From 876d9ee193b17390a5870c633c73c46aebe9df1e Mon Sep 17 00:00:00 2001
From: Jeremy Kidwell <bots@jeremykidwell.info>
Date: Thu, 12 Oct 2023 18:50:44 +0100
Subject: [PATCH] fixing structure

---
 ...Religion--TRS---Data-Science-in-Action.pdf | Bin 2038588 -> 2007042 bytes
 docs/chapter_1.html                           |  24 +-
 docs/chapter_2.html                           | 345 ++++++++-----
 docs/chapter_3.html                           |  22 +-
 docs/chapter_4.html                           |   6 -
 docs/chapter_5.html                           |   6 -
 docs/index.html                               |  60 ++-
 docs/references.html                          |   6 -
 docs/search.json                              |  79 +--
 docs/summary.html                             |   6 -
 hacking_religion/_book/index.html             |  60 ++-
 hacking_religion/_book/intro.html             | 478 ------------------
 hacking_religion/_quarto.yml                  |   1 -
 hacking_religion/chapter_2.qmd                |  14 +-
 hacking_religion/chapter_3.qmd                |   8 +-
 hacking_religion/index.qmd                    |  46 +-
 hacking_religion/intro.qmd                    |  45 --
 17 files changed, 428 insertions(+), 778 deletions(-)
 delete mode 100644 hacking_religion/_book/intro.html
 delete mode 100644 hacking_religion/intro.qmd

diff --git a/docs/Hacking-Religion--TRS---Data-Science-in-Action.pdf b/docs/Hacking-Religion--TRS---Data-Science-in-Action.pdf
index 446532ccc0573bf86ca625a1d3de0439be97a7ef..3f1604da2b11166e6457909edc6c99abee079649 100644
GIT binary patch
delta 94105
zcmZs?V~{Q|vn@Q@W80qDW82(g+k0%A&)BwY+qP}nw&(8mocmSXQ{VZQs&u8&>7>_6
zdZn}3%fHv7RvS2_4;h*az{twU!N&*f=;UCmZw>9Xex*4UO~MY_bwd3Ks!AHa?i-8%
z91qm?>r^W?VC#CEGUW4zEI)Z(@u<E6#mHo{?=e>=f-GFl%d7DHc0~}+=(PkMy+4Mh
z^9!rm2ma@$_3a~MW#EOs3{+en_H^I^^%}nmDf;x*<LgRP@<0|J&~N)hPL`W^SSZ4Q
zH@9l1R$*s6fu=o=*B*T1?o;E#X7~Q}lrj)g(_$sB=ys;kEEtE{zA7-4`sW^3|IZML
z?A|b|%p?L-Iq<CuSNy{st=G(PNXuwp%PU;x<V0$H9UbqG6!)^(>-^ta$<#ix#|)U|
ztCu7sGYZ&T3Go~VK<(wm8CW!b#$PHX__-P-rWpqK&`U@jyn~3#^iLvM)gCnF<OT5D
zq7Z$}D_JqxBS&vdVfhzGfB8ZPJsQT%-f4CH*%|$Wzu|eveg_qeFmk<~(bE3OFIjO%
zv@mC79vOZ;2zu-frXw|u!*oDd$m4DOoNR&vv5Y4S`c8w405L<O0@R=sav&l{$wMMy
zd?pgT0C$A-@XFZnC>K6Zqb+~fE8oe4S~Hl{6I_{YlK@~k3R~1s4o@==!r}HbKF`P}
zn-QHfyhM!gjQz3Rm~1a(Y_X^LT9e61^1#HWvzBUCVfpxM<B_&F>{te!@u;NAD;CqM
zIl^o^z&SV=KtI6ooMN3UAqh-3W$8BrC!-h)^@$$JP^yioMY+N6cu)oML>d>WI#e0o
z2K-dKL^tyVL23bN<j%7Y)-P9_DJ=c|5wwzZyg+y(FgI*M#uO}MSb23xT)7lUOkVrH
z<PWJ(Ub6c*<&t@*@pg<qj`n@9bMs@hpu|_gRemHQfD<g@C^F$?OpSXFuvpd~WgzNV
z2h_Uq7=_;}xp15qmloR3xwe53EdGe-6>s^qG`KjK?;JGfzrevy^`Q*PdxcIeAM-Ma
z;n=k$HLK<E%$uB?x-E^%9Ofxt4c#qi9{o4*-58?!o)V_avyaqJO`5`uLwwOY-wTBD
zU>0tv0p=r9o<x={`TGaNpWa)&zHa_ARXE2oYupEYG7^&V0>_vPNP{iGGHXVfqH&cG
zRv~O;=jH+Rg1>H49C^42rBe758K!)}(g@GM@foMcdGhJ_nN!%cF9`$-GIr1UFt^%D
z+(c`IxcU9}s0=F>=|RNUF|jwn=Z;E>qR?5*0LS<JW0ef74X#FOgeM2=$Ia;%%%a#e
z##VN(ik@D@kBBArq~0CcRir1$W9Bx4Mnlr_tPFYw!QV}Jgz{`0td~wf*J)!P1U75a
zf^C01y=E+si`kgMUl&|e{*XV)*6Ig1BYD&XmKSwTdOFcmuG29AtPvqEaF49TjXml@
z0X1ML(-gzTG4H|$V2iq9eL-B>;l$Jyu-3*Qi)a_IX&$r~3eHd4iYwNw+qt(s<|pjv
zTv>M)h}O>v{(J}|XYRgjG5LxFMY9c48+<N!N<D|(3X{hP$OQ)MG<em8EL#NO3~D2x
zk|X5m#O9Z>Jeyuvs`lL%!Ap2jc_0BE03&A&7QNPt3Dw~vSxYO$dMV@h)^aIoA_7{-
zq-7x~=Jltk`On?$2R?J5ahMBxpc3nWPBA9gF8fk6wX%y9>Zj)C7#(1xZ7H#&DNgly
zWJ7#){>u$M#tf-19|b-1tnHo?iVu7-2;nk4oL_IVAl96z*eVMyC$p0Jz4U*+05s!g
z!ne-V2Ct{yYQ%)rK?xT4xuhUmB_h3hxl+_t_K$EuOOEXnE2+$mj*;1SF-S?y${8(6
zvN<Do9$anq0JW%{Da<d2w#`rRj_gY0)#5B7{Zei4AtT1dV7Nu5B4^*EBas$v+jMBK
zw76RCwvzHV8|S;5h*=v}y@_rcfOWrHU_(F>F4W1FuUrE2dti6=VAG_Qhve#%&OTik
z&XPru_NS1i<ivr~=}?qib?xz4ZE=Z}czK|A9}fDyWeh)eO0BRfnf?t}+((OfH^CDi
zeaE-PQ6&;$UGxE)P<#D71TO7(uWSC3YYjd797Vv*<?6@oB9LjKK%H36>Y<snm{XrJ
zD1-HhW_a?Xev315%!4j^yr3clJBAUem1@QS?%NF+Gpo)Sy6Z}%o=D&^WPiUqX=~T&
z=xXs>;GCGGaA;so;4esl#gVJws9w(p&T~-E1+=k^(f<XNe=S=B3usox1V&6!05co&
z|BE+M$}(~T46t1f)b|kBmxC_Y=I6jrv8_Z;!5p<w*MojPfIeNEIu}d!J0cnD#MS7m
zl9&Bfxe)kA;S~SxT{Z#>-t*O|Y1gU_Z-J0qW+Nx-l5MJZ%NBZSUv+ks;>wCZnV{n{
zAUjY(tXh;TVI@(31a@B4pMdK^Z&bI1&StsmZXFKl#Y<zS?Bf&jZHgR(2L&%w2Q;#o
zM1=5KxS(Lf=hB^}L2}G#qfkS2IEuMSGtXsSpRplf%ZLgH8pY1N4B#x7Wt7!a-T>*|
zI;L@;m3pos#27SW`gWe;SM?HiZ!q3NkNlWQKv~R|=tC?3t?(Bah=)mbZz~how{9KD
z{EXA`Biih?k!(<v-$g1o+TUH>4n9=l{vka6a=Z_dtiI+@q$RM!yjVN;Y9qI5qm7n2
zIO}CIQtU@k3}HM1q>qRXlFB%qqF4UF=!hGI?kwT9*%ng#Molt5FIx52EZR4(hhJYP
zf1my<BnbZv^#5N>SW*~+;K>@S5DOuQL>*RR)IYxm01LvMpZ`nz*c%uz#i3y(+A`x_
zzX*On18;Ow1dH)eK-d`mMV7pwmv|P6u)oV8X#WQAPT;H0?E3#7ge`#q^EZH<{r}wp
zRhrx}ByC93U(}Bf#W6J9z=D2$9iW+5sgriKT~TeL|MtMX3GXOz#=IG@q1r5qic#Ft
zXZdQ&&-Ue1=FcT<-`mcn&-aPf`@`qw)8ofYPRq_WB)es^L3o$9&sOC|W<v|YiEYkE
z&-L&i!EoT`EBMO^zfBLIL5KDS1el~jhanuCIK1?$f$aOO<p)?oi#p?fnK;LPd<0=&
zZeV6_hr&qes1VysioLFKW3%c+zPr);2KlC63rS&=z(+}_qxr|HSpKJzN$MTdhitG{
z4`|<h+<Dt<45`Ed$b>q+Xa1;ZPt&TD_dha&DI#n4Q{WvKhEk}a3L#=*y5Aa`AWu13
z;Wa<{Z}+a=A5Guh++W|1+CHCTwD2}KRyOY4mKS276OKOz@Q6IV0MNnr40hkcb9x>C
zpTArad-W#D!vIFT&*$4`)%!+e&!J6T;(4-8K8}-?0pMCGG=M0#bxQ~zkF<ml6&IYh
zA3NY#Tp9JD+J0nxU+=Q7#x*+w;^_UjTUm{}xg5vg{g~sRx~d%{{F@BLYI(uPLwiHa
zyDJIvLb$c4bu3@;&^XuG?T&fKe`X0_ny)?<lTvtNw&tOkxE{a#+PLKo_C}kiA71_n
zp|OX{fe}M}(z?~*g7Npl2F)<0_&qU=rJ5!~kXrps?uD}|COrV?^qTrA08Dv`L)2~*
z?g$&z0Jo-w8;RZZ1XiIESz>b9Nb>3~Z3GOPB;nXblo1^l!Gx@}Wi`Q76#56Wp58X0
zSBL{ZiN&UFIljrQ+_AoYQ4OxPb1_Nz5S;MqFiB9d!Oa?P2fOqci4QG#sXq<5Xidic
z7?@XLZJ)c3AU}a=P?a92>~HSNZN2IcPPbrws9)zEiV!g*k%+BefeQ(X^CT<?s@7a6
z3kaZ)(qhhe*aYJ;8y>5xlBr6m;|d$m8p#*1hy91V*`rL@VFv-U4#Nb4TSxxcC2xIW
z6mJu)ZGaN^?*&84+U63G0B?e@Zyh#hHqsj0Tc&YkK-YZ^^$%3ev=F5yC`oYZ`|hNL
z1opPGTlwp3o@i$yku}?^GBuSFB0MFyc<|h+9^>0F$w1h`_ElTP&ajb11%Q<+!^9lG
zJ1tMmNnSLB`XsX!o_xp+q0hod>6GZ`7D<ID3G)~pt*K{y$@rYQD?T~4nD?5~7vfob
zo2R7j46!8d0AsLur_oMx^3+i0@8X!C5@=14e${^LiGzlup*crL`#0R|261A_zpZhK
zm6|@>yt>Y8Hp!(vLpCkYx$+UJi^L01C_?H%L@pGsHsp#5m9H0co{j>ZGDjEnv#6lO
z*R?ivUkEo*SlwX|S&s*)cC7j2w61P}S9E=n&a7i8`<i3GTqwA8!}P1^J$D{TYriOq
z38E(}Ic0wo*JZI5C&Pu648kPGqqb&yJwW`KkS%i)?3Wf<GT1S+qoO)}#~LKy(CoZ~
zGL<$A-Xf6DNd}^K0{TUP;OOrYQVR480~LDpJ!O826aOLIknW&#q=ktb9iG`Gn^Y4V
zy3*WVOwK~$mFU>}NZ`w=pr{Og@gs3D30u}V9EF7Nsy=0M>^+_d^9;qbO=0R<e$D8R
zf$>3TVO-l}D3MrCJws=^u}cqt1O;lpXZf_njYp5el$(Y`K+2<f7w>{(GIswF6%-<8
z*Alf(-({BNjq9)cV)QM{8XJgWU--s)&HT0W9|mQ5s6!}Qt&=b}zKpMnrtmIf;x_lm
z%U69*)wF`}b5&J^aAK))Dv8hDGg&zFp`#jV9pe!MSIgDwX5<cXRJwM6kaSApaq+@D
zsaftBu`YX>D1=JFp$)Rq&+}YOuM=uofjLj8b7C^C=LguU0smhyVtK$b!{Wj^`R#tZ
zC6=V9Cgh&-*t*BtS@;Mf`Xed6dN8djUG?09LU6tHPj)U_PAtJyTIK9ORrX`M5GI;O
z=3iVb{tmKROwzpskMwr{io`_0e%rfSbMsf=esdB?hszOr`4#R{9dWBDb!eGbX^;uh
z6*I3h*Z|R<H5@1XFEsFU2VkWRj}Ac!ZdV@L(j2nLHQNzS%Jp7JDNjddyUsW+KufxR
zPd@9Nz;l#T?`8@oT~}{s=tVn-^Itj&o)PzDmzB-pwuYQz<#P*?vPG}`*qof5yQI}2
zuK?uan9Bp}DOp=jy&CO*P}hSiI7^gLhfIL_FT0a9$cQmR!511!mH2!Al>C6o2=`(9
zr}IA<og9>boiSlTo)4gfEDC4Z?e1{6cR&b%^mDkY@`*8UC(HxD;g_?2wEpBdx?oa+
z<))l0Eed)g2w`EJ!W%3t`5V#$fjJCdgl;cx@N91o_HYtUyw1_NIu|x@WAFT7#g~Pl
zgyuEx#|*@d#LRjY$gc(T#J+R-T%W}hzW+c@+*83cZhzi6eFMJTViitHdd8>D?qyhI
ziNN46hupj0-`}kH?@f)YSrQm6ARUyqMJ&G<4jFylTl8OzM?rRxx7zj85DFeWV&hUu
z;S>YJl}G}yKmU;TRZLJkAs`H7<P;13K^-Iw31PO=AEGeE#FxE!5yB)3e0J^O>%{T#
za${9F6T?y#TL-NFT2uIyYjBW4BFaiqF_`4P5Z%f}aw9t!B$P;$?I~<{o3`s#p)Kf@
zuZ>)a$-o|EumpNhconYv92jpN4_kN#QlqcU3r!S5froo_W-`_0Hj|c((1NP)aZWw9
z8mV0i!x=2zR8$S;l}xQZQcM{8=3?X>G;3H&V&@RODhk-mj9j@_jZ|i_E!jdY$WlSK
znzJ@Mo$H8d8kjzt8{)hOt~TL;NcgKX%Z}3yQ=rzZ<$)voVj@~z^Y@f7fh@%G&8}rW
z3V7*5L1N_n9!LIg(~ndKY(gY)lcbzLfa!O*qgUjMcIU}&9!-1_d6`^w{)0KmTzlry
z^#TX2U>Sf>e`r)@Kekghe8Tvz-oN#hq{8iM2SMrymJN~QxW`G!oz=MeE-9)O*n5vC
zMs96N)Yc5Ou7h%9C;=w0>iL2f3r2CdRn|H<%Qu(`CA5W$<D$$AmWsNM1Je)%x5^Q-
zufQ?p+OhoE9v@pEXa}?=&(ZEJ_)qJS-#!Wp^p=46il(m{r+NBW0+gMy?xkT$cmTPH
zka?<M&}}Oxxi(R+f{~>`FlwI|o~gyji<F_#i0=KMZ<XIh)Ur_L9aT1kLBG_a95ML}
z-BJoS-eopMFm+2wVy0IczQn}EKFqUQi=z;zwI{|RzG1zT@+5v05tQ3`8K!HA07uqJ
ziWnf<&~zLlR@^ib<AgZ}Sha1VOkiS<w#BsXl`5^dTol+*2MKYOwDZwyYH-h8=zV!`
zs5neD0tc?WoTxueRf(sZw;jroYY74<)DAXPy`U^1i$W(Xy)$-|$<ZhDvzaZc-ws=0
zCc<|DxZ$iUHcs~SBbiTtoU2n{Z4}c-{tgfdU8{E4e8-RWP~&X#!8TsrqE?~feJYyu
z%U4it#u+{P;G~#syoKgc&7y#UANnRq!?vInh~BEe3yN3Us$f%bM#ZW*30JkGMa`kr
z(R`)$+3^~-8VyRci(T`&1RYAI+9o%RXPIMsZSDjfh$wFVNgEkbIX@SRAR)VPTN&U^
zRkGZh@ebza1z#?17DJh?3Cv8_=c#EUilq|IIki}t-?+hhpmY=_uBKi`KfsA)1TrjN
zRW%-fZEZbLE6_O>L_mHaQGAuSTeTjyKw-*Kj3BBiIXZ|K?f<LI%pJROELVjr!oTxu
z9#yKSI#@{T!8#LJ|AbN=%CbCkiUg2tnN`MMug!4KHwdTEl+C0-4sMw)1|l?nTt_K;
zoI(_5!QUj%zZ)-su5@TuJj#$ld6ulhpo96R7nv*pY?U%?JUp;BFBh1w&Y1x@c;<oD
zjo8~nTOh<T6t}*zIX9L#qFfr~rHG{=kpe4Do{G3WPb^%48Jj01&WXKtSpvW@5YGZh
z?n`?Gs+>$tB2M^#nj7wG3g*s1??k~c)kHf-OPU#s@Mxq?yFpySAWcixTv&7>P|65V
zK0bpfzK%?9DCEXau@+Z6+_S3fCN-u+B^}>UcdHa769Gf(E6$A5ygiQZw%oro8#7ni
zI<o?Tx(uYrwFm<Qo%I;}+5)Hmx?|ZnX9G4uB#dU^>0-~6#A%0~Y!_i~oc+axxs-f*
zh>zZEQ0Sm0{oCF$f`7$9O^|(!{$oM8xF}TK=vp6`DoOD0w0eyL+Z}}<S!G9rhz))G
zmxKw{1g)h<0)u1IMSh^gaeSP6!{IsNBxn0}gzof$0BAgK3KL~0r3t`dNSaz7aV6fY
zymF{z-cCZo*St<#nUa-Obg6$7xUCh1DG?`AC%PQA+jqKnom7NPX_DT#+(b0~t>!NI
zVR;j=ZFnOuOjW6lFR=!y+M~QcT<H&Pd2SW`z3K||*jX{!wiVRq610ZHK(m$=n=)T`
zc!hpO`TP-x6`J;_DjlGXuD4jYMzI-J*%rl9rjMeol-V;R-&SwZ46aQv&d9dASY?Ac
z#FA(0Pj@EONo8y}JDbYk4Y4gOR*DGx4#aa6hxrwS^5%ybD^-pLoO72~%L3cwM@8dP
zdfAoZ{y9E>E%2^}7x+{LOURBm>%(SP`L~NxQB_L2(zGIJ03HCu{&$^uD9@r3C6|g<
zNwQvh{fS3avvW$Qv~vlAvemBa-^GOMgPM!9Xs5>N<X-CSLpKFy9M(rl-X{Xb9;PUv
z-YH-=s*UVTu1U+s*tSW*>w@F9wt-+CcmK%5@r$xg>1m4Ulk~DX-7_I6TFv{@{q3`f
z#>MibZ#13a@EpLEmc>qJ?ay2C`ml#vb<uOU9tBbW+26a3tixA>41$tUfu3_SRiz5s
zFx?dl7Lc7@x}!6Y+dn<qW-Dvw)~vQVn&%tM9BukbQnzhdnK;dy^kNBWO`O&r2WY$&
zs}h{X!@EXbCdxYdKi?eyAckco+W&DIOzB1@_{U%`@X0}0*jW-NH246$)z?%>A_@cq
z`J=ksbS{jyrYV=ZJ2Y}VeG8_o-JS2>?UiI5ou74;!^=nepV$t%K3dvSmNd;RpY5N9
zZF)X#>U>|A7?#Tx<vRGfZmDvXq5AHvUtXq>J^e?KVaeACjKh3=j=m=;Q^uzW3odtz
zeLCffC_Qy*6dOaFUMK*Y&*L;R!YTP(2NCvL+`VQ_9MDL+l_|8t-Jm~zV-7>vw%OlX
zrq&H=TSC|wLOk<e#G;Tg;FdR|mV0>@tRJ9$L;0zX+rS1K4K2Ev%EtOAdI@*1M%nD^
z6E*2$<RD=Pf|!c#k6Bd@qcFsZzyhU2!t!LeZqOs}(*isN-i85)hAFmomLOH~_eaSs
zM>q%Ak_K<VzfFc9YpmHCv@1i_%1iyoda-0Lk+-mMV^_A2EyM$_1t@_Fv7UfKfE1^j
zlQ=D&eDhxJo1et#>_Q$#Nq4z<7P?-voZ&2sr5vqOcBAqmw(g>82sH*ZPj`*>+yji=
z$^EyCFr{xYDvto7mXY&#Wo&<-yVok!BFC3fY2eaf3_<&~WoT`My)_J_xmz2ypr`k?
z@b94wvWXB_oDF}&PAVdfLcyQUZ4bgD3G(gcp|LUL^$o@9l_m7T*NZ3B6}27e%A;ym
zi(uL1VMKWqrU;`o$NyX3ex$*|*baDOa~>X>Ip`CeeM7*aC6?3Q1J%u~SWs-5HLi`W
zGw>F6;CuE{_TFxZu<+IAx+$K734xcmj0l4Z6TRF>v-t!Q>?HKW3q*)fDT&50?&)0M
zcp3%tHYb62QlI2fmS;|UiF^*bRC&waNR7i{_{xKH*cth#qKEMN(&1q==;}!vT5@~S
z)b<u1ww8eM295v?rO^``bNZlL@pw^Jf=T<7{Gy1(X=4&?Mw_i9m8;$03i*Gqf`gQs
zN;W1o5qzd779Z6*?)Q5`R?#MU_rgZDqqX-L$yi(gKN&Y82W;ibk)7SxgfwG#%kmBN
zXk^op|5-0tVNHTCbcZuO?h%<9lL^|eCN8?+ivqwaaK;vM3SniGxBpsvRk?xCA-twH
z%3AN$F4j|?ryF@;&(J69v^u*ADw4rxBaETu--V;A`Pfo1>%6WuZU-_CBOp0VZJMu7
zo(v>fG4N~W?UEwCd|Ze%e1)<)RVN$_e{f+M>1nI<+?pyH4v~g%z0^hIF0T-Q3Vvf~
z%@`2W=a?%aV;Z+=3`f4ZZM1y3Id|-|LC08#4)LOnp*fx<5@fxzhrSt<e`MC#w2wE~
z`TWbJ(TZ)`^*tk_S)zT!JL>EuRYj9wJw<>u1F1?$gr+NY0liK4Y@VC^DRxzB-<aJ+
z4g}1aPtr)K<mzc5Mup_`QnOF0)>WJZCmR4VtMo{Oa>`fT{mOTmsbglVm1%AoTqqoT
z6)*mV1(##Yf5JQ!1<5WVXiyv4ZG)?&;lspekZGmhfPO~)`>O%R6f#$+7&bilp3h2J
zi=WTeEd7=<zs*faobwMtG(d#no6EzVMfr`xuw0V~@AA|_tmj#^AnaVoz!yI+#uczA
z1@9<RMn#+Gfjh%(R1lpJB{!m=<+BOn=W*}S;4j6!Dw_`SZeNJj4fZ+vS*B`WAsE-v
z8ZAl-q3CTUJO@E=a|}x8Aj@JVW5_GLc6HM4eyfa`uC;#fpx|TFc{{J_*@?DbX)<{W
zg$wms`vBYb8{Hz{v|P)6eNOT?u>in-YY!IxY#(654UaKAWFHTtwt@I?7MsUB7R|Vx
zImo4}z&&^@liN3<QTKF=a&}f8X;OMb7BA80752D9e#jlfRuRX(YH`LemlR@VPO!y0
zJ7KjQK0qbsS!AF)o5AO|XwikzGdQB6_jOr1RMs2m-GkKSH*Hm#A3bu-Qw(68`NsW#
z)@!9ZKJBduS)|!44bbGbeA20V5Lr*v&XTk1HRqW%wOF#L&-&4{oCq`af_AmwucV2<
z$&>bO3U-XRS(oY6JXj9w8>n<zi4%{nYDdZg(z7n6IRv#s8T2M|_4RP6KB|vKbcVMb
zk$qzkB9IWnh9%*heM*i0YzH*b7)c4!fh!mOO^41n+o=rBdArWu(GJB2uu8sA<+<~0
zWCTp!FWf)S-#Q1_=j^>Z2+Rc6qaSEe4XH_#BYy-ezP+a9czz$KeEra<F2jUC=RTd~
z=;?mHKbq;!>3#F+E<dEC&QJ+F$1KxV#Pk5RTRvgbwT0pOnhtcQ=>P(QTN{YydKi!`
zA0TGoTb(~zoRw@JLm>2oQ3E1;0%vnRR-gjB{pTB$2KAOhyJ<V916N9wd=)X^4~q^Q
zanzRg=KLA=qYJRrgk_KfVWz2?@TR^bl-qcfCxq224c;V1WtDDD8&fC;FB+oo)a0kG
ziwK`rIqJZ;jNZ^SYJlE4iTrwh5g_E)@jr3dD##M^p*PD$yWD$E=`U{|a-Io0Z^vKA
z81Z#T*~B>=2?VsJBQ`J!e<;;|1IJ))wJf#LfMJ!e(!Er%RvYz{4WscZA3*<Rt+v`J
zw_j?p)FvAmKvv_RwlBI-*cW>1_v<QANp~yTlWylAJ#2C+Fav~Hyz<P5gbR6<I29sT
zf?a*Oan%>~ZA*%IJm_*U;gygy@Jop;4>%Qa%84Ck-}6X|EjKtBdC=pT_l;dC4OgB1
zVcIYyr_H3_o{)a_Skf@52lYr#lO9PMgFmg9G~%6*UVfM~x`vunOB?e@PLsAZC4b?9
zp7J*u9oQY#fCcQ1*B9p&;(H{;pf%RyRjiD9pk&859@z=}b_=fah=)JW5W^E%3X`OF
zd*u>YtH4l6((~+ND3z3Er>+9lo}qFK=X6ps@KqVBlzR8bM7YJ<H97$$Tya6znh=}|
z{~EqD$PU?iVbaMQyyuYY1|h^@2{WdHj{3M}Z;Z3Mq62v9;4eyEG^s12nU%Kh3%33u
zGg3h)N0&QXbr_eYc%~!`(*Wrr<!y^@>;3)&cIUWpS&KN(<hEh0jXTZG-!nJ>g|wzn
zw7g=yZFS_?s-BqD?zQ8{wn1&&y>!(ATJ!hC54L8D_&xT~N4|u_z5?JSY8G+UF4&B^
z#L&eM8U#qUm+K%H5o$^;4PPOb=%^_U^VV2@ZheyY-l2dn>Nha!`$ajEKIM2vLRTCs
zT}68H!wuX;?qr23DSm5uZLVU8A{?zy__Ek@XcK*a)=XS%1(O`K)xcLkpDU{~rS3r(
zvx)}s&kNnn@UifPx&-KX13{5tEXP+OBTd{ACgSz{OaOrXh_SHz52E&8MJLYxlqSdk
zEKL7H<Zz@Z6@%M?)P17%NU(~oG4z`O6AYLbxSMh<V{c$O`cDc+=vBavk2(!H!Pccp
zCwKA{=KQ7fgF+=ngbIP@oBmvc<j><i(U;o9!^_#3@Vgh^=l90J{dJdaqsAPEI=*e$
z6Y$i%+t+hpV$dU$WE-I5AwVTu^EUFaDrcs2p+)H|;Sbikc2fVu8*tVHN$W40#eF3h
zi=s{mmiZCVa#d#9{JNYQyq9&B2_5sq$KI&2j?}l;>${Ajo0tL-mpO|lDvS72{h`o_
zfQE`;c~VDnZ)qn)WEH8+H_L=!QS%~Jv&~+D7+2HVD1L88o*V!ra*o^H_Lp{kdLf2n
zZ=$-PDdg|H-mBsG%d0R7?o=h?t3t-0nPIhgjrSxqqG>u~?Og<+WmfZ~r(*ji^6+in
zD&WzcIy0C^+I9hYxkmUO;`TLz@Ft;7-?Y{GNZZ7PP)iGEw|#WbDe|BNCWzQ0X%|Xb
zBLz*)n2@d<)@(onK!tL5eprq6*geUz&T6-<P&hx`O3*_Ir|5_*bdFGqJS{Bk7y5GW
zLjW+bb@qpDdVy?~zJWbU=^`O@by)UDgp@v>>!A^ku6D=>wvsaOFdjuM%UYd<oNem3
zhLu*Nmdh}_0FTEU6s1FXZ?<wcabqFhgVCvr)itBsBrJfI+-Jl-R4kr9uXzKq=qZ;?
zmH8zUxE|rLL*k|U;5WL^B9T)=m=way;+74iAvo4kgl;lkFzQpS=7qXO^JzFsK)?QD
zkSwF8HKk&r+59E+*B~xMJ{Yv+sDbE-yip-9I7XU)32N0i-$^!=CHYJLH<IMaR-4d$
zw<p2JC_Dgj1yTDx1wG3l5i)AR=vOvpCR8dskLkIH19XAC&1t3CZEL$3JnjN2d*F!y
zdcnNBJhgvj(RTfaL#IUf<{CKo*h91a5&KN)X>Z69h^mAcJJu?AcNz5Oxl@V1)Ja#0
zy({W8sdz1%?bm`T5@#;aaf0e0Iu}KEK|3K@$1XtG4EZ9F{!lf2z<|fhS}kTHL82b|
zkeBZPy@~5cucQp~E4QpY2hqsd>?2%=<mHfd&;zqm!>y{z0``MPOE!zCI0msTNF>%<
znW@~P5>`X3csUvf{%2byMCMIt0w2x2S|dwroDiVyV3FilLB{C>>+<HFXYyU6s**iX
z69XtZ@jFYZkv=n=nvYiXw2#;9*69AC^VV7DL^ykJ7m~}U1^)%Z3GrvlFYW8kx!d>X
z%*cI>_|plJ>ftY`#_g|@hpU<yIWDNLw~}S77;gP2A}~_CBKz)-hZuy7WCE0kim>5c
z5FeH8lH9j1#SQVLAI)R{oeGeyLBqAJWe7m!w<gzJd?jO5M?6*BbIdgKDL#OPbpo-1
z3w%OCc!bp0Cr8ttYD{2iNij#toA16QiESxb6dDHE*YULV9utAqeoxt4OZ`g`sd2?}
zR-ZaFj5WR|$SqV)PMF(7?QtvTa#naE(HN`++p^he19jj|a_7UC5Ro_IyS09DmI<IY
zJeav|mF&LP=ox^;uzTKjV^kmilQelV?;y-YeTFJEG_JE~5xG4kDV56WE|Z@s9a;7Q
z=eUnv%+{fp&8td)e)%Ksy?UXgV#7FjY58)#>DBYb^XmF`N1SZbqCjJ;gCxlIQ`|lj
znou9HfOU3-Vi0-EF-70SYCbNfJpfpZuRU&jC`)?5C2?~u_FWJuyC)vPuW_=j9j5b}
zIDojNnps=!oz%+XuJZ0t_c&6%9g4RQCvbN*ud0>COGy~c*KZwbd7NJ^M$&*;b($9>
z%TphXS6fzm+VZSQsr{4Ul|md6O&yt3v9Lk4mX)3s%CgC2L>_}#uVarxTME!T1&W~{
zUj{8t9$8m3hOI6x60(_QVx0vd!BiQ)n3X9$QJ?T{1<rDV_pu-j*E8*5#!8*7>V^Ex
zcQTkPp@W7pzW4;U0Np~_a5~-R5{rsm4!gTBObLhX8QL(WuL-Z}&CCQHd)}nE3kALp
zJs&@^NNaAVH#3dfeRa;L<OiVhQtaIKZEKY0d{>X&cH#<(HPGzyY4C7CHmzK%Q0;5C
z42kduvgS?}M_~r@_&b;@<zBtTFrUdlPX|GMBja7Z>q6!Bb#DG{CaLH04!Ma=CUr++
zT<3PC3znupgq=Ne#5=-|C{~6mw}NOS&yavNX%c~AhTmczr^9fC?+rMQniq{mnik7q
zu^iYhbu0ZHF}nX$CT1&%Y{2%bE@v<K)N#(++{|L$9^$Zpd$hG22B8E!eX?kx0>)ax
zo8u;a$@Mp|Qc!`+N{!Sq(&Qr4s!QzwJB4IS0wlggWIFDE0}Un~ybbgGjV#ii6GGQB
zcp()K{UTzz5yO$RK@9-spSQ*diG`-K0=kI=_FM~n@bsLG%Z#P;ESEt77_<HfiB0S{
zl+E3ur`{N3hdg0}dSQeS>#hQQ$(FK+oBWaIKk#-clm`SgM-1alxJ6lBYYknJ5A?c4
zS9D#OIqu|2YhWWDWerht=D|?*1gA>YF~Nw+IZXZO6+4>ULMZ@AH1vZCzQc+?-Q<;3
z7}01}(Jo=?x!d&G;3k7S)<xHBwfe`GZ(Te2tXVG)UD`4<RfT@w?bhQ}c}KLI3>uEu
zH<PVJ!8(@Y-*GeYCFLD2mhflANsi4u#8j5(plN<RE?Q>YZue-;4OkmV14OXdb9#ou
zp6QHDVo)x`=8S;c%Y<vTn!kA|pcwS#Rx<KYU^Dbr+n|>Vxq3}snyaQQ>tDM9?QXNL
zR2;PjC*P|l1R|?DkRL1-&bx|}KTCBp+#7q}t0fXASxP?;t0xqrcy%*7S;4v?Va2#U
z`6V5O`5;O*jV@j~pWqLLBCbU_T4_{v_fLeTD4Wt<x1RtLERw>5o$F6e;vDKDH9gd$
z_{pxMFuK^UyMsZ(QNc{Nq#MUZ%+Z6Q3NhQ4b{OA7XslFx43eIo_uw`XLZ3|1PX~d{
z&TuoD>OTY5I)l~6+kOp4*5T@o--->A2q}_Ryr5L^t)?zFH(cSMNf|t8D|`BQ#)4m|
z$z4yjK~{jBU`Dh-x~F;A@zgFqlSkOGJy-W)s<~A|(-qF?)S%e_#J8}(UDu%8WSc9#
zs;Gc5sU$)#MxeF3;MuvESqpb6Ul9H%`b_+;T+Z%He+bP43?a5xHRpD=4&wJOsYD#y
zIzU_rs^yTxy=8tmCsT?ey?JTpsskmNYYffs+Zcm$<4U8%UFb9ggbAFi>u#7Q7xBX#
z{m(Dy)O5H1g|soJ023k(Hn>>l_@>PMdudDHxkdrl9<U*Hzo^|Jgc6y)f`JrJtb=c9
zwYSke*G6AF&Nfax&-UzK#mAF{HycWAJOXPK4~}$>roH_^rx$_|C7|V*RSO$2^=L1+
zI5Yd03TS=Pg{d-bL?0Z;_TXmO*MHelrG#<%Rm5<-++W-vIR`l#>Z3fa5V06B{qXs9
zd@umuzS|lQ_#s4@i;~p5(Wu0TGbyALl8EdJ=l|t;QWIa!lhX0QG;i!sB$0SM!^qwC
zC(Lz!K+M9W@iMx)1f0{_9~K%cwiMg5!{iL1FuEou)KQO5wB#qEysP<d+TzrW+(!)o
zzq#VuS<Jv+9zIB>V8mEjk%!OIlaY@06J`kjg$xULpu)VLBVqQ?>`3spXy}VUEPq4k
zYxfzQ_*L9iymr;qh>?WR%ACZ{+`-BIl7bmiaCp00(~F`YMK+x0y_TNmMUiq-3YUQB
zx_m8z1*JI)a#opm2VR`Z;<pI473clz1DzPrN(;-e+2qLPdbeEC8|nzGSe3O+6&(w}
zEc)bbeg@sZ1JR^h966M<<?&$rF5{5}4de)W*x;H*V;v#cscz4g2E(cJZMCI#d(+`m
zjDO4_A-nibFwM!V3ih6X6c$_*^RddbRVAfXP*d=hHp&2oEr;k>;b?2Y)C|X2UkeY?
zee|R1HFE_ecc9jI$6pOg=9Izq@5?ek<!fUJWyG&7j;Lryo*Uddv987LWz4abB?%`h
zx%Pc7ALr1{p=|X_vF1kd8^inh*4NI`%IUzYrV3oR3~v`9?1{0{uc#ayt870QR;XlK
z%Yi1Peib?#@8`-gz6f4bYAdV0lm~Td2G6lT0#BoTH3PH$UK96!rzW07&qfL06nb$Z
z&cazNLwi_n+-V5Nc}Q)R(m$#>I@YdwAoiUuTA*r`W)%q|LYyW(M}+!&F7kaGVoKKi
zRV7%bLn|1I6s;_6v!N#aN~wn~H&hrlEbe1r@R|W#jMM@U!D5Uz{fa-+%fAG4n`yce
zOsmMO5W_wm-kbPlZ+KO~(7yx1O~)^+eLcDTbXA_^qf&c6lxe4PI9;n%Zx&s&K!JeV
zg(Sz7_O~;84^qZ&VvSO-J~)D^K}|RY$ZnO-_SG>O!s$v+iY<JZH-dku`C&Viw|!x>
z9}tBS?lHi163|H=OvQTtp162w$h0aX!C;}Z;v>NKvuF@^E3+M0c>e&13ECB6-&lZb
zxslain!q!sD|H~HtOa64g^$f}9{f{U=0rcQWPCvT*Nh_+CND?h99T~yl(4fN@S<kl
z7mSZop|^y1f7x3eeO%Dmqn>3q55@b+tSzW}j!qUNu9QSsONa^=b-l{?r3i#|?6=86
zx-z#@>I@Tm@;sS{Hp~LZgK{RVH|^Vns+8C8>Mvru46hH^&?GyChA@ZvVTp<-SIT>%
z0~S|O{_=8C`|*@BOHLr}S%n#5%?n-%%l$ykbKiQH{l^nr*^&btnOvNjL$rZO{XdQt
z?E6d^3<U~|Z4{Z=?7I#?Q^4_N3Mf`8BZ>1bh)~quLru*k`+!|{G+p<~cm@}T$Erc;
z330i*TwjDPD7y@8*&7|;KK%tEhZ~?Ml?Ps)ss4@oVQanbBWYgPJ|$e5SaQLWErxKM
z+h1yEd`EcM2xP&*hTp5R6S*Sbx$uz3T)ZArzIT{uA2r_~>T{gb|4T;XBurW8fEj3j
zdL#pYFfjaAer$Pk1Rjq8W=LTOf+q)MVq<SP1qHbUgR|;hX{hb`IJe~h*H=v;N{2-G
zXTz8RHq_Ds1;QH%%<xZxN8Un`3}Olmt509&{<-i2w4rgO_g|(U3>^PGxNRQDW?hQH
z0Lt%w$_JJfXC+WfNLXL#D!;b3bK7z|ZSem#XJq;>^AJaE&=yB+FfMvv#{Z}#QWhh?
z1c{6-i%b&Skbr>UYd`BpMGJop_f_8YXz2Ck7d{@}yzahI*doCQ0l#muljL^TqS{fS
zu#@;oSmL{@-1$ml!JR$iM_4fR(<>t8#b$+^MeN>b0=@j$8Tg|>&3>0aPNDP%y&a3x
z0yj<Pa3N!Ds$<FjDnP!!L?o&9Yk3cq|4Zu?%IHm6>bn*wB-aPUfIP(S^9fJ-`^iFv
zW?reiHWQLT6GMY+77(hvG^Yfdb^;tBuCC9#`x~lYk|1q>l>x+($q)P)t}df@MMFZa
z7xpi!Sf-p-u1XOx@*+Nn4~n(>4hp=jfQYV3qn|o_MYIQ^%tjcgR#7;jQ(PI>K&d|I
zK?2u935~R<ob`)Yq0(@tjIpF*W@Kh-sT6Z<S|E%^wHrr1H2|8=oNhUqKIpN+u|<_c
z-@HQ54yEnsLjHCb(hVi{kR0ueg)dGL7*+l^t~FC~R;0e#X4zh4bL>eTVLDgrJJ|>L
z`i?hFn^K%$m7=c}Rk8h_vs{A)l`QIc^SClgfjnx6xK4@W9k2qIiYI5<OYn39ExnZ|
zj;Zo2o51cw34kJ<e$;dtlLSSGr(s&nVRKa!T{!+|5EL?6r;1z`5I0gr8T#>Dx!)Bf
zqHri^E@A@EQiQ4Ed|HNoSuMt`-;n@fq#s~<0=q4|LG_889~h~XyA?Y2Pkm%(dPhS2
zaiC*E2G_oqiK3{1V|kRp1&M;RzoN8o!xGU@;h0#pHy|?r_7_E{+@Z|K)@Z><+W`IZ
z2|w%olYr?!8MLTOFs^7pg`l}hj@(>15DxJ;JcA8KZ|xBfv(8$83R!?7N2IA+_KH&>
z|G=3%!`tTLGpD*wbV`#-8rD;?XDH0rBlf8ZuVO^<d0O^ZgJ?L_u)rrd?pfBA6XAzi
zR41aGHGn-;b;^iO;l53?sEohMAp$$>JS^+tx1t*<R!ijI^;%r5{)=e8Srtv_#dJ|Q
z94BmNJ;sY$oYe1-4#~X$)r4CjWmZ}~b_X%x3^D1sy*bztSG}bmvugazOmg3CTq><X
z?dpeSlygtHcvo1H@27)#V%OD5LRDHKw;$uNJAeoLY6=TxfX;VEFT*#zs=~i3Gs4jn
za9>gl?3pm!7kjLZ4oUJ&GJ%h@UP<Gn_q$iNZvuIxVCm57lDOy^gyN2Xb<NG_oBTI5
z&sR^a<R%qN?)KNm!`lpfj*-FJ17Llk^-HmeF)&w9$UNKh=kx1};~!76W1Yin5@`T8
zPMMAYBLh6YT1xmG#DYkbsa{YZOn)!uP1xXL^TGs;-cA>}^6j@2G0tiGJd=!U>z!~e
zbcT2KS6PYQB&^htFoVOx+OyvlQt7_&mlmpCt?_Fdb;MChtyieH^VS>fo%#kW?a($D
zDWN|Lx2Z^a^J27T;3d$oe__{kEfy9J<bwFtp8*Ke7@SCyU{%q-&Kb1FnyzDYYHu9}
zjjzalgYyZvq$gqRfhw&kRLge2tOkb|&p=e%)5f)F!8Fn&FXUxFlzx=@J6+GU&t^`c
z5itP}Z_o<jecCP29psL2WtFZvBqGP(&!*eFMM|}`jpdn#D6sCkh+%A&#%j7Gjba@a
zD*#j6aMG6i4Z7oNOLbevvzSKO8R#wTu`}P1U7y>xeg?sqviLABg{~cBBm;}}E=U;)
z_9jY+JG&~wtc)6BT)$(pe2k)5%=ZSXo%$*E1cDz??giajP7NqVtZSm^Ognm@+#-G{
z(Zn%T(=cVh(k?!hY4KI}h?h&xQy~e%`|$TPj-a9kl3u+}mCW6w7;{G6AdvWi>OoPT
zPjVS@J@wXVOSk$m623u@%LJ9&)QnuVM0|kz)<8d?Xen~NGNshSfe`~@YR2}T3q^k~
zIG;xe+3Qu)pLUzY1d3J*2FJx^AtxjL_#(H{D%cy4mq3tGsn$r2w@4~`SjC7dbHO6W
zDsyRb|EA)-4Eark*WMzbj9&~xtI$R~I6_Jl@TXHWfBt6kI&3`1;4-VUQ^+VrLYZsO
zHm%etlpwD3F}cYxf1VmJh(kt&H!ns`)y1|QTRJ(=eE8UzV2hfVO7d~=SchJG`A?e1
z*nrf&b=-Op<w#Gj@NH4j&Of<*;H?N2vt1$#wte!_wmqEA(U{#5qfw&ivjE@R^MR_w
zyC32|t_bFO@_Yo1&z$QKF_odUyg9P%bg9~Pn0W*O-z4^4q(}&eX4{mqi9r#<Qu8t^
z0qS10F0=3CH&NM6bWA)@I6>c20>(}9xJKomNFk5(Jeb1gY03>80GAmS%G+`izrJ?(
z0se;Pd1W|XLbS8REom`MWL-UD81}xb73Id5mTuagxin@;Lfpa&aN~Zm==diCo-Q=j
z%wtB=u#aJfIUohp{=J_jwBciznQWpG)DHO&Ig%(s4Kwm=SBqY*Ko)WO*2nm$>q~45
z9dHTXVd!PF$$pN{mh#}cTP^7pU(H;<?qgSf{EByyAh|;>AK--w{kZX*Fj%KO$Mp#)
zHjwNrf`rctZaqiroW_GED$5E3`rg{<UOmbHForH<Ly`a%`tnbQS3Cb+WsVp$20%4{
zL!oTSv)T9)yrbEPbkEY4W;|6AGNgQ&aww~$G;EWoF7BmQr8^3<M*sV;67E#A9b@sT
zN$_WAFj`E_-0B}q=V-d*_)SH$!~7@O8q9rA>fM-+Ri#KsqTJEh*$3dYKv8qUS|nR{
zDNgHV666cWYZN-Pc;g(bd$0RztMaC731EzYX0Dt3r;d^+y3pRFBsPE+V%>aSJs(1u
zkJSL3uXD~-aV~yZl#bQlnJ@LW2ufw8&ktRCM2r44EFT=kFdU4fDxqsu(wer8W2a*Y
zzU$;5-0R*6*taY?W4$MrmaV0d^%}PzsfEmvM%e)*I@o&{ATNj494ioyYbS!348dxx
zjZVUMSXorcF`1pm?kar*v0K-?$~HQyy*m`EbS{V-zvUL?oN`7-yObNhN<MKlPxMNU
zd}?0}BhpGO#gC`$ZA<?9N!BM2rqaDs&N5F?sA=6;v3;Yeq?z0z2FgZlq4VBI-f>Q0
zmSKkcYpS6cskOL4?Opkl>AmGKu{T9035*!rYg{Dr?IR^92}}rERD@ncTf_(Kq~SFi
zlk*wE`MJM**^MB+ayMl!35*Z$RmM5tKoIC~fh`jJJV#@`{A+15K(Pv;p|R<X4sHos
z_cfG{X?SoIL@fhV*|Ed=s+fw(rlH9&xBVtOBMUF8d)=ep(d6UOq(&M1`m^$YI3~KP
z%$Vg-5JsEJ$=@y-=U4xZneGxD3%wEvRp2aL9r#C#N+_t)y%gzaU0nizD^}5i<s#ZC
zZ_Kzu;)ysuEeq2@rI_=D+BMy<Bu}r)j5a*O0ELy5pLq|3q`!&0c0cJ}kY4-bUSdc%
zV-Q%|NmI_N7)Le=JcLJ;B&mg)t$(%7(~i>7*`t8DxJ><o_qWE8aW#3AF6-f-Y#M9h
zh9_d~Hps)C+z%*Oqi*8=rIl>|O)H;Mz*?SDe*MsbFmnE<l9Pfv{0jwu({`6#b4nne
zLDTK$hX^)?y_S*ipND{Y8ohne4$i|Dr6JQe8S_g8%Sh5%<EGUqj~?|UT_2r8;@cQ)
z++DpouTTG;?Sqqt;}3+Hi4KBE;vsC2*og;Y&PuA%quj@PukSMn$crF(j87n4P;UZ+
zb;?c>{{8S>>fC!Oz7oJ4Xvw42$D@5SPZ=wpv#i{DFHt|4Hza}fI37+QL%h$~@AmPI
zp}Uh4g1CTQ&RD_fKbD8|_PMfWhEDm!*YNJ9BOTBcz)I}MZ-J<rN_l5)v4J}T2;0bH
zs^o5VeE5;Kfm6n?Va=f9MorUn5M{CD@@7KUnbY(7jiS{^$shoK71dZz_c~JAcHza?
z&BGDHPVzFUTBl#;qJ6E%cbPFrRAb!2V~R38;)X%!^VHiCx1#GSx$}I^l+@MX;bSkE
ze;LAOhJ5067pM|ckKJdhFu~Mhq)F`xp$mLRS;P>~fczV72c>JP2X(NU&KvMgk@mC9
z!s;6>4jFB%8p#2iqD(^XB5PS=)8pUK4S}rz;xny|cTxA!iT@Oz1B26j@2u)7Q%msp
zYOYV;I?YcGwCDPflk)FN+>W3m)!vewVghS7=256A`M)%beN-nYU~N>I+id4+4f4c=
z*(ym%P1LQ?vPkr>rfA3;dyN`y7kKIJPalTR6KseHm81cbT6Th{U)ugUf<dIJN@gc6
zsxqqNm=YG43yovXio9H&xG$t|YxudBxgFz4dhdgYV(Jvj79WjK{cE76aSOp{{@5Ud
z4;FW{9!A9yTuyw!vP&R}^N?|6+%8r|CEzW%e~99jwP*#2zLH~yw%S@tf{#_#siVg%
zkBx@P*UtgN@bk}C0gb$Q)D_|`q&;9HS0jHDvm@;Jk+ZT_n*X+;DKNvE1*UwRGexEA
zlsC_5PqQK=Coz(3qzZ(e3AT4goY>RL>2(5$$B`DX;Porr2w2<7vP-NdnnR&U5F%MY
zu>aPRxYqJjHqVM-Z9tk8k~Umech{u^3O1^gd&>X-)6GYY358%oMnJ!3H5WP=I(&y$
z$|JKG7*LrvO=$h$V;w6&R8*DnuL&DNhm@&qtL0(^<N_Tys8gj&chqm`F%yIIix$ZR
z?1!%uF%;QiGD#3?BBMr_^-Ty(Tu%>i5Nf!@cs)y~StjY^gO5*GLrd;<R&}6yE6Plg
ze4qhN1`2oCRKyaa%F}Y`behLQWyhtfmo1xB&CrY_?Z2rrS$NSdlvS9dVIs#Gprch8
z$uG@&4EwFoCIai^!?kSmPbh}S2Q3ue7g?}hq}I^#X}*&mb*MDdNJ+?)La31A8I}j`
z%R2_So9@H8BjEQPd&5I#<E0V>z0z@f79|1NR~A97isi$n>GEnBq0Mw52t(^7s#JN!
zn_eLsHox%GIr9*ZPWgH1ul|O*0Wm1yh=mO3rMTfoLiCH~rgv(5cg;GyQ>(@SPAU-I
zriuC{^pNI5JQ_QLua3#<K3hK@nSHYCcRWA6vU?M-C**-)!l0?Ey*gzM+2vPWULFBn
zqW~tgAO1;npS!_`+U>80hoct)K-}o#37{!s?_EYc;6*ns@|JVt`)%UG{)nB_eo4?$
zy|=2Lb9N%7s5-9nr<n$zLj?*o<ooh*uAd}Ji)=1vJ*i1W?RJmPgTLp;KbEWYM%$v*
z9-k5SX3zqn(@G?|8}2(18upc$uK?i6E(6zg$`5l+oHA`pch{t=dy_W3%Z54INMxOp
z40ZG8yK~^?RZ^myu&Ntz+`fIJ0tP9?vP6Z_0PhiY%Ki1ns3B=9*++(eShZY)Pp=gO
zS1!s-wEzhg!mQ}#xZk`TAL%JA0<UmDTX<(Wuivqb4d+Y#;lign(Sf;oG#g-uWZ-(x
zzn+GK=#STG=VPeT?k+>4XU#Kb%b^hcg`#n$T7260NUal1F~A33Ml>lhL`@YARrEV?
zgWfe<o|_rL&XYpVlM^k~=1HH^>GqVJ5mv}=G~Wf;p_@U}2DMUk4es8{AYAk-DD1_n
zh9W9p$!|j>T<3GtO}YU8&n^I69kM@v8z^hz87WuPF=yz;k*`xneHun4;c)#0W+7k4
zqs{pyOMA_OU5dc7tsc(D1e&|a`v$QB#^pS&qqPIWp`4l54p*~ez;|s*w+l*Xd)j<m
zSw2H2a|?oX;{Q<fPSKe~OWStG=-9TMj&0kvZR3e;+w7Q6oQ`d~W840F@BJS9-{Bf#
z&N^9Z%~7-JzOR}_eaGv$=J)>KGtd>8{UhW^>wIMgdOq;G%VSZO-;(YNaKj!5cs^!y
zf87_r8eFqoz=|nZ&sgHrac<)s4zs~A2eBy~$?!I+feg?hL|B{>%D#ZK`^;#nK|5*5
zRerz3bpH$00y2!N4FOJ=IOUNlAJXb;ysA)XjLOe$U$kh?ZZB(kU&@~XV%XOs%Qmd8
zA0%dxyj)^$k{x1~Q(npk@b&MS8Z|Sp|DGAv$*{@oVEfLHkG-{pXoVkYERb8`#ofjK
zvv<1^;)Lhh$;oZW*SroZ*PgART^gv}rd?*`WYdTIgM(SGX2AE1KO3Ga(zbCe-w?lX
zT0Z&885-Nv?1rhJUtB#>pmtU?YvGXK7wY?QYA8!Gb=#8C?Feo&U}<~d1j!S#p>=xI
z2ma$i=b~KRRW0)*Ev%!qYD*R9u+)I+kSWy6$7kpLtC-e53D@)5Kf$?hj^0^XopJy+
z7<&$S^F}FXJLZpDE>HS&iF%WGwYq{Vqz1*#B(N3%ZqytsUZqg5*Gwq)VSnMiM>5V)
z`xc=#Z*tm%rnn>p(6~2$XGE!H$0Wj&QdQUrDmWIN&U>J?IE6D*>u)ts=IPk!Sj1tC
z?)H1^?mP`Gl+vA0N(qC}6t{GGy?t1E^5H9Xy&Z1DA78%Umst1jfw7ad-rRSC{tFN5
zV{2aifR>vV_p51d&&u5Q*i=^JiU!5UkJv1rL-mzQZHXx%AYPD9=Ikms+m!)-*yqp6
zMuNvPpNvnNL<O}^WG?q?sSq#p#G{Lcy5QB^$Yr8mul$!hpA^_BUI(zxYF}mG)CS%j
zD4z~p>G2CIkMaJN!qKNG_y#9ArZDME7;L4yGdp<MPR=2n-QtuR;g7)KGlG1L4yMhk
zf``Wu*`)9$K=!rngexd-EWfR?kO=O$;hoEwge?xTL_-{^E=N6$?gL|CoM7d9vANOW
z5@caq$-Psa7K+r<OZ%1EXh|0+q>)6ka$aK6y5i|%6TwK9fH#wi#e*?M_$<Y&J(1W<
zYqGQge`hU!rN}G4$^B>3-5~Fzr|gJ{$i-^5aKYmqVDx8t@~DG(^7_(*Oua1!-P_nb
zQ+H);y`TX~zK$Nl)ro++VAJM6E%}6fhR@dfvLtgPlMn7sVdf0@6UBnf@#5qN0Y~?f
z@|5x-f6qZfwTzfGAu`9pIg~6I5Y+eMfm<?qX*@Ut`_Cb07nJ{@*(F~nj&8;+dW~`m
zr2j|0Az3H1GAy=vyK7wmRVc~=bfb!Dilrt?q~A9ONQtV^QS$qsJatRv*=6iDL;{iF
z_?C!1WI{n%OTWDXnC`VM;Bw=~ip9I~{#cYWGPjS9&Iq|_XnyV7_pZk`$YSsr-~V#Z
zU;kIyRGotWs?I^Ose}IdUt=m<feH$!Kn1;Ql>DVhk<QzLjs(<+f{ufLU&3n-{WHMv
z{ewU^Yy1DAaQw$O3}E^7|F=BnYERUXw!?4zhqCe{xmc_kusif4`9~A(-z!Kh*_Qq4
z#?fyFl0*`*N-~4;IXhh)#HgatfpFK?>mhyFFAGN8gYToeeSy~%fxF|KiQZm+J^$Cv
zZcTs-fB)fms!rXsK~ElFI~FEEU<@mrV8u{2y*>e&0E3W~B<o|XedVhF`B3D(g8s@&
zUF&`%{U8CF800t|G7*{#u+!sR85Ye3+`4F$eDrqd?9@%}%$!<wGcm4>T<PydROE`f
ztE$!`yFla%GlLibgAET=-W}DDpCIt1DgN1uJ4?k|*_9SQk9zJ~fLgRH%%`dUb8{c|
zv^Jg5Shy^&!_CK8=F8@cnHSf@*OtHSYc=YJ!}7(e<tF`0ca$XyfDW!mB-`4~S20ga
zH4d3W(Z9XsBS=Xo5<t#g(hnQe^lm~et_WA~@S|S&cGVW;2|Zdgqm`;MB5kBmlUAdr
z_OoPbwrzf&ou9^DNR8}FVylX{Qs^#tQBp|}M{qdd7^fmE+*-$ztp|rD-((kLNy2@m
z*i!%XP*m;rIQ*G7U|)pIcEn;O7kO-frw#<-*e%RfQ-W7Ta&!L?#)D<e)zf1^g~6F6
zcy>BMXe8iFXO(S(=+E*Uewx@|f}vytYaI(gaVFW@tIDvjDetORPJxJCudmEAh2>E}
zj7!8oHKb{ZTst)8d^Ze~zRRvE50y%b9u;(#@M=mJ(7-oO5ouibYi)2~&${VV*2-4E
z=Uu(dlVJY(A|3%wxGUad#%8c<xOHKN$ai4QebN$5=qGJ`b-|@AOKdP)^IB$`kBG3J
zVTCzmXU*t-6s_yGaQbr+bSi*N+xwop8RfaY2a}u3eSRH23KCr6zB#@oM_uQ+UhbyT
z*@bn7@1{~XDYFFJ<hS20G8e?PFBaCabeqXV1=i|}G;x=u6;`o_h|ygW2=B0vw0fV$
zbRyQxFTF)_SvQhG=B$w9`V?bEPS4c}D2M7=6+|HYW5>D~I0O`H4=Nx-1{JB)=_gW6
zccVf{0Rq;cZ$BR;9h#kUr8KwCT0OIr`?T#xgHAso%9?6b#x9qHi0{{xB}41q%($SU
z!Kg%&?A#)e!QB9-3C}MScP{k3+1XjsiQgK0Z<4aMYQ%OrD#kY|rsIyu7HHp#Lx-j!
zvLeRU&BNCkbxQ&58zX?>zxl9#%(%-LxaETI;Ro;RCCTasK5_4yGscI+iATNTLEsK^
zbx@U&#q{>pgbBwcRaM0jmr>FmghY&8vKo$TCWkzyUQtU3$|FK6kn|)7l$H}$DFdTa
zo6n2#_Hy2slPxLG_4*_UbofT>IpaxD=YDCa^>3Dp$Q-0oWbOi}2J=^_WN*uH&|4IO
zIPawvx#JCC@Q5=HyBHuezTiqOhyvf|EL!P%ll}&9sS3sl{j0ySr<QARI}kV}l|QkO
za<Gc!fSQeubCNBny>_+b^gTK~)wba7<7)3)rV$OnT@sUYDwI2Yk3~e_@I<fM>Fva(
zk(Xkxq?&Iwvjzid7L22tMDz`)d3n6)AXcpDK5V8Cy@X=@+BDn-S#%S%V|aoeHpp&b
zQ_T!^bEbLE7{4GT^6@%FN{bRDq{v6wnOBf#j+un>=`ymxYK`OUSEFxT5?qxn*4HWH
zI^y-$Oi%*#dBRfineH=6Py6@ENbvi#E`2o#sbG}X1I__WJXPhr&JuLP$Qap4izO&j
z3@Q*zt58dGrNr)HK`cZp^NLZ*zXweet!}a*5#b|abaH;oS-Ka)srg#+xdm#9><$PY
zw&a3mhng68z29KkDHdb1r~}OmdL}b+gzc7uX=p}FaGg&a#e65LVh)lNZ&j}=#7;yc
zo;VkI)QJI{-Fm43PyB>b1z~eYQ6a7c&7PrXc=jD8>R(Nuo$!L^A7-<Cb2xC+dEh5`
z`>b`*N}U>iMNGo(7On(X=-HKi)xU__NdIo0ndh`UOrEx$E@8T(4#S8!ABO_dM_tN~
zboP^7AG19&g^}$M@5^yr%IwhWl3T&%&_j3Lb1Vh?R;joa7dDA)M>qfQk8<atNQk6G
zie~%u6CA-_E>$N@il&yF3UO(=xa>)O=<V@0dvz4ACx31crxLy%cz+O%Pfgo1CQB?A
z_bHV-Y|bI3jI3+TABdPKM>m?QDp<U@)6e2eG}Hva6X@@l?VcuP4m2XzJO8-Z>C*V3
zB)dcao1%gRod{cNXZ4`;&`4;kzm0Rl?ywK^IyjJyca5&7HoZKbOhHxks8h$oQubh*
z$w}1Jrvui72JDzjd}}X`LV@pEE{{6C7N~4?ZMuBKu`o#x@~9S62VDQSEGD5rA3crB
zQNeTq@!t`syqa~EM`b20*35mTmo6JB?*9d-4*pGV3$~~sq_?4FlJLI9vOaxnTHIk%
zfqB>NmYh9RGX9{PX%2z7k!{TLn3q~1Cd)vVTG<I^-2O-aRDYnWwU|+hO?<mk$54&1
zWhFQiamS(V%XYX*wCoZMFX*qPoC#Oos;2MG<2?}1uYfm6eKW4_c8On1_-J2Nsv-gy
zY1miCy(2uQ&e!%UxJJ2a5!T=IO|fXlUjDL}K(CYD9dHfi^uio@Yzb+1Z+X<{ix0gW
z*VFf+<J<q8RspK9J0HsA=ky2Y<@~&>!wqG;pgpl(1$pZm?!P3MW^eHagQq@VK_#*Y
z33o2^@22AzrTTYmEAxaV=MUF0e}-)U@A?T7`E4$t-}|5l5(HO@>C!$TjG!_tts3^I
z2)Nq25Sr}HX;pG_)I?x1mAN`kHgVN0VS0@0?6I9Z=H%+#YcfhY5M0|i8j3`Sd6c-D
zNEtbtEg8ii_xUP`a0OLPPXc>q>r;hj;G{A?j7fwb1&cJt{-`Eux>JKsX=M{|Ugwb5
z<So3qw9kfY5yG*C{OF!|on5qeYKLvvE7IH11wYx@1#jCet)suP@hRt*=dIDxRLi%9
z-85AzGETQyOh`UA^t4yHEh`SEcx*W+>u|$;^J8mWe?0cJ{^t##N4*eNqG>!W2>UZQ
zYon+o)A41A;5NDkTGg*}^G%$Dbz_(M=uN8Z@#@I8O_%gVC+dzS*}fXn^y_8g`$q<g
zAi@8VC6@n^WI#E%fcja`h_DFaMuZj%AD@WdU<(tL9RIiG|B^73QfQz`DU6+6GR74J
z@CXI=3kq&)pY7N4&Ief6TAIm!SEncXqEi5=abZW`5itBsh`iRHd*2beDC?pB7mW3v
zUmlAb$Vm;G2@8LjcRzSs-unr%Uu~=RzwI(AU~uU_iI6(Nfvm7VAnVUhe^A!{pkT<s
zn3&js+f6?gpx|||{Gz}A)4Zn`;|KlUzM5!oxa2O|j_|aupTM*(xEfPXw*N~fVMySC
zFeC^RCLnD8A;kdILJ?je;fv!tA&<VYdqLRUOj7?F1BC6rE<*r2$N!T$9_s!B^tB^*
zzyAX!rA+yOz(NcJln1uA=mGoiv`f_uc*87wZ?3a-65x`QJ!E7{1BB?4B+V~w{}pn_
z51&IB20v%TSe|9lo_99O<FBr5p5K7L!vO{7LerMP`NPW#aYS7@hjIgb=)&ue2vp(=
zz=!wWv<_r9GEp62G8q8P%!>jVztz)tv(MpVJkoO<exSY|xJ7-~>(a3xDPHA$;`pr!
zS#9k6i%DQqIktsS2tMky%Ul4@RZyQ#0iHx7FZ<oi;vll8XFHi2%iH4$Fu&W2%AmA<
zOjL^rbqs_Q5Wy`*BK(`If<G-)$Wccf;3E_g^Lu2DXtq59`}D^92+bmQ{8W?a_i=xV
z54qJ4HXK$gSk7Z*TUokz_g}3%R=9QYaUlXg3JfH7^}iaCWL6Ijfmpdks;ps-TdJ?r
z7g?Uvp%=M+6p>Rd=v>^`h7)U~nh*XYN&Fm)k|<}<pm_>~gu-8Jl3}!ww;6q5{2FVo
z&lrkKdCvX`ilK92#IR<VaLy$JV%GL-cE)t(I0RI@Mz`f(O*kG@l|@On&Iy=W;|aC5
zhADT#)gkF7>4tF#_#m9={&5JH0N?ZN;X$Y=b(5r2GYHph@xR*FTo}qbK5<(5I{l)#
zepqjRW13c+CnqY=Aqyn^Olvq<T7&ZCoX`d&`reFZwDkjI9gGBvInU!JonRkIVYPgb
za!N|fQn%FYy|P!3(i3KMm$AXoiDQIs4hH}JMrf^vH0A>r$q1i~CS}8)0$Ahhi2vx-
zCW<?guK9D`SAUBI&GbXa{i@N$unO;MULWL^^W87uXVt*?M<oLextXXm%LuJ;UX52^
zDu@LdZI=wwJE<_qDH;PvbRfc=Y8fx=A1bB0f%>v#3Sg^yhh&iEMhj(M^@u5*5>b2g
zbw687P$Thxe5iOufmRS6J78IVOx}J<Mh8gBrsiryjZ|&t>Lwh&k&u|^OubueWfvk=
znQ}oYwLuq(TyHH}7@|dy$|I%iFqjyEO~S=#7={kbk|n$|%1UQ6SiQWe8!jrXH`6O0
zM(Z|N8#N(IlmdOiUATaXF|r*f)kH%9`S@3pTmoGsfvB+2iC?;%2@uuU*41ifd}p~O
z)bUsv6u`mhLv2_rkWO_v&sxhn904x`T1w4k1pw%bYXwEqokkTUEap>x^n@e_JG$2+
zL79p(eCvETevs2j<u7z9SREmqmZl4ys_@UW4PKrJnZA0x9%?*0JCFFe0t}!YDjaJN
zjTn4&^^S4m-HSd#0n28fngt4{l$DQ^^B8F6^9ye``E|#=J%BR#^SW>P<fl6_mo*!>
zXL09q*T-u@!i_|MoQSGmxIPS_gWt^s&aZZ3!P~3<6}M{zj}{ws)lsVdeihE8n<gw)
zxkx=fGhhP6c_q#D-#B{6*6u-(;O;93Mi}!w9#Hv-9R&}Ft|YLKeupJZ&syUAIYc*S
z265wA=*Uo?oo%NK1}Gc0w$55|*&QTxgS?ZWcVU>Z<U=z~kf6#4{Hm-DE>dMBaKnwn
zZPb82pOqokDcBya@rCN5yK<xM?}oCvv3k5s62Vrg9lSXUk$=Ism>@6Q$Fptu<1T~M
z*|>pZ>Dvxi)K1ebQ7fO9OO)2Du~*$c(6>>dRiAQ|X@eI(vCWaWHXQ|bl1sJ4hBff?
zu~RqarC%}OoQw(+CN;rOMs(xertjQCm=TrH9M`u$JB|1|P36w#7}%y)X@q~PLYR5y
zT1Sy{lZaW8mFPEVRkaqo28EkDlj$eflDQTREkFWzM;fqeL_B9@en;9}TMLiOht#=2
zZ9-(pPV&p)xnIC-n(5_tcp_dhHzy?dh`11e1CH@mFAQFYgjiS*Quv>dx|ly1nI{V`
z9T$-ND?ITOB6XO`T23qBBJiwh>)Chs#Vzp&g@W3zFy<J;DJX}@J<IhSt8sX@EC!r)
zw><;G3boKR=m~qpS^(JsV13w_46d>kqm@W~+IOgbiTCCAGq`SktOiHE!>=~eWA=Y*
zPMdum5erb(Z6adJ#)2D`W3M~ae!eHV-}&+}P+FRs32(!*qJAA1C)8{^Jn%H0+Fyn=
zl7diJS)82hAT+BSpZ(VDNOA6EPnx^*CFcTk9?;TfPozED6nU2@E?6T`x}w|rpW}qQ
zZYl>K<zL9x2*naxtPsii8^c@Uv$R064H6mz<1|gx2z9$7RtxD^rrFH?afq)G%C>}J
zt5~t$$7v2_Z-;zbw^a?s5l6K_N~JBde#Z%U`A<BD*U1{8)r18bBocupNVd349Y8SC
zExt7p`^EvzqSf;#PII7nDdeNSk0boLaHnoC_hoCX5U!<)wPN{{I@`Zr9SA=Lp+lYe
z$$r4o)9&C(DCF~ea>5_h*1ozk;$?O3?Ai;+I=ya(<m&NLS@&C!X-lb6-t)RfZ+k3)
zRKF13{g^uww+cG>C-0WyQgOto1W<mqcGvy#mfzovp(=6J#iL;vuiN(b;<%ew6erd%
zb{BmSz$X4hrHmLe7d?DVmGK0m)$WlbrdUgESIYth6}-n(Nd~`5-#;dzMJiT=wDZX6
z;thc=X@0(+eO4rVwt1vTaT>1vR_ht2lXfNM<=49(k55+`R~=c;2lu;u2GD7ET=#3h
zY`aj8dL&}T-%t;wDdjfeMzW2p$E+=a6Ys4U%P&e=9cSgEW-AxD^o@R?zF8P))QXNM
zPMuq}DZ@B0Mc2%((UjBs!Eux7C(mKeZKLKU63qlm$1Xh!TI`}MQFTCZb5x^pN6(zT
zBY<XoLlv`N*!<!)+3PHJNry^BKvn03%t5YdUyYHxhMtm)qZB+EKIr_xqEVIlkHt1z
zjmorOvPL>)Fz8FgMA~2u#zN8~!1T<3l>wk&G6~E6!D8Nk&Lq<)ZA=h68L?t&Dc<}%
zXihfDlJ1y_fCUhsBfZQt&>hkA+WxVLfY<x<ft7Qt+B!aUtJPx}5qTYY>y-@jFul&9
z#@q?PfxHxr!HgR5dkq^>$Ewql5qvOVBDl?pxs7<r!-@y@f{byf?)f>a)1h4H-sd@q
z!}R5HpMkvWpN(VEO*UncF8TQwGq>>1kJaEy$<2RpDg;0m9AAtfeWX+i+zcbx>r@Mb
z8_%us0m@Z^ijxZtz%7-WuO?-sO5JwtbaX#*v#Z<Qxm}b4|9v|R4c<B}U`Y#02_w2A
z!(B(MU>Q$wmp?ELVAcbd&wQG}rp2axedFB=j6Ui9E!IrtIAFTn;yCcewDraBG-?|P
z(;YCT1-PaS$2N=<u<Ws6=OSP7&0)jb!JTYs`eC}t$G@$E<uaest~8|n1Ao~|g&i|S
z-#v_#^9!N!5(_AM&BNg2d>ZX_<`y_*&eynHv#*SXrop^24`gn*rjgSP&@qq#{ILvp
zKxf9@r=j4#c)G0!>pWf?9u|jio*QW>&U7fc5GK56DHA>)(!aq-7>V=#my0GU5>Nns
zv9fXf|0|G(x>E7`ZRr0jkSB;};S>9ia5zE%QeZAsCHBEw%@b;VW8f@f!~g+Za{efH
z4=shXMAsIX){?!A(QCVuasdkF+Z$2}+5rH6_ZP-C0HLmK_u}~GjT@kUb+~}4ISpRn
z`Aw+O!M4TYRMxey005MEa(^Gkq|Y2~xwPM<jL?R&&fjxX>V1;(fAW6b*>L7)DAVo3
zI8HioNmXVnBaLXj0Q4h2>BI6S6p+uqCD&={7oDmJ{lDM7j#n5-{=N8}2{FKHk?A&9
zc~MfoU+e25aON`;0I`ha`GX5YlcKG~{LL;)LEP-yB<qWR3V>pYgTHG+-xs2m4BZ9p
zgT^zc69lQJ%gO!mOU%=i0{S~W{WQGyO$yx1k+CfOnG?a1kMHzPk>kR|YdRrUIO_2c
zo_?OoZC1Rr|J&o?@o9N_s3BPa2}oiE8)0~fAPc6Xp|W;em>H5mqE%rd)@9`GtTq#T
zH<~os^iHs-5CA7Z$fVpi=D6ndp`<j<qdzM$JB4~cua7eWQRM`iK;PlDqMr*s5Hp<(
z?=|Xp@9~8z4+^NZpM*=1qg(mm<BH0kPc3VL*EBD$AkK{XpspEUW_&o7x&(S`LxHTM
z9wF0?<e;F-6md!oWhAD0M~zDXn}bGB-ie)Chc`BE0#H)o9<LUD)xQ4K=cr95Ly4n&
zE9ue1+&y&;H6~(0HsqJrW_e+Qihjo2Cz?VqT3wcdc+o%V3|3{gLMNHO3%0;=+ma@+
zcBFv<or|sVgGC#H-I7k)F!FD?VS9@#tP3WcM2Tes9=sn>8Yw@)j|rYs!ob5k7MllT
z%{XdIIzasBCJo_a(BU{k1%6$NCSxz!^toHA!S8#R^IO6pO7NwT=Ujcu7<l1dW(BzK
zRQW&j%{4Wr0umpAHtMuvG1^m{!ubV#7H~so?sqUjA_;0cL)3=a&H5nmeg!&zVt4}#
zPFnm!Opr(hD?nHfZs71w_i$JZR1?N#N)9Et?g2|G=4^dWT+z<?L<Ws7t*GHN;~Hkj
zg?$2$r&&ys8UtD`1eviE(;5&3OnuB10LL56MG(X7(cjB1k)qo;Zh1dd7fnK<9H-uV
z7f}Wzf}k_YO?Zc;(uX}eSp$)u?9ik<sUU@egy39Xjxq^&Rbc;-Tx@`5z94BT9|1!f
zQ-E%pk!GZ4r|Sre1|JC%!qaVJ()hpIJT-)S+jckd;&^-8%%^+XHB@^Op`o2^r+)?S
z&cto!EOn#}jDN7c7}<9$L#|L@RRTJl)g)t-_tucfCc9IZAsm<@yJy#|%&)+w>8Xuv
z?Zf#|vUK<fKr$P~5kbtaGiSe$42Mg8G64qC>1snk$FEaeK@YfoMn9J(%+NzLTX)n`
z*-tMiBgcWCrLwkT2SSjKkEYNIG2whrRY2`V9cy8`*(oN)(1Ejg!5amo)L>RcbM%q9
z2YFMIHGS~My2?glT->fg74exLfc))Qc|-PO1f!DjljP_o^()O~u($)VE{%Nc-2o)2
zG3dttc6~@*0XXr-bCJ9OyJpPAeTerzh{7ng%df9Q-8t}uq#vGZ|3FhTV7}4L-~pe7
z0HV=YpL44cMFPeL$g{l*Z^S2cYKr+>;GfK=wD6vhOkZJ7uO1(t(D&KB@8&+=jq&Zx
zb^#T34yp+64cw7$Pi%jOf(e(}Rsh<VvRZPvx!lkC$LqQh3A0rhNTB9$!(>l)G{Ye-
z&!<}^+JpKKv3?~QLNUBS8mG<v5gJas=-hEPG#L*F-9AN)y=i<+qU7Iyk(3i*5dAgB
z2vq}TQ;nWZCuej(xPwA6rphUY=ohrr@N!jTJ*Sd~rXfaj{U?*opkXFb7XTzc?iR6e
zG1|*a(_c$589b^R9Jo{vla{xAt*=?1y5l}Ma!@Q3#vqKg+-+lsgW(y<A&aL8m@4R7
zV;(47e?&=I342Eh;gbc%<qkr!aFZ@M#-Yvoz7gx7$^P=wf}T5;qX$O#nu=XGRMXQI
z*T&^)3{PFna2@@r*)(9dngZlU4M-z2KsQtjj-w=YO))#FsiT&gf+)Jj7Nw~2(?vl8
zk|f05p|$PDCvCVi9bQ)v2#Hwl4G9qVVROaJz})Qg)7VE0G+|1ROo&%_nIbhULE2dZ
zWD0uO@~VB*nHokyv#uq>tO&Rr6a1tsAmmcHu^56VwQT%>Tf&f{@_=0KAlEh+V%)s~
zjW(;ON263oOJ)-Tp}`J#IjgLtMP4<1I?r)NSx299+Hi=B<zo6`hy8s{g<i4Sdjjw3
zq?2-IZ=hc<45Q-aX->(8CP$NNayiIuN=3gpb@Ox$pX}&a=|^6WOcsH)pMD!DBwEsB
z{HoiNXD`1;r1h&C03b`5Nt`(OqI=jEoc&xnh^^rqWHo-g4JwaVUCK8Fx-=hiq1HgT
zcC6|svy|D^er$N?AFZy#p^Al;lHL}mZb6hNph2sVtj%$qsmMG1d+TZfEEmLDajAU>
zy4~hc0JGETyn;Q94D6!9|Dv0I*WGK!+)TSld{wAe7I$wH1mG?@lXH}BGtVzyrCL5I
zS<6bhL;x>lAtyz_Bt*jNI*rf}42`I_`P8Pa;rd$9aL7Wjj#4#b<J}Z}>y70Q<9Lg)
z<V7mc7&&D|<8Uz>xmiO^*@hE_Pu-k>xz_Rzhkp9A7^VI03zWPQRM%zw<HT@w$Wa}Y
z9;a`M=~t098zAW2$#L-t%1K6{Exy3UQ%NO9n`48(8hY0wiY4S=p9+%O%g>;c)CL04
z1fDd@98W5TxFlS;bk)M0M!!|%f|Vz=zK?uNMl66mgGxc!b}3`ty@FZJ_?797>P80s
zm^M<KfmIc)`n`2lJ7t1BjGQy?e75m3CGm41`2~5&1<<kQU#+n}tlPO`s|RUS^>@7W
zu?MXrKP>`cr5!5OVXj6btHrPdZ)nKWrY~#Dna`-EC*^g<sbIa9_}V?^Jq`@_7#e)o
zP59bc7ijR8CI{!Jn|YLkwU)(LVLwdJSzuoqSm(sIGu4(Co#?btBUW15^D0A!Mwl5u
zVR~nM0yx><lqFI%>NvF@x~EQYy8UcH`YL0`#jGl5>6m$JZBP!E$g$h0{cG53oN<sB
zPchvk^xo|4=_=w(CSv*vwwcI$e1nbLFyjsNk{XTM{OcUYcXJ3vE`2!JQu+=ZY3<j<
z(o<~X?v5nJZ$@23>y(R9RzfT5syJh<zPbiWAKq_$Z8mw}h)>lG&`qGO2k7(JvY^r1
z`<{O&r|@^48KwX^A;_4<526bED6RZ`-`c-(@cTah>*ogf_0E6NI5d#|D<4i3A^@if
z5t;qLnAusv|M?_=AVkQj_5Ys!$MOc_VrK^i%p%u7{p`v-N(DS4W;`fF{4WH@e?*l-
z;izlKK-4u9&Yg6YENH59mo`)sK+;CrKOP443#bTK>^IGlDoyHOYB$%doekph=pDIy
z;}~%rAuA4ifU1o+sq&g_sV%(J5Aa!oZ%|PM0R?a6*Ze%tgKul^*SviGqa9$hjYCx#
zWx06`Df_n<4ZU=-)D(pn$EQ!f@mrm_TK@S~x%R#2){@NW>4Hvh_gh>Rz(2$PgW5p;
zwI%+Qa!PN5QvxEHy@@6Av~=ycwzju7W4ipUZvNq0m`3F%Lg(8}yu<NMkMCUOfy4J%
z>Zv#RB=xW*QsE{89N_E2N$+lkcVLae#wduX3h9shPPuxR;I~`W*RPh>1*9q`fD*$b
zcZSq)rK!H;8(TKh2Q?BMK>8PW?>n#Gi%FJim?$})A3tK#hws~MK?<LM0GH$IbWZAB
zr2q60?LnZy-O|Dg-f6fS`%zWxrHdAWvS9@rfF0<uDL&^@N_fGtbB=j7@FR8iebvD#
zen!x5zvaQBKT|ok(r@!2uBi26^6~IXb%*bfiwNGVNAXzm=;LDna7<Vx;M-%p+e$OE
zb2ioT^%HYzbg0~xaq8i~@5rSh=ONKk(=p(f(quPaQ9z_(Q!FZhJHK9?kS5P!Fi<=6
ziD4N7Tz(UEKg=@@3t1tp=4K3!JO8rbK2m(7byY;s&0nw^a;CT`hiImL8LyM~P`yD>
zL%cD3{cMv<yW~+9z$u|w$kr5DnrAAOAK9ybq?bv*{R%b~SmRQfge<^$k1M9Z3dB|_
zWcL>=X*D|KMX_E~C`Rv)Z+rPLCEi^%kU-!Q!K;r3bB};5L55EYb)smea3#A8a9+s?
z%1#oT$X!dI5#3Ko{5yFja$dxLtD2xm<0X?YtX_kuwmN_WIN&1MZSP4T8n{gS^CNi-
zpFFA|2EwW;J*Q5kHDb;PN%=%rFw9yGF^~dmsq8T(h;H)YRf^Elee|%1Ya~!~ru48>
z)1WaxdMIjk)$1V3c%BX>sD*BH=nuL5f@3L%`krKI`rhh#{3abad<R9SM27<v+1cbQ
z5}08eJto~Fph`12!g-S%T}D&l6anTEBNm#L#Em3P3IF*ajIGkIRZEbt?kTI{*nDnp
zue)#LVQpZ%(U@MkNTx!NU3Id9Rk~jlQr$L&R`VysbdKZItPYwS9l21$axDd7aBFJX
z6fzA%pbHe7(6!0S*YdmPOx=2)v$6!8rJpd!wmOTzKSG0S6LXoeF{l8ucUeo-Bz=pe
ztQK)>ppb0TPnIJL5T>J9WO`ww^dd+`JVCmC5GE@Guetjjt)KA4mq6iq>nE<+E{jm6
zNsI5L*}9NbgMXE~N4UPD(D#8A5uW?q8VF=zD@A}Sh@od!E86_r6vLf%g(pJupn-(U
z@{~n8z~9VbO@kfnOHq%7eQDD8vyG&Wed$ak#QiKUT7zk=TDO{WTLOnl;2hcpl)gi%
zrQL<8>Gnw@WLuB+5YNZLVt?6`+jZk)bl{K)sX-w9`-OY5NEDA^!sW@+D<#F;&J#_=
z#N=thOz10hul;gwSGe_uIbWHJm+mJ<8=4+BK#-DP6RCUO)+0#YM~&Q;tM7ca7xf1u
zKI<g^9c;&-O`4t$OxROmiLKuKmUnYHGwUjasC;q-gG9Xq!q-TEDXqH}?%ky~-7y1o
z!^?n`jYvoggz=7)1>rg48@qCdRCHE{hZ%>A=+hmj*4HK}KLBO7Q=wlZf%E87UYr~U
zFn^==Ji9!ejUV%mZYnoQZfc2Ty6=c+C!#xNcX&8_pbi^Lj@pb@h4dn@e-cH%4Wa5-
zPtj7nA-MhRTVDSnIyZ_l!6z$@U{<xq5K8hemE9QFv?h#Y_IsQGQu|#nlJvTKJcCFH
z{emH7m2CX!)h%-~{8zS!O5?4}_y<7>KnuUBN>AB+DNvU~&V%diuc+YyzqEC6esNsU
zC+^cNr+hW8p^k|d7)tMxlD<U|^eS$?8@Ny(@tt8P59^C25h6iyiOJO<kB6(>O<t?;
z=~U?Tx<q-`kmnPqIHJkLvvjp9lqY|52T^p~=b~4oS@Fy|f+>@@^1a_5xMgW~K#!kz
z%*56_pAh_5^wGJhfcRh!zLf{aFC0eiqudLlW}#+$p0$CKByh|Q|FB3Cs^&<LH~Ef@
z3+tq(HP=j?i#@(s@*k1xe%rhb&_$?o&k)%j)wfAW6pojcfB3UXQ<YX?_{M*Z?3WE+
zVf`b}Fy<O%gM~<(rmHhYy>%uB0jyG!O8$`4_2@%x;YBia1R|?%a5yS^%1Raq1&*?G
z7ICz|r|S6yK-Q5_KX$~QyTeGZlF??>cb6<safz?WaK{e&$+oF+57e6j-UVl$;K>z%
z+z~t_i$Pd4%}9L@Ejduzbz3IcbRpUF{rWy;@&-3A<431Hl}?5wf3V#?0dL+;jK70?
zGR)h$l+jN1avPaR>dJLmNlu%1VU?Q5E<IL7^Dzs9%8K=T?N*Acom*z!Bnhl{O9grV
zo#lU<em$_}`Bjp)k0hKNpCJn~3{=_B??yK(jJEii7rr6u9};x(-(!bMkBLdmrYA1A
zBPQw<`kQ`$(l8#lF2>J;0TjLHDec(T=9I5)8rXQ{pM)S>)Sf*4IyT22I^H23IwCaG
z*->7N?&6K}CJb=b^%IWlUNjPp8(q$uaN(t3IkDPo$}`%F)6}_>@n78Mf~F+%H=yY(
z#(g}gm}u(n!GB=c8ASfQ5<xFYZZj`<N9v679rvgjx?m@@&q3BW1iTa3=d^j7U5|Y&
zbzu<B@Tc8Nn2mZf#+%<;OGLEO)$+EInzf`StXs%H(urEtC{M?)?^YdEWsd?$(W}XP
z3Z*+EH|C_Zi!3Ocb3mPbUUrGwkjhVk&o$ZuU8jR8Jnn$?@AG=Kgx<ciJEa~_)IJ_H
zMdkJ;S#mLbhR^KDfPzBT?_GtRmTdp;=fa7J6l`DeiCncb^n0^JwOiBX!sO@DkTgV1
z>y?q9$G5>>sk4!X{{H#fx)#Q84lDDGpBZiDYt!m1U6TrmpH1=tmQ!HbOOV*hv~E!j
zIZ(aK$RW9c8%F~mLAwjDeR26RVZqp!f?#kX<zV?KBSHh|fZb8>>d~J}z6;tpTa13q
zlf`$<^0o!9QjzeBJ^1(PCMc5Ub|H;vboWk;WKxP-qM99f6-x^mcc7hRb!a3DL(R&_
zJ1Pp^ed|duMe~l!-G}fmW{@!5@h!VD=YUi<5j*?Ij$L_-@QY-vv$zngF?L0AsFOkA
zVDh)L&B8)-fIw=?bGtG;KCb@z{c+r|^31*gD(o510@U$+B}w*zre$Iy2p8^^%kHWT
zQ&0iSQ+W?g2Sh;7Rz#wJ+{o~EC^hLXx$1(@=wjA1^`B!j<og<I*69pQh3ciBm*$ol
z)HbAHZl=`O6%XsbcuuTXBd2Tc{I?UQ#P6+i3Fh4}0IzCJsd1_*_ncdGR*b;IwfTiM
zZL3CUK1oWyLhD+V^HhYG;)!-@-A>lNDsbS033^@eL9k)5HX0wv;Xp+of1`MMr_O~D
zespfx8FlZu3lVuuJQ)Lmmz7ybP_gkX(&oQm9~uTmX|GgG>cS^U04?EMLOn09Jyc}J
z3;uL^IfbUa(nB>3Rcp+S7OK-7ri}~<ua!h=#zr7?qW0>q(u$h%4(<}NCpxWt@s>hr
zy`N-^!7O%DwvK?I;N0RB+nt`#Ir~@<z&+vYBh-J#(xk_fK*J~VVB!87IrRV1-*jZ+
zH~z~KIYF%MAGv|RiY^jFB?314{B(|3abl30D+&MloKC<?N}{$fnL8<5^Q2cga6HPU
zq`rD^OG^Gj$i)@<J@oJaalQTb_<rb_QDLEK#XQLvfn@abG^Pd-r>uP8Js9au3!0OU
z-?9d9D^E*|2VEF<y8gEXel@uEu&7*yUfuEM<AMcFy3T=?3E>=n;nykxZ`ctrf-$1+
z?|51C*ZND{MHDX1wAcE&0jDre6?#nZ+V@$Hi*VBzz{kylyLX1$>_RX~lR8}3W4t~-
z3drI9;xQR7Ge(`C&v%7xPLj6>cyg_*Iad&%@&y}M-}aOe5>-G&AVFe1;sJ-3!Y?|O
zD~YV-))Nyls274bLamw#yGTeT9H+vS__7qLd>}+lu$W1$Ms3Rq(lOC0^hRPH0pS+I
zNr6?G2Ok2r-&#?DDMME<Mp1|ZGS?chUA0o!qDE<f1S{8?MA2wlkoZw9now3?G3W%y
z8vbo<90G4*r@DxIlggz=x_})oiGHGGZnG;q|9cHb=Y_@`^O)WR!rJHtWk(Cj!rQoN
zxLAq?o@M<Qc6zxX1rsVP+BI#7c%EZxBh)N{ev*Vf6HKrKY>6OAEc$9wYW_j*m{>YS
zzzO5CIA(V?@m+70EQdHgZiQxpU_K0RVA7b#J!hZe34+<7%YJYAWb0Kh6{ts+?Rqew
zKK>@TbrC3+ex-YqVHfNKjCyA;q*pjVK!>@jT8zI!ake8YZL0pojOm7yff6gy%XXw#
zli__5gXG1fnc6Nm6I;~8XPU{rsmj*ZDqb|apS+&b<_+@&COXuh<>cDB)<*=O_8#~w
zz-o|FP0j<2kFF-vh|!!|-K-SXcQlH9>K`}kLt)}+#e(4@4-pL-VNDD}#l!NmY+ig(
zg4bX3hC2>q4Qu|nRy@yQsTeOf>|fmg{kKwBoKQN%imu#P8M?JhH<W*GNIgekG5WB=
z^g}H#X-8MCA9>)H!P6pSVJZl~44+#5lPK-PEr`29Yg94VVn&|oWBp+&47MCrsLMQ@
z8=bs%pt-&YbN8351jPzNZcawtc$NLgzDa`OCLob2l67Rmy2dJkpW)(42!~k)gw`>(
z64^~lP|2wn=aNo_bCf!(raaAF>$?R!sma-04O9qW35lHUF?0L~O-B#lo)^LwS~)wY
z=dL(|fRq30E>gIfRwltZr~N^%&3+h2?+8o246xD)2{Mg2W@mP?agwoAQO&ZUHe%kl
zIW2qCj2ag|AyBOiMVFST8I+buChtR5nX`|pG>CnOUpt~gY!+OBzRDDw3+y!i)LB@W
zC*KGeeOa4*5kAWf-H-(E;_Xzi)ZY%zvrGJV#2fj3V_jN85GOJ_v<)pppIM=1aNeV_
z5GF;~mi?Ap%=wDeI(YYeWDxL@e7Msnd8n;UZ#D1amXviUe<r*+5840s)4PP{fyd8D
z%8gI;`{*~3KhaiR-pcrIALi37eM@MjX<!#Up4H79=jRJZqrCy{cOH#a{VR_CcCUmW
zKe<fR?QB=;^bkk0fd62_&M0N+Q!pr-7gzjdmg`jz3t%0x-u_lc9Bs`by9-)Bxk4pM
z$@-8G|B<!f;Pp&Y?T%=fYaFsK+oJ7-GZg1@jwY$Eqd5bX@-_r<spUmtc9T9uqucCi
z!GyA(s5SEk**z6t?Jm6!ucu#NuDlptHoEILsa7x*s#wV>Rzt9dbD)AcoShpfHvQfF
zTV0_&jCYxw6J7g^6;g}d6>%Nw+-9z6gtK1XX<Tp5YfNA^JR_C6VTauo3Ano3|4@-*
z7fl1-S;}NyHnAi+&8kCLRF{fda#u)+PT%q@6C~R4<kthRPd6b0+qXDf3N>|)FlC=q
z-}pJ^S~on?5dYi8hdR@Js*Y;CsFbjH7Q^RHm=LkzyMD^yOF!)vRyM27fxcvHcwWyi
zXPpqsH3+1eOAx8PO(ecZA+=*;MJ}t?#%%6p(8X)U*b<u@MwM;Yb2Fy&c_)``jKNwP
zM-P#$nqEIZdCLI}PdqUE7SB%U`Op4m-15YFR|}zJIQyE@J+okkp^sCKt<}pAi3?Q)
zfi8z{?p<q2-Ma@9xa74S2Mc0UIDd%!1r{12n7+qf?_J#R+*ye8H?cjelb{8@wF57X
z9fk)yjfoA|X~Jj5NLC-bhvz(r_B1qoJF)lcF3EKONNdXY%y^{_S4IlAE52t!xK!js
zZ5))t<#%nBJ%bpQUio<KlD;YWi~cWsm2^q7f+`NpF}$;?{JSXUkQVDTdh}84(;=K0
zyRb!aWf4Fh&$x8UDdx}OYE>NppHl<tQjV_D)nEqB*Lhi|;L@P;Ww=uHSvMt%I<3=q
zX)`pyq5Eqi75`{l%;lA8#j*SKt9xY5>FfJbzf*!LhDI!(0pTIO`g@ICQ!A^&`g&}U
zTaO0+bM<Ffn`QTv7x&PR%?9RLvP%O){Oe$}k1v%&-944R*GgW#G=K7r?_uYYos9h$
zW%^49Dt^3Xp7~zhnTf-GJ)YedR8(X6xHm5#*e`1*wUi*8US3~a>AfigMKfu;Ax#NX
z!_A}GEagwA9;GptW$~eXN(tQkM9^n^c5nahf3m-4TCDpMJ@lCNvs&1+vg{<Pt$0-?
z2C}PiL*0ZOrM@<T-twSSG!HeVuk&TRDWjj9aihl4*mFw*30d%!8#NpZ69cY~5%Cj%
zWADnTzLuq7xlwD^&$uJbe05WsPgDal`nHW&Zs-NQ;&>tak_#;bpC7+g1T|(`_<KrY
zRgTVM*Az#Hbq8Sz9!GwZI1VicSx+Lwg05_(#Y=3fhsy(j0tPW?L0e6H={0)qTb9_0
zd|r@+W^k?^iwtomS3_g(_O4rmm$CtX+8w(toKEt7)^IX)nFL)|9%d59bQkF8_F*y$
zBrl_;k|_yW%<$@@(_s}Du4cH^GUSetKLvVPFkIyHr8>E2*!507xUZ@7b4M$TlDxEA
zPaZ=)px|#+#U+p5p}<+ty}9S#C*-G6zwuMzPfMVbDl4-D%CXVzgU_Wzq<8WS@LHhx
ztRV3xsVuZG1}`gn*rfn9%(a|VPzq^xcphM8Vj4e1ib`8saTVp=^4$QE|MXjF$q%S2
zXFkuF!vgn1%iufTo&2z2S%Qtt^}wW>Q&Kx@yW8)pasbFo)*j)1yUd{fW+D7jMgHqE
z|3Bj${~2B4NIU;$ba7W*Z2D1*333IWpck+Wp(Pu;-#Je5-7A%dMyQ;+#fQlf)=3tX
z^up<z!uZ`umtOy^!+`$uy?b}x`@XRAJVVIX`~BqgdVW0t(O-i$nm%*P@2xjcQ|kKa
z`*BVnyvAQ^rI#)-jf%w|Qvb1Oq45=#|GqwKBQk1LE&}Ogl(hIX^AFDOejI5+`2F=U
z0+FVpHE{@g+8VYVpGfKuWuvkeYqY@D>j%u8M#TXG6);L~pF^br(E#qvp~8XyjQZSK
z0o!!w_b7L4oX3`jNnpb*!9>`y*|01ANtiIXPJA|!T}*W#%qxtvJ#5p5>p0<?_P>Q^
zxPLf7)V-^LCiI^zkin^<=u}CcNPwTBFFL}*;A9=0#F%P}=WicF?^F!G|AGc~o2LYI
z__B)sMLa_@@|x?{IxeLD$DGszpm|loX}az<!;DaI-$MX&cqv-iSH#*>Lk8erDr>Jw
zP$;|iStMuxFCQxSo6i~pU;0YW<7j_yO;|)y=EGG$3n#tGznF!an*0i3Th)0rw(@WL
z50BeNxgG9!lhQma&{z`PZ45}|2)t=<HPT*k`<B9~ODd1n@SdjGqRTP>MPxg0BB(}H
z0wo_+f14I-zCO-;bP;EyQffcdR}5pXoyNQh_~g=rh6-SZFD%YLtrXp-53<TuQdYQz
z(+fr~g4n0%7?S#3=zh!lq=}WJ`ZmK+ydF$Ty9?2ha4LFg`j1d1oGIcZxWez8QC3Ly
zJ+r@_rpsu=R%HbU;-eq|{8da?ppsR*<-GVVc;ZM=5K4L3;ChSQVGCgn<N{JBEbg>R
z<C2y)9TkRzxme;`+cP!&mI`*iokD^+3#!gaTOGDzwIl4h3Bk&8HA+U_TROv$3bEgk
zjGuX1Om3(R2Q-H*g*DPKTUcgZHYbxN8uFr5gh&!?Jtl+2OKz|MzYtFHGzh!-e@|2$
zh~_A79A*a>cf6#e<$vsT$*$Q#=r}&z^@&06=z^?F^_9B|FtUD5)X9^d+F2R2r6_i}
zS1zc$v_i+Xtyu9U6$6}>Sr(N6_^Afnu}J5k?E<E)u<NpCcgzVv4e5yMF(mC~-|MPM
zIN6M<PLs1k4+IzhHShMGSo>~d)nmG#mCB1hZ-Z}9>?`-2p6bsDrDcyLZAWt;@RlLb
zzwKd(id`l;?|s+OuL+XZbun*hr(BfcxpP#aHL{px-7?aaIa<arx3Esqr1uKKR(w1N
zI{ZFduln&L%Q@Z}6AdVpQcZ(dCRpCuCZJ?B$H}-30z7pAZ)=?o{>6*gbq_2*3~2A3
zDgvt0X9X7;ecE5kqJ7`gJ5r@2=6C;Q_9<xTUhxqm=6t98$)5@__&)Fv@B>?p`fd=U
zh3Zom<M4{A?Z+U^85B&#&v6HebIjhY?w~6OSB?=k$*=!$bBn>E?7S~TOxe>SjPR(f
z1*_=VftpHTL10AFn>k>nXj(rTvaKq+h5z$Fnz<s7rls69l)|_Idb&JLLh%A7P`CPV
z&-e``K|iYTKm7|BF6=-33men_b1)s^OxPc=-}M+264nc25ElIy(4?!!CIXJVVH_|y
zmmfDatxgf#y&I`Dj24NmnRF%+VNEvc6pecKE-idh@MM0>$p1X}Huru@y5>KaIsP}|
zoS^)=2QiuL-ddf9PyhZb_kLmju-wOl7i&K)ybJK-)jkkP^9~56+wcmzIe<^5{cD&X
z8#UH;xcE!?nq6xwytWZ%#rIBIzShlQ8<h^=6Kv^)nOBagID<QkcTf2u9EBH3D=;aF
zwB*UEKAx!wPY2FF@xgsyzn|*4+Z_Q(%)N<C=<ojl5^A=E_n{4Lo_}W)9v*58Og&HG
ziUhnYrkB^=4g7_9r4XfER5Y@;qvVIt;&nvMkmDQHFE*YLnwvE~Ls*6vdh9pfx_<hN
zZvOiX+DMIg%Ss)|l){IM7`Xu`6^T_EOb8xe11*cxQFB$>+Mo2~j<2Z{978KfbWQ4s
zD-&BfIxkpRV!XV~@T`6MXp5Gc!cuV3%?{`eeQ?CoC_0O@Yn=5wg-^7U5&?-+8gOsk
zu@)V>ob2Ty%A>RHVUJBfCZEs3Y@(4*@9&0oWJEH~inU6;?>35nK|)Hgg+TFvPaD8x
z5kd(wc98uueYWOsP-(e^OW#C9q61X~3Im}9B?QNM&bT9HM%cCv9;Oi7NZkmt9uHWI
z5>PP}26VW(|GRj@?di(9w$d|IF*1dL$(PeDN81GUXPO=>I}+zz6o_=LL+LoXXp!G6
zy$&@Fsx_xR-loVB%x#&u4W1%QUI@;Q7^|H~PHGtf8g13-NybotOwxM}sEAd^><tWB
zmB^+x@6W{f9wdX-U8Ki|Xq;rfAplJK73z5rcR1K+gHhvy&3F<*81m(am!-CXDB?_(
zzBK@omuEH{mzOFJA`e?SF@qD(C&PnfoAMvs>pF3N(aJBA>Q>vr!&HORNn=c$sgVB-
z#Z-}LG2vn04G{H0q)%VAVLcQ)Ln{?k>k2U&>G_G$WTIZRC9O{meN#ytVGF2;2y!2E
zgdKwlBw9?Yq42v~i;%JqJ7FAbV~LlLV(xrzjH+oKMNSS{{4td9JD)2~EO@<GO6x#m
z2&C{_78FqlNk$vwv(VM?sQ;uCVzW5P4Qs1UQjdgC!{+dIeRf%PVQ9^K=x+(iF4fW*
z2pRNdk!{X%Qo<<>28L+dKm7;%)9+1vx)nRQ1!Y8bPZY{*&vw|`G@66Oa1Z{Kt7WB9
z?#YI%tQ^WJcxhjEG&CD2=mKdcWv*3==<E&=Q&nltM!}#k4~YoA;b~N_P~L_YJd@Sy
z1`P?k@B4;kIRSqv%-%R0GDm{CCeL|pN?h_h%v2>ys@C&jXh=bx^a?<LsMH#&Zbq(~
z4`L6035_H<d!wJSW1DoD`X!>+Y|E|d^4Snm9kt=SyGxuz%%VOYsE2mz^3JngMs0?t
z1Etz~*<Fe}@#o)lYTw^YV0cW+L690v4EHqtv*nnD-67B@EslKE9R^F%{WM&%as6e#
zS@oz$*Xp0l@>J2LwmJc<n#hjgwa2{_ytC672KUxz3l)xkH8rF5Qwze&45^Jnoa*)j
zQWp_+X=RwVUyZ4U|J}HM?3SS2&o0d&v0>x1oW4)i0>KT=<dGGFf2S_x{ndBBPveO~
z7x>3}^+rB0eJFE~L)7=ZV0ejmB6#A8>J`uPND1C9ZVJ=(s{sPCSgGiKqGOVGhZR0+
z+=<B;&qs#gX?CWxDuF}#&*U+V2O8?HRWMg;ND=xfm#PU>8WYw<#Ny+vQ<~LvJ<~Zc
zIlT`OGe;J|ulPwvY-NPDrALCq6Z^tM=|QRde^i}QkS1ZXty|N!ZQHhO+cu~9wQbwB
zF>OrSwrv|{{=H9}vv<@@-Bv|ZWxknfttVF~%hjrJ>5L<0qrAcz?nrRR7s?6t!s4C&
zBQGLM7ZZ~W7eLeCia9y#QXlB5WM)}2o3mi-dL6AuvF1ujR?(KUxM44VW7qV@<#e7U
z4S5go&&bysC|shc@v7KvaYq_|)wY3|L{24Xf|$me%Ik4V8Z%-M4^fL6D_MRXP|ZQR
zv^oc5HDgKME*cigZ`g;iJdDnu@6YO8zF0DwldY7#9zfIaa$Mbu@3o3O>XE7ew{4|L
z!f;pNIOK0ZjesOU%#Oyj`jifBK03LzSqcfg6=XJzjK>6wgu2(|I9GhL?8(t7rI^ZN
z9kUT4Ow6{dsHL*}NNcJDn$#w&!TuN6ax7E~7H@5Aiuo$Z+T=aR6CSfz5+O6ZHuMdD
zWIdZF96$owr26d$llx5}El@cMO3H1FhLg+AF0+jvMzMCq-0HP3QI55ey6E~G(F5dg
z-DHM_yFMTGqp-mJ9Y(}IPIPdOE!Q9aNp7CXZ0|9j&b3pk9w)tvL-^7`ni4v?InSNa
zwXyfLH^`$H-BR<`D9M881dwTBxL<+FUE{wi7XY%0Bv3s(nqW2Xhjpjc4rnZV{TW!y
zLJ9`=+;#<gD%13<I$!L2%s(7WR4+&8CZ5hCx8oQ>@2|wI76=(<VP|D|4=ZGOAMpZt
z5VzF|F3gO-I2gE2(>#h~{XpIPddf~r-Za|Z-P6(hhzP51zCYgHU&<8NS-#$WuJ*jX
z5dqF9x&O=oN4`<-K?`YqM=P@Xml`)ljpi9aEpze)rGLU3zh79tY$|aXWAXL{G?3N+
zcyI=U@CKtZ_DTRr$gRl9!sV?AuROJ`z?8~g1}Tc2xaVlG%{k}SOnVO~nV^7$Jm2IA
zY6Z%h_@AM|Q2QfQI3)94&6IM{LzqQuK>&$xA3X3xSHm{El3-?0ls7LCE>=C{aMBXv
zmzNRb#*r?{7w~h)i1=Ma-mubp+)>VNDo%8^p~AQmzC_$3Gol(jowu7#5B#?`1_rX#
zf4K@1WZ9U?d^T9ebhp71-fqWoxYTsp)90z2C%4-kw5<GWHMDyBA6OZ8l@ehB>438`
zMU&-i<Q3&zB=0ap#JwZ-#-?F7%|tB8+Yyf)RhEwJ;#+OMcciNZEJRICiY3QCF1OdS
z^{)ZV!tFIBC2sIeUk9=^gJF}4ZO$6@wCGI$10SA`CN2XAf@jmsMUb2v49#cC?@aRa
zjoyT=Y1`YF68TZhYPm4wvX;c7HUM#RCB=I|w*sXLDBtDml`>6+;ykI6j!fmEX&YwD
zNYzddT=AQF84No`eEX-7{K`QR(9C6Jp5>Ng(Zzt7MBGy;Ke_#8(Q8!b8>+-T_g#Yq
zh{l(E#=+A~nth=o(6kC4xuBH5miV6X*fVB1$d}l*SGR_$Q3g?)GY@WCh2_uzQf+j5
zBnkKq!eM!0N`Rv0opf*VZ;z+Qrl5d7;;qYoUhn$bJZGm+!&j2$ATA5Oj&%$IXtif-
zsFxri`k(o_uh-`5FLRwjmTm%org3)`-tZQrpL~P+Urd4(7Dj@fXC7H7$=I}lCK%K-
zWGZO#wDu`91i+E@|F_Fghw)_i6Jmm{!cEp({ee4Hq;7fN$NGI41lWm=fn`>yJU817
znyXPJPcvkEH7-fr!RSnXpP^q{`Z4$XYU%MU&ztGI;a{dzn5!n|#QgpUByk)yR_{$|
z|Fj4&e>Z@*=MnHd%%vb6F_1hv(23&9Hc18KrRD)D3|6?WM80tE>_xPqoT8p=lDCj=
za%R8QKv@Xh2yRY)Oo|#cFaj14em;FZ9xkG4aK1lpwmY_NTrbq6?+Ej_>8k_)AB=6E
zRnG09bgJ~a`Y}vo488O<a6_MQW^W(!uVrMVZa-xf_3M`yIvxk}4fbkMUWzzT9~yu<
zw+8`!27ynv!87xwi3hp67oV>;)96l26zQ+__llY4rSxX3gA4C>(H$;K6wo#-Y?zrn
z=?Amw&ZoEMr_YH8g$aY)-b|H?2W;c)SGW{+MxOO58bRu1frtRcX52c&!Aze_b$`5p
zSVM|>l2xt!7uy~;jg;B`TH{ZC9%le~O>29^pWdgYhwChl-BvXGo$Y{M%)qL13*fGU
zOm+mz#uKt1>rRkmK%BpLLG0riAY6Lf^0ueT4r_Nz;<ig7{T=H^1mrO;{^cHh@*SeE
zw++{eI0w$;KEHnb`<HR}wM!6m#AI%iVQah|QI#y=&}8b1a!e}_ZLBBOk*WdWRo9Er
zW_E{9Ks38fb1jeH-V0M$l9r`DOlh0jJ%4dG49D<v`V6c+r_R2&J0nvc4?U0NxBN}O
z;!570-S*eU8&rsWNi;x>9c@fV?mGyRtA<b#dLPZ=or`qb-uoPi7UrmjG``i^CLigm
zA+%L8g0L0mfWrB8$Zn6;a#{s=m7N^4-XpHDB!=8>>fgD?uCj(!pO|h`D6bF_p|$(O
z3$2UBxDHW}mqQe4khJ0D!q8x(4@$qIOhNA4cpKx<?DIroYqo6nC$)qHZk5R>Udaov
z9+=COSb(Ysay=pE3!tt+U%UJR69XHu{UsJBoudEv7&B8+qdtsc7OMrY^|SotI!g;o
zbvqi!!ou9NTn$wMmAem@Yp6riMA&5s>w6$o0>`I`XJqDMaf2x7UPWYWIf}S7m6HU@
z>e+i7|6V+KJkTr#aoNOF<4ij@c?_)uPL5<4%dz4W#m)q3$Ib*PibErjq?k2#t3_0D
zhS$pNB!B){4gotmz3T}OXTXQQ#yl${=M>LpcUYyd4uLSsWGWUJMB<s4Z-<nB-fVwf
zl8tb}?zFVts|gTuaE}7+TaBV6Yh)@c$s%0AS(k`rE}ey!R4~+axjoyoC4XMk2{8v#
zAqZAp!1M{q8G*^Vwt@=)7`=LHBzq6MNgBu~?auf#xF^BwCn*9tNqjNX9zgS&rp&o+
zsp6G*7Y!R?x2YK6!nSlr@AlyA?RP^IO@|<N>5uazKf}OMe5a?05Kj6R=}j7e4lbpt
zIjroTfsecQxx#bQ`0bWQu@Aze#EbSXNq}#N2cg9!m*@2)m5J{qt<Zxyp$*N+$ktzy
zlfA}FEn)$Rd^vz>9EX=>USz&0qyyrf<{MI$@G#DdgVgR#e2LlUQU=%}sE5-$a~zSH
zEx=enJUciVN&}#?-pmXJ_%gKWd47Z@IA1tc9EVExV~zrzjZ&Dv4c@eV3?g}{+sN88
zcz%IgV6%UB4CzJXHExd+oyA8ob^<Y80z8Qfz1NXfeGt%o9&YrcP|j*AO^snC2$9dy
zU_!)(F}cs_q(C&rDeBciyzOpA3}x&ALyW1JOILHr*n@vfE&mrS4R$=+%ubZXa0?jh
zl^x7^l~^bh6TIeEvUhp>*gKI-Jo+ph^lI`mq1#oiQs7X?8PGu7xQjS{RzR6LxP|d9
zk$Qbu917s0U~(sc1X1CL*Iuy7UV`<d2TC;JLDHU)4mTLOYcW~)7TKdE;F*niewB^S
z7VRyDB(5)|R#=7f#Qv-6Cb)-g*uiSG;}zvCJ2=6$^<-c5epLT;%t5})e|%0^0TaY3
zmY}t0vbr>Ky@mA;W*L}S8~YBb@;3NvYesXb#vLG~90<x!-#dJu)!vPbe7^e@PHpEs
zh%imNv-CZu^!*?{oGKbbb})Ll7B5@SZYoc;l6}&K_&Pg$<Iky6lAUKUIi4|{8FygD
zDU4tAcz!Kj;FKVG{if!|ZBkUEeh^h;?_JH#6mqPKGL1WeNlLl}<X^55QLLQ;nuxU3
zWqUvt`9@d~(N>DO?0g)ZCJH|_@6g%VI)Wi;YV`SCN1mR#4Hl?@la_;aO3$eOAM>qx
zzsf*@hJ&e-_J-ku?Y-|x-mW&8lq|g@DC+HhI?c%z^t3Pg=Lr)ba8S(`L@v?aQ9*=U
zx+BXBix3&qvGSD)%D=&*F64C~L84^dQgQ(I;JSbQX%WDM+~*;z*SVeZN5p^pEQJHC
zT0OaMc84j0%*6LBt7N521-uBPT}jK$q)QiGYEUsk0}1Aq^W8@5GeV&|4nqSiXa<>G
ztx0iCl*K%p0x$E0e?wJ=*+TkWihOY>t9#~A%M9RbV<EhvM}>!B%#akhLd7Y?Vod}5
zenf!B4(mpKD;E%@HYKF9d+n#Y3Wor(c!FRqn@?KitI<I&7m{9w9J7KJi%z5qDncwc
z4speZA;A_3M(&k#0=dP2s8;WAa;}3MqgQvyd2Ou7I7~D>vg<Wmt1Y-pa4I?Ld@D=Y
znz?!t!jVN2wL3jTnpPA}z0j&*QR4#sUdtemR(AO^`FBQ8i$VWVEsuADpfs5n9Bels
ze8Nfr-m?#L{K;hrbUdcBWun82o%MRhy(d!R0-XX~Zi!wXUz!A_d`g-uKMv;Idcii_
z8&?Wu0XuM>WDF9v_E1H!Hu_1BmK?)(>7Tk9$Hapk`#Xr}_pI&*Q<e+*kRk?<0a~x_
zw(vqN>B!!>DAi3xN?e>=&J&01Ivy-8A)Q~jFY%=7yNK!zks+p%I`?B5Yl+7urd#uk
zycw<KZLW|aN|p{$esM!N+VtvL{fudw!rv@eT0bAug+r;yyEfD@#&GKsbs)Cc(FSW!
zOrdpVLl1k0p6e*TuO2@ep%ei06nzGHN^t+FTEd;+WI5z<g1<JY${Kd%E*CEyd%9sT
zlbWIiMuGpV8{t%;NIQT-T3h?lm8R$<4BY^sVEH%X5mWcQlPbF9mPNBZ84dWoxy0#N
z`#7;hA}zLK)`+dNb9c2{qTzm62+OcQ$&>4dVZ43hHJjN?G|c2s;?4>vBPyQT)+<4}
z;@tL$RwMeP3%M=z!$O9y^?p)%U{EWql$hBRlRGiMY$irh_tB|w;*r<%$lMLH4sk%w
zTxan5QAwzPc&osMZ1Or$i@zZoMHuYPG%*+U*|qb9G{9L$GU6l}6=oWNb)~w{f74E(
zzq0q(|1MMvd}PfGe|OY3;!feQ`aA@1VMspAQKJxRAk=_oB+)mRcif>;ywangls&-n
zoJp@~_=ZHis0zFIA*LYaa8Vnb%WtWc@R;0uvY+MWB2|_Jek2_ZXe)oLAqMW@XT$;C
z84x*Bi2tkG{0~k6z`^;y?RD4_vAAvjNvU=5w@UcSQI8l1)Bv=D3OU+;YvjFAgiA`|
z4*&KQuSVNAdYzGz0y-o_%ix|BXWVUYct25yk@9m!CirbY$6)ZV688D<cKb1tm+G<8
zAy&m!QJki*!t2H4{?`0+Tb%5tG)_|j3!psGPwrWN!GNilUe%*&@97n!CE-7E)6ll@
zz;{a}<ov=H{)T^ZfH5Pex%_yN2~s5-a>~)*wE+>Cq+!R0qxwlv<(fk>H`^`)Z&L1e
zoD%PKTGF4s{5|R`X;DmY@)j*@Q+hh0u)~nHq}8U|Jl3mXmIW$2idhc%OY9JZ3h=h^
zaCs4)67--@JwjEJ22;KSBtv!jGDmnzi+7O+2_$Vn(&tCOWp7vRLry>5-39Rmq=s#_
zGyH-5NHfE@LcJdODpfTBn?5i@A*8l{5IN3zWGR{KMtQo37HOVt5Z;Am2qUgel%#(0
zNs4@$86knz_e-Ma3WbS}B%#+Q4PgK69E^H>nv68xN-S6v8NcO{JC@B7DhjSuR72I|
zMIvM9O?|ka)x^F)nP*k`i?IwQ=GRIf=%tyPDW-Q$4fL>=4D0)xF-+WIb(0tCp0OkO
zX<5WV=|2LEh{bK=0F$nAX_mC$ig_o@5J~nZKZ>)+Gr1rdF*G>BB>Up8CcpzFjq#2g
zwSkpqAKa`K<?;ArrFt4#Vb1FE=NkE9<0#%&@MGfeu$023g0O<{GKw#obL*v4Tl2^;
z%T@mqu(*P(%|FB4-1S~q!caZFA*QzOL3CAg!Y7XjvULEHg|?vngDi}r?*hmtcf439
zU6xSDg$@wS>G#n>4p4dpBY^xv({3yCsd@eZS$_x0D8%qM=edIiVd2z7hXl>YdR5-=
zprtf)Sh~g$b`*`FD0)!y4jfufSLOQvhZ5JFo6Q8yp>N&6VTilW`XH<R+2+w|yMhD$
zSG1gxGbr(6iNujHhyKD~R0q5&e{aJ0sfJmP7^^dhOsV3sOKhaoU%;2!5B;QSps`7^
z_^}YO-8@c&yp<b~FU-=Q{D*LIax)Eog#R*A6kHR`pnr6T`81ZEN?Iiur5r()1+{_8
zx(t8ZLHZWC-MZQHz=u*8Z`t_PUyrymkA(ON@U02a>VCsbM>!dsR(zO~@Mu5IAui21
zEK^X6vp_TALTju7MSw3R3LAOYJ;wIZeYK{r%gh=Lij(ReDb|s4gp4ZxUZ`jI4<2z|
z7SSGXG!=-YH52v%zIJH#1Q=&m)f3rBJ_4+!eB4{S%s3?Xw6g=k)nV=Xi)mzX5a&!<
z9(P5z7*<DllQjfQ3kP#~<c$?&BW*W<`=qfmTPK&uShsoEKY$zfGITE$vL)IoG{>g8
z(9Fm?gKQ?&4b_(BsIUR|^|$hnWf$tJ({SryP^z4vZ++G2N@<o>m+NRJ1R5k#4vceM
zZ3=p%f#sy`qU(EU+QtAMxERTD${O&7g}|dDmYRpivvUhqzt>W;s|iFsvzWC%+17U1
zFm!q{Mue45O8}QM`j~}?z<N%CswFL|=pxA|vJMka!Iyn$^v)3ds;7vMWZ4mFduvBX
zyLS}NI@d6;h#E;l7M$M1KSS^t?k{ypO~3V*Ex_%)ep7em&~B>H3~3qaSRg(}G%|uf
z7r+D+JXSU*Mi$k1(C~gVx;xWM<$Hg|S}9GeOO_vVNdOA_(R2W13Qof~(iy8hoQm_Q
z%S?&GtI7;K$;`SJZ#8Af#E>dptsapd<&u0Jdw)E%%+o9U4%9Z)>Ju3V7lhOi68}l(
zK<8o`$EU9-C5-EMlihzaVGa+_hYzi~Kt6C<XXU`KbJtF1XksC0Lu((Xnf>P38aGFx
zXbkSBzXxzoxcFf=Fn3}*;xh|mQ}wrd`qyBLjaG@Q{7P9m(@0540;(Wporiw#t!A}y
zulIvc7+I`Nw^cpmc<hRbLE&378V(vN04VJf%_PPw&Kd?(gnD$J1+Ahe0@6i*LOxy{
zEjT8f62G`8JR7^k6S!p|N5Rr!I@#6hzo6*`VgXZkuQBkT`$U1__V3NILHQJOepS9_
z87bMV)7RXZSp7)S_Z-AnN*dw=E^)VZ0@1prCqhj{a-*0G&f(tOdd~qC{kcj(a^y&R
zr2FBfC0Ko0UW7PHhSS(fhMRB{K;+^3G`w+7iW(%LL?{mGvBsFL-%R>Q|8S!@3JwFT
z-vDu!HWRCS4#xUUU6t%Y^3tbQg_cUmr9Lg7%ub)`y;idw70E6Y-fhU-pwJ<ERXRF?
zjcCGX!<*qg4;;SlMp{LW)h{~<3ZR;;Q8vnfxXqT+7sm`c`d2kEgX?`it@?rhycjZx
zY1FOzx)RuhA8WOz&hMv&X<bPDuaJ2De~a<anuTk7<%yRG1c74%@9h_&A0PgJGCSR(
zK(b;spj$iQf*ql)ZGruVhVLrXukUcVDv$ctcIy{_a8;{O3;QQU1=%?LI*vcv(a-+t
zotu{Zv@h04X~jfNM=y%S#Eiy!6Kf?}d-QG^KO)OOd~a97+MZ1s^OJa)uC5MVGyt81
zVIq-axUz^t9~MxscbEjulr1uSo^q#@%^ZtF4Ve-Kqa6IclX_%PKus;01%Q~PKipaV
z3pr7z#eDO9_x4_vW|70;_kHq1|JO_~?DFIICLqvg@fQkGoME*PYjP|~j~OX}g?dfV
zbrf`YmjPO#=T2pR2`OPzK&%JeCBXU5cS4D3b>Ls2f%vN4X9c@WY>|%CYtpsfHk!Cg
zPX&Fl_`-V{frU827>%Z32Rw!mLwzeGcO)L>;P|LP9bTv-O2!alJ(6S6^pb@XVn20%
z#*KN_lrq^!IRT1E8iGuVv{^@W1f>B67e^tvJL`<8=?Yd)3!rIaYkNmvwt&rwF8hj~
z&YOYy?ITHZ=6dbi7je|o;}*`G8#QJAp%yuWZe`C5r<0vtF30b0RH3@{4;DVo@v3LZ
z>X~nb4EHxp95ia<HMz#q8rWQ`rzpxInd#UV7~Q$3N2ZYBV`!n1X05*!c!tS7MPeN0
z6bQtQYQ2ybO98SgXME6`9e`J44ndA}B9A55X)kM~Z80skqS+h+Dklh78>t)XgP`XZ
zn2;nJB`-{;Wv1Gqr1W>XUv>GpsiZ_PH4*C?EMt=ja-w4PFGxLIx=oH3k(0V7DURA1
z35+!@Pg2mG(c}E?IBq|)1|ZjO^{<mHX`QSe)@fBjHQ$%P7*}>m0Kkfq6Xoa~c!=mF
z?>6t}P+izC2Ndso?K^TDY$<Ye`^n27_aBOD525?m{2M#WwT7OYiSZ<zw5AW31vg5Z
z`Jp7C@$o4P|6jHaAa{xGz(NMIb`8mI>Qcz2d0qTc=Yq!@CEO2!MX~BVeh5p2Q+rrh
zi5iZVSVJ1#6xMAGS^(&)zsr0|@HYZ~@ewwORVWxpkE%4*?nyQuf%o<JS2`u>{#A(M
z#j~8hBnmt544!jhZqyq(yDu1V3=}n~JRP7x*Pto)jyJr)2=OvyHnLcHm|@?>gnTg!
zs$cZFcaAyQb*S=M#FseAyhykxlRbIY4iE^XuRSbV-j~w5AAlxSa4!8g6Iiv}8rJ2s
ziiCawulUu2Lk<6SiF|g}>NjM*zjxLL(F`62Ac9_Bu|LK!$O=$zW1MkK7e?&q;B8xB
zyMHuxa0s(b!lmO)Ma_!M@|n+1r^_Cn4v0Dqns58zM`xbmxU#yEHcmhWlV1+;FR}Rq
z)M(;Hy0Y#}>LCe!ZXKu<FuneJ{#v+&PX=INV`BfGkrP+A|Dz{P`qvX{f&nA{gKne8
z2kix!kB|`jcTcQ(C&zxW##e<#xN%|MD775)x01MubAbRQ5A%D10`!$2OTTCLgXi-j
zxaH>dMuk4D)pi|=O~<_AjUdV4EKA-NeY)}tfT9Kn`&ped=IZH=GjKO*f4wE??ghKF
zC1C&J{Q899hljx)chuF!g(aVDs!t51T<tZO^#RU#x!aLk$Rb&q(&qDD6kLwA0cPa+
z9BB<<Sa7}HIC<82vGO?Ha0saJN!^5$7c4I~WM6j{qU+1hVkx+aSYK?ZFl|CtrmCK6
zT8IS%5u{3QS{L}9nT`8ayn2pzmt%fQbFul}asGH``qhC2V`W3L<Lr*_s!-b)y`xwh
zhW+?(e_tJui~#e0y)aj>Kpbt>*OIfSH>_Dx(59vsWsf7HawMg5m2FpiGP5Ozjn5R!
zLuz3o|Fkz$MrTt~pSx^j<rN<oETGOIOS%A%%nL0p(oi-LhNQg<TZ?E^(XTzf18XpG
zEt8%$9~X`++H>ZPqT5y_yihuF7JcRH#F|(-GNjT}(b!%;!p_0~)bHo%2Mb{&LPB`<
zw_mppC?=Uq<o--ncL;f(Axww`w7Z@Z!F}q|p{bDqLH&*YF6gKG&xtSszGGu`NB}o1
zwI5ti1m8E_#BKp@=qx<M3G|L3fps}qz)Ovt=mnzpUMIP0JGm?8GA6aB>1tD<Q#k{X
zRD5{||Dx|HO@fkS#E|+d59Pfl1bbi-enzktHHvcu*Ag*+SUJLUCpD%63>YP@E$Vgn
zOmW%cZ6qwDz_XW;OU8)P!pkp?0Z^gwgDV{!lwpN(mrA~WjVW+G$|OhYZ$gM|L3+Cg
zDCjs_JHa~YRiwJ@!6&;5?p3Ubc#wb%GqEls+EwzJ=hPA{2qP>QZjsau^yl+YVBjH@
z$Y6g`l}dhSYEn^FCiu1`nk+dH!)?g4PeI2fjT|`D|Nh!%RumP(Rk8bLDXE8i0S+Q8
z5rggijjv1-fN7Z`O~=rXy+Ou{te4RS>{W(t2}i%IhCgzt^K0HX^q_~-%HA{?M_Jp)
zo8xyoF}0-y&(<+SR)&`M7*|*D47J3o4mtZAjJ5W=<5>~U7t)uGO${Nw5s%8(jls=3
z_9OCKBoCF5GBO?{n%<Qr1|TOluT-`#hWwlD7<djHMMBq+otnlVcU1b`0g;#~pZXh%
zt@DEQ-*UVtm~#(~HjVH53md(IaZh2O<L^#ZgO1W+zBj2EQ^iwXlgAI(TN(E>K!pV#
zU*fnu<W%rCH*rk_{(U6%j3u3({678gqkqbJDE*KJxxp8#9;-(`FhH6-;}}4vmv1Pg
z0+bjmdL5M?9t;asme|#Fh#^JEPk1S_F)g5ZE0cZ>$Tg6gLbNl&yO((`k6QXfl#c<E
z^_5M<-Wf|+ZUd&47Q*I|kr@F)ehQ=xla_nw`573N=DskTuj*$g3I8Cu>N~X9qU}D&
zwOj%0Jb_-G=Pr`80Ve4}<vh@m#e)7~;>G_yMzVT8>w50V+wy?@ozf%G$hq<uFaFCH
z;HTXEx(B|J-)*A*iGC`6H}P&`+Iw>{G}b-rsb4+kI^-c13q|_i7(VU0#$LN*<zA|i
z2oGP;a&>idRcUCb)0}(OVlkQOz7UBui*Y8nYwksNy-bJI4H)9e-v1qguUJb%kGbi7
zYrP=XbQA}<FIx;(%57_crqkzP<I@*15`4mU&STzV^BHFSiPX7MKWvbX!}sy!6(g`e
z#2o_|@guV@-+uNh5xQskNC6j>({QiS_^91X*OFQS^-kUk8Wiv0HOQxBUsop@n+tDd
z$&L<_yB;~W0|dD?&EDf)C}>x?Z9qk0Vc+95=X5N*;9yK-*F;A#6fU<ytNJZZ&3m)b
zPrc{~^wnNt#mHD@=)h)OQ&UEPM=@~|#;wZ3u6{uei*0u1oX*8qzaD^ZL;zE$W2ZfL
zycgd;kB-4>{K}B2vdAjrUsrD+N1uL~ORAMWl!*yX0H7b(m|{#wHZbLEltGGP3E?Y8
zlV)Fst)m7&nG7(c3@uP?uH$nNpPb)NX)B4>6mcTu3E`H1Bj@$Iid`=Wp=wNqA#A$N
zjZs@-$Qedj2u3il%TXqi_)=2H$-<+&#X19sU8yK9L5UGkNa#PzP+3OQUX_xY7=g(u
z)ut!}0OJcx`C_~lMpmiH;jpNIsRH3^<y}9R;#g_j{lPK%8#sxwB~4wn{_#0LIkeKd
z3P+c95mljznno(8n_-v2b_~kM^Tbk33@;UShPL=|q$0s($GN9|VWC+*QTk&0v2c&C
zr|405UOkkqtjZ8Bmhzz<`IL5&PecYPAs7!#0807Vdt}Uv)uGgs%U}H~<p<%=W#(hh
zXj1!dG!zxmW-s|gka{v@&|+h1MJzYCrRJzFfL47}s^oHAjwRukMfrjO2tN@kRK%wM
zA6+$~V4PV|^h6f08tU750Ik!PnBw>O>HXWQtCfrMeRC~g$~w9Aq%zN7WN$fwm<DVs
zfS9T>hxH(WHPBa#lXU9vX^Fo+H1s9BJtUFZ=)eTe*x(RQJ{n$tEKO6-@AjZy-Yvwn
zr$1<x`S>k<wD_3HK$eeKT9`6Vu1e5*ZXJQjeOqPJjroSXpPxsLGkEfG7;=I{6sg1^
ztdzVrvD?M$SAiF(nzR>{%*u!g-NDcZ&<+;X*Z>o-QnK1&Rtl`h81t#Vy&I31Zz9@h
zy3%W;KosFp#cfo=?LkHLB^Oxs+8SPvK5LV5BUktfHe*9|v6gthXgjt~`=%s(FC%1V
zP-1@gtmho&VUx_f<=PU8PyGg6fkkm2>m$=kfxAb;UFK}az-cV>Fi@E?_(F03=yTep
z{=j@PQ$`rERdL6KBe1sDO`>Gv9kDeTMi1ZS0O~J-S@}RJu$wO{SmXgOc$O_y2Z_&_
z@)tzYXPrgR_m7*m5EQDHBhxyawN8d?kXC>$8l$&@4rE6oqo*-C6BMUu*W7F5wjv^G
z=B0w{Elr%C8A!BgdK&9?=Ff2eB+({uJaZ*YkRWdubhH{5CY$(GcpT3?2NKfaPyuCe
zK~$)Q<JKh-^&-dY8uzHDeMuO+s*Hu)yK`FT&S^N|9YF<qG)v2N8+ZmD<j$gLT)DFL
z=tKy=Cly>)oVQ0H+15uum6MVs|5_~B3c+C2s}WBtro6A%4&ZKRrZt)Z*zg6R9|UtC
z%~X{85F<qkM`(9wNP|`7+oj=<oh<@gkH=KruJI#X;t)O4kOgYVJKMY+p!BonhQ&jM
z9&+|55o4Z*X<7f)G$bQe5pqgffjIWXjU;}cyQcu5=a3Lk6mj|ZJ#CBfl&^yX>WWq%
zSlggAg@DgIPKr%9tRgx9_!wmNq#i6^&^yeJyrY7}Yu-y(@om8UE8<ucRPUftSj5o~
z=#Ux`t_O3HDLG9^c+AjT9)G(FRYNl!2CD<cx(MNTS{fj*mLd}`O`+Z>=%nMqFHj6>
zJ!mepu)kZ?M4gV`sP52Q*dDQ7g7-a*ruKKPC7bNwcM)vl4HPZ{Kz<jq{)3Lf@$u9?
zt&wAYsEXXt6o(j&DnWyp2(Dg2GalQ2GtFC=Y{IhzN$GJt0Gn)h%box?PP-QWy~~Q&
zreJt^-yWehR6nm-B!!Ou7V{=CIveJdagMUIqKwAwwwnXD!*;oBO9t=Cj)pMqglu-J
zFN~h0f5<TBsY@0DP?WKJpl+ood;$D<q2i=oa$t$1d|g}&s`<60_S@F|$A8O#tFu?a
zqf>VVi9(-eMa7!m<vS)<a<SdPm+%h!)!D$D{M2!xvS1fj0`;!`D&=zmeVqh%5_}+t
zZxVcYnBJVn-E9To8RtE@y#s1n4^-Zxp#{Yw=-P#HAtq}CFeWTN%s(=eI9UI^S&0j9
zMQ+&3D2Z1pYGEnia2{w<=|T+7UsG>_j4_?H07d_`;*iDCwQtJduiS_DOlF*QPO0sE
zFMFeopjm<RYPJtC9r6SpOhMC==UBOBd5&g#Qqv~ml;nwMPE}pIsh;{#GowV-MlwzE
zg1HlxXIzjC7=E&-KwcRaw5r|eUR~34P&EXP-(f)EeP-_e_vmlTLUGrjX;9$Vuh?l&
z@)Dg4dgPk$W#)#(khoi|=ol_!s9%zh?WBekk^;5hp(p<-@L^&Le*dq&|F80%g+65l
zUK=;G!0af9fD=lKf(!=eNqm9-W*d1=lWJG427k+jRw6e$1zXM-uOB^?KgMMm`$y0o
ztJEQ+GOPNgYnqMJfYKvhd3~h&apy{C7rgN!k-0DXi_f-9U*`{IxHS_SPMtt?ygK)t
z@WS=6gb?Ru?#RRdUBjkv>ua~&$xZ|Am(r54Yv2;=o)NrcqXG>0T2zrOKiH%%dM2hw
zO|{i6M-I54Bf|Z7G`e%0^7IrT-jY&l?r*)uW?Gq7SC%JB#<!RqD79Fk>=8$*>%IOn
z-C3!T?65-%AYj`}x%-&${c(S7N!+@#l^J8#Hb}k1{$}G}HbY*PY$Xl}!2y&U?n)Q;
zHj&8z0%SAK$K%X;Dah?CbcLs0^~K-Uvl0|Hu%f+|l83w<@+xVtflIhOc?^t_$f!75
zs+1>+3g*-w(}28e$mPj0Rn*-(+#ri=ZyhU6szdNInps#V{?b|+)Cf(hKb(Vvr<R3!
z{``v=G$<0W`fu|G*Z<ArKKq5*cJ>SH$sx@y8lD2c#Kz3@KTm*1+A<C}9Ed$H8n+1H
z<Y5kdjFE+Mvzwd$G}nYyBnIcL@(5oaIeXp)tt2&gU?JVh*R)(aj@!kP_!*`k08$tK
z-atHp+cQ4>m!_G#>(wQme;hI)u(4}-+YvQI+cMTp9DaQmqi)1=(qP_w-*&*7(5z^P
zC>!z(1QBa&AWApet2$nMAW3#Ru*8g@!=f9uO+j?hh_i)ACh+LOS|l<ueR**Hx$J;i
z?KJu$y_8yULB}KP;FLU6pA!89t`odgqe{Q<qNEfJ_5A>mRDM|GFQ}?9;ZMa>1rGwu
zG~)Uk)y>oRCyoP{#B$;p5;_19g@2cRqM{n~-h%8<obEoP<jnV<>PE*J?Fg4ByJ|{Q
zK2_1g=X1SNI)<2iZS2P=+qY`9dGlm8otAqtJAnMV620CZoIKvrDI!;FgBsWHMhdB6
ze#t^fGK$Jz6%%bisZq;(5oGk(ggpk2>W~1#PQ>=U5+hRH#?#Naj|_mI#Y^bdM0mE}
zFN`;?ovmYz&LMh$sC*k+ta@K=Hpwc)^BsyGEa%m~7YcJ({JaVC@8d(0rldH-sd{=N
zak76<&|mm5t1xRZ3}0H>c_|}^^Nx_|`6=s$y<iA{xalT_+v8mMdb|2czW+sb$ZZJ+
zi;FQGV2)5dFF6Nbd<O7T-BW}^U30X6q{;u&j^@*)N<%;>1O!C{Zd3;%9m&ws6dleN
z%Qj@s|6{0^AveLncvA0EkqExDkK*~;Qy>m^slg9=xc3C#os{5zTJ&fg^lGk^k;k7V
z0g>fd)B1rM6Y(c^YKN;CxSQzHk;rzq{F#4tdOKxZPnTN0jsi5z<zTBI#XNNF`%g(%
z@T%JVJHaASL}@lz-PT6q51z(XOQ5l+j}bMrp0cU4EqnSZ1+vnL%;99p=3<%QGiN7l
z$_NCoC?5CsOwE-*K~4ES&ek+Q8Czki-IRNs3r@zDyJFo7JhB{wZ`bLN2}LGtSaWqd
z(Cck#5TrIpNdW%P78;{0bpdr|xEu7xnXaD0D~N7Ia>KTenIbGjsg{!}qrPe-s8R}-
z&rHoPa;<{kX5v3;{<vDO<Pbvn!E#+)Cp!==zBqK#Od6&zIL8`A*!ws~l^N;4YG(wB
zikt)xc?v)FP!o!|JjPWL&CALL?~#R8)$nX%{HU?XhXGQd?}-;JkrqN_wPMybyOkuC
z3+z_P4CBrnvs~r#=Es_R6`pfwpK;vGlsRi_ME0xN%y4k_*Lq|-t`$@V!W);<fXS7C
z8Ev3l*FM&pN$k)v=0)Uj7iv#7j}ZfphQ|_#5YD{7FKIeOLyqgcBA-(2h+#J<VYQ6P
z*mR^(5di14?}?$>-1aFs7uNK>rBT5t)Db%<>VMo?Ov4xDm8DeQjo(`2rus37pFsx6
zZR)MZKwRBZHsU5$cCQjMbFa&+Xd2t`xM+AN@z6!gi`p4Jeuc0?`Cg`Rau;(}ju?I`
zmtd!T8iRGj=xk7JkUzaL+Aj%(vjvJqk<RAU7XpN1eD_qt7I|Jqa*kGKVbjhPuyc?H
zF&#(%Nz2ZEavTvXTt?124;|9|tL-*+m7gBnxZ)+t)?~bAoxj)dMDX9=C5P!PmP?9h
z4-Y2x#R@PEQeF8BBl~l<3mwI0@QvujCb?)Db9o$9t~ZvL9OnCHALaqRT@V>0Mnm5A
zKY%OhK+)n~5b4~MYW^kE9_o{-Fj)OLPfh7sH>euK1>QKl5>6d%)DUmJFsZ$DmJR9f
zs$S!~Q+A0H6O}G7JMeTZ*V+TOs>~8*N*DOR4__=)K49kb?tW0j7@bH5cG!d!hl!EK
z#*-BEWK2rJ))pZ$mE2Y8vlXLYy1Lbk`G7Qav(L9<x$gJLuJe6$@Zh8Q!He`Zdm!@3
zo%|ADoYdtfFTNP$y{COJG2MI0tT;$LsUQr}`1KM6!e<Pc;q=Ye7`ld~G`tH65r(hU
zg9IT#iIjnjq2(g#t~iJZo}jH_wuG;xI)wSgydST?qfY~(;$dHEGIN+rZ<>!JZ<v<S
zzzu?LP}oQKr2n?!kf;5t3S6vg|FbCk8w=XwjQv}cr@H`6KYsv23L_FS)pJ<<I;>y)
zr>~h7f^VEA>dmXH^>YbD=Ax6)pJ11hm-AR@x|rKyS-AN(7W_O)Lc5KVdokEO2le64
z^Z9wQe?PnN+P8h4d^o#?oXt^P*xFmc8Ai4Ew8U%zT&iofr|!E`B!?M%<}x`I$DzK$
zd9ViYWQz2P*y0e*E8|?6@cQi1!)mt(mLESsE|Lok@$U-^D`7rMH5=aejFj=KE9L>A
z1(f#5oRxtzmkQzAR0hkgA`WPr_1!rDIzO<)Mvkge6!xPai%}bzD!6UX5}<{JYFMFJ
z9uqhKM?GqyoI)v4(D8!lodx8OVBE$iG?08wORyN8q>`BC29JR&<3~!XHOm}O(sbo@
zqnYw{Nug2E%m<B<GM$7KTh-~A?txRvq$W-8%MsPz8#&IDbS)#(ti^1QEgWzskj`ih
zq^_^&_d3@qRpPd5vpxcla8c=sGNWFWGL^9ayQBc%1J6X%#M@Y9Cm*GQ`Y_4-e4fTk
z;YH$gIOxd3MNKt_;BGuBhd{i&I#ysr155!y<Rh!fQmJTYYtiGR?tw$p28&>HDY5S0
zgt_E_a_>T#3Ot>Fa}Ry+9Puz9liB3SW^$`B5Grh~Ok;9wJYrau6^ghdS{?Ddg`9to
zY(iG?p=WiP%|ea4OQ;f+Uj4IyhtP&o8sCGLQ)=i+HK{H3wYIVBNRzuNMmgfvaeuD>
zqoPw-u!N8WmIQR#O$_EF7OC3g^k2R_!6RdzZJiatxUd9)GwRr|JiQ7;^TCQN{iZMS
z3O`whvj1kl`);34OxtU7zwc<GADJKkHEgyPJPl_jXDd6q7eHV9eJ2kCzjZs-n1Ju%
zXEw|Fqz=;xmOt7aeWC#*8UpZ55e5ZL)p_3!Z?9<|3b^UtpF7SAbvvj{9@A3ENX<=4
zfS=b{ksgqUrhDlKDMluli;V0ZS*-9zzwA<j(n;XGQ^ej{VsrbWp#XHn5#?3@xUJgH
zFCy0XI;NxtJ@S#1TsBCmV-m)qmOg)a6GvDmSyxrp5%C)tjpR`@fZ*(5>uF_iV28$}
z_i3ev5}4eWNU?+}JpHh^;}}#PB^CT1ceDdl=frctZY1<;80Rl28hwvfTc;mugN0<|
znQ*Q@rp1K8G4#gdlfMl%DI7Ebix4V;g`vM^wcI)?8LupAWuc$KhwbrFH!^tU;%XEb
zFHJACIB}&10p!}+pjtzOOSCcPOsv)@R5P37F-}crts^CDF>&pIZ))cV&_4gtA*dVW
zJ)9@E(pqcgl%?@w4pEv1bTwZTqZS6D89`{wL9dx26VRBzonQVIx)k041WiMgX{P-b
zzg^K$owBDpl-XCOsvRjTfMaV+EQ^nTYntXOB@!a}4_WSFJ>mUEaog_6n@<z0`NApe
ziX!UnRVmsJV}e%JN0UtbqzsJg@t`W$quBk>@+{A$CxwrWLPc^WPa3_VQa5c0r9FPf
z7p!Edkz@xWGJ9>R`;%AzQjHbaPw{_=9@R9{pwv{Wj8kpljwlh05H;HLWmv7j!)nit
zxY-q@J{oOqTK#py>_>~o=No{sNE8^{Zgqjk^@;FDnv)mwrB3mAvcxD7tB#z5fV&ll
zUl_c}9ZT`_YCySV`SxK!=4E@b8At#7>#a4FE~a>2wZ-HU$5Px1;OHQnzUGP4kYBHC
z<Uas?A&QX9X{!)USPwxkw?E|gSZ&2C*{7S1g1j8!Z8e=X1idFhrH{sJMaDMC%08;D
znGH<W`tjHLlA2{MyU;Mq$UrvOy06CBB|5Sh9U*gArNM@3i1O1Cnl4}tyS|!(EDe%z
z%&ERvGT$+^TqSuOK&r&N-yV|9fXH1O+s=Mk@M_L6Hd$y1F+~o1<TlFGxmTC1WTqyG
z$wm|ED#PQgvaFJI?fa9Yrp|^#mkxblXfR#^^z=LY^sLi9)o(|^zpJzN0%CQEuR+2-
zLx+}qOHETAJ3D%g=nbQ$Ks?uvtJlY)yS)zcRtv_j5B<avz#RW%rG3j&w`l0j=oY{{
zF?#9cT(owSY-aWA#Y@Vys`DpSAUE;vFlOP=<yRXt5nN+hI3ZhLygvT;J93SR9Xv6&
zc@|q`$-w#j>#pac^VF<`h>E_gT2>Nt4SIdpQ#fYU`O$Vr^Vc8VF&b(w{Yh47%k#H!
z?|THYf;L-Cz}Xho=){Eb@4JBoS>_JL885BUtE)+yHH$56Vw@)bA1)}|nu?gpQoOv3
ziPO#nn130pQZiq3xd|`|YX<C?ik8khF07MAt;!1yc6i2I6TSACLvEF+M}5QbWExN*
zW||2%X57TkQdEV5w%~2&vS!XyU)mB1OWLpX(lXi&fMQ@?QI=?ky0oE3UI|vpntA&K
zFoEAs!zSjt))YkJNX7NeNwK6IMpYbe(@ddu^22nl40f^%YP)H5G%mG;b#_|ZLoPrg
z=ZFQE4Y9h&9q4=IDRkletR5SC5?DDcGW*<ocH*fP2ly;GTpQV)LWgdfd?TN=(zEmK
z3Ou|5fY=ExodQ0SewX;(DWp20KGiVMd8iOaW24c@#PJc{GgI@DCW<p%IYu-#(=_tV
zFA9s5l#iCPRh6uy5|Q-O7)oQ6(6xQ+CcgB@FZ)Z^*CL+f8ofyl1g4kG>z{l{EkeUt
zWc&MLJP$oM#(Z1_o4+SWR!q@N&GMVXvSFJIfN&Xztrd*&7?=wkV3SW@Kb;yDcdp5x
z-r)PDtgCW)Dg)V<!@CN%Pm8Fr4KjPhvt@)0IRKQ>(bl88qe|Yy%SHcgWhD(78lJ$%
zRTaFg5{yWfnf^SdTbd`HS>GIp!jo~BshLpm*S3#r2dyasiDg*8Cqf)5peX3-*G792
zU|V=9QvH!Z;6m@zGa%^7B;%=FkJX8Zrw)K<UXsy`J5I~gdLRzX<o$ZE4#R8O>weSF
zy|T5Q(}B_UoZoYT?lX+>M^(|&sf4Q)a>YIrh;-HyD}n|X=r)ljg~uG|n!!ugVq}0t
zgaGP1=A=-NsT_-(=-LbnjdBiATy{1XaMJ)zUI`6N`X?T#jY(zaHCp&QrIr2SVlkOr
z;3tC@BZH_3z9<(>cLVVY*VMv5A+Po~#-&Oi8Vknyhqg2nDeE#BG0G6#>!+BZgr~ny
zIy=5JGwITP1-W9<>?V6N3K(U;XC{w6HeV>8^F%wt7qOP-ZtQ((o~*@R#hQ0mfC~#L
zzqFYv(Cj2;j?}on{ufxxIRdS@VX80|zX<nMo+Mg2=Qr0HcO5<!ang4Y)ukpMVF&>U
z?fm{P`8#XrASjMczz%foFmEy&Yi(gtIw@*yEQ5d$0$gAo*ix}<8<2ACgaUqToM<R(
zT2mPRUp`-Af_cn3pOqx7ZT>uL00A7vlgh3F{p(I0*=p88F-Mt{fXhJ|YR4yZu=w)6
z3jYG3?UN?I{({NDnU+UaQXT`wNE*^jiCYv1O(@7N&JrD3D~D2>>SMIR5d=$ThyB}k
z`j%o_HRFeVTh|KRjoXCx*8P9o?uczMv)A@0ZY73yb=g~aF~1>lya3ERL3+Eak9GeF
zk6b&K3|_1-CU<4GI^0KDx03B%oGoT8yqZ19RVA%dIxM5KPn_d~&4~F_-IJ5xXm2qV
z)J_Dja52Q+Aegy%+dbqoK&QmqleD~-LgRqShjAr6IKaG=Ah~_R>1Tg%|Bwq3<Uy^U
z?10%N+6gNLAj1L52LQ)<^#Zm=DiuBBApyD?^ZJNg@nmNu6Y39OHpu!QOJj5~nZP;2
zI};fJN0T@j7x~HVreG!5;({O+Vatr72WVunOLtm4451n~ygbPo&!v$g)x5;bo)YG4
zQeHOe-SzqvXt&l!<J#)asx9;N=-V%pXkfa=a)Ul#!kreb^JFqjS!n~go(#(>%tI`l
z$TRm?l<e7Kb=p8mm0@ZfpJzV<r>OX8N=GOtAdLUvv5^6o{<pX_MY}Whk{$Ny0qp^l
zr$JPz)xRGVI1%U!#1^BtUEf5xWb}uFDX-Af`$nHMsO2eNGLqNlb^E(BWTW{9!`@}{
zC(oYX=h~t7rWZi}^L_94@z&-Twa}fM?r>A*`&g6ukvM<l`*Fb}ceL?9xAaE<P~xzF
z%Af0C1elHhP+j-hWg7bS2xYuvv|l^a`R?#*m;O`tjW_=6k38JG2-q`i%Fwm7rC+;c
z9?@!g>cMxRD?k!_xEv+ja?$-(?oZOULx0<UIEzkM338O+73F?edC`CNfmxUqf+O~(
ze9KK_R=w@J!iuP841gU<#HYmvh>{6Z>y}#4&-{v?v)f@}C?9Bn(x9H_LJ5`-Kr%H*
za3Z)=kYtDOUR^=kys#qq_1-DBr}jA}NWrr5@JNa+OK^1IsU)R_;JRE&a|ovvtsU$w
z|0LnSb{NF`y!0UpqO5PY?74-TVW*Ark4E;yMB|ehDZgB{@}!Jy%Jw(}@QFb0x5~{p
zu;^yn25Aas-RnN5jP0{EEi->5TwJ9}_xzn>JxPyV*p~8YsPBlXaH515MM9F#@k%}p
z?U!3J@1iAUmF%jr0aa|QpMl^yif$m6W1*WYceISu^*b5gt$4<I%WI3X^<zqMF4~(2
zJZHY~RF;7X4T*`^ey^t<z)i}$6mEvpm?EZ%to6w&cZ|zBaMAUys%O1b4qia+9>!96
zsFls(lRXgZIBr>alGJEGE#=EOgOs%ECW-SeZ9{ykYwI8pyeLzSNp2yS>2xrq9^`O&
zwl?+e6%h}{BcT|>pF+{NXun}BCVthrIjPA|^h&Dq1_=wz=DLi20P%ez@OhV|+VKL=
zUt4F$eh>z;PC;e>X62n|)VH$XoWBUF+IBLp=6>T}E|v35M2jdDl6z}^TEXd&W*mF@
z0dFK|%_=YSfFMq6p#^4|^)=r43?3p$hpM3`F)`g~;u^i~KeexiI_)DJu+$Ttus+k*
z3zvdPJKUWo2?)=cfX74I&PC9%hu=AxNXjor!xyzVx?shu^^L8WpU|<=V_n(Pe`2k(
zi8@9@Gr81cF$3XuzAe**3Fab=b%k~039%JL#HDC8Bchqe?l9cko$au%jA5&%BP_KK
zqW@HZWRQDzQTKuggyRh%m`5z-L{3xI-_<teu@|KgiR=V}0K_<=Ad^`uVPJ}dLh$dG
zN(3uJ_!q3RsOR#l#e&P545n0qmtRCadYFBYhl1nDnd!vlV!~+VVK{ZcU5ZZx@psee
zCl^=dVdu6^@k{?@9Ys@ND0xPn7ja5|cScg2$u2$xqc@8~p%CAXt|OPXiY&mWj8JQ)
zN#vzH8BEbX0DMACC#7(kpcj<1(W05k4jkEj+dAc1m;D`!?@*}g{&IVVeu@?-e9)rm
zqd{%*x%6)l10LjZ(hQ&b6tXH$8qa8%iTM3kd5EdkY=$@Wr{g((!kiK`M9*T9>bJ=;
zhkluZ7X|J}iM8ihwKm((pq}SGT^LIvaYgl-wxx!y7yy=@tvz~<$1X8*ATC*AgKoXY
zQ5>ydg`Q#s-+}eVrlCKQRi;->oO|tP&`#Vzon_Uyspj-SEATR!4q15_{Y$&$9KBcT
zq-#ak|E`irAA0m~etx}>LF?KWYg+1f2gBI72B-(Ks4d6=I654JP7|)Y6bqxzP$1ZX
z_q;Lg5P+z&Fwxr9i*dx6x%8CXEX(e)M9dw&fJbaee`hUKM$T;Yw3LZ#=cE=+ZZ~-e
zi277aS+MYrjOKjR_ax{a^jS}!#M@Zc8}Pcc=?+kNgr6u%Id<Y9#G3M;^5(P|gmkmq
zCe!*;O+Z(jQ+#XQWOf?ykI%ixN?$&hKs(qe444xM80T7FClo61w-5)lyXao!t&|^d
z5cw+spS_UraqFeAcphH&@w=Q(U;;Jx=mEM|V&ak+V~2D;FpP695SJ@ZN*A^!g)lYp
zo6;;Zz~}EAVfFpO>eHSQjavHfa^6@?pdomHg9#Qe{Zj+Hq-v%5z61f3Zq~3G$%<1O
z2cRRlQaa6Aqw6eODs`;B;Ofo{J$6znZ=buJTjO~Og11fk?_^57=lKIB(N;!YtbPlX
zigs-XF_UO{RC<;!@4+ZhPtL-iwt<hek2d}sC!(j_h??oIeK8ibY5y`5pBKIH+J-{Q
z0dDRKu=?idPHiN2TX7Na9j+r70@j(LX+XC^(*gaF&vOO(A<CkpVr)&2ud6+ush)}s
z*#L=ofqe@m2&zSeuK6PrHWt3#JZ`QG?}TK5u37Q!0uBFHAv@2~JW)!jy4#LyF{5JE
z_rIjIchDZ4mY6*Xtt=k6zsNUEr_9e>)9-iEqu%SQuh$_aMV4r7r5E%Md$q$t0Ki-I
z(J&Y4|KaMLqB9HDwb3{o+ji2iZQHhO^Xu5Qoph3pZQHhOTPJ(1|6=bmM%B%%yE$tX
z-bZ>aN7CR`wV^<xg(VS0egxh$e&Xxb&UV6$qRhQ6BlwBuot98cRqUwaW_4A@!JJ`_
zTQs4vndkQQ=or-(GU;>lf~{|CB2}IW<xz1;k8r!IJwDC>Rc)abZAMG$g^-!N^b4qe
zsH!Xf*5&bQ0<-+-IOl7vwuzJn6s0PgDrnU_Ju+?e=lt@Si$2tBj7Q2{Pos3hdVlbQ
za%%5Fm+87TdH+k_=E^8$wy=b%p7D3%`#|~aLs49JfZInc!gswlGO#L%f@PGYc}xrR
zS19I*k|B&&CPJ30%L=T14SBwct*qDEcxdH;*7t|o+WUSP@bJ{be3!f?;PH|l-AC{9
zm7W_L+Ba<PZK`x!$ky5d`n5qKqSp8R9*%hmT|0EJZnC0_sC3|lBSicpaoX-D+ase)
z@$2YZ{VM>CZ}gAHbo`=GE~a+zwEo-<BiOyi=}{HcBA)@UFXOwDd(Bd${WRGiL7DQo
zB}Vm9Vr;3&z#?E9ImdjPdUSc#m|nm<D;e&)Z}&}~D+tV8;`K?*sEwY?f>W=S86bKz
zo%;p3v9BropL0uM4Gl%}kNjH-8X-;@bw0=c^*Hwnp;xBT<UhlyGA$@S;1t{c#ist3
zAy6MXm4N{bhzaC~E%J5%F3t0U4pXFz)5tY_q^c-7mD`53J1%ShmL{s`Qs$!)y*_&9
z-w%2E*`ELe&$p}}FR%0Y-LK33yGg)D!D3sPEIq<8kGJ{eox|H{w$ha(RrR&(#6G~{
z>U!hoj?zYIwO;8BYKY-{*SE)2?|2oZdho~DdovTvIpq0(odp`=h<OMl_7$$DUa=|=
ztNA{|K}Yz36>|u=@eh%{nLR3^j5|Plpw}7<SqzMy-v{At*6q=b1Zx$K9jyz7GzE4q
zpjH~IZCPNnwKKSVG=T{iu|3G)4hP^1AGYBk><xrGLZ9$~m_d!a?+QK-wKSfs324AB
zlD$WN4tR11Aq$h2k<M2kQW0OP_11OmnF?_JSKP5xO5Ajf@0B?p=~m&|8#h_p@FqeG
z7h6NCpRQPq3bcJ)X{2}&oz@pbWLu_pqnQ8K=9i5DsgFX_V$p>{${(HVy?8(aVFvK(
z8x_UoX{&a&)2h%zli1{k1w(V%h(?6a95*9SaBEAabw|xkItDsVRr!#XNt70Y1Ure-
z0G9tIA`YKF9?&y>!=MnUAuCgxve<hJbPLUgS%t)`#j6QM#he$1B2oTBI;3*Va%s^@
z$~fhFaR5|@j1Yg4JE`ToDIUP-7ju!jXcO%v_D?u8jw_<uUSNNW2=0wgNv)A*{FUU5
z+zU;*E$3$@olec5<ar>TOz5h2+NxIM$Ay{rYdoI-^3pq~A~aOF5JLy-_8m%MZ_K3I
zxj@YeSa}bUL@8DyA{@QQ8Fx>)7jYC)u$eolS&f9?4I%A$Ke#qocsu~~8FzoeRn0~>
z+?Qh4qseH{oeoVO`6@Bqg`57LzywDQmJKy*h~Xyr1b26DO&%$yTdEvTGzYh;;XqhJ
z$F_J7G%tiL;Y0K>IYMelel<kNaMK3-)rc5&yjOS#WhXrsD_gmfTj^W7n(~Q#4tLn#
z!k7}DPZBm^UcHaDgap83g4aWv^H(YP;KiId@_y1NxpU>m%wIZhp9I4Mq*Ze6HNEt3
zDT&*GHd|+<0^DvKmtH5{^Cav&!5bVx$2NJRUyVqo`FqCNQ2dSk8wB&e+qUFGCGpp$
zQ)tR29^ymI*Lwr7wohZ1SL#(JT2P$Dw{)jiscMe?lxqd%!RrD@b2)pU&r8Lfhm;h(
z`OuLB(i6yJ5stUa7bbKaX_w$%iKjT8Rt2-jtsz!-*1Yt_l~WmaC*mAb$HOV)7(A(h
z4zOXLO8%vA{rC;nr-NRe3IC}(8dq|vO^BlXJ=}MY%8#Bl^dnSwkSRoXID+4utrU1u
zm~WF_`Ep6r>e2`Jy>V{Kd2YY^_-6FX(^X2Q4-Tl3jR#{TYzydaZO`BPe<A%4NX^pq
z_kow#yZyMn-%l0IOZY#x^z2ggGhC&q5+B4hF$Qx9wqlvPO_7&%Cb;T7n$LDrb@=FA
z;YB{j>pogkAP@)tJYO&Nmlx8iO3^8qP5xOV{V|39h9U(7+8wd~c5TX6jHB*e1gCT6
zNDy$<+-HVZ%ePbd(*pvcTN6G^^*gmVOB|H%dUX8HJI9`eEh+$7d2<3)S;CsdalnL$
z+?&hDBYY(xbT=$N7L3*jtgXWqtqF$XXi9iyxFFuJbr@f+dAdW5erPzoMSzhxxMN{V
zXhhY>hnpI3egn+xi|@c~UC}gS*iqbQX=*OxTYQARDWliS#(vjnZ)S(rwYSjH?l2TV
z^;@%2pQU&A>kRQ-A6rL=;gT$NuHpB;%*r{H6|?&i7O<A-HdgC>t|(<+9nDOFq_F*q
zd#AnRtT_40)+(X1uk!QnQtUdPlfV?2?hS9LQcnQjUzpVMaz50sKxfgPJBAIzL2r+L
z)oXrk&^nQkpE6k2&Fjn!BdFV9s02aPzDX>ScCjqsZ{6uob8J1eWqs)lzF?NzkBYeJ
zjX_4$gsnuj&HO0KOFW{MHOkS(E|!MOQRHprA`tJr)pf^)nTE4a=j1V)!`36*`VA1|
zjC`d4+138ObD+q^v|^6RqjnNQeC=*vnqn^9TcRV{n3HvaN1yMNjdjy6>!kKR;FfWC
z!fD=J)foi>$jWf71l|l?9}XBLq|H=l;#;Ye`gsquNrvH^%+sRzM<yW(pySF?=!?^x
zLec%gSk=^|QMmkLVrAWUDCeNyLb3}G2%#RpIaiho@v4ld*?wYJHFsYFg(cKROGf*>
zn5`{UiS;4U1TN&vpuVx`c`hYlu7lO^gv`6>mW{Q<SM-}G$oi!gC8s*eTF2jD;*(b2
z?h|UmC;KxDvPLdr<G1s7QT8R_ON?}eYmCmKl}VP?6ZKT9?h_t-S2z0)+G%C@`2tG7
z*PIXpjP(Gt)M@_kCdf*ygF&eEQ_3*z{O?r)d%M`eiTJ6f*OKp^-}ehYl*!Rzu<9vf
zyhBVs>sR9gEy`S}uuGFZ-{-egYFAuDTJUlSrS&aj7jS?bL3S5+bWD3N=npxM<3Iw(
zUcI!vp^|eHP|wvltbXP~w)g1Q-xLSXa%CbJ(ryZFYFXP6gVncGr?g>q+-#vfaMf_V
zG+sFH?XS*#XnkhYDPlFb0mZ+Lg|z_MkN70~bTWco=UR9IdKY=!PuvNGMoEK&TizRN
zsO~y+N+P+sEJf^y<$tS9b}@Yu-z7gCV2V1^;$u5T!HbcODYukufx`!Qb(#ZyDHnIH
z9fpO;@iSfUi);^!V+Kwx;G?97HFL5pUU9+3L~J4JJugR$4KE&;nD#hkI-|rm$X7~-
z6EwX;H?QD*dpQmR>77-nuVa%AqquJVgY)5Z?vlh=G3TY?XFskzWq3z2IjO5tz{&sC
zfpn9+9^NjpGFLzUaKebvR-Qrl7gXtXOm4vUaSm{rZ;boz$waaL=VZdh^na`hFLk8t
zH#m^HuB&GVYC^6d4KO3Y-9RR2>$~8N5KHG<FKFD{5bmE#BTOWTMccF0W@9J>iQStA
zsxZ!b-v*W*-bpEdzYh-3J}~Bow|C~bIry&imTQgLX16AW#BjgglR}CZ6VqH_-QJA*
z=3l$h0HUutTb=akc1)dj36!1PBbTG};isjisro-kqit_Uad|JxKHQ*;qUQS41eK|~
z>IQrY1(=7f4{9r4;kAUmG2%*s9|Ne5=U4(#iys51^TC%FQPyF^05OtYj_%j{`}U`a
zZ-IeT5#xm-+74x@^3!HZ&hVlC_&;f}jPL-khpM?z>L0uk2w^6~>1WI?*#QYi<;(^_
zfDL*64@?RuF(gH~XY-`d0nJy2&0N1$w7#rsyMJ{za=yrYQbdGR!JNHFcjm81RkVe7
z6fJK+kkLWvq2fWp2aGTH)7~%t7W;(FGp>@l!;|JL;>FKJ!o{rh#bCGs>rS2dBJTi|
zNq7p3Zq&t*Fw3DC)T5_((IX8KS|2{o=b!TP{MYPikkgA68tF2fuP<rY*%^U%Cgw>S
z1jWBUX#%YzwA)FzEB>g^d^irWmNV<_N$swOa7+ni92*fyr!Y-+R1~&>uNzJmYbVvu
zS1n%Z@X)=V%(nW6H4gFpI3k+JMEsC@zn?^Mn?R1@;IP-$e?`15=D?pD$_}fY4HSer
zGk0yC3$}uN!tF08vc0J6EhRrAj^n#D{wDB%-^ckxvD8RJ_@G;Z_*_(6V!(Zx9TQAp
zuY`w>?ToG(on1T0X)aJRo9^Xu@{33NJN>}P!3c?^1)movqP>SJ(6w+^Qz-<vShE_P
zh@t2pTK9r{ayh6xF16AeHBvR>F1P&s*@P5G@e(*9*-h>{#+U76KhYKKSH*zQ;zP5j
zW>QJD+?p^<Rkem&H;^-ehEy5EDE7*)d7EDn?}6M&@v_J9A>-o{elE=%vMtS(0v*k>
zVVT`)0@*y|H5N>Uu=5)KI%p55#+#y_ip(Ohj=eh24-B28OVY9QE|BLQ(J6GB{Yir9
z2#3{*#)3D08gd~$QLSrcwN^aO(ertQW3midqM9#Gz9{ckd{rGGD>dp*>~2VJWTri3
z=iiq)jnU(AEblQZ<5O?2+{}KXZ@i53Fj5vQKIQ0*Q!PnpI^4(fqN4(^{B4m5IVSu`
zcT+IK@w9tv*3)l2HAz=PYlMaHw(ecWWYtnqf3~b_0f*#R)3xCA)_`JzZGGb@^PM)D
z$j1MdqkN#hv?~y~fJlHGY><%C>XE@qW_oq<oY9eQH(jye$u7lFrPAFX0T&qR>YGV}
zk~{Xe{P0I?J;w|lW9t|I`xnn1C>pNS({gfnp32xO1iV<~3@3n@is+o!`R&Pa-11iz
zRb^-cb?8nytF4_teaY8d*b(Ms%9yS-JH&j)zdm4vdLlmb#eeCH{Tbe}IsaS=z-~_~
z%G|VEK@-q6qD*g9-f5}ZPQCQFfwrQW?CE3%LNK<I^roan)gd_m)|?(lQz1|8VTlNP
zCK9Y045gEZ;`=Ky9HsQdrWma9pxGe^Ck+J@eX1?X>g=Ag7tFxM<uGPXejE#$wY$Mw
z)^mVxD)rwftW#?2<Sp}IN;N+^_x|jw^!h=^c@5N+ioHP)&mo{x5{iTH5m|?PP|qqq
zPfRRI_f{*tteFx3Eo<Ja8D;~PuUlyb0Ztv2%e2A7XO7c!{C5O=xWRVFOPXHnT5SfF
za`mzAl&ZJvmPWOBy9~DIOPuVQ$s7Y_tPS+?d=%Ddkk`sRD4&V)DTDZNH<3vqORqo=
zHMz?E7%f|YblpyCuQq40YJIZZuP4p<qai-)Gx}KHnAsRW&9qoM<I1rq8oWU++->cO
z`D7H<`-b(M3a<|OW9A{Imnn$!fVwhCDzX~&aCqn&o~Ig365&_~I5)ag(p9EakDh!m
z-|<C-aegCihlYjlJ2(1RNN|i4Z-+-D2KhCNMOL<{<rP`-u`X%nikJd{7qJd#N@q0G
zpeyadhpz>I`AC$&oMO`9py8+=CY!C{yY4FQAAOiaF#}58nNb-r+AUwfnENz|?O6wp
zKkka?g%=EDPI*C2@qCs?IlA3bg2Yw(#swhNOQ*}W7$#-q*U+ynIykvWqn#n8wIoeu
zlR*8l$+A)A<}4yU@h|F(1`DS;%dps06)E=!D>E5Drqrq0tX^XCwjG@|6VBlZp(bdj
zrj`mD?2vFBqsu%nHK}#-glaV_0S;*eD_iTGlth!Q(Fj%IJl_mufUfcwLMD<pk$MQ1
z*$w(KB5DKiBwT>nfIdDbUIC6^UZb5c;!GqoE|7rrn(^&)1P1d*>%6fDXWwo3l`tJ=
z62S<dw2Wm};{)E000B4mg)iMdf=9oHVh}#s@$hr^wNA_RC=$h`afY9?rY;6wY-5pY
zTgg0B$F@P2+mWP7$*cus)<MRi8oZtEwILypf`In4q2v>GWij>5pH0G6w>A>a8Yb0f
z+;(Ro6q`wQnFg2O5Bxx`SO(W{6`g{)cJl^6;O~9$oy;9U?Glahj#)ssK0AV^tgX}K
z3lAdAC!!yL>at+XvWOdE7X{cg6S0M2MhvhcP6IW}i0;lEJ!t^OCa1BcWKb9Sb}G`c
zy%}>pyRIiuFZgA$d6%Q0x4zj9cFvA}u2nE5ksukR3N`vZp`fYGDdX=Do6X|Q)4ogq
z2inXOOitt-fyaulu+PRkzLgk{q_ZdHvR>qM`2AV#(SYv+N0;_=K`QtgVv%7GVdo|%
zH~mM;#;l!eGlfn!zF*ZDU@UVOlT_1IyR{&#Q-@6)qrJMtNYCDi7NyCf^n*4Vsd<pu
zl<#~n8+uXoO_HkxHcK(nHXYIS?t%%BkZzd>ps0!;8jp49#DJ+VIr3)%%%rQh%VW7u
zHrzqP51Z(6no?ca&HX6~&>ocR+ZEZ9r@PUVAODc*GvCcrOkrNL=P&*sxru7Cvc9tN
zMH{4jloTZq$F^;L-`K=NyDjg4Idh}ltYhPxO=snv!<ry@GvsI2^s8P<j^qQ(yU)bD
za^)}TxxzsNMeKO4p>IZo;hZ1JKSJa(u@^U7xlI&#2^OchF^sZ)aydD)2brQ^EE#?w
zMi#9SMHoagM*NBY*_-l@@L{u5q)Al1aP2T0u*5>M+bl##L`k}--E*7d6raQk;n|VP
z^H`$O&ws<m(8Oo++Y(C*P1yjB93v}F-2zJQ>O9v5qy%y;w>2CWMMkz0jOSO-Nymbt
z(vEbESkQy6Ch7v7m#XhK<Pkh`QnStDg&g*|o2E9MRUC;j3Yl=!!B2>TtP1&5^~Ww$
zbJzqY1;;VGR$fDJ_V>F5Rq?95-k!zqzs$3{kxcbZ6_3ZJ`@Btu_)iV8Jn`xHPCv#z
z9Y~K#h%X}7b}J=3*Ci)ms3%f+f5%)O4LMm?B2hW<8dZKIRU2>Gb@KW}Y8+*<dZW}=
zRuUi_NL!zeTiaSb{JRhAoy6TRVsP<Ch#md@vIl@P-91tM_o%r#fdZQfLx|WODt(F8
zEPaWd{sF?om{?2mgZD8o{9iX4jY+%x|4B^x$p<wY!Ac<Z7ga#D4ZNDHw2$N+aLbaO
zXImH6?`F=*7twT5{s;S@u!34lbbP&kgJOGc*5UYYYX<bj<?K^jDsDxu{>~0+S~J?7
zCBlvo$%!EG?7q_rL1}aRRjSUPb`4-}4+ex^KOKFE+xygDBBy<}mWwsi=B?)P`8v??
za$+z)hK9DE;7axhAYW(kb3q=5a?5~9`>&Pp&dQ|X#@4GGIUztEBd*azY!)X@kQTBC
z`JUMh2*`g0!q)oVkPcbw+<z7N`x7G^O(8THz;NyfFYXcso)rsKa>=oPOdACke*rXT
zKWnc4*hUIk7{s6pnGtTwWGfwGn%5p!d@<3aRM*SpKH8O%xEsm0TUkC=H3L+fjtXBN
z(%Vo@d6m31YmbGSR{fzyQ$Q+<NIymwTt7W|xIZn_zH?2qyAW5GJ_+cL?PKM068v7B
zv1yhedsDKQnZ#J^Pw4ph^`yEK!T@YZh&`yJojVZsR$^GfpB0SVX^g;*j=Rs@x{&AL
z0X&0y2FXJ-@SDX(42eF9dcTKI$g4aK$%<t0M@ww(1SU|3?lu*hxa`eJpg5Y4R7#}m
zvb-&jcZuSm6QLj{)1@%w%*agtVjkpN)~L;`%CP=fINovt!7`1WX-JMTfdI^Q_Sc`)
zFVB)_R}YjbsuP1Qx}yZkZmqyi<C1@i7TJWlmbTh-lJwGT4UNJDveZv=^~3T{Vduv`
zkY)RQ29TgBK1707_ZY+Y?0IO?S(Wl(Ws&jrGD2+k@Q&;?fc%X-JS=9*)hw1YO3U#M
z`)y&T7-O#6R$!_iUM4@M5DEA@r~w0W__!`U>bwe=@d<_ST|!^Tp;M!sTG$<6%PIX;
z0Wb44tmN%SZ+<%;;ir>I>iA^cU)OZIgaD3>@(&LREYL$Q34NZ)3GG!&eRF@YAY%1R
zVNJ2OV%%Jo+C@BCnA_^a1M_)lh#}1W^oc1&U>twL=~=)`Ii60^eHtKBch<|@;N!h(
zL>g=N_=wreNJcR81`Fe}AAOhK{0?n$-`T_&+Pi*n-#yw}pRMpJt#kse9yPyCJrI05
zPli-)V@)nly^Yqg;}R(YG-Cv9o$j;gmarK;)-9Oszzr?In$AEAB6}_YW~YewSK=YS
z6>_u^eL|3fhLXONsS5zyH)ktT`I244n);6mp03g~<+r4gJVW6&G{L8jPUVVAChaC3
zk1I1l8}Z1cOmpY21`YErqYZ_Br)(6s^23?HA)*$CYDue@p5aU`p-{f3do}5N%vyPc
z-`ES%BXn<*{Ih=Zna^LW>7S2C^uWSZ&tSbr3sc!6rL|79Uz-r@08r5D*3|qGd=yY7
zMz;S+4IT(}JjeLym_Hq>+3_4(%;{%{J(V#So(#ar!SKHsz5gTg*!X_|YZ^rVm3i3y
zSLR{hNvogHB>5xrIE-W7Gj44{p*qR0aVNWFW!~nIynL`sNDKowIU)YUtKBg2bw=lw
zMlQ>vfU#>+bxb<@rX@ei4Zc&yF<Ad<nab4Xlj8q;Id*9W$bRnHdEM8i7o`RMa%$6_
zN3Arc_;7Le0?-Df2(1tvSnZ?PRS^cQob5p9bYNegSq3RkKY3+27v!vz6Y*CBA0O2U
zjX-u>YcFEKy*hxll-GBBc?7TMdvMuyyley*8y?nL2}-e`S7)e?<L-Ap@nA2_;k4#2
z(X38}w!4P`GF0kk{RNmMMks`MSi{L2YRT$Hd9ku4<cwxW*3ATonBqz8(!0$@<~NE>
zanQ|p0Zb+wFu^9OJxbN)`-w0<rd@a{4a>GRg`|oC63B-1PG%+-t`onJ77E115EUjk
z&IAjmqR8QXhsu{wWN2%oky*owioPU`tA~JDk_eju%=QNAr6g#}Lc#JWP)@PBA2;i9
z|FtbUMf@V=;6ak-BC}m?S=1tBK&-03>bQ!kXm%zODX#hFWs@wzL@WM#x^ff)T}@4p
z_;F12c5Su&E{hn)7+c)xA~<qkm50?ai&G+mR4e+{*)}Jbj(8-?{!)M$^j@G%U<KkY
zQ85@FK+&R?{56?aQ(^8xL3CdV*l?IFdHC2Ob(nL<Q9vW#x6VbO)!OaZ#Pk4z&<s4I
zpAJ+xcuR(^4BQIh%^^+uJ#VnD_V-<z+`&#c+fXAN%b1ldChcswLtIkQaLhv0P8$<-
z?X57~nqnauB9Dgz(5DM~@&V>76TF!v9C?#GKv~Lys!$ebU}y4SI%x7$e<C2~^eV-x
z>dwnWl6q6k(pBbiRno|dz4IH9zLewI_Jap*DRFIDuZL7{s?Q-}`XaTMZrc8}wU|HQ
z?3;vU90p?9>wbCu?X%yPl!^CKm#hC!E-mm`k4VfhtD?)#cfHvshGyzV?}B2HCMtjf
zc;~Q<4tu<we^_sjjI(qJ`qmbmU|I^QU5Z@8Oir$MT2$}LvwW3#G&(sGHI=#8Ivd19
zg*CWRekLn%j-sE96)(2vPLV>cc9lB@A&sT;_t)kCJ*J3h(3y91IgTr)DxI}LT`<5R
z+d*}qdbofbedGMY4H`~jt4pqoKn6tzaKa>%6VO;k0&7$8h1G;{%4}hWjw^H%tsWz8
zWmOGXkK%e&1SHdhA7Pp7AmN~N(+Dm#=mbp+S4P&urk6(jn=&|oA_fiyGbvExg_k@%
zLAtFfG>2UkPL7D;9m4NOD-O*e7Ku1>f%S0|{`>EWs)*E^SkvX4b`(>k46bn!@Z7;#
zTlZT<w5&R@daL=I<=&v$uO|da=WkviYQPe@4Q$lWgrL3~9p2nkNrd^xTC4Fu*L>4&
zwSog1sl%5fzFWQ54FI1O(WUoXuhJg+Q|6{0qTo!PnWoju+QW0j?6le{Gq&M6_7$6#
ztr@+qynprzpVDRrdf9?qSpcpiAQ)nX0>eiPKjBV}Y|BZ<TzFQYxF+xdCuovxp%x3?
znV`~UA+mf!9GZmd#SuZxEgY-FHuS97q}aOsI4#+TcaYk=?6Y>_xKnp6f@Mqjb#^>l
zshg)sFCHzW>Z?y`WYSkPlE#&GZb|e}bEKvVtKimj5#jMTtyiUwk&e<BAZR1Q0*N9H
z3af*t!sz*TEaB6WSWSaYO#{!?c#3TPMUb&*EMO6?zcCwsbnq!&2uj?!Qyrd)XRuZc
z5kMFASk@xXH1i4Gc(QuJ{FS^tNj(5Od)Xem>jHM7G#W29-%FmI13Tl#ODU#&VV5!-
zvHU{9l;h&`cO)>N?ZGMt@Tn;1>ug$Kc5I@IY00H99@Bv)q#9<}yd=$eHpj3AWamG2
z+*6gp2euUY$aNKa;EI+Yn$3uivN8A@dx{LuRlvdW&X1DgE{Q&Hu~{FW8#6E3vll#Q
z`|mX+3I_?a7Pgu}Ok<q;Z=JtvX-`3#(r9fa5$#~yE3JZNE$-hGfbfcsly6Pe++^?D
zG3$ba1Wtjn>srTCG9m#>i*$0twATQhMEW4*$T`=1wD~^#;kqgsIeAdnL~vi18!Px=
z#C?pbJ;O1t_jo_!VlcahcT*Fcz4_VA47j*2;?P=b+8<MQ^aGZ6CMV?O*-?I`b=Q6m
zN9sF4@#dE@R|}R^KnLSWXC#`DNn~Nb@p!Sn#R-Wphw3iJ%{n?4)&_mNW9!&i*cxba
zZmkdgsH=;|j2Zfql1GpEKYEMICX~isTIHQu8N-;3y!xqrBt6=qNygnruW>bnt}32m
z>uGkcg~@gwHpO_CEU9c5PO!XorK@v%Vy_+Z7fVBkH!Dj7fSVds@U+JXwNjoYdOxb?
zaixAvML#+vv^@>}u#YVWeSU(YPpL#A7GnP`zrz}K8!PFl6~s7-A@PbUbqHx+s{;Ic
zdtM6^<6oJ{Qw|mP<%&t)*di(uTHB~dhXz5vtWt!3sXIMTLyAG4Sz*an-V}<;2~hb&
z*ho87<fu|6YwUmJnTTL=E;CX<XFAVDF5YPs(MCQ#P8EXl*;_*0;!6f^ilPxjH-JG<
z+Ey37qx&g;jHzD&W@NtuUhm|giq-0c5NQU03_J-c`%ZSkQ#(Je^uxAFdjIF5fSOvl
zgaY&5i-L&_PN<0uZnKFEUQ7xwQ)0dXIe?Lc_5b4NKb!vn8|;t9^8`U;HqZ-ruz+$6
zVspKrmG&tvoV%69b{_xfI(4DH$;Q)+S<Oum6)TpvPUen;Dx#=gFU<txo75SaKY*8S
z{}B&gp2I72c>cvZgL)+z)*q(Y@(U(Xd0&UT1%9hWJ}P8h+yM{|IIa-xa!x!sR4#I9
z8M(X=4(K!|;M=p*$WJ?wyxD*m>^K&pB4!K+XykX1P7{5bs(cgFfLAk#oIh2U&b!AV
zAPe$;GH=4Jv!fwwk*vx)=4ASQIyK|I;)w1e0-+{OIRRrs@z*=XxqNaPch?~a?$sK6
zxWe@o&&v?wn*t*7EFyLULVM}UP@Z0agucIWHos8!3W+7$07mdPU54xs{v;poyZy~V
zm_5SKhRHpGJfSWnjF?-SnBW}J{q3>O*Q>G5%)m*duF|l!OMa|#sDf6@ae{d23*i~$
zR!j*wpEmR0lf~-NHfFSqq)Y<%35#q5D|wfvUXtDdp8$^q*fXj2B6jABp<PyWi<@A-
z5IPPJhf*ET(79Dep&`L0{->To0S8d(+<IG?<@ScfjTs42gp)Zp2|8P0cZ?xcEFx7N
z?v@(l(Fe3mIl8D`W5H+JjpaRF80pOiM}3Oc)Gq&hxJ`8~yGJSa&CO*_IwIxN*ik0b
zpvPM!L_pUNMqTb?dRII*ro>r=#wsiHrI|jG7;D|GbxZNSsVvWf*1;$PaOq22_;4i>
ztr0+aj98j8)7L(%)~vMepI(g(?e}tf`XkkidXc18L>$CO_e2N)OIw8uImoN1k|#gW
z^ta!ikAp8t%>sMT_Gv2`s{{~*U%8gUhlF=+Jb*dm)2iwK%ifiQzBnqVKXfIg%`|0Z
z3!2`T%-Qbutjcpz07-9kk$OdUH0=oUqHbi7{X10WCo!@_w=gHnKiI@BOXVp#=eCu^
zi2P)?R|zH6Zdp>W`^$)xvBlCNsnyfwi!dfLw)16B?re}i&s!-oYHm};T%crRr8Wul
zwt#Xa!3E+grep+ZC%g%j^7sq~b7rh*U}JkWWi~iG6A0*ECeRUQj_jzM`(yVPs~WDV
zT8el3S~c*4!jd6YeU%Tp^_6;eml;Jcl9pnZ&kHq50}h=X77a=Doyi@>j5CU6Xj*2}
zO!J!Wn@%{3s%HO!dwOujFKn&zS*$|Uj{&i(k~8grp>{6j(cQQ<P72|ljUm^Iv<v;^
z>1|iwIkx3r@$@OMDaRNkAZ@j%n2scx1T9ba?69N=AdQJ{oz49G<2`;a&SG^5U@|)o
zD$jCXWOn8RUaHR8#In@%tj+YJ^%aTV`VjBQT(4X^OLWCn>~}DK)Y<O3gYc9{2mq!?
z<yHsRRsnHofAW=*B_K)@>Q{7jTFNiNTV>&8sdO(BS0^4+*`=jmi%Ld?in588M^xMv
zeWEpoww~PF;YJ9RK@^-9N~y9pVy$Q_Qfu0tzR(9k7mhU4$6G}g^2Me`O(t8E&0CUq
ztW}aJUR+{M0l7~#Km1U?&)u}PMZkb=6;4@K5i5Epf?JU@*}RqkAzOn<Y|`ANY7%<B
zX+EEn5WVc8p0_4?>5`}+cT#b!pEZ`wVTZcAB6^!Ms62%!cp?`k>uniUhrA%6?{5oq
zxpr-;JQwdWThk?F@*3vR*Nus`taWdaPl~}6?4VHO<h4xJ%Bx#BpM2J@KETWm*KjNc
zMc-MedR{KR_wfBzG-0}0<Z@vvta|Luxq%_9`KPcT6&2Oqls2n`68%5`;wdNLj9YYm
zCq*|^oBgtK->$OIYu2%Smsry3AZV<B>up}Tsz#ak=hRo8HqR@IJ=J`cIW|ElD|<Q}
zJemo*mG)O6(?7558!<<iViA)Pc1Y!1N4g?bNpUY>f%iD><Ps_TP?KK9^K`B=a$ey)
zvpji}zn6>H{%NM;n#0Q%&_<^Tg<fGGwsD+3_w>9BSjPWZ=Yd&LcU{rQ0sp_!kLKi0
zPTJ3MPW1pXD^=j<FQ|ZO0eV4eYxh(6Nl({l6K&>wc%+spB3sXp$w;?VKn*w6E}^^?
zC*>FOLDTyCQ7FprsvcfS|9VNMKb`4asJbU+O|3g8@vVxQAViWBgqr;#z2oqysx-^Z
zyE&hl2^fpD3!eX6UDV4e8W6udb?&;n?`ZdlcB{5cQp7UMhV88%aD9(nJbfMi+?Yp@
zeEddAIb?<R^nLk!efgX(oD$;yUgay9Cy16|6cfNRZJYUgeYNAg6W=Om7}X+4NSU%^
z*o#aXR#@zYzKu)+|114($yc9S0#88X)CU`%2>_M<uB=jnIr{r$SJ3B$3MyN*;I0cl
zriJX>v_X#Ng8wq9Zg?HXT*vQ@T;IDGcEP;oS%p3`*Y*nnMVULh`>%}eKm7;g&P*ut
zs-HMsg=F&^UIg=V%Be=>!+jcMLU-I=-V+M^;GXmQu<YNdLhF^So9tK9Vg-aykwwtk
z=>V$~vnBW|6ibIqgYbKm>k$&^$JgD$m)rb$<tG;b<rAZ<tH&f$kdcdQlXPMtC;pD2
zvbSvaz-ycI@_H%#3lGBet8l)AM>9M$I33Dhc)_*{U1?34x45;N6mnwf)NhZwoxu^0
zU=vdY!$y#G<l={NPvNGWU}UTsrn8o&7(jar%TT$0*Um}@GF?;nEn#JSb({bRj`p57
zS{?yMFSjJ#WX7|fubbnA5~o0~fVvp~j#HrBANJZA;{HP;H$L_8!Kd~s>-ME1Wj@x`
z;?_ykT{QHwC#|~?nLz!cl-z!j3R9WZt97&Lj3*aUHCQVqYB}1HFm;;CMYEaj96<0;
z+;+bfRnWtB*3C+7og5jXlm2hs3C<`}Wr6I>rh~54@q{vbDsfzvmbGtebIs-;QpLXu
z{VVA%0%gjwF89#2{hwbwmy5=EpcU29aD`N|;9s3BdO8C@VW=|9P3NyebWa#klk2uw
zASNLrgx{sUotKw3C`F@-J627eMF5S!V{3r*UR4e~P<`Rrk;XYA(28nC4ar;C98OCo
zRp4TmOoR>5O=kL;2*(_;iUF1p69r9wq&h(+1|6BU9_3XA_u5GA<HFNm`w`|C+7d3m
z8410Nnb6oN+lTZ`o%W?y*f=EASSOUD>w7C-$4Bh1cexv-<2MG#?yUSSPC!>j9T9Zb
z0H*PWDsTzB)DT#v$`NXD_%O=Ll~pSXc={Y$>KX+!HQ3t(mc%I0uL&+3k~CeREk0pp
zpi@DTsXLMAd{}3U6o7P1ls@N4@@iRVasK!=>+*91*u0x9x5v4vvXgeSu#LreDPPQU
zgE8h7sO^%!{tf-&fDnL^6@W=sP;KT==qd)`b?%rE0an%Ddc&xqa_MboK;)9ktL>?p
z#NB8#s${C2+*NWg2lYI&<&DOg)a)>9eBO!5PN{#txeU`b1&XW97}S!#O*XgpmU001
z?@c0=%+G{|4jaRxNO-|C#`1i+%8i4<SvNG>uPM@=BVu`=Hnm;40Z?jaUv}1mSADEC
zW%4I8r0Vc=Ao=n_yK@rJ0o~Fuq6}*;x4>{8gy_e`(&%xreD6=K7;Q*A*;Y%X^6m;|
z487I2@4D*=Q1!L?I?x=FmjOnfxdsbp?^A_mgu_wICWg?Qm+o}b)+mspvDi}<vWIf$
z1_J2g8eJTIv(cxrG9Y?_$(#Qf1Fe_k?=&BOsEQ*fklNJZ<CvuATmKxmJXZZ*e9Bv0
z8;K()*+9ig=T7HlmIZ0^t4ldXmELuRlhfbNxDcBK;wGs&a`8DhQL&X&##|R>u%N>M
zOJg_+Gqhn2R*xc<A$Z;fYRZ@c5)zCTdUD1(oDXQy+g$Y_`2ed(0oqGe(y_8wWhF^g
zW$VSsvH+!Ju{$f~f4H^%X-9Myo=6cHAjKbhsS911pL6sGww!rgUGL&XLZPii{VNYW
zyf{2Bo!z$@@9d(5>55NT?$3de5e@*-jp+>9!wJ9D^ZcPtF>X*%tws1K)yuqF;M$UC
zM<i`-VA{FPCBwyxSs8fJAvaniGfR(1M@?YmP~)npiuC61r84}G?;0yOv;}*1z?Bf3
zHV3$=|6>R|(Ex1QAOCQYN<)cL<)?x<&h<HLmkgQ%(d?Gnp|O)z8i)rD?}*J$_n)$2
zMnmF%cN_NqDvSOP)5TYTQ4Wj|_QErmKaRl-*PJA~NdSbDtH>oRL^~xS;G2EL1Ys6e
zig-QoHp=wvC+*YFY7fKZ>)~v6dw9`<{p87CGH$YHZUs+v^R7V3*qal$xLVTtS!N5E
zuR5H!neX0+_V?{Jd+aaNztDe;+dR9C_Um-hddoY|v&gyAnu7b@xN;;owArN!S_|<|
zf{Z9X2f!`Z@cMh-bymM>Ou9W-)A@FXLvt^jLuSf&IzhQVu7|%f%Li^oxrR$Lr0KM%
z(qr2(e~i%eTgjh&?%`(Mv~vCfwtz4*{HIcyN;H6m0uX=|cJJ!rpXnbxoXZKqi~|aX
zJ7pUH0Vjv}0ucSxjYK{l#kcVxG|0+Z_SpM^cZcmnZ?)DS02@1~0>8wvROf{=v)DHk
zPN7I=U66N?YlvT1&pLftlUr&2<_7Om(L8J>Nl|VJm|C3MVVXX~UX1-&P|skDD;Wr(
zP3=saQ`HVp;DMR{2kA*2K)^=?D2>bYGaz={qkZ|O`Fi3>tHMkC8Gxf=A0U(M4+s4Q
z7-m;yHWmDWB1^ekUG{M6SMLFOG8{4C5Be<K%(3;|*dQ2k_zv<XELVcwH?mM?c_KE<
z+I9olK*&xqDvff}4uQXn@&=;)6S*cQJe&cSyvj4eW7;URhH$+v?SX3#SQHJXks5BJ
zM24{qrWMpp;R9AVFkr`R$+_23aUT|1p}aXjZOD?4)lRBqwT>)0d_Y{UE~`RS76=1}
zW{c1yQR$-=<<cQNNoy@gEM&?hfyy4!@)PCcCTeDJ88@Z{BPwkbHN{l2`XLN+@TI+3
zEuTqpPmi|_lAVV?;ET(ulfK=~F8dNq#7k7a70iFX^+l2-t0eIMU=iX=Y=lrgdsYGE
z0rK0eZL0p|@l#cuc4{sGtaP$hyAM8$pIogN0}3k(Qu)dWE7UosN#m6))$ZHKK_h+t
z`SQTbsk7(!WU2UY_<!Kqc!HcBH(l?+^(5&p|KG&m{P_>a0sqGl!&RYEZh!%H%M-d=
zAmEb2jwBym0umWViG5&BZP*3$y4!$#R57l6DdN(HKV}!_*9>2P|E0Kwy;`T1Pj=2U
zGfx*HgP7_RgMKAW#E>G`(d9mr;yr2+>a+#)!Ep$sG@Mpx(3bFkVCnQL9NoSCBWw_$
z)__PIK~aG)&R}BGU&op`^?)v&#sr#2EB?i!xY!M%jI$JC+(jO?8jn7CQh5I@j(Y*e
zr;>iR7*y8hRHeJPaqc487wBuSbm*4r)={L{0*!5Bg(LdhxUKn4(UZWjVk_XCZoNrq
zqLsEn8|QJmZaP&i!6*S%P;>9^LQw*&Z1H&d#CtzN8J!=;f8QHRYA-T0S?c!$N&sA~
z$EI=RRog3wu@(c~f1O0&QSr$@SXmSEVQ~TM98CWkez?@pw%?a<^P8?Ftf5~5DLV6m
zLXW<fFd>iDC5=pns*yCV$un)Lv>inzEvJ7!zxSn4Di9faavQ%IB}ZVRQd{IKo%&Qk
zqY@=&Dyv6iK4l_PA@?moqa$L5NKMl$or!Dx6FK4YfR#bgMwKm@?+9p2Phv9}Gf@JM
zr%IJ1F)8sOuk3T~YhvrKe8AuFXB_w`2_p&QL22waLzq-B+XAiaM-Y)`$OJM_*ovv(
z_spn*Q#0WKuMJM@sENd5K`Hc^K?rk#2NH?xodH3MVCA{JnEx5$o`}s0m;@U8MZ^?Y
zg?tZECI+Q5D{9y#2LdQUNC*98Y%G?9u5J&6Ex_aFjfgZuZ=}Nj1hoe$@CW#Sb_V8#
zraq*D1e!2;r|Hok2^D^XX7v{z%%8Y9AX;)tBFIu=4!FK7U@J+@9e8#&=bQjgV_mRk
zQjW9Wd;!~|QKb>0duTA^6m~)(p}$*-bq2A|G3*x5__9kTg1-PgwP|1k(y+gvy9v8#
zEItzR2sNNGc*&1oMXorNr3ay~R<NiJXrQeGcZwu^W;7QPoZK~8RA6WDXoH9fCksGf
zp`ycp69eT;7`e89+7RK5D5Sx{73y}i3nOh7<Jv)Di)vIzepfkMh!>;cGoUTXiHQ0p
zA2<~1VZz``|HcMfCFMVXFky-p|0K<8^y{HBKc&HV0E3mZI%i}^rn8(?Q<78W)Y-r(
zY=)SpVY8|$n220k5Fq!Um5>Tz$bykl!J!M*Qz-%qjlcr+Xh6$x))ChvOI3i75lA=?
zCjE*~DH;L)hr(Y5p5tz@h&{Kkz<>$z&;KYI>kg}N(2@@T%5!P?7+{5LVUX}id@0G+
zG=X#i95OgG^jFm1y~oVuKB^L(HbaIDo(%?P0Piyx0^d>y%%r>_n9?xaqA{p=R2eg<
z504ijS#X}`U+Jwd)vgY%ZCa%QwnlA{l~6y+<FeqNmiZ16)e~yG*D6c87#9ntWh~01
z5}I9bh9!1@7hIXe303eIw}Erc=ZNkx$&i>4%moG%S%Q*9<%7^|3^ct!nPnD*>sDN8
zj+f!z7Cjs;ik+Je`@bz)U|u*)O1)79qMQy{@E{N(?xNpkzD4ZBJW2*9(!K*xu&pq~
zfj?Yt7D;+BQ}_tFm@!-y-1hu%h&E&4t3G>>h~y4H;ds#%1SB(~3qLSCQDJ0aey`Y7
z$jPaYKMC~Bf`U7V?dFWBi0Jh%`CrV<9mdoma1_**>+x49h6C&|>hlw;u|6?KbP}v2
zMtNKN^1mR^`7i=9Y%M-OZg5c0T+$hkw}>MCj*Kh>;cLYKeOj2RD8ib>E1(`t=Lw-K
zL}9c5=HXELxrYe3pz<wES^V-r_oa&7{f!la=te|ou~P?nNI8~3#;*O?KiwKNZ2h!S
z>!GiNO!(!x^*5J2zIZ!)-zu4>Gu5k00Biru_*EY>PjkE8{ob4(Yje8vQkoZ=Pw!;g
zI=&7UD*zyWa``JOPOq=8)gK>9H(e+70a@FC?o7Y>wGEX_cG!)*)YEQRefe%*lKI$e
z;}!XN0yX~Xl5O3|t1(wyTR-i;Pzv7~lV|KXz{6+}ImoGTtCW#>^g#|VsP~JX_As5|
zm_q(><ERCBAU!wGpl;d(&<r<o9+N=T;lV{DE^RdqiVRO6=m5(Ow?FFZeTs&~bUTUw
zq$^YZ78lD7yFcn>LNi2~`Wk6Li!E9J&EVfA`nfV-&|*y%FoAj2jHWLQ<8qC0yhWEf
zyFv`=7EGvXGjKy;t$MiC*gKB}HL_-~*+U<J)H@AyG42M!Lv6%t;Q^GGDbea_&xc3^
zQNLCd3^PnP(EWiCv=&W7htSu~%m+4pnDl>uy!1$5radf${Ri%p!rYg6H+aF<r?uM(
zB;^A7l3l^GiW#N@_26b<C^bmEI8cUUph{n1vF#w!8X<>9&irfpm_UCY6mK3O9jWd7
z>I4`5(%b1n)zk4C104?MsWa#ev?($03)rY}=+nooB_L$W7mzAAm6m|;$$-NEJS~gg
z5^#DGX%MD|G)|1b9MjWkvpy_Sej_GoG`EVivM|L(MykPmvO?zzv52Y{Noa1MKFz`7
zNG|nwmjPK@oz*I=ke3EM*PZqCE&A^M=8`D7mT-tH*?TOhNnJAHO`=790`W)WX$m&S
zz7|1>Zspj^ci2rREa?}pFltl)<T16kSo8zY>ePyj)?rjGi85UE6|nlw7IX!e8{xrY
zj5Y!zAw^9NS_gXsp|Z^<wLv=_20#kMuN>FLs`JsjcN?9~!!V>p31dD_DhU(C^Y=oc
zt?6?C>V)?l42YMlu!{vnui{#~jOk-M3~=F@A9cV)RN!uWrbwd1-7S*=vwLCVH4=l1
z#cHOwPGN-f|4KW7<dXX0^K}k>7xeQef?ImOdg3qgqOT>X?acAe_Q{0puFCFH3w&*Z
z)(nIH_4m`mod)7G)8lLIMophEDc%2m-M9YI-CTV1dY_&@9on#oY`-&8^!<JH&DKeE
z&Xh~PTp|0JDXY3QaczGOI9gqwv`XJ+(pT{=WPnB2DwGk*bMyDc2Umxg<YV|cAASqX
zJ*%r~$(w@W*G<2bFxvuPW#+_m_d5XBdvVR_x8ir?+PfbbeKnu4-@kj+x2>}J9~1ha
zq7Mme{6^IgXucT}a$3bYtn3Pdekhuw>c5vXj_nRrHJ^oT*q#0cIDV=#02%Aii=sc?
z$1M)`ebyfBo|8Sdi$pq#c#1>$`DJqE;W6iask8)t7`bHU`@e_n7R$bm<X>XB31PVh
zEiSu)y<V2+Y#%ls8Y$ZHq17)sm1j+}tAau??)bK*r!GhS&fPw4ZayC@R~yvr)VaU|
zRgcZ+Ebn8x0(vz7XirlX0)ii$kaXElor=K>qgYwlljBUqTs3ENN7`3){QSOWFZi0_
zNgcjl2mF0J3Ex22zdyuY5~@maX`e)UD&0O+Y2V%SwlAx2Kt0*JUlnN40ohy0_3k6L
zUv*{Z_u633-4kjR?#nNkdh}|`^QN5Lxm(pK_qMnD`70`b;Q2|elEYarT4+0CcIx!p
zMvw<$5$XyUe3+d&dIZ5dCAD6*@h-MA`WnYkrU&Yxvgy)w@o-3aznAJA+L*979-+B5
z`W%(oGwQb=Q<}%Uy`%UHI)$5si1&>6y;+`^rsEI(m==8(tVhA-A0#8xNR2MiN$p}w
zw)xSDxw;A9P<_+!`e>#%-R%DQzUZT78*ky*xKpD<zN?_?{JV#tbgnCm$7!@k>f38%
zLE@@lhf`Mn1~>c23`yQP;OvELcc&rjL#uVTK2w<g#`kJ%9fN!^u>3ywmK}0`=mU9r
z%GWgHq3Hf%;x$z26hPMWG2x%nu3W0QFaojyqu37!biPv0wj7Y5x-}ZN^d7z|==ubo
zeEC-VBoO*Z?QP%M&?|J-TwXl#d((Zy=Uk2D_i;YCp|4j$2YIe%N)(90Z$uc_72KUG
z?CbPOhWK{u%35A$SKqEQ<0KEly*_i*rG?>qHGR82tmCrU+jW@8JMVQqet8{dEGkiK
zG<X02%e?Cp2eeopaXvCL$iVdH^e~{{TW<H)pd^p2Gh$W+Bgq-GKq0hV8@@nIH(vun
zpkkX41ap|e6}}m|SG#t-=}xvf4(D`~qc=NtokHq3T7TQ8+`qpzZ6!T^_$J$5kFhFU
z@>E}!`#fI-?Q!hRdL_J!FqrQDno4*Cd3Xen=c-1E(hZv2{2e|vwC`%3A>_C8M=wfn
z2g{POyzEapAtKYtDHcgY<_?ihM1BVIQK&d~(mu?*m&h}uiz4zz)al=Z54zg}2EM)}
zC37UE3TD1sEUh3=-u~o-<~;4rs3~n-o?>T}P_E9rJ0eQ^Ef)iC1)E<GDphrPau5NK
zI=>MQd?g;!)cGm1{-wATd>~4Ax&h+fVe%SIhPc^L=tE*Yp$*{l0vUvTvc$DlAl>y`
zhpuS6nbGoaKtP0fp1%OMH_vpMoCm63H0xYHI`nOQX~@1mBLDu==G&xkY1h>M+9Mxa
z6b`_^MH})Fc&sftwm=}0soI*dch3Zrrva-vZ#0!cQs}CVl|qo@Au2{U3Dh`Fyve_p
z5n*+vF<wkICk@>Q-puj>=VI+SAKwak8etAXl1XKbk?b@eBAgFkf=iAYofH30b3*c=
zxb1at!7sSXk}0jm6L|!)dnGz>$kFyIm_K?LA*8)PxfulJBb(Zf{Bt&Z12Bw3-*xnn
zg1;@Rf0@a=K8V>>wuqPSDGhy@vL^7l|JcXmi%e0J1I<c#swb^@;_7$T`7wCXFIBEw
z@7R2M!3~Z3e5DdHJ^1TiOT-sLD^q1x(N<EprsmqEohu5Crcx*etCi&DE>C31dek$A
z|8j6<_ehkR{QO$FNJsxOpYHCw8Y}MYlrIIu*7Yk6n(?tp7BM3^|LPs)(RWv%pO313
zizsf{okZ{O;gzbcpv>~=SFx$y-D;5mo)~N@2j^XO7?Bf8idFrOCQn>!I}`Ei$p<)V
zY!mwv?an%~Cfy|bD>}r?#C9eC=aW@k`-;JAI#U(znyjr_Du*TE0JF458&~DSarxKK
zyLo@*+@JfU?P)z`1HSYCdc|Ex9%Aja^q<k@_R04%<t{Dt=W(a(YITF{Cb;?Wx?Q|c
z;g8(vGP?7S=KFI|*R=j^P?htfbw^l#;}y5Y3l<_;U%rr^A}q!_yj9DqTi5E+>le#L
zflZSCM8`U<XURbuz(#XQvr5^_xp{r!N?~S0t>O}!WNeUe`1$@*M#iU|XYwliS0k44
z(4n2;^ma<USL12Sn=Q`2#0u7O_aiY#sh<#R@-lhsfZUR26h)hczAqnc4uzBn`Sqm@
zEphvwHq@bjO~XyuDX`jjqB3#KZGke~H;aes4Gn2XkM;n#v=7`af5z#ZKC5@og<Q_<
z6Sa_0{p<>CIzGnI;s~@Pj$8R+LE={$+<aV?;~C>^#PGM}&9mh-$gH-c$VDHN?-`x)
zcT2eFDRX8a(zLQa7*$CZYt0d20(N?$!CmZ~Ep-?{*$xDSYIi`7Io-*!g3xj)Z+^ym
zu(-_9;+6#{?4rW6^gdt2Xog)+qYytmIr?3u0vfDQ$d}g?Gi|H^YTX+C?ChXsg1J8O
z-96NRr3e@CNIq5NhLf3k;)PSLhkw67T{x^o+v??P<#q#dexAC_>|W3`UhZURk!ZP`
z$fGbp@iO-T;SKI@+<RjMEyW30OX%jKOk0Ir*#LBgmCy2kYfFhv@(MX2OO-X8tps*t
ze@m_B#z|hR0>7PmEWyi|%O9zT&KQYI!_V}F)z`Cl-)&Ig20C_XVskSVm_z=5Y`tT6
zCPCM>J256U@7T6AaWb)O+s+-^wr$(C?M$qRt#96U|JeKa_Vcff?xXw1>Z<ChTIaeh
zEd3y#B(Gz6&VPJo(L&qnkaS1RGSXr@qmyN^Ia9&Eck2&nNHb=OsXM?@$VOdifRa;r
zh4_7m<*)(4H@U36EcMNq8qA<i^$*uAHfHi<uYSObJ(<F!Oy*$wK}qpWbao@BDd)29
zPD!pBgNu!eZ?E?!c$;x$KX4P88A+7Fmb!w+m*@NvLb24&W!EyL*B-9mFVNk%MD0uh
zwX0}d4xyTAiQU^5B40-NtQU*c*vuEEe=obs8T;o2nx+hf@5WY0CS4n8t8XW6@C#;x
zyWkmVB|RdPb;kVt$Gg^?<iBFkb??uz_LgzKd84;=AldsV*$!X+CxD#nQyf1!2C;uR
z@vBrXM4st2$>6#7w~-?C27yJCFLZP!p3j$Y7*t8}!dap32yb7~aUi*P?iMG+<^v;n
zR^JgY>(4mL?}-Qs*u3H7Aq&ky_G*Pzu59e>r*k0vpHox>lBB@WD|+?b!un&OHebhw
zPe0_g8%?T4{<l7V$?~gteWO9JwwLpd`HM?1?Mnm29-pQ8worE?+_utP7VICb@Ha+~
z>yYlulOW(M8TbU!#$jwQT*>6qt3w%;RyM9cXF01ICNy3R`bil(QKPxxCM``JxdX9k
z_G;{+28Zqn%=^twZt>;WLA=DujM@NJ?p@)BIp{=`hfhI9DzZ^N`H@&`_DOZ$blTnS
zNweio*Vb&-*|rpAo0wEPvA2w*{#*p6LaNu$&W4@i=VkHV^{qK`vIW~3SA^H~Ph#mn
zc9U=WXP9n<yn8{u9CCsiN(t1V#G()IjRf}B6_b}0lP02V@K-@N4`%0Wt~^<Bp3_h8
zd>yG38<V2VxFuc0=26^a(9MV0@5fziJ-OH#!4>abEm~h8BUt!@=yBB!b~0ms6vHMN
zPZh!M&6cOQC(5Zr_9$Uv+fCyPnN?^YKN>#(=To83PB-)%XK<cXegWNgmppc(YDIz`
z5iB-Ov!5XeUku<dBccKhG$jpMwo?}8?4Ao=JrSe!?LL^2Adk#0TS9iTK6Fca8hl{V
z!$$&;ryJf-vVDVf$fuG1@2Rb`qltmd|FrB3En!(X*$5d4|EI;v%aDqV2EYd6VCPJA
z$N}I1nV9~A-EY>EvD+F$?mkg_VA3E|c2jx8)W=f4B<3Q8#=s?DNA|8ORxs>~!$`p+
z{pPP-lW7<?Sllx9!GaH+KCke6JP#~d^E86rG^fNhwen_0Su65mV4CqD8ONarh)i&Z
zhvTst<M!qeraoFj8d?j_M+4qo-70ugg#(P}B9JfXQ*N)$p+-SYu&k(X`*#4VQpN`m
zcS%vP9i?m_j!9WCafL(Vf-wncKe(iJM8*`P$YCFhda(ix)>3FMG1|a%rjTv>ee20h
zmak=8ErIMBM`P)D2LF&QRrqN8wTfcSi%av09Dl&$Bo}6MpZv*<G?4S)M@i!z&an6h
z7=%Qfh%UnlSlZ+cc4=EUR158oMPVv1rrW5%DEA+Z6yO36Q8^g3W(m4*R{#Sf#4A9C
z2QRcesJ}gbC7LtD9dtB6Fo`XP2*mHlZP+OjxHdQ*9%~a$Ewq>6ieLYL$QlxTd(ahI
z5PkV=gqT>J#_hPE8gS2V5;_;!9|t33pH>50{s+7UdMJ-^Vut@5*D?T^r*vdc+p$g*
zZ%79+4H^d7z-(X`yeWuKHV(-}^0lH2nIt6Z;l~V<H^~e%2-dFvM&>Cpf6l<!A`w&Y
z*sfqjlNfPcF5zKl9aR90Gj<0W_XsnUbHJX01P*9cIHOaPJaDPcgby`&V~xU8-8{MQ
z{9Qsbx>E(iN{VQc(n^*nl@biapDM|pNtR@gmQmT9)-jkBfH80M!~ZbQ0FU*85aOJ0
zTYomqDxynifaZdAQ$oq+H%P5VF#PCHkWAHjxyEP)21-^5#E=jS0DhGPbU_MBqsG^2
zf;2cNx$T850jRhix_S8+Tar3Uq6y7dt}LGe;vy67$0?!gX;B>}g^#X=KEaLc!48Ve
z8j%r*qgGpIQHG?h@ZIRBJ8Rs`NOKBXOWqjRW~^P>gd&1nUMyYk2)>c5f~|}RzDAP<
z3L>Y0*l^1mA%4I&!0C(9cIhu6sJrhc@8uWdN7`>B42(?+fga>D#8Ff%*Dsj%>?23{
z5=5j<I2B??z7tDiC&wxtA&V3M?91YKi?Vwdw}|^GNqCNACdp6p%1d*d4ug<MHy1t_
zM<-)URdG>6+XIA{*DE#hfKPRho5y9H{-RtK$f$`dRnMB&%o5d)!<x!8bi^w{g3=+P
zLVau!Ku_sDI&%f%wlcOs%j`KtgJyTHg*)||CDB-IJerbF9m%5rwOxCp?7!urtf2ME
z2tNeMO*x}Uw%G#l!-@lu%cdm}6L7{goka(OEAiSLr1=!-ek^cR_vAdLwk`D72ko{Z
zB(G!;*!jL~?jqx1&4N{VeSF*=Z0=fPDxg4u0}JRuYfb<8ANhH^MAx}=rrq~#X^~3I
zbc*$&gV!Z;L0j*xJQ8P2#-%pT)kr3R_~&3WZo?KZ%eHiYCBr%c<7;_AVok@pG@Y4;
z)(Pous)g#<>8xnv<c&iJ@%5R^;Rx5d(o_@r5gi~YtF7U=;J7Yp>yt+iQ`L_9{QB>F
zft0J6=OS}@Q|-NNLi&wuV)~tFZ;=Lcr|tXOM48*3@x<`?4o)VtC2{?;n<dFySe+q<
znEmKf^Ln9NQyw<d4vG9+kJ#<H!tZwLLhZ9F(R7C!SzC;K^Kk<Juq>fo?64WqH+C?j
zXf806$LHT_4#Yc!x5--|OdHKcT>o5LfcLsdEslS~2BE^^5v|v6kQzlNg(pkNm>%FK
zao!~K>gZxtBcc2fbygvh>LKymP}(nsy%#1gh6`<upn16xtlv)_3j{QIp_@<GrakIx
zf$%Ihg!M`XbtegqL^UhcT4ga?0zaDZkz%ylG<gJ<2t+vX)=&`4@nQp-=<VnofSaWQ
z$V!9AOb9L`Bz*_-EoWnSq~ej{9yzoBe3T1<i00dMozN`F$C7EdiTn@W!3JDx@)Ltk
zJZ}llPRrKj4eFi5S5OG(LK>$LT8)IqI<LHG(m0s*P2_i!K_;$pE)=lbVubUyk<TXJ
z`G1AR3Szpoz%d(%aI*>6jB+^$125Ugvw0OUL00Ry7TbHeMQ!OQX6OoMG^g@s#Ctcr
z^5s7IdQy3pcA%?5G5&DIT|Vh9j2cG2SK{PH74}P-#Uq!Sit+cEZccsdc>ry^66<pE
zt!+QlcN;lfWggD<jtz0)l4$<I7n3^eba}eme;&OatxJ^G*XmuAa}enA19f%zv;hEG
z+t+INH+>SVN;%NNq+O6+l2j)U;DGRFS8ED^?yh&F&zI-@Xeoyr$L{yjtK;+C{@HD&
zKk)hOaT@WOUk9K!V#aN`qDTMji=F#<d6E0@S|J^a&#&!#_A0U5qP}AI_be6vQ^339
z!^#_a8SCIFI=-{OUb#RPs0WL5;AZ{9vuwl>LNo%exANo~SFqQ-=^968Q;RKwTWmBU
zLHVEs^1gxpYsF}jh5RA<%wP2}`pp7F<dIUaQ3mm-1BP^6%+si~6ep^)17p*N{b~2}
zetVW7Rc;rq=;?cZu=FIA>lo<!%sc3IzrASY{^X_M9Yr^?Wni%k#KmO~k#7uw9bxHd
z>EG9R%&(E_8HK-iq$=sEZCi4WPIFuyf4{|%Ki%|0f2T0>rqJ)$2mkT``yl^8QmMnH
zs#n)_fg{8tro19yrEuD3m70Zn*3mq)yo>&494y$gXjC}ZTl%xRuDa{obI2#w`$FMT
z|F_Cd4xy6jmf34eU~ohDVMI!M?%Uff9fD(3uxpd@sG|6!PjugVBD5Qd(PXItKW#O+
z?D*d|QIfw+kqUo%TFN_3qcK#cm$}(~C)z#~mlM0Jm8+1psswSv!AM^|EQ_>S%fh>2
z4%1j=YVT<(o<Fh}ZUkD|-7O@|cFGc1#br&nJxp$t)lRsO0M%`<Yb#BxKCU`X-+XO{
zF}R*5U)w|-8f+^N=GlWg*wa+{ZE;rHo~O!dIPy<q;6D%HAG_1j1RD8i9deGN$4Ppp
zv08`R9-gSZI#Nb^j{a^njl2vlw!Ra1X<Tg=f5}KvNH3_ImJ~&9uINB@Ed@+Nf$a!G
z*qEtWjsr^ffq#3bsLV2;s;NDiO_-}z6P2PJts8dPMp)4eD?sZJWSvy<LcVcht`QYJ
z6#%kxF{sJ-3!Y0CXNkvkaJLt4a|Bk5Ba(VvP``H3hB&FJh1;~FnUgEEL&HKlRtAzV
zuzcJ~F6`T}g(=#oQ-vo@c1|316-qb^f8*^$*(*)z0NFyi^qHN!Ea_cg#WV`v3$dGp
zF?Z~JRYvdIjkn12ek)}wpKN9DIsNbw0;*>zwU^tSwo{cAemy~kJVP(KWVmM4(ArGK
zH|mjzE9!ai+h*7Alxi;v=!#X$W9f3{%}sH%J?1z~Wm1K1j3{L;eDksXejT#mX9Lp+
ze&Wns0g|mfjzoa*oM>+ty0-cvF)rA5j2=%<OAnJCACaG&<5&Q2njTI}`<`8XpFPe$
z3df-%*l2RUp-Z-J0Z(e4-t_A%Pn@FdAKS=ka*d;suMO&NoI{^3ERR$lZh8X@EY3Q*
z#pxIWT%*e5efBN34~d3Dgi|d=uC&yueg1^n0UgJiyUTlM+B(`b#N##V23GfwHu*zU
z^CLzR`^IA5k1?p-kYId#uBx-7^@g=pS9dr$`4%<NdzZkc)*bg_Q%_3&dHc}MmaL3$
ze80X1e11Emx2qDA0G}UT0(NIxjauD*-fjleQrTt}?4q%ibkQ*zPo~o7DfdL+Nxt44
z0V^+mnP5_PCY5f5Owwz+QZewurRua@D(cRp-9c{IuNQYEF=@NTH%NP6-@9J<_;7uh
zT$?C=Jsoe3j+UmL+!EZvp0_H}Qray<qx~#WCmasABlaoWSd?7eu~BYSaY*p30Xa6w
zL|GH~=P=Y$4MOuHg=5pMRiupJ!R<0W8pwzL>a5EDcVcyQUM~f)C0+nV&;!$GV$$Q_
zz6T=T%zsyX@A>Fn<F$!~;Hb#kWB6+liSk=de}YF5)eO$gXotw`9Fn9;M$%PyrUZv=
zbA0{E9W!0?Ms_T8y|(E@jsHSz=Pel_LDm(S;hV&CBGWeUyw(|w`=8!Rk1NAvwS`Ml
z$$&`M_&H6RBO-kZF2nOtc5xzZ+Z~d*S*X1y6fj{5p74k-dc65$&1a>}*3PJvjo8gK
zu91gR_w8T_MOto4es12P5*7*r<!;IO>!+fp+wlK?2g1h6`43zm1+p>zN9mtVl8jv;
zBT~rq2P{En1vNk1lUPI?ieiO?%yXcs7x`Y1M6^%{-re<2-`%G=iSUQL+o{C#HG}%y
zD;tjRo7RiWiQj*g)>qQ&dpkzL3c1|a!%*j4r$z2|r;7pq%<Y#3rG1!)Kh*f}zj${A
z2r4#!F%A!PR{|TMt*gwc1#A=|Jv)#jD0jYU+au%JPftsckK9PaA3PKQ3O7`@%oc$R
zg2_(38n&87GlyqY<u4f*{18MZCRY@NxD$RIxaJbOY0jl+hi1M^ef()T`jw-PL;w0K
z;01Mm%R#wE?KTR)?&OA2>R?N0Uoze`2jvJzC5e>oM@lS-TP2C)NR8q+0UyTobB^Sx
zI1lArka<KC@|TO~FPB%;qI$^L*J3@RL)f9Lhb2PKgwug6XOLp#DmTK6YC%?#V#1FN
zjU91YAy_L?_0m%jSS!&2ii`QgAcP8Wq$;usvzy=2NWADUIVg?ObNwEH6zWze+9=Av
zF-`$fAwA|Po%|f~eBF?Cnx#cj<+#)4UwWkECGre|QE22PlBYq&7|y>M08`ArHS-xV
zMC6p`9!Td==`(UjT|u2zZ>HvSqF*ggzBNwMkCm5J1&HWVPe~)xz6kGz=)@h{#d~Vw
zkI%Qf>po2mYNL*v-B!GJ#f1fEJ3mfvP*cmW`u|(^_#du?6v)E-A7qQp!mec?BO2t5
zS1xfXeqmYtGJ;6m6XHNe^aj5mPONMhlZe-RYwmHPE^Xdjq+ZOv2*>0<RS?MIuQXW<
z*18IX;`|;jEXhkTmqNVU#)kMoTCddVxtPrCpf40UCO6>k+0l#lle4YnU$5RCC2tNu
zOjD0;ypOnKWg;IBOG9th9-5p_L7SS2nBjjY9S^<LTR-nQF<mEaw~W(n3F%^423?JY
zfmuZ8ikqb*{FGC|P|AHh-+J<r7121fN+PP)frPp@miV7I>hJDn4t3Yc!&*kAEP|1?
zli*5Y#lC#DrdCaxD%BkHT_LACFxWVY-~TQJvrCxHVf;#Fs{rQ@BHh&re$gEwT7vsU
z84L=|21ZJ<6h%xKR5yETkeFjLvlUz}v-sK*qsAYFooNFE^@>s&#TBjn7IE{`G^rIh
ziq3cXFVSZEADwPeAUorKfG|2q3RatpXkxqfG#_hBQihWWiqQT`7KC-Sg%+c_I$Gm!
z(MDN^`QN@RGvYtto9vCHpL4v9c<5R#Y&NwM&aS_duLj80Ic&B=@6R^Wh&<p!OR|eJ
z6gief(Z1D28!~@KjyAk&0EC%+=)bC5fxW<QcW-X|N%rh*{@}sK>PtJl9FB77^SYC(
zlV^I;bob7k?p%ibHk;>C<N7qxap$#_EQU`n$Hoe|+WpP8r^;I4-KNmP<Kng9<n88*
zodKq%RAY7x`U3~5GnG>Atj-2lpLM{1F%z!uq;WmA@1&&p1;gw~icRhNx!!tgZ7J}1
z@S;&8hn@sI9xus+cam_BWu7;oJM?cJTa#g~f7qUg2qTR-zJ+?!LthWxMjawE)hw-u
zsjP^pDZ&v_JC-zfRMY_)l!q*`fw42WnAp6@ArW?XQgZ=~6=g1RwzebJmXIUTew<P&
z9u&m@QRxa7f^3kmEQAwYmV^dPM*|RJKJ){m08ut5o`@w<xX0h#o)`~qxtS9Vmw10q
z^c>8kp3^wOPgv}T4gtGe{VmSGim4$YG#>NJl#1m|zr7j(ieaGNkpv$>w`fz?4qpS;
zHcV+n#QqJmjVOk<JB&AahE%Tb^w6T-MR_N(tV;!)3u${&h!pDX!4at(aS6CliAWqp
zoBWLBfh?Azb@B)?C(ygldUxCXGH>Z5Ad9zc(EH^rPJ@_Bvo&sb>B1h9;=kaGFpRnw
z9iD_Ck5EknwLM&4^2&zL=D-YuJ3ild9!!fm;6d1PP-VAyNxRuD$8{U-{a)nJru=W6
z5QORf8S{+)0d?xk%UKN)qW!q>;tw8;Rw%H@bVdTzl4|Meh%V{AG<_^a#X&bh{CtX>
zof`}S_kH`g-;us>-LyQCe7lM|?Mu|PIMZiUqwvext8yM-CQiO-ykUT}O>Ek6cprx%
zJgfJ8_pr<1clBbipFS;k{3rkp{c$%}mnD5*da-X5Y*@c)>5K-1s6K}znf#LI$ejUn
zcgBZeTFBmD9rt8`mp-KP;$0w^byPNEVhMWvN~{<-Q1{MeL=l^W;H;UAOLE3Rr^>WY
zhB1G&^O=Ba&Gx*V)PbH&QQ|E2P|eRML$^#9tu8!nhPP}EuPD?lY$;P@i?l3IEej}c
z#6iEFJoqOstTII%pq?l<LFqLja*P?8^o!<iHj>&&$$)-u-C;hr)L?h<%39seUV*S5
zan=7Vu>Xs~0ogd&|G$`<PLj0MrXZTw^B1~iNs_+gy;L6-3J-|zVhShGTsCpEju0)(
zy6)kwTF!mEC<Hs+^hJ)BxLeOg6^D-3#BuLv&(4Tai&dv-)aV!Q3X@mtdrKy|$x73s
zWYX7#Fx%8|WvS(h2EHNt_x;mTm(Tkb@ZhZc`i;2PH|G6boK3g4hZlq2>QBr#L$2_Y
z)U9dvCvVOlgT<>i>-sd4b#KiTfZT!EKY!AP>)Xwv?b`Zqqs^<v%GG7|?)99|eG-77
z+r-_vV|oisSk2!|cf1Pj7?sx8Mp1sgN$XoqzDbSCjPDUU(8(RQ$6d>=-wc0CfoC4J
zktPKy#0;7g)O-(dV|;?K;h@SXmx{kabVO2IXvXSg5A(e!1+7j)TyQ#rSzSjxAP4#*
z?HS$T@SFh%E5A71#}22^(5IY{e076z+x>4?L`-jJ#`F4T6NCbDG55`yD160ACm96q
zPz{X7y5i#v<B^R`M2uC%TH~TbfLI!irc8JL2AE6T{|vB{OU<Y*2{B@9Dh%xKvb5o`
zKPRFu*u&v^lEwJGi<2TR9>~5S+?*dJ5aBmJiB+PqxIUz2oL-Wb#`e`v(g$lvQ4s`6
z@BiJ}7V3ctRgh%E7mHzvRX{R0tb4?Qh_}V>bjO3^n4I+g?d~JKMJs2wBqXvV=7rKc
zf2KUV|JMPU_z^Ul5K0!&5o!G22_c?H1z0*FCnqz(61E5@xUKQr^n_BR5<@3zr*z`i
z`TU~?UTI1?w}<6OYR|gsZRI-Rr@cn9@Ar@Ic%7^N*6IFN35J#BKMs?15@oCg8R0^%
zU(xw(qvnUAuP~(Yq;XEyFfUx!m}Hjd>p1&m5I>(IKO+z@%=hcbx)W%p?`-OuuP9CJ
zYHHnBBZRGR#Y?{~Em6F<TiB(F%r`uZVZU9};OA+9H6f`Lh2!rI_m3?-UY@?!ofGuH
z>)kW=ukx+W6Y+HO`8kjgf>i$?;~k97NsA&|E5vT~YGNYOL@h6zZPBf&OkcUyughh&
zLrpI<j@(!Jtjn3f8)5u)g&~EMV0)N`%J&diVU@(n%lHBnUY~UJ<i%7`<4oXldBEnE
zJWy>Wlj7AFFv>m7JI*B$01$MNN0tB{=gH);LL}FLPm@XbBRTc_uz0bE^Cw%xaf2K>
z|6MhzppKYC-li3xo(<#q2GaB+KO_kq7U_I3QxKNP1x5-i9jj;#ri_MC;*^R~A|`+r
z{jJ<c#2_Hxmm6CH`|q%Vx!Lk0`_Uj?_>RhX%wS%4n!MZ)^f{qH*x%a7mW*ZE68VsU
z5patcyQgomB`-WM?2>1@>Td?HNdd5Npmc&bO>hU}PNz`I3~MgE=Z#K_ZJ~5<k%wkI
z&CWVY`2<~RFQ1@#AD1ux?fbI+o2<z|*_l)A<p9J$8e`T?bxp`lu9gIBoK4OdqHo_X
zN<L-%NO51XNDit>AF|-!!%51OykJ=gTYKe118BOZUkR*W^l9CSZR>(;k`o!{h}?vv
z0=?6>pI5i)t5OvwM;uR!!5K%d&JwMC^^qHkRmcrAYVW21I)vm)v_mcs3GGa*>U4wp
zgNk7wl-@flUUVEE?TiU3^>ooA1XL1+Y7yl?8!P=N+Ngq3NXP_0vd2+D(AQo8De2>{
z4{GN|%%{S^@&#t8tA&!g=LB)Fe@mytg+y(91LHGy|My;W`|AL?(N<1>_nx1gszeud
zRq~V0{s&kTooDX<9S|@}ShiH5*nj13EG+-midDuckP!>&rZbm#G)7^mi48xj?i_ZY
z!=;h$()_@PVu`fqY0geyTHt|EXdg2-wJX>|BdG#cy{%PKm9?%#DZ03_M?>;b<U|&N
z$xMa<is3og$0mSrclo&RZ+P(X*Gc7@r|<4n(}DvlL+INAaQyYQE*f=3K5}hv8|h%j
zL}9xY?6KIAO?uI<{>F>e2mdmAS{K)+pw!}Wk=>Zb#kA2#w>|#cbSl_W5iBx**6GOb
zx9oXHO-YKel}ia>^azE~nX7k$pYr{+z_UfE>eq@ZZ0tP~Z$tuT<`5=QDEl1f#xLno
zh)~ryKa{Z(Ak5fFb#_WTDg&Y35rkA8d{c<y*y@7^!~<kL*eI`>GzBsj*(pU`0r)34
z@o0;zx<Wt(C=Cj~Vw=swJDbg^Ga_JBaHk4Cc320)ZEtbiIRfxJZq8!T{*36iQx5Zg
z$NgW}**N~+RF;xUD!ej)0m#h!AHTY24;XbNv#00iI9%ljx?Aew^KOUof7V7SmDMUV
z7;efH>T1Tjt#Ngzgh8Zs(2|6LKaxc;%4z??T5gZlugsgCn{Uph{7VBaT9!_>KO70n
zK8KiV9iM&lGR!7k9e1B}U2FKBxXFqnb@Gs*gm<X|YuB;RQhA+5mWpqgh-PT6p-F-j
z#u8CErS?Pvy!{wAWU$U-M!h<=@13u=gJ+|PI>kFi!^IVnC@sV8AJ1q&@9>2A9T+@f
zoOIm}U#ZEE!%1_5#Xk;8za7SPQBZn$oJA$fvxpVr*{wFBu$^{d<Rqjc;9}*+%%;<r
zydv&_k1nqZ7I(=){qwgKho=%A^4ekEN35?I7I)3J)Q)V5{7n3^Jv^+$l;pnw1muX&
z@JXelq2y$fgNc8XP!cF)5}`yy$R|~9CFdRQa?o&4agy+Gh~>h6!pJ1h5dS*5?&@R`
z?BV5?CDOyk#=}X)qt4}4p_fB|PZp;{gZWtklv67Tl8cms%>{_9kjlk6&`IU|?Xo++
zbFl&|snNY{x!#EGSwt&`^-%Xt;3ZW?M#E0zt<ELW^Ufa&EEZENEMr<yKbKoF{`Yy4
zGsI*{O%)r<)|YrK`5(EP{WK-$3U&%Gl%&gwmzXC~HD$YnUlnM|RhDop>5k;8OWEat
zxaB{n<2J;uid+?hmTakmV2g>%B&Z`IN({@?sdJ?AP|8WAgo@>*sG}!PJ}<?>vv}Mi
zJ*|Gc9?F)NU5n--u5s;=v{JUpZH=~%cIaU+AZ?x*uqMo*R38F)Eul=|yLe8gg$x3%
z19K2!@IS&!YfCp8KZ}zYwr?}8T};M+bwryXcpmHbM_s8}dK%voD%bft6h&4=qoKBV
zn!|90I3d#nrua{@`!u1eFI7*klhD1$p~g<e)0YS>I0&APq$`vLj;(b%Wak<?V`&QU
zEdhi311z_<Q=CPHW{VY^O*Dz4``xoc8`edu6DUt4Jgc(T7DD2e9|rcQgCHG1mj=#)
zt%`HQ*~O>NviMP0Xd`>tE5EP_RP<1(4RI_BC+3AzO5Uy@&1SG1A5w1EL4BeqXVgC(
zSY`2jw7kP@CYvb(?gFyurOV|eIFp`6{A&8FR@GJOB7l$a12fC-ft=ciyAu*p<Tk}v
ziWgn&JC}c3zG+!qvhWsDyd!Slsc=gKJ0SKWJe?ilEC6K#(dTDZ^Z1y@Bd21{3eO%J
z)FF450eRfj{Doi0zTg3fp!BQ~U(4t3RAYt)lM6UV9gx%xwz0VBlA$!qUmiF9{iXe_
zBBqlJ)RcdUz?R5e1d~Ii+02t6ycq%K?{6z}TMO%mfwo?KdknR@-9(ANoczCZ-`0Pc
zETnJtTAvuI5%&KmeY%+$dl(yw{ncg2+~~$4-Mih>R$xk<bK&|@Qhf_~drLnjM%A<Y
zb?noii<qCI@oBpkgGGN<@+A^@nV9$(o%I0fQf8?HO|aS-u3)`oJZ7YZ{zdSvhq$b)
z{#t0Kr@4!KF7y%6L9`13+~3|oG+@09jZXvB^=vR>w>H#gJ+>O8-73s*BG^jyCx{Ud
z!egJ?=Q?NXc@8J-qAd*!6w|DD0asP)g9R`IX~C7;+9wSeAVP=wH$BW4+|0=wW!~te
zJ2+0OBJ{$(?#W1RZ~BR+?RN)F4$VH}#}%OH1jpC)pPl=}^*a***Ze;9b{qIOVgqT%
z-!m$<wbg;!;-}|er*-kWj_0%$hD0breb|2#z#742`u_?^zF%1)P?H^@L!Hak&(1vz
zcd5;aJu3BoC6#6Dt&nt19lAx*K^%i-=e)#A%a4>7ECJ5TpoK|0O=TX-^?gU%wMXgJ
z=`s%vxoYmm^eVoA0|0X>Wsdvl^UpIEDMX+6aec#T)5i6&>rV}BMRjfUA<<Ov^#WQz
z%Gg)|rRxCa(Va=x^<B~}s!5{d=&t6>tM3QHvQo=I-2BQ+>n4IK!PbQVH8Z-FvF@*w
zH~OtknsfUN{deq=s+**q0G2bG_F&JX%t@0HyUkB-9t=L<`;F5*=B}RIRG7`l@{wp+
zS~Gew2JUMC%BE;zm9kQn?!dBbbn|P2`vR-p2fMByvAN?xZge%rONmtBJo$Y9P1ukV
zAsnU;#Naj|g0Jac2^_c)5nJSJb4Jp;NYZdIVKBE)TSgpQ_mA}V?<W>y4z<_C+5wzz
zi4~z5Lg7rHLfpPWTsm%~@jAQmaPs1N)Ce{1cR!y)IX=3|WS8fLd~02+)&tT=zZ~OC
zH3dixW3K79)4}ML0EW@*T{3OWSL~0f5}{|Zs8B&bEN;lKB`=1!V;b_)fIN<&)Gh1f
z*E{9?Uwct(00>Eu0Bo>M)Xkx&bMUdL_>DRlSC0`ek}YJI2N}WLj%VO{z^m(FrvBil
zH0IvmX-gHweYXDqAu;CekFvH{!mr%H81Y!!U9s5y?sg_M4e0<IEKpci{*YvWI4~J|
zRDcVUt7IyusDpb4Aa*s92)crlZh3_Sf1SOc9d;$Fgh-siwo+KfJKRdEyD*2~CH|ss
z04){h%8(zFWpU1hb#`M5GV;TAHz3owAPx>4j`5=7mQ>bq#AzojymaqX4r5?SICTn2
z;E&a0Rf%J|%BnTxz9uaE2=5BEwk7`@Fq=Fhxn|@~);qzu`<mS96-UlteP(j}+X0d$
zj?;3}^^@AAS@Oj$EAmk`RjffSnBm_@SrA0v^^a?E!nJYUEf)&|{O9TSqgL4o{edNV
zUaZKGpgru&rz%gR$>>O>?t0A-Gw=GW7q`3Z%^M1YShBqO9VAY>oUeymJ52<iw{kXb
zY_;DvhV{OmS?h0+ZZ(CO&jfjHs`k3}y<${wPJNW(MT4EVVpC-FNk3~J!qSuyJOi14
z3loxHbPiHU;|c1Lqrg9yQc1}Frd#yoyO(^CiZd2{D7wdsM#4C05FKX^9!on!aV~Gd
zuv19q!?*YMJH=-QMUS54B92D%G1Vh(22a?21g!U^cR;!1q4UCTB19DI8<-aiIT<PX
zDd}T<NN_3cXqRaCGGCflt<%$}0DS;ZFaENOg_S+2z>~+Vt;c1T>51F(Vp{aHnu+N;
z$rooz%b7beWO~)={^tvAF!3*vM9aHcR9e8#TAxp@siQDoMgLNM4EBnyeH=K?pOBKD
zK~ifYoFt)%<lr#>R)a1cSglF2(;^Xg0E$Oh^b<2;oebJ>l{-QcIJ)IWAL{~O@s|(z
zivsZ@j~EY0+$d2toNs0b*PO<NVa-$A8?J>S;gX!e1FHE|;O{n}xN@@(?2x<)6{~rB
z&a@TtpR6B_;Un7mM}PMXn#jpJ7~gse%&aQe|LJ-PbTSq5uZQQn8Lk+zYe9Q{k+z&q
zf%6qs5MLUQ0gCqxO6tBNa@_s`l?l3rF;Ul8QP(bqI`J8iM8@Jz{ag^P4;Yj7`sWU>
zi=l!s3{aG+s#iDL-hL@3jf_~2D+JxqZL(ut8wTE32k9ZwZ#q&&$UzGeX=+rERgn8n
z(ynE-NMR_oziQ&8x}SyQCw7D4B1idSyR#0^_9i3D{yNg|HgNDX37oP7(lTpuo^=n+
z(3FMnK{g_H`EnK0)kf9Y3ZEBt^{iEJ)RUP;CXd!1r2o(ftF8Sz7yPqiu+=vG7g!sC
zCs`ChiGp(--uAh*kPDvsoZ|e+Xu|+ZP!n?Nt~*?)iY&Ol)=We5KF1iWH-d)83&vr0
z+Y%yRwxbX9X@IV46&=b9sJAz2Li}C>_L%=C7lyhAgrs;99DiQ}igaP|d&A>l8D=we
z)XV`=@$kFttHYQ=s;9&IA&@?kIBrme9$#Gd)IQJ+zrd062r`u@+MxtHv~`YapOj`(
zfeTUVZ6Q`Uj=?gCk4B|H{4gl)`yKUb-k~cmiLf65-92FFo%eSi&@lj9%>Z@%t@m94
z;=^wIKI^(uj(d<=TmR{aMYn=<Cd)HRg5PxA$xnhmDzFFX>n{pEI7vg8vsrl?c!ar_
z`ev>2Lx^O8{7na|Xgy!BU$ugsIkoY<<oM*lPbao=ZjJNq7ptr#d`P^nLmpl3n$gWq
zwe5v)U{UwzCG(&#5bJoQu_wJB3PP?7l$4%9IK@yS8rH<Y>dYBkk;=mxR}V$FQCi;^
z$kWX4HjRs7|Bj6IlBKSp68_WxEU~&QOMfXIFFqlMb4G==#14{jf+^I4EoX!uL9roM
z3YXsW<fcQXLN97FOvmbRuuyth{by-?1tpo>-Ow<BFnB`@%uTXXG+LwI(3S36m2Qj7
zfk9NRVP0DpPLeu@dvA4WE0d?idl!})T-<HnXkV^m9GE`a-<@#jeVpaHe3Vw-bsR7Y
zwGJlwg$EZ8`Ne$Nx_LY=Dfza2#{1OomgH}+cuyjRb=vi`y-PK_pKrf-V(!T*WpuK*
zkm38M*z}h(F!Nm*+O@FAdQ|dK^%@cI5;t4J_gI!}Q!PIFd;{)byLLW1C}A}SZB(26
zS2|?mMC$?srK!*^EUW#g<buoDSI$hYhpW*>LvSg={%>YfdkG;s8?@-vo)KDwxlBpu
zKHT=~_}}kzAYu)Gdt0Ss>u{>~dp6fs`diOw_geHXFxUC7=~PTBN<;g1>G^lgn`{W<
zio-pBRoAr->|tFLE1F}RG>}L{NZ*&;52A9VO1^eu3If8sg*Ks@e#71yto3!Qd<t=0
zZlk72`6h(dsKPdNRMTd|o!2iqoMt_@Pphee*ha5>>8^ZUBLY<83o5fGSPWd8z~24A
zhy3S0&><0%c+~A%BqlV=d26~t)F?|Z<1XF)q^2<fhJj0ANs`v!T~R%N<`x9lg<Lyu
zQDdZ67TD0qh$~Ubk{^*CY|r^LdVw6o|B=yzxrcaxyu-Mt5euW%dyUkLv?<E->Pc3_
z$Otsx$O*-y&j{8VyzfX&ICeNzLr9z5k3X6M;+luxDV_G4xy^m%`u}R&b(RuB0O9(m
z-6A#NLlBd^hOWQ*?S-Z2^skElw7blsv_d{Naj@7VC`4w-tBB0<&%mZU1cMF#oGHD<
zu@}uwk}(fe%snw)!6bK<{zhYbTg$Oho!hfYwB@fEPb{Kv|MA3{0}DL%Plsp;KPr?o
z;Oh$s)~JzhTCOL0io@E^OU5A9Ui1s^Y^K0eN`vIV_2P$%6<HGXShW{BsTjBu?gs1l
zUelBIuiFN(hND9WV>6;Z9*ret0my)e;fSV$1w8t8ob=~0L6Z7p{3@OqU%R0|*Si|i
zBj5#{ua!s;bwiUDj3!pjSsiy*?Z6E!5KC7!z^kZEhf|u?&n!=H7#8h((!^@v{>~U0
zJ;SMu?>gKmBC@HtigiU%*_iD<JCvtVu8|<Q$a*Iuwe<HsMOVa#$b@6uMA^YzBwbks
zp9}}L3cHl=$BQAZMXPj%6^>@@#LbW{>f-T02DDmwnEiU*6$Bh-_dQX0$4rSA5Rdq`
zJM>w5=|dEM-=3(wws-a2IFY*sq?SGHM!x|(-DiDnXGhLv1MC(lBLM+2#pa|uomcRV
zS~S9qdjrzQi&=MMsJ%N@Q6D1?uVqUuVaYAwDT(i&0R^GGblGu!W0^?3jL34Eto;s}
z*~0geS10A*<OGUbK#%Iw6%$525b{@hn0~zcGXwD@`dFaaR*+IqM8Nw81No9eqSY|f
zF`G-Ov7WESlIQL3r<Fx*)8zR*W6u&PRK3^awlZ+^bYn6|BSnbSmZ@F5v6+FzWcTfc
zm@6-pkM-&7cDF@((^X3=6+p`tj!BvIxm8D*hqRWQ?f!(*Ls=Z%+&Qxg(EAv#o}b{%
zh4~0^-;}Sk%RzUyZy*=2>vwk8_ERE&|3!W27QgE$JCiVUerkBFrHPupjc))&`46)G
zPkJs|9?!bNo9L&aBuM(zr`%}gW%ACBG@73=?BoqRb#VLj*UKa9<o7o7p=_y3fa#4!
z4<3`vU#vtt9IDz|Bk9fDKqZjO%2^u5o42Yg`1XdT&G9hlUi&2QNsSx&kZ@V~CaU03
z!EygP@<ZP!=XyU+?=;ONEMro9sK1O6YKvMb<}|r<<AUXE9$C%~`HzG|DGM=LVoY6<
zMi64qnP4V9`d$$=o&@;EGWs8|sz?*e>xDxhQcq*rr|-9)t@x{dftXMI4E{Szi275I
zpnZ-JgmoG1LHcw%Q<7k1etT(;FkP?y^(}W{q0WSl&&RP+n`?aV9haWEo2%fGdL<JI
z9%Q>j<>Dh@u(FoQn<%LA^pUZ|-p)^)L6bxmva35v;;)GGTJ+n=2JP|jD@uuRyIUMf
zh#l9<-=9XZQsn}|z{xQ5tOJq#djb*riSQv`zMQE@-RCTtk2^B03k6iDO_@JCPrig?
zP15?AvRle>IK~VH1#h<LU=p~z+GKAT^5h2k6h;eLKP}sQG`q=SmvP`AAiR6HiO9la
zW~#ozpdNk3Yd?3YylT>79jLNv<H`ne?iYO4e#P4Dx->q311sMAD%tgR8lT%`o<HFW
zt|R;4$-OTQnP+mI`&TMiF4|`0Sao|z1G(I-hyT?!*eUw@vmrXj^#4h~qxr>brNHE$
z+?)N0rBX+bBG#Q6+%gh$H-0Pmrrx~~XE}Gq$I7i7M<ne;v;p&GupT1nn}N<!kg8t$
zdRy!PX_U{>1$=-6991n$G<0J2*{gr7iZd3}#(mJL=Xeh4$C(B2rnqZR9J(+aB~Ul1
zX-uG76~ssQj1rDGS={)m4K1Y7&>%D`Z}1f%W6tPty6HjcHu?{s60@5Nw>WxaT<u%<
znyJ1XYW)sYZD|_QwEa{3#qrz>c#lgy|J&1ReN`df3v{V;SLARDcXEp7*Gc)#YW2AX
zJ)WZ4<_pXcu?8g;HD~VM<Kc6XKqc%tc67aQLgABRrm`h_8UQe<eGS9h1Ifmw=}9w4
z`uFSfIVAi;imQ<(xk9<rf~lzIop%1Lh--aq%TdlGSB0(BSsVF(;z5<_ejHlDaFgCm
z)(f!u0;75^4^iG_0^dOw$JxAguF_{%$&uf1f6*Mw&A<T~yA!6}Gy02cCwaL+XVKXs
zV$Fs~$lmF*lY>y2z;_+p<sZHX5h|?3-q*yPWl*^lhJ_n$y4%{e%$?aQI(kY(mk6z$
zQU58;|5ieoAl2rU$i~hcl^en&AAoR(7E|g>2FjGMB+7RooK0)+!u>^$47{W>M||fv
z;r&d@)<zjJY}^hn(CuC7eENvYH=|z65`3yEQn@uZLlEdlorIl=oY<7as%b$+Cj&-P
z86?aaG*94-*5oDoM4BGVCX!dQ9*JMDj5T~9CiKeoKH+>!pSa87NtuwGRe__0#<iqf
z0*!pHSsdgwP(cb~vr2hXX1*!wl258-T56ERI=s8dEM_OJz4Y!JYSu@&v#es0<2QrM
z{uuHwlccN3l}v&@@>Onm24rK*73X8QYQ@+(Vf6kW$o+XT)C`f)ab;ZsO4abvHI?ly
z2@YsO+VIhIS~ls_K+moq4cP~t<5TbG0_Xjn8fBc#B#+{md;aS3fUF}%i4jbOpt`FL
zv&Y^rJDp=c)jl|Ac~nM!FMm7^kaG~#q)<`Ot|U3S(_06q8MLpd#zagvXreoe({s~7
znV8;cUQKYFMXdVWl}oDOqF>5#^FS1vOGq8w2T8u{Yq&SI2`ZIMrxq^}^SEX41J#YW
z)hFEs=dN!#>nC(&tz80IRynouFzX?*XWC8mXRS-<gE=qwG_eDJp}t6}1M11xi1@rk
zOd~5amV<^FS5NYYi%GNEsA(XmSjnq{%bK4N&<-9jf9F*Xtn>?afL{&>I@GUgTe^f`
zBfKfLRiiZjq{(u4VqZ-2YiLK~1_nxuAG&Nna4KA!2pRr)_zS#z8#&PIDslXoc^Y*v
z^eeGMVsh|phwD`;TQil1n3}?8w9`6iz*gm+yn3h1sQI-c%X`vMi6~deQ&(?zV6xOe
z-UkNpK8Ulv9TfdB+E@{9K=_2zloTL&p2ZMFb`Aam9MyZUk5bXs9&$#66Nr27jy6yp
z2a*n*)Q4JOZN{F*!3LsTAj@aO)G6{=rZ6f^!g?}yP}o3oDm-aXGjII<c6~{4bgvAa
zyjAUg8Z9Go(%rMa$AZ;fV#f&`DY@#aSBCDZAM-F6vghj)k^lVXIs}sb=Ed#%JFEpJ
zP&+ZwMR0L}M@AG%OVfsm3y4uuH>Rv_LL;w%4pdbPjYy!)Vha~R;BgiH0qE=9v3pyK
zmE-Fo_27SQBPRW{b*A4k&iR<X<aNzYQw|Fl*qxr9ly+x0+3jKYS#|QFfF?Ta@)gUo
z!BecdrT`x=LryXc?}!-oj2FfW#RJ(t)_?M(Nh?-ZzGlvZ&BxbEL&89@JLZ<Se)ziZ
z4Ph-LtNLHk7OC;_0P<AZbpSGL98(>`$A|A`><kBRx1BK#JnhGP6?Ef&d5E1U6+{`p
z-P&UZV21%^WB-?9B~Qik0I-0uv9tZ}Eg&Z=(|_O-#hy^wXsa8~w3C1JNhaFF%$y;;
zu5qMUsh@O_Xo$Pr)GPWFF<hT7>AP2hi#Z7ihj+Y46bi#L!shiQ4a3Qgr##!9-%T;u
zdN_8vo_z^Adm)aeW<1{RzXe!8r?&7=p}|!vRqdc}obGaYEj}QD9Ejz$N_DrVD7J(g
z&<{miI0slDW;P@67>>eI^I<sy$2rH@?B=_I@ptej@B@rOcN8C`>s9=Cr(8kbRu73e
zgHcfA&v-E!X$GKGP%F3>vG4;qhTlkG5PaWT=KfZGjhl{Y9N+S_b7NGf<T4p8jD(EL
zYm<X4OP&)&l1igMYymTk&EIvp`-gu}kpO=VOs@W^i*_3ZJf6d>Sb+|_7V54Pf__wI
zZ}Vn*Z&!nDRdaOM)Te~BL4>M%fkR1v35&_q?VO&W8{z|yE{&$Y5nS(GoM0WCnp567
zJ0P?|Jv%fQnj5#xyGi>5mOPVFBQt?vnU62GD=S2{wktWn{CxjL3R$>h&)?9{Pidz!
zxv0$Ztrtac;Um}4dJV4ABe#_)4BA~IuD3yRVe(ebe)?8wt@0EMyKyi;rmJ@_E6{+?
zEN`Z-&;dw8;kx`aN^4ejNZiQi!4V_Wy6`n)Ygl&l%n+GT{y|fHmU@jf>r1E0pVk0&
z0xiFF{h#$f(KSPBm=-jxkm^2F!^*nlH5_aF7Qm+8x*;wTk2Puw_GW<1aErkvlb1Ec
zaTu4uOd8E`NV`!^8s>5ItA0-!!Er2vzP~yWMpUqVej3`(LE<_ITG+@D!#efLuDsqo
zj{fTafuJs$*Ib!3@<OHQdV3Ih36@6YOyic3%OZPV+?;9|dJj`9$G5OC!<z!H>Lqng
zBtz4w!M^ov#U|o*VaNB%V8Lh!0G=;B2c}!>iQ9^6$+7e3U$)2-xS{nAhR?0z-_@K~
z#TF4D_TyaTsJ-S7Z$C8db@+Vp!Ff5${wfDEg{LGEkE1`7u>^O~-y8B@4y1*I$11`D
zKkEigxnAh5q(ar=dfSaP5xEHKgiHfg*uC#>#`ev`rU)F&hraz?QbNG{2C8XWNwoFt
z&qu}nSQ#;&8^hhPg^75KPehqhYZApun#M_=%*JbJyRMf*zWiZmMp6q?_qM9*QLLO%
zwMq-TQ9=g)gKFL-kzEj98t2<W822>X48IEO(}jHu)<7Xe7j|;jG9ITg?x4iL;ODJs
ztmuZ&so9xBt=D!BJ#?<4>)}_YznA5T6BG98LTB}0U685K`q`?~n6k$3OcPyEMNeNn
zB~!DRZ+c!CbGW1^KGP;^uysrKZ$qZyi*er_z;R6qvwm6HpSpp-PC%UxgPXcUT`CMz
zUJw4b#(sEZg2@N8&P^I*I(^P7RbSbwa@$y}4P@rYKxL}aj5Q55%Gu~5LBYXkQ+Y66
z;F3-i=cZ_PdnbA`-qN3oyJftphS^yd1Ad!9{LN4D5ej{A1REBi#Jj0Gpn!Y~c_yW4
zofiCs10PE5kWGGo1n(6Yt|fONusH-&&T}0vm~IT2FN<B%O;;H0lWIetn96OHRUc7z
zq%iLIS$&g*(9x>dZL76|z;-o6RD<;<MacBVg)-~X6dP5t&xQw|818**l`grNHbddr
zwO>Hll;?6G1N-!Hg`LF6vW(((qzQ!D;Fy};j3M5_=cUFGPD^9Xqa)8}(FFxAaRon|
zl_;g7FZqWMw!)SyXms|cHwDoyu=irG()w*@1~Z~skWCxyM0ssQL4G=6e)U@-`npi}
zzL{d;9Ns<T9*nf@r9iq-Sz*n>%J%*Nb^8f_U=T%$YdkdJ$Ezz^m><5<80V)M*mCJV
zy64d5F$+O?nieFM#LR?~H&zOS^>2ytzja3*iz_bjYZLLj096QlPboB_YQm5aSBfWb
z^v}&7w};QVg=3xz;8^2M2dwbfF8zFJ8k~*|-cnu@PQXV*gPA~UPsrKpC7#LNV09tL
zLNMF1;fr^XJW`nDq&f7hH<ovBcQLV;|66;^=HvhNTLkMo`i-9ef{qZV@zcw6v8!Q^
z4bGje?OPL?!!acxBMuh$9ft(`j)=?1h={<!*=GNqWGW0l4i)NL1#Q!&2;x~zm?Wa3
z&x!XJ5)$=?hj=|(=V~&Q?aRo1!f<StO8!M&TwHXt(+=jq54ZTk3aJiMVpre}bfgCf
zhkXzMX3LL~6MOpIeJ>ptzK<s{s9=xJ(m;SBR`s|h1{GqbE)ak-kFK|+yM-3OFoanv
zg^6$UR6Uu&pUNtl5ydw>pyXXu-&3vH^(nOh%~4Z85Xm4r#W`T#yDzd2Wf0<h3ke*R
z;(>J`V8)R|y0u0_)8nrzRh^|B4S$t41i&OUP-d8K{5>IbDZ&HtQa^_<3eiQd&>0sV
zdryO8&~0kDB~;jLAN4VFDF|XqYT_K<rMu;H&kkUB?SS#@vGd_ouAT=&TBVV|U=+gi
z=d+vh2SavEr!0iNEaK(PV)4-;V92Gu#^h5B7PU%}BM;uH;36xH{j2nor#|QX-qAO?
zhIdukLTB|gnG*w2l-Y*D*w_`4D<o#+B~ehH8RR4i@is4;xEWC%xM4N1#nq`+Ee154
zxXLLI7&AFweM^4vL;jh&`+=6iIJy4=CIWMC{_Od|t4wV@%<)KAiKv?0y8yEZK*s}%
zE*`vvz$v~!$g{U1C7vW|R%_kz(s4U>%YJv8#F8jBqss^+QaqTmlsg$>gS;rDU3r&l
z#TP~JZNGG*d~5$i^hEZiTHovNsiuhb1X66&^O(pXd+bYHJmRKuFceL4lO;ghbIfM+
z+!(&EZ%hi@l)M<%lyU!w+r`-#AhQ=_$(p0(w83IvZR^KOSb#<ncKafZ3IDObPnPGm
z?~8(&<fjOPv|&D3b)C-^2zPU-K4RHo_~U($L4^x$Uue)(^pEeNq-0#kv_0RV-zZk4
zI96QWXd-4?3?lzzlca?Y^uQ!A+!yL;L%6g*Yg<IF3Kf17A$f*s)iJzV?C*V({9V@k
z*eZm&d<RX5gSCQ7!5#(X@d&59JqjH6#ROsb(>)4YSTyKJ!`em%;~Tb*+QFC)?ixDz
zF&VDTd8Jz-`(t_&Ab|8Ma358&&}+M7IM)W}H;H%5HNNvtw>W<j@6rIn>atfPc~%x{
ztRBJi#lAkfyfo46JczG@{lWKU2*Pr|80qjRcyi&NgO!%BQZ(j?nfdFD^;h<l2)-LV
zFB*bCmo!;iTbBn9^-;6g{G!0q&CJF?9@4teDop6jzgSmiLW9$s1`6a1)zTHkdlajH
zqhgwc1grvFNHjtXA&MZopL$}*rfHKgQOsWC3YnlkBroyN5m5@Uoxx+l!(Xv+Y&@xT
z(iYwCyw2Nv#f{08PHP;tr91<giDa9s!o22y2ot7*;Z6#0Ou!zgN|hsd7tyzf2+D4^
zD6<8G5XuXI&vUFE0<EqO2oy0scaC^k3V6&u)YafD#4IucamUSD#W+BA9%G7;bI+pm
z;ZNxj?Cd5#gnM)=t(ZX7ibrtyrY?>RFmGfUAy_75`)!ew5GkskM(Vb0<#gUFM65x;
zK4X|j66xTvBbK~JeQ&GVP|b9nMilYmFbQaN|CZ%UoS~JDdmwK{lvv6FaFM{$=TbK2
z|Fm^hL3KslmJaR^+z;;V?(XjH7J>&mu!B3nA-KB*cXxLP?ydm>L7UruS9Ra&`?9NM
z&9%0?EZJ+!Z{*JqeBclRGvhEbLyP-Rc)BVCo5SJ{x!U(t^+@Z#2XK37*so$2q>r=`
zedYDd?WiRR{L|vfz<W>F)BS{K1<kxi-l~?6-=e>K4P?36(4>pj@zc;$C8gI|j^YY<
ze0^f2{-#sdOmmWPG!#1TPyi+}8VQ-^5m#7w{ip{zvxs#pE3V+Ff^#S}mC02$2dnq5
z=(A%d;U>>PC^1E1oQ^J5G!qmM295s|LP4<`#JVX%w)|8TnNmbKzB^Ld(b9LG5ba99
zB%Vg83LMlkq{S*Aqd4ak2jCuPovuO(Nf@RaSf}XlXrT3ssKTau{Eb^-TIKf{NU)$m
zy>wi!DVmPBl5k_qI_dj{tJhLe(E>V}yjuBz(%a3-A6d~u$jR4yjHq3CZTM-Ow)vmv
z+32J8cCn%4uVdcm*ENl|a|v_DS0&Kl#VaiBDe%*ium{qM2pfEd)%AWo%D3%!6=1EC
zx2ClVt254%Gudm}92wB`=-2baJtM`oS#(y0FR<(*$wB{Ek>>S8c1P!v(?QO}$wNM8
z#qRt)pyGO~I;RUvH~bYU#$1?J#uDMGztIMUuT~e{VOxJxPqQO?nCsnZ%^U6;{LZWY
z7vQt7%*~ScYNmjDk=iR?$5XYN;;GKAXM@wC&dA}6nDoZwbsN_P+sRO>yv5FqV@Tk#
zw#W-xd(4bPk3*9nRaL}JFs}=&9-&rC0@cx4`DLe<wZ58Pr+Q-;*^|@0f3jwX_LKL`
zI%BP%?L+pQy%0$UpE~jP!*4T&<$Ha)6#$NH7e@NKPtM9%D}H5+9+g^nW-(+H;ojPJ
z5vX6;iX}qAE)^Hl>3HNOh*LhS1GRgt?F@vF@=$o@@e2&rG!oxomY2VEbhun(5b0+j
zaQe!ACEiRxW4_TnByyd@ej5o%<8<)`pZ_>;9GIT*s|ZfqvCUKqGtiUf(cMEum<M#I
z!vZ@<+ejNZyCW;38ByTFk<nJc{M!>exGW<15nZ8Vhk(|o>YtJWl)R4k^FH;5XEs^_
z;ehe&z=;XW;VvCCQx~Z?H26Y6?oU;?dr>?Ua}t;%$B&N#lmv}=d*oiP?+mKAyMpH&
zLE~yaX@$rbxMT0!V0NVcs5?=zqyU*+6^YRJLH8bOJdR3^k^-rt3TMnPH0yYhJw@cn
z)O<z?#l|8zS#l=)OXYML1|K8+aNwsgi$-bU{wSmTv6R7t9X1!~UvLx(=(v%X_ar}T
z0&vnhe;nyN9%$qn1Q|F|jE0vc!SC@GV=pe0CUJoiq8=&H(2j~b9BXUEQ~<-B%e3ox
zXFcQ}S~zO;0|SN3ye))VP=A2pGhH?|`5iU{UX5zEt@Oy2o0{V~-7kpNn_@$Z*iGm$
zGo_7k)R#HDF86zNNdE+VIi9L4sw&LHxT4>UAEh7T=YkF+gTB+VM~FdW9mSJ)N*C^B
zDA9%-b`3)#3VvQe<1lW{zyZE-tXksnY7RQ1*b|Mtn^8w9DJYB-yEgpfD-+~ry~8LI
zsEOKYZ@*yxNpkujV8wJ4eNBI%o$svTeQCcE-3{N_-%28Gracs!2qc0V8`^a7_;f3v
z4E_Q8ju1Ki<_JL^3+{k^wXqu0ZEgRPpPr#LnR-$Ea#<|8(eQFu^BwRbo##W7sc98g
z`x+=uESARtb{K#uqSu1Zs8+2YADK!3ELz9~ne?jUlil|CbN>DqSFd0%1;?rNIm=wr
zJ0D|jXl07jVyfL<G2Hsr8@{4Vs^4B|a+8;Z4nK^sob+1o>UHZlEk4zrlbyfDdAS6_
zO?y(QhF#Ybe_6*l*#=|<1cLb|Y1DE^WF&5pn5t~X?MR3QQ8#`Gk15yF#BJv2X$*v7
z$rij~7^Y^qzd^`9;s||yV#Op;u=a`hDSzw8;{(^SKQ74~SRgZ!xUH&3;n!gAWGv)S
zJ2jpcPR3Ykn6scI5Dl-dZ<kiavzwY#6aD$=7c_tVFF{PBfD|DA#+JbK&}A5!6M;E^
zVOy6Mn}4SN4^=51N;;bhBRLTKj}<revHTT+s14o<pDkQ*T8TaW$Hs+p;Q>>CpG=o@
zr)mzHY|qgLhw4`?;raa;2PkcFBFXEMz=`szSlc|naL`hJZc2*TL1Ji2n$a^F`Fht|
zE@3sf@aCsJEDsdVebmHJDCx#JShC!zGG7<RWz7D#@1<dICBVOAG=|{JyBk~72tYfC
zO3j8!9{#D^CBv{wJuf_T`uK@cV-ii#Z$fhFuXdk8rPeEhv&l2!Y8Uw%Ynq$uXuX<9
zz8I&^tB&;34P?frqupAXfi9pI`OF5*t8TKrhj6yGBMNBu2;b3U^~ylU%~fI~D%pdr
zW%H?6)-f{^n0>zZWI5qxDZFe~!kPKCf2;TV8UCLv#ge0sO21|C*_<72xaq!vdHstn
zKM~xMFdwGuUp<3W4ODTlbWo@27@^yeTNYe0_OWU_%!*Rv#-R|j?B|o!&b_zw;$CP}
zat)%GT9QD4@XQ!ob=aPqZ9VR4kBTV?KMSsHgQaw&Hb}&qj(lm>AWn}6V~X_XFxR*w
zE?hb%v%8x-LtbUk8tiO_H)+y}ZBNx~Rl6)ryNj^4dZM#S=Pe&;2$4h;KT)q{Go!gL
zo$q}qh;bfMdA!1#s~PuaA?fZs%tUOL$PYRFKpfZ+5*Bq@JtdukAYM2H9ak!wbmYD(
zq1l)O6W$HnV1^#R0&>dYDU8B=baKip8*Pj;q52*A0GL7p3tf9#V$y&U%0r^FEBkda
zhJ$!Ukc0?PV5-H0FMVCq&o^30$!4KkxgAi%p&AIJ6dkL>tgGiL<NL{3!Zjjwq9=$(
z2CNDEJ0DmU(!FC@&KD5!7L2K#MB)i!nGUMoInwzvrc7kO8hp6)E%wDU;81QXC(6ij
zp3{@>hD}<pjvu*+tG~$^g2tZPy09r~AzxpYW(tmKXNNTKy#{r0hwUnyasM52_<Q)A
zx@v+-Kfi92C%S<I!s^{=d6%50+VJ9W6`<U`U4V88pjS~?vInVjP!jHsU}#3%(61_A
z?jOnihP!Gd>ig`7ckaInw}Kl>^{$RWw5^*RQfYcNUvGcx0`m_uM|A+VE>#dyWC;22
z=gT@Tr`NUNQ^Ug1F~5COO@W)Kaj^dAN;3H^A!}r?_srAtFeiGTPzY2>fg&u51mKte
z&ys^h*GP3^DyF1oG%uGm|3oC5f9EuB6>SHp?WHxMm8+EC$@CPB<(WZQ9_!I{#}~pF
z8ZGv!no~lNJJ^`w$HfoJ^h)aJW&V&gK*KIdAU-_s1ih-k?Z8ip>TBrG{m<yELFq-i
zYx{nj)$4fnuZ+o!*x#1(`=(_=5a8Q{xySaE{-J2Ua-4~iz^o#*IyWNVwsXv4EGBUk
zO1Z;Cxz+|rpN{sEf)Re7S`#xrJ9~n0mny#t%1KY6p7&SK2z~LHi|zfLj~|s1<wOlV
zlY=c)yob%(@C(UUzbQ`kOz)miGD63xgIp3GB#=}Xl&Om^E2};mwVxf|3oJAZSg4q%
ztz40_I906avTxfBULZA(n#4VV2;>S_L`sc=-=Ciroj1bqsxyXAuTFLN2X6Ybx3#9v
z?>}{ok6`x{4aQK%dN*VyTor%C-KesTOUj_v!(b<j$&deh7&$Iht*qcs=O9XV|2e%R
zcc%jduFIjaX3cnurjjrE44^2xuX1VlST5j6NN`vXS_v;nzw>9Lea9@wZvF<&$b+Cq
zsZtFYhKYx;YIh%eTGzKgI`V!n$C<KVsdfI=xP5oP!qTsm8e)iONa6kK<uY2G;ND8A
z0?AM%o2n+O!<o>(T7rXk&aRbU#H~~?PP?y%n7TTvP<@bUHk(kA3-F&}(xB~XQz90;
z4W7IZrKUO1U2iNcFWit@yu`5*`@OljpI?P!^Ej;_P#0kgsJrMbn#NbjLnB7A5XU3e
z4Ht;2D~IYQCW=8h+HWANMs=RgiuHco5(={J@V5P_tkH+U{Ht65ISVZeL~Y%tyrSdU
z=j3U~d|`B!ev)Ko24vJTUNP#K{B|WtFH-uuICC0I^hq{xpgSE}Sv#;rS3e#jCoaFX
z^qU)OJFVqO)5KCeYp(p*0<6|}ckD0HugB__(kztCUTqUuJ1&P|@cb%y@-YB|9gtXP
zXYV10P&NBW$t5CW{tc>}9dQ?~`(>4PT_eQh!D;bN5qQ>u48XNJW)Y(^w=A1rkTVM$
z+#Jy>#C1TbXFfR4FSJ>Q^~Q2Xr*Pwx;>1_m498a<f(i?Q>katwgy%2sOMgq5rJoG|
z?^3e8D6jR8)#}pepU`;<J6_f5(Gh!L(4asVS%Mg@`@TC#CUMSX&nXu>?l4m_Hdmn*
z*j279#GT_X0Pmyr`5gA;ls$j<?X}_pu9|FMpekL1?p-jMve?eFNblQ(Wuv9Q3c-O`
z&HY+Dk$lx30TWUia<qV}$kDY%77z{yx<apQh4{;F>!dx$HU3FI`u(LYM;bP|n;S%e
z*E<zLHCIO2LjehGab<t|Z%rwoBAvg`i*7&=U^mwzK*C=MZ+!rf;EuH(%KG#fm6LJ1
zNtQgiK!i3{RDDz1*~edum`DLz2hxgLBL&hjk1{94nA95n!kmb+6B@28s75h}8W#*d
zvSmSuUPRi>o4mBI8GwNp%XBS0e;Z?@T^yDZ)-Q=ujoAEjVP6YM@YzLqf%uz}iLe>Y
za_!~@n81RtXX8R7^f(vb<x60qwCZ0fWlD5qT>@Vr7Q<(0RCWofbxgX~xpv#9FbB*I
zYM*Ox?;&}2*tSjlTsCoUDLr*IK>E^KRs6mesNSBZC%-5TtFn%&qDsvaJh)^odYPZ#
z*HV^68#5D>V>PPLR!1KlAJm~M7&Rlv#wh6lwdBG`8Y}$V@J*JkwH9|yf|dcGWbY;4
zEnCr#y<$y)8#?7F>~Y{c^UjhFOLSc3hjDtSM|1XOpo0H<awGrKbNyB?#V(2soTJxJ
z>UnAdhkvS+`J5|h(G2^|nGQKG!rYNc7ipf?AI}kt2pq*sb6+gms8crsdJ2XQY?dfc
zlv|UO^F>fWXqCqoN5b$Q_$)J~!ma_`JIazh7nzLDP;uqD@I%~!`&+_jzEF?2X60d^
zupRQ=OGR-8i3kP%_8+hBf)N_ejA2eU%~7W_C!Rj(TmEO@5jrx;){$<+o_YtR1C@yN
z27PZb@<UPn(A!`e5|NnpRZBD+^LYjs5ji`=0I&Wn;^t6CB`%4`zJ|-RJ<;z(!P3Cv
zgv~*s?sX}7&@rjZquCn(8?#C3AV%jTi~*F;J1-DgX*(R+s^%-qoR1lrnon(|fwYv|
z_TGkCI9T%3^!xIBG1}ik$qbY!5{`m2MVB%me9~#&Knh+`=w^b?@(9j*5F<eK6i<G5
zG^Z3ut59xS#ljT+LBiicYf|SF0!%WEx<~!z*@aD<Ifl@Bon^rUTV9F7I9DwhlR%7`
zRzuzL&N(S!!mLU*+h)D04gu@7oh(2c>o|Yuk*({oNVC-m1QkX`8!5L;d8FoDruSQr
z;|*(bik@Y4dFbjd^^Y3k=@r1VDWH+wA5;8`H^r6u?6l5-j0fKz1+6kqXgVEA0xhEX
zljvfXU*uZl>_Rc?emI)xnuhk8vvXeDY`2Q|*CST$QRwrnpOlU)$&<B{^InfzX}aW;
zzD--@`jv<!<t`2#sr^fj7Bn8vGbG5|`1vM+`iH3}rJaUfql2YAVz&Z7aH4@Q=s}D`
zK?;UlV^)y+a3-@4yjew|H!2jNgB#n|^}!e;7$l^p(kYUos7M#3V;NQr8JX@LSB?o8
zTlqq*l)aaV&PGi2Kmw;AH45HQ^7CYN(wyh|aYzsLva6aQ$xq{SBzIC(;=k%LG3!@Y
zpiL-BN3~PpzJ%>&5gr2$7jL=!WwsfR34>v;{J%hV@iE4rSj54-5TFJMxHtOR3q&U}
zQEG-8HbGTI*;N-Mh9=y|TO~wGn{(pI<z~s2DNQL-xp_!EFGwr+tl}~K{*hV<Euxix
z)$?$o6)wf0R!1v1<TMOl?(k{eYYf`-n)tM|+?-t@BRyWCpKJyoRu+pOSBm785*v~$
zAkr$PTaNM6V4mOL?a@DQwHNO_xIvJvtdCk)2xbotM*4QO;aiyR`e|)O-Fbv}Lml?t
z4c$(&TtVzX2u<a9($8PBmhRv$b(zLOo|iTG!C!LaXpj)=<QF;PhtDGEB+dTXE0F?E
zT#fb@WQTkSD>X2H{4)yFl9NdZ@?OZYX5Fd$sSQO0#i2r@oHtBib@652@?A59)X%yO
zTfU!hwG66Kn32_Vx>||ytr`E^OkJ<KW<LKFwt%7wCd^t<$3tgM94;Qg=R``*JGic;
zjlgA&VVo}kvBH$1IA5?ku)S#&GnU2$Tprt?uSNq{c%i$132j{lo^6<fz+=&|_+IOR
zegQ&!V)Y|v#zjD{BEfecp!<-$f<79NdQWR?pVtRk{YF)dMt)SgxHus>CU@V&^`A4s
z^*+OJnzGn6|E#oKPeL-53j-e(mJI6``{L@j%4k|-l+>Xoz6Kwn&IQ*)MHm!Zms`nH
z`X6OT-Y!c3gwV1MP3d79LhFum)lI1VLvU@@NTB$}VBCREWSwg?dQK`lxt+TNqs06_
z;}`bMLiBVsRYvK?tWHS;Fq|y41-sXYY5U3wibv=hznF%=#1~xG*z3^!_WdhCr@8L)
zG_7OFsz!wL?TJT5x$I{?J`oSsaY3KIU4%O_rHNZW%4<hXS!epUXH-589d;7N<f_8q
zi`DHT(bnqucZ|`Isi5Jbk@4J^Vewj-Q64>?@@ghmO@u^8q{FIRwGmGvi51SQPxfn{
z-gjMsLq7h~1|5OcJOnn>6HcUowFXgpqPmr6R@jL5qYa@v9K`p`56^_o=LgR>XB>|(
z9Is(uM*>dR=Ll_ZQ8piY=NCkwA+ceyD1*6}I8lW2K8)1ke0!BntftJTr;Q18j2~z2
zfy<6F{g0OpcD&dAvGfB**b*30^hFp0*Zu+IH@DA|xSl)%VJUM{^4_AHCr0=N>c++`
z&+9`=Fd959=aGeH5?S)i>U(O<OtRPA_NO-hkAuc_5dN<zWcqh!uP9VJ+K38vGRn-P
zLup)^c|oVU!$zN*+!I47KUdMkXLK@3eu8LASk$9eu}pR$p=)IZ%Vg3>Z91**FQQ!e
zw^vCS-+itTXWui#NwA+fW$7vxC=+A|Z|<N1-XeTQr0D1ysY6B|TwvMl;W1WFUs+-R
znm>n!3eioUF*|d>zSq=BE!J8A!S>W-RQ$r>(G%-hlsu@kpdK6u%|0BmW<qI&%gPpZ
zo2f`_w?jicm!qj<2G2*1;xt$yw0}BVJwKHBwo^Xn^K99vZg&p0xXh3Ga7Sm+Kgw6+
z=?lsQ`z&@3T+?|v5kFb^)9Yp2+!sXw?q8RC==r$xxZOafT}^#vAG5j^6$;P&^HsOy
z`g7RFaZ|a2jm>4(K6nIT*i)rF>CmR6WZphT8s$4Rb;zBZev3U9u}!)KK8f$B-8;@V
zp39dlU^Icnc)kr(uXq36QD1apH?>K$=r%x#++R$XGkxBWaM}_gH6ub}L{lu@p5cUi
z{2S%&y!Tu8szk{W9!px@tS!skd70B}vBO-WzN5a6{j~8r?%6H6|NkVh_%CBI%GBs=
z5C#Y9qsjZf_pW!5t=IR57zji>{O#NT2%0#^PpVu<4*yeUzUJ9%P!Jpx7tepOET~e!
z#US2~fa5>1jhkYSC<u~^Cy^D8I#q8F#QBlp`A=$W5afsX5&jqUhAP$RH;504i}(Mi
zJq~_@+Myu1c>lNK*6;?1^&|BkwMR4OCg=g;BjovyJ($Kei0&ie`_DaFw?Q~aP+WZf
zd6kdneNKft2T?+Cad4!fVS(_FdHGnR?Ofg5NqM+Gs<~x|KExycO*_)5Z{&vAis83q
z!sdh|b)dgRVxU>BNZMoE5{x&(Tz_l2E9b0bUh>6;<>gt3X>@7@zsMR=uN*d}a7nG=
zxpa|2M!~o?5Xl(tM<14<eNxxwXk7hral9yQHCa1Vo)B|Lk0i+cBu38t7IBh&&LILI
zf;+mI=rtXCi7%<mSwGJ`WSEypjeo+5H6<^RP*WD+D_f*MAh}0_WK`ilsXXj9sysb-
zhR}B+C8fT|Lvph)Ipko3Kthba$90^|B@mpg`Cnx!el0|*Wv3u{1vDZ|F&c9kv({_Z
zevezYLL@s8LFKcXoggb<t=~Rgnvr1u8f+PCq0ftFkb@8v0xi{${J0dUGW)P<%dUyI
z@RYp52r9{c_F<_?xmJ^Yx!IMq(UTR$C2*6Iqd>$LRFy3WK<t-Sd6n+rNLh!#rGrJ*
z)oi`azux^teYQ<jTE<pJonu!Q${iQb*wA!MUKm^~?e4RU6PkIBNWnPShge_=I2quL
zkNgdB35J3n=k+e?ah5Ge0<{cbYX_OtHhJ{rH`jvN5*mrZC>U8XgN0{MkovH6gE&Aa
zP2cCXqnHILe)JF-_ppAyAA?iI`kt?qJBZFQ@+;cTl?6N*PLtl!L+KJT#0tffBTNi_
z^w&a*9Xh&qb!P3(u0o7h%^yMnT+*<IgWi>C#uIo05KBij%E~JLJ^&CSE}fYsEXrq8
zY~0AJJzr;P6HE6@g$?phCG*(ft2qK4W#9$cFas@7f~$)^k3#2uWAu}bsieEBCLq+<
z-jLD6)>`WwSh@%=3rtxw<;|GX8UNg>+Few5T3I;usPJzJ{*u|D;+um4fPO)i*oY2q
zZg&Z}$J4ck!bwbwy5U8yMbF{BMBmKYT+hqI0pG+CXeYy3$q{%ZZDU6WyEIGbE;%@7
zwJ^dycvWh~uE^HKAubKa&_=H7kEW5<P_ML`ZiUa~S{$CyR3>d<vxX&J<*@cU)??^y
zO+euC&Oo03H}*_~@5)#);AfjyMdZ|+%)!SsP)+bt=ooAhjO^HP8M)1|aR2e47dZP(
z(I=Yh=K0|&zH?hFBdu5qyJ`tUT}$^%yR@^Dj9-sBu%fL#YuZ5n6syfbSLA6L3L?i}
zk<n1~CmjD)P4+NZmgl*6jSszu9blu_Yh)Ar7g`p67F+E4>la4?Ah8QN5M6_r)|?qn
z7;1HK^s7jHMeG=9%M?6*qNQ7(0lbl0A5&sD9*Wc$f)q6k7W+7Adxdl;@!U6Q+i99$
z^DK1s3e`G>K#YfN9r=I3iXHMdilqXhV_LqPnskFv>m~x=DhE`haZba+Z8~+<n7<|t
zhA5j$i6@W0Ukv12fzWxIEZJPRD4&jWmh9|B8=L4djnE&=;AFZaf+7oKh-AH>wjX|Z
zQ@ibDH*t(Z1g#>EtVTu)4veXml-VqHf}?BU5LNv|hn)AnHM6Do_>^7g$H-YwJ<QC@
zGzXebrCAaXZvO=vm9)jaIuQ3g78>An|MUb{JB<B0*+zI#fhGql8m|Y0@6q+J1}vXV
zm$-4hwTbZRPtYuv-0#jdk8aIZjQ&1o-mQ@d|HG@o`+Rn@cN}yA{tNCXClPu_F~ZpD
z5C1jh@E#mtg8zK);=bHo;lnLRwZmR_<J?vbNw+ygbHBY;=kM9smNwNB{~p8g3r%y(
zacMFn^2n2N4hRhmFo4@zd^Go0j>xSoLh+O&FnQ@npw~vJNup&!l?{oO*-a}L4jqSb
z|2JpKVW;d#TshZ<{GgT|Lh~v2_a{<R+QMHUk_(utnnTHuZTfB7aqifkGpgu?jCk-2
z^bF=m`<xlgEA#XgCR;I{G>wyVbq+jcG|9<IqM)fHVSv<ByU&|@`F*eB1S12<&Qp7V
zC!4`!1G+^sw(|C}#MpwGu%vkk7m7tc;pcU_lg8$<r4`BwQ5jOo*~^n4k+`f{md)ze
zyBg9a5$($=nTdIlF^m30FNw!&Ce4YF4M{NAgpQGSxG{d$OF}(s6eF>($$z$$n@KPX
z>sRehi3IZcXR&q6TwdtgxDA%=b|@in`~ECor=+)`bP<itFW{`k!(sAPF{Vw;gy6o6
z2DL~BqKBh1|2h_y*YI_w{sRFwd(SFP=9FLwNdp5(u7->j0Y^M95gw@s^&V(47e#xi
zb>|d_z)VhJQ3hQ<D>SKS<%R2hLYc3$gi;o)v;*{6BFEHVA7i{$^;;Q+6vnvMKrem&
z1pDmGYXbcmj@DrRC~>Nee;0_838$ZHZCM)s5OKWkr^5##$U=naP-(cY)|`L~BP_s}
z{o=}bF`E^ZxjFapS;gwHn@j8L&iJtShviE7L!JEk)nwbsHq88dwFb6f1(SIWV{%jS
z92*ch`&K(Uzax&P9CXEKgul0#96PAJDr!rm7vvnFgIvQ%txnF`FJ=8%>`NN$XGxeq
zQG#I<Au@f@q^985<@aAa)vM(66}7}*yb!uy{A!^F-=uvZ>O&6b+s2EKoEJi9Q51G!
zv}G5DR*%td$T)0ELogxLq7Wlh$iRC-Gnl|y-|xoO@Sprx@<Ze^sS{X{D<yuk6GQb_
z>}9ndu4M>PNloZ|Lt@M=X@8YycQbX(=?gwz6dVtDXnTZPI<vOD)gcaVpB-tIpmKY0
z8!zQOSl<1)c;->r>>!S@QJDIhJ;_2%$^X=4xR_dFV{;n^WUHiB3_i&8kG?3pdR+rw
z*~)O|fB!33#-#L{Uwjx&J2*Yf?<ypUySNJbnX^1TKX`{|rwgy>4iXUw@cX?@`C?n!
z%J=)&hr^uJFdHMq!dkTIu4}lHAUb!ErY&LMm;Zfvb~X?r-rqf~Qk=M`^HfG=DpWAw
zj0RD*g1I?og}}l)?9JsB<8JE1pP2=iEWi*F8AngY>kxwF*>VAy{0A((fiGQ^Id%6U
z!%c4&CD`iBb6>?yg))s7k_Zb2h`6ib*N39|vO=6P7kUMJ9SW{G2|CiUrf8f8a%XWS
zx$q?d`264pRN{NX{tf@en&bKw(5?{jExW^uPtgA#dOFcG%P<G?-=2uGsGD5?q9uXZ
z$f?DA;D__zF{4(YqdLsdKFAunH7<xOcI8`n8l%D;G2`fJO>dGHT$@>Byii&~|J68-
zX9iQ3h=DK>m>~9T#kL=mz+|7wbQrd>TmMC^YZM0`66qB+X5q@Fp30${+Tf1c;0s#Q
z{`GSm)hpKTLWLKl0um!(P{#}Ka$1|G+^{cEJ9tq$SVv+OHANyZ#`QaJkGW2&?Rpv3
zCxgk?@HK3QqhFx}(}l4e*Uh!~3&yY$`tTpnz-Z5daN>gF=Ji|0Kd`H-Ab$Y2(^37u
zAnBiXXF5SV<(N*_=pOxqWReC+WIqWvS(^q4jnCy`6}9I`P`5cgcnd3lK<RG1=BQqc
zSn{L7Jc^b(TDj%+vpSW&e+GTNh*qpWaC0>4tYr8er}<mS;{vUyE!#>W`uCW7a^Yu+
z<10k*r(OTUfFur+M20o*>Wjo9>uq1RgKvNHt$^xGFsSq2tCV@QuV{J8hT?JrNd*1U
zenI4BBt5K}6f2lSA(9?AwUJJaz9EowS+lD?OFV2UcsPh~k~7c*vR`gawr$;Cf;Yq7
zYf^8=^+}jdeVjPE(}V|P8rBNi+~qTaMY7JY^eVqf;^Zxp%$lE#!;2Gkc`*=zXVXLV
zR1rz$rqYE2s9wOmmMP4hYqg{47w*gVa-LV2g`QIkPOnT_aMeI`>>F6b2XG@0)(t)T
z#$h=9maI5fqBtnGm{8V0)$(F|LuD~St<iLAutn4DD08KRzLn5W=~e=K;y?`P`5!g}
zeC+=hPNV7VY{jChZe`6PCutyP#>r*DYHexB$7;^TYi-71&B0^N%x1=8#>T^G&dXuR
zD*Qh-`F~ral46p8KGydfsbZKUQ~)RUe`9TNrFg4qYvF}mJwpVN!h^wi+n!SExC2JL
zFsd#lJunY`Dy&Lwa+nWAeJJDW`V9*Uit7@XI)>$TFZJE_uH`O6vzeI$goN!|w_Pu{
z6HeJ~msxJp-qS+EE<{I*3^{v9yji9)ITYP|!+x=tdx}HAexGqI+J}Up?2Px$h4N7%
zQT1;&e+2r|U63D}Fdl5b8FVEKg&9^QiiIg>C6tAk5gy#08Kh#ce;PE@HCGz)S??1o
zOmQRy<~aia>zl?tsk@m(8Z2S2y9+tcR6=mOVCQ1*#4``AL8jb?$f3k$Ga&c6sK_zF
zcW6U;&Y}#=<1VdcF5T`nFtE-;x&>rQY?Hdw(daoS=y#K;<azKCMcPp*UmBkMPR~x>
zMXtn`Bb=~uk={=m?Ex!%WtbXJy#4v;ush*{!@l_}(*x=9Mq4L*aqMQ4IegYp9=E&u
z*`%vs>2Jg4=bW0TBfqu!3D2G_jIGTrArfxODDNtue+`5CH2$ccN05CB0g&nQ#L75<
z6i|@V^5rnX&yXGHeBP{&w`&%XuFA2l+OWNsTUY{f=%ukn(3{v&6JnC7!AUrU$NrlJ
zdx9y6P-J#}`#z2vk{pCQx+Ue@pIABg(pE9DW|w?zCPr-zJ%Gjdv|E?#A4+;>Y+pSh
z8V`Y40tOdCmGqOCh{P6Rj)~*y=+qX!O<XzfYc>e;@}g;}tASd!FQ_W}LFWrs&JPu=
z_hd(F_i5^gZhq5Z5$T6nQC`s(5$=X1u?Fg>_Y!1|Ah!F#_v>{0suipo5$`Y7wA9$K
zAyT`*zXd{r-2LEt{7@&-fI+lLTP>a>Q9D2>2U0N|x+y80Zh$jE)H@%)YHkQ)1=yO9
zRm4Ofy7`+*Nm9)!g1Dub@FVn;iR-}Wwry;ZlIvCK*hGhK9gN-;0$p?P6F<m~uJ;tq
z`tD3$?pk@W`A2_4{Psq@^^gTSrC0aN1hk?7f`954YjSFhz%3O{jtHZf05H08NGg!~
zhEm)sUn=0kUm3W(qr?j}72NFY#_f&c%?Bo|9+;+1?c+-0+edL02E&QqPW4xYoUzp;
z^2AT~BdO+~h*q_kyRuip-IEwY;+m>&<4h3ld+(25BO1cj^f@@AcR)Dx+CNeoBB%BU
zbZFSTG1Z9*b*Cgy<Yk+Wk2IQbE&!O0e1-uLAwI>&>%|g68j&{)qZvE&6ZC&DN^MfI
ztN7O{*v2%YtJM(PcPL*7TqSrR3o0;!q0HP~_*+2ph)uo6`pqFvM_1unvX{v1=G^h^
zy^Or@-T0^Fj)R_ILabK_zJ11N4<mf6xVOT5Xn#ZfuC1I2i&==Ge=CC!WPsk;N8){Q
zUfB9XL7dp2y_<GZK5NL#@ZU-x$P)1lKa#i-i47}~1RL=UllmLqZ5g}`r8dvM2_D_Z
zAxPKHBl;KmYtN%m+pN4qz$Z{J$sUp^*&$|Kb^2zOFx9YFr;DF-Y#4&7lVsSoS@fp8
zzPT{XQNMq!$Q#n1Pb-ja!9XPsW~Z;^?vFkTyiPv%KCs*+Buziv<DTV4h5{SSh#4yp
zbN?CfJwx=RWT#m+IZJ4S>Q$mi;yq5u$G*d|^Q0=sC!^}S&9<JGa$vW6Q~NLNmRJ)8
z4F<yseTG(KdlcI$dn<b``<RPB{Q!L<eKp^0h*GWs?0l^}2cOUOD8O;s*%&CWjlV6u
z?c*x5!!||QS*0bGg`Z`UHC=yA-hs%{%OFT7(IvOUSBBGk9<f2wZ^l^(W2B4wWG007
zU2FwZ+<#ch^@XN)*i+)@Uc)3DNC|n=kq)0m3wcxt`)pzv`gkQzC=m;7GGuVc_*Fgm
zU~?CCj?oi+nW;GwxH?F`@x<n74mp4@-n47YXn{&#Li_uNeOzyM%Kxi(T8kSZqfJyh
z`>JbR>XE@E^VbvP@q!LRyVkH42Zte6>2(jj6ki{=_VH(a&zgtOcL<k~gYo}=PH}TL
ab9MJ}wX#NJXJzH$;Nn80qLNaPM*Lsi5`D1%

delta 124753
zcmZs>V{qU>^DP|Pwryi$Zfx7O{mW)!V`FY?n;YA<Z993Nd!PH^f9t*9rlw9;4SLS$
z?&+X^KAp{ewR)hbq8M<L04oOvyPzPPtDB3tu|1sE+NHL9+&&jV*R$pUvUsxi8NAkk
zQXb=s?yojX0Sfm09G{c#o<D!YR}%@TbBLO=89lJ-2qmaq2ZpK79+pS5I6m1+j7}AE
z4ZaIIa!<dBJ%s=-UjOeTUhS&Rma7N0*?HGHI<g#tt$_FZrnpoy;B>yz#$LoWTIY9H
zRxh;A8Nw!HTSLno#P*42#PH<umO%jUdibzEE`T4cAx*7n&r+J)@E33QWY8cLmQ>Bu
zVKv@(?$L}3_iz}NbgRK)n=Xz{LX)~Tq{1$n9RsG{IqjrZmo=eBoNsur+h7U@ef>s|
zFQ_|u57o(bHroaPFnz^x7;nq*>}pu+&?nh&0o%v)D#R64Lo$<wfbwr&8ClMWhBP3-
zy(z;v@%{zkM6=^E1<uR!nBRJCFc|yjYYlm3HvHV~#miSEw7iHFUJX4<_uBzs2yck+
z#R`t_IGRcLkYQ@(R$n^;d;A>9A2fA2XPJ#|<*v%xentoisP(;XK{7(_2L-9}7z2m7
z4hBOM8|v&YoK5&SK0F~siR>7K(U`2shE00+2lJITJ1)<jg~oo*>X<sv4(8(>bVN>t
zN~er_IYZvM8U|cXLlVYJPlP~ls_sjE+bUL~hF?FnH!m>MP0C9P;t<~$Rf0-R0xEkl
z71H?#?9!1R0AAhhRmWuA<T3QIMMto-KH1wTe=u$R7Wk;%e_U@DR+f&^)?>iJw_mN=
zVVYgq%R<c3{;rCTzFsQs*S<6J5w85BLOCtRm=B+49fXzJC)aVi8n_mpQAeNK-KU}p
z>5e#wL?KcELQcW)TNn&N;$PpLS0-&i2~w$Bj;%;JVD9%*8+S&AymISXdesQi3=jZO
z7B@E~AZ_OgCy<KCay6U0z!lc`N&=ypalxu<p4KjD$tx<i?YA&^%DXO%dV=e%?tCq;
ztptO=1!15qg)t6<RQU~A!+FwQ442)7uHxqN`)nrNJ@0%z&%8v{!WhSVp>>nISK!7d
z{r9sH@E6Mm%Sbz8Qr8YC+&!{O2ww@wobW!a{$S0d^iDq5?}<zx1<5En=;S*UiC~`;
zN3mLl1CEeZqE;>CUzNsT{sP{F9pl(0lWl&c2hTd*A=lRJDX4bHL%M(}I7EJ*7jnFw
z6+DS{rlD+7S)H>FRO}aGE>WzyaGafS@9Ysa;1o<gRcSA?9Uj?Fn9}SVc7E0}r!@Yf
z@viNLtwL@XAtyZr!~aXdwsx>v=g(_Or9HV{dWXM3MplxaY5=1rKBQ05q`&&4l_lZE
zGuN^RV&f?5ub{JR7gRxZ;Jcr59WuPd`X>a5Q&w***o=T0Uh+KOEEIgD#KocgLg#J`
zKzOxyWV5Pxl#57aoLMAAN$FsWpztB<bQ*1<7fT~7rSWPdsaM-Cp_Wclf7m8+8sk19
z*OfC~I`q1#vwj)8^;^c;{up3z|FPdfx^`KZ_xID8{KHzXi`M=y;S$MRqP^|&u^;+}
zlud8ZN|xOvk?NX&il`_j_w!F#-DCeI;I#Vq$JRl&J}(@dNxo3uu1{h^H^E(?+53uw
zw;)~@_Hvh4&Zm>t8V=7kRicv-&8`uCL&N-%bMivZ8f21O8zY~^`ufr^5mM;vpBq->
zB#r7Sf-Md#<XwIK&OoM1$NH7!Deu+3DC9<%yR^P*HU$oWW=fS<xm5UFdsshN;Me4Q
z*CFWY{*9Y~v)5A9SxMi1Ya5o4AZ69A^YYqP<ztQ4ztOC!nEWhCFSnYa-~Kdp(BLXV
zCt^9mDlis;cBqQQXQSC8%TN2b@dB(HJ6)6H++)Io){ZVA?fUxW-65TJ`)MM<V3f)f
zWmi4Jn)nS+GrSy^X^yn?`k4Pnlq47l`T457;_VzK5_ey4X4_F<VvD_<U5$6|E!^kp
z^y6?He`=D{!85iuln`1zxK4I(AJ%MtA_`3sl*5@jnEfB({?EwK#0HL=E%o*n5f#A8
z_J47@)_5!fC&K1E<|jDeol}-L-Xj=n{0iwLQr*e$Wrs=cw`1*oE8Xd^T=7v5iYR;0
znG7ZEJ3-IhW-eb#!^5%1=zPy+_IF-M?&;gx!&m?L)qC#7l(wp!bH_4$%oX^B0dr56
z7Ji?xVctxQm%s%f5a(q}Ixq3;l<4cWl%Kk-h15IO863842&{g-ybY!L#X`N;VCo>Z
zoEKBXt_n}s6Tj3;`P-=+mycJ9_d75wey-7$)pHLTuT!J_C?j|>!p#i~O5)Q!R}7-Z
zYV+;;L2`$O(n#MI@)O<k+}z$R?BCkoB5j(N{t)lkM{DB{K(eNtDDDC3rJ8)gY*V6J
zFls)Jcwc<Pv3GRUOf-^EX_j8;mgZs8@l&nRna@D3)iIHtCrRw&pl=?C{HH7;85y`8
ztR}wyYAtbAD)m}$f=u1ppDp2=tjYOA$v?A#ZAEhrWB7)}Txlz|buNb_{t@bVTtNmT
z)ZAR$Ms)@OtMl<gY-cT^o9xZFyL66b_Wpb^cOXawau$uShjx=YRDT~Ff}9Ns14`&-
zos-13B=_yFB*y5_xDNbLG#14E^bhq_j_jA%hy!Pf?nx_-dovIaYlG%0{D@%l`V0VZ
z*SRRtVZQ&(U-oiOHR}d%4NkJ{kl$~`?Y-vS?Fxs0YA!|Y9No7`Hs7WJCL}Rpg$$ED
z8OH2&?*_7-rNlFl{PjN!MJp)BAbmChW#U0Rq381i>FDigtN12DNH04KM;vhRBvU^l
z1cKv19I7fWWEjQLRBo9r{@ZF!?iWrZ8@UQ)s_l7XoE#M&OR(DptQ)L%EJTQ6AuMpN
z0NXc!8@G>StdcaaK$LmzOgvdGI4rbQ5*p%3hsX1@loC$?6J=K-H~1fCLgRw#tQjS2
zMRl4{!5L~`(N{E}$SUI{iT{Nejg&7p@ZO<{Ahf5HG|)|@EVO@NKp)J1^mVZ4s7jtZ
zs^7_ej$pNzs|G!Oa+r`l)B2f~Fyinr;Nk)dT4A$SI9aircYZlTB?hUn$73Wij#JDe
zto+jwr?FKqnJp(der?ExM%Ood3+^nNfr*cpm`j2cNQbjlB+-Z|{BS5yF$kkKN8k|(
zJJ0ylcJe1VIzQCw;q!#CIlQgE+5ed*BV=M0tse7Kz`NE;Gb5DdsITtlO2ugDLq{}l
z_%PP+O=hUb*ha%dpL$P_PmT26ciuhYCo~WmJY<3g^JgYw%szZGkx+~wJNKkg>2JgQ
z-#h%^HSWGj`cVuc78ll&n``(6U9c;f1&HYuK{z$cwOc<lK{O}QRZp<$iB`l`wy_08
zycmzt(n7lF-?qN*wS~GA_5lwwal<D-<hEdYkRp`8L$tkZuE@iX#Ww4(K;w78xe$sz
z=qXkxLR-};%%I9^jZAZaxl{#t2-MSDRmQaug0Ilk&}5S}t*H2nT3^%S8fpj@=}OpQ
z+_Ht%*&2(E!fAsKy7v^3i}x(lq)%c>^ZZ!Cz;E;I^S6|jD;x4{g#&W8d;bGKd9$?*
z6<3VagZF5OeRPhbs8F4;5i70)ll#OZdPsRuNLxN;g=6ta<3Ot@Cg1@MY6UVSKaZIl
z;mGg&R5G=-?HPj==Pn27PZOUkXnUezM}zFPTAsFyeuYDq#yR0=dA2U1!n{fqcZvYi
z2;JQ<l59Fw<L>%kXFiCn_^BPI6W#>s?-z}!9IHgoMJw8ir$1_P2C)j$udk0CJs^D=
zwfp~n?&L{jeI=v>IC%cAcz9A>Aa0!nVe_5-0lHGAP)b@jemBtemjh%cCSk_GMw!_6
zw~7ie^til>btFibc)WzFqN3k_?g8I1>!UM*1h9<lF?-Y=nvS}kw-XGw8{*5<f3h$b
z_+iX)f4V-!tCJ-bk9s~)J+*s4K#+m>v<)0HA9;5N!A3CDIPvme-+OG!Z_um?m+iwq
zf7RteC}`a5WeG)6o_FTP7rY4{mTx>VMz~se3fJ|Q`Ick93ectaR8Z%RArAibbnm&M
zcTMDT8bqGy+8cvUafnb-#@ys~(>jZY6zk3@YzD%Qao~+*oiydpS1w*zGmuxt5&&dR
z=LaU8=teXmS8PF<7bkDbZRXCD0vN_Z)6z1L;mlD-Bt*(}rdno_MyOl6gx`6AD<lXD
zA(C`H@(>xQFDmumI?Zz7wYF|u)lQlXP2xhHvOJ9&w0Q3H3}S>F-Igz?+Af`xQqoe%
zt=&;2t~#@&n7S#$_F2>A?h=#;T!1)#L893OP!?R3FH;mo?-SS}-dJ=u_23ogV0y4!
zUEkyLo1-uKixtrhLes-*J9V78F>U!Z*#~@o>ANY%U23sAAcSmN!JUWn5Bilc+}x9j
zR9wU{jKM;2i<Uvz*1sj`D>;N-sw+1Al#}x?p`QE)hX3gc(Ek-uo>bClbV`7o?f(+e
z5S4L-eioF@d(1D;O#c;I-EtS<Aejx}Hd3TUb)P+uZ@`E<R=N!D??8Q5M@~B7x<9X5
z9&x+e+i$*rGl!b}PF|ttt1nnW5tP3NL(&D^GBm91w-3$WQV1j<{|d>$cTfY7nu)iD
zEuVS524#>)0ku*qEkP&qV^&cqLdD3OY>@mtSj3!k&t*RTC-t|Pm$JGHxH7!{97-bo
zWPNx>_`$h8H4FE(=;oouMC6c^x3mV-)FmD2aO!a!HGYoPCBL|-%X`SovZS?nT_0T!
z-I@<Hvl9mfSAGBH4*yN&sB7I=^g<N$@?)^SQrGJU=G?RmSu6iWDP-_Zon)I=y>@>s
zjxY?>a8z>ZVq<0KGzlP^vY72bTH8NtTPE@31^G;{iXVKTj6aUrLqwyIoX?|2+JJlD
zbCDWc1%O0b*IfM1qyFzn^RhK0z>Y$bh7|9kMr{Kx$n*4_@Ba@NXOf5tWkVh+IUETW
z?v&KWSKK#f@Up?{{{xz&%tGA&M=%46xHmM98QJrF3INOOE~(1#Na(47;h59_CpXXk
z43MgHI%3H?P$xez-+rCSVr>WJ?SMVwIawI7_4htC4<~?s1A7(%BV?I#R-mSuGaTw>
z36K2h7wdrIlf@M9l(O^T=pgj%D){;M@$r88e4X2}{e{47+iViq<sYzFxt`U~f_&_l
zJNzGV9UvYG{(yctKHhc!{2Gd>zQI7*8;WTop^#<94-KbNUfvDApcNG8$Nu+fL0OW7
zRj9x@SvVSeSlVH627TpTpEN|<TlUh!-IP;JzCOnd&5hSI{|jbKRc<Gw2Do|u=iZXF
zI;s!25HA1Yv%mOeqY@DjinWZ;N%R9xgD}$FsnjDs0Q6f*<z~Nz-mAeCGzsN}-Pwdx
z0IR3<ZJL>K;oIT$$<zP6?d#M1?ek7A-~$%x+97lO<IZn+DK0JL_-lQ$4JQlU@mS?^
zGShYEyv7&s8vtqIuG&btBgd!@`1txLdRwm)x@pmpaUW-zMQmp*6MCi{=tWUlb6|}_
z`e%y(8P-N*93$dOK^N)0+^T91%lw40#5X<p+r|HWyS#!>cM*}--!;p6SwkOG;yV$R
z>)e`6p!%|)drL;<k#vj1zMe*^6QMDdIiOhUrtJ-Yh)n)~kba9zsVb2uW4swExtt;&
zar}#*sz%8_0(bnc%}bAW_;S0cgQxjQgZ9y?TrpjGf2<z2F(~=lE!Wt8fj~U$>Z{N{
zKCm3OWQ5Q~tjz<CHI{oJTPm~1HIzmpg2LR9t?21x!jvo~LE5z?9|u0x(}J?KWhKc|
z0`42UzQ!mCoSz2(2}NAWVEc|;tZoi&ub5qa{=sgx<y7zFZ5}_zAfh|m0#)uGtQcb6
zbW<#D?V+0GEsRW~>qq@!dZ!Y)f(Jo@iSR&VxKs|&&n<;Po5<}aDkV-DjKN^F;1FCK
zn=TS)LXVdqi4w~p!Pq{vaFM?`Pg;<wUhHnuq7R<W=rRR3A(Er({$3z$yAJ!c0^bmo
z%|rv;p>%0~aP@)N)}acPRn@;?ajzK$@+gM{I%E^+C21yf;F?m0^fU%^e^C4VfEPi6
zl0<NR>`h-x<mf!VRfDY)i}JD-TeG_<(@-l`;3>o*!Id-x1`jgh5SSHwJ$D=e@RQ1_
zb{9|VGKzpl9<ize2$`t(2l>^=6pSCx#vHI1KgM|ZB{5-&1|K68bPVk;Sf3!Sq$Z5c
zls=|+`UO;9X3A>ce%sKrL9$-GFzBb;zpZMCbi9tz?QvmDdg$MG#YBcPR9VGiSw>o2
zql`}mwKYz1(lbX|SJzq1Bzx3nDx|xeYn`I|{CokVBBUNf<$??IBCl!C#CzZtIjIqt
z3iR>5%L<zPd>S$_rSMav^*smE%=l2MCz{Sq8|xN+$ZSq?QtR0&JPQq41dD85vq3h6
zmd?TI&J|^|LHA^*KwFz3@!71z$aiEUx?1G=)YfdR{RZ(x!B=ny3aSdE{MFW%g|Rg5
zzy${IroUOkS%A-q#GnLMpMrR4?+Tt35L5~7sqErP4)&=FN<2V4i(QdCl3AFjvHQB5
z3Y)fQm)qB-MMCwDphoKPksaWIujs<#b3=k2O8v}h$-sy%{bgc{GA2%*Wyx!g{%2I6
z5$wI-(2J*1uF@fVf0FGNjLWrcB@$D*J~J#3BI>2MsC}t_tyzuBOmBSy7RR~5US(EB
zs#l!|k}$8^HHSH|)%FyiNn&F&8*_W+VFY&~OkY8nQS}-p_LwM~bJOgOG;qIqVOXPn
z!H7OOX=*WyyxuTIpsv<ZAE|<YTUt(WyRYDBW2vn7)jFr{@!{0svA(HcvK-tep&1UG
zCN6Eb>1044a_IM+Q#G9A*$7`z!M?S`1wa-Tj1b80i&Pdg9;~cW|HFO<<wN`R))4kL
zXNl_<cB<aE65X6hbcxK;(zv1~zhrG5_3b}4yE+%=n4CDaKlbm>5KXVaA6C<5A-$Vt
z(lzvM1)_@R(Wd8?Ef7S%Dg0CvRRJ=<+FR|1Ch_!AbFR`xvx?Dj^@n6rc7dP<M2i(9
z>my9l3`4$uaY?KZ57^dH{5?rI%gi76w6(=`WYN|4eQ&W(2IGkU-!0i$C=H#p%nHw8
z{4!+E3fdQX9du-~?bd<xceddo>@;j=ea*bBba=RF=bWZZ&KB#dixS@Cb{#r$&Rm{@
z^LxH-w08_g*511WvG%G}`p^WZzT`|p;00TwY^Le|@b-TMi>e_`m>m{rpkg;Q$p5?K
z8(i6^SLnZrrPNMH1;NI`&60#5BM4}tNg!Hwd%Ilh?vp^Hejn_pe_;3DiggCy$CdEB
zaS<uLc28jTE5NbaSQ~#0lgi7xLbIOHuIyfehTa-R2VI?><>Xw%6J?_ny<2K_yd~mn
zAzlM9CG(<@qKX+TP=BGwB})Y2X)d;z3j<<vec#B-zJ&VfJIBDPxB>p`9zcl54CC#z
z=FX}8Ln%fTMg&~aIj2T2AYeW216Lb=(kvP;h#;AJR!0cI8zJw@9wz{|9;8*g<!X7c
zpQek*?10kd_X5e>0?c2io&@QeGcK9W{{EYz66<(WNY>HaT?9QXRvFyTu=)2c6iL{>
zpPMzv*P+D32J-4}%2105&47o9hZ<<<%FC{l^5h7ebJ0#cdG*vt2fFiL6!Y-qtr;8w
z^eo3@`f$ZRvS66tN5#sOK1SJ%`Uzt>N^J6pzc>tb1fnZ`k{+NI{caH0tmMVc<(<FZ
zO(C-}^ryhhOn~<w9Y!f@UTFGd+7{$)2M3n!_;X1UMj-SD81-?uJ_Il|>c#9hC?#?4
zvBt={rfuGfwaFDq)U~d~cacS;pVi=SVl!mR0?P&I-|)AC#pkf2sD;(dQKRq;E|iE}
z+od%H*^_<~N2!ZA*{zbf2UKE;P-;%1R!)3WApZ@N!Zn{~`1oaF&@!N};gA(<5hC)D
zJ*Nr+kNbDI09}juia0<Oy)r^uDRO+yQl<xjK-D41R+uSZ5YE}gHEz{sY+yZlK1S-`
zX<bx|$|NKbn#$KS2L<l?^Fxh|)oLh^60cEE9t(DDZK=ulGZxi-O;n~zj|04c>QF-H
z!jA<`u?Z%I_$F%z@^!zgWt&lnb<4!!I?eN3xu)vi8k_U8o(kyFS^rkR{sjBvz0E$;
zwa?nSu{5Fj6UwUr6PIb)hY3a?(M)9_h3dEDT<j6h!@=c#L8*wag4eof#W;Xpvll`}
z)hYf>VRCn&;@>vkd*yB1>gp>qI+}W((Ade;Z4~FpN6j(Q>?v}|O4T7gcrlBY7+8`M
z>Ffy?Jn!xAeE{%!6#IcjxD9zb3*O@zC3j@QH4+HA_+mw0Ct6qNV6ZG>f`ZET$;K_o
z5#&%8I?C(WWmq+}&gOOCkr7yP4i>BxWY!j|`hx_*3<pMVX8c%<m`g4lg^yrlKTaKS
zw4J9#C_#PfglQ#U$=rg&Jq_-0JOww)?}z^r6TSN@@+Y7c*k#fZ^Iet(RJpFVC~I_e
z>0F-4GM_PkAo%!HH{1{iaW^S<n;ZA2;7hzJd(}+%;D<nZ8RH+yp6r_7AEx+Jn>17%
z)c)w|O6<>;?Fenkb+vS=lA<~5nspIslCpAf2cJBs8&aN%74!swiNL5iP87~VgPeRx
zdnkM?9s^W)o1nH9s9_FVl_W%c<z^S)(wt$p=3VH=N)sonizP9OHm`8E=^Vw|`y%~u
zr$$N8Swd~>)eRP`Z23YV_vD#AShN+{>`NdV7h|&|c?($mY3fx(sY0DF7)B(<=Z)l{
zs_3*a4&+mdwdmYAF@9ta@Be|}Q~V{Pr7B@ZbPMR5%Y{Vj2jL=9IgFG){)u2?)tcu=
zLrLU{lXqt8L5NU$FM~vbN|mcmBMW1f2yK&-j=Kmt?PCE;p-HFsr!cCfK>>jTi3k!=
z^*e#ZFPpd*|1`}Rl(3rAWu$&BNHmh6oK1csb#6i>-%MPzEPN!Ch*oma>PAm;ZvLMR
z^)7%QJ4ccR(%guwq@58>@@qmhkPa*d^<cMi=I=j_<!14?cqcr;nM~s*yQr)_mZsK~
z#FQNHze5D2bV8;i*0P<^u)DpL7C$razD-<r87URZSfsCp`V=|27?~PD$R_PpeCU05
zwBa?yge3d-1P$Spw7{)Sm6$<j+~)B2wyOcKfqB1*b%=P8K%Tjqhb<>M`x82rbF|GF
zQS1{VHQoFqU#*qMgfV^A?13uAFTHVn$W61J3q{rR6f)6-wlK(2HK>Gw4vT%O19?9!
z4S)2I6)hTUiga^^mdaE4zKO-^D75l)S;?cx#0oK%;}aidA6`XpM5!meu|ksKv@!sb
zy((BlPd2}-9!p53pOO-^c#urFXrU299iANeU{7I0$-&8kx18+->_?_e63VI^YtKSi
z2B!2;&yenvuB+Gqo?{r5j{M-8Qa4rWSy?QZJg#$TP;>W)u0B0QNv4KZ10{(jz1b+@
zx<lQn(kl&Jb*O#0Oe%b>x)Z5x+W^oGl8MwMv+APMC4a#QSul`4bwL=}?ax}kc_1T^
z*!7yBVP8W~ed~t*>QbK7AWFBjqL$DT)yZn6hsOIMSnD__ZTs`@-qz0w<<|+D((}WP
z>}KQb?V_q>Is066&1bKFP=S`Wm>iCC1YeTn>BkCck1YlC_FW6vE-7TV7brkDUxd8a
z!QW!p*|I&prjNO7fo0pKst$3pa=wtiCg7f61vzfSfc~^wVS#%S#MUj<a8&tv>0iau
zvhTF_WNQpC2c&<ht4{|@#s&pp6Xr-R#GKQUgj@%s=w^j#28xHCE|Sgy%W^T#GT1sc
zvxz^kpA`&?i!`d9YEIbHKkR^Qm3PVD&*_#0uBCog#wN`V(Fhy{(5RkG=~(9GSLaB$
zhl*GW&ZalU+4Hv`$sO)mS?_j`&e<C7HTUMX=8P65#z%YItc}_;%9jnsDa7@x)Pm89
zwXEjP+ejkLi^426Q>Xe>4ZXeZuMPl&^O#Nl|4wjI4dam@Q%!!u{3j&f;AT&<RuKe@
ziZ?guBJu?VC7$lw>!N-E9-~NKnTMy`FHg7zh6Y`L{!*g8!IzeX>A8z@03t7Q4`cNa
zC)WDU0R6AJ4Q3&JhTMSYC{A0>MJ8nC&WRGvfm;5JfNu67!`;K^h!jC;(`f&oYtZf<
zJEs1utmddVSivG0_FF>Bkb3>BerjNMNHHe@Y7PO?Aoc`mi+qF2(likBs=Y0UpmPQ;
zD}i8kXgcQ0zM&Z>>v9+NV%Oj#q+%#?63X^!$Z9A3wD~nSGgz3xoI9oW*2JchomxtW
zw%d3kLz4TpK7Oq}YC1fM<W~pTtr?f*J}izD8C0OG2GrJM$7O0*Zc2|po*!TuUN_F&
z%K1x!=Jjr5{Vv8Ck+ROOAHBgOc)2-Kn`x=vL4DyL+71G>LWBb%(#W|rEGI>uTizd#
zx#+h*SrA&w^-+QtpMNvH-fI>V*j}POW@tA=Ij0TJy6%X!#j?KksXH<GQS-MEH6+>t
zIww08yWYP|y{Urc&2a6mGb@2Z5&UR2!ZI#NIKkCQ&FHZTR&6{E>>;S&whW!E(6{=b
zG!G}s4$O`I4uJ#A0e&$etMefRoU|g6cyt1e0)YrZvLL^IV$^nKqQ9d^d*&#95t=2?
znacW(T8bz;wV|2+3nAjTOCUm0Uy(uE{D)mlLKU*%P9wX&>l~%QbBzWNR~U@vf#bLA
zU4;_N7>zEiUdeN}cEQH?i1dIy$;+q>G{@N=rRzbp$8G3g=yJb<nTsdoaLkvw<>`g-
z2ph=c%WpYDA;z0U)Q^~hB~sirh@015-%6nkhV%!o@kHby3f|?DdCab06w#1*qFAcP
znbrL(A6T`3^E8N1T?YU@)$Bpsi40LePg!5u;Gc;M?e9jhB^ZqQg&s`!yOGZ4)s;5T
z+<GBkRdlQ2AcG9jf;wDssZ@z}1L{cps~(9it`LTvu4{vk&2cock$1HRD<2fCSGgT+
zwCv=o99IV8B{V`u2Jdy>_0eMrU?Lw=3DIULTD|egICYX#Q%^v_14z+2e?D<-LP+F(
zW}kKou}2u1;Rt8FU;EFV@*;!ib7z)b1-F%%aL{~t;+DTr^u1e%%#3gA>L))2li9KY
zC&1zp@5j=u(jq8@XV2VvJq7t93(H-UV)op}=q*yoBA{;@UBw0P)Cr6CU;>RmXC*OF
z2g4EtOZn|mEmHwZtmz0Y#B6huRL-=(y3xwjwt^kEbtcxr6zFF?Y{{`~vk=Cs8yLyI
zR=oI*1uw8J2Jc{~jF~a}3V_Qzmvk;`HS1?i!x)Ukdt#{td%gQuxH*=~8rw2`m)k%g
zZlbSj_l4vGh49Q#ff<)OTup93C7y`oGMqtBmstMh4HgiXlh2$k{OtTV_$+aJ(dPy4
zT)c^!sDRrnOLl0<;g{ky<(Ywx>JT0@u!His(b`=9VP-nOvzmXvFek5YWXXMnA{-)z
zi@|v4zn0b<;14p(vFj@7dRrXt5{;Yn9j^4@KCx$4dgnf*=5E%$GFzelxuzOJRSq8k
zY7yMMH48AbO>h>KyD4QjcV5TGv^#JK5?b27Ix|CLjP6=Mrh1?#WPrUn7h!foe9U~5
zX_(-NB(!wKN-$!p_*;w3LK9yf!IQWsaGc2P^UJMXKKJ?GXy9b%uI=9|2bgu;%qjR1
zM4qy;Tf9KULinwChwh-mH;K3{7PMaCP`ZxI5(6(hVAAicA}#u1@cVnMQh_D*;jeBZ
zGwEi-c=ie=gf*pvCihe`JNuO@KKBt1m*vqGrH7P>(v3b56N^*_d?C#h3Di6`r);yy
zVOG|}n*uZAc3YACblXodgPnQY{$OQ`KGXpbH5J`&tIAQTzX*LhQM-PP+Lh+V4qx#X
za{|*}1jlfOUQ9<P|7yY(7&c0ybb4$bOIN(f?B%MbxOsLsh)&pJY`9mi0Q<(p0k!~m
zPdXxvl))IK;x?Ur?*Y$CDx)fA+r9&1jrMaI3h7b@ahhJu-ajFJAPzyDH3vvaHB(JG
zOQ`u%yThooxsqq(hf=?-3!&SIWD^ZjHGmL1^O3>8NDKzmU6xPoirwpv)0|wraH1Yg
zndkC6@6PqisENDzyL;vvkKgXOyN`!~If&+rLrp4?H7aV9PtaxekL*IvA90G0->zD`
z%zAWzOC>;n_v`t5L5LXe`s}4OO>81i0kSHz)>--W@p&I`VEAtj&jzMi|JlU@6kxj&
z3|0DW7jEA~7S_FZ`rIBfZdzr=@&dIcz`6sKiF-1|2RWn9>fR)y>d-*Qd6j3~=pH<4
zc$+uMkwoHlF_#y-^i(DCUhBoAz}!{bBP>qnC)|;V%te}VjbA?B>_)9u)97r!e2Tbz
z&ms%Mgn!^WgZ_F_%mS%S6AW3V26i?mlyybeK%}K8l?pA@#S_`~Is|aN6kmKM-#@-e
zx<;=*c0aMO6YEgBCAvMSg(Rn{H?iuYRcaN%h*;Yl&z-fU*c5#Au5^9%Mgvsi=mScJ
zF%^9DR=XGX%N?IvWg^2EtDTniWVQ<iL+(Q0eVHpbZs-1`yE#jb*q%zx0&t5r&z)$r
zaJSiG34)cVjraS9BN|Y?6$Gb~zK0`jda)B<l?2+z;|WKd1nIt`_q7Dt(_;yzT`q-?
z)Gf@oEyehwc82MVPCZ^VY^(3;mdT??C)Va{gql=*c{OxNkCklN)AUJg*usYDROi~}
zY+d^48|O3#@QI|zzwtd;fk684^q@qa6MaFAnSju0gMu@8FTttoe$eb4+@VwHAJLYC
z{xFNwbX~i<=P2ga25O4|mx%p&KUg;_8=;(pDkdn8X5=G4H5eP1PS2eM{&@x_WitnE
ztP8j?L(9`Y;WtEi6NPTCyE&tc?_0gX1=`I}q_oj;2zvJXjS2Sk1Xj<V@-%d62J_p6
z#TGG<`xt3RD}0o?yvT-AR6|AU6iKBg&`uanN8Kk7pN^47`n_8!3<#drSQmszX4xhB
z2Zx|h)|3jD*L-)JkG<OUQ~$OF?mF>r(wzKTy%q&o4GJU*wdabW9)0hndPL=31_Vf(
z#r^dPHev?gYw$@;04n{(YG^i;sv?Jdj|kO;<V5EhTYP}~8^-7(SXgf5LQc!blu-K9
zlDMG1{8cTXZg`%BVESb<f`{_3W^g!WgvN)aM-sF!qJI}|mDp?3;F^m7ciP`m%U!8t
zD6tn#h^B&;?lS>(40tNL6=}x*I0KiWX{${!OAhCOATGRvc?MpQMe;ac{#(sq`(M=@
zfP;<ue^%iSb!6iRT2Q->H6Mu6u(SuMS#Tgg$w0elS2K5iTl_4LB^a0@1%w!|qT(E$
z=(Y&Qox!X<ieG7#q6W+3x;`3DB#Ho^+xV}F!`I=52U7oTf`IQ+Ywu?R=Cw*I0tUtB
z1s~A{{>^S7dUI0$AeAd%{?IHrAFGqZ|NI#K?BP=Ftx!2skh|!{hxf0mH$=<7Y#l!v
zk*Yu35t46d;)^!L5*$zWUHb-^7H#-3iA1gJ?%3?aLj3+MAFtMbMwoP}lqKZRvNfVx
zv$FI#L&?%*&rS1x$Cv8tO>JjzFv-wYiR?ptd3AOKI)0pX?7zwZ2`>Zm&sWE2ixgc-
zV8sJ?9l|{D&<@-wW{!&DtG!utw$w4Seq7y&Q*$+)aLyi#`(OY{eYJE&C3j2(r}6`o
z9is48jD)U{-7KV!cfR`z_Ut5<Y9;0#<itRVxnDt+<c3N`KYI&~6&Gz*3U45TWxcF5
zY1G`9H~+<F<!u)5Ss??u%++nfI$fa`Wez?~Nr<*+20xd%M#IP~5)HM=HYzv4P+QBT
zrvMe+=zZmD9s9#GG-7ZzPvv)IE0qrsGgFBBMncl9mop)et(<ZMj%VJ#IkVw%fi7b@
z%Q|~jhgDoYklPap>cWvSFkcng$eF~qzNEkFb#23bCKgrz>n&fb7=PjiY}2MBO%-%g
zT}Inagn0kFZTWUhLZLd8e5Ajb@_IgA#aAA(xa=dPh7a`@EQO`>HdUtPtNHYE2ykJu
z*gpm@nu?^9^Q9Z;AJAR%!qI3dNuw~c#S#*paP-W<(v=(O-m`qmE7LdN-+$6<dK|-z
zzF+xYkn<S<dSXtahB|Iz%48f&Ntj+oPE<xeZnDbIM`@#n+Wt1=_wJwN^E%0?ZlL5u
zX-CV5iB!S3r1*>zjo(t0xme=kP)x6fCU5hb=7SS!z-v+d-83`8e<`NBshH1(tImEl
z7~WLoNh0ax>U>tzl{g3tO;glK&^^g}i9HKay9iPR{z_M>cE{^lBSksStyL4XP$!vT
zj`;c=G1|C~^(d&azKN*13z3ej%{*a-NM4QVhCi}+HQea<F5*0SH)s7Ymm?;1fsY`0
zFEvwp)WWTgm8&8ICHU&9K+3!;NfDsC(`)z<_ebQb@nC`OSVPw31n1(eCM4svM@QY8
zsYfsc$U&c{H%y-!&L|?RdfKCC@@w(?7JF+i@+O`;9uLmpGDd{MazTnt1ZRJbS-Aa3
z$c;NROgQ`gsg;1uFzfRi$NVfL2+T>JFn`-+-?pMJ;fM;)DdQb?2m!CP_OHBktr=Db
z3_F^K{%{#WOieLrpDpIE=PHmplw9Xu%^q5Sdwvl*^C;eYJAE0N_4i3-;MMK1N&`Y&
zxoK?{hnt+r(Q_MRX(Gac9^+%%!ZA6ZYpr^zRai>`$`clgA9p*0hOLm?9J0uaDdO7&
zMc5parbYg$8y@y@$<b))A+h~AiX_k|r!DDJQD-sUEYC{d_|Rnt1vcAo8b?2lz<3Xs
z%T1I`TBKtuba1DEf>#rLC{WSA8jgNcGjbO;Br4RYRq~5h4V`3HHU_UIEmK5}4y5^P
zvrs!2+ce-Gu9`j?_kKYyhIMtRA7b@SbbHiHO+IH%|L#d8W;T)bz#mju!bCN@cP!V9
z=ArH2$xhf3BptGfo&T8AO)CBv;EV<sVQm%ogqrShOd)EfFq|@I)C~$(_LChDmsTj9
zygxt;4tdw{(!A;2N}8@UWoxqFy0uggPswYTsxVW^_Fd+*I1oHvQw-MDILK^4GZlW6
zQeePq-;L>%cl*{nqAi^yUaK6;IEU1^xclf*cDS;mCxrTjO&p+#HPbs3K&k`qggdB%
z13towcUrpT(Y=d}Y9o`{#ZY)g5Ct;cgzcjb1h(2J+<EBJz0>mIx91n0RN(0Cm^ix=
z$5|_EWfdl(>FwR7I=h1ASeuk9NQt6i$)J8)JVm`G$j)56a^t<_*OHQ;DqR}z{Jp|@
zB06_z?UaaD=d0=cE&2i$bTdGRVx%SkODY!KPvY^}Oob8@6*>A9M~!KPy=YF~MLI3d
zb0=k#Jo{lZYm-o6<Y*i>+UX{I$qe+P!NY$CU1igl8w!v$h63`$@~7hp#J|?_&1iFq
z7_gW(qe$54#QX2tI*(tV_h^#$Pw|2f;4JEcR_&a@Cil6R>fwtw3#NfC<@OQQzSq(u
z3wtdBOyoy1RP9c|=M#Bp(TQS`C-x0Af2Q^tM0xoiSAl*W(72lJySp2rNo^k9?tdA}
zkdO82->xW(%AFcd-`q!k1f~A~T}SfxfRZ?MM(Y)eQBE?|iiIrb@3ohF+obI~Avmbs
z6e*P<sZ<*{^t#|l;-LqIX>vVyt=l>M7tFoypwt*AgzsH5{Tp-|=3`$>LFK)8BXr4K
zg77;ViC&aNncSr0JWh67t2{a6P6gd>^&eKYMnJ3S%s=*<2qfE53L2A`eVa9{zXmHB
zH;Yp5*{99uGIz9?-J4l|OtO$(f%}(V&Y}dJsY+_OKKhPpo~Qt2eI|a3VSbAv%fU^L
zV|783vrMS)3-8I}|Dw}Msunh(xF)g$dY*L~i|+%vQ!65hk~wCCTjbj~8#^hXscfhA
zvvFG%YHcS6gzj+9)ZCu~CP_CNU8bHi#my&8NZ4<d9pEQ56}$5txJ)}-c!GP`-)twm
z6xoZPXnGYW7@>ea{&Sm0m<$g-JA0HdRj}m=JFiWfzJdOD4;9VZaPJoyz61nP*vNQ>
zkkB3<{6y~`*)Z?;#<`MJNP3a4JwKjajrjgE%W>N2dIo^J6F_>CJiQv0{9QKArwz+J
zIo(Yw{r3mIiqH_dr<b30R{!)^qZlsBk8flZf?JiDzX#ABf;oL@;rq@RIhJ`{#Fl8I
zaCJ1#hd1l_=3#|#L7_G{d?#Rpy7Rg`n|&|10wZY?g(@B%9J%i4GA4ovC#Ht&AwXfS
zA;XKRn6352uB&MC+_Ce{BwNbrS*V=rFH-TH4veI@O5c3%h}{!DWKukG_tN^Qtt3=z
ze*LLSx(>7$%R$!3c5(b>vvADA6%ccI(a$N9;dokEwU1@DAblVa4<I9b(I+h}((O>R
z-8Z^Zp@cC+5ccRKb$eiM)X9O%`=_`MU-FaLDwerq=hxCfNIc?~dUo|>b$Gxx+9A}>
z3&R82K^^DBkBm~A*ongpSv)%CW<A-_P4Xz`02h41OHO+g{(SbaU)sOt3B7`L!zYG?
zKJb6uX2|{TFb#~IHI<bSZlIyXA~!Jg6CRE_$?q5)a9!s@>3-JSK}LbXy2gzwo|iti
zc5rYTKS0q=b}gO%A@FeRiH#fyK{|$(-fl0MFT7XBY~<Nfw8EYwxAT(?`UUf17dH3m
z#)p6PtJa)bNScZgc?e{ksJ2Bl!~Xf&F@z;B6<ai90dK`k5{3YjAOTra%MSC2?%e9j
z_}=&(Ncn8NJS8?4Z5MIcCy02Q*o)=~fvpbWzF?NzJf&|PnSQ+zjB|_7b08~vcb%|U
zhIvZq`VO2VO5NOjTp^)uIyL|S$51afS*G|*SU7zocfPu(K7Xhx)oFwjD|Gns;`jT$
zQO+NW1BjsePepcmeQQQUd8QEq0@^BkyYqqpJ?N@m*1e2c@D~w}{$?lJIKIO^ujo;c
z*A-V_sR{|Y`4vr{IE-Z@?q&<X%?qWkyAfIpY0!khB?0zMgJnPH=hEVq53TxX>w{9l
z5)MP7mW+;db!D~Gx<F#{%2y=b1<l>gy`TiCt`0#1&ylE&{3*J(;1H|4vTdpkIXfbd
zwQt4$oD>pH^b$cw04X|(__gBqAt@jqBD@k>7tSY*_57Pc-;PIk-22p4@QAaLyD7`%
zcdk{NvZ5+^OiII;242}<${ZS=<wV4vRVmeaIBUfBGO7%wHK*uA<pkrOnYqmyzihq9
z3JGa7n9|jc303$eI*g=j1(F9=T%N=M6~B%7KZAjGS(386IUY!_^lb~>lC}yhi_(o|
zik*Ew0^FlJS99prk?pPB)`m~ajc>hCrL$4_Of`7%s9rC^8^@>%UhoChXZXRfZT1j1
zmqKkzf-Cg6S`V~M{jq#2G0N*84W~_uG)845KSs#OdkDnHp_ay7%l4s)UVSnFGJh@L
zXOIG`y&Y*L!#>07`=8-feK>ld<4o74DRyxFR1G*sbamp^J0fQ+m9Rh%RmnSUAtBhO
zqi3i=k0yv5XdY$ORsFKGg<&^bPV6K}Jo*P!!Ve)e7R1(1`myj|{O751->Bx_&l2-v
z?@rY=?O4V%VN5l?CvCFJ?Kk;=8o#MQ4fw`7>_d4ywCw=Q*07E8mt31`lTS(WIvxnf
z47!O55xS=jRGcAQk2+1i*>)JM8zGArE>lp647P0hiMpGzi%mHc54*jV6K1Kh21m=c
zs3$)ZY6y=U*ku@^9^a<r_UxU{Ck3^w!z`pbLZ;&I&wpzSBnEVsPC9J>U<C8C{I;5E
z22pb^GQdKpfVT<XDP1mYrZmk@2ABK%!9j4%(D8~Mrkh46Pe(<^rjeaiXu{E^&o`2H
zB94r(MP~!-eazYR@co?88SQjx-9-A|&_!7cuf>tb7`?S=s;aV~HFJRK4v*1ZwO2KI
z-(|3aTq@}}(a~0=6q*h&e3R6Q$ah4)Ad~#b+VQl?S14#>`nS<gFWQ(v4#i}`uL|yh
zL(1P9R$~Mg=|njjEAbt~QkFn09!V5d;1IR7VaIG6sU-OwLt%JdUp^1Nx)AeoB0$pB
zhI@xCo#n4GYa2~g4)=}=h&(8oweh~Z`tZTrGY%~M*J_i8tMiRbCmb`shB6NOkGIeY
zaaq@}Taw?%ZMRPN`U*sLP`v^3v=Utc99WajH$1YNN8Ao!AIz2)q%cJLMxh+2Kd#@E
zn?O^Y)sK?4cX@M7ON9>O5W5=pB0)+agWEKa4A`|?BPTvR0dTE0Ub_D#tL&*PjBtYu
zQkRqf82f)ZVE@C2E$sH7V{xGD|GNVcf|ZSnr-k|x^acXyPXqj1-_un^4=DGQvHE{m
zl!NWRHlJW%0@0uxslWcyUDsj~2WA0>P|RHC{W1RyvNnCG^MAlx{{>fNfNj*JGBd&<
zB+07agR^jRwlv6r<G>&aFTZoLa2tLi)|l#X{x2I0P?rB~5>~4Y-cqX$@q;-Pxd|R2
zRj-{8FBK&OLKJAKu;$DhhysCW3_MJgU?TfWiqbso(K6^R?07!Cf8BdajT`T9yd)dM
zllU_}?20E~)!@hfc|#GLAZqpPH?vgCcb${0sF^9b86g=XldFm+t!bNsOjjt9?XbX#
zD4BM2mj8g-s6>dGB?wPyxC1+(xDT>6N-KIF>}1@{iwf8YSKXJyibHX0K&kbX2J`a`
zgxl_v36f!G!4sRv5yR3Mas8c9bpnePzl|gA0Uwp_(m{`U_`}3N4Hms5p#jT5rs@8m
zCVb@mj|@#?1>r@ykLLd8krQI{zYMv&U;bhjR5$J#bA&A5T9c^9^H^5uW#JG_VS$?Y
zjMu(>yNE#e$apLl%DC~OGIG8(UxHbM8fp*|o4O?qXS7+Ts%1cGNZCpCB~gsqSXxV&
zoOEPT0y?(4H56BplgKnC@=rI3Uh2ugwcDcj0QajU8wv({P8bKWmH<MyEgVoqu*8i*
zvfLp)71}_sv)}^xq534&i%34stVOIlehhI{XceHxY+Lxf!;##Rj;&N3&PjHVV~U1Q
z&91rJRpJLDn>!+n+aXCJ_vl@w(Sk!$cXi07u&Ukpc{@>wcGG;!Oe&l77-i)wuPPPn
zp_3!Seey<g)2zfScpHmIeo)C-8MYC%3HxFnzYb^ZCZq|BGYEzWTAt6XoXihbRTj)A
zFhQV}049j_X4+euT+?}}eCvRZ1k4j!6Ld~0B|+2GQ)Ma3$-zVQXV$b_xs;tkld_#Z
zMWnP+q_tJ^kHR#L*O8_;*Z996khc+?r>*$j4?LbPgI^ydlHEinc0B5TOGo1itlT(h
zCa>;kbuf*@#8*Lw5TS9J35<&AeQV3joREO9TZHNRjMxWD9zr;(+{`UO=<(^>`er)z
zf-M(hXVP>7@UHToHnS=M^X%TuQp$rk;oHIF0u9+nX5Zxr*n4G{Q1j*K6Vx(-niU+6
zC7KS(<jMkduAky?aTNP02`d78Xj~WFQaDw~kj|TJYE7hJzciorh?reY51ce<{bm3g
zG{NBV;=yCdD=Vw}aM~aI8M_Zx740aKi@%fRA!ahg%%lE{yKi}j`d>@m#r9701nB7;
zAZ20>lv6@n_b~7pOoP5BLYdZ%1K2o2-zJ!c-G1*%IRq$rZDvx$HYYv5F3#}k{8~P4
zOxv^O>i_ekyulPQYN7sO?Q4=_niI1~`GYrgb(0^9y=r>B0mjL#>R7^E@6X5gxrTzH
z19!*YjbYC3e|1b@M1BX)@lSuHeuP7C0v_+S(!ciO!DP!c&S{XRzLx%uI}qUsz=w?7
zOcYrP?k&!xSuqK{CzxK>+-F%ENEqyDa1}m}p06a}f<uS4;(MuJ)_f4FsZ=~&C~CYq
zn2jYq*{B=DF=+Gr*fDza9A{Yl6Y{C#kgQ@j9dczcOffFMEosNhS$4`&3X*5{2JFLy
zI+#WFj^!H?hSIk!5!S<Fb;q_=b{!1-gC0?6U?cZ4g5w)owDtsDzp1@=HBt|dHM?2@
zV~jd<J2?V96D%jm_HAV1uymCOpCBIa9V{)(y~+sLNa78fS9^CrE2s}>cXc2}rNh?5
zU65my3H7TP3DR}Hv#mkHJs@~o1dg(yikot^nGY|{R<@32aLn{Fv0B>Wr@x}R{I+j?
znS|mf5W!zdUwg<(hL)LMl5rL6O%&7g^lIWR510|2W(j+|CGgG_hM;%M!4?FA<IX9L
zKp$@ANBt#e>Ede7y8NQrVZ@TF6CZ1>A8})ARGKewcy0Y6D`S(c5J%vB5&E2SP}M?7
zY&=g^EVyPKcfrXemIQ(BM$=eGcOP;)`(@mg<^*age1|NT0jIca7`N(({fr1=h<QZa
zT<GS+ntB-tK?bDOjP5-ZN>I<cpT&qe8yXnSyypv`VYNZwN=kajXo-G+%bYZe4@RVC
z5yh8kb<xpo(<z_U36LqDvhedLpE_M;l|IxG=ajmgY*8tDr!h5(A4Z}OCzT@L-C{+^
z^tg6V(_u%}dFJ22B?wR{pQ7}b=g*M_i09AgE=@(q)Bq@se@fk?h|^0S*1W{>*^t*0
z=kFD|m6c3lFNRM|8UOQ@r0+UBA$j7FxnJ#=<?9`BSCM@dkk&Q)UI;_fr5Fa&IemTa
zgKxVx<Fh4bk!|xT&$klrOk3<TfDD;j1amcYIgZJ1A^eDy$=y=f7T0yUQspzsI}S-;
z6ZarnBn%MPx2A3oQ3tbCzsx9r`qizg?0W$d<(;(0G{Xf`jD00wJams6v`$J)@;J{!
znF3xmBCuhI8POr49k=ltn}=WEAB0{v#tRjsJ6k*oj^i}8we!Z&?<>Yp9!y#3HiNmV
z<5qN}?R-$T9%qXl5nDl7(&LSymUQ)dM2=WP(tuI#LAJ<-pHWV_t$I)^(sRUEk_;`{
z=#yIwL6s(D_{m2<)063MT5Hs>OAMeNWVqFPP1c9$^mV{A_l?Zh)usblv-aej@-$I$
zPqq~4nS$i1<~e1sv8B}Y1u-L%_BMr#Cm3(9T<qqDCr@yp2Nl?hC-82P>i9KCqTovw
z4ah-9<#P`mLE0YkV-*m}rEyB7?S$4lqkqiQ1d`u{bc<|OT0;J6yAbOCTi%}aNK}`P
zv~0JSg<tN>KozB2NdFk1*1!D5LPzDn-!5b7YGFE9W<JcT4EvhH>S9MeLT)g}eP32a
zhbg0yT~TrWzMm=S?O#Ld9%=s5rk9-_0zfFjB-0$9CXHQ;gY>N!E75t6@>WZWF#k_8
zlq{Y8@ty)IswztP?$WRZb54Bd1YUe>6?j!SdsT@=(8OMX<fSV8K&3aTL}hV}69Y>q
zn_Ms}*F>$%ohaYn<ib|qxehx>NfJKT)cz;BA-#M}0=KbCkh=4pz6Sm;i4(Jo7ohM@
z)I4EQt%e_G9Gdb`J_ZZD#mbnZg~y7ys;PgXh-w%_?izEaweO&3epSa@DtpylHWk}6
zG~=iEw6j~IDr3K9`Es2;l*NyuV;k;JEA8?WwJC(_Dvp#EEQCK=hb=nEl6WC%6)_%*
z@0&I<<9AP1|12b`WOJ9yGzlT?(b22&yqCDZYECbb1u=baKXrX%c2YB9A;_RQ$HlW=
z?^F9?Aw=<_!VSV(!#`lAEN=x^T`rK%E<)6+ZUrf{dsB(xAOwNeGT!k!L72lO&Txdw
z0`0j<`0_UBA{}B2YukN&ymHRY8&rR@=!iPVYA)K+Baihpd3Ci73)^8~_iapWK0ZAE
zrbo%s$*0vxgEEATSM6a5B0^o&ajTQxSYD?yuzkD^7%)JAw|d7+r(8@ail}b{=L%3M
zN|2XiNG1{+lMO8V`ZHIc?O6yf&$KRIj-4=ZF%VzDHrZzAUBc^u_qG_dl;29rr7U*G
zjxNYbre_OAZ$L}@(C5yStH$3bP4G{m<Z-~VeRsjiX~dLM#k<3lJesfrI+abcg!_kS
zMx^_eYofZrk<HwRx|qVnA8oPsbdsOQx<-N@rvStMN7Xk)2NE?~XEL#^$s`lowrx*r
zV`3*gv2EM7ZQFJ-v7P+6-*+GW`_K>Fr%ta`552l-@3VI`>P-E&$YlO+k;xPT*2EM8
z{z(JM!uCIElO**na8zJz_&T#{8!^~Kt;@#;32X#sB`vOn=ApWlT6dSYjbr~&X<;GC
zpst`sY#vKOZ!9<U%)P5MG}Aj{zJ42@#jU$*?Cu)&!#yf%D(#YwTkg`kI!@!TS(t@K
zx3I4~FB4%^dg>@EGjSlmcn0$e7ya&qk2q-Ap2xd8N~p$vqyl(OZbQnxt$}^Gs%0`m
zWO3fc(Mkr}?jY{?Nww_l*8Udh8b{SW*ciJ$I>L|fBj=D;m{`QxkK&jn`}p0boWH?v
zsdZhsXVtS9A<Rnv{;6!niHEj>I3#2%9HIn=I635}4XS^O{e8}vKV^Kf?IfF75gk@$
zID<`{)3>por5BjxuO5Z5ucD327jX&3iA{TPm0luNfsd^GtHyjs1wHkgoE_WF6i==4
zA%a6yAFXqZ(5LIF>nHhoS5W>$NlCE9)Q5zY74}}O<EA1`b-RAzd;+E>W2JAK4^!qd
zTt5VY4&v)@MJ!!inXe7efK!WijJ%s+CQ<9ydi2!>*8{cbDA+N463(Wzb+y0%Hj$1%
zzUf=blc*c%$Q5_CzTVMxFK$NV;Xlr(vbMz!?Z%1&jd>z0>>N0Us|N^Ct=Il`GX9lI
z^9Zz|8Q*e7zP51kaPP&+7COt{Ve&@5eqg{#V5w?~91W{2LWPCU(6>-~c9Mx?dUh@o
zo|1m69RT8M!7+tLrqG+Q9gS*M1>5TDENVmbtLNzpvD3~A+#Mg-Pb45}dH7cZTpH-Q
z#wEz3s^!aBF3f)f)ImlP7KJehZ-DdfEQ~cBD<qSiOb4NHI>CyUBNNV*FWZG=;H=wY
z1qiRMT0p~`H2G7XOb+nCGdD2`NJ;lVkx{8Gr~+)x-D^#Vv(BPwsxfE6-q50?cRj&b
zQg(dE6H`Cbs@Fq{u_2jz$Gp9;MJH5Bokz4LS&$PGcuCsg`9n{>ZEfV=)~glLse{-O
z@5GxJcxrITucghwOQkW=<PbvB_f$GK+@L#ef#yq|dX}d|{KYW9Nt=wgmHakMV(2is
z1q#^4m`GxfF7p3YuHHP6lx$)>s_KU!(Qkv34p38b8!Ig$bweO9ZXkY2P1f?6-cN-s
zQ@VwOq2~=M$d%YkIR8jRMn<Bbbohs+)*jgx-=aTE9AYFPPn`=+2T9ZBk+u65WrgRu
zMYeVD_zZ<bUdKjB9`wk1!|rY;^|$B))g%xk(etM?lK6)<lYn-sRYFX<a<W#%l2*ku
zrMRr!4pn-5mZVcDEp{Qe=tH@0o=SLl#-j(px@rv^R#-uE0*14!qLpAdsj;j~o_UvI
znV@q2R&1vf$!VBTJj%am5kZ(2_utx1NsdLTI_9P`0`3UH$jF-Z5{%+<Qhjn75@kS{
z20=x9DMJhC`~qoi#n_He$wNtkWH|-fR!pO&&yb1iu@Kmc+>{a*79k$U`26DB&eak{
z2IQQNt%kfzkL+irS;{BvZ7gt9hWI?`vvW`db|S>1q0{H=Fvs}L{(i^gong1-@#cxw
ztpqVP4Ni)JuB!FyI=0Uyzw}sj@(e_O8dZJ($uw`fA(;9duSZ8?mwX?&(I-=%7HoU>
zY4k%#zNFYY4mxkQNssTNf5dmKavA7-vM?`uq;(5hMRPyanBSdB;pjsVE*}^A%G)#1
zW_?$&8k9|-_IX^nd%yFI={4MF+BB6*+6WwmZNND01fuy^f>xrCUONPfrU8zH|7>SM
zNEZ}nvt}#~&0G2pm@@i2cyF3$ETyOjW}#n*$E>{z3l(El6r+yXcTQA5!KR58$x%Y#
zyn~Lrsj*_tRh>0o;Jt!>7o>Q$*n+cX;>?u06CEQ=udYu7usLoJX_(U5MMoOKdvOKf
zObra$+zCxoy|&D*Ml}<9$O0n4YZ0(VqN4mz&fC2~5l$(GEG3>!C&FDj!XDRh2F4AH
z!luf}C$(bOk9OkU?T9JW6%?e&d2-ghTa^3<*X;hT2|be*=X6V#A&%t;iFWzuBf$tK
z*7pq`^dhw^&~u%$4&R1Qpx<K>Y~FU{*5jEVdZSV}UlaYHkAOJ#;R28dpu*f?9c|Xc
z{vu3xp><6?UpXUl(6P$IT5i!v2RUQUFLt=t8)Lqadz~GO(gwy*oXtOU2$l1;=7?SF
zUg#?bWu!#zEpz_FIJ0@cvBJG^J~USnuy(O~K)NUALOiQ`?R-7He)P117x)f2Gd1jH
z3&+~89<*K3;z|GeZ3nOh{k)jcUB4Rwpn~&O3LCH_A{y%MEB9@kq~X=@Onl4=ijv)t
zD<J+V;3M2k2&8GiuPz!+HY#Q>IZK{5>Gu2J-$5i>nV}=u3$t#ja3T+mgzUIWCZt}K
z*u~{L*V!4OrqSK!Q2&K`FfN^Gy1~N8kkN>1jj+bj<dfUuu?M`{Cx(r6Z9bz0HIpqn
zI$1ukWFxMvz$@VgYO}2u+Oc+icJEv-4SM5xwy|?!%{8pT$h4%XX%xh0G>cx>H#clU
z{l!nNWU%9Q!k-9i7i1kc`-A+mHX)<-!XE0x$oQH*drwq#JWH88?og&BXgvSJ&3K2}
z2*&$C`E3Nkx&d&(`{{c|xVV_&S|yO_74|+KQb~)tF)PpJ<ixU|-Sn?Nq-p%uY9@=d
z34I=wckB<>OK*9L+!-2sN!6lVxD4zWsP$0=-<9xgnGCL^$^4l-(efENK?pU{t=^_e
z1h`?7yl|x&{w`wys*|FkA+H$Ro$$n;T1?3qMarTQA$7p+{LPMJ>7YU21G$=%HTd6H
zL^`+OvXZogFzwITK$(+m=NREX^R4qGg~L^vip-hILWCk*?ef;~oEAqKMf7t)Y!*i_
zR<BO%KBH*wr3gjYrk~Ci0Ce3}NrvUs6Ub!G-dYRS=}v+31)J)uaCVO%%P^b8W@k<7
zR7-5E7bgH(p+4(^zaIbSiKY9!b2G5L?NWWU&(c4zcBxhgvXwcQOaJ+PT^lXh<IZkr
zxKo!5D9X$S=MB2BwjG49VfU)&IbS@{AbgbN?1MGt-2}U^aYoWHjHOUk)Ggk7addH*
zey%t<pFO139zu93dfV(NaXM9``8>IpO!rj?{sB;YqVtH+6FsA{v7q%|a69vfdG8D=
z1l^4uZzs%+#s+K|XtWI8^RZND)jj+*H>PXTE=tHiZs)G>%&4}gT+61CQ2FOH&9G+K
z-ptaEi6S0(<bA@0aTfKOk7cMVx0+D%?-as~g{(@RTXvnaciCAt^SHC{usYk`yjsxo
z;t=Tb{gE=^WQeu3HYC2j>!A7@wQg*`b#jq2g2&eVygFOzb{gS8ho~x5xXJB8f6lV%
zjD7M<9|Uj0len5KMaQE`jQ64W#7W*gKKJ3e;IHvd%#zdK<`f-1J)CCr?Y*KU1&(r6
zJmPUEUd(OFZ8e?6O{L<QA!OYVN+-+hqE9`E+OS^>xx3^^7wBjUxk>t9D-@KJ%;hot
zou6qSk|*ppBVGnYih~IE52BZ#<iGWXH-vN`<g<esdTsuE*SgdK&euSeOkYR0M?xi$
zIXxiN21z_Y+C@Kuw%#EZX#a8l7aL{$U!XH)6uc>B6oN$+l=VLZ)j&`L0c2AI0n~y%
zC@W)J3^Ykoh#yo2IJ^QlP9u`*(;+@#TpGmxzT-a{=_KnAC=~rk%QE=PtfFC9VsHm_
zYKqZpMa`4($h)ahgojm*lVZQl&wM>Y!{5<-8>?I|sI-Gvueac|pB>9#BcEr+es5b^
z-XCP2FJd2W9?y?ex=CGc8#?7(9QZpo_oEeRM|8SVN!uY%R2W&zgsE>UElVHy$Ol5V
z#WWXgs_M5#Nd%!#1mBjDghHW+fiACmMR-h0o3<6}n1A<&&MqyaE=*|^r-S`ks8s<T
z_}TVo2WskV;>-B%aAPo$kZ1_71-&s<>4`i~%A%OwY}pE~if%Oc1!RlQ{A9uv0q)i9
z7|lcIv)XjJ{r=KCPNxt1iEm5uhOVrGPs^UR?=|S(Hp^FTXPPyyJkV!MfZtfcL97d_
zPubjI#kfQ^*>Com&)|8!D4&v6;sNMrrjJ94;n`UHC*Sl6c57CM&!{j$=&cnLk!hlg
zYg8*ewO=LLa_kC*tUZ<2eM`mXqgvI36#e!gOJZ`fng50pOtHwrBQ7*vS$MK2^A7cX
zFO53O6q{+k84D>@PWUk&0SpFNEJsdNauLNAx#)a{nLLGCsg86ljBXw}MZGYoJ-NCl
zEYjLHfyl`~_KW<s&{S>NDDb!9fQvdTn4l*b$y~>jTa-!S?zSjAVA!?#mR%sI)%!W|
zLS}kg0Q(3jNF9EdJk1`NHs1pkq5ZJ8#96-3qD>yjHK3XT{!iBvw>&|7z*}8N(7=Mp
zZOZJj_rpVl&NX-b)(ZYlEPq%0q0FUVw+QQ^E`g`Oyz}H4a$gKpJ#~S#6%!mtY_mEh
zyT?HPmu~q9S!d0dA#}Cl58)){aHs?Ti>BilMI*%H_ZI9=6fW~~2$Ao;d2Xvis<LGj
z9?PUp+ASQZmv~Rg1S8Ulz6=YUSHbDNF1&DXmL%&8&dP9SrUghk^~~WjZ8`MMD<F7#
zzeviqsgA_qTvKTOp%ik)Ok>IPO8l0r!NldZn)hQ<T`CD$OLW&hXY}>`j<W>~j2A!$
z&o+YxFY2t2Dau2J+w<;ZqGE)ywkVTibXYAX6LXoeek{}MAck0#s*GQy=MnG%F3gyk
zSJGkv3I+qiH50Nk1iQA~&BojSbXz)%otc?gt?@z`o)<A`V`+3_1qH(s1*38E2vaEV
zZ`Zz-ppc-(MPu)=Y{{Z~{oD{RypjXoX3S8+%pmQFgfMVpBSu-?`G9xhkUlWTLo(_Z
z0tvjCtbi^F&ZWJ!%84>Ep)Ac6w+xYb$tI}bnD~9ma%j+g_y(;QrPME|99&5hMSd!N
z7RN6_x$2}KXD9ofEWsQXTB%1IRgr1PmOc~{VwO@)sc$p8M{*+-H*O1{?N(W$6+S6N
zgszeaq(2i|WQbLRMMjU?>m>P70T7DL(cSOIOlyg{61LsAl-Yy7OaaTZ(K+f2wkY-y
zrPtJ$AE|`0K8(i3xUnato;q5S+b`@MOREvr@D+B<Qqf<3JEWM6qPgZzBVpl)43YE3
z%1hC)*xB$?NhTYO)E;2byk=m9psETj^CwTDuO$n{N5f%Id-fpjdKu>~cBMGYV1~ep
zHOkZAXl<3PtSR0-Cg7JR7rBw6I5V7=AG^1nYzdX%fR!VMF+TB4jX{j<Y~YDQoU??<
z`W#hkL#*nIA%wr`r_iWevfKEqqrRO|OyqXCGjBO!JQ#&__dT$Zu`s9GLWHpg6(%EL
zp$LtZR04{1=J)S-7O9P}2Rj<uoLq!LWtX9t`ANnvC{TEiM)t26bDKgSC2xIZD}Q;;
zm4436s;uusZ%tLZ*OS1Hd9%Ur1?!b0IwsO0dCi9C(9!GVKwM5OWc*u7q84Ih?gcIj
z<d^9f9)$Zy6(~UVj~YpK#}p(?u|BguA;C{_8XW@gptOtRgwOJBoA1+{0JfuTJ%msw
z*|*js_K8!2`3v-?>ExWZ`qwGxFqwEb>du+eSjzJ{W(3XW{JK;oig3@+9Pqu?o1VW*
z!uM*qTKI9!jA<O|pm9}Sb{5)Bg|rxd=9~s))B@7kwMu}0X#d(`rhgmS1Ymb%<!VPl
z8|}jM8$^tQ`uJ%o9V8mZhFU_83N_pomvkuAcz2Y-mk}an$`z5zDvM$IRA&$BP+hza
zNE=4Va)xGrnm!MpplVZnLB32{8KJMGph1T2pJt>3V8lqAy<NFYmsaEO;K9D_Sw!^?
zCwaw2nnnQp{2ca}e8f2|RgL1coqpa{s+LvrHJ&eP4-KpwYuJ~t>x66s6+PkkV(}*q
zY73h*F7ifZWpDauH%Ev<Q*+*k(v(M>>1f_*h4KnlO2hL6a~-6<SHkvb1uO7c94rZL
z-%4~eFQWB#>rt+ht3G{Bczl>#^~ftM*8IA-8V)$kwt!s&xmS^2a$dmN_kIX(yyaRc
zzQFkx;Ayk5Z^Bf<*r8mg-qZI`xGdXYN@9T=2P<X#@8a)on~tFhbe^!&6$cf={Lc0S
zaCE{S5n|8L8KRO`hiM+fICL`v#q_5mw>c_omQqz@h%GZ@7Ja`9I<c%S*NIq&fl8-&
zN^xL0;YZnCXP;L=ec2UiMy~cEB(<mQZMdpo+xUw8K$+qb{l+iZ%>g?v8vROb>vxqa
zL;ZI9BsDy#spe9W(r%SzM*?YG+$-zMb+4zyNuTBV#oC8b0q(6FU6$yAHSFpE1nTWO
z<z4E2QAYxHHm#eGg|0H|aYhUT5bfHmFg75Hl~Y>6^GIxq_Zmr90D(mP{z(WldVW+=
z1>y!tV8I&+e9q<NA92O5{WfuE%EF|{u*KUD2@&d@TiP5rQ={ice*XcDPvZKnkGMRX
zy*OR4a=-pWe!tpLN8A3;y|B`f!k~-GC)zN*`@bTB@n}erL8ziMx3b*Z3l+I}mPg>_
zRCB*P6Uq7BRi+<%N5)yWLwo<Vko@a39>nV9bQSHN@3&u<X4P!s(!KMSA6e06!ezOo
zob#Yn-Dxx<?BlVRgkw#IYYAH%^7nZT9EQ0c?FiibO^r$RhP-E>Z&_6;$AdHFwFP>j
zpV^0vX4a&*-VI`Jxpbf^TeMA`$b=KluAwd7vF06Z9Qd?LP#=l8?W5F3_l6>eQClC7
z8N&Rs|EY<RLTgaTzJJ-hG^NEsA;Q2H`nj`CY<*twO%a>H{x>439ThH#iw34GC^i$S
zDK-<@${Li7@qh5YCK5Q9cStyyX3@isGytTTF_G=RTYgQUk~W#5!3@D8WSS6Ja-Y9`
z)pG5K)sX*Prd>fLYjP%pNr8d44mcY8yldhE*^9T*`p+sTJ4?_P&ECX71KR_{@hOsh
zr>AFW!mWUvgM|BAU~>(8F6ff-@x1?UFh>w_18fs=1Du^PD93+l#DO4*AMi~QKM+U^
zzj6GJ;|pn1f;Ykw1pFSBJ^c7P8}OaW%&<Rc#sM8Islpna1Yl%k`#+Xe2U;VML~JM>
zQ(qRZ%B+vz<Y?hvwC2-dW-B&tykte^vR};+eb*lw0Y-7j4H||7&~TknVMfC!OaT+h
zBtDE*q{krI)RrzIx6ci)m*Wqw6Yq~(r{}9YEpm058b>#tuMdVcacPfk%75BLus;Ss
za&~vyO>9MW`X#I>NG$cKoOzu^?m2rFVE!1##3#7^=M3cT9~HYF%Xk$pIL?k_MwN18
z7NMPW{GcwZGWP^KEYVrCYgQ=4x*aB*H{_@t@t7eIqMQuw-=HKZMYcR$Z?E@}w4fUm
zm^A5laHbes`kSO;Lt?^wN#B1$>eygFlW_y4@Nls^E<9*PXmSifm1(5iVo7=>c%D10
zR$CTv1-s9;JiEVjyckP&v&E*|V{w53ZNePGdsU1*{BmVq*iURBU7*;^i3ABMmZ>&L
zRvcf5siK=s$PuBqRuO-_(2X;$jKP8qWZkfkqaYe`C3y{Y!N?4%YhW<LK+T6-U6Op6
zh7MnzL?LSBRjtJJO+2K~s=rjag&YBMOB(BxN<0@1>0t=lX16m#u>9Ri0={>>(;{OX
zjtfOeZtS&v45s>Mbmg@{{Ec9_f0AWVMi9bJpqWyHURchto7}5|_8aK$LgKyfsLR-T
zTd&j2!G5{aia}zkF_`1_i0+0>2kQLJN8GyhM*k4bSNgjiY*V^}<Rm33IR2QIVGTPI
zD{#KFUGl&v-}`~ghIWvY&ECJFc8l66duYc}=(SH0wz08^D$bfM2i7`5T7tX|>ZVA_
zfImG<PaLXW8Rv2@PVhYvoIai)UF5tYbD6p4&u?!QKJ2Bi4WLz!eS?<2x<SBJO2#IA
zD(s)6Jihhsoiz#2mLpKz7PXqi$0Jir;8q2@H6DViLZmB{U(xoQRY8U1D>Z{rLvRi&
zrQDE%<Oz=;_N8j1t*@@05JDSRFJ*$&0eG=B!an=8ov=&vFr$HRTo^bxz9!K2%fCGc
zxLR1ItT!U(m1hD9_^TUd7NB`-ghWM03;gQ}8;~&y6l<f1t?ST4yGxM+P)t&kFNn=M
zki}3;g7>%oVypfdH-qsanydCluG27d_=!T{#Pi9CO*_xfMoG)$BTtxp>Mtwz8^HCD
zlTKA(U?_$RqU0z|_637Zh`3c44NzDgIB2j&jT1XXn602g2W#(HarpM*T{SmfYOeVP
ziHH>pI=*l||NYd7q74jFeHLAWpk#~Ut;T;sVEHF?|F@Kwu}@4s+|SsA{Hc>BIrBq`
zSt&ly0y(!VyU4YHu;DKq*I3oX5HO?tIP?C>m@O7y=>_Gmv-gj4sd^>k22<k*TF-~K
zfKQ^LE2c4<J}z@H=+m3y{pv$BV^Q}*jr9Ck;;?oJ>mp)*@#J4u7GEO~z=^B|iS0|{
zJFH?ZaB%Y)3)xWvXeI)5>VCs{`t4O@s-@3@8Pt>bc{C5BPWQU>pg7M=fOVI3-FsnT
zRey{u{c*Ms<JR-lykMX~pF!zR%4}!vaEl4cq#%Qoqxv^)9rARTvqO`#`R=SQzp_-5
zd#z>H{7HWAO5@1`?1~^kxEm;NrfI$eC;9l$!8{uxJE4bC{h#h7MzHg1%?X!(lLk6y
zMVcRf!Shz|W;&1)jV55}0M^I;e|mVcP&p@i6ku_i{A*x~;C%*<94d@UL^PZzwb4uF
z7ygN|$l5euG#D&MH~FvW`A-hE+=&KCBP}V9B<5TNG%cAJ56;>11ooAikux)z5Zucb
z^-a`LU0g}m`xl%Y6lO+3U4$DfeK!qWOTbw~=6i^th-$Lvg~oRi07|h}hSD2TwNt!o
zr=OEof|S!zyn?TIY7rUMNnfjFQR@(qGc&xBpX_YG_!wU4>nK)+TA27c7+>F}h9-FP
zSI5tU{4Zm?f_GR~f}B#5zxJM;_4P-28BLD!j$0`fzu+nxvCKRFfXR^>^gqOH*s;y6
zSa>d6JNoT~qD;OGIKPi$7&c2_vu>z?XQ^!*FDQO5ecau|2{cjBYVdqRUih&7fCDqt
zYMy4uASe<9vmBB88x+nJVR+d6T@R7;0#R?SMhogwXl3lBPsm$Rr{yPOG7fZiBvbvK
z*6Wto>d4$db9#140<;_3wDi-&z%R{?tz$N$xrtS1V=+)^V0LnP;gfS(Hlwu7{zP^3
zd{tbu={8~iEWN7U%0_duGUQ*3piG%S`m{>(bA=_+p@R&3YS&7Jh98d5WTimzTR+Y`
zr-=o;MetS^1dB<Url0%y#c#jHY3ikHpREf`Kh_xp3;C*zK^8MuYkT;A4J*YEEa9Y!
zzbMrN7amxkfa{h*hzY)%6ivTsf}*9b)5Iq57Pw4J5c<1Z^S`VbM_7vIE;3onzE4QP
z|9f{n!l@5%Zh>%Gwbu4yoi3cumQAa$Kr&&z#EOM&RHS@09CmrL-8$y*eK?t!ae%wF
ztpOr$*LMz%-9DM;_#EKvJ>PTc*DjK5Dbx!3_jsvnfpZ!7^5vA?=Z=MlS+JQXn}#y`
z!fj?nxfeqZUE{m-!9HvS@snnDWs`Va&$k<s{iKoz!FJK};Hys~|KV(L9(qwaoXb4D
zRoyDHXVQRVBe_FW^Gz7vOr}ag_)MyaM=?cW!5oysLuNZWG+1TB`#)umd4dn`n+l{m
zfAz~$foHrr@|l3EC)W|GEnQIxrJt5v&@X3M>Xn(B0aYmNM~cx|gmid2N})7)oW^V@
zmXQtEbtTY({d5EA#WAy!EPQ0F1;R(ZG0$XoQ@xd1A(1($OEb0wD4Tj1nmKhUvYPJr
zj-u_Pc}%$-WSm3-c;7O9lwJBv_L7t;I3haQ!2Kq3#Q1aaiU6AW$(6(ziGWA34w;Tz
z*0LNfehD=p9tp{RIJn;i!lYJ~;Ebs^SdL0RC&WQCqTAn2mE`jkj(FiW9=v2^CR+d4
zZ$dmwlvEjsfCY3>6J?}iX%GE&+XCT6!0meQ#7{p`ZXBJsR_`<q4!I1tevWQ(F}h6u
zVqg4mAkKxuF++zOo&yI|v8uGC`0u>v@vc+DuY(^lv7*4-z{3BOcft;8wkno5_qdMZ
z(0^38O+Z<8$wbuX#2z+C<$t&j9$Pb;Kgf5Y{?vVNECOJD8Cwj-yu_9A+Xx|FZ<O*w
z984~BtQ9JM%R|Z5544!|vXYn>&39e@v!UaG|AUJ4nfIYj^StqO`FBUf>bH>-i(#(|
zLqi>hwQtfML(U+uHz4QymiIiX-;M}Pc{|0prmMZ<zk<t0AnlW%E+FkYU|Bn(Ht5%P
z`78Bn&;d@Vy}@O#Y3#2gCclz64*yER(dH`&&LTChbDzoN`+(PF$pkQ{DB`}?BoVjQ
zeW5?oU~<Dz%9-^7ejE6toof&gWAQc_9!)J`wDr=%QP`8*g>}^W*d}&(&$nYwtH`c$
zn{1tCS9~W)5BKgzx>9o5<#u#s2D#owyAue1yfip`&WeCHa3Vs2qW`HOIRR#t|KpW-
zpe6A&AONFts%jdaJaB9e0xY5jMB;mkN}-Ja!nwVgE~%f<VRRRMB?`VsTU$BVSn`QQ
zl9j|jQ_R*Hxf~#Eu)8BBts400>HbdB{)wfd+q)9Ab8r07zCKn&R-FzlfBnH%=xEvM
zvMcRYRK!;VxN?3Tg(poNY&f*s#1BygG0)zrm1w^c@xC*^-&nGzt0_|NLEDboaY&RT
zFCq>7egbqt-dO@O$K;StKgCxmtLN>?FTFo+9v+wE2*uwIy2eC+=Uk&rj-uSyUa!XI
z``e+9aRAg3j{9#q2sP@u2Gcw11X*6~CyUHyx(PJE7!i3@lc?8QDgmbB%M<!@mqx&k
zmKOJ?cM2glC#wJF(AeEZlsAe1_IlXK0Zrt04LLc^YV!3v`;P;U$({Zdj-aF?t@gvb
zSbT4<XY-dGV*)JbVsU<Ai-{ouis^B1l;rruGJWiRH6x8;YEcgZ_IeCyA-VvRfZL{D
z3egaN(eyF}{+^p9FSmKgUPiUaE~)XTtr|VTVIP`%V9?T$r}9pEV0YLUPNe66!?pVx
z?v&qJsoe-Xs%)k52Tdz<))ZPHL!`QS5m{a`v^y1bH)GAs{-}k|ElXT5IaPmwR&Wy;
zEmr?syk9jT<#RkdD!436)Z9k+q*|oWK}|70{?k}B$FssEMUR~(u>cjm#5KQdC3*Ak
z-fusLA$FHnPK)`WAq3_=S&vW>(MVNk64F`UsKsZY(Gnwn>dNON<w;{K&+3*6+J7>f
z;xBdu=nrNLl4{{ArRsGJ(tsA&L?Xrd-;sg5NMp$Oh<^_<Mx(mzr&3s6ps0u9VH3pw
zBbx+7lb$bvZ9Z~IqdX}K=IFk4ly2eK|Nbdz7uEOQ-m5Gf^B@F43gdKySG*hu9TRoA
zN%!!}8cSJ%k!XbpZjRjaHdCMh4A(PQ55aKh<t`dMjYieqA>L^kJyE=Fx;qWNUIr*+
zgZaPMkgkD|j&|^=bd<yUrnC0>>CgWFin>(2*L0B<xoA3d&$SRC^n<e6;OXs4KSvp?
z!?K-nj;L`_)T1&``Q&Y^`7I7t>VF{ge~eU4TZ9R-emG^qD$g7GhuTiOc+R8riTOgM
z6d7_4N+b<Fd76U~ovo3_UdhAxgZW_{UrjLaxE7+da<?zqj6H!;7d?FoYfAF~mLkmw
zF7`H&X$&4C2YF{Zh(z&vI$SjP`a9OPGs4;XI}B$BIy4pflVD+8?B;rW_7@_Sb7vak
zJNrS*uKLy;E8r_-=oLSm4@#r43i|7a#FITJ3}H6)P&~d&r^nZ!(=`?Qm)8-zh?$!_
zxj`8X6YxRDH<@zY3A)22F!cffIdv^q@c1pt6R=?i42(-Df-FrKvjs<eh4u8Z0zzEq
zc`9>zCQw+(_!tUJUp?k01$o3?^r<?=f2-NV*y>OgH#ogu6q<CZn16gF&cN;!#Z8{Q
za88mjXqNYC5QRO)hrf4wRoxR^eUT=yFVQycVlR?h+LQZ#%uA!*2M%HYMQV-M&sAT7
z8xR(}fm~EKkiIbkQ9t~{Z~SmF?Q&ZiA!jz6zVXMG+CIqA26T67nH)BkejmXY3@>>#
zQNlp~Fi|#8bT2fmTb9l10QF)xtA=xhqW=bee*5R?75R`2cwBh+FvfQ<UjtEC+bqU?
zFmyt>Key}-gXAl<t7-sflWS8sxtuOKCL3BJf90q#5P*$ig-e_rsDwiwUCgx&)dzRL
z;Jk`g`C_?&Rn3~eBUSCXQaa-9sL-GMbbk~!253A?LL@(W2=WP0@!qOq`HDevsQS+4
zQnH#r9l@X((&XeqH47U`**S}nuTsgvGGHTH-jYcckWmt;ibMb#CyTK7Fx6G25u;LU
z2AAT-KdkaF$uqkd*0(ILE%A@+X$U6L{h&I_j<#|5!H5h6P$hE&bcK|yaes(ieL{q-
z_`IWpevo!0r42)~uo18QiANsy{lwQn7Vq{_1zX&ar2<8En+jdt`mL!ds{NCzDm-l?
z%VFF{v!zRGJ&g}Q4@$r_KsHbaNgyG7<{s)+QI|=N1?OL32~|{lt|ljZlfXkiCo=mo
zYpFWW>o_pv^87nJ?&rq-MDB=_06*X0roM^`=#xYXh6iZfb`u-qfbY+O(E5BVy4GH(
z_w?goT67X%7rGsH3p|iypfjud(`$yxG^%*saE8T3;&1}~LCx-v`dbD2>Fg9zEC#9I
zWDkUUfg_)?G8dSuD%~o()ofFA(f6EJRN-RiX+$-KcSc6-alc~>OnDsFNakjZo@_k4
zleY8Q6*<J(7;cQu%49<LE9JtZ6-;wBJ<t=BCEa+y(3-m!-@ENZV#$j$@)<0x?47@?
zlawATy)ytAMuEbNdkzVZh&G#vD5hE?(1jFP9<b~@)rk+xu*y<QwQ2*IMp;TdG%BXM
zhnao}uQVFg8}j<{@;bW^8W}#qA6gY!G8P-{#)8-EWzKDMh>j4&0xCxsMEjKspIT>y
z6?v;zv2fLSFEve#{%Z$bX$j^PIQozgjMl+Kkjnrvk3%X@De=l)p_su))G}MH?7@td
z!9tSK4Hj}W&B4`=K*;TAx3;IDJ#&yZm^hVeCRL76buvLac&gvd&^w^1d=!B@Ldu#{
z(q1D(X5UnpzHNc!oIh=#W3<G1KUhE)F1bJY3LAe0V>@Afo98Eh*{3d3ZwBVlHymuS
z00mqO^(eIA_tWs~@cLHnh{)JkY*={|6ZmgYOd`cb<zXG~-+LuQmZNirvxJ+a+7a6Y
zM&n5)7-X%KxUR_7%$+IsLL?E>p?_P|%jK4+Q8AQU%Nb-2Ip`bC>!xE#uOilHnU+_m
zzE~5qRG?kMO5Ap>6dHR_5`93F+x1YMIsrmoYjh%hIGlUeIbr8l_)@JczA8q0YY<{+
z9pNc=iZmgZFZwRnqhZEXfLl6`-TODZC?4_+`B=0Cwy)V;a3NTw6Cq(OL3fndZFu{X
z*;*t&4*m+8Zk`P0iTD9J@*Zxv(K&eU$+qWCD>`W1gpttta!6Mr?`Z^@8a+~$lLtgO
ze6{@nCxvb3Q$_NF1CL9BPid=GTIIfr|9F?y1|@&u*)@8#`TD(w>c@KUrn28coX`E+
znSmdPfr}X-pY~0r__GxMSO1?}eUbce@^h8tl^rG-vo?-gF2#r%ztI@A+8WzvAV8#E
z+-WONqEMSO>zQ9t!5L}{-Zhwe{?6%PzQDeLcUjT9wfNrtvUzd8B&+-WaRDT;@%Ts$
z5&sqChY2mYLoY{3*37*4WL}iz<^4RTTj;qgPs$noiJDXnp#Lwv37>}01fPaT@BN*T
z{=e$SATS)HCNLc2=&G+r|HCemg0iwTb&VibK*GK006NbupCB1ja@GHN6DU2)f6qzE
z&muR;&!TW_C85NjlLKrl^#8}|LUST=uldXBg5nNzF?s;#HUkr=^*ghU?WB2_wrM(w
zm8f0m>-?zwO&uFs+_)B7rw+zsBS9=u+xbE=fa8s`|7(|zgFX0j;Od2j*DJECD$LTF
zD$ox(FCH)LlQ|!wWaO79dONl^c6IVcn8N-M;Q2_hgj?L^DZ%5_!n)jc8NBnl6NJBa
zfUEn83v{$dor}@wMO!da(swYwt}eX1#+`VMd@{;>(&aNY=v9}=eSWw<@12H)69exp
zs)6^CSbmQ>1cEggmLG5Dx9$6Uc7b^a8hs!(Xp=s8o$v*4J|9E$Z(iiLMFb{$AB9=~
zR)LaVxKc>_@ySxL)-9O;Jw*u8+lkxLoi>({zc3gFw{EIBZx+A~lS3y5`JHsLBn*jl
zdg|CUfc?s{UE`$$^}Sm5)it5Ti#kjM6fI##Zs;L!4a%MF-p^~$<5|*+Je$!I|5fJv
z8oT&Ud*R0<yHcrAOH|q>@_b@e3-1Pyb=!8t37JnYfq8>Fm!9SA`QiOEc3T>-R*LpH
z&Sv>eMw^-#7Gy@ZU|Y?4vtlf(A;&Gv-aRK^&Q5qQx6SAhLzq7aQJydqegtlpty<Y0
z)&T9p^w{4_kVzxjA7@VGsW>!h>Q(BQJ5xjRuq~X4|7AyXb4*P(%v7#YL@5W<B*@Jd
z#N077wJJxFns*RSBCDEBIO>&<secJvF2A)))D3WX5OYksRVvT!LSlLoorLbQ(ooJ1
zvIg(7QfH6}M!Zne3obzKWg*~zM!qlFA47ZkW&EiYV*XWcR!v>c6v9$TSn?yj?2vd}
z7_oji#hE~8UKSfVvYa=O_68M@0}m;Pwe+2Dr0fXp#|!*5`3*Yiz61=rQVtBFSlot6
zA-q6N+>+zKGqYASJTf9$5FM0J*})yEo>$&jLymknBZcSZevE05mTanng?PPIs?N<b
z34F0~IaELfS)~N2Ox!QC3Ey-$t!-9tHFrbONaa?vq1ny@6H1C{bP8@@#!G-e-ZV7O
z41~$5MXywMB-J&3kFw-<FQ=r7xLJdl@Nbk7rke17%|%p;#E<GG-<;alEweMjL~i3A
zaqyK)5t+t98^}XQvq8<17|LdsQ45)?^$gQ{Qps)i{TYT<3C#^C?R*=YS>4R)X&R^_
z<!zz0DD|M+fA%dl@eX<b-YRT~ahwJMWvKSH+7niWQiKzdCDI}GZ~ez5v$RZX3%Lx&
z0m?8q3=*``C7NLTC59r*&^}qAIPLZz4A7AL0W$+4Q-CSWeE63#T_9{`kpDIlU63j3
zT}`M9)d0x5k4_?D|A~2lsEc?$9%Kni=&zXbF$~6k+uVp2yBQ7)-fED$A=LN(sURsk
zWYXSRR$okj58zW)3zuQ7`@K~uUM<u3f|ip}cC?Ew%}f=BJ~Sl$U0>`(Up8Bmje^w$
z!9rQ}Gjh7kvH;ic^4MxTUE0s)$Ili`gztE-OxIBpebSPxeZp>$cyy5{z2xXox%O7$
z-W)W;TjZH*w0MJn2yFJDAr9(L!cup^@hk|ZooVXvPt!KkyV+1`{W<7Lp-%x2EXQbi
zjVY>Qv?_Dwa|*0gvDjRv2$$O#owaGbG$d?^T+W{YS?o`LzQ<N9i&zIY-PVMwE9PDY
z%_W9HnZvevL1KHAYu{D4Ba_lt+-xRnQX|idxeV7P;asl(4w#*<#Op11L**MI#jYYa
zyr+1h8g6xd$UN|ZsI~rZ@IqVc>q45SYRZ}|>&feqIkQH-g@z=cpQgClwF7N`VIU6k
z3!Q%4J@WC9Q?tr5GY;7!#vu!>ls7Bo*gD;kT5nr5B1smP*T(rA@>UPUL@5=-pDG;O
z(Z(N7C!CJ~uZ%5-#+-qLMteo^D?H?Lv!-fX1y&0_<!Oa&xE)N|dVRc-r5!&z?zCT*
z&i3rmli9i(2SZ`p-&V!kB5t(1D?JxapkNS?A*I%Hde(eDwt4Ry$J5=8A}hf+Z&k@F
zG@dHG97ip}O)=>;%nZ9+3<|iq>lEg>YR#7Av<o$W#h3DPe$E1Si%!I3+0)Dnx-8^(
z$0kQPdF^WLTc=5UBF`@{z76Cg;oSW&<1A;X(vr)$HfYlyPwV&vn=~?Knv}Dc>%^q4
z40={{uE3@y?2#W6#JzrG?9k2cloQ!ax7uw8hc0*%Y%vnzEKgmLyvF2AL5Cerjl+3}
z-?9yGAAwps?&gu~%D?I?>*AtFMkBaGYaT0WGM=7@fyz6@(y#IQPS!l+PmBWQ=vHWY
z7g(E9TZR~|Cn6_VQNEX|-HQw@;3BiEGg~6eC%P=fnb7Yg-K@f?U1!qe{mbk%5G7di
zXa7&d(wrZZ@n76(Lpu0o)rMi(cuN{huZ{;`EK2LzUhDRvU;$UGvmM^r*{QQ0Mrn#y
zh)r6F#^rb|tNtVAXpTlRrd|P$OTj^I^|CsIl~#}|HPZRILp!W|WQBJO)vY)r$@k*A
z=nWz(A*bl!Oo}#yr<I>CE(*yWTSvwmny$l3u=0Qjb|vbH@=^IdWs2QM5`#pRmZ%6g
z$0KYNd>(!R7uFh<pu;bK{I#o5*|d_cDb><9r25Fd0=Y9^dUn~ZUxh!mqlZ7bgJn=<
zO?EN7gEh(>-N#tji$An;T#G-dZ{BOjaVE^ROTO5guCpGltaX0(R6XSagH-q!Lb8%;
zzu%OJ(|3dRXM4!(T^7s*zjRwB$`S@BMVzS{Ci7}a9k*fH9WA~n^~CQ_IH}ZG@u&q<
zE(eD4?oZwna2U59#pdi9=0aMSE4dqq3JT5%8m1EA%&s~bBsuv?)2dQ0Z3wp_<L7u*
z9Mo46wjx!ddEA`^$DodFa<mW4toujEmm12~XfkG|7ur6Ws$XZd7ZG`M#$14s4T0cI
zZJ0kLn)spz9ZJg`k;LNS8($1Hn|UDLXW5B;3Zq|@aLb%vok6E;EhT~^C8t}Kq}lV|
z0(Wof-;;9GO3)d<D~}#eCfrCxZ=Gpn)}nkY3(p-{`{v%j6!=c({T8{iF^r>4I%11l
z54fKqLVlfrjk4k+Y@=mOy0ilCTC=$0<d8Vo9K7dG`P>NqbW({>>4~NUZe=X(o%!w+
z7^7zAxNCe4m!zz+9}be6%;NYZ|Ioy@7^Oy`l9WI$$oL1`3s<CeqF^p_c#$AuW6Z%w
zcuujLfLkh&218A#KgP1@6=4(K@h-L|&3;m!2Cl9rJN0EqBdkEOEMx$9CKPM@`MB9k
zu<(h)n)X4ds&Da&n@Q3v%2uxDQ(0UvQZ!Lrq-gVhCcw5IE=f|3)h&`1pxMlB97fme
z=>BHnT)BrZS%F)9uW?5AiS_p>!Jv#7X0GuirEQ!|){r5lN~qUeDAxXMA~Yd+x;8L9
zsko>CcRSo+ls#`ba`x3+FB0SMjJ-bdv;LSUvTc!AO4(O?R_9cdo!E2i%I#QBG?kJ&
zw<s8<yr1SefmL^iNUd_r{@Ib0*4v<j$}MH9iC5-|Pjbr1S}Ht4qqx|BW5#3Y*xELL
zcUZG*9-4Z?#RnNX`y#&U8JCnOD#;Fm5f%Ar)%1EpHO_eYT7IraP`|GmFK?)+mYMk^
z43=IxWjv;2Woyy$FI4edxQUeH{)Y+n?fjNg{;J3W20<*WR3Taei;7xWe19rQnRMA#
zAKAcfkDV@2AsX;Xg>iRWVf6ao+tpyx`L?6B2mwX;-!ghE?Q3of6YKw(t#Y6#74@G|
zSM3<FJ}_z*mL5mo?-__kTK=Cfp7OQ579+p2FDx{RvQ{$RXmrhRW|&}ZuTeN=ptLG8
ze|hdtTqfGbF$LCJRpF!V^#zl6=Z5F!f%VIsUs`=?oTR&Zo55*Vz{&Z|oCL*tC7PBF
zpj+PHY8s*VEuQhn{`_i{>`Y+EeSs++qbfh(WrZqY55ucrS*$^>n6J?<JjX5AZ~c4L
zYMs0YuO}B~gAu~ynCCLvHoI7RN+N7glh-lVqYxu)=f~T_-W}tbQ4waIi6s~uzHS}|
zIw<L3NtKAJ0k!VX!^eU@H_3e<9H}-3V8Y>NzMoPb-29>FKbnn&Ek?Nd%LNpd+ACPG
zIhyddlN~m6KpPx>BvUbETrr=SKW-6B>6Im>WD2B`E4^Bda>3OK*m0z_-<`-f66|5R
z9W}iL7neBPUT1MJrW|$l2u2Rhx0%M^%_4k7%QAtfY`9b_5*ecbex~QDfL}5Kz*O)a
zV+7kmKh%$&)s%9gXeyTyQ4{PaY4l_4x(0iK^NF(z-6xH4+Nrkjgkq6yD$hEE%DaBG
z2-7%b43qjP`1A_Hlww&>%#-kioKd!o*97BYo^eK+iEqE=d@K+La#dU|2+f}>Z;?oP
zve?HyYQkftd){r#P$s+cZX5L10HIZRPiZzq%+(0gs%^m}Lnb;Cx<tsujDC^jd?wva
z#YWSAhjIq3j+n`YHSj$fKTg*CSu@GgDN**-3h+13_g8rbYs*{dlboXcQ6hx8oGz3r
zlHGrc2Djt3>(fp<)EyfjQqM)ErIIc$iqE@xDrh1vRrUZr<?IYpA(NRu0l3gymvux8
zqK5#B8Kqdel>6I2<SLxBw)tUL0_zzab?oxrp!!2}qJnbJ1S;EhGWjuJkdv7>G%ViA
zewdn3aoqBQ(at_RrQCiH_i#oRWH@6WI+R9tIl{jOB`HY*^ejhIj)@e`2c8AJYH*dp
zyGHBwo8;qc2W?ZLcp3!E07L$ABBB!fd7KVec}OOz42!F?73ct6-Od5|9YuoSrCs?X
zk&rd|iZ@)9R`|J;kqRkHvcjU5%C(UvT;lLMT@pz`n(IalOKgKFsy2@q*$Isv2(2Rv
zVQox#<@IDyu2U2Qc9F3A`PqnjU8!F@Y1?chNVdg+)H{k<*sMkC0M4=-%mZ4XBC5-P
zKpwl9H{~!UOiQJfukzlw)K<bNW2d!Gojl4r9Zi|MeE9V$DXF>kI?5`V%Atl-bMkeE
z<D5ISh;fk>V(D}*jFePGe_|r3l;E$z+KqInE}RqHl|ypGdj2ISynyapKijF7$HJ-t
z*foEHH%|-CoIB$kz!@c~|M4H3L3>O60hHe6?Ne8$j8lUj@~|t9$AQ_v?OHr0hg$+u
z4sy|P;ivSx)<>E8HIJ^>4jEkl9$+k~n|MFFY2^Z0ax!#Mly!aTyT`kCq#2YM7(6p9
zO*&2~s$PfwKvnX7ZRy>4g(!9ZNmM>;i97RhSM_I!uj?}xP&oecS&2CJA;eUKl}8d@
zt!w7}etz3{;9_JK2&P90S%*M7aI$1>PE`EKAOmD!d>chebNg&^=WjgEkHMT5b;$zB
zso&@0{64+@G1&%bUgyDoidN)nR5~#_hi$hmR>Xg^*yoIwJzr&GHbsg;5!A(9X1g$(
z(PJJiX2azN2rtW&a*)e7I=cC#QYql45{mWKDEnl><`nEIH@FX5MpwyT{c$E$<@h`;
z)abB<cdL}LrK}$}hO1Rw!K>$3T*^HPw$tlCk!~KiO$4$L8Op5nyR4T9E^7KBP82xS
zFjO!Qq;}S&lhQ(bEgNN(b*T`=(Us&VH7xLxz(O2>^H=vR+97cW+DXpTA0ux_Bc7=p
z_iV8%IuTPfQKgmuRSNt>74>Rq0e;>zhDUdy0KxR2PJF@>A=L(MTKVsN1#xZv%+5jf
zDj}xJZxBunf`mGjAwPw3C~RxXGZ@|0rZd+4E}oP3{-u(RsWf(baKIEqZQ!yFR(Wpz
z&_#X#3TgFAE?gjF^Sb+7BOA>;wnx0hz6OwPav~V<r=2xFWBn4k>0s}&GVfm?bRaKA
z7G(3(cGaX@eCNUfH44v3OMx8dOIBoah6@dXuHv%wx{Bx%IF3bpC$xieq*%_hdT~#_
zjCPN-yf*|tdVEb1P31v&@tDffk}5NM$bH8L+Qm6R8W%|?Ns~=Ek{4N>e!4~nnMaIO
zMnYbmf7+DUP>6tS=p|~G<qhKmcNlRN(#{MDsQsyq;aE`P8>1RUUbJv(QvPLGfN9Ct
z^g}Vp4E7!TlANlcQkOcsR`GNX*XgY|f%htL7pfKMPK;^bdm>DZCKGjoZ$%g<FeTXq
zs8O1dH=Ra_Tnjkwd%6_$wl{f9MM<G)O>I45M*8L7G+UfKzmQ*Ev0hlGH59nbEb60q
z1$W%En4Tl{i{M*N%~8;9u663k9U5q7J@-qwdZ};f+>*PwEoEj=^7j4d{OUB;dTBiE
zC1lxWu7mH*Nl(nZPpoli#WbJzkv&R*$|0uyq62O96T3B(J8N_&ZYit)k`q)*>^xhQ
z$9|1La;;@2Le;;l8hD}-r_2ELGSKz>qukwj;<MS8A&OU}C%G)MRPVf%B4qYm9DGl3
z*A3=t)(I+l`02GN<+9nmGWcGX&BP3D<KNq0YLjp!Z63IimFU(jkG)!6X|*We35<71
z%KF<y1uLH5V(zI6Gud-Jq2_7U8>QvIhW@-j%LR@X+`nw6DfiXy(OTa(1eS*05?SR&
zmbmJ2B9&T0Q29@zzKQRI7WvI5|HS#8(?*jO)ozWDaq$Q6#-adgGxhMP*cj|H<Dlnp
zgUC6+b#$4xi9E&|pcu4q<iR@!w0tYK&f9T3h<llXh*_nOXj}bokeibnpvqbXiB09(
ztJUWci5km<R0kYyOTZ9lzBii3pEJ;#si}T1%5QK?izEEbiqL%%xw{dPj!mkbZydHE
zOAbs&%Ngg}cLMzMOeIA`9_?rKiqCuk&&jgxNHi=ew@1u=2Hr3{=He%MLveX3C1exX
ze%NE`iRgAfNtMi~u4OpY<iDP{2JWLiez%2%-e@!Pj!TGmsSQ&eJd{(0lzZa${)T5>
z&%*00&l<KK{7-Fsa3QH*ZW_xTG>gJNgw3Nq>3|oy(c7Kd|Kf}c|IxW5`H%nffql3d
ztyOi|@cd>3lY}(!6YV=A!+%sR03$Q=|BpImXpBWtwZLy2QN4Y4R-Uf|$;12>3$g-!
zyrGAov~*o=!tmK85rc-Wn6Tjm`G|87OD?&)ccswz=B7@o|JkHPdG^_NFogd)*Y!Ac
zN!Rsx?e_fIl?dyvNfAw#HRW~Jny4vta`XIjfG4=ZTWPMH)INxc1szcRwrr~Q5s06(
z@pb#N%lqR9bgZKMz&^lHQ|NqXIEHPYf#Q0w`Xm+bZbBJC#rY1}rI$oIic0>?v`K9g
z73Lc-;d^TJ*{)7`NV0EfJ2^8-0vT=zDZrS_ieC9nOozg`=e`*2s;~3ipi)=W(=vUm
zfd!#uLpdPR31k;W=dldzSHMD%1XeL|qiVu*6x1wL@eU3OGxNj@_DFp=Z^smJr$WS|
zF_MkP1Ub0VvsrjI{yatRO>RKlq>yIk*<B4lexrz8ecWq~8mZuT2>a3KDsACV6=quk
z6@-nVsJ$UUBI`b68mato^<2c+d|4Iv-jRzGPw|&+&?Jz&0I>*6Fy&VEeGO`2@*#k7
zS>syE2^fS;$~q!EZ6CQ6*VxQZ7#Cix@QPq|J1()(Qr~rfP{S{bE=)CbSms)yPBj2R
zV?CMRWc({WqHn5vrxcrBZp$w1c(}<FS|2pE{a8AONRE6SnAIWB{V1{OvPxj{1y(rX
zEK_xqbdIoeeF^JeSDEXY#bKsuiaF2KF=AA2W*C$A<G83+z$=2c`FHf6e`fKQ3fb|`
zvUrwdWfOvLt?d_O(Q30)T7ZH&iSq+bG7%HF(y}Kk2lv&N?2+Q^@i+7IZ;`7M{-n`t
z-&AR{Ml2IJB{dxSc+g=F#<^DyO$_c;`~p9RkP&wOR+c8McDk`y5H#L<!zDZG#Nb^n
zUEm4&n;c7pZrsjC_7wevH^k0^*4EaUpXA<iV3ML8a;KFA4-u~2U_iu)=`{t0*|R-#
zyzX9#X{%yU?If*}{vbsD+f~x=z6rZ!G_FIp>RB0s#3b-^0M}xJ%vybKpT48(;!MbI
zD)8D-6ue$7kd@n6Bjn!IDY_Pq{1{N5l9zgSQ}(`R6)eX*{D@wmG-E0m8{&r<)ZsN_
z3f)h9w^R}_x9XH1U=YHb^Y{%YpB%ifw%&`%XVgLI6xDt|MLnhZe^i|Vb7oPyZG(<&
z+qP}nPCB-Yw`1G3Z6_VuwrxAP{hd>{>U_Upt*U3QXOB7Ny6()uuGW@FO2%^Heue}D
zYaSAPMF-WNsA;;J=?7DT9`ApbMDK4W`-~Xhxr02NzU46El99Id&LX*ii*cR~b$B4Q
z{>_%>>GRz}JB&MN&i3hqxL>xYS_0fO?ab)`JprQ;RpMbB=)NQ1%=~8lBU;(MXMP6A
zr+Rdw5Trq07*uZJ`sg^5{{BqoOr0E0Dm2g>T)@<^{l!Dn>01Q^z>ebg6?yaZ2&YN&
zJpxjV`%4FH>yWAgS|`c@8C1>9VSNjGOK`#%pur1OOddMPZ3()v#PmDsaypb>F~Z!J
zaJ#(&KJVU-fK`Q+yGzl9FL5Mo(y9`pu{vXz185=EME^(AOzI9@rAHU0NQWjeZC8IL
z`7>e)@be514j-lY-xuLKF%E2ECe%OEIO~5AGTEB#F^BAk|Jp>qKo`g(Ln#es{eiuK
z&DqIWaHC4*aJcGz1Q;)fBazugez!6q4{>XbR8s4IcfUqf{(PGHzwhX6f0x_uoZo!x
z-7qku<^p6eZfYuZzK)KRG;)Qzx;B5j+~@<GjoO#M%Isk_4FTl<(A|hy1w=aqMT;@6
zpJQFO4lJpI?rz%-<#7Ctj1}oO#PQ~<YVU-T-no3u?chgI;OOo8LtOw>1hq6Y!nbBJ
zVRbw!$CQ@V1T9&~(E~W|)-cW6w6_wh6KosH&bQwiUi8rb+mIUU@eZ}4;O`|)n|s}J
zDQ3Uud$y(rMu0bRPx-t!D{GF<PPl<G8^UI?xqIwGtW6AtAa7G!OQ5faZXtf-6@vH5
zxlzW4q@goY_gW$0M8)%@u+{!)BomW11Yi_sB`zHrR%c`DcQ75^7k7BUG?s><(6+=U
zdkeNse6_zi74q^N!_4woG<&5&5O`Ei>~*WEoq{J>HDH^QTx{e6GptA^9a}OGyT$mk
zg^=0^<C_mSUU~EL6hEJk`mnjoh)hajz7gBO9+8_BVxi*E*w6wJ5v9q-I6(pd`ffOs
z5GHUCho&ylYZ~XPLO*tTowj^zIlNpjKkuE;@{jt@Qmf5As77bstu-@~7?aS4lFpR5
zF!^NZ2LREWSjTgAEcths?a`W(KN+qGI7+>e3QW{w0S`ueu#;7Q{DBohOVV+N^rg<d
zwTWfLDkaWck}&=dpPKvO1WKDV5FJ4z$E?z;N%+H<v!<))JcE%@yi1Q9OVyk$wqD0P
zqRGX_OWX`kyfMtS);$FXyjb%uyBMo`vFw#4LI5I4s-*>(xDfmFE<$E8t{^lCBzEqU
zebCS9Y}#IJw||dgU05$8*eB7bZ<7?Tn`;zqa=8=_szLM}q+VHox<(We7gWKBp6Pk9
z8~%tBxMEW5thJ^0u%i5-ysI?(l#I692!a$7k4Ede+&Q)5pOtIE2qGr1tI@M071?p2
zW<a4PUCO=QAgMOnI|QbC%AExj|8-CGJ|!B@Z>3p$L6GtzEkoLjNBpgED*`Kc$xTFL
zNl`-eDp2tPCrLA|({9k~xgm6MciwTu(UYwVAN|f%bxZK9D<xPV9whPdZG=2wM0Avc
zuGA=51+ADox5cEJE{()z>~^DzoMped2Y^b}<?#_csn>bj1munE%FeP@DR*_fdZspo
z4lFf41jx`bGDw<wmM;AF)^W*|B90V{0+QtB?bWDb5~m4qts=SfbbZhl{tu+fzJY;>
z&HF<p?<WUN9HFRB%`w$Q^J|p{Jw1s<+-kHI9H?QUjGkag(K!^6ULe8;tly?I3!sPz
zXNaGRldnlR^DY$8<;t8J=gFy~hRkRVekDSJc2R(s8DY7FpFh?)?JCXV#!&4?<cdlu
z{~M7T&98Y07#>+iB(QQR)fEkpwh(zvk0?~KtfJ6)%@CbXX9cICyegY&hGVLVvs^#5
z5K~b4p$MJ{R5Kd=&2lTq!_%|81gOfNd;MEqzc`38JD_+QglWlNIjBK(70168aW{>i
z!?eBZ1tv*Rtr?Ub8)CCmSFput4&O*7dd9OPm|f|r{uAKMHRXL3N9ku@uy9|{Pj$M*
znStN%%BjZ~K7~Kc)o)O`12NTiU9UD!<;SL@m(H3}FtnvvD}(uv>@=5{4zS73|J6ce
zYK)pLv`N4a;Y`@h0B<#UA+sE+DQite4EKB&F}zBYZ_IQz?mYtyLDR~B8zB|MzWIVh
z0>47*;?>QxYaK?J$0^Hr5BDyYs<xr{GwX^2r{<fNmF8hX6jSGSY`^vckMCr>J9(65
zc!oN8{9;J#!enc4F)LDM27uj@oy%KtF(bQbjeOoP0&uL~oKLr3Ow!Wz{|nzeyhYO|
zJPtbu?*3M&byoKVoW{6SDlejooG9E*;oLY8iQ89L-B|pZRY+^OXe(KEKw9-U=k25M
znnFZ6mf^K`qj-4J8u0E)u}OTH1sNgA_~7l~k8PeQQc|y|cU_$<0u%&#qNsu0g6SE{
zgB$IUn=O#PSVe1xHdx9}m(-ALsHiQOYl*fafABJ!B*Az|T*uh4^6gacz#H!NU+Y)-
zf-R_8M{X8nZ870G&G@$m?wgs_$_Ru{Q)|;aIR#y0Lye^8SHymVUHwg~iG9}_18<D}
zy2JLCRbmHf!a-5FwbNs5YbS7cvv7n^?p`__!Ox2;;~uY&g5Z1P4>-znI8N46pP~0M
z>behi7mEWXG_ui&p~`d54pb-dLG2mXI<@SE8aPFT&mW*+PJ!mmWV-UveW;JIte2<9
z+HW?g755zK_ZvQr&HaX$D|wyzpWz40{2$dP8GwoNznip=uqW&`*>AUNXA!D|;d4Qv
z#FX;j;xcu10`R*ks~F_wibB8r%sFu*4yD;`XrKcAF4u@EKlnMe4U`HYK>s`<0{tvG
ze1|F6-+8TmejnL+JY?bvMsEk=T+?%UrF~*n<6R%Wa7e#>9Q4VfY_9?Gk`|=T9`5q)
zu;@9#J*K477w!yw+TxKs@BiMcqSOFm6>~gcnV0^$n(1^OsC5r62}URatHKUk7@R02
zqLZ=)XUm}K*z5HVc{7Nn7C7nL;B_|ic%u>O@I~GzWGLU;y8Tx8%(&}r_28@eyTBf!
z)VB-U|5yM=4ghZc@{<9~*nUswesNAh_gdsYUQ7ufq~^}NV5j@Nx~FHgP#9(XjtVge
zx)6T4``GY1WJ%y$AJfKcf-YQGzt}ra1eaO{zzq<H?EP_c3G1yU7RQ545Rl*15cz8%
z{1hKF8|5=?WF#%PSmdlZYlmI!%tgOCaku+-FPp|)KQXn@z&i>6Jv|<!Ch)!_!Nqzi
zdGGeR*NB}FFUR(arNDxWcYxR_>J3x777@u%<d18=GG9%>A5O0WZ{BG=mOQ8^IrV|Z
z70}AMXuRlFjk-D2u8|AfyTaF;;p&qnVM~%jPn-u+&uFf@3=^+6zQhICEOgAQDu-{-
z1Iju>|9Ir(tQA>67rAB+#K41P^bSO9ra>i3<!8nGr=L2;*J&0iSU;3hV4KVsZf_uf
zkAh^&^)<_wD$;cJ1>2b++-7dhHWmr;pey#ZiP>6~j73f<fx&3<WFmdJ>U2Bn9-D4S
z#Vy&=-}FX@bISYn{1)*tqf1hRCj%#eyI{2^qrLex3@ZU(0mU6gTgJ|KWf_2-*wR=-
zVXT3}>IIAFg}PG;hwV~^=4rdO3v+=tYxW3VbB?#KL<=-tHr7c}D?J6;btBcV6d85S
zx1h`sUz8~7T3)$N+?i*^%TAF9XW6rQMi`a%xZ>Oao;&Da#$i6`DG)91Zyk$%QNypk
z*(P+CJ?sg9y`i~ZzpXb1sb%MbZIQeFpv3)yq814yIe+4dN`GV%NtB#Vph@ujsIA16
z#D9Iljr);w@Qr;v=|&lIdZ`RT@lojXJXk-L&Do<A;^PY?0~dtmTD_+I@pXRPzw>rn
zuCKMg(#dQdd+6P2H9cfax;YS`5;*B(TvhdD^dbf*coB~j4^>h)rfCMH`6n3}t*dZg
zIFD6Zyxf8Pq}TisuNIX3V^PlJ^CrJLt?f~P^7>gt_v={KOFJu-+5hoTZu$>0A!VfG
zZ9`p>n}hZ;zaRPSrOj{Qb2YbL8<xH5S#Bxb^mNcrR#dloPI)>NuPf@NZF^dK*44Nk
zPzrf~&oYw7-Q2S2oC#1_-5-Iefd?7vzZtaW9$-HDRq+=Mx*{WVFq?%XOz+Yvj5qTV
zV6kR~D{Y@n{A)Lo?v}dpe|1V?ka~@eTC-(yDn60BYf4`JxPqfk4Pz$tHi7$~VsPQx
z<Q2{_Y9z0d7D~Zw<)1MhH1QVe)uK0#S)2p`8d5mC@xaT8SlAqXak~w*K9Myy#30)L
ziWrAz=o&dmCL-9sucA|U4~Yf5$;1B{@HLgj-Pe>&`ne(%3d$#a4QFpRj>?IRWX@-m
zBTc}RjVq;>I8;Jzd1XROhvzn;0Pb^9c`}e^Lq9J^yz^0}E2NUI3x3H5Ko^R<pjt-*
zG>CdwaH<G(X)ib+b5hm)Rqy&RgjOR&hy8h+Ih2Elm?ox4EE}ARtrW2cUkyd)S(SBU
zxYHD@GqT4>`ztf@DbP!RRq(DU?!xi+G=nHo##ghFw}5JGbnt>pO3=g`=vIGM&dNG2
zMd^r-alX>k&n>bWx0i1-igapON3nhZ^!?>It7G3d5OL&~beWF8!<QQTi2gOZJ_a3C
z(1kc|eQUFjNAdN&9~D>JrNy(C<Q7tmGjT6TREjpRCRtQpQh2g2BbE4PlgKuR$c27q
zE6PkVS_^>gDaAdip2eEa+qJ~Hlk3<k%kOjcy@)xifn1!Xm<?4X6sU#=S2Z;O_NnVo
zsZDNd=!2~Sm85^G@C?-sPhx{TBDXXEKemi8iEp|fFR(>mInE>I8Sa`pCY4*wXai`O
zSf);4Y=@{8b^KLz$@z*yM7OP8I0wItTL7T-(h6r^zk1ONR{<q7og5JPRS^-LU0*Zk
zcG%n>Y1v0WG9w$qYD~|zOTsAtZZ{}X5pTH+YYQV5Wn?g93|~FcHxcTIqwLC{%~KK)
zp5{()?NWPEpT-Mt<Fjt(`M_W6)TK}K;mtj^C?Xe}E5U#ESRyHV?~ohOHXfhsI|-{K
zfz9eux23S2QxY>apOM?tty~eit{Z}#<?cUYeql%z!%z@yIH*vaG!%ycY@tkImuJ_~
za<n8H$4WmJM=<gA?o%m)vgg#vl&0-VakZTP5IU*E!sCy`y$3!h=QCgaGSXMQaW3v-
z^SW>%4V0q4SMwlVJVr~-M1M=3CRR;!to-h!|F`BTBtqufeV$28#Gsa)LQmWHH-5qC
zOIH4?4m7o96b~GPnf)KRM*;XR3NKshKOtY(?*G|`ky_E^?TP#$6|@6>M?ajs#y?3C
z`Tjw6Or&h@nlv1?NR=k0x_5lq>kSlWd!8YHZu+^teY5v|@Amy}|9DA#8JOvP18q~8
zj)T8hY4O^;FCX^s_<6sWYOl+Vb=<h!;5bPT00sClIPiyLZE&=4o(N<|8nh|j>hVLo
zgm`C97<3@Km{@!?To0f@UX8s>T{UL^`28FJeJ8d0a0>I<)`U^6u@S|I+U7oU=)N-K
zL|@sw7d7_w%&f>j++Zh9O+5L?Qy3&7%Ecf+bj8OKOH;llwLZHv-|RVoClP3z9VZKr
zt^+V4gLhc6A*<;4=QM=lKPL3ssGmT_6<o%ZIn@;w6;7JRv3Sh2!TbWv_oQ3mIgAYw
zI`eE{WFoCZ<$_g|`G}HR4j}yxF)9(R@}l0=;et0+Y$~5@HNyq}Xuwyn^NL4w!xwxv
zHd4#RO&f5-Aa*rz43&G`1^85J#nIOo(*x9fAEx<ikaFqC#{BS(W2P%;?8FU<4&<6L
zX3(&Tz4*<5bni}}X<K^DhLjpd!R<x<WmbF#`X4H^gLgI!4PlIX^KnM$I}BdxL(dn2
z*8CDY&+NK-yntx@j;cHJocp?i3S+{%Dh0p;-euG|dNmoabFl4r)!GY*cwhyoCIQ%!
z3*qz()p?=RE)yI~Kc;*uuDqnc$C01+F@tyMxv){?c?gfB1RT1NfoI5sI2{QC4mhq5
zVJRwg7Se>=2w!|v*w@~xdJ>zUiz3uCAs#;~c{1ERA{f~@MKyR#$eAgt5}n5Qm1Qlk
zofYTf6tM~z=n`;@G&1h4$w77G^?;rLsj5u*gfx6WS_5r-Ob7n<tOyBgr4{ZCmw5#J
z)g#PV?HS3hoqMZsiXCSkkpAI?1x0zq{;9Lrk%%bxdi;z*u053u)UoZp%AGR=FI5yh
z)EDw0HTZVaHbqF-_HS&)%b3OJkl)MLEwAO-K{jyb+yrc=ev7>Z8LY=w;((#lp>3pN
zY7rOlO4Pz^q29~|1Sr_3Joz!WccFq}MU+<>TNv%tnTam7vAvkRdF3S7Q(T>TMReaN
z+|to&KQANpwX~Ewg50;>MPZLXjepBZ`!ELdU-=j$8T)Jxr}WY7;74VDvOg~PFa?iB
zm~!MS!|uYu*YZlk%TrAY6ahxoKp|p;wKdDI<B_~z{I9YIg5u2qP#~@@coLUr5(?CH
zPb5fD1SFtkrL5h=NCfOuF)d+JB8WRz;~Rduo;q5|5?@jJVi#}di{bhjxH%vEVm7YR
zUS+U_kzAGo;|U~c_ec6g71QDs=VZ#sY|6=3oxr@ps?|lXYhn?EJphy>%cFd@>tB-A
zMchQVc8K`;OvouLGk?jNY`FSAtMJC@)tdd2iFBWQ5X$Z71mE~BE`1&|u&>SGq8wan
z`_n5N*;sORGqCLh4KKd=a`Qe_i!e6--Y&vWqB0k4Vf#&~8%5d|LX?Eq;@}AixMrr~
zt63_JS6V_xT+h~+MFYOOME07o6SXXh?Vl!2y!<)R_y))l7wMl;(%E%HqvKM(Y$FkH
zHSvcQZGfcVY3eC|X+oxclUt1L94;sP%};jiDQ1MExXp{U(aRV&xghgXRtxPZ_+xu)
zu1T^w&#G_4J<nQ3VWl8|@pcw`hR}?+hU4*!J}+7IvAK9%)C_1zMf*&lgD!0tG5HR;
zT`Co-Iop?ihf_6M-)=ZqmsPQwapK@YHIg7OfyXFSoT9i)ifFgi(x4Z~x8D29JY8>;
zwe)i8s4WFoim5+%<Ho0_<Lt^!8jL1*bnMZmCwo+~G_YiIz<A}kympr!6DRMD#|(L8
zqHWYFAH4puX$rv6Dy_6(Sb&Ch53wn9)Zk!4TjwQ(zBLn%-tg=7I)F05Ru96@^ZfdK
z(+is}9vy3}^$)F*jV5Rsj^#5}RV&lml4&6tCZM}YW7LMsRJ3A364`_$FkW8GsBs_v
zwo(*+q|Hd{z#FYOkl*etGOz@@x6(!t=G@D~G8uClqy}L9KKz@iE9*QB<A`k4jrU%G
zS}Q9o<Efa$nB7c?bpDr1NW9>~`)0abl(kxc_zT_W0;Oz2-nYMUrsK>pM|CDCirB{F
zxRs;OQk!mS&d?b|macd1E9jU8x+XK-Kc;jW!Tg9Q59uVzvby9C*uIB9h#b{`lyF|_
z44CoGc|G8-Cqq5q^<aSH^j+0XtRu&x_?K-t=;pE?2H#5=@G`6<Fn@}~0JmA|y9n!?
zVUhSaAye<_Pn2NY2FMi3DJ4<%?~+y`e?W0Mu6zZeTs$k>oi!(HU(kS{(rW`a>QQq+
zy2e%5AMHg;?cdVW2Fb&z=z%52wO%8<-k06*yX1g*^q&?B?yzoe41HIfA75YtcgeqY
z==|XLigYdZ812R%iX{Xh5j)m@EtX=A1Ix7z=^286kJ;1+n2orEqA#kZ;0R9ZL_1Zo
z(mL8SF|r2j!70cr*DvkwCfyMD>aX)D?7Vx;hC52>wegVrwAYfBVW3H3FEY*q^sv9K
zsGvB#=v)E=fL;^@KX{<q)OBiyN((#sFqdRNbil6>hM$?TK!%@T?^GLCB7Ng*ujAex
zN`HWX=T@oy|AU(7pH&3F&h=ko{0dtx*0}X*+u&{*>{Gp<2iRB<)dGFgHUiiS(g}0p
zV#-K`^(-IIn?M<nwB0z;2sKMMHI4nekVxs5NZkCN-pC_??q8!nOHSWR0=|>EGaS7?
z?+re$!*tr!Qe@p-x)`?V#*a>nZl3Kv?yBdwfSew{o7>yQM79{BI;1#lL#;Gdb&`fO
zmIpT`sOMtor;D0&?n9Ie7MnztIfHax_;HGoZ;1hL4k7LY&!?usZPr9C@BZfoC9)oS
z)pSX1o~>pYM~*{?`|hQ}&A{4)TJbA>))%&K2KqV5{vpcVZ<5~{hkO3pyu>Kp1zi8U
z4w7zpcTc{o;GJGLJ;L+@!U;0@w2U*&i><=tjS3K9a)QB<8>@ySax@Xz7;cKDs1@(T
zbA1?w2^NXl5-w4d@&+Q0b+b9~F?l)Ma{6!{LoqA<f`Y?BBA`aB2?%l$6|}xKT`c*$
zWJZ0iFcuw1staL4sq`ja(NeCyOL^}TfHigvBGrvwdzp{7dZE{j;!9)Jp+ap-Tw<)Q
z@&iSabZJTTH8Wx`!n_FxR<jNWO@I1Hn{gY$RyXO8kSzD}UJ0d4ouECO3)D;(&+dz2
zUu(EW7t=H)iUX^L!wN+ONva0^we`SXV3Bx<RH1vbNt!0D8p{iNpVM;IlTct+0}^DN
z21$^$v9<K;8f(^Lki=8RL`Xo?wUe~d;kST3s=$AlPDhQr9cHiNT5h2^8&Vi%^D&pe
zf}@4ilVNO^Lx_&CzxX%u-qH)zV{^2tMlP)FeikVWQF9b|KeHo724F<Oqu}-2p_)&|
zU?*5r{WZW+4;AFz<@T}s^)N002WYxlu&DKeoSh{s&d!T{cwt4`(LCP7KfqTfmb6<R
z0WrG7x!MbAihCh&O*KQ;Qd<X!mMl}##v@)(7T-lX@f35p!=FUhX|58$Dyp2MVwZAq
z!it+f8qfHhDI_2!Sn<?+CLd$q4G%k;0<T^fEs{Sc9VOO>p;+`fi0MWD3!qc&E?Z>6
zrCv;eBg2t1Sh3|G85EChMp~C%Z6IYz92!;mW~oZ&g+LJ_18(Q_dX85`5<pLhWbG?b
zC%rcaOAU!(8AJ`10xs0&6R;{*$QQ-7^)U6YD0?XVuUL24ot%aU=7Erj&MVwmuW^P8
z8LnqFp~g++{IG;?B<2k{9e^+-r7J|LZ5hRPLtN65b&rOE1ladc9yFYsqL*cijp%wH
z)_oDnVK}#fRwPCxDnj25qa$<{nukX{i>ekO(vaxUHg(=FQU@(zqTo#O`qv#a5Lj+R
ztNSaS?b)Wl2d{Z+8VrZ#SGxRI*zCg$dM)<A$F=%kXwDEZI7WOA7a;xiAS`)o>CZ#l
z>k<bUraOegc@K^!*;83%#e#hW^%0P=h(l4vUnx>WIndiH!w*hoGP5DW!U}znnaWYt
zN`j59J3_}@ib<YB{VnRzgQZr(j5R%bFT#6JHfV_mAk3?SqTOsHLRd_TEkyFMOqGa{
zF)FZgMk2xmorbG0X#kcr^$O@^oG@X6!5#yl9f(D+T2dtiHe~lu^l{sB;a2!Trpt2Q
zIFNq+Vre&L$YOH*?Kp5xZD@StA<ylV1v2wsB&zVK>5Rsjg0oAyF<(A7MY>2(wg-t*
zvK>(8C-S!d0nC^DBiMl*CorQ>R(Jn}2kvh^Voc^c@+-;|f53<R0NQ@z&6?#4z1)II
zIZ=0#Cid>RSUj#Nr<lU%`~#IotejXC_%nzi3@P7Ye8fhMuL9BC^xB>HEv_nQgW6M*
zkKmti>_Mr95b^A=Ued@4-oqbx9fm)3d!rJw&;rU+0hO`ow({w0k)5i90BtL-!ey2e
zTrL%t2|x;S)Ia-D#k%vy)8;lVqW(ukT#mQt;j*1OM)}dkQRmIW3R&$(nn14mt|iV&
znW2=U9Frm(+MKy!>q_h^Z)>mkl6~xIlNW>xY~|ATPmm_wR)+8=Te;Xc{5sAbwj+R}
z&#R-BUC#$|Po_TJb=L<lS7qa6z?^B~dz#O5dw`57GypzmNz>HF?|%5$rz)z*F!9>j
z^RSDS%dU$1z3WeC@2<nWR3PTGzxflE3u@;mlW?){)qw2+gHxQtfgbdAl(gr#qJ^zz
z)#F~peHejXN%_fmIe>OTAlR$|(e@iYPj7T*c?fc1Mhl7m^Wx!&;Y1@F*ysC&0M;+7
zug=PW3Ls_olyCUpNGM1g<ua<YJA4}Eji*waY4lr+9n(7E@82^<!&nk5j7BRwHU;W9
zb&MqWt{S3cV1+gXR98_lIb|3RM&qh17T*!(1eP#EZf)L%xnH<;8H!LxN@j>ReIkk2
zHgB0@?ceE-y=@%IE~*bZxwkbom!?+th|u&<5r91ykr`+{r(Jhq8)Cq{`OgB7Zk7am
zZyk2s$JQAyXoSGDviA44Blc^%>)_)oGK%Iht6rr70_(05=9K4Go3iItY!X2a)PPUx
zIqfeoH3EA3<p_mJ&&jEdBo0U4xsA}5&kx=M`JG-)ypJg=VQn?^z8Gwqp(Sej;Wagi
z4Zz1*^=I+#>f=1hHbHUw_j*lL^Kq%Vl!0^Dny)B`M(qZXQx{AX#|maFu`SsEg=$2t
zq&}@py(5-V-e3PNQV7qM>YO@mq|H~Hm&Sx)tul^j;6eq?6ABG=aYK!^4C;2X7k14F
z`;vTVcaFoAK2AZj{O|p3jH$oU<)lQ>Xn<PvYPK=i$@pM=Z>+tEnCf61Ukxqt!Sp)s
zZ3!c7>qe0d>}Skq-$S<b_k0^DRcX?@bQ%n$+pEl>1^DK6mSeCt?)j>D7nhSL#gxQK
zC)^DuN~05No(mOgqtLq;nCQo6$Ab7i<p4AeiBCp^8+?aArRQu9+mX>2vBY-udceZ?
z1|16aa=wiuiV8Rt4M%G-jBX9xeA4z(KZT(V?GQ;NDO|%_L59mxb&jd`gWwm2dC%6?
zWuKV}oN7b8OpXw5@wW@M^*<R<4}**yka4^+TvRGge7N?Wnswqgab&h>@U~#r#L4pB
zasvrpGVcrL1LTLCEDzl80aaxE6F_O;3OY%urj%><zm^?kJ3AKGJ<!9&axVHpn7zL%
ziV3?so|o4Vq2#{Ss#gu7n4CpzB%H))J_6g&n7nBP^j;kc={CD#&r%a6a7mjrN;y@m
z1xXPIBDb02ZgklzIAo+){_C?4`IT5yF>sC9IcHlJWO3U)oKAHm*0Kd`&42}yQz!7H
z{<Cw4=`+0+?wctY+T;mmK`}|@Jmh(1d8}sFlUPkSXpaT^W*4;bJ)`<Q&jG1)1!n#j
za;k&kIXjBGg5xazvuzb2i3zjlIpCW;P6}8L<HwA3jcIA+V;R>>y^xA;`JHM5qJ;+Q
zU`aSVpynAe%&po>O^37Zf8FL*yej!uZ+NXSFr3-y8;RaH9Vi#=34NqR5WnL6JX#lY
z%B$|}<VCnq|KUH<un)n%Zb~DmIGK6k97A&?y#Yxb;ygAw(X4<r_j&B4n#0(e_7jmY
zLW9kHoUtmpkQ*Jz;XJ&ac<q*N<?t@MF4PGgXMzd?F6$t<RzCdjQc}EWoio@?UY?t*
zpK2{R{!9YiRW>iV=LDe;mj(LoM*~*->w*7#u%ZGpC+<P~lgn}Z7lw6(yB34ne#K{?
zB%s*LT?T^*8vqpU@9Vs5AL;9$P?{aeIZBT3_N55nn*5$tbAE@(beVHLKO}-GrKGCR
ztKjqdHC91<7r@@_Gt`Rk`qto;(qfYlt@UfA=j}$t0x3h*CVsl&tT&M%=4Y*c-uoT!
z%(OJ=*!BoX;@bs&`I~_Ki}&jthd*ZZ3e-=$&ABep^f$7Z(K2#3(g*Y6+cz9H!9uU-
z!VgTjXtknWS?>4K(c?(!46fAoeXjD^v_`~`mUZz7VhhVsZm-{|%y%><{Vvd8k>Q>=
z7mshYQ@Z)1K*?Du)d7K!i$m`jE#Wf2X+I2lddR_-h@#kln|IcroA?g!&ieI;!Qp5E
z?(FmKa=%L1;$T;*Fk+C4xe&!fg$Wu-;4?1bLmlHP@ww~%#WDN}vz-3wU(wC5QuFA4
z@L-?om-GF3W(V6oix;yyVqWQpVf4ia{d3CtCO78c5(m>-5QtbNnS{(HJRA!sRgIWZ
z<<g8|#M3Q)&WE1U6D)^Oee-;{*+-;;eVAxbCQH&(LN$u>`I|o)Myh*j3A7<hZ7lWd
z^I?3cU^`U)c*0<9C#CWQwP__CqA7C`r!#9r&*3b>%@#-Zgf${=XK@%c|NX$tDTejO
zA50Nr#t<Ejd1I!;eaF{S&1eoF%?LSuW9^9+IS&t(l;g_s{>?1sY#{IgI_avgL()1)
z7b158Jzmjg6$L-KQ@A9{`sJi$zB6^jGu$aPL8A7rgO>kqS=kZ8jAa}jJw>2+4A|O5
zy<9`;)AFdv&Q$1h)(?ws`*1iNHhg={Q9(WTT9EM~j9|y_#t?M3Ic0Z%VHfwtj%bfQ
z6fA7Sa^%l?y{UqV<q`4hn*~wWfZ1fVj-zKZ(~F3p;sMfL$8JNo?FfGg)+5VNH2hS6
zyto9OJ5vzdX6I7ZU$Bv)6CQ&$6oJK0k)_W+8#kI$FRSDf-*tLZ2(NW=Q@wu13GDJW
zUdHpguW`{*p!p@#owJz$Jhd1`)H3+xLYr*P>%*1@eB<_oXDF(el)`Mi2F#Sr=qa3E
z4v~uv{x|;umMzi$PEJS>cn_N+WyfBL*14sDat}?dSxxxJd@HJlB65Cl<_pMqi$yDG
zmAMs@J)4HI61<X$n=p2jv#R|I99?X?)A6)UZvEu}9w(HIfL$Aq^xVz{t(6x$g3zV>
z^q%b~mKW^8jCngqMS#zpEEs}1!{RlC>Srk(F#OmTs|cbXRhpXs&nNC%>FjMoF{#2Q
zY?0^^z^P;jdpeuOZ-Tr7GD&1-iy)Orsj)JW^Hior7;+2cAwfHlR!q)UI25sYfNUE}
za^3!$<1hb=@_P?JAJogI8(sAe$$F{HyK-V4CW^EiN^l{NARCKPtcnCD>IAt$P<t&}
zj&yox0sOw;sS+(?1OMWEBJCZ$R1j_^)Dgv~riP7NA&1S-OLDwcB$1Qi{=A_4K61S0
z08)7Gx=vbKrL{K<GIhj?=|SKnY5|Hk;%E8b3-gb$V!a1|I!CVFneK!eVQm5xQk#tR
z!y&IFzTT6U0gIWR4MFsUhk0{G$Auz{hkgDal!?~$^sQiaAP@d{HOS9A|B?6~>U)3T
zNLC?JhC-nM1|=&?+aG6bB?L)!&RWo44yKtOK)PLrOs7WO363Xrs>H2ENlY*W(ahIV
zlpE#OB7_Qv#m88YYgQ%cRw=us;qMM79#(?(iH@kGjxs0C7!|&O2#-k>`g};B?;vuP
zugr1QMku0e!LcmSR9Vl;?q;MHqAor!zYoG{W~q9Oc-KrCGK5I*nq4I89wjCjEr^@J
z^OVn^I;|8rj3VIM-BXu7mm~}Fy#1@7l_E7zF&GNSM=n6qgjfxLurkQcqNSA<-p=n)
z=3?vKE5^khOt&aTRjVQglWXqKCJu(LVyj9mP~7LjG#eY%f_2+Zqm{r_BBeSPr;K1;
zh80dMFL{P6BOOSUiqNG)@pSx~A7+IFt!InWUqn{?lo5`c#h`tx*nfgUA<aPUkt1h6
zkXsEP90C>@0*>B?v{#xlzgMxGwar48?iH(`mG4%7?gB3}huPh<5xPt1rxc2y{smv`
zi?sqN%=(JZSF1D*`<e0kRE*5PD1y3s(V}gZ`4U3nb$q%KcNHc^^VQR6X?!m<-f`E;
zDA`x`JYjKwY9lGVa8G0^S|u7};LHTLEtM0H1-AA9E_{F3|KulXBXiE$^AsaQYkSf<
zcle9YI}^$O9C9wq?-s=8$c|`CINTx=B#2caT3iazGXH2U0avsgs+hUjgxd?O-i<I3
zO}E;iOqf<5l8yolX&II3i<Y*X6(@I7rg$yfc9ap!c&1_gF|6pBqo(>)k7lG_`KAg`
zcm*b;&8`f@?kqp6L*&#Y9r8(x9gLzd^nxB5^k)Z`>swTWjKwGH#t1sQE7}PJ3AftG
zX1kVm4C6xAacP>M&7DR`7!ul4L9J9S$|O)uRx~(C5nWNDGm_AvLpwUAi1My{JAk{r
zh1PM(x*L*Cr#I-6C;hkTcejm31|J7NdWjfln{uEspv7PtrOt5Wdd$1xc1>X9f`{lC
z$JEo)gtg%Dm9{qfgs8-mh%4qM>6py-7(Xo(ue8X9N@?@x%KTPWh&RYSUch<!{#*rN
zadwkGhvfs;9nxI<lsY!RJ$lN_?>3Ev-0_J<q7<o*0Q_Dxq*Ts5K2T7&Ll^>Ji+SI1
zgDP?7QMM#x!5eWYXS17WdY4xd>^wm=eM&l+Ya1Bx2IoK9v3oCN6!{0;6$pNd6-l_W
z4!oSTK0z#EBVl5U5Kj?@17@88zK1I^;Ec%nNuDMmvq*1!??zW?X{|j3ENw(*Js9Sj
z#Ppz^3J27!_arMCf9uQXK|BlKu}~FlBjV%5SJv7<MAsy-8V3$iWNRE_*U6{cUwv=j
zty!qswz2^^l2~Fr++&m5HKu5s=-7(0@~3v-s$d+3;5FJkTtXhmC(8#Frnw-B{k@My
zK8k&V<x+^Eb3<zEV2vuv<IyQmHfC98Mm{3m%u2!AR9LE70&+7ap^*weCoz%cM4@zL
z@i1WP;UN6;XX}QmuvfypS9b=9Vw-zS#mZ$k`(1tj1@7KElwupy{CZ**m0Q2cO3FiU
z^dF79J-TI+&)2_YL&&?jWn*}CY1d+`@$xZfB=Mqre|W;S>*BubcC0<?TSK<h;+aKz
zSac~POUnhfaiB?tJ1+$ojA4?6k}7RxDR#dfZglQK4k}w`Z-j(1RkZ*`7kjbKP1C<?
z#u0S+LkWNMD|P(y>}m~j$9l`KSbTRSoRAZPofFBw>hUYed`euUcF>RByA#zt9l~<C
z|9l+Il=Bi#1I$h$OX8a8nyx#XD`vyE@{hG?9&78h;pOj7SN=o5#_zd~&o-^w*-Wti
z984t`UkA@N9j3#+jxIeX;NADit<Q_I8S)s-|G{ice=1w?n?fjv1h`P69+S>NMq;J|
z%QrT%q&oKl__(9=CSK<leW1h{%yh|+O&sD-E%KNfv^96~!*n+Yph>pzE1dE+xy$|5
z9_qn>Vo$st`w-wxExJY{^0PtsR#C=58@D-@#RA@?umz&72a}Illf`;nhW1Z~C`%7@
zrn|TCG`J7aEmbL7yhYBITvHn)YPh8kJxX0qb+q~RMWPYd^^iukmD#t{_2<t}UgL+c
z?RGmm4Y*%ov(&A9c(ERZ(Z~8@s(b>jlif<_EAvTu${|2k=?H9vN7(VaG5lzOj_Dyp
z7{G&8G=zt@NqmHDLw5KL7d(Q+TK8CmU0f^Gf3e<v!K=}o;1O!p?ziYbfAF&8`{VXn
z9k6w0qtM5$ZGdr!{Y^)|?26n1$v*x7``0n@2wa170F9pU%eO&M7~@ZWGgDo*Z8+J1
zHMZ)8PCP(!eJ#>!DT(*fFsA|s2dS7lm1oByFo8t&szUa`#Jovw%k?rkoj29}I<aYj
zOq59)KGxlb9{bd0&TNrc`vvQhm4=I8N6597xm0$|Wp1(j+)8<jc&2?sVq8I;Ye!Q}
z1NXD*JxnY>`0VBAWl8xXVX3+U)ViM>-?8MvDhkk$PbJYY^%H^?2(|OxuNt6M`ohy8
zOl~~swa4$WE_-6N=|JJ+iX>D;PaMWQ*NrZOEcKCu;IvSdPLpr%3r3fsdSQ~3QDfrZ
zy}Cg_^@vf%N6ovfSFPze@zuzZehhuszkS&%N@MXsX5g(Z>8^3_8a(rze!K%!zeBmO
zNdY**CfU79KjxVkXeL1IemPl#oN`j(+8N`rjTwmaa9is4SVUN^a$VyT_tF?V?Lytb
zX0qz4U82Rj)=Gy*R&Y5Tez@CsJ&_CaD@Kp#!5THqorxE?1&$X!1AWAHmsVHV4{$j+
z1?+DVd_$ZNw>baT?{^dX)VfhTu>W*u4~MpXp|rMsp+4G!Fta2oG5%xY*jfMEyNxwr
zcPM&0t?>#mym1qX8jT1-{QPg1-yABNH5LriiTmnX(NWQ{5t~k%uEm)*XxzE^<k!LH
zgZ-GdtAH@~w-+1VVD}n5=49!u^J8x-)^pZslKNuro6~}i`zAEob>Qo9PrN)YsR4Z2
z!2==`pgu0=Vn6kL@bw`?!GmpBf;lAjk<IZPf(Xrw$Q0V(K~d?x9<y!QH&(~IPpuif
zf)WpJ)zrs6e>;km@+XCf#wTF(d6ZdUlK!0OtjX{sdN(~Z<Un)gukNF<QpwrsFQ_o^
ziGE-xE>bQeRD@ihJE&=-z3y!{BFH@qFvL_izy)%p=c-wZxKn|cX^i3z*re}L{=J{*
z$ar>}Zc!Zscj5&H+5IhNfq)G|yr5D6@lEA%omNnt!qfuz7fF;eOJL>(K`qEYJXu3-
z<D`t6)T(88C&`7rh|$n8dA##9`X0ZMW|ldQ4_k8ysbfjxUs->}f?8+`+E9sRL~U?A
zz&zmzu0($G4pv*rYcrdIHMZ2FHjm1F=tuq%8rx-5Y++yoaP!fE;<!{f1*nO&yx*&}
zAmdl1(!a#!)?Fgr+<{8Iiw4aCm7RI~;;rB7Es{LS?pFU@mykjyE4eAS{RpI5OeIP9
zj%4<5?u7FQplazyLThftqv+{6_T-=y5Q~7F<oDgkH(5%M!Bm<=yAHWLbbWHNL3YRz
z2Fc86o~P!|N(6FiS|-@^R6B4xp~yyv4>SBzO(G7r1iwyGbZ;huZ+qz%fn*jpqjQCA
z8ez1^B^k)OeDAP}j4WMv&CQ$wL64iYAqSZlml>M-S9p?;B3RNhN*)_#8IM;VU=%V)
zhrAN3;@&wn%#8`QnUMNop!~AyLF~L6=O22pOrq=Mmu;VeX-}-O-<)1!%t#2PvMilU
ztbfLnnlXOL368VeqCIhQD`?bAcIW+<-O;!e7b&|*^nyNR2MizHjCP!sQBN$?u$324
z1W$l|g(#SgB|EotkAJs*`HpT0(AUwG&a5%+?Ww^CE#A&6<0n-FN`?%7e8jJ3rB*gZ
z#(2zIi?V7Y);hL@hT=*t9>?xLB~Mhtw;P0p7ZpOovj2$-2Uo<5hTbTjnxW_|F=W(t
zD=kzif_dj8#owFW6_cq{I*^uC>8s$CdmtUb0C(K>MLvENU_dOh#rLQOK>pIRSY6g+
zuedkWGIrE#F>esp#ouCEHxIwYnyIexj1(t|o*N{4x^Qhgyt|hv`Z?jsWjpDQdS3ro
zxqw+8t_IQsr+oIW)_*C<D#v$nn`C5Ua6W$Q@*qa(D1{vIuYgsQ*4OD2vMFGr!?4sT
z4y}UOQaf+Am<r@I$G&$2Y#FA_EIq`H0~ui6Khdc93{M*1ey&qqd$9DcU<EcU>5x+g
z$G_+mF&YUKAl)RDZ!gk1*qij}>e)10wlv+JU0oN4`yT0g-|-rwc6F0IxNhngfT9on
z1eXs7brr9yWLi~?8!#vc4pxUjcPU{O!|E#vo>%o4{<9Ywb)9$uY_7wf+FCgcx&$6^
z46=UDj;6!<SExaYqHn9)NSk_lp7`J=K1+UYDKK!u?)+@W)1JQ%nFURlsn+{x%}NvG
zmx?uW+=X~$I(o8`wZNP5UdN2fXldyXnr`H6Q0HZxX%M2m%;JEJ(FCM{J_s<QiFJpX
z#CMw)FmTQx%KOg)2DM!*J(|R}Cj@y`lPl5eAm=EK%A7N_{nCuUm$Aj+@fzIeLgT@z
z@0&D0+@H<ZhMAk-&<Qm=(iJe8NHc0Aam^1kB?3^HL!XAZRFu38N@GN6AXiisCbaJy
z8kN*G*{vN()}rN&dW6rhJmJFqa%EkA{7#h13en;R>A+I~`%|*czl!BK-fsOH({wii
zt1{B7!L{{~i5eW6&m_o8nvo6jJmeC{k@>Y^q@y_M|8yl9b)pua;c{3|HZ0IqqzJV|
zk)(nGivNMP;^F~ehY1x4gZF5~8MHE8!cN%3CKf+^rLV<`HCOTOj6+w&3PPHuIzHoI
zz!r+q>RKXz4{qwX<M?V+nEUeh9U-}t2$Fc)wbw^cSoNq9$yL+Ej3zC$2Ct#BpjMZT
zF|QKLm2uumZ<o^bPFH8C-j<f{igK=&4rt5%%?==KY*-9Ord_=Cd)G<T*hNj|5p~(3
zq=bgu8<W=3{={yvfs4c;A^}W}62(%%cX*$G$t@0W0BCAxJ?H;sm4r~C$^QduGymVr
z<Ud$DX5)WhZOy$8;9rpif;fb&w$JE?am|&Fv#4_3r#J2)S@EI~&t7zP$a#`o36xIy
zS*@HtScinWxwu^cKfgKsLP|MqKbqR<orfG0$7963vVKe@RpzsVqPW3%K6!Q>z8S=Y
zW?bAA>;dpt_<G1i9~AX^x}+hk10M(uTzwrJx7JDwa+raNfv8SN@m$k~40+SF3}@}7
zfWf7oP_1JPt)Qp#T7`w!kyzxk@5h;Y<{UuzFsAS<B}v+e2Efl(CH51JG=?0z5S(nW
z%oo+`m3K0b@<JeFZ;R16&QUe2_ewl!{z3?Z4p4_j#{$}-4oE<ZKtk+SKp3brAhtQi
z6iXTy2x3j=q$UUnUHDiLPCL6cOb+I`+LeL=#@M^XeuG<WpN+_!gvz~Fn5xu6?U(jT
zknm~si;bHK&%MQ)?o+!TEmW^Lu}I#zpgItRJa-(HKurVEK+q6kA;%UtTH;LEE?t6A
z2l&+7zD;^#`|2(nBBrD<!4$v|VTLpeMcpjjT@MUw?45)zUHgMsY!aro*gSdI^=4rQ
z-ZvRQMee|;y(@vM`Lk1^*8}ra{E4IebSyF(l0@-mFLWyrR=mWzg0NVV*Vo15%cE4%
z%IqVlDDtYTR>*wO>G4YBP)gj#X|^G*0i3iK_xC?ku|Jbcg3wMc7vjtHyD9?R{LR9H
zQKA)=UF4@9&q7V66sJ}y)_U{&UIs$9w3&+51&R9D${Nk<yE~1@&C!M^iW$y;7THfL
z>d&Id256zZIF(dYIB0K#lUy{q<sL!4a9AFa@a!E{xj7Yy*<7kMR8So>)s$~>034yX
z@%J%_SO~K@O><y%cE9odk?=LbftNl!&gXl$N%LznDf<_e%HmPWtI(i>d)-Mmsc2Q}
z@pMbbeQ8MxCIu^?CsXx7Fg%8(4?NL4CJI|oB}q%=5DJ2xZohDPn!1D+o*%%cJN<hl
zJQCxgAx(QceF`D-neiX_@IK)W0XGf?ES|%{xwc=9gBv&E0Xky6#iC5grlsl)rtzvx
zDmx>MebbmQF>+S74u;KGFwl{eK&a^Cp;W+&e9DAj$74cCoJ{a~rbF4kXp=YM9Q4~}
zrtUSXz@asZ)lFd{kX>oDGER$V0%-FF-E30yIm*M6xxUpf@iEZGz;^W80bubm4af);
z!z%ujXoj%gHt@9Jb5DZaJ5hE>yQ_|n*N~V^$c<u_c#+B`F%$6<WJ1BLCN+3ol<gaG
z&?~666xPbS#maVC{=IVrDe!SlW~{_>bQ0$Mn>=(WD;FGiYAoE9K~j9RGZTdYSK)}W
zZyh<qFC#{ri#G^te+InB05xgm(<~V(2sE_+Qj3^q2|LccNgiJA?P~6wZT3JOYR(9&
z3G;hH9+{cmsT^$Ocxg2Rg&0~_R!&k^$4SLY)!ll?Y7{RD9iGzYJjRfNGABnX5kzDy
zsbNH5AxTC#DLKm5a#t}VDag|x8kKckeo<iwyn0X{zkM<`D$U%@07h~!zE<LjLE7#f
zIAb5SYs#$c_nur|n#7ACEOr+~^Fheo3>|7a%_&`7oNB5JwszX5Dcwu^tcxev?b&Sz
zU7eb4HDZ6+#&qo}pDL<A%dR((DON1Y=j%H4;5go3Soty)8h3O_1jF}g4@)yQXhqF>
zTm3|NHhQNd%e2%S09kFP^Nvg7NBTZwbNG&u)?(jIa2uI0M`O<Attnlq`5RjmbXRI3
z@7u12gzF(uHO>Or_tZGrsDsm2nV5p$j#s#8xe``eiq&?)t<Q4)jj{5baGSx`%jT!*
zP~V>|4M`!oHe>VQJ?{#UBGSM4o@Dl=gSxeK5Iq~WQg*$m0NF2ej@7+=^#&4C507eC
zJ8N6$)r8yNST#z$naC{P^C|(UD!nF+sOxBWGxF1J@UCFMcCMsBv7|*L(0a5onUZUB
z!ws)RP`L$C6~8-tiqo^d6PH~N+_m`+RF8khAmcH3CDmnVq_n;J;2i$$4VW6?drb??
z!{t)U@hr%42K4>}t``Xjmg3WUKSXp=KinurI5)mB6IXUP4oB}J6LD+uKPCH)6~i~f
zl=mew&VL=<SS2@H*x`SYu>;&1(U!9Alv+wlOR?WHBB|cdIoi(n-9A#jkqa^eN>cy6
z8N*Bbqf~cGoFZW&B63H*y20l*gXy=O>`ZL7jyCzu01i!r$I|t}P9-qP`tG?>`rK=_
zzryu(B!q0D7$ojvd|K|V^O|I<w|I^WI5s*NMXIJJsH;aEkK2C<$!CajDcm|cM=VcP
z)99=8O)_yU0JkS)!5&O6PCgLxK!p^tZU(PJ5}Y`jdeElCDX>(kavk>&^{y;9l_)bn
z7}Zl0+elcO?}+~XjT;g>)xR`{KqTsPM;&Sczi<{Qs7X!MC86yzYQ158u<US+oLeq1
zP%2jHlk0K!xaO~b<lRc?N1Z+7NqQ#gl*ZEng`T=y{XEX)I-prO2c8{px_4|xK7M(?
z#rPud3Bey{)$>1N7lidcWHuRqk(2%Z&fP7|DZ4{<*q#@vPmpEh#au90AIe$u)9SyQ
z5%|pMZKe3nUkozJML)yacJn5Y=;1~>C8G+YPw^So&L=#10y}6$fYY3xf>wjm_lL*t
zgXWLN#v4T4>9mdu(L&!TyRiDsZ9p$jkkO@a^<z@bdM2RsBx{)euzv@}_YTO>LgU^T
z2%7>3(Vz5&RU83pM4+bEO+c@;MdhSVtxVtc1Cl=7whQ>xJljBuprZO?bp=z3_@44s
zr{hm9$dpkM%9u#vOxvWX-h{3Xk(9WnMs+Ls&<$;Sv*dc=x<6-n%p^@niOls3`M|2D
zSp-Q#t~4O!=>a)VMoMFp##-Ol+gL!bjr9U^7(X<Uvx(}D(l%&DT01{z>>!sY9j{yn
zh7k5+CuPoS0}}Ik(}BhEDU6-TQoN2k>%Y+A(UdUhrrF+hfME?>Yct7jy&-KUNF7pI
z5ZwH#cxt83wMv2%LLE%4a5=-x647f0noVj6KSKa*6WBR7g70$>EL5SPjLpb{nuYHn
zp+Zjb>N}4fZrLbklZou7L!ouvMZ1(yp0_S7TB=BlO_{2MU<bZlO$M^%9}(Gj@?<-@
zu&5A_vmvu0f2{Rc@L$_e=ZHeI^g>(2lq335(<sZ$yIq`|%2$bz19rAgq7RA$sF5!P
zudRToHAOHpiBDrBZ8Clef7w$JS$eSE36||nOwuI^IozoZ2OZ=tGCCRN2PMJ$s{4m{
zJXvM8YF;LqvsDhp1nHiLcpG)~E*Rs@b-`7v1<R4n+Bv~A@%$H+vWaakG{r$QtyT_^
zH4jlOk*j<D>5kao&wQptTKnz8Hb~olwrT(?aaadCyIIID-OcfOgdk0Fu$ai|B>geP
zBV()x>IM=JPIF2#eGhH>p9T_0(XFmP$2B4mFb>6LFA@Pvi0(K&z#JdnDPh%RBar^I
zpovTVW%(hds6%mz1bsS&9}02Nt3%O}b}mW1^|oDjsp6t{|CQK#4)}VK5hn-^aXnxf
z|8fGW^rH>TdV1_a+i7$YGkYXE{UaS~F!ZnFr*Wf07cwlXqXrH*0QuujXY%HvY&~|p
zNr~^TGJBfLz(D11%eAPVz=pRP*ThcqEhNcosUxtOOf}=LszJ)4*Ctfi-|ym0o6crE
zX%}B5U0v?BO7FGHtT@G)N3`oNcrJiUvUT-;hgxD0j6|;R1OCVb{XskhW=+qXxHO0O
z6dof`XrB!yk@Oe6I%Iz8Z-v#dx*y&B|D)_3qbpm#t>4(L*h$3|8x`9&E4Hl_+pO5O
zZQDu3w(aC*?|shwpMBo<ez>izwm#3<+HB()zwz|ZyC3LBC5}e=!@W-NNX_TQ3C17*
z6^YYNT&2X}XnwT3?oyA3Ool&-KS^a(rLde6_Qy-r1?I-3ltU(8*=`Q$6svD%fu_|j
zKI>D(or7sCG(&ouOik#yIHNmDU*+dZRLNhQmoQr&5m&l4O39Z5hdix)2Ea4xZhH%t
zm<tP4Qiolv&zQIw$R+Q2D(1r*-d8*D8dLLc4}BO=b*#|;h(?){zeJ;7lO|?{e>o6L
zsEvF%5d5?JQ4oUK0368)CI+&Ho^A6L(NXFw!?E{CM?^W2r1fxTSjdR!h_f%8i%&pM
zV)SC-H*!NDh5WoJ)715RtlIg(`Tl<F^m2{ydpZ?qTDA(dt7A_OW#-QPb9Z26s|sT@
z<?_uQ&;j(xS)YWW`*wP8bLy+P@!Wel5I9`n)79^00nPT2+O+6sLt|&{Vps*}{9q;e
z9n<H?&$v7o=D)E5l_ou#$#2=Ns`Y5zu@iUbA8UlF2?;eZa{|K2YCf>p(vH-|c;XwG
zr7KGI4wfWsTr4HfIQT3=Itd<81WLsi8m}%6ppdO*_x77GI$p(9oRuK3_LX$_3==S{
z!~c@+@eXw_in+5N*VK3J)M++ejZiduP3)KTIhBVAuq*Tl!=5`lx_dRCl3Z7ym_3SV
z(syIYwt(j$RM2h;`=acl%M;ZKs|V$l7us6NcLrp9FQB1Kqup9ufvy(&KBI(2Sf9`d
zkT@f^1eAMz>@(Eel41&U57=5hkW>TPgg4(ObGgyY&SOH=)suoVsdt5S8rnvlyr>Ym
zn#$6u{BU?Bu2Emz5anE7>nyixvv@J9XF0nzf~&q#j$Eb*{;3?uOuSc67T}IZC+Lo6
zr*Le|4>hn-q?o8_27HU>gLXPgx^FHAK#QR=HidvVzO``TTsbwLhBU+GDw={N)x8|p
z6V;E^q60GbVjul2(i8R*1F1mG60|g{(N#H@&82-o#NaHPU$skoUKKnafhO|2x-@Sg
z8A8bIX5ahAUfN*=KvE=9t%|m{VzFv#^N~ZlXQRoovJ~~kb#lzB#F+@ohAF-jVCaqo
zxfcePA?zDh@-X3Uxaeb147p=*@Z%ayBK+oRk|u^AMw8IGPxzY_Ar_6_h|U39=7A?3
z!v=z{oByP=m*Cbj`@Va8MO5kS4MZ@9V3Q!I%l#fuqzT$SUMe#|?)Gt<CW7g~hCv0W
z28iT`PCKc83q^yon?r92HR&5a;1kpnQ_3*$2aoyF8nmYG60DbC1I?bLuYn4EGNpIE
zD95*oNm2Tch-l{CkjHPS>3zsS6;N`H36kxZxujWeFWlwP^+Fj*VF9c(UYSvu>BxG%
zP9VD5gFK{}aR_%U$9%7jJ}h_&$q19i@qPq9n#lSc>bp;kYd!{?_Q&e409{L{vqk|O
z3vIJ}D9U2r%>v?-)9A;RR16So>vY6U=iAl_l0T;tMRzU;+Yq8_oG}Eke*xK0ToxLi
zo-{Zmkk-MY@gt9{oT!T@#)V>Ku}^qn*dMu>vf+M$jC>_quLvRa9!~l;z@Dq2!<WD>
zdyh;Oq<uzHp@S_mUV3t-0T4lUj^=pE@1B1-ja+SQGM6vXzNdR_x}G`WfwOd3$5S_-
zBx^)i(3Gd_5Z;V8jG^jT0C^Rff|+Ik)u2LTc}Wvhc`$T)x#Y-S7y5x-*CdA-N`J1#
z*CDx?@7`ayXeP>{_iJ(PgyhpT!}zFBo!$lYq{M9LxhuLi7?fKm16F4|+Akd1x9rV=
zAKs~Zd_?Na=FJ2>Q3)BZSlp-wAj|k<RH~(557v=vbAf{I)IJP<|EZ80Vm~AADvZ)B
ziV<8gU}PP{qxU#y!vXiT!jp{*@eYM&u0)eXlAHHh`i(mV&7!yNPXbFCjFxJ$)8Tm&
zx}R{9_s3=-9DQ!v77*=1O55M~t6nFn$&g8lt?`cnvGFnEnGbcR6S?(zXhu3dVegzQ
z;z=e?6KF)EZ1PMsetOUOHFI7hQY(BTK2E>8W%jDWXT?@!IS;OvoQpeQQL>B-2i^jg
z+8SCq{<2sAre*mMc31^hMx2d()GTocd*)GErr6f5Z>8+e-uGUC(y(>;+vixO7^W!K
zT4=2W1s{o+Z~C(-DVu;N3>7s#k$?I@7RKajUwkqE%l~W#y&@T41o+{EJY*R#{`@{8
zvje?><p<~b#Z`kO`~&uK6LaQ4l5E^C45OWJ3%95AYfxmO^SU-vZUd3T#cyO^6=XNn
z7@(*+(44V2Ua2?}BIW$c+KcMOYHHW#{7IG-O3`*Wn0lqSB*#_xgtiNk2E_#0p?P-)
zehY#I>%Sh0<?l9?f&O35)u1+Fy(jY5DRKv@y{&>Xef0mX5Yl%{PzJw!k{Z&)NhIl;
z4Ek!vXzkpHb!r?PUqX{l6QmO#(@8yc-Bsy2d2)MxIA(aifs=z^fIJ_+%q}Y;(1uDa
z4H1|C9gK<FFS32MrcWC~@x=iUylyIZxIw^5FNt(9m1u*;vX(Xrv#Rpgc!`?fu+rF2
zZ96@_Hw3#hkQ<xS&_s`ghA0VGqOa&a>Gvr!qyXq%NHf~vuq_D|fCe0Zi%MN}<~N`V
zOb4<n^DQ8r=u)*UfDm+?dh;MGoT=67ELxMm>b_;tAnP@1>OItmt_A{H6=9ow#HB^L
zMk>HR)Ns+N8AoGBQ@p$z_GWg*USaAD*&}O>@K5T1rY6XgxLavL<5bqw57e{dHTHMh
z<iEOR{t13w3*old8&8##xDhhZDgo{SXPr74uuGJX+REyQG=lnA^y?!KNCva<NrzOK
zJ%I;h|I1%X)kl8YW~2b9s{%M#4YqkdkAjw=v?wK&Skdo6P09JY%lRQEU^P+NN<18)
zKeKO+oqebdt3{N7f71!_Sz<MD?&KY1<(gnP(qzVD*l_BymqyWeb+Pg-bR+P=&`MMS
z6@cz(`)q)R%xTyHo4D;J;m-<NXpZh+6R?Py8g`ubAldPZLVXADAl&pzeTP${f{d{k
zKo@hM<W(DNwKw7PjF05@!xn7lH7MQO^|Nf@W5Uqi7cdg0d@I_K*07PjE9zcw-1uQ8
z9{odHNxZCIGZcq|8mQ!|K8}+g?`OHIc5?!R-vqQ<V}PfYGIt4QkZIO#`)KAmi7igJ
z5@S$sL!0nPotq#a0>R*EgFC?eiYeklf$$H?jTOrebIH!YU3qe*bT*<GOTT(|G#q!s
z+VoGSJP}8?I>lGV`bE$g%qen1CAKuF8;-PE<Ku=6I|A5p8Z*aMn^;p=HFx2~^dI3u
zeB3V+#K1}pA?t?piZ!H$Vj{Q*;VIc-noec<z-x8FHVrTU$b&@3&n)&R_4L6e%8`!-
zAMJ5F>Jj%t3OPdnKno<aOwk_^aVQ4&h}%Qx{--|GXb4rL<I(U#QZsx0r5aL_p1Bb_
z`@wxFpW7}7Vw4-1o1~k=H`Sr1-<Rjs>(90}ZU=eOd4=qQ@bv6q8AH8_U1wsl{V(`_
z85>Nc+F&Svj9_W=ol`;<sJ9?@7Sgu*2lquQ{V~WS#Zen^FB|#0RD|{4I_vEH-rNvX
zE^3WQ{2FxnY^To3^Y$Em)ys0G3t`I{>)1Y5tt4$C_aZ$P_XX-(1;E!mgH89U72u<q
zWpxcZRpNpcS{igSX{j5k3A8^NY0Df*Kk(S22Yj%M!^^CT*eE3tS2^>wn;p!5#VcCO
z9~6q0jEtlE*0Wjp(hV2vHz-j4`P16%2d}cOUu<>Qnokf~QN*?2%xZVrkW7CbCrQc{
z`FYI?B=@K0C<@bYOak~7fLD%}^qr~e^9nJ7w<zwP&Xwh#D^f<r|3$`MC5IL1?~*ey
zKL~I7ghU7(qY1R>hr70Zk#0ZysjDy`oI5}3x^t};*NxtvG_0Z1K^4pL&aw^m60At_
z9(?>#^zouz#fxys`%`xFGvJ#qtnb^M=TcD-t`<s4rcGA{@ZS9~3CHPh7ZAYKoiisY
zCCeM|Vf1qCMl&J{MJqT+1`VEm8FvKYqnh{~h^WkPmf__6!7-P*a4ggB{l=u5>-4ql
zWY(?cAgojTuu}}YF`3F|XE|U+VG&v9CEk5O-6=3dZV<y`Lpe<<>b8oOLz(Hh_BW2T
zu?!Om2rDQsp+JT*DMsEk?JYn`ZA?iNUO+H3;}JezRNB2`@iixN>0}|VsWi<Ltnuw8
znW~L2cBPfycu5@u+*lIS+t$+PH*?^?Q#~drH(Zmjyq(PAt=M6TXJ^wE!law-5R+x{
zz;frtg*>*P)Gtv9Q$7Q}vfJ)}gjb6UMI^IZRPONTLQ#Bm8p@V4|3N?n*mxR#y`*pt
ztgAw`9LG`OD?Xh&X@?*7!PI%MF{(N4&tte;VM#|{|KD*)S_pHYTEjFRp6=^xL*NN!
z>89=`--l0u+VoFUhBFmLWfnPiom7il`rBv4wW@Y*rRg@5x9|q*?N05tR8)XethGh<
z{(xA;4r;0cW>5Bi8%P7lMx16~MYZ$QeH2I%RvO}JO^j^i!BKyBHN_$NKp_>`>Bm}0
zpkq^9<p+lTQnDgM>(2huU=S2?E5B&mzY452Ue5}e3Z8TkQ?^OaE0u-&9I2j_U8tKz
z8m@ksUkFG`wV-e=b|WskCP*re)q=TOjv3}5d1E2kZX$I1uK5l)G%njT{xP|n8f0lC
z0EyyU>r^;|5}VZUnNVD;iTw#5GRpA@xx=VBbF&?2fwKUul2ENIO<YDeQ9XZ=ywhcE
zS>E}(oe{g+SH<PsO`QXs7ij(NSI7hBWLqPx7;pEvhA}1lU73~%8bXd8g%x{!xu?2a
ziQQ3$@Dx{8Cwm{D%W8&8U*Y3790C71Xo1W-%7l#Mb{zO{9?!V#23UZhVnU?1aL|R$
z@~HE=#d(W=2<<H^Qxs2%?-h~M<vt{NFy`mN+g1~ASdyPD0ZLQ5nVjXEJi+6-^k=S0
zs9ohfjVzW^pVaD4VYJ{s!Vurk(zwfj`ZWp-X^|8x5F*C_2H`Rc%?lKf^L?egZCLZC
zg$ka>JV5trX9PFL01dJSMA0+-C`2y#^ckzoNc7}JtZYJB;L6D!r$d=YwLolo>jY<b
zqjg3H_sbf}3h2$qbx<MPS&<35l$@bCES6Rsgek&u*jo{Be4#{fd|8P?G6WO2bVPm!
zEc}oKEPxe&{WkWLfg{Au02B9l(#}glD`U{mK`d4L$*fh<5ubIs{}q@S>eS)n4EKzz
z+NHKYmM11%!8=B4E1oD)<(!^d)jp)dX1djEQ5-Zo6PjW5PP!yNxd6|wqH-QvljkmI
zva#W=MmoQ-u|Wd0bFnR@L6fF)QARdkaRiGLK|1-@tVCEvqLK#dhh_?I9|BH$1s99U
z?}SSCX4dF_5rvmH+2I02Ga4Eevc^T#8nhsE_2Q03`~&?V)rSSk6;SL;1<bnR6?_*P
z`9C$Tr{$(@2!CGV-Kw5fFT1=S*#;MO{s~S3X8k*+J-!hd5zv6u5_QU2J@q95DIYR$
zg`(F28-|Uc?RMsev9KzTi2bx-gbe;Ni@zp=+PIa-4RX-M&G_J4j_(0HB;bCq6;1J8
zADg_r+qu49@7|raadR*Y9Q7U>3@qH@7QxxRG=DlXkg*%gdU8V=T#XEs6_vd+57InE
z*z8iPHt9_907O<m2l)oe3YzLuZCN;$an2Sr+eQ54o*-@M){uvCU*qGI2itW3pHKUH
z?}j_aXL_m9ROJIWejwV30lF|6zk*pm?rP9j)64mB>u7pcFv);~k5z=?Di1M%vw)8M
zbBg%((uc+4YTCnXG5yx&ILB&3yp`y#iB$Djy6?|!00r$7=`XoHrPIOmxngR|kEa~K
z=WrAaq6&7DF`Xs0Ku$OUvBqMqyx_kVW?R7rLk^EHPu$5)k@H1Ekg5*P#Gk=L%l*Q(
z!>q$()GrX)DO5AXoWUB)0F*h}{3+MTcz5SWsrlTR=uY2noOy8-{q<m&m&;*_21$6y
zkHdqn0IVpexLii=Q5eK`<#g*grLpAl@UYkCuWM~pb<U%UIixP=o(fva9d_P6lsROM
zb&$lEUtzet&9{n%p)AJ&zv1GHv?1U{;v`5%vAa~02nQl5ofQZPSo`Xbn%Dxb95$e~
zP2}d>bEY0fXo`G9s(wsEOp74z!=3(+F=0|?0?4twxyURfX|j`2+wa4jbmmUfIsB%o
zTLo__G?3~A-LMq+)*QvQEy1e2JQVL?Cdlh%+FK&2c*Jz-qJbm-P@H&_MTv;qzT-3N
zdLpEjMl1=vEbkm;okd~8=3IlTJ7`aP+BtS<IbPn<X_ym%Mm^BsbbzfLQ-q{w@B?l8
z5>T;KY%q;Y`p2-6C?U7JhHBh#;DpxpdR8FeYPQWMP3z2F6WVA{*|a^YABl`$&fXy>
zS%|==Lj;b?DIttVq;3@{y?b5@IekDsK$%jl8!}k+jNdXlFu1cNnJuCGTgu?Z*gAY5
z467ak1BQxe^=~9xE71iOR4MSWNCu|LI6&ZbFOF!eAw$};ahJ;pcrvE0`}gIY#YN)z
zjj%SdNgOyFN>0@gXyk=6t7vif=%5%XCEPfN%rx-$=KSyLs~zw!erx&!MoZ~v$=fGZ
z^(Tkh=8I2pm=2R2jh*+d1ZSTHpT@5uT+%b5gtN;sJfu=pis_OQu+KbuD1}FBB>+g0
z2TWc|wX#Yun`d+~b${s>Rc%Rg=l+kOo<d{E!VZ?I+j(D~7SluR-p9(aFs(SC3JnB1
zujqk-7wlIYRVvR@6DxYtp?Fz4>qO<cB0VbbS2A#>E7_2i@|6Oi@$oP$$3PZG_AH5Q
zZb6yv;8#?2$qpqRAbCRdAxJ_>=l}<NFv;SMWGde$F?l<T_P~R|BX{hXenfS)Sonrl
zWToTW;P4e`q}pEnliObYzO^CydUK8^&q=nB)a#(^Md%rx0iKG~i?acGGQFN_CC|9d
zVCBnHjCq&GgKw3+*T-Kb^170$pBq(SLpl#bl?I5%GL{<a^~y7O)K7MO*#H|VOBAx#
zg_nf1DJwhk;fG*pJIAUGdAo-b5Lq2eiy%47^ZNx)`!C^`^lmO1*$M2lXJ6f3m(XD!
zVO4>DTxbYFRECg9BqINqG1tJo=qf+P+^on!{@lH)7hPT6{4tTEwD(nsycO-`?Z${(
zV8$9bUQPKx#&gDxW#N~eB*3rY{NN0eqfu{EMf&qqrIVZRvV=&{{$31f=9>tB9Cz6|
zF+rfDrZEIl;z4Amp)fhmsCltTP11%d)OEBWxQJ;_47%xY^OCg}T}G6J<3k@&ZPv3b
zEzowu!tXd9E;P3LaPn!+O&q+j5xPOYT^%Yp-(8+p+Uk9s6UmiEZ2<c02G$r#xdEm0
zeYgW-c8imnGn-{K!X`}Kg98_hgq)q<MO9vfx|GH>6?K>}mey{$@5Vi9<2uTQRaRJ7
zeqd_3<m=C1M&rzIGcp4>PJp_`{km)KgIX8&jGODX7z$=!7QEzZOj(vp$5sAsDOJTl
z19!yqlzDT)6HcJuN`R^$1AhYCjC*E2i1@Z&xFy}5#jc_@bXVTW%b_Q9_2#3qard?3
zfiO0GP*jErW-QCt;S?)%kMSmMc@8E`^*4FncQWztcuRy|d0LXp|7`zW;FE|qAyj(7
zU#BOs<}$N6H)0AmN8>{>OSsP1OEY4^p2vP-EY=^c5(?J#aRPvsI?7qfS3)(aAB}82
zkngH-DYxJ-@>yivoO&vlD^FZntL705d8I+nV(IRAN7a_5=kbp}%niBHE0N!HBH{wv
z;Gat^t!vsDz`v<hDPLY66jnuLZ;Y%0uLT{-oam<mO)U#L+ZhbG3{~%|vBm3rvvhfp
zpMXiPFKK7+2?FHx%PE0HI_d66Nb|_t!>^W&zXu%7z-=X8;3(VCpe0tRzX7t;j>W|M
zDX}Z(sv_Mimr#Y4@I)pWT7eL=_(<dtUi}7kUkbcJW}$EZsR`xd@F82Po;ay*XD;};
zn}lW-&j@Z08%xmXONcRbGV5=`vhis%buu_ey;jZ^unbGi7Ij5o`|9gSh3sRqh1&yT
zZByw(SzI*H$9>1Rcv2i14ZO=&b^!-V1RoG@hhawl;0kLp!!zF3pq-xWUy{xW)qe)<
ze;?;D3B}+P^F(|ode)%xG{ZUC%9DQ&N%GM(otL>Yj`^FmYbW-vV{?nQH-RBzcyU2S
zc;Oxad&h_^A3PnO?l@mQyO1Ee+}E~g`{%#gLnq;zhO#h#$T}*&gC5P{_(TE$F5KQv
zADo`f8yosT{lsIcH3T4o(nwQ*u4q|tePs0oY4X+xmSz~k1Lm6KvTPe~ywCx}hsulE
zqG8E}BoL>f41C+UBFYOT0mbfeF8h*KOXVrjERcj>t{bTICw#zYY2g8`wZGiVKlEc8
z=CFkt<L}#vdk!Q$1si_S<ZPV*7`;YgSjHpGA9>7)&%}s{7gEA(NLB3i)i|TElL3{D
zSU*DX(uIp-v-b|R^b=!ktV>CFRYW;ln-cR%{4y<aTc=aTE=97-&~h46J2(8xmJyN@
zhsIsOn|uIC5iH-IKd2c~VyQ$O4m2gW@6k?T;8!W!hN1QD{oTbA`_j4rSykU94pIVG
z>XUJi6sb{bm72WOsUdeOT+DwjlVFQ5@+_Pjvf~NTQ$FWl^ov&(JqbElCW7yOBTxr8
z_TZ3PP^m^q3Lj4dA)M>`h6RH9>`5c-N5tR=c!b162Gig9{b&tLqCjkY*_v$`3~r^%
z-*yD+<LK+L`V}w)Ex$MmK!;EQYenE||B)YEKn-}-3d>{RmoORIdbA&Z#a)6E&x30u
zY57<)zWel(TolUUfs5LN#IQdN_Mqguk&m!BtvWJIMJrb1o=qQyJr3<hd&Z8YVVO$|
zTWq0q@k;#hv0J($?sm)18*J3`GflkuUZWs)vGj7iLLsXJ21SJ$P%xDQ-pJ>bm;H{U
z1B+g%?<X9paujjH$gdq;7IwO8OUkw!bP>?1os{W<ib$M#*Jre$a_C`Udx5iL5ZE#G
zyWKhiqJgqiL_X_ED|@k)NM;oV9MvIYB41T8M{hZ%fUd2uO$l;VN84<|#{gL1t~4>e
zHsSi692HaI72~rF5P)lAGn=E}YL+sFGO=)w<n&maEqRy|6<sj1GWRaz4l?}KEPVfy
z@>{fZIF)nBWei>gok@(d_2dJrY}<Qog17jZV=I$}{2HC!&t)u{mL>4=FD=Eb)$>Nv
zK*=DW>gQ_Rw7(qgLuhc_pL5ph`V_=9>(fD%QP@&JFj~wY03ORPo*amt&WpvAxV~EB
z-3M*547I4^hQdphkfhlOexV?|Q^B;GR;CEJAgj_iyt&RhICst9*Nd1OMRv&w2${`{
zl~X-!6GftIgt5{J1x|aq)M+!GQ|49a7B^=^C+8_9#?Rtc+-FSgC7>iqEprmr^$ROX
zUt}hNcMf3$QDfD54o7atka)Q%*k>fL^_JdbPzi%J-jF+2#b8w{+MYocH4Y}DMVFz;
z+g)-_@sjw=y#h0#XqKWd(tIYS&CWN-L@nv(f2OXiY+qAX=Ko0q`Tr(@um$rBD&dc=
z5nSB3ILe_kLf5Guq1cmNc8<@d`g9PV%aZm_n_4^{{a;x!k3+}zLrr5%mk&GRxOuS2
zO<a{SlIcv{`&O^#=2EtU{r9Zke-T027ZK18Ry;|4IpVawTHP=JX7TAE7<Fhm3Kjy&
zMn*RDY&LdSumgH|eLQ{%;+9>=%BW!7zCXOK(qUYhe4~r^cVj52M&Cq6Jt`%6)0l?g
zs=qF3>Zsn<hu=}NL|z=E2c?j35hXch9e{cQ6C;4*1}8ogl8KAh*ADDM+RI#2Pg1_i
za}Uk3CshGmBTKFV><e$tV}L6Kdc%AV3rOr(F9fS+%AV(mmxsx>p~B_sK+F+iB5BV8
zr8YNP2QlcEbcAzC8+HeShC<u18qG|Qwe6=W#;iOlJWANV=nl8v4>c6k^LUgct#^?#
ze!B;H_lA+4l}Qp_4{hH>Je()|4I<52)?30bG9~`9L)0D#00SSANx|O@gyKGtbSXC9
z6IbdJ@O<sOyErY+sB<zM@dqbBRdv2hC(x5AhF(AOg%U}eOQOGcLsi$8ZbdToH&jpN
z_m9n22(2Bwj3gDaGYRuY9S)wjqUe7O7kDDQ47h?VHtU!+dEHA=<jY$^GX*ux3$YJ+
zWs<>Q(qxhY`~?J;G)=Y=YWt&D^IpPf@qYmU7uf#>g7rUNKwvJ&>+#<}fKw4k;W009
z7o89cm1)Ok_S1=Y?JpqUkoy7x0*cGOfFSGt1cE%Lq%R;i{09h{Q~<W~45pDNmW)kE
z67q{`XvVGj4bWQcjYy>JceGn%7;S4R0x5QA*j3~V0V9X?YYIoCGmv^!$bI~H1^OW~
zXFOmg^en1iCN-G9lwr`XdbZI#kZ?@56*n#S;Ym-03Trf5JVbN+;MJtY6Vb4#$-qQc
z<(=Tc6?^Or#{J3+e*v0r2IWcA$BjFeFB9E+NC%TQ#2TADERfA@2DHB&!Usj7X1M&H
zK%fcs4-n-22MCt7PhL$I==v7QuSppW(mkxbPOo|Xd=7m~JVZLD=0)@6*JXG~{MI>a
zPDsVLVckPHI9e-!1pEaAXMX_!-TwxH$}b@Bve7v#0R?tgm~0EUEd0L$0mffI;Qu!e
zIOP8Y1jPRh1Pwytz5fA%48o#V5ZA=zv6gvmUwL8;At++1a1H>XAL;ytWFntuesMGG
zhJb^KBX^|fPH=hJaL~#-RH@@apYS;ul=?4*uYB_SUl#^n9SW}J&jTEx30Fb6(*RQf
z{VWw}mj`{!Z`E6_R6OFFecFEmf#xl=>h_bPF<M?#K^frk@(T!}hbkTZ7ZBJ0{sRQ(
z|G$AC{tF0X{=b31A^TrIF!SF)uoljB%cWWye&<iq2app-gq)gA+@&bg<7_OM-^Wm;
z#F+{Sq*F89!}`p2S7{UGfk|#0heM~D;PM&?lkrSgkekw{>9|JPAsB&)nD!&0nVzj#
zuzsibFCef_|GxnN))+GNyFN90qk=CC?&ES=cald5o!O={KwV7Kgj&*;K+>#KS8BxP
zVDWxty}F0f>qo>&^m{!8{{VDrS5zZ9gV{h?H3hnnZDbup>oKKe7is$n4;q>{EQ@Ax
zlpBK4_)m;EsX<J~kJ`qQw%dUB8gD@E?G-VhyeWt|C+#jnruomKMt?F!U3uWJV_`i7
zk)rU_QwW%1fDZg21TP2YshJNPnM)tLg7353b=XGc)_ZB~w*_6(g@gjqQ==4Ew2dHS
zrQ!Tp^I8@a`FfK}@~In<!%1taeNoYVZZ1x5h42S2dzuB??wVrX1hOf<;yW+rzJTV-
z{EmjDb?Q=OApU&$L6<n2#dT9Ti><lrFe2V>cnH9Y0JP#+{u<#H;y=bYkN~dC2!nH_
zed}SM3a{a+Tk#TQmaaCz$d8|wtIW0|PsEP~3CjEF@cIu~JcIr@oUxByTTiDB!zM-!
z(ffgvznk4F+_UJcUrwM-65H{*&s{I_X$PUNesddQusM6WAZSK8!5*{pcoz{Yly{4#
zjpBfs0@x+sgoUTo9!V?b_jZ*wtGUP45M6oFfZtZU@~A2ca&K6|KMB75z=JlU7w&U<
zX<&#xPxU)$?JI6k#@O2a7D6)mOSjrptE&-F9{bm>Y<yf|3ox>0n~h+d?pu%G9S(^V
z%_b4~rB_C`+dELp(fow&J#z~V_h~&a(VBHrBZI9~6RSt5TIRyJSy)}U6}C=$`|G&d
zN6XyJZyv3}BfZI)(syyD3%H3Y-Zywp#<1>xPH#5)cz%Vy4AB2|l^auI4$1%*^vDya
z(|uthzPQfcOf(JA3f`WG&=PPupL5xz6jsHIhhj-|cbt50@KPvnrD&?xm3^D63K>1s
z=C*nC+<^2w&90!4Q#yWMK5=E=3sMQ<jJXKLb%2~4e0rb`WdAEN)1OK81U$w_M&IPX
zaZAR^z7+z`nPE=6-)6}(N>x>O;#Sn-dn{aDe=0d#8rCjtq!aQlQuFn}Pt6dcGZCjX
z&aqAVZOy>)jyaR6_3Eoxe1KK~hzncdQ*&;d@DEalvAo2I+9!L>3vL(B7huc@oqsYg
zf!Y4c^qq~K;eWn$g?d}m87tiRE!u}~$`QXftDY3{*R{=GL(Q;z_w>J7n9!#<Q(cb>
zT9h@eY9o2ntl$LaCzC7xufP*If%nUx=&sYFrI**!o9DUJt6e&8sEbSovA$K6u8l%Z
z0^p#g`{?Bs)b#MV-K6xs89<u@2lt2PtJTx9)f6fG#yUBh+xAY+y5er_R_lugCkE@`
zo2XaT)pK(o5*7G7GCLmf`=}#5qm}c0hr~BqTKX!mH}2<F`*b%(2qGk6&mDncxZPg3
zyz!nJD5HZ3$M5;VgGsbXoO=XpYvr)HxAorp9k%#^0y^x`FlD%<Pylc8eW6UUtm&fu
zx7D@T$U={uI-pUFL{U;xfpXNq95j2I=a=;o(Nm^))v{f4`u^-FMUUzDv+L#o%*Z~|
zy{6G0KbMhz>N093mDlVn5xz|amC_j&ETwezHGD`z+}IZ#;^h@Dzgc-^@syU^mX@}*
zHH+4f1@_mqF<YAN4*@1#EzjkG2?bOSvHU}FDO}%Gt|$#$ZxDyv%&7@)<IE*d@(=72
zTWZTx?rOjctm9M#WY_cUlMmzxEXZU^52#M2g9ZSTwjw?=p+J=8Hwz`hlGl`J-yg5K
zERT4bwo@8)m(8S(ZwosTQM<Z%XJF>>=wH}y!Qqo3bWlEi3Io)&90SBgG*S9}s6+h(
z31?N@BCz6m=^QB{!vx~Q&8W%Z4N1iEP5Wij3%J9{g!0&y&1hyTd=lKy!}VpNBXKB_
zQBZ+f3y>k<n_U)Ra*vwBbV=%cZ-i4s*dt-%(BYS>RnfzfcW1YiYJSH?n2F|)KGTM4
z;zer-fX*7fD+5#oGD#8in9&2uxAK@ZLYV`9e=KBbU`|jm_e^Yt!-_H^-AT|+<lftB
zy+hNKeWQ}CDO=RlQqI0EY@(hS_#smH?S`9G!|8%{9!=cgDDzO-h2KeU;jr~Og{($>
zvOIUAhz8>v$nfXI_uOT({fJ>}6yRFb{P|&4_E|aKVmSb&o1*6yvbxLe**A%_7o~Xg
ziU;n15qQ{OWY_?GMxE4srHx)Qzw{Koc+fNi`0%_wx(9xSu$90KnR`A&d68s8>FrVB
zFhhi5j8^5i8B~{k+7lx!QK1;m%l9R$Agb1H2nWnrZfGX8`UGN9V3V3Dz+RFtIz$so
zGyPl*%36SoTzPfEng_UwsC^)ZOZRYdgg?WQAco>VZvN7n_L9}%2mA~V+YNdt5Z~(X
z{4heFX}TQd{NsRqb_#872gA<rGM0@xOXsj11wcvQ$rJ(#m?B+Heb0k5kos!r6vnME
zM3wQ6d-2cH@>fd&bq4e!Y(q#sawp(2u(7q1RyqM?Yg63$GgyO4X0RZHvrMD*?jjs&
z_bjbA?hzXlgY{<d>87Rw^_U!Qg+b8_Skp0K4)xMpJ+Vc6Kf}2ZqBR7fUW6kt_MvQ*
z7~)lT*X+b+Vq}8iZ+eJ96HJ>SXU+QhZs|J6a%EJ;ZR55DT#A?4txjXo&k?68nD1Gk
zr0fC5H3^NH@KkNiCxjS**&}%Fk`7y<^+UwGI$@OQ^g>VP=~2(-4@7_!$Zy7mKu}6I
zyPa6M2PA7=6v35f${}-WvSY=ZBDIUVuCp!j2;5ULm4ib4iBJCD-ga#Ux~0|}b)<_^
z!pWH<;4&NDtB2cC5YWMvR1hqe<@t-m0FnS*w0T$5aczH~1ZHYv1O`PRJJcAxcw1`g
zb&`PKdpP8l;|(bUY3@o8Y>qLy-w?8uywwkk#IWVfLzO|vMJe$Qt%;3+&nN3LZH3E3
zt)6}F7@>GS?}2%*>(dKjh$eJw%#CJ*9@z)JX<7R;-a%v=00TUH!KCEC`YF(LXiNby
z=bfTQlc)?XBp0nrQ{kMiwHkXHb!EAZfd?<@^qtllm1#odSc)lKiCGs#5?$;y<U%0O
z$1wHVpah5SR?)^p3w<SL&aW%VkxG@~zZhh8F@9a|p`VT7E*DMBSA<);Z`QL|Pc!*q
zx7TPfrz{_4;0mLL<Q-R--7`EWPh<du>=%Bu9OOA)l&gQxao!5&oQ%7~uXy$^diHBC
zTpUK9!b)1(?s)28_pZ>=Zl|F&koH2Tzg{k^*|92|#6G2(0}`>jJRrT@40(0rsbCCy
z363S<!A=JZ)IEw!re@*2zi}lu{#^4TZOxhE6r-u1??tVbm71UX6C*W%j?2v_f6wU9
ziA~g5H5t6h+8Pa0qPw&9lXR8#_|JFPz;x%5U|w0R&y-%lc2O}<{FDoW2Xo``z{ek~
zUkU`e2}PT+|2n`~zYMHM0IUrE+KHP`?unYGhwHdSd-e_Lca9Umo&bi9X(pP0x64*+
zB}aJs1m|*I$&hbr`%V(zoR=^CcJ_EP=+vd{==+)bqx-W*8*n{mf-f6<8yxQONy!Pt
zV<L7@d$pQl%!|@8_I8dc;D==U6Oj_kdo*?k7laTnRonMn6|di7hdX_8lTLSSXiUCQ
z44=~9C|szs*NQXKh)*`n`)F0_5ZHmk?1l|hzi3~n-J1N@F&E|N^1a!N7d9F=dB%Q1
zzrvWkNJ+__$hUY%l0tb;VI_1182}TuwJZFMdzHN+TMMQqJWFaT()Tj1)K+?{HB(0E
z(vnAj;-SEJvQyBtAx=*}y*hY4WOuEvtuScfxI9TR(#ssL*vduaRjB%bGVfhzseI8f
z+yuJ=;##R#D8EQELux^<H8}^r0`3S*<dTEp$TRV&(GSg7Gf4IE3if9G_*hh>$c^yb
zSk?JfNml>TI}|<%5F`wBQr16y8t#wWRlB_ahVK3AsSh84@O@ZaRYbY!kZDz$VvS*h
znYyw|Ojtx>W_<l7)cf9N2^D;$+`ZWKfwOKW{dzUQoG8Y7>t9`OEgc5TTm=|8WfNWx
z5aFeStjKu{_$Ub(^GlWl^<f(hjCT^Y6GYdzw<uMGVWPM;!ft^95`Iv0THd1)C^JWZ
zi~ID~sHhxQ%%O~^%pyo$62u1^erZD?!ONx85)S*fDUYX)%C&Yy4JVpChxNq!4}bBc
zioS*@454P_!fN#kHv&uilygSD^FPG+q)`h+scASrBBP<)i-)gOX;Erl<8T;b2ppJw
zg>cJ7ZFM)_fk!fd#Q)<WkX$y52c1m#m-R0b+rP9h846soee_5h&uAZ@42N7<U(uCc
zU*eybTB0D<94WZB&%m}nGHbaA*^4*3JC{zZn}b*}Sxm_Lh{hjr>va)RX}5ib7g_22
z)kK78$J9lF2&`^4h5P3~-B3ubxR`v-u3)xIKC-nu;zpDf+^|?a09#xNp^if!;-gR{
zy{>)5*WN4%rwZ!b*E{_XYA~kkCM<oltz<l%V*&8{=?XfMka~xfrnsuS*b|w+IvjC$
zw17lhD!bx-F*H;h(@SYomlz$9JIxNVx|zcp;TBcl@gK*#j?1vD2ttC+4H&fkq}CI1
z`xYT;JEz>i(Tt+dfFNO*!oJMTD*OaI*;jD*Qr{!$B;SIe1oK1Qm*K`{2%-7KVmec<
z3i0w{XrX=1hj$wGagR*%0IJ3ti*s3sU0%4IS@-kd+NtwIT@P!-vgZqCA<Ps+w92_Z
zE|`a#za2Xh+KhDe?WgVDsjRyY4oBiln84f`DVxoODJj?epxioMf!P9g4*w|+?0@;&
zeU%5Mf0c(0<u+?v2Dr0Zv=?7xA@O-o`bPQ~Ffoj+R>Hu?$<x1DU54N|Ya7HvRS>WT
z{^){XEPJ68-k^whUdH9IE~cT)+845}B8;I||L3>F2p3k;se|g#3#E7(hSa{}!wiSB
z3`x7pKOE}Mkm?+VerJGL`2)0DHyFyXCAI#{1E8@82Yu{h<MX9P1V#~c$g?W;lr6UU
zbyt)hyq}z&Y(#3w<9m4(v@Z1YbJgDLcsVJyPjGv`{F~1k7cP{}!GPy~Rpw|Trn&zS
ziab65x*hzse1#J|gwEbJXtD=&i+~K=iTi-2Cn&R{e1evJVz~v72kEMxH}^{#>mOqW
zf$>aYe70ZWbp*>XS)g0>4FcGvlHxMdLS)M%AM*wX6O-YzKyB)AEatF_>#+YqrJzNM
zp{Wz?m^J&+@6%guTnwIfR*-ymt)OfoOO#pUy&Miro~=RQ*nK&99OQuO51Y`S4b5^7
zHCyWGzuF8hC_WCjw4=UdmQ9J5Kxg_AW-1dr@>_iUnmc1_$yx5>YT|ldO)ZE<j1Q}p
zK`bUeN4W&Gkc@BdRofSWZw95@9<2*{<63Rn!$o?Q8pggd%dxKq!c~&l&z@PUFy0Z=
z0}YL2+a;r+Rvb}`_a%<cHHT?Lw}5;R@wZ^wE1H;<N&z#u@Am0DL({ldr$5p9aDE3H
zc?thWPxxjpkP0Ua6TO;l?fjEbsFCq?<JvP)#d_*uCRRTCa7E;J;vHFl>)=n$gSWpS
za3f54M8^ke!AxfG&!C0pV`2@S8w*6G2CewoiW*1gcDD!DjHoo(n+yJIOv@0<LXs*4
z@%ebLb#fT*k@aZVshs#uyT3on<euHXDh12Gs53*MMy8)0Zv8pzjn99VUFLfRCm}Ej
zjsjc1?YZ4wmBKHhIT?QzKD*IA{_rO+?~qs97GLHT<0!24d)z{uXBOLT-`Pc$pWxN_
z?={b)^_*&g&^1qYE+7)H5IbxXpYkgxBo!d+eE{&5;_DJOEg&v*4vFKm1t^B!1<cnw
zY>>u3DM)3HxnqisRs``3f<%`v4Jau=C{AC9B}wH)K;yBn7=_6KxG|oJF))bU5(I6)
zw6<oX`&OqF8su9a7lQ`%9O<%Fu~#bdgeeJ`euZK5ZNudVIMM~m_@i9+P(4=FXw$}g
zsdY_%G&)lW8(_|7H-}4lF-B#mfS}ufe1_s(FwRYcvR88KILWCENxrDStEW*x%2L>g
z(4u13JBhV^equo+*UrLg;A39zL)70j<yQ!g{Id03<zec_j5SB)lG-?)WclVL<wmA)
z_R)7zwrZ`cSpUSzdB^aB`wK|Dk%`g&RkMNF|57j}hxkJw0nRm~ZFaw8i;t>TT6|gv
zUC`7KU#s+bHk2TD)qeynMVAE|kE{{jUo*C2a`RX0P=18gow5h@BXisu2N55Nqw9i0
z1ri|f?{=Z2(S3<4FV8o2t5kQ^FA3}8HY~_1-4mQg#!M@kSG+x|zVgnN)07VrdXqX;
zMt>B*N&NCZ1cXkiW?TbTt%SV)GAbqqk;89WVR>BSZi2_BX@no~z&;kQ`c<;7PuKC{
zc+ZddJrN_Lk+DRsTdv7cMYUj|9u)g0P>VHET7qW0d??;HA6kaVT<P!ofpW-)ddh5v
zhxpu_Gy*nDc8Z6h!H`K<A;zoK1!hng9{~?&2?#<;Gk}(~ua1MMgU{k__ihcg(9>ik
zC*Ec<EVwVF{K6@#&$ViniYWrFaHLP^+8v!<63C4BDe;R%eqG5VJ6z`SEOHJ7Xn<wM
zYyw@ERcNtX`q(-JM$~Ln$a^a$uJJ<l$e(Scs-pN%k?HaRERAi9{c1rD`0r>5DxIYE
zs?s9tV*oDCG8e%N@kwqWhRj!GjPxnRq)B`FQSEzIylD))>9l9OP{#3+!4P2^`~znh
zlHu&THO*e^-WqSnR4rx(^`-c?NzU3$$E0`KGUZIQ-;W0_<0?nyx`lYTta9ghN$J|Q
z#wT<Zcoi<F%Bi&!g6GX5zuS*oIm7ExCYPkPFafcM%spiUdq1}K8v^C`Ik0ct)kP6m
z?cvtmy^Y}@>6bE(Gh22%A42s(I50UVDmNxND_yMsy=aqsF$~k__sV+>^mIWW$sMQ}
zPpUD$KNFP-v?poD5~CIp1s5GTsLMPe*11GN5qEaUe_Go80r$3XDF($c>Q9Nn3ApdP
zr~yQDmaOo4gB;d+hRKp2n#XOJ0c#BIc|)m2CXY}bQ?)JlvT2M30PI$ti2FAzT;?q5
zz8q4PcpTLDHJ~7dp;)Y95XwPMVpru)P#wF2*w8Q4P)R^%5I`Y&M4%##h_*7f((a-P
zM#!h7!#E362ZxOHNJ=ql2dOF^E(UwUN&zW^)9?@ta*}wG6K9ywxnuy%Hs|#{sCUYK
z-Mr&s5PelfQ(IKzwYEI;P5RHvkIr+zvKA;+^ZOz0pFZCOlzK@s+ybgu8El(-ASjT^
z2d+S-J@rDvyODl47EB8Y=@tIpdM41s3@C&Gr$VVD)-5qB%^((XKLvU2O}aVkO#$gI
z{OGYQriOA0xG=c(aBO_oV7GU5_4&MQ>`=iJ%=i9=Nw1&6Xc<R<N{0-UYR(cj34jgs
z>8h2qcKniWHHmc7(^1kO<<IL+EAnPeZp|tk2h{==zmWE*>Q^%`=ixCDW05yIhHsu_
zQ{)GC&3b#en(MfQ^J+aLR8m8r$^f8BlN2e1L`hHbrLNXSt<94&jYn46=%I-<S8jYw
z!VC@cc(5#IQoBRY2=VOW9C^2=9!Dx=zmCx<c=o4Ot(n4~GSlyM{SUR)ugM-+VbTp?
zhD<$OsLE!GGX7=EKd0n+=uXeBE%zrlJm7Sf&(Bh-)1k-y^9NWZqz=C*n;KAq877xY
zx)w7!!b?14)4W&K^nP`Xsdzg7y`;8P^xgnhW#nee8#;Eg6<yKwn50Oad@Wj=CN85B
zxp79bg+E|Z9@THC6S5gmPBUtg&?sB#C+eC+_l)8EVCsGQkS^?PFqfOcP3gUBcP>AJ
zXyn>`I(TY_6$J?A$hX6q^m}HVn03RNoE4v#fDp+I+iu%oJM8|belt#Zkr$7!H7N6D
zxLT=SNlu#^ud`HmtTolztzG1&AA3q!pdL1t`B^ch)HrsLGj7Ce4ku}!0&{b%Wz2ZP
zf2n(voLu-;_~Vm`GU=oAVeKAg418nkj4n}zwl8{@c8(0U9o=PFHyv+>HaG*!g2Xx?
z_jVXIH&`O$_|U<a?jq|0)E<X(<?jO&nB#9PW-{t9-Zua}lAudxFYokL8_AN80Gyal
ze^3H^4WAh~#0Owot_^)%UG_n57Zms%tmEa|R8w1>g~oo)FAo$7{}ki;nYl_03d$V#
zCcG3;#?rDZv-A`Q1p>*!SC6)>KOdKdP=ZE@n^F0g`lCluiWFpHTGTr48f9(Pod!|#
z1b-hU$%VsT6L^llq$Piuv;1rMCQ*Yc`fG*r;`YY@;+YT%=`sj};@1vmK5Vsi%uf4Y
z{+Cj8v)@xZf%@`%+q$L^A65MNI(B0fzy@#_;st2-UT?1Rd~k1g^Ll@@yuFXTo*!<$
z(KLHZ60L9O4jpNZJhBX~zJ4tAwX}0IB^+wn11?;{H`8wkCJTmn8E|>q9IClD3F(Hy
zY|Dd}YA!J+Dm(gW7zuc{cb^|$3D|tv#+sUtE(d7rQ7zl4-E_hrw|=!;@ju}%xlfl(
zyddHgd}q}`?8U7?I{Kr#=`k}m(dO^JBkY(p$1P$+vFrQFg!Jnd2o@9vj~XYGgtuA?
zK0sq^3N2>l{ZCDKaYqGoGVJWgH-6W<Z~Rec(2qUW0fzBah(oOr9yC_@d|fVwy33hx
zj*Jc+&EzwBFBG~{;S#0p*>%p^L^j3x^Tcb4)V%IxJQ)dg<vhm3F*iT!CQp;PE$3Ez
z^5i3;)wk=E92t2!DwAgk%W}vMB<rC>ZUN)Cyr&h{=xRbF!H=bR>>!e)r>X1yat2v?
z2iq(QY8`4&iz>bTZawlpGer5Rqau7Zw$doZR^5^$ri{=A<Y%?5hT_BAj5ueAG(B0I
zCYy)bMk~I%UQ=+Nh-EFb-nZJFU8Tk9vs@%%GD}G2GIiwt7Vh8uqh_16DMeUXiVPT|
z0*_V~CiNXSl!L4?)^49Hm0c)Uj5J3w*LPvhlhEmRdmmz!f-1t5y7yFgkR+u<+~v?m
zX-6>2yA7K!PHXZ|+tpK&RF;}!9>BcY7N`N)Tb!<3TsR}*N<QKb({U0;U}apvGU$Jj
zD;LT54Ns$_SUiXy@oT2j7@}-=3<6Mmo2#4>El6P;--krKO?rtDJB}Y@o^Cp$15=)A
z8<IPUlLw#FY{@c-%Ei{dY&jp&zUV{kn;vb(ww507s869+aiWfj+do6WToEqP#u@FH
zBm*_ON@nJzjJ%{?SfN@~`Ge>A;PTgq|EL1?x2&4i=HJ;PHW}-;!;l`-?)HGvwt3Kz
zTh|OVWQ8WI!IR32bW$-heIqNDXBPG^kKBF7MJE-H&S%l&WjQ9rfhVHltMlj}$~y)P
z`KWNyF=Qc7^I-~cX%%9F5cxOZj0~+bkv3e=G&|zd#h-Z>rM^-%_*D{gp#=R1gNVkV
z3wU*trh2>D5fstu!_#aze9M3eW3b+clU|hBY(9dUg#x}3!Bn#b>3umt^)V=Zlddqu
zfW=3}*BVCexc-0`kQ4}c$KB(wI}n-V9^7M&n4l}kn)1@B9=MtHBYb6lPO%V5BY8Kt
zlRS2bkG4?qW2xzTRGP1A_QH_i@0y@;R>8gDrBTxQ+Spz?UpH{tg=T<>?(_g;WhJ};
zX@3TNP8n7IA>-YW`}!?&!;9^PhAtW1&he-3oLPw=?E(f#6L{4P)N;G&?9>5r@BC)P
z@uMjPLqb2@mF**FSL8(NtSh<qYB>^ogR~9CXEY5C*mR0)+{x323e4S(%QaX6=Csv!
z;QN$oiA&0tHO-fr!x#YU7&b_GPAe2Ffp{a)jhgjd+YwayRoW0stQ!;`7A-mGW@z4J
z5stMzeO4kiD#(_VbrnZ-6#ULXNzl*@;B6`ut@}2M9G4Z8**B#eIxxegdS+(J$<!_t
zP$qtFuL4r#Xx-=rcg{C7J`A~hk}85qiv8g9e-H<l@L7(&r-lG3u4>QEFNHY}7Eat{
zu;Zk)8bT2K+5~|HP^us}(T4jcR4gRw_mzpT8|WO~CU5*p9kald4EqBvBOm2m)e7-K
z*J>&zNXv&h7jb@z*mUt|nBhMtlH@%j)V`|Yh!hLn(()hIde>Jw#|1TUrUfTq%JUda
zj0QH&f^BOH&nN<XM=`A|i1-D8ry?L$7By?!lrjP~gTkV5*#}ZyuiO>qPeLkQU5jb5
zh`@ij>G<BwFk<cLrwM6XA(3mud>7s7ADGsl>{pl}$d@YP@7yZJP<b{xd$}^DQT{!+
zku_2Qq6;suImieCcUOWJldWVCN?=JwZ-uQGsdc!%v8NB9rqZU?zApT(Tq_g=Zxu~b
zXKN__g4)=#&{Au|qt~)PWlV{hrX6h%;ArB_>Ap}HVb_5k){m4!Yj4f`9KjU)<Qx{g
z^0K4YSGN-~=sT?GqGhB)sDoO<2;v<7WZjmaZ6KTneQLT0t!EbL(m;H+77+y2=rD@G
zW11-%=^+c4v4iBu;-2?o4#h^kX>qcRpsfflw~#0ka?RH$R;LL9T2`Y<#^UDt-j^q-
z@#X-}Bn0ye6YCeK>iHsU#kw#HEg@OfYD1=gO(6}ag|U1G-mF*|y!&R6#)U9|a>+1b
zbmlPraxF3JzVhekBG4eu6s@V($=|cN%x+dUwP6i_DYz|6-l?>O?I9NPQ$v|y@6sW9
z;KKBk=AcsEN|^*x_J|>MBQ1$=_XT@Efkl0;MgUI1!v?KyaQ&|7F%_fgOe$rTj=$s&
zs}Pl$Txmp%!aaPS1N=c7vf~`L&>U*rBED5Mhi#4kP+@yRZYi0UDr`2<aN0%u^Jt?6
zVTTMLk-u8+^wg+57!=BLHgaFRA(nxsi9!C1^Wk{zWZ&6}2GeS@<RY*0Xk#BUMP75R
zc~)rugFI_?R}_y0>+(*W^3DqK2STfYy=k1CSz-ejk_^K%|90w~a~k{DQ8jd@p0vR6
zO6uKRvb5$*{l~M_%Ij_kQPjlUT&L{9$*MPi`l<b;PS=?kZ3T7UJ|l2Uz{+L@b+%p%
zCdccx4xULQyrNHG=0JHfhKS+k<T0}Q)pF##wLdXSuT7)(KAqafH?V$>$?}7j&>H2J
zH&S?fK}N)6YwJ{nb5QhYj5<0ONXeF`hjfFdSk4BiHrE0vp3FF8j8`v7R~<`oUC}A>
z4xkJM71p=FzI$EHr~ikpw+xDF>$-(;x8UwhaQEO2!QCB#hu~~9xVr~;cM0z9u0a}i
z34S@xd+Yt~Q+2){yQ{max@+yV=B~Bo9Al0F=N5W&pDfl<y<!U^bK6XeWA@=l0CWp$
zUHngRjF<Z#@VmJ<6iXI?QiUNgdkZfWcsqLUVW~9z|MKL2-Z_}qAh5Ii4_400`~RZG
zQ6E){0T!h7f2tM{XFS#nxhVe}z~+%_Z`I8`A>X@<hPZt4L`Cf|LEb{`DMi&m@OwQR
zu^q8Yn!LY<p9Icg<k|ox_}m<$-iFhFA8B)SzlFgrp?_`52lhiw5Gj3D&H}qe@xzJ2
zVoa|dWS~6+u=RFZA;vlAC6y`gV(96Zh?+ww@;yCT_UU?U9gov?Z;0>{eRR%9BNj~|
zmiAh2b~x<F@7w}o_SIi|)^W1e^E-5upU;ahC$*TPJTUJ9_(puqTxp!b%eW3^zEcKz
zH6Ex9+p5XD608fW?jC4e>J%$HHYYg)lN!xLQyS&+&6d(UR>i4>3J67`zWg4q##wD4
zPK3>rFKk?E*~!3**Kq5Uo{DDJb8VikUPQ$mb6Rt|gSp=?<A0gb6u(fck3%Cb&wufe
z|6{){dHNswb+!vU@Sn<<R*-uKi_N64w)=O5bm_OTTNjSfla7>jr>g|8BF1C!2R+=p
zl>0k#zhIMqTQr3?^}Z`pU$2bnkopHh`w;G~qn;90sNIk?RboMTWBCtG^4K#&VQ)nu
z_Y!P1pyoSw(-_>&OSgX=$^NKwNxYqYU3lF_A=KwSe!(SHL#DSw@70U7xv%`;a?b)T
zXX5J;o&yH;d2&s7y_2AVNF5htFe)Kj-tgzHciP)s6KD)oXFn+crV}M!)t={YTMFdv
zVgcGVfk5z6@bO`}R3RCMKPWCA)H9anJn-`X_;2WqLP;-g8$~2><0v%XPlJ{(6-cU4
zNl|jb;6JO!8&KqTXYbRZH7*W&*pW)K@W(NENX*F6&k`mrY8(8xwKvhGQG>lPLb!iQ
z#=P=VnjtL`P%%S?JT*F69zKZ`!zB~@>MnCNI~wMi&(*6a4p(b!VKzubJ*QRMA?@}F
z=)e$?`;dC;aGwoE<Px!tt_eJ1aY1FzaEQyvI@R$lK#dK$^A$@J4JboHV(!!SHDwzu
zAp@12e5v^hQ`SurtH^FchHnr$@8*Yvh}js*(ay-q5P=C*U109RV_Hth8vN&)va)(8
z(PCf`TwVRCfkOAHrx0DQ*Oe{a9u$lPgnk;LU|2(7=&P|uKghDYrQ?v2a6bJU2!>zl
zDuT;O&UkZ!pW7QMLICHp+d9%mMb~O$^)r)WvvL0iR`j<58WrXlG;^N8UxSkB-fo(J
z(gNmS#t>m`s*d*PWC*c>pQ3Y9Z0vBV*JYD4S~B$j;p_wDPf8H-fLb=CFh|)9@F~Yk
zD|trVx88P*fkGLcj;Ugei<(M@g%Q2YsP&1@X5kmbU-(#_@3IXVs>%sO+<g>y6gzS*
z2b&7zhfh32;(jK%_xX#L(!lklmCI+`tE@NoC_$Nl3ODRjSGOx$6;grvv^Y+<V;1qc
zDR2#~u9I}H&l0jJ<bG^acB`(FKw4DkPHFCdP98~CfE3}-DQU+@deS#XySt@pYMFvK
zbr=ms8YU}2J37LVJC@XpI$DWgcuhGz_Kd~9GxEgfQ%YhZ1F~#fCEnQ7!rpjBxWGcm
zVOF-i)q&sTp$6&3e8V|mQ?<QHU;EB$h_~)_XY-@yvVXBYega!ySiWpBplV6k<<HmE
zRSf#+8(RL3T0ibkMa5RZ4G3rY3T+;Z-}VXHIr9ly57HIN&!*Hdg$s9e>Bo3aQbyZ5
zPa<tb@zzdp_6+4l`<J{E{uKT>3YMp}^V;c&>)KE#J0dY^(8`q#wYhtf^M&nDQb@Iw
zeME(3&a8;zIBbkiNG1{s0I?G{l()%%Lm<L^@1ZtuQ*Qmd|I*M45|qG$Bhe+ykNTrz
zTt=`mfH{k+pl8a4m!RK-n@!QQx8oF*D-6Fn^6Bt8*_nJe1VTtR{8GdrLh|SSxhKzt
z(2dG(T#|===<pTQVfM;#8$n4NgNad3?{4`VSiQ;BWo*yLlm@O!;FMTZxTIC0nWkDU
zgJ57&lf4<m5Z-28pT~FrGpS;6Dt-I-?GMF0v!{DS`;u!P*X%7XojPZj=Ca#O5eJk-
zU3RH9!IE+2@$TsA%P(<4DUuY7>b)TErR)>>f^%K{_pIYtOohaw6fdPC1AYn(0kE#W
zW}*UxNtj;=VZg5if%S#brW{j<ls_iI7=zDe4Y~mwT5NsX<bAL!wj@*v@hgl9c`ptc
z0S%Sg%W69UqHFR#m_zD}JE;ugjziwQ3I?hXn2t_Srr(dO*(0gjr49pL(KLClYbI(=
zgIqDj&fn<vvL@3Z6aJPAo1r3BBY)N~P5xsrS9t^Hvb2h91kiCFOisue^obd6&E33q
zN;KcREQy_0ymK?Q-hh+F5M9w*l|%&&iW7|xBe$3J)U066_>_>M=fFKbiud74*;6NW
zoOmRjgRMIVsv8X{L}Ew9yjAcOE7-#c{$N|cN@J1I`r8ns-~9#7T@abZgL18O_?tI6
z=p>Tmj9kH=0MLBC>QL6z4L>TqhO;1noi>kJ-r9-FakLp4KfRCejx?PNG%PiwtkJIu
zj(3sBpQzWN4agpQi10<T=u+*XEuFz5m}|bk(bqz4G&Jk=?K<QgV2ZV_qg1clQ?k_r
zT@G$t3+%I;;W8G+B!>iZnm`xcPVd^&EH^oB``I2O14G~D=vPP_jANA27_-i96yE*S
zsmr3+m=fgR4FaiPM(-YV$4DraL?6{Amldhk-Kukbs|<n9sQe12Y&>HOsoI{JIQeyv
z?7D3~<Ub#YL3k}gz^qy9MIZ?>tX90ZO5)QGyYcKa(v^<lOXD1rYYHynS>0a5^_{Nz
zntI>w3hY$HqJHh<A)`als`WYdfyc2%;;OiE?=t@aiK}@Edz1p%UQ<b7_fUf~N)i)5
zQYLRi$e~w`GuQc)Jf<AY(+mV}`@GD=R2G!aT}Y%-#+c)BHmo;qV-N|Y5US6mJK_oW
zepqP0waa=r4S%BH3()fk?UYWWv%G2;3@V&42iA*L^+S4;7j&bS9zU3hP8_`2+B+?g
zTOXc6g|2j0eLoEUV7KQpA-s4I5pEZrd)8KpICtb%MKt<PSx!#bKt5;x;MW0#h_Tqq
zi@2cO6{@@be9OprgJ#(Jlm9Q9J_{Eq3+X>6DggmzN$YRlQw+%wZ~?aeLMYGlChQ5h
z(K|0xZxKVuBHsN-KVvWB4o_%r1Q3!(4W*dXju2lzk!e?)Q)kRB&n?7b5&V!7{HBOi
zt|eSiO{dtUWD|dslP*y3?(F%(=;N7E6@vr3pZGqVKl9Mh<we@G2^TLC*cCm`DK)Dc
zyR>})WNEIN8;_jbxUs)^9$zQW7JBv*KW>5xGFCdBXYjR1y`U(6-csVGHasFm=QhmA
z_{tpUa|?MdLfm*3skd#MYpl4u<fA32f7mVBM}MmMB!0S!ycswMDva6Pc6qpYMV;%q
zC^k=y{~7hY7cax|_@Pu@GA&95fhaKREz_ADcuNsooVq*r@o^5kJ+2l#dKIWva`L*D
zbAB>@Is)CEBz8V9>65_TCc=|TrzSS{GDr;m+%>ckRQN?S{rprNeupI^2N5{Y(D}SI
zbR$dxD9aqjyc-e^7Lx>ol0ju9B|bea#E@*=F#-i-2pCcRGHc&qe;Ga5r4!WG0a-=B
zyL>&=aK7nF8#`IzQi80h%nd7olj!q8OkPmYehf{ozO;^G(@z1HqF88>>0lq%htthR
z7kFrpYwDQ)n80WMTqt@3jd)SArf2le7Q~$EEf~HSlX5(qnDn5{)uqjN9oZIvrLP!^
zi7qvqKL{gTNOfHbot}6bC>ZhW-sFveZ!?~CdRbWI;bod6IkPSeqV%$i5`0}LW!Iw6
zhtj(3WDKNc^jxow3moamWKMqGYoDfEjErVtk>wz)4~XtZu}}R3Q@$y~7wa?YCY-xX
zDO_u1biCJr(I>DD$T_{7{A9J09#`c!w5k>?VUj<!ZYEqr`VLKl&6|nw_=8~x=or2{
zEWYVI61&sYyhvbvrYwi<aMsl>nI8z{bU$kA9=$)NwZTPswQ}s$nj705RdLGCw>4?t
z@-(Z|rmx>OBEstu2VYHQg{6`BnhT0G`>LJdfuhneZ_`{QN_cD&Jsv&u+odRp48Cea
zZ)%FF9)B=>x=B;bA-<rA1UBXwfDJS5%J&hiXWHS;^RQG68PWk-)O(X?AKI|#s+9H1
zQ7KN^2A1|RHzHNQ7GQ1hgK1{Gema!08tv?s+Zv|Vzx*aQRhUf+;b(Nwy4}WhW8SIx
z2?2^mX7zpj<`(K?V3K+o>4_Go(D@l(nYGBZFV$V=JxQSP4)?e0VV2u85I^p?T+eO6
z5u_~SrHg^nNQ^LudoW9{P-p@JKNtQZ&ZHU&jpf6a)V(*AOO(Gp_w~S%=t~ULu9K#;
z4nt`hm7?vXN<>ea<R2$xX<kb+xRB4hjdcW)(YX++s|X;>i4YK}bskvZ;u{D%gFrp4
zye-)_Y$md2PgeIjStctHP}{BS?g~dZt>kP#C-$C-Ek9OWjo}#7dnjz=@O+oR`++G>
zbS1XVQfgMfls(Hl)A!`dFhWrFDquvk3BS{l_o~O@ibR}TXg?$G&9rK$`Y9cg9yY$U
z14fvlqp6FKgKJ*2NR>osBeSpGO_YY=*|S>#VK}2%(2=&KG(HywAV3k})?Hn{w2*t<
zznU;D$Y6J4*kFYNnSc86C6nmNlyx#0kA1i`X>UcIrW_-uGxb}s@nLMmSIdfs;zfJy
zT1D=<Rmkp1tD)mj|K-cPEYe1n*)G1-mFPg6vr{WTjQ??3@#ojRD0dxkUcNpHQ31iC
z<jN(17<YW3{<+Z^0IjOGv$JI+!h!%ES1ik!TnFI!vvR#7A;7;1L}<f?wv1Z`n~DlX
z^%rcQ3lrac&*3vBWR9l_FeG9}*<&|HG%nl~7wW-{TQ~9ZW@>6BAT7E0tz(c0S6rwz
zE$f2Yi|4~x@{@$1J1yY+i}AQQNsAKRJ614yZr8`X$qJPLqdFGyR5=fvdUli^rc@+$
zf=5t0kUZ8PnW1fWKX!-7b=g5A$D&N0I!GZuj1KhC<7&8VN|`!}JsN6`*W(PXJUP=5
zJJ^ehAfup^Cp5T*!U<&mrg=kR2ti4{fG_+<(f(hUBWfTa&O%8L{hP*9$)+nQr}q0i
zw7Ybo&C5RnsbMcB&U=}?Iy*eZ%3Wkr2w~_dE44-;;>d@!Whl#F2&`~5mrt=)>?jt}
z4TZ1B=;ndqF$jVksSznK&9L7GC+j#K!e?&=6AZqYmZ0X7=|QIcj)I>!SxvlYuA0EX
zX;dMXcIEM1z1|s6`j}E(7qF8LuWM+FUN!yx&$H+NiD{X4wb1Cn<QT@VzkYviqvLid
z{<j|-l051sq@tN_^mgHT99?o$Oj`%Dc(SK3gQ34C?-a)Vbe|PAaMqg-|Ljyrnv1ur
z;t_jEyfReImrylTkb6n3EWE8NF&}JbOf<l@qw;uOIOt%l$B0Ib<HUAUSYhQMj+<b}
z7U3}hB+M8~(r@|hYRT$W3(Z}SG|B6Hki{g!VC}y$i|We_qm~nUebW3XaKOHIhAJZR
z5vsISAEBx|ZCXHq*^qFKi+NiI$NzOY`fn|*)mS7R^oFdnxWVWv;s)%v;LW3eZ`dDb
z$w$Afk*%qeQqYqCN%*n5P@vKk>9Oh*$Eq-3s?ldP$TOpvfe;p;^FydVv|9(ny|*%>
zNpPm6zJ6$1){JmlYFb6MUM^DOVrDnkgOPmDvO4<)1-G~)AMg6rrlSmpAlb9(Z;Gx+
z1}dYsPWH<8*Z4z9lKHeE$@c}U`&0H*ezbe<QZ3a}DW=vjwZk;h-K8iA1J#Ljd?8eT
zP!6E%x+^=`UbYqBmlno{x^O19(q@Q-eBrXv#7Q6++BZ~$Vh(W`tjmNmrR!v*s@ht%
z=(TEdWb6sLnaOq3<JkhywG7Z})s!fbN_wN2xh^CRIQ=EX4Jniyi>;h)AkcP)q;U^K
z%pTj_a)J<-dTGHR)sYksaS^JtWf%aOyFKm<Px>pTcsiG+oiU*Y-3yHSDSmI@@-t)e
zc5R%iH&d;@ZTegu11A&pL*#2hE?B?534zrd`+(y){Vr{!|FTQ;SJx)4d4Ub9$<qTi
z_XC|R%$BEa?6!%pxaM;G0cT5YS9j6NsRG<dPf2ywM2-tgOQPA(c{~J>ex3z-C&-ne
zTz7)<TiWS}E{FHbdCVO>$j5rvO;%pY;mQ>ZBg>ksy57`!W5yXB7nn9A9?|w0lVsFM
zr64NZ-qs6`q8>}z5FOizH##_YmB-KtOQ}LWz1C#m6Ly7|@8nJ0W>rXfD-Jh?G<J@^
zd=Q?yofa)tA|n~jc8Y&gD`f%c>Dt^Gma=WX<mEHc=FxqKR+_VZ*SR2`IRA~kk)9zw
z?&dR}2bUaunip;KQE~hfHMuvL(|6`v)e>h+VXu*=Tu0DV?vS@AsoX4UiD$1S8!-Ca
z#y-Kl&NOM|ac<OKjc}a$KEqX^8e70TW#JJ65!|PU(QH8tkqnUgso(>6MZ(?SvJN1d
z&T}JsvW&%^R~jC85E40l5ZkTd^c>Ckn~*oiMKi<lH1nLv3dlu8i}iKmFD_ZWmtPyF
z?gXQV89JE?5DSEr?Wz|CRuA?8*iQtX?vtWLVVMgajG~3%vpk5IvV(m07kTgx=qbJ>
zte)%3v_Kc`C$W!%h={d@9z0Ebdlr%DoQc{r-pd?*Z5VfMyf<m>oTOKVuOu%j@n07f
z-t8AQvFRK}it@6Mj}Kv4v=|N*55B*V0N`Y8ax4FK*8RXm84>zJaEh^-af-3iUm)38
zS^lG*nBqA~gbcVwjv@4ZMG}AT9eDXNNBNM1I4g1Ge~#crQ-#@1Kl{Fg>qJKd&yJTG
zhy`)~3G>ce0Q-H?_ok?7T89=<_sz9Zg@vKVr#KI@(U<qtJT9P*V2;}6+o2MCuUad@
zDk3^$aOy`oWwufevYX1paH-kZtm0`B9&dOCgW9H3K*0E@ZMuASHaE8W-zq0-%GC?T
zKuApq&c}uOap5)Bl;BG`L$I^{U-bYEuKzO2-Tz-5@LLEc%3~5-D2W-a!_C=)re!g<
zgpoq)jI-QoLlsKKm0fFWe(?LRl*DV81AtGFnL!jg_Iu`t-}~aJ;pG{2HK_i9<b2|8
zYSJ%DCBW)DM*4S|H9cACbKg)_<565yM!6MZXE5;1?)zff??%Ut)@wQptV_N_0_RmT
z>&%jIIkzt|#EA^KiQkPBF~cd`mAG@xg>f%vGRCojN>@Ky#7^Tn)~&HJ%OIhC{*7E?
z5A2R8*0I8Ambr8OhL_q71EDRPWpw78gWPX%O$bTbN-=-&bfyo^x<_VtGtRu2hK|4g
zsJjN3l-$^y2d?)PDK~zXo(a=v-}HFD1T{lWwhx?mh-D+1(__m^t5*I>>OOE&27#E*
zS`&n?j^B4Ho+9KR&Gp~!ykC6Zx2KOlUC%~>!f>jLh3?^>uJ?C9OKY6_?CCtg_z<bb
zP6pkp04sZx=xaM`SK11=M(ymg-3?3YX%rx)`}tem)ZWeIoJq|uZ*1|hL1X!Et^v|j
zzpQeg5FsFxf~1wa42~O8;W3S>DGdDRSJEDo>5#<a%}Gt+bRe)G30<hbOK|_D?M+uu
zu0wHdUl~ZQz`G|}KxHu+^%I89E|QoFS!Z#^-@>?<xI(hUJ~oMIElE0uvb((|l~MvB
z2R{trh5WXSszvJtNg4O%GCM*^JkCVOJdleGb1u`1gK*mw-4Q!9`}hZ4+AjGbC;tY|
z2@=*{;jc?6Mq_`GOWt)@jjvb6SexD&LTmiBlHe>Ldt8<%LNMU<XH4!>X;fv6w3N1v
zOh<umXqUE_;T{6HQv5<VJF*iwp3(-;%LUu^);gcPd7XA=H8bTQ^9__rkayI(b`IZ9
zy?hME19RoBq60OST{hg<hHo&1g=Hia_bI9{-!bo6W*tGy^r|E(I5Nb;PTxv0?6#$e
z%4juEP+#z?v|4`-Wx1uu%KY}{CZ)eEDy)G+u|7wCn7)rd3-iAGtk@;k8X<WB+8lzu
zwJtK<lkK`qCoy03#AQxjM(E?hTLyvXe!7#t)WDeJ=^w*ygWM?e9Nkfsz+U!Lm#_Cu
zk}<07ng0?%!86AM45iBpW#OhbQG7ZKC-lb-i>s+5U6}~*FhfEN7%QDL^m8oZqGt3R
zvuC8s2oF@y%`SwAcOPQ$a<l#mK+I2sD_qnUgbb=;-|T*clJII9i}(`mn#(YV7|!`y
zDP*@JgKZCE?$}GDi2T|*(b$T5FaP9b;8ObqYWc|__=&@;{CPmPL3nScvM*W_a$bW^
z7d?W}^}6ua2CYy(3pmHsQdh_J=-btlh?xs-l?_iK#{?t&h3Gn~(-cx8(De2Ob6wRe
zyuLYOwesOXbY949#iyw&@+MGG`X&y0fAvMhMdfh~-LYfsd#WAXSRl~>5d*b|L;S*@
z#X+ongA4jp{HIAjd@o^%C~Zo?LKH8==uA6gZrxIf)sEv%Bw6jA%JZLQZ?zW*-zCZy
zej%4}Oc1g%D8yTc#*~{u0b9-s?W<YF-_|TQC8EE@h}=lKInL}@ZB}x{|3FeJ2|ssj
zrSr$73~c#vW%v8{r*KbpQrk83{mnN{TNEG7arO*42^Vq?YYNZ#A&1+6FmqzBuTmUt
zhBkDBRKyaAG~gy(2<xem@Gt`>2qLT%CQVf?TPQ4?rkP%OZ3~h$M1V3^nf<TtL)CFs
z`R9k~T-8k3^~)|U8E4Vo1*~@}?mhY_vFo9K@VHG!POLRu@whI^eNr2<e3}leL5@d6
zSd*NWRz>Wz(t^=_d3f79&9hwhie0u=U=_|Mx-tJMBnX;Lz!I*QdQ=N3m&b<LwuHO=
z&Szx=b7<~*G>s}4RA&(&R#YV-mo=wWv;GZF@AaGen)Or&TYUl|?wR8Pqm_)wW@Urd
zGP1e3rI%S)RReM*!s(QcrBavvJc+x_1$vVk6|Y2^^b#+t+tw>0V-fY;zieBq|A;!!
z0RPn=xvx8BzrlsJ@$ey}Yy20t6O4iup#i*3?nFN}xOzWBL6zeyugef+Bv)GSlp}qO
z2|+hmT$w$S9d$DH`qo?}L0;JAn(uY==*p$dD<fJd<7QS<vHA<_4wBJ3v+8;Lv%E?w
z?&<AP(8Rtbg7(G9!t)cbr=F{+ls5Z}(<=BJpSh_(B2+Y&x%u(y-!lww<kEuHrNJ?A
z;!Gc95F!j**xSxGwc0nS+4$~N^2((ypw0LF@2~jzoHsq<!QBv@ezX9QSQX)|*T=`R
zw*43maqv%Hlt?RUW}aDmvUMl|UQak)**M`5Vz74KH84ay*h>M*m>=!Y)R7l&W@tZ?
zp?Xmt=1y`<sLAuw&#OIBS&zCE9&SST>2{tDk`}o1(?Y%<&a<V|C#aMSeUElm`h82a
zp<mcgcH3Pae5+C7V0<Bo!4&)x+NS4iaE?f2*x{oEA%`6B(I5O{l8-Hm>Z2_Q<LNPW
z#7%Osr69bSGc*hw?ZNyAjLR6)@^@@&ps9`iTPHn>CU1Fx5AjvUt6ws5!gZ#5Hl0-k
zvWsEoOQnzn<}uNgfmK0j))vHuyFJ<*cy8@cOnE3Wqg1FzN*fp%HUg?07j4BOnNQ%Y
zy2awgxpTl?+mVf#r5)!_3;QESfkCu_uOijB$Nkge0?QHrb7UYEq#75#ZZedl)W}bx
z@Pb2x?a{{8d7-jjBEKvl%Nw?h3)nk~r|6~HTOhG*LgWZWrbXr5YMdrgHV~#o%}9nF
z&2&?wrw$}tC5f?UR3TJ_W*g!6ADfy*Hc{0fQ>ct~T)bAH9d(oqDC?j^9qvI%p$VyL
zm$cipS^`?YCp9e>QrI6#0~~a1xoQUuYRYFa!7w%1>X*bru}R#_amnO-cnH~R>jP$+
zI&FWxlp9NOT_{T;*s6nsWOZ5tw*K-B1S7;$z6D^|S<PYI{!seF5b}s$*-?I`H1@m<
zOJ9-y@ZI<J8WW!R;fyZ6_nMdRYwwJ}cbVZrCDFkkz)FT61wBKb<4t>2H76xiM2|cM
zu`Cf@H)9-JCB1dm4|i*L+chORGlxPC&EPq%zEQUn3>8x(jr^klTyCXLgSsw~P@Fcp
zPa7{0i8J6%G^i4dY44j*OqT&k31m?4!m<L7pkfwZ0l5ijxg|<XlCrKK`7BS4MT9?w
zqgKfb&_)s6T8T`^o_TE}<s(m~2VFmq$>7J`+-T5JE8yRb-W;*ilvx}>z1R13H@!-G
zs%hAkl`aw2p6SF&ir3U$K_a&8B(M6EM#`cm;aDw_U?fM0Sc~S#lKhHp1Og6qM|&hX
z|C)T~4-hfN<24sIMEd>K&^Ri6nrKw*R7wsWa6AIdS;(Xe#77)>hMRRgRzwh$M-o<N
z^65i4PT5+;)+`F$u<e3CVvAy9@NcvX@DedvcAp(SAskobtle)7+U)(n3tH4jJw%EB
zySWr;P|-49WC_W;aIlN(UKeI`<t&zw{NomvL}TVtD$CCrZa5!aLC9ICXEtYqqc8U{
z0NZJ&aZXEuxrC{NH!%^uLK7x<F@px(!#5}9nMcs<ucZsT-u2*J9p^-_L2~8vEtemS
zCdasP6NyY0BLhUl@@&R6ush%vtUV=ED>C!wR=hX(9@n^yYqMDqM5@}M&Y4RBl`vKA
zPE^HeEfvEq3>!A<Rx9NU%)5HhtclfC0HMbXZ4UhymK_qflIdG6iLQoUeYTy)=ydhO
ztIf!cH*%EuLhi(3s_<|fFJ$)2GqRc(uJ~+hF>7=Ud==kr_m_zv)KuSue68U+QIl+E
z5e4<g%4NM1&yQaHMx6aF=uZh2l^NtF9h+iMoN{DAY^^4+$aEW)V=bb=u)}QN!2Ts&
z1Y>EkU`C5sG$=4W$!V(9T?0Mcg_kB?{W}4vsSc7G4Gxx7l)0HWXM9+30=*789v(~n
zUVe=-;L3-Pc~o-c6=)<a)iW>dn;1<)WG8bg?QKvNujgOqF01>hEz*xJq84OhL?C0I
z8Xiz#&(7w0noN}N>C2i!fO+$8;MAdB$2kHTQWZw72x}>hC4tJenarDj!5(WJ4mAdA
z?)XF_o<OJex^bH*txmT#PO+tB)7L6OYie-Mp*GaUo??wa8=4WnBGqYun#cA}F%iRu
zZhx4_@VzVsRK<c*cyf?Z3YEn`yrpY+S(ZNsqaTx@?wNL)D^${F@<>Klz%{?L?dY+`
zRFQWo9c$o^KUa(<S8C)Zq3I@YMjp$Hj*4V`k4qc2sK_h1EV<G>=d<Evw&J)??4H}f
ztho<q`IX<JQx|IpmJjx^n}bxitA*|B(>v>UyF?Bjes)LFV`uK*F0{G2GzfNE@bs}L
z+L@^4{f<ZaOP%e{QqSiI18{e)jc|FcfC{SF>tx^T)Rh%jQLYlpMD-F(zhWfOP#9Zq
zLNl9KWjLyHzH*P1AUu&X!G_x3Ok3YG;>#ut`Ln8_GZNv$T@<1kR=SRMNX18zXqeUG
z`=@sKbl4e!G<NOO!FN+#*HtrOUKP(D&&VeDEG*F{9d}t_n?bZ7Md6Ube&Pc35BJZu
z4dL(J8<tJGkGH~OFB;6zxD`{{5bfE!$VIG*fpAqmAj^k*AEF0008(>DOYL8_bT*d%
z$wa#;2tv9k2%Ebph+>k#*;Br@5zzo#9RHot=}g$KbD@1$JKaK94DSdV{gcvJ+1vXd
zdS2?pF)OS!gmn5^N=Hk}=nY-fu?F-S$jPbPOr<^2Dv{7|%DgBiz`i8Sy~4iS!w92V
zR+G?#8t_mBj!pff{SIfa`-(-2217pMaW@BxRebTDYM%~Br4cdSsaTy@9}Uql%Ggp9
zM)fGZ%X4G`g|Yiyt?hK|nrQ~Dl(IPo_lIK6p8UU`v6$I~9zOn@$a56x6Qw+T_%<d1
zV}zkh7U0V;rSM3n^9m%I`}Ck!Y63!^w>G!v)Sh-O`0%Eb{sBW2xO!bXA8&3lUWW~7
z^H^qZfz<Dq#EAh%eGSoZr9eiJVUfE3xXI9i`70@=JzH?yFK$l)b*rrx8Ls8XU&x*9
zSq87TG{IbP$_mYL=yQCxj;Zz_w<VSI{ktUS2se}hwFebXx<VLYP97UY56PU_@#<!W
zViJ1-5mGd%2te<X8N0JatV#9f(1@fs&cCm(fJlL$sYyXtoUC3(wSKY&yzOarb)#-4
zwnwjk<VZ=-oc)wCa{;mqw>U#^+^qUZNdP$?b+FXpFQP*X*u7}u78;@aJt0_F=->?P
zj8k@g!-^zL@{$_Mb(eAW285?fjSOkHN1KC+{e2yOF2Tx*6I+Wz(|Spn`X9fhx_h%*
zfRIxft!rYr8O!K#J?S)>E4l|C>>l{DaS>P(F<orr{oTT!vuyVQ9D~xhg<9w@t#%_K
zl`BR#xN`g}t~{}Eu?@r~pOHY(LV=N3m0x$z)ElH`6}eOJhD%5fe~>ib5DfU8J2X=4
zoZbDQ20b|6luo8szpjQVaO)&T+!9$90=~6H8^0%w^t2m&`{w_ISGOEY3q}!WvIjEX
zP$$NW*jiCc<L8Y|%?y%mI+k(pNEMYa66k!j)=F9`<YcYBqTULeys?fV=pPa^D2zv*
zJa~3-nsrTqVeT+-TPdUqrbkRo9jf$moU&gvpJJ1hysFZlqeLd=Oryq&kJ{P30fL&r
zl{0sQ<go&B#TV%d>Q^M6BK^*pruf^fi9x45jVIc=a>pHqcq(Peo~5!28*M9IgAzNA
z4*=fh6-NTt>PW23cFUe}$?PkwYX;@};2O&Nn??Fp=$btvVam^VL9R};i5%$d-!Iy~
z#-kJOhcsUIr-RA;+!y%eY*3V?1h6N@ELDJer8mTv8zc~_A4wqGr7x{@#J43&YZ4be
z#XeT^o+Oq!hV%)<I2X6|mlD}1jBzGwR<3@w8!h7OU&=tJZJqRVv%V3l^7repZ6z-y
z|MNT~p9Ip*R6~!jt(%e6j`GlVKG$0e`P11XZir&{dusYed1NkayeV2+I6(a{sf&F&
z*)$<tIn0IxaZYG9bO<SVFVA&944x@DZIqqY2MToQ9!~)qivHWN)k^`^n%rht#X5wf
zvyzR?T#qAnqG5%{eJtG<kpCQ**B8xE!HP$Aj+<FF*xK>clXJ<3$A-v(Fl}Kq`W7!_
zd2T#-eH)`}_E_bH7kcBnBk&n~s%mBt{xf?Be%9PdE)%CZ!RwM5pCVc0pvrQVP1byS
zY`GG~Mvyul@}bVV3Mp4hgOCJIkg%lS8sX^0nKB<k(@??_Htu&D$IdLD<5CfoQG3}`
zY@N9`I1!%6CC`4dK7-TUIXJyJk+pm47KX>4KbBwcdtEn;b^2UgA^|v#yy_c#Kz}jk
z+H<<Pde-QknVG6?^@f{gANEcqHPIdsAr70<;(&KhV^ApPhgmcgp~AMcYngNHe5O?E
zh7S7&aiwU0y8rNRaJNOD9pT$-s?MLAOHGO?_nOBf+GkNCjNmY3nVYS^i5_30ny|bW
zs0{1|u!f$_SZ(d_eqaNygu5(WCt_|$)aG1HG3*(#UZ`d7CEwUE9DdQh=i<Pqc{>(c
zs3?Wc+?8*1q4j;O*CKsC(sDv)b`)U?^Fr@Xm*1vDB?IP=Puotf)yem6npo=OK%k-O
z4DuS=xgL4_=i3z#r*-8cm=`g~A&8f(-?Y1Qmduf2QeTNz4sh_qHR_2zu<|N5TUR|x
z9A78KywG&sY7k_1P*BU=q$W)9L`di*ok(_rYOnYGJ+Bz*C&sX!utkLAu5{TlnT87c
zdYp2gm*X+JybF@Q6Gn+Hmfzp#cCJDcXtK*B$<Z=wd0s1CkZKbOv?C$Ygr$BhRurL0
zHb6VwH>=~`!hb=`Wq<7aw_WRhYL47IEdMX*a$kMIevJjG^WtNFa-s@<V*QHQ=U4f~
zVa1ujQ~D2eimO(0p8-%Ht*w~Zu%TlVLKdf>JWq2?L6;%58(Emc4u9w{aqqupi|;jS
zFt;3YV_`c%{yf<_eh^cng{^AJzXdY##nTwu`k?`$XLL0u100{|rN=-hUd}O`+z0p3
z27RA@!hl%ZRsyc}qAu|AJi0OmULyJBUajTd<w`hp;E-Iee7d~4^!X7y-s>6fK{&Z5
zE|b}K<n0_D-km~=FQij^r=NPn!zrj{1yhxW!~xEO2)x%6is;GLV(mk{4=p(kG+XrL
zAWtd<Vyat606F;0*qlk|&b<CCOZ#j&1&x37*rw%o1GbxH1ekxpc16^ebmZR`)<~36
zagd{Y*L+8&Q%$Zkvq*MXtdiQ6NVb*5t!p}n{Jn5>*(UWjo)*KDG=dZn*=Mhcd*K6Z
zEO5lTY%pCB_PF_Y=}Yh&;W9z{4N=REi4sEMU+%mp0PN58NhG`XJc3bi9;oY6uL!l#
z<?Rbmk1d`)=%!o2%Tsl!iB$LLg0!r<VZb00m(r^}@j{Npv6_a7z#dU$4V=NAw6lq@
z^+v*^UBUJ8&!x+^8i=X=I+lxp>LpNZUWak#t*m={76160D$&COr&GLtz8Apz3^9ID
zQ{4Gc^HJj&tiI^A;4!Jc2s<hG_SAR{gYMGG7)k@yQt@di&CGy@meZn9PWg{H(u$JS
zT6yr)W8HUH$^m+JEyjZ0v6^1T5v|{-^mD&SahPeEQG1ttx<QwGnyl5P8zj(6VtrAK
zirh5hDUny)LvG+FSXc3A2nyfSK6IBt1WHHUf#upl{`~P>9g&6v?Sry1fy6S1qbKV|
zb)xG0P11!Tp2S9laLB$e1^Et5G1Mq+$zA0x;yUY5#ge_u?N4?m*bS9qHg2RGL^f_%
zZqDud20GS^mS0lf++7hDk<rT{JZ68X?QMGf-unwiUO~x{tHOevhN%_aVY-T)@T%iI
z&(3G5<GUG-;saux2Eo;g&6Mtb2!3S-YIi;(OMZ=o|JMiS(w~^-ACSS9l#jcv+V-L<
zgV~Ur`QNHNDNYG+AH|;kBH;LZWwr17;5FYM_<k9Ui>D=^6@ZdPG(-HwI6OV*RE+Za
zj?Pv$1cfeg>E4zv{oW77FRy+YU=L{cXuouhhe$pvQH!W0iQ2JpMB;~JmQJLgegQsr
z7JmgR-$S{ah@H8?jMo5#x@LxKGU5&Y+{cp!evWmpW~sZ&R_%YYJU&I3Ys{i8gexTF
zjV?I>V>%R_5&%<g&m~x{S9doZsUuTwi(0#~p^+o$aYb;m9nyF<_3A1SMauYQW{3&G
zRAs`PA0$d__F0J;m215@2mfuVlld>R#LoV|RHT%MP%MHJ_tB4rx(`4`!maDi+m{?}
z4-ad^Kcpqw)Ex?XHl?bt?*Qv;Rnng0Fp=|>zaMLWhH2Om;S#62!;@NV7hK|qKH@Q^
z^_<u7_}8H!|IZj{oK`seIBU|!H5Q)Uiv*K*fRaDU-Rb@kMY3vBz~F)ksBW<px^J=l
z`5e(yn}z5<LrlS$AVP*@Xa7gpg(_ujm<Sa}n2_yfLG!y1k}&w?^u}hLd;2jag+m2_
z<;uIgo`)`;BaR<#D_9~w#8Tgx+2<~?So~gD%<6=L`if;@eh5`<x|z24bbmoprwevs
zwVSj4Ceh2&bqmG^?#Rsn-(3(%%?ResA_v=2vcbVou~Na7ku)x+!BEh5$tM*2e5C{E
zCl2Q|EJ(yH&sZDF&93GmbYD>KbTf?WXgycrY9M73inWu}SjVuL)Gtb}l9)d5o`<JF
zh@SrH3s2(|^*3NWdZiTU8<*g3)i^L!5S7v%tJaxB*-I|lxLL*(8D@PbK@yXF)xcRd
z^<rrps2WJ{oEmE#q{@hBAeL&B`F>?P1F9z*kCSE|mn?pt_=&TGSwjh=gUJwRpX5<q
zPB6gB(s(1r@awgGeJc1Me7iC58HLFuc=2SD6d7{=g+dChG^OK^Mb{lzEx%^%(3iXM
zzCk9!9zFiIi1?2e<^PR?FKFUkT_(PypwrDwYFHDp5+fQt8ZLYj^PP9@DoQ&uH}`Q&
z_P<}cc|@wO`frHN2|X*Xr)vb^|A~WDOlwU+WDBcZJE`Qbar|RTKFd|<W7CoEfCCQU
z+Itp+aaqOn+NUu(T@jFtwWIdIdR|@Cf<CvQK^^nee+Gv3-@qIP+kd3`IR8st#Z`Ii
zAJK%4e?;q`)@tW8M0rUWrPpJf#q?Hfc+dRbyS6)$1JoKd*d`;-<hKdnriB8+&ZL&@
ze;P1$DlUPLVwj;Kg(NkGHOJbhp0uH^->!miHTzj;ibJ8}2BK+g5m!}8Jj63ip2i&<
zM}Tq$0PP7HhkhXo2h)-A(DnN-6<a06UK8ocKv`!20yuAhaJe!0*?U4hg`-YZUqWwd
zo9wnGB<)hFo~0ic2Cy^X)nYn|*0yZzySRS9W3DX%iZ@|x!yz2M!+Cnu!nw(w=|10|
zO;1D(=62nl@*nk*wh3?S&+Fggw*zTghW~)1DR#{ZJ5OcO9zsZZ%YE_wWd%7n{}*3n
z*V>JvWBXVjT#@S9j|E!db+3H-|5>2h`mB08%DY;#wytKx=(B$wRCSHVyU_i_%3m&x
z94((+?M1Y`+%~+y4bqt}Q<P&-FjA-{h^QdgcoK|31ONy3e~YT0=~>6DNv6Mbb_H~M
zAEA5zG@6VsgNpGAi>W>5q$3cBxIKQ1tQ&-Kc|Y^K$E&wMWOQMPB~7NP_B5u;$ixGl
z7xh|+()k?d^w(tQeDS4WK@o}lf+W_#8DwnKMx=Epu%BA}0wW;~q_M)(jFW-rAQqlg
zX=zJxcvAXh={9LxzXTz4jT33`G8bz~@I_cWI`9_1d`d9LXb5R*dGXlMFlq3_doC3)
zb5%XaM2r>~XySw<SXv_TEQkj6IBIFwHgH)TLU36RJZt%x9tr$R?d>_@Utge#=wm{V
z4q!7RCBZNcx!uK~KH<r$yAuPH*2pH^d0c}Mf2-+`O~L60I7OV+Au);TQt7{i!l{VX
zkWN@hFUtBs)MLl!An71~&5j>X8y@=u&|hZ4!d*(Cp^J-|X@J9tn{Zz8L8JAElP>7Q
zP#ky+5An+TcaT9y{k~KNKk)07p`nP8Qk(uH{wESG4pthm@KexS4fr^4^ECiA6l@P%
zRU(|i9=?Q#((^h{wfak<G`O6Y4TR_=%pY_Jt%?Kqa~_Gl?mqB-czgwfbLepjxXAz?
zC~FUh)bvFtG8jmBgc6RHM6S~OZ8B2u!Y~eKG!X$bjL0aUG++av1a98X$oOyOK9bzc
z?Q~9!H`5n|`S5NQNiuPOEd5ic9Grw1+AlO@%uuAj*tARUNfDb~7Y;wmPs0+c?y#4m
z7L@LFa14E5lSa}!E_>JwGT`pB5OtJ-v&91W?LnB5NO=BWdXSitp{l(LK`PVP1zvQO
zM?a#;13aYL;_JS4s9|Oxc1Y2J*Cncf!F87u?MY`qQG(kZq~&P=M*@jCT#l$RD^|8g
zR+%RPQ{5Rwf-@;bQkBm$g);v6^Hfe>ZD69IiVI#i3n=I~O-Blgvk6<xLRqu*RD7wY
zE9rzYL`UMU>*p);YPH}Yl@l8akua&OIU9<hq@s55K)urx&d}+Hn+>Q0NK?2YR=MFj
zLoz*o$xGY>@~zSU7LP9)4bZVMXg!x^8AiEQh&5t`1kZl*f4zTA%&q<sGUANN>JGzV
zToi-hfn;LFGEL4q`%*vZtR`j<?i`e3l(-0DtdLdq6pD3dp~^!!pp!)nhjNWFr<*Js
z4;W;Rtbo`-;80c}McAR+FA4EEOw4akVZ=31jTTjbYao;W2vZ!%*FSP+d@|JQSh}2<
zP-vdOY+Agu-VvuF>))Xo<1i|*+?Ce9@mCk-2@_$yH7K=UlmrkO4fz!+n64!oCoIZM
zEhRT8*jed(>h_2Dn<h@$Ehr7Cfr4Tj(BRw)IfXt7!x;Nh+-!(8kNq>r)ATrdcq;A=
zRR`oHzQae)1sffRPkOz;dos`f(lDr+6di9AI?r^kxSB;APgZ)aFKUn0?kNVy^igFR
zJ04QtnlgsBzU@10ch=v1bA!7(<20dD$`;IcZKMYYAKXQ(7<Rqd->beAWeXabKRj}u
z?VQ|SzJM&J-jKn3J=;et0iEhG&->l;s)Gf?@VhZ!Wy!p8xEyDE@@T65JzX%z=SH*v
z2k31evD``#PO5isI6z$~nk=tI_duK%-n)5%F0js2NFR><33VQfM1_7S#Hq;HZ7u#M
z^_9wz0z)T-)`3Q}6*@jo4@f$Ml)b=c!`9<g<^Vo~D2eg7oD%XmEv*U_2`Ng|;d@Cj
zyvc#!RQzheJ_d0J5_4i1Tvnt4<mwFkYW@Ml3)#=RnNIMc+=pM`>kb;QlR`U1tYI-D
zZK-D>34isMhEZjP5>E=2!zkvboqn5z!nQ<@OCR|(#@@e5O?P7y3;)<mE>f3DSk2d0
zZf1hprJqjQ^Y=V~!!?0I=!xkwY@YdMfV~1>s+OK>Py~;V$3VX6^4S~x>md0%RWwn+
ztlFJgsGFPMx*+6QeI;6SM*8v?RBMF3RGxebil;~PrU*hs-fw@U{7D4*t2zE;Ua3gT
zndBVlPmb2+c7K98$PW~^{>lm1HYsk6Qs}H5X$v7Pl_;YRIU*b0V{+Cd)XcefV^?DV
z*BI^O^Kzk7lu#me$L>*OVh<EeNMB~ZyNAsw1V_`0%V$!+1V@{FgOM$bi&j3)vy%M8
z=Bk4~&wE&apOjZ4kyv2Em{*jByZ^Q7b7?vkvWD5p_DuR|KkWpVxvY}$64j@&<sNcz
zXkpe-cEFf-z*nzOx;0)9rzX7I*gWt8=uQ<Vf}q}FaVlJQQ8Ec)pl^Dhom6cqm87wf
z;!C_Z=iu3e*Ny6bNUN1LMq#8Wb4(9Ip3cwEF*f#_`3?=x2t}~5${U6iL1xO<0)LLB
zd$HKcA<<XoHOOK1tD%9>42qo4Lo)fZTa#sO&+XXrVas%CtD2%g-ctg*bPfi9Uy?ex
zyo$DcZbTX7PpR@35m>bk-chR#Z{tk3h=BRlVnr<ZI9wI0Tu6Um5fjw7bYp3*TGfcV
z#$$_kJj$ja#!arJILS_QZjxZWvc2axh^op$s^<X#rEEVwDNrQD)@1W0l9=Az>GZQe
zy0mN5Eb;L3=;E^hiXfq30nZ|E>x~nj^Q{i)3GVIpHs|G&Y2kzgK1lU>(IYpifpMj;
zw>a3=V2^Us0G09iCNb}fGY<W1aW6Al^PU!`rI%U4N*7@)zm3?SrmP|#>+Xrhia$xS
zhCZc$LZcFmu~mKpGe40>CHQ7Zs_R0RQ`XmOzJcx3aAThQp|~trh*TR`EfT%$xe12D
zFK75uSf)C>m_bq9OXK`hN0E6IPC1U_?^SQ1yMKE(T{l#lZt>kn-C5nJ?z7Wr+?h4A
z<wma5PZ~(jo}e?|>As&>Lz!I}e`;%rsuU8P;u?LYNP^z6Oh&WT*PD6$IzqMT-jW~(
z%p{wH&YEwsrw)a+PY8CvgqH1s_NeOz7kki?0h(s7eWUzVV_P+nQxjIQdR7lvvkYY-
zNo0fmw0~kjC`;PM05ijgRHev453xbZ3aLod7WGEk+NNc7y(3vN$gZi91u1kSiT{)q
zIW8#B8TqqKo9EwO>&Dg4fq!g$ptGDEki$c_i$N~S14W(6C;m!+RX3r@Dd>~eES%s)
zcGmmr4)41?@v&$9>%+<^Nqa@vSV6_PWrTBQ)<)9)*qNW0S(!xw3J>O9Z2Bu{tuGW2
zx^*yrG|~%KjgAN~#0U=~<>O;548FI8(w&V-=w_vlGlE45Th%C7ilO0It3X^gzy@Sg
zyz}asxnkp#D`*1?K*{wt<%)~lgH2}j)^I1<<i{RPj;Aj_-tcE)_?ueTzfinb|5mbn
ze=K@?8{#X!vB1I5_658K8GH1zN$5%qMS8)9pf>LqrhU&3F3XnB>&&mFUQh?xszwK@
zWd2Gq#Cj)8WEpgN$eF7!xguzRL+)`TPcM!4&{mbiL&5_-{p<I%Qx$*S-$3UVXpk0y
zhE5R$E$G%h0_!C!r?0)dTjzgKK&fOgs;_i<B_ST;Sx-n;(7#{47wG>AX!koScDpzJ
z#UFka#5XCQ!^PA8px)#WdQrd6R=r7m<II_`IH11yMby(hNnPSc^Z2d_(bkqjvN>Zy
zh-ae!6yiA`w!;<6!Kx5i6J|*erJ-+ghc|0mATKVKtEe(IaVN8_^2sC<(b@Sfr=&wW
z<QeKJh<Rg<DVrlBPwCGH6yCAvpLZI^AteI0Cn|G8)ECk(0;LgKICp*LRac2A1AkmQ
z_1=8$V<!yrP+oMCm?ON*-nu4cNNZ)%HyFp-SLhjl#d_CP`lb=oA!x8PM_t}0qrS7h
z^a>T&6GbJrfn3l4dS0Oj&XgwVrN_W;Q|dUqlphl?^D$08e0jFBi{gLh1*6KM`=%RZ
zQ`#d9Z(|Ql2iI9JI6M{9p3a$gK{PV>e)xHF3xm8?*RRgWs~_10UaIEb=r9gI_1=G<
z?oP~r?8^9W*{24RO|IHlkJvLr{=%K*;cd%8{neu;*$&E=P9nQ44YX)ecJ;jC-0i!1
z=pEJ({CAJBrC-giH`uw)3+jB%?AIieafHTHEGz8Z7?<~?K)0v{J}-Y4pYl`agkysa
z=9-5n7`p%$C59v=YuA>6=94Ew=nI?`0}KaXtEQ{wt<d)=;KUGBvupS-`P5YHA0Pbk
zzgbmOEz(5GPu^|G;9)|4P|CKl_QY-=<v-e<qH&(P?7+Q8^b8$es~qjlffcm}H+P=$
zlMbD!&Iv=To0pJ_E+L^yk{fwSx=D~WrQfgL3meig+~{kYvZwWyXM0_k<$0T(Essq=
z=FoOYnS(?2(j$Fbv+&d#zwUk@Sz$(JmyhW7ED19e6T!us2V4uD8j+ey(CUV!B%$%4
z?pu2I&g`~|K7=)Y>>E_U0HsUNj-z!<3USIg`mm!{f|E6xr+)v9cWY!Yk3TU>*UmMy
zZbi6jZDn}{lfnMgJ=xmc8c*~|h`=WByF>jfV~O4GsM2PuFZ!2?Ms)1r+&Hc)z=XYq
zQ??;G{o<$Qb$d1wJ#v8=$s1VV>7657S%J6vC61e)Pjqv=olg1b-Xfb3)J}CiuwULW
z>gbkadE%b6&`uvyo+%dL+PN{f<`Ks4wt_u3=Gwuy+5f)0uX}E?YCuUsn}`gUii<W@
zE#Qd^E>zkWt}05zIh(Hj2J>#GE6Y20t9UxwHF_3a|8De@pL?Csi@*8Mo!ir7e6*`N
z8C^U7tPs*e121*$sYDA>>h2BH$K4dmygV9V)XMm<i`ft(*NLgk3gKBG()YT(W=%F4
z35;3;eV$o-4ZFk?-&ZOq9-adz{>VkA{<XWKBd702r5s|ONJ8z?b*C@r)BQyiI&@jK
z_EtOu3KenxDwg`ybh=L$4x>ll=iugjR}r5SBs-k_Kc6!%BmIaK-BFs`eB2(1Wxpvm
zWAE@I!qxF}ty=pMUa%2dH@$Dn;^Ck-AemFCuH%m)V7NY|R+y2Dg#xmaGap}K*&~}n
zTh1MQ)c`9Rps17eY+?z(;RzxHgF;~4OToRR!1c6XF@eUeKLg9|%OWc$cKeyx3nA_<
zTu3xOwZ;E!o>AP5<4n0IhGw5#`bE45nW~JPbVUT8v%tA=gx-GGSAQOr)kZ-45L!0u
ztg3AE0|_e^G{k0*1YEXtwm{w5`yG18)tZlga$A7Vi}1U9-0=*noJyS9?k>Bi`U;(X
zM9gTsU9a`#dDQNyYHoZFfDq3wzt|7*$KQ5)P@Ua9uLDTE$vTy`e8L`)XXW~=X_*Vm
zFlE7{^RpWw<*eb?4t6`oE>c(n;-I|ElE0nw*ixmh^usz$08B>Lh!*<~uM4O4#?^=J
z^8HlaE-!LQmm4{!7(S}BKG_WB1g|!L8LVyN*&7DdsltbmJ3yqow(aJY*F#Z{c{o$J
za%~{I{!<W{xs()I^y-Iia_IN=E>8qE!reS%`Z`2<`m^XZ{+|!AF@hnd%ww>*DlxVQ
zWU3!8xvE`2Oi?c$)&F7Z9fLEAwk^<(*|9pdZQJPBwrzfK(lI)=(Xo>+wr$(C^K$O1
zdi74-`(vYa?O$upHTPU=j5&sqUNQKNdaPAMtNizgO5s0ua&PW);lb{1W4eX$C$$%8
zli~)y%v552ltlX=O27Xi+hHbB5L#=<iWZDAQ;EuCrWTuYATZGwVlCx?v(0!pl<-*F
zOGUVJeuly|%2>LwbN0Sd=!`iEU$7t3&9v1YT+wa)xAy@sn<zl~eRxu(Y4>EcSC{m~
zlY27bmX>(R3>o@Nq#sD$Z~UY5xIz`r{O+YH|7y_!K@#-sp>4HiDbjU4<bha!>W-wb
zgwQo(`|U~nylAy_lWpiD)@s*o%_`?nEr38a6aS(l#Z!$92GV3=ObowzbFjL;xtU|E
z?&BEFpy>cmPV&&bU1ZsI_LnO|IevEWR}BkR=jhhp0l9v4om@Lr&qdQ-mvz1blNE;=
zXl0!^=U*@-tJ=I6j_B(MAX)F3RdWU0etBEPqduMO8t86!50Bt+)7YB8V)%PX{_5h*
zj&iA~wzR@7A`o2wzh#WOWBs%W2eR6>6R@m$_bqW7^47j?$H=6eZBcTT#GqUyp?#zb
z@QS)y)U`dz*5&!9t8U%KbjPV**V3_K^2C++BJ`I8@uZ5tKBr?rm!UJKT;lE8P34^F
zm?ZQg+zxL465Fx!EMv`D^tj4=6KuNR!->c0(PNzozyGdE(XKk4!Z!y`^dU|!_>8B8
znJ)woNxAM8vB4H-e|!VxCLD`b+&Sa-od0{@uu}N#cbRTjxyVvJIFBlXbA+P%L&Qm)
z|JgWAi%dd-x15YCG1cMYbRs8no0K<3FbsKzg^-S@mHauCPPC`*9~_-0F*`?F`d(M>
zmL0UJKHjCEJ;#lDLRm)37I1Y}gCt%MIQlh>#X6wu_hSMwhmAeRy9tXu-oI`^#WQIq
ztFm)!b;Y*l83ZL=qGtgb&eF7H7bAi~i}@5_vW&ZR@aOoKWj0JkCi<{(jps5Tb;-U|
zf{V9=Azx4pQ-=X50r>c+pe)}_3(@#kDGJ+o@SvP*DTaag_yCUoKPH!@DVMn3@(n^&
zPtQmvnfL{oEG&Ko6gmx4{jo;8a9q3e=v?1Gt^V@eOq{1vvT`(YUPjj@6x`kc2_D=m
z6ldFfC5OeIJr2(K%_jbQ|LyfLHP2a%bA|Uv%Iixqxto@rdxN$l8uEdXQCLH<OimV5
z0<dn7#)sRopwlT7$H_b-5nUAI7!>CT;uv91k9h`<hmMg#f@nvEP@E4G^AFNv%a=3$
z+pMuXh-HI;hiz<Y)BkPq+b4{@7i8IQ6^vTDB8+w;gH$S|fL?<04=I~b#W)w?Y*7gh
zMBBb*C1Vhl7cJ8~D1cWOEqIcG7A4l-3(#*C*<w=|J<I{U1geptf*aQ`0!e$#4)Sx1
z1Z8)SD_YrMU6cNLRWnj77!UXK2l1u{EmH1-j$}7Z4{=ZXP;a*g)LAzQy$3x~T~J_s
zdfqfR${B9uw@kJNe%)T2^t;0|0pMCNKnN3gDb>?z=cn@(E-lMUoN@d!+%5<KIN$`l
zB$37r92XX()d`;Q+9X26AF=@YkLF;u<bDq!CSQG&FVpO|`l>j+Aax4v-2lfp)k#TB
zJidpi5I>5#=QfzQC;Ck~8u-NzQg&+zs~v%#YTVEJ(Zmf%zw%>@+mYogYW<m@#6AD*
zWQ7WuAsQGXLsuHdcQadZn}qJW^#ZnoAB6Wc8OJTR1LLrg5u9L26=;UbC@o;5BKtj&
zU!x`xpz#oXsz4E!5(q;%5|WkTm&NEjL#sOa^c4oA5t8N3MJ02IVI#5Ah-oM?V!=>S
z5~b$?L0oA=iSy+td7x(V2MtzgmbU{3G!?GL;Wt4A3~jLv(NqZ`xzOk9u>gmt*Lll7
z<~48}Y~V-g{WV;3DY2vA=L>+~BSNSLL#)(lc+*O>0#iQ18!ROTf$(`hxD6M)?!gI<
zQt!k;mE|lzolX^TP~zr5&IC&YP5(q?+(-;j8Ztr-LMUSY35BR1;``$&;zW@hlZ=8U
z4}};+d@3oe5YX|v0CU#!2LfQU&s-D-ItxxmR0&ZlR21wi&=HKLHkW%CgZ2gfD3B~C
zKC-*q6cHAztS6MQ289z!txzHmY@#3`UC;(s%l_I1e|;~6HS8eZ+xdhshmZ{;MkiDV
z#)N~N1b!A5ZtUL=t$;#EL=nk-%uP`@8#LE&upFX@_$P39awA#5>@*-4*$Fg<&LV)?
zyohognbc$(G+1=Q07Tep8;h-59K>+Pg3*9!UacQA_}+qfh7b)yiM0Zz0j=Su2eh`u
z={Usv#WYz9RyZWxP-icWRk;9dT!TKq_p7<DZ^!5L`qJ~sbPMR_b^7@91lZy`HwUS&
zN(aaasLF^QZMBirJOe)7?7Ked>a6I^K4vQ{)52C_Y|~<1I3~r^4!1f@o!BR9*#(ZO
z!{kd4E$r8$8{SUR21_0?Hb)1fb-ZZkeldBYYM~f^;%UzsjoGE2zoNI4k$G0RAr+1Z
zUjJA#B^1mtS>YVi?|!s%geTo!w1F}J(&AFaS!%m|3|yjMNCG6sR}c(tJvseHj&6RC
z+v|q_IL&4AGZK!AN!zIY_~C@WE$1h1g4*Qd37WGY)#NPH8miq7oTKPSI;5Gp{b({F
zgzip;2gmBpYWAFjuz?wK{?YV2j_{tsi|bEkIA@o69!f?Vw>>y<{-Zc<0IZ$}4(b(`
zT3-afX*Qmh;{+hL^BTPQ#V*9?Wh*wO(m6LL9|fHu3$0G$hRjULZ!!&0NNb|v4@w>_
z2TajxrG<j#+#ENHt4$RNiyDZ6`N3~zBuE(hHU$KJ?4lJ?=vg@M;^B9>0Egr^K4XvG
zN=N`7K3-gIIc`UIk8(`E-8@1n<=-xg-rijRU-t#xo&f&F=d6K7B&Y3?fvbx1Li)4I
z``eoafgI2Gp$*HgGH<W*n76mSl)kwWhtHMUPrN7MLldiCZ`rDV)KpZ8!~Q>9Bb!lR
zaYVBpj$a<l0tZY#>R5#z@u(z8s8`ewW~_tmLVq7611rGLyg5fxl6mHwD)ieM=p^0;
zWR{%Ps{vj+Cj;=ObQC^g_8}wpWe5IzpQ#IsC!4%x%QsG^ZaD%3mx#bS5U%#Kw#%IZ
z7l6kx{{=w~K0@rC_x{`4*k`}%+hs%7-Y$bhX`0MeHJ6{*%)viZ(8N*oaHW5@Ne#G`
z92$xqoJus&D!U~Q&+vQ7TcbUy0y?%2Z5+-mD*#A@`84z85^cZezgOLGbZZobVH2?l
z3yOURem#HPK$3q)x(kk#4)AoFx;(t|ssKn94dJ>xVEeLXu$z7UI7C<Dn&aMY{?y&a
zma1MdM|d+y7pPBZ5D>cY;l!V00D3EQ@N8UMJ|W44(@sRHR+D*O%m@;EHk#%}GR--+
zCjt65!5bTlq=9+rVx?mf%+WoqKf)0{fk`F}<Q!q^tuk{#dw?f_Fz<XGK7lO7D*dUA
zyPFe%nucpLm3Ye^oeO8q^V(_TdvjxbeV4;qnD5pS5x}IVyUV=~ztTMSqw2Rw$^u}D
z{l>=U&3!`SvuB3v-QB^@>kRT^oL493RKVe2H4te3X8dIQRPW27Q<{S~G`Fx4qixr!
zXf?{}{Po<%lhZczK@i0(TkY37yfg3X<MkTaOkz}>O-kSKN8jGTbN_}_ZsZx6`TduJ
zhnFg_V9P2SFp3L2oj1Kuq335vH2jxAJyJC0;&HRT8Yi$^JG%LGr~hU5?3z(H1kn9_
zb^?68F%XOo=R{8bIdOc^4c^0YL+4&IPDl%OyFYEx<Vo-p;k-R+a>%5jXD)DYGohO6
zF@WtFZcthiPZ74r=hF#p>K^jE_j5*XLjlEkb$A$x+IXRcKi<1bvm<ipNH=T_(Uj$B
zZ6?uW5cZkgLv<5DB`ED^<t`kW0`#Ayjof4Daj^c(%|eeP393sytbUmdLHc{LmrVXO
z=@}o4;u0`njm6z|0p=RD)=O#n4(%xZ8}r7vuK#HxR5b&wv%k7m1;l`;f&PI7>aTXu
z%EW@|Vc+&=T1eCU{j7GEqF_}26hxsBopy1B#Rf%l{HW4gf88SrWcI&&DnR^CF2hPG
znY?j{)j9-H(*x77+ujr}HN7@oXT{l~Y3dXZ6KVg<Sl{Ht7DAgzGhMFPXJSLW0saZz
z>7-(_LP`i@Qn2AvX%gLrRMWdE&od)Bw2wp1lwJko(1ga-M|K-FL;Lz&otnb^xBNsb
z_D}dVv8lU`jM{~eW(?75LjVaHMQyX)p}HrH#@5h_(Y)81Hfn?qHjeoSvE-IFkZ7N+
z@aWM*<~-^mdaI>7Sxi^Ypfs_P#s}mzKh#0jq(jAY@|FObQ7fa_b!YutdaksnxRzt0
zimhmoW<G;0>vu=TMxa`{Qdy$aSwh52!Wl+kW~<)xHo9EUTmO3DFhH8gzSr82Yz}Tx
z%SU<DQ;Eu6uBXz#E{5|TiSJLBoXynuM#gsJ_Qo1B_QmN))d^`TEX}PlbwjNNf;Adn
zZL=H8Gj?sp-|MB8lZBqu70H`T<Er<vT%gG*Vn1&*cAUydNmOl$u{l&iaLCJ~5#`v{
zl`6@VLk<VVTQL?jiU6*18Z7^4pwUUjwVo-s!zHWF)fl3lJJZ)9C2fn#0}QMQm$9L%
z27{?rbSH@Porl$NVIxs`E2`~dII7nh@@HUXCmbJ=q87ri&Y0^XiN0&cSmLomW2NGj
z=gpmPio?VA=F9xsK@1?mTsv}5V@>kus;!|_v)GNR+J$gqHXZab9e?O1`v`rQQ^}jB
zM^!`97s!uxRP6r^O0%=E{(to|92h4X^ZyGOGqU`b?%josT<m5Oa_6@I|1#vUwc|4r
zDH0YmN<$ci*!1D_;&spC<r+5hc{h-L6|iM4s+8Y?UAg!N746uNP8uzYWy&XE_Y8@G
zB@L-EFA6R#&cTtKiM~ez$1!^F)t?4u41dYS!ioIaWH{at=815d8E|=X#JtMBcN%p!
zA{u5sKDdJ?AjbPN2=qUid_KO(jGD?#WP;M1hQJg$_J@L0$Cb-OxeQ6>HAXF%9w8sa
z`W-5D4BiLAS~B8*B1N7^drW4Xki&?X2EGaQ`rT{MM2@=WYwr}^0-31XjqdatL)J)%
z$1^W==7;v?Z-0|Ia)420BN8pjW-U=r8?O{0jYB-T=S?s#mgx_vXA01t@SwCvBivA#
z-LbA%_!OSReLde|CHM*5mk<=OkYkkah^TR()a;ywBG`&1^%jE2dRUPdk`S`ke7Uf<
z;Id+}#+ukz2B-~4Vi$>}pwmd2o)khQdTs4_afLKkMy0Bf3II1|TpcZjpOk5$eR1<#
zrL?`IgeZALZK1M?3M7#fjMP`bgrqn12!=A*0pj4S{PMTpi|mFLqVC-l-<#bXNugMQ
z;qX4WLhbO4q3~2B_rFRAb$_D;0l`7QS1Iox4Dk&HNx?xJ$8Qm`nFB(g=Q(9>5h6FX
z#jqI7*Tpm(oB%@E-6s8Z#0oN+oWVP$6yXOJb%Y@0<Sh6@v}P<Y;hN{6yyQQ8c_4oD
z*5LDqJ^vI*bOU8;Xir7wYUn?r9b=)ufP6D$`gumg5ox;0sf=823_>nxk0S7wrrR{v
zw$efxyBi;@G7)3>W)^A62#d1<HN2kn2Qz*ETg2TPvj-qq3;Wx>j~|j!Q%~NXC(_d#
zM^wWSL^pJ!x7)CRb}9?qvAC6toJO|Kppawg(Oyi{GF|}+{Rjdr!JI!rd#wHkltIQf
zMx)V-^ZOhYSDI#|qkTwmaKew&59H5uFEotaqA3;+oT)6xjj1>%=V48KpH$5qbHop1
zIMb`*l?gy0nI75Y-cc&j01=;qae5g^i-x0{M_#aG9!1kI3eL4wb|1Oc?e7H7MDRe;
zsXBCOVXl%D!hyb!JJL=&F)J)0ty^~43CO^lKjdUTx=99HK*8B}aoIf-%!I&@ReBo~
z;w6Ox*@(H{b<haF<=vd&{*vhs`N*O`NNV+VBzgksu^6J@)XYn`#`7U)2;+HStw@!`
zd(UsfFwO&mnRyjTMSI{-W}^~X{0XBlSSfqJYDb8U!GF=j{80Xjp<qJM0fV+I1&^wp
z$CYlnErl|`_(MwK9)T^eu3ry}h*HKNq>ZR(98S`|KGEC`!Ge_g#zWqSWt{+5(&d2g
zub2b?_q(%Ta@YUd6x<j$(v1@&3lls<_Hb(I^R>CFFG^5A=f@T>b_?)ztA0H0RRT5-
zm9<mKg6FbIQpb+(ith;5Jadpq6TV7?%&hlgZ1yCEdr#4Pk!N5Fk(<NB7a^s^Y0M?P
zqWLiat^9fvPS!jR+oe1vrXqN5W*Dp}Jn0lbf`=D<FF}GBB~NT3FdG)BEhMPHN$73T
zw2AK^gA+|8po{b~IsVgMd$lwQS0AYh<B7LsJldQ8xWPrqG;Epw`2O*JIPQ+DGz%OF
zqf&D9^n$%uMw+Gx=S(D_^~b>C2|K)}H0C2s6L<ZJo##(X(wWti7@bYqa}=p7qoqjz
zqrw3k=~|YfgVib}cG2(vjbG&K2lF=zV07MdLPF90ES-Np(v)pDIMNkZYczeAqR;<!
z_;~q=K(<9Yb9Z!m3~YYd+Vb=4=+L8>PeE0qq6E$e#tVq4m}&U#e7@9IUtj8He|&zp
zUwxzDXB=NX4}BjOVGQ*58Anq&zHfljz6;=6Yr@~wFPFEgH&3D(0VrwB`EAr>aMTf>
z&h>)q>^RdSb48Dq#jd5f{i?oq_h$Rctxmwxz<~Xs{Kh-kzw_~}Lpax}`H`14UQBqi
z&6az|#Y04%JHXq{qyC*ynepHy%ifksjV;7y(+?*7R<L8OG*@&q{lQlChZlex)Nz5V
zMAAum1kGF_;{-aHej3g%H|sASh6^d<P**e7=~JJ(_XbenJ7_^T`K4Puu#wnw{AYxT
z+pT&2%M%Oy?clGijt>X-uH*OE>Pw90Y~PPh64JLyf{sEhm?wX0JI%E1Nmlcx@q=t#
zsK@z$+j3ZyHykA1mLJj|BVB+hLe+zbA3*MsHT6x1o5m%*%7t=9p_yzXoAj2G21W~G
zc1P{B#@_Z(@C!yox#1^VF}_nas7zb;{r<%_SjR-c+-%zeEh2&s19@AVGL=7>^oqx1
zMi9p3^)0_gSSKHE9=P)eSX-IKDuzH_;P?<eJup_&ieQWP;Q-3t2rYoLH^MmTku^5f
zdUNOSMJ<-FJ(1_ok%P^=<K)$%v7F4MWOBB>+;=PFFQ^P0RVyM@p@w8mi07=a#aI)N
zl7GAD4nkc-+7}=si=rf_(&JuxADuAIO{z_MRP#7-x8PU~NyBjG&1Q3<MoZHs^i;Wd
zy28JKFM)>RPRD&c4*@t$w`B(bjv29B3>2$a=2QQCYR!~NDc1ZndeA*siijmw#Nr#Q
zdd({G-R7e{s&KZOO0q-vYH@4KNgcByX1J;5%DT#o;qc5%;@wQit~j{qY(UU0>%gj<
zB#^f4Qnps_yFg{|Hhr8lZFN+z7XKn#QDfDW`_^1sN)fkYI|Ssa;lZvJj`rec_6lwI
zR30`f2R<4GX;1jfK^zsZu6wv}$NFmaNNX~sWGpGgG7v4e`M7@9!)jGUCn#2BaMU8(
z%M8ly7&3DB8-6Ve)EZROfy*Jm&&y=1)|k5)6&#iO0@qEscXd+wC&==vJ-Hs9jn>*P
z6^7QH0E!U~Jiv&mN$Sv6#eH%uN7fYURR_XAg>ctP?s1Z>ZCIm%r%fvp?FM^`ZGyU=
zXjsLxd6pl+&g?ymSDBRU?+48^fs?zZvlA17cm}HbdTD`&6K2a3r+TWoWWHK$-^wC~
zW_){@;11$sbsephJsnM>mL{U!4HS{cGPl`~`VNOnWWY7+fyO_1!4L3H05TsRSrI=O
z86BCcWhfB+;8&=2iv^LtHs0~l^|aDTLN9QBTCD_eb{nbbRoeHPILT>SeP#6ElltnD
zx*Omr7TuDasg#pBYdrF&SyF8EQsUx5eZa7BecTbXMIMUXGl%>Puq)sSM{c8jStbuA
zrQPaI1&9C&-wSyfHvi@>Se#9IYbCvKL+TBIbi8w{eM>y)Lho(Wo_NV(n`STpo3GS6
zO$n*<D&uf{Fp}UYqfhpOk4TpZiz%76Hzqa3F+O|r#Y?>qnzp^Z`eu#T8E#g$ooh8W
zecUx6cFhN-C$94@NVVBUoGq?Qrf#C>S=Dh?0dU6Zp)!ghmF_{EP`Q}#H2M?Yb5S=R
zc}xVH_qWFyqH)uMH(gKhCQ9`WU1UZzZI>Q+)U@##s3-qfO_pz5rR~?C%2l5Y4mGA8
z)WPg77QHLsG<fyfr!d$e-p^!Z3<S5tvQ@>h*$FJ#jMF|9Sv(4K>EFdN?dY!q9w3x1
z0W#OPpY|I$W*e`tOi!wIF*&BO@`zKd3_&ae_P_5MR-c0)gideMwB1!AH!5k0)6T4q
z82Eyl?rI6@>AKFLPOX>PslXemMQuqOp7WP+ZSJy&tA&gW*(<vrXsVi!8@@(VP{s8r
zj^q+1j@&qg@Z<ox(H*K(9lj4dazJ1QU`bWs*OIo}+C<jTl|TcqBg^RL4_~@0pe-ra
z{QIxWVZ3FfOY^0OQLrGra|@R$jx{iBmh(9`7AHkzSNus`Cx<sChx(5lAo_>v29W}X
zrj%~2`aTZQz2>~Z!-}l{kf#~Y$pzK7MnYb{7TfwVUgEau_gpp3I@v4Vt*NCBfL$fR
z+6i=PDK&W63sR92-<^zF#V+(KQ0#(Hc&cojREcP%e!n5E)cPHCR$nM8(e}&s??7yY
zp?w=4BlmJ(ajrbp8_b+J@>91t56oHs$t7DdcQlu}-uvCf##=1G=KAI@ZJkfx*SiDU
z>sO0zMJ2xS`|AhLdwr`T)p%b901q%U_m6%!=sidnHT9dAR-tdwDb*R}q~4?aD?^(i
zT!f3ucF^#TN&;Jsi_vOy#W-AnK17<Uz-pArY7aKaiE_C`cwkCJ&u-w+Md{qh=kcJv
zdb7hD0O0t7E$;rv&qQYLbl8T2`k&K*PCblRjMMylFtG%et)*}t3IH}KUsD;)V)V#Z
zc``Q533lr5oqcg83CjUaHC0Zr92ca?s3xK^0)48JTK`{V;$ojKjsxS@?|;YeD<|c_
zo;#E@+#<s$hln!&Qrhjq$lqAeVxNCZ2Z<3>1O&1#q6VX8uif$+d%l@Db#v=Cb5KL<
zuT`)9`Xgv-UwvyCKmg`KF&szddJ<w={X~i_k}`V*Bzyg-u%Q79qK#W#Ey>aE1R5D&
z3gX~U{a)Yab4B!#q0cIwy>+igraxWYYf(NWHdhXMl65;(V+1?<Id$tfetO-C!RTmZ
zZ6gVkwNs&DOpAQatS0P0zKloZdsM@%rIsaQeY?3?GV7rQHUK(d12eYQ685t&f3BQX
zJMmjk;)_*oO{sKT03oxQ%~Ev{cDezM)lx7KuL#?pd=+9fq#itL3u;xqgvul&@Gx;7
z5_XdcZPpUj=l4p|OMZ2N`4NFCzwSz7cxl%+&tF0{nk_nY!rC%R-HHv^P&yY?ck_mn
zmdibh<Y0T;4rn*?(N_Aj2qwxZ9tlFeBuB^D&<u1Gs9eU9SLU4GNe9TNSU|?n;IpAF
zl@EyzP~@K$8z=vf&Q==Bf<jAG>sUx1yw#&Jm~%-#RDf4lUC>ra8wcwtJf^#`guN-;
zb1G@p8d}+(;<^++sSVJ?OR<(FCyO$jSXfVWtlO>N0%R8FV(I-Zp*qLJV726S72z!T
zg9hu9-Yb>W#-DxI6!C_ZRosM95tC`n==O$9<O*?W@t2GSZCw*qt!Hj<4lQ{()J?ZC
z8PpmYLS=3ZVS>m|sZuIgVf$T=lJK?!CcEO_KbQK=Qu_TfMB|)dmvn42*8zPFC~9l@
ziEdZ>TmWRcS!VQB*eS^Z-fsA~U(s(nZaE<);iE~V=|YDW)({u^o;JG;QQN+vi`scx
zbft*jO3mNg_R(RQ?28vx2r)Xt@AnmD6c(6iGbkyoU~X7L9A+=t-ZcUKykB2vCF^zt
z%KA?$K5JV4ippgoXv_Z`C}M;N7uKWY8=*600*Z4r%;uSEa>QY!A<ev#LFFl2R|l$J
z1r$<WbL4mxOI+`&KcTRSKlL%A8}T$78n%bD{Ii4u<dqfPRp`74l)f*O&L61(N!%$)
zymC8*Ij?pBS^yt=O5cbarwx$mwSsv!(GTJ=0LY;)xcL9t6#$3j_}&Qkk3A<F%YS(a
z=>DCtDr7_pz4DFFX`&UAlbsC^N}YiPvu&=RgY!PJFr#)+nQu*P)}Q#YXHZKGke7Gr
z%p|24NBAbxn<IBd`|7r7X0BHK+L+fLTK!YN)2xdmGt=-Od;9fJgdND}T^71@x-0s8
z^mMmo{kiv21gL1Eiot*UP`|sr(d!pyy$l|HTH6jLCAht1pSW~7{@t-`2S(JZKh{6T
zyV<`{brO5&=lkSrv%c(R?)Vg+^eN|w7s}x7xIvpwDEHyQG=JLZIH#~+SJ74v*EDlm
zXrxUmkKI!Bas@o7*(>|37-Zw!WY|&s+h0~Po=9|%4Jf=JB_k%2BuZ%L<ACVR3=QU5
zK<MXUnMNHX%LUyNq#oq>DeNX9?Dhc793EU!7f#AYoUq4EXqMXuF_SLJVwU@h@=r}D
zuISXy@a#hH;6?_Sn|N=eik~oxFsI*YFmcBtKKG<~Z6m1A3v8qnLOo)A+*E=!?4%kr
z=Rk4S6aYwe4Wvw9nkZznmvn_tpRzNChZc%F$X#AGcF9opc!EK(qZj-*Eet)Zw~2y$
zNEUl(i6|tPd~_E3bpxpf^(2h^!1?t&Cj;a|1F05CP7&LmMaKFD^4dU7K8!_dppl`i
z6mL#xqwYk+hgXSbHto~WyEp3F8%+Txh$&%LonQ-NZnQl_`<=D22G5E+$-nAvsuIT5
zrO%w!o6K74RSf5SANT0jCn(r9v+n;2qwgq62?$3g1F$gvS8Isw?2KI?BO2tkcXoaX
zeI!UkpbYe%d~y?Rjv1)ZgKo(}RtS2ZHzB~@K>ClD(Jc|@hzzqe*#$o5E;$<KQ%%i(
zy$VYR;wV5xRhprric-_Bza3-Z7DIBe!hk=asc)W~x_&WNVSBB%!M88(vI`CXOf$cZ
z^N%=m6=Gjc3#0cFn+$=r>~z^Ax=7<NCU+e*_YTjRl{O3ed-jPeS)pPz^bFm(^~wF9
zMRzO7hjHhG{^Yw_z7>plOQI2I)p%6zJxPsr0*T!?>d$XWN)0!fgK9?Q%&Vc6qmW1w
zrS3ekhI*}v3XE*@E#awsC^SNg*3jf>NG+l?VH{}H<WiJpqy!&x^j&Y{Fa;LqJT^4J
z(qDwX^32eLh=P4%5Hs#=oU?UPS6Q>J&O8nb#+Pm0P?wD}x%FtgJ}BB2=BT|tKnNT1
z;{K1}PGS6y(-}L<e>t7$Ch1!53Sx<0eW16V*P+QB{tJZ0PC{>5D{A8KP8pm*xB6R(
z`SFx7?rC8df-y<Ta+m2z!P4i{p}`Sm&HlcTeJa;(1{A5c-8*oxK1=Nw_{jUUFV@62
zudev2Pw?-~K!acHNPmIJtdD>}zWyKwa3w&HBR^C=BJnik2=K06eOpRVy{xxgzj+)R
z;xY^I=vemAsk&*gb1Si7{$;XKbLQ5fvle@9*TmN`VDG!842-_WTQPU9J|J(ju{*l+
z<{dmtkA|GH#HOdv^IaVrYb`g{W_#mIjcVIs&8TSGazV<t089<V*x3VL?H3*a)kk-i
z4>QAubE2li3|eHcW1<6;icCSUujp}kIm2`I2oM$^e56iZ?4kEIgQ<|jY2Q{{7LO2`
z(iTzzaPg}m;$>o7_T11=481+Pf*8bUj#0gBSnm~K*LTG4QG@+c7H%&Pl77Td@Yc2_
zg|y6p6;YCArmUeRq`kv+W*8&@hTdT%sK|IsLlaHi@5_iJDT!%(e0YRr6s@0LaE{{d
zcUbC+>oaWBog=a5U#HvGXWXQ{4B;wFi{m@$j5&SPx#9?2fhONalEX9CM3_F3r3Cda
zgcyeIJtR?1&Uan}p+et5`s+gWbF%9B@$H9$niO8Hpzus_<Aga2H-Hnj4WBDC@#!{e
z5N;~pun;Mf6C_u-3e+NZDFSg6E)9VTb2uaH`EYKQ*hjCd{=!iTy4Syjc7dISuD;zf
zyhhqCt>rvZ{j+?5D(=xcs9}*i(bf=Y%1XmdW(62c3lL9>%ovTVq7TEhEtH%TQD5t|
z%i-VZQf=_B&TrHGQ^aeBKHN_ovs(gcv+lwFN0hR${il5p!1!NXow_sn)<KL&Kd#*c
z#PE5<nQAND2z$+tgB#Zx{QOzDfP&FP#fTq|ESM*Kps-#0w?~(|b^P;MTquAw#fELk
z>V^i!Xm=Wao$Yd$eiY&)hsbLNNW1(NUMJ^Wu(;<EZdbXdS5KS40BiB3#yt+Q#SK8K
z5mo$j*!ErD4=IeLutz2?EUS%J(kT+*T{C`fr$-^RE2sAE$eg!2l!{sPy3<h%^Hm@d
z2bLg@a6x|eo`y#j7MW})T#b%C(dY>;Y=Zh7CGqs_)@$Z3JK+6piZ(&E&7p(zT%n+<
zkoG(|%qj$(wfDRmfeNHAIGzFzhKeGDt>Q1SB38=vlu(?ypVZoaJ!`46k`x|8|Iji#
zQoX40@(^7vPKNaO8IJh6{tY#knXq`LfB*c^02Md%e~s#YnlitC*#B*FsQY)rYE2O7
zd-9{T$x`(sJTib7OP+(TlgpJQLMaqB(u9){tzmk;je7GtDF*zMc;hfPW4l{0>o{3e
zX8+=Qd|=P6(_LdT=6yV>jam6-$8A(l))!aG=iloSlpC_^^Qre$pCZ-2e9WDm9K~MA
z1E!{YmoQ%*a#{qwb+aGV&Wv~J$uCpiU%C1#(Cio2ruL}t**B`DFd{XRCrV5&Zdm=y
z)ZOL2@Vj(3JEG)p*X(k1J1Uz_CLVd=32u;p%E?p7V-31OF0NbsdkPDV6>Ywmbhjs(
zW|2cDnv+IL?;L)p8jqd-zSQ<iy&MJP08`G|w5XLRo-^EvCwXC_mmprUXt{MJDJab6
z#B~+gLNTtyh#MjxS|S`3bIWvvwLFt7NC<ktL9IdixpxjV7`;6r<mSbt2E{@DneO91
zgL8<OgY=~!fBGfEW52gU3l#9(uyE`8fh6QWlZYHfN1;VV5z>$l*E6?;d}jc_a01K!
zO#QN;f#FETI+G=%eKBT~5y{$NNznmXaSQ>5-YLr8@6z8HAVZJ5cPjFlWxi*JgHAG7
zAF0e}|2qRXW%pGVerJG$VO&45Z+~&FtnUnPf^f^n86{nQ!sry`85Iak5>`1x8SZkn
zJg{fbqN#^q(tF0UYv!46k3gKyzM5DX{kw!3!97+4sSNWNoXf#0E=Urg|JOdD65g;+
zcTfCF?j&LFx5h${om#e5rq@c*if;P7dTiCy9%tkD{D?A3=lw)er2d58aoAY-{TmcX
zxNgS(6>}W_6LYMr|2>rx<*fG@kwULN(Yc%c8fn)Sf(H^cT1#3!kyt*}a13P^hH9}#
zI1hJK7r77j!rFKg&a|@>q_r$3Yp=->e;$OI$tQBUHMzRr(^nBW<|>;Ps6;eblNVpx
zzj{UO?pERh=K20SyFWZlT^{T_*m3~w8+G|Ek6m8MH};0SldNWDkq57q;)6{#li$uf
z^Zl%2gEXp1h<0DJ+_TqJ*KJpK1fTq_N~}j*A4xCyBYc-v^apPuz3LJr^LY_3l|+}|
z>9U5}h}73m#m-FhS~b+~MoH=}07q%PjeWH*6<Hkekt@7XF6rJ0aB<*#0T_T><~Ulx
zsen3ol^&FaI6{cPsR5KiNBJOl+B9}f7D6?1BfiI8uUy)J71;h$+37j?Try}blM02v
zxEw!(!fj>sT-rD(LD(e!w!pyP?7%=1hH&M)Z0-O|a!N`SbrP#Sa0b|iOl25z+3>mC
zHf&eL@VOe$Q%emHbk>UQ{$F}TP(e{>PK?9DjJD6wc2_2&0G=N5%6A-@E!lwYl&zFY
zCK;rb$JdJJTkr}3oKSamw6l9+3^9!JnQ~9I3*ts7=tA$b2ku^={9oVr+lHGQl$|j}
z^97F>fCf6Y$VSD()0FTFdyR84@5}EKnorgsQX*L|l7s4>F<D5+z73l7v@ioH2WQy|
zV=w{~kyJVe<~Ti?9lMfTLS6A|Vhr@i0gw(bIYr=FS?Hy!?~rOgUr~KQwE;H7|JdXn
z9Y?2wm+@xeSYc94I0eIrPQj<?9JUbf4<W|~a53h<DvVImmMmAwTEEDFtEjacmk;e^
zsS`sLlUNM@GYKrgXD{FXeLtU&@}=uZH}ll%Q6~Cov$NGM#Vw>Hg;ojM0ubI1eUDEC
z*A^N0)q`_)9WpW4!WZP-1JYFy_spn4b5YX!0v2QAmG{3#1N6J1C7MG0u4q~QJLG3K
ztb!P^Ah(^XiNkOTN-eFqkqj5mgI#gz_57oAvJ?`LE`+Z9(mC!80(()ra^6EcHUF09
zXgu3CR#+J6R%^)=bZSbS%ckf-FqzvjP%u0ti5Lem3a}s6|FW2@8f~l4=Z}%7biu70
zEo*&rQU&B7IQD9tB6+vr7_9?o%ilO>6v}_sv;M?_102ronOvQpR4$l%=?!+;$TX>A
zKB^og;gs18>R1x8)lv>KtJ4qe7us2}Mw70U!a-QF3jC5myi0^ATm^i!%JaD2HrL8{
zWKR8O9kfE+8OqrZwm1#4%2~<P5CZ;JkY8SqA7Fpf@F&v)<b}Fg0rVmd@2^N_@f1z2
zJ2W2WVe1CxpJ6H?fdF)}V+tg$X4l>blzB9b`0Myy+k&lXQ;`(!>lVmcvkM>E%eodY
z{mEkW`xbfe46?BffTnHf)BhfI4tB=>PYmIId|6Bruc6QZtc?G?s!&$64`f90J5s-&
zF=d)2hW$f?92aadX*+IGjsBZ#Mwpua`AYpVmGgU1(Fwe|v~FE>Og1UEO8dw=BMwW(
zqgJ(QS>(|DvDR0sgn?O65z!>^Oiy1|d+tG!;qD1yCZXB+@#5o~Xj{bY3ZrFf0`N}v
z>$!1D_G)|k{gRpFKsfQ#0YdJTN%r!w+DoP!mQI&~VcZn2x}>(XVxqd}?Qzml$*xM{
zYLcj%Ns_pgeh81LK<8Y+*zc4ni!s>S7!P4l7Gpq%S1;ToI{{0la6s8fxbUfvSvY93
zWWp1JM3nCzCvPD}Nr;9T0ZkTc1Q5-PTPtA81IN4X=+{5$v52s2cdsG8$dt6Y@bZO0
zfgpLZlU<>|YRI0;>v!)U&7O$cf$M|5tblomZQ+nGLq>!h)g%Q=B;Er~vAr9VA|s>z
z9Ph%L8svHOc(NWh=f4~!fe$a)1zG7~x%J@(x2X0L4sd}k{F;o2owPdG<<W$u&*pPO
zo&EW2+!>j%Xf}C`u?_Ctr$*(W^S9WJ8=b#vy6J@d|N5NFDL44|+yHh?*8kdFDDr~Q
zP+oex`V)t$5|O;8I5OkBs}Od^D~~r{KJW`2-5fd192$lNL|C#;L=x0c?H5|o=(Z?&
zgI39zZKZmKc6F1lmUiR9=Hlep_Tj^q8}zDE*XG;i6JIC8+{mPsclFMbwlyB<Umy=z
zykxr?ph5=|E0NRmHaMhK5tyaEi4qnr5>LT!`<GHuRE)tfHG{JoFZR|2cy_YcikgEb
zyhX5!Pl6|{m=Nx_-<q;-itLF%aTV%&#5L>L+INR8O&&kY3-#>tG4|y)qF2M?rhYxU
z(Ze+AU^+VgCz0Y?ON*VJjEI+2wk(y~^S0pzFnPok5rT?71{y?cyznP}C!#xbBf=CF
zorZ#1ZJ>N)!C@eE8Ec?wyQaGoNz5j^SAbJ6{x4Q$Tq(3%xLkw`E|2040Y-MI!VtMA
z^{}<DgNlQJ(Y2kJ()i^zcnCAC#?9iZ>t_U6EtB*>#C7g=YQMoiFbu4ikwXo!Y*|zW
zfX6a{naah;M)<f8sgB%H>94ioF6q$~Ijv^LD4;i@$ev!W(e6gnv`z^bB_nqkuL2%E
zRyuRJFJVeRVL!GYR6(R{XCC^LVV={9mn|S&QZBz(LAlIwUe*eOEhAlIqUcbuTG?LV
zt_)&c*b2EZRZF-->_us;^k5#=imWjLa9gmbU|r_=R{&kmHlJNtyR>p1!HNQ1@*>|&
zxkm*WT|7h?S%nl`OiZ~}1s7eOOhKCZkCGyl3U(6F4yg!fSDPZrQMM1&mfn6APf@X7
zoX?`QL^a|D@jAa7!3q9_pC-4u)^Ef@<c~6&p!4J*=~x0Uz_PhbP9T4!FaD!1px{b#
zN}cy2n}hf3s0g;2N$*8Z|3T)|y%n(a3H(O7c@GXM88|$qZtRSkSO^fY*tb!DX&syn
zN8Nm21PHeT_bv{T+fzPfuV`<)@hJ}Q=6lz1<3s|t@LwFC5&@{}J;>ED&sOMBKn6Qy
z8SdoydJL6ZnmxzT=Z*$8KFBgat7QW><b^DzgT>L4k}f&(@xJ4mBG;Dem8Ia&Scvg9
zc}Snj9;c;7ncT)?heXPi!~>|BFmz;rB~-9#yRutD>B^9(HcEV+<jD@%q6O~tM_2+X
zBhq0H5(iUpR*2Ov*?~Q|j3&F$*oOmEF76KTs>Uj&gIiDX$jL#?@qKv!=Ym($q5Zmd
z60)CgDlNP^*7flKCZ^Ad$CzJ%oWdBfttG`X#%pOug;ckQpCLtz#A|oUVZm0Q0guIQ
z2RE*i{24A|AIJ)VeJZGl_q|oFA<}dU=JdF?N|>*}8vR=2d74*&V$KkSJq)Xvla%7&
zsR0P2$QDaYi=^EY-7pPcJ53L}ey~ev%oNA9oR<h1QG?f9#=-5TK_Z};H}~pDy;?7Y
z#k>qC!;i<}xC^@e6#W7B&+V>u#1A;j%sV^V=Uy2v-y%R@vVC*yBSb{7<j3`h8sn+z
zd|{&+ph?M2GRE&&5~eX!r9n``7a`wb=yBx#zNhT^!0Q~qt<?%JY(|=E4N$FGGZ^1V
zKKiP@?INh8uZN-7Y;PzgTMl|g-w4xz3f_G31Mf2sfbT(r9xscrtO*D))F_3iz13IE
z5pb74=;XwOxLuYEGqV)_rr+01CsbhIpIVB)FjY_n6@g|$acIi!2~h?N;UmFzSROty
zT~Es+A?$Y5lb{1u>3IDhoO&|M)-wbJQ_<V|$NHxbapMY;bwY@07Eexn;|7~ZG$KC^
zZB~=B`b0v{ZEX+e`3p-f_goeXUB*dmxIgI2_X%+WzBG3gNq|41hb{wP-p1H?RfeU<
z5SFqJJ0%hi;q>s%Smg(9OGFnenh{=1*`GS+)6GZbaKZruFPy4T+`@Z#f1p`HooUq7
zvJg<3a;DtY|86z#{LVUBi}|WNwO`)}^_d{ri8BLORgU&47_g^vWegZG!FU6bFb*&e
zF|Xr`kRPG{g|YFr69(e&DK(nJ*LX<HmNMPevK83T?_dJ%3tp*~oaRmJG14z2%45zS
z7%@}fDxClfOd`%umz(hooVGO=wAu-PRfWNfpO5E0V55_VbIQ$^>3z|HTY&4dgH6Px
z)4BW%2suj->iu(wfe`Y%++n{NVunSGVvHL$?fi>KE?&1?qj&Ob8Da~a4}H1lrH~+W
z@<iQB2z8`CM?bJgUqB&Sh2Y;uH<R%g(|2E%PCNhwOnM%=81~Hg+xBx%vc+JapYtDG
zzH<E&Dj&DP8Xq|$+`UIdC$qP+ytlLU1@XLdG1M<gUO6KwmY9<pNf;>wfz++)5~ytq
zmmLhA;3PYkSSO3fQ1Z+2%wLU{B%fz5%k?h5!v1~I{rG1w4L~R$6znE}GAe_N?cU)v
z8|VNq`iGVp@gf7im`2;RbreH=yTe{I$L-ArD5eT%IC)rPyR9_O9_5-S5v~N60e9@d
zOMPYmt-f`Q_b&gCl(IJ@L920i?dr=YDa0pA$S8kmu2bGGRB9hHwGfm~sV&4A;Dq}J
z;>8-S$nOglZ1h*Jt~hA|QN*r25<p^^C5Zq?)TTdGoA&6^-11vd)Uxx4WEgDA#V&;7
zt+lg6fQau&XWhFVDQ*n4;hC1bo>;rrc0U%O?Y07OSj1(6nK70Zo^CX<Yqw9TfA{R&
zoKxHxjLyg;lMukNk+D0MtrrWJ(C*^gMi=8w{?frF>b7rWPcT+9uv`p6yazomv3>$%
z{DxVc7-aVI;Fu8AWxE1CKs;Fvq%Vz&dTP%aFCY1rLM6-*c54&1{DF3>iFMOUS04l1
zxt&ZXJ7MwV;XMr`>;P}}e|jeiL>)>Dmg?&g4JBUD5q;vhA7Q+Q43R)vR4_q~r<-!#
zE#WK{<oFoD>Goryw5Qr@u=u9$>|Ow@j|YRojw_4WQ=o%GC;38uZalWH&cyoK6{UD-
zo#>{-=OWF18Em^O&r1hMGuXOdFT>%h{Ob=uz&eF?_t=8UNh2ji+a1G0d2Fbdw192C
zk@aU251Hjc@h*F2=;ckLh7Sp4&&LK4NMKr}SEXa!J5v&;e`%g8nka4MLNf)xfDq;(
z=I$+RZl)x*$C{(Hve)<YCJ}iTjrko@fpZ!3D_;SF_re=fU6`PDHL=dW=(6MDQgafA
z`%IwY8q=)K5Odq;K<#o#=57PQeZ-nN7Bv95SPMyaL#NnJS^HIa9D3e(9ZbBZmAmqR
z;Q#dO7o1ulb?vg_@>D`C5266<PuM+=$c%eqQwHQrrZ`Lo!9%;;jU4TWzMKC1QPmnC
z+L|SK9^~{2xI!G((jw0xE>7G^W-y$2&Vj5PcqhvH->^eS;SOuTm6vy%*?XJpb$!$g
zpA^qe`t1!{TEE25lWDbegR)zLcUe{<+zDl@M{sN)AyyS?;Te`|G8ur<c22XA7a_l~
zjkmH!w77A9<r(NfSQ#BL9{V0^vA$e7W`ndjSs`C8<C0&S7gw)Es}?xeC28vgC$twn
zT5}sm9J@P><U9-z-CJw^7qgBA3j?nW4d1+@i*WoCWk1}#+bYbR2A=SlneYbcFnpOX
z#uU!g6_Q6kB^Fkxr4%5iIckt+csAIZr9+q<dYze@ASXc*+=A4gO(EYTt}=9U;)$c2
zJXS!>-bv6mq~B&|vscy44c=$Sj33HKVUs7qqF@;sr=P0jC}_umo?5}{%+YxPTYmVv
zmlfQZo`UV*FSM$hq_Z$1+A6$R)v!?`45t^`S@l+jNA2fv$oERkn8Q9j=;I}a+q8nA
zSkWX7bi>A4u#wkeI%>Z@@mC&rLVEaGwLqw`l{sXO9L3M74?Iw?HX=r-t>Da~6tC`!
zyY^K$jvGC7-1axzSbEeIcid7q(=Ta-M^&s|T9k)EJ04o&$@iAMewu=2x<3=!(S;Vz
z&iw8pF)6-|Fa3a4Hz2eB5R@)=jKI<k<QZOq66XbE3Q>q_@vm5p8O=OWnheFV9~o`4
zv4xKvR;okHjr%lC!oweXFkk1KdJ3{}20(0Hp~DaSs6p63aCJkll~;Q{X~g%83Z5gN
z-;iw6AI#+rzNi%1$Xe27lo5(bZv@nlA%|_gn;iO?fNemcP$5s*5eg-VrlTt2=?Q#1
zS#ItE)oAZ6Dd;A-NGDg$7#UhR$-cRD;=}Deu=eUMiszpD@x~pLrLJ%7BgD>ZJje2?
zre*vZ8nVr`f~$X*B%eYBG$A>e&^oVOE1LOP)ukM}Ock^<gh6U2PR77Po`=m1c&PfT
z<pW;YM-Nb5ULok++BLgyaEx>_CX|wmGrcA^{?8DZdXgNkS6DLi4`ZMOOGc#5>f$uj
zh-49|N%Voz^I(zOg7O-EN>h$W(g8Yp(g7n<7e8lNgagLX@02B^>j#vp+^c~wE6ks)
z4fO+cT6>?iy!68<5foqZbJzEXRc>|<)XyzkeD46Bk0*_-3&U!2BHb}5-*tPqdsO3G
z0@Uk@sVjjDF$z*Fw<1^%#?DGu=(N8$hsK4@>F&NI+_6yI(LEGOB#N*c8D{0);pVi3
z4*c^)!n7PC(oH$NcCLQg0kH_1ttAL-PSLKVuCan~M{OOKd{{<2+s3D~CBTHdPH$_x
zU<AOxw|2{<j`zd^79+5rl4dJ&rD<5e$dd9)ZdKNqFa<Z9xCu^C+O?2<qt7Gxa>wKe
zFx3^N9-D2+plIuGxb?NCt~<R8+vt}M{`%0J)N=V;#WZ>?bW?NRLY?jsjq0t{{-$9R
zEd>e1gD4YJj#%KkzVsDC&yYLt#MO}Lgb#3ge&pC?e)&2Z`DCf%giA+`!9g@w0lS*Z
z^%pDrQuGh>6hFm||5$)ltI%szsrMlvIIiBfZ}SoBSzT;=AKWXM6WHdrYnxRd$8we(
zdCkKA=R#HBz(7x3YyrqLSU7=m(Uq-0O(0?U>v_BGjMEw71x|r~>#+>R4z?4&cL*RF
za{m&Jk>F*y6+bxZ%Nmq@RcZGfl1kW>`5ouB*i&OmMB9!2TtlE2ma@07bZt}`lEB0G
zQ_`5dEg>tyf%37>8VOwBHNiz>8_F6<k7q#Ysj$&wJB}4;O^kaj5?0d4@Ui{P10(zC
zRfQL9s;w*TzD>E)q1y%@k;+tGED?a7DvT)55sm*2i<bVFt_<rGKD^_809l4@WI*Yx
z9c5>hArcp=PwOXlB^KsZdiPUP(??p%VHhh&Lb{!uya6T&W<0M?E;(OB*nGvhPSzaa
zpdDJ`Bmsj$&Ao!8s`3+j^vXS63B8PrM}sLK#L>8V=MOLY7Z=1pkH;@r&vyV(6j}}A
zw0b@6nCCgf!F25Xsp&sV9%D!bg&wD{`m)m*oot~RNQQ^CI4u0pW|QSUebTR&?3+5J
zrjvOHl}RBDcY8~NAS8VMqS3$kyR`hZu$qs<LNxU$xMj8nevCW(JmBi~4_jJ!TW`fN
zfM^mSU&PB&vfXZPJ3Ki9GkL(J=n8KY2}BECBP3*g*8ZGdyMA&9Dg<N`F%Q+mjUPA8
z1+OPc<ErOu!_vrk<#8d4g<1LK2C{%7zFXliuYJxZt4+C8{DEz!X$NcFYK1_ruXM^n
zemb#;&>_Cz3Lh0?vn}Hg(GZE;IV`KuhSTO3iKUbqztpO#i#Ups)h1xGlR}8`tQ_UR
z>95)FP+|7UyMMn9QUw?w*j?@b(A;@|(6(m-^z$dumfW<%xVxcLd(VY>ARDW?x)v$J
z)yoMrANQ_AnetaX(tgCVM!SYovT!e<o*P^6coF0pvyqeh6S*?j5$Mn5hARYcsIHil
z-y~s-0E3|<i$rru>IWb{spbX=ogifNy-~xD`U@4Az=`zR?gB|WuT9W$bs_>W@6uN1
zGjX|uUy^D4rpzqW3<IiCjq@uZAlM_P-6|?i;hzi@X9wxHxXMWl?`bSNT6p2g<h=Ns
zrz6T+SX?^h@*e7P1lpEWrdY(n$`M)hlJ=Og%$i1Arx}-o`o87f7w>V|vTDCTsM0b~
zQ{%%M#6AQK2HoL##&?(URA2oD5(}pF5i%TDS0|0>Cf?UceysvY8d26w7;y+v$dglY
zu`gjes$uakt~r^4AI3Cd-)=kzNqYpbdCQyW$kKL%N=07##RBswYi#R$hm%o&j5x~L
zo_lPV5#o=cGXOj3VADE^99-|NxGiI9L8$PwKL&p^-bj!infJTL>~<|@CH&vcy$h^g
z9NA_`$3|CWZl?qoEZRNToyUIhcg&i&B%d6@)+^kEG~_~~ryhujD+~YHY8akE>6RNv
zRPlJoPqy`5=mp}e)m;_pj91Jol=7XgER-9(1pj&s*95@f$lLD9)WcKejLn_QJqW+L
zlo4V6x_4TT&%*$#Zu`vM`2hv+^uZ&K-<u@6e!3=+8gpmd<Ka*Q>06GRk)ulTQE*OW
zs~PUw!dG#T_1l%4sXd#3k%D@*dFzr_rRMl+4f~RiE2+%ej5mgiU)xIF#;<4*!CTx>
zc*9xf%>ghE^EtD-xWqdfrMEkv<L=;!9b@0${{<l;zPM`RIa(>&U!un;6nLO}O&`qk
zd$jgn|BxV+|1Ejeii25aI5%WHTPgpv=&IPdQ9@O2bLk8Qk#cfq!arvZGr!IY=o8q~
z<P37Ec5!1eacFJO|A(z}49+Bow|;D!Pq49V+qP}nPi${&Z*1GPHrm)WHp%AAMZI-z
z)tyh%Gym%8`7+bf=gjXM>c2T>)%xKRqE8$B1xrizaRuf`CJ|y@C3Ur56+=7nYy%T_
z#2rG{!UaS3Y+-w-sRkYKp3#EUy?)HSs{RvJw%v!A5>=HSaYioVe3}y_!s2xHMLK1@
zfY8^3o_hQxqih{EPg*5fR?m{hY<+p>OqD%Ei6X#kPc~SZ=-lOhbA~?HF|{h|d8lP<
zlF4I3cMr^ct32NO>~jD5h_s}d`DLY?j_DMAuFXR|KFql_{1^|?N?~qjBIh9zpErgK
zd`LPj{ZXuu*DR^;Io{j1Ha#?CDWGBt$+CW^A7XviluzcgA)VYyn8$bwdZOQuO}nWQ
zf%F|UPj_W^JwRmAgwT3k3Vo6D6T~_8SRo3W4GD--lIABJZQkICZ_BZEU-{<G6T%*`
zgd6AWEmBLtK%FQnSNB&5WCnaaMge%PuQ@2zkus|>$wu;ra6-VYRFeIsTloX2l5b2?
zo8e}C^D5E*D{ved%_(GIho?&G<neGXtxDDAZEm}w&qEq%=`OO-+^hU%ZHrGAX&)I%
zItJKi2Qy(083yt5-`%xjdF*=umx(LX1{x+E*(e205h9*xqq5`e{JFA07T<_C$IX0#
z%xdp-l|B!*OVE|SVIkL;)uiWW*#K2R^%0Nv@n;7U%<<PJ?d{qAtD*RXs^ckqw40LH
zPn3li?kl^BYP2hL%!jZIsX;JD+)^(J#1P<~IrUOs(WRBBcn37P$6gQo3DNj~EuNB>
z+nUawYkI$U4_&M1oAsj**P3HLd`&$gT6Xx@ojCtEO2J*6&)hcb8Xdr8mrsXKyY(+N
zUVL1YqmDnA@0@eYi^*<%#R*LiL~&C7Oz|MESMg^hpXiqDIfTpl2D8a=aoXWM^^(Bl
zR3dNZ=a|*gMEKFNDJQDwKadFX_4TMlxfrD#H1V3Uow71*wbA!&01@X_JL|O#@r(P(
zxd*4eZ<@6h_2Omi1fQy(lBW+G0=;AYVYU5k8bgE^yc7*juqAiv@>M4D@ob>3xs7pN
zMGMu|GJ<;{meCg_HP#JMBQ?KR(UgEW@qwmkAd|ECB|(@GQlKB0`u-?As?<V7uOZF#
z%VsX|RFC>sm4|`tv+}W6*p1NWN<3#pVC-iN+=DoN#t3?e8n!L$y6s{-{GcP}Ndw6w
zLOTi-Z1vrsS9|~Vx>;QtbM(+kRb)HRgM0E;I-WnK*O-I&F(jX>i&h$}B-4OQ)j#K8
z#-qm9g?;e#`Dvs+&{X%=2FBQIWD(j@G~DU4!Ubh<ir2I7t)+fUxy+FVWd8k!@H)1;
zT8)!39kzt)Oa(=vPqIFCIeSwYL=Fv`?0?Gl=S*eKGO5aI&SQT2+O$aaw-)*{-Fx9f
z>A>u_XC}gRW&-V=_0HGYn7a{3ocwym0Ke=`=Xn9+q9c?aT-08p{WlJGP;ZOpJ0acn
zzPV+y-Hlwu0*3pH_coYm%NM|g>@dBWa=E|k))<W=@mlH{=%%|4+MLfT)rP4~z=&#d
z!+{<uV9A2mTI&z3NWXcGs$(K8F!h=1(2~xv5r%zShtAm^#msJ}98d^s%@|`f2JZ%w
zEqPW^vr7719wqCsNUcr9FFJpgCa<VI3u5EsUO8HF=rNxuO(iX>LaIq@9?6&&%_;NH
z{MM=Gt%68%qI>oO^7se)pg+znIDlF`JsnNAM>JiNH+$!#0n&aJjj_NKPd8gErzKNE
zyl|Jxm`)?qUM5}^@ktu!s6?bfG^~=r{Tj(qL6y=zsoiZo!$YgAyS=U({T-`?DsTRb
z1*&4M#PFPkr9(HuVVSYlKR2RlBi+=#jBDxG_~cCWQ?Y0%GQ~}}E<5JHy%#`6F?;#f
z=Y$h*GiF1};e0pC6+g#m3-1P2eU_6~wP^deE`X|CvH9B-b<zcx9gYzX^)qkj=)B3L
zMt1s5MB~mkTn6cHevp8>IM_6rX-OK3+B|!#tV?7<4r@D!7Yxb72vcx38VJhFEh4-q
ztmhtEPP`KZ)~!<LPZGpM)N&zWeL3zfte&!`lY^(MiH8ozth)0jF!VD11VDbGp)irw
z-0hRad-~U*07EXYl%o950bZ!if!KS#&zA0kU-?WP(9`*As6LSII0X5jqil%e-!R}N
z2wj+eL)!FYL@+j9Pv~BEjyGX*SfKVTm1v&LHPfbHb>$m<nPR&406*=`@jK@==cma5
zeYbd~#H;#VAChdSg8ZDz<-!O9DVh_woiQ$QrL6~{=|LutHicaP*W6_Y%YG<ta0}3M
zA^3A%+&qA-&{IW@<ytM|ZTxI>ayb?)PiiH5=56(4=%Q;5v3b!P_m?^#lKtyTV*T$H
zRaLG;hkyO9lxQ(O_0-=%3i8y!u0|cTM57~nShALORNGHyM&nr^_ZE4&cXysY(m09m
z+;s<k7xWB}fxXZCQ-IemZH1o~W3v-0`d4nM-OJ#tAOh|Pw&*CW$sp0SZxrVmCD7x5
z)+a9|(Z%2&k%Lbv7OcVS*TpHLOI~rf3I_X6KE#`&T(ut#b-}kG7U(lC&Myg1mvMaW
zJb(KWdrw|;YAAko%$u=i^YQo5(v#<pTQ-eczAb*?g2r$2F#TWp*ndV#2>)-1+1AJ?
zk-47n-$%esLjKLaUU=hv2s8vPP;`*n6j;sws~3*{RN30}F##N~pqxLIm?+b`0035S
zPR?&+&VT4{+-yw$D@U=^2igmLZ1ow=yQPeKR~*7)qL$>*Koqv;#erwE$5Z!OoS|`2
z$|c=5i5}a`6d#+X%rgf?1WjfiggJ-|1q?P5^I+>lcS7jZL8{mHYo`$+5BDmE<s|2&
zy9;dkn+OI2(OUJ}4&m1IKA+8bO9IG=995@U@9jp-8Tk(KXu<_`pap9AI}V@jFkGu}
zuP0Q#r<w=52t35;NH!E0XdImK@;c{W``4x5H_AWYfvOK8J2KrFJ3`ZSIBb2M7Lgsu
zGw3tUyC^Z*<6qZe$Xd`@2U@*9AEG@XKPVIOxt!070{h2}7?J0cx+c&m6>x#J|8lI`
zoVx$WP-5o?-l7mO@=^jdyHXFXR%6?A@lj?IeAeR-ZrfZv9^CHT+u)trHZNOe6wvmu
z2uw~;35a14aM`yVnto|U>U#;*ClG!))@Rpct43y5<qq$x(62EpFU>e)ewNL6$bx@}
zUTOGtw7*5PugBqAT4L`XTbuxK@$k582Sb&j#$E%B5@4@CvJzo?^<zXRGWs|j&N^GN
z$7&23tR^<(Wk(|<LN_{~AvQZY!+5;k!ehT;talKq5n-NP??+yV@bE($G6*>!-cj)5
z<N75`it88FaO~(fknj`a`zcI*)=#Wq*wJtxzQbh4&-7cEG}R|GaIXRN>;N1@x`Fh=
zSjI68<ZCK+xUB@bK{dk~##IgMYdUs>t$@uy`(X~_Y-V3O_*Tr#aQiV2Bi;u6HNZ5g
z>tGJE<}{+~7#}m^H2!zeFtgw^5qv*b12G*A{D25EELOO<QIZx0R@{h*^BUv;2q--c
z*{k|(_O2(%jlsV$R97Gy?*<$^HVPQgz|EiiFfilR6{;$84F?U*yNY&B1Cn#vNpii&
z>{ZvAIrp1C{?;$({NYbti>}Ud_&o|Q*MGVuM`tavzJKBQeNz)q*E@GCF!x@2Q+4{@
zQ3Gu)TlPHkju{m){OcXKw$lZCf1kY}A~fPHFg0omx~SMzgZKlC6X39VE#M^@7kEvA
zwK!iNu&d8a)o=b&7s83`f&Vd*C@%W%wch6R+4u&TmO#Lw-HnU0D8Ov8Jg9h5G8g}k
z<zxLqlLQeCt8*;G>P7}y5UK;GOV<!2#jk&(QYQL#5;ZB}y%Yp<^2!!sOABz?c5pFz
zJiyqkV>Ht`yi-{q-;#((5PsCvKsa0tk8X`I^3aPjj2PttmmdA^@xzoI3Y76zw2i-W
z{Q-DSA=)NnsKT^f@NIh-D*h>`SbSWSZ8aUR`n7+U5}ozDB3|WgDNID`<<98^Q%3vs
z#o(<Tt&H&W+psT7EE)c^4AzlV7FCv)kW!X<v}M&)@s(8p3lnnPbEiAeH!)-}sy?ev
zd?6kGh$by+CxkGz5jyawOeezYtI+1g&zE99^ja*8)tIR8dnBa~A)Ry{{L<ann|2!w
z=Y_pOh9HvK-)S|&^8^E+jA8B1^0vO(7wdn(%kr&@0Cz;Nvzp<NWMmTS4?k{0ovhWW
zKl4D%bPO<n+kTga+Zvq}JT%?$ag`hr><fo}qT&{<dE{3zjk0cG-mBfSx5_weRC-RS
zLt$Gg4slwPbFq_V^Sd15t$QQd6}a4LwQPbs+OVX-IAl4Q$S3=6&g;$#i?3W1L^I<4
zfZa|l^2CLO#SAWM8x><Tc<4HzOE^AK<vapKP!uKLlu(zU$-EvP1t&?HQ2NiQ;r@>L
zFD<>KGIg^>$j%SF)zRj-7q4sp2cA92Cej?g!D|YinaLi_-DjpDsWH++`|Cz3WBEIJ
zukdH)tV8cnQ8r!$^rp6g7geciCW@g$VkANsIFZ{&f04kg?p-C}Qc|MuP=SnE)dbgI
zzW^!l{`jCXp|$>~T)*4vV!9CFoOYvhy4>6tQcp2aL$EE@aQ5CK{hYD2|H0>U+vL5-
zUYvKi`KM$%=_zgaVR?R%Rs2EixD`}@L_wj-R{AR)t2gxK+qlrO;h^Y)ZYxlDD6dZ-
z$iyRD=dxDs<fi{Myu*8QsiZ4J(pwY;Ul$J8ERNwu5gnwX*`H(0m#eKnREg|DYe}2a
zf}cyhO-scR8^ds7G6XsQf=<GTOdi!_PAoh#t>7w{qUBgK4=PUxDT(eYT*Z<dWvi>c
zSJ@QK{~UCoj-jXEhxB*^RaRbJ{n4a$!RBsb!&52?cK3tAQMbfwp2mbA5(&=8#1#BH
z5OAvI=TCK5*fV0?5rEPbBEE1T;V{&$`(W-JuM_)()QK4w;k!&(EEB^+i=5KC429&C
zWT_%`dk7cwv%S4hC84JI+dM3+(1!cMCN(0GHegkUTFFPnifs?N)fz2h3sW-~uXMFM
z0$YnfsPJf*lo%G)g7k-|Xxey<s+tS1BR1J0<nk9`O};Hod-N+PhMF3)w|8o4x@rw8
z%kp|PA{(jw(o};y*jlkP3~Q3c7Te#WwK4hA5a0;PcTCfv<*(Z&M$>s{hJ-J6k7jsd
zQ<roc`GeSC*13-U9+4$39NJkZTxS(xStaJvA+oMaDWN*?&F29PFEEEco~Up@9*Uwp
za=U6Ms{2#00(x^}Fl88)5*O}BMUz&>{)<0tghZmn?pAnN!30V0A29O4wtJ71iD9eV
zu)k#y>%afU6|S!%bnvG-EbGtPzl11|QkyiFvFQGq^z!tXN&$Dm%lRiiHBmi9lfbj8
zP4W;^92`AZMI!_E62MdE%y7;N1T`*4K7V<TvO=uDTcge1ONC@K>W3fb!4_%HY(mwH
zNO^yc2z`A|k5JQ0PuFBUO<OS>ou<elg1Rz<*@f#txmJ#;3uWu_l7NDO#DrQsIVrWg
zz?r!WZ1MHbo-(6J&5=>~`Riz_7ZG-JGzBV_B1jn|(|0Uyu>F_FlExYdFdQEhk+RVH
z25H`blnbB5iA--U{xl&J^@7iXDQFL$6X92c=Lkt#etqb<>O!e)@<`2P(3AGku~J%O
z3f>&fp0xl)TrYiV_h<B`6lZOBUy@dGigWBy0Qq=u+aT&2+S9*y%xAW9W-|=5ANLbd
zRGltXhH*jq!if)sYcyz7z*1#|_4cJX;e9DyFSSc#<B%*&%j^oMnXq%w*akP?6?JDV
z`RKs-_q>5+ksvM4I3jNT?;a=eFTsz;J=TLgr%l$HL^hGe79x*yzb}S1BnQkk-ew95
zW(H{fMvr5yA(s8xkJG#l`Bj?^m0Tg{FiEIaJQJA~=;bj$U3*Y9aB*F<w+og`PD{X=
zw58eD5|#0r5cjVCLf$f}U94duvr)U+F1uQ}ET>MRudS!2jj4e_=c;_2;pYN&nLW&d
zgN)v1{>SSvXXBXCpx5>75+c002Ob%vUejsx^gAUgoQBz_P9&f%wA1NuF;_0Ux!2Y-
zYlw0ae$-cjE7x-mSb)?%1Tu!x3{^ORbUr*l$|k_&Mv|pi98`y9HUYlhFnjW26(lBD
ze5s_jI8&PGP2B)9X19HECX(I=u?U1}fS0lR&i@R52S{?x^A%9)RA`{If`q<l04bDA
zL9R?KtHL76PZ<Q^(>1jnuC#hXWmWadC{C&lvQ%(WP{Kb6_!L_BLHV?4B_)(Cs-!ht
z8wfr^B@7{13bb956r7W2uHsgfu9UmMg`gjkO%NS%CTYW;#riFa+|j~>TDq`al~pb1
z$mLNK=fm}OIgHwb6`Qck)qH8<c853+PIZC!Z;2zW1WHi%AO0}V?kiUtAwP<<^IGp@
zqCmj&_K)uo@ExuqMpL(Tr@he~Rds_Ji!r0m{kT~?ePeD#uVX5EzlYmPeHgW`B_*x`
z2gxq@AREF`?jV$c^Hog`4b&uaHeaj(m6)#^=dfuDx#+`0X|RZ1<3kT&=`Dn+%$6bH
zC;;s!^Mo#lJ}_JUV7MhnTv#|LX<N?pM1fDDMq#oL*wkVjX48r)|6$v`nE~~{AYc+2
z@#e!*K2&<(Az2R^U`34IzSEIw5|fYgwW3I>Qd83fiFbB<6I7JP?t^|f+CbbEA6Dr*
zthipM*#8J|!#vHF!9XhMo*QI9%C%7s%M95)d=iv26z6o_mgxi9Y~$-?2qRziGK_fL
zqMPOiY&K^KiCph(TaX_VhD&?ULWpkmT0$+bGKn)D1}m-23o{f+LUoT=9rlz=04yJe
z9Qsih0C2J8!mvU$HBQn~Sg|;V927^Jhpv2H1KyJ<LDh|Y?Ax#I@`rc?{^CaSjtBU2
z`L^pK_|E!8*XPDB<N*-2WD-|P8<RH<@$kNB73(h@XVJ}OX_xV!kW$J_QG2|D=}Tsu
z)8?KMXAB(Dw9tX1F9~uW(hGDv5@Nw6-3W0U{HfIQ>^Xn41dP`RGbgaxZS~qz^NjQ(
z(GT$U0nOP4?RST7RDgsTP3smKLVaB;D#ipBuQ4Br8;glW(52eAW7J<yP5H#%E(A{?
zy7?iQpEkd@{`gyerpW<}1En(9Vlg`A@nkTAo6lZvy;tSBG<*h=VN&ZGgjJ!g0+{#%
ze(@hlEc_8El3=)6UI)|4L>%MlBz%hsCn9$kc@idZJF>($FwZv_D}JpVy!{}J^VwR`
zL`+h2aE&5D_cWchp69`v9Yv8JG8sT1l{>q@^Dl`-EBe1M@)8Q;unOX;yQBD?G_yCR
z<Q?)1JUsAb$4yQo3mI7+E7cS(3?cY)gfz5NAq8~3(^v{`Xt&>%OloPV4k?F{cBbA@
zcE5z_^(d00=G6&l+QKnLeICz*?HQEO(Gf8pUzKkQ^r(}Ceo8_ye9P&{%sdqE`L*Rq
zL*MSAm8Q&Q3aUO0$phltY?YFIT1&JvxN#7TqM$)rSx&Ii<H|zRP1FgutFBj|CX!+&
zJs6=toms^@nk_GSGLCVaVV25t-Xt^%TwQ>9K+aFaDiEF@laIbi?e`(R)_pV^Wm9!!
zR4=kbXk)#urDDV9{#9`}qdh;++Y2}(Oz&#ohO850&6h^+a8fYeoE#y=Ep5HXR@Q`h
zyoc*7Q&3PE6}33brftA7b(Ylm6L`=tLER`Ola*S&WFsWOFqElzvB<LIQeG=@nv8mV
zF}XzPrO5Nl8IIyQBXBYq<r8;Jq3VD^&4&%bxnVCb@6*_O6iQyLOuz#KJ^1H5k-wr{
zQu7{s6ZH&)#c;p8Kr|BI3%cLw2(6p+%R%;2Pcm+^oxj5K4yoFcy<at|*Blv?ALciD
ztg(bM?UCh}UwWy2h_q0@#YTO5wcpiyr9j@*u`+kqNs}EQ!K$SQ=m|KUG|&B%fEwv-
zfocnS+3j=%Bcwwcv}p%^Pn0n2cNeya>TbsikVv|J@8F0{YjigzYItT>FjWa=YwozW
zihsiGw?3!-g*&sO7_Z*6H)tE(#QO7EZ98k+ORsc0_<ViSv>^o!(QQF{XdOpKNjBaz
zK3hGwLWhgY@#r{wQ8sdMo>J*g$8E&=64TDoJUB<oN6b}EXLSSYO{M`Sg64?=2+{gl
zkuSI!OKe(LwZQ3ox+HAPc4S^ogVJd1DyRmo#y!<g<^^&owz^_78^4#)BXEngBIG<6
zO%zhPJZmgNr$E#nnnY<{0@5JhYwIq&JPw)|-D7#st)7*)e2Tb{^^Q@uy$?$mU*q8$
ztoHBgpI$KS4-<g%{#_YA(X6VqBeJzEIM+~-qLwsDLxZ=7DFj={>XIuX>0n_Z5s){0
zD0VN?(`)_a5<lfctN&FdSG+1tWs7+KSdS<LJen%A`9Y))L1t!B>h@_Mv!O*36QM}?
z+Ck_ruBKb5ltxgfEWpA_PaxQcQaX8keq9tWka{s~A=UzgO1*qcH1zn6PT&S-aszTZ
zmfH3Z^u?*i{j?05{nuC5BwAfFyPL1koEGE6$@Idj*r?BRXrBItUCJH1*v6Ep+0VpW
z5kZ4lC9vbU(=mE9(CFexhK-G8j{suroETDwFSh;jAa&NRX1oIw^tj`82(}ujlcOV(
zB{NQ#=?#GTH7XhpZ(t`Oe0(|CAC($IKH%P|UG_mHLnRG-m)df37)j_|LiYwHMC0#C
z73|UDNpJ~K{h1!Q50h$#alo-HandQ{V?wid6=VE$kQp6av;BYKiNCk$yAl(cerwa|
zRm&>GC|dG7Db3tD@_ol@%1fT1HI=}TOT`~F;ZFjGN~xu(@6`M(q8Y*zjXs22YN4#G
z^);||_<ISS&STfq2R>*rZd-0kZwa*#W>6tP)y-I9z<3ym1+VF9Ph^d#;*Pt8Y2Zjv
zF7a%z8oIDNII}&exKt)wo@~G-|DA@9m6noQEb-WvF{F;sm2J0oK6IYWOjy6BL~z9A
zgNOsc9UmF#Bt<HLsok$T0U_h2WvX9^16xIF?+x&7NRNa1@wQ-VJ23aQY_D^*#gC4z
zxZan4)-MI8ypBodF-_FBrfGV?+)AUb;g<EM9RCqq!kO`&pq@f%2A+^rE~Bz#UFgzJ
zGAdWxud{^dU<$iqeZ}p;MKxoz(5R2bUHJj|hy`S8CJP>fB$%fNf6xphoyckC0jCtq
zo^~kTI4wG!iLy%n-l%aLbp4IkNz>;DjBrXe?i*-55<UZ8I{|%rBtt&yLlxyG70B)O
zmMjVRad3%p&XzCY--i!b!@$v&Lu={>gYmL=ji~&b<sURXFnum!D%DH2!MNc5MtuV8
zQkn*j5T1|7y;b5HvVFt|hX!Bh8ZbBHXapq?l*k2>l)=w9NPTy`I2~#+KnkO?jyrlS
zsokFWeZV*$%;YtOaJ84~1o+bhCm-gNvlhIY6cL&KZ7qba=L8SHlE?fLOy90kJR{u_
zD%VV^P-$dQ$jYVj^`iQ{@D4jKdKe5GJ+Ru&{{xyslNF-ll&XTt*tQ!EW?!o?<%c-8
z>-4#!80PVWAq9yYO`opkh?<V<CLLxSJ`juX!pF%;i##%q8W%)Vm}-TK7g0uP3D}F1
zMBh_sM~e?qEdXa_Q;dzChhCzL6<+paLO$-_%WvI@AYzcvwrHkgcS5JP-`)m#u$Dbx
z4F&u#DA+E)u_D`D%6$FJT+e&?IfV`Yn;XaQ1@WB#j0!i70eP?5e?2-8<NKlLRsX^p
zsJIWA-kbbUD*reDjy49@I*4lLR7_W%Y0G4=C+xb5E#lw<EJkLQF0?i3a24a84`r!Z
zl>-%MW>j_osO1>(dczb)<_`m3Gh)X4=fybxw552<;&=Cf8E|Z@@<YEg&+V0+9NtjS
zDo_Nl-19zyh8TRLj(T~1N{70>rs10y3Ytee??9r2znUJ!;b^U{kb6e%Vla6{;Bpkw
zOK@q-A+n9)*%|7EKwg>UYhLc3<|nNX{Nhh`DvR@qORlvXf3DBXa|{L!$-v(Uidu0b
ziHrSeSpz<WyIm&uFlP4lkJOZKCq~gjo}<BqqnP!ro|~@JJjTVSqRdV)p@@g~#KK~K
z_$k2jiBFi&&x>KQlruIz1>x^<!7*$hV-LO<8B%aUTQ#eN>(R<)G$SNkWZIYO@}VjG
zIzCmpS%R^g_r<=hnJxn^skY!c>f=Z}XgciO-Oy8C_TcXO*>*c@tU%~cq)%Z8X%kEc
z;Cf@p3p-$Boy?kE3yh~l#X!qIlFO$4vJ`y9NIuEbSfhuA>tl!hL%}cRQxKu^>ECme
zDT|HRhqqfT>r@=X=e*cFc*w{xrYIug(ip4V>hHZP;xmCa6g301GZxe8Mdo+Bb6X~S
z`scTr&N=HLRK@2+b_sD2@Z=@K|EXTZ%$}=W#dGzQ5lT54WF{otgx=J|UePHRzLaI=
z<%6|zr+k&`H%hX)WBB3t+k}VzZ5Gm47uz1uFbUprAffI!EzeG$DRghuJ>JVUcyDt+
zkg{vT2VUFrUcU$EdzZzuwhFO12uZMQ7B6Lw?H_1kR6~V~5fKpr^RG@vO?J3f8t%ie
zNDD<-1uXgjM+@FIk^yc&Ew+`f&%$ciaM5)IYLiv*Z3!A#Aa3=iO2q`Fa1Nr|qL{i$
zaCAdD_Z#<OSMZBItnmZ~!ZVjQ_H&47d7l|LG*|e|iv}L>VzH?*v*FDf9k6hych+RW
zRi8@u{gHAqTs#nR!emJ%F!p<C(~mG0-1W!uxAY64{U=ZvV>-;kP|$1~<&v3z4bi;O
zipmCE((7%U{IRuU=xwF6CGOXw0{(P&)1G>JZ0md(<nK^D?hVp_IHOq^n6@Y{^_+*J
z1Zy*XGq!VJ!e0RZQjMUkfKrX+LSx%txMSf>q(3P*8Ht=-j^ZwZ&)x=-RDWK63()zO
z`8ieqt>Nvr4VCZybM{8?2zh;I5u4Y*&hK<xA%3WxdW&-3m4S1CDzrwsHJJo@=7lDP
zo^|Jm`*8UEsN_yB03H$2D3ex}N9MPrq=W<uEtWKpqPmnIQi^zL0#aLCju6zI$gr-;
zXngoBmAv$c9;5MM+Bf}6^o?NvM)7<vs&*P8@1=<(Mm}gKYAJ|80S@h_`m{-=r5RJt
zkBTQ{I($#c`^or?nGtZJ^qo$xn7^=5z2ZM`9w&6yV;+G&`_AjKmFL~z2OfBUrJ})9
zR-gtz_sam}-L4!RSiP)2IE^dCZ_Rd${#CcxG+5H@)cd6*P4iMvu*UJ_2#2RFu@qf+
zDf7q$sRW1z_FSjzT3!p!`vB`PV?9IXILDR)k%Xi8282tCaHz|!f&oMAf;$NA`;)Zz
zIu<seQsZyV;VUss0h@%O*?0x>5Ly%}1M?uzdo)IZt4hY{c#=|c_ncNl0qJ*ie(kG;
z4{XR^+&lKS+F!HE`MI55@B!75tOSd=nRw%FZVXbi7JClxRY}z<Bb2Op_;)sfC&v?P
z`G3j~@|<1vYJ@Z>9pk>}Dp|Z8T~fQ;t(98aI9t5li_ZQ%EXLa#bL6P#DX-{SRwcgz
zTk{kNljt_Y#e&TA!*z<Mi~>1Yp2-f(yZ0A_hps*I`W?ExaViW{rx6)iYxv=E6T$)H
zmi-EAihhfZj($i7`UhEODb_vGZG0~T>Y59F$R4uUhgHVQQ9K}2F~faY5DLn{uUrx&
zic%@G)m2+L!U%D8TWOB>bTH*9_3Xkx^~O2VnhAWLH_nBaa{D|6*D~Ln9{hnr8np`8
zIx<3|BsTdP1_lc@yaH;PRu3L8DRmjq{aecXY-=@j9M2mI#QaHYK@69qGEfsTY!ZBP
zhlU(PEbkwdTf~c4X8N-;4GMGZkS^Azq#_ztECR3#+mVD4<RB445d(#GWvN`i8vb1G
zXzA~iD9>B8lD$z-Z9jGDQaa)Ue3jLI{y%ZI2OyEb0ExMLovw$rdx!anPMPnmxqEWl
z1NpN8Pc=vMER6i<2Vew#Q>KrQzD;<Hm)tzyPoivS#l&i0HQ0hC6H7|32gpt!ywddB
zu&t8bOcx&>zd{$QJ{b<D*2aGTcNI|GtB%h?f`^RalBivvH$A#L_beE6E!$thwmLPB
z$Tad23ykKGzjPvh4F_Qu1*v*htgq`p{kwG`$s6^BxE-0u6}-0{+Qlj^*al(cH7YCI
zAzlJ=$mSZ9YZOt2NWN_iUb?@x-NWfD2cv3QSK&?!SMk&G30fnGjmcaD`a&TsMda&J
z@nf67jBi>x#L5U6DAN=#s0W+i#ZSCQEZtA5YuE%5MGTSs&?9tszi2T6FF`nz^}>Ee
z%}3mpqq}u=HAL!VG%G+2uo5H63VL2PO0p@-|3lT$5=tEBsoVn7^Fr18&u8U{R^RO0
zy%z}3f%5%unIH~4AOiabTuIpVXIA`}IoBmhW7$#5d%xs(nn__b4WHBHoYN&zwx?Ur
zB-$An+BO|+-SX*tYc^Bqb`hNitUCTmC)F~qk%3dDSdy+ZQwJlT0STKHUf@KWk|-3G
z{e0n?W#qe=SS`hQdawA~kM@O<zs8SFR1*kh_3-&JN7sBhGsd|GT-*s@-T-`EO*roQ
z-jiF-9jqEDM;qSfh2mV49AXNI6dx5~&pC6UvS2Ilmljwp5}JkFLZVYR@*p*bxpU88
zCSm4D%M_?EX-+Ay=M?t|GTBnd8hm=aUw)Iln{mAlRH&80l?X^kvG3#3hR5|;0B59|
z79M<@z1FQhR7!I|&KWEpnF5kxK|mM$6H|X2!ta;-j+ku-JcJrlpt2O77le3NY%7Er
zyYelRGCY#D58VS{ZLx&cE<KBVY3W?!)=qFDF>(Pa@OY+lcsX|pOCg<zdmyoAS3lV9
zI;#f~<EJ)TRt7l5V1p%+hJK|51?r)n0rRjL%yj;Kv>+NV1SUWT)8g+q6&1%7M%D*C
z4UNCPnlKw$W(!{-4I*4=G6qX&tZ)_2&^(1g$ke?MO+n=x7`Q1J20+$Z9M#&uzXhlL
z4uq`ifgF&OR5o4;&u;B)O^2{|eax<=0jva!|Gv9qascZi!lpf3Y%e_i6!}yyd{ldY
z#8c~>hoJ!~Q>V*j8dITb58&yg*>2Yqp<>*1kpzJuyD8g#Xc_LiR8DiIhl!#Nn=Y|a
zcepv&XmIRY^&vZ&r(y!E4m^D`r!4CzM~zH-7FtiuVLGl#t7XLn4O5B!9E~fgk#D1>
z$R(L+IJ^B~&6*t9t5h@h!E1RTD6cPXpRO*7^JWKfAJ|dfjvGJ8qHK16$RCun%T!Iy
z_qtw9FsiC<xoWjnErn9rw7_<_<x%`d$9ASjhq81p`>GS}g|OT29ht)-9Cs*rvm4x$
zHk#&yi<W~AEtA(zOMa~)d-Qr}xlsQr1(bnGp?01YOJPsuOyQF(l`f{i7lvgVi-6mj
z>ze{9AVN;I+i}R3JxoPrgsVWshLL;0<$I|b)*Z#!bR6FigdsE5(ElY&kYA#>gIXzh
zxPFIR6%<8mXIo(@X<2LM{pdVxXKsscyR%g(+G<v|y1#aeC2kEb#U0W*1Qm=R>i_dM
z%b%UlVjme6vwcdTz`bEUz(<-YwLpzR7MBbt;+lxFaUf$aV?mQz%*a>3p}Xi>xSss!
zJrNzXG%^9|munCgYXv1UFl74af&8oMwIX~83q<r%LsA_}S$ks{fe<`G$dl_kC<?Db
zqqbn5TsfWw9@guC9oDMj1~#^pJ10xcslzWY>Eh}K`${2Q(Qa_h`wJ0m9gbDC<E$Xi
zgyJhjIKWTp)qbJ;z-v#sOU2wmj}0>yC7slCbvp9_J}(22?My8oKqD%P#MN7$84}Pc
zTLXKT4lL8C;T`-JJKVJjdQ=++&UvX<hj0+~TMcIg^D*I=B0h(4f#T#?_3!ycOg^h&
zTt91R#{vrtGc$$KNMbZYa($z6>DCnRpEQ`iuc8&4FM2}|8|k!y9YZ8c`sDs03)YNS
zsTCw&H08r!sd8!fvS=E3CrZ0VW=rHNn<}1Kzli7KkN8%!8;J12VLfq=AMjVOR>*<_
zj2t<NHO$#4+-EVpkUkCx3$3rZ_w$CP>*X|&aAjy}lPO*nTq<`;$_Z0VsTCDKHF0pN
ze{EU<ItYAk)6AW!DBXY^I#{jFXbQ)&(BLAN)r_nP#sV5rIFTW8=Kv&nz!)QEKELj4
z|Hu~Khl_YsXCSsEKa<nvV=zr^S0X;t?@UMi7b%3zkZS8?QHVG_Mm||ffx8ZA;$&e?
zF%`DBx{<lX7<?m6ylAoVvL;So-neEU1`S6@I#O%Q^oo)rUI8{+>(I0Z$q9^O<W0%e
z-AC}7^~*FErO%(ig1g!3xy2N2BGm^~O7=NZ3&M8PfLMVi^|J)%^(<TKHV0K!m*Iy2
zmNOa)yJ-nQ=neOty<buqKA?Pjiu$1Td|1K!F@4i4^DXvCKVsG|<&Cz1Hh{3Lv;Fd=
zN3e2x@Q;`=)Yb2`>8HGRj+&hV3PoeQ56)7^Mbo4wjnD?E$G)3lxE7^XN>-<zFV})Q
zrsT2Z(9IM)@c{0}S>I}^ULArOm<0F&v-90aF$q*?v;%4}V#Y}Hgg)yIpK2N~raEsm
zDS_%0c!OqP<OfGV{G~!5CtFjZlRAR*pt=ls<k2UX^TOOq7Q<DIA`HE8PHJPI)npfG
zZF_aK(aqZoJ$#v+TU3<Uxk#=|>(YVJi@nq>(B}LO+4X1C5U%qq9gT1HrbL8X^O|}l
z4w{0rOHqspPOszRaf^>n-<_YgzKqZj{sww7;nT4N6w<+`a9%5r&*ff+)+{?xvMPl}
zsHYTf{Y`N)PLkg{EaOYI&=c-cf~-T?1a+b~&hH~l=!=xktX;2kb$p$V$db4;f_R_|
z`Aha-Z+pwHVI7OKajv)y2{@2gtDQ>R5G|h~NeQ8xl*t^%!Yb@c!~tJ`#sS~a!=&Tq
zd$h9Nq*<H8|9%df+-$2e=$Wa@up{9De6J-3-I#Ijw*LEC$e5YJ$*8~%OkWsGUtm(L
z{?cp?WfJVL0vsM5hbItSres6=b00%CBV+s3Mm#(nQx<YW(H7D051OKzv4ndj!-1F^
z(5U~sdpnYqQ^0OXP0k|CWk!C|(!6#ms4Z^{4$}Sm^<oHQx$%Dq1F5~<SkLUD>_u@I
zzk3=|BlliEJB!sRRt7JKN>vq@h|#Ds-lvaEf2*L;f7h&!@ILwaC(GfNmA&zV@%#Mi
zB-eg=F6(9Q)L=-kpUE!k?d;A^H!4q84d&DR%m;O8$i`Ku?z%uz(KNx9|H&rqEf^Os
z?%AZyAl!n&zLP?~YNf37*Yo+O;P{C$>;K`R;`|Q|B8BMx%oF<$C)U=-*VoI}_vZKV
zwZjQTWn>l@f{VZvgaMMHg)sep_46IG@a?&3n-u~`Lw{SaIMS6#;V9CDqyU`XEw2AM
z*(?PR0)TUJF{LMUqf?|4TK<Pyi|fB=32kwf0Dp9_?^5P($Xoge1^^{p#1}yQUGU#V
zuX0~N7x;I<H?V~~JsJpL0_WuZFQ-@A3lQ)E`rZ8QDhQzPz4PCr@e2lE!GAY7{{ID$
z(<kEqWZ#GWYe+y2PA;zhdGn<@(0V_AZ!5$y6437#Slh2~mamuyN?x_Q2efWfy3=By
zuKPkK3rT5_gh2c=6P6mJKxst~7E%)1a&NY2SwZskZE1OE)wzQ8@7cNG1+}ev*$!Z3
zgmqly{(9q^6NJrX58#WLu~6JWqluQ^m;@$7VvyM*Vvf*lVB+Rxq0l%9ytJB@nPRms
zAY~oxHW20=5HxYepB-d^a4NKIzJ6I{c+c4%A9k@)`peIlG^}A${M>YQRLJFa&fFw$
z;dy6oJIP6?cF7)dAsjPlDr?L$S*Qk@3XT~}Y~Lrm_n;0S$N2G*vPfglCcjF+g#sHs
z$mU98()&972nDrCBje&xy?Y<`7t^umN!#kYB<>*diqm}KGq2C|rd+$`eCC*<PBupJ
zvPFgt-b{@BE=A@DWB7Z-!K}?h+SSl*aI-M>{LCcntQJon@(j%K>&G+ZL#Y%4Qhq6x
z<XcuI+TGyN@BX5I$EJIJjEchai3Yv}dnw)<qGY1r(!|Ez%E^;%`b3+M@^f|{Tpe`e
z<&)iSq2U8E$zmV%n#WafaL;gmppn;UHHxL+^<*$c8lZ_Akm@BfJ>%`6oor&&?DWo9
zE~K^n?rE8MuNf(FTPQa~O)8pSgV@^_9G@2Km?6bRvIVmxor+_RV$Wm`69f9-O@)ZP
zTd%sUvQ}GaH)*k*U>u3a7nZ3kQ6Zm8N6Yey^UKTsvQT2J(8iFzqf)ftV2{X><(Hf(
zK31JmHCP6t`8iqSLWP?uOI_{<M@3&+{D^LNkZ6`X#j?s`sWaBJZIs%0U7y~-x^8}%
zFnWcjIhi@h&9UwKfog;N^b<Hmmu{3U^IM4xcAcVKo{RZT5na%C@bWnxKM!LskH`Dz
zaG$zR6(C2S$6={cG3Jnvkau}<EO7MjxTKh$1$d18&3ZJ)kiExqFgcy2O>5~d0zJ<?
zt?nstsTH-kE@)WUH#}e~X|QioRcda662hiRE5QME9yEx~{82UbCli>FAI|+c7n=j$
z>8Ic8asBkf1#(M!>Z<ADiwgbW_x<=d7lIDw69pWK1_%d$vO262P=I8xpR#`!B*69`
zq*=p{gF-PS3W}|ctefqDa|K`MS<es<^v8B38A>6Ihw$A)<JCxHe?6SX%%&RP>Yt@P
z$@OooyuH2Q&4aAZ0zq`<v25V)+VNj<G-`yewKirvl>X!-QB@yg9Z}%UJRSsKpJd80
zcy62i)z7tL`MEVcbeI?OFm1<@X)R}PcFuRu!AdmX&k?XWIxrBn1s_}+$<+{;ub11H
z&>E<2XtHSC5+=m30X81YY0^42GGR9Cji`dR4D~?y`xe5-4j9LH?OH#l?C`Umfm)hM
zPNou_gMDR%ec}2c!E?`5pI~e@sf3yLs9<`D3dQwA(61b<5kq^{8aJb0ynt5Xb+%4G
ztuPHjCuZy9*M>yd=9)gI(;Z^`4+z7(K}zrZVLikT2XAjGyNDKQ<CufrCpF=zx19lJ
zLF_%WwUNl+c);)ko4>vGJyz{VM8)P<4BbfhX4$OR*D?sfFl)x?ew4h3--fuH5crWH
z_17q5c@LrY-QbeRs$u-qeym3JW-cDxHLf?BvAYdb`ny_L_G5*-gA!XpEAhwG8AegQ
z0*K!>jgw0JK1#=UH51x0W0dX97yrQi9KOOg+vZH^4FO-SKbKkTZvjaEWQ<<gPrRV(
zj^Om}(NYIGGG#_K6X7D)VP0-j07xICWs1l{2{2uBpP|l{<(z5Vhx{;|(H>JlKGOaO
zHBL}{1<xN&C(^HQKAQb5S{;_Da~i|1OwP+z4x!_F=OU!PBgZ}i+tzNEILi?KxjVuu
z^c~&7uLF4#WAe5B{TM2G5s{K`Lm2GRuTp<csR_J2lr_|j!wX4<o^4!;Jo>jEhk_I&
z4$2Ofo23Ri2`LVhz<o&rDo(8rp_999#c)IL4<o!EphZWWuqe?Fhzz<KR5BJ*lOZl>
zslO<HOsN&pG=e(3yXb}U)z4i9q+=E%_&x2zdja{7i+Oj9k@J>}#&S0B4c#|?J#CE|
zw{iNP-BtZSiEwHdligt^@BtTli+U;I(X<;@<%yqnpD%74Ax(hJ+(SuGO1GwU<Z#bL
z5@?R~PR<-~%ETzcE9UGwHlA(U**)V+FigjCF=o6>lAv@zo<<+QlH#kO`H9d^sRa95
z7YO`7n=B;KX)T(hx&;R&cLcMIVgJ?{xjc9_dGBiDMw%FRV<`48-XhtltQ7%^G#$JY
zI-8WYtVtt1aIa6s2?uA*^N!M)oQ^d0VoDIBEAJyhN}HF|+snAtx~uPPeT?b#M9mBG
zRHlHpzQnW4<cyc22tbbh>*SwrHGcqc=Lf{Z<}ri*;k#(dBE#YNBf*x)4FCv+KYf;^
zH1G=;!H=;{v<mS7o9501>%#Ef+Q@vY<n$_B_vF|vW`4SgGxP%d-X8XBuctM+dBz!*
zdkclL(`XZh<Z<miVevoOx_0MF&}Bz%-{Lgb>G@c3zu7L>GrQ;zL{1dZl4mbNCIb$(
zDlhRk01Ym@;xk^3$~L)7=;-sE8{(Y(zsNVBKgdahjfIPp7R{!Yv*B;OxWJ)(Y!r*U
zG%0)?b`^BsZmfQ8CXO-skH}-v4>m5xig^!@?$rxEFkyEcWOC)T9ddYTU&1pk;iAC6
zT1w|i@ff2Z4KX1syKx{~x3&J<x&+45z%&kDv<VTr5P8ea`a7ZM5**5`WR?<w;rck`
zT9=f-p=eMg4tYDItGmI>K+xu&jmC7XL(l*TLJImi+WCS<QL)pNsm%PwzV=+N@8<XF
zJ?otpei2CSs}d&F&V{%W%{dRZkn|-gI&R;a2;&r3fgxh=YW?t0I>dusp93x>EiicX
z<hhJ>;p}{)_zxyHB!A}mzaIPJ|I@9xrm!UY(~uNnm>Z`(4Mq_pZ80o|)dZH1pO9aC
zHD7kaQq`6mJ}Yro{}(D?A7$H)LFIf|0cCtj6Lx2dI+EmW(r)|{hLJlgf9wLZFT}v%
zXz(FGj-$y&*}(TYwI$H+^96W`h3^{J)=B=%;|Gg<ahH?8bTOIh__wY<!O5JRX^ymP
zcL0yU3=8A1Qq?R<MxdbD>-1`S=g*QV`PQhbA!QRc*819?bmw*R_#en~M``22q9-Cj
zIzP$(kgcOaO%zl_*wkCc%=nK#n?paBMB_;ZT@;f35`z;Nms=^3;rR)~3x?U1H!5SA
zV|~CT^nK^4!-IPI#fTlzIz|}mQC>suSaVvRNA5H<J;>WpFsX@oSjZnslu8-&A!l8$
zs0EeZFSp8k8a$xE*U{Q{v^+5Dz=d2oB`nVM@}z9)tcL8n%a-Qf@7x``*<tu*peGG2
zqNQR2))^f^4dTi{AKU_rPVo~Dss<sW8iO#8Xr9!=OSxa`W^2xQ%a~j*o(;rlzCB&7
z662K{g_m+zeR4LIBsidnirvJ)Ap~te`3<632v<4zjfAzIv;wc0@O%9jX!JP^3>RCI
zv(q7O+H1}J!|@zcmGQ+s*6@s7tE^s96qPovNv+|{Y&T9)km3i#Q5Xt3zuDZj=XS1s
z!D$}&ce!YmB7Dk8OZ$V>5Ke}yIz^qGec7D|>D2G1(I3`nJ&WhXpH%^)u7Z7zg?}{p
z?(@tp9G?gx+(S!N&+IAaQe>mrD*05@61rC++gLl6zbKe@r}<-#0Q_kmJoZVwU=#JP
zU&*bwWMUH>-HW5Z>n{+^%l8nR8mPCEdAPK}v_{s`a#?h}6{J-?8;+b{m1S&iBmN=d
ze^-zef4Wtx0&w8R4T=(vS?Yvv2#*)Tu(!#zg~1V}8^k`HGL&?W=p|j?^E|5-M+JQF
z`xFEJ-Hu~80FCT%2;p_fq6{k8<ZBZ%!&kdXrigPxYoa}Y-!esiF&U$j;TEC|ija?a
z-|RA|dAWv(w4$_39Kty|pddl`rDF24#?tn#WRTPi&+so;`8irS$4PKX+w_QEBk_)(
zilM5{x)rm!ddf<oLPZ3PbUhCz6iZS$%dg7biPn6WYU<(X<UT)R*Xag%+Acz0T<n17
zRq}+ccrs{U!o?;1#z6oB8STnK@@&Rmr(`kgdo0Cc?mAXpf!*BTpdd6>8oJ1D>E6b>
z`nR1_M7v0DRKH!*gq|Wyp`J!+4jv;c@&yJ^@}S~&;Hhy5#+87#T%>pvMBM6)g8^ln
zTB*1vma|j_6;@?wi3~QGZ1Ak_a)RLec%YDiuVD`;HMF;V4G0b{r6or0fCs3P{@Zt3
z?+Aj5FMjbn1d%teD0BSC77&W=gs>TKsAOHA^hMsM+$GCFzVRlU$6Cuq!HldunEF!h
zvl*W5hSsEV#}bysjI4Z+3!R>#4_8rpO1hF=OpwSf)Uc)7*0fsA)QN9nUz^me^eM!i
zZa@Jd-iWc-?QxW#932JXKsJvd_$@`9VE=5-9=!yfuQr#BZY=fudhsK-zJ0~?j91z^
zuXa&(aglNHsc4R`wXd~5_Wqc8B)+=w|7jPsdHgcYX=;6iyX>CO$QIOEQrNzs1gCy#
z<ad%Mo;1zpT?-k=5F}Dl*u9s1eat}>4o3`xW0lV=n^iA?V5a&(#!1eLw5TMcJ57?M
z#Wg1e_u4+qF?ePClG|lCH*mc3_v5B0CEkpjOeV}Fj8$d{U_}*2X9$a1U&Uwv@*LFD
zSyb%Ocl0R8?*2F4NeW`os#HnSib)L>&mM3PSVl3n(qI?A_4lSxn|(Qi+iCwB?+Xp=
z+ojq8t162#q%4o(gfXIibxe!CR|w^RMx~KWNR&+^qwtMbLWx--qoQNmpkdG?Iw!`Z
zR}~^Fu{K-pT2P<}Sv0E@GxJMMnVjkeOC1qk3DcPhT!;sh5@<tLG^Q0B`olvHU_d$(
zE8Sy8Ma~h^a8?SDC#c38HT2OhfJp#-ooTIAF-w~OBMup#x<Qw4=6b`uE`>VJUj7&#
zSjc{l#_oMzUVOv&``w>J31bTYzWCvTp2TbSK!6XBubSLx`CU(1!fq8Qm<o8phxgE%
zb(qR^`*j~sCHP<_5viw^2;Mk$P7uc}l2LI5H-;MX{r2s151@Xa<346`?FMf9Uf6yh
zpjNZxDb`)>i-dY99F}F|3N!?vfDO<)5hDFI)woC+u%(>DlfK}%3A-Bi2R+yf#*Sy$
zS_}n})8)u(y=LoUug?1#IlAeIT{}t1FAr7Anl!Zu<~>FPXbM%ZUXaGg(Th*NLi19K
z+x4>GC?O4)gbC|U+XhnT{{U7Tav)|JBdjWW&1`^+QvkGlZf(UqY*kFBQAuY|^P*1<
zPQ3e;JTG;94~ll}$9q8~8XF8Og(Dh>)R*VywX|2a7uD2+kJ~$O)`Z8cCvL9Y&>;^`
zw8mC!V`X%p_VyM_miHoESddxJGbduPSse!|YBari=GA{M1O;axrT~NMlzg!ZF7;5r
z$E&+FtbU`$6pA+8B+cOZO4>6l;<ZpL-@uR|oDbcypUd^WSU9p=RMsUKp`@5=J~rzS
zo^$UW&R~imwzD#(l<dv*Hpgo02awWp@K88!9%O%>%A;K7&uS9HIv~ChjlT6&GS>7W
z&Mj*eX?$Rmws9aWuLGlFvd(>BEmKcG$c{_lNFdEKmZTqTc%{_k)7Rs-hYd8}?U%xP
zX9a=`x%PZ#4th;-J)Drk3ABdf60)%6hR&-p<!MC3MLR@UZi6ruY-90JdB2c~ir*hD
zXXYbqSm~^(qpXFy6heVPU&~>Xge8SP{^XfTJIJv>pyPEB=>rK2#G<<^uBYYehYA|$
zf(?lk!qigCCz}^qJEIxEQe{*Q=bG#1@{V@#<IVNZoMAs3Wufy-%PH20Qp@pnvByo7
zARg{X3x+#FFJlU`JeSM~mnzcG2E8qThqt-tC6+Q61!YG{1rtBRrk^lJMeq)Fq!HsM
z@~d9wIZfr}T!D(DT@2{icA>)YIjs55ugeRc?H;rD3Ogu&H{b2kg7!U@Behd%tIhg)
zr<HUY^Kmq#KZrY|sFj9`erFn23K90Y&|ijch0Mc_;S1LGSy=;)&=ZCfB(!m!xQf@l
z8v9%YQ55KiV@XrUD_DxV7h8h`>AI<Kpxi)tkTlk&jlWIsJ;>?+pYMq?iKyzqd`I$<
zmn!2J>VeSKUW!YUP)4$hXsfV!+Dc>%HA<XF8#9_kaDKUeWcUH1rj7%K`UlAb`{%Om
z<|F<Dtjo{TWFshZbUtB(3kwNB19vc>fLNF5@0}q|w{5q-v)t9EPdIq%La|(FdK8#2
z5^+qGazMDI={%9+_q7s8>Hz9qX6An=AQLkXHuy0*kEP|%bwvoR$qG!Q33Iu3C9Xn6
zO400-AfvjOMRL>Wfe>cjY$nQkdAZ2IR3S2d$Y2f8-ynH*4jgGB?Dsf4IKR)n67K9@
zJF7}R+g{<!{7Ts}GBePnHqQQEVdoVNchs%%sL^{3MjI_!^dUir-b08IeS+x3i2m0S
zy^R(jdKZj72qJp4A$pC`GHUeBaqiCdd>7|tU+ul0b+`As-u1k{o--Rc+*epQsK7EH
z=t$X6EPx=;ep|Poal7*T9*CKBV%_%fg_}uKOjFbB!Q%XqcY-A|j7(||6+dq5$J2ej
z5s6E`#K&&xv?uh5-`#bHAOEY7lVdB;xumiY?=8I(j*IE#?G>|i(sDo;;3|Bf^^Kh&
zZG1GYNLtpD5fz$p{^P!9t9=-#ZeI^5!IZM_z0Pnq=Dx1?C%<Tyv1HtAR$w5fOdQkr
z6NMsRT%Y?V@XOfJ|B8wsm&~y?YHf8h3XhH1#d~hbboNG1<7{EaH$`ZDTBLC4VB<aL
zEJ3E0#bRMBm-@58fY{Ly4WT0;RW*r?r2f`WV$%}7eBw!x&0e)n)2`hGR@*GVxWgKM
zTj3a_#Y*UDkb+sM<7!oFw3Ny8A=0C-Hx!q&+aEa~Khuti(;@;}Q57b8r-spL2==e;
zjZqmyrKY^qtd)CBlfKeXBD`gODYS4?PHFJ>X`i@;rN*Zz4*hGfxBIm7h#sSB5R-0$
zEiq$JK8u^DCbXN5%i-{7Ey^GGEgBB39p)SSrdFM5)JGXdjf4p5mWUo`<w`ij8Q$;^
z$K>Icr!++GX^CMC{!N^oDzeWls(#5N*}H?i&cHw)X5=J3-6$nhPgSYM(Zpj|XlN&n
zb-As+GiIg7L^Fb0Cay77JdE)OR{{6d=Nmf}%f)>+!S)>fH^A+$N{kKod_Weg&R4rc
zJWp7t@ML27u5y~8%OZA~P)6+}sgNxY`s~+QRvYH2B0FA+q(g8tIzHd8#G4l*ApYf)
zaa#}vyeO%zjz1=x&rB=tV(ENi@zY&bR9B=fW77iCaZ9}PgxL|ZS3VacIxYQo&Lvf_
zQD~d6R^;f>(h#1$4&Ebx=7)1h%JnM`$CfvgAXe;`6z1gVD-P74*Yk^a1C$($LJE_Q
zIMf%~REB%3adwE)J;r#LRiC;3K@;-hPycRZhwzEunRg4m$}@VcktJEGtyJA5`Z1Vy
z@f&u2Y@J4@CqW94XvgNK3MIArHJ9S(U(||{Yq=MjlFMw^e;A)p1O8+D{z!^68DWnS
z6fkNb9~ypb0m1uPEUAyv`xs?3dwRxF4@-1()SUbA{<UoFwl!Fu!K4Sar+YVcdr=-B
zJ=arcaF>Y)5tDwB#(Zq-U0%=qmDW0Y_3w*)$~ySP`W?jzOLH8goKn~Y+HuC{%IRK(
z9F+JqOk}F8;{4GDIH9t`E-T)pSR&1#TDNqO{xs4e{<CjxrM%*#C?c1U<!H7k+r##@
zKHNm?Zin9o+v4WHQWK*M?F|YsA!V=c7{q!;{$3z1_)^Az(|4~^ORZq_hAeBjzx`15
z_Rv3mwIoS>AqZ$?Wg)o@U6V!gD(gK8Lp`SVkSw$_$%B6YXf1*g)z*tNrgyk9xQ`mv
zgAJz@5cAxE)LcB#9AWtS_?Y*o51lsKKGk%(Q>G}7>pk*tSfv&JFg~GMji%r!@1C%$
z=!~-j44SG^`ipRIN-2z}$Ot+mDLxG!yS!1&xcT=Y%cJODm+fvA<<#;?l!hR6B?(y!
z8%d=QSKu4K31I}PuV+S*_YEkGQ%~Njn8NV~#>DL_FWLWc25bSZatcm5hl1RvE><Fs
z3Qn|QCh+Y_ML#=vsTf4$;ZH6-qQx5Fr!i`31c9D;e044-2K&^>hOa=wf1Nu@t0lQv
zE+nyDJuz2lU1r)o3#q!~hg{`J9o>y(CL;a;>%+j=oy7{V1EbFs*C&4AJ;X;!4`Gdd
zx+0agyBuiFRvNED4kDN`Y3a_Z$rT~&x5{OSB$z{9{i-s})eIg!Sfj>s4M>A%6Z7M|
z1;;1%fwYy~8LBU$E`l48nCG@<KTr`+ect1+V|g#`h=bi97UV3RLu~F(t_jE*L){!i
zE&YI>^Iz2ZH9o_W1mcHhbbR(j#B_~F<wOnAa?EoTV+X??jPqYayFZ^bN#qtQBmM3-
zQ{v_xG+xZ@xB#6pOtn+caTGQ7vEnVCPS4Z%9$Xt~mK6}SIKU>3Bm74;lp>@<J|s~}
z!84B6v8}OGF9F|!4R$zZWx`J(sS!%rf1?e&|6*J8n>=9kX?)ij!zjyP*#T!MT?*ZI
zRZly!s6?;I=P8}^%Fx~GK8KvLN9S$@=J=DvXh%i%Kq#&=8+I+q_=<}?7hT*rCG+0n
z1x&bR#6s^Yi}2_QHQ%;#$XuYIZ2i^LLT|R)zlDUSOeN;Ga!1S-fv)o<O2vIEmdH(D
zdbUwS_>gfL_ffuMc#-r8TH~aLIe*G&V$;)#T<vXAUTEYipg+WY<}KCkd`Z)6)Dl8d
z;GMd9{-52={PsSxxqtOSk6cHqV;{qv@cdHK)J{t|)JbP@8*4j_=Jdv@D%mC>g*83J
zrK02gDT3u`W*GQQFQ<~pH*RIwvalQAB!an*rmy17_@cfayO)h{IVx9zV&>b>V_NFO
z{uC+}9t4QUZYtkb2%{sw9!9)xHq+V9oBA9?I_uD9bxb%U9V1&=Aee-McR|Dl#N;%Q
zzazw!Wzoy89!tw5cRypvEhqT=Po=~0=Ei9XxVQ;EvZpj)7F*aDwW%?7g8*;V5x#S3
zlgaU{$TG>k7NRWN4HbIsZ>ZF4$Wp|<WM01u2U2!ScFa!n?+IPl*l^`ll&fvolGVg^
zTrWr4JlOsHovy6$SMC3H<RDL;{4a(^{y%d3GZU{a7`q-k;Le}C$+^3GkTvMfo@Gse
zt@6On<e|%dUm_)*{x1&1NFpV2pG)ug-iuB02_)S*6a)gE{9n_o&D2SkNuBOSt|Px)
zJ3kU8>t<NV0mcsdiL3b2*0`ud-pBog0439`o#e%tzpJ5CJ&V*fnRTM+<cSQ7wZwJN
zaW#%w|L>oV<cg|%DGj`FD5Bw{@Kf-2*iYj)KN=%t>q0uoTy2?^EJa3KKH=E$&`_Y$
z=f`Yhgwlifm%KP<s+3<TRhaU?IPZQEb7FW?uxWG+Ob@Pu=?UJ((*1(OhLrwt6tG9V
zWgLB+$Bd)sp#0!CSP%F4elAh<elx$7f^Z0P5mj&u);5ugFH2}u{MA4g85W1n6hT3o
zeFJ;M8O0=HBjr>ys@E1a)cd(Kd=dpLt47e%g~UO5iKx=laOgQhYp@&PpIO6y|H@~C
z;e`3G5TG8wmX7}MzJac@I1}mN*(Qq!q*Jbe&9n#)aoeoSqNx}_wd$i`?7?FwbFQYE
z!f4nlXA3$eentXzTJcX`Lx~phl@ZVIN66xRjA%j@OCLP4!IdJ3Diz1Ym9_zF@UJPq
z;s`!ZP}9o$i>JjjW@Dn^(z87sR_E3<`KRk&%NKI*k`KvvLkU~oellGtMJIQ*9ynO|
zd<!O8JA*O4CloFaXleqm=F}x}mWG8Yq_a|amRFYxOw(n&OOo-nj<8+L6DUrK32I(^
z`YWk|U?H%!#a&iBA1Obz&JTP7Jl=Pf<Z#=4^QEP*0nJd>(w+C#1EDNKvC~UXjMM2B
zi^}`BHSx2Wt&~hKWuBo^ZiBAvaeybWs+Kp7>X0gT>xo>PudO@7NKu7U^ggO6*~Z#1
zoeZ~9N*Ps-)9Dgx^^`)UA+myi#gw6eFEokiQoKH({QWh$?pu@YMdVO8P#RdFz59D6
zPjg!6y{BP%yt&<VF9A#I(62a3nR-Mqu|g%A@nXnxCQjsVfGKg#B78JE#p?L1tn94B
z1C}8bC}>J=JbmDhX>P%q+^FE6Qjxudkwy{bG|}r!;|n=SLdsQES28ivp?yi{FpKHj
z);dSz=Mf7_!`H9%=QQ~Mb#;t@MuezZSy4*1UP80*<lCSUBJzZuH=^@lbqDBTM}(p*
zehJ}?;ppapoGs+*bnalw==8cF$o|s_B(qN@dHaj!JD1+GQw1SV@>Gn3x8bz;o*Pu{
z$Z2-``U=+c@acz`QF467je&Mb@9;BY9E8Usgo;A2@A5rw)c+q~Ey=QIZd6BasR|^x
zslK*skCwM|C~F8`y$!e!y|Q}tb%&IGGNuhE7<B(s^qu5aJN)uB9}t+a2n#UN_7?JW
zPimUJ^?Avy&#bLF`6s}FWm(X*m)d>p<{z;nWixYDrk(hjd1|DWZdZ5Z;);!rSX-~g
z$J72<l5vy5NR?EecjIx{_}0%=_ok42q7+cVH4m&O6p_xN<FE<EI4eXO_@N#Bs0dmJ
z*nHtn`iG_coK(5+z!E=R+-{@R6kSgnBawo*hr{}W;C_;Et3d%P0=^Ff@gS87W0mwZ
z$^I^r#}zO_Y?@(b{8yqC@7@xp*1BqxIb#>9Xgq3vU?L9acap+X33F%()iOD(V)%B6
z=u0O$Y1IB6J=SrgLkIwT--Ma1vIM?)4Dj}*K`)SeQGak548Fi^LNZk_5=|*fv<fd7
zy{rqMF3lqP*4Tw9j~gDVzllDs%1u4yeX)|FtLW*U(Kh6#=+y-`2wFct{`zsq4Zb;F
zxq2iIx(Ef<W1t51IO`cmw(^N|XZ<tk<rV0j?rvw1A;IYyW?<wGrIy2=$JyvaZ!U|2
zr}{Y9#J}}@IL1E**9<@Vqy+<+33u9nwKjN{y-~|UZchZ6hKpjNIt|%L6C@4tKdFP>
zYCO07)I6PQ@n!Is46?9R`|iu9mhgs^g*kA1b!0C9HAU<o5Yb7y52E|ADW4c9PgR(C
zDWbpvzwtE?^RfH_dGtFkAJ;sT5H0HkuLyR53E%3KiOjqT25Vh05b@ih@scVp{yNG1
zi?{UsOyTq;XVu#1`h{ktx!jkkbuqEDt+sD)TjkcO{R240_x&7I6%3R#K{ItR-oi}o
zJ9rN4yxa8kFP!<cEP5_Mqp)EV&*rdRvqlv5T1!}#u2x2b6KeNFA;6TEvYR{9N}NnM
zNMEm0Z;3Uxtj8RDW>+a|^IhAYv)II#YVMr{?p3f9zLDluKKnoytddxRy>=?GjCApQ
z?7~_M68Z56zcY@Q%ofA;!#|(rRPA3}^XCjeh<0j?V=R3*C3<1g=JDU9I%bpRf4&Su
z@aSiA_5?XRAJ=S_q{{B85j`zKVLq&rI^u|CLED!PLA+)Y)`(YCs7i{-ZhGUlMSN0y
z`D8}M%oWtOct3p~7W@V4c2o9R7Mv%hT}x{e2Jd&`CK%hXGkC)~?=?e&wYLjV8)O+s
zv@->lr`bd|k8HXH$pw~<Ge$lL4nwdFbG)niAvsnfMcM2gZ_FIk4LAAt9+&5lg%#D*
z{Gmc*Oq!TLzR>+}cuxN7<o-QQU)HRpn+COvzE03|^9!;il1;;{8!wOZm7ArCu7cky
z(5khmOw4d(M0QZ7aJlfq<cfDK!fz6Q&6wFH`lkgL%gMpc1Ls%6y~*434>{LnS9_Y$
zjn_|4i!Xn^Yg$;+;omnr8k{|+%W%7l?Bhc%;i>UHq{q~;KQdK6JB;ldxrWz-7GgCW
zUZq8QyAze&Qv9M2Et_weJimMosy{kA>G7#QCs~~fKl$|jKv2o0rpGdfCMK5#7`g4l
z5=9)WkE~r5Q+Xl)vG3W$+NRQk$8P<VmYg1$TAPzjv)6dd$4%x={g`DxV$c2C^J4UV
z{>9#?WF;Mj_s-(LpU36T*UH|%W^NBVw1rAIAvP$-!Hq|x>_1Y3T`vx$A(`2_y$t3n
zGZP6>h-{vHvV>7NkODjzlfD8dVFcaLdv*|ivMQuu)p?2g$2UBNv2F%<89j7HxXl~@
z(^QbGVQDhg9%Y)>T=g)Gsh!5(&J>~Rc4xxnx}IbE!TGw?ugh|anw&Ywf*v0QEo7qv
zYmx;e8)t3wtyWkA5(V#^8}Co{&<Ur{pkd^VUMpvBV@#>vI0G#K3jhEFc`i}$m>n|C
zpobruEf~z8TN2or=A1q9>|$1OMiO%c>)vY40_uf?W4N!W`vzHUbkh0eyK4r!^FZC0
z;QisuYfNAUq)OgniFpsye#L@j*5>(bPb#)lO~vD?#0-X#caND?6N^3D2!ZGeQL07>
z-XeD|HkHd90#gov1a~fP&!Ry;m#T2CD%{yHewKf2&H8uU_1(0WNo+@$gydzWtzP>z
z4P$NZu4MQk@6#|6!S$9Y_ry-Tv6hq{;W@rwSVpyS8X^mWKhKZ&_Hkb~O@ar0za}Tx
zx6-pZ?tR)^`{h{6bZvl+F4;q()6hihRe$C%E|->6SAf{6M#RCKmdS|MiyW;FvVLy)
z6Y|@Flw8*jUY?%mEVcXh>_5G$-1se=I-g@WIfXg4yJn`xxaaI0DtIK_hS(L%pw0Ok
z9j4zDo{aAjgzXVhx4ompFA*#Dl{<UZ`!WXw50ZcvX9_3GA(S(bS%<xHeES74$94%N
zldPmte}G>Rzr&8gjv{alW@{IaU)EL562Sa5I!5Qq4eh!{Q(Vnk5!hN0?DO{(df4VL
z-&bfE?NGM?j+ReEgR($(o2wOFD>lzomRaqS3s%raV%cgQ62IdP_-12vhb$cU{9lec
zB2=jWkSez|^FR(G^frNIe!=9IZ`e-ikz5P6h8x-B-TSc(3JLTzd9>9m|9^j;Ax}io
zT{A!oiXygllH#@!)>0zk5J?GfaXV`}X%R6AJ1E5Ne)>&JOzzqLw<Enj0|df>JP}RD
zWPrE;X^H>HHItb3b$?|@*LUzihVWf2&sSCK5PB*CYeJ`BL~>lXVSZ#T0WXrBjV)h5
z;Z&B;n$T(nn@Y(jm5Yr6_25=4{#HaH41g3Fnx@6;Xb&3~7#I{BUHa?X9hDh$__to2
z1fI3|SFb`w_B$SC{rC_cVGaarXK`WmMSXarc4;NN6M=_@48*@+RsDY4?Y;{*GDcYi
zXXDh!tJA31k~DT1li2vjcHzeMDQW$r(dvDr)kRTU5X15W=cx_1H<ha0?1{a{`e4~p
zTl(WCOszI#8#YoyU3;GsWD)eI-EuQbS2kE%pO3pjAP<N=D9C!*Av8ctca&fgKWlGl
zbuK8L(;XcDa}0vtXCsmpne6_fy(DG-9!MYm77cDN-G-RHj!G6CffK<e(a_VTLO3)I
z(i|bcsH;D`$Qm2g&4+|mQahOQ=)6S1qrlr3lkgYad;^Fn^?KJrx#!yi%qpTLS-+lb
zOMlGH>@IVxU895#1L&da94-U(MuXQ;V*5OUtX#|NBOFz~?{3E+`dJ;VQ=1}}J2ab?
zMYlug>w$qdFPkc1k1AWi6xR}2QrFXmeFIKllvnR)L9upyfQ)aO(y+|_=2+{9*)8sc
zMU)%^<4OkPh0Wfb^FZ?PERRUe^kq)m@bVth!tA1HPCl#yz}@ELd=|47jLSqU==)_Y
z7gu-kJSR@G)%L>qu<3lir}W5PXe0@8mo%0=imH2w_A4?z6d16M;=hi#`+ATrY$lg*
z{kePjdT5mT{m+8*`l6hMk&+W6cv7HoO&ZRvF2m4VH=3N4SV+BlJz5OC+sM4U@Nx<a
znM#J=8>BfvM3rvsweq1IVS}N`A=SV;!kd*mE@HK%BveU~^pWU5>qLxuUifXmh$;PV
zKO;p;Im8+*JcjDhyPZo@O-TK3$w8U`m#?m3N3m&b>{_4-l0*|M%RSu@^r6SHa<t;O
zS-=(6#x<y3h3dc_>{-lmtE?XLGhEnBYOW7m`+@{uPJ+H)3zZiZd(yru9OfafIBaX-
zYsJWj_9iIpfS^a_Ep)&UK982vZG@7lS0L%51<JTD)D~jI1WYs!Yml!81$>hWwHMO*
zlacXj*+?l4+xG0Wc4QNsac#(*5V}z%dpHh^<uQM2JHQR+_1Ir*fQqTtBXd-G-xI4p
z1w2^2>!AGV-;kC&O;9Fvy@hHf!C9Sgiv?Dxi!=V!D>s2<1rQITeAda)w^_loeXwrI
zw~J|kS!Fobgg7C58ude7u$o<$t~#)es{zc1=DM~~XHhTriAs_hb%lE!z;A8TWuy2l
z4qs}XCVEi_!wR|l)kV8CsWnfNpwaX|c|Eivr%;4-{Ab1I>MDnaWyA>KtxH&>9|f6;
zdGk^}=o1q%Q@xbmhZDq&32%D|Y!ko$xp~FKR6p5D)iQcGeQs5GU+KeD3T8E3^Xdd!
zYIV6&CG%EYz3usS+1|W86&-yZE72{oHJ2@~o#2em%G-R-8<{ZWeuc%CS;fHKr$rHR
znCbypjvuytZWV!jX-;>EJDr?%MSJH6TH140p6M((=N?qo{-*b0jlMLeC#dvkTFitD
z{38T6#-S~IKZ&G|veX}bR;IdQEYM43DxWLV<N1wkx}v=M0QtbO_=L!Ut9b<AQ(Wx0
zHwlB^dx%&1rJJd3{2|R!4FbNJ_FYRqiycMz;mCuf<NW{89%fEx9vB9YT9%y9ojiUM
z<}~1D&=IxmF1(dpLc9OR{7@Uo*pvt;wb3@^?YYYpFNHkyp!a1uk$V<;$;?u40$Spc
zj=wC-Vw*mA7Uj0e=UjY3y@ek@Kbn7+Hn?fNJ1xUBS}-F1^>rxL_i#Wa-OE*Jh4xvn
zAHCO5!An)+zB*dle`H&KF~*qw`Yz)Mkuj;v+ZeVwWnHOItO@=xsTbnCxj~uj{5B$L
zo{=(2ED404)%I-gy}{ETZ0#qgO(dJ!m6L0d-YnN&LW`_Ga?!$Qajl;F^iv7rkA5!H
z?i@Nl2R_Fe=zXxkDK!TKx=g=yEzV$xAwcPWM2EW9k=V;WCQDcK;x4_m>%LXIkUQ<p
z`>f_fw}mXqZ7=Y%q7T5b{OM78w9uvM{#Zzb<-#8sxj%8@O0uPq64pGU{ar!RWrRd^
zkv0BD4TMTHuRW<ZH#;`E2KU6PxC9|8pep+jGoYz!=J#p5ZX60s{7%dcKAtf6=r=w#
zsXZ#t0>bzz>9UIH>5_`Q9HsHwL)CRLPaOSxGeI4ajqNpB?eD)hx;-@3*0MJQE2;GN
zT#VS3sapO1c`<Tnc(K&~^`hqXlJSS{EfEue-}as~<?o3%TCuoxnSKu@MLtZ(m#b{q
zhJ0+P-8S_l8+CU9{L<xrFqJx?%)f`_{J|Nx7yp~T1Y$ey-x!KwYn~lmi+3;1hfQwx
z5wuKQI=sYBYq)k&q7eo)<#Ic=&8PfrJ(;JOle1_+j5Ex#W628k`oQSc`Q*WYk#>WO
z>UY~wGxNXGK)uZC@4pkwWGAYtjdw>cu5c<rd(#a@AL#d=#%NTiUYsyi9CcsS4s6u~
z-Na1Fc3+hzOcn*1yTICgfUBV`5A+QK=J#*bN!&Y2`;rFfkvput8_lrvnH&&^L`+g#
OM4E_;OIcTi=zjqEQTZ1D

diff --git a/docs/chapter_1.html b/docs/chapter_1.html
index dbb00ff..c93eee9 100644
--- a/docs/chapter_1.html
+++ b/docs/chapter_1.html
@@ -84,7 +84,7 @@ div.csl-indent {
 <script src="site_libs/quarto-search/quarto-search.js"></script>
 <meta name="quarto:offset" content="./">
 <link href="./chapter_2.html" rel="next">
-<link href="./intro.html" rel="prev">
+<link href="./index.html" rel="prev">
 <script src="site_libs/quarto-html/quarto.js"></script>
 <script src="site_libs/quarto-html/popper.min.js"></script>
 <script src="site_libs/quarto-html/tippy.umd.min.js"></script>
@@ -155,12 +155,6 @@ div.csl-indent {
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./index.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Introduction: Hacking Religion</span></a>
   </div>
 </li>
@@ -227,7 +221,6 @@ div.csl-indent {
   <li><a href="#is-your-chart-accurate-telling-the-truth-in-data-science" id="toc-is-your-chart-accurate-telling-the-truth-in-data-science" class="nav-link" data-scroll-target="#is-your-chart-accurate-telling-the-truth-in-data-science"><span class="header-section-number">1.5</span> Is your chart accurate? Telling the truth in data science</a></li>
   <li><a href="#making-our-script-reproducible" id="toc-making-our-script-reproducible" class="nav-link" data-scroll-target="#making-our-script-reproducible"><span class="header-section-number">1.6</span> Making our script reproducible</a></li>
   <li><a href="#multifactor-visualisation" id="toc-multifactor-visualisation" class="nav-link" data-scroll-target="#multifactor-visualisation"><span class="header-section-number">1.7</span> Multifactor Visualisation</a></li>
-  <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
   </ul>
 </nav>
     </div>
@@ -579,7 +572,7 @@ div.csl-indent {
 <dl class="code-annotation-container-grid">
 <dt data-target-cell="annotated-cell-10" data-target-annotation="2">2</dt>
 <dd>
-<span data-code-cell="annotated-cell-10" data-code-annotation="2" data-code-lines="1">We’ll re-order the column by size.</span>
+<span data-code-lines="1" data-code-cell="annotated-cell-10" data-code-annotation="2">We’ll re-order the column by size.</span>
 </dd>
 </dl>
 </div>
@@ -602,19 +595,19 @@ div.csl-indent {
 <dl class="code-annotation-container-grid">
 <dt data-target-cell="annotated-cell-11" data-target-annotation="1">1</dt>
 <dd>
-<span data-code-cell="annotated-cell-11" data-code-annotation="1" data-code-lines="1">First, remove the column with region names and the totals for the regions as we want just integer data.</span>
+<span data-code-lines="1" data-code-cell="annotated-cell-11" data-code-annotation="1">First, remove the column with region names and the totals for the regions as we want just integer data.</span>
 </dd>
 <dt data-target-cell="annotated-cell-11" data-target-annotation="2">2</dt>
 <dd>
-<span data-code-cell="annotated-cell-11" data-code-annotation="2" data-code-lines="3">Second calculate the totals. In this example we use the tidyverse library <code>dplyr()</code>, but you can also do this using base R with <code>colsums()</code> like this: <code>uk_census_2021_religion_totals &lt;- colSums(uk_census_2021_religion_totals, na.rm = TRUE)</code>. The downside with base R is that you’ll also need to convert the result into a dataframe for <code>ggplot</code> like this: <code>uk_census_2021_religion_totals &lt;- as.data.frame(uk_census_2021_religion_totals)</code></span>
+<span data-code-lines="3" data-code-cell="annotated-cell-11" data-code-annotation="2">Second calculate the totals. In this example we use the tidyverse library <code>dplyr()</code>, but you can also do this using base R with <code>colsums()</code> like this: <code>uk_census_2021_religion_totals &lt;- colSums(uk_census_2021_religion_totals, na.rm = TRUE)</code>. The downside with base R is that you’ll also need to convert the result into a dataframe for <code>ggplot</code> like this: <code>uk_census_2021_religion_totals &lt;- as.data.frame(uk_census_2021_religion_totals)</code></span>
 </dd>
 <dt data-target-cell="annotated-cell-11" data-target-annotation="3">3</dt>
 <dd>
-<span data-code-cell="annotated-cell-11" data-code-annotation="3" data-code-lines="4">In order to visualise this data using ggplot, we need to shift this data from wide to long format. This is a quick job using gather()</span>
+<span data-code-lines="4" data-code-cell="annotated-cell-11" data-code-annotation="3">In order to visualise this data using ggplot, we need to shift this data from wide to long format. This is a quick job using gather()</span>
 </dd>
 <dt data-target-cell="annotated-cell-11" data-target-annotation="4">4</dt>
 <dd>
-<span data-code-cell="annotated-cell-11" data-code-annotation="4" data-code-lines="5">Now plot it out and have a look!</span>
+<span data-code-lines="5" data-code-cell="annotated-cell-11" data-code-annotation="4">Now plot it out and have a look!</span>
 </dd>
 </dl>
 </div>
@@ -876,9 +869,6 @@ What is Nomis?
 <span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"chart.png"</span>, <span class="at">plot=</span>plot1, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">10</span>, <span class="at">units=</span><span class="fu">c</span>(<span class="st">"in"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-</section>
-<section id="references" class="level1 unnumbered">
-<h1 class="unnumbered">References</h1>
 <div id="refs" role="list" style="display: none">
 
 </div>
@@ -1122,7 +1112,7 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./intro.html" class="pagination-link">
+      <a href="./index.html" class="pagination-link">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Introduction: Hacking Religion</span>
       </a>          
   </div>
diff --git a/docs/chapter_2.html b/docs/chapter_2.html
index 22df6cf..0003e48 100644
--- a/docs/chapter_2.html
+++ b/docs/chapter_2.html
@@ -115,8 +115,6 @@ div.csl-indent {
   }
 }</script>
 
-  <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
-  <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml-full.js" type="text/javascript"></script>
 
 </head>
 
@@ -157,12 +155,6 @@ div.csl-indent {
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./index.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Introduction: Hacking Religion</span></a>
   </div>
 </li>
@@ -218,33 +210,8 @@ div.csl-indent {
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#loading-in-some-data" id="toc-loading-in-some-data" class="nav-link active" data-scroll-target="#loading-in-some-data"><span class="header-section-number">3</span> Loading in some data</a></li>
-  <li><a href="#how-can-you-ask-about-religion" id="toc-how-can-you-ask-about-religion" class="nav-link" data-scroll-target="#how-can-you-ask-about-religion"><span class="header-section-number">4</span> How can you ask about religion?</a></li>
-  <li><a href="#q56-follow-ups" id="toc-q56-follow-ups" class="nav-link" data-scroll-target="#q56-follow-ups"><span class="header-section-number">5</span> Q56 follow-ups</a></li>
-  <li><a href="#religious-affiliation-c---muslim-denomination-subquestion" id="toc-religious-affiliation-c---muslim-denomination-subquestion" class="nav-link" data-scroll-target="#religious-affiliation-c---muslim-denomination-subquestion"><span class="header-section-number">6</span> Religious Affiliation c - Muslim Denomination Subquestion</a></li>
-  <li><a href="#q57" id="toc-q57" class="nav-link" data-scroll-target="#q57"><span class="header-section-number">7</span> Q57</a></li>
-  <li><a href="#religiosity" id="toc-religiosity" class="nav-link" data-scroll-target="#religiosity"><span class="header-section-number">8</span> Religiosity</a></li>
-  <li><a href="#q58" id="toc-q58" class="nav-link" data-scroll-target="#q58"><span class="header-section-number">9</span> Q58</a></li>
-  <li><a href="#faceted-plot-working-with-3x3-grid" id="toc-faceted-plot-working-with-3x3-grid" class="nav-link" data-scroll-target="#faceted-plot-working-with-3x3-grid"><span class="header-section-number">10</span> Faceted plot working with 3x3 grid</a></li>
-  <li><a href="#q59" id="toc-q59" class="nav-link" data-scroll-target="#q59"><span class="header-section-number">11</span> Q59</a></li>
-  <li><a href="#faceted-plot-working-with-3x3-grid-1" id="toc-faceted-plot-working-with-3x3-grid-1" class="nav-link" data-scroll-target="#faceted-plot-working-with-3x3-grid-1"><span class="header-section-number">12</span> Faceted plot working with 3x3 grid</a></li>
-  <li><a href="#comparing-with-attitudes-surrounding-climate-change" id="toc-comparing-with-attitudes-surrounding-climate-change" class="nav-link" data-scroll-target="#comparing-with-attitudes-surrounding-climate-change"><span class="header-section-number">13</span> Comparing with attitudes surrounding climate change</a></li>
-  <li><a href="#q6" id="toc-q6" class="nav-link" data-scroll-target="#q6"><span class="header-section-number">14</span> Q6</a></li>
-  <li><a href="#subsetting" id="toc-subsetting" class="nav-link" data-scroll-target="#subsetting"><span class="header-section-number">15</span> Subsetting</a>
-  <ul class="collapse">
-  <li><a href="#q57-subsetting-based-on-religiosity" id="toc-q57-subsetting-based-on-religiosity" class="nav-link" data-scroll-target="#q57-subsetting-based-on-religiosity"><span class="header-section-number">15.1</span> Q57 subsetting based on Religiosity ————————————————————–</a></li>
-  <li><a href="#subsetting-based-on-spirituality" id="toc-subsetting-based-on-spirituality" class="nav-link" data-scroll-target="#subsetting-based-on-spirituality"><span class="header-section-number">15.2</span> Subsetting based on Spirituality ————————————————————–</a>
-  <ul class="collapse">
-  <li><a href="#nature-relatedness" id="toc-nature-relatedness" class="nav-link" data-scroll-target="#nature-relatedness"><span class="header-section-number">15.2.1</span> Nature relatedness ————————————————————–</a></li>
-  </ul></li>
-  </ul></li>
-  <li><a href="#calculate-overall-mean-nature-relatedness-score-based-on-six-questions" id="toc-calculate-overall-mean-nature-relatedness-score-based-on-six-questions" class="nav-link" data-scroll-target="#calculate-overall-mean-nature-relatedness-score-based-on-six-questions"><span class="header-section-number">16</span> Calculate overall mean nature-relatedness score based on six questions:</a></li>
-  <li><a href="#create-lowmedhigh-bins-based-on-mean-and-1-1-standard-deviation" id="toc-create-lowmedhigh-bins-based-on-mean-and-1-1-standard-deviation" class="nav-link" data-scroll-target="#create-lowmedhigh-bins-based-on-mean-and-1-1-standard-deviation"><span class="header-section-number">17</span> Create low/med/high bins based on Mean and +1/-1 Standard Deviation</a>
-  <ul class="collapse">
-  <li><a href="#spirituality-scale" id="toc-spirituality-scale" class="nav-link" data-scroll-target="#spirituality-scale"><span class="header-section-number">17.0.1</span> Spirituality scale ————————————————————–</a></li>
-  </ul></li>
-  <li><a href="#calculate-overall-mean-spirituality-score-based-on-six-questions" id="toc-calculate-overall-mean-spirituality-score-based-on-six-questions" class="nav-link" data-scroll-target="#calculate-overall-mean-spirituality-score-based-on-six-questions"><span class="header-section-number">18</span> Calculate overall mean spirituality score based on six questions:</a></li>
-  <li><a href="#create-lowmedhigh-bins-based-on-mean-and-1-1-standard-deviation-1" id="toc-create-lowmedhigh-bins-based-on-mean-and-1-1-standard-deviation-1" class="nav-link" data-scroll-target="#create-lowmedhigh-bins-based-on-mean-and-1-1-standard-deviation-1"><span class="header-section-number">19</span> Create low/med/high bins based on Mean and +1/-1 Standard Deviation</a></li>
+  <li><a href="#loading-in-some-data" id="toc-loading-in-some-data" class="nav-link active" data-scroll-target="#loading-in-some-data"><span class="header-section-number">2.1</span> Loading in some data</a></li>
+  <li><a href="#how-can-you-ask-about-religion" id="toc-how-can-you-ask-about-religion" class="nav-link" data-scroll-target="#how-can-you-ask-about-religion"><span class="header-section-number">2.2</span> How can you ask about religion?</a></li>
   <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
   </ul>
 </nav>
@@ -273,8 +240,8 @@ div.csl-indent {
 <p>For this chapter, I’m going to walk you through a data set that a colleague (Charles Ogunbode) and I collected in 2021. Another problem with smaller, more selective samples is that researchers can often undersample minoritised ethnic groups. This is particularly the case with climate change research. Until the time we conducted this research, there had not been a single study investigating the specific experiences of people of colour in relation to climate change in the UK. Past researchers had been content to work with large samples, and assumed that if they had done 1000 surveys and 50 of these were completed by people of colour, they could “tick” the box. But 5% is actually well below levels of representation in the UK generally, and even more sharply the case for specific communities. And if we bear in mind that non-white respondents are (of course!) a highly heterogenous group, we’re even more behind in terms of collecting data that can improve our knowledge. Up until recently researchers just haven’t been paying close enough attention to catch the significant neglect of the empirical field that this represents.</p>
 <p>While I’ve framed my comments above in terms of climate change research, it is also the case that, especially in diverse societies like the USA, Canada, the UK etc., paying attention to non-majority groups and people and communities of colour automatically draws in a strongly religious sample. This is highlighted in one recent study done in the UK, the “<a href="https://www.cam.ac.uk/stories/black-british-voices-report">Black British Voices Report</a>” in which the researchers observed that “84% of respondents described themselves as religious and/or spiritual”. My comments above in terms of controlling for other factors remains important here - these same researchers also note that “despire their significant important to the lives of Black Britons, only 7% of survey respondents reported that their religion was more defining of their identity than their race”.</p>
 <p>We’ve decided to open up access to our data and I’m highlighting it in this book because it’s a unique opportunitiy to explore a dataset that emphasises diversity from the start, and by extension, provides some really interesting ways to use data science techniques to explore religion in the UK.</p>
-<section id="loading-in-some-data" class="level1" data-number="3">
-<h1 data-number="3"><span class="header-section-number">3</span> Loading in some data</h1>
+<section id="loading-in-some-data" class="level2" data-number="2.1">
+<h2 data-number="2.1" class="anchored" data-anchor-id="loading-in-some-data"><span class="header-section-number">2.1</span> Loading in some data</h2>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># R Setup -----------------------------------------------------------------</span></span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">setwd</span>(<span class="st">"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion"</span>)</span>
@@ -290,8 +257,8 @@ div.csl-indent {
 </div>
 <p>The first thing to note here is that we’ve drawn in a different type of data file, this time from an <code>.sav</code> file, usully produced by the statistics software package SPSS. This uses a different R Library (I use <code>haven</code> for this). The upside is that in some cases where you have survey data with both a code and a value like “1” is eqivalent to “very much agree” this will preserve both in the R dataframe that is created. Now that you’ve loaded in data, you have a new R dataframe called “climate_experience_data” with a lot of columns with just under 1000 survey responses.</p>
 </section>
-<section id="how-can-you-ask-about-religion" class="level1" data-number="4">
-<h1 data-number="4"><span class="header-section-number">4</span> How can you ask about religion?</h1>
+<section id="how-can-you-ask-about-religion" class="level2" data-number="2.2">
+<h2 data-number="2.2" class="anchored" data-anchor-id="how-can-you-ask-about-religion"><span class="header-section-number">2.2</span> How can you ask about religion?</h2>
 <p>One of the challenges we faced when running this study is how to gather responsible data from surveys regarding religious identity. We’ll dive into this in depth as we do analysis and look at some of the agreements and conflicts in terms of respondent attribution. Just to set the stage, we used the following kinds of question to ask about religion and spirituality:</p>
 <ol type="1">
 <li>Question 56 asks respondents simply, “What is your religion?” and then provides a range of possible answers. We included follow-up questions regarding denomination for respondents who indicated they were “Christian” or “Muslim”. For respondents who ticked “Christian” we asked, “What is your denomination?” nad for respondents who ticked “Muslim” we asked “Which of the following would you identify with?” and then left a range of possible options which could be ticked such as “Sunni,” “Shia,” “Sufi” etc.</li>
@@ -345,15 +312,15 @@ So <em>who’s</em> religious?
 <dl class="code-annotation-container-grid">
 <dt data-target-cell="annotated-cell-4" data-target-annotation="1">1</dt>
 <dd>
-<span data-code-annotation="1" data-code-lines="2" data-code-cell="annotated-cell-4">First we generate new a dataframe with sums per category and</span>
+<span data-code-annotation="1" data-code-cell="annotated-cell-4" data-code-lines="2">First we generate new a dataframe with sums per category and</span>
 </dd>
 <dt data-target-cell="annotated-cell-4" data-target-annotation="2">2</dt>
 <dd>
-<span data-code-annotation="2" data-code-lines="3" data-code-cell="annotated-cell-4">…sort in descending order</span>
+<span data-code-annotation="2" data-code-cell="annotated-cell-4" data-code-lines="3">…sort in descending order</span>
 </dd>
 <dt data-target-cell="annotated-cell-4" data-target-annotation="3">3</dt>
 <dd>
-<span data-code-annotation="3" data-code-lines="5" data-code-cell="annotated-cell-4">Then we add new column with percentages based on the sums you’ve just generated</span>
+<span data-code-annotation="3" data-code-cell="annotated-cell-4" data-code-lines="5">Then we add new column with percentages based on the sums you’ve just generated</span>
 </dd>
 </dl>
 </div>
@@ -403,82 +370,220 @@ So <em>who’s</em> religious?
 <span id="annotated-cell-7-13"><a href="#annotated-cell-7-13" aria-hidden="true" tabindex="-1"></a></span>
 <span id="annotated-cell-7-14"><a href="#annotated-cell-7-14" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"chart.png"</span>, <span class="at">plot=</span>plot1, <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">10</span>, <span class="at">units=</span><span class="fu">c</span>(<span class="st">"in"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<p>Use mutate to put “prefer not to say” at the bottom # Info here: https://r4ds.had.co.nz/factors.html#modifying-factor-levels</p>
-</section>
-<section id="q56-follow-ups" class="level1" data-number="5">
-<h1 data-number="5"><span class="header-section-number">5</span> Q56 follow-ups</h1>
-<p>caption &lt;- “Christian Denomination” # TODO: copy plot above for Q56 to add two additional plots using climate_experience_data_named<span class="math inline">\(Q56b and climate_experience_data_named\)</span>Q56c # Religious Affiliation b - Christian Denomination Subquestion christian_denomination &lt;- qualtrics_process_single_multiple_choice(climate_experience_data_named<span class="math inline">\(Q56b) christian_denomination_table &lt;- chart_single_result_flextable(climate_experience_data_named\)</span>Q56b, desc(Count)) christian_denomination_table save_as_docx(christian_denomination_table, path = “./figures/q56_religious_affiliation_xn_denomination.docx”)</p>
-<p>christian_denomination_hi &lt;- filter(climate_experience_data_named, Q56 == “Christian”, Q57_bin == “high”) christian_denomination_hi &lt;- qualtrics_process_single_multiple_choice(christian_denomination_hi$Q56b) christian_denomination_hi</p>
-</section>
-<section id="religious-affiliation-c---muslim-denomination-subquestion" class="level1" data-number="6">
-<h1 data-number="6"><span class="header-section-number">6</span> Religious Affiliation c - Muslim Denomination Subquestion</h1>
-<p>caption &lt;- “Islamic Identity” # Should the label be different than income since the data examined is the Affiliation? # TODO: adjust plot to factor using numbered responses on this question (perhaps also above) religious_affiliationc &lt;- qualtrics_process_single_multiple_choice(climate_experience_data_named<span class="math inline">\(Q56c) religious_affiliationc_plot &lt;- plot_horizontal_bar(religious_affiliationc) religious_affiliationc_plot &lt;- religious_affiliationc_plot + labs(caption = caption, x = "", y = "") religious_affiliationc_plot ggsave("figures/q56c_religious_affiliation.png", width = 20, height = 10, units = "cm") religious_affiliationc_table &lt;- chart_single_result_flextable(climate_experience_data_named\)</span>Q56c, Count) religious_affiliationc_table save_as_docx(religious_affiliationc_table, path = “./figures/q56_religious_affiliation_islam.docx”)</p>
-</section>
-<section id="q57" class="level1" data-number="7">
-<h1 data-number="7"><span class="header-section-number">7</span> Q57</h1>
-</section>
-<section id="religiosity" class="level1" data-number="8">
-<h1 data-number="8"><span class="header-section-number">8</span> Religiosity</h1>
-<p>caption &lt;- “Respondent Religiosity” religiosity &lt;- qualtrics_process_single_multiple_choice(as.character(climate_experience_data_named<span class="math inline">\(Q57_1)) religiosity_plot &lt;- plot_horizontal_bar(religiosity) religiosity_plot &lt;- religiosity_plot + labs(caption = caption, x = "", y = "") religiosity_plot ggsave("figures/q57_religiosity_plot.png", width = 20, height = 10, units = "cm") religiosity_table &lt;- chart_single_result_flextable(climate_experience_data_named\)</span>Q57_1, desc(Variable)) religiosity_table save_as_docx(religious_affiliationc_table, path = “./figures/q57_religiousity.docx”)</p>
-</section>
-<section id="q58" class="level1" data-number="9">
-<h1 data-number="9"><span class="header-section-number">9</span> Q58</h1>
-<p>caption &lt;- “Respondent Attendance of Religious Services” religious_service_attend &lt;- qualtrics_process_single_multiple_choice(climate_experience_data_named<span class="math inline">\(Q58) religious_service_attend_plot &lt;- plot_horizontal_bar(religious_service_attend) religious_service_attend_plot &lt;- religious_service_attend_plot + labs(title = caption, x = "", y = "") religious_service_attend_plot ggsave("figures/q58_religious_service_attend.png", width = 20, height = 10, units = "cm") religious_service_attend_table &lt;- chart_single_result_flextable(climate_experience_data_named\)</span>Q58, Count) religious_service_attend_table save_as_docx(religious_service_attend_table, path = “./figures/q58_religious_service_attend.docx”)</p>
-</section>
-<section id="faceted-plot-working-with-3x3-grid" class="level1" data-number="10">
-<h1 data-number="10"><span class="header-section-number">10</span> Faceted plot working with 3x3 grid</h1>
-<p>df &lt;- select(climate_experience_data, Q52_bin, Q53_bin, Q57_bin, Q58) names(df) &lt;- c(“Q52_bin”, “Q53_bin”, “Q57_bin”, “response”) facet_names &lt;- c(<code>Q52_bin</code> = “Spirituality”, <code>Q53_bin</code> = “Politics L/R”, <code>Q57_bin</code> = “Religiosity”, <code>low</code>=“low”, <code>medium</code>=“medium”, <code>high</code>=“high”) facet_labeller &lt;- function(variable,value){return(facet_names[value])} df<span class="math inline">\(response &lt;- factor(df\)</span>response, ordered = TRUE, levels = c(“1”, “2”, “3”, “4”, “5”)) df<span class="math inline">\(response &lt;- fct_recode(df\)</span>response, “More than once a week” = “1”, “Once a week” = “2”, “At least once a month” = “3”, “Only on special holy days” = “4”, “Never” = “5”) df %&gt;% # we need to get the data including facet info in long format, so we use pivot_longer() pivot_longer(!response, names_to = “bin_name”, values_to = “b”) %&gt;% # add counts for plot below count(response, bin_name, b) %&gt;% group_by(bin_name,b) %&gt;% mutate(perc=paste0(round(n*100/sum(n),1),“%”)) %&gt;% # run ggplot ggplot(aes(x = n, y = ““, fill = response)) + geom_col(position=position_fill(), aes(fill=response)) + geom_text(aes(label = perc), position = position_fill(vjust=.5), size=2) + scale_fill_brewer(palette =”Dark2”, type = “qual”) + scale_x_continuous(labels = scales::percent_format()) + facet_grid(vars(b), vars(bin_name), labeller=as_labeller(facet_names)) + labs(caption = caption, x = ““, y =”“) + guides(fill = guide_legend(title = NULL)) ggsave(”figures/q58_faceted.png”, width = 30, height = 10, units = “cm”)</p>
-</section>
-<section id="q59" class="level1" data-number="11">
-<h1 data-number="11"><span class="header-section-number">11</span> Q59</h1>
-<p>caption &lt;- “Respondent Prayer Outside of Religious Services” prayer &lt;- qualtrics_process_single_multiple_choice(climate_experience_data_named<span class="math inline">\(Q59) prayer_plot &lt;- plot_horizontal_bar(prayer) prayer_plot &lt;- prayer_plot + labs(caption = caption, x = "", y = "") prayer_plot ggsave("figures/q59_prayer.png", width = 20, height = 10, units = "cm") prayer_table &lt;- chart_single_result_flextable(climate_experience_data_named\)</span>Q59, Count) prayer_table save_as_docx(prayer_table, path = “./figures/q59_prayer.docx”)</p>
-</section>
-<section id="faceted-plot-working-with-3x3-grid-1" class="level1" data-number="12">
-<h1 data-number="12"><span class="header-section-number">12</span> Faceted plot working with 3x3 grid</h1>
-<p>df &lt;- select(climate_experience_data, Q52_bin, Q53_bin, Q57_bin, Q59) names(df) &lt;- c(“Q52_bin”, “Q53_bin”, “Q57_bin”, “response”) facet_names &lt;- c(<code>Q52_bin</code> = “Spirituality”, <code>Q53_bin</code> = “Politics L/R”, <code>Q57_bin</code> = “Religiosity”, <code>low</code>=“low”, <code>medium</code>=“medium”, <code>high</code>=“high”) facet_labeller &lt;- function(variable,value){return(facet_names[value])} df<span class="math inline">\(response &lt;- factor(df\)</span>response, ordered = TRUE, levels = c(“1”, “2”, “3”, “4”, “5”)) df<span class="math inline">\(response &lt;- fct_recode(df\)</span>response, “More than once a week” = “1”, “Once a week” = “2”, “At least once a month” = “3”, “Only on special holy days” = “4”, “Never” = “5”) df %&gt;% # we need to get the data including facet info in long format, so we use pivot_longer() pivot_longer(!response, names_to = “bin_name”, values_to = “b”) %&gt;% # add counts for plot below count(response, bin_name, b) %&gt;% group_by(bin_name,b) %&gt;% mutate(perc=paste0(round(n*100/sum(n),1),“%”)) %&gt;% # run ggplot ggplot(aes(x = n, y = ““, fill = response)) + geom_col(position=position_fill(), aes(fill=response)) + geom_text(aes(label = perc), position = position_fill(vjust=.5), size=2) + scale_fill_brewer(palette =”Dark2”, type = “qual”) + scale_x_continuous(labels = scales::percent_format()) + facet_grid(vars(b), vars(bin_name), labeller=as_labeller(facet_names)) + labs(caption = caption, x = ““, y =”“) + guides(fill = guide_legend(title = NULL)) ggsave(”figures/q59_faceted.png”, width = 30, height = 10, units = “cm”)</p>
-</section>
-<section id="comparing-with-attitudes-surrounding-climate-change" class="level1" data-number="13">
-<h1 data-number="13"><span class="header-section-number">13</span> Comparing with attitudes surrounding climate change</h1>
-</section>
-<section id="q6" class="level1" data-number="14">
-<h1 data-number="14"><span class="header-section-number">14</span> Q6</h1>
-<p>q6_data &lt;- qualtrics_process_single_multiple_choice_unsorted_streamlined(climate_experience_data$Q6)</p>
-<p>title &lt;- “Do you think the climate is changing?”</p>
-<p>level_order &lt;- c(“Don<e2>&lt;80&gt;&lt;99&gt;t know”, “Definitely not changing”, “Probably not changing”, “Probably changing”, “Definitely changing”) ## code if a specific palette is needed for matching fill = wheel(ochre, num = as.integer(count(q6_data[1]))) # make plot q6_data_plot &lt;- ggplot(q6_data, aes(x = n, y = response, fill = fill)) + geom_col(colour = “white”) + ## add percentage labels geom_text(aes(label = perc), ## make labels left-aligned and white hjust = 1, colour = “black”, size=4) + # use nudge_x = 30, to shift position ## reduce spacing between labels and bars scale_fill_identity(guide = “none”) + ## get rid of all elements except y axis labels + adjust plot margin theme_ipsum_rc() + theme(plot.margin = margin(rep(15, 4))) + easy_center_title() + # with thanks for helpful info on doing wrap here: https://stackoverflow.com/questions/21878974/wrap-long-axis-labels-via-labeller-label-wrap-in-ggplot2 scale_y_discrete(labels = wrap_format(30), limits = level_order) + theme(plot.title = element_text(size =18, hjust = 0.5), axis.text.y = element_text(size =16)) + labs(title = title, x = ““, y =”“)</e2></p>
-<p>q6_data_plot</p>
-<p>ggsave(“figures/q6.png”, width = 18, height = 12, units = “cm”)</p>
-</section>
-<section id="subsetting" class="level1" data-number="15">
-<h1 data-number="15"><span class="header-section-number">15</span> Subsetting</h1>
-<section id="q57-subsetting-based-on-religiosity" class="level2" data-number="15.1">
-<h2 data-number="15.1" class="anchored" data-anchor-id="q57-subsetting-based-on-religiosity"><span class="header-section-number">15.1</span> Q57 subsetting based on Religiosity ————————————————————–</h2>
-<p>climate_experience_data &lt;- climate_experience_data %&gt;% mutate( Q57_bin = case_when( Q57_1 &gt; mean(Q57_1) + sd(Q57_1) ~ “high”, Q57_1 &lt; mean(Q57_1) - sd(Q57_1) ~ “low”, TRUE ~ “medium” ) %&gt;% factor(levels = c(“low”, “medium”, “high”)) )</p>
-</section>
-<section id="subsetting-based-on-spirituality" class="level2" data-number="15.2">
-<h2 data-number="15.2" class="anchored" data-anchor-id="subsetting-based-on-spirituality"><span class="header-section-number">15.2</span> Subsetting based on Spirituality ————————————————————–</h2>
-<section id="nature-relatedness" class="level3" data-number="15.2.1">
-<h3 data-number="15.2.1" class="anchored" data-anchor-id="nature-relatedness"><span class="header-section-number">15.2.1</span> Nature relatedness ————————————————————–</h3>
-</section>
-</section>
-</section>
-<section id="calculate-overall-mean-nature-relatedness-score-based-on-six-questions" class="level1" data-number="16">
-<h1 data-number="16"><span class="header-section-number">16</span> Calculate overall mean nature-relatedness score based on six questions:</h1>
-<p>climate_experience_data$Q51_score &lt;- rowMeans(select(climate_experience_data, Q51_remote_vacation:Q51_heritage))</p>
-</section>
-<section id="create-lowmedhigh-bins-based-on-mean-and-1-1-standard-deviation" class="level1" data-number="17">
-<h1 data-number="17"><span class="header-section-number">17</span> Create low/med/high bins based on Mean and +1/-1 Standard Deviation</h1>
-<p>climate_experience_data &lt;- climate_experience_data %&gt;% mutate( Q51_bin = case_when( Q51_score &gt; mean(Q51_score) + sd(Q51_score) ~ “high”, Q51_score &lt; mean(Q51_score) - sd(Q51_score) ~ “low”, TRUE ~ “medium” ) %&gt;% factor(levels = c(“low”, “medium”, “high”)) )</p>
-<section id="spirituality-scale" class="level3" data-number="17.0.1">
-<h3 data-number="17.0.1" class="anchored" data-anchor-id="spirituality-scale"><span class="header-section-number">17.0.1</span> Spirituality scale ————————————————————–</h3>
-</section>
-</section>
-<section id="calculate-overall-mean-spirituality-score-based-on-six-questions" class="level1" data-number="18">
-<h1 data-number="18"><span class="header-section-number">18</span> Calculate overall mean spirituality score based on six questions:</h1>
-<p>climate_experience_data$Q52_score &lt;- rowMeans(select(climate_experience_data, Q52a_1:Q52f_1))</p>
-</section>
-<section id="create-lowmedhigh-bins-based-on-mean-and-1-1-standard-deviation-1" class="level1" data-number="19">
-<h1 data-number="19"><span class="header-section-number">19</span> Create low/med/high bins based on Mean and +1/-1 Standard Deviation</h1>
-<p>climate_experience_data &lt;- climate_experience_data %&gt;% mutate( Q52_bin = case_when( Q52_score &gt; mean(Q52_score) + sd(Q52_score) ~ “high”, Q52_score &lt; mean(Q52_score) - sd(Q52_score) ~ “low”, TRUE ~ “medium” ) %&gt;% factor(levels = c(“low”, “medium”, “high”)) )</p>
+
+<!--
+Use mutate to put "prefer not to say" at the bottom
+# Info here: https://r4ds.had.co.nz/factors.html#modifying-factor-levels
+
+
+# Q56 follow-ups
+caption <- "Christian Denomination"
+# TODO: copy plot above for Q56 to add two additional plots using climate_experience_data_named$Q56b and climate_experience_data_named$Q56c
+# Religious Affiliation b - Christian Denomination Subquestion
+christian_denomination <- qualtrics_process_single_multiple_choice(climate_experience_data_named$Q56b)
+christian_denomination_table <- chart_single_result_flextable(climate_experience_data_named$Q56b, desc(Count))
+christian_denomination_table
+save_as_docx(christian_denomination_table, path = "./figures/q56_religious_affiliation_xn_denomination.docx")
+
+christian_denomination_hi <- filter(climate_experience_data_named, Q56 == "Christian", Q57_bin == "high")
+christian_denomination_hi <- qualtrics_process_single_multiple_choice(christian_denomination_hi$Q56b)
+christian_denomination_hi
+
+# Religious Affiliation c - Muslim Denomination Subquestion
+caption <- "Islamic Identity"
+# Should the label be different than income since the data examined is the Affiliation?
+# TODO: adjust plot to factor using numbered responses on this question (perhaps also above)
+religious_affiliationc <- qualtrics_process_single_multiple_choice(climate_experience_data_named$Q56c)
+religious_affiliationc_plot <- plot_horizontal_bar(religious_affiliationc)
+religious_affiliationc_plot <- religious_affiliationc_plot + labs(caption = caption, x = "", y = "")
+religious_affiliationc_plot
+ggsave("figures/q56c_religious_affiliation.png", width = 20, height = 10, units = "cm")
+religious_affiliationc_table <- chart_single_result_flextable(climate_experience_data_named$Q56c, Count)
+religious_affiliationc_table
+save_as_docx(religious_affiliationc_table, path = "./figures/q56_religious_affiliation_islam.docx")
+
+# Q57
+# Religiosity
+caption <- "Respondent Religiosity"
+religiosity <- qualtrics_process_single_multiple_choice(as.character(climate_experience_data_named$Q57_1))
+religiosity_plot <- plot_horizontal_bar(religiosity)
+religiosity_plot <- religiosity_plot + labs(caption = caption, x = "", y = "")
+religiosity_plot
+ggsave("figures/q57_religiosity_plot.png", width = 20, height = 10, units = "cm")
+religiosity_table <- chart_single_result_flextable(climate_experience_data_named$Q57_1, desc(Variable))
+religiosity_table
+save_as_docx(religious_affiliationc_table, path = "./figures/q57_religiousity.docx")
+
+# Q58
+
+caption <- "Respondent Attendance of Religious Services"
+religious_service_attend <- qualtrics_process_single_multiple_choice(climate_experience_data_named$Q58)
+religious_service_attend_plot <- plot_horizontal_bar(religious_service_attend)
+religious_service_attend_plot <- religious_service_attend_plot + labs(title = caption, x = "", y = "")
+religious_service_attend_plot
+ggsave("figures/q58_religious_service_attend.png", width = 20, height = 10, units = "cm")
+religious_service_attend_table <- chart_single_result_flextable(climate_experience_data_named$Q58, Count)
+religious_service_attend_table
+save_as_docx(religious_service_attend_table, path = "./figures/q58_religious_service_attend.docx")
+
+# Faceted plot working with 3x3 grid
+df <- select(climate_experience_data, Q52_bin, Q53_bin, Q57_bin, Q58)
+names(df) <- c("Q52_bin", "Q53_bin", "Q57_bin", "response")
+facet_names <- c(`Q52_bin` = "Spirituality", `Q53_bin` = "Politics L/R", `Q57_bin` = "Religiosity", `low`="low", `medium`="medium", `high`="high")
+facet_labeller <- function(variable,value){return(facet_names[value])}
+df$response <- factor(df$response, ordered = TRUE, levels = c("1", "2", "3", "4", "5"))
+df$response <- fct_recode(df$response, "More than once a week" = "1", "Once a week" = "2", "At least once a month" = "3", "Only on special holy days" = "4", "Never" = "5")
+df %>% 
+  # we need to get the data including facet info in long format, so we use pivot_longer()
+  pivot_longer(!response, names_to = "bin_name", values_to = "b") %>% 
+  # add counts for plot below
+  count(response, bin_name, b) %>%
+  group_by(bin_name,b) %>%
+  mutate(perc=paste0(round(n*100/sum(n),1),"%")) %>% 
+  # run ggplot
+  ggplot(aes(x = n, y = "", fill = response)) +
+  geom_col(position=position_fill(), aes(fill=response)) +
+  geom_text(aes(label = perc), position = position_fill(vjust=.5), size=2) +
+  scale_fill_brewer(palette = "Dark2", type = "qual") +
+  scale_x_continuous(labels = scales::percent_format()) +
+  facet_grid(vars(b), vars(bin_name), labeller=as_labeller(facet_names)) + 
+  labs(caption = caption, x = "", y = "") + 
+  guides(fill = guide_legend(title = NULL))
+ggsave("figures/q58_faceted.png", width = 30, height = 10, units = "cm")
+
+# Q59
+
+caption <- "Respondent Prayer Outside of Religious Services"
+prayer <- qualtrics_process_single_multiple_choice(climate_experience_data_named$Q59)
+prayer_plot <- plot_horizontal_bar(prayer)
+prayer_plot <- prayer_plot + labs(caption = caption, x = "", y = "")
+prayer_plot
+ggsave("figures/q59_prayer.png", width = 20, height = 10, units = "cm")
+prayer_table <- chart_single_result_flextable(climate_experience_data_named$Q59, Count)
+prayer_table
+save_as_docx(prayer_table, path = "./figures/q59_prayer.docx")
+
+# Faceted plot working with 3x3 grid
+df <- select(climate_experience_data, Q52_bin, Q53_bin, Q57_bin, Q59)
+names(df) <- c("Q52_bin", "Q53_bin", "Q57_bin", "response")
+facet_names <- c(`Q52_bin` = "Spirituality", `Q53_bin` = "Politics L/R", `Q57_bin` = "Religiosity", `low`="low", `medium`="medium", `high`="high")
+facet_labeller <- function(variable,value){return(facet_names[value])}
+df$response <- factor(df$response, ordered = TRUE, levels = c("1", "2", "3", "4", "5"))
+df$response <- fct_recode(df$response, "More than once a week" = "1", "Once a week" = "2", "At least once a month" = "3", "Only on special holy days" = "4", "Never" = "5")
+df %>% 
+  # we need to get the data including facet info in long format, so we use pivot_longer()
+  pivot_longer(!response, names_to = "bin_name", values_to = "b") %>% 
+  # add counts for plot below
+  count(response, bin_name, b) %>%
+  group_by(bin_name,b) %>%
+  mutate(perc=paste0(round(n*100/sum(n),1),"%")) %>% 
+  # run ggplot
+  ggplot(aes(x = n, y = "", fill = response)) +
+  geom_col(position=position_fill(), aes(fill=response)) +
+  geom_text(aes(label = perc), position = position_fill(vjust=.5), size=2) +
+  scale_fill_brewer(palette = "Dark2", type = "qual") +
+  scale_x_continuous(labels = scales::percent_format()) +
+  facet_grid(vars(b), vars(bin_name), labeller=as_labeller(facet_names)) + 
+  labs(caption = caption, x = "", y = "") + 
+  guides(fill = guide_legend(title = NULL))
+ggsave("figures/q59_faceted.png", width = 30, height = 10, units = "cm")
+
+
+
+
+
+
+
+
+# Comparing with attitudes surrounding climate change
+
+
+# Q6
+
+q6_data <- qualtrics_process_single_multiple_choice_unsorted_streamlined(climate_experience_data$Q6)
+
+title <- "Do you think the climate is changing?"
+
+level_order <- c("Don<e2><80><99>t know",
+                 "Definitely not changing",
+                 "Probably not changing", 
+                 "Probably changing",
+                 "Definitely changing")
+## code if a specific palette is needed for matching
+fill = wheel(ochre, num = as.integer(count(q6_data[1])))
+# make plot
+q6_data_plot <- ggplot(q6_data, aes(x = n, y = response, fill = fill)) +
+  geom_col(colour = "white") +
+  ## add percentage labels
+  geom_text(aes(label = perc),
+            ## make labels left-aligned and white
+            hjust = 1, colour = "black", size=4) + # use nudge_x = 30, to shift position
+  ## reduce spacing between labels and bars
+  scale_fill_identity(guide = "none") +
+  ## get rid of all elements except y axis labels + adjust plot margin
+  theme_ipsum_rc() +
+  theme(plot.margin = margin(rep(15, 4))) +
+  easy_center_title() + 
+  # with thanks for helpful info on doing wrap here: https://stackoverflow.com/questions/21878974/wrap-long-axis-labels-via-labeller-label-wrap-in-ggplot2
+  scale_y_discrete(labels = wrap_format(30), limits = level_order) + 
+  theme(plot.title = element_text(size =18, hjust = 0.5), axis.text.y = element_text(size =16)) +
+  labs(title = title, x = "", y = "")
+
+q6_data_plot 
+
+ggsave("figures/q6.png", width = 18, height = 12, units = "cm")
+
+
+
+
+
+
+
+
+
+# Subsetting
+
+## Q57 subsetting based on Religiosity --------------------------------------------------------------
+climate_experience_data <- climate_experience_data %>%
+  mutate(
+    Q57_bin = case_when(
+      Q57_1 > mean(Q57_1) + sd(Q57_1) ~ "high",
+      Q57_1 < mean(Q57_1) - sd(Q57_1) ~ "low",
+      TRUE ~ "medium"
+    ) %>% factor(levels = c("low", "medium", "high"))
+  )
+
+## Subsetting based on Spirituality --------------------------------------------------------------
+
+### Nature relatedness --------------------------------------------------------------
+# Calculate overall mean nature-relatedness score based on six questions:
+climate_experience_data$Q51_score <- rowMeans(select(climate_experience_data, Q51_remote_vacation:Q51_heritage))
+
+# Create low/med/high bins based on Mean and +1/-1 Standard Deviation
+climate_experience_data <- climate_experience_data %>%
+  mutate(
+    Q51_bin = case_when(
+      Q51_score > mean(Q51_score) + sd(Q51_score) ~ "high",
+      Q51_score < mean(Q51_score) - sd(Q51_score) ~ "low",
+      TRUE ~ "medium"
+    ) %>% factor(levels = c("low", "medium", "high"))
+  )
+
+### Spirituality scale  --------------------------------------------------------------
+# Calculate overall mean spirituality score based on six questions:
+climate_experience_data$Q52_score <- rowMeans(select(climate_experience_data, Q52a_1:Q52f_1))
+
+# Create low/med/high bins based on Mean and +1/-1 Standard Deviation
+climate_experience_data <- climate_experience_data %>%
+  mutate(
+    Q52_bin = case_when(
+      Q52_score > mean(Q52_score) + sd(Q52_score) ~ "high",
+      Q52_score < mean(Q52_score) - sd(Q52_score) ~ "low",
+      TRUE ~ "medium"
+    ) %>% factor(levels = c("low", "medium", "high"))
+  )
+
+-->
 <div class="callout callout-style-default callout-tip callout-titled">
 <div class="callout-header d-flex align-content-center">
 <div class="callout-icon-container">
@@ -519,8 +624,8 @@ What is Secularisation?
 </div>
 </div>
 </section>
-<section id="references" class="level1 unnumbered">
-<h1 class="unnumbered">References</h1>
+<section id="references" class="level2 unnumbered">
+<h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
 <div id="refs" role="list" style="display: none">
 
 </div>
diff --git a/docs/chapter_3.html b/docs/chapter_3.html
index 888493d..156d3ba 100644
--- a/docs/chapter_3.html
+++ b/docs/chapter_3.html
@@ -155,12 +155,6 @@ div.csl-indent {
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./index.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Introduction: Hacking Religion</span></a>
   </div>
 </li>
@@ -216,8 +210,8 @@ div.csl-indent {
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#administrative-shapes---the-uk" id="toc-administrative-shapes---the-uk" class="nav-link active" data-scroll-target="#administrative-shapes---the-uk"><span class="header-section-number">4</span> Administrative shapes - the UK</a></li>
-  <li><a href="#load-in-ordnance-survey-openmap-points-data" id="toc-load-in-ordnance-survey-openmap-points-data" class="nav-link" data-scroll-target="#load-in-ordnance-survey-openmap-points-data"><span class="header-section-number">5</span> Load in Ordnance Survey OpenMap Points Data</a></li>
+  <li><a href="#administrative-shapes---the-uk" id="toc-administrative-shapes---the-uk" class="nav-link active" data-scroll-target="#administrative-shapes---the-uk"><span class="header-section-number">3.1</span> Administrative shapes - the UK</a></li>
+  <li><a href="#load-in-ordnance-survey-openmap-points-data" id="toc-load-in-ordnance-survey-openmap-points-data" class="nav-link" data-scroll-target="#load-in-ordnance-survey-openmap-points-data"><span class="header-section-number">3.2</span> Load in Ordnance Survey OpenMap Points Data</a></li>
   <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
   </ul>
 </nav>
@@ -249,8 +243,8 @@ div.csl-indent {
 <p>The most complex aspect of point data relates to the ways that coordinates are encoded, as they will aways need to be associated with a coordinate reference system (CRS) which describes how they are situated with respect to the planet earth. The most common CRS is the WGS, though for our data sets we’ll also come into contact with the BGS, a specifically British coordinate reference system. There are dozens of CRS, usually mapping onto a specific geographical region. Bearing in mind the way that you need to use a CRS to understand how coordinates can be associated with specific parts of the earth, you can see how this is a bit like survey data, where you also need a “codebook” to understand what the specific response values map onto, e.g.&nbsp;a “1” means “strongly agree” and so on. We also saw, in a previous chapter, how some forms of data have the codebook already baked into the factor data, simplifying the process of interpreting the data. In a similar way, some types of geospatial data sets can also come with CRS “baked in” while we’ll need to define CRS for other types. Here are some of the most common types of geospatial data files:</p>
 <p>CSV: “comma separated values” a plain text file containing various coordinates Shapefile: a legacy file format, often still in use, but being replaced by others for a variety of good reasons. For more on this see [http://switchfromshapefile.org/] Geopackage: one of the more recent ways of packaging up geospatial data. Geopackages can contain a wide variety of different data and are easily portable. GeoJSON: a file format commonly used in other forms of coding, the “JSON” (an acronym for JavaScript Object Notation) is meant to be easily interchangeable across various platforms. GeoJSON is an augmented version of JSON data with coordinates added in.</p>
 <p>Now that you have a sense of some of the basic aspects of geospatial data, let’s dive in and do a bit of learning in action.</p>
-<section id="administrative-shapes---the-uk" class="level1" data-number="4">
-<h1 data-number="4"><span class="header-section-number">4</span> Administrative shapes - the UK</h1>
+<section id="administrative-shapes---the-uk" class="level2" data-number="3.1">
+<h2 data-number="3.1" class="anchored" data-anchor-id="administrative-shapes---the-uk"><span class="header-section-number">3.1</span> Administrative shapes - the UK</h2>
 <p>A good starting point is to aquire some adminstrative data. This is a way of referring to political boundaries, whether country borders or those of a local authority or some other administrative unit. For our purposes, we’re going to import several different types of administrative boundary which will be used at different points in our visualisations below. It’s worth noting that the data we use here was prepared to support the 2011 census, and make use of the shapefile format.</p>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(sf) <span class="sc">|&gt;</span> <span class="fu">suppressPackageStartupMessages</span>()</span>
@@ -310,8 +304,8 @@ i Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all
 </div>
 </div>
 </section>
-<section id="load-in-ordnance-survey-openmap-points-data" class="level1" data-number="5">
-<h1 data-number="5"><span class="header-section-number">5</span> Load in Ordnance Survey OpenMap Points Data</h1>
+<section id="load-in-ordnance-survey-openmap-points-data" class="level2" data-number="3.2">
+<h2 data-number="3.2" class="anchored" data-anchor-id="load-in-ordnance-survey-openmap-points-data"><span class="header-section-number">3.2</span> Load in Ordnance Survey OpenMap Points Data</h2>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Note: for more advanced reproducible scripts which demonstrate how these data surces have been </span></span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="co"># obtained, see the companion cookbook here: https://github.com/kidwellj/hacking_religion_cookbook/blob/main/ordnance_survey.R</span></span>
@@ -395,8 +389,8 @@ i Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all
 <p>Guides to geographies: https://rconsortium.github.io/censusguide/ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistical-geographies/</p>
 <p>Calculate proximity to pubs</p>
 </section>
-<section id="references" class="level1 unnumbered">
-<h1 class="unnumbered">References</h1>
+<section id="references" class="level2 unnumbered">
+<h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
 <div id="refs" role="list" style="display: none">
 
 </div>
diff --git a/docs/chapter_4.html b/docs/chapter_4.html
index 1edca7f..10e0ee6 100644
--- a/docs/chapter_4.html
+++ b/docs/chapter_4.html
@@ -121,12 +121,6 @@ div.csl-indent {
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./index.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Introduction: Hacking Religion</span></a>
   </div>
 </li>
diff --git a/docs/chapter_5.html b/docs/chapter_5.html
index d18ed30..c79e907 100644
--- a/docs/chapter_5.html
+++ b/docs/chapter_5.html
@@ -121,12 +121,6 @@ div.csl-indent {
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./index.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Introduction: Hacking Religion</span></a>
   </div>
 </li>
diff --git a/docs/index.html b/docs/index.html
index 8dbfba3..606fa94 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -32,7 +32,7 @@ ul.task-list li input[type="checkbox"] {
 <script src="site_libs/quarto-search/fuse.min.js"></script>
 <script src="site_libs/quarto-search/quarto-search.js"></script>
 <meta name="quarto:offset" content="./">
-<link href="./intro.html" rel="next">
+<link href="./chapter_1.html" rel="next">
 <script src="site_libs/quarto-html/quarto.js"></script>
 <script src="site_libs/quarto-html/popper.min.js"></script>
 <script src="site_libs/quarto-html/tippy.umd.min.js"></script>
@@ -75,7 +75,7 @@ ul.task-list li input[type="checkbox"] {
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./index.html">Preface</a></li></ol></nav>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./index.html">Introduction: Hacking Religion</a></li></ol></nav>
       <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
       </a>
       <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
@@ -103,12 +103,6 @@ ul.task-list li input[type="checkbox"] {
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./index.html" class="sidebar-item-text sidebar-link active">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Introduction: Hacking Religion</span></a>
   </div>
 </li>
@@ -164,7 +158,13 @@ ul.task-list li input[type="checkbox"] {
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#preface" id="toc-preface" class="nav-link active" data-scroll-target="#preface">Preface</a></li>
+  <li><a href="#introduction-hacking-religion" id="toc-introduction-hacking-religion" class="nav-link active" data-scroll-target="#introduction-hacking-religion">Introduction: Hacking Religion</a>
+  <ul class="collapse">
+  <li><a href="#why-this-book" id="toc-why-this-book" class="nav-link" data-scroll-target="#why-this-book">Why this book?</a></li>
+  <li><a href="#the-hacker-way" id="toc-the-hacker-way" class="nav-link" data-scroll-target="#the-hacker-way">The hacker way</a></li>
+  <li><a href="#learning-to-code-my-way" id="toc-learning-to-code-my-way" class="nav-link" data-scroll-target="#learning-to-code-my-way">Learning to code: my way</a></li>
+  <li><a href="#getting-set-up" id="toc-getting-set-up" class="nav-link" data-scroll-target="#getting-set-up">Getting set up</a></li>
+  </ul></li>
   </ul>
 </nav>
     </div>
@@ -200,12 +200,42 @@ ul.task-list li input[type="checkbox"] {
 
 </header>
 
-<section id="preface" class="level1 unnumbered">
-<h1 class="unnumbered">Preface</h1>
-<p>This is a Quarto book.</p>
-<p>To learn more about Quarto books visit <a href="https://quarto.org/docs/books" class="uri">https://quarto.org/docs/books</a>.</p>
+<section id="introduction-hacking-religion" class="level1 unnumbered">
+<h1 class="unnumbered">Introduction: Hacking Religion</h1>
+<section id="why-this-book" class="level2">
+<h2 class="anchored" data-anchor-id="why-this-book">Why this book?</h2>
+<p>Data science is quickly consolidating as a new field, with new tools and user communities emerging seemingly every week! At the same time the field of academic research has opened up into new interdisciplinary vistas, with experts crossing over into new fields, transgressing disciplinary boundaries and deploying tools in new and unexpected ways to develop knowledge. There are many gaps yet to be filled, but one which I found to be particularly glaring is the lack of applied data science documentation around the subject of religion. On one hand, scholars who are working with cutting edge theory seldom pick up the emerging tools of data science. On the other hand, data scientists rarely go beyond dabbling in religious themes. This book aims to bring these two things together: introducing the tools of data science in an applied way, whilst introducing some of the complexities and cutting edge theories which help us to conceptualise and frame our understanding of this knowledge.</p>
+</section>
+<section id="the-hacker-way" class="level2">
+<h2 class="anchored" data-anchor-id="the-hacker-way">The hacker way</h2>
+<p>It’s worth emphasising at the outset that this isn’t meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values, and social action. A fair bit of ink has been spilled in books, magazines, blogs, zines, and tweets unpacking what exactly it means to be a “hacker”. Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation.</p>
+<p>Back in the 1980s Steven Levy tried to capture some of this in his book “Hackers: Heroes of the Computer Revolution”. As Levy put it, the “hacker ethic” included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn’t just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. It can be a more substantial ethical code.</p>
+<p>This emphasis on ethics is especially important when we’re doing data science because this kind of research work will put you in positions of influence and grant you power over others. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows some sort of social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I’ve met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it’s possible that this can quickly get out of hand. It’s important to have a clear sense of what sorts of convictions guide your work in this field, a “hacker code” of sorts. With this in mind, I’d like to share with you my own set of principles:</p>
+<p>It never ceases to amaze me how often people think that, when they’re working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you’ve been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond our true apprehension of a situation.</p>
+<p>Presenting through “facts” an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you take up the hacker way, it’s vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I’ll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge help to build up a community of accountability. The upside of this is that it’s also a lot more fun and interesting to work alongside others.</p>
+<p>Far too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There’s also the potential that when we make choices based on reliable information drawn from everyday reality like this our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of “learning by doing” and it’s this approach that guides my approach in this book. This isn’t just a book about data analysis, I’m proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of the commons. It takes a bit more time to learn and set things up, but the upside is that you’ll gain access to a set of tools and a research philosophy which is much more powerful.</p>
+<p>Here’s a quick summary of these principles, which I’ll return to periodically as we work through the coding and data in this book:</p>
+<ol type="1">
+<li>Tell the truth: Be candid about your limits, use visualisation responsibly</li>
+<li>Work transparently: Open data, open code</li>
+<li>Work in community, draw others in by producing reproducible research</li>
+<li>Work with reality, learn by doing</li>
+</ol>
+</section>
+<section id="learning-to-code-my-way" class="level2">
+<h2 class="anchored" data-anchor-id="learning-to-code-my-way">Learning to code: my way</h2>
+<p>This guide is a little different from other textbooks targetting learning to code. I remember when I was first starting out, I went through a fair few guides, and they all tended to spend about 200 pages on various theoretical bits, how you form an integer, or data structures, subroutines, the logical structure of algorithms or whatever. It was usuallyweeks of reading before I got to actually <em>do</em> anything. I know some people may prefer this approach, but I prefer a problem-focussed approach to learning. Give me something that is broken, or a problem to solve, which engages the things I want to figure out and the motivation for learning just comes much more naturally. And we know from research in cognitive science that these kinds of problem-focussed approaches can tend to faciliate faster learning and better retention, so it’s not just my personal preference, but also justified! It will be helpful for you to be aware of this approach when you get into the book as it explains some of the editorial choices I’ve made and the way I’ve structured things. Each chapter focusses on a <em>problem</em> which is particularly salient for the use of data science to conduct research into religion. That problem will be my focal point, guiding choices of specific aspects of programming to introduce to you as we work our way around that data set and some of the crucial questions that arise in terms of how we handle it. If you find this approach unsatisfying, luckily there are a number of really terrific guides which lay things out slowly and methodically and I will explicitly signpost some of these along the way so that you can do a “deep dive” when you feel like it. Otherwise, I’ll take an accelerated approach to this introduction to data science in R. I expect that you will identify adjacent resources and perhaps even come up with your own creative approaches along the way, which incidentally is how real data science tends to work in practice.</p>
+<p>There are a range of terrific textbooks out there which cover all these elements in greater depth and more slowly. In particular, I’d recommend that many readers will want to check out Hadley Wickham’s “R For Data Science” book. I’ll include marginal notes in this guide pointing to sections of that book, and a few others which unpack the basic mechanics of R in more detail.</p>
+</section>
+<section id="getting-set-up" class="level2">
+<h2 class="anchored" data-anchor-id="getting-set-up">Getting set up</h2>
+<p>Every single tool, programming language and data set we refer to in this book is free and open source. These tools have been produced by professionals and volunteers who are passionate about data science and research and want to share it with the world, and in order to do this (and following the “hacker way”) they’ve made these tools freely available. This also means that you aren’t restricted to a specific proprietary, expensive, or unavailable piece of software to do this work. I’ll make a few opinionated recommendations here based on my own preferences and experience, but it’s really up to your own style and approach. In fact, given that this is an open source textbook, you can even propose additions to this chapter explaining other tools you’ve found that you want to share with others.</p>
+<p>There are, right now, primarily two languages that statisticians and data scientists use for this kind of programmatic data science: python and R. Each language has its merits and I won’t rehash the debates between various factions. For this book, we’ll be using the R language. This is, in part, because the R user community and libraries tend to scale a bit better for the work that I’m commending in this book. However, it’s entirely possible that one could use python for all these exercises, and perhaps in the future we’ll have volume two of this book outlining python approaches to the same operations.</p>
+<p>Bearing this in mind, the first step you’ll need to take is to download and install R. You can find instructions and install packages for a wide range of hardware on the The Comprehensive R Archive Network (or “CRAN”): https://cran.rstudio.com. Once you’ve installed R, you’ve got some choices to make about the kind of programming environment you’d like to use. You can just use a plain text editor like <code>textedit</code> to write your code and then execute your programs using the R software you’ve just installed. However, most users, myself included, tend to use an integrated development environment (or “IDE”). This is usually another software package with a guided user interface and some visual elements that make it faster to write and test your code. Some IDE packages, will have built-in reference tools so you can look up options for libraries you use in your code, they will allow you to visualise the results of your code execution, and perhaps most important of all, will enable you to execute your programs line by line so you can spot errors more quickly (we call this “debugging”). The two most popular IDE platforms for R coding at the time of writing this textbook are RStudio and Visual Studio. You should download and try out both and stick with your favourite, as the differences are largely aesthetic. I use a combination of RStudio and an enhanced plain text editor Sublime Text for my coding.</p>
+<p>Once you have R and your pick of an IDE, you are ready to go! Proceed to the next chapter and we’ll dive right in and get started!</p>
 
 
+</section>
 </section>
 
 </main> <!-- /main -->
@@ -446,8 +476,8 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
   <div class="nav-page nav-page-previous">
   </div>
   <div class="nav-page nav-page-next">
-      <a href="./intro.html" class="pagination-link">
-        <span class="nav-page-text">Introduction: Hacking Religion</span> <i class="bi bi-arrow-right-short"></i>
+      <a href="./chapter_1.html" class="pagination-link">
+        <span class="nav-page-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">The 2021 UK Census</span></span> <i class="bi bi-arrow-right-short"></i>
       </a>
   </div>
 </nav>
diff --git a/docs/references.html b/docs/references.html
index 9a81cb2..d4bc756 100644
--- a/docs/references.html
+++ b/docs/references.html
@@ -120,12 +120,6 @@ div.csl-indent {
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./index.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Introduction: Hacking Religion</span></a>
   </div>
 </li>
diff --git a/docs/search.json b/docs/search.json
index 6e5098a..b08cfa1 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -4,43 +4,36 @@
     "href": "index.html",
     "title": "Hacking Religion: TRS & Data Science in Action",
     "section": "",
-    "text": "Preface\nThis is a Quarto book.\nTo learn more about Quarto books visit https://quarto.org/docs/books."
+    "text": "Introduction: Hacking Religion"
   },
   {
-    "objectID": "intro.html#why-this-book",
-    "href": "intro.html#why-this-book",
-    "title": "Introduction: Hacking Religion",
+    "objectID": "index.html#why-this-book",
+    "href": "index.html#why-this-book",
+    "title": "Hacking Religion: TRS & Data Science in Action",
     "section": "Why this book?",
     "text": "Why this book?\nData science is quickly consolidating as a new field, with new tools and user communities emerging seemingly every week! At the same time the field of academic research has opened up into new interdisciplinary vistas, with experts crossing over into new fields, transgressing disciplinary boundaries and deploying tools in new and unexpected ways to develop knowledge. There are many gaps yet to be filled, but one which I found to be particularly glaring is the lack of applied data science documentation around the subject of religion. On one hand, scholars who are working with cutting edge theory seldom pick up the emerging tools of data science. On the other hand, data scientists rarely go beyond dabbling in religious themes. This book aims to bring these two things together: introducing the tools of data science in an applied way, whilst introducing some of the complexities and cutting edge theories which help us to conceptualise and frame our understanding of this knowledge."
   },
   {
-    "objectID": "intro.html#the-hacker-way",
-    "href": "intro.html#the-hacker-way",
-    "title": "Introduction: Hacking Religion",
+    "objectID": "index.html#the-hacker-way",
+    "href": "index.html#the-hacker-way",
+    "title": "Hacking Religion: TRS & Data Science in Action",
     "section": "The hacker way",
     "text": "The hacker way\nIt’s worth emphasising at the outset that this isn’t meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values, and social action. A fair bit of ink has been spilled in books, magazines, blogs, zines, and tweets unpacking what exactly it means to be a “hacker”. Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation.\nBack in the 1980s Steven Levy tried to capture some of this in his book “Hackers: Heroes of the Computer Revolution”. As Levy put it, the “hacker ethic” included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn’t just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. It can be a more substantial ethical code.\nThis emphasis on ethics is especially important when we’re doing data science because this kind of research work will put you in positions of influence and grant you power over others. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows some sort of social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I’ve met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it’s possible that this can quickly get out of hand. It’s important to have a clear sense of what sorts of convictions guide your work in this field, a “hacker code” of sorts. With this in mind, I’d like to share with you my own set of principles:\nIt never ceases to amaze me how often people think that, when they’re working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you’ve been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond our true apprehension of a situation.\nPresenting through “facts” an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you take up the hacker way, it’s vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I’ll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge help to build up a community of accountability. The upside of this is that it’s also a lot more fun and interesting to work alongside others.\nFar too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There’s also the potential that when we make choices based on reliable information drawn from everyday reality like this our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of “learning by doing” and it’s this approach that guides my approach in this book. This isn’t just a book about data analysis, I’m proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of the commons. It takes a bit more time to learn and set things up, but the upside is that you’ll gain access to a set of tools and a research philosophy which is much more powerful.\nHere’s a quick summary of these principles, which I’ll return to periodically as we work through the coding and data in this book:\n\nTell the truth: Be candid about your limits, use visualisation responsibly\nWork transparently: Open data, open code\nWork in community, draw others in by producing reproducible research\nWork with reality, learn by doing"
   },
   {
-    "objectID": "intro.html#learning-to-code-my-way",
-    "href": "intro.html#learning-to-code-my-way",
-    "title": "Introduction: Hacking Religion",
+    "objectID": "index.html#learning-to-code-my-way",
+    "href": "index.html#learning-to-code-my-way",
+    "title": "Hacking Religion: TRS & Data Science in Action",
     "section": "Learning to code: my way",
     "text": "Learning to code: my way\nThis guide is a little different from other textbooks targetting learning to code. I remember when I was first starting out, I went through a fair few guides, and they all tended to spend about 200 pages on various theoretical bits, how you form an integer, or data structures, subroutines, the logical structure of algorithms or whatever. It was usuallyweeks of reading before I got to actually do anything. I know some people may prefer this approach, but I prefer a problem-focussed approach to learning. Give me something that is broken, or a problem to solve, which engages the things I want to figure out and the motivation for learning just comes much more naturally. And we know from research in cognitive science that these kinds of problem-focussed approaches can tend to faciliate faster learning and better retention, so it’s not just my personal preference, but also justified! It will be helpful for you to be aware of this approach when you get into the book as it explains some of the editorial choices I’ve made and the way I’ve structured things. Each chapter focusses on a problem which is particularly salient for the use of data science to conduct research into religion. That problem will be my focal point, guiding choices of specific aspects of programming to introduce to you as we work our way around that data set and some of the crucial questions that arise in terms of how we handle it. If you find this approach unsatisfying, luckily there are a number of really terrific guides which lay things out slowly and methodically and I will explicitly signpost some of these along the way so that you can do a “deep dive” when you feel like it. Otherwise, I’ll take an accelerated approach to this introduction to data science in R. I expect that you will identify adjacent resources and perhaps even come up with your own creative approaches along the way, which incidentally is how real data science tends to work in practice.\nThere are a range of terrific textbooks out there which cover all these elements in greater depth and more slowly. In particular, I’d recommend that many readers will want to check out Hadley Wickham’s “R For Data Science” book. I’ll include marginal notes in this guide pointing to sections of that book, and a few others which unpack the basic mechanics of R in more detail."
   },
   {
-    "objectID": "intro.html#getting-set-up",
-    "href": "intro.html#getting-set-up",
-    "title": "Introduction: Hacking Religion",
+    "objectID": "index.html#getting-set-up",
+    "href": "index.html#getting-set-up",
+    "title": "Hacking Religion: TRS & Data Science in Action",
     "section": "Getting set up",
     "text": "Getting set up\nEvery single tool, programming language and data set we refer to in this book is free and open source. These tools have been produced by professionals and volunteers who are passionate about data science and research and want to share it with the world, and in order to do this (and following the “hacker way”) they’ve made these tools freely available. This also means that you aren’t restricted to a specific proprietary, expensive, or unavailable piece of software to do this work. I’ll make a few opinionated recommendations here based on my own preferences and experience, but it’s really up to your own style and approach. In fact, given that this is an open source textbook, you can even propose additions to this chapter explaining other tools you’ve found that you want to share with others.\nThere are, right now, primarily two languages that statisticians and data scientists use for this kind of programmatic data science: python and R. Each language has its merits and I won’t rehash the debates between various factions. For this book, we’ll be using the R language. This is, in part, because the R user community and libraries tend to scale a bit better for the work that I’m commending in this book. However, it’s entirely possible that one could use python for all these exercises, and perhaps in the future we’ll have volume two of this book outlining python approaches to the same operations.\nBearing this in mind, the first step you’ll need to take is to download and install R. You can find instructions and install packages for a wide range of hardware on the The Comprehensive R Archive Network (or “CRAN”): https://cran.rstudio.com. Once you’ve installed R, you’ve got some choices to make about the kind of programming environment you’d like to use. You can just use a plain text editor like textedit to write your code and then execute your programs using the R software you’ve just installed. However, most users, myself included, tend to use an integrated development environment (or “IDE”). This is usually another software package with a guided user interface and some visual elements that make it faster to write and test your code. Some IDE packages, will have built-in reference tools so you can look up options for libraries you use in your code, they will allow you to visualise the results of your code execution, and perhaps most important of all, will enable you to execute your programs line by line so you can spot errors more quickly (we call this “debugging”). The two most popular IDE platforms for R coding at the time of writing this textbook are RStudio and Visual Studio. You should download and try out both and stick with your favourite, as the differences are largely aesthetic. I use a combination of RStudio and an enhanced plain text editor Sublime Text for my coding.\nOnce you have R and your pick of an IDE, you are ready to go! Proceed to the next chapter and we’ll dive right in and get started!"
   },
-  {
-    "objectID": "chapter_1.html",
-    "href": "chapter_1.html",
-    "title": "1  The 2021 UK Census",
-    "section": "",
-    "text": "References"
-  },
   {
     "objectID": "chapter_1.html#your-first-project-the-uk-census",
     "href": "chapter_1.html#your-first-project-the-uk-census",
@@ -91,32 +84,46 @@
     "text": "1.7 Multifactor Visualisation\nOne element of R data analysis that can get really interesting is working with multiple variables. Above we’ve looked at the breakdown of religious affiliation across the whole of England and Wales (Scotland operates an independent census), and by placing this data alongside a specific region, we’ve already made a basic entry into working with multiple variables but this can get much more interesting. Adding an additional quantative variable (also known as bivariate data) into the mix, however can also generate a lot more information and we have to think about visualising it in different ways which can still communicate with visual clarity in spite of the additional visual noise which is inevitable with enhanced complexity. Let’s have a look at the way that religion in England and Wales breaks down by ethnicity.\n\n\n\n\n\n\nWhat is Nomis?\n\n\n\nFor the UK, census data is made available for programmatic research like this via an organisation called NOMIS. Luckily for us, there is an R library you can use to access nomis directly which greatly simplifies the process of pulling data down from the platform. It’s worth noting that if you’re not in the UK, there are similar options for other countries. Nearly every R textbook I’ve ever seen works with USA census data, so you’ll find plenty of documentation available on the tools you can use for US Census data. Similarly for the EU, Canada, Austrailia etc.\nIf you want to draw some data from the nomis platform yourself in R, have a look at the companion cookbook repository.\n\n\n\n# Get table of Census 2011 religion data from nomis\nz &lt;- readRDS(file = (here(\"example_data\", \"z.rds\")))\n\n# Filter down to simplified dataset with England / Wales and percentages without totals\nuk_census_2011_religion &lt;- filter(z, GEOGRAPHY_NAME==\"England and Wales\" & RURAL_URBAN_NAME==\"Total\" & C_RELPUK11_NAME != \"All categories: Religion\")\n# Drop unnecessary columns\nuk_census_2011_religion &lt;- select(uk_census_2011_religion, C_RELPUK11_NAME, OBS_VALUE)\n# Plot results\nplot1 &lt;- ggplot(uk_census_2011_religion, aes(x = C_RELPUK11_NAME, y = OBS_VALUE)) + geom_bar(stat = \"identity\") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n# ggsave(filename = \"plot.png\", plot = plot1)\n\n# grab daata from nomis for 2001 census religion / ethnicity\nz0 &lt;- readRDS(file = (here(\"example_data\", \"z0.rds\")))\n\n# select relevant columns\nuk_census_2001_religion_ethnicity &lt;- select(z0, GEOGRAPHY_NAME, C_RELPUK11_NAME, C_ETHHUK11_NAME, OBS_VALUE)\n# Filter down to simplified dataset with England / Wales and percentages without totals\nuk_census_2001_religion_ethnicity &lt;- filter(uk_census_2001_religion_ethnicity, GEOGRAPHY_NAME==\"England and Wales\" & C_RELPUK11_NAME != \"All categories: Religion\")\n# Simplify data to only include general totals and omit subcategories\nuk_census_2001_religion_ethnicity &lt;- uk_census_2001_religion_ethnicity %&gt;% filter(grepl('Total', C_ETHHUK11_NAME))\n\n# grab data from nomis for 2011 census religion / ethnicity table\nz1 &lt;- readRDS(file = (here(\"example_data\", \"z1.rds\")))\n\n# select relevant columns\nuk_census_2011_religion_ethnicity &lt;- select(z1, GEOGRAPHY_NAME, C_RELPUK11_NAME, C_ETHPUK11_NAME, OBS_VALUE)\n# Filter down to simplified dataset with England / Wales and percentages without totals\nuk_census_2011_religion_ethnicity &lt;- filter(uk_census_2011_religion_ethnicity, GEOGRAPHY_NAME==\"England and Wales\" & C_RELPUK11_NAME != \"All categories: Religion\" & C_ETHPUK11_NAME != \"All categories: Ethnic group\")\n# Simplify data to only include general totals and omit subcategories\nuk_census_2011_religion_ethnicity &lt;- uk_census_2011_religion_ethnicity %&gt;% filter(grepl('Total', C_ETHPUK11_NAME))\n\n# grab data from nomis for 2021 census religion / ethnicity table\nz2 &lt;- readRDS(file = (here(\"example_data\", \"z2.rds\")))\n\n# select relevant columns\nuk_census_2021_religion_ethnicity &lt;- select(z2, GEOGRAPHY_NAME, C2021_RELIGION_10_NAME, C2021_ETH_8_NAME, OBS_VALUE)\n# Filter down to simplified dataset with England / Wales and percentages without totals\nuk_census_2021_religion_ethnicity &lt;- filter(uk_census_2021_religion_ethnicity, GEOGRAPHY_NAME==\"England and Wales\" & C2021_RELIGION_10_NAME != \"Total\" & C2021_ETH_8_NAME != \"Total\")\n# 2021 census includes white sub-groups so we need to omit those so we just have totals:\nuk_census_2021_religion_ethnicity &lt;- filter(uk_census_2021_religion_ethnicity, C2021_ETH_8_NAME != \"White: English, Welsh, Scottish, Northern Irish or British\" & C2021_ETH_8_NAME != \"White: Irish\" & C2021_ETH_8_NAME != \"White: Gypsy or Irish Traveller, Roma or Other White\")\n\nggplot(uk_census_2011_religion_ethnicity, aes(fill=C_ETHPUK11_NAME, x=C_RELPUK11_NAME, y=OBS_VALUE)) + geom_bar(position=\"dodge\", stat =\"identity\", colour = \"black\") + scale_fill_brewer(palette = \"Set1\") + ggtitle(\"Religious Affiliation in the 2021 Census of England and Wales\") + xlab(\"\") + ylab(\"\") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\n\n\n\nThe trouble with using grouped bars here, as you can see, is that there are quite sharp disparities which make it hard to compare in meaningful ways. We could use logarithmic rather than linear scaling as an option, but this is hard for many general public audiences to apprecaite without guidance. One alternative quick fix is to extract data from “white” respondents which can then be placed in a separate chart with a different scale.\n\n# Filter down to simplified dataset with England / Wales and percentages without totals\nuk_census_2011_religion_ethnicity_white &lt;- filter(uk_census_2011_religion_ethnicity, C_ETHPUK11_NAME == \"White: Total\")\nuk_census_2011_religion_ethnicity_nonwhite &lt;- filter(uk_census_2011_religion_ethnicity, C_ETHPUK11_NAME != \"White: Total\")\n\nggplot(uk_census_2011_religion_ethnicity_nonwhite, aes(fill=C_ETHPUK11_NAME, x=C_RELPUK11_NAME, y=OBS_VALUE)) + geom_bar(position=\"dodge\", stat =\"identity\", colour = \"black\") + scale_fill_brewer(palette = \"Set1\") + ggtitle(\"Religious Affiliation in the 2021 Census of England and Wales\") + xlab(\"\") + ylab(\"\") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\n\n\n\nThis still doesn’t quite render with as much visual clarity and communication as I’d like. For a better look, we can use a technique in R called “faceting” to create a series of small charts which can be viewed alongside one another.\n\nggplot(uk_census_2011_religion_ethnicity_nonwhite, aes(x=C_RELPUK11_NAME, y=OBS_VALUE)) + geom_bar(position=\"dodge\", stat =\"identity\", colour = \"black\") + facet_wrap(~C_ETHPUK11_NAME, ncol = 2) + scale_fill_brewer(palette = \"Set1\") + ggtitle(\"Religious Affiliation in the 2011 Census of England and Wales\") + xlab(\"\") + ylab(\"\") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\n\n\n\nFor our finale chart, I’d like to take the faceted chart we’ve just done, and add in totals for the previous two census years (2001 and 2011) so we can see how trends are changing in terms of religious affiliation within ethnic self-identification categories. We’ll draw on some techniques we’re already developed above using rbind() to connect up each of these charts (after we’ve added a column identifying each chart by the census year). We will also need to use one new technique to change the wording of ethnic categories as this isn’t consistent from one census to the next and ggplot will struggle to chart things if the terms being used are exactly the same. We’ll use mutate() again to accomplish this with some slightly different code.\n\n# First add column to each dataframe so we don't lose track of the census it comes from:\nuk_census_2001_religion_ethnicity$dataset &lt;- c(\"2001\")\nuk_census_2011_religion_ethnicity$dataset &lt;- c(\"2011\")\nuk_census_2021_religion_ethnicity$dataset &lt;- c(\"2021\")\n\n# Let's tidy the names of each column:\n\nnames(uk_census_2001_religion_ethnicity) &lt;- c(\"Geography\", \"Religion\", \"Ethnicity\", \"Value\", \"Year\")\nnames(uk_census_2011_religion_ethnicity) &lt;- c(\"Geography\", \"Religion\", \"Ethnicity\", \"Value\", \"Year\")\nnames(uk_census_2021_religion_ethnicity) &lt;- c(\"Geography\", \"Religion\", \"Ethnicity\", \"Value\", \"Year\")\n\n# Next we need to change the terms using mutate()\nuk_census_2001_religion_ethnicity &lt;- uk_census_2001_religion_ethnicity %&gt;% \n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^White: Total$\", replacement = \"White\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Mixed: Total$\", replacement = \"Mixed\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Asian: Total$\", replacement = \"Asian\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Black or Black British: Total$\", replacement = \"Black\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Chinese or Other ethnic group: Total$\", replacement = \"Other\"))\n  \nuk_census_2011_religion_ethnicity &lt;- uk_census_2011_religion_ethnicity %&gt;% \n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^White: Total$\", replacement = \"White\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Mixed/multiple ethnic group: Total$\", replacement = \"Mixed\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Asian/Asian British: Total$\", replacement = \"Asian\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Black/African/Caribbean/Black British: Total$\", replacement = \"Black\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Other ethnic group: Total$\", replacement = \"Other\"))\n\nuk_census_2021_religion_ethnicity &lt;- uk_census_2021_religion_ethnicity %&gt;% \n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^White: Total$\", replacement = \"White\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Mixed or Multiple ethnic groups$\", replacement = \"Mixed\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Asian, Asian British or Asian Welsh$\", replacement = \"Asian\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Black, Black British, Black Welsh, Caribbean or African$\", replacement = \"Black\")) %&gt;%\n  mutate(Ethnicity = str_replace_all(Ethnicity, \n            pattern = \"^Other ethnic group$\", replacement = \"Other\"))\n\n# Now let's merge the tables:\n\nuk_census_merged_religion_ethnicity &lt;- rbind(uk_census_2021_religion_ethnicity, uk_census_2011_religion_ethnicity)\n\nuk_census_merged_religion_ethnicity &lt;- rbind(uk_census_merged_religion_ethnicity, uk_census_2001_religion_ethnicity)\n\n# As above, we'll split out non-white and white:\n\nuk_census_merged_religion_ethnicity_nonwhite &lt;- filter(uk_census_merged_religion_ethnicity, Ethnicity != \"White\")\n\n# Time to plot!\n\nggplot(uk_census_merged_religion_ethnicity_nonwhite, aes(fill=Year, x=Religion, y=Value)) + geom_bar(position=\"dodge\", stat =\"identity\", colour = \"black\") + facet_wrap(~Ethnicity, ncol = 2) + scale_fill_brewer(palette = \"Set1\") + ggtitle(\"Religious Affiliation in the 2001-2021 Census of England and Wales\") + xlab(\"\") + ylab(\"\") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\n\n\n\nThere are a few formatting issues which remain. Our y-axis number labels are in scientific format which isn’t really very easy to read. You can use the very powerful and flexible scales() library to bring in some more readable formatting of numbers in a variety of places in R including in ggplot visualizations.\n\nlibrary(scales) |&gt; suppressPackageStartupMessages()\nggplot(uk_census_merged_religion_ethnicity_nonwhite, aes(fill=Year, x=Religion, y=Value)) + geom_bar(position=\"dodge\", stat =\"identity\", colour = \"black\") + facet_wrap(~Ethnicity, ncol = 2) + scale_fill_brewer(palette = \"Set1\") + scale_y_continuous(labels = unit_format(unit = \"M\", scale = 1e-6), breaks = breaks_extended(8)) + ggtitle(\"Religious Affiliation in the 2001-2021 Census of England and Wales\") + xlab(\"\") + ylab(\"\") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\n\n\n# https://ggplot2-book.org/scales-position#sec-position-continuous-breaks\n\nThis chart shows an increase in almost every category, though it’s a bit hard to read in some cases. However, this information is based on the increase in raw numbers. It’s possbile that numbers may be going up, but in some cases the percentage share for a particular category has actually gone down. Let’s transform and visualise our data as percentages to see what kind of trends we can actually isolate:\n\nuk_census_merged_religion_ethnicity &lt;- uk_census_merged_religion_ethnicity %&gt;%\n  group_by(Ethnicity, Year) %&gt;%\n  dplyr::mutate(Percent = Value/sum(Value))\n\nggplot(uk_census_merged_religion_ethnicity, aes(fill=Year, x=Religion, y=Percent)) + geom_bar(position=\"dodge\", stat =\"identity\", colour = \"black\") + facet_wrap(~Ethnicity, scales=\"free_x\") + scale_fill_brewer(palette = \"Set1\") + scale_y_continuous(labels = scales::percent) + ggtitle(\"Religious Affiliation in the 2001-2021 Census of England and Wales\") + xlab(\"\") + ylab(\"\") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\n\n\n\nNow you can see why this shift is important - the visualisation tells a completely different story in some cases across the two different charts. In the first, working off raw numbers we see a net increase in Christianity across all categories. But if we take into account the fact that the overall share of population is growing for each of these groups, their actual composition is changing in a different direction. The proportion of each group is declining across the three census periods (albeit with an exception for the “Other” category from 2011 to 2021).\nTo highlight a few features of this final plot, I’ve used a specific feature within facet_wrap scales = \"free_x\" to let each of the individual facets adjust the total range on the x-axis. Since we’re looking at trends here and not absolute values, having correspondence across scales isn’t important and this makes for something a bit more visually tidy. I’ve also shifted the code for scale_y_continuous to render values as percentages (rather than millions).\nIn case you want to print this plot out and hang it on your wall, you can use the ggsave tool to render the chart as an image file:\n\nplot1 &lt;- ggplot(uk_census_merged_religion_ethnicity, aes(fill=Year, x=Religion, y=Percent)) + geom_bar(position=\"dodge\", stat =\"identity\", colour = \"black\") + facet_wrap(~Ethnicity, scales=\"free_x\") + scale_fill_brewer(palette = \"Set1\") + scale_y_continuous(labels = scales::percent) + ggtitle(\"Religious Affiliation in the 2001-2021 Census of England and Wales\") + xlab(\"\") + ylab(\"\") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\nggsave(\"chart.png\", plot=plot1, width = 8, height = 10, units=c(\"in\"))"
   },
   {
-    "objectID": "chapter_2.html",
-    "href": "chapter_2.html",
+    "objectID": "chapter_2.html#loading-in-some-data",
+    "href": "chapter_2.html#loading-in-some-data",
     "title": "2  Survey Data: Spotlight Project",
-    "section": "",
-    "text": "3 Loading in some data\n# R Setup -----------------------------------------------------------------\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nlibrary(here)  |&gt; suppressPackageStartupMessages()\nlibrary(tidyverse)  |&gt; suppressPackageStartupMessages()\n# used for importing SPSS .sav files\nlibrary(haven)   |&gt; suppressPackageStartupMessages()\nhere::i_am(\"chapter_2.qmd\")\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\n\nclimate_experience_data &lt;- read_sav(here(\"example_data\", \"climate_experience_data.sav\"))\nThe first thing to note here is that we’ve drawn in a different type of data file, this time from an .sav file, usully produced by the statistics software package SPSS. This uses a different R Library (I use haven for this). The upside is that in some cases where you have survey data with both a code and a value like “1” is eqivalent to “very much agree” this will preserve both in the R dataframe that is created. Now that you’ve loaded in data, you have a new R dataframe called “climate_experience_data” with a lot of columns with just under 1000 survey responses.\nOne of the challenges we faced when running this study is how to gather responsible data from surveys regarding religious identity. We’ll dive into this in depth as we do analysis and look at some of the agreements and conflicts in terms of respondent attribution. Just to set the stage, we used the following kinds of question to ask about religion and spirituality:\nThis is one way of measuring religion, that is, to ask a person if they consider themselves formally affiliated with a particular group. This kind of question has some (serious) limitations, but we’ll get to that in a moment.\nWe also asked respondents (Q57): “Regardless of whether you belong to a particular religion, how religious would you say you are?” and then provided a slider from 0 (not religious at all) to 10 (very religious).\nWe included some classic indicators about how often respondents go to worship (Q58): Apart from weddings, funerals and other special occasions, how often do you attend religious services? and (Q59): “Q59 Apart from when you are at religious services, how often do you pray?”\nEach of these measures a particular kind of dimension, and it is interesting to note that sometimes there are stronger correlations between how often a person attends worship services (weekly versus once a year) and a particular view, than there is between their affiliation (if they are Christian or Pagan). We’ll do some exploratory work shortly to see how this is the case in our sample. We also included a series of questions about spirituality in Q52 and used a nature relatedness scale Q51.\nYou’ll find that many surveys will only use one of these forms of question and ignore the rest. I think this is a really bad idea as religious belonging, identity, and spirituality are far too complex to work off a single form of response. We can also test out how these different attributions relate to other demographic features, like interest in politics, economic attainment, etc.\nLet’s dive into the data and see how this all works out. We’ll start with the question 56 data, around religious affiliation:\nreligious_affiliation &lt;- as_tibble(as_factor(climate_experience_data$Q56))\nnames(religious_affiliation) &lt;- c(\"response\")\nreligious_affiliation &lt;- filter(religious_affiliation, !is.na(response))\nThere are few things we need to do here to get the data into initial proper shape. This might be called “cleaning” the data:\nIf we pause at this point to view the data, you’ll see it’s basically just a long list of survey responses. What we need is a count of each unique response (or factor). This will take a few more steps:\nreligious_affiliation_sums &lt;- religious_affiliation %&gt;%  \n1  dplyr::count(response, sort = TRUE) %&gt;%\n2  dplyr::mutate(response = forcats::fct_rev(forcats::fct_inorder(response)))\nreligious_affiliation_sums &lt;- religious_affiliation_sums %&gt;% \n3  dplyr::mutate(perc = scales::percent(n / sum(n), accuracy = .1, trim = FALSE))\n\n\n1\n\nFirst we generate new a dataframe with sums per category and\n\n2\n\n…sort in descending order\n\n3\n\nThen we add new column with percentages based on the sums you’ve just generated\nThat should give us a tidy table of results, which you can see if you view the contents of our new religious_affiliation_sums dataframe:\nhead(religious_affiliation_sums)\n\n# A tibble: 6 x 3\n  response                        n perc   \n  &lt;fct&gt;                       &lt;int&gt; &lt;chr&gt;  \n1 Christian                     342 \"33.9%\"\n2 Muslim                        271 \"26.9%\"\n3 No religion                   108 \"10.7%\"\n4 Hindu                          72 \" 7.1%\"\n5 Atheist                        54 \" 5.4%\"\n6 Spiritual but not religious    38 \" 3.8%\"\n# make plot\nggplot(religious_affiliation_sums, aes(x = n, y = response)) +\n  geom_col(colour = \"white\") + \n  ## add percentage labels\n  geom_text(aes(label = perc),\n            ## make labels left-aligned and white\n            hjust = 1, nudge_x = -.5, colour = \"white\", size=3)\nI’ve added one feature to our chart that wasn’t in the bar charts in chapter 1, text labels with the actual value on each bar.\nYou may be thinking about the plots we’ve just finished in chapter 1 and wondering how they compare. Let’s use the same facet approach that we’ve just used to render this data in a subsetted way.\n# First we need to add in data on ethnic self-identification from our respondents:\ndf &lt;- select(climate_experience_data, Q56, Q0)\nreligious_affiliation_ethnicity &lt;- as_tibble(as_factor(df))\nnames(religious_affiliation_ethnicity) &lt;- c(\"Religion\", \"Ethnicity\")\n\nreligious_affiliation_ethnicity_sums &lt;- religious_affiliation_ethnicity %&gt;%  \n  group_by(Ethnicity) %&gt;%\n  dplyr::count(Religion, sort = TRUE) %&gt;%\n  dplyr::mutate(Religion = forcats::fct_rev(forcats::fct_inorder(Religion)))\n\nplot1 &lt;- ggplot(religious_affiliation_ethnicity_sums, aes(x = n, y = Religion)) +\n  geom_col(colour = \"white\") + facet_wrap(~Ethnicity, scales=\"free_x\")\n\nggsave(\"chart.png\", plot=plot1, width = 8, height = 10, units=c(\"in\"))\nUse mutate to put “prefer not to say” at the bottom # Info here: https://r4ds.had.co.nz/factors.html#modifying-factor-levels\ncaption &lt;- “Christian Denomination” # TODO: copy plot above for Q56 to add two additional plots using climate_experience_data_named\\(Q56b and climate_experience_data_named\\)Q56c # Religious Affiliation b - Christian Denomination Subquestion christian_denomination &lt;- qualtrics_process_single_multiple_choice(climate_experience_data_named\\(Q56b) christian_denomination_table &lt;- chart_single_result_flextable(climate_experience_data_named\\)Q56b, desc(Count)) christian_denomination_table save_as_docx(christian_denomination_table, path = “./figures/q56_religious_affiliation_xn_denomination.docx”)\nchristian_denomination_hi &lt;- filter(climate_experience_data_named, Q56 == “Christian”, Q57_bin == “high”) christian_denomination_hi &lt;- qualtrics_process_single_multiple_choice(christian_denomination_hi$Q56b) christian_denomination_hi\ncaption &lt;- “Islamic Identity” # Should the label be different than income since the data examined is the Affiliation? # TODO: adjust plot to factor using numbered responses on this question (perhaps also above) religious_affiliationc &lt;- qualtrics_process_single_multiple_choice(climate_experience_data_named\\(Q56c) religious_affiliationc_plot &lt;- plot_horizontal_bar(religious_affiliationc) religious_affiliationc_plot &lt;- religious_affiliationc_plot + labs(caption = caption, x = \"\", y = \"\") religious_affiliationc_plot ggsave(\"figures/q56c_religious_affiliation.png\", width = 20, height = 10, units = \"cm\") religious_affiliationc_table &lt;- chart_single_result_flextable(climate_experience_data_named\\)Q56c, Count) religious_affiliationc_table save_as_docx(religious_affiliationc_table, path = “./figures/q56_religious_affiliation_islam.docx”)\ncaption &lt;- “Respondent Religiosity” religiosity &lt;- qualtrics_process_single_multiple_choice(as.character(climate_experience_data_named\\(Q57_1)) religiosity_plot &lt;- plot_horizontal_bar(religiosity) religiosity_plot &lt;- religiosity_plot + labs(caption = caption, x = \"\", y = \"\") religiosity_plot ggsave(\"figures/q57_religiosity_plot.png\", width = 20, height = 10, units = \"cm\") religiosity_table &lt;- chart_single_result_flextable(climate_experience_data_named\\)Q57_1, desc(Variable)) religiosity_table save_as_docx(religious_affiliationc_table, path = “./figures/q57_religiousity.docx”)\ncaption &lt;- “Respondent Attendance of Religious Services” religious_service_attend &lt;- qualtrics_process_single_multiple_choice(climate_experience_data_named\\(Q58) religious_service_attend_plot &lt;- plot_horizontal_bar(religious_service_attend) religious_service_attend_plot &lt;- religious_service_attend_plot + labs(title = caption, x = \"\", y = \"\") religious_service_attend_plot ggsave(\"figures/q58_religious_service_attend.png\", width = 20, height = 10, units = \"cm\") religious_service_attend_table &lt;- chart_single_result_flextable(climate_experience_data_named\\)Q58, Count) religious_service_attend_table save_as_docx(religious_service_attend_table, path = “./figures/q58_religious_service_attend.docx”)\ndf &lt;- select(climate_experience_data, Q52_bin, Q53_bin, Q57_bin, Q58) names(df) &lt;- c(“Q52_bin”, “Q53_bin”, “Q57_bin”, “response”) facet_names &lt;- c(Q52_bin = “Spirituality”, Q53_bin = “Politics L/R”, Q57_bin = “Religiosity”, low=“low”, medium=“medium”, high=“high”) facet_labeller &lt;- function(variable,value){return(facet_names[value])} df\\(response &lt;- factor(df\\)response, ordered = TRUE, levels = c(“1”, “2”, “3”, “4”, “5”)) df\\(response &lt;- fct_recode(df\\)response, “More than once a week” = “1”, “Once a week” = “2”, “At least once a month” = “3”, “Only on special holy days” = “4”, “Never” = “5”) df %&gt;% # we need to get the data including facet info in long format, so we use pivot_longer() pivot_longer(!response, names_to = “bin_name”, values_to = “b”) %&gt;% # add counts for plot below count(response, bin_name, b) %&gt;% group_by(bin_name,b) %&gt;% mutate(perc=paste0(round(n*100/sum(n),1),“%”)) %&gt;% # run ggplot ggplot(aes(x = n, y = ““, fill = response)) + geom_col(position=position_fill(), aes(fill=response)) + geom_text(aes(label = perc), position = position_fill(vjust=.5), size=2) + scale_fill_brewer(palette =”Dark2”, type = “qual”) + scale_x_continuous(labels = scales::percent_format()) + facet_grid(vars(b), vars(bin_name), labeller=as_labeller(facet_names)) + labs(caption = caption, x = ““, y =”“) + guides(fill = guide_legend(title = NULL)) ggsave(”figures/q58_faceted.png”, width = 30, height = 10, units = “cm”)\ncaption &lt;- “Respondent Prayer Outside of Religious Services” prayer &lt;- qualtrics_process_single_multiple_choice(climate_experience_data_named\\(Q59) prayer_plot &lt;- plot_horizontal_bar(prayer) prayer_plot &lt;- prayer_plot + labs(caption = caption, x = \"\", y = \"\") prayer_plot ggsave(\"figures/q59_prayer.png\", width = 20, height = 10, units = \"cm\") prayer_table &lt;- chart_single_result_flextable(climate_experience_data_named\\)Q59, Count) prayer_table save_as_docx(prayer_table, path = “./figures/q59_prayer.docx”)\ndf &lt;- select(climate_experience_data, Q52_bin, Q53_bin, Q57_bin, Q59) names(df) &lt;- c(“Q52_bin”, “Q53_bin”, “Q57_bin”, “response”) facet_names &lt;- c(Q52_bin = “Spirituality”, Q53_bin = “Politics L/R”, Q57_bin = “Religiosity”, low=“low”, medium=“medium”, high=“high”) facet_labeller &lt;- function(variable,value){return(facet_names[value])} df\\(response &lt;- factor(df\\)response, ordered = TRUE, levels = c(“1”, “2”, “3”, “4”, “5”)) df\\(response &lt;- fct_recode(df\\)response, “More than once a week” = “1”, “Once a week” = “2”, “At least once a month” = “3”, “Only on special holy days” = “4”, “Never” = “5”) df %&gt;% # we need to get the data including facet info in long format, so we use pivot_longer() pivot_longer(!response, names_to = “bin_name”, values_to = “b”) %&gt;% # add counts for plot below count(response, bin_name, b) %&gt;% group_by(bin_name,b) %&gt;% mutate(perc=paste0(round(n*100/sum(n),1),“%”)) %&gt;% # run ggplot ggplot(aes(x = n, y = ““, fill = response)) + geom_col(position=position_fill(), aes(fill=response)) + geom_text(aes(label = perc), position = position_fill(vjust=.5), size=2) + scale_fill_brewer(palette =”Dark2”, type = “qual”) + scale_x_continuous(labels = scales::percent_format()) + facet_grid(vars(b), vars(bin_name), labeller=as_labeller(facet_names)) + labs(caption = caption, x = ““, y =”“) + guides(fill = guide_legend(title = NULL)) ggsave(”figures/q59_faceted.png”, width = 30, height = 10, units = “cm”)\nq6_data &lt;- qualtrics_process_single_multiple_choice_unsorted_streamlined(climate_experience_data$Q6)\ntitle &lt;- “Do you think the climate is changing?”\nlevel_order &lt;- c(“Don&lt;80&gt;&lt;99&gt;t know”, “Definitely not changing”, “Probably not changing”, “Probably changing”, “Definitely changing”) ## code if a specific palette is needed for matching fill = wheel(ochre, num = as.integer(count(q6_data[1]))) # make plot q6_data_plot &lt;- ggplot(q6_data, aes(x = n, y = response, fill = fill)) + geom_col(colour = “white”) + ## add percentage labels geom_text(aes(label = perc), ## make labels left-aligned and white hjust = 1, colour = “black”, size=4) + # use nudge_x = 30, to shift position ## reduce spacing between labels and bars scale_fill_identity(guide = “none”) + ## get rid of all elements except y axis labels + adjust plot margin theme_ipsum_rc() + theme(plot.margin = margin(rep(15, 4))) + easy_center_title() + # with thanks for helpful info on doing wrap here: https://stackoverflow.com/questions/21878974/wrap-long-axis-labels-via-labeller-label-wrap-in-ggplot2 scale_y_discrete(labels = wrap_format(30), limits = level_order) + theme(plot.title = element_text(size =18, hjust = 0.5), axis.text.y = element_text(size =16)) + labs(title = title, x = ““, y =”“)\nq6_data_plot\nggsave(“figures/q6.png”, width = 18, height = 12, units = “cm”)\nclimate_experience_data$Q51_score &lt;- rowMeans(select(climate_experience_data, Q51_remote_vacation:Q51_heritage))\nclimate_experience_data &lt;- climate_experience_data %&gt;% mutate( Q51_bin = case_when( Q51_score &gt; mean(Q51_score) + sd(Q51_score) ~ “high”, Q51_score &lt; mean(Q51_score) - sd(Q51_score) ~ “low”, TRUE ~ “medium” ) %&gt;% factor(levels = c(“low”, “medium”, “high”)) )\nclimate_experience_data$Q52_score &lt;- rowMeans(select(climate_experience_data, Q52a_1:Q52f_1))\nclimate_experience_data &lt;- climate_experience_data %&gt;% mutate( Q52_bin = case_when( Q52_score &gt; mean(Q52_score) + sd(Q52_score) ~ “high”, Q52_score &lt; mean(Q52_score) - sd(Q52_score) ~ “low”, TRUE ~ “medium” ) %&gt;% factor(levels = c(“low”, “medium”, “high”)) )"
+    "section": "2.1 Loading in some data",
+    "text": "2.1 Loading in some data\n\n# R Setup -----------------------------------------------------------------\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nlibrary(here)  |&gt; suppressPackageStartupMessages()\nlibrary(tidyverse)  |&gt; suppressPackageStartupMessages()\n# used for importing SPSS .sav files\nlibrary(haven)   |&gt; suppressPackageStartupMessages()\nhere::i_am(\"chapter_2.qmd\")\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\n\nclimate_experience_data &lt;- read_sav(here(\"example_data\", \"climate_experience_data.sav\"))\n\nThe first thing to note here is that we’ve drawn in a different type of data file, this time from an .sav file, usully produced by the statistics software package SPSS. This uses a different R Library (I use haven for this). The upside is that in some cases where you have survey data with both a code and a value like “1” is eqivalent to “very much agree” this will preserve both in the R dataframe that is created. Now that you’ve loaded in data, you have a new R dataframe called “climate_experience_data” with a lot of columns with just under 1000 survey responses."
   },
   {
-    "objectID": "chapter_2.html#q57-subsetting-based-on-religiosity",
-    "href": "chapter_2.html#q57-subsetting-based-on-religiosity",
+    "objectID": "chapter_2.html#how-can-you-ask-about-religion",
+    "href": "chapter_2.html#how-can-you-ask-about-religion",
     "title": "2  Survey Data: Spotlight Project",
-    "section": "15.1 Q57 subsetting based on Religiosity ————————————————————–",
-    "text": "15.1 Q57 subsetting based on Religiosity ————————————————————–\nclimate_experience_data &lt;- climate_experience_data %&gt;% mutate( Q57_bin = case_when( Q57_1 &gt; mean(Q57_1) + sd(Q57_1) ~ “high”, Q57_1 &lt; mean(Q57_1) - sd(Q57_1) ~ “low”, TRUE ~ “medium” ) %&gt;% factor(levels = c(“low”, “medium”, “high”)) )"
+    "section": "2.2 How can you ask about religion?",
+    "text": "2.2 How can you ask about religion?\nOne of the challenges we faced when running this study is how to gather responsible data from surveys regarding religious identity. We’ll dive into this in depth as we do analysis and look at some of the agreements and conflicts in terms of respondent attribution. Just to set the stage, we used the following kinds of question to ask about religion and spirituality:\n\nQuestion 56 asks respondents simply, “What is your religion?” and then provides a range of possible answers. We included follow-up questions regarding denomination for respondents who indicated they were “Christian” or “Muslim”. For respondents who ticked “Christian” we asked, “What is your denomination?” nad for respondents who ticked “Muslim” we asked “Which of the following would you identify with?” and then left a range of possible options which could be ticked such as “Sunni,” “Shia,” “Sufi” etc.\n\nThis is one way of measuring religion, that is, to ask a person if they consider themselves formally affiliated with a particular group. This kind of question has some (serious) limitations, but we’ll get to that in a moment.\nWe also asked respondents (Q57): “Regardless of whether you belong to a particular religion, how religious would you say you are?” and then provided a slider from 0 (not religious at all) to 10 (very religious).\nWe included some classic indicators about how often respondents go to worship (Q58): Apart from weddings, funerals and other special occasions, how often do you attend religious services? and (Q59): “Q59 Apart from when you are at religious services, how often do you pray?”\n\nMore than once a week (1)\nOnce a week (2)\nAt least once a month (3)\nOnly on special holy days (4)\nNever (5)\n\nEach of these measures a particular kind of dimension, and it is interesting to note that sometimes there are stronger correlations between how often a person attends worship services (weekly versus once a year) and a particular view, than there is between their affiliation (if they are Christian or Pagan). We’ll do some exploratory work shortly to see how this is the case in our sample. We also included a series of questions about spirituality in Q52 and used a nature relatedness scale Q51.\nYou’ll find that many surveys will only use one of these forms of question and ignore the rest. I think this is a really bad idea as religious belonging, identity, and spirituality are far too complex to work off a single form of response. We can also test out how these different attributions relate to other demographic features, like interest in politics, economic attainment, etc.\n\n\n\n\n\n\nSo who’s religious?\n\n\n\nAs I’ve already hinted in the previous chapter, measuring religiosity is complicated. I suspect some readers may be wondering something like, “what’s the right question to ask?” here. Do we get the most accurate representation by asking people to self-report their religious affiliation? Or is it more accurate to ask individuals to report on how religious they are? Is it, perhaps, better to assume that the indirect query about practice, e.g. how frequently one attends services at a place of worship may be the most reliable proxy?\nHighlight challenges of various approaches pointing to literature.\n\n\nLet’s dive into the data and see how this all works out. We’ll start with the question 56 data, around religious affiliation:\n\nreligious_affiliation &lt;- as_tibble(as_factor(climate_experience_data$Q56))\nnames(religious_affiliation) &lt;- c(\"response\")\nreligious_affiliation &lt;- filter(religious_affiliation, !is.na(response))\n\nThere are few things we need to do here to get the data into initial proper shape. This might be called “cleaning” the data:\n\nBecause we imported this data from an SPSS .sav file format using the R haven() library, we need to start by adapting the data into a format that our visualation engine ggplot can handle (a dataframe).\nNext we’ll rename the columns so these names are a bit more useful.\nWe need to omit non-responses so these don’t mess with the counting (these are NA in R)\n\nIf we pause at this point to view the data, you’ll see it’s basically just a long list of survey responses. What we need is a count of each unique response (or factor). This will take a few more steps:\n\nreligious_affiliation_sums &lt;- religious_affiliation %&gt;%  \n1  dplyr::count(response, sort = TRUE) %&gt;%\n2  dplyr::mutate(response = forcats::fct_rev(forcats::fct_inorder(response)))\nreligious_affiliation_sums &lt;- religious_affiliation_sums %&gt;% \n3  dplyr::mutate(perc = scales::percent(n / sum(n), accuracy = .1, trim = FALSE))\n\n\n1\n\nFirst we generate new a dataframe with sums per category and\n\n2\n\n…sort in descending order\n\n3\n\nThen we add new column with percentages based on the sums you’ve just generated\n\n\n\n\nThat should give us a tidy table of results, which you can see if you view the contents of our new religious_affiliation_sums dataframe:\n\nhead(religious_affiliation_sums)\n\n# A tibble: 6 x 3\n  response                        n perc   \n  &lt;fct&gt;                       &lt;int&gt; &lt;chr&gt;  \n1 Christian                     342 \"33.9%\"\n2 Muslim                        271 \"26.9%\"\n3 No religion                   108 \"10.7%\"\n4 Hindu                          72 \" 7.1%\"\n5 Atheist                        54 \" 5.4%\"\n6 Spiritual but not religious    38 \" 3.8%\"\n\n\n\n# make plot\nggplot(religious_affiliation_sums, aes(x = n, y = response)) +\n  geom_col(colour = \"white\") + \n  ## add percentage labels\n  geom_text(aes(label = perc),\n            ## make labels left-aligned and white\n            hjust = 1, nudge_x = -.5, colour = \"white\", size=3)\n\n\n\n\nI’ve added one feature to our chart that wasn’t in the bar charts in chapter 1, text labels with the actual value on each bar.\nYou may be thinking about the plots we’ve just finished in chapter 1 and wondering how they compare. Let’s use the same facet approach that we’ve just used to render this data in a subsetted way.\n\n# First we need to add in data on ethnic self-identification from our respondents:\ndf &lt;- select(climate_experience_data, Q56, Q0)\nreligious_affiliation_ethnicity &lt;- as_tibble(as_factor(df))\nnames(religious_affiliation_ethnicity) &lt;- c(\"Religion\", \"Ethnicity\")\n\nreligious_affiliation_ethnicity_sums &lt;- religious_affiliation_ethnicity %&gt;%  \n  group_by(Ethnicity) %&gt;%\n  dplyr::count(Religion, sort = TRUE) %&gt;%\n  dplyr::mutate(Religion = forcats::fct_rev(forcats::fct_inorder(Religion)))\n\nplot1 &lt;- ggplot(religious_affiliation_ethnicity_sums, aes(x = n, y = Religion)) +\n  geom_col(colour = \"white\") + facet_wrap(~Ethnicity, scales=\"free_x\")\n\nggsave(\"chart.png\", plot=plot1, width = 8, height = 10, units=c(\"in\"))\n\n\n\n\n\n\n\n\n\nWhat is Religion?\n\n\n\nContent tbd\n\n\n\n\n\n\n\n\nHybrid Religious Identity\n\n\n\nContent tbd\n\n\n\n\n\n\n\n\nWhat is Secularisation?\n\n\n\nContent tbd"
   },
   {
-    "objectID": "chapter_2.html#subsetting-based-on-spirituality",
-    "href": "chapter_2.html#subsetting-based-on-spirituality",
+    "objectID": "chapter_2.html#references",
+    "href": "chapter_2.html#references",
     "title": "2  Survey Data: Spotlight Project",
-    "section": "15.2 Subsetting based on Spirituality ————————————————————–",
-    "text": "15.2 Subsetting based on Spirituality ————————————————————–\n\n15.2.1 Nature relatedness ————————————————————–"
+    "section": "References",
+    "text": "References"
   },
   {
-    "objectID": "chapter_3.html",
-    "href": "chapter_3.html",
+    "objectID": "chapter_3.html#administrative-shapes---the-uk",
+    "href": "chapter_3.html#administrative-shapes---the-uk",
     "title": "3  Mapping churches: geospatial data science",
-    "section": "",
-    "text": "Until recently, most data science books didn’t have a section on geospatial data. It was considered a specialist form of research best left to GIS technicians who tended to use proprietary tools like ArcGIS. This has changed significantly in the past five years, but you’ll still be hard pressed to find an introduction to the subject which strays very far from a few simple data sets (mostly of the USA) and relatively uncomplicated geospatial operations. I actually first began learning R, back in 2013, right when open source geospatial research tools were beginning to be developed with quite a lot more energy and geospatial data is my personal favourite data science playground, so in this book we’re going to go much deeper than is usual. There are also good reasons to take things a few steps further in the particular forms of data and inquiry that religion takes us into.\nRecommend https://r-spatial.org/book/\nGeospatial data is, in the most basic form, working with maps. This means that most of your data can be a quite simple dataframe, e.g. just a list of names or categories associated with a set of X and Y coordinates. Once you have a set of items, however, things get interesting very quickly, as you can layer data sets on top of one another. We’re going to begin this chapter by developing a geolocated data set of churches in the UK. This information is readily and freely available online thanks to the UK Ordnance Survey, a quasi-governmental agency which maintains the various (now digital) maps of Britain. Lucky for us, the Ordnance Survey has an open data product that anyone can use!\nBefore we begin, there are some key things we should note about geospatial data. Geospatial data tends to fall into one of two kinds: points and polygons. Points can be any kind of feature: a house, a church, a pub, someone’s favourite ancient oak tree, or some kind of sacred relic. Polygons tend to be associated with wider areas, and as such can be used to describe large features, e.g. an Ocean, a local authority, or a mountain, or also demographic features, like a census Output Area with associated census summaries. Points are very simple data representations, an X and Y coordinate. Polygons are much more complex, often containing dozens or even thousands of points.\nThe most complex aspect of point data relates to the ways that coordinates are encoded, as they will aways need to be associated with a coordinate reference system (CRS) which describes how they are situated with respect to the planet earth. The most common CRS is the WGS, though for our data sets we’ll also come into contact with the BGS, a specifically British coordinate reference system. There are dozens of CRS, usually mapping onto a specific geographical region. Bearing in mind the way that you need to use a CRS to understand how coordinates can be associated with specific parts of the earth, you can see how this is a bit like survey data, where you also need a “codebook” to understand what the specific response values map onto, e.g. a “1” means “strongly agree” and so on. We also saw, in a previous chapter, how some forms of data have the codebook already baked into the factor data, simplifying the process of interpreting the data. In a similar way, some types of geospatial data sets can also come with CRS “baked in” while we’ll need to define CRS for other types. Here are some of the most common types of geospatial data files:\nCSV: “comma separated values” a plain text file containing various coordinates Shapefile: a legacy file format, often still in use, but being replaced by others for a variety of good reasons. For more on this see [http://switchfromshapefile.org/] Geopackage: one of the more recent ways of packaging up geospatial data. Geopackages can contain a wide variety of different data and are easily portable. GeoJSON: a file format commonly used in other forms of coding, the “JSON” (an acronym for JavaScript Object Notation) is meant to be easily interchangeable across various platforms. GeoJSON is an augmented version of JSON data with coordinates added in.\nNow that you have a sense of some of the basic aspects of geospatial data, let’s dive in and do a bit of learning in action.\n\n4 Administrative shapes - the UK\nA good starting point is to aquire some adminstrative data. This is a way of referring to political boundaries, whether country borders or those of a local authority or some other administrative unit. For our purposes, we’re going to import several different types of administrative boundary which will be used at different points in our visualisations below. It’s worth noting that the data we use here was prepared to support the 2011 census, and make use of the shapefile format.\n\nlibrary(sf) |&gt; suppressPackageStartupMessages()\nlibrary(here)  |&gt; suppressPackageStartupMessages()\nlibrary(tidyverse)  \n\n-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --\nv dplyr     1.1.3     v readr     2.1.4\nv forcats   1.0.0     v stringr   1.5.0\nv ggplot2   3.4.3     v tibble    3.2.1\nv lubridate 1.9.3     v tidyr     1.3.0\nv purrr     1.0.2     \n-- Conflicts ------------------------------------------ tidyverse_conflicts() --\nx dplyr::filter() masks stats::filter()\nx dplyr::lag()    masks stats::lag()\ni Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all conflicts to become errors\n\n# better video device, more accurate and faster rendering, esp. on macos. Also should enable system fonts for display\nlibrary(ragg) |&gt; suppressPackageStartupMessages()\n\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nhere::i_am(\"chapter_3.qmd\")\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\n\n# Download administrative boundaries for whole UK at country level\nif (file.exists(here(\"data\", \"infuse_uk_2011_clipped.shp\")) == FALSE) {\ndownload.file(\"https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_uk_2011_clipped.zip\", destfile = \"data/infuse_uk_2011_clipped.zip\")\nunzip(\"data/infuse_uk_2011_clipped.zip\", exdir = \"data\")\n}\nuk_countries &lt;- st_read(here(\"data\", \"infuse_uk_2011_clipped.shp\"), quiet = TRUE)\n\n# Download administrative boundaries for whole UK at regions level\nif (file.exists(here(\"data\", \"infuse_rgn_2011_clipped.shp\")) == FALSE) {\ndownload.file(\"https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_rgn_2011_clipped.zip\", destfile = \"data/infuse_rgn_2011_clipped.zip\")\nunzip(\"data/infuse_rgn_2011_clipped.zip\", exdir = \"data\")\n}\nuk_rgn &lt;- st_read(here(\"data\", \"infuse_rgn_2011_clipped.shp\"), quiet = TRUE)\n\n# Download administrative boundaries for whole UK at local authority level\nif (file.exists(here(\"data\", \"infuse_dist_lyr_2011_clipped.shp\")) == FALSE) {\ndownload.file(\"https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_dist_lyr_2011_clipped.zip\", destfile = \"data/infuse_dist_lyr_2011_clipped.zip\")\nunzip(\"data/infuse_dist_lyr_2011_clipped.zip\", exdir = \"data\")\n}\nlocal_authorities &lt;- st_read(here(\"data\", \"infuse_dist_lyr_2011_clipped.shp\"), quiet = TRUE)\n\n# Download building outlines for whole UK\nif (file.exists(here(\"data\", \"infuse_dist_lyr_2011_simplified_100m_buildings_simplified.gpkg\")) == FALSE) {\n  download.file(\"https://zenodo.org/record/6395804/files/infuse_dist_lyr_2011_simplified_100m_buildings_overlay_simplified.gpkg\", destfile = here(\"data\", \"infuse_dist_lyr_2011_simplified_100m_buildings_simplified.gpkg\"))}\nlocal_authorities_buildings_clip &lt;- st_read(here(\"data\", \"infuse_dist_lyr_2011_simplified_100m_buildings_simplified.gpkg\"), quiet = TRUE)\n\nBefore we move on, let’s plot a simple map and have a look at one of our administrative layers. We can use ggplot with a new type of shape geom_sf() to plot the contents of a geospatial data file with polygons which is loaded as a simplefeature in R.\n\nggplot(uk_countries) + geom_sf()\n\n\n\n\n\n\n5 Load in Ordnance Survey OpenMap Points Data\n\n# Note: for more advanced reproducible scripts which demonstrate how these data surces have been \n# obtained, see the companion cookbook here: https://github.com/kidwellj/hacking_religion_cookbook/blob/main/ordnance_survey.R\n\nos_openmap_pow &lt;- st_read(here(\"data\", \"os_openmap_pow.gpkg\"), quiet = TRUE)\n\nggplot(os_openmap_pow) + geom_sf()\n\n\n\n\nIt’s worth noting that the way that you load geospatial data in R has changed quite dramatically since 2020 with the introduction of the simplefeature class in R. Much of the documentation you will come across “out there” will make reference to a set of functions which are now deprecated.\nLet’s use that data we’ve just loaded to make our first map:\n\n# Generate choropleth map of respondent locations\n# using temporary palette here so that 0s are white\nlibrary(tmap) |&gt; suppressPackageStartupMessages()\n# palette &lt;- c(white, \"#a8ddb5\", \"#43a2ca\")\n\nmap1 &lt;- tm_shape(local_authorities) + \n#  tm_fill(col = \"surveys_count\", , palette = palette, title = \"Concentration of survey respondents\") +\n  tm_borders(alpha=.5, lwd=0.1) +\n  # for intermediate polygon geometries\n  # tm_shape(local_authorities) +\n  # tm_borders(lwd=0.6) +\n  # for dots from original dataset\n  # tm_dots(\"red\", size = .05, alpha = .4) +\n  tm_scale_bar(position = c(\"right\", \"bottom\")) +\n  tm_style(\"gray\") +\n  tm_credits(\"Data: UK Data Service (OGL)\\n& Jeremy H. Kidwell,\\nGraphic is CC-by-SA 4.0\", \n             size = 0.4, \n             position = c(\"left\", \"bottom\"),\n             just = c(\"left\", \"bottom\"),\n             align = \"left\") +\n  tm_layout(asp = NA,\n            frame = FALSE, \n            title = \"Figure 1a\", \n            title.size = .7,\n            legend.title.size = .7,\n            inner.margins = c(0.1, 0.1, 0.05, 0.05)\n  )\n\nmap1\n\n\n\n# save image\ntmap_save(map1, here(\"figures\", \"map.png\"), width=1920, height=1080, asp=0)\n\nMap saved to /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion/figures/map.png\n\n\nResolution: 1920 by 1080 pixels\n\n\nSize: 6.4 by 3.6 inches (300 dpi)\n\n\n\n# subsetting ordnance survey openmap data for measuring clusters and proximity\n\nos_openmap_important_buildings &lt;- st_read(here(\"data\", \"os_openmap_important_buildings.gpkg\"), quiet = TRUE)\n\n# add pubs, check_cashing, pawnbrokers, SSSI\n## subsets\n\n\nCount the number of churches in Local Authorities\n\n\n# OSM data\n\n# Note: for more advanced reproducible scripts which demonstrate how these data surces have been \n# obtained, see the companion cookbook here: https://github.com/kidwellj/hacking_religion_cookbook/blob/main/ordnance_survey.R\n\n\n# osm_uk_points &lt;- st_read(system.file(here(\"data\", \"pow_osm.gpkg\", package = \"spData\")))\n# vector_filepath = system.file(\"data/osm-gb-2018Aug29_pow_osm.pbf\", package = \"sf\")\n# osm_uk_points = st_read(vector_filepath)\n\nGuides to geographies: https://rconsortium.github.io/censusguide/ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistical-geographies/\nCalculate proximity to pubs\n\n\nReferences"
+    "section": "3.1 Administrative shapes - the UK",
+    "text": "3.1 Administrative shapes - the UK\nA good starting point is to aquire some adminstrative data. This is a way of referring to political boundaries, whether country borders or those of a local authority or some other administrative unit. For our purposes, we’re going to import several different types of administrative boundary which will be used at different points in our visualisations below. It’s worth noting that the data we use here was prepared to support the 2011 census, and make use of the shapefile format.\n\nlibrary(sf) |&gt; suppressPackageStartupMessages()\nlibrary(here)  |&gt; suppressPackageStartupMessages()\nlibrary(tidyverse)  \n\n-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --\nv dplyr     1.1.3     v readr     2.1.4\nv forcats   1.0.0     v stringr   1.5.0\nv ggplot2   3.4.3     v tibble    3.2.1\nv lubridate 1.9.3     v tidyr     1.3.0\nv purrr     1.0.2     \n-- Conflicts ------------------------------------------ tidyverse_conflicts() --\nx dplyr::filter() masks stats::filter()\nx dplyr::lag()    masks stats::lag()\ni Use the conflicted package (&lt;http://conflicted.r-lib.org/&gt;) to force all conflicts to become errors\n\n# better video device, more accurate and faster rendering, esp. on macos. Also should enable system fonts for display\nlibrary(ragg) |&gt; suppressPackageStartupMessages()\n\nsetwd(\"/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\")\nhere::i_am(\"chapter_3.qmd\")\n\nhere() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion\n\n# Download administrative boundaries for whole UK at country level\nif (file.exists(here(\"data\", \"infuse_uk_2011_clipped.shp\")) == FALSE) {\ndownload.file(\"https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_uk_2011_clipped.zip\", destfile = \"data/infuse_uk_2011_clipped.zip\")\nunzip(\"data/infuse_uk_2011_clipped.zip\", exdir = \"data\")\n}\nuk_countries &lt;- st_read(here(\"data\", \"infuse_uk_2011_clipped.shp\"), quiet = TRUE)\n\n# Download administrative boundaries for whole UK at regions level\nif (file.exists(here(\"data\", \"infuse_rgn_2011_clipped.shp\")) == FALSE) {\ndownload.file(\"https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_rgn_2011_clipped.zip\", destfile = \"data/infuse_rgn_2011_clipped.zip\")\nunzip(\"data/infuse_rgn_2011_clipped.zip\", exdir = \"data\")\n}\nuk_rgn &lt;- st_read(here(\"data\", \"infuse_rgn_2011_clipped.shp\"), quiet = TRUE)\n\n# Download administrative boundaries for whole UK at local authority level\nif (file.exists(here(\"data\", \"infuse_dist_lyr_2011_clipped.shp\")) == FALSE) {\ndownload.file(\"https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_dist_lyr_2011_clipped.zip\", destfile = \"data/infuse_dist_lyr_2011_clipped.zip\")\nunzip(\"data/infuse_dist_lyr_2011_clipped.zip\", exdir = \"data\")\n}\nlocal_authorities &lt;- st_read(here(\"data\", \"infuse_dist_lyr_2011_clipped.shp\"), quiet = TRUE)\n\n# Download building outlines for whole UK\nif (file.exists(here(\"data\", \"infuse_dist_lyr_2011_simplified_100m_buildings_simplified.gpkg\")) == FALSE) {\n  download.file(\"https://zenodo.org/record/6395804/files/infuse_dist_lyr_2011_simplified_100m_buildings_overlay_simplified.gpkg\", destfile = here(\"data\", \"infuse_dist_lyr_2011_simplified_100m_buildings_simplified.gpkg\"))}\nlocal_authorities_buildings_clip &lt;- st_read(here(\"data\", \"infuse_dist_lyr_2011_simplified_100m_buildings_simplified.gpkg\"), quiet = TRUE)\n\nBefore we move on, let’s plot a simple map and have a look at one of our administrative layers. We can use ggplot with a new type of shape geom_sf() to plot the contents of a geospatial data file with polygons which is loaded as a simplefeature in R.\n\nggplot(uk_countries) + geom_sf()"
+  },
+  {
+    "objectID": "chapter_3.html#load-in-ordnance-survey-openmap-points-data",
+    "href": "chapter_3.html#load-in-ordnance-survey-openmap-points-data",
+    "title": "3  Mapping churches: geospatial data science",
+    "section": "3.2 Load in Ordnance Survey OpenMap Points Data",
+    "text": "3.2 Load in Ordnance Survey OpenMap Points Data\n\n# Note: for more advanced reproducible scripts which demonstrate how these data surces have been \n# obtained, see the companion cookbook here: https://github.com/kidwellj/hacking_religion_cookbook/blob/main/ordnance_survey.R\n\nos_openmap_pow &lt;- st_read(here(\"data\", \"os_openmap_pow.gpkg\"), quiet = TRUE)\n\nggplot(os_openmap_pow) + geom_sf()\n\n\n\n\nIt’s worth noting that the way that you load geospatial data in R has changed quite dramatically since 2020 with the introduction of the simplefeature class in R. Much of the documentation you will come across “out there” will make reference to a set of functions which are now deprecated.\nLet’s use that data we’ve just loaded to make our first map:\n\n# Generate choropleth map of respondent locations\n# using temporary palette here so that 0s are white\nlibrary(tmap) |&gt; suppressPackageStartupMessages()\n# palette &lt;- c(white, \"#a8ddb5\", \"#43a2ca\")\n\nmap1 &lt;- tm_shape(local_authorities) + \n#  tm_fill(col = \"surveys_count\", , palette = palette, title = \"Concentration of survey respondents\") +\n  tm_borders(alpha=.5, lwd=0.1) +\n  # for intermediate polygon geometries\n  # tm_shape(local_authorities) +\n  # tm_borders(lwd=0.6) +\n  # for dots from original dataset\n  # tm_dots(\"red\", size = .05, alpha = .4) +\n  tm_scale_bar(position = c(\"right\", \"bottom\")) +\n  tm_style(\"gray\") +\n  tm_credits(\"Data: UK Data Service (OGL)\\n& Jeremy H. Kidwell,\\nGraphic is CC-by-SA 4.0\", \n             size = 0.4, \n             position = c(\"left\", \"bottom\"),\n             just = c(\"left\", \"bottom\"),\n             align = \"left\") +\n  tm_layout(asp = NA,\n            frame = FALSE, \n            title = \"Figure 1a\", \n            title.size = .7,\n            legend.title.size = .7,\n            inner.margins = c(0.1, 0.1, 0.05, 0.05)\n  )\n\nmap1\n\n\n\n# save image\ntmap_save(map1, here(\"figures\", \"map.png\"), width=1920, height=1080, asp=0)\n\nMap saved to /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion/figures/map.png\n\n\nResolution: 1920 by 1080 pixels\n\n\nSize: 6.4 by 3.6 inches (300 dpi)\n\n\n\n# subsetting ordnance survey openmap data for measuring clusters and proximity\n\nos_openmap_important_buildings &lt;- st_read(here(\"data\", \"os_openmap_important_buildings.gpkg\"), quiet = TRUE)\n\n# add pubs, check_cashing, pawnbrokers, SSSI\n## subsets\n\n\nCount the number of churches in Local Authorities\n\n\n# OSM data\n\n# Note: for more advanced reproducible scripts which demonstrate how these data surces have been \n# obtained, see the companion cookbook here: https://github.com/kidwellj/hacking_religion_cookbook/blob/main/ordnance_survey.R\n\n\n# osm_uk_points &lt;- st_read(system.file(here(\"data\", \"pow_osm.gpkg\", package = \"spData\")))\n# vector_filepath = system.file(\"data/osm-gb-2018Aug29_pow_osm.pbf\", package = \"sf\")\n# osm_uk_points = st_read(vector_filepath)\n\nGuides to geographies: https://rconsortium.github.io/censusguide/ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistical-geographies/\nCalculate proximity to pubs"
+  },
+  {
+    "objectID": "chapter_3.html#references",
+    "href": "chapter_3.html#references",
+    "title": "3  Mapping churches: geospatial data science",
+    "section": "References",
+    "text": "References"
   },
   {
     "objectID": "chapter_4.html",
diff --git a/docs/summary.html b/docs/summary.html
index 1420c3b..bad31e9 100644
--- a/docs/summary.html
+++ b/docs/summary.html
@@ -102,12 +102,6 @@ ul.task-list li input[type="checkbox"] {
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./index.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Introduction: Hacking Religion</span></a>
   </div>
 </li>
diff --git a/hacking_religion/_book/index.html b/hacking_religion/_book/index.html
index 8dbfba3..606fa94 100644
--- a/hacking_religion/_book/index.html
+++ b/hacking_religion/_book/index.html
@@ -32,7 +32,7 @@ ul.task-list li input[type="checkbox"] {
 <script src="site_libs/quarto-search/fuse.min.js"></script>
 <script src="site_libs/quarto-search/quarto-search.js"></script>
 <meta name="quarto:offset" content="./">
-<link href="./intro.html" rel="next">
+<link href="./chapter_1.html" rel="next">
 <script src="site_libs/quarto-html/quarto.js"></script>
 <script src="site_libs/quarto-html/popper.min.js"></script>
 <script src="site_libs/quarto-html/tippy.umd.min.js"></script>
@@ -75,7 +75,7 @@ ul.task-list li input[type="checkbox"] {
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./index.html">Preface</a></li></ol></nav>
+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./index.html">Introduction: Hacking Religion</a></li></ol></nav>
       <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
       </a>
       <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
@@ -103,12 +103,6 @@ ul.task-list li input[type="checkbox"] {
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./index.html" class="sidebar-item-text sidebar-link active">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Introduction: Hacking Religion</span></a>
   </div>
 </li>
@@ -164,7 +158,13 @@ ul.task-list li input[type="checkbox"] {
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#preface" id="toc-preface" class="nav-link active" data-scroll-target="#preface">Preface</a></li>
+  <li><a href="#introduction-hacking-religion" id="toc-introduction-hacking-religion" class="nav-link active" data-scroll-target="#introduction-hacking-religion">Introduction: Hacking Religion</a>
+  <ul class="collapse">
+  <li><a href="#why-this-book" id="toc-why-this-book" class="nav-link" data-scroll-target="#why-this-book">Why this book?</a></li>
+  <li><a href="#the-hacker-way" id="toc-the-hacker-way" class="nav-link" data-scroll-target="#the-hacker-way">The hacker way</a></li>
+  <li><a href="#learning-to-code-my-way" id="toc-learning-to-code-my-way" class="nav-link" data-scroll-target="#learning-to-code-my-way">Learning to code: my way</a></li>
+  <li><a href="#getting-set-up" id="toc-getting-set-up" class="nav-link" data-scroll-target="#getting-set-up">Getting set up</a></li>
+  </ul></li>
   </ul>
 </nav>
     </div>
@@ -200,12 +200,42 @@ ul.task-list li input[type="checkbox"] {
 
 </header>
 
-<section id="preface" class="level1 unnumbered">
-<h1 class="unnumbered">Preface</h1>
-<p>This is a Quarto book.</p>
-<p>To learn more about Quarto books visit <a href="https://quarto.org/docs/books" class="uri">https://quarto.org/docs/books</a>.</p>
+<section id="introduction-hacking-religion" class="level1 unnumbered">
+<h1 class="unnumbered">Introduction: Hacking Religion</h1>
+<section id="why-this-book" class="level2">
+<h2 class="anchored" data-anchor-id="why-this-book">Why this book?</h2>
+<p>Data science is quickly consolidating as a new field, with new tools and user communities emerging seemingly every week! At the same time the field of academic research has opened up into new interdisciplinary vistas, with experts crossing over into new fields, transgressing disciplinary boundaries and deploying tools in new and unexpected ways to develop knowledge. There are many gaps yet to be filled, but one which I found to be particularly glaring is the lack of applied data science documentation around the subject of religion. On one hand, scholars who are working with cutting edge theory seldom pick up the emerging tools of data science. On the other hand, data scientists rarely go beyond dabbling in religious themes. This book aims to bring these two things together: introducing the tools of data science in an applied way, whilst introducing some of the complexities and cutting edge theories which help us to conceptualise and frame our understanding of this knowledge.</p>
+</section>
+<section id="the-hacker-way" class="level2">
+<h2 class="anchored" data-anchor-id="the-hacker-way">The hacker way</h2>
+<p>It’s worth emphasising at the outset that this isn’t meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values, and social action. A fair bit of ink has been spilled in books, magazines, blogs, zines, and tweets unpacking what exactly it means to be a “hacker”. Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation.</p>
+<p>Back in the 1980s Steven Levy tried to capture some of this in his book “Hackers: Heroes of the Computer Revolution”. As Levy put it, the “hacker ethic” included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn’t just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. It can be a more substantial ethical code.</p>
+<p>This emphasis on ethics is especially important when we’re doing data science because this kind of research work will put you in positions of influence and grant you power over others. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows some sort of social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I’ve met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it’s possible that this can quickly get out of hand. It’s important to have a clear sense of what sorts of convictions guide your work in this field, a “hacker code” of sorts. With this in mind, I’d like to share with you my own set of principles:</p>
+<p>It never ceases to amaze me how often people think that, when they’re working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you’ve been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond our true apprehension of a situation.</p>
+<p>Presenting through “facts” an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you take up the hacker way, it’s vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I’ll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge help to build up a community of accountability. The upside of this is that it’s also a lot more fun and interesting to work alongside others.</p>
+<p>Far too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There’s also the potential that when we make choices based on reliable information drawn from everyday reality like this our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of “learning by doing” and it’s this approach that guides my approach in this book. This isn’t just a book about data analysis, I’m proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of the commons. It takes a bit more time to learn and set things up, but the upside is that you’ll gain access to a set of tools and a research philosophy which is much more powerful.</p>
+<p>Here’s a quick summary of these principles, which I’ll return to periodically as we work through the coding and data in this book:</p>
+<ol type="1">
+<li>Tell the truth: Be candid about your limits, use visualisation responsibly</li>
+<li>Work transparently: Open data, open code</li>
+<li>Work in community, draw others in by producing reproducible research</li>
+<li>Work with reality, learn by doing</li>
+</ol>
+</section>
+<section id="learning-to-code-my-way" class="level2">
+<h2 class="anchored" data-anchor-id="learning-to-code-my-way">Learning to code: my way</h2>
+<p>This guide is a little different from other textbooks targetting learning to code. I remember when I was first starting out, I went through a fair few guides, and they all tended to spend about 200 pages on various theoretical bits, how you form an integer, or data structures, subroutines, the logical structure of algorithms or whatever. It was usuallyweeks of reading before I got to actually <em>do</em> anything. I know some people may prefer this approach, but I prefer a problem-focussed approach to learning. Give me something that is broken, or a problem to solve, which engages the things I want to figure out and the motivation for learning just comes much more naturally. And we know from research in cognitive science that these kinds of problem-focussed approaches can tend to faciliate faster learning and better retention, so it’s not just my personal preference, but also justified! It will be helpful for you to be aware of this approach when you get into the book as it explains some of the editorial choices I’ve made and the way I’ve structured things. Each chapter focusses on a <em>problem</em> which is particularly salient for the use of data science to conduct research into religion. That problem will be my focal point, guiding choices of specific aspects of programming to introduce to you as we work our way around that data set and some of the crucial questions that arise in terms of how we handle it. If you find this approach unsatisfying, luckily there are a number of really terrific guides which lay things out slowly and methodically and I will explicitly signpost some of these along the way so that you can do a “deep dive” when you feel like it. Otherwise, I’ll take an accelerated approach to this introduction to data science in R. I expect that you will identify adjacent resources and perhaps even come up with your own creative approaches along the way, which incidentally is how real data science tends to work in practice.</p>
+<p>There are a range of terrific textbooks out there which cover all these elements in greater depth and more slowly. In particular, I’d recommend that many readers will want to check out Hadley Wickham’s “R For Data Science” book. I’ll include marginal notes in this guide pointing to sections of that book, and a few others which unpack the basic mechanics of R in more detail.</p>
+</section>
+<section id="getting-set-up" class="level2">
+<h2 class="anchored" data-anchor-id="getting-set-up">Getting set up</h2>
+<p>Every single tool, programming language and data set we refer to in this book is free and open source. These tools have been produced by professionals and volunteers who are passionate about data science and research and want to share it with the world, and in order to do this (and following the “hacker way”) they’ve made these tools freely available. This also means that you aren’t restricted to a specific proprietary, expensive, or unavailable piece of software to do this work. I’ll make a few opinionated recommendations here based on my own preferences and experience, but it’s really up to your own style and approach. In fact, given that this is an open source textbook, you can even propose additions to this chapter explaining other tools you’ve found that you want to share with others.</p>
+<p>There are, right now, primarily two languages that statisticians and data scientists use for this kind of programmatic data science: python and R. Each language has its merits and I won’t rehash the debates between various factions. For this book, we’ll be using the R language. This is, in part, because the R user community and libraries tend to scale a bit better for the work that I’m commending in this book. However, it’s entirely possible that one could use python for all these exercises, and perhaps in the future we’ll have volume two of this book outlining python approaches to the same operations.</p>
+<p>Bearing this in mind, the first step you’ll need to take is to download and install R. You can find instructions and install packages for a wide range of hardware on the The Comprehensive R Archive Network (or “CRAN”): https://cran.rstudio.com. Once you’ve installed R, you’ve got some choices to make about the kind of programming environment you’d like to use. You can just use a plain text editor like <code>textedit</code> to write your code and then execute your programs using the R software you’ve just installed. However, most users, myself included, tend to use an integrated development environment (or “IDE”). This is usually another software package with a guided user interface and some visual elements that make it faster to write and test your code. Some IDE packages, will have built-in reference tools so you can look up options for libraries you use in your code, they will allow you to visualise the results of your code execution, and perhaps most important of all, will enable you to execute your programs line by line so you can spot errors more quickly (we call this “debugging”). The two most popular IDE platforms for R coding at the time of writing this textbook are RStudio and Visual Studio. You should download and try out both and stick with your favourite, as the differences are largely aesthetic. I use a combination of RStudio and an enhanced plain text editor Sublime Text for my coding.</p>
+<p>Once you have R and your pick of an IDE, you are ready to go! Proceed to the next chapter and we’ll dive right in and get started!</p>
 
 
+</section>
 </section>
 
 </main> <!-- /main -->
@@ -446,8 +476,8 @@ window.document.addEventListener("DOMContentLoaded", function (event) {
   <div class="nav-page nav-page-previous">
   </div>
   <div class="nav-page nav-page-next">
-      <a href="./intro.html" class="pagination-link">
-        <span class="nav-page-text">Introduction: Hacking Religion</span> <i class="bi bi-arrow-right-short"></i>
+      <a href="./chapter_1.html" class="pagination-link">
+        <span class="nav-page-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">The 2021 UK Census</span></span> <i class="bi bi-arrow-right-short"></i>
       </a>
   </div>
 </nav>
diff --git a/hacking_religion/_book/intro.html b/hacking_religion/_book/intro.html
deleted file mode 100644
index c374a89..0000000
--- a/hacking_religion/_book/intro.html
+++ /dev/null
@@ -1,478 +0,0 @@
-<!DOCTYPE html>
-<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
-
-<meta charset="utf-8">
-<meta name="generator" content="quarto-1.3.450">
-
-<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
-
-
-<title>Hacking Religion: TRS &amp; Data Science in Action - Introduction: Hacking Religion</title>
-<style>
-code{white-space: pre-wrap;}
-span.smallcaps{font-variant: small-caps;}
-div.columns{display: flex; gap: min(4vw, 1.5em);}
-div.column{flex: auto; overflow-x: auto;}
-div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
-ul.task-list{list-style: none;}
-ul.task-list li input[type="checkbox"] {
-  width: 0.8em;
-  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
-  vertical-align: middle;
-}
-</style>
-
-
-<script src="site_libs/quarto-nav/quarto-nav.js"></script>
-<script src="site_libs/quarto-nav/headroom.min.js"></script>
-<script src="site_libs/clipboard/clipboard.min.js"></script>
-<script src="site_libs/quarto-search/autocomplete.umd.js"></script>
-<script src="site_libs/quarto-search/fuse.min.js"></script>
-<script src="site_libs/quarto-search/quarto-search.js"></script>
-<meta name="quarto:offset" content="./">
-<link href="./chapter_1.html" rel="next">
-<link href="./index.html" rel="prev">
-<script src="site_libs/quarto-html/quarto.js"></script>
-<script src="site_libs/quarto-html/popper.min.js"></script>
-<script src="site_libs/quarto-html/tippy.umd.min.js"></script>
-<script src="site_libs/quarto-html/anchor.min.js"></script>
-<link href="site_libs/quarto-html/tippy.css" rel="stylesheet">
-<link href="site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles">
-<script src="site_libs/bootstrap/bootstrap.min.js"></script>
-<link href="site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
-<link href="site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
-<script id="quarto-search-options" type="application/json">{
-  "location": "sidebar",
-  "copy-button": false,
-  "collapse-after": 3,
-  "panel-placement": "start",
-  "type": "textbox",
-  "limit": 20,
-  "language": {
-    "search-no-results-text": "No results",
-    "search-matching-documents-text": "matching documents",
-    "search-copy-link-title": "Copy link to search",
-    "search-hide-matches-text": "Hide additional matches",
-    "search-more-match-text": "more match in this document",
-    "search-more-matches-text": "more matches in this document",
-    "search-clear-button-title": "Clear",
-    "search-detached-cancel-button-title": "Cancel",
-    "search-submit-button-title": "Submit",
-    "search-label": "Search"
-  }
-}</script>
-
-
-</head>
-
-<body class="nav-sidebar floating">
-
-<div id="quarto-search-results"></div>
-  <header id="quarto-header" class="headroom fixed-top">
-  <nav class="quarto-secondary-nav">
-    <div class="container-fluid d-flex">
-      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
-        <i class="bi bi-layout-text-sidebar-reverse"></i>
-      </button>
-      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./intro.html">Introduction: Hacking Religion</a></li></ol></nav>
-      <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
-      </a>
-      <button type="button" class="btn quarto-search-button" aria-label="" onclick="window.quartoOpenSearch();">
-        <i class="bi bi-search"></i>
-      </button>
-    </div>
-  </nav>
-</header>
-<!-- content -->
-<div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article">
-<!-- sidebar -->
-  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
-    <div class="pt-lg-2 mt-2 text-left sidebar-header">
-    <div class="sidebar-title mb-0 py-0">
-      <a href="./">Hacking Religion: TRS &amp; Data Science in Action</a> 
-    </div>
-      </div>
-        <div class="mt-2 flex-shrink-0 align-items-center">
-        <div class="sidebar-search">
-        <div id="quarto-search" class="" title="Search"></div>
-        </div>
-        </div>
-    <div class="sidebar-menu-container"> 
-    <ul class="list-unstyled mt-1">
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./index.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Preface</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./intro.html" class="sidebar-item-text sidebar-link active">
- <span class="menu-text">Introduction: Hacking Religion</span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./chapter_1.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">The 2021 UK Census</span></span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./chapter_2.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Survey Data: Spotlight Project</span></span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./chapter_3.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Mapping churches: geospatial data science</span></span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./chapter_4.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Data scraping, corpus analysis and wordclouds</span></span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./chapter_5.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">5</span>&nbsp; <span class="chapter-title">What’s next?</span></span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./summary.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Summary</span></span></a>
-  </div>
-</li>
-        <li class="sidebar-item">
-  <div class="sidebar-item-container"> 
-  <a href="./references.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">References</span></a>
-  </div>
-</li>
-    </ul>
-    </div>
-</nav>
-<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
-<!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
-        <nav id="TOC" role="doc-toc" class="toc-active">
-    <h2 id="toc-title">Table of contents</h2>
-   
-  <ul>
-  <li><a href="#why-this-book" id="toc-why-this-book" class="nav-link active" data-scroll-target="#why-this-book">Why this book?</a></li>
-  <li><a href="#the-hacker-way" id="toc-the-hacker-way" class="nav-link" data-scroll-target="#the-hacker-way">The hacker way</a></li>
-  <li><a href="#learning-to-code-my-way" id="toc-learning-to-code-my-way" class="nav-link" data-scroll-target="#learning-to-code-my-way">Learning to code: my way</a></li>
-  <li><a href="#getting-set-up" id="toc-getting-set-up" class="nav-link" data-scroll-target="#getting-set-up">Getting set up</a></li>
-  </ul>
-</nav>
-    </div>
-<!-- main -->
-<main class="content" id="quarto-document-content">
-
-<header id="title-block-header" class="quarto-title-block default">
-<div class="quarto-title">
-<h1 class="title">Introduction: Hacking Religion</h1>
-</div>
-
-
-
-<div class="quarto-title-meta">
-
-    
-  
-    
-  </div>
-  
-
-</header>
-
-<section id="why-this-book" class="level2">
-<h2 class="anchored" data-anchor-id="why-this-book">Why this book?</h2>
-<p>Data science is quickly consolidating as a new field, with new tools and user communities emerging seemingly every week! At the same time the field of academic research has opened up into new interdisciplinary vistas, with experts crossing over into new fields, transgressing disciplinary boundaries and deploying tools in new and unexpected ways to develop knowledge. There are many gaps yet to be filled, but one which I found to be particularly glaring is the lack of applied data science documentation around the subject of religion. On one hand, scholars who are working with cutting edge theory seldom pick up the emerging tools of data science. On the other hand, data scientists rarely go beyond dabbling in religious themes. This book aims to bring these two things together: introducing the tools of data science in an applied way, whilst introducing some of the complexities and cutting edge theories which help us to conceptualise and frame our understanding of this knowledge.</p>
-</section>
-<section id="the-hacker-way" class="level2">
-<h2 class="anchored" data-anchor-id="the-hacker-way">The hacker way</h2>
-<p>It’s worth emphasising at the outset that this isn’t meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values, and social action. A fair bit of ink has been spilled in books, magazines, blogs, zines, and tweets unpacking what exactly it means to be a “hacker”. Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation.</p>
-<p>Back in the 1980s Steven Levy tried to capture some of this in his book “Hackers: Heroes of the Computer Revolution”. As Levy put it, the “hacker ethic” included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn’t just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. It can be a more substantial ethical code.</p>
-<p>This emphasis on ethics is especially important when we’re doing data science because this kind of research work will put you in positions of influence and grant you power over others. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows some sort of social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I’ve met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it’s possible that this can quickly get out of hand. It’s important to have a clear sense of what sorts of convictions guide your work in this field, a “hacker code” of sorts. With this in mind, I’d like to share with you my own set of principles:</p>
-<p>It never ceases to amaze me how often people think that, when they’re working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you’ve been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond our true apprehension of a situation.</p>
-<p>Presenting through “facts” an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you take up the hacker way, it’s vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I’ll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge help to build up a community of accountability. The upside of this is that it’s also a lot more fun and interesting to work alongside others.</p>
-<p>Far too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There’s also the potential that when we make choices based on reliable information drawn from everyday reality like this our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of “learning by doing” and it’s this approach that guides my approach in this book. This isn’t just a book about data analysis, I’m proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of the commons. It takes a bit more time to learn and set things up, but the upside is that you’ll gain access to a set of tools and a research philosophy which is much more powerful.</p>
-<p>Here’s a quick summary of these principles, which I’ll return to periodically as we work through the coding and data in this book:</p>
-<ol type="1">
-<li>Tell the truth: Be candid about your limits, use visualisation responsibly</li>
-<li>Work transparently: Open data, open code</li>
-<li>Work in community, draw others in by producing reproducible research</li>
-<li>Work with reality, learn by doing</li>
-</ol>
-</section>
-<section id="learning-to-code-my-way" class="level2">
-<h2 class="anchored" data-anchor-id="learning-to-code-my-way">Learning to code: my way</h2>
-<p>This guide is a little different from other textbooks targetting learning to code. I remember when I was first starting out, I went through a fair few guides, and they all tended to spend about 200 pages on various theoretical bits, how you form an integer, or data structures, subroutines, the logical structure of algorithms or whatever. It was usuallyweeks of reading before I got to actually <em>do</em> anything. I know some people may prefer this approach, but I prefer a problem-focussed approach to learning. Give me something that is broken, or a problem to solve, which engages the things I want to figure out and the motivation for learning just comes much more naturally. And we know from research in cognitive science that these kinds of problem-focussed approaches can tend to faciliate faster learning and better retention, so it’s not just my personal preference, but also justified! It will be helpful for you to be aware of this approach when you get into the book as it explains some of the editorial choices I’ve made and the way I’ve structured things. Each chapter focusses on a <em>problem</em> which is particularly salient for the use of data science to conduct research into religion. That problem will be my focal point, guiding choices of specific aspects of programming to introduce to you as we work our way around that data set and some of the crucial questions that arise in terms of how we handle it. If you find this approach unsatisfying, luckily there are a number of really terrific guides which lay things out slowly and methodically and I will explicitly signpost some of these along the way so that you can do a “deep dive” when you feel like it. Otherwise, I’ll take an accelerated approach to this introduction to data science in R. I expect that you will identify adjacent resources and perhaps even come up with your own creative approaches along the way, which incidentally is how real data science tends to work in practice.</p>
-<p>There are a range of terrific textbooks out there which cover all these elements in greater depth and more slowly. In particular, I’d recommend that many readers will want to check out Hadley Wickham’s “R For Data Science” book. I’ll include marginal notes in this guide pointing to sections of that book, and a few others which unpack the basic mechanics of R in more detail.</p>
-</section>
-<section id="getting-set-up" class="level2">
-<h2 class="anchored" data-anchor-id="getting-set-up">Getting set up</h2>
-<p>Every single tool, programming language and data set we refer to in this book is free and open source. These tools have been produced by professionals and volunteers who are passionate about data science and research and want to share it with the world, and in order to do this (and following the “hacker way”) they’ve made these tools freely available. This also means that you aren’t restricted to a specific proprietary, expensive, or unavailable piece of software to do this work. I’ll make a few opinionated recommendations here based on my own preferences and experience, but it’s really up to your own style and approach. In fact, given that this is an open source textbook, you can even propose additions to this chapter explaining other tools you’ve found that you want to share with others.</p>
-<p>There are, right now, primarily two languages that statisticians and data scientists use for this kind of programmatic data science: python and R. Each language has its merits and I won’t rehash the debates between various factions. For this book, we’ll be using the R language. This is, in part, because the R user community and libraries tend to scale a bit better for the work that I’m commending in this book. However, it’s entirely possible that one could use python for all these exercises, and perhaps in the future we’ll have volume two of this book outlining python approaches to the same operations.</p>
-<p>Bearing this in mind, the first step you’ll need to take is to download and install R. You can find instructions and install packages for a wide range of hardware on the The Comprehensive R Archive Network (or “CRAN”): https://cran.rstudio.com. Once you’ve installed R, you’ve got some choices to make about the kind of programming environment you’d like to use. You can just use a plain text editor like <code>textedit</code> to write your code and then execute your programs using the R software you’ve just installed. However, most users, myself included, tend to use an integrated development environment (or “IDE”). This is usually another software package with a guided user interface and some visual elements that make it faster to write and test your code. Some IDE packages, will have built-in reference tools so you can look up options for libraries you use in your code, they will allow you to visualise the results of your code execution, and perhaps most important of all, will enable you to execute your programs line by line so you can spot errors more quickly (we call this “debugging”). The two most popular IDE platforms for R coding at the time of writing this textbook are RStudio and Visual Studio. You should download and try out both and stick with your favourite, as the differences are largely aesthetic. I use a combination of RStudio and an enhanced plain text editor Sublime Text for my coding.</p>
-<p>Once you have R and your pick of an IDE, you are ready to go! Proceed to the next chapter and we’ll dive right in and get started!</p>
-
-
-</section>
-
-</main> <!-- /main -->
-<script id="quarto-html-after-body" type="application/javascript">
-window.document.addEventListener("DOMContentLoaded", function (event) {
-  const toggleBodyColorMode = (bsSheetEl) => {
-    const mode = bsSheetEl.getAttribute("data-mode");
-    const bodyEl = window.document.querySelector("body");
-    if (mode === "dark") {
-      bodyEl.classList.add("quarto-dark");
-      bodyEl.classList.remove("quarto-light");
-    } else {
-      bodyEl.classList.add("quarto-light");
-      bodyEl.classList.remove("quarto-dark");
-    }
-  }
-  const toggleBodyColorPrimary = () => {
-    const bsSheetEl = window.document.querySelector("link#quarto-bootstrap");
-    if (bsSheetEl) {
-      toggleBodyColorMode(bsSheetEl);
-    }
-  }
-  toggleBodyColorPrimary();  
-  const icon = "";
-  const anchorJS = new window.AnchorJS();
-  anchorJS.options = {
-    placement: 'right',
-    icon: icon
-  };
-  anchorJS.add('.anchored');
-  const isCodeAnnotation = (el) => {
-    for (const clz of el.classList) {
-      if (clz.startsWith('code-annotation-')) {                     
-        return true;
-      }
-    }
-    return false;
-  }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
-    // button target
-    const button = e.trigger;
-    // don't keep focus
-    button.blur();
-    // flash "checked"
-    button.classList.add('code-copy-button-checked');
-    var currentTitle = button.getAttribute("title");
-    button.setAttribute("title", "Copied!");
-    let tooltip;
-    if (window.bootstrap) {
-      button.setAttribute("data-bs-toggle", "tooltip");
-      button.setAttribute("data-bs-placement", "left");
-      button.setAttribute("data-bs-title", "Copied!");
-      tooltip = new bootstrap.Tooltip(button, 
-        { trigger: "manual", 
-          customClass: "code-copy-button-tooltip",
-          offset: [0, -8]});
-      tooltip.show();    
-    }
-    setTimeout(function() {
-      if (tooltip) {
-        tooltip.hide();
-        button.removeAttribute("data-bs-title");
-        button.removeAttribute("data-bs-toggle");
-        button.removeAttribute("data-bs-placement");
-      }
-      button.setAttribute("title", currentTitle);
-      button.classList.remove('code-copy-button-checked');
-    }, 1000);
-    // clear code selection
-    e.clearSelection();
-  });
-  function tippyHover(el, contentFn) {
-    const config = {
-      allowHTML: true,
-      content: contentFn,
-      maxWidth: 500,
-      delay: 100,
-      arrow: false,
-      appendTo: function(el) {
-          return el.parentElement;
-      },
-      interactive: true,
-      interactiveBorder: 10,
-      theme: 'quarto',
-      placement: 'bottom-start'
-    };
-    window.tippy(el, config); 
-  }
-  const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
-  for (var i=0; i<noterefs.length; i++) {
-    const ref = noterefs[i];
-    tippyHover(ref, function() {
-      // use id or data attribute instead here
-      let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
-      try { href = new URL(href).hash; } catch {}
-      const id = href.replace(/^#\/?/, "");
-      const note = window.document.getElementById(id);
-      return note.innerHTML;
-    });
-  }
-      let selectedAnnoteEl;
-      const selectorForAnnotation = ( cell, annotation) => {
-        let cellAttr = 'data-code-cell="' + cell + '"';
-        let lineAttr = 'data-code-annotation="' +  annotation + '"';
-        const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
-        return selector;
-      }
-      const selectCodeLines = (annoteEl) => {
-        const doc = window.document;
-        const targetCell = annoteEl.getAttribute("data-target-cell");
-        const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
-        const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
-        const lines = annoteSpan.getAttribute("data-code-lines").split(",");
-        const lineIds = lines.map((line) => {
-          return targetCell + "-" + line;
-        })
-        let top = null;
-        let height = null;
-        let parent = null;
-        if (lineIds.length > 0) {
-            //compute the position of the single el (top and bottom and make a div)
-            const el = window.document.getElementById(lineIds[0]);
-            top = el.offsetTop;
-            height = el.offsetHeight;
-            parent = el.parentElement.parentElement;
-          if (lineIds.length > 1) {
-            const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
-            const bottom = lastEl.offsetTop + lastEl.offsetHeight;
-            height = bottom - top;
-          }
-          if (top !== null && height !== null && parent !== null) {
-            // cook up a div (if necessary) and position it 
-            let div = window.document.getElementById("code-annotation-line-highlight");
-            if (div === null) {
-              div = window.document.createElement("div");
-              div.setAttribute("id", "code-annotation-line-highlight");
-              div.style.position = 'absolute';
-              parent.appendChild(div);
-            }
-            div.style.top = top - 2 + "px";
-            div.style.height = height + 4 + "px";
-            let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
-            if (gutterDiv === null) {
-              gutterDiv = window.document.createElement("div");
-              gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
-              gutterDiv.style.position = 'absolute';
-              const codeCell = window.document.getElementById(targetCell);
-              const gutter = codeCell.querySelector('.code-annotation-gutter');
-              gutter.appendChild(gutterDiv);
-            }
-            gutterDiv.style.top = top - 2 + "px";
-            gutterDiv.style.height = height + 4 + "px";
-          }
-          selectedAnnoteEl = annoteEl;
-        }
-      };
-      const unselectCodeLines = () => {
-        const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
-        elementsIds.forEach((elId) => {
-          const div = window.document.getElementById(elId);
-          if (div) {
-            div.remove();
-          }
-        });
-        selectedAnnoteEl = undefined;
-      };
-      // Attach click handler to the DT
-      const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
-      for (const annoteDlNode of annoteDls) {
-        annoteDlNode.addEventListener('click', (event) => {
-          const clickedEl = event.target;
-          if (clickedEl !== selectedAnnoteEl) {
-            unselectCodeLines();
-            const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
-            if (activeEl) {
-              activeEl.classList.remove('code-annotation-active');
-            }
-            selectCodeLines(clickedEl);
-            clickedEl.classList.add('code-annotation-active');
-          } else {
-            // Unselect the line
-            unselectCodeLines();
-            clickedEl.classList.remove('code-annotation-active');
-          }
-        });
-      }
-  const findCites = (el) => {
-    const parentEl = el.parentElement;
-    if (parentEl) {
-      const cites = parentEl.dataset.cites;
-      if (cites) {
-        return {
-          el,
-          cites: cites.split(' ')
-        };
-      } else {
-        return findCites(el.parentElement)
-      }
-    } else {
-      return undefined;
-    }
-  };
-  var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
-  for (var i=0; i<bibliorefs.length; i++) {
-    const ref = bibliorefs[i];
-    const citeInfo = findCites(ref);
-    if (citeInfo) {
-      tippyHover(citeInfo.el, function() {
-        var popup = window.document.createElement('div');
-        citeInfo.cites.forEach(function(cite) {
-          var citeDiv = window.document.createElement('div');
-          citeDiv.classList.add('hanging-indent');
-          citeDiv.classList.add('csl-entry');
-          var biblioDiv = window.document.getElementById('ref-' + cite);
-          if (biblioDiv) {
-            citeDiv.innerHTML = biblioDiv.innerHTML;
-          }
-          popup.appendChild(citeDiv);
-        });
-        return popup.innerHTML;
-      });
-    }
-  }
-});
-</script>
-<nav class="page-navigation">
-  <div class="nav-page nav-page-previous">
-      <a href="./index.html" class="pagination-link">
-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Preface</span>
-      </a>          
-  </div>
-  <div class="nav-page nav-page-next">
-      <a href="./chapter_1.html" class="pagination-link">
-        <span class="nav-page-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">The 2021 UK Census</span></span> <i class="bi bi-arrow-right-short"></i>
-      </a>
-  </div>
-</nav>
-</div> <!-- /content -->
-
-
-
-</body></html>
\ No newline at end of file
diff --git a/hacking_religion/_quarto.yml b/hacking_religion/_quarto.yml
index f47ad38..226aba4 100644
--- a/hacking_religion/_quarto.yml
+++ b/hacking_religion/_quarto.yml
@@ -7,7 +7,6 @@ book:
   date: "10/16/2023"
   chapters:
     - index.qmd
-    - intro.qmd
     - chapter_1.qmd
     - chapter_2.qmd
     - chapter_3.qmd
diff --git a/hacking_religion/chapter_2.qmd b/hacking_religion/chapter_2.qmd
index 063db21..7d89c29 100644
--- a/hacking_religion/chapter_2.qmd
+++ b/hacking_religion/chapter_2.qmd
@@ -8,7 +8,7 @@ While I've framed my comments above in terms of climate change research, it is a
 
 We've decided to open up access to our data and I'm highlighting it in this book because it's a unique opportunitiy to explore a dataset that emphasises diversity from the start, and by extension, provides some really interesting ways to use data science techniques to explore religion in the UK.
 
-# Loading in some data
+## Loading in some data
 
 ```{r, results = 'hide'}
 # R Setup -----------------------------------------------------------------
@@ -23,7 +23,7 @@ climate_experience_data <- read_sav(here("example_data", "climate_experience_dat
 
 The first thing to note here is that we've drawn in a different type of data file, this time from an `.sav` file, usully produced by the statistics software package SPSS. This uses a different R Library (I use `haven` for this). The upside is that in some cases where you have survey data with both a code and a value like "1" is eqivalent to "very much agree" this will preserve both in the R dataframe that is created. Now that you've loaded in data, you have a new R dataframe called "climate_experience_data" with a lot of columns with just under 1000 survey responses.
 
-# How can you ask about religion?
+## How can you ask about religion?
 
 One of the challenges we faced when running this study is how to gather responsible data from surveys regarding religious identity. We'll dive into this in depth as we do analysis and look at some of the agreements and conflicts in terms of respondent attribution. Just to set the stage, we used the following kinds of question to ask about religion and spirituality:
 
@@ -120,6 +120,7 @@ ggsave("chart.png", plot=plot1, width = 8, height = 10, units=c("in"))
 
 ```
 
+<!--
 Use mutate to put "prefer not to say" at the bottom
 # Info here: https://r4ds.had.co.nz/factors.html#modifying-factor-levels
 
@@ -331,9 +332,10 @@ climate_experience_data <- climate_experience_data %>%
     ) %>% factor(levels = c("low", "medium", "high"))
   )
 
+-->
 
 ::: {.callout-tip}
-## What is Religion?
+### What is Religion?
 Content tbd
 :::
 
@@ -341,7 +343,7 @@ Content tbd
 
 
 ::: {.callout-tip}
-## Hybrid Religious Identity
+### Hybrid Religious Identity
 Content tbd
 :::
 
@@ -350,11 +352,11 @@ Content tbd
 
 
 ::: {.callout-tip}
-## What is Secularisation?
+### What is Secularisation?
 Content tbd
 :::
 
-# References {.unnumbered}
+## References {.unnumbered}
 
 ::: {#refs}
 :::
diff --git a/hacking_religion/chapter_3.qmd b/hacking_religion/chapter_3.qmd
index ddb0e5d..ec72fd9 100644
--- a/hacking_religion/chapter_3.qmd
+++ b/hacking_religion/chapter_3.qmd
@@ -1,4 +1,4 @@
-# Mapping churches: geospatial data science
+## Mapping churches: geospatial data science
 
 Until recently, most data science books didn't have a section on geospatial data. It was considered a specialist form of research best left to GIS technicians who tended to use proprietary tools like ArcGIS. This has changed significantly in the past five years, but you'll still be hard pressed to find an introduction to the subject which strays very far from a few simple data sets (mostly of the USA) and relatively uncomplicated geospatial operations. I actually first began learning R, back in 2013, right when open source geospatial research tools were beginning to be developed with quite a lot more energy and geospatial data is my personal favourite data science playground, so in this book we're going to go much deeper than is usual. There are also good reasons to take things a few steps further in the particular forms of data and inquiry that religion takes us into.
 
@@ -17,7 +17,7 @@ GeoJSON: a file format commonly used in other forms of coding, the "JSON" (an ac
 
 Now that you have a sense of some of the basic aspects of geospatial data, let's dive in and do a bit of learning in action.
 
-# Administrative shapes - the UK
+## Administrative shapes - the UK
 
 A good starting point is to aquire some adminstrative data. This is a way of referring to political boundaries, whether country borders or those of a local authority or some other administrative unit. For our purposes, we're going to import several different types of administrative boundary which will be used at different points in our visualisations below. It's worth noting that the data we use here was prepared to support the 2011 census, and make use of the shapefile format.
 
@@ -63,7 +63,7 @@ Before we move on, let's plot a simple map and have a look at one of our adminis
 ggplot(uk_countries) + geom_sf()
 ```
 
-# Load in Ordnance Survey OpenMap Points Data
+## Load in Ordnance Survey OpenMap Points Data
 
 ```{r}
 # Note: for more advanced reproducible scripts which demonstrate how these data surces have been 
@@ -142,7 +142,7 @@ https://ocsi.uk/2019/03/18/lsoas-leps-and-lookups-a-beginners-guide-to-statistic
 
 Calculate proximity to pubs
 
-# References {.unnumbered}
+## References {.unnumbered}
 
 ::: {#refs}
 :::
\ No newline at end of file
diff --git a/hacking_religion/index.qmd b/hacking_religion/index.qmd
index 2062215..7d124a8 100644
--- a/hacking_religion/index.qmd
+++ b/hacking_religion/index.qmd
@@ -1,5 +1,45 @@
-# Preface {.unnumbered}
+# Introduction: Hacking Religion {.unnumbered}
 
-This is a Quarto book.
+## Why this book?
 
-To learn more about Quarto books visit <https://quarto.org/docs/books>.
+Data science is quickly consolidating as a new field, with new tools and user communities emerging seemingly every week! At the same time the field of academic research has opened up into new interdisciplinary vistas, with experts crossing over into new fields, transgressing disciplinary boundaries and deploying tools in new and unexpected ways to develop knowledge. There are many gaps yet to be filled, but one which I found to be particularly glaring is the lack of applied data science documentation around the subject of religion. On one hand, scholars who are working with cutting edge theory seldom pick up the emerging tools of data science. On the other hand, data scientists rarely go beyond dabbling in religious themes. This book aims to bring these two things together: introducing the tools of data science in an applied way, whilst introducing some of the complexities and cutting edge theories which help us to conceptualise and frame our understanding of this knowledge.
+
+
+## The hacker way
+
+It's worth emphasising at the outset that this isn't meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values, and social action. A fair bit of ink has been spilled in books, magazines, blogs, zines, and tweets unpacking what exactly it means to be a "hacker". Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation. 
+
+Back in the 1980s Steven Levy tried to capture some of this in his book "Hackers: Heroes of the Computer Revolution". As Levy put it, the "hacker ethic" included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn't just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. It can be a more substantial ethical code.
+
+This emphasis on ethics is especially important when we're doing data science because this kind of research work will put you in positions of influence and grant you power over others. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows some sort of social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I've met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it's possible that this can quickly get out of hand. It's important to have a clear sense of what sorts of convictions guide your work in this field, a "hacker code" of sorts. With this in mind, I'd like to share with you my own set of principles:
+
+It never ceases to amaze me how often people think that, when they're working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you've been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond our true apprehension of a situation. 
+
+Presenting through "facts" an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you take up the hacker way, it's vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I'll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge help to build up a community of accountability. The upside of this is that it's also a lot more fun and interesting to work alongside others.
+
+Far too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There's also the potential that when we make choices based on reliable information drawn from everyday reality like this our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of "learning by doing" and it's this approach that guides my approach in this book. This isn't just a book about data analysis, I'm proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of the commons. It takes a bit more time to learn and set things up, but the upside is that you'll gain access to a set of tools and a research philosophy which is much more powerful.
+
+Here's a quick summary of these principles, which I'll return to periodically as we work through the coding and data in this book:
+
+1. Tell the truth: Be candid about your limits, use visualisation responsibly
+2. Work transparently: Open data, open code
+3. Work in community, draw others in by producing reproducible research
+4. Work with reality, learn by doing
+
+
+## Learning to code: my way
+
+This guide is a little different from other textbooks targetting learning to code. I remember when I was first starting out, I went through a fair few guides, and they all tended to spend about 200 pages on various theoretical bits, how you form an integer, or data structures, subroutines, the logical structure of algorithms or whatever. It was usuallyweeks of reading before I got to actually *do* anything. I know some people may prefer this approach, but I prefer a problem-focussed approach to learning. Give me something that is broken, or a problem to solve, which engages the things I want to figure out and the motivation for learning just comes much more naturally. And we know from research in cognitive science that these kinds of problem-focussed approaches can tend to faciliate faster learning and better retention, so it's not just my personal preference, but also justified! It will be helpful for you to be aware of this approach when you get into the book as it explains some of the editorial choices I've made and the way I've structured things. Each chapter focusses on a *problem* which is particularly salient for the use of data science to conduct research into religion. That problem will be my focal point, guiding choices of specific aspects of programming to introduce to you as we work our way around that data set and some of the crucial questions that arise in terms of how we handle it. If you find this approach unsatisfying, luckily there are a number of really terrific guides which lay things out slowly and methodically and I will explicitly signpost some of these along the way so that you can do a "deep dive" when you feel like it. Otherwise, I'll take an accelerated approach to this introduction to data science in R. I expect that you will identify adjacent resources and perhaps even come up with your own creative approaches along the way, which incidentally is how real data science tends to work in practice.
+
+There are a range of terrific textbooks out there which cover all these elements in greater depth and more slowly. In particular, I'd recommend that many readers will want to check out Hadley Wickham's "R For Data Science" book. I'll include marginal notes in this guide pointing to sections of that book, and a few others which unpack the basic mechanics of R in more detail.
+
+
+## Getting set up
+
+Every single tool, programming language and data set we refer to in this book is free and open source. These tools have been produced by professionals and volunteers who are passionate about data science and research and want to share it with the world, and in order to do this (and following the "hacker way") they've made these tools freely available. This also means that you aren't restricted to a specific proprietary, expensive, or unavailable piece of software to do this work. I'll make a few opinionated recommendations here based on my own preferences and experience, but it's really up to your own style and approach. In fact, given that this is an open source textbook, you can even propose additions to this chapter explaining other tools you've found that you want to share with others.
+
+There are, right now, primarily two languages that statisticians and data scientists use for this kind of programmatic data science: python and R. Each language has its merits and I won't rehash the debates between various factions. For this book, we'll be using the R language. This is, in part, because the R user community and libraries tend to scale a bit better for the work that I'm commending in this book. However, it's entirely possible that one could use python for all these exercises, and perhaps in the future we'll have volume two of this book outlining python approaches to the same operations.
+
+Bearing this in mind, the first step you'll need to take is to download and install R. You can find instructions and install packages for a wide range of hardware on the The Comprehensive R Archive Network (or "CRAN"): https://cran.rstudio.com. Once you've installed R, you've got some choices to make about the kind of programming environment you'd like to use. You can just use a plain text editor like `textedit` to write your code and then execute your programs using the R software you've just installed. However, most users, myself included, tend to use an integrated development environment (or "IDE"). This is usually another software package with a guided user interface and some visual elements that make it faster to write and test your code. Some IDE packages, will have built-in reference tools so you can look up options for libraries you use in your code, they will allow you to visualise the results of your code execution, and perhaps most important of all, will enable you to execute your programs line by line so you can spot errors more quickly (we call this "debugging"). The two most popular IDE platforms for R coding at the time of writing this textbook are RStudio and Visual Studio. You should download and try out both and stick with your favourite, as the differences are largely aesthetic. I use a combination of RStudio and an enhanced plain text editor Sublime Text for my coding.
+
+Once you have R and your pick of an IDE, you are ready to go! Proceed to the next chapter and we'll dive right in and get started!
diff --git a/hacking_religion/intro.qmd b/hacking_religion/intro.qmd
deleted file mode 100644
index 7d124a8..0000000
--- a/hacking_religion/intro.qmd
+++ /dev/null
@@ -1,45 +0,0 @@
-# Introduction: Hacking Religion {.unnumbered}
-
-## Why this book?
-
-Data science is quickly consolidating as a new field, with new tools and user communities emerging seemingly every week! At the same time the field of academic research has opened up into new interdisciplinary vistas, with experts crossing over into new fields, transgressing disciplinary boundaries and deploying tools in new and unexpected ways to develop knowledge. There are many gaps yet to be filled, but one which I found to be particularly glaring is the lack of applied data science documentation around the subject of religion. On one hand, scholars who are working with cutting edge theory seldom pick up the emerging tools of data science. On the other hand, data scientists rarely go beyond dabbling in religious themes. This book aims to bring these two things together: introducing the tools of data science in an applied way, whilst introducing some of the complexities and cutting edge theories which help us to conceptualise and frame our understanding of this knowledge.
-
-
-## The hacker way
-
-It's worth emphasising at the outset that this isn't meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values, and social action. A fair bit of ink has been spilled in books, magazines, blogs, zines, and tweets unpacking what exactly it means to be a "hacker". Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation. 
-
-Back in the 1980s Steven Levy tried to capture some of this in his book "Hackers: Heroes of the Computer Revolution". As Levy put it, the "hacker ethic" included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn't just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. It can be a more substantial ethical code.
-
-This emphasis on ethics is especially important when we're doing data science because this kind of research work will put you in positions of influence and grant you power over others. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows some sort of social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I've met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it's possible that this can quickly get out of hand. It's important to have a clear sense of what sorts of convictions guide your work in this field, a "hacker code" of sorts. With this in mind, I'd like to share with you my own set of principles:
-
-It never ceases to amaze me how often people think that, when they're working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you've been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond our true apprehension of a situation. 
-
-Presenting through "facts" an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you take up the hacker way, it's vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I'll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge help to build up a community of accountability. The upside of this is that it's also a lot more fun and interesting to work alongside others.
-
-Far too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There's also the potential that when we make choices based on reliable information drawn from everyday reality like this our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of "learning by doing" and it's this approach that guides my approach in this book. This isn't just a book about data analysis, I'm proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of the commons. It takes a bit more time to learn and set things up, but the upside is that you'll gain access to a set of tools and a research philosophy which is much more powerful.
-
-Here's a quick summary of these principles, which I'll return to periodically as we work through the coding and data in this book:
-
-1. Tell the truth: Be candid about your limits, use visualisation responsibly
-2. Work transparently: Open data, open code
-3. Work in community, draw others in by producing reproducible research
-4. Work with reality, learn by doing
-
-
-## Learning to code: my way
-
-This guide is a little different from other textbooks targetting learning to code. I remember when I was first starting out, I went through a fair few guides, and they all tended to spend about 200 pages on various theoretical bits, how you form an integer, or data structures, subroutines, the logical structure of algorithms or whatever. It was usuallyweeks of reading before I got to actually *do* anything. I know some people may prefer this approach, but I prefer a problem-focussed approach to learning. Give me something that is broken, or a problem to solve, which engages the things I want to figure out and the motivation for learning just comes much more naturally. And we know from research in cognitive science that these kinds of problem-focussed approaches can tend to faciliate faster learning and better retention, so it's not just my personal preference, but also justified! It will be helpful for you to be aware of this approach when you get into the book as it explains some of the editorial choices I've made and the way I've structured things. Each chapter focusses on a *problem* which is particularly salient for the use of data science to conduct research into religion. That problem will be my focal point, guiding choices of specific aspects of programming to introduce to you as we work our way around that data set and some of the crucial questions that arise in terms of how we handle it. If you find this approach unsatisfying, luckily there are a number of really terrific guides which lay things out slowly and methodically and I will explicitly signpost some of these along the way so that you can do a "deep dive" when you feel like it. Otherwise, I'll take an accelerated approach to this introduction to data science in R. I expect that you will identify adjacent resources and perhaps even come up with your own creative approaches along the way, which incidentally is how real data science tends to work in practice.
-
-There are a range of terrific textbooks out there which cover all these elements in greater depth and more slowly. In particular, I'd recommend that many readers will want to check out Hadley Wickham's "R For Data Science" book. I'll include marginal notes in this guide pointing to sections of that book, and a few others which unpack the basic mechanics of R in more detail.
-
-
-## Getting set up
-
-Every single tool, programming language and data set we refer to in this book is free and open source. These tools have been produced by professionals and volunteers who are passionate about data science and research and want to share it with the world, and in order to do this (and following the "hacker way") they've made these tools freely available. This also means that you aren't restricted to a specific proprietary, expensive, or unavailable piece of software to do this work. I'll make a few opinionated recommendations here based on my own preferences and experience, but it's really up to your own style and approach. In fact, given that this is an open source textbook, you can even propose additions to this chapter explaining other tools you've found that you want to share with others.
-
-There are, right now, primarily two languages that statisticians and data scientists use for this kind of programmatic data science: python and R. Each language has its merits and I won't rehash the debates between various factions. For this book, we'll be using the R language. This is, in part, because the R user community and libraries tend to scale a bit better for the work that I'm commending in this book. However, it's entirely possible that one could use python for all these exercises, and perhaps in the future we'll have volume two of this book outlining python approaches to the same operations.
-
-Bearing this in mind, the first step you'll need to take is to download and install R. You can find instructions and install packages for a wide range of hardware on the The Comprehensive R Archive Network (or "CRAN"): https://cran.rstudio.com. Once you've installed R, you've got some choices to make about the kind of programming environment you'd like to use. You can just use a plain text editor like `textedit` to write your code and then execute your programs using the R software you've just installed. However, most users, myself included, tend to use an integrated development environment (or "IDE"). This is usually another software package with a guided user interface and some visual elements that make it faster to write and test your code. Some IDE packages, will have built-in reference tools so you can look up options for libraries you use in your code, they will allow you to visualise the results of your code execution, and perhaps most important of all, will enable you to execute your programs line by line so you can spot errors more quickly (we call this "debugging"). The two most popular IDE platforms for R coding at the time of writing this textbook are RStudio and Visual Studio. You should download and try out both and stick with your favourite, as the differences are largely aesthetic. I use a combination of RStudio and an enhanced plain text editor Sublime Text for my coding.
-
-Once you have R and your pick of an IDE, you are ready to go! Proceed to the next chapter and we'll dive right in and get started!