Ęb¬ă·ńň'ĺË>Ôó¤ü(Ýgy¬ň‰yŚůE*ĂăL•A<á7ăóÓĹośŇ0ѧ{c%C?Ő™®ăTŃś.Ţ<ÇXťß@y“”ĎkÄÝüĺS}Úóö<â_±7Ź1—ß>ôr#ţĺ_0ŢçSó=%s“ę÷UÂ<Îsă{12ߡkƲ•%­šdCěË1^”‚ 9ĚErX#çsóWđűćľ.ueLCěä·{óUťłüoTż(~˝Ăąż‡ř—CśĘ!¶ä0gČaěĎa~šCśČ!VňŰôžżý†řÇoFćËr6ČýEéßAňÎ!Öä«ůÍőÜ;A~O±1‡ůDîcĺ˝ ˛¤ý–îżxząŢ”,/«ü‡A/ţ„dy]´˙ôĚ!~ć0źÉ1†Î-ČšCLĎ=&yž”nRżO†9wŽß˙F őßl~N6@ŚĎ!nç~«z‡äÓsWęâksl~7Ö’ü®··ÇƢµ‰Ęm¤´XzOյѤm'G´Ć‹ÇÉI=0y_Z_Ľ0úďÎĐýIş7Uő6Ý)Ňi=]ĎTbąĘ›/žËóŰŰ^–5eסJsĎrkK湪Ëă:â3VljŞĎsŚťüV¤—ŽäśĺŤĐůdń!ŤîşGG‰Ö•%˝’wĽä2:_s“s˝d—a˘5Z´yżMz3ő×ő`ѧóQş?ZuF¨>Ćd~3Ů'ŢďPůˇâÉ„qÜŹ“cbOŃÇuc´˙ľě(•ëPY[Uf¨tc^Qi ]Óö+sˇ<żaéëOÖĂş+WŠĘQ÷JČ[%O“ňš}÷źXÇÓdľĎ’dĎ\Yň·J–ĺ˙ ţčzZçóóćî•@Ó—™ůω÷ßUçÝ@ŰËQ şúoňş “ŻóQ ă>ť÷¤ë˘üwtŹe1·p˙ ůţ{őŻJž˙¦ůţ[Ľo¸ż„Ł—é_Şűľäű@ç&ĺýYzbÍéżáüoÝűP2› ’ŐďŻő’Ž–¬ÔçEŃxOňĽ*zď¨Ü+ĘSůRţ_UćmÝ{Sş<Ż:<ľć·€źÓý—"zWřďż*zĽ˙‚Ňó]ÖÇ|‰ß˙őôMľ§DŹ<~çÂw”_Tý߉ţsĘCe-ź{Ž=!>,Źy—ß‹ě1ÝĂĽĚÓ÷iŐ]ő0góßţuDëIĺ=ˇşżVť'•žŇ‘<žŐń^ńzH7H¦oëţŐ’ý Ő·rWşđ=Ţ‹#ZWŞüĄĘż^ů׊†Ąo(]«2”ń2Ý»$˘u…äŕůE*ĎółUçRŮîrťMú\(şç(˙ëĘc:KéjŐ#ßóDól]_¤şgKÇKt~©ŽgčČr§Gň_$Zç(] #óWęú<ń?_4x~¦čś«ň¤Źůł˙®5Ź'«>Ëś˘t‰Žg¨ľĄ3TŢhžŁs+{Šî3a]á÷XůbÝŕż3~˘®ŹŐ}Ęsś ű3)ůOŹĘŃţjT˙lťź˘2ÇŠÇQ’ď¸čú•;Rů'D可ăTö4•=6âkeŽU:*:ZšÉŁ„5˙NěaâyňŚÖ1Ę?X:™îVçŃă}¬yüwĆ÷Rţ‘şwňtéwÉŹĐőţâqęĄăAşwĘńŘ?: úĽŢ[|°fń߬=Hç{(ĺí¤˛ű(íŻÄs¬ÜÎ.|‡ŮčíŁş+¤ßrŃŮKewŚîďíŇoźŻpéwÂw•¬»+í¦ş{čŢľ.ýNű΢Á2ü^۶z»¨Ě¶ŇoOŃ ÜÓs{ĺí&š{D绫ěÎJ;)-S9ÖÝZ÷xľEtmew/KŰëHKt§ŹÉdşlŁĽ-TÎľ/ľ8şżU”°ÎňßťÜN<¶VÝÍUvsĄm•ś®7Ťę,ŃŃę“Ţ&*ł©KżmrďRŃaŮE:Z=¬EýţĄ›©ţbń^˘ĽĹ*‡5Łßgt#é‘-łH×E<6QŢĆJVkd˙­2¦ő˘ă|ɲžřŮ·ž)o‘ĘĚSą…JdŇ|ŃśëÂľ‘©>ĎąŻßŃžĄ2ł]ú-ćŞ?ŰĄßIfâ^¬ë*ÍVŇŕ>“kE2ĎýuD{–ĘĎPť™şÓE{¦ęŘ7—g(­ĄătÝ_;˘oőÇ}$'şô[Ë3Ł:Ó”ÖR˛{S$łŐ7Z¤1UôÖRąIŃőT—~ŁťůkŠĎDĺŻéŇýpyśŕ:k’őF¨.ŹŁ]úŤiÖëŇ﹎séw‘Ǹô;Â㣲Xűý$Gąô›±ă˘ëa.ý–±•ł{#\ú­\Ţ’ÉŞĽQşŐčŇob˛Ü`•éŇýí›’}#Z]úMÁA.ýćŘ.ýŢ`Ąş7ÄĄßeĺŰw¶ú¸tKű^óí{Y¶?$ëuŹ®-őpé>Ŕöť´^ş¶='ăď۵GőlŻSű6’}‹Čöx¶˝mďbŰ9ţFü] ŰR8ęçÇ»ÜS\në—k‹eőß0ťGŚż Ś!óŮĺ—˘Źl‚±ucČľó›+ĎÇ\o>Ö3A{̵¦aî¶ÖäÓÁw*ě0 ëďI÷Ž/Áő°Î‹µćX¬ëFcý;óÍ«Ž„,Ă1; e†aý5 m»ř®µîP¬ż°ŐCp>cü¬ó‡ î…LC°†Śuň ¬[†  A›…ýCżAG!^ ‚Żz yč×®DÂŘŮóČč3°ö5WĚçŔŢ0—€~1€kĚ“űáŘöčŹ9iôŰ~4Öţ}wű`­ŰýŁôî[Ŕ|zĂF}0NőEĽę ™{ˇ?÷ĆŘŇ óż^¸× kŢ Ű ë ^СÖD˝ŕG=1źďůÖr=˙‰fÇż'ěŇ}«Ç«cÝu{`ľŃńˇôęŽu}wĚ˙»C§îď|uâ´áË=°ë†uq7ŘŞ;ÖÝ0îvĂŐ ńľ늎˙"amŰą[憰OćrW‘íżÇs¸ôÓŚí°K|±|Ű!_;Öw°_;Ú =A‚mŰ1˙iÇ|§~ÖŽyU;úQ;ńÓh÷6¬AÚţ€ë‰ÂT€ąÚĐÎmđŁVŘ´sŘVđo˝ZžĹq őř€µnĹřÖ][žÁ9|¬öoÁz¶ĺßHǶ€F[GĚĺ[Ńţ-k[ +lŃB5âoË[HĐ·óĂfđj[ţ„][Á†.ͨߌ9U3tlĆÚ±6m†^-hçfŘŁň4Ł4ŁíšŃ¶Íđ‡¦{ŽşţÓ»4a-ÓŚÓŚviB[7ˇo4Á¶MĎ6ý öm† š wúVúSýmaŁ!CSꀵn‚Mgüvýi$b·aÓú/‘@ŻŽµZţ[‡ďÖˇWý-}r‡yôęX/ÖżŹź¬#Ć׋ÎŐ`łÖ~ő*ńĎŕ]‡-ëĐŻ†¶ŞCö:l^C¬=ęÔaÓ:|«ö$řK ţP»-ŕ©ëAµ …«fÚ©†vŞÁĎj× ?Ťľ[Ăܵ˙¨í$:°} >Yý Úż†ľP=«°WúV@z ňVĂ=ôËÚ8Çş°Š8R…}Ş/"=„„ykőgÂUŁźŐĐ˙«č?UĚ“ŞX{V1G«‚gk€ę+H'†rUĚ=«Đ·Š9Có*|®Ú!ŢXăT)Ćş*Ć¶Ę ÂU#NWŃ>UĚŤŞÄ„oŽġ*ü»ň뀯®bü­Ŕ*Ôˇ-Č[ŮMXkâ«ŃF•›‘®P9č_ÁZˇ_® Ý+“uŤąeëŇĘ×6ş¨l*¬5d«` ¬Ŕ§++¶şü†đŇh— ü·‚5…Xp´Oeý€Ç.#^Tŕ3•fa°? lbś+ç•1˘ ?(#ţU`Ď ü ü/¤ű‘ĐË׆|Ź«F›•aßňíHçë2—1N”ŹćúßÂg#–ŃçĘX«–‡Ëhź2â_1¨ĽťpŰ—Śue˘h_ ű•§C= }°Śu`ůná°G)óŘ2|®Ľ˝đŐŚ´ŻOą¨'bVý¬Ś>RFŰ•‰)ţ ŽÄ ˙GőÝŢX8mÄľlV® O}»pÖçś3±Ű%řLé9áź˙ř” _ v)‹ J>;ŤX:P¸iŘ´„R‚¬%ÄÎlVÂXş^¸čă”Đ–Ą‹®ą›•ŕ×%Ä…âRi/aˇ±~(a XB,-,ś4ěQBĽ)Ý)üôAÂ^7M9á%´y íYj^ú—kKë ti’ĘŔ—ŠIĹO„ą&V±ˇ4LĺP§ř ¤_é^"<6új©›pŐçůú€xjźG|öënŐv…ĹŰ…yFż÷mâ±o ĺHŹřmʆm‹O 7Möˇ:"váďĹTď+ÂNĂ'‹đ•"|«x˛ňŽD‚]‹đŤ"Ú˛¸Lĺa÷"Ú§6,"ĎB‚Í‹ű }śp×[ ?ŤţXö]‹÷Ä|÷\DŰzĽ6q¸Ä^Oíé}/\ÓÎ˙ŚöJ>ůßÂ*cLđě÷‘Đf âh‚ůO‚ń!yIçÄň˘ť<¶úzá”oWbyÄ„~ź y 6Ë Ť’ŁEç2ĺ± úCrµpͤŤ6ók–E&gŁLL3Ć“í M“#TľăńÖ§ }¦0ÎĽż3ŇžHŁ&Ë•G]‰Őž# 41Ň» ÓL,6qÓű)Kĺ#~'{ űŚvN1E%G‰6ďŁo% Äíë1ÓÄ[Ż/ůŃ·=Κí6KXhÚüK’sx8zĽ5Ë?úÉŮCřg1¦$ÄĂo­˛kŻÍúÄNGJ 3Ű€¸č·Ĺ§%ČR@ż- Đ–ôçÂ{’źVôŰă±Óý_•#ľXŔ<8eŇ"n;Á•đ­ş˙sŃťúuţE|¶ÇR?#Z…ďęţĺ1Ř<wJŚđ=ŞKlňăHXłxĽ1üČc ď=Ěe<š÷•~J×Äc\,Ŕ—<>ůGQYb“Ńý ]ĂÇ 7«>Ďá×Ä{ŹŐ&¦™křÇ-[}‰č‘'b„ÇZo'ş{Ž‘LË đŹ«fą˝T˙X]“Ç2Ń#ž™8kř¨Çłm‰ł¦?Ťv-`ě-Ŕż CUfądâůţâIś5۸hÄ…âŽÇG÷MôbŃ ^~ç±ĎĽ&f™¸gřoˇUuw<¦™>@_^_¦2‰>1ÍÄ;M<ńiŇűéŔ:Ä)Ôűh鄾íńĂg« iîˇ|ĘvľĘk}˘hcŽâq†Ă>B4VŞ>ńĚÇKŇZ!ů÷‘śxü1ëóL<ő)*Ďă˛Ď˘E,ó—E‡Xč}UŹl⣉w&™řčŮ’Ťĺ›<ƶBlńxč=%÷áŇgŽä!n›xăíUźňëLĚ3qĚÄ#¦äG<Ţxşô&~xcÝŁÄcLőŘčMTw‚ĘPVâ†×˝Eo#ń™%™·î/möł­”¶Vţxé5BtÉgC°Ń¤µ@ü‰uĆ8çqÔ3•櫱żĎŠóź zzYS9ň}Pr’>±ľÄŹL<őSŞó3Éůk—b¸‰Ç&Ć1Üă’¬2w‰ÎÝâűYçW’ůçĘg!–{<·aĽ®ň”ăŰ:Ţ"÷HÚărŐ{H4ż«D9~$ţ7‹qÉ׉çí˘w‡üŕZÝ3? ‰«6¬ôo$Ó7•TąsĹ‹řč«$Ç7$#1ÍÄ/›KĽňůĘżE|‰µ&¦™8ibž‰ą>ÍĄŘhb­‰ĺE öŘćÓ•‡qČc–YöxÉ@|/bzî<•ŮUĺĎT›ž,Y.T⤉…&^™řćł•™hź%XoĄŇyş>_2­rÇč¸RtyĽRůçI6â•R˝űtďX•?SöˇüÄQc\ń~Í{űčüĺŻt„Ę(y÷‘.'‹Ë?|¸Ę(›3Ťů¬Ç2SžĄĘ'ö›Řhbm‰O&¦×°ÉÄPď->Ĥ߼ôÜGĺH‹8jĂXó|kŐßYi÷˙*C¬ó6şŢGçĽOlń†:’÷"—De6h÷»ľôâ=b‰ů%ćzˇäÝVrlˇ˛Šî¦ŇwkŃÜPôŠßşĘ›Ą#ńÇ‹Ug‰h=ňś§ĽĄ˘9Mô6­őuÍDü3ń˝[ŞîlŃ_(9‰Qž#:ë¨<Ë`­ę1ĺ D®hĎÝ™’aŞř Í1bşčź;Y4g(ÍâšÇęzŃ_GúŹ˝őuͶ zĂ”xNĽńšâ?VçÄRŹ‘üă%K—âžÇŠůŹTŢŃš+ýĄÇ`Ń©|¦JCT†˛ _ęÚ,š¤ßOrÔ˝a™ňÄfÝîR|4Ź­:7,t_ÉÄÔKĽ mřęAşĎ¶ě®:žÚcŽyŢCůí*ÇwŽ»=fąÉ­Âe{üsUy,Ű[×Ô%=âůߥŠd©ą<.ą¦ş|çúI(ëëcA•«KţŽ@kUťşř&iyʱć@ňR¸çqż)ź‹łż‰¦đÔ^§ÔKĺrmDڱŻ÷ĎP–XęUăץ›a’˙%Ű´}˝l8xüďű·ŮÎc™?V˝÷t伄8íż(˝#Éđoéö˛î(:ďéśô‰[~[uß¶ôćŹDă5Ő};Čď1ČĽ~5Ę˙DçďJvĂYż)ކ·~_×očÚhľŁúĽGĚ2qČLݍě+*÷GńyKe_Uű˝¤qČ_í‹TţÔyś¦ăŞs‘ę_Ńg2,óůůÎźK\ŠÁ^)gęÚ0Ó+UćDÉAšçEtWęú4Ow)űTŃ[éRĽöi*łR׆ç6gŞ,ď†ŮpÖ–NQą“tM|íńĘ?>*sŠh~ÝĄ8ćăUĎt9Q×V–řácume>ZůÇ©Î1ş>IuKńČqYĘcřäT‡6:\ç_QťŻDő'>JçŞţQ:*>»ßüŐ9:J_u)ľů„(ßĘ“6q˝‡Çˇş˙eĄC˘úëÚpÓ_Mę|€Ž‹¦á§™żżčňzOŃeą˝ĹÓřî«sĂ;[9ĂRóz—HŢC%מŞc×f«=dŻýU‡i…Ň^Ę?$ş6L´a®wQ™\Š«>Pe ĎĽkD“őw×˝m]ŠŁ¶{†łŢ^ü¶q)žyąxRFâ” ›Ľ›hěŁrL;Gt÷ŻU~[É»‹Î ÷l4 _˝•Ę<†ÁŢUry«čś‰Xĺ­U‡˛lŘçĄ*żµhlăRĽóö*łD÷wŠęn­´UD›e7w)¶{™K±ÔL‹Uw”ę‘qČ›ęz“(máR¬ôRŐĄ ŇÇEQýĄQ]“y‹č>ŹÄ §lčMÄc±Ę+lXĺ ÄőÖs)^zîÍs)Nz mŁo×FcC—b»‰ž«zs"ž–Ď´n&­§ű†˝žˇëŤ¤ď|—â·×u)nš˛K<+˘e¸ę ”?K´¦GuH‡řbb}׍y*ó%ńgŮ©Şoef¸g=]çFczTZ”Çz“˘ë©Q9Ę5Yĺ'ëžÉµ¶KńÔŁrÄýNŃůxĺ>šu'č|Šč­©Ľ‰â;IůŁ]Šťž$Z“t=^iM•5ÚŁE×pÖĽ«:#Tg´Ž%×HÝ&ŮGčÚ0ÚFc •ĄëaJĽŞ´FDgŚK1Đ]Ŕ-Ťx TݏC= *ż†® Űó‹ľXßőĎôEąŢXĂ @<îŤőK_Ä—>Xw¶aÝóćžĐ«'ćÁmXŁw`­Ń†9Z·— ʶ‚O;âZćĺm Ó}š±Îm˝6ÄëfŚe-X#´@ź&¬‡[nűŰ5ăşiyŔĐrźaâM›±mÁĽµ ~Ň„ąy+Ćď¬5›0ߨcN_Çx\öúm² ó›&ô‹:Ćĺ:ĆŁfŚOMU¤-´—0ĆŚć"ލ[ýe؇{sżŢ¦×εŢ5č\9`@)Gĺ]$č]'VňްOoÓÍÂYswfŔžV1ÖV0Ż©"żűVo |k˙ ůeĐ©ÂNŐ‹.”{{¬'ÖúUâÜ`źę•Čí*1«čcuâHˇce|Ŕn÷č÷¸ĹZłvąöčý×Đż‚WĆú¤Śë2ćbŐ==żWî/Â>şŐA§ň"ŇeÂjN ÇęŽÂą_Ŕţ•·ë“ bis$żw-u˝<ŕ ą˙m¶Ş\ö„e™ ń«˙*WţÄ<źUzZ{© i¸CřGéżHÄov 8J>¢=%ޱBÜć(Ĺżăz^ŔRúűÓĺ™É˝v‰ILžÁ9ń°Iůă°­Ç# (cîĆ}/‹ŹjŹÖąČŰ)ŕţą+‡5¦ßĂuý€ď#ľ‘Ş"楎đ‘Ňă.ěCKĚqtđ-żwčÇÁ>Üçµ|îcŤUZ"̱9^ebç_üGŔ¸qßVâ‰W+ÁEbŕ »ß”Ř3Ř9!ޞW]ŔČoI č–0W÷{ÂNÜ 2yŔ…}?źCŢna?ŮŇ™cŔď¸h‘˛faů0W!Ţ‘¶"ú5÷;ő4n vńűŞ0nEř[ý«8Q85bŁ“ß:ŹŹ#ľÉďĎ _KŢş'wĽ±ťEâmWĽVö°h~źUbż® ¸HbfEÄ>Źź/{śÖˇÂŁm+v  Ź×Ű3`Ţ!ô{šׇyIň° Ř˝›B9Źë"Îú$g=ţé•`_Źq"věEŐY/ŕ‰J¶žo«>č$ű%čw|Üš·•` ÷Ř+bŃłÁóíĂzď>É˝Á/ééqÄ­.ŤX;â”Ďyç°«ßO“x§ÄŠ]0vÄb%y¤ŽpŹ89îš´ě1wÜÇ3Ďz¬ő%ćŹx®Űť<® v÷ű~ľ+ü¤€¸â÷Ň„, ŹwžÜÇ•űP·çńWl/bu‡;>अô¸2b‹(ď·D‹x6âŞ#î‹zχ>JĚ$1#?Gív˛îŁÍ<ć‹yó‚ţÜßŃc¸°~÷9âɰĺ^› ćc…[e٫ʑ.÷pőűg.@ân [ç"N±@Ěú‘Ç™m/‘š,ąĆ +EŰȉs#Éă·hWř‰ß«4§sŘŮď±IśĆŚ÷ŁC_!fŞ€ţPčěIl–Ç"oA,Đ}.ŕô?!6ęmá˘s~źHߞďż@ڱ9×ŢŁD]~ď&x­&}ď<ŮŚ} ‡úńreń&b´ ţÎ} ÄĂ Žů˝/‰—"~çÄ ›Ç˝ˇ ń7Ďö¶řSî§T‡8±GC˙ň8:Ś‘ÄAĽźÇtŃo!¶‰ľY :úö F‘¸´+÷±ó˛Çő=Ő#¦„X+bČ}żg肍ý5ßC}-Čë÷ţ|DXôCŹëˇÝ7Ú8čďŰöe]Ó~đ-Ź52¬ŘÁ˛±>7JoÚŘ#ľĄŽÄ˝/ELbq…‹ó±0./Hfb•I#^‘±^ÄN\Ł:Ź©,ń6{Kţ+uĹŹ4±!ć†řöÁ ŐÎÔ…ý¬&Úlç ¤ó@µµŮhšhŽ“Ľ|ďA|ńU»¨®áŠöW;¤:ËdŹMŐÎg©ÝřĽýŐ;Kö,›,—MU›7D Çbĺńx ÚŚXĆbqŘç0Öyś cóî’…vKűŻlG[´©źÓ},˝mʇňŻÂçť˙/Úq†ä:[|hŻKÜ*,ŚďÓô˙Ž Źç=Fm̶ ŻíŇ}#‰%š•™-}(ă‘ŇąwĐŮăcz…:žß–ÁžcňBhKoËĹÁ>ÄŹx[.ęÚ3´3ń«úŮĽpîß›×CěÇźŐfĽĎ±‹Ďóź ö&fÂcvf‡űÄ7x›m.„çń¶rŇU|=¦ŚŘ¤7U—Śy#=‡ă˙ú*óa¨gřŹáš|xŇňx wŐ>ÄGýĆĄXł!ęźl÷7ä|ŢILĚű˛ąáÉZÔ#ŇsŹOů›t`~lęi­+FążňZhwĎs‚ÚĘú«đoŠ?q?÷™ý>ŻŁť<&čNé˙G]sÁg»W»Ň.#xĚíŢ,˝^“<†‹ř™lEžšžîoC›y|–á˝Ţź˛ôü•î]/_(«­^zř~ń°ř˛ /!ŢçŢĐţďô€ô&¶ŕ.ŮĄ(Ţ×IOú2Ö‘Äy\ŢâKźB\÷§?ÎÇAw˙>ý`Ź_y>´'q^?ĘóéA, ± {y{R.bŔ^‘oç±?RřˇÚőAµŐ’ű-µ1 ĎÉwŠ?ĺxFňçt»ěC:ôá÷äŁÄ’üŘĄ8­'¤ßĂ.Ĺm˝!0–{|Đ˝˛ď+j×Ô®ô)ÄŹş_÷.–]î•lß•rŁh°ń&Äë+[űB? Źť-»ost~4ř€/ąxÂ>őŞl|™ôľGz6ĺAµéÓ:?ŐĄ{F’Î:ŁôCéu­ěk8ŞÇeÇIâdčOWŞĚŠĐ¶ľÜ’ë éö ÉN˝ľ'Y/ÝëŇý> żö´K÷éĽNeż)yĎUşJ2rîµ§ly‘hÓöʆ·Šţu’űk’™ţ{‰K÷ľ¤¬Äű°ýż.^"›^&;^!ýĎSß.˛Óµ’—ô®Q[R¦sÜ*ż÷ů—‹ß·Ĺ‡ň§{çŠ'ëáŇ}@™Gź#ľ…¸Ăd]ěŇ=7ďV=b”čŁyü ď? ¶b9ĆŠď‹÷·\Š%ŁżĐ'Žr).Šş~G2^-Ů ĎrÚ•¶8K|ďĽgÉ6śgś)Ůďréľś;Š×%Ňá\ŃąDúťíRěŽa±â¶?Kt,»ź#۬ŤŻ}ćčúXÉýUŃ ¶ęTŮ‚í{†ÚóëŇ‘sň uŹía85ÚŕHńÚŐĄ8.Ň?@ĺŘżo×}ň8_ú[]ĂťŻë}ĹgÉz˝ňŹQ›ś,y‰ŰKüÓ˘ßńd;î v’ývVýÝeďSt}č\ ^†u[!>;Jß=Äs/Év¸K1O+TĎpMű±Iôż Äăt—âěNUî'۱΢}˘ÚőHµ«áŞvPÝ˝EÉş·lyK1l—6ëź%ÝOw)ţ‰÷vQ›ťâŇ=@Yf‰řě.ůiĺjĎ“ĹgĄę~Yů”ðp‡‰źaĆÎR;-ý­ü ťîŇý.Wť3\Š7;ÚĄľ´©ä'ŢčŮاÄ÷PéqÚěéi8¬Ă]şŹć!:ßFĽ‰ß9BĺöQŮM•··ňOP»0oŐc}¶űv.ĹŚ‘îV.ĹŞí­6ÜD2*Ý–HVĂ®ží—bäöV{ěäŇ}9w“Ľ+¤ŰRŮ`ąK÷¸\!y g8ÂMTf/Ő9Z˛3VlíRĚć2ÉŵĘ~*·0˛ç’í —âşNÖąaî Ă·Łd:R6ŰIşď!>ËĄÓĆJ;(Ź÷ćştŮ-%˙Ž*GyŹ‘-¶ß˝$Łaúf‰÷b]łÎ†âaĽĂ\ŠŻŰKĺ —6Ez.Sůĺ.Ĺ˙mŁvZäR\ŻçÉć†Ń["—+o]ŃÜNrl®ó]¤Ű.‘< ŇtÖÓő†’{ É·­K1t›ĘNŰşt/Řm”·B˛­Ů‹4ąć]Ůo[Ůť6;\zmŻú¤ĎuîÎ’yé°L÷·•;K/Ó…ügHćť]ş_ě2µ©µ­aŮn3Ek7ŃšńßHv`Ţ—"ýŮc¤ó¦˛=ëŻ#^´×Ú’™X9bČ樞á·ÔqnÔžătkŮeµ믯d2-qi›očR<âÖŞgřÂJó”7]e¸/I˝×íĹ.Ýoužl¸žîYŰłÎěHĆ.ĹIέ/ɶkéľa ßh¸Ë­"= ¸@iµ-ď v368â˲ÓTC—î»Jţ“dó-uś)úK?óó˘3G˛.Śě0JňNíŮ.ő¦±’Éä*ŰĚu)ľqRt˝©KqŚf3Ň›/ú #YčyLĐőP­}ÖŐ˝Y˘=ߥŘÄyŇgQ$÷—\ŠďśáŇ=aůL‡>6D2rşl°ŽKqź¤1Q<'©e`˙©ë5uźtéÓÄއŘOuM7ňćRŚâ<×yŰŃâIú\ŠĹ\[÷FJ¶Ęź)›Nw)®s’tčçRŚĺTŮŘp•ceٱ*ż®ę’Ţp鳦řLVšŕŇýrǸ«:R冫śá-ynxČ’c°K÷çµăš.ŤăE‚Kq kŞŢ—âY™úÇ$ÉĐߥ{č®­ăhÉSUąAJcD×ěkŘĐéşŮq˛čŤ˙±ŇiHTĆl?V2Ś•ý‡ştĎ^ž÷˝5]şwďšŇs´tź,ZĂĄ·áHIłGÄ{€ň‰Íě®ëI.ʼnQ}ĂÄŽQ{Ś×˝q‘ţ"Űt¸tŕ‘Şł¦řßfŃ^#’µ§l8.’ąźęX_ ,1–uT¤'ŻŰDßđ¦¤×-’ŁŹKńąĂ¤ ë÷w)N5ĆÚö•m†ČvC\Šĺ5lj?•âRül“xtDífřŢîMĂćR†vŃěĄă@É>8ҧ‡ę¶ątŹĺ^:’wE<{I6Ăɲ|ÉĄx]Úş,Z#tݦ:\ş×±•ëéÚ¦kö‰ĺ•Ä·_¤_[ÔNÔŁ9ŇŃpŔ-Ęď§2vť^“ň»+?>7üoo]×TľGD»Uy”µ•gÝştl‹dm­ć¨n»řň|ĄÖŰíXËv@ÖĚGŰŻÄkÁđčľmżDŮ×pÄ|¨tÚîÇőw0ŹoGi=eďFţ#h6ĚMŰ0jC\kÇŁô۰>oă9ć†-#[ { â\í[aŻ`â±Ű°Ćkއ=Ś[@łťęÂuý¸‡ů[ âhÓSHX«6Á^Í8Ö˙‰ówp$ÎyMXŰ7ÁM?Ĺ}”ŻaŐ4 ÔŻCÇ:ěÝ„ţUű#ÎQ݆±»ůyçÂe w íÚŚőB}VŘ—Řď|SŘC¸ŽµD•¸oĐ®]Z5Ř·Š8Zëö¦ÜŐŹI†lµ÷Ăľ»őBÝ*d«BĆÚ·‚¶­˘U× {s/ß*qÓohßß«†Ůc¦‡kžźöŕĺŢŔČQÝ]{đ˛>Öş¬ŞđŁĘÝÚ«·oŔ’sźŰ*ćveřa<+Ż…˝m+h‹ęă8b]]=PxňŃÚóö¤°źqţVÁzż »TnDę§=~1ת FB»–'„ýmKĐ·ŚąRů-uß÷B=î?Ë}…˱ĽkŘG·Ň_űÁCýIŘăÖď »Qż´R{ĺbn]ľÂ…ýz‰Ż>=ŕ’‰—ćž´ĺÓ´×ě}8Ç:ˇL 7Ö%â§ŃžŐZŔ{WŹ{ëjŘq?KoŇ>·?GBç>ąE´A1 ü"®á %Ř˝„1«t>ü»ô,Ęě°çEčRúNŔjű=W‰F{—öĂńŐ€Y&~š˛†tłóűĆ–qa_Ö—µí Ń} k”â^üŠčËEħҴ°p m\Bű!§ßW·®˝Z!{ň˛pÉ'g{(ă÷’E\*"¶$—ŚÄ'ß"účŁ~Vâ…?Öţ­đKżżëáČ'=Ć{|¸NF,µÇ«Łźř}bŃfÉÍ{ť[Žv÷{­®a—ŘsżŻç8?\ň =‹pbŻą˙¬ßĎő!ç÷MžFBźňűĎ6 }UŔ§;ÇĽ"!¶}ť{‰aŹäŻHűG~ˇtbÝŻś1夌ÜO7AóßÁľůׂÝň´;úVa›`#î‹Jü2÷Fő{k^lËrÜsŇă¦_ ô}ŰuKKź@źĺޡ޾Äíď w„vô¸jb(ŃĎ Ä ;J¬7Ćfî™éqéÄ Ż6ňr?=čęőľUĽ(ßO=<®±ŢcĚÇZ~OΛd3âQOT}úÜ=ˇ}ý~×c©‰+¦O·KÜq¤d!†toÉz©xĽDJ,'ńĽÄ‚ŻË8AçŰ:g]âkçˆäq@DgĄî1oˇh-s)~{'éhcb–‰×ţžôd]bł‰y$.u7—bበ&&u˛h°ĎOí±Ň‰WâL‰Ýś%š´ÇI’÷\ŮíBé»™l}˛dŁoÜ+ÚÄ@˙î…m#[ tţťdŽ9ý¸^…Ľß`‡jdk“ě1׳ńÓ0dGßô“Â=·ŹxJá0…ÝFw|¨ő3a™?M÷eű1ý^±^ó)|.˙“ěéőc^@ â­[ŘZw3â{żIÇŁ^B×úä'áHĺ§Ź” $ý#ÝźÓFĎyáŔ» 8ř9ý+}-"ćůeĆÖ9~ áň^§źôůi’?Úř˙°‰Ú üJŘŠ=ʏł_§Ś×Iß/BĆ-˙.Ů?Ę~1¶—˙Aź’­#öŇ1Ěźr˙!c™˙Âöod˙]Ú˘Îę'őěď"n$Ż°ŻżÂŽúýýG)ö¶ÖőÔóSë/ÁWľ|]Äă}lđ,çw`űW’Ý#^ňKlý âŕµdăŘîg´“]Eľż@ë9ô}ťë?EVŮôně÷!÷?‡¶0ßÂd*ţ>NzE߉oźiÎĐ:ŕC!׼üüź…ŹÚ 'úöŹ^Eß۰Ó=Čx?ú˝E›‡éűSd{Ű˙ąďâŘy˙šŇáq|-Ýn§í‹Ř˙fî˝….Ŕ—?€Żř G+Ěî%Üűş`'Ç»ż`»G8=nÂö·q.L¬đĚÂ4^Ü?DoÉp%úČnÂŞ]­°ý«Čr4ĺ7Çś;¦ř%dű9tn ó{ônEź+ąw/4n‚Ît}Ś6Ž˙ľ9Ţ`űvRź3ŕ÷PČřŢÇEŘŃ3ŃUçÂ{j >ý‹hŁëŔG28N÷ĄëRžŽź…÷ÖUŘŢ[ý¬˙Óý.t˙»8Ćú2x^r]QŮńx|r;×/Łß÷i{-2ßK˙‹ńŐÉô®SsßťČ,?;Ţř 䓜Â4_MŮăúž…t]qň>ňś˝G°ÁŐĐ’Ś§„ś/q?ţ¸)äÚ˘›B®Ă*Z—‡Śµv]Ź5ěÝV[ŕ· ŰÜÄţL|u%4äç#諱±Žós°ÍfÚ G}*ýÔVŘČ[ŃílĄcá@ĎŁÍŮŘě:îŻBń< zŇy#ô¤ç Ú_Ś ŽÖ±°É@űÂëa®ĹG§ÓţDd_CßCŃű{đżzGs3}Nö±ŘďNú]rNËE!cëď…ÖIč­X;›ź˙Mč¶Újwň­-ŘödúËţG"Ăáđ»}OF˙ŁŮN§żl ÷`t»8äşś§Âó"ô8ůO ż{hČ5:Ýk9_Éćî#ŮVc« !c»7†\St 2-ĆNnsŮRq¶Wȸk§ż:’ç„1DzßRäZĹ5Ý›YĐĺ0t<–{ł?ž|ąŽv+BĆ'Ź…´˝Ůĺü`tsß:–x}÷ćÜńĆ®ß~ô=ť˝tŽNřŇĺěĄÝa´9ąĹw3űőđšďĂO÷V˛­…ÖI}ÖŁźî)¦çĂK¸QawćţČ.. ý~!cÁñ¬‡s}%ţ^ĆőU´Ý=wGÎC8_EźeŘiEČ8wÇQ;NÝcář1ąű‡Ś«^r-ĐŁńÝÂk şĚó ;›…|Žc–.Óó‘ŮqÇËŕ±ŢShł[ďő™DŰ˝°§dÉţđ1Ĺ'#ÓN!ăŠ`ďeô?„ë‡Đv>ý÷¤Í î/fS<ďÂő]kVȵBĂ{×ÜÖËă`üs8zH.á"ď;[.…Žc‹§„Śá_2¶|!ü— ű<îĎĄý"řIǨ. ąĆ©úĚćŢ4úΠ˙ÚOEţiŘf×ÜĎK‘qař.|—qÂSe*tÇŃßq˘3ńÍ8xMâľăśçBß±ösˇëq?ą\ŻIĐŢŮd˝B®Ĺ:†˝ű|fA§ąĐpśď Î÷ ąž©tŹěŽŃ•ľĂŘď|3ybČ5]'ŇooúMFŃ2{÷w <ŘŰ8îągČqćř]—Ďy5Ał<»po¶;yŽă©űŔ×őoõ.ČĐ™űí ׺…Śáî ­.\ďVŮqÓ= 2ôO—ďN!×ýíNŰîl= çŽḏ̌c´{‡ďÖ/n.ŘŘ1ÓŽîÄŢŹ{`×!\+b¦› ÷ĹĎ1Čn‹ĆqáŢŻ[ČäV´ďrýĺ:čµ ÝŠ{µ…ľť¸Ţ ßh4rŢ ~Ž+n_hßľpîŘäęqĹ !ăź[CĂ1ĎŢϱǎGn‚CČXiÇ!;ćܱÚM…óVđnÉąc•‹xčz®;ÚńŢ …vŽ•v]śov«ĄM÷kh[äUO»ęqĎÚWr˝ž¶ş^*đtütG].ô«,đ/±•ă¨[ÜlßÁżú4„7>±Oďßۧ§}fóŐ|{÷Úą…Mm6F~eÓ‡˝oú— ű~ř„ a{7ękß}ͧ}í^/›kz]g›˝W÷üÂBó Ăw,äľ´˝Ů±›=OşL¶ÍžĎ]µí ‹‘Ž7[ízçg,l¬oűźŰöGs‡m¬O{űFi6~lŢlgßmďLÇÍö\n¶÷®vöţÖΞ˝mě=»ť=sŰď’öm­[ăÓlď€í{Ú±°Ő6O7Ű{A;ëŰÎľŮÚŘ»|űĆjűŞąë‡vlş¶µů®µÝomôÚÚxióŰ›-Z_mábď1­Św+á«?´kö>ŐĘćľ&kÓĘމ›ěŰş­}ßµ—jl7ťo›}'6ŢnűŹl3»6˝.Űú4ýŰŽi®°ąąńPB÷A»gsPKű–i˛qßt™Ű7@Kł[Ë{l®ú·¶ß@]ěeÖ×ŢŁ[Úś×ŇlŐr÷Ě--ŢZšM[šÝęŤ~ĂK¶·o°o¨zűžh0»4Ř»XKÓ­Ţt¬·qß`ßŕ ö Şű‹ťŰ·_˝ői0ÁŚn˝ĹLŁÍiőf—úłm›füÖŢÚŐíŽŰěPoşÖYÜŐÝgÇoŮŢd©·q]wżí-Îjź´ýę„w®˝Ŕ†ÂÓ¶˝b›} Ő™îµćżZ‹Í:á˛í›°ÎxÖžeŰgv,ě÷ol{Ä6ó[Ýî »]c6®ű:áŁk.˛k—Ú¶)Ń©yŰ6áŹď˛˝µ©mJ5Áë일öŔ„Ż9<á’U‹[µ­kç$Ľr­ŤˇšcO¸eÓ©fXŞ^-ş·&üxÄj«Í÷¦»Öü[cĎ׋Żę_Ů^¸é˙KíjLöaš×%,˛0ߪ›]csgő©ćuőý ®:ŮŞc]-´äű“ťżkÇ[şfTŞ)^cßž5wµć‡{W¨.ŰžŐO'ězµĹl•ŤÇę÷lß9Őலج¶÷€*µ5żWŹMűކşĘâ­fV´ ď,}•='«­_•đĎöŢVeă˘ę9»Ö.aÇ#óµj“WŮł¨ę× ˙±ć“’ěŞu]mńSe1^ő=Ű„Y¶ř.›ÍŞě{¤,ü·ĹH•É\mßlĺ˙ §Ť?Ő'/˙ČöG&Ľµjˇ«Ţx¬·m<Şn˙mv,?śđëe{ÇŻ˛óŞťáů ˛™Ë/'<¶ôű›®[¸äŞu ^esQŮćÁXÝćąňű©¸ěT¶y¦,<şŃ©¶÷–ňú„i/osţ3pŐ{U6ţ«‚çÖű’k—ľM8ň*›GËćż*ŰĘ}F\µ¸Us;Ö·±Z¶1Rž}UÂť—ĚVŞ;ĎĄëŰlLĆšěž?Nüź ;®ZĐÂzKőVUö–,%éu•m#Ŕ‡K—RÂCGz’ý90Đ7',uŮěQzlř±Đą'mÂe‹FĽ'[[Ě”mŢ©”]ě[´dqQzÓŽí©´ ěşŮ.ÖţY¶}ÖöÂËŰűXůódŹíţ\ąě#lúS Ď^î’đë%aÂmî)™ÍU×\6.٢Ů,⡰ă–ËöŞi^: a­c˝uŞ-­Úᕲ·ĹyÉbSőĽ….ŮX. ŰmĎš˛=?Jň§Ĺ[Ą]ŹřkáĂ… 7ߪFąpß±.ůiĐ–lĽ—ěý"Ööž‰¬CŔLK‹ŤŇµ¶ Cü,Řó:đÖÂZ cżúŻÁ’·M¸âČ·[v›KU_<Ú\şŘ¸QýńX×[Řx{Ž”ş$śzÄż7ĄľQFůúÎ|˝Ňl^ů9řkóOĄŤë’0ąëţ<ĘÔě9_Ú&áŚ#ž[Ř~ał˙řV W,=„%·XŽxsᨅµ6{Çמo;ü·dĂX÷ú‚„‡ŽŘű;ťď¶ąąâ÷ŘâÜ„ŁV]űJaÎÍźŞá1˙›’św~p˛S¬«ľ{Űř®|.aw+OřäĘ7S˙éć|8káž§»WĚČž6 Ű.¬j¬wţuňC¤˙ĽmŻ$Yc­ěŽ:Ę(ěµ0äĘ%±wÜXÜćŘXKÝĆWÄčK&óKÄ@ Űm±1ĺK®5Ö¦·÷?Ő˙Ö˙ D; Kţ>9ʰ=z[›J‹‰Jó­ę[GÝvL:Äşň+‘Kdaä… ¶Źů‡`ń’ź{˘żŤŐXű\¸_ůDŘŢ˙ItcíěŻa©#^\ń¤\Ž.řW˛Ž@7áY•»`ĎĆą·Xqá÷Í•»';DLđɱvąđŰ_%™#fţîÝ’dŽ]ľ¬Řśě±Ý˘ńcúŰř­°xU}öŘÎćáX]¸X›ß#¦úĆk‡G,ą|&ěöŃɱý¶Čđ+hŘś±ń´ Żoă»Âlk®Ű¸Źy{ď®[5Ácţ€đČÂĂ ‹-Ě˝°ęÂ`ź‡÷a‡štă˙ÎdŰwzÂa?€­tüHÂůŞîłâX9_.»˝’d‰uÉĄż°˝×pÝž3ÂŰÇőç%oÄśťw—<«ŮH:ÄľÂzŰ8¬°ř޵°ekáŐwŔnŇOăExÉ#őhúŮXřya°•C±—áżEřĐć¸mßžömqň/+Ý‹ű’W¸jááĺ7ĺěŽoWĂ[}gÁW±ˇx“/„iŢ€®ňť}ʍîvěWŤÍ¤ŤůX‡ZSĹrB§>?ůGĎöNë«ďŠnňŹ0ńÂŚ .,bGy˓ު‡s:NÁ¸7¶˘5ý…Űü{Âmk ŢŠ]Ďăˇ×6é*,uÄáKîŘTřůí“Ý"Ž]¸íqČ*śŰ˙¤v±N·âE{aŕţ˝fü+[V†\Ç]X¸·‘uH˛y¬Ażg’YXŕX˝™ăRźhKáôÁĐÇ<ĺ‹gůö\‘l1ÔŇç¤k¬źmţv»˘%çíđWËÄ_ŘáXwÜŢ=Z|•l1óöÍknK/áú;áŁţi‹˛ I˛EľÂ ŰĽq©Ę•°ńĄZéŞYi Ëű%1$Lźr&ÓV›ň „Őłw­[+6ěý%bš…n[ăI8żsh÷dâ±˙Â$ŢšdŠz w.<·Ť§Sל'śźđy?Ç6Âč=Ă5éýzŠłńqâkŤŻ˘Żú Ç+lű«)Ö˘ť¤˙÷ą÷-פËeI·ČçČ+{(Ž„E¸›óű’Źb˝ôłigă-Ö'ô…$Łle!™pđÂ˙ ýŤ´?vŹa§ŻMcV¸řągď ms‡đ×ń˙ ”ǤĽ… ‘O˛ O,Üľ°¤˙[¸wHŠ™hë…Ăĺ÷Ż<ĂŁů>1tú™}b®ÁZü ěŞ0ůÂĎź ůPxbűö‹Řő“‰ akĎĹîÂu 3}~»[,Dz±°ĚÇc§őÜ;[ ˙-Üí~Č{2˝}´?»íÍ=›ß"–Űíw ˛ě‰lÂĹ í č{SČ5çĄ˙>ŘńlüŁř˝Ť>`ŰĂáů8<ÓO˛GVéxtNĂŹg@ďëoË—Âx?‰Îťđç%Č,ŽF‡+ˇ'ľĘwŘ šÂ9OgżyVÁăä‚ěĘŠmÄg_ě"yOŔ6’OsÂóČ´:(˙@óő@ěaĎÇ Ma|§ “dY‡Ě+CĆ]+&aÇ3hŻPÜ §Ľ-çĘčżsŃgîKvŤ'=CZcc=ź4çlÂf{ ç™Řv!rě…ĚÚ„­ ýťáĄ9z&ýgc?µJ{ůzv˝÷…g®é™7==`´u®çC v–xD\®ôţZcNqݵĚp˝%¶ mŮ{-ú+†”{0 ůj±Ý4újë„?wB>{FLxkΧŔ{2Ő%y#N|6:¨ťđü˝á3 lźÚGĚ»|0}ű kk|'ţZoĐłNc˛'?ęC^ĎëţIŹ—¦Zxë÷ą®őă7“®‘'9±Ţ~-úwE>­•ü9Ů Ö‚ţ÷~¶ć=D~ Ľ„Ż˙[éş°­Â# úÇÂUĐxßµ‚†=3#Vů›¤kÄ˙-µŹŘÓߣӧđ×;bCбXź\ë&żÄöü‰Xoa|ö3ĐŢöۤkÄĂ~“äřŰŻ±űPěúĽ˙„ĽźÓď7Đ^Řć'’ěŃOŕ÷Żł6řť~‰ľź${EźżŠ,o"Ďoˇ/>?ář+t »Ô@Sň, ¶p¦Ż Çۉo¬ť.|őˇ%<ż0ÍÂÁ˙Ú˙‹.÷ˇçsčđ-mîÂgEÖĎŮŢ…ćh/ÜýÓ!áň_FţۉÉý 6˝Ś~ŠźwĐ÷wČőxČů#˙;č' 9GŕçěÁŻ"űŘćčţ†ë?OqqćJôŁ<7`é˙<6x’6ŇQq¤ţŚű÷ĂëčđBČőĘ_€ţČü ¶ţqČř÷?ŁËKÜ{›˝ýG ¶¸—í2lű˛˝ËąđŮ« w+Ü®0ßcÓçi{gČů ELţ˝řĺü%;ľ‰LęwKˇťcč%ç‹ŘÎǗ竼†ĚŇ]xÍ۱íu!×Éľ;ËĘŹ¶Ü±Ň.§öЩӏw;v˝™ž¦źäx ™ĹçCÚ>†-DNa®źŔŢw ó»ČqmÔ_xÝ ň]‡ţŹÂ÷ č|ź~ça»ËńďU´ű<ď*č;ţ8]ĎÄƲ×őčv=˛+¦…5ľšM|ď~%´ż‡Śô¶ ÇučzaČXwŃ\Ç˝ďd–ś7CóÚ^N˙çč/{^F_ńx*ä˙±pěů•Řä*ÎuĽŮ”gp7´Ż ą>ü‘čvýnĂ>WaŹK±ŮČ'Üîć0µ' Óą!ç\ĆţÜBÇŻźÇ=ů˙´Q˙CĐńś˙cAÇG ÇŘă"ä÷|‚őČt.çÎűě»ţ7†ěsÉp ÷Źăü4h^Ś˝6A×sWÎçüxmAß-đtĚűĄČ±‰va§CĆú{żsĐm rI—38_YC±·Ź…ľ0Ŕ§B˙(îm„ĆmĐ;Ţgl%ŢÂźmé(zž2î˙TöçŃç(ôŮÍ-lnŻ9đ<®@óZčl€Ćlx ˛ËĽ ž:ź…-Aóčß3C®˙ý}Ú»ľÇsĽ’vęwręţfě¶Ž{'qMqż–cé"»0ĘÂYRý(Účšăć%űáČľ.äzëKB®#~|ČuđWŔË1é«Ńý$Îł/^Ž×^‹ÎÎ˙ ü¶=Oăx_Úéxoü¶ůÎ@Ď č.Ë‘SxÚ#đŐ*d]„6@S2{ęąG†ś×°ë˛Çţđ;]WĐöPt> ä<‰´źĎ~#×÷G×hs: ş”cÉî8őeŘýĐsÖ@Gka”÷ ąľ÷dYŽŽ«ĐďúéşăÔ×pĽţXr]|Ů{.~]Ź\˘·«‘Y:íĘ5—sżóVtßqŰ»bç˝C®ë˝6Ú =wľ2-GÇy!×Ů^2ŢzŰ,h.//š»pß±ŐłC®u>űÎCß˝č ýĄËT®ďK»ĐŰ]äWÇšŻŔoîŹy˝?Ţęďó9đ‰|Ţ1íw–č;6ýčěŤw ąŢöÎ!c–Ó*mw§ßĐ‘­&l¸/÷]†éč° Üžâ5 ˝gCËewLµř <‰m|w§íx®Mî2čîďť 3#䜀)´Żôߍ6žC0Ův ‹î9 Ł‘wFáţ,üărÇÇO‚ß,úíŹ9»NâÚö´›Ý©!×?ő26ľ¨ßv!Çĺdě8šSˇ»sČ㩱ž„_&„\ |Ľ¦„ś×0YÜćCą?>CĆţëú îÍ #?ÚŁiŻ{#°Íäsv ??:ÓĐs<÷·§ďŕ1ÓŽÓwŚţńÝcCĆÎďŚ;…Ś›ßنsm·ó †ŁĎXdͱdíDZëä¸üÁČ16{cC®Ií˛ŽŔžŁC®Ż]ôÁ0xŤ,đňvB®=Ž{“h?›ô§ý„˙â7¨@g@Č8íA!×sr]í!ă¸uŢŤv#B®Ë>&äć=ˇŐŮhwąÖ»čő˘ĎPöŽąw|ĽÚőŢ'äᎩ ­ţ!cíÝ>}ѧgČŘőíčçý{†Śqď íđír­pׯwČx÷Á!cź{ Kočn‹¬Ž—î,Žăîr=r··ÓęB[Çm÷äZĎńíÍ!ăŢ»6ńjľ‹5w¬t7äن6Ž—îrmnéÚšľŽ‹oO›mŘ;&»GAfť;ćŢńáŽď˙ĆOŃhU8n2ŢÜű:-Ç„;†˝HĂqčŽóvĽu#×7Ţ.d\xÚ9ÚűúyGÚ¶ĺĽ4:†\?»MČřë†1ÖNß1ÎM!×őn€^±cá¸áżhú=Çh·*ĐuJúhÝ}eÂŞ(… “ţĎöZÂUcsCňq\~YĎI8žÇÓZ·p°ź"đ!®źp13&żKa)„/¸/Ů^ëýâ)LjŕĆÚ¤&źÇ:©ÂĐ“!l¤ÖŠßENłŹę/F,ÖůI~aââ˙™k}M:Čw ˇ5÷gđ‹Öč§áĂgŃOńřS;~:ńŽuK”léH'áÖ4Ö„kŽĎqَkă'â‚4Ž7&LŢÂd·±Ů5ĹE\ţ3>9Ĺ{ÄŁi<·K±ë jÜ O벟"żëOëzÂŰhÍ÷é´^ë”.JńcN á$> ÷¤ßużIă$â!$żbB-ówÄüü±` ­Ł ŞuëY)Ł~F[sšp‡1.ô{´|­1Ž*âă6¤Ř¸¶Öě…»ř>ÔŘĐš’pn›Ň¸‰X/ÇČȶ őĄßö±ÇYÉO1ÖŞRLF¬ŹŤ•Ť~B1ŃůĆ'_E?ÉţÂĂh}]Gďs#ŰÔÄ/ţ÷ú,d^Kx˛’]#ż1Äč´•ĽZG?Âî?—xÇřÝ;É×±ŹĆNŇńPř ¤ďą/a˝? »%¬Ě'iŽ‰Ř‚±)n"öâLl'źi~YŤŻLşÇiŚž‡Ý5‡+΄M°1káIÍwÂ?hl ßaq×űµvcďź&Ygn1â4ÇT¦Š˛(…iA*­q˝ťhEÝô»ë—Řs-rmÁöű§x‹‚É6qŤ]ňź“îG÷Dŧ~?Đz˙Ą!a*ÖĐ·&ů0ţŹů2üŮśü1úÍě+t^Rż­j-\kt…†dQL pX˛\˙ţIŇ-ÚWŘŔ•)~"UkQZÔşýkřvý<ľő˝ňŻd?ťk]O±ëjť_kéÂę7[ůCż×Ű;E\Ënazâkhţ=„“Ô;†p ’>Q~Ů~s˛_ôŐdhč7*=#4WîBߎÉ[˙łü‹Ä#Úř]ü˛ ä:ˇ§#ݰ#ŰWú~‹¸Ú?l­—¨Šk~Â%|’tŘ*Í zfwH2)öŁĎ„WŃď ëđkSňŹb%Ţe‡Ńi|k=8b]ô]{út!^ô­ö*ý´Fű®)žÔWń'<ČŻ“}âZüĂI׸F q®ß©ÎFÉú&íź@ŮîCtF˙»ń™|ŞćHżóhŤű•ô,kĺZkÔúĘKÉ~qÍúdóřG:–@·ăzŞĆţÎČű>vzű˙@ç.ČůX:÷˙žŹ'ýöYâ+ŮâsIľ¬Jń}’=ăÚŻć–Én1&Á,ĹuĂ-ÄčăIŻO#§Ć¨Ö€˙ťlĄëç%Ü„°"ßü®q­ąđ)âWü…+Đúâzt‘šżőűČqé8ÚĄ šZ›}#Ťo­í ď×1-¦µŢ1*â)ÜÇkaëÜ×dźdslŃýI–Óý’M"žC„ó›’ě±] <»b§;đ…Öŕ7qţ 6“šn˘í›ŘGá7n§ďIÉ‘×ŐĐy ÍI-zŇ_ó„bó\üŢ#ĹUôŹ|VŠŤăň…ÖôF®ŕ{#vŇ93ů$¶9[ý5Ĺ@ÄÍ\Ň|vzלË=Ĺ™đ'W˛×Ođ‡ôµďđX ó-ř߉=ßÇôŃŞ0 Ż$ý„·ŠkąŻga=§a×'űkM1ÎGJ4â{€ÖŐ–G˘ůtâçŤzh-Eźë°©ćôٰá>)V˘ěç!Ż›jljMMsVr߇µn}x˛AÄ}hťMsŞž›Š/­Çűú lٱŻ÷µßbaîF&›·c¬ÜžtŚ2žŹŽ5ôŤGÍ ľľ+žzĆ]†ž÷sďqh‰źÖŽ{§Bç\ćÉ-ŘOóů!cऻăKNÇďgB_×>O4˘ý> ů?čµćţrý{mĆĆ· ŻxhNĽy^B¦—‰ź@g ~ÔąŢ/îAÇ0Ď˝łŘ6r.ű<Ŕń Éo1îDó.čČ÷'"«ŽŔCz\|áß'ńŁc¦4—ď˙ «ĆçMČŻźŠśZăS )F4ΛˇůtÖ˛Ă;Č9#äZ`ú=ůF臜˛ç’kÇJţË‘_qŁőŞ·BĆëHîóđ©čř:·žËBŠ=­«®F&ɢµ@ŤMč~m÷Š%=×®ÁÚže/Ů|mŮ19§ŔĎ×âWŕ“ÉȤ÷ńĺ!×#|ťľ‘űHävś†ôÝ?I7Í%‡Đvě4{çůĐó5÷ăŮ_€_Ö ź|©qőşß­{čżţ’ăDÚ¬Eɢqü2Éď§aëQřRóŻćŻBĆŔE˙Ý9żÝŢǦ˛Ít~ş˛Ď™čv:Čî—`Ű›ˇ˙CÚ® íyřě#t¸˝l+|ĘRt\‚ě—Csuęź7ËđáéČ zLČ_Âwh®ÓxÔ;¬ĆüLřřsRďŰ`µŽösBĆ0*´fę8ń›ŹýeÓ#áďŘžˇµ˙® źŹÁač!ŰĚ ąFćÎčą ąµöŻőŔ[±ÁýŘí<ü˘ďAÇÁĚäÚô˝ş+°©âKq©gŰqĐSÍZ?Ôş«Ć•ÖčôŚ~$dśĐá!˙7ąâEţ_ŽN›ˇˇů]ďyz?ť2îi×Wq}5ň/ąŢaŻ‚ěš3´¦x0v—Ť57Źŕ|mAŻB®9źMA—u\—]<¦W Ó±ŘÄéëúľlşw>ňĘ7úţq\ć;Ŭ0焌=Ůß) ą~áéô[‚ “c‘G±ĺ¸‹ 5…cĹÉmŘD2č;{4Ź–ü´72iĽkĽěżŮȲůçA$zéyˇo3Ĺ˙Őč3Ţé;9¤g×.ȸ–v熌/Ů 9–A˙0üḚUřŕh퍎'ŃçQě1-ä9x5ôumRČ5$5ŽŕXľÖ¸•ż/ĆS›ążŽ6—áÉ?ů6ÓďZxlŔĆËńÇŠëNŽFDZďÔ1M˘y"4tň;&k"v_ÉąÚŽÂw‡b˙ą´_§bł=eĽöŔ·{†ŚŃđ9k ú(ÖŽ ą–ăöČ>!äš’#“ŃqíĆ„\{q6™2îc"~8/ä˙w|Ď8l˘ľ3ŃEüf‡Ś̵}hż dĽĚÜ‚˝e Ç;, ą>ˇăVC‡E!cŕ¦!»Ćňxî­ůżěăx!r/@źuđßđÍHÚí2Nh{l<ź‚-‹±¨÷˙KđĂŽ!×tUĚ).vS4=d\ŽÇÜd<•öjłÇŰě-ţł ď3é߻Ϥťl30äZžSńŽő,¦@CóŰbî;nĺ l>.dŚŇ®!ÇŢŚŤÝŘƆŚI96űBs'ôťKz>L¦˙úÍCßmB®™©óîČĽ»OǶłBwâÝ ÚŽSŰťó%!ă˛zsĽSČx®A!ăçd»~!c•Ćăăyě÷âú.Č'9÷@Żť8ďrÝŃ´U,ëÝp(¶îň75ä¸wĽ“tčr­ÎŃ۱bc°ŮBl1,d\Ő l:—v˘3ą'rOľ‰†„\ăv÷c~Gúě‡Oö‚÷`duýF†·} :LĂ6]ˇąr ßDčŹÄęłz9.i tDSóß®´ď´qŚśă’vâř‰ë?NG~Ç iëŚŚŽ Ű~ýo,:ÍâZü;>Ó ×™´ő¤Í ě<úó°±ó[Őq‘ýB®ďęx°ÉŘşGȵK· yžwLÝ„k­ŽĂŻz7ȵţôëÇý)!×bÝ6dĽĐdôéŤ>ŽăšĚ±ŹQÇ> 纏W÷áhOG7€}wüáxČŃ!וŤ†BO˛N ß­;Ş8čDÉ 9ÁqŁá5ßÖ`§!׬ťP §k]áĺ¸0·ĂPŽ{qO±1Šm*6Ů‘­hcÇšMć¸_Á?ýéëř8OŽ3s9CĆłuĹ#CĆyöÇ?»b7Çéő„Ćţô—źŠSŤQŹ_ÝÓ÷żż/ô‡ĎŕqŽ?ěÇ=ÇŞuÇÎŰŕCÇŰy GŽŽč8;dL\ßç§nĐu,Yׂ,ŽŁěť®čŰ?ŹDNŃď}Ç-ö伙k áüÇaGÇ”9ţ{WÚř¸îKß~čÚ7düâxl98äyˇkČsÁČk»öDßŘľKČc gȸ@÷_Ç1ĄŽcÔy[úx|müŕí1Ű?äš­ŤôQűIČăşl2Ćr<¶î‡ť mš‘ÝuëW°ű@xéşÖ®Z…\W}Ç„üß—­GČBÉÜ:äđ˝Ç\Oř¨ÝpxőäŘźĎ>żv×9´°őĆvŽě Ç ąžnt2~Ô1Ť}°}otkŤú†Eź¨_{xoÇő6ôďIŹŻN!?'{řő 9Žz‡\×ççn´—ĎGAOüşŽE§)dŚhGäl˘ťăP;„\7¸9dߏ¦Ź·ď żÎŰtÇ˝ąć1č8LÉÜÜîÍŘÉmĐiߊľ=BĆbv żéÍN!Ď~żMžcBoéŇĆk.· 9ÎÚŃÖńşŽ·l‰¬#Bž7˘C3ú·*čŐ„^­iŰ’}3ýŕŃ€MĽo§q­ş6¬ŔŁy˙ę6mrťbÇŞzŹ—Ú‚ńM»kF·ÄŽjW2–¸Cȸ×®5ÁĎíŇ Y]î:ďv7†ŚeíĚÖ‹óţÜwţŽŰm[°‘ct›ąî6j¦mUČuŤ»ŃĆíŇ ›é~5ľuśmCČă.!×Ú®9µ/‡\Űş}‹:µ-Čë8^Ź­.ؤ]Č]·M=´kCĆ#7h9n¸)äşĚ._ěĺ[mřn­čV!cK!c›8Żbď~lr<¸\Žmn «\¶â†ăýFhU…ó !×nĂqąp­:äšÔŽŁ.CŁşŐ!ÇDą g;î9_·y5÷]7ďëvŻ ®UŇÎé¸\[ׇ\űÚĎ]ďʱԥPÄQëŻß[ěţnh1o˙Тʞ/_ÚĽqý…!śą.„c7Řg×göĘa2MłoŞiöŚśđk{”}bŻż·W{żzż }{·ÚÎŢS]mSűa¶Á¦{Whß{ý­m?ű.pŚ= –Űfô¶ů«…®ÉŢë.Ű®77šL=í°·=»n×ěŁÇŁv<ŮöŮůŔÂČčw1]{śfa~‡m'Úf|»Ú3»Űzk{‰źmÇGŮŢčwú܆Ôf;7Ů»ďn6şţ#őëdöîdďMťľ°6öŽÚéY&kmJ8ÓöÖ§ÓďěŘž,î;ĚťďLµ±Űťl×M†ćR]ěfűo¶ţííť©ŮěÖö Űżcçk¬­ÍíMîv¦OóEłŃj·ÂöŻ[Řťkm_łsłU[{µţÖöwŰţŰ~nÇ&{łńk}śm˙¶ţ&o«íúŢvţ9µ·-†Zo1÷żl÷ţc›ÍżMgŮ5Ó·­µomß­­?¶kfǦ?ŰfßwM&_ăM¶‘ęe·˛çe+óqÓoíş˝÷7š˙šě}ĄŃ|ŇňZŰ·°sűhüĐÎ-všll4šÝšîł0}Ř®™n-ÍGŤÚµ')1˙µ]{ö+íŘ.4ÚŘhiô,nO±cáÜ˙×Îo¶í¶˝gçÓu×nö«7ű7ţČ®mŰčÔ¶ŢÚ5X¬5­1Ű×l˛Íľók:$;¨¦µęo×ü&ĺ.Tkë–j|WŰ;bŤŤÁűŢ©˛q[}MŇ«zĄ]łw¦ęŐ¶·Ř¬ľ3Ő€®VľŮ´ÚâŁĆ⼦oj[ý´mf‡łMőĄ¶·¸¨¶ů©Úb§ú‰TKĽĘĆ|ő=\ď—ňŞ''žŐ6ţ«»S[×llUżk›ů§zdŞK^m±P%>JüU»ĘĆ\µŮ¸ĘćÉęcmÚýĆŽ­]ő,jy[lV›Ť«d;łg•ůˇÚć×ęş$WőóvÍâ˘ę|;¶y˛Ę措»©ˇmótµé^u`’˝jžť[ĽUŰ­ş×¶7RÝějŻUϦúŢŞý­<ʞŤÓ˛W™ĎË˙RÍîëČ›řCŞ^6_VYŚT)dŰlN¨ŞLúŞ^v¬.»YÜUŮQ~Ŕ6ëW5+ĺKTŮ<^eóI•rH$Ă©O•˝T™OT˝ÜšüRÍlŐŹőżŹFoŁSmsGŐ$Úí—j‚WMLtc˝műUeŮ{25ČŤWů k[eó•|8*ĺ™ÄzÝ’á)ŰlÎ/ßb›t:•~7pÍ擲ůµüCóß_S®FéW¶·řŚőĽÍĄőôQľ]ŻŞI5­ËĘĂy%ů˝l±k…Ë>äšÍe%łqyvĘ[)ŰsIu˛Ë6—mNQíňXcŰčÄúŕ“ÍĘá97Ő…V=kĺżÄ6FŻdĎײrSţf{嚬N}Ę I÷’ĹFÉć˛ňŠd—Ăň·”'S˛x*}”h”l —$ó¸k_—ÍĄy)7EőľK·¤śž’=cJŇÉlU˛y«¤ś‹ŮJő·ů#ÖÚţ,Ů[µ˛cĚm¶Wľ‰ěmßn%›ŁcMőđVîÁ”cë6Ű,=śr|Ę;Ą\Ů ÖR?/¤ZŰch+»)—ärtľL9Ş^˛ůĄ´mŞ­•Ęߥܔ’ÍłŞ÷^•ŽcrŤŮč5/Ů,ĘeţUmkĺ,•6ĂÓĆBÉâPµ¶KĘQnŚÍý%óI¬ĺ=;ĺF”Űu›ŻKöí룏Ç^»Űfă˘d>ŠuÖ‡ŃÇž•[•˙Nu—c^ŤńŠőĆ›“bÝěOSźĘ§‹µÎ-NU+ĽdótitĘŃQ rŐ“Žů6˙˛M9Ę5±q_Śž»%ľ%›żcnÉŮ1Ů"ÖW·÷ŤŇзبüKĘł‰5ÖMŤĹRĄÍż•ć'ŐÖVNrŽ*‚ć°dGőŤuľµo‰ĽS‘ň§*’cýl•ök„ŰŻüüžŁS~O<7Ůb­oĺR)§Éžł±®µrÖCóč)/ČŢMcŽ“Îm1–MçXŹ[ůRĘP~ź=‹cÝf›ób~ƶÍ1GPşŮs1ć͉‡rű!—r”ŻcĎÁ'¶Ľ”_óöú32ööź¸6yeďzl§\ĺ%|Â5ĺx k/|˙WŘŰŻýzŹ${ÄZăĘÍPn“ć#ĹacŇ5ňTNŮlĘťQţرÁß’Î1OvPŽČ?h«<ÍÁô‘lĘĹřMňY¬#­Ü·ěm!Y^ íµInŐ>–¬1Iç&۶°gµrmc-taŕ‹-_HzĆ|ůFů2ĘPÎc´{őC‘ŤăóöNâsĆCoÉmßŢ1×LÇ/ÓWř]ĺ<=ĺB\~ż¶ú\…˘góvĚĹy^oˇÇé\uëc®›dý"Ů.öů^_C÷]ÚI.ĺ \‰MŻĄĎçzl,ŰÜŹ*lmsYĚőR>ŤŤÓXźYňÚűlĚ%|śXüŻ"ç;Đ}z˰źňf`łwŢ+eďě1‡U9]ÂŢźË}És5ň? ŮôTdř51p22Ú\cM¶ÚÝ•{ň]MűµôQţ€r?·÷ŃŢŢg”—7ĺZíÖá÷[éöP{Ź9UŇG9+Đá<öÇĐ_ro®ňđ”Odóp‹KńÇZdş›(wöl§Tĺ4h,=ĺé­Ć˛µÍ?±F÷,řĺŃÍEFµ]Ăń č_GVĹȱđ™Íuĺ­)wc<´V ĂuĐÔÜŞ\C͇ĘOU.ŃAŘäRŽĺĺÁî6ßĹĽŻ•č(9&@g=×5Wí‰,‡`k›˙c^ëBÚŘ·QĚë< ~Ú/€ç´•GbʱĐÔůŞë‚Űs-Ö? Ű/€×Rěăú‹o=zËg{cŻŮŘ~%~9ëĘßĚţS9Ífl«µ'áú5§kííoŘGk“z–V$YŁlZŰÔWť’ž1«™sŮOď mákď6q ´KrĹśżNđiĆĆm‘©3ľ’š—ńOďjXKžP°ď¶Č20őŹůuЪ‰­˙+íł°µît”CyBź‡\#]úöItbŽl~Ň:Ü?RŰ'őďÔ7ćéţ5Ů=Ö$_}ăóLC˛CĚkn‹ĎBŇ/ćłý-ńŽů„ĺÄ+Ö`Vć;…ö˙N±ĺůgŠĎ/hď!Ş)eoźúĹ|Ę˙ˇ]´>ß!×*Ťw“OŁnHü˘n/…\Ľ‚űĎaź*äüG’1úąű*_ŕwč \ŐŹBÎqŐ5ĺý9”{÷q’ÝóŁŁ<ź#ă»ČđGôQÜ‹©oÔëCxýúJücŤtőy•ţ˙I|¶Öĺu= ńyćó[őĆ…r–~L˙§iűFȵťŰĂÜ?G®_¤}ôŮ›čô3îŚďB–·ˇý|÷×aknlĚĎúý߆żěq+ű·‘çihŢNۧ’ź<÷4ć"ş^¦ÍońhiŻ\źđÓ3ôW»ÇčŻö÷Óţcöoáséů(¶űQĎ#ôŃńsŘßŰĽ_Ńľ>7Ă÷ěü,÷ŔŹ"ŰC…6ˇłçô=rMuĎľ??M[É}Çjw$2Ý>#«bF9Uw ď;Čń"r<Ž]>†×slEŽÇč'ç÷ăżŰˇń mžC~ĺÔ\ž·#űăč%~§O@ëäWnŢučsÇŹs,ľ7°=r^ęOisň(Oë*h^ý۸÷$2Š®ň.<źďqxzNčđ»şjiAÎłés)vx…×őČů}tw»)gCą.[ |7b‡ű¸¦~ĘŃąš7CďAöWĐćzîI¶Ë í”ç˘Ü»+ŮÎáübčjŰP°Á˝Č¤8ŮŚ˘5ňŢť Ń÷Bütúťď« © ĺş\„}7Á_÷ΠźË!şoAďĆs+ݦďőč¶^·r˙ZŽŻ,Đ<óÓˇsr¸}váŢńčqzČ5Ńe‡Sé#ľÇa˙ó±ź6{GŢZ/ÝsT/ ˙UĐŚĘŁ»ľę ý®ˇß đą ˝nˇĎěĎ@^És.Ľ×cĎăáăą_ĘóŰ½MĐ8g‡\+üZúŹŤđ^‹>js"tÎFŹĐo3<¤Ëˇ!×"?†kÇ@o#˛n„ÖáČ­|Ť Đ9†sç©ý|z>ý΢˝ËsrČyn§±r˝ôőĐř^ˇý)đ:_«í*d8Nf;;ś‹Ě'aÇ5\?~šWW†ďÖËýCŕwtČuĐO ýáČ{ň鸂6Nk :¬ĹÎÇpí@ú¨í˛ëoBÖ5lgŃîPäđ\Ü}Ů/fNAé¦Ü‹EĐ_C˙#ą·}ö‚ŢbÎa[] ŁľKąvúéÚô_‚®‡A÷ ÚĂ=ď«\Ťś‰üűĂc6Ř:‡ô_Íi·?í–ÁOhú¶2*Δë˛wČcčĐ‚ĚsmohLżŐŘ%ňîŹMöƦËĐÓsÖ]¦ȵ<äü¸˝Đç`ú/Aľy]÷äX÷—…\ËÜó_€Ť„˝ß C®·>‡űÎ{&ôö˘ÍNĐó|= 2Î.čî´d©˙Ĺgjȵ±=ďĘófv…–çŔůůÎđžK˙©!רžÁ=çµsȵѝ†ň&ě1‘>~}:´&†\ëŰý0ąĐn<ý'úÍ.Đž€ ¸7ă˝ =)ä:Ď3čçů‰ĘgK»i!çÁL 9g'®{.¦çíP ?~S±ď8®M 9gBí!çyçݨźçvxî@oî9-çăýú˘ë ěŇžóáůž30äzŢ®OßÂýŃ{.Ă óz±y~šçŠ8»˛č÷–öđö\őń\ĎIčr>—ç©©MwŽ;˛ď˙ľ!×ÇöĽŤ®…~˝B®MÝ!äĽ ççůΫ´ÚsĎ1ű¸¦ăvw Ďďm[äuľÍ\krîH[ŽŰŇßótͱٞűâxüú1ó˙ݧ}ȸzĎ]iľ›Çáyő…¶Ž±oËľeČą …kŽ‹wě|[č8Ć˝&ź«ő‘6§ŢiĎÂ;íűíLűž;î÷6]Ľo›ůmG{ŢŞ2“Ôš‰Z í;±«}ov°÷ďN6ovµďÍVöťÜÚŢÝ[ ckďŢťě{ˇé[;¶ďűVöľĐęŰě±ŐÓvÍěѸ.áWëěZ=?흲ÁŢůęŚFK{OŞłďĺ–˙g×Í·u%ünÉ]ý™‰nßÔ-_ ˇÖřÔŮs´Îř×› ·Ťó·ţŰ–¦ѵźŰ±éŐPgýÄÇľĺęL5Ąk޶ţF»î'ŕIoçi¶¨3jĚUż0ľďŘą}S×Ř{o•p«łýç “'<`őyÔ˘¶o޵ ‹Ycý«íYTű•]űWe—Őv­ę„w-Űł¶ęc»fĎĂa mn©^ś0kU6/T »ŮÎí]Şü¦]VÎŢŞműOSíçŁ_uiÂň•ĎJu‹U#ZÁęížé[­~ß‚˙z$Ěʮ űń~ż´M¬÷l›’0h ?±]öU6_•ľˇň|j>›]býęcÁ1 ?63a«Ç&¬`éÜ„I‹8=ű^­ü,a§ĘÂţKż ±rÉľoJöîŞÚŐcbrUM¸&áF…K,=—0fŞkJ Ăő.Ř&]·ď—Ů&ČćÄJá'6¤ÚŘ%űŢ(MKmJ¶ÜJaÎ.xmÂÜTľ’°LúŹôJÉ,,šÝć˘| 6°wą©¶ëž„›Š8ag„Íyl”µ)}ę$WĹ?Yłż6á˙¤0vodóBÄó§bţ¬¸ ĽŐË kq.ÂLý+„ř0áĄT›Tő¤Ĺ[X$É" ę #&_ ă1P×…ô˙îuŕ¸V$› źQH4źOuÂÄš¶ć_a„*m8á,‡ńł„ąÓźWŚNí+eďÂX["|Ź0G–rtjëóŢťl$Ś_¤ńLÂHDL€Ťayô˙Ŕ’˝¤u á LŻřéÂ8h]ęĹq3±Ní÷R U.Ş=,LŤřČßńľpDç‚ŶNkĹ_‡„OvFk=™o"Gk(€Ľ›x¶PŮe6ľÎĹĆjĽܞôŤřá<&ŇOë™Z×ŇŹŤ·›VČľí*&Áw|˘ë0OOöЏ…g>MÇńżÓ÷E>­aę7đWŔŹÂů?ń7$[Dě†ĹSÄ*m—l×n´fîköÇÂű¸kZŹue{­˝ psňU\ËךžÉ'qť^ke¤h=KľŢAë@"·l©5Fł±¦˛ě˘5Q­Í Ł`Ď áŞ´Î©zÂÂHaë˘.·§µ*á~âZĽhß•üëI?LZOŇšď1´;9ŤőX×Őd‹X˘[ÓZR\[Őş»Öx®€‡t{‡ë’íˇdßoĐ«Önçkş¦ďśŻˇqxZ«Š8{§u…µ6(˝´~%€0nZÆ^˘9‹MëX#{`×+ŹXsY<µN¬xUĚ /"ZZ_>«aă:âLŽH±ר_H:E<’Óą-ů=ţ7ő–$s\çT|- qť4Ö@•ží±›Ö¤íą×Ď5„ďřĽ´689őŹk+ŇGńZ5Ž´Ö.ßkť^­ă˝„ŚÂ%Ľ‘íeŃşŞ°Zwź¶Ö Ź˙W-¬‚Ťź“Đš¤Ţ…ţ]µž¦8¬NĽĽŢn´«˝SĆuÔÁ©źÖ†˘´6ˇ±r çZ'ďD¬Ř­-Ů„\˝±ąÖ“Í7ÂËE™„¸ţ±Sq0.µŹë‘íS D~Š1Ţ~GÓzďÍřőÚkęę=ôŤ‡ć÷(רdł¸†*=»píúé[F!­9ËöZÇÔ{đ•Üź“ü©učóÝ“ź"–b×´VŻřž‡NÖ<®'.Nńk÷Aţ=Ó˝ř_·ZkŇş†°>­‰-ůç#d_›â+ň†a)ׄĐďÍö~×z~šúGÝ?Kń×dǡ·~Ka]5®ŁëÔžĹqM\}k ăŃÔ^ë ué)~â:Í?‰őýŕĄőW­+iđăD+öפ֥ľ Í_͸®ő=hkýňţ$W¬¬u5­!č˘9Akńúz äúżW#ŻŢÍOĆFđŚăĽŚ=µăëZ÷{{čÝţőD+bd3ŤMÍ÷úÍĺ÷aëZ_\Wż› ĹÎż€†ÖänDŢűS<ĹuM­Ü’ĆN\”ľ'´6úGdÓşľ}_yÍę(Ł~;YE_żŁ˙$ĹFÜÎÇÎOBçŤäźŘç%εŢs7~ÔúÖ±Đx=¶ÖzŽ:¶…–žWô˝$Éë;ËçúíúUbXzh>Ôď˝O¤8Žńú`˛oävÓţyřĘć'Á[˛śA=G$ă9i®öűöZg9]nÄ–o…­˙kÝ^ŹNZ'ş9‰Ü˛ág™®N±ýô"ö| »I&­«ś?^@Žá/{Ý›|-ÝâüđĂ˙üüçmN…ŽÖßßaŻő(ýžĽŻGĎ„­őŽŁ<ľękł×ĂCăýxý…{şö>;=‚Öfü©ßŇ/ÇŹďc›K±żěń$÷źHöŹcí.亊ö’Ď×Ň4Nµ>w-~—/µN­őŃÇđůőŘC¶˝śOnˇďýČ´‘ëăúF|˙0rÉúťzş»ŽŇMë3+’Íâ<.ůţŻs Łß٧AŰ×ń´)6Ö!‹Úk˝Lëú[k9_b{]; ›śJŰgiݵ/ýîţchŢňqß$ď2ěřLČ˙©-ý>†Ć*d‘µţŁ5Śrm-4Ąď…Řt 6ZN7ŁÇî g«ßD××…4–~Ýű’ŻŁ˝OFWÉŻyÄ×çďBWŃVÜž Ź«‘ccČkÁgŔGń´»ćG!ý®ŻüK¸ćë ŇMń¤1ˇçĘć˙űht˝ »lyťÝ×_O-řU´4§>ŽíĺłÓB^\.ÚżňgË&úM|öÍĂąw)ľ9=OgCCs—ž­ňżĆ÷!×ňťĎýŘ‹ĺëe´ýăBţ˙V_ŰňźňeŻAÎs±ÍRÚ«˙jÚŠĎrä|ž…ÎC¶ ȱzLźŚ˙ö ąŽµě¬1w"ľ»m¤ßŻđăękŕúşË:úÜ̱Ö÷Á~ľ.}:Ű!čvü× ‡~÷ךËŢĐśňš¨dĐ×řüAČu§ŻĂ&;q¬17Ý÷DďÓ )=ǡĂzě÷fČ˙Y+ßk}ăéü<˛ĎC^ÝÓ;ÖĂbż®ŔGí5´®t0şÉg°×QlŇUsÜ4l¦6úF¸vGŇ÷@öëC®ë+;hŤKďqkń‘âÓ×h§Đď^ÚťŚ=VĂÓ×ţFCËu9ĺ3_<š~gŃNţ™Ýó{›ŻËnb??«ŹŢ·&‡4´)&}]Ú×y§…üżČs°‡îOÄfǡŁÖÉF…\“w*zů:ç‘Řa!ľ»k{b—Ç żkČărwäőőp_;ť éy9zĎGöôťÎµQŘé(d”=çˇ÷BúśrmŐí‘ů¦˙s{-ü ąö®üżvÚ#ä5TůďÇ!×–/ŹĹŽš_'ŃV}´Ž˘8UL?Rě‡ü~ ţ-e˙Ó¸.;Š®˛ťŻéĎÇ.;á—±ď.$—Ż˝÷ąVî<üÚ%ä˙÷Ţ%dĽÁ6řa[äÖőĹřBÇ{…Ś2–CújüűzöQđöąĐ×˝G‡\ËV×·C˙%\r}ôˇěuŢő{Ó\îŤGN+¸._Lá~?ř FgÇŚ@?źŻ‡±MGΉ!ăFaËđqĚĹĚ1âŃHťĎ˘Í\d×Ö^ޱSŘ˙äŘń\NŁBţżě!× _бWÁ¶=‘u ôv ą^ëä1'®ăčëGę~oô Żí Żý8tŤ˙Ƈďâz„<Ž [Wön+Ĺo_äqĽ‰ă0v@¦č=ŞÍÎ\×ŘÜz>ďLEŽa!ׯ2¶F׺…\‹u>ńąmPÁżťCơŢdí2žc}:…\ką;űN!ׇśĚ±÷Q ‡—l©XSu yNw¬Îk­˘ďĐń+˝C~.ąí›C®Ú>äxé\ 3kCĆDŤĹ6çý°O—‚:ź@űř`;ě҇~BĆu8n¨Mȵ†\/×ăKí{ŇÖuň˙Üw ąNiű±CŁB®/<š#wDţŽČ.y;„üí;r<#äú=ŕ76äú¦Ăą?$äŘŽăˇč="d,IĎÇÎ6řÁ1eíĐŐq;ÍđčS ×‡6ÝŃ©öQđé`är­O·Y3};°w¬P˙ń?˝C®]ÜŮú…źŽÓ™^ŕŮ-ä1ş çáŻ9o{Ú÷ 9¶uĎçë¶!×í ŃkňjyţŠ8=×}dÁžjł-íCĆÖą_zŕk÷AO®wŁçcÜÇDW¶–!׸­ ąVp»‚ľîÓÖ!ăôcŘvé×~­BĆüůśÜňüăzt sŘ:äzłŽ ląnsďÇI[tiĂyMČő}Ű„\c¶nűÎ\k2vpäqű´-´© ą–«ău>;4…\ÖÇ_/ôh(čұ`ÓŽĐ­9ž«Âw±Ž.›hv˘m'řŐ‡3MżTĂ·Ç­‘µÝŃö¸v۶yűĽRrĽu ąŢm›Ç[Jörȸ̦‚˝ęBĆbúŢĺŞ^‡‚ü®{ŰBßZÚÔîׇ\?ÚűÖ‡<'·CÇVaŻbßęë;ľÔăÖiş˙=ŢC®áëôŰřVtw˙UĐ·*|·†q9ä:ÓM!×"vyť‡Ç_SwCȱÝTĐĄrÝçşk »ż]—†żžô)…\'ŮcÚíZC›–yJý«ĐÓű”hëşÖ„\‡ŮeqYťG}¦Çű©í+Ăwk87p­’űE;?—Áĺwyk ô|s~ĺBźJÚ× ŁĎ™µ\ŻąţłëTţŻóRȵ±+B3ÎKçsçU´Yц- tś~cˇOçî‡rÁ&~îíŠ6Ş,đŻăľËW‘}Zż{ď|“BXnmwű?űDüŤ˝‚ťkŻĎÚŁ˛MÉ7¦şÍí?6wßiî´oˇ¶fŰ6·ŰvĄ‘±wܦcĚÜ´ăOLܶ˙ľíg۵oíxžńůťŰsŻţ0»vLŞc¬ú¶őćżÚĺ©ţn}Ű7čřŮTĎ·ćTŰźf›ń¬µy«v}Şe[·Ř¶ev|ĽmöNYóąÝłoÂŁWc´kż—jňVż™jĎĆzĽżłăŮŢŢ“jMćŃ·o š'¬ÝEv­mŞűZó˛ť[|ÖXĚV›j&üsí&»˙ ™OŘçěřL꽞žjĘVďf›ÉTţ‹ő±o™łůr»¶$µW Éę3l»Đ¶Ąv~źíÍŐ›¨ť:ŤZŞö­Yž‘jŢV­¦ë°TKµl~­2–eűžÖfĺ›lo:•—¤Ú‘eÓˇü•˙Âöf‡Xóô:Ű ż˝ u4Í/e{w/żj”V?Őó,íš0ßĺÎÔ¦Üöl»Z™fݞµ+÷IXçÉ~ÉÎ…7ýKÂď“°Ó#}PµCUGUuË ©Ń)śđă!ŐŇ´¸©|'¤šÂ3Ű{¦ęNĆšťfĂX;ÔlR˛X©”^ö-«ZšÂaë<Ö Öô,Ű„¶VýDŐ+Ťő/ďďýS°ÉOŞÉXi±ë˘>¶úŇëýĹÚ‘ß§®á5 ›¬zŁ•ćóĘź%\fŧ S]~[¶3~•6"VÚâĄr XďĎÁl/o+zß•ÂL?† »%üpÄţŞÍŻ–ąňŰl|VZLÄşf×XëŃřU”ěë3ކúË„!Żě=Í•Ż…X_3⪇Âcß°ŕS•fcŐ9¬´÷X'riÂV ·[q%X㻓m*Iú©VgÄ7h›Ť™ŠoÁG #ş&Äş‹ŃFćŐšŚ5mΨ°ŘW ÍhŁŹŔp™®WÜśôÎ5ÖË-ał-†+ţbm<Őň‹¸vÉ˙cđ¶Â†ž™pąŞcëٸ‰5 Íg±ţ d4»ĹŤď%Ěc”óę1ň±–丄ă­»?8ŢÓąvršŹc­ľ§BŞ (žţ÷¦iUľ«x+adUg/bĆm\«Će‹Í!Ő¶űeŇIhŐ Śuŕ„_üľG'Ć#ÖśôŚŘiáČ…˝´q#,yÄhŰśńÉ6źEĚş°řón5ÖĽ[ĹątEXճҽ­őâ®·sáFĄË4dW “ż)ů$Ę"Ü·0É‹oa6#á-RŤ´kč/̲Ť“Xsđ,芞pÁÂÖîź®EܲbMŘÁSn3bă‡>öÜî>Ę),ţŮÉf‘¶ĹR a"†ŔţGü˙„íŚöÚŔŢž ±NÝŁŘLŘRócÄÇÚT]Ĺhźß%ý㽿ÓÇâ?âĘÍž±Î—pŠ7%la¬“&śäÁČúx-JŘŇŠea˙~OýúúćVŽ€ĆR#üűb˙ť]cA9!-¸®ďĺč·"á¶gbVř¤S’3bţőÉ7Éo«]•b8Ę+_¶CźnÄ€b±cŠŤ8ô=&ś˛°Âú>ü;6˝ [±÷Q.áŰ5Ź:^úÝ$§Ž#źěk±Ë7ÄBďë}˝Żr˛GĚoÝôűńÄDsČy ĘżčźöŻ.Ě•ps?‡îĐ–ÂżR.AH:GlyGô)âÝÂýW ő|)›ă#Ěë;Ě -’¬[ëcý+ů5¶y>Ů'ć ü?ţ+Éeű±t?<ţâ'Ň_á˝~2ľÝâ,âľL±ăG1%Ěógřţ}bÝĆoÄźŚ-nÁĎuří*Žëßč3ő>÷ú|Śë±­d¸.ŃŤ>ř}Š•ŘO6|yoF†±›d˝—ű‚ ů8<ěqŠO2ntýd˙iä^ř>áXšâ'Ţ—ŤĂżGž·Ń˙ÎD7Ę÷qđö˙)×Ďr˙7řNxî’ťŁ nA˙—é?ÇOá§;ŃMŘ[a\…‹~(ů<Ę­ýÜ'dlń3řÍs.~ľźbO›Ç#6RüV"›0Ŕ+B÷2dů=ÇÂ_­ń×'čpSA××9ľ&dĽé%đ»>ş~6ű§ą÷~{ú˛Ë!×Äz˝ űÓđX8 ŤĹÄÝôż»\€Ü¢ ‡' Źc÷ďa“lÂAüýźĆnňĎo‘M4NćX44ď>‚Ě7aÍ!ĹĚŘKü.F.ŮůIÚ9Ţűěń˛ şűźť;Đ]üNŔfŇMľ?ťëŹĂ÷9ěs.Ľ/Łíúqö—"żúŢŚ-¤÷&dý1ţą‚Mt„ď–ú.d=›ÝŠMÔîHřKĆóŃA˛;Wöż?ť‡Ín„ď;!ĎĎ ł®o„ß™đ¸Çţ,Účüjtż úş/ß߆ÎëYv8ŤóKC®ßµ™ýe!×Ć’g ÷eŘěg!ç˙EÜßĚýÇ‘ç-č݆=ŻÁ§˛ŹbďPdą9äł-řŕ |s 6“Ž'†ŚOćHq°(äštçÓţjdÝŚý/FÎáw!úmAÎőĐ?_ęľ0Šç`›Ńéxx^DżcC®á¦řĐ<ŠŻ%řđÜ‚-W#Óěż®Ŕg[°Ť°nÂ<* É‹ŘË1銵Cŕ-™ż~>Ř=NäÚ1ŘN¶P _wľ]Gźë‘y}ČőÔŽ…Dz°fâ{zž@_Ń=»ďŻŮđ?{r-µóéďuÔŽ†Žú®BŻÓ }aÇł±»cĆ—cŹkCĆ÷«Źp_Â3îOÍŹ7ŕżÓ°ÇRěµ(ä” ł4äúqň×č.EÉw ´WĂK}"ב赞r¬‹ ±üŹc[Áő5ŘeúĘ6+ çgă[]ßl@–őČ­ţžOu~8ŽŐ—>{†\kN4EÇ‘o-ţ8ů×±Ŕv ü—#Łç]ĂůŃČt*´ŽÇw‡Łë,řďĎ^˛/¦˙2lq$:ěν=±÷*¶ăŕĺ69<äún» §®)öĺúJ謢ßbä;0d|íY´9ľ ÷lř‰Ţ4t›‹˝<ďŕô9_Éî °Ű*d? _ěĹńľ'Ńî°sCF!Ëtlć¸úÓ˙«ŻtÜ˙E\— &"ăôő<„…śŻŔ‡ÂtŻŤčą+÷DtČą{ŔC÷–@kiČănzrnŃ\›rý¸=đíňăNzî€îâ!˙{ln¤˙a´ng¶ďťŃy/ě°šý>ȱ&!§čďíŘ~&<„ŰrľÍNŘ}9ô—†\“nWlş›hŽĄýLdr~Ě´Ű%dś´ÓY2®wNa[ ÍĐwĚř$äYťĹč˛kČů!˛ĂDh/DŽĺŘk>ý!çfxžÜ(čtçľÓér>AoŽGѧOȵ1]_Ź-ÇŃ;nTçă öq{{čôĂN®»ç2´A†N\ë2NżwČ5DK:0d<żč¶9'˘_ČXíčŕ¸ZÇÚ;>۱Ý=Ů;ýnŘĐý7”¶= [çëk:×±ý]hß=h?Ž9wĚosČřx·‹çT8Ţń·mBŽ#÷‘ówÜzWäm 9GÂó+îúą>í ô+í}×]Äš;ÇW;îą&dĽşçÔú:ľŘq莧o*đ(b”]?Ç)»}Zč6lćň• 4Úlč~+úkŤŐ±ÎŽ nYĐ«[.h¸ěŽévl´c®‹ň9†Řqďµ…ön·e |śg=ÇŽuvü°Çnm^©@Ďuqü¸ß/â˛/]UŕQQhçíÚ_×ĂŰ”ţK¶ĘBßš]ÇY»­+ ĽK…vŢĎŻUx8ö»šëÎŻEřŽzúöh˛y˛ű›Úżµáv”…Áófz“»î¦Î+ĆžëŐŹŘŢčU ßű˘ímĽWŰجľŐÎWŮv¶±ł¶UwŮ&Ěîn¶í›ę—ö¦ć±µ-Ým›˝S–m –öIxÚŇĆţ7ţ“ţ/ţ~đ˛?˛íŰn ±rĺ•!ý‡ů5v˙ŐţKŢľ—+ʱĆnĺ˙Úfü*g«5+Ź ńÝ#^´U:®<9ÄúĹâ'Üg°®_&ś¨ţG=ţďřŇ„5Ť˙K~h˙›˙S]¸Ë·mű:a$+„-5ţń˙Ł7$Ücü_ńóÁmšţ×qo íW„Tcx˙„KŚős׆ôßŕÂk [8v}ŔIÚ7HüĎęíN1ţʶpŤN-Öé]®őp‘6†*Ö…T[Waů„)z-¤˙¸ý{H5q…qÖđź©MÄł˝Ă}a2í{$Ö’FÔě˙óX™·B ç&¬ đ?co6TťUÉ˙ öG‰^Ä ‹% Ý­‰füźPa$…ŮftrHř@áo„Ů{űOĐ_´ž 3řýţgXí„Ů2[Gl¨é¤˙‹Žxł÷ŻXgV¸¸óŃo4„<Zş6zŻ‚e˛‹1±–ćbÚIáä,Î"fLz‹÷$´®®p^§…„ŐOx8açţż°÷»¬*Ďţ×[ÎyëĚ;3ĚĐË ˝ĚyvG@zlŇ$˘46Ŕ; ˘‘Äc/I>M¬Ń/ƨ1ĆŤ5QÁŘŁIډŃďÓ|÷ÚçyÎ}ĎůŹ×źëşŻ÷”˝×łÖłÖúí}{­ť}Nů±ą.M“˝Bűcň±ŮKö/·ôx·&zײ'í\ż×#돛s“=\ç§±Gđ?'Ç=ĆëýxŮ'—}Q[ź{™ë´ÝżËçeż[öxeßć^ţ]ö”eâCĆďű˝.ł§ňh/'ÇÉ·#ĽĽ|nö`aś÷~ĆěĂĽč=§ß78{çíu9Ěë–ůś}aŮ7»%ńąźůĽŤ®]=n®Ç·Ćíëý,÷Ą±żqfś«Ţ[´ű8‡ýžĽ˙™Ćźě‡ůËÄg;fżŮ?%úárůşń˝qÜţßÎł_+{I˛ďč»ă:ô~#Űű„ľęuĚ×Â'>‡ńýz˝3˙ż=®o_vöąäń~¦×%űŁ>çĺ‡÷č›ţů—˝Ţ_÷ůýÇ˝îőöçżŮ›ôŻÇgÇuëý+ůśŹxżîú”ź˙.ĎGöÎä«˙Ęřřţů§źóc˙4ńŮ–_ňň~2ÎGďsúĽç䛞§|~áuČž&÷ö^şŚűĽ÷ääú}Ácgmăuř]ď·;˙xöť|ČsűŹ™˙íüÍ~|ö?Qýž§ą¬÷zůé±_;ngŢ›¸˙,—ńĎI®Ăs˝?ŢëßżŰóň/˙łë ă8˝Ď!×-{5Ţćçd?Lös„÷ëoüś7űçŮńVŻWţ.{&îň9Îű˝^/÷úŢëíΞ¨Źy=^ĺőz™˙ýŠç%żÎ˙VţŹ~î=lóäą~đşĽÔ?ĎăëI^§ě-yž÷nĎů{='/ő˛źçç˝ÉŹËíyŁÇÉő ŻJţ·Ý;÷ĚĽËŰ—ËĚ˙ý,/çą^f.;{%ňżýż!Ń•ĎËŢŚě­zµ—Ţ—»=?ěźeßYřAžáőČźgż\öM|Đë•Ëx˝Çx˘×1×˙wýóśëě×ř†×÷5‰ĎĎ{©çő©~Ü=ţŮ›ÉsľĄ§zÂ+x›·ĺEžß;Ľžáy†–__ç}˙Ýöt/;>ĎľŚ«<ţ•^÷Gxňż+>Öópz˘żć˙ě1‰ű†Ţâm{¦Ť·ëIž‡Ë÷ĽŮc^í9 ?Ë•‰űç^ăeç8'î÷řDŹőčÄąu]˘÷ă}›—µ—yâó ĂĎrJâţyWů9Wyý/ň˛/IÜS5—˙Ýđ,˙{™×1rxł×÷*?>Ç|ˇżľ2qµü÷doÓ…~ÜĽmĎôósŚó=ć…‰{]^émşÖż»Ů˙>Ěű!×7űNôş„1|SůßęÇĺ<źëežęí źĐý«ýű ÷S˝:qÂÇřyOóăá1r_ä1űt/;Ž{sŁż~‰çňz˙ü\oSÎÇĂ=Îs<ĎőOđĎsśłźăř[ž§řî//|.OňďsÜđăä:źîÇĺóŽI|g.ű:ĎÓcüő’§Sü—'yťsţóżßťŕí?ĘŹËßď9ĽČëxQ˘$ü@§$ú|Nôş?!ńů‡ąť'{ŽĂßtcâ3oűé‰{†F=ógŮ·‘˙˝§LôQ\ěń/ňţб’Çͱž—S˝źOMÜ[íĎýY~ţů^ď“˝ÜGy¬«˝ĽÓ=ör‘¸ĎصŢŢ‹˝+'%ţ[úC$ŻJÜ÷0ęNâŢ~Çyěsü¸SýĽÇ{ľÎň><ÍĎ?1ńyŠ‘ź9Oá9Ó˙žâu;ÁËŘžč:2qŃđ?äü·žź =~—¸×ßąţ:ę˙n~¦·ĺ ×ůţy5UçŁ÷[|`˘˙îtŻçÉţÝă=nŚłĽŢŁÄý™/ő:u‰{žćÇëĘýwIâ>¨ô×—xÝc.śáźG…ęxŻűI^Ďcź[ţ¤3÷Ň:ßż/¬đ:ĹoOx‚m˘Gd”¸wę!^§Fľ ßIřńŘąŚ<ßç^„đŠť–čŮ–řÜÔüőáţyéÇUiÇçV‰ű,š¸§bxNHOMôZm÷r+Éw>.ţ 8ü+G%î‰hž—˝Śđއc)ÎŹľ;@> ˙MřBŽ•%îaž•đÚ!ńĂ·•Ë=&ńŮŞáĄŮ–¸˙ĺ~ţI^f“č…90Ń»~—đ‰šč')˝EáGŮ.1÷K|^ęá~|ü»ř^޶eâ^pá©9Ŕßš¸ŕ!ŇWáI9,qĆčç86Ľ%1Hôoś¸wräŕh/ë‰ű­í/ů ŹYx÷ţŰščż /Zä,<2Ç$îŃţŠčď1.ÂŰŢHÜS4<:ůߤŹKô§lMÜëó°Ä}÷¶&î7ľśmţ:üyÜŇă‘čç żßQ‰>¤đĺÄ÷á!;:qĽ™č}Š1u`âstĂłŻ ägĎD?Sř<ňń[=8ű1űH[Ă c$×awÉmx™ňń1עżÂň Ďyř|˘?Â;އý÷"=8ŃqP˘ćDM”>¨đ m–÷ű$zăJô„)›8b‡Ż%Ľ‚{z»&îłhRćÖÄ1v»żÇmöúDŢĂ‹9‰~жçţ]JôrDÝÂżt„ł5Ńă;ňyŮČŐČŮÁ‰ľŻđ2–č# OPřˇvOÜży«ä?ęľť­‰ž«-ŇŹ$îCÇGßCbĽ‡o&ĆĚf‰µNrÇďíÇoN|¶uŚŰČsä*Žß%Ń/^ŤđEźď+m?ĐF©wĚŃ𸄷%ü:ŃďűI="NřŹb nJś—ůܸ愇)‹öD~ÂÇłü~9Ńă%r±9ч}ĺF}Çs;ĆÇ^iGo`đ=<„[¤­Qß`Čîr^đ"üqęÁ ŹVřč4Q×/wŃ˙Ą^K‰ţż RŽúŇÂCĆČ_ĚűčĎe9'ţĆ1áuÔńmŰEŽŻ\xŇÂ;¦ľ('ütŃG+r^śíŚ×áŚńc#Ćy”ýĺ®ÉߨSämMÎ[‘‘ÇŤrľćp1ѧ¶ }c6ĆjÔyMâ‡/mUĘ]•ó"ö:ďďđÉiţÖ¤śÁ®Čý@úi*vÄŹr5OËň7ú#ÚąŠşF}§=‡QÖbÚ‘%kRVäsAr|Šľ żg|ń˘Žäóeů^Y±8—6L» e…R}ŹĂÄ˝i5wËr^|c0ćËşÄqžżŐ´c˙GĚřnQľŹ:ĺ:n‘‹ŇŻá_\‘2VŇŽu ˙ĺ qŹăřn·Ä=rŁ.1¶ÓŽ~Í%9ťžËiżĺ0Ń[uŤľś—öFě8/b†—v-Ń9ú.ĆyÄŠňiDZ}źĹ÷ęůŚůˇžĎđžĆ¸Źďg¦bŞsAbÍI~†ň}ä%ę1Łn+rľ¶gYâDąk~ľút5—ÓůĘńQʍ˙´6ü¬áC]•ř‹CÇžăcVŽ_‘á]ŐśLűuŁśy9f]Ú±Mę÷Ő2"_ęŮŤ¶Î%Ž)msřr‡ňwVΉ÷ó[˝¸ËrlÔ{(1†iÇńe/NĹŠ2"ćŚÔ9Ć`řŽŁMłr޶/üĚłrÜŔËĐruĚN}¶0Uf”ŰĎü?Ť?“v¬‹~>]§Q’\ÍLŐ9ÎM‰ăgÎßĎËg3SçĆ뙦b†o:ɱą,÷QźŤq}đ}řIü ó͸}˙oÜÖ]…KŰó0äQţĘSĐí˙ }Â1‹/ďĎĽřq¤íëř Ç,~ z?>żrĽżńÂßşçú.Ľ˙*ôč}xߡůçă5â ŹÄßźAż€î„4ŢCx:Î˙ ôe|v:ţľhĽźńŕżđ~ďĎăş:8żčű+˙ÇřůKń÷±óŮ»ýpĽţôxĽ>sĽźńÜ'ˇďâőˇwBď‚NrĽ“ˇďŚ÷cžGŰć~6Ţ[ą/÷ŹŇxďa?Čľę·ŕő‡ C×â=r7_ăď[ń÷|Ďŕj\ĎŮ_âő‹ńúďđ÷ڱß{6űÁ˙5Ť÷7F›çđ;sţ0ü}ţ^†żČÝÚ;ű§řűč5P®ĂŹĆţíą7@hgż?ď%î;-”˝ŰČăÜŤiĽßoöV;Ť÷6Î^đwŚýásďÎ…Ŕź~]ä$ďĂŰűÍń~żççąsÜkţqŻ{>ąš; Ť÷@~t‘űĎżážđ[]ßÄçŮ{ýFč§^FëţđźCďש÷ŹŐËĂysץ~_ęŮěUF˙Í>Âx™E^f2nsżŻî{Ýožë˙±?>—5‡ß'yżŕěµĎ{ŻÎ˘ łËłźHă}Ś_—ú˝Źg˙*Ť÷ţ=$őűξɽ︟ë÷xÎ~öÜΧŹsÓ﹊<÷űúŢ9®ög÷ű$? ÚîţuŚ˙Ľ'nöĄçý_gÁ˝Ľ×t^ö˛Ł/głďůî4ö‚˙*ő{ŕÎţ˸­ŮŹŢ·éâ±/ľß×öexŤńŮﯜ۾ë÷žÍţôě7ĆśĚţú™§ń>˝÷űḵŢ+ţëÔďÝ<óý4Ţ'řÚ±ź´ßăŁß“7{«ł>{Űżäő>vÜ—ýľµŮžý Ůc3>ß:>o6{ďĹߣRď±ďýóĎJă=g1fł˝ß×cŻßK÷;ăňű÷Źpśa.őžóŢoç*űëóz~oá×z]Ýc;›˝Ř9GŕVď™˙'/|É{÷ľőÇúwŮ{ýö4Ţźc¸÷ůçľ|wűá˙8ŤýÚŮ'›÷KÎ_öĆľÓßgßţ_Śó×űŃ1îú}hŃ—˝Ďţ±ăśôíz†çyÍű÷žýű=vîßĎKýŔ…~żŕ·řë\Ţ…ž0ˇßóöžźěˇ˙[Żűß{ťžâuü}(Ź÷ěß÷yöÝŘc^ćőĘk(*ĎéŇxŔ;ĽźrŚě/U{Ý_Ćţü;˝nąěçŤű¨ŻĂiĽ~s~ć…žë7űçQ·ó=/9_zóŘ3z?üÓxżá—x;÷őşäv_>>¶ßŰ4ŹńcýřłüűóüÜ܆ÚëžË<1Ť}úą˝'¤~Üö{“ćvĺń´ŮŹÍůÉëňÚěĺř¸îŮŢÇĘuÄ|î×äzç}}­çfŕ}×Aü‰÷® y­HżG,Đ_zóÚěËĘ}vŽ÷ďüřłŢ˙˙TŻç)ţ>żÎkpÍę÷ÝËcćÜäu{äś ˝ĽÜîĽĆaÍËČőźń\ěďą8z\VďóżÂŰĽěí;ĂËÚćíÉë4ń˛.ńöäů—×€‰3[ýłüo$ŕAö±çĎú±v…çŞő׹oňË÷şŮţÇĺô^Ďü:˙;ţżxŮůě‡Ďkyp č×±d_Ŕ;Ľßňz†|źö“q[óţŻýç{{Ž÷YďÁţ˛ÇÎ˙/ň×ŢWůśĚÇíŢŽ=§ŹŹłěďóôk 2Żňż/d†ä˙ś˝ô¸®öc9ç+˙ż°ŻŽóť=¦ýž¶?縏ż«˙ő5 ý:†|ŢĹăz÷{ç{čĎůwëÇę×R¬%îů»ŐĎ{¨—}Ź×ç‡^çŁÇíé˝íŮżý׉kGţÇ˙~ĹűíW;Ż_x︌Ü˙˝ű=˙_”~Ícć­Ţw™뼎îqňżű|Űßßçőűą÷íü8‡}nż4îç~}ÇO=Ć}ăüők!>ŕźe÷˙ö>X稏ť×›ä5"ďńăóz€Ľ㏽Ü˙ăÇ}9MömîŰňwŢţű˝.±ž#·׼!·©Ďĺß{;~4>Ż_?đeoĎ_ů9żôúç÷ßÇďŰ™ű2˙ŚOřx™I“ő!ý÷÷{nňZ‡Ź{>ŢáíţoŰÓ},ü§—˙1˙îď«űÇ^׼ľä ŢŹßöÜýłç÷ž§ĽŽ$Ż7¸ĂŰü~ěo{9ůżđ\ć6żĚóüŻg®óŇxMĎĽýyÍL^»ń•ĵńvßďőŹő#9‡ßőţú3ăĎxűľîí˙…—‘ĎÍk3>ťĆkFŢçŻ?3Îi_ŻXËó)ĎㇽMßňşĺrţÄËÉůÍëuŢée}ÍcĽÍëň7^Żü÷MţŮďz}sű󸳿ţ˘çükóOüonë}ž§÷ůqů»Ľ&䱞ó»ýÜWůńůĽďxąyŚ˝Ë_ÄëóbĎk¬}ů°—ëŢîőý„—÷ oS.˙Ţ7ůř7ú9źv˝Éűěm~Ţ+ĽźbĎĽ®y-B¬ŐřŹó~oçýłßńó_ěuú —˙!ĎŮ]ţ÷s^ÖËý}s/Ľ~$qźÖěĎë0~ßëţFĎMÎë«×čÄÚ“wűű‡/őşgŻľľĽĚëv·ë&^ëĺÝá}ő&ŻOžg/ôsďőň_ăÇľÚăŢăm¸Ćűç™ă÷<˙/ňsďňĺö<ŰŰ—Çaöj_çß˙ž÷ĹG='ŻńĎ^ë}śë–×H\âe?ŮăÇÚ•\ż[=Gď÷Ľ˝ČcĽÎ?ż;qĎÜ{<§÷zýÇsđ*ďź»Ľěú±Żô:ßŕß岲ŹÎëűd/˙Rďăç{›růŹđ\ćsŻňĽ=ËŰktďçF˝.öóóąOđ>Î1žëńóÉsć%^Ż\߼"Ö=ÉëqŤÇű-˙îiŢ×—{}®óăn÷coóQ·zYWx˝ÎOÜkřoWîÓ|?=Ó·zťsNbÍŃ«?ßĎ9Óű,ź{Tâ¶6qâóĽľg%®™Ëßćďcíkäé4Ź{bâÉUâÚŁşŽőśÇšŚ“$Ήë•ęő‰\V‰kĆNń<ť&źG]Nö¶ďyĎÇm÷˛ŹJ\Kr”śs÷”Äý†cěďuč$Ž%î«|’ż>ŇĎ©üśXWëu¸§t-í<#qOÚ“ŇŽűńž*y;Cňw\âţŮq^´-kŞÎôöś”¸&ăÄ}µG^—XoÓzý÷’zF.rîďuŠă·{yµ—źcîŇľn÷í÷s&GěĐɉűřť¸Źo¬ő‰µ&±ž)×A×őlMÜ{ůČÄ5V±fâX˙ëŹbmÇń^ĎŁüó&q­ŐČß–¸mÔ#Ú}śżÉű6qýÓüo¬©:.qŤ[ëőu8Ç&®©84‘_±¦éH?Ç÷h="q Ä1~^ôĹ×O쑸>§N\GmĎ>ŃÝ×HĹzÍĆ_w‰ű‚Ö#Ö›“¸—ńiÇ}xs}bÍG¬g9:qÎÄšÁ×—ĹŽőN±~!ÖkĺĺמŹC×P‰ë–J;îw{D˘Ź9Öm—6Äú]ţňŁ×qÄŠCĄÜXcýqTâZ™üy¬ŰŘGÎ=|ęśXÓßîő;(qMϡ~ľ®S8LrżMr}„çăÄupG'î+»-q˙ŰĂ÷>DĘÝßßWRߨO¬Ůz@â8;ĚŹ‰ue±N&|Ć[ĄĚXŰkj˘ż¶%îqśµŻäýđÄő8Q~ôŮ6˙|“źą;PâFÄú¨śźX÷p¨ÄŤ±üŘ–¸i¬­8,q=É69'Ö;Eyű$®‹‰ń»OâZŤXcłMڸĉő=ű'îźuÚ?qmRÔq‹źóîŔÄůc+âíăďw•~ČŠ5űz{{ĚČSäď Ä5cŃŻ{&®O ?¬ ÚGň˝Wâú›]Ą='®•‰ďwI\'~ň}=î±6As¸ßTY%®ą8$í¸żtôQäóĐÄu$®áŘWr·-qíI¬“µ7üňű%î>ű(#ÖRĉőK±aŹÄý}÷K\ °5qťJäs«Ô}ťÔ5rmŘSÎŃőK/új_iSÄŢś¸%ćt¬G‰ňĂk×Âűă!Ö DÝbH¬/Ů–¸n#Ö3„Ź~ď©c#ŃŽčŁČ}¬ß1”Ź Oňš´=<ö±î#ÖI„ż}Ź©ĎŁžŃĆ˝ĺu¬SŮ]ęŢ{]ź}»g˘<Ö·l|…??ÖűDţv•1"ż±#rţîX“°AÚł‡”»WâŁđÂFG×óÄ8ĐúmHôµÇZ†đo‡?ÚëbME¬+‰5!±®%·-ü˛ë}Č×…Ĺş‹đŃGbĚĆz,őÔÇÚřlsÚq˝‡®[rt FÔuŹÄµ±nB×ŃÄů±> |·QćšôYpDsy‰ăcÍÉR˘?}“ÄŮ ĺF›"á?´®µ‰:.ĘëđG»umA>gŢË_–ÜG?Żórb­Gx»#W×X„/><ŘŃ‘ăő˘XˇŢůXó>ők*47á%?~řÔW¦ľ ?vÔ'|·±ć r˘ë(˘Ěűë仨‹~u]óω~őXÇőŘŕ±ăoôä>Ľč‘“PxÓĂ—Ż~ęXËíÔ6¨ß><úkr~¬ÇJLő^ëÚŽđHĎůqĂ©2bŤĚPňc|)íč±Ö>ďÂ=źčĂ^”2ÂW>ęđé†ßWýţs~n¬/‰ă˘^QVx›ŁĎÂß?›vô–‡ď=ňm\M;ú§ĺośŻŐď#'ęŁWďxÔ=ŽŤzG=ćE SqŁ,Ť1íŹo»ú•Ł<}Żë†RĆpJęźťŠ5'ŻăűđľÇ÷C‰­lő€O{ČŐ«žďx=í»ž.'˙7öQ^˛”fß˙€4űâץ™˙ş%Íüŕ§i沛ŇĚ^ďI量OéŁ7Żý`Jwż*Ą;'Ą˙}JĎ}tJ·~zEJ7=5Ąë7Ą'ŕ~ój´ó·ţ<Ą+‘ŇĄoIé±řťűČŹ¤ôđ÷Ąô°żJ颿Hé‚«S:睍2ÎF=řó)ťţ}čî”Nűô·)ťúą”NÁ9§ŕt2îIOú„ß<'ľz2„ń}Âąbž€2ôrč,ż­Ź#t„ßçÇŁ˙ŹCYǡÇŢ]!î1ż„P·îüżBý[\ [ä¤ů1„˛ę_C˙ýôčZüśügü„Ço¤ě+~ ?ßţşz~:"Wه@¸®o˙KüdĹńGă>ă¨˙}úÜâŁMG˘®Gžá7íľˇě\=Âďů#^†ź6˙ }BŢż*ń3ć Đ˙‚PÎa¸;m:ôőŽ?ôFu>ôTčDüL@˝ůÜÎ˙½ŘÁkýočEĐÓˇ§áçÂżAčĂQ·‘űźˇ üô čĎ Ä?ŕýĐ[ˇ›ÇÚ˙żˇo@ř~˙Ëp+‰|műô)ן@hĎ6ôŰ6ڵ­ż€~ á­żˇ˙·"O[ĎÄm"ĆŘ~»®oűŢ˝::Âď}qíÚíÜç˝ú~ź;!´aôá>»Đî}N‡»}đu´wä}ď_Ačç˝?=z6ô(±÷F콏…*÷h{á{s{=z0„ňöÂÜŮóËĐ+!ôŮžhëžB7{bśď˛÷ř8„>ŮăućÇwA/„.€pݏ®˙{€G»ú*„9°;ú{÷—BhÇî·B?»#îî×»? z,t„v쎺íŽzě†ľÚ cb·„0‡vC›vøÜíčJíÚí"óa7är7ÔoWŚß]!Ç»bĚěŠ6íŠq·ëpK€vmÁ\Űň×Đ'!ŚË-č·- aLmA· -[0¶¶ü6ô<ý´±¶ ?[PÖ–í~łnAŢ6ŁŹ7coĆxÚü ×g!ŚąÍ†P×ÍčżÍĎ„0ç6Łľ›ŃĎ›Ź‡vń[CĚĄ]0–v_vA®vy„zďňűx± ćÁ.·»Üaďr „±ľ ćĆ.Ű\›ĆÚ„ľŮ„:mÂXÝôuŚŮ„±ą ăyÓBg›07ˇť›^1^3˛é&ůßt)„ą¸éáĐĂ pf~WnÂÚ„y´ ¬Ţ¦nD~7bŽmÄŰ~Ůy´ąÜřg.äs#ÚĽĚÜq»őß8‘ÇŤO„Đ_QîFŚ‘Ťç‘‡ŤŕŐF”żyŘ€ů˛,Üđięľ9Ů®n@^6ĽB;6 Ü /ž &ÂxÝđPčllŘ€śm7,Ťµ6­!WkŕĹÚO B»†ńşömý°†ů·†ö¬}z'„ţXĂYO×Đźk·k7¸Đ®5°pí  XC?Ż!Źkç@§kk`óÚ!Ôc óo ý·¶Żt° ^CÖ#ëżáú „~\Ź9´ón=ĆÖzĚő둇ő÷BčĎő·ë1żÖcľŻżÎ…z­G=ÖcÜ®ÇYľ¬Ç®7Khëút }ş6-ˇĎ–ĐžĄŰ\č·Ą[\'KŕîŇu.đw sf ^B›–ĐK`đ®ŮKŁKhĎćďúq ý¸„~\Ş]¸0ź–0——Ŕě%Ě«%Ěé%ŚÝ%´o)·}Ľ„űáĄ˙†±»řsÚ»ř3l_üwýľř#×·E°q×ÎĹ/ş0˙˙Ć…űŢĹŹB`Ň"ú{c{ý˝ř.¸˝ëÁ"ú}ă{}ľń˝ű‹Eôűâ.ŚóĹ@ČŮ"Ćů"ň¶ůşř úś_ÄÜ]Ä8_ĽĚŢ.‚3‹ŕĚâyr¶q°ś-‚ý‹ ‹ČŰ"r¶ś-bL,ĺÂŘČĎśYÄxX<ȵź ÷1‹¸F-nvmrŠ‹ŕôâ‚k8ÖĆÍĆÍň¸€<.  ¸¶,üŘő#î3îwáZł€ëţÂ×Ćk0×p~á“.Ü.`- § Čéćи±€y´^,`î, — w»^1^Ăą€|.ŕŢm9]@.ËŚżppcoą\¸Ć…űůĚ«pdY@N0Ŕ‘…Gş0ŰÜ- · “ Ź ‹ Čë®k `ĘÂB.0Ż000vq7 «.0gcrqżer®yĆęór6 qÝb¬˙C„{ěáżşď!®CŚŮ!b ·¸Ŕ’!úoţ˘˙†č»!ćÁý—×~%_?Ż`^ ĐWwý› ý6cpfđ}7Ŕ=őŕď]č»ún€ëÍŕo]¸' /ź?jđÉńZćÁÇD¸'`ţ ŔĺćĐý9x· ÷”·Ź×6p_9x˝ ÷ÇÜË Ŕ©ć×ŕ•.°jđrú{V nwan Đßô÷¬`Ž pÍŕ÷Íý=@Đßë]č÷úz€ů6Ŕ|<Ň…ëöólp® ×ď®kŹ×Nď S]§¸Ŕąú~€~€sĆ…ëĂ`» }>8Âu¸ }ź×gráľl€{ĆÁ`0{‰0&ňsµ]ˇ-"0˛_˙ N–§„ą>Ŕx`Ľ 0^ßüVŕ·ŢäýĎoĆŇ<îIćóZoŚŁyŚ›ůź¸~ěkĐÁ×yŚĄůşľďúž ÷0óŕîü}®oş0Ćúç†}ŐőĺńđyŚ·ů/¸0Îć?#ÂoČyđŁîXÖÇ]s#ó¸Wź˙3_ž…ű„yÜ'Ě˙/׏׫Ď˙ˇëÝľÎ= cłŽY^żţćńZő^opaśÎăţvţ5.Ś×ůWşp-żCôR׋EŕÖ<Ćńü \Ďqá÷C˙ś´,Śĺů[\`ŘüS]Ďy-ýü]ří3˙[˘« \OćŻp]îÂueţ±®‹S˙\¶ţ™m¸—Ë{ĚăľgĚ›żPtśÇüÇĽ?Ç•÷8Ë…yŃď ÷&çqmšűćqmĘĎ}›Ç˝ü<î“ć÷wmsĺ5ý¸gšÇ8źóć1¶ç1¶ç·¸píšßčÂýň<8żŢ…{yđpă{c{ăz~ŕÂřžÇřÎ{Ěa Ďá:6‡qÜ??.+ď7€1=FΑs?uýÄ&ÎýŔ…qÜ?›żQçľăşĽźÁÜ·}_ĆôÜ?@ř 2‡ń<÷w"Śé9ÜĂÍaLĎaĎýµëÓ.ŚéţŮvŞŹ»ţÂ…±=—×íTôa×űDďőý Ţă{daĚĎýˇëÝľĎÁŰ\ouaĽ÷ĎăĂ=ă~wϽޅűę90yîĆűĆűÜ+\w‰Ŕěą;\ŕöÜË\űsż-zľčy.Ü3őĎďËşÍu« sd×ó9Ě…ą§¸žĽÝäşŃ…y3w ×~˙„'ş0‡ćďšÚÚü™»ě7ó¨v îú},0źňľýł *ÂĽé÷ČÂÜé÷ŠČÂÜéźsxŠ sg÷ý>Y¸žĚaÍášŇď1y47rmwáš2‡űč9ÜGçgö:X„ëIŢbn×VćÝÜľSÂőf×9ĚĂĽ?Äćá®1ů™s›]]\Źs‹ýsC«.ܧĚáş3‡ů87+šĺ2ŔüśÍ{/ŕľestö®Ľ˙Ĺą~ćűcŕţe÷.ł¸ÍţŘ÷ř‘čź]żł?týŔő}®Eł÷ąľíú–ë›.ĚáY\›úý:ľ.µŞß‡ă«ľ/ćóěߊ>ďűbdýŤ s}öŻ]ăłźráľ©ßo#ëăSú ó=ďÓŃ?;2 ×´ţy”řý;‹űäYĚőüČ~źĚńYĚńY\×ň>!˝Ţ%z»č­®·¸Ţčú}®słŻť®{ły \óf󞯽ĘuŹë•®»]ŕCŢ7¤×ť;Ѣ—»^ćÂős÷űł/‚ŔŹţů!\?űgb†ŔŽŮۦfĚ>k'z¦ë®łłO=Uôd×Í.p¤îçµ.ü†}‚ëjčń®«\¸çě÷<ÉÂuąćçe.°dö1˘K\Źv]ěz”(ďŢôĎÍşĐŢä˝Nz=ÄuŽ Ěéź—©:K„křěé.ÜßΞâ›fO‚NUý3EóŢ9ÇąŽqĺ}]Ŕ¬~?—jJř-Ô?g÷ł`YżLżŤúýQwá÷ě졮C¦t€ \›ÝφőĎJÍÚ˵§ ÷Íł»»ŔµŮ]][D»¸6MiŁhĂ”Ŕ»ĽżĘěŠkyJK.Üźô{ĘॾjXŘ?o5˙|š÷" űgŻţrJżpý·ëç.0rć?]?ۉŔΙwýŰNôŻ.Üűô{âdý‹ëG;Ű?6ë‡Súč{;ŃwE`qż×Mî©fîsĺ˝HŔáp¸ßÇĺ.0¸Îk~›ö{e}ÉőExÜďß’ĎŕwD˙¬×ĎěD`óĚ_ą>ĺĘ{|Âő—¸Üďo.Ď€Ëýľ.™ŇźíDśRŢĂĺý˘÷ąđ{ąß#ĺ˝®÷LéŹ\ŕzż/OÖ;]ďá~®ßWč-S½ÝĚ› ?őýŢ;Y`|˙ ߬ßÝ+zŤëwDyßš{\ŻÜ‰îá:Đ?«7ëN×®—ďD/ۉp=č÷uQáwUżWMč…SzÁ”žďşÝ…ëÄĚsDĎv=Ë…ëÄĚ-˘§O)ďO{Ń™'‹nvÝ”ř,ŕ,Ü‹ö{ňěL׉®u=qJ¸ĆôűĚdáÓď-4-\sf'şRtů”.]*zŚčŃŁ§„kSżŹOÖ#]ŹŇĂEۉ.] Âőlň¬cÜC÷ű6eť#:Űőŕ)ť5%\ßú=|v¦Ów"\÷fNť(:aJš®‹ýţ>ˇc]Ç:WިU®rJ… ×Î~oŁí˘ŁEGąŽá7Bżď®©3‡şpí÷IĘ:H„ß ýľ?YűOi›k«k_®ąýL¸ÖöĎsžÖî®ÝD»şpíí÷ďÉÂőwf“ ×ŰţąŃÓÂoŽ~ UŃĘo®Áyď ‰] SŠ®yלhV4ărËSŢCč˙Ł_ďDżrá·NżŹę—˘_ţ[ôs×%>zZ¸ćç=śú=xţÝőo˘uýDôă)áÚßďwł3áúßďŁúˇ ×ý~żťĐ÷D¸ć÷ű'…ľăşJ÷ąbĎši}sJßp}Ý•÷źůŞëďv˘/‰ľčÂýBż·KÖç§ô9×gEăúŚëŻw˘O»ţĘő)×'§ô‰ß żt}|J!úëĎÓxݬŹîDᾤߏ$ëCiĽżPčý;ŃźŠţÄőŢťč=.üí÷›ÁýJżGQčÝSz—čť;Ń;Dow˝MôVŃ[\oráţ¦ßĎ%ë ®×»^·˝v'ú=WŢ/äŢß Üőű]Ľú7čU.ÜőűŚdá^¨ßëć.ם;Ѣ—»^–řĚč—¸pď3Ů/ĺ…˙?zľčySş]t›ëV×sv˘g'îăÂ}RżżDč–ťčé®§%î)’őדE7'>Y•÷xŇ”n]?Ąë÷˛Ö5®'şp5yžoÖă]¸§ę×ůg]9Ą+¦tyâ>—¦ńZű¬K÷&™ÖĹ®Ľţ?ö_=.Bs]”řü٬ \çďDç‰ÎťŇC\¸_šě1 Ęë¤Ďpťľť&:U”×6ź,ÂýSż6ZuBâó-C±®ü8ѱ®ŠŽqu®Ľľ5ÖŐg•S*D¶ĹzÚí˘ŁÓŽkTŹü Šuť±î2Ż+;$ířŚąD±–.Önű ÚšĆë†b홯šwŹő†ŢWÝe×>.OXH‘?ś=ůB5÷ĐSOŹ—'ź{NĽ|Čő×úËá'Ţpý5O÷w«<ůĆ«.»âq7]~ýŐ7MvĹ“Żą9 8ńš˘€ŻľńňËn–wW_wĄż[I~=’×&Ż y]ĘëJ^×ňş‘×­Ľ–¸#‰;’¸#‰;’¸#‰;’¸#‰;’¸#‰;’¸&qMâšÄ5‰k×$®I\“¸&qMâ·¸…Ä-$n!q ‰[HÜBâ·¸ĄÄ-%n)qK‰[JÜRâ–·”¸ĄÄ-%n%q+‰[IÜJâV·’¸•Ä­$n%q+‰[KÜZâÖ·–¸µÄ­%n-qk‰[KÜZâ6·‘¸ŤÄm$n#q‰ŰHÜFâ6·‘¸­Äm%n+q[‰ŰJÜVâ¶·•¸­Äm%n'q;‰ŰIÜNâv·“¸ťÄí$n'q…W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W#áŐHx5^Ť„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxeÂ+^™đĘ„W&Ľ2á• ŻLxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„W…đŞ^«BxUŻ áU!Ľ*„WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU ŻJáU)Ľ*…WĄđŞ^•«RxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„W•đŞ^U«JxU Ż*áU%ĽŞ„WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxU ŻjáU-ĽŞ…WµđŞ^Ő«ZxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„WŤđŞ^5«FxŐŻáU#Ľj„W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ ŻZáU+Ľj…W­đŞ^µÂ«VxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľę„WťđŞ^u«NxŐ Ż:áU'Ľ»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»ěďť_KÜRâ–·”¸ĄÄ-%n)qK‰[JÜJâV·’¸•Ä­$n%q+‰[IÜJâV·–¸µÄ­%n-qk‰[KÜZâÖ·–¸µÄm$n#q‰ŰHÜFâ6·‘¸ŤÄm$n#q[‰ŰJÜVâ¶·•¸­Äm%n+q[‰ŰJÜNâv·“¸ťÄí$n'q;‰ŰIÜNâ ŻÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰMüí&ţv»‰żÝÄßnâo7ń·›řŰ­÷·ĎϦ4÷?xżšńď–ŻĽěćËŽşęĆË®}Ţý:ë˙Ýcu¸.sampling/data/rec99.rda0000644000176200001440000005011515033732352014425 0ustar liggesusers‹í˝tŮÖ6|Şă‚»» w;ÝI*ťtčNÜ38Ő¸»»»»»»;!nč˙śęę®J&ó~ď˝ć~ßZ˙]kßÎŮudŰł÷>Ő=3zĎVµś[9Bl­Ť ±±ĂźÄÎčĺVź[±%NŚ9 kç 01. l„¨ţ dx!PaPPQP1PqP PIP)PiPPYP9PyPP%ŰŁ2¨ Č TT TTTT TTTT‡Cžá AŤ@ŤAM@¤ą<@<Č ä ňů‚šš´ ??H µéAP Č µµB@­Am@í@íA@Ať@ťA]@]AÝAˇ  ž ^ Ţ > ľ ~ ţ ?A@A@AC@CAĂ@ĂAČ  šššššššš šš š ššZZZZ ZZ ZZZ ZZ ZZ ZZÚÚÚÚ ÚÚ ÚÚÚÚÚ ÚÚ Ú::: :: :::: :: :: ::şşşş şş şşşş şş şş şşzzzz z ==˝˝˝˝…ހނŢ"@‘ (ĐP4č#č( ŠĹ@‰ dP (”J}}}}ýý$.&ř-°ěA G)µp(¸‚€E!;(('((7Xň‚ňň € ‚€Wx€Wx€Wx€Wx€Wx€Wx€Wx*‚€Y€Y€Wx€Wx€Wx€Wx€Wx€WX€UX€Uˇ)x€Wx€Wx€Wx€Wx€Wx€Wx€Wx€Wx€Wx€Wx€Wx€Wx€Wx€Wˇ-x€Wx€Wx€Wxş€Y€YX€UX€UX€UX€UX€UX€UX€UŽ€Uax€Wa4€Y€Y€Y&€[a*Ř€]Ř€]Ř€]ař€_ř€_ř€_ř€_ř–€a€a€a€a€_ř€_ř€_ř€_ř€_a'€a€aa8€cá X€eX€eX€eX€eX€e8€c8€c8€c€a€a€ař€_ř€_ř€_ř€]Ř€]Řž€_ř€_ř€_ř€_ř€_ř€_á=€a€a€aá38€c8€c8’@Ŕ˛, Ŕ˛, Ŕ˛, Ŕ± Ŕ±ĂőÄ›€cplŽMŔ± 86Ç&`Ř ›€a0l†MŔ° 6Ă&`Řüš€_đk~MŔŻ ř5ˇćš€a0l†MŔ° 6Ă&`Ř ›€a0l†MŔ° 6Ă&`Ř ›€a0l†M¨»&ŕŘ›€cplŽMŔ± 86Ç&ŕŘ›€cę® X6Ë&`Ů,›€e°l–MđlňË&`Ů,›€cplŽMŔ± 86Ç&ŕŘ›€cplŽMŔ± 86Ç&ŕŘ›€cplŽMŔ± µ×,›€e°lŽMŔ± 86Ç&ŕŘ›€c’SěWĚÍŚĆ[c0h<¤ˇ˝Ć×Űź7H#WŚôĽoôŐjy‰ç˘ ö6ň† ß ë4[ŤVăoÝ@ëîkÝÎNăç®ieťćoťćŞńôőđŃř»yňnĎ‘ńĽý‹őîb ó· ­÷ôĺ˝yëB=Dň×Ébcť ťQ^ŻUNđ×=ÝĽˇŹĽ'N7ňÖů†>Đ:ß çÝ ˝^#ńś5FŤ_Â2vŁV!˘QŻ)Ąˇ†Ľr˛+°“›Á¨w ˛ęž»ĆW«g0ĆţňC÷ő÷¶c]†§:ŮDĆ@^źÉ[…hdsCĚÂąęä!ľĽRŢ ˘ó‡´Ř»kŕBr$›ŘťiŻç ;¸k´Ľ!вÜŃť98Ävó–Ť7X˘đ6¦…-Ç8»óđŹF« ´î Ž—Noőűéü™©­;‚` ¶„4fpŹcý…Âg`hÝy=ÂH>Śž)¬ÍĚăýÝ<O‹ ŕ–Qćżň}x}2ąÂ„F…ĹšΚăŕc3ĄőB4z_-Î6XéÄxFy‰8ÉŘ s‚ő EBxĆTć'ĆóçŤAĽ›źN>=DŻvĽ\=QX·ÎőA]•‹4†F?ëŢ>(Obv2Ęó}Y,Z…!Ęž™‡jĺ«ä¸1”üCćů"eş+ěhçá+ nďˇŐčfÓňz± h­őČŐ‰†GŐŃűňF«)uz˝Î_îŔ@bŃĘ:3†Vă©Č`č­&˛ńĐY —°Ż—k2¦Q®Ř¶ž«%ě<ů`?kđ{2üdč<őĽźčs/Ťµŕ{ꍞrő…kĺĆÍ÷ ŕĺÝ]y±]p“Ő°ĺe?8óŤµÄ sĆńç=ô°ŻuOC€F>‘ ő˛vě1Ż÷ŕĺÇ0’rĂ@ŤżG†.,E×fĂ-ńl祑;?f6hm50ń­cg/ě(¶«–é^ľ(w:kOĺĄe޲ĘĺčĄÓŠĘ"§—Î_,őr>tË Ó»[Ugs ą2(Đq°¶Ř˘‹—Nq€‘ ŕ//6ęaę …{b9’ĂČŐK/šK"Ďş“8…íÄR‰˘xéy@OÖ?C8»˛ˇŘfšYđ ĘerěbČJ˘^FC†fÝŐ›ets?ë-óXbö@Wi´#oÖĺËŮĹ[QGÖéäT`ʱ·ůzňÖ§l©µFc`´ŇΛE(nŔ,˝¸ë2ń2J®3ZRĄ& žŃ*!«„­3ôž0b€Në«ŕˇµDEĎdµl)‡ †h.dÓzł‹˘Ę9€¨¨Ŕć!lŞ·JˇGş÷§óţOĹj>@Q[ ŢŠěŽ%ßÝP—ä‘ŤŹŻ5Wřej+ř´bGëií:}9ŢŐĄ'P4S‹Żśš3V<»f°€uE3±ß1×LĽ l7ëŃH zTO7_Í_x¨á)ÚDĆCČ2Ďóĺyh˙Ę Đü•gř+/0ĐZŔ“ď)ά9Đźzĺyzc0˛éä/Žη•Ër2ĎËË7Ýά)’ń|q4kK¬•[«ńV¤2WiČîu€rÉ3óŚZń‚+«âŁ‘/Řá|Ą"bewA/Y]?ŤA©®“Văď©c~°8 kC‹§|°>æŚ<Č»yÉbč=¬ ýôĽN«J/Xž eéĺĄzě/?4ĘąËIp F†ěć˘e—ż És‚Đ ÉއüA<4#YÉÓ#˙(óVĽKŰ{ Ŕ´Š·ř›%uYkÔbŁ  ˙¨VI'·âÄ‘1ŚŠ6™Ý7Ĺ0ďâ6Z>ÄęŕŢĎje«n§ŐÉŤľ#úXdy¶\AY,§"-KŠ‘ŹvZXŮzĹŇ ˛g]µâÝĚMĂn V RSŇOăŤPćĺ[™Š‹ŹNn>ý4Ú Üoyyěďá+79¶+žexłâj»ÉűeĹsO+'Sg ^ë{HĄ7°ţĐÚ+ř±÷Jnńlă@éÍ>3/P§ŐÉK ™:{v…Ń(L*n›"C®$âPńvĎŮ/ók(6%Hăk_+1/_~ىrmÁS@V‘­üĚw1­+xEcäčg†Ť&ÓXÖĹ›,±Żž]Gĺ”/2XŰ®xOžRk6‡`9SN÷v~:Ą×t¬˙ °ZAçď —…( ÉŢ`@k“Ć:˙Ś–b,ô@Zůąvrźi †I—n#»Ógŕa…ŃCîĂ mż¦™|-đg¶’s¬?®EŢ2ŚüŃ˝ů»yór±˛ń×YCÖ_‡ţD.şţ™.Úv:OŤ^řË•HĄłäHťlŃ Z_Ë[]‡ô4r)Ăă`ö}€51ŕ*ˇ‘cĐ…ŤŮ‹+ą1rYFÜb8đâwrć`É.@~ÝěÄzä{Ůóć¦&D~‡{-k˝ąřşËw 'Ôv7wŤÖOnř’Śç‡Ě­UL2°†Ĺš%Ă­sŽüZ"@«xˇć(Žäć‰ÝäŻm\‘?q/ËX“,Řjd49ôŚÁ¸·ů* l ÎFĆ0°×¬Vă ż ·±WĽE \Ăxs[ôŃóîŠo•0ôÖČqěŚbťńEŹĘłU Gö^Š˝ŤłÁĆ~€źŃşcČ€aCkĄ÷U”@W±Qf_‹Č%Č^äeś»)ľ pyhq ňŁâ•,F¸|ëýü3.d«˛–Y:+Ŕ¤­šˇ~e˛0cń™61ĺĘcá寥´Ľ.óJŕZţjMÁłĆ°Äł~ß"Ić§L)–Ţ×fÁűëĽ@ť>Ź÷T´Ď×Ă'ó~¬(eŽ‘—im.k4ĺýe?_ĺÍâŕiŮëÄŚÖÔëü2ÍŇł·z™¬i`‚LŃe0j2Ď ôŃůi2i†[¬‡Ő‡vM3ą'g_#řĘŻúŘĐz Ĺj-űzĹÓ¨xáëEP0ô™x^Ýá®h`7Á@Ťő»ż¬xĆOĺ~âkc+Ç×QŤB°§‡|Ës~ŠÔďÂrBËđí˘«hÄ@楣Ť­Dut~Š“‚Ä7W 3)żotv^ńŁq 7‘4Š ˘ Ćě­¦·\ m |@+y«`ka+Ve Ĺ»±őë[Nľ‹X'gő­šö5ŤEdŃ îŠ¶>Ö[•C Nůköę˘${ŹŁç3LQĽuEw‹cČđ˝–…§¸—Úâćl˝\i<ŘÁÚ^:i´wĹë('övEDî ‚X°)ŢŞcC˛eű¤[ „ >Ăďgl/ţP¦<•'đ¨”r·‰Çz/Yo{ö*IÎE( ŇO6¬/×-<öUĄĽ s”ü*…˝/ßőá^oUUĽđČőÂAdXßl9‹C("C^ÔZü:ÂúvĎEäůiaÖ ÓĐą*_ÄŠ<ówŽžYđ´Yđ3ňtŕY+ľť»Żü~ß‘7°oç¬/®í´Š_:ŘyoÉ­ż×fż!7íiţý”ř»ęqfľHě7©îćߎ±żĹqé÷p±fľřŰę*ćg&iöÖKżEn ď+îĂ~{H¤9lîTé·[–ó·H{†Ęçłß:˛çâď&żšÓÇÖ›|Ąuă¤=§š˙(žóŚż‘ůµĄ}^J{ÄĘg°ßJ3]ĹߍÍżÁc2˛±ř[ßPiî8iźŐŇúÚŇ\"ť!őgň‹[/ŮE҇=ϱÄA9^DŠ•ö'ýns˝¤s9.ű‹´ďK9öD}Â$®–ä®­ŘŰÁ¬·‹a’Čzëß}%ĆąQá·PÉn’E<¬2ǢHëĄuĘ}Ą¸ý±EáÇU2Ö¬j—;"­—âüe&٧Ę>ce˝´Ďxiţzů\qíly?qŢ8 Cľ ZÎś-íI¤ç/%›%›®—°ŕ.é·ZóP…×+r†ŰÖśa‰ą0iďrĚ‹yŁŚ„©0Éć’ž kWË~¶ć‹rŽí5žČů*V˛‘ll٧ů|q~ißPiž§¬żř7‘ö%ĺwéŚŐ26E[/”ĺ˛č)Ʋ"¶"fCľ[(ç%ëłÚŇŁ$ꬒđ0NŽë\K{&ŰEÔĹ]¶›5¦§J±jÁ¤ ťU[Ž‹ D®—Öż”ńoŃĎ“îŇ<)żŠr/”ýźÁ?‚”'ŞHëżJň>Süý’Č9ąŚŚ31ËHöx&űCĚ ˛ÍD9b‰\g¤ýVŰĽVÔ}ś”Oä:bÁ‹xĆ(ɿϺKľdĎŮb.·ä K[l®ôA¨„§q =‰$÷xIIwkŢ·ÔÖńŠša‰ŤŐ˛ď-uÖŠg©>Šľ‘ę˘řwśk¬9c‹BĆq˛Ü–ś%ň¤XĺyC¬ąßRw­ţ{&űZÔµ”·ŰI~ź*űŃR‹ÄšŰA:˙Ťś˙,ąS”ÝAîWČpĐYü­A‡V€®2Ć™ř€"A . c -žóř„¤¨h3¨4!6ËńŮ‘|+1§5¨.ö, ţ)đ×áó>“AMŔ/FH΄‚Č4XËЇćGë†qsPy4~ě\Ä<ÇvµŔÇ¬Í Ţ$ŚSŃ{áÓÖĂg¤'Îfť0“µ+h$űPMÎ y°f4öŮ…ż+€ aŻ ¦öáÔć}U°1×d9ú`ÍRÖŻB[|BŘŠłÁßĚé°[8Ćť@Đ-ô ń÷;¬ďHoĐP ö=‚ĎxĆäÚ†OŘ<Äú%„dďm¶77óp&9 ş ‚,ĹëˇK= ľaĽT×lkbä(nö!q5ëĂô$Ő@Af»±ů®8×ţ'L¦“Ňü#’_Űä ôBś8•3Ű›ĂYÜBrcîŚ9¸öř<ŹOł]ŮŘ6‚ý: „xr†oČ„ě@Öł7ęćřĘÇüŠ•ÝĂÚ­xž[Šźy Gŕ!ţÖŘ"r@^ň ¶áĚz8#. ‡?Ża/.'>‘3T7 ×2ÉżqÎźđyb~Nj`ŻZ„¨n¶}gČ: '«6đ“ úÚ0;tĂzä9rAŠĂ™łŘż pN -üFüĚqlS głx‡=]€U{äź >ÓăČM˛˙><;]ţÄ'|Ŕ±xĚaŽOÎdöłź-â$wAČĂô[>bŐňŰUg/¨ÁĂ2۬§ÝM|za.Ó2;N”žĎŧ˝C,ölz€ŕ/ŰĹf™™>+8Řš+j¶]lŕŰ<,žš×©BŔ ąKvf ˛ůČ,?xJ1(ăÔé>±†üÄó^ŕ{š÷óD})nO¶cŚý1Ż•ů†ĺ€%Žá˛ź$»źČq¤řđˇ=Óa:h¨dß­ć¸âŘ?a˰;©›TLľPs<:#ŽČ ö°AÜŘCŞBRÜĆß,Áľ˝%y™€9[ŘËv/öGĚ:1ąYţcXg¸ŮdŽY'čŔ b>.Ĺ8öâp>Çć™ŘWTËädve8D Ě®°›ËťU1‡aĺăK Ąf[r8Óu,tČ%ĹJc 3 ŰCłś,Ž–p‰ĆţČeŞ­fr‰ ňŮ,0ëh‡Üŕ‚ˇ‚ŻÉ łí‰ŢŚ;ó‹Á>ŞŮfŮś·s47űŹCĽrŤ°‹O`†¨q>ěŔ]Ĺß%QŠ™ý¤BlÔlΡ˘ß‘§ą¦ćc1› 5‹ Ďp'$˙Cî5Ě<ŇşV2Ç‹3ŐuČ ް—+Óľ´Cí ×$_eÇ3佼îf›‘ öf]H >OÂr|ˇbyyŃ.źYV{ĚłcX؉żYmBžQ1ěĂ%Y~ĆÜćśÂ±\Ęj öS}Ćü[ř»"rł["ánŞ7ä#sťj>ó@ĚuůśĂ'7ČŚ–í`7{†?–Ż:›}Áő0ű•áŽĹ:‡<’źíĹ03Kňën3žYĚ1ź’98ď>Q÷Ĺ"żBŠGřŇ‘áě81×ĹçRl!®ň3™±^pÉć8frÚP|"ŻrÝĚëYíă}§ŔçŽć·ąŃć|Ćâ•ŐJ &ť#NW wó<Âę-ę‡ r‹ 1¨Âٶ貱šÚÇkŘ—mřűěŽř¶ż ‹gŘÝ–Ĺˡ–~E#Ůi?ĆĐ™Ő,v†™×0ÝÄŢE'ŮuŤĺ'ÂôĂą¬_Q±3€sG6ď6h¬¤Ç)łÝTEđŮÖŰuÇ=‚sy¬çÜYsڞýrM3ăKěqXF°Aľ!A{ÍxPŐ’|_×|¶C9‹˛KµJ¬;ŔşŘ!ľTČ ¶¬Ş‰ŘŻu«/¨’ ɧŰÍ~·eů…ĺ*Ä:çmö'«XL1_Áž*†ô]dě>ĐĚ#čĎT—%űş›mĆ1}X>ź*ŮrŚylżÚ?–bXç̬7čcÖŰ1e‹¸µE˝sI^Ö÷0|2»´!â?3HXţ@óD é<äo{–gYěŐ‘ö] ĹňËé¬`yŰ–ĺ?V3Îč™ěY]\+ťqŰwb ?U8“c}_^sżĂ|lűÜlG®ńŕĐEЦËk°—[Š© Rś •d*kîŹěK™cĺ^ćO›+ě(ö.dö%ËĚάF‹ąY#ůšĹoubÎé Ěńf˙:V6ŰEĹz’ţDĚ;Ě/›Ź~cą€Ő–CYnj$ůŤĹË% Ŕµj 1÷!°??p,7#ŹÚ$3V‡šsłXs™LĚ ÷Ŕ˛ÍÉö+%"ÎÉ"ć[*ÜIě™ďP·Ĺ^ňs¨?Ü3łbŹÉtb} Ë=Ŕ çj¶ëwÄ3ĆęšíČrÇj=dWŮHú?”|Çr»—L2Ű—ĺl÷*ů‡őžčUČ{¬a~bµ=›ărłěb.®,Ů˝ă•ŇľĐWĹtŔ~ §bßŔüÎň&Ë5,wĽâµŁY/UCsĎOX-R›c—ëcÖU”ąŇ¦ź¤›ź´ŻŹd[†ĹÖRě°Ü]ßܰޚůĐ–ŮŘ1ĽŰ|!‰Ňúć~„ůW¬UĚ6ůĚÄÁ–Ă0đmóѬ7ł%9,ťËú|RČâŤá¦–¤k süŠwgđ—Ë»¬†\7ÇĄhÔ$Ő[ón‰„–XţŞiöŹ-ÖŮÝ2ËAŠJqŐZŞq¬7Ć}ۆĺ…2’˙ďKţëd¶7ÇlËÖ ¦ą‹’=ľIsW›ăH\‹žß‰ÝwÂÍçŰÄáÜá’Ź†Ů=Łş„źBf[Šş”@Šžk^Ă1˝H>›e~&憭ÝćłěßJţcw Öcá^Ł®d<·Eł9(Ů1d ýH>Y9<Č%Ö¤N’=k —xOa5á¦YOńn7D˛A"ŢE™ťUÍĚrqčŻUlâJµÜ,3'ÝŹ\™mŃŘAgńţÂl_ĎĽߪ ć=pGQ1]–›c@ěe™\Čez)±§ź'Ů PŇůˇ#ČÜOs˙#ćm‡L&;ň‹í i î@c$?±ţ˘ľ ¬öÉ·¸‰őˇ¶´Ží‡\dsÂlqĚîcRMpś"íĹŢT6ÇŰ—c9`ŞY¶žÝĹD~â>ŹÜuiţPÉý%ű{›sµ-îö*vç5J2Ő”ú0ö޶ĺĐ›Ř;›y¬çó2âĆ•őţ 7ůͲq¬ö n[ű'{ßÂü”CłÜĆŢ%ŘIi#Gi.ă38KĎC¤ů~Ň>ŽŇĄ¤Ď2Š=™ţ ٤ő,Ç0Ě3©Ěsí¤ůyĄóU‘ÎÍ%ť™WZ§’Î+%íŻ’Î°•“řś9Děů,ë3{‰ďd¶Ź¸GubÎ=D’3'1÷E¤uΒΕ”d)`¶·xľłtV1éÓVšă,}˛ĽÉň?Ë“…$[ąJűłą,ݰÜŐL’Cé…žDá[…~ě󑤓ĹţlmQIWGé“Ĺl‘}í Ť-ń]Zď"ń\¤5–srIsŠJ2äö(#ťˇ’ÖŰKöv‘ţv’ÖY|Írś—$ŻłtĂ»A:ĎN˛­˝´®Ş´?›—[asN:“ÝëIăśÄěżśŇ>ĄĄ9NŇ"cÂQ’‘“ôaź"Ƕ˝ô™MÚ“­!íí¨ŘËNzŢUˇŻŤtn6‰Ç>Yž©¨đ‰ĺ9“‹Ý[ 9ž +|ÁÎË#ýťC±·­bŻ’’ůĄuDzfńe ‰šCělçnDĆ}k陳$·Ť´;·ĽbmYioKnQ)>móś$ťËK{Zdł`ß^á‹Ň3‰,Řu”Ť]Ą9E$ům$»ZâŐEvĎή°±%® Ś˙cűĺ–žW’äw–xą$ýOŠ)ä°čiÉ)ů¤}öHë]6´%rţłřĂ‚‹ŢĚď7¬:X°M‰Ś˝2’Ě–Ľ’M3ĘIv±äĹBҧ҆™u’¶’έ¤gYň)lśŹČ8[(}:)ö˛`„Hö*,=kDäŘar2ÝŢ9/˛»ŤźôĽŞdc6ŹĹn^ÉŢ–¸"’<–=->bxĎIä8Í)ůÍAšë =+CäÄ)öĺĄOéĽ|’Ně9ĂIQéygĹ~ŽŇą*I΂D®M,2Ył+üfÉ_ą‰ě[Kě¬đOI‰ç@d|[b>ź´÷D®Í9‰śo;Kë‘k3›g©±–šjńűt‘öTIĂćoţÎ*f3ăčďňąň™7™ă4+gSYÉ”ŮJűfÎUJy3焿“[iǬâ!+{g•s3çóĚ±Ş”5«Üý˛ĂßĹ}VvĘ*÷göýßŐľ¬°ĘeÚ3+[ýOÉ<'«XĎ\ź3ŰÉ6‹ą™k¸ĺosż2·ô9N—MĽ ÉőNUÉ®Y%‰O4!!­Ď“[1-‰Gd< Îs‘ô»Ą'Żî“†vŢd[ŃPRiâ2»ě2dp32oy^nÁ飤ń-ŇřMň¦Ň rgę&Ň˝_cîń™edF;.ď~g2đ˘i];ńÜľ™<8±OŐ`Ň)2ôŚ/éćÜxŤz«ęŐ(©Đ,©Ô)ަ\—ő¤wąTŇáăk$ˇ“Ę’ĎnsÜĆlÜţ{#É®Ľţ„öéD&uďËą|’l*űŤÔűYťŽOIă‘j˛[¸JĘ–N"ŢoĆ’ ¸ćë¦ď–‘\•?IßauI-Ź]dW©-ÄXâ97ŕYŘą)YýÇ2÷ëZ6~·M Ď…DWó'ńńr"ËwőâŠO¸Dôkîi'ků{ę“K[>¤L—á$Że®ÂĂ+\íĄßI€iW`q72}G ˛Ľ§›Ęßć%镾‰Ě»F6?ű@ht%˘Ń” ćç&•{µRťÚ; »zŹ4ÉBĽľ^Su2ŽŮîAÚÚ’´ö¤ßŠ8Ň;Ýź´¬ÚťÜÚů©\1€Ôîq„lę}ʬöčG¶.¸Fž]čE*Ö>DtßNß\HĐň`Ň2¶&ąד|4Ü$M÷&ý&zŞ<_m"#Ň–“·˘IPŁ»äÓ’·ÄëdŇ(’p-Łćľö#ˇÓGÔ­/iXbéŢę y[˛YY(Ś„˝iEV.YCV~ ŻŞe'ĂU&~ţî¤ĂÉOdđ}=qďÉ“R‘+HŰăH›}UIłĹ±dWŮ’ä(î$wsu"šŮ”ĽĹČŔGŐHŁ“ů‰ˇá-ŇvT0Yh»Ś»ąáéĐí(™[ŇťôjqŚL1LăjuÎÎĺxĚ‘öĂíąŠąŞp!7’^oęsąőCIín•Č˝+_ÉÝ •ą<‹HżI•jĄ¸ˇĹ‹’ŞůîMď-¤ČËR¤ŮŇÁdćŹC¤Âćz܆ !¤§)ä‘]2çÓ:étńŃO!ąăţ$Íďö&5ž&g›Ů“A)6$ôhaR5ű[2ziŇwl2é첛\÷lO–×iLNn(MúölÇűFĘí[IÚM!¤î„ë\×Îţ¤Ĺ$o˘[Ĺ“ Éq¤«osâągY–óŮşş;i6(„ĚLň$ţ×^’níJ«NtKţX[š{е/ąÜŁ(i˘}HF“ó\«N^$_GÖĺíMúĚ«ĎńŻŁÉ’Č.ä€!”ĐMIů’‰®ÜŇ/ą/9_µIŢľ›ÄĚ˝J‚ÂŰ‘ŕ\®Eö$ąęY®€Mé4|w7ęŮtľ7é4t©ß.?)÷ř1®ŘF†4 'ç ·&‹‡rܰÉMlćU©BÚ_+G¶ÍĚGčé¤ÇĹ?Éě®jruýfŇ$é"W7öYPŕă„4;áDšÍľJB˘Ę’€4^ľ™Y˘4ńńL:}|OJű=ĺŠ©Ž‘˛űńÓÂČüFŇřsŰ…t ¨Aş4™©ęÓ˝ůăĐe˛cq y4ć*ѨRçů!2°íURÝ~W´Č|˛jVY2lä=Ň|ŕ*ŇqTnnFZŇŐ}9ś­1ńżTxŽÖš÷-é˙u‘«,éîŕG"J}$ÍĘ7"†ÍőÉŘ®IÓ×5ČÇ„iD˝ô\ 5ą˙jiřą9Ö˛iţÓ‡x_Iž]ϵ^;Ťš–#>{KşTś@ü'!ői2Ć=BUâůXR">”4ô{|}"—k©|e#ą]h!™»€¬Ż°‡Ô*Űť´OH'>ŇIÝ­7É騤fŃ…äP7ńĹo:‘t-\‹ËŰbé˝ ;ŮţŞ™:tŮxĹŹ,ÍS“¬{~”¸}ďHß-^$aER?[;RÖ­2éuf$yÔť<Q‘TëŢž4Żň€<ą3’;v'݇M$%+Ž&-—o$­ČERĹ]btµ'†»­HĄőŰH9˙QDSĽi3!‘T=»‡»Qí 9pT}ş™l¬“´ů>Žř5˝CÚ5]B|§ř^_.rEĆ_"'ł' K^ĺĆĎIjîFGu&;÷¸“?ă˘ČBçĎ\ďŠőI×CÉäô–¤9öÉЇl®jŕ ś=ĘUą˛\u‰ŚXß“¨o– #Ţo#^ű“]ą<ÉęůIĺÔYäů‹<Şá^ËÉÚ«sH`㙤z§Ö¤É¶ó¤6z~Oć“"í‹“ŁGź’5nŽ_Tת] ló“ ŁŰż eÉpŰW¤DŁęÄ=Ç1ňŁPҡj bţťŇj~÷ÎłéóŇÔü¶{ÉMŞăóĘłŠë“ĂŐüˇÇΫů=&żSó—+łYŠĎÝ:®Ś­Łćw)va`¤šQřş‡ŚšďŮâĆýkÔühUĚč/3Ôü|ýÚö9Łć§Ěđo˝ŮIÍ ›6Ţ8tTÍh4mw/5ßďÝÓęíK©ůyÇňN±Ô|Źý×RÎ Póc­¨\`8Ö%$ţśż_Í?|?d^ĺ{j~I÷”ňnj~_áAąňLVó'ÔŤ›1_Íźé1(ÚFÍďPź»Î¨ć—÷>y-ŰS5aaçy=ŐüüCĆѧÔüôÝcęăóńÔEÇvVó»HçşÚ»j~Q缓‹«ůó;ź´×>Wó‹ż^÷çďq÷Şü¸—š_ýr褦Ôü@×íGZlTóír$ś|­RóKź˙ś–XOÍOčéµ+࢚ďű­đ•Ä?aŻë/$żRóÇÝjŻt|Żć_V,rŐí§šźµš^)TFÍkroËůvŘéµďCŐüý)•F ˝ ů·´ë>d'ÖE. ëó zw)çŢm¦š?3¨Â[śwÄ9Íé Λ[álÂDČ}Ä{Ú~˝ŐüŁ%‡}«ćß]§‡đ|mOë†=ó.­™;Îi;ĄÔOř)ŇĆ?ďŐNj~XŮQ°>´[Ż[Mŕß53KŤčí¬ć·ĽQ÷D˘š?PöqÔŃ·jţăŰŇ3ömwüđ1íáĎE›žĽEôɵ©âăYă5\*ć÷îy±Ađ*ŘuëńµM¨ůn›ŚuWóŁ&5ŻÝcđł ôLNř·÷®Ý‰oÔüâšOć)‰řós9e„^Ťň5/đsl®TuŕqUî«eľCĎaŠä= |®‹ů‘o=ü?ÝHÚSó}¦lŮٸž<4úlËŽj~ĐąíSě*Cţ&)^§»ăy™âyßz#Ž—^đôzŚ}·ÜŮYůŕľk.o+5"¸ZŐ’ŘwGź-ˇŁ—K˙č¸ü§/ěŐâxBˇqj><0lNđ7Sß±„ítŕţv‘ń`ŻŻ&]í <[]÷'âí„épŕŤ†jţúÇ)ÍJcÝŘ3G‡E#<ŵZ \ź H*_űŢürw&âaýäüZč{nkż‘] ďşŁšD}TóG·×5}đ0ĚćĎÚźM-ęűNC\Lh7{m<äř4ŰqjěŃńĹŔňŐü™GŻ®‚?7Ç6-íĎ!ę’ŻĹŰB®ÁŁšs°ËţByŕëń˛U)Ďצ}™?ţÔ5cLýV6°Ďţ—©‡Ż4Âsî{Ž"°ß…SÓW"oćvüąâ¤š?\ݶç Ř­oą]çňýPó#—ťiů?}[W쪚߰6RĄą˝ËÔo—r­ŐŞá¬j~ÎĹ›Ť\XţÓ_Ůa}…Ţ>:KÍ›Ž×BśíĎ=+p=ö‰Éeh>­*ěp#ßÖËYOžŢq+đ4átčBŕsÍ•ĹwS”÷N‚O4Â2ä§˝~ůO Čáe^UřÓÝuIĘ®ć»ű|­P őŕ6ŮÝ7q÷rž]˘í5ä÷GźŇSáßµŐ[\ć`÷ĺko\˘ć;? ňą=zßďĐxöäť±é»äĆyŹUr¨Žyý=ş/<^Ż};-ěş6˝jż°W— ęŔÖ‰~ݧ·U¨prlľ5«˙ĹĆëě¶ÉÇ»!/ďŰ©ő><Ď?j‹ yĺĎ6Ţۢ ˇţtň˙ů§çł‡OS^®_âÚçßąe?őäPů\^ĺD]Ů:Ůąy ęTنŰÔ,>š–w 뻟71ňŤ¨X8_;Řmć“ă·îďÓÚć~Öř8vđNëĂß!ďÓCŮ] °<=üÜl<ß¶ęȸš°Kő0÷ăČWÇĆöűü~O)ZsµŁšźŘţÖfďC,.RfmÇy째^9|“żŁŻ őiať6é Č[›ĎĘ»ěpŰăô öŰ™7ŕňÖ”áýś>ŔN+„y†ü0»ž}ü@;äŹöë_"OJl٤üľaL“™…ŃŹ,Ô´T…}vž0=??^ć¶®ęňÜÂé¤0ň˘Óăgą\`·öë˱‡wM±®7ľŞp!ô9\eĺąsj>ŞčúúÇ ĎŔ' ˘Čź:_8»,l{âV;ÔżiÚúť8řwŔ„Ô s/7}‘Ďq´ĽAĹž!ż®]RÉ}Îć ĺM‚<3·W˛ĆC˙ńëö~Ócý’ QÉČ×K_;ÄO×Ŕ^ĂVÔ[…Ľ¶xý´®{‚'!N@ľ<ľůEűÓ{k5«»"žĎ_^°űúŁ]6r˝\\6Ě9müż§ŔŤ ťżŢ+÷ú÷…ýćÚ;ąy˘y­˝ źy”»0ňĘó>áošO#+?m]yc*™xľ<úžÎTDţŰS»ż!yěĂĆ-G!.öÓů»¸—{Ső®Ń¬zwܧěĽŃmŚOmÔőó«Ě>îÜ<”:qzpb­1ĐoŰĦ“ÚG˝®µôújäáÇĹÝ× ‡ü^h‹UCôĽăŮ ˙.íő?g4Uó]K±ż‹>jG®ZAřeAχË{ _Ü3XŰzĂű˝ťŐof¬JŞ yŹ×_Ż <,Śš5mâ~—iŢTä•Ńůy‰z´Á1»1ňž¬pn<óŰt‡>ůżĂnŰ[ś=Ţr(y XşżdĂíŔ9ÓPĎf&śŇ"^=9đ;Î_ł2ér«üđ_ţwKo ?Üžć7çăXÖ—ŮľľĽ° =ű%äĂ·«Ü‡»Âî§ś§|óWćčô-;âz媋'WˇďX˙@a8~Z±˙ĺŰČg÷ęüĽŢăé^ű; CŢeX±'ö=53Uóx=˛ögÜZô]Ťď-»ě:¶ëŠĽłł¤o»ňđĂʉcöŹĆů»JÜŮë‚~ŕtžśyĽ=`÷ňMë"/ž»=ĄÉ6ô…ď6Ô?yőşÝűfŽ!čßG¬ÝłŻ!ňÖ’k=Ş Ţ_Ź^ĘOč~fC!ôŁgʵ· yáY¶f˘ď8_«ŃAňá–s6ä›|ذÎTřuCťś„ůË–N u‰¸Üą§° ęđť`uż\+Q¶f4 ŹŘ÷`GźQ·3í?śż> ~8:ż˙üIđë‚Ŕ«1ea˙}Żo_¶Ţ-Ü{¬9łp3íđĎńo·|îuô3ü~Ú{—ýFŘmVËw‰ÇQO»żĘŮçű6ČwpÜ™R°sŹO k™Ď‡Şžr(Áňaöce žľŁă­E°Ç"ÝĄsŔĂő1źéđ÷UŻ’5.!ďmŕj79î)!%»śÄyŻ˝öoŻü] ­č튺iôű±ëü˛żăÉCđď¤Ő҇{„}ľS§ÝŔŐÔŁ%ĽG?zŕ’ăÓě¨'öśń{‰ř\sĐQď…şş#rżíÜA8t°2¸OśůôvŔPŕâEôĹŰď¶@żf‹ó ®MncôGŕäAzB­qď9>DŁžűťŕ6Ô‹{@č‘SCnŔţçm‹÷xŹ:ţdűćýÄúüe+YÇúKĂ©ŤXßáç˝äŃŁpyăyq'ú‡ń9«Ük‹8|çŇhÁ0ô«K¶Üë» rť¶úţ,bó´SňPÁ‹~ Ż<şgfawô3Ű}zmtBż4Ň8·.đprÎÎw¶đ۶¶ŻňŢ…_o-2”ĺ«ăń MJ!üĐäć`乶׆ĽĂüĎ^—Ś9‚şŇvôJGä±—«önpó‚ľ5ç=Ů„Ľµ»ĎmôŻ+BKö܇ú°uˇÇKPÇ÷]‹ĘÖă0˙·W/Ů#ߤ-jRóguąßäčŁć;ťéxýó‚űż†ţKĄ%ł>j^«úaeű9Ë9@ý\0˛fÝGčsĂĆĚ>ńŢËżGZô ŁŞmyťţs_:u3ňÝÝÉcľť‚śWN¤ŽţŘń8Ąçd-ú»Çěľ:ňjGź ¸÷Ľ.Ý”3˘ţlť®óX\ňDŘ{y0ę­qůČc{ěOžąŽú6~nŘÝEđÓf÷mŐóŁŹšń-ćŕ*śŰóô¤*vXßŇ»]{ÄÁŐ­yGGÂn—슼 ü®ZďßPŹ>h]ąˇ©›Ń·­¬â°ľî­ËghĆŇ/ř,ě1¬:î'[—9¸ľ>˝[J;ä» >­ \D>ž—Ľkë"ČŹ›®\Yvq˝Ą|ťÉ×yQ#zŻBľ;őµdŔXÔ§'-6ěĎî€xŢş¬›ůg§Í’Úˇ Đ/zęrmG˝šź˛eKĘä—Ą‡VŤD>8}ďtřvÔóAS…îD~î~ÍĆ;'âcҬÁ+&ážtvž1f2đ˝jE呯ź†ďjV ~™v¨ä”řy–_TŃmŘ˙ÄËŹ›ŠÖGő\W¨9ęýŃAÝŽÄ=jLösöVĂ=¨QŰŞIčÇżOąę…ü7˝•şúQ܇ —޶BݸQ~u›ŞČc'ŇëŽ{|jťŘô0úąc_+lËě±äK©ŠŔý4Ď ŐpOxč[Ş?î‡nć;°ýĎůŮő“6 ayvń˛X˙$W÷ö?GΤ~çßçŻó$ŞşâŢv<ˇuĂ‹‡čŃWv®ĆţňFîť„{tAGďAăóVż'ě}ÄěŰü_#/ľ-<$őá|𦊋+±ű÷s3´ Sę—Gż5ËýLłZŔ狸uE> wLt3´°ĽWˇźZw:N;5˘éťN=ťŢe3MosëBť‰4}ś×\·či4f۲ÎĹΧߦ8ĺź×±>ŤIÝřhéŹ`ú!±«űë¸24bĂŔn #č›'Nh۸ĐO馎Í÷xŇ÷=Ż ä.Ówkšřś±ľoţąÓňW%idř´FNdŤlňvĺłYťč§+ŰNfď?“ľ‡Żwš~¸ń Cőáé{ç\Ĺ]JÓ”Kywz˘ˇŃŞFBv=Ąiĺ„-ąÖ„¦Ä˙¶®ý^ó„󲎉4ůg·fK ŁźďÔ*±Š~ů¶8Rź—ĆŤ~>…ŹţI߼Ţ~®Ö'kľV~Őč)MÝ0¸Á4ű˛ôÓjuÓq\Š&ś6 rXíLÓ;Ô%W·¦‘ťľ\ż˙}ť`×đÂ#úîđ—ÄÓýh¢ö=_î©JŁűŤzí¨˝GÓö¬ţ9<ŕ;MOkššżč šPűô«­‡¦ŃwĆŤőr5˝EÓVý˛'üMŮű¶ÓMUUúîN‘Řâł?ĐWÔÝęřłýęvĚąň»îôăĎqŹ=¦źŹ÷p?qa=ŤÚ\w‚űѢô˙yBťbui|ɡÁ}ęݦ‰G7µű1®>M,“°9a\s›ôjőÍŮ64e—íŹôô]ŰŻłrěKŚC]˛ŐBÓď¨*ĆzĐ÷;OzlČń‰ĆéćwpLĄô{”Î~ÜČ24îÔŞëUú< _gLŮ?ş:MxľsĎyUgúmí0Uż}“iRĄŘ2Uc ôkďűżʦß\WÎüzh0MÝ©Vbµĵ{Ó–U´ôĂÂjš ÓŘ3ĆđyĎęŇXužë[T¤1ŰmÖĚű#ś~zőu7®4%e]˙}iÚŇŽëʇť¦É×ęě=‚Ć5:Óáh­íô}ő‡ĎŐiě­–“óŘŃđűťŤŹ†kčÇý‹V­M_»7>±#Ř“ĆW‹Méć_Śľ>Ň( ¤ë3Äyó-v-ľĐ7‹ŠŮnř°“ľw»´í«ĆžľÍÔµŕŽ˛4śěŢŢyů#úqa·GËe§ń#“ e–n§)_> h.ýÝcMÉ™Ťidą5ô1_i„OůyĹGĐOܵ{ˇ# Ń÷'^Ó6ťZŇ7WV×Řpn!}—ăľWő)ýč;]źĎgç‡Ň´yŤ‹´ ˙ľčggw’ľ=ůŰţń45ߺ䪡/irĹ]Ä禩O†Ýď\w<ýTÂůŃóůiü¨ÖąUÁăht…m÷ë ©Dăvî(0äv }=ţł‡Ý1ôí<›YaĺnŇ$Źäś»_Ó´‰uÜÚ/żBÓnçüšŔo˘é‰Çâi?hü¬üý§OŁÉ'ů ÂÝy4ž/âšýĺú>˛ďšrS`˙†5ęńźWŇ´'B·Ű…Ó¨&Q»ëĎÚISoF%lă^ĐÉ_ôÔ¦tź<6gäIúѶjˇxŻ8őýuxęJßvMhf_€F$´Šx˛çý4/°őM÷+ô{˝ ‰Ăg-Łßg.;ö Ü.š´?igxŐW4…>umjú)­H»Vµ¶Ň´ç…‹5¸Ü”~;Ůnľ!{Můęş|FöĎ4ń~›üµŐĹhb…“Ö]jGăöŢyč?Ťľ˙8zçé«MhLr҉=ůKŇôř¦ą^ݡńN_óµčHż¦ Š»R’Ć.žyy$éMĂ J/9ř‰ĆżŘ4'ś&ßÝďž{D5šęwĐ{dţ˝4ú¦ť{ăĂ~4~HLŢÖŲÓ/aď›×ą“Ť¦¶Ţ6ŕt×4íţ˘ţ¦ é««Ýş)EżÔh¶$ř Mźxweˇ]khD÷AűzĽ^KßLO¸¬J¦ >ÇÚEľşH_NĽpjřšîôݤ2?oĚZM?Ż~±đýYäÝž§¦ hŕO.ĹĎ1&MĄ źŐî÷Ą)÷WmˇĐôçgä›2ŠĆ§ők¨ą;ś¦$|Ôyy+_í~ůTkšdŠ\3¦™?Mm<(ięçlČw+ ßﲜľíłĎ¶Ŕ×|ôS›őË7óˇźcźx Ýĺ@ßV©mÓg9ŤŢµôËýi14ÍůË• ++Ň÷ߊT1ş…Ň—mžtšAVŇo{÷ŚťGß•}•#äâćó°BhôĆ>‹“»_Ą#Ż]ľŽ¦ôč±9ľóNúeUÓ%ާń{Ϥ?mOcbcç—ýNSzv˙ňĆő#K/´đˇi­Ö:ŠżBß$ĽąşbkCš:5‡ý÷‘§hŇřŰĆN­KS®VńĘ}ΓĆÔsŰ"”]CżtÝܰ4ýZî®ęČŽ4ĄŮż*ÉEiŠ©U•âĂŃřŞŤZ$ŻéEăs> ~CROö·ß„FöŚÝquŘšö˘Tä«24ęĺi7×üé—="{·¤Ié_w5±€¦U‰ŘI‹Ó÷E§_é_EOă:Ťíq…ľÝPűřÝeĎ‘—Č‚sĐ#i\›üď'ě¤I›K?ű¦jCŽw{2pwMőÖß\ţ3ŠĆŰĽ;Ţôč8šžS[˛\“‚4]Xß,ĺÔ\˙„¶nx‡&Ćhú¤áašöă¦ćîÍ>ôm™µűĽšľ ‰;U>Ń´?ŤË;ńiˇŃiJń…/Î\Ľ@Ł:÷ŻăÜ3ŤĐ'ą\%Ăč§—[9MJ#—~ż˝Mśg×çĺńĂôCÄ@nîc7ár»ţL·4±_Ă*ž•úŇoÓrŤ´mô‰ľđľNŐăĎhD6űŮ7/˘‘uSý§MË……Fv/]{V˙ąôs‰Z 1—égŻĐ%/˛÷€\3+Ęú´ŻCç‹Řďóĺ=>+ §©óÚj6ßüH“Ę.yl˙Ä…ĆŢrv“ÍúÉp<¬lá©ôÝÚ NµÝDăěŹŢŻ0í,ęJ‹AĎ #˙µ ÓĎq1ýáóúčúíĽ¦u›ˇßhJüŮŘbSŠĐŻ}–vŻ<¤%M[Yuraż03ôŃÄ…ekÓ¤Gé•?,‹¦á~›b\ŢK?‡]>uł”ý^a;ú´Ôů[rw]Ü‘&OZyÜÔšF›†ŘTwý¸~A˙nNłhTŐO÷Š·ˇ)Ű6ę”ʤéŤ+úEyFҨώwŚéKiä  ś,MăvM[v5:'ŤÜ°żxÁČdš4ęŘű«ű~Đt.h¶pŹĆŤďŰó[ř{úeyB«ź şĐt·ÔĽEWLĄ‰gŰoľ”w ýôdZő!MŹŃô>÷zÄ=ťMăšĎHzaäéŰčĹ3ÖlB\%Gîn^˘íhşĎÓ“˘? žđ6®çĂŃI‹>-Ąqn׫•˝M蟣^iFÓ†ŚnQ˝@6¦*°{c ódä¶s>ŃŻŐ#jÎ9†~/q=|ö{úćĂ#˝ţBš~ëH‚jk ŤÜýĄHďđ×ôm‘ŽŮ{ś§I†đu1ôGlšˇňôŹ=źď¸żMŐÖ>ńÖšôŞ˙ťĹÓWGF/Ţűy }·aWץ‡Ň¸Ô)#Ű«GÓÂo5-ŕň zîĎ3gfšV©őçŰŻâijP•9«+\¤©ţ'úűˇ‘ăĺŚjĽĆGµťrĎOcŞ ­ófŇ˝2¶^ďÁýiBŇćűWj]§IUË ú´ůúáŇ7/g|¶µfçE4Ĺ?˙Łnď&ŇÍLőç[sh\üđ›»s9Óx»„Ń·¶ˇńIű‚“ź ďězćKş3‡~iBť:áôóŕăNžo§i!.ÍŐ9WŃ·ůÎ O^A_–­3şAń"4ĄoŤď§^nĄŻĆµęwjóSš°íXs›S4îrLÓrWzŃ´ť’w{]Ł_Šŕüţ9čÇË×Ęi€~s»Ű𾍓iĘů^}NN(NŁň]]Ó}sźcA«!oáĎÝď6LŢŐžF猫oł ýżó‚ę“oÓČoQűkőíMżÎŞ2ň[X#ú3aň˝'ÉiTš§ó™UôŰŁ2•Śť.wš/Eýg†şEʦ1ť»Ą .@cç^6ZH“·űů˝ËMQ/űÜ)ęU‚&Ożćâ1úÉăŇĂ1đKĎą3+MľG|q|51÷e÷A_¸_’ľ©=˙âť‚©4ÁsČÝĐĂMYştoÝú±ř¦eö˝7][vęÔf}3|Úň©ůchDLřаf4|űŢ KŇÄ\%ęŹËźF\ăó/ň7ĐÄ Ën•vĄ µ.ýśíy‡~¬ÖćÉóµăéŹŢOJî®c¤ďçţL»ó1¦o^Ôďë‘Ű4ˇFEďűmťčÇú«v?^¦îéşÂ“~]Ő¸ż-š&iťh¶ç5Ť,^ńű¤Šłiš]“.µ«húë 3ňÍ+G“Ö¤'N÷˘?´Gťzl™IZ]?ń˘[=ú~űęű7·—¦–—,zuÎ)š®jčn»fMÝĺÜëŠsWú¦kËź5‡Žˇ‰U;<řté*}WÍ»ć&ďľ4ý>ßh‰ż}˙ěXĂě#źÓ/ö±öůŇhôÔĽą˛ŤEßŇáNŘń­Łhâ¤č¦×› ˇá×Çľý<ŠFś­î9î˝ Ť¬%ôĘ=o%ý➼íô€¦ô‹ÓôôSk†Ň7—ěăC.Ó/×=B_¦^¦ÉM7°}Ůś¦ýń¦Đ©4yÖîçÔÂ4ľç„˛UCsŃoĄÎÖ¸–ď)Ťëľ§yµ=…čç´ˇWî¤čit§§ĹRZä źď ŹÍOßöÜ7'{úÜłľ­=ŇlMrrt/Џąőçęou‹ˇîVŚXR&‚~î4şđśbwi\ĺŹaĹ[Ś˘o]~®Ť‹ŃҶ1ź֠ɧçwíđ§-ýş(©ą{×M4bň±zeŹó4rFóñŽč‡C§?(°„Ć/im¬_K|™›¸ü<ýšx¨f›s«iňÇ4lČ;ÔĂÉýĆ?xJcëţ8~¸ýP(O77](ŤľÜáă­A‡ĐÇnÚóŁIK>\h7Ş}{yRĂ‚mp˙Ý<í0úÁµqŤ17–Đ/w&Ř÷šĆ<ôckő4áĎ­Őň7ilź"lŠT¤áËćzx§ ýW¶Pž%<}S%ýC×Vč_§~y®%ú±™‰mĽM=ŮsµCéRôÍŃt7Ǣ}i䀖ůâbJĐoůśW×pů~érVčřµýšgĎęxQM¸?wuzsŤ~÷čĆßHż©ű UŮtBźUsOţýé—G›N|'·iĘÉ-OٵĄ©*mXŐ©ôËŚÝ'mJ>Fź”gq—×Đ?¦µígCSźçO™wfM)ZöP‘îchJj|ëŇϮӯľŻvš˝Ž¦Ćohj‡~)5ěŕlS¶E4˘ż¦Q‹ý4ĺŇŢţ.łŇä+"^´^Źş—żF¶aOŃwÎżäSĆŤšü9­kYš¸¨oµIs»ŇĎĂ.…zn`˙~śDţďyg3těŃw›wÇÁ]şöhů7ßú Đuĺß‘Řopď~vý›q–;üŻ&ý˙ĚÇj˙'dgpć/vÖ~Öż]˙ó?B†ţĄíţÓń?lţ˙Q·_é†KŻ˙|‡_Śâ˙ł@˙y@ţ–ů%aő?şç_zřŻ;úż–Í˙sŃĄrż3J˙ëY÷˙˘ŞńűâĺĄĘß'ŕżđżČh˙ŻĹú”}Úů.ůß'÷˙šŐ˙÷]ÚŻďp_Uţď„ůżßńţű]Ŕďl>q.úŻ]—qX˙ë&˙ßĆ(˙ż9óŐÓ˙ý÷ű·ß”%~ť÷˝żĂ™˙Ä7 ˙aŃřÇ^uü˘šňß0ń/}ů›kĚď|CűŹ_c~Ëˬßůvˇý?}AýMµăwĽSţ'^Űýćw{˙° żzüëîíżôääŐűo‹ ń?śG8‹˘};öé ¦řo§v‘ö:Oľ˝ż4rđĐůů­˙%w;÷ ÖgŽş§GŇŘF«ó¶l‚?4–‰†@˝&P~ę0pđ€níűÔ”†Î˝;jßąkßAşZ9ýúfäŘűküřöµ2)ŕ4 ßĐŞ%ŘżšZ5˙÷óç÷G™5íÜ»ă@‹¦¦s—Ž:Ví6ë1b˙5Â˙•ż \«sampling/data/MU284.rda0000644000176200001440000003702614520143732014255 0ustar liggesusersRDX2 X  MU284   !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~€‚„…†‡‰Š‹ŚŤŽŹ‘’“”•–—™š›śťžź ˇ˘Ł¤Ą¦§¨©Ş«¬­®Ż°±˛ł´µ¶·¸ąş»Ľ˝ľżŔÁÂĂÄĹĆÇČÉĘËĚÍÎĎĐŃŇÓÔŐÖ×ŘŮÚŰÜÝŢßŕáâăäĺćçčéęëěíîďđńňóôőö÷řůúűüýţ˙      !8FB < 50ŤO;1&* ™! A  Y   tv )  k   B  6( 8<   F1   ĺQ#i" M$.0  ¨1.  $1d   #.   J v #  v   3.   X)& ^<  8 TJ  C'  4>6 2(+źN67$' Š  >!\   lw * l!   >  4* 6<   C0   ÷K&f# J!+%  ľ//   "2j  #- !   I  v &  v  /. ! U+ %]<  5 JH  @#   ‹Äź†o`Ó›‚ń¦wdúśS7"ů¤aJýđŁ?čo€ćĐł%A%Y»<«đQ6:©&DľN'ÜâEłwI;\hKů·1)LqZOŚČg:ŽvŻŔéfa8EIV5iQMŘÂ+Qzqş[‘…rNNHF Ź/•ą¬đCzĺ*=łéBWR?AH@}đvŚB#+¦Ř5RZ>MĎD4ŚÜď’_%#'.;,iWB>Żü€J9SĐJ?m%i&RL?V[Ńdyż‚?€ZO2yW5K˝$RT<„.Ĺ›„Űp5ZBdM4<d™˘L‚Ĺ1iWF“ĐVéËů\†ç¸¶°1?^|E5[Ă5*2( :=<#üiP7."{)6Ď@2'â?é     "                                                      ) $!. !$- && ' #     #   "&     # $ , .#%!& $"  1)))=)===-)3-3e==)=391-#1Q31#=-3O1)#1)-71OU-9#3#)1)Q91-11)))1111=1))111)=1;K11G1K131)-)))#1)1G11=)))1)1--)1))=A3A)1131G1333)))))))))Q=1)=')#1))--))3))3=1O1)')###)1)))131;!)3')1#')-))=1)111)-)'#A)-3#1'')'1#A1)1)11)11111==))13))))1K3313)11Q===-1111-1K#))##A-A)#)'))'=31)- W˝ú#o–ůň>É Üq §± ÓšP ¨0ökÁ§ęUć-Ć ¶÷güw±Ö˙O!ź©Ă+ bq/Ű=±íEĘĂÂĘ“ĽúúÍVi] VŰ'ŕş:- Ő'9ńß|çě>STVád?¤~ŮvLrń ö`vÇ@ł×)Ő•• ‡CAaĺ¬@˘ ·â _Ť 2ĎýXbľ‰ž:Ú~~ A+nŰI‘77UŮjb" `…o›#ëľő†őŁ(®vńuˇG›_ćEŞ(}Xť˙d4†Ý(Č~ů7ÄĽÍd¦™ Ó —÷š×ÍA •CÚ–†+ô“±“ŻÖ ţ«ö-%Qäŕ f¬­CěŻĘÇÝü¬®©ÎÖž—..ŕlł’\ü  óŽ`?m ~·ł Ŕk+%héĺs VŔ6 ź U¶×Ó3• ŃŞ¸ő&) bt DŹ}kćup*/PZ? i˙ ňć$ ] n WtľÄZżč‘ í'ý±Ĺú$¶ÚU˝ Ď˘Z ů¦ë)~?Ć äë­¨ ×ý†ü ´dJ…ĐVü±Fî $› 8 ě 3 “ľm ŕ®)ĂÇ l ąpj$,žoČ!4 ë E¦Őé ůŠŰ_š ‹Č 0ľ ¶M’ }-úŰ_,ďËÝ 8É2¨ yŃeÚV±§pó¤ëóĺL ý·¶FCN°+vm€3( R´‘6Ó­])ÎÄ´ ‰ÖL2Y1:4ÜŻôÄ^ ť [¶đ§~ ›"82 â W*Ĺ 8 n&dfřĘ ¶ A _Ó?E×[‡Â'ŔĆo¦)!Ż*KwȲx#\g®'w±·Á RR >    !!!!!""""""#####$$$$$$%%%%%&&&&&&'''''((((()))))*****+++++,,,,,,,--------...../////0000011111222222222 names LABEL P85 P75 RMT85 CS82 SS82 S82 ME84 REV84 REG CL row.names 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 class data.frameţţsampling/src/0000755000176200001440000000000015033751703012657 5ustar liggesuserssampling/src/init.c0000644000176200001440000000066214520143726013772 0ustar liggesusers#include // for NULL #include /* FIXME: Check these declarations against the C/Fortran source code. */ /* .C calls */ extern void str(void *, void *, void *, void *); static const R_CMethodDef CEntries[] = { {"str", (DL_FUNC) &str, 4}, {NULL, NULL, 0} }; void R_init_sampling(DllInfo *dll) { R_registerRoutines(dll, CEntries, NULL, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } sampling/src/str.c0000644000176200001440000000021014520143726013624 0ustar liggesusersvoid str(double *st, int *h, int *n, double *s) { int i; for(i = 0; i < *n; i++) {s[i]=0; if(st[i]==*h) s[i] = 1; } } sampling/NAMESPACE0000644000176200001440000000015315033731766013315 0ustar liggesusersuseDynLib(sampling) import(MASS,lpSolve,stats,graphics,utils) # Export all names exportPattern(".") sampling/inst/0000755000176200001440000000000015033751703013045 5ustar liggesuserssampling/inst/doc/0000755000176200001440000000000015033751703013612 5ustar liggesuserssampling/inst/doc/HT_Hajek_estimators.Snw0000644000176200001440000001202514520143726020172 0ustar liggesusers\documentclass[a4paper]{article} %\VignetteIndexEntry{Horvitz-Thompson estimator and Hajek estimator} %\VignettePackage{sampling} \newcommand{\sampling}{{\tt sampling}} \newcommand{\R}{{\tt R}} \setlength{\parindent}{0in} \setlength{\parskip}{.1in} \setlength{\textwidth}{140mm} \setlength{\oddsidemargin}{10mm} \title{Comparing the Horvitz-Thompson estimator and Hajek estimator} \author{} \usepackage{Sweave} \usepackage[latin1]{inputenc} \usepackage{amsmath} \begin{document} \maketitle <>= library(sampling) ps.options(pointsize=12) options(width=60) @ Consider a finite population with labels $U=\{1, 2, \dots, N\}.$ Suppose $y_k, k\in U$ are values of the variable of interest in the population. We wish to estimate the total $\sum_{k=1}^N y_k$ using a sample $s$ selected from the population $U.$ Assume that the sample is taken according to a sampling scheme having inclusion probabilities $\pi_k= Pr(k\in s).$ When $\pi_k$ is proportional to a positive quantity $x_k$ available over $U,$ and $s$ has a predetermined sample size $n,$ then $$\pi_k=\frac{nx_k}{\sum_{i=1}^N x_i},$$ and the sampling scheme is said to be probability proportional to size ($\pi$ps). The H\'ajek estimator of the population total is defined as $$\hat{y}_{Hajek}=N\frac{\sum_{k\in s} y_k/\pi_k}{\sum_{k\in s} 1/\pi_k},$$ while the Horvitz-Thompson estimator is $$\hat{y}_{HT}=\sum_{k\in s} y_k/\pi_k.$$ S$\ddot{a}$rndal, Swenson, and Wretman (1992, p. 182) give several cases for considering the H\'ajek estimator as `usually the better estimator' compared to the Horvitz-Thompson estimator when a $\pi$ps sampling design is used: \begin{itemize} \item[a)] the $y_k-\bar{y}_U$ tend to be small, \item[b)] the sample size is not fixed, \item[c)] $\pi_k$ are weakly or negatively correlated with $y_k$. \end{itemize} Monte Carlo simulation is used here to compare the accuracy of both estimators using a sample size (or the expected value of the sample size) equal to 20. Four cases are considered: \begin{itemize} \item[Case 1.] $y_k$ is constant for $k=1, \dots, N$; this case corresponds to the case a) above; \item[Case 2.] Poisson sampling is used to draw a sample $s$; this case corresponds to the case b) above; \item[Case 3.] $y_k$ are generated using the following model: $x_k=k, \pi_k=nx_k/\sum_{i=1}^N x_i, y_k=1/\pi_k;$ this case corresponds to the case c) above; \item[Case 4.] $y_k$ are generated using the following model: $x_k=k, y_k=5(x_k+\epsilon_k),\epsilon_k\sim N(0, 1/3);$ in this case the Horvitz-Thompson estimator should perform better than the H\'ajek estimator. \end{itemize} Till\'e sampling is used in Cases 1, 3 and 4. Poisson sampling is used in Case 2. The \verb@belgianmunicipalities@ dataset is used in Cases 1 and 2 as population, with $x_k=Tot04_k.$ In Case 2, the variable of interest is TaxableIncome. The mean square error (MSE) is computed using simulations for each case and estimator. The H\'ajek estimator should perform better than the Horvitz-Thompson estimator in Cases 1, 2 and 3. <>= data(belgianmunicipalities) attach(belgianmunicipalities) # sample size n=20 pik=inclusionprobabilities(Tot04,n) N=length(pik) @ Number of runs (for an accurate result, increase this value to 10000): <>= sim=10 ss=ss1=array(0,c(sim,4)) @ Defines the variables of interest: <>= cat("Case 1\n") y1=rep(3,N) cat("Case 2\n") y2=TaxableIncome cat("Case 3\n") x=1:N pik3=inclusionprobabilities(x,n) y3=1/pik3 cat("Case 4\n") epsilon=rnorm(N,0,sqrt(1/3)) pik4=pik3 y4=5*(x+epsilon) @ Monte-Carlo simulation and computation of the Horvitz-Thompson and H\'ajek estimators: <>= ht=numeric(4) hajek=numeric(4) for(i in 1:sim) { cat("Simulation ",i,"\n") cat("Case 1\n") s=UPtille(pik) ht[1]=HTestimator(y1[s==1],pik[s==1]) hajek[1]=Hajekestimator(y1[s==1],pik[s==1],N,type="total") cat("Case 2\n") s1=UPpoisson(pik) ht[2]=HTestimator(y2[s1==1],pik[s1==1]) hajek[2]=Hajekestimator(y2[s1==1],pik[s1==1],N,type="total") cat("Case 3\n") ht[3]=HTestimator(y3[s==1],pik3[s==1]) hajek[3]=Hajekestimator(y3[s==1],pik3[s==1],N,type="total") cat("Case 4\n") ht[4]=HTestimator(y4[s==1],pik4[s==1]) hajek[4]=Hajekestimator(y4[s==1],pik4[s==1],N,type="total") ss[i,]=ht ss1[i,]=hajek } @ Estimation of the MSE and computation of the ratio $MSE_{HT}/MSE_{Hajek}:$ <>= #true values tv=c(sum(y1),sum(y2),sum(y3),sum(y4)) for(i in 1:4) { cat("Case ",i,"\n") cat("The mean of the Horvitz-Thompson estimators:",mean(ss[,i])," and the true value:",tv[i],"\n") MSE1=var(ss[,i])+(mean(ss[,i])-tv[i])^2 cat("MSE Horvitz-Thompson estimator:",MSE1,"\n") cat("The mean of the Hajek estimators:",mean(ss1[,i])," and the true value:",tv[i],"\n") MSE2=var(ss1[,i])+(mean(ss1[,i])-tv[i])^2 cat("MSE Hajek estimator:",MSE2,"\n") cat("Ratio of the two MSE:", MSE1/MSE2,"\n") } <>= <> <> <> <> <> sampling.newpage() @ \end{document} sampling/inst/doc/calibration.R0000644000176200001440000003420715033751703016232 0ustar liggesusers### R code from vignette source 'calibration.Snw' ################################################### ### code chunk number 1: calibration.Snw:21-24 ################################################### library(sampling) ps.options(pointsize=12) options(width=60) ################################################### ### code chunk number 2: calib1 ################################################### data = rbind(matrix(rep("A", 150), 150, 1, byrow = TRUE), matrix(rep("B", 100), 100, 1, byrow = TRUE)) data = cbind.data.frame(data, c(rep(1, 60), rep(2,50), rep(3, 60), rep(1, 40), rep(2, 40)), 1000 * runif(250)) sex = runif(nrow(data)) for (i in 1:length(sex)) if (sex[i] < 0.3) sex[i] = 1 else sex[i] = 2 data = cbind.data.frame(data, sex) names(data) = c("state", "region", "income", "sex") summary(data) ################################################### ### code chunk number 3: calib2 ################################################### table(data$state) ################################################### ### code chunk number 4: calib3 ################################################### s=strata(data,c("state"),size=c(25,20), method="srswor") ################################################### ### code chunk number 5: calib31 ################################################### s=getdata(data,s) ################################################### ### code chunk number 6: calib32 ################################################### status=runif(nrow(s)) for(i in 1:length(status)) if(status[i]<0.3) status[i]=0 else status[i]=1 s=cbind.data.frame(s,status) ################################################### ### code chunk number 7: calib4 ################################################### s=rhg_strata(s,selection="region") ################################################### ### code chunk number 8: calib5 ################################################### sr=s[s$status==1,] ################################################### ### code chunk number 9: calib6 ################################################### X=cbind(disjunctive(data$sex),disjunctive(data$region)) ################################################### ### code chunk number 10: calib7 ################################################### total=c(t(rep(1,nrow(data)))%*%X) ################################################### ### code chunk number 11: calib8 ################################################### Xs = X[sr$ID_unit,] d = 1/(sr$Prob * sr$prob_resp) summary(d) ################################################### ### code chunk number 12: calib9 ################################################### g = calib(Xs, d, total, method = "linear") summary(g) ################################################### ### code chunk number 13: calib10 ################################################### w=d*g summary(w) ################################################### ### code chunk number 14: calib11 ################################################### checkcalibration(Xs, d, total, g) ################################################### ### code chunk number 15: calib12 ################################################### cat("stratum 1\n") data1=data[data$state=='A',] X1=X[data$state=='A',] total1=c(t(rep(1, nrow(data1))) %*% X1) sr1=sr[sr$Stratum==1,] Xs1=X[sr1$ID_unit,] d1 = 1/(sr1$Prob * sr1$prob_resp) g1=calib(Xs1, d1, total1, method = "linear") checkcalibration(Xs1, d1, total1, g1) cat("stratum 2\n") data2=data[data$state=='B',] X2=X[data$state=='B',] total2=c(t(rep(1, nrow(data2))) %*% X2) sr2=sr[sr$Stratum==2,] Xs2=X[sr2$ID_unit,] d2 = 1/(sr2$Prob * sr2$prob_resp) g2=calib(Xs2, d2, total2, method = "linear") checkcalibration(Xs2, d2, total2, g2) ################################################### ### code chunk number 16: calibration.Snw:157-176 (eval = FALSE) ################################################### ## data = rbind(matrix(rep("A", 150), 150, 1, byrow = TRUE), ## matrix(rep("B", 100), 100, 1, byrow = TRUE)) ## data = cbind.data.frame(data, c(rep(1, 60), rep(2,50), rep(3, 60), rep(1, 40), rep(2, 40)), ## 1000 * runif(250)) ## sex = runif(nrow(data)) ## for (i in 1:length(sex)) if (sex[i] < 0.3) sex[i] = 1 else sex[i] = 2 ## data = cbind.data.frame(data, sex) ## names(data) = c("state", "region", "income", "sex") ## summary(data) ## table(data$state) ## s=strata(data,c("state"),size=c(25,20), method="srswor") ## s=getdata(data,s) ## status=runif(nrow(s)) ## for(i in 1:length(status)) ## if(status[i]<0.3) status[i]=0 else status[i]=1 ## s=cbind.data.frame(s,status) ## s=rhg_strata(s,selection="region") ## sr=s[s$status==1,] ## X=cbind(disjunctive(data$sex),disjunctive(data$region)) ## total=c(t(rep(1,nrow(data)))%*%X) ## Xs = X[sr$ID_unit,] ## d = 1/(sr$Prob * sr$prob_resp) ## summary(d) ## g = calib(Xs, d, total, method = "linear") ## summary(g) ## w=d*g ## summary(w) ## checkcalibration(Xs, d, total, g) ## cat("stratum 1\n") ## data1=data[data$state=='A',] ## X1=X[data$state=='A',] ## total1=c(t(rep(1, nrow(data1))) %*% X1) ## sr1=sr[sr$Stratum==1,] ## Xs1=X[sr1$ID_unit,] ## d1 = 1/(sr1$Prob * sr1$prob_resp) ## g1=calib(Xs1, d1, total1, method = "linear") ## checkcalibration(Xs1, d1, total1, g1) ## cat("stratum 2\n") ## data2=data[data$state=='B',] ## X2=X[data$state=='B',] ## total2=c(t(rep(1, nrow(data2))) %*% X2) ## sr2=sr[sr$Stratum==2,] ## Xs2=X[sr2$ID_unit,] ## d2 = 1/(sr2$Prob * sr2$prob_resp) ## g2=calib(Xs2, d2, total2, method = "linear") ## checkcalibration(Xs2, d2, total2, g2) ## ## ## ## sampling.newpage() ## ################################################### ### code chunk number 17: ex1 ################################################### X=cbind(c(rep(1,50),rep(0,50)),c(rep(0,50),rep(1,50)),1:100) # vector of population totals total=apply(X,2,"sum") Z=150:249 # the variable of interest Y=5*Z*(rnorm(100,0,sqrt(1/3))+apply(X,1,"sum")) # inclusion probabilities pik=inclusionprobabilities(Z,20) # joint inclusion probabilities pikl=UPtillepi2(pik) # number of runs; let nsim=10000 for an accurate result nsim=10 c1=c2=c3=c4=c5=c6=numeric(nsim) for(i in 1:nsim) { # draws a sample s=UPtille(pik) # computes the inclusion prob. for the sample piks=pik[s==1] # the sample matrix of auxiliary information Xs=X[s==1,] # computes the g-weights g=calib(Xs,d=1/piks,total,method="linear") # computes the variable of interest in the sample Ys=Y[s==1] # computes the joint inclusion prob. for the sample pikls=pikl[s==1,s==1] # computes the calibration estimator and its variance estimation cc=calibev(Ys,Xs,total,pikls,d=1/piks,g,with=FALSE,EPS=1e-6) c1[i]=cc$calest c2[i]=cc$evar # computes the variance estimator of the calibration estimator (second method) c3[i]=varest(Ys,Xs,pik=piks,w=g/piks) # computes the variance estimator of the HT estimator using varest() c4[i]=varest(Ys,pik=piks) # computes the variance estimator of the HT estimator using varHT() c5[i]=varHT(Ys,pikls,2) # computes the Horvitz-Thompson estimator c6[i]=HTestimator(Ys,piks) } cat("the population total:",sum(Y),"\n") cat("the calibration estimator under simulations:", mean(c1),"\n") N=length(Y) delta=matrix(0,N,N) for(k in 1:(N-1)) for(l in (k+1):N) delta[k,l]=delta[l,k]=pikl[k,l]-pik[k]*pik[l] diag(delta)=pik*(1-pik) var_HT=0 var_asym=0 e=lm(Y~X)$resid for(k in 1:N) for(l in 1:N) {var_HT=var_HT+Y[k]*Y[l]*delta[k,l]/(pik[k]*pik[l]) var_asym=var_asym+e[k]*e[l]*delta[k,l]/(pik[k]*pik[l])} cat("the approximate variance of the calibration estimator:",var_asym,"\n") cat("first variance estimator of the calibration est. using calibev function:\n") cat("MSE of the first variance estimator:", var(c2)+(mean(c2)-var_asym)^2,"\n") cat("second variance estimator of the calibration est. using varest function:\n") cat("MSE of the second variance estimator:", var(c3)+(mean(c3)-var_asym)^2,"\n") cat("the Horvitz-Thompson estimator under simulations:", mean(c6),"\n") cat("the variance of the HT estimator:", var_HT, "\n") cat("the variance estimator of the HT estimator under simulations:", mean(c4),"\n") cat("MSE of the variance estimator 1 of HT estimator:", var(c4)+(mean(c4)-var_HT)^2,"\n") cat("MSE of the variance estimator 2 of HT estimator:", var(c5)+(mean(c5)-var_HT)^2,"\n") ################################################### ### code chunk number 18: calibration.Snw:263-267 (eval = FALSE) ################################################### ## X=cbind(c(rep(1,50),rep(0,50)),c(rep(0,50),rep(1,50)),1:100) ## # vector of population totals ## total=apply(X,2,"sum") ## Z=150:249 ## # the variable of interest ## Y=5*Z*(rnorm(100,0,sqrt(1/3))+apply(X,1,"sum")) ## # inclusion probabilities ## pik=inclusionprobabilities(Z,20) ## # joint inclusion probabilities ## pikl=UPtillepi2(pik) ## # number of runs; let nsim=10000 for an accurate result ## nsim=10 ## c1=c2=c3=c4=c5=c6=numeric(nsim) ## for(i in 1:nsim) ## { ## # draws a sample ## s=UPtille(pik) ## # computes the inclusion prob. for the sample ## piks=pik[s==1] ## # the sample matrix of auxiliary information ## Xs=X[s==1,] ## # computes the g-weights ## g=calib(Xs,d=1/piks,total,method="linear") ## # computes the variable of interest in the sample ## Ys=Y[s==1] ## # computes the joint inclusion prob. for the sample ## pikls=pikl[s==1,s==1] ## # computes the calibration estimator and its variance estimation ## cc=calibev(Ys,Xs,total,pikls,d=1/piks,g,with=FALSE,EPS=1e-6) ## c1[i]=cc$calest ## c2[i]=cc$evar ## # computes the variance estimator of the calibration estimator (second method) ## c3[i]=varest(Ys,Xs,pik=piks,w=g/piks) ## # computes the variance estimator of the HT estimator using varest() ## c4[i]=varest(Ys,pik=piks) ## # computes the variance estimator of the HT estimator using varHT() ## c5[i]=varHT(Ys,pikls,2) ## # computes the Horvitz-Thompson estimator ## c6[i]=HTestimator(Ys,piks) ## } ## cat("the population total:",sum(Y),"\n") ## cat("the calibration estimator under simulations:", mean(c1),"\n") ## N=length(Y) ## delta=matrix(0,N,N) ## for(k in 1:(N-1)) ## for(l in (k+1):N) ## delta[k,l]=delta[l,k]=pikl[k,l]-pik[k]*pik[l] ## diag(delta)=pik*(1-pik) ## var_HT=0 ## var_asym=0 ## e=lm(Y~X)$resid ## for(k in 1:N) ## for(l in 1:N) {var_HT=var_HT+Y[k]*Y[l]*delta[k,l]/(pik[k]*pik[l]) ## var_asym=var_asym+e[k]*e[l]*delta[k,l]/(pik[k]*pik[l])} ## cat("the approximate variance of the calibration estimator:",var_asym,"\n") ## cat("first variance estimator of the calibration est. using calibev function:\n") ## cat("MSE of the first variance estimator:", var(c2)+(mean(c2)-var_asym)^2,"\n") ## cat("second variance estimator of the calibration est. using varest function:\n") ## cat("MSE of the second variance estimator:", var(c3)+(mean(c3)-var_asym)^2,"\n") ## cat("the Horvitz-Thompson estimator under simulations:", mean(c6),"\n") ## cat("the variance of the HT estimator:", var_HT, "\n") ## cat("the variance estimator of the HT estimator under simulations:", mean(c4),"\n") ## cat("MSE of the variance estimator 1 of HT estimator:", var(c4)+(mean(c4)-var_HT)^2,"\n") ## cat("MSE of the variance estimator 2 of HT estimator:", var(c5)+(mean(c5)-var_HT)^2,"\n") ## ## sampling.newpage() ## ################################################### ### code chunk number 19: gen1 ################################################### N=400 n=100 X=rgamma(N,3,4) total=sum(X) Z=2*X+runif(N) Y=3*X+rnorm(N) print(cor(X,Y)) print(cor(X,Z)) L=1 U=5 C=1.5 A=(U-L)/((C-L)*(U-C)) p=((U-C)+(C-L)*exp(A*Y*0.3))/(L*(U-C)+U*(C-L)*exp(A*Y*0.3)) summary(p) bounds=c(L,U) s=srswor(n,N) r=numeric(n) for(j in 1:n) if(runif(1)> stream xśÝZ[o·~ďŻŕ[lšrx-‚ľÄŤ›Ş1l%5šřa,Ťí-ä• ]éżď÷‘ĂË®veĹVP Xá 9Cž ĎáąR˘Nh!FčˇVX«„>ᅔʋ ä`Ą˝ZYĽŇ(#¤Ňň9é ŢMŔ#ToůQ(ɧJi<˝PźA(mzˇŚ‘„§S$”5ž5žŽ„rś|n'éńŢ‹Á<Đ&HćŤ,> ă€P a=´prŔÓg †:ףď„óA  ö=‚đŇZ©^CH-…·«¤„÷Ž«„ĹéĂźľůF,ž]¬6"6Ŕ…ĂzľDŚčÜ/sS¬íÜÄňĆć·ßŠĹ‹«‹ÓWÓFü‚ćÓgbq2ý¶ođ)xÓjłzFy<ť-ÇÇża|Źź ¦SÎ’ÓÎĚ[ĽŻ0J‹Ă_Në‹ë«Ói-TzqňźË‰ŢO…ćńŠL¬)qóÝęôâlązĎĄH/ŻÖŽŢ$ˇ'”8‚´˛‡‰ô=©óŰń¸ąZF»ľ·ÁEFŰ6ĆýmśJ¨ń÷ńă”®aĽí.ÎóŹ`üq\±xuývĄˇLC-ŞdńŹĺŮćĂš:MëĽ#éŕv%µ»’bÁw$…ÁÂ\mŘ''Ä×YÎŇ>(§Ľw9Í9ő®śĂ 9űĺ^w"ěW¨ŹKŐoµ ›żoAŐAĺ® ý® đ­ G:jÔĂ;¨˝UŤFŐŤ*¬e»˙žUaż ĘďŞÜ® ä«*…ű„g5ţł•đŔEŁî޵[‚^\ă¶ŢËł5p&s•I»2YąLżiťŮ:#h<ňŔД֦ńÎűü0šžńě÷údó…>YöźvĘĘě*\ď*áyKă?B÷yę&¶¬nďęZu?×SúüęŮ_˙ůÓĎ_?9>~.ű­m;Gś§Óúôjyąą¸bDŽ+P·˘Ůˇ trńÓj‰%›„¶í*osłĺNöFóĎ ÖîK C2Ś=<ŢÂŘâőŹo˙5ť¦Ď?Jˇ‹,7x_Ęł=hĚWÓ¸Y^¬žŽ›Iť8RčÎtďN–›ó‰/ţr5^~XbËüx˝ąĽŢ<ĽidßOç˙ž6ËÓ±ÝNł1eOľÇ’ŞÝ#Vřů“'Äy&LÚżo ™‚¶z´Z/ë‹§Ëwď&¬—꺎ĹÇĺęz]ÝX‰dľođIJĂs' Ű(â> ÷˘ŕ„řŐČlăŔ6t&̰/Ďľ´·żÜ/ěoÁŢoqŃrvsÔ!c=üíË$ůŁa¦łOű ňcšéߢ[]lΦw"ZEňţĺsĽ1É:¦~ ‚4! Ćů‘Ţ„_XچsaHŠ€3 gÎ@ńgj†ÓŔyyg‰ bAĘe‡ć+©X߼#]ěňΑxpşů@ xáşž$ĽlŢ‘ű(Joë×@ţ ˇ€ ą!H#JČ$KBˇŠpPGąo0·ćë}„ŤŚhdD##ŃČFšăž—q®Śseś«â\çŞ8—éŹj8ŹYÜHx‹»čYC'2Cé }ÔĄ3cJG cKÇĐrÇŃr'DîZÄÜÜV–Žd>ž;ŃLr™zákb X[8Ŕ ŮÂŚ6•;žF•;ѲćVϰ®p€µt…Ť.wxŚ0w°]f€Ç ™>Ź2yl?—©óH!çqB¦ŤMč3iěEź)kĆßÜv,Âç¶§éÎí |¦‹íę3]ěZźéB·!Ó…jC¦ ͆LŠ ™.ô2]h2dşĐ]ČtˇşéBs!Ó…B¦kÓů@ę`qiZsVÚgŇX7šÖÜAFŢg➇ ™ş↚;:n¨ąĂ­–2LĚÍŮ6÷´”™0/eć€ÇWąhŕć–s"–‚§ď  m»8âćO Ź“T§Â ë[ĺ:ëçv?őŰá6Ö<Ó"ëńĺ›ńěĹ„Bء)ŰĹxŹ‘ŕ ŽtŕZÍďŽ:2I”°°ďM7čŇÍĆí°Ćm[aćö™űô~ ·éŤ ]ŇOC°x&Bl°´í×ÄhjĎă#ń}*ůů% ÷ĺ<Őđ<4¨yăB{żŢŤůşÍü'˘żŃ7Ă=@ôę5‚ËälC×2~%cľ?äÝp ľ„ODuß$DžÓ|“\rŔďŤĺ„öŕŚ5…ŰxŇ4ŢşĄ¬AÎOÖ¨LďWĂ2Ý_ŤËp ˛fžë÷Ągăq^ çRŐěŔG&k@—ŞFç>¦5¤KUă3…«šYJŤĐŕEŐÍ»… ¤‘CMŔËPóđ2ÔD5N—ˇđiäPxáBçŁŕ¸ľrČĽ(Ȭ€C™Ç৤.;^Šäëł!‹.A˘čµyWRÂ6Qg4Q—ŔMÔ%róŇ%s€](M‰ÝŻcr‡×/%z[ÉŮxSr6l@Yr6lDYr6K%„Ać€a»älŚÔ%gc¤.9¶¦,9ĂvÉٶKÎư]r6Ol™ěJYr6ç’ł18—śŤÁŮ5•­‘ť´3ÜöťÖt`7Á»řZo»a4ńˇ‹©WúaŹĄĽď—Č9Ä:Ď(­K(ŕ‰űNŽŕŰŤÎ^Ţ0ť Żŕâřb&#üĐÁM<ŤI⦯‘j‘ŕľ`q¸ ŤŻ7ňö*loMeÄÍJęεRô®µ>2Ům—Mţ‹ümô¶µę·śî'üŞkk"éÚ˘Hş¶*’®z`đ益íVLă­^ŹŞ×…RDoé«×ăUkőz°_˝čůę-/e[oé«×˝R*DŹč›"Eú¦J‘Ą\ kŞő†•‚^"´;ľ” LÇKÍŔ­\ŠnĺR5p+‡rQÇ”&ŰsĎś›W® ¬oďš<™ľëĂťGţµ|5Řu'łŔ~„cß»óć|K›Ű˛¬vçĄ3–íT‹G#€3űöEÜĺőśbÜ0|y“§vŞA"/ĹgŞľ©WU©Ţ ‰*Ő›ćý~)±vĄzHŞToŽň)6T}ę®/>Őu]wŃ@Ó“µ@ţ˙‰G*śR »§c ‡6łܦFuš'1nÄvĆÍ_łO•źtuˇUĂěď˛×‹>Ż~ţwđ.v@˝ŐD‘šŻ‰"ÔX jňŞdMa ˛şL‚¬n 8euSü÷Źę¦˙c¤qvŞ&­ŁjŇĘ˙1©I+˙Ó¤&­ń˙MÚÓU’VhM•ś5Ä˙K©I©­KôGëS^sXžţ=/żź–ď? Ë wNĽly°¸\>„yLič=ßâŮůř~-¶˙yŕhŕżĐköŘ%Δ›őgËó „ó…$ޤkťŰ°ç›ń|yúhőŁŹ¨ÜW›éăϱRŞ—ÍŮâőĚ®F&3Ë“nIç5KłžŚŔ{ńţN×4îë^}Ő÷_µ·5'Óë[îkš/Nľ{Ý=»>?;®VĽ˝9ů°\ ü/’ŁËłwxxRlńë¸QdHúׇ[W>i°HŹć1Ľş//§3¬ÂxľŽ—N˙2k9endstream endobj 44 0 obj << /Filter /FlateDecode /Length 718 >> stream xÚ•UMSŰ0˝ó+|«"%>¤(=pęLĂ ©b‹DĹ–]ËŇ_ß•dC’RHOч÷í{ow•óĺÉŮWN:ĂŚŠi˛ĽO¨”ŇDĘ)fM–Er®­ţŐ«2ťpĘQÓÖ+µ2ĄéR&Ń6:U5Ą±ë¸+´3këŇ»ĺŔÓ„Rś Á<|6Ç’dÉD ĚđW}éaŤ’Sż!FáS‘Đ)ćSÂ'xńO&‚`:ĐtB)!čňŮÓĐ.…[†ęűř[©gSĄś˘ľŠÚ¦ś ®­şŤ‡Ż ü.*kčČžNÂ8–RŤÎ8ŹŮ[]ŞN@\ÎĐ}oóÎÔŁx–%Î$“üÍ2LhŁŐ-Ŕźs>ŇŹňH•a*ˇl ÝĆOşMřvŠÎSŠtą6ĘĆ}•˛9ę­ÉMŁ NĆ_»xU¨NĹh§»¸PĂU“NŔůşéA(8nmŻÇ°ŽÎüVv!ČPŻŮŽb´R:Źô?ˇL š¬çŕnµĂV»`É(ß÷ŽÜ‹W]§ňÍŕăAůbá•î^č^ÔUÓwzĎlŽŚÍËŢAkĂ8@V°Ů˝z7ç¨n˝•at> stream xÚ•WIŹŰ6ľçW¸7kH-–•Ŕ=¤MH zČäŔ‘8c6Ú,Q™ĺ×÷-¤—‰O‚¸<>ľĺ{ _o^\ľŤňYćËh9ŰṲ̈aś,g™X†v6ĺěsp}%R9_ÄqĽąWuWiś$A4˙˛ůs&f ™‡2]2ńߺ҅5m4I´7üčÜŔ“›ľ­q”Żu5—Á­Q Ďëy´ ĆƦS•±FĂîŔ[Ą˛ĘńŇöGKÇŐ­šG7JE8_$YüŐnÉnÝ V¶7÷{Áe§) ľ›/˘U€®bĐš'×ř tŐ˘Xwl\7˙ ĐCÝ8_ČŔ˘¬%ĺq xäĂ}ÜF=őééÝh düµzpäd;]˘řQĽ#ÂĎŇ1ü®z„ ŞRli’E5'Bódt9Ą­m١ A§zͶxÖöŁ[2MQŤ»¦]ß^«kC®xéÎŘí“ăz° Má•¶ŃCr\ľM˛#ĽT€G®XŞßć‹4JÉŐč‡k]4ĐzM} Š<1ˇ•;ݬUĹÖsŔó°3–':óuíTÇăms˘8rVßtŹ[ęVa[ë‹Tś ™Â÷><ůËZ~ů ułĆoňóŚíúÓÇ÷o:’ö«5U©1Čřč38ܡ`ö‰ĺ‚đ Ó36ÚŃ•;fIá!î.ęIfyâé?zE1ĹđP  }8Ă,ßÜžŕ*ĆEęC ? M(ĺ€RîPBu÷<)ţVńjĺă‡Oă׬ۣŢjŽl·µu,ö âŘ'ĘWí2=•ćJıî5gŻUć#ڲŠ%h -w/ˇ Ş)×M¸ÚGeůĂČĆĽ“¤”[¸3MADçËĆGY2N‰™O‰ÄÎĄUŐíŕÎź…|~â$°čŠŹ?é‡uĄ›[»eHŇĎŔÚÚuŻ;¤ÎĐŢ´=ž™†˙ň%HĆZ2CţŻýôWBʲGŘîĽPÖ^"„~[µş§$sMąĆÚŢŕÂř űR?;ůgŇ™L°âGÎ5‰e1ĂOE?,bAÇ˙Řčݨ*ž˛ă<–Á/˘ź>ú˘™1ř)¨qVęÁÜ6ĂŁPx€5Qśňýđß78á„#Š"Ľ¦Úá}×V¸sÜS;ę)"€<×3\ó â­=˙ZäŁ@šC8V‹)§ĐeŘ­Čب€Đ·ců€Ăśű#áS ޵ý7c›-¤ď˘V]ě˘xJpa1öŞpl|¶RNÔ±ÜwUkŚęxlü 7mĺ<3ÁŁbheNh¨*fťšę~9›ş†<¦ă%`o™‘´5Č :'pYJSł‡śÇĂ`5h‚+&?ˇéĚ7âaUĺv7¦ÂŢé*NĄ[yoĘDZi/¦ě=xţČ> stream xÚmTMoâ0˝çWx•ÚĹ$ !Ů ‘8l[•jµWHL7IP‡ţűő¬V=MžßĚĽń s÷ëu;ŃU··őČŮ›=w—ľ´“ě÷îÝÝĺ]yil;<[[Ůj<=?±×ľ+·v`÷Ů&ß´őđŕČ›¶<^*;˛~&űQ·‚>ěţÝţť”MS >Ů_ęăP·ň{=éÇsć@öd”ôÇöçşkźxäś;`ÝVY×`Śs4˝JaÓQܡn«ţއíˇ.’Uu9\ßčY6î>Ľý<¶Ů´‡.Z.ŮôÍž‡ţ“4>DÓ—ľ˛}Ý~°űŻŇÜŃör:-d0­V¬˛WŃÍ˙Ľk,›ţ8ăŤóţy˛LŇ»đşĘ®˛çÓ®´ý®ý°Ń’ó[Ĺ*˛mőíLrź˛?ŚÜÔqůĄă• â5F8@ š=@Šđ)&°  Č8Ôą€ÂĹRx u€Dş\j2H—†ŞˇĐVÁą0CzL]ř Âb°ct‘I ©g$`htŃ‹0śĆ\F„áŚ0ä†sę‡á jd< —Ię6ś»őńzgóńşË»ţę W ¤qČ’Ł+—ź#ö•ńĚÇkÄŢ .‰bŞsťŹré…¤šáćÄç†bďmŽXúľ„Kß7ǵHß7Géű„űľnb§>&jĘصäuśŻĽú•ń1ÜV™÷•âÜăâµÇ‰Ou$ŐźqWčS/%1{\řxB!€§ÔK(hH©—TĐ–ćž»J©ĎĎŻv×ÜëÁ=küŇ2řĄUđKĎ‚_:~é$řĄÓŕ—ÖÁ/ťżŚ ~™Eđ+7żčˢ/ ˙lěˇŰŇ(/}ďö -+ZXukoűěÔťE?Z„ăćĹŰKýqíÄendstream endobj 47 0 obj << /Filter /FlateDecode /Length 764 >> stream xÚ­VKs›0ľçW0L0‚xŮdJ™6“öŕéLčÉńAĆr¬ HŚ$Ň:ťţ÷ęÝ8)~L}AÝoß®V\——7ÉČĘ<‹2«\Z 8ɬQ Ę­raMťŹ®źF©3/PŔű0 ç¨~p}ŕ`HšŽŕ ·°Ć#.…Ŕť•_­Đňň4ݱĐâÇ“Şî8VpJZFçpŽ·ŕaËďJŞľ‹0ń˘0<ŕgRÔ<•˛ŘjWŹoŮ‚ĽkŚúVőňFđ— Śň€±NşĆŤĆÎÜőĺ1׏ăءKłrť ©3rş L 72ĺeI{$ýZU©]â0Ä»ZxF$ crdvb…{;Onš:°î6jVYAĂĎ•Éa§ĽŻrč“ç¸)@¸‡OÎ Č\«ĐCOĺV©W‰óĆĘŐĆú6ú„îĂ8!o2A/aÎk´K#&Š>$âřtÖ…ę"ÝA*ÔÝ.‚?Ťŕ|!mĐáBßa]čľ›˛-LĄ¤‰¶/$›Đ·ŮÝRö„Ĺł_®¤*ߨɄ¶-eüřôV˘ ]Ö5PVĆŽě6ĄŠÍN’ŞWp%ë7t,Ţť_˙˛ÚË*(”UűN Ö|±=ěŮ* { ëlEŐăyńý[K1לçŐá­ÄĚŠŰrĂź&OA×S^`ćIS}úÔî,%m4bÍ’ö ŻG…ýG(Ů@(­6öD¬ŹŽ#>+%×5Ňľµ˝#ü'gőßŕĹsGčIM‘žµÜ´‚ľpNh‡ě¬4´¦ N˘atĆ8ă? žč‘?9@śt?Ţ랡V[SŇˉG^ŢCQđ)öfĹJěŃů}x„_S5Á¶5ÜĚí7cZFŤOťÄ­ lĚŹ ç}ˇ€’%ÎUşĄě~řé‰éŮL5ť-׾î¶&ÝÖ‡Q~î…|ŰĚ*ůşUµU±ô/ł\sŞyPČß9…Á’É4L íÍň"¶ö–u§Ag–źô<ĹJéâsyń„ŕĂ1endstream endobj 48 0 obj << /BBox [ 0 0 468 504 ] /Filter /FlateDecode /FormType 1 /PTEX.FileName (./UPexamples-up5.pdf) /PTEX.InfoDict 18 0 R /PTEX.PageNumber 1 /Resources << /ColorSpace << /sRGB 20 0 R >> /ExtGState << >> /Font << /F2 19 0 R >> /ProcSet [ /PDF /Text ] >> /Subtype /Form /Type /XObject /Length 1563 >> stream xśĄXMo7˝ďŻŕQF†ßä^´ ´@+=9Ž:Xۉĺ6h}ç“KŮ2GŮŹ|ó8ÉázsnĽůdľLż™/&ĎÖ%SŁMÉÄÚl đÇŰĚý•ůĂÜNŻwż˙üĆ\î&g[¨füÝ]ŢNµÚ”M(٦hn&ďÚ ^G_Ŕ0Ó;üË|”Ţnaçś·oť‚ Ů|ťŢ˝7Î|ś9źŔůÉ#Áü˘z<öćĆčpŚcPµ’˝!ˇ•<(ÍÉÎÍ„ěíŔZ řŽĆÜk°Ĺu‚z?Şłf+6„UĎ;N1ń™Ń…!§t;ůŕm€yĚŕIäbcVĽ¬x¶!!Vľ`\”QWV´yF×U…đŃPűŘĐĂ(Í‹Sh.MÖ¶ą‘Â~= ·ŹyĐLŐć¦X4‰˛j2E0SNhŔňxś]°sŤŕśőMńŇ1$Pj¨!|ĹĎ„\U›Í śďŞ„Ź‡F/ŕx-č(Ě]°8Es—&đŁú"~9ś­ šł·)(fMˇtMˇ0fĘÉáÚNĆř:#=úhdäŘ’l®0o}”mťuj‚.‘Ý’őiíϸĚÝVi3[Zś>ťĺŃ9HU!|4}B(í3áě,Nˇą4Eđ(ě×Óô ľâ łjĆŚ fM¦¬šLĚ”“Ă9ŘĘÇÜ| ŃŞmYń˘o’JšĚďř™«j&UÓU y…tá‰4Ľ@:ŽÝ\š˘K8P§¨_B^ ¶yÔ„ż)fM¦ šDĚ”“Ă…;6”Š+šł9ŇŽí-°şśřP´!öŮ3Â] 2c»÷GZf±UDÚĚć–J%ŽÚÂ…ŮVmFÄ–±Ą_üŰ=żiÇÎ…Z`/y¸W¦aS5ĹËŠÝ3+_đ3鳲đD3Šű†;Ď.Ź» ÎĽ‹ShŢ›˘Ťm¤„~î§Ot™«fXĚfMˇtMˇ0ćô99\>x~Rµmáż·dĽ/"ÔFĄ*xčĘÔ];Ű’ÖţÂ)+¶‚H›ŮŇ‘ö«­łó MŮ2¶ô‹_b;úŤéÓ=ÇZ%äq.¸“ î=ĽëĺFŁ•%4ĚĄ÷“ŻÝVis pkŐ˛ÚFÚöŞMhKďżÄvô瓣8P<Î$ÖL+ăeʼn’xĺ >Ľ: ş°pë*„Źo…\©–§Ě>ć´Ő¦“’žl6Ů i¦ňľk–Bĺý¨©ŐT aˇś.Jźś©…%r’>Ú’đ*ŤP´f<)3˝/djŚh‰áÝ—jď/޶źŘ*"mbk‹ÇÚ¨Ű:*T›kËŘŇ/~‰íč·l…ŐsWöç’)UŻPęx„öćÂýę+Ű®3se.jëm´ íÍEúĹ/±ý¦­˙B‹OŤNÂ)ůC xQáŘ‹´ÖĚďř™­Ŕ¬ żaDEđŃ­ŕ†›ŽÚHŐ'´–¦‡đHQ·ě„™–ŻKB• ń`ČI΄U‘‚™rr¬¶řăŘçGĎ—GźřAĎ_?úĽëZmí÷3*ĂS‡_¨Ú/p$đĂ ©• p$pˇŇ ‚ßJ88©;á@Ř›vŹÂ@xs1~zeü ôú'Č(sń‘š \đűŠ˙P žř ÂűďâĆl>ß]ďvw·gćâÓôă pDÁ§OŔń‰÷»w/€ăa.đ†¶ŃˇŔ»Íý™y˙o>_˙sf`Űlîţ\ÎŢ›‹óo” ŃáË1y¬ѧ‡ëeązOˇ&,s|Ô¸Ü\řďďŰ»HDßl‚“ .~šÖć…a‰%ŕŇp¤pXľ; ę*|ĄG, Ijwżűzf ÂŰÜÝw%ţâčˇPrřy€aä9ňéŘáH€śŞd!0ÖÚ×N8ŕ†ÂâK Ţ˝1v‚Ŕ n+aśĹ·Ěă…9Ű —ň(_ýŕý –Ź%<źL¬áżO‹B88X#ěiŚßá Ć\+zJ*Lđć†íµh`Ň1=ý›”o’endstream endobj 49 0 obj << /Filter /FlateDecode /Length 114 >> stream xÚMM» Â@ě÷+¦4E6·{Çć¶ h'l'VŠ]üăÓ ó¤¤ÄąňXX\đži!¶±ćş&tµdëmÂp›—=v«ß7űĂč9hÔá즆řBÝX«Â—ö> stream xśť–wTSهϽ7˝P’Š”ĐkhRH ˝H‘.*1 JŔ"6DTpDQ‘¦2(ŕ€ŁC‘±"Š…Q±ëDÔqp–Id­߼yďÍ›ß÷~kź˝ĎÝgď}ÖşüÂLX € ˇXáçĹŤ‹g` đlŕpłłBřF™|ŘŚl™ř˝ş ůű*Ó?ŚÁ˙ź”ąY"1PŚçňřŮ\É8=Wś%·Oɶ4MÎ0JÎ"Y‚2V“sň,[|ö™e9ó2„<ËsÎâeđäÜ'ăŤ9ľŚ‘`çřą2ľ&ctI†@Ćoä±|N6(’Ü.ćsSdl-c’(2‚-ăyŕHÉ_đŇ/XĚĎËĹÎĚZ.$§&\S†Ť“‹áĎĎMç‹ĹĚ07Ť#â1Ř™YárfĎüYym˛";Ř8980m-mľ(Ô]ü›’÷v–^„îDřĂöW~™ °¦eµŮú‡mi]ëP»ý‡Í`/Оľu}qş|^RÄâ,g+«ÜÜ\Kźk)/čďúźC_|ĎRľÝďĺaxó“8’t1C^7nfz¦DÄČÎâpů 柇řţuü$ľ/”ED˦L L–µ[Č™B†@řźšřĂţ¤Ůą–‰ÚřĐ–XĄ!@~(* {d+Đď} ĆGů͋љťűĎ‚ţ}W¸LţČ$ŽcGD2¸QÎěšüZ4 E@ę@čŔ¶Ŕ¸ŕA(q`1ŕ‚D €µ ”‚­`'¨u 46ptcŕ48.Ë`ÜR0ž€)đ Ě@„…ČR‡t CȲ…XäCP”%CBH@ë R¨ކęˇfč[č(tş C· Qhúz#0 ¦ÁZ°lł`O8Ž„ÁÉđ28.‚·Ŕ•p|î„OĂ—ŕX ?§€:˘‹0ÂFB‘x$ !«¤i@Ú¤ąŠH‘§Č[EE1PL” Ę…⢖ˇVˇ6ŁŞQPť¨>ÔUÔ(j őMFk˘ÍŃÎčt,:ť‹.FW ›Đčłčô8úˇcŚ1ŽL&łłłÓŽ9…ĆŚa¦±X¬:ÖëŠ Ĺr°bl1¶ {{{;Ž}#âtp¶8_\ˇ8áú"ăEy‹.,ÖXśľřřĹ%ś%Gщ1‰-‰ď9ˇśÎôŇ€ĄµK§¸lî.îžoo’ďĘ/çO$ą&•'=JvMŢž<™âžR‘ňTŔT ž§ú§ÖĄľN MŰźö)=&˝=—‘qTH¦ ű2µ3ó2‡łĚłŠł¤Ëś—í\6% 5eCŮ‹˛»Ĺ4ŮĎÔ€ÄD˛^2šă–S“ó&7:÷Hžrž0o`ąŮňMË'ň}óż^ZÁ]Ń[ [°¶`tĄçĘúUĐŞĄ«zWëŻ.Z=ľĆo͵„µik(´.,/|ą.f]O‘VŃš˘±ő~ë[‹ŠEĹ76¸l¨ŰÚ(Ř8¸iMKx%K­K+Jßoćnľř•ÍW•_}Ú’´e°ĚˇlĎVĚVáÖëŰÜ·(W.Ď/۲˝scGÉŽ—;—ěĽPaWQ·‹°K˛KZ\Ů]ePµµę}uJőHŤWM{­fí¦Ú×»y»ŻěńŘÓV§UWZ÷nŻ`ďÍzżúÎنŠ}}9ű6F7öÍúşąIŁ©´éĂ~á~é}ÍŽÍÍ-š-e­p«¤uň`ÂÁËßxÓÝĆl«o§·—‡$‡›říőĂA‡{ʰ޴}gř]mµŁ¤ę\Ţ9Ő•Ň%íŽë>x´·ÇĄ§ă{Ëď÷Ó=Vs\ĺx٠‰˘źNćźś>•uęééäÓc˝Kz=s­/ĽođlĐŮóç|Ďťé÷ě?yŢőü± ÎŽ^d]ěşäp©sŔ~ ăű:;‡‡ş/;]îž7|âŠű•ÓW˝Żž»píŇČü‘áëQ×oŢH¸!˝É»ůčVú­ç·snĎÜYs}·äžŇ˝Šűš÷~4ý±]ę =>ę=:đ`Á;cܱ'?e˙ô~Ľč!ůaĹ„ÎDó#ŰGÇ&}'/?^řxüIÖ“™§Ĺ?+˙\űĚäŮwżxü20;5ţ\ôüÓŻ›_¨żŘ˙ŇîeďtŘôýWŻf^—ĽQsŕ-ëm˙»w3ąď±ď+?~čůôńî§ŚOź~÷„óűendstream endobj 51 0 obj << /Filter /FlateDecode /Length 149 >> stream xÚ31Ô35R0P0Bc3cs…C®B.c46K$çr9yré‡+pé{Eąô=}JŠJSąôťś ąô]˘  bą<]ä00ü˙ĂŔř˙ű˙˙ ü˙˙˙˙˙ý˙˙@¸ţ˙˙0ü˙˙˙?Ä`d=0s@f‚Ěٲ d'Čn.WO®@.Ćsudendstream endobj 52 0 obj << /Filter /FlateDecode /Length 218 >> stream xÚeαJA ŕ˙Řb Í>Âä Ü]vĎĂjá<Á-­,ÄJ--mo|±é|Ťy§ĽbśáÄC®ČB†ţdyĆ-źj /;~ěč…ú•ć¶Ä2xx¦őDÍ-÷+j.µKÍtĹoŻďOÔ¬ŻĎYó†ď:nďiÚ0ŮýĂŞńs ü’#źVľśH€ř…|ŻÄ›śŻFoý;ŹsŠ+lqÎ…¤ŕ÷Ƕ÷d,˛6Ş‚ÉşY'=alp µľŚ+ů–‰Ęč%ĐĹD7ôťpëendstream endobj 53 0 obj << /Filter /FlateDecode /Length 196 >> stream xÚmŽ= Â@…'X¦Ů#ěśŔMXŁXüSZY•ZZ(ÚęmŹ’#X¦Śo[±Řf–÷ćůa5•B&x#/~,§’Żě+ĚEÓÇńÂł†ÝN|Ĺn…-»f-÷ŰăĚn¶™KÉn!űRŠ7 !ŇH”ë›ČꇨÖ+UĘ4jôdcŢ‘‰ćM¦µ-ĺ­Ť@l_ Ϥô"j‰~Đ' f& Ę”Ö74.WHÁe °Ę4ů˝’©A— oů \s`¸endstream endobj 54 0 obj << /Filter /FlateDecode /Length 181 >> stream xÚuα Â0ŕ+ ·ôzO`RL'ˇV0 “8iGE7±}4ĄŹĐ±C1Ţ…:”Źün83ťd3Ňdäf”ĄtJń‚F“Žňq> stream xÚmαNĂ0ŕ‹2XşĹŹŕ{H¬¦.X*E"L0"‚5)oÖG1o`‰Ĺ©ąsaAőđ ľ˙t7;ž/¨%KGvAÝ)ÍNčÁâ v=˙¶4ďG÷O¸°YS×csÉ˙Ř WôöúţÍňúś,6+şµÔŢá°"ŕ§<€ .L)'¨rfë˘Îů;‰î“őÚGpĺźaF¨Ů]1Píő˘.š­ä;Á´a?2ČyWL ÇąGő•9^ÖţÄjoÉó.GĄň¤8Śť¸2T‰Já‘=ă"b<čXL’á-Ϋ(UM+®eĘýw1•ëŇEK[ĽđŮzŤAendstream endobj 56 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚ}Î1 Â@Đ‹Ŕ4Á9IH,¬„Á-­,ÄJ--mÝMođ¦L2ÎL‚ö±vY~ Gc 0äG8 q bÉD9ěŽđׇŕĎy ľYŕĺ|=€ź,§Č9Ĺ żÜ‚Iѱ…Ă‹Ę_­ęŞ˝Ćâź^cŢÖfě“8y/âű>Éß_[;bĄ–â Pső®fm]vŇ¨íş”ľV˝i».Ąo­VÚ·ĄĄÜ[e¤ÚŹ2‡™Ľ ąt6endstream endobj 57 0 obj << /Filter /FlateDecode /Length 156 >> stream xÚ31Ö3µT0P0bcKS#…C®B.cC ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. ě ň€ř=2>çgŔ˙˙g`†ŕńzŃp=×aÁ˙€ř&fá„?Ŕqý˙˙ţ˙˙˙†A|.WO®@.ďűJĎendstream endobj 58 0 obj << /Filter /FlateDecode /Length 230 >> stream xÚ}ͱJ1ŕ9®X&Źyw×Ýl ś'¸…pVbĄ–ŠvbÖ7[ńEâ(6W77V8±0/™É̤möf‡RÉľíö@fµÜÔ|Ďmcq…×w<︼¶áňÔ˛\vgňřđtËĺ|y,/䲖ꊻ…PLdK?˙ł“ět4ýg1:üVuČ&*Ţ Ëw×#ďú¦şŢ%č{"ßo¬×OÖpş‚($ŹBňÁJ(D|p¤0hÚůŤĘđŽ®řšÍs^>Űą3k¸•ý ÝđcÔ¤RýP5ż˛¸Źy>éřś·ZsYendstream endobj 59 0 obj << /Filter /FlateDecode /Length 154 >> stream xÚuɱ 1 €áŠĹG0O`Ż\opÎě čä Nęč čjűh÷(÷ŽblÂ-ň…?ńĺ´šaUź—Ă“+”>·$?Ž¨Ř–ě*_Á†5ŢoŹ3ŘzłŔÜ î šHť1DŻ>‘1Cf$t cˇUIa.…Č<5ľĚGa ĽűD"JLKLü“`` ?:•RŽendstream endobj 60 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚuĘÁ ‚@`Ĺ0Áy‚Vq :f‡ N˘SuěPÔYŁÓ7|;µÁâ4kuhľýçgd4GôOĆ q¤ě^Í·=@’Xˇ” fÜ‚Čćx>]ö ’ĹC)®C 6Ąčhż[®¦Š —¨ˇ’}PíOmĺwjŘŠně•ÖîŘÎÖ¬¶ŐGe·żrŰşµInůOsá•&yĹ?Í…_ä[ßć*o©&ť+jIÓÓhň»‡iKx—‚»endstream endobj 61 0 obj << /Filter /FlateDecode /Length 180 >> stream xÚmĘ1 Â@Đ )ÓxçnBVÁJÜBĐĘB¬ÔŇBŃÖÍŃ"^doŕ–)BĆŮŐBÁb˙FĺáSĚřTŽů÷ś@ůžúęĂî…ąF•śó R/đrľ@Ë)ňZâ†?· Kڍ6•éA–}’c‰EŹî-Ű olĽ}´Á:X}±“·"jţł&x±űoÂvÁV$öGCÖë* šŹ~‡™†ĽęőfŹendstream endobj 62 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚmŽ1jĂ@Eżp!fʰs‚¬ÄZ1®d˘"W.B*'e »Ťöh{AĄ ˇÉ(&E óŕ˙aříŞ-ĽŃ]{öŹü^Ň™|ĄşXär8}RÝ’;˛ŻČ=©K®}ćëĺöA®~ŮqI®á×’‹7j$ąô€•2©%32É« ]Ě„hzŘdL˛¦úsÇ×_L˙ä_ŘÄYŁt:wĚjh^Hů;„F´U.Úo%mĄŹZ”ö-č/LRzendstream endobj 63 0 obj << /Filter /FlateDecode /Length 230 >> stream xÚuνNĂ0đ«:Dş%Źŕ{â„:&Km‘Č€bj@°’ľy?BFiŽ>@UĄJÖOöÝůîÜň˘¸‘L—˛ČŻĹ9Y^É.çwv™î/·}ăUÉöI\Áö ¶ĺ˝|~|˝˛]=¬%g»‘ç\˛.7B>š@TĹ*ÂvPU‰<ÜÓL_Ă: ŘŃĽˇy;§3‹ýóÄd4śŃĹ0 ˝ă1ő¤iČď{±•‰O¦K[¨lűŁ5LQB}!ŃżŐ‘ßgěŽlO­4 b ó¦űçŰ’ůÜv›endstream endobj 64 0 obj << /Filter /FlateDecode /Length 228 >> stream xÚuαJÄ@ŕ )¦É#d^@7!ą;­îN0… •…X©Ą…r׺ë›ĺQro°`łŕ‘ßY#\qŘ|,˙ěđOŰśĎ/Ą’…śŐҶŇ,äąć7n–šV2o˙FOŻĽęŘÜKłds­9›îF¶ď»6«ŰµÔl6ňPKőČÝF@fŘ*ńÉá;€á!É…Y$ ť‡rHôT Ö'Hq‰ŹÄ8(ý)ĺŻŘ Ýp^wáeđÖç ŰĐ *ô ˝LÉ1j ˘~-SŃ‘1qř‡ě—x 0hăD^)㫎ďř Zz endstream endobj 65 0 obj << /Filter /FlateDecode /Length 179 >> stream xÚ}Í1 Â@Đ]R¦Éś¸‰VBŚŕ‚VbĄ–ŠÖÉŃö(9BĘÁqvE‹y0˙3LŞűĂĆ8ŕI3Ôî8BŞyŹÝęŠírj…©5ă”™ăůtŮĘL@¸N0Ţ€)PR+IÔFdęĆŢ’jIW˘ZČE,×Î&´¬ *>¨„`…óîíĽí۰ů°ţmôÔţł÷´ú˛$jĽüŚĽĺKÎaj` ż†Uŕendstream endobj 66 0 obj << /Filter /FlateDecode /Length 206 >> stream xÚUŤ1jĂ@Eżq!foÍ ĽRd\ l¬Â`W)BŞ$eŠ„\vʶGä)U8˙M—b3űŕíĽ™µK­tÁ™ßkłĐ×Z>¤iyWůĚâĺ]V˝řGmZń[ľŠďwúőy|żÚݵżŃ§Z«gé7Љ}'8ł„Îl€"M !#ĘT ‰pp‘›P\‰©Ť`‰~ŔԅƲꌀE˘Św€KŐ¸r40Ă€€0ćďŤâ‚ß=ćO%›ňĐËAnŞRZAendstream endobj 67 0 obj << /Filter /FlateDecode /Length 176 >> stream xÚuĎ˝ Â@ ŕ”nYúć ĽÖ«˘ µ‚7:9“::(şÖ>šŹâ#těP“C…îăňĂ‘Km8ˇĆrŇĄ#:&xAk%Ź5ŐĆጙCł%kŃ,ĄŠĆ­čv˝źĐdë9%hrÚ%ďŃĺHDĄĐëbćfţRú›ŻAˇ#´JÓAŕ©;=L•â—Vi„@ …&Ş!`®”ČnOY—őoň .nđ îRđendstream endobj 68 0 obj << /Filter /FlateDecode /Length 178 >> stream xÚm̱ Â0ŕH†Ŕ-}„Ţ–´ŠSˇV0 “8©Ł˘«ÍŁĹ7é#t¬P<“ŕRt¸ŹűďŽËÔ8źa‚SW™B5Ác PąË‰Ź~q8C©AnQĺ —n RŻđv˝ź@–ë9¦ +ÜĄěAWX·ś µÂŃ ˛0ă-‹‡FV°_j,{üáÍâ€aý€Ń—ÂđŢ˙é\wî¸v‘ŤŤđpzQĂčI6đ&‹]+endstream endobj 69 0 obj << /Filter /FlateDecode /Length 176 >> stream xÚ=Ë=‚@ŕ!$Óxć.dŃ@ bâ&ZY+µ´Đh‡ÁŁqް%gů+ćËĚ›Ľ@.Wyň!É5Ý||˘4™gNó¸>0U(N$#;NQ¨=˝_ź;Šô°!EFgźĽ ŞŚŠÖęš®łÚ~ë3§ś ⻂|¦ž°4Řš±4#\YüŔި]gr¦1äőÄWOŐLÉ$ÓÇ­Â#ţbVOendstream endobj 70 0 obj << /Filter /FlateDecode /Length 197 >> stream xÚ5Í; Â` ŕ€%7°9‹őm`A'qRGEˇCˇGŹŕEz”ˇc±ćokB>ňbwÚÝ!›Ü—˛ÜéńŢÂÚ&ë”QvGű¨Öl›¨ć˛Eĺ/řrľPŤ—¶PMyc±ąEĘQŃ·( 5Ň•;Ў‘iŇ?Í’ä•Ä5™Ó-7€î- ÇÇ«yľ! ^P+Ě<§$r4ˇ+n ”Ź„¬"©IŤD>8óq…?áUŃendstream endobj 71 0 obj << /Filter /FlateDecode /Length 216 >> stream xÚEαnÂ@ PGNň’OŔ_ĐKH@b!Ą`b@L´#n¤vý“Hý¶Ţ0öe`¸'Űwg»ČßFJ)—SŚ)Óg†G,†’§šęĹţ€ł 톊!Ú…TŃVK:źľżĐÎVťÓ6Łt‡Őśbö%71w%;Ă]Í®Źű:$δ &Ŕ´ nKoW1ň]Đ‹pż©uű˛tÁF@u¨°ŢF˙jü§ďM0ůŐ>ÉŹźÔ)č” čÄNŤĽ6Ş˛#0Ëľ˘ jÜ×ńŁÂ5>Ý[¦endstream endobj 72 0 obj << /Filter /FlateDecode /Length 224 >> stream xÚMαŠÂ@ŕ )„iňBćÎÍâ´‰ŕy` A«++µĽâŽ®čŁĺQň)·®;»Áló±ü3ěüj:™-(#IorNjNÓśNPĺ6Íh¦úŃńW%ŠOR9ŠŤÍQ”[úű˝śQ¬vď$Q¬éKRvŔrM`şŘčČ> stream xÚmŽ= 1F'XÓxçf׍ VÂş‚[ZY•ZZ( vz4ʞG°L±ż‰?•ä13yLâ˛Ţ`(‰d8.—,—mĘv}ô‰¶z±ŮsQ±]ŠëłťbʶšÉéxޱ-ćcIŮ–˛J%YsU äf”÷7[qňá(hžĘVŁě ¨©[“©it'äzS¤í•Č[Ś vý».Qô*šFEŠńńQŻ"xĹż ?>â¤&žTĽŕwse–endstream endobj 74 0 obj << /Filter /FlateDecode /Length 203 >> stream xÚ}Ďż Â0đ”…[ú˝Đ´´Őtj3:9“::(şÚ> stream xÚuĐ˝‚0đ’[xî´‚âD‚ŘÁD'㤎]…GăQxFBíĄ1čňKűż~\Šq4CCM1Ĺx ŕ"֎ʓ¨«ÎJŕ[1đĄŽËŢ®÷đt=Çx†»ý=Č ™W3ĆĽV“¨‚¨ôTQÎScýĂ6ÔCC5Ä5”źQ·š±•>ŐRÍ›p(s©Ú5MŰ’‚`_ä=Ź´=ÍËĐ?ÍĄËčGrúĄJ‚"Z–S°°ňZ¨endstream endobj 76 0 obj << /Filter /FlateDecode /Length 154 >> stream xÚ31Ö3µT0P04Ćf ¦F )†\…\†@ľ –IÎĺrňäŇW0´ŕŇ÷ sé{ú*”•¦ré;8+ré»(D*Äryş(0Ř10Ô30üo`üŽ˙7 "b‡ ¨č?2úEň`Ä˙ýVŔĎŔ`€ˇ0ü‡#H—«'W ^čFÉendstream endobj 77 0 obj << /Filter /FlateDecode /Length 137 >> stream xÚ31Ö3µT0P04S02W01V05RH1ä*ä22Š(™BĄ’sąś<ąôÌ̸ô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ň ü ö ň ö öęQqC=C=2Ăp\ĆŕŔń†   \®ž\\Ő8ćendstream endobj 78 0 obj << /Filter /FlateDecode /Length 273 >> stream xÚuʱNĂ0†Ďňtyß @Ą!°ÔR)`b@LŔČ‚ 5ŢúXń dcÄŁ‘˘çŇ҉Áźě˙¬űî&ĺ~uD9Ő´WÓ¤˘ęn |Ŕ˛–0§ę·rsŹłłK*kĚN%Ƭ9٧Çç;ĚfçÇT`6§«‚ňklć :Aň¬P<Ę‹ŮaîŔ2÷Đ~˝z`łôj0:hoTĐ˝ Yˇ“,ílR7Ý"fSíŇ®_‹řǢ‡ĹâŻá°®@śľc9´ň1XĘ·ŁĽôtíX ŽużĆ(cąArł°â6yŔ.ßź!nŐC˝Iś@­ŚqqHÝf Ř`Wž4x?l˙„Ťendstream endobj 79 0 obj << /Filter /FlateDecode /Length 199 >> stream xÚĄŹ=‚@…‡PLĂś č˛Čź bâ&ZY+µ´Đh«ŤŁxJ Îd)č-ľbß›yó6šĎâ¤3šf%gtÖxĂ0e5 $¬Ó jOaŠjÍ:*łˇÇýyAUl—¤Q•tĐŃ”ŕÔîŔg&Ě›ß}NÇr ŕ5Ĺr^± ťĹaŰý2Ťó†ż¶ă“Ę®ä`‘Ő׉i˙`ś•Ź»r_zHé&=ĄŻ| z)3”óWwřFHH—endstream endobj 80 0 obj << /Filter /FlateDecode /Length 203 >> stream xÚuŹ1‚@EÇPLĂLś č‚ÁĘ1‘ÂD+ cĄ–&j´ŽĆQ8%…gd•B-^6™˙gţß‘;đĆd“Oý€\ŹĽ€öžqđÇ~ŁěŽƨÖ4 PÍyŚ*^Đőr;  —SrPE´qČŢbt ÇLR~3&0 Łč> stream xÚ31Ö3µT0P0W0S01U01QH1ä*ä26Š([€%’sąś<ąôĂŚÍąô=€˘\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. | @ Ź…°v¸:QAŘ˙˙˙˙A=řńN€ą ?@J@#ř€`pě`ÖŃŔŔŔĺęÉČ\z> stream xÚ=É1 Â@EŃR~“-Ľ čäg”`Ł#8… •…¤RK EÁJł4—âRZ„ŚÓ(śęŢ‘Ž'̨–Íi•Ş<¨śE‹3ćö÷ö')ť-µł CŚ[ńząĹ”ë9ULĹť2«ĹUD‹¸CŇ#őMx‘fŔx˘ńi‹çţß î€,ślä ő‡* endstream endobj 83 0 obj << /Filter /FlateDecode /Length 102 >> stream xÚ31Ö3µT0P0"3#C…C®B.#¨‚)T&9—ËÉ“K?\ÁČ’KßCÁ”KßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEˇţ˙˙˙˙˙˙Ă >—«'W ˛©$Ěendstream endobj 84 0 obj << /Filter /FlateDecode /Length 99 >> stream xÚ31Ö3µT0P04F– †† )†\…\@Ú$l‘IÎĺrňäŇ pé{€IO_…’˘ŇT.}§g ßE!¨'–ËÓEźÁţ@ýú˙!Äncŕrőä 䄬eendstream endobj 85 0 obj << /Filter /FlateDecode /Length 179 >> stream xÚ31Ö3µT0P0QĐ5W0±P0µPH1ä*ä21 (™Bd’sąś<ąôĂLŚąô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. @ŔxD2?@ě,Î&ĺ¤=`¨C"˙€Ťů ™? ĆŹaÄdĂjđĆŽa¦›ěĐÝ„lÔMđąIž$bş‰żź‘ÜÄ6†ˇLrązrrШAendstream endobj 86 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚuĎ1n@ĐŹ(VšfŹŔ\Ŕ^Ů’ŤĄPXJŞQ*;eŠDv mʞG tŘ Ć.’ć­4#ýťżJ—نc^ó"áUĆŮźú¦4—aĚY:mŽ_´­ČĽqš“y–1™ęŔçźË'™íËŽ2%ż'PU2µ|„ţ (ßÚ2w(Ú¦E-zD6¸BŰđFĺ”{ íDŘIÚť3ę?Ż”űmgDíŚj #’× AŹrf#érµŃNNŹ,t']´÷cÉá^Ţal Đľ˘Wúqái7endstream endobj 87 0 obj << /Filter /FlateDecode /Length 170 >> stream xÚeĘ1Â0 PW"y€#Ô' MKUJ‘Č€CĹŚ X)GëQz„Ž U‚€ Ďň˙ö8eSŠIĹ<Ň e ž1ÉÉ5ß—ý ŤrKIŽrÉ5J˝˘ëĺvDY¬ç¤P–T)Šw¨K@ô1c5ł ™0|2 GÂŢAôĽw=˙ý ś§/t:źpZßĐi|‘óř©­m¬µí—˸иÁI Ptendstream endobj 88 0 obj << /Filter /FlateDecode /Length 229 >> stream xÚmбN„@ŕCA2 ŹŔ<ŔÉ™X‘śg"…‰WY«ÓŇBŁ­đh<Ę>%aś™KĽKî6đegçß]B}}µľĺ’k{ox˝â·Š>©®´.­´Ćţ6-Ď\WT<č*í#ýĽS±yşc]Ýň‹nyĄvË@6CG'=D"ŠŚş,2ůdíf‹Fzěé-mĺý©É™Áé1ş:šđ;Ý_w1Â|4™Ět4łhćn7öµľ)ńxćńÜăM> stream xÚUĐżJÄ@đYR,L“GČĽ€nb.r6¸?` A+ ±RK Eá*ď-Ź’GHąEŘqľ‹‚˛đ[Ýý†ŮE}Ţ\I)—rVɢ‘ćBž+~ăziĹRšz>yzĺuÇá^ę%‡k+sčnäăýó…Ăúv#‡­r·˘69MD^őH…jO­ę@‡±IÉGJä˘3&ţ`ËM´·S˘™ řń—|0ÚŢ8‘oćF ˇ¦xoÍí2(đ"~řBł9~…ÚĐň}B@BTB_Cm˵c1a´H9ćóÔťză x×ń‡kendstream endobj 90 0 obj << /Filter /FlateDecode /Length 214 >> stream xÚeĎ1jĂ@Đ[¦Ń4'đJ–T¨±@±!* q•"¤JR¦°±» ëą’n+¨s«.*„70‚,ĚýË0łi˛Čr‰$CĄ™dKyŹyωf‘^őáí“ËŠíł$9ŰG¤l«­§¶ĺÓÄl×ňKôĘŐZ¨hÁYqžb~ÁOC~O¨•xCH7Lü-…VhPjeŢLă hAŘ€‚&j˘Ψ\ďś5Ó™ŘÖë˙cîtsŚĂ·|çşšń¦â˙ţ*fëendstream endobj 91 0 obj << /Filter /FlateDecode /Length 224 >> stream xÚuϱnÂ0ŕ‹2Xş%Ź{âD,Q*5C%ŞNŔČ@Ő®uÍŹâGÔĹC”ë™va‡O§łěűoQĎšGŞhI† 5†NŻXݤYQ3˙»9^pÓ˘>P˝Bý"mÔí+}~|ťQovOdPoéÍPőŽí–Ŕ2GpĚĂ=ľAÎ&ČnÄ ňč<ä?ÜCžţĆ Ţuj„Ň«…W=AP!÷BzŮO˛P˝˙SÜđBé%­í$”ë¤bpŤR«l°J–,łLaî ă´ś•řÜâĽp.endstream endobj 92 0 obj << /Filter /FlateDecode /Length 247 >> stream xÚmŹ1NĹ@ D'JÉMŽ_ňC~Q­ôůH¤@‚ŠQ%Z6T«ä({„-SD1łQ ŃĽÂcŹg¶íqwŞíő¨Ńm§Ý‰>4ň,mĎáF»öGą’Ý őŤ¶˝ÔK=\ęëËŰŁÔ»«3m¤ŢëmŁ›;ö d ´ płÜlFaůŚr&Ş@¸©áGÂPĚŮŠÂŤ>pßOĽôcÝÂë˙(Ă{zóU­ŕA¬L/”»Ś.˛ł°ßÄŢ©8óđ’Éĺ|kůŘës†endstream endobj 93 0 obj << /Filter /FlateDecode /Length 202 >> stream xÚ]Ť; Â@†GR¦É2ĐÍšD„€p A+ ±RK EÁBŹ–ŁěRZ㬺†8Ĺóřż‰ÂN< €¤¤¶¤¨Oq—vŹöxP~WŰŽŠ…=3žŁPs:ź.{ŁĹ$Š ­%TWRU•ÎE:ĺ]ţČî7€ ®ĐâśŇŕÁp †§~nµ(->şGCW;]Ý@ýâőGĎŔ5vś*\â jwR]endstream endobj 94 0 obj << /Filter /FlateDecode /Length 251 >> stream xÚUʱNĂ0„/ňÉ‹Á˙ @ŇTęd©‰ H01 NŔČ‚µÍŁĺQü=X1ç¶bůdßoßݿꯇŤ´˛–«NVknä­Óźş/b+CžĽ~čí¨›gé7şą§¬›ńAľż~Ţuł}Ľ•N7;yé¤Ýëq'€‰rTÎLÎ6çlÄqŞ#¨T%T©âÓ¤E… µ§öjU$Tä;řÍxŘ™VĚpya"ÝQ1ě|r9±@Ĺ桰“™afË6ŢM¨˝q¸@…RąţńĹ{ćÔ¸éúŃ ĂÍŠ˘Ież€ş,yZĄ,[č»Q?é_Wuçendstream endobj 95 0 obj << /Filter /FlateDecode /Length 124 >> stream xÚ31Ö3µT0P04ĆĆ Ćf )†\…\†¦@ľ –IÎĺrňäŇW04ĺŇ÷ sé{ú*”•¦ré;8+ré»(D*Äryş(0|`ţĂţ‡ý?‚Ř?0ŕü öęÔ?ř v—«'W Ča*‰endstream endobj 96 0 obj << /Filter /FlateDecode /Length 187 >> stream xÚUޱ Â0ES:ޢĐ÷¦µ±ĐI©Ě čä Nęč č&´źÖOé'8:knh †ä@Î}7D%“YĆg¬XĄŘçn”¤ĆE¬¦68])×$÷ś¤$×Ć’Ô~Üź’ůvÉ1É‚1GGŇ łćxos «Ťď*!‚Żą…ř¦÷~‡ŃÖů˛ŽZoź(kĚ ‡˛B" PőŃđqă>´.î۶ř{€°xcA+M;úç–=Äendstream endobj 97 0 obj << /Filter /FlateDecode /Length 118 >> stream xÚ31Ö3µT0P0S04S01S06QH1ä*ä2 (Z@d’sąś<ąôĂŚ-ąô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ň˙˙˙˙c$ţ!°‘ ¨ř˙˙ Ŕb\®ž\\ĎŠ>Ăendstream endobj 98 0 obj << /Filter /FlateDecode /Length 187 >> stream xÚUŽ˝ Â@ ÇO YúÍx­w8jotr'utPÜęŁőQúŠ5I-Ôĺů$±f2›cŚ-ZÖá)+GZŚv*Ćń™˝Că@ŻHí×xż=ΠłÍĐ9îŚŕsT/ĄÔ¨"ŚkFĂ㇠ZFQ"¶Ă7!Ř\LĹ®{»kwĹ; #e´%ç(đ®»iőÓÇÜ›^/ŞaTtY!źÉ)yçÉ@,=lá M>kendstream endobj 99 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚĄĐ=NĂ@ŕąXiš=‚çŕŘB‘,… á * D)S€ µ÷&\Ĺ7ÁGHéÂňđ6.‚DIói5ű3o¶Xť—k]꥞ĺZ¬µĽĐ×\ޤČY\jąšw^ö˛©%{Ô"—ě–eÉę;ýx˙ÜI¶ążVV·úÄ;ĎRođĐN>`aö}x3 H‡”V˝ŁmH¨ńâbŚ&oĂNúhŕ»h:€+T¨p˛=Úüq::ţϤ‹ş>ľF›_˛/C2ă1eÂyaÜ:ÄůÜčă#fśŹĂÉ`ÖĹčx–!7µ<Č=cendstream endobj 100 0 obj << /Filter /FlateDecode /Length 191 >> stream xÚuÎÁ ‚@ŕÂ\zť'HĹ Á ňÔ©CtŞŽŠşEúh>ŠŹŕŃhłkeͰüł°;ÂűSrČă#&ä»ttń‚Bpvd”‡3†1Ú[í%OŃŽWt»ŢOh‡ë9qŽhç’łÇ8"h¸reˇ)ˇŻ‘QŔ¨5“ńźVzV \ż4Ů ¤0°i:“·uç“űÓl3%üRk-Le00˝µĎöĺřăćËJÍKŔEŚ|ń}xBendstream endobj 101 0 obj << /Filter /FlateDecode /Length 193 >> stream xÚmĐA ‚@ŕ'.„·éľ4ZŠ´Ě Yµj­Şe‹˘¶i7ó(ÁĄ qš§ 3üo~f‚ů4\G3˝C˝|:űxĂ ŇąŤ|pşb"Qě)P¬ő…ÜĐăţĽ H¶KŇ9ĄOŢeJ5 jPĘRÍČnî|Ŕ-`ŇY€››Ťs.°9Ä`6.°Ż?•ľđgÖ[÷ęÂ@KŰ´Ö`UfíŠ lviÖ)ąŔ–üʡ™‚öŢJŤě渒¸Ă/V±endstream endobj 102 0 obj << /Filter /FlateDecode /Length 156 >> stream xÚ31Ö3µT0P0b3SC…C®B.c ßÄI$çr9yré‡+[pé{Eąô=}JŠJSąôťś€|…hCX.O†ú˙˙0ü˙˙˙cŕ?ŔŔŔ &pö`‚Q"ęp˙@Ä#ř`pě`â2QŹěżpOţaŕrőä äIVRendstream endobj 103 0 obj << /Filter /FlateDecode /Length 242 >> stream xÚmбNĂ0ŕ?Ę`é–ĽAě' ¤Ş˘X*E"LSadČ`µy^ÉoŔ+dc$˘–sŚT@•|źôßů»89šžŞ‰:ćšňÉŐ]NŹTĚ8ŹŃV4Ż)[ŞbFŮw)«/ŐóÓË=eó«3Ĺyˇnr5ąĄzˇ°é ězČí^˝ĹĆAHśż ^Ů_öźŃk˘O mb¶2ń{Ë o)ŢĽIP¶X—’5•”`ÓŃj´5҆uiSyű˝˛ ®9iŮ^ZĂ&­WŔ‹ÄÁŽW9 ő+żĺ§űo w }:Żéšľ˘{{endstream endobj 104 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚmĎAJĂ@ŕżtx›9BŢ šFSŠ›jłtĺB\U—.”şjir˝‰ä(s„én„ˇăË š…˙}đ˙łšâ|2»ŕ)źÉÍ$9?ĺôJĹ\z¨ÝĂú…–e÷\Ě)»–•˛ę†7oďĎ”-o/YúŠrž>RµbÔµ·đGx×+Ł$qP-Tô Şú8aÚ ý ¦Hń«Ú”@\¨fńgmŁ{`Ü%íNGőP¸ iŰk,FťÓű=pk0Žjluo-9˘Ôđţżm·Ë骢;ú[Ę|endstream endobj 105 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚ}ν Â0ŕ+„[ú˝'°˙‚SˇV° “8©Ł˘sóh}”>BÇĄń.EÁ†ËÇý$$q4MćäSÄ;žQĐ)Ŕ+Ć!׾”28^0+ĐŰQ˘·â.zĹšî·Ç˝lł ®sÚä°Č ´Ö Ä,¶5yoÔ“ÚfťJN©Ń­>ľăŐTĺHA¶±-ŁÝIÓĺ?”ň±6*‘ʰ<”+Ľş1­ÁvL{°ůµÔ˘yőˡŹË·řäťjŇendstream endobj 106 0 obj << /Filter /FlateDecode /Length 245 >> stream xÚmĎ1JÄPŕYR¦É |sÍĆ}!°®` A+ ±RK EÁĘ—Łĺ^aŹ2Ĺ’ńꉋ6ÉĚĽy˙‹«ŁúT–ĺ°’x"ő±‰pÂ,ŃÎ\@Ç_łŮčs/*g.ů ů)¨&éÖL“ŮřOPëăvY´µ‡ůĎě`nî ˙,ß{ŕ·ůOÄ›Mx±[l)őz»i˛ç&µ$©vŞX?zÎŹĚňEË7ü }„tŁendstream endobj 107 0 obj << /Filter /FlateDecode /Length 163 >> stream xÚ31Ö3µT0PaS 2TH1ä*ä21PA $‘śËĺäÉĄ®`bŔĄďĺŇ÷ôU()*MĺŇw pVň]˘  bą<]ě˙˙˙˙ŻHüG#ęěę˙1Ô3Ô˙a¨c¨ĂFT0üc°a`řĂŔ€•`?pĚ`â‚L<ŔAđ‰8y0Ń€Lđ˙˙dü˙ŹL€Ĺ¸\=ąą7X^´endstream endobj 108 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚmĎ= Â@ŕ‘irçşY“€V ţ€)­,ÄJ--mMŽ–Łä–â8“mR,„Ţě›d“găbF)Mid©Paélń†y&ĂT'ÝÉéŠóÍžň ÍZĆhĘ =îĎ šůvAÍ’–Ň#–K޸vÜ07·}ý> stream xÚ31Ö3µT0P04SĐ5W05P0µPH1ä*ä26Š(™BĄ’sąś<ąôĂŚŤąô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ň˙˙30Ř˙˙߀JĹ€ NÔa!ţÁ‰?#‚řI0#;‚x€Iđ#„<‚hŔ$ě&ß»˙˙˙˙‰z—«'W !čVŽendstream endobj 110 0 obj << /Filter /FlateDecode /Length 156 >> stream xÚ31Ö3µT0P04QĐ5W0¶P0µPH1ä*ä22Š(™BĄ’sąś<ąôĂŚŚąô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž.  ?ŔčC Ő10Ř‘=<50đCĐvzŔŔ G!č†qŘM„‡ŐD¸qXM„Dr 2\®ž\\&Š;*endstream endobj 111 0 obj << /Filter /FlateDecode /Length 172 >> stream xÚuĐ1AĆń…ä5Ž0ß]cŐ&k%¦P)D…RAh­ŁíQA©;Cńš_ń˝ę˙şş  V:FÔÇ:¤i]ŤâčyYm)5¤ćĐšÔ¸šI™ űă†T:"$•a"X’É ¤µB$Öž?!ä›Ä#rljŁtÜjžCÝsehx. MOÁ ‹ŻľßŇ˙ąą{•}RľČmU@#C3zäTńendstream endobj 112 0 obj << /Filter /FlateDecode /Length 197 >> stream xÚUĚ; Â@ŕ?¤¦ń™¸ ‰«` A+ ±RK E[7GËQr„”)–ŚłŘh1Ěë/ňÉtÎ)—ZEÁyÉ—Śî”Ď´OCç-*2Îgd6:%Smůůx]É,vKÎȬřqz˘jĹH€HH¤C,â10ęă\ŔÖq‡¤ŽEĎ˙qRc,ŠS4EB€č¨µH<,l«)®o ˙Ëđe@äˇß®±ú¨)]˘ôšîúXĽí!í¸ŁuE{úł/^qendstream endobj 113 0 obj << /Filter /FlateDecode /Length 212 >> stream xÚuϱJÄ@ŕ_R¦ŮGČ> stream xÚ•Ž1 ÂP †q(d°Gx9ŻĄOA ZÁ‚N⤎Š®mŹÖŁx„ŽŇÁ!$!ůżŤ'3NŘ*Φ|IéNYĐ>±Öç-KňÎůŤNÉ—[~>^WňËÝŠSňSNNT ČD'Ň i!Š4y;ě‘·ŃGwpŤ{c×ČjCeč ß s»]Ř—ĘžZž†ş.ţ"USł“‚9©-­KÚÓ¦ŤIĆendstream endobj 115 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚ31Ó34V0P0VĐ5T01Q0µPH1ä*ä21PASKLr.—“'—~¸‚‰—ľPKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓE˙ó‚ÁţT‚zó !˙HÔ±÷`řÁřţó†ú쀶¤ „|P±=i«‡u âÉDŞ)öph‘<„ÚkrF=ČAď?0ţ`<˙ꎆ˝˙?ü?ţ˙ ě@‡sązrroXhIendstream endobj 116 0 obj << /Filter /FlateDecode /Length 189 >> stream xÚ]Î1 Â@Đ\B/ 8ĐM˛(ÚЦ´˛+µT´“čŃr”!ĺbI qáÁ23ü;čŤö9änŔ¶ĎvČű€ÎdC)úlGUgw¤IBfÍ6$3—2™dÁ×Ëí@f˛śr@&ćŤm)‰Úť¸·2Ď©\^ˇsϵ2¸Î÷ŻHĹřQ‰RńţQÖOţř—Ö5ÉQŃJrµěhč MťŁíÂá„TĺrŹLĽ@ł„Vô˝Ł@ endstream endobj 117 0 obj << /Filter /FlateDecode /Length 141 >> stream xÚ32Ő36W0P0bcSK…C®B.# ĚI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘*cą<]ę˙70đ|Ŕ ßţ€Áž˙C˙`ĆĚ00Š˙˙˙Çäč§3˙a`¨˙˙Žą\=ąą˘&[endstream endobj 118 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚŤŹżJ1Ćż00…ń v^@ł9ďäŠĂ…ó·´˛+µT´[¸}´> stream xÚ31Ó34V0P0bS …C®B.C ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. Ě€ŕ?É&™iN‚ěaţ`˙D~°’Č700nŕ?ŔŔüťDţ“ŘŔČä‡$Ů€‚ë˙˙˙˙7 “\®ž\\yendstream endobj 120 0 obj << /Filter /FlateDecode /Length 122 >> stream xÚ32Ö30W0P0aCS3…C®B.C ßÄI$çr9yré‡+Zpé{Eąô=}JŠJSąôťś ąô]˘  bą<]ř0Č@A@ 8~Ŕüá? ±q©ŽŘ0ü˙‚¸\=ąą(CE`endstream endobj 121 0 obj << /Filter /FlateDecode /Length 150 >> stream xÚ32Ő36W0PĐ5QĐ54W0´P05SH1ä*ä22 (Ăä’sąś<ąôĂŚ ąô=€\úžľ %EĄ©\úNÎ @Q…h ®X.OĆ ěř   P?`üÁđ†Ř€¸ôE6Ś?ęügüđź‚üc?PĂ~Ŕ†ź˙ó.WO®@.˙§Wőendstream endobj 122 0 obj << /Filter /FlateDecode /Length 196 >> stream xÚµÍ1 Â@Đ•ir3'pŤ.#BĐĘB¬ÔRPQ°ÍŃrʱ0EČ:? ędŮł3ó7čuÂ.{Śô¸ňʧăH‰ĆrCqJzĆGz$ݤÓ1öÇ5éx2`źtÂsź˝Ą […RĘüâë?´LőŤşćÝ3Ř‚ćrÁĘkm‚¨„;xÔÂ3ęH†Kv¤Ř@%Żâ.ęýoÔ nn—**ŚÉŤů@Ă”¦ôDrendstream endobj 123 0 obj << /Filter /FlateDecode /Length 108 >> stream xÚ32Ö30W0P0aCS …C®B.C ßÄI$çr9yré‡+Zpé{Eąô=}JŠJSąôťś ąô]˘  bą<]?0ü‡!ţ ̱˙`ř˙˙qązrrĆ‚Q.endstream endobj 124 0 obj << /Filter /FlateDecode /Length 177 >> stream xÚ3łÔ3R0Pa3scs…C®B.3 ßÄI$çr9yré‡+™pé{Eąô=}JŠJSąôťś ąô]˘  bą<]?đ`Ŕđ˙ý†ú@ú=ă:†˙Č77Ř3đnŕ?Î ßŔüť˙ţÇŔD˙a`˙ÁŔN˙``˙€ŤţŔŔţ`Đ O€â˙˙˙˙7˙˙ŹNsązrr#ßendstream endobj 125 0 obj << /Filter /FlateDecode /Length 147 >> stream xÚ31Ó34V0P0bcs…C®B.C ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. Ěř?00ü˙`˙D~°’Č70đnŕ?ŔŔüťDţ“ŘŔČä‡$Ů0˝ń˙˙Á˙˙I.WO®@.‡e%endstream endobj 126 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚŤŽ1‚@Eżˇ ™†#0Đe6 &na˘•…±RK Ťv9Gá”Tâd)H¬ĚN^fţîţů‘žĚ¦đ”ÇšŁ€Ă9ź5Ý(ŚE”qŃßś®”R{cRk‘I™ ?îĎ ©l»dM*çćŕH&g8^W‰S­śQdHŕVđá•Rľ ň!J*¨- Ŕi~ nNű/†oońkg»Íîő$AéÖHĺŠ> éáwlzZÚŃIKÚendstream endobj 127 0 obj << /Filter /FlateDecode /Length 196 >> stream xڝα Â@ ŕH†Bˇy˝ž­uj;:9“::(şÚ>ZĄŹp"ŘŠç]qĐQ |CB’?Šű2ä€Ü“1G!‡#ŢI:R°«ařm”d$V$f¶O"›óůtŮ“H–$R^K6”ĄŚĘŻŔ¨\ąUW0÷Â/Ľş%>Á«°T¨5*č´4hy~“˙Ě÷ö˛Ąý¦Ýß> stream xÚ31Öł0R0P0VĐ54S01Q06WH1ä*ä21PASc¨Tr.—“'—~¸‚‰—ľPśKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEůĂůŚęŘ0üa<|€ůĂăěĘđ?`0?Ŕ€Áţ€> stream xÚ36Ň35R0PacCcs…C®B.# ßÄI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘  bą<]ŘČ3üPŕ˙ĂÇţ?n˙Ŕ˙śýó3 ~Äo0˙ah`ţÁŔ€‚?PłÍü˙˙sązrrjŮF„endstream endobj 130 0 obj << /Filter /FlateDecode /Length 195 >> stream xÚ=αJÄ@ŕ¶XfßŔĚ x{›`TńSwŐ‡•Z * WîŁíŁÄĘ6`“"8Î%GŠŹ™ů˙fŠ|q~ĆK.ř4pˇó‚˝R^j¨çĺÔ<> stream xÚ36Ň3˛T0P0TĐ5T0˛P05TH1ä*ä22 (Ad’sąś<ąôÌ̸ô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž.  Ř W á Ś@Ě Äě@,˙˙?Ă(f„ĘQ „ţ0‚pC sC3=;˙?°f.WO®@.uH–endstream endobj 132 0 obj << /Filter /FlateDecode /Length 153 >> stream xÚ31Ó34V0P0RĐ5T01Q06WH1ä*ä21 ([@d’sąś<ąôĂL ąô=€Â\úžľ %EĄ©\úNÎ @Q…h žX.Oć ěţ`üŹJň`Ŕ‘p’şŤBţ`°ŔŔđˇüÆç˙왏Iů˙í@’ůĐ.WO®@.1cendstream endobj 133 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚU̱ ‚PĆń#‘k[çęŞWJ'Á rjj ¨Ć†˘¶űh>ŠŹ`›Ph—ş—jů ˙ľ@ Bź\ň©ďQŕ“ŇÎĂ#ŠHE—ÄčłldČ—$"äS•‘g3:ź.{äÉ|Lň”VąkĚRj×_ś śŇ.Á.X ,g0i)ŕ <ˇĄ©ˇp¶&†®A†=éjś|c(v‘kŘ]ţb=ŔĐ(ÔżáúO¨ÁI† |FŁ?ęendstream endobj 134 0 obj << /Filter /FlateDecode /Length 233 >> stream xÚUÎ=KĂPĹńs Xxłv(ćůzËíËb ­`A' ÖQ|A7©‘|±€Đ~ŤLťďx‡`Ľ7UÓN?8gů«áá°Ď!ńAÄjŔÝĎ"z$Ąěr·ż~nîh”ĽdĄHžÚ™drĆĎO/·$GçcŽHNř*âđš’ WUPń÷6ľAß´4ćđŠ5ą§q ‘ţ" bxŘ%âtÇqżÁ_ů®cůGŲh;˛š÷L€ Ëtč5Â<ţfúOk…2·|âµÁ+ń–ZlECÝdŃ ±ď(°çÂŃIBôĄY_™endstream endobj 135 0 obj << /Filter /FlateDecode /Length 210 >> stream xÚMν Â@ đ)(ˇ«Đ> stream xÚUÎÁjÂ@ŕYi® Î čn˛Ző$¨sÚSE¨GÁ˝‰ćŃöQ|„x ‰ł˛Iéĺ;üĂüü=ÝF¤(˘N8 ^DúŤÖ!ţ qިŻÝiµĹIŚň‹ôĺśs”ń‚öż‡ ĘÉÇ”B”3úI-1žQY¦ăâŹŕAćgŕ//7śŽ4gËZŽvŞ*Ě 0‰ĂżŠ+ă]S‡¸CEÉ@QsüϰFŐě,IŤqSn/Ľ'¶’gCţbź^m‘mjg`ç1řă'>ÚźKřendstream endobj 137 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚ%Î1 Â@„á‘@„‡$|'0‰+AA˘‚)­,D¨Ą ˘ťŹćQ<‚eŠ`śŤĹ_ěě·°&î# µÇL_M¬‡H.běÚŁ˝Řź$I%ب‰$Xp• ]ęíz?J¬¦Ęu¦[>ŮI:ÓIU•uO§Ă)Fh~đß!;Łó:cňĚŰዬQÖ‘‚ôź˙)H˙ĺpIëH]R·YŔ#őH[¤mé(ś˛âl2Oe-?uŕC endstream endobj 138 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚUϱJÄ@ŕYpa ÓZ7/ {IŚ(ČEÎ;0… •…X©Ą ˘Ý‘ËŁĺQöRn!9˙ŢÂ×Ěěţ3›źź^¦:×LORÍ -Îô5•OÉ3çZcçĺ]–•řGÍ3ń·,‹ŻîôűëçMüňţFSń+}bĐłT+Ž\QM=`Čţ.If °`kCtŤý3Ü›íŘOZm°ťé\01iůt3(N‹í¨ä¤˛˙g7ť~Ü`O=ŮNcË–ąŽ3\‹Cpl:\ rĂÚîÓ u%ňoGĘendstream endobj 139 0 obj << /Filter /FlateDecode /Length 175 >> stream xÚ3±Đ31Q0P0bScSK…C®B.SßÄ1’sąś<ąôĂL ąô=€˘\úžľ %EĄ©\úNÎ @Q…h ĘX.Oţ ę˙ł˙g``üÁ~żůűĆ˙üäŘ˙É?`°gŕ˙¤ęŕÔ őN}`o`üÁŔţ¤›™ÚÔřFŃ¢˘˙0°˙˙˙˙? Q\®ž\\ŕ  endstream endobj 140 0 obj << /Filter /FlateDecode /Length 172 >> stream xÚ31Ó34V0P0bSK…C®B.# ßÄI$çr9yré‡+qé{Eąô=}JŠJSąôťś ąô]˘*cą<]ř0Aý? Ář˝ýăů† ö@C˙ůA2ţ€’@5@’±D‚Ť!™dţŔđPI¸ůĚCdţĂŔţˇţ˙˙˙ “\®ž\\^ĺÓendstream endobj 141 0 obj << /Filter /FlateDecode /Length 130 >> stream xÚ-ɱ Â0…á gđ 2ś'0ą-Ą™k3:9 TGAEçćŃňfÚ˘|Ű˙—ŐŇ7ôlXUÔŔ:đ˘x@='eý;ý m„;P=ÜfĚpqË×ó}…kw+*\ÇŁŇź;Zä“Fy2d›ĺĎd“L*R!s™ÉB¬ąËY°ŽŘă ,P#Śendstream endobj 142 0 obj << /Filter /FlateDecode /Length 189 >> stream xÚťŹ1 Â@E°Lˇ70sÝě ’@°ÜBĐĘB„€ZZ( 9ZŽ’#XZ:IV›t«ţ 3ďOĚŘÄrÄ#˛‰xjř¨éBşN%7nt8SjImYǤ–’“˛+ľ]ď'RézΚTĆ;ÍážlĆ@TđJô ř@ đhxÁ«jzeŤ/¨ š]aöĺŮáýÝ;żíÇÎAdDÉ/ťak+ÚÎ?i¶Ą”T“‚RSĘ"§…Ą }G«@endstream endobj 143 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚťŹ1 Â@Eż¤L/ :ĐÍ®A"EŚŕ‚VbĄ–‚Š‚…EŽ–ŁäÁÍ$±ĐNxŐĚgćýˇ1‡qß„l">hş.§!Ǧ^íO”XRÖcR 7'e—|»Ţʤ’ŐŚ5©”·šĂŮ”s Î@ t€h~//iąÝKxO`L®Đ“tIVăçßxĹ?üŢůĽ¨>ö‡©(=C±uÚ•ż/ń@ŞĹRÓr•iniMoEËBsendstream endobj 144 0 obj << /Filter /FlateDecode /Length 131 >> stream xÚ-É1 Â@EŃ?^á ¦xĐ™‰‰mŚŕ‚V"ŃRPŃ:ł´Ů™&Nwoľ\ř’ž%红V\ó¦xA=y1žö:Ŕť¨n×w¸°ççý˝ĂŐ‡ ®áYé/ ­tň‹˝4č’M22ÉDłÉT&2+•<ĺ*ŘńBŰ#´endstream endobj 145 0 obj << /Filter /FlateDecode /Length 94 >> stream xÚMÉ=@PEáţ®â®ŔĽ™x¨ý$^!ˇR Ą‚°{ ŤäTß±4J2:*5ˇĹ4ĺ¬Ř`ö˘Ł˙Ć´"žfšűą@ň¶ BJJ7"”Ľď몀Đi ‹endstream endobj 146 0 obj << /Filter /FlateDecode /Length 94 >> stream xÚ32Ö30W0PaCsK…C®B.K Ďȉ&çr9yré‡+Xré{€O_…’˘ŇT.}§gC.}…hCX.O†z†˙ 0XĎ ĂŔĺęÉČ[\wendstream endobj 147 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚµ1 Â@EH!L“#d. ›ÍşŤBŚ` A+ ±RK EÁBb޶GÉR¦R×l´6Ż˙ţPtĚ+îǬƬ5$ťIi;ŚXŹÜf˘$#±aĄI,ěD¶äëĺv$‘¬f,I¤Ľ•í(K~ |[äjż„W˘‚opGĎŕ ŔÄ!´—S‹˘E¦ /‹ňčzů´ĚOľ6x+Ó¸YŰ~ĺŐÎÜuĐ´ńí…ć­éÂŐ`úendstream endobj 148 0 obj << /Filter /FlateDecode /Length 121 >> stream xÚ31Ô35R0P0bc3SS…C®B.# ßÄI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘  bą<]0001;Ëń˙ ˙aX*6T°ý†ú˙˙?Ŕ0—«'W ľťNÚendstream endobj 149 0 obj << /Filter /FlateDecode /Length 228 >> stream xÚmαJÄ@ĆńoŮ"0M^ป'p÷WóSZY ¨Ą ˘`eňh>JáĘ+ŽŚóé5‚E~°;˙Y˛¬Źšc­té_^iÓčC-/’łź+9¸’u'éZs–tî·’ş }{}”´ľ<ŐZŇFoj­nĄŰ(Ę-€~‚Ů€8¶#J^ÎQě0CÜc…0áůîČDĚ_úźžÓÁďř:ßsöNüaçü™r$_΂[-> łŔ,°, %‡s„'älĎ"łČĚńĄ™aAZŇ›M°żČY'Wň Tźc|endstream endobj 150 0 obj << /Filter /FlateDecode /Length 235 >> stream xÚuĐ1NÄ0ЉRXšß`3', ZiY$R AE¨€ ´ŘGóQr„”[¬0Ľ„‰"OĘŚóÇ“ăîČ/Ą•^—Ňź‰÷ňŘń+÷ĹVüÉľóđĚëÝ­ôžÝ%Ęě†+yűxb·ľ>—ŽÝFî:iďyŘ™-­2Č9QµµŐ EëPőE6‚f¤LÍôV»&‘ĆŕđĚÔb&e6‚€§Ńf“őŐŽó‘ňY (yâ/ifU ý°Ĺ_ cBüÔ¨M>Ő‹ý‚¸ź™°yĄ˙€‚޵¸2_ |ĂßÇ›jhendstream endobj 151 0 obj << /Filter /FlateDecode /Length 188 >> stream xڕν Â@ đ+ At-('đ®¶µťkotrˇP?ÁQđĹ_ÄÇč čý‹­łů‘äIŕőĂ+FŠĂ!Ż=Ú“™ş,ń‘o)Ń$ěG$'¦KROůt8oH&ł{$S^z¬V¤SBĢ⊠ŘŔ©ičA«äf°1ë€h‚.p;»Áö`ŻZ  \2đoóŠß›˙Ây™ł54Ö4§ňý`öendstream endobj 152 0 obj << /Filter /FlateDecode /Length 226 >> stream xÚ•ĎżjAđďnaÜ Î ˝s=b!j W¦J!‚`R ěnÍGąG°´8ÜĚśEH:›_1;ödĎyźSpŻĎnČyÎźíÉ9)¦śżÜ_6[šd?Ř9˛oR&[Ěůđ}ü";YL9#;ăeĆ銊ÇŔŚÇćҺ„ĐpQ*Ĺ+j .+xsş7á”xÄ•‘Íç–Üđ‘\ }µrÓţ† ”żř´•R ţ/:tK­¬uéîNTc¨'ŰĽ‰ŤÄ'ňˇjěiT”2®DĄ×‚Ţé+XŃendstream endobj 153 0 obj << /Filter /FlateDecode /Length 101 >> stream xÚ32Ö30W0PaCsc3…C®B.K ×ĉ'çr9yré‡+Xré{ąô=}JŠJSąôťś ąô]˘  bą<]dęţ7Ŕ`= 1S—«'W fp"¸endstream endobj 154 0 obj << /Filter /FlateDecode /Length 140 >> stream xÚ32Ö30W0P0WĐ54S0´P06SH1ä*ä24PAS#¨Tr.—“'—~¸‚ˇ—ľPśKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEA†ˇžá Ö3Č0຀`ý™ PČx€±±ą™ťŤ¨Ň‚ˇ€!ËŐ“+ &,•endstream endobj 155 0 obj << /Filter /FlateDecode /Length 107 >> stream xÚ33Ń3µP0P0U04T03P06TH1ä*ä25 (Ae’sąś<ąôĂLM¸ô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ő˙Aŕü˙‡Îj-Ô\®ž\\~,Üendstream endobj 156 0 obj << /Filter /FlateDecode /Length 209 >> stream xÚ•±‚0†Ź0Üâ#pO`Amd3ALd0ŃÉÁ8©ŁFgúh< ŹŔČ@¨…«Ú´_®íÝýýe4ŤfĐÜ,ą ą¤k”µÓ„íĹĺŽqŠâH2@±5§(Ň˝žďŠxż¦EB§‚3¦ i3 €5C8ZA–›Ŕ/:LĘ^ŐÁ­űpšôXpžŰôkÚF¶­±bIF°Ü2ŐéqžËUśNĐC¨™E>Ş_…ń÷c‹đ+v·dŻóŻĺínÔâ&Ĺ~VźPendstream endobj 157 0 obj << /Filter /FlateDecode /Length 260 >> stream xڭѱJÄ@ŕ? LaZ áć4‰ÜŞ[-ś'BĐĘB¬ÔRPŃÖĚ›ř*ľ‰yË+Äuv˛g!–Bŕ#“ÍĚîżÎďúnŮńÎ;ÇÎóMG4÷Zlyż›ľ\ßѢ§ć‚çžš-SÓźňÓăó-5‹ł#Ö÷%_vÜ^Qżd RPDZT†¸R´öR ĘOÔµ ţ@ů*Ť(ŢAWEÁ],řR‚şIµRę5ú7P­Ń&?”2oĆ(~#FLŘŕgČü5=dF#ďzv˘L;mf–Ä&,—mXJ[°Ěa Ţ#ĺ }Rş:%e-vÁvS˝•Ô=U:îéśľšes–endstream endobj 158 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚ33Ö31V0PaS Ss…C®B.S ßÄI$çr9yré‡+špé{Eąô=}JŠJSąôťś ąô]˘  bą<]ţÁőBýc``üßD@.ý0Ĺ˙L1˙SŚŔĂ?UŹBŮ7@¨`JJ=SüPęŠýę (<öˇ9ĹńPŻ@=ómrüC%hACž  !@ y`> stream xÚuб Â0Đ  ·ôĽ/0­ µ‚ťDŞŁ˘łý4?Ĺ/iLsqŤđ’»INÍĆŞ ś&vŞ)©9ť Ľ˘‹ĺý¶O4¬4Ę©ĺĘFQę5ÝoŹ3Ęjł ­ioK¨k2ýč DŇŔ€§dFLƤ1’(­C8^Q€„ÉĆDđąďɰ|pĂ1ĆŰ˝Ó.ţ"bř˙yŇ€Ś)™gëşk¸×żŕRă?Uź’~endstream endobj 160 0 obj << /Filter /FlateDecode /Length 125 >> stream xÚ33Ň3˛P0P0bSKSs…C®B.SS ßÄI$çr9yré‡+šré{Eąô=}JŠJSąôťś ąô]˘  bą<]ţ˙˙Ďř˙˙?TŠńó bü78) Ŕ¤Żs‘)hčb y.WO®@.!»Ą7endstream endobj 161 0 obj << /Filter /FlateDecode /Length 244 >> stream xÚuŃ?kÂPđ{<0p˛ Ţ'đ%ś˙€ ur(Ávt°ÔŮ€«ę•]ÝĚGČč|˝¨X#yîřÝ=8. [~›< 8˘€:˝ű¸Ä°ËµW”ĹÇ|ýŐ”Â.Ş1wQĹĎôőąú@ŐŹjHŻ>yoÉŕçŁ1 Ă˝¸ 8hFăx‡]Ę*ń›1ć•řá8§ľyşŘTBź¤,a Pł —Ŕ“M ő2Ü< ś fepŇ\$ŔIÂÖ5+zŰG4÷V¸Y5D NZ@fWđí¤'c´ÔŇÇýoĘŔQŚü¦Â!endstream endobj 162 0 obj << /Filter /FlateDecode /Length 243 >> stream xÚUĐżJÄ@đ/.0…űfźŔMNÖ?ŤóSge!VjiˇhkRů\AKÁTÖ©$EŘuwöŠM1üřf`Šď`ą·<’…ÜwŁŹĄ>”w%=’Ö.>úĂí­jRWRkRçnKŞľĎO/÷¤V›SY’ZËëR7TŻĄµ@fŤµm óŔ¦‡íĽĹĎ0 ŕ{dľ¦ĂĽŰÎ=ő4]LŐ3ůȦ€aŇ@b·´liş@ĎT|`Ä“MLjbËŔľĹ4źLő“˙1ÂÄdtFŔśW$®Gś á*Ă.|×Ř™±ťŐtI˙6D†cendstream endobj 163 0 obj << /Filter /FlateDecode /Length 239 >> stream xÚ­‘±‚0†Ď8ÜÂ#ô^@D'ÔDťŚ“::htŤGáxWÚśmš~éÝßöú_LÂyŇxJsNgoô(ň»ĚéŠIŠîžÂÝ5‡ŃM7ô¸?/č&Űń~IźĽ#¦K¶ CµĄ ÔĽ*x1F%¨Ŕ)dBśĂč ń‘Š…¬ŞA«Ńź8çEĹjGîU…Ň(ßNkĽűČ4Ş,— ~ĐjÔ…}Á<ŰCż2[|Žţfa?­-ČŤÖžĆ3ë ń“­oŚ×śČľ}°]Ń=ÂUŠ;ü”K‰Éendstream endobj 164 0 obj << /Filter /FlateDecode /Length 167 >> stream xÚ35Ó35T0P0bS#Ss…C®B.K ßÄI$çr9yré‡+Xré{Eąô=}JŠJSąôťś ąô]˘ĆÄryş(ü‚ ę„úĎŔŔřż,ĘŔ ˙LńSĚ? Ô0Ĺř™adŞT Y;ŞŃPű ¶CÝuP7ČŮ˙ŔÔ ™….ĵ—«'W ŽK€żendstream endobj 165 0 obj << /Filter /FlateDecode /Length 256 >> stream xÚUϱNÄ0 ŕżĘ)Kˇ~h{=îÄB¤ăč€Ó @°!ZŢĚŹ‰čF%PŤsw ˛|Jě8¶ç‹Ăަ’ćt0ŁůŚŽŽé®rŹ®^j°¤EµËÜ>¸U㊠ŐKWśkŘÍ=?˝Ü»buyJz_ÓuEĺŤkÖ?€ĆŚ!ňÎf°l#>Ů3ZÎ;@Î'€ç7Ŕîx ďÉ&Ś&Č–Nm9R0—!ˇG/aEďFD+E$˝Ńڵ˛MX‰ż„^É>a‡-úĆü‘M˙čű=¦×:upÇ´–¤-µiŢ}őčGŚA§Š^{s¦ywÖ¸+÷=ź†#endstream endobj 166 0 obj << /Filter /FlateDecode /Length 150 >> stream xÚ3µÔł4W0P0bSsJ1ä*ä2ńÁ" Fr.—“'—~¸‚©1—ľP”KßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEÁţ?<@Ł0˙g`ÇŔřŹůA büP˘>€©T*L`Ą€)‹`J+ŦF ĹţżHĘ‚Ťârőä äWÎr°endstream endobj 167 0 obj << /Filter /FlateDecode /Length 307 >> stream xÚuŃ1KÄ0ŕW „ăşv8ČűÚôÎbť ç vtrá@ť˙…?'â)Îť¤Cąř’ŁâMHřH^ÂK^Yě/Pá÷ćX.°8ÄŰ\> stream xÚ32Ö30W0P0S06V04W0µPH1ä*ä24PA#SLr.—“'—~¸‚ˇ—ľPKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓE±ąťŹA†A‚Á‚Á€ˇ€!0€Âs ˙ţÁz ´oŕcŕrőä ä-#Şendstream endobj 169 0 obj << /Filter /FlateDecode /Length 204 >> stream xÚmĚ; Â@ŕ . ´Vf. ›Ť´1ŕL!he!Vjiˇ(X›Łĺ({„”Á8룗ĺř‡ůÝéĹQ—Úš’ş}Úi<"ĎČĹ÷f{ŔQ†jĹ{T3ŽQes:ź.{TŁĹ4Ş ­5EĚ&ˇ€ş6äüĄ…°%/_x÷/PAP02gřýÁ0Ҧ–yp&îî¬dBw›:Ś+0đÁüâ}¨ATľyóMŢ6Ó˘5lö–˘.Ë5˛Ŕi†K|¤řTŁendstream endobj 170 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚ31Ó34V0P0RĐ5T01V0µPH1ä*ä21PASKLr.—“'—~¸‚‰—ľPKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEůĂT‚D0Sť$ę00|`ÇŔüąAľů;˙ć ě˙ĺ00ţ* ŕÄ?8Q"ęI&ęPMĘřbŰ˝`Ëßśq ä ă ň ĚŤęţ˙:]ţ—«'W ČckAendstream endobj 171 0 obj << /Filter /FlateDecode /Length 182 >> stream xÚŤÎA ‚`ŕ'?( ‘ś ”ýüşĚ A­ZD«jXÔ.ĚŁyŹŕŇ…Tcu€ßć Ź7f: 5ŹŮđPł™° ř éL¦ %ż—ý‰â”ü MţBbňÓ%_/·#ůńjĆ’&Ľ•ÎŽŇ„ˇZŔ{ČUe5ČTŤĆ©¬Ö-Ő‡W¨6ęŔj@-ĐÉĹóOůŻÓ‰;*`{ú^‰ž[bŕTd7“ý w§”§ÍSZÓ»=endstream endobj 172 0 obj << /Filter /FlateDecode /Length 253 >> stream xÚŐŇ˝NĂ0đT"ÝâGČ˝u˘~n–ú!‘ &ÄŚ Ý7č+őQúíŘ!ĘźłŻń‚ŠÄ„dĺ—‹ťł˙Ęl4¬ć\ńŻjžU<ńsMo4ťHQÇúćé• Ů{žNČ^K™lsĂďź/d·K®É®řˇćꑚgáʱ‰w_ s=Ě˙‡$ p8E €.˘° (±s‡×…˘ŔźÂ4Ž2ěĄ*ȱÓ| ]ąŃ6&âÜ´LčÎpßŕÚ‹Ŕ_ŕ‡ýřËÇIHGN!ÄXĘ>±] łŹ7ž#†Ýfćýß".ŚÎF«?«Ç^Q 3Ň™Ö Ýщb=endstream endobj 173 0 obj << /Filter /FlateDecode /Length 244 >> stream xÚ…żJ1‡gŮ"0M!óş·`D«Ŕy‚[ZY•ZZ(Úşy´}”<•aÇ™ąăôP1|đĺ—?üâéáIO :˘žâ1ĹH=>cTąPc;÷O¸°»ˇŘcw!»á’^_ޱ[^ť‘ŘÝĘ™;Vŕ8Ś‘?dmgPÇj·\R…q :“dÄ„*Á |…Vbn¶;głEó çdö1Öo( Ř÷aăhDB˙cüł!ýD[Áo¬1żEnĄ ౦ä%ięÝînŞ6N:ó\ŇZŰ` æ]H›_ŮI<đ?yë­śendstream endobj 174 0 obj << /Filter /FlateDecode /Length 175 >> stream xÚŐĐ˝ Â0ŕá–>Bď L*)¸j3:9“vtPtnÍGé#8fś—:čŇM‡|ä~ŕŽ3z> stream xÚĄ‘?JĹ@Ć'¤XŘ&GČ\@“HňBŞ…çL!he!ŻRK EëÍŃÖ›ä¦L2Î쮂°áÇîüűľÉ®9o[,±Ćł‹w565>UúU7ż–Řv1ôř˘÷˝.î±étqÍďşčođýíăYűŰK¬tqŔ‡ ËŁîŻ|˘QŃŃ’“CD–F°ł"RcB|&;¦JŞť ŔĚĆeÂ%wąpUľëö3Bú?OűţÄÂ|€ G(ú‚^±'€f ‰]âTHżŘŻđ“|X9éʶĚÜ/O8E.‘> stream xÚ36Ó35Q0Pacc …C®B.# ßÄI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘  bą<]ěţ``ü˙€ů˙0f˙˙+†ÉÔ‚ô€ő’ ä0ü˙ʉaŕ˙˙˙@Ç\®ž\\ÍŮĄ;endstream endobj 177 0 obj << /Filter /FlateDecode /Length 107 >> stream xÚ36Ó35Q0Pac c…C®B.#K ßÄI$çr9yré‡+Yré{Eąô=}JŠJSąôťś ąô]˘  bą<]ě0ü˙ʉ™aăÄ˙„Žą\=ą൉Ăendstream endobj 178 0 obj << /Filter /FlateDecode /Length 232 >> stream xÚíŇ˝jAđ WÓÜ#Ü>·ÔŚ‚WZĄ©LĘ+łŹvŹrŹp!E¶›üçT°+‹ ó›ŹÝ-ĆŮÇvďŢXÓĹqöÁt;ćÍń';ë±j-->xsúŚÇéiNó©Y-×ďśgOŮ‘yÁĚ+ç#CYEI şO$RáxŠ%4DJʤnď«Ň ó˘ŁŘŇ×®U¶¤ HŞ@Yű$߸»Np·â§¤D@Ą(€ţżŘAx^ć §¨ĺ9ěĹE…˙ÇÍŰ„ÂĆip xśóś˙vÚiCendstream endobj 179 0 obj << /Filter /FlateDecode /Length 184 >> stream xÚíѱ‚@ ŕ& &]xúŢÜHLtr0Nęč ŃUy´{ጠ„zwŔˇÍ×6˙Ôd4”’™JBG´ń„qlfiG{Ř1+P¬)ŽQĚÍE± Ëůz@‘-§˘Či’Üb‘¤‚µ©ŇÁc®|ćÚ!P÷Ćái ŕ±®!`{čř.˙TĽĘV6ߡýAÓő_°yÍŔ4Ő8+p…o âřšendstream endobj 180 0 obj << /Filter /FlateDecode /Length 231 >> stream xÚµ‘±‚0†kHnáĽĐ‚±0’ &2čä`śÔŃAŁ3<šŹÂ#02Î^KL%!_sý{˝ţ¬ćI‚!.qaĽ@ĄđÁCT±Ý9ß +@P% 7ş ˛Řâóńş‚Ěv+Ś@ćxŚ0> stream xÚÍ’żNĂ@ Ć]u¨ä…G¨_.!MB§HĄ•š ¦02€čś<Ź’GČx•ŞŰąF:ˇ.§źľóůĎçË“«č†"Jčň:ˇlN錞c|Ă,5˘<WOݏ(Ńm(KŃ­EGWŢŃÇűîÝâţ–btKÚĆ=bą$(“#ýŃĂ!@5@÷ŠřoJ ˙§4ö{®aäÁłĹŚňßëŽfJ®`o}4Ľ‘.lO­%ŢwŁ‹m_…mt§˘e4](z†`_ëTŔU‰řµ` endstream endobj 182 0 obj << /Filter /FlateDecode /Length 266 >> stream xÚÍŃ˝NĂ0đ‹2DşĹŹ{pBóˇN–J‘Č€D§¨02€čśĽŻ”7ŕň-[+U9.¶«*SŐ%úéě;Ű˙ăëlD etu3˘2ˇ<Ł—߱ȥšPYúĄç7śT¨çTä¨ďĄŽşz ĎŹĺ+ęÉă-Ą¨§ô”R˛ŔjJ!7 0Ľ†xóŚ bf Ĺ­ięfŘŞPď ě¤xÁŹ fŘ BîdYqë  iĺ`ËćurĎóă?3ýźďŠđ!ŘXĚ>1źˇ “}ĽűŔŁ•íčA}űő»˙ŐŰa˛sc!C:‡Ý9ŕOżD(f§SŔ» gř ‘düendstream endobj 183 0 obj << /Filter /FlateDecode /Length 169 >> stream xÚŐĎ;Â0 ĐtőŇ#Ô' Ť’VbŞTŠD$02€`nŹĆQz„T d¨jś20őXö“üYśé™žcŠš+ă4xRp“s?¶aqĽ@iAîĐä W<i×xż=Î ËÍČ ÷ ÓŘ Eá˘^ą6ˇ–­É±Câ‰:_ř:WóŃ«}ßÍO_ /h‰ Ćmú ýIž™–¶đj^¤ďendstream endobj 184 0 obj << /Filter /FlateDecode /Length 259 >> stream xÚ]Đ1NĂ@ĐĄ°4ľ;ŰŠŤBĄ$\ ‘ŠQ%Ú¬ŹćŁě\¦°vY)˘yŇî·çŹÝT—ëk.ąć‹Šë57 żUôIőJ/Kn®ćäő6O\ݍ¸×k*şţţúy§bóxË[~®¸|ˇnËXĘp8™ÎŮë…HDŃFä#ň°Ô々Ú~Ŕţ¨¨7ö'ÉQČ”´^;LKZ+45qj@.dętÜÇv“ů!¤¸Ç"iíĐÄĚôehÖ”ôÁjŰ]˙dV絳˝ÍSuž‡č ±ýő?h©›ÓęgĺcfKxýşëhGżÁ•ˇZendstream endobj 185 0 obj << /Filter /FlateDecode /Length 186 >> stream xÚ35Ô34S0P0RĐ5T01Q07SH1ä*ä21 (›Cd’sąś<ąôĂL ąô=€Â\úžľ %EĄ©\úNÎ @Q…h žX.O†ŔOţÁN2bĚH$;É&ĺÁ¤=¬“˙A$3ä˙˙˙˙?†˙8H¨úANň7PJĘĂç‚”˙Ç`$˙Hţ˙ ŔŘ`˙đ(Čţß˙ ýß E` qązrr:é“pendstream endobj 186 0 obj << /Filter /FlateDecode /Length 187 >> stream xÚíŃ1 Â@Đ  Óä™ čfŃlě1‚[ZY•ZZ(ZÇÎkŮyŰt¦Ž»‰… а{üáĂŔŹ»ť°O!ő¨­(VőhĄp‹ZŰ0¤(j.Ë ¦匴F9˛1J3¦ýî°F™N¤Pf4W.ĐdI ŕńKü#ZX€řă+üĎŢ8äŻČ’ ŕö„wĺÂ6î .n źŹÁÉÁNĂő<sUĂv‹öÁ848Ĺ”Ěđnendstream endobj 187 0 obj << /Filter /FlateDecode /Length 270 >> stream xÚ…Ź±N…@E‡PLĂ'ě~ >ÄX‘<ź‰&ZY+µ´Đh+ü™| ź€ÝK$\gfŃX)Éć°{÷žúä ÚřÂĘŞŹýŃĆß—üÄu%űB·úáî‘·-‡k_WÎeʡ˝đ/ĎŻ¶—§ľä°ó7Ą/nąÝySĚ˙‘ş…Čí‰壼Ł'7¬ěe†"Ę0Ň›0ĹDr„ě“92•ăDÓIŮ-٨l‘ÎčđŢ+s@!ËĘŮÂb4ĐHëÜţfoöqŽ!ţ˙C»?ů„őI?b`6ĹŔ|ŚtC t} lL™D2r1uIU'‘TuIk*’ÖT%5P%5°­!Ä.>“ĎZľâ/1˘¸ľendstream endobj 188 0 obj << /Filter /FlateDecode /Length 310 >> stream xÚ…Đ1NĂ@б\XÚĆGđ\ś8ÁM,… á * D” č"ÖTą–o+řlé"ň0łłDQXOš]yţţňôx:ÁNđ¨bYâÉĆćŮ”OG8›…Łű'ł¨M~eaň ž›ĽľÄ×—·G“/®Îplň%ŢŽqtgę%Qm˙3˘ "Vě–ĺĎŠ<łźł•čXú1f3j îÔ„MĹVl!e±y‹ şo+ =Ěď¬Zy·Çę˝ĂÎČ[‘ÄcoFG\{SZ·ęƛЦQ?䍉`߆µ™=m˙»•;4ëMŰ?l½ťţś};Y«íTťjť¶Ä­őj´Ó©Ú őIP×Z§ël§klku釾2#}UJ.´҆RĚym®ÍaÉ˝ďendstream endobj 189 0 obj << /Filter /FlateDecode /Length 253 >> stream xÚµ‘1nÂ@Eą°4Ťo€çÄ^Lh°D@ÂT(U’2Q¨ÍŃ8JŽ@é"Âüoł QRĽb53»;ďżş7´Ô\ßz™łÁŔF©}:Ý*NiSpľřń­“B“µeN“9+š űýŮ}i2YľÎSŰ`î]‹©‰HŚÁżH§ Ž"áźHâHľ)Q®Á -h @XµDhŹ Ús‚‘’`¬né`”W„\]W·äžcKů<[?ŢűčM˙'˙Çűż_ďĹ=ýÎÜ?¸ř ŢQă‹ŢčŹé“^é×»¦wúgMĚ…ů0'ćĹÜsdžM®]ťşŇ3č’¤6endstream endobj 190 0 obj << /Filter /FlateDecode /Length 247 >> stream xÚµ‘˝ŠÂP…o°L“GČĽ€&×ŕ.VŔ‚[m!VjąĹжĆGËŁř–!zŽ×‹‹¸Ą„Ż¸Ě ™9_Ďv쇦ú©íĚjÖ×~Ş++ż‚WŠĎví˝¶ü‘A!É·fV’ ’SÝnvkIłˇâ=Ň9ĆRŚôdŚ9=ČA|0&ŞŔŃĹŠA IIĐ”WŽŤ1#‚ć` EGpń`đFĺ(ÉńA~zâě=ők"ŇĽú˙úťüŽĎ»˙˝«¬7ăţŕ†ËÄgÄĽócŽĚ“ą2_ź5s/ťú úˇ'ú˘7úËď>é•~e\Č—\ÄE“endstream endobj 191 0 obj << /Filter /FlateDecode /Length 137 >> stream xÚ33Ő37W0P04¦ć ć )†\…\&f  ,“śËĺäÉĄ®`bĆĄďćŇ÷ôU()*MĺŇw pV0äŇwQ6T0ĺňtQ```c;0ůD0I~0Y"Ů˙Ić˙ ň?&ů¤ćDĺ(I˛ô˙˙ŕ"ą\=ąąVI˘”endstream endobj 192 0 obj << /Filter /FlateDecode /Length 301 >> stream xÚ}ŃMJĹ0ŕ)Y˛é’Ř–G_]x>Á.]ąWęŇ…˘ëôh=JŽeĄăü? ÚŻif¦“tßź ChĂžŻ6 §á±s/®ßŃ\¦ĽđđěŁknCżsÍ%˝uÍxŢ^ßź\s¸>ťkŽá® í˝ŹŔo@ŁB,DŤ¸'€DdZš"-š,-ÚB/6¨3"x‰š˘äç”™ś®—ÓĘ®k‰í ËpŢ7q|Ě$păFúćšżČ »ůdíL™@ÚAvüZ´HĄŮFÓ¬¦YM«5Ţk|,ZdÖěIłeb4Đj`Môäłg!@ŤTt¶«`[ČBÍ».ŕA8ă˛EţőËwĚ•b«ÔŠW˘’üÉü'îbt7î}tű”endstream endobj 193 0 obj << /Filter /FlateDecode /Length 228 >> stream xÚ•Ň= đ×t y G('«Ćv3ń#±‰NĆI4:—Łő(ÁŃIÓľú¤H~…ţiżŐŤE[ôLK;¶nc<`’ďgŹŘěqˇ\Š$A95˝(ł™8Ď;”ĂůHÄ(Çbe–Yc6ş,wh*ŕúŔ´.9)"1RH HP+wh ľyĹ›(¸/*±†řPč#qRDŇĄLůSőÜ×ő¸c_˙˙˝źčć“˝®˛ŹéPčŇĺ[Ě+^« —& ĘIş ¬)J˘˘t*Jl)sĹŞJ¶SŕN2\ŕîŔU\endstream endobj 194 0 obj << /Filter /FlateDecode /Length 209 >> stream xÚł°Ôł0U0P0b c #…C®B.s ßÄI$çr9yré‡+[pé{Eąô=}JŠJSąôťś ąô]˘  bą<]ţÁë˙8ëśőżÎJóű˙ ,fn0‹¤ÂŞ˙cŮ5CXň˙@Y ÂbGb}ŔÂúe1ceýˇ ź˝ěH,ln~÷ĺ ź#BBđPŚş`pÎb€±~ŔY 0SFYä± Iť—«'W TŰ4#endstream endobj 195 0 obj << /Filter /FlateDecode /Length 233 >> stream xÚ퓱 Â@ †S:Y|„ćô]Ş‚ťÄIÝÄöŃú(>BGńLÓZD''—|üą˙r7śŃ¦©;¤©M CA‡ş>­ î0đYÔÔmťŐĂŐśŐeTŃ„űăU8A5¤…!˝ÄhH–ăŕpÉľe¨Ű ä§P±ţóď¸Vr˙…{ÂŮźyą%źŢرWáŰ K¶ąŽp,ěŠ+ľçą&űÂuaĎJNE±IŢM şś4y0犉%®Ţ­ŕŘ^žĂů ŽâAlćH 4Č—¬6eOć†E8Ă`ň|endstream endobj 196 0 obj << /Filter /FlateDecode /Length 270 >> stream xÚ•‘±JÄ@†'¤LsʰóšL® ś'BĐĘB¬> stream xÚÝ‘=NĂ@FÇJišÁsX[NŚ©"ĺGÂTPR€ ¶;®•ä 9BJGZí0;Ţ J¨Řęifw<~ßEqžU”QAg9•—Tôă –)fTŤűÎĂ3Îj4wTNĐ\IM}MoŻďOhf7sĘŃ,h•SvŹő‚`Úć_Ŕ ühv= ™{H™× łďńžˇ±ÁBĘ [rëˇ%k‰TťďË3¶ü·š.‚ 0=€;  ý Úż€“űv>ň;ö»ŐbC _Ć\”Éő¶Ařf #Ťŕc§—č,'·4/+;h‚ťĽq1h¸¬ń?7p%endstream endobj 198 0 obj << /Filter /FlateDecode /Length 243 >> stream xÚµ±NĂ0†/ę`é?BîŔ‰dSş`©‰ Heę€hÇ XI-ŹÂ#dĚ`ĺ¸s‚ştĹËgý÷Űżî·×~IyşŞ)x ö5ľŁ_‰XQ¸™&oG\7čväWčEF×<ŃçÇ×Ýz{O5ş ˝ÔT˝bł!€˙€śČŁ‚™Oޱޖ!2J`@;€÷PŽPČ<˛;…‘GgČ3E9cĚą*lĘ0´9Útüř / Îŕ Ýěi†Őnʲm'ľ©ż;)¤ř–),ĺbČß^‹ěJq™©Ý‚§®ŁzµlŃđˇÁgüÍF‹ľendstream endobj 199 0 obj << /Filter /FlateDecode /Length 110 >> stream xÚ32×3°P0P0b#S3K…C®B.#C ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. ŚţĂűć? ŚC 1˙cř˙˙qązrrŹp^Úendstream endobj 200 0 obj << /Filter /FlateDecode /Length 162 >> stream xÚÍË1 Â@…á·¤Lˇ° čfqCĘ@Śŕ‚Vb--+'GË‘<@Čş!Xč lľâý3©ť™ŚžóÔpjŘZ>şíÇ„m:”ęL…#˝c›‘^…™´[óíz?‘.6 6¤KŢNäJV- đ-r˙eÜByDˇz 7˙«˙U}Ä`‡(řD,uxIé0nŇ·WR héhKo©b“endstream endobj 201 0 obj << /Filter /FlateDecode /Length 248 >> stream xÚeĐżJÄ@đo \`^›BĽyÝÍ] ç ¦´˛á@-íÄŰG˛´ĚŁäR^w˘ůĂŮüŠ™]ľ™9ŽŽâ„ Oůpj8>ĺxĆ˝PS5śĚţZ÷O´LIßpśľpuŇé%ż˝ľ?’^^ťqDzĹ·›;JW\×…ŞËˇ~ lrŻ&V‰÷g¸îľ{„ť'Ŕ´N2¬;säŔ8GÖęĘvn=§·őĐŞĘQoĺb]pĐ» ~‹‹Ż^¶ă8ëőí®Ř:úg00ěś7~Ęžîż®JTĄÄŮ Ďľüś4s”M^!ŇyJ×ô[ÍX'endstream endobj 202 0 obj << /Filter /FlateDecode /Length 136 >> stream xÚ32×3°P0P°PĐ5´T02P04PH1ä*ä24Š(YBĄ’sąś<ąôĂ ąô=€â\úžľ %EĄ©\úNÎ @Q…h ¦X.O9†ú†˙ ˙ᬠ—Ŕ€ ăĆćfv6> † $—«'W ÷ '®endstream endobj 203 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚ˝˝ ÂP FżŇˇĄŹĐĽ€ŢVn«“‚?`A'qRGE7Áúf}”>BÇŚނŠč*3$|9ş×î†ěłćV‡uČQÄŰ€¤}®+ę5“Íž†1©%kźÔTڤ⟎ç©á|Ä©1Żö׏¨8Ux·čă”Ŕ*ŕ%V7±38©“ÂÎ \Aî&°rOP ĺdeyÜżˇ>Xý ?c\%éý#řëŁćË'q¶(IŤŁ©fÔ‰µNšÄ´ …)endstream endobj 204 0 obj << /Filter /FlateDecode /Length 131 >> stream xÚ3±Đ37U0P°bC33…C®B.c# ßÄI$çr9yré‡+qé{Eąô=}JŠJSąôťś ąô]˘  bą<] >00013Ëń˙ ˙Ař9łůĂ ó˙úóCý˙˙˙aËŐ“+ Ět^@endstream endobj 205 0 obj << /Filter /FlateDecode /Length 259 >> stream xÚ]ĐÁJ…@ĆńOf!"·."ç ĺÚÍE0p»A.‚Zµ ¨vµ ôŃ|ÁĄ‹ËťÎgH0?ń?p´¬NÎNmnąĘŇ®×öąwYUşĎąĺ‹§7ŮÔâîěŞwĄ§âękűůńő"nssa q[{_ŘüAę­…ŮČB´aD4%;>Ú#îp¨§Ýŕ{%*eĚdl”é§W”]čH˙‹ůOË·ž¦…dfä 3Âױt˘K҇óFĽoćűĽłMŘfl=łoÂ,"†EĚ"pLΉ~WІh–FšĄFł*Ö4×€& !Ś3ž´DWţËZnĺÎvjendstream endobj 206 0 obj << /Filter /FlateDecode /Length 238 >> stream xڭбJÄ@ŕ?ěÂ4y1󺉗‹[8O0… •…‚Z *Úš<Ú>Ę=BĘKÖD¸Ňć+f™™¶ö‡Ç+.yĹG\×Ü4üPŃ -˝Knü÷Ëý­;r׼ôäÎĄL®»ŕ·×÷GrëËS®Čmř¦âň–ş ÁŘ`#úÁ¦” ĚJT&e« 0m´ă?H‚M¦ČFŹ3âC‚ …P J°@¤#ßJ“˙2 ‹_â.N”^‘v2%5+w:ů‹gY9–ş×Cbě)ű@;ä@Żůf,B‘MĄ—B‘~2ŃYGWô îřeßendstream endobj 207 0 obj << /Filter /FlateDecode /Length 171 >> stream xÚĺĚ1 Â@Đ [~ˇň/ »1F“JL!he!Vj§ ˘uöh%G°L˛î‚……7pŠWĚŔÄj RVsČŁÇ BşRäJœϲ?SVÜp”’\Řšd±äűíq$™­f’Ěy˛ÚQ‘3şĆ´_@ x6˙ÂÔQj‹yţÂka´–D DŤ~Ťü:čVđhŞt—Ť%¨š´¦7ĄTmendstream endobj 208 0 obj << /Filter /FlateDecode /Length 290 >> stream xÚĺŃ˝JÄ@đYR¦ÉyMĚť˛pž` A+ ±şł´P´”äŢ,÷&ń ´ËAȸł›„ĂÏΰżÝ%“ͦ‡GÇ”RFűš¦štšŇRăN2»šÚąö{‹{śĺ\Ó$Ăä\Ö1É/čéńů“Ůĺ)Ůůśn4Ą·Ď ܵç0ťCţ v ţ-¸ô¸ń0ÜypiV‚ …p-PŻ‚¸ŘLđ"(J€Ëv×W—ŔU+ov®Ś‡-ă“ßúcDâőgUŹâ7({đ_`üú7'4»¨ż ÁlĂ…éâm¶sކH/@םb€±'۸^U Ţ¶b°ćĘUŚVl˙A1J·1×vĎŢ€g9^á[9×^endstream endobj 209 0 obj << /Filter /FlateDecode /Length 267 >> stream xÚť‘±J1†'lq0…űŢĽ€f̰pžŕ‚VbĄ–Š‚]ňhy”}„-Ż86ÎL˘ś‡• Ů/Ěü;“üq«Ó5äč¤%×QwFO-ľ˘kHfçrćń×Ú;r Ú+Ł®éýíăíúć‚Z´şo©yŔaCŐ 2–i¤´ĺŻ™5şŔ€z„>‚¬%k<&ršĄ,«¶`vŚťěd+q3Ëß’1«^+ü ô\úoxE<@ŘG*Đq ÷ů/|AüýoŚŮ¸=¨×,¨˘8U(`‡Ř´ fA-©‘pśűžçÚźąÚ¤PŤjí"ę{mś¤ÔIš€‘ă倷řYRŽendstream endobj 210 0 obj << /Filter /FlateDecode /Length 351 >> stream xÚ­‘ÍJÄ0ǧäČĄŹĽ€¶‹µ‹§Âş‚=zň ‚ =řu“mÁëŁärě!4ÎLRuD¶„™ÉĚüg¦^îW¦4•Ů;(M}hęĘÜ-ÔŁŞKC˙Q•\·jŐŞâŇÔĄ*NŃ®ŠöĚĽ<˝Ţ«bu~lŞX›«…)ŻU»6Ŕ_‡GzahBź ‚Őď„—ă›t ]ć2 ş‡¦G6Da)…ĆhrűĹĚcf÷EAż1ť-Ű?pλëŰŐł«÷łî I}Ňš6ÄĄŁP€gOén ŔâÜ’ÝŮ'ű+ít‰c˘Ź„036u! č’ˇAŇMÄ"9Ń%űČ} |Hł=¤X9ŃZ±H vą÷]Ď˝ămłE=L‰QVţgÎq)ĎśŻďRţT7éŘD]ŕăn˛¤Çó c»Ć’|´M É'bŰ<Î%řŞNZuˇ>ÚvÔendstream endobj 211 0 obj << /Filter /FlateDecode /Length 142 >> stream xÚ36×31R0P0bcCKS…C®B.#ßÄ1’sąś<ąôĂŚLąô=€˘\úžľ %EĄ©\úNÎ †\ú. ц ±\ž.  Ś˙˙30°˙oŔŠAr 5 µTě ü@;ţŁaf f€áú!Ž˙``ü˙čŻ˙ ČËŐ“+ > stream xÚ36×31R0P0bc#C…C®B.#3 €’JÎĺrňäŇW02ăŇ÷ sé{ú*”•¦ré;8+ré»(D*Äryş(0°70đ˙o`řʆ™†ëG1Őń˙ Ś˙Ăú˙dĚĺęÉȸ§‰ôendstream endobj 213 0 obj << /Filter /FlateDecode /Length 252 >> stream xÚíұJ1Đ;¤ĽÂůÁ|IÜeŃj`]Á)­,APKAEÁnćÓ"vÖů„”[ ű|Ď]°\k±äÜ„[Ý÷vGÜXN n2rבî)M‚Z/W·4mɟ˟1ůc‰É·'îńáé†üôôĐEň37Ź.\P;s0 ]*îËÉđÔ\ćT3Ť&‚ś0ţĆ3vr•ŃőŠ‚şHM“¤ĺ%Á.,äč^{ŘaK uÝ`†m)4ď‚ĺľ`±BĄ°ŠOĹÝŠË5䀳¶Š"mDVô‘řÇ_ĹĎ—ĘBŚ.¤fY/Ă«©ó/AG-ťŃ!A Bendstream endobj 214 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚíѡÂ0ŕ[*–śŮ#pO@·@ ¨%0&H@! $¸ńh%Ř#L"Çu€…D´ůţ¶—KzŤzµŮ˘ę˛™Í"\˘1’CÝĹtíőŚAÝ“SÔiźÖ«Íu{СuBă ¦ ˛ĺŕłU|0Ű€ů‰Ř–ŘB%/Q@PxĽ·ŕ_ĺQvŘďʲ#€rO‚ű ^‰Ëç7\©ëꑆýăgpÓ÷x'A~^ÉĽ™ąP˛Ů/ŔnŠC|U¸ýendstream endobj 215 0 obj << /Filter /FlateDecode /Length 249 >> stream xÚ­‘±NĂ@ †}ęÉK!~¸5Ç©©*ÁÔ1#ćÜŁőQú3T9l× ęČÝIßÉľü±‡Űë5•TÓUEá†Âš^+üŔ:p°¤Pź3/ď¸éĐď©č·Fßíčëóű ýćáŽ*ô-=UT>c×€Kxĺiôi$Ţ«Š@v”#W@Áťř!ç'=rĺ4ŕ8 E\)™ćGCÎ †B1Š:‹6ŠÓ˝bęĄ:wZąK˙Š??˛"XÖi=Ěť1w«˝fůbpęYś4?Í]óšeä[›ă©ÄßŮÄt~xßá#ţ°´”đendstream endobj 216 0 obj << /Filter /FlateDecode /Length 288 >> stream xÚŐѱNĂ0Đ«2DşĄźűHmÚN–J‘Č€SÄÔ22€`%ů4ŁŚý*źŕ1CÔĂg[!uBbňîbźť»Éčt:ŁŚFtr6ĄIFĹ9­s|ÂblłÍňđiőóÓ%cLŻlÓňš^ž_0ťß\Ťt—SvŹĺ‚ ŇPiYÇÜY0ë„ŮŁÖ-$F°i nüQC$««­Ťö‚ťl±ŹréÚ˘•ČîWFĐ$Ť\E‡aë×}!î~"Ú÷bŔÇ ö€?Äqë˙Á®·®Q®uć{3}>t^ ăuCaĘÎź jëŹeG)…Am´«ęÝř˘JżIăŠe­Ĺ[W.Ü翢jŘ„7ýĽ,ń?n·Ůeendstream endobj 217 0 obj << /Filter /FlateDecode /Length 303 >> stream xÚŐѱJÄ@Đ )SĐËü€&{šś‚şpž` á¬,ÄJ--m.ńĎâźÄ?Ý!{3»Qä@Áf÷íf™™ĚŚÔ–Úˇ„RÚîŇHQ¶M× ď0Ků6ˇ=ŐşşĹqŽń9e)Ć'|Źq~J÷Ź7ʧGÄç ](J.1źÖxĐ4Ú´0J[řĄ _2˘ĽZ k€FPTO3öžáĂş c :†łđ´«łńÂp`÷đ śÁĆĐß 9 ©ëĎđ›Âôę´Mđ–»aĺ/wěł™~Ű·×ö9ęúÎoČ"ł8\âł@súwű°ďÍN¦ŕUŔÂgĘ™Ť…™;D¦vŕń:ôţ[ř¦G`Ç'»qZ<Îń –ÉÄendstream endobj 218 0 obj << /Filter /FlateDecode /Length 185 >> stream xÚÝĎ? ÂP đŻ,d°«ĐśŔ×ÚVt*řě čä ‚ Ž‚ŠÎŻGëQzÇNĆ÷:x‡üČ—@ iż—Drj*ń ćCDJb“Cíb˘qNjÍILjn¦¤ňß®÷#©ńr©)oĚ™-ĺS†݆/ž–ÂXĄSeF·Ô•+^ˇ+kŰŞ»Ťd%ôA˘č3đv×X}Xţ´řĹ~äČö"ő7i–ÓŠ^¤Ds.endstream endobj 219 0 obj << /Filter /FlateDecode /Length 281 >> stream xÚuĐ1NÄ0ĐĄäĆGđ\’o$"-‹D $¨(PR€ [mr®â›#¸Lvq v š'Ů3ţ3Éęě´n¨"O'5ůsj<=×ćÍx/—5«ĄňôjÖť)ďÉ{S^˵)»úx˙|1ĺúö’jSn衦ęŃt8ä€ĺ©zŢ[dŚö yDńŤbDΰtÁ‰=Z¨b‹ťč°M΢ýÇűyqPűˇ©“Újë•e^Ś5X*ł>ěYëŽYžĚ:#•őB´IjĆ!ĄMlGŐ-ƨéÉâH]$?r>Pçäcš6ňźA§Ů ÓěÖ~˘ţĄI"v¶ČfD7¸(ź0ćşl@/]ćŞ3wć×„Śśendstream endobj 220 0 obj << /Filter /FlateDecode /Length 191 >> stream xÚ35Ň31T0P0RĐ5T01U°°PH1ä*ä21 (XXBd’sąś<ąôĂLڏô=€Â\úžľ %EĄ©\úNÎ †\ú. Ń@bą<] @€ň>’dF"Ů‘H~$RLÚÉz0ůD2Iţ˙@ŔđD1a’ڍL˛˙``n@'Ů˙0°3€H~`Ľücŕ1(¸l@A˙ŕ(ŔáÍţ˙8¸\=ąą~@‡Řendstream endobj 221 0 obj << /Filter /FlateDecode /Length 203 >> stream xÚíŇżAđďr Éî$7/ŔŢĆeQIüI\!ˇR ĄˇćŃîQ<‚ReĚž ŤV÷Űův¶ů¶™Ö[mN8ĺšĺ¦e×॥-9§Ă„]úHkęfd¦ě™ˇŽÉd#Ţď+2ÝqŹ-™>Ď,'sĘúŚ0eQÄ"”ďüĺ˛ÇÜźŢŃńţń3‚Ď?Ł(%V” śĘUŹč… Đ’“n(6áÁY4nú+|×<>čČ­h‘\Đ şEŚ&tj8­Úendstream endobj 222 0 obj << /Filter /FlateDecode /Length 335 >> stream xÚ}ŇĎKĂ0đWz(ä°ýĹĺĐţ@ęĂÂś`‚ž<'ő(LQđ–Ŕţ±Üöoô/Đw(‹/Éëh3&ô‘ORHż}ÉEv–ťó”çü4źńźśżděť®¦|–Ń«ç7¶¨XňŔ‹‚%7¸Î’ę–~|˝˛dqwĹqľäŹOźXµäZk ˇÖęz şĹŞe & '˛©N°ĆM±ŮpŽLŔĎŔ7Vh°2ŘzŤeĘB†—C(,JXÔĚ:ňĐŘČ6tě°%`Ö©‡ÖF¶ˇWC`ÖÚv‘1«KÚÇš Ö’°!”đńKütŘĆíQŘN6GŃ%íAů÷>"đ1î0:@|€y×eĽfx~®ąx ÷¨}P@QS@ˇÜéC))NIGŚĂ%ĹSÔ¦HS Ýýą ‘]WěžýĚ%™Oendstream endobj 223 0 obj << /Filter /FlateDecode /Length 259 >> stream xÚíÓÁJ1ĐYö°0óĹťĐM´H b Vp=y(žÔŁEÁ›ý´~J?!Ç–Ť™Ię,ôęˇ ’™ŔŽŃćT_&K'g49§‰ĄgohÇqS“Ń9őôŠÓ›˛clny›vNďź/ŘLď®É`3Ł…!ýíŚBü Áď%Bŕ:ˇŰÄÔF¨Ö»X­v±\xŕże©2ůwîö°JTŹ˝đďţĂQ\E§ËVĆkÉńk çđ˝úĄck!ÇRRµĎ3–{WrVú©Óąň•PĘĄNůâ"˝śĺUź+Ćs!Ď=7ş-]đ[–©OĽińÓ}Ďendstream endobj 224 0 obj << /Filter /FlateDecode /Length 287 >> stream xÚ•Ń˝NĂ0ŕ‹> stream xÚĺÓ»JÄ@ŕ¶8MŢŔśĐĚŔŢ„°ë ¦´˛­VK E[7Źe°°ô $Ź2EČ8gfö‚A´ł0đÍ%sů'™ ʦÇ$iH‡Š&’””tŁđÇ#[ËeĺŰVw8Ď1˝˘ńÓ3®Ç4?§Ç‡§[Lç'dË şV$—/%¸Kó DŔşýżásĐĄ0­GbŚÇڷ鲸fĽV Ć[÷ÖďöŃ1>8Q†«.ěÝ„y4żšT1ŁbÔ<˘[϶‡. ęĂ| ءř üĽÂşŻ;í‡ Úý \tő~Űś9ů„“ŮAƧÇrŕ×:ösÂLnŮ˙ĘťrŐnČŕ™7ĂІűÂbÓ„/ǵŕiŽ—ř »ĆËHendstream endobj 226 0 obj << /Filter /FlateDecode /Length1 1394 /Length2 6009 /Length3 0 /Length 6960 >> stream xÚŤwXÓ}×?‚ i¦7ÍF‰twH‹H¬€ÁŘĆ6‰ŃÝJw)©Ň " ¤€ ©")!ˇ˘tĽ3îçyîç˙ż®÷˝v]Ű÷śó9ő=źó»~ă˝zËTDŽ"41h‚X$ T30Đ€ „($ŕĺ5CPżő^ ŹÄ e˙ˇ†C@$ť:„@`Đ@Ý{( X––߀â ĐÍżś,P⎄ Dş4ŕUĂ`˝pHG')ĎßG ?Lľyó†đ/w Š+‡„AĐ@Á áJĘ €¦AđúG~y'++&ćáá! qĹ‹bpŽŠÂ@$Á h‚Ŕ#pî8đgË@C+âOk˘^ ™˙Ű`Šq x@p IBÂh<ÉĺŽŔI٦:ú@#,ý¬˙ üs9@°(ř_áţx˙ „D˙r†Ŕ`W,í…D;(ĐHS_”ŕIBĐđź@ Ź!ůCÜ!HJü*ÔT1BHţéĂ!±Ľ(‰úŮŁŘĎ0¤kÖ@ĂŐ0®®4řYź:‡€‘îÝKěĎp]Đ´÷ß’ wřŮüVĚŤt»‡ĐQ˙!©˙Ö9"@)Ě )  đ„9‰ýL`ć…Eü2‚ŞI=řzc1X © „/ŇAúxă!î wáëýź†J0GÂ@(‰ü;:IŤpř-“ćŹCz­A$ú źźťlH cĐ(ŻĂŤXĚTS÷Žą…Đź–˙eTUĹx˝E$Ä"âR $)ĽA:řţ3Î-ňO˙á«vŔűY/é˘ţ®Ůý ř˙lđźÁ 1$ę"€ü˙fú]Fú˙źůţËĺ˙GóźQţW¦˙wEš÷P¨_vţ߀˙ÇqE˘Ľţ HÔ˝G ­†´ č˙†Z"~ﮎĽçúßV„´*hGĄEŔ’˘ Éßz$^鉀ßB`Nżió[oţsáPH4âŹüů!y@˙e#mĚ…ôÁ“¸ůŰÁ“VŽđk?ei©ţY‡†˙Ü>q)i ‡xHĂ'IR@o0iMáĎ_ěЉ˘1’ Ôł/Đü´¸P ‹ü©ü#.ěGJü‹¤¤ËżVđDŔď'00ąçę–ýJv‘ĄAyĘŤ´ýŰâ"¶Ô„.Ť»ůÓě¬I˝'šď;Áš¶Îm†Şnű93o·Ľk¸k˝$wE¸5Wąˇq§»gĆ˝÷8®M\j${h™˘Ę%[Śë"»ĹM×NŁwě2ĺ˝äóWuÇ»D>¦Íşˇ-†­í•%YúĚ7->4!4A{Í–¤¶YţĘýčú x!ćndŞaĆ5=ůÖ@‚GÝ\O_ď}'ç‹č@]÷ Ąo,&Ký(ëi$E«§bźĂ[íšCkn3ŤÝÎ&˝Q~bBG´‹‹ĚkŔŚ4şi”Ě"ńöT¶z;73[űŞĹŢT Ľţj}€\ňQľYáĄ÷R˛Â ‡îL×az§&ä„C—ĎÝŽ©` ^p^)0ťőčR„¸uCĄ—Ŕ±:‡Mzt|ˇJ7ă{ń kMőňßśő~ÜĺqiDíÎđÚm»ňŘĐĄß\é Î[şâŃď÷g›:H3şö~ČČƄ΋Ľ?žŁ9é 5{>Đâ@öńK^™w¤ţČ:_D{; ďvöÍ…ľ˘ąĂ˝ŻułŠJŚ“‹µfyďz1_M<2Fçeá\™¦ €Ř&Mdó 'Ç fŻŁ/Ť}Cň ˓·7_»°éF™s8ômgXN¨ “9kj2z;Ůë |ľVÜ9Ůȶe>¸™Ž·ýüóěćĐ9oűX±ĽŇŠÇj®Ů%Đ o©ďCě‡+đOŢNĚ’ŠQ Qçl=Ô‹ľ)#Ś·á;)Ö H¸Ő°íśr[ť5öÉőV_0çQĂBW^+Ëíw¨‘ÝD¬ňiZóGý=;(Rd‰Ĺ‘E}Iki¶Ű.=G?â`Dm9ó†|ˇ$·äŢhz/>ÖE,ˇśŻBjçü«‘’şńw4U"łúVwž@?KŢαÝpfVÔs©¨ľz»«]?®µ~˙aOĽyjĺnćĐ uh¦ÍŃ‹9ńĘěDwwźţŔ‡ŞśťĘŠÂ„JpúŘp+lŇ—ىMÓ†ĺ|ÉlIZăć`ĺş0Q±›är=Ă:gÖ†4‰IOُëU'€ľ˘Ôśc±¤[ĎĘu@%ÄÝËî Íłf&N 8¤U0¤ľuĐś¶v`U 7``–ŕmůţ¸ś.ŃíIy˝?eŰ]‘;WcXąrÖu;Ú8ăݸ:Nc©Ý|3¨ľŻâ ” Ăg–=cŹGF'7Ą~D\g—M~+üČÜŽY˙–R|]OßCµňeSÝqäĆř…IĹä‹ôÝC{Kç—ľKľuľnŮ‘­ľ&łÍł9ęł™sµA3j¶o Şń„muR?QÎÎ7NŃŐá©oNé°ęĺÇÂějâ¶Â¸—ŻkÍ’ŕç0AGqS8ĺ©tÍÇ+vŕĂüQ? íłőǦ”JŕJÁÔ´[*c]ł^(ţ ý3MC„gyÜćq _?ŻĐ´ł>uUd>Š3cş÷AىĽĐŇČQd2eâEĚĄJíUě2ËE6×5ę»Ä‚ă—tb#Ł\!ΔŢŮŕČ-đ@°YU˛…ţń”—Ąž1`dk!ő›Ďd=á\ł*|5ÔBˇśř^b˛ÝlBׯeŮşşµîd\ÂţŘ=ď‹Ć›ź7°°|’ą+ú‰ŃJ„°’ž¶ZYV-NĚ˙RH7•&€ëPťÓöĎ ËQdąÍd:ëĚĂśvŮwŇ·ĆětM  ÍaRź_«Í+Gaş¤čz<őŐ_·©¤EŐűwqľ1ˇęGÉrčö\WçO @çUONd}@NĹ>~żĐńľ%6řăˇ(n†ş’‚kKĂú±EéqĂçť‹+Á%Äç)Kpĺ”—ÖćFx UźĐę“‹EĹ}’mTeŢNĚĽĺ ĐbĎß“Ůŕí ŰÉ>͉üUĚ’ąłĹü«ßT«ŹEč]Zž5ĺt§¸Îo=Óă*wŤžVž±+PůkůműťćŃvjaŤëş5EYl{,ÖŻ˙[ů·HZŞŰS‰*Ç2®*Ä÷;ź˛ÎÂ[ ĺ®-¦ű±P '”´bg[vS/7˛Şi] H4|“[yżsÖG¨wî«4]»ĘěŢXŹ˝ýb«ł¬-ů˛;5NOĚ‹xĄŮďľŰŞW]žOśŚ—ď[[şĂĄp. hď^őč „ !KfĺşOĆr~Śź›vőĂŻX5g;‡ú>Ü}IĐV[;·ŃwŔ•.Pź_>ëŁńF":ΧS”Ę?¬`o]:öy)ęť « GíîçGđ3Oú«Q§ }}ÍŃČńţNă –Č÷ťR|©–GăI‚¦v¬+™Ŕě•™ ĘR¬É2Ĺâőtąz¬–.pső“Ý Č3_?ýaĺO_J_^č×Rč´}ęônMŔĄ"ł I#ŤßWĆŰ^Ů÷Ô:ŤŁľ´Îb´–q'ú«sĹ‚&“ŮTË› Â<Ť`6“•‡x‡¤Úü‚ww]`.`L•в?uřŔňěŮ€káĐŤhaµr·­ľ°ăŰŚë:ů™ÝcigqĎď~¨ôęôă‘ cďž0ߌ_Á*}+ŻČrWĽLUőx˘É’r6¶ĺBő(x°W%Ź›Ž˙˝ŤÁ"ë[úK4tL»Sq¶eĂăFÉU¨éˇ7^ Ű%¶D)Ť@V"ĎIP?ěË ¨ßĹ4 űŕąĘ7r§bž6ôy„=ĄîëÄ—č–¦- §1ľĄËÁˇŔ|Ćąrvűäěĺ›ŃWÓ0w•ő!f©6&ÚĎ—m± ľŘ3— Ńf[ MÄ<3~śn“Z¬hhűń\<©Uńég=O7‰ŽÂąWv„bβĚŐ¸7>Ă–oy#@ů ËńĘÍI˘â¶s¸šBŮ\~NmĹRÄĘä­éš1ąŠ˝w=o†řşZf­Š|Ż3W$âíńP߸ś÷ÇřFe&»D|‹yj΂fD[Č%ŁŤ—OSQ_ŁgśîjŮCĎěB¸d 2xřg6{mÍ%+Ü2jÇv6¶Ô(Ż==uo‹nÜq>ˇ'‹UJË=śęWámX–-Á˶Úô°Ëi‹—‡>ŻVBµŐVĂOČnîm_EPçX!€ďżc뉇ʇ%'N»‰-yĎR­'a2óCĽî¦Xř\(ŁĹK‰lyµŢÝ`đ]v4ů}ü’f°Î.uőIBN3(9ęă¤rčŐ”zżEušŃë2rçýnÖžnáĎrćÚ®U*8Ş}{_8­t°˛ţ–xN.ÖÎ&±çGúݧťX)FřA۱µ"­öř†–ăý¦´W첗Tnňěśű޳Γ)Ř żsŘë„»´s5†Q*·Ň1`IJK´î·§uŇH›¤!+Q/ÖĆz"+Ł,ž¸ťŹh¬xšTF7ýäX`xĆĂËfvânÖ^ÜÍőë‰RŽč»B˝úĂ]9ďťł&É ş°öÜ—Š mŚŃÍQµş¬ĎuÂç<_č8i(ôGÚ$Řŕ€f íľšżę}Éźf-ż~ˇ0ülúÉZ—5Me*`<vďŘ«ÉÜ$ĐeŽ"ŽôFR×Éţôu`[XOËí[cyĎ»řW‰3Ăk®ŢîĎďwsľgŔčČÔÓ«Řhíä.c3"™Ůk’śĽĹĹő”š%é¬ç÷§żTłťˇP1Ęĺ/Ăżr¨âĺź:ÂŞĐľ&ÎG™&‚Ť˘ŐŰ𫌄.lK\U¸§±eĽš”JÍż®./Wă\ŮŮ×p8ű^gé@ĎÄů¨~öHĄÖ1L+ńµˇiO¬[|ěGó[hŘôÂŤqµű¶jŞyóŃ+•L Ó¸¸aű”M¦,”[ż¸Ít[ş!‚=[o% TĂůY©x NŮűNk›¶y‘é2˝ý-?ÓSn˝éS%Ťe=öĚLľˇ­Č|Ü]që·1bžX‰V˛őŹVćKÝ‚)ëÉíópŔÇ{*´e‰ć2Ý=]8A,ţ<ÇÁŤÂ]”K1Tm,´5 gÎI÷ «`y_ďËőlEoőťž…Yđő§2ló19ÉX ŮVŹľěśaf mbnGi‡‚ÎÎjd±nŹý·WI·L<7fVă(:=i¤†E'ťJ®S7«kł®ĺľő’Ăęü^41ëž_kÜ,”u;ËÜÝëäĄ7]{˘)'oušű&ô‡_‡ĎćÓ†Yš1zĆm31\ŰqwņJ't­Ą'żöhUĘ‹"n2 ~řO‰ę·Ô %őîÖOo.+i\˙n‚ŮKľ¶6ďr}:w;AŮ4ďĐG—Vŕt[űîYv*&_á¤ws%uy€ţ” ›‚ĚクÉĎľ%”~śYŐzÓC˙"‘’¬!ĂŚgEÇ`wŢ+š,C†YRĚ€Ó§˛ˇíz®‚˝4[7%9e<áă ŚKňŃ •<ô 9+·é `!—%BKNáBćY­ţţDş¦8,_X5O¤ËÎâkĺ꺢´hA@ÄË”+L×űŞ>GiŰĐŠŹ\é´ç úá2ĂU ÔľźˇŃµV#.Y·7KtšÉšŁ\ň|ĎęÂć.ĘčÉ ďŠě–»N鎅R+FŚn.…©ř°<ĽK;ďe{IXÁÓ.ćĹSsĘgÚgJQ‡;«N.Č(…*…|ZR§|KĎa}ŰK=é}Ű9WË"»ë ĐhČiŠŹ‡˝ŽýÜĄ˛$âéĆ?ŐÍ6Ă^“čUQć[öR~ţ…ůăhU(y{ Fl÷aŤÖ“6ykGh€YđFý†öÝľ±ü6 ŘoĐlv¦ć˸T*ü6ţ0Eű~U0ÜĹ{cÉÍËdDľ‡kŢ“Va[$Ë=âě­!&ßJ«¦ŻDXúŘvĹ’ü»«ĹÄ,©~FĘ‚Ącôă3'¸A±ŞKm|`M3_ ›ď-g‹My9ř ˝bó­Ô ’h  ŤE±B4*—őň{ĎeńY4ůŇ‹ś(K\¸<•äÉľAź›ŤYDşß_iîśGJĆ^çąĐ79óIţ/hÓ|D*aútgC¶kÍÎŇç~%úőŇÓ“gť˛ë˛…Ý+,ÓşÓŢź«S5Ő¶¦ĐH-Č7hv㩌źxÜő´‘ąî6TňKîËőÜŐ]ĎťtŹŠ^äq<âwŽ=…. 2+řbŘ…a#‰ †.Şaٞö5^ޢ٬´<’´÷[ű&kđfŞHILJšz"F*¸·pęi Ľ”‚NĂ?ń¨ŹŻ)čUwqÁľż~“¨¨vZd|aŇÔ^íŚ;Ô¸\ß­Ó´!˝/|¤cö‚'<ödĐ+ó}ř×Ä3Ú)XdÓň©Ě)džĹ÷aöˇfÇ´lÓK:Ł‚Užë˝QÔWŐeKȢéÉByČĎ1AX2 <Ź{{®Ůé§*qĽ„3ʆO€6Ämnxňn–ÄŇ–ß’iä=ë.Ĺ™ď‡ĘďÜŻ±Ń7mî×ë}I~+Ô ¬™Ä<}°r˘ĆZÎzélĘĂîTÝF÷đ]?VˇË<îőÂôIǰYęŻŔµĽwÄůOę†hÜŠŕĆŔn91ŻÜi?řýľu _Q_xűéňŘ7Ńňčľ}ζé7…áW: ×{PIž;fŮOÎďňęC‰lTPSâh­Lt«ßk¨4”I™,In”Ć)üýĘŽőS3…ÎćĄuFŻ+V±>qő«/ü-,kwä©ÇÓ˘6VĂk)YŮ˙ Ť†nendstream endobj 227 0 obj << /Type /XRef /Length 421 /Filter /FlateDecode /DecodeParms << /Columns 5 /Predictor 12 >> /W [ 1 3 1 ] /Info 43 0 R /Root 42 0 R /Size 228 /ID [<2085cec1a7edeb2f29829c5c9f5dd866><30a69273a1544276f7b8f04c3e8cc2e3>] >> stream xśĺŇĎ+ĺQÇńsÎő+—QfčćWŇb¦!ŚňűgfYP˛eCĘr˛P¶6ö¶6’Ĺ]Ń”…˛a°!+%fćşß÷WťĎ˙ŕ.^==ßsÎsÎsg‚źłĆäŕ˝qAÂľ;&šu’Ś#ůAŇš#»3ĆčĚz`t.Đžafá?ŔLĽÂŻx)ůżřÁuŐÄ/Čě>KśŤĄX‰éřsĄĘ>Ž`ެ9ÇSŚó˘©[ŽĚ€űB\‡%R±Í×r]ś°M&‚×c†Ľúš]MÄĹVĽĹŢůőöQzUŚNŢ•†ÇŘ =ß’÷Ň7ł&ý ;*] űs$˙ć…ô“I°µXź}ŢUř˙Č„·Ý•»…Uöp‰5K’ßDşíę$_ĹʸÜj °_ş÷w°…s^Ű(ťü!Ý‘űäI'ěíőçĽMÝ<ĘjĐâ7vőúů±żdRX3L<‹TqíĽń€ř·ďĂŰke×O ßőo°C¦=&ď «ÇΙ$"^¦î ¦×őČ ť8A~ZćmTćů_|-7N<•Ô-šWŹQQĺ endstream endobj startxref 66327 %%EOF sampling/inst/doc/HT_Hajek_estimators.pdf0000644000176200001440000037074015033773102020204 0ustar liggesusers%PDF-1.5 %ż÷˘ţ 1 0 obj << /Type /ObjStm /Length 3982 /Filter /FlateDecode /N 52 /First 415 >> stream xśŐZ[są•~ß_Ńo™©”ŮŤ;JĄj|[;»ś±ś8qć#Ń6w%QKRO~}ľď )R–gT{)هhôpî8 Ý ťé”óťí4 ëlRťď‚6]čzb§†”şÔ)mÚť2#ĐtÚˇłSŢtv*řµťJc\§vúN+‡ÁˇÓÚcpě´MčOť/‡NG—ŞÓÉŕĄîĚŔÁ¦3ŠmgćĂRVqšô‡Îi‹ßŘ95w^Gđ2ŕ7áL€_Ýy‹Nc:ďÁ†±`-rŇ.Dç:­SHHC HHŢÇ΀äÁ:ÓYĐbĽŐ”x±¸ě,iXDEđĘđĆéÎB$Ú°2ŃC,%E9`,ŻŚŔ f6ČX çţí÷żďú§«Ëm' %rşŘć4c#bi’¸Ň¦ Ç¦Ł†Ć¶‡2Ç&¤66!#[š fŔćţĐőŻ×«Ó7‹m÷ÍÇO»ţdńyŰýWBŰ#ж¸Ün:7~ą8[ή>źd¸äf:PĆjĆőŻçk  ťţ‹Íęz}şŘt#3'?_-őqQ×ř4_“Š ĺ,8O.OWgËËŹÔ@&yąŢl‰G Ŕf¸îˇ©ÎëóĺËův˝gĂŕSRŰ6𾟏3*´5żXd!5“¸}ń<żĺE@]˙ćú§­°C¦LáMôŮ˙ey¶ý´ˇ1eIﳪöYn°jwXŐÝ-¶äÓ!>Áľ-|Öö.źjâSÝ7ź&ćÓÄ=>MŘç.ľŁR+Ś"y}QÝ0Ş0Ş Łp€űfÔ·Ś>śoůő÷}öÝŰ~űčĺËçpІ=5ZěăĹćt˝ĽÚ®ÖŚ"]Ýě,Žç“ŐŰË%¤¶čśk}g—w„ś/ţöęO ç‡]jhěűÔÄÔ„[©ńÇ©±‡©9y÷îń_€š7Ý%g¸AŤö©Ě­Ô„ăÔĂÔ<9yööŐ3QUh‰ dö©ˇ:oˇ&§F¦ćů»WŻ_?5OŢíĘ&ŢŽu{äÄx+5é85ęlţüôű‡Źi7;˘A:»AKÚŁĹßjĂ~8NËp–?ýűwoß˝«Ů!ưµG qn!F%Fď˛Ő5Ţ!żXžm3˛E©ě:Ó­łblsۦLp/Éý`Bç¤`pż6Ą{ýkSşÚ‰K‡sşŢOúF@–ßI ˙mÝý˛ĐftH鞀ŢI ŁnźQ»Ď¨ŮÍč´0 .6~Łnb4Ü;Ł;ás2ä;ě}Z©ůŐVšŽYiĂÓ}Rüë·ĘęĹďá(3„ô ‡ńŻí;ď†uhŢýößކu‚ă” _äč®ÜţďÂBăŹbŹ—>, ejő˝ř˙<9™öłËŐölń›˙~ž"І€Č„¶Eě šçLwsDZŽcqľÄ­ŽpÄYµŹŮzž¦“M/83ÉS×Ŕ»öq·4Ϟ߬8A± Í’ÜĐ̢jú8Aä7’ßčşŘđ›HdÄKÄK¶K®AájŐDry$Tµ@#Đ t˝Ŕ 0 ”±JĆ*«d¬’±JĆ*«d¬’±JĆ*«e¬–±ZĆ"4*ÝHHâöś°njÖىďÇ”"\x_4•T 5U,UUőU|T}Ô^y€’L}HTĺř!„Jd*I¨@2ˇR…Jä+W¬@j±RáĹJd+L5•H4V ŘX)€|cĄRŤ…řZ*ŔĺRY®–Ęňđ¸TV‡ăĄ˛8ü/•µá†©, oLeexa* ĂSY×ćrD~`h(+ł4”ĄYĘÚ¬ eqÇŞ\YÝsLYŢsLYź…ąˇŕ“ÔAňô«Jj¦Ş˛÷¤gŞ˛ů¤‡ŞR8˘kŞ’Śč“ŞěďćR,DVĂ Q*băä«J¶Ął*](`E°d5:ŻŇ…‚$´ňĹQƇ$Ž2îAŢďĹׯĎl_Î<·ç¶›™ńpFÚźďk2ő}dž/s{EŰWĄ<$ń˝Tg$Š—ňDq_c5żľÔXM´«ů¦Ćj~‡©±Úň‹M]ë^ѵî]}­uĽGrO3śBŃrhąŻ†F›™Ýs>™›y$V 4ô ©Ű™9 ˇ]˘rl2ęŹnăL.͢™IćK3&x‹®č#_čŔYRÂëč ÂT>X0Bśii{ćPËÁĆcDâk‹ť¶Ć-„KsîńÇćI—pƧJ!YöŔhť 3%ߤ"¶ Q ësűŤWśyPd$€= óÓ»ČE¶ÍŔéMÂ[ #„€É´ŔÜvű­»@ÄŹA†|ݰ;L*ŕpoYoęi§gŻÄ—áATŤMiĆôžEnĘi[l.ĂŚ!¬jZß"ój!EÚŠó”§öbŠĐ‚©"gIbmb N ĎóđićQ4­"$OfÉJµ!ĎŇ˝vB‚L1/eFŰÁÖZŰĘäQŘŚľcŔ"}ńpЎnzÓŠ„ĎiТE›líNW”ě«S«Ĺ2˘‡ ]Ł8y'ŘÓ•]y"oꮼ‡Wiቼ©Ľ‚–¦ôĘk}ÓÉ´LĹWžĎ§ę+ĎçSů•çó©ţĘóůT€ĺůĽV`)oUk°”»ŞUX‘­ĂRěŞVb)mUk±pCU‹±Ľş7Uc9}=äsözČçMĹzČçuĹBŻÖ‚,ďÖ‚,ś/>CËĐn¶DÄÝ,7ýâż®ççý‡ĺ/ú«ëu1?]ăýęrŃ_ń~ŐůâĂ6·Öś¸ż:ż†,.–yží§őbŃo˙±ę˙ąXŻî&2xßhC: 3uĂ&vîbt×îWbY.ĂÝxÂîm´­đ¤oŘA{Oř Ká°Ä…ŹłôÓz~ş}J+ëÚ_,/©ŘĺĹň|ľľ#Ď©{ QŽQ<·ÇłŮçyďűăglľ_ľGďčĎLńË%Q{ˇ»|2śÚ˝d}ڧŻŃeľ˙\Î-yŘŁ9&^Ől˙h˝”ôxľ]tß<ţN`nHúŢ!Ď˙vĐż†ß|;âa™oNďđřruöĄŻOžĽ›=˝>?˙i~yąŕČOËM‡/—/0É«łřé°ăÇFłűű7Řî3m˙ţí·rżóěú”2r—‘Ś8Ŕ8YĎŻ®gĂü|#—2˙ÁźĽĐendstream endobj 54 0 obj << /Filter /FlateDecode /Length 2324 >> stream xÚµYKoäĆľëWđéôű±‚ń"ĆÂ@ +ĂĽBÍPZÔPKrV»{É_OUw“CŽz4’˛95٬~UőŐß]žýí{A3fgJf—ףŚpÁ2­%áŽe—ëě÷ümsw_´Őöf±Läý¦ ďšöSŐ]^n@ k¶ˇ·ěúę®č›6ĽŰu”.K%ň?ËŰĹ—?d WP,[rFłaĄýX˝±ŚÁWĄ8îmI‰Ń<[jE8•aŔ»ú LĎuÎč_ńÁäśr†s—9â4×~4.(ŚĚ–’ óÄŁm»j]â^%Ë lxţ ą­ú2tŢ/–ÜćÍý®.úĘ:Ş~„댻ň2eÝĹ…Ĺdaf4QÖf4¬řKˇS8(;J\D =—Đj”¸NĚ!‰łŁKěc&pžśAňq›üô  Q“żÁF‡Ć Íyčü1qřć¸9˛Ţ$.R¦ÇmľßÝÇűéĘÄdÂ&Í ü%JpĂaĂrÖ*‘.Bę6˝) XÜ ĹÓߦ®Š+ŘSztDIy‡éôÜşA˘h#?-”Ę‹zWvá˝ń8Ȇ€l §„ČVj9ŽŇ0l8ŻŮxĐuHk)m8XŃíĘ“ö |´ŘżwÝî..ŃoŠ~x*ĂňńtľłęâÇâXnĂk±Z5í:č 7ÜÄî 3faN&tý“Ę»N·)ý6ŕ}Sŕű§ń{µ]ŐpÁ_©üľm®Š«Ş®úŞLń7§šP¦†‚s` P(˘ŤK‚bJ±†hí¦ ¸Hé=@śFfíň6ĺ68Q{§đ*š‚ ±fD}ŠŻ,p†}š®$u‡Ŕ›;m&»`§v‘B‘†Ďüë¦L’[bµ{őEĚm¨`"ă±Ŕ ŁE«¦ňÝÂ`±¬PććčèAÁ :ŔŃ'ŹçĐý‰wWŽ­zlľ$ÎfÁŸQCźÓsϡŁ&BüŢsTu$ŘVşqźmb;ĐĚL˙őPÇY€SOľšáâR¨ j“€ĹŮ?.Ď>žáů!ľĎ¸Łr™Iđ8Fňluwöű4[ĂGđśD€Ű|đ˘w™7ěőVgďĎ~>űs„™ŁŢĎ­T˙ł«®®pµw<§\µ{‰ÍTÉčN1ŽŰeCä‘B Ü?Ä&‚Eŕ!µ‡ĽGapp4°ÁŰ`ÇÔdŢUDł÷pĐ“Č&ŁW Ś1t>b'\Ľ™‚źň/­1“ Đy|MĹČÄ÷:‰ěÇ€]€ĄÁ$S:H\nâŽß}€CíS<ß5M‘s®ă~‘‡ań@ źhSú|*&Z^9ýpĚAsëŇ'heT}‘rILB’@_rHL ‰˙•2oH¬¬8•!Č™Mż[,Ť†bůźp^¸˛”‘ĂŠź´ňąŻ˙1·ä=čIcVj6çu,ćI“ú‘·sO éúÄđN¤Uęé´ŠĎTt„W1ýŐŹ§;$9CŔŽ…Ă‚‚|ŠăŃJé'9r4ŽsQpü„ÚÍóÔ.^¬vz v7UűÉ˙…Ę< H€ă,8<÷#JT“ňV¦t>lŞÁ7÷#7<®mĐC•4d ą°QľLdô ;–/ËôŃŽu~™\•°y‘6M´9罂—ú-±. ´†ű—Ňţ ÓJç fV|‹‚Ç,Ś|^2Ý›$ś Ĺ•ňaa˘ž÷I[Đf$Í˙¤ŃŔä“É|ëqŠv».j_>¤ůű‡ŚJcý}“/dęPöhËţ.~Ěx‡Ëśă8„ArMK YłÜ'D~Ü ćĆ;xxëĘ}‡C竢ó…xĽĹS“Żby2ć®z¨˝ęoRcJlĚLôa9u)™EOéŰďş]Qcáßü”6ŤńlŻďË(?˙Kx_ů:0ú]?® Ăâx›0ňŮ$Cč7Mť6>ë÷ń~"\‘DPű˛pe®Ć‚€şę&rÎDěşrý&čŤ;` Đ€˘…áÚĄţ|‰˝QĎÄô5e"=ËPČDčĄ4]"ÖRďuÁő7"ÁTýžŔ 'B}9şMĐcKŚq˝)Ô`B^ˇü†ĺ>LäÖ„®ć ÝßRy»b‘˛ ŹŰ¦_1ŕű\®Ăü ¸ŮÄŮ#ĐWGĘXµěU ë6n=Fqë˙DŔ®¶-oЎPÇ~Ő´mYýĽú˙©°ÜţźÁ뫏7†ň<¶µ ˙g+Áhą2[´u»ężŚż@°ŻęB‹–ž6e‡öq\d |QH¬đíÚbő%ĽaV€mHüŻ‚Ż#oŚËx{öăST2€‰ÜîWQ|AjĆ+Ágżn 3ůňŽX#ĹoˇN^ďâ0ÜJĆ!r˛@@k4~)?îÄ˝tş {‡˝r™ŹÎ˘ŮĹő#ÇŁ¤W‹ď‹?ň‘OéăÍD'řĆŔ„\ěŚ0üzĺoŽCŚłGe1Űm×c ËäŃ"Ż›dŮH)Äaµ‘«6^ l2’e0“3ŻřŮäŽýl:bŇ*˛Ë 3Í.˛Ž7ĚîŢłZł]wóÔ|¤¬ŐxŃC TȰAős?Ź7i alÎUű»ä$âO8¦©şŕ3Ąś¸/)CŢ‹m44Í zÖ­Żî=„Ţ"týŔ±´3ůC¤ =Őąt6ę çgÇ%öʲQYŘvŤĚ‹Ť®~XSN´Đ%Ôhüű˘dĐ 3ŚP=;°ť$>úý–7e!endstream endobj 55 0 obj << /Filter /FlateDecode /Length 696 >> stream xÚmTMoâ0˝çWx•ÚĹ$ !Ů ‘8l[•jµWHL7IP‡ţűő¬V=MžßĚĽń s÷ëu;ŃU··őČŮ›=w—ľ´“ě÷îÝÝĺ]yil;<[[Ůj<=?±×ľ+·v`÷Ů&ß´őđŕČ›¶<^*;˛~&űQ·‚>ěţÝţť”MS >Ů_ęăP·ň{=éÇsć@öd”ôÇöçşkźxäś;`ÝVY×`Śs4˝JaÓQܡn«ţއíˇ.’Uu9\ßčY6î>Ľý<¶Ů´‡.Z.ŮôÍž‡ţ“4>DÓ—ľ˛}Ý~°űŻŇÜŃör:-d0­V¬˛WŃÍ˙Ľk,›ţ8ăŤóţy˛LŇ»đşĘ®˛çÓ®´ý®ý°Ń’ó[Ĺ*˛mőíLrź˛?ŚÜÔqůĄă• â5F8@ š=@Šđ)&°  Č8Ôą€ÂĹRx u€Dş\j2H—†ŞˇĐVÁą0CzL]ř Âb°ct‘I ©g$`htŃ‹0śĆ\F„áŚ0ä†sę‡á jd< —Ię6ś»őńzgóńşË»ţę W ¤qČ’Ł+—ź#ö•ńĚÇkÄŢ .‰bŞsťŹré…¤šáćÄç†bďmŽXúľ„Kß7ǵHß7Géű„űľnb§>&jĘصäuśŻĽú•ń1ÜV™÷•âÜăâµÇ‰Ou$ŐźqWčS/%1{\řxB!€§ÔK(hH©—TĐ–ćž»J©ĎĎŻv×ÜëÁ=küŇ2řĄUđKĎ‚_:~é$řĄÓŕ—ÖÁ/ťżŚ ~™Eđ+7żčˢ/ ˙lěˇŰŇ(/}ďö -+ZXukoűěÔťE?Z„ăćĹŰKýqíÄendstream endobj 56 0 obj << /Filter /FlateDecode /Length 739 >> stream xÚmUMoâ0ĽçWx•ÚĹvHU„dçCâ°mUŞŐ^!1ÝH ý÷ëń#xŮö?ŹźgěÁÜýx]OTÝmÍ$|äěÍśşs_™Iöss îîň®:L;<S›zś==±×ľ«Öf`÷Ů*_µÍđ`É«¶Úźk3˛ľ'ióŃ´ž‚}Řý»ů=©˝ŕ“íąŮM;áŕľ7ĂŢrľ›f¶ĆnjĚ-ůeúSÓµOLg~ĽŔ8÷ă ăâţČ)okŕ çA„8 ö$`I\čÎ×3`çAfŽă<ČZ]Â!‹„ę xNkÇyăąăĐđ"ś7Áż _Ąă“§Ěq âH`ňáö•‚núĄ¤kĚÂđRONH=CpB:# =Ń%8“88QA~ˇ!*ÉzĆśřĐäT?!~Ž> étw©8éÄy*ásŁ¤ĎŤ }nÔĚçFE>7*öąQ‰ĎŤR>7О˘ G]Ľ;~îó¤ŠŰ<©ň6OšßćI‹ŻyŇňkžtčó¤g>O:ňyұϓN|žôÜçI/|ž´ňyŇÚçIg>O:÷yŇ…Ď“.}ž2îó” ź§Lú> stream xÚmUMoŰ:ĽëW°‡éÁ5?$R. ¤d9ôMđđ®ŽÄä eC¶ů÷Źłk›m‘CŚŐpą;;†wź~>Î|żŽ3óEŠ_ń¸?O]ś5ß¶‡âî®Ýwç]Oßcěc]=~?§}÷OâľyhĆáô9%?ŚÝŰąŹ×¬Ź“B|Ćś‚>âţ)ţ;ëvÇw%gĎçáí4Ś3‰ä§áô–’>\ ‚‚6ý§ă°ż őEJ™€őŘ7űĆ8ó 1ż’{Ć~şđĎ`W(-úˇ;]ľč·Ű%=°ůńýxŠ»‡ńe_,—bţ+-OÓ;qü\ĚL}ś†ńUÜ˙I--=ž‡·B«•čăKŞć˙ľÝE1˙pĆ[ÎÓű! Mߊyuű>Ű.NŰń5K)WbąŮ¬Š8ö­iÇ[ž_®ąuĘ•MúŃzQ­ŠĄŇ)V†€Ú(TŘ€ŕxżŕޢ žjy‹°°!ŔĐÔ•µZÔŔ2ŕP="¦ZdÔ0\ĂG©R\ˇ·”).–2*ÎШa!„UĽÄ,†łÔŰHđ° `+jĐĂ.¸5Nα@čâ°čĐVK-ŕxź%ôÜ3š% A°YÓ€zˇÎšÔ>kP#¬ł¦ő™5m0WŁoš¦Ăľžj­®§Üý·ť.†ĐZˇŽT$X/©)n)ć#W—„o(ć“oŔRZŢ $K˘p4’ŽZ¶-bâ\­1¦Ü°Jä ćP"Gń‘XÔQ¬‚i/8şkÉ^€ÂZqŚ:ZsŚ˝š9”d š­Bů Ž)ßsLů-ď7˝ćxĎJ›ˇľŇ`ŻažÉ˝)fĄÉ$†µ’1™¸ dŃŠcŞCZCů<Ł7Ă3JĘgózĚnřţHȰíáĚYÉšäTśŻa…ŠďŻĆ,_»ś-ź—Oë87Ë}ęŰKÔ´Ü—LląoKńšň+Ęg­JĚâ.ľGZyóş‹VđŹc­48¸’ďĽäŘWtů]Í:P~`ŹáŚń±–rZŽq.nÍ1]Ç ÇŕS˙ć/©ßP•ýďuöż7Ů˙ľĚţ÷Uöż·Ů˙Ţe˙ű:ű?Čě˙ ˛˙Îţ&ű?”Ů˙!d˙‡&űż1y–¦ĽÍH·śn5ţąă)ş˝ÝyšŇ“Bď˝x#†1Ţž´Ăţ€]ôGoáőńĹ׏Mń?®Xęendstream endobj 58 0 obj << /Filter /FlateDecode /Length 695 >> stream xÚmTMoâ0˝çWx•ÚĹ$ !Ů ‘8l[•jµWHL7IP‡ţűő¬V=MžßĚĽń s÷ëu;ŃU··őČŮ›=w—ľ´“ě÷îÝÝĺ]yil;<[[Ůj<=?±×ľ+·v`÷Ů&ß´őđŕČ›¶<^*;˛~&űQ·‚>ěţÝţť”MS§“ýĄ>u;áŕľ×ĂŃq~:fc_0F)l®»ö‰‰GÎąÖm•u f8GÓ«6•ę¶ęŻbŘŇ"!YU—ĂőŤžeă.ÉŰĎó`›M{č˘ĺ’MßÜáyč?IáC4}é+Ű×í»˙˘Ěťl/§ÓŃBăŃjĹ*{pÝěϻƲéOŢ(ďź'Ë$˝ ŻŞě*{>íJŰďÚ-9_±eQ¬"ŰVßÎ$÷)űĂČM—ĎńP:^9Ŕ ^`„މŘ ¤źbr š€Ś@ ‘{@(\,…RH¤Ëˇ&€ti  mś+3¤ÇÔ…Ď ,;F™$Đ‘€‘zF†F˝ĂiĚeDÎ(ó0śAş1a8§ÎyΠFĆĂp™ nĂą[Żw6Ż»ü·ëŻÎpµ@‡ )9şréń9b_iaĎ|ĽFě-ĐĐŕ’(¦:×ů(—nQHŞY^`nA|n(öŢćĄďK¸ô}s\‹ô}sÔ‘ľoA¸ďë&vqęcâ ¦Ś YK^ÇřĘ›!ˇ_Ăm•y_)Î=^ ^{śřTGRý÷w…ľ1őRłÇ…Ź'ÄxJ˝„‚†”zImiî9¸«”ęđřüj'pͽܳÁ/-_Zżô,řĄăŕ—N‚_: ~iüŇyđËČŕ—Yż2qó‹ľ,ú’đĎĆşíŚňŇ÷nťĐŞ˘5Q·ö¶ÍNÝ YôŁ58.]Ľ˝Ń»á‚ňendstream endobj 59 0 obj << /Filter /FlateDecode /Length 494 >> stream xÚm“MoŁ0†ďü ď!Rz Ź|U‰ÄAĘaŰŞ‰V˝&ö$E 6˙~=HŐUAŹgŢż“ÉŻ÷˝ź«ú~üĚŮ´uo$ř›ßÇĆ›LD-ű t÷  @ŤŮö…˝›ZîˇcÓÍNětŮ=YńNËkŻ`T=­áRęo ŢæřôeCîźúňÚ•Úç(>”ÝŐŠć™ ˛źAćŠţ€iËZż°đ™sn[­6u…c´^0XaÁhî\je?ě„îĽ0bŞ”ÝprOYŮ÷Ĺű[ŰAµÓçÚKS|ŘdŰ™›óřäoF)ő…MZł©}ß4W@Ś{YĆśmG;˙ë±<śń®9Ü`‘;‡äKÖ Úć(ÁőĽ”óŚĄE‘y Őąˇât¤baĄbi<Îg®bĚĹw­ü:/Ť]×ĺvťYsäâ[äâ+ä„#Ď]íśôň‚â9ň’8D^osâyMěîÚGČ‚X o‰ä‚îBźÉŕ5Éŕ‰<řÇ»’Á˙Âň kŁ(Do9Örá,ÂqĽB?"tŽýEDqě)bbśW$ÄčYĚčM»>sb×gEějqŢ(ŚćĂ×poż$îÝ}IdoŚÝ·śn-p!J ÷ýmę«ÜĎ-ţřOĂÓ[áýL‡endstream endobj 60 0 obj << /Filter /FlateDecode /Length 740 >> stream xÚmUMoâ0ĽçWx•ÚĹvH U„dçCâ°ŰŞT«˝Bbş‘ ‰B8ô߯ß{ .Ű@ăçńóŚ=»/Ű™®Ú˝ť…Źś˝Ús{éK;Kîşŕî.kËËÉ6Ă/k+[Młç'öŇ·ĺÖě>Ýd›¦yÓ”ÇKe'Ö÷$cßëĆS`v˙f˙ĚĘSŻfűK}ęfĆúVGGůf–ąű\b¸ŕ·íĎuŰ<1ńČ9w…Ľ©ŇöÎÁ|Á擬CÝTý¨„íAW $«ęrGř]žÜIŔâíÇy°§Msh$aóW7yúÔ÷ĚźűĘöuóÎî? sŰK×-`ăθtJ!±'™cřŔ8őăŚ3?NaśâOśâ¶<Dg!Ŕ;IXô ôŔÍ0z)rĐĚ@« kĐpČBQ]^ŇZä 7ž!‡î /˝‰ü ňU ź<ĄČɉ#“ÜW şmĐ/%]cXß!őÔŔ ©gśÎČ€žhŚśIDś8QN~ACT/čsâ•QřŠřôQ¤ďRsŇ ç©…ĎŤ–>7:ôąŃ źůÜčŘçF+ź­}n4eE=zG~ćó¤óŰ<éâ6O†ßćÉŻy2ňkžLčód>O&ňy2±Ď“Q>OféódV>OFű<ăódRź'“ů<™ÜçÉ>O)÷yJ…ĎS*}žŇĹőÎđ—Źżtx›ŕ˝>zĺĄďÝ{O->tđÄŐŤ˝ľĆ]ŰÁ*üŕ3>ýcŔčąţ¤C§~endstream endobj 61 0 obj << /Filter /FlateDecode /Length 900 >> stream xÚmUMoŰ:ĽëW°‡éÁ5?$R. ¤d9ôMđđ®ŽÄä eC¶ů÷Źłk›m‘CŚŐpą;;†wź~>Î|żŽ3óEŠ_ń¸?O]ś5ß¶‡âî®Ýwç]Oßcěc]=~?§}÷OâľyhĆáô9%?ŚÝŰąŹ×¬Ź“B|Ćś‚>âţ)ţ;ëvÇw7{>o§aśIä> §·”óѲHř´ĺź8‡ýřU¨/RʬǾŮď0ñ_xů•ŮË0öÓ…ŚxµBiŃÝéňEżÝ.‰ÍŹďÇSÜ=Ś/űbąó_ińxšŢ‰áçbţcęă4ŚŻâţfiĺń|8ĽE°˛X­D_RÁ4ű÷í.ŠůGŢRžŢQhúVĚŞŰ÷ńxŘvqÚŽŻ±XJąËÍfUı˙kM;ŢňürÍ­S®lŇŹÖ‹jU,•N±2Ô@  "Ŕ–,Ŕű  đ őTË[<€5€ €¦¨¬Ő –€ę1Ő"Ă †á›×cvĂ÷GÂ@†mŻgÎ üKÖÄ §â| +T|5f©řÚŐŕlůĽxZÇ1¸YîëPß^ę ¦ĺľdbË}[Š×”_Q>kUbwń88ŇĘ×]´‚kĄÁÁ•|'ŕ%Çľ˘ËďjÖň{ g䏵”ÓrŚsqkŽé:n8źú7ĎxIuř†ŞěŻł˙˝Éţ÷eöżŻ˛˙˝Íţ÷.űß×Ů˙Af˙•ýtö0Ů˙ˇĚţ!ű?4Ů˙ŤÉł4ĺmFşĺt«ńĎŃíŮčÎÓ”^z­čĄŔ1Śńö öě˘?z Żď.ľ~lŠ˙P}éLendstream endobj 62 0 obj << /Filter /FlateDecode /Length 1624 >> stream xÚ­WKsŰ6ľçWhÜ Ő ^ wŘCÓvšÎDÓ™čV÷@˰Ś"’JíôĎw%R‚%yRL<űŢoW?-ŢĽű•©‰Š”dr˛¸ź¤i$9IŚ(\,î&ďóVOCÎyŔŁé_‹ßá <á2Š“dB,ńłŁ`©„§H!˘4Ť'!Ť„Š-ŐŁŁ‰ŽŇ$ž ňĹ*<čJ7y§ďěvŰŐ]v+C’÷uYÖS–˙Sô×ëz˛4¸ÓĺµGqšĆŹă^ó'Źćq$•đjNFš'±jžů¤ŃHŮ {YđĂ4¤R7„SŹp ŇIr‰tů:á•ĎT$I¸WÚ“aqtH$#)Ř$‰DÂ,Ń>$Š©ś„,’[˛9’–a)•ÚłÂńW§,Jaď¬É¨Ď`1!PĄę˘đŠ‘´q‚GśZ;"¶Ä›ę hůăé„˝Cž&×(ŻtW^ŮKąB")éYÁ=:6ĺ¸C.UD!©BČ »8w«˘µ(°ÜáÁ˛nÝnL‘ŐŐť»ďŔ=AíÖ+}ôč†ÄÔ.ó[űt ˙ľ`őjŁj`0%¤ *5†!ńÍ0$NÁP ä" C2Ál{‚Ąµ†R€ˇţîAŠ*v!ťL)Ş’qJya€îĹ źĂ(xě˘l¦Jf3§A &>-`ůí'g#©o}˘$˛č…Ýšxó‚ńúŇ1iěC lcd„/N @úËSî /wgëÜc‚ŠÚű"&>.Đ…cąWßçÁŇž€ú;DJwxĺ÷dşËxŽn|…„ŽQ¨¨¦ˇ ҡ®,°Ř3·ř­nľÝ×p±Ş×›¶vOtŰ뼫»mWő¶Ľłk‹`şąŻ›µ=±Ŕ¤»N7=óĽ:sĂY,żőcßą†Ęî$FŐć K`¸r¨¶(Ęň†# Š4 Ú|˝) tŕÎâŔcN*űE,t—t*tr{“WŽ@2T”@ëE„-Zëx`e¸) ¬ôHŚr‹'Ěq[€ń&Vb˘vßęňˇČ«ő¶ÂNP,‹M^]‡™B§Nö%z—w :S‰QŚ©^1sRŮŻł/©=1vス˝»vÍił-ó®¨ŃjÔ |Ɖ”îVž f j”ó˙٤TgAç ]ăZ€z\uça‡!ßUŢůCI?bóSXví-@Čňql?TŘ5DߍaĹĐ™ĚŐ|™BIäM‘ߖ·_j *Ăť´ö»¦0<á“ŐÂTŻ5¤ś̤Đđ`­s'ĽýĽµ]Öşi°¤q‰öńÓ/nľp_ÖëĐW ›­ëćlßÍYĐkTÓfLŐÚĂ{#:_âíĘîÜX+“€†`_őảÓ.Ćx1¦u’&á7é1ÉŁűˇě¨eÜłşÄÁ§›ÖLfŽj(¶…;vĄ…K„dĂÜ´f,ü ÉŚŤÔŤŰ?"*Ŧž1,P<ÂÂú.zhž{źw]ľ\ő śäŻăđťýÔÓn]|Ő'^T#'®7ĹcVTË2ĆşsÓÔ·ům±Ó†,ęŽŕ•Ug”›gĄ®:cŕư{Śęe÷η&G]¸š2oڶ•‹"ňľďŁÜ§Hľ\nq–µ†Z˘IŽmŮÍú|X6z7r裡nKąí/Ü O üˇî×'r#u–ŃSžn۬mi–7ŤÉśg3łĚđ÷·3âθËIüYĂĐ$Ş>±wżJ¬1ŞÚ±«‡U×çű2ďP˝+ ŹxBo(Hľ:“Ď4kôźňŮü í± v™ –-ÄšBTÔŻĂ/ó”Ńëůč^ÖwdęŮS@O3ÓĄĎŮĂ3úą˝Ęq‘zÓe]eMUŁ&ÍąĚgdÖ~n GúŽÓďe¬Ů‘Šc<‹,ţŢXýV[„FÉ—ĺőÇÚuÔđ}Ţ”®M˙Jl˙šöx/öđ q‡ĆgšŰ8ĺ˝}ĂĐ#ü?ldĂfŇ^^˙«.«¶kݦ˘ `žqç*ö‘ť,ńáąG€}HVض9S–×ণ—˛˙)ihţőq};N¬OĹŤÚXâÝŐ¬]ŤÓěpęU1L1ĚX$"2±śľůeńć?óQendstream endobj 63 0 obj << /Filter /FlateDecode /Length 1007 >> stream xÚ˝WMoă6˝çWîE†%†ß˘¨‡Y¶(şęÉëBVµŽ-â]l‹ţ÷rH:¶b)–ŤtOÉáĚĽÇ7Cú§üćöO‚Ą’Ę  Ćq$X"BÓ ˙LĂÉ8T„Ź…ţŚý\4Ą›!ź ă«‘™%ăYţK€” ŃÚ×dü¦«ççvŻÇ1 «Ĺ™-s=%łě!/mí—…®7°ý™6YFfŃşZLaÉŽNĽÉ¶·âką° ŘbĆď‰OXr~]„_#ým]f#×µ.žĎá<ĺ‡ă‡‚ÖuŐ45ÄZYš†PD»)˘SăŃsK~xÎź%‰ö’äĽÂ’gßq4Á|6ęˇIžˇ‰ ˘ÉfÝ€ŮAĚŚ—őâemQ°¶*`~tĄ*řP¸Ľ.?ŔĺÂĺ˝py.·}şM3­˘Y6×o ľ!Ψř ŽËŶ˙ÂÚíÓŠMĘXŇ©Ô[Ţ; U˝Çڱ°~rż–·yé?Ý»ŹbőĹ}<ÖËőVźěăˇŢďŮŔ˘ŹĎŽâSĆáĘäaă4qp~2 «đŢŰÓ@˘4aĘÚs”¨ÔŔC|źóĂ8N’0ďpNbLG¶ŮXČARD‰ě$‹Äłl6ác$ CíŃž;gŐşbFÂ$ń´ű˙ŃĐzłőÂŢĎ۲é:Lo«wŮ#¨­Ů.]ă…AEűÚ1?37Ăa¦Oę>Ę““tĺFŐĘßUwüL‰üsQŹ˘*˛wCO5w7˝|î·/ËÂ'˛łäě—ęÍΖĽţ;ÎçFĄMíM_J¶ő»Q^,ˇÍ4Şl €śŔÔĘĽĺ¶uN°[z7­fŃhPK25D˛]±qmëńQ'0ušLlďNŁ?é‚mťîY¨l ÷˛`҇uHk ‚‹ŽÁtLpż€!7Q-Ď‹{b_"—Ŕ|»wđĐöž°óűꊯ=¸3^8?ÜB&sȤ7éné˙n»i7×úŻúˇ—öAr·oëĽ.Ě ¶÷şp%LLJa§§ˇ% ĺ ąö•čťď;"Č6şDAXžKĂŢN`zăŠňX"ŢE"“.‚_ŐŠ©{ău×Ę>¬„yâ_ Ľ%‰j [h!UGîůčA'^Ł{ł°R<@‚»XIZĆTvůŞ»wŚŘW$検°N‰I~ 1ś*iËťŕD¦—L ˇŠ+"•|'%捒bfűă„â˙MM&sˇ•’Ó+@»Nŕ+̸`D–JŢAaqlîXvF^ě;Č‹rćĺ•ŕ+;”ÄśIDĹ{u)Dą”^^Dtü ©@)3Ď]óěUJąH–¬›űüć?[ŚMÇendstream endobj 64 0 obj << /Filter /FlateDecode /Length 353 >> stream xÚĄSMOĂ0 ˝ďWäŘ 58źM8p !$.âi™V` jł!ńëIš 6T>§ş¶óžýlź™Éń—Hc-©DffŃӀĄjdćč®2K7­ŐĘŮu¶ü"Ă{čŇ>¸Çlş>´«iM*|ןd'QLrFŁTv=˙ô>t›bmíÓĆ•W8“Šé˝ąB€jB°"Wu};§¶!‘ű®`pAçîĐ1śZ˙EWáĄD"Yc 0KŚč=8™áÎmďJ—|ŚîWbúnۆ×:ŃĄ_=÷~˝×ŕ‡®J )Źă&Ńr˝+n < ś~ĆDVRiJcSߊ\J2 eĽ¤G9%D©4ęŞůł?­S¤‹÷©´:Śď?;%ĄŠC#@ұ%8p§Ŕ0D"(• ăa6;‡ ¨Ć±ëLpšSâ…îÜ®X3‰j®!$gK8™™É7ßřśendstream endobj 65 0 obj << /Filter /FlateDecode /Length 218 >> stream xÚeαJA ŕ˙Řb Í>Âä Ü]vĎĂjá<Á-­,ÄJ--mo|±é|Ťy§ĽbśáÄC®ČB†ţdyĆ-źj /;~ěč…ú•ć¶Ä2xx¦őDÍ-÷+j.µKÍtĹoŻďOÔ¬ŻĎYó†ď:nďiÚ0ŮýĂŞńs ü’#źVľśH€ř…|ŻÄ›śŻFoý;ŹsŠ+lqÎ…¤ŕ÷Ƕ÷d,˛6Ş‚ÉşY'=alp µľŚ+ů–‰Ęč%ĐĹD7ôťpëendstream endobj 66 0 obj << /Filter /FlateDecode /Length 196 >> stream xÚmŽ= Â@…'X¦Ů#ěśŔMXŁXüSZY•ZZ(ÚęmŹ’#X¦Śo[±Řf–÷ćůa5•B&x#/~,§’Żě+ĚEÓÇńÂł†ÝN|Ĺn…-»f-÷ŰăĚn¶™KÉn!űRŠ7 !ŇH”ë›ČꇨÖ+UĘ4jôdcŢ‘‰ćM¦µ-ĺ­Ť@l_ Ϥô"j‰~Đ' f& Ę”Ö74.WHÁe °Ę4ů˝’©A— oů \s`¸endstream endobj 67 0 obj << /Filter /FlateDecode /Length 181 >> stream xÚuα Â0ŕ+ ·ôzO`RL'ˇV0 “8iGE7±}4ĄŹĐ±C1Ţ…:”Źün83ťd3Ňdäf”ĄtJń‚F“Žňq> stream xÚmαNĂ0ŕ‹2XşĹŹŕ{H¬¦.X*E"L0"‚5)oÖG1o`‰Ĺ©ąsaAőđ ľ˙t7;ž/¨%KGvAÝ)ÍNčÁâ v=˙¶4ďG÷O¸°YS×csÉ˙Ř WôöúţÍňúś,6+şµÔŢá°"ŕ§<€ .L)'¨rfë˘Îů;‰î“őÚGpĺźaF¨Ů]1Píő˘.š­ä;Á´a?2ČyWL ÇąGő•9^ÖţÄjoÉó.GĄň¤8Śť¸2T‰Já‘=ă"b<čXL’á-Ϋ(UM+®eĘýw1•ëŇEK[ĽđŮzŤAendstream endobj 69 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚ}Î1 Â@Đ‹Ŕ4Á9IH,¬„Á-­,ÄJ--mÝMođ¦L2ÎL‚ö±vY~ Gc 0äG8 q bÉD9ěŽđׇŕĎy ľYŕĺ|=€ź,§Č9Ĺ żÜ‚Iѱ…Ă‹Ę_­ęŞ˝Ćâź^cŢÖfě“8y/âű>Éß_[;bĄ–â Pső®fm]vŇ¨íş”ľV˝i».Ąo­VÚ·ĄĄÜ[e¤ÚŹ2‡™Ľ ąt6endstream endobj 70 0 obj << /Filter /FlateDecode /Length 156 >> stream xÚ31Ö3µT0P0bcKS#…C®B.cC ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. ě ň€ř=2>çgŔ˙˙g`†ŕńzŃp=×aÁ˙€ř&fá„?Ŕqý˙˙ţ˙˙˙†A|.WO®@.ďűJĎendstream endobj 71 0 obj << /Filter /FlateDecode /Length 205 >> stream xڍб‚0ŕ# $·řÜ hA%1!ALd0ŃÉÁ8©ŁFWáŃxÁ‘ÁXâ`›|Ă]ÚűŰ‘5°]2hH}sB–Kö&žŃr¸jиjíOč‡(6d9(\G.éząQř«™(Úšdě0 Ô„éĄ9F˙"­şZ ,EĘÔIIQă«Úx Đ%Şą˙ŕUóě˘4d]Ô†G­ mQţMSĐĎáež[ň©p )yX$ł>ďń“Aă&ŕ<Ä5ľNÇXęendstream endobj 72 0 obj << /Filter /FlateDecode /Length 230 >> stream xÚ}ͱJ1ŕ9®X&Źyw×Ýl ś'¸…pVbĄ–ŠvbÖ7[ńEâ(6W77V8±0/™É̤möf‡RÉľíö@fµÜÔ|Ďmcq…×w<︼¶áňÔ˛\vgňřđtËĺ|y,/䲖ꊻ…PLdK?˙ł“ět4ýg1:üVuČ&*Ţ Ëw×#ďú¦şŢ%č{"ßo¬×OÖpş‚($ŹBňÁJ(D|p¤0hÚůŤĘđŽ®řšÍs^>Űą3k¸•ý ÝđcÔ¤RýP5ż˛¸Źy>éřś·ZsYendstream endobj 73 0 obj << /Filter /FlateDecode /Length 154 >> stream xÚuɱ 1 €áŠĹG0O`Ż\opÎě čä Nęč čjűh÷(÷ŽblÂ-ň…?ńĺ´šaUź—Ă“+”>·$?Ž¨Ř–ě*_Á†5ŢoŹ3ŘzłŔÜ î šHť1DŻ>‘1Cf$t cˇUIa.…Č<5ľĚGa ĽűD"JLKLü“`` ?:•RŽendstream endobj 74 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚuĘÁ ‚@`Ĺ0Áy‚Vq :f‡ N˘SuěPÔYŁÓ7|;µÁâ4kuhľýçgd4GôOĆ q¤ě^Í·=@’Xˇ” fÜ‚Čćx>]ö ’ĹC)®C 6Ąčhż[®¦Š —¨ˇ’}PíOmĺwjŘŠně•ÖîŘÎÖ¬¶ŐGe·żrŰşµInůOsá•&yĹ?Í…_ä[ßć*o©&ť+jIÓÓhň»‡iKx—‚»endstream endobj 75 0 obj << /Filter /FlateDecode /Length 180 >> stream xÚmĘ1 Â@Đ )ÓxçnBVÁJÜBĐĘB¬ÔŇBŃÖÍŃ"^doŕ–)BĆŮŐBÁb˙FĺáSĚřTŽů÷ś@ůžúęĂî…ąF•śó R/đrľ@Ë)ňZâ†?· Kڍ6•éA–}’c‰EŹî-Ű olĽ}´Á:X}±“·"jţł&x±űoÂvÁV$öGCÖë* šŹ~‡™†ĽęőfŹendstream endobj 76 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚmŽ1jĂ@Eżp!fʰs‚¬ÄZ1®d˘"W.B*'e »Ťöh{AĄ ˇÉ(&E óŕ˙aříŞ-ĽŃ]{öŹü^Ň™|ĄşXär8}RÝ’;˛ŻČ=©K®}ćëĺöA®~ŮqI®á×’‹7j$ąô€•2©%32É« ]Ě„hzŘdL˛¦úsÇ×_L˙ä_ŘÄYŁt:wĚjh^Hů;„F´U.Úo%mĄŹZ”ö-č/LRzendstream endobj 77 0 obj << /Filter /FlateDecode /Length 230 >> stream xÚuνNĂ0đ«:Dş%Źŕ{â„:&Km‘Č€bj@°’ľy?BFiŽ>@UĄJÖOöÝůîÜň˘¸‘L—˛ČŻĹ9Y^É.çwv™î/·}ăUÉöI\Áö ¶ĺ˝|~|˝˛]=¬%g»‘ç\˛.7B>š@TĹ*ÂvPU‰<ÜÓL_Ă: ŘŃĽˇy;§3‹ýóÄd4śŃĹ0 ˝ă1ő¤iČď{±•‰O¦K[¨lűŁ5LQB}!ŃżŐ‘ßgěŽlO­4 b ó¦űçŰ’ůÜv›endstream endobj 78 0 obj << /Filter /FlateDecode /Length 228 >> stream xÚuαJÄ@ŕ )¦É#d^@7!ą;­îN0… •…X©Ą…r׺ë›ĺQro°`łŕ‘ßY#\qŘ|,˙ěđOŰśĎ/Ą’…śŐҶŇ,äąć7n–šV2o˙FOŻĽęŘÜKłds­9›îF¶ď»6«ŰµÔl6ňPKőČÝF@fŘ*ńÉá;€á!É…Y$ ť‡rHôT Ö'Hq‰ŹÄ8(ý)ĺŻŘ Ýp^wáeđÖç ŰĐ *ô ˝LÉ1j ˘~-SŃ‘1qř‡ě—x 0hăD^)㫎ďř Zz endstream endobj 79 0 obj << /Filter /FlateDecode /Length 179 >> stream xÚ}Í1 Â@Đ]R¦Éś¸‰VBŚŕ‚VbĄ–ŠÖÉŃö(9BĘÁqvE‹y0˙3LŞűĂĆ8ŕI3Ôî8BŞyŹÝęŠírj…©5ă”™ăůtŮĘL@¸N0Ţ€)PR+IÔFdęĆŢ’jIW˘ZČE,×Î&´¬ *>¨„`…óîíĽí۰ů°ţmôÔţł÷´ú˛$jĽüŚĽĺKÎaj` ż†Uŕendstream endobj 80 0 obj << /Filter /FlateDecode /Length 206 >> stream xÚUŤ1jĂ@Eżq!foÍ ĽRd\ l¬Â`W)BŞ$eŠ„\vʶGä)U8˙M—b3űŕíĽ™µK­tÁ™ßkłĐ×Z>¤iyWůĚâĺ]V˝řGmZń[ľŠďwúőy|żÚݵżŃ§Z«gé7Љ}'8ł„Îl€"M !#ĘT ‰pp‘›P\‰©Ť`‰~ŔԅƲꌀE˘Św€KŐ¸r40Ă€€0ćďŤâ‚ß=ćO%›ňĐËAnŞRZAendstream endobj 81 0 obj << /Filter /FlateDecode /Length 176 >> stream xÚuĎ˝ Â@ ŕ”nYúć ĽÖ«˘ µ‚7:9“::(şÖ>šŹâ#těP“C…îăňĂ‘Km8ˇĆrŇĄ#:&xAk%Ź5ŐĆጙCł%kŃ,ĄŠĆ­čv˝źĐdë9%hrÚ%ďŃĺHDĄĐëbćfţRú›ŻAˇ#´JÓAŕ©;=L•â—Vi„@ …&Ş!`®”ČnOY—őoň .nđ îRđendstream endobj 82 0 obj << /Filter /FlateDecode /Length 178 >> stream xÚm̱ Â0ŕH†Ŕ-}„Ţ–´ŠSˇV0 “8©Ł˘«ÍŁĹ7é#t¬P<“ŕRt¸ŹűďŽËÔ8źa‚SW™B5Ác PąË‰Ź~q8C©AnQĺ —n RŻđv˝ź@–ë9¦ +ÜĄěAWX·ś µÂŃ ˛0ă-‹‡FV°_j,{üáÍâ€aý€Ń—ÂđŢ˙é\wî¸v‘ŤŤđpzQĂčI6đ&‹]+endstream endobj 83 0 obj << /Filter /FlateDecode /Length 176 >> stream xÚ=Ë=‚@ŕ!$Óxć.dŃ@ bâ&ZY+µ´Đh‡ÁŁqް%gů+ćËĚ›Ľ@.Wyň!É5Ý||˘4™gNó¸>0U(N$#;NQ¨=˝_ź;Šô°!EFgźĽ ŞŚŠÖęš®łÚ~ë3§ś ⻂|¦ž°4Řš±4#\YüŔި]gr¦1äőÄWOŐLÉ$ÓÇ­Â#ţbVOendstream endobj 84 0 obj << /Filter /FlateDecode /Length 197 >> stream xÚ5Í; Â` ŕ€%7°9‹őm`A'qRGEˇCˇGŹŕEz”ˇc±ćokB>ňbwÚÝ!›Ü—˛ÜéńŢÂÚ&ë”QvGű¨Öl›¨ć˛Eĺ/řrľPŤ—¶PMyc±ąEĘQŃ·( 5Ň•;Ў‘iŇ?Í’ä•Ä5™Ó-7€î- ÇÇ«yľ! ^P+Ě<§$r4ˇ+n ”Ź„¬"©IŤD>8óq…?áUŃendstream endobj 85 0 obj << /Filter /FlateDecode /Length 216 >> stream xÚEαnÂ@ PGNň’OŔ_ĐKH@b!Ą`b@L´#n¤vý“Hý¶Ţ0öe`¸'Űwg»ČßFJ)—SŚ)Óg†G,†’§šęĹţ€ł 톊!Ú…TŃVK:źľżĐÎVťÓ6Łt‡Őśbö%71w%;Ă]Í®Źű:$δ &Ŕ´ nKoW1ň]Đ‹pż©uű˛tÁF@u¨°ŢF˙jü§ďM0ůŐ>ÉŹźÔ)č” čÄNŤĽ6Ş˛#0Ëľ˘ jÜ×ńŁÂ5>Ý[¦endstream endobj 86 0 obj << /Filter /FlateDecode /Length 224 >> stream xÚMαŠÂ@ŕ )„iňBćÎÍâ´‰ŕy` A«++µĽâŽ®čŁĺQň)·®;»Áló±ü3ěüj:™-(#IorNjNÓśNPĺ6Íh¦úŃńW%ŠOR9ŠŤÍQ”[úű˝śQ¬vď$Q¬éKRvŔrM`şŘčČ> stream xÚmŽ= 1F'XÓxçf׍ VÂş‚[ZY•ZZ( vz4ʞG°L±ż‰?•ä13yLâ˛Ţ`(‰d8.—,—mĘv}ô‰¶z±ŮsQ±]ŠëłťbʶšÉéxޱ-ćcIŮ–˛J%YsU äf”÷7[qňá(hžĘVŁě ¨©[“©it'äzS¤í•Č[Ś vý».Qô*šFEŠńńQŻ"xĹż ?>â¤&žTĽŕwse–endstream endobj 88 0 obj << /Filter /FlateDecode /Length 203 >> stream xÚ}Ďż Â0đ”…[ú˝Đ´´Őtj3:9“::(şÚ> stream xÚuĐ˝‚0đ’[xî´‚âD‚ŘÁD'㤎]…GăQxFBíĄ1čňKűż~\Šq4CCM1Ĺx ŕ"֎ʓ¨«ÎJŕ[1đĄŽËŢ®÷đt=Çx†»ý=Č ™W3ĆĽV“¨‚¨ôTQÎScýĂ6ÔCC5Ä5”źQ·š±•>ŐRÍ›p(s©Ú5MŰ’‚`_ä=Ź´=ÍËĐ?ÍĄËčGrúĄJ‚"Z–S°°ňZ¨endstream endobj 90 0 obj << /Filter /FlateDecode /Length 137 >> stream xÚ31Ö3µT0P04S02W01V05RH1ä*ä22Š(™BĄ’sąś<ąôÌ̸ô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ň ü ö ň ö öęQqC=C=2Ăp\ĆŕŔń†   \®ž\\Ő8ćendstream endobj 91 0 obj << /Filter /FlateDecode /Length 205 >> stream xÚmĎÁj@ŕ_<sč>‚óQ‰iZ &…z(¤§BNMŽ9´$7ÍGŮGŘă$f–+–ů`f`ů'ťĎŻółTşäE‡„~(ŤŮ=iÝâűDEIѧ1Eď2Ą¨üŕóďĺHQ±]łôŢ%ď©Ü0đ TžÓů‚őőĎ‚ ÚľQmĐ÷} WG?p…j2ü6µ€ęNŽČ`ÇÔž}Ĺ}gvŔä‚öµjčPhCLQmŽQ€˙ +ŕI.˝•ôI7y-qˇendstream endobj 92 0 obj << /Filter /FlateDecode /Length 199 >> stream xÚĄŹ=‚@…‡PLĂś č˛Čź bâ&ZY+µ´Đh«ŤŁxJ Îd)č-ľbß›yó6šĎâ¤3šf%gtÖxĂ0e5 $¬Ó jOaŠjÍ:*łˇÇýyAUl—¤Q•tĐŃ”ŕÔîŔg&Ě›ß}NÇr ŕ5Ĺr^± ťĹaŰý2Ťó†ż¶ă“Ę®ä`‘Ő׉i˙`ś•Ź»r_zHé&=ĄŻ| z)3”óWwřFHH—endstream endobj 93 0 obj << /Filter /FlateDecode /Length 203 >> stream xÚuŹ1‚@EÇPLĂLś č‚ÁĘ1‘ÂD+ cĄ–&j´ŽĆQ8%…gd•B-^6™˙gţß‘;đĆd“Oý€\ŹĽ€öžqđÇ~ŁěŽƨÖ4 PÍyŚ*^Đőr;  —SrPE´qČŢbt ÇLR~3&0 Łč> stream xÚmËżJAđOS¬Ls/ î<{ÇŢů§ń FđŠ€©,ÄJS¦P´ ą€/¶Ź˛Â6ĺ‰G>÷ÄÎ Ě曙ҟV—šë…žkYjéőąńUĘrőgż‹§…Śq÷ę+q·)×Lőíő}.n|w­…¸‰>š?J3Qŕ©®V{X‡‰u¶îGÁ†>‹vŰ×ŃvŁÝ}1’‘=@nšČ^í@›Ć2"Ýu)âő÷'Ńn6?"±2±ĹŇÄŔÄŁü‘…˙ÔrÓČL~‰Qendstream endobj 95 0 obj << /Filter /FlateDecode /Length 151 >> stream xÚ31Ö3µT0P0W0S01U01QH1ä*ä26Š([€%’sąś<ąôĂŚÍąô=€˘\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. | @ Ź…°v¸:QAŘ˙˙˙˙A=řńN€ą ?@J@#ř€`pě`ÖŃŔŔŔĺęÉČ\z> stream xÚ=É1 Â@EŃR~“-Ľ čäg”`Ł#8… •…¤RK EÁJł4—âRZ„ŚÓ(śęŢ‘Ž'̨–Íi•Ş<¨śE‹3ćö÷ö')ť-µł CŚ[ńząĹ”ë9ULĹť2«ĹUD‹¸CŇ#őMx‘fŔx˘ńi‹çţß î€,ślä ő‡* endstream endobj 97 0 obj << /Filter /FlateDecode /Length 102 >> stream xÚ31Ö3µT0P0"3#C…C®B.#¨‚)T&9—ËÉ“K?\ÁČ’KßCÁ”KßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEˇţ˙˙˙˙˙˙Ă >—«'W ˛©$Ěendstream endobj 98 0 obj << /Filter /FlateDecode /Length 179 >> stream xÚ31Ö3µT0P0QĐ5W0±P0µPH1ä*ä21 (™Bd’sąś<ąôĂLŚąô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. @ŔxD2?@ě,Î&ĺ¤=`¨C"˙€Ťů ™? ĆŹaÄdĂjđĆŽa¦›ěĐÝ„lÔMđąIž$bş‰żź‘ÜÄ6†ˇLrązrrШAendstream endobj 99 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚuĎ1n@ĐŹ(VšfŹŔ\Ŕ^Ů’ŤĄPXJŞQ*;eŠDv mʞG tŘ Ć.’ć­4#ýťżJ—نc^ó"áUĆŮźú¦4—aĚY:mŽ_´­ČĽqš“y–1™ęŔçźË'™íËŽ2%ż'PU2µ|„ţ (ßÚ2w(Ú¦E-zD6¸BŰđFĺ”{ íDŘIÚť3ę?Ż”űmgDíŚj #’× AŹrf#érµŃNNŹ,t']´÷cÉá^Ţal Đľ˘Wúqái7endstream endobj 100 0 obj << /Filter /FlateDecode /Length 170 >> stream xÚeĘ1Â0 PW"y€#Ô' MKUJ‘Č€CĹŚ X)GëQz„Ž U‚€ Ďň˙ö8eSŠIĹ<Ň e ž1ÉÉ5ß—ý ŤrKIŽrÉ5J˝˘ëĺvDY¬ç¤P–T)Šw¨K@ô1c5ł ™0|2 GÂŢAôĽw=˙ý ś§/t:źpZßĐi|‘óř©­m¬µí—˸иÁI Ptendstream endobj 101 0 obj << /Filter /FlateDecode /Length 229 >> stream xÚmбN„@ŕCA2 ŹŔ<ŔÉ™X‘śg"…‰WY«ÓŇBŁ­đh<Ę>%aś™KĽKî6đegçß]B}}µľĺ’k{ox˝â·Š>©®´.­´Ćţ6-Ď\WT<č*í#ýĽS±yşc]Ýň‹nyĄvË@6CG'=D"ŠŚş,2ůdíf‹Fzěé-mĺý©É™Áé1ş:šđ;Ý_w1Â|4™Ět4łhćn7öµľ)ńxćńÜăM> stream xÚUĐżJÄ@đYR,L“GČĽ€nb.r6¸?` A+ ±RK Eá*ď-Ź’GHąEŘqľ‹‚˛đ[Ýý†ŮE}Ţ\I)—rVɢ‘ćBž+~ăziĹRšz>yzĺuÇá^ę%‡k+sčnäăýó…Ăúv#‡­r·˘69MD^őH…jO­ę@‡±IÉGJä˘3&ţ`ËM´·S˘™ řń—|0ÚŢ8‘oćF ˇ¦xoÍí2(đ"~řBł9~…ÚĐň}B@BTB_Cm˵c1a´H9ćóÔťză x×ń‡kendstream endobj 103 0 obj << /Filter /FlateDecode /Length 214 >> stream xÚeĎ1jĂ@Đ[¦Ń4'đJ–T¨±@±!* q•"¤JR¦°±» ëą’n+¨s«.*„70‚,ĚýË0łi˛Čr‰$CĄ™dKyŹyωf‘^őáí“ËŠíł$9ŰG¤l«­§¶ĺÓÄl×ňKôĘŐZ¨hÁYqžb~ÁOC~O¨•xCH7Lü-…VhPjeŢLă hAŘ€‚&j˘Ψ\ďś5Ó™ŘÖë˙cîtsŚĂ·|çşšń¦â˙ţ*fëendstream endobj 104 0 obj << /Filter /FlateDecode /Length 224 >> stream xÚuϱnÂ0ŕ‹2Xş%Ź{âD,Q*5C%ŞNŔČ@Ő®uÍŹâGÔĹC”ë™va‡O§łěűoQĎšGŞhI† 5†NŻXݤYQ3˙»9^pÓ˘>P˝Bý"mÔí+}~|ťQovOdPoéÍPőŽí–Ŕ2GpĚĂ=ľAÎ&ČnÄ ňč<ä?ÜCžţĆ Ţuj„Ň«…W=AP!÷BzŮO˛P˝˙SÜđBé%­í$”ë¤bpŤR«l°J–,łLaî ă´ś•řÜâĽp.endstream endobj 105 0 obj << /Filter /FlateDecode /Length 124 >> stream xÚ31Ö3µT0P04ĆĆ Ćf )†\…\†¦@ľ –IÎĺrňäŇW04ĺŇ÷ sé{ú*”•¦ré;8+ré»(D*Äryş(0|`ţĂţ‡ý?‚Ř?0ŕü öęÔ?ř v—«'W Ča*‰endstream endobj 106 0 obj << /Filter /FlateDecode /Length 118 >> stream xÚ31Ö3µT0P0S04S01S06QH1ä*ä2 (Z@d’sąś<ąôĂŚ-ąô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ň˙˙˙˙c$ţ!°‘ ¨ř˙˙ Ŕb\®ž\\ĎŠ>Ăendstream endobj 107 0 obj << /Filter /FlateDecode /Length 187 >> stream xÚUŽ˝ Â@ ÇO YúÍx­w8jotr'utPÜęŁőQúŠ5I-Ôĺů$±f2›cŚ-ZÖá)+GZŚv*Ćń™˝Că@ŻHí×xż=ΠłÍĐ9îŚŕsT/ĄÔ¨"ŚkFĂ㇠ZFQ"¶Ă7!Ř\LĹ®{»kwĹ; #e´%ç(đ®»iőÓÇÜ›^/ŞaTtY!źÉ)yçÉ@,=lá M>kendstream endobj 108 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚĄĐ=NĂ@ŕąXiš=‚çŕŘB‘,… á * D)S€ µ÷&\Ĺ7ÁGHéÂňđ6.‚DIói5ű3o¶Xť—k]꥞ĺZ¬µĽĐ×\ޤČY\jąšw^ö˛©%{Ô"—ě–eÉę;ýx˙ÜI¶ążVV·úÄ;ĎRođĐN>`aö}x3 H‡”V˝ŁmH¨ńâbŚ&oĂNúhŕ»h:€+T¨p˛=Úüq::ţϤ‹ş>ľF›_˛/C2ă1eÂyaÜ:ÄůÜčă#fśŹĂÉ`ÖĹčx–!7µ<Č=cendstream endobj 109 0 obj << /Filter /FlateDecode /Length 191 >> stream xÚuÎÁ ‚@ŕÂ\zť'HĹ Á ňÔ©CtŞŽŠşEúh>ŠŹŕŃhłkeͰüł°;ÂűSrČă#&ä»ttń‚Bpvd”‡3†1Ú[í%OŃŽWt»ŢOh‡ë9qŽhç’łÇ8"h¸reˇ)ˇŻ‘QŔ¨5“ńźVzV \ż4Ů ¤0°i:“·uç“űÓl3%üRk-Le00˝µĎöĺřăćËJÍKŔEŚ|ń}xBendstream endobj 110 0 obj << /Filter /FlateDecode /Length 193 >> stream xÚmĐA ‚@ŕ'.„·éľ4ZŠ´Ě Yµj­Şe‹˘¶i7ó(ÁĄ qš§ 3üo~f‚ů4\G3˝C˝|:űxĂ ŇąŤ|pşb"Qě)P¬ő…ÜĐăţĽ H¶KŇ9ĄOŢeJ5 jPĘRÍČnî|Ŕ-`ŇY€››Ťs.°9Ä`6.°Ż?•ľđgÖ[÷ęÂ@KŰ´Ö`UfíŠ lviÖ)ąŔ–üʡ™‚öŢJŤě渒¸Ă/V±endstream endobj 111 0 obj << /Filter /FlateDecode /Length 156 >> stream xÚ31Ö3µT0P0b3SC…C®B.c ßÄI$çr9yré‡+[pé{Eąô=}JŠJSąôťś€|…hCX.O†ú˙˙0ü˙˙˙cŕ?ŔŔŔ &pö`‚Q"ęp˙@Ä#ř`pě`â2QŹěżpOţaŕrőä äIVRendstream endobj 112 0 obj << /Filter /FlateDecode /Length 242 >> stream xÚmбNĂ0ŕ?Ę`é–ĽAě' ¤Ş˘X*E"LSadČ`µy^ÉoŔ+dc$˘–sŚT@•|źôßů»89šžŞ‰:ćšňÉŐ]NŹTĚ8ŹŃV4Ż)[ŞbFŮw)«/ŐóÓË=eó«3Ĺyˇnr5ąĄzˇ°é ězČí^˝ĹĆAHśż ^Ů_öźŃk˘O mb¶2ń{Ë o)ŢĽIP¶X—’5•”`ÓŃj´5҆uiSyű˝˛ ®9iŮ^ZĂ&­WŔ‹ÄÁŽW9 ő+żĺ§űo w }:Żéšľ˘{{endstream endobj 113 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚmĎAJĂ@ŕżtx›9BŢ šFSŠ›jłtĺB\U—.”şjir˝‰ä(s„én„ˇăË š…˙}đ˙łšâ|2»ŕ)źÉÍ$9?ĺôJĹ\z¨ÝĂú…–e÷\Ě)»–•˛ę†7oďĎ”-o/YúŠrž>RµbÔµ·đGx×+Ł$qP-Tô Şú8aÚ ý ¦Hń«Ú”@\¨fńgmŁ{`Ü%íNGőP¸ iŰk,FťÓű=pk0Žjluo-9˘Ôđţżm·Ë骢;ú[Ę|endstream endobj 114 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚ}ν Â0ŕ+„[ú˝'°˙‚SˇV° “8©Ł˘sóh}”>BÇĄń.EÁ†ËÇý$$q4MćäSÄ;žQĐ)Ŕ+Ć!׾”28^0+ĐŰQ˘·â.zĹšî·Ç˝lł ®sÚä°Č ´Ö Ä,¶5yoÔ“ÚfťJN©Ń­>ľăŐTĺHA¶±-ŁÝIÓĺ?”ň±6*‘ʰ<”+Ľş1­ÁvL{°ůµÔ˘yőˡŹË·řäťjŇendstream endobj 115 0 obj << /Filter /FlateDecode /Length 244 >> stream xÚmĐ1NÄ0ĐĄ4ŤŹą8I±U¤e‘H˘Z()XA»ö 8W‰DAÉr„”)˘5c‡H€ÖĹ“5¶üż\ťťÖ+.¸äĂU͵áCĎT•2,¸.ç“í­[Ň·\•¤/eLş˝â—Ýë#éőő9Ňľ3\ÜS»aXŕ˝wŃ>:@ć~˛^M€ęą¤:ĚÚ_6‹ů¬;â~±qá…ÉLÇ ‚Vrď»ëđÓJöX&{بäČ#’‰Izłc&ń4ĂÍ˙~¸ŕg'ňŻ.żýŃz¨w'©ĘĎĘ—¸ě EJsY#袥ú´}×endstream endobj 116 0 obj << /Filter /FlateDecode /Length 245 >> stream xÚmĎ1JÄPŕYR¦É |sÍĆ}!°®` A+ ±RK EÁĘ—Łĺ^aŹ2Ĺ’ńꉋ6ÉĚĽy˙‹«ŁúT–ĺ°’x"ő±‰pÂ,ŃÎ\@Ç_łŮčs/*g.ů ů)¨&éÖL“ŮřOPëăvY´µ‡ůĎě`nî ˙,ß{ŕ·ůOÄ›Mx±[l)őz»i˛ç&µ$©vŞX?zÎŹĚňEË7ü }„tŁendstream endobj 117 0 obj << /Filter /FlateDecode /Length 163 >> stream xÚ31Ö3µT0PaS 2TH1ä*ä21PA $‘śËĺäÉĄ®`bŔĄďĺŇ÷ôU()*MĺŇw pVň]˘  bą<]ě˙˙˙˙ŻHüG#ęěę˙1Ô3Ô˙a¨c¨ĂFT0üc°a`řĂŔ€•`?pĚ`â‚L<ŔAđ‰8y0Ń€Lđ˙˙dü˙ŹL€Ĺ¸\=ąą7X^´endstream endobj 118 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚmĎ= Â@ŕ‘irçşY“€V ţ€)­,ÄJ--mMŽ–Łä–â8“mR,„Ţě›d“găbF)Mid©Paélń†y&ĂT'ÝÉéŠóÍžň ÍZĆhĘ =îĎ šůvAÍ’–Ň#–K޸vÜ07·}ý> stream xÚ31Ö3µT0P04SĐ5W05P0µPH1ä*ä26Š(™BĄ’sąś<ąôĂŚŤąô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ň˙˙30Ř˙˙߀JĹ€ NÔa!ţÁ‰?#‚řI0#;‚x€Iđ#„<‚hŔ$ě&ß»˙˙˙˙‰z—«'W !čVŽendstream endobj 120 0 obj << /Filter /FlateDecode /Length 156 >> stream xÚ31Ö3µT0P04QĐ5W0¶P0µPH1ä*ä22Š(™BĄ’sąś<ąôĂŚŚąô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž.  ?ŔčC Ő10Ř‘=<50đCĐvzŔŔ G!č†qŘM„‡ŐD¸qXM„Dr 2\®ž\\&Š;*endstream endobj 121 0 obj << /Filter /FlateDecode /Length 172 >> stream xÚuĐ1AĆń…ä5Ž0ß]cŐ&k%¦P)D…RAh­ŁíQA©;Cńš_ń˝ę˙şş  V:FÔÇ:¤i]ŤâčyYm)5¤ćĐšÔ¸šI™ űă†T:"$•a"X’É ¤µB$Öž?!ä›Ä#rljŁtÜjžCÝsehx. MOÁ ‹ŻľßŇ˙ąą{•}RľČmU@#C3zäTńendstream endobj 122 0 obj << /Filter /FlateDecode /Length 137 >> stream xÚ%ɱ Â0…á#ÂYú‚÷ LÓ´‹…ZÁ ‚N⤎Šn‚>j#S®AáźľżvÓf.ĄŘJ\#un&gË]•őç˙uş˛ó4{qÍ:#ŤßČăţĽĐtŰĄXš^VĘ#}/ @›ŐĎH5LTă;µIc‘4 Uô%0ćĘsÇ/Z)µendstream endobj 123 0 obj << /Filter /FlateDecode /Length 197 >> stream xÚUĚ; Â@ŕ?¤¦ń™¸ ‰«` A+ ±RK E[7GËQr„”)–ŚłŘh1Ěë/ňÉtÎ)—ZEÁyÉ—Śî”Ď´OCç-*2Îgd6:%Smůůx]É,vKÎȬřqz˘jĹH€HH¤C,â10ęă\ŔÖq‡¤ŽEĎ˙qRc,ŠS4EB€č¨µH<,l«)®o ˙Ëđe@äˇß®±ú¨)]˘ôšîúXĽí!í¸ŁuE{úł/^qendstream endobj 124 0 obj << /Filter /FlateDecode /Length 212 >> stream xÚuϱJÄ@ŕ_R¦ŮGČ> stream xÚ•Ž1 ÂP †q(d°Gx9ŻĄOA ZÁ‚N⤎Š®mŹÖŁx„ŽŇÁ!$!ůżŤ'3NŘ*Φ|IéNYĐ>±Öç-KňÎůŤNÉ—[~>^WňËÝŠSňSNNT ČD'Ň i!Š4y;ě‘·ŃGwpŤ{c×ČjCeč ß s»]Ř—ĘžZž†ş.ţ"USł“‚9©-­KÚÓ¦ŤIĆendstream endobj 126 0 obj << /Filter /FlateDecode /Length 193 >> stream xڕα‚@ ŕ’.<} L— &Ţ`˘“qRGŤ®â›áŁřŚ—;[pqÓᾤ˝´ý 5)+ĘHń+•9ís<ˇ’^&Ą|ěŽXLפ*LçÜĹÔ,črľ0­—S⺡MNŮMC±€Ä  ˙$z1Ú1Ţwxď!"Ëűâ>ô<ćôZ™iá&łN°?â>cíH ăRa¸ĘÉHŽ'c Ë:ÇŃ´m™¸O,Î ®đ —şYKendstream endobj 127 0 obj << /Filter /FlateDecode /Length 201 >> stream xÚmޱŠÂPEď’âÁ4ů„ĚěK¬® ›BĐĘB¬Ôr‹mM>í}ĘűËâě}VĚ™;Üą“úł™i©“ÔĄÖS=Tň'uĂů9&a˙+óNüFëFü·â»ĄžO—ŁřůęK+ń ÝVZî¤[(˛€ÂĐŰ f#2ł;ÜJ>ÂPD´Cv@Z }•„‹÷c˝C  ¤7¸ľĐ'Đ* 4u‘ö.ć7úąmp Ěb2rćcŔňÝÉZţI÷_ţendstream endobj 128 0 obj << /Filter /FlateDecode /Length 154 >> stream xÚ31Ö3µT0P0asSC…C®B.cßÄ1’sąś<ąôĂŚŤąô=€˘\úžľ %EĄ©\úNÎ @ľ‹B´ˇ‚A,—§‹˙ű@â˙Ć˙˙Aűźz ńHđ?°*;&pő˙˙˙š4A€ĹđkŁa˙˙˙[~ `1.WO®@.ňĹ^Łendstream endobj 129 0 obj << /Filter /FlateDecode /Length 253 >> stream xÚ}ʱJÄ@†˙#E`š}!óšÄä”k.pž` A+ ±RK E»#›ÎÇđUň(y„”[,g‚Ť˛ěǰó˙˙ĚÖŐÉzĂźňqąáşâęśźJzĄş`;ëłźÖă íZĘď¸.(żŇwĘŰk~űx¦|wsÁ%ĺ{ľ/ąx vĎ’€4¸lnfxYé•DdöItÁ§S¶n\Ĺ#7@efd=ş`’El6X4jB*˛`„éáľfŔ}EŹ_éh0‡íb•ôj“1SLÍ€,xÝ>v*‹Ĺ!*:MĂö–Ƣó˝:ť˛?-y‰%ۧF‚Í@—-ÝŇ7ăč‚>endstream endobj 130 0 obj << /Filter /FlateDecode /Length 161 >> stream xÚ31Ö3µT0P0bcSC…C®B.ßÄ1’sąś<ąôĂL ąô=€˘\úžľ %EĄ©\úNÎ @ľ‹B4Pe,—§‹Bý ř˙ť¬“Ś‘ň@dý ůó˙? ůű˙ ůB~°o’äAdü ÉŔ$˙É?Häz“ő˙ťř˙˙Ç˙˙I8—«'W zúendstream endobj 131 0 obj << /Filter /FlateDecode /Length 132 >> stream xÚ31Ö3µT0P0bcKS#…C®B.cC ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. ě ň ŘţĂÄ@ňx@ý˙@ü€á?×C1;}pý˙˙ţ˙˙˙†A|.WO®@.üŘO)endstream endobj 132 0 obj << /Filter /FlateDecode /Length 169 >> stream xÚÍŹ= Â@…_°¦Đ#d. ›Íź B Fp !VbĄ–жnŽ–ŁxK q\‘`eďŔWĽďńЉŁ~2â€cîé!Gš“·š¦ÎO¤j‰Ô .»m÷Ońë1üęâţdX÷„ČVîŽ|ą˘-M -č§úXendstream endobj 133 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚťĚ;‚@ŕ%$Ópçň.¨H)L´˛0VjiˇŃV¸‰Wá(xŚ…[Ć_­Ĺ~Éüłó‡Á0ŠŃEź_čcäáĆ=’ą2Ęb˝4gA ΄Spň)§-8él„ôŚsĂQąyŔendstream endobj 134 0 obj << /Filter /FlateDecode /Length 115 >> stream xÚ31Ö3µT0P0b e¨bČUČeläą ‰ä\.'O.ýpc.} (—ľ§ŻBIQi*—ľS€łď˘m¨`Ëĺé˘P˙˙Ă˙˙‰zÁŔ<Śú˙˙˙7ń˙,ĆĺęÉČî{\Wendstream endobj 135 0 obj << /Filter /FlateDecode /Length 171 >> stream xÚ˝Š= Â@…·[&GČ\@7!Q°1#¸… •…X©Ą…˘ő^,7đć[n±ě8šÎČŹ÷WĂŃ3ä‚r„Ĺ9śAl&’ř]ö'¨-Ť\Ŕ,¤c—x˝ÜŽ`ęŐ s0 nĺąŰ =śî=Cężbq䙣Ň1 SĄe¬”ö‰K•vI'ě’ö‡mr˙/)Tžňě8R`ßűľ‡ą…5ĽízfĘendstream endobj 136 0 obj << /Filter /FlateDecode /Length 155 >> stream xÚ31Ö3µT0P0bcc3…C®B.ßÄ1’sąś<ąôĂL ąô=€˘\úžľ %EĄ©\úNÎ @Q…h ĘX.O…úňţ˙¨˙$ţ˙$˙˙ĎŔP˙D2ţ˙`ß$ČČů@’Hţ“Čô&ë˙?:ń˙˙Ź˙˙7 “q.WO®@.‹Łllendstream endobj 137 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚ}Ž=‚@…‡XLĂvNŕBL¬H·0ŃĘÂX©Ą…F[ŮŁíQ8Ą…a†‚Îb^2ď}ą™KJ)*%ł K†w4÷Ň‹ó +‹ú@¦@˝á)j»ĄçăuE]íV”ˇ®éQzB[Ä_PĄ ˘:…đá9o’.ęAµ@9(ˇdq%ź»7@â'aʏý/=ßµÓGĂ.^¬ÄTyhĆ ‰”pÁ A!\\[Üă>ťP:endstream endobj 138 0 obj << /Filter /FlateDecode /Length 200 >> stream xÚĄŹ= Â@…g°¦ń™ čfI"¦üSZY•ZZ(ښͣä[.(w“€–‚S|Ĺć˝7q4HRYs_Ź8Ö ů éL‘WCNâvµ?Ń$#µá(%µp:©lÉ×ËíHj˛š˛&5ă­ćpGŮŚs” V,ČS*7;(& A‰]t,ľŕ -Ŕ•ÇýGTÎŔťµ@Ű8×=ÓF–>Ľ®á ˇŻ†ľ$ÚńĽË_ČĄ÷ŞůF­Ń<Ł5˝ŢŻěendstream endobj 139 0 obj << /Filter /FlateDecode /Length 158 >> stream xÚ­É1 Â@ĐźJř—đźŔÝuŁÄj!Fp A+ ±RKAEëőh9JĽAĘÁqc!Ú[Ě™Ií`4-ĂÔËŢđ™m»îjw쎜{Vk±«y\Yů…\/·«|9ĂŞŤ˝e_Hx’+5ĐCôŃ8´äÂ#‚$ŇRC®ˇąš\őˇě¸˙B˙"¨żxo<óĽâőőIwendstream endobj 140 0 obj << /Filter /FlateDecode /Length 185 >> stream xÚMË1 Â@Đ‹Ŕ4!s7q5Ć@T0… •…X©ĄEÁĘÍŃrŹr‹ń,,Ţ2łó˙ÔŽg©D’€MĹ&rŽůĆv‚=ę×ţpşr^°Ů‹ť°Yă—M±‘Çýya“ołYĘ!–čČĹRČůr¨ęGB®ů7 }Kď˙´DŤ#"×eZS¨ˇWˇ˙!§("P÷B Ca÷Ł}­˘9Şť6A«Ş=> stream xÚ31Ö3µT0P0bc 3…C®B.cS ßÄI$çr9yré‡+›ré{Eąô=}JŠJSąôťś ąô]˘  bą<]ä€Ŕž˘ţ˙˙˙ @üżA€ĹH2…‚ů`€ťhŕŔ ß €AţAý~ [@ó˙ Ś˙€LxŔŔĺęÉČţ:B„endstream endobj 142 0 obj << /Filter /FlateDecode /Length 148 >> stream xÚ31Ö3µT0P0bcc3…C®B.ßÄ1’sąś<ąôĂL ąô=€˘\úžľ %EĄ©\úNÎ @Q…h ĘX.O…úĚ˙ţ˙`˙…¬˙Á $0đ()ŹDÚÉ? őţÜĆđęd=”˙H2˙c˙ĎŔĺęÉČÄŁd>endstream endobj 143 0 obj << /Filter /FlateDecode /Length 186 >> stream xÚ5Í= Â0ŔńW:oéúN`úĄĐĹB­`A'qRGE7©…^Ě­×č ęء4ľŘ”É? ‰Âé,&ŹžQ@áśÎ>Ţ0ÔÍÓ[}pşb*Qě)ŚQ¬ą˘zÜźévI>ŠŚ>yG”˝•Ą:ĹôJ•^ý›]S |Á-,ZHZX:Č^<rś[CÂ×Á准’qĘz¤b&Őg¤aě¦QŚĄŔ˝†żŔ•Äţ$›Lăendstream endobj 144 0 obj << /Filter /FlateDecode /Length 174 >> stream xÚ31Ö3µT0P0bcc3…C®B.ßÄ1’sąś<ąôĂL ąô=€˘\úžľ %EĄ©\úNÎ @Q…h ĘX.O…ú˙ `Ôđ˙?ĂŮaCÄŮ00~ @2?ŔDv`˛N2~¨+ţߎ ż#ȏߏ``’ ?ꇓżżG#«ľg``¨?řA6 Hű†@Ržˇ†ËŐ“+ Éťm˘endstream endobj 145 0 obj << /Filter /FlateDecode /Length 176 >> stream xÚ}Ž1 ÂP †S2Y<‚9ŻĹ*BˇVđ ‚N⤎Š®­Gó(ď¤Ď¤c‡|?!?É'ăéśSžčä3>gt#Í”»Ő§+•žÜ^wrëŽ~ĂŹűóB®Ü.9#Wń!ăôHľâ"Ć…ôPŚ‚˘x+š—"B I Ŕ/ >Ў€i`¦$fŕ_Ł…$hЎ¨†˘Šj(ŞˇD{Ł{-ĐĘÓŽ~ćęb°endstream endobj 146 0 obj << /Filter /FlateDecode /Length 116 >> stream xÚ31Ö3µT0P0V0S01T01QH1ä*ä26ŠE-ŔÉą\Nž\úá Ćć\ú@Q.}O_…’˘ŇT.}§gC.}…hCX.O† řA-Âţ˙˙˙€ř˙4‚Šv@  Ăą\=ąąemH™endstream endobj 147 0 obj << /Filter /FlateDecode /Length 103 >> stream xÚ31Ö3µT0P0W04S06W02TH1ä*ä2 (B$’sąś<ąôĂŚ,ąô=Ląô=}JŠJSąôťś ąô]˘  bą<]ę˙˙˙đ˙˙˙0 âsązrrŹĺ$~endstream endobj 148 0 obj << /Filter /FlateDecode /Length 99 >> stream xÚ31Ö3µT0P04F †† )†\…\@Ú$l‘IÎĺrňäŇ pé{€IO_…’˘ŇT.}§g ßE!¨'–ËÓEAžÁľˇţŔ˙0XŔľAžËŐ“+ ‰;“endstream endobj 149 0 obj << /Filter /FlateDecode /Length 203 >> stream xÚť= Â@…_°L“#8ĐMLRŘđL!he!Vjiˇh'šŁĺ({„”!qś-–6߲ó`ö}›ÄĂtĚ!'<8 9ń1˘ Ĺ© ĺ»äp¦iNfËqJf)c2ůŠo×ű‰Ět=ăĚśw‡{ĘçŚŢ@в¶^m ´­…ו„ű•WĂ·¨”x:ô däTLdOń”€_Öű'¤X`–*şw]!WҢqťµ˝z¨‘ş9KőUóďĐ"§ }}ŤdĂendstream endobj 150 0 obj << /Filter /FlateDecode /Length 141 >> stream xÚ31Ö3µT0Pac S#…C®B.# ßÄI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘  bą<]Řř XŠí¸˙7001;×ńľÄójä‘Ô®˙˙˙Á˙˙˙?Ŕ0ĎĺęÉČĹFJÜendstream endobj 151 0 obj << /Filter /FlateDecode /Length 222 >> stream xÚeŹ1N1E˙*…Ąi|„Ě đ.›-V Ab $¨(U ¤A›ÝŁů(>BĘŃóÓ„,?kĆ˙ŹWíEwĄµ®¸kí.őµ‘i;ŻO%/¶ď˛$=iŰIşó®¤á^ż>żß$­n´‘´ŃçFë6Šx0ڄʬ íÍŽX⌾T†~ÂčËϰśfGvÄlŽâgŘ×ÎOČ —Ŕ<|žđHTGÇ‚+µ§Ë‡D5˙WôTŚL3ü*١¸=·‡2š˙Đţ‚˝,·<Ę8hńendstream endobj 152 0 obj << /Filter /FlateDecode /Length 226 >> stream xÚEĐ˝NÄ0 đ˙é†J^ňń @ZÚHH•îC˘L @°Ň>ZĺáƧúl·ŔźDZăTĺe}Í9W|Qp•s}ĹŻ}PYkP·ĺ|ňňN›–Ň#—5Ą[ SjďřëóűŤŇć~ËĄ?ś?S»c„€Nz¬DČDF‘âM&4=:4§WâLě• Ť«hLşVĆÚšÄQ—5Aýâ1;Í,ňw×Ki üs°Ä™ăÇ…ŕ Îdw;«Ň-Ż—y"źÍ§\ŰĽ>ą˙í[z 3áVc4endstream endobj 153 0 obj << /Filter /FlateDecode /Length 181 >> stream xÚ•Ď=‚@ŕ!$Ópć.ż bâ&ZY+µ´Đh ŤŁpJŠŤëL±hë$ó%ó^5YşĚ Š(áÍĘşÄxÇT˛HN)Î7¬4ŞĄŞ §¨ô–žŹ×Uµ[QŚŞ¦cLŃ uMţÁÄ„B9ÓĚĆ›‹‘ńGĐ3aç(if ăMŽĹ( Ś/˝#ěŤ`Ëc„÷—V2öOZËżZ;ý®5îńÜţtýendstream endobj 154 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚĄÎ= Â@ŕ‹Ŕ4{„Ětł&)!ŕBĐĘB¬ÔŇBŃÖ,xŻ’ŁxË’qFEĐÖćťŮ}o“¸ťv)˘„ZŽ’RGk‡;ŚSʱóÚ¬¶ŘĎŃÎ)NŃŽeŚ6źĐaÜ íOäĐiá(Zb>$Ă\CČĚßČĚüÇą.ě5ďŞTĘÂş)ń7˘ ˝śůPĐ €ů\č)'…ß'ĺ-,e›ů$9óŇ‘• i«ĚŚţ `ľAYŇ Öš G9Îđ-˛c—endstream endobj 155 0 obj << /Filter /FlateDecode /Length 241 >> stream xÚmŽ1NÄ0E”"Ň4ąž @’T––E"Th+ ¤Ř´±ŹćŁř)S„ ăÍ“ü=3˙uíEĹ5w|ŢpWsÉ/ ©í5ÔgűýóüF»ŞGn{Şn5¦j¸ă÷ÓÇ+U»űkn¨ÚóSĂő†=6™Ě@! `dŐHpŃëłÎ糢˘˘Ś°0g0ş°żp ă†\ĎŤF<'ź"D´MÖbLz[‚Îë€őZj6]*7DEńă?°?(Łj”A…LP5ăË GŐÔˇµ(O•Y*GŇ@BRć ›č ţ5pIendstream endobj 156 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚ•Í˝ Â0ŕ+Â-ľŢ hÓ NB­`A'qRGEÁÉöŃú(}„ޤzW©Eqń _Čĺ~3°#ň) ľ¦Ŕ';¤ťĆ#ËI~š×Ďö€ˇCµ"cQÍ8ŤĘÍé|şěQ…‹ iT­5ůt]ăÁ‘ Ů'é`ś010%p1ßŕ ­‚içBĆt*R¦—€t 2;nB)Ľű˝˘¦•×4㪙_T+~Ѭý‹.ś:\âăM†endstream endobj 157 0 obj << /Filter /FlateDecode /Length 213 >> stream xÚ}O» Â@ś`q°M>!űz‰I «€0… •…X©Ą…˘­É§ĺSü„”Áő˛WŘp w»3s3Y:Ę'sĆĂ„łó1şPš»ˇ{¦~s8Ó´$»ĺ4'»tc˛ĺŠo×ű‰ět=ă„ěśw Ç{*ç Ó(¤DžĽ`D:„ťy#jAÔ BQ»SQ]9h@ř”˘9…׆mđĆ 3/"-PI˙oÓ™n•§ ŐŞË×ŮńÍó?|ÉR3{żľ‡6ŇnÚRűúć}Z”´ˇëĺnendstream endobj 158 0 obj << /Filter /FlateDecode /Length 245 >> stream xÚmŹ1NÄ@ EmÉÍa|HB’b«‘–E"Tjˇ¤`í&G›ŽkřéHĹü 4ŇÓŘŁńnęóv+Ą4rVISJ{!Ożrݢ‰˛ţ~9Ľđ®ăâ^ę–‹k´ąčnäířţĚĹîöR*.öňPIůČÝ^(ź‰(`)3SÚŤčçą1›É+-:%ô8p'?, ó\üú‡%ᔀ^ĂŠ‚úH˝"Č4źť)ÂMˇń©úP¨9%7ąHič/üŠ!©Ż Gó«dLşâ!n&{„ÁŹČë•|ÚŇöÍ J™MřŢc_u|Ç_ž!r·endstream endobj 159 0 obj << /Filter /FlateDecode /Length 107 >> stream xÚ31Ö3µT0P04F Ćf )†\…\††@ľ –IÎĺrňäŇW04äŇ÷ sé{ú*”•¦ré;8+E]˘zbą<]äěęüőěäđě:¸\=ąą{-=endstream endobj 160 0 obj << /Filter /FlateDecode /Length 218 >> stream xÚťĎ1NĂ@Đą°4ŤŹą¬—ŤQY AÂT (‘A‹ĂÍrÁĺ 3AzšWĚJ˙_¤ăć”kN|yą9á‡H/”–v¬ąIű—ű'Zun8-)\Ř™BwÉoŻďŹVWg)¬ů6r}GÝšĹ3J•~ ZýôŞýT™MčĄŘa.ĺĘ)Ąś- ™oö̤Ĺ/˝ó`t™śÝ˙ţRôř27ČäVÖŻ˝ifđöíh·ľhăŰ`+-·RűˇÔŃŇěNç]Ódvg9endstream endobj 161 0 obj << /Filter /FlateDecode /Length 147 >> stream xÚ31Ö3µT0P0b#SC…C®B.c ’HÎĺrňäŇW0¶äŇ÷Šré{ú*”•¦ré;8+ů. ц ±\ž. ő˙˙˙˙Ä˙ Řć Ś„ † ‚`|$€lthv›b)ŘŚ‡6 ˘ŽäŁ˙Q Ř.WO®@.ĚŚ‡rendstream endobj 162 0 obj << /Filter /FlateDecode /Length 120 >> stream xÚ31Ö3µT0P0b#SC…C®B.c ’HÎĺrňäŇW0¶äŇ÷Šré{ú*”•¦ré;8+ů. ц ±\ž. ő˙ţ˙ů˙źń˙?cŔŔ€ęÄ˙˙˙±4± Nŕô%—«'W ž‡äendstream endobj 163 0 obj << /Filter /FlateDecode /Length 177 >> stream xÚ31Ö3µT0P0b#SC…C®B.c ’HÎĺrňäŇW0¶äŇ÷Šré{ú*”•¦ré;8+ů. ц ±\ž. őř˙ü˙Ŕ ˙Bü`°˙W$ţđ‰ü{Ş1y ꑉůŚ0˘źń1Śh†í͇ÄqŃ|ĽFĽ‡Ťď™aÄ Ń𕨠‚l˘č·?`ż!°—«'W ±,endstream endobj 164 0 obj << /Filter /FlateDecode /Length 197 >> stream xڕСÂ0ŕ›jrfŹĐ{Ř::"#a‚‚ ‰€€îŢ eŹ0‰XvtmC‚ůÄßöîOőhŽ)¦„Š´¦TŃ^á µ˛aLiâOvGĚ ŚÖ¤FscT,črľ0Ę–S˛iNűf‹EN†`ćŇY9†»Q‰¶3p‚qNĘNŮ3Ľ˙¶ßO0ďÉn‹ßč¶ ×ÄZż’J4˝&}ţ5tĘň›¦y+™A˛ý ˝-ŘĽ+Ô€łWř2>z endstream endobj 165 0 obj << /Filter /FlateDecode /Length 236 >> stream xÚuŹ1NÄ@ E˝Ú"’›a|„$ŐHË"‘ * D”H»$*âŁĺ\!GŘ2HQĚw€‰ćÉă˙m˙©«ăćT ©ĺ¨”şćDJŢsŐ ‰gő­Ü?ń¦ĺx#UĂńmŽíĄĽ<ż>rÜ\ťIÉq+·ĄwÜn…™ĺş2űĐĚĚ4w„C0Mý€¤LúNÔéL”túAř ¨9ÁçŇ„Éa=tCą6”8y€ÇF˘Ě›ÔaĄOÚ2éý/ňaÁ<Ăô&ÄŘůE>oůšżĺxvendstream endobj 166 0 obj << /Filter /FlateDecode /Length 124 >> stream xÚ31Ö3µT0P0b#SC…C®B.c ’HÎĺrňäŇW0¶äŇ÷Šré{ú*”•¦ré;8+ů. ц ±\ž. ő˙˙˙˙Ä˙˙ˇęęđ@†H0 zÂţ˙(Q˙˙—ËŐ“+ +ňT¬endstream endobj 167 0 obj << /Filter /FlateDecode /Length 191 >> stream xÚmĚ= Â@ŕ Óx„ť¸ ‰‚Ő‚?` A+ ±RK E[“›™Łä)S,;Îh%Xěűfćůh<Ą” }ĺ:exĹ\łTż:8^pV˘ÝQ>E»’mą¦űíqF;ŰĚ)C» }FéËEÜ$ s­´ŕXB×^H”Č©ÁĂ@ž?|Źbe¨®źŕzY©E—â˙đTZ_Őq×-`öRĹ!a~…„®K<.KÜâj/\endstream endobj 168 0 obj << /Filter /FlateDecode /Length 187 >> stream xÚťŽ= Â@…g°¦ń™„Ä"•#¸… •…X©Ą…˘­ÉŃr”aË€!ăN;±ćď˝GÓY‡®âg!źBşR¤ł@[]/”ňw%äŻÜ”|łćűíq&?Ý,ŘőďÝĺLĆą©ż+đx•“Ŕ—´€"ҡ@±y‰Rx Ś-¶0ޱéŤţ~Đ*ž?˘uîmÖ˝rç!0±eĄć] ÔEÓ`ç%ĐŇĐ–Ţ*Ĺszendstream endobj 169 0 obj << /Filter /FlateDecode /Length 182 >> stream xÚŤŽ1 Â@Eż¤¦Ik—9›°° Än!he!Vjiˇh›äh%G°L2ΦĐÖ…}đgŮ?of§óÇśęťĹlS>'t#k5Ń?ś®”;2{¶–ĚZ§d܆÷ç…Lľ]rB¦ŕCÂń‘\Á¤"iJzŚDĆ=á[5/”ČjLAOĺQ~Ńý‰ßʎ@«B_ŐZŻh4čĘJ—â5ˇÎ«µ^RMuZ9ÚѲuEJendstream endobj 170 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚ31Ó34V0P0VĐ5T01Q0µPH1ä*ä21PASKLr.—“'—~¸‚‰—ľPKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓE˙ó‚ÁţT‚zó !˙HÔ±÷`řÁřţó†ú쀶¤ „|P±=i«‡u âÉDŞ)öph‘<„ÚkrF=ČAď?0ţ`<˙ꎆ˝˙?ü?ţ˙ ě@‡sązrroXhIendstream endobj 171 0 obj << /Filter /FlateDecode /Length 189 >> stream xÚ]Î1 Â@Đ\B/ 8ĐM˛(ÚЦ´˛+µT´“čŃr”!ĺbI qáÁ23ü;čŤö9änŔ¶ĎvČű€ÎdC)úlGUgw¤IBfÍ6$3—2™dÁ×Ëí@f˛śr@&ćŤm)‰Úť¸·2Ď©\^ˇsϵ2¸Î÷ŻHĹřQ‰RńţQÖOţř—Ö5ÉQŃJrµěhč MťŁíÂá„TĺrŹLĽ@ł„Vô˝Ł@ endstream endobj 172 0 obj << /Filter /FlateDecode /Length 141 >> stream xÚ32Ő36W0P0bcSK…C®B.# ĚI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘*cą<]ę˙70đ|Ŕ ßţ€Áž˙C˙`ĆĚ00Š˙˙˙Çäč§3˙a`¨˙˙Žą\=ąą˘&[endstream endobj 173 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚŤŹżJ1Ćż00…ń v^@ł9ďäŠĂ…ó·´˛+µT´[¸}´> stream xÚ31Ó34V0P0bS …C®B.C ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. Ě€ŕ?É&™iN‚ěaţ`˙D~°’Č700nŕ?ŔŔüťDţ“ŘŔČä‡$Ů€‚ë˙˙˙˙7 “\®ž\\yendstream endobj 175 0 obj << /Filter /FlateDecode /Length 122 >> stream xÚ32Ö30W0P0aCS3…C®B.C ßÄI$çr9yré‡+Zpé{Eąô=}JŠJSąôťś ąô]˘  bą<]ř0Č@A@ 8~Ŕüá? ±q©ŽŘ0ü˙‚¸\=ąą(CE`endstream endobj 176 0 obj << /Filter /FlateDecode /Length 150 >> stream xÚ32Ő36W0PĐ5QĐ54W0´P05SH1ä*ä22 (Ăä’sąś<ąôĂŚ ąô=€\úžľ %EĄ©\úNÎ @Q…h ®X.OĆ ěř   P?`üÁđ†Ř€¸ôE6Ś?ęügüđź‚üc?PĂ~Ŕ†ź˙ó.WO®@.˙§Wőendstream endobj 177 0 obj << /Filter /FlateDecode /Length 196 >> stream xÚµÍ1 Â@Đ•ir3'pŤ.#BĐĘB¬ÔRPQ°ÍŃrʱ0EČ:? ędŮł3ó7čuÂ.{Śô¸ňʧăH‰ĆrCqJzĆGz$ݤÓ1öÇ5éx2`źtÂsź˝Ą […RĘüâë?´LőŤşćÝ3Ř‚ćrÁĘkm‚¨„;xÔÂ3ęH†Kv¤Ř@%Żâ.ęýoÔ nn—**ŚÉŤů@Ă”¦ôDrendstream endobj 178 0 obj << /Filter /FlateDecode /Length 108 >> stream xÚ32Ö30W0P0aCS …C®B.C ßÄI$çr9yré‡+Zpé{Eąô=}JŠJSąôťś ąô]˘  bą<]?0ü‡!ţ ̱˙`ř˙˙qązrrĆ‚Q.endstream endobj 179 0 obj << /Filter /FlateDecode /Length 177 >> stream xÚ3łÔ3R0Pa3scs…C®B.3 ßÄI$çr9yré‡+™pé{Eąô=}JŠJSąôťś ąô]˘  bą<]?đ`Ŕđ˙ý†ú@ú=ă:†˙Č77Ř3đnŕ?Î ßŔüť˙ţÇŔD˙a`˙ÁŔN˙``˙€ŤţŔŔţ`Đ O€â˙˙˙˙7˙˙ŹNsązrr#ßendstream endobj 180 0 obj << /Filter /FlateDecode /Length 147 >> stream xÚ31Ó34V0P0bcs…C®B.C ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. Ěř?00ü˙`˙D~°’Č70đnŕ?ŔŔüťDţ“ŘŔČä‡$Ů0˝ń˙˙Á˙˙I.WO®@.‡e%endstream endobj 181 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚŤŽ1‚@Eżˇ ™†#0Đe6 &na˘•…±RK Ťv9Gá”Tâd)H¬ĚN^fţîţů‘žĚ¦đ”ÇšŁ€Ă9ź5Ý(ŚE”qŃßś®”R{cRk‘I™ ?îĎ ©l»dM*çćŕH&g8^W‰S­śQdHŕVđá•Rľ ň!J*¨- Ŕi~ nNű/†oońkg»Íîő$AéÖHĺŠ> éáwlzZÚŃIKÚendstream endobj 182 0 obj << /Filter /FlateDecode /Length 196 >> stream xڝα Â@ ŕH†Bˇy˝ž­uj;:9“::(şÚ>ZĄŹp"ŘŠç]qĐQ |CB’?Šű2ä€Ü“1G!‡#ŢI:R°«ařm”d$V$f¶O"›óůtŮ“H–$R^K6”ĄŚĘŻŔ¨\ąUW0÷Â/Ľş%>Á«°T¨5*č´4hy~“˙Ě÷ö˛Ąý¦Ýß> stream xÚ31Öł0R0P0VĐ54S01Q06WH1ä*ä21PASc¨Tr.—“'—~¸‚‰—ľPśKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEůĂůŚęŘ0üa<|€ůĂăěĘđ?`0?Ŕ€Áţ€> stream xÚ36Ň35R0PacCcs…C®B.# ßÄI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘  bą<]ŘČ3üPŕ˙ĂÇţ?n˙Ŕ˙śýó3 ~Äo0˙ah`ţÁŔ€‚?PłÍü˙˙sązrrjŮF„endstream endobj 185 0 obj << /Filter /FlateDecode /Length 195 >> stream xÚ=αJÄ@ŕ¶XfßŔĚ x{›`TńSwŐ‡•Z * WîŁíŁÄĘ6`“"8Î%GŠŹ™ů˙fŠ|q~ĆK.ř4pˇó‚˝R^j¨çĺÔ<> stream xÚ36Ň3˛T0P0TĐ5T0˛P05TH1ä*ä22 (Ad’sąś<ąôÌ̸ô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž.  Ř W á Ś@Ě Äě@,˙˙?Ă(f„ĘQ „ţ0‚pC sC3=;˙?°f.WO®@.uH–endstream endobj 187 0 obj << /Filter /FlateDecode /Length 153 >> stream xÚ31Ó34V0P0RĐ5T01Q06WH1ä*ä21 ([@d’sąś<ąôĂL ąô=€Â\úžľ %EĄ©\úNÎ @Q…h žX.Oć ěţ`üŹJň`Ŕ‘p’şŤBţ`°ŔŔđˇüÆç˙왏Iů˙í@’ůĐ.WO®@.1cendstream endobj 188 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚU̱ ‚PĆń#‘k[çęŞWJ'Á rjj ¨Ć†˘¶űh>ŠŹ`›Ph—ş—jů ˙ľ@ Bź\ň©ďQŕ“ŇÎĂ#ŠHE—ÄčłldČ—$"äS•‘g3:ź.{äÉ|Lň”VąkĚRj×_ś śŇ.Á.X ,g0i)ŕ <ˇĄ©ˇp¶&†®A†=éjś|c(v‘kŘ]ţb=ŔĐ(ÔżáúO¨ÁI† |FŁ?ęendstream endobj 189 0 obj << /Filter /FlateDecode /Length 233 >> stream xÚUÎ=KĂPĹńs Xxłv(ćůzËíËb ­`A' ÖQ|A7©‘|±€Đ~ŤLťďx‡`Ľ7UÓN?8gů«áá°Ď!ńAÄjŔÝĎ"z$Ąěr·ż~nîh”ĽdĄHžÚ™drĆĎO/·$GçcŽHNř*âđš’ WUPń÷6ľAß´4ćđŠ5ą§q ‘ţ" bxŘ%âtÇqżÁ_ů®cůGŲh;˛š÷L€ Ëtč5Â<ţfúOk…2·|âµÁ+ń–ZlECÝdŃ ±ď(°çÂŃIBôĄY_™endstream endobj 190 0 obj << /Filter /FlateDecode /Length 210 >> stream xÚMν Â@ đ)(ˇ«Đ> stream xÚUÎÁjÂ@ŕYi® Î čn˛Ző$¨sÚSE¨GÁ˝‰ćŃöQ|„x ‰ł˛Iéĺ;üĂüü=ÝF¤(˘N8 ^DúŤÖ!ţ qިŻÝiµĹIŚň‹ôĺśs”ń‚öż‡ ĘÉÇ”B”3úI-1žQY¦ăâŹŕAćgŕ//7śŽ4gËZŽvŞ*Ě 0‰ĂżŠ+ă]S‡¸CEÉ@QsüϰFŐě,IŤqSn/Ľ'¶’gCţbź^m‘mjg`ç1řă'>ÚźKřendstream endobj 192 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚ%Î1 Â@„á‘@„‡$|'0‰+AA˘‚)­,D¨Ą ˘ťŹćQ<‚eŠ`śŤĹ_ěě·°&î# µÇL_M¬‡H.běÚŁ˝Řź$I%ب‰$Xp• ]ęíz?J¬¦Ęu¦[>ŮI:ÓIU•uO§Ă)Fh~đß!;Łó:cňĚŰዬQÖ‘‚ôź˙)H˙ĺpIëH]R·YŔ#őH[¤mé(ś˛âl2Oe-?uŕC endstream endobj 193 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚmαJÄ@ŕ †@,Żw^@7{IŕŞ[8O0… •…X©Ą…˘`!l-ʞʰĺ!ń=uŠŹev˙źmÜi»äŠk>qÜ,ąmřÁŃ3Ő+,+nŰď›ű'Útdo¸^‘˝ŔšlwÉŻ/oŹd7WgěČnůÖquGÝ–•ʦ쀂0c—M8ä; “<“˝‘Ŕ‘p¨ţމ‘Š)i.{Qž'\~‘í~PŁtŽJ÷óh)ôLŘá?%`$ë… Aí)¦ßd{f•ŹEPúŁýÁzá‡ăő˘Śď&ĺs*#jô@ç]Ó' Ę]Öendstream endobj 194 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚUϱJÄ@ŕYpa ÓZ7/ {IŚ(ČEÎ;0… •…X©Ą ˘Ý‘ËŁĺQöRn!9˙ŢÂ×Ěěţ3›źź^¦:×LORÍ -Îô5•OÉ3çZcçĺ]–•řGÍ3ń·,‹ŻîôűëçMüňţFSń+}bĐłT+Ž\QM=`Čţ.If °`kCtŤý3Ü›íŘOZm°ťé\01iůt3(N‹í¨ä¤˛˙g7ť~Ü`O=ŮNcË–ąŽ3\‹Cpl:\ rĂÚîÓ u%ňoGĘendstream endobj 195 0 obj << /Filter /FlateDecode /Length 172 >> stream xÚ31Ó34V0P0bSK…C®B.# ßÄI$çr9yré‡+qé{Eąô=}JŠJSąôťś ąô]˘*cą<]ř0Aý? Ář˝ýăů† ö@C˙ůA2ţ€’@5@’±D‚Ť!™dţŔđPI¸ůĚCdţĂŔţˇţ˙˙˙ “\®ž\\^ĺÓendstream endobj 196 0 obj << /Filter /FlateDecode /Length 130 >> stream xÚ-ɱ Â0…á gđ 2ś'0ą-Ą™k3:9 TGAEçćŃňfÚ˘|Ű˙—ŐŇ7ôlXUÔŔ:đ˘x@='eý;ý m„;P=ÜfĚpqË×ó}…kw+*\ÇŁŇź;Zä“Fy2d›ĺĎd“L*R!s™ÉB¬ąËY°ŽŘă ,P#Śendstream endobj 197 0 obj << /Filter /FlateDecode /Length 189 >> stream xÚťŹ1 Â@E°Lˇ70sÝě ’@°ÜBĐĘB„€ZZ( 9ZŽ’#XZ:IV›t«ţ 3ďOĚŘÄrÄ#˛‰xjř¨éBşN%7nt8SjImYǤ–’“˛+ľ]ď'RézΚTĆ;ÍážlĆ@TđJô ř@ đhxÁ«jzeŤ/¨ š]aöĺŮáýÝ;żíÇÎAdDÉ/ťak+ÚÎ?i¶Ą”T“‚RSĘ"§…Ą }G«@endstream endobj 198 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚťŹ1 Â@Eż¤L/ :ĐÍ®A"EŚŕ‚VbĄ–‚Š‚…EŽ–ŁäÁÍ$±ĐNxŐĚgćýˇ1‡qß„l">hş.§!Ǧ^íO”XRÖcR 7'e—|»Ţʤ’ŐŚ5©”·šĂŮ”s Î@ t€h~//iąÝKxO`L®Đ“tIVăçßxĹ?üŢůĽ¨>ö‡©(=C±uÚ•ż/ń@ŞĹRÓr•iniMoEËBsendstream endobj 199 0 obj << /Filter /FlateDecode /Length 131 >> stream xÚ-É1 Â@EŃ?^á ¦xĐ™‰‰mŚŕ‚V"ŃRPŃ:ł´Ů™&Nwoľ\ř’ž%红V\ó¦xA=y1žö:Ŕť¨n×w¸°ççý˝ĂŐ‡ ®áYé/ ­tň‹˝4č’M22ÉDłÉT&2+•<ĺ*ŘńBŰ#´endstream endobj 200 0 obj << /Filter /FlateDecode /Length 94 >> stream xÚMÉ=@PEáţ®â®ŔĽ™x¨ý$^!ˇR Ą‚°{ ŤäTß±4J2:*5ˇĹ4ĺ¬Ř`ö˘Ł˙Ć´"žfšűą@ň¶ BJJ7"”Ľď몀Đi ‹endstream endobj 201 0 obj << /Filter /FlateDecode /Length 94 >> stream xÚ32Ö30W0PaCsK…C®B.K Ďȉ&çr9yré‡+Xré{€O_…’˘ŇT.}§gC.}…hCX.O†z†˙ 0XĎ ĂŔĺęÉČ[\wendstream endobj 202 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚµ1 Â@EH!L“#d. ›ÍşŤBŚ` A+ ±RK EÁBb޶GÉR¦R×l´6Ż˙ţPtĚ+îǬƬ5$ťIi;ŚXŹÜf˘$#±aĄI,ěD¶äëĺv$‘¬f,I¤Ľ•í(K~ |[äjż„W˘‚opGĎŕ ŔÄ!´—S‹˘E¦ /‹ňčzů´ĚOľ6x+Ó¸YŰ~ĺŐÎÜuĐ´ńí…ć­éÂŐ`úendstream endobj 203 0 obj << /Filter /FlateDecode /Length 121 >> stream xÚ31Ô35R0P0bc3SS…C®B.# ßÄI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘  bą<]0001;Ëń˙ ˙aX*6T°ý†ú˙˙?Ŕ0—«'W ľťNÚendstream endobj 204 0 obj << /Filter /FlateDecode /Length 228 >> stream xÚmαJÄ@ĆńoŮ"0M^ป'p÷WóSZY ¨Ą ˘`eňh>JáĘ+ŽŚóé5‚E~°;˙Y˛¬Źšc­té_^iÓčC-/’łź+9¸’u'éZs–tî·’ş }{}”´ľ<ŐZŇFoj­nĄŰ(Ę-€~‚Ů€8¶#J^ÎQě0CÜc…0áůîČDĚ_úźžÓÁďř:ßsöNüaçü™r$_΂[-> łŔ,°, %‡s„'älĎ"łČĚńĄ™aAZŇ›M°żČY'Wň Tźc|endstream endobj 205 0 obj << /Filter /FlateDecode /Length 235 >> stream xÚuĐ1NÄ0ЉRXšß`3', ZiY$R AE¨€ ´ŘGóQr„”[¬0Ľ„‰"OĘŚóÇ“ăîČ/Ą•^—Ňź‰÷ňŘń+÷ĹVüÉľóđĚëÝ­ôžÝ%Ęě†+yűxb·ľ>—ŽÝFî:iďyŘ™-­2Č9QµµŐ EëPőE6‚f¤LÍôV»&‘ĆŕđĚÔb&e6‚€§Ńf“őŐŽó‘ňY (yâ/ifU ý°Ĺ_ cBüÔ¨M>Ő‹ý‚¸ź™°yĄ˙€‚޵¸2_ |ĂßÇ›jhendstream endobj 206 0 obj << /Filter /FlateDecode /Length 188 >> stream xڕν Â@ đ+ At-('đ®¶µťkotrˇP?ÁQđĹ_ÄÇč čý‹­łů‘äIŕőĂ+FŠĂ!Ż=Ú“™ş,ń‘o)Ń$ěG$'¦KROůt8oH&ł{$S^z¬V¤SBĢ⊠ŘŔ©ičA«äf°1ë€h‚.p;»Áö`ŻZ  \2đoóŠß›˙Ây™ł54Ö4§ňý`öendstream endobj 207 0 obj << /Filter /FlateDecode /Length 265 >> stream xÚMŹÁJĂ@EoĹŔ[8ĐĽĐ$ŤA„ŇB­`B]ąWęŇ…˘ĐEÁů´ů” ;#Ç›*ÖÍyóî{wćÎquÔLµÔZ§ZźjÓč}%OR7KmN~&wʞlĄ¸Öş‘₲íĄľ<ż>H±\źi%ĹJo*-oĄ])L OÄ[ Ŕ`;d1ëa¶°3X`LpŔM6{ä{xÖSĎś°Hpžî|tOĄ0Ł1lą6Ě ůi4ČţÓ,ěŔe3zŤźÓáw™ťgRŇô¦SĹß@v伕+ů˙cĺendstream endobj 208 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚuĎ1NÄ0бRDšĆ@ň\ślÖBT––E"T ¶¤AKr®â›ě!eŠ3ł šgiŹ˙_×'aE5tĽ˘ćŚB Çź± 2¬(śÎ_žpÓ˘żĄ& ż”1úöŠ^_Ţvč7×çTŁßŇ]MŐ=¶[‚b—….'0SÉ2*(ŮŚ`&p ŢÁőBě!Ît çĽŕŇđ_čÝ_čRĄc§Ř™%Éž 6{6Cń!I¬c“Ä)A×ô?€Ö«ĚÁ“ôXZ1IÁŘËN+éOVë”ůŔäqY‰-Ţŕú m9endstream endobj 209 0 obj << /Filter /FlateDecode /Length 101 >> stream xÚ32Ö30W0PaCsc3…C®B.K ×ĉ'çr9yré‡+Xré{ąô=}JŠJSąôťś ąô]˘  bą<]dęţ7Ŕ`= 1S—«'W fp"¸endstream endobj 210 0 obj << /Filter /FlateDecode /Length 140 >> stream xÚ32Ö30W0P0WĐ54S0´P06SH1ä*ä24PAS#¨Tr.—“'—~¸‚ˇ—ľPśKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEA†ˇžá Ö3Č0຀`ý™ PČx€±±ą™ťŤ¨Ň‚ˇ€!ËŐ“+ &,•endstream endobj 211 0 obj << /Filter /FlateDecode /Length 235 >> stream xÚmĐÁj1ŕ é^=;OĐd-‘ő$¨…îAhO=”‚ĐöX¨ŇބͣíŁř{ô°N"¸Q6>fB&?™Nî'izŕmf4Őô™ăŤáZűŇ||ă˘DőJĆ zâ.ŞrMż»ż/T‹ç%ĺ¨Vô–“~ÇrEP@X×ěű8ő \˛˛IU{ó»ůÁ3ĚbĆYăĄ1Ezôč$ć'i=SË©†LÂB„p6Pu Ž–8ç:R†Ł ˛Ž÷›[4ß9޲áéí…ĂŽ&ÎČ&üZÚú'­ăXÎť®ÁÇ_đ%°mĽendstream endobj 212 0 obj << /Filter /FlateDecode /Length 260 >> stream xڭѱJÄ@ŕ? LaZ áć4‰ÜŞ[-ś'BĐĘB¬ÔRPŃÖĚ›ř*ľ‰yË+Äuv˛g!–Bŕ#“ÍĚîżÎďúnŮńÎ;ÇÎóMG4÷Zlyż›ľ\ßѢ§ć‚çžš-SÓźňÓăó-5‹ł#Ö÷%_vÜ^Qżd RPDZT†¸R´öR ĘOÔµ ţ@ů*Ť(ŢAWEÁ],řR‚şIµRę5ú7P­Ń&?”2oĆ(~#FLŘŕgČü5=dF#ďzv˘L;mf–Ä&,—mXJ[°Ěa Ţ#ĺ }Rş:%e-vÁvS˝•Ô=U:îéśľšes–endstream endobj 213 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚ33Ö31V0PaS Ss…C®B.S ßÄI$çr9yré‡+špé{Eąô=}JŠJSąôťś ąô]˘  bą<]ţÁőBýc``üßD@.ý0Ĺ˙L1˙SŚŔĂ?UŹBŮ7@¨`JJ=SüPęŠýę (<öˇ9ĹńPŻ@=ómrüC%hACž  !@ y`> stream xÚuб Â0Đ  ·ôĽ/0­ µ‚ťDŞŁ˘łý4?Ĺ/iLsqŤđ’»INÍĆŞ ś&vŞ)©9ť Ľ˘‹ĺý¶O4¬4Ę©ĺĘFQę5ÝoŹ3Ęjł ­ioK¨k2ýč DŇŔ€§dFLƤ1’(­C8^Q€„ÉĆDđąďɰ|pĂ1ĆŰ˝Ó.ţ"bř˙yŇ€Ś)™gëşk¸×żŕRă?Uź’~endstream endobj 215 0 obj << /Filter /FlateDecode /Length 166 >> stream xÚ35Ń3R0P0bSCSs…C®B.s ßÄI$çr9yré‡+sé{Eąô=}JŠJSąôťś ąô]˘  bą<]ţŔd’ńü†˙ Ś`’ᬓ6`R‰äÁAňI68ÉŘ€L2`%™‘Hv0)"˙˙G'!âP5Čş‰ A€J$ă˙ `G@%ą\=ąąM˙x×endstream endobj 216 0 obj << /Filter /FlateDecode /Length 125 >> stream xÚ33Ň3˛P0P0bSKSs…C®B.SS ßÄI$çr9yré‡+šré{Eąô=}JŠJSąôťś ąô]˘  bą<]ţ˙˙Ďř˙˙?TŠńó bü78) Ŕ¤Żs‘)hčb y.WO®@.!»Ą7endstream endobj 217 0 obj << /Filter /FlateDecode /Length 106 >> stream xÚ3˛Ôł´T0P0aKSs…C®B.#3 ßÄI$çr9yré‡+™qé{Eąô=}JŠJSąôťś ąô]˘  bą<]ţ˙˙†€ˇľačcWüĹĺęÉČ3v\‚endstream endobj 218 0 obj << /Filter /FlateDecode /Length 244 >> stream xÚuŃ?kÂPđ{<0p˛ Ţ'đ%ś˙€ ur(Ávt°ÔŮ€«ę•]ÝĚGČč|˝¨X#yîřÝ=8. [~›< 8˘€:˝ű¸Ä°ËµW”ĹÇ|ýŐ”Â.Ş1wQĹĎôőąú@ŐŹjHŻ>yoÉŕçŁ1 Ă˝¸ 8hFăx‡]Ę*ń›1ć•řá8§ľyşŘTBź¤,a Pł —Ŕ“M ő2Ü< ś fepŇ\$ŔIÂÖ5+zŰG4÷V¸Y5D NZ@fWđí¤'c´ÔŇÇýoĘŔQŚü¦Â!endstream endobj 219 0 obj << /Filter /FlateDecode /Length 243 >> stream xÚUĐżJÄ@đ/.0…űfźŔMNÖ?ŤóSge!VjiˇhkRů\AKÁTÖ©$EŘuwöŠM1üřf`Šď`ą·<’…ÜwŁŹĄ>”w%=’Ö.>úĂí­jRWRkRçnKŞľĎO/÷¤V›SY’ZËëR7TŻĄµ@fŤµm óŔ¦‡íĽĹĎ0 ŕ{dľ¦ĂĽŰÎ=ő4]LŐ3ůȦ€aŇ@b·´liş@ĎT|`Ä“MLjbËŔľĹ4źLő“˙1ÂÄdtFŔśW$®Gś á*Ă.|×Ř™±ťŐtI˙6D†cendstream endobj 220 0 obj << /Filter /FlateDecode /Length 167 >> stream xÚ35Ó35T0P0bS#Ss…C®B.K ßÄI$çr9yré‡+Xré{Eąô=}JŠJSąôťś ąô]˘ĆÄryş(ü‚ ę„úĎŔŔřż,ĘŔ ˙LńSĚ? Ô0Ĺř™adŞT Y;ŞŃPű ¶CÝuP7ČŮ˙ŔÔ ™….ĵ—«'W ŽK€żendstream endobj 221 0 obj << /Filter /FlateDecode /Length 256 >> stream xÚUϱNÄ0 ŕżĘ)Kˇ~h{=îÄB¤ăč€Ó @°!ZŢĚŹ‰čF%PŤsw ˛|Jě8¶ç‹Ăަ’ćt0ŁůŚŽŽé®rŹ®^j°¤EµËÜ>¸U㊠ŐKWśkŘÍ=?˝Ü»buyJz_ÓuEĺŤkÖ?€ĆŚ!ňÎf°l#>Ů3ZÎ;@Î'€ç7Ŕîx ďÉ&Ś&Č–Nm9R0—!ˇG/aEďFD+E$˝Ńڵ˛MX‰ż„^É>a‡-úĆü‘M˙čű=¦×:upÇ´–¤-µiŢ}őčGŚA§Š^{s¦ywÖ¸+÷=ź†#endstream endobj 222 0 obj << /Filter /FlateDecode /Length 150 >> stream xÚ3µÔł4W0P0bSsJ1ä*ä2ńÁ" Fr.—“'—~¸‚©1—ľP”KßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEÁţ?<@Ł0˙g`ÇŔřŹůA büP˘>€©T*L`Ą€)‹`J+ŦF ĹţżHĘ‚Ťârőä äWÎr°endstream endobj 223 0 obj << /Filter /FlateDecode /Length 307 >> stream xÚuŃ1KÄ0ŕW „ăşv8ČűÚôÎbť ç vtrá@ť˙…?'â)Îť¤Cąř’ŁâMHřH^ÂK^Yě/Pá÷ćX.°8ÄŰ\> stream xÚ32Ö30W0P0S06V04W0µPH1ä*ä24PA#SLr.—“'—~¸‚ˇ—ľPKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓE±ąťŹA†A‚Á‚Á€ˇ€!0€Âs ˙ţÁz ´oŕcŕrőä ä-#Şendstream endobj 225 0 obj << /Filter /FlateDecode /Length 204 >> stream xÚmĚ; Â@ŕ . ´Vf. ›Ť´1ŕL!he!Vjiˇ(X›Łĺ({„”Á8룗ĺř‡ůÝéĹQ—Úš’ş}Úi<"ĎČĹ÷f{ŔQ†jĹ{T3ŽQes:ź.{TŁĹ4Ş ­5EĚ&ˇ€ş6äüĄ…°%/_x÷/PAP02gřýÁ0Ҧ–yp&îî¬dBw›:Ś+0đÁüâ}¨ATľyóMŢ6Ó˘5lö–˘.Ë5˛Ŕi†K|¤řTŁendstream endobj 226 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚ31Ó34V0P0RĐ5T01V0µPH1ä*ä21PASKLr.—“'—~¸‚‰—ľPKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEůĂT‚D0Sť$ę00|`ÇŔüąAľů;˙ć ě˙ĺ00ţ* ŕÄ?8Q"ęI&ęPMĘřbŰ˝`Ëßśq ä ă ň ĚŤęţ˙:]ţ—«'W ČckAendstream endobj 227 0 obj << /Filter /FlateDecode /Length 182 >> stream xÚŤÎA ‚`ŕ'?( ‘ś ”ýüşĚ A­ZD«jXÔ.ĚŁyŹŕŇ…Tcu€ßć Ź7f: 5ŹŮđPł™° ř éL¦ %ż—ý‰â”ü MţBbňÓ%_/·#ůńjĆ’&Ľ•ÎŽŇ„ˇZŔ{ČUe5ČTŤĆ©¬Ö-Ő‡W¨6ęŔj@-ĐÉĹóOůŻÓ‰;*`{ú^‰ž[bŕTd7“ý w§”§ÍSZÓ»=endstream endobj 228 0 obj << /Filter /FlateDecode /Length 110 >> stream xÚ32×3°P0P0b#S3K…C®B.#C ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. ŚţĂűć? ŚC 1˙cř˙˙qązrrŹp^Úendstream endobj 229 0 obj << /Filter /FlateDecode /Length 162 >> stream xÚÍË1 Â@…á·¤Lˇ° čfqCĘ@Śŕ‚Vb--+'GË‘<@Čş!Xč lľâý3©ť™ŚžóÔpjŘZ>şíÇ„m:”ęL…#˝c›‘^…™´[óíz?‘.6 6¤KŢNäJV- đ-r˙eÜByDˇz 7˙«˙U}Ä`‡(řD,uxIé0nŇ·WR héhKo©b“endstream endobj 230 0 obj << /Filter /FlateDecode /Length 248 >> stream xÚeĐżJÄ@đo \`^›BĽyÝÍ] ç ¦´˛á@-íÄŰG˛´ĚŁäR^w˘ůĂŮüŠ™]ľ™9ŽŽâ„ Oůpj8>ĺxĆ˝PS5śĚţZ÷O´LIßpśľpuŇé%ż˝ľ?’^^ťqDzĹ·›;JW\×…ŞËˇ~ lrŻ&V‰÷g¸îľ{„ť'Ŕ´N2¬;säŔ8GÖęĘvn=§·őĐŞĘQoĺb]pĐ» ~‹‹Ż^¶ă8ëőí®Ř:úg00ěś7~Ęžîż®JTĄÄŮ Ďľüś4s”M^!ŇyJ×ô[ÍX'endstream endobj 231 0 obj << /Filter /FlateDecode /Length 136 >> stream xÚ32×3°P0P°PĐ5´T02P04PH1ä*ä24Š(YBĄ’sąś<ąôĂ ąô=€â\úžľ %EĄ©\úNÎ @Q…h ¦X.O9†ú†˙ ˙ᬠ—Ŕ€ ăĆćfv6> † $—«'W ÷ '®endstream endobj 232 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚ˝˝ ÂP FżŇˇĄŹĐĽ€ŢVn«“‚?`A'qRGE7Áúf}”>BÇŚނŠč*3$|9ş×î†ěłćV‡uČQÄŰ€¤}®+ę5“Íž†1©%kźÔTڤ⟎ç©á|Ä©1Żö׏¨8Ux·čă”Ŕ*ŕ%V7±38©“ÂÎ \Aî&°rOP ĺdeyÜżˇ>Xý ?c\%éý#řëŁćË'q¶(IŤŁ©fÔ‰µNšÄ´ …)endstream endobj 233 0 obj << /Filter /FlateDecode /Length 131 >> stream xÚ3±Đ37U0P°bC33…C®B.c# ßÄI$çr9yré‡+qé{Eąô=}JŠJSąôťś ąô]˘  bą<] >00013Ëń˙ ˙Ař9łůĂ ó˙úóCý˙˙˙aËŐ“+ Ět^@endstream endobj 234 0 obj << /Filter /FlateDecode /Length 259 >> stream xÚ]ĐÁJ…@ĆńOf!"·."ç ĺÚÍE0p»A.‚Zµ ¨vµ ôŃ|ÁĄ‹ËťÎgH0?ń?p´¬NÎNmnąĘŇ®×öąwYUşĎąĺ‹§7ŮÔâîěŞwĄ§âękűůńő"nssa q[{_ŘüAę­…ŮČB´aD4%;>Ú#îp¨§Ýŕ{%*eĚdl”é§W”]čH˙‹ůOË·ž¦…dfä 3Âױt˘K҇óFĽoćűĽłMŘfl=łoÂ,"†EĚ"pLΉ~WІh–FšĄFł*Ö4×€& !Ś3ž´DWţËZnĺÎvjendstream endobj 235 0 obj << /Filter /FlateDecode /Length 238 >> stream xڭбJÄ@ŕ?ěÂ4y1󺉗‹[8O0… •…‚Z *Úš<Ú>Ę=BĘKÖD¸Ňć+f™™¶ö‡Ç+.yĹG\×Ü4üPŃ -˝Knü÷Ëý­;r׼ôäÎĄL®»ŕ·×÷GrëËS®Čmř¦âň–ş ÁŘ`#úÁ¦” ĚJT&e« 0m´ă?H‚M¦ČFŹ3âC‚ …P J°@¤#ßJ“˙2 ‹_â.N”^‘v2%5+w:ů‹gY9–ş×Cbě)ű@;ä@Żůf,B‘MĄ—B‘~2ŃYGWô îřeßendstream endobj 236 0 obj << /Filter /FlateDecode /Length 171 >> stream xÚĺĚ1 Â@Đ [~ˇň/ »1F“JL!he!Vj§ ˘uöh%G°L˛î‚……7pŠWĚŔÄj RVsČŁÇ BşRäJœϲ?SVÜp”’\Řšd±äűíq$™­f’Ěy˛ÚQ‘3şĆ´_@ x6˙ÂÔQj‹yţÂka´–D DŤ~Ťü:čVđhŞt—Ť%¨š´¦7ĄTmendstream endobj 237 0 obj << /Filter /FlateDecode /Length 290 >> stream xÚĺŃ˝JÄ@đYR¦ÉyMĚť˛pž` A+ ±şł´P´”äŢ,÷&ń ´ËAȸł›„ĂÏΰżÝ%“ͦ‡GÇ”RFűš¦štšŇRăN2»šÚąö{‹{śĺ\Ó$Ăä\Ö1É/čéńů“Ůĺ)Ůůśn4Ą·Ď ܵç0ťCţ v ţ-¸ô¸ń0ÜypiV‚ …p-PŻ‚¸ŘLđ"(J€Ëv×W—ŔU+ov®Ś‡-ă“ßúcDâőgUŹâ7({đ_`üú7'4»¨ż ÁlĂ…éâm¶sކH/@םb€±'۸^U Ţ¶b°ćĘUŚVl˙A1J·1×vĎŢ€g9^á[9×^endstream endobj 238 0 obj << /Filter /FlateDecode /Length 267 >> stream xÚť‘±J1†'lq0…űŢĽ€f̰pžŕ‚VbĄ–Š‚]ňhy”}„-Ż86ÎL˘ś‡• Ů/Ěü;“üq«Ó5äč¤%×QwFO-ľ˘kHfçrćń×Ú;r Ú+Ł®éýíăíúć‚Z´şo©yŔaCŐ 2–i¤´ĺŻ™5şŔ€z„>‚¬%k<&ršĄ,«¶`vŚťěd+q3Ëß’1«^+ü ô\úoxE<@ŘG*Đq ÷ů/|AüýoŚŮ¸=¨×,¨˘8U(`‡Ř´ fA-©‘pśűžçÚźąÚ¤PŤjí"ę{mś¤ÔIš€‘ă倷řYRŽendstream endobj 239 0 obj << /Filter /FlateDecode /Length 351 >> stream xÚ­‘ÍJÄ0ǧäČĄŹĽ€¶‹µ‹§Âş‚=zň ‚ =řu“mÁëŁärě!4ÎLRuD¶„™ÉĚüg¦^îW¦4•Ů;(M}hęĘÜ-ÔŁŞKC˙Q•\·jŐŞâŇÔĄ*NŃ®ŠöĚĽ<˝Ţ«bu~lŞX›«…)ŻU»6Ŕ_‡GzahBź ‚Őď„—ă›t ]ć2 ş‡¦G6Da)…ĆhrűĹĚcf÷EAż1ť-Ű?pλëŰŐł«÷łî I}Ňš6ÄĄŁP€gOén ŔâÜ’ÝŮ'ű+ít‰c˘Ź„036u! č’ˇAŇMÄ"9Ń%űČ} |Hł=¤X9ŃZ±H vą÷]Ď˝ămłE=L‰QVţgÎq)ĎśŻďRţT7éŘD]ŕăn˛¤Çó c»Ć’|´M É'bŰ<Î%řŞNZuˇ>ÚvÔendstream endobj 240 0 obj << /Filter /FlateDecode /Length 219 >> stream xÚ37Ńł°T0P0bsCC…C®B.33 €’JÎĺrňäŇW03ăŇ÷ sé{ú*”•¦ré;8+ré»(D*Äryş(00`ö˙PĆ"Ś0C=Ă~d3ę@Ě˙˙@ü˙˙Cö ŕPł?PÁ ˙ĚřŔŔ˙Ä8x€ýq¸¤Íţ83qČře0‚w`Ś0H+Čű¸p3Ś2¨ĆĹ>ă˙ ňĚŔřţ˙˙˙f qËŐ“+ ‡ŢPendstream endobj 241 0 obj << /Filter /FlateDecode /Length 142 >> stream xÚ36×31R0P0bcCKS…C®B.#ßÄ1’sąś<ąôĂŚLąô=€˘\úžľ %EĄ©\úNÎ †\ú. ц ±\ž.  Ś˙˙30°˙oŔŠAr 5 µTě ü@;ţŁaf f€áú!Ž˙``ü˙čŻ˙ ČËŐ“+ > stream xÚíĐ= Â@ŕR¦É2›]4Ť‚Fp A+ ±RK EëÍŃö(ÁŇBłţ€‚ĄĄ_3ďÁŔ¤˛%;śpśq¬rV]Î3^KÚQ[5넥J_ájKMbÎmEběz‡ýqCb0˛$QđBr˛$]0€3ŕ_ Âú;—ąŽëţ|" _ĂŻ?ÝďzOř{Á«ŚőŞútw}đ/ÁŮŘŕdĘĐö•‘5Í'«ćĄŔXĐHÓŚnFŹŹendstream endobj 243 0 obj << /Filter /FlateDecode /Length 279 >> stream xÚĺŃ=JÄ@đ )ŻÉ2'p2°Dl ¬+BĐĘB¬\K E;qŇy­…ĺ^aŽ2EČ33ďźÂEô„ßdČĽŻÚ»ŇĄOu¤mYę­ĄŞÂAßĂîöžÖ ™+]­Čś…c2Íą~z|ľ#łľ8Ń–ĚF_[]ŢPłŃIÚ%ae,ň*¸=ë˙cĘ<üć<¬6ęFąç<ě â˝Âö˘ňČÓ‰Y+ćČ _ŕ Ş^L˝ubŢŠ¬qîđ‹ď,÷?vďóMÜectJ§č¨ÄAq´O8Öç‡:ę®ŃG±ţň}-˘˙ ôżČKHçÖ~źcŤą‹˝DÇ='ůů0t[°gž7׏ŇiC—ôÍâŢĎendstream endobj 244 0 obj << /Filter /FlateDecode /Length 252 >> stream xÚíұJ1Đ;¤ĽÂůÁ|IÜeŃj`]Á)­,APKAEÁnćÓ"vÖů„”[ ű|Ď]°\k±äÜ„[Ý÷vGÜXN n2rבî)M‚Z/W·4mɟ˟1ůc‰É·'îńáé†üôôĐEň37Ź.\P;s0 ]*îËÉđÔ\ćT3Ť&‚ś0ţĆ3vr•ŃőŠ‚şHM“¤ĺ%Á.,äč^{ŘaK uÝ`†m)4ď‚ĺľ`±BĄ°ŠOĹÝŠË5䀳¶Š"mDVô‘řÇ_ĹĎ—ĘBŚ.¤fY/Ă«©ó/AG-ťŃ!A Bendstream endobj 245 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚíѡÂ0ŕ[*–śŮ#pO@·@ ¨%0&H@! $¸ńh%Ř#L"Çu€…D´ůţ¶—KzŤzµŮ˘ę˛™Í"\˘1’CÝĹtíőŚAÝ“SÔiźÖ«Íu{СuBă ¦ ˛ĺŕłU|0Ű€ů‰Ř–ŘB%/Q@PxĽ·ŕ_ĺQvŘďʲ#€rO‚ű ^‰Ëç7\©ëꑆýăgpÓ÷x'A~^ÉĽ™ąP˛Ů/ŔnŠC|U¸ýendstream endobj 246 0 obj << /Filter /FlateDecode /Length 249 >> stream xÚ­‘±NĂ@ †}ęÉK!~¸5Ç©©*ÁÔ1#ćÜŁőQú3T9l× ęČÝIßÉľü±‡Űë5•TÓUEá†Âš^+üŔ:p°¤Pź3/ď¸éĐď©č·Fßíčëóű ýćáŽ*ô-=UT>c×€Kxĺiôi$Ţ«Š@v”#W@Áťř!ç'=rĺ4ŕ8 E\)™ćGCÎ †B1Š:‹6ŠÓ˝bęĄ:wZąK˙Š??˛"XÖi=Ěť1w«˝fůbpęYś4?Í]óšeä[›ă©ÄßŮÄt~xßá#ţ°´”đendstream endobj 247 0 obj << /Filter /FlateDecode /Length 288 >> stream xÚŐѱNĂ0Đ«2DşĄźűHmÚN–J‘Č€SÄÔ22€`%ů4ŁŚý*źŕ1CÔĂg[!uBbňîbźť»Éčt:ŁŚFtr6ĄIFĹ9­s|ÂblłÍňđiőóÓ%cLŻlÓňš^ž_0ťß\Ťt—SvŹĺ‚ ŇPiYÇÜY0ë„ŮŁÖ-$F°i nüQC$««­Ťö‚ťl±ŹréÚ˘•ČîWFĐ$Ť\E‡aë×}!î~"Ú÷bŔÇ ö€?Äqë˙Á®·®Q®uć{3}>t^ ăuCaĘÎź jëŹeG)…Am´«ęÝř˘JżIăŠe­Ĺ[W.Ü翢jŘ„7ýĽ,ń?n·Ůeendstream endobj 248 0 obj << /Filter /FlateDecode /Length 185 >> stream xÚÝĎ? ÂP đŻ,d°«ĐśŔ×ÚVt*řě čä ‚ Ž‚ŠÎŻGëQzÇNĆ÷:x‡üČ—@ iż—Drj*ń ćCDJb“Cíb˘qNjÍILjn¦¤ňß®÷#©ńr©)oĚ™-ĺS†݆/ž–ÂXĄSeF·Ô•+^ˇ+kŰŞ»Ťd%ôA˘č3đv×X}Xţ´řĹ~äČö"ő7i–ÓŠ^¤Ds.endstream endobj 249 0 obj << /Filter /FlateDecode /Length 281 >> stream xÚuĐ1NÄ0ĐĄäĆGđ\’o$"-‹D $¨(PR€ [mr®â›#¸Lvq v š'Ů3ţ3Éęě´n¨"O'5ůsj<=×ćÍx/—5«ĄňôjÖť)ďÉ{S^˵)»úx˙|1ĺúö’jSn衦ęŃt8ä€ĺ©zŢ[dŚö yDńŤbDΰtÁ‰=Z¨b‹ťč°M΢ýÇűyqPűˇ©“Újë•e^Ś5X*ł>ěYëŽYžĚ:#•őB´IjĆ!ĄMlGŐ-ƨéÉâH]$?r>Pçäcš6ňźA§Ů ÓěÖ~˘ţĄI"v¶ČfD7¸(ź0ćşl@/]ćŞ3wć×„Śśendstream endobj 250 0 obj << /Filter /FlateDecode /Length 191 >> stream xÚ35Ň31T0P0RĐ5T01U°°PH1ä*ä21 (XXBd’sąś<ąôĂLڏô=€Â\úžľ %EĄ©\úNÎ †\ú. Ń@bą<] @€ň>’dF"Ů‘H~$RLÚÉz0ůD2Iţ˙@ŔđD1a’ڍL˛˙``n@'Ů˙0°3€H~`Ľücŕ1(¸l@A˙ŕ(ŔáÍţ˙8¸\=ąą~@‡Řendstream endobj 251 0 obj << /Filter /FlateDecode /Length 268 >> stream xÚ}Đ1K1ŔńWn(ĽÁűĹľ/ ąT‰„ZÁťÄI…* nwâËÖŻqź@2ŢP.ľäR0‘:ĽđK2äONäˇ<¦‚ft Iť’šŃŁÄTŠ RGĂÍĂ3.*·¤ŠK>FQ]ŃŰëűŠĹő9IKş“TÜcµ$km™µúŚlvĂÓ2JP;L5o<š-ÜDŘw0ąĂġ ;Ě#đ3đÁ“9¬~cÔóŇF°<ŕ cpĽGÍh> stream xÚ]ŃĎJĂ@Çń = ĚeßŔť'pIĹ€¨ĚAĐ“‡âI{ěAŃsöŃ|”>‚ÇĘĆů»Đ|hż|!ËîĽăš[ů-[ľĽŕ׆ިŐ{µţŐ/;Z ”ž¸í(ÝÉ]JĂ=Ľn)­n¸ˇ´ćMĂő3 kŽ“ž| @5A<,„˝P f<AĐçgĘ·ŃďáZgľŚ)+Ő/W¶Ą #[ĆÁ-Ąź‘7ű–ži&ëU·ŔňŚbHžžŕhľçümýźĚ°Č–g€˘[Î ÍCźDĎCźDßBĎCĎ ž> stream xÚ31Ńł4R0P0T02W0¶P06QH1ä*ä26 (C$’sąś<ąôÌ͸ô=Ěąô=}JŠJSąôťś ąô]˘  bą<]ţÁü—«'W ‘›44endstream endobj 254 0 obj << /Filter /FlateDecode /Length 398 >> stream xÚŐ”˝NĂ0Ç/ňÉyÔř  mĄ KĄHd@*bFHÝ’GóŁä2vjîü•Eb&Cô‹íÜ˙öť×ËË«ĄČĹR\”bť‹"ĎĹkÁ?řŞÄQú.íÜË;ßV<{«’gw4Îłę^|}~żńl»żĎvâ©ů3ŻvčŃľQ˘5@d°ÖşµČ´ÖG`ĘĚ·ědQö¸bB\‰"dşÁW› Ć'ś‰ş1é)ŕP’l$bÝ.µŻDƦŤ9†hbY´±pť‚ôÖ>bP:Ä`VE•S`ŞśŘĺtˇł€ÖĆčÜL©t„Ç9”3)ď|Šő bvóŘÔ˙ GÖ˙Ns@9ŃdSLç±8:›pÜ Ą1¸ eĂPQJn„gCĘ‹Áű9„RŢ@(đo!ŢDľE˘ĽśiM«aZÓj$MŘźÔ|›h×f•‰ÖŰöj¨cĂ•[ÔčŇćBíď’ĄKÁď^~ďńiÂéűü¶âü+8éjendstream endobj 255 0 obj << /Filter /FlateDecode /Length 146 >> stream xÚ340Ô30S0P°bKcK …C®B.  ßÄI$çr9yré‡+Xpé{Eąô=}JŠJSąôťś ąô]˘  bą<]ţ?ü@ÉbŘě˙€Ě˙00Č˙1˙7Ś2‡ó?Ĺ łáÉDĘĽ,MJIŔĺęÉČR…endstream endobj 256 0 obj << /Filter /FlateDecode /Length 190 >> stream xÚł4×36W0P0bK#K …C®B. 3 ßÄI$çr9yré‡+Xqé{Eąô=}JŠJSąôťś ąô]˘  bą<]ě˙ĂŔ" ě`üÁţó ó”)˙Áţ”É˙ˇľĘd˙QĎe2˙¨1ÔŔ 8pfAfśiA&óśióΔˇśÉg2Ś2G™Ëe^L¦ý˙0• @ “ËŐ“+ »Ďöendstream endobj 257 0 obj << /Filter /FlateDecode /Length 287 >> stream xÚ•Ń˝NĂ0ŕ‹> stream xÚŤtT”]×6Ý RHçĐÝ!JwĂ0 0Ä C CH#%"Ąt)J§"-Ą H*)!Jç‡>>ďű>ď˙Żő}kÖşç>űşö>{źs]7'›± ŠÜ˘ ‡!B"˛5]  @DDLHDD”“ÓŠô€ü'â4 | pě0Ôň&¦BŢuá0Ŕ=_P ””JÉŠDEDdţ&²uÔ  +¸‡A|8Őŕ^(ÔĹyłĎ߯0/(##%đ; â A@Á @„t…xŢěyŚá`(‰úG yW$ŇKVXŘßß_äé#G¸(ň üˇHW€Ä‚đ8~Ť ĐyBţŚ&DÄ 0q…úüĂť‘ţ pđ€‚!0ź›_¸Ů`¬­Đ÷‚Ŕţ"ëüEü9PřŻr˛‚Â~'Ŕ`¸§†‚Â\ÎP@_SG€€`Nż řM>Čő9Ţ~·hŞ@7ţ™ĎŚ€z!}„| żfţUćć5`NjpOO éCô«?u(ľ9w”đźËu‡ÁýačżWÎP“óŻ1ś|˝„MaPo_¶úÎMčß1 !"#*%) €x `Wá_ Ľ żAŕŻđÍ Áh/¸Ŕůf H0ÔróG„öůAH„/$ýźŔ?WD@ Ŕ F!.PŃż«ß„!έoî X‹ÜČůőű×›íŤÂśŕ0Կ鿯XXŰBĎŔ@›˙ĎČ˙UUá´ ¨8@PTFR—HI‰‚˙YČýÓČ$kÜᙿú˝9¨ż{öű#ž?áüł–üFşĎż•n#"!ľy˙Ďz˙ťň˙“ůŻ*˙«Ň˙»#M_Źß8Ď_„˙yB=P7ŇőEŢŘ@~cŘSÍ!yWâőőüoT ş± ĚĹă_ őŃ„@ś  H°ë_Šů+núËkPÄîýőuEDţ »1Řýć âs#ËßäĆ?˙ÜR†;ý2š¨„$„@€PD7×|ł’ 7Žt‚ü2@XGޤnĆ 8ĂDżîTZ ěăëéůűçőńňˇ~1ţEţDBż‘ěöE nŚř[#7Íý˝ţíz$&šž€ĺ"Ýj#[NŞUýW†>s®gň ˘§­ľgdřiĽUŮáó#•´ďČg–4x•ż°^˘7›ęńcŢ>1l>ş°l4˛ŇL45LŰű©xSĄ®‡™IĐDy5čŇ;Č,Ě» łýgľ·Ż4™A!Ő‰·V@]Ď«ŻŁ'V W«$ď_ĽL4M° {1ĆYŕ3NĎŽ‡d&ŕŁÜ ¸5vxô™2ďÓ5ë˝ÇüDÁ[‰b%h«ѤÓńŔŮrQź†; VôĚ؇”G¸ĐŞké÷č&ŃĄ%sEclIÎ^×ZJÖG&aHwßln Z}đÇ µPĄęŐ´-U¸Hę éG38Áž–\XŃ˙ĽMĆĂr5]§űřLŘŃ\6–&hżrRĎ€< ©·Z!ó$üábÖ=%QϤÄzrheą|ő lU— ďA(‹‡®üÎßńP~‹Ĺ’ş‡ˇM¦©/ŚënĹ«†őÜ=ěI`ŮS>© _?˙gݤ /¦ŁO\¸&âËM/v¨ůŐ+ĚUJzćÇęX¶™—űĆ räŠ8m9ĘĹ ř×IyZři• ¶WBśĎî‰GnJOť˘ýR5u OBOľ˝fĎ )˙Ž›˛á˙ĚŻü[ßXĚ °aóŞ{SÄóž…űmBwś$ęńő Ťznbě}Żő€9ÜËcÉŽg{WŽfÔ?|QŢüRßb†ś°”Öëĺ3@$ŢWZ˙‚ľÉEĺ'ŘŕŚ—ÜÚ4#Ű©gí”…¨×a5=ĽsľÚE*i#ř.đ-NüCXúĘ#Î z^ŞJQÚŘ7Ž/ě·čm3`fcgÇTL­nÖc4wkÇę̢tSÜĄKőC§B‡VŞ+ĎxNÖ ‹%ÎŮ‚-a Ö„ZĹĽťNpě‰vŤFÚVąűYÝTÉl4ń&´ŹďF˝¬FÝÉ(ĽŔ%©gś® p”Ë"Ç?üşł¨¨x{µő–̱ŢH¶źŕ—~E Ľ&&Eę|ĚÍÚ:ÁyŤŢLŐßJż:L*1ű’·&Wr­Ëm*xŘŽłl=Ëľ~]łQ¬ń$ľúőŶő#»ôO—G¤±}Ö·V4ŚĺŢď­'ĘĄ’±÷JňôĽ¬»Ď¨¤Ć6¨(çâĘŰ6N˘jÚ%”ľdťíW¦[Ý‹Îáˇb•˙‚›ĹÄÍŚ5®ĂaÎť&‰d…-˙„.łĆ:ŁüĹE*ĎßmÚúéÇ(íĎĆ[Q~zدǡ˝SÉŤi#3ă˙áĽ+Ę ŰëŃĎ+Ü·­ĐŠ-žeŃ=\Ě@ç’ÂU *-ýŹ­•‡žö_g„~–ŇűĘŰ#<ä-¶+ÓĐć±›Iѵľ5m}łiâ\jݶŮÎ88»ŽÎź!F«MV¶[ţSë)îĂ-é)g©Ŕ:u^ą¤;ő€U츨şLŰ9żŤÔ§.6/'Ž"o»?ëň˝ę»·6ő˘ČxtŞ×•<™vBYţśőŘ»8e,40QČ˙¨EäýĂPůĹă¶Ťýr+'<ń­ŽwJň?dU±ž+´š›ąZŢâ2şBW°‰†~ťiYăŢÔCH2¨yäď^lA˙ĺĺČÁzőm]ÇËŰ‹›Üʱ Ŕ^šE±4ŮžîýŮäöGçöÓÝZ Y“vlGĚţ¸A«RUôrq©ÓÄJ+žč˝ŁôĘeĹs€”Ńg‚‡C÷ËĄq2'%ŚLŃušË˝Ż^ŰŞŕ·OVýɇÁ”ÖNŕ-"'•_§ŔŽ2WÂ2>nćšK+™Ű·ÖYŮ>-;ÚłăF'\đš}¬)PČŠXŮ2›ś'Ž«‰RCä”?KÍÁÎŞ$‰‚ňÇYf4(ÂňÎÂůű›9«~Ú“©’˝•âá%Ž>Ź  ą/ů4őÂű]—áĺ•Ǭ,]%áóŢŰc¨]hŢO{ý˘6©uęoş÷ŮÚ%Şy|6čťĺű5DšďúĎYm*şÔżčĽ‹WBkdŐ‹—a[‘“&Vţ2˙TöÁ§r® ÁG<ę""Ż÷J÷śÝŐń©­Ăň*Ógfą˘;ÍCďţxI !ň!{GIsYîsšXT§_ÜúŔlŁ26 š5v/ZgţógJz5›‰Ą‰ÁpÁ.KT9´žąęÚÍąeM ›đ‰‚ä.Ú ŞŻĄ˙ČĚ#ąěÓŐzîä.ĺhŰŞOeö–˝â.|ůxh÷H]+ĆŻŰđÎč/LĆWă‘íř’ň×ßOŞCđ÷ŘŐ×Ű]ĆsÔ ´đ1a~éź•a‹Š3–9­áX·-˛ů+? oTWŤFµ=lë”u¦+ł ĐíśŮ"+çˇ|‚Ä Hč^;—ňżr¸ ~ĽěGÍžÍ?ŚÍ¶ę˛”„ÍěAN/Çéľ6ÔDĘžđĚo‹ť0ü çWÝ—ďpTR$'˘őСQď9ߦŮ&Ň«ź×:Č­*jo¶Ô­ő®¸4˛e¨Cצş2áÎĂ}.tqôľ˛p±|T:łéŢláäž (—¶Sw‹űÉÂCç2ĺŬ§~SßĘ㮬.«ÜőTkm#J¨÷„Po ‡ćb]ç„čŘ„÷wŘ1¨ä×'-/lË’ňŮ Ŕ}ôEmŤUd5ěVĐOŚňH3cż*É™žoB} Ď;AKśÔÓŠ#Łß¶yMBŐÚż§Źął=cĄ^‚×*ó™N $MHĆ.Ë3rż'pHPďÝëĆ„ËjL ż˛ u#%9dc2˘+źŮúźË=ĚRSy•›Ćíf[ p~~_<÷€yGCĄKˇ¸fHv@W Ô;ĘofńÁz鹣Ŕ,ł'hšŻ)Ž>ePR&Ă*®Ú¶ż¸ą%6"¨ĘĄ˙“5m<Ňo_s­Đj!ĘŰ;ĚĚúeBxYp [߯F»‡˘Sú2>\`ű–cřsräýéOĚ€h4Ć.á@Qŕy+ŚCx’ұOŐůíí=ĂĆ…Üţw…ŐśŁĺF±A6$ŞĐű|zś{!›ĚdehűQ.Çé\g„<Ó2+ü#ČŮ(ůI_÷Yí˘8Ľ‚Üv1kŇ›¦݇ůćÚ–·ć" ¸—rŞĎsë”î&ęŃ˝F/ômŻb»2ćőjgĚpx@ëw×PuOč¦ î\#¨ćĺë‘ YqP LPWţö'j<Ń<ůËşôHß×gá>Ň^Ž!}ą0I“°b‹Çü…ľşS[óç±lőÝ‚©” KRÍô»Ćˇ;4P¦÷ŹoÇu0ȦiQ[.\FľýˇU‡¸Ťńmn…a ´^în$´`/Ą4ůŇOÓp&ëÔţ¦† Ҥ›>ą]Ő_ĺ§cBüś2­Ý†bůA2Jc}ć熙÷Ü<Đ ü-D"mí© íۦ:˝BŤ”WIăÄf^O“7‡<..ä§Ĺ$2.ÓiÄÔŹ%TČ$ŹÓÖŠ,%±P ˛§ÖĎ {8¶çQ:1HçôK3%(ŃčÚČ™Úw®čd„öl»Ů§Ł\ »x;źFůŠNÚ[Üźą¤’„ÉĺAůyPÎ T;®Vę${NaMéoEöŘ·W›I|‹rśű|)ućuŮQŽ ÇŇŇżxFô`yµŁ‰Đ¸Đn/őp4E€ŐhÜ9y«ńJăťMVĺułšÝďĄiĆ€śěĘ®µáľŃç™)&w §¶čS˙Ž´~ŰďPŞ †oRL@Ö˧L=§/ JĺRáEŐŞAé»cÂý[T|}ß+˘MzÔmĄŢ^o~ŘsZÜB?9­ eX2GŇüĐő˘ŠÎ‡~ ăˇ*8ć· #ő˛˛•~ÝŢUŘTáö°×¸˝5uŃńfčăéäŘçšÎwŤ”Ďl-Ą(ů˛“&†W6sÙϑžĂWG. Nş2,źa·ůš jőmLIXŮ qŽób‘eëŞopnAČIŽ”F®ún¸'Q´Q§6Ç™Ş(°?Yf mYoż€ŇĐđůóÝIĄäžuĘzÂ4Ó4‚jŻőŹáL·Ö–źÓ•»‡öi?©\‹»ČO¬7Ó-UÝx{jµiÓŽĄJĂR%ážhŚľĹ Q”IădGŐă˙ę]Ö4´áµ' P˙P”„ž]©gČÖÁľ†öĄ€_Ú€Óf}čŔ?3›7Ś)B†S&ź‚îg–S¤±dčE˘Rpt˙R®ÎËŇ{źěPĘŔÓţ52VŔ&Ź+Ú´ööĹ0¤–Áłl[@SŘi9“9nŚ™ý®ÔmĄwn$ŮfÁáGé˘D«DúőŽ<š–ŇZgó5ŰâŐB=¤c2ŚZŰRůšÚjGeôÇĹ*¬BƱ¤śŕG:E˝’ÂłßŰS»z¨ôďVBT‹cpú†8ČeÝvVęĄK:$n1Y.w Űů1#ęוpëéôN&ĚFµG¦ Ďěr TևƯçCĘWOrďç°âú"SŚŻ;XE,h9°¤ąmÝí_Ě™äÜ7"ux5ćžĂ˙şlŽaqŽśÂ-h±IjľĐ|WĎĎ›ňa2Ĺ=—a<ńŃĐíő…"ô){Âw,=‚}üŮ{»_¦„Ö÷âb, łjÁ­ŹPT~5Ź…´sŚ[ř“űdČi܆Lëuśřcž8ű¦¶ ”t‡xÔ8łtůhíë9ónYŠ.v+V˘Dť*༉bh6˝•¶ŹSŮŢśşřŃ„!­Ě'ϧ԰ĽĘvř(Yh •gkŕšD§71\6¤účý kwaIĺ˛S[­›ToÔ÷M8žŚrŁ“čŔŢâĂÔĐrsŰ&—[eŢËBĹ?pŰÁ_Ń;`ţĂB«EŇi7ö¸ÍŠ<…´}ź¶Ś Bš˝BsMžz¸–mŇtRřa{×>&ęť›]Ľŕę·5¬†˘Ń·*’Ő_ľS2MJ}ú"ąM,PNZ|EŽó"8YŘž4KpaX=Ź^ËƤżlČß_Ł3t¤śIňrJÍ4`řGČ-‘›‚©ÂP‰ş÷jÍýçó)\ąľ2ëç8ŢYCęň˛ó€†ŞxŠEÇV!±–ýëĐHđň.ç—۲ɇr8•ťÝŘ${t­+ íĆL#TtéW«/˛ă‰]Ź“ÚŽěÂNa“ädl ӄçXăĎwÄy{\ő_§čâë:u’7­¦„^MlĹë…-ÎľöUÓ‹°ďਔm–7ôBOS<’°UőnĹĺ#ň¤sFf«-ż‡ P2ń®ń=J`×éÜt¶łHV0µ%Î÷„(ýňÖŻgŕbĘĂ™`ď[m¸yé6Ťgéhăŕ/F=I—dV42¤ĺř`ˇĂŮn™KSüAýˇSY…€őŢEyeeŚfˇÉF~qX¬Ë€8|¸¦˝U¶.Ó“fމ¬Ř»nqĺô“9äFŇâĎš˛¨R—Ô—ö¤qEśi^{őŚ•|6Ź_ŮĚh<ÚÔlú¤™–ů¤CаXĂĽeX]Ł6pDÂ'—CČOHn$9W^#{~g6?Á·^ažš›u„ű`®ŇÔ±=ŰĆVŁÔĎ?¨KIÇ^[ś¤_YBë¸3sć1]ďĐwsćP˘!ä-ă;/*>uÎMśz%·5^c}~Ňý.S”j/TŰWëÜęŇÔ÷ë jč…>NŘy-ůÁŚrhăv­^*ŘY¶rő"QĘóŕh:ČT=nîiŘóŃhOŮÁNCÖCií’CČkY÷;{91ͨä~dŚM8-Hă$”ËU ’_˝kŁŁć÷]{-É=z'B( m+nK‡L–§6áÄ­Ăý]‡×;žc»őűÜ"wć¶„aó4.}úJCx“Cy$d6ŹN}~ź ˘Â\Č˙Ißô4¦>>/© :ö ư_ î(u‚Ü)É $ź¶„J;°^‡bpćľsZ°ě05ÜÜ×c¦$dl¸µż×ČC…QşšZ­ ~&ď˝qoü[…=âäŃȨ­ëń—Ĺy伏Ř1ÜáĹĺ-ɢ9±G‚+ŻZIOęXEZ§úcć¤Ä[t®tw3€|Š|aęZ î’[ŚrőúĽyLď˙žo˙$Ł ĺÝŔ˛ăƲOE"‡ętŔ®ŹäŚ»÷¨ą‹°‹ŢwălNHöb˙ =ökDé5diŇŧJُwE ô1Ťďé•ÁF–_*_‰»-Ćúąäj˘RyŔŞŘú+çëá*ŤźĄ PłëŽük·ŢĎĄUM/R Š"Xô¬źďż„„RL~2săű•d©HϵK8>y“Łł˝ŹÔÚq”0¸ČńĘi}ÄáŇŚW•p5¬);ݱ˝'~)ëKĄjv¬ bŇ_I- |cë$vđjv1ńEjÍH]ĄÎ*:®nřŚ ·§[*—%6c‹Äˇe.Tw$ďôgŚ˝g‹KÚn\ZčSH•[ 7ç é«,ĎZĚWśkéwěf›áĽx«@áî§8ŻXÖO W ĺ*u˝ 9ťK˙a„gŻP5şň‘>‘ w7ŻÚĆi7Foă,s¸ĄYȱÎę~A n>#˛=1.ä±]ýyďdMI)żu‡7÷›XűĽńF=űÔ¬U“]žŢ1Q±[\‚ćśS¤îe`HdĂhŔńŔşŽL©ňcO/ÂJĄý¬5ň¤íşĄ,5µŻĄľś»jý4Rŕ ¦CRU;Ó=v5Ř-ŞŤřJs@WôQâG|Ó6=U7Áćo™Iî{E@wŃ{"e!vhĹ/ă­‘ÂßÄć+KcŁ“\bžŘ›µ˙av=xŚWobßęŤÚ~`t{čŻ ĆČ ŘAŃlä›ŮöÉĽ1űä9Ą9) EÁbgŕ5}uI\.ąbe$©úúÇÎcđü—;&1{–˝Ydň”O&=-JÚŁ ¸÷Ź·5•/şZP¶Ä6zLćäQ±ŠÝÎśŠšk€SĎ8®{¨Ş¦bn´‚†­ň} )ÇmĄE¦328‹»ĚÇQYž­}ňbýű°Ŕţ,"Π»ĐTn’ä» ľ<ř^1Ęj™8wĽ˘Ú´¦čłrÉT°eVé> stream xÚŤ·Pśé-Jp îŢÁ‚hÜÝÝ-¸4ĐX#ŤKŕî–ŕÜ-hĐÁÝ=8$¸.™™33çĽWuouU÷ż¶ďoŻýU˙´ŻT5Ĺ, f i”™ŤČPR’c€@ •–V µýGŽJ« rvCř˙e!á 2…>Ë$MˇĎ†J€Ľ«€ŤŔĆÍĎĆĂŘ@ľ˙Bśů’¦n` € @ârAĄ•€8z:­¬ˇĎyţó 7g°ńńńĽůĂ fr››:”LˇÖ űçŚć¦v 9őüŻô‚ÖP¨#?+«»»;‹©˝ ÄŮJá Ŕ µ¨\@În Ŕď–ʦö żZcAĄhZ]ţTh@,ˇî¦Î ŔłŔlrpyvqu°9žł4ä*Ž ‡?Ť˙4xřëpl,l‡űËűw °ĂΦćć{GSO°Ŕl¨H+˛@= o¦ż Mí\ Ďţ¦n¦`;Słg?J7H‹©Lź;ü«?sg°#Ô…Ĺl÷»GÖßažŹYĘÁBbor€ş ţ®Oě 2>wOÖż†këqwđţ˛;XXţnĂÂŐ‘UËěä ’“üËćY„úŹĚ pyy8xą 'ČĂÜšőwMOGĐJ¶ßâç|˝!ŽËç6@ľ`KĐóŞ·‹©uvůz˙[ńß•Ť `6‡Ě@V`Ô˘?‹A–âçů;=úŔgú±€ż??>3Ěâ`çůŹů#fUÔ“ÓRgú«ĺż•ââ€73;€™ť `rňxž|˙;ŽŞ)řŻ:ţĺ+ç` üö»ŢçúOÍn‘€ţŻ aüw0eČ3uAúnäš?±ý?óý—˙?š˙Žňeú˙V$íjg÷‡žţO˙ŹŢÔlçů—Ĺ3u]ˇĎk y^‡˙5Őýą»J °«ý˙jĺ ¦Ďë ć`őLif6N çźr°‹4Řdˇ †š[˙I›?ĺZżÎěR…¸€_1Ď^@ŕ˙čž·ĚÜöůqyććź*S—ç•ţ1ČßôĽT˙]‡”9Äâ÷ö±sqLťťM=Qź‡˙ڏŢlĎkjňřÝVôŮđÜł/ŔâŚú{ĐÜ|V©ß˘?€UéÄ `Uţń¬Ş˙ «Ć?ŔŞůâ°jýŤřžcšţŤ¸žó=_ö˙HŘž­AŽĎ{q`űG|ö˛ý>s“Őá_ Ŕ ů'ćsťŽĎ Xü-bęţ—ĂsÎ˙‚Ďj—ż!÷s4;SëpXˇ˙@öçô˙‚ĎžŔ˙š…ą«łóó°ţX˘çAý˙q-‚@ sÔ…YąŔ{›Ú÷m7Őb¤îĚ;Ł‚§i7şěĚŁFČĐ>©I㍍ŹJĄz٤Ťl:•Ĺťn˛Vçν·ë(ë=9Ż)Ą÷¬(Íâfź®^L'z_“QÍb6Ăäę¤Sđ;÷Á¨’GbuˇXXőiĐbúĽ®í™Ot§Ă+‘ć‘nSîčŞ.ů H@Ƨ}´©m1ÔÜáş zť˝i´ežŞśAĄ Řq‹šŕ^…µ>04¸ŚÝKŢ ď¸sFĹ–$“äDŞŇ^ÎĂxČ.v;ĚŔń6“Á=´č+[cHą©Uţi˝ýóĐđ}RʵI’eŰ‘×-9¶ă2žxšsNţn„ĹRV(%\Ó€pⵋvO:XöËjĹ&ą­|h°‚çâtĎÄú«BŞśţX˙{ú7J BŹĺŰGľ ´íŃn–!°­Ż«S°·Ą>0řZĺőÓŘ}\ľ˘žÖa×çMłnÓ.Zć{ßꯋÍÁîN«ÔÂ`mř>€T¨wĺíĹůwŢŃ!N·čTéŤĺµ^·ě“Q{wĘĆúµŢ<˝&JÂËÝ~ăʧĘcĂPqŔRč[áŐfĎuĽ>‡áPf}:čŚxMÄŇNU±´ŐvIQgS˘śSvw@đ­ŇĂęVuwčőë6 ŚęXď…PŞŠ/ =ŮiĹ=ׇ+Ľđćé‡pkŞŠż<§˝pä—:Găl #Ţ wSAë6öF0ŘugL)i[EÓÚ—w‰>=Om0h?!6ŞMlý˘%;’aކtúře[‰P2 ß§ď/8¬HIÎ?aPęwS>™=AŚńĎÄÉ´ Öîú‡‘ęPQĂn7~Ěü˛núsňVÚE%žá}Rí©űĎ÷(ĺĎS˛Â—’ľ—gČKăűô’1ÚŽëkŘlŢVY•` v˛™u´|/5ő1é°Ž.6{ÇĐí®tj®O´I:«Ő…áńĽ±BĹMł&Öh ÎŁCxç‹ăřČáÇĘtʶ˝MpČHĂZ±ăÝöŇ]Np¬˝ČÎü(űJűz9Ŕ˘ńUŁż€g^ľf!ć˙›­;7¶Ż{ OGłć”W}’°‡¬íGiË}s—-ľ{[p GŕD ąKť‰‚Ô.,0záLÁzF뢝 "ČŽ/Ádµâ}-bä ű{Š‹Ë—pT ćű»Ć¨¨Â_4!BŚd¸Í_‡ď< ü¨qiҵFÉL‹üký?r©±żĺŕ'úŘD:(¨'3áÍvód®-G\ ÇMoě¬Du ¶đ÷Z)U> a74›n\qž? ť’sN˙ěćąTemÄŘ-«°Păcp1ŻîŽŰ@˘tĎń†±ˇ7ÂŤË—Ą´Ż°:›ŠŰ´­¤·źáÇý‰ÝW!xb¬]M0ú9 ii„µ»Ëo8řÓV†”€*Ăôí]RŻ–€ĺJşd’$™-6цV>X ącŇ^I4EţZó¬ °pvs–ÜNë9ä(ž¨µîľ5˛˛¬uŚ´wKfɶ‰Uz5žŹńF›Ę“TŹüZŤö48ĎB‘Ö,í˝@ůFbŠ[D&ĘAR[Óŕ}ŻĺË´śÔ9K9Ć}Kb!X%Ž)Ú¶ź*±ťJ+Uč: ßľŠ"¦Č:”ďA•e sV»Šk WăşĘ§Id¦čË*<Ęe‹sDt-¤ćž§ňČCŔőFMĘź<÷&OË@QU$ľa`PY˘rWCéj|:ľ(śŚÝ?v¶órçăś µNĎGÉŢ˝HšźS>?ł^5IG¬ ŤD4?’ě/*& żCµPK‘—ŁilMéѤź'"°WwÚýΗ×'JđłśĹ‚ă{“RĆ]·IhĚv—?ĺç@ű|ř‰Lă ‚[¬H2Úů¬Řtßš§}ŕ–â‹–1čçJ¸S­8ś“Ł=”.â2{ŃIF‚ű8M<ľ¸%©ŘB+ćĹ”Ůî(ĚjŮ}Ç]" SYD *dŻ‚__°X'§(ŢKŮh#xd ?g ҬIÖVü5É婣ŕăĺ?yľ•zćłŘE*°·ŘÖŞôZŕXěŇś•÷kŰŐŻíhxśá0ůĺ–óččŕ¤őRI[§4óŠĺ;®3t/=mżş˘–ÝëăĚŹř÷…0ˇ¨‚ÜP¶»Ţ“Ďf»0Ů­0r‡äĆ ߦźO°¤04 č…Zć\Gß$6D#!}\XŠ’ß:ĹŇ"ßýęS."WG(F°ě÷Ře—¤O qČ©]śý°^Ž.›řĺ‚޳дyÇ⼊\ Gq.Ą˙‰®üWÓŃ%Ć^PI€W{ĘN¬s%ćÁú$Mˇx)š",kDÜwţfq‚KŔěęťż iÖä(›+7ĐĐĹ$5á"Ů'Ł5‘ľ†€3{­~˙LĽö3¶mŰç–¬ţŰÂŤ‹űĎ •ö‘+˘«ĆbŻwçşŢ¶Nuá żˇQˇŘOKęĂUDwE;ú źUž…Ł!ę.'ŠýâµóZ¸üţޢCY€j;ÝŹq"ˇ¤Ăq­í*ż™XB¦Ă?Qy<»:¦wÍG €tÂŤŐ%¶v==`b˛ÝaĂĎŠKó¶.Ö˙éQ«–ńŽ›Ô`ţĽQ|w#q1^pč`G5ŠĆΦ¿«ß˝Ě.YWČ—Ng5=LÚµ ű†Ď\öôZ?Úűćˇ|ĘJ ťÝR¤ł˘Jně>ˇxA˝[/ÓáĘs«H;Ć~ů|ańN5ݵ»Ćä‡Ń,ľ“@Nc:ůFÖL¶đ¶ůŃ‘@÷Ë´łžFŞ4AZ6Ú†aŤpµ€ˇş¦ľδŁŇzŘľú%‘ÎďĎ·˙Ýx”ůłŻ­źâ„č÷ăň/čĂ2B˝FeÖó ¶U™-`^ł©řQ#™'–řdĚC"•댷‘'6U[ŇxšËmăčohLOHÔ—Şď\,“ęó ć ěýÍmŮ 5L»ďCGv×ŕý»ĄBÍN#ßHT:ť÷Đ…üŇĹ=”ËĎěźNwn7Xjüřőió^1qKŘhu¶ t9›®¬ú`9#1ŠŹXói¶EáĘ1dúÜ1/htP,‡‹~ÁPi›x ďj9Î( bbF%ąĆnelÜSě˘ÄČ‹K**€Ř‹ć1pŘüx8l«áŰ.vk]ěě‚Üf‚ ˙-ńŃ6˛­eDFĘzšnO?(Ź»^Ij’üqK”/ňUÄ@T1ŃT3ŐPŠv„éŐYőš„±űZK „Yj‚d'8ňYÓlsĄ“k»Ş©óˇť=©C¸ěHÁĂ‰Ł§pý«1´Ľ"s?nÜgBgŽ6 ?"´+/ZŐšÄrĘn´î\WČźMO.+\Ú[TY©›¨şžË}ďkŻóaźůG҆ب—‹‰‹™o\VÔÂŻ—fQ<ăD—6VŘÔ¬-é°Î÷* R_ĘRíN"W­ dLĚ^\™Rđd4ŃĐŻţ4ŇâŔ­rʨźľ<Ý:—@ *{rëŚlľ´yƉIËľ[#źjÚĺb)qáď0 eŻ nŻ±ë¬Ż5ÇĂŮ~„Q-ĽľxBÎ_:`†˛ĹĚł’°Ä› †$'.˙˛˝gÝń^CÜźH‚dŽćྏýÖ·uTČ+C‹î8Úöő°żIé˙TňťĂŻ/4ćţ‡¤\ŻJ˛ZÉ›‹˘ÁŻRý¶%Q¦¨y^ľËýY˙tîOžmtP-d%q–_°"r»w8ç…$mlŘćě÷ü~,›XÎ }@ë9ßă–řÄ#cÓ’vű•”óV\ŹćéÇŔ!M&cwüĺÝ µ3ćĺ« JPąŔŢ0†˙¤NK‰…ßµĚ#P*mc¦`U”m»±0Ú\ˇ]ęô2~¤ąŞ,©kÖń»Uň wOõYË.ěNö'Ź"Y,ó… ŤwWŢ——š-śX!]Ů_b‹•óN§±Ö;l×{-Ö=şĺ¬Ą„‚ŠĂ  ťš ]ľč­'r÷Ç™ě+Ä•Ôč…ˇđéŹ}ú(Ő©¨3 殿<[´Ôl×áâž˙‘4 …“–} č yďÓ¦«:ťŁÝŢGżďµ:q`ďíÖÓŁ eĚů‘aŽ#™^3B’€f,µť^eÎüxŔIN[\Ü ]r{šNüňf帖äśJ6}eËWËZúĺ{ÇQÉ%Y*v:„4fÇ4…Sżępł­ ŽWB®RçjűI©Čô‡’‚u6Ő˝CMwk r;·ě.xäyŤk÷bőV!2‰ß”5"Ěű٧ZçśqXBV¶xf$bŚ$Äs6"÷Şń„Vśă&LR~â}°sŞ g7\éLW‘~TŘKÖ‘‰O:˛Ą\˙@3ŠNű‰‡Źm˘ę§ńD)¤°ň$"u»«@š™IWÇtžďl@ĂŽBŁ?Ĺę1ëČŃs¸©§•[î„ĐŔk’ă řt-†V‘¨ĹŰ?ЉcttyIv‹.Uxeg[l&ę Ćt>" ĺ•Îî9REđ~ˇŃ—âóžÂţĽ‚¶fu/ÉFTV˛Ł‡űĐǬ )s´Äěžň!ąËýđbů“łŻIş ę§« űqp˝(\,‹Ö%ÔČ­’˛ÄŮ ŔAXóżîů—[!ͧ…üNđýÖž +őŹŇ‚zOŮăÁ~=>?ËšÖP¦±q/4Yť;QVťŠőš´ ä×ßoŰ•ÓÚy]3Lý\ľ'JŞJ(s*4®üÜ‘˘ţˇî0yťLu°aK˝’}‘ Ş‘sç#ŹĆđt!kOŠçű&iŇMKDRÁ8ĺÔ° óWÁIňçł„ňÍŐ}™ńěîD¦ Mš=9Ą« ĎH ^NV%rźę¦Nęl!n’~X„x(:î(Żí8oQĐě6ůCvË Ô­l˘0& !ôLx™ĄwďĽx±x4śéBjiÂm/·ż‰n!7ĄEš*ů‡}I!ÄŁŞůܧ]UmR¤Ł˝„*†ňNÔS‡Â|Ʀ:T(ÖśĆw°¤“ˇ()*ٰůBµ…­}‘‹čóEĆ»}BO‰XżR«&UřvBÄ|Gr Đ6<Ť0ßyGu—i!|ŽíZËĺVAµ_Üâ y˙}Gás4L<™~Ě…§dŇB'’˝N‘1u‚Y¤éSŠŹ;¶śÉ:fEŘÔÔǴߥť™Eţ٦#Ç (Ý®§hű1ÁćT +"b0lW „ő*:A‚ć–|~É4BŔČÓx*;f|Ł&xAÄćć˘âߤů˘nÉv†+ŐB×ĺ.E6¦&ČÂVÇ‘gÇÉS}Rp€bĂMč‚ů[üĚžoµ^ˉ—yúôEŐ3ö‡˝öě}ąbFĘ–ŽUä§ŹÎŁB¬5ťtlŇšľR†?Ú8r׊5hÉ蔚=Łóő$ ’PĚn·š‹˘™PÄÜńňQŻŰdńPňĄ¸·ÉítśC9o”†ŚI 5ĂŇý^§ąU‘ß‹¨y˘Ú¬Źgl$˝ëFKóa®6WÄ‚QŻ Źť}âĺ7Ť űá‰TńŽ$„gş°B†ém×p¶xTÍZDçP32#ÝH€&OŻß»Đ8ę´ÉőţN»ä/Ľ”řŤ”µ}íÍÖň÷MEÝ9dyô6ŃOf;ŚB>Đ(Ň7擉@ś> ]Óiuý7AíVwň¶É¤ë\JFĘXiâ ź©Ě%=Ku·ŘďĽ \A…ËeuĺpXRďď‡čzđçű‹ nŢ)¶°ëWg¶5L$^Âň843 ŠÍ ѡµM«“ŐPM“˛É3NŹŹďŘ?bnt›‰sʇ­xdđŚO€uŻf!^E:Ag#Ă`ţDť6¦źđ© umťłšđîß?ą8…j–wXr;/üTL¤ă×Ń‹čmKŹlY•4(ZG…‡Â* e˘‹ŢsĚśh(óóć漶‚ß/ZCÖYeŘžV7Łť¸_„D5ů§í GpŁoőř1¤ˇ¨$ –}ĐuçúĆJ5Ľác›^B"¦a%Z•/ëAk^O¤¸ěÎ"*]B¨­'4÷i{îĽ`Vϡ.d˝C™,9dç łnŰ;mő‘cárí6QSÍżž d0ůń)UMm qźţéš3‘‹(ąÎ¨8hő} žŇZ$Ś™K†ŃD䛺´R úzŹ˙ăĚŐVĚc—ĺâ¬Ü#LW?¦EyAKü±Bď!Žľ"ŞgłJ˝xW#MŹčkâ°!îýW:‡ë¨ŁqwRřŤýr-§Ü7oîĺ4»iBŁG=3BO_Ȧ8‚[vź´Í­ł ‰ěč–ÖrĄ{Vř[ľ`©ěÓPF ż’ä/‰Ä† ¦e€EÂ3%ÚĘTňHü58@e¬z'BöŇńŽ?txĘnČăAÔĘéV©ĘŰĆHĆďĆEžĺ2l&xSg¨¨Űśő–Ôµ[Âsߦ`É?Lźáo,¦§O7qK®ZF}Ý-éy(’ľĂΧ)!Gňz˝#†ą‘P¸émb%n1—U݉ůx|-“c¶{xX„›Ü˝'ĺÜJą¦ŰŰôeLĎZż«,Y«Źvy?%˙nžťäĆozŹôć(pεIşŤ1ek•–ŤL‹±É°Ý€l5*nâzNd~ł‡ńíł eŇz×N!7čőů4F0 ™ŇŽ%ŠA5UßÁ>üřëfý5цaŰ)aÇ_úQŢ( j;;•m=B’oCHá?ő#Q{Ě&^{`'7Íl Ő‡U ĎYu+„±†H˘;Š°Ň‘FypŕŞĐ´u ýŘÖđ4öSčp:ôözb čhíůý|’ÝL§ďđŠ3ćOÂif[ă:őĎYb@fyľZ§ł}ŤW>1ďMőŁ”Jčé•ÔŰßš&qţş“LÇ<ݦÄk·đ‹ř&+zsfxę˝>ŰöŠÖźEfľetlł«nSÁÖOSíĹ·>1ĆĐŁm~i)›ë;^d™Î•cűěŲ*é·žJç…‹µŤEŻŽ ĽCp¸%^†8©AĆÄą´şěś ®äľő4Ż;qÄ\ś)糼nŠtJ»ő<ˇeŹĘGÝxçeä` ~˝â <„ómú¤>ˇŔY¦ń쇧ń7µ©‘zEzŻČ¦.÷DFËóű˝_Źi5!|Ť‘đ˛ˇ0Í~¶%|!ÁÁĄßSÖq!ŃKMůŔ’ée\gm¦á_›„Ö&qMĎ8MBôj@ c<ľJ ·éă_'^u ł@ażµÝ:ĚWϦ‡mńꄿ@ylőČUóŹO^‘{`ž&·7Ó3[ĺ˛Ý \?HŃś&s(”ąŐx‰µeä’}ôŹô–?‡ŔmŤi †Ů÷;ÍĄ©ĆńTŽ UEŘůwÚ ü”9O ‹Ä×÷aż ©@^‡­_B\ŹW.íp*ą&L†qĘN˛9$_ĹĽ%Đx¤gOí•‘#=ąŘyWë錹y)ú=]@Îş?ÎńĺwëPřş'ŔE"ąÖăÎ9gĽCŽS'uř ­€çEŇŹóŇCÉY™rJÂ,N٤¨Úk·„yšőÔ!-éµHP’ő¸ŕôq¨"ÚĂę“™¨"-ž`Z`=Q‡“!ăÇÖIpa)ą!šŔÄL\U´ü›‘‡łEĄUďđ;TwU~‚wć¤ŕďz´(Q]śBmŁŠ‰v‡wQ–1±GĽÇSŽ#Ť¤÷”Źbmţ¸±¶’Źâűĺ|Ăb ŽS_·öbëâ1o˘ćzë7‡¬gj_Ş|îďŕĘ"ű~Ź×UÔ°5ź˘Ó%÷”(«ŁÂóR˝îłmM{ŘT~ř‡đ 庼»rÎ\sĎ cář•®wá;¦xţ§h[Ä/?{V‡¬Ů÷TžÄ¶sŽ7«}ǵ羺äCWOw”©{_XgÁËßNmD1ŹOß-üz†ă`îfľŢtüz¤wi´K[…řĄ™˝w‘´‡â żűn~Śx7Ş˘Ř)R*çTW Őb!iÁ™«V7( ĺJVfCÖc[·3fĘ\ŚđMGůŰ// ă_ĹR덜¬ÎăŁ)v1ńá˝OJBOňiË—ŹÜw}Ą6;µô¦H¶ÓŰ5äiR¨ě©=YaęÄ›-\.biú&DĺЧóË }#–%·­ÉÉ5"3HrÚqv& Nr{: Ă˝Ď:úB'ąmˇíŤÍăćÎJl×+…Fę%ĺ$ź+ Ý’Ć%vÉMéŇŘk`÷ —îóÍhžÓÜKŢ–›Ë¶Qa‘Ë…;îÓâyűE‰ŚGőŮŮZÔĂ w§ KĎ•}ômčNßŃâ/i‡—ú7 ~śý5 Ş[×–4K8Á9=‰S7mË<ďL 0ćb7-:ţAZ»BNšăľđ12üŰaKłRSۆĐkŃĆüÝ«E…¨UNŇuÝĎÍYL-:/ü\Nş|OöĄéuݨupÜÄŁł8FŐë`ň,€óöîśl¸ć¤›YíĽëŁÓ­m™ŤĹř·çi-™Y÷ľŢ×u&D@˙óő‡źŃ<Y©(“?¶Ä›ĺ9Raľő!`Äưjz˘ä1ońTEÔi`˛†(g?áţâZ~,św†"¨fôa˘Ô)u ˛ŕÖÝO…~ANé8Ď™‚ż‰ţÂľaK‹Íµ­ łřÄ_ń6$+Ëdˇnäş°Đ“i‹ń;UţŘśĽŁöO޵đÉ×F ŽÔÔŮB‰ąh†ŽÓ{"j»´%•mlŕ¨{şřQ[ňVCf˙ HĂAŐfţp†O˛Ěř‰×»VĐÎáÇÔ#ăxÍç·P’‹Ă˘S«,ůe‰ ·Pjt"âüÖ݊ްPL+Šďw/ŘĺĘ…ůMz?çKxîg´‡xgŰ,FM·34ťIbŁ~ß:Đł•óÄWJ“đµőΦ•á"KfRĐÍ_•ĆřšůŹ_Qôţ(ćâĺđhaµ¸lHöµľ\?ĺgšoĚÖ^ߣčĹÁ+ŘĹqö[t‡ŃzEÁ5'}łEŇ]/=ČÓˇőńż1X‰ŻĄu(3`«IĄÍ>˙D•»…đ(eú ,au@ć»RĄUDVSţQJÍi…$ÜÜ G©R4íůkźDüęqZśíđĘ~®řä¨jˇľ/– ®˛-$é„‘ťŤÂ/éź…Ł«Ţ|~C‹ÁËýžCI(vɰßPĺĽsQF±nł°7Ušî}1ĽďŻŃĚÚŕŠ…‹ĚWKLű„‘ SŐĂsŃrňBĚ$‘yŤ¨Čě ¦úT_’=Ă”©!ś*ëŢW„Ä®ôŽHXĆ|ľˇC˙±',dŞ«C8v^RUYRŇ–!ŢÄ—R“ţe°0řDŁúžż2ŰVO_}P%ČÂ8Ë}ßSĹĘfzgşÉłxL;ÁQ#׆…î.uhżo żzpŤ¬°F¬ĐČý¶‰ćřÁ~č^A=/áfVöMnŹ9RýJ3ěÜ8ŮÝ~༲ŰâřţdM4>ç0˛RA NîÝéŰčőYIĂ Ôx%Žź(˝™$˘’R«×$6»Ëĺó»˝ńLŠŮń ĐĚmĄµ_ÇŻł@‹ í/”ÎyĚ“^ĐT }C˝ÓąJfeŠ^DO}KË<;Ř2ÍÔÝ4ŘíqNÎ>âH°ŇMÍ{HòŤK'¬ h•NÝ*" Ł7zÝF*ZM¶Ş—‘Ů^ř2RĄ/{Ŕ$ČäÂűđĆ y<†®CďUbęIŰşŮ{Ć$í ®2|f uř‡DE ä3ŚWĹťą1X]#ňşEcřÁ†[“ #Í;¬!Ł{23Ż™vó=ł'c^ëâýćä&‚?ËcQ/G›ř®-ć˛ĺ~!µZjNiZě ]ĹZŽ$çĹÝ čÂ$A…ŞŃcwÚÂŔŚ‘íú«nQúčęş+Ě«âұÁć_Ą?ÎYhŇŕ# śu ć-»•?ŘÍcPáZP’dIł­ +ƧS6ˇĺ!źí±ôYݶźín}_ ńGÁ¶ ×ä+…żڧ2±·,6eŕćkÄÖ*Ł˙JkáFQFEˇ-;“ôŠCz|ŮďSfíÂÄeʰŢĘ$R©Ăô6¤lZîúŁ[–ŔżÇ ˘48ńć=ݬlůÂůp%3)žEŢ"ľláG;íŁHČř/Ä&DŁ;öŕ…´Č/0ÝôŠĽa'p>Üw,ŢoćĐˇM÷“aEfĽĽäŇ Ô/_żŇ©uć°ąŞçk |hA§ď¬špŘŃů%ŕ^H çD)ĐÓÖ–V@Ť ŢU¸ń©6ńm A+Ý$’«( şĄĎ¨ ađ#łkŻrV&lbH6ęRéţ1ţţs™›a“@łGřaÔi׉$ZÉňF†‘—}]@wŕy ÷f»ÂňŞűÓWéĄE°KŃ“'ݰę©?<ĚBËŻG3ëtnĄŰbŘ’dÝű=ďÉiäBÚ÷C:csůH"~Űx\KóK¦vŠŻ…%rÉřťÇŽĐ Wͦ­0TRt[ÂÂ5v˘š[zJ ’ňĄ/ło?÷Uě¬W3ŔńĎŹó0ŽŠü:öś®»YPú>ß7™MĽžsT¨čž•d8e™q„ JđXhëŽ]&:&áÇíXsa˘–ĄO-©ţä˝~ůúE̵NŐpf16ŐŤ:ż`Η)Ă€Ş!ööŐţiá&YˇĘ Ž´łé6tĽě–‘¨/+PÂŁx#YŚŃĹUľď’ŠÍÓlőgű×Î]BO­ŘqňlĐSXSÍp~˛Żj^éˇňř?8§ŕT6řČŻŹ¦Ń©6ő´Ë fR¦ń$‰ajŢ_Ú‰Jâj}gtÂÖ>mMRˇ˘01=Fđ F<ęěţUŮZĎČóY|'W_ť†"+¤ŕ[ťď‹$9YŤÂ'ŰT‘´ZQ˙µ ˛»kÉţÁ×ý®  Sřxß»Yę”·éú=_ඉ¬/ě‰ü@1Ý@sŰ\Ęw8ňÁ{-ý‡Íˇx┏˘JČ@aă Zµqť-Eö¶źĽ”ât»ŃÜÖ¦"®9ë:ńDę(ýą0·/‰“ś#•ŃŁTf`÷¸©‹ÂÜ´'wC’ľög ~ĹlqfůÎQh×6_ľćK=NTPŮ·łqËťżĚ§NhöýŔČ©Öúç*™Ŕ„€;^÷§=z¨ ܨÚqË™â Ůw¬»ĎU=~‡RSłZĂÝŐeQ;Çř‡®ľ=FCo(ŞT9̦𷎺"Ý‘ôźDjŐ=gg âVXQhsŠÔĘ3ôi©éŢ)XfVźd^¸xćYôóČn!B1uŰαćÔĺXlM'FN FبĂ>[‡*‡ˇ˙HËkŹ2FĽK|×<±B˝Ę˛éôúÍ6ŔíbŽ\Ś|âî:7{ź¨řŽUđŘ7ťń  ?}Č9·|m`\ëM´I% oPZęVÖĚ=<ĘCřÎřE!2öůˇů1˘!+ŽóV9mˇąŔPÝ•.nPl؉ ±wĹçH1Ţ™eĹc ě™B1!YdNJ«*ˇR“®6MOśßJJS”eż\áĆľâc­$SHHd/CA]¶sąU§='Ľ‘»ˇň|¦%cRGĽJIúň׾4©Źd}iy­ŐŐź\ŕÄUD¦Í6Ť őrB†a ůŢéçv–‘ĄţkLĆĽendstream endobj 260 0 obj << /Filter /FlateDecode /Length1 1515 /Length2 8220 /Length3 0 /Length 9237 >> stream xÚŤ·PÚ-ŠBÜwÜÝÝ%!Ŕ .;ÁÝ‚wîî\BpHîAË#ÉąçÜs˙ŻzݦjfŻîŐ˝»÷^˝«†š\UYĚÔÎ,mg e˛°ń$””äxll,llě(ÔÔš¨5ř/3 µ6ŘŃ bgË˙_ G0úd“AźxJv¶ygkäćňđł±ŘŮŘřţC´säH‚\ ¦%€Ľť-Ř …ZÂÎŢÝbn}Úć?Kť =ČÇÇĂô; fv„€lJ ¨ŘćiG5@ĂΆş˙+ť jĎĎĘęęęʲqb±s4¦g¸B u°ŘŃl řŐ0@dţÓ 5@ÓâôÇ®agu9‚Ok ŘÖé)ÂŮÖěxÚ !§P±Űţ!+ţ!0ţ:řwşż˘%‚Řţ™ŘŮŘlÝ!¶ć35 "­Ču2@¶¦ż k'»§x b 2~"ü®S€žü«='G=ԉŠbý«EÖ_ižNYĘÖTÂÎĆl uBůUź$Älňtěî¬nÖĘÖÎŐÖó/`±55űŐ„©ł=«–-ÄÁ,'ůĺÉ„ňŹÍ p±ńňpđ˛Ŕ°›‰ëŻôšîöŕßNŕ/óSŢžövöł§&ŔŢ3đÓЧČ €::˝=˙Űńo„L!&P€1Řb‹ňOö'3Řě~ş|Gŕ Ű“ö€¶_źżWoźäejgkíţý÷ý˛JiĘj)Ë2ţéřoꏏťŔ“`fçb<@ĎÓÂűßYTAżŞ`ű'TÎÖĚdűSíÓ1ý§b—ż@÷×pĐţťLŮîIµ`Ý?"×găb3yúţ?KýwČ˙źÂeůż‰ü ’v¶¶ţí¦űí˙˙¸A6k÷żO˘u†> €’ÝÓŘţ/Uügh•Ŕ¦g›˙őĘAAO fkţ$ff ' ç;ÄIâ6U…@M,ţHćŹ]ëרYClÁŞvN_oËSŰ˙řžćËÄęéýpzŇĺČéiŘ żŻń?ŤÓżë˛5±3ý5wě\ÜŁ#ČĺéęźŔř4 ¦`·ßʰ˛ŘÚAźBO={ĚěQ~]3;€Uö—éâ°*˙Ťx9¬š˙ .«ÖߏŔ úźôĘ ţ/řÄ…üä°Zţ|еúźČNżáż:3qvt|jý· źÚţţýĽ€Án`”…Y;@ËšŔ¶«*1"WćÍqAÄŁ”+]vćń|dhżÔ”ájĽFFú˘ÂGé…> ´e§˛¸ĂUć·ą3ĎŤZ˛:wÎ f2és2ăŘŮÇ ŘéĎKbŠYŚ&ť$qRţ"Ç~U’—]ĎELÍű5¨1Ľhkzç\i°‹ĄyäÚ”;şŞŠÓq‰ů´ÖÔˇÍĆš›\çř´Yk61ďÖ#őĂ’•?P(v\ŁÄ»Vľü>0<¸ŚŮGŇá/ď°yL Ĺ”$–äDŞÔ^ÎEżË*rŮ˙đĘÓXK\–ţÍ}BqěfZżÎ>‚oHĺ°ÍVÔ–sž@ŹźĺţÇ»t±Żu$ÜöéFjŁź6ÔVźů/Ëź“=ăĂű?k‹©ŕ8Í$=Č?•mc#®´î+Eh­ iÜŘůĎ?<ź#óĐbŹ&/›®DŮm&€-8Ŕä^>˘9Ô%7M|óŃ'˘×<ákŐÔuAx˛ůţëMS<‘:oü0ń;«/ĹÔÂ~ź%FL‚F™CĹr•”ć[ňyźFa7łňń©"f¨7’˝°>1Ë«f>7}€ţĽě~Ą\J«źčľĎđ3žŹŮW˛Ś}†ËKěňë8´ě0Ł:«8'Ń:ä±z˛ ;%őˇž»%Â]NÓóĹůY|µß.'’Őh©{ň "_L”ԇіżÚ„wČÔÄy>­řS<Ž:¨ )r˘cŤsÔ.Ŕô)_Q°OŐ€;-„NZä-ř;zc §»×g|•<±4—jH 8„4p[.ÄWw¬† ěpž·Ä‹~%‡#_Ă‚1Yh{ŤâíxCĆĐŢv>ęÓ ž¬ľ ÷úČq*‰ŔĎą¦}[ŐëJµäˇšĺdŇŘł»›R”E-U\jN¬âőĂÝń+‰ÚěĆń^“ó”)ˇĚřGŃuýWŃć€Çqb)Î3>ü-ö‹°+äb_/ęŔrI058S‡‡m‰7%śé™:o)rŤ€áé&Ęwár÷¬ÔĚ?2‰W`n…Ńk›ÓhF.ÚZŇߌ%ÜÂ+]Á×éŤ u‘wb(˛t K­‚ s%2MÎä()ąě­©ĐŰ6şC(uîzqCöďĎçú+rµ°Ó _ËZĽŤ˝ľUę5ť ©r5ł{·Q˙şÖĐ-µA%·ţË•ŚťvÂ*5dŽ×Ă([p ¸-Kă;ŢŔ±'ťĺ‹‚ş^äĎÎ;Jçŕď›{Ö “mŐÇgfCűÁ nűť3ŰYČ9/uČÚŮ•{^,7Ůŕ><×Ţé±;îŤÚKktUšˇÂ]ćNÓˇUss °·xSťăá÷`˘ŔKŕ8ŕâ|1‚™AÔ®s3d^@XI¶×ď_,xŚŠľ š<6ăś±jBŔř"UĄłş¸WÔw;8·˝¶M=×Čł Ă©2_*[ÍNůŮrŹĚ©ěŤß¶ĘĺdvKTQđb!ě‘—M¦Kĺf±Ľ ’… |Ľ?ζ6˙Aó°ŕZUx4íĚ%ˇCDź™Pŕs:6Çťęâ}Ž]ČÉ.*Ś×_<5zćPF}âł.X^î6Ći4,f×;žc]Ůů^}^ůŮ\ÁN}Żě=#n75saBprë…vĆ”#zDIŽ)‚m 4}¦|u‹_Ň2řť>­}ßń©XČ­ş<Őv’łD©Ą ů}%U´ĽFjI¬˛Eîę]%Ú'Ó…pÉşLa¬TX1ddÇVří¨|Ň\ă}ÔDb›]ßľ…túňµ }źn¬¶GßÍY- >ľ`śŽ.Š+őéíé;<2rˇ»}…s×Â]pĂ^Qa3nh '7UÂémWPĎ!ŻSź˝ÖĎ}É|Á·Ź›&@T; †‚áÚŞHmY)¸j?e%dJ™ >P.”ťćcű†Ć!÷:U ~ćJä@fŮĎ~‘®‹ŘxÚ1ÄŻć¦Î +śďŞ  t@Jůđ=¦üUĎĆôéc©ŤÓ˙Ç­@8¶ZZxđíAÄ,Íńá^Kb-¶¸A°\¶µq#}N˛ ˝O±(nl»‚ ůSAý4 0¶X#†ŤĂ÷U%‹Ńmx$=ŤB‡}6dĘ ĺHüM:‚˝Óüáüv7ńŔgĽáłJ)Ů·ZńźíÜĐÖś/Fßď¬D 4Ś…šŕâqs× v0‰ Z¬čys_‘}žBÝ®s5”hD$PŽáźÚŇó Ł6ŔĐŠ_`\Ľ ^ÜLÇÖľă>:«¸âUŻú"’~0ăâŕüŰK¬q®ŮŁe@yK˛ › ‡Č»řöbL®9N&űĂö"xą·îű;Ł¤Ęą” üŚOÇđţ’óö%nřp®ÎÄ­Č䱏“Ť‚gpVhYsęÇü‡­ ; yKˇĆ ńŚy L›ĎD¦iٞŞČÓ 6&ď µ/ďŐÖO °!L}Ž/ˇémK+‚šĽô»cIĄ°•NÂŮă ÝĹ>&ÄűlŻć)[ŤS#ně« Ě9-I•“Iiý±;’43×úŢe2Ö-„ŢűÎJ“GuŔ,˘<É}ĂNöÉ›‘YőŮíĎÝĹ%>é-ŇOđkfÚ_Ł/ó;8? ťrt};c«čk'O‘Ě™FđGŞÂlV˝éâµĐcźuLčőĹiL Ł0vČ“J›&ŕOPwÔ†—’Śnó îK)ElŔT;?ŞĹŰkűăÚÓđĽásXÂTł“űqWŻť„ŁÄţÔs č¦!+ż[ő»÷Ş6[`]“ŽqüxŤśˇ®írgp~†p\ńöbÔ«Ż„9Ń~ŢMćđţľ#*ÉW„i;ÂĽÓc׺¦8!äęxŹ){“hĽ–˛©đCłX„tyj’žMfÁ‹ŃH¬Ây§Ů–Ř#a"3ň·ÄósňŮS_$^ĘÜW9ă·ěΨáFOąKÄ´Uč8-ணőV­Ń„ŐřËiaJŹůÉv€GÎ0v˘hkŔTÚŮVÔx5Ďű–q“\¬)/hąOŇ^DÔň^%D É®tĚó¤ĺ˝­‚Ě…br€őÝFF76CYÝ ĚŢŁě _Á:lĹźúßÔôŔ`nůĐÖfxĂťq< tďžNÓmmKg—Č|zv=¦Ő-Ĺ\Żś+@Ľ”Pĺá*‡ś#ÄĎčEůT/aż˝‡§ˇ’>©‚“»'WŢQßJ´áŽIâ <”î/EJę}Gn ­ŕďç-Ďr5)J2Ě鱲ěH\—ä ‚ł"`ňg;.ÄéN«—Íň"䔄_WÇŰ«~Us¬—蔥Î[˝u÷!Ł)x‡Zqź™¨ß¨Ş©ŁďMŰÂ…ţiď%``Q"Ë##,xD×ü§ ‘ŘÉ*7ÇŹ<đj^Ű-đsňšT}býËň7ť°˝kń™MňÂ4ß)ÂÄNžăk| ?' ööĺ’ôß´‘ÔJjłŢUđ*Ń[}:žG¦W˝@¦ffµĆô‚î#6Ą<´ßâů&¬j˘Zt­XTĂžDYť&‡t¶-Ç‘4„ IÔW[Îż 4V´líG)őɨ1GU.zˇvV|4ĂóµŘ\ě󺼎wcľ»oą· RxpIđy!şV¨qK¦ĎŚťtÖëâŚvô§"h?=쎶“ŻI×Í|N5«ćoš5[ę[.˘Ł‡©­LĄP‹úNmüYżß1Ţ@µ‘[lrĂ™ěcź÷w„@U~:DËw‹˛ ňŢ #őĹiìz(úP‹ Ě9qz!ňľ§Ň /™$CŤ:ÓŐ5séC¦úě·Y_É\UŹ~µQ«Í‡gÓJôÚP^@‡Á6îŢ%ZŤľµBLV\y”‡ňíĆ~îÇWnĘďĹ´%U-ˇ!jőbU1š ĽÓ ăĐăČhĹńĄf›6 M*ŐŁ_¦±»(żúd-ý-M‹Ś¶iĂÝkkX &ďk莤ZOÚ.ۨ筽J8ZŘ{ę¶óˇMŹ%I´nŘü/eC)ß_Ăué{Ö´ěK2Ű« Fn úęť:©@ü8¨FŁ…šż18űڵyFŔ=˙}Ţ}ßg0ˇ‡´bŠ;uâův¤âѵy…ňbFkŢ­-">Ba»ţĺ^Géőů¬a'?V(şŚA5=âšć+ŢÁ¶á×”%ubôŐ{‚„úłx[úy4ń‹I.-ÔD Éj{ák´€­<ßj¤{BNľ(Şö™(YdÉ!6=w zşám“7IĹ)ÍUźď¦1·Ý™¤ľź\źŃľíź”÷ C»ě?CşŮPa^f„ĐX?’B 1Tó5ăAωđsp§™B˝·NjżßĽ™Ř r'ČZ,aŚ §‹Gts`SřąáżŰwżüy®Z– MK-lŁxÉ dUňO(ź}5âÚşřó}U¶ÄŃďI5„ĄŐâs[ś Ůśaᤠ>o "´‰}>ˇYsąÄ„¸}ůYh¬§¸ôĂ+‰ \Ü5K´Ű­ďŇkć5'MžzĂ×ÖőŕYV×űţ`ĎźÇăűF­Ţ­i)śődŇÝ5˝tÓL&g DrăĄ"YyB cŃĺŕ:ÓOĂRÄŢiQŞaj ę TsÓ&ewÝ‹ h=r!ó¨Ě±}i~‡gŞźG)ˇÝ‚sYďĺ›Ýßob1PJAą6ZŞ<źŤůĆ1ÎŻ%thŤ0łr‡*V;éVcä°ćËâś 2F“Ć⣦7b Pă(F|óş!u°v-Č ~Ŕ=uff˘›ĚBĵ&˘PłŁ÷gŞ»č"/++ř‘“ľôő^öCôHó™ŢP& 6ęŰěŹFÉoŔµ5ţ~$Fˇ;şóůĐ˝@ÝË—hĄuŐëóĚú. ëÂŞ>Đ\A$„ÖC©'MäůÚŞ_źÍI镉­kÄÂČ´”ČU=ŻgLĂ× Ţp]´šŚBěäÄ-B`ÓŠ|ĎiI¨ĘŤz]ŮYTcA[[!ć$˙)1:ţ)?zG6Ŕ(÷™%¬6ÇzNžÍ Ą3es¸˘ŃrŁ l‘ŘGás0? ÉŐŁň0'ŢaC‰ňî1~‹•‰ŠĂU$Ex/Q ˝Üâ^Ľh|YvdŠ ťPńNN‡F_‡×¤ÎĂ›lކ¬q Eű"‹Π͇A íů˘‘u€Ęń™ů­¬đ¤0 cn:ĺčGoSmČŃ*!55W)vËć¶oDńvĽťŮE ‹@~÷䏦đÄ kÖJ{ęÁm_ëâěÇĄç ÎřJŁ×Ü)Lw…DaŘÜýß Ô„-ÍÎŐ1‹~jv˙PÜ>ě/ZDŻĂšöDvB'ë‡+˛ŚˇëŢ*)MŔk`§Ţ=bë`!3˛«őT@ ż ;›c5ěŕĆ$Ó®áÝ“Ň &†‹2đnň’Q0ąn‘*==»ŢoâŇŃ ZŔúąŽWÍĹ– ”-ń¶jó$”áö(ĎO.UŹíËŤ™e #ýÝNGę:°yBŹ™(ÓôOe*Fç\(!›ËB”ĚčŰ˝ôě 2}»Mhú¶@Éwy—c‚†K„ˇ&ŤNk1!ď?<ĐSd|.¬ŔŮĽŔJđâxř¨ČbéW·u ôüq'— ŔĽ¶§$NŔ0†¨.© č)&aŘ×€+ňËžm‹»MŐ‰ş3qŽĂP20´©ę±?˙¸–‘Z1č7~áľf>Ż)\z~Euő¦<é•n‚§$pŠs,˘Pár¨ń|Čťk™˛ü–žú°‹B1ÄIí)±ď wfŢ˙Qż2+đJ3G‰ÂPŽEVYK:e>!äd"´Ż/ôý2›rŞ"#«uó«uµ‘r‡]ě9”šŁ4×óĎc:ű®}wÇŐ$ßâ§őúňúĐ%R<Ľžş×ĘŔáęţ @;Ĺ%©ůp†INq&t$¶–f˛<¬ôŐĹW3s“Xo{ŻË˙â,ťú‹µßűˇV‡‘¦…‡Ď¶9/Pö:š:A§‚¦ť}TH-MoúŁSRxŢ{!ßŔŕ6ESëĺî,ř­컣ž©Ü řicÁŤźO•ŕßňyç݆ žřľfżŔť.ŤŐĂ*Ď+hľF%ÍB°őQLBRF}ý.´KCiw­ôăęš·ź¸źÂ*Ő\Bq:ĺËü-EńDBv é„56eú^.n‚¸ź’e»®—ďÇTůbŐŕĹľ­A‹Ż‘ćľSń›ú¬±Ĺ¬IbäĺĘ•™d°ĺ‹äÎČ+ç"´ě*jă ‚¬sKŤSĘâşk»7o)şŹ÷˝ăëý{ăů÷Žq±ëÝŮĐö;˝¤ě>{(‘|Ëôٶţ=Ů@Š˙PÉFW‡xF‹¦‚kÓŻ( ţ€»ŢbăŞÇě]lŘu;ˇ¨M­3řMU/zĐ—E·Ň|3Tô8{{§ŘJMvJK»í@í¦3¤9lĚ »©ÔT.ďmObď;đ˛uź‘‘¸˛«±Ń©ay#Ę7›Ş˙ŐűŚg)Y>ôćHxď0\đk´ŰdlĘŃ|uýt´¨Ç´jnÉÁĚLĎÔřqč?aOáÔÁ!ű é…†2ZłššsVŐ}ń u§¨IÖf:Ŕ•ŇP %Â#PCŰ›Dj}—5G~ő‚óěŃn¤UĎmz..’đ{4ˇł\uÓđ.1†12{‚ŕESSBw÷„HçYüÖ Sk Ç-ś×ńúĎłi×ŕĺb–Ő*™a'¨Ä^˘',;WŔň]q¤>|ÝrűđŚL›–o>S˘_‚qň‚×kţtmTŤó—^üZľ…ÔöVçS˝ĹęKB2áĎ”3-ÜxÍ"RĹß9żo±ˇtsD$ĺČ~M”rcJMŚvőłMŘUE›éČ^éçÝɵ¶-–ľ•ëyŻ;\U ló ×؉ćÜj]ßâ•÷®öŰô\ŐOÔÖcgĚw ű—üňÉéś3ç·ÍB8ŔcpůŐ§ĆŞ¨ŇJHË|wŃů ZV({µ9ŻĚë÷öÝ‘y6¸(Ô!…oŐ˘_†UľşGśähl]DŢ—#`żńź;Üf*ĘËvţDŐőíĹČÉ˝A×^Í-nYM‡ź'{‰5‰›×Ó˙•*ąŁm€¨éÁ (Ą+ná°Ňţóó ”ôb'ŞťIäi˙=]źŽmŹjvEç+ÇĐ,×QB)¶~,ÔŚ‚uŻ”tŽSôB8ô¸·Ý.UXw•}fĂeńŘžcƲŤâAd™3nyČô Ă‘ 7ŠŚpx9ŮXC!jműžÖŚĆHňyÁz1­°PG•MříAMˇ·ŞDâD!/ăäç”oyĆ·SëhëÉw2™îvŘ ě•ť« 6Eďşkhł%Äűaővr÷qè jĽaĄÖńĽqî[Şa454đA6ÇÇĽ)Xö˘«ď“cĂ%Ż’ÝżżNĄ˛ŃĹuźű”ßSĄ¨wČ‚0„1Éß˝L„#Ędc“¤ŁDVŢ~ʬ}wrň˘řÝ@zg@͢Ĺá˘Gé‡*mö}fĂĚxŃţÓÁÝG‡@ ‹dꜢËx2€Ŕ9Lę8,úúUŚďĺ…X·AĎü~rT–Ž÷Úň€^\§×ótÉM†w©i0şOĹZżŻi/**]+7-‚”‚ZF®­č’Ş‚u›C—˘ě¦ŞŢŕjĄNşľ,‹doŰípnF[6Ą †ó‹çűoµuą7–xbŞ0ěźuŢŻŃkÖ‹¶čᲔľ‘ăi1&Eź”wwc!Ř얲؎ [ŔűIçuý)jţî°—ď%Siif6lŁĚőřxŚÖHŢo;ü9ŚĎ_E~‘‘~g$‘Ą~†çő$‰ÉÎEČGeĎOýWíňk;§ÚJ Ł"9~ö·†ćŻ”z]Ţç±6˘4+ň–Ýž ËM§Ü;˙ Ű,!Řręjz[÷Ž‘ű27#Ú/ŹĺPL$MQ˘üd3Đ‹YÝmĺ”‚Ž„´şuúe§}6Ô*ęľ{›’Ň8Ďž°îXp‰ŔĂ>8h\0yˇđő{—Ň T+]t7·‘ÍXÓ <ŁÁ}µťřä+§óüžRĺâf·8r ý·Î[§ĹCĎÄR¦ďçHë‡? Á‚u>Č ¦~‘éYˇŢcČ‹0ÉdUc7¶g„ŘďĚ[ÁDq›op“á/şN¶7”ĘëJăŘęa#†m2&|ů9·>>ÖÂ|7–ßä_Ą;Í•qŐtiXĐ^«™ěr¬°ĂöřúönĄOšźu–Î(lą|®µ@-ÇřÇ‘hfM^–ř›Ë’FĆ[~ű0˛X„ #Ďţ䟊D|#Í5‡qeÖHQž™ŽęŮs—xŽ÷ŚŐhëĺŁVÓm™g[ľNćĎ0˝”ŐÖŻk”śö0÷ÂÔÉâEW‘F{HĆ…•'É$,zinLě`‡”›<ëüśüÄTlHc˛î}ň¦Ýń nX{*~>w3ë]_Ž E™:é×Ň·IŇCK}2kĆ8×oZáR‚á!fŐ(2B­ĄŞ9÷˝5KĄTŮîŞ1Ą±uŽ#ZiŤńö)ÜÉuFę3Ë…(QJÔFµŠo‘Ţ—€N\mwb!r‚°VąPDE0'HĽÁ‡k›|NT˛1É~mŹfWRÁ×cóĂx,n±FĆ6"M’ďŇ©a¨ ź|‹ÝVŔ#Őˇ~[UHHTjĂŮć¶‘›©˛ş7Ré‘—ŰyüÖxëţş Ü?˛Żź§âéA]‘(„Éu¸ňŁztŃ…TŢ—Ó+r9zk¬>›(ó‚üáh‡¨$ *0ŤhŘäŠ(…›ąKév©«ŻF&b+< 0™4'¶2ś‰vÚŕĚŐÄBbxľ=âB­«pÇdú°vĄ©ĄŁtÜVGŞt0 ˝ ]}đĆń[R$é·Ă¬˛‘R,Çuç–ę!‚ů@w"ÚúÖđy˘Ţ2™`ŚČő¦fîf^Î-î7®=3Bů÷xú¶ XXfdŻ´X)C‘ź=ŹOŤî÷8Í<,Üę †o—Č\5S–VáuB ؉Oäď÷›Ć˘Ŕn˘.{=ÖŇHu/0>ËźźźDfĺŰŕZşRy:{Ź2Ý‹É, ł.p™\Ä9ë'űľľ,/ż*±*@c+bŢJ8 †Ďif1`|ÝgŔü–@NľĆ ž·ż‘™„‰ĺ5Gă­Ä%»ěű&˝·ŠĹÄOů$Ăá›»¤š5˛» YlŻ o–9e?íBYÁ›ÖAU;mÄŠ ôZýlżźŚĄ |K9 v h@|Š@{UÔµÔNČžvńŇŠă;pžŮĄ)GHR™= O SŽ^ˇÉeJc’… ü•ř8ÂýsA7ÔÂ2âŻ8×ďHüéŃŽîyy2‡zňíÔ’°˘»ÖhćĹ_‹ź;ŞgďŃŞBňř–Âî3IÚ2IĎ_ů"÷ů !Áş›‹Ç^š.U†fI.ĂíŠÉĎD™Fľ\¬w‡}™Ńţó*›¦ËvG‚O4JFĆNxJ™>{Łž(¤J ¨‘SŐ&ăöĐLו‰Wu«Ů!uí…ń˝ĘĐ#E˙Wý‰<ŁŐĺÓÉhdŻŢČm|ťsŃ]¶–Ňbź,íĺŃ;űá˙jšÜGendstream endobj 261 0 obj << /Filter /FlateDecode /Length1 1634 /Length2 9714 /Length3 0 /Length 10807 >> stream xÚŤ¶TŘ.Ś»; îîîî…âAC€$8/JˇXˇ¸+PÜ)Z¤¸»S´@ˇ¸=:3wćÎý˙µŢ[Y+9űŰ~ö·Ď #­Ž>§¬ť› PÉ ăäĺâČkęńňxxřąxxř0 ś` ŕ_0Łur‹ý—<h {¬aOvšn`€š'ŔËŕăăáđńđţÇĐ "P°ör˛hrÔÜŔ@(ŁĽ›»/ÄÉÁö”ć?G‹-+€WTTăw€¬+âdk hZĂ®Om­A}7[' Ě÷_!X$a0w1nnooo.kW(—ÄAŠ•ŕísčˇ@Đđ»a€–µ+đĎθ0ŽNĐ?q}7{·5x@N¶@0ôÉĂl„ž’ôU5Úî@đźĆpţş/ďßáţňţČ ü‡łµ­­›«»5Ř× ě°wÚJ\0Ŕl÷ŰĐu{ň·ö˛vYŰ<üQą5@IV`ýÔŕ_íAm!Nî0(Ô ô»EîßažnYl'ďćę Ă żëSp‚mź®Ý—űĎÉş€ÝĽÁţ öN`;űßMŘyşs‚ť<<Ş ™}ńţ?Sý—˙?†˙Žň#ů˙¤ä ýˇfůC˙˙Q[»:|˙2x"­'ěi4ÝžÖüż¦ĆŔ?—VhçäéúżZUőÓ"Č‚@_ŁTÉÉh§ăłuü“-↿· äę¸Ať~?+ΧŃüŹîiµl]žžč%˙Pź6çß)Á¶nvżWŚOP` Xűb< ůIřó>í˘Đ縹Ŕn°'ŔS{{7Ć ¸mť ¶ž®ö 'Ë'ݰ ČězŞę?/ź0€ŰÎ BźV÷?¨/€čáů4¶ż=ůÜöNOkđ7Ŕ÷¸yBţřžWk[Čp?=‘˙Č<n÷§w ÚĂţAy˙B˙äÔßđSFwç?E >E]ťţÝŔ÷¸aŽŕUö”ćíöO §¦ý€?ݲ­'ä)-ěŹ=xÁä?^5 Đh‹1?ăf+î\ŢzőYö™7çöä$ă¶q*+§˙<¤Íó5™µ2#tr!›<Đ…·´©Čr.ł@sďĐT‹Ůś¨ŰrpgůNo|»cnŚäëhÁlM/:%§Ě÷€{ŹŁÄ&ř/jŚ9ž"8:y„WŢ=Ę>5˝Ą‹CŻf¶użW ©cŢ•NpĆľ1)šb̵ɜ&ŁCqRˇ±śřŕNť_LdŹ>Ҩ˝cÇ<Śĺ/ôľĆ÷özÚoů“´śü9â9ÁĐ8“żÜnŠé¬qáŇ×9ź&‰BšllŽ÷Kśx\»|é•Nz1ŕ†î*ݎůvŢ­śd@8󳯄[‰U% D–:mâĘËBXż j·Ý^nąő¨ÓŢů=ĺ‚čDÍ#` ŰÔń ¸±Ó˙¶Öcy`đ+çĺ«÷ÍW ›ťĆEҽҔŢĽlŠŽfŃK‹béTď‘KaĂĆ@U"<ŽŻd‘;dë ˙Đźr7Ľ<<ĎE»á‡(Źs~ľ ,Ż;Ńj_›ÂźyĽ{ Ňz´ůňţ䤀ܑ_Ń*ăKnşbçëÜ•8f]ńj`ÝTX)÷ÉZ9 —B‰/ŤF†ŕÜĎ>†BUĺîíŮŠhóP_çkŃ|®#ŁrĂ–xÁ|]Ú­ĂÎáÝş~ą°Ń »ÝŤ‹˛29 â=7Łë“rď(ą·ëÂṯŰő‚ł; Úiʲ1/7§»ŃmčČyŃş6ÄáHŢlw|Ľóć(UU`d.ś×ěUß“ 1§PŘMţ9X†äőyC0_u›ö VŢň[ĆŚď‹ô–m F?(äžé\ĺŃű­ÁuĎN oŇ~ßdđ•› ô4Äߤ!‚Ţ1ŃÎn8[•dyý`ݸbč-[»@Y•>ő¸zŘ?„$GŹÚĆĎC$5r¨((aÎXŃ(d'zśIĚrŢđË”ąˇ‰k”¬€šWŻťKD”IšwÂ3¬*HG}żJΞđQÎŐ÷•=űMľĺTÉ8“”E;;ގÁGZe–ő˙×fa+íű“P4Ű]ü4¸M‰if˘ţ긔źz„:üŠą´áśbUřĚřkc˛8j¸•\Čó=Őňj#pj°R8¦na”xˇ* úŁ±î±Žé•ég‰g1Ü%8JłŢtJ‚IqŢ?őŁ*CÜt%FvFŢcbŽ[˝¬—Ȥćĺ) @K{”hŮs=®m¸žËXpxáKF=óđŇ–šŃ|Őă8ŘĽąů»€¶€­YşĺĄjsŢçăšĘ;âŻßmc(xV©ĘN5Ő®5łMy> Ĺyů ­’%Í_u ŮčřÉ%/“2 &LŐą–ÔÉöX;9H•Őý­ŁäźË)gµaPSů.˛ŕD«3żęćÓć™4z_!¤ŞĂŔşÝ»ůÔČ™HşfÖAv0ąęŁ@#WšeâctH˛©ô‹ĽT"Oăđt{©ů‰Ż‡ĚoĹ3!-'rJíC ŰGěŢ.ě˛ńGŃ/#Ĺ«Ň=ůęI$¶¬dßF/×ÍO˛aĺLŇ%›oG{~Ý‚Q ĽŰż·2äŘi™Ë7¦Ś,h_ĚŚĘóůĐěŠxĐjŘMTl>s;xąĹkÄý¦_NR¦ę×ÍZž„NÓ\7żk†×c—}ÁŚ=ĺ_Ć˝×WÖů^¸•vŻ*,ďĄUżázîćĘą)ˇŠŐńâ ÍĄŘˇ•ôű ]†v,¬¨”°‹ĂÉ4Ŕ‚ú§ŢL%ś“ź+ł”ý®ŠE÷É$ű™¶X†î[Uďµnjgü·´ 8©Ľ^¶Ó®ÄČNu;úé4÷]{dڧ_•“”».Ů0ņ̌"łÝX}8@íÉM\Mä¶?ďε1ę(ňĺŠQɲęËip—(%9$,•Ď’Í_˝Ě‰WřëŔdˇož3A+ˇ$СĹŔĄÂ…‹řëÍą%ŕwö˛lYź ŘČ>©¸ibZÎLľ7Ć_} |×†í»Ąąű(\–SeŃvi ˢA“Ńć=Čĺ΄SúI“TĎďKMź¶i‹«©9;¦Üűü~Ů ŃőÓcłßčĄ tPéu]—šTiÎH.ÎXŽ2pÇ5iIżщś#÷8dĄčXŹ€¤‘¤°­E)¶±´M'“k·Mi<Řç é#™ĄŮ‰Q>ißŮ/ÝśJlťÇsݰśk ŕ G‚űĂE$Č1ń¦1Ń“äg|ř+v?Ú%P÷r&ŢRĆžTźBeUÇÚkÜŹÜpŘňsö0Ń×çeĽÔlĄ[}±•qšě}6îÁŻÄý—K‡ĐCĘuůIvŤŚÄçr[‡Ł&q° *´ą´łÖś8ZEDĘf¨Ă»_E8dKšK®ÉĐ)­Ý¨+]äÚܦľk7 y b9Ttf.ŘL”Ç.Ó6î–N§L3_żż¶Đĺ%ý<ôP&şáaTŘX4TH€Z¸üČÚ˝Măř˝ëDC-Ďß)›ˇÔČň#ń˙¤‚]W楥ĐŁ©,,Ę ŞáôŔ”(;çđl ň…ŹTCŹSŇ®;•PGß>Đ.Đ~++K6čő*1he”Ç´Sweş=[Ëý<©ĐPü™~rc^%źŃŁ©Ý÷«‡×w±u>Fľçu řă™t/čý^’˝/r÷:íÄΤző.íb®· "'úšÜh*_.Ď{Ó+µ“ˇż,sŃŠśŠQ˙ ź3)žß±?• QşaAÁś{Łuý^|m˝ĐîáS×íř„rĽ»U¶Öh‰Żs“7{ Ă KˇE%ę”ă°ĺ­"Śöň×Úz‰uG” ÝtÚz6ĆńĂ‚JăŰčĆůů[/uDgEźÇBE#á™­6Ë©±! Ć×bZI…‡ýĐÉóľ¤ű0¬ *ăĺ&>o‚ćw†)>ăcÂjď寇ڢ6ŹůŰtÚł ‡Ť»Ů”^,s´ň-}Ł[…5d®›ÜőL‰­tÉÉ€ägě®…ŕÂÎ2_Ó*¬ŕ•>ŁAŤVłşp\%»^Ú̡¤0ý®ßôĽ—đ‡©0°µjŽ«0ćrÎ4RçćĆĽˇbŢáŮĂ›“Iő°Ži\•7ĚŐuRđĎueFpUç×`Śů[Ź)fŬÂ=Líw9Ž#ŕL¬*ň=Sć©Eë®zîźĚ& ©Ő4n°%8QM#—Ť%EŁz'çŰŘŚň:-*uĄşŐĹ —t›•.đţK’V•ů/Đ­ sá›zý6Ž5V—šżç $ţâMe"„¸Ę>A“‰şľžť‚źŻ"˛ĄµH–\BĎOÜ҉ldüßůÎ`˘ŃĘËÓÄř“Ú˘q=Ż?<¦ţÎC Ć\łqŹp€ę¤Ú1pŽ›_TŽLĄ3iH¸A"hÄb4˝LŻŮ˝áčI©g·éóó-ö ŁY·¬–ó«ŚĚ~ő‹OÚkŞ a˙¬ő|6˛ŰŢ:źV„|şŔ#e1’ĚÁ{žíJx[ ÝTĆW˙ĹËźŮiĺ× 7ą™˝O‰č4)™^´<×» ŃJIˇXË«7G%Š«ÖfQˇöW:>Á*9Äű¨Ćwµâ‹ĘÔ3Dx¦„türżçĎîľ_­«ÓM–™ˇ\ń¦Ś¤ý°Ôş-u—âVk‘»?«r#E×;°ü*ń!ÖĺŔI™Ul '*C{CÄhôD_1EݸúV°:Ő‘G{ ě9w]ÇhôčÉYPéÉYľřÔXćĚšfŠU jU"­~?Z$:ŽF%|ŰRŽ[÷,¨ř–Űą b­w>—6pĹ*éč@É2¸żtkFפ‰Eeµhw¦ZPŢŠÔ?Í5oJůSfL\IÁłüu^ éBĂn׾ۄÉj!sj`SbŢK#[®XXF,:%Ô˝Ě"ů'kEýyěG±ş\äʉ°§ľs‰¦ň]#†TŇ.č‹®ű»WeyA“¬Żę3Żë OącĹQőÉ™±,šÎŶ`HjUÎ2űD>e5aŠ,<Ęâ#ÍÄ‹şoKú’\ ›N˛đÄł$â=žç¶đˇŃ´§.Ö3xj–Ńy¸ŹVĄý8F[ĺm`ř-Ťĺu}ŚřndőČiĐ» µFŻ\śęKô 󨥸‹ĚňŽŇ!‚iáúd÷T-?ľ}îü)“XN–‰ţÁúSm0'Qšlmô«=›1|­&Ëi ®ˇ~Ńjv lęďĽhĹB𢪰jfVĺÜ­UŃn×ŢĺžOęt ú'ĺˇ(V¦Kޏ[ďZ`†Ě9h» Ze6Ąoş_Ý`•‹xú.1@NS–Ď’=1ěm=şŮ;}_ŇŢ/żÎĽyvŐqcČQßËaýî$¦ršÓ®ŃłČRa ÔŮŁR2jđC!‘úäą»ÖĂ©,µţłÇ/,+"]ĺą9cśáŻą8jě1żM ˛µor±ľŚÁZŤőü’d&kŇ­®Dj W$ś“üá—TN›ł*2Kń!öŁa÷.Ťz\éç÷^ĚÁ}®\ĚĂú›] -Có ß;2áD’<,Óóůšň^×˝:‹úJeOAKĺtÔ«şyŢŹ·¸0î7Sk±C'kŻ«xËÁQ–· Rî(ěßíKD¨nŠ{á_Čk^[ÍYL4)«_ –ˇx÷´ m©B<Že‹ůíeCWMńť‚}±µ}%<ËŤ=vľČ˝ŐoüĚaßaŘzëmĽg­RÎxšD7ˇÉŤďrcé§1„¶V„~‡‹Giľ]ńĹ*†óŇgO?XtŽyhÝŞ3R ś˝Äş,LH–:dýZp–¦ŽĎBh..—¦ŰóWxĄ'ÁÍ»×ěíűu Ýţ|ë"Şjň+Á>ż$cqăqôţ–ťpţىâaQ ^?×ţ:mÇX7›Ú˘ľ0NÇ‚)u%ĺ.ň±ó"6ÓÄĽě®{Ů÷¨/M-h ’ç)ZąŰ B^Őú¦řŻéÉ*4Ţ? ŚíH§ĂŤ2_^v_)‘Ú)kĚp°ď@‰?)?¬Yp7–xÔv# \Ŕ;ľéĹýt}ýśú“_¸B L‹˙Hđ!={WsNFĘ™UÂŘŃjKł5Jęq6oŕńĺ:p퇑‚ÚtŔűćfÜŔń"yHt?éW~Ůţ]L{˛{„Ôü„$ť@qähşÉVő‹Iú‘w9 8J—7ć;÷—S‰TâičűzóžPlĎ)ˇGÝŢű±TćL]ţ~qúĆ·4üg4úCbĄÎ6ř?ajďPąĄsµŹ2¨ F+!]ä>/>7ˇ´J±ngwÖ˝e^# şµă™íäŽ)‰ăqh13:•D›WiévŞžŞL ˙Łé6ű!3§tLşŁő‰ŞoŇąĚS%ń‰µĹqÍx%¤MŔľIIˇÁ1n‘k®ĆŐByś“:ěxĎő\«Q‘xLţWĺ%¦)f¬2°ÎŻkęℿuÂŁ~öăáÁt]Đ*ŽTéĄeşOgl˘aV@ łIB]_d¸˛ ®á ž*¦9MZřÎ҅ԝ؀‘ĺ-€ÔôGQ6Už@’É:s:˙9b´c~ĂżŻ?<·”2ě‡ć "ÎuV‹YăŁDpCď 3nĽY+NŁż{L‰­Ů9Ó6U.G9˘zôaf´Bݰ9›d0Ô•…ă6éĘtÇ"ľ—ŢlžźúA"Bó{4¦kő Ł Ĺ‚íK§][‹şdůŹăfNô1ůz$ÉßÍŐŘ5Ě%Ţ©e/ŐP—Sž’Ř.ß]ă |µ;óŽM—Ý%„ ÉpžeŘ,pUÄ ^Q}Y«˝řŮÉŹ„Ť}ě adÄ2˙pű™ŢסT‰—*hőG±—–O< jHŤł’ćN.ĆlfiË*¸nLvŁŘ)H‰î<[ÇƲ"s,:¦¦;“…=hÄË{KföÇ×a¶“iWóšyŚÝ,…"9–uScń ʆyľ‡B‚îsírŞĆ^Ń[ŔI¨ńîkÜľM‹`ÖĎĽjW/Ś?xííăŐ–¤%¶še¦ú'5`cG˘„íꤶ‘©â"¦¦:±‡ë§…ľÝot2rťGW!"Ż9ľ…lđî´];ڦ^Jř×–{9ëµ™‡«ąŠŔ4•˙Q(JĽŃĚŐá&Ť“îîöŢŕÂ7¦¨‘Î"ŇÖ!ž3»Áł|g$×d/KxË[ÜŇ|â/DŁ«Kę Ţ…ç\‡EaG'‚ź”Ě"ą¨ĎÄ?TţZ\ĽZ´i ř™' BlŰcť"©ů8Rh˘Ô}/‚ŤłíŢ“Qěßö¦źČ¨ -Şˇf\ł=L¦ŢXą§?9Ş:<ÚsŤŐJëÉ({&ĺ‹Ü&Ц˝+7íŹ$'=¶(&żo‰r=64m9š˙üÝťŚBržCÓN§ćŞD®± ±Ă‹~I"RÚ"Ľ¸[·7ÎĎ\ufE(ÖŚDfb1ó0‰—|ÉLL¬żWĄ†ü"Ű1†FU—Üí- —š)śÁQV#tŮ6÷‹°\îdĂ×Ý ˝U{$w·ÁI D°ž7şđq{đSŔ83‹l|-˘ěÎgËG(‹»?››ĄÓń{SÔĄ ĽÇЉžbOä,-ú˝Ěבč‹UČ#QčÝIâŃC×íM÷q)»›™$˘ÝÍË\HňkÄŁ˝%^[I1yřo±MČńÜELYOÔ/ ˝g€Ůqš:w.wRÓsGs›—™żôłO¤Ö^‡×W ę¨Đ­WÜÄ*űj¸ŤJF6«gIQ)„‰]TWŰÂYą˙*8×tĚďA®\o†…Ú´Îş1kÚK˝“O­wNîz-ŮĹ©_MŮ›]T˘Ż|şH2)ŠŰvĽfz¨N¬ýIJG>ĚeŰěSMźpaź)&ĐvÁ5bbnlŢë`·B˛rą%ÁĹŕK¶)ö٢JQJTž…@ł×OG ľĐË;ZjŞ€_]@ç›#a]±"›ââ7\ľG]v…µ§ýéă¤ěňpQu>`÷YĚűb¤6ĺî©D)qIeŕ˘|uz©„) )ËqÍ:oŔ©2ťÖhö˝pâÚé^ŤÎ"RŁC¨Á=˙dĎë<ˇ–W¨uú—‘j§ę*…ą»A!íaÖ_îui’AoBě<a:ăŻám¦<ŽéâŃ%E±‹=Y­łxŐ0rómĘů÷üâý ¨ščdă6„&ó޲ńtI,”7ĺ»ÓĹ,űď|sĐ„űVż~+WFc,}íŰ%*Ź §Ę(SúÜS“şXĆ›ńQ e2RXţâěé/˘Ý-™Sž…h&>_ěuqf†ýÉ»ÚđžłĂ‘č7«(‰‘[—ý“ąCvBŰbÖĺä|V6ěĎ×ÖěäTńźé8f)^šN\[ľůeu;ůcĂřV›ůô-P•'éiÜŻLťÓüśŤI›Yë*lĺVSćĺqÜF8ţ=BW`żŻÄ§>Ă-ÔŔRř•^×hVG–™óĂ„Äņ \]q5±ąÔUx j[Ö&Ëd‡ŘíÚh’¤Ě €Í’5i®"|O.¤ĽhNÔýÔҧ·lđŕss×Wj9ܲ‚â€đ™ł~©äirÁv Ľ˝âjÁÔÄýbĎ’Ń$)ÝűŤ—*mHo/çwŰżł>?eŃ•ľ¶ ŢiY$“Óą^f¬}TXOŠň8żL„¦ľ&‰ú’źz5<ž_xšâRöÜ«¬Aę¶o j ĺ˙䵲­HPe%ŁČ)mBůŐ7—<ű»Á§™k—rĂýĚÓ€űJ¬çtZßä8°mŤÜ%Ą‘şŁ=˘1“ßôxHár–™™K3ÉOQú,.Ge~Š/;m8Ł™ĘrĐ4HękgC=-PJ¸’ë»s››K·ˇÇüŤ-NÎ-|¸6T¸´˝h©tëŽ&ˇUzWą®EŽmĘ Cb‚ŃQőEóf.őiŮŔ¬Ykž°ľhBGagňçÇ÷±H«’Črq Šgđşt“ęrürk+˛}«ę¤¶çk9›§ťmĹăr†iÚşÚŞÍřx\+Ő*zľ/@DSçăXű;˘,[wĺ ŞđŚw;Ôxla!QţWĎżË9źG7ż=P¬·˙Qh„• ńůřŇ—ea8 ëôÍLŰĄöřm±ž˝ÄWÜO/F%pDńUŞ‹BˇQYľ%6o©ďš)ÇÔ§ow2"{y6@9‘ʰ4Śř¸Š€6č=ň-#†ąÜlj `śp~ôŇ·Ýf›ZśŃr»Ă•zĽDY]·ôçPYýęiZÂS÷٢ľ$jŘ´]¶."ł­yí\w“[i.<‚ý+Žłě–gěmÁdŰJ[Żß‹‘ÖĺřčLŰ]šµ9ü0&„•NĂżéOžąË6ĺ»ŕ_“`Âĺ ťűč#{Tź~iŠ;÷qlÓ×ësĹKĘÍ·6ŚčĄwm×čú´!\@×"p0é!_úÇknT}°vIZÝ\ŕߤCóGáRŤč)ę‚ÎŹĄ'#Zy#´ŁD͡C1ePs{z-łÖéůÇh8 ’¶ŞIuNÓ©ę|_}sóZŐ«>ŃcÁ¶r…A¤P%Αň«šENŠ*„Ů‚QÚÂçł—Ďě¨?ä8‹%ąęlTĂGŠžűĎ˝G4aĄĘ)†‚µ÷’îĆ_%¸ÍÉ“ÜQĎ~Ĺůď…ď2`n6Ô˛ŃîŁŢ|Ëćŕ«ń©hI#Ă#ďń,6¦XHł ßy$9yŤá €!L«QÇ—ÎŤł3ŻćŐŚěkSł¤p‡5I¦–ńiŰ Ń÷†·‘SĆssÔ"á:ôT¶w–j'ťŰüiaéľ\ 7rĐ·/PbüG¤&äČÝpć„`†Ă3ĹOi¨`zý†~ZkŞŘźqGwQ‚Đ×µ˛Ś’Čv¤ÔŤĆčQ^/4‰×ŢŘ(¸Âkĺć‚Ć~‘ą g¶…Ľb79‹G:űâ`ݡîlvŰ8¸ăżĎ ŃňÎEĐfZŤeĐ1üŢ7 “ČřŰ^Esů>–ľf©ýËŘĐŇhwÇžç@ęŤuO=ń}ś±&bpî M°ŁźžŤ;;iŠľ+¬é>Ú+ÁÂćî#õMń˛Ťé—ľ«×ŽŻóč\ĄÁ¤))ĂżPŁ@˝‚}Ö\č5ëÍë 0 ń<č)‰UÖNűŔ—gFEd`öȇuÂĽ¤Ă9w%¬ędüĹĄSu4Đ–˛µ(ŮŚ'Č0~:ĺ#˘úÄŇ˙1Źů‰endstream endobj 262 0 obj << /Filter /FlateDecode /Length1 1375 /Length2 6042 /Length3 0 /Length 6984 >> stream xÚŤVTÓíż§$F‡4ň“f)(Ý)]Rc ° ĆČŃ(˘‚” ’ŇŠ(‚„4”„„’R’* Íťúţ˙ď}˙÷žsďŮ9Űó|;>źçL𢉹„š+ơŤAă$ ’E@ĂČL€@¤%!)  çŤř# Z!°~H Zńżé5°Ž Ó„áfF4 ďď @Ą¨ś"T^¤ …b°Š€&,é Iú4Â$¨ń Ć"Ý=p„,˙:ÂpŞ  /ţŰPC!°H8 Áp!#ć cŕH.ř!„Żzŕp>Š`p`` $ ĺ'‰Áş+‹Hś`†đC`®ŔŻvc ń»1I `áôű#6ǸáaX@x#á´ÁÁíŠŔ„Ü€ąž!pÍţclřÇ@řk4Túďpy˙ „D˙v†Áá” ŚD»nHopMŰP„`h×_†0o? ÁCzĂ\ż ‡Új¦ŚĐß_ÝůÁ±Hśź¤ŇűW‡ŕ_aCÖB»j`P(çúUź&‹€¦ ţ˝V/4&Ť˙svC˘]Ý~µŕęď¶D#}ýzšYD żeî @ä Â@Á=Ŕż‚[ű ~+ˇżÄ„úĂđ>ŔŤĐ" é† ü€đ~°€Ăú#Âđ˙]ńĎ \‘pŕ‚pG˘AG'nî„Íc‘AŔuxPňëóď“[®´wđßćż— Ö˛Ň6T×űÝđżUęę /!%HHÉB(TJ 'Âţņü« Čßľzh7  đ§X”ţUpŔ_Űţ‹"Ŕ?ccEÂÜ" ľ ˙o˙vůßĐý+Ę˙đ˙¬GŰßŰű·Vř—úha(¤wđ_z^ýqěa @˙§©5â]Ť®HÔjőp0ÔĐîŢ˙"ŇO„p5Aâŕ ňGnů‹`ŢH4Âă‡üő Pä?tVÁ˝ʆŹżUiţ™R Ǹţb—”¬ĂbaÁ Š 7Y%ĐĐôÁXŤÁ\B{a€ úµO9(Fřú¦N˙–Č(`ÂCőűţŹTp,–@´ßP ÔńŻűoV#A8hb żrĂóĺŤ7ű•j\‹ýJĂ‚‹ÖD$đŘF˙C:Š4‘çYŃ3Ř=µ´ž6†O Z»ޓĽ'řµşW·ęSLŽBŹť’͆@ă¬ož¬©UuňPqKX¨.…žř†ZEy‘Ö7ë ćúú_¦3ÉgŢěĐ Şę,›z;¶hşô\΀ú¸ěÄËDű¨âÁ<—ÇŁě|ä8 JQ¦í ú‘Ý˝a¦ś3^ýd1PŘúéBĽÝ¬ÔÝŃĎO-¤üZ88ěŘyHw™Ţ]Â«Ż¤ëł}Ä—NŚŘűĹKj!rĐ ›§’żŚLľüBĄ›ôâ’űÜĎőfęÚ^´¸N\Ĺdú“Ąńň ŮĄĺcČnNwĂ&~ڧکć%_óńyŤýWĎĎËĹGęó_äTRÚJĺÝĆŢž ŕUžäŐŮáHݏ ¦ĺ5ĄŐ¦3zŢéF]ŕÍCŢÎć»,mÍDĘ2+ÓtSą¦ë;–Wßäâ ßpvĚ…,ł±q~ĺ˘ú^-çĺQ:Lây-pĄń“ď: Ő¶×%ń”%'•ȇďO{âŕřRĐ*{EýŁÖq)"Ů)飷y™[8űyH‹¨îxµ§/%¤9oܱ{ö:µĽsĎś‰–:]´O%­š<Jń%uŃşÓÓ”Ľ™|2Ęů®âĄK+ň˛Âoż©Ň”yx٧^FD;/ů€9 öÝŢŁH«ÍĹÖń¤¤Wr­łçÖčöç–ĚęV­rK ĺć×jëmjOuc·Ű­S-µ1&=‰ăäőE`N[« Ďh{ü5]YbÇĄďšRîe¨sŘ™»űËЬd—µÍŐĎÍ \=ÍI„Žs}Ďł™óJ“gÖˬSä:^ ŽŤ M-Z<<) ?/ 'Řć;{ßDŹżJÂ)e`Ĺ*Č«ź®-¨s]uúÍČTłÝŚľśGäÖłůI%µÉĘ™ţ÷äŠ ÍüI—±€úi˘Éţ„w‡>o¸-Ż“’Ňłrnnň\=ßÉ™ďF¤4ł_ă MřúY瑾ú±EŐµ_3<ŘŰ”¦YqÍ.©D«Gr é“ŇZа cI#XĘŞĄ–M¤¶‰óńם]_„g¶7P[őnőç¸ÍîJAŘť§ă_[˘vŔÄ{_L™ďŢÇMb0nÄzÁŰWř˘0kűxśJĚqµv,@­ę ŁŐ{ŞÄ[Ŕ˝ľaóZ/HŤ¤ç‡ő«ś'݇ŁĘA“meÍk˛Cd1©;[ó†WShXĆ; ÇR…Iw†dÄ% ˘mßŢ 7PŞ€ÍâÖ3TîjVů"|[ÓŰß)禬’ČË-ˇ ŇÚŘv?=¸Ăš”/*Ťy3Wń ÚvD ‚3–<<©ŽěT‹RĘŤzÝĄ§ÄÝrsxKĎĎ]™+ ĄÂK*iOzV¬‘ČěŽŘż“=ß5 ŚŞW9%Ţa}˛˛Éźţ*7–LˇŚóĘč™Ú9‘G&ÁtÔňqaź«Qţ\Ú›Yă]R‡ y©O+ÂQrmäW­ ĽIyűC¬î˙¬x§Ux™^‡a?o©óKÇëŽ&ő8ĎFşĺ7 íćś“ěé9ω:d Ŕ:m„L4®që.ąt;¦oS*c Ţ:‹Â-öľĎrH‘^=Ą~h69ĘSM¤úqťNçłCPA·¬ÂËG5aHźbŘĹQî~8ß»f`’ٵ“ŠŃÉ!{‘†Ď·HsŠ›rëçćtęßN.’»Ôb#Áą…]Ńź¶ŻüĄśžWćĄN§ ¶Đ>T.ł„îa·ˇ"°­=ůSűu1ěz|y*;Wúfk(3cöâ«§ŠĽ?‡!čB{ţ ÚŠ)Ď0ĽŻ´µŁ:Q©ĐăĎÂ'Ěě¶…DÝhČu‡ŮĘ=ó9"¦˘Éš’ő »NŢĘš´¸{Čdô«…ŃwJŤµ“ą]DÖRđŐ~Ď•VPöŃQŃiÍţŚŢŽ:ĆÝĽăäŞGkłÂÜI~´Ad ‡rçS&¸_ąÝŠbýJY#DZVÔ)·¨ňeq ݬ–[‡ĎĘ®|Č+Ţđ‡ĎÔÚ‹[—3 BËpÔ{Í, ł2gÖyKCŚĘH›s¦–ćěµ8ް ł’W3”w}N6úpÁź©8g0ôĘ b|ń)Ě)ăůkó˝–Ö2Đu͇ĺ’雸!=ĆěZĂŤ›Ä˝ÔńQwÜ:—­ľVڦ”O{Ę[Áś`=-?vů$đüĂlKĹ\Q‡çA{ŠËÜ.×2¦®aÜÔ}ö†bD͆dRk­!µă¬ř§Ą{ÎUŻjßb"CŽö*őňĄ±w˘žO‡ë=čĆô…hÎ5©N*˘$%cÁľ‚e>Ţ€z$‹ÖŢmuŃ_~!Ł‘Î gqű‘GꝀ IŞ,ÍƲ(vs?ýusâűË[˛ţᛋ×yĹt4"w^j‹ń3€‡9#ÖnŮ=@qd¨áTÍB×÷rPSbJ•eVĚşäÖą&łŇßCŹHr}P!| …M–®ňË5›8şŻ[ŮdoĄIáźkżk ĂŽÖ×نçĽbw›¬n<ßł»{SÇhÖ4|Dé®Iď–ć€7Ń—CF ź.Ű·>ú {4qNđiÓÇŠ›3Dž)« %3ŠŁ‡@yvn<ĂJŻ˝¬˘‚Ťĺŕ™@b–9ăÎď°Ebí[ťµeW[’ĽĐů¨µ=<ś«Aúxe+/;žríĄ'żX×|W·ýĂ1|J_jDpÚrYšĂ`#ýµý"Ω@-Ä}J«ŁGˇŔóĐc=;×§ËYęjLÓ’«•ľ€jEčÖí Ť±l‰Ô˘_K8›2·YOi rč.~ĎĄT¬¬Iď“úeHjW8ńبĄçi‡íÝ–µVv$aK´V– ą–ć»DV\á[SČĹ— Ü<Ĺęő„÷|â(Vö[˘ŢŘ- ?©´U-9¨ď.‘=,]axŤ×kĽ;N˘Ěü>řÜrôKĚ Ć«Ďë1ĽjRJ-ę*›oZůĚâ+A­îN=âá5G?}%µB4ćý@†…ܤŠM‚Ë{™Ď;a¸źě–SRv’‘ɳ׆ĐđµK)†®v}a;R±Ö?2%×dŠďĆHn68 ö‰ňT±öjN„nŮ©TmÜżq׺MüŚF”VóŞÉ´×Óá ~–:Dż‰y?-÷3ů×űĘ<ßX49sTLśÎ¶pĽľ iI† Bđ˛„Ý×Iw˘đUisőń§ű2ľ÷ůM_~C%Öżđ9–6= ů vbUSľ®‡Uó¸á â˙âóÝ©ŚÖ‘‘ dâSŠ.{[Ë>Şc˘âHlśG>ýťuéOĘłŽxÚv¤&Fý~–Rhn.Z€TĘĺĽU{f“5ďÎV5‹Ů¶˙ö#Çő:‡WňRü±Ďšom?Éł,ľ6×bm­,H»–=>¸eůNQóĚ—¦ŠÁÚžţôÝůo‹Ľ9ľ $Ell&3DďpWwĎ{/T·uőÍ™‰<—żMŻĺBÝąTo'ˢūܞyAńp»s,‹eż€˙|‡9“™ZŐş˛TÇ4y ĺµQoz>ßiaú!ÍŻ×boíú{í’Ę8…ńNueó—Ň:7ÇBŚË)˘ś4źN¨_ťW*w_3¸FÂ/n\Ľ5’"ě¬ßűě#‡‚ä­.ł‚Ţ’`(…'EŃ7ot†aŐOçC·ÖȲhögcá}eţ ú/<5ء ŰÝŽÖň†gÄÂŐš˝tW.c;#ŘÖ5Ŕ‰¬]äN”7ËW‡.2Ó4Ţ…ş·»;Ż?¸|;WăźNUs4o励¨s*×-ކ í4Ö˘\ľŇÍ2ôßKb)ć÷?E¬|žĄX˝šĎ€"ď ă"?»űj„Ý«×Pş§;űiň5 č•›'iKÍtJ rń{kđđ üÎĎČ0Fεzł_îëh$pŔ;gőîµ »1ż˘Ň«Ě§­*‹QŘPpiرÜ,Kőcńů0đs:h{ďL÷›¦ŮGʆG$ń‰·¦çKKU@Úâ_÷T§^'=ęj÷– ˘c®¶žő_~kHŇb‘‘›'Ëí“-qtźĆ»(Ícö‡î˛Ö–ŔďdĹĂNAKX[ßš×<Ěmn\çsĆĄ-E٦rž÷)ü6ă/VÓ&á"m ĹůµľžÄôŽťÔ^V‰¤WĽşĘx†R)Ýjżńhľ­öd¸Ův3­E~ÔŃr«ë›‘sÓ„ŤřDφ$¤čÍ”±ClÉé§#r: óí|—±Ţáćî3űî|ÜA'»”8t¦ŔⒾٖ†˙%ްćţJDSJhďQŘýŁ«óĂÇě™!×úm-Ň5–#]ŕĂ·_ŹÇę˘[¦ä°nŤ Č<|"ůâą'ĄL7ś‹om\©VYk|îýé/e«;Ëš¤#°¦?ѱ;’’q­ZŮ@,Š–[L{ę‡$µ Á§ŤüłŹÖ“ôt“«ÓÖvçhŇüáܦ ůGŁfwYŠ’–-ž@ł«źź•·°+1{™é ‰W´/čŚ:iâQ|ŮYZŕz~ťŐćÖ:TďÍ®C_IA/x™Jyë¸ŢYd,ŕâ“ůÁçőUK—]Or©®öÚöA•P×'b[¶{ϻŋ{ó :XßE·Y ]nčńI{řĄ‰˘:”ČŐNČ(G„Ć4\ěŔ E§ńčK^ťĺPćiżˇ;Ë)±sA"ă}Xj`Łí7Zx=šëvú2̱,¤źĎch©¨üí¦ŚšČ4^:ĺź‚-b1¬ÖŇřgésĄ5Jť­c{’‰~ťmfsé­Âôa¦»pGá5NV ź*¦żź>ú4Ň3pt|A’ńh˘Ov‰ú=n.Ő7ąîÝ»ěoYDZ$´K€;{Őeý¨ąĄĹÓű_čŔÇ—F¸0oÚq¬±'˛jCDÜ÷BŘĹ*Ĺć`ZçişS Ý¤ł{[*jŰ.Őç©‚l„ ÷ró…‘Ă ¦†ËyçĽa&ÚΚwâ(²ĎĎŰýL~)ňôŢeźč˘ë·čWkűD…Ëů®_*÷ĎWě%}Ś}äż)ŃG• ô ¤š«ˇŽösĺÚ»±ťŐç>Ľ?€~YZ¦2yŐĺe9ŚúGpČÄ­y.1¦—„˙8g!ŹG¦ŔÚ2)I+›±JurO1/Jž˛}?x2JWÁă6Ňç”’d»Ö ±Ń-”©3šŇ%Ĺn°‰­·G)…ĂĽ#tLľ…«ś8Á“ĄŃx±ü—iö’G°Ř{zĚĐ/žE ö¬ L¤uT–CžT…şÄ˝Ą÷Rő ŁMÇŽďäy¬D÷÷—L±.”(SCŞqEN ´˘Î׊)eßPÇ­g?ZKÝ_´3n°ěúywí"˙ ­é‡FzŤĘôl˝fP]ř†ë'5šM–0/`eFw…)dŐÇmŚ*<™Łřއ™$U»śc3`»1˘†ČŮĹ!÷TŽÝ|JP—“$äB,ű»ŮÝIľ2<\ÖqÍ_ˇ»ÉË©¦ż|^P†ęęÔ`x‰÷đł“TďFŞuµgY«ŃµöfŢ/D ‹ćS(D)W_{ă ő€R>ЏL!%ţĂ­Ç%ôć-•šĹ´-Ź)ąjK ‰Cćî+)ŹBđGëµh›oSÍ)¶ žcŇďb-)WĐY3 µ†Q­Ě‰˘,ĺÎőw 4‘b/H[żgdn±WxvľźŇýţĂgźřsóíŕwťý7oŽőŦ5µ‡ßÝvÜ”a˛š ĄruóŢîIM'i5˘2[ŕˇĐ^|÷ą>Ę{îĄĂź.¸Üľ·«UqSáG«v:ĘvŞt9<ÔXÓçY†·…śťŕ·ŁA]*8Ăq'Îłě{tNÖ¤ýy@4•ˇ ©ç.T0ăąžů6_ş}< _•™+ŰH&2>~îXA='í˛%¨Î©*PfäZł.pYxÍ/ĺ¶É;kx‘íٶ˘TÚńĄďľŚd}źdě& äJ¬lŞÚŢęÖ×±™Ń:Xj1•{ĺĄr=_”çýđŰ—^W”ŞŇÚ„0Ň|9zÉ˙%IWWLS01Ŕ.ÜúÚ+JéNiRcjí›ĂQY Äą ë|4| ŐXÔSDV\ĆÚ^üřČ©´!/§ü˝e÷¬Ç]UoÖĚČśM .żŕµßßŢśL‹txěćľ"ŻVś]fÉ»I3ţ¤×k˙Ćdć`]3¶ÎńYÔpúţ†ýM"†JŤ&m—­ŢF¦Ä!w$yë{çó¶ő¦žU^®u  )—čź%śĚ.Ľ5¬nauźŚM·tĘ–M¬ĹG?Ě4ÓóĺÓ¤Ď![Ňc{ó–ďGó9§Ť¬ZÔÜÔÖ°^ÝÄéz´®5™:S‡©»ŰŕĽÝÄÎĹXrwýzĐű)Ň{`2’`źPr@.’şV93„#čÓxע˙`ŻŹcG“ęC~˙ŔÁ«2 . âçP˘ae>ĎÚn'ăÜvrµ"ăTÝŚsgÝéň$yś]ů:«cŘKžb©çÇ9ý°ő2˛‰ćí–,= lNAŹHfĎśßkóÓÍ,6'áű‰î]µś.±,ôCDňb^´M {9WFľxŤ '­TÜ_tžóžÁßMťë“–“¸Ĺě]>˘Âµ=ăp‡„f”¸óŐŹK qĐu}z’ćFíŻ%#^l±ˇ¨1©Uś_6Vřż2ËMŤ8,ő‘ĽţeÓňżg8?˝ ŘVX‘ÔaltFVŕđS<ďf+L@nÎŢRŕ'ń`hý$÷«¨ň»ĂëfňÜéřŻb”ü9ŚÁmj5Ş>ˇóµq0ňŁI Ó™şĺlŃě˝űíBjs~ŚîL>˘č”ľĎnż®;ˇÁ4?»ô=_šĽěąV× "k©=g k?ë%ŇłQ„¬˙ĆĂ›zó$äzȱfoÇCÁMóTj›ď_EůĂš‹•8n´@îď&$o™tÝ}k@fm.j{'"¶ XaďšA)ĂY»oÁú1łZ7Ç_kFÝab%Ă#běitĺ˛e˘‹Đ¬A‡Ô“şůŚ«yô›%"É®Ó5ńí›ĂÝÖ”r’Ń—î‰w[a®di}ŤÖËŤŁî>ßđ…á}`ĺKEłˇ Ťb‡ÇşěWn˘NclăfD«¬e_ŢH=bO¬§¨‡É­oŢî xÜeNĄčÚ4¶ĹŞťEh ¬%ߏ˘ÎSł8eżŻ|átźţ´!čÉĎ7¨ W©š)Ű«QůŮzŹMÜÔôÎŽ>¬Ţč˙/ukŹendstream endobj 263 0 obj << /Filter /FlateDecode /Length1 1466 /Length2 6544 /Length3 0 /Length 7541 >> stream xÚŤxTSí˛6ҤJݤׄŢ{ď˝”B$t*MŽté €é(U@ş EAz/"ĘŤ~~çśďü˙Z÷®¬•ě÷™gćťyç™˝“p°š(9" ęHZ$”¨č™X€ PD&âŕ0…ˇáĐżq"s¨ †DH˙CĹ Fc0U0CÔC"ÚŢpH—IHa Pęo"ŇK  ö9ôÚHEġ‚ôđ÷‚9» 1űü} ŕ†đ@RRüżÝJîP/ŚčŃ.PwĚŽ0`‚„Ŕ h˙„ŕ–uAŁ=¤…„|}}Áî(A¤—ł<?Ŕ†vCQP/¨#ŕWÉ}°;ôOi‚DSę/ Ň í ö‚0"Po„#Ô €Ů`˘Ą 0đ€"ţ"ëţEŕü9HôŻpĽ‚!~;!¤»áC8ś`p(Ŕ@]Wí‡ć€Žż`8 ‰ńű€ap°†đ;u0@]ÉĆTř§>Ä ćF ˘`đ_5 ý 9f5„Ł ŇÝŠ@Ł~ĺ§ ó‚B0çî/ô§ąn¤/"đď• áčô« Go!3ĚÓŞĄú‡ţŤ9CŃ1 ”¸¸ő@ý .Bż60ő÷€ţ6ţ†15z =N2 Á0'(ć(öĐ^ŢĐŕŔ˙4üsEa4Ŕę Cý;:†:ýµĆôß ć°bä˝ţueQ#÷˙7ýw‹…L--Uµuřţ”ü/ٞ2Ň( ,@ 8@BB üĎ8†`Řź<ţĂW á„Hý•.ćśţNŮ珸˙ ŕź±ô‘ĺBÜ˙ú} ‚yýźĺţŰĺ˙§ň_QţWˇ˙wFęŢpřo;÷_„˙Çv‡Áý˙00ĘőFc¦@‰™ÄS- Ť®ÔćíţßV-43 JgŚ˘@˘‚@ŃżpJću4„ˇ!.©ć/Üě׼Áa¨!űu‡Áx˙eĂ Ä sAa¤ůŰĹĚĐ?÷UC@Žż†MXLöňűazŤY‰A©t„úý3@HDc\NH/˘_Ť…B^`uB˙˛ýEţŔµńo“††ůkţţ…a wÂő/@X „‚aÎěőűGÂo//Ś÷oeaŞů{ýűV…úA!Ds3HL¤ëËČö‹Z%F_őĽĄĺÎŘ$«ľ14燧.şřŮ“žĘöŽĎ醟ÎVDMĚ1ň\ŚúÝ“¶ˇY€ĆRë[U>(üvňĆĘâdp“x»JqlÄLfoŤkÍ•j1sőP˘[dóĹFńŹNÔć=©]€; ôŚś5ť ”ßą‹ýČŠ•}=duźüńm/ëLoS-ł±ŘŹ`ŹĎ•ŰăéżůŻZ?Îúôémnß‘ă"ťžńçQ”ôTÚ¬ůĂ)ôÔÉąÚŻPXśĘbg*éIlô%îQś‹8ö‹ß§ŠúŰ’DĽťőUÂn?S7„łx ;YĚ_ɦ(8( µpôY]—˝‡ďň ~}Ö«˛Ľ“Š8Ýj~Ű>Ô}„d?`¦ž K˛ěČŢíÝI~Ą%vöžÂśWM]§óŮeÇ“ź8b\ŹkŘÖ©!sOQjvś-Ń\„¬Đ”â©î&V¦>ˇ•ôCĄĐ)rRJ· ćóŐâďžĘ…7ËĄK†,ŚÝP5ÚK$Ż[˝eÔýí¦m’ב0­NAç=ű2}.m´h¬i7éjKŮj2ěHbf±@ů§YeůűN ßÝ× ĺ” Ä%Ž1Q%ů*y›<Ţ.<Ärqj"Z+čĚÓíZł?jŽŁ¨żÔđ8:ëaúDŮZÇ$¤=9Ç) W—üîc(ňńłĄř[%dWÝÁÝM OFŐp˝Ľ+ŃĄ˛“¨fŠ"«ÄéwE–†jGş?„Wí„[n=o©@j¦hý&é©w¸şÚ˝éÚ$g—úwŤłŐŇŽK÷łZ§°Ożs¦­fÂď¨_ÓÜŃĄŔ6v˝`QĎwřRú|,/N˘ra(-Yčć&dňqšFFđ9ŤŃĺęíîß©>‡+0Axc؞ۆWoŘTşG­‘ż‡•EęT N>fŐş˙ręĺ÷MşŻĆ?íĐS2Y]ďľdx+rß­Dóľ+Łśşeö470hőëWŁ`°Čşz9'˝óDŇ·šżxť˘iˇTŢý|E¦žuŢ|qü'‰JëU„9˛ţÉ‚Okţ^ŠBw}Ú™´Ü:ąáŹňü‘ý«eUť÷ŮŞľ5ĄgÓNďßô1VIź-ÁÍ5Ru!˘'ťR´ăĎ Î%ëp»M“ŕŕßTŇş0´Ĺ®¬ĎňťË¦,uÔÔ p㆜‹Dř}Ob‰ Ű323ÔŞw Ô­A1<$>±Şi/ÜÚóy“8f]Řň}–ĄY—Ă Pq7K3"*ő¶ě˘!×cígĂ !>ÉÓüýě l.fez §ő» Ë©Ľ3‘>¤hĺ A¨ŕU ˛TĹbý1Ű#‘ôXÓőzI§Ö(żQitD‹OdI5Pbjc’ô!a¤qńeŠqŔ3Ę“§íŔčq[ U—ʧ˛kEYY+ĘéĂ;3Á—ŻűçňQÂN\*'ţ‹+µv/¦^(Ú@X$Së ž\ô7tb®DÝ äh÷V)ÎRĘ?Âů?"Ő-Ňn¸ażĎâ%• đıžgĹŰ;ż|5:»5uŮnűýgüúɱ …$<=qíŞ…32ňŤPŇĘŠ-O6«ézo(ä{ąú`nĽ~™&¬Ă„­P&q+ýĘŰË$–HIě8K\YŽbˇČ±eşJđ<+±n¬ťíáÄyĄŤŢ@Hp`ĚD˙ bÁTß#¦léŤđ¨bpaµaęót`%‰~E8Öˇ ę[ĺöQ>«ĺò~1MřÚzµůüÚč@D¸"ŰćtKašź{˙Ř]OÍt!Oľ6ńé¶+Ă3Ţ~W/•d°Ĺ1Üg5ň8'|“ÄxŢâ%~ŢţF¦¤ţ^ĆVW˙šĎ,‹”±?ĹcšFÍŐÁ«€TWú}Pî¸ô‘q¤Mő§‡ 6Žđ č§_Ö­şŚń„ácĂ-öŤW-Ň×ďy;«ÝťÝ*qäŞîYďbhi"~+Ś%ÄŘŕřň88z®‹1~ö#-±űđĂš{W ˝Eş¤’cŻOç Ă'ŤcHŞ9ěę¸]Í’÷­:OOšňy2żâ{ćó}X€Xů§÷Ť´•äÜ ˘´Ó{˙Ă^@±xŰď#×iîXÝřYÚ¤|°ĚQáSť%5.%w„UÄĂŢs7}‹F«×ű곲[ĘÁőŰnő =řKĆűÜóÓc¶Âłä@lĄ«{î‘mXńŁdŰđ ÷äÔŰ˝ QzŁőVęç‘…ţy”A©‰ó>wnň=H*.`éMSk„<~˙r'EŻ÷Đó3fš]ôěCś+MhvÔśˇJöŹţÁ&›†x0qéÉ~AľM#¨P‰6éu|_Đf-÷Z‹ůô—Ą8Ťňj¸D¸ŽŁUŔ˘WÝľÜw ¶@®JŃá(K=@)w]Oz]Ô!}€ĐÇú¦ą€IÝž{‘A-±Ş7XúEČľ-Őö=šâî™\6ź( ,GWÄÔż]¦źmVZKkyÓ†wÖŤjţ(´Ć2 óCŤę1?4Śĺmx …V$–:•vbůşÍGÓ±\h‚x$Ęal ă=‡áě@ćŮźa U=ć °®Ű·cN6%űű‚şJäŻ]ýMłÂzÍÝ6ž,Ó{–˝sV˘O>I>ś}Ýš}C*ŕfŰşiŚĘbP/úAkr_fÔÍ2$3ďţ©ř~ݱi­G…H׳}ť3J?×ÂVAB~Ľ‘ >PíĚË/Ä=Z9pUÝCŁ{8"açM†äiűq—ě¸UÇT`$tA_ Ď«&v÷‰cô—bäăŚ@•Jyöź8ę„άă1ĽĎ­XS7]ăŢ čŰ)yŽ<–LěÓń5rţ”ŕ‘Ë ˛Ą$ ś‰‹ÉíŢ­#Ö5Ź}púöúęÂť„ %±á Úd+µOj«°{aô ưćńú¬ď1Ë>>*[Ě_Đ2"‡iräĎ— ¦"ÓÂ}¬Ň3GNA‘[UŐ»8×O!UEŚ`mĄĆ ćĺ;ž±ŻwÁFŰ‘StÚ/[f-‹ŠşEĐZţŔŢÎg‚¸Swcű¬Sď3G¨ŢU{žâáQŁÎ[Uţµn1ÓçĐË`Ęq!¸UL1‰ă$Á[øg)ýµsS~ =[šwdQ—Ů {$şG*…śâ*ĂÇHÝŚ&äźWJqî©ŐáÝ łÔéĆ^0oâ »wňN‰yž‡qp6v«~Ú´=Ť˘'+iAwt‰T{÷đxŰVVţĂşôH%›÷±Y¸›ő Ő˘YôePVĆ]Ľobć‘»Nfq;ž†aÁçW“Ar×ńŮď3ÝbA†=x[¨l©/e,Ź×ÖSy‰ůś=”BůkůßČąV ‹Q¨ †Kä|ť&N9̨i|2ËD"¸-¤^ÎČ8Ř2 ,Ę@ç.·Ća ˛1ůB‰]č»H CźB×fÜ=Ž´Ł‘ еu+a×vG;J“!"e+FÔŰ]Ę,ć§ŘŹ¤ÔŠZi’\ĺŮ(޶ň}oÖŘ‘‰‰ś—¨8OuŐ‹ć)gm­ĺě)•WÝ^îđ|9v4Lvgéőâ„h#~KtqŇřč…ˇÝ{Âęţ“ľńÓŐö›LO˝ŠË¬“?˝ń±˙LÜŢX÷=ö´+Äď\ýş€1˛dgL0¸°eôÖ»´Wĺ´Ç:ŐG˛Ň µuŮÉártĎß8±Ň޸Ă;ÜQ)'´é‹×k,ŮĘ­87÷(;€Ňܡأϩĺţ$˙Ó3yKÖ·îéĐ/CŠŁpR‹k¶IsÍ­‹<É÷n´ÓÉ7eý/rő˝IŔiř;%ĺ}‚Ąý‡Řhý†’޸đKňf7%-›hZ˝ ˛&“˘Ťk«Ś9D#Cx^Vâ4g9Żícćd®+"Ů Ýç«Á ŮĄŃľâ'çť, űµ·Żwć*ůŇ ¤ĎC›d řsąďˇdłĚ¶Ă¤’ij߇ł©fč\ei]D«I÷ŞŢ×ÔĐ;0™ŐŢ:V{a˙O ďyEˇŮŁ'uę_Í. ŮÚtFŁ˘řŞÚ'­:ˇ” Ŕ*îô3##c¶nŚłľÔ›ŰÜVZIńpű_dě§©77”Ć 4Ą^<]¶ŇdvĄ6®´i6űF ŰZMdÓ“ň=eW/ꎩ:wlΠŕqŚď?ń¤…Ęp‘C]®-Ľ{ _9ďlî¬PCgÖĐĄţ~u3®ÖÝNL¨ Á‘)MŔâ¤á&ˇěTFZüUŘ {QĚSY 4ŁÍŐţců»}°1!Y8ţ|ČýňPŠ}a7§"đ Ň[[B$/„|§áŘgy§ë4DgÁ"0WojQDáCbB…ޱÍ7i z* ź;IÉ3oZ®ČÜŚTľ“B[fI™(h¸¬%ŁPŔjZĽő Eł‡sffłö""EnÂ2îç­)b ŻăB˝BőĐňţr5QĽ«a*8ťlI:Đ}ŚJ^u…p®¦‹ľŇô«“÷OĎg ™tðw# S9%Đh2ě3oŤgŃů“‰™WŢ×´: 0Í>căč× ˛7Ň ţ\ôvőŘ[5*75–4ř„3ôGݤ”ŕŹ™˛¶Me_Zźń Xi '\ęÔ˛ĹP~á|J¨Ąľ)­ĐŤN›MÂ313…ô“ş «¸[—|®2sł¸t+)ŞčŔ¶¬áŠxyŔ\J“KFÝ-kVöňţ‚¤.ŇŃuőT„~ŃŰt*‚ŽnŞşn-t­Ś*í b+kŘ8=Hš!*´¦žŇŤs)Ŕ~lu›şžg)&áF˝ô3ő.GľfĘ},ţjżŃ ST ĘożD3_V]‡bu-zöćČľKŮ­–{âăt±Üů'\L,îÝN$ĹŐ¦ăzU{>$WľöŃW=Ż Ż”Žeż0[2\‹ôí±ŹUƱę,ŠÄŽłŮ®O|söü)¬sRëç–ń`“ű9äů ă¦ÚźVŢ [Ť#4řöe×ţöA™GJf`óRą–8­J†Î!ßÚbźďwćkL­÷!3x6‡áPě86Ť'f ® KUx‘ţqGhżiioGŁdď{ţvT/MŮR+ź9đmľ1˛™…×ř}ć÷=^·|©BŢĄŞĂb€O˙&Ú)‡ŰYŃ×Ć©ÔV÷ö[fČ9—÷ÁP5«ü‚Čw`CmOű3Űéit<ÓYŻ [j f.…ÉŤË»ČüŢní܉1ę#8őUvO^i˙aňŁ-?‡íC%<Çáč,@¦Ą.SËúľÚ‡›o†wb=ĽĘÝô_)â)vC9_?IĎ„÷ ŢMÎ7z9Ăöy¶„<Ś.ç^W˛Ń 7zeŞASÄ.řzçjQe÷Žą¨ęZĘľf~Ţt1zÉpzTkż]ŕÚwH˘gŔ÷ˇÇ"_ ]yŔ!H©p_źŞ0.rójőśFmźj.I«`#¸ŃFpw#ŮŮź›.&s0bBĚ>đsRQaÔSçcáĆßq7a{8!üްî9ť'‰kpţ Ţp·zeÉ2Ůz<&Ávţ˛Çˇ$ Řśąčô†¶84Nhâµ®{U ußüëkř±ŚCÔękýŁő3cąőÍž.…7Ćšµ¶lt^ ĎnŠt”Xe‡×·»ŽĚńNmZć főôĘ÷ŐŘČ.ĺ%UÔz§Ě=9X~kYČGĄ…Ä;n1q¦Őö¤ÓĄX^`JĹóů°î^Ö°¨ôŁYÁO[®ĎHő“5»Ď„"8‰Ś˛˛¤WgĎvXĐMGd+í„®Ćî†N­Äs´WäŰĹä>ýËyŢřzq×PNˇ[÷?găŮ’kI FáÓ'x4}îgĽ'"_rÖ鏗l¶KŽň´Q¬l“"ż¬ëŔĆ~*Ůđň›žíŰďxa‘ůéTќܳR -mÓ®Á• rµ ŔZ-‡Cyá9ÝŽŮ,Éaöp˙»â-Nçě‹™«QAĎZúá GmŐśEĂŇÜͰą Vv -vű¦p—Đ~™â%O5Aj92"ú w>pŤĚžě>xiďî;6cďÄ{EšÁő]”ą|vź“ńZkď ž­gěë˙Ź®”endstream endobj 264 0 obj << /Filter /FlateDecode /Length1 1374 /Length2 5974 /Length3 0 /Length 6920 >> stream xÚŤtT”ďö.Ý(Ýŕ €„0 -)Ň ĂCĚŔ0t7RJ—H HJHw„ Ý‚*üÇ8çüç޵î]łÖ7ß»÷łźwď÷}žŹťU[ŹWÖaUBŔQĽ >~q€Ľ†ž±(€ź_Źź_€ť]†r„ţ ±@‘®0\üä‘P0 SŁĐ8  ćć @"â Qq~~€?˙ĂHq€Řf Đŕ¨!ŕPW"vy„łfk‡BoóŻW'„ zřPôÁďr€¬ €á 0Ęę„Ţvč! 0(Ęëś’v(”ł8čááÁvrĺC mĄą<`(;€.ÔŠt‡Z~ Đ;A˙LĆGÄĐ·ąţ‰ë!lP`$€8 P¸+şÂ n EĐ›ôTŐZÎPř°úŔŔßł€ř@˙¦ű[ý‹˙] †@NÎ`¸ n °9BZJę|(OÔný vtE ëÁî`#Ř řÝ9 $«Łü;ž+ sFąňąÂŤüE>eE¸µ<ÂÉ GąýęO†„BĐÇîüsłp„Üçď·¶ů5„µ›3đ ćâUUř A‡ţł…˘ÂüED„ÄPÔbüEŻďĺ ýťý Ł'đóqF8lĐC@ý`6Pô‘Ź+Ř @!Ý ~>˙;ńϰ†AP+¨- NôvtjógŤľ|$Ě`ĘŹÖŔ˙ë÷ď7s´Ľ¬pGŻ˙Ŕß/PGYö‰‘Ďź‰˙ť““Cx|xAÂއ Hýřý“F űŰ˙jUá6ŔĂ?ݢŹé_»˙ç_spţÉĄ‰@« ŕüŹČÍř…ů!čč˙[ężKţo ˙Ĺň˙ů7¤äćčř;Íů;˙¤ÁN0GŻż´hÝPhh Đ6€˙7ÔúÇ´Pk›ÓgUQ`´dá¶h1ó‚„řř…ţÄa®J0O¨µ6 ±ű#™?ń'ż¬ćCµ®°_ßt?˙ĺĐţ‚8 ż®h]ţNAŃöů羊pÂú—Ď„E`$ěEÄŹ–“€°0Ŕ„6¤5Ôó·’@>8…. gôŘ Dż®Í „:ByéW‚čä7$ťř­ôÎ˙Z˙v4ę …ÍN# ˇöŐˇÍß*e=x7Fđ–VZźĹ÷D Ł8f^ůŘ©ă§+O¸Č=µ.§JŃţT>>íËČ}řmÔÓ¬#3h1Y%…ˇŘł&wĚ›;půĄĂĘbŤ`°My¶F~” Ă|ű©)Žéý†Ó?ÂD;·Ţnć˙luÝ2y¸p~%cMfDĹĘf·îaEł˛­ ŤÍČâ(¦©núŞOĆ~bEĂâ.äšcč/˝ÖLăŇú3{{RO_?îŞ)§¤§ňVcÍJ죧NČT«wĹŕţ*_’—ľŔ)ścŰ©z ÄÄÄ×MĎď(b˛’…Eb&"µn«ŤŐ65Ź pôK˛ţđöÂ{Ĺ.5r«©ź¤Ć÷‹ÂĘ9!;^E ­›­'Ď”k7–f’ÜG÷ŔĆlYQz}ÇtCťśň9‹FŃ.Ç*·4µ®˘–·:Ňâť›¤`ŁćwtŢ +Ś›ĚéÍ«W“řę,@cň?Ú}Ů4“§NAA'×ńÄhs&]ůdo 3hZÎ@ţźiĎ0·â>†â–銆啊ĆiäP—H „rĹjÎ9{ÉÄŞNńy[@îŃâłĐzíÎčŕ+’ĺJ˝eţĎf×ŔĎ_>ś*12ĽVĎôGńĂ%!ďĄ#Eů6ä¬Zuc°Ş|7Ţ*bVëQuóŐ>¤Ĺ  \Ćâ{4ś2F*ŹK2Ü«ŰÉšN¦ }j*˛÷nńvYgy@ŁŠ—TP7<—Gă˝[áąrMi•Ęű]Sĺ9í šÁ# p‘ ą‡zúi~®ęËŢ›N†¸›nY„ź„ńťŢĹşĽ†ŕ¸CŘďĄÝdLI¤ÉŮŻZ΋|c)„R<—Ď—ZHn†0Ţ…’—pƦľ?‚čĎĂEśí>Ą›ĐŰx{źĘD=d$¦šĄżb‹4ľ¬SCŁ\N‡ÉÚ^…ăÄlŽśň}Ůś´]Ľ%ÍJTŁ’#źô¸/Ôđ.Hć'™źÂ)0Ť#€$´ßDĘ %9©puäÍšh˝b€$ÔÇ×ô=Úuč_ Ô×Y],|gö&2ďE^jŤŠ°P÷śĐ6% Ö§rz6Ŧ(đçcN;ËýćX–®KüRXyś¦%Ä™CÚ·óä˝n|ăGî[˘.ŹÝob%Źq˛É9x©ÂĹĹ1E$%ˇÝiťĆw˛/¶WÓâÔě)ŔĎ•Pâ÷pĂ{6fq´ç0ČŤ‹xéç5FĆ0ôâ–\â+ ‹Ş/ŰŻ¦[MĽ˛đ]ÉŘŚď°żd-M®[2ľ¬Os'^fjőVťđ;…ADď‡püď7®śŽ‚:QëřžőF6˘đąeĺŕ±;#ÎT#ČPhŢ€¬Zl6Ę)k©ťoŃÔOňbMô'é\S¬·ú˘¨żAľkNN“4 ęUĺ@lL…_ŕâ`&í×O ·ź ä|ÝÂE]0ެő˝Ů›'ŰénŢgť6z(˘x_r‡%i·Ó‰€ë0N­•lDĎFUp´v˙ leä=SérvÍ(ÎđXMXxŠAă®Zň†Ë¸'ĺ&]ˇ ¦j[tÓĆds«§U¬†ŐLćŃ?⏭Lî§ţ~=7o‹ďŰ_©TőTeĂá(‰ňy˛@jÖDFÓLloÄqů˝¨;Ă,Y;žV=OCŚ–dĂ>ŮS“4˝ëŕ ÷ďF°řŞ0¶¬~ŠJ>®ţ}ů—+ßq´\ogqHßŰL×6)Wf%OP9&Řáđ|ăcrqľOd¨ŠÔ}H˘mR¬·¤3.IŚÓ¤B}Äáăő`|˝:ťîó­ťş‡©o›™2&‹K&¶_§ncLĆę/ĐP–»ćŕ<´C¸0e˝0ß[ý¦‚‘Íô±Ĺ_'ŃbłA]Úďq3Oł \/•bčĄmÜ-ŁôÔÓ8-T˛fĚ; îŕ‰śiľő˛Áš/–™ˇa@'7g§ăýˇKBô|óĂh”~űť•ňłľ@¨âĘóüŘŤÍ|'ć~9túI¨0‰{iŚä °î‘®›ű :}Ĺǵ*ó™ HÝŽß.+ítIřŔPúčlšnS.ĄŘ|ÇŮŕŽđgvů*Ăô¶®·NČôKŕ‘µ_¸]H„šÔ9ýňÓ„ †?ٰ@˝üʸ,Ôg{&SÉÖk‹ĽâÝ.šwź|-’WR(ظŐqÄôŤÔô&ĚVźŢg‰¶gá­éšöh“ŢŇzz÷~Oä]çK©âXʵőF/oÖW“ą$Ň]LۇŻúPŮ3ŢĂŁ'q´xÔtnäň"©VIŐŤ+wއŰéŔ¸Îďé+HŰ–S™Y«ÍŁÎS«€?`Ť».ťORnÖ ĆrÚş]ú)"+˘•‹Ó”[…Ép%eĎ÷fTK5ťý «×‚_›…Đ!uď2Ëćľ˝Ţ4'Ą)3ű_h€’łrśöoSDîŐ=c˙`Shúř%údčk†Ď$’m3öôvů|t¦pP¤ĚĐô6šq \6[†ŕ9ʰ~mĚ ŽŠč=“÷ëč+ ·¸C#ďWş*RiC€ęş€žpvH ÓM6©Cs`ºS‘OŻÓĄhŠ Ô@çî ˙ąĄ:ŢłŰBYÜd<×Ě:˛:‡»üŚŘľóÉqMŚoęĚUiŚV‚çK·>F“,frľě±ÄŃ]z–gĚŽU«)<°!˛?iąßZh“„]4ĄwÖS}Ţă'4p­( ěÎoż(ߦŃöxAťC¦"y&&źă…cůMę—ĎD—]đ ©1}@ŐÂÓ‘s‰$– νµ9ާ„ ´v»'LĹÝš$ĺŮ/(WĆI»¦äSXĘÔnŞvI¶Őm™–5G´~jěš Ô4gѮڲ ńH&úuˬŮűH¬fjyßajݵxi×Ó»ÚťV0"ř©‰M$U,JÉßVĆ’Zěë•ĎŮą›Ý'Oö ˇ„UßŔ6|ą…Í3ĽYX‡}Ă$»ŽiĎUÔMÝ(I]”ힾτpŚł˙s”2ŐĽÇT$ĺ©€V© á)}Á¶ ]˘ý»wçKKßńµň1ËÉăŠk#c´çq_”G«`ZIÜfţx|^ń~’ň`ŠD¸SVăú^×ҬŠzzvť+©yŐ‰e÷č&^ř“IžÄFÍ\]ŕÚ·±7qX›_Yţ$«VŻ.ĐrN/nĎ“†Ëí±÷~zXü=Ď|ׄu‘úÓśŔýgŔ«lrśĘ ÇŃBôÚNřVXhŘç {|đ¸ ]kSV]U^–źcGçJ„Řżt§đ9ĽŘ_ň™ßćŤpŕQŃ";­\úi8RxuŞ5,®,@sä#ýÖĄŚwańř!î3§C+®Ü—«(‹z„îÄ×ĹaS “|€ŔIőé6ö»ˇF]ȲO\ťsČ' :¦Đ˛Ź[ÂŹ ő1L2—ĚÍłZľ ŐëA­Éí}áË^Jóű» ă7¤ÇšŔĆS»B#űŕ=G;Ź úu«őťÔĚYi}_Nť X*ŻÚS±¶1Áaź9>ą;Ćjň©ßˇ+Tš—FŠ˙•ęĄÚ&Ż®,·öŁzV·¶gÇu\ó]Óî8ćĐyď~ţdóţn–Ú˙VÂđG˛,ó:˘Fňő" aʏş”͡á¦ęŇ9kRUĐŹů;˝™ëŢs}mŰĄÓ˘c§1s]üîkş ¦OkYb~\ĚőpĹň4‰ű^=/3%ŁŞ]ăĽĆ°AEţnDľzٹόű `Owf˝‘J]>öĘŠ†*ŃoD8ómýâ5&f§h x«+¸ż]Z»PŤV™îFuYŚ‘FÎD’­7cg˝hi»Hçďë'VÁ*%Ť‹á@‹9Ƨ#uL ˇ%{<ýťGŞžWźÖ^$>kŤáÔ:ZbĂŽ“ 7RŇß2Îxs—Ź)„ŮŘç« 2ÜľĄb\~î’ŁŤaőVQŘ{uÄžjcł‚Fć^ĂĂŚ·sń¦ä˙¸cŮ †ś}›š» ÔµBbb%¨@L»ioĄÄůČ/84ŽŕX¨©¶Đg3đbѨŘÖč|· Y¸0hĽ-3ÂčľwH·Ô˙đJT'‡íBUß1,(Ýx:ëś'=¨ihźl]C^ĄSť1ÚÖ¬Q>N”dą&;iy}łű¤»\ ÉĽ˝ÁĹűíč5Š(ŇËţ<Č[WFT[‡"¤yCčgÚ;]ÜhBŤK‘ŕü–Y–)oŚ0şš) N™ŽŐwšôj0JŹđŁqŔwz›çKë6¸Y§3dĹo‰öÉý7ÎďçPů.×'i ߥ<ň'éĐS'ľ;U §ćž˝Ě]‹Ű ’řo¬zĄ:… ö i*<ŰqΡgtCĂOˇÇî®$,4l¨)ʼn¬×ç ĐĐů)ľ^łĽUĘ#'$í¬ÜŘÂîdŰ_˛ ¨Ţ϶Qjćçk÷T?vřaáý‚ \X‘;¬g˝şśĄ^„d"2‰fŇäŠht–¨ ë8ľő¦çë켊~z.•sÝ©á`Łóž¬ŃĘOťC:?×/ć(Č3Äłą&ČŞ7đ {<Ťß@KE<şŢ"Ć^-·#ФMÔaÜ#!9Ü xŹJAjú˛č)ćE§ęĐßžŃĐn˛ú™PąnßË“÷ß{0 ^’˝ í:+¬HîŞ}ŢjäˇÍÚŁŤ vŚ7ÉŐřR0W#*+Ôˇ˙B;Óą23[c´ä‰n{er7~ Í~$ţíĚVăŘŮbwBřťĚ?đFÁ°ąhˇz1˘ŚFcÇ×n¦ľ·řqěÍZ?K–'~ĎŠ”4­ĆĹHŰDęqĘŐ-ÖtŚČlűLH8×ĆÉu Âe+µÝáłômü]NŁąŚ˘ż~@]çůőɬQ$ĹýÚ¨§úWm«+)Ĺyw»űeŃĹőŘô¶Vxמä7w±í=±®·_ĺi‡xĄPQ‘ďŕşNS&ş‘ë®´Gůý<şśĺ¤dFĹż§ß-”IŽ„YnÓ×TřF…–´«Ł ›÷™k|{®vëK5ď§šý§/<« ~”q`Ą=u>"fâůřr«»j•Á^‹WS¸ěÓ JIąu-«%©m˙ş()Š;ŕńü÷>‡uľĘsŇfÇ|Ł=Čš/ňúą©9ŮHęáwޞhÖ9đë~Ł·¨ňĚó–Sdúc&Ý FKˇşŮÝ bźĽ¬w>° R,Đđцř-ÍđĘ ň„w-¦Ý¬˝üŢ ›űÄĐĺ˙1¤QRCô]ŕlxżăËΩ1yA×u Wńażlż–nÁa./8Ţsý7bBM”Řeëţ‚=ú"dĹEE·żfNŢ@Ô5˛ćđTőZşÄpwŐk‘ŮfŚôtŚďhGÔ^31ä°Č”ev<\¶O¤ąuő"&ĘPr Ě"H•”[ďN2™—ôŞ­mÚ‰H˘Nş¶>>Ýâ~đhWSŁmżŮęłţ”!„¸MAţĚĂ’ŇŽö¬Xé,UŞfĘľŕ‚w:ÜűŢđ„Řűi9‚îsŘA°E<ĺ؛ʲ˙m\ęYđcś˘%ŕˇŕŁ:Ůľ5XĘ<‹úrŃ;Yş›Ç3!¤‚ZÓ'˛kr Žťă>›®Žw)GO@˘€µQ‡Á Ëčú8/ĹMĽN¸f™’NFŕGzĚ ˘—‡ÉDZܰÖn©ťz¬ĎJbűÜŐąTËěRĺŚNXáí¦E#ąćX˘ő*Á!ŔťÉ~ĽR&čËr`R=$á…âËLĄ(=C­*Đ$¶PŁOjóiúK„:ń…ü‹{ăvéDr_n÷”Z§!Ç>ů~«őÉÂč×ßÎŐA,´´îeÉhm9A#?„fĎóńŤ•m;V÷¶ ś–‹ĺŢĄcPS]KŽŞaíń*Ťw?ň&h;i*;>·D@˛­#‚ÉMNkkk?Ăďz!w:¶mGˇł+yťeV“l\EŮe:-bóY,ş ŃG_ú¬ĺŤ&2j•zvUĽGJ¨ň— FňÖđ#Îm"«ŘµŐtČËćC^î®ÎĄ&|˙!bIpkfşmČ% FжöUđţý«:(ĆŤi‘Ľf(˘ÚQµ8#\3ruzZÍ Ť3Ę:€oNţ•ôÜxK”­Ĺ˘rđ=CČÍaSţ´r‹•–Q˘ŕ¶¦sd™†îůµ5ĚĎg»Ećî)͢9@ţ»Ěďš ďî{A/‹«ńşQĽ)›c¬–±YűŽMâ)ç6Ňéiź"ĂžŘÎţ˙unoŁendstream endobj 265 0 obj << /Type /XRef /Length 486 /Filter /FlateDecode /DecodeParms << /Columns 5 /Predictor 12 >> /W [ 1 3 1 ] /Info 53 0 R /Root 52 0 R /Size 266 /ID [<8064405e9de39b491f06974ec77e6efa><1580ea95e9570bc0fe05ad04d379bd69>] >> stream xśíÔĎK”AŔń™g×ÝJmM3ĵ-*Ë›)5AÁŔ(‚¨cž’ţ‚ s˙G—ĹDP‘@ »ÔVp=¨¦ *$ZôCsmľĚsőŢ><ĚĽďĚ<Ď<ďŠq?±Ć$p×°˙=†SÉţŤO>vŐ‹ltF?śŇBśăńWÄmĚľvÚKX‚đ>žÂ5¬Ä&\Ułx:Ě {™}âsŚ˙ÄߌK)#±ăXĺSű&Ôl5~ÄeFv´µ¸§N{/`=Îá4F‘*Ů*ŻuěŔ«XĚÉowá6VáŽzë;ΫʜGQëű?ămÜÄ,Í$ń7uܵ=je?»‚üżpŠŐF‡đ,ćńŇ?ö0ä%íjÇ”Ę} /‡[03á–}ÖBĺÍśŞ­Żv!d'1žÉ‡^5>‹·ę–}·Ś«őý˝©Yß Ş“?±Új8ż4'ńŠĘ«Eݲż»÷S»đíŘ kfÔř,^Ça\¨•éOĂŮÄ?s/tÔżjĹw<9Ş:­SuWźŞÉ u*ýŤřÚNŕvcNőŞë.¶†î’gĉU•UG=Á^Ő!=ġĄ_Ýř#ĽüŰ[ÍÚ%FüW˙ÔYĂwť¦ćµ/ť <_óĆ™úâL>7G9Scę endstream endobj startxref 126693 %%EOF sampling/inst/doc/UPexamples.Snw0000644000176200001440000001104314520143726016365 0ustar liggesusers\documentclass[a4paper]{article} %\VignetteIndexEntry{UP - unequal probability sampling designs} %\VignettePackage{sampling} \newcommand{\sampling}{{\tt sampling}} \newcommand{\R}{{\tt R}} \setlength{\parindent}{0in} \setlength{\parskip}{.1in} \setlength{\textwidth}{140mm} \setlength{\oddsidemargin}{10mm} \title{Unequal probability sampling designs} \author{} \usepackage{Sweave} \usepackage[latin1]{inputenc} \usepackage{amsmath} \begin{document} \maketitle <>= library(sampling) ps.options(pointsize=12) options(width=60) @ \section{Examples of maximum entropy sampling design and related functions} a) Example 1 @ Consider the Belgian municipalities data set as population, and a sample size n=50 <>= data(belgianmunicipalities) attach(belgianmunicipalities) n=50 @ Compute the inclusion probabilties proportional to the `averageincome' variable <>= pik=inclusionprobabilities(averageincome,n) @ Draw a random sample using the maximum entropy sampling design <>= s=UPmaxentropy(pik) @ The sample is <>= as.character(Commune[s==1]) @ Compute the joint inclusion probabilities <>= pi2=UPmaxentropypi2(pik) @ Check the result <>= rowSums(pi2)/pik/n detach(belgianmunicipalities) @ b) Example 2 @ Selection of samples from Belgian municipalities data set, sample size 50. Once the matrix q (see below) is computed, a sample is quickly selected. Monte Carlo simulation can be used to compare the true inclusion probabilities with the estimated ones. <>= data(belgianmunicipalities) attach(belgianmunicipalities) pik=inclusionprobabilities(averageincome,50) pik=pik[pik!=1] n=sum(pik) pikt=UPMEpiktildefrompik(pik) w=pikt/(1-pikt) q=UPMEqfromw(w,n) @ Draw a sample using the q matrix <>= UPMEsfromq(q) @ Monte Carlo simulation to check the sample selection; the difference between pik and the estimated inclusion prob. (object tt below) is almost 0. <>= sim=10000 N=length(pik) tt=rep(0,N) for(i in 1:sim) tt = tt+UPMEsfromq(q) tt=tt/sim max(abs(tt-pik)) detach(belgianmunicipalities) @ \section{Example of unequal probability (UP) sampling designs} Selection of samples from the Belgian municipalities data set, with equal or unequal probabilities, and study of the Horvitz-Thompson estimator accuracy using boxplots. The following sampling schemes are used: Poisson, random systematic, random pivotal, Till\'e, Midzuno, systematic, pivotal, and simple random sampling without replacement. Monte Carlo simulations are used to study the accuracy of the Horvitz-Thompson estimator of a population total. The aim of this example is to demonstrate the effect of using auxiliary information in sampling designs. We use: \begin{itemize} \item some $\pi$ps sampling designs with Horvitz-Thompson estimation, using auxiliary information in a sampling desing (size measurements of population units in 2004); \item simple random sampling without replacement with Horvitz-Thompson estimation, where no auxiliary information is used. \end{itemize} <>= b=data(belgianmunicipalities) pik=inclusionprobabilities(belgianmunicipalities$Tot04,200) N=length(pik) n=sum(pik) @ Number of simulations (for an accurate result, increase this value to 10000): <>= sim=10 ss=array(0,c(sim,8)) @ Defines the variable of interest: <>= y=belgianmunicipalities$TaxableIncome @ Simulation and computation of the Horvitz-Thompson estimators: <>= ht=numeric(8) for(i in 1:sim) { cat("Step ",i,"\n") s=UPpoisson(pik) ht[1]=HTestimator(y[s==1],pik[s==1]) s=UPrandomsystematic(pik) ht[2]=HTestimator(y[s==1],pik[s==1]) s=UPrandompivotal(pik) ht[3]=HTestimator(y[s==1],pik[s==1]) s=UPtille(pik) ht[4]=HTestimator(y[s==1],pik[s==1]) s=UPmidzuno(pik) ht[5]=HTestimator(y[s==1],pik[s==1]) s=UPsystematic(pik) ht[6]=HTestimator(y[s==1],pik[s==1]) s=UPpivotal(pik) ht[7]=HTestimator(y[s==1],pik[s==1]) s=srswor(n,N) ht[8]=HTestimator(y[s==1],rep(n/N,n)) ss[i,]=ht } @ Boxplots of the estimators: <>= colnames(ss) <- c("poisson","rsyst","rpivotal","tille","midzuno","syst","pivotal","srswor") boxplot(data.frame(ss), las=3) <>= <> <> <> <> <> sampling.newpage() @ \end{document} sampling/inst/doc/calibration.Snw0000644000176200001440000003675414520143726016611 0ustar liggesusers\documentclass[a4paper]{article} \usepackage{pdfpages} %\VignetteIndexEntry{calibration and adjustment for nonresponse} %\VignettePackage{sampling} \newcommand{\sampling}{{\tt sampling}} \newcommand{\R}{{\tt R}} \setlength{\parindent}{0in} \setlength{\parskip}{.1in} \setlength{\textwidth}{140mm} \setlength{\oddsidemargin}{10mm} \title{Calibration and generalized calibration} \author{} \usepackage{Sweave} \usepackage[latin1]{inputenc} \usepackage{amsmath} \begin{document} \maketitle <>= library(sampling) ps.options(pointsize=12) options(width=60) @ \section{Example 1} Example of using the \verb@calib@ function for calibration and nonresponse adjustment (with response homogeneity groups). @ \noindent We create the following population data frame (the population size is $N=250$): \begin{itemize} \item there are four variables: \verb@state@, \verb@region@, \verb@income@ and \verb@sex@; \item the \verb@state@ variable has 2 categories: 'A' and 'B'; the \verb@region@ variable has 3 categories: 1, 2, 3 (regions within states); \item the \verb@income@ and \verb@sex@ variables are randomly generated using the uniform distribution. \end{itemize} <>= data = rbind(matrix(rep("A", 150), 150, 1, byrow = TRUE), matrix(rep("B", 100), 100, 1, byrow = TRUE)) data = cbind.data.frame(data, c(rep(1, 60), rep(2,50), rep(3, 60), rep(1, 40), rep(2, 40)), 1000 * runif(250)) sex = runif(nrow(data)) for (i in 1:length(sex)) if (sex[i] < 0.3) sex[i] = 1 else sex[i] = 2 data = cbind.data.frame(data, sex) names(data) = c("state", "region", "income", "sex") summary(data) @ \noindent We compute the population stratum sizes: <>= table(data$state) @ We select a stratified sample. The \verb@state@ variable is used as a stratification variable. The sample stratum sizes are 25 and 20, respectively. The method is 'srswor' (equal probability, without replacement). <>= s=strata(data,c("state"),size=c(25,20), method="srswor") @ We obtain the observed data: <>= s=getdata(data,s) @ The \verb@status@ variable is used in the \verb@rhg_strata@ function. The \verb@status@ column is added to $s$ (1 - sample respondent, 0 otherwise); it is randomly generated using the uniform distribution U(0,1). The response probability for all units is 0.3. <>= status=runif(nrow(s)) for(i in 1:length(status)) if(status[i]<0.3) status[i]=0 else status[i]=1 s=cbind.data.frame(s,status) @ We compute the response homeogeneity groups using the \verb@region@ variable: <>= s=rhg_strata(s,selection="region") @ We select only the sample respondents: <>= sr=s[s$status==1,] @ We create the population data frame of sex and region indicators: <>= X=cbind(disjunctive(data$sex),disjunctive(data$region)) @ We compute the population totals for each sex and region: <>= total=c(t(rep(1,nrow(data)))%*%X) @ The first method consists in calibrating with all strata. The respondent data frame of \verb@sex@ and \verb@region@ indicators is created. The initial weights using the inclusion prob. and the response probabilities are computed. <>= Xs = X[sr$ID_unit,] d = 1/(sr$Prob * sr$prob_resp) summary(d) @ We compute the g-weights using the linear method: <>= g = calib(Xs, d, total, method = "linear") summary(g) @ The final weights are: <>= w=d*g summary(w) @ We check the calibration: <>= checkcalibration(Xs, d, total, g) @ The second method consists in calibrating in each stratum. The respondent data frame of \verb@sex@ and \verb@region@ indicators is created in each stratum. The initial weights using the inclusion prob. and response probabilities are computed in each stratum. <>= cat("stratum 1\n") data1=data[data$state=='A',] X1=X[data$state=='A',] total1=c(t(rep(1, nrow(data1))) %*% X1) sr1=sr[sr$Stratum==1,] Xs1=X[sr1$ID_unit,] d1 = 1/(sr1$Prob * sr1$prob_resp) g1=calib(Xs1, d1, total1, method = "linear") checkcalibration(Xs1, d1, total1, g1) cat("stratum 2\n") data2=data[data$state=='B',] X2=X[data$state=='B',] total2=c(t(rep(1, nrow(data2))) %*% X2) sr2=sr[sr$Stratum==2,] Xs2=X[sr2$ID_unit,] d2 = 1/(sr2$Prob * sr2$prob_resp) g2=calib(Xs2, d2, total2, method = "linear") checkcalibration(Xs2, d2, total2, g2) @ <>= <> <> <> <> <> <> <> <> <> <> <> <> <> <> sampling.newpage() @ \section{Example 2} This is an example for \begin{itemize} \item variance estimation of the calibration estimator (using the \verb@calibev@ and \verb@varest@ functions), \item variance estimator of the Horvitz-Thompson estimator (using the \verb@varest@ and \verb@varHT@ functions). \end{itemize} We generate an artificial population and use Till\'e sampling. The population size is 100, and the sample size is 20. There are three auxiliary variables (two categorical and one continuous; the matrix $X$). The vector $Z=(150, 151, \dots, 249)'$ is used to compute the first-order inclusion probabilities. The variable of interest $Y$ is computed using the model $Y_j=5*Z_j*(\varepsilon_j+\sum_{i=1}^{100} X_{ij}), \varepsilon_j\sim N(0,1/3), iid, j=1,\dots, 100.$ The calibration estimator uses the linear method. Simulations are conducted to estimate the MSE of the two variance estimators of the calibration estimator. Since the linear method is used in calibration, the calibration estimator corresponds to the generalized regression estimator. For the latter an approximate variance can be computed on the population level and used in the bias estimation of the variance estimators. For the Horvitz-Thompson estimator, the variance can be computed on the population level and compared with the simulations' result. Use 10000 simulation runs to obtain accurate results (for time consuming reason, in the following program, the number of runs is only 10). <>= X=cbind(c(rep(1,50),rep(0,50)),c(rep(0,50),rep(1,50)),1:100) # vector of population totals total=apply(X,2,"sum") Z=150:249 # the variable of interest Y=5*Z*(rnorm(100,0,sqrt(1/3))+apply(X,1,"sum")) # inclusion probabilities pik=inclusionprobabilities(Z,20) # joint inclusion probabilities pikl=UPtillepi2(pik) # number of runs; let nsim=10000 for an accurate result nsim=10 c1=c2=c3=c4=c5=c6=numeric(nsim) for(i in 1:nsim) { # draws a sample s=UPtille(pik) # computes the inclusion prob. for the sample piks=pik[s==1] # the sample matrix of auxiliary information Xs=X[s==1,] # computes the g-weights g=calib(Xs,d=1/piks,total,method="linear") # computes the variable of interest in the sample Ys=Y[s==1] # computes the joint inclusion prob. for the sample pikls=pikl[s==1,s==1] # computes the calibration estimator and its variance estimation cc=calibev(Ys,Xs,total,pikls,d=1/piks,g,with=FALSE,EPS=1e-6) c1[i]=cc$calest c2[i]=cc$evar # computes the variance estimator of the calibration estimator (second method) c3[i]=varest(Ys,Xs,pik=piks,w=g/piks) # computes the variance estimator of the HT estimator using varest() c4[i]=varest(Ys,pik=piks) # computes the variance estimator of the HT estimator using varHT() c5[i]=varHT(Ys,pikls,2) # computes the Horvitz-Thompson estimator c6[i]=HTestimator(Ys,piks) } cat("the population total:",sum(Y),"\n") cat("the calibration estimator under simulations:", mean(c1),"\n") N=length(Y) delta=matrix(0,N,N) for(k in 1:(N-1)) for(l in (k+1):N) delta[k,l]=delta[l,k]=pikl[k,l]-pik[k]*pik[l] diag(delta)=pik*(1-pik) var_HT=0 var_asym=0 e=lm(Y~X)$resid for(k in 1:N) for(l in 1:N) {var_HT=var_HT+Y[k]*Y[l]*delta[k,l]/(pik[k]*pik[l]) var_asym=var_asym+e[k]*e[l]*delta[k,l]/(pik[k]*pik[l])} cat("the approximate variance of the calibration estimator:",var_asym,"\n") cat("first variance estimator of the calibration est. using calibev function:\n") cat("MSE of the first variance estimator:", var(c2)+(mean(c2)-var_asym)^2,"\n") cat("second variance estimator of the calibration est. using varest function:\n") cat("MSE of the second variance estimator:", var(c3)+(mean(c3)-var_asym)^2,"\n") cat("the Horvitz-Thompson estimator under simulations:", mean(c6),"\n") cat("the variance of the HT estimator:", var_HT, "\n") cat("the variance estimator of the HT estimator under simulations:", mean(c4),"\n") cat("MSE of the variance estimator 1 of HT estimator:", var(c4)+(mean(c4)-var_HT)^2,"\n") cat("MSE of the variance estimator 2 of HT estimator:", var(c5)+(mean(c5)-var_HT)^2,"\n") @ <>= <> sampling.newpage() @ \section{Example 3} This is an example of generalized calibration used to handle unit nonresponse with different forms of response probabilities. Consider the population $U$, the sample $s$ and the set of respondents $r$ with $r\subseteq s \subseteq U.$ The response mechanism is given by the distribution $q(r|s)$ such that for every fixed $s$ we have $$q(r|s)\geq 0, \mbox{ for all } r\in \mathcal{R}_s \mbox{ and } \sum_{s\in {\mathcal R}_s} q(r|s)=1,$$ where ${\mathcal R}_s=\{r | r \subseteq s\}.$ The variable of interest $y_k$ is known only for $k\in r.$ Under unit nonresponse we define the response indicator $R_k=1$ if unit $k\in r$ and 0 otherwise and the response probabilities $p_k=Pr(R_k=1| k\in s).$ It is assumed that $R_k$ are independent Bernoulli variables with expected value equal to $p_k.$ We assume that the units respond independently of each other and of $s$ and so $$q(r|s)=\prod_{k\in r} p_k \prod_{k \in \bar{r}} (1-p_k).$$ The nonresponse model can be rewritten as $$q(r|s, \boldsymbol{\gamma})=\prod_{k\in r} F_k^{-1}(\boldsymbol{\gamma}) \prod_{k \in \bar{r}} (1-F^{-1}_k(\boldsymbol{\gamma})).$$ In calibration method it is assumed that $$\sum_{k\in r} \mathbf{x}_kd_kF_k(\boldsymbol{\gamma})=\sum_{k\in r} \mathbf{x}_kd_kF(\boldsymbol{\gamma}^T\mathbf{x}_k)=\sum_{k\in U} \mathbf{x}_k,$$ where $F_k(\boldsymbol{\gamma})=F(\boldsymbol{\gamma}^T\mathbf{x}_k), p_k=F_k(\boldsymbol{\gamma})^{-1},$ and $d_k$ are the initial weigths. In generalized calibration a different equation is used $$\sum_{k\in r} \mathbf{x}_kd_kF(\boldsymbol{\gamma}^T\mathbf{z}_k)=\sum_{k\in U} \mathbf{x}_k,$$ where $\mathbf{z}_k$ is not necessary equal to $\mathbf{x}_k,$ but $\mathbf{z}_k$ and $\mathbf{x}_k$ have to be highly correlated. $\mathbf{z}_k$ should be known only for $k\in r.$ The components of $\mathbf{z}_k$ that are not also components of $\mathbf{x}_k$ are often known as \emph{instrumental variables}. Let $w_k$ be the final weights (obtained after applying generalized calibration). It is possible to assume different forms of response probabilities: \begin{itemize} \item Linear weight adjustment (it can be implemented by using the argument \texttt{method="linear"} in gencalib() function or \texttt{method="truncated"} if bounds are allowed): $p_k=1/(1+ {\boldsymbol\gamma}^T\mathbf{z}_k)$ and $w_k=d_k(1+\mathbf{h}^T\mathbf{z}_k),$ where $\mathbf{h}$ is a consistent estimate of ${\boldsymbol\gamma}.$ \item Raking weight adjustment (it can be implemented by using the argument \texttt{method="raking"} in gencalib()): $p_k=1/\exp(\boldsymbol{\gamma}^T\mathbf{z}_k)$ and $w_k=d_k \exp(\mathbf{h}^T\mathbf{z}_k).$ \item Logistic weight adjustment (it can be implemented by using the argument \texttt{method="raking"} in gencalib()): $p_k=1/(1+\exp(\boldsymbol{\gamma}^T\mathbf{z}_k)), w_k=d_k (1+\exp(\mathbf{h}^T\mathbf{z}_k)),$ but we calibrate on $\sum_{k\in U} \mathbf{x}_k-\sum_{k\in r} \mathbf{x}_k d_k$ instead of $\sum_{k\in U} \mathbf{x}_k.$\item Generalized exponential weight adjustment (Folsom and Singh, 2000; it can be implemented by using the argument \texttt{method="logit"} in gencalib()): $$p_k=1/F(\boldsymbol{\gamma}^T\mathbf{z}_k), w_k=d_kF(\mathbf{h}^T\mathbf{z}_k),$$ $$F(\mathbf{h}^T\mathbf{z}_k)=\frac{L(U-C)+U(C-L)\exp(A\mathbf{h}^T\mathbf{z}_k)}{(U-C)+(C-L)\exp(A\mathbf{h}^T\mathbf{z}_k)}\in (L, U),$$ where $A=(U-L)/((C-L)(U-C))$ and $L\geq 0,1C>L,$ ($C=1$ in the paper of Deville and Sarndal, 1992). The g-weights are centered around of $C.$ For $L=1, C=2$ and $U=\infty, F(\mathbf{h}^T\mathbf{z}_k)$ approaches $1+\exp(\mathbf{h}^T\mathbf{z}_k)$ and for $C=1, L=0, U=\infty,$ $\exp(\mathbf{h}^T\mathbf{z}_k).$ \end{itemize} We exemplify the last form of response probabilities (generalized exponential weight adjustment) using artificial data. We generate a population of size $N=400$ and consider the auxiliary information $X$ following a Gamma distribution with parameters 3 and 4. The instrumental variable $Z$ is generated using the model $Z=2X+\varepsilon,$ where $\varepsilon\sim U(0,1).$ The variable of interest is $Y$ generated using the model $Y=3X+\varepsilon_1,$ where $\varepsilon_1\sim N(0,1).$ We consider here that the nonresponse is not missing at random and the response probabilities $p$ depend on the variable of interest $y$ which may be missing. We draw a simple random sampling without replecement of size $n=100$ and generate the set of respondents $r$ using Poisson sampling with the probabilties $p.$ The bounds are fixed to 1 and 5, and the constant $C=1.5.$ Three estimators are computed: \begin{itemize} \item the generalized calibration estimator using $Z$ as instrumental variable, \item the generalized calibration estimator using $Y$ as instrumental variable, \item the generalized calibration estimator using $X$ as instrumental variable, which is the same with the calibration estimator, but the g-weights are centered around $C$. \end{itemize} The convergence of the method is not guaranteed due to the bounds. Thus $g1, g2, g3$ can be null. If it the case, repeat the code (considering another $s$ and $r$). <>= N=400 n=100 X=rgamma(N,3,4) total=sum(X) Z=2*X+runif(N) Y=3*X+rnorm(N) print(cor(X,Y)) print(cor(X,Z)) L=1 U=5 C=1.5 A=(U-L)/((C-L)*(U-C)) p=((U-C)+(C-L)*exp(A*Y*0.3))/(L*(U-C)+U*(C-L)*exp(A*Y*0.3)) summary(p) bounds=c(L,U) s=srswor(n,N) r=numeric(n) for(j in 1:n) if(runif(1)>= <> sampling.newpage() @ \end{document} sampling/inst/doc/calibration.pdf0000644000176200001440000054334615033773102016610 0ustar liggesusers%PDF-1.5 %ż÷˘ţ 1 0 obj << /Type /ObjStm /Length 5401 /Filter /FlateDecode /N 95 /First 777 >> stream xśÝ\Ű’7’}߯¨·±cB,Ü/ˇ‹µöZňxdy,YăZMIÜiu÷ÔŽ<_?çÔ…E6ű˛±±-`•™H$ň$PTŤht#ŤiLŁPÚĆx߸ƋŘř&Ő„FJo›FuŃHk%®5ŇEôTŤŚĘ4R7JZŹFŤRĽoĄ5Ú;’E'ßŕ˛kdh”C!cŁĽB!´DçF §ĐąŃ t•n´qä©ŃÖm´ â,:ůFG¶ Ť‘÷cc8ע1Ú˘łlڱč¤ă4®k¦#6&܇ ôŃŐJ‹ëľ±` ÄkúĹĆZ<¬[@L66DSŤŤDtă„Ç}Ó8éqÝ6N9uŤ3Ô&”č Ż Ťód"â“Ä>Q@E.<\AŐŕ˙ĽłřnP€ő ®;|z|ú&(Ë|’ąOřŚx¨lMP6şF%AD)ŔAŢPäP¦dÉyVŔ€ăK(ÓaĄ´L*)ŔFă{Ŕ8CŻŕ–Ö˘x ”Ť‡^`Ň:^eëˇ$Śt é< ĘNcP(; C 4 ‡á ě‚€  ]@`Pö J ěi %˝‡ÍPF[X%(  ÍŤ ‡:‚rhi›Ź ťFˇÁŔÉč Kĺg@I¬8ńořCÓ>˝ĽŘ5©MĂŇEó‚u¨˛T9)r•†VްŤ®J3(URŞÔkW·TmŞ˙ńŹMűýćňí«]óŐ'O›öĺęó®ů·?ŹÁĎęb·mb÷Ô竳őňŃĺg´'w6Ú…ňT\@ż€ČrśŤ©ý‹ŐöňÓćíjŰäG¶/»Z±ŐűU}Ƈĺ†\l©˙Ôć«‹·—gë‹÷™ĚózłÝ±M&)éQćA5 *Ă÷Kľő|ąŰ¬ !\b\Śęh÷lŮŃ“îíwËŹ«¬ĐżdmŞśo>‚ďGI=Műç_wIФ‹diŰźÖg»[šXÖóTP1ÔÇ© ÁAU7gçä„ř¦ČYëc9e/§Ľo9}—Óű©śnOθ'§¦W 1Ě (|RĐR *Ş  ÷-¨= ¨™ ާ‚ޱĺ>0iDéŐěŞÁŞ™ĹâV…W¸oAŐAĺTP1TŹ-÷ÄżČĺ +ÇmNQ¸´{Ôš Ź–ŰUľýřůĎżýŹß?~ţüřáńž¬¶o7ë«Ý冋CŇÁx¶ŤŽď//ĽXCk«&†ˇ/łćŮůé?˙üő‹×`çĹ#ö¸‰bŹq”›x?oÎM¬ŔŮ©0$š†źˇVÇ[Ů©¸wcwć€ÓőČí­GScWÉŘŇÜÚŘ{Aí˝ŻGnĽ]~Â=<ďŰőŮ4ł´2ĄĚC/łq¨|OgÓę—ůh¦«ú¶P„2łáË :ąUDÂ˙Ž!‰Üă~†ůűäXŢ™cw3ŽgÁžyÔ%PWWA7JÝY¨pH¨ÁTśFLv/bŇ“HÂ$f9o3I®LÄ{ě€ÉN&»0MÄ$¬M˙îALuďbŽÂĄŢD‰Ő‹Ť‡ËEČ˝€,G¶«;˘ńcžĺθ‡ô®Ŕç ňé—÷źzöăë§XŢxí‡=łşëÉęŚtdq—bäăĆŁ3Zözf^Ľ~ńňŐ·)ô1f"ź0Ť5°|eÇfGĎłóęÉ7OľůSŇÍ8ö™QŽš˛#ÜqvÜavÔ<;ß=|ôýĎ;_˝łöů v:Xń8;ţ0;ň€vţĺé‹ç GcĹyŞ9aĆ RĄ8ĄZ1?Żďu±ŚwžxńĐÄ»ˇ'’LŢÝŮ+]łßD7ň†ˇŹ6SÝhy˝SzřěŮO_?L~ŔŽB{Îń©Ąű:)Z—A˛Ąš>JÍęŇY™&“1ú6é˙Î2¤™âíę ĺ:°…Ž7·„F_3–`Ż·„źţéő‹gÉŤťT¸g qj ú¸%čĂ–p ýĂËŻź‘źGĚĺnŹ3ĺG_2ĺá%ÓÄf Í ŽăüŹcbÄ‹[ăDÍ%ns÷·™Qdp“¤ô˝Ć^˝qqýpź‹VÁgwŕx6Z|ë ŚŠ •(ÖŠ€{ZśUrˇ¸«˛ŔRoÜ"ĺ’Rc +¶đZ~čdĚ‚ľ‡D~I"ń$Ëbx“ĹďSY˛¬%TšTŐua®CŃKn\p/Q˘tÂŐ+ц…îÚXo»ZnaĽ^88“TŹáŢ Zŕz®çŇJąŇ709‘>ť劋 Ë˝;<Í ĎÖůŠ´,˝ďľC­°úĺô ni*MÎS™ŰDpaGőľťé•˛{BO#Ę= t—rČW.ů$Ę c° î¶ÁΡDĹMÁ•Ҥ¦Cż‰„Oµü=×­{µSJ)„H]nÖí˘©żZž×_4îż»d0ąśm޸ąšZ:·đ]Ť0—Éż%mç2·H˘*ę˝+Lć§ůHłqÖQźĘˇî=§†U©Ą ¤i.! ŤMĄËt÷Śă‚“N6Q‚˘Ł0‘l™ĹśA'‘ذwIĘňŁhÎqzeŞËĘh5f¦,Ú×IEąěMËy“”A÷ËzÚg·¦ślş rN­a‰yňô ÉA$"2&ť+>ZRÔ®đ°€ő`”e2`Q4čĂYáy„×µ“¤Ě‰ď”8ܶŽNŔ&έ˘˙±đ6 ŻcZ4Ţ ÎS” ßuW·ÁÎÖO);yr·›v>‰t÷qřÎđŮă;Ł®ăk^$»Iĺ‘NZŃŐ—>đ^ˇ+ˇtËĆů“ÉN‰\ć6ť:4§GW„ČĎŽŇcŚĽĄt < @é$çD4&8ĺ˘çÔ"}YH®Ç,Ą6´‹¨“˙ś,ApČy%bĘäĹ«ÂąŘ ĹĄŽGÖmÍĹ8öËă]ii1Ąµ_˛Éˇ{w/OĄ~z»ë{‰Ů¶w•ůRK÷§íá·Y»a8™ †”Ü<\ňVčOnś.sÁŽ…fÁn<9°äfóŇ05Ś`ąď¸äžÜ’űKfË—–=ňa©JٲG.ŘͲ›c7ćŰ–\ątßŘńAąŕÓxĽeéŘͱđˇËžŹĚE:ýÔß`ß\€¦Áµ© GžL*Aę6¦ń Fxú/îş‘ˇç˛×ŠIFdŃă©<ŚNÄ7.Ťs §|OŔz‚Uťó<°śÜާńCJWA0d’éZˇhO *+y=!âÎś1l×Ň'8f7TPOy÷óqű}"‹ásŽa…›´>öÔS0Çm%Ľ :íţ14 Az†iű­‡:‡ff С3lšp öµ®é!„ćH,< Ü·$\p x}S\€B6עŘL€@đśŃ'üŕšmî L6|s NH(áôm†zP š}ôp(YbÁ·‰z Ŕcu=PŕkJ=P@"{ `ů Ó (ŮxűÁ^‘~ŻCňé}pާö:řFÓpŻCŤö:ÔhŻCŤö:ÔhŻCŐ˝ęEŐÍęG©Ę ÷ËUŤĎń8]Xa´Sá“á V5>OŻjő·ŞŘ LU脉¦*râX8ńcĹM<ĆXa&˘Ş¨Éňm®źsżĆç|I¬Ćç>˝VŁpUfŞŞxÉ‘@á€/U´äH Ćçŕşb%ĎíşóhAáŔó łÂç»j…ĚZUaR ťŠ’©ŐčbW”„é¬*J´V%a>«Š’DW”Ä ş˘$ĆÍ%1Tvm/ĺŰ^Ş˘$†ĘnŰšŔýK¬ĽÉř÷˙”^î-TěĘţ*˘şşč‚îŰ•˝ŽRůCLٞ,ß­[DľĽ¶0|őmÁJńÂ麆Jb·ď!S6w!ó^lnĎ–W˛¬˝ÔQ4Läšé;SîČQ]ˇ—µéäČ×5ůÎW\Š—R“aóÜjµ©ô¶Sńđnf4×»öéáső˙ĺ/K83đmťe.8ŕŔˇKI÷ÉC3XsňpAĽź!%D<9oB6'd }¸{˘>% ˙/d™†ŞŚ¦×¨–,ŻYŢk–'-ď~´ĽűŃňîGË»-ďľ_Ţ Lűĺť^®_Ţ íú< ÷űĺťď+W^¸Ř‡~y/ˇ_ŢůrrżĽ—Đ/ďŕ%ôË;x }Ľ„>^Bjđ\ĺ…@Íü¤@ öy@đRs?ÔŻŞÉXŻÉź4ż 'XŞ& ©×‰×d©ÉĚ4]“?qş& Q 2°®ÔäS5ůcůćwMÁ)ľ^ľŔ×äŹefˇ|h 2đĐýaĘęüaęęűYžř¨)8¨‘ź#d@ 2H $PSp‚çGĘPc>O'_  A†V C×xĄk¸—ÖŠd@ 2xТ\FjÁ7Ü ‘_j ŽÉ«dpǸô~Ău•oűĘë˝6S·]—†ĎŔŠĆt°0+âÂĺÝcU S‘±Áܢ‘Ľúȵ[µŹÜĽŘw“1 Î3VăĂTÉ#öřF5s°§â㧨ô`ł‚ŁŐ;)Wď`ýf¨ęt›t`0»ú äkĐM ®A7­Ä la^U6ÂEoGeőÔ±¶b!âÉ­˙ ůP7 Y-IňެIt±ÇÂ;Xps ůvFżb“ oçě!­˝%ř·§%°·Ůąčc–I«ŁÍáé·g8ŚŐ3Ă TčC/k†v`†vPˇ}i}Ť©/XđŔáBź2őNkż€v W2ŽV»›`ÝáÉ®–[u·1 j‘ µ|Ö‘&™ąÁÍQ×8?s<đ™ĚhÝ… ):ů0ĺdĽĹ(dJÁĺMťŇńŃZµ«żZž·ďÖ˙˝jß]~Ú´ë‹U{‰˙{|Kâ|őn—khŻÎ?mŰ݇ÍjŐîţqI/˝Ú\ž¦řÇn>Š9 ÖSµŚucÖ_1Şż_űÍúř¸|»[˘N—O•Ę…r_>3•oôňč¬xţâńÁ‡Ĺ{Ń.·płëíß°Ţ|h]nÚ_7Ë·«4Ě©–‡>‚ZqËY+ë‹wë‹őî·äž;E­/`›Ő»óŐçí§_·«]»]\ź/7'ę,6Ň"ĆPĂ#ÂëĚîélü:ň™bnáÖ®Ó[ŃH–řjłţ¸:ÝDžGâŢ“uşţ&Sqü6ú! obů%Nę«{‹łËt¸ó—fîEÍüeü&çă%¸ą¬ńčc‚–'0‘ć‹'˙®Ěß#,u•ß ő;!~÷e׼}ńrő _ź_ž]×ăű—_˝Z<ýt~ţëňâbĹžÖŰ˙žŻż‘WgďđŃĘ…hţú X(ó×/żLo‹!Öb§Ü¸ÉŤtmĐâĺfyuµ:î–çŰô†×ż–bůOendstream endobj 97 0 obj << /Filter /FlateDecode /Length 1350 >> stream xÚ­WKoăF ľçWľDnăé<4#ÉŰŘ,¶M.zŘÝl˶ [ ôh’ţú’Ă‘,9Š{04ň#ů‘Ăsoëqď—+~á{·¸úágĹ=2)tŕ-6ž0śEZ{ĆLĆÂ[¬˝Ďţ‡dź-ˤΊ|:SBůIľ¦Á6ÍÓv˙MÝÂŞ'úuń ŕ…'‹µ–ž+íÍŚf’k‚˙ÔěźA[_đ„ľäR“~ =0«?ŕ— o¦9-€Î„ŕÜ˙ř”ö)@­ľŚ˝ĹFTçŢLĆŚ‹ô:ů R~±ˇoSeů–†ő.uNŕ‚E‘0 b#±Ą8DÝImš|Eü!đ¦(iЧ $1‹;y‘—iő0ťÉČ/ňĘ9™¬˙nŞúćSX®ií ×ü1«wč¤$‹ Ë:Y?ARJů»âP`ö˛ˇžiq[ÍC€‚Y4ŕKÄL@ćŻ)sú«2Mj7Fžě`Sě÷â=Z• |´úĹCłoë×Iť8Ť298eŚŁCrîöŐ”_AťŃ(«ăjŔ¸aA ZĘs"Ľź;HťŽZ‰[ô-ňĄć#ů“š]ú’9 ugŠ˘/ U±H@eË éx˙"?ŃŘŔ‘hm@ěĄ /);B›’(űgŞ ¬gÉrźVóѲŚ7]lUŤé Ë0qŚëfČ0…­@™n»Ł<,oaĎá[q˛|UĆüQ3±nŰüGB‹śč.˛ôiGhfŽćޝ͕Ô! Bi‹;–ć˙ĺjĚOpębb&U1äUc^§ÂÇÔbĺţ.©p |Ió m‹2KA v„?‡ŁŻ¤ýţš\CćţőÝő;˝âb¤úrz¨ţŕÔÉě/Ńţ)ą¸KČ)«w©PpŁX¨ÂaS7TăŇ}:¶r«˘uěmYŰSŰ Îg’EůŐß.żˇ˝sŢRŇÁJZDJ:˛˘ó9@ŽdětĚĚşO#w=×ör)wµá5Z4y·Ń&kÄÍÚ"«ËlŮ@÷…yŽsF>ę~C…Ś,VĘ^¬"˛ľţ4ťi©]›ÇŃ-}Ęe–Ż1Á‡Ŕó‰Ňý€źÉűÉ É Í1ÇÇY;rßĺsY<üů‘TÚ{KŘë׺ó=ÉXŁ}čŔä®3ĘFůŰŤâo`Wź§a…40\fč‚˝Ń\qĆV=7[Lß?·'ŕFŹě¨3Zí4Cě!üK^uÇ+śŽ\qRúÎAaUY0ňěAXţ2iŐs`Ün9oŔ˛O+ ^FCě6“ó}šoënY›Ĺi7řĄ„» ô9űJ{?҇3u”ďď;ßÝNşÇ‡Ö+2ň xÁŠ ć TµZG˙o{Ĺ6ˇ3ݢ=jĂÝ”^7u N.%·9’ňŮ·‰l5Nz˘„ł‡ť„.ŃY…Ę^V¨oßʡ„W Îş. Öń  ˝řŐf|'@–;Ĺ{ŇDBuřsÁ°€a¬aË-kĽdmy+Ć•‹đĂ>©*ŠrľÚ%e˛˛¦ĄĂUM»ż7 ÁüÎÎpĎŢčRÇLËŃҸ/Ö©óĹŮim€żé:K\}Ďe/Žţşö "ĹĂí˙÷•rź˘8Żä ĂqËĺĂ×đČŽ´sS•ëc¸=WëˇÖđo‘F)˝Ož:úU/švŮyǚŜSÚC˙%OŇînT|zŘ÷îĐH/Ç>vrÉ{)ŐBťŇ˙ŠTźŰPç""ć,äŕ2Ľ#x¶qűW˙NÚËendstream endobj 98 0 obj << /Filter /FlateDecode /Length 696 >> stream xÚmTMoâ0˝çWx•ÚĹ$ !Ů ‘8l[•jµWHL7IP‡ţűő¬V=MžßĚĽń s÷ëu;ŃU··őČŮ›=w—ľ´“ě÷îÝÝĺ]yil;<[[Ůj<=?±×ľ+·v`÷Ů&ß´őđŕČ›¶<^*;˛~&űQ·‚>ěţÝţť”MS >Ů_ęăP·ň{=éÇsć@öd”ôÇöçşkźxäś;`ÝVY×`Śs4˝JaÓQܡn«ţއíˇ.’Uu9\ßčY6î>Ľý<¶Ů´‡.Z.ŮôÍž‡ţ“4>DÓ—ľ˛}Ý~°űŻŇÜŃör:-d0­V¬˛WŃÍ˙Ľk,›ţ8ăŤóţy˛LŇ»đşĘ®˛çÓ®´ý®ý°Ń’ó[Ĺ*˛mőíLrź˛?ŚÜÔqůĄă• â5F8@ š=@Šđ)&°  Č8Ôą€ÂĹRx u€Dş\j2H—†ŞˇĐVÁą0CzL]ř Âb°ct‘I ©g$`htŃ‹0śĆ\F„áŚ0ä†sę‡á jd< —Ię6ś»őńzgóńşË»ţę W ¤qČ’Ł+—ź#ö•ńĚÇkÄŢ .‰bŞsťŹré…¤šáćÄç†bďmŽXúľ„Kß7ǵHß7Géű„űľnb§>&jĘصäuśŻĽú•ń1ÜV™÷•âÜăâµÇ‰Ou$ŐźqWčS/%1{\řxB!€§ÔK(hH©—TĐ–ćž»J©ĎĎŻv×ÜëÁ=küŇ2řĄUđKĎ‚_:~é$řĄÓŕ—ÖÁ/ťżŚ ~™Eđ+7żčˢ/ ˙lěˇŰŇ(/}ďö -+ZXukoűěÔťE?Z„ăćĹŰKýqíÄendstream endobj 99 0 obj << /Filter /FlateDecode /Length 739 >> stream xÚmUMoâ0ĽçWx•ÚĹvHU„dçCâ°mUŞŐ^!1ÝH ý÷ëń#xŮö?ŹźgěÁÜýx]OTÝmÍ$|äěÍśşs_™Iöss îîň®:L;<S›zś==±×ľ«Öf`÷Ů*_µÍđ`É«¶Úźk3˛ľ'ióŃ´ž‚}Řý»ů=©˝ŕ“íąŮM;áŕľ7ĂŢrľ›f¶ĆnjĚ-ůeúSÓµOLg~ĽŔ8÷ă ăâţČ)okŕ çA„8 ö$`I\čÎ×3`çAfŽă<ČZ]Â!‹„ę xNkÇyăąăĐđ"ś7Áż _Ąă“§Ěq âH`ňáö•‚núĄ¤kĚÂđRONH=CpB:# =Ń%8“88QA~ˇ!*ÉzĆśřĐäT?!~Ž> étw©8éÄy*ásŁ¤ĎŤ }nÔĚçFE>7*öąQ‰ĎŤR>7О˘ G]Ľ;~îó¤ŠŰ<©ň6OšßćI‹ŻyŇňkžtčó¤g>O:ňyұϓN|žôÜçI/|ž´ňyŇÚçIg>O:÷yŇ…Ď“.}ž2îó” ź§Lú> stream xÚťWmoŰ6ţž_a-*o¶J˝P/Ů4`Úöa€‡H¶h›,y"•4űő»;R¶śČŮĽ)ňŢďž» ›l'lňÓ sż–7ď?ĹÉ$÷ó$L&ËÍ$`Üçi2IYâa>Y–“;/jËŮ‚‡ÜűµóoCź16»_ţ Aŕçś[Ş_ÄW¶xĘ˝ŃűO ä$~Áý0š,ÂĚŹyfŮ~źe‘'g‹(ŠĽuł?tĆ}ť;fŔŕ5‡®F5µ˝Ô¦¦ŰăGěiő·śžőŢ­UËŮ@-s*ť??XoŚXUň ă¬FĽA^m„Á›ŔĘxpfűŹÎߏEO2ŚJŔ™ŐŚ„]F ĘJ®Ť= ü‰­Óę ‹bY:"±?TâÎî-!b#†sL)h" äâ!aîgIŇS=Ě8Ą×bX0VÖĄ­ęN[#bOč V®ű„ÁŁŮ*DöĆ˝˝°Śűq”śGÎşĚÁ0ŰřŮÖö(ZGrwQ—î‚Íńz­Ô¶€ ęa†‡ę Ă FÄQJFÇ^š]C”N„rJŢéV?"cÓľł7X4ňŻNTöóĐ6+±R•2HEÂçcyT ˇ3¶€[y¨ÄZîeŤL«ÎĄtłłŇŐEDŘęĹ1_ă×”´ jţ¦{šîz~˛őŠ |qTŞvW»f/¬ł~Pö¶mÓ\]Őő‚Fý,=bł•[W~/!śä|СčPĂ!=ÖfÁÚv€řB·=v#Šś‹m`C1%[đ©ž^Ăăsˇ®žÎ‚Űŕşjtě)vJ5˘Ĺč+ĆG[č;ýłŽ2 E0żż2ő­˙mĹ9eŤ§Šsžnzďż:tÓ^Bn±Xn@x]*Ř’šö˘‰Ďós˙>ŰR§á¨hçý“fÄĂi™˝4é)JżxÇ”R٬‡ Ď@ű˙\ÔMcDĺúٱŮI±F ě^‰x-$éů*ŹJ Z€p“b¶häĚűćç6Š ˙{űÍ[$űÜ»ü·čY‚śű9n§¸#g.áÇÇĺÍ?ť¶s´endstream endobj 101 0 obj << /Filter /FlateDecode /Length 1054 >> stream xÚÍW_ŹÔ6żOťxČ"âłŰIŕˇ*T­„hQ‘îNČ·ńe-˛É)Évé·ďŚí„d pęKű4ödfvţű·4Ş"ývAýe{qőšQA ĹU´˝ŹĄ$*ʨ" ľlËč:ŢîÍ&I%Źoh*ş~ŔK̰o7 ĎâŇ3vmÓŰ~转m<ÝéÚŢuz°Mĺ';콂®kĎęĐd“HĘĆ_KăÎôÎ~Ű”¦ŮŔađâ%űÓ}§Á·ö~s»ýăę58˙5ś4g„©‚uôć‹ZÄśŚR c¤ŇËę¦\1Č)©íu¦˛młb2•$SbłMiwzh;HŤ4¶Ž˛x×=âΔňq»ĎŤ¬®˝ĚiĂóŘŘjŹtŠÇŢe…ŻĹ@kWÜqü‡®˝ †1'‚˛ŕk”HEř2ÚërÝcBÓÔYĐw¶gLďYş ßvíááč\w±K:‹ťF`nĚ[~ Uĺ2ţŘ{ú"\ŻűîÉďż~:B¬Ď6 ‹oťg4xĄşĺB•]ÝPIAý-8čYO=:ý ía@ ÇfĺÂl<t÷7Z+GŃłr3ITž/zc<ÁQŔĂ»#p؉ßŇj(‡A‚ Q íĘ™¤˙¨żP ¡yfDR &e&ăśĐٲΡ™?Ë(áą/Ňi¬4: Y,Ăůs“§ń˛¬ţâ: UrÂá3h2ěĂĚő!~ }¸®mct‡g+t,Ǹ)ž§a˛EeŞEÁÝÁ*}ěź…†th]‡sXGĚw‹•/k‹LôéňńýPý—ýઔ/]¤DQ•Ł”d˛H§K!˛Ů%ăjí ÇžXm hżtŃv¬%îű7^|Ě6QŘŹ+ééEů´úÁŚĎ’úź #Ś“ĚÂĐÁ`fĹtÎť†‘KĎ1ĘĆ$/ÜpŠ"Ć@1ţíLbş÷Ć‘Ď~¬¦Y›ŢÖ¶y\v`čł×B§śćO'ë{Ăp¶čźLßëʬMŘ5» ăč|ź¦|G?Ü«…L^‰˛mĚĺh§¤Öú±Öúhú‰íűŻÖT˙ŇőńÇÎ1“PµR#ş6ŠśČŕGŃ ĺq`ĚĐb Ũa'†ôŹŰYn‘Î$Ťö•÷ÖB:\ŻgŁtţl;”;Ú"Ái„H ˝ ‘Î’±‘><ĹgÖˇȨ"˙94„rµŤ8`h€FŮxú5%xë1sSZGä,3*Ůyzq‹q•}‹§P6ĽcxtýéőG<ĺöBĆ=‡c !<ĆÂŮśV¤ÄH'˝ç~ĄüÍ•č¬ü <•˙±p rŠs}é1 ˙,˛– ÷"NĎáôwŕĽč…$€Ý„$‡Ĺëě§ ŤWŰ‹ Źendstream endobj 102 0 obj << /Filter /FlateDecode /Length 739 >> stream xÚ˝V]OŰ0}çWDUŃ ˘ĆľNBóI •išĐRĄ‚PÚ„4Z›T‰ËŘżźżę,kĂCyhj§÷ž{î‡OŤ­ÄÂÖ×#¬żŻ‚Łók۵<äąŕZÁłE0F”żąŔ."ŕYAdM%+B¶Yť pää1ř~~íŕ-/l a$ý¤ýge…,$ľxNO†Dnű% YěűlG¦]ŔcIŚO-aś’6¸lÁ0 ö"ś= kHlŮÄá ‚<Ç1r2UĚE˙eďy:tOhzîÄAŘ&­ÜYÎÂ%ńçŘÁLFěŕbwőqbÝĹv‰ě;h;Ř)]“¤ŘAw±V±6±=bgNkM±m±méŠ]…µFL Ű‘ýGĆRY‰Le4—1ł,gĘhżÉŇ*U¬ÂWµßŢ©…*C“ô%Ö9űŁg¶ŘQ°`§1ŢFĆx×ZŘŔ~ă^]a:$ÔĹh„=ł»ţ~ÜßÜtT÷¦F__ŢÜ™ňţ}×ů8±E+A7ÖĆk>ţ„_Hă×pµ^ň.P÷˘’dž·9wÓ÷Ő`!ZE)¨o›·_íă‰ožóB`Ő·a‹xň¨+ÁFŁ‘ł ›qpôv2˝endstream endobj 103 0 obj << /Filter /FlateDecode /Length 2246 >> stream xÚ­YmoŰFţž_!ř Őôľp—¤pÄW®E€¸8;/hŠ–ŮR¤Ž¤â4ýóť™]R¤Ľ¶‚¦_ÄĺîěĽ>;3K±ĹfÁ˙~ÁŽž˙şzq~&‹$H´Đ‹«»ç*‰XDL\$‹«őâ˝÷Ap¶üxőźqßůĄn‡"ţ´TĘËÚrÉ˝¬Î‹Ą/óŠ®/·Y_6µyoîĚłż·yV•·í„ÂîhZóú)¶ďĘzsŘ ňz˘‡P:“hĐ„xź áLa™ié˛zí`Ć“@H5Z•µ ‘‹U(-˛»}ťűHµđĄb‹Ł…Ďy(eÖŃľlá«#΂ŕ‡ú çM ´šyT‡&0mB?4í§˛˙â_Ý7Ű]GqxĽk»Ů‡„âáiß©€1}2 :Š'¬~¸rpňe¨ ¤ńÜŰŤŃă ‹„AT„ †îËXzŕ…P„ަ¨ Ŕ!˝kűlűň“a-łĘLî–ľ˝f·Ż,nq?ZA«űÎň¸*«ęTܬwŮvW˙‚ĄńÄ»ş·rťĚşĄň@ćË«ěĚ“3FżĚ,6ҵ0Q 57ň 3Ů•_ě,ńÁ@ŹX­ĄĘ†Aß–>Ű.«2k3+€/ŤřĘn«ÂrB`ôK0á3™#7M[ćä2ä3č×Ô…K˙,‡í}IŹ}łď^-}Ťµ'Ś<Ŕb[~¶S PĆ€ríI°Ă6đŁ8´15B‹ÁţżŽŕ8Ź~g)ŘTđÍ)Ú©É-\1?ÉÁÇÄńĘÁ/ B1"ź€FšĂđŔÄç:ň.đˇÍ#.–ɰ[„ :ĹŇ©…’HĆ$%b/d ąŤs¸7 D(ŕ$¨eŕW8 k˝ľ139¤}oÝn“śÉD<–sHŕ±k»ŢoÚuI(Ň^Yç$!JQđşk›ŰěđŮ—E1U‘„âŃ) Á®f†’qZ ĐŞ$(9+(řwôĐŤĂjP5 ǨŁĹČ×Ú·6oCÂŚ"›man žŃkŁ&÷*‡x_hČ~‰š;dĐBL"ɰés(–ęGĽ!B:ZL¤’Ĺž˛´łĽ6äá´0éP0 $WÇGbŞ—bĆťzMeé@¨ ŃD{Jž,×± âh¤9s¨:T']őHĄ—†ĎS¤ j…‘­$o,U2== *Ť†`!4 ć03ćĐ r!Ŕ Š’•€s8˘1u&§Pa'óL8•BÍáRşńÂx4Ź‹3 ˇĎŹ“Ëő€Łđ$Ąz.U2gÂoŚÍŢOýA^§xq±Ü¦ô×§aČÁ1?ä}‡ |‰GzËrýĘ$sWxD PŽ&ϲŘ%M•ŕN'ÎëĐ…#OjęčE޸еýJŘvňaʉ±AkkC¬šĄB(,p™˝H$ń ·*˛ÖŚ·EßP_µ†˛Jé˝-·ÔX&ËéˇűXďsJŰřŠĹ ×m l‰L‡éAą·ŻÍëÉlqÖ áÄX°źv>Ú2ö٨”¶7$čl{~|CŇ“–<ˇÁ6ŰęłĂ¦Á8žxĂLP±¦<ÓLýHÖ g˘±F÷kSĄB5»¶…ߦ…Ňj{ŰzÝ"ꔵ 'LÓ]A{ş6m±}ťeËg¶F‰ö.±c7BŘŔjhÖC)w÷ĹHÝůšŚĘgQśś…†fňţ–Šô0;u|k,‰•.<¬V…i*++ż¶»mKŰĘ9YfťË†é:DĚţîĚĐě=FÍp®Óˇ<¸NxE˛Ç—?Ú "ş~¸„ŔCȇ>8 WÍG¸‚Šă+ńeŇĹąg SXW‚Ě ßWx‘µČ‡ĺ‰Sń•śŠ|+ćµ™~(űűß.@łă˙wł HŰW=ş‡EŢĎťóxBÚÄ ?bbćÚ=&!¶ńŮÜöYiWł<ß›;%Ń’8KŽUéŁA{Ë-Ń0ĚDÝ~K]ŁŮ’ut…éY‰¦Z\|ąkŞŞÁ^ö#űˇčoÚl‹'ZڨޓM·äwjłaR<ŤqR†„;lęę7łĚŮx˝>żTÓĘňEpčIţ?0O)ď:ÍoËzŤ–çřÓ’;ş)­1[µćťŮwóĹČ̆ůâ@Ś›‘fÜŔ/ jCµ=v†UćoćńÉÜúhŚfăs79ĘřŽ‘…eU÷ żľ‚Ň zÇ„÷z%VgÍłŞĽKá˘x·°ÓęRěHďńV3ŐĽ¬éFCę>Ăě&Ußżűž#ó絪ҟßôeU—UéŻÓ˘Ţoo‹#Řái{e†UaU¬!礜̣4„sw^3«ř!ËĘ2Ϩ0đ|:j9Os‘ć2ÍC”ść*Íu :mIYś0´DĘrpµyň‹ç¶ľ4Dż?łfý·nł‡ÎZoös×Ó;;Ż‚Eŕűő)ôÎ…ŮšŐÁ* Ž˘4ŇźÔĐÓĄđóľKÉíüăiGs§±ýT6C—ůGřĆŹyĆPŇ6O şîŇkĐ(嫯Pç Wmü‡˘ÜPŮę»g¸lRę+)Ët”÷×)?Gż¬(ż®l+‹óg¦Ĺ=ű¦Ú4j멡GÜďĚQˇŹD3}Poşô†Ü÷ç˝w”˛¨1š§­oÂ\E «Ţ#_Šó·ikoäąQÉÉíŔd®µµ§ď& Ţu$'¬žgž§öĎú*;nşŐő€2nµ¦Óda´Yaw^ţóżo_Ŕ^ży›ňÂ×'`”ó÷ĺÇ4ĎżËɲĘ~6|ęOž¨ ‘ żlql?ě¨ŮŽ×W/ţ é endstream endobj 104 0 obj << /Filter /FlateDecode /Length 900 >> stream xÚmUMoŰ:ĽëW°‡éÁ5?$R. ¤d9ôMđđ®ŽÄä eC¶ů÷Źłk›m‘CŚŐpą;;†wź~>Î|żŽ3óEŠ_ń¸?O]ś5ß¶‡âî®Ýwç]Oßcěc]=~?§}÷OâľyhĆáô9%?ŚÝŰąŹ×¬Ź“B|Ćś‚>âţ)ţ;ëvÇw7{>o§aśIä> §·”óѲHř´ĺź8‡ýřU¨/RʬǾŮď0ñ_xů•ŮË0öÓ…ŚxµBiŃÝéňEżÝ.‰ÍŹďÇSÜ=Ś/űbąó_ińxšŢ‰áçbţcęă4ŚŻâţfiĺń|8ĽE°˛X­D_RÁ4ű÷í.ŠůGŢRžŢQhúVĚŞŰ÷ńxŘvqÚŽŻ±XJąËÍfUı˙kM;ŢňürÍ­S®lŇŹÖ‹jU,•N±2Ô@  "Ŕ–,Ŕű  đ őTË[<€5€ €¦¨¬Ő –€ę1Ő"Ă †á›×cvĂ÷GÂ@†mŻgÎ üKÖÄ §â| +T|5f©řÚŐŕlůĽxZÇ1¸YîëPß^ę ¦ĺľdbË}[Š×”_Q>kUbwń88ŇĘ×]´‚kĄÁÁ•|'ŕ%Çľ˘ËďjÖň{ g䏵”ÓrŚsqkŽé:n8źú7ĎxIuř†ŞěŻł˙˝Éţ÷eöżŻ˛˙˝Íţ÷.űß×Ů˙Af˙•ýtö0Ů˙ˇĚţ!ű?4Ů˙ŤÉł4ĺmFşĺt«ńĎŃíŮčÎÓ”^z­čĄŔ1Śńö öě˘?z Żď.ľ~lŠ˙P}éLendstream endobj 105 0 obj << /Filter /FlateDecode /Length 695 >> stream xÚmTMoâ0˝çWx•ÚĹ$ !Ů ‘8l[•jµWHL7IP‡ţűő¬V=MžßĚĽń s÷ëu;ŃU··őČŮ›=w—ľ´“ě÷îÝÝĺ]yil;<[[Ůj<=?±×ľ+·v`÷Ů&ß´őđŕČ›¶<^*;˛~&űQ·‚>ěţÝţť”MS§“ýĄ>u;áŕľ×ĂŃq~:fc_0F)l®»ö‰‰GÎąÖm•u f8GÓ«6•ę¶ęŻbŘŇ"!YU—ĂőŤžeă.ÉŰĎó`›M{č˘ĺ’MßÜáyč?IáC4}é+Ű×í»˙˘Ěťl/§ÓŃBăŃjĹ*{pÝěϻƲéOŢ(ďź'Ë$˝ ŻŞě*{>íJŰďÚ-9_±eQ¬"ŰVßÎ$÷)űĂČM—ĎńP:^9Ŕ ^`„މŘ ¤źbr š€Ś@ ‘{@(\,…RH¤Ëˇ&€ti  mś+3¤ÇÔ…Ď ,;F™$Đ‘€‘zF†F˝ĂiĚeDÎ(ó0śAş1a8§ÎyΠFĆĂp™ nĂą[Żw6Ż»ü·ëŻÎpµ@‡ )9şréń9b_iaĎ|ĽFě-ĐĐŕ’(¦:×ů(—nQHŞY^`nA|n(öŢćĄďK¸ô}s\‹ô}sÔ‘ľoA¸ďë&vqęcâ ¦Ś YK^ÇřĘ›!ˇ_Ăm•y_)Î=^ ^{śřTGRý÷w…ľ1őRłÇ…Ź'ÄxJ˝„‚†”zImiî9¸«”ęđřüj'pͽܳÁ/-_Zżô,řĄăŕ—N‚_: ~iüŇyđËČŕ—Yż2qó‹ľ,ú’đĎĆşíŚňŇ÷nťĐŞ˘5Q·ö¶ÍNÝ YôŁ58.]Ľ˝Ń»á‚ňendstream endobj 106 0 obj << /Filter /FlateDecode /Length 900 >> stream xÚmUMoŰ:ĽëW°‡éÁ5?$R. ¤d9ôMđđ®ŽÄä eC¶ů÷Źłk›m‘CŚŐpą;;†wź~>Î|żŽ3óEŠ_ń¸?O]ś5ß¶‡âî®Ýwç]Oßcěc]=~?§}÷OâľyhĆáô9%?ŚÝŰąŹ×¬Ź“B|Ćś‚>âţ)ţ;ëvÇw%gĎçáí4Ś3‰ä§áô–’>\ ‚‚6ý§ă°ż őEJ™€őŘ7űĆ8ó 1ż’{Ć~şđĎ`W(-úˇ;]ľč·Ű%=°ůńýxŠ»‡ńe_,—bţ+-OÓ;qü\ĚL}ś†ńUÜ˙I--=ž‡·B«•čăKŞć˙ľÝE1˙pĆ[ÎÓű! Mߊyuű>Ű.NŰń5K)WbąŮ¬Š8ö­iÇ[ž_®ąuĘ•MúŃzQ­ŠĄŇ)V†€Ú(TŘ€ŕxżŕޢ žjy‹°°!ŔĐÔ•µZÔŔ2ŕP="¦ZdÔ0\ĂG©R\ˇ·”).–2*ÎШa!„UĽÄ,†łÔŰHđ° `+jĐĂ.¸5Nα@čâ°čĐVK-ŕxź%ôÜ3š% A°YÓ€zˇÎšÔ>kP#¬ł¦ő™5m0WŁoš¦Ăľžj­®§Üý·ť.†ĐZˇŽT$X/©)n)ć#W—„o(ć“oŔRZŢ $K˘p4’ŽZ¶-bâ\­1¦Ü°Jä ćP"Gń‘XÔQ¬‚i/8şkÉ^€ÂZqŚ:ZsŚ˝š9”d š­Bů Ž)ßsLů-ď7˝ćxĎJ›ˇľŇ`ŻažÉ˝)fĄÉ$†µ’1™¸ dŃŠcŞCZCů<Ł7Ă3JĘgózĚnřţHȰíáĚYÉšäTśŻa…ŠďŻĆ,_»ś-ź—Oë87Ë}ęŰKÔ´Ü—LląoKńšň+Ęg­JĚâ.ľGZyóş‹VđŹc­48¸’ďĽäŘWtů]Í:P~`ŹáŚń±–rZŽq.nÍ1]Ç ÇŕS˙ć/©ßP•ýďuöż7Ů˙ľĚţ÷Uöż·Ů˙Ţe˙ű:ű?Čě˙ ˛˙Îţ&ű?”Ů˙!d˙‡&űż1y–¦ĽÍH·śn5ţąă)ş˝ÝyšŇ“Bď˝x#†1Ţž´Ăţ€]ôGoáőńĹ׏Mń?®Xęendstream endobj 107 0 obj << /Filter /FlateDecode /Length 494 >> stream xÚm“MoŁ0†ďü ď!Rz Ź|U‰ÄAĘaŰŞ‰V˝&ö$E 6˙~=HŐUAŹgŢż“ÉŻ÷˝ź«ú~üĚŮ´uo$ř›ßÇĆ›LD-ű t÷  @ŤŮö…˝›ZîˇcÓÍNětŮ=YńNËkŻ`T=­áRęo ŢæřôeCîźúňÚ•Úç(>”ÝŐŠć™ ˛źAćŠţ€iËZż°đ™sn[­6u…c´^0XaÁhî\je?ě„îĽ0bŞ”ÝprOYŮ÷Ĺű[ŰAµÓçÚKS|ŘdŰ™›óřäoF)ő…MZł©}ß4W@Ś{YĆśmG;˙ë±<śń®9Ü`‘;‡äKÖ Úć(ÁőĽ”óŚĄE‘y Őąˇât¤baĄbi<Îg®bĚĹw­ü:/Ť]×ĺvťYsäâ[äâ+ä„#Ď]íśôň‚â9ň’8D^osâyMěîÚGČ‚X o‰ä‚îBźÉŕ5Éŕ‰<řÇ»’Á˙Âň kŁ(Do9Örá,ÂqĽB?"tŽýEDqě)bbśW$ÄčYĚčM»>sb×gEějqŢ(ŚćĂ×poż$îÝ}IdoŚÝ·śn-p!J ÷ýmę«ÜĎ-ţřOĂÓ[áýL‡endstream endobj 108 0 obj << /Filter /FlateDecode /Length 740 >> stream xÚmUMoâ0ĽçWx•ÚĹvH U„dçCâ°ŰŞT«˝Bbş‘ ‰B8ô߯ß{ .Ű@ăçńóŚ=»/Ű™®Ú˝ť…Źś˝Ús{éK;Kîşŕî.kËËÉ6Ă/k+[Młç'öŇ·ĺÖě>Ýd›¦yÓ”ÇKe'Ö÷$cßëĆS`v˙f˙ĚĘSŻfűK}ęfĆúVGGůf–ąű\b¸ŕ·íĎuŰ<1ńČ9w…Ľ©ŇöÎÁ|Á擬CÝTý¨„íAW $«ęrGř]žÜIŔâíÇy°§Msh$aóW7yúÔ÷ĚźűĘöuóÎî? sŰK×-`ăθtJ!±'™cřŔ8őăŚ3?NaśâOśâ¶<Dg!Ŕ;IXô ôŔÍ0z)rĐĚ@« kĐpČBQ]^ŇZä 7ž!‡î /˝‰ü ňU ź<ĄČɉ#“ÜW şmĐ/%]cXß!őÔŔ ©gśÎČ€žhŚśIDś8QN~ACT/čsâ•QřŠřôQ¤ďRsŇ ç©…ĎŤ–>7:ôąŃ źůÜčŘçF+ź­}n4eE=zG~ćó¤óŰ<éâ6O†ßćÉŻy2ňkžLčód>O&ňy2±Ď“Q>OféódV>OFű<ăódRź'“ů<™ÜçÉ>O)÷yJ…ĎS*}žŇĹőÎđ—Źżtx›ŕ˝>zĺĄďÝ{O->tđÄŐŤ˝ľĆ]ŰÁ*üŕ3>ýcŔčąţ¤C§~endstream endobj 109 0 obj << /Filter /FlateDecode /Length 915 >> stream xÚĹWQo›0~ďŻ@YŇLB$ö0©]4iy­ŇlbÄIPD@şvÓöŰçł 4ŇjÚClî>ßů|>4i%iŇÇ Müp/7X“lŐ¶tKr—Ň4Ő0-i¨Y*ŇmÉ]Hłn˙JÁ:îúú,;ľI®Ôő’«ąű‰â(©6Ć%ŰwÂ%ŢîöIů([ţđŕ%ů€ÂfLřCÝ4 ¶^'Ü(^VĽ|/ ľ'^Äź Ě< §{ k)ńăhÁÇ[’­ăťE5d}„QR@”‚Č]*ߦň.Ř8ô—Ę?śĽŔŕšőĎĄOÜcĹ…Ý> ˘UK_°olžś«mđţo'.kőaˇ`yD¸Ľ0•ő7©›ÄÉCýTĹ]S›´śŚŔü|jřđš¸b§¸ý]Sŕźz÷>?l;Ó]ĽŰ‡üp&‹3/wätże«Âzrçf†ť|ýÁ -‡˘@WÔmˇ_±˛…çovư ·Á›#Í䨥RSA=§źzVpŞ'˙&Ń‚Ç4Ř2NŕG)›/`‰nlWŁźMď|(°sŚa`"<Ň_™©’h•­‹ť«É† 3ϡŚFő|4y*Oü–<7|hŁ1LNşšExX@„e†ÚgÎăiMŐéÄŮĎ6r8wřc(ŠÍŞSČ^(ôi¶™ĂtĂy]4o[śşVf`5Ä…–’o×ŃLĽôi[kDśźĽ?·°â%Ëk’‹WěI]kv"wd_Bű.n˙ކµwGÚŰÓ4âŔâQŻÉŰ4G‡°vJž¶}ä€Ďvö<>}ŰŞěy»]?ćŐµ|'‘ŤĹˇ>Ó’+áeU»¶µ’óĘŰq3Š>Ş-·CÓ#ЬĄY#]3 sŘşÎ.ÄćQb„ 6ä ¨\Ţžíyq3kčYVď#üĆm q…lĄÍŕ˝LkÂęłVĂgá=E¶ux?ą>CĄ¬…ĄŰ‰Ŕ÷µa×k_úyWSÜjlZÉó_ÁFáE'Čż”m]'RŇP-Ý@–NúhÔ:jÇ|Mp7ů¨/١žmś_ť”˛”(¬äżńĂć( ¬Jă9a Y|Ö nč—ŰŃ7ťŤU۰$ĹĐUc(. «äqí^ü•ÁWendstream endobj 110 0 obj << /Filter /FlateDecode /Length 1867 >> stream xÚŐYŰŽŰF}÷WóDÁV§ďއ,śőlddëMŔŃp,®%r"Rľäë·ŠÝĽ´ÔŇhf’ň2ěn««NUźŞćĐŮűťýí Ďo–ĎľúVŃ™#Ns=[ŢÎĄDH=3TĆÝly3űwö—ůBq•­ňöUôęű_ű…úÖ?ŰuáM±Ş«?ţĎ,Ű•yµ żM[nó¶Ţ˝ĽzŃËěPăJŔ_†âĎqş-ňj\^€Đ/yóeŰËüĚ_\˝cBVWÝĘ–ß}ő-X<ú@g îcÖ[©ąh*ę?6×Ď9±śrĂŠçĚú]#äú]Ó {ľ©wË÷ům±\×Ű»¦®öóÓ}uS„aSnń…ý&oËşjř 4"áQAÁK‘90 ·/7 ÂĽQ~U)#ťTgđ±çń9Ž›Ě:Đ‹—~±ŔĹT^ýňf&J–;b&©ă÷÷ř  ÓDĚpG5ÄăĺLB^`Ó™ˇĘS‰$ÇDŞ®PđI¨Eŕ<ÍčÜkMpc´e–j#śm§)á^ÔYüâ‘ůGÜ&{cn“·˝YâđgűÝíŢ8ÜëFĎyRpiDńśł˙'Ęüˇ(«4Ęę)(»'ŁĚ/E™ ĘťQ–jĆ$b ’”0É˝Av:ť˝ţśoď6°»Đ& ‡Jöˇ#´wdą.›9¬¸¬b đYôšp‚Öâó}Q»|SţVÜř…Lć,»Ţĺ㸠ö ®°dŰÚ?×ŰŔ:ÖY–mć(ĺ_©° ⨪«]ŃÜÁÄâż*۵Ý”ď¨Ĺ®¨ć ѢŁčcÄ)ĺ]»­w[0AŃ™/„Ě‚JTâŇÝ®ľÎŻËM٢íECUnĄ˝Şż‚|Ů‘jëbÝ˝ëuÝ…"ĐQëd‰eôuzŢ&bµ¶xí ˛ĆÇŕX±ŇD:ÓżÖ$ő:×˙ž#ұâ>6­wĹ#$˛ č7Ű&ĺłÄ{ý»Äţ–PÇz.pÇj8'ÚšC-:ňB‰ÁŤw”ó$ČDŤ7I-Riy;çŮË„Wp®4,NŹe‡Ş1GůKŰb…(®óŞl¶~ ­ńĂ÷ĺGüî¦×8ůâÇ>TFCŞ7í®ĽŢźH1.41@Áđ_Č”’%±TŤî+šP#5ň\$$1fŘĺż)CŃG›ˇ'Ačiö iqĐëŘ{čzŹÄ4Cęú&ö\@T¨…#ŔLŃžj"şDyo;뀄MŃvN`ľ @öj˘jŃ u”˙ůh˘ Ü+˙`é*ÂĆ@§ň Z 8I@Ý’!~źÖĐ?$\€Ě˛t(ɩڶ"™04âCĹ4a^%6 07PÓí}§ćŃ `ĎipŽľŻ>F†6I;ĆŔ˝?áČ`çËä ŁnH®®¨JhM>Εʰ«ľŢ„lVđYú˘Ř´'š/7*ü’$¤Ľ•Q ?$ěxxtň±WF>T5Ö€OU°«Ú|ń#ä®c,¶;C(?$á¤)+UɵzŚ%řźBÄvzë/ÎhX×aűÂ&ˇCT"®RÇm·ő°ßXf«0kű t>]t†G«î”ČMާňÜiâ*M‰FЉ8HŔRĘ,Őfë¶߳ĽőÎôŘR$ľ´÷DměZNEM»sÝ2ł±îÚu´úG 0ď>•}8BÝv6`č]vFÜ9ąęé¦ÔÉÁ‘»D(Üń©ÓˇŔfJÎ&BŻŇđ‘¶~€<n€†ĆÄŁ{H›t*‰äc,_5&QÄÜPR”¸¸ÔĄ‡rq6q੯ĺ#š·“5;Ĺš(9ü÷Ö'PŮř¬É›fżĹë>.vMx‚Ř$wáV§‰Ö‚3j ľ é\…y‚)üĹÔd=ŹqlNşíoŠ]Uď7›NŚÁN]éľ„4~ÉJŔQńŮ”bŐ˘›Łüf_‰_÷ůĆŰ:Ů€ŕ˙äăÎA…„m"”®’ŠX˙4·" fúH;»Ű’uK¦Ł·ŕüô–ďQä’®ȤÝö 6¬pă'Ř;_a0Öa™*tíý7‡:ŐÔ0ľjőŕM ˙~G€ě‚Ö[(ó§mpŹďp3Ńń]â_©»…jćďRÚ8»TÔěȱĐňd’Šă>1ľG(SnÓ—äz\Ż#ß Ů“ČoNą?Ć5ć"›Śž~GŇ'6ŁěĐ%‡c±˙EÉâdšJµnt!iśi|4Ř´ń9STT†˙ů^—Üń‚Ł ô ćß0ŃŻ—ĎţîúSÄendstream endobj 111 0 obj << /Filter /FlateDecode /Length 695 >> stream xÚmTMoâ0˝çWx•ÚĹ$ !Ů ‘8l[•jµWHL7IP‡ţűő¬V=MžßĚĽń s÷ëu;ŃU··őČŮ›=w—ľ´“ě÷îÝÝĺ]yil;<[[Ůj<=?±×ľ+·v`÷Ů&ß´őđŕČ›¶<^*;˛~&űQ·‚>ěţÝţť”MSÇ“ýĄ>u;áŕľ×ĂŃq~:fc_0F)l®»ö‰‰GÎąÖm•u f8GÓ«6•ę¶ęŻbŘŇ"!YU—ĂőŤžeă.ÉŰĎó`›M{č˘ĺ’MßÜáyč?IáC4}é+Ű×í»˙˘Ěťl/§ÓŃBăŃjĹ*{pÝěϻƲéOŢ(ďź'Ë$˝ ŻŞě*{>íJŰďÚ-9_±eQ¬"ŰVßÎ$÷)űĂČM—ĎńP:^9Ŕ ^`„މŘ ¤źbr š€Ś@ ‘{@(\,…RH¤Ëˇ&€ti  mś+3¤ÇÔ…Ď ,;F™$Đ‘€‘zF†F˝ĂiĚeDÎ(ó0śAş1a8§ÎyΠFĆĂp™ nĂą[Żw6Ż»ü·ëŻÎpµ@‡ )9şréń9b_iaĎ|ĽFě-ĐĐŕ’(¦:×ů(—nQHŞY^`nA|n(öŢćĄďK¸ô}s\‹ô}sÔ‘ľoA¸ďë&vqęcâ ¦Ś YK^ÇřĘ›!ˇ_Ăm•y_)Î=^ ^{śřTGRý÷w…ľ1őRłÇ…Ź'ÄxJ˝„‚†”zImiî9¸«”ęđřüj'pͽܳÁ/-_Zżô,řĄăŕ—N‚_: ~iüŇyđËČŕ—Yż2qó‹ľ,ú’đĎĆşíŚňŇ÷nťĐŞ˘5Q·ö¶ÍNÝ YôŁ58.]Ľ˝Ń‰ç‚čendstream endobj 112 0 obj << /Filter /FlateDecode /Length 3743 >> stream xÚĺ\[să¶~÷ŻĐäIžT îłÝΤŰn&O§mśéćö@[´Í¬néu6żľçŕ(Q^9ń¶/EBŕÁą~çŕŔdr;!“/Ď˙üëĺŮgŻY:I“T15ąĽ™PB.ÔD•Pxr9źü0˝ĽËĎgśóéj˝Úćĺć|ĆĚt˝*ýÝĺÚŢç ÷ý:[ą‹+{ߏÚćۢŞrűLLłňü§ËŻáíĽűvJ“45“c‰îĺżřq¤3N$†HXđ#‘$2OŚő­ ‚I´fő€ź#3°D+](_¸˛;E𠶎ƭ*‘´K+¦h3}é†Ň`ů:Qçőâżó2&ŐÜŕ¨I„FŃDíĆ˝őădgśHáő‹Yd"™p•ö9ÔĄG&X21ÜŤzyŹNt /cŠÖk$^4í S c 7il]°zŠ™ę­ŞËNĘďă !Ş/˙@6(9B6"Í®PTBě÷“ÉDôdpI‚ÎĄ-EL Ľ¬/6¬<Š»bCŃ Ŕ˘Ű·‘Ů«zĚüéHP§Q3RţâXŰlu ŕ,Q4RůÜčşMđsH–šĚ8E07ň«UíĹŐ6«Šµż±Ě«;ď4ÝŤ˘r^±8§Ó˛vĺý2÷Ď«»¬ŠčfŠ—édrÔÜóěM„łŕÓ%{śvîx ÓS=zŚŚĽ«zżF¦“‚3jRRĂ˙ś6é šG)˘F?Őë(UŚ!6Ů7Ő)Őooh RwÝĐśůE†9«QrÖĎBÎ$4ćt;ŚŠáâ0v„RĎËźŔçiĐôΨăřtHóökL‚‰7ńX˘O&ľŤ°B'†«§r /˘>9EľÖp–PîÓĂ]ľÍc`Ϧ܉s}˙ŕIc¤$žžtĄÍvüé“˙djĎ!ČÉýj Ýu@ÖG2”Âű/ÎgTéé&ňV0Aľ ¨sgP”類»Îěł›§#ťc[h‡"sĚA̦™Ůjcd ŢŁ3ä˙ăŚěľĘÁ#łm^(Q¬ŠŞČ|¦úpÎô4/n«»2ÁéŔg4Q)ęG ĄBw›Żň-`ąßjX¶ě2÷1/~$śŁYá *w3˙ĺľ3˛(Ýç}™ĎcŢ››DP <ˇZŽvćYA;1ƋǼ?íN5äűčç'F8?icľóý¨ß˘«#){¤óóĺźžHgĆ|y LocţqRJCőb˘q3ő˛Ę_ä×yYfŰ÷î+ş‚…»¬ÖÂ(ΑĆźi2‘­ľşŻbÁŐiúç:„o,¦ÔăVż÷uwŮ9äÜďÎ]UŇł>…-Uęúć]q{·đ˘ş^o·ů"«ňyŁ”iX8ΔwëűĹÜ%đ̉ÔŇćů©ô–ÝWcÓ·«5.ęaĺľ®WHľ`tzłŽöŚ„×5ęő6RJ$żgű=ěct'G„·Ç #@15ÍD¶Ś^Ż—ËńőĘÇČŇŃ»ľ‰pŮ<Éb9ĚbčŕÎ:lQĹ’ca^XE:˛EąH5žTăIeqR©ĐŕůHÝ=†ÚĚą0ĐĂ)x¨ ëWY§´« řµ.´+ÖŤQĆ"tOe±*«íý2_Uéé»l[dW‹ĽŚQ†Ń&š&Ŕ řÁE^Ĺ’*‘hŃ }0Ň~XtÖŇ´ËŚvgVč°%ąXŮ%ŔÝkĺ`ɵzą’¬ŻŞ¬XY‡ěĆmýĺfłx_¬nk$\y.{ ¸bdv ’xôčiýŞ ź×¦˛,€Áť®»5ż] ij ćĽôőAP?żŻ˛ł!łŮ®Ż˛«bH7/­U6;>ź˝yB€@ĽĆŮČşXĚ(éý¦§™©jdzśĚ€€»»·3€xţó}Y-›Eŕ8BáŻí^^4Ĺ/ĹrłČë!ŻÝä{w}_˘śěĄ•>łF|{߼Čęđ1€"Üü;{Ű8ţ0Ŕűđ| 8"Cű.~›˝µbYÝĆŁŤűŃN°ńŧ Ř ysBćĂť9xÝóĺXvúr Ăô>_ŽÎľ1čü×Í@ †bC…U„ů_vߏÎÓyoŕ· –ĘŹĘďŁsÜ#ÝSÔ¦5ŹwcëŰüďőG›1G éŃžĚýjו 6Ţ•©„¶™ć‡¸2ŠÔóe)ňeJC¸”öq)C\j?†˝› âŁđncú`ü[̉ХzŚX˘^LµýGö ,“F1č€äř ä>ȯ銍>Nl1PJ[ç‡`k¦ľNŠ×uˇĂ…ěvw÷önÜűuÁ˙ź1őp‘iź˘n{š “tš0°Řô19F÷P[;›¦ý~9Ł…fż7¶Qť#@ČŁ9Ŕ÷©ÜqÖߨ•m 2Ďća6đUČDő»2ÖR-q:ĆÜŘŹ?h MŘŕŹ/:&•čúÂrĽŰ"§˘Ů"ŻÁ ŽîĽ‡žóőąá-ĘőŇŹ[ůéżp÷'Ľ6Sfˇ3ęşá±k‡‹NYW803k*ŞŃE8®ĎÜ!·źnN°çß E8”«DË„łX#Ŕą-ŞŔѧ۔cs4POž(¸Ëűărµ×ńŰNgüI¶ËŹj‘űÝ«4ŚcNVKSOÚâ8Č‚aDóü…g7kĐ%…­Ż*ĄřżŕĚp/ ±mŔŕÔ<#.˘Y˝6ô#4ßĆ΄6âAłiń*ž´»~ime¸żkĎű$ő&3fÄşdź˘Ţş0nq0bĹá˛ĐĎYF·´eÔŃvˇ?’˛řŮß/Ď~9CI‘ …•˘ŔK!9Ő¤“ëĺŮ?‘É~Ťl©™<ءKOÔ»ZLľ9ű—?˘F6ipĄ&Ó„0}ćÇú(nĘ6ńR`ĘNŁ…ü9j!Ţ›ZŘő4‘C…؇j Ó.BäOŽŠŁă…1od ˘*ŇŽ;čŽ^Äwˇ%x8…ń„‹CťěiÂÚ|ŁŠi[ăšMŞ)đŚń6Ä»Eű¨šÂ &ĺ!=Ą#8u\mn`UAEdŔö¸8ÉšzÇËRóa,VGů©Ť „˝m÷áŕN†!ŞżřѬłzëV‡7±vłŠÄńn+ŃQ‘‹ö51Ą1‰hgřłSö“ńĚ1ďŘ*nllNLŰ(íeߞτ"Óż¸ź˝Â.1^»–Ýä´GůšWqňv·üeHtčF¤L4Ť4ł€żöٲRÓMćú‰l[<°ŤFđů·ü]±XřA.µ‡»ßdŰŐ<[`rŻĹ”¦)sŤP3)?8Ăogá&Gég±íů0ËuťŃomNoaGKCA„/Š&¦Íd_ˇ âg3ë‘Á˛]­b?JŁ[yŤRt¶;GćÚ^K˘"„8ÇŇŢŚ,f™&Ń­¬-“™˝kęr§÷F f«ôîđK(&0jN“r=Ď=ßvo,Űl¶ëě5ů®iťěÎ*±–µlť1"-L$rĎî—IJé!ö?|÷á {¬=39Д̅=™µĎÓŢŽ5;ÓÓÍSË– ·€ś=•aăçČ4ŚĄFśÔÚbHŁsČę4ôj”Ą©§9â2fľďç)‚ÄĆhŰÍꋪ˙A?Ź;Ý)šXľÜ,Š›÷î«ŰOŐtqŽmŇ•ű‚-¶î9>üěuŘâ şĂö‹ŘE^ş»ČôşEžŘę:ŢŹW×qęť­íŞëaá[……oßlĘI]î,…Ya˰¸¶łăĂyVe‰5ç p #·ňpPćnúnîţŢDŕ˛ř-ših“Ókˇü#nb˘=—ú§3SŃŕ×4h4: |÷őÚv®Í(PWóş™m룝Ý~Kűŕ&»˙µXöÜŤxřĄë12ÜVi őM,üâj;ĽY×ýµNBXfÂ{ľĚ–K9/Ęj[\Ý{¦Â‡˘şsĎ6Ů6[ćzJ÷„»Űnéđ] đ´´0Ę•9ń8…Ô}ç»÷­jŘ}©¦ďÎ%‚(ŰĹ[+p딾Ź'D†-†0ă­í#´ę3wwę YCH¸×ůŹ@÷ *ońÓ÷C%HP©ŚźB‘‰iű©Ţ  ă6|Ď›ÚčúI §cľÜ˛bOŢ 9C3Qt Y›NţŲ8cÔQNMzL‹đ§M>7ĺóhăoă{٦\ă!Úžš‚BJ« @WhÄč5-8ą-ls}Ĺ ß7®â˝!˘]úݧéăR=Ő!¬Ś ¸A%öĹÄ9ô$F˘O`$ú@Üśä"´g'×€2ŠąMľ*[~Ă•đ§ŐđžSZ¸…˙Ž­‡¶Ý»†ö Ś´^>í±6üŲ(˝ňĂÝĚßÜBŔ±[öL¶˝yI˙|< ÎM0!Yá˙ů†uSvI&ó÷Ëł˙ ’B2endstream endobj 113 0 obj << /Filter /FlateDecode /Length 696 >> stream xÚmTËnâ@Ľű+f‘’a؆!ÍŘXâ°I˘Ő^Á˛–°ŤŚ9äďwŞ3ĘÔ.WwWwA?üzßNtŐííD=söaĎÝĄ/í$ű˝;EyW^ŰŻÖV¶ßž_Ř{ß•[;°Çl“oÚzxräM[/•Y?“ŚýŞŰ@Aöři˙Nʦ©÷‚Oö—ú8Ôí„ýYGÇú™ŔĘîPFil®»ö…‰gÎąÖm•u &9GÓ«6őę¶ęŻ’Ř#!YU—Ăő‰ľËĆ­ÉŰďó`›M{č˘ĺ’M?ÜËóĐ“ʧhúÖW¶ŻŰ/öx§Í˝Ű^N§Ł…ĆŁŐŠUöŕJşĽî˦?Źy#}~ź,“ô,Ľ˛˛«ěů´+mżkżl´ä|Ĺ–E±Šl[Ý˝“ܧě#7u\>Ç—ŇńĘńš# PMÄH Eř“XĐdjÜ @áb)<:@"].5¤KCŐPh«Ŕŕ\!=¦.|a1Ř1şČ$ŽŚÔ304şčENc.#ÂpF‡á ŇŤ Ă9uČĂp52†Ë$uÎm}\ďl>®»ü·ëŻÎpµ@‡ )9şréń9b_iaĎ|ĽFě-ĐĐŕ’(¦:×ů(—¶($Ő,/0· >7{osÄŇ÷%\úľ9Ö"}ßu¤ď[îűş‰]śú8¨)cCÖ’×qľňfHčWĆÇp[eŢWŠsŹ×'>Ő‘TĆý®Đ7¦^Jbö¸đ1đ„8BO©—PĐR/© -Í=»J©ĎĎŻv×ÜëÁžµ~iüŇ*řĄgÁ/żtüŇiđKëŕ—Î_FżĚ"ř•‰›_ôˢ_ţٸD·«Q^úŢ:Wt&p ęÖŢ.Ú©;!‹>t Çó‹§·"úŞ#…®endstream endobj 114 0 obj << /Filter /FlateDecode /Length 741 >> stream xÚmUMoâ0ĽçWx•ÚĹvHB˛ó!qŘmUŞŐ^!1ÝH ¤íż_Ź_‚—m ńóřyĆĚÝ·—ÍDUíÎLÂGÎ^Íą˝tĄ™¤ß·§ŕî.kËËŃ4ýc*SŤłç'öҵĺĆôě>]gë¦î,yÝ”‡KeFÖ×$mŢëĆS°»3ż&ĺq÷GđÉîRúş™pßęţ`I_Î3[d·Ećý4Ýąn›'&9ç¶7UÚaăL)l:ŠŰ×MŐ zŘę!YU—ý0rßĺŃžo>ν9®›},—lúj'Ď}÷á4>Óç®2]ÝĽłű[ivjs9ť2V+V™˝íhý˙Ř ›~éńĘyű8&ÝX®˛­Ěů´-M·mŢM°ä|Ĺ–E± LSý7—ĐŠÝ~¤&–Ęçř U´ –2´XĆ(p‹m“ˇ¦ÂÜÂÂâ ËXXś(W°8X&LR4â=z¨Ĺu«kTĚGEĺďm7hçáË8KÉc`Iu(ŕ!a <#śG´Ž »>ĂÎn-tJ!]O2Çř`śúńăĚŹSŚóř#§¸­'śâ,<Ř“L€%qˇO8\Ď€ť™:Žó 3ht ‡,Ş+ŕ9­uçŤgŽCwĂ‹pŢD˙‚|ŽOžRÇɉ#ɇŰW şmč—’®1NĂwH=8!ő Á éŚ4ôDCp&q"p˘śüBCT/ôŚ9ńˇ!ɨ~Bü }ŇéîRq҉óTÂçFIźúܨ™ĎŤŠ|nTěsŁźĄ|neEAŹĽ;~ćó¤ňŰ<©â6OšßćI‹ĎyŇňsžtčó¤g>O:ňyұϓN|žôÜçI/|ž´ňyŇÚçI§>O:óyҹϓ.|žRîó” ź§Tú<Ąłëťą_ľűĄămÂKz}öĘK×ŮŃ=·îˇĂW7ćú"źÚVąŹ{ĘÇ˙Śž‹ŕ/@ĚŞXendstream endobj 115 0 obj << /Filter /FlateDecode /Length 1487 >> stream xÚÍW_sŰ6 ď§đőINc–˙DŠŮ´».·í¶k{Ű-ą%Mó [L˘ž-ű$ąi÷é”,9Š“öi‰ @˘“Ű ťüö‚†ßźĎ^Ľţ•‹‰!Fq59»™0J‰j˘©"Ś›ÉY>ąŠ6Óëł?€Ńô ˇ° ž!·›éŚ'‘-óéLR­KüĺQsgýĆçiGYUdóĄőGëR”S®ŁĆNYTMg,ÂEÝ„¦Ĺ’p®Ű;żŽĹăΨű»b1Łîü=« ‰Żž{sĂIQ×EyK¦3‹čźi"Ű“ĽrB÷žĘüO]¬6ËŔPeeľ^ˇ%“ÓśÄÔŔ‚ÇŢ:fP>ť e˘ű˘ą[oOTÔ,ě öř}ëâ_;RŕĄi˝, Ŕ d{ž‚2ˇ#ë^ÜÍ“–5ĂčáÍ·¶´U!q”‹ˇ3ČîYXŮă®!Üąő.ÔŁCNí,ŞF I”]d· Ź©Đ’0š 1ýc˛† a–qĂ{ #… ăŠyËqkS­çŮĽX6…5PSÂkďßśŚXČ(QIܲśµšçŢ˙-ByëďÍŞpú‘ ůĹćžhÖţ”yҡŤ‹řФ´Ýč™˝X—u“•čîر„’„©€Žň¦ťŽ$OY/%¸NŔޱ7F¤éq2˘Š­şŚ‰GTHb¶ŻˇŹ¤"4Ń; +‹é%Ddë¦XeÍşŞ=íP„ĽV›mcs§¬«]Ż•}­3ŽC¦p …+dĘGÎčžĐ^˝0ŞłŐç9\ěÓ Ď/÷ ćđ L¸©žěvÍ$!IŇ%ţ‡ń´JvO# ®÷j»˛!ôY‰Ą‡J©jKéńaH¨!JcE‚M®ţŹ\ŽaÂ`ÉL ÎdKżű `ÔL‰Ćw'„—:7`HĘ3<ÄŮĹÄpčlťÇcč·„O íŕ;OĂ+- ˝E ˘T[1Ô °k¬˝x˙güg+ř"`óăFµUZG+ MÝÍąß(jĎQ®żq»ÍŞPĆ­ Lů6ű¶ĐÓçgôµ†‘D Fˇěv¬u ®ÚŰ‘ŠCíĆöTĹţŞô¨"CbŃnţ"ţ­ŠÄh¸ĚŽa‘•;` iÚč%D3=ĚX‡ńvąDܤŚ~żń™Sěçß"«mHÎŞ]łL!¨üHcŠ ąČmĺ:ĚI7»áoßúťd‚ĂĄvÍ:xUŹ»múĂŘXĄ BéC“”$ZwÁ›ńL1C „”°0„A­uÜ?Mg1ŹŁ÷©ôĂ"h Î˔흫ÁůEZÝf«U†€˝?FLıDKčlÖP˝Ňz»Bˇ ”yBŕCĘŹ.^U۲¸ Üôý"—©@‘r]­ž+˛H7>đUgŮń%Jµ’ĐfTY:„ôŠ]{MÂB5#ÁhEĚ3®ţđ]Wa¤QâŔÝĂ x›ĺ<ŤD˙4e$> ý&EoÎgoы׸ƿSôÎďůóÓľ§Ź…'mĺ˙«}]öË·Ţ]Á7H«Ň]űŮú—˝:wÔ鞨c°2x•U_QpóH`ÄVnNíáţ®(‰WŔŕÓ×-ţÚÂ=Ń;›®Âł—Č ŞĽÇéł/ˇ·Á¬#ă˝OQJ8 ŢA)¸Ă$ß"!9USµ;I4wďy¬¤Ŕ…h,•ç®—Ą ‡ş+çOÁÖU}ďżtO˝Ô*-aŞ wIůŚrăµňT@e'Ą“ó›7Č1¨1.ţ?n®ę4e×Wź®wĚPéłjÉËżńĂŢíá,á„Ă…őÉË'·Ů{܇•m™‰ˇ0ÄřŤMbČť^3řĺěĹfÇoendstream endobj 116 0 obj << /Filter /FlateDecode /Length 652 >> stream xÚÍV=oŰ0Ýó+ˇťČ˛H‰ŠU€4:dňŕŹdmF&"KEu)úßË#é%jl'NŃĹ<šw÷Ţ)NćÎ÷‹ŔŽ_‡ý[8‰źÄ8v†Ź ?Śbç:}„g¸p¦ť/ÝÁ¤SZŐ«ű€Bý îĂđ‡ĘŇCČOix®/$8[Ďţ­ĘşĂQqř@Ü=Č(6Î RĎś-ČHĐŃ´˘=L»=Ô`˝Âi,čř÷‰ “WÝ›dÖüIPÁÖşč>DÜy’ŻXE‹CÍZPÔ‡0HqŔ5C4cĹ<Íů j$Ľ‰đž,eš{+&—%,-¨›——®7+ëbQQ5Ŕ˙ĘônčÍ!܆3>?N؆3:;„ÔĽň‹:ĎÁ„Đ AÔ‹H»ŻWĎ”ěfČćŞěiP©\ËZ/E–ĂĽbmL®ö™Ě—lţ¤Ęw>©äea‹ßTľcřI°ŞÎĺßJ´‰íĘ%łíe¤ńu ŠY`•Ňf*Ka¦uĹu/3SĆDWaětĎć:[%E˝b…öü3<ťĺĚ=@pËĎŢ!c}¬.3tąh݇fôďîFtńu ¦xuîíö¦vä….2´ÝFyK$„>˘Ło´v™â#Úł'Sü6™Ćď)>A¦GŃŘ@€8BÁZśVÁă—›«gV.ţż”;>ŁrŻ ńŁŢ)Ýđ4é†˙^şá‡I÷őÝ{«ćÂÔÜćéž˝d|đŞ#:Z/óÔ´k¸_—ë:×_›]·ô>m4ńŮőŚąéôŔsďQîqŻĚg` HöŚ´¤žěc?Á8„ôۇs˙V˝Ť÷^ÍI¨ŽKěôÂŘ'd`ĐPĐů6ĽřPËü„endstream endobj 117 0 obj << /Filter /FlateDecode /Length 193 >> stream xÚmÎ=‚@ŕ!$ŻáĽ čňS $Љ&ZY+µ´Đh˛…‘ĺfx“=%-l,ľf&™LŹCö9áQŔQÂŃ„ť)LLčs›ý‰¦‰ ‡ ‰…‰IKľ^nGÓŐŚ9oöwTä ”€Ý×pź< ŃAZ-¤Ý@:ŇÔh˝M¦,ĂŃ™ňTYő(űÖPĂ zăőG÷ăßŘ IaévíÁU.R8Uk®čĎÍ ZÓ˘ Bendstream endobj 118 0 obj << /Filter /FlateDecode /Length 187 >> stream xÚ…Í1 Â@ĐR,Lá^@Üą€nڦ˘‚)­,ÄJ-m5âĹâMö)Sq79€3ŻřĚ?ŠĂ<ć~ČQÂq̇.ě6µźý‰ŇŚô†Ł€ôžIgKľ]ďGŇéjĘ!éoCv”Í^a JH˸ěçř;%ü˘‡ŽB·‘Xś[O”ë ÔŽgUđ[ĄkM•4FF~ŚúęŐxçĘĎ•€ÓěBTđ hžŃš~; 9őendstream endobj 119 0 obj << /Filter /FlateDecode /Length 172 >> stream xÚ}Ě1 Â@…á‹ŔćbćşŮ…č ‚#BĐĘB¬ÔRPQH!šŁĺ(9‚eŠÝٵľęđ”(E!Ť¨/I )ŇtxA©M )»eÂ8E±!©Q,LF‘.év˝QÄ«I m%…;Lżđ>?9›:Ŕ^ÖÓj¬šµśŠµť7óś’ůNÁ‚˙÷Ö=¨»Öj •‘Av†G ąĘç)®ń ®E‡endstream endobj 120 0 obj << /Filter /FlateDecode /Length 266 >> stream xÚUĎAJÄ0ŕ?dQČÂ^`0ą€v:B[ˇLaÁ.]ąWęR¨˘ĐU'GËQ2x€‹É˘t|MUĆŐG^Â˙żdůéyŞć*W'©Ęçę,WO©xŮ‚†t,¦›Ç±ŞEr§˛…H®h,’úZ˝ż}<‹dusˇR‘¬Ő==z­Ĺ€Č!ň|ŻeŹŁ2ŽL»Äń˛ä[+1“-˙2R•c;“–íë¶2l ›IÓTšőAp©ÝfŇvŕî@tc[Ą§Ö čŮ˙ư`ć)ôĎaTzÄCY?›ôŁ´‰/C ÷EĺîPÚĚ5ˇ„Ű&„së~´ˇ„oŤ eźôs*ÁP%Äe-nĹ7ă7x`endstream endobj 121 0 obj << /Filter /FlateDecode /Length 225 >> stream xÚUϱjÂPŕ?ÜáÂâ 9/Pc0$Bjˇ;u(ťÚŽ…V2HĽŕ‹ĺQî#dtíąÉ`]ľáżç˙áĆÉ8ÉxÂ)?DĎxšňgDżGNxšő/ß4/)|ĺ8˘đYb Ëo7»/ çëKşä7éĽSądĎâ蓺řů@7=ćĘbTŞEV´žÓŠUŃ?âI4ť›öŕ´őMÔĐâÚç;žŘ@ę˝AŻęmQŤSuj#Síęő}7µ÷ÝČ~Ô9ěĚÜ`^ą©ŔBË× č©¤ú’tUžendstream endobj 122 0 obj << /Filter /FlateDecode /Length 182 >> stream xÚUÍ1 Â0ŕ_:˙`/PěMC”v(j3:9“: U:ÍŃz”ˇŁIÄ!Ë7Ľď‰é8âQL#NN"¦#Ç ˇĂDňkgĚ%˛- l©cdrE·ëý„,_ω#+h§‡ö( ňŻż ß0¬R‚GéC:k3•dŤ¦V™Ş4PÖ`  {@ű1Ľ˙€ˇgy9x–Ρoi|KăZ”Cf1Ź.$nđ ń˙> stream xÚ=ͱjÂ`ŕ2î’7hî čźäÇ6] fěÔˇtŇŽ…*:H|±é(V;Qű¬›X¶’¤\FjÓŰeý%E)ćM“TĚ‚k1ĺRvűO1Ĺjޱ™ľÇ}H9S Ü ÁąB†4řĹ7Z4^ë7^óŻć¬üđ;r<×˙ťŽĚȇ0Č)¤ ĘčŹz§»!ËB–e,; eáŁ__ß=FĘĽ”Wą|/Hdendstream endobj 124 0 obj << /Filter /FlateDecode /Length 178 >> stream xÚ]Ě1 Â@Đ )Óě„Ět“MBÄ…Á-­,ÄJ-+łGËQr„”Bt ńóŞ˙á«|Ť(˘śú1%Š2EűϨR.#Ę’ď˛;baP®IĄ(ç\Ł4 ş^n”ĹrJ1Ę’61E[4%o!¨Aü™u4§x@ŐuŚ/řňŘÓńYë¬qDówßűk;Ôp×pŇĐjh´WOü: ¬đm 83¸Â7ġBendstream endobj 125 0 obj << /Filter /FlateDecode /Length 216 >> stream xÚ5É1JÄ@†áo"đ;ÉMB˘™……uS,he!Vj)¬˘°•›xĄ9ĘaĘ)Bp’ŤÍSĽoÓ\^]sÉ-_TÜ´\·üZŃŐëK®őůĽĽÓ¶Łâ‘ë5w1SŃíůëóűŤŠíý WTěř©âň™ş##„M~!ÝJő‰Ë&Ň ­zĺt9FěaĆôąőąu‘Ţť"řYa€áĚ b&ÄőĎ9ă1¬ÄM¤‘J·°‘^-}´đ‰?źʰ9:o,”U ŰŽč;˘VFendstream endobj 126 0 obj << /Filter /FlateDecode /Length 205 >> stream xÚUÍ1jĂ@Đ/¶L!]ŔXsxµ^¶¬"W.B*'ĄÁ v+éh:ĘaKĆxl%4ţ†oÝlÎ9üdxaŘüa苬•2gëĆËţ@ËšôŽ­%˝‘štý§ďó'éĺë3Ň+~3śżS˝b$PT§h»$&wĘ;.CŐą Yw¬ţĐ ˇA ß †ż ¸HD†‘)Ô€ TřC‰8Ŕ!ö#Ç˙ř_˘^P=”WťĽÉDC)´ö­kÚŇ V˛Ašendstream endobj 127 0 obj << /Filter /FlateDecode /Length 212 >> stream xÚMÎ?ŠÂ@đoH1đš\@Č»€NbjŁŕş°)´˛+µ´P´ $`‘No°g‰7ń)S„dgFA›ď/ę÷˘}q7`ťÂo:PhŠ>‡Ăggł§iLjÉaDęG—IĹż|:žw¤¦ó/HÍx°ż¦xĆ@@6/ďcGÇÄP‰Âŕ”¨!×R^!Ş'“ĚâTH3=™âŤ,ŃšĹć×R;÷â…gąX˛Kž%Hs$h%Ƣuőg·+> stream xÚMĎżŠÂ@Çń‘-¦Ů70óŢ&a…Ă€Ŕ‚VWŐťĄ…rWšGËŁlgé–[„č¬QsŧůMóťľyK)¦!őęúJpŹ©á1¦Á°ą|îpśŁţ Ô žóŚ:_Đ÷ág‹zĽśP‚zJë„â ćS‚ ş¶ŕÄŽ˙Ô¬jußkÉŔzçäEŞ’ĄňĚ «¬°Q)Ü]ŃČx’îÄŽ/ĘŐ¬eQPú»¬xĎŃžc=ţrÔ_ÇÁ»°0’%tŁ˙Ŕŕ,ÇŢ!_‰endstream endobj 129 0 obj << /Filter /FlateDecode /Length 104 >> stream xÚ31Ö3µT0P04W0#S#…C®B. ‚‘)T&9—ËÉ“K?\Á’Kß(ĚĄďé«PRTšĘĄďŕ¬`ČĄď˘m¨`Ëĺé˘`ÇP„˙ţ7Ô3`‡v(P†ËŐ“+ L5*endstream endobj 130 0 obj << /Filter /FlateDecode /Length 119 >> stream xÚ31Ö3µT0P02Q02W06U05RH1ä*ä24PA#S¨Tr.—“'—~¸‚ˇ—ľPśKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEźÁľÔ¨o€B¬Â@ř €a—«'W $o&|endstream endobj 131 0 obj << /Filter /FlateDecode /Length 218 >> stream xÚeαJA ŕ˙Řb Í>Âä Ü]vĎĂjá<Á-­,ÄJ--mo|±é|Ťy§ĽbśáÄC®ČB†ţdyĆ-źj /;~ěč…ú•ć¶Ä2xx¦őDÍ-÷+j.µKÍtĹoŻďOÔ¬ŻĎYó†ď:nďiÚ0ŮýĂŞńs ü’#źVľśH€ř…|ŻÄ›śŻFoý;ŹsŠ+lqÎ…¤ŕ÷Ƕ÷d,˛6Ş‚ÉşY'=alp µľŚ+ů–‰Ęč%ĐĹD7ôťpëendstream endobj 132 0 obj << /Filter /FlateDecode /Length 196 >> stream xÚmŽ= Â@…'X¦Ů#ěśŔMXŁXüSZY•ZZ(ÚęmŹ’#X¦Śo[±Řf–÷ćůa5•B&x#/~,§’Żě+ĚEÓÇńÂł†ÝN|Ĺn…-»f-÷ŰăĚn¶™KÉn!űRŠ7 !ŇH”ë›ČꇨÖ+UĘ4jôdcŢ‘‰ćM¦µ-ĺ­Ť@l_ Ϥô"j‰~Đ' f& Ę”Ö74.WHÁe °Ę4ů˝’©A— oů \s`¸endstream endobj 133 0 obj << /Filter /FlateDecode /Length 181 >> stream xÚuα Â0ŕ+ ·ôzO`RL'ˇV0 “8iGE7±}4ĄŹĐ±C1Ţ…:”Źün83ťd3Ňdäf”ĄtJń‚F“Žňq> stream xÚmαNĂ0ŕ‹2XşĹŹŕ{H¬¦.X*E"L0"‚5)oÖG1o`‰Ĺ©ąsaAőđ ľ˙t7;ž/¨%KGvAÝ)ÍNčÁâ v=˙¶4ďG÷O¸°YS×csÉ˙Ř WôöúţÍňúś,6+şµÔŢá°"ŕ§<€ .L)'¨rfë˘Îů;‰î“őÚGpĺźaF¨Ů]1Píő˘.š­ä;Á´a?2ČyWL ÇąGő•9^ÖţÄjoÉó.GĄň¤8Śť¸2T‰Já‘=ă"b<čXL’á-Ϋ(UM+®eĘýw1•ëŇEK[ĽđŮzŤAendstream endobj 135 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚ}Î1 Â@Đ‹Ŕ4Á9IH,¬„Á-­,ÄJ--mÝMođ¦L2ÎL‚ö±vY~ Gc 0äG8 q bÉD9ěŽđׇŕĎy ľYŕĺ|=€ź,§Č9Ĺ żÜ‚Iѱ…Ă‹Ę_­ęŞ˝Ćâź^cŢÖfě“8y/âű>Éß_[;bĄ–â Pső®fm]vŇ¨íş”ľV˝i».Ąo­VÚ·ĄĄÜ[e¤ÚŹ2‡™Ľ ąt6endstream endobj 136 0 obj << /Filter /FlateDecode /Length 156 >> stream xÚ31Ö3µT0P0bcKS#…C®B.cC ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. ě ň€ř=2>çgŔ˙˙g`†ŕńzŃp=×aÁ˙€ř&fá„?Ŕqý˙˙ţ˙˙˙†A|.WO®@.ďűJĎendstream endobj 137 0 obj << /Filter /FlateDecode /Length 205 >> stream xڍб‚0ŕ# $·řÜ hA%1!ALd0ŃÉÁ8©ŁFWáŃxÁ‘ÁXâ`›|Ă]ÚűŰ‘5°]2hH}sB–Kö&žŃr¸jиjíOč‡(6d9(\G.éząQř«™(Úšdě0 Ô„éĄ9F˙"­şZ ,EĘÔIIQă«Úx Đ%Şą˙ŕUóě˘4d]Ô†G­ mQţMSĐĎáež[ň©p )yX$ł>ďń“Aă&ŕ<Ä5ľNÇXęendstream endobj 138 0 obj << /Filter /FlateDecode /Length 230 >> stream xÚ}ͱJ1ŕ9®X&Źyw×Ýl ś'¸…pVbĄ–ŠvbÖ7[ńEâ(6W77V8±0/™É̤möf‡RÉľíö@fµÜÔ|Ďmcq…×w<︼¶áňÔ˛\vgňřđtËĺ|y,/䲖ꊻ…PLdK?˙ł“ět4ýg1:üVuČ&*Ţ Ëw×#ďú¦şŢ%č{"ßo¬×OÖpş‚($ŹBňÁJ(D|p¤0hÚůŤĘđŽ®řšÍs^>Űą3k¸•ý ÝđcÔ¤RýP5ż˛¸Źy>éřś·ZsYendstream endobj 139 0 obj << /Filter /FlateDecode /Length 154 >> stream xÚuɱ 1 €áŠĹG0O`Ż\opÎě čä Nęč čjűh÷(÷ŽblÂ-ň…?ńĺ´šaUź—Ă“+”>·$?Ž¨Ř–ě*_Á†5ŢoŹ3ŘzłŔÜ î šHť1DŻ>‘1Cf$t cˇUIa.…Č<5ľĚGa ĽűD"JLKLü“`` ?:•RŽendstream endobj 140 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚuĘÁ ‚@`Ĺ0Áy‚Vq :f‡ N˘SuěPÔYŁÓ7|;µÁâ4kuhľýçgd4GôOĆ q¤ě^Í·=@’Xˇ” fÜ‚Čćx>]ö ’ĹC)®C 6Ąčhż[®¦Š —¨ˇ’}PíOmĺwjŘŠně•ÖîŘÎÖ¬¶ŐGe·żrŰşµInůOsá•&yĹ?Í…_ä[ßć*o©&ť+jIÓÓhň»‡iKx—‚»endstream endobj 141 0 obj << /Filter /FlateDecode /Length 180 >> stream xÚmĘ1 Â@Đ )ÓxçnBVÁJÜBĐĘB¬ÔŇBŃÖÍŃ"^doŕ–)BĆŮŐBÁb˙FĺáSĚřTŽů÷ś@ůžúęĂî…ąF•śó R/đrľ@Ë)ňZâ†?· Kڍ6•éA–}’c‰EŹî-Ű olĽ}´Á:X}±“·"jţł&x±űoÂvÁV$öGCÖë* šŹ~‡™†ĽęőfŹendstream endobj 142 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚmŽ1jĂ@Eżp!fʰs‚¬ÄZ1®d˘"W.B*'e »Ťöh{AĄ ˇÉ(&E óŕ˙aříŞ-ĽŃ]{öŹü^Ň™|ĄşXär8}RÝ’;˛ŻČ=©K®}ćëĺöA®~ŮqI®á×’‹7j$ąô€•2©%32É« ]Ě„hzŘdL˛¦úsÇ×_L˙ä_ŘÄYŁt:wĚjh^Hů;„F´U.Úo%mĄŹZ”ö-č/LRzendstream endobj 143 0 obj << /Filter /FlateDecode /Length 230 >> stream xÚuνNĂ0đ«:Dş%Źŕ{â„:&Km‘Č€bj@°’ľy?BFiŽ>@UĄJÖOöÝůîÜň˘¸‘L—˛ČŻĹ9Y^É.çwv™î/·}ăUÉöI\Áö ¶ĺ˝|~|˝˛]=¬%g»‘ç\˛.7B>š@TĹ*ÂvPU‰<ÜÓL_Ă: ŘŃĽˇy;§3‹ýóÄd4śŃĹ0 ˝ă1ő¤iČď{±•‰O¦K[¨lűŁ5LQB}!ŃżŐ‘ßgěŽlO­4 b ó¦űçŰ’ůÜv›endstream endobj 144 0 obj << /Filter /FlateDecode /Length 228 >> stream xÚuαJÄ@ŕ )¦É#d^@7!ą;­îN0… •…X©Ą…r׺ë›ĺQro°`łŕ‘ßY#\qŘ|,˙ěđOŰśĎ/Ą’…śŐҶŇ,äąć7n–šV2o˙FOŻĽęŘÜKłds­9›îF¶ď»6«ŰµÔl6ňPKőČÝF@fŘ*ńÉá;€á!É…Y$ ť‡rHôT Ö'Hq‰ŹÄ8(ý)ĺŻŘ Ýp^wáeđÖç ŰĐ *ô ˝LÉ1j ˘~-SŃ‘1qř‡ě—x 0hăD^)㫎ďř Zz endstream endobj 145 0 obj << /Filter /FlateDecode /Length 179 >> stream xÚ}Í1 Â@Đ]R¦Éś¸‰VBŚŕ‚VbĄ–ŠÖÉŃö(9BĘÁqvE‹y0˙3LŞűĂĆ8ŕI3Ôî8BŞyŹÝęŠírj…©5ă”™ăůtŮĘL@¸N0Ţ€)PR+IÔFdęĆŢ’jIW˘ZČE,×Î&´¬ *>¨„`…óîíĽí۰ů°ţmôÔţł÷´ú˛$jĽüŚĽĺKÎaj` ż†Uŕendstream endobj 146 0 obj << /Filter /FlateDecode /Length 206 >> stream xÚUŤ1jĂ@Eżq!foÍ ĽRd\ l¬Â`W)BŞ$eŠ„\vʶGä)U8˙M—b3űŕíĽ™µK­tÁ™ßkłĐ×Z>¤iyWůĚâĺ]V˝řGmZń[ľŠďwúőy|żÚݵżŃ§Z«gé7Љ}'8ł„Îl€"M !#ĘT ‰pp‘›P\‰©Ť`‰~ŔԅƲꌀE˘Św€KŐ¸r40Ă€€0ćďŤâ‚ß=ćO%›ňĐËAnŞRZAendstream endobj 147 0 obj << /Filter /FlateDecode /Length 176 >> stream xÚuĎ˝ Â@ ŕ”nYúć ĽÖ«˘ µ‚7:9“::(şÖ>šŹâ#těP“C…îăňĂ‘Km8ˇĆrŇĄ#:&xAk%Ź5ŐĆጙCł%kŃ,ĄŠĆ­čv˝źĐdë9%hrÚ%ďŃĺHDĄĐëbćfţRú›ŻAˇ#´JÓAŕ©;=L•â—Vi„@ …&Ş!`®”ČnOY—őoň .nđ îRđendstream endobj 148 0 obj << /Filter /FlateDecode /Length 178 >> stream xÚm̱ Â0ŕH†Ŕ-}„Ţ–´ŠSˇV0 “8©Ł˘«ÍŁĹ7é#t¬P<“ŕRt¸ŹűďŽËÔ8źa‚SW™B5Ác PąË‰Ź~q8C©AnQĺ —n RŻđv˝ź@–ë9¦ +ÜĄěAWX·ś µÂŃ ˛0ă-‹‡FV°_j,{üáÍâ€aý€Ń—ÂđŢ˙é\wî¸v‘ŤŤđpzQĂčI6đ&‹]+endstream endobj 149 0 obj << /Filter /FlateDecode /Length 176 >> stream xÚ=Ë=‚@ŕ!$Óxć.dŃ@ bâ&ZY+µ´Đh‡ÁŁqް%gů+ćËĚ›Ľ@.Wyň!É5Ý||˘4™gNó¸>0U(N$#;NQ¨=˝_ź;Šô°!EFgźĽ ŞŚŠÖęš®łÚ~ë3§ś ⻂|¦ž°4Řš±4#\YüŔި]gr¦1äőÄWOŐLÉ$ÓÇ­Â#ţbVOendstream endobj 150 0 obj << /Filter /FlateDecode /Length 197 >> stream xÚ5Í; Â` ŕ€%7°9‹őm`A'qRGEˇCˇGŹŕEz”ˇc±ćokB>ňbwÚÝ!›Ü—˛ÜéńŢÂÚ&ë”QvGű¨Öl›¨ć˛Eĺ/řrľPŤ—¶PMyc±ąEĘQŃ·( 5Ň•;Ў‘iŇ?Í’ä•Ä5™Ó-7€î- ÇÇ«yľ! ^P+Ě<§$r4ˇ+n ”Ź„¬"©IŤD>8óq…?áUŃendstream endobj 151 0 obj << /Filter /FlateDecode /Length 216 >> stream xÚEαnÂ@ PGNň’OŔ_ĐKH@b!Ą`b@L´#n¤vý“Hý¶Ţ0öe`¸'Űwg»ČßFJ)—SŚ)Óg†G,†’§šęĹţ€ł 톊!Ú…TŃVK:źľżĐÎVťÓ6Łt‡Őśbö%71w%;Ă]Í®Źű:$δ &Ŕ´ nKoW1ň]Đ‹pż©uű˛tÁF@u¨°ŢF˙jü§ďM0ůŐ>ÉŹźÔ)č” čÄNŤĽ6Ş˛#0Ëľ˘ jÜ×ńŁÂ5>Ý[¦endstream endobj 152 0 obj << /Filter /FlateDecode /Length 224 >> stream xÚMαŠÂ@ŕ )„iňBćÎÍâ´‰ŕy` A«++µĽâŽ®čŁĺQň)·®;»Áló±ü3ěüj:™-(#IorNjNÓśNPĺ6Íh¦úŃńW%ŠOR9ŠŤÍQ”[úű˝śQ¬vď$Q¬éKRvŔrM`şŘčČ> stream xÚmŽ= 1F'XÓxçf׍ VÂş‚[ZY•ZZ( vz4ʞG°L±ż‰?•ä13yLâ˛Ţ`(‰d8.—,—mĘv}ô‰¶z±ŮsQ±]ŠëłťbʶšÉéxޱ-ćcIŮ–˛J%YsU äf”÷7[qňá(hžĘVŁě ¨©[“©it'äzS¤í•Č[Ś vý».Qô*šFEŠńńQŻ"xĹż ?>â¤&žTĽŕwse–endstream endobj 154 0 obj << /Filter /FlateDecode /Length 203 >> stream xÚ}Ďż Â0đ”…[ú˝Đ´´Őtj3:9“::(şÚ> stream xÚuĐ˝‚0đ’[xî´‚âD‚ŘÁD'㤎]…GăQxFBíĄ1čňKűż~\Šq4CCM1Ĺx ŕ"֎ʓ¨«ÎJŕ[1đĄŽËŢ®÷đt=Çx†»ý=Č ™W3ĆĽV“¨‚¨ôTQÎScýĂ6ÔCC5Ä5”źQ·š±•>ŐRÍ›p(s©Ú5MŰ’‚`_ä=Ź´=ÍËĐ?ÍĄËčGrúĄJ‚"Z–S°°ňZ¨endstream endobj 156 0 obj << /Filter /FlateDecode /Length 133 >> stream xÚ31Ö3µT0P04V01T01S05TH1ä*ä26ŠĹaRÉą\Nž\úá ĆF\ú@q.}O_…’˘ŇT.}§gC.}…hCX.O†? |Ě˙äŮ˙7ŘË˙Po˙˙÷żúó˙˙Ô3˙˙ń‡ń˙‡ †˙ ě¸\=ąąJ'˘endstream endobj 157 0 obj << /Filter /FlateDecode /Length 137 >> stream xÚ31Ö3µT0P04S02W01V05RH1ä*ä22Š(™BĄ’sąś<ąôÌ̸ô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ň ü ö ň ö öęQqC=C=2Ăp\ĆŕŔń†   \®ž\\Ő8ćendstream endobj 158 0 obj << /Filter /FlateDecode /Length 205 >> stream xÚmĎÁj@ŕ_<sč>‚óQ‰iZ &…z(¤§BNMŽ9´$7ÍGŮGŘă$f–+–ů`f`ů'ťĎŻółTşäE‡„~(ŤŮ=iÝâűDEIѧ1Eď2Ą¨üŕóďĺHQ±]łôŢ%ď©Ü0đ TžÓů‚őőĎ‚ ÚľQmĐ÷} WG?p…j2ü6µ€ęNŽČ`ÇÔž}Ĺ}gvŔä‚öµjčPhCLQmŽQ€˙ +ŕI.˝•ôI7y-qˇendstream endobj 159 0 obj << /Filter /FlateDecode /Length 273 >> stream xÚuʱNĂ0†Ďňtyß @Ą!°ÔR)`b@LŔČ‚ 5ŢúXń dcÄŁ‘˘çŇ҉Áźě˙¬űî&ĺ~uD9Ő´WÓ¤˘ęn |Ŕ˛–0§ę·rsŹłłK*kĚN%Ƭ9٧Çç;ĚfçÇT`6§«‚ňklć :Aň¬P<Ę‹ŮaîŔ2÷Đ~˝z`łôj0:hoTĐ˝ Yˇ“,ílR7Ý"fSíŇ®_‹řǢ‡ĹâŻá°®@śľc9´ň1XĘ·ŁĽôtíX ŽużĆ(cąArł°â6yŔ.ßź!nŐC˝Iś@­ŚqqHÝf Ř`Wž4x?l˙„Ťendstream endobj 160 0 obj << /Filter /FlateDecode /Length 263 >> stream xÚuĎ1JÄ@ŕ7L1đ7ą€lţ hvÉfÝm ¬+BĐĘB¬ÔŇBŃN’éĽVŔbKŹŕ€X"$ţIX ‹ůŠ7ĚcŢ<>HV<ĺďň|ÁÉ’ogô@±%N~onîiťQtÉńŠ˘S‰)ĘÎřéńůŽ˘őů1Ď(ÚđŐŚ§×”m`©Ĺ ‹]‰\ ­ęśjô—çÓéÚ•ys¦Ç 3[”Á¶ =©5ď=¶€ů°9L‡ÖÓ@·ŞjT TZ€©ç)…°Ç iŹžv@J˙ľ/Ńc‰qCS86ĄvâKdSÎŽ›J4ú{wSý˙¦×2´Áî¦âĎ&Ľř-eč$Ł ú®Ô„endstream endobj 161 0 obj << /Filter /FlateDecode /Length 199 >> stream xÚĄŹ=‚@…‡PLĂś č˛Čź bâ&ZY+µ´Đh«ŤŁxJ Îd)č-ľbß›yó6šĎâ¤3šf%gtÖxĂ0e5 $¬Ó jOaŠjÍ:*łˇÇýyAUl—¤Q•tĐŃ”ŕÔîŔg&Ě›ß}NÇr ŕ5Ĺr^± ťĹaŰý2Ťó†ż¶ă“Ę®ä`‘Ő׉i˙`ś•Ź»r_zHé&=ĄŻ| z)3”óWwřFHH—endstream endobj 162 0 obj << /Filter /FlateDecode /Length 203 >> stream xÚuŹ1‚@EÇPLĂLś č‚ÁĘ1‘ÂD+ cĄ–&j´ŽĆQ8%…gd•B-^6™˙gţß‘;đĆd“Oý€\ŹĽ€öžqđÇ~ŁěŽƨÖ4 PÍyŚ*^Đőr;  —SrPE´qČŢbt ÇLR~3&0 Łč> stream xÚmËżJAđOS¬Ls/ î<{ÇŢů§ń FđŠ€©,ÄJS¦P´ ą€/¶Ź˛Â6ĺ‰G>÷ÄÎ Ě曙ҟV—šë…žkYjéőąńUĘrőgż‹§…Śq÷ę+q·)×Lőíő}.n|w­…¸‰>š?J3Qŕ©®V{X‡‰u¶îGÁ†>‹vŰ×ŃvŁÝ}1’‘=@nšČ^í@›Ć2"Ýu)âő÷'Ńn6?"±2±ĹŇÄŔÄŁü‘…˙ÔrÓČL~‰Qendstream endobj 164 0 obj << /Filter /FlateDecode /Length 151 >> stream xÚ31Ö3µT0P0W0S01U01QH1ä*ä26Š([€%’sąś<ąôĂŚÍąô=€˘\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. | @ Ź…°v¸:QAŘ˙˙˙˙A=řńN€ą ?@J@#ř€`pě`ÖŃŔŔŔĺęÉČ\z> stream xÚ=É1 Â@EŃR~“-Ľ čäg”`Ł#8… •…¤RK EÁJł4—âRZ„ŚÓ(śęŢ‘Ž'̨–Íi•Ş<¨śE‹3ćö÷ö')ť-µł CŚ[ńząĹ”ë9ULĹť2«ĹUD‹¸CŇ#őMx‘fŔx˘ńi‹çţß î€,ślä ő‡* endstream endobj 166 0 obj << /Filter /FlateDecode /Length 102 >> stream xÚ31Ö3µT0P0"3#C…C®B.#¨‚)T&9—ËÉ“K?\ÁČ’KßCÁ”KßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEˇţ˙˙˙˙˙˙Ă >—«'W ˛©$Ěendstream endobj 167 0 obj << /Filter /FlateDecode /Length 99 >> stream xÚ31Ö3µT0P04F– †† )†\…\@Ú$l‘IÎĺrňäŇ pé{€IO_…’˘ŇT.}§g ßE!¨'–ËÓEźÁţ@ýú˙!Äncŕrőä 䄬eendstream endobj 168 0 obj << /Filter /FlateDecode /Length 179 >> stream xÚ31Ö3µT0P0QĐ5W0±P0µPH1ä*ä21 (™Bd’sąś<ąôĂLŚąô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. @ŔxD2?@ě,Î&ĺ¤=`¨C"˙€Ťů ™? ĆŹaÄdĂjđĆŽa¦›ěĐÝ„lÔMđąIž$bş‰żź‘ÜÄ6†ˇLrązrrШAendstream endobj 169 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚuĎ1n@ĐŹ(VšfŹŔ\Ŕ^Ů’ŤĄPXJŞQ*;eŠDv mʞG tŘ Ć.’ć­4#ýťżJ—نc^ó"áUĆŮźú¦4—aĚY:mŽ_´­ČĽqš“y–1™ęŔçźË'™íËŽ2%ż'PU2µ|„ţ (ßÚ2w(Ú¦E-zD6¸BŰđFĺ”{ íDŘIÚť3ę?Ż”űmgDíŚj #’× AŹrf#érµŃNNŹ,t']´÷cÉá^Ţal Đľ˘Wúqái7endstream endobj 170 0 obj << /Filter /FlateDecode /Length 170 >> stream xÚeĘ1Â0 PW"y€#Ô' MKUJ‘Č€CĹŚ X)GëQz„Ž U‚€ Ďň˙ö8eSŠIĹ<Ň e ž1ÉÉ5ß—ý ŤrKIŽrÉ5J˝˘ëĺvDY¬ç¤P–T)Šw¨K@ô1c5ł ™0|2 GÂŢAôĽw=˙ý ś§/t:źpZßĐi|‘óř©­m¬µí—˸иÁI Ptendstream endobj 171 0 obj << /Filter /FlateDecode /Length 229 >> stream xÚmбN„@ŕCA2 ŹŔ<ŔÉ™X‘śg"…‰WY«ÓŇBŁ­đh<Ę>%aś™KĽKî6đegçß]B}}µľĺ’k{ox˝â·Š>©®´.­´Ćţ6-Ď\WT<č*í#ýĽS±yşc]Ýň‹nyĄvË@6CG'=D"ŠŚş,2ůdíf‹Fzěé-mĺý©É™Áé1ş:šđ;Ý_w1Â|4™Ět4łhćn7öµľ)ńxćńÜăM> stream xÚUĐżJÄ@đYR,L“GČĽ€nb.r6¸?` A+ ±RK Eá*ď-Ź’GHąEŘqľ‹‚˛đ[Ýý†ŮE}Ţ\I)—rVɢ‘ćBž+~ăziĹRšz>yzĺuÇá^ę%‡k+sčnäăýó…Ăúv#‡­r·˘69MD^őH…jO­ę@‡±IÉGJä˘3&ţ`ËM´·S˘™ řń—|0ÚŢ8‘oćF ˇ¦xoÍí2(đ"~řBł9~…ÚĐň}B@BTB_Cm˵c1a´H9ćóÔťză x×ń‡kendstream endobj 173 0 obj << /Filter /FlateDecode /Length 214 >> stream xÚeĎ1jĂ@Đ[¦Ń4'đJ–T¨±@±!* q•"¤JR¦°±» ëą’n+¨s«.*„70‚,ĚýË0łi˛Čr‰$CĄ™dKyŹyωf‘^őáí“ËŠíł$9ŰG¤l«­§¶ĺÓÄl×ňKôĘŐZ¨hÁYqžb~ÁOC~O¨•xCH7Lü-…VhPjeŢLă hAŘ€‚&j˘Ψ\ďś5Ó™ŘÖë˙cîtsŚĂ·|çşšń¦â˙ţ*fëendstream endobj 174 0 obj << /Filter /FlateDecode /Length 224 >> stream xÚuϱnÂ0ŕ‹2Xş%Ź{âD,Q*5C%ŞNŔČ@Ő®uÍŹâGÔĹC”ë™va‡O§łěűoQĎšGŞhI† 5†NŻXݤYQ3˙»9^pÓ˘>P˝Bý"mÔí+}~|ťQovOdPoéÍPőŽí–Ŕ2GpĚĂ=ľAÎ&ČnÄ ňč<ä?ÜCžţĆ Ţuj„Ň«…W=AP!÷BzŮO˛P˝˙SÜđBé%­í$”ë¤bpŤR«l°J–,łLaî ă´ś•řÜâĽp.endstream endobj 175 0 obj << /Filter /FlateDecode /Length 247 >> stream xÚmŹ1NĹ@ D'JÉMŽ_ňC~Q­ôůH¤@‚ŠQ%Z6T«ä({„-SD1łQ ŃĽÂcŹg¶íqwŞíő¨Ńm§Ý‰>4ň,mĎáF»öGą’Ý őŤ¶˝ÔK=\ęëËŰŁÔ»«3m¤ŢëmŁ›;ö d ´ płÜlFaůŚr&Ş@¸©áGÂPĚŮŠÂŤ>pßOĽôcÝÂë˙(Ă{zóU­ŕA¬L/”»Ś.˛ł°ßÄŢ©8óđ’Éĺ|kůŘës†endstream endobj 176 0 obj << /Filter /FlateDecode /Length 241 >> stream xÚuϱN„@ĐK¦ä5ü€ ď”E–+’uM¤0ŃĘbcĄ–­ˇó·ř”élé¤ <ď°ĆÎćîŔť÷¶ĺYuˇ­ő´ĐmĄŐą>ň*eÍpŁUy> stream xÚ31Ö3µT0P04ĆĆ Ćf )†\…\†¦@ľ –IÎĺrňäŇW04ĺŇ÷ sé{ú*”•¦ré;8+ré»(D*Äryş(0|`ţĂţ‡ý?‚Ř?0ŕü öęÔ?ř v—«'W Ča*‰endstream endobj 178 0 obj << /Filter /FlateDecode /Length 165 >> stream xÚ•Í1 Â@Đ )żÉ ä_@7ߍb…Á-­R•ZZ( VšŁĺ(!ĄEČş*¦wxŐL1±î ŽX4wĄĎZłňNčHň®#ŽGżm{ ÔĘYRs72 >ź.{RérĘB*ăµp´!“1B`Râî<`[ߦ@¤‚_#hÚVáŽgÝ0n@ ݆˙DX}nKŘ phfhE/997Šendstream endobj 179 0 obj << /Filter /FlateDecode /Length 187 >> stream xÚUޱ Â0ES:ޢĐ÷¦µ±ĐI©Ě čä Nęč č&´źÖOé'8:knh †ä@Î}7D%“YĆg¬XĄŘçn”¤ĆE¬¦68])×$÷ś¤$×Ć’Ô~Üź’ůvÉ1É‚1GGŇ łćxos «Ťď*!‚Żą…ř¦÷~‡ŃÖů˛ŽZoź(kĚ ‡˛B" PőŃđqă>´.î۶ř{€°xcA+M;úç–=Äendstream endobj 180 0 obj << /Filter /FlateDecode /Length 118 >> stream xÚ31Ö3µT0P0S04S01S06QH1ä*ä2 (Z@d’sąś<ąôĂŚ-ąô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ň˙˙˙˙c$ţ!°‘ ¨ř˙˙ Ŕb\®ž\\ĎŠ>Ăendstream endobj 181 0 obj << /Filter /FlateDecode /Length 187 >> stream xÚUŽ˝ Â@ ÇO YúÍx­w8jotr'utPÜęŁőQúŠ5I-Ôĺů$±f2›cŚ-ZÖá)+GZŚv*Ćń™˝Că@ŻHí×xż=ΠłÍĐ9îŚŕsT/ĄÔ¨"ŚkFĂ㇠ZFQ"¶Ă7!Ř\LĹ®{»kwĹ; #e´%ç(đ®»iőÓÇÜ›^/ŞaTtY!źÉ)yçÉ@,=lá M>kendstream endobj 182 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚUĚ=‚@`6$Ópć.?’`# bâ&ZY+µ´Đh+śL9 G ¤Ř°Î nńľĚĚKfÍâúQć!Ć!^¸C”ĐîëUçdřŁř†®ŔĹźŹ×x¶[a<Çc€ţ DŽ–eI ëŰÄ™p?šďדéÄR󞬱§öĘ?ÜjÄ+ RĄ I}ëi*»qúčÔD!™jUÇ”Tť­ˇ©żÁZŔ~'dŘendstream endobj 183 0 obj << /Filter /FlateDecode /Length 222 >> stream xÚmбn@ ŕ1Dň’Gź @ C§“ŇT*CĄdĘPeJ;vhŐ®GăQxF„kű˛D‰d>á;Ýńăňńˇzâś×ú”WôMĄőąµörú˘MMŮË5eŻşJYýĆż?ź”mvϬý–ß ÎŹToŤHČčNî [`ŃCZ,{µĂŞ3ďVÜZµwŚĽ ł™LćRżD·Ă%ŹÚ»ş{F:™ÉlZY<ŔŹßŘFăĺÉxmăžÝéhŇÁîWŁ˙őŢĆ IÄÇÓxLz©iO˙¸Çńendstream endobj 184 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚĄĐ=NĂ@ŕąXiš=‚çŕŘB‘,… á * D)S€ µ÷&\Ĺ7ÁGHéÂňđ6.‚DIói5ű3o¶Xť—k]꥞ĺZ¬µĽĐ×\ޤČY\jąšw^ö˛©%{Ô"—ě–eÉę;ýx˙ÜI¶ążVV·úÄ;ĎRođĐN>`aö}x3 H‡”V˝ŁmH¨ńâbŚ&oĂNúhŕ»h:€+T¨p˛=Úüq::ţϤ‹ş>ľF›_˛/C2ă1eÂyaÜ:ÄůÜčă#fśŹĂÉ`ÖĹčx–!7µ<Č=cendstream endobj 185 0 obj << /Filter /FlateDecode /Length 208 >> stream xÚuĐ˝‚0đkšÜâ#xO `âD‚Č`˘“qRGŤ®ŔŁń(}FB˝ЄĤýĄ˙~¦řópE.-¸K =şzřŔŔçěšh.wŚStŽřčlyťtGŻçű†NĽ_ç„NągL‚\kĐZ—ÖĘZ™o¤’-ŔT c Úš[ŁâçěŰş8RőňfÉÂ_yOwyö_ľŞµ6|pd‚mAÔ&˛Â:©­•QV&ňŁŇ¬ĐöëíP€®$> stream xÚuÎÁ ‚@ŕÂ\zť'HĹ Á ňÔ©CtŞŽŠşEúh>ŠŹŕŃhłkeͰüł°;ÂűSrČă#&ä»ttń‚Bpvd”‡3†1Ú[í%OŃŽWt»ŢOh‡ë9qŽhç’łÇ8"h¸reˇ)ˇŻ‘QŔ¨5“ńźVzV \ż4Ů ¤0°i:“·uç“űÓl3%üRk-Le00˝µĎöĺřăćËJÍKŔEŚ|ń}xBendstream endobj 187 0 obj << /Filter /FlateDecode /Length 186 >> stream xÚ}Ę1‚@Đ!$Óp™čBBE‚Ha˘•…±RK Ť¶.Gă(’‚¸Î.ZHÄIć%˙ĎŮ$ŚÉ§)Ż) čŕEÄŮ×QgLsô¶$"ô–ܢ—Żčv˝źĐK×sâśŃ. ŹyF •R 0ŞýRG5X-ŘťXÍ NPSĎnilˇÓ•b“ŹEOţŇ&¬4>ŔíŘ=źĆöźVgÓWŞĘXłĘ(ßę9nđón endstream endobj 188 0 obj << /Filter /FlateDecode /Length 193 >> stream xÚmĐA ‚@ŕ'.„·éľ4ZŠ´Ě Yµj­Şe‹˘¶i7ó(ÁĄ qš§ 3üo~f‚ů4\G3˝C˝|:űxĂ ŇąŤ|pşb"Qě)P¬ő…ÜĐăţĽ H¶KŇ9ĄOŢeJ5 jPĘRÍČnî|Ŕ-`ŇY€››Ťs.°9Ä`6.°Ż?•ľđgÖ[÷ęÂ@KŰ´Ö`UfíŠ lviÖ)ąŔ–üʡ™‚öŢJŤě渒¸Ă/V±endstream endobj 189 0 obj << /Filter /FlateDecode /Length 156 >> stream xÚ31Ö3µT0P0b3SC…C®B.c ßÄI$çr9yré‡+[pé{Eąô=}JŠJSąôťś€|…hCX.O†ú˙˙0ü˙˙˙cŕ?ŔŔŔ &pö`‚Q"ęp˙@Ä#ř`pě`â2QŹěżpOţaŕrőä äIVRendstream endobj 190 0 obj << /Filter /FlateDecode /Length 163 >> stream xÚ31Ö3µT0P0bcSC…C®B.c ’HÎĺrňäŇW0¶äŇ÷Šré{ú*”•¦ré;8+ů. ц ±\ž. ő˙00ü˙˙™‹1 ě`â‚LŔAȉ„=`ŔAÔ:\Ä?ń‡ÁDÔˇ˙ÁÄ Q˙˙˙˙?˙Q ±\®ž\\Á[Éendstream endobj 191 0 obj << /Filter /FlateDecode /Length 242 >> stream xÚmбNĂ0ŕ?Ę`é–ĽAě' ¤Ş˘X*E"LSadČ`µy^ÉoŔ+dc$˘–sŚT@•|źôßů»89šžŞ‰:ćšňÉŐ]NŹTĚ8ŹŃV4Ż)[ŞbFŮw)«/ŐóÓË=eó«3Ĺyˇnr5ąĄzˇ°é ězČí^˝ĹĆAHśż ^Ů_öźŃk˘O mb¶2ń{Ë o)ŢĽIP¶X—’5•”`ÓŃj´5҆uiSyű˝˛ ®9iŮ^ZĂ&­WŔ‹ÄÁŽW9 ő+żĺ§űo w }:Żéšľ˘{{endstream endobj 192 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚmĎAJĂ@ŕżtx›9BŢ šFSŠ›jłtĺB\U—.”şjir˝‰ä(s„én„ˇăË š…˙}đ˙łšâ|2»ŕ)źÉÍ$9?ĺôJĹ\z¨ÝĂú…–e÷\Ě)»–•˛ę†7oďĎ”-o/YúŠrž>RµbÔµ·đGx×+Ł$qP-Tô Şú8aÚ ý ¦Hń«Ú”@\¨fńgmŁ{`Ü%íNGőP¸ iŰk,FťÓű=pk0Žjluo-9˘Ôđţżm·Ë骢;ú[Ę|endstream endobj 193 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚ}ν Â0ŕ+„[ú˝'°˙‚SˇV° “8©Ł˘sóh}”>BÇĄń.EÁ†ËÇý$$q4MćäSÄ;žQĐ)Ŕ+Ć!׾”28^0+ĐŰQ˘·â.zĹšî·Ç˝lł ®sÚä°Č ´Ö Ä,¶5yoÔ“ÚfťJN©Ń­>ľăŐTĺHA¶±-ŁÝIÓĺ?”ň±6*‘ʰ<”+Ľş1­ÁvL{°ůµÔ˘yőˡŹË·řäťjŇendstream endobj 194 0 obj << /Filter /FlateDecode /Length 244 >> stream xÚmĐ1NÄ0ĐĄ4ŤŹą8I±U¤e‘H˘Z()XA»ö 8W‰DAÉr„”)˘5c‡H€ÖĹ“5¶üż\ťťÖ+.¸äĂU͵áCĎT•2,¸.ç“í­[Ň·\•¤/eLş˝â—Ýë#éőő9Ňľ3\ÜS»aXŕ˝wŃ>:@ć~˛^M€ęą¤:ĚÚ_6‹ů¬;â~±qá…ÉLÇ ‚Vrď»ëđÓJöX&{بäČ#’‰Izłc&ń4ĂÍ˙~¸ŕg'ňŻ.żýŃz¨w'©ĘĎĘ—¸ě EJsY#袥ú´}×endstream endobj 195 0 obj << /Filter /FlateDecode /Length 245 >> stream xÚmĎ1JÄPŕYR¦É |sÍĆ}!°®` A+ ±RK EÁĘ—Łĺ^aŹ2Ĺ’ńꉋ6ÉĚĽy˙‹«ŁúT–ĺ°’x"ő±‰pÂ,ŃÎ\@Ç_łŮčs/*g.ů ů)¨&éÖL“ŮřOPëăvY´µ‡ůĎě`nî ˙,ß{ŕ·ůOÄ›Mx±[l)őz»i˛ç&µ$©vŞX?zÎŹĚňEË7ü }„tŁendstream endobj 196 0 obj << /Filter /FlateDecode /Length 163 >> stream xÚ31Ö3µT0PaS 2TH1ä*ä21PA $‘śËĺäÉĄ®`bŔĄďĺŇ÷ôU()*MĺŇw pVň]˘  bą<]ě˙˙˙˙ŻHüG#ęěę˙1Ô3Ô˙a¨c¨ĂFT0üc°a`řĂŔ€•`?pĚ`â‚L<ŔAđ‰8y0Ń€Lđ˙˙dü˙ŹL€Ĺ¸\=ąą7X^´endstream endobj 197 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚmĎ= Â@ŕ‘irçşY“€V ţ€)­,ÄJ--mMŽ–Łä–â8“mR,„Ţě›d“găbF)Mid©Paélń†y&ĂT'ÝÉéŠóÍžň ÍZĆhĘ =îĎ šůvAÍ’–Ň#–K޸vÜ07·}ý> stream xÚmĎ˝j„@ŕ aÁy‚SQ W-±8ČUW„Tą+SÜq׺>šyóBš…[Ü̬… ¤ŘŹůa‡™˛ŘT[ʨŕW>P•Ó1Ç3–’g’Jđţu‹éĘÓg®bÚîčząť0­_‰ó†^sĘްmôą´‹ÝλÄK=š lhBNbÄ&Yb‚MzvV j±ëDmW•ńN«ÉčţWů®ţĄ“_Á}5ü–™Ń×jüiýo/*eČ>ť×rĆ‹s‡OŐóY"źĆWđ®Î€›g˙¸Ôń©Ĺ=ţ@rüendstream endobj 199 0 obj << /Filter /FlateDecode /Length 196 >> stream xÚ}Í1 ÂP ŕ_ Yz„ľŘëŕbˇV° “8©Ł˘›´=ZŹŇ#těđč3É$‚BřHţGň’éd67‘‰#fKťcşQ"ł&úrşRVP¸7IDášc ‹ŤyÜź łíŇđś›Cl˘#ą©,ŞÁ pŠÓN3żEĐxLíuH… ă%Ć=ÁbÔŹË=ÓÖSZ`đ T‚šI™4łJáĹVč^…Ż´Bóąůůç ĎńeßőźhF«‚vôý}Zendstream endobj 200 0 obj << /Filter /FlateDecode /Length 202 >> stream xÚ}б ‚PĆń/‚łôť'HEš„2Č!¨©!šŞ±ˇ(hJÍGń"űŽaRüqŹ—?č üno ®öôµçéĆ“˝>g×F;Xďd‹łĐŔg·âÄS=N[q†ł‘rŽté©»’8R„EQdHh^3ĂčdxaŠ;Ŕ|ŤűÇć 8ŁuW;7™©–&†Ą(#”-ŁzúýH_Q+˙—2şšŤ›Ą”)eĘ #Ęń’)µ_Ż,ŢVW"ăXćň—p¶endstream endobj 201 0 obj << /Filter /FlateDecode /Length 167 >> stream xÚ31Ö3µT0P04SĐ5W05P0µPH1ä*ä26Š(™BĄ’sąś<ąôĂŚŤąô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ň˙˙30Ř˙˙߀JĹ€ NÔa!ţÁ‰?#‚řI0#;‚x€Iđ#„<‚hŔ$ě&ß»˙˙˙˙‰z—«'W !čVŽendstream endobj 202 0 obj << /Filter /FlateDecode /Length 156 >> stream xÚ31Ö3µT0P04QĐ5W0¶P0µPH1ä*ä22Š(™BĄ’sąś<ąôĂŚŚąô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž.  ?ŔčC Ő10Ř‘=<50đCĐvzŔŔ G!č†qŘM„‡ŐD¸qXM„Dr 2\®ž\\&Š;*endstream endobj 203 0 obj << /Filter /FlateDecode /Length 172 >> stream xÚuĐ1AĆń…ä5Ž0ß]cŐ&k%¦P)D…RAh­ŁíQA©;Cńš_ń˝ę˙şş  V:FÔÇ:¤i]ŤâčyYm)5¤ćĐšÔ¸šI™ űă†T:"$•a"X’É ¤µB$Öž?!ä›Ä#rljŁtÜjžCÝsehx. MOÁ ‹ŻľßŇ˙ąą{•}RľČmU@#C3zäTńendstream endobj 204 0 obj << /Filter /FlateDecode /Length 137 >> stream xÚ%ɱ Â0…á#ÂYú‚÷ LÓ´‹…ZÁ ‚N⤎Šn‚>j#S®AáźľżvÓf.ĄŘJ\#un&gË]•őç˙uş˛ó4{qÍ:#ŤßČăţĽĐtŰĄXš^VĘ#}/ @›ŐĎH5LTă;µIc‘4 Uô%0ćĘsÇ/Z)µendstream endobj 205 0 obj << /Filter /FlateDecode /Length 107 >> stream xÚ31Ö3µT0P0QеP0¶PĐ5RH1ä*ä26 (A$’sąś<ąôĂŚŤąô≠ô=}JŠJSąôťś ąô]˘  bą<]ě˙˙˙ĎP$ţĂ 0,ÁĺęÉČö•)endstream endobj 206 0 obj << /Filter /FlateDecode /Length 197 >> stream xÚUĚ; Â@ŕ?¤¦ń™¸ ‰«` A+ ±RK E[7GËQr„”)–ŚłŘh1Ěë/ňÉtÎ)—ZEÁyÉ—Śî”Ď´OCç-*2Îgd6:%Smůůx]É,vKÎȬřqz˘jĹH€HH¤C,â10ęă\ŔÖq‡¤ŽEĎ˙qRc,ŠS4EB€č¨µH<,l«)®o ˙Ëđe@äˇß®±ú¨)]˘ôšîúXĽí!í¸ŁuE{úł/^qendstream endobj 207 0 obj << /Filter /FlateDecode /Length 212 >> stream xÚuϱJÄ@ŕ_R¦ŮGČ> stream xÚ•Ž1 ÂP †q(d°Gx9ŻĄOA ZÁ‚N⤎Š®mŹÖŁx„ŽŇÁ!$!ůżŤ'3NŘ*Φ|IéNYĐ>±Öç-KňÎůŤNÉ—[~>^WňËÝŠSňSNNT ČD'Ň i!Š4y;ě‘·ŃGwpŤ{c×ČjCeč ß s»]Ř—ĘžZž†ş.ţ"USł“‚9©-­KÚÓ¦ŤIĆendstream endobj 209 0 obj << /Filter /FlateDecode /Length 149 >> stream xÚ31Ô35R0P0Bc3cs…C®B.c46K$çr9yré‡+pé{Eąô=}JŠJSąôťś ąô]˘  bą<]ä00ü˙ĂŔř˙ű˙˙ ü˙˙˙˙˙ý˙˙@¸ţ˙˙0ü˙˙˙?Ä`d=0s@f‚Ěٲ d'Čn.WO®@.Ćsudendstream endobj 210 0 obj << /Filter /FlateDecode /Length 193 >> stream xڕα‚@ ŕ’.<} L— &Ţ`˘“qRGŤ®â›áŁřŚ—;[pqÓᾤ˝´ý 5)+ĘHń+•9ís<ˇ’^&Ą|ěŽXLפ*LçÜĹÔ,črľ0­—S⺡MNŮMC±€Ä  ˙$z1Ú1Ţwxď!"Ëűâ>ô<ćôZ™iá&łN°?â>cíH ăRa¸ĘÉHŽ'c Ë:ÇŃ´m™¸O,Î ®đ —şYKendstream endobj 211 0 obj << /Filter /FlateDecode /Length 201 >> stream xÚmޱŠÂPEď’âÁ4ů„ĚěK¬® ›BĐĘB¬Ôr‹mM>í}ĘűËâě}VĚ™;Üą“úł™i©“ÔĄÖS=Tň'uĂů9&a˙+óNüFëFü·â»ĄžO—ŁřůęK+ń ÝVZî¤[(˛€ÂĐŰ f#2ł;ÜJ>ÂPD´Cv@Z }•„‹÷c˝C  ¤7¸ľĐ'Đ* 4u‘ö.ć7úąmp Ěb2rćcŔňÝÉZţI÷_ţendstream endobj 212 0 obj << /Filter /FlateDecode /Length 154 >> stream xÚ31Ö3µT0P0asSC…C®B.cßÄ1’sąś<ąôĂŚŤąô=€˘\úžľ %EĄ©\úNÎ @ľ‹B´ˇ‚A,—§‹˙ű@â˙Ć˙˙Aűźz ńHđ?°*;&pő˙˙˙š4A€ĹđkŁa˙˙˙[~ `1.WO®@.ňĹ^Łendstream endobj 213 0 obj << /Filter /FlateDecode /Length 253 >> stream xÚ}ʱJÄ@†˙#E`š}!óšÄä”k.pž` A+ ±RK E»#›ÎÇđUň(y„”[,g‚Ť˛ěǰó˙˙ĚÖŐÉzĂźňqąáşâęśźJzĄş`;ëłźÖă íZĘď¸.(żŇwĘŰk~űx¦|wsÁ%ĺ{ľ/ąx vĎ’€4¸lnfxYé•DdöItÁ§S¶n\Ĺ#7@efd=ş`’El6X4jB*˛`„éáľfŔ}EŹ_éh0‡íb•ôj“1SLÍ€,xÝ>v*‹Ĺ!*:MĂö–Ƣó˝:ť˛?-y‰%ۧF‚Í@—-ÝŇ7ăč‚>endstream endobj 214 0 obj << /Filter /FlateDecode /Length 161 >> stream xÚ31Ö3µT0P0bcSC…C®B.ßÄ1’sąś<ąôĂL ąô=€˘\úžľ %EĄ©\úNÎ @ľ‹B4Pe,—§‹Bý ř˙ť¬“Ś‘ň@dý ůó˙? ůű˙ ůB~°o’äAdü ÉŔ$˙É?Häz“ő˙ťř˙˙Ç˙˙I8—«'W zúendstream endobj 215 0 obj << /Filter /FlateDecode /Length 132 >> stream xÚ31Ö3µT0P0bcKS#…C®B.cC ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. ě ň ŘţĂÄ@ňx@ý˙@ü€á?×C1;}pý˙˙ţ˙˙˙†A|.WO®@.üŘO)endstream endobj 216 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚťĚ;‚@ŕ%$Ópçň.¨H)L´˛0VjiˇŃV¸‰Wá(xŚ…[Ć_­Ĺ~Éüłó‡Á0ŠŃEź_čcäáĆ=’ą2Ęb˝4gA ΄Spň)§-8él„ôŚsĂQąyŔendstream endobj 217 0 obj << /Filter /FlateDecode /Length 115 >> stream xÚ31Ö3µT0P0b e¨bČUČeläą ‰ä\.'O.ýpc.} (—ľ§ŻBIQi*—ľS€łď˘m¨`Ëĺé˘P˙˙Ă˙˙‰zÁŔ<Śú˙˙˙7ń˙,ĆĺęÉČî{\Wendstream endobj 218 0 obj << /Filter /FlateDecode /Length 171 >> stream xÚ˝Š= Â@…·[&GČ\@7!Q°1#¸… •…X©Ą…˘ő^,7đć[n±ě8šÎČŹ÷WĂŃ3ä‚r„Ĺ9śAl&’ř]ö'¨-Ť\Ŕ,¤c—x˝ÜŽ`ęŐ s0 nĺąŰ =śî=Cężbq䙣Ň1 SĄe¬”ö‰K•vI'ě’ö‡mr˙/)Tžňě8R`ßűľ‡ą…5ĽízfĘendstream endobj 219 0 obj << /Filter /FlateDecode /Length 155 >> stream xÚ31Ö3µT0P0bcc3…C®B.ßÄ1’sąś<ąôĂL ąô=€˘\úžľ %EĄ©\úNÎ @Q…h ĘX.O…úňţ˙¨˙$ţ˙$˙˙ĎŔP˙D2ţ˙`ß$ČČů@’Hţ“Čô&ë˙?:ń˙˙Ź˙˙7 “q.WO®@.‹Łllendstream endobj 220 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚ}Ž=‚@…‡XLĂvNŕBL¬H·0ŃĘÂX©Ą…F[ŮŁíQ8Ą…a†‚Îb^2ď}ą™KJ)*%ł K†w4÷Ň‹ó +‹ú@¦@˝á)j»ĄçăuE]íV”ˇ®éQzB[Ä_PĄ ˘:…đá9o’.ęAµ@9(ˇdq%ź»7@â'aʏý/=ßµÓGĂ.^¬ÄTyhĆ ‰”pÁ A!\\[Üă>ťP:endstream endobj 221 0 obj << /Filter /FlateDecode /Length 200 >> stream xÚĄŹ= Â@…g°¦ń™ čfI"¦üSZY•ZZ(ښͣä[.(w“€–‚S|Ĺć˝7q4HRYs_Ź8Ö ů éL‘WCNâvµ?Ń$#µá(%µp:©lÉ×ËíHj˛š˛&5ă­ćpGŮŚs” V,ČS*7;(& A‰]t,ľŕ -Ŕ•ÇýGTÎŔťµ@Ű8×=ÓF–>Ľ®á ˇŻ†ľ$ÚńĽË_ČĄ÷ŞůF­Ń<Ł5˝ŢŻěendstream endobj 222 0 obj << /Filter /FlateDecode /Length 158 >> stream xÚ­É1 Â@ĐźJř—đźŔÝuŁÄj!Fp A+ ±RKAEëőh9JĽAĘÁqc!Ú[Ě™Ií`4-ĂÔËŢđ™m»îjw쎜{Vk±«y\Yů…\/·«|9ĂŞŤ˝e_Hx’+5ĐCôŃ8´äÂ#‚$ŇRC®ˇąš\őˇě¸˙B˙"¨żxo<óĽâőőIwendstream endobj 223 0 obj << /Filter /FlateDecode /Length 185 >> stream xÚMË1 Â@Đ‹Ŕ4!s7q5Ć@T0… •…X©ĄEÁĘÍŃrŹr‹ń,,Ţ2łó˙ÔŽg©D’€MĹ&rŽůĆv‚=ę×ţpşr^°Ů‹ť°Yă—M±‘Çýya“ołYĘ!–čČĹRČůr¨ęGB®ů7 }Kď˙´DŤ#"×eZS¨ˇWˇ˙!§("P÷B Ca÷Ł}­˘9Şť6A«Ş=> stream xÚ31Ö3µT0P0bc 3…C®B.cS ßÄI$çr9yré‡+›ré{Eąô=}JŠJSąôťś ąô]˘  bą<]ä€Ŕž˘ţ˙˙˙ @üżA€ĹH2…‚ů`€ťhŕŔ ß €AţAý~ [@ó˙ Ś˙€LxŔŔĺęÉČţ:B„endstream endobj 225 0 obj << /Filter /FlateDecode /Length 148 >> stream xÚ31Ö3µT0P0bcc3…C®B.ßÄ1’sąś<ąôĂL ąô=€˘\úžľ %EĄ©\úNÎ @Q…h ĘX.O…úĚ˙ţ˙`˙…¬˙Á $0đ()ŹDÚÉ? őţÜĆđęd=”˙H2˙c˙ĎŔĺęÉČÄŁd>endstream endobj 226 0 obj << /Filter /FlateDecode /Length 186 >> stream xÚ5Í= Â0ŔńW:oéúN`úĄĐĹB­`A'qRGE7©…^Ě­×č ęء4ľŘ”É? ‰Âé,&ŹžQ@áśÎ>Ţ0ÔÍÓ[}pşb*Qě)ŚQ¬ą˘zÜźévI>ŠŚ>yG”˝•Ą:ĹôJ•^ý›]S |Á-,ZHZX:Č^<rś[CÂ×Á准’qĘz¤b&Őg¤aě¦QŚĄŔ˝†żŔ•Äţ$›Lăendstream endobj 227 0 obj << /Filter /FlateDecode /Length 202 >> stream xÚEŚ; ÂPEoH!LăśřŁ‚UŔBĐĘB¬ÔŇBŃN!…۲łt î@Ë!ăL@,ŢaćĚ»·µ{¸ŁŻŰá¨ĎŰ™ lµĂfOÄܒŁą©ZrÉŚOÇóŽÜp>âÜW!kJĆ‹/źLnRüQ;”Hˇ(Ô+€Řű­Üp{ÍçhĽŻ€/ O ¨.†ęçę«oźk> ą¶´¬4¶ú…Ą4Wč¬&F&ž”™äRŠ˘Ş§ÚŃ$ˇ}¨xY&endstream endobj 228 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚEαjĂ@ ŕßd‚ÁzöěŘ)ÍCšB=Ň©CÉ”dĚĐŇnĆvČĐ×jé‹:tÍ&É=Žűîî$%ńÍpÄ!ř:şădŔń-Ż"zĄXŁ!—Znh’‘yćxDćQâd˛żż}¬ÉLć÷‘™ňKÄႲ)—ÖłŤµ[{˛v§Č­Ťőöđ+ďđOPy5Ŕ‘ Ć@®˛äĚ©¤äUíđ·-G˙[ůŮ;zżĘßŕµ[*ö‚l”ăŽBÉ;Ąv\ÉĽHer”;ĺSúľH‹R §Z88 ľ~íKôŃťßÍa{endstream endobj 229 0 obj << /Filter /FlateDecode /Length 176 >> stream xÚ}Ž1 ÂP †S2Y<‚9ŻĹ*BˇVđ ‚N⤎Š®­Gó(ď¤Ď¤c‡|?!?É'ăéśSžčä3>gt#Í”»Ő§+•žÜ^wrëŽ~ĂŹűóB®Ü.9#Wń!ăôHľâ"Ć…ôPŚ‚˘x+š—"B I Ŕ/ >Ў€i`¦$fŕ_Ł…$hЎ¨†˘Šj(ŞˇD{Ł{-ĐĘÓŽ~ćęb°endstream endobj 230 0 obj << /Filter /FlateDecode /Length 117 >> stream xÚ31Ö3µT0P°T02W06U05RH1ä*ä22 ()°Lr.—“'—~8P€KßLzú*”•¦ré;8+ré»(D*Äryş(Ř0Č1Ôá†úl¸ž;¬c°ÇŠí Čl ärőä äÇ\+ßendstream endobj 231 0 obj << /Filter /FlateDecode /Length 251 >> stream xÚ…±JA†'\!Ls­ÝÎ čޱžšĆ…Á+­,ÄJ--íÄ;đĹy‘µ˛4ĺB–[çO"hŁÍWěżüßĚěąÝf,•4˛s n,͡ÜÖüŔÎéc%űÍ:ąąçIËöRśc{ŞĎlŰ3yz|ľc;9?–šíT®j©®ąť ŤfDT„żP&E—{ĺh+ç•9G2ËĎD~ţ>/BGŻEđô$E7č~ }§ř¬ť€źK…ŃvmV›:¶Ľ«$ę,HŠ@•%ˇj»}¦W”}ţŤałÂzHő‘ ¦OŘ#bŁĽA=đb2ńßăŕ~|Ňň0Žendstream endobj 232 0 obj << /Filter /FlateDecode /Length 116 >> stream xÚ31Ö3µT0P0V0S01T01QH1ä*ä26ŠE-ŔÉą\Nž\úá Ćć\ú@Q.}O_…’˘ŇT.}§gC.}…hCX.O† řA-Âţ˙˙˙€ř˙4‚Šv@  Ăą\=ąąemH™endstream endobj 233 0 obj << /Filter /FlateDecode /Length 103 >> stream xÚ31Ö3µT0P0W04S06W02TH1ä*ä2 (B$’sąś<ąôĂŚ,ąô=Ląô=}JŠJSąôťś ąô]˘  bą<]ę˙˙˙đ˙˙˙0 âsązrrŹĺ$~endstream endobj 234 0 obj << /Filter /FlateDecode /Length 99 >> stream xÚ31Ö3µT0P04F †† )†\…\@Ú$l‘IÎĺrňäŇ pé{€IO_…’˘ŇT.}§g ßE!¨'–ËÓEAžÁľˇţŔ˙0XŔľAžËŐ“+ ‰;“endstream endobj 235 0 obj << /Filter /FlateDecode /Length 203 >> stream xÚť= Â@…_°L“#8ĐMLRŘđL!he!Vjiˇh'šŁĺ({„”!qś-–6߲ó`ö}›ÄĂtĚ!'<8 9ń1˘ Ĺ© ĺ»äp¦iNfËqJf)c2ůŠo×ű‰Ět=ăĚśw‡{ĘçŚŢ@в¶^m ´­…ו„ű•WĂ·¨”x:ô däTLdOń”€_Öű'¤X`–*şw]!WҢqťµ˝z¨‘ş9KőUóďĐ"§ }}ŤdĂendstream endobj 236 0 obj << /Filter /FlateDecode /Length 141 >> stream xÚ31Ö3µT0Pac S#…C®B.# ßÄI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘  bą<]Řř XŠí¸˙7001;×ńľÄójä‘Ô®˙˙˙Á˙˙˙?Ŕ0ĎĺęÉČĹFJÜendstream endobj 237 0 obj << /Filter /FlateDecode /Length 222 >> stream xÚeŹ1N1E˙*…Ąi|„Ě đ.›-V Ab $¨(U ¤A›ÝŁů(>BĘŃóÓ„,?kĆ˙ŹWíEwĄµ®¸kí.őµ‘i;ŻO%/¶ď˛$=iŰIşó®¤á^ż>żß$­n´‘´ŃçFë6Šx0ڄʬ íÍŽX⌾T†~ÂčËϰśfGvÄlŽâgŘ×ÎOČ —Ŕ<|žđHTGÇ‚+µ§Ë‡D5˙WôTŚL3ü*١¸=·‡2š˙Đţ‚˝,·<Ę8hńendstream endobj 238 0 obj << /Filter /FlateDecode /Length 226 >> stream xÚEĐ˝NÄ0 đ˙é†J^ňń @ZÚHH•îC˘L @°Ň>ZĺáƧúl·ŔźDZăTĺe}Í9W|Qp•s}ĹŻ}PYkP·ĺ|ňňN›–Ň#—5Ą[ SjďřëóűŤŇć~ËĄ?ś?S»c„€Nz¬DČDF‘âM&4=:4§WâLě• Ť«hLşVĆÚšÄQ—5Aýâ1;Í,ňw×Ki üs°Ä™ăÇ…ŕ Îdw;«Ň-Ż—y"źÍ§\ŰĽ>ą˙í[z 3áVc4endstream endobj 239 0 obj << /Filter /FlateDecode /Length 181 >> stream xÚ•Ď=‚@ŕ!$Ópć.ż bâ&ZY+µ´Đh ŤŁpJŠŤëL±hë$ó%ó^5YşĚ Š(áÍĘşÄxÇT˛HN)Î7¬4ŞĄŞ §¨ô–žŹ×Uµ[QŚŞ¦cLŃ uMţÁÄ„B9ÓĚĆ›‹‘ńGĐ3aç(if ăMŽĹ( Ś/˝#ěŤ`Ëc„÷—V2öOZËżZ;ý®5îńÜţtýendstream endobj 240 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚĄÎ= Â@ŕ‹Ŕ4{„Ětł&)!ŕBĐĘB¬ÔŇBŃÖ,xŻ’ŁxË’qFEĐÖćťŮ}o“¸ťv)˘„ZŽ’RGk‡;ŚSʱóÚ¬¶ŘĎŃÎ)NŃŽeŚ6źĐaÜ íOäĐiá(Zb>$Ă\CČĚßČĚüÇą.ě5ďŞTĘÂş)ń7˘ ˝śůPĐ €ů\č)'…ß'ĺ-,e›ů$9óŇ‘• i«ĚŚţ `ľAYŇ Öš G9Îđ-˛c—endstream endobj 241 0 obj << /Filter /FlateDecode /Length 241 >> stream xÚmŽ1NÄ0E”"Ň4ąž @’T––E"Th+ ¤Ř´±ŹćŁř)S„ ăÍ“ü=3˙uíEĹ5w|ŢpWsÉ/ ©í5ÔgűýóüF»ŞGn{Şn5¦j¸ă÷ÓÇ+U»űkn¨ÚóSĂő†=6™Ě@! `dŐHpŃëłÎ糢˘˘Ś°0g0ş°żp ă†\ĎŤF<'ź"D´MÖbLz[‚Îë€őZj6]*7DEńă?°?(Łj”A…LP5ăË GŐÔˇµ(O•Y*GŇ@BRć ›č ţ5pIendstream endobj 242 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚ•Í˝ Â0ŕ+Â-ľŢ hÓ NB­`A'qRGEÁÉöŃú(}„ޤzW©Eqń _Čĺ~3°#ň) ľ¦Ŕ';¤ťĆ#ËI~š×Ďö€ˇCµ"cQÍ8ŤĘÍé|şěQ…‹ iT­5ůt]ăÁ‘ Ů'é`ś010%p1ßŕ ­‚içBĆt*R¦—€t 2;nB)Ľű˝˘¦•×4㪙_T+~Ѭý‹.ś:\âăM†endstream endobj 243 0 obj << /Filter /FlateDecode /Length 213 >> stream xÚ}O» Â@ś`q°M>!űz‰I «€0… •…X©Ą…˘­É§ĺSü„”Áő˛WŘp w»3s3Y:Ę'sĆĂ„łó1şPš»ˇ{¦~s8Ó´$»ĺ4'»tc˛ĺŠo×ű‰ět=ă„ěśw Ç{*ç Ó(¤DžĽ`D:„ťy#jAÔ BQ»SQ]9h@ř”˘9…׆mđĆ 3/"-PI˙oÓ™n•§ ŐŞË×ŮńÍó?|ÉR3{żľ‡6ŇnÚRűúć}Z”´ˇëĺnendstream endobj 244 0 obj << /Filter /FlateDecode /Length 245 >> stream xÚmŹ1NÄ@ EmÉÍa|HB’b«‘–E"Tjˇ¤`í&G›ŽkřéHĹü 4ŇÓŘŁńnęóv+Ą4rVISJ{!Ożrݢ‰˛ţ~9Ľđ®ăâ^ę–‹k´ąčnäířţĚĹîöR*.öňPIůČÝ^(ź‰(`)3SÚŤčçą1›É+-:%ô8p'?, ó\üú‡%ᔀ^ĂŠ‚úH˝"Č4źť)ÂMˇń©úP¨9%7ąHič/üŠ!©Ż Gó«dLşâ!n&{„ÁŹČë•|ÚŇöÍ J™MřŢc_u|Ç_ž!r·endstream endobj 245 0 obj << /Filter /FlateDecode /Length 107 >> stream xÚ31Ö3µT0P04F Ćf )†\…\††@ľ –IÎĺrňäŇW04äŇ÷ sé{ú*”•¦ré;8+E]˘zbą<]äěęüőěäđě:¸\=ąą{-=endstream endobj 246 0 obj << /Filter /FlateDecode /Length 110 >> stream xÚ31Ö3µT0P0V04S01T06QH1ä*ä26 (Z@d’sąś<ąôĂŚÍąô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ő˙˙˙˙Ä˙ °‘§\®ž\\şâAŠendstream endobj 247 0 obj << /Filter /FlateDecode /Length 184 >> stream xÚmÉ=‚` ŕ’.žŔŹß‰1‘ÁD'㤎]…Ä‹‘8p n #ˇ~ $(}úö­ëL<źL˛ĺ¸6y6í-<ˇÓvf{¶ŹÝĂĹšĹ\¶(â]Î׊p9% ED‹Ě-Ć4 đ•ÓžgŹö&ëÉ{ôĽřâ!1îĺĄqú?µ\ŔÜ PůCÁµ#ýA“dZz–4Ŕu ×,işÔu8‹q…/ÂaoMendstream endobj 248 0 obj << /Filter /FlateDecode /Length 190 >> stream xÚ}ʱ‚0†K:ÜÂ#pO`iŔ‰1±‰NĆI4ş ŹćŁđő®ŘîK˙ëÝůÓdąĘ0FM•j\iĽjx@½%\îPPGL2P[ę‚2;|=ß7PĹ~Ť¤K<ŃäL‰•sŤ ´Â9×óËy|Ą9#l K#‚vÓś_ó[ąZ˛˝äC„N Ň_‹¦CŁ•čFôŚĎ,úa8č—‘[NÔřXT®®ţQ­€Ťü÷âŠÝendstream endobj 249 0 obj << /Filter /FlateDecode /Length 218 >> stream xÚťĎ1NĂ@Đą°4ŤŹą¬—ŤQY AÂT (‘A‹ĂÍrÁĺ 3AzšWĚJ˙_¤ăć”kN|yą9á‡H/”–v¬ąIű—ű'Zun8-)\Ř™BwÉoŻďŹVWg)¬ů6r}GÝšĹ3J•~ ZýôŞýT™MčĄŘa.ĺĘ)Ąś- ™oö̤Ĺ/˝ó`t™śÝ˙ţRôř27ČäVÖŻ˝ifđöíh·ľhăŰ`+-·RűˇÔŃŇěNç]Ódvg9endstream endobj 250 0 obj << /Filter /FlateDecode /Length 147 >> stream xÚ31Ö3µT0P0b#SC…C®B.c ’HÎĺrňäŇW0¶äŇ÷Šré{ú*”•¦ré;8+ů. ц ±\ž. ő˙˙˙˙Ä˙ Řć Ś„ † ‚`|$€lthv›b)ŘŚ‡6 ˘ŽäŁ˙Q Ř.WO®@.ĚŚ‡rendstream endobj 251 0 obj << /Filter /FlateDecode /Length 145 >> stream xÚ31Ö3µT0P0bCSC…C®B.c ßÄI$çr9yré‡+[pé{Eąô=}JŠJSąôťś€|…hCX.O…ú˙˙˙˙â˙Hěó" Á€ř$`@±ŘCLÁmQDý˙ ˙!Ä( ,ĆĺęÉČćxôendstream endobj 252 0 obj << /Filter /FlateDecode /Length 120 >> stream xÚ31Ö3µT0P0b#SC…C®B.c ’HÎĺrňäŇW0¶äŇ÷Šré{ú*”•¦ré;8+ů. ц ±\ž. ő˙ţ˙ů˙źń˙?cŔŔ€ęÄ˙˙˙±4± Nŕô%—«'W ž‡äendstream endobj 253 0 obj << /Filter /FlateDecode /Length 123 >> stream xÚ31Ö3µT0P0bCSC…C®B.cs ßÄI$çr9yré‡+›sé{Eąô=}JŠJSąôťś€|…hCX.O…ú˙ţ˙˙€L€ĹŚÁN|Ś? ę˙˙˙˙ă?*űŔĺęÉČé f’endstream endobj 254 0 obj << /Filter /FlateDecode /Length 177 >> stream xÚ31Ö3µT0P0b#SC…C®B.c ’HÎĺrňäŇW0¶äŇ÷Šré{ú*”•¦ré;8+ů. ц ±\ž. őř˙ü˙Ŕ ˙Bü`°˙W$ţđ‰ü{Ş1y ꑉůŚ0˘źń1Śh†í͇ÄqŃ|ĽFĽ‡Ťď™aÄ Ń𕨠‚l˘č·?`ż!°—«'W ±,endstream endobj 255 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚUĎ-Â@ŕ%&c¸Ě 迨¤”„ P‚$ޤu˝Ö’[GEÓev›¶ ćKŢ1Çî»hŃ8ş&nLŘ-;CFąXďŔA_ í>ˇôpŠÇĂi ş?!ĺ—&+ŚRĺ"c˘(ɉ(§N+ƵGÍSro‰›‚W\ŻŠ‹"­ŕЬćüĎ ¦+éŐtI…–đߣmĹ›h5|Ö ¸ü‹˘dXB]/†qsřş‰|endstream endobj 256 0 obj << /Filter /FlateDecode /Length 174 >> stream xÚ31Ö3µT0P0bSC…C®B.cs ĚI$çr9yré‡+›sé{Eąô=}JŠJSąôťś€|…hCX.O…ú˙˙0ü˙˙˙ř"ţ3Ĺţ70`řHŘţ@‚ýŚ`?€#^¬„ůŠ^°Q`CĆ-YÉ ˛śä f€˛ Ô$ę˙700€ F"Ŕb\®ž\\ć„wNendstream endobj 257 0 obj << /Filter /FlateDecode /Length 209 >> stream xÚĹĐ1nÂ0Ćń/Ę€ô–!ďÔ &HYj‰‚Ô •Ú©CŐ @°Ć9jŹ1CäÇ‹KŞŢ ’őěĺű{iËŠs.y^,ŘV\.x_Љ¬ŐŰśWËűÓîHëšĚ[KćEďÉÔŻ|9_dÖoĎ\ŮđgÁůŐ† ůĆHŹLd€ pÝLiŕˇ'ŇîAi ű?’NIű¬ iďÚ&tZÁéŕ0÷^gú±Č…ź¶X{cąţ‚Y7‘öÉ01ÖŢńż<¶5˝Ó Ż endstream endobj 258 0 obj << /Filter /FlateDecode /Length 197 >> stream xڕСÂ0ŕ›jrfŹĐ{Ř::"#a‚‚ ‰€€îŢ eŹ0‰XvtmC‚ůÄßöîOőhŽ)¦„Š´¦TŃ^á µ˛aLiâOvGĚ ŚÖ¤FscT,črľ0Ę–S˛iNűf‹EN†`ćŇY9†»Q‰¶3p‚qNĘNŮ3Ľ˙¶ßO0ďÉn‹ßč¶ ×ÄZż’J4˝&}ţ5tĘň›¦y+™A˛ý ˝-ŘĽ+Ô€łWř2>z endstream endobj 259 0 obj << /Filter /FlateDecode /Length 236 >> stream xÚuŹ1NÄ@ E˝Ú"’›a|„$ŐHË"‘ * D”H»$*âŁĺ\!GŘ2HQĚw€‰ćÉă˙m˙©«ăćT ©ĺ¨”şćDJŢsŐ ‰gő­Ü?ń¦ĺx#UĂńmŽíĄĽ<ż>rÜ\ťIÉq+·ĄwÜn…™ĺş2űĐĚĚ4w„C0Mý€¤LúNÔéL”túAř ¨9ÁçŇ„Éa=tCą6”8y€ÇF˘Ě›ÔaĄOÚ2éý/ňaÁ<Ăô&ÄŘůE>oůšżĺxvendstream endobj 260 0 obj << /Filter /FlateDecode /Length 124 >> stream xÚ31Ö3µT0P0b#SC…C®B.c ’HÎĺrňäŇW0¶äŇ÷Šré{ú*”•¦ré;8+ů. ц ±\ž. ő˙˙˙˙Ä˙˙ˇęęđ@†H0 zÂţ˙(Q˙˙—ËŐ“+ +ňT¬endstream endobj 261 0 obj << /Filter /FlateDecode /Length 167 >> stream xÚŐË1‚@…áG(L¦áĚtYY +ÄD ­,Ś•ZZh´†ŁqŽ@IaGhôf'_ńϬ‹gÉ‚#}SËÎqbůléF.b27§+e™=»ĚZ3™bĂŹűóB&Ű.Ů’Éů`9:R‘s)U*µH]JóíŘý^‡żwźř¤Ôč¨%ÂH«´RQCôŞ/ťę‰~ú´*hGo8‚endstream endobj 262 0 obj << /Filter /FlateDecode /Length 192 >> stream xÚ­Í= Â@ŕ )Óä™˙"U F0… •…X©Ą…˘mńb Ża—Ň”)®ł‹¨pŘůŕ˝)6 GqBĽQ@±O[ŞÎSQ6{Ě t—&čNąE·Ńéxޡ›ÍÇÄ9§•OŢ‹śŞŞA â‹î¬ě†q“©ÍŇÚĐđ@# ~8 ©ˇ¸ôŽćÚŘ7Ĺť±ÚÇłm'cČúđh„˘ü/–ämŮý˘:ś¸Ŕ“^[Őendstream endobj 263 0 obj << /Filter /FlateDecode /Length 182 >> stream xڥϱ Â@ €á”Y|„ć Ľ–¶ j;:9“::(ş¶}´{”{„ŽŇ3‘ŢŇŐ!äŽH–ÎóĹ”ÉÄ”'tIđŽiÎűo•Źó ËőŇő†_Q×[z>^WÔĺnEĽWtL(>a]Qáś3-c'4‘aŠÎÓÓ|` ÁBAőž™I=E’zNGţKCö ¬8e  śŽpެ“‹&Č•×5îń űÚlÎendstream endobj 264 0 obj << /Filter /FlateDecode /Length 114 >> stream xÚ31Ö3µT0P04WĐ5W01T0µPH1ä*ä22Š(™BĄ’sąś<ąôĂŚŚąô=€â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ő˙˙ü˙˙†ţüa`üč?’›îçrőä ä—5ezendstream endobj 265 0 obj << /Filter /FlateDecode /Length 116 >> stream xÚ31Ö3µT0P0VĐ5W02W0µPH1ä*ä22 (™Bd’sąś<ąôĂŚŚąô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž. ő˙˙ü˙˙‚ę˙˙c`¨ü¨ć`°›˙pązrrléIendstream endobj 266 0 obj << /Filter /FlateDecode /Length 104 >> stream xÚ31Ö3µT0P0UеP0¶TĐ5RH1ä*ä26 (A$’sąś<ąôĂŚŤąô≠ô=}JŠJSąôťś ąô]˘  bą<]ę˙˙˙ĎÄ˙а—«'W *›endstream endobj 267 0 obj << /Filter /FlateDecode /Length 191 >> stream xÚmĚ= Â@ŕ Óx„ť¸ ‰‚Ő‚?` A+ ±RK E[“›™Łä)S,;Îh%Xěűfćůh<Ą” }ĺ:exĹ\łTż:8^pV˘ÝQ>E»’mą¦űíqF;ŰĚ)C» }FéËEÜ$ s­´ŕXB×^H”Č©ÁĂ@ž?|Źbe¨®źŕzY©E—â˙đTZ_Őq×-`öRĹ!a~…„®K<.KÜâj/\endstream endobj 268 0 obj << /Filter /FlateDecode /Length 187 >> stream xÚťŽ= Â@…g°¦ń™„Ä"•#¸… •…X©Ą…˘­ÉŃr”aË€!ăN;±ćď˝GÓY‡®âg!źBşR¤ł@[]/”ňw%äŻÜ”|łćűíq&?Ý,ŘőďÝĺLĆą©ż+đx•“Ŕ—´€"ҡ@±y‰Rx Ś-¶0ޱéŤţ~Đ*ž?˘uîmÖ˝rç!0±eĄć] ÔEÓ`ç%ĐŇĐ–Ţ*Ĺszendstream endobj 269 0 obj << /Filter /FlateDecode /Length 182 >> stream xÚŤŽ1 Â@Eż¤¦Ik—9›°° Än!he!Vjiˇh›äh%G°L2ΦĐÖ…}đgŮ?of§óÇśęťĹlS>'t#k5Ń?ś®”;2{¶–ĚZ§d܆÷ç…Lľ]rB¦ŕCÂń‘\Á¤"iJzŚDĆ=á[5/”ČjLAOĺQ~Ńý‰ßʎ@«B_ŐZŻh4čĘJ—â5ˇÎ«µ^RMuZ9ÚѲuEJendstream endobj 270 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚ31Ó34V0P0VĐ5T01Q0µPH1ä*ä21PASKLr.—“'—~¸‚‰—ľPKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓE˙ó‚ÁţT‚zó !˙HÔ±÷`řÁřţó†ú쀶¤ „|P±=i«‡u âÉDŞ)öph‘<„ÚkrF=ČAď?0ţ`<˙ꎆ˝˙?ü?ţ˙ ě@‡sązrroXhIendstream endobj 271 0 obj << /Filter /FlateDecode /Length 189 >> stream xÚ]Î1 Â@Đ\B/ 8ĐM˛(ÚЦ´˛+µT´“čŃr”!ĺbI qáÁ23ü;čŤö9änŔ¶ĎvČű€ÎdC)úlGUgw¤IBfÍ6$3—2™dÁ×Ëí@f˛śr@&ćŤm)‰Úť¸·2Ď©\^ˇsϵ2¸Î÷ŻHĹřQ‰RńţQÖOţř—Ö5ÉQŃJrµěhč MťŁíÂá„TĺrŹLĽ@ł„Vô˝Ł@ endstream endobj 272 0 obj << /Filter /FlateDecode /Length 141 >> stream xÚ32Ő36W0P0bcSK…C®B.# ĚI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘*cą<]ę˙70đ|Ŕ ßţ€Áž˙C˙`ĆĚ00Š˙˙˙Çäč§3˙a`¨˙˙Žą\=ąą˘&[endstream endobj 273 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚŤŹżJ1Ćż00…ń v^@ł9ďäŠĂ…ó·´˛+µT´[¸}´> stream xÚ31Ó34V0P0bS …C®B.C ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. Ě€ŕ?É&™iN‚ěaţ`˙D~°’Č700nŕ?ŔŔüťDţ“ŘŔČä‡$Ů€‚ë˙˙˙˙7 “\®ž\\yendstream endobj 275 0 obj << /Filter /FlateDecode /Length 122 >> stream xÚ32Ö30W0P0aCS3…C®B.C ßÄI$çr9yré‡+Zpé{Eąô=}JŠJSąôťś ąô]˘  bą<]ř0Č@A@ 8~Ŕüá? ±q©ŽŘ0ü˙‚¸\=ąą(CE`endstream endobj 276 0 obj << /Filter /FlateDecode /Length 150 >> stream xÚ32Ő36W0PĐ5QĐ54W0´P05SH1ä*ä22 (Ăä’sąś<ąôĂŚ ąô=€\úžľ %EĄ©\úNÎ @Q…h ®X.OĆ ěř   P?`üÁđ†Ř€¸ôE6Ś?ęügüđź‚üc?PĂ~Ŕ†ź˙ó.WO®@.˙§Wőendstream endobj 277 0 obj << /Filter /FlateDecode /Length 196 >> stream xÚµÍ1 Â@Đ•ir3'pŤ.#BĐĘB¬ÔRPQ°ÍŃrʱ0EČ:? ędŮł3ó7čuÂ.{Śô¸ňʧăH‰ĆrCqJzĆGz$ݤÓ1öÇ5éx2`źtÂsź˝Ą […RĘüâë?´LőŤşćÝ3Ř‚ćrÁĘkm‚¨„;xÔÂ3ęH†Kv¤Ř@%Żâ.ęýoÔ nn—**ŚÉŤů@Ă”¦ôDrendstream endobj 278 0 obj << /Filter /FlateDecode /Length 108 >> stream xÚ32Ö30W0P0aCS …C®B.C ßÄI$çr9yré‡+Zpé{Eąô=}JŠJSąôťś ąô]˘  bą<]?0ü‡!ţ ̱˙`ř˙˙qązrrĆ‚Q.endstream endobj 279 0 obj << /Filter /FlateDecode /Length 177 >> stream xÚ3łÔ3R0Pa3scs…C®B.3 ßÄI$çr9yré‡+™pé{Eąô=}JŠJSąôťś ąô]˘  bą<]?đ`Ŕđ˙ý†ú@ú=ă:†˙Č77Ř3đnŕ?Î ßŔüť˙ţÇŔD˙a`˙ÁŔN˙``˙€ŤţŔŔţ`Đ O€â˙˙˙˙7˙˙ŹNsązrr#ßendstream endobj 280 0 obj << /Filter /FlateDecode /Length 147 >> stream xÚ31Ó34V0P0bcs…C®B.C ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. Ěř?00ü˙`˙D~°’Č70đnŕ?ŔŔüťDţ“ŘŔČä‡$Ů0˝ń˙˙Á˙˙I.WO®@.‡e%endstream endobj 281 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚŤŽ1‚@Eżˇ ™†#0Đe6 &na˘•…±RK Ťv9Gá”Tâd)H¬ĚN^fţîţů‘žĚ¦đ”ÇšŁ€Ă9ź5Ý(ŚE”qŃßś®”R{cRk‘I™ ?îĎ ©l»dM*çćŕH&g8^W‰S­śQdHŕVđá•Rľ ň!J*¨- Ŕi~ nNű/†oońkg»Íîő$AéÖHĺŠ> éáwlzZÚŃIKÚendstream endobj 282 0 obj << /Filter /FlateDecode /Length 196 >> stream xڝα Â@ ŕH†Bˇy˝ž­uj;:9“::(şÚ>ZĄŹp"ŘŠç]qĐQ |CB’?Šű2ä€Ü“1G!‡#ŢI:R°«ařm”d$V$f¶O"›óůtŮ“H–$R^K6”ĄŚĘŻŔ¨\ąUW0÷Â/Ľş%>Á«°T¨5*č´4hy~“˙Ě÷ö˛Ąý¦Ýß> stream xÚ31Öł0R0P0VĐ54S01Q06WH1ä*ä21PASc¨Tr.—“'—~¸‚‰—ľPśKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEůĂůŚęŘ0üa<|€ůĂăěĘđ?`0?Ŕ€Áţ€> stream xÚ36Ň35R0PacCcs…C®B.# ßÄI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘  bą<]ŘČ3üPŕ˙ĂÇţ?n˙Ŕ˙śýó3 ~Äo0˙ah`ţÁŔ€‚?PłÍü˙˙sązrrjŮF„endstream endobj 285 0 obj << /Filter /FlateDecode /Length 195 >> stream xÚ=αJÄ@ŕ¶XfßŔĚ x{›`TńSwŐ‡•Z * WîŁíŁÄĘ6`“"8Î%GŠŹ™ů˙fŠ|q~ĆK.ř4pˇó‚˝R^j¨çĺÔ<> stream xÚ36Ň3˛T0P0TĐ5T0˛P05TH1ä*ä22 (Ad’sąś<ąôÌ̸ô=€Â\úžľ %EĄ©\úNÎ †\ú. ц ±\ž.  Ř W á Ś@Ě Äě@,˙˙?Ă(f„ĘQ „ţ0‚pC sC3=;˙?°f.WO®@.uH–endstream endobj 287 0 obj << /Filter /FlateDecode /Length 153 >> stream xÚ31Ó34V0P0RĐ5T01Q06WH1ä*ä21 ([@d’sąś<ąôĂL ąô=€Â\úžľ %EĄ©\úNÎ @Q…h žX.Oć ěţ`üŹJň`Ŕ‘p’şŤBţ`°ŔŔđˇüÆç˙왏Iů˙í@’ůĐ.WO®@.1cendstream endobj 288 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚU̱ ‚PĆń#‘k[çęŞWJ'Á rjj ¨Ć†˘¶űh>ŠŹ`›Ph—ş—jů ˙ľ@ Bź\ň©ďQŕ“ŇÎĂ#ŠHE—ÄčłldČ—$"äS•‘g3:ź.{äÉ|Lň”VąkĚRj×_ś śŇ.Á.X ,g0i)ŕ <ˇĄ©ˇp¶&†®A†=éjś|c(v‘kŘ]ţb=ŔĐ(ÔżáúO¨ÁI† |FŁ?ęendstream endobj 289 0 obj << /Filter /FlateDecode /Length 233 >> stream xÚUÎ=KĂPĹńs Xxłv(ćůzËíËb ­`A' ÖQ|A7©‘|±€Đ~ŤLťďx‡`Ľ7UÓN?8gů«áá°Ď!ńAÄjŔÝĎ"z$Ąěr·ż~nîh”ĽdĄHžÚ™drĆĎO/·$GçcŽHNř*âđš’ WUPń÷6ľAß´4ćđŠ5ą§q ‘ţ" bxŘ%âtÇqżÁ_ů®cůGŲh;˛š÷L€ Ëtč5Â<ţfúOk…2·|âµÁ+ń–ZlECÝdŃ ±ď(°çÂŃIBôĄY_™endstream endobj 290 0 obj << /Filter /FlateDecode /Length 210 >> stream xÚMν Â@ đ)(ˇ«Đ> stream xÚUÎÁjÂ@ŕYi® Î čn˛Ző$¨sÚSE¨GÁ˝‰ćŃöQ|„x ‰ł˛Iéĺ;üĂüü=ÝF¤(˘N8 ^DúŤÖ!ţ qިŻÝiµĹIŚň‹ôĺśs”ń‚öż‡ ĘÉÇ”B”3úI-1žQY¦ăâŹŕAćgŕ//7śŽ4gËZŽvŞ*Ě 0‰ĂżŠ+ă]S‡¸CEÉ@QsüϰFŐě,IŤqSn/Ľ'¶’gCţbź^m‘mjg`ç1řă'>ÚźKřendstream endobj 292 0 obj << /Filter /FlateDecode /Length 183 >> stream xÚ%Î1 Â@„á‘@„‡$|'0‰+AA˘‚)­,D¨Ą ˘ťŹćQ<‚eŠ`śŤĹ_ěě·°&î# µÇL_M¬‡H.běÚŁ˝Řź$I%ب‰$Xp• ]ęíz?J¬¦Ęu¦[>ŮI:ÓIU•uO§Ă)Fh~đß!;Łó:cňĚŰዬQÖ‘‚ôź˙)H˙ĺpIëH]R·YŔ#őH[¤mé(ś˛âl2Oe-?uŕC endstream endobj 293 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚUϱJÄ@ŕYpa ÓZ7/ {IŚ(ČEÎ;0… •…X©Ą ˘Ý‘ËŁĺQöRn!9˙ŢÂ×Ěěţ3›źź^¦:×LORÍ -Îô5•OÉ3çZcçĺ]–•řGÍ3ń·,‹ŻîôűëçMüňţFSń+}bĐłT+Ž\QM=`Čţ.If °`kCtŤý3Ü›íŘOZm°ťé\01iůt3(N‹í¨ä¤˛˙g7ť~Ü`O=ŮNcË–ąŽ3\‹Cpl:\ rĂÚîÓ u%ňoGĘendstream endobj 294 0 obj << /Filter /FlateDecode /Length 175 >> stream xÚ3±Đ31Q0P0bScSK…C®B.SßÄ1’sąś<ąôĂL ąô=€˘\úžľ %EĄ©\úNÎ @Q…h ĘX.Oţ ę˙ł˙g``üÁ~żůűĆ˙üäŘ˙É?`°gŕ˙¤ęŕÔ őN}`o`üÁŔţ¤›™ÚÔřFŃ¢˘˙0°˙˙˙˙? Q\®ž\\ŕ  endstream endobj 295 0 obj << /Filter /FlateDecode /Length 172 >> stream xÚ31Ó34V0P0bSK…C®B.# ßÄI$çr9yré‡+qé{Eąô=}JŠJSąôťś ąô]˘*cą<]ř0Aý? Ář˝ýăů† ö@C˙ůA2ţ€’@5@’±D‚Ť!™dţŔđPI¸ůĚCdţĂŔţˇţ˙˙˙ “\®ž\\^ĺÓendstream endobj 296 0 obj << /Filter /FlateDecode /Length 130 >> stream xÚ-ɱ Â0…á gđ 2ś'0ą-Ą™k3:9 TGAEçćŃňfÚ˘|Ű˙—ŐŇ7ôlXUÔŔ:đ˘x@='eý;ý m„;P=ÜfĚpqË×ó}…kw+*\ÇŁŇź;Zä“Fy2d›ĺĎd“L*R!s™ÉB¬ąËY°ŽŘă ,P#Śendstream endobj 297 0 obj << /Filter /FlateDecode /Length 189 >> stream xÚťŹ1 Â@E°Lˇ70sÝě ’@°ÜBĐĘB„€ZZ( 9ZŽ’#XZ:IV›t«ţ 3ďOĚŘÄrÄ#˛‰xjř¨éBşN%7nt8SjImYǤ–’“˛+ľ]ď'RézΚTĆ;ÍážlĆ@TđJô ř@ đhxÁ«jzeŤ/¨ š]aöĺŮáýÝ;żíÇÎAdDÉ/ťak+ÚÎ?i¶Ą”T“‚RSĘ"§…Ą }G«@endstream endobj 298 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚťŹ1 Â@Eż¤L/ :ĐÍ®A"EŚŕ‚VbĄ–‚Š‚…EŽ–ŁäÁÍ$±ĐNxŐĚgćýˇ1‡qß„l">hş.§!Ǧ^íO”XRÖcR 7'e—|»Ţʤ’ŐŚ5©”·šĂŮ”s Î@ t€h~//iąÝKxO`L®Đ“tIVăçßxĹ?üŢůĽ¨>ö‡©(=C±uÚ•ż/ń@ŞĹRÓr•iniMoEËBsendstream endobj 299 0 obj << /Filter /FlateDecode /Length 131 >> stream xÚ-É1 Â@EŃ?^á ¦xĐ™‰‰mŚŕ‚V"ŃRPŃ:ł´Ů™&Nwoľ\ř’ž%红V\ó¦xA=y1žö:Ŕť¨n×w¸°ççý˝ĂŐ‡ ®áYé/ ­tň‹˝4č’M22ÉDłÉT&2+•<ĺ*ŘńBŰ#´endstream endobj 300 0 obj << /Filter /FlateDecode /Length 94 >> stream xÚMÉ=@PEáţ®â®ŔĽ™x¨ý$^!ˇR Ą‚°{ ŤäTß±4J2:*5ˇĹ4ĺ¬Ř`ö˘Ł˙Ć´"žfšűą@ň¶ BJJ7"”Ľď몀Đi ‹endstream endobj 301 0 obj << /Filter /FlateDecode /Length 94 >> stream xÚ32Ö30W0PaCsK…C®B.K Ďȉ&çr9yré‡+Xré{€O_…’˘ŇT.}§gC.}…hCX.O†z†˙ 0XĎ ĂŔĺęÉČ[\wendstream endobj 302 0 obj << /Filter /FlateDecode /Length 188 >> stream xÚµ1 Â@EH!L“#d. ›ÍşŤBŚ` A+ ±RK EÁBb޶GÉR¦R×l´6Ż˙ţPtĚ+îǬƬ5$ťIi;ŚXŹÜf˘$#±aĄI,ěD¶äëĺv$‘¬f,I¤Ľ•í(K~ |[äjż„W˘‚opGĎŕ ŔÄ!´—S‹˘E¦ /‹ňčzů´ĚOľ6x+Ó¸YŰ~ĺŐÎÜuĐ´ńí…ć­éÂŐ`úendstream endobj 303 0 obj << /Filter /FlateDecode /Length 121 >> stream xÚ31Ô35R0P0bc3SS…C®B.# ßÄI$çr9yré‡+Ypé{Eąô=}JŠJSąôťś ąô]˘  bą<]0001;Ëń˙ ˙aX*6T°ý†ú˙˙?Ŕ0—«'W ľťNÚendstream endobj 304 0 obj << /Filter /FlateDecode /Length 228 >> stream xÚmαJÄ@ĆńoŮ"0M^ป'p÷WóSZY ¨Ą ˘`eňh>JáĘ+ŽŚóé5‚E~°;˙Y˛¬Źšc­té_^iÓčC-/’łź+9¸’u'éZs–tî·’ş }{}”´ľ<ŐZŇFoj­nĄŰ(Ę-€~‚Ů€8¶#J^ÎQě0CÜc…0áůîČDĚ_úźžÓÁďř:ßsöNüaçü™r$_΂[-> łŔ,°, %‡s„'älĎ"łČĚńĄ™aAZŇ›M°żČY'Wň Tźc|endstream endobj 305 0 obj << /Filter /FlateDecode /Length 235 >> stream xÚuĐ1NÄ0ЉRXšß`3', ZiY$R AE¨€ ´ŘGóQr„”[¬0Ľ„‰"OĘŚóÇ“ăîČ/Ą•^—Ňź‰÷ňŘń+÷ĹVüÉľóđĚëÝ­ôžÝ%Ęě†+yűxb·ľ>—ŽÝFî:iďyŘ™-­2Č9QµµŐ EëPőE6‚f¤LÍôV»&‘ĆŕđĚÔb&e6‚€§Ńf“őŐŽó‘ňY (yâ/ifU ý°Ĺ_ cBüÔ¨M>Ő‹ý‚¸ź™°yĄ˙€‚޵¸2_ |ĂßÇ›jhendstream endobj 306 0 obj << /Filter /FlateDecode /Length 188 >> stream xڕν Â@ đ+ At-('đ®¶µťkotrˇP?ÁQđĹ_ÄÇč čý‹­łů‘äIŕőĂ+FŠĂ!Ż=Ú“™ş,ń‘o)Ń$ěG$'¦KROůt8oH&ł{$S^z¬V¤SBĢ⊠ŘŔ©ičA«äf°1ë€h‚.p;»Áö`ŻZ  \2đoóŠß›˙Ây™ł54Ö4§ňý`öendstream endobj 307 0 obj << /Filter /FlateDecode /Length 226 >> stream xÚ•ĎżjAđďnaÜ Î ˝s=b!j W¦J!‚`R ěnÍGąG°´8ÜĚśEH:›_1;ödĎyźSpŻĎnČyÎźíÉ9)¦śżÜ_6[šd?Ř9˛oR&[Ěůđ}ü";YL9#;ăeĆ銊ÇŔŚÇćҺ„ĐpQ*Ĺ+j .+xsş7á”xÄ•‘Íç–Üđ‘\ }µrÓţ† ”żř´•R ţ/:tK­¬uéîNTc¨'ŰĽ‰ŤÄ'ňˇjěiT”2®DĄ×‚Ţé+XŃendstream endobj 308 0 obj << /Filter /FlateDecode /Length 243 >> stream xÚmŹ˝JÄ@…OŘ"p›ĽÁÎ}ťdłÚXW0… •… j)¨hëäŃň(ó)S„ĎD…m>†{çüÜuuěVZj­G+­ĎÔ9}ŞäMjÇa©îägóř"›VěťÖNěÇbŰkýx˙|»ąąĐJěVď+-¤Ý*Đô@ ŹP„sŽşř‚&ľłľ[ D>#E@˘Ç†rIő~2ű> stream xڕα Â@ ŕHÁB}ŃĽ€Ţ]ő¤“…Ş`A'uŞ(¸ŮGóQî|šTZčŕŕ‘ű†?$w#3°i˛ÔhdČŽéhđ‚CË!Çá·s8cś ÚĐТZpŚ*YŇíz?ˇŠWS2¨f´5¤wĚHźPQžç®ÎëY’ 4aĐ:B@Ă ¸Ç8 ‚—1ľěn -ˇSQĽüRá-8­đ d“_Ń®Ó+ČJ˘_<˙!’Żtůâ<Á5~lúQ-endstream endobj 310 0 obj << /Filter /FlateDecode /Length 265 >> stream xÚMŹÁJĂ@EoĹŔ[8ĐĽĐ$ŤA„ŇB­`B]ąWęŇ…˘ĐEÁů´ů” ;#Ç›*ÖÍyóî{wćÎquÔLµÔZ§ZźjÓč}%OR7KmN~&wʞlĄ¸Öş‘₲íĄľ<ż>H±\źi%ĹJo*-oĄ])L OÄ[ Ŕ`;d1ëa¶°3X`LpŔM6{ä{xÖSĎś°Hpžî|tOĄ0Ł1lą6Ě ůi4ČţÓ,ěŔe3zŤźÓáw™ťgRŇô¦SĹß@v伕+ů˙cĺendstream endobj 311 0 obj << /Filter /FlateDecode /Length 237 >> stream xÚuĎ1NÄ0бRDšĆ@ň\ślÖBT––E"T ¶¤AKr®â›ě!eŠ3ł šgiŹ˙_×'aE5tĽ˘ćŚB Çź± 2¬(śÎ_žpÓ˘żĄ& ż”1úöŠ^_Ţvč7×çTŁßŇ]MŐ=¶[‚b—….'0SÉ2*(ŮŚ`&p ŢÁőBě!Ît çĽŕŇđ_čÝ_čRĄc§Ř™%Éž 6{6Cń!I¬c“Ä)A×ô?€Ö«ĚÁ“ôXZ1IÁŘËN+éOVë”ůŔäqY‰-Ţŕú m9endstream endobj 312 0 obj << /Filter /FlateDecode /Length 101 >> stream xÚ32Ö30W0PaCsc3…C®B.K ×ĉ'çr9yré‡+Xré{ąô=}JŠJSąôťś ąô]˘  bą<]dęţ7Ŕ`= 1S—«'W fp"¸endstream endobj 313 0 obj << /Filter /FlateDecode /Length 140 >> stream xÚ32Ö30W0P0WĐ54S0´P06SH1ä*ä24PAS#¨Tr.—“'—~¸‚ˇ—ľPśKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEA†ˇžá Ö3Č0຀`ý™ PČx€±±ą™ťŤ¨Ň‚ˇ€!ËŐ“+ &,•endstream endobj 314 0 obj << /Filter /FlateDecode /Length 235 >> stream xÚmĐÁj1ŕ é^=;OĐd-‘ő$¨…îAhO=”‚ĐöX¨ŇބͣíŁř{ô°N"¸Q6>fB&?™Nî'izŕmf4Őô™ăŤáZűŇ||ă˘DőJĆ zâ.ŞrMż»ż/T‹ç%ĺ¨Vô–“~ÇrEP@X×ěű8ő \˛˛IU{ó»ůÁ3ĚbĆYăĄ1Ezôč$ć'i=SË©†LÂB„p6Pu Ž–8ç:R†Ł ˛Ž÷›[4ß9޲áéí…ĂŽ&ÎČ&üZÚú'­ăXÎť®ÁÇ_đ%°mĽendstream endobj 315 0 obj << /Filter /FlateDecode /Length 209 >> stream xÚ•±‚0†Ź0Üâ#pO`Amd3ALd0ŃÉÁ8©ŁFgúh< ŹŔČ@¨…«Ú´_®íÝýýe4ŤfĐÜ,ą ą¤k”µÓ„íĹĺŽqŠâH2@±5§(Ň˝žďŠxż¦EB§‚3¦ i3 €5C8ZA–›Ŕ/:LĘ^ŐÁ­űpšôXpžŰôkÚF¶­±bIF°Ü2ŐéqžËUśNĐC¨™E>Ş_…ń÷c‹đ+v·dŻóŻĺínÔâ&Ĺ~VźPendstream endobj 316 0 obj << /Filter /FlateDecode /Length 260 >> stream xڭѱJÄ@ŕ? LaZ áć4‰ÜŞ[-ś'BĐĘB¬ÔRPŃÖĚ›ř*ľ‰yË+Äuv˛g!–Bŕ#“ÍĚîżÎďúnŮńÎ;ÇÎóMG4÷Zlyż›ľ\ßѢ§ć‚çžš-SÓźňÓăó-5‹ł#Ö÷%_vÜ^Qżd RPDZT†¸R´öR ĘOÔµ ţ@ů*Ť(ŢAWEÁ],řR‚şIµRę5ú7P­Ń&?”2oĆ(~#FLŘŕgČü5=dF#ďzv˘L;mf–Ä&,—mXJ[°Ěa Ţ#ĺ }Rş:%e-vÁvS˝•Ô=U:îéśľšes–endstream endobj 317 0 obj << /Filter /FlateDecode /Length 194 >> stream xÚ33Ö31V0PaS Ss…C®B.S ßÄI$çr9yré‡+špé{Eąô=}JŠJSąôťś ąô]˘  bą<]ţÁőBýc``üßD@.ý0Ĺ˙L1˙SŚŔĂ?UŹBŮ7@¨`JJ=SüPęŠýę (<öˇ9ĹńPŻ@=ómrüC%hACž  !@ y`> stream xÚuб Â0Đ  ·ôĽ/0­ µ‚ťDŞŁ˘łý4?Ĺ/iLsqŤđ’»INÍĆŞ ś&vŞ)©9ť Ľ˘‹ĺý¶O4¬4Ę©ĺĘFQę5ÝoŹ3Ęjł ­ioK¨k2ýč DŇŔ€§dFLƤ1’(­C8^Q€„ÉĆDđąďɰ|pĂ1ĆŰ˝Ó.ţ"bř˙yŇ€Ś)™gëşk¸×żŕRă?Uź’~endstream endobj 319 0 obj << /Filter /FlateDecode /Length 166 >> stream xÚ35Ń3R0P0bSCSs…C®B.s ßÄI$çr9yré‡+sé{Eąô=}JŠJSąôťś ąô]˘  bą<]ţŔd’ńü†˙ Ś`’ᬓ6`R‰äÁAňI68ÉŘ€L2`%™‘Hv0)"˙˙G'!âP5Čş‰ A€J$ă˙ `G@%ą\=ąąM˙x×endstream endobj 320 0 obj << /Filter /FlateDecode /Length 254 >> stream xڭѱJÄ@ŕ?l&ŹyM"&`µpž` A+ ±:--­7`ákMgé+ä ĽňŠăÖŮÍ& XšćKf’Íěż]{Üt\ó)ťp×p{Ć =SŠu¨ÄÎć‰V=U·ÜvT]j™ŞţŠ__Ţ©Z]źł>Żů®áúžú5đ(ü6S¬ßü`ŤŔ쑊-Ě— oŐ¶¸áÖëĄd‡ľŻ IľSňý03a‘™LlB".€żŃ!1ÍúOx˝&ÂpcÄJÂ&ĆHů‹¸Ł…¸Ű…„ťrI)ĄĚÜ” _ň,v0źšőů{lŘtéT–‰é˘§úî”Űendstream endobj 321 0 obj << /Filter /FlateDecode /Length 125 >> stream xÚ33Ň3˛P0P0bSKSs…C®B.SS ßÄI$çr9yré‡+šré{Eąô=}JŠJSąôťś ąô]˘  bą<]ţ˙˙Ďř˙˙?TŠńó bü78) Ŕ¤Żs‘)hčb y.WO®@.!»Ą7endstream endobj 322 0 obj << /Filter /FlateDecode /Length 106 >> stream xÚ3˛Ôł´T0P0aKSs…C®B.#3 ßÄI$çr9yré‡+™qé{Eąô=}JŠJSąôťś ąô]˘  bą<]ţ˙˙†€ˇľačcWüĹĺęÉČ3v\‚endstream endobj 323 0 obj << /Filter /FlateDecode /Length 140 >> stream xÚ35ÔłT0P0bKSs…C®B.S ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś ąô]˘  bą<]ţ˙˙˙€™dü€ţ3 eR/i& 0Č ň‚d“Ě`’LĘ?`üßŔđ˙Á@!ą\=ąąAfl÷endstream endobj 324 0 obj << /Filter /FlateDecode /Length 244 >> stream xÚuŃ?kÂPđ{<0p˛ Ţ'đ%ś˙€ ur(Ávt°ÔŮ€«ę•]ÝĚGČč|˝¨X#yîřÝ=8. [~›< 8˘€:˝ű¸Ä°ËµW”ĹÇ|ýŐ”Â.Ş1wQĹĎôőąú@ŐŹjHŻ>yoÉŕçŁ1 Ă˝¸ 8hFăx‡]Ę*ń›1ć•řá8§ľyşŘTBź¤,a Pł —Ŕ“M ő2Ü< ś fepŇ\$ŔIÂÖ5+zŰG4÷V¸Y5D NZ@fWđí¤'c´ÔŇÇýoĘŔQŚü¦Â!endstream endobj 325 0 obj << /Filter /FlateDecode /Length 167 >> stream xÚ35Ó35T0P0bS#Ss…C®B.K ßÄI$çr9yré‡+Xré{Eąô=}JŠJSąôťś ąô]˘ĆÄryş(ü‚ ę„úĎŔŔřż,ĘŔ ˙LńSĚ? Ô0Ĺř™adŞT Y;ŞŃPű ¶CÝuP7ČŮ˙ŔÔ ™….ĵ—«'W ŽK€żendstream endobj 326 0 obj << /Filter /FlateDecode /Length 221 >> stream xÚ•Ń˝ Â0đ–‚ě#x/ i*Uś ~€ťÄIťíŁů(}„ŽJăŮK Í"&…äHr˙t˘F*ÄÇ8 q˘0šâYÁ Č€f4ăĘé óäžę ×´ 2ŮŕăţĽ€śo¨@.ń 08B˛D­uĺĐ uf,HW§‚ ôĄlüfëç¬(şzĄeő§Ö~űüćަŹŘô§ą_Qš@™ńÍëő6Ň+L®6źńeĺlóZąš˙«›v,XżŐKéP~ď‡ŢEÔşeŻÖ©úN=â’ą«vđ™<›Âendstream endobj 327 0 obj << /Filter /FlateDecode /Length 256 >> stream xÚUϱNÄ0 ŕżĘ)Kˇ~h{=îÄB¤ăč€Ó @°!ZŢĚŹ‰čF%PŤsw ˛|Jě8¶ç‹Ăަ’ćt0ŁůŚŽŽé®rŹ®^j°¤EµËÜ>¸U㊠ŐKWśkŘÍ=?˝Ü»buyJz_ÓuEĺŤkÖ?€ĆŚ!ňÎf°l#>Ů3ZÎ;@Î'€ç7Ŕîx ďÉ&Ś&Č–Nm9R0—!ˇG/aEďFD+E$˝Ńڵ˛MX‰ż„^É>a‡-úĆü‘M˙čű=¦×:upÇ´–¤-µiŢ}őčGŚA§Š^{s¦ywÖ¸+÷=ź†#endstream endobj 328 0 obj << /Filter /FlateDecode /Length 150 >> stream xÚ3µÔł4W0P0bSsJ1ä*ä2ńÁ" Fr.—“'—~¸‚©1—ľP”KßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEÁţ?<@Ł0˙g`ÇŔřŹůA büP˘>€©T*L`Ą€)‹`J+ŦF ĹţżHĘ‚Ťârőä äWÎr°endstream endobj 329 0 obj << /Filter /FlateDecode /Length 191 >> stream xÚĺĐ= Â@ŕŃÖBČ\@7‰¬ĆJđL!he!Vj)¨h«9šGÉ,SëlĹ3X,ßňfâu˘VsŔmnFlzlşĽ é@ĆH¸¤¬w4HH/Ř҉I'S>Ď[ŇŮCŇ#^†¬(±µĘ>ńl \3X~ZPCAůŹ©J'BEH?4€ţ—şôuâ7{©-'żROrď%ËxşVÝ™‹Ă·ąCŮ ď qBszŘxaşendstream endobj 330 0 obj << /Filter /FlateDecode /Length 307 >> stream xÚuŃ1KÄ0ŕW „ăşv8ČűÚôÎbť ç vtrá@ť˙…?'â)Îť¤Cąř’ŁâMHřH^ÂK^Yě/Pá÷ćX.°8ÄŰ\> stream xÚmĚ; Â@ŕ . ´Vf. ›Ť´1ŕL!he!Vjiˇ(X›Łĺ({„”Á8룗ĺř‡ůÝéĹQ—Úš’ş}Úi<"ĎČĹ÷f{ŔQ†jĹ{T3ŽQes:ź.{TŁĹ4Ş ­5EĚ&ˇ€ş6äüĄ…°%/_x÷/PAP02gřýÁ0Ҧ–yp&îî¬dBw›:Ś+0đÁüâ}¨ATľyóMŢ6Ó˘5lö–˘.Ë5˛Ŕi†K|¤řTŁendstream endobj 332 0 obj << /Filter /FlateDecode /Length 198 >> stream xÚ31Ó34V0P0RĐ5T01V0µPH1ä*ä21PASKLr.—“'—~¸‚‰—ľPKßÓWˇ¤¨4•Kß)ŔYÁKßE!ÚPÁ –ËÓEůĂT‚D0Sť$ę00|`ÇŔüąAľů;˙ć ě˙ĺ00ţ* ŕÄ?8Q"ęI&ęPMĘřbŰ˝`Ëßśq ä ă ň ĚŤęţ˙:]ţ—«'W ČckAendstream endobj 333 0 obj << /Filter /FlateDecode /Length 182 >> stream xÚŤÎA ‚`ŕ'?( ‘ś ”ýüşĚ A­ZD«jXÔ.ĚŁyŹŕŇ…Tcu€ßć Ź7f: 5ŹŮđPł™° ř éL¦ %ż—ý‰â”ü MţBbňÓ%_/·#ůńjĆ’&Ľ•ÎŽŇ„ˇZŔ{ČUe5ČTŤĆ©¬Ö-Ő‡W¨6ęŔj@-ĐÉĹóOůŻÓ‰;*`{ú^‰ž[bŕTd7“ý w§”§ÍSZÓ»=endstream endobj 334 0 obj << /Filter /FlateDecode /Length 244 >> stream xÚ…żJ1‡gŮ"0M!óş·`D«Ŕy‚[ZY•ZZ(Úşy´}”<•aÇ™ąăôP1|đĺ—?üâéáIO :˘žâ1ĹH=>cTąPc;÷O¸°»ˇŘcw!»á’^_ޱ[^ť‘ŘÝĘ™;Vŕ8Ś‘?dmgPÇj·\R…q :“dÄ„*Á |…Vbn¶;głEó çdö1Öo( Ř÷aăhDB˙cüł!ýD[Áo¬1żEnĄ ౦ä%ięÝînŞ6N:ó\ŇZŰ` æ]H›_ŮI<đ?yë­śendstream endobj 335 0 obj << /Filter /FlateDecode /Length 107 >> stream xÚ36Ó35Q0Pac c…C®B.#K ßÄI$çr9yré‡+Yré{Eąô=}JŠJSąôťś ąô]˘  bą<]ě0ü˙ʉ™aăÄ˙„Žą\=ą൉Ăendstream endobj 336 0 obj << /Filter /FlateDecode /Length 232 >> stream xÚíŇ˝jAđ WÓÜ#Ü>·ÔŚ‚WZĄ©LĘ+łŹvŹrŹp!E¶›üçT°+‹ ó›ŹÝ-ĆŮÇvďŢXÓĹqöÁt;ćÍń';ë±j-->xsúŚÇéiNó©Y-×ďśgOŮ‘yÁĚ+ç#CYEI şO$RáxŠ%4DJʤnď«Ň ó˘ŁŘŇ×®U¶¤ HŞ@Yű$߸»Np·â§¤D@Ą(€ţżŘAx^ć §¨ĺ9ěĹE…˙ÇÍŰ„ÂĆip xśóś˙vÚiCendstream endobj 337 0 obj << /Filter /FlateDecode /Length 255 >> stream xÚÍ’żNĂ@ Ć]u¨ä…G¨_.!MB§HĄ•š ¦02€čś<Ź’GČx•ŞŰąF:ˇ.§źľóůĎçË“«č†"Jčň:ˇlN錞c|Ă,5˘<WOݏ(Ńm(KŃ­EGWŢŃÇűîÝâţ–btKÚĆ=bą$(“#ýŃĂ!@5@÷ŠřoJ ˙§4ö{®aäÁłĹŚňßëŽfJ®`o}4Ľ‘.lO­%ŢwŁ‹m_…mt§˘e4](z†`_ëTŔU‰řµ` endstream endobj 338 0 obj << /Filter /FlateDecode /Length 270 >> stream xÚ…Ź±N…@E‡PLĂ'ě~ >ÄX‘<ź‰&ZY+µ´Đh+ü™| ź€ÝK$\gfŃX)Éć°{÷žúä ÚřÂĘŞŹýŃĆß—üÄu%űB·úáî‘·-‡k_WÎeʡ˝đ/ĎŻ¶—§ľä°ó7Ą/nąÝySĚ˙‘ş…Čí‰壼Ł'7¬ěe†"Ę0Ň›0ĹDr„ě“92•ăDÓIŮ-٨l‘ÎčđŢ+s@!ËĘŮÂb4ĐHëÜţfoöqŽ!ţ˙C»?ů„őI?b`6ĹŔ|ŚtC t} lL™D2r1uIU'‘TuIk*’ÖT%5P%5°­!Ä.>“ĎZľâ/1˘¸ľendstream endobj 339 0 obj << /Filter /FlateDecode /Length 137 >> stream xÚ33Ő37W0P04¦ć ć )†\…\&f  ,“śËĺäÉĄ®`bĆĄďćŇ÷ôU()*MĺŇw pV0äŇwQ6T0ĺňtQ```c;0ůD0I~0Y"Ů˙Ić˙ ň?&ů¤ćDĺ(I˛ô˙˙ŕ"ą\=ąąVI˘”endstream endobj 340 0 obj << /Filter /FlateDecode /Length 301 >> stream xÚ}ŃMJĹ0ŕ)Y˛é’Ř–G_]x>Á.]ąWęŇ…˘ëôh=JŽeĄăü? ÚŻif¦“tßź ChĂžŻ6 §á±s/®ßŃ\¦ĽđđěŁknCżsÍ%˝uÍxŢ^ßź\s¸>ťkŽá® í˝ŹŔo@ŁB,DŤ¸'€DdZš"-š,-ÚB/6¨3"x‰š˘äç”™ś®—ÓĘ®k‰í ËpŢ7q|Ě$păFúćšżČ »ůdíL™@ÚAvüZ´HĄŮFÓ¬¦YM«5Ţk|,ZdÖěIłeb4Đj`Môäłg!@ŤTt¶«`[ČBÍ».ŕA8ă˛EţőËwĚ•b«ÔŠW˘’üÉü'îbt7î}tű”endstream endobj 341 0 obj << /Filter /FlateDecode /Length 305 >> stream xÚŤ‘˝N„@LJlA˛ ŹŔĽ€ĹgErž‰&ZY+µ´ĐhÍ=Ú> Ź@IAç‹ á·ě|ýgf.ëK xQá®ÂzŹŻ•˙đ!đe‰ő•Y^ŢýˇőĹ#†ŕ‹[ľöE{‡_źßoľ8Ü_cĺ‹#>UX>űö)Eŕ§Ł‰żŽNŁČGG#›"qhfHřÔ8ľĎéäfEĘAEIĹČ=ż˙„Ĺ-Î’%$©#쵂H\ŔŐWčfäą  Íhg™…™cgÝşi†ą8iZţG«`©s+´¤É,25×ô\iÜ`2[Ě[¸¨ČE3)Dä/ţbZÁť1.8G •I¬łéUuužRŻáŤÍ:îXÔ&ĽoÝ´í]ÖŻ"MşÎÝß´ţÁ˙éýëoendstream endobj 342 0 obj << /Filter /FlateDecode /Length 228 >> stream xÚ•Ň= đ×t y G('«Ćv3ń#±‰NĆI4:—Łő(ÁŃIÓľú¤H~…ţiżŐŤE[ôLK;¶nc<`’ďgŹŘěqˇ\Š$A95˝(ł™8Ď;”ĂůHÄ(Çbe–Yc6ş,wh*ŕúŔ´.9)"1RH HP+wh ľyĹ›(¸/*±†řPč#qRDŇĄLůSőÜ×ő¸c_˙˙˝źčć“˝®˛ŹéPčŇĺ[Ě+^« —& ĘIş ¬)J˘˘t*Jl)sĹŞJ¶SŕN2\ŕîŔU\endstream endobj 343 0 obj << /Filter /FlateDecode /Length 270 >> stream xÚ•‘±JÄ@†'¤LsʰóšL® ś'BĐĘB¬> stream xÚ32×3°P0P0b#S3K…C®B.#C ßÄI$çr9yré‡+ré{Eąô=}JŠJSąôťś€˘. Ń@-±\ž. ŚţĂűć? ŚC 1˙cř˙˙qązrrŹp^Úendstream endobj 345 0 obj << /Filter /FlateDecode /Length 162 >> stream xÚÍË1 Â@…á·¤Lˇ° čfqCĘ@Śŕ‚Vb--+'GË‘<@Čş!Xč lľâý3©ť™ŚžóÔpjŘZ>şíÇ„m:”ęL…#˝c›‘^…™´[óíz?‘.6 6¤KŢNäJV- đ-r˙eÜByDˇz 7˙«˙U}Ä`‡(řD,uxIé0nŇ·WR héhKo©b“endstream endobj 346 0 obj << /Filter /FlateDecode /Length 248 >> stream xÚeĐżJÄ@đo \`^›BĽyÝÍ] ç ¦´˛á@-íÄŰG˛´ĚŁäR^w˘ůĂŮüŠ™]ľ™9ŽŽâ„ Oůpj8>ĺxĆ˝PS5śĚţZ÷O´LIßpśľpuŇé%ż˝ľ?’^^ťqDzĹ·›;JW\×…ŞËˇ~ lrŻ&V‰÷g¸îľ{„ť'Ŕ´N2¬;säŔ8GÖęĘvn=§·őĐŞĘQoĺb]pĐ» ~‹‹Ż^¶ă8ëőí®Ř:úg00ěś7~Ęžîż®JTĄÄŮ Ďľüś4s”M^!ŇyJ×ô[ÍX'endstream endobj 347 0 obj << /Filter /FlateDecode /Length 136 >> stream xÚ32×3°P0P°PĐ5´T02P04PH1ä*ä24Š(YBĄ’sąś<ąôĂ ąô=€â\úžľ %EĄ©\úNÎ @Q…h ¦X.O9†ú†˙ ˙ᬠ—Ŕ€ ăĆćfv6> † $—«'W ÷ '®endstream endobj 348 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚ˝˝ ÂP FżŇˇĄŹĐĽ€ŢVn«“‚?`A'qRGE7Áúf}”>BÇŚނŠč*3$|9ş×î†ěłćV‡uČQÄŰ€¤}®+ę5“Íž†1©%kźÔTڤ⟎ç©á|Ä©1Żö׏¨8Ux·čă”Ŕ*ŕ%V7±38©“ÂÎ \Aî&°rOP ĺdeyÜżˇ>Xý ?c\%éý#řëŁćË'q¶(IŤŁ©fÔ‰µNšÄ´ …)endstream endobj 349 0 obj << /Filter /FlateDecode /Length 131 >> stream xÚ3±Đ37U0P°bC33…C®B.c# ßÄI$çr9yré‡+qé{Eąô=}JŠJSąôťś ąô]˘  bą<] >00013Ëń˙ ˙Ař9łůĂ ó˙úóCý˙˙˙aËŐ“+ Ět^@endstream endobj 350 0 obj << /Filter /FlateDecode /Length 259 >> stream xÚ]ĐÁJ…@ĆńOf!"·."ç ĺÚÍE0p»A.‚Zµ ¨vµ ôŃ|ÁĄ‹ËťÎgH0?ń?p´¬NÎNmnąĘŇ®×öąwYUşĎąĺ‹§7ŮÔâîěŞwĄ§âękűůńő"nssa q[{_ŘüAę­…ŮČB´aD4%;>Ú#îp¨§Ýŕ{%*eĚdl”é§W”]čH˙‹ůOË·ž¦…dfä 3Âױt˘K҇óFĽoćűĽłMŘfl=łoÂ,"†EĚ"pLΉ~WІh–FšĄFł*Ö4×€& !Ś3ž´DWţËZnĺÎvjendstream endobj 351 0 obj << /Filter /FlateDecode /Length 238 >> stream xڭбJÄ@ŕ?ěÂ4y1󺉗‹[8O0… •…‚Z *Úš<Ú>Ę=BĘKÖD¸Ňć+f™™¶ö‡Ç+.yĹG\×Ü4üPŃ -˝Knü÷Ëý­;r׼ôäÎĄL®»ŕ·×÷GrëËS®Čmř¦âň–ş ÁŘ`#úÁ¦” ĚJT&e« 0m´ă?H‚M¦ČFŹ3âC‚ …P J°@¤#ßJ“˙2 ‹_â.N”^‘v2%5+w:ů‹gY9–ş×Cbě)ű@;ä@Żůf,B‘MĄ—B‘~2ŃYGWô îřeßendstream endobj 352 0 obj << /Filter /FlateDecode /Length 171 >> stream xÚĺĚ1 Â@Đ [~ˇň/ »1F“JL!he!Vj§ ˘uöh%G°L˛î‚……7pŠWĚŔÄj RVsČŁÇ BşRäJœϲ?SVÜp”’\Řšd±äűíq$™­f’Ěy˛ÚQ‘3şĆ´_@ x6˙ÂÔQj‹yţÂka´–D DŤ~Ťü:čVđhŞt—Ť%¨š´¦7ĄTmendstream endobj 353 0 obj << /Filter /FlateDecode /Length 290 >> stream xÚĺŃ˝JÄ@đYR¦ÉyMĚť˛pž` A+ ±şł´P´”äŢ,÷&ń ´ËAȸł›„ĂÏΰżÝ%“ͦ‡GÇ”RFűš¦štšŇRăN2»šÚąö{‹{śĺ\Ó$Ăä\Ö1É/čéńů“Ůĺ)Ůůśn4Ą·Ď ܵç0ťCţ v ţ-¸ô¸ń0ÜypiV‚ …p-PŻ‚¸ŘLđ"(J€Ëv×W—ŔU+ov®Ś‡-ă“ßúcDâőgUŹâ7({đ_`üú7'4»¨ż ÁlĂ…éâm¶sކH/@םb€±'۸^U Ţ¶b°ćĘUŚVl˙A1J·1×vĎŢ€g9^á[9×^endstream endobj 354 0 obj << /Filter /FlateDecode /Length 267 >> stream xÚť‘±J1†'lq0…űŢĽ€f̰pžŕ‚VbĄ–Š‚]ňhy”}„-Ż86ÎL˘ś‡• Ů/Ěü;“üq«Ó5äč¤%×QwFO-ľ˘kHfçrćń×Ú;r Ú+Ł®éýíăíúć‚Z´şo©yŔaCŐ 2–i¤´ĺŻ™5şŔ€z„>‚¬%k<&ršĄ,«¶`vŚťěd+q3Ëß’1«^+ü ô\úoxE<@ŘG*Đq ÷ů/|AüýoŚŮ¸=¨×,¨˘8U(`‡Ř´ fA-©‘pśűžçÚźąÚ¤PŤjí"ę{mś¤ÔIš€‘ă倷řYRŽendstream endobj 355 0 obj << /Filter /FlateDecode /Length 351 >> stream xÚ­‘ÍJÄ0ǧäČĄŹĽ€¶‹µ‹§Âş‚=zň ‚ =řu“mÁëŁärě!4ÎLRuD¶„™ÉĚüg¦^îW¦4•Ů;(M}hęĘÜ-ÔŁŞKC˙Q•\·jŐŞâŇÔĄ*NŃ®ŠöĚĽ<˝Ţ«bu~lŞX›«…)ŻU»6Ŕ_‡GzahBź ‚Őď„—ă›t ]ć2 ş‡¦G6Da)…ĆhrűĹĚcf÷EAż1ť-Ű?pλëŰŐł«÷łî I}Ňš6ÄĄŁP€gOén ŔâÜ’ÝŮ'ű+ít‰c˘Ź„036u! č’ˇAŇMÄ"9Ń%űČ} |Hł=¤X9ŃZ±H vą÷]Ď˝ămłE=L‰QVţgÎq)ĎśŻďRţT7éŘD]ŕăn˛¤Çó c»Ć’|´M É'bŰ<Î%řŞNZuˇ>ÚvÔendstream endobj 356 0 obj << /Filter /FlateDecode /Length 142 >> stream xÚ36×31R0P0bcCKS…C®B.#ßÄ1’sąś<ąôĂŚLąô=€˘\úžľ %EĄ©\úNÎ †\ú. ц ±\ž.  Ś˙˙30°˙oŔŠAr 5 µTě ü@;ţŁaf f€áú!Ž˙``ü˙čŻ˙ ČËŐ“+ > stream xÚ36×31R0P0bc#C…C®B.#3 €’JÎĺrňäŇW02ăŇ÷ sé{ú*”•¦ré;8+ré»(D*Äryş(0°70đ˙o`řʆ™†ëG1Őń˙ Ś˙Ăú˙dĚĺęÉȸ§‰ôendstream endobj 358 0 obj << /Filter /FlateDecode /Length 207 >> stream xÚíѡÂ0ŕ[*–śŮ#pO@·@ ¨%0&H@! $¸ńh%Ř#L"Çu€…D´ůţ¶—KzŤzµŮ˘ę˛™Í"\˘1’CÝĹtíőŚAÝ“SÔiźÖ«Íu{СuBă ¦ ˛ĺŕłU|0Ű€ů‰Ř–ŘB%/Q@PxĽ·ŕ_ĺQvŘďʲ#€rO‚ű ^‰Ëç7\©ëꑆýăgpÓ÷x'A~^ÉĽ™ąP˛Ů/ŔnŠC|U¸ýendstream endobj 359 0 obj << /Filter /FlateDecode /Length 249 >> stream xÚ­‘±NĂ@ †}ęÉK!~¸5Ç©©*ÁÔ1#ćÜŁőQú3T9l× ęČÝIßÉľü±‡Űë5•TÓUEá†Âš^+üŔ:p°¤Pź3/ď¸éĐď©č·Fßíčëóű ýćáŽ*ô-=UT>c×€Kxĺiôi$Ţ«Š@v”#W@Áťř!ç'=rĺ4ŕ8 E\)™ćGCÎ †B1Š:‹6ŠÓ˝bęĄ:wZąK˙Š??˛"XÖi=Ěť1w«˝fůbpęYś4?Í]óšeä[›ă©ÄßŮÄt~xßá#ţ°´”đendstream endobj 360 0 obj << /Filter /FlateDecode /Length 185 >> stream xÚÝĎ? ÂP đŻ,d°«ĐśŔ×ÚVt*řě čä ‚ Ž‚ŠÎŻGëQzÇNĆ÷:x‡üČ—@ iż—Drj*ń ćCDJb“Cíb˘qNjÍILjn¦¤ňß®÷#©ńr©)oĚ™-ĺS†݆/ž–ÂXĄSeF·Ô•+^ˇ+kŰŞ»Ťd%ôA˘č3đv×X}Xţ´řĹ~äČö"ő7i–ÓŠ^¤Ds.endstream endobj 361 0 obj << /Filter /FlateDecode /Length 191 >> stream xÚ35Ň31T0P0RĐ5T01U°°PH1ä*ä21 (XXBd’sąś<ąôĂLڏô=€Â\úžľ %EĄ©\úNÎ †\ú. Ń@bą<] @€ň>’dF"Ů‘H~$RLÚÉz0ůD2Iţ˙@ŔđD1a’ڍL˛˙``n@'Ů˙0°3€H~`Ľücŕ1(¸l@A˙ŕ(ŔáÍţ˙8¸\=ąą~@‡Řendstream endobj 362 0 obj << /Filter /FlateDecode /Length 257 >> stream xÚ]ŃĎJĂ@Çń = ĚeßŔť'pIĹ€¨ĚAĐ“‡âI{ěAŃsöŃ|”>‚ÇĘĆů»Đ|hż|!ËîĽăš[ů-[ľĽŕ׆ިŐ{µţŐ/;Z ”ž¸í(ÝÉ]JĂ=Ľn)­n¸ˇ´ćMĂő3 kŽ“ž| @5A<,„˝P f<AĐçgĘ·ŃďáZgľŚ)+Ő/W¶Ą #[ĆÁ-Ąź‘7ű–ži&ëU·ŔňŚbHžžŕhľçümýźĚ°Č–g€˘[Î ÍCźDĎCźDßBĎCĎ ž> stream xÚŐ”˝NĂ0Ç/ňÉyÔř  mĄ KĄHd@*bFHÝ’GóŁä2vjîü•Eb&Cô‹íÜ˙öť×ËË«ĄČĹR\”bť‹"ĎĹkÁ?řŞÄQú.íÜË;ßV<{«’gw4Îłę^|}~żńl»żĎvâ©ů3ŻvčŃľQ˘5@d°ÖşµČ´ÖG`ĘĚ·ědQö¸bB\‰"dşÁW› Ć'ś‰ş1é)ŕP’l$bÝ.µŻDƦŤ9†hbY´±pť‚ôÖ>bP:Ä`VE•S`ŞśŘĺtˇł€ÖĆčÜL©t„Ç9”3)ď|Šő bvóŘÔ˙ GÖ˙Ns@9ŃdSLç±8:›pÜ Ą1¸ eĂPQJn„gCĘ‹Áű9„RŢ@(đo!ŢDľE˘ĽśiM«aZÓj$MŘźÔ|›h×f•‰ÖŰöj¨cĂ•[ÔčŇćBíď’ĄKÁď^~ďńiÂéűü¶âü+8éjendstream endobj 364 0 obj << /Filter /FlateDecode /Length 287 >> stream xÚ•Ń˝NĂ0ŕ‹> stream xÚĺÓ»JÄ@ŕ¶8MŢŔśĐĚŔŢ„°ë ¦´˛­VK E[7Źe°°ô $Ź2EČ8gfö‚A´ł0đÍ%sů'™ ʦÇ$iH‡Š&’””tŁđÇ#[ËeĺŰVw8Ď1˝˘ńÓ3®Ç4?§Ç‡§[Lç'dË şV$—/%¸Kó DŔşýżásĐĄ0­GbŚÇڷ鲸fĽV Ć[÷ÖďöŃ1>8Q†«.ěÝ„y4żšT1ŁbÔ<˘[϶‡. ęĂ| ءř üĽÂşŻ;í‡ Úý \tő~Űś9ů„“ŮAƧÇrŕ×:ösÂLnŮ˙ĘťrŐnČŕ™7ĂІűÂbÓ„/ǵŕiŽ—ř »ĆËHendstream endobj 366 0 obj << /Filter /FlateDecode /Length 262 >> stream xÚµ‘±JÄ@†˙%ĹÂ4yËĽ€nnŕŕ pž` A+ ąJ--îP¸B¸«Ř×\_đSE;ň%ë_űtňřBë–Ü=ű’ܵl“kořu˙öLn}{Éą ?T\n©Ý0`Bůňđˇh§"ŕ(»Ů vě3…,rŁVç ˝(R0§(™şZ1Ěľ‘Ź?ˇ^3šAŃď RŕWÄ^ţS…ăML j×3ô)0}1Fč3‘őąfšĹš l—iX6e–§©î*y’›XŢ i}l±éćM‹óŁ«–îč S-zYendstream endobj 367 0 obj << /Filter /FlateDecode /Length1 1400 /Length2 6424 /Length3 0 /Length 7374 >> stream xÚŤt4\íÚ6ť ˘m„ Ęč˝÷ťčc F™afô 5JŃ B]=ş zťDŻů$oŢsÎ{ţ­ď[łÖž}ß÷u·çą®ÍʤŁĎ-g‹°*#ŕhn>8  %oÂ@ ź€•Ő†v†ţí'`5‚"Q0\ü? H(}íSŁŻZ8 îî đ |Ââ|"â Ŕ‰ý D ĹE°ĚĐâÔp(Š€UáꍄŮ; Żűüý °C8>11®ß逜  €á€íuąî;ú ŠöţG vI4ÚUś—×ÓÓ“ě‚âA íĄ9¸OÚТ H¨-đke@ěýł+`ŕCýĐGء=ÁH(píp†A pÔuŠ;ÜŠ®»újšŔCW(ü/°ć_.ŕĎá|<|˙*÷'űW!üw2A¸¸‚áŢ0¸=`s†•5yĐ^h. ·ý;Ł×ů`0Ěls ř=:P–ÓŔ×ţŮAÂ\Ń(Ěů׎ĽżĘ\łÜVáâ…ŁQżćS„!ˇës÷ćýsąNp„'Ü÷oË·µűµ†­»+Ż!ććUSüąvüŰgEB HDX€şP/ďŻŢ®ĐßAľ_îëü}]®€ÝőPôúŹŔö€h¤;Ôß÷?˙´řř[ Ř@íap‚WżvCíţ˛Żď óĚ@×ôă@ż~˙zł¸f-îěýořď+ćUŐ7PŐÔ|đgĺĺĺ^€/·0ŔÍ/řř„‘ë˙ÖŃĂţĚńąjp; ö׸×çô÷Č8ŔţG Ŕ?ki#®™ Ř˙Mtsrýŕű?ÓýwĘ˙ŹĺżŞüŻD˙ݝťÇŮ˙ü?q° ĚŮűâšąîčkh!®µ˙o¨1ô/éĘ#śm˙;¦†_kAnďüŻc„ˇ”a^P[âđ]ţňţš3 ŐA `ż>-7ô_±kuAś®?¨kNţAŻĹóĎ–JpÂö—ĘřŻoŚD‚˝ ®/ůÚ|ů®ĺh őúÍb€—Ž@_§×Ëův$ÁŻĺ Ľż|™ü €×ë?L~€×ç·ůŹ®w$ňZ{żyq=ŇßöoˇCˇ^PÁÄ"ńıüIĂI™­'÷JżÔëŠq2·ďňŁűŮ-ÜDŽŇ׏çGr‰Ý­¤ÓKJ쇲“Ś—ľ›u•¸aâuëĎý.¬^č ­Ô|ĽÓ1»)WŃNŹOÇm »ęwéćgětŁłIť5ÓÍ]ô–N6ů‰g›ŠWEűŰ©ŢбÝŐRa ‹·ĂÜQ†‘ćÁů_XłlŇF©™qĐÜôxś·÷ĽHľŤÜÎřɨţâ˙V”@žŻé<ôé¨ĎL±?Ş™ćŤ)5ýŤĂŰ˝Clľňk/Ő©Ć} ŢDÎIŤ§‹<‹éš|Ň7ŞaDĂ CŤ|RČ-•¤ŮD~Ż9Ă’Št6‚ hl÷+đ|ꞍŘĹ—°tXĄŃÁO/YčőăL0m|lahŢÚ1lâô‹‹ˇç‹úÖďÂp$EľŘ |qŠĂ[RŃÄN´Ăe™Ô~xg¸O?Ë_IŻU™ëkÖţ3‹çÍ1·ă Ç0ż7hóYń6B/Q*ťţsťĘµřÝíËĽ¦X‡nłŞ2ŠCĘAR«F±đĄA4˙•@|µČ÷'\jüTň·¶‹ŇD»Ô“ç@Ł.(:C,ăAÝ3[“-&şˇv™[#—iŤŰoe¦twno nCő"TŁ-Fžó¤¸Z öŃÉĽ^ÝIG¬‹×ŢuĎUŚ75sI±äíWŤLŻŽ &áU9vĹŤŢYAkJ°1żzZě© łż­×âć° ôŞ?óNłŁGÉÇÓ] Ô{7=ćËÁŇΧ,±ĐťfúšÄÓKlSŤ@Ő˝HĺTÉc%őěňóW´óC<Ú›…AF#enxšę¬†đG* ĐWŹ<4&˛?BÍÝńR4÷tݢ%ŤâÔäĆžŢÝ87ŕZÍŢgŰPV¬Sa1ŞĎ;•őpzŢŤ®ĺQ˘óó)j2͆ĄJ¤kż'z¨ÁČ+zFĂĽ‡7DZ¨*7)Ň8&#Í\”ž“F…ŢzŮí)Á9´î,DŘD¨¬L,oĽ|Ć÷TźéŞ+—źłj‹çKź5m¤¨¸Iě‡ŕΆEwqkź¨eČyaÝ]ě…ŕł™~łÎ§*Ěhś-#BŮŞB•ß$ÝżSˉZ]# 6°ű"žĎŤ }ütŇ,şüâŢKűrľú ;\óµD\Đ:ľČ6XoŔ’ŚýŐ…&•#Ëg&ĎYţz–zřgäÂ=żźC··5qâ-}‹Lň­ž ¶ó¨$z’W/OůéśđËoŘOWzîŰ85+—ĄkçŰ9JDŰʰÄ:3?Ţ*˘oµÇZŤń==$ă<ťÝŮN˘"$çŇ×]ľĹ˙¦>űĽí~üŤąuą…nÄ“ńµ[‹3u­ÂÎ4pŤX¨‹EĂŢw®MŢÉ‚<óG÷„ďš=6TzÎŇQ©•őp‹ťx śGńbAłtń-µů!ç‚쉏Ŕýü7l‰Âožěń'MăŕŘ´j±ÚîÁÓ‚öŞĂŁ,ÄߍsRjyäś]úĆß6ڇŞNř­˘óP!67đŤcˇĆŇť*EbGx©ł7…I-Ń´bµß [CVř§C'é5XZCëÄ)ąWYä¦Mť{« í›Đˇ÷q]WĘ®Q*ͧ9ńbEBXO¬Â0B”Ý! q.ěبfđŃWP­±˝ń^3Ş@řfÚË^t{3Ł9Ö´Ĺ If—łý—=ťâ,qŚÝDNb‹Ţßý =7/˘š‘YoŤŮŤŰxż­š Iđa±z8ş"CĄ{¬+/ű†i@ŞÂ·$‚Cž›ÄJ ő ŘţcPř˶üE%ňĹL§Á f^˝}^%đV7–*ŘŇ؉ń8ďwŘ gچődwzVéÄ%íO˙u{‰ţ¦ĺ$Ĺń˛ą#ĄÇTKŠ[6ĎOw §\ŕb˝|)gÜaÇ+™ç”…f¤qXĹŰíÄźľˇEńÚµfĽ}ŢCvű•ĂŻ§łěąÖů“"ę.s©§|l°AK„źą© ŚĘ|JńO¬Twc#,Ă̡äÎňť‹‹ÄIŃô‚‘ÚßźËÍgáwꤥ›¤ ůşńÜŻîĽ‡ć¶ ©3˘YŐńŚŢÜL8­ůůÓŰč=ˇ’i¨)ĆčOý 4_?ܤ* ió3Ó{µđ^Z(X-ŠüÉŰ4dÍ!‘©C‰7Ó’“Ńgý®‘05‡.°Aü!Ĺ bł;řřI4{ ‘±-ĘPíDj×âl$…TËoűŐ [őóGoőn©x‘ÔÚ˛“ÎiÇ,ŃŐú2ßq>5X8u•¤ś`îîÉíJI ś ŁeËŔ#ÎŻCvčq1Ý)óŽž2¦Ó«ĚOňŐé6˘AuC&Äc)Z…e+#ßÝţšżxFŕ=źÁщÜ-$Ę XžáŰEŘ:ó WR"Đ–«ÔrD8oĹ.ŤK§ŮµżŔoŻÄ©AOôHŐ4Ś0'Ľú=°lzRń:M{8›N¶ľĚáöZšu©™ĽrXă,—&ëCş× kä•ňĐ Î{eŚp„ׂ€@zŘşotŻŃ,۬%ňňi˛ťˇŤŕUDTy:Ť.ŽcÝ~ÜŮ“ŠĎqěëĂí“ćłÓÄL¬ô†w)šžĺÖOłIř×ęĆ3*šŢ‡dçs •ó)R› †Çő[™ËDŁ?Kv=啨‹"Şb^ŐŁ{ç2ÓZ’‘ĘQrTH‘ŞmŽě2ć#jŠC$ä ’cx/Ť=›ŕď}ľgŢ(;iĽ9 .ă(ąąM$<9´ęY$iR̨¦_‰-ĺÖ6úľëÎO˝]dĘTieÍŤśtžáÜ­wĂ|śJťľL7ńáëó,4ŃMMLűaq¸Ť:~ř™ć‡6·» ťČoŽB–mb7ŢjëU¨“|{'Ąś˙ /gÝZ(#Ô‘čŢ… |É0u3śJ°Ł6bIŇŤŮ-o.‘%Wp‚mq‘“ÍP~HkGÝíéíí§J‘ĺ»jpGŇą©|Ąőť;™Ł1l55^÷užÄj¨ˇíĽTZą?˘-éąGśnX·#IĆŮ]¬¸ł’4aJÖtHĐ –Ü΀ŞX¤çź®b߀b ?{v<ˇĎ÷ęÇ4 XmU&t'FY©×7žzWzĆr%T…ŕ+ěĺĹ,ęuÄY7pŃ^:2‡<č*ÖpŠ"Ö˘zöşG-ä­ăç ĄĐď6;$Ą$™ĚQˇŇUžë3ç ~÷¨x7â9eGP2S°“đWŚJ›öEs$~”Ř-ą˘Oó¦’§Ţöŕ”j*,×'ö?( źĹЎxIwęÁăĄ0<Ń‘Áö¸É° ŃÂ\mĺn8’1­ç¬‡JÝž–(­yA|uJQ†úna‡Ëžúă.Y ĎË-zˇ(="kŢ©łŹľ¸bÖ%˘ó”$1‹m“Ę·Ů4÷úYŤ^ńtgĆy€7ćőW’$D Y{ť$n,iÉË[ď ťrýń•ZWHŞ‚ĚĂUő;ú•S6¶MHËÂü¦˝vÝVtÉŠśš<ŤpGr31)×okčOý^Ř*@ŤyN6účÂňë îmí v˙dzÂŃxžýư"JpîđZ)LĎ|ş˘»˝ŻMo;;U»Ú¤}Ň^ł·ş ňÂ_Cx3U׊0D÷˛Ę»¬Ws‹ ÝI×Ĺż{Ęů˝])ˇáĆ Ń>Z?Ŕ»JýAxì€H“tl‡QŰB[c@ôcŻ0KůYBą´Té÷Ż˘{ŐĎďŦgŘ®D°±€‡' hwłĘÝľŢs%ĂN&:ęöĎ挳±(rOĄźű'ĹĄ[ŠŚÇŁCÉuEń‰·+$‰X˝śŮ‚ió ĆŢ‘îČ;,JÍ+WfĎ1ó›×Yp-ŽĽ—EBŮ n9+ălţHäb"jň«u.á{W}ÖZßçS´†ŢńÉSZ-©Ó×Ă"T¨îAxŐÝǤŐCo=ůgIÇź6ŰňÚě(q”žâÚ ćg ¬]Ψ°ađşj‡´˛’$ŇOHĽk@ÇŻc&‰cFR»®mmPś¶ú{j˝˘$ăĹđĹĎ(ĘńŘ&-92™.EËJFä®?>şňńsţÂńó’ąÚ;ÔČ"X iž1tA §ěŕMđçb@°’ü9‚Ň–ľůÄĎ‹ÓH’˝?Ţ=śz˘|Ęź‰hÝk!-L_^SŃ$ńî1ů!©4µ»xŐěöĚ8@ˇ2űÔĺÍľÔ7čŹ-ëŃ*Húç‰9ň¦šš{Ëß-ű±2Ąą˝ćˇW>źNČĹ|•ÁŠ™ ň‰ę…c#ĆÎC¶~ýâ· ’ŤÚȢdýw}öŤćKµ­çĎS6g†n>pĺ Ł`ęÎŻ”yńSĐűA{o÷AVň®­BďX–ÄŃ&J[cYNPnG»)‹\euVaĹî0)ÔM©Nš«IOó-ţă« ŤŻ˛÷ü&ŤßĄDŚđęhWŕX& p‚4—×Ű.§;j8üGn”¨ŻkŮ3ß:ýnŐh_¤€âW¨áŇHË M—ÁëŻďŕČ\M ś%:»5ÍǂيřPô˛®†ŠRŤŃ’¦sü5ŕY łŽţńxŮ2Ż]đ’˛[~$Ă‘ţá›I0%Đ&¶Dx_FYf ł‰‘Ď%=;ÄűÖYÄšÝ$)éąX ĺ$“ĐcęĘQ¶XÇŹÎéčN `ŽňŐV(ĘźÓäňÓWŤ7Žů)űW‹Ŕ€QÉfŮĄŘ–dęÇşţ´°Ç¬‰b"Úý{÷|b-[[žcI»h~ŇÔ^;ö~¶YŃBş…‡€Ç9p /8ydúĆ0ĆÄŇJíőŽřßîF™ÔŐőăy˛0XéůĐâ™öşs¸-ĚűgäG·ń–ô·ćRżéPΓ)ö´ÇyápâŤlµáĹłŽ%µ®ÄęZH~A´őÚ FŚŮĄJą˝ţXCˇŢVT¬Čd:Ő8ŞˇHŰ= i9Ţ`_‘Ě6ˇîŚĄćI[cT]Ż#P˝މń´P=ć°ő†ŽČI\"Ëg†˝µĂ…"zLötĆę % KŰ)ÖĘfćWú<ío3ŘüDÜÂ)ňĂ4F_ÓV.s¸,X€ŁŤ “S[dM]¤‚ńć—>‘•1ŤJN% ęąÓž>j2k+ŠĽ[O†Ťő•w Uř[ă$-ى2…ÂľSĆľĎiöMk…vl\]ć_Çv÷ç%Íy۶‚¦*M˛)ßÉÇ!v>-¨X]Íe^ŚąěĹÓ˝ŻßŇvfb™H{ą«wţEq´!Ý‚; óxYgߎăţ–Ç…ŕó˘%uŁŔ~3±&—< ¬đ-dě-$St«oK˘ŇËrx'dZ;$ą7e˝ř…Łń› –ÔŠá8śÜ„ńžŠîÎňF*ěó°e*łeµ|c9IŮÚµĄT©2á#™Rňu{ ˛Č{Ä=*…›·ł‚AÚ(MˇĂ ŚţőšŚűîĎąŁ1…Çě­<ΰ4WyJĄµŮj•L&ßű=?Ţ&ÄĐÔ=^ąí{Ü’Śî‡&đĐA:Ěąńňĺ2ä’[îch‘t“űÍ”/Í@s ]ŢŁ(É-E5ř’Ď˝9íyvŰ &EŠ7ô"I…™cW¤íŇŘC#ź¦kęť—‰I –â©HatóŃ4Ůča:dQĽű>CSH=;´ć Îź»ÉĽŐ…˛`K*ťZ´xí´żÝŞ_©ąë˝5kuĺxň9YoĽZrw řŃ[7Їä­)jˇJµÇm™ö¦_šŐČᢌmEP髳hdP™±ŤSŻ@Ú‰2i°HH¦ÚÖ1’ŢüT>·Ř!7qýť¨¬··®ţ¦{Ůn€‰ĎUşËú„€×řÁ3SÔ;”{Ä`śmDĹ„’ZὉ̾sCHbŻQ ±­÷Ě”:IlĹĐšÓô&hsč„P˙C˝ţŚFÝrG-%vCĐřŮۻϫśkmP{´eWľ›âjľ•Öl€ľëŐč˘rĚěä%ŹfíóoH‹ö¤ÓČ>;¦ńśqJĎyř-ĆI ’şD`]VîL˙•Ś}tëMínˇI71ĘEI9Úb ňúđŰ!Í·g ’őôë4zúÄ7ťsö)¦őń^úOHHľśÚč©’ÜHô -±ú#b®¤•?í…dĺ"đłWżŰ¸Ql`ZĐ5Źq=ŚaĐz‡žăŇßł·Âł˛¨Ę=}^˛Á­Ňc2zâS±ö@Ĺ//ËÎ*iˇ¤ňbjě6ő:Ú>6ßÔ˘J žó3 Ď˝L±ś·ň`RÓ¦,p=bÜvqwBďťÄ”JŞŚ Xý“‘É•·ĺÚÔ»ź5ö9.óݬËMů#×Ăý"°}—ĺÔ!Işz'ąDnp[öY’©×Ţ)?jŽŠ}ÖoŐbnN陼ůč§:Gču.Ę/sĆHÇ7žőxuĽV­żŠŚ×rí—4‹‰ Č\B;%°L 6b<-Ô3Ëv:±oČŹúIA2D•Ć~ÚÄüÖí–ĺ{›±˝˛Ćą‰.|ÎĄ‹@š«-äńÖÄwżqńR#líXů‡-†>·ş§UmöŔ*ĄŠŞ·J·ď ßDk6T‘–ŞŰ`V4¨íh«Q+Ś˝v«óh¤quĽ#’Ró&pNÄąźîbOČV‰čák_(ş »~ZH¶âN_NěŇ2ŐÂĚHÁ’ĺ“äĺĎ·(wüMŮłľPŃę 4cJŰ=[Ćł~{ŕâyî}ăkqů|“EĽż&Ď ± >ľÝ ¤LěÎÓ.Ď)ČËj~;÷ęăCYÓ°›ó÷,™.7šňŚL¨ČĎ,Ňo@ď¦EVşWW‹®“Ě»ľĽ%StăŠďhd;™!1Đ":X@á´ˇÇxÉ޶ÔI|ۉ;Ż&Q L°PT|ďšÁÂÍ!ÍŐ˝0“ň şęÉÓÉâÔP0t‹sĘÄH)čó úúÇPí}üĐđ4rĄÝ höî{ŕŠ`˘‘˛á]ł+¶ŘŠ9jÁŁ)»Ó rçÔ7śţ)[xźSß ĚVLúĂ÷ŕP*®Ü$źç+<ł¤9ŚťvéčŁq &9O]–]H‚ÎHxđĆ ™µŤjĽ ÷ro‰łKQö±‰:k˛(G'ĘMqËS4t0Ąs›ů#…Ř tţţKkŇ}ĂlŇ‹š‡´ţ‘Á– Îł‚®˘E¶tÂ%<Đ"e ôż×ú•žl7¨K]źF÷ R<ŕ­5Š=° Ę0)ňH$Gş©<Ć­Ôg¶ĄR ĹříS;úŮ‚o9žm¤ Ů6?/ηű”a§»ŞWn = ÂÇ(j-çîvó˛$N#Jđz…¨Ř6ghÖÄ•–p'c$ĽĹň ľß9Ľ ˙ŇąÖôĆ…řSŠÍ ‡ş‡ŘdŽ÷ćëí››FÖ,ÎŰV6ú;ßR\Úć9$ĄäúŹüč!Pzą3ďÔĘghůM`żuěqCRůS*6Çů)3ť[˘†JšlĂË´! ĆĎOőȡ]wĆź‡ržb5%ŕŇqă(›­W˛…X3ý¤Ű0yŁpŻŮo‹ › /Qrr:Á.Űü“Ú8›±Ą~X?­|ﲀ űŽO#)÷©Š)µz1{˛8°ŕŃĄcĪñůßÂ$Ľëu1ž]Ú„ë¶„ĂPęĚĘű—B­íDĎ@—n{TµO”ײĘďŠŢŢ9« =˘QĄf­pu®îË’‹cc`8ţ^Q_.ĽHa˘˛ž/Ł; ZhŢ´€<Ć`'p~4ëké5ę"Ú˝sčfçĎŽ–®ţHˇôrÄ.Ă,“—6SóŘN_Mq‹ŃŞěs¶ˇľŤw勺(ľuŹ3M$oKÎëg˘raIF>âőĄŃó9´yr©Â%źŔĘ*¦^ÖU)Ňn÷ç,dÔ‘ű!2HěTúW[’yăí—uŮ;÷ +\Gef×ÔǦĹ©ď(j4žű´qŔĹFÍßčüč?2Š]Yż¸HłŮFă ť{ß$ňÂ’ăŢU7=ątÝoVŽZÍQβ—ëJ(«~yyë¨ŢŚź3Á•ŘînM#[hmhňŽG· »ç`µ@Ă'±-űŢF„Ç41,ÂW´;ĎyP¬5°ô?a74uendstream endobj 368 0 obj << /Filter /FlateDecode /Length1 1440 /Length2 6267 /Length3 0 /Length 7241 >> stream xÚŤtTÚ¶-AzGZiŇzďE¤÷.„ ”B¨ˇ÷"EĄ#Xč]é MQ”*é‚ôţŃăą÷žűţ‘dŻ9×Úkí=çćb70TvD8@5p” H(PŐU·@ ¨(BĚĹeCąC˙Žs™A‘Ţ0\ć?ŞH(uSŁ®‰ş8ྫྷ;$ IČ€$e€@€(ý7”¨}aŽ]!Ŕ=ęMĚĄŠđ @Âś]P×űüýŔ ဤĄ%~§”= H č‚Q.PŹë!`w€1˘ţQ‚WÎ…ň”öóó{x !Î |?Ę`ő†"}ˇŽ€_#ôŔĐ?Ł sL\`ŢĆ'” \Üa(Üű:ĹîE®wkéô=ˇđżČ:ý«Üźě_…`đßÉ`áá †ŔŕÎ'; Żˇ#„ňG ŔpÇ_D°»7â:ě †ą® ż[4” ŕë ˙Ěç AÂzE¶*ËD÷ ”7K†/}ĎŢđR}‹Ă‘Ľ‡ĄE¦ˇ/ŚďfŧŠóĚ-üa`ůă;’Ą>ľ~OzI“žOÇ;sO&VžoÓđ«Uš+—ö-̏׳n±,LäůË>ĺ˛EćQ*•,`^ĄPifV-„maBśNď‰EmHMť }34t÷‹ŹCŹż˝âČ ©řŽźľî÷Ä·âŰŔxě%-¨qă˛wC ‹Äăž…Ű-"7Ľš‰µ>q­Z~rÜCżő{s„§űŇ}Ţ­˛´c~‹rćú3D/č<Ă,ź˘ľŇů |^Tz É~ÉŁE;ş•qÚIUđ*Ľ¶ŹoÎŢGë)Ąrć(ˇ3b“‹đž…Iĺä:gŕÓ1¨®ˇ‹{íđÜn“Á6n&0~zDÍŮîj=N{·nĽŢ,Z7ÝŐAŞtH?t*u`Ą‚yÂ{Ľ [,uĘl o´6 Ň,áë;–Çł#ţ‘Ľh4Ú±Â3Č檬@fŁA0ą˙^+đčnôËš€ŰŮĹ·‘ř$ LÓBµţ®CR˘9„_·"n­´“KéŤćú ~ůéűtďÜÄä©Ú–V-ťŕ‚&/ćšo/ľ:řV8:dń! nO«â^“Ţw·ť`Ý|<ŠyőŞ!v˝Dýab%ěëó-ëTĺűY/I㬳…É1ęFC˛owŤÖ’e3Č8ú%xű^Ök3)Ş˛)Č:»đuLh‚›IĂ _˛ÍvÉE(ŃŻěÄäńRłÉ}ÁŽKÎa âŹá ž0čK#Âý˛Ö‘5ËËr>Ďą¤EćR×´ÚŃ8„jť Ć×ă±äO“D±Á—żB—BŘâś|“Ĺ€Ugo6l}őcwg­¨>ĆęqjmWń`ŰHĎř˝;ë‰6¨ÂőLÝ»Äo±_o‡Unâń.‹Ľ×ĂgÄt*-ţYť¤Ü6řŔZńAčÉŕUpvč'I˝Ż|}‚¨ľ’űĺęZĽ÷gŇu­ÉçcíŻ7Lś^Xwlt2˝ś]CÎÜDR¨NŠTuZîAľ©ö• ŕ—öU°Vâś8a.čO|ßĂ+M¶ťMTś§ď;µDéÓ”WÜŚ¦čŐžuţ^óŮYťzU`:<ŃkŽNűśyLUńŚíČëhĘXčýd1jđm|¨ÜâŃsۦľůU“„VGŰĄ…ďHrŞŮÎäŰÍÍ\,ÉąŤ.Ń•ě"ˇ_gÚVy6ô,ďjS’ ÝJě"ľĽ]ş±VsIW@ßőňÖĐâO—R#¨źvQ´M¶Ł«=›fwxf7Ý« ž5éTĆuŔLZ˝PA/—‘:NbÚ DîfU-+ś$Ť>ÝÖ®LZÄ{ô95íĆč}· …ěŰšŐÍJ~k±4•˝;XĚ™ť7ĽÄBd% ëŐâ¸ŃćŠ8ĆG­ÜźE3_ŚÎíZë`¶NĘwîó “Î řĚ>4„©䀀V¶,&gÉŞ"4PYyĄO’sđÓj â Ć‚ ÖuĘđÂĆÓţÁV®ę=;2ŇáI^ľ›1gŃţ´ÚŹ3νŢô^ěP»ĎĘĐW={gďµ5đV°g§˙´OrŤć›®6{§x Ż÷:“Ü :đFë]ż9« ç†ç˝Cw Jé Alz‰ŇěY©›J_ćË„}¬ŕ^LĺU‹_íĽŘqrSe!¤±/¨ĘšńgLšĺ‘÷Źé6˝űóĺMX±7Ůó*Ú‹ żŘ“ää§őú%íafëUqѰśń{1: źNąŇ3ŃłŹp4°Ď9d«¦@Ö3—=?ňČ­Iá“ŢŃĐüE[A5ŚPÖĎGą·]–jßíüĄ<-[µ©Gýĺe<Ĺ©Q†ŞiĆôŢ óÄf*›ę$”»ú~\B¸Ăˇ¶Öé<‘˘Y CŚă óKíU…/*ĚXćµGŕܲČĺŻú8˛^Sy8ÝßŃ-ăD_nA©Ű=łIVÁKő<EâźÔ»şyÁ_fň`Ů—†#——}Ĺy)—Ĺť‚A–Ëmu¸™”3"é‰ď&QÄk®Żş/ßŕ)§KLĆčˇCŁßrµdÚ&3¨ťŐŮË®(hm´ŐŻ<íÇ87±ç©ĂV§z!śFśéÜ|d˘…Y,¦;łĹźwlŔřtݲř›<âťĘ•sűN}«H¸´ş¨v˝©§RgYJł#đv`.ÚsF$ŽŽKz{›‹Z–iíłĺąmyŠ}!; ´‹>Ż«µŠŞ“íaUD™ řVKĚô}{ÜZ⢙Vű¶ĹgŞÚů=kÜŤăéQ6š%DťŇ#ÓĎ)“qËrLśboÄnŘ'ů«őďôb#dÔ'Q_YŃşQś2±Ů1UOlýľĎĺä¨*—ĺgň¸ÚÉźťi‹Ĺáďłl«+÷Č—ÔËĽ×Ő÷Źń›YĽł^zć 0ËâžľÓśŔ>$!ťm•Pc;XŇÚTmĎ:řQ‘†.廫±Zě75Śáëaa‹gť^\ÂŐ÷­ÇíŁěV }™! °máąÂţ<úödŰ?$ŤőťčýÓŔwrVX!”Ă€ŠSË­æ…üÁ7Ĺ5\cFqA6$*0í;z\;!,dĺh»1n‡é|'¤ó2'â6ŘÉ(íá@ďiÝ₢üNińMN¶”×ÍűČwóŤ0ş¶s #ţ…lXÍY~˝âÝd=úWč…­\¦‚~­ěNwXĂŹŐ€ú‡ôÓ7n_!©çĺPŚ9 0 lpOáÖG‘ą‹ú¬tčŔ×'aCń©ZËŃŁ¤/>Ó&%al Xľ0Ôt ŕjěÉÔÜ-šJźt·$ŐČşkşM c~űŕVBŁL¦&ŤĺŇţETËOÍzěŔ-¬x´ą–1Čz෉Ȃăe”É—AÚĆSÇÎ×µěĐfÝüĎ[ŐŐľ:x&7ź1RevÚP.‡Ą1c­Íě­›yM"Ě­ ßBÄŮW›Đµ4×ë5ÝPO/K™¸ićů8mcŘco_p™hń}afl2Ó2˝zlĂxRĄtÚ]­!p)…•*X@¸#ŁÖ|fÄݡŤ¤¤€ĘÉQ*oPŠŃ,I‘ÖD×FÖÔ®Ł“Ú·yÓŐ1íĚRÜ‹$Řţ8v§$踳͕č‰sI¸lŚź7ŔizŰĹJ ‹kÇ1Ľ9«¸Ă±µŇJâó4ĎQŕ·Á—N|ÎŰJ±8šúÚ™ÄaË+]ÍDĆĹ÷w2ĆŇŘŚ&śŇ6›.ŐßŘäT]Á°k8|_šfż—•Áü°6Ü5ú„‘žbvĂrě9ńëĘ´ýóŁždü&É 2hż¨ĺt|¤çřĄń}—b…dÄÓ• ěóő¬ăď"üÚ”}|Ţ* MúÔl%[®6Ţí8.n˘žTŢc\2GŃţŚÔő¤Ž)„} çĄ.:â· 'ő´˛•zŐŮSÜ\éq·Sżµ9uŢőz8L‡“éäóř§Úî7MTë•Oäm-%©îä¦L"# /ěć†3ź˘}e‡ÎŘ ŽşŇ¬]źŕ·î´Ô:SprćEŁĘ×TZüńȡ$‡J #}WüăhşŃ'6GʆT ~dŹéňČú]ĐĆĆOźî~VLë[Łj Ę4ÍĽBăąöA=‚™|uů­P…[č€ÖĂŞłÜäZ+ýňW•ő–« ›NZÖňhq—­cô(ßĐv\~Ä’0ÎÂqWň–âW’\łŕĂ,âwbýw^ K©!MĘÓůÚ-±ˇ>R¨1Ů ¬:Űrµué4JčďJ”ٰ…ŚăHß9)$!şužöId Ď~ďĚčéŁÖż[U^,‰ĹŘć¤qÝĆ7HŢ,í’h$g¶\îąoäË‚lXSÄo ×;ž4Óť5>óżźg Ľ6  ö­} ¤•gÜĆź6 MAë:lÚ ăČ×řĐÉ'Łí}io‡aQ­kŹ·ć®î°Ăßş‘ĄČbŻBU€cĽ<ŢëČaĆVSňĚ]Ľ"čÖĆÔůĎf,©Ą;rw—WŘRÓ„6 l \Rčő&GĘGAÔĽÂz~¬ -©ť·ëjtSŚľ '’Q­wďŰYĽ›^níŘ@úđ(Ď{ú@©ůß߲·$TđŠ˙´Đl“püwÔaE‘NÚąKWN%ÍĹPÇ^ÝČ8Ř=Í5i>.~·őĂ.6úŤëýDÁ•Qkx <€Vßꩌţň帢iJFYčó´Ń@Y $x±Śďypš°iŽŕÂZ˘ŽťYŮ'q°VgřPéÉË)!vS˙‘ź!äP#WySůáR5Ż•ZígóéÜů>ŇkgxC^9Ăjr2ó€ĆęDĘE‡v!ѶݫĐ(Čň%>×ŰňĎń˛xU•Ý˝¸$;ôíđVÓ#Ł€ŇŻV_d&’{ܤtŢ?¤ c/Ś<7Mz†3ńl[ŚŻĎE˙Uş.ˇ®cďEóJzčĺ$ÉHě AŘâ,ţ•7¸†ȱŤ§\ľQŃ8pE>^lJwOÁUŃ#O(DHĺŤÎÖX~AH'ş$ö)®C\¦ółŘOŁŘ Ä4–xß“˘ő“(ÚżžBJ¨f‚˝Č;Äń ˛łlšNłx[&ŘéR/žÝ˙6˝Ë«óĽé&H\tÚŐ76pß%aŚybMzíUFUÂJ6“ä$µŐŞĺ!6&ą>¬ů~÷B™Śű«ô SšhŐŰwľ¸Xk†żáÂK^ŐŹŮďŕä/ x%Á1˘`d$H)ĂůÎB‡«Ó2ź¶8íťZĽcyĄ€őÁyEUU¬F±ÉzaIxśó{ HÄHmg»L #Bz 6Ó˝UąsŐćÂĺ+}Ŕ˘#ś5eUˇ)m(5ěËäŽ<Ő¸ňě/ý:. ežŮČn:ÜĐhţ¨Qţča— a‰şyŰšz]ਸw>§ Â5›PQ+sv{6?yg­Ň<#4?ç?l®ĘÔˇ3×ĆVý…Ż7aPŹ˘Ž˘®$EżŞdü6 ‘źŕź9Ä+Z<ˇuŚě9•ň#$Ę»µăĆ‘[ĂĄ}ă>L<şGş[5čČ–ĺŢ÷ez±"O”k_BĎŐŚGoc3‡{+oŤk˛AWă1ÂVĽpÎź4DóJě`Z]¶‡źęµŔÝĘîś1BSĹjŔ˘eɧöŤŘ#­ČŹÁě!Ş{ĆźüSÔEĽ¬dđ\—jź­ˇĚuÚk´ËdűĄŞűź)ç’xor{ťŰ Ľ^´¤ąâ¸`U˘náéX·á{ <4=ňIęśwzÍÍĚHÚ µ Ą6Âö—.pĆBa$b Do(K<`gˇ'ŁBöućŔˇQ‘˝ŕĘä ְ»-4ŃýńHDčËÉ,K{•evv`Î$Çű‹HŤy7َÇMĂĘ'ön‹\‰Ź´|Ŕçp›KŁ/±> ŽŚsř¤C2¨ĹW5›–ĎŚSNĎŠ„Í-Üéô×RÉŞl»Ń/\Ó»jť˛3ăěâ=20ëňVú9Ľ?řË.ě7î·“†<ÉeŤ•ttąń§s˛ÜÖě+¨abĆ«žL^í¦,µbOdQŘOŃyž8oţL¦ń\¶*ÂŻ8· é¬_eQΪɉؚѡ„„·Şe—jHäSĆ÷ńČă˝$ž™ Ř×§áÄ÷R>Š‘.±ŮlzSF˝’sk§°9QŢ7tXđ'Ďšš…łĽ;ăŰ3}ć"żÁ@ŮT9í­g÷‰GBČľq§ZŢ=`f-gżL¸ě÷Ńř¸Y¶ô{ŹĽ.Ôšm¨Źď¦VňG•TAen‡´Šw—ĐţĚśylĎôÝĽą‘ж‰íç•»g„&O<Ó:š®°? ?ě}óH„f'RÝUíŢěŃPOôně‡>HÚ~%ńÎŚjxýnµA2ŘUŽą|ž,é±8dŞ–0÷8üŮX,Ä»|»1'^ l»dňJĆíöN^lk@Z?*Ö&‚¬†ŹsÉć«IO¬ÜµŃQőý®µšâł)äß±™€°e¸M¦/PťôâŃáů®Ăç•Č…Ám˙>·ˇĚźą%nŘ:ŤŹEĎĐčŠ. PŢŕT ™- DPĹśiÓá@•™@ …{ ÍŹc âPşŕ#÷÷7±, W‚ŰŠÝF7*˛E‰ÇmˇRölWĹ †X\ůo_ź-ŰOŤ´ô™) nîî4±ĹRc˝Xɨр<‘sŽYż7ń­ŇŚyś::fërôeq5ďm z„°~A.ńtN4[SÖNz\ĎlźŚť“kÓąÔý‘˝ŔşŁp'\MłQ<ě.…Ĺwż÷ë ~oďěgĄżyż,Ë´ľěÍ[™Ěi 2ÝpA裸öő0řŚb’Í ÉśďîgĹ}ŤŚ$˝â†.aĽY˝«ŐKú1˘ŕ™w_¬çřfÜ)uł˝!~Ąv&±’¬Xáż"ş€ţĘőj¤Z}ďE#Ě쪫đʵ˙Ó‹ęćçO#Yőü­źŢü1ňľJiđů …ÓśpÄ·4GYj®S˘Č1đáë<ť­]”涸Áyžź€P^{*§s+AytŇĺ†Ět×6îŽŘ…ڵŠŮ‘Y“)RřÎŢ}ÓŢłŐŮÄĄ9#y™1+Oę°¸î=>RŇ^’e©TžÜŚ L@Kź«lKÜĚ.Çž˛Ő´´0 ź!»É`ÉÖW^žµŻ<ÓÔďÚzl†÷ĽEžËÍWa$Qˇ|.&OÇôÂĺ"äd.Sě§ť|őćrć*Ëbo<Ň.ÔIµĆŇkłî+EJ“Y×®ĎóŞI4JđWŕËÚ9O‰ammtMó…vž9ů§ďŐ˛ű—އ›´î˛ ‚LşÂ(GűŃĹŁ(Šlţ·ôC˝ĺÉľ`*ë`ŕĂ[~‰˝TgŔhâI»ěE›«ŇJ?PM%ĺ{/Ě·'!–B pż®Rź×m¬b˝k´üX0Ť]äi§ůpš×žâj˝gĹ`ţGZ,!+Fý —µĎäĺńT*Ë,ş™‰ăÂX…©J1, ą&±ÖěkŔSéVňľÝ)Ô¬vAS}˝wWŇ\uă^gHč–üĄFy& fŃ-N|žgr`|z ©Zf<Ú'H/źę}"˘ ô«ĂÖ~=iî©ß2źNě¶ĽüI^™v<Ýdġ,®"éŔ˘‰h` EŇŢ(öę€zhĺ‰ýH™ŞÚ9&VXđÁĆ / WT %­Vëń‚B°Ć|§C5 ť)ĐýÜć˘ń¦/ť~p[có^ŕŢß?uŐť˝ňF÷ÜÎç–‰kÔ®´Â’ÓŞő;.ÚP >Nʞ®ă]­»´Xˇ,>Żě.ܰţ¬Dů{<ĚKd‚ŕ?Ł]0&<% Úx—L\i;k-–fq»Ryhű¤M ’@©®˙Ô_%ĘHąÝŻvő+Ů:/ČžŃí-öŇŰúPý‘Ьúݎ9 &Ź' ›ŞáV¸B‘lťĎ/»W ¸ŚJş“BĂDîed¶ŚŘă0­¶;ś;[ć°N,r 3_ů~R…Ë`$0Ě ]ifg[@Í.ĄÜăKČĎ‚‘tń˙íŰîendstream endobj 369 0 obj << /Filter /FlateDecode /Length1 1825 /Length2 12084 /Length3 0 /Length 13253 >> stream xÚŤ·TśŮ-Lp .` ÁÝÝÝ\‚5Đ@cŤtpw n‚w—ŕÁ%8 .Á]™™;3÷ţ˙Zď­^«űŰU»ęTťłë¬Ż©)Ţ©3‹›AL@2;(3; ›@RYYžť ŔĆĆÉÂĆĆJM­†Ú€ţcGĄÖ9:!v˙bH:‚€Đg›úLT†Ř>ŘŘ9ě<ěĽll66ţ˙!Ž) 3Ř  ĚP€ŘśP©%!önŽ` Kčó:˙yĐ™ŇŘůůy™ţŰ‚Á¦@;€2j ˛}^ŃhP‡‚AP·˙JA'd …Ú °˛ş¸¸°mťX Ž"ôL0Ô r9:Ěż[¨mAµĆ‚J а;ýéP‡C]€Ž ŔłÁl ˛szů`gr<ŻP—WĽµŮýIVú“Ŕřksě,ě§ű+úw"°ÝÁ@SS­=ĐÎ lg0Ű€oe”X ®P&ĐÎě7hăyŽ:Á6@“gÂĄ2âŞŕs‡őçd궇:±8m~÷Čú;Íó6KŰ™IBlmAvP'ÔßőIA¦ĎűîĆú×áZŰA\ě<ţĚÁvfćżŰ0ű`ĎŞivř’—ú‹ólBýÇf‚¸Ůřx9ů¸ ČŐÔ’ő÷nö ?śěżÍĎ=xyŘCěćĎm€ĽŔć çT' 3uüňňř·ăż*;;Ŕ l €,Ŕv¨˙d6Ě˙ÄĎçďvĽg{–;€í÷çď'g…™AělÜţˇ˙qÄĎ˝ę‰+Ę2ţŐňßN +Ŕ™“ŔĚÁÍ`găâđ>?xýwžw@đ_uü+VŢÎřťěw˝Ďőźšť˙Ý_Břďd*gé‚t˙(]źŤ›Íôů‹ý˙Yď„ü˙Éüw–˙«Ň˙·"™66řéţ$üü@[°ŤŰ_Śgé~€>Ź2äyěţ—Ş úsv•Afŕ¶˙땇źÇAÜÎâYŇĚě\,l\ÚÁN2`WŮ;0ÔÔňOŮüi×ü=p6`;Đ;ř÷óĹĆö?ľç)3µ~ľFśžµů§ čô‡ž{öCQ47€Uü·éOÄű,Żż/€UćÄ`UúńXUţF|ĎĚw˙ «Ú?čyÍĐsśÎ?ŔŞű7â΢÷7â~ö=_.¶Ŕż-ż÷‹ŐěoČÉ`Ů?_Ď{ů…ŔjńO?Ď•Xüľ©źůĘsAŕÁ羬ţź·ŔúźĎ+Úü>ŹżýĎSÂúŻőŘźW°˙§ćçîěźµ1űăą"‡ÁçŞ˙U űs1˙dçaF6@'Ëž·ÁĺČńĚpűţ— L?8:>ëäŹů}ÖČđ72ä 2Eťź… ZU¶^WŠ»0oŽ !Ą\ëp0Źć"C{Ą'Ť~Ä©g¤/(ËĚ÷°ËZµ«H8\^™;óب!Żuăşd&—ٱ 7‰™}ş|1ďqEB9‹Ů“­ť$A&PŕŘ óŽ4«EÔ̢WťÓ“¶şű{Ľ ^ˇ ŻBP«J[Geaş żÖÁO5h“É€Ć&÷9!mćOŰč€őHý°d•O”ŠBm7¨q.XkýCKŘ=¤ť~ Îţ›ÇTÁPliÁ).¤ ­ĄŚűĚçýO8&˛¸bűf˝%«ôI×Ő*§–§űt Ľ–Ć ć‘ěî7¤ŘöKx)ŽYą[áf‹źCČáúE⯜´şSÁr]+e?I­B‚ݦ»'Öî?(&Ëżë ¤cRŽ~,Ý8đŞŁţĺl ŰL[™„˝!ÍHNżçeÓGe“±tI4­Íńž/ŲUCäË`łŻ6''ş38Ą\µn»)_÷ŇĂťë1笭M‚fÁˇ"Â. 'Ň}­fÉóS^öí;ë`­ijU¤¤»‹íŹKĎ ×Jý¶|Ż2÷V[î_ksŘ#!ňĎE{íáQ„,Ía”e‹ë=ˇdĄ°1f0…*Y%·ç~ßú ­0ś¨Î…~‘·űîű°ź*¨öµîĹďđµ&Ű-xúszŃ`E晞NDš“ßú*pŮŠłŤ<¨qÖĎ61ŕ x´1ć4oŕđc˙ŘSNŘx+†iéĹŚ»H÷ÝOu `7.:˛U|íĽ)3‚~ŞŠxú×ËÖBa?dţ˘íôýsN â×géÂäďŰŚČźLž FřÇ$Zy«·}ţĂHő§Ú”äż`7ę3ŇşÖ€§»=§·±ô yÔGf§M.‘*-eSr"R^ÇČ‹±˝ş‰­żj«Ř­ôĘ, 1ŰŮMښҊžĆm–Q&>ôť.şG¦ďiجŽ«u`xÝ®-PqS,‰Ô‚rh|Ľpě™'÷Ěg Ŕ»Y#Ô^b.w­Ýł;µłµvRŁ(XE ¬»gRl˘6Ą„ţcjšD°úÇU'αčhë,&ĂH“¸OÇîĂMČ@´á .# Äő=\śĄ»ľŃ[ö#Ɖě©ÚuŐCöt©»ô’0¸6W‘!ó9ąšŰ÷äŇ—ťVźÔ½YďĐ66öźR°–mř6Üu–âě«Ď3Ó2ä(´®–üĚę)ę}͸rr5ň1çąÖoťńŢ*F3,Í 2şŹá#éDVร[^ĺ©N­şôŠúLx6ŮŁäo›F±h‚DAă3W±Âˇ{ćMőBÇVŠ÷§¨¬m.W¨ŤÎm© °Rů×™{˛6_Y•†}^/ݤhŚ˝c÷÷Ă}Ťg—öqćłĚ‚‡‰ĆבVsÄŕźż˛Ę<”&÷iB;:P‡t2řׇľ¬Ý^Ö­Šúá.lÔjd}€Şą|šú!€B–j:Be=E kŢ!%é$$p»űŐáÝZĐÉ h¦äő=u€®™ßzuŕlM$ĐWóÁ««Mé$ş˝ÂJwŞĺ°J|kŘú# ‹şăe´fFsĐš‡ČtkViE‘¤-du“m”şÔ+Ű~ń•ٶ‡%—H8N8’ˇ‹Ô—c1ęąFÜl¦;a¸&Ö^'Šl˙Ě)HRí'ŃŰ$N ş»#ë?żx 7AY¦hş»e„Šz.r?xŻ™Îc ÁmľuËóťcŹ‹H•Ş9Jüâ[í›Á­ĘˇÇ)@Ń@< ¤+;áÁ~ýdŞ%OT ÇCgä¨Ly 2óő X.VŮć00\rť= ‘~sLmqv[¬°4dč”SśŻňÔ?˙®ć‚[÷Zy?—“‰ˇ®'ܙۋĄ8ź˙Ë«ĘLJ`k-6Hw÷“W O‡—bۡ‘V%ÁhK$ŇâkO‡÷pŰ©µD\hߣ͡K܊ΞĐO9ݨĘ>xWgśŻ¸^ľ2ĚTi”i&xtuîÖRŞj&PÁš~Ś]«ĽĆž k´ ˘K®˛)D®›ÔóŐšĄ'+q†4c‹ÉŐâŞjş/Ű‚Ă~•Şčšä’ö&;ßÄŻ»%ařA»42ďé"PsînbnľŽF9„˙Jim6:/Bž4‹¬˝2»^Yřá†mů*6Ęš5®ś¦‚űâĺŕdáMÝĚwż*ćU%]-˝b“.ťĎďGŘ>ŚXHO[WűVSčôv(Ĺ´Ő_g÷Çj&W^¦­”Cln5äHě&ÝŇz@š}}O÷NAâđKéá NňQOr@…AęƱ{“߲J9MâëŮMv±ş`V~XÁţ ů{c2î T_Ú|5żł*ÂÂŮĚ™ó¸:¬e‘˘¸ˇV»xUůËɱÖ0Pß.š$ZÇWÜëVąA\Çë­Ę“]s«Őż¦Ŕé»ŐŃŰ˝mţ¬µăŻPODvČH>@l ĘzOŕľ4bžRĺ‹“łE$Ůç}…nT9zGŐË:RUîË\ B%/V‘QnkśÂ+aU—ś·ŹĽÜ÷ˇoçr4Ť”މĆÖő˝R‘,ßRWľśÍ /$b`÷Ť]…nľÜ<ádłzŁÝť!µÇ·Au:ĺyú™˘A&|uh$Ľńńőî‚RĽ ‘Ş™j’‚=j~ˇf¸ĺ!Ö˙~V/±ő#~!Vhhoó]$•=śU™oGß®K74\¸.ĎS(žţÜp? řŞ™ß;|ě´ŁŰśa䕪ß•“ÜC:ş!KeE•ú± hBńzÖ_¦Â• eW·íŤ=xv±x$›V†Ř\ł}Ě Ą+ Xđ‘DNa<üFŇH2Ż×řhO 9Á`ÖRIÇÉČEŮÂĐŻľZÉăD(…®Şm‚Ól¨Ř¨]­)$S|ůw·ŤF™[Ľ¬˝•&Ķ•vˇË ÷–X~ߣ·®HkŁđ™LĹ^‹9ľşv•}b‰ FĆÜ'|{üI/âĐŞb]Oc©uť‰ xđZm±ňÖÉ<ˇ67﻾­Ż©5;¤ŠqË9ddkŢ·Sz/Ää(‚I˛Üᬛ&řAw_>7­o:Ţń«ţb}—˙ŕÓĎ;5ÄŘ9îGłŁŮ+{ѱéňŠtóÉQ|ÄŞ˘Ů&m„Kűŕé3kÄś€Ńń,r,şyĺ ˘9lL,ĽËĄCż˛‰™·‰U6ËcănÂ$ç…†îÜŇ‘~DîTŹţæżV؆­ŐĂíŁżŠĂZ8:ˇ} 5FPř˙hŃÚ4"+m9Mł#ŠČĹ]+'6NĚX㏠Hč‹)Ĺ5’ Ô hîí´Ż:Ż4™Ą'^oĹCZ4L~.·soT4´ßĺHh)9PtuŕěÎ_4‚–ĄíĆŚ{NhĎQ‡˛ĺŽoIÇŠU4'°q®9Öä dґʉ”‚vŢŤ,×L V\}ďźËô˛ŐNße>©Sţ!>ęîdědâó9rţˇŰ©Q Ď(Ţ©•6ůóşLh{ ćŰ8é®’d›ĂK}Yc“—@2ľĽO Tt+§†šś¸źj§/ŽÖĎ$(KžśŰ#/¬±a˘DS2o—†ĹI§¶¸Y ťÚ ú‰ĺ8ĘľV‹Ú´×V›âál<ÂĽËż:§!ç.î1CŮ?~g}ÍÚk"śżô`}Ǻ鱊¸;‘IÍ ŚţÖ·~Ď'KŤf?Ú:¸ß× |"0•xk÷ĐEeŞî»OĚMQ÷ą™-1üç‚XER˝÷†ĘÔ>Á—>٧µOgN𤙆{•Â’Ç9±µ~ˢ7;űsîH‚QFń­ŽŢĎÍĺâ+ÂXˇ÷hÝg;<’EĽ˛›Rn‰0o$„ů©.Nú÷ ©Ňxb/n,1/(PČAĄ‚;Ăľ“Ú˝,…fŢW˛ŹlŇ)?FQ`ňVÄŘ7ęóŁ\Á•áZĹ/cG+Jʰfí·-ňT^¸¸¬Îę§/9q8Ř>Š~fůžŻX{)čqqˇŃÄĺŹÜ‘Ů] ’s4Ťµ†Řf˝żÖc¶ćÚ)o)-ÜPšogŕĐëđBo>”÷żű•ƱLTţ=?>őqŻ÷=Je2ęLśé‡·&M5?ë5¸ç7’şˇ0â’o~íÁž­:靈´ľöŇíşŻLěŮz8ýŘ­eČ9ůdŠ#•Z5ň:ÍHj=˝Âś–±ÇEJ]PPŹ Sxs”JôňzůWőëpâo3éʝͫ¨é–îěGĄĺ(9hRíĂŃŹĽ+ĂLÖĎ9)„?Hź©î&$#ÓíK ÖXUö 5Ü®ÎËoŢp8á‘ćԯމ×ZËĆSQď7íăľožsÄa ^^çť‘üh()‘ő#b§OxŮ1fÂ8é/ÝĆˇŚŽĂ`ą=UDśˇ¸“ŔVCz Z0iĎžtu‚f•rЇŹmüÎ[ý‰\XqůITúfK‘8-Ť¦†ń,,×QźŠ…ęý\$«ë¬=gĚţO]ÍěR‡„:Xă,G@Ń•8ZYĽ&__/$†ÁŢé%É ştţĄŤu‰ť*ăŮ Zl¤Ýç¸0·±ŕ ‚Ŕůz/˛–ĹÝďŠZoJř^˙üśh/-Đć2”ńy‚@Ú->ł»´[Xţb7¬@ˇßřx0A‡^Íőh%n7®Ç…{‚eÁ˛đ rł”Ń^ć<۬iťwgBţËőŕĆ#ż|x‚ľK7ĹĺÚGA!ݧĚń {ďnĎÓ’†U”ilÜs VÇö‡/äGâ=&{­ýąµw6ĄÔ6îçlź€ŢNŰńRď$U¸őë—O·DĄßś¨ŮM^%Rîý°~łśy'¦žuë©€F˙t.§OŚçĹ”0é¬)*%Dď;śtd—öwŘrWúseWvĽ»3¦á“ŐŽĽňĺ·O|\¬Ę¤ž• ío2…Ťy^÷!Ŕ"ÄBŃqGů¬‡ČŔ9;B&7‰é™M/P×3 CÉ…ŃÓŕe}|Üů°xŐíi‚«©Â¬/6ľ‰­#×}I‰*ű†v%˝Â{3T ňĽK97©řI–ŠöŞÂ7QĹ–<ę96µ×ö–Ś~Őa|K&Š’ô66Wص:żą!býűŁ­^á§x¬ĆäŠÉ·ü›Áâ^#„Ůúh?Ü 1™„]Ť";K4Z˘;VłyŢ"‡XI,¬ó‰‰noJ!´DÁÄ’Ľ˙xî&•0ߎd«ýĹčMśIđ)ÉÓ…[Ţx ł¬ zűśľ2ł(dhŘSr‹Ńlą‰}ýEđsĘź1¶# Âz ťxŤćŠ–xvÁ8BŔŔ[$7ft­*tNČîěôÖ·)@ăE͢ő w˛™ŽÓm’ÜÇŞ3km{ŢM7µIˇ~˛®hÂçĚéΡđ÷˛cx^•şM‡î¦©ÓçcĚô`Ř[­ŮUşRĄOIëÚE/G…Y«0ŰiŘe4Ľ¤ NZ9łW Ô©Ih”ݢru%óPLnÖżD1˘»ŕ+ć˘^}”ŦŁäJólÚh;†!r=^+˝6ĐMő¦Mq® ˝UuCµZ ˙ś÷#Á§-Ĺ“ąŇT >đĽV;óĐÝ{6ý‰Xéöu0ďt~™,Ł^Çp¦DdŐjxűžp#2Íźo ź·OHĚ›”ɵľv›Ä.>rüzňęŢŻŤ– w _:łHr謢žL6„=ˇ‘ÄL¦“ńl8˝l6 G•µßś„´š]`JűŮ'®˛ÉČŁeN“™ »kn°}Ü!ÜůK%5fĄpXŇ>ńwC4Ýxţßű ň®}”šXDŮj—™gÖÔŤ%_Âřó:€4>ĺ™äŁŮŰC«V&+ˇÚoŤI&ŹąXŢşRąĆąncź|ĽÖi$Ę*¶ŕS”Ĺ3:d«ˇđ›…ś»Ń8 ô#j·2žÂ'+Ö´¶ßĎjŔ»l99„h”¶Yúó8.ěť*ĹÓhë†÷´¦ĆÖ-Šóę”ĚD"ĂB`‘ŇĐĹî8gôŐUř˛s h-ŕwż¬"kŠ®ĐoL«™POÜ-@"|SvDÂyĐ×{ú˝éSPŢơ–őë¸pcĄţáiťZH"˘b%\Q(i“Ek\‹'ëżčüL¦|yci?ˇ±KÝsşů‚Y-‹N$źaôe˛đ`€;Ô˛uç¨ŮSž…űC§±ę»Ü«Yz㓢dUŐţú]ş{¤+®xnÂÄĂ‚€•@<ĺŐ H()2·,±č75ĺBôµnßÇ™‹őŹ{öć «°vpŹ0mÜ}ěfĄyM±ż{öqŢ+ˇş5: ÔJtÔSu‹Ń…ńěRhﯡŽĆÜJă×÷É7ń\3ÝÉktR…D=ŽşĄÍ‡ĆżK˛7m=i™Z~6 ´ˇY\Í–é^hęÂz›·Kőf ™BJ &&š H¸ž¦ě˙0ĐOi¤”|+JŇe†Ăp+2ËvÄaŔëJwĐĚeŹVţŽŻ•„Ţ™›ôłÓ°‰ĐĹÇ%Ő`¶#†ëzĹ_‰sŚ b" ćHńTôÎŁ$Q9&|RvźŻÍÖQß„ľ7‘#>•s7$I*?uÁ"xůŃÍďf/k»či—O/ž”]IV”‹dtăié!Ĺ]M‰˙ú†ö4xł­)Śĺ…lŽô!˛şÂöpâHńktqx˝źŽA7?×)!ż ň(3td&»™ŻOéłŕ© u1~Ty‚ŁjE}âđÜOÂŘo-öľIÚůÜJ9Ć@ŹbľđöűŮWÚuCNÉɤ9°,Ăť;|űoŻĐş í$Ýď/ő#•ÄĆü…ČYč– ĆĎ]°ťb訞,Ý$ßĎçĆҦ޵=ĐŔtY{«xs}¨ËŐD ď÷§^ç¶n­ ôQ?z0lĄÎą­ý‰K›<ÍFĺhůV^:żĺs™yÇł29Âë;q­Ä%>~ͱ©ŢzŮ˙kü+ĐuNUâýjŕ×T!}Pú—Wk|UÖÎ}ů@ľöŠ9NŰ,Š.ňk$ó_•ĚsË/ť[˘9`4Ż[šig(iLµ_Ń$>Vp©«U_!nç ď9ź¨ż Xxŕ‹LX7™MöńÁ Ń ălν‚)‚±wGżiĐ©Ŕ µĺx‹fJŢ®)]Rfţš–`!=°łĘA&[‘)ěAçdpcîůÁ€U?~Żhµj©ä Ţ!Nţ’µî1] “]Ö.Y–“Ó<¬c ŘR|ĘĚl”śÇ1}/“¤#ŞQ+”~2°kţî›jwV§ľ™ËRmÇmâ&mç&ĎpáIU«.K+ąZ7)ĺx“Á®{E˙ąžä¬—o;<Űn#é±c&|Ǩf_cݧóăé{>˘8’âÄgGécΦülĹęăuĐÂWcă/PŔ'šÓoYJE¨ŃRľ<Îo˙Z˛ŻĺlgŻ#ů0{EĘö*úőźÄTBÝ8‰năA••± ś,ĎX˘¶‹‡ěwćť`G4Ľ ‹ä j6n¶e°µe-ę×ő•Úł“ŹöŻC~Ť=ĹV´‡6&^çëČ:0ĎĽÉî[ŐľżâpÖA÷4w+R¦=±ÜóX>˝ş„H—žô·*NűjŤbžĽő.Mߦ<µ¶#뾊çyiZ^Ňf F¸«ždg&¤ću“ËçÔJ·Ě! Ď4±8-®Ť&N”Ŕv˙řÓ±,MPh(đ‘:¤~bŁw5᱑XB0oÚ4LÔDY°ÄkĽ8/>†¦¦4‡`jÁĚPGăC4 r@µ{/f e"Ńѕޫc ˙¸«z y=HyúP,gÓ҉kA…I?–`Fő:ň…_Ĺ÷ůÜůŹÝ#…ő5‰@qčS+T˙SŰa46(ĹęxşBkf•oć@3_ö†éŕÇÖm’ŇQĽ·Â˛ę~¨wĂXĐâ—úl~?LĘ{§Wó7š<ő3a•!= ËôčEţŮ´®ëHűëŢNÓ(N#yÖŞ™Ş>ĽńÓĄqĘĹK±gúz ¸â2@ŮŮ•]IčŢÖóÔءWYĚŘ.¶oľťNŹ„nűţO‡ÇÍÜ=ÎŞ^¤1őł•˝!’Ô'Lú[ix#‡NÓŢÔJ¸sÔËx[ŮU1‹5 ÇÉă©3Äť(Zĺkp\-]üłó/Žŕ‚RÁůbck(ŕ9F<%>Ţy5«ôĚî=$©$©á—‰_)cNŻĎ îigEŇb ěń`'Ni§-ŕmô}UÖő(š‚ĎĹń´@ôr(—˝2«×L_ş@>¸{ÍŽ÷ó'ÜŰÍŁű…ţ »Ý­žźůoP 'VęŐ¤˝żPćy5V±ĽčÚ˝5Ş3Ďď7…ˇVÉ@Š·4:únMN„ňŃ-ŹHioCxAh`ű©Ä×i [čptş^lJLÝś4Ĺç-üÂî5"y-‡łc˝"mBýÍřke*y·0™nĄÖŚ/YŁTI~›;CůĎ iĂůÝ ŢÁź%ą&Ę#fÄ_kü=ăŮ>môŚŻ°±k‹YÎvT«§Ů„{ą4[×ݵdÉ_KĚó›łŇWżcô|¤™™@iűôÁ€tę–É›™A¨AŔ÷1/Â9Dí®ž()‚s–D†i/Gžćüź1Żę˝P)©LIJ ßđ`ĺ9¦˙*ˇŞśŁZsľV3·ývř3˙Ű­öŚ©"a+ó¨Zş‡,;ŹďŻI ,_Ôfçí5ŤlzDŕőúż°*ßßµÉęqU V©y1{/*™źŤmn*‚E+’Ě€!˙®Ůúéąő…‘vĹ ľSk[śbeP¸\h°Y}­uóĘJ"žCŁ(?ôÜNĽŢ çűĐ6›•ÁĄT7g’|ڵ¤á÷p° —ń‘üuÖ®¶;ęŽč3WPOňË”=ř˛ś«aî«$ÍČ„5:Y…;ŰP¦+mp®$örÍ ÷ěMżĎ9FĽőç-Fv6#ŁŔp<…QŤv÷ôë×ýg+NŇýE4üšR·ĂP·pŘ?-ŃZd ˛úéc©˝Vz¨Z S=(¤}taöţ9±”^¤ĽA¤–áEß•…ŃÄ}wŇĎ»Ś‰íF¶óô‹RÍ˙×âW“ a¦üŁ‹ů˝˘ÜŻqQ†fY4R>Ĺyś} #˘×ăëZ ř,Í>)˝ąÄĎI쿸uwwQQQnTŠ0^@߸Q-ŃŢĘ]]®‡V}w{+xâĹ~ę´lĚ”{rëőö8‘sÖNýŚďiö޶—·­ě«˘#ľ=hńäĹĎk$›ĘwX{Éă "Ä=%ůVľńĄŽOXMěŤú*´,jşhZŘ/ł6$mŃQ=Đ (ť,ú·,üFá7žaĆ$˝,uÍŐQE;3˝bzőĂ1¤µč^vřŚd˘†\ąűJ>‡ 6˛.N/ŤÜUJ°źu’1jµo…‹·=i6P}O©ĺ€u“ôH,­§Ă––ŽXS±űhbV!焹€|1űk<)Ľ+śl'łâ†nޱÖ6 ó=§ąv÷´ b9ł\{O;ţÓďbŽqcM9 ěý,/b5®ě-‰c#[ĆDs 'Ž[ďë¦Ý¸×)v2ŕšŕ¬cĺń9ŕsĽD˘«k6kť,%%*Żý´dRż]Ç*`őÄKJÍĺ"(uy]=ĺ-YŇŞeÇ|bĹ›Éń ´ ¬ˇ,řq¬»¸ćOÝĐiÚž®UnHŠqo^±ĆÂť©ÖÂksyašÄÔßX>ÎbË?BÉ‹ ůî"3§ŻÉTö®ŕ2 ‰ó?,S”wă<’±”ĹÂčÔŠ‰/Ň]·s«$sQĄlY†ö]f\'°352"\L‘ř¶|-7m–`f/‰ ZrúsŞĽníęföšĽŁn–8ůp"Dď!{l*Ą%ow[Rč–P,דŰĺőšLhoĎ÷BCpí¸w2fŻ©ygć T8ö1 F"ă”fÎTÇ]ń‘®nP@Äîi‘ďF Ó:$×Heéľż ®đ~ęŔâ0¸yˇ\p‘!žő€‰q¬…Y°j¦NrůÁ€kÚÚXĹtO3xÉR]!…-ĽŤS—M7•i†ňsȰ}G†+–}€Ő\…ěă«ĺšc¬aËÍPÁ†śiR»ă‡K…ڬµŮĐ‘BF•(·:lO1=7ď玄8Ë÷OŘ[-Ö©|yĘeSů¨ď4»9íE” ät›ô•%¬‡őşßoFú×ll*’ѡΞëWrřć ýú4'˘XQk^]ĽhTŽ-ŤAf\Ů‹\ewfçmqj%ŇůC&W”‚4 ˘Á Ś1<(_·ş…Z7—•:qŽő›!»ćő O­"QęL!Đ» CKŽŕ8cqÄByŰOXy_y'`ă{‹O~¬Ú<8śčÝwŃżŃ-1Ɔ©é‹řD‡Ó9Î{ě‘”#­_Z8ăÓH¦’?©ö°ÓŁBśVń łXÔ@&,kÄ”«Kls,ÁÝFř0č’ Ŕ›Ń¦ňÜ®ąE÷4éČ ć¦\Ůđ˘řň{9˛y}Ű#a.şäđM E÷ĺBÜ“ ©÷ČÖ­ÚM Áś¶_'ŔĽďĺtŻflŽDqśK łÓ Ń›ęß0|pSµ3đ» ŃŘiÓE3}ž“ęh–‘'vI‹NÍt±śk‘Ë+d¨‰……) Đ{ˇc@vZ|MÝl|00OH˛Są|¶ťŔÄ:”ŢwĄöłzÜÁ"řÄ-ďĂřdŔ!/ÜE4]Ę·Ú™7\ŻÎßĚ?v ’XďóĘ‹Ç#ÂO`©ćfk™ĄŽ0{µµ-<śă͢ë `Ş5á#Ü•ß{}”Ř)ŤŠj®Ü>ÝŇ ©y±ă¨Ţú}ťZ'·ŚŻ=Űq·GÉőÄͬćűa0·_Ó´‡(/`ÉY¤Fł)„âľ.Sţ‚AląWĘ$föőpZ«ţ);,Çěe$ë¸ÓŽŰĂđĺ’JnCQRQäNa#EoŹ“ŢSŘh”ŹsŢxg̢—cřÔěF27ŃJж´¸Łžđń¸*CPŹM*uÝ yd‚`Bcrń…Đ—h„ÎÁWý¦f,›ńôýťŐb±É˛<w=Úč<árs…:DvďV,¬ 8™+š@í+ (đ»Ę+|äqťim ۨCü˛Tľ‚ă”hľÖÎ%ŕRˇ“‰k*ŃťV6? ?"ą ÷ę·OĆŘzĂńi佚ú';;m˘M32î&š…ŕĂ÷ăq4čȨîËş^jâq\RdGţĐ59$Rr ·)fV%Ă8¨ą«Ťߪl5Km?vŁ„ľĹůp4ćąĐH{Zn‚`¤šňťLŃŁF¤ů­ôv3Ó[ĆnÔ#éµJŘWÔ +±·^k ]mËÍCNđďj¸M«ńÔi>Ýß“üä.ÂfşEP' XčjyQŻŮKĽ´úą’˝žíúşŘyźWlđŢÜ{łlHkNŠ  cňÔUXë/;d!O Ę‚ Ą”Hée&¤*dJ ¬Ď/´ŕq/Ďgô‹KEhH KëŁĚäVe‰ oâsĂď´oîr×ëĚó¦sĽ§‡~jYy›3ěUí‹Ů%!{©O2Ő'˝éşe>Ŕ2śĐ˙D"Z¸:Wh•r €#ĐŇ·ťrżpâ(EI¬=ň)— Žĺ·ěO•š×•šÂ™ŘqŘÚHăSżžA-h‰ćŮŹőÜqtĚwü÷ Eă'­¶kO €J˘†łľ ť+ˇ‡ó žNňGľŞt ®Ę‡¶ VTi'vH–Ç[ŢŘ=˘6𨑔ܧhQŘ儜ԥsůÜÔw:‘Ä8VÔ‰fSVě ‡ůng‘ěţIŰ8¸5ü4ćx]ÁűüĆĹŇş^÷ţŞő= ~lŠ:3ëSü…®ń"o~-©n•ĚkÔĂYľą«a™čŢâ&:“ddŐü‚ 5 Rq}ăř»,.Űv'űv/Aç/řß6áŹË׾LRşź© %˘ľBwTë‘®,†{1đkd…sę\Ę»-~‘¸¨Ś˙b"ľÚ¸}Ő|ďËąOľÍ׸†Üˇ…iQŢľ¦î2ÚČ.fĚEX`=Ţĺą„üř:ĆĂ•“č/V{v×ÇC’°ĆE×…1YŻ3Éjľzđ?™10úě·±ćľoáufô§#s—ňúÝŤV?"qˇÖߏ˝MwĐ€]6ÇÖIď‘܆S^ŹĺČ=1b“·.W9Ř1Y·B1i…śíSżůŘô…`¦Ą­|€íËŐW§Ç—űqËCţŹ[¸ÖăÍŚÎVq§[‚·Î>ˇ.±Îęż|qŻ3ůžâ$ç´i’ ľť«Ľ¸Š)»Â:H÷Y6>2ő–néµBňYßŃRÂŔśóUŐ;Ú侉%°2!}ľ;JŇ8{É^j {P ř}Ló¶ăŽE‰.Ţߑ͌“’xřQM˘yGV!ŁiŁCčŠäŮŹÔÂĺ ,úaŐťhw¦łĘü˘6ĽîUŞďŃőtřËŚ¸Ń×ÝlĈěi@Ó¸Šń!Äć‚hŠŢ]–(ÄżD(Ev?a5Ü—f>δ]‘éBúWâVwÜÓ[¸ş˝WIxřGŕŹÇ~Qt+býxE´ËŹöT±ÁOܤ]mČöUyڬŤ˙k‚5 Ž‚Č˘„ű\O+éŞ/U—<Ť^ď™ă{Š• B0Ť. J %Ú§TmÚîŕ‡`´w[ ĂňbßOÚ_&McŚ8‚¨â¨ůÔ&`{_În“Ą˛±—ŕÂ0$ŚlřđpLII˙Âרă—É˙~qĄ®ŔŻŮŚ`á9¦•Ô!]K;ř"´Ď ˝źypqă[ýˇ˛D˝Şĺ+MňzŽv ă»čřÉa“$iVtŃ]‰ŞgŇŕg(m©DŞ?ÜĐSŕS¬·ŐËR-ő7ëév6‚G/ö ŃäÄ$fiRMOýÓ7H]yRß‹ zJŕacXÜ}Ď=NÁ•čdß_měKĘÇĘyzk’.“ŕMKľČeDZí6 ]čA؇ĄŚFţő…>0ßŐoÜ3é—'V˛ęѰfXLż±?Đ‹Q3.·‹řůMÁ6Ĺ„´g牬łi¤;7ś]^ďˇôŻŹ; °¬t>öŢrËy®Ć§"ö®-#“wS »í=ŹŠŻóâ6PŇ&Ĺi…{X;EŹŢcŤĂ6١VÖ*ó]Š›óćÄ =[îűVď…ȇ#“‡MHły4”=ż%!]Ť±o4®î%·ń01Ó©§q›kľ Aa‡ #âĎ:_aőµň± Że[9ˇ—-udčt“›+×ďşőŮG¶’l“Ů ­5 ph˘Ća\…?uvb˛üňk7afµfCNeĄÇSǤޜ†öůäpëimŻť'hşěQě}—ĂP*±+DŞÚcmyÇ^§9e¤L‚ŠĐů Ďug{îćVGRăD["łöÄ‚Lg12ě;dt řTÄRŤ2áR˝)^kĎ ´%ő -z×}#ŞZi9űYől·Uˇ@îÄ»Ľ)VŢŃă  ´_đńęeIją“>¸Í%›$^t2P*Ňš#+Ű Ó ď÷´«8ŇâčŇ“ JQ’%nśÔőZóŮubád’¨Ěť–.Çż†ĘM]Ü—Ţ24ňUÜÔPD@Sđ Ý%mĹ<žČ'·J?3ÚŁĄ”DGś˛7`d-`š€ç§dwć Šg~ť[qPZÓgLs«59¦@‘V‹Y¬ôÝź‹ D)nE‚^y«O Ą{µ9â¸;z© xúaöá> stream xÚŤtT”ďö.Ý©’H×ĐŤtwJĘ 0Ä 14ŇťR‚"-‚´4*Ť¤tHJw(0ÂŚsÎ˙wî]ëŢ5k}óí˝ź˝ß˝ßý<+“ž!ŻĽ QĂĽ|@I€˘¶¶şâ XYŤ Č_7« ÄĂ ‡Iţ/€˘„@ů”@Nhxą„˘’b’@ @”řî! PyCíÚ| 8 âIŔŞwóó€:8"PÇüëŔaË ăůťw…x@mA0€6áqEťh rÂmˇ„ß?JpH;"n’üü>>>| WO>¸‡,'ŔŠp@O¨ËŻů•Aݲ2ĚNîę !< ~ő§ő€Ř˘®ÝŹ˙ĎfťapXŔ_Ă łł˙5„ť—ż1 ęîQWú Aąţăs€ "@q1!q â€řÚ:ň˙*oäçůřĺFMŕwŘŁ†€Aí!¨?‚O7€đđ‚üďŔ?-€ÔC 0‚˙TGą!ölÔň= ľ Š{ŕŻßżß¬Pô˛Ă\üţ˙˝_~y--S5yî?˙;¦ ÷‰xE€(ş KÄP/A˙¬˘‚ţířźTu= üÓ-ęšţŐ±÷_pü'ŕźĹtŕ(ÖB˙!ą%Ph‹züSýwĘ˙ŤáżŞüżHţß ©xą¸üsüŽ˙a+ÔĹď/EZ/JÚp” `˙ 5…ü­6ÄęĺúßQu%yŠĚĽÂ|@á?~¨§ Ôb§EŘ:ţˇĚżń/©ą@a=¸'ô×·•ţW Ą/[gÔ÷ĂĹË?!'JlßküeCPrúgĘ0[¸Ý/Ý Š@ ?ÔęQ– @%P;ďofřů`p*€š9`÷ řµfżç/Á?ęÚzyx ţMÔˇ˙˛‹ń…ŘĚMĂmĄÂťjĂŰŐň´>Ľ#ŇŘGYČG‚Ľ#EÖxĺńÇ«©†/rç5ߨĚu ¨X;}ĐQpGľ\šů°^ÇXď'|Î˨˛íŔNžľ=GźL ¸ cž&mBË7ÍP`,őčAÓŁŹ#ű˙ĐΡǕ4˝¶k6͇íŢk1Ť6ť÷«_çjQŇIě5@4űŤ6DľSłç}uM [‹·ŚÉÔyά)ýţ’ Ő§ŠlĄo Ľ›ľ#wčĆńHą˛T?ť’0n•ÉBÉu^©÷Ţó;`Ő»r{v=ĺËśČZťSÇőÓ=+&.÷î—ŤÔÝxűĹZŞ©Ožç'¤Ę §TK±ś®‚-Ýóůđ6„Ĺ46~%,Eľ©ŤńDߏ°ăËxü†đŃEǤWţyůĚéˇä“ĹÉ%śđđÄĆP]Ő×Aň$Üěş×BEU…gOČc3Ý#±c'˘ ű;[oń<ćË'×\ŽMeÖLťzdR …ŹAlçwvŔÁ‚ĄĎŠ{‘źcË»şŢɧ{Đr~Z»ş wł4•R Ô]y ô˙Xđ©ŐÇ­Ĺ_ś6xĆfz\ôPpˇz-.:Z©#tBëz6;NĽ©7đÂLţŤ„şĺ!fpFęĎdęÇŘ,ç#ÍO®đ OřŘË»Ş!űÔY=â]÷îă~oö.©˙4Ś)gt” ňĹÄh^®á§!iqj˙°žY:wjA°ŁćRçE¨Ş©ŮW’ú1Ĺ9o‹ęË‚Ť ăpÄť ¸'÷Ńě»ňHµ*ĆiźjśîôIĘ»ÁZ9ˇ5—™‚ps*•lęÝO_Ś„&Ô=2 Ă:Â"Ŕ~)ëÝq«’`“@€éëÝŠ 9".4­ňŤţ_jKv˝J{$+ĺ$Ň›Ź>>~ť4|n4B@ۦ“?G*IBůYůŔ…ft¦Şż.iRK€ŔĘŃĹč\ęo‰Ôß>Ű} éG]qU,+N\$@ţŞ'Ţ‚ł©Ť˛Z‹É—ˇzţ{Î2­<©ńOąę­ÉéąĎÖRýíWB~Xňۤ$Ę‘6ˇŞŢě89UĂwF”‡>emfD,;ĺjŤ lë}đ4Ď€1w{'npooűÄý±âi·ÝІ>27*oQÝľćŔ7/•ł©kŁ&qÔşŁm‚´”Ń”N©ŹÂUXżßđiŇŘîř›ßqţdĹ÷‹Ă±ľÎBĘŚ“<ô''VIîiíĽ«¤‡ě W®Ô/bh×:ˇäP¶ĽµIčK˝G0´µ2SňP˝¦Ö’—ŰÂĆ´ŤáˇÓ‘"Ćô„/ˇŰÝĄŁĺZ,ÂÂ}b6Á™#ëăRvĺą˙=Ľ¸ásÓ2§CâÓwřăزČvÂa®ěXŤv&÷Óő[ňB˝Ş|›w[;iÖáDŠęčWÄ_ç;‡G¶¨A"o±xxO—şĄZ~Ň>(NVśďéúŃyX8ëejĺΧ‚îPÂjb±ŠĆÄŤŢ_Ăú‚ŇČĘ~ DŔYJ¶vS[{«µbŠé‚ÍÍś—¨jeô͆YgO ÝxtH‹{Vr}üŽË.$ČóĽpŚ šĘg9ĂŇoz †<żŮ¬ó>ąa_iYôś~çŃÖZ6sË{sri“ŇF8ŔZCHKńłëEa —k¨›„s´Ź'­…ĽŃŁ:ÔĹR‹lýÎĹŔ[qNúŤę×sB†1JĹ"řęĺłť »˝ ŰkQÖÍTąÝa}˝+}˘´xrÂędĆěÄ:R“(x Wc“\ا ŐČT<3´ł5 23ëW¸FÁyp#D€‚9)n'\ąPnz+Ö†ďĆp‚QfĽđ4ÄôB‹Óź†{÷|Š-űnfô#0‰NgKL‡5˛¬-$7$)µ®µ®b`Úm‘©ĚÂ÷Á¨&˘˙ŚH-‡c Mqôlx ꆇ»iÓđÔ¶­T|{.©÷𡻀Ý0@‹đµî­®Đ÷€.*˝z5˙/ĎwřËhöZ÷‰‡íłH Ć#ő¬¸äÖI ks^„ă)ěö¦ÔźĄ í—ŮŤíőtpĂEő,U˛Ę>|‰đ’&µ ]q·E}?ĺáäMµŽÖXGІÜőĺŚE–¦ţ»!o˘÷śn ťr‹`yrB7ˇhřć5ILo}’ţň·˘Ž+U·ţÁÝEÍH QěŹ ÝŽaĆĎĽ”Č®…Î\§ÇU¬óe@Â:S%^#Á ś65>YšÎéOĦ,ˇ/ĽĺżĐKŢ’ňVÓCŚIÚf”'ŮĐěn: ¦Ňµ´^÷¤¦q-–jŞ^žŢY'ŁáÜĺαíŰ÷ĺŮ_AR,BşŰËnő ŔéÁyžŢ{?ŮUĺćŰ\OŞ›r×\#ďzAÁ)ń‡Ť`­Y{ >#5»1A`ő#÷@tĎ´]ˇâmL<–Ô(†Ű×¶4Oí|žqŰeEGř“n´”-ĺ:E$9FĄEHĺa[d´ŔŢÝ&ŐđŔć$aăA3ÓÓ•Wg`Qĺ/ďzL´‚ăČ`ě›–¤˛Żi64ÖkŃ·şŻÂěn ĎäKG’´x49đë ‹7ĄZľ5.ĐD캎Gu¶?ěç¬nUIżN >;Âąž qÝJë¬9$ŰTĆ4čđaŁxĚLlLHe¶j^Ű1•‘hĐŹG4˛óBz$¤ó(îĂś zŰÍ'ůQ}C°‚¦±”§_ľĹ€‘/)ŃřĐ+,´ćşYtÎĆ\?YqĆjE©<ßś˝'=s?6ŚČíÔÖȋߟ™îż›iâH …Y'„é}4nS©f[Cű.÷=F«2-mQmĂflý^ÄĄµtrNśýW@óIh%{Ůľôá”±ű9RxžÚ3ÉjšËŘs;|ővś{–,4–ˇ(8”şĂś/s°?5¨p;„ŻčŐĚž)8𮵎öĄWć,uAĎ\0vÜ|ڱ”AwS,‹FšP§^yd=/ß[áâ%ą‰.9÷ďĚłJX>xóŇ-‘%í.žwQ˘¨~ßXô^łT1YBdüîă› ö%qú›>窅L»ýzžŞ î2iś~ڱ@áĘŔ_­¸í2¸Ě¦řŹ)Á˛hX˘k“ŐDš]9ćľń¸U‡ßĐýÚŻ‚kQۦ}a‚łÂ)髤 QŤ®…µ,I†c«ż&8Ą–ęő±ĺ¤|ÍĚ3w)ŕ]÷ľĹWŐ’(Y’¸U[& z0č˘]Ďč ć—ýţ‘šjRu7„"ÖFFďrX_Íć7 NtęÚÍ˝ł¦ě"Bż ůÄ€â%Uxđ12*zęŚ0Íđ3sŔČę{Ńě86ćŹ^9ÓÎëJ zŹňr¶ěg›ĺ|šŇ1wXë,Ń÷9<Ç48ńX®üŨž´Tář©§±slç”ŃŻUĺ»5ţ'0îŹĂŹM‘b™µü*˝5+#č^őBUO‡ŹäCźă,vDĹ2ÇlqgĂ!˙Ö›sţ«ä3(R3Űő¸cĘCČľ[t/I–»ą>83›Ë}ÔR=9~~ţľćUĚ˝™µ™É. ŇéŰY ě‹Ť2÷&Ľ¦öÓJ¸Ŕ+ %ä`ë×z“¦Ż\qfČ›×ڱhćsRyęŁ 2C#ÜřX6ç‚ětî¦íťÇťĽ)đ5‡#ČÉ`¬ÁŰhŮ·1\ňe§SĐ×=ʤ(u¬Î‹LCM^í±éŠôf*/X<i0®Űd[©a΄íf'ůČ74Ć–§ŘT?=W|I°cDťâ¸řôB˙Ű˝»řßIĐJú:Ă…ÖĎŐۦćKä}‘`w§ =ţ@¦]%JÔé~7bÁv9ˇ>uXǵdf oB:•ŞAĄ\Ŕç„ţ%—ĂúD9cď–ó¬vĄ´+R÷éË‹ę}&N^u5~,'ăa÷ĚcľâhG»ąŮ}ć’xĽ—•·Ç2váł]ň‘ ȆźŇËćý"ĽE«’ ŔĎî+É;ŁÍĺ…_*Ńϵo&řĽőĐ˙čÜúś ëe4űŔń©ó‘ĐąII2‹Őa/ËţóˇK\«đć<_Ň™YNâ"ÂŤ#Ńwś?©$¶¦°AŻă‰úďŚUĚhv'OkĘă-’™;~`®=L’ËyBÓôaöýő:›´o^ ެűÇâNLU•´Ćk†˝¨É‘™ď… Ť8Đ'šëňBKĚKßć+Ó.JÓC5=-Ëí´icADŻü—n4]+‹¨Üž‰SĆČ`×cŁ;nK‘Ôy„fůͤómˇ QüŮčĂźßóEÝ93ČzőçÔčľÄ[˛z??°ŰyüäłBe%˙“§Dr‰f2]Ä]Â;‡ěs‚ÍIXÄ céÓ&¸‹ńL’ĆP¦2-v]’ ČÉ5ŮĆ|™4\„:°WhwE_ÝŬ4Ö¶ńžEű97:›R ż{tqđCŢO§Ŕr+ł]ŹCE›gŇÂ0LSă(î¸y«w©b?úITr‘Úącľőă|r§;<$ŻĚ.m¨ŢŃŮ?ŞP,–yŮŽĄ7$‡§DJůř‡ü;|˝€ń»î[˘ó8ž(?ˤ ł±&”‡NČn%ë°,G=ŔÖ™źĂ>óŁľ&ěŐÍĐ×ÔĽXČ)}é-ÍüÉç ¤RCĚv“ßRÚrz÷ÎE%kiâ^„°ű—GLr±Ť‘륻/DÖH" ĄAŢ1—¦Ź®x%řĽr9_ ­ŮÇ8ş®t/¬±©«‹ęµÜ×ăőě6¸Ő¸=äqű@rĽöžżş4őÁ­ÎȰsťxĽ+N¤?‰µLł4-żŠËş¶€ŕÖ$LʬfěˇúžŞÇŇrâ‚®Ť ËĂĆ÷Ňv‰Ď‹é O›2Âň2+Ł…ŰŠĄ«1hvgw¬ůŻžáKž™4ŰćéĹĘQ(T;2ÝľX-UľyŢ30lťHfrOášbŔPČ‹ćĂ×^މ¤ü!î›ďĐ[ş´}Łi6а’ôłÂÔHÓłĂőřÂÍ`ŹCń©€7ú§ĺť±]0Ůmök¤ĹT\V6íîŁĺ±áË[~âÁ‚ ¬řxäPÔÎ_Ç,L¨×!é±){ŤŮö,ÉÖÍc#f(E®ž×˘˙ Śy»ń6ŐśJ:¸í¸xć+.gh˝<Ő ÝĽoHuúYýăě¦z×ÜŰŰ»B|럆Eg•dűŹŮčŽR{Ky'+Z†(×9Ë ĎNs)DI‘ź$™‘dM®śĐR5R58©wاĹCs"ŐĚÝă»= |_Ňß8<Ó:@w°y\ę4Ó¸ďżĺh–”ŇíŢ)VŘqµ«Ů=É«?aQÄëŽçí‡1Ą‘—ŃĹôž˘ˇ˘)Ż]?źYÖ]°<]ɵ ůęͧ‡ľFjP@ąŇČ Í«Ă/¸´ú–Ďř…—Űâńűn0ŰÔJź›˝p¦›ľŽ‰—1iEÚł+öaţL]Ć©x¬B†3ÉŘşZĎÝQ cUŤ_ŃE©]č~72şR%ŐŞő7Ũ¤~8ŘI¤@ŰŞąvď«ß¦r”…ˇŐ\ęĚuhj‹˝a‘s;JíXş$x‹SaúĐŻI)G|‹îŐžĺüĂgBwý<íÉÝôý F‹©‚UBËĄ¦dO^žs©éýČoHśéZYĹ|e=!Ń?C(•eŰ2Ěň&}çg‘ŚQŻ“ĂăW•â&ź iÜ|Ľ`ŃnCýoVq݋ׅ–§ąSkKä±Ć íŃđ`>—<ű4tŢgbp#üĐôÄÖ‰¸´\†Áů§…y;„âĚ ^m iĺçŐ¤ňD&ú_ä#ěËEHç©bŞfC#«¤šĂ"/Úîib,5=#”Ôáđ)(í¶éĄ %ăčŇcřÇUt-)SŔL4žńŇFťt—:k;˘Wúń ¬ˇĺś:ÖŃ:M©ÂŰĄn3ÍŽTd϶É?ءYp˝'‰ľ›N€—U–ĆôyŃ‹fŠb›=‰×lŰ*O)ąµ ° #*Gv×NF K7Zµ˝o ^üAVŇł‹e_fč^˘4FçŰ'msąý0Ľýjţ‹E¬}2ě‚W ÉŤ¨ŮoęŰçy1`~"ÄÔ|Ý˧K7ĽŠÄÇČbĚg G4UŹ®ăöݤ'l&YĹN}Ţ5íxJ‚˘#—¨¬‹áŚ*>ÄĂČ› 7íâąÄÂôŰ–âýđŇęÖ-öŤIź™đS\Ͳ jOű–cîÍT>JkÁzDLĐZ‰ąm Ę G9UŞ~r{蟆Ą}[úi¶ň8ěĚw’ěLó 8»'1·iw§ ڵxĚđETáç¶žň Čůvtcf\ µWďzf yŕĐG c»O{ąÇ!ćĹ›d0żűzZÇ"ꂎë«ä0Ü·â5ÖpHĺ{E…b¬@Ü-şŃŞŇjÓ†ł×čL¬:JÁ5ć $™® ŢÍ,!OZşü/ăß•ÜńU·][±~–iIfž|báĂ,NĽQe®˘ l/Q°z&†ßCĽ6ÓÜ”KŽ^{ę™íýŽŮé ńrlzâÓ†Ç%Ď 1°ęĎqîł-f ˇn*ý”ÍŞd,đ ˘Ząž¦(ŹśąÎ6OŘ7×+ţóV9nŽ{Ě{†}š÷ŕůŃ1¨¨Nú ôÍŔ6µ_Ű|9ĄúĘÎ1ż˙l ­čŞcvËg+5'bů`6ŁŢ „s±Š{ÔÖHS`BĎšyťH®őľą{ZI]s™vfŁyŰisűÖYné ŢŽř"ÚŃ żu$üĆ K0¦UôF“űé9›r—ěć«­8^VŻ6ď1úwč˘wĆŃŁíÄxp†ůJž»‰u ƦóŤţV8˙J‡Ĺendstream endobj 371 0 obj << /Filter /FlateDecode /Length1 1473 /Length2 7451 /Length3 0 /Length 8448 >> stream xÚŤwTÚÚ6)t§ ť ]‚¤t§R 0Ä 14HR‚ŇHwww—4*‚„”¤HóŤzî=÷Ü˙_ëűÖ¬5łź·öűěýĽ{­aaĐŇĘXĂ-!ŠpâáČ©«+‹řřxřřřqXXô GČ_f«˙Ź9W´ÉČ8u8  âî @Ââ q>>?źŘżá®ây°Ô ÎPĂ n8,rpgoW¨­ąÍż–v+HLL„űw:@Ć â µĂę`„Ä ąŁŘ  ·‚BŢ˙(Á.i‡@8‹óňzzzň€ťÜxŕ®¶RÜO( q¸z@¬ż4ŔN?ĚxpXzvP·?v]¸ Âě  ŽP+Ě ™áł†¸›t•ŐšÎŘź`µ?Ü€żÎâý»Ü_Ůż Aaż“ÁVVp'g0Ě łŘ@!ME5„‚†Y˙ ;şÁ‘ů`0Ôl‰ řÝ9 (Ł # ţEĎÍĘęŚpăq:ţ˘Čű« ň”`Örp'' á†ó«?y¨+Ä yěŢĽnÖ÷„ůţl 0k›_$¬ÝťyőaPw˛ü_!HÎß6[ Ä'*" Ę€¸ ^VvĽżĘëy;C~;AżĚHţľÎpg€ ’ÄjAţŕřş= „«;Äß÷?˙D8 Ŕj…XBlˇ0śż«#Í›?yů®P/Ŕ3>¤ö@ľ_źŻL‘ň˛†Ă˝˙˙}żĽ:Ć:zFŞ\˙Ű'+ ÷řA żD ˙VŃC˙ę‚ďďTe âűÓ-ňţŐ±Ç_`˙k88˙,¦GŞ`˙[ä&|B|VČ/Đ˙YężSţ ˙Uĺů7¤čîčřŰÍţŰ˙˙¸ÁNPG¬uG @ŽŘ‡Bţ ­:Äęîôß^e920[¤ A>Á?v¨›"Ô b­EXŮý‘Ě»ţŻQs„ Zp7请™ĹÇ÷_>ä|Y9 ß7¤.˙¸ŔnČaCüľĆ_‚§öˇł‚[˙š;~!aŘŐ썼z$ř‚j ńú­l/ Ž@¦śý6pWś_×,*ŕŐűeú„Ľú˙F >$„ţđÚ˙đ:ü AČJ®˙‘ąnżá?úµrwuEú-3$™áߏâ±ÂYś[I„ŘW‡´žWĘĐx7Ć%1’ĎŤřăąfŘ>…ióŐxÝô´%ŐbĹĹ^˘™}‡†¬ËyĆçůßőúZoÁ3 ˝â7[z˸ą»3ԙߟ´ŚsŤ(ن‰˛Ĺ \űP´č˘;ďK[Űöé˛ř±U÷,$x˛’*Ѝ„¶j´wV¦©‘ÓŠě­é š,ő6„N)Ů2לb_|}e‘¤ńŽQU˛ý'Ţł‚đËŔđŕG˘^ş®¨ ŹŕŤCć—"‰AZyA¬ Ź9ř×™»ď}-źČ*q<›ćH(ŚŰHí3ÜĹ«vÚŚŢt/Ńhż[|ť&ó)¤–NŔ9ÍB{´ă{MŐIĐG•Sú»aJô {­±ĺ‡Ź3îĺ+µňŃV8ö–ŕô×hé^Â@â§ďîĎÓűčóÇ0”ÎWŕlŚ6Ń  ćí <`Ý7b°~ó¬řyTŹm§Ę鋼Č$Űݧ·zÖҵţś”˛× Y¤§äF¬BGá29ęę Íą˘÷ĚŁI›xĹÄ´0Óuč8ŮĽŻžZ›ăß/˝Y)SĐé5ź˘xnó‰>}—óŇţ'iŮĹkĽ¬çÍěŻÝcÇ~g¬T˙­¨.ĽÝw»HŞÓmfˇ07S¬ćóω$m¶7,ÝąfŻLŐE°•o »dč‘ÝźQű!űš%´+zdčHvĐ&ÁÝź«&Ů«e źyDů°âĺŘEź4ĚŻŻč[—‘˝–N®8ۇ^’=2É•îřG‰řŠ–Gř«Ą‚™Ř©ľĘW(VgäĂßuLp)Ś_•+ŚâŞ"őĂ[°dŁ´uX᥍!ÖĄĹ&)/ęV|ĺÝ|¨ďsđ>[ĺÜÇýˇaÓ¦‘sÜĘžü~D {ýšĂŇЇŃíúé‰XĹ HëOm¬z2jV´ŰÍ Úók^ó~4ß+ZĄŔ˘ý‘O /‰xŘz,âá˘acx/ö;îL8$ŹV‰#ýŠŽĺ1Ä× ®*{<™—]ҵ2ݬş··ÓĂ 2Y Kli5ýľűđ»~˘Óž[˙ Űcui›<ý(#ţŽęńWâ[3ŔÝ8­‚ŕ Ę1ĘMţwK¨+ 2–Îj!ʉ("ú¨žBv´#\ú+ÔWEGq’]|Öű/Ź b‚öyĂi‰É0âBHߪ^ÝěŠözď,0.őëgßŢçJx»$^ÖóŚTĺš(ÍoČIBrEťwiÎüzďn—†“"nč1Z;ô‘ń®´š€Ă>9{Z»u;úşhĆłî]6¤)"J˘kAQ‹TX©Z+ÚéÉSŁ;1m4k—Ű-ągE‚i†¦Ś9–  Č4+ŤëHĺ^ŕ÷ Ú”+)üš¦TÖ‘łúÖć´gc WčjçčµĆcCť j<íř «óąŚAW«eĆ%&!gÇ—Ěř­ë]aL†×=äa»7§ó}ĺ9ś$ÔifŔ§Jv¦q7áę=Ö¬•ž6đëuOkĚ˝Rę5sę>ś?$¬˛@çE},2H%^¶ľ$ŃýB1pčËn?ÁŁŞcüęGÇ5“űËďo›şżľÉO‚éŚĎν ďUÔ{ívĚneŢb‡dŇÓ´ńkt?řŘčD~đ]{aĚďş3ꬨŰYaS€‹ö3g†ż ˘PČR0T‚żŮźů–‚˛›]ŽĚ…śĚěĘ,¦A­—9đc0ĆJ’łIßRŢ]tĚeč䡍đŕ¬C#Á…ŞhĂŐĄ]´‚fи×Ţ)ě–r„n4đŢ>‚HşŇJvąt5+ůGó:塠†g eëŞBčőFSŐŹ‡ş[E)‰e,EÇţg¨Łřć0[ĘŃö;ëí˘geţAxčŚßK!9CŽśWIÁyA—ÇácóÂ)ţ§¤ů‚ü˛áRřh}…ÓŁ'.Ą,GĎżJ–•yŤ Z ËŔ{Ćł+:Ţę,hÜ›O§ úVףtĂEŢĹĚOx™Ôrf>íŠU”mČiŤ+B¤Í–­nRSĘۇ‚ľĐˇ§¶íşŢR.>‚áU•ĄŔ&yŠÔ›[Q±ßV0Ǩč¦ĹiŘĺ¬^Wŕő[/ĽCK:4’ăŽŕe&‰Ą§?t lĂťSďeˇ‘Ůčüü9¬#@¤ yčËLC•3ţĆăěŐ’0ÄÝ®™‚’ç'ř_ÍńZ¸)’ÇC’a um?ňFô\”Űĺ9«DńEn\ĎéGš¨Č…)C r7ú«_;ńIîŇíhT!lĐ2˙¨j:-ÍDÍ Y8Yz˙ގĄË—ľ×'4o}ňE7)ŇM4ËúĆYku˛ž6UŐyôl†Zťą “Bŕ™Ř.yŞ]hę€6gSp¸‹}…äŞ ú´‚š;y^‚öP6XëŃpţ“ŃAľĎDĘOSôIĐgĎĄ÷ž|´¦đ@_b蘆śq ¬ľ¬5‹&‰;o„«ďq„3Ý~‰-#î^ź9Ţá*qrŰ/Ţ Aă{§ŻOŢ6JËÓé·,cÖ #E‚ŐĽéu QŻ÷ÇáxńmKbL¨ćN‡öí±Ş˘ŔHF̆o*‹–bÖ[)‚éşí÷:Ńk,ę–a‰7bJöĚGĂ/ăA÷D#çÔ“ł® l˛?Ú„-ŮQ,ľ4\;pJ›éZ>jD‹'ĎYËűFD“×rëŔÍ!šCL˙a`xw«ÖÓ\®“J#V|zÓřy‹5…MöŚŕŚđĺŇF ¨ć…đÁIůąŘ¨Nĺé´˝Y÷¤~2 óM>Í›ň•¤Bd4f¤…WgcĘMÁhO˛Ţm-A†(±{jżĽ°H¬Ož Lď‡ŢE‹çŃť¶-űŁG uĽŮ|U„=V<Ů y‚怗9ŻC0x(ľß’đ ¬b˙¨!t<}AŽČiŠĆ:uTI {¦ŢÉęEľÁĎíŻGÁPÔb¨wŻ+!"­uyERO”c{,±µâĚM*k<ˇë¬đąmÔú.ßÓ˝¦Ë”¨Kç*W*[ű‡IŮ‚H%;$őŇ—Özh^d§Śň7sŠ ŻBŰQ–p¤ďŹä<ă§ď÷çj= żú±˝§¶,¦¸ů°}ÍĆŕSĚĎÜvÁţˇcA€Q@G\%GÍä–-cd‘rd±Š3ÂiŐź=^?î^ű„qďk}łé!)®Î<•‡ŠÖ ”,í5‘%tŁ[⏿Ů*¨G­ŁTąßiÇ;‘;łŠ<sY&҆+żą®3HÄUç7÷Ć^şŘQčË®WoüŞly×c]‹/°ßčx\ż6;=Á8,żŹz6ę×[|ăĽŕőd˙潄 :+@š k+ʶĂgܱş0!ěüp‡;kfĽ†©1˙]“L˝śbYJ˘±SF¨Ů˝ÇŻxHňÜćšă¤hlLićU˛¦?Č>ą©t§lŢžŐ&Ź™ö–‹m-7t[$˙Š×SąĆQ¤¬GC¤8¨˛ß9!řÍ ¬†0dM8°PTßďýHľźčáČtĆ&|”ú ŞFôśHbŚ˝ä52şľÎëMeczł#uŽęb¶atYÝŤB´ůś­&ÝÝâDŕŁkűx†}sT2·L˙Ľ{ŰgFÇ^ĆóÜ˝,ʄЮtÉ>ŔśÂ/Îe&Dýüöék]Í´IM˛śťÇŻË:˘ęZhÖ˝‰č|AűŠ}%ŁX‰=/8"ÍUýUxÎ'Ó ú¬,Úß|•`Fs ââ;Ě'ëJ­Sωbd—PE^TĐŇľ~é¬őI۵N®Zš2¸ŕ`ę"űśž5ďixůMĆ“-=C¶f!üţBŔŔ’\¦Oz,Dň€˝éY'&­›CNî7řV­CČů‚4©WČTŇšBÝ›:²g¨=kńgŤ*R¬_#dŹîSęZ˝›Ź<Ąz8ě $´á$ŻźŘj7ł­Rçpč?\ŔćĐ:Ăfň:ů!v1“o­Ú®(VőpÍí:WěŞPŹ˘Ž“Â:Z?ľ¦«’««rś~C1XŘřÚ’ë’öpc*=ŚĂáb¬Ă g5¤ĺBü F˘®×cۦÂ[ N ´Dô÷aF!¸Ż—¬ďYÝ żÖľ¶ŘĆ0™ŽbëżÝŮocXS¬ťťJ±©•mśłY íýXŔÎRS‘¨ý…Ĺrę1eź WĽ™V°Ěäş;}qŻ˙Ś-ń'ěö/–”8UüÁŚ:K3ć™uüˇľć'(§´iůŘ»ľę+˘ôňśŐ:Üçë,Ŕ’Űr"ťąĎsć9Z>}ÚŁ+.÷fÔ9 ˘€vł-ňťźxŐ&ŽŞ±™ŃheÁ&ÓőÝśbb/Ť·2ňZö0í:™ĘX"˝EŃđař»qlĽbYJ…ą¦Đ s«rLťÂT~ â~GĹĎ©úôlŤëŢ~›Ă (ďĎHh®éŞŚáYݦÎn8‘xoYZO‡6|–ĺńÚ…QĂr?”%ď}yó­ÓÄ·şyWč¬9$3ŐúÉ?eR•önP›U7w}pü/nÓ¬„wîŰ÷7˝SjEµdo–7§[ŻÔ.lË5–Ň[ň(®`”důmŞ”gL;í%§sćâ$ářOĚŞ80×t‰E[‡ź2ŐĘŘqTíHR—›ĚQDQmšĽgŤ_JÔőh>»eˇQMŇމ\clľ¨Âşˇ‹fn›Ť&S–â3ö¶ă`Ţ’8z&‘XÜT9u=C´ĺÍ­đĺčâˬÁUßä J@ţŘĎľ¬ËuMŕG.(«ă=ÄC("–yˇzü6ô> e6ů w¸˙ćQµä—ËgëĚŢt™KE\Qěń^.|Ş?Ö¶{o>NÍ·!Ę"˛© qů­Ś„Ľ^ő „˛9âĎ–Ą8!:.k ZCúç9?,Ů…'ÂĄ­ĚÇís.qL—Ű fís‚ŮŻÍĚŠ_Ń‹[řŽIçßÉvč_aÍc±f7ŁŢŰřxz6s{QL©×Eź˙ř¸ŤJ˛Ęlµ>”ŻK%·Ń©s.䡺©p 5řĚpٸuŃT[%áň– ţ!ö&"îZ“EÄĐ'á$ißţyU÷ľ­±KkČĆţVlŚnmbíʡᩤÍěTłut_*ĎH5F퀟±OJ¬‚9"ťNüÓa™Ä×€{)‹8!<ĹĘ.TZJY{ÇT"?ů ůKÉb÷ÚvŐ%šŞ=ĆGר˘˘\<7-Íf D<»‚“?),Ҩ6ßČůÄ?źŠX»G9s^ş,ĐŤîËîŤŇ»‘¦mń÷Ct¤¸ßřćľ7Č­­©Š÷aq=şf?]ß 1úIWR[%đuhbtĚŻěĎ罂˛ů¨w§Jďc_8ô™đą©űŃÁ<ŁGf¤ĄĎëD}cS9?…öDáU§ç“&˝Ů¤sݍt…k^ęÔ–žD7äµ–ňĺMŠÓâS‹ă·g,r·ďŮŁ|Í®7v:brgjŠTłřŘ ‰Z S,u ǡ“q¸Ó¤ŘŻ/Ň#?¤lv°Ňt9Ĺ,JSŔʡĽ´˙8ľ4ëUŽűŁÂg‚‚. .Oşođi™óľ^,Ř•^btOhÚ­oË•xüŞP1>»°™™Áh)Ě®sçomŔ =ŁYĄfád*!}ĎÍł±U8b·9 µâ‘ČíšüŢů€|ÁUáĚ2¸ŕXu·|źÓť‘R}ÔîB8™{ý:ź&‚T¸ďsž¶”˝Í©QÁj˝®ďj[ű}Křµ$3ľŘnřô}hö±(ě]›E% őü,Ű|}Á>bâo•÷pđ/>éĹÜnÄ3ä_Ľ˙9&iľěCnŐŕ¶ööÝ-cúT~•„ŮĆI‚źŔm±VŹ}`íćOď÷kĺ ŃYŚ3m•TkYĂX-‘ČĚąRž[zoKÖkşö±7·ŕ8 'g«–1˙ýâµô”ňÁŔń3ď5Ű=©’Ósćóge‰ÄF ľň m١´jąÇ  jÄ÷6gľyÚţsZĘí6Yă ‹ŻÜ®/Ú‰m_±etN‚X/[ťŃ\ŤGIC_1y!!ěh"Ľ·7üíG>Ť5.^Ç&âŻÚ#{e.íF¤ó8Ő©ž§Scf†»ž˝×‡ĹÚň¦”©=Ď‹>gĂxűtúF?ťL¨ëť*#ę´ĽŢí 4)ٝڕᥣ"·ýˇdE€Ą¶Ť—Ü×Ö·FâNŇX>8ľjqi\ĽťÚe&ŕĐě´7v€Ź%­;zÍ&ö\±¨ší¨žőĹ$'‹ĽőĂľD!oŚa1Îů¶¸š·ëŤ{˘y=h@‚6~l9]Ú_D7ó†yđŕ%˙ůLIśž IŮűĽÖńá ܇™0iµD,m©—¸O_„węŞoŻu€żź_öŃö1:¤ŘĘ©ÍÄâ|X¸b,śHČŠ}8áHĘ”¶“%Fž ¨nßfÔ°#0]¶T9x¶ S j°ĐŰu+|VWO2¶”9I‹]­Q±2›±_ă‘˙6Bě^€÷€_S;dL•yjoĄ›lÍTX{fşĎŘu¸ë_ÔóS|7ô€sźś´Î›o·Ă˙{ňîţ˝Ű"ySndfë×/If âűęNFş"´((ú¬ĺ´$©č{ÂuvëçÝ6/â".Ú¨;őŐ¸CžUöŕG€xŚ*l7Âfm}+tĐVšÖ 1hÝÓľěxÖ¶EÝPo,Sńw¦s lŮĺâ˘ÍŁ€në®wčÚ_>›K1ůdKz"˛ś¤~y 5GńNŠíĺSĽ«°|\ňÁBUÝLĚcźŚT—ţÉ…‹ŞW‰3Ibľ?şř-€gĂőQ4B¸†r źöçćWřń„ňecČĹR=ľŻ0徊[ůâ4–>:Đ](®ÎćásBĽÚCSýeúwĎ]O窞N?«óâ­łÔźEa–Eo> uU9#‘#^)ÉđŰcÚTłť»!PČŹ.>•QĐ–ž@×>Çd{ŔšĽ’ť'2a_ć’kŰG„ĹbÝ=[ÜäŘ*ÔjĂ üÔ0ßqĎ`ŻSÓ{óI5Š ]†VaN×ŰÓÜ7ËUg±s®8[üąČ/#ěţáľ×pöĆŃÁ#ŇIŚ»§E”ŁÄć3Đ úĐšôË…ŻĂRűb°dŰŃë_“Eb-;ŐŢŘd 1ső¨Ď>·'Ő Üľć‘^•Ť'7˘ą3ű¨ÍUO]_d‡>FłÖî|‚Ř~×-ۉľŹă×öĂAkClŃiyňJ Ň–őN˘±Ëš›íIŤ*͸1›Üîużuă~ő\—ÚaˇĂGdCqŘ›ŁĘŽl˘˝oođKľçE«Ľ 'ˇ Íź°%ě2±Ăď×-i°x”DşżĎĦ·™9řsŠĆ4ť¬×KCĘX´1©ňZo{ŇH¨c {çřPđ… †j;V«ť|zéĐ3U¦jŁJUŘ(ѶłVÍ Ý¨rÚÓ”ÜۉŮ7ďhWćV5"úŮĘžŽíDnÝßâ|Ăhó9Oś*;Y¤ŕi$“ÍDoPRúý$ĹĚŹk*Ůą?;lx1&ćŔv0Ô‘\SÁv2•oaöc;ö ŽăK3_ŚÝąĺň\ÇSÁJó<źµoÝ®­"¶Ç~ ä\ 'Y8xr0r¸á˛ă#.0ÖK©ˇčSL¬NÔŹdж€hľ>Óp“0Ŕć(2,ŹŘg׾Č#č°ó ľšË8‚»đĽÚn·q:ŁfŽ|É…Zú)=#’ÖQq,noIů´-*iKůść”'÷Č8ď.ĐË⥣rŰq±N[oćé›t‘¶”‹úF¬„Č{ý’{ĽâzďŮßţś*/čM±}„ăó4Ü©f‘üz7ř´€ ’\a8LÂ#=×D,ŐUJ®( ąpĨä[q¨÷8ㆠňÝÍ{ťěÁőMţ°,Ď ąŇě~ujŰ, O‰Ü¸ď3´4G_™˛zëçw­™Q 3ŽźŻbZřŰź&}¦a¨XĚ’¸aĆ ŢqöŐĘŢ=Ě_˛ZŰ.˛ŠkAłű1¸Lt(˘°§wҶŢbŻ—/¶ą@Güµ%VtďB,ýŞ\'·F3Đ®ČŢiš­î‡ü”š¤č¨úföíů0ÔPPő ŢWýĽÎ§Z*ÁŃloďŃśNk† Ö_X©»d1&aUĂäĘĘÓ{×<[Ȳ®ĂÚ©0”Ş“tѵ»/‹»~'TędÚ·KV69DëéÍkňµýVëKJxWÖ:_㲮šß]”rŤ&g6ČnďÓ(ČĆßNgYdP˘qđS)ꋍ7™-˙4ľk ó\—Ř&$ĐĎ;ĄY2kÇ Ł•rŐjŁÓçţ€‰\šendstream endobj 372 0 obj << /Filter /FlateDecode /Length1 1347 /Length2 5948 /Length3 0 /Length 6867 >> stream xÚŤwTTm×6)ˇ„”4‡%fhn¤C:&``ˇ[:%¤DPQ–’”. •úFźzź÷˙×úľuÖšsッ]÷ľö¬ux9ŤL…U`G¸&í% Éjúú:Ş`‰‹€@b似fH/üo9Ż9ë‰Ä ĺţ˘†…CĽp:u©ŹA·ĽQXKÉĄĺ@ @ ’ý ÁĘę$ ĐnaĐpOr^5Ś»?éäě…Kô×€ `YYiˇß‹„BĐ€>ÄËî†Ë… S  ÷ň˙Wyg//w9QQ___›§ë¤((ř"˝ś¸'뇿š nđżz!çĚś‘žXL1/_ŕ($ŽöÄůxŁap,€Kęč†îpô`˝?BŔź·€EŔ‡űÓűW $ú·3 ŸąCĐţH´€@˘ŕ€ˇ¦ž—ź—AĂ~!(O ÎâA˘ Ž8ŔďÚ!€¦Š1ÁµřgžP,ŇÝËSĉúŐ¤čŻ0¸{Ö@ĂÔ0nnp´—'ůŻúÔ‘X8wńţ˘Í׍ńEţ-"hâW#0owŃŰh¤‡7\GýONEţŹÎ îH‚d¤ĹeŸ÷:‹ţJaćď˙m˙RăştǸ\#đ`$Ž{‘zB|ŕ€Öřź†Kä`0CB˝G¸MţOtśŽřCĆQ‹ôl@8‚ĐŻçd0 ĺ˙ü÷”EokZ™čÝř«çż­ŞŞ? P, ‹I‚°X Ć‚˙Č‚üł˙đŐA#0€Ěőâ.ꯚ}ţ¤ŔźK"ü;–Ç^8 đŮmA’ (îü¦üo—˙ÓEůßÉţß%izŁPż"ţÄ ‰ň˙‚ŁŻ·nô1¸…@˙7ÔţÇ«bP°˙¶éxAp ˇ‚v‘Z,!’řCŹôÔDúÁaFH/¨ó´ůCńÄmŹ×ďü’á¸ýřw@ 4űµHb’R‹…ř“㆓$@0nă`pżß4DEĐ/ś €+=@`°äż&†ű›u‚¸ąA~©É˙ęŤĹâr˙..ď_ňď……ĂýŕPňÉq ôf¤KUdó÷—*,ľÂËbťűń¤KL>«Ćájň?ľ‰?»7ˇňvtĺŢŹyšM±â‹zWK«ý\Pć=9†Éšfb„㡠!…ź›žzŢb+ş­­ÖwÄŃ0'†Ż &ÝľĘ#‚ׄ<ö–ÇrÖµäů*ŽËťż’đ×m‰\!HĽGÂ(##~… ˛E%§ĄBő•ĘČčĎ}ĺí‡lšë?2;zĹ$;§J…đ‘ ™,îş9ŠŹÖÚ¤uď #ĽKmŚ'ÉáÍ‘aŽž}FiĺşkY']ţ ű™ęôŚ«¬ ×ÔAˇ.¤ţoŐşS2ó);ěF°¸…jWľîAÝ~ tÜYh©ŽŐ÷‰• Ţľí2rîľ—âj/ľČ `B0áîuŚň‡ŘěSęŹÇŃ70ú.§.EśzĘš@âx±Ö>ˇđcA´ĹSWű© «¤Â˛đggiŠîCË7•ŇD§W^´Ş9´‚ř§Á'a:¦`ƧęË·9ü&J¤$gź‡Ţ•陉ýޡ\dRY”ŞYDgK!‡÷XŢPjŐ ĺęÖHHť&­ÖÓ¸ÍJi´őx¸ńĘĎŰĘš¨d÷ăşMŰňäzěŢÓć§„ý ™*)kç«töwÖyÍ*» T3wÇĄŘńuč‹mBÚŠK]1óI<ŮU‚mŽeKó‡gŻ)OKOčá 2ł/öňşÁĄ=Ę*äň&˛ĎÚe×ŰźI’ůEŤŐ&ĆRťQkĘÇ (ŕäαŹŇi d«Î"㇗Sô®żh%-IŘGtł˘çć~2JľnŢĚÝ&Ľ†?Ũ«ľâfźe•Ç+fř<\‹HRŁncʸź1UŠjűlĎ:C÷ýs‚gĐ<ĽI§ôŢlćÍđyů©ŠrľNoĂA8˝ą¸@H&čÚŢ#kJŢgŹfÓzMÇr}]Ś$oŢ“´®ĺ†l®ÜMMuI$žÓ“ş!k/sćpµ€`'ZlxWĄ<í>ng}"Âg[BłZuč3'µ÷$=|ÜăčÁŽęš| ™Đ0;¤/c5ݎzzÓöeŰżqH™Nu7JnjGe—ÚíÔ.Őö€l콓˛ç©OziěYĆ;âkţgý±}˛DÝ=ýĂ›XňŰÇűˇrsĄţd×[őD˛YnĽn¸›EÜő˝ÁµâµhbE*L‘µ˝Uaf˝Fű˝…^Š@mĚZoGo/¨¬ő‹ąˇ÷¤ÁU,­CăĆŹ¦qC•»Ëë©Ëެ«-T+µC‡}‹;ůÓŁő‹yň9/X …Ţ6N÷îł—ŔgW÷™–Ó9“±¬2ˇXńÂ3ş#áuĐň¬…Şç,›b‡šŹRmă<çx’ćGúźoö Ve}Ó™“źßâ7jňϨ×îŮz'‰BMʦw’m$±:{ľr{n÷E8·äůčÁ`Düdv«¬|ăq@2lŢxÎuí`€;Ş#ký˝čF]÷ÍOa˛ÝĐŻ'‚§eÝ.»Ů«ň\śů ŠĄěîK÷B\ ߨrq‘ö҇ÎÓW«dDvńáýD1hÚů»ě†ŚŘ/Ăö˘„­×.IżąÉ¨ŁÉ˙:złm=âµcrw4ýŐ›·‚ŁßěU¦ŮR›KW­Ň«DöiչߦŇŤ?WK1‰Ut4çq[f"üĐ„gy’gŘ ł Ł !f|b™X>?ź%x/ubětżŔڞîX!<úÝ~yÉ ¸ýGiëŰgŠQin¨‚“ AŤĎÓúm†:ąmR±ř쇟˛VŰV‚ÍÓťxu¦§@d±Ä`݆9IŞ vŢy»©;ëšm!kÁ )ç`q˙f·ˇhçŘ'äfl$ďfň×…ôŃ(3ç‘ĹéÉw9Öŕčs&(od(áľťď6I 﫨űw zaűůř…Ç"Ęßđű ‡%3ĆŢÝşĎ÷Îů†*Ý+ýújŐ^‡ó·UŚy\üAYşŇФÔŔ’T§ájGěĘľąŞÁR) Ú”ĆLfrĺ8é,7öĽ‚XqžĽ1óS౦Él|˝Y1ŘäÇÓ6Ôač¨Ő¬ňd;„ýç zś\Ŕ‰gŃĺ/çâsěso›z.Eó+"„d‰nëRň;SíËz“´Kc vŻRúÍW}“w‘ˇç7Đv?I|­H.i2‘NÜf ®=LŞËŻŰ©˙ľ?žôóO׸UYŢg˛Śýű”>žźÖţ§űÓ^«š4±5†´â;Ŕî”va ż§]Mtß>zH^o:Ćl¬Üe~AxĆvŤ%DČFU$ …ë§2€ó’°ßJUż­iˇŠ> vÉݲ đ@o{dągoâß[¨{T°ç›đ:i{ ¨¤”ćEXYjťk™‡®ŠťçÚłąüŠ_ąWĺd,Ąĺrń·ŽÁ>͢WźńĎ'ůDłşą˛rĎý;Žę¦…Hä<ţŻ"±xˇÖő3µĚ0`ĎaAŁ”ąS°=;ZÍ·:aČ˙zŞĐ}Q‚VĺQĄR(oÚÍš—ŤŽ„”ö˝aŕ–¬KT»}šŚÓ›mh¬˘¸ă^6 äś~`_ÖŢCŠF•mpµwÝÍÇ‹*Kŕ»g?ˇŠŇ…0“Ô"bş×ýüŚ‚[”_yÚ%fŹ52%=ŁÇF„4éx§2Ű—á<+n#±X·™e]SŞäSŘSˇ«ĺżĚCĆ˙˝Gi›\xO ŢŮFĂť~¶™Ď…2$To÷uäRYŁjx‡(uśŞuťŰŰ÷ť 6Ň2ŚÄ%üŮ,e›Yě?ëÔŘlĽVú|wş¶DlěŻŃźĽ+)«¸˝6$Ţ3˘Ű-ÜĂ"˙Spµät.Wn}ăCŮ^çĄŇŕ‘äZńS†‰v%¶÷É‹N‚3ĺnúŻşCŰ“/îYĺPŽ…oP~Ą¸¸š,bôć©·O˝Ç0jłG`Š9çk ˙tÔ?÷ WÖZŰn—Ď—ö]ú˘˝źGŤr:ŃUÔşAg‘r4Q‘cś2mú[„ţK˝—«ßKĐ‘\q۸<úiÜŁ+{ˇľÔq·d4Ý˙Ę8W1‡pPxi.CâáĘ90‡ĘF=ĽaúüńF;źľq‹j’őQóx†zF5•ó¤…PČŤ&îHúî–HćM¦'9$płŁďóä8ĎĘE:–M.őĽÎpw™ć”€˝Č ›ĘbůÁĚŰÖń‹$YÚJ˛˛WĹnëŠ7í±ý ˝BÎG¦VukNńňIWĚŠ¸ĄÇSm¸ )í§ŤsÚŔ‹#_aňMJĆxŠÎ8Ĺ*…łąÝ•¶6'qűüëV#Qü~Ż9‘R…•ÓžOžt”M}±^ü™7ŚŽô–šLmŠŕ/ţâa{ąáŠHżçŐĺ*!ÖŇ0‹‰Ž‘µo O“n}JÍČSť&Đrˇu`ŔűJoߣ»źŤYŘ›SŢWbŕ!îµĺý)É·qi‡U>–Ďžd"L…E+ŘĂ"cŐčĂj? ‰†Łç&@iÇ=9Gw§”ás:ć[˝TŚŕ6©©ś÷»Č ”|'7ĹéňîTIÉdĄź›%łŇ–Ň*CÍÝ*ŢR`CÉqˇy‰NŔß{|ÇÖ¦É.*Ü@—SSH~yÔ6&\ˇeIŢT…żżb'•Qý侤#—¨noŁVěÓĺú•“aŰZʢţaͬ©mGţ}“Ťďj;‡đQE®FÎ @aVÜ:&+N¸oŤ ”Ę:FősJűʞ¬1şŰíŁá~›Źťĺ÷Ź”ŢJî¨Ęwé–ş>ZPŤß"˙JŐEĽĘŔ,2°“*BZö›GĘă­ď†)®9X¦Ź«˙Ćr€B¶{¦…Ů=⡋zˇHNŁÝwµ o—)]uN‘Ä›łú=Ą@đ‚%ÂŐgý=±¸÷ą„UňîřŮjM}ŔĐÇ鏍\?T“i‰uHěě¨Nč·(eršČBŚ}MôŢş®Ćöšě–ôpMfÝĐŤ—Ő/ŽX ”žk™—xŻ(a×´#ú6,.¨¸÷ÝIßYy»lx†•Ú1ë4]孔㴅ěöf«ĐŵúĺO^"J­Žk#c•g!´"­ć?ÄoŹeďŃĘ@bźŽ®‘„ᙑ“#ś€|®”J§RçýL§T©1ťît‡ů÷ěaőŚ|ý®ň vľp«ëv™E\'3 äuŻ,g+—J>Oô…˝ůÚ¦7$9 Ö¦‹GČrŕ,Ů˝r¶łĂćyŠüŹŹ%©'ĽSđ€pą~»)v ¶[ţŇxńQ†¦Î[Fëţ(â-ůbŽ3‡KťMsöZB‰Ů¨-ü§l?{ˇ%jé÷r¤Î1dexĆ úëŮč¨T;őýÍĘ)ĚŐů†{ńňćCmDmąĐťŞ•Ł7•\řaoŽĎ—S8ię‰>Cźvźć/ířšŇŠólŤ†?>ź«ąÄ!hvÍ>÷‡şř-Bâ°Vť—¦A¸Ď>céMŞ!”ŁUř”T4MîĂsĽx«2®]çĘ‘x(ý áÉř7ţU’8ĄÖúéG*–{×hü¶úU¸őIłiž^,Vµ­6hş µĽ#đ­“šŽČ©@Š ysĐl˝¦4©Š0-đr Ýż#›ö4pH YCxUo†ß`8ăsÉClčR‚Ý8–4ŮŃě9Úł˘kgn}2Ml¤Ę§‘V ®g[¤ ,}/ʦ›Ů»¦ö¨ÓĚĹAdH¨'&ńň´ZË 5łA‘4rG/͸]N)6u)µTöŘ™\š€tc Vč»p!o¶Ý-BŞväŠ/ŕÔ«ŕŐ扅ëÂ)ő./ĘÝ~EŔnW˛¦© nÖ‚»¸‚T¤™Qąw-›ľ‰ Č©u`‚Dť͆˘ěŞL^'ŇYË4o‡ÖŹ DŰ€ŚíäźžpÁc˝‹ěeë^cłŽÓSZ) ŠÔMzµMŮŻ.mŐ ¨A•A‹j†Ôđ[ËÓî™ţ8Á“Őm©Ń‹KźcmÚyB­Bv-•ť«súË%˛ ŽUhă仢ŹkE‡ÍWçýýĂÄkŮ{;/-XĘ á]ö»1ąö) bŽ ›ŇGŘ3Ť÷‘ďË×6 čUi[ž/‰z}§^H°[Y`ŹĆoőâvҬ—Ű©v‡Ţ\7íş:ŐŃN5ŤW¶—ĺş\ň0@ýÁ|„ë~űűďoůŢÄ(DQW"&5^qşż°{ł­Úśó^ŇeWŰ&??OyF<¤qĘ«Ł÷żš č'u‘)Í ˙ąňłňÎ×ŘuU-¸\E„GëĹéű=ş#“č˛Wéó#%'yíÂ]âö .É*iůźě X 5·sčm6R=,¨ć…Ťč˝·eŐvŻ.~ÇőĄG/»(€©ůą¤=8T±čĽď%kí—j„Ѥ‚ňO‹~é%Š"QđËá°'ŇŹ?Ž‚vť‰­}t·>-Đú¸Ô™×ݤea”Ţž- ¸(ŚĹĹ‹ߥě>ÔŃ[¶ď´…Ó}dű ůl6¬ăŞú`÷kEFć;ÚkT_?¤(® Ś—Ë'ÁĆ,G;Úµ?ť Ć W>ľHÎ>¦×H|ţŞă´AٰDř‚r4)äiŤ ˘÷ Ű˝Ľö±^-›ŰܰnÖ×ČëJ–Cý G+dsŠ«Ő‹¬ô“űG“©ÍO¨Ăl÷ ”+ُ ·š†đ̤ovg$kk˝ňÔ×ěż5·&[EÉ,6…Q’2SFŻëWŠkýß6(\gâĎn¦Ż'ęíŇŁ$Ę{ ‰!'kźc1ˇ‡¤5bUĄŚéÉM6¦ÜčűôŘZ‰AŃ÷]»šzc %΀·Ć˘·ş5 ľáŚÄ‚n¦îńř;mUałč¨z?ňWóh G )Q‚«LPžn"Ö(źb«ŁÝÜ’ŢÁg’î†Č+W|f*Xľ~dpżC#'§â9ŕ“kŰ?ĽŁťśdřÁŹ­ţÎ8uWCB©Ę…ĺąŰ·4w‚Xw„čuú•ęMxdňź>cqć™÷˘mřŤńËË|‡ÂąÇýŽŠ¬+Okűúů‰ˇs (ÖybÚôÝu§ĐÔěĚi­oD…]'·ö*ű^հвh/‡[ŃxŽŮ“ł.ŰŰ÷E–w®˛ź¤wWćÓĆŤšŹE妏~ ’ú ±Ţ h~3™ Ó«/6ÚŐj?nŚaoŤăÉZęŢ;‹)ąúń~—ŽV9ľ†OT<źC;SÓkŃÔ ĹĄăŁŢäëŘĆFůÖ\A({úâTwX˛ |PžV ą>ŔX%~ń®IŐ†ÍAŻl'ßGX†Iúać챚„`±gę‰&Ĺô-ÎɦJ|‘E-ę^ Ę>ĺ§%Ᏺνć™4mÖAőŚĽü«ĄËďoÂ_~nE˛I¦řł¨×đ?W§ŔÚ‘ˇk„µ(‚ËĆŐoĽ†Ó¶Űíd§ßT|ŞěŤş/˘ëU?řĄ1)mşV”€ĘśA1DJóó¨?ôřzŔµŤć§ß'¸#&V—ůł]Ň‚níV÷ą±ődž]ś*eŃ>÷çÎJ|I®¸ť!d5«MjŘěüůµźŤ®Iaj$˝«NľzJžď㻨ŰIÂ-RšN‘22çíśÁ#éţĽIĄC;ŔAE2ëŐ9[×–­ç2Ŕteť/QČĹCwĎdĄ_Ş“dY_L€­ÔĚü «¸®ŁńĂóT°[ŹÓ“% Ç0 b×ŕ ;UšŰ$Ë˙/R4endstream endobj 373 0 obj << /Filter /FlateDecode /Length1 1592 /Length2 9932 /Length3 0 /Length 10990 >> stream xÚŤ·PÚ. ·â.âÜÝÝÝ!X!A‚Cq)Z´ĄHq—R 8ĹĄE‹;ĹÝ]í9÷ž{ď˙ĎĽ7™Iöú–Żý­=Z*u-V k%H†˛Ů8R*š@'*-­¶=Ôô7ŚJ« ruł‡€˙Ă@Ęd}Ƥ- Ďv*0@ŃÝäy|‚NB\ŇöÖ6€" rCĄ•‚8{»ÚŰÚAźÓüë`°břXţ¸$ś@®öV`€ŠÔäôśŃĘ ±˛A˝˙+°ę,ČÎîééÉfáäĆqµedxÚCíš 7«Čđ»a€Ş…čŻÎŘPiÚvönáZ¨§…+đ 8Ú[ŔnĎî`k+ŕ99@KA ć ˙e¬ü— ŕďŮ€lŔ‡űŰűw {đg ++“łŘŰl °±wÔd•Ů ^P€Řú·ˇ…ŁäŮßÂĂÂŢŃÂňŮŕOĺY €Ĺs·çfĺjď ucsłwüÝ"űď0ĎS–[KAśś@`¨ęďú¤í]AVĎc÷f˙ëf_!ž`ßż{°µÍď&¬ÝťŮuŔö.î éżMž!Ô0[ŔĂÁÁÁ'Ŕą@^VvěżĂk{;ţ(żáçü}ť!Λç&@ţö6 çT_7 ęęň÷ýOĹK¨@ ŔÚŢ °ŮÚQ˙‰ţ lţ’ź/ßŐŢ `ÄńĚ= €ă÷çß'“gzYCŔŽŢ˙˙ą_v=E yMćż:ţ·NRâđeĺć°rňp€żIĆ÷|đ˙ď0ęö—ńľ `@ŕŻjźÇôŻŠ=ţ&ĂßËÁřďXŞgÖ‚ ˙܇Ăęů ř˙Lő?.˙ ˙ĺ˙Fň˙-HÖÝŃńŹšáŹţ˙ٶp˛wôţŰŕ™´îĐçP<ŻřMő@-­ ČÚŢÝéµ P‹çEŰ:ţ{Śön˛ö^ ku{¨•Ý_lů ×ů˝eŽö`:ÄÍţ÷ł`}ľš˙Ń=Ż–Őëç§Ăí™’T çÍůď”2`+őďăäáX¸şZxŁ>_ňłÄđ>ď˘5Čë‰ěl`ôŮđÜž?ŔâŠúűFϤbýĆţĽż%÷çkřÂĂ`·±÷řÇ„‡ó€¸»ţđŘÁĎĹţŕ°CţCź]ś˙Qs^˙Dŕ°ű€\˙Ň˙×ě¬Ü]źóA˙°űy°˙’˙ĽU Č uv b%ęđ%´ĺúł©'ëưČ8í†ŢFVßY×V÷[L¤ĆŞŚŕe×K‰”N¬…5† ń9Ęß˝ĆZ¤¦$Ťć;ż{łͱŤfÔ™Q‚Ţ‘ü=‰šr2VmńMż?Ý ×/aż)Ňf»¸ócŞçâ^{vËyŐô”ΆOmhlVń*ˇÝ—ţdŤŐ‰1*š Í±Ěś$˘F„˛’#3á{˝ś¸¸Çů4ňD©ŔŚężËUŕk¸Âw3éłX®ÍéÖNLClHDţâgpŚÎWr;U‘pÚ·¸`ˇwĆ«Q¸€ňËűV,¶mÎô*{Íhp}WµÇŕlp=;JOÚ‹»žT]RŹgćJ­†_ŐlŤ ­ćz "ßnł‘\ląÔí°qxO6'đłć 0a`·ŘĐá{Wë˛8đ˝—ő*ü}Óő@ý÷µ˝"±12[O “ŚťQÔÂĽ`:ů{„Rč\»H‹eÎ#…˙Á"LŘ7řDň–¸šĎÁaČŹĐ;HvČ“íwç_Qw¬Ú¶2=íč‰cň«údůíýńq>±—ŚyF8CNşLÇŰśĄxz ˇ/ ş‰Röă• 6éoJĺÄUž™“>šną®ŤéĘ(ă`o‡<¶CÝ ťćwdbĄÁi:NZŰdË*ź+ĹAâ4wČ•rÎn"ź%÷Ä´ě ^¦ŹÍL…=ťecO߆‹—Ĺşó”’ ¬‰~Ś\ý2>¶ý­ +Y‹é>ż#jďy¶&m¶Đd[«K5¤çĎ©]NŤčqzQnó»P)[˙¬\#…ě˝Yę˛ÇôKŠWźßPq$vä@|nhnFŻúi»+ľŤ ŕ{ţZúĹi)íZTš=)Řýxéľť#ét(‹H(XÄDžIµF.é÷ć˝ nź[Ëb‡)y´N=éJX÷Ć?WeÉů\ ě‰ôÓ'®3 >´(ÜŰ©ŢÖNů®«á0?_({XM:á#Ř×¶ÓőS«ěď:u‹×ëM†WĺW8-XŇrĚ~@`ş5˙¸‡Ôť“´śÄnsŃ•éŘJ«.Ă™#Hˇ+Á¨%©Ěľ)\JpČ[*•%‘·|•é(TéűŁŤáUÓŚ>X1‘9ćL(żŻć $»Ł[›aÝLďÎRâ1@ɢ‚f†;5ţ^{ů‘,ˇĂ{]eű‰7°,»Ú´őĘšE‰,®ÜËaĎ„‘=ˇLţĘĺMń*mÍꥊĘô¨\ŹáĂ˘Ń §ň§&ź‘ŮDÇŇ›2ęNEŃJüŚ®dĽž$™˙–.ă;ř­ţy{b–śŁ Ą˘#M‚‚‚ÖfŮŘľĄ­ę™lŰ­Ęđc^×đ…DfFĂúş‘Ř„}gçŮUÚęO!Ů7¨ ď,‰ÎŹs$!®vI· Iî'ďBcĚ™}IŻ@WSďĚÄmyąHä—Ő-<Ć|u†đÍ>Âë5,R0•®÷ĹâTĹ«0÷Y:† ů.–˘Uhplëę Íä´ EŽcbŕTޱ©e­Řł´đó—MQ„ěw…‡Ů~1Y§–]ąU’˝ÖαĽűЦçČŕh[Ů‘9gůł"v‘Şa»t2uň‡ń݇ n áçÁ7€2UÝ‚†˘Á@˘›éë\7ë¸t0¦OB2REŢVľŕ™r–±Iöq©+ZTAni‡Şt[´ŠźŤ‚˛ý#]’ÄŚ-é/°#Ż7l„"JĽ¬ZÝ©°ĘĆžúPľZś„ŃwŹđ¤€ĄźŽąt[u×ÓµôłĂ0zsŁ!tíąAJ€GbüŹěö(öł?ÝQ,łH:ĄPůűş}$ä &?qÁ˘złťe{5®.BčÎ#lFÜyZ3ś–žÉĚVhIţˇ*„ČŽLzđË"ZuĹ›ő2!f>gă|O/bU9TÓE™Ęˇ…í$|˛Of Ť©ruWżŽw($ĐëqÇć üîlgî̇kłÔ·g˘BÇ,>µc´¨ćŞĺlŻýÜ‚±§ú‰qô€˛^#‡Űď—¶ŹN ±u^şŢusŘc™Ô&Ż|Ţ˝/rö8íŔČ$OH»śé)fs•xKn0ŞŢvCK­Ĺ_]óĂŚľVŤVú ›=.”׾;‘ä${Ë€6ŁzU+´ň«Ŕú±Ľóî6±ë~™©%J¸·0'e­7·ô…[{ˇ€pť\<†”ŚyîN®ÝJë@7-ľFҰlD˝µ{u ;$€¦ô]+őWË•şŔ´Ŕľa¬›@,˝ůZĆľëśŢŤ jrÁ~żŰřE_r=Ą8˘Mz†)ąŢb'·'N‰ŁÚ&ͧŢîĎŰŢ}5˧Ľ jµÂO¸ß‡ôşdqLŮ Í˝Kc4ŞQ"’Ť5R:Ie™Jěµ Nb·MU—Öfy*ć!ůáZ´Ú5ŞMJ|ńUČĚšiSűF®é÷ý=¸. ˙–ę2ô‚č«őŰsÔaYX™ÜýłÇăqĄöÉ—ň1ô_ęDa 5ć_*Ě®@ióÖźR3ÚŤŠůşéÚîłí†Á™čŐÄ;ôóť_µ»Nčő]&–ÓŘÁfŕ”ŠĘ9hČ ©â¨_¶˛Ś~D­¨S%÷U’­[ž?6}ťnąÔ Ţ}Cp`®\•g‚‚ČÖs– ůšoCĹ@Ü[ËäÍIŘ'¸´ŠGäÔNy ej¦ÇÍv}”lr0úâňía‰Ě˛…Qd°Íµşw |X6ľß.’Ţ}­ĐĽĹ–.5—ä9ĎěŮýćő/ycęń2#Äk`ępÚŹęµ@©ł('źbłäĂYťű‹Ă¨Ż¶ ç%^ř,PQŁĘUţˇ$9·ž† AJM^<şČ['ďJFű:â(ʇÎK˝âhĺnMISrMI Ço{^5fŮÓ™‚_xTŠŞŕ—7çńŽ˘păš+^Ö‘ß±;䇭ôĚćPů/™'îIÓé‘ö—®Oič7Ň Ę/m˝„*äW´ ôO˛ÍťĎ€ńË\S±ĚÎ÷Á AťČmj÷kP UVe pjô{1ł%Sł°y[ĽÄş7Y˝ńB±ćźGŠ•$#–ŽyAÝ_;(«PE“·żi8'„“”ĺŚ3¶‡ÍĽ©+8eŹBŇ"¦G7mĽ\‡Â+V;ďây•Ő„Č0pČ}nÂź×p‹+éK~­Mßxś…%”%üÎ…<łîuT–&éwx†HŢŁş$Lw»ü,[ŹőJi1·“ĺ©»ŢP€ŕ´8‡Đ¸ĘSU澇¦82śŚ×Ń S446ŘťśUy”ř`ő@Q~Ś]ó3KQň©+†ŤăUÜî±:ţßzş·şÎ‹ńtcT?R Röwan¨&`' 6ŠÇ?+Ńn[ČT5ěSÜĄ‹HÎ*j!ŻŻÎĆ›°í0Ť4•íJ X7‚ßhą>z[đTÔ äJ&G…(ŕcÉLŕKĆ `˘ĂÄy—ÇHPŻ3DŃ2»Bę¤ĺ"r±ÖÜđ]ń˝RQ7Ů:EçF§ŞŞrF^żŻśsě˘K­z%Ľ5÷ńó)čŁÍŘş¬Oşy}_˝Xîš‚ڏ–<]ŞđBKâ!<Č ĂŹ”)LfĽ¦[]ĘŐsfQ!Aĺíݲ&x2ÎŚ—eE•çuŚl9˘ň8őö»Ť?×rjúŢŹÂľ5ją’TěŠ-z·1¤ J}=ť˙űËşúîşXS´Ŕ‰iZ‡ˇ>*ů¶Łh5ů'äˇ8Jł›ŻŃBŰ_†OVŇ«¸:#ťď<› b™[Ę ‡Ź¶şGň{2€ßNëfAí¤}°ćî×âp$~đĺű”řS ‘۵{MçxIŮłĽÇîĚ-Žsp ű1˘ŚŞ†Ł°Ě¶ Ędĺuz’t ě[P˘çřlÚź*[„âř;|˙Ľ5 ~—µáßŕőróHď+RÚ8Â-î ÓłĘQ̰Äç,:ĺ;N´hĂ˙Ą3«ň]ŢÝ‹#%sQ€ym1m§yjîáâ··R±&ŹÍ˝¦Ć÷o™ń§4ľ˙*Vµ´ cžoi[ Í‚PŁÄÎé ´řŚťŮîÚY¬Ç˝sŻ•ÍKîÂŐżő^l(-…»ejë·˝|âÓç4é|Ç],˙q(7}úôŁiǨ‹2ň%ăÎÁŮô«‚ÄÄQ>Ń}ĆŢüł4yđ»,¸¦â >ę_épMavNčJOßůŤŰFaŕÝkýóD›Ľ’ڱ«O#w̸ł…ú2űE©Xýl»ż¨ÚG»¨™çµř0Űç (ŞÉ¶Žć1Čé~ÎJl+˘3—mF~klFƙùHUÍŮ áőř˘E˘m€ýöŐˇŇ%Pqt®+ÎÇôOŰ*3⢌Âzvćë*-‘˘OÓąOo~VtĄ'ýŢ75éżô+’rŤę'ěŐî—čßFł!z€ű—¬î/„E=ޢt9ţj8A;»SöęÖx‹äáĘt"é9É 4ˇ4”vż]M€´qw0†Ľűď“FĎĂčúL ®~K!ŻW q”\g”Z‚Ą–Řó´ PĹ$vŃď9jO‡䮺KAťÄ^&ź[DWŹ>uÔ™:fŢĽŕ´amĺŚĘ a±¨ŇÓăÚ—DW«jt(śĘO ťŹR¶‹i ĎČn©>ˇ!iéw,rT —36Ű­čM!µrŰ4ĘJ×ŰĹĎłÍÔhĂ1šj!ڱR„=ýXĐ" UdđGĄ`Ď«®Đ Đb5@u>ť—Ç\-?ŤQżÎ@ ~ńÇĘľ1K÷ę yˇ“ĺMŻźX×*'ţRg ‹WÍ2-tkáRô^p@×ěn@t˛P€IĂź`ĽÎÚwůKgΧ÷dŔ}]6ĂćĹŕl~Ř…úrńwF™w‘ü/ďu 2n=+ŹmqŁ~ŕ»L®X;Ľ*h¬ZŔŹ´CréC+Čhq`°6Š'˘*˝ă‹_Ł®đÓŤ€Ű,˝Ýžú¸ů…űçu+OÖjáFÚ”N:µuJpĹOkˇqv‹'LŐX×Ď$Ý+f/ÔPTťXíĎŢ߼ôCümsŽő¦Klą>FáW¶ŹU>*zt˛(QúÁ˘śÖ†Ť¤č.˝{—úá>]#´ÖhaIX§C_ >xn^ĚŠů’vĄĺ@µyAR‹G ü:¤Ł6¶4‹şkŞ;ŁĆv[{§eqÝÔ˛DŻqžá  Ʊüz›?*&i-ĚO•ʵ˘ËŔ§@¸ śŽĐő˝„µ‚©9{d?*«–×Çř‡ăéËÁ‡¨ %n¦ˇ %VâéE˝­ őčˇJ‰R9ún‚î…[šџ2Gś3Łňę|„Uëz¬_dŰÇf2îy“#Šşk®Űć.#,N“yú$ŤŞ)ŁÜaOŇ*Üž…Z~ň’oÍXťÓŁÚ^âť—ž“j‹’ůâ^”˝oŘů¶9ýÚHµ.«|Zß0`š@Z»”EĎgÔ e1—.gtTń5µ-Á¨#W¤C%ŔíţC[Jj%oťź¦4,ö•ó»ěIŰĂa ÉÇÇĹl¤8Ď·\˘ŐČ@ĺ$1ŽŃ°IG5ăÜć_NQodýć[ý´;ĎűuľĐXŕ@y\oâ?ĚE†Őfíq#ú>8)‘M{öÇŢ^˙TDn©” ňeápŔzýŽn¤óóJ×ÄvqVyy߆˛B: ŃŚŮóżš÷M?TęđSÜŁzĄŤŽőuđË©şQ´xi”w1rvÇŐ9ËëŇć´Ř#żÇHq†Z⣸ú^&˛3ŤŢÎTXTjyGři‡3ó}Ő<_L=âÜęŻÓ*+zíşsJ†U¬@ţBĚô¨´Ţ¤sKł…@Łn5Ęi“–BkĺčČÝĘ©ś^-ů!…źšÖ@ oI8ż\Ş]V•Bžä{5ťdߏ~łpoVĹ„„zIÄwő२;9[č0†ßÄvő“mÉŹ5ľ2H HÄmc¶{ FźF=WŞaĎ /_ŰC)/Q[~G;vÇÎĎŤ‡,M¬Ăú >ÖrřBŚ”Ť‰VpڶZőA¬óUŮ7 äosXĚN7Ş–âq4řJĄ˝Ż™i2ěőkŞúË7čµüHß’MżÂĽxKL°&Uéyŕ>|ZÁUɤŤčPťşŠó4_Śď‡Ňv›ŘĽ*§ő€ëÁ,@łflmf^)ŐöĘÜlť! uŇśa &“6CLdJXµ€RĂ8&›€ť´HË^ńYf˘—e¨q˘‡Ň]ýqŢ^eš^EđĄnP¶|ŮÖ' &t*źW2^16XŻŮť˛nĐ“GKďÉŤ ‚Ô 0OÚšÔSŕ ›Ľ5eŢX®#“›\ rn,.căâć«ČĹlŹT̸f§gßddГޠ?•ä犭ܮ.ŚÝ\űiý&úÇě#[Ií3›:űkň˝€!ÖHňűѱëÔRţč<śu.!šëĂ2áBű¤¦1eZđ¸âÜ‹Ú)ÂÄą+ÔĂpî(¶) ů´ÜúŇŘü¤ó"paźŇťěf”•°ô†Ń[z0šk÷C­đ°&đ%1ĘĽó)äŮŐw{EŇ;0i9”éÉł¤´ĐÄ™˝ěáÜ'_ ¸Á+đ°Żôe!}Ť´QÚľÉ+N]’?ĄöŐ$c˘¨Ç­áCMŮ “CľvÄAű®¤eŽ_,á ă|LÎX0+eUt0Úćť aóaÔÓçĄZćĂ%âiĽ;9:*lÁéfŚ­ĺVJ»~ołÄ¨cĺî÷WÝŐ_G ëçĘĄ ‚Ó‡ĚKŇvζ⭟Wń+«r.ĎĺY'U.e®Ţi˛ŃŘ+Ź>«ĆĂ‘wAKťžť]ň14_źľŢ# [„6Ľ&ÉúşH˘Ń}ôAQ/rGÓ|g¸q7[>Ă eW'= ăţ;‰€-—Ë6ŚAl„Ô˝ő—×7ë TEHť#˘ó…á8ŢŔńýĆ.˙·fÔúr¦őńF0a°«ščE{×&;×*‚ĄŘĺę.7~ĺI\žW’ &ÓŠóíö»N¦ĹŹř<6öyÎN®™"k©nŔ—QpŠh64v9: ’V]sm+7hĹb.}¬ S&•˙Ě‹Ŕ$âę‡đrőpj(ą@ąG†}ç”$qQŮŻ} -7N­ťJ;•Č×z¨ޱ@¬WÝLłBë°ÓîgŰZc!°ďVč`«Ŕś—ś×`ŕ†?㩱÷z’ŠOD"^3+ú¤Ó'5Ź4€óV4Ö°=b-Ř·űqUÇźd?ű¨ĚÁA ׾“ľă!ŢŃHîÂă’ýŕŇWÚ—™b96PźDŰ!x Ů$‰0p±(#DÎĎo@:‘Mľ¸©©$¤T1y=®ä/Żćˇ)ŁŹ‰˛íťÂáÖd©¬ŢËľUë&î|ÝńŞk?mN ^b¨› Jr'@ N·F4'nă€]]5ç†Y%u^‚Ílń’Ǥeä¤ů¶6üŃ·sx(f~­÷ĺŽ]\čP+Á:» îÔě˙jÎ8>Ü!ěÇ;ńÇn-†ęáŘÇUÍş3,‘ ęDú´÷ł‘íZňňÚÍSICjĄřđŃý”üßőrC;ńrÔ•:v;éIŽgsżą—gÔçÍG(Űé|ň*čL 8ĄĄš¸ą ^ü2ĺ$Âď’Ë\P„ʱ|ux5ľęš†ŚćŁN.ĚÔŢEi_˝ß®#É„­čn[ťě«w~Jáp)äË{ÄÔŰFÍÔíDC´ţ´#Čěv޵?í^!ŚdM«{FŻşÓ¦¨ďžď¨ů*`âß‚Qń„ëî5\١ŁáÄ;ĄµC±J¨×Rí0˝QŕůlÔČ:÷~n}',c•Z_0![§^űr<ř“Óă×lú¶˘CwmŐe‰_‰šK˘ŕú—Č…B§nü¨’›;jŤˇ3™,Ď<¬â2ôW”Úwß1:U qW±*y˛·łBË€ß$-úk×Uu:ő¸@UĚźçĆŁźl°WăixR°=Úč¬pƸęA‘[$Ô{ďK7 Ű˝şŃ^ş2á¤ŔŔăZY„ąľđ%‚ČŃě4ňC$úŃn…(¦\|“‰•˛ŐMşë ŹE ÓXŕuąŇ2W©Oí‘xž«°qVúއůřĽžA2×wNˇăTÝ:¤z›†]¬ˇJ¬|’Äĺ`6›šłÜăŕÁăDŚŤ"öZ‚¨HöN¨,& -ŃňAăhX &Çq&ĚV@ĘPMv=¬»‘[’ó _Á†!Ż?ÚŃ•Błżl8Ľ‡çÁ¸şříeĘ{ɺƮÚsqŃÔŇ’§ü˨şe׫e¨śV©'2„Ń$ľHQB^KĄűĹꕸÝ0«kU˝Çu$ľ˝ŔT1˛ţů¨ýŇ“O“GiŽńŽŰçăQ¶&›ŘUŃ„młvěS4nŹT6ÉSč.Te÷\˘ ^Ż ôšPÁ_&ę~ĂńÎÚ\ŁË c]@F^Óřµ…ĺ˝k(b¬˘GŠĚ1Ž'Ô2›-j <-á® m4s'­”dA÷欂ČăÄúĘIç`ŞŘߦӲWµűfŕéôýRß…ŃB§żŠŰËă~ž fҦř´<ń%óĽ3-ť´ůđp¬R·2í´Šw1 µĆ˙äzé“4ŞÎ„ŕř¬÷űđĹkNTçŘFłâ:lZÖo.öQÁEÖĘ÷|”0âez ‘\ÝtQ“ ÍúôM*ĽO¶Ů’ž‡or…çć­:cáŤĆÎ.$S9nžĆâ‡EH…`2|6!‡݇Gřěô‰ć.Ä\¸źę(ľ/ŚÜnȦg&貔ŕRy@óÁ–glÁ×@Š QŠ6-Ěó:Ŕ3ĺľ0«běoÚd~W!vÄšp˙ë„-@<˙Đ´nć­ó 31`?ŕćręřŕx ^ŔÝĎŰŚs˛ŰĹlnȸ}´µÚt/ ×­š~o­Ź°µÂď]:vUO‘í˝óEÜJa9SIÓ˛đf•& ¦ÝékňA´ŮůG˛IďWŰQŔÂÓŘh–E~éqÓ OµI‡¦ íŮŔŠ+†„˙NČ[ endstream endobj 374 0 obj << /Filter /FlateDecode /Length1 1411 /Length2 6234 /Length3 0 /Length 7200 >> stream xÚŤvTlű7%1ş¤e”ŇŰHAéîN©16° ĆČ" %(€ ’ŇŠ(Ň]"%!!!H(©Ňü§>Ďűüź÷űÎůľłs¶űú]q_ń»î3!>S 57Ś+\ĆI@$ÁŠ@ #sy ,- K„„,‘8oř d Çú!1hĹ˙Ą×Ŕ¡8¦ ĹĚŚ0h ľż7" „Č)BäÁ` ¬đ·!«Ô„ Ý€F’@} îŇŔřc‘î8Â-Â0 DAA^ü·;P Ç"aP4ĐŠó€Ł7 Ţ@ Ç˙+„đuÎG ”„˘ü$1Xweq` ç4‡űÁ±p7ŕŻrĆPüwa’! ĄŇďlAŕˇX8x#ap´ÁÁíÇ w-ô &>pôcĂ?âŔżZ„HBţî/ď_čßÎP ň˘‘hw é šhJâ‚pâ@(Úí—!ÔŰCđ‡@‘ŢPW‚Áďġ@m53 ”Pß_ŐůÁ°Hśź¤ŇűW… _aMÖB»i`P(8çř•ź& ‡ş ú=V/4&Ť˙sF Ńn_%¸ůű€¬ĐH_¸žć_đćÇeÁ`°Ľ÷Â` _Á-}ŕż•_0!˙0ĽĆ ”C"ŕ„Ţâ°ţđ0ü˙Vü[@ @7$ t…»#Ń€˘`8âŹL<Ľ&˙úüçäHŕ–íüŹůďá‚lŐ¬µÍŤÄ~ü•ş:&—’JHÉ‚”PžpűwS(ňŻ,Ŕ˙řꡠŸd ]ú;ဿ¦/ü×b˙ËC`,(üÁŔ˛`á ň˙Móß.˙7v˙Šň˙ řçŁíďíý[+üKýhˇ(¤wđ_z_ýqîa€ţoSřźu5‚»!ýQ˙­ŐĂA ; †v÷ţO‘~ÚČ ¸›)óřC•?¸ŐŻóF˘á¦?äŻ(˙KGŘ*áŃđ#đń· NXš_©…†aÜ~m—”¬ŠĹB„$Y BXC7xĐoA’h Žŕ$”D`°€_ó”Ap_B× đo„@ Ă2ú’Q‚o×?ňU (ŽĹüţ•Ě‹%,ăoşrý[ţ˝ůpxšŔŔ®E{ľŠn:¨Tă ”XTZ¶y("źÂ6űŃQ¤‰ĽČŠśÇőu0|\ŇŢSťć=ů׿¦mH5k<=qN1YnL_|3ôt]­Ş›‡Š[ÂRu%ôÔ7Ôú–i=q«ľP®Ż˙U:Ó|ćŔ.ť Şî˛™w1Ëf+/ä ¨OĘŢK$Y%:Ü*Ęs}2ÎÎOŽ“ŕˇeÚ ˘ŰŰeĘ:çŐO„m$Iâí¤îއĚ>ł”ňkăä°gç!Ýcz7rŻľ–®Ďö_R8W0ćŕ_/ý±ŤČQ'|b‘Jţz02ĺęK•^Rľ÷O?7Z©ëúŃâ:qÓéOWNĹËoVřČ®¬ž€÷rzo2lá'xĘj^ń·ž<•×8xýuQ.>B_€ŹSIiďĘŢěťé^ĺi^ť]Žô ľ-Ż­6ťŃëđnu‰K4yG8›źYÚž0eV¦é.8ˇrK×w*ŻľÍĹA&ľ3äé”Yecă|/ĘEő˝Z,ÎËŁt”ÄÓ$p­ůŁď ŐŽ×eńÔg•GďÎúâ`—řSŃ*űEă6q©"Ů©éăwx™Ű8yH‹¨’Ľ:ÓWŇ\6“ěź×>číŢł`˘ĄNPI«&ż Ąřü`٦ŰÓ’˛•r:Îů¶â•k;ňŞÜď ĄŇŚyÝ× #˘ť—rČP÷v˙q„)ŤőÖrÉĹÉä6¤WJť‹çöřÎg—¶Ěęv­r+ ĺÖ×j›jOucÄťö™¶ş(Ó>ËÄIň†˘÷PgÁí/ Ďiűü5ÝXb&ĄďšQîg¨sŘ[ ‚ýehÖ˛K‚:Š>ÍŹ^?ËI„Lr˝_Ěłýä•&Ď6Ş—YŻ,Äu˛6z0yefŮňŃąHi8« žĐŕąďÂ}S=* #¦Ôˇ5ë ŻAşŽ î Őą¦±™,fűy}9Źíç‹ÓJjÓ•óďČZ’1® s,`x‹Ă)ď.}Ţ&qG^78µoíÂĚô…zľ›3A¤4PăIř:«óř ľňˇMŐmP3<ŘŰ’¦Yabź\˘Ő'9”üQi=UXČ©¤$eÝVÇ&R×ÂůäëűîžĎÂó;›¨íDĹ&nó»R`v—ąřZKCÔ.x˙łó]Ăűoą#iC &ŤxP/y _fíśLR‰9}©›P«j`´úĎt‚x˸76mkő‚ÔHú~ŘĽÎyJŃ{4®4­ŃQÖş.;Bő`w›/0oÔéK*Í!ËdWádŔaŇÝq‰‚HxÇ·¦ŃĆJyüŇF†Ę]Í*_¸o{z㓤rnĘ*‰ĽÜĘ ­Í- ŕŢLJI“óEĄ1Mź*FÚŤ‰ńbĆ’G§ŐÝĘCqqW”roŐöčéswŽďb5řý|Đ‘•ąR‘*Ľô‘’ÖÉ´gĹ:‰ĚŢ˝ń[)Ń‹=pŃŔ[ *gÄ»ź®m ¤żÎŤ!S(㼶 xM¦vAä±i0µ|\ŘG§j”?—öVÖdŹÔQBŢg•Aá(ąňëÖNޤĽ!Ö÷‚V<ĺ×*ĽJŻĂp·Ňýą«¶«E=Îł™nµ)ˇÓâˇK˛=çůăc±BÇlAh·íS ĎÓ:jp1€‚ť ‡ëqY͇Áá´‡Űtô9ÖąuWÜ? Nč;ŔʷĎoá–űße9¦J9Ł~d6Ś9ÎSM¤úqNgÖ1¨ WVáŐăš0¤O1t—â8÷ ś˙m+pšŮ­›ŠŃŮ1{ž†Ď·LsŽ›A rs:î¤É]nł•ŕÜĆ.ĹżL;Pţ\NĎ+s™R§[[č*—YB÷¨×PŘQÄžň±óˇşvL=¶:“ť+}»=™±Ŕ÷ú™"ďĎa¸Ô™?¬v‰bĆ3 ď+mă¤NTzĺɬđ)3»]!QŻÚó9ÝŔmĎRqÎc6é•Äřâ3¨sĆ‹Z‹ý¶ö2Ŕ ÍGĺ’é[¸=ƇěZŁÍ[ÄýÔńŽ·’ÝG«Ö_+ĆSËç<ĺ­ˇÎĐľ¶{üxÍŃM¶•b.‰[G¬€}ĹUnW“Ś™ßD·tź7QŚ©Ů’Lk­7>č:/ţiĺžsݫڷȣłJ˝|eâ­¨çłŃFş ˝Ä+4‚ZT§Q’’1 ß ˇ2o z‹ÖńeŮ_v)Ł™Î gyç±Ç¤€ GÉYšÍUQ~ěÖAzmkâ5‡«Ű˛ţá[Ë7yĹt4"w_i‹ń3†G9o®ÇÚ?Dqd¨áT-†B7ö‡rP3bJ•eÖ]Ěşä6ą¦Ă ŇßCŹIr}PŹŔüŤ…-Vnň«5Z8zoXŰfo§Iáš_hżm ĂŽ××Ű…çĽfGLW7Ź ±öF„đîÝÖ1Z0 SşkÚż­9äMôy×ŃҧÇţŇ> Gś3lÎě‰âÖ<‘gę—Ą’yĹń#ŕ`yvn<ĂZż¬˘‚­ŐĐ3Ä‹ŰŇ đÔć+ú…Č|łŤç%ŕşHHjφĹhG?'"úäIy%óMb®YÂX ®~źűPŠëĺ4:|Krł< ÝÚĎ•ZćÜ}‰y‡}>ąîĚkHřąö9 €^ş*š}ÂmG$}Č/éNţEÚB}ňß@Ć÷żą«MTbKł1´éiȇuS_4ĺëű.jž4VüX~±7Ó†Ń:62Ź‚}JŃeďîh9Üꚪ8›ä‘OëLÝhö“2$ĘĽ+žv©‰QżźĄš›‹$•reµîĚl±áÝÝ®f1ßńßyě´QďřZ^J ćykěÎ3@<Ër­…ÖĹFŮĘ‚4“ěÉám«·Š*çľ4U 6ôgoYż-óćř&±±™Î˝Ĺ]ßcő ^Şîčřd.đBţ˝–+Qd÷J˝,‹çžrgćaĹŁťî‰,–ÖF &sµŞ e©®9ň(J“qoz~ß9aúÍŻ&1±{ţ^{¤2ÎaĽ3=ŮĄ4D….Ć­qG`ărŠH†FgŤĺgSęוĘÝ× Lč  #h3ß&_ěXް‹~˙ó ’±=ćý%Á OŠ˘oŢč ĂŞź.Gá‹e‘ěĎ'ÂĘüô_zj±Cvz ślä ω…«ß·zé®]ĹvßdŰĐ%^ě!w¦Ľ]ţed‰Ź™¦9:âŢéî¬}ďúíBŤ:UÍń˘µƲ޹\K0¦88Âdhݱ~Ëő+ÝĂŕ˝dv‘b˙3xÁÚěĹščő|ywůůĄ°ř;×oÚż®…Đ=Ű=Hs”ŻÉXBŻÝ>M[iĄSb‹ß_‡…_pyN†1r©Ó[ř|_G#Ö˝ —dŢ!Ś`$~MˇWYLű˘,Fag@ÁĄaĎr[¨ě‹ĎűˇźsA;űçş&lšć<˘ ŹIâcgKKUÚâ_÷Ugj“÷tzK, Ń1WŰ,řŻľ1$iłĚČMŹ“‡ćČ–G8zĎâ]•1#w/Ö•Ŕ’˛âˇg€¬©ťoM-s‚‹5gRÚ ^”m&çyźÂźa+žŻš6ĄwsŮÎP\@ëëY@T˙ÄiÝU•zŐáë_ĎQ*ĄŰťŃŹ;ęNG[í¶2ŘÚäÇť¬Ŕ±=ߌ\Z¦lŧúö1$!EŹ©hfŚcJÎ>“ÓiXěäű¸Nôʶöž;ôáă»ŮĄÄ!ó–—őÍ·5ü/Sť‚4“*á-©ˇýÇa—ôŹŁż,ŽJ`ž°g† ÚY¦k8® Çz@Goľ 8śŞ‹bÍȡ˝—yřDňkÄźž–2QD»Çn^¨VŮhĚö˙ôŠ—˛Ó]`x¬#¸®?Őµ7–šaR­l v‹–[L{ć‡$µÁÇÍüó6ÓôtÓ_ćlě/ФůøÍňŹÇÍď˛%ŻZ>…dW?Î*ocעö3Ó7Żi_Ň™(ţvĺ´…GńUwiëĆEŰŘ!t¨^Óžă@IA/h•B{Ňŕ‡,2tőIŹxďS{ÝĘu,Đ—RŞ«˝ľsXuĄç#±Ű˝˝ĂâĹýy]ßFvX]ąÚŘç“ö4č—V "şĄĐĄD®vJ¦@9veBĂŐDˇPtŹľ,čŐ]ažóIZMŤů$2y‰Ń‡Ą:Îç°ŮĆ;ěŃB\ż;ae!ťeĹĐRQůŰϵhĽrÎ?YĆ`†/JŘHăŁĎÓ?•Ö(u·OîK'úuw’Hç‰ †´ WÎÍ"®÷‹Č»•đ&ϵ¶k“'NelěÜŘÜiq™,HŹ´$^‹Â§op±‡ó?ZGŽýś‚Í­°ëzązřmF Ô˙I“ăŠ'Ľěç$ę|÷p­ tÇcĆaöâ¶ 경‡ń-ŠkU5ŻŐtäÔUjx»{@Lçp'>–ËE¤'»bńÎĄCtSjÍÄíşEí[–7éżş´«\ľG6¤Uř§}é.%)ÔâġĄ°™â`ŠÉűéăĎň§=—'—$ʧdW¨ßá>壦7ĽűWý­J`8–„N Pwżş¬5·´xúŕKŘäʦ©w‘#ćTVm„ű^»XĄŘ'¨+Mo*¤—ta[ECmǵš•*Čö ţNn±0bTÁĚp5ď‚7ÔTŰEó®cEX6ë˘ýĎ”W"Ďî]ő‰,şKřĄn@T¸śżńĆĺr˙|Ĺ>QŇ'ŘÇţ[T JKŞů±ęh?÷q®ý蝬÷Ń!ô«Ň2­é뮯ˡÔ?‚C¦bąÄ^ţ㜇 =›iˤ&ŻmĹ(ŐË=ĂĽ,yĆöýđé8]blŔ95Ůn˝l«[*$S)f4ŁKŠ Üd'¶ß§;ôŽřŃ5Ý W/8u ‚ĄHŁńb%ř‡®C?ŇŔť%Ź+ 1÷ô!ź=‹–.20‘zÔSYŤxRę÷w•Ţ{ gi6q’”ç!˛vÓxp°dćâR™25¸WäĽD+ębRL©(ŰDo´‘ýx),%ô`Ů޸ѪççÝu>yZł÷Íô•éŮz­€úđ ×Oj`h6Y¢ „ݤշ9®đôĹw<Ô4ąÚő›Á!gßS9A(ř” ®&KČ…0X ö˛»“|ex(ąŞă–żFw'‚—SM•UH†ęúĚpx‰÷čłłT˙ć›jϲF7#“ÎVŢĎD Ë3(x)Wgó<őR>ЏL!5öŃçz;Vežf9­`ÇcF®ÚEâą÷šĂTĘŁôÁf=RÄö[ŔLkŞ ď„ôű”X[ę5´AÖĽBťá­vćÄ Q–r—‰$M„ŘKŇöď™ŰěžÝĂďftż˙°Á9$ţÜz3ü]§M? áͱ±Ü˛®öč;bˇ •ŐÔ`,•«_ôvOn9M«•Ůĺ‰2?ä^4Zŕ*/:C-ş RTĄ–2Éŕ+ŢňPčÇ“Š8Za®­@é8‡/Źlł—Ôz[3oĘdşĽs|±™šł5-iąGť}z;Ç­YŽLR^»ÁKc´@™Ô—uË\a‡gétľŚ?âŚňŤ¬HŻŹŕýDiÉ5ß6źmÜ3´ŞęĐ,Dú†"®áĽˇÜÉ,h»w¦ţ˝î OSÎ1»ôĘŢř –ĘĽę:§(ĚĽ92GˇB»ňĺ›ópVâ‡Ç««ôŁ”Z{^ž+CëQh/ţű\d‹=÷AR‰áĎ–\ďÜŰÓŞ¸­đ#N ];e7Sşj¬éó<Ă’ŰRÎ^čŰń°.Śá¤çYö=D:'kÚ(ú€ˇ nŕ.T0çą‘ů&_şs´ű"35Q¶™Bd0wň©ę&ő'i×m!uNUÁ2#·š Á«Âë~©wLßÚŔŠěÎwĄŇFŚ/÷e$ř(óp/Y0PbmPŐńF·†ĽžÍɡĄSąW^*×÷YyŃżsą¶˘T•Ö6„‘ćóń+ĎALşşbšB‰öá6&I*Ö”ŇÝҤĆÔÚ·Goe) SänđÓđ7VcQĎŕYqëűń“cg҆Ľśň÷VÝłžôTe4­›“ą\}Éëp°ł5ť{ěĺľF\Ż8żĘ’w›68jň @!®ßˇÉtţpC3¦ŢéyÄpň.Úá¶ CĄF‹¶ëöo#3â$IކţĹĽ˝™D•Wë4ĺS $BÓŮ…&·FŐ-­ď“±é–Îر‰µůč‡Yćű^ˇ|Zô9¤cJúěnźBóýhfs:ČŞE-Ěě ÔMťoDęÚ©3u™ą#V€.;-Üá\Ś%w7n˝›!˝"# öY¤ä"©ë”3C8‚Ţ2Mö,ű÷ű8uµ¨>đľ.łäĘ ~%VćóĽă¶q ľ±›«§Š0Î]p§Ë“äqqăﮎbb/y†Ą^\fśäôĂ6ČČr$ZHtZ±ô1°9=&Y8wy§-@7żÜšŚ$şwÝj®ÄŞĐ~S”wóŇ sd¦HŘËĄ2âče-*ś´Rń`) Ü}Á{÷Á§i9‰Xfďň1¶[;SQŽI$4ăÄÝŻ\Nü¨Đ“´0zä`’ąÍ†˘Ć<¨âüĽą&đýýŚyî›GĄ>’7>oY˝đ  WŮ +’:NŚĎË }Śčßj‡ ĘŔŘŰ ü$Žlwîu¨„Uż” h,Ó®«xa¶¨í™á ëK—ÎĹWžqøß$}Ďns>ĄZĽ ą–¦“`Š2•{÷µť’są›mÂţŮďs[ň­É® Ń–<·¶…wÓ¦Ço%¦ĄoŽZ„š4°Ę»<Ł.fЉGSWÎo>ß« Ú. `Ŕ–Ťß3´m}»!aó^¦9çůMí-ýVZ˝ź?‹CŻďqúbfŨ\ßd´úŔzUÉęD'űÎ#›¸µUjHćş·OQť`Öđ uNĂ; ›‹×W‰ Ş®ŤGGµŠÝL˙*ËÜô˛e§®RáoŞĚJ?*iYŹrIö˘×Ď:3)!DDÍŞŕ Ú×{FřÂ|˛“Ŕtľ‡Áăôh{·če›Z`Á 'ÎŢ·ÝÍÜß<”%1Růfĺ?™MR|~Č~ô?ńEÔendstream endobj 375 0 obj << /Filter /FlateDecode /Length1 1614 /Length2 7807 /Length3 0 /Length 8895 >> stream xÚŤ¸T“Ű6L“&UŇ‘.¤7é˝—BIč˝H“ޤ ‚ô" ˝7éM"éM@ů˘Çsî=÷˙×úľ•µ’w?óĚě™=Ďěd……Q[Ź[ÖnV‚ĂPÜü<|ây =~>ź ź! ‹>˙Ť˛‚]8Lüżň.` Ť)Qh˘xę đ ř…ĹůEÄůř||bá.â Ä Áx ‡‘„,ňp„§ ÄŢ…ŢçďG;Ŕ/&&ňč·;@Öěa ĘěŚŢ„ôŕ ĺůŻě’(Bś—×ÝÝťčŚä»Ř?áxp‡ ş`$ŘĹ l řU2@č ţS! @ß‚üË ·Cą]Ŕ4…€Ŕ0$ÚĹf v w詪´`Ř_dőżŹŔĎĂ˙O¸?ŢżA`żť Ü„yB`ö; ĐRRçAy €0Ű_D  GűÝ€(ĐMřť: $«˘+üSäA ´ t…™÷é)'ăŚ[ÓwŢoÇ׫ ť}$7äTFU©}‚·[qÄfĹNúëfgáh7·)KĘÍŚ1Ĺ ’ćč“ŇWßÄHŐ$0.0IFBá´_L˙v®LxčµTPT˛hźßç±CLťÝX˛ŞŐŰ:]ßoYĆٸň ŕ[Şe·™:Xj˛=E=ŽÔíÄ]mÉ™LŠL/fËý4đ·˘(ň3϶‹ľŚů¶yZ{m,[n ŤHjźđ_µz3DßjÚs?µcl·»•-yÓCĄöÇÎ!…Í€˛xSH>´´.V¨I~ˇ—2ÂŔ¬¨ŢrÚ€ďŇ5dWwúťßě\âńZ Âr«VóqYňLNp‡”mćąŕjąF«7Q¸ˇżń7Ń;ÄŔ±Ú i†]áTZmŽ*wÇŚůEC$yőeÄáY÷ýЦŞűĽOűĽ@çŘůAJ˘—nÚ‚ó_xŤµ„?xĘÂŰ«öýv6-ŚžŃ)i4↛<^*< m Ď5‰ťĘ5ÖVkó#}W•Ieň4„,“#ô׫EíšB´ž:aÁŤZ!5Äe|"“,$Ď B”‰ň~$čz˝ˇ89±űę6fI®ŕ0*ą–ëť–¶"—<¸=íűŁŁw6 )`Ç&⹸RiŐ÷vň­ŚA4± F+ňŚ-fS-ü걓–őŢĎR™i@BŃ<ôŃ‚6\ńĹ"ő†ÖÇ4NügŘfsڏ»ç?ęGf¶&ÍáÍA–źëYŁÖOŽ-ČEˇÉ±kőČFÖN޸•KŽtFýő÷@â¬ĺ˛ýٱęeŞŔV=¦‰Ř­ä+WK ˝BYˇă4a9)ňĎąn°Ďiú«řĺi±UŁ-LĎÇĎ‹),xq‡ąý}˝ĂÇ{űe˛'S¸^ÜO‡`^€#X˙çŐšÉ/SŢ%ÄšĹA,Čď%_łŤź–5ó¨‚ÖÖË çÖFú‚ŰĹé§[ŇSʰöŽmP9ĐÄT2ář­p·.«B\ÝŻCŐbqzKlí=FÄ9AgÝyŁ‹đyK§D~µiĘV{ďšŰ ‘'y U­J‚'B‚Vˇ˙Ó* Ń‘fŹ?sLüP7Ģl!ĂĆKY^š‰%ČĽďθU•2,=Ż«˝ĹĽQß(~Óú‘Łż5FńáĚVľ-[Y÷z;wcŃ ^şËQŚoŘl;]ÔĚ<5‘óŕó Ó+Ú÷ąę$˘Ł§łŃ Á“ÚQřÝY¬˛Č u•üŹMjó§'uY©ßđžeq}ú 2ńLînÎĎÎŔ$¤°ŇŠş¶ć–©Ćă»c{MÇvš9Z5vVŚŇ+ęĎ#µ•^¨2¦Äˇ`6 ~ţţÜIÓ¨Ö¤cOiFrKη:ĺ«SőçnĽ%Ý=öą©QK2>,Ů«ë]çfڍŇŻPç`řä‡ÝâÇ4:ëM”ĺ!9žŻ(|cçÜÜ9ŕň‹ËËfxź¤X Šůřn;AŁwçoů=Őjć9ö• 8=tV[ľć«›ćţ&“˛°/QÁ.ń>^v–E-Ž,u\GT—Ďf%űZŁáÔŃR¤rQT$HÍŽÎč®×˘K¶ŐžÔĄ2“7[ÉăÁPc @{ű˝îäŞĐ/ŢůęşYŻ őnÓźĆL†ŢÇܤߗů÷u‘´¦z?Ndű–ĂĘđĂ«?,ÓĚ4Č®%5v¶˘ mU#*×9fúČŤ\úçÚś5ŻÁŕâŘ»‚6 w§ą°{ *ü"řE±¦č±î f>ú™ź5ĄÝ†Ňö;w"ĽN6E{{|ÚóźŘŢ8zę§Gv×:÷mĽ\¦yV8d/?@0ÓŃ”Ž)ću«y]?\~Ńź÷=ĘŻ)ľ'5ôV!śžsďĄXTŻËč”ꋸc…ÁžÚ…‡cNÁ#Üá .ţĘéwGDÝŞPőSlÁŔó:mnü´ĺ¸•Mr̤u Ě˝ËĎ{A•-Ĺ©(ôđĄmŘQ<&ĹŰBľä óOl%/{ƱpÎ7đ&ŚÉ[Ž‘ą5/÷†ó‡ËáǢ±=jî:ö ŃLN~K ’ žéČđW#];UDę†~§n®.ś‰ďB#Ä,wwy ¬ăš‹­ŢęÔCîűÓ®!:Îzbđđ XBž<ĆÁ±Ř÷'†ßrmÜĎŐĎŮĂ(8łĺWËÎu*-Št\đ/ÍŧńyQ—(żĄ_~đ,˘>vXl¤ó•Ĺ+dňŢÓwŤ3Ćy´ą]‚(UOľ÷moxp&Fô%>ĚŁVx¨ř1(¨Pâ,-úVµęvŕ˘5i;ÝÎ#źŔ¶ 欠Ű5˙Ö¶ů¤Ż%]śsxQťŢk8š˛[,ŕ(ÁEďMŮĚFŔ16'›`ß]©Ćąh¬Ö…őٰŽÍkÖôdH–~޶6z»zJż%‰Ľ;-îłúČÉӝ㯖 ˘kł‚Cq¤ä«ů.ĆŁ»‹a?|20Râ~2 ٱ7Ü~¦č{~5á#u•Žţ=Ó%¤ĺŁÝŤ»…L;*dń][Oä$¤ç˛GČ<Ş|Ô)ĺăX* D.ß$’ńmŠ(á Ą˘öĺĚ}2žŻĽJEttýŤ|ą)(ÝĚĺ¦H,¦űî`"Zšví€×ŕµigÄńţÓ0xŤLeŐJJŕŤŐá¶ě„ż`áŠĺ×v9ĂS?ćC1ĹÜ&Ş8G)ÂҀҭWŢî·*¬H…Ďó•öí'Ű«ż’KŰZËŘ•-*˝łÜúěÝčá 饎Ĺńǵx‘ ayqcűüoµíî}$(;y4­×“-|şÚrëţk—ĽBłř…N7ë/D-µU—ŃÔ§íţçĘĚ7Ůt!ůŰŁ<ľ9Ť#·‡’ꋨçGŰ”†Ó’@•UéńAR÷Ę;í©1p¶–đŕňnşăľŹĐmb—™ť}‘îE jh•éÖdUu~™µđć‰1ăçdđр̔ŠÄč†iÂP^eëâ•čG'ę©ř[’ž™š®ÄŔ$Ľíü˘Öž‚Ţ,”fMţűp¨Ŕ;˛'YU‹0jŤ R˙:˝ÜŤ“”YX-mP4.nZěk§e }_<ŰHtĺŢk}żŹäŇHOŢËó6†qÚ˝Ę;7Űł%\I ’ň€:É?O6ç]¤dšÁ×@1ŁUVˇNËÜ·5¶Í ĺšĺ[©OH°zІ;-jîŔNŤź<Íń’žUŘ ŇĽxŽŮj#¦Źö~FmcÂţ„iV”sëE[˙°S‘§ŕ|%ňcpˇ˛ÚE«ŕľŇxa‘@ÔJGącűÚ[1r żű…{ŕ,Ír93Ąáň¦-*óĚˉŮɉŮâńN%Î!Č®!çGYł<ŽQŠĚńŕɥ૳—PťŠ×‰:i86ËE”ĽĚÚ“×÷őźµ)Ť‹âXýś’%Oh‡íPč|Ä»ÉuV‹ńfő’śjĎ t>^5 ŤRŐědľ9b㤺~f Ź ;FIŇME9óâK 8Ópd ŽÚť6Vě%/Żä^x>d_­°f™ó.ˇ#+dlŃaČäĺcE.y4žÚmĘ•r•MąĽ8P\:0CŤĎŮk©ÝŹÖyĂüůťÔ]Ú/Aź2Ą·meAd‚%´)$ŰS÷§0X85|UîwÓ¦}ÍĎ’·}łśz«ßŃJSÉ×ń:[‰ś–¬=ôIŹJŢSf;_‘7OiBpŕ”sśáţĚ´zNžËQ5ĹŢř##żö¶†Famuńł^7ÖşŠsůĄuAMŚ:uSdµ»ľ¶«w<˛Ąi´ňB;ë÷«ňâ/«”ľ\h35«Ť„†r•¶L´E(¤ůJŮ“Ďđ!¶ttéę5öNxbťwŘMTąă˘ ÖożíKXOQnnČŽá«×Ľ}˝l˘BoĂźX»Ň¬ŇçÝŕܧki2žNCňđ5łRŃÇ…ŮcCZiÄ1žçřËƻڋ,JRÍA]}˝ra¸głg9Ęj3ÚŐ~š5f 8ŞVŰáňř‡úT^‹v›’“)IQWÖŹŃßĘŠüÓOŮZ®—­}u pIđćüÍ‹ČW4śěr'p×§"‚ŻüɶkŽÝ–·ŰOýŐ>ygjL. JŠŤ.Ö8¶ř.nD#q—ÜíAÜs˛Ô[Ć+·Bä$DSÄňh/«JHg3ęç…xn×#©v±Ď ,ÖŢ'HŤGţĽ}ť TÓŕ zâ)UĘąh#ŹÝćgI܊ꡓu©Ę˛Ő]ô$_ť`šźžĎd÷ßWÄÚ ÎŃNdAˇH±Î\•˝$ÔěĎťŚG3HÔ»r0}­–ř1Ż—śŇĺÖ:xxőMŢŻZşýip3o‚“°O%ß“M\¬S®y5ťÉ>¦ÉŇq©ťŔ#ä×Ĺ˝`řS‹ĺO%ŚCîWÂZ <ľ/˛P;ţB䇹†+ĹŐ\eäůőeĺWô"‘ŐČ ô·ÓĹ˝ž›ëďńôłŁä…®”]ěW pß“ 1dž˝Śbő©Ś­mŹÄ÷K^P.Y›ÍząMśS«á¶ľÄîHŤ* ^]ŤćÂŽö2,F,zt°†Śî=ך‰§ w&á‰zS~H kÖF=– ×”t|¸˙“-hó} 9Ĺ I‡„ďŇĂçćţŻ@Óěâ3ŕŘ*éłçˇ«¦iŹÔÂíĂpŠ{¶ňşŠ7I)d˘2őË’SlŔ3&–^5k5ß/ąVŹ]B3#H|OX®ß‹Šń\O6oÚкɸüŰą¤‰ú… ÂYˇb§ĆŤÚOĆ`ö§ŞJ›âŇ]¨¤™8\=­P/‰Ł€ĽłYţ—R-‘'NF?śňs‹[±Ť+Ř‚ßíÓPe’RvIľű¤ůYTnëřů‰D,ě QÚC:ÚŁ”q›q˛-Ą—˘HŐˇć.~šm”¨wŹĹq”să+Ţ0šŰ˝GdYÍźb(ómiÝh^`…⚤śý|ŕGPś~McLS|ŻýJDI~oô˝Ł6©ŘĘq9SF§}˘\䫨‘žYíýS 9KťX­âDÁďíśr·«]aÍŘĚőKŞ˝$Ű7ŮVZ¸ŕźÖ ¤2w,Yůr‹QęđľĎ_רdIĎtöőoOTÖb2í:¬a[Dض\mŢ.µÚżNéĺÂ!ŰEÝď*©4ͬ §yšółXK&ŹE#^5hń+1Ş vĆ 78tĘś¶‘×fuĽűXĂP‰r€Očf„ËIu]ŔHě‰]Šoq=€lěâ}Ý© *¬®ô¦)DZ$´ Ű‡©°făt?nš0»ŻßŚrR=Ň!+Ćäe5Ç \HĉrégâC<•sŚGe#>§Č ¤Ç^ľJ–¤’ůęZŘĚ­á=‡ÂŰŤ¦Âc÷žc8?ZwĐă62˝K‚=ňô[}ĺů€TŃÚĽ»ćú«j ´D<‚ůBKhI{-Ä˝Űj,B‘/’QmQ0blÉr}ü»ýłźb|UvŠ˝ě&©źv O<NĘČ˝)ůÁťŔŐH­ďG;Öwđ  ©Ţ KEŞÂÔň)j×ňؼśďâ•Îč}d~«sÉ0.Ż~š{ß@ĺ‘ęN𥭀ţąRĂbĆ-Vb?¦5ĹÄ'á>uÖ8¤=Dn—,ć*ôÍö@Ó¸A`¬H&ĺ—´ŽŃKĄ¸!ž‘‡(7Ź)qW[ťx*WSO«»ďôm™KŢŘpmvj…ZĚ@+<ľ° »Ż ż“'kWPcĐÂŁé łË`·—q·°+0Lě2Űö÷ۉâWúţ•Á’TVĐ­Ś oE+Ăm˘â~ť¦ˇ×Źuý†Ĺ|ł wmže`šexyŽ&L1ă€ůË>;M ˝´Ő•ݵfďy.ă'uËÓčIűařÚ“ń›ž2=7ŤË?đ˙ĄĐ­"ŃňN9”ô”ł)†2˛zÄ6Ţ’)űxŚĎm›óąŐumŰŁkk)Ő˙śÍuŔ‡’ńÉgÁKľšhęî–7–So“îqLĄq{×kĹ6VjM˙ Ó-ęŞ!őř°µ˙ \§˙Ô]Î~,~ĄĺZďş9+éS 4ĂćđĚK˘±*UŐsĘ\ńÓ­ÎÁí„K‘“Q˝ ®Lµăµ`r*´{ Ąwľńž…>pŹc‹ˇŰw±8ŰQÂtO Ö•0Uś tÁő~¶YhzĚF‹·Ş±đ[ę—M›pťw´§‡•zĐ;ŮŽ=g€8Z/<7 ˛Ĺ€? 5/sÍ»9‘!›W«çTŠŰxŹVÁ†äRÔp Ž»ÜOA}†_ňÄ'h fĆžôó‡úČDÚ®cÔô:XëúŰxücN-ŻË&PÉ#(©Ô„őji~ff”"vw[jGäN’É˝´šł‹‡ŰŹd…`ŞKJ‘ÉĽ™ŇWC‘iËÄ@$OŤ&ďOů©f,őüµĘse!>_0˝ŤţK=Öď™eř_ůÖ]Úľ”>-dP żxŠĄYnŢ.mům?s˦¸żyôA›¸Ú„µ;4ŞO‘Ë8˙Ůę†čL%•®ň"˛1z!24ţä'˝wJŐśďO•vgS<éKÜâ¶Č@iŰR|¶NV_}۬ äÉ|¦©V^!ĂëČDŰ&[Ą©v™©4ő¶Ď!…ĂS7|96ZóWŘŐĂwY†„»ÖŢ nU<ĺŔ“ ˛§~ÇĄßuQұ_xŃ:í["]UßMÔJëÔŚK’©UôÚTqa.¶e÷i:ď¬ĺÄő źGA”Ú°‰đ¦Â3šXK§–FyO ąč›UrĐËźKˇB%saŔ58‚ë/«mJiś ü0é…gcqłĆű0~´ĹĚ”IůĽÝK üän±ç ©QG”ŮińX¸Ő:?Ű!z:r LŢąTÝ)× ,üş"vw§!Í.źfŃhš©™"V±™‹F˙’dJÄJănާ WĽsÉ@ęf)pŘńAŐ]Їa÷reYíĎ;q±PŻ«ĺ_8}_('ĘůQ÷níLĹn|qäěyááąfůĂ„Ďo»¬—S›AkŹz‰RÎĽNŢ\ʦٴEuĄy/µÜ3řCč5©;ŕű*`+ďX{—A Ä”úzĆuAů(~łř* %€[©=H-ç!ćźOć™U÷ҶŻĂ%şÎ Űe*®“D餱en•SL~sPÚ~ÄŚ/|‹Ý·í‹‡‰éŽy\·R"đxżÄ&uŇYň˝iÓNĚ‘µT.űč”ţ˛Á” ˘Őę(H3<9yJŚ/–ćž˙y5ŹHĐőřڇ™„řx÷Bű«îTFڅȏ›>3öXo2›ý“Ô-máţµ/Uß˝FŢvÖÄľaěu^˙¬ÝĘU<¶Śîx¦Řv]—ç+ëo÷ÍP¶5bŢŹî¨ĆĎčńŞÉ‘\8@Ç ń|bĽmőú݆˘ y»j žą±»ëżČ&b°“c¸z@˛-bYÍŠ7čó Ű&׬ӡ_h^Ľ+,Á˛HZÜĄđ‹oëő›@ĎçąŐCĎŔiŢî&éúm„dĂy«‰. Ö?’¤ +JŻ©éÇąë˝çůš¸Ą?5{ŞKĚu«F>(WöiĺĘ2­#EÝ2˝č¸E&ť$öŃŘąW<¨Ë49ôĘĆj•·ŁďˇŹttcO0Ţ›č°kXzÎęň~užË"Lş/|ęQĄýÝĚ„WKňőZ¨-©ŐÔ[”éuĎdý­6ÜNMďnµűÉÉuÄ]Ý/©íő^ &<3°m˘ž ŕ®_Mş7ý.FW×Ţ)MŰQrL-˝ëKq,6!~ĄŔôfc\`r3¶í\cŞ<6 šŠrĺ‡üňRaśUŞ8áD?{oŰ3„bŮM”b¦f—ŤűŃ«1Ů“UL@Źů9Lş´ˇ0ÜÜńr x…iŕfYůExŹÝ_é]—Í ÷}5ęµEĹMUńŘűG‰&eŮ­.1ĺqŻŰśťÝ÷^IS$°‡!ćĽÚ.tUçW?,n·Şg„-›éb›–7Ć H޵ďWo-ď¬ů R}ly |Ĺw`ŽŞ’¶j'ű=3fuÖÁpëf¶q§*vŹýĽ@Źŕ'Ŕ5űhŢ÷fT}¤J}$_+ÚˇŰďů›\©=&‹Śo·¤ ŐGř§ŕ]Ą°@IłĚ•Ęa“WęP}]NłĽň}fş™Ĺ][^Wňě®÷#SKŮ4/g× u+ŘşD§-EľNתylůaXŹW[Č ľŕ(\Ł– pRd[˘Ú(ńęM)ʵ©Äőň$šś+÷/V›Ëˇ“ÓStMÄŤ{7ݤ2uÂŁ šôáJă_¦¬~7wEŰŠźźË~\Mân{ČÇ}›@ŞäEž¦JŔ±uë †ŽÓĽż„^ťSĂsřgůw: ^°ť+&h«|ŐTŇ’<Ş'QŰ„ĹňHÇľÉ'P0&8;Xˇő°6°ĎXw°‘Oź{ĹhĘD_ááR̯፜ć<•Âô´üfu5Ó°9Ł/đ$’`ż”9+ĎŹň!“kpąÁ™×ĆQdY5E¦Łs§č\hę— n]€R9Ťµ¤Ş(SaüÉ$‚㚣ŃlĚĺźňđPŃ,O÷Ś@^Ł<9«ÁÚŐD5-˛_`÷ąĎćľöŕ »§ä Öˇę“K>ŁÎXse'",r¸¸`ËxWł\>ń¸áŕ¬g ĺ.ÍĘ·0ÎwYĺÖťĽ¦Ăy,¶T;ŃTľ˝|ďiP*ô1˘%ŐĂoł,¦ł*4L? ř&$םîO=5ŕř˛ş…ÉěŘŃĚL’+RŽň`MňöDúč˛MAíëô®łóÔą8XÇLóL–8¨d„łzćě/H˛däü€<Ă9X—÷2Á­?â‚$ť;¤łĺ˝UóúR¸kBÔ±h_,řÖ’WYĽNű23›ˇ† Bqž­f˝ÄˇLş'÷pĽxĂ˙[ ŽÇN˝íWéň±7řô«Î 2ŢDrú±ń๨’ö]:ÓĽ¬ß[<ĄÓÄknŕ#Ukˇöl›,›ňaFQe’ëŢýĂYüÜűď,bi_>Ů e0ł/°ÂIk=¤ßŢëđăöYZT Áô6´›ČÉ/–ĂőŁŔ2‘žĚe =_şŮćôŤĄďŮłżäK˙đŢăâBaKP7̰ōC˛6ć¸/Ľ‰aŻ=)L(/ożÇž‹z§®J“(Ă1˝˘:¤ęŮ#ź."K«äx„ˇ‡Kŕ9§ć"ąLLq[l"vęKé_$W—úń¶ö[•«-†ş»Go!< é—sŢŞ Î>_ĂwĘW™Âü<,/T÷čĂĺ ¦OŞźLć™ 4%Äi’-X®ł=îôÍë_‰2oô˝ Qő°fxŹ$ĽqPĽcL÷¸ő¬±/kCĐúp>K¨ßî˙ľÓNendstream endobj 376 0 obj << /Filter /FlateDecode /Length1 1423 /Length2 6347 /Length3 0 /Length 7322 >> stream xÚŤtT”k×6Ý(Ýŕ €„0 -ŇÝH ĂCĚŔ0tw#!]*eR‚H§t ŇÝ]"(đŤzŢsľóţ˙Z˙żf­gžűÚ×Ţ÷Ţ÷}];«¶ŻŚ5 ހŁxA|üâ9 =cQ?ż ?ż;»> ĺý &b7€"]a¸ř˙"Č!ˇ`“ŁĐ<  ęć @"â Qq~~€?˙Ă˙Hq€<Řf Đŕ¨"ŕPW"v9„łfk‡BoóźW'„ zřPôÁďt€Ś €á 0Ęę„Ţvč! 0(Ęë_%8%ěP(gq ĐĂĂěäʇ@ÚJq=xŔPv]¨+éµü  v‚ţ™ŚŹ osýë!lP`$€a(Üťá·†"čÍz*ę-g(üYýá௳€ř@—ű+űW!üw2A89á^0¸-Ŕćh)ŞóˇĂ\Ü *ňQĐŃ?-ć(""$€ş ž;ŕŻňú^ÎĐßAĐ/=źŹ3Â`곢˙|\ÁîP éőóů߯@ €5 ‚XAmap˘ŞŁa¨Íź5úň‘0O€)?Z{ ˙ŻßßoOŃň˛FŔ˝ţˇ˙ľ_ ‰ˇúcEž?˙“•Ex|xAÂއ Hýřý»Ś6öWü˙äŞŔm€‡şEÓ:v˙Kś™ đďZš´jˇÎDnĆ/ĚA?@˙ßR˙ťňSřŻ*˙/‘˙wCŠnŽŽżĂśżă˙Gěsôú‹€­ m Úđ˙¦B˙Vj ssúď¨ Ś6‚ Ü-f^żĐćŞó„ZkĂP»?’ů?ůe5GŞŤp…ýú¶ łřů˙+†öÄýýpEëňwŠ¶Ďż÷U€CÖż|& ,#‘`/"~´ś„…> ´!­ˇžż• ňÁ(t =ŁŔ$úu­h5uAżWč]€PGč/gýŤˇ!'ÜÍőoí 3†>±_Čżş¸!‘čÜߢA·úźőďOę …MM ŹBí+Cľ—Ë0zđ® ŕ-,5E%wF Ł8&_řŘ©ăg*ŤşČZZ—ŇőĄi}>2áËČ˝˙}ĐÓ¬5;h>U9…ˇĐą"{Čű˛çâ¤ŐĘbŤ`°M‹Z!?HÔaľmiŠcz?Ůpâgh›ŕFŮzÁU“ë†ÉĂ€ čk*#*N&·i+Ć•m ŐklFO4MwÓWy2t…‹?—mĄżđZ1ŤĎčť›ëÎîęL<~ĄÖţ^¤4ś’žĘ[•5·/é3=ub¶j­+‡¬đ7ą7) wé ťÂ9汝*ÇALLlńôüN€b&ű>X$fRë¶ęPu}ĂHżG·ëOo/Ľě’·ú)Z`,p·(¬”˛ĺUܛشŢtĄT˝¶0™â>¸6fˉÖű|H×·×Ć)—7oăr¨|KSë2zqŁ5#Áą^6řôŽN™°üÉ´ŢŚz%‰ŻŽČ4¶ŕ‹ÝÉş™ý3 :ą»ŽGFë“™JG;A˛žňçřL;†Y¸ďďc(l.iX\*kGöµ‹ĽáP.YM;ç.XŐ( “7äŕô™#f…V«·{_Ä*•ë-ňĎš]›gO†Ź^©gű#€řáŹ!ź¤"EůÖ÷d­štc±*|ć×Ţ*`VëQuđU?¤Ĺ  čťĆá{|RŹu ”Ëšô±tĹăE/Ş6¬0žÝćRî©ú~ćNaL—nÁn{4Ix'>ˇČţ#Vĺ«RV±¨ ň¶ÚűśŢH)~”?CÄc,YŃŃ«ÜęŇ©ň^î»Ă˛>#wɉúś~w:„ ¶…üčóň^uÓő+» ĄaC^‰]ě·­^]Ôy,}¶‚ É Ž„ó×V]śîi‰«ÚăÍ%ĺDt@ÎL†j¦OŮ„”´Ýó x L9é7Á˘źç…m ©ęăq3őž¶pó×ę_|ç-Ee!»ŤA — ÷ť.âéĆ;ż–1˝¬B­Ô´›ßĚŘô˛ČhÍ&ZĘx2âxŹő9’´ÎŠĽ őôÓś­řś»3Ýź q7Ý0? ă;ľ‹uq Áq‡|d›ż—q“5qđ(LÎ&xŮxVěG!”ćąx¶ĐHrÓ‡ń–¨8”ü gQ\úsđcČžn˙ \ÄŮîk¦ ˝Ť·÷±t´Z/#1Őý%[¤ńEŤ*írÜŰOÖüü 'v}€ŕxźďdsÔ|ţ)”4'I•J–|ĚăľĐÇAŇWd~ňÇŔ Ž’ĐnI´Ô”˘ĺ·+˘µ P_ÓO hű% {9PkDgyľčŮŰČpĽäüô*eaˇŽiˇMJ@śOůÄTš3LPčĎÇśqúň»cI¦.±¤¨ü0CK3ŹôóÖ“ON¸ u_¸o‰¸¨;ŢĆ=">ÄÉ%çLäĄ Ç‘†vd´ßÉ˝¸jŻ¤Ĺ©Ú‘‡ź)˘Äďáö†w>¬ËáhÉcďń !ŇĎŻ‹6ŚßcčÂ}sŻ ,Şľhżśi5úÂÜw&m3˛Ĺţśµ4¶ú}Ářţˇ|ÍťDzéńĺ[5Âä{]Ăář?n\9u˘Vń=kŤlDáÓ‹JÁCwś©ˇ ĐŚYĄŘT´SÎB ߼©źÄůŠčét}ś<·úĽ¨żAk^^˝ âEyO\ě{żŔůŢlÚo_ĺoGőěĺ}ŰŔEť3ެ}~»3C¶ŐŃÜżË:aôPDáľÄKĘv›×~ĽjŮ€žŤ«ŕ`őî^ÉŇŔ'¦w‹ąU8ýCUaáiuŰŞ©k.Bâź”š´‡Î™f©~iÔQŁ'ŚÍĺVO{żV5–O˙?®<µ›úÇőôŚ-ľowąb…ĄţŇšĂA ĺłTôśŃ¬úɸ®ĂŇ{ŃwúYrz¶<­:-CŚľëÉ…#|rÇÇhşVÁçî?Ś` al9Ýĺ0|\IüűrĎ—~ŕh+ěąŢÎá ş·ž©Ńo$RŞĐĎJž¨|*L°Ĺ! áůÎÇä :ŕü”ÄP©űDŰäµŢ‚JěL$1N˝2őW„Ź×‘ŐĘLşŮ[[5ÓËꓲĆ^żÝ|śľy1¨?GCYꚇóĐᔓlľ·üL#›üĚ–pťB‹ÍNuiąÇÍ<Á*p='ôC/cín Ą§žĆq‘Rź5cţip+OädĂ­ç­řâř‘Yt˛Óv:ŢĂMXŹDĎÖ÷0Śéw±?X)E}„ !.=ĎÝŘžÎăÄŢ/…N< &q—#yČŚ4k§.o–94 é§]JPÜc¶ľł™UU˘Č:ôxá€ßĎďu´‹ÍíĆ”/«‚p°á"+ěŇ۵Ií[—,đ=—6íu=Sw M6'ôłÚ«ˇ÷ýWéĚ©F(^?¶MÁZuŻ‚iۢ/cFůŘŤcÔBsCHĄuÓé•ĂŁŢqLAk‰v˛˛ů|-ĘĚ"ĽĽđĆ?¤ř&/:±ż(~˙V^Qf€3˙Ŕ@`şA“@XJţTý»5mź[¦¦hޏ?űçgľB×’şŔ/ Fó\l›*_Ň–y‰´&Iţ9ě ¤‰â?dČ(c­±łrě捼O K´Ë›pĽÜ˛ů©ˇ›Ĺ»ő€˙Ťâ‹™:ăľń7%Ź‘·ýv< U–*#Ü2ŹRIc©+łG˛ůSťýdŐ ¤ÜCÚ®âçŰ„­©µMďĽ6^YătĄE17Tpů¤ąXąY_ ˛nđmE¨Gi–:øŚĄD†YwŚHWźúô:}Ăǵ*ń™ HŢNŘ,y×ć’8ĚđîńéÝş,"\RˇáŽłÁáYvą ĂĚćöá[Gdúoŕ‘Ő'Ü.$BőęI~Âý€+6,PÖš.& ő©ůŽÉxęŐębŻ·ó†¤í'ß Çĺĺ ×"nŐC1}#5˝ sŐ'vYběYx«Ú'<šĄ6´,ďŢë|!™GGą˛ZçĺÍúbě%&‰T;Ó&Âá›>TfŔŚw˙ŕ E<-^@5ťąśHşUJeÝŇťÇţ:đ9&n„ó'ú÷¤Í‹iÄ̬•OŁĎŇ+€?auŰ.mOŇnV ‡^î}lîpůŘMů>Féup†R“0®„LÝŮΤĘ;M§yĂĘ•ŕWf!tHÝ»Ě2/ˮן’Ň”ý„ĎŐ @ÉY9Ž»7)"wj˘Ř‡Í`ňőW'1G}߲|ĆlëqÇ·Kgb˛…"Ąű&6TŤÓŕ2ŘŇĎx„˝8đ«c—p”E@źŘđĽ_Ĺ\˝ĹęxżŃUŇHŮTVô„sĂD™nrIWĺď‹|}•)ITIŁzÚ¶'ůĎ,Ôń˘n ĺ`r“ń\3ëDČčěoó3 `űΤĆ×3ľ­yŞBc´<ónăK É|67áóN Ý…¨|cv¬jMážĺ‘Ý1‹Ý¦"›ě’¤)˝łž2hv‡g°„ĐŔőý»ŔŽ‚–óŇM`§Ô9d<’g `2› ÇoR»|*şčę€WDŤéŞžśN"±HtĆčŞÎó>&üHk·}ÄôşC“¤47™ri„´Ýa\.}ŽĄDő†©b‹dSÝ–iQs@ëJcŰL Ş!‡žpŮ–ĄŹG"ÉŻCzĹŢ·ŕŃr¶–÷¦–jóçvť]Ë-1…‰‚_ëŮDŇŢýmĄ-¨Ĺľ]úśžąŮ}ődJ\ö lĆg‘ť[Ź8Ĺ[’µÚc×3íĽŚľ©%©‰¶ÝŃ÷Žuöć€R˘šńʤ<Đz'HxL_¸iC—dD˙áĆŮÂÂĽD­Ě’@ňř×UÄó±Ú3¸Éď‚c”1­ÝfţrxöţÓĺŢ8‰p›ŚĆő˝ö…a¬÷ĂzzvmKéů•I%÷z^yř“IţٵŞéšŔ•ďCo5ľŕ°6Ľ0˛¸"«T©,ÔrÎ,ny ~M.»ĂŢőőáëůO·MX穿N?¸_ĽČ%Ç)R‹˘×v·Ĺ¦aź)ęôÁăb&t­N[v©S~^z†óňQýs÷şźýóÝź™MŢe-˛Üň…+â›čc­~q%šŻX©2—NÜąůÇ Bx¸ŁĚśM¸˛'—ŃćµÝŃo{óý¦@˙ &ą9’ĘăMě=vBŤšEźřçŻtLˇ%_6„C=jc™¤/Îł¦´|?V>Z jJmůľčĄ8ł» :|Kz¨ śd<¶+2˛Ţq´óHfĐרY®m3§fÎÉč5˙|rěĹR~ђ޵‰ űúŘńÉÝ!V“ŻÝíˇRĽ4’Śř/Tz(U×yue¸µײş5GÖěqőδO¸ă<…&縺bóţa–ŢśPöČđg!˛$ű:˘Jâ;uź a§¸ş¤Íľ<áşĘÂSJEĐĎ-ą;]Ů«ŢÓź›7ßMÄN·ó»Żč2ZÖUłÄţ<źîäŠă©÷˝|öΔŠ"¨dtŤ÷Âű»ůę後2î&€YnMy#Ű}ě•Ě •cŢŠqŘú%hŚNŤÓđVľçţ~aíB5XaşÝn>D9I¶Ú€śň˘ĄíH$ťąŻźT+—0~ šO3ZÔ0ɇľŮáén;Pńڏüş’śŐË©u°Ŕ†/n¤*¤żaśőö.Sł±Ď7d¸}ăűąé{HŽf†ĺ[ĹaźÔ;*%Ś ň٧@z 3ŢŹDóg0EďµÖEs0rúlúÔĄ®÷ ÄD°ťQ&)ÎGĆ|ΡqÇBŤ7‡FMÂC^‹FÇ5Ĺüä;ż]ÄÂ…Aăm‘F÷ŁUęuc•řOŻ$urŘ6TĺÜ⍧łÎYʆć룍kČ‹LŞă>F[ĂŞJµ$ –k˛ŁĆW7ŰO:JµĚ›kĽŃĽß^ˇr!]ěĎ‚ ±uĄEµu(BÖ„®2>čâĆj\4N±Ś{c„Ń…TŤ›ăpJ·.ФW…Qz„ü|? ü ·~¶°j›Cp%±)´–Ňü™»Ţcđ]pź×­ŹI›{8Ĺ•á\K$~úđô˛\6uŁ•ĎÚMĆÉŘąbwBřťžóFðąhˇz±â¬:cÇWYn¦ľ·řqěÍšf%J“~äDJVâbdě>&ő8ćęk:Dd7Ď’N7srí)sŮJn¶ú,|ůW÷TZÁ_? ¦„óěúhĘ(’â~u´ĄţeóňRoÚëü»í„Ý2ŹéÁbzlzKĽ+O Úىv˘`¬«-—ůÚ!^i&TTä[¸®”InäşK-Ń~WSś”̨„OôŰEŇ©‘0‹ÍY0}Ő{ßčĐRâ/v5´a3>ÓuegŞ·N*yżVíZ&{VČ˙,áŔʰt> fâ9śÜę¨Xf°×âŐ.ąŔĹ4¨PTjZÉiLiŢ˝.N‰ćP›ůŃ‘aöŮa•Ż|ďŚt‰Ů±Ŕ¨J˛â‹Ľ~fú”l }˙‡GDI ë4řU·QŞ4ű¬ńČ™©Ć¤Tg!T3Ő©;J쓟Ězg]bކŹ6Äoh’W¶—'´ż}>ăfĺůŹ.ŘôUK@»˙—: ŃSáÝŽĎŰöƇä]W1\Ĺűýrý;űąĽŕxĎôߊ ŐSb—¬ú vę‹˝..ľíř-{ě–˘¦Ž5Źç=Q—…K,y{­™mÖ@gëČ–vD5á5#I‹¬AIvëĂEű$š[—É0Q†7{Â,bTYHŮŐŽ“ Żęęú­ ©”k»@ŔľÚń÷o@»Ş*műő&źUK†âfyąS J;ÚÓŹŔrgA¨2P%[ćmě°ÍáŢŹŹOźb[–r č9űĄđŔ㎠±¸é,»ßG$Ł‚Őp‰€ű‚Źkd†}«°”xćőec¶rt×'CHµ&ŽdVd?:¶Ťř¬»:ŢĄ<ť‹VzG,ˇ«#Ľ7 :áš%Š:Y_č1łžď§ĆqĂš:$·j±BfĹvą+_R-˛Kj”2:a…·Ľ|Š%Z«ÜëĆ{Ç}^ L©…$&+¬ ±Ě„P R“Ô*őbsUú¤6_'N"Ô‰Ďĺ’ďŤŘeyČžÜî|gťúęű˝Ú'Ł[MřĄbN iŻi'GZ»xĂ 9š;ĂÇŰ7T˛öÔ±˛«ąç¸TěĺŤP&5յĠ*ÖŻRńHÇco‚棆Áľ’Ă3ó@$÷ńr0"Ü为şz~× ąŐşi;ťZĘo+±cëá*¦Č-)Ôi›ÉaŃeY;8é–·–3Í"ŞVěÜVöxCU°żH0ż‚pfYÁ®­ŞC^2”üŚőłÉ fÔ)?C§÷¬ŚĂ1 •Đăăv‘!ąĚ˝—Öăf«ćRńö†D˝Óq‚],έë#ś˝­Ř} ¶x”ß‘ ‘2î% ˛hĎ«ÖXʆ/őđ3YŇŃÝĎËa%TźîŘż!ô{KÍC– ]vdŻŔ—XTU<ţÝč#ý‹’>[15%*‹}ţ*Ö  Ä©w‘aČOnˇ§žmĚ»o70[«w¦ťŽE^ť›ö/ëŇ7ÜÜßJ9Şz2ikşä¬)ÍÇą ś^?ßżq~4—ŕÝA˘ReWX ą«Ň˘ZEĚL4w˘ű3»%{Ý—–§†‘#§Ś=Ž\±cBtę]B„G0*ß-ŽE»l[sěSn|ě'|C!uą/r0·ABÜĄ–v¨cwćHRüµó&ą%Ç´¸ľ#ś>j3ŹĎÜâGÍÄçZü®yŃ|"[IęFúĽâzżĎPµĺĚzJ<’:ŠëĂ}Ľďň¤őŢíŢ’J[1¸ţJ¨ŰkćJ¸x|ăiąßě ôlk*·ÇÝEÍb9§ĺB‚w±tĆáŁOđz`ţŁ@Ţťě˘76Óą¬ĚgŤśĚá1OĚW®çęU)•ů‹Éđ«'GöIÓ…×ŐŇ-Žg*ŵ»&ěqiEâţ )°B‚x‡řXąŕPBła1¦=3ŇpdM@ÖĘôí=eŽÖ{†Ďn¶|×ępż¸baM;4ŮâşI±őrč“>I+˛ˇ˙č§Öż­ŘŮÎ9 x±ÍĂ|ŁüCË!ÝŐŇđÖą¤e` oY×3ÜB RS«ęůÝ×ámÖ-–\˛9üpąa#űn‰©žź=xÎçČs5ÁŃDB~bĎú©`p@l˘©şJ 0B&ÖŞO”ňł±_čęá BČ*OĎüKçôŁoZ“UjŚjĽ*Ĺ30óˇŃŮJ[e‰4é Ű›RľT!ű”@nÓ˘^*bZÖ;ËťPÓňó,ş#jR™+¬Š¤±y`K7u&7™ŞŞ.¤_üSA8‘Q‡şÓÂČŢĺ'w¬6śŽ„řń˝ÚlokÇzÚS{2Ż~¸K+ Y ë¨ĽţBFCgy¤#ô8^4‹ÓJúö¦@Vfű‰’ď1m 悦paÇŔ‚fQÇđ›CäR;źŽr )‘Gćs ŮEÚ&…źźK“U”ĹĆđuĘ7ćă„ĺŢ´”řîşîł‰WSÁ7)Xw ĂăÜĚÁăQýN–ŹvéXĚ*C4|řrŰ÷Żî†óú:Đ{NîVÖ1Ťo#ĚI¸;ŚgÄpĂV<ű^fž-{P=~6FÝß,­‹‹ŰťčĆuO»8~ťqś6ÓŁâúMنyâËíˇg*rż´őd…Ůű&)n6agßÁHXĽ©ˇa-Ş6ŃŻÓôÚ›|i~pĎňŐy\dO˝ż—í˙"Z3 endstream endobj 377 0 obj << /Type /XRef /Length 621 /Filter /FlateDecode /DecodeParms << /Columns 5 /Predictor 12 >> /W [ 1 3 1 ] /Info 96 0 R /Root 95 0 R /Size 378 /ID [<9ff523b039c290e2d77d868692a7cb0f><08360af64288f7584817180b31c7907f>] >> stream xśí•Ű‹ÍQÇ÷ŢçĚ1cpĆe084ŤăŢ™Cø%PSž<*ĄĽxż<đxR.!ĺ~a…ҸÄH.!LîŽŮźu˛ż˝Ďyř´ZżőŰ{­ďZëw‚‹żŕťËĂŹ.D‡ďg?˙׸ű@źťŰç'ă`1˛fpdv ži‘u xşR¤Ůa.žéxş#k÷Ąü+lgDćš°ďEú0 {á8VŕPř»š±GÂŹ3˙—iÁCţţ7\ëá 9a&|.OÁĽ<Ą.˙Ž‚ßR>U{ä6ÖÂp8Ľ ďĘí›$·†ĆRËS<ŹŕQxX2™?ATő­pBĘ!,Ćn×Ụ¶»(Ę×?» ›¤ŠŚtĘÔ{ çK&¦^;ç,”Ńp–tÄÔř §ő&eÜɤgµż>–†Ą®…ĺĽőO—ôîü { “Yz)'÷Îɰ,ý˝—ÁóÜxL:b]ľMâĚ „6ÉÁ&ÖT˘^w›ńŇ ;¶É?„—á5ř˝ŹľŞů8Ftk•Ž›Îuжé,'S…mMµËY‰1 sRWY´ş ź¤žşSłrľ;MÔ?·}m¶ź‰&s “&Šß¦ëkšwSrł[nÁBüî 6űJoÝY Š­'†Ýɶ”´ kdέłďaĽOpď†vŃߪX)ş™ć»`Łh^IďÚ.„ŮřWŔNȦ„Ř;ĄĆN™Ą7p Ľ“ć°ŞŐjâůnWwd+Ü˙ö™7{w) ’˙*â-’а {mda<Yš9nśŰćíŘlSq31{#[řJęÝN  endstream endobj startxref 181092 %%EOF sampling/inst/doc/UPexamples.R0000644000176200001440000001141715033751700016021 0ustar liggesusers### R code from vignette source 'UPexamples.Snw' ################################################### ### code chunk number 1: UPexamples.Snw:21-25 ################################################### library(sampling) ps.options(pointsize=12) options(width=60) ################################################### ### code chunk number 2: entropy1 ################################################### data(belgianmunicipalities) attach(belgianmunicipalities) n=50 ################################################### ### code chunk number 3: entropy2 ################################################### pik=inclusionprobabilities(averageincome,n) ################################################### ### code chunk number 4: entropy3 ################################################### s=UPmaxentropy(pik) ################################################### ### code chunk number 5: entropy4 ################################################### as.character(Commune[s==1]) ################################################### ### code chunk number 6: entropy5 ################################################### pi2=UPmaxentropypi2(pik) ################################################### ### code chunk number 7: entropy6 ################################################### rowSums(pi2)/pik/n detach(belgianmunicipalities) ################################################### ### code chunk number 8: entropy7 ################################################### data(belgianmunicipalities) attach(belgianmunicipalities) pik=inclusionprobabilities(averageincome,50) pik=pik[pik!=1] n=sum(pik) pikt=UPMEpiktildefrompik(pik) w=pikt/(1-pikt) q=UPMEqfromw(w,n) ################################################### ### code chunk number 9: entropy8 ################################################### UPMEsfromq(q) ################################################### ### code chunk number 10: entropy9 ################################################### sim=10000 N=length(pik) tt=rep(0,N) for(i in 1:sim) tt = tt+UPMEsfromq(q) tt=tt/sim max(abs(tt-pik)) detach(belgianmunicipalities) ################################################### ### code chunk number 11: up1 ################################################### b=data(belgianmunicipalities) pik=inclusionprobabilities(belgianmunicipalities$Tot04,200) N=length(pik) n=sum(pik) ################################################### ### code chunk number 12: up2 ################################################### sim=10 ss=array(0,c(sim,8)) ################################################### ### code chunk number 13: up3 ################################################### y=belgianmunicipalities$TaxableIncome ################################################### ### code chunk number 14: up4 ################################################### ht=numeric(8) for(i in 1:sim) { cat("Step ",i,"\n") s=UPpoisson(pik) ht[1]=HTestimator(y[s==1],pik[s==1]) s=UPrandomsystematic(pik) ht[2]=HTestimator(y[s==1],pik[s==1]) s=UPrandompivotal(pik) ht[3]=HTestimator(y[s==1],pik[s==1]) s=UPtille(pik) ht[4]=HTestimator(y[s==1],pik[s==1]) s=UPmidzuno(pik) ht[5]=HTestimator(y[s==1],pik[s==1]) s=UPsystematic(pik) ht[6]=HTestimator(y[s==1],pik[s==1]) s=UPpivotal(pik) ht[7]=HTestimator(y[s==1],pik[s==1]) s=srswor(n,N) ht[8]=HTestimator(y[s==1],rep(n/N,n)) ss[i,]=ht } ################################################### ### code chunk number 15: up5 ################################################### colnames(ss) <- c("poisson","rsyst","rpivotal","tille","midzuno","syst","pivotal","srswor") boxplot(data.frame(ss), las=3) ################################################### ### code chunk number 16: UPexamples.Snw:163-170 (eval = FALSE) ################################################### ## b=data(belgianmunicipalities) ## pik=inclusionprobabilities(belgianmunicipalities$Tot04,200) ## N=length(pik) ## n=sum(pik) ## sim=10 ## ss=array(0,c(sim,8)) ## y=belgianmunicipalities$TaxableIncome ## ht=numeric(8) ## for(i in 1:sim) ## { ## cat("Step ",i,"\n") ## s=UPpoisson(pik) ## ht[1]=HTestimator(y[s==1],pik[s==1]) ## s=UPrandomsystematic(pik) ## ht[2]=HTestimator(y[s==1],pik[s==1]) ## s=UPrandompivotal(pik) ## ht[3]=HTestimator(y[s==1],pik[s==1]) ## s=UPtille(pik) ## ht[4]=HTestimator(y[s==1],pik[s==1]) ## s=UPmidzuno(pik) ## ht[5]=HTestimator(y[s==1],pik[s==1]) ## s=UPsystematic(pik) ## ht[6]=HTestimator(y[s==1],pik[s==1]) ## s=UPpivotal(pik) ## ht[7]=HTestimator(y[s==1],pik[s==1]) ## s=srswor(n,N) ## ht[8]=HTestimator(y[s==1],rep(n/N,n)) ## ss[i,]=ht ## } ## colnames(ss) <- ## c("poisson","rsyst","rpivotal","tille","midzuno","syst","pivotal","srswor") ## boxplot(data.frame(ss), las=3) ## ## ## sampling.newpage() sampling/inst/doc/HT_Hajek_estimators.R0000644000176200001440000001043315033751670017630 0ustar liggesusers### R code from vignette source 'HT_Hajek_estimators.Snw' ################################################### ### code chunk number 1: HT_Hajek_estimators.Snw:22-26 ################################################### library(sampling) ps.options(pointsize=12) options(width=60) ################################################### ### code chunk number 2: up1 ################################################### data(belgianmunicipalities) attach(belgianmunicipalities) # sample size n=20 pik=inclusionprobabilities(Tot04,n) N=length(pik) ################################################### ### code chunk number 3: up2 ################################################### sim=10 ss=ss1=array(0,c(sim,4)) ################################################### ### code chunk number 4: up3 ################################################### cat("Case 1\n") y1=rep(3,N) cat("Case 2\n") y2=TaxableIncome cat("Case 3\n") x=1:N pik3=inclusionprobabilities(x,n) y3=1/pik3 cat("Case 4\n") epsilon=rnorm(N,0,sqrt(1/3)) pik4=pik3 y4=5*(x+epsilon) ################################################### ### code chunk number 5: up4 ################################################### ht=numeric(4) hajek=numeric(4) for(i in 1:sim) { cat("Simulation ",i,"\n") cat("Case 1\n") s=UPtille(pik) ht[1]=HTestimator(y1[s==1],pik[s==1]) hajek[1]=Hajekestimator(y1[s==1],pik[s==1],N,type="total") cat("Case 2\n") s1=UPpoisson(pik) ht[2]=HTestimator(y2[s1==1],pik[s1==1]) hajek[2]=Hajekestimator(y2[s1==1],pik[s1==1],N,type="total") cat("Case 3\n") ht[3]=HTestimator(y3[s==1],pik3[s==1]) hajek[3]=Hajekestimator(y3[s==1],pik3[s==1],N,type="total") cat("Case 4\n") ht[4]=HTestimator(y4[s==1],pik4[s==1]) hajek[4]=Hajekestimator(y4[s==1],pik4[s==1],N,type="total") ss[i,]=ht ss1[i,]=hajek } ################################################### ### code chunk number 6: up5 ################################################### #true values tv=c(sum(y1),sum(y2),sum(y3),sum(y4)) for(i in 1:4) { cat("Case ",i,"\n") cat("The mean of the Horvitz-Thompson estimators:",mean(ss[,i])," and the true value:",tv[i],"\n") MSE1=var(ss[,i])+(mean(ss[,i])-tv[i])^2 cat("MSE Horvitz-Thompson estimator:",MSE1,"\n") cat("The mean of the Hajek estimators:",mean(ss1[,i])," and the true value:",tv[i],"\n") MSE2=var(ss1[,i])+(mean(ss1[,i])-tv[i])^2 cat("MSE Hajek estimator:",MSE2,"\n") cat("Ratio of the two MSE:", MSE1/MSE2,"\n") } ################################################### ### code chunk number 7: HT_Hajek_estimators.Snw:137-145 (eval = FALSE) ################################################### ## data(belgianmunicipalities) ## attach(belgianmunicipalities) ## # sample size ## n=20 ## pik=inclusionprobabilities(Tot04,n) ## N=length(pik) ## sim=10 ## ss=ss1=array(0,c(sim,4)) ## cat("Case 1\n") ## y1=rep(3,N) ## cat("Case 2\n") ## y2=TaxableIncome ## cat("Case 3\n") ## x=1:N ## pik3=inclusionprobabilities(x,n) ## y3=1/pik3 ## cat("Case 4\n") ## epsilon=rnorm(N,0,sqrt(1/3)) ## pik4=pik3 ## y4=5*(x+epsilon) ## ht=numeric(4) ## hajek=numeric(4) ## for(i in 1:sim) ## { ## cat("Simulation ",i,"\n") ## cat("Case 1\n") ## s=UPtille(pik) ## ht[1]=HTestimator(y1[s==1],pik[s==1]) ## hajek[1]=Hajekestimator(y1[s==1],pik[s==1],N,type="total") ## cat("Case 2\n") ## s1=UPpoisson(pik) ## ht[2]=HTestimator(y2[s1==1],pik[s1==1]) ## hajek[2]=Hajekestimator(y2[s1==1],pik[s1==1],N,type="total") ## cat("Case 3\n") ## ht[3]=HTestimator(y3[s==1],pik3[s==1]) ## hajek[3]=Hajekestimator(y3[s==1],pik3[s==1],N,type="total") ## cat("Case 4\n") ## ht[4]=HTestimator(y4[s==1],pik4[s==1]) ## hajek[4]=Hajekestimator(y4[s==1],pik4[s==1],N,type="total") ## ss[i,]=ht ## ss1[i,]=hajek ## } ## #true values ## tv=c(sum(y1),sum(y2),sum(y3),sum(y4)) ## for(i in 1:4) ## { ## cat("Case ",i,"\n") ## cat("The mean of the Horvitz-Thompson estimators:",mean(ss[,i])," and the true value:",tv[i],"\n") ## MSE1=var(ss[,i])+(mean(ss[,i])-tv[i])^2 ## cat("MSE Horvitz-Thompson estimator:",MSE1,"\n") ## cat("The mean of the Hajek estimators:",mean(ss1[,i])," and the true value:",tv[i],"\n") ## MSE2=var(ss1[,i])+(mean(ss1[,i])-tv[i])^2 ## cat("MSE Hajek estimator:",MSE2,"\n") ## cat("Ratio of the two MSE:", MSE1/MSE2,"\n") ## } ## ## ## ## sampling.newpage() sampling/build/0000755000176200001440000000000015033751703013167 5ustar liggesuserssampling/build/vignette.rds0000644000176200001440000000051015033751703015522 0ustar liggesusers‹Ť’QOÂ0Ç "jBŚŻ}Ő„} BH|!‰oä`·ël‹O~qÁn¶°}ص÷żÝďţ·ě©Ĺ«˛ ^aŐš˝Ö:64ěsť%,`M{ŢĆÓ¬đeŠÚŚT:|¤Ť+_N†řIcQ˝šC,f Ś”Ëef8ęMŹîřY&©–Älń|ŢQs]w“!ďň5áëbž*9™…Ůrť´äj±$íî .r.D«µ6 ’á ;Š$)Ô©$Ť˙Y:Ť'–>ŞĄĄ3ąĚĽ9ĹąâEčµv‘7˛B'§ýPىóŹzPµˇâŕu‚µ+6śôEŚţ…±0‡¤6ěőݵâíťő0EŠüÇ>ŔíF*›—5•Ü„~X;űá>mŘď÷_żÍcĐŢ‘[ĘöŰl÷ 5ýńòsampling/man/0000755000176200001440000000000015011321161012626 5ustar liggesuserssampling/man/cluster.Rd0000644000176200001440000000511314520143731014607 0ustar liggesusers\name{cluster} \alias{cluster} \title{Cluster sampling} \description{Cluster sampling with equal/unequal probabilities.} \usage{cluster(data, clustername, size, method=c("srswor","srswr","poisson", "systematic"),pik,description=FALSE)} \arguments{ \item{data}{data frame or data matrix; its number of rows is N, the population size.} \item{clustername}{the name of the clustering variable.} \item{size}{sample size.} \item{method}{method to select clusters; the following methods are implemented: simple random sampling without replacement (srswor), simple random sampling with replacement (srswr), Poisson sampling (poisson), systematic sampling (systematic); if the method is not specified, by default the method is "srswor".} \item{pik}{vector of inclusion probabilities or auxiliary information used to compute them; this argument is only used for unequal probability sampling (Poisson, systematic). If an auxiliary information is provided, the function uses the \link{inclusionprobabilities} function for computing these probabilities.} \item{description}{a message is printed if its value is TRUE; the message gives the number of selected clusters, the number of units in the population and the number of selected units. By default, the value is FALSE.} } \value{ The function returns a data set with the following information: the selected clusters, the identifier of the units in the selected clusters, the final inclusion probabilities for these units (they are equal for the units included in the same cluster). If method is "srswr", the number of replicates is also given. } \seealso{ \code{\link{mstage}}, \code{\link{strata}}, \code{\link{getdata}}} \examples{ ############ ## Example 1 ############ # Uses the swissmunicipalities data to draw a sample of clusters data(swissmunicipalities) # the variable 'REG' has 7 categories in the population # it is used as clustering variable # the sample size is 3; the method is simple random sampling without replacement cl=cluster(swissmunicipalities,clustername=c("REG"),size=3,method="srswor") # extracts the observed data # the order of the columns is different from the order in the initial database getdata(swissmunicipalities, cl) ############ ## Example 2 ############ # the same data as in Example 1 # the sample size is 3; the method is systematic sampling # the pik vector is randomly generated using the U(0,1) distribution cl_sys=cluster(swissmunicipalities,clustername=c("REG"),size=3,method="systematic", pik=runif(7)) # extracts the observed data getdata(swissmunicipalities,cl_sys) } \keyword{survey} sampling/man/UPsystematicpi2.Rd0000644000176200001440000000170614520143732016200 0ustar liggesusers\name{UPsystematicpi2} \alias{UPsystematicpi2} \title{Joint inclusion probabilities for systematic sampling} \description{ Computes the joint (second-order) inclusion probabilities for systematic sampling. } \usage{ UPsystematicpi2(pik) } \arguments{ \item{pik}{vector of the first-order inclusion probabilities.} } \value{ Returns a NxN matrix of the following form: the main diagonal contains the first-order inclusion probabilities for each unit k in the population; elements (k,l) are the joint inclusion probabilities of units k and l, with k not equal to l. N is the population size. } \seealso{\code{\link{UPsystematic}} } \references{ Madow, W.G. (1949), On the theory of systematic sampling, II, \emph{Annals of Mathematical Statistics}, 20, 333-354. } \examples{ #define the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #matrix of joint inclusion probabilities UPsystematicpi2(pik) } \keyword{survey} sampling/man/rhg.Rd0000644000176200001440000000325714520143731013715 0ustar liggesusers\name{rhg} \alias{rhg} \title{Response homogeneity groups} \description{Computes the response homogeneity groups and the response probability for each unit in these groups. } \usage{rhg(X,selection)} \arguments{ \item{X}{sample data frame; it should contain the columns 'ID_unit' and 'status'; 'ID_unit' denotes the unit identifier (a number); 'status' is a 1/0 variable denoting the response/non-response of a unit.} \item{selection}{vector of variable names in X used to construct the groups.} } \details{ Into a response homogeneity group, the reponse probability is the same for all units. Data are missing at random within groups, conditionally on the selected sample. } \value{ The initial sample data frame and also the following components: \item{rhgroup}{the response homogeneity group for each unit.} \item{prob_response}{the response probability for each unit; for the units with status=0, this probability is 0.} } \references{ Särndal, C.-E., Swensson, B. and Wretman, J. (1992). Model Assisted Survey Sampling. \emph{Springer} } \seealso{ \code{\link{rhg_strata}}, \code{\link{calib}} } \examples{ # defines the inclusion probabilities for the population pik=c(0.2,0.7,0.8,0.5,0.4,0.4) # X is the population data frame X=cbind.data.frame(pik,c("A","B","A","A","C","B")) names(X)=c("Prob","town") # selects a sample using systematic sampling s=UPsystematic(pik) # Xs is the sample data frame Xs=getdata(X,s) # adds the status column to Xs (1 - sample respondent, 0 otherwise) Xs=cbind.data.frame(Xs,status=c(1,0,1)) # creates the response homogeneity groups using the 'town' variable rhg(Xs,selection="town") } \keyword{survey} \encoding{latin1} sampling/man/regest.Rd0000644000176200001440000000505714520143731014426 0ustar liggesusers\name{regest} \alias{regest} \title{Regression estimator} \description{Computes the regression estimator of the population total, using the design-based approach. The underling regression model is a model without intercept.} \usage{regest(formula,Tx,weights,pikl,n,sigma=rep(1,length(weights)))} \arguments{ \item{formula}{regression model formula (y~x).} \item{Tx}{population total of x, the auxiliary variable.} \item{weights}{vector of the weights; its length is equal to n, the sample size.} \item{pikl}{matrix of joint inclusion probabilities for the sample.} \item{n}{the sample size.} \item{sigma}{vector of positive values accounting for heteroscedasticity.} } \value{The function returns a list with following components: \item{regest}{value of the regression estimator.} \item{coefficients}{vector of estimated beta coefficients.} \item{std_error}{estimated standard error of the estimated coefficients.} \item{t_value}{t-values associated to the coefficients.} \item{p_value}{p-values associated to the coefficients.} \item{cov_mat}{covariance matrix of the estimated coefficients.} \item{weights}{specified weights.} \item{y}{response variable.} \item{x}{model matrix.} } \seealso{ \code{\link{ratioest}},\code{\link{regest_strata}} } \examples{ # uses the MU284 population to draw a systematic sample data(MU284) # there are 3 outliers which are deleted from the population MU281=MU284[MU284$RMT85<=3000,] attach(MU281) # computes the inclusion probabilities using the variable P85; sample size 40 pik=inclusionprobabilities(P85,40) # joint inclusion probabilities for systematic sampling pikl=UPsystematicpi2(pik) # draws a systematic sample of size 40 s=UPsystematic(pik) # defines the variable of interest for the selected sample y=RMT85[s==1] # defines the auxiliary information for the selected sample x1=CS82[s==1] x2=SS82[s==1] # joint inclusion probabilities for the selected sample pikls=pikl[s==1,s==1] # first-order inclusion probabilities for the selected sample piks=pik[s==1] # computes the regression estimator with the model y~x1+x2-1 r=regest(formula=y~x1+x2-1,Tx=c(sum(CS82),sum(SS82)),weights=1/piks,pikl=pikls,n=40) # the regression estimator r$regest # the estimated beta coefficients r$coefficients # the regression estimator is the same as the calibration estimator (method="linear") Xs=cbind(x1,x2) total=c(sum(CS82),sum(SS82)) g1=calib(Xs,d=1/piks,total,method="linear") checkcalibration(Xs,d=1/piks,total,g1) calibev(y,Xs,total,pikls,d=1/piks,g1,with=TRUE,EPS=1e-6) detach(MU281) } \keyword{survey} sampling/man/varest.Rd0000644000176200001440000000347314520143732014442 0ustar liggesusers\name{varest} \alias{varest} \title{Variance estimation using the Deville's method} \description{Computes the variance estimation of an estimator of the population total using the Deville's method.} \usage{varest(Ys,Xs=NULL,pik,w=NULL)} \arguments{ \item{Ys}{vector of the variable of interest; its length is equal to n, the sample size.} \item{Xs}{matrix of the auxiliary variables; for the calibration estimator, this is the matrix of the sample calibration variables.} \item{pik}{vector of the first-order inclusion probabilities; its length is equal to n, the sample size.} \item{w}{vector of the calibrated weights (for the calibration estimator); its length is equal to n, the sample size.} } \details{ The function implements the following estimator: \deqn{\widehat{Var}(\widehat{Ys})=\frac{1}{1-\sum_{k\in s} a_k^2}\sum_{k\in s}(1-\pi_k)\left(\frac{y_k}{\pi_k}-\frac{\sum_{l\in s} (1-\pi_{l})y_l/\pi_l}{\sum_{l\in s} (1-\pi_l)}\right)} where \eqn{a_k=(1-\pi_k)/\sum_{l\in s} (1-\pi_l)}. } \references{ Deville, J.-C. (1993). \emph{Estimation de la variance pour les enquętes en deux phases}. Manuscript, INSEE, Paris. } \seealso{ \code{\link{calibev}} } \examples{ # Belgian municipalities data base data(belgianmunicipalities) attach(belgianmunicipalities) # Computes the inclusion probabilities pik=inclusionprobabilities(Tot04,200) N=length(pik) n=sum(pik) # Defines the variable of interest y=TaxableIncome # Draws a Tille sample of size 200 s=UPtille(pik) # Computes the Horvitz-Thompson estimator HTestimator(y[s==1],pik[s==1]) # Computes the variance estimation of the Horvitz-Thompson estimator varest(Ys=y[s==1],pik=pik[s==1]) # for an example using calibration estimator, see the 'calibration' vignette # vignette("calibration", package="sampling") } \keyword{survey} \encoding{latin1} sampling/man/strata.Rd0000644000176200001440000001055214520143732014430 0ustar liggesusers\name{strata} \alias{strata} \title{Stratified sampling} \description{Stratified sampling with equal/unequal probabilities.} \usage{strata(data, stratanames=NULL, size, method=c("srswor","srswr","poisson", "systematic"), pik,description=FALSE)} \arguments{ \item{data}{data frame or data matrix; its number of rows is N, the population size.} \item{stratanames}{vector of stratification variables.} \item{size}{vector of stratum sample sizes (in the order in which the strata are given in the input data set).} \item{method}{method to select units; the following methods are implemented: simple random sampling without replacement (srswor), simple random sampling with replacement (srswr), Poisson sampling (poisson), systematic sampling (systematic); if "method" is missing, the default method is "srswor".} \item{pik}{vector of inclusion probabilities or auxiliary information used to compute them; this argument is only used for unequal probability sampling (Poisson and systematic). If an auxiliary information is provided, the function uses the \link{inclusionprobabilities} function for computing these probabilities. } \item{description}{a message is printed if its value is TRUE; the message gives the number of selected units and the number of the units in the population. By default, the value is FALSE.} } \value{ The function produces an object, which contains the following information: \item{ID_unit}{the identifier of the selected units.} \item{Stratum}{the unit stratum.} \item{Prob}{the unit inclusion probability.} } \details{The data should be sorted in ascending order by the columns given in the stratanames argument before applying the function. Use, for example, data[order(data$state,data$region),]. } \seealso{ \code{\link{getdata}}, \code{\link{mstage}}} \examples{ ############ ## Example 1 ############ # Example from An and Watts (New SAS procedures for Analysis of Sample Survey Data) # generates artificial data (a 235X3 matrix with 3 columns: state, region, income). # the variable "state" has 2 categories ('nc' and 'sc'). # the variable "region" has 3 categories (1, 2 and 3). # the sampling frame is stratified by region within state. # the income variable is randomly generated data=rbind(matrix(rep("nc",165),165,1,byrow=TRUE),matrix(rep("sc",70),70,1,byrow=TRUE)) data=cbind.data.frame(data,c(rep(1,100), rep(2,50), rep(3,15), rep(1,30),rep(2,40)), 1000*runif(235)) names(data)=c("state","region","income") # computes the population stratum sizes table(data$region,data$state) # not run # nc sc # 1 100 30 # 2 50 40 # 3 15 0 # there are 5 cells with non-zero values # one draws 5 samples (1 sample in each stratum) # the sample stratum sizes are 10,5,10,4,6, respectively # the method is 'srswor' (equal probability, without replacement) s=strata(data,c("region","state"),size=c(10,5,10,4,6), method="srswor") # extracts the observed data getdata(data,s) # see the result using a contigency table table(s$region,s$state) ############ ## Example 2 ############ # The same data as in Example 1 # the method is 'systematic' (unequal probability, without replacement) # the selection probabilities are computed using the variable 'income' s=strata(data,c("region","state"),size=c(10,5,10,4,6), method="systematic",pik=data$income) # extracts the observed data getdata(data,s) # see the result using a contigency table table(s$region,s$state) ############ ## Example 3 ############ # Uses the 'swissmunicipalities' data as population for drawing a sample of units data(swissmunicipalities) # the variable 'REG' has 7 categories in the population # it is used as stratification variable # Computes the population stratum sizes table(swissmunicipalities$REG) # do not run # 1 2 3 4 5 6 7 # 589 913 321 171 471 186 245 # sort the data to obtain the same order of the regions in the sample data=swissmunicipalities data=data[order(data$REG),] # the sample stratum sizes are given by size=c(30,20,45,15,20,11,44) # 30 units are drawn in the first stratum, 20 in the second one, etc. # the method is simple random sampling without replacement # (equal probability, without replacement) st=strata(data,stratanames=c("REG"),size=c(30,20,45,15,20,11,44), method="srswor") # extracts the observed data getdata(data, st) # see the result using a contingency table table(st$REG) } \keyword{survey} sampling/man/poststrata.Rd0000644000176200001440000000264614520143731015342 0ustar liggesusers\name{poststrata} \alias{poststrata} \title{Postratification} \description{Poststratification using several criteria.} \usage{poststrata(data, postnames = NULL)} \arguments{ \item{data}{data frame or data matrix; its number of rows is n, the sample size.} \item{postnames}{vector of poststratification variables.} } \value{ \item{The function}{produces an object, which contains the following information:} \item{data}{the final data frame with a new column ('poststratum') containg the unit poststratum.} \item{npost}{the number of poststrata.} } \seealso{ \code{\link{postest}}} \examples{ # Example from An and Watts (New SAS procedures for Analysis of Sample Survey Data) # generates artificial data (a 235X3 matrix with 3 columns: state, region, income). # the variable "state" has 2 categories ('nc' and 'sc'). # the variable "region" has 3 categories (1, 2 and 3). # the income variable is randomly generated data=rbind(matrix(rep("nc",165),165,1,byrow=TRUE),matrix(rep("sc",70),70,1,byrow=TRUE)) data=cbind.data.frame(data,c(rep(1,100), rep(2,50), rep(3,15), rep(1,30),rep(2,40)), 1000*runif(235)) names(data)=c("state","region","income") # computes the population stratum sizes table(data$region,data$state) # not run # nc sc # 1 100 30 # 2 50 40 # 3 15 0 # postratification using two criteria: state and region poststrata(data,postnames=c("state","region")) } \keyword{survey} sampling/man/HTestimator.Rd0000644000176200001440000000146714520143731015401 0ustar liggesusers\name{HTestimator} \alias{HTestimator} \title{The Horvitz-Thompson estimator} \description{Computes the Horvitz-Thompson estimator of the population total.} \usage{HTestimator(y,pik)} \arguments{ \item{y}{vector of the variable of interest; its length is equal to n, the sample size.} \item{pik}{vector of the first-order inclusion probabilities; its length is equal to n, the sample size.} } \seealso{ \code{\link{UPtille}} } \examples{ data(belgianmunicipalities) attach(belgianmunicipalities) # inclusion probabilities pik=inclusionprobabilities(Tot04,200) N=length(pik) n=sum(pik) # draws a Poisson sample of expected size 200 s=UPpoisson(pik) # Horvitz-Thompson estimator of the total of TaxableIncome HTestimator(TaxableIncome[s==1],pik[s==1]) detach(belgianmunicipalities) } \keyword{survey}sampling/man/rec99.Rd0000644000176200001440000000302215033723615014063 0ustar liggesusers\name{rec99} \alias{rec99} \docType{data} \title{ The 1999 census data} \description{ This data provides census information about the municipalities of the Haute-Garonne department, France, with less than 10000 inhabitants in 1999. } \usage{data(rec99)} \format{ A data frame with 554 observations on the following 10 variables: \describe{ \item{CODE_N}{municipality code.} \item{COMMUNE}{municipality name.} \item{BVQ_N}{code of the Daily Life Basin to which the municipality belongs.} \item{POPSDC99}{number of inhabitants.} \item{LOG}{number of dwellings.} \item{LOGVAC}{number of vacant dwellings.} \item{STRATLOG}{a four-modality variable which equals 1 if the municipality has less than 100 dwellings, 2 if it has between 100 and 299 dwellings, 3 if it has between 300 and 999 dwellings and 4 if it has 1000 dwellings or more.} \item{surf_m2}{surface in square meters.} \item{lat_centre}{geographical latitude of the center.} \item{lon_centre}{geographical longitude of the center.} \item{NAME_3}{regional administration (prefecture) name; a three-modality variable: Muret, Saint-Gaudens, Toulouse.} } } \source{ For the first 8 variables, 'Institut national de la statistique et des Etudes Economiques', France (http://www.insee.fr). The geographical positions are available under the Open Database License ("OpenStreetMap contributors"). https://www.openstreetmap.org/copyright } \examples{ data(rec99) hist(rec99$LOG) } \keyword{datasets} \encoding{latin1} sampling/man/UPmidzunopi2.Rd0000644000176200001440000000173214520143732015477 0ustar liggesusers\name{UPmidzunopi2} \alias{UPmidzunopi2} \title{Joint inclusion probabilities for Midzuno sampling} \description{ Computes the joint (second-order) inclusion probabilities for Midzuno sampling. } \usage{ UPmidzunopi2(pik) } \arguments{ \item{pik}{vector of the first-order inclusion probabilities.} } \value{ Returns a NxN matrix of the following form: the main diagonal contains the first-order inclusion probabilities for each unit k in the population; elements (k,l) are the joint inclusion probabilities of units k and l, with k not equal to l. N is the population size. } \seealso{\code{\link{UPmidzuno}} } \references{ Midzuno, H. (1952), On the sampling system with probability proportional to sum of size. \emph{ Annals of the Institute of Statistical Mathematics}, 3:99-107. } \examples{ #define the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #matrix of joint inclusion probabilities UPmidzunopi2(pik) } \keyword{survey} sampling/man/ratioest_strata.Rd0000644000176200001440000000656514520143731016352 0ustar liggesusers\name{ratioest_strata} \alias{ratioest_strata} \title{Ratio estimator for a stratified design} \description{Computes the ratio estimator of the population total for a stratified design. The ratio estimator of a total is the sum of ratio estimator in each stratum.} \usage{ratioest_strata(y,x,TX_strata,pik,strata,description=FALSE)} \arguments{ \item{y}{vector of the variable of interest; its length is equal to n, the sample size.} \item{x}{vector of auxiliary information; its length is equal to n, the sample size.} \item{TX_strata}{vector of population x-total in each stratum; its length is equal to the number of strata.} \item{pik}{vector of the first-order inclusion probabilities; its length is equal to n, the sample size.} \item{strata}{vector of size n, with elements indicating the unit stratum.} \item{description}{if TRUE, the ratio estimator in each stratum is printed; by default, it is FALSE.} } \value{The function returns the value of the ratio estimator.} \seealso{ \code{\link{ratioest}} } \examples{ ########### # Example 1 ########### # uses MU284 data as population with the 'REG' variable for stratification data(MU284) # there are 3 outliers which are deleted from the population MU281=MU284[MU284$RMT85<=3000,] attach(MU281) # computes the inclusion probabilities using the variable P85 # sample size 120 pik=inclusionprobabilities(P85,120) # defines the variable of interest y=RMT85 # defines the auxiliary information x=CS82 # computes the population stratum sizes table(REG) # not run # 1 2 3 4 5 6 7 8 # 24 48 32 37 55 41 15 29 # a sample is drawn in each region # the sample stratum sizes are given by size=c(4,10,8,4,6,4,6,7) s=strata(MU281,c("REG"),size=c(4,10,8,4,6,4,6,7), method="systematic",pik=P85) # extracts the observed data MU281sample=getdata(MU281,s) # computes the population x-totals in each stratum TX_strata=as.vector(tapply(CS82,list(REG),FUN=sum)) # computes the ratio estimator ratioest_strata(MU281sample$RMT85,MU281sample$CS82,TX_strata, MU281sample$Prob,MU281sample$Stratum) detach(MU281) ########### # Example 2 ########### # this is an artificial example (see Example 1 in the 'strata' function) # there are 4 columns: state, region, income and aux # 'income' is the variable of interest, and 'aux' is the auxiliary information # which is correlated to the income data=rbind(matrix(rep("nc",165),165,1,byrow=TRUE),matrix(rep("sc",70),70,1,byrow=TRUE)) data=cbind.data.frame(data,c(rep(1,100), rep(2,50), rep(3,15), rep(1,30),rep(2,40)), 1000*runif(235)) names(data)=c("state","region","income") attach(data) aux=income+rnorm(length(income),0,1) data=cbind.data.frame(data,aux) # computes the population stratum sizes table(data$region,data$state) # not run # nc sc # 1 100 30 # 2 50 40 # 3 15 0 # there are 5 cells with non-zero values; one draws 5 samples (1 sample in each stratum) # the sample stratum sizes are 10,5,10,4,6, respectively # the method is 'srswor' (equal probability, without replacement) s=strata(data,c("region","state"),size=c(10,5,10,4,6), method="srswor") # extracts the observed data xx=getdata(data,s) # computes the population x-total for each stratum TX_strata=na.omit(as.vector(tapply(aux,list(region,state),FUN=sum))) # computes the ratio estimator ratioest_strata(xx$income,xx$aux,TX_strata,xx$Prob,xx$Stratum,description=TRUE) } \keyword{survey} sampling/man/UPsystematic.Rd0000644000176200001440000000247114520143732015565 0ustar liggesusers\name{UPsystematic} \alias{UPsystematic} \title{Systematic sampling} \description{ Uses the systematic method to select a sample of units (unequal probabilities, without replacement, fixed sample size). } \usage{ UPsystematic(pik,eps=1e-6) } \arguments{ \item{pik}{vector of the inclusion probabilities.} \item{eps}{control value, by default equal to 1e-6.} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). } \seealso{\code{\link{inclusionprobabilities}}, \code{\link{UPrandomsystematic}} } \references{ Madow, W.G. (1949), On the theory of systematic sampling, II, \emph{Annals of Mathematical Statistics}, 20, 333-354. } \examples{ ############ ## Example 1 ############ #defines the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #selects a sample s=UPsystematic(pik) #the sample is which(s==1) ############ ## Example 2 ############ data(belgianmunicipalities) Tot=belgianmunicipalities$Tot04 name=belgianmunicipalities$Commune pik=inclusionprobabilities(Tot,200) #selects a sample s=UPsystematic(pik) #the sample is which(s==1) # extracts the observed data getdata(belgianmunicipalities,s) } \keyword{survey} sampling/man/inclusionprobastrata.Rd0000644000176200001440000000134314520143731017375 0ustar liggesusers\name{inclusionprobastrata} \alias{inclusionprobastrata} \title{Inclusion probabilities for a stratified design} \description{Computes the inclusion probabilities for a stratified design. The inclusion probabilities are equal in each stratum.} \usage{inclusionprobastrata(strata,nh)} \arguments{ \item{strata}{vector that defines the strata.} \item{nh}{vector of the number of selected units in each stratum.} } \seealso{ \code{\link{balancedstratification}} } \examples{ # the strata strata=c(1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,3) # sample size in each stratum nh=c(2,3,3) # inclusion probabilities for each stratum pik=inclusionprobastrata(strata,nh) #check for each stratum cbind(strata, pik) } \keyword{survey} sampling/man/UPmaxentropy.Rd0000644000176200001440000000742614520143732015613 0ustar liggesusers\name{UPmaxentropy} \alias{UPmaxentropy} \alias{UPmaxentropypi2} \alias{UPMEqfromw} \alias{UPMEpikfromq} \alias{UPMEpiktildefrompik} \alias{UPMEsfromq} \alias{UPMEpik2frompikw} \title{Maximum entropy sampling} \description{ Maximum entropy sampling with fixed sample size and unequal probabilities (or Conditional Poisson sampling) is implemented by means of a sequential method (unequal probabilities, without replacement, fixed sample size). } \usage{ UPmaxentropy(pik) UPmaxentropypi2(pik) UPMEqfromw(w,n) UPMEpikfromq(q) UPMEpiktildefrompik(pik,eps=1e-6) UPMEsfromq(q) UPMEpik2frompikw(pik,w) } \arguments{ \item{n}{sample size.} \item{pik}{vector of prescribed inclusion probabilities.} \item{eps}{tolerance in the Newton's method; by default is 1E-6.} \item{q}{matrix of the conditional selection probabilities for the sequential algorithm.} \item{w}{parameter vector of the maximum entropy design.} } \details{ The maximum entropy sampling maximizes the entropy criterion: \deqn{I(p) = - \sum_s p(s)\log[p(s)]}{% I(p) = -\sum_s p(s)log[p(s)].} The main procedure is \code{UPmaxentropy} which selects a sample (a vector of 0 and 1) from a given vector of inclusion probabilities. The procedure \code{UPmaxentropypi2} returns the matrix of joint inclusion probabilities from the first-order inclusion probability vector. The other procedures are intermediate steps. They can be useful to run simulations as shown in the examples below. The procedure \code{UPMEpiktildefrompik} computes the vector of the inclusion probabilities (denoted \code{pikt}) of a Poisson sampling from the vector of the inclusion probabilities of the maximum entropy sampling. The maximum entropy sampling is the conditional design given the fixed sample size. The vector \code{w} can be easily obtained by \code{w=pikt/(1-pikt)}. Once \code{piktilde} and \code{w} are deduced from \code{pik}, a matrix of selection probabilities \code{q} can be derived from the sample size \code{n} and the vector \code{w} via \code{UPMEqfromw}. Next, a sample can be selected from \code{q} using \code{UPMEsfromq}. In order to generate several samples, it is more efficient to compute the matrix \code{q} (which needs some calculation), and then to use the procedure \code{UPMEsfromq}. The vector of the inclusion probabilities can be recomputed from \code{q} using \code{UPMEpikfromq}, which also checks the numerical precision of the algorithm. The procedure \code{UPMEpik2frompikw} computes the matrix of the joint inclusion probabilities from \code{q} and \code{w}. } \references{ Chen, S.X., Liu, J.S. (1997). Statistical applications of the Poisson-binomial and conditional Bernoulli distributions, \emph{Statistica Sinica}, 7, 875-892;\cr Deville, J.-C. (2000). \emph{Note sur l'algorithme de Chen, Dempster et Liu.} Technical report, CREST-ENSAI, Rennes.\cr Matei, A., Tillé, Y. (2005) Evaluation of variance approximations and estimators in maximum entropy sampling with unequal probability and fixed sample size, \emph{Journal of Official Statistics}, Vol. 21, No. 4, p. 543-570.\cr Tillé, Y. (2006), \emph{Sampling Algorithms}, Springer. } \examples{ ############ ## Example 1 ############ # Simple example - sample selection pik=c(0.07,0.17,0.41,0.61,0.83,0.91) # First method UPmaxentropy(pik) # Second method by using intermediate procedures n=sum(pik) pikt=UPMEpiktildefrompik(pik) w=pikt/(1-pikt) q=UPMEqfromw(w,n) UPMEsfromq(q) # Matrix of joint inclusion probabilities # First method: direct computation from pik UPmaxentropypi2(pik) # Second method: computation from pik and w UPMEpik2frompikw(pik,w) ############ ## Example 2 ############ # other examples in the 'UPexamples' vignette # vignette("UPexamples", package="sampling") } \keyword{survey} \encoding{latin1} sampling/man/Hajekstrata.Rd0000644000176200001440000000373114520143731015373 0ustar liggesusers\name{Hajekstrata} \alias{Hajekstrata} \title{The Hajek estimator for a stratified design} \description{Computes the Hájek estimator of the population total or population mean for a stratified design.} \usage{Hajekstrata(y,pik,strata,N=NULL,type=c("total","mean"),description=FALSE)} \arguments{ \item{y}{vector of the variable of interest; its length is equal to n, the sample size.} \item{pik}{vector of the first-order inclusion probabilities for the sampled units; its length is equal to n, the sample size.} \item{strata}{vector of size n, with elements indicating the unit stratum.} \item{N}{vector of population sizes of strata; N is only used for the total estimator; for the mean estimator its value is NULL.} \item{type}{the estimator type: total or mean.} \item{description}{if TRUE, the estimator is printed for each stratum; by default, FALSE.} } \seealso{ \code{\link{HTstrata}} } \examples{ # Swiss municipalities data data(swissmunicipalities) # the variable 'REG' has 7 categories in the population # it is used as stratification variable # computes the population stratum sizes table(swissmunicipalities$REG) # do not run # 1 2 3 4 5 6 7 # 589 913 321 171 471 186 245 # the sample stratum sizes are given by size=c(30,20,45,15,20,11,44) # the method is simple random sampling without replacement # (equal probability, without replacement) st=strata(swissmunicipalities,stratanames=c("REG"),size=c(30,20,45,15,20,11,44), method="srswor") # extracts the observed data # the order of the columns is different from the order in the swsissmunicipalities data x=getdata(swissmunicipalities, st) # computes the population sizes of strata N=table(swissmunicipalities$REG) N=N[unique(x$REG)] #the strata 1 2 3 4 5 6 7 #corresponds to REG 4 1 3 2 5 6 7 # computes the Hajek estimator of the total of Pop020 Hajekstrata(x$Pop020,x$Prob,x$Stratum,N,type="total",description=TRUE)} \keyword{survey} \encoding{latin1} sampling/man/UPmidzuno.Rd0000644000176200001440000000225314520143732015063 0ustar liggesusers\name{UPmidzuno} \alias{UPmidzuno} \title{Midzuno sampling} \description{ Uses the Midzuno's method to select a sample of units (unequal probabilities, without replacement, fixed sample size). } \usage{ UPmidzuno(pik,eps=1e-6) } \arguments{ \item{pik}{vector of the inclusion probabilities.} \item{eps}{control value, by default equal to 1e-6.} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). The value 'eps' is used to control pik (pik>eps & pik < 1-eps). } \seealso{\code{\link{UPtille}} } \references{ Midzuno, H. (1952), On the sampling system with probability proportional to sum of size. \emph{ Annals of the Institute of Statistical Mathematics}, 3:99-107.\cr Deville, J.-C. and Tillé, Y. (1998), Unequal probability sampling without replacement through a splitting method, \emph{Biometrika}, 85:89-101. } \examples{ #define the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #select a sample s=UPmidzuno(pik) #the sample is which(s==1) } \keyword{survey} \encoding{latin1} sampling/man/UPopips.Rd0000644000176200001440000000236015033727256014540 0ustar liggesusers\name{UPopips} \alias{UPopips} \title{Order pips sampling} \description{ Implements order \eqn{\pi ps} sampling (unequal probabilities, without replacement, fixed sample size). } \usage{ UPopips(lambda,type=c("pareto","uniform","exponential"),eps=1e-6) } \arguments{ \item{lambda}{vector of working inclusion probabilities or target ones.} \item{type}{the type of order sampling (pareto, uniform, exponential).} \item{eps}{control value, by default equal to 1e-6.} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). The value eps is used to control lambda (lambda>eps & lambda < 1-eps). } \references{ Rosén, B. (1997), Asymptotic theory for order sampling, \emph{Journal of Statistical Planning and Inference}, 62:135-158.\cr Rosén, B. (1997), On sampling with probability proportional to size, \emph{Journal of Statistical Planning and Inference}, 62:159-191.\cr } \examples{ #define the working inclusion probabilities lambda<-c(0.2,0.7,0.8,0.5,0.4,0.4) #draw a Pareto sample s<-UPopips(lambda, type="pareto") #the sample is which(s==1) } \keyword{survey} \encoding{latin1} sampling/man/UPsampfordpi2.Rd0000644000176200001440000000222614520143732015624 0ustar liggesusers\name{UPsampfordpi2} \alias{UPsampfordpi2} \title{Joint inclusion probabilities for Sampford sampling} \description{ Computes the joint (second-order) inclusion probabilities for Sampford sampling. } \usage{ UPsampfordpi2(pik) } \arguments{ \item{pik}{vector of the first-order inclusion probabilities.} } \value{ Returns a NxN matrix of the following form: the main diagonal contains the first-order inclusion probabilities for each unit k in the population; elements (k,l) are the joint inclusion probabilities of units k and l, with k not equal to l. N is the population size. } \seealso{\code{\link{UPsampford}} } \references{ Sampford, M. (1967), On sampling without replacement with unequal probabilities of selection, \emph{Biometrika}, 54:499-513.\cr Wu, C. (2004). R/S-PLUS Implementation of pseudo empirical likelihood methods under unequal probability sampling. Working paper 2004-07, Department of Statistics and Actuarial Science, University of Waterloo. } \examples{ #define the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #matrix of joint inclusion probabilities UPsampfordpi2(pik) } \keyword{survey} sampling/man/samplecube.Rd0000644000176200001440000001057114520143731015252 0ustar liggesusers\name{samplecube} \alias{samplecube} \title{Sample cube method} \description{ Selects a balanced sample (a vector of 0 and 1) or an almost balanced sample. Firstly, the flight phase is applied. Next, if needed, the landing phase is applied on the result of the flight phase. } \usage{samplecube(X,pik,order=1,comment=TRUE,method=1)} \arguments{ \item{X}{matrix of auxiliary variables on which the sample must be balanced.} \item{pik}{vector of inclusion probabilities.} \item{order}{ 1, the data are randomly arranged,\cr 2, no change in data order,\cr 3, the data are sorted in decreasing order. } \item{comment}{a comment is written during the execution if \code{comment} is \code{TRUE}.} \item{method}{ 1, for a landing phase by linear programming,\cr 2, for a landing phase by suppression of variables.} } \seealso{ \code{\link{landingcube}}, \code{\link{fastflightcube}} } \references{ Tillé, Y. (2006), \emph{Sampling Algorithms}, Springer.\cr Chauvet, G. and Tillé, Y. (2006). A fast algorithm of balanced sampling. \emph{Computational Statistics}, 21/1:53--62. \cr Chauvet, G. and Tillé, Y. (2005). New SAS macros for balanced sampling. In INSEE, editor, \emph{Journées de Méthodologie Statistique}, Paris.\cr Deville, J.-C. and Tillé, Y. (2004). Efficient balanced sampling: the cube method. \emph{Biometrika}, 91:893--912.\cr Deville, J.-C. and Tillé, Y. (2005). Variance approximation under balanced sampling. \emph{Journal of Statistical Planning and Inference}, 128/2:411--425. } \examples{ ############ ## Example 1 ############ # matrix of balancing variables X=cbind(c(1,1,1,1,1,1,1,1,1),c(1.1,2.2,3.1,4.2,5.1,6.3,7.1,8.1,9.1)) # vector of inclusion probabilities # the sample size is 3. pik=c(1/3,1/3,1/3,1/3,1/3,1/3,1/3,1/3,1/3) # selection of the sample s=samplecube(X,pik,order=1,comment=TRUE) # The selected sample (1:length(pik))[s==1] ############ ## Example 2 ############ # 2 strata and 2 auxiliary variables # we verify the values of the inclusion probabilities by simulations X=rbind(c(1,0,1,2),c(1,0,2,5),c(1,0,3,7),c(1,0,4,9), c(1,0,5,1),c(1,0,6,5),c(1,0,7,7),c(1,0,8,6),c(1,0,9,9), c(1,0,10,3),c(0,1,11,3),c(0,1,12,2),c(0,1,13,3), c(0,1,14,6),c(0,1,15,8),c(0,1,16,9),c(0,1,17,1), c(0,1,18,2),c(0,1,19,3),c(0,1,20,4)) pik=rep(1/2,times=20) ppp=rep(0,times=20) sim=10 #for accurate results increase this value for(i in (1:sim)) ppp=ppp+samplecube(X,pik,1,FALSE) ppp=ppp/sim print(ppp) print(pik) ############ ## Example 3 ############ # unequal probability sampling by cube method # one auxiliary variable equal to the inclusion probability N=100 pik=runif(N) pikfin=samplecube(array(pik,c(N,1)),pik,1,TRUE) ############ ## Example 4 ############ # p auxiliary variables generated randomly N=100 p=7 x=rnorm(N*p,10,3) # random inclusion probabilities pik= runif(N) X=array(x,c(N,p)) X=cbind(cbind(X,rep(1,times=N)),pik) pikfin=samplecube(X,pik,1,TRUE) ############ ## Example 5 ############ # strata and an auxiliary variable N=100 a=rep(1,times=N) b=rep(0,times=N) V1=c(a,b,b) V2=c(b,a,b) V3=c(b,b,a) X=cbind(V1,V2,V3) pik=rep(2/10,times=3*N) pikfin=samplecube(X,pik,1,TRUE) ############ ## Example 6 ############ # Selection of a balanced sample using the MU284 population, # Monte Carlo simulation and variance comparison with # unequal probability sampling of fixed sample size. ############ data(MU284) # inclusion probabilities, sample size 50 pik=inclusionprobabilities(MU284$P75,50) # matrix of balancing variables X=cbind(MU284$P75,MU284$CS82,MU284$SS82,MU284$S82,MU284$ME84,MU284$REV84) # Horvitz-Thompson estimator for a balanced sample s=samplecube(X,pik,1,FALSE) HTestimator(MU284$RMT85[s==1],pik[s==1]) # Horvitz-Thompson estimator for an unequal probability sample s=samplecube(matrix(pik),pik,1,FALSE) HTestimator(MU284$RMT85[s==1],pik[s==1]) # Monte Carlo simulation; for a better accuracy, increase the value 'sim' sim=5 res1=rep(0,times=sim) res2=rep(0,times=sim) for(i in 1:sim) { cat("Simulation number ",i,"\n") s=samplecube(X,pik,1,FALSE) res1[i]=HTestimator(MU284$RMT85[s==1],pik[s==1]) s=samplecube(matrix(pik),pik,1,FALSE) res2[i]=HTestimator(MU284$RMT85[s==1],pik[s==1]) } # summary and boxplots summary(res1) summary(res2) ss=cbind(res1,res2) colnames(ss) = c("balanced sampling","uneq prob sampling") boxplot(data.frame(ss), las=1) } \keyword{survey} \encoding{latin1} sampling/man/checkcalibration.Rd0000644000176200001440000000272414520143731016420 0ustar liggesusers\name{checkcalibration} \alias{checkcalibration} \title{Check calibration} \description{Checks the validity of the calibration. In some cases, the computed g-weights do not allow calibration and the calibration estimators do not exist.} \value{ The function returns the following three objects: \item{message}{a message concerning the calibration,} \item{result}{TRUE if the calibration is possible and FALSE, otherwise.} \item{value}{value of max(abs(tr-total)/total, which is used as criterium to validate the calibration, where tr=crossprod(Xs, g*d). If the \code{total} vector contains zeros, the value is max(abs(tr-total)).} } \usage{checkcalibration(Xs, d, total, g, EPS=1e-6)} \arguments{ \item{Xs}{matrix of calibration variables.} \item{d}{vector of initial weights.} \item{total}{vector of population totals.} \item{g}{vector of g-weights.} \item{EPS}{control value used to check the calibration, by default equal to 1e-6.} } \details{In the case where calibration is not possible, the 'value' indicates the difference in obtaining the calibration.} \seealso{ \code{\link{calib}} } \examples{ # matrix of auxiliary variables Xs=cbind(c(1,1,1,1,1,0,0,0,0,0),c(0,0,0,0,0,1,1,1,1,1),c(1,2,3,4,5,6,7,8,9,10)) # inclusion probabilities pik=rep(0.2,times=10) # vector of totals total=c(24,26,280) # g-weights g=calib(Xs,d=1/pik,total,method="raking") # check if the calibration is possible checkcalibration(Xs,d=1/pik,total,g) } \keyword{survey} sampling/man/landingcube.Rd0000644000176200001440000000321414520143731015401 0ustar liggesusers\name{landingcube} \alias{landingcube} \title{Landing phase for the cube method} \description{ Landing phase of the cube method using linear programming. } \usage{landingcube(X,pikstar,pik,comment=TRUE)} \arguments{ \item{X}{matrix of auxiliary variables on which the sample must be balanced.} \item{pikstar}{vector obtained at the end of the flight phase.} \item{pik}{vector of inclusion probabilities.} \item{comment}{a comment is written during the execution if \code{comment} is \code{TRUE}.} } \references{ Tillé, Y. (2006), \emph{Sampling Algorithms}, Springer.\cr Chauvet, G. and Tillé, Y. (2006). A fast algorithm of balanced sampling. \emph{Computational Statistics}, 21/1:53--62. \cr Chauvet, G. and Tillé, Y. (2005). New SAS macros for balanced sampling. In INSEE, editor, \emph{Journées de Méthodologie Statistique}, Paris.\cr Deville, J.-C. and Tillé, Y. (2004). Efficient balanced sampling: the cube method. \emph{Biometrika}, 91:893--912.\cr Deville, J.-C. and Tillé, Y. (2005). Variance approximation under balanced sampling. \emph{Journal of Statistical Planning and Inference}, 128/2:411--425. } \seealso{ \code{\link{samplecube}}, \code{\link{fastflightcube}} } \examples{ # matrix of balancing variables X=cbind(c(1,1,1,1,1,1,1,1,1),c(1.1,2.2,3.1,4.2,5.1,6.3,7.1,8.1,9.1)) # the sample size is 3 # vector of inclusion probabilities pik=c(1/3,1/3,1/3,1/3,1/3,1/3,1/3,1/3,1/3) # pikstar is almost a balanced sample and is ready for the landing phase pikstar=fastflightcube(X,pik,order=1,comment=TRUE) # selection of the sample s=landingcube(X,pikstar,pik,comment=TRUE) round(s) } \keyword{survey} \encoding{latin1} sampling/man/balancedcluster.Rd0000644000176200001440000000337614520143730016271 0ustar liggesusers\name{balancedcluster} \alias{balancedcluster} \title{Balanced cluster} \description{ Selects a balanced cluster sample. } \usage{balancedcluster(X,m,cluster,selection=1,comment=TRUE,method=1)} \arguments{ \item{X}{matrix of auxiliary variables on which the sample must be balanced.} \item{m}{number of clusters to be selected.} \item{cluster}{vector of integers that defines the clusters.} \item{selection}{1, selection of the clusters with probabilities proportional to size,\cr 2, selection of the clusters with equal probabilities.} \item{comment}{a comment is written during the execution if \code{comment} is \code{TRUE}.} \item{method}{the used method in the function \code{samplecube}.} } \value{Returns a matrix containing the vector of inclusion probabilities and the selected sample.} \seealso{ \code{\link{samplecube}}, \code{\link{fastflightcube}}, \code{\link{landingcube}} } \examples{ ############ ## Example 1 ############ # definition of the clusters; there are 15 units in 3 clusters cluster=c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3) # matrix of balancing variables X=cbind(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)) # selection of 2 clusters s=balancedcluster(X,2,cluster,2,TRUE) # the sample of clusters with the inclusion probabilities of the clusters s # the selected clusters unique(cluster[s[,1]==1]) # the selected units (1:length(cluster))[s[,1]==1] # with the probabilities s[s[,1]==1,2] ############ ## Example 2 ############ data(MU284) X=cbind(MU284$P75,MU284$CS82,MU284$SS82,MU284$S82,MU284$ME84) s=balancedcluster(X,10,MU284$CL,1,TRUE) cluster=MU284$CL # the selected clusters unique(cluster[s[,1]==1]) # the selected units (1:length(cluster))[s[,1]==1] # with the probabilities s[s[,1]==1,2] } \keyword{survey} sampling/man/disjunctive.Rd0000644000176200001440000000105214520143731015453 0ustar liggesusers\name{disjunctive} \alias{disjunctive} \title{Disjunctive combination} \description{ Transforms a categorical variable into a matrix of indicators. The values of the categorical variable are integer numbers (positive or negative). } \usage{disjunctive(strata)} \arguments{ \item{strata}{vector of integer numbers.} } \seealso{\code{ \link{balancedstratification}} } \examples{ # definition of the variable of stratification strata=c(-2,3,-2,3,4,4,4,-2,-2,3,4,0,0,0) # computation of the matrix disjunctive(strata) } \keyword{survey} sampling/man/ratioest.Rd0000644000176200001440000000217014520143731014760 0ustar liggesusers\name{ratioest} \alias{ratioest} \title{Ratio estimator} \description{Computes the ratio estimator of the population total.} \usage{ratioest(y,x,Tx,pik)} \arguments{ \item{y}{vector of the variable of interest; its length is equal to n, the sample size.} \item{x}{vector of auxiliary information; its length is equal to n, the sample size.} \item{Tx}{population total of x.} \item{pik}{vector of the first-order inclusion probabilities; its length is equal to n, the sample size.} } \value{The function returns the value of the ratio estimator.} \seealso{ \code{\link{regest}} } \examples{ # population data(MU284) # there are 3 outliers which are deleted from the population MU281=MU284[MU284$RMT85<=3000,] attach(MU281) # computes the inclusion probabilities using the variable P85; sample size 120 pik=inclusionprobabilities(P85,120) # defines the variable of interest y=RMT85 # defines the auxiliary information x=CS82 # draws a systematic sample of size 120 s=UPsystematic(pik) # computes the ratio estimator of the total of RMT85 ratioest(y[s==1],x[s==1],sum(x),pik[s==1]) detach(MU281) } \keyword{survey}sampling/man/fastflightcube.Rd0000644000176200001440000000337514520143731016130 0ustar liggesusers\name{fastflightcube} \alias{fastflightcube} \title{Fast flight phase for the cube method} \description{Executes the fast flight phase of the cube method (algorithm of Chauvet and Tillé, 2005, 2006). The data are sorted following the argument \code{order}. Inclusion probabilities equal to 0 or 1 are tolerated. } \usage{fastflightcube(X,pik,order=1,comment=TRUE)} \arguments{ \item{X}{matrix of auxiliary variables on which the sample must be balanced.} \item{pik}{vector of inclusion probabilities.} \item{order}{ 1, the data are randomly arranged,\cr 2, no change in data order,\cr 3, the data are sorted in decreasing order. } \item{comment}{a comment is written during the execution if \code{comment} is \code{TRUE}.} } \references{ Tillé, Y. (2006), \emph{Sampling Algorithms}, Springer.\cr Chauvet, G. and Tillé, Y. (2006). A fast algorithm of balanced sampling. \emph{Computational Statistics}, 21/1:53--62. \cr Chauvet, G. and Tillé, Y. (2005). New SAS macros for balanced sampling. In INSEE, editor, \emph{Journées de Méthodologie Statistique}, Paris.\cr Deville, J.-C. and Tillé, Y. (2004). Efficient balanced sampling: the cube method. \emph{Biometrika}, 91:893--912.\cr Deville, J.-C. and Tillé, Y. (2005). Variance approximation under balanced sampling. \emph{Journal of Statistical Planning and Inference}, 128/2:411--425. } \seealso{ \code{\link{samplecube}} } \examples{ # Matrix of balancing variables X=cbind(c(1,1,1,1,1,1,1,1,1),c(1,2,3,4,5,6,7,8,9)) # Vector of inclusion probabilities. # The sample size is 3. pik=c(1/3,1/3,1/3,1/3,1/3,1/3,1/3,1/3,1/3) # pikstar is almost a balanced sample and is ready for the landing phase pikstar=fastflightcube(X,pik,order=1,comment=TRUE) pikstar } \keyword{survey} \encoding{latin1} sampling/man/balancedtwostage.Rd0000644000176200001440000000362214520143730016437 0ustar liggesusers\name{balancedtwostage} \alias{balancedtwostage} \title{Balanced two-stage sampling} \description{ Selects a balanced two-stage sample.} \usage{balancedtwostage(X,selection,m,n,PU,comment=TRUE,method=1)} \arguments{ \item{X}{matrix of auxiliary variables on which the sample must be balanced.} \item{selection}{1, for simple random sampling without replacement at each stage,\cr 2, for self-weighting two-stage selection.} \item{m}{number of primary sampling units to be selected.} \item{n}{number of second-stage sampling units to be selected.} \item{PU}{vector of integers that defines the primary sampling units.} \item{comment}{a comment is written during the execution if \code{comment} is \code{TRUE}.} \item{method}{the used method in the function \code{samplecube}.} } \value{The function returns a matrix whose columns are the following five vectors: the selected second-stage sampling units (0 - unselected, 1 - selected), the final inclusion probabilities, the selected primary sampling units, the inclusion probabilities of the first stage, the inclusion probabilities of the second stage.} \seealso{ \code{\link{samplecube}}, \code{\link{fastflightcube}}, \code{\link{landingcube}}, \code{\link{balancedstratification}}, \code{\link{balancedcluster}} } \examples{ ############ ## Example 1 ############ # definition of the primary units (3 primary units) PU=c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3) # matrix of balancing variables X=cbind(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)) # selection of 2 primary sampling units and 4 second-stage sampling units # sample and inclusion probabilities s=balancedtwostage(X,1,2,4,PU,comment=TRUE) s ############ ## Example 2 ############ data(MU284) X=cbind(MU284$P75,MU284$CS82,MU284$SS82,MU284$ME84) N=dim(X)[1] PU=MU284$CL m=20 n=60 # sample and inclusion probabilities s=balancedtwostage(X,1,m,n,PU,TRUE) s } \keyword{survey} sampling/man/writesample.Rd0000644000176200001440000000104715033723206015465 0ustar liggesusers\name{writesample} \alias{writesample} \title{All possible samples of fixed size} \description{Gives a matrix whose rows are the vectors (with 0 and 1; 1 - a unit is selected, 0 - otherwise) of all samples of fixed size.} \usage{writesample(n,N)} \arguments{ \item{n}{sample size.} \item{N}{population size.} } \seealso{ \code{\link{landingcube}} } \examples{ # all samples of size 4 # from a population of size 10 w<-writesample(4,10) # the samples are (read by rows) t(apply(w,1,function(x) (1:ncol(w))[x==1])) } \keyword{survey}sampling/man/swissmunicipalities.Rd0000644000176200001440000000300114520143732017231 0ustar liggesusers\name{swissmunicipalities} \alias{swissmunicipalities} \docType{data} \title{The Swiss municipalities population} \description{This population provides information about the Swiss municipalities in 2003. } \usage{data(swissmunicipalities)} \format{ A data frame with 2896 observations on the following 22 variables: \describe{ \item{CT}{Swiss canton.} \item{REG}{Swiss region.} \item{COM}{municipality number.} \item{Nom}{municipality name.} \item{HApoly}{municipality area.} \item{Surfacesbois}{wood area.} \item{Surfacescult}{area under cultivation.} \item{Alp}{mountain pasture area.} \item{Airbat}{area with buildings.} \item{Airind}{industrial area.} \item{P00BMTOT}{number of men.} \item{P00BWTOT}{number of women.} \item{Pop020}{number of men and women aged between 0 and 19.} \item{Pop2040}{number of men and women aged between 20 and 39.} \item{Pop4065}{number of men and women aged between 40 and 64.} \item{Pop65P}{number of men and women aged between 65 and over.} \item{H00PTOT}{number of households.} \item{H00P01}{number of households with 1 person.} \item{H00P02}{number of households with 2 persons.} \item{H00P03}{number of households with 3 persons.} \item{H00P04}{number of households with 4 persons.} \item{POPTOT}{total population.} } } \source{Swiss Federal Statistical Office. } \examples{ data(swissmunicipalities) hist(swissmunicipalities$POPTOT) } \keyword{datasets} sampling/man/inclusionprobabilities.Rd0000644000176200001440000000212614520143731017703 0ustar liggesusers\name{inclusionprobabilities} \alias{inclusionprobabilities} \title{Inclusion probabilities} \description{Computes the first-order inclusion probabilities from a vector of positive numbers (for a probability proportional-to-size sampling design). Their sum is equal to n, the sample size. } \usage{inclusionprobabilities(a,n)} \arguments{ \item{a}{vector of positive numbers.} \item{n}{sample size.} } \seealso{ \code{\link{inclusionprobastrata}} } \examples{ ############ ## Example 1 ############ # a vector of positive numbers a=1:20 # inclusion probabilities for a sample size n=12 inclusionprobabilities(a,12) ############ ## Example 2 ############ # Computation of the inclusion probabilities proportional to the number # of inhabitants in each municipality of the Belgian municipalities data. data(belgianmunicipalities) pik=inclusionprobabilities(belgianmunicipalities$Tot04,200) # the first-order inclusion probabilities for each municipality data.frame(pik=pik,name=belgianmunicipalities$Commune) # the sum is equal to the sample size sum(pik) } \keyword{survey} sampling/man/gencalib.Rd0000644000176200001440000001350114520143731014672 0ustar liggesusers\name{gencalib} \alias{gencalib} \title{g-weights of the generalized calibration estimator} \description{Computes the g-weights of the generalized calibration estimator. The g-weights should lie in the specified bounds for the truncated and logit methods. } \usage{gencalib(Xs,Zs,d,total,q=rep(1,length(d)),method=c("linear","raking","truncated","logit"), bounds=c(low=0,upp=10),description=FALSE,max_iter=500,C=1)} \arguments{ \item{Xs}{matrix of calibration variables.} \item{Zs}{matrix of instrumental variables with same dimension as Xs.} \item{d}{vector of initial weights.} \item{total}{vector of population totals.} \item{q}{vector of positive values accounting for heteroscedasticity; the variation of the g-weights is reduced for small values of q.} \item{method}{calibration method (linear, raking, logit, truncated).} \item{bounds}{vector of bounds for the g-weights used in the truncated and logit methods; 'low' is the smallest value and 'upp' is the largest value.} \item{description}{if description=TRUE, summary of initial and final weights are printed, and their boxplots and histograms are drawn; by default, its value is FALSE.} \item{max_iter}{maximum number of iterations in the Newton's method.} \item{C}{value of the centering constant, by default equals 1.} } \value{The function returns the vector of g-weights.} \details{ The generalized calibration or the instrument vector method computes the g-weights \eqn{g_k=F(\lambda'z_k),} where \eqn{z_k} is a vector with values defined for \eqn{k\in s} (or \eqn{k\in r} where \eqn{r} is the set of respondents) and sharing the dimension of the specified auxiliary vector \eqn{x_k}. The vectors \eqn{z_k} and \eqn{x_k} have to be stronlgy correlated. The vector \eqn{\lambda} is determined from the calibration equation \eqn{\sum_{k\in s} d_kg_k x_k=\sum_{k\in U} x_k} or \eqn{\sum_{k\in r} d_kg_k x_k=\sum_{k\in U} x_k}. The function \eqn{F} plays the same role as in the calibration method (see \code{\link{calib}}). If Xs=Zs the calibration method is obtain. If the method is "logit" the g-weights will be centered around the constant C, with loweps & pik < 1-eps). } \seealso{\code{\link{UPsystematic}} } \references{ Tillé, Y. (1996), An elimination procedure of unequal probability sampling without replacement, \emph{Biometrika}, 83:238-241.\cr Deville, J.-C. and Tillé, Y. (1998), Unequal probability sampling without replacement through a splitting method, \emph{Biometrika}, 85:89-101. } \examples{ ############ ## Example 1 ############ #defines the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #selects a sample s=UPtille(pik) #the sample is which(s==1) ############ ## Example 2 ############ # see in the 'UPexamples' vignette # vignette("UPexamples", package="sampling") } \keyword{survey} \encoding{latin1} sampling/man/UPtillepi2.Rd0000644000176200001440000000211414520143732015116 0ustar liggesusers\name{UPtillepi2} \alias{UPtillepi2} \title{Joint inclusion probabilties for Tille sampling} \description{ Computes the joint (second-order) inclusion probabilities for Tillé sampling. } \usage{ UPtillepi2(pik,eps=1e-6) } \arguments{ \item{pik}{vector of the first-order inclusion probabilities.} \item{eps}{control value, by default equal to 1e-6.} } \value{ Returns a NxN matrix of the following form: the main diagonal contains the first-order inclusion probabilities for each unit k in the population; elements (k,l) are the joint inclusion probabilities of units k and l, with k not equal to l. N is the population size. The value \code{eps} is used to control \code{pik} (pik>eps & pik < 1-eps). } \seealso{\code{\link{UPtille}} } \references{ Tillé, Y. (1996), An elimination procedure of unequal probability sampling without replacement, \emph{Biometrika}, 83:238-241. } \examples{ #defines the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #matrix of joint inclusion probabilities UPtillepi2(pik) } \keyword{survey} \encoding{latin1} sampling/man/srswr.Rd0000644000176200001440000000116714520143732014314 0ustar liggesusers\name{srswr} \alias{srswr} \title{Simple random sampling with replacement} \description{ Draws a simple random sampling with replacement of size n (equal probabilities, fixed sample size, with replacement). } \usage{ srswr(n,N) } \value{ Returns a vector of size N, the population size. Each element k of this vector indicates the number of replicates of unit k in the sample. } \arguments{ \item{n}{sample size.} \item{N}{population size.} } \seealso{\code{\link{UPmultinomial}} } \examples{ s=srswr(3,10) #the selected units are which(s!=0) #with the number of replicates s[s!=0] } \keyword{survey} sampling/man/MU284.Rd0000644000176200001440000000237214520143731013711 0ustar liggesusers\name{MU284} \alias{MU284} \docType{data} \title{ The MU284 population } \description{ This data is from Särndal et al (1992), see Appendix B, p. 652. } \usage{data(MU284)} \format{ A data frame with 284 observations on the following 11 variables. \describe{ \item{LABEL}{identifier number from 1 to 284.} \item{P85}{1985 population (in thousands).} \item{P75}{1975 population (in thousands).} \item{RMT85}{revenues from 1985 municipal taxation (in millions of kronor).} \item{CS82}{number of Conservative seats in municipal council.} \item{SS82}{number of Social-Democratic seats in municipal council.} \item{S82}{total number of seats in municipal council.} \item{ME84}{number of municipal employees in 1984.} \item{REV84}{real estate values according to 1984 assessment (in millions of kronor).} \item{REG}{geographic region indicator.} \item{CL}{cluster indicator (a cluster consists of a set of neighboring).} } } \references{ Särndal, C.-E., Swensson, B., and Wretman, J. (1992), \emph{Model Assisted Survey Sampling}, Springer Verlag, New York. } \source{ http://lib.stat.cmu.edu/datasets/mu284 } \examples{ data(MU284) hist(MU284$RMT85) } \keyword{datasets} \encoding{latin1} sampling/man/srswor1.Rd0000644000176200001440000000160314520143731014546 0ustar liggesusers\name{srswor1} \alias{srswor1} \title{Selection-rejection method} \description{ Draws a simple random sampling without replacement of size n using the selection-rejection method (equal probabilities, fixed sample size, without replacement). } \usage{ srswor1(n,N) } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). } \arguments{ \item{n}{sample size.} \item{N}{population size.} } \references{Fan, C.T., Muller, M.E., Rezucha, I. (1962), Development of sampling plans by using sequential (item by item) selection techniques and digital computer, \emph{Journal of the American Statistical Association}, 57, 387--402. } \seealso{\code{\link{srswor}}} \examples{ s=srswor1(3,10) #the sample is which(s==1) } \keyword{survey} sampling/man/varHT.Rd0000644000176200001440000000354114520143732014156 0ustar liggesusers\name{varHT} \alias{varHT} \title{Variance estimators of the Horvitz-Thompson estimator} \description{Computes variance estimators of the Horvitz-Thompson estimator of the population total.} \usage{varHT(y,pikl,method)} \arguments{ \item{y}{vector of the variable of interest; its length is equal to n, the sample size.} \item{pikl}{matrix of joint inclusion probabilities; its dimension is nxn.} \item{method}{if 1, an unbiased variance estimator is computed; if 2, the Sen-Yates-Grundy variance estimator for fixed sample size is computed; be default, the method is 1.} } \details{ If method is 1, the following estimator is implemented \deqn{\widehat{Var}(\widehat{Y}_{HT})_1=\sum_{k\in s}\sum_{\ell\in s} \frac{y_k y_\ell}{\pi_{k\ell} \pi_k \pi_\ell}(\pi_{k\ell} - \pi_k \pi_\ell)} If method is 2, the following estimator is implemented \deqn{\widehat{Var}(\widehat{Y}_{HT})_2=\frac{1}{2}\sum_{k\in s}\sum_{\ell\in s} \left(\frac{y_k}{\pi_k} - \frac{y_\ell}{\pi_\ell}\right)^2 \frac{\pi_k \pi_\ell-\pi_{k\ell}}{\pi_{k\ell}}}} \seealso{ \code{\link{HTestimator}} } \examples{ pik=c(0.2,0.7,0.8,0.5,0.4,0.4) N=length(pik) n=sum(pik) # Defines the variable of interest y=rnorm(N,10,2) # Draws a Poisson sample of expected size n s=UPpoisson(pik) # Computes the Horvitz-Thompson estimator HTestimator(y[s==1],pik[s==1]) # Computes the joint inclusion prob. for Poisson sampling pikl=outer(pik,pik,"*") diag(pikl)=pik # Computes the variance estimator (method=1, the sample size is not fixed) varHT(y[s==1],pikl[s==1,s==1],1) # Draws a Tille sample of size n s=UPtille(pik) # Computes the Horvitz-Thompson estimator HTestimator(y[s==1],pik[s==1]) # Computes the joint inclusion prob. for Tille sampling pikl=UPtillepi2(pik) # Computes the variance estimator (method=2, the sample size is fixed) varHT(y[s==1],pikl[s==1,s==1],2) } \keyword{survey}sampling/man/calibev.Rd0000644000176200001440000000736514520143731014546 0ustar liggesusers\name{calibev} \alias{calibev} \title{Calibration estimator and its variance estimation} \description{Computes the calibration estimator of the population total and its variance estimation using the residuals' method. } \usage{calibev(Ys,Xs,total,pikl,d,g,q=rep(1,length(d)),with=FALSE,EPS=1e-6)} \arguments{ \item{Ys}{vector of interest variable; its size is n, the sample size.} \item{Xs}{matrix of sample calibration variables.} \item{total}{vector of population totals for calibration.} \item{pikl}{matrix of joint inclusion probabilities of the sample units.} \item{d}{vector of initial weights of the sample units.} \item{g}{vector of g-weights; its size is n, the sample size.} \item{q}{vector of positive values accounting for heteroscedasticity; its size is n, the sample size.} \item{with}{if TRUE, the variance estimation takes into account the initial weights d; otherwise, the final weights w=g*d are taken into account; by default, its value is FALSE.} \item{EPS}{tolerance in checking the calibration; by default, its value is 1e-6.} } \value{ The function returns two values: \item{cest}{the calibration estimator,} \item{evar}{its estimated variance.} } \details{ If with is TRUE, the following formula is used \deqn{\widehat{Var}(\widehat{Ys})=\sum_{k\in s}\sum_{\ell\in s}((\pi_{k\ell}-\pi_k\pi_{\ell})/\pi_{k\ell})(d_ke_k)(d_\ell e_\ell)}{\hat{Var}(\hat{Ys})=\sum_{k\in s}\sum_{\ell\in s}((\pi_{k\ell}-\pi_k\pi_{\ell})/\pi_{k\ell})(d_ke_k)(d_\ell e_\ell)} else \deqn{\widehat{Var}(\widehat{Ys})=\sum_{k\in s}\sum_{\ell\in s}((\pi_{k\ell}-\pi_k\pi_{\ell})/\pi_{k\ell})(w_ke_k)(w_\ell e_\ell)}{\hat{Var}(\hat{Ys})=\sum_{k\in s}\sum_{\ell\in s}((\pi_{k\ell}-\pi_k\pi_{\ell})/\pi_{k\ell})(w_ke_k)(w_\ell e_\ell)} where \eqn{e_k} denotes the residual of unit k. } \references{ Deville, J.-C. and Särndal, C.-E. (1992). Calibration estimators in survey sampling. \emph{Journal of the American Statistical Association}, 87:376--382.\cr Deville, J.-C., Särndal, C.-E., and Sautory, O. (1993). Generalized raking procedure in survey sampling. \emph{Journal of the American Statistical Association}, 88:1013--1020.\cr } \seealso{ \code{\link{calib}} } \examples{ ############ ## Example ############ # Example of g-weights (linear, raking, truncated, logit), # with the data of Belgian municipalities as population. # Firstly, a sample is selected by means of systematic sampling. # Secondly, the g-weights are calculated. data(belgianmunicipalities) attach(belgianmunicipalities) # matrix of calibration variables for the population X=cbind( Men03/mean(Men03), Women03/mean(Women03), Diffmen, Diffwom, TaxableIncome/mean(TaxableIncome), Totaltaxation/mean(Totaltaxation), averageincome/mean(averageincome), medianincome/mean(medianincome)) # selection of a sample of size 200 # using systematic sampling # the inclusion probabilities are proportional to the average income pik=inclusionprobabilities(averageincome,200) N=length(pik) # population size s=UPsystematic(pik) # draws a sample s using systematic sampling Xs=X[s==1,] # matrix of sample calibration variables piks=pik[s==1] # sample inclusion probabilities n=length(piks) # sample size # vector of population totals of the calibration variables total=c(t(rep(1,times=N))\%*\%X) g1=calib(Xs,d=1/piks,total,method="linear") # computes the g-weights pikl=UPsystematicpi2(pik) # computes the matrix of joint inclusion probabilities pikls=pikl[s==1,s==1] # the same matrix for the units in the sample Ys=Tot04[s==1] # the variable of interest is Tot04 (sample level) calibev(Ys,Xs,total,pikls,d=1/piks,g1,with=FALSE,EPS=1e-6) detach(belgianmunicipalities) } \keyword{survey} \encoding{latin1} sampling/man/UPminimalsupport.Rd0000644000176200001440000000244214520143732016461 0ustar liggesusers\name{UPminimalsupport} \alias{UPminimalsupport} \title{Minimal support sampling} \description{ Uses the minimal support method to select a sample of units (unequal probabilities, without replacement, fixed sample size). } \usage{ UPminimalsupport(pik) } \arguments{ \item{pik}{vector of the inclusion probabilities.} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). } \references{ Deville, J.-C., Tillé, Y. (1998), Unequal probability sampling without replacement through a splitting method, \emph{Biometrika }, 85, 89-101.\cr Tillé, Y. (2006), \emph{Sampling Algorithms}, Springer. } \examples{ ############ ## Example 1 ############ #defines the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #selects a sample s=UPminimalsupport(pik) #the sample is which(s==1) ############ ## Example 2 ############ data(belgianmunicipalities) Tot=belgianmunicipalities$Tot04 name=belgianmunicipalities$Commune pik=inclusionprobabilities(Tot,200) #selects a sample s=UPminimalsupport(pik) #the sample is which(s==1) #names of the selected units as.vector(name[s==1]) } \keyword{survey} \encoding{latin1} sampling/man/vartaylor_ratio.Rd0000644000176200001440000000412514520143732016352 0ustar liggesusers\name{vartaylor_ratio} \alias{vartaylor_ratio} \title{Taylor-series linearization variance estimation of a ratio} \description{Computes the Taylor-series linearization variance estimation of the ratio \deqn{\frac{\widehat{Y}_s}{\widehat{X}_s}.} The estimators in the ratio are Horvitz-Thompson type estimators. } \usage{vartaylor_ratio(Ys,Xs,pikls)} \arguments{ \item{Ys}{vector of the first observed variable; its length is equal to n, the sample size.} \item{Xs}{vector of the second observed variable; its length is equal to n, the sample size.} \item{pikls}{matrix of joint inclusion probabilities of the sample units; its dimension is nxn.} } \details{ The function implements the following estimator: \deqn{\widehat{Var}(\frac{\widehat{Ys}}{\widehat{Xs}})=\sum_{i\in s}\sum_{j\in s}\frac{\pi_{ij}-\pi_i\pi_j}{\pi_{ij}}\frac{\widehat{z_i}\widehat{z_j}}{\pi_i\pi_j}} where \eqn{\widehat{z_i}=(Ys_i-\widehat{r}Xs_i)/\widehat{X}_s, \widehat{r}=\widehat{Y}_s/\widehat{X}_s, \widehat{Y}_s=\sum_{i\in s}{Ys_i/\pi_i}, \widehat{X}_s=\sum_{i\in s}{Xs_i/\pi_i}}. } \references{ Woodruff, R. (1971). \emph{A Simple Method for Approximating the Variance of a Complicated Estimate}, Journal of the American Statistical Association, Vol. 66, No. 334 , pp. 411--414. } \examples{ data(belgianmunicipalities) attach(belgianmunicipalities) # inclusion probabilities, sample size 200 pik=inclusionprobabilities(Tot04,200) # the first variable (population level) Y=Men04 # the second variable (population level) X=Women04 # population size N=length(pik) # joint inclusion probabilities for Poisson sampling pikl=outer(pik,pik,"*") diag(pikl)=pik # draw a sample using Poisson sampling s=UPpoisson(pik) # sample inclusion probabilities piks=pik[s==1] # the first observed variable (sample level) Ys=Y[s==1] # the second observed variable (sample level) Xs=X[s==1] # matrix of joint inclusion prob. (sample level) pikls=pikl[s==1,s==1] # ratio estimator and its estimated variance vartaylor_ratio(Ys,Xs,pikls) } \keyword{survey} sampling/man/mstage.Rd0000644000176200001440000001711014520143731014406 0ustar liggesusers\name{mstage} \alias{mstage} \title{Multistage sampling} \description{Implements multistage sampling with equal/unequal probabilities.} \usage{mstage(data, stage=c("stratified","cluster",""), varnames, size, method=c("srswor","srswr","poisson","systematic"), pik, description=FALSE)} \arguments{ \item{data}{data frame or data matrix; its number of rows is N, the population size.} \item{stage}{list of sampling types at each stage; the possible values are: "stratified", "cluster" and "" (without stratification or clustering). For multistage element sampling, this argument is not necessary.} \item{varnames}{list of stratification or clustering variables.} \item{size}{list of sample sizes (in the order in which the samples appear in the multistage sampling).} \item{method}{list of methods to select units at each stage; the following methods are implemented: simple random sampling without replacement (srswor), simple random sampling with replacement (srswr), Poisson sampling (poisson), systematic sampling (systematic); if the method is not specified, by default the method is "srswor". The method can be different at each stage.} \item{pik}{list of selection probabilities or auxiliary information used to compute them; this argument is only used for unequal probability sampling (Poisson, systematic). If an auxiliary information is provided, the function uses the \link{inclusionprobabilities} function for computing these probabilities.} \item{description}{a message is printed if its value is TRUE; the message gives the number of selected units and the number of the units in the population. By default, its value is FALSE.} } \value{ The function returns a list, which contains the stages (if m is this list, the stage i is m$'i' etc) and the following information: \item{ID_unit}{the identifier of selected units at each stage.} \item{Prob_ number _stage}{the inclusion probability at stage 'number'.} \item{Prob}{the final unit inclusion probability given in the last stage; it is the product of unit inclusion probabilities at each stage.} } \details{The data should be sorted in ascending order by the columns given in the varnames argument before applying the function. Use, for example, data[order(data$state,data$region),]. } \seealso{ \code{\link{cluster}}, \code{\link{strata}}, \code{\link{getdata}}} \examples{ ############ ## Example 1 ############ # Two-stage cluster sampling # Uses the 'swissmunicipalities' data data(swissmunicipalities) b=swissmunicipalities b=b[order(b$REG,b$CT),] attach(b) # the variable 'REG' (region) has 7 categories; # it is used as clustering variable in the first-stage sample # the variable 'CT' (canton) has 26 categories; # it is used as clustering variable in the second-stage sample # 4 clusters (regions) are selected in the first-stage # 1 canton is selected in the second-stage from each sampled region # the method is simple random sampling without replacement in each stage # (equal probability, without replacement) m=mstage(b,stage=list("cluster","cluster"), varnames=list("REG","CT"), size=list(4,c(1,1,1,1)), method=list("srswor","srswor")) # the first stage is m[[1]], the second stage is m[[2]] #the selected regions unique(m[[1]]$REG) #the selected cantons unique(m[[2]]$CT) # extracts the observed data x=getdata(b,m)[[2]] # check the output table(x$REG,x$CT) ############ ## Example 2 ############ # Two-stage element sampling # Generates artificial data (a 235X3 matrix with 3 columns: state, region, income). # The variable "state" has 2 categories ('n','s'). # The variable "region" has 5 categories ('A', 'B', 'C', 'D', 'E'). # The variable "income" is generated using the U(0,1) distribution. data=rbind(matrix(rep('n',165),165,1,byrow=TRUE),matrix(rep('s',70),70,1,byrow=TRUE)) data=cbind.data.frame(data,c(rep('A',115),rep('D',10),rep('E',40),rep('B',30),rep('C',40)), 100*runif(235)) names(data)=c("state","region","income") data=data[order(data$state,data$region),] table(data$state,data$region) # the method is simple random sampling without replacement # 25 units are drawn in the first-stage # in the second-stage, 10 units are drawn from the already 25 selected units m=mstage(data,size=list(25,10),method=list("srswor","srswor")) # the first stage is m[[1]], the second stage is m[[2]] # extracts the observed data xx=getdata(data,m)[[2]] # check the result table(xx$state,xx$region) ############ ## Example 3 ############ # Stratified one-stage cluster sampling # The same data as in Example 2 # the variable 'state' is used as stratification variable # 165 units are in the first stratum and 70 in the second one # the variable 'region' is used as clustering variable # 1 cluster (region) is drawn in each state using "srswor" m=mstage(data, stage=list("stratified","cluster"), varnames=list("state","region"), size=list(c(165,70),c(1,1)),method=list("","srswor")) # check the first stage table(m[[1]]$state) # check the second stage table(m[[2]]$region) # extracts the observed data xx=getdata(data,m)[[2]] # check the result table(xx$state,xx$region) ############ ## Example 4 ############ # Two-stage cluster sampling # The same data as in Example 1 # in the first-stage, the clustering variable is 'REG' (region) with 7 categories # 4 clusters (regions) are drawn in the first-stage # each region is selected with the probability 4/7 # in the second-stage, the clustering variable is 'CT'(canton) with 26 categories # 1 cluster (canton) is drawn in the second-stage from each selected region # in region 1, there are 3 cantons; one canton is selected with prob. 0.2, 0.4, 0.4, resp. # in region 2, there are 5 cantons; each canton is selected with the prob. 1/5 # in region 3, there are 3 cantons; each canton is selected with the prob. 1/3 # in region 4, there is 1 canton, which it is selected with the prob. 1 # in region 5, there are 7 cantons; each canton is selected with the prob. 1/7 # in region 6, there are 6 cantons; each canton is selected with the prob. 1/6 # in region 7, there is 1 canton, which it is selected with the prob. 1 # it is necessary to use a list of selection probabilities at each stage # prob is the list of the selection probabilities # the method is systematic sampling (unequal probabilities, without replacement) # ls is the list of sizes ls=list(4,c(1,1,1,1)) prob=list(rep(4/7,7),list(c(0.2,0.4,0.4),rep(1/5,5),rep(1/3,3),rep(1,1),rep(1/7,7), rep(1/6,6),rep(1,1))) m=mstage(b,stage=list("cluster","cluster"),varnames=list("REG","CT"), size=ls, method=c("systematic","systematic"),pik=prob) #the selected regions unique(m[[1]]$REG) #the selected cantons unique(m[[2]]$CT) # extracts the observed data xx=getdata(b,m)[[2]] # check the result table(xx$REG,xx$CT) ############ ## Example 5 ############ # Stratified two-stage cluster sampling # The same data as in Example 1 # the variable 'REG' is used as stratification variable # there are 7 strata # the variable 'CT' is used as first clustering variable # first stage, clusters (cantons) are drawn from each region using "srswor" # 3 clusters are drawn from the regions 1,2,3,5, and 6, respectively # 1 cluster is drawn from the regions 4 and 7, respectively # the variable 'COM' is used as second clustering variable # second stage, 2 clusters (municipalities) are drawn from each selected canton using "srswor" m=mstage(b,stage=list("stratified","cluster","cluster"), varnames=list("REG","CT","COM"), size=list(size1=table(b$REG),size2=c(rep(3,3),1,3,3,1), size3=rep(2,17)), method=list("","srswor","srswor")) # extracts the observed data getdata(b,m)[[3]] } \keyword{survey} sampling/man/UPbrewer.Rd0000644000176200001440000000166414520143732014671 0ustar liggesusers\name{UPbrewer} \alias{UPbrewer} \title{Brewer sampling} \description{ Uses the Brewer's method to select a sample of units (unequal probabilities, without replacement, fixed sample size). } \usage{ UPbrewer(pik,eps=1e-06) } \arguments{ \item{pik}{vector of the inclusion probabilities.} \item{eps}{the control value, by default equal to 1e-06; it is used to control pik (pik>eps & pik < 1-eps).} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). } \seealso{\code{\link{UPsystematic}} } \references{ Brewer, K. (1975), A simple procedure for $pi$pswor, \emph{Australian Journal of Statistics}, 17:166-172. } \examples{ #define the inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #select a sample s=UPbrewer(pik) #the sample is which(s==1) } \keyword{survey} sampling/man/getdata.Rd0000644000176200001440000000267114520143731014545 0ustar liggesusers\name{getdata} \alias{getdata} \title{Get data} \description{Extracts the observed data from a data frame (a population). The function is used after a sample has been drawn from this population. } \usage{getdata(data, m)} \arguments{ \item{data}{population data frame or data matrix; its number of rows is N, the population size.} \item{m}{vector of selected units or sample data frame.} } \seealso{ \code{\link{srswor}}, \code{\link{UPsystematic}}, \code{\link{strata}}, \code{\link{cluster}}, \code{\link{mstage}}} \examples{ ############ ## Example 1 ############ # Generates artificial data (a 235X3 matrix with 3 columns: state, region, income). # The variable 'state' has 2 categories (nc and sc); # the variable 'region' has 3 categories (1, 2 and 3); # the variable 'income' is generated using the U(0,1) distribution. data=rbind(matrix(rep("nc",165),165,1,byrow=TRUE), matrix(rep("sc",70),70,1,byrow=TRUE)) data=cbind.data.frame(data,c(rep(1,100), rep(2,50), rep(3,15), rep(1,30),rep(2,40)), 1000*runif(235)) names(data)=c("state","region","income") # the inclusion probabilities are computed using the variable 'income' pik=inclusionprobabilities(data$income,20) # draws a sample using systematic sampling (sample size is 20) s=UPsystematic(pik) # extracts the observed data getdata(data,s) ############ ## Example 2 ############ # see other examples in 'strata', 'cluster', 'mstage' help files } \keyword{survey}sampling/man/UPpivotal.Rd0000644000176200001440000000232014520143732015047 0ustar liggesusers\name{UPpivotal} \alias{UPpivotal} \title{Pivotal sampling} \description{ Selects an unequal probability sample using the pivotal method (unequal probabilities, without replacement, fixed sample size). } \usage{ UPpivotal(pik,eps=1e-6) } \arguments{ \item{pik}{vector of the inclusion probabilities.} \item{eps}{control value, by default equal to 1e-6.} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). The value eps is used to control pik (pik>eps & pik < 1-eps). } \seealso{\code{\link{UPrandompivotal}} } \references{ Deville, J.-C. and Tillé, Y. (1998), Unequal probability sampling without replacement through a splitting method, \emph{Biometrika}, 85:89-101.\cr Chauvet, G. and Tillé, Y. (2006). A fast algorithm of balanced sampling. \emph{to appear in Computational Statistics}.\cr Tillé, Y. (2006), \emph{Sampling Algorithms}, Springer. } \examples{ #define the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #select a sample s=UPpivotal(pik) #the sample is which(s==1) } \keyword{survey} \encoding{latin1} sampling/man/Hajekestimator.Rd0000644000176200001440000000231614520143731016102 0ustar liggesusers\name{Hajekestimator} \alias{Hajekestimator} \title{The Hajek estimator} \description{Computes the Hájek estimator of the population total or population mean.} \usage{Hajekestimator(y,pik,N=NULL,type=c("total","mean"))} \arguments{ \item{y}{vector of the variable of interest; its length is equal to n, the sample size.} \item{pik}{vector of the first-order inclusion probabilities; its length is equal to n, the sample size.} \item{N}{population size; N is only used for the total estimator; for the mean estimator its value is NULL.} \item{type}{the estimator type: total or mean.} } \seealso{ \code{\link{HTestimator}} } \examples{ # Belgian municipalities data data(belgianmunicipalities) # Computes the inclusion probabilities pik=inclusionprobabilities(belgianmunicipalities$Tot04,200) N=length(pik) n=sum(pik) # Defines the variable of interest y=belgianmunicipalities$TaxableIncome # Draws a Poisson sample of expected size 200 s=UPpoisson(pik) # Computes the Hajek estimator of the population mean Hajekestimator(y[s==1],pik[s==1],type="mean") # Computes the Hajek estimator of the population total Hajekestimator(y[s==1],pik[s==1],N=N,type="total") } \keyword{survey} \encoding{latin1} sampling/man/balancedstratification.Rd0000644000176200001440000000440314520143730017623 0ustar liggesusers\name{balancedstratification} \alias{balancedstratification} \title{Balanced stratification} \description{ Selects a stratified balanced sample (a vector of 0 and 1). Firstly, the flight phase is applied in each stratum. Secondly, the strata are aggregated and the flight phase is applied on the whole population. Finally, the landing phase is applied on the whole population. } \usage{balancedstratification(X,strata,pik,comment=TRUE,method=1)} \arguments{ \item{X}{matrix of auxiliary variables on which the sample must be balanced.} \item{strata}{vector of integers that specifies the stratification.} \item{pik}{vector of inclusion probabilities.} \item{comment}{a comment is written during the execution if \code{comment} is \code{TRUE}.} \item{method}{the used method in the function \code{samplecube}.} } \references{ Tillé, Y. (2006), \emph{Sampling Algorithms}, Springer.\cr Chauvet, G. and Tillé, Y. (2006). A fast algorithm of balanced sampling. \emph{Computational Statistics}, 21/1:53--62. \cr Chauvet, G. and Tillé, Y. (2005). New SAS macros for balanced sampling. In INSEE, editor, \emph{Journées de Méthodologie Statistique}, Paris.\cr Deville, J.-C. and Tillé, Y. (2004). Efficient balanced sampling: the cube method. \emph{Biometrika}, 91:893--912.\cr Deville, J.-C. and Tillé, Y. (2005). Variance approximation under balanced sampling. \emph{Journal of Statistical Planning and Inference}, 128/2:411--425. } \seealso{ \code{\link{samplecube}}, \code{\link{fastflightcube}}, \code{\link{landingcube}} } \examples{ ############ ## Example 1 ############ # variable of stratification (3 strata) strata=c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3) # matrix of balancing variables X=cbind(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)) # Vector of inclusion probabilities. # the sample has its size equal to 9. pik=rep(3/5,times=15) # selection of a stratified sample s=balancedstratification(X,strata,pik,comment=TRUE) # the sample is (1:length(pik))[s==1] ############ ## Example 2 ############ data(MU284) X=cbind(MU284$P75,MU284$CS82,MU284$SS82,MU284$S82,MU284$ME84) strata=MU284$REG pik=inclusionprobabilities(MU284$P75,80) s=balancedstratification(X,strata,pik,TRUE) #the selected units are MU284$LABEL[s==1] } \keyword{survey} \encoding{latin1} sampling/man/UPsampford.Rd0000644000176200001440000000223114520143732015205 0ustar liggesusers\name{UPsampford} \alias{UPsampford} \title{Sampford sampling} \description{ Uses the Sampford's method to select a sample of units (unequal probabilities, without replacement, fixed sample size). } \usage{ UPsampford(pik,eps=1e-6, max_iter=500) } \arguments{ \item{pik}{vector of the inclusion probabilities.} \item{eps}{control value, by default equal to 1e-6.} \item{max_iter}{maximum number of iterations in the algorithm.} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). The value eps is used to control pik (pik>eps & pik < 1-eps). The sample size must be small with respect to the population size; otherwise, the selection time can be very long. } \seealso{\code{\link{UPsampfordpi2}} } \references{ Sampford, M. (1967), On sampling without replacement with unequal probabilities of selection, \emph{Biometrika}, 54:499-513. } \examples{ #define the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) s=UPsampford(pik) #the sample is which(s==1) } \keyword{survey} sampling/man/srswor.Rd0000644000176200001440000000171014520143731014464 0ustar liggesusers\name{srswor} \alias{srswor} \title{Simple random sampling without replacement} \description{ Draws a simple random sampling without replacement of size n (equal probabilities, fixed sample size, without replacement). } \usage{ srswor(n,N) } \arguments{ \item{n}{sample size.} \item{N}{population size.} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). } \seealso{\code{\link{srswr}}} \examples{ ############ ## Example 1 ############ #select a sample s=srswor(3,10) #the sample is which(s==1) ############ ## Example 2 ############ data(belgianmunicipalities) Tot=belgianmunicipalities$Tot04 name=belgianmunicipalities$Commune n=200 #select a sample s=srswor(n,length(Tot)) #the sample is which(s==1) #names of the selected units as.vector(name[s==1]) } \keyword{survey} sampling/man/calib.Rd0000644000176200001440000001137514520143730014206 0ustar liggesusers\name{calib} \alias{calib} \title{g-weights of the calibration estimator} \description{Computes the g-weights of the calibration estimator. The g-weights should lie in the specified bounds for the truncated and logit methods. } \usage{calib(Xs,d,total,q=rep(1,length(d)),method=c("linear","raking","truncated", "logit"),bounds=c(low=0,upp=10),description=FALSE,max_iter=500)} \arguments{ \item{Xs}{matrix of calibration variables.} \item{d}{vector of initial weights.} \item{total}{vector of population totals.} \item{q}{vector of positive values accounting for heteroscedasticity; the variation of the g-weights is reduced for small values of q.} \item{method}{calibration method (linear, raking, logit, truncated).} \item{bounds}{vector of bounds for the g-weights used in the truncated and logit methods; 'low' is the smallest value and 'upp' is the largest value.} \item{description}{if description=TRUE, summary of initial and final weights are printed, and their boxplots and histograms are drawn; by default, its value is FALSE.} \item{max_iter}{maximum number of iterations in the Newton's method.} } \value{Returns the vector of g-weights.} \references{ Cassel, C.-M., Särndal, C.-E., and Wretman, J. (1976). Some results on generalized difference estimation and generalized regression estimation for finite population.\emph{Biometrika}, 63:615--620. \cr Deville, J.-C. and Särndal, C.-E. (1992). Calibration estimators in survey sampling. \emph{Journal of the American Statistical Association}, 87:376--382.\cr Deville, J.-C., Särndal, C.-E., and Sautory, O. (1993). Generalized raking procedure in survey sampling. \emph{Journal of the American Statistical Association}, 88:1013--1020.\cr } \details{The argument \emph{method} implements the methods given in the paper of Deville and Särndal(1992).} \seealso{ \code{\link{checkcalibration}}, \code{\link{calibev}}, \code{\link{gencalib}} } \examples{ ############ ## Example 1 ############ # matrix of sample calibration variables Xs=cbind( c(1,1,1,1,1,0,0,0,0,0), c(0,0,0,0,0,1,1,1,1,1), c(1,2,3,4,5,6,7,8,9,10) ) # inclusion probabilities piks=rep(0.2,times=10) # vector of population totals total=c(24,26,290) # the g-weights using the truncated method g=calib(Xs,d=1/piks,total,method="truncated",bounds=c(0.75,1.2)) # the calibration estimator of X is equal to 'total' vector t(g/piks)\%*\%Xs # the g-weights are between lower and upper bounds range(g) ############ ## Example 2 ############ # Example of g-weights (linear, raking, truncated, logit), # with the data of Belgian municipalities as population. # Firstly, a sample is selected by means of Poisson sampling. # Secondly, the g-weights are calculated. data(belgianmunicipalities) attach(belgianmunicipalities) # matrix of calibration variables for the population X=cbind( Men03/mean(Men03), Women03/mean(Women03), Diffmen, Diffwom, TaxableIncome/mean(TaxableIncome), Totaltaxation/mean(Totaltaxation), averageincome/mean(averageincome), medianincome/mean(medianincome)) # selection of a sample with expectation size equal to 200 # by means of Poisson sampling # the inclusion probabilities are proportional to the average income pik=inclusionprobabilities(averageincome,200) N=length(pik) # population size s=UPpoisson(pik) # sample Xs=X[s==1,] # sample matrix of calibration variables piks=pik[s==1] # sample inclusion probabilities n=length(piks) # expected sample size # vector of population totals of the calibration variables total=c(t(rep(1,times=N))\%*\%X) # computation of the g-weights # by means of different calibration methods g1=calib(Xs,d=1/piks,total,method="linear") g2=calib(Xs,d=1/piks,total,method="raking") g3=calib(Xs,d=1/piks,total,method="truncated",bounds=c(0.5,1.5)) g4=calib(Xs,d=1/piks,total,method="logit",bounds=c(0.5,1.5)) # in some cases, the calibration is not possible, # particularly when bounds are used. # if the calibration is possible, the calibration estimator of X is printed if(checkcalibration(Xs,d=1/piks,total,g1)$result) print(c((g1/piks) \%*\% Xs)) else print("error") if(!is.null(g2)) if(checkcalibration(Xs,d=1/piks,total,g2)$result) if(!is.null(g3)) if(checkcalibration(Xs,d=1/piks,total,g3)$result & all(g3<=1.5) & all(g3>=0.5)) print(c((g3/piks) \%*\% Xs)) else print("error") if(!is.null(g4)) if(checkcalibration(Xs,d=1/piks,total,g4)$result & all(g4<=1.5) & all(g4>=0.5)) print(c((g4/piks) \%*\% Xs)) else print("error") detach(belgianmunicipalities) ############ ## Example 3 ############ # Example of calibration and adjustment for nonresponse in the 'calibration' vignette # vignette("calibration", package="sampling") } \keyword{survey} \encoding{latin1} sampling/man/UPrandomsystematic.Rd0000644000176200001440000000207714520143732016770 0ustar liggesusers\name{UPrandomsystematic} \alias{UPrandomsystematic} \title{Random systematic sampling} \description{ Selects a sample using the systematic method, when the order of the population units is random (unequal probabilities, without replacement, fixed sample size). } \usage{ UPrandomsystematic(pik,eps=1e-6) } \arguments{ \item{pik}{vector of the inclusion probabilities.} \item{eps}{control value, by default equal to 1e-6.} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). The value 'eps' is used to control pik (pik>eps and pik<1-eps). } \seealso{\code{\link{UPsystematic}} } \references{ Madow, W.G. (1949), On the theory of systematic sampling, II, \emph{Annals of Mathematical Statistics}, 20, 333-354. } \examples{ #define the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #select a sample s=UPrandomsystematic(pik) #the sample is (1:length(pik))[s==1] } \keyword{survey} sampling/man/rhg_strata.Rd0000644000176200001440000000541014520143731015264 0ustar liggesusers\name{rhg_strata} \alias{rhg_strata} \title{Response homogeneity groups for a stratified sampling} \description{Computes response homogeneity groups and the corresponding response probability for each unit into a group, for a stratified sampling. } \usage{rhg_strata(X,selection)} \arguments{ \item{X}{sample data frame; it should contain the columns 'ID_unit','Stratum', and 'status'; 'ID_unit' denotes the unit identifier (a number); 'Stratum' denotes the unit stratum; 'status' is a 1/0 variable denoting the response/non-response of a unit in the sample.} \item{selection}{vector of variable names in X used to construct the groups.} } \details{ Into a response homogeneity group, the reponse probability is the same for all units. Data are missing at random within groups, conditionally on the selected sample. } \value{ The initial sample data frame and also the following components: \item{rhgroup}{response homogeneity group for each unit, conditionally on its stratum.} \item{prob_response}{response probability for each unit; for the units with status=0, this probability is 0.} } \references{ Särndal, C.-E., Swensson, B. and Wretman, J. (1992). Model Assisted Survey Sampling. \emph{Springer} } \seealso{ \code{\link{rhg}}, \code{\link{calib}} } \examples{ ############ ## Example 1 ############ # uses Example 2 from the 'strata' function help file data=rbind(matrix(rep("nc",165),165,1,byrow=TRUE),matrix(rep("sc",70),70,1,byrow=TRUE)) data=cbind.data.frame(data,c(rep(1,100), rep(2,50), rep(3,15), rep(1,30),rep(2,40)), 1000*runif(235)) names(data)=c("state","region","income") # draws a sample s1=strata(data,c("region","state"),size=c(10,5,10,4,6), method="systematic", pik=data$income) # extracts the observed data s1=getdata(data,s1) # randomly generates the 'status' variable (1-sample respondent, 0-otherwise) status=ifelse(runif(nrow(s1))<0.3,0,1) # adds the 'status' variable to the sample data frame s1 s1=cbind.data.frame(s1,status) # creates classes of income using the median of income # suppose that the income is available for all units in the sample classincome=ifelse(s1$incomeeps and pik<1-eps). } \seealso{\code{\link{UPpivotal}} } \references{ Deville, J.-C. and Tillé, Y. (1998), Unequal probability sampling without replacement through a splitting method, \emph{Biometrika}, 85:89--101.\cr Tillé, Y. (2006), \emph{Sampling Algorithms}, Springer. } \examples{ #define the prescribed inclusion probabilities pik=c(0.2,0.7,0.8,0.5,0.4,0.4) #select a sample s=UPrandompivotal(pik) #the sample is which(s==1) } \keyword{survey} \encoding{latin1} sampling/man/rmodel.Rd0000644000176200001440000000404314520143731014411 0ustar liggesusers\name{rmodel} \alias{rmodel} \title{Response probability using logistic regression} \description{Computes the response probabilities using logistic regression for non-response adjustment. For stratified sampling, the same logistic model is used for all strata.} \usage{rmodel(formula,weights,X)} \arguments{ \item{formula}{regression model formula (y~x).} \item{weights}{vector of weights; its length is equal to n, the sample size.} \item{X}{sample data frame.} } \value{The function returns the sample data frame with a new column 'prob_resp', which contains the response probabilities.} \seealso{ \code{\link{rhg}} } \examples{ # Example from An and Watts (New SAS procedures for Analysis of Sample Survey Data) # generates artificial data (a 235X3 matrix with 3 columns: state, region, income). # the variable "state" has 2 categories ('nc' and 'sc'). # the variable "region" has 3 categories (1, 2 and 3). # the sampling frame is stratified by region within state. # the income variable is randomly generated data=rbind(matrix(rep("nc",165),165,1,byrow=TRUE),matrix(rep("sc",70),70,1,byrow=TRUE)) data=cbind.data.frame(data,c(rep(1,100), rep(2,50), rep(3,15), rep(1,30),rep(2,40)), 1000*runif(235)) names(data)=c("state","region","income") # computes the population stratum sizes table(data$region,data$state) # not run # nc sc # 1 100 30 # 2 50 40 # 3 15 0 # there are 5 cells with non-zero values; one draws 5 samples (1 sample in each stratum) # the sample stratum sizes are 10,5,10,4,6, respectively # the method is 'srswor' (equal probability, without replacement) s=strata(data,c("region","state"),size=c(10,5,10,4,6), method="srswor") # extracts the observed data x=getdata(data,s) # generates randomly the 'status' column (1 - respondent, 0 - nonrespondent) status=round(runif(nrow(x))) x=cbind(x,status) # computes the response probabilities rmodel(x$status~x$income+x$Stratum,weights=1/x$Prob,x) # the same example without stratification rmodel(x$status~x$income,weights=1/x$Prob,x) } \keyword{survey} sampling/man/UPpoisson.Rd0000644000176200001440000000210014520143732015057 0ustar liggesusers\name{UPpoisson} \alias{UPpoisson} \title{Poisson sampling} \description{ Draws a Poisson sample using a prescribed vector of first-order inclusion probabilities (unequal probabilities, without replacement, random sample size). } \usage{UPpoisson(pik)} \arguments{ \item{pik}{vector of the first-order inclusion probabilities.} } \value{ Returns a vector (with elements 0 and 1) of size N, the population size. Each element k of this vector indicates the status of unit k (1, unit k is selected in the sample; 0, otherwise). } \seealso{ \code{\link{inclusionprobabilities}} } \examples{ ############ ## Example 1 ############ # inclusion probabilities pik=c(1/3,1/3,1/3) # selects a sample s=UPpoisson(pik) #the sample is which(s==1) ############ ## Example 2 ############ data(belgianmunicipalities) Tot=belgianmunicipalities$Tot04 name=belgianmunicipalities$Commune n=200 pik=inclusionprobabilities(Tot,n) # select a sample s=UPpoisson(pik) #the sample is which(s==1) # names of the selected units getdata(name,s) } \keyword{survey} sampling/DESCRIPTION0000644000176200001440000000167315033773102013602 0ustar liggesusersPackage: sampling Version: 2.11 Date: 2025-07-10 Title: Survey Sampling Authors@R: c(person(given = "Yves", family = "TillĂ©", role = "aut", email = "yves.tille@unine.ch"), person(given = "Alina", family = "Matei", role = c("aut", "cre"), email = "alina.matei@unine.ch")) Description: Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators. Imports: MASS, lpSolve, utils Depends: R (>= 3.5.0) License: GPL (>= 2) Encoding: UTF-8 NeedsCompilation: yes Packaged: 2025-07-10 14:52:52 UTC; sematei Author: Yves TillĂ© [aut], Alina Matei [aut, cre] Maintainer: Alina Matei Repository: CRAN Date/Publication: 2025-07-10 17:20:02 UTC