From fa91bb4ef181bce280d7e8a3d5d23411ba35e1d5 Mon Sep 17 00:00:00 2001 From: "Joshua C. Macdonald" Date: Sat, 3 Oct 2026 05:25:16 -0400 Subject: [PATCH] docs: add IVAC serosurvey demo notebook --- .github/workflows/ci.yml | 15 +- CHANGELOG.md | 1 + README.md | 7 + docs/assets/serosurvey_costs.png | Bin 0 -> 44165 bytes docs/assets/serosurvey_population.png | Bin 0 -> 66031 bytes docs/assets/serosurvey_priorities.png | Bin 0 -> 106134 bytes docs/assets/serosurvey_tradeoffs.png | Bin 0 -> 132441 bytes docs/guide/serosurvey.md | 176 ++++ docs/index.md | 3 + examples/serosurvey_study.ipynb | 1153 +++++++++++++++++++++++++ examples/serosurvey_study.py | 578 +++++++++++++ mkdocs.yml | 1 + pyproject.toml | 6 + tests/test_serosurvey_example.py | 142 +++ 14 files changed, 2079 insertions(+), 3 deletions(-) create mode 100644 docs/assets/serosurvey_costs.png create mode 100644 docs/assets/serosurvey_population.png create mode 100644 docs/assets/serosurvey_priorities.png create mode 100644 docs/assets/serosurvey_tradeoffs.png create mode 100644 docs/guide/serosurvey.md create mode 100644 examples/serosurvey_study.ipynb create mode 100644 examples/serosurvey_study.py create mode 100644 tests/test_serosurvey_example.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 9081142..d7f46a4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -22,9 +22,13 @@ jobs: - name: Sync environment run: uv sync --extra dev - name: Format check - run: uv run ruff format --preview --check src tests + run: >- + uv run ruff format --preview --check src tests + examples/serosurvey_study.py examples/serosurvey_study.ipynb - name: Lint - run: uv run ruff check --preview src tests + run: >- + uv run ruff check --preview src tests + examples/serosurvey_study.py examples/serosurvey_study.ipynb - name: Type check (strict) run: uv run mypy --strict src @@ -54,13 +58,18 @@ jobs: enable-cache: true cache-dependency-glob: "**/pyproject.toml" - name: Sync environment - run: uv sync --extra examples + run: uv sync --extra examples --extra notebook - name: Run CSTR example run: uv run python examples/cstr_study.py - name: Run sklearn example run: uv run python examples/sklearn_study.py - name: Run assay cost annotation example run: uv run python examples/assay_study.py + - name: Execute serosurvey demo notebook + run: >- + uv run --extra notebook jupyter nbconvert --execute --to notebook + --ExecutePreprocessor.timeout=60 --output serosurvey_executed.ipynb + --output-dir /tmp examples/serosurvey_study.ipynb ci: name: ci diff --git a/CHANGELOG.md b/CHANGELOG.md index 91f60e3..4a69129 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,7 @@ All notable changes to this project will be documented in this file. ### Added +- An executed IVAC-oriented Jupyter notebook compares synthetic serosurvey designs across cost, overall accuracy and underserved-group accuracy, with live budget/preference changes, a 15-minute presenter route and documentation figures. Install notebook tools with the `notebook` extra. - Adaptive trial inspection, explicit failure reporting, and bounded retries with persistent failure reasons and retry lineage (#151). - Incremental grid checkpoints preserve completed design-point/replicate evaluations across interruptions, including parallel workers and incomplete `Study` phases. `run_grid(max_retries=...)` provides opt-in bounded retries (#151). - Grouped surrogate validation holds out whole designs or regimes alongside separate row-validation metrics. Prediction and recommendation expose observed-support diagnostics and warn on extrapolation for GP and RF (#152). diff --git a/README.md b/README.md index 71b9069..bf19be6 100644 --- a/README.md +++ b/README.md @@ -99,6 +99,12 @@ front = study.front("benchmark") # non-dominated config indices study.compare_phases() # per-phase hypervolume and IGD+ between successive fronts ``` +For a complete 15-minute demonstration with live code and saved figures, see +the [serosurvey design notebook](examples/serosurvey_study.ipynb) and +[presentation guide](https://jcm-sci.github.io/trade-study/guide/serosurvey/). +From a repository checkout, launch it with +`uv run --extra notebook jupyter lab examples/serosurvey_study.ipynb`. + ### Protocols Users implement two protocols to plug in their domain: @@ -147,6 +153,7 @@ pip install trade-study[design,pareto] | `surrogate` | scikit-learn | GP/RF score and regime surrogates | | `dataframe` | pandas | ResultsTable export for analysis and CSV | | `all` | All of the above | | +| `notebook` | JupyterLab, nbconvert, matplotlib, pandas, pymoo | Execute and present example notebooks | **Core dependency**: numpy only. diff --git a/docs/assets/serosurvey_costs.png b/docs/assets/serosurvey_costs.png new file mode 100644 index 0000000000000000000000000000000000000000..9461133904e93d928b32e19e4feb25b4a0d1c35a GIT binary patch literal 44165 zcmd4(XH?W#*FB1sscmzcFoQ}Vq5?_~L6Q*)5F`pBph%7?K>-QfWGYAs2 z5dq0rvXV2BC7-z!`@HWN|1-wDW84pyF`lPwyQr$)-fOQl=Uj8`Cl{r~)~(*TnnIzh zqlurDqfl17q)?WY{rLy}Mu(;L5&j`yeoon3-c-l@_O)Bulnd9)Zy1@H8|iEAwbZ_4 zrf+J(&2j7)2iK9kdgkUg%mg?&jsJTEhv_X{PKT$1coWL18{#Tv6v}pA^1mfCi+e99 zl*nP)*;5L4{Q8@2D{r|pvoKs*vGMGdU5=S_<+xooPuUIng)|I% zT~0n6KMqg%+bw_n5&yDs^D4c?e{oQAaUf4ie!i^uKmYXud6ShC%KJHvD{o(B+DuB& z(=GZAY}@8}<=H`-XS?}dzke@kb?43-`7r6UJ<<;ulSWNBFU4FbPcd$w()I|LU;OaT z3N!xKv-`pZA7xm#+-!P(xks(A=8DXdCrdb|Af zISB~~I@MyRS*@g`M8Bpf#n?CIO4@!_R#u^@b~m<^4UB@y^er1VzIuJmNhf=An%?wS z&vb8OtU}Bc{uL`%c3Ga-VO>^|ow-~1uCnd)m@C!)#Ld&yk(Ww49*;coOJ^GCwav3w zW*BmtU3bFza+02_`kikp)Y8q_awlwOCc^FJrs=^Ke2?q5=ew?6y;?$6cWR`=y7dXG zQPr-mAGR?u#h!Vzo{fh`VQ_FTLNU_AaT{kq!?h)%G0aQWQN(K}TkK<#4QnGr|6ZDY zv+cYOt;3B~CgM`8dS~|S{x7dWFZhPmnPl6|ZDC*togVKC6Bo^y;Lym5QhR4fqYK;~ zj9R*4b^EQeaeV4#?^8zlgvRlPks8@{65`@6&!0cUh{W8Oxv?Q;UKGe-uLe} zY)1-MeU2OZm^7ukzmB?|_iTDNk4Z6Hj^*9%U6he!o60B-=E+8Na)z}r)!3b6`vp6p zi7(d~`iz^>d8=FWSzgNc3xveQ9W|SYi$y5;h!yysye-{P=vmt2aP@XZr=AnPA^Y&w>zkT~=-S(7ktWGzu!}3J_ew*xPCvOi{xycBJM_!8kTwHvi*q6`q z-Me=rc+1KzMyn*Ae=aoPH8I%a%NnqU;<8Km{$CWA7^UdT-u4T(=~H$EqZg;Arb2!+ z&}SObvTZ4MT&kl~^M4ge_aSUYuT^Y^Zx|>aM+i9;1zO zJGaYZXF#lY=>R64IS;e+7 z`sV`e*vtu^V4I**n>KB#i0=FP^`%a+PaW443g!F4x%1~G=y&hlO;B7ZT8|xxz+UJ# zzP;JWWvkQNs@A!+G#zJ5|5^TYs##Y_ zPgS`5Epu~)Vjo_`*JoEbjeBsYMf~`2$DLE7IpeL|Wt=(9XCW$yI)_vev_dtf_;QY# zx94wE%eW)k+}s=?8}!g>Udr(7*|Xh0hTGDuMniGtqkMP`b*5*PH|^RLiC{2n$+GP< z#$GAKYv#&T6&BqM^%h$2_?Va1_x)SW>NRWJ@blS|GuEIwLZ#Mwt)GyMl5S~$qLh>~ zP8ElShK4)4s?q^BHkpsQPS>di4)k`H2d!SW&b>fO&YMeHAzt%bT3VWS-krSeLsBofRrMqpzy+x zfjz>(fs*#_U6@SgDMlF_|MvkdKv7k2jdJMEoDw$+H!(#9-!0aXJHsau(t0tfduQNF+DqEPbRKVq`e6X&Atn3)`sQ53f5RX9U0e|c(I&e63& z*!73hHYd(3amC5TG_%f>PfvGwM%?}6yrXmE@#8<44<5|ds|ka4rYM*dOZ zI-!NhHEY&z2iQ)i1R_%+x3i*&xb$Z!HpSP zuzkfAGJYqn)tddabEm;sK$cW@-I{y7^&cs6a`{B*L}hEuU|>mh}sHL)6j1}%e4?{x3jHQ#A_ z%JbacL%Yyphmg%g(w+I;yLUSy0qx3rwnsAc>CDX5ZQFD+#wsKqzI$imm|{KA@7+4z zoT&R8yL@kpMPpMF`~1lPI}$qTw&RLUllr-iTPtldIn^`tV&2^uZsqCQ!+-1J71Pnq z;+_dUlqzoBlI>0D7Tmk{>|s~GEh9V?9FNRpUfM8+z%42rm2f|#Z7o8%Q+9OePKwK` znQf~Z(=8%V?>t`%S}j*{Vqm8>?OK(fSH9o4A*EQ5C2wv<)u5J+_XwBtUm<@8a@OzcQ3Y zw;t<$_3wi!Nzn0KtK+VnnfmdAB*y94(NYT>%(Y!s{(PnZf2`XX zj2!DNb)Y(0^-_qWXHA+}m-$$?EW1Y56<Vlxi!eLEHs>YMfXFBTYydGW+o4xIH8>LXhZG%+|2lB zsfcQItQKl)w7PW<3ymHb8HvaZ(#&=2!4X4RET0jp6EaFPqt!H^iwf!yG z8UQ4`ob}#UVym-+?r^GLu-y3G?Y$8kT|HH52UbO3TN- z^aTu0OdRKwerr8$(3EL&$^GyprGffn_L`Oqt3;f24iS;;EzAMC_)Pp>2e1eFBIM*J z2b;F;*%OUw8jj%M@|Nl}-Ooa$>LIuJoV*=F4aeD!P)&_l&2Z$+%a?meI+PEUDu1|c zSCU1a5=zVQrnYr^1QZZu3P^j^3EGl)=nM99#4hbu3>t0te#$(x8x+Q+HH*UlMZ-gKT7$S_r=cBc0 zsDjCeA$)`+$*@?*XM0YDo^xVg+rR&`YKl=g{??l)lVZWZd}n z52H>0vC-6}z%{Q&ItmNR%VqcN+qZiC`WiZmqyo?$$?o#U?7x3lDPTXJLG=zCpnSjh zd0_1c%HAQTr%!pG$Mm4^M2z56Zz7U^}vChoY}JpX(dJ8-17Z_FJ5RdUZ?>Y;F(Fni8R}V%}nYv z-;Vcuw1MfkpTGY)+IgT6`KMLB{AQuw`umN@mn<$fWR>!Ix=&pD0&o$om3 zxaAmi2Nsd{^qDi!&nk}&K8=$cG&~$Ii}DimX)XN3Rn%H64E#Z`C-@tYUoA87McW zwzgJq|1I#0@WC)Z(CyqhMMo$4>)bTo@gjd~{*V0aZ^#+c*NHTH97kX+?L4xVe5NHL zfg1_8azMj!=v)67CwGFGnlA8#f0l}wjP^tJn2sXnH!_S?Q>{*%p*tjl&OcpSF!H!d zE9H)KqrEZWsamfg#n@-auz)#x&z?O`IkSHJ`}oNdnrs#VIq2+@O%JI-$Vdzg;*9)# z*n>MZHeuVRw*rAr&B4r<`e>yb;7N!U)UVS=(1G#>7jf z(~qRMHWDcIb5#LvJzmew3}y2LcJ)+-?Ly>oPfrXO0-WYt=|X^>_ZP6h10uIQ?e6YY z$m{tMA2iissBw4roa4+GUhrwNjNmolk_{rWE(Z3h&mBsh)D8p6^DfMcw{%q>9h|ed z(^C=BGtp%yGXEne%@h=@;JdFC<@;X3r1D>o;!Jnmf6g)Z$3=5|jKZpp`}dcR)-rA5N$(W$ zL2WV9SJ2xEL8XQHx#GO|nk?IyqMM8-ZoH4MHQuOk{t1)n?zX9}K)x2$zMAMlt)apA zPTtgWQ7LbkxN;cs3QX)1%?7m1o5p%7^IWvH98oTNdv_`!_xPZUp4JP8lFveZ{`1=B zEo1ung}D_*x z3_>aA&dSQcvIBrE>B}cECR?o-Gc2q^M~bwlZu|Scmv!DrF{;yyFPWQ5;2F4oZ956d zb+m`T-W&h=>nhNASVZE@wog2zrP%Z$?bwVF$K2~5KYsKI6Jt|~QjXAl8ojxUe^>x# zx70<;c{e{-lePCTs}ZeHo>T0qDLjMCnZ5(Zm+Tdhoby+qP>zTPaI^vb2w0E3v9cL? zu@kuVBB85IGlzz+KNmXCvk=j@Je;ck_5I%jCFfd$I9)J16@@5Z zSImwlMEsHjWSRo$v*1>w`vo;R&#iWGcMdgy;)y_; zL4MjK!#Rm84z9lW+%RMm?o18opQM9@MIR`X9*i6v0eQe%IE&Zlzq)5s-V&v5v*U@A zQzZ&Fm3D9C1`p)sTx$S$9uY%sy1UI6^*OhM+iDH1-uk6kD$HSnJ5=NI%*^_~#Oq6| zj^KP9K6XqN$&7aU^FIJ^rfYzM<981Dt;Zhjp}wzOicI7 zL-V0~?HcxTA>gwQ9zG29_DV>O@>_tE68a?twUjowY5T(b(9>OfMdM4CErWCsB9*6v zh1<%0Spi~8b=j*klq8%4gj>Sp4!~?nE(5R?b6h&dSO{ZI-T{B-&iM=NLQAo=;xdg8hCZAPxNk zkB#>M663rZ0Q?X{^2gfkFF`lMv3R#EEmI==J=Lvg5aaN`uRS<50z&v(v5#j6G-ks} zc>wzX!~kL(p933lePOrsaysAA`lD+BJVy2O+zMVqWU=(vcGKp|yl3~#w;(ElR;*zl zlo{2Bn~9T+jg~z(;Tvqj+)=Tsz}=($K_WGT{}#K-tE zZ&R5H{{rwvXA8>rnnjE9SobX%fa#55WZf#PRv2$@UrcAe(4lgHBUFH9S&NdY${`*tQ1K z>}cSQ9wc)gA)6GP(tv1ac5klbJTxeO{eW-+2L}i91|Zo8t8bqGHS!^X6%JG0-@8vF zPx;2i#;P~6%!HBniLYO?rV{CmB}7`3&!9>i*bf?w&od^G7$ov4zbLzQSFwt@y{<*& z<;Mz#Q}ip(0~yz#3=pVib{A0en0@!IW143@>ZL($7Xr%PcqM%vVj9ruiHz6(S>?U| zGTwiEVF^^K!N8g2sQ_S=$RO3NJon8Ebzmg8S8KQ>=CB zBTZ}a!)-n(^$#~nnF{>V-iEI%J>FMaS`|+Zi;klvPJWmRiW=A_EUb=HR*Rg^(j+;r z6sxWVAw=Qtr7Mb<1EI0Rpr8f6l|E8nl+7hS)M6`>)xwotOYcL$YM5(bMFO+7veHLE zo}Oqh5aCe0Eju8~k+J{^B8TjlNlwLy|?Rq3+d}P?0HwQDvYg7*@$Gx;tukd?Z z%8>Uq`{|6BUA@mQ$SI!}-_d6}knFk9aN1Kgjr~iyz1&2Mp{g+VKsldbdzH}j!~Ans zCWnV*_4M9Af{wvq{xYu2uU`>N>uev*W3rDz*$;=r5Z(*iQ#2U#gj??IjcZ7_I(O!p z(#-N^Mb^&S#x@0JNOOAI0mK@eHLy`vSGOL@oE$?31FF|qXri?U9jL`z0B|%ylDkU$ zExx{A;zS#snemww*-D{ok~vEe4Q&cM(F|7QQ)x<-40w1QNU1aY#C+2;AtFyA^3f!y z0ae(_z@QVrhqC=jg;8aqJLtKOeM`2zeY!>Ep;)3i4(GI|F;tEKyV2-^LPFfL0#PhA z90^`~@C-cm-5K^JrZ#4`Zpq#IV;!^zP9ic0+-$u+W$!5E!^0-lhMYo*s|j<^gEvPC z`VCEfMs$1C&HmwO-HH0b{5hvyh^9xI(pRqEoqst_5+%kaN5^9=q_gUxS%Erov zJ)`P_h4N2FGf5gvrL%I{H74oHXUr$Ha&1CVS2U zLvB%^rkyR7l3J`Xm#)dE*zTE|ovp${&Ot691TF4b4wVM6L1L6&;myT;&~GF&t;auC zq`sqkJ#~&`bbX?9^XtE}=&O-peK{nf6n3elhNz0jof7m=Deu<+IZc9G9iV6hx84C^ z$YMdLG*B0DGp(OUPyWTMDsVMXD`*v{M`$Bx$2WuKCEcC61w1rU@cB}2Q)6RMc7x3` zNi-PH*N~=<0kd?~AB=(a5)RNsmn%v2**sj=x;-EupflHhes)wQQp`zbViEsBlI0(s z4zn20#<>;4w{@o$`|-=8OfmF9*f`m=qPCws@+U^>`pC_l`BQNv>AQW*EECn6K3M$s zEK)TBcL&X`$#}TgTTmuBHTAZS1-kL9mX%OH%b=N6MtROopWx^Bj&;>SccycB zKa?x>LDTIIkppz@J+9Pssx2=lpaFjzU+}LPjYY}19C!6^oMV^Hr7o23NB0xvk%V;a zCBhS{_D%_<%jJdT=`>PeZ``=3tn7t!&rXW~C5z}0=qI8HeekIg;cZ1X6(Ovt4rKjk zb)pADYVAp`RhLu*eUAaK2t8Ta5gD6xhz9w(8Y;Fti{I;@Al^IQujYRIn6Jg<-KW|& zzwt!gbEZ0Kbr$MXNx`h(YlSQ589H;)_Y;32OFW%AI1jZOx5Yw(tJ= z=N~zDCP-;m;F8U;Q}QxG+5y==08JB49I~lT((h~^1JgQE@}%+AnU%DP46D&20XaAs zB4X!7btfzY@BX-vZYIw9T+_3{ZXRIOYjYWt;o{Rtl@s3bcfPobQJfv|aX9q4aNOU#X3s;fvc5wul3 zfd2Y-w_}MO0|JS!eSTzQqym)@DK8Y*)&SfT8eBrP26sz>wiCg30v5e*Agv!#OHU$Q zGZ0{ZG)d4+sDN-tZIZm&ZjR!7zU|Y~aa8PX_e<*KP(Bn;#@6jVagiLi#Kc6>@P$HT zh(;#)$`B#7nrnS(8yVv3TYmyz)^~HoB>=$t&OFqsiBh?oYT_@vFlz>WAqmc-2=Zn= z(oVY&AgDr))qLG`>pjr$J~vzVH|gqFPV4k_=}9+nr_eW-7Hb`FD-g6EgDgs9SRX#q z7%WJnQC))LlP9uw?z{z!PO=)+AwAidiGjkge;(98+lWpFFHG-kBl7zMqX6N|)c`eQZkIjUUMQ;^p%G5y7G;ZEz_%43-6xJVO_vHyUU8hpK1!s{JMZ8wvS8l7 zzanZ9zvb1Z-Huasyp|n>|0Zhb+Q|avcejUjA~+<=v)co4;&(vN;3AETGjv&Wy`7BF ze%elYAKzMAHRJbe*sy{0Qt~aMAi7G8ZpWK&*CuvMX(SuC%OW(K@}%Z(T3Q|lAJdPV zNd#63(kh_$qztsRU7tn3TRt(xhjj`d`^EL~djaHr>+AFFoJZ;Z1oc#Iz_YHlQE4%F z_OM3&3?A>kPVDsg=Zr!X11(_mYiSR&v+X115^Xj#v^IZsj#96!v(nK?HLYou-%&7_ z?C0GV4!s#|2L!GZ_cL^;chH^Deb?S@W*M;AJM*|3GQf7kA20e06+-He9Gxq}WWwrO za&mH9wC?`+_DG+RSy;Hav1DO>GFzd;)zuYm!h3{q@+n_)e%ywIiAouf9WW6vH1yRa zhG8xD1Oe?qE3=s#WN65j%03_yU}Q20_@ZBf#+lE)g6%@qNx35sHMVa(tRywsh^T6Y zm?@cBYSvkF3>^&l0o9kHORmGE@CjMHsNuM2vtP)3eXj)~T>D{d1JX?8-0k+xV&7s5 zdwfIl(sdL;sqH8$aS~wO)$eYbj$YaV4K>hY4$v>;$4;El=*nmfY~$$cbA%VZ3Q|Ct z`gsBnGiOH%>4Ub>RXI7#@-x~Wo0`<{Mar?fN#;Fri@jKbsyD8X<_?4P6O9=_K@OaT zJ>^?>_nT7T87ah?A2MPn=_&$P9=h^24(f3gvK-H3@_EGf*6rKh5Jp29xwbPYQ+<7t zfg4l<7Nq?J!eMr(M=ksfh0WSU&UfH&{-BPpjwj>k>jBn*g=#Gj7RJ zL%-mtJxd0n%HT`fE7%(R1`YlFe@MJIcx^D-)6-L}^aF}R2+Fe|B*^+Sv#@`2aynXD z_lt;#kY^xJ@|cKmLQz&{isLWZ*Ly-`z|=A(v*-#RYAW$3N*P5wL@0CM<#?1L>qVjDmr3tL)5 zim#tIal-4mM8N(wJ5;0kZ2N4|N{4Uau%KXL#?jX7ZB2#F=nX}n??f6CV0RojM-@_x zR4fMSs|5$d(Wh!37%&bP6+Ao_aq-nZX&MD<#4lH%MpnO(%@JIf%_e3HoW?S=tCGOq zNxv2}8XdZeAPkkx!#FnM@`x{9WWzltEv3-U9GRS~b=h}DA>+y<{b|oA zlslAJAXMwYv@R+Qy6B});iF4L)j=QGNe7KccA?V#J`LZ94XHuV`l>ENnv(@W0o#)0+FXQ>>bDowm87QxM-@5hA&hOY1Q; z{KmI`d$E|(4~f4?Yn7SkG794fn~DB%ywqZ%U*&?|30XSv0+8zM$-K;A%yzQrHd$z5 zr$LI2L>mFsr<^pzx&rNcao*{{Om;s$=#17a!+TDg1)%=^-uuMO*9;t2^yV9Ka?X%~ zaOKLCTgMJj@|AJ4x9;2-L32Bxas+g$oUi3>(BI2ecHYp!Z;RH%{kf0w_3u&AL<1Q( zbn&GFsqkp7=GWHprRFSkkV=Evi@F+=s9Smn&LOCmhm@kcAdCj1sZU%AX?ynLtz7iU zdi?sa{ILK2;vXsR_9B1$`iWZud&%O@|NCG2UqCIrgqScpxpwiz`DT8%PCCqT9653% z^~~bmIS4-4`2WGX{eSn_|Nr>vu*UV2@TZJI0;y->>cQL%MvrgfRF5LoBXGPj90o~+1g&m`1!KHdaSn}E z9|4O9h&K^T_Os;&@>CObo=P2X3%Jv|$~OtNJM#0DaTDGISTh*58X6sNB@F5Hr}A>M z?I9Yg!9kW3O3EE=G*A;Ht=N-1yvtGs5Erae*P|&RF$+}fm>k{j0CkYK*AzgakK5D zn(8B8w%S;YOF(>MSj)cFTqj4U>HuK8H(Ss0nl@hn$IEyBy&X3Dmpm2Jg+xFPCYuPL zT8V9qu%Dk2!{O=~yp5LnI`{Zg|Ov?>8-C!z>R%;;PJqkAy<-AWB*1^uudcqAXXg3`WN?L`ANEA548H|q)_cJrI(4fe?Fy~}5?#Tv|SJc#ey?f6d zvnWo~iwlWSgrHgUz5pS(b%c>T%7#Z}`k{^Mq0R|p$?Qtob2{MPKlR#o0f|)s&FTf@ z@I!Ax>%pDM!ouR+$8zAnA@ojQYe9Q~tF-5CHkv=apC*PT7K*hbW|(XXW^9cA`i|8ecvnr zaSy#&g+Ro?#VIlL(kAQ}k0qrDj(ov(ICq==AVC`VX}@G8 zyadhjXsB2QAeY352R@pSnMqC|ks$FOQr@<3mkI_U1&7p`h>AKu4Zj{6=eiEhU5>^? zvQF_%0oh#jyN4R`tlNA zG!hzzL2ZnG?p&BmU<|lpXgw4JlcBQ3yz=#xN7qk{$KBUg)zKL1rU(K_BAYA?(JHx@ zsG-sjX^r*tieFDhY0TS~x-36G+pV+!j|GvHl~#cs!!0iA;tNC%v`;NJx2A9Nii9%b zV?lz3wDW-cdm+kX&5lTt6#8iY@?XE;@aR&2#{Dun6385!&^x(xNb8wM&zBy`X_}jN9h$9oFhe!@2r#msf z6cQ5RwI0($1LiWES``SMoO7L()th}4Os=u9rk3fyJ^dSk z@47i;A#I`CS!{n&Kp+BrDM%ox%J$6}R<~~6q{T;rj9GVj>sGzJc^x{{&;FK-$edQ~ z+uw_Q&D}KgD{&sc_ZOtX-YRe8bDWnth+n{Vw+>IrP9$A zKeOoGSk2YzDfw1XA6(_KXrihkMB*SqB8mcsdWKie9FBDel-AFMg_4jViBl|Mj92!ctGKFDH^-iJ@m9Ecwa{MtJgx9%#_l)w^Os7*9FDie>*=R` zZ6~r7m4Gu#r{DOJ(b7s_i4cH=?O3@O4s*pE_k*)yVkATDJbKlEO2p|~w{P=#a;l{( zpi7eluoDs-OdXxToC85{c%>`#>7J9vA6YL}QpyGZ@1W9f_yMC zKCWnF6t#c<{-P%xQ2F{mBBbLrfc_3hdA&s8^_p~Yb|!|`VheJrEg%Ak%`E0Z$_6(< ze}}<_)L7UgB7tZ6p#G-AoJB3r;tIzK_u@YhDFET zZU-)meLfuf29`zsW~Rk;y5r^LJkynup990D1x$jZ5}?uT9WcsoB|pObX=8 z1GJc(%vf0innnzUL`m3*My1K4>%6KT16xXeMWv_-^Ai7+-gfr9z~(;3j9wQvI6r+_ z4X^MUY?^THQhEVwvtvl`eJD#FP&134Zz#km;DrH&=zt{TZ=LkJ@`3-?%H;0`$aXVn zeESN3nv^$sFv?>%uoKCyPyn|;?LeaGnVW2>g$$bnFXWYHKR(FAYi2vu;RynM+HnAq zS>+`S+Ys=x_MUdb%fk(?pU&P||LY@e?OLVRPRt%PI4=ZD7PztYg@*-5D6$?pBthh5 z=s%uoHVXfMDLCkoMzMX+uv^{!`B}UF`3C&xmGM2-3;0D;jp73yJ$lq^)wO4Qs6|9k zMNW>MV$u`=gw~5UBXp5S-vru4*uW)01X0MI<o<51D7vR zw1IbJu@;IDl@ta@aV4#Lg-mOU(b+u2JJhpv)>Lctyi*Dg(=yu99Ezj~m- z6OS$tzDc(UQXm_{;o@HYxQP?LHe$meH`tj>R=Ng)yV2-?l14S1Il?Q=F<`@E+AK&u z15*-l*2Fmy0cSm=eDR0`Y%0MVL!e0 zdoEG~{Kktu$pvq&Tupy1963cuKwJO%>t*)Be1oQcyBW7$+1_HWW6Z@rScuynSSES9 zDg-+ow8#NSI8?jd*SC@No7f~!{GwqWX0h@n^(&$$uY*VUAv!P^c6-QVJPWu16zwTw zJ;h-0jR;E(Ja+|Ro%iRX=?9DBP-%!M+o6k*KkO&4SsFCE%3P# z>hizVh`+o95I+taiPC;10^cS=0FtsNRcLOkaz_fE(U+rpMC`7>hCrr_*Z_$zpo4yo zJn4u?2MBtbXrAEide4yeD|g-b%LOQX)3$A4n5m*wFh3X69G?%gpHCx82GlZ8mna5T zPiF$5?FP*mX+*Kavl_g2Si&~(6hlPzI8IySPiLUdPZ~e}+`C8Oh8-0P-;LLkjJiS5 zu7EFvm{kdQ9>gL6>x|W?n)VunGyU9zX13fNz2G>4 zSSBMkhg4Id(7kgrQC+oaRXrMg@UoIcd;_x=+viv`O7ipb%^UKI_~8Zrbut;3uTR@U zxseUp>}x;y{X32z+y~-x;o0vwTMBe(L>7iv@BX@Zj^s#GTE5&Acs;$kR0jdH9napp z<%`EuLemqh+l|>+#~ry%_{Y~}W+N)ZV^7WiZL*U)#T`wnd?vzAw_|E9q_KM8hm0%DYXw#rcceHYV@L|%&^Zcz6@OnNQ7&Tq)tcG;$n|*q2$Q}u@=)*Vn6Eg%^8BAH`@eIs{=HCp44?}gx)kFVZYez7&5VO0L%Np4Jwo~eV4o52L6A;L z;;4)K;>pM_BnpszQ+hJBuu?0S)oLv1+V*tfc@=;qj2?XY!D?;he;Zu|GtWX5$Q^+i z)Two1$Lc@-yogq&1>hgS{}3y1_~=>zt8;hC2#vo0+ard19E}Tf3#SbS%#cvQy9jh9 z&PI?r>n{J><^%P-#GzxFJ(p4)-_iL$7s-q+RstNR3Pz>-aC~8Y#*{|K03nFmei~b- z{@WJl-k5TFwm+LD9jEd3#s|$Qv{P?UpS81{n-|)a$TIo-A9dsc(&R-g)-o9WU_UxC zA_vm41!(}XUJq96a@wts)msj}1iHA~6hI}OM6kvxV$Bt{7eQn7VUSxi|H?Z4UvNOw zPK@YBu*QadxVKW0PH56l){Hcitngm=>iPh(#gBP@N5_PPHa%p&5D6VQ^i4L*T%(Wc zmMr_@%i+84!c`JOsrtxn#6u{Hwv^!LmHY`M*m-CY7Iy4CK$(~`OuF&yv@QI#^n~Nn z{B?7cH;R0CC5MNHdtr|a4I6!HHQ2Ntzrdad`eNwGpQA3p{BII%7`WKpv`YTtiB;Sm9-mQc?W4U-bR)jc}1d0S|ejJ5xNGMz+Jzk|_a4-h%uYfaA2{1rfKQuWcd3lzXEQFzvv1hIA z4+E@w*&<@0PzrPVS{2qU04YeWIT z?z)AAg)sv5@amO`u3xWTy5DT}$IXGJcVr@24z#iwcH+@Ki)>h7&>hq!J$0zzPZT6xPHTR?oOSpO9(R)nL9kTZyY2Dlzj0|s_4qLkoCisWB55+f_I8e(!2 zy&&EA?3zC@2nDs93^)dZr!e$|Y+Hbok!0E;OcV{$umO1>!`B)bEmEobwC}Fxve`|s zTDeGU2b?C4gW+bRhMZk0;43b=tcU3T1g+zL-2gxszNQ2_ZRnx6jQ(&j(a1<$B3jax z$rGtN z;OoYvfxCwzv8L@=R2**YaQf>vE$;sZ#?@ax?>X@5#XtZ5?5|zcVSzCYEU%@d6`=Q9 zvXvwygeEQy*OjtkAP^Sj!uV3pNNOYY2qlM}J$m-!ukRwN-l{dQ9+C_3*C4+!kv{KY zj3T9nB6{ACQRd4@Z-g{$^?uP{rBU1ZI2Bb4tCAsTcBZ`AOF%`HHba?VP>t&Py*xhV z=JtR9+%hwBOHEBxd+r{1GvL<;x_C&jyxhEL6S2w>UknPE5>z}Niwg(b(2sxh>dYep zU`%R|D?5wC3m58+hp(UjlICCb{B$G(non44xYi{sPfp;75)F&wIwX`hc9%CFkh)kvXE6&tb0_Zpg9qOB&%wBnyLcd_l7%Db2Hel=m_RtB zAU4gSfyFx~95SfI%g5m5CSpI5d<1&4P-$OGPY%VRn^jm`%wGJ*x}1wwuf9Yl^$kj$ zJRymI#KbRk9^WHLlJ)xy%p#E8L-(7PxNQiedHwoz`?qhduhq@XlUiF_+dDc~+zLj_ z4`53{U|*m{1G8y0Gw#@dUKKUxXwjObpFVvG6<`1E?OQLgL##3)-+Fr^;5rK1wE%Gq ziIW2ZP72!k$=BIRz~`DWtmHu$==aWahTbggr%0N{f#67t=R4?%3fSJ~}p( z)ePwLV9hpZZiZj`EV#X*NS!3S^78WO7R-2J9BdBFA4m#{=>@ty&0BVZtJIzx$_T>j zupxM_LF3zakRsjDufsRURTyx~kDWPv`UN;G&Tk|P%A{F zT2s)_pi1tg0GA+G88OD5V>$So8zMBZg}_^_fCwSlGN8Q@+8z;YI+K2g0sZe^Ka7lx zDIn&s53c}c+2Cr(74{aAq4Pj#A>BK?G2{SOa56;NICRpm6We~R(w!T@{7X^7YXrm@`Spn|UpGA@MF`yAv!LI7I}gCL{?U-BTTBczhU z7=p);QZ4E@nYx66AwhTC!bV4&5FtH#VJjB=Weka^66|Q})~)374dP9N!7czz0TvpeBhXrkM4%EDAN>R^xCg#VImk!^ zPNGQmA}Adl9mycnINFpd$)a5-ejWY&QJ4)Oo(KZLFevsKlT1KlX7Dn?p;pJwO0*mV zP7-c2A(Oe_LiKUi?qMZffBkwDibv%YzQnE`U(+mCD$%n^^@oB9cPp_K0mmgOv;AC& z_sb47o%=HysudgL=}3?V!c5tHKb<*bC4CLh0)y8?G61wpriMeTfvQaeEz*N&Cc+xI zC1h-@ibyC(dxnJ`?BrS*GHX78tV_)JsQGm~gT(U$>VSxzhGbaO0PaKVAs4)ke&{_O zhG_sGF|P#^G%ea?&&|_8>od?eiN~>rCSEj3?HUgT{>GUPeSy zhs{jDy!$1bHTnqA<7+l;h;5&`FA3ZXTV^-R73*Z8q`10!^d0R0WAv{)P6q zUsbd!-?%(fK91OytC`0xUYOJ^p7m=_!gm+g%RRw`nt? zgNv|hXc{<`HAq@KVBw%&r&g@q5~rPUC!bnsXvF2+4oQ>VZ2f4W9pqVCGy}$F8TFSZ`EllV`#G|(Jq-7NU)DfC# z=C}t*AP|{)aj^(7DS&(w&3LfMA1IzsQ&Tf$Ohm4ORPYcNmedDdkbKf=@5#b+>2ouG z{7M+Ci1U?L{)m$rbJ$=_=ZFG>v+xGP`(1j1zrJz)4kebCSO%65M&5Mbel!(i5pVAJ zE28ky2mm&Iy}ZIzaQq`2C$HcP`~2bC0!JU_le>{sRSasixU}^}NRdGW!Fj+Kx{5(t z3uLVD;NW0|hIKh**gEBCreHay}?LKJ?%Lg_TlOL#1{kLlq?{96iS3259mTKr_Mb zOp*4Iu6|fb3J**oN)RyQs%eZY{ySs@hwqb0eM#Nw99o(ZjtN-VzM$vA67j|AT_5Sr zB_vK@^&f+mMY*vBU==jd?Skg-=+IPLT93P#h(VsTGct!i?IPDWQEB8dBO+3bW7LbC z24%->-I?5+f9vY%1nY&lVOS!b$-cx=;Yv955GrK)pSal1JX}kZa(FPGY?04{M`IBd zNT&_}s`cWlkrAz}xDgD=1ov)u6{=miq5#LHQ_$Q=P%!lSh-yXVs)#TPrxy|2U?s1> zgt}|ECA9$ZA-P!!rkGbm^nz!Q5DXwLa+^i)P9dY`W^{Gu9{(_aP!bKqTu8)u7&_VD zvcbrV5^>=7CzcXFlz!GChsw`J9ok+q$U|GVd%<{uNCW2aErk$GNEd4}YO+=a$`grG%N!(;}ii*r82fl)OZ+i0hs5 z{>4z-7UJ!YWiuIrQ~DzM69!^DqUZj8)P!Ywh11fwevt|0(|IM;s9I0WNu({u>ECPp=(*ymZls$63>l$gQw!pwA76s-K=_H!=wv)( z?$2N6ghH93E3VQ*r!MpRzia5hQ2s~@u>$E7)k~PPga}Q8khujDM2JX3Tx(*v>UW#m zWs+azT@DFY1g;(i`bbEkYwQ~#jbfDyb2w@CQN37#4*MOH!ETCnN5PPq z9I_O#Z{rR>U`R4}vt!T~bO-I0KA>2@I9VVL1q9pf$G^WnqY2zNLQoFLxd8MI|4hz5 zSZ%QP<7V&~a#0RWSIC!Us6?c#hDA(~ELwaVO2NLvC&?5Cl&-fJdOIX0CWg68ClTlz zC6*4vW(ZbR{i~rV1KO#8InH+2;Zb2CP(`6NNG7)cn^wnP7b8{2&dzS9@ZGmWNCVC{ z0JOB89nP~r&NRwf)FUWyMZFu#0&OR}2@)J_Ip9V^-<+W@G`VRfalGKs4{&nEZfU@! zPp?oa4&rhb*f}$WacSX}4q^^5!OdQT7D1;bOoWQU&=a4>Sc&g34xrkL|CLxe|Bj`q zsGUn}af??bRBC!KGGZS@0ysV{0UzcDqKl++^8KlMe}qT~OVW`;&Oh7DMw^B+LGA@n z#c+t+sdLbZxmg2Q!hx-(F;Ye)- zb(0QLy1(WO|^IQ|I1*6G9#i%qYt*yO8)K~mxZo!nmKn)*lL=-Z3 zu@>jzm;3q+m<5ih&jx5okXa*`D^&cN|5Xo_2@KGb)<^sRs+=O=4u)69bg+Fom5(o# zr$I6xs}1@WYAv+~jV^}COz=W0 z1+!afi)-m%tm5u42=T${@9ZK-ImrTh4eti700*u|0tkNGLU5r zZp#Df&!}KEOvANM>{w|4(HA3Dto@x`Q?8Bgzt68vdP1-#lRLF=dy^um)UfgpxJL{Q zC>T$mr z`f)E@T&~7T<7HJ)wvVjr8XAhlvl90+2E=S5M({5^K>SKDCdj#yyEK3LA2%??0P`C{ z3+qe(e>8yJdqnil#ig;-vREpB8F_Mtk{x6Y9t00^t9$fvb0pm@yLU$sa|lXIIZzN; zT(H+j#0=@z`T120o`%1QJU8hkqtQx#Yj) zT1gx*mb+HL0Ec4u1|no3Qa7oKDD(S3om6p;&u@3kE&fdaT57K z7kuw`^P*kL={zB#@Y2^aT=*RzjDIP~d~gjQeH`fHAbWAziHqyxa5ukq`4~=X6$abz z=!bJBaK%a_v9*=Gx(Cz}hV#^LCDOIO28Z}2>WOXj-fu_3$isq!ek~Ik^*$5 z+RzXGt?dS^Eul#e1ogJhDB7%@5j<5S&Xk6@3P%WYO{mg-cLT!P|2Zf6amb(F_ zr8T2Ove1Z$>e`#%8#4Qmt?dvSxq^T+I{-JJF0zhym!APa=ki?n#~;1uT@$eoJ#Wp* ztH1Yn>QTYbwalsa@=O2UarLNcRlpt7We$FMHmVXz7e4&{T~QtMm=-_(%L7F-Q6!$r z5aEnM`$$DZ7a+0v)bAIMh+vI>`6=#(_Ti?O++K&p-=#pM*;EGfLMFS|J%|XJ@5*cq zm6zOdNq+Pg;2T#QfeR%8>73Qx-d<>?KM@m2bo1=jO8-1vq#Fqwa{)=V1x3;rZ{stn8;dz!~E~g}t&q$ZGKyM1`-8 zE+0e9j3&2#(6KF7`#OI=;M9FZr@tUBp2Ja0Ro2=55YTw*mM!R6DBx~m2oyyJPW=~f zOn6|@M#r91nwAxiy{y1g@?lfcq5hA**EBy5v6G6_2L(7$SqH=zBdG8St3!}KzG~4@ zNO7?z;Rs6`x|F7|zxfJf!(aFak1Lthie$V7IxZQ20Nhdi_#5NrckNC{s08&M$7oC> z_$!fRQH?^$b+%;qt|dDgqLF^ziT@taxq4MM#CRgBgQS$f)yhJHaN|P7r39*AtFmY| zkO7*8^NTY~l>AFk(xQljQ8G+Vw0zPJLRptZnnxWVF9v**L3iY5X9ya&&BwLB_h%)Z zU+nyO12}D~sPstq^+^SG_ZJ_?;cfvEK2C76-Hb7<0;2C5 zcvZ=uL@f?%GGrMt_X{&j8NM#DWul_gah&=u=L@qM-o67@1u6pznRO)YG$>sc{vwtTn z3}sT4u|j<1)k9lkd+;^r6}AO2owK3P%6)v5yyR|8oNJGl+~} z=nZGB+#3UN;Wok+^a$VgIb5f%&ORQ|Q^MjXMb3uj%Y@zKnn9QiQO z#>joHn6)Fz$913C@dNd`Az?W>#~=`v9eZiSig1r?%v#U3hEKxzHpMkivk_{$Wg-Fh zO{hf)65Z*j5`|`hn0D&oiw;@JaOz~Tjh=ONb?J9WdyA_I&%$NDP;M5q;#BetG~NN< zNmZ(HW&P+$Ou7bwe5sZLdUlk(-hmF9P%X3}>I0kazS3vpgZA%V*|1Nf49#GTk%W-` zhYIle8bYnFyF5lcA0=Z1vBT5YIz|Tu1}f(v{J4Hf=o;sJ(E$OSyuR%mr*t2&)S zMb|J#13O3!~DH3SkCpNfCc(5hyy6d*c>yQ9X^6w8y_0l>7uqzEnnPoOb7pubIxiUg>* z6)`{7PaLEsaAKw;15cjn5Yd%Iy?TXkYs%#<7d-$3nM7;w(3>IU=*Ba_zHpJ9n#EuZ zCna^aksgnV^RXR~J8r8S{{dNoAUr|#Qi?ELzN{6QeTbad&LK_Y)*#CXo{=x}LY!Tn z8wB$b<@7{|_~}_dNsR>C40%3jps8sPjIH@~Q%Do(4j^zS0m7+*vIQsUn5f9e`j(dP z9okTyhzCrO)UiZz!}OB?6haS|gul4rMCt_rEUNODu$C!Mhq5@I>6Gw-ttg1nG{tfh z+-@WM+B7|6++)BBMj2zSp^Tel4<>j5h6qxtpqe4D;N{NV=z3un7FUdKx z;VUa}q-5$Bb{_G6p^=sz4{T}B zlbc^Z!3v)M{?SS0e9fTn)bxfL*)1UnFM!uDmu>`DW+#OY;UhQ4c$5zbYXIG4?h?L5 zi(R~doesAOWkudeUvF<)Z|jloU=M}3jJgj56v9qNgD1j0w4WQBj!q3DKW5ZLP4KgQ zW*8VZZSd&i2vl8OUTto6Kv}^a2)L+_O8$+|J=Gh{l8OP;NH|<8=&u)le80#rm`bkz z1i}+}A8R9k7+yaPkyEv*`N%i0v(CI0pG#6Ggbo;S^3K?{?!f0L_1>2-`b5Fup-}dU zKKbN6`~MK$W$!Ai7Wg?w&`ZFKsSl@7FtlV$-qHObz1!1J5AnXH6yiiw9CF=-=Og;8k`7R&|VFm61V(N~{t_j`d^TNyVUi zj>YMOy){We#UjTLcY*xlS8-ba5kV{;R;_7%sM_{Qs0*^ zdOQ?fB}>d7xvKdABxB3?_zLT2BrkK%y~%)M2y0~6Hj{9iC6MHAI4Cqi^}4uB7d&@=c2oesscTUc=I}oTD1|V_d75* z*k*kmVf{oBm-@qjg))hIS%+}1bdD1oNL)2Z-N433J?SA52AZL3S1NJAr=X_BX=iHQ z5OelyZd#B#LHYMXhw9x}5PzhviVJho93!&7`HN1}hqZ<_)7qV({oL=WpDTN5JcOE* z#h^*+4WzWKz?b8#(y1JJ;l_fu9~70{cW zn0i~GwpwIMP}{jw2wcyXT<0|g z1u8YuA*0)|h{T-m{2)lXk^4~7g_fH1R8nPzt(V(B@}VMAE@7{bNABq1&4z%}xDi)s5`6m}-M;475r~7ZqtI%=v^WV&GHY zq0Tit7~pMivYWCzF|;n2%5s|GD#iA1EY23Q_jeT ziu9U?%vhj2@$o=9?iN@fFBav5b}Rl~1$)p=*s#1<1pBfGICu?Xy3sm_MTm0cNnr*S zxDnV5=2V4KGLE*l&7cZWwl*PNq1F#n;|}Fk#F($w7I6Li!MA`8gL7?)fev27Cpsb- z<;5aiZh;VmnmJJT3@kQc?Rw$%ou?!9c4%Cb(rzT6eMJF7mq4u@ZvcrvT~`|rxwMIN zY5c80z$zy%^)PR__5`JZ4gGgYB{@69gw}>c)q`;%U;*Aa+0<|3`EdQ7Bcn%5CoV2d9a4yY zqNb3<;L>O*z`rjb`#*P4xJ2`FpTmd{wuTnB=Ycm-!?@jFKE5X5A)I4rc0jPMM0j{oEVj$dqI&T54Q@;v`ekhZ|u#mqXlb{p> zMTU0i&M>g(noGl821P(hzQK&q5)|ro+bVpF>C5cDagfF9`iCR%520~mpF_e1+I^ba9D0rIN) z(4OD~$Vq{cM>PUT3>I^sNaSqZhrEskwLwwPhxRQ-`P15|N=x|ASUxofHwHUqc|%Cz+Ehi=NMLwvTFb6TtOk0%Gv!Gda9AzF}KzC7@2 zK}f}~_=JqW{RW7eS6U&ZC;b?KWEbV2rQ{i3-+D{=vjV6Hq^kLzB|)`!77EGv7b8al z)uE1)n={_P`O!h5IgW#XqOUy%O}&_d5Mg%;#QjS+02OX+3>3a8P+v(jbAQK91hKi0 zn<1IhyDP7xo7-PmjMmVJNCSLAe<1R~+_2FR*em!mC-mL%?O(c@hR)?#}fH`|YV z;!U{%*_G&~P_XFe+c$3vo%2rA4>~nKxI=Xl^014}wHaolyW5(CqDNjVe9Yxa>XT#i zLWqu5YzZINjGh2_%TG*9Z3w3R5#;cIO$NCjTc>I%PBBpMUAMrdT2jbbyzT1OFiX34 z=j3MUM#{V=L_PrlPHco< zyG_7UHni9_?uXYYrGyyO9DCp(s6)i>{Xn-@0b2@U7)O+eG$s=g1%x1!1)Zg$MThXb z727FZKWDTHVn%8PLnJ0Ni8krme)j+JdMqPb`f$>*2ghw|5_soEpIquQL~>#zKT8Z_ z{{SwXIFh`8ku@T48n_FvP&;)UPUAV~ACgM}{N^0^Cm|WNd!FPBVFr6yxrtji0idbf z5VQ^_cbBE(z{8}2Xs4r3orfq?#I`T61%pwJ6#jOUpD@OaEm$_L8Ew=#s3W&Xsq#i+ zHt!P3DkS9I3eIHVs`u z{dwCtBGRe;GhIUX;zrwZ+-=Z$L%77^02Nkt?dtAcZ&JQ|B8ldy8ka_rSbT-?a@H!Y zXJg#Zxwsd4=y!0=(Lxcq427@6^}?t+M92uAL$3jF2$=dMFnqi_*z*K~Ae-j{Tr({7 z6o9C0m%jR1O=PK{J@b!`D?=+KbUM5M%;t9HgZU#D`|6<`>=k0|azJ%$(?=R4iM$ep zbhFR@e<^AZhR8YDAQVud+zJGyjPN77H|l1{9KObw4^}^aSS3L~&Z185pAKy(a_A+&)>PINL#{o3nO&I<4&VRsNpepSuS}XM&6MBY^BDWmBU-fu7 zbV4yGM~iqNbm&_5@NxwZ3s`n)1+<|>c<1vhu|0(UlOvbTin(<0B8!SV|0Rq4hmERl zYO?<)5pDGIbReA%@J4W57n2iQI)5InC-%VJ4=zFdkd&2nCL(<;= zFBZ%JmCs!}4^r0}boY?~(m&$|;d6{#`glzDAixVIElYXE1t>~|-g2gRY zmtTq)zm3llSKM3kTsd#c%$XgrHjXKYd+e4hiP)Tw{B-ku?q{p~J?U$9AK8?vIjZ>L z88=Cn)pd1}Lw2hBet4CaAG*VCTatU@+@>zAqNe^KpHoeLshy5yHh00szr?yxXBibe z)E%e({^n!HByDu^??rR!Xi!Twh&0V01}j7WU~FQd;wa$g!%trv-wgPpiP~U89;Uee z09z$4i8!ucP>^X?pY6Vab zhXYND%gE58iwI(&D~3a4F!R)uHP=k%BMMy4yW`xS-M-8+*@i=7f-arXi0`TL?{5ob zd80;Q_JW1X%L_Jd7VAbB7R~a8fDX6v>4g!8hbcFu+bn=|@@#-~?%vC0UK<{Vdq5u! zlI)s+8^{`LbG08D=uLz6zXWG4(STj96@Lj0CQi;~jv2S{($!IDB-oSo=Gamm%X+ZI zR0;h#cY_TdiylS;9J=94fhw24PhE-S;1o zubdL$w{(BW7-q>Y_MP+KgS~6D`R3EFnD668nRG8&iN9j0?OzLCx^g(; zdHiKiq}z6eMUq5*~7m5YEIRv$olf-Q(y9) zKY#wa&8{h_f3LjXg=>rOrUwQ~P?ZiHM0dlj&Gn( zv}h(W9Vb-j{-{=mW(dEPF~)`OS8BmNEupD;c-0OsJLXx&h;>YKV3up$#oB3n--|FY zjX2*MaL;gQw6T4f&`U7S4D$KRw}Ac%FC3X?e(W6cp4eI9{33MuM|I{|>GS&+X5&nt z)qV&|GK2LCDgi&;NU!JVccRnk3r+tQo7dWcCpjrGlitf6PO2T{115TwuD{=JF8*a^ z_O|F(HR=Ad)T~d5S|1;b=I~IGVS^OE62eP^BuQvr!73Ow^`W?FPB3$?+_r>NZY`cJ z60?e!2v>~O`mr_02FU3wRi~|h!Ox>#ks>Vder%?jC9TLV!jT7Ojr38IF~}4nBF0EY ze4l-YGgQuQMr)ZH+dlJsM-kTr)!n^CWEu=jOwJ-WwS%^NPm3P&lEhWsMq_2}B6re8 z>2f}H@olKsoA|4z6o4*^&|qu;`*``AjciExn12WI)rpO?0A;o9U1>!JmZ7JHADGi* zqtDHVR5N#h8q$Y@nZ0xweFgjY=Js*TNTM&pi0sBTDa2#eDBsr@XJ=*frCwS$jBJV- zcsk6zy_|vU+YIWfp&r*^RgPP=b7}*glw-00-;lS;l#YRBnFrKDiUV1{RkL#O zWtrW@8s#*{m<}iMr##??U776%6zn5^c{;hBRNX$$QoJ-6CMiY*3qDVCpVRomk0vLn zthN5~Xr?0il;=mNC0)D3tjnFP@f7Blwn2d`}XsR~n= zy)1KKh8gMT?$@q8Ojn(^CKRR^(;z>w1~SsbuNWN)j~QU8)NN$q7I+^#+R(>p9RDrL@4 z6BY0*zC z-{#*L6m0|ECEb{<3r&K0dq!7_<#3B>wt0U!nExUusyFxh+S=M8N-^{;?j6>^)>eXk zZ0`P7zpiOw4&nAD9Ol!rRhFdI`s~T-EpTm|VEtuPR_EEIdVEchv`)v-iE~utP|Z~p zA6Sb~M))jV-9V#Sn5LHtu#+elrzx@u3Ptd3p|1X25$WQ4!Mzo1UMq38MtwL{ZyxHL zPicO*H}K_|ZXpUMZ)0cyiv!t``=fEv^(E0m9;ny7sW0FeD%5Qc_st6#7)CKGS|+Z~ z1qRiLX1}f~Xf0DsHBKfbCU;Liz4{ldR^)-6$}r{KhTlw?_wq~b=kLh-bUr-VQD#?J zGs#zZg2yC{U_V$$KGMJj3h3MjCyk$#>qM$p5?@fsr zd=+ZoRbrqi9~ZUHoELllzD7V_NpvS1d>pd>N|^K8_?%EV8Rj7uB}e#APi8nL?^89}@%GfqrE4K2PP5QfIE#QDsdf`8g9 z=5b+@o~I`yolUxG&$XC;HopO%N&(BYt52wz8u= z%giX0=?#y*IH4JlkcRHyyN&J@JC2lY^Ti;%E~sjBNdN$fIT`UH z*@tWK@s2NUGGCx9iP#V!OChv~=kRM=-1yyc0L|y&XJ}ef4%+ku?>eO}1&Kut=z=-g z%LP}q8c`xN5j+;M&gzyvRBuM0Pn8P5n2oq?9bAMohoY6Q%Xe%^4060M*6!+_fI6#t zIBjy)Sz2o)JdNA#!q6#jI#GeEyy_>xW4VNl0c1*-(}^YsxRsV`aXc1e7YUguP^z+W zvC~Vw212+8l*MqULlktYe*M*TbfDd#oW6ow%_53>!`pZ*iY?}-2nd>jwb+d?h3-$L zo3c1reWgiuz>m$))p;2zy72s86#9Z%;IR@$per2hw#Fu&xDA26Ub|?H9XQpb+KziCeLybFq^5Ox_JpGq`g|SXE`j1*=%M zY&t*S9mPzwfTP|+A^#5C4UTNd4{6LR92$@LeB*I4K0e;kx9#pb1U5Z2UUjqw#?ITn zod^e5@Ft9Z+WkIKaGgqPX7cG_fo_R%^?N(V3=|#DhJq>{U?VBWMy-chl7^OmT-^Uw zipf)B%w+q%pBrlF#@XiAfuWdX^0*sM#%ZF%v@5>6GtYWR(bV#nHU3y!I~0w0cNM5n z0lZ=THzE!m+7m)mJjZai#PMHX;t_8Nv`r}faD%nf=?Tfb4@Cd@tG+)LozjB` zA}@()aJCWdfK5u*M`@^V+%U;eRcnFZ0U2VaK=Fh&caf~Hd|XBA4l9B_yVBTYXuYii4@c<%RICfA#Ak- z#zpNSm*itQl88(|s$}J!+za`@MmW^WE~2eaz34xIczDSOhkV?OFFVgPKx7R6gP-os(QMGI)AKuV~>YKs0#9^mIxI={* z9b}-fJM{75Ty!Ui2JO;>WGert^!an>%Bzax^yTaO$R?%h20wTiAshmoM$ssS8Ctod zx252)Eb4u4uNwh{t`^$t@BOWl`MR6Vc;ljlv;Lg!9ep`UB^v2%+jVLW>koo zT6vndAXoB0l^9?IC*BkTjnk64Iz(XcJRnH19_kELg7WyAu>*OX-u8oyYT3Dv9+W^_ zivgy)6K)~U_iNi@tSj?^P3VAO>8p7nZR-RaPRFYP+U`VBXP$p4t6vTWsLQ3 z4!NitiU6HV+AVbyW6aU*F!)m7>L0{XZ@GF2LdZ~!w}e4}sEvkEZv?w zVn{me!Q!and)@O_JbN+A6Cr? z(P|u-yiMd}!c}{=0z5v)jsN4=#l10m4R~nI;jGerr^FzSw$|Et-Yh6;{1*KwSBR{T z=QP|Q0X)E>pyoUxyi!f<+GOOe!CSJAe}w0^ zsZjNBc<7=s@%rjw#OH~P%sTP!9n%o4$qpE1kgI1aDlnIe=rDSq@>VHW4mcq>u2IBXAmt-DH%vn^JUxpx1z46>GinTD` zPl|k>%l&s(20k9qUC+aEWw0)IN!jz{(9NJ$9jtfH7I%SNwS?p2xV=6+v+=>`Dfy$P zXa%y;+ES{E!2xKYbVD7jER;|&=Tvn^mcl;-oh8k`dOh87L?Wa-s84aE? zgMpzSj7wKyvQu`#<`;`r^6CLO6#{9r?C<}6?c6hXpiM|$iXge}C+C6$({0(kR|ng4 zeoG4`i~y5fmcj?;p~*%#vIpmcj__$!h`;m^{6s8VNKb%$+|)xfQy%eOo>z8%&C`1( zmzu$5WPLoWP;Wi_$(k_(Q*;ORPSzdx$jUtpOzfpc$p#J&yHnYAJ$(dsM&jo2&4xY* zRNYb6Qa&^U!y?5Q>W~~>fsojl7YoGN8^298|D5Q0U?~|J^3xMB@eALpk4;a&7Yo~b zJxYU8eX|%N%U*it87xQkuzzDwv~^P-p0?w#53G~tQ_Rf`u}1Kb_C7hU^R49Yy_9<> z_w^kbY9H3I?yo90+_Gg$@=LllkwhyXAqbfi{Rh6Va?R|c&f;sFJe}=dT;KBt`2n== ztd6=(*V)yV)&GI<*oa~CK^sTWNCadkyQ}w|Zm(e;aCoLqL61|egN`|&e1r>4WBx>A5zX;R9K$(XX@<25q`EKapyEkZ2%aR?K7ik|>K(|$qu zas#fTy73DNGZi6Nvi#-A^huNdhS02J#N8Usn%V(OR!2YPRP{JoUi^#O<4J1u*d0#6 zfy`$s>tZvaQp;`|0Dy@9-hJZW8pnnqSuQSOE6(#R5If3#t4G5j}#MjH>b7$Nvu2MPSSXf*5Y zMFY2O_Jz1lMJU;S9``N1l-*_QAsWS^@EFY&!f;a!q1gkrbkrzdSMTPpR>EQ<07$;& zE-*Ct?+rzx{64OFG{@!Mg%!7%FXSH-|1@oJs=B=A&x`Ro=fcEx4d&zItzii%w<7n)q3xQ1 zIZc%r-tO@U38}sAsHh5EsKmUqfQ)7|oFj116_i0BO+Jr=CwK|-T_OdR_`Mn$*0N@y zDY!s_%57T$h<4@P?Yw%V9`YQ9g%B}61;2psuKv(8gPaKp8G%^(;3d-p7sQ4`;=M$` zi6C}kp;lKu{wOhsvzuV!(ZHXY(8;_AQ>M-$MRzXLeS1QQ z9>UWcM*YTCGBOs;1Ph^26i^G-msxaZ8>Hwrqy^EY8KKB3j>h0MvP!x>BWJugTJTMZ z;wJ1=s1J~sDwqf&Q1ifx)~lFDE24Sn>NF%^xUT}3uG>g0jZIAv{8EHj%#m($BN{+N zBs>Y51$r-g%$ylDk?FkC!n2^` z!zo5!<=2~MU5LWgIcoCEhZg+&@4M|4f_JV$@=cHxfRVX=A0Lv#X*rR)yF{wo(tQJ& zZ}R(q?Om>aO$w*;(bI6FfDzD-H4uY5_{2HD=x|1NwCprP(#wH%aSb@}bY#Qt<&o=0Mm?vm@;}C_Mf$+GdU6TD< zA;L?PGruE4s07hl5OLaLExjQSbT8SU)&Q5fgJ%Qn-Qsafoy3FlNmnVzMw(6dp!cg^ z$MRUIHTx_e#0W;fpoP|^K4=#Z44c{zY-Es2G22&|0%?FHlG-#(=iU>bOKDfY(7;Qu zUcEp@qETE?kF!@OHq&xbKTmKv%?U|yeh1_3aR|)E`r-h6WXDe;)R*D&JupU~B&RVu@?MOO5WjXQn1xsU| zbkTt1*out1v!Toy(0Vc`8`_9nO~C0C*4Qyu5dJG(s*BPr3jHR74WM`#z?qDRAi@ey z!|wt2%W^;^Zf_7A_-ZgG+IwF;VC5qDw%{#iKIzU}n0LgTISAnc!J#ccZX{zO#r)2k zi|5KpL*9?08y)H07=}Og=nWZSSkD-@n6RZtR>-eYz;N9 z(^>X5lqH=B(%w|`$aaH-XiTZBvL}6k4DDUg3TX{{!xMhendXDOeE+okKB(a`gIvd; zs1640*gsgb8gTX=$V-jQm`io|@tFU(1Og~K$^E|M)jeltWb39+&~5Yj^mzB zDKpQH(o{o1jZf|)fw}Q>2KfRs8nX>+Ac4DBSb>2OVcA2i*@cM1&64*rm#b_&Pf$hA z{?E|qUn$o{2S2AM9$|TzW)=}B`AS7EPab97Y92bnf?em9C+`iIqD4X%@%u;|u=tLV zSa-m{vj7{6nXs?|@@18|ONCrkwM%;9uiwvUJfT#mhuX(h%XVz;X|c=|J818V=c2W} z6(J!PpCOz|Y0aITiyX)uGDS5{fk)nApDxx-hZUi6TW0~C<@Qc<=7Pdg+h*|5bTJ>H zxtCFJ@qW~O5j+O8^)iG4>gXy`f;+i#ib+8EZ|+uvepmJd`B!>rJ3XN_Gp&Lld)5KG zWjgILv&9^G%@JARMA#xCdRLbnfNnTvDVrlM#|1;ZC}<~pQIgVjYIsIBgc`L3-cs;= zau(D2F=h?n)t41fp_PHX+*_LDe-;i~jiMK-j|2s=qM@O|8~5mr1Wv_sBpyw8a-ORL z*VAH|07yEP6V!=;8@7;rr6H&9dZOU}%aPCKxQfhO)tVBFdE$rPt(eBIHtO2>1Qd4K z?%$YIxwAbn8L(sTb|BujQy#siLCPX+5?VqUNRZBu?&P{5ZX0Xcve6&?67xb39`^uY>*4u3g4tj8l)6S~c3 zcgZ)*T01;6w_)-foEdG*nb@LsOFIC8feNs=lHu{cJk1AJcyX#U}-#P4QH&dOT zpcg#-tot|UkpFw`@c*eRxzjxAKJYpyTiprWpn*RybLt&`PE!JydgT<8Baw+2h#tqHP!io-jaQA#Fz5ZMvi%fYk^a3>!j2|Mo5US5vUI4H*ms$p@!+J~LB~xS>%p# zkE(;2dA(8$n3@O{$OYPeA>-i|?gVFr_VpcZhF9oqsNmO@jt-v#H%78hh zd?D0oALOdV`U<36xQ%pvpyPBvetA~U-SyEr+8j4fM}LDD*Eh+Chim*K>r|VePSGeN zl^xEf0tQ>Hi+~)aMi_~1D9Do&aoGKj`x@8GBG!`tmI#;ovPUHLZ3W>R2&>#ki4cH? z;3DQO7nkkaC@`k{JLd04j7@mRe+?e)*$+%^gPECG8_{&oj_`mSEGL%$33V54x9y|O z;Z+L=Ow$?aI2{M>l)9yzf+&j+&3LZ@{9hMF*4l#Cn1w+_82U&UtK`6|u_t?{i#pgM zDmS)wZN)D0|<+ZPyAf#*}NEt*5WlF?A zfzN(pkSN9-rf%{UZF>+lQ+T~(=~8##hYg@!Y@ms8wMBoX>dYdz_R#2odg-+0i^mla z?pFB@mIn-)a>_*0*P30}B%|#4Ew>HqS^MPSHH$e!3E*%mAyTo9B2g1@Xo$4y8mp50 z8kVMkPj9izesyp{Cy1Myg`X(pKR97o#nEn&O{R3pk#|WH_NQm}r-M6Mj7{(Pls*#) zbxh$Wgh;le^Qm>mUhE}Fin@EFlluT>wiK#mb)1y28_k`KKFfL_hWD*Zf@F`7Ut%lm zz|!4N@(1g_mSErPIpiiX#D{^IYQkB5Q-(-z5xZF zI#?9Cj7FlJNE%VoVIu0V*#A?4b?FCZlEILrp=F+hRGU{{H#}J9{#-UL7AQ_(6%rqa zV`jk^S}^Lxz}1s0uN4jSq-ICq{;3X*k_rgI!!v-$fVXku)!%Qukq17jdcXBl?v@73 zGD)hb2D2WdR=a-@!O9%zs9+*r!ZbV`X@Q}7(Aa~@g7Z+G)Z+?sPzx7{`wNKEaW^d& zP3MSipF-^QD&8X;e&cp=xZHa*iF`sD(w+hNvWbbNO^@>vUT&jikR+(*1uAz+yCz{L~1 z6#B7>7J7y&v4mbNz9MYwIiyBefw6(HA5p1>K@mU(uSa>oW3+wD0&fwpfb)80_LnuA zz4v)aY?^Jr9xeZfZa1JWE?^ylogfGDZmS@QL~dKgO7#BkeQug%Xy1mRZ#)*Mq!6s# z_Z>C*!Q>N=uaSI`S5MCv$<=JXA~4g$*%Qa9^Es`kbXHR3v}lasfnB-sFnX*=z&l7x zuOA40?UmIPC9XAnhPjxSGJmJvJALbQAUrWhDiR-YrKgYaLnId?p_#oE(l1I3S(3q} zA~)miLCVHsk+XyWp5vDtuHAt$Km&Y0R0lX)YLrYk6eC+K8qFO}uyaN76_)vpIiw=| zogUEplHp^JgZRuL#M+p2^{Lggz3R8g8Zw+@XsL_E#&8!xuzb5`3TN2Owc13pY8u>} zqi)gpZ`rbhz3YSc4^l3y{pNkIIG=so?SUxjED4^g+5=`_1FtF|w=yI==&vEKT_Qco z)m7E|&T4{6^79cVtwGzFWF2-ZO)T2XNo9ae)_VS zs~wG#x0#YUMtWf6o7fm!-ecf~0rEZxX;_Ggvf7k{_eVvP@2n0c#as3nYj{t}6dB zd%G8?NBKvTz!J~J=>$5e0~{?$VveGX9$9yS#E=_#mJz1I;gPTq9=%HYSJCZbN&m=X zGH#g09S5&7DXXb&pr9@`Iav{5M!xpvmqzv(b{prS|1O*_zj>&wZb)AD%fl%=7MyRG zJ$v<1$cVx(qvekc0t9c6scwKP8)JhEo@0-=!;dt9oW80wd~E3rJsuHqWGB#MJPH;; zm-tl*%@P|$*hX6++99zm35JL)DAZ`Z4uGWz48Z<2$z)aP6YN%L8@iZ7LIv{hq~(wt z{_>0%w>*RZK^4W2eU`h9{Y)G zx&+S)#{=iMJ3r-FhORNwY2&R9{w&?_AFpo5LWSyL5f@zSAii zBt&Ai!f?EU4ipYo2@aLCc0`jy)0R=ALLw4_m9G!M>FegeE>becuwazTBee2T^B*lz zVU93^Fgke426Q1VBt;5-sU``P_Uysih-*7xEbLIn0ub7_6K0YWR1{QTzH%URFlv4F zqxgNnP*!0vAP+m>hK*T&Zr3SBAc0Yr%AU&G)!c=V&_|(a_y0^Hc64~`x>T9tNuCdx PfS$9);HMj_wjcZ-%N;}H literal 0 HcmV?d00001 diff --git a/docs/assets/serosurvey_population.png b/docs/assets/serosurvey_population.png new file mode 100644 index 0000000000000000000000000000000000000000..d03fd3f80929e6cf2551adfccb9f9c2f9631acd6 GIT binary patch literal 66031 zcmbTeXH=9|8#X#>jEO;wu_6K%6h(@FfPg?^K>-En9aN-O=^YajP!SLm5NRsCOD{uF zQIIkqy$k~?y(?AvcWvLi@A=NZvkq%z85Cxo+0WkheYN|!cUAu4md*P%Qz(=zGMCOP zQ7Autq)^sB`{_sgjhQ}m5Pyi-U(m2uwl=bNx^8PoxpLj!#=_d(!t}-gM?+gXQ)??h zUVeUF0qz4P_VzY*Vtjm-|N9AEYg=Q!+kO*x5z5atmo)7tl)d5PzjZPWcOFyLQ7AI! z|4?y`7-Kj&?`&CHTTwr|@8_Q*fBmV%DOPTB{a?o}ZNIvFZ{xddciVSyC*IY~TzBZo z4;TJ;s`LZ?`FYz9XAk`P+u7fO9u}^BtaHyEv&xcjRQ|Y7kghW}aojFmz_?q`xQnU$ z3`py-oEbi|Gs&3?~E;Z zk@nwq?~d=S3^;N0sPysU$Ae;jJ0hFtFxnavyRtMt`u5(Apx9^64hUIypK0qHxto}H zEKNJVZn>N4WL;QT*nh)!N-{)qHAKHn-_ugWCjT1y#@nfdF4OTL+?qE$CqF#Dvw07% zdt2TH%I%1cMti*j>3$SSAM5ky&pQk45*A7{GH;mC^DO5-uzg)Wu-TzS^+uw8});+G3Wc(}!k;xIOwjmgkB7VaPBylv!;wMxJ)YW=CG$}= z36EIa<|kUtZIj@T;kx?lcF?0oc7+!x7T5oMtDSj56w`N=mEn~?$aI!^P@kSM4f5q# zPwA@q>D4;Q<+gO)rsaic9#6STmp<^7Ex#Cya;uALlDzf#Z^>^TH%wD~e0=taSfwh( z$yPB|T2IK0wx*3X3jcfCZVUc;djsXkKwV@?LBZ*i9CH+k8munYC4s;nu5BN#nscf84dMk7q39_wEWwa$F9S9l!=T zxiZtFFfi!D?y2Fm{MCo4{6k%&c&^i=o{-6hSB6bnfBusKz- zuinux$2;gy;C`c*OCioxBOp;P!`2dDfHeuPW$ z#ou_N6YT8lKBHy2_}#7SlBqSJd^K%t>PO^4RA^6yRGndBv}G9P(>85mYtA&Nf)< zWox}}Ojh>F%Zvfm^wV2)O5HUt2C%8(4RH)qi=A)n;?vWKlMN2a$guWr>96+SjIsRq z`JJzr%T(-(7cW%Obwpc|lsOre%_o^yMQzU;*F^*-BqVUDr>El68nt&n=nQJ<-L{n2 z8&OyyE@!nrCab6}i<)LAt=DNij+HO+XO~Ks3+1gk$rQ}3sg!HhQr%kUp(kE9IFKpI z#U-1d5Z$^kJsAA>ajbl#SXEC?&xHM>M~@t4h7@&+>}77 zAs;*x@LddvBNLuh}^y)CYg!=sa2D` z-q&@?JQ+Xz^wUYNwb?fPNHN9$Ej--8crXKddg~RwBS+q8yu9{jL!8{6m}{^9Qk&h$ zt?|M6+b18twbiBiAalgBFPwF{;+IaZ%>GpwB zX055F?)a68+0iz^RP%|hLZiZ0uYRANx1{GW;}8ntF7b@a&CUv~_N*;=Njp0iY>=A^ z`DG99UhJe(`-Ck_RHHc#A2{%8s4XKp({0|$XQZZDpyqB`|F0}8=EZZJ=E9+NUq8R| zQ;d;jR?pC*HQVcyOhmK{@EcUzbd}P|x_M7C-saOA$7w^y6;bo0>-txEhMN-n{`qCk zQ?J4LXs5)8m)2JvKG!fK zYUk$kPsHiylT3rDXk+Ge8oY&>J@LUoK{u8cM>C|C=Ek@1@$sdI{j!_eWM-_RsU53X z?l92b>u$vw?lv=YJ(qiKvd20*iWV-y;;}fBIb;yGy6f20R|WIkZhk$UOSDTeGPe>} zysz)YEf@6O%4)WgOXYoX-AGFc3%l2HX5iz;H+86vv_m|CqsK((w^kM%dK02O1;p%! zuGxvtO-%`O=385H>lU42xE{Uo^l_5G*#-He+ufCyL(YD@+j*h-H-YFzF zo|F(J&@@4vluVEp;oM+jTpuO$RLFebss}1YMRvWUxuRqO-(Fswf)7T6Ev@=CHa6a@ z>e_p@#LGvTIXgSw)R@O<7SJi>EB^8q?OA+D|5C$6c0&zg`5!*wI9aKrYVel}haOk1 zIPLu9*|{d0*&;im@M&2}T^N1alG!488AYp4Z~kEpx%K#j|AqwlNCC6v#K0s6F|Sqk z(6->FJuXrUeLKo&G#*ACUa>p%u&{9JP7%WA*_B_1DvCr(THy%m0bg2r_k%LBEPh?Gu#!|I(eDoILP`SLHa zxxBq}>C#KhP{H*!br#zwA17Y@`j|rTou}f6DB#!%lu3PkyW^>tLo$bHOS0u?C8%&Sa3>df#5dw({GgvD$ew&~J0GAU75B}kO-GqP&+`ii4D^E`;T3Do#f5qmK3+n3XngMob zk1%?4^vZs-aGvSG`nFepZXla=bzpov&26Ia;sxK`EhuTZ);*HU-Unqi|MH76I$7@Z z@*hcGd~V9Eop)J5Y9)uM{2@vy&Tt~?&IqokGuJ%Ss=L^;oxfFYn0Ij7Ul(d-*P zyW!~U+P|OM%ggJ+g$u)Y$NTs01#&QZKX21>y8;-}AXtCqQP_oE6*P#E?h>kev?Pzr z)YMd?#C%uGjd%Blu^^pPr;Ng_bxLFYh@exaU4H+@@lSmTL>`A z+&Deq?%khc@iym#sFAhd z4<9~5mkFa^PX^>klnv%Wb8aB_%4^NjE?TqHy|Bn(G#)Uvs;Q~Thz0#S9UJI4FK^v4 zt*J@bZ=X<>nDAs*p|Z!)oWqE~`+JU;!-cQx;nO=+w&}+olktLFS~-d7`eo_mKHI`Q zF3CIF+4?SbQlARl3f?4-wgIy(r5(onp3qmwIs~gr~cUU7FhS zEW`P)zxlxcu$DKi=A`Y!ao^jwDWqt4;|zVR;*8d?6gi=up5y%anQDsK_YQtZpgM%d zoLejB{pYnJ;M{#^nK>G}>m}wR&RO1?BYP|;J6}=#RzH8W>L_;#UCXF1ZbkUu!Ph2T z1({)O*aFVIej*lZq1)Kbm;gL>yqEwsYflsBV;|gd*f@Z8ME3DXl!OQB`W`KJ@86eo z+6Rc6{iObnJ3lh}Y-P==cKY;YqrtZ?pLV3Ur`xVByBK_Y z^`~$^f1UICx|VU1?&3nlB70MVvG&Z0z{3|U20DsevKwAp_{CRjZDEj|>d-Cu!vIbU zYm~lv&L)bD@nL{)r^0QL-(HRFRY_5!8AT~bF1)Q(PEv}yT=M9ZV*2J+SDu`F{bE|s z{o5BNpEem88GfU>2vb2?%N{1{NqdDzF(HO)wEL8d@3PeLmpe4~Jl+>|Q4(~k5)ckS zvU~#`L(hF0Eh}-*1a;EAZ=LwavUBInR&m@6s*4>LWgbi4PK!qUV`3F+KD36aWiDtf zpDu2kE1L1cN1aSS&(1uv7Ooherh(`*Cq1L!>^6hWH8-t1i?Fmj(QMxG_l3U2NnXPn zWzi@t1M@Eh9+D=IML9vfgJ=3%Z$S8h?;buYr;@^{Z{?C^RUP!il!B?f{mBj?P3sD} zx~!VemvEeBTn;| z3rvb34dxy-m7RYKXtW;?wHo zh`%>MQ>v4uY^6-x0P&T^*`oQs1_v?`)IWdz?{`!QwG6%SnY@BSl+cI>3w~)Yl)!A( zx3~H?mg6+Dl$MqnMX^b^-qe^x1@-&*@mluf64zOa0qV+&KcXc)1X9Jv-FW``>o0*Q znN1$^3@4)!oX4ziKF;+v;IFik&-Q~2J?7SINQTcoEbp^FH_CRnn z`hLxT$+ET5FwU4)H>4vpL$CLZUFiOE!=`v`>l-&-D8^pe?kfNqB%fc4UdS^=r~38; z1B)rj)pbkjzEho^tab=t!P0hGD=R5IK&gDh8$+F@4b8X9O(}o7sGyjRro@Ja$7u{N})KVp$-p!``Q~6yME*5 zq0CCQ?63?R_>qPfp9K%xFVV)*F(L=N+q;rcwpz5H=Ur@q`mlRaey@<3inpP=hsP{B zLwL{pWKYKX_wSi%B$mdqnFmbid1(uxXU^zgkJr3>aJa~E+{_boIe#JDx9dxnU40e7 zMQ7HnTjx5{aFI5dqMngg{pitXou*}7#?+fRyZo_~^uG75sd^Kv~KX`Ua8MPJEmU$Zm?>O$MBoiyz!dmOcFyId8pWa7z?jucuGMOQm(HJ-mT zKiLM+pmx$fm0gCk*{34bR-XE0o+an{8y_`QE<^~E!#mwo?4k@lm%qShm{olC`#KO% z`m*QJ;$1(#mOM*3cjxDcsMY?Gv;S-!{#wcIWU}aAePaBQ1FBu(`Fpj;Mz7rPRp@ zTXx>`XyrLKGoykAwx6pF49Jf~q;9#?pmHnj8O_R5L_{RlqC=qE z^@K{|wXZ9v=u`9aPKj4S`Se@A_EzrNu_FczCN4JiiK!{y%XjbI9R~sb^UpuGwv1Y^ zirL$o?ty5-D|7$u-K!cJPyV`dhc*+T3rx((eA1!XG?+`bZUb!?`rf*pNm^AQR zd-fy%)er@W?2n0(c|9SkE)C#C+eDta)DxUgoss~|mF7sm0YzF?6ZtH{7AK{amX=O%aGXDQ@SuQ|pC^Zm zuoR2;_3fuc9Y$2V$3v%DZ%HP^ZQ-V88`rb>-nIi~4B^v{`n32kc4){XyK8*Z0Gn-h zjhJBNz|T95o_^f6y|BR_;Y!7+r++F(~NF*ssmRD(ubTMzP8o6Y)7 z#WcD#MI-YCAP6g$dbZIO>TLUOzy5kaU%!WGbar|=`&-o9+50@`RgJMSkI0>m6nEk+ zUsT514T4Y9)YOc;cqU;w`60wg_o}=bzi2{hq#!+_tuJAmvuqN_pmug>bTnn-FT02S z+{E_bo$nsQz23qW?br0KIXXG1-}!M%GvtHW$sT>wu3YUh!O?0NTwvXo7sVXDgaK)@#XdMtWzcQwu`913mr7t6<`2|#3(2LH%%Ak`c%XTf=B%y2DAV0 z{G3ni0YmA=ANeDXXXI|8Jj~yId&6yNb624qP$b`QYh(oq`yw=?CZ48ee{4w8DO74q zOBkGTrSNibRWC=hMYlfU7B@tJ*cy65r{Lwg`3F%qFS&6V8O630x}#(Zc`kqB-m>XQ z5$ixs?U{r2ON#|18zPlQ>%uJSNPbg`Lrib(YVe_J}o=q zVfd1z>z~=R1-E*=X+6`6*pa7;{EEyxfbO0|dw6(2P+I3RQdL{pe>cLU5(j(+z17&% z*-|~?@IdYyG&sriHWZssS@GqYpzJ>~?R}UuCGIrQjM~_Obubp8nKpKNM6ipUF{J}a zf3zv257Gju>5;>}e5D`6CyNcfhEu6jF16G|Ah#OGGu_?Y18#S~X_DgPLZ78 z`8kM(nWy(Z$5~BuDbjr5)>2i*Qu2vHTZ!FJqaZ4`q3RQn$Feg=2EeVg!jFXrLV|#n zD+%BTW++x8U4_|Lkkl^so;bn2e|iIArluvIhSN=~s_RF&`DB*-3&4Alu_Z&!2N%Pmb*m&#q}1Z!fqNvp73y%wHSF5qw-( z8sd^_;GqkB?Pja_i2SEzr`@iFij&(l2wp=a! zHK9DMSFG;&`KeyM{NS9^CvP7gZFfHKiFfngS1bFb%k^W}ndM?fYB=SxPn20nE!;vS zjL-=XGH)f4A?8yN!gIvokMrAwap^O2xQer zV`9$0PS3jXL`LzAAF1otuRrCv?7U_F>5~jY4WJcniO3LkI$5PI!MgR7f+nye+OiJh zM$)xDl@TG$&}(&J@G@ikNmHX_ zAfBr`tO@RT!@%GOV_)@5wAZR)ndi!w-h;qcC<%Qk(*k`a2VB9k2aF*-sHW?59FpK{ zi_Gri*4%o04>R-d)snidfT_{BN9;Iz)wEfSB&8#MkUl=I<)Y%6eJ2(t^5-MFKG*gJ zoM0p^B?{)x7Z1qm8jGfZ0}c!}B`6q=uv!jR{bzOLsgkkU3RgwT$LTJe23D>S9;r5l z-|GyK|CS{!J5TxT7ZqS!Q-|&szy4cT;P-ta^?lzQ<>yzFT3gOuSOI))#)k<@l5(2Y zUh~j_PVKv!r#elu>MA}Ak2huElMj2Au5qWhxpjB(aP#x?TDfM1Up=rLw+73FXTS+b z*36D?>UfyMOuk z$C0B)Q?41S0EtJ?DWe|oehHRIkUJa90PWxK$>iMHBjSnV68OhLT3*d}=H~yQwsa{C zeZs6QJ#s-}oZ2__6iTp0Z4XL$BKG}hl{pCN!d_(_Lp&EJ(;d#+*t~p~wK7%1Cs>@A zhmN*_LOw8m@SJzw(8+Mq4H2Qdy47D^w1Ui%x?5DB>K`s-uIB9|SsyM^|2ZiDKdI4Q zRpl_4DlA9j69dmu7zo~bY9yD&1buyNI{N>!Pz7cZMn;LLXyV$%m4kB$0sz{NmzLzM zZVmiPEgP0Af=6qE=~{ZqfZRm>7?{UZYoMmqs<1*sdAZ0VwSO?K*u}zguEUsC4+2|j z-*X&I#^}JUFCSkI3_^)B{5I18mGU_%nW0&yzm2N6l~=vq`Sa(C7BV0-fO`s+yT)m+ zJ8f-kJ-viFYx{q#VeI&fYK9);Ma)Pd@c+|j^)#(G_!u}`T&jBn!w=T=2>LjZq-MDp&bNnB6<4bf!DujH%#%oJTl0Q$WO^o<`GVz|v~mH{Ijd_~VaS+|xO> zW(?76n!`x5B4JRI1{Du7MQ=X7zISnsZaq5?Ze3kjS^3;i>`gEv&16gX4w=p`I+!Q# z+-h-k|N3rEOQyj^9?dKchSLJa(W4`fmqh|bA+LV#lOsX4UOiPjb!S#z258yN33C-1 zc{v?XPZLzPHg}<+Q61LT*OyUpcP}ooAAV72H)yo?6g|_x$9oG)Jix9-T)$6m@newy znu5gaV|Wino^Z?(NLOd_`6`8DL7YDLA0tp0)rN_n&@uP;@lJ^^uQm`3k5;6Xs_`5! z!^nVl%uZFDtmW6(PC&hJ-fR?`Wb?LkF`JLKFI?l1FwNg_(f{Xk7>Xb3g3EfEnJDic z1N8uNG%iPVB=jiFj?>i-OFy`ZgNj0O%AkVDk5#-?lV+ccLNFX|UD}|mAaP{4Rj!ZS zZHymu{|~T9CE_0?UOjk0TAKKzL~S~B`0#l5m5Is83}}>uF!mH2fVeoRCIp8E3Ts)pb7iZL@~Yby$kX$I$U zO+ps!Cke=hibdYqb8W?GoIzFB)6;W>Y;(%0tAL3<@{_ui)L2R$no{){HrYnVptB$E zY%@xcS5LcGeiDwcR+=tV4Y*YuT&6k4RGBn>PfySL{{F4-ZiFD1`?CB&E-;u+zuV9u zuA1h-DRO%0yArXBo_J!;N!_+Z%x*R>qz&ZsLbef2361IIUT@fEr$Eqgx6}aK%V77V zpl}TV%iMqPfMBZJTfNe(Ql2`EY1cPehqC)mCrx!`cGT3!p#d~P?Mnf5V1V^OKWM@! zkK4rN{5yD-+P&Y7;N{cdCZ6-zIt(Vz1lzS~%TsJRMIvc17x;rW;X;}d6xeaVWI$1s z0gP7{NBM}a8pMI!pRn!TP_iR@kW1SnW*&O3%&N=ed!I@JS75_70(`y#cnkjZ0oOYV zGRB3Dwc$sm52zycs7y?BM!K%JU=RH5l=gOwTq}kaRA~MUbN?D^cCn<6KUqg9Fn@q1 z`|#!}>lx>hH4cOxrX~PVNXyE~=5@9r50G67Z_4nA7c@{E3j^W9b!*sAv|)e`IZ$aJohSU+K3S@gZp(G5A>0H*dzujmq{XdvKnhzk)_@5W;-&+U;|Ouw$-= zEUYd~vYPp32S|;@tX2NInb>1=GV7QQoByjuwm`_S`nI%W>`E|-B`uwRE zF)@B(4kM>?0`{M_IZ!@ch|?=dtk6i`gU8l==M@yP^+l|vf^&0(>ax7DG7Eu$5FwY55_NF&rWY4{ zx%CcsQDKR~eo@FMnbY!~?SETj=NSIW&STGj&pTL;$`{RAMd6bc-TZdw`(YQY+%@_Y zE?X1Xy2yqYpFXeg{Omt7o#q*)C>vaQ#Ra%0!F0508=?ZwtQFi~DT$=Ibx*ER%_0k3 zwQGDA=f+b2NQdrmM!noCR=@i{f`$?fRU?{&#X!|)<${XKYrI9m<4EsD>wvyu%o_kbRilqjE%ix<-tk`FYbdk9xWB{8FgH0GhWt*H*w>pP2o*WH&;2T=yduE+OR_+ zJ>NP5%VxST)vpD$>&=@t95RG)F^U{o@~!plo^Wp4w$0ZSq{Je-u9i1D)%D|_n}`qq zIIab_t_(9AHU2qbMx^}UG^oG+Ya?rVea9#-XXhAH3E*l?>*BB8V3nyPcHH%YA z(>ji%6B;)W+jj2Tmkj-C7pf=ulrhqN%@8S=@?DHugR6Hf$*bl4iCyy45Dv`9)i6|N zVxa{+p4d1`7bTrz(JP|F&-?GcpG5m2G6LP|baVGi!Q$Q{g%fKHwN~a~f$H;gU<-4o zoQq@G^*L4yF-t~~DvTx-L^mL8672_Xkql4>9;t&<0S7j@*w&&{7nNwX+ih~#Po(<; zI&%)X3`vgQr$};x1S8;ajo>H9O0y<6VUGgVHKR)Anlv1;>?zg7+Yk#6@_z~lUm|Xb zEwmg674db?1!`!ZN9gI^D&$?+z3^4K!O&qacIktVh#d5lOYX~ zjie8awq8LwZoy*gVq;53&p`jbl;z>yR}JN1;PcFy@x|Ml-lthq~ zgsy;X-}^4hU&&AG`8Uq?iuER%>A*=dd|>;Lt4QQgDErSr%E#Aj@%LC44u)$_=P!K4 z52=16DL0|SR>&a#N}+r_19fhMLCq&P7(1ljk^K5?>Tmys<0cM}YqAm{zg92`MF|Qv zQTItuC`cv8q(R5Z&tS?Rd|emw=~eQAzqb}LdtdK!JWRg%{&idYA2%zJ|9*#;c|>Ac z|NV-%`~NGhN8)4u{gOZbpU?XJdz(X0d;a@%d&l=H{`cGe&upG)!Q8tLkg8+v0ha`}) zgOMD=kp#v-u&sYSzu-iL!%sIaz<=@l_Gy!hQUIHT3M!wD)%J&L|6RNT8AzryJwC3S z>gi->r;HMynyhjhxbT~Q*=h{CNxBzYaD^6KxB>%Rf1A{@&Hrs%B=aAj1tPRU93j|)i8&axw4*eyY>mcf%m)lj%?#m zgaTClT))va;g5n*n9SjW8PG%&L@0w36IM}Wtr_}K7dto6$y=ZzaGgAP^5Xg%q7whR zEKs8al4cm0Kdu~a0#_6euo0=IH3d%jh^x49cBOwh1p*s(=EH#q?BQ^<88VJ8=aE=IDcGOY^l@Gl?h-t4fUJlsfNuu**!&v5I~{>7PLgW$pU zfrkm3BQdxFo9j?DE^gU(>S1_zIK#F4&6^px4{||2ZevS@AXa^niD;FG)&)ZV9cjzR z05$GNGgw<)(JFDx<$w>Q=$McXf5ft@K#?$%iNeACQS)@P>dW}_AJ8|0BO+2Eo2HtoBay7oddW2M!!iZmO!y8+)*DgSwh`ZEuO5BV3p4V$($@6mUEj?V;DLl%Er$uGXXw=>%~U@P01L=YT;6#n?* z4|3_iAdzakr%tJmFe!)W(1+)abO%sS9^E39#^~yYT;n{qEtEK|o1;p|oL-K+a@||d>kItSu=YXu>#*G`30D(xj z@LWGoa}Lf4tE9UI8yj0PkQd^&pRab*64*>4Lx@wH0D6GmPk5EU{`jtL+buu;+%UnQ zIv_2knx>_S)KN%iXsGG}WXfUCkb!w%^AR{-yImSf_tc$a@|bN=4kjY;lFmbr*6pM~5_&1tikqX1D4*xWdnwlz1L%w1pyYs2shegD?`xGVu zAR|41`neBDgxw3s*;_0_a@%@^^8`IhaG>oZlR@?4zSa>nOKl4jnpFJ6%H3f5?TlBdosQHSN^n5o)yn9feTRJQvT8=n3{iA6~b^pjWOY z@=L894kFd^DT-Ox2@UgSgQ}n-1J+<3viZDj zglWd$%sJMxirWu8oDeAUDAX&muNp!|PE`VJM6fko*Ic!f+^(oHq~;j4(MW@?u0kg< zZjVBLV*!uKPtLaOudcvbHR=j)+PeQF$n(wYE5HoG@t4DbK26a^#Gr+-(|YE|>7f&+ zh!iE5QR1oPnyyPVVqy`lh4hdmngL;=0`0VR8KkDxCXy(j-{lEuus9|V#P9{ec8YO) zjimU}QZMs4lmI@8GhD18k{zttD`sz!7b<7;U@!lhhd05R-(mf~AtRd>;)k}w4?V0G zTXAa(KP34W=dYi4I{=u4E%50To18&bUwE*xyu93bBq`wmDsRg0_cwAw9SWUr$G)ja zwS%U=+mwVUXr4|Z`miAVKwmr4me7}sy3rOoX`w9(*hB4QUS(lUXc`sx%Ljqn6vrRIQ0L8VXp0&oviiVhrMD|0v_jkfG5ECLblr&Yy zcS`sVWv58T?f4w_Bd3#-69caJ=B-=Ru3mkJBwsR_O##ta#B4wgnlWMS5XZ8Qi39oo zSA?Y10gC@|Dq_GRGq7v@uQxL+XP;u7vohuvJ$g zdgTyeL=PdT5T}6@$ERmpvaspm(b~AcKEa1+$>hP=|4JuBIc9pzD{{n@DekS)7blC9NbVC}<~M6_CRW%SQmxyTtXH zJcAYu4Gm_DqFi46(o{*0MG~tZg%_Wi&`KbuOGMT*j`oO0;gA7cR>P)$3~UEqKrS#a z5Z9MO7^mmTqGpR1b^zeIrq4F^5Dwh%t4Ll3p}P{$00CFQQ-S=7H9ujc? zuXYTYfx}q4B5)3W{cEs@I6(E2^qL!hbVMXZTrq}3KQW?lKOmqDRWa9YP@YJ@JX$#) zK?uphk-6BapB`^6d4*|82~q8dQNMx-MhH*|KW;T*bMW}-153i0g!Lr&l;)g~~M z7^@6LaBW3|UP)RO;)~qt*RRPu1%#~_9H(UMd@HSD=S-3hPst*73C^2uUm=jH$^l_{KmMH%H`%5LWt=iv3cT5ubW@(1$IT{)k-Xs^3 zhVGbzg&*lDD?_LG4g=1(C_!{-!r`5*ms)Fg|Lar>5sq;eyWKx$Ku`_3DY@c^+U+ph zcmW?Jh(pM-Qw4kGD(FTI28vLPy-4I6f~gb^d&3PE$;F|Ved3O(#1k>D+h4LgRpUtK zH*e*ZQ9>1IhDzX@1DY{}Qj@G-R*F9o09J>vUpE7{0S?UIB?;Ch2{HtXvU9kz1*7w+ zP*DM-lJF4;aVO~_!d`JNB2V8si)csm*8QiiWv5;A->-;7kHg%Uri>D{UrUyu+{Pb% zAclsR>r5gUIDt2r2tvRczUjcZLe_OZ+%A6G!13)&_FXb$T4rL8}X;XF|RxZ^mni-Q9UCn5b46eC}o9L zSE9~z+`W7EW!N07nWr^3z^sI)V@sw*#+%OO-E3wv=37GdVn%s73De+4pfMRUiOTPG zJ;4yB+hLE!05y4Ylp_fF1kIV|jtwd#Y~4N9xPYj7Y6H|8U+M}$4Jd`oC>Z76=M>PH zF_G~yaS)hP8rtU;8ENqI#z=X!{Qj}JNwiHr=(gvAZuNM~GXs39jbFZe`4qG`3x_?| zr0HFZqVM!lW<{zP>e}wIvgtbUfwdYWtb}XwOd8PPnlto!29$LOY$08GtTVUfcz?vo z4?5^3e!F?JG-jE}@S9*gOjQ$S7Z+xdz_<{Fdl!QZ=*Y*{y5`G8kH?i|n9=)`b6V4OnvOXR4MH(-X7DB^Tt2NxScdvp}sV`oXGiLooqk zJi=5!5`DWx%pGKf4Pwfv0(&LR(+i}6X80r!<#-?317wea>%qJ6KDStVp*Lh|UNnji zWpIAX%GTK*xHzK)gwBLh-iv;b>%7P(D%ugDFH?a#D41&S=I@_{qYqrZbm=4rtTocp zp?ucURcTN-!&iDZqnzJxm7oV0^|VL9ptRGDIDPAd>LSY1&AsV2pOnYUjI`8nNsM2Zfg0Dr z%4>+)Zd6L&a9DKL?V9(H+znf!A3nSmJW)&W&JOiJH{8<7B;VzY zffmEKM#mOXRg!{035fT4j`Tz&a2GlwWW>SY0nuvlR1S-W(|(Riv}D>N?gHAe6}rqLR-QjWhe0 zSGTivzVDGjXGGltX2IGs`J0Y=!GAkfBs8-!E|L~E3;Qc<0kqRBa1Qa1C$sd;AE6ZP z7qxwR!#OMU$BzP8SZlj|dEP#9RGpROKal3fpPep_7BVss58QzGCeFb>#u z+BM^Jv>zHrMC)V|0jA;oF)=YT%Qz@RYQuFJKIqayO-k13B@D>^wJy2yPyd;T9cQ6$ z?vCXYT*rSgcI(gL*IwPWP~fAkD;VS6b1`-mO z(2|mlJc}?L{u95=$KR>+&j>aSfB9OwYnpP?+rawl#@o~n;M}taZanp3cmMLI>)cop z5Fb)eGC`z8J}EEY(RlfL5XK=dQqL7)ez9Y}WIq_S8YnMgSZc1_fcQVO9JupOO_Q2v z=4`G*2W9g0;!DwHLgi~ZYF0756@<|RT2AwGWw_)PR-3~3(PCtj0AQNIwk`)d%(+W0 z^gZIzc=@bll6c2Z(vQeMI&k#4VAfMw!f_nPiVOgVN2XfUbVT+5mDNCJ4}x|22X(y0 z^55+f4ry({<-@Z-cAJ9g3;BG3sF6n|fK*Enf)5h#VtdqLTStU7$Q7C^dSHZv{VrQs zJC8TpU6JVo48PC`c78+#c$s*{=J{JN0jCH^iD_IU7q}Z&m!}nA?WSKAPs@SA88vJ- zms$XuxORF9=qWaQ9{Hj?aeJMSLOm=I#&$k+Nq07+X^Q~G));AF`i?eDr`OM}ee2yO zL(n;KmBafttY2SSib<`zs?p`lqc4Hl%o<+&6T8M@B#;J7^R!$D)N6$c_6nnDOoqYP z`}uS2!AV;=Gbs<~`-Pobj~j>9kVHg$$+yqGL9tJs>?J`R$7zJ78m+WO_>-`=jPyH0 zV8Yjc8bFL4_z<2%*zsJil22HUNF<1hcFB*gw`2a{S*$jF7SKdMx0`u7tTiS;(#+2J z+VGmcF;LIIsJK#`o_Z4KJq)&|DcDPYJH1+Wo0Uir$opBwqe{sWb(YxtGL0lo%*Mtm z?(sN7=^0$(~TDQp(lBvKE>VMT$AP;jL1~usr$Dv|lGX~wf zb!!dxh3GPJ@ssxrqdV7S$3YkmvWk2O`9~FVB=!YqxHzNA7x$Eok+)~Ui8jfjHE`2+DT_-W)3W^i_;!_2P81C^kD2(pN@j;+HzOeeoar%wnm(4 zB!^w@bm6WZkpZ(1^vaGyyK!9_&p#rAOMUs%oTxb~Nv%K}h{ggWz&s>C)2I{qFA$xh zGEliRikdPLARv)=Fh(7)FlpNlO9L+H0R?&%O-HffSSn%L5}E=F&#vr}?gWuYWn==W zZW9vWfyy=SYHCg}Mz`Zc1)%{G3~S!TOM;7B0hlLmS!z{6t||O=YHTbAwKBHZ3n(ZN z|DR%@1eckd4qB9iHWBwxBX*EE)XP1<)M>_AVWLq0gR+fjYx>IbD@3bTrr$pSS?VVkO)p~r!H5CCffFr>INvtr5I zKh{G#;wOyeRV_y}_*yi@UzC>aq^U#YwI7waq98sVK|TaQ$y!v$vEe<+cD{QfU&51T*?v_(5qW=A>8~M4gjR&g&AYy zX`i6c0}=`b*VNefkt~yCH|E9UBgxo7&b8O;$SuZd=(w8UwAYL!{Ls~_(!K0RT7Gg2%tH-JG1}Uhl)MNGa^Stih*>;cCJJ;P{3o|{M9&8ncOFE2fqSFovNFJFGlfk@8T6Kbh8 ziMpl%GD##MK$!cXLvKbZ9yBL>Mrwk_$!zTeZLMUcQI5F#G|rT!>M@6X#&yQYsr&2U z-qT!a#vFF3*KT;Y9iHE{i#Tv3`zZEU#FD}lL$z+~z8{ykK_I`?Ll7ihp+q;sB+v zwMpdO6n?ZA#*`86%=K7uB>pnsTl4L88%|*;GQ9j&k(OsCkrjIzw@NXyv03HoBd8J! zhzUl1Nl{U8_a7;6Yf!utNR$HN6ubh*Vdx5^`(f}q18o4W56e z)Cno`Mae4RSqQY}X9R;BXKtrP-7$Oz>A4w^`sp{GIAtV4L7qy0gJiSz?0rLQMr7ocXqx`?r@JM(T4-O|p|u0(aEB3*?LyC@KvS-fK_Isf;B9FKE15 zb8A}tenpn%Doh>{O@IRtwyvPB?Wv z7F6f2FQCF$d9ESEC%|ooB7o0W4_h9&vqng(r;KX%zQepjdEW}wUd(g3fV?Pxsb)$3 zu@n7PlNZ|`MSA`%h(r|}j*FONld1gu zj6-m-z_tQvUmD$rav&N>lL(iC5;397(4!R_({`M2n-qW8=(UgsOAk+N;n3U?9XiL@ zI_z-Dak4woN_<>KUS7DT@C)Rs&Ju&6xjk_`b(#i6)EgaC*)Rda+D{4sOTYCjpNh_} zL1Rf?;K80GeuD=BzC>LT^@5*1(n$D|CM|+vkuoEy)$nRucWm%nlBhP3 zsdCNLJ)l;SBP04l2R1p6w^+TIsQUcjh~u+K`Sc9|VS%oa!QF+)hliOLP?C=@icGBd zzTrtPqM@6Fb!XH0FNW$veBthV=sGbuB>K{RRA{@Ys2wYI5Z%3+&`TITXhbeNi2 zUVp@s9(41k=Unk<4W4pwk)|STqC7v$L9{<-N+ zDEs8WCuQRMie)l%pkLA&1jl9%lejBUNuHY)HllZM5U*Xx$FL@~+3qDuu?a>Ngr{|& zmlA75>+qUa(%}7$VR6wr*>(EX`8TV6qbfWf!_$0aLt8am7qS}SXv^9VOjuQBbFyjpaYoWxN) z-i7x0=b=<2n!N$^j}EKpFJjA*$FXn><5?fMEF83Z27ZErP0Zu%Fw0jVd~yfV#$8@9 zh(2<X>uiMRcHzXF=0*dMT{Q*lzeqG8h13hAWk_2?Z$lGP#Q@_4S4cC^&PtdzxT8>s z_}p&C84KwQs_v-i4%TxWK0;uN_w_`DXh|ZX(%ezEA+Ba}h3gLpru~Qb;%=CL?>SC> z@x6DCUtB=Qtoc%Yj4hSwK=hMgI(~2A?<%CdZp_vl)Aoj|=PB>8nHOH~+VXS&QM0ly zBZup`G%g~OUssQD(uZlbjcLC1NeU)pI2uOW;|m@5sKbF5*{W9!2i`h?q7-y7Yr8S67fEAT~u=gO6ZF=Mh5{bnUm<2g?$0g4K8zQ2f z?vs9*aWO-eN}jAnPdhw*^$E<+e^eK-{<9y>Z8Q9~_2R_@P8n3MvRLiWYBhF4VtoLV zjO)3*ud}Tp-ZJe8|2eyX+DzG{?~bI;78awg_ixt{?-Yt2pA3evuNIZNSG;>S3@{~| zU)szf2#Fzs$V6x2=`5Z~i)EJAKtYS#7qV0rf#GIq_{!QnuG~9J3XIT=w?9o65w>VH zA0G&h5Ax`p z?^$Ac-WB4m!K8wn_5u&=xdM`iU_dJ>L?7yzs$*Bdt$kwliT92a(5{{Thb3Pt zScodcVOXe>OkgEM9E=w$;cm>d8w4CbeOdz_vISVIe_-G{FJY4J+YKp}02h+RhMh|o zEV0~(8GG&8GjLP~h_{iF9$G9SRw~elh^M{%?$4VyH;VMQ#~`JlgG#Cb$}V*66~))? zC72}{LqWQz)>iwX-@Z8T@#7F4@a(Tf_)4UJ0-~AV(#V7Eh}Y?XQ9yYQ0a^%DfbWbF zH~uk_DF|dft%=3y-tDpNVhIqP>tLi4>9X1S&ambPyahoZYltf`aIn(Jx+z1pKRmye zc#K5~xMK>~9T|{AY%0g*(^*ytSCyb<#EVlXQGLq}LwIBll)w=vPb5VWtG#E)x<^5P zj2n>02f_?=Ab^~tXb{Q5QjiD5tN@!9;aP-_ZwG1+j%gr<^kHjEE>kWnQ#%XlKyGh7;szua#ze6~NN&fnt z3-5l~yjfsWW=S2)pciIWo;DP?T%&F>34uq12m=SEjvR+)mHGWaAp96gMc|=48P(O& z4?nOE>u{2T8RI=_hTB-hZVu!NMPhEtU}mVn8&Z$z3dRV8Nv}DK$&zJDAU`nr5d3J%Go!7jIOTyD-L}<{zvw zfmXvfgCK+8D4M%WQBzf2UPQ}+1c*sPvg(QOr9V1{>!YQ5l2Z@E5i5oxMlpvBa}qIa zt4jhz=}F20T|TuIu!_(707okZHjg)QT8!zv+Jl=kc|u8*{GKVYB^DM+q){ziaGB@K znXVTW5ShY^M)#j+A+Zy?%$eBMSz;t#4!~pR*gR$$?Ca;D@tN3TEa&^&xELakmOGCt zy-gvmV(Pg9DlDxBr$0+b0?rYh<*^U1lvhpCRlPKko>Rovgoy`J4uwF>q44HS zTMz>fg_Fe-DA|MDxB`1mIM5DMZ*fokGDjG#8Vj(dq5i1BPMX`VaB8iBoh`gWq zpeN`2-v7>f?{D1kj>{P5UZb16pZz@F?{}>=*PL@LepOCMz*0rSEqPIG;|1j zhUTZPq}85YLTf*5q(8elXXfgQH%q^L?VVkx#(>Q=Z|i?=WXS0$X*XR0%WgtsSoQdB z@r)Yu7<@_{%bJ1ZwVwTOTG+YD!F4-Vs%mn>y-Cbb>%@}dQFQ}uE$yYVS!4y_he)JZ z?=E0li(2lNLm{6Yk$bIX%alOOf&Mn2$H?per(YRQQCVupwjUGVeM&op3FI zgJ}OSoIBC7g*PoTNl@=_mmU^+fQKw=y@URR z&~E)l9i5xmc_V2>Sf1orJpl4I;m$K_4N<-Ae7dQ_hpgm2zWERh1ZOE6YiFUaXz z`d}^R;IMNAg`*-=$%DY5%C7J2QEnuO5{^s_5C5)a2a}lP!)D?D9QW{#D561 zh%;e(l5U=!Ih06^YSPH7sGRuaE!DybdzX^t%Lw&L&VWw1q}q=@)C4`KUP)bapw4Ia z{SdsK7xN1w><;k{fW(>9Ipq*E|1an+rLwA7ARmpY=%lpUcyFoa;gZ%fhh08cD|mIT zC3=HY@+-uuf&?XtVlb%`R_)m8Q#yi7tBLm2p4E{n9VgRc&)>P{NK*N;l5Ps@I3ynW zp;n6>1Qz+l_$5l0N@p+k;oc;OWQMdA^iP{mtaVZ{s94(|I!O8gmbHgkq0O#*+Q5TA zz)ly27uq|Xlb#B~m-JLxqQGuEQ0*rtEHJ(?vR(tfQu&dPP*6Yt|PN z8s%9Wn{H#X@bXGA;ctCHH>FkOcjQR6yGHa6yJnMp6$4pF!&~>rx$35o#~(%%KbL}d z)n1Kc!TdYEw7~agvs1ANarmcjA<8r!XR^Rmf=Y<6*A-pP%QQX`dce`4 zctoJULE;nJzI*E47ShHfSMf?BaX@PBzgWW!;`p9mW22%6 zap_7J3UoYn(|;Sz;WpFK(YOj<5^2#>$#YWShockk`s~pJ7?G`y?W26V2x&;sCLl0S zyw*ShkSjG#$Z|twHpQncpl`t!pfYvt4)F}DQIO#t%t zFg(ImCRTegL%&pS#q9-~4uK0uf54@=p?s0jp+~m9fI6xGGJ*LK(IOOEB1z)|L*0tD|-#w3o>nSq76>TI&Q^PuEUZK^x;VaFq_3v+e(!zi~B=EL#Krp z4{u_PGGj`Czm=DCAUn0lRIMP#($BHJQyYiuyRW_RM&#O+S4XYT7x0SysiCektG{EfGrpD&ezjVgQb{vl>T zxs#v3Jz=jQ%mv-ZesM(u+~-Y$Khvh#v8H8|AqTy#|J^L{&x^<5+v}AqC62j+t4;%f z9ZZjTK!}-KxF24v34YHX1}chAX;8|G0RVi`if73Kx>>9w?4dwfQ{s$kj7h3rH%Q4f zGY#fa`9qq?XRBMy*z;hn3}EGGZHJ?3SjCf=xP;S#^$BGHT_dIVlx;0W9~vrtmO1~~ z(7}m{UqQ`c>qecplh9)>9A9CrIVDoW(|9F0UCCFqwUD-?lAg6VC3J{`=@J-cHb4k# zBA@;>mmF-yLB~gZ(1>jzDc-ABJh^}G!RSBU)H8T|f!!2AowDXykIz1TQ(B(f7v-YKkIfRgQKbAR$NL! zclsQK(Y`*|ByE=BjbhH7ynemSaAU!av_03YQfJ&FAp3CquG z8{lmPLGSUCCz1_U_df(D@U4uFuNkXvX7;If7mi)q;_B6_Li;iw@KJhl2v1#rcIh?e zp5GtIFFd%ZTh^iJxvE+BTzoV1A2#zU)^PlwMZf%&*K`=PVO~{ZQJ73gA!we&N&-(u zdm^$3cD4IlaBK{z`PN%#aZM1*Cb>7cL~Kx^Ew>hUmxfc^DLGB6qTuhy%_1*)vL+6F z+c-;~O%@nV#j%~bNI(`9`3`dOW|01YCC>v@Na4L8&w|2}w?H4)o=U9`&(E3XAxE?> z)L!Jmz6Z1M3@*l+Asps|`cB-2-K5ik1grO(M+KyoDFV6dfphiwi~ZxX-@aPkxP-@V zly$ge3mf60K!3<2p=o8!_V>&+ zakbD7r9br`W5(9~!1f7%?1s>D_*3?|=d&mzJ3HOfdzMg~Cj2fM7->HRuyYeyzQ}WL zHIhK9TQ~Ghenm=D&Ui(uv(D`R%vQyYOD5VZz}mf5+qZfBi)P^tg9gNh~8YI%ghoOOBp)W&BcIW0B>c zjPgpK9azvHFyZMT0`9_gbn7fmpy#f!7q5Y|w3u7Y!{%)N09d?frp<>K0?h6Cyd1J= zXh$dfaR5#Rxj9sDdmeWzSmtBZ(wOT>$$hXmv|l$kH9d#t{Sjg;D1VYVixcau`P@>H zEOAs&KP1S=){Uoht5=m5>vk!``BfXQ%`WT$4t93(5pMYi5VJN%ffz=FhLw$$$SG_X zTl8^hsiPH0cmOC@RqdBx_T7$Z0>1`vy|Y782Dw8l1B4IMevF+6#jQ(13j0 zv3<?0-nel?`4NnO-d4q_G8U$~K37?}mLvjz zjOUG`C;!qA(n6)oeDBFKUgN~P)pbb*hnn$IZwLUjqFf*_o9vZn!l9~6n0%E0m3QFM z=w-&T4c%I{;Yow&2dE7!R<$SpoQ>Zcp4#Dj33tvn$-P(S;Ibtm3l>Uhb&a0;c#=hK z12pHi5aXqFRdbDHpp$Vu@))2HXHta&C{4-x&wag>f-yMyk17=hdH1eu!Pm>UZL*g? ze#7a#4&h@PTnr+ zc=?7{L(${p6LPiJQ@BkQ5on}YlAM`Q4wrbAGjmSYRWk_wCM~v!^9#7HgoRS^>Bpua zN`Z~C%vVbdz5v@+0(e1HNUL^|>pa1k?aWgsB|kT5__x1}oqNPL*XdxgP6=ZBu2Zt1 zX(4~pkCTNSLPyk!#ao4W~Em_PA!odNVDO^@wj@AfkrKF%J`agP%#aiRoCe3cq|%w>YuRQ%KPlJ`i$PZ>2YIkl1vY2O$0q->g7BAwW=yeuBPYs8#CZ^G#D|m5n_zwV$$}7By-lezH^q z4E4`M@Wocb8JS%4j;VCT)9~Unhm)RFubKsD{(xsB0U@w!=T5n%hzgYhR|y*WUDxj7s#D@B_ZcXx6i>r8qBck(@JD8-^~ z%Ev`fbj`SY@9iE0sbp;U>#+*j)I z>G#{b{v5W)00)Piz=Y(*&wmM)^q*g&P2p>AzKOr)Nnb2P9zT9eLeNJl zf^GQ_4F`!kPjbV44+oPLi}YCbfOQ>yf(Ek_wyd&s8oKqHijj=x-$0K3fkd{pnJ*%( zX`d)e7VEe=k~bfDbmI-0@)w(SVIj&%lNH>kWy?Lj`9k8goaxEt(V)vI1W%CvP1o|L zKC}{!c|K;tzFEH$gD)c5%qs#^LeRApB0$?rx0A<@hjZEsY$w53<~jPstsVI_%Lx-@ zi$o(E<~$s>_LoltT_wLi{m@So@t_eWJV!~~O`D?~(+d|eyN*5=70ojz@@4f))@Hx+ z_S+X@eKaSf50&{r)HJ?ax^~(0!^JDbnex4zEh9`4{F%rf-H`ayJ#EzUx{$NIOx1%v z>bq(a$@$uAhYuf?L|4PwBp0F9IK3Y;5qA!dkMlJ_4HEiKh&EOo74@7F+=U~T<{YH4 z;DVnPG;qQ{!L>fDnFwUUw_qKBNXS%;q(0>8+)@uqlH9amIj&8<29@jxp1_(YVE^)$tbL#@EoQ>ZWw1{Uvd9fi+yILCvn zh3|2RnqM>pAx`uc&ZA^~!YkUGVerG36q{bF;sYSXjD$&_a0qOFb+_kpo&*$+3 z@6afbEe^a~5oZbTALD9t&s)S5KFHQWftn1Q@-A~omWC0JG%E2P-dm`gtQjQBJvUtR zPuRK!Z51MgFPDl*Bhx{|W|UK$_D#2vyp(2{fl1rwcqc!-OUr!jk+R=ASg=FNsNHW9R--wc&E#e7h*hsbiCm0N~`)jSMl1twNhOvUd$l zaQp$K_)*tCI86IVbf{Wz91I=6pIkH9oB3yl=7jYg^*`pHyf zdM>p(HOgj+q8&847q^gMp7FC!esij)q~z~dZz75WMR=uDUwnbiX9B4EMKQoQq%Vwgn(d7bxwd8IdE0HB{mggWXAzJdmEmz2w4gzSm~r_eaW~&9*pk2p%bWIAUREc3sT-1M98Rti*WF&k z#Nc;58D|-r`F-3V@CUP9^XAP%M!eKp$|~AXIbP2&S{Y2{vHr|{0_tD__gC|09zy=7 zI;j4dtL#NNFKYU$1d;UAJ#uCfiXIRWh(kR@-___q0MLBa$x5^2G%EIcBv;`^d4|+3 zo#8oQzC|_3R4REI@JQpEr8OJtiH*Eo6Ewp*L#n6xdbw zi;7g?ZITI*FXYq1;HH7uD~8l_AEBOHjfVCXApixhT&u7X2TD+(eFu$9+j*Egs@$?v z^boj~;6}4vvJbg++@bN;JSwdZd)Sq{T}nj#cb-Sj|h5zmA{6CBxgh2oLQ99ulD{|^$+!YdR5Jmc`HXqQ$hph zwR#O4JjK9EP-RYd#`0W#Y|v#mtXIZZEk*N66Ll+OnC!9nsu8p9MZ$?xv?SyUw9C%U z-U{VH21LS#GreIjp2wJOCmUfqg7ZVoJTE|=|n#;^4L_-<$a-vyZ;eZktu zrpn6-=K=nze&>YMNlA-lDmn1|;^Gv-lqR3-+=snzD$7*SOChsQ@7@E+@ap=`1_Ia* z1$o^DTiyMbEyOlOJ1Qv&&qSRIFH}74zkOq`J5l zLp(yD!#92Okp)B{G9g4$eb8YK0;$;b_vzMlz?POIQaRtMCk$-D=2GaxbJ&`Tv9R;O z#knK-7Cq-pBLtH54h9Bohm+V&@9?r1`jQ&Wu=~qSy6Hn>>DBVkmxFNQ@D_oYU$^`qh{?(YkQ99|U43bH5cE(w0ocj1AoG z^fl+w8RX0%ysc|2TO=GMHP?TArg2DnpQa_-jJ~+gUjl+yn4(6D>MTnzHJX$LOGG0K ztx3pxE*jN!Ca|om3@UZ&g@BTNBq<|EEJnOJ_3iiH zZ)lFWFZ)URTZ_SZL>OlE2@yY6&KgWwcpPb5-*3#-zx+bNA>#z#`$bGAW_p7Hs z1XR3;{Q|o*46I!feNmH0&BbZ771GGLuj=~mpz~=F<301>1oE=2Ob5|t+W3%%U&P8u zJ5IVyX_y%t4%tIEm*V*mU`Ew;wX&bz+=cfNOc|1Y@}WDZQB{osdfBi`*@-*3OX5$O z-M}DqI1bwll41=dP+#6vLPVJyX5SvyUp~7O>U9LES~zM|jg18i5T+=q4reci^HCM| zFd{YNaIzlo{Bf$$YX#^~j!)5ba0JnnXG-nr(WE~ZQY9E;+kbm{7_N#nl*Znb=F$=N zvGxhEkATzCVefH$xR<|U!AVUg&KbCrgJUo|y7~P+<(HPwmspFeH{ujLdq?Um#R`y- zkr#aRYd?F#V1ObEgCYKO>THvCNcB&hHh=zFQ3;E43`VyyhNwkvZ4hRqJtbyBBpP6B zkc15v=Vl;DR!?a~0|rZ8?Y=sy;UyVqz4A&PRpAqQLJ_AbjLMJ_wXLFO6hr`fB?~Sg z3O!uo<3y{)QL06^Ae!{u~8Qu`i0{^x0{%X*x=ZYp?;n5)DU zxdhyP+Sj;+_3x@6rhg%w5$nqhq(RsaRTQgALU>su45XDMfkosxUa7;?lB9k&V;??~ z5{P>0i^cWAjS`FzI}syS8;{q&@h8Vp8;$o5QapLWe4D%5YBw9)7iIrIsovT0g$ON#2Gh`0_#uSd6b^p1PYl-1_Ml*9Ui$)>U(n9%{*@2+jM zy0;UdSo2DRB-J_^#&{JpaTiGcCP!xUYB)!7wD#!*Tj0HPTsT(=N!$dq>T<@`v=>wi zNo0HK91=*RLzcHcp1d9_F)vNhan5(jr93C_xuWSSPI7Fyy0CVFeJ4*-aO9nsN~Yj$ z2rc?ar4Q1r+z()1kN|5ZnJQR#(AsQ#Y4p{wppzDdGDvYycZ@!E$E_ZqzBW@XEU9d| zHO1f8WZ!bj%~!ZwVZsUa>~uZ|a-{-D{lDw$PXWK!ajcVXglX_8ujY@ym@dWV4`;WpW*T^!9m#Q>Ol^FTY{=y#b;$_% zKoBA}aVAe1@nd(t>;Li*-(!3sdiuCWIeB?`51x?kBtH#F3;zHfQYg)nQ{(r3i7rV7 zl^`MN?~p%A`h|+2`pGXo|NIVw3ObrpUq}JG4|>`}|Mkp+dsQQAC!p>tJ1W&Ko(sy9 zN#WEbLKCK#-$10Ic1Jsa5v9^RMgRD|%p2N2^w= zGq(1T?B$d&C@LJSMBNFO8q}<5wg4pb+uIo;lt65=8x766oj$7(Gvu$k1k7CXHL(bM5%?uKgdu1-iJU zR}C<@D+emPz62^j(?8Fbkex0$A?#uU8V8j8$g-yEoP(@{ZDJ7uo`;y%cbPp{?P8!g ziIPAd<)DJD)jMxSCGR)>I-W{Yx~5+fz)KHiN@;oKrQyp$fk**{ikBaox?g-C0`+F~ zG)Te3=%o^4X{2Z{m^^!)@Z_A}J^h&3Nz^fz$QG-*D3D=xlH;oh&Os8UMl!KWY%0ys z8`iv*NDSzgH0%P&RwFqH0gx^~KK=p;l~_;0JjtMj1?~!3*ZrS|ZTuY6kY7a|opO5B z2PpAzKNB_A35Y_9Rmep6&nBk=4b2pjq9D~kQouNdz;h-ItzpM+?;F}&>;+L{c)C+* zR7XAI>C7Bzj13e1Lsp^54f60&Q&w#qS|T4<0v~6G2oaS9d9Ax|Vy>kL%|frG;{uUq z>q)VJ|G;#)Yi#R0xx3$AVI!;S?c2AflsZ5I5F-n_zs+gcP2A1aN54`{d8H!dB>$jzZe zHXu)%nX`!_?u_0oD960X%l#|rfPzjl5WPBckIWWw-etZH1MdprZmrYEY(c3F!TR%U zd9QfC{;$go7%lDDk+zF`LSb`|7p*X(0PB(*9dLWlmy36|4OuDj5W+TvBY8=Un46nq zYjI+u6r>J=6_|bvoBq{ybrV|u>pquT{~IZks_Q3TBjV^FJ6Qk3q%Yt^X|!_bJUX&% zI{TqQd-AmCTsAh-tm$9JH9XcTX=Kig&klV(`~4A3UL3n69f^t(4_j;E&4a_iwlu(l zYX`m?CYDEXG*NdukH0^;tKkQ%Y2kI>TMH@=d#Wi<7R_w$87^zh=`*t#eX<9}bxjW( zjs41kX21K_y$FcscBw0Akx(B3Vkiv}V9CC~4&p=!rzOqI{CG0reY?~@SZ!_{H^eHAvbk#gUk>E$o=6OxpJJc5oAhhr8dZq)# z8lA`?z{736THD>zD^YBvil6SOA~h8rDq1W)5oeLc)wnfy?Gr=@2QVKwzKP z$(Pnu_I~eQZq9Egz%+k=mPFe6U;pe6s&$E4rC{Zhc9+?`+J?m!dNu4=Ey*eF3~9q* z=$-P3-chFhxV*3EDh;90?X+L@Eue7Ve9syQ{c0V%u%g=10& zL!MyYl!hm7y;YqtrC^ULSGx7~OEDvV{q0Sz=^afEay_k_zco33#<`K+aW5Vj|hT|Lp=3!cF!R zS>T`2{PxW?DxrkY>}P=xeVSy z7w0ENeUKCq5w>an(B@ZSMc*<7{l1E-i?>LfW>r*Ezt zy#S)SqP0~P{^|Qz6zRoS2?$XuMm^NWs!^zuBlPm1YTqwfw13pHQrHPmlNRR)%|Otg zqurci;7DWV`&N!2$Ajf=bC|RgTPqI7!a4dLoFeYZZ8r#tC$=1$>JL*?#YD)zF z@sc=*4$@r8R-e%3H>X9dg;IH-Y$c}l1tRY_%3i;4t7GmfULYe*hP9AVIB{1v!*WTq z&OE%K*O<;uCFh+5GuG?(VQ1p>mpY8Q+w_bkA-T7H+(^inQ8}@5;v1P#ZAY zRb}7lObq&m8cm>ZxBcp__QpyB%(V}$JDR&S-|v+!1lq+7yGhY3^cNvQ;@5rjp*FT* zFU2?zkn*u^m9Yq&%VS-+3(6R`Eo^&?SO-n418;XK>NFJ_jV&2ixH1Pit~KfPMfzl# z*pS|aWXF((kSEbUSkdT!7E_RBmd_+7q{ZCS&hGbd8v*J7ubVAs=IBOOQO1#+VH?K> zux5*C?#lnpZvxl48*o0o|6x1J?%lg3Ym?2vcG~w>MjO4Dp&jeTtHz;4p*%{Z=HzPv zM275nw}fgW4?i2LSq6?0^^j5{iV2gz!N2gTU!@F~u$tW6k3-0)2C-<$|HFW!mv09z8tkP@O(BH&(G1y z6>Oz|%LDUCrj97)5#7E#$5?c`>M(;f=iJ7Y7KWBB61mU4jw?%kUGo;-(IUA+b7tkZ zI!XaZ20x@NJff6U2JYA*Ziem&`JLeVGM+(H>{aQ1XwQ5J<>m=ETzlFL>kcx@%VcWc zRSSaRBEPFp#95QJL|_$}(rNgmu5<$%P&5*nPllcg{&x0!48AMdxpclg0XYm>`0~wj zZY3<$+yJ?if}BnaKSA+ei7vT#Xo%uux~-{uRO^pso+)@6eCb^xsKS*wm;Cw?dY@H^ z?{3`Si*j`euMo7c?zc0ZBS$crg53aTw(*5nFvIDhST zeXY4TH@TPf0YTR8gAaaLH{RHQ@1tq%ZFPYOF}u@OKoF^{2!mIU_(zO{;T0^Jy>DDI zhqH98p){uQ=sV}sG}>gjUQL4{H*5~2e81wfXxOgWnY%d`gav~lXo8tWZ)xdM(Ji>S z>mBD_wI8gM4bLg|GD^TC5}18qv-E^oT_|g_B%LIIB%*LGq}mxXg?L>H}0@!|5259^T4d3fP|ABTTkxDK@*x@DT7r8%e>Wh%soo0T)A>F zeZKUE-|iPlKJ;SaZKyxgrIl|Cd6w!hx72a#Z*_bdv&*+_`%Ju3F;H=`W>WFmk~5um zlfkJCydfZ`F6{C2G*10p{#OHfVL#R9hZ(tNZtn8naYr3-8`nU)HojW_`jv)+^{=ns zD5wD^qR5@wnyY7M&Lu>tuvK&5P=JpLIdV)fQNW?MD5b7Lr?8O)d#y1$@}r^{+T3>S z4&yK=s>zWe#6j{PAfk@ZiIx>J8~8(#kg={4W%XZ&&I)*uOQi1cT{jmMTIRX4`YEe#2 zw`f0x6)KylszW^5Os*f2TAT@HU~2)=?;E{%zy0a;siO^T%U*4# zoV^G)ds;aN*rnvVd$~ zfo~og$nG;M8NK3SP+e9{;lcuM{G;)-@{N=dnDVZ2FX!1!rJdSw;IKaaXK#uxI^xks znbprSwqgf$Y7a5AV3E@<%&K|QY3)f%wEssdDsFc|M=lfD21+!)`K7m0dmbULYHrbh zyJKBG(a>QeJ-w;WljwaHoTv{;=X&5b-+c2f9W;x?R97Y$aV!_MVb;aYfC=7LhW5+b zRn*#|1Y;D(ZyaasR?1@F zD^1ew0c8K?lSMHq^DAbo^=>_xTgg^msn#yHXYx(s4hNOb_`TqzeLaX#%6e2fft~pk zD1+qdeMp~+oa?u!Lp-O~Uw!pF3?h{>TkH5B&Ypv`#2U&tpoxX`bzMPK7s1oxG;U&f z!UTl7X{`7N>>m3(xz<0#D$0&(DZrp7#MSlV`{KZYtJ^4xlX#Vv78J=%>ynF(x=du2u`Qwh$c#*+or=F=j<|*{9J30pZ)|u3>9X0Rq3YjwvI-00scc;cc03h>`6R)3# zxDHr%I<5PA5&mv8mfcoIkB(LcoBsn_f)R!Lgl^ zF^v7IF;L-)*V4)ss>NqEC5;6^RqFMuc+WiiNIt1rsEBA zzlTCW;u_BX#PPo$Zdf2dkM8$WhR(@58vTyODARY6WFdU9d{A@}>uTt!D+{D7+QRDH z4oR?MMRwPH^{ZKeuoUh|nDdgumSC`bVEpmdN6bb>r_W4Th7Q%mh+k+cKXD4H)nc9c?1HXAQZ*QvS->@p4QvdTY(R7fULXiVq)MnO;>2K z$}24oXZQaIz8%$pHB`daL5=i!n`NB0_5g&a-SWU4to0gJil)_R0c_xhc>l6EAue>f zkyUP%NQPlJyU$^F>C0$I0&aiK-%lb2>eWF-G(}KE^b%1Ac(wX~*0E@C)Ou1TfB*N! zK0o!*3lm0UtjoPi>qm@d!saC+G4O}GXlPdc{E>8coZQPe`7xKZ$1UmZ_PYAbtZ!*z z^#yuN2CtR1pggaP^v#$TrUO2g3Oc8vpECBPu$MYmG&5AHIS2#F45G3ru$G(Y`rqOB z8bpvfy_38agvE_-DcW@LX{<@fLM~7r+gj8x2Qko?I+TvRDMZd?S5$e^y*dRg3nep` zCR~*XjeSWF5;zLE=dOUY9P~c?z)c_x2j{ao)FgYvNkP1up?=YLuGw+jkdSdd%wjvRyP!wk~iY}q8^f=|a+ z>>}?CBoPF1TMK-Y!L$*Mb)~~0=To!Jo?*?6dmj9*==dx`4JyN4W?Nbzhv>53o+CL~ z>@idtf=p@XUBI*x+l7$LT_`RTS5y!vbO1%y(h-8<+kN*^+6qX^iX z^i~K`oZ_qBaMdI2e)UF9-zjWNfYQ&jMaYhKsgfe=mYN9vHd*GU7r&$!0*ctu!QEJC z9mYw77&BvwNOPLuhe-`?Ax`k@6yVf3vp(2Z{RU2nDSU)9U1 z2}A3#y%6q3%n=J%S}E2wMQUWhhu8GdpxLrZlge8%5EUeRJ*j}n0mF%jcqe#yK0kAT zb27B|h0hM?{O7lFNmC@`YhH0Rlw&pvHS8Iy89C2L-bYA1S$t5>-)3#ccjHBC=6(&~ zV2#|_!1(-sXzC?5F_D(~KXIdlZPz=lXliSrdo@0aX}A2es!hepPeK6aBAJ})k*^+W zBr*ryq#^zEFQ#Rf(m6_@L0^C{ur;H?RHnhJ{06pV7kT{RwuuQ(qLH|5pYdChe;(Rg zbQe{J8Vd<6HQG_PJE-%y@Tu`~^3g;R5NuIw^q;CIIR9)H9v^?{D2pO5J$jGZapMWC zi$v8SP8i?l5~-@o-tj%t2h9NT@hfBE^L7C`wVNuH3p#Wx+;opaQBw>H%1J}OvmJMo z5nEr&(uLX7W-5B*B0IUZv_)S4)N5 zu`=%AiXT(-X7S(r5^hqTmkPkiFU7dlR>L4wr?8AN?2S9zpox9#D80DiCpcu)+wsc6 zV7@MLHMX#KJV`rUg5dN?dQJBY%lQhN+VXdHMN$N{;S{Yk>@K&N-Dv&<=57sX2NB=- z4I_D-I2!V_+68i~B~=nti?rF+BHD)a>>dh>Tup)wMv!kBobot_q%7@fmkuYRbh&%CES4Su3cJYMuui;h_#2D z;`gmhjkhfXwkU;ACV9O0k|3soS>*K_Tpx^ohb4MIFLAL=30+8E)iWaJSGlW}x2NaB z#yu)(d{fShra|dQ;LPEyaBAo7JIb`sEWixjwfY!OX(9<=sn)7;HF}5P4<{J-fbqMW zm&*)sRn+P6doLF+l-7!eGWjP1W$r#$Eew{Pk)ptXvj{Coy6evfa|@PK13yYM7RIWt zNu2q))tT4*IBw#_+%VP@PHQoQgJ#zgI2fTlD?iFuw(y>Gqr;}Ut^vIFVqJMz> zAQ=`XcbDtNIhoN2JWZ7u$I^CEYTEMA2Sh5w#vvJC^Ht+)jyHR*u6rvlZwI7F^~_cJ zlFHLgZ3b=`nQyFzRkw1(Hl~76M9?3U1c{0X(nVr{ak|-@`~Cb|UUI*F-USom6aNBb zgc^1urVPONx6}2!y=ruXUZVM*j2iir4NA28Xd3oe4)%&?g3jnA zT^_FDA$uh9e>4JeszQUPex;A__yz+(akS=!Q^zi30*;y>3Tsy?O9rjq0Ub2H4st zrlTq&YHnAGNV7%f5NxKkiLS=VySgIhEiWPb$iYM+WQ!?`z{`Gm{H0I!?pk-!&<^=`#{n}MD!n|L^#W`-T{6(rWt-HB$X@5~3FwUPI{gnE` zEvkg_S?J=**LA~99j2oIlEf_2J3qX!^VI1e?C`yl@7f!ebbo)fm%TLe={t|J`&F1E ze`f@NHX0_M#vzqlKpt~tSS`I*=f5RY%OYzw&zo1?ch4bo*Q*;;#JG1K@~s$_t=fG*?yHe zN3ntp z^8q^HpiKRMxU@b;q3B{AU5(aa@4%p!Vp#Wb9K)8~YKAb?EXCvrI1xx%$%`XuCQb!Z zq-NeJz;Sd--H*<9WSI&d(ICPpU352z%_qGXH0IuWJ2z?4TVo9*>nbSLG6_nl{8qd& zA{L5feJgo{$>mZ?DL)V|_vL2I)+VS7Aw0j7&7r#07kY z6!|iw1a4m*9qrF=E!)e%=`d~Hou3xm%FVSq)=D?hd8+Xny+v7<7YT&Jeyp(p(ena} zI-uJd(Fzyn*2#HBfY~3zp16Weu7w(IOgZ_9yF2B}(}tpys<(PNt#f&HK@k{nZGV+l zUfGsb>v{iXyRpt^{f&9pPI6F?%@27P%{D&BqN<-v>N!B)I^h1RF;HZl+q3!SeU=VB zE`PHUmr}!)i8jr?!Dc;$_>bT(ee=qj8XXUXb!W9D71>R9Keu=o^&P1f(cec2G}8HI z76Swtb#S78+A{L5-d2(md-U5y3oCYH@Xno6*A&Gl>5v`Rb2TL?(J!f~tnt{G7>Qb> zlu}>0<^+zIDTwCCAi}|2uJ|?7gU#yCoiCzIM5lJ6yC@`LG14s(l53ZSKy4Ejq!BQ1 z=~m6M=A|dO?bF{Xbm=HW|r|~`w+SOWUdnO=*Zj&!gSL*E1Meci{7AZ9ECQ)7> z2&Y^>FazptRjCADw*T7|)pEyX=U(<2W>5{-<^oyp9oz*YyeR@ah*g8A$hapvIa_Y~ zt=5GHl^04;PM#(9fB+$#roc2*OIumheCJUboKj%aQ=PjLrGsXlVLsAkfwfeZon}AW zg`aL!srgw&>gG{Hm=_+mrcW{={j;O$Q0c>|kQj@)O^!Q&JI;IvsYMtM77KZY=tS+> zKTH8xi-St3xZXwTNHMfE?KaIDS9ovGRCd8kRz{1vwrp8{F~C7nQo+;!%`gQGvm6)z zFPBE<2L+e+_V^dfoD?o+>{fkYuk^~brn<-{O<0zjJ9Y(00_tD^Pj+*m8%f9 z^lZ)#dby3qy1&1zKNvs%)zX81<4yf{Y8?LegLr=bnLOlw=B=C98}coK3bympd`~lE z1Q2&o`FaITqioXI%jOL`p$ZWW7H7NvlaGyj-n8yhla{y(;G=qh1xE zD{cm-w}KNjc`>rX?Lex-X1N$o%G74Qp}+FekR_TGa!6Aj?f&g^)29|;$1Bu;i>vv8 zG~ahq-yvnxaLI?|qT55<0PqMgXP?_RhN%`uixFU?J;|7pmk0+&x)5t9gPT6=IfC@i z1zw)TyN-$0_^{HF!Xe~J3=wWQ>CrPI2RBW8*&s2uVI;Pr+It1%EPKB>YC6#x>|<3x z0T}Es@{(L`>eOjsNsIH}1J`B3R0gd?mU$HTG|nI7L7m%w?^9%df%xm(Lc#&goqF%0 z5fR}=eK+O=lbZfOyI7#hjg~mpLwz#*W@3JEIT{He{^&K zI!3z_U|0^r@132iWlhL(p|8#yxUTDGSFF*o_Qpb)*axyi#luq|RtL8@7AbE*p_Cx8 zM@UUN8up=vQl5nEZp{h2Xp%#*vSC|`SUp^&*&=&y_I$)V1P3}Y`0*_?@(@$^$%YOo z-i=&VFCGgBFs918VzBZOze+%^)d6$Oj#2!4T7D&_| z`JMpdR?jgT&y3$28atX~E^fwPmWmT@nM_l}D90FG%Y%}_ne$=F8zx+%HgSE}MSUiz zo!O0coDyMpS+=}^piHvICx!FMHRv0uN$cb#&!1o;1A|Za8GC^)k8dp#Rd(d4QGz=4 zpWzRgAFx!4KpM6k&&)je+i#bDjD$q55iWDNxzvTM?$$?-b*OK0m0CAcAGGgd-y zEo0G#=F<9=nIE@Px=NuMYPYs+7saU*b@UUDwB?hpaondpaYG6a&CB+=HKH2?w+8rlK#xk z$0zZRf{ObWy1-c8)wK)YPA1VnTG4IZT>jX!=cbi!QQ5lEuCcYiLb<@Oh4GhwFywb_ z_^F{g8LcMA`6n%5Agb`GwXi4U_5u0~PWwZ!6tjvm5VtpVk_a`x3?~X}8eg+}r^_jV zQvD=2ljBO)uA>R%a>}6N#oUZ_d8?>u{`y@e^OFb}a=KtL{(x znR()Up89w)RG_-Mi^SxB4yQ>MlmM3xj93z?=dB(g^<#P(o542MuXn;*6~JYD-=|5s zCh%dLL|ahHy7%$Jia);Q317gcR`|J+y$F06=!k;6~F_Q;f{Ib z&z`U?>p$E(w7DGIgo{>-Xp8xbpGzhdCo6O8n%%}2ns>_Mpa$r2{virttuY<@P-Ejw zO?8bGWO4vopgQq(?@Jcp?4hEN7^jL&Xf=^C2?C7E{x1iirtgjbav*KJwqK3V7LJXQA^^XW0jk$<1ulqdt~8~p^3K9269{R9qzyV|5;A` zZ~1aUi|98J>=_uAipj%nPKG;he>FXeUW^_yFaZ3a&%zhCuhv6Bj|Cw4sT9x@d4>!L zO<{JHfnfzzJ;@2apV`S^`Tb}-xsoCES>8mzj#3io6V}`eLH~?cK^$3)ZzHLA@ar2b zw{s?R^fexT*2fFS)%n7+_P8jqOh~pS?jPS5(zV@9D2d(&F@!vtsfw5R{x z{E}Ekh@fIlJ9Lh>8pQALk4v9@f8hQtyCyep++b(5Ka99i29c6o(w1=KcyXrBAlmWC zhVw>3llPlYUbM0NOO6V)L}cGm0IyDbVR~>oC;@Zo z{*ofny9`VK_+*_faTI7``j6tb>$*j?R^BG7Fb{Yx&?DtnEhRC3%=z`O%KSezom$pQ zP%RXY#Bu2Wh8G!l$gww-ia`1Y4-Z*Yuq9O3lS66_YE%^ZUNuFD)G74FX%vSxPf6IY z3|ocEK5xTT7%{vkhUTiKpK0ERTqB^LXQocBGyl}e7CSM8$VC{RjCW)2Ee<2`kR~g6a%=*lI z(JZ)WbCSm&?i$oc$@c7u*UntyKXAp8g}giWhe_ z4}(ojmW6I1y^G%*N(|fIuje+7AdRxMP1Qd6G zv=6|qk0!y4YWM=>tns{)04_`X3*S} zU2SG~lL2%r$ZbHcNcr22m}+EBJ1-8f&f6G=WI-%f&D|sMrxqN&1}O*U*)RjOFSvR* zeRmoByrpKXD!0*Nv?NVgheWkxe`)BGexC#-tS%Re=97V_Tj=icjT3ffc3LtU;gBl~ zjcMTqDtm8bW9LxqS&+|8*cob#NPHf(&@*f+d=9}1W{XAv82wXx#Gdf4I%6$K9QynA z7;27LCVWT3k&DS^2&ak>q9DLY&Rqvv;_z?3n6 z>N|LabPjm^6uub)AhoVA>+FI?>34Y#WCJ-PIyVPGU&CmgaHx7R;M6=Ca_VWd3+5u8 zSB-S{uu*sZ5%ie4U8};91S*chbBhIK;K~ymcARm4`shzFhR939cs}Z{6X25BQ??cz zl|=N&60I;(eEs_qO(L*mi@TkUS^BoFC>GPnvoM}Et#Ou;4IhO|lMeg$*1#B&8V`vv<7Zy%;|3eZ?Be+eluWHjW zZcNS$bxHVsYrJQ8pyErfV(gyaIg;^8v0t9d|2%7;|9wV0mj5BJ`PL8UoeC83_4V}(F_N4F z)3H`HaZn@Q&Gs`C1r5el@v8pqci-h|j0!X5G%gtl=}wYb&42WcGnjEfmB~fVbG#!; z-a`YCxgfoP#!^5S#LiC7))6!-Uhs6u8oHO^Fcrn1FbgFj>+bZ`qn}@?R%a7omq^`cj zx^w4F)tOXa7Av}pbyM5x!e{;QxA@F)q#x*o0jT)bAMd7ZWDHErGQE>2b}Cr&bLUHe z_jm|(_C!fb^Wj|XeZ8XdrdPA>{XDOHuEhzoU%aPYJ$qhx(Sqxe8jwL0Z(h50solLE zHM&49ng=?DULwagK)>E#pl9r;_`@*xKDVN#z4db@-D$iqFA&u=XO#bEf2J5--L^D8 zNQ=N1p;sE89JM5|;B}KtdKLRWP8Ga;&+#8iV@FjoO>7x`pyX@`^~l1hbfjZPotV@3 zaKO3)EoeY+`v&;WpE%aG->iGy*4Z8kH;pPwrx5s5wAVr zrLJLLm%icC@m9^Q4TCPe6`%P_;6dxHTen`&yTfiP0o^~U#=ZVh1py(;3YA5%&LEVN1eeG$7f zY}Mq5U~wX8_ugXA13fuG8Uz1f&9D1Fsi#x zDY8u)Bt3luh1+oZA+PI;1=EDyEQzi40W<6Juy9Es<2*p+CVR4@<n|2*B~yl@p2@qy8R=+G zjn1G{1_F(xHs0`tYo&my8cj?z>xDZsc`O1&h$f(r7C3Km{g zJPQ(PYxDZu=8lA}B$tHIfw{I4BS$+b7yTpX-#kcuYoEnmKL7bI;hWhklqWv7j;C_V z&o1U9C~3m@y-ixRYSruHOOH14*Zk)l%JtY$rz-z=`}(sani`ZIK-3+1MRz|<)n-HN z*)!cUTc zY_=$4Q|CP^YDi18eY9hhpw3%CiGb;4S(JSD(#|0*xh$EDb#$p~%Gmg7R`6}*<;bAb zINT2z1xqo6@1J5>ECD_Psv^Yn1;6&2z`W3jdxz4mxw5Why&Bb`+M43a5}s5(!`bc= zSJM=59=?#O265expr404A^ROFGX1`a5eXbzN`A#noO}2C^&Nk18s~rk&Gc8Q*K5MV zz~8HNv}1tH-{Lq$KMOAOBdi5 zme{Hq8iQMRhBEQz2JvC}xH@R1hAm$F@*i1hl*mg5u7^4?f>=d^<2_}hMt!gYGi=Qr z7Vtz3c#8KtNgs&!ge#n__iU3-(~l`SOb{NscyZ4@RlE`+smxNMr;icAmk=x(sZ=B~ zphNG3-09OD;(WEDXN}eE0w+;jpfZj5a63V#H2{=aDTU^; zEYm?$Bqdho^v<0-8TP6|d(%A%NQIX!E(p(6V4S*nm-mQgOk;)>S-nB$?L3D*R+)YB zQb((rsi-_khcFUgK|ajDi$Y&%e>H<&BFVKT-yqlw8-AN#)Bp}@+ zKK#dafP=R^^Bx1m(BRJ!J%{5+NR=e8M#uBlUw?f-;BE?c5+FYizT5-0R${9~)5hGU z^TqbiQReW~B9>xf7Lo${@P%#91;Ci(Cdf}UvCYBt9p9VXcuI65i*zSwqugfqXLSj- z&xam#J@CDRnS}hkhg8s|A z8Z~MU%TRo#t5>f|+Dx;1yXKK}?Rs^0`T8X{fqUhqAkh?hege@l-7*W=4#e-RIP!~z z|9pUFGH{5`#Pxt=80uMy_wJ%7=Wc??t#)s5_R#KK=_g1AZ7no?LvY-qZ(z2FpY-SB znYRx#ENn14?DVt|XOC;Rw3e#iF^W?w5GbixN+$rkb~N}HFfat|gIc^AfT-z_*GqlS z=yRn&Wu&^h;5RtIj1^~LeW&lPvl5NoKYbKgImlRuLrX#ooe!hRamjFJ0diw1obaaM6AY-A#d6cn-yieH{rykB6etQCoYR02F z@)Y1p36)6wuw%(OO`9R&uxzY8=~pr*6yCb)XEx-a$j{0A%pB?RKSYx)8u2j?Q-Xy7 z>cQM;wOZhYboukQccKalge@1Pd2_dtiB}#+v#Y%PvZez3uxL@=5!|fppFy* zy9RL4Fld_D2_orFI_hZVVEVdu{mPaoOUUB$^!_$ zTEdnX%@=}PFnVSF(tQs*hfd<2XjqucscGkuIrb-GQQP$HlM^RS%fzYGyN3Qw2 zrU8-5r57J9nvsD>PXbkTPP2K>p5hZ2@WaXJoXBd1YK9cuUX)KM*Qd|AviB@|@N&+C z#e{|9o?U19KVOTGL(FA`(Z|8v9j#C+LvBRopDoVl^3e0F=ZMt-U9#)G65I=++~wej zQh)R)1C5;8yA5YGDbct?^d0A$7nJj1W8e4345^285Z1|CNYJX6^7B5fg+Irgp^aQe zkMQbQzKk15ES3wm3q*zKSUm0L@p?zs5Ge}Nlgi_=kNAnR%4QdA8e zL+{2B8G`vfTr@fhQ;v<-QCvhR_aa!SRwLiZq9QS$*lGztm>d8(#csI)AbxOINd1R* z?A*yfy-@#Hw9Qa-eB|zc6tfy*DEaMQL(!*&YEugDN@?A?b#)Uilfk>{aNSM=GEB4I z168usxN$%~*AhQxzgQOr7I5f`s^Y&rxzgEJuezzJNzd=)uTPLmqv=$u6Q?*l8SuQi zG6SZn(h3_zy`dn3Mi=Ud@8w5~_;eItD^pb)qF`36NSG%s<=eZmJN+joK7Bq*!2$0O z>=GqV7KF2cVCYYVr7%du)~~b=tG{J1-0|KYR#&fzmqO5EwcS1>Ee;6|W}uzpl{VlW z$RGe(Oz5l*esyMfYDQ3xGu+LFs=FlNLX1UY(mu2cfNn3B%s~iS#pkQE%mT{YS6B^R zzM&jEA8eH1pE8-~KUL|L61=Xn)jc^D46)7^y~`Pl*KbO{|1l*%)O!&|>@ud+|9Ppj zNyQX_%38h|%VDEqzl^iQQQ4}@AUb&S&{STeq%O{dD{rXt=pF4_jq{nig~IWLuc17G zgm4;tGZ&z=!O%fEya;SK510X{R>MMsL5%pUyf5*7SdcHwzgA2XqtMiy(tsXWCq6#@ zX;SDeO>ozFK&=xHkG5C5Eql8jp|N>8$xKB^X_L=jf*~ zv*gcTjYurNJqE1;FH~+V1GRlMEyhll0{c)U284P$-i6z*w9-4DSVTzhh z+wBK1!qY}w!G&vF&3e>cxGXx}_F2WWYAFnU)D-JAwe`13Reb?c)yb_GGHQXR;^SBu zA^h#sDNS4=RvF@cHR2dc6p3xKRVWJLT#ktZ4SE;}ELBiAngmBS9S~F0s?k}=32d)? zw~z(*BD(oYas29&^qVIS;SpTUVJC+zC^vrCOs}M^{p>xQSiDP*lB8D<$liJ)U{c>= z(~i2oI0+qX^*&|-1tWZH_g>V!8eq?H!Do>{pQ=sLhte%iPVnlXz-AzBYf$3tDhqDb zgCX$~xTu)iD{p=v91V}f9x%lkICouoi&-NBR3lUL%Wt7`OK&^#eB?QQ`d^eQG{8sb z1^?a@o$nnqmDeH5k2)qciz^k{4J@+=soyg zo(==})V#oCyCu()!W)+;O?z-CqT_;H@qqM2!3$VVxfeuhG5|6 zl#{e?g5AQT3EflZQW!uT)zM>kiofrHQNqsWhh^r(KDHPDvCz)YIr2lqV4)?8LecA; z9JJVY?n6_g6w?&d_Gy%=vfveJ(xHYXppE0Z5#De@Y40FGJBEHNoCQ@*qXQ;UNQdyj zdy5ex1L4mULCO!Ap%(&7O;CXj?Evd3R2~K|nxMZ(p^#D9u%UJ&7`8hG0)L*4!;EmBoS6MOo{P-PE!ux2sVx9;O=M(a}J`)LppptfKHp#|~E4^c+P!B{_yd zy%0IvIxnz2_`k)(=zx<+mBmv|q?4l~msY#cty?ND@9l*|RiyM#XgKV~H{h+huP?s0 zd(#eC?CwL<(Q-4-YI5*kY5iBcvondqp>?}CheDDSdiJ)z&IvR^aI%`HdNcA3f12e6 zlH+j2L)oZ9L)b+LxxXoyqm`GV>HXmtQ>`ObX3xjVY$$M3KyGma+aK>CMMS9`_^nkNI?)| z>#oBWx#GJnhC2PmaW2P?AD6)qyCXXV{HepaG^hfr^Zfqw_4CsR3k$QFDPh6I!cF|M z3r?)G$O~4SRbn@ZzX3cAv{#=T%BxbB>P+FnN8q++Hk`Z^YueDBB#3H;#^Mq{A(f4n zf=W6%)T6t>{$D_GtIFF3pm555i?*+*Pr!c*MOnTq53$}sI5!nJ=ho^IZoFFkr|(Yn zp-{~E+!uX{RMpIa&AiS8tvr1KTvz)ar}QcB2R%_VEPKx2Sa_`9TwL77PXY zA#Ob@XdyD6ix6S};;S39=+?lHV!2det-k-q#pq0&c=q~YNF|CdqIdTetaLWZ{ za^%FmZJul^2sEJEJLA&(AD66zR0_+5pS?CXkZZq|Nyej~CN&QqHCj0a++_oF!ljdN z4NChvNChPu38y%9qLyqGCRtR`+iG=&X9IIG){c{_#X}(DnXrqMM7>+RJG3|3#)fu) z{yCgLByH$)&YpW0$PQN^BRe_gWdR7r(#Fe(WewIOQ5ev_6nRdKj~6&?d>Yb_T?(i6 zt{46yPj5sj@{g?Au5THwOe@*qhZ z^!N07ZX$^E z-V{M)WF*35hHXDgmQc{`-u>ZHH%usztgxLi93CQFK2E4MEYbfBCme;LH_@N~{>W8C z?s{4|JF%tHTTk=I<28^KbZjbiHVno`%B;9ButPqce9@SbVN_* zagzzejOah0J6`sQ^jw0kx1eG12$9&bG0uul!&lN-XgpT0g(Hgk%o*)M+_}5@2RJ!6 z0KrbK(e*wKjb$nCutVjQo#3m@2g{CYm`HLZuxY%M+4&VnjCxFXAD&ePk1NAA0)jLq zQLM>T9mfrHY@&vNp8{;6=&J(;|MMLE{U%fv8KP}ag{7S{p1$!YWT~4d)<1~1xe|pZ zk}a4>lNW7OWMU#Nmh&Vde6>bqDtP_uDSJYA z;ZmopA7Px32DX8oV>e1`GKB#)L)PrX*EPa-L|Yz)C0kVn<9s6Rh^a@~4>0ID{Y9T$ z98J-4DN3o|{ zkpdU?>I*G_Y(cqZYyhXzzklM~%BA-en`mp9vODHxjKNBP158HhfH?TJL1$A?xwL@jhcEv%JIy_Io@+ucQ~cd4z=Kve|ipAzL?(k%-kJ{X-wG zB{^y!h!H=ZILP2Ok`b=}wK%CYU*pQwJ`w+1#-BbphWV;Hak$ToS8m}3)zypCa;FxW z<#~!kSWp(7cf;_;Bx|qzXbr)ZKmpOG^a=eNEM7&0lgaSFkmOE9m z!8ZWDQn!aguj@U)d>v_MWgi>IgWJi6$s~JxyAw@$IbIHoaHGDRcjt0H`D-1Zy7R}x z&tw6`TJT0OPxJoS1f`@`9dE2a1)w+{*zX zzq~Dh4^O<0#iv%{mw|cttG~b4k@AavU`Te3mz!NXE%AAV-ACcz%@Ug%wdDlpoEx!H z4fJ4bvp*I0fb6=3>7L@e22v-~-Dj0)^jlZ#9$rb>ld4HrYe1M#<8yZ+ki@me4w_L< znC_A7R9b{SIS9=F_u*HxJeP(VprQ zb*OD{BXB0`!mh8LPR{_1%`d-|@4z;$%)giG_KJWN*JOUOe!x0m>o{^b?G0h74)|G3 zHuU9;9>P&_x`(Tt^1o`?^|NYa091#i)}e3HzWQfUKwf=sT2AB&Hro&Z3utXJ-9p@C z5xxN;vqCJL1|qtEv;3<#1|yM9iU^lE-XC`?DQW17Qk8|>2B99P=6cSj0wgY}-c7XL~&;fvIN;%yeoIdqFKdLZS@Qh1Gyy`s1^j>$* z$!lhJhL}?QvQX|_4!#|_>Hx;k^4egIP!)lcO^X*GzM<0k@*8G;a-_bYdEaAz6U?We z%QqVp^z5D2{e;7d1}x-#NV{FNnCAJFs6gf+OQ!Xunu}@!{^zuZ>jAnp?#W0-* z8(h0iF>SQAmS}9CT~AnA2@V11l9r{OA+D{Cjnfh_%D39bVYO-kz6%f#&?PLWBzc2> zz1wfleWPgGEmF8NM|5w2fh_Iu!ZclfTXH9sk*Bf>kqEQ`48de=1?-xM|5Fu03TVD+ zo=(5{3C4cljUF--b_ppuIYB0k&&_#Z!PfTfAkh$w)dJ%FY&(po2Gs#==XCd@_K^3D!|R_BRGlWxrQN#Ff=fvkv!f4 zp^K18SOF5J-7BvF>(dHb=-$ZX1Gs|-_zrCXCU=9EzTGrd0vQCykkako=m{0^7 z(0WS_BJyK%s5?p^%9E%cbHHE6g*PP=LR>pF%+~-FT();J2b(^hqzPc)7MXzl0qBu(wvhgsI{K3S8 z7JZ?1+zpyCz7AZjjB)trAA$TPJ`J`1MgI*a@m6TJJv}Me%R)Lr=K(kXjdX`ESq0(g zE|EY6RW3Q8QQ1S2Ui@aXj{wg`b{VT7>EGYfo_Yz7L(&S`+(9mjm`wQC!dT zTEN`5$<__O3Ri#o?0Xhz%&4H-0Ksz^SI8Esp2s#OL zK#oei7KQGUma^g=HiI%kGJ$_DHYQbST4yIsp|#%b>fbE-EDy4%OuR%9hHQAsBwF-@ z#Q}=^!Q0MNN3aq2o`2u&D8B1_)q#6BiE@yZOOxiA{PF;E)5;CnVGLCP6?)MM5w>u4 z$N(dqsKjmNm)Wl2v80Q_b={Yp3#S;WmasV?mqbA5S6cw+eygC6hv^qBK&3$xKsnD2 z@7-2NsBlJDOWHUf)Ga7H;99c_LvSiji0!;J?7go8=YN zvLkadX@>~;*9pOWZ_PW{3RGtT0~`an13+!bX$D{9Ai8W)_R-c40Fks1<=FH43yFw< zCJxis&ZI2bIXeh9KAL#|SK5e?8cwo3)}|ucDZh@)D;&z{H5VkWtCUPSkXmZRACE>f z7ujXb7n(COGukk7caXs3<}rjoM4%u62;PMazPLaDZw^Z+z~;`C1&=L<{|2ge60uMh zivkB3BLjaLI$8K2EYg+R91ZhuxN1uzc7UnlnfiUhXz($;a{WK=3nD&_#$a0<-HUNE;17&-hB`2wc0IS= z@*fQy7=R}p-WyVUVJQk~9l)(ggWfPm{6lrRDN&2>8xXLkaPHSHmnCKSVHp$<2`Hzf zJ8@rjn#60MhxBsFI3xpZ-dA@MP|t z#P%2rY2j)Q0M)SU=m{+xF1O|D|GB2Y8`SX#Q<4r)8kLPFJWnf6c$}{+o*$3uYlQH?SGNEcU76L2#J3@F4KY>oo`9xA)F1sD9HrbU&tq z`3V>9Xoh{2aP%(0tx)Y;_Lu`&1JJqauQ$Civa$e^@HyoL#b^eiM~i_?&9!z5Pwsdt z@soq&YO0s@0d3*a7VW~8MLe~HhtcB)elh*twfobPPG30Nn8r@JtZ6&@+kphdW0sr0 zH?_=k-EC!R!M0ucj~z~*E`7GsiCI7Q(qfj!+3LM)a4O<~^N??aR!_UW8@o}C-Gm4u zapnV2amBn6*cTAa)W%2ZGsW4z{IUqZF3NC^4{#_6@ApM2@?)u37q9C`SuoS?ToLA8 zA{^1^499H15A^%gwp-y;Au?D}rEXwg zz`b&1O{y3pcb(L!&nx}wD`#V>s;aIh#2#p>K9#)JXjNXAyL)C&_$LtpC|8zTsN2eR zf-98ncJvG;Lr4jLl07}Mn*zn5I%Bhv(8_@sETvc4vZX1`1!%YH{x0xqxu^lSp%xz` z<$oU!Uajx%Qa^zPv+g($h@(xt4hZ#H_3W{~?qQqfOhU5P_OnLPEXL~9I-q5!vsd?| zXyNP}QFAV9JKBz3u@}#_Y+4>mt)-a858;zg7A_XVMTB)v8VWuD$30WgF_p^YN-62; zw#J!GWkjr789AI)R@N2gqOGeNYd<^Gu9e#QI4UaYKVSZ=PT9if$`pR@ljqN`0GZD0 z>T*O*s&&y+MK^$XCKluU`uXC;eQ%G4RqJYN-@JD12h4CZFE=b~*rcUp4ae22Tep5> zv(qsaa4EDuk1&Fl-O}2cedEU3E1lzkg!G#*KZPQb=OeqN}df*Y9n4_Uz`p zdm;NjpfrG@DRjf8P3N0mpQ~}PLIn)UTg#ItSF^IRP7LVT)YjG#AC=e;!(XjeNUv|Or*cb1v;NgE>~|&8A%N0kclu+t4SuWD zv1St!6NAIUSSs`vy?^Q_z^FE>5KoJ14mP;u^AD-v2?vLU_On>m^Le~JUp*IIyEe>o z&!0b^$>Rm=|DYexPlF$-HM@L@y>u=QaZ3N41F}m?#nHuUXP0ljuqSAw>j~l!c4BYN zZpnJ~Y=4VDkX2Y{gT~uB-ptbS4Am)IE>}Y(X3AAF(DT~xm-kaQVU0Hoo^7nH&tf(9 zx6pp^+H+8Kktm*Yr;i^$zNz|Yg^i8P4HR?O9~kV?c>X)M3OqvwvN>=dBPT~qUrri) zt-Fq!UESQW)6-3X&@2?DE8i9R7zMxEfv`yKvaZFQlp@E+k3ll!aQgJ=>$PykzL}S2 z;p*z@f7cNQ%wJE=9>(Zp&UvVnC&6PHx{0A?(hD-=RV(kzWdMhoFstkz2A4ebIm#C7-O!7S7pSv{YLj24GqmU zio``Z8k%3v)6i@@yZKlAWJjY(9{wX_bxFla-dx|x_J)NXjr0wx+a~5#CPp_8+vr(X z8kw8%a&mKX@~|H^w6eNwDa6HP`k&w6G`BF|a`PO=o6u~zEum^jLvtXA{Ih{#eb0wx z0}T!3;-3n3!K0nFc6$`t%OwevT0MU1FkMIOh7sKlhl##HjqK@N?vY zCyzdFJn`E;pDz5yW4FV9+;TgAcnk9ppGN1k@*w*(U2hZq{MHnwb1nV-=R8t-+>$e?xQKoay|(zj3QNvf-zHf1|N|%OC&#`m=?E+rPi2 z@fZ8)=YM~bb8y$;e}DbD|0%hnAAdN@8d`sRlV;FWU@zo2m%ua>U=VsOfJe`wpvI^n zid9HRjXG0#GP%60?08xG#jYFD%lg^c@5{=J2WvxwEczb23J>@7@nND2cjTrIHzyh^ z@e2y7=G#s*zZAP~H9M@x%)%l`4dgS9(ys~ZUs(9;kY(B)RTCs|g{qce_>x6B@QmOq z4?(u({Go$1=d~`aUv>GhB#qoxB93!kM_bb$J$!iW?oYocB&cxj<wLneXNj z5D+l9|M2YfQV-g;3?px*SjBiH8;YCM^HYBs)CNCe+IH~F)1MzaPK|O-Z%B4o?BDNs zP*BdFQ&Td#w!6EVeM8!3nrdy87WO`;TwynFxm@84%Wi0z0qZb_o!zIk!{m(4Ur>{?>4UaJc(6dLhB1D)($Q(hMG6f1qa`R_N9T znHR;f%15avielye{PWK|n=v{(Vj1tHi+N0YIW<%?^Q_3@=^F~Lc>GeU(4llmR&?>5 zgo~Y{V?3^cO*tvJprC;1VfHzb6pMaIX{-lzrr%%N;@Ar3pZOcyEPp)zVBPBH+(Xnu zz9*HNOLm+~pfX>Pq`rFfYR}%iktr#s7z8aYR#a5fZr*EO?Y3d#O!=`V64XjRj*&jY z&uj@p%KN#r8sFU8#7tS79&9OcUbgCXo>yfL^6`nlnjYvDFzbl9>`B)ucq=C^N;+sb z#I}oDbWqRh(X5%!CYqow_;Ymt@6z|_I?o9_ye8MRW$jGEk4H;{Qd3jg3LUdK zHFBQPA33^%UdSpYUCizGv?%S!kg{-@5FuKYb4;u}JaYJ)r%b+VijVp>rA1ngG~epX z%SZz5rwD{8>p$TT=dwGQN z{_*~7byaK%@qayvH^pnK#!fCXAH~z9Ty&M? zaze!JZy)M*T~oBP%lU0TX95Bt&StEwRWzxuPj{>{U-`(9Baa?El3V@0sF-JE#AnyUTk6@Cs&k(y_Ro924u5!guXPEZGK5V-jW7tc zzpPj3Q(@)E;!XM2e%!NrcetAyO;fUV_plugwPCJ2##J-lMmmV!R8B?ZxfF|crSHk; zeH>~rh96(GdVSR88T#;2Ovvi%Q$$AJz(AB-q-3mA0FT4BH=BIVxvpvmDv75lFY7lZ zCutWanHR4pQLVnb+Y_cY}>^tyFOWt?W`-Ibd3q|IKkpWJ+UV7zy(jf^63?xDmp?8Oe9DDiq8@MI z^Zfbqq)`&($UXYBVVRRicJjFC`drIFtK~UUUk-JpMD=W?)SK^3y7Vtx_$$k#^%bu{ z?Fob!BO{|jucv^ALwlBK!$3_CGlfGfSW_bD|ZJEYwauE;MloDc) z)5_c1+gVcwB05b3H_6p5nW)8{6PC~JotWTqb#+xtP`So;tFgQ}i{`bF6v@OEn#C?F zQdIo!$+^{>&a8ahie2*ew=+on^V?pYEZ0F-Gc&WOB+dN5?Tlxi_s?OEn=}eLFJ&Og z-p+i|c@l8;?AdzLY&@MP<|}^Bo4O0^(}!N(XG}XF{&*jURJ3eZ)^HA9ufO!IWQX+o zM|-O#x%I?l=9k}WI&?rU%y~{VeqKFUOC-EY+jYg)%(=>s!*jMh+q}+7{fZwuw{cm< zn#i};8>0~afe4b=3;+C9idg<8m2H$fmy_;#NnAY0eJkzF%CK{;d8Q8b_5qhY4%~S3 z_;J;j(G{*z+|;K%fBf;ZC-d{ji1Bc1dh`%B{`PZ9;;9BCcxpq0{d)wfD_j|btWpl# z4_{Kgc=7L8*R@p^E-tS0vDvR5k7;&BL@+hrjSLYkYVMOQvGQdZ2vR8>yPkjcdrY1C z{_P&CpkV81`zc-T;^N}>rZ&Tk6;^Yjs*l@;>mwgG4bEk@XjPr&H173$@QRuwb# zSGWRx+jHXjq|=@gS6`oYTDaX1dBxvCq&YZ4-KO?#;gX4kMVb*(d=v>rI^*ltUl@J+ z{3fceuTLs_6HV>q^%8gcFMn;9)=`$Xp7a8vX`L-vjy1#d9HOG4QOL;pd4(AQf84uw zPg<|auf{5fT;wmm?;{)3s5wEEMOL;>s>=VQa_t^k+5n@ih!h^hIE9Kh9k#14+)~$q z)5=_AgU{TsDbmr=N$;3Ie)bR|DbB8>yY1)l{n}2?^hU{hB3M*HXi^OvBgVAxxCInr z<(R8XZHDTZtLV*^=O^l>1~l8&{mF4*>lC)vPh%h0$Z!dn@xG}wBz*|-{c6zX`s-#4WZ19>u zzW$-1Sp2Fnu8|t??%l=0smhbs-_Nz|dVT?_i9`6^YKZcz^yf;Z7cetiS(+_9B?MH| z;5bnjP0OpV(qVvWQp+|=EH9VPQO&&7)R=NZ%=g5#3zL(RLQV@Q{N`N=cwa2y6?{%h zbE5j}=X*QaoK{COyOD_FRnzs`()1pib>zGx8Lduq*(7Rtb+YV`XWhnMw?6gv_m?h9 zv75YExH8vXY70QbXH(d!xji ztP6sh{3KB=v+}v@&BdwyC4hvPnFd+)j@nrH=*B2%dd*ym_bN&;SC5bygepsoj*fm? zQexfn_Ls5Nbn!cP?&P{8%zb_^S0mB$_Mh2W;Ry+9ZLnZ?x686LRUuX`QtEyR$?#FK zVVsny`T3U5Z|>DJE5|F}K1HllACIa_;n! z`=P8Yw_Yuz{3g%M zwDGvBPVY1Jpcjz2apMLxVyeH2UQFws`}a?tI8kmG-COn~-k>hDt;jk5tO##$&1tia zGXxA!6;f|r;xq5kz)tePs^ew;V+Xi2^CG+$gpqfyl&10MRmNb$uw8rgH-T4s_ULZC zWQP^B9{qG})AoZ`E2zwt%u3?fK_bhefXSyF=S+I@larGJ16-J>BMfz*; zpel7Hn4lE5(w{>5ky2(+#FBbi0O<&rb``YV%*7R@N4FsQgL}ADOG1}dCS2Ey6U>|9 z6r+|&o<*>~JyC2sq4l^w&0%97HlMz1zH-dfHyKz>t9~q!K4Q@&@3;f6gV^h?u{#E9 zajNN2@1xVXG;*%*GteJf2J$@6-_hAA9WMT)HpTUQX=&g-P7T95@U5(^g2=A%KdB#<)@9C*&oa1l;K^p zoM#o&!*u+*^sg*mzRM(!5SA`pnN!cpe*Wdlm+H`h`Kc)$vITB>OcYFeT^q0BDz2Wf zOB<=R>%`^kJ}z?_Ss>6i-+o#@PhfkrOvtS!{=&jS>2&>?$|-41<1Uv2r>?uTPJF<| zW!H3MMr{#Y9_95$h+pFme|emNHlAZlzs6>))u3V3Y#jX)x-FrSPk_DG0|hOkSXo(- z(l^i;{OI@2RHW*Zr5%hqe)#a~#&~6}^=gRbD7C$)vF})~d+DyDE8B zLsIwS9aa|yYi=Gob^W}%UT<%2jXS$)v5RvaPmq8)e=`aQXVskn^R5D;IOJu&ihBv4 z9E-WRxugF2<##%PuNTC`csrV3U3%;>)|jX+P-3Cl8H4u=t1+`sl~juaOgoLFz*a@y z=VkaLW98)G!-pGQX3tG@oBPbp%$)8lu;+|coaVn{=R;R<(KIwI%9xLYCj4@HJUAra(tl?*^`-(BX# z5Okx}X>p2n**`+3%q#NbG?$)ZMm}{zL4IjrQk@#YfW@(d> zm6Joq^Cv_ptYV2FLe^}W0q0g1N)|e@Ox1(xUZyXFW*+=lR6Fq1QWQ%|ihKhPR+Zmf z2Cy}rWD3Pk4lA=QiC!zInd3qqERi)Ea`KKUcZKVr?-^0L@VU1 zz1;mBhiWsNP;7V)7IEeSlSQJEQRI;H(?$m;3g<()M-|aEDJ5wH7^ez7DqJ>+l=S7% z*hFJ{a^Oev7bEfP=+c+RTr((W#^~xv^G(q7y-hpl1Z?E6^DZS9Eyc}^wt5dq&nO@+ zg>HX#lLj&(%rD^eyq#L{N{Kw0+F2&5y_VOnU-xUjqAqH! zF_i%T<5^esz8ei z;B|9CRM_ytVd=or6zmZ?UVRVTZWDfy&$LY;)3_zeD0CmqZT@uz=se@q7w0$3j(4gT zI?nqsbrw0RlbHA8P(SUu>NM6>*nWbvn53y49U9EA8U6GU+Xz+CT2ewH<-~4e6g7_p#YEI(qw^i#kxxZ+qFk0_S6!MBtJd zL}G<)$2GzWg>1(Y+}zwuCWM=SNj>WVIz6NmlQaYgxq*jFOV(7yvOsk^U&Q* zjOCZD27;UB6Z#*_S@VmBO=rkvsqZcULP~cMJDkt`Dg4PGi}8{lQ&WO!gM?eeSdgDe z!}ey${A4s`>WWCrLZyQlJ2t4?tW#ll-{qWzvI_3$S*ZO0?WN0?*-+u_cE78tx`Jqy zM}cdKmOb>?J7?p0t#^N@AUT|8BrSZYC;dobc2_;xsEq4xe%i~f@4`crZ!&PAD68yEqNV}y{V@^YvjL; z?sWOU%5fBsOoPiM@tv&c7P%i8=f3iS(l}MLytvp}z+f=*BZ@c#YVF&?!f11W1H^R7 zjSkhBHqQm1eWnjfza?(m_C~EGSv&iHn121LMtKeHai?<@QjZWiL_n`3+pgZdtQQ@i3Pid&i<;`+zy)Dy8`D)7R*1NtW}8Ze~4Bm82LyIXp5VkQx844!&qiZ zwR?VyR$<<)#+a8N5y%3J4t)U@FkV|-$+g+q+SAi>#N6Dx_wocPCHtX6VJ4;}ZPEUg z#>FUr38IUAyFqZSV7D(VjU;>0o1kr}2M@x*s#oitF-@cEkG(cESeKlWbA}>kVv>Nb z!}JP2efqSuMKE3@tpvy%D5MchKeL{*C2#szU%!s^#bqINEx|#f`BucbiFreyO-_(X zyFtBQRn5g9z^F+qpJS?h!>d$n+%+toKEAew=oDQfENKt@Btu%kV1$>(!{rA znFa;&o5o>}#W~Cl3kq$gxy`h`d0uP2a$cU(_bzb1e?N%%e9`i_F!S-_SEvAxG1@MR zmr&_yZad(aOUL1EL(%a92Z<)93A7Z-z>uuz_u$4lg=2Wm0_7yzv_f8WRc0T`H> z5-8%Bb#1)jcv$u1nGqn#)9#lA9cE&18G%NNXtUYx|7oe@s4EWI&x3nx$H6l%02JFY zLJH888EP9;{XSS=HB?_Zc>j>gr!N{J^He}3HO2ArXU9qh8!4rhVr|ZPe2Wfo8PlbG z`UzfCdgtbv8fjdwB7oW~Apc2zekIa@r0G==iU>t>WWL)qd)Z~fYpwC5c`k6oZjPTq zWP7^=&z6yHvW}FqsEQpF{pm-2mjIr5Mc43PIa9muZrnr{CV#&`Qc9{3g_y%HwF$(F ziIY~EF2%1uk+nBruG}l*pHMjsR3Riu4zA$o4DRp9!20G5{mdX-N)5zIb8f!> z4fsl5jRho(u+8YHqUf<8FFzY?kQ4H9a&l4q-c)La)sS4TqgmaJo(DFTm&0afXPf@s zvfE<<@ZMrUbi?7}KNxY_hXK3>bYau|Rnb6%fr-V|BYu;D@jW^N)dAs8pZ<|wYG97` zjaO7Qh@V4!OHgA^#8jKh^6ZE~Mh(!j^>~LMASGH}O)3)tUKT8Hoy!N?uZ^+ZlZ(p} z#R5Sm=4Ft(=mg9({MxO^v+6E#_N=S&=R#gw(QB~zFSiP|gZ^PZs@$!qz6zz5Lc7UV z1o8QwNu%;FI*+yS-5(po-d-9AGUwso)vrFFlQsv&QdNXDSH$Y;hd0la`?0X9rAxCd z+Vm}tNW*09Lx1q2+1z-#8cR_LoMXgJN>Q z)Fa%u4!bcfGLNF&z;dz6aY96g^Y08Ql{zz)-93zFku5maQ4iSHyQORB=9oE^Ttyg$|F;_U3~88ic`m{ia){zZE=1d{UPm-oz(9W)c5B(c5xk~)B- zz}yW=nZJm(Aj^ZLY<%(O#@=N@F5^ZF&Inqg(Evm4GtTlN+-vqRSJ8)s$zH-D9^7_r z5Sc%#;6w5Em6h4y_++|naj%21&yI=fFIg_3$)V%Br{YLpr~_R z7ouB7b$+~45ul~xH$|#WTTPdQ%^7-2 zH>6M+nN#LnXQjhLHAw@^+!S+742ebguP(*o z&La?ICm|LfqMGw3isDht31`ZtlJer_&6|7Z=;A}HB))|L4W<7I8 z1#suiLTb@`mt*NE5p?~ji>8OfGn3Q%|5@cjz=Iq%MjfU`pr{VDn-?o$%L8?r?q!v` zm~GZ6hikVP?}$An?$x(+RTTspzvx6qt~^LRIlQcYaPT;|5DIIF7-5#gm$z)*d=-S^ zuR9nMpsd6I#t{~WQ>!3m?@6T-r%yMYP*2yFF2rLMYzyNkS{goS`YGj4&ECP4XA;IgGfa82CwuK?Cb83289^|k>_~mWnazB3j zC}chC13;%YS330ch*;WQE}Fg{Xw1z!MAW(M`FDYn!2k8%7pFP4^YQ?ouRsxL1A`mk zbA;bzbMe@@5emTE2c-H8lUAN|3sB3X+3a$j?_@WBil>?e zAWBEDjKlnR;)4g9(Nq-b+R8lrAJyip>*{y4JgZnjFD0s^UZ4^VJyEN$P2!Zg93%_S z>CI+$bfXZ3M3mTBM=+Cd3uih{a^aks*Y3lIoW9*9sh22x{}LG9TcEXSLh$=QS2#FU zw~y$25TzQ!#dnZaK&~=k)abarY`jY9O|>-Lhpp`ga^GJY)2|b42j#(V=Jt8&9tMU) zkf*osN?NmCl%tlE8zn#1+x<0BOoLzGGYDnJ?IEp1QVFQsp--tg@md zeN$+dUm3_d)QQnEG}SITd`}g`;J4L=H?SytI%o4 z5)$rPX{N{%J``f)(o$Q;4!0lB=BxIl$NQS_v}UlY;sNCX{1U;CKx2pi13^SK8_#3B zXk043+1@P#BYhrhO!bVdBtToHPTBV8?e_?MMsy``hUK?6bK2V4;&QYd=f@SnY+4Gw zUAv=@-x^<6`r_~%*+)nEy^ImmB7Ui%)Qn8ys8Cwu0-u(EeH@wAjLuD5%LLaVs*}+VDFEP zeM%Lh&b-?#t8&oG;LE#*kiDo(h10dd1fRPvZ{4w@r7>2XnR55;U5CYfe_qc!XhlG% zhD7*On4rZ7>AwRAHuC!J#iWa?;$mvjQjEK<*3pkbDjw4Z8k zX%VP8W8t8c)=&oR)mc>-V1fhdg>b=$ru8 zwI<`S9=h`fBid4K+?)LMUS{#SN)NpkIaHw~Ns|blME^n&kHIdrXHhb4uko`y+@DYv z1&OYS@Z0FShq10xb8|{V^^r}001zX?-feQr|Mg!+hJ&L3ApFz~5T33f*#{ro`BdvN z`MYtYt#^MRCD!d8$uHg2cfk2o z6*~pJ_)Nc)+XzF?CCTj4rAw@$qQDfWe%2FRoo37BBME}VUJC)%*&0upbJUYnm2YjN znG+|1)n~>mKmdlbb|1b7p}K;)Bm4~^Yj3qAad2@ZYj}OQ17bD?rRNG2$u-2HcMi3R zh{u2u5#Vfe7Kyn5`>)^O>|C15ii7h)&wXkHq_w~4cXG0GX^3a#v@d_{+_eVG90|lO zNd>(>5f~Jkl@cWlfV=j6`pK``=pw*`L3t`W=$ zIp3tLOk~p+)Y$&m{o166ZaHtUyN9o}%X2Loc+}9C+n9w9VQJ~}W>lEG635_@C1wHE0 z4eFYC>(2JRGYoz*veb2{ePj97^=%t{ZHL^`?F%QmL~bE)-*k43Mf+B*qr?gP=^ly6VqcAj`Own4YFbKOLAc;X~_UJ?QTeDqaH2`WAB9*k?DsqI2f>z;= z-H!H*z-3=1FzkltuL*HRrouMp}52F4);{PGC^y}SRvi~SaC={lW|^N2zQK@hz_gs^|? zc;-m?$1(r?ONMc{eIxW7j~+bu0K^W#`~|UFz}Z3Is_CW^M8P81m&pi?2*7Av#&_TX zw1h_}a^P*iU}v(=-&vY5GHuV2hD~MNkD#2CfErfWDPmBk7Aj;di4Kvwt@z@@U0@Jj z#wo;7!^iaBF}5h1Et0fve-0A!jF$RU-p*E-vB zt#EAYXp%xs%Q1l9l@7^*5(TprPt_FcBU#r(Pn|kdVu2kIh3(xz8lmul$Tg&E9Uy{h ze#_28?HBrgsr_UBQu|v?OnHy(+_^IfxQJG7q$%D2orjYjw*g@-s=vTxk=L0O^8C3W zU^*{_r@t<&+iH41`f)$$!1swE2zWaNfL1*$z%t2ZuR;{&ZgzzOU|)961Ab{48Gh`( z8i;=HrIt1Shp?~vxP1|xH0dzc)m+m32ykfRoN91Z>#fz1mXJ8*Ny}TRfXh$MS$7n4 z8n-+vY^$TI9S)C0!z~5y#tltUxTJUH+qY8$9cAd(&;@;Qh0Dg1XVVVGY96vg^(TOg zze`myiaLEF>g?k?{!q$^pygem%d?H~>?f5~e71qTjLY{)ae1T|i;kAW8xtc{f&8ga zQc66+=Zx0kiCnTYCsa2l>N=ORYMPq_Sx6d)pJ#DtDXN`Na6`F+p}eRKdUTF9ZG73w z_HhMHH3LfR(MtfkQqaazs&dP#T&rjn4z0s#wkQhM_fY;2V`1Ax`z(BUE8QylXB4xV$-Kr7b_WWr2=)kpdE z?UYI%R`2k!0hS#*cHnj-seAVCk4B%R!oD9e>f8#^R~tSD_#)m-y>v2^gJ6$Qfdfn4 z(9key`S;&{Zvg!abE58@l?2uF87L&f@E|mC6@R}udq?FMH}^FLVVlP1+&aXkm~M@Y zuHQX@{iroIxOwiZHt7HXWM`^*>vZirDd3!_#219UIdH#A^OCKrQ=X!Tl_@hT{qVsi{Na11+Vps}`P%NC-w zfa0+09V^Q~&cL&02l;-csyD-(cPZCrKDo8SWckI5!^AFRr`{)%75rGie$1SfX02qM znog<2l4YRM_bx+3_t9fO)LS23+;x~6 z4d~}D^Q4c4&q;y`OHl$rf_9T%@|+ga0M!gSA~s_Mi5?c_vQ*(7?we)aodoL{{n`WI ziWmCT&tIaJw`PbK0E#B=X*6^$1fWla$;VcOj^Y_2^EEZLei@%ozIr z6uCc0nF|TG5j`Kg8$}K+R8xVyC27Y%4bsjfI`#-vj*J;3Pwzc+C>fiTAmLITSy@>= zlTTL>CsfeB=mqwo+<}Xt7h~w(i*Qs%YOOKbhu~ZxBo1-jk%Wtef>;ztKlS?cYxe8_ zSQv4W#Pm^`2AZ2#AJBeS*xI%Lkz@e1!0dE|$|4gS1IS%C*Jdmz$|A0z?L}DS-hm;s zeoPef)DFvI*)^4&f_(wuwdo?YVd+V05YJfI*`=roV5oG;<6(XH<(FTGi&0K;SN4D0 z1*Z9)0|_pnE$fdS&n5<8h}HBwGLkSjX0!( z%i4~l0PEf)zNgmsd0_v_l<&}Sq+C~K{fB_QY5j6RI>wrJ7nRNdy@lLp%@Erv!2<#2 z(+udANXM~roSd~V1{@9#hRI|INnF)zy7r|bC=xDjAez)waJto}_#JQe{hDn3F5+A7qQy8DE(I_s4Ovttj|a$$S7c?T zVaVOM!flac>RT-5pk2*W|H~*nx6ln&N z(6OqtInlbgv#xV_B#ZucM?lmN>}I?tg{_Ab9=B)c7uOCC+OGuG)MQP-{dO(t{N10T z;M9yfX}Pjw-2e=hD7xQ%k?iYr+xBNhKmHAJQ0jHM?lrE7E_~i7kJ44|=_vxLGJo_O zi~+>xp_31kmaQ^g>v}y&t5B%KwDkS^08dI05Dx-*u5?UU+gT5h3ofkJ)qp?wZs8fB z4L5lLJR;ol^Ye$$TI!F$PvzkZv)IR+ooWC^Rswu~{q>he8dT)894>XU3;h|=>uULq z?b~aIt#lE9s!_IG_GL40`#guO+W(+wm9C^~0%0H~`5P?$87U7PGuUP@^AQbmAS6It zAN%3h=!0~{g}V>KT0?Sqcs20@bP!4aP9quBuJazAo_rwVs&gI|V_DdH4f7f(QS!bbU*z5Cb#K+J0{}QJ8U{x~)mSEx$$3C&pAwDsIu{ZE zzu`z~8Dj%56DtM1w~rr}b`o4*hpX~z9|K?0?RH2;iUaS;*BrTetQ`-ETo9B6PXNwD zLbBHTN_R$yAdhT9QwSu%%E>8%4(}R}1Ik-0a4#`d+v|_}0hY8W)HNlC7j(JIozZ^t z=1u7!qliO!U0t2%n|^9W#Rs%#da6a>Y4eaxc%?%lMUKOzM}Yrir!G&x%DGufC#QnR z6<7$Ge+m1=Q5!27US}y&M>0DAP*^@+a{jWgaq^ozCwGtVzu3hNpcp zSNsX1svla|WyL}VYxwNn4Lf&S!Lr)Yugi>2eWrGFqpHz$n|J0#qD0YptCW_NjX)l3 zY)v-+5{O~X-oI~O6#C|iuHPr`<9GEHw>Ed^Zri-MJWZ;Np3Z2kC*&EVjuyMBNJI(# z?@L+;y~2D-o9?AHrpwrP{3f3cv-TgFK&c`l1H^<4lN;P_5{H~;o<;b~;j^AZ_~=ql zAjftEiL_-PZ2y~TentlSg6Lt`C`!f@oE7xnvVY#)}Ni*m6n*}EU z7G*B20z;>)xnFN%S$GNq@QC0WK&)g!-7i2?|7V^(@6YyXz1gq-~LHQ7&%M~+7LypwI4 zNIJ?FdnEGgU3+3FtS6Eu{Vt>X8Imgn;8aP|PR+Q}d%3qGg5}ES8!c0q@WnyqtD(VE z24qwq6cMWct<6MNB3IGE1)@v?a{m{4Wan8A~2-?`F>&bhh! zOgamk6gGvJr;667@xen&$e;r!qW=E-=b#axsijKegLgv5oL7VIthCoZ(Ybjut#CcG z`U7h`v$Fd&RMa_}3TLvn-p=mXMrML^E0LJwqol5(A04O-IZ2^AYnLJueCCL@cDGf( zIZUNxjE>c#hbjnZ3pz^y%ssM9MO9T*jy>b9UEtzD!l&vI^IQYk^SuWTCV*y$!;cBh zYm(j4#R0WE9$^DFZQCt)oN2ckHNkU)9!VOT=Qki^tTY49ci%$rMTVgI=`hKG4ii`L z643huqV4g1u4a%98Bl77VFeZu1#I&WhO|rb!`6LlV+Ir%^H)M@Z8wbBDa+HMv(jM+ zx{9@BWn~K;c$vu#4x$&B_{v%fXlV=Hn3x`onf7mQDdt!?lP~a|c@SG~DA3xfUHFT} z8S2S3&Yy#SP%sHoVQb&T;TNh0|Blqo?2J;jw9xyc!B@S&E)`A4aSEGOL5|DHtTMDu z(UP%M=;SiRsS3aeh+jz&hnWw6Wd!PvUpMqaA{i`b6vjXcPb6+WvdA@M-bI)P)*FljFTkK4cg=;#z5 z?c<1pNZGw03Xj`kfod2x4V)MSEp(#@AoSJ~JLQmmUG7q;wF!PcK#_DA{%+OU_)$fQgfnvx?sV#Gz*$if)C)8hl-K zQ~I}{)OggnLM`(%fBC7Z)4{p5vTLxFcCc<|%Np7sG6G^00NMqTL&-wuoKT8i`ciJZ zM*5uL@_j~!@`d20Q%O{arMJG0%< zW?*|mMdTYp0hrsyvx)dtAeCIfjeGm>G+RSR@nCw>nS_3dn6206fshg0hi zG?#%!t*Cc;g1NRJoIJS4QBBNOP^AO>T;aV-D-dkMm`rc*!LxQ(y^qVnsHcX3tR7B1Tt=0%Gv4Lqou+$`~&CpL9z;VbggOQeVAYWM~XA zTs!AtF|4(qx%te-b&^q~roCMq#`~nn72FZAb=6d2Uhm_=ckrRCP*}fYq29cJFmOQN z^YkOMjYBQku1z2;kFn}~sRlo6h%zJNRV`Z2!bLo?5N}TsU!7)a<+#C zdtj_a^n&mzYSV6?-wm${;o}xUg;>9btS=<}16Q@H! zqwh_-;Fg1RLeft_ow)*NgqRa2o%_n4q3zd=^1c-YS0soOIe;fqmkY~g#YJgV2*Iww zx`387(niD1AE_St{CNz{#DM2j`sXm_H{&iSUuEr)t*fF0a%kB5gxT@gkhUXH~fDnI+F0w6Pb(T z7(k#nuwtaX96fq;&%uKx9(T1t09sM@7)(Rt$3?}!_d5(b3yIREOk-uT%*4d*l~o8r z!AkZmB(xxB+-Dk)U$q|*+uFT7)nJJemJU91O|DY?FjXPjOcSo}qnfSg)hZAc+2-B+ zm6GzjH1YDo`AkN}z>MNA!eL1#$#ciqZ|Ae-i4T$x z2iP)%zHylO`V!PeJdiI&m5SfKeVYMsHblfxiE8cqH9onhp16HL2$Bkq41W~_4>alD zfm$W7#3bQO@^Zj`J!){D`CmvAwgxuVep4SZ{RVDIfykdI|A^-+kP)yCqrDCaQhRUM zYV<5sa)SI34<_VK?207{W+0E=Bbd&j(XkDIHCIc$`H$+&XsjHupYkyEalv0~f&+ldQ@v)9Kg&u8`3 zg=rH@?j+N+2QBXm6pWLnPFBM{ov&~+Ra6rpOn4ueP&rYss*901PU(wq@C*ok0rw6hGW4Na=6QQN0 zRr-{X+TW8L;UnNert_e$(pMqBm^Lt_cr^np_Ufx!S!JLQ#3J(bRmf}$5$!Eb7MSF> zBpp*WF7_aj_DQ9Lt3=PX9F(EdBRGku8DAP%=LOS*F{E6yKK|T3RT%Zlz;uh1UKw;= zlAPhG4=_syyj%~LY;*1R@844-X=O_5zaN2J#*e8cSdh2s45A~B2qX9ypzn)IcsmZ! zmmzU5)v&DJAg?kCX4A1F!$8Nffrn?LT` z{m*$boz3h7yR~CfM)Bo^Ur;9pU7%s3*-~M!_zcDU(8~H;yFjvsHc~3OMg@LIYQjQ17q~sZ3AT3+7t~C zY(xWDfvt&F^ds%=-8xQCz6v^ZGMfvn7+TX;rt%;}pc-RV`_3ys8egMU1nmfh2Q%!o zJM(Q@h-V3m(^j2DXt(Svn!NSM9;R&>6jc3`oFa@~tsA{T8mSc4;FoV9&WO@O=vApI zPzvka+{4|+iESo+{?2jbyrKj>EO#AQ?qVzu=)mxQJ2N)TkG9Ig5tveSO04$lpj=mx zzGeQ{^2|^;;>+MbI%H@<=V1ctdClX#!9jy*(ez%eCC$x!ECSvlU9pf$4UD2BAVqoMbd=j>d< z$QIr1i`}!h< zww?|v-z|IghSc>?c{sTU?LE0d=(XEN+Y!S-BHIWoHL%;w+=Te{h&~Vb_63qy^vPZ* z5_{?C6YGaj83{Wcq7j*^i4yqTtg9dvE6xU?myBvdnX#OiN%UFKDQqaT zVd#%oN$4LH4P?X2K8Vrmx$(|;fF!UQ@sNnv04*8e^V_=ncJsS1CG@|Q&W5+U$crEz~z#(0TD3{dUa{8g!EbXVdUW-e8$8e z;-G+PeI?toU@3bmjpYv+{_`3PHkYxlpE4CL4abu<()$?X{oEn+{)8RS;|b{N?F|P? z&&}t3u>E%Smto>da9i^iI(Gpl6?jC)Z=iWy*bA+JkHyTqA!=L2j?bKBd*K$Kp;_ay zemjpO6^rDuZc+GDg=Pa>b!ZRi_l6<95K6>|ot{Tm!tC!~U*Bb~n}YsMLo|hd1JtsT6A)e;iN^nM=f;#x z>+}tI%g^;%fV8mrbjGEj>uSIQB?iMy;=jE9_#YR8+ZD&YXX23JTBWKMmX^RjTtjEa zvbZ5$Ah5xe1`9&zf!w+T)flJ z&C;}!+yXO;9*Qy^ztKG1O-{AgRiN6QW%dzeTQOaqLqwp@{(jR&nlmj###QKBVfB^H8L21Y3VDBP=UE$YYOuiDZTnJe(~jXL|NBPs zNg;6DpZ2pI?qF`X=$<%BLsPrgVkSn5j$a|)X8bCoztjTjpsWBE9+SjPIY-@J|8)g5 z+#if{(qP!&6Z<|>!J+sWOPTmLca3MJ1~L<;$flnrXZiBV;w&_rbyKWaS+FVf5w7XqBr4z6`|@a|fvx z;M~YqE0P#dP#R3(8YC7v0-J!vQL3ZiR3E_{vJ&RVcoL@3pb1;zy<-7$P2U1 z=ti`lHIYLzIy!mlxCbK6HSE_AlUa)6UADJTag ziZ1ELUXamld>?b5e}1a*v22EwjRcL`!8pz6q`&yhK9+2wXTyG5s%uQnFdL&=f3V7# z!+aSwD=d5_GYElhsexR;>ahaisL%f9&E7! z97exxJ@5j&VpP2GNNT&!;40j`R1zGrM>6aIKF!dO~ZT9Q*gW+8WdItMQp`I8fTQ2H;lK(kg=Xwf6dxC|-V zXhvqysmFpKC1%fSEC4y^Vf~m0GecMvrdXGiC1Br3e3MATXVh(+8uWMT92`|_072l4 zH-`|)UdVDlD(`q8J>31c>^L-pWKfWJt8SF63gkXbL}**Occfzn;9l1F7|WmY7@mPO z;uVO5N?jhKj+{ICBm2Rh<5U_E!Y9_WD*&!F!}rTf!9fYxIb#Kf5$?3x2jy@y6Ko4A z)IB?Q-pHzL)~TxytSX1juK5{fJn0lR{aB4fNh{t$CAl9!~yXNvdD%e zPw3tqR4~2^n_W(+5q7fibUy8U1b5FNT$R#<_ka-|mj5s=E% z652t*6vDE$1oj5%qe+v-Z}v4- zN`#|tFh-xKmUH_h;dO?`(Z#@--v}zX+K&uYUwAJ%UM+(a$*mfS<Pw^PbtG_5c0byst)Wd-z)yX_ zX~L|DE7K%eBVl^5ScQ|N^!s{k-|?HS7wWD8RkXz~2@eb}$OtHur!bq5=QZP9T^%Z- zPS$+=6t_~R18C1aLc&faC+za|AYLJJz<(_k!+qN~5qvj?Qk^X)u% zkez~ArRzKbKc0|o#mkK`#2~j2Y$Fj2qLCtpD!|b^Jv~hhU_iTq;i!YOuhz}EM5_SS zNG4-C`7UTotyjO>6Almz9^Bo;NP|IIvw~S_WwVZLpWqe9I0D^HI{eBNuw6x~=O)-Ltf0c6>82Z@KW+E|J`F=W zNJ68+9dNKnS{)PsNebj#pVU#3lcBcFV9?o+j4mK!M75uQRP5=on6QizNPs^r6lfhRKF+mU7$(@aMq@k|LGVlpCU;0+UN z1@6qphsXbdj863fj#PCW;41`$<-#{*s?O3|G}TNLbmh6KQ^yh;b+u&L)5wN$pCINM z_bP|c)Ux`{g6*3(pXxG6Jw1Q#Se8*dc=zYb=j0+wZQ)|eA3J$0YsL~DHIM?%7>20% zrk2P1GAJMzCE79+Q!&!y3-H(cB@$f5m}lrA}MOa50dPA_%qcgv7G(a!VzAY3~p< z@f%!pWS;|_MMIJzDeSQI_&tPCvt7e^YU^5OM~BtqmrFX^A0c>$kbS8UD9q{}$E@v# zi%f8g$9e{#uLVJ#66YlIDW6Lt-=@Y+EFKi0l5ZVF9q9f2%@AwPr3(B2Ueja0TVPXOlcxX5(7dqGf;y1V&-i0#_egAr}5>+2Efi_)0G9RavvX4qeD<45CgdDEZA;WIo_N zXE+CeI_1$k%^<;^z_BV-2S)EmN7T||-jW3{5g$@%m%imkaHP14?LG|klj_66b)L9m z`hjGc@R6CInjg=~`72`#vxGijPPeAvUF$4Pi!%Z(PF$L;7VEWW6lse#=}O3LoH|FP z%7s41xi@mC?c^+uqn94(=8ZKTu_S^pWII+=7;v^}XQJPLt*xHZGyYNVO~CcSjMzku zWKmoUPP5fM0mI3^hWQ!Jxty&UITCFe&=SdV%B(X_?~b#+`M$ni1jhzp<^PDtV_{`} zmI^kVoMHhHHyU18aQsv3Iw@s*&pPd8X(HxL>Pq7@ne{~ZckGxJpnJTJGlolij=Lzi zoA#COoBI}gqBL%%UG^i==qV}G?W3}b^8?S0-RGO~+<7_RMv1mkzi`)IS(&6`>2jTC z(2=Qi-NE3)glnrF9h{#Q(^kk7US%*f7J1!thJ6Dju8O}x5&J;_>W{#~7>7exG@w-B zoH3QWmx`;)-D`CH<4VY;zT{laPbRtRyUYgtk7;KfU*|V*co`ZY@{)5C#To4{%2n3% zppzgCEh+aWlM8M(iJlb^A##fm#+CxPb=-j>gOjRJinv(y>c@iLJy+eD^)A#Gg6fsZ zke+0w<@sNT7uxXS1#C@dtIm<@$DK_HnpPEXTF1nR-LLQ7EEQrRB4B54PpLe!(|f1m z97#JOQ{~49@?ZTvEt{yBuk`TYL)D^wYJ1DiG*x*SF^Oi0BdQw8pR(PwT+%WpS8SYq zg0P!&#c4S#_VUqVieHBsmgdgMdrovZ&Pe%}r>w7Il{z=pItgV|MKVF^3^O$G zj1)O7l2i2@-3^pUd4$=X9Ct*Hl2T#B>7*F@xrPZDKPJHOs)SIi`d3SSGByji%+$uKB<5s57bn_l;{Tpp!uXx4+Lv+N(cd}WH=6fXN(2V22`L+tT!?9RDc5d z5wa&sg%kSWn~ePR9^k!cJWAV& zMlEhJ8LLCC1}JU8%t->ed1Jt{K&afL)DXS(;zbE6$Pj%W)V2X@+<+3?xa7nf(h!ls zR7IGAU~~HuHHa`qe(b7WzMVmhB_}`^a_F8*+9PfL~j3^KH{fp~5>mqb-=zI4L!4RD{ z2uWd~%Zt>1jh}aS(5#3~$Z`79+A`<6cw2PKInt5R6IQXJ&0GM`rv;k}|Br zXtXscMiHd^`!K2PIJ6+LIt)d&8vayy3g>l3(RzM(t6>Syn@oG%EkAr{-T_E~g2 z>KJGY@%kQv%pyj;eEp;Nvkt=FKr<5>fDP1xznWaM_?{1N%Ep2;OaJRK)X^?ellCZc z6)CC_elp-glr9+CZS*`0pH>&IjkIlQ2u>h>8s0I!kStZ$2T`{r$F~$swhssYx}h1p zZ;E|KpIY-H`YGzJS8aK@3Ke_!+_%p6B)8yC5V!+xFeEci<%)DOZ0Ig6e&zupbLT}l zi)-jkfV{LKjuM5aVb41Kj*opx+oz8%;27m_npM$GVo3Y0W5caKY%QZ1< z#$xcZ(&3 zNrSuqXNmlc!YbQ4J}3tVR!S@U&9Q3L3<0SS;Rs9yjON z*DNZ)wQ?{xYuy=R%%3d>#&EC zU?aXDdWJ@x(61uXE$ctC{y52r(#w5u`ONyWY23fwtXDv*@n1H--!jaM*M#@=pP~*N z*y65x&qTMUtvx->uHZ{rC7Ae4y!gVQ+{*ezJO4Ed4{}Uv?tc9x|GQHuUv2E@QHa_r zMh{QE_hbXN^e`b`1QgW1ZEIe9^-VsA-}Ua)zRst-u|;sMrQ5H^g8vWre}TjKU+P=@ z_J+H)R7AHX%0$eMRo%JaO}$68RMckMZ|{~HxzA?2eQ-wlw(*!}JZZxUHR8gh#cWez z8iPKl)MlEwLmBWO>C*CwG|dG#2*vhlG29-#IWfOHqGj%Kd(I zWGO8P*tVvqPO_G_Xx7nQ6kblR&u!J&+hp!gdisr!%b;*~eRjaX({mVmt5Oy`sQr-} zC2D{2ZjDvRkJ+l3xh0uoe(Tg;L1QZp*$%t1^wQ4n~}xRG9l5 z`9Ln_$$7n@f0AA7^Y?79>)LrWs(c1!?&d+)IsjGA+U7p|>%KP8XP z6(dt=8dKN2Kei`lud$o|wtI;rN-TZfFx;AaS#9KJcZOqI(p7dBnYEXzM0Z#*+&B+i zx!Rd%Kew~~(%Hc;g^BaoA@y3U_}I#4@!)V_qEB7*Ua>Y{hlHM)G2s1DB#A z8iV7wRy5EOl7|h6IB#OsBhOa}zF`V1#-UGoMv1Slm#)sSazgkM89oRL1wsnvVmJ=Y z$E5WF!mKv&EC5*iS%9hV^r_-dL-d0hVzo#&4Zuzv$j`3$u!b`wCfQB}#E(HG1u&(3 zJjZ>}+Q`R`w@`&_WnUs1bpap)WhnehtaZWuqC_WroUC5jL|b^GXmXg5v|ZwLRq9j- zP6X}(PZTK&sKlsRF(H7PySTX)wWd%5fi)AI2Yk_fnHKa*h> zG8JIK0#??OJJ;;fkEY?$$JHsP@@K#?y?1;EA{V@T|4bC}X{zY!saE}*6WlG>EydTW zS9?5c)rP3uu9kME|Ff^r%Yco^&3y!lni8vn;L@sL5E(|ullF-)JXYvqx}YiDqb&)! zA&Ew*v4ak(xXKw~t2JVFaX=i)&Qg#au$lN_d@cR#nt1^#U>gaJg?uBzriUmpwAzG} zArpUCvH7D_uSm3e{Ze4Jgd<~E!_)2~mISxME`a#ExhAOf zdA~T{uqKI3N$NFeYNS?UikPeL^`)T927B<7ex%*zXWNn0ceZ{jEJRZtyDtvx{l{^xv`(5q(ol%7wi{FYR+47j+r}bt+ ze}e3Q9@Ny7l1F}>=H`=>IW09g>;uxxE1@6hnc=9mHPJXZLabJqcK!^CPz8vP)uA-U zITQ&nJgA|5{-Q;GsCJ3;L1p(Cn-9LgY7}EafVqj(rNRdPYJFi%(h6vt(C$+71wDDn z)k1AdBNQsxrGDH;5kuB_a7x%t?|4=vwSBZ!{+~#{pTNthQG))E=4@L7F%EF6_7vUjC2ST z!~(o+*GB+|)gFg1O^QlX$Pdc^PQ+nRukIm6!Fg(OtjnT%@w&aosH=g@`Vk0EW3-SY z3bR|xRY&CzDlx>g44*Lo2Q);MV90_gV?G{$8-M7DeIA<{S*4FHfvYk*bJaMmB>7z* z0)M16g-2zY>Vk0R4Cjed;^XXw)G1d4W?`^FP)nPpCZJx0@{t@KvHAtRz3rusJf}E| z!F!(`JSfBRL{!lM<-%jzOlll^Q>%*Jf%qZv(E00|mEPfK2Tg$y61k%bJ1f=&Z}~W0 zKB4|(AtjJQQDVe5QZDErKAMr%cBIrmOIdz@|K*BId&}%E|16*=zpabU`j<~rny6E; zO7}!V{B}g|oebz(KuG@alY1X-4<`c&p5rjIn$)YT5j6J7yS*V1dq7LT@^)%31i5rv zknfoSpP>T~m%!^Kq3(3XpUX07OKUugZUq0*P@J3jSvZ*JX4S)IjeJT#9z^apV7q;U z0Y0YSsa8BUGTA%?X8z^39H%i0d~M~o0St60cxIuMIy{7*sYhz+`|Z+5h$QKhwSls7 zVUIpDv-&lTz=WA|mGgph(I;iJO2^Qog}v;s{YK#KN?yO#Z7Nzl;qg`b#HjzgX3GCK zbDKhW_I~txyoKeRXt`KGDTp0UW+V192E}xHfaA4 z$Dj;KdAIfyr6nRmstojY7;Hx9Qb`g33^U*SJ7OAz?0Sy_S=IyjBm2Ar+^qy`3SAa2 zDQpY(dL_s`WDG9Pd_7I@6p~ZL%j@LGF{xCmiVlsj^ikjnjX@w7_TWfzf@}o$me%5icFN+w@w%{SGX)~h=N}lb zOcg&XK(7lPi+5F1xE3iWTR!sIr8?p;k#$3WuB#XEWIINGwJUqmSk1fYahqVwl! z7{%s}fKxXbYtUce=3(4h%83Jjb~W!w@y0)Ia?vKeI@(P@A>P|o|M60k>&JZKSAqR} zogs>kT>hwfaI9_vI-~ma7EKE2`~92h#Bz5vHqK_Up8rIB#@N@m!nNn>SBLW9rx_PV z0}HDxm@mrOY+DAoLMDvvZdO=V@$mMX?ysx`s!y)9bSTaltU6 z-pUrwWD37@HG;4L&0)kTE`9pYLc2Nz+5>bJqII82kc$W#j3vvvaw?L{zN$2sv z_~#=Z*Pnl{Ou`iEV+n%8wnwC)89uNa?7Xg*gwj;GD z_KOgdE62>L;*>zKQ5p*6*F3gINkvRIRAI!%5I2RA>IkuOxRz{(JCz7A0WQ4TTk|*6 zaisgiT#znr)w6p6HeLf+N8b*m29PdexF<-zK%p!sk_q-fu*Qx;tQC^CBU2YLh@)gs z0NrR<5*kmcVG2LP6+^z=Y(!MhB|vB;^&P!l(B^J*{ptH!`oP7DR}DV5d}gv5A$FFLW4PV>`nJR1Vvgg_a7T}zh?kZEUcY@u#T@lm{0BvDw_o4-rINUR=g>1p*Xl2GW1#c@DC zUAep)R2X#=_aX>Vq6Uaf|t@40RDe}9Ffs`sXE8&p;N5p)>m>f*mhTxLfAqBC9*NJM&t$& zccFej=E=VZf|DX*;u4G&NDp5jZUY7v@*_gxUA{e~RdhL=?D7qnT^Ad3rZS_H;tkl7Tiuz<`85u+4|6>1v*zyV z{Ta{PcTO?6Xx+wW2xB}>C6?ug-W8SFcVx&REj@kp$mgF^1wJ0H-@J)d43ngRE;N4z zq>NDAc?>EzK)uIkd2uufd5TY^sup0i7f?OY$=|g6^P^+$sJM~+^su2Uw&+I3o~>Xx z4nT)d3#a(gA2>&Se64&&-ro)P_Lz5F1L}lSaobg42&@oj(4K)Jry52ACJUrz(fMCQ z+^pHN2`yc`;irt1oL-=mYYx){X!eQ@Q{+e=1Gn!nAYxJkKv$}vd>jTHs0RI*lmudU zeLgD#t4TG*R1JL!UMZgjSZqA_V6fPrT4a?A>vnq@%1Vt552FN+fTI#Rvj@;{Al5O? zb5myZq1vV;{y&#ku)3cjDz>~kjd8S1=9_8XN@ul47kAQjZG>tZD&+Zo{VE`NV5_$g zZ@u5-tjp{ zajPITi5XG8Q;j%QkLkrp|ip`xqGQx zC9eYQHL$(YLkD3a(9F@Xjlz)$*~QMr6G@};J{8}M;#RK^NV%>37h$DJPu2UDrq!wO zwMD75I7MIFcS`=dbz%u2FRBV&E~^%$J2QC%%yU`$qW#|C z(!rvZ7fcxYMrT!-gg;ulf|vVq)|-)?OL(WYjc!wAu6)Ec$dSptk4Ao6R1jN7s$JYA zuNGP!4Uw99v7oZ7yi>VQ{x#GWOUf08t1Vh5GZep+aY<=6EM=Tn#1+sOuoX-1*eA6G(@ug4hO+6vaaPAT z?4vYA7Oy2^##^lvtHUXulXdx824b(f_LjO!Sy5J7^7;8Qe-OCV7vGnK?^(CrB3 zhIp1T1Mt^$12rjm){lnV7F7$YDm#5veb0@RqgCBsomG>J_pg_7RSQUHtSFM`))ts( zD4QvxU}v7Rf^kwRInp&2_!+Cr^DJ-fnozN&F;fZ?x0NDg$8`C0MqRBRj;#IS(f;n| z{<&jQ@fLk|V+WQh7Q8q#@9cuc@a9{*mabV+?Ym<+{s79d$iPe@j!#~!>vfgi_~fm| znA)u5A2NQ`xA>Qc<``VD(0jo>@Xz6qeTl=lb~DHR%H-My>$85=_Z`THO133}z?WBr z;+L`BP;AiMJCme=;@zPvo+AD1Foe+X)sqw+0wgFMDg;p^w-}H_C+#% z$qU;k0$9P##>1jSc4ovo1WVrrI4<<(epsU5 z5%lJyY_DcP@wB$!3Spp#0j$DbM@kgr)^V5r(iV)w`-2iZe&Pj=#S_;#M}Q7?3_mrJ zO2lX!a>U_3(~OB=TsTl-8ZfW#I^84m75U00*i6uDmXd?}DK<%mK2)#X;iTn7H&#>2 zer`LTZkn6S3VGHw_-9r1az1R%O_GY+`#7yN954~X>);uw3w5g;MJu0ragKkReC{!j z(p@1W>vrM71>@1I7sLM~Po~(9EiV4?)3x9){IIviJ5@$p%-vcp&t-g6tJBCUC~nZn zzZ}qW^aES6?cqXoWp`ojf~dfeAt$zkE&21jfB=yao3xEIR1yW72^xAs0}8jmO{qu3 zG&uPYn&abBgZo1Vs#Jf72$7FCJJ}I-CsH>6ZMjdPFi_sl$9bKdCeW@?7Cj{*n1^12 zvwW*gMn#~08lVuv5eU|_do($*mngO!SH!JNKFBN^skbiSMJ^oxOO3X|sk{5SP&!t_ z9{2#wIBiDAXm@6AS_2C%Zhw9dWK=&sy$H!03CSs^7{bG~ia`)Kl8%UqcBO4mI}Kk~ zecYW4ha$9zt+gtho}CM*%O{f_iU~QOTvf2vdki5Qib@p-(B7t%-t$R)*TZq~%YIfF zYFdi_XfIaDlTd(|8*eiKz(>2xx@e#G8zA>bYF5p-rje}F3#b;c0wTzSN)tb8MsbdF zvbuhr3R$9^cpasD5B*5+5s%=Ir^H?g14rJ8xi8JUphrqBN;;p=f+VM~H|yGtjGuK* zHV#uWSnA`we%&I*%g|7#PX=5@U+uM`cU|i8_Y+XlTA-d#T~)mHnf;hTLxzKj0b5a5 zse+DLhMcA}ft(u^6Mr}kk8H=D+(ayc#bG^lNy`nhRioprMuOi;+?>1SlGFZFM9qW= zcw(F=uz8>Z1|H{|!dXjcW=Yr8lV692E8w3dE&W!E_{9~V0&I@@FL3XyGP2_$zvyLL zBOJ^&X!8i9M0ZK%)1dKMF3KYZ-c?m_)78k-7+(PbH@`n((Kq>Y3Xp{+!lcNVE4zWMF zj$K^I%zprcQg|&vi-17XP)t*}GZdAp4xU)FL9#yW(>k09w+|yz9Nk4`S#=@;gXe_~8zzPA&h2lwsqi?5?t3bR+sj!|y8b3eaibSHDA?~EKVHDz& zA&$_4P30MYpP*Zz{5MD3+w`MAiLoD#2Jo^_<5h?L^p2#96P!`iuMx{<1JVQf5Jig9 zrsRGy$?}~@djjVXX<=F32)3h8dTEYRy~;1w+$nuNT;*{R$+s}Q-Igr=VVV0bEi*IL zAvV>t@y4dZGhX_A(}?4qx>1SfUoRd5{u!rD{%&BKJ)cGaIpRB0uDSl+t4^rSDFPk{ ztQYp{6;=%We3E$nWhZG0Vi z2OT{t$@yC>AzPnqeeD7x#N5-o*Ojj%ECHd+#zdaX~L9^|Ak<28L9bS{zj1Q(qS zC8OgHde}k|P=<9p?ZilLf<6W{#yECpCBP}7a>&Pw$yKM~#ta zIMo-q>$9UqRz}4_wl(N7fcK~+oT%x(?i!CqqEfa84YevnLc{3sdr;V_z%WAZk+dja zj?{UU;~F_D=ct2#TM`cyT_B$}o~<;d$k6A&<0~%?@b+1?9pZiTG#IY84=d=IS+5Dv zeQ3RLBnLn+vNN_P0=@Ie$fa4b%1ER|+H=?^N2lQ{&j4mF-XIx=vh24BJ1=XM|M5D1 zh(ao0Dxz#%Bpz>U7{{*b#CeR}Wtg!kkV#s^3}k@96lVIm<=WXuBL66+gG4Jh@W^A1 z_e8^ENM?=YV>I$r2Xkde-2+J+aXx1p)SZQJJ5d1auQ+53J>Chb=ygm+bW&Xjl5T9F zBElH?SeQd7hWO2%6ejj*2#w_ccF2pk9U@L~0NmQt)XRmuijGI8+Zqhxbe=C@yv_71 zDgOE7DJE7`f4dcRZ#^4hW5AES;VCzkeIc9Kn-PEv&7%Es+gcAhh~(jdr>WKCy!x6t z>)iHC?ZkP+zzon}#jzFB|M-?~hIb1IK(0XigD}&3W|nJbIIM?0a{AZcE}&0-G?E(Y zKvZnu#**gW>YjbNh7GTm|N1KYzx|R%M!GvvdF6;$f)XiYz0B4AtE+wqNl@Q!Kw{ZZ zG)jX%&I#Ec^!WV;-Lh`~8fZ)Z?pE|`;^Xx1o%r9-!7LuK9zR=paRY+D4T|<6aev>o z)&I&r9&TcvBRZ`J2^wg-)8+VICiDN}G^PddKMP?LjpovS%>O!kPUYm&zxlMY?tA;l zi#qVCFDZQbN*tSD2@0sB=_NodAM_Es`Pz}5@=yY^Ooli z6OHrh6Kw=8q~m+6E?N*E5H&|4Or>w}ZLyuA3^$2Mr;~}?9xmE*$NKhY|2)^S!f_y( zH|BVHjL+togNr$)++;=1f1akdfyIl7E!(K_+k@;7cYgMW>2clf;tjR};z#bX>yj=~_^2fJ+M(FB){~;0^+77{7cV`R zVg6!<72FTFNg6M5B6A)U0ucfzqPj7PkR+OFbqf|PA~ti`iWS^aQc@tmh!#B2U-h2QV zV^`qod{Yk#COm62@{&|_XdkGwfwJ&1G1+Z7oeS(=+#6Oi8Xf9=G8+73c;XE-vvfk^ z2CINA9H~cTrJvZgoiRK5AN3#3y04@;mxTqI3vAbpbNe}Q`i#(HRk<_cm1=cQd+x2` zV!YJ#_VIo;UQ}%>@i}XL0AJK%OF89zezkeyf!Bsav(2)Xa^h*1M-600eoNacWc=~a z(wy%~Tr&RN%)@$_5800_tn^=GCipY=&bZxsHXiBudo^HhM${I5O4CRK0Sl|6tvY6@zEG!lnA^bcq@bo8UWNOvm;0@(x%P~DMu8-N4Ml!Y5lwR z^1MmmBGKvzK}$9!NFmJG!N1@((q1}g!WT+}vUqExHG^Iq20cTA6jt=W<6Q?~?+B`H zqQ!{q3R&-2oMbJpKQi$K?n<(zQO>TD`@FR9*Y15C~=D<`z$c!;=bVg|d@&PdJ zyAt{o0uaz}YQTC;20ru-CMXv(9EKuMbOt1BgO&oA@L`yiSYvsLsEZ;MLTF^QejuaZ zv4yU8h}q`&XD{?o%Kzp{Wez(&b5`EO60)C zfpfbx9i2BV1$?56l7(6D`7XORVP6#dh6<8oti!0y$UcXq9k_}%6>7xtwDT=w%v;m* z^LF-xXKd1ssl7iX1(xqW_UH0*h|YSOZ7&iWsjg*M^F*+y@M?6lNu8x;x{!)$y;JME z>_6u|-l*{Bq1pOBem3V9aIU+QyVvfb#0K^a|2XSZJG=H<>j{6JOi%2gS4!qch^mNK zr^fK90QNj8GF9|E%ImFBQ<;`NyaHqwNr3KjuDrhfAlY;clF88ViDBHoJpxAn#+qp@1$|>Q&=Q9H_Ewi_QBff+&j+O z-pgBCdr$Z6xO8GjS!XEu4kNYsLr%k*Z>mlrd-oc56ckz6SjnhGsr8j*tadq=J_z0V}Us81s*aG*dey^%+u=syupk307Jr#b~v&J&gE zSUbZY;xOP*g(jg9DGE6WZHv{8C(|jI5F%~xS!$@$u{U@`_681U0RthT?stX~N&kp$ zMa68*% z3L0Qp>CoHsLXULoV0>w&LEtSe1TfI@A(7LkPY>{29-CHHuELJI~}9V?|Prh=GO>QTRkSv)x{xSS=o-li|gTK6gpnR4M&hM&(dt<=a)aLR~); zmd;=#<@Q}OyKy_4pJm0brQ8#homgI{!Nvw3O<)vdJ>gce|drhH$RsQE!VAnWu04c}e<+Cv#tTtFUaf5qi?{&pYks+%HT%g5YvAb7X4xY@P`2fqm6h5Uz5AHbXue6g50Lt2RT+(tab$HW(&|9mJW ziJ~t-)x){`)_+y;Q^C>UID}rqGFmXKDrK^B)k4gNq{KTFE)J{ipXrG5!5G7P?H!-- zzHrsWpzg>!tC{YPMC);28-9V`1|DXC|M!QC9pa$-$Q0?tQ zj5kD&E6wX;;hYHM#i+uIEO&(K6XXLZTDh*X7 zB|k5}Bq5gaqeR5Ydi&$Icb8;XPo$>g&W_{D|7)W+jl`liPY3`?&s&yHqdaarMqv=# zr_51^7uN}#5|!V%jC`$MQ^~oFj5^GnJ{m^5zd4NoDX)$EPgBSMA`|E-W9Phm|6T!p z?+WCN(b!QqD5MFit-6hAAn4X9dL9=jjCdoyu;bjv<}5^pC{P+@{H_g5g8(7c)KMEv z-YD!mf)(J5RBvo-od3rk1l^OfjPfi{9LSE?IU1f3%rE=6`yYrTlm~sKG-=)u{Ji{% zq2ly5V|MX6P5eW2C?X~(kwrm4p}X{vI|_*iTzr^H_m)0@V%Q$;eMweZp&@uo3ko<7 z-9Q-oMid?5@Vc(7)ICNR7F^^+1LN2vM~BNqpD?X^&LbX2{a(L~cGjXZD=()O7gSsE zT@8W)v3_WrCym0wpK|GxBsCy@GzBOQlhO2_PMUM{2*%~n!zE9 z^$R_p*j;0|2_)H{6LfLz`X-sOT2j|fYjVQ1^nUi;XYD=DZ8*RAdd7z))=JFPtfnc?zg{$?XdQYnr#FG}`w?=g)Gwq0x5KIY@c<6h00!(l5D%-}^d;@2KT zNt|J0LQmxV{mz;(OH{3jLo@nOMZnjdK#(W!8blO&fh9nN#DhtXa&JUn>T1ZeS0v<} zAdl$Opj&UstV=vg-re&Ro^pj8uO$^Q?=ccfV$;TfO3*{fdw6eeFOx;-!GO@z0NbGa zDsEkefTcvj)KE!~Sq(BkO96`2v4O;fGEXt-i8S3F23V++uSgR(Nr(i%-`xK+t(|h8 zz`!l8uCB|s>z=@-EjX9?EPVJ4T8-%uj4+L{92ADHVESn%A}**A0*mF#mu>~)*x^Gz zCfo%0s|Ueo6LB&OTyo8GLSm7a8HZ^~_n~4fYHDiIAAW-%0s_$ruuyTZ-APWi_8cgu zDFx-^t3(#-YUykmtVzRo@YeyL{$aCpBdvqvUCOuCuHnU;aB8-ajKg3AXR+gkp@;7MbcOBwrO)D`&6f>+a8X@14Q;^4QwSPRK>)!C_$^s6qTJK4=ML zh({ePa#8srrG3t`7?mp(IBj+RA6A@V=?Ghp=$H z__u*|pUBL|G{N?d{!84`(~}kkGa}fr13Fi4rZd`}XbxTA${JW=eoU|!dy94kpE2p} zs)6K>j(r?oMllVSX$~}GM#g{wDar`5+kI4RtsZWtoDOfA*Bm91^crqh5DmLs!u-rM zA0C=Vx?b+O`i6!|w4W?x{8%dT5^59&48I)N=)fo@^p-2bokkHp9tG^Y--8!IqpUo* zOiQHL+oiv`c_Z``BBG*eCQ2s7#l_{kYr6`%C3a%Y1`_-aP!13&XYltmUbuaGt?>JY z&TL0l*p7ut2D%LQ=7Z=2E$D{EIHGCFm0I^)4@;HDg45n!AdSxsc_%| z4u&}^`$+NdduhGyo-o6nWL~LQvw`+IxZkF z*8A<bbT$MVv;?mkn>uS`WHfl}JNh(oCNhKK zIM2Vp&NBv+3Kajsuf4l_bK8C^^~iA;CCU(7977N}_AsU!x(2g9OwQa+pB<;WfA~U) zxcA*Dak$5WIc*^@bzrkk7;c_~*uDkZ~=tqRHgRUUk zEM-bULahZLRs~`&j4>;f%fD0U^!cZ)qVfetM=TE()Wwz=eFqO5c;}BPVtfLdt*@Vc zx#G&*8UV^@4j>KYq$k?h}9RnndEE=p|H8ai%L zAS1Ow*y0}bDCChqAt>mXfo$$zx)+gK$80cZSJod14Fob2-fGtR`SU{*@G*2k=I~s~ z4|$Zm-{QSOh713m;=$U81=0J{TU|L7bZ!)qKwc#Lz|DmDKaFm(?b)gp-#^or$92XT z&5Pz9o(Z8mydp|$jBf*(%NW5(IF&|r5}?pB~{MnKrIs# z6Xr(?{@%BD%HqB9UA{@ab6a>HX)c^|8O*d-`yW>(A_UxY?(ZAi-h}uTbgat3LqmF5 z7fM@?`{x;ZMm&w`05C+MuA$I=*o+1FQ;rY>^3WByWZ=MaP%3r0P3 zI;K7_j}@dlHp`87?1zHrjt4jQtXoum>Haxdy>ci1&Z-}bVZHgsRIglHVX>Vkt`IRr zofNw?hMW1Z1QVY1qhay2!^xmsledL}Zc-@`T_Pvra@+y< zCGPZk{Rwn-9@hOx8c$Nqt1qg5L|XDBGAhaR7UV;+kn$09KL_-V|7A_WiLe>r1%Y z&svjpkBWd`6W2fUbsp zimOpl{f!mE$#WSmU0E}P?HjV4wXI#gZ&TAO&R8V$vo475_?8iy!Iv%Ebu0G+!>y*U zJ`l7dK){9M*b!nc*Nt%tESSK`QLGXe6f8F{*`h(T0 z4Y(vY=0HIqh|$uoL!R1J)^w`fs#V6ysrWWHY-PQNQC1m>9I+O-HF*k~kei2x2bJ{B z+_T}WDQl2qzloq{OuR$r)7uZ%uHWliuswU&n`tuKS!xnG0X2dAk$d`WT9Vx{8j8B> z%20hlwk5=i9TEy8#EbuYxCa~E$&pdTN#S8gt2;>OU_)kfb>3drpIPCpT_wA`y`Bz- zGQ6BO57euKjA_JFhi?sj_9Jb@kM(hCa}TB%8$HQ6Zl!uQ|Llk4@+tKTHuq8v&SvOy z8rh@IsbsUgV1Fa#S8n>MS91dLZ|G+fKYc2=84H-3^4gCI zEmDM(l#;0)!;3HubrXd_QWzG6ULphzCk6HBB*O`BKz7IjbU4v42S7~hOEU-%QjHj_ zZXC#Wm<|pOt9{r-3d(Sb*~``$AU1%D_8f%nv5;!*TD6dLffQ(f+lpe7!+%244(T%= zNvhGCQA9t*wx_41(NJu(cV*}=X-W7f{V{-v!)OL5bcu=piq^`?&L-#liO1<6Pm%ql z?LXbr6{F}VMQvXR?55@IC68$(K4yZhUO0y=q~vb>I`&8D0+jLQXIaeW;D+0`@{r!0|8R0#0=u^GlO*nkjLTRd~I0v5cE+2 zXtl*CWa)(566ovzzV)D%#$u$jS@K&0Ix)rx9itp^qhNeZ*BdL0kAhe9DBU9%b?8x7 z-V+O#Fl7uwED(tj26r7neydIS4RXSoP?#gcP~+G!F9e!Ul04dgLX3|q9%skCIY0Gc zyGZ&U8F*$%eBlVkW{Tg*|6oq&#CjLgE zGuZ0Bp0xU;G`uB$)#}yLq08t)XvjfI?u@wp7gmaxb?=tXj%23`S=0Y0?Ksa%K>pHf z@%NAh2?CJBIF-8E2aMaZ`hTa3iOZnrUBRcDb-`1ieCChpbBe##QM1x%qrPANIK8aa za_-|egB??br>chh7c=+MRj!+k{QioUkKAXyJrf7g<@OI7p{XnXrG~i85&81pAPmNy z|CYf0M1;BT-@o3i_`k^^%>SDl;-ob`+`qr>{|0T{zEIXXH8dmglQp}x$Y#bqyP$C` zy@vTOLM^g?4md|gHcWIJI0Q$TYJIMzq7Yy7d)q9vf;xH6#RW?z>#yIcZg0^LG^sg1 z`^1eTD_^&fJB&;6e?TWd4o@5!C=QBiKQRmqA`;6=X6PU0RW!=3%JAsVjd3Vuu$mB<$+Q|s@AsJ^UG z(&_&8SgmriqhsyjK9LX?t94Hdy14g&wk7Lt`H{to`HtOp1C~OiIvuLHbF-Yr5-_wV z2BK9cR4L$#XkgV3sHQ#uSmI&Y!{MW#lzb8CDA9aQXW6qy!+^nG%4~PhQIZPssl9EDg28`LBxxnz^zo z9zIJs$cBgZ+~v4_s(D}BK%3N=B)*EXn{S`aNnf>`u`z0vUS|AxSB}yQ&QqfHZT{zL zVP#ri+I`bj?M=0gPiX9;6);X)+;{VfxXYk>`zrf}pC3d)*Hogu7;e97N!kI!p5hQO z4Z4KMiB@lDWE2&k>quy<1{$EUMHj%o5s`b|q8x}$y9`?^1lXj-R*yUW0JWvhE&?`& z-tC@N>;48MuHLi8>@2#RX%ZH?{}lJCk)fe-xB`X&`ttAGSsMNhdJc>eD?^#H{qid> zL#Oe-&t?J-qSytjsaVL%sut&;4oc@yMy^y383Gafj_M~xwGB9ZIq<;>Tr9ndS6(pB z`WP2(y3D@(?$F}c|42)kxz9v3h7PWMeuPf?Z)k z0Y;omL^*!WqLVv1F(sGmfux22e`yL(GP?0X>TrE>%kG2ksm4d!Rf(p|re}O&!VJGn zSTzv#hw#O2LB<*dG2)xk7<%$?0SlL@^v^jd233ln`Nt=!Qal)d`~cb9^iU0#ZfviW zlmRb(w2asjL=OVt(DiPG>=9La0bUC!D3OjiEhIokOPT#J-QT%fIx{s|?$Zy)6ob|A z;_D)dXH*N`H<@SVJ_}F#(p4%#7$GH%F@$&t-Tmc&~GPkIUKq!`jW~??lgf zi)7dUQ*CsOnARDK8Ozdmu+utT7+pw_e;g6JM+gK3Ix7fE(j>&Cd3P<*TcUFynjs1t zpC&kGL8l)O3LTv>V+OHTR15}vNPdSRj|PBKL_@dy1fB;42oQdTm~4|>Lbre|>x8Yg z@`pc>8I5n=eO}=E(Z8BLD!%?m4B#)Wo6|_*XK?AOOh4JLGYPCQMs0u55?G%*$aHwl;Ul#8CG|(wc|9FH{7^h{Y@<@ zjwVjbw~If%g%wksTw6hC%;lKjz;(3HCeRjm(`YVZytn9X^6(%of^s48aHu<@w4(>X>gdSSI zM63*LMOnQ)9HP@!e839`A1z7nN>(dms{+8wUAI+YEY8KyLu8IUaGGxuk;X*9O zIdQ8EvC5PQPmfT-;ww!Mw8@yX(TKuFVJPEq-0vP-2jOn2O>1s-2U9T4{+WH zA`aPSiIC>?(si!iG#l;PH`fjCNwSCvypL@F$;Rc}#Lbu3$c}%<888AEYmLwLF#ss3E|{|>D7Dmj zmy_ZTs1>GdV39#Tj-J}FN{~_{G2R5ewQ{UKv=LDY^Jj}E6gsq3d-)r*y6%Zc~ zoXL3N>{N|O$7#oxCmxf}Y1`0XqB?`&AFGsf-~UBZp?=*Z>rdk+f9_vD-}8yG>dQx* zbs+qQK{!$9Iy5yj9UPgGT)f{eg));Z{i4iYKSrr*2K7)9!ThNVS?4J@AraXtS zfc`B+n9LM=1eRTV{v2%xo;fRHy1mVs)6 zudnR!yMzmoa|iT&Io29>nHkniiM~x9T{B4rvP8+3mMY=j0$@dftNIy24=BqgPq;RD z%_9;2ywD{e;(dI-M!Rsd<6HrH=K*dQahaSxNmv&^jp!yg-*5%;1>&G?Kzx8~qs1KJ zk0}vm>BgG#TV;P~*l8dfjxH5E4eHmy`r;qtJuv#UbeP1D4DN)O=BkQbgKJMkS; z4GBUafdK9)-~Yk$17U@U&N)cNO@B1>BaTxhMT>M*;j7~4=tOFi2|g}!pPS9cy4Ay) zi7_{_oD}tTjx1-0r`mnr?#&eHuFWsJ!(dnyHl)o8S$xy`)k&`?Gmi8Qd*zT_SBBq| zrwq4SPIbO()2>>)K(#ouhUZs!wZB+a1`iY;wb79n7?bgA98t#c^D6 z$Rhr-P~yw`YcM^rz++N$&d$7dfdEF(HB)dg0Rq_CloX6RoIG8D6)9LGgFz8IkXVrq z9sAc$*ix5O@Ms2w1mNoHxh33)n%UiB1lvcLV&8Cp(7jUd*bOqg$IzP5G>;KzSSb)S zFA0b)sS5B_6jH*P`Z6@6f}4sgDmY3agE{z;r@7qK5W`uKr3F5uAkzSUf{O65DN+n@ zqkJJ~T-tn88r%yB8Nmvs_$=-=e8WnR?jB7>BU(8>28+n=g`#!ystGEXybVAy4 z;04YG*^{>iKoc!D6h#pLIoH;;wMuSXjvRVW7W*j??EHTeaBSdWok!9zoPU(#KK{Wu z3xArNBUB;d!ZLIWVV50uK;k&f>p-;(f9z4TD$p#UB$FTCcSOHEG!4@#U=2Y@03P$| zs2;i*>=Q-2BH8EtljUzEXe5B$cNIm&IUl`O-t|{i3u6mT8htju&0hJzkEx&U_&e|Y zu$AO-@*9-nc(vx@q^H{FS&Y_u%}d>FyUg9zg$zE{&E%La%MN<#rbV}(nF^_SppRni zg|ym;#qk5Ojm`(;rL-Pt%emjo57Ze~+x5Ptre^PUC)iugI~5L#^0Fsw&__W0z2{S? zG`Ran`h`7e)Kj4Wjm!r)mI%GSS$i8w2a4W-Bzzd}w+K5PxKcTmf=Xp^VY+tNHqLwZ z?osl4V6PaoJb7o~McU&j>NTCK=1p@ieCEbN@uSLXS-xHC+1qe)C=9&AL2P|&y_D|z zgcwPGO&=Pel*Ekw3k%oGzk2gGoQ1;}zZ?ygJbPHK zX`1pHqXY;nTuOjwS+n-`-!q_w*$(0s3Y;j~0{<;D(^_k$KKGtkQ9{XJ_wxJCSxSo zaVwDT5W^F^mYsLkZf*YM_Ponfv9+Dsz#O3la{!;p^0@ z9rM?glwNLizAWV9h5p%$CrVH42RdIKab*d(ph|xf&g`m)*rN8;HbC)#bVm^1$wf86 zjP$Aa?unIkTUEC>#N2@}EV8EmXCJRE7I!qBe=KfeQLEN#1=* z*WMkwM}xL;KLxaFm#qHUY_kV_4JvvcvJ8O4>BLTvvy}`I8H;;Xbj@9UkxJHu^LS`Q z->!}TyS>GVACA06Tt6Bs{PJLT{>Aq*_m(-{72Eym$ozyu6st5E73%7kFn>gtPYcw^ z?}fkPZiwgB%!z;SPl)7X;)GhxtITYmGMm@3Pil*SuUop+T=&)uiepU!BI$~uJ+Rq-XH4Ex)0_HK;TJQ)vvj=NP)L}5ZfNiM#9>$0( z50)?(?7}V)-)*Jl2yT5~FRJ<>r}>XXVNQFIxxr}lpSgB&xiybgXd{wD{Zm#qvN-8~nQT2tKuS#O(9S~eC}XNy}U3C-|MVk9E!1KE6*^|}a9gvUC!*Y_}Q z9fh53Pi(zAg!-G38osvV-%=?E=i*wu6LOM;Jp0Om4Y%;)IfsKeMnKelvXryfUVB@Y z$Ygq?S+&Vz81Pyij}izpM-d0G$fjwbBCn!J08%!wLLk0Ab;-(z4=P9z9wb z$HhM>PL@&3BBtm&D4D38BD5W4Gx>2q%uPO!a#cC*s7P{gDf76|)k3duH#@?bi% zQBDP}|9)q4=EUECSg0RGHxC_XQ<{sr2-frM4BuJ*;G8ZKPz=)Li;7@!?-D!B6dO;^ z&d#PX43)Vl#2{oQ#^;a3StNLH-k-7q*D>dUEDc1DLLo5qp_CIp2W>ex`{N*w)$iVm zM}FJq$^G*AqIUbh1e$*Q&YA|IW&nsP>}b9JD!$0%T#uDWm5A$Bt;??U#cZ7ydJT#; zcM>xMB8l`K04yVduSU>l2f&&jViQpn-SwqGzO>7jU7*Z_E#R5WLhqF+I3;&?Px0{q z@&!?0hV@~XA00@+AuXpX*eRupX2h{*VvBs?@8^$EN|+xnS~-fu!)ZM`2+jhwk2n$@ zGP}^5Ki0%G_?xXu(9%X{^?_do$NHI5~8sYR$Fj# zO|=kggYH0{-X?H^a(S1dBIT%;KFwy8We((+ooH%tK~53nrA*(xlHJ&OOqLer!lOfx zE2M6}$z@`z#^%ZX06UQH9}Ma)$wT~jTd4XHRc~r!rl*r1ogmUi*D2?|nq+C{$tfL{IMW7@ zW8l&*gl`B{2;8wBm&J6=WndwsvI0&-^d`#g3YZ+xvc{uKp(d3LJ* z@DLs(#lyko{x{wHm?J>oHm0pqJv>~X!Yz)fpex~~kDt>&cyMTZ(FFRT1FeXZUz=iH zql|My6_*!iSIW@r0s`h8j^4!-p@Tu9bKENY*Dowjx_qi?@l52kXpOCntD2mnO}5^} znd{dkz8F36LC)7?OxuxYrd^N>KjdEQv$sMyD1DOGyU;WN;6!WkW^^G%_2d zrSk>yNtbrF1u78C*SZ<`f--u-TkmW{`0oyFOZ3dqN1p53>%B#IHAI@>C?bg*rch46 zxMl*|k3YE&JB-;6Nr+o?L?G55*Z&`_ zeLkX4^i$In1Cdqb!!tyfVbbMns42}5IG z@RAT_$%oiqD#ow|-c{skL$`cJ4tUsVi-6@M0VF648<^;H;2(D`U%pK4BtR0FsyOwa z51?7^_&W|k2v?`4vtT}8f^K#Uxv79r@@Z`AbC`)5P{=l7HUmLWFA946vmzR zb~wG58>J+B2NDdpJh2O^uxu%d`^uFo1m&a8RE69^WvgLGjhSY`2@!-WyzBi={(U4d z#GoDwZli+&`w0X<$fF5g?`lO63JfTobVeNh&^_4BTib7p8u_;tS%d(Rp~st)FF;59FZ|ClIwV zc+OAk_L?~Qo*@npN(&NJ;0&qizBqoW?@weY7?%ZF{CY!@TJ6Ih z{*PXO@jpvQe(MEjb-8)S${jtXtgM_d(%ai-uw#|grp?Wzhu9GH@iK3=gG5p3Iefaw*G+946#5$@;?{HgU+legRF-M`F8pBE$XH_`7_@*&s34(&!UHH$ zB8>qG3QCuNV__g20)hcZr-Xon1p*36N+>EIAs{FvxsMZPezE`f*4}@7Ykhlt+qLGM zcNCwv@9Vy<^E~QgtY51kq^h95CO~y8hfDL}?a&*`ZEAaNJ4^g!qyKZq-O974y};j2 z%r$0Qn7XPmmZ34y_SWW`)UP6WvwC|YA-P$SAqp1?xrvBGZR8si6ogvBsOKExQQGuO zi^^pjYa_(`_IHf`ZO+|z)?h*H8>Oc?Yz{|UcqUAvFa@f#qx$x+by?0n%Uj2_3muE56g4ftuZ|3~u!750vF+i!=f z!glVA+5Ptn_zn+Y`r3&p@6auR=&NZ5hv~xTSrb(ZN6wZh8fKWtRCsuNTxL2kc5Lga)~>ENjLn59Y*)1G z>@(#>XJy|0n%UUu&2zVF*!jSU5L?yBy#_hfKXWb)S*RT;WI2tfhgaOim(IR&8x~KL zIcM+IrLJ_ikW;L+ZQf4}{fwB;@$Xc2ynWwnud_=7&aV-N&nAzIcWDV!kI#>2 z)6eu()QI!BsmkINuFSKW8VpoT!GiX7c83AsQ(n&C_vWelthp3bh@*s#y&`k8a}+=Y z)oF}qdo(eJ1PebJqj>H?NK;RApGaGeDQ|@9kbx40%MgEp28em2#C{CIj;jAiJ@u|u zPefXgWsb5VklZNzK^lAnu!lN$$GDg5u7%FD)ER6PHysAIwUzj*X9=onW`?ifiSR|X6I1>3*LDJe0|hh!E*Q(IHdW| z`>!84lMthQ_#oeNvW`K3qBqUAqgn_5Pkap4Tn*fi57AYcqBe|e02rq|@!I<*#Sx%o z8|-+usmgrWvSnfrk5Vq^ZN@ftQ2aO`DYU#M@w5b|EtVYt)?mOly17j$XSnzSB50cW zu7u`;NFLy%@i>D~4$fr$xqc>$6I+=4AMRx^f6Qd`jr2FKi==CEblBZXXf9)X_)6G9 zjlS!$R_CnLCWeCDq-Vbm5#ZWg>BMVKNn;@A!N4V_Bgle+yh*GXT*L#`h3tPc$qeRV zN`^;);Kw-u%6g52O$-eQAk!xl=m&qkrvy`AyN{N-1|o0R6krj_D`6k8XkzE!NQM+( zKeZXSN@M`X)h&%|!W#0dSR`HnxwmgMnib`&Fvt_AZ$yO^ zeWk8buXum3e*SrF&<~^vfs;GYi%t9i3Y>2+kF0KxS5xz)uR|j*i1NO1qx4WNMJ|9i zr>VchhFzXAyv@fmO}R4xdZFPWc?TtZ#hHUJqj|7YFoET2mHqa$Xu=vJvMIlrJe3X( zBeLI226Zq*b`Q49*sfx@&9Stoi@@sjK0PLh#{uyQ2Oj>FYltii{9jurq}+=(GU3^z z3YS9QAKfj(<3j3kkS=So&<7WfOh9cy@%-b~Qx*Q{4brcCUD?;IGk@}svVliFzABaK zzXw3z)U(U;eIrqB@&Oc38k>1Wzq;m6oK~Bd{A&4E51tuO`q|u_#4%R=b}Ibz`hwJ% zwsv(Ao8{*-UIF?X*q_|GGQuQTCitK^%o6n=QhRi>lzppXlsIq)CD)`$vG_hfrA;Vt zWNTLC_bm91nIuYX>+KDX>w%qe=%`MrSJy|Qnez2F^S?3eQ>?#) z7`FS^KTYv@iEcttZ6dnQIDB$Y06C=4!JZasK7|RLWSB;=uZc5}CL)2q>Vk7;TkgR! zRN2$xE{vYils+Gd%)!W*S19MPm1dyKMF?%Thm;guqT6X%zHP_O|uAuZ_2AmY&_rvL)vkf-7tcX{vOq&8?BG!P_6lM$(@1c(5IwK^?0 zUr`K5ASo~C3nUwc5uk?3-N4e)Qarx`+cmXyAyUqEA$O~w#;JkH{6LsQ%^3lFCyM3K zfT9C-cmrHpP4uc6j@DhXOV$1o=D@fi%SI@u27u2fDZ0LI#@}qYix`XV_szct)w{Y5 z_v*tQCWp^ibQ*f615g7gpes?#_^9e6kjL{HWVvT#Ykr88K1-zFb_sDy)09Vb?=-D5 zez|)sckTCBy6npedmf$p#Y3mwL@sIQZ=AJR9-buAyLs>B{@`CxGg-KK)bi!$X&m{H z=NaATMxQ18M_%Zz_$HQNx3h551|@Z)sCESVPur3K8PZUCs0IT$TSCXaew=r(9s^cp z(_w&iD7MQVqgq>8PU9<6TDDI5&|W)AOoCX3tK=WRhCI*M%VTlH;Hp4|Pd13`bf^x( z0Dof-h7dUy{k^TTtYNw&6qPb&agJiGXrX<=S?Nxrw`nc}wiZNx zyq6t*hhCa4EkMrzREs4WJ67wrV`rGe>9S^QH>LZ4{ke8+9@+7M`N9iwi!RjXif5J1 z&+ZWRSdj0gvyTyrWS=!t(&^*&nifAJy9U+>)IIx{W8339G$kg>5;|ey@REaHH8)tX z`iqP_!|3MK;e;sr(dw#~@mT`{D>vJ2k$%@enc;8CrwqrRsJ2H}>g`(Xz}D6gb_j`_ zEp;$C6eX*FY#JOKJOCQ~=0ih_3ehC!nDRhS3Q&e8od*~ljiX5gIK5PrC3<@uY~?us zcOEt>R^suaJrTe~7kp7P0EsT%^z@k`s|89GQt@}|pR@hO&D2nZLWgIVi&Zu>NJ?i?&63olb#$oj#|(V+fDhQP;4$B(Q9bUz*9k@ z4HURQZbIq&*Dr9@$1s2$j(uS@r|XmScZJEg8DLYWB(DkFN|b_wlgg>()(-NW(KJy! z7c~g1x>e;!g@oxru7G6_V1R<5&5=?WNGJ=i50*7PYGBPk>$EL@Ij!SwCI)gxd;Z!-%^(dt^+~KzWzPn04Rx+8iMs!%ml+h5K_};;3|6R>YWL#;*)KsK=p^415 z+1OdOZE~useW9hpJK&Tq;i2>3X5E9tbxRrR=js>Os7AAPL!4nWy7`kd@)RtAKVgY* zQdDkHk9{v^1=D4!VfL`Kjg4r20NXnAQKbf|{AkV)=w`z4=<|c(q)nO>;w;uI?m-mjIyB74;0psKl0_KhAj!X&YN_mK6+Gz0z7Wxz8kBu zh{DFk=8um;smQpQtIKfvxS7Coec`-5E}U@y`jA1&=~j#5yG&p;v(ou{UNpGhRat8n z%^`8-nxY$)$#%($|m@k9IiH7!C(t@r@Juc5IsbYhG?Om(&d5oH&*iVdledy@c{z6(1 zL*XA{?+6~y^VH3k6)c~&r8K(v;5gPuHBeu{-{t2z^V2q;2z4VA|Err5@~4g_ zMnpt#28VU=Ue564Z%2>~U7FZBRz|0Kpkl15q^oA3jWB{|T`L1?oa z$`S}2NlHyQS_FjoZ8!<^_ptP%0SMrv-k91_RuXE1Q^W@%2@6`#2<*!(A!~6gkxtl> zKh3`YaYe>(*rzGjfSy9cF+Y>y;Ho@Yz#Y|r62lRlFYuC3-;OZWcl!WouhEAOr0SL8BExdGZGL+PizkfJUO{!`8szvVV`3dR?CB-o| zk!8PSZaBuQY-}{}rXm_Lyu1p~4aI_ReQRym(q&U&LH1xf5sh;Q0_aoqhM_bWodK zp}ts1)*~vhxR7~(lgE#91@EE4#|~-! z!UX(K{LAO3$F+a!3d6Cxo(8f`KGLgxDww^lg@?n^(%zb^+DvkFi6Mo9S>L=;O*#ui z2zWZ44=FBAAO!VPWZ5{sE!ABXo zLC5EN;}kW;CNM4b4N2g8izd_aVp>ICNynG54M@J()ruF6keS}~*Mj95V_5~YamfaCv4UkbW@W-v3+7x3k(~0paX7&^U<6=d zaq;tgmv(9TaRvye?dCCAyj8a@YiiMlYo$#}myn{ORekHUM7}lTn3h_9$;@T#-X`5iYD-{-M%twGY&*s1*+q{)lP?ymmUYKje*4`_b+?rLzjKZ!OM9O0fwL)IALT zkemkpg?cl5cyxO&b2$o%jmkM-No14AgIbkl10Xij2EiG$(~xrt1wWT@59CAGknV%+B@Y^P znluaHU+X)tY3uWUcr8b->WdSZJ`#m|qI`iJ8$1lnj}5GH=9qh1b$CZBdR3 zv3%{?&~NG!V8Ht@9s}!JJi;wffiiBBqTe7pK__sLMgoBJrYKILU_kaz(TpV{QPVgcoEL~5M;)vK zMOTP=8Ad6t0C@d!=j@tGs7?j2Cjxrnl^^C~(#b0z6046kgOKX0ZOzmD`X1_soGKyY7fem2Ufplsy9j1Ab^P@T{B! zxfVZ`+_VhcY>wj&gDsP3^)R_JtV)unbkF|Fo=$yq@@Fv0&R54@|FNq+RKH3bpcjkR zBXok~ktI4SLzaboe$%qwAHl&NzH$NowDQ>(PWuaP zpFHnQ9*A`t@VT4x^}jOS&*y>3{r&6zU~BvTzTtc4kzM%KR=NiAyKG}W9qrkQOLrpG zD{I;U{~)dR*R(4>2*wX~o$~*mUZek|5QBeB@vF((n{St$nXI4W{YfMTF%}oY4dM<* z-_IG~;H}vB@^@KMBc!iD{_z`Lq30!kpe9|AvnJ72{@Fl#($5h3%RR29e;LCFlAy@ z<2|$4R(t1$pH)|KT-Oc7ny-oDL-`As(%Fd^kB7S~&mDr2t+lsT2*8Z;Tt{H*7-_r@ z6fU+4qpOW54G+McjNDx{=-@e`HZGQNWnYwPH^{ki4c%E?AB{fYGzBAX(H<#eXtxiOJPXha`v-k@RHrdvVYc5bS|(9|`$jC&dmoa&OqM2duXmJczAr zZBfF;rLW)wfe7y{2*9xVs*=CZqY>#tg(AvFfMnC+SQ-!1(a|B=c!X*g_a>rqaL`gn zY$vx2>OL{dy!{6?4}e$-aODQ*A}PZhl?*dW%H^pY0m4AhO&--vt$@~^eSA0y1>BGR zejjAWjShcL2Hw}I)>`P+-26>T(Kkm*!h%bS>oN znZ9PCr$XWquBpslF(??Uw}qcys|EfC5BJ3HslVPEGkqg2h3Wk?KA$d*BuU%$+Nr60 zLz%(vW4-KGG}H9Bn3X-cKG|zl_QjN_&ESN37t4XlHBzh*Hb9^|22pb|3A-l|?#Rf9 zhB@5c2>?ilbf&oqAQ>pc0x+1O?|x6VU^9My46xq^&VslMKwy#hV&t!aqq|!7Y+e-L z5jb}h0B>oFCoa-WJ8>OyVlE~@eDuZ`9!Lm3g$T^$IKQ4Kg`R2cxomI$&!2vrsQN@c zmfM6%*GB(QA(YA#`Y! z*qZ=Y9vydNUGKtG>L0{;MStEw&A+UNJuUR+1816=azaz0*-*C6*XCQVl>V(7saB$l z3*Fv7oX@|1c~8T5et&c!0@UKb8PW3{u` z{wdQ69-DNSL|Yomh|q%PiQ=44%~4+04$~bc%h`U9%EN@SRqdB5ux?58fQAdM2R-m` zr|rTIhmcqu<+XyM>#7N;_i1k|vO;z3f($O|^q9M*_h43v(V*{-O1;P&yEaYi1?DL7 zyZtk9`%V=Bq@pf3s*_jn^&(eni zKW-W*zi%HO79PS&JBzG%kV$P>NU?G~k|2Hv$c1ksC>-Jkv?qfgflVHVO%Jhf*Uq&|1Us2P z0T8r}!Pubz{EQOja0F8ibMd0uNgs6}nfuYm??~FC3bq5WvSi;b;>ckolM>Y85pjb$0T>a+WtS)ELV?g8NF*E)Ie~ECg|={IP0l;Yf-L95yi4c_QFgGsnghSqL2< zDBlFQP)~!_vKAl8@9Rk#pGg=p8nEqamEa=;^l#D~5Ah8V!GI@9G{zbc$%Sih=81*x zbk-f<3*Sgkl(!SE`v^ZP*J~Uz&YY&~kgZGkdi^0eh<8BOxJKM6l4-@WDeY&tcO>{S zCB^W?+eAf0%SZ!KBk{*T=diWr;}|dL)kAljYBonj>uS8SPBiQIl$6*t^m={Xj(+*m z?vR0)43;^aY92W_+8%#UVpv}#aq?cqg0J?K#Wxq99*tw1cApC!9aouVWl({@;n*Jm zqDNc*9E{UatVoZEiw@O#H~%*dLycudLvUu5kmVwlXWBd7_i*(b+(yz`MCo zJ;EFDKw$~gVc{coSQW|(UQ`p>rw}F!e!h(ItWTZiH!gObhHG`IDNjdlIHu8 zjt|F)Kb9XDW~3S=qHmT1;8T*}qSHlq6cjDc2azI*065g(I85lFK>ouUn_*StY9F`S z468=b;8tqXQ6ib}E=IBf+}MK+M5HdLI@j`hGcfeEI6|@v$Qx&zGlV*}u(mA1_Bd@Y&LDCz8K}|7!c@V_5F! zYq&RiScHTel{R2b@ zBOTJv7kGCh>Z?^*(ND50WE{cj zSpn4n4!KTj;zh?j*rjw}kAGwJ+oFqPTs{Co6nXWqQTh~~X*}eCgbD&o4fSu6Gdu#F z8C6HjN-7A@JaphbLE;ccl>`FyX{72Anay<+{?4!d(1S`has}WBgh=1}snJ{9!2t{d z?gV*VjooGWnl&V655kNgwqXF`iy$J`YI zlQ|3OMMS@JdgfT~8q`sT3OXq-IyrtaM$>GoQ-*|BbL6(4tfjD9s!GM1miHwbP+Gun zusNeQ87;l0(vXL>r%EI_Kfc6ze6T95XLO+0F-|A?@56$lvd_mdtKO`wOIXKmq)Bra z0Pb{m`?4-!XSZkyPm8few}$No^+}5j-!P9bnSDT;=YUSo#8<0+dTDM31|XBA_=M}& zYtG#ya;9n&587%AzaM|uW3wg}99tNP%V@`j(JN3$KMrePZF8;4(dmre2x9&bOjbPN zg0*?;bQT!&XD7NsA!^~r;|rM&tv5W-#{_Uk!}_20Tv{j+Eve@M1IS>*>EzY&1CJ3X ziXxq!eWcS;mdeTB8WSlh3h_Y^xU=?A9d877k_aT1mb`q)^=2n>8zMbwR|%iEhJtA$ zj;rIr&}n?c5F2v3JQmzsS6N@LO`1`F*&dDf&?oOaD+h*#K&@?_329I}U^k;dKiDOr z#oM$GB8;EZrwDvv8bTD1Wcl|f#C9f)sjo>g!pX^tyeX)nNC;SxBMLx`Vv_M-CNC=X zu5sST_1PL|JM|5u(E^anNzrlVGnHd!zfaqR@wvt6t$Y$P<0cPB7qKTPigls;xnWiv z7a@H&RUzPXjn=6?ZNX5H=cVt^lB%CM|3K*DV^&YF**T6xX(wvcp;ZJ_)Ao3tRXi}FFs$rAYb)0$$dS&8$Ezn-BjpM6U|G5 zdJuJ0L!NV1{|H}|8&;h%rsKSW-JRy1!f3`1QS(0y8R6hfG+M2H%wMkdl3M2XhK$Wh z|6xq$;RC*oNdvMpJBCzpFb7nW0Qe?FS86fIDDl535y~@2AJa=F%$5*dh^JAf z=#T->^q{&XvNM<^>TPYDP$59>0&j2}JcQpcOb=-==K{aH>XPzF`^v;Bs)kmHlvMaC zbmn5P(QS({@NSRXP#l}6Di^wyTzM3|e66*XwbY?F^X9}bp2&J3B=_&+I-vMrIp4SCG%AC}^WrQZg&;AWMK2%p^z@KC_47{Z zp`oDn%ZjXO`5HK`gmF-QnCE&%Sd18QG4vk<(G%hhjhRPP22#DWf4#26nY{XuF{;TL zsFiIoLZzkudlMA3W>wrh94UMxK*0zoXuD`S5HJrMf<^A-tV9ZM279!oa!%XZr$u4_ z-68Q5l*I-H6CULeW0$Xj*eaG<5bZ6ADGTd=LcBRm*N6{@EB_@D&B^pUIG*oStm`SgPtkFqIL<(B^(ueJ z&!)zIdZ7ylGs*Su!M8Z@^XM_E{`455*xZ6R!N*Fev%V707R^HjtZyWJ>D9-f?IY9& zoF{gj^4pBm-?>W4rOzwf1EQ4O{e(vgjkccXION zS6Sn|s5XPvTLZIhgsZ>OWIR=AxuDIl^qL6AI$2u^7L+ViR)E-BCP+Y)=XQ`h8xlOuI!# z?T6N8?LX$kx@xoL)o1&buCUd7oi{5{=9$we1_V9Y)w(a0g6dx0t0Gu4ufODkW_+W9 z?{akPhXq5TYHs@09ZLFexp}zXp)7UQE3AJ}+b#{J8p@;t?7pzw8=`i23kt64OfJ&A zaQHPuO1@T7!Omi7X=8o1{A4uvRz=~0qG$NKfNC~(HfO!%D*LV3A?n^b9YZ`Wq8Jfk z5|YPR0?3nEWGq(-3ACG4cnR$fp0gz2BVCBJF_o5+f*7kLRp3_t(QS~{I-!`wthBTJ z8ZyxE&@gIXnBW?>;4v7yKZv;`Gy(;l#~iz!5OO8ZmDWWi1ertoRP*J0w+|1-usvdS zz?@4@AK2ButH9orjKfSx@u1BF3NxaJVT2`UE(MH`jgpo3?r`Pf#tm-eB|nblWKwhPLrmg%96WXbn5P1mFMT@!Ft!dxZ{K zrKn8G>^Z;p!N*XS-9a0BbwtP+w0h{>tXnRFtFO!od*?jU=@p-fL7Tm0&6~b&Ma{!y znz8pSR9MrjA}`psy;`##-Tb?STb|rQvJV_9r;x$V-G+<2y<7%Uz@7Zkz}fvye?TZm zWP?2=(G-tE*=dVFYSe8iKvLg46xo)Hl7XVL;61;!NxzrSl9t8+njT=0^*d0i9eA>- zQ04?g0iCdRQY>}E{u1pIm!2Bw&2z*tQN$5riv4<S4jhO=SSYs#x3HMEQysG%hAVZS{Ab4uiU5SwAKU zT*9w?BwjUsacTPJ)_Yt@JE+^1shf;*xfa+exTo;xQ?H(KD7GOTa9LcBM~O+ayA#7W_kIt zi1$m87~!Hv`qiAi*4T^ndl{F8R)F(EH4rt`|8a`@9PmbPwlos!O}Lcq(W52L%?d`o zETp8%NPRR@)R(uM|A`1dEnL18@V!&xhVslG-w~Rq0FB}VKt+f#NkSi9hAN9nH>gR? z^v>A>sX(XbaQNV6*#&TPBe9AG-;&URM$D3znhpeP-?vyVxG!3LEG_dI3=;tr0#bH% zb`*?&?uACwA?}|7MerelI7wYfAaClVJcmmppbjV*9BEPt3AAX82LgN$*^a_An&3Br z#t{^enEC-ZQzY=k4oUWeHPYuF{~kJUk2GU|r9gBjW}JJ%^&1p>QB*|JCuoM2<(rEh zAtEO|iJgZ;{Xn>+Hs1E&&mJeV>!5*Ojko|3qp^7)qLbYKo7G z@ukKg?dPVRyZ$~h-;f9EK}3XLNEZrH@uDSjU?Y22?}SB0kz~r5#`cE1*!wA*4P_3c zzL6FuPJ#kZ`oGa_41;I@0dYz+lM7&WEw-Us#xytOpK)$&4s4_%zsFxOL>A@Koi{pMvDc|sFR#5cJpe`=Pk_}h@0n>1xYa`MA_B6HOwWhM zEKIA-9UzA<$g4eEwjOCG%)!IS(3e0r*M?b%c)a2vgCS#1cTdloULJrZq$e;bzY~jf zOOQJNM$-RKVgVeANTEUlA0lr3kQ_|F0Epm&-k=ic&%)`S&e~DOgrL-FsL-HuN@$Qs zU@*dlm~wkGv}n>LY-SVzOREuBl4*I%+3EgJ3ZLv9_5_+WeHu4_ZXv4WhxoZufXGaf zHmMf4x&R+Oh0Cu4H1y)7OI*_>9YzAu4Q-L&8(xc1oFoUP$U%y~L8n235kS+B&K8=M z*MX}*g=k@Amd%om`K2iUOr5t4_C zKugiiPgTG(4FzfLC=du@KB|PlpprpI#8(F);S0Ql}rQ=SPPlh7&0K#E@!A)fs+5or;hfNdpZgnRkN_ zzJiShD&m54^TzFjh$A-*MJ+YNB+gnPbZ7E;RYDW_fd82eaHQ0GAZ7lSqg5#76X=p7 z9a1O7=US)x9oOoe560l-3m&tfB3T3zAiQ9X>!UZA zSIn?_cXpFbq~Uyq(X#)_6N|?i%=n9*Nob&{&6OY8v&B2~G0$&FMjtQ65FCktR?bvN zXR&0`fBydG75e|kHW;MakSwayP5-6ZXLxPNKfm4AadbgK@e#<#IevATO)lK_@5}>s zDUbgT4k!5ES8)6>HX1>()SqTGMA& zQ#E*=U8gu0VLxcJBQVS6QnAZ8smtJlmL}^Jr=~@uS0(dfwL7 zKQ`|JlVH*Ni%s)+ZlUbAR@;<{s8dkJ_q<4+sgZMAH?Bu2B(N%Ga!{--dFGn2v4flC z=1A%Y`!f)Pz2g@~-x@%;E6vcN+bj6;W8*32p6@TGvl6h$QnRjOnO#G1j6)2EcyDnFI zItV~w)za82bA_U`?S36TBP5T6e7=1P+v+B(w93Z#tU{F^XvuiWam7{#NFl(6^)H~xOE&F~ zfw~Xmu{ZELB>zxF9@RL6ZPf|I0!ele2T%eC5=>?t1;=`Od#65nbCD9{aHu#TJc@uS zGJy^a4Z((am>h@58kb4Wo30}liEjZC$))H|lX|vY_gRh4L}OX|KT=|FeGZ7@Kx~z; z#@rlz7xALf`3rE+H6kV+;50TN?FYlJ%kcK_zEMRR!oP&|)8{0^ya#P=!Ob$K?M-^ir)PxUm5u`s6MggflT zoUQK{z4@3}|4qegV)AE5#^hz4g4`oz_Td4Xc@2pV10rT6Y*Dw4fdI7(Hy(umq3Spt zGeD_PG&S1!I-a}dj}2|8!GTV>Hx#8tUIna6m{&mP2}2fg1V@0Fa}&^=ILJ^&QGSwj z2&ZT#DsG~y0lh{*=BOVrOjOe5PUaQWW7du*QUDeL4}CnE3(vR4X39+Vo5j?HrOu!XMQEF|BwO?T+|jpx3RR?@xM%m3)>D6L-BNM@(IU{x?O`B1^s zWG)#~>5GG?tU>8{$E1-a1ke@3S&mTh?ZnGt-I0zINHm2DVgj|8W-e}h2kgF@DC5a* zjjP26ESQ>HR5lnr->esG@)9?Rw45;K()?=Bze-qVBmp2_6BaAoGq5`Kc8E=t?bXga z6U-Tlry<)!YFMTBD=D#)$xlL+xyVdmawaaCp$~$ z{gN|vsf~KDKWlTetL=^0Yw>;=&mkmvFfzr_Z@-DZ2+7g4C;Rzx;29-Ik9r<}Qe8+b z@aH7d;m6OPBC*c+nl$JQOB4T1nqeXn@sOWJFGh}b)ZS5WlzxoVM?pf38SF@8SwOZ$ z0No2@s_-JZ(yTr$iqtNUg=jmiS&cjoW5DkRu|m>->j`0G=+yD=`OhCbZrFaId7+Y@ z!K^(R-8b@9c0Wix_I-`B?5+JSiPyJnGxJ!p?7*4psly*P56$4W+qe6WKmYoDyGPHv zjr3f$wLhvI_iZ9qDCx3z`)kRnK-+Nf_QLn?SD_3n0>r6`G;$*0ar)=aOYZk#zlG2L8S;&aOp85w3$NT?Uv8|LpstzyFqQXo zkS+Tv?d)!zZ-2VoeLmq6>bw!-dl4f4_xgDqwTWwSMXn%B;#pn;EW6OBnlrG)~~XwN9zCjfq{Xo4t+*XT*m%FyX1c9(oDPx1P=Yk=-#+}`(fiT zD6WpeZO*Hk*)>2Ke#ahB9MWsSCd7csmYtuZGv=67U~cLqm?hz`#=t z>3p?G`ZpsvUncp7gqHzh%C_!oZM}>(=sN2U911r;j7FeW^j3^IXJ=OgzwiR%FmN50 z!^7DD_ed|E1R{MuI@%o&MXU#xkkDcZmxN<#Q@Q>&vufU`ycM84W=Kg%0TSA$R|_X9 z=(H7#b5O-+iw9AfhIwOxXm&X(hqMPE)SKM?pnIiuaD&GJ^%| zx%0ob+}tR)aU(w?x!I*KDR;vWyUE$El0EKj7WwwcYx_boF`7yc4SK3UYF@ zyW_96ZGCb{*hRV-_n&jUrs4t>-=!p1-cT;=u8W>j}~77VZUrc?4ile!EsyXQW{N_E@*;aPMP)HqXk+7J{IA`|jOSXd+$+AE+>fe;R1X#lpg=j}r41 zEO>_btA8$8@;4#|o}wq@KT_> zSA&99qP4PCp7ZMK;nLTyFUxK8Q5uTd_2Mpf-7nbP|HjXk2rgW>Q1-}?RbaP0)6+$j z0Tm_o%*-wTkG7MAu}Z?`l;^ND*5e@vXD(m9{0WFj45sHbST1q7J}@x2?OUT)UaG^3B!R z&Ewx2IUJYQ>$|mg|HDJGmMmSOy65zv`o9|6ZT9as;odJObMtLSQ{H=p$Xh=$l=flE zW?`&ey}EC3a79oV`!Ov5XQsQftu{$Xt;0?H@&l5jnIpFu{lmlZ_`F6I7K>e6Tu`me zhADZkjg3uG%j;LKb~`yq%-Zv4i%Z%N1YdnWe(VL|fP!HmX!fNz2VsWj%s<_5a2?E$ z4pWm-Fm2q#R>H}}^%;gMUhOo_aA}vgtvoLw5_&N=9$uwqz>WJqKc5S${ZTwGSe-9% z8f@fFI6>Mf>8(>}wEMrlSsNK0-2x7gcGk+u%B@gn&YC^DO?$qaOwOF$ICPE zii3K3Y_wOP;A!7E5TJ}*8@Ki)upFO|kOipl92^}taB=N>^6BZRZjm*q$&|V0fQ#`2!N`xM4I32IRV zB_-JtCpayd9{)Xm8F%4}7o1wf8?%Px4wmF4;F7;6*oK!WOU;LUy!38L%GQi8n?{># zI~o?cjpwok#7S%t=b>&!^QZH z1oF(9JGY(xAP&=x&Q4QFpL0OPu-VSV!-8oi%fZUN#Feo`@S_6f((Ig&-rR>h)q|)X z9>5^QtNLJ;hlj^oL^qy+8WdAc#_W*V*c>X0I%uDn89#`aRCu>Q(XPFE^(wDs+;Te4 z%B4~fq>*i&pp&tHu@xStajYDm-IwvPp+K@6={pKv&L0p<9m%yb4?nK4uj;+^(iJPN z;8CY&*OW9h>8A<8bPS-PP6dCN;cT}2MMerTHL&<-w8u`T0bXEJi|}CF4i3H#eZmNk zoUI6@PIWaXkHXPHPdygd?aSsk4<403`cn!oDWw{KtUx|Q#Ol8kec zQ>O0l9Bg*FD(GI`QuwQJr`;e+NamTjccWQZ7{+(}dEQ^Yrr^vOX|i5YdN#wzJYlkf zMNV$O@l@{_uVpu#pQT@x-Y+C`bEcMf1&5DvzDnGNZ<_t48+U)O(=I9cF@;)*J`iGR z*5gb?Rg7S&x$x;-N5t)MgdaPyO0JAliybLkJ+kxuAo`lmZBg})okE7>-lgk9lJOal z4k=F{KZ?=G;8#;ux5TxvTno^Vg#o^DpC0$Ti#si?A%40a5wR(gMLIkeB-zXhl^@rr z#cHrQwWFPLyg?_3RxvjkH#Zi8HL5ks-LcFQ#kjKB{qkZoq!4QAx0i*D&D zr!b``HSFngu?;>4|41ni(K|)6dD41iExKZV4s$`t*XETc#lwi&qQW;{y0vy{ML^wC7hp^xWh*(X@*;-24;1_2o5P@T9o4)Dxrp>**^!hBc8vB4e9yKwU zvw8F8MaWO;{^K0zwkW?p;x{T8osx(h1$s?Y2YMA$=Cf$g_kPS<2ueaP&q*^W>ET$C z=`Pz7JM3J+z_ILRQ{+RDcgN-lRO(;GXGus%ysfFZf(PsM>({u(*Fj{XqGpV|cW%da z?ybNX13zx1Ic6P}nJZPT(tIasXZwnqO>6k*S=yg(t#t>IaSSN1l`Na_horR6^*_dR zu3f*rXYbw_oJ+4)&K)l8YH!mE1ix;&w!TkMJm6$l)r0kQu{4r7s?7MV;je5#;~!RG zXPX``K~J(u##J&QG4UP5L(oVp!=w79W`fZ=8SeeZ$$1Re>&!BI^bNOf-=2Nr*0tpm z8Rt>aoR5l&sk+BpA}@>5DKRNxb%KYC>#)=6iTl?gHf($_UYVV`Z1Ggb9GBR|SGVlC zd8zL9`1(8VHuthNk3hL%rk;ulEBf-kg37*b2f*Q5nUz;S747QP zd;Xl2EiP~6>+OAEV8Dh-$)4?{`2G86fJRU|Z^h(5Tsb2gp;os4lvlY~i83zFb%u=A zT^QLamU~x)M=c=ldXmSP52YW!mLKqlYZbH8#5y~NnhSfN?vwr2R#THMR9zqvo_0@E z>o4vmEIXlv{m#kB31q9x3*%odf~}eMfgTUrg`;~+m#XYZP!C9`Q{8h`X_;eoIehom zE?i)AH)Je?IAJdiM%Sqcdt~g)M_-60L3Q(-1HJP<9sj<1w`0Y972^{puDKm2+G}Y0 zwfqyTy$)YSp6Y)&XU*`w-N#-aaQnJQ@nQzUMCgc)#%HW-Xwssw>M2_DN!8#FSD z5{6c=For+A*ubA^WcU`B``NpysEtFfzb{yKeU@xcc+uyzTW&~jJ~+k5e^}YwcyFpt8%GJ z50gk!`b)}flC7|K{$-Xzv}4@d+zJW`h1mD2*(Hkb8#vtPG=rQ~NV?;-;?OzGOu+-k zUklASaIB&P#VY82cfCIbH~A>EUOh18-)-)!9hQ*ihc%m$o{o)TH9B)k+yabL+*&;9 zC?51OEMeuvIQH#vOI3aysH#YLV$hEFCB$xV(&W z*4CGzqj{hXxQ+sITVwb-0OwR*q8NA!m9U<L;Z*O1En9CZG#`98k?6`3KX|daoJXhqg z!GNIOWy0J?OGn>t&s{bUnd7iF^fFVb>zJBXj^ek)+E!~+`@1LP4U^7yviRab$Hlne zH{mLwvKw14-IEbuM^?tMv9VuXl)%aDyV)9FL4$wa?%fP_8P|lA55Si;h>Nermht6J z$$f1A^%%=}e%4Uu9qkFFI>w0~I{FKH^{4OOS!d3iDSCRyQt}**OImx3K9K#fd4J$P zF~@DebTfMe9FUFWu^w21TltE*kGDPH>IDH?m~2Y&v1j#A|1F9j^?@GaA4$gR~Pb8tLCIJS#( z>3lHbe9}g2+m6i=5EOiw11FpmG&i0g+tzb(&O-S6ZfNg}jvc!Qsoluv=od5%DB)h9 z9(`elN{AvVQV>aj&=n&fw$SVqqQ=^WdA|@QKLx~ty5qIAN8G73x%}vLc3OU!MM+5M zJaT(@ubn=M^PfsMdPjg9M`2imt8Ou(JU+LyTmn9FAMgQcs96`D+%5Z(0D~jY1N1o0 z;K>7-gZl{&YSnAvupaP zcQXKeUqTImuoN!toHJ+6ykHlOYi$cpNlSZ%3-t~q?j0%Tzd*e{gSUo`FVvMAcJ5pS zxby|upo@Lzt+mi#w)B_6f;1%Q`9o?HYhXIqyO~edoT2R6e^e+aB z)=4af60ryg4FO5^(2-je*mt~T0T=xOdT}zTTsa<{oa{w+1%;oZ=X6ci1~t?*PtPJO zi<-E*hS-3i>=1%1=LV<+>aBNDB0(4q*XvVvgYa}?1j$h-n_(cCjp@Oj&>P(}rdB35 z*ZKbWBA^`X@T*0(u+0-NghM9lxZ%OR}Kl}C2~?F#z#b4f(-rZDtm+P*U{h| zLw&wV+=`R7H!CT~W1nM9h_gue%o+G&(VkUAwk`sp!Q0@danQp3k*w(z>0i zgC7s=jEs4{epH}!=PU&Hv?R#PnKLIPHI)WdyiGQg!6KW3MjRVXQt6I>p6>2XJw3PZ zq~3?NmJW4L`|rTV1GARDUw2IzbA!%7_@AJcy%cAnI}Rb`0|&?y2TRQs1X2j}edJ)I zVB+97N^*Mokp6cOCtV?W4jk#L#LSk%xq20c3jexdLpmt+58|2p+}1YR-rgR{#>M;h z??-IH49p(B1q27Zit_yfUhGnR*fPeyyG_aBPEv&_A|iqevUS+}pJB12*u;L3i<24e zn+bf)9e4LRwg$WsulmD91i3)8AH@_raO|~H*jKIY(o+)EDt2i_NoMTVE-dGIY)r8-UnPnTOsbOzlBKj5eWh;%c^M z)j6U?wS-HJ>PNI@`w_K#)WSl@^&zI89EZ0;aab^Z{4tNyaPO_c!rgR&5D1CRX8xv4 zn|4Y_Ji{Kl%dWc+v92;@v(~}_IsI$Jy6fImvlx+L$XB_5?%`&MI4yynL*e)?n-)%HtxzQnsAD*hZMM#VrK4b@9_aPccV;kowDub7=>HEiqU4AW3{ zZO?w|rtZ2Fg~WfA*&x&QnXX?vUT>td^@skLRF_zN@~|NeT(;a)g;k`C!oRPF7xk$-6Fjpq`PYCJZ?_`g@||J8NknI1E9J=W8MC)dxf{d;YlP)kysU?e9`gvG?wRl>)V zJ$nF1e6}1~-s{}_)E&pKdhPtM+~M89X$NPsi!52PLQ+?Krt%qGmZkIi`1$ASTFhEu zi-*VG3x!YDVQaauhc$;jzMF#vo#E~4d-ZHk&z3J8*KW-Hz1F4GKW0g78*60t4Lb5? zNn!>|@vK*6g$$3?l`CEIrLD@`es%78t=%V5bRZ<;dq3db&!hL{ZPOEu&15|ODSVY4 z2|;0dJ)Xy`XJ)QdT6QJva^!Nn^dz3yjftN+JLjW!Uc@0yyGrR{gCtFZcDaGrPdk!7 zfBU(d%e?1po|a6yMctl6I}Yjdg>@4jKDB?FxyyO3@>sLv_+uNc2K#OgAlw3@uG>A? zo3skwEd$`(rCq@y82|p0=H|Bm(nbJz>wxJ4EvrTnkC@mBK>3J5y5R0U7u6cRnRWYOY_h@T zivTbpR(K{zI@*vw?AsO=q&WSHLS6>102F&&r zMo5n}VhgyrZ)iKOHdxrOVN0EvszH19{az^_O-028@S|NpeYy#C6MQKtnVI{69lyh= zo{mEELIn4yq}Z9tOYyUo2$mS zpxVN7^xd#^N`G>5>WR;iHa+jws`2ewv}}osMfdAPTOX+#H#sc1b+h8*gTM$!Rd?)% zzs{6gRbMsy+)l2Tdj_Lu@T}$wtVR{_$(!48MfdE9%Plc@e2%4HVrcCdbMsVWC!qY& zFwH}%8?U|!PUp-<;I2}T6Iz0C&Ta$CU4$;+5?qWV^AzvdpPy~nG6&;(Z^8qFKezbs z495)C)OB!6uL4YSSYMhqGv6U}{~XwTB+&8}&&@+LTM_PXKvE0BYma1@tpMJ03DQ>n z>dzVXCcv&;L|YvJ1;~34PB;%!o0^shqrxo2sO0|9!P8gL(9nE_3}Y{_EHsFVj~qGD zYW+g)ZdRqIv6z|tsb+L$FV31=D`5@pd{pqf*L-TDDCes19gsMFvvc(H!^T?ojJ;+9 z*wDULBI%=38&a-aQ^<9YQ6JwSx`eN^=FcOTm-hlrhb604QQ`3g732uQ;ZPl1!!zeVYn7S60+H`N8-uYfkh>`F4bIvYuzpm>W6do@4|r#n{bCd!vS?Th=?2{X#}J}vRVH(O#QXU7 z#hT_f{ct@Q=P){uc;IBif~#O{sR#z#@Vu^WB|;Pnk?;oE&mDK_0lFOpWo3a^eb@8n z&HvuqQtv0vVALJdUtW3(WNt!R@&91&&7*qm*SOzrnGz`yAynqfQ^l=>%u~oz8A9ez z$yAXcm3d0$Oqqp{LPc|#rxH;rV*^5^Qs;H$-upSv`R`e0owLq5=U98~dv7<@@B94> z*Y%#RsS|H3e$!Sc>9$`>dMxoA?$G3dX_mK7@t)&#V5oe~40%6)L>11MuyneEt(1R) zx9XZK(j&!eEpSF%3dRBFCltI1!YN@teY&E|?|Vk0cI_A*q>4hvV~P1!Q2-nVKsi|v zi3`I+5`_l&@PigD8z^%X2hNyb2m-$a`1=@9Z8Ih#O6W5CzAga$$9v}WZ+NtMFDyO) z{nGrjUhy~A5C{Ad#%r(1!k1c*mi_n^QozE6M#aVs?C|mW(J8exG<3{|*E->7pZLIR zCwwItr%*)Iq$^9kI>&zAymosh!h@knil^~l7Ui5itIKrR95HMHb?1gY*QVa7)mQOc zd(0rI*LW+N)6OsEn=bznI?12XUo-H&MwdV5jY8iw_lhp+zqz-b+%Sgzop&E?(x{O= zv>d#qxiTGpwz})A#O-OOOFwB4XkP(t+tZEcMj9+?GH-38Q@A)Vfu%@ ze_6tpXHLOA-*~TkS;X-ZotRh?c{W&(y1Zykw__-5<~}>6AwCqZs-q?V$WKDI!?yJ1 z-^hdlU9(j^^3x*&@|FekS-FnE*~ehSp(xdgBW_zB+;9tuZ}r6~)WmQ@y}kYFQZu$}m(F=&ixLo9JsX{4zH>BV{+)wq01aofu-&UPXHL~KiuDMG&3g3<=v2!OP*FN-B3FDj{aGT6t zj?mo%nbd?0_j9yP%rg@nX~V{iQ3(lZGQD{C>RA$(*TFVq#6oTIlX71AeCm6p>sLjY zFd-ROdsxmS+ck#*I(9M+xg5o7bpJMU^c>|*)Cu!}dNsLH#(dwg_^QpYz_IQq)pU&) z>XLf0Av=4PXdC7o1}mthTw8G3J@+$9zKX(Q-@au_ZJLk|^W0K*YP=S0o1gaK_9s4@ z*N=%Hx!jPOik&-%M2YMVy9HjM6I}w6bqF89yEGI_IvfLouymvqNaF;jkN>(U0Q>37 z4}@R>*I6{F%^KRq+bgvhDALJ0lSa9t2=B6orj<4UoS(| zU6>ZxjAL@*D!V%eNu7HznOlwi?(a@I``&OFgIVhCW}BF zMIk<;Rp9&J_wCxXQ`6}9GCy3iSsz6S4Gqnp2i#}oW(+*;iSFS9AMGX9gJKJV_9Kgq z=@(5gu808#{r>IU069n)-&%_^h$}))$uU8zt*o?G{rt2RQbo3xKtX&o20Qm05n$dF zAa%*|<=(_9*!_bW<@?&Og9n?i%&PJl^vqV(2au3ENR)oxrcd7emwSK;3fpn0?EC^AGl$h5#QioG0%63Kx#AS}5la(4*sl6r5NKoO)+7AkocsIMvQvJ3 zO>ZvC1I*6}o2jtFu>N8Bj()G*GP!UReg7R}2g-wJ+-bp#JwDEl*w@OAL&r~y(H%+E_kSvS|z-GbLb{sHhP<1KTI3KS}0W^&%0e$<9{=2_d zQq>U`;sf$>9P(-Yjd341V_82nlnV@>c(*kT)Ma(~?>X@NcVc6!q1tdB@SK5K&DYn^ z-0N0+*x#k5#j)i>K$QW8yaX9Uxdx299+~f?)y(qQ)6up8>z*dHD*9+NRtL1IsvyVM z>E<(y@*5A*J7Af3?$IYyR0~t==gesnH*?mkJvl4QQBOU4_DsYbRCd$(s?Ls5%24t3 zo>+57+FR|f>5&b+)$W}w^4@ac$b}mtmIW6c`fUvzvM?`q{9f&O|K#inQ9PHA*$K)& zQ8z+cF^r1ls5UnCY777~Z}y1xZFHLq+1XC1{PoM%iqrkIbK)s!X!(7LH~ z46J^njB3C?X%QYxB-zBj@--{iGPfWAwg1iA-~^h?!_8nXWELQLQB@=nJaDGhyj&!6|vT%*&dkuPh!g`uG; z^!Pv?m(G{$qU-uGqTpZjQ{Z?p_$M7Uv{MO5AHNZMPbWR^Qb6439^;{4*@C? zG$n)M+bej~NYR$g)2yEA-Vr)=Jg*~gY&%#CANFSZ+Ub7Rx7~Dh8$GM(vf*#?622sw zZ}%P2UB_s`M(7=6Ku7wz9l!SQY8zw&OUHD0C&UM|^&b_HwE$l0o&cxoMH^8RjpjnH?V6%%> zOnm(29~wUiGst~c#UekFr@-0D)|Eeo_q=Yyh6vrN%y2;0qoFweXgasv38MpXL3L8A z%yFn$%N3jZofj{3c)-ylJBa)v@R!ijn>TB_%|$ZZ0gBJe|J=s>T_aZ<9=FmtIJ5Xi zwJW1WChjr{2o4E(SmfAlw||a12l1yD=Nl6DyPc*7*HweLFE1O&Rfk{bgV8qDH~bPj z`oKYhHrkl=R(wj^{S%T}vre4}lRwaaY#DLgc<4z#_^8Kc=DDQZT&)wY+1%p6E9CUi z(fyrDlQk5Jd&a#q#|*Z#9Qbv|^0xUA*5;?DRB1DO>7@nRCyw!b>HH_F=-2b5s~bth z*?E9a*I8l1Th{Z_tmYE^wKmvktd&Fn#gp^^yMh6; zzh&Ij{byMRL|5N_ejex4tOj@3(ZZ(1i*vu`Ce6}(J}!8RW;0#geOWhAtk}<;+cw01 zNXy45DH9ji9*8@$>C}~vEjxD9AXK+M2rISaRw_qot96t>(uPpyM)Q2ElOI6PElY#w ze7@?3`6Wo|p{XwpUkg8)a`tRg+TLz()Z7lWiDi~!AIoVBfq$1N-xLd|Es^VZ9qExY z*LgIGqURSTYf}`)zy>}ab||5BxPPlOpCK;my|>3*%$ay)UUa~x$7kC&HvYX&YxJm5 zaTm^N@7%k0qm64$`<8=gWLDfPHLAXRTD#GnpS}!v93Et5n0`9*vDrgS!62vYbsyNO+R3iLzNl6L6H*orN{c{s1 z>h;fh<~XmhEZHNw>0Mi(v*BQwaK8LBmxdRQwMCaMTHN085O`NF2BfE~3k+;UfwA+@ zp)CmD+nbpLi0G|D{;Z+;;|FAhul{qhOLXFOIBM>dSWfHh2(g;2)6byioWvX>Y2Soi zfduRocl5JaWdr11tVJIgPg20qf*(4?rng`5qo^o6ili6Ie6CIAfcP*fphJ22&%_qW zo#@i#-y;{d_PKYud~qkedyLdQJ@{++%Ns6Df~FK&MCLcmczUj5hl>|7Tz~YA>OWxm z^tnEyk8miCj2*)pvANjL4#=cS8(R1%Ja{J_uKl8n=PQ%~E z-#!XrK?k6sh;yNHPA*D8MSf`K-P@`(_g*UQPrLAS*&jYsSE!`N^*&X>`iL>u6kNFa z$_WMpRUIxQ8~Uq;$!=bCE)j|xOSymq`#&+AU zAFDcTzQv8NsxS!fq}AS?I}MI}UDrBi%I#!`M!o(;)bb?Y@kUNtpT| zwz*k&#h^2fpFQ(yfA2tBgOaD&XKM^WFG0NdW#}>y@G7ViCL%OyIc?nnXc6+dw~4Hd z@h&fKEEZBb>!mrowep=Oarw5#)iTr4ljgvQEa_~$T_-yi8CDGF!8Vft*{8Y?m6d({WA|^~Tnh}Cl+eq>CrUd)Eh!9qMR`$Qr@4{qTHor_yw3_xQFv}V zvg=89?==uPw7l!7DTS1taVebV*R|{HS+h;p z7hz)VKBPZT_T0yzXJ5I@kY;xI$MLlrG+VhbVZ)w7?X~MZnKLuK>*%!AWn(YiT-~{a zx@qI%ne8Xm)B^MQUcr&BrLEm!)T%m)sCwyp{I>ZLzwVx!7N)Iazb$aY;yNi3`JK9p zfpC)2y+b5%;xIz(+~DsP@Ox0uIo-xl6pN~%R}{FbG1QO?mbU8xuA@vA|P>tjy7Tr5RCve z){4@?i!03Aj6BdXWf9I~Qo@lJeiOgkCR4}dks%Wm=dAqPKy`7+~i%87z& zb!x<+Z9Y^;)`p_bzv9h`z_{{E`Y9wpjjjXw_25Avef z$wJP-EG{!%(|0@$x7SoOZ=cn^Fa?N&dd2l2_aRoo* z=-8dL?1%H{1YzoQKN~zLY`KiHRX1>jsDB7o~+}0bpGRC z75)@NXmG+Ds9H{)I>i$k@8+|ChssAWf8CosgmhudWKkp&6N_r)?1*di8}!ox)v4A` z%ZdRN*FSE5ytH}2%yiBFiTT#_+&vI zPdrvJ4P40@Kfiqc;&g1fp8x0@1 z^}Eo$ed?8-Y#(3Wnuf!Yg91DC1fj5FY`c3`F zo@VJjDC|CI|LrvG)K~@xUTdP;es(1^A-E+}X9S$tD=u`WVlWlEzNWDkrph6ufZoLFFLDz0;Q`>%Gpy_s#s7flH{t27JX~8pRdpd_7^SM^-YfagC^2_L0p_$BUAPHuKr{hjMx zr)$0c{A(T{oM^+Ar-vTgy#A5Fpubt?# z-idpD6cq^alFT(_L;lK3&Nvqx`MV_R1$c(1?!8*$|BIT8|FuW@e@=;Va$r*dMe4pE z37VIme#_g|WU+p)R*hbq|L275@*AoP=xA^!DNkmDJYV zdN|4#O$lAy@w@Az*=KBD)V+DLV7-}TY)cW@{H(U`$(3#;Cd;ZSeO775qsn1p%{vRZ zfOSp>;Hl#mE1zIL{{?<2z3OuR)Q{iZtRG(4FDZ*XOEelcnEMagI%3teOY5sYbf{Ze zUZu{o(CyIu$Lsh)$Bx;pzW3AX%RQzJY}LTEt!3=6AU99pGI7oBoaSq-&TTNs*7gMD z5aWef$!w{tn`9b0F&J1V1}a^}69ZE8)2G1}uXOeR2}6Xa{?ClR`=4JaayO;sQ+t^@ zXREDSzh0_GaAhHMyBHfkT;_Hlq;wZ`|DEXQDr4upTkcG}bW5%cK~q0{$~KQTU0t@K zYSpTWLRm=Cc@00;l^xy73!E=Dc<*|o$(OgJ9}R<% zyi#<>{@VOE)j14{9TFdfe)PC;=am%H*X_TKcaJR5%`d$T#Jpn7N6oq49@RB8EUE4H zI3+f!*+Vs-5iy+}nG_!zjEvCt%*%vd?B28eIf!dv8HAfGnur8#i`r z@RUoZ^3*rc>|oyZ-uKd)lxld12UV57nwp=obPQI@UFnQb0 z;h)_Q$7d!bK3fnCEK$RA`(ppwYqnE#2v-G!d>6LhF@yaUzPeVk#h@)#bDL;s3ARdd zP6Jj#JZ0>QSEu;o{f7)u!;rg`3tEK1M2F+@X@;RsFl+u~3v=_S$KH@{ zlt*)uFEpa*TaO}C;1W8*XgYqKX5T8G1rrYxMbhD1UsEo=TT@ZmoJ6+`P`@PqO|212R`cEt`f;W4K(e* zglqjH2W@+~q?eYt{n!19L5by&dioF<>i3AZl`;cUsxGZt9HF&zCsmkkI6UYrA4Ht% zO7rxR@g_Tu&AYWOgLalbZGFzVCJKoGe4ChH;SJ)Oud%eTIbe5?qEPxD#k|g92{;Hd zrhr;^=~8C|vqmQMx17p5vm|k2@n^8^t`)Nl3P21{Q@ z9sg|h{qt&)4eIeS?(lc6h`1CTWaD4K|6asT8^l$=S$xI2ds$Ta%xK&NIFPSc>yO?} z$L2)d1k-!#0;CU1f9}_+tMl+@%YHuq0i^7}FbadaoJSz%)fGK+cU@XeQHcn(f}q;?b>ZjFpI_cu z-dU_hkle=@Y&+@Z(sulu!Hx}^Hr+~{Y|o=*dO|I6VDN0)H;wCXsq2U3xv8kt??Nj+ zW_SY~8=iF7awnv=@+eau{#L1f|GNHe>RzJMr;~Rl+S=+GjW@tqLh3afmh&+_Ot2@U z^a|VW$|7gyZsmW9rqym@Tw9hIT^(|hNo{iwRQCl`kWCj@n)~jZ3hpZ=GxT{q>ERKx z%?MtO^C^o7P`R==-CS+m(aYuz+WQ|d#Ix)TmS@&W<|8ua4*s>FLknCcEady4ZOD{( zEOGbz!zv8@{N4KR$62Z6Fjsyd1wI5YV6p?h*z@sl~{m6w+;UYHtOjdTJX_IFE@OAitD zh$a9RoQ@2@d+b`hTD7~}5`2L1s}7M)n8AwzFAsYkJPF5{LoewFBjSqBe-O1V;A;T>UU_+Gt7izmkh)Bxf+N8!p;^? z2wkL=@X_=(pnWhcLg7sE5{fvbL#l40aJ$mrMeBQb1K z5G`?a9SG0IxzW`=s;W@ZG-;Ugdx?UIjt9T(BL^^MU;&@MG;`C6^3u-2bpqlXr+{GW zPPK@NayQbBGz$c+Pgo zY)iq^RPnSl9P=t?+uwfZ)9~ptj&5$g%G!HzvD?CIsOMCp$5n?UudIy-yq5F0mHc!K zPo6q814|$RFkc(TO8rCD63HoVke=j^B|rmqe?3HSKb|=zP}{djNy9#`>e#U(Qa83C z99OJ&%7Kt^gAoD=y9@9-%`O>&Uaks~OYibMx{|1@tE;Oh)I}S~&_7CeF`AF#p?b=o zExKzFZp?tgm=xh2mNwuI=0-!@?{LTQEQQ!*(x9Qg;KIk zwGXKczzbtYce(!hBEE)+L3Vc`ChQqSFySDSv#56D=E9%IsXcC=b?L3I5d$bvi`}L^ zde2!UP6wySI4$IBK*HXA_DowiS4IX;%K!3Z6QE%hZxpb1{1Dapyh=YBbU;v1>SqSS z3>kLst}QQ-o7{PXr;Jq4JCzg{*E;2g`&{S4rwJ3F`jM~!3ILt_Ko0MuDOGws`FX_Z zJ&ra}ZzxCccjNE7A5wnWY-uoa1qQV1`PJm_&ODwu+j@ALJ>5(;6rI;~e{=hS-TtfL zjg+G&&#DZ%URJzt*~g0}Lxw);@YB?H(|Xmz{U&g)0{ko4-j3}Su}T3Bx7)RvH>>mq zs+uTZP7zBAp@qy*tTAwwqWhp`$$pjCdf>#KdiyL5>u#T%T=O};xjMLd$fsi1+U=Q`7;gooo7YaH4xiY-QxRJwS)GSX~i{ z6!{J-a7iy+CxxuvsxYeaT5I( zF5$)^l41E-Z~%~B{&?=;fA6PPcan;;?j=vjc{$U@JIT3k(*>_ol-!QBV;u&S+#J}b z)0mw`kDKXZIzysM@>Y8CpR|OHzkEUesd3R7OuMo$VTZw)$yZwoj?Q$YT2$!k@ZKv& z5e_PzIG+P^Mh+jY{n0a__tG8Y56Ef;;4$i8(xr<9mkph=3d$)-wxA?XQ&-10h?N$` zR;SIfhb7ZMXhDNFyI28A`qpecK765M11Lyo9*wKCp!J^R5_P8ZvI$F($qE~e9kUNw zbwK&OrXwdu3upr6G2Mk;&c|PN6E01iGVdTn(7EwJ3-i4tT*7?hwFH5^N<;P}-gAv0 z&WBMPjirD?K*!{mwW8@@&t|%w4uVP!Nb3F7cLcgg$@?MuvQK;=c#_dQPNutO-~D^^ zxVS#(tlo8K(zI0<7tUEXVf;d^x&d3XHKSFO#CeUp8Z{epW$e+KJ1%I~q4NN!+D-_i_J&B<3j*LE??Dq&TB*8<7}Ogk>;> z>andw;f}@%b(%ndp|@_GKzpPVRM_ljYBFWYl)*H3|P}0?A0qg8epHg?R zP0D`Xn_*V<1v-aJ%A_`%p?L%Y)O6tcNg)dx0e?H&oo-zebDBzCGb77&Hnu}RcB8!i zEWw|F-@o~8@ZMgqb79(-S8GOi#sp?;|It~!SGVJtr!BPgvHK|BoQCYn>|=ki_u}w1 z=eTy1a<}zmAW-10WG0{3e20eyR0{U$>+~|Jj#3i{mYB?eh$QS z9~v^WFyqPJq$q?6F)*(#x%NnN+k`xO@}%GF*{!EWuHH&^e0C_tv zotoFIMewn+^vSVr8zxEfq&(&}cbW{@h{OW)5P8gC!1=T^v!v`_unxiRqNx`A_7cL^ zaJFgK-jtj)#6P&-6!ZKX4tcTP388uA$`wkKIorCjXWfl%{5XQSwOhAt-(DxZ&-`i< zG_8tKTv9y!_oYamd)^*q{qt|_JCz52Ui>93xb=MNlEp`^UPzDLQu|jImsvHw7r8xp zn{hnlNB^zcS&nDg)}N%B4NCqp*rAp(J!(VEYS;H0KI85aR*e94(g4QjX6A586Rc80 zzi%UV3Qp049_q8Q9x_%m_InrHAP7jYK%Dq~uY}HwKGNr|EXb3il0A0(%G?c=eDn=aXhvVV?kUos>@cRz5!d4Bp@86|^PJ?{=( zB@iNK*>Bgw{_y)8d9jRltOm?MG?p3;)fu~n_}#9vW}xMl;lqbVulwzyv$yZ+GPUi^ z`_@~tIOXB^PBeOzh)Rja_~z?x8da1#E}Z2#Fuknk&B8xN+E@CXo-{N_*VacS8pn|! zO#o0UPV?$E`<1h3&Jmy>ch$uCp$GlZpt)rYRMqSO#gncjL8IbDh@-73!;*Q@qFCjj zw5ikX2R)?R6{ISSF`Z2*;jOPGmO&9$Q~fFy6lVx&eL-NP9f!u6@rQJtES~c|Qzt_R zTz-P=w}#9;ib3oSjK0=BdZX_>3c#(D*fUA`nEU)p-J454t0KjzAb&!Gv!+4L`<@c$ zWA+b!&g<;icG7N4@W{xrEjjmgiT{Ii6@~I+aNsb%>?DeOsX3PXDRD)vTRS8q1X)|d z?ZY{)n1NQC8`7!y4`=9g6pAW}Mo}e>NWoIH2a9Y;RDG2`^D+GdO3c=e{Ql-}w>miz1LOJ{svHao@4sSU;ec2yxCMKZ#1h5Cz(9r5SgmHwy8dJjHo*8g zp(G=ppW+Urn~KGuwoMhLa(3Q!>?^em6kYc4TkFKT$w98ip*Wa3eGCIX#ELDR_K_pu z_pU@$O`)Wy^bs(uL==ax4RYZDc*Xi|rUAP6_*uVa%f1MuE=B~-kE7rN=$JzxaX106 zZ*GLV9v8Hl#HTKfuX+gis%!3UDmVn?RW!XImQ;Rjp;g#NE&VO`b+K1v`n8WJ@aa#5 zN9TUWyuY1-PPs#|zZvHF-(V&@In8lqixqSJgND1B!$F8sUb=G5`B<$b869pAn0eiIyDQILpD^!$YV&`ne3G$?PwPP+Br8=rDGs4l)_} zsL0;^9Bq+($HZ86u(_rt_dYBcC1VYENZ`g{hbGP@mA+SDdD~5H>pl%{?KkZEk6qK8 zgZ7QCJidA_$!}KY(5;Ej?X~KTsr#d*n`Xe}qJd4TkGZn*bX6Y?&8Tj8FhrLrumdx? zt+!R=XJwtZ_J(OAyEhky-oipTI>wwi$0#s zllhNLgoPqDks14WQUU}bkgy5bN=S?J@hqE}G|18wO}$-g+GzLT!%gMeFp(?Uwo+U|N4_DknBBK;3%vRC!$-aRZFTcWx6n@79I?*^3J!8DB*200%gj zclN?&^rT7qOoy(-kuin?N-w?M=#e9JJ`QEe^+V9*H`U}ooG#rSR%YPFqr8sajol^y z5HM=dgeH#m`q;8t2>QGd^nKT6I1D0)RY>gCLL0jQQq3Q(M#FT2iWA_K{G76LtUw1Kz|KqD_)ANNyO zjP0l^l%wxJQfOT)iE@c~-)qWw&@gh}1`;*)^w~3hJ}Dm0y_nD%+w+JKwWmh7`#yhe zeIdJ{I-6 z+`YTi{_Y8`svE9(2WR(p8h2=BZG;injl^eypufJWspY_Ym#{WMO_UT=@(d2*5B_v- z=Q!1t>23P8DH)4+{2jOE{E-o(ezwXm`>Xt-8HlZkbGR|W%1)2%SNWKm{O?`OV-{O2 z|J#sN{`c`mYHptHcOmq>Py?MHyl!CAO~V2PZ27mR;2+jyT~rFHUA=z2V`21*{{y!e zE&j0o{`!Bd>G*$*;2LbGw$AE#KcNj9P&-L&u_xT4((LKg@~7j%zsq3A{JGYDm&VZ1 zD{7dX?NnJ`4zJa#89bF?AklBij`~h|mR=n@bf~RNf0W(aW9_ilHuYWAEWNe`lp?Ea zusZ*l{};#LpyCUI8~wF9m#E^aS5>&qHk;OiR;pc=*2b_T_?t_>kX{e}FEoU=s9UvP z+b{b6T?zb;0`31B-R=L6Y(nCG>Fayq1?j-I)=eiQeIF(3~V}e=f1vc z%*T!$D@6bg{oIs6me#tQ6;9X(CHRAaTMpmwa5IUKpc2R~om0d$POnQCZ=3_)Ihuq(`?$gnv36;Eo86KKdTU%fhjXQ-;Gx`cl#QZ@lH#mvQE-ugo? zaM+48PmwRyxJ}J}!{-JJ9$W)Fm?OI-V&N6myRTBk4h#(*w@(gN9& z22@*2U-)dO3OawW^7VNhSDCP|Q=MrO!dIeyVupt&_udIm1odCM>??dVq4;>&1NSP4 zorRTCetvmT3o+cpwqKu|r~#TVhM1(NI$M3rtI(u@;-4-~4TGQPG54`W&_ht+E}-!e zWx^j@M~%VA$Qy>6kc)|aoQIE?3CyZGcAv2&Q9~h)(auef@X~pe26jV39iEUEl^fJc zCJ&rTd7{l|Bfl8{dot_VVDO|a0*ES-qWZKzoPb2ccr~h5*JF^BmHbW2BpG!3BrYH7 z-xS0Er%Pk8w2MAiyc@iQ4?9LvtY_2f_-xryRS{;#$nEt3G94*ty&5&HJLbsW#EFwJ z0z%-OSAY!mCuI+wkUDZhIcg%Y`x4zK{_y?X95N`|sh8*6dWdI45O{{}Qf3ofWJ$&F zE;G~6WOg~QX6y=;=>&@ad}yy(jG>qnxIZsM0&bCE>AijXRuLaurP(oOP4AkkSY~*R zl}dvZ!(Tnw&0;(D*|e!YmK@@P0t{1442|S(%Gyvem_uWApe$iNb&Tnf36fme2dh+i`oK%Sceo zK(HqkQG$@H0*C2{OO%>@<$P^)wM0N!Oq|#dXVbWwL1NhC6l*)>W{(Jj(qWGEKZK2$ z6R;>fVk@o_j3{ICB~QhEK)fIdlAT3K6swr(zc~?iEZq4MhA#Sf9Z7glk6I9jc*5XBhg!NCD+~21*JuYTPH<_y$Ig4WeM>qTK$gxs9t7ET7E-&OpU{yW#qg11!H<_7 z`4-Qk7!fphb%Tn52ojxqg%!P(@3^n1Y3uf;U7Z%LjpawEAtgi`| zmZ1RT_8$BcS~+$R?vsHZcG}$nCh>K}U1)%UvE7u&u&}Ui7$DAXdCeS6MW1rC%|wS@ ziNihO@kGA$7ag>O1ofm10Zrz*jHqlr)Gq60NOKT zd|2cyJc%_q^R7M(g&}LBXd#ifSP&{xG(Q)mQXkh+HgKSd-&TvsK7p6>E|gWTq3U`uWN-XXkoE6dw!rm_7T{H>N45aS{xB&7|=0bQKTA9W3j;3?oZ@Qf)il zxCqpK{_*x`$ib=#&Xd3$C`gv*VX3gP~O5-Fi-aBwi~5 zd=(4Y)F*61=?Cc9#fOZ9cmukSL%m&x1*JrT(OzDbX9Ed(Xg2qz$B&$lN=_-i{Eac{WNmC z1+lN(@u}M)+k61aU@J$Wuu_yhYk}~gHbq^Gwf63#N180%^{AZL@_s>XIG5#w=SQ}R zTZ4GH@eYqL_!7&*)} z>j3Hm{uOW6@^lYxhJN^&y)H0p-MYF+q5ndZa>BA$F#gm4yZV$C)}3{5qG0js293@sxa@DY+i=$p-Xq-M#Xr=uMCp7Q7`gZZkiE>Hhr#0gFfPvlRxj6wh zk{6Jqar*qwgSR*N`mV)&P0c38cu}{YKgc|^&1Gfg1ENU~)k$v1HOKo_ z$w=SqKJnz|hFm+xXPl$+S5@pmQyN#^?U_w1AdSbjl_cfZ0(dqM3FezB`eomuHHW4A zfiidp-t2rYIHGB3!TaG`vzjq6O|g(W{b>QfwYo*2nFaaT))5-EdrAjcRy)t}tEwDB z$Ta^(7_&!LtZ`eh!sp&{MyM-Vn07aCV0$gY?x^j#J^V9|Bj!El{fy3=S`FXp6FzJf z2=)ff8jcH4g?oMue=a|XTwGj#JG(>uJyLCd$^VA_P1*-tims;Z zZH>h^31x=czMc#AN{ETq+hyH;KdDl+qQbc1^y?kJ3ajW#tb`r+YNtgvAj8!cC$lPT zyL9W8bft!xVvoLJSk}W54-YXGJFuFfz?nFb;8C0PS)o7iNb_)AMU7Xheg#Oc7F)JK zk(mj7s~3V3BQt*_-TkSIswz>G0y;_C^?Dh`T}LqQ6?ui7g@mR5+_`g)yvk0w;==hT zO=_$YLYA|N1=_OxPggLmt%IT^Bc`1LhIap(P}a^MXSPTOwx$>~Ne<+u>OX(}xu?BR znrSI&SP%Btc5?GsftT?5jfscq>7RS0-v3EH=TC>p1uNJYyuURS5i8a-H(O5p>7p@sFY}BX*-h)nEY!cG%?%#CPTyyMdD6!5iSzM*s(wrF zvcod=4Tq)|Esbxq*6NaHG@S1^O4RcTTByueu%JEoZCl55;(Sy*ndM;Nl**YE^QZtQ zx}9U2CdtOPsty6L^lSYJj{pgxhKiPq5xv%@W5P za|#+MS_1i5CDZt-cnY_5Jp~Ckw+>pziEal2lD=kXHEPrjIfI(AfgU8u4_}7QfX=H+ zX6U_dO8gbS#!A>aZ5~ZlcwCOuu^16d&o=Ho9jsp(I4LeE8>nLxMbv~+g91=9{Hs5g z1hM|-oH}@2z$&)^> z<%nWY$|2ACZT+#z&e1)5hZ;Nmk$4MyrPo7bTgj$+=5YY(MKn5;?RTia?3y9pq1#ba z_|2n*B|e+RaxE-j3cJj4;XIH42R^n2dM>DiyT}l@;-aVk$1q*d{Y?ndE|Z3a^B`*T zC%%{V{FZ)sGyTxsCe=Hi7ZDYP-qb}mIMRl-xwSX1vx3BJ#5`N#kQe17@-K1w&i)_ z2ktiheU?M9S8}^8S&F#Wg+vfrDjzh1i>2N@P9|?Y&|N%W=z}M^9X{C%(*eKskP+({ z5YU|ZHt>6I)`6lhJSUO_~Cli{A z-`P5|{oK?)RdQX@O$^6AVB8C5+h77tUpWRRyE4_Boj+A z6R4PQgZtYZL>sEy;g;nl`K5P*s^!wsm7_FwkvXPL?k0E3Y}?MAeSfAs?a_MjpR_IE zLH~WbjhUt+VVo4A@2KcR!RopFhcBz-C^hvNIF_K~q1MmAc0~RlTC6+q8{W!2$+v5P zW{Xl!vX*|h9!&Z$dB;wVfW01()jf%2`0%pRt!|CR2ehN}hb#7f9nmfL>kl?duSM-| z5GxwHrg__O)DT3)TjqzJnMn&PDMDNf!554EI(AfwSM*_-s+_6lbyj|23_yN0d+v|w zU8frp5Gs>Ffpf}GGtO{F=%sx7?lyYrhk%yv|Ekr;ij*PL{dc$9;o&=X>_7v$+wRf; zV}r$bxrRdFPsb#qMUZEkE_!?0R~`jT0hebL#z$=$wGvqp$`a5_`X{kWJB}r~(lT_QHPx_hTXc= z%U3bO!9nNryTAcstfuho$PCfZ2HM4s*PHtzqOM0G@Q3f$h|)T8 zP)b0~^nQNS-vUx7k53HN4AfjA9CO8=uWRHRW9#0;;1OK8BRZ5?^US}=nMf&&Ec?E9 z_*lYnde53Y`#4ODsYl7Rr#E_nKu*a^oD{b%h{!$NNB2XY9@fnPMrD82vhi0!LK=7vcLx|j8Fb;=k=)X<)8V=ytCIcb%DtiX&AcxlwG`bm3ThR66yhqB$qpbEXWZMZFBo+CZ>Xiww#LQ_+nOJ$ zkbJwZUjt?yl(dW~-&KxMGANz^rRT~DB1BKo-0e`phYG7$ZZs)~Sy_sLu~IAkOf`Lz zM?SfZmmhfoR5S#z2 zQHR}18UG_v!AYIe*1i1kh~PInq)INz9aMKqzP>dJ%mFD~uh7Abt2NiZwasc^oxppp zB%_1kDis>3d$dQsQhqPb4IZ~I-rzh-3iFDDC&f}o#V*(!Uai)@$+$)mDj}pD=}n^V z;#0M+JYA9DsnU<`Y_ixL)*3}dKT2cX@~Lkbe3LErB;Mp3GSQtFfzc`n-KOp;%5Z}L8+|mlR?1}=sqndk zt{`UUh#L!24b9cY_Wb?{Lp`1ETKoiP1kWC-VTch~5}We!RXmGZ=Tic{-*sJ)M=8OQ zCw_exICTa~UXQz@E)#aR{f(Lqf192@?a-1*&y49S>c>B+AT%nwyc3r8>Bt+1YTt0c z?Sh?sbMFhY*4*PR-^v4UG1V@)0Q)fm1ure}SR3)1qR;Tv^rE-OP6C@$S7ZiH{rtlM zoYLv_ij(*xG$xyWv`^?T^|mhkuj;q&=6-BiiP~1&-B*;&+wLiG0q9nHEi(Bh*sU1o zgPnifZZgJLfssX#$SKyzI3ugeHjYFtMs>%m%uLB7xc)&yCOVonx;28!f5iyUvQ7N` z^WmeJM51^~`?4pGGmH@I^+_RU=@kXQsZqN74-J-2{V#Hdf#R*4zk6DIr+;K2b7EJ9 z9o(T!l;q1jou)LV3QL(~GDIArb?cs8ijMd$QzsSRFhF=OsxLKCTIzCew*Uj2Ajgj) zU{`;*Yr9gx}(Fz)*HY4Up75uQ3s;wq$guMBQXrI_8o=U$jX?7M-ACz=0r z156aqF<*R@sc#pGQ!haAEX{p6hWx`_U1*w)aT=^c0IXOnadO(ZpospbYY<2LIlBrF z)tFUC3F;zUHX!nK6oU9lWDqVu4Go;C5*RRs9#d5jW<1R3-l(ZFN#fZ769a~aI_Yt^ z+j4tui74ixdvUDQBuM}~cg&I3l9RRNDvvJ;gZKtEikftaiWN z+`6Vqj&@m;W(uP=F#m0JeQ@MKL5>`Tv0syv4Z#lg#4A^qBmaFwcdCV{2Kfv%R1?@? z85`0_W+dS|4+kiUaaGlVquR4*IZ~7J_8O=8 zv@8OBore1Zka|@v z_~De?ZR@uL1O&*>U&sa;5OQVg+rL!Ip7lIeb+)XwRHU8HPF@3ur0cb z4sw6F>sLH=43h@8?by5b)Z^Lmaqva$Sws@t5C%6K;c5(e{E&2_N_yzv=nDHChu4kr ztp7fOJY~%Bb$R)%S{s`Gaay*l0e9%hOQ;eA3Ld9=wdDfJ2C@T5}N}nR@29Ncfm0bm_FyK zc}%2RQIMVL(86(O(%zN-DNQB7e@#Sr$%4R-p&Y|eT&vDyqbfks+naq*HXz7q_vg?< z$Ae|2%VNKqCL|P&Z`m6@22s}*MCW0UmAqT#2WfHHAk!q$vxij20P~60ZK+9l zJzbeDek2dGlo3xOXzrN@MftVy?oa#s?D3%w> zc?nn{IQ9CZNa%^F+oOS^WxSpg@on3-jsIH1GIl33F+~W(9WQAW@aMuM15H#Vl5OGZ zxcK;JY*35e-d+nc#NEC{MhOt?CG0#%iq7mm5jU_`idO@HJa;rSTvfo^4Q#wgHa1d~ zLxZ_Gq;w<%nk^;wKn2=@G&1 z`%j!`67P6E6V0L!s_bbA>}HusqUZIaul$gn=%6HV%*Tj2)!$R6>#n@5YZhqH6v|ppTE=c zsLN3z-paOak)`a?VsbuI6#j0<+{ZrFTw6LQ(!}LUL&i6^+3wNxKVPkfhtRgAh%mr+~mB992YIJYU1h(t3vg`)nQqst+~} zv})0t~;QU#I%NyD&$9-Q;#ut@u4c?&mgjvg|+B~}c1tK_yDt}#! zPO10rQ%W>jqr_G2lENV4NYlDK?L-kGgM|2k8)+e` zDCw))T=!Oy?X*sTk_KT8f-!X?+?= zF`naKQghX!qR(iuH?l6dfP~oXIx~*xt16)U^4aC{WoKunHM803Dr3a8h%)G>rS5nu zHF4Dzu0t8Fj@#=lmn-GTokYlHPCsN>)M zR#A}Pg)J!L`)95SKIqe{b5~gDTB2)~2f=VDTn5be9{D-Kd*enihl=Z%dsc&sThc4} zK9n<7EAJN!$KVApQ$vxs3W8aWU88SrCdxGyJ?6J8p0Lrkm(IiAxD-NRI&Et{KA^>4 za+pu>t*~%!`{9W9_*0uG$H{4`EwiZbvaBxo+=d##Xv(x@DHj&*3*p9<4womq&a`3} zw@Vk|S;KM~JkGtD*f^R-KGy9yr4Q9%97UC)5Y;U)d1Efx^aN7licq1S7+12qQ|F>^ zqXT-=3=Na2F$zc8GGn% za`J?Ex42#@m%$LAgK7AN-SbP0Tob8SFbVhE(*o25NJwBI40v(zzkU}N&vM%`{tvbu zyS#l4@pBmvunzgk(_gOnX_F-nIrSmrU{E=$wLMaF1|AsgarpC@-HI5~;Ko!?0KPdf zCxMPc%x-{QX_q^AYDiC~OiQm4TK2?3-AMP}W9_!*zS_2B%l#YsM2HpWh`4YA2XOn` z$jzIFhkOsB>pHgeTqwuuViiv@Vt5&!OW5YWb?a6du)QwTQsXHo1wkqIKHnzw{ByJ6byUn><|!k;BC>lAp4QqI|H5kD>m^N z(k|6%V(_~iN_J_NNuni{70UDMFy&@_B=KTqqyZ376=(8s*c9i*9SNXrPI9~_$^ynD z2rZYLJ&Q0lLC!?RfHG;)G`wKEP)V9$u7!lGQt={HQKy*krl`j2Y?w^uT^t>uq5ubJ zeN3PgsUQ$=ZSL;-;1SYI$X@_tV&!SM9nojo}qM(lrlR z1H2LdcMs?n($>6?t==IIl$91oeEd$wTP)lbuutpsGkEs{NS~kp_D0Vnh7LB2&ZW~V zO={qVEV5{-_&q_(IWfdc^$l?WbW#S8;9U9#(F4%PNbmqz2g+PYgGw%u1}Tko;Q zd-dAb>w(XKd6O*+zPjeb*X27%{HEc*btiMmOtB)#~i2dNtE=d8)_TzQuVF zo!si=YV<5wJ-aZ#<;cfADK5GX`DfU2P0_({${rni_S@YW&5uIozv)vNWq#{jp#N0o zRihGy4nvh;ZEfAB>(yho=XEc=T8fU+@Aj!bp=NrDnt>qfpfx0(Rwb3#($TTwmO(8f zuMbHIe=FlpQM&(w^VR_`a(>ZZSec4pKZk>|hDWAe zo2TvU;xcCZc&`s1X3I@R**(D3wdd3o@2Xi(m@osHN_yV|2M$M$zPi_$bLX6WKYp&g0}YRg z1xx<MME=#RnYh=Aj=cwb|$mMDi$Ug7GQws3(?{9yt79>R{$zF!Ma_eObe4 z?gldFOf7K*Fp4&q-7?>ac6J>UwZo%67Py9J^@m@wRJ7njM#bk%TQ_=Nm%FVVf}#vW z!l3BGlV#kv791RGm-*7{*Yk0zp3fGZ3OO0kzo_SEIL)QLGrQ>OO^d$O{a(~VbK9ei z(+WS<-hrqkzF}E@+s>Vr9|-%_^4qBgEVmZSRgms8hP`<>ig3Km85z@$jXitlNMB^4 z4HQfARcEEZOMBuiNx6Htu~m=ggx~z-o#XgBmsucJMb$S)994#Gb*DuzIbAn8Y!nK}yy!0=jOe=D#{x?7^?Bz|@_8l73lE7I$v1!ngxs`jz zKt@*tJQd#a-1+mztc`Z=-ks$f0XqQJSY62gj8tsq%wYla4;*V?Vv>?~t#bKSn-#`2Su8mxo#M&7RT0`|Nw0}ub_P{x6U`o8x;uTz zbFX26{c-&i6cp&qBX!=p*|e)ynbJDQIejD2*8V4+96fcaI;y}Wz51dnq~Z(H>)gJ5 z6$JxY6{d|W>lLtjca^}gzHIVgQzy>X+{eW=lh{<{(CgHcYv}p2obOMJ_%M(E)hS+* zap2zX&gypT?AgT6hhEGdXm4*Y)h(~55Y+Rpzj{)GvP@IyRN6NoF)3-CR#e_MO}D4B zjaNn;G_J*|33z?Da9QwcgcLISh8pzrM-)A7ib93{IOkWV&+>&N!f{duUbg@J^L7DE zsiKvmmoGoQ*r;#B&x77`x`vFLcKVq;68hkefk(nT=M;tOt$EJv;JT%XzH~O0*-_@9 z2EQ^ty$iHnXnZhb9)*sEMNZs(uBSb)u^d*^#P^RC+{7Z3hg*1)U3%EbwC{2}91cW`hZC&=w|;Kz%c#EDsK1DEIN zlv-0B61X!Y?&^*8;Zt5cW#iVW<)WT5YBlLMb_JOWY@8jwd0kpraPrZsg~l?=Z1UvE zA|saSm~H1DT7I*6;L94dQDfy#Sm9>sH_VGjhlfdf2j6z=9_DcQn*6$O#pPEmeTDK1FFR2}w!)ot&=R zJG%eUpF!ug7k+wDMQK@MU~}`%MfZPYNBC!LXPqD4=A`@uJPz&g_s_yXpfF0Ne*DCc z!gP#;%C(4$)EkQfQa<}sTs)ngI&H=yJO*UX-yz;=2?;Dcdi1z6WrpEa+x5eH*T#+xZif&l442aOoW&D&N=oln!K3XJsCyii zg(@wP^F>w#T9RurE|oNBusH|42|850xV)&Sly&d+O2hcy0aH%aLRtEMXP5l;*^|ee z4;5?nt+TUpyptoSDD>s)=^WZ(9MBS;el4kF?O^MfBrhR)KDRZhPGaR?@N?O#nE2P1 zK;?DG+d(;O2Wp(RcK^T;2OWUInD?b;Qa?q>PcEXUhtF4;n@?$S7~M226(gl(dI=mh zn_-ZhE@1Wb@v&Op<|CNHnKc#e^cbEwTctd`p7{Z7+0xfWYr^6b-d_$W_J z#^l=*;CG2HT$sv4K}1R}uu=&=Mzak<2ZubvW*S*CC|q5aM-VyGx53WFX8oC-guc_B zSa5&vRPqq$DI7!@T~cGCtQVk(_&L8)gG_qM{TX_CQ=9%=Dl=64n70Zf-htQJ=UW@c zK6qqE4tdo574&LrYg=YY@yvQ1nupATqZR6>DtOWC$WRHlBf=IFIa~ZauVlS~1Tm@t zOjKkD-N;hh$TzKLH2fdW%?Y$LB30<@!dIkNgi0jCVB(e~7cf}vf#`h@2~FZ{p-39` zD{Y|;w&!pSs{BtX?y&Zu$zZeMLtRKVS7GZcD>F(N81!TacM0RN$-?cHAAf(|4s#;@ z(&pz~KxWq|X5wy1UtJRGnR`3SK$bk`{c#f1u?HfF7AXlTgtCpV(R0RSTUNc9{9Bm15Xfn!~LeGS-#M3S$GuoV{; z&cas&EoU7P1`rO-@Tv4quc&a?6B0u5JX9^@W;+vACZer|R?8F>22MItTS{|8d3_TozWXdaa;3=C3pb4>}HqR5HUyL|1M ztH1w366j`jT@Os%qi$~AMnn@3*i-NQw;D77;F2RqRm7mopS9lmZFh5cdSzpz{Kh@; z#uE(>xBDRAnUt;0BU5=QC20+Q>ErVqIUm5U?@BE%nJO6AB5#bIc;I#GfCb8^vW^b0 zxjBJIeSxPA$|E?yb?BpigD(7Bz`(;UqACnAnApuv_Du{c2Gm+^W+pBwG9cm=IJ;Iz z^=g{~XCritkr5psY8Y4`FrT8~9XC@$s>gsk76^5OPu<+!mg(UaHkUgKeW=6d&duAk z`lHtN_8^SNOjLQH_RFZd_p7Uc1i2xP@Hlf~qfCNBvlYVv%wn*dTX%OLtI=g(Wu%CX z0IHmlLT#|^yUKe#@^@lUPGe5q1zno9Sb+o@+NSg0PobXDG9^-jJvGwvPCgLAJXZ2< z-jhn6C#-)vP1Fcl$BS;@{{Dyg1)Xn`fdo{~@WG(!)|bO}Z8}3$xu~Uo`2cJwcS1p0!I>fAn3%+&k*QEp-FXpQwb~=-jn|nW6i8n8G zaPYRCnZ-2&WypL)ol#GtirS17+G+BHjO~7Y7ARglPD&&B9?&p&i7#BMGi{4ktyyzG zH#0k%mDzxM#>Qm(t)UAo{k}bMts4N}6))Gp;bg(ca7njBa1>m#abpW7qE#L+#zU=eNm8UN1Av4 zTG-7H5NaS6W)IwAT)*4t+d*CqICYd1gQ;=^-VUD>cr3&0FD6}FH*eO~nY~ac8&Wnd z*spTM2;MK*$z#XCb*F|BR`r+pZN#?+{4GL_WXH~(#8FrnFzWvLq7+nCrodx{FixI_ zZABhSZ3BuKASx0l0g0h6w3O^h#Ocm1@$u!gBPPSi6pZfex4YP8PDgSlT-hc`L_yloPF2StjX8tM}_g_V5ogMR@rdROiM literal 0 HcmV?d00001 diff --git a/docs/assets/serosurvey_tradeoffs.png b/docs/assets/serosurvey_tradeoffs.png new file mode 100644 index 0000000000000000000000000000000000000000..968e4a82f243681bf681fd28c1067da4bb885b94 GIT binary patch literal 132441 zcmdq}cRbhq`!|k1r5&ZLGSiT)fe;d*r0g9+lAXOrg^bWJ8lsG3r|gW%Dk^zJ$qdO} znHlxHKb@cJbGxp8e}DemZtt`6-HF$8JRZk!U&kXF-{i>AXR7k5)93*xM)%N1J(7h5Z{-EJ06&Q=cgM}!U?5;`of z`?8D66=z9dVY~nR4j~68OW}Zsar_cu)0I>D&IGaZA^AV5)2`Q}2`Yj(ee#5sNBn4~ zyB>pX`}#@mA&nH?jcbxx)${nn)}67%NqNyn>x~^_7FYl(3l9FfInM@s>ojv2@_M@L?6sPLE>FMcdb{(#Mw_RPqWogcihEwMJ z1HZ)?-VYzND*dbt$&dZ~S#3zv!OAK$KAvr_l9u1Oc=4-_eYI4?c_X9k%*>WQ$y;~X z8`qHMJ#TMSYT1}@<;stWaR~OY6b^Pz^+k2I+%#RxtSg?p(4q!I+ zpvo6Tto~{Ha9nTH{L`mTJ?2L5-^LHEe#tj{`lM6pF}?g}`f_(g`S#VnclFqI zVb7kV!^Iv`d)81>Hzg}=%hJn}Gyly@jCWT?nfR|TW*Zc|y~QO@$H=H1d3(q9|MR8p z`g-lb?c3FPvyEOc8NG6=sr>ghQ>44PdTj*jQAJraQFctPZ8Wcg#IU z#Lmp@nnE~J4}N~*k!))7!ab&6@fn7ysu;;g1aN4>#Ef z+@8grxQ0WXzb{I8>pGOO9F@<>v9M9jy>=gQ2WdfDP|reyXKR!c}kgo1$q3x4&F z{{D6D?(UD;)S~0#Zys|W-z_Le%gxPw#mz1F<;x@I&!4|wXO~_0sV1CxJ0oLIrf#nC zbRb=8iE~=p?}UVe%inU0(#-`jDdR6EC$DrAJ5m!D?Cm2@Twgzw-}U3i1u4(pYh~X| z*?)gw5$byQ$Pq`ZfqmTEavQI<71$)}zr6H3Kfl@fQ51*t+dtC-RD_O>&R}W5O1u9VOROQubZ6w5+PBStn?a|9Np?D6^dPsQqp!DK_HAuV2CE9!bU&=PWFE#LM`y z$WArR*Uzwhas9_z@$2J<50ky&!o&G8HQ#)yf@zzQ6vAKF1+4KMO}i7>iN)+V(Ivg_ zz=33)Y=g^9N$aw*vXt4Km6bP>pIR5iS@r$N-l{Q{| z&&Stq_cJ!@)ip6ohmar@!J@aNAD z?%TKTb#pVrr%#_QQ&LWcHd@(j7cDHFkdM9j*>*NIwyBN z25OV?Zv5kv4Xc+y}az>INY3W)_~#|Uex7v#>2ydC%b4e*XqmDUjKhf ziSIsqz>3t@*Y6$4tz`Q6Kvc7xgD^ES+w(xwhP%t7k9Pa6UFll>Zv!{6jHE9;JZLJH zKCfyuWS;m)+)*z5Q-4ETfw8}fOa8$_hi(tsw-=r@|Bla``ex!Enjr1HdCQh9F?u%5 z&$x_zrW70(XNGosTN+68SDr;tP5q-3$+n$(!&a@ZO##JjV-J*B9UUE4uOr*ANQZZu zY;%^r=1mq+<&hQqs8#ABZdFjTgz|Y_jJLkAk-5Fdf!TLyj#1RQSpjaQ|J@<8yQ(uZEXfB2~zsiwY9?x^FdpfPgeS^1m3@YU&H(GeHEAS z4t?9MGX0HQ)%fldi`dv$$?RltiI*kl;9Q*?XYRkzQQN7HN4qzgn3xpQ)qebVw(RfX z2zJra>aq407U69kVR!G{dzN4E@TfzEAeO@uEulSoZFL@ShviKXmEC&Yqt?Qz(VlD}z|2 zdEFK>oyMM=hFpQgp3_h7NqN4gx2&s=;n!|^_N;C%eqC#O`xy)WdZ60ak;a!Kv{}|gQK^o z+mBycAlR!>!gV-$=OMGeXZkOnw)OQ)ObClPbP4>Po^JX2ZzwHM#IGV_X?kkv&}AtS zEl6<(grWm~E^)R@E^oX>GA zANGqkDZcWgc1})SK4!Rb_N$@w-AluNFCMxO$aD8KV)`#)n5$GX}=Sj7CSaSc6+tha)@5N4le6 zxa&YwRaHTl_xn5h`Q9tK`qOi(&~4btcI)YE4Dq$j^8AzEKJ1<4-nt&%-f=rKU%h%& z?ARBqljrmI&ww2F-I&&23sZdsGE5e}D<-ep&Ss9S-7F-#fL$EcS^HaMhjFE!(ac~| za&B84D!r^P!^jOC8X^;#Bbldo2=cAB`xWjJJOOFck1-%LmuA!j|eP= zqT2ZmY;OLR+`+oO*>5Isu|>AuH8N*6b5&zsr+A@g_x4oXF!(0=bB>2H?;TP)@b)V8 zhQNr3vw3#gb{#%B2u!Hwly?dnXg~gWINcM146|02Q)ctMwR@6#LO4ZfX zKZaW}>EsBWoh(`2^W)-$Yo;Nwr#E7zjN*1zwTSE ziISI>Bq6);Dk=!w)~#FbNjN{%nc8{%N#W3^N5?j9+$d{imPGMkW@Z+#@7O77^YtXk zb&k=iU;x*RT+i)0B?Sx%v(C5i3p4ghJk4D>YV%dVO8npPf6MgUzVnaTxK1*@?{~^F zE;o3PAmPldqM{O(t$F9Shu~k-k$Z>C*9X#3@-mHHiS3}LcO0VZM};BQP2BCPji^F> z4fFkHSYXZAWL2zOibj=%FYJDy9zu-@OX6b_%RJ#uaN1hH#~Q4D~U8WY~vsZ z0Re%}PtUT7OGt1D32lAyL9B7!= zfOcTlRai(D`EG@$PX{$t3h#RO1?~?$jr0D9>ae9PNCio5`hG zkZ0Y(2Z(U$OHNMCZJHd4MG1$Gh8mLn0$}j_u2=Xy+FOc&x(Xz4ho3EkQQ@w6rvr zR{=>?$8~4=rpU_DO`nUm_|me`S~PG4MIyCMxMIO9Z{kqf=lSU;QeE;`(-Ds!@4k5H z5(xsmRKug9>SFkHQ|5aC@_VpG-)!LY3BV7@Eq*90C{P&rnr(_J0~qTC#MfxY>u2|x z9ZsFirkYtHYv6V4^fLqYLD)$fI5|1>^!2%b+Hj$FW&X`ibVmU^_ko`sJbE;4Smg3& zLjK0qpz}g?WCi#m?KPw3J=)RHLBp~QZ$IX>-hE5Zm3qagVGpABuByzTcV2t;?u`k) zf?c?M`qxK#>}}=#q@<*3l%*Im>#te&-+}g6O;7ezDff5zOrO1t{^qvgRB}H4nEN@O zKmBpNqu*azH}GuTWo2z`Fc%@TSAO*6()|@F$y>CfX?=o1e#SR=$%D4G;j?diQ zUg+<1;=hpMV)2+4N9{Z4d>3cbt)1t`JFUNEw;s#9^zlIwAQg&d=r);!$E_TUK?K=t z^IV2B3i3!6VcS`%?cwQpdL%C|Z~N5L6id5^+qMhxwqNcZP}K^Kqotu?K&8;wl^}HN zn4#_W7YZ(epHH;5wnm@v?r(9t018Q4$e<>Kr}THcspk^QDKXU?P!)Z|wF<-@9vt~WyIM`=2n zX0G1$&EMWW=WI7ux`oT+vK+M+ zD70&T`uqIthW=ix7PHpprfbxW=NDnoH=4IUK0!?-OA>CDHFz&Q9y}F=X=fkm^orHg z*78NH&-4HHcgINd$;5W^XY;p`8iRsqSvNnvu=Z9`BIi+uF0I8JQwIm(a|u$sDre6I z#l*xcOx1B}%S_LWeovnH-dbp97_rpwc|rmgs%`DQyf9^LvX_LBExIP!d`tM=OuHj{ z_sXYt;EqLeDy4e4Kp|)UQw=C&{XHk^KHgzBVqZ!eEem!(Z z={G8M;dQ>rC`e@+;>#WQJL+;<$AssncnOU^+-Dy=6PyO)SGM@@@na2;V3?hzP+T~( ziH~~5mkM9+g8t_HjDCwlX*r{W7iM4>u=-TI8g+mR( zm0Dr5xMo2|_CJ6An2og+^izyI59HijVf!-w*L(Wo(bPgo*I_NQEcsxX+x1bbM{}xw zw;inZ({LaCnro7q=cA|?{LHW@N6T$tvWKPN9KZ*kB^tw4Hu1-KqgcNuZ)|0c?)|K^ zMz1^3v42H!5jQ6i+Xx(em;0;+-@l2MMWO^IJE>xUcbOex_biArvQQ_*Ba{j%zghrQeFz?c2B2%}UUX zQv38frsuHRmGjoGU3)p^xs1!;EmTA?pbTGI6Li?@31Nd>k#)5Ztf_^zc6MiO&Xc4> zs{)kTqT*XdMNysF1lMrF7EZr~C$(-4U1jMm57GYXb{wDfTbb2OofGYpe0OU?VxTcm zmv0_8#m4sK=WFY>r2KYsbF&uy&1<)&Rc#6!?zY#e?qD^9!lW)~O1o)O(~=~)Qws_T z@*{y-7+E@!BW0q*F6qK^sOL^>EH}gg!IoXe z{mPW@&&;@_F5QcD>2?IL_gyN~5I$_Q<8@obUq){^FAtB94g)c`w`1)6?{YDiLvUG8}#?4ndL~z*>L4_Hy3n{Y%R;* zKi}8HHrCf`EZ%H(nYz!_?abh9K99m*Ef&PH;Mej@eQ$af8kBZ@v>09oi55?lzvz4% z2Gq#HTc)JVfOQm)SpnB^or%TTdA$!u6VQN2$d=zP?T1y5yTyUNUI@Rh|YUOX+KE)w_7{ z;ii?T&Siz2VI|wjdbEE0n@ogC&5*+~(f*~6es(`akQO*dT{&Hg*rw*?( znB9=f(*UdVZ18842D6ppDDX#kk+^b#x^VhFl>}AT#%+MEgvJK{Hc8|pr zP4(5W{#=r}5Z_0|&~e76KAM-};+SX-ZvO_lAVGi{gGxHx|C&89#@jf5i&yh$^a6X* z;7T+$ldBA7G|NxXf2eP$0x3n z2=U&%osd+x4j!b#?%XMUW%u&RN>h%}F|fyB2l@KjPv&;1O(rivh$cBX@w(J?U2Xa4!B9M?0XpfB=oDsVUvMd`{ zx}p`GoDPq7m4{>VK|0!d{J4q1(|cF2%;hvRc0l}S$glXV_SH_(^LNTjD+H;P#aVt$ zsYUzTvRz9Om!7T6lYnqjH7%>y1*me*U%b%Z+Z*^AHCpJb&C=dWR)b3gJa^xJPCXO1 zEnk>O}+ zYMPv!ycKIw>T=p^^ykk&fMe?xd%z{f!OvXmd;E-Z_mSwfl;;VA7lVRMa59ob2@7wN zeKW9(=jsxvZsH=L7)F9?2M34p+B^5}hmxu)cnoMIAG0q{g)V3^on2eLx`7|KMCz7x z^`$1WkQw+#2J{q;aRgoZ6d#J>aAM=t>4C;R>&oF@jazTIuf}I>1icLKSTJ z@~cdql~Irb?`1e((*-OWLB{eOuP#Sj@KqcA_-ky87T76!Sk$gf%lw$^iKuU2?&j}q zah*DKDu8Yc`LcK1fj&GrJuN@cRelKN0*LR)O+yVonu5m7XCH{1Rwfd~9oKB*kW%|| z;waQ)fbH0)CVqb`3hWe=l+HvQIehrwh1W#3ncy&XN{vv_^XF<01BBjxe0X%~i|TPx zfNc*zg0Hhs%lT~A=q0u@Ff$*JMs*c(mOG6NmKHY2cBJ_)>mjqX(5G+Rx)lJjW7}Rx z3%x*%&v{|e6ugs@xB!{6`Isgr%^!m-$SFF~&undNqn|R~{TS5S*OyusR(|CS6i@o> z2J7S(ezp2~de8Q?iSm~E_x1I~?AQ`kmtO!I3{@Y@~8FtlzI6Ya}6eN=>^C%&+Xd9I?6mXl|!OW9x~N9HRVgsGkDvC ziCoM>zYTb`ZbvCS0a#5osr19kIUaD$`1SFXnY$YIAqS5_zjw^;T1(J}X# zimf%AH$W=VUCWcIzNNoQO(8cE>vWTF%f_i6V6W_--B^^D_XaGkr7$&@Q^3jDIov3x!H*~Z!kabu zw%>PDF8>Zcx9I?(TrSPG!`I<%uL39dx&^K3#Q(mwFP9cnYCQ3(cv}8`uKt``lT?1c zAXVu7?Sup&X`!}b#o~({CP93Cmgn{7^qVefcyes98)1mZ%TH`-n|p91nli5Hy6L!) zU3UdL`ludsDG{@fLS?V+we^3y(kJHXALcav3-gm=;NAMA!nA6A%xo^|)uPPz3~l~w zuh+3ByW2GrCb-&+WewVX2P`(KyUxreXd0uOX8cb%9i>u%gO!?e{3Z$QGnd$h z83o@HT1-CI)RVDA;@C0sp0`2xY%WmeAVA69=5)=Cn>MK{Urv;8UJrcy2I4~U>=m0@ zvq9fWuj@rhN=sS0xA_XZ0npZMt$Mcsjufqg!NmNEt0DL-S;rfRvzig-3Lx#k?i>JIs zUX@e$K1#>)zjgj_Ybp22!Ute7G?QsEx?eLd?* z!bI)zP5zG~5QMV^Ep@D-6s+tmpNx%|bY>sPA4|_IE`K1C=&Iee)Dkx*9wRyqshl-@ z2>x9|Lls^Z>EztIuicSL)1TJ}-m-6h0MtiPR>wY|CJn`07h#RFRmZ8-N{b!<{%P!Z zuLo--dOWBD49*AK1WogC?SPw;lhgePddpM6ZQM2eP`|Fx$thRI0OwFg`=9q-oJrSl zr8yK|o>cE7#o&q8NbdR~2hMv$na8XHF!-ptIT#7ul9#cRQ#X5emVC|5oNxFPZ}yEs!2rN*aKZ&<)IjDqTeEKz_rG_w`(WUHPh&rnASgjTx8YM z&K4aDMsjO{i}8L%mvb0L6eGV5!;+6^ei#!Z5V)>fk4c64AhgvXM{8?7KU~J)f_W0J z4?%o59i^$EkQvFsI$!!HuV*f|)g-NbY3DTL)hW+VMbNUUjf-!fb z!leBeIlVnSfm@!px^0&Bn3NspF{153O`=SH?&x59^XAP?QERO{X0elsim|#Lsbu4WMikVaKswVr$bek*My&Jn?L77`o^ymKK2FE2$zha5ba%lp`=f{C&F z=mgfltn;UxiTtHLwJ`%DgHS09Vz7JSXIks)H`B6+s2Sue=zCx#Wz(B63!VR*ao&oj zcI{>V4lJeH@$mv@GnagHq!b~68SLMD#``A2ysx8>NySZg4uEs}u85q5_5lqsWe9+; z2VRGOnEr1(j2*(&s}jWwOeVHY%q3@8gFvzF?pdlR@aR`ez#eI9pB;j2WqLY z?KR-oscE1BJ=<;7plhe)sW)>qcNylFkzRmTN0$DPW^|8gNyaAwPL4DMXJH8Hvb2$I zn4q>#u=O@VN?JO#4${=EgtP|`oM?`@jq*%;g=^|ZDyhQ>i=MNTlUw69))s_ z12%@nqP^(l&yRPEut~aXgf&*(sLt-M3fq@qlvyJx18L#?7t$1tYfgP|YD8k+g3 zFF-ia61YcZW{C^T?%}A}eTQ zW~f6llg-|DW0YELNrfsE*M zlS&zgJ^Rl3Pt`E*A0NoASj?#I)7A--Z{U#e*$LXuSLk!{LF7!g z1GV#g`}M01pN)mZe!SB(4SS$R(%u|xD>(DBzu)SnFj(MC&f~_`s?d1fy*m}-1kD+G z@9BOq(Em|4elG}|kSK4=Fw@f~yBeLP$!l!hC35+emIGkDS?$eT6qkWdj|!~&@cZAw z0nQj09OS{z@}Q=MXS@Gh_B>=!&(QSTRK8%<3`jq#tE-!Bwp}g+X$k(ZSL?PgT^8{E z{quBBfB${~LBW_|*b@47f4-!t^2eY#THUYL4zzP9XWFef!nR@!zs$pDYUbi*X3C@VtQbj^yqA*OVEG?!0 zGLgcU&^x$v;fx*EUg8v$i|Kst!R9spb20=KxZTxHYL(n2M!#R{Q zMvx){&TvY_-()Dty+s8DjL_wE4d-x2Z|mxmc%K5bb7xW{RakAWNHrjXtr~h(Z?Zk6ProF&COc5-iJ3y$8czDZ2cjrZ*mK)|g zt9OxVj&W#ycAGMAv8N9u{Bprc>F4>mox{>8B8@>UnYw5CVSnnHd2$8b6S2CZWeJ{S zP?OX+9}gQlwa)>aT;|`hb3|je9K;c3(#n9A!vK%Kx?y2)QF*KaLUB!@F2}0S$akMk zk?rMeFqAvWXU{6YirmU6lO@s(Mh*mf63eTnw|9-9p&=Y4XwhrD=SEL>cob{=QI+|# zb_dh z{tGuf(kSotTcX8cjeF`o+Ikg?Fx}{e5ZusaM4G}yerA@GNWkp79UHrz&?~gP|8<_a zv(Szeo<(YNRbyS<2GnplIAK42{0P9a+qZu|mxu@h7Z;a?@HC949>n?h?Cjg}PY)Z7 z4i9Izs+t1V1C8>`UYwhqt${oC1C;#8kt481dVajQPMP)v^SF21ze4LTBsS|CrBcqn zXrSDr4v|VMZFz=`gxxNuheXT3LA8SyC-5Up`Bs|HboK!F<55?^U%3?>y_S`gHOAYI zHKQE2&L^FQ`V|UfmxKz2;0mczJqI$!vVZ&b?Hat_D!7nq2*iMttgSQJ$jwIc`QKlD zYbX+}rLLKjvI^g&UVNBA%ePprc}=TGBd9v0V3>)Hhf`6nzwqzx3MK#gb!u*vdja^oWW0%xn>VZB$E0|x@NsNG z!GQb}x3fqn*PqaX`>&BJOALO?<%BOwfbXWj#)l{0TITsuqcMl1Z2(N4TgPGAlR1l_ zm(9?o;QY$x8ka+1xDIvNl>AJa+05LEw>LQbcv`;A#QUMDQs_p*mJp9JMwaU!!j~Yp zk?2h6)ho!L^ubQ~>hBfB?!~zqw5-8mc5Sblnzo~|BQ(SUA82p6FkU1r!fyv*EV$Yt z)`Mk~?Z(Nt+Ox2v1-pFbjZ!@etXt%eRnibX=i7w+-@Cg1v6U}CgC<1$IfaK~bdBF2 zs|O|X900rM{@SF8`FK>Qx82<)l9p7PW%zHQObYRcb zbd)#;p$OJ%x9?xkH#7_>zlD6`UG`JakQ)4 z@PTCr$7?yQioPd2;^KO#f3f^r+x+ZySs&l^V#9L?qjF%DHjM6%Bwee)MY+$8ByV`m zg;>CdYf7=O@*-(9o#|kH|Neavh^qnTmYT+k8#Zjn?)%z;gjmj(sa*&KBAzCY*E!$i zt(rM^sWBmY*17buLm9L){#jXpabX^wQ^JX+avP^6d*pk5{d(Hs1*9MdB`wa@6Ux|DOxzK)^1iTp}0gk1Fh_+dG0-l+nAw=Fo}tY?N7*;6c-Pf z@AfyzG>z(aD$y%-Nx$OiDp>dtJSGe&+*j3ea&mGfp@h_LlU4W!%d3XmL{~`cSOFI)T6@9L@&J>7-tM1HCGXz7Q!~)nPX8KoT>&l`GyDpxcMG|wkjd$<{()HgVn38_8N6DeHCc#M1V09h&+R_d+PJZ-&{DFWV zx?UjM-iV`JYP)pwrY4ZRq(htTg?vtt^4%8@bnwHfW&QW>k^^GcIgZi@RG7h9HJAv8 zP<;p{M5>`E!^#NauO17NPg=?Ll3N_8sBiL5j)BIq6G>FFC{<)Ys^QU!_E{xgth zBQ*|NuNS3f2d>LPc}H+?a2Pg&_09JBdS!w0#cC51w9v2VA@Q?(v$=GMn^ZcWy9Ck6 zr3EI_tvme@?gN82RRqw|PBGhiI#peekba97mOitslW#gBT)wXWIrRLBmDBoz-rh4L z#l)lYYCL`REF*L*W{96PAT#kK}8gyn!*wYpaslzOn~L#L=-JWd)&xBTBpX=uu{rp=&p8Y=FZ+W^>=5u%)>6T<=m(hR~g=*26LMUT(77lyVEQ1J$k zhD@nuWeHe?YI zq9wcVsz@~r{9OUO6#EMCXj&lNeEUuo(3b0IX_k3*2sK`q>a8IN#IjO*o)_##MBe|O zA9*XV7>EGeC*CI0P6?+JkurB=f)MB8Z&^ZHhzZW&2i|5c`}cQ=GJ^urGVX5I04=HL z^drQpNcst`EQ{U>_}w!+92Uub%Iq9qvXH$N$)j6$?yLd4f+lG``Qy#yt}+RN%$`xMyoZ#@a>G=J7EF$+ ze_GND;cbAuo9KI7Vq#2i*WmQa|ChIa?EHbC+qEkcM7UkQqW?M9GQ>1&fp;mXE~SV7{Gf|1l2YIh6DN zQgw(HnS;ot1Y&o+X81Z=sHI#Wk2-x)cmPdpTy~{tTK@ z(C+=d-N;1qmZ(39>6ghn$p|e)3BmbP@2`JBw|62!<9L0~rfz4L)bG@0z7$*|-Rdn6 z#EPBzHxsYBx{SxPCr=`P2M$GBa(*j8qNTf)T2GM4AyJ<}OlN^joJJI$%n}Qqz@EcN z5PUS1|H7xg@cHqHT3YGotU zzVCp%pAnN`LpyDC0+y)-;7hd<78Zu*|M_#zc6Es}kmZmZ+kLk2f#?<_I~8yK_c7u3 zSBHR-hRF~ZL0~2!#+#ac`>uAV6t^%yfzpDI(mKexnavMhfT9f|MS6ShKECf*X5@Mi zf#prTo<@rf+0-dSBwu&AmU#`mL?b=l2F8A;dPmu8qgJ#orEQIzG~j_9mTreiI9V_N6$J);Jc2o?b)-R(|2Y&0z&DByFYxO!BvRLojh5Z zS5$NZY=!)lxaZzqA0O7l#=XA?IbQf6JUpkcTOl-7v%@V5bL|c-LWcR5x5GQq|Fk-( zay#!JX^Nce{{_r_)NLb_s=OC3-ezcLk`YGp8**An0HHs?NERlmw&l4`7!MV0c~I{M zwPxqR3$hZ5r%u*RBCBNzX|Z3GTaZYHQa6wZ2A>7ggM1 z^m9o5FKl4+d2-WZ;hnsEnFr~G3ub0(P_4!6DmRcYSahOdw&x}m2i zv6Sg={|U9SU=D4Cb~ZoXouaQ8AmcAj-UZUVZ9Rd1{lC7{zu(_^jKK5#zrHBjR%6zy z3*f(hWNiHZkH0x$z5&BpT8|VI70r<0B?5q8s<0!|`cqzo6{_+ZkR<`%;E2#QGa4ur zfdmOPh?-(#$V_NmTu8`_0(T%??N)5+j7ZFqbdXd1`zH(P-s zVIdF%IfDbiq1j4eC?{#`%c@NPqd=RPojn5f{2GF!cLM{uW{UZ~4&$&Pr9sjd|5H4ByhR)isUZ9oz- z`M)=v|ArhNiy;S9)^BAC8Tpn=O@RAe1t@sk(69xr63M)B&X-&RHz8B53qP)Jn?x6b z)V52)X-`#&Q-9dZbqzH&wY*IiAnts)e`Esz{-egx^S{eaow0WHQw)GCN?nGoVaNt^ zUy$vYNf%K0#R%Msdy=kvLPn*5sAVjeCDMCwG1T!IcgBSWVs`c4sQ>p@4MnU@D<;+3 zK)FG_Y&{{M|6(l&nNIr`SZ-g%F)t(OHoB1@pVcJbd7Mqw>bw5$A1zeg7;{rkXOnPR z3l(bvLBk@l(V^ncrM!MSJG(r%s-)SAZoYs}&%ulD=&X1KEgE9E5QG2|soNr4reC9iF^Lu_bKuCHk5C2K+e2`d}+Whn3B{#6 z(6@;0xQsfuk(M^*7clo`qz{z)878){0kA+aBnWQRge;B1%PNa)^Ku&j_im%&Kh18^rb5xkW>3`Cxq07Z?&Am8p|$5??*<)F@)+S?nOc>}_PTi zPEm0^i8&F!w-(S{c%=@ohp~d(d|O0R$R8jFKw*#&L~n2JmEmSya{jEPh4JdutBk!z zS@tA3?M0DQHmwfXz9xX!4J?-IS`OVqKf|4zY5NNebS*(@$)x^l*9Xgtfe6gb&i45I zGqnCUZvD{Z&%41`Qe2_jVx(YszVaU<0@QZxm|cvmf0~jK;{R_&7ujvu^a!N4H*p1? zI&+4ecoUFmFWd8vr{}e#1PM6#k~zWY<>dUrA<4mCK-zF(Ko^ zQ<#nA5CTa)qqB=tAz)Koubo2dd?P{T>T7BxoPViM$wC#)5a72;jO{O0R8UCI)|?UM z<5M2-*gWLiorr8V8OBR}gohS3I5=oFFGwyPbqja67DLA-SIZJR%#4VEowyVYZ@d*I zM0&|dH@@y7$lu{BT|$B6tPEXOR~Mh_%F2qu#fvGc98y?JOvjEDqJkNOp|`LeQrThZ zl2f9$@7zf$v~RKty?K)X+(py65AmCrdI!v`HD~Ioj(LWWb5LK>qKds{Gn#rygNWD- zo{jgzA_wr$!}hMp^&it8wBR0K&k@Y2>0#)uq^c9hU> zv~6!O0=y;sAQ4aVRC%91G@f#(R-0F;vah7EQWAWa>_STzjk#FU&#Y;^RXuW{84OH^cXKgtTlnS;ZS(s`}hX8%kKv#1Y+W<+_+I{FmAX5Wf9%Vwt%Hmo-C3UMVj zpzZcRzmd#NOBs*_O<$ZF3&;K@0&r(uGlNvPaB_keSba5LLgs)#3rKludM@TC4DveR z%H>sU6r3Ew-ARncBYHN~{qHX`xhnC@XgM})zumO!81-3bo{d02Df~RwuU&J=czEm9 z?iP$!ME@G-@81NnO(jc~ZI+P)fp!ULFCehWPt(*M4& zXwYiY9pxPw9~a_27taGZtEKk$!jyR)8AgBbAmatH!Ir&giGDjkZq@{lV|>kp>Rr+S zot>REkO;L4tw7@5qV*7Tpet%teF!B`3dyOG)VM*kos>*Bth461A1z93{Y}(*oORk$ z*48y$6~oaCQ5Ey$80EQl*dh!fga+R zM#sk15r_Pi7tD%kIy;S!p3>>#If2I*`$@BT)+eT+A5$56urXLfKfxV9O~&rFj@zO> zX{gXMrT_fG)vmslx~UdLAD^< zn!jAf=^w3V$%^bxo7fEQ19PeOu=J1!<`-=I@PWa+_U3J~71#!0M$ACAs|ODZje^lo z;(nnS44$vNQk;@>;1fwlqpSHI#PcyQFzkDz#mvqJxnzj4N!h^QLG5fK!rH{TDhI~Y zdpy&#vy7L#r{xuK;iNmZLe3z%{G)lnrcm**&cMC&bonKvg z-C?|@|7CS3Grc8Vzn3a2;!b3y3+0168aEfLXMiS=hL_SPji}O#Wker#R{9`I2sK{TgJ<6BxA(sqd;pdvHHNRxWupu|9=(#y@6VCMq!K$ zWVanIvhTcer*r@QfbjMrv^mPb21EstYVSes7A$aw>>ksLG2S?NJ7-;sKjd^z|=oTV2EG&K@b=4VJtT+#wn>=00-9-n9F(C)%WaF{YWv3 zS$8G;(SwK7S2iQEJo$EuFhS&C`Y`r6!cw48*<^o#5NaKVMTKZ_iJg{KH01>@W(^_f zK2GEB?_V$rg3eX&RAR#NvCPd{;v>DfeTRch%4FR(2cgki1WUIjFoSl zTkQMFvXKLk0xE)Zjw0+<70SLlpL84Qaf$}G!P7PilunP8*>6+~QkIA&*nQRu3Aq_D zPaa3|h_}oADCVUH7K2>Fx|j<9ye@yu+GXEW7K)~A3O`V};ostjFcS4^JQ4(iTxN&0 z$+IJfNt|;6ub+X~9jNvvkqj`u^8Y7;lA9&=DQSM0_Z%1*rjl_S$qi^PtAZ98?mdk1 z8WfT&62r;>%x zPD&pI4S09!h|VXNW#hjVTYIRZr|x> zps#NV(^{{@X+OLOVUV_yL)U%+)ed#M9CV;v4AC{IE7}J*TYB&4?%j`DUqIKVtedOh zweL1A^VlwX!mI!#fTaP9V;kaWuQ9vtUt2tnpEUTnA>?RQf%v#i*6~dBFGjfkBYv`TJ%aLsJ>0TdQ>74fTsWP*l zwR29&IRFyGFd?oZzJ|6DW~Y>wz`g$wJz)L3xcfL8Cik!IKb*Qy zSV5Wl)<3GAAGeTVR?17bE3YA8p$JeI6WcTd za_t9GAPuf+*z6F&lMWm;2@^8MeUgxIv)Q0VLBg}U4oQ{KX z?VOIy>8I*i=$4E-CrKam3k-Nt{Uv@8nQ7CWfJr7Gz|4sD8utjOj<6P{70iIf;>lK({i% z%-xO=>VpV-#CQRb#Bw0Vr8s15EDskN#?Cbe(cq(`xKY*KZkXBufGyHx{%+@v9XrC< zx28W=a_@OB@z`gb_i5=0#yI}MCsE?WsVWzY1Le1hYiMYM8ReW-IF=LKI{)8ueAGK{ zQPY$n@*d>C{;pZbL>oi_^uH5+elq0m6wDkdBj=rgsBSg%BRxe;#^K8A($A3tNu=iw z(5i&hi%G-?7$T;)zhV@#tsGJz*o>q$uNXnkQ+#h3m7xaa0|KNp0DdGRAZO}hnNwG5 z^lUjtO*>1BPR}1}GqXKdRTu*mjcsv>+;-VLAu%p4t)+&gbkro_D80INozQORcYH@I zKSgu+V`S2xB@`3VcsT^ad0r_n<$``J*|m>bKQ*OxvMjO~G z3@MyE`2!9I5zr{Jq+)l6jsTp$G^yu8BK3#{FekFB5CMsai6WKc+>&;FQ2BFF&?PFh zgJtfH@=S5uTgjjyK@z3n*1J^=al*{3Y21sFIc?{uvT5ZF?c7=L|6eK|^?-TJ6eVx8CYaVlKT)H9ETQscje00BZLh*?|=qYh~PvJ7YsAt|M85$~@U$Gzl@d^>4Ur|qaGZg(SEvyN^l zfkYY4N-@*-=Ji!-D#G$p{B0rGmWv2XP|W3hyM=7$!DEVH`n4TaS6A0iH6ZCV_2VCA z!*@P4(c+LK8Z_{AY6mHIdP{ER_0G)A#eT{~1-Jo*dtp^c-bBxRdGyWyuq*Y2SMN}N zMH86oYHQ8VB`plL5#)R&i-sqLC|Mc=k>+Eu_XMTnYuM5_tr{=>#8K}T-kw6{J<_ir zbvYyMz}F~802j>I_9ShVzoH@Wg9WroK6oz zBPV}3IZ9>?0f*Zh44<-+ySb*W&K#%^#o_t$=dWK)RM^}S$9D=Du^n{ik^=PM6RgDf zVNX@=ufagC*|*%#ibxwkyOcWY1U_wnpqcWCRe-qBW1@T>a>aVCwWM<#u~w@Yv!yA{ z6jF}v?(W__^&vr;70ehPMIIrBV=H1}5q~$u0bLlJqqNqN?j?p82sIWr{YNO}qwPhi znM)ZNcW>Xm3R#7xbFZ#C&XvLh=o*3y+M!E;Nvj9#jeI2S&4d?|>f)C=WajK!lNr^Q z83AbZ20jZaSQU5<*;-TkckSA>6VtXXU4~51q{zz%xE5I28?++`$F*>h;}gW@Pg8&+ z*&0s|TCwABF~E6si$8eBdA>_-5sx-9d~a{>gKThH@g3j3z}MtS4oDnO6J4GIj}CV_ z+z)wn_=hx3z&u!tS~yn_~7BbL4P0*UPJPWn&%V)4wWdZ zEA+kg~^-TPGGB<<()C93rC$(7~Xs2}9uz11KPQ0e0_eavH1C zeI7}j8@ai;Cwid{)nZA7Ln~y!E~M^Q0X?{(3PRJ&Zu0^3kq8JxP2#93-cbno%Mh4yQ|_QoUg1by5bm`wPdUj zAEB14NQ;BC`pA@vT_~C5H>BTnEiR5ZRzUw6B;y}IS|rdX)j2Zmh$=uH$b%C*A(@vh`20|8rs=XJR7SBTwd2Tw)m%{VL)bDQmCAgR@HW&`fnVK2W%-&(6upfu8 zVT=J6ssOr38tug;C8Y9%R{0j@O=`*_rZr-49@mjiNeBT39qsa;>rqe1DNz zD+xHwS*Obil2v!K+vhs@9S8&|A(TVTHR9L{^6U?IrHaVSkRf%_ijnQTB{)en)ID+*7d6Qg^>WoKe+*7_Puklpy$CCxLI+S{!NAEW zZ0tQIYKr_f@oWVrLv1GyJA;#8P;eh=5R%a`Z6v4GamD#w2N1avsa+!raWW)IX7N<& zI7F`0!@Rt_H90U-m2R>gJ!iKB&!D&4{~sfcnuz`dmEcrr1UY=%fQc4YA>5s-}@Z2C;Q1FKp%P2Pm)AMBEKgegL%c{Kki5--rfnlMKaF%Su4MMg*W$jBI* zvywviA1I|Z0%Qn1z2iS!0ZsB5hCP+p$YZteip;PlDP`Dx1$HJtP&X`Qcq+^q=__0 zh(t0Z{>QoXyubJVed}B6TmSW~Z?AVfPj8QH-}}C=>%7kMIF9o;e-c>e_*wxjGAVb( z&;U)kk2^R}>@8t8VPIj=f55;XbS6y4-nhCk*8z@_jHE0Z-(|j8wqugPeXK?D`&kHBL-Lr3BAW(_@#`kevlOGiM;^vPdqzw87De<)X|awi$p{RUf_qfUWvVA2$}}zCPcXj zs&SAkq~?WqU>+K}9{Ju3Sp_?oZWLypvKlN)k=jk2J2!;NcihTa-z1zDlW{tLlA zQEg?7hxwH&SEiu226RCbBEIdZ)2G(~X})sxDzV@ny|ld^B~?M)rl0R|z}_2)#Q!YA zWpJ@NE2btwJv~T6$WH3|Mt+#Oj)P>97*wVhLOL5aX^Z|ev=0-WK&S31x35#sGcz7q z8xR#xPG!Dost-V8p8$H zkIk#VC=$pZFVduoT`P4r7|r?TM9qsy(<#N&pp+O)=|l!jYR}CVXiUC81gU!C(-xx~l|L3)NEgReU_xs}C$qW8Z|7Jxj z0?7@W1b^i_c)HI#&*`u~-AMWJtktaKC313AujaEs9DuOg)h_I0^^`O(TSJk!9jWuF zkdTO*78VvTiU7m=_vQAa*BqB7$G)`dyOAb;x_Y?j!cBD0nZpK6WZA!8pz+6WIN(wK zCZYiU>-B$XyZnD<*8Km~yUv4pzH>Y=Syfe)VZiQdo;!h?`V)p#Q*f;7<>%#LC6=6` z<$YmUwE?f4KPTZY&`!)+q%bn7BW4B0p+Sm^~WQ%S8>kj?DQ!I_$ z*Pt*+7SK|&`31H#=)u3{%lL%aIn_=`xAVsCKu{ROx1d_IUyO{5sIFr(I}5IB2e#hF zNoC0@lc8vUQ-@a%()e3800f9^wTJd$@30H6!x5ri{rkO@r$64?bms2*jAmiGanXNA?E+? zv9OH*dC=!gW)t|(J)~vmNW0(s;loknvGdT7cAyXlXi0VTWDu!kAaA=-1Oa!LkCWE_ zPoFT+v@gw_){T!dxR1iZ6a5}{;7bTERT1xL?0u3Llu|RH8HEZZ4J6BP?s}(84&G3d zA_Xi(u0|PQs#%tHtH*i`jT-bRA%rePdz%38(DQg0P?cCeJjQMJHd}UUPPsl>dGGlD z4G-;KfBV$p>NLg^7=j{Y`vV_ksHqtSdBDK8%xZbZSMu;ROz_4orQ@AsC=A?Cis2H5 zoaD5GPWa{)Vi%Wy9kwY%mUvly7bgUQ$C2256$_Y< zR0YUS0}x|B06=_%<`sFITx4erV6rw@Z~i!_VXd|I~3{XO99kXUNF=$J0eI z&45kLmxE!*K+=(?^al*AsYo7qmXVdU(zu_XLWA(sN}fK|lJ*Ba)8^vl=9aX3Y*9-a z#T{X`Mk!4o$Qh=Br@`Kr;Cu%~4L_lkr~`@fSM?p5jz?RcR|%CgnH)2GU44oGq(JgN z#7{$EQ--vRi+Po~FJxWzSa8Iq4gs?B0U4oT6e_*rKx5#iFzZfYI1Vb7&UI1Uh$}fCCB{6T_Jce9mqTWNH zId0sznNre!@7LGz4xd`+s*%Xmy5Xb;+|0pj-n#V(wnTyrU;}8h4U^xar8B|4Osr_^ zgNID)rxi2MxK3ocfBkxyw*{S5(7p$wrv|D&9h*VGtIYIw{ek6yVPQf@`mF8kla$`q z)(S%7r5S`Sa(YSU^{B=yDX=c6K|07W{j8GYiSu6?Ks_X+!*{aQ?L*fyx*_?>ZYOI!cN>LB z%iUuiVQ{Nff`v`73`#i&KuGc7*?RS`+rfnpI0AfI2ll!AF4V2_px=gIk%jNHhGf3U zb;z>AQmbNOqGlo>|e@x$AWXB(S&Ajout@9 zUw$z(=R|K@eG7NrjnJA+MTY<>%^|2EX&AIlq>mmQZe2TovepHo06-+qmB@x5GtFg( zOW|=m59u?IwKM|(Q1qBd7oq764mQCaG$f;r`he6{*NUJpl6D8ZbLnQZlKt-3#W*Nd zsTN5fiMw9=j%yXZDUye<&?0h6PV)W=40#5U09->E{#b+B1ynPg1906wElLwsQvSe@N-sMj_N4+{%`qgBEXybJZCs5SS)F-SuJ zD?4Nuh_i5EKom~lNZ1V!EMdzCXECaF4hAw+gVm2V;a|<4K5;Q@OFRl?#;y<|AaTsA zEkL^fHoIQbHRu^?djL;J>%kbp%6liU50uPR%!RQz144=26PmFesCq#E!9%E4A-5P( zXpFv3%?B*39B5EdmTcX;nJ6TV={w8D(kE~<6}T|UUYA%s5T$-p5`x&^qaIV^m(d7p z5Cc#-E2;U9_&|m=m0~vBnMpopkF&~`2jzRI(>QhvHZ~Vh;ShzK7GU5!cyht5p#=3) z)|L^p%FaeQOwT#&3lNo=j5BCfDj}d#(nKIYF-Ac0Pms=7S#%iU;OTn+X$t#gv@GD{ zNeEgAWr0q-6kSI_iHVCLQdqNe`)vlk7)FB!s9zLCT3A=-Y+M6!M%adnd@txdXVit> zsNy5v3vbdk$7P2Bwh+(L}YtO;C+&@fyTmk$iq0;tg(+J!WEb4&v`lo%xH>6q6H6w=p6vKlC^1 z$4x5+SS~UxN8)(GFZ*04p^1uI;(TD%76=)<%8yhf!U#=n4CfW}j{Hq+7g|L64At=Euu+f%B zmIxz-YpoINTV!vtAvu zL+GWS0JonlsLO-g-Ktu&$%DHW_a|m z#nG$$N3Tqz^bA!g(U&-r%NKd#A<29CA)eL$?ZipNcnzYuYkwlO;RjH*P?J8nz7pgy zaHDPVyBzcn^_X5b^5R&J`4^5JRp@}NahOod{lF=?;5y&wu|neVe>G(2otQF!uB4di z?%A=vJ@?P!&2iU9-$U*;??jE^3HkuE60x~>_8*OQU4=dm`Lyqv_46;JTr{cfut0kb zj$Eldd)Ys+L1@~)Z73Uzb|Lh344#OvdRXYIQ~LBq?1Mayv1gY&rHLyi3P+rjM{Zb{ zvGT^hCz6puTT>C!+?47VoV%P`>|}EG--ipJeUaxKT>&E$kXs&q4R&rtWmyL5?J`GZF2I%V}^7bA?I(RfV zIQULU3Anl+1<;CyIK}<}40XUDlDXH7N1%`C^?2ppf6li~ zo4o$$rA-O@zq;IW8?_4i&+V0DJxvUf^q(Vpw5*YDgiDumY^N9>Ms0~^x+7~Ay7{+> zc6uK1A3WL%9@A#2PqRZIP z;=&KaT$OxK2uRh;cPqmq0eOc2b@2l~j33;1u}Fd0QO)`kbl)XNcA%kd>J+9nNc^b9 z&Yd-|egx^2VsRLTK2rD3ojMhC$lbjRTQWt7j&-35bkiMQJ2JpU4QD_=9qN<0k=GG_ zi177TAmbT2W$vJzm+;%>kMFNcw60Js&RE;Ll5tK=F+-X3@OE|F?}X180HFaQ^UR(d zeW-~O;b8n&IM|pZSd8R(JrGKWB&dD>4NIpeKeV+OZSj+_QBWIg zfKuA!$H$GZg{)lscIyVCdNSU5@#4j+UD$WV(j@gdcTRImKtW#`88)1%tTH-~!atG8 zC^&}nP^J<`#uI#TLi`;u;i)umazlrA@O+e3fC);l#Q8&udsLfi(~qrPy0$n?%37c6 zp9-GcP8^dlD8n4^Ik+UuCy=k5F=gt7JAdJ;v}wiAz!S}g^eB+pHld75ID7i^1XO|n z5WDE2)b#W7^PUtgEeX{>!o@76=Dw2^H4S}#@LTS|@2=HMF)oG(18UnvS5We$;pG=O zsz=uTHZPqxAT`B#VD$IM8*@f+vass8a^~?04gpoh;1I4CiBJh7iSoNj#xcY~n;(4p zH)t6FyK$^ag&oG zR0Fa2Hx);JVR1s1p>Nm(1{}kJc+sOtMIKm1Hc)`!5!4E9p6w_V(izLQVJwFv9{?p7 z&vHVL9UfY=yfS8yKS%71j^58p88;VR$7eBH=X#A#{l>p)#Gfpy>2{-VU;g>#SV$I< zS+t0kb{9eQPz_t-SirsDK$=LT`8}Yv$Qm?J05^lWz&Qt#d?CpA$YzGhP*DC6-hToX z&PNI^LN;Ky+b6+H>_Jb3e+;aO2h#N8N61nLT}$^1BT@o(s; z?_YXn88{jAwsL^DC`K874*dbCJ97os4tH}=e*Wuasx4_X6f$mltrI+a;Bt@8BANQ3 zs*PV7w{TcG#;%#5>fEfZrnzOqh9I_w#w0|A$&h`O)d6u$7W-0ll^>8I6&pY!;3Z;l z+8v-;GfD?!tE5{c+W}K6-vF9if=WySd&Vs&4tfb<4S;N=+|`wffIW+bw5ib3y$aNc z1&2m+#(-w0r8`*bzLz8eNIhE`c`L#qOU#b8f$F&4CL!gOe>hZ%*k*>aP)T?`M@u3e#%q$;{K0cSM)eL_5tCRy8CZ(a1Zd)LSczXhT1ot-}*cK({CmOOAyOf3jYIQiM(J8}b~N>*ME zlA@;O22~lR8Y)fLxvD^kee~&j&04nmMAI7)G!Z9}De-iA%FrRmgO{a#2*}DX{AB?N z0TFkoecEjD@oyhK#JTK%ITV`Zw1pP2f78^kxzl`CE{>?^YZN!u05fOwd#Cg5B?C_T z(^ZS?G-fhAK}Ppw3~t?7*zNK}VVoAO9=E+}qD$44urO|*EtfA}CZlJ_+NNm40+FI7 zR}+))riQT}-5?ZEHK7^5w;2VW@vNr9i2P^{ItE!IIu#)oO20oZsb|(&KUH1bOEH!= z6=E-}fA}eHap%q}CY|tK-aowO zyVY2NS-!4yTsI2xKrF`(D0ylNP`Kz`mgj)x3mQtCHq=-i*pl`b==F0nuP1mZFd-wO z_AipN4-IaLOg^CMJu>}xEZ{z z$1{!XoXcgeEzQ3YyL0sUw0T>0T~a9PK^_H#fhXh|lWq&KpZ|uu*SB|ZG0l(zgnvQj z7W-60bHI7Xq%c#Bm+6M2Gs4~iB;ICS-EnM9+kwcEK~SShUgV8sdx2s{z(Q;SBJ+x7ld;uqf2-SLGbG;^ zzRUro%XFgw3QEHnWDLN%8>0yLOe+L~gX+R_F+xjw$5=6u{KgHk+9t(+&{Px^5gK|7 z=L>Qj^{58YigEV~Tu*j_?u9^oNboq|ZbD2xz)FS%62}_y!B+)diU{uqaTgX zWRlna&C1G3QkRTjhoR!7Wc{>}GbfpH;}Uw8Gm)OPdp<1MH0ywl@eP|N>D{vt=G z#nF{-n6&*VlKy(Sx>(UmEiA0+&#5e{-b;t7U$ur@0c@*~h_GZBF&>D{k9^|P51xP~E zgCNKxjY5kJCvy}PF~e%(HdpZoW^yP_E3V|ZJviFITQ})kbDYf^>G~16mL+ok*ztpjajI`bZpS+dp;t9hF3yS5^nDc$x|t4C2}IB z9Sk#&+TZ)2D5I3r2TK+0kWw$a|7ZKie1Ve*l8s#yQ~Ub^!3Lt1=*El(=us@Cg#`sU z88od0`rSz2iv!o5ke(tpLAAwtonX$Lh0QzH=N8;NGQz{n9kz=fzcf){@$i-;t$!(` z&O*$sAhd^S4gZlrUg)7nOh=v|3PWr5kqsYeyLl(nF~9)Z6aAkIN|?fXTgO5~*qJT; z3=azdXb;Tr@F3AnOg?pfz`5-Q@erMX(-7}Uf1^A*=jZMp$if5%t!r2i3iz9_V>f^Q z9vT>kg{mw}`fpjVAuJGK9*I>Pl3GHo`wY%)wh$a>oTfX%hTQdAl#uL^`41A1mpcEB zK(=Ue%XL9gKs~mosM-=lr*^AONL?kWRE!(W4hCz`Y)V!HAlrW$pem-Q&b(Ct zi*Acn1>F;xKK%qND2gA&H%r-LA}{_tcIn7!1goRRV4;7uO+0kdtEF6sQAdq1>A9-0 zJ~}l@VeaIN22DuHX5;@VdTfRaid2mMcL>_}ARQfAT1Sy4Ad5dcM*!ep)?eIGTbIEi z6@k)oIWtdEQZwq7oH9%ekk^oT0rT~B=B<$h2@VfI>!Mm@>oTSj35jQ}{Q}S8=Q}j? zqRE?+!F0E|_3n&Hg^3G5Mj)w~|ksMTjVN8VE zLG$rP(Wb^jWh&Xbh1a9Bgze=zOfl7-b7UhD5vugb^)et}K1g>Eq2z3ZxY+#t`{YPn z+i&t?PhC2gT(3|66JhB+0f!$%OoeYgT$Nq2gyaCAdVajqnT#a7uBymqP=Sdh{BIVX zD_;5m+jev2fU}?^gM*C5R{0JofvlcisTfbWBa~j`rjOOxjGi;VP&@P7CO|4j-yfZR zfhY;u-tFlJA4H`N!%${NLLm^bc)K+ibNG;$g=sTuvG?&ek8qq!5fMqgI}AR6N@kD_ zfr!a_Afu$(LL>g)AhtVn879F{B9EWM6x6+s64|=~a*x_J+x29lQZzcUKX#iDv7p$H z8-Z~drJrpAH$rSIQ6OA|S6CGYX@Xf!oKGDRpN}GM1@#T+RaZJC35RbC= z#+zMt@Nmcvjw!~Pc6aE{tX{)K9yRMF2KgBP7f?+Fba9xfgPPiat%R(@(oenhj(Y!7 z?$N^GOEW+W)8l~#VBaA37#6W1o=Zk1W%uaVmrq`J)teEfVap(goH4E?K0cmEJlO^$ zZ$3Rw7uoRLUIn(8dzbh;)YI;9sgw2t6T+`fSRLe^*fLF(eG&Q+*s1|Xxp=AV-7Ksc?&{8C-(Z7L&375`j2B^b!X{93uR-wBhN~B+(n}0rMcM)o+dKMs9+JQeIHYSI`xT0^?** z%60%5rC7+^K)Z+`SE)xR9ml+T;5!>U(gxJTR3X7@*7@-fW(dFP-rSWD%_cdYykRk6mOU>Lu>hZPd9GR@6C`=1n!$ zRgqvpm(0PSs9I^((+^3=qf1hTr@hdUMSDF18Vmu?(vTv&jkViNmP^|GE?t*|r}z1dUF5U4xo9T+V`RVg~Y6 zb{WUcsX~@60!(+0OwN5L`&C}k>-=X*XH`JSso!Ua_G^%MQhGQxE_lNy|5Q-W$3kD+ z8R>ar2~M0LXAXiMBq)z@XVGc$=}@8Rb81&aRHI?#R9r!wrBinX zK5N6?h6THQw_==~OtK%pi_)H8od5zN@ePWKC&(Xz=kO3UCy#}Wey|xxzXQ+XDv^}jj&ZNFBQOmMMmw{mx&+gq0iFo`J%MBik5~QqEo!W4^qJtk z(Wo1W2!u!iG?tl^lA4Ib$P+Dn71NtBUGXSl;zLMvaaP!2th7lVmR|rIBPlroIB?<1 z+t;tzoy9<%38Ms2K4-au>efRr``QUiD|rZ$G)OL4HY7tqkdxxX-P6*%Y(EL&>A0TJN> zN!%3-*ntG;trNOB^Xu-+zGzV0ayi;&|B_$fLx1U{&1K-Xpc|h|J=jmMn_Z9(1h=3n zfA+6nxGfB+B```7SJmYefOo6nkRmVqp99bwKE^Fi%YFyly0Kt!SrXi)VAKwq2~RQ? z!7-SB?-_(mP&EjkcWirmPVfOt$QppUj9*ghB&Spe^CT~C<6c}pkz((@{c8m;VmRuDW3ey=D3q1e1 zC}*#Zroz=o{4A66mKOdrIBf&5e)+v~LE1pxVJ~!9+12*p*&1J*8>}OSowHBp8lMNq z{+Dxeji+V(pI7`=$;Se$iS+ENzrU8L=JJqRvbo)1;TMy2US49XV@F2Y6&|h7cRzpe zqT#o>W2=k(&kM`!zH#`#*@6b*GDsL0$_BN2@9A$3DUU(LL3so+(0Nd-|MQf|e#SLC z#O23T_kP;I{{vlhJxEquJpNS+c8b)k%{VD|A=QAH!0go%@l5bd1`e?7Ub|D;&%g{ZK18!ysV`^Ln#5b<<-QVzEG@?83>;t!3>c=4@~JFeYMd^ULYC z6FENHaNW7j3XIe4la-YvR}7Np*;8$Licix5^;#+J*!jpv`)|&tPKhJWqEdy~e4}`A z)eD|YA+%8N@_lic0fM}mDtYgj~} za4Aofx!ExWLMQx{{5h^NLOFNAOBfZEaiNajS=AVsX zb3Qx8VX_BfEqM8qbSf}Ol5hcQ)ZDulE>_B8c5N&uLui`ZV*yM-pjYzZtJ8`##y^9* zC}11Y9Ujo#!9sjCnLVM(=K{}FIEF#jA76od0erarMt8V@kx3DPs&+3ztjm{Yvk37f zNd$EhF5HMhlU~M=fTGFj*P!Px7%LL$lARGQj*OMo{+WB^u$ZZP*r>~}@9PVCJEw98 z>sA`Xm&N}ht0cQf_W3DCg_P&d?6)3R5*_96?5>5m-wqRPrCpI}=b|FxZKE_I!}AxM zntjGmxbWTkxz7r{cef_JIGae+Pd^HADR*Nv- zg2t5Y>l_SI^YfM9h#9Q>?d*UF!_!f-4Ygf6a{8{KQNKR4Vl_224Z%X^CmK(r)YKYP z_&xDO(&c6`c-1_5HJzCqtcinjQ>YRkZ#`Imm-Ka{=WjXu&Lt3a(kJnHyoQXQ+<68G}9gUv^=;QXLn6m+8Qk;b8PnBA-tBA@eg+i0yt+E#q4oKqB7fg- z-eL}<;u2w_POp0!1H?fvm@4YBAQX28sG7UOzZ*xr!-{q}x;p z{T~!Sv8bePf*X8lEe|QLp5(=+Zw_KoMHI$=@yl>Cv4gvOV5uhOD8Pb$*iWSXUlSrM zYe{bjgTn1a#rxl8r{PRULMW0}P^kS-#>udTBJg2|UL7u|4Y~Hdl2B4I!fYVO++E%% z!q%}KRc?tDBjmXOMelqyl|Z4Fn3K&PJVt0N{@6o zF8Z;Wv#*30_%xZKb@wG$vCkbHDG)@S;qsIV;Z5hj>Jt#D@RPSyB(eZe5B+{WnvSh%lJ^p`GSz-OnwAGEu1+}t z{ES}ALJe!&s7cUuoEGyRcBzb6YlYP#lS?h90Q$kK7UNlxT!AjDr-H0$K{?R&sVDI*MfOVZAX9DJ#Z)VYz}p_SrKj z3XskqiYFx|ZU$sRkp-{V$lFrPL2MLp?6SJ#!Ze)$`o#v7^KS$2#O3f*gu7(IoDR6t z6$`3Ty>J?C;O+FnUuIgh zsM+)npauVBBF5h~>~z&jkf4*1VQxq0EyU=Nt%a}%la`Zv2`CMI1s=vZmPSVLPsEPs zB1d>mx55H#2~SS5r>Rmhh*;I#1^`>PIZr_foN;jc34HUUm`t5)k@^-UOGrb)1ijr{A^7f47*7{A`6r8R-hW5yBr`^R=c zCe{RhxODaE0kt(KO9gQ1CpDtC$m+mcEKXGp`I_R$O522eSL>>u=&BqDA6AR!KYOd? z`}g*8dNi}SNU76-*m|$yC>nY_8YeT8wnSK{z6RTof-Rqx3cR=^qmptsP+iz~Jl^zbCKDJCAT@vf!yQ?S`VB*q zD;X-$#AK2SD$2wSOKZM;SOkgGcfQ?orXsMmdOQ(uqDQ1mfBpN+C3110qm?1gs4`jB>> ztzY4dH{Jc!51L#ym=^pxes`JKcmn1q* z#P3t$J#tRCJt@q;Zg8Y>mRD5K!5A^Ica=wtITB?a-)C4~j!DFF6!X*v6q(p&-fha) zQBHZE`o0A3WEg?|$9aFG72m#oGW16wy7L<{4(2uohqEFEk6sTA6`U0;)E~P{Bp4gQ zMc-KclXDe2O{BMq6owi~7H0lnHU9agY#Hz5A?l*O5r41Vd3&6j;zbPFZW*M}RiU6a z+cQO#7PfjnEPnFj)-~y*XtThg8KeA1CGLnS(XUd{82$4n@tm--t=Fq~*+8X_!@mUD z`0*cB33FOAeb@6MCNB}`so-6Kx1A`achuO~Ty=Y=Z6DYh+U)i{IrsG*{yQXR}oKl`EscHIy}3m!o(`OZf16xn2a2KRT(TBFk7YR zpJOT8E!QkwvgDqevYZx3A~-0!8t+(sclB`AJP?13-OFdTnK*P+RXurU+vy(j5$!K} zJ`DQ=8Dc$!M7w+KtM9ZkUeNZFzYfM=&M#JI!jWJH*L)*=m}cS&s~!H^fw~X=mu$Gm%5_0aGZI%{eSM2B)hKUvzMb`M_ zkQ14f!pw<6>6qB24>acivQ8yX%iKIFU8M?49d96R^E380o&n3Aj3ZsSnK4L?N6-F zOP0AE+GfmbD_SybC0Y|LKKQJ^il?=BU+=_bDD51Odgj695lMv#g{7P>SSFz6d1|NB zj=n_a?QzVuxmR#|LuBXj{cq0%e!bLioS)+Y4P( zfq*$Ys$%fZH-DDID(A`E%xv`C*?1!@FZe^M`=kpv_(e=F8EgYV zyHuO!__}F}wz!q!LO$Nz@^Gl0+YRaEUh=& zmkfYCG#;ry{i}lzMuo2PV%s%tBcDWB<0YrlJQ2EjjselA#+iN&+}d%;4jygwdm{5j7Z^83yB{%kno-u!%(nsPzl(20SR z;QMv8I(+?#i|;s{TJdgLN@f4#nw$r5>2j1wR9@EO045qJ<~M*40Y9CeXn>Pmxw66A z7r6`VdngK%Q9yhRk`YxsBEYm?C}rp549j*8x+eI>kFP<_6=!}hcO%}A$y1E&6$PKU zYuNsLpZ0G;#Q9Fsbdk2(gF;-_!+iyi$tGSpR2awUcO&Whk@^^N2YVF zT2c=441BHZsBW;i+Wz3yN?OT_S9EeL(SoAe)Z05R^x{RCZwr5-Ny`p1bVK7YHmGBX zHDmYeThlb$D?N2I$imPl?FlmI!*Bcouv9$a;^Kyoo+-i95J`Mu$*UYRuP8)Uf^B#~ zrRN#Bynm#Y7ohM6jR%i6I5ecvLTgNOEFr4oK%N@C2Bo&eki^JS&jSG znsw-D9D1^Lgp?!71+S2k&I;DNHPIS~oUxISxE2nCiwMM}F&wcj$*$vd2hK>My~Lb~ z&puB?B7zro;RsZ6gtJ-=rSr%S&8&QW1i=4dzE;oApLHOLD8F<-6}QF914SPh;Ay#= zk0^tre6t^wfqObX^KTY#+kV2=%wUYC1+uzr*RD7~neoUXWuS-0Z!ah>pwtr)$)%8GT!w{f-m zQFJs=Aq;Y%4KcL**)yYc>yD0~pi_|@4vR-UnG_$t0TtNuGGVK4_b}i9FOhT=-eR&Y zT~9kUqf+#4+R zxz1&xV%mR=TTv1F@^l7@M%MB8n>S6o{0LQwM@RM05?;ok1;wMWrDc=yTdI*!6H;Uo zGTj+RYrJb8WE)uH&}=tJrMR*4RS{l*Z-_;0h5b#LgYyA*-D_MiRB&sMJg74DJeQgNLc*7 z0DH=-s+lE0a~@xaf9I5fdyuSkV0EDP{<$6wps>Y)EplihKeH!t#2be z5k+6a%@4Y4*JGk4ubk_jRruSm)6e5_P`-fib1#VX1Q{hM48Td3<6s8or#N34F&j~_1X6Bz4EP&sxux!a+4Lf|sC&q~5DS_54D z3kaaypUgS@H9fzp77#`fuE`#Y%o`Dds>0})Bf!_FOeF-=Mt-JfUkARLk+uBHy(^+E z&?(z2>InGS)s<>rV!dXKXxBpQn+`QJ@UjChTW+Nnjd;_(5B-Tp_sYTzdC9@NHVUYO z%Yll{Y8xSa3kuZdXdBW{=P&jHuEa0{F<_)qVq?_--=rT(nGcmcp+dHqP+M1qilbQ7 zBm;5rHb91{FI?BJUr#vW%Yjh9Q>-)$J|HlL;2%)Bz_Jq%+p=JVW@BrM)66hkZYr~l zJKC1Ph=*zgkzk~_$qK_+(DsFkr&$$QB2_&vK1ob<`&_?)II45|9VD) z9Gc*U_90PExp&V9I}r*Q0%fuAF2R5ny`w|YIF@2DsY`IqDra>V5~lVHp+tvXBmoj# zfDtm7>E%108}9hxL@La@#)^Q)tGjUs{u5%@oEQ(mP$hh2Z!K>Pbkom1Q~T- zVAvS?q;C_AzD2??9ap5}0w^gg<_jkCZ6qc_5bUK%^-d_fT&&;*ju{y8vsrd}t zrqh8K325n}Rytj){4IiWH_am71ox#5Q8fcP4!*SzZG?$KvaN*&4pevdRR`{N9RP~K zAQmy_B*I2?xN7xxqqnx;C!}R%jW=&T<$lu%>yeL-Z@P$xgYQuh>u0LkS!YkEHIihEc2C3+OEKt9 zQ$7ZlXw{ydzur-<2Pb=8dvXe;NR7b%ys@(m2>2)TOlse6rb+7@-yS*3c_a`PnPEqV ziYF>EeQ}7_NU{zF?5SGUc&LS56!6HS~dW&yiHRD zAr+njYypa)a6$*o-foIJcFCGO)g!y5C`0Ib@pP0Rq1GX zX%xr93u9A@1aSBk*akHxCQO@-?}$tSt4}Kw#6cg_@Y^HuDG%7b{c=}fzoiAh`xRv{dD+G!2S-ptXZ_==DKzeJz55)P_DSP^+cp9ndC1W^NT#bJS?O}nqp z6Q3=jXnR)7`|EtuOOqJ&%;JG;TcCQZQHk{YTAVL-7)2Y9cCWOB{w+dSS@J<#Im@ z>RnUa;s2+@N+#72=-?7zld~s-md#~i*UVA#uIpL@GyxLao0yowYc!5YOi?L3`Kh(_ z7GannYQHXHe}$nx7VS*vWhsvILyod(A$s;B|Yh?6Ot0 zxG8#Lhvh*7e#>#h9Zg$~?m~z$0a|E5*JZe4M`Rwf2biF6RyZ6A`SrC!kmJ zhCUjlhDUkSZ{YO3iJQ49J_Cv7G=__H=JlK+*d{7>rWwwi8za*HO)dZA+TQ+tQ}|XQ z80`O3z+y2d3YN{|6b)rLQ`j$-&aXMW_6fOb#e2Dzb1I zBIP0)4iaA!Qchjj50m1&E!CXcmVdIpC}#F6x@oX2Hb`49EIlGzYwpcCF9YoZ-Ceey zw-ob@OaJ=uRoFP@6_z2P@%P27eEaINyw@Su{!#@34QV_|*?PaYHyDJMo1fp{YJ)R= z>shCieG0}SUFqp~SdaifjQHjD8V)8q$&e{A|Q?qIm918#Sh6HxyY zX6mE8P}tF^z3GkYoJ{3~TV0$|8$L!at)1knvMN`F$%gJGqx-uTn^gk#ejZ^T!vVGv z?2l+w^*UTa@aW^RyDJr0j{|=k=o1~(h0unyro)yOL^Fq0>j?Z zDBjf20NcN*#1rpH;jUSH>6+Y&K7RjX_ksQThw^k+$`0*#KisJ>sq5uqt$NBCt1d=I zr^uw|Xnr7j(=40Z$d2q-Xt8X&}v1F#C_j>@W)D_jJfpC+W8-a z8pk{1ne=@x_OHV7LQxkX4yt4;(n6f9SaV>>o0FD{p1c15X&NgpgI(_hgNdm&2 zhaTuYXs=eKZexPi?}*%yk@4QibnYfT?LHT?o5!UogKZD7v3dxlJzAYJN|vL;pb-w0 zL#=B^lkmO1#*7Z%;+MusPBcRb)Y(;PEkA|fQs=TZWPH%Gs#VJ^yc5!zm9r-DXw~9O zPdk8Z;$9(pA0}&tL)HExpNb7Qlz6O$!_&Bw%RKrO{TtW(K11KVJ<{ggJIHmlk`KL8 zMZ2C>EkuItST78#owyhjVjg`{MMa;@5N2u^ko<_DrUNf}R=6TD2=IXK$^~)g>?^uB zIh5qSJE}IH_j}n{@%-Gki(_bN;!;2@Xw{REm*0k_8h|y4qYLoC$uJDqM~lAA5?>Z! zdUehMM%@A*aJaort2T0F6by%@lYH#I-o2~%+Z_+pG*tBNE>+fJo9fdG0{gNWj{4H| z_r>M8xT3PpsziAah6bP0E+Ba9wiB@lIac&~YivKlo1Tquvv^8wG8ijn09c&FScuN< zFs@F*iX(3Gn&-y};QklO%Id+>IzrsS?fFKKZR(}&wVp%of*l&!ST21g<|dnj-oro( zty(zXh-;ymy9ZM57I;f~Y@zXJn3kG1uXuR?9<5O+hW(=b@)F4Tv^V!1UmM{6+)HP{ z#aj&=HC)Oh#Kq~LrB;N8)-?$D5?bESF}ENvBQKOjoB**ACBN&Kq61G2i`#Q`muwO9 zJx`Wh737qo##^FghPg@NZBG8bqk%ES<19`=t%J>4M*tbq`)d~uMc zCLvQkv+qINj!>*zV9iW2Z*87&t|KMv(-3<}ug7nL0__IPD8Qs+x*A*v43aa@ zP>-c*;rx)>41wifZp#eQ2!NW~7UJYV&Yy_9s~in22M?yByJtG)hT_mFuc)Y~m*8ZQ zt$U5skh!CFPb<6%qxVB|X?1$Z(JQ#t#2!4&}O|_eNU3I>-?<;S)Drz*9IYMXxrVHT5 zaD-GluGkN1T^p{cPDOuiA~^R6uPF!@& zv!u&|i4r@!^0?#^x0lZ{4%g*|WRF;iG_0%xWpaBHq6Vc6rV$*Vo19B0t^A5sr=iam zx7LB2&wm@|g?+OPCdLGf-@A8Dy8-S{=|orJ6$KA0i1@*XeLn`w*yXS0;0@OR^s_K~ z4F($h*P4 z6^(jx-%U@8Q+uP5MBIMtF6(z^lhJ)`1R;$I(wj2=srm+^9g&<(F9ADSgx5OUZp6`)Hn2BO$Nn>Gr;#7k=XQ`Y$Ewc{^ zXxYEtY;UD=(*VZ>wxJyq?%*=g-hZUnqO`bmA`7EeGGOOr5_$>ZKQ)>$r+uIjoYvjD zkjbyr3PmCDIY*mAN#(({F7Jg8LaycBywx=LBerRK(boJB;T;)AMB<+dXTMP6%9G08 zzhBtIAs-c(R_JrPROco;B@(ef%ap#DFnu}aw+HS^L3a~+DLQ(;T5D&gMbZIaaKIFn zXyttjlDW(=QLE+1$m1tZVkY?ea>hRo(TY<`DU!;$q4d=w>9*N3 ze4V)oO-Ox)v<+B1tydOS0K}&o0|!|wJrPY!55QOzO(iZ0KH}+Tkx}{ zd9#|*DuW$QT(i6zHNA3YO>dU6a2e!=aP_eJ=@lsl{nP%7V=GRAJ#Z?9sBI6qr58<^ zrLf63vSEDGekF<%Ks->;1lKCw&zah8ueP+O~B~GJ1ZiFTo z;q=zWBJV2b-O?&FkqMsu$sEXzOBJ&U@s=Px@it^EH4yW=mkF8L!B;ZuO)Vlk_>-?O{m9&nlPXWWS) z@Jq=%n+Og8CaQue9`Hnau7T1900bz-R=X~6*w9&Ob*AQ8i_XT;0^Lw0{}G=?mnUeSqs$T%cbMnS1DF8`W?TT3{5AHiz+~boe8dZFN`M* zd{MO3E#uH$4%z9Bh$!aDdgBk@SC__jJv7*>WB<{A0xn_=?zcUv#;lR+^?<;kbRit* z>{;<~JS@q0=vtsE3C1@uO!=jG9Oz z0Fgg^8UFRSDiVD`(CFI^1uo9#6vy}32h0Pf!&Ne>#42gH`tZrAoLuU@%c3iZmAq>E z8r3Y#_ilfeoB4^Ki^+LJyB-B0rl{(`Y4dh@2nEtd+>fnX6hW{ z{=d&ciWzCU72^i}m-rVXc@rgB-X7^=WPJNp5PCSA+kHlnc#($8whhG}x$n`S!OXmrG2>AIH0%-PdvwGAe*Ufxgc1JcXQYinvHii9FD=$XN2BXJKPAdxh zy-nVBHCz?d5E5??oNMarSAqcdmQRHnFo=E_nNBnr4+%J_i&Qm&i&RvJmTZVG7jgh2rC zDS_lEOwARby*L()0iq;16P~UOFBN@bhUH2sBq-eKkm)Aey48Ury()P^@s9StAKJUB zr}w;C#bz!>Xj$R-%37`KeNhpNFgmh$m~4Q1F97nC;e7bZ7N z8`z*Zc(>aA{q|BTr1>O715VzzU71l5JL`1daJtPkUmqU=0$H+Poc%G-hdk)t+_gCa zKx8FRA5@c}kKNGbH(WN_zzn)KeRdhZ(Fr-D z+M`(of2faJA9xQ%GaYmgOHzJ|n~>tT7Ne3id&?B}>})khdWhC_>810LWKta&*0eJxg+k8;`4##=%sZ-{oSpR|H6qf+`E1@H&4K+N z+NWG*W~v(=0!$2)|IvJ3*vU5yy@u$=GvLID#W)UMb|Q6{E-3i%QIKfhvV(un23(Ka z+HvSCIZ4azxO*b9h(1SLODtQ79uR1WQnUN}X05NgfAhBZQ;5c|&KS|&_iJ4CiQ;gz zqcG)40&$@HWj<4*yQ=5C5He^Ihd5t_cFPbMRn|dx@#(Y*9oLk?DGI$T9kBq)ZXu6! zc#EpV6zf}m_wMU3RBXRPG9_kGB)%h1INX&h#{Aaz;Q%C(@dQwXp@e(HrAvD!`AgW_ zu6;s6>*E!_eeDhm-a%Z2-6tMJF&$e5n7e^P2SN-ixQ7n~Mk>UZW+rIuzO2B{7(Su4Ay9mCA z*8inVtn1m9ish@1Ot!WiB}78VFe_$GbbxY^ONNtCQbH1e zlTu8q;5Y`e$&Bg9-L)OJUR6mT^)mJa3ZzT<`T6K~Sfup^qr1U=uPKC~j)3g+#7STR zNx}2>ZGMRtFi*`pqZu1fc!1l1KzPA@?(PGj^fi)v zIT0*ENgrylc4C>pV_%Ai`F>mD=$FUO*{td+XwD9_|9t7WVZ#Z4ew`V{{nrFTd`(a(-E1QW!Js zq+LW+sT(Uc9lIEH^2vRYjMR2S=SAGxA3}gN@j)>Bs$+W)Nm~pmOwbCvf5N(Xw5rPg zL`l9{zGI~KqlaZ@QnU~$#d6X=<&33cXli-{^Jx5 z2}op}b_LL7c&#b9$GqAobN$8*U4TZhc6RdJnjJcN$MTw)b(j=)6 z=p2aE0S{2uj>PZ;@wrUPuYP=YY=%n&e)=h#W-|iEkdR2sQ~pjI&POl*U$U7RkF&WC zA9k;sT~KGs`GmNVVZESZPAE^)1>OoM4!J_4zyigPu%v;gFBS^u`&;~`h6|_v z_-9OQ^uN?CCb7BisC{CR$jU(4RDOO~dQMnjju=Zg|2drhae$SSSRjx(!#8s22a8Ox zkAC9-oi@0GBT`F5f~Egf|ItO5&0f6mR~Fe$49dn%HI70Xt8};oMI^&G&H;h~H-j8D zNme9uZyl(T2sY>+M%OEam@L5F+L)GI4i@`O`C%@fSn(<2ZVpd)JcZWd%!b1fXN}5* zuXx8baDH876L7(A^Z!GfLO-72fBvLqDg4X{h;YI5D;tRaYrck^?!AmzTdR~%_@$Lj zn%2Va_md0+=Xm>NcX!-Lw@8qVBvlZc0m$YfafB5tX<*7VE+ikrDgqA*R}M8xI(#P1 zx5!C=Us5`orx#%B8piNf7pO=&K`B5z9CT!30R92ax#_?`I+srjUP8V`>oqs7;MTI0 zNxOFPcw%CSi3xo!b&1{A7Tuw`ngFZ3!MC%Beq_Dx=!iKxd3q-Basnu(1n}0NYdxRm`7>v+{`HsRF`*Y~|5aF+=-+Z8 z-Fs$}TkLlr`yP-`HGurCE(T2C?#x%+qTAPbG-ZOfIp$UsHgv7IGgcu++SP?N1+dNtOIiKMCUQ2tEAp*{eUYQj0o4FxWfFSxG>*7@7}?C}EAh1d2-9)2FRR>6?17QPVC5H-}I%b%4;6{vzB!2}AdS>;W`X?OE>e{wATWSaUp0 z$lF$`fCkLaE(qa{+Ll!AGw?*EP3gndE~o@}GA=o!b(m)V6{ZN2+J#zB*nY{Q1-;C< zX*`R<(UVC4ATb^pd*Azi^TpCwX+zZ+KyYErv_VE$AJ z0IBo4=s3yB$I4(J3;O~2sLJ5`H#2CZA+4`ZG}vuH(w}eb2+U(p`XJ-tX_Ut#rrd{i zJ|Ur}Zv6A-KVcR#Ue;Ad8@t=u)}X?~UlH#Kd?N0CCq8Y{5AVyQ7JM{i7E}{>PH5}d zw(vsgDv`IOTi6~#5Bw-E$XBa@7@*cIhENuszf{tMkZuIHiH@7wu0h(-1$^?`!WL2)8TYAPH%m3YCOVX^E- z-3Q%4X6ysZz0zJ>rCb3nnV37JR!Npg-CFuy49mIJ#Ffw$7{$)f)&bE4oq4Q2#G%n% z=Z461+)UD%@ab`uk>wTJK$6?tQw2CS7K#6~!Jd_aYeh#Ig%{o?2ux2@WTecPY8zC+ zDlAlkOPs^#$9c6OEK{4rTu7pe#!N&@G~Ez_fG`Szgjb9N#BM!V?NV43_;CdR934c@ zyLVb@zWC?iS;?}@30K!zWNW4&-!~UTIyDlTMn8g_0{pjo?A`K-q-imwnqYMRO6_Jj zgD{tVC9jbWD1??cY*&ytIgmFBH9-|(p)vSKWX+n3LN#=OGS$?}A|pphO|IW3Z+zH> zmK$yex&)*E+{E0$J5-C0W?|4K0o&-Z!V$THM{9{^q`Y_U3d>2xEB@&x1WmmZtv(V@ zKVp8t*|TR!g651>5DgIo&<}F-0B`7s+$iFXLW1n3CQdIlWuq!Isj)ylczP`$722IH zRb323K+-z6NWfm`85$Z!b&&^%XkuhQ!jHKN%_CuW+oR~i1g5})77eKgL1)L0r(lC7 zqZ5RQB!j_Qnt#begrK`+CtJ86%+Qg6wCYn=R(ohAj@IB_PGPS2#H{i!Cp@Mr-rlun zV^GVlQo=hxA}g&cu{0%(2zg=n$}nodHp%1enaF$htiSJcf5|r7i;A+sKy$b5U@#mCr3Dm;R| z?#nM%tQKvSU9fPWHCB7gS4iq@<$(Az_fyt0bGW%#g6Q<`FVI>_vUuY4Ne&J4V$R~d z4V4zVckfQGwGI^EDMXkirzxCp-MC7bTkc?QudnjesFIHrXAy?v4T=zI4La~KSfbyE z`~+TW;2GQtlW{`kv9D-4ol#9Iu{ag{;M=Rb3C%$Dx18!dl3O(?d{uEpm)}^v7_ zeJS1DCa7h~2eDlognW-=rJ(hD;@qtJ8lB3l(ECCsSB<`pD%?$f$!jRoph{fo@bG2! z^z_F6E1b;{)iW(Wk(3hOH-RJ=C?t;$e{DMXZZ_}2g=`Q-ZUc1|v9?GiZ|U2@ND2J7 zB5Wlxs4$^xsD_%OA7Idy*n7fcvm-Ah(q*F|B#Q-)J3B?K${S)1ye;2x>0~vfbM}c_ zMtzIQ=4R(U3{e~6TLHq~?AbL9U=wmWWY7y=52}jIGwmFbhE@ZC>}$uay^rM#Ee26N z9hfSMUs9*xA{7@Fu2}88?Jj67I?^g2CyB)zJhCgI_w%hEfmYzi?*X|P3{*N=J z!PhDt2LS0}JnjuCa6;WW*Uw(`iy}OBVH6GJ={X_XpeG%gFdL#y^m?EHa3rF}?Z6pW z4Zp%FG@mFF2wc}H3r*)2;qrQwvq*L6_k!h@hR@2!{|O>Q0Oi`ky9WL*gKv#a-sI<_ zO#>SLP7syxJs#NhIGZjan|TLLrr%dHGLYX_m8SNKtp$odV159Y{KFntY7dnhF!K18 z(dRVO&l>|7YMN#SWz-ClR_ieCRisDpobHr2LDUt#{{nru`Urtf%6 zLC%3Hs>$}i>1r$f!4~^btxaO?8l_GI0*0Py3lMG~Q}U7$fJgqsP$8L1cuK)Q-{p&k zz_3{W(mY+(oc%$vaS7w_I$MX7$ouwPn+)Db$`z6nR*}WgkQe{rPSoeZvUXTY@$H*K zl}QhOOPvVGLacFBo`<^}`Ze-}gT9GDAd3}nbB=8k3dRpmIK_QUrDeukzofeQ1tzMa z&oL=wN3l>t8cuvgpcmUt+PW&*@KSo|hxz@*DTTg*>sPUd0R$4#8fYq>nIEbA|dXG(b$BP6Wp+^acWXLHM!_q`c98*=_;>W!svaV zK7v{D00Oe-P-39RQ{J~P?x%5O77k4Wfk#pxOw+@iwV zZz&j37fRm}!2#8>4DMzcO>=_m76)E`cbyirppgG*9y_*Sq~VbjsE|^ApM?#CtWTdd zt-jWOR#Ks$*Dz)BAv2)RZ5wI`Kx7Y+?q2lH_vB(&X{Z-Tj|VCN&A;1+JD1*n`jiOP z6^j!h3AJhG9wk^H4ivPH9s9({S)ilJEa=p%Yn^b(+?=}a>XxYmbTmMb5eJurei;d7 zNTdpsxC-h3(gjeY0bm7Fz;8K{(*qhlc8S{TsUF+>bN7as;qnM)U_Uh&lF<`B;z)h6zx%moNB^JlSV7eFkgRCE+=A75dQdl{+t5;(*~z*t2^#8%bPH z5Z*^h40`6Xk`P^HOHQ9&5gEMGrR&457hNWrmB;8AD+(;&STb zxb46Z3;9k@8=wLn_}^M3xA(X95WkFlilhM9U!E=1K^`G07%QkEPbkdtvZJ~PH7De; z9neJ9lAafm`U6nQRseYsez<~uq6`bRM0AEdc_a2HA6|sY84=Be{f#=8JFiat44nGtC zbtYBW(5MhW4I!ps%8|=;w7&YZ1>hlvJ%Hm(voXN(8E#)qMfeKl(C(P_(B#xtYd~;5vM^ z9N&rQFYf)VZ@${LI~e$)*hS2j~}5D9k{0nDtW@t&y;h{y6ZoCff6B^3_^A2A3z+l_!aDXP&$ia zzi|1~^m>c^bae$hQZ{NIpxcpeFZ{kP#`8C+mW*D7)pv!B<Nw) zG&;JVt-ZbWRZsbyUO5A!bZec$6t6&JFW3NvyK?pF?U$B5%KMd@kImEq51Skg}FRn%mE z$`E}1I(BsAa`YP=&po%CqVe0bL?Nx1Yr7)=W~V_q>+kRla7j97Su`OZU;)t5(6~h| z1+@I@F~k~p@b%-oc-6hy{=CNw{x(berXbE%j33y8rKfYIWdsd^5Lq?nteY2Y2-71F zSVS$TFPjvk92Ms8JMrtUN&;@m2l6znl6}&5Zc#thA%w;h#bVBB)2UKx+710MlrA|hAL0*MCXGR8HjGL!&g`v%caUDZp^y?F>CA20Y?t^=wuI=J8-Tdyn$iB5H+L(n zxiAnney`<5MR-G0kDgDWrtWdg@0Ha{SYV8M?{0DD!vmEP*c?Bd7b%kfc5r$tQdI$O znoi6MGF+jzf*MebObAc7r?NLVAy#qZ5 zSVjKl_A4tN*Nmz*`*aw0_TJOB*=K3MBpPNb=)&pKj(?gMSK~C6oC*)yphRk2oJHuV z-RPfBoP1OehUUN;jXG&{7oZ(6YrrPn2^gaSpZkxYSykKdERcI5694%p0f842_i(j> zwBHluN}@iZkEnPAohqcHw(ZM#wEOAi#S8nzlzt|Pgb>+zo9+&PUcv{6 zx;tSpJ~{*>cMkh=BZ@d|N*&3k;$z4HfzE-Lhko?KUGS{?3yvXhY#0|$t|)j7x`?kj znXKsYUz|tWNEKQXSUk$I)l{+J)!m<#bk1BYhxSdGPmp~$j7(rGTqiK38nbhcR-p94 zUJwh96-DTT48Mloh_=8&u(GiavlTaCffJCA%ZIFBV8=8je6i#r9?}`Rpl0nt!K{TI zufHB`!%8LURf#v~>gr-|s3WIg?ohC4 z!`cV9m87Al0`svOy@(#R4zkCgC?UHm3XuxQvTYG$z{7oUjH;V}Pgxx@iGCTBfLQG5 zXL`t4f&w~mhX6Vkfc!OR?8px#H`Z54KEOlYK7FtT4uFO_k(~CW@dz5Cx;eHO7(cI{ z>KwV|ywWXwY10aP{mf2Gy3}nlO5&NSY}YR+XF4AK#x>payFImO@*=b_@3CLiQl;+n0AC@XG_p5}4H139daZlf*<}q4MpPC}U|j1KIN zzj`mpb%Qz|#}Tnc9^M!3|b!kUHjaAUCO3HPjLf<30ZXI5@QO?5eL`z z=X^D78ENS7FZJbOx*VXymu~#nyxtTqMuUjKC1q|8G04fTF6HUUGQbqqpTHOuL%}!% z#$#8ykEdpd4Wd5j(7900@l$lZlcd?iWDPUjvNqv$z`2r-11^5)5uIm33f{Q$=~a`t z(fC;#GkA9TvU0qq66YR;V;;N*6s7gBCy>Rm6VMj^h0ejDV!PW*F&pdiQE*DYz1oqe`)_2921h)x!cc- z4_V}lyoL!Vs(sV|jfyZozObREJzEny0HVz-$JUF9xwxRZl+uSXH4bu*O+XY;0jrYp z#aHh@3>_7Kt!hx?N&&M`A|qSJTWhXYzyfz`XjmLA5dJ^(Cc3H5vRyZrN^sIIpff%R z#Tb~u?3UkVkq1z(?jCRy>YasR9{&vDuPPH0eE-_UCn{wO7G^b4fisYPWoS+tHxgbOt|3K$Iv{d$BA7sdYZwqNUOHQR1CK0+|ljqMf(y6jncKAu4Glf=a@)2E z^9Sv_N7qw|0bM=q2&Q^=XY;!@^qK1+;ojlST-Q5%_$?{{RiL__)$&tH8#S7mwB=7& zft5wI8KT{>n0_LjYSj(UfZ(+Rl|aMB60>lD?|}aOyEsqI@4O`3laD&Z+7)d@69N%? z8+twEzqPxgk*Rb+&$*XiYOG9`pubpDMZkbIS^|f`T>%``|VE{s_l-9*m6MQRuzA7V6?? z$S%RjD?*Q)0U4BVZhtp4hPw*Ad6GowoSJ}i@ z^1_oRPoO_IF!6M9QZepOI#r8{CGWjexih^&xP O2KX@_WL=a74c9giifCY>e1VK zN6x1C@~i=I?0bqI!C-j;jVHTm|4BHFpl9FiYZu#)cm!ZEw1vs7$;jn-Pew9i7MRHm zeEnJpOOFBcQ!?bLn@WIaPT|%dgO4kXRAGAw?NRMd>Ub{fAIS#qXRNlqhE2a=OcO zjRwS*jp={fVaoTBU|g?yY+(a%DnV#uGWWfTBZxHusHwq2zeDCl4 zH0U#$qa=@tx${BhNqIO~_3eY@#7hd zKQ%<%`H&aybE&{D_p0ZCHM{=VdPP)d*7WDPl6K$vQtcHk=DIS#oA_cemDYpo2apY8 zO#^Y(#vcO4mLPW5_vl#Yi`TDncPO+Y$VBVSASPo~k=P$m_Fa(8yo4%ANC^lwBGdn% zT*{)_+S+H$gl03HdS*ZF%}$1giN1}tl{fHP3$h=_hM<^yj;1n-*t z!IrLa^i5iXuUm2U%;2REJQW-T{9KJha2*)r;8Z8M#}D!xU+cBS8Xq6MUP~i;wpade zrwEi+$|!fVu+4UjdSQWdk?kJDCq>8AWGx54coSkPkiL`)q3_#zTs`v&3z6^>q7p!E9>CQh zhsZX*`7@aGFuXsMENO%DSJZnZqpAo<2yHkN3&O$D0sFv*sYATG(s4fFw2O$`1-k0NIjpW93reXAcx# zOVxZ@x5VZ(p1I5(#x@z^HOpalac*(ml#L_|b?X8xFJX5;azXf(UU(>5G-?B2qo z$rQ4MM?Eb5sFKqkUcD!9@Zb*Itn$-9p!O28w@_3+yriVW@b&x!3nKADZJ=sJJK}#^ z#F^*cKR4Q(vAq=%5)a_u14_zTY^*LYY>-9+Co*Z=pk~-chvgDP%^+U#T}k%RsnZJk z6G1z(;OLFUo2FleDE4CD4&-+bo^6?C&Fv{R12>H+%6v+LFw&77g4bq0kgO^Ugo3cD z3PLL|be$>1;>27NV~*kyX=x4TH;7{;ZrW=X=Ym(D;2EVLj525Q9eVb%?bbj5uv(=q4rGMRxyINgDZ`3tKr6if^j zDoB|Ycv*jXH~S?0Bh_7ICQ;X-V!5QG{qk!$Iam)fGo?M-xh&J@AF}xQ#tpT3Cn=!& zWNDkt;E1YJx0}tLt)Gqb#qcjqlvu~qIyn#89GZFdTyC*9F`p%s8usq-!K*!wTE`6k*tZ5dTT${{T!|$^VX<&SD7*i~rD%qs zn_BPY;wLYW2aAr?8I92)RJ4TX%w@tuf#((>kic!UZ+C|`f1&(4@8I4uVt7@d3euAu zSc!i)yMR6jFK-m8^Q`Oz_!CA^(p-z-U$8(>3B<$x_0t&j2_VgJsL^a@1e=K-OsLaX z`FXjlg`cNi(UWE*+Zy_NqK2_lUi3Yt!}tY&L!2D&@C2Qi7<09#p3FF&#VLFLe1Xn| ze8@+aK#}AjKro#;FxOjx&nQXsd;5cE;Frk5Nk_Lx@f{G1GcA*D%sCr7%M+QA0@~Oi z6`vrlQ5zjDg)JXVt*dV3{OiD#bB&hp@$z=u+OzB>$`zMNRp|}{x>Wx@Z}oy@%MKix z*gFf{4(t#$hn`Mif{TBigtHTKJPdZWxL#Cxea@zN?i+{7w@`y6B_(ke;_+LyeSHg+L>UnZhQmTq`FhSBypd!@_xKtHH_pR19EXC!66N+5k74I@I{>CpP|+jagKBQ< zObLbDTm|x2wioSAZe5~iW>LJ))adcSqaXm`GrV>T`@a?j%iKp5-SF^FC-BhN?>|Md znS$g-V-qhiaQiNR3kc$fQ-kRl?i48L8V`9)Qq(#WFw?fqzjWjLLGR!z9|34`9;(;H z1Qb$GZO2rO=ivyVLu)=niiN2n3@TQNEWJY~B3$6gY8-1(gmoph@_zI%HYm^l5)usp zEMftKp#YzZp-xl^8NnEsN=nG4eh#7=z*m%XD}~6Fu6@!lN$jNPTnNjcmY^M(5=+RF zgL#naK@V1&<5iS00==6e@gLRAd zkaPnWm6hBa2%X_W1uA=3P8xs`eN9G8GgL5igy4ht3qm-508}rO0JjCHHKT0Vj|pMu z0;Gg=#HP!ohV}LBZg_Ig&&%HUe>b2V-59{xsL*VxLpOQB1{@HXXaUK}l1TSn!Is0< z5YODbsChFdpCiLA==B(8rS2l*6=Ee@wrwtBnlSDN_^8EfPhjC}h*Dxvgdzrq&~|5x z%%q&3l$;0XGS=|;zMf>vQr6w>?x@?X$cV8m%08j}U|^c@EP6phlUJkB5$I}Frr zD<~<05*ShU5rIbqH)YB}jTu`!6|Sm7ryCwZ2b;FNe=<{g#EIDkOapZW8=r(K>=U1w z;;A6Vb&(1V)#y4U%00rY4+lL?R0djPXv31Y9d=WEBP|9x?v*Nt3+$4|JYQsJI4H5G z3N$(Pl$Rw4={4#1$oMX#MBRx=`ea3|zV@G9{l>||Gsam6huV7#g(W>(U0q#0iZ;@< z;FX{T37Q;VP@QaG=sDy8@H)DB?b^bXV+$56_}mLKV;QzsDAtBiH4xNJ`F%J-5*>2^ z%`#tY7}wCwu8pbZ&QRgK29~KKuG3FsKBR3v z+Iexl5jZKVF{y%JeT9i3wVTeN7M65RIj8LD_qE@Uvwh&Jzdp5(sHWum(>eAbNX6>SKec^1<~4Cc_Y zQ>OM12HSYx8Xq8yu7S(38Y~YI->k6v6FU#2qZ+dMv9Dnh&MsNF{1>}{Hn(@odY93cof0BCS9hMco0-NvPJ zEn*y4Y51tZiD6I`6S7#okWqKtbRl1JDC`r@YRZyTJqL&b6ISm~bMqpQoSOj%4%NM* ziZdRka3nMz+0l5Jz1k=Z5c(}z#33UX{N@KPVRGywraiiHf2;Is=i78^EzyY*hECCn z@L3UU0R@@!Nylsg(os|*zNiZOGU5JJS&1qVx!SBg=u6lrs45N=Ah19SmC_HkxlTtK za;EklJ&>Xnl=SydU+st5sl3Qn#0BO%s3|BS5^xj%hegHHu%$duq+K4sU~n*h(5L?X zejS|K!Row>Oeo^q2@|Be5`U8u0i@eR0W7>>!%Ks7C6Yc9_eJ4zM1Ep2mQL&lu~%>3 z9!A)YG7i0zQ18o^Ni%{(@P6!5U2qfZxiCVMic3yh=9KQez zI)y`rRy0BjSom~NaM{0x=n`Vy3WUJj1qEgZ%8jI^!Q&%bidyD2gEre|H2#8GKzA_T z6XCV9V!6-@Qck?@ty^zC+ubTZ3AJj?Nx2_<{pjL%MtfTqZztji)f65*RJsr9Gw|Aw zO#!uD8MipIgcq#{spnd^J9?0gv|lXvzvGh(yUx&|1Hq!|aQ?)I0+#dyJ1LC9RGUtJ zRmHTGhJ<>q@GSQCYy1rF5lzRSVG3TmNhM9CAlxlceAvmnY&PUwxbVnS9Rwsb%&1{8 ziqQ6WjDKx3$usEF-X4zbVadB4DnzA$hVY#);o+DC5+|iWeAldI-6j1q=ViiCo-_- z>NBUP8BCg3fqjrExC5IjHXoxy%bQT{;-zEP4}e0OQUE)e9kh-(031&uJIQ3mLqO@X zmq`7!VeaisM&D;rRkkAJ(Jm`m{2!lDu#`-s~_*=RjlhYm8g z>Nm;%B|-re&4UL7-!L!ELLi1EB%}lu{ecP^e%gIH!y=h8@t3*rZ<7SkT zg$sbKwwU(I*~`O)ik?fyEC`R$EoN#fen?!{)iN|IK%ENO4d{FCx2WW4L4TplLV42a za~RLs=-=kJC7_|8;1JkMsl_;xcPA{7MwDo)Ve;W!z6wzEvuh0lQxz`dKWeJ@s^>Sc zK4j+EfSH8u6a-+QQy31UqE@KusDj>PTEJLTPh~8<(38Y&%ZDgNi9&659wqM~J{iZV zORXk_&mPE(vjpHrI@gbDKg2&Lm0tU8*L!lniPp}sXl1vtf zkY_7uF5H;<88iNiPDTCetdl(y6f zL`R{#^FviBgby0?YycC}5Dk_*l0}4*iRXnieyM-F=BTI`S3-3_7ca98!<)}bJ>4eTh6Gi59d zZ4G*#)%>`asR+>9eEq`gY3Fu^27%Lr=&=j*yC+BSNHQCgBK*BP+7zfjbGFRL>4%A9 zC@ZK8AjFwMgYmx)-QJlyE6D2cgzd_!{_s7H=tjp)P6&6>^`xWfI`+VMwIqe z%4Q?7j|eFERdKefAjOTy3PB};&LMaYHSD)oT&OGbJ&)G(+K58N#e9o*UonBE8~m6K zXodJfP$<56-(JC^mXOu91UrO5SuS$@ef@5XV{fWLvK|7tm05Ji=y80mb zQWSvNJ30?tXRUsGumeiHM@_ATey!n{nZa+ofsrW;-+qqHIs@;kSFiTt8Xr)Qzv-b` z9?ECNyfzb@GFN{89V6S$@#|wQ43BB}P}0MhM*qg<6AdJ|JTeqK4Nh!++ifq`p73K`5U*T#hlVLT znpOWlW4xr1#7ZC#Qb9&#VQfB3i`dYyHk=AfgfhG+6k(he?0btm>u3?f}8o!RU8 z8}##O2>T~Utz0k|?FyAr@J12YgQTB(6>-hPF@V+AgGx`!W{5;Ipo*0=(c1(M$KA6> zVX=Rvrx<(I*?icX3X$j({Jw0Y*y!koNvN<>WSKm~;Of9wjrQCc9YQwDS|aI-3l=eMbTwNE8rHh+|Z7ltf{ch)CM8_kWy1 z%n}f}MCWlh`l?BnsYIte-dpo;se!4`dvZ7YQPSQ1;B)O=cSoc#;OK_N>_qoYrr?pN zh^jt{%A!pdaIkZ4v$cAN=|Paw(qu3<*jp*O;M^ni7!w2rq{}gk&JXMWVmCm`>x$!= zXoMfW*f&!7T^qIloN5oa$6sZ~2>dNNdi5mYVb)8_w9r`VX)w=S5{jwVsE7UL3mg;w z;-9Kv*{`#dGW0J=b8XxPIzbiUV2VCQ)#`(>gt((IC|;2fkrwNq4gK*apPwFNf0jmm z)P@U_y>5>ZVtZz;s1j1*07g0<+SUH-d-Pq}bVu)Ha)Q1RA0b~6jOk=f!qUTvgj)8M z(Q>b&_h@?Kay;Gem8sW)FfwINR~A&yVrFe9c(#Z}&Ym$t9S@jF5(=|FpTGa`%(LKd zgq|;r|Mp^<=HS@>fLD>DOb7HzEXNF2s)5*OEzp1f00WZ5+2fP5cUx=1H?Et#Iqt%VlaO*onk?y-MS>wNzm0|%7f0{%4`EAtH)e|VRA6!2^f%%$%XA+A z|DUtPuILwRXAtG8!MR)UZ&|WBOji(l3z)3+I{FxD2Notpba1!l=H|xN;u^g!#sOR# zhEjoY!Jx%!C!-BoF4E7#%Gn9k_+js+HIF%U*X);d#htnrl23k}zi82iqhpV1|fFNr$2S%P_XpJ*F%J+z~JmJXftE9|G?veQnn~gDXuUr$_QOG+H4bT zUVL5mBOX)wdWM-EZN296PMb30wWGUs9x<3R2r&s~`cta5<7)GnJ%H{>0^c=+K|jDw zv)G~lV!esPrC%*8JVR&NZXcW#o;Uc9;ZgHDCqqQ=3)IC-6~a=qpoihT!L{p`<*p3g zKc!!{YvY^b{y@XA0H*Zk@w*zo1PlAsRn{UsVgI8a#P?*c$%vMqA`~_CFzPnS zbw}G6jll^JLhHf&2lObmcuJ=r&ROCUI0uBK_HJmqpn>D)@ZqEXaiRrlDb|nl*5&Um zyd?jrQT@VezyI>A>4yZCJz-ajT`RR5HQ_wj;OrS){f9D_p7f`o)0-Mk(B7k{I zG1eC2Fp?I~tqt@6kqz-kRyI$#?(zK7ySX}+?`0V#p4@xys)S$QmqC0{_y)7Nrc87M ztalJtTEpM0gSr(1T`EyNQkW_7l-$@!Z0z~2 z&whulWQ!&)^Ze7-_qUtpBnjj1s(ei(y}o^HxQ|*N)dTU8$mcn(Zorf$QNAD{-A7Y| z|J$?t<})@k_`)bh!Osh9rR7_S(#IegK-NW=c?E5?Pol(#^=6kiw{y?h{<=R=l^ms} zy~T6*^QU@g_oOXl`eNSU1LD!NGF;3#(PIvj3<1UIU7*ZH9BUmtJ#0CY&Pb1rdP@OA zTeJE9$p}$=r}YEt@dj|yZ0fiF0J1r;{ei79JoEUq%9SN6VFmhat8U)P_M1Kf*L?=k zj|k@%LaT*sR2$kNIo%#NKZz&oC;o?UtJlN6`;t&$Gz2DiE;V1j_hT>~UIVoI8zNN^4W zNHj?S>C-L&im8uX4})2GA?ZbxAe?eBu<2vZ4aFc)3YjuU{02-IJ)$8#6|l&;o_zlJxZxL z{mIA#zHn!nQR~v&OVq^aDBvjL*tec%xx6u}52#T6!Ig~E3Y>0~>pFDyKdGLddW2$5 z%YHz|xmJf;ru6@0GTq=!0ro5m5x)|Ra|QAW7)SrkY_qm|m@J<0YTZeq6G{@VX~MFy2CP0P9w@q-rES&7kL3$& z83Ey$74pfJqu+B^j9+}0_Pik^5DU2UVUqp4c|!eaH>Wk~j$8byeW4f%9t~sTU~)lv zKArT!r*&7-hYugP+o9JwwVlUH>lG4LkTWHu1grp&=Ls1m|Ew}C_5UaMvy}U|U4_Mi zzONf$*<(9kG%jpvbE1F3yO?lvvGg~zBlVjB1|UF88{tQQJ+1it*5@!Ijqy$G#SEzC%7{KBAA?ojq60)idF2+v)XoP+_9f)>@J+aF;E^2lb)^A z5*jCfNN%rTGlxN&NBdeXMnJEOcM2x&FvhA|3qH2kiPBXM%!c}t)qFGchVoVZnGz5T zEzJWIqO3^tBzV7fvDkAck{3-#<$dqmelEpx8i&?L{U=CN>?Il+M0RepA4d}v-#)(0 zKI2*d1QhGl)XIiM<|wANMsp-thxQ_b_G`#*IsQ_^$@LC8y!ELMb94M7dq1s#ZZTMU zy;myY$%`TI#7SlO7BEZ>hN9w}Y<>Tp^4KkW?I=twO2xkjXCP(U{d%1#U!kpE(^OnK z*UI8Gs3!+bny2M2XrWdsQLP^rYZelZrkd2}f|UByZ@AQ}?fFZZ8kuARvKKYvr;^Lj z^U~*fitTXMebr?BxA*KPMIF#;NO_h(BGV32X1{smGURj9*#)yN#`^>#B^VY6M=>i3(k%SSI8}*$g#$sOgN)7Ot+$ZIf{+GGT|BQHZAazqD zV2_|WRg7o@AO^HalWm}Mbql+j(X{a5XN z_wi7b*reW(xse7V0DJfoksV0JGn_~lzM^p@aRs&^W9`Kl;oipRv#5FjI71f5VHk`t z16K^smMlIn@oS@^i|CqCexC44XyDW9XGxx1JeaNxT$#ufMG^;4}d%lN)HH>ROcitL**k34hf+~)ew~b*_6)&mtwBu z1+_8#=?9mJ1nignC_DbLHfHR)Q*ey}TldXYAI=N5Q?2XUg7hK#X>Du(a{ON-4ST zW?`S;;5dAbT9x=8Nv@JRWj`*pD*u?_>N9@5Ux8%zpcP%!0U$tLNF#?(NoS+_(!djs z`H~H}f}Bo8+7x4{qL`IQo_#LdVZ+Hh%MQ%>v~<(fQ@!s4CQEGnHczf=9Bj+T@Fev| zS%B*B`rN?4FbblBVmTK5Bk4_Ee`KT_2*jcIAvn8w=l2}Ytopsz!-gGb5ablS%~^HC zaHB8EC1IqwQ4*Pd0ESFbKI}_Wc13ED9%3%}I}kB4vVWti&%~SwNqEZrKUz`ER0!Q9 z3wa1&S6w`%-Rrqt?D|jr&$?G=0Q@`A24Ts;vg^b$5>r!q=JN2QB}y>5>5zj$B@35* zLrJLv{Gn!$k? z^WsICdjp;e-u~8=-Nw3KYLQY_S`siK+irRYO?8?f zs@K4ypf=cb(yw_<_B4J~IFaSH_uM_d{Iz!a4R^=#-M0(>rGxgb4fr$S0qYW5Cf;H6Jos2L6YP4T zYjK~i`eoKZjZdwt$6cw%c0G}IFtVTN2_a!RGNiB>y~;fr&Zc}x7+z$v@hF3T{&+O} z_fy*Uk>f?iy_7txnY19=KXv7R3fNra!nivMKz{i+*=|?FJ>L^MJq${p8K`Cvrm<8< zU-D(%E|+NAL7Uxbrc8#Z|FL@|PP=|V+5&6Q#XE@YY8ky!nyGEO$UMe!qI693D#@*Q zo+N2pFu!`^Mhy@Fp!f!9cUS1ukDlurnqoDs-BFepIlGx}%ynGbs^-tTANkJb14xMV zI|M$PQI`y8w~u_yv*1f_FAHPR5vG?8#*oGyQT>kp9h$g2TPJ<(FK--P%!3dXQ9oe^ zk3DkM;!PPFJq87+2JjrV{$lk^olT>gBG&%m#m+u5w*@N?^q|z-U*S(PHvvMO>@oX^ zI?xb~T!3<`RIijgfBq6;6Dsit;J8?-%hX#jFfUgn&U$6Eza$J3%o*zDl6q zQL3HO)KlP@QGty>A^+`fD>8S8f6`kJr_2eXgRli%*i!A2R+LSOl7172#jZmmH|$cg zodyn5L`0G)7fpHen*SWO9v~Bf2KLG&MMc8NuY-{TL-teb%GTxKXYpiX({;n|86gTj z)MLN^fk>T@wdtbL1_iNfkAJJSa06Jj%%d9u_7PS>PWQCK4I5+n(|L0qjlVw-+j0^x z%C&u5+Gpb*ACz>PG&!34_OZ+2P853sxue0G`8hx>A%MY<9J8paw)Yc5No>->F^!?0 zWzB!|=;V)T*@iTVhz06OrTbp7*Z01Dy;XWvILlUG`~U#9-RBD!;ck|k7u%o120q%^mv`q<>qWDH$e{P8j7N^0kZr8NoGYiHTn%=C&cHBv>rP6lNP zB&T>;s`UZvieR8AXarJX(y0Or-;qzuIv$X1?DF)b()OZr=bjmav#F%Hx%qhIaYMsE zo+mfK1YK^PAar-xvIJtVrB8Yl?#H;X2*>UTKq+W?ySNu1BmUX9#z*9EPt5 zXka=Z&C+3q1vZxds@{p-nd)65LK!`-Y=;%baD%Xp~y~JViHcM zZ-2jw=`O`;0i=AQ-bfM6N5%o*L`TzIj%io-9L-wvwT~;hxFq*11VOyWvi@+~_R41Q z?$)=TGtb_aYPIfYMWo#Rm-2m6GQv{aKqA-7Ayf{ta<1%XnYHPovGco!mok0a_g`r? zvaJ`!qz){$#ER8oHJfo0Y)B4@_~X})n`goN0Ub+8Pub)GS>u%HpD!dl>{X_fu{o$A za~t?3v¥{hFFD{ewWc@ioXF{&PkyxDXObtA_CvoI+Qxkbl!13nHiGq=G^|J7gB4 zE{7o((Tmklq7_b9v519*rU@WPxX1NYIW82VyzQ={5H7g(+rp`Z z6BanO;85R;AqK#ngAoD8ogOs%iKFv##dNmi_xeOp)uK3*uxK`W$NF|`Mwlh4_jWbt zWGa+V6Rm{~lFo4?xX0&qCa^q5-!124SRzZ3W)Wi2Y7HDD?7gz+M7o=k@B<3IRk?MJh@)&xG>4|_H*dbGk;!= zq-+AZkST)EoNTaTBJ0;b$DM>Mt(ZoF)0su;PC7|x)FjCh=tY4pcpsJzO|az!5%%FY z*L_5%P!0K4<<|+rp1O_-SBbs(WuhMYTTnqZMnLzL`<|}lK zT69kw`rSUa=$u4=RuNqruXfO!kV`>&`q-=2WsCvSrMdE`=PWku znZ`8yC?88F6isf5*o|)=KK+}NC%dm*uj1mTtK(Ze*-|!#Uk*%k4RzsLBL(MW5Jlal z7Y%-SC!|CS@i`GL_9d!uaCN{$H2}*KJ4oVIe zcMr}}2Byi;N{JtHM?F>=Drb`}!Hg^hDG$);)EODeRI`T2{QO%rCBnm>lI?VZ;Oizg zIV5n=^_=V>+ebh;v7Og~hVuyx?fGz=$w|SS!QC@>d~O_gt@uLy77;66@mY26A^`&4 z7R-9^O6Z`tKPh6#y$*Gb`6g*gXw#bzG(xMEBQU%nU=t&W(u7)T000o6Q!eaQ_UZ8b zw22{Bh`I+<=8EX0MO>P}I$7e?fqrSq6M10i;kw``GN?i2<@x z@;op21Ut!3IgoWc%KpKdX${+y1PY=JOYf(JHnbf80rxGT^tjiS7yOe{DiEon+;|C> zFZXI20}d*zAKon`7D8>I;Vv2g6KQKB)g808|WM9t(l^mGOSe8@3@er+ct z*=dFfFyxRl8Kq>U!uXfDgz%C+74D;S=#-&h+sj&pVdce3C7F@0u(H6N(}xqu5+zok zf@dE2LD1Wv_lX8NK|2XWUfzEG9xERuwK6$TF#bYBiE1ZU7us8{Fe3q|IbouVT(8$4 zKDx=~b@+#eYVqq0#n-JXN09k0#+Yl$9LDsZ`Go@9rt|qa&M~)U4>sIQV5>6^fkIP& zyO6yk&rZMvBrFjm3m4GggTAx2At3|v*Ntt#BX@~S;H?q;He_sKLZgmtpz^19ZlX3I zQz{;SWr8qaC?U10xu>hI2mS?c6_~{B>TQj3ZhbcqOgmX*a%Tc$rCk`Dmu3i=xDWtv z=7;~?lZ#?M3Dx8$Mx9h_Sn{17+z0w@r7#56!PMj#fQ)-VREY|@>kosak@4b&g0Jd;C$wWN$w z<1e0V3Ojvf&?08tXo$4KvRqG%_IYc3Zw!d>KgWbpkZpkVkGs0+VZtIhMFEqA%vzm0 z%pfkN8k%~QM(7r`6A#F3vNUZ-K8;jZHWKM|2Osn{cC;by8M^C~jpP+W8%jWN+{K5v zHzB%!6bF%3-qs1+j=_L)>V>9=i;Y6x6m7>nohg_DJ{?IzcmzilU5qF`#@!~mPExXR zV{MioDRNP?-w>YV_<(zA0ZL)e<(Yb7^b?pWYmC;tAZEdr_&?EN{Dqnj!1%_ zlF2;GCKGhIf~Xn_tvz6SL7DWRfhXM%?A;l}%@iL;W)jWcE4YG#bTOVjAudr)gCirw zM>f_)uL?gJYpq6(I-Ol33vWDjxWurHVz@p-ze|Zhx*hL#UgV)1l33?CO!RD^qRPe& zp{{pwnx zO0P~`Yo;4d0LxsjDi2u{V{zHF)3QIP7n{h#-`xM42@Gt&JLGF)(cD9T? z-_^W)Z@YIlf(vMob3%k4yw!(8FQ>|}Y-DxU`4@7s;KD%R0@o}_Hq~i?=c1Jg? zTMPwFx}iO8O?5AZ%GvflWYibKPzr5Wbl(>;@u2s#)t{&Cq$9(Gg2*Ak6o}jpT?i4x zUoAjb64o4&`gg(+uL0{S4cLmwft!I*cf$QeYVA&%#ai_RDm91&0WU3FASKU#oKuWCl7+@DCvv@ zoHVEa`8B$(M-fe+fz;iXH0d6`aSu3@HcL=^L-kr&*{mYDD(DdHv}YF`kNI4C?WaXa ziB$8kh^ixZ1NzwvTPnu@@R6f4)`Kfp+Om8FNno?-6+&3E73910qRkTz&8%2h885hY zEniU6(}sqel2-i0-AT}L!iR^vq?q1Q=qYx(FLFT()B;^p@h?176ykRh$ChJ7j~SK|A)8tjH~kOwtg3CVmFBf zD{72j0YpVnP_Z|}f=EZ~SP-yKtRN75tPyM=O;E8R0)hoa5ykVL zOP{@;{hs%?&w2NUmk&vdthMfS-`6#-ImaAh%sMfj;s!ZkUt`0=9P_y`eTI*iba3~BdkxdhI+`>}S7wGJp@!Q5@608UUm%w_vDO*P7(X}?{A&IjA)|7f%0X!ZsU*@+>iNj_&QjW zxP7dxRkz>AkXRfG!b_UdoC_rbv-EaVg!Hn1V0dlH{*dO{ISmbUbtfw^Xvx!G*W4M0 zNAuUmv5gy9`GgHfr#17*=&(Xl>AZ&InIKO9)`N3FIy6V%;3^FjY z7QgZ2MgdniDQ}afMCRRf@_qqGDiTQl68OtDUY?zYuP<==0)wIG>6*?#gA$f)OpZF) zt!(LqB%gyMyQjm$!)L86(~_|pw_!?0(6bbgyg3=D2iTandl#>--U$;)eJEXi5D{ve?^zWSf;yPu<<$YVuHN+hHd ziB5pCSca;Jc?M6K^u&cX9ipeh-|#KN|q{ln`15J?>SJ-T=%#!&mmh|!2*aw z9v$J|cc4>FuS3K7DlGQ)37#^yS=*fNdYot-yYLGN#VmTxOc|g3xJK+>AEd0=cUY(3 zE)LPAN;n`IGn<;NiqtfkOMY_j|)Qp|VJEomWON>Mo2Ue~^*2!=j2;??;oz zZc6uQ2?=0~q<;4``i%oY+#ZKA;gl`yVtz16&G4~w8@M~ z5z{RDd>{g|xzCO_8Zcl$pAm~E%6u}BiH5{DwS)vpiB!1Y!?R@X`2mpy13vjZZ`z_@ z60BK-yHmw2(vHHIn)VdUBtd$CDrr-Yt{*#**+`KiZ0~XwKYSMEBb)?!46meyyG;*F zRJQZ3asX`?l3AZjf=}$nRcNzyGX?mQ`(q*&_yYFxV9T(k6Cxo6d@e;9(5JRY0CE_) z$n7rj?@M&QgeMcZg2c}3H6XSV*R}H)9n@fsiBn$00ZAq8^BO3w*ELE%b$v|JS;L&R z1_$@N*uwvJsxi3oe*5>^1XYXXiGIO&%UqFLTz)nub_j6SY6Y{cRYb^Ru_|X^ zc;?D~!Ij&bb`$rh1HvbjE{5B2ID{;ly{M>*qUj+BWx>-j8(@rMrYll5=KQBzZOe`(xJl46WJu<|-j4vn#vLzJ7` zTD*aK=bsnPTwt#ihvv4Iv<)R&ZosI9tSbMC!*QDjoFzC_fn@oepKeS;q3l7vPqsy3 zoDn#R@IBxhiS3S-`u%Z(fKNn|u6$Zv9@b&N!sqy?o_2`?=P6`FtnKI9OS?2^;qg1I zRI>M(&L5KAz}(dQHNt>Fs(KJEk zwiop-#j4=BMQ@Xs;TvVLr$;E|qOgEm*1Gcc0X^W8%Ld|#GYtZFkrjXvICZ%3eTa_s z&x)&8u9RcrA#S`Nm5!f*xgt@6VHiCs?MuB%oB1c+f5Ih)NQ;-t(k*JSLA zFotB?PrGl-9>VEXCtlbi+7Hp}HmMme zj5Qb_d;9in?GcdIsBq7-(jbS}l3Oxz;jj$z5!3fCwYN4fi8qirLAEtRvA5PN!)Gp5 zwz@*GEp%~{$sd{*Oye27CHjgt9{W5maNKsV=O`M6xiPmWM}eqBic^)8YdQcYBktCu zV_r2K#*6?MRwy(r`>95fiB5@%S!GwxOSMb{((7Y9yv4=a)P0&*LCK`I8g+S5uTv?a zg`G1S0zje|8E^-~0ri#JTu+H`&hZ$GkWotUdDAw|=X`!Ac5MFku31oh!rb*@@gy)$ z_^F?~*T_n<;}Fe6xgo_C6(W4hL(mKo3%3TThjEAv=YTIfOT-_{*BE8K;}&P!s9ayYj+9fx(@ce%9#py(Nja7Y$H~FNyPXQE9#uukqg%wq zTb9)N3aoZe6HcFb)%y(I>#v~zJH4` zWP7spz<*CYY1BvdRh<^r8nb zG#qUaDEWp5oSZ**>e5l+lbq3^FhLRVEn6QSmJ>bl3%;a+65$M$?(|uL2pLiZnMDqH z6P;@3Jxqf1c>*{oN}84K9sCxy_Z9axz)lk-!36A8dXNt@6m?ik$F=yWdB8_!=$$eo zfs$ss3&V_#iok`Z;e?lZtB_V)NO*k0GPR$tYNU<4rmdk!1H@kY`f@uJw>g=9d@eoa zBSvoHrScG^oy28wk~?7fWlOlR8th}zGP`#l4Gv6}$sqWz(D;3sUXBO;bJB=%=+~TD zV!Q!p7Y;K;=pbax(z=shNapKEjn7e)nWd%LeusBl>(}B$%Fy7Tpiug-;>$xWE0|on zqD34V$$0KC(EJonQ6@Ef3gx5%|I2C(6mFU&TPyCSad#1z6Zb4#x3d2HIUWhuzPpymQ`)6$!; z`O2a}gC`I5e9>#qHH&g@X&V(H%FCHW)(T{ES{Hdkf&MYqDHcax#ULy4NgEI2p51|;5(20apzxFlOM^9^RYp&0)+wz5dxwkd5PR3U}Ea4^NnOMl> zMR&juB;88R{77;GRBZ9=C;P|4C4&OVs(-zEgN2)iLJ>>+&`*G&cJ81y_v&Ct(GCZ`qWb?p61U#6kh9FF9qUh!GPqs0OhUZg7vS!dllK?BA15ZCjO6 zJykU6A2ITh`Jyr{K=d0p@_gelsOa0mLSh<&p$v~c$qG{)7D%EyJ2WGJPT5=7a5Y?T z*-M$jWK=dRTc@o>s#uOY5@fSzJ6Rx(r^kJ3-H(IhWpktZ>NBjTC6w)2u7~LENks%G z$bBscAFCMI`+E0I80zQCf|Ha|rUQ#k8-{=9nVmU7a`$#ru=@YW&5fJXGrIh>e8d?? z>AJ38@~k$Ss1=b{`t<2PH*V~#%~s@eAWg7zeSdIrk4}~i6(LPX{lya+N(VZz<0k{- z)9E%^q2^e3JNy~}-=JJ{5zCj5IPGDL`8R{B2YfP^KXndomu}>sQ9tO_-UrWZ z-_9;u-{g9ZCy7wJcAWg;$SGpiM~!xF#5AstM^g6P`k$)TOGp~&GQ<2soo&}pkA}VV zM2VKHJitSX+?U{BQErw6s>VXRZ&~)3*G+nihF?wu&PX);u|?H9h+Y&O zuBFkD`=7K^=*c`M_`^AzlCq9E_)dT9P0MmGe6jGPA&QVg8O=5@cg8z!%a0}4$2k8+}&dxbi8X%h`n7!LP>PbogBL=RKtzdsU1*O2ci`|n8QJPu*Z-HWjZ6rpGA#5#mO8-{2HREZ~pn0b(`?gU_IT{T=xKX`EP z@l@qQ4nWrag_Izp)~Hau>xa|j!O#&tOoqj12Cz4rU+9^5DNoV^Qf&yxKL+Gk;j7%{ENV5Jw#VaUno3vY(h{l?78}ZwY$_9Tnrxb70NPe{2TJ?NXhJ715q`*#$Ppp_SN?rnzL8+V z+^YzCPOLQX=GRT#ECnebmrP?9bR^7fxFTQ|Nv^Md-`TFVgAPpQ^!b-U_@s$|9<#)5 zv!ps!mwZ*ma0Ut~f;NQzJAv3B_`L9RcCo&zVl*l2n_`YiJz#mRmN`oT8W2YGLZ4q< zBxxC@uhbZ1e*Jb5lCD|t<(C+5#1P%oMCB(2fUVLz%N#55)a?+VAYmQnkv)sNUderVkl@ybs+-p zGVwCM_nA^6O}pJ{$SYzcC9_ooP$a{)sy!_196H4ki^=GOCyklfF~~)M@W-5_>4sI2 z-?7FR{0T)|77VPCAS{WdGs{+~Nw;$n_LYV31wRpaWbB<7i*bP=>meRnHDVPw6g|ghIPC{Vz ztH^C|%g<+&CKe#160WgK9e}L95k5j{6)I8d&}<3^5XBTgi$v zY2{T&}ugQ<^?WExq>ok)ipm>nCOJ4R>i*e=yGVm-~66 z52n8Vl-&IHXaD#-9y9LNp~1V4rW=fPaVc(~Fq_U{HDPB*M}rM>Si`D;7o-*vf*5z4 z3o4?0>(tl`bb~Q2uJToW;;Wdp#v32# z^Ge&`m~*hM{^{33l6RG@FO>YLTqhh1F-@G`yph5%j)<7on)N`fC3ROgm(?mFBME;h z(Q32rl;Aj&a#w$-VsQOnnzT}hXE`c-W}HKzDb3={ez&g~jT;pKIp=7c=~}NlwI$iU z`+JC?iLg?4r)}1I=+L695)MGc={+PRP5^HH)4yC7qEpzBgp^0o zcQ03eH=w6vSW`Xp6nyKyG-dWVO;b5-)PMTvI5MJZVB0j?;;-L@84(HO6K+HrQ`U6O z20oO!e}yoVuqU{l*KN5p@kh-H z7$_Z|DNDI^I`95oo~`gvu3op!zRx147-H5!wLLYy*+v~GwzP&6iU}cZYNsi&ONz16 zGXqZ6&E!Zr$k;-FW89Qw0HR^wScT6X)2Z?Gmp9g`>Z0_Tu#OF^(YUC*Lvtf^0e zHh4(W%%4V7`MT`^nV^j0z0^v^@9(6Z!_H)xsg8wu-{pfNLe(kO870Z5yStl7)j@vVx4%B%4yF zPsR0(m7B4HW`2A~>oE_AxY97k=V_mKxQR!y%mt_33-1D3Jr6&KO+yd zrdnrpyAJid?o`K(izf2e`2%CPFp(lK<0_R*5nUVZy5rQ|KWS*_w%gZn!?HmN!BTuASz24>?Glo26bwySODhg(l7~)}YFI4Y=;t}e z>V#zT5r0gMv+a)z)dE_H223+NX^j?r@;fn+fq3ZIym+RGrz)rBG+a|oYq_7d0eTX` zW%(MMUjk%qvj3OQFE}xcTQ*6uzODI(G9Bq0J~!Zf&vr-p8U-)D3(HJu5{5;Zz1#CdxtLg6-fYj@ z_T$I3T8&8hEp@WJKlESI&jBekv)@6Mv5dT6cm0#&NUZrqwpH-(DVj1p zUQi!I_^y(iaK(zhzUnW8l|x~~gaf}dN4bj6V6G)mwPJOVmbSrfw}+=?ZE;(gZ3WbF zP+aFW_4Zo7K0~9!QutdKgHJcGv^F@qLd|dwy)Efhik*4n_`&WT9=V*zf|r?&)V;e< z?_u_ZVxp#1Nf3+s=NZQphU9yI-%^H%Hw9@f{!6!xzRPtQxJxa-vzw;-tWV3DD?;?X z`GDo3xsZh~i0TQ`m=1-Bio@5?wP^|NlP9fnE+1rFsxZNIhbBcG?aXJ5Rn@U8X z2uQZ3>@$QhuJe-$D^bJI(sQ?&b(2P@b8@Y)Y@&lj+?`F5qD= zgeX9*VdyFsv*hHwsk9_$g^VKX2K79)KKqR!jN}tCBkzjLw8lky(M|W|T0lx+0^-}F zxcp9$qpseuft|L~r5aYc48xMUbl?5X6mW!?2`=RB-~U6gUVa&cLutB`_OlCf@ADz8 z1{bf7g@N})C0zuudLSwsh_MDZ7co9C3oVDq zlKIoDdj0o4j);d7)jQ<1bhTcYN3FaRC} z#~$%P=dChY4y+JGA;3YEYUWsi0y)nAs0=RKLo(BSI!I^~dXD#F=C;Pk87( zKT+7UlA%^#fBcq1He13<^#8Gdj;yGk(Sei#7b`|OSRL;c&{rt`f(jte&Y62038w_0 z@N61TW}WGHFp)i|wz_mFC|d^VOxTDr|4F7hLUFx*i=K_P=6?^6`1Bb&VS;^qC%|#~ zbxQz%B=t*wMBF4LC_7(Qbq6q`Z_-N`)-vYCzzW=5MdD|iB-y!6{Aa$U%EX*hOkA*p z%s=8bUZsyoSOlgzZbw`C{beHfnRL2^b9YxEY~w}AGW z84*rizDHB+x>d7<0|PZp!2A#DJ8tv+p{r|=lbYi6UjkC%2=wX0Yc1cjc6i1dl279J z;%-^Y*v!KnBm&p}Pt-*4vkCq@aT1QvY!8CD6j~-qVVvT52eSckxDbKFhel3B>N;A5 zCRD*^ye@;eVL7l}q6h}zcFPtQ+Lrg9oa{O|Yr`SW_`<_b=SVD<@;kZK0&!Rl|MY4M zYicPMesI8|U0Sb%UGt9PeJT06z_#MC&i=HC7uda*-q4}vt~d5kT2dX7L21L!Bwi*- z4a;$*UbKk!5!V#&504s)xJKqy-Pp#YiZDTP&%HcvPYpUwXQuJVn+X$-)S0wA`16Qg zYB!yANjmFtz~awvm_f3yK+w)!e~;bAoEpw!Qzd>lwvTIQv_EI;nHkQBDoZtH59Zp6 z3zu2=Os9e8`C`^Hv_@8%!(#sirTh{2`~7XL(mv>Imw-6p)2rY4I-^+cA^j;Fq}r{1 z6m9LbZoTm z_oy#qJ89~`>Rq63x#2kZ&nVakg|xE8ix%h!kkHNDXyi_7P7WE`zW2a^-X^5Ne`+Fa z%yt@4tug+xZ(bus`bO69y875_xlghmz1)1Eux;g+`_0Wq3FE_n(Jf>EH@&~h{}JyD zX@+PxP%*@fhRIFWA)@exurO(T2m!`KiLn~+iXUQVA!{$boxYLgquDBOCMUWni+S2Q zg9cdvMvxy(U%2oTV1`Z4AqxfFH}dHVlwcwSzorSTgT1)6)20OqU2u09-52G}#+PWt z9h}=<##%%p%E07(nzAmH z1IgP=gl{L3j3vz6@IN|%a3wcyelwz?!YL8*RAq5mTEW1R^wH@j`j=TNe&#{isCb=X z|CF7%O6=w+sj1oxxQ#NrjYgyZ9(`JJ!f8~JuNtP^KI@is@4R1Dzm(@6lsDPj^kR$ogR9ieVlNOtDrGwH&UkPXp+igd8HLiyO1%!t7Y`e^Vu> z@8Xv+K6XJD)Rvo2g2`N-g2&jz6^vWE_G9gLKfDY(^5VBAce1m?JfrM>!CeD9`rl>8 z?3v*tqB(FyI?Jl|=m<&jS@=Yi`0HouN8JMW(4F)PH+Xk@KQni#=ijd(?OP62_11{h zlQyRjJ zC2Z396v{C-z3P0O>zZ}eD>S9#OmAwbrLsdofls&imtOYG){G<1oew+K7ta%g@1NMeM2g!f&r>Au0}(RI_<9No<;kH{ ziDRf>C7Q#==F>H9wWXGWWp2+}n9C)P9|uwdwySu@0X|z=wWbLT*`gb_`g@t_C`l=#fh8Q#GDZX&?1-A$S55sB=5-Or7?A>>v_Jd)7`ER4d(st)TBwuZO`gEqvJZF&E1RhOR|0`lnfy3 zN#^qV)m(f9EY<<|e(NB~M_O@Gu+)_zq|Ag~obwsFpOy)zvSlw4xgyju zxy-Y_xxv>l*t9tHm(Rmv-x_Vabw%CfOV#GU&ssn~PIL*S&ODXDgfN%61U<6-psFX& zp7nqvfVi6asa(eLaOCXjI>I%dmH^Q~nn!ij3WJ^Yn>{^s3O`>My4SQ^Z9R-U6YW9Q zDW!L{{m%?&+_6R9C1pSBx~CD`~Bgc)2xTY-zZ=GRbfAk;*hIB?8?_aK5=yoUx4*I9|T$Rs|YfQ8zVUXD?4mX#z+5jbd&c5n~1`bfzIoE}x zB-X09tGsOe{w@j|{--AH?*gH7fPnj1j6->y(A8#vVe5nllT1$si+!Du)9XnBo zzLHs#NtQvWEUqEse?=*C2qFNqD)~B7OakDIORrL(PZ4dvQAgV(kRtuqblullBUyb`7{ub4&#~@F@70+W7cuo0#_r>A*t?=HtYC z1=<`;piS`G-qMD1qWLA3S<;6=pm9GdNOF>W7FkB}7Rs!K1-lZ5cH|4Kr5o#0_>3O5 zAh09|+g$2iZuk;TvPHm{0Y8=TThsiGZimh)4Fj5JlwLtP0~E6Wr@MH)&6;#91=^(8 zA?kRocbV|@Ha%3rA{8^5cwHPnt;yP?qi90g(p z+QRr2$6omwLfuNPj4v9(AnUs28ue1CwJRoJ(dK=h8bt*KJz4c_;@=d_)<`iTRwAJ867rg^rdF9tt2?ka!>hqyZGAs!)t zlmi>DRt)40Rdne!?tt1WT1`UMv&4Zr_WYOnbsS1#C6j>q`38|T4Z#NIvTA!DYWUxU9gUpz_M0CJJS>jCYWh5-Y5oc&#tdLc6HcwHxsl| zWhN~YJ`E2nOR0YQ)`Halpb!jcD6=Mc<&)0kZL2+&=^qg58&D-+MKK0aCRT0CPTU)J zhLAyJb<7>9hibAJrIk3|2{)AXR4#Zy9MPyeOa-HwU8D-u!E?msme3^W{1uL}v^*y9 zj2$oXB=JQO(Vy52B8>|qGjT#?Ea%F)&Rn}lr{7{!Emk_>iYvk;7Hv`FKDv7$-p^nf z-MfDuvxAC<+kDFBw)ce}5ngi6;9-l&zH3GYtQ3)>Fcf9Bl7QQqv^!-wFizp z8#|!bsB^n^r4J`56eVZ`kMsR({TXdHYRsf;SX>7Gl?&8J5wi7zMxT|m1(^nIEeI|Z zfu;$W5B2%jAm2aqm?tcG#aNV_{rgXch!($zdBU|PA22=!x~I!b&R6TAq6DdRP=RfW zWEIOvru%olhm-X*KBj24P;0q2;(C{Ktt8F>Y12thjhge0SQ>JZmoTvodT}mTiSzCu zRPBd&PGDfdq!RJ(O|7&A#DSS1bavLlHHd@GRe3LI1{tde4v^CsOD{(H_aN8K^(o}v z*{n7L8GBOqBK9M5>)vv9O6@0O(XkzLsV=38&F$I0|Cvj#thGNnz;&5Q%OrQE`kW1v zJ5Uq$MqE%keSr8ZEFl?($CNNLyt7CeT{^7lO!)Z~4hHey^6-n|$#7s1?-&pWmK`7# zTfpiRK+QVSjqg-s%2<0IOXj_v$mW>BtVz#lqVk^h zx;bom3D)AI0$<&lV^6wyk@kb_IsEw<87Yzu`D@`y1znxWiVXKyeTDsKGS2N3G?T*M zVP8EF=;S|rY+iPr%BQE3I=F>C zsziI^FvU-Cvm4_AWulbK#F3}L0VZxF4D373*&e>(OTCC16f+&gG(`BhQXwH#wIg9LM;P;_*xtGgFR9$3IFr7Nv&`336~(0W(OJ zT!7$Y_!-5&mU_$AaIqKDWR9_kp1E#6BhVynkXcP3myRuL3$F*H)O@#b&HnopygYt} zpDKe_l*j8ge8^F-m`T_26=cdbRWGrgDZsb&cUag$6_+t8_PNl|G|~~taEGii6;6Yz zZ@))pDnq>-y?ESTWj{W4Wwf@%a*qyvG&jTEceSXRl6JjhvN#=X?a!aYx_;J%&mI)v z(_kZH!6TJ;2%;a(6~oB&8$Osq{1j$TEm#00FP zCzvPb$sST~R!N^qokjV8cy!e)KYln^P>o2_V#K=5X|0Vnss-q24^W^RKZKj-&GN9I zu~MKiz)mC|yoG6WYRy3zt{Yp8)>qaI4c#Q}{aMsshmLfTk1PL;o%Ebdhs9}%gkTpb%~omPNeS?bxQyUwq;gbJ4N{)`dfTF7t#`tL1W?`C|Uh3 zJ&KWzGQDMOWn8{)=B;7XJ9u0gOicS~JGHRYMl_g}Z!eA_X*(F%BVn1Cy_ra3wKfUM zpn$5-ShZKEU4x|`2nT=8^F=%z#yT&`RiJ)xn`6N10_cegr@j`pL8wAN>XRa)P8j6c( zk&pqSh-N?&Lhr2@x{f` z>)mCmxY~@lfFBg#F?MLgf}sS7f~QqjGlf~3+z(ySG0)MunSYL5o|)FYd-w2IOO6{S1(^i@I11| zo@?XPkH?>}mO_oc?cZ*y{eC3o6tx3h3{V7&H5jgF)vA@%^5x4n?^exQ+FeHfee-Ty z7w>WaEumDFgJuh{7>Eh?OGw5HxITzyr;CNUQ+Jix3ncC*r*(CCnVI32HV|3*(@6F` zv3PT|AvhqMil)#6yt{p~RAsvgB+M`_rm(>={SQ!F3w%wt`SS$mQhI(o*47 zE-W2WX?gci01-vr`I~_ZX%);TYgmzJJHWm)bB}iW(3B1pxzK`&p%zOkI!B-0{jC+t zBPpa(lD;6tI(j#Ama6nTaVj)1XTx@$Ik#4rz&`pi3Zh%LzhjsYI)ANNgE*ha-v|9R zbY_G~NyPW+Ej@OQTJ-G47inAyM7Z7ZDi-{clB2J7J9p+##Q)uk=lfe zKhpSSZ$NqSLY?>rnVH*VSRuDc=Ol$Kn406%jeZzF&%e4WciWN}>C|x_kZu01y=56p z6QoIJr;l51#l80*dR$9pG?RNstB()p7)?GYNB`1sO(v_FlZL(9wQKiXR#>OH3+$h*MZi}B&Sflj_l9Y1$(%!AJ-NyIE46MpW9`BL0}WP4bQi$D*2U&BFLK3w7BP@vD%ZvA$(W>=vxPGiGblI5@; zbSqL*Q#*g<;V67Q7j)-$mvrmr#xnZX&vH9lNN+f0A5L$S{;&Us?XmxwX1eQ4+3zI( z_m7pDe|=nEf59*k3Yu6*zB3{W<|4;WN;2~Pe%4iM{@&@VP(;6(ZKdlX1XD6%YJS{s z07zNeZ{DmNYs>FlW?}gxi^7xnkIIweCk3BAK|N(RJu8iBAveRU2c!#GrWi4gY^mO+ z&5Xw%iSiSbYe~$Hl<+2dx6z5uaj9vxajD%0o&Jpm^8dd6_zX$cXi(&2`21k|`blpU zvS$?*qX^L_OjW-SaL+lnP2wrvwAQM}tO#f(Tz!7biYyEoMO}LoJyw>P9y6muzCq%1|sK+X6Bh_!oKMci(+{()8%ujbXM^Q8CUF@e} zVS71CCi*Bs=KqHo6J_1bHcWHkNWy>ybnhPsev~fzJ zS*F>DFIBTD&@v$YgjaLL@*a+H%kGCI`473wK(;Kc01dz{t*u@A zo6TQ7CGgY=4-c_m(u^|A(MYilJoV_{Scj3F50$Uafv;qw^`w!dexMR22BG_{p)2NqfBxJ&fF8SHBoQri2w1b`S0%2WZ${% zcj?Kw|Lw=j<6C(6`se?Xb@JbR&VPL#iAKqa^ap5lID&uSpsKB_vwmJcUsam`DPTPA zDMz%&I*9WHNg3x_=8ect%PwT|SR3N!G6GagMxEr1--oKxC($mHEs80l(PqYtoCb_H}j=Q`k|~Q@~B< zeZW0yr#20k)Z(H{Q6n4D`E-=Y!zaMu&L{l+Ogw*4W@`)Wsc&G5N!fVwPAvdJYL0(aY zRzu1Cp(utR*@GK!f5p|#G+HXOcBg^ZN1RUprJXVY7U{bbg<+702l1$3jM>(bRgzKR z=08aQ2F~qC#)Cky+Vq$#MTO8U`Dn3X7kwpPA^f#v`d#C9IWzp@FGbkEb%6Mq(&T74)fZLHwW`C0v?Mzv>ipt$#pTNr3#2HskLjogFK; zcfAHTT&~Y!_tnh22z?6ElC6)*%bjdjw|EXg~CtIB9g8usR zPqQ1*O>REqI~*$+0#Y>|;{SBmSeZlMEj|H!)OlSXGZIPJ{40!rELIw?Teq&0h}8|! zIvR$<4v#}Y*wSP?N9Io* zI@rGGzxZ}Q>)oVagT+-l>3e_+XJp*={=mDg+HU7< z7HD-j=TbB%^SqYVvO`903*}ohuPzPHyvSsvCM{bUklX53U~%QhNL2NqdRkhSDcgQ` zOrs2vpRNyY1&8q%^vM;Xjl+)4;aXN!6nV*bb(UgcuIhLH1)B*RD{-9sF#nW&|wr`x$dS)}Veut-0O|kMAe})W>J~MyAm(Qy*JKe`lfBupP=Lacwu_8(8cC9@qMcnx= zhFc)tKiCh&?+nWA?z}k0o2qa;H9AiXTstzJs)%{Ng{|k^L&Z2ANe;#7PhGn%y9e=< zhr&+N8Wgc{+kVR1Zr4^Gh7|nrUk45>8Akh;6~fOS412s%{z&(_ z+AGv;=EtAVaa%A^pE$LXP0)#hj8Jg}RC;bA-&?r@_m?YOv@TwwKD2Yy`{R#4`U#5P z9l=b$78n1ycCCL~U7I)&J0e=FOVzptW=9 z&AJ;jpTjFX(v3hS+LP`N1m#_n)I0jD;scvGzEQ{7>tAo4Ze})z&jWAoI-bzk!sH5| zFEznMll=`J(gBPceO3Ggev1>v@IM(D;K}_^n>)|MLdjS2$-$*jXC_<=X{h(&q_C6P z6B_(o=%x1Wvi5nm%vIxK;X<82vdBZD19yeq60hGu;`D^`BJSQWI)De9y!s>#Y%nLs zNMQDE*AfwsZg=U_Z;p@F;&TNE())~fdZ75J!k)=vF$3Kh2|O*_MIvdpYl}hY&;0ow zLZbij)pY1`ZTcU+!DsBiN8kxuz5$d3nJs)k*_W0rS;?lFSHuhL#whzQ$v#=$tMk-T z`fAIu%6r6?pQyb;bF(Ox{#@V={mefoaMuTTc)F`9_j%5kjC*vsW%&;~>0ZR$)7_&~ z&dXe>mNN?aqW|ei+EQ_aHQdvV$`*9*pNMd?h^^XZ#IMs1=m!lk9pAYh9g8u_70@N@ zBBz;}#=h0i3(fy1(CtKhEH*znh5dZf2|}$e>C0uLmKq6_En%v)j{C$u>9s76t^)=$Q^C6%1wg}Kh*e7ZThq0hbE zkbUUn_veWDh=tl#Rke)w3+Vrsw+PVA}5c-gT8SmY@)~ zWijfuscc*@2_~PWI^M4^ODP$7NYcQxdtq2z`i#_Po5QVnqujJYvNv13T7iZeyhVINtHmXk!W z1TtrMRC3s*`BcN2CZtRW%l-jR#vZ=Dz`|lXH zH-VF2-@Og&6Q!QllrE4)h_tZ|$3td_mXB>BDFt1aawI^+xT~$-8_eDV%W>a!)7R1Q zycjs@FFHenr5ARC4`vJsnS*IxQTXp7vKHHtm9W|#iqQSGxE9#J#Wwt(6tO(Ns zw8QRGd2@^F!jjJq;z3u1Uw>v&j+XCSnLMk!x#7Wq1JTzIdhK2PWC;p9Fu-6G8yv5h z^_MS{5d)u-$*3k$g0a^r>o|-ZXko*QBAn^Q-{<6N{cCkXOljM;tv1ua2W<|# zX)a)pe20uIp!|iX)|ruaRJiCj_n0- z%wElv_6dvG;4bW2^9hy@4>p>k!-<{@t1FleMkuS&swkr_wGY!UTL0QmU7VQ_ftd-x z6`j5Z2fJ!2FfrJGsS)8`nboO%$RS%F`zz$I&@9DV3c)Nz)S7FbmzV|&IT;*G1~E=b zFAMiRD%Vx|Nf4Rl#J!|Kfq>h~L~B>F=cBbzCKfrw6ipL^*Fi$LXO)LbI!o40A}mvg zWg=6-3ZBOS@J;aAp_aQdeo>4+!YLI1Z(2q+!i>!q|H(LGcA%1EZAQ5fMTM5S*Ybvb zx}9&6!}KN3baSiyn5c(;3UaFa1X<2hz3;weto$ergG6dWut zp#=-k0}D@a^t8IVKiY(3-hA1-hX=VgOyhQA2u(4Ju_nD8k+aL z_4Un(5ukbdH0f8)7TP#^^<{A{H;*~s@9D5|N??q*9g}BH`^Xlye{5zQ^eDA;%|OaA z3D5pleh1rP>%S$a<51|WJc(HBR(bm`@K_MhF(rZ(t@Y6n1 za9-Z^ghf70tf!E8>2X!!=-g+;(Kn{4T^$T_x^k-{Zr?ToZ4LvnUkTj8<~=p-SQN*0 zYQb{l`tcWLaVvykF1$POfy(gG$k%)k%CRa!6hP~_NjM+os|Z2jJ8mL-rOzGpNi+AH z?pys%3^SyEOg11BC_#*IT8JW(C%`Z*u3guwt1=3=DB4errgKe_mCZpR`eSNDztsub z5I>x(UUgybrD4T~*31qFlp15q$l#MFpJT>xkk*iA)xA(L@}lW~=Eb^49#(}O*9^91 zAR4^QZ4;b8`t$xv-$13aYPp-fK$-lPo5QT~RDB>0{NpQ|ThIxKY0)i_lsnvIz4ygg zdMUHS;Eafoqd#RLi{~BZ9^@8R{1$#NY}e(gLDTeELM^73g(=p^=52M&IM7V*y)=J0 zLCfoEL+x_<-T9o^90b8~RR7Ld1aCOZO%BAaYPdF1%{JG5N}{+2)+{xgSj(_K+vhYK|*x*K0Q~u&sA# zwYKlVSikoMh$yzAYs%oG(oKvR4wNxE{Iz8~3) zecrzJLmkPlC;^>q^~3&P{px_R5vjA`ZU5Z4v%|s|Gc7v9X;1JB2g)c5nO zM&*Y|KTL-dPaHI0_8#b3ymUN7Eb-=D+6fG&PIsT=GC%!QncBcZF^EQtwzb`@u_$Jv?Gvj)zoV zuPl;wDTBmf9;}~I&nZ0wWzd-?;#I^bW;h<+vBzMgDlUg;D`?{7X`*U4ErXtetGEx3QhgRnyxqV?MT7w2f`P)fb~=lvpz5kom6;ol;J>|c2iAM-!E|EatrZQrn46pX=YmgcR?pB*9@EJ3yIuQpfrjDo zfPxzOy8@y7xgf$WF2l*5#hV3jdW`ZlbUQ2SYs*_ZrcUF`&Lb)CqkxAOoEuMy*8SIC zFYuOWU*3P&)_EN^hMkl<5L;Y)W^l1q*m_@qlgsgR+-##Vw~t7B$xz5K$}FrwPQZ_H zDk7ZCov06OVFCEZ-8gT>&G}HErTTdUlJg1raLEuT$;Q6SQ_<`|>&B`GL~bW>oNME7 zEjY2j@J!741b~7RlzWu<_y_mu)$4Qwis8Yyk~&Y+udgfqV%K?wjm=`v)2n|jVDkDs zJ&zNvVhu-~JLU&ti^nzKA(ZQ!7^wyEZ}kwM2LZ!yGj5MFi*Ed9TsRCgI|!%lSTSO$ z0h%3PmtWzTMC{tSd-q_i+14Hu^&O^Pi@-F ze?d0QfjMFaFU(|CQCzoc@x zBh|`ap=ydC-M?$I)YR0S@R!>TtpR0-8#rG4q382fSK!4gk>?ZRja)|cJwW1jlG|qP z{!KoLaUHJf(UM<*Ys)U7*w`U4s&fKm#YNgGJl!tC3UDvj2^rYx3DsM|qi4?+PSh7- zY2#FFMcfS-;jUmQ?RjDV$O9(16rVbI^5>y>pKRN=Z@+jwtPEx+4BY`Q@meBA^SUGS zjtyOPQ!Tn#++A6%W5B*^a(+# zrCaXu??Gn89&x8&Q4-#Fa1?dq4DN@Yb=%CBddOwRo_Rvz(kHD}^l~=w&IiciT0J3Q zj5#}{D(#DRu}}SjYBHDOH^WKErS_3M5*A>iRZ^Lc(owOE%?mh ztY0%<=CO!$@{YIKHGxw#7H_i5Ezg6xXxU=8qxB;7Lx1<@W)J=`>rLk8IBPqr`wH;D zq+y5+Gsn)ytU)(riX8U1~2>|DYF-VeMiPa^nRTv>vHtN?On+;x~5K4 z>@6Zp&yJ#sV4~=Y0#57`-0z_HQHi6P`Mo9<&wjI3!+5Rx*3@FbO<|X%U7F7fq8)wD zf4HCq9{mFK#oYE+$bc3!-PqTAGFQ&En4VSd#yxb611y{7Gk_kE2&kOUTO|{@Tx_+=|D@6pb~Z<*C9#B+FEyaFU}V5Lh5B5BTjiHQoWms% z<}ejTi#RWf=Nu&}jZ^D)sXrQ~(nbnLmC*9^c9S8EJ&7F z3@6qbp5gPK%U1(E5o0Q#kuW&zlD0BzYm%`mbU7K(C&GR)Zt}#jTr87Tzk28t(PgB| z;FVARX_l<@qC)Ql@j-ay67zEynBuhdyAYlF;xBa@#MVX>;~N+b z5mc3cO34dax+rIWHiU|?6QUV0bE$z<^F>OW^~45S@!F|bb_`s$<%ZPZCa6G$oyRD%>vDE|GDY*!IgXC~>KiMi2jey5nrMjP3uW$-im)2`FO!@l){qe{K5z|8J}Q zU+=5)fg$hU<%rgjv&b;{=X0?oOCwD;B)qXjnDT0oU*DgSTaEiON<)OjOHn^;Wgmzi?qCaMx;&Qx8?{bxMtzAWS zaiuwdz=KXj1vRfY2<66S*H2Y<_@RD~!ClH?xfqdDX9XD^!F8TFPGg~4hQ2jIBOjWn zcTUt%gCbg_U%n?pul6oLpC}6}<6ba}gIY9A9x$$z^LBPzv>-FzZeP9YzIp9^#p|17 zO-Plog;s^dp@9|CtG^7QB)39hikKw`aqZsZ*-)hEP-&aURud6td{>YeSf;l*9Yb-& zKvo{gx0LyT-?!2T>ExO?%GEV*_TSRIr2fi`!SMnyZ?yDpG4&&l7I)1cf=*%8ce4Z7 zC@>x2#KmRSR>Tk*U`v7<1!R}v#IqUt$~#%Tm<$Nfud-Pqv=xDbkx9to+>VJ>MWPX2 z!eBcAQe(~V*fsy#m(*WZ<4hOQ4&rIwyqaFBXBJYs;Skl?5)z-az#9m2|1f{wi40O4 z5ZTxjWHAYa!WomEjO=y_O+TU|NuCUBCQrzNR?jn;=W$)8SfQ1@9peniuB1eZG#J$D z`8J)X+kin);G-F*qTi3^`5?M7gHLh)=!qy`W@o=I@%YOHh>OL(S!+!=O;5E9Z@9c0 z;35}Vu}iN_D^K<{i{?3Y#L?Mnq}dWVo5(Zl%^xc6q{F+|+~O1Q#tD&6 zRm8zjq}bYSBiw8ba!1ID!(Hf!Nrm!`>{+<7GO>Z>MLZ~m z2oi@XQ9eXeI}8YrZZP+T*;xsrqcc0yCu|hPdoKzJeAiM=n5w}tSsq%`XViBm1I;v9 zpVDv+x+Ms-jO%27$gpE)8!q*7@rgmh6C&xO*~xTBrnsvv^{>C?#?=EeiD?ckLO`dF zCc;N1cM!vNm8OThLkeOkhsqNr*?q5K@UZ1XK`hZpI^yNw(N;{dz_ui+S(@IW55=dV zDu9utg7JFwcVny%pe(5!ZfoqLma~}d{%x#z7=3ttdoF5Em=)k<_3P})vzPYy!EJYT zn8^6xcH)t>e_oN(o#J~|8-7@4yvpUHz1016$>=uz+_V){L^aC?i$zbf_pECCYG`QDx}%{WN3LrB$!z*mIH9L6z4=;)*E zIsfck*{kwV=2`#96oSIoMY{z@lAQ;nmyo>bWfW+C1#7<7Du-PudUJ*fNJoQAoSw>n z20Y!3d}hF)Gdlqarrv4Nvw1!Clqu^Bwp%4le@PIMyEKU4YXhRfBvU;M!e zWS}#VCk(t14$8%tNRTO&m>vii1FpSmcZo4~+H^`@p#lJoR*3RI;Aa^c$PUaSOK{20 z|NY=1Y+LCv?GZaI8N0O7-3TsbYxIe$uf4p47&C6|g1~JdrqOZ9>w#pICs7>*Us7Gm zfiM1Wtx@9pX_k?!B1k#TZ6eGSvmx zT{oH5j(%o#(z2DlZtd|l6M&12GxN*KFP}%$zi<2^-RRsyMaYc*Ja}v(N6`7_xcOOK zUCV?>`rym5m*+1A8rF~SNqea%>lN_xbhR_{quX!obEV6<*3CWV=WCv_%WgAc;FS*s zd4FhW`!Ali)4}{MyNtGqif&{P<6?)#e{<(!7mXH4uk>@Uw1Iap$P}4f`1mU52thT+u7zK0HVVux)3CV0d~%ud{Se2oboxS5w{+>x4h zE9L%k3#&2n^qM(5`R?enrcX*sx6s!L0Qr0M>eWcz`3#mHOpFWmT$?KK__4!9k8IEm|P zWAc*4rHMznNn()8eX>_+Qlnz~5!7)cr*?l#HHUe9w5X_`^xIpzSuZQkv+OIwQd|j(M@7wpiHh+|PVd-o5;OhzO;795# zY`~q(h;PoM_YQsA`v(P(q^MX{fBMiLlg+a{t!*QJ{K;<&@%l%_|3liFNAv>x1-uvFvr_bklU)OmZ=W!nAaT<}Pww?9Qkt4$-`7K+vgo;jBSs#{D z0p*l}l-GC`R*~d7@B90Tis+G(q-D;|!)G%QaFsjbi^Q&stxM%8?g)Ff?_j&a1yjE5 zS|G0i)*L`(N&B>=M(7(V0G89e-{-3Hqp@yHWD`mT@>Spxr&;!hPS&~8t=Up?VLJp? zCO+GrmL0?(Y000-CyoyWF&#`&VC$$`Dc2Q!APum{wTzi-Jp0U?ExBXX%yQcw8mg|b z;KDG;pRCmJ{6EFsO-m$&)XlYX7a@1)YkqG2@K|^5Ppi_#1X9ktnBTgazUn_x!^Xu% z53g<+{S>HMRR(UwrOcW&i}yj=Ao~T)YoAhn zmVf#*oESAg@<&S7b8)|Wyz8`HUTmLQTfl<(BZkLXDd zQ7^v&P80-1MzC*Y=gWGm_}H=CFy)wHYTB0jc00}aC75){w{L$Nx*<3`+z%+x9pB)v zD|Nux;3G%&fJ95lB&IA}*aI1JJp2Ur!~9oGdAd` z7^fa}>VulDZXd$YO_)>{N{+f7KNWD1+E+l-52#*r@ZiC_YEuegtJ)?lgdyFEhsJvuOd9vo$q!5+iztrZ=9~Zr^j1oU|65{QCE~Ejjo4n>LO2 zRmco8Au5(sRLDzL8D<~xzJp`;`Xjg6Psr*JRmonDt=TklW_!xM!AFk4yu8?hz@IDeDn-CMZtcRmaS4w~#R34Lm;L>>AE;85?L%(zle|blQPEcf z+8N@KwoFM^*HCeIDKpFHRIs)z_rb#PIyx&G_8;>Ywn}b2cgUAUuR(!-N`kjf-x zi2>=~-HRk2+5&xrr_V8GU}yhs8S^0JL&uU;Q6n>L;`KWo>YJtQX29tEcaaj;+6EZT;`! zG>=96mU9ahWK%O+JXoxUbAG zUj?$-Zb}*!X70IIeg(WC|5M~l&7Gn3#7*v^f&sSUGbIHaj0AVcnkmbiU*kRiF;Z9QHX@(IupmO;ePu_vi${+h z&H7a&>~CjVKbJ3|1{A%=RLQ`fEBN)BH>y&tc~;9M-KYO}1;)^FwShU=Z*oXj?Ad#1 zr42C=Kyp!W=ZyNS-d($@rPpTCkUjhp&HXLEHVhw;d-oBG&`x^8T#0ANO%3ipfBux0 z>?lI$guXVWdfZk2hMHcstK8b@d473fy1|92V@C3~3ioTeJDvLnGhXf*V9UxPu*N8Z{Pk^*C(E#i;2z%os3~N<)nC#5Lcx{4_5Qpv8gdX7V+4*m&Lt$_S^-J5kTJQ znCkuPZ@G)#vf_YI9~NtR7Vgk1A7QxHMa8sy;b0;|qt1B9H7gsEx&uW9jeMdo6ITwY zj6QDW)=qW)ch~ljnQ2spB}+%}gLb3i=8(Bv?d|PnaxFSv`*vU%)#E}P6*=66L};e5 zuGL!Xamy8hkD0db)QPpDg4gn^M`QQaty>3H4Gm2`dbUI0BIWw5Ep)9)kB|3bnw{AU zPU2!IgR@vRNG7Vz^on!QPS|JJAGQ{xIg*6iqg~P8E<^9tZ=$e~cwuPZ_W`)13_)MN z4bf5w*|tU{YKl%Oa>Bi0l&UK*Qu3h;n30=1*ZDY^kT~<)I_-{JNxb^-;K&w)o1bK@ zU!kHH`z{L<7{9(kL6ksH)we@tp)t2#Zl9Zn3k}WRZxMTK+<~+^298JXM2(%uM90Ls zqqAnt{7Q+v+YI*sxpk&G-T=EN+3JirFa7OILjC9$SU`2=tXY;})MJG&iIf+&;DD+6 z>X%oy^3G1c7XhWWU%ov0$dMyof$*|9Ue0lIY>*9^tX!!fA_>yi6)%rpShu5_`tl|3 zdw(I??IVhFv1Y&v40onOQ`8_~dG4vbJ>`@Jo2Bd3u&}hT!=cHR|5SSgS%=h)Bry&A zF+l%)!kLCs<{2;$mW%pz?Yf;T@n&vr7i!cY;m60jh!sHh+yc0&+ zYZ0>z0Mm<~yaSd9;8wX=RMcA}dz`8#pf0;lQrPMdHXjp)%rQNy*Qak^^LRVsz)I6| zlgh>+tx7>yv+&C5zF5A@k{(a|%Ffb?d;A3oO!A?yQVPSi&o(=(DII1;5|HIaW@c1Q zk5m72HorUcef7q_Rwi#{+OrO@#SfsUqNUXd9%KBbul>PTzGSkI6xFY~*9>Ga=dYM~ z_;yy7?BGMnZXnd2u6uf$9=DIseq5S+E+MM-tL~20t1IH2S2Z+Lr=IfaucE3N8Bz=R zls!4Q{7h`AX;;8|V0Pk?xbxOmIwFZ1`n7&aQDUl|`}7~X@nd~*IOsw^m&+Hv1ujVb z?u1$lY8c^lMw=3k^2x!3=ktc;ZY{6eAX7`$Czw-nUjDaEB+n|ZO; z3o4UySIntHB(@YK^7C}>$)v+?f1Q9OYJonqhn~lHvW2(ba({oqcBAaq?S>yo2J&?a zG2;n-&Rg&SWfP3C?Chjp(=s8=eR}ut$zf3g>@@FfiuzXg?vnP% zk-=+5oJxRjxawBf4a4zWKhr zKX@?nsNKG)U4$>gk#VzBGtZa}ZhPLOq;6g^k13=)C;rM65ex)7H*cm^z^+}vYfLK( z;@0HowY94d89octr(KkLzi_W%Px2fW=eQaMeFDnL=KNL}3`Wtg{rK^t_N(HRz09Km z^$LbUZ_-WBmMY6g+#kVe5}utmdHVu3eSDSL+vmH}7SC#p3%`mYvlh>->bBuiyM`^F z!kY>=;#GzVwj#cu0bH{TK$YcY72ylRUX@O?BG5S*M?#NA!ju+?4~t?lL*Q8@v7d(eKK|aqXx5xSF4zzvJ^| z*RuOP#;@&90<#?+UuKhD_qYfSVCxfqeO@DT>(PG!)Zm9To0k2!(lU1R+vo3o4>!N} zMt-SkWcNP0n-rw4Fh#E++~!+1f%EOM&%u`oOB0 zDqmR7Wuz}J)3s~Y=S4-?*~5nqAGz$NT-}((jGxUyGBA?6>RV+&Cvxl&1R6-|*P_eFxr+>>o2Xsx88m9Z1;Qk>+N?9G4Y1 zq;6W*ULp)+(*6}sWeq?Xli^vu#=515Ac}2U`YK>9vD50z<+oh#l0*Y~>grScO5&I` z;i-`R>f))O60i&$gmXHa7i0-4?l(Lg2VC z&0|78-Az99CZ-mfg{9ZbeOkG5#y=5vHceM<7xiN2(IZEs6>%+8ysbfB4Hf zG9 zw63!}zpyj8cWbGE0=(7J*hQCHNo_xE^1^+$BfLpV{U5X6HxMJgvWI0i>G=g+7}uEj zOjCPgGw>Bx1h_`>5$$t}o6?(p&nfqG96HA<_u5%qT}hw5tTNjv1;$jutm2lnXGDDK z9=&^)-rUj7_^|Tj1iSokHt!36@SpO`sIvAhyRURa*S$+ddV&98EHom)3F0YB0$Wul zN7`JU5qCMwwJcytQKez>g@W~3p097VtWzr5G2~vo)PBtzKNbggxB%LMzn&;jE9 z?F(hXJex!g*55sP;`A~?0jJrP#G-%hPw)EvSS<|~`GwQ3V4GxG-GfYqhRA)gnl za(1r$+%RkU^c#4*?K^a+&BTclQ+0mN_|UH)M-ZAja&?1dlnL`{<@R^iz^v&!F0jst zA0GUuIYsLEQ5vYadF!{rLtND&7tg3E_(~zX0}64oLrs^X1)I#w(l^y**%j1aOwSmt zU$0G}Z71(1?d9$BYf_*_;fC5gIzBmwdn9|^^;4%$zabkApZJ1`i^>{UXk`)qNI;Ew zRzBr(I$W^6GW)ac{?(hvf|uFbZwIaib`MDweMjt|!ibQ$z$igIQn3Yk2tgf32A zKRwE8b#YoCx|*eQ!)(s{3V4-~=t|j2-CVeO*lfhI0QpWU5XVlUl}A@K!EcO2Q2g=V z>)j!oLTuYWRPClEIFj>LJugAHH4iyP7DOqm7p?xd-=+wQV)TF#e8{y&4jLCWMC?+z zv&pgf);T_6ne3jxK;b}>LMvtUxfj&wMq+u#`#*cPEEpxwy>(V-!n#}cB<@r^Q(w}Z zu{*sXoJrTWE6841Q#_Tle#{2f9`|3oSmYJGjzLi@$$pdDXthi6@1-`eHX`og@YsX3 zfd&8m+ZR?)R%+i@N0B&GJ#@k2BNMhWu25foD@QH6Q!lM0$%-PO=HWx+3@h*3rHiWV z&;@5LrrT%+n!ch>WJ;%9;rYLv8&mX}8rChox>hEmWur4SXh{=7o8e6@836V{K3`rU*>aJGpEcs8%XLe_2(ZKFJJDOm6hdMPnr{b zDh|3e7KTIz!Gmwub zdQX@S$+~LQs<+;##0>t+H19~XRB7)tC;4scjDHw4_CrpZkPfzWoYh-Fp+7;UOgGdt z>SAJIXi;zJ33AxjBB#;hO72^v$hlFbHwz1`$}U@{Y>F@3AV!P6m>n>vsl28BgMY@& zakhrwzqXnM*m3gY$&2yv=0n;^;+R%dJW)%{w#eIHo-<))vHfCy@nC-GrY*f88~S*X z_bPUnqGZI?&_OfkFSIXkvz(Kx9`J6_4Iyd2=KSBuW}t!7%h}2OY8j5c2os$tHyqvHlspzyY|f=#T`nhiDj5}Tq8Z8;7-f&y zV8v=TFEZ+ctb;g#&<1{b?qon-b!z0djc}*2zjEGEWY_r74}J2@SFdau&rAOBdAmT; zp`SEu)6R#)t+#cc%m31O`f^ltbgUeTV21S2;Y{XRPD@0L`{s~9a`ewtmNgk6a$r@(%X zk%hM4JicvX2fm?gWyRCe9l&8U*)3hYdM~Yoy~!vHf#(5Z(y1Z{T+?HM>wu4+J_!g& zzotd3l@cx0I;vYJQXjmjV4&qcoReht?gW>h+-zVTsssP*P0M}q={pk?j%GjQQfK))aqtFLxrcDfcLHf5 z(P}9e^dB;$4WvV+K&&)f1Qs3G;3@6Aa^}t5Em#;x$0jdX`S;%oN~XOKTsPmut)v6L zzZJBFSRwMasg+c2>XiE*YV*me3krn?V1A05-V$Gw~as~E;5m0 zFiLby&dc$cRWDYY{4%oRfrfZ9>SH|fW!!?%w&{9Q9$BU!%8f#advLP1S8%o(cHL@H z33bpR`9B4dE!?EX&OEH$hRt^O(W9y49h3zFfLuagsFj~GZ1`|duo`BSATP`w;sW`D zC-L?s0xhr?%LXp@EOm2nxj`fqcN#3H>X$!i|)>S(|b>4k%yXDguu{vuvZZz4jVJwr*Q5~NwIJY|LN`G?o zm>w&qU1uNn@84fSd^3=}5`6e@Tc})dW02J4k1vcH_Mydy^OKZJUg5eg8U%D@Eqb|J z*mdZTaGCbw4^I%|MvNRe#np8bM4>2sl7=mXfTg$)Z{zUe`}Zt_2!a(t;oDzLtp)us z#^7#5l0wki247pV-dii*zu!e(Zbwf%Gk|?VLzQ?+`;Hyk+g3?Kn1TNUvbt2gF=kCx zKA1>*Sz0>SVF+d;C2!tr6BPpLMn)7U#F&f)?mlmvx*x9CvZqDVx?u<2xxak)umixn zhxQ5u4U1?Wz>>Vm2&M`N0Z!?G`SoF~H|cGgsG=f6@n}bAsAYWER{vx3R;mSVV#9?(2v$U3GW-Qiw`&T!XHQqTDwoB-Xu& z?zE(ErB`y&pe5D$iL2pK`}^weXsfi8Gq_?;2wazF;k1~X+{FNckItr1T{u{qo&QNU zEX>Wll#wv^kUD=xjB@=oSku_Cd-9Q6-zNRJ_o zm(~4(zend(U3MB}uGW->#hM+QUsU@Y%(pK%SO#nzSYU;wi)6(R6We8k*(94}y~OzV zV0+s4ZvX=GE~M^096o>z?da%e6}N8vwJSMe-rf8jwfp89&)~d~k(H?ic6O>9o@J|6 zEnZ<2Q#xJVe8r^ngAC~$Q$j{XjXkk$d2>~`1~(Q12o3QB%ER%cdHm(izD z>F{yAPt(Iw++zL_WOwa7P)>f#`Hb`*^93bvZ1lHat`Ftw$yut?XV0daT9Yfk?U9;s z-iNl9$K4t`X3X&Q6AgP5YOeopp;rVABJBWm_0VbVLi4}{Q3C8=K^1Uo*+A7>7h4?k z_iwC0;$jBBgHr7c6obyj#hp8chC$IT<@t_xc_UYD!sVwLDNBQuGV7MbNgKJgB;!&? zKmTc&gp=1C7J9qrt4Ke8OJ%b|ym(o8pY-&}u+agWw(PhEP%)kQP4?P@_BRqr90mE` zi~I~p(u~R$vm?p+TktFvUvo8fRvgjUd=aCt$Ak6vinUv?va5O3veqMbmW`g1;tw5K z6rW~r&_6o*lw;>_?zPJNdFS6(rpIk`Tf27f_kzz|Ix873Ufkz^sYArm#?IzXnm;D+ z`@dfmN;mGL|Mx%s{M)9pNSprm*Jk7Y%a3(j`oF(LHFAO6L?1%!|N8oW_d?4S=gTPY zrn}3lV0-GvSm;X~cl>#k=)57Jrj!i_D+V%+`EwJOQ~yDO&alvb z-ipR(FeDfFj-yBW9xzQi5(1VwHP}}n^89u%%VwoEDhf(UJMcLApWpD3gKYqnIKCIp zDcW}}CF#!QZKh9~wr`^Mr;i`aKtv?0p)MBU@$ePJfF%(R{DXo_s3C*tTUlEdUFx`- zOD6R8pLeI~)=28rdGFr64t+X^2f|CfATVu({D0XQhX28z@4LCH8=uqEM?sduBuP$A zm816V{Ld%%*!MXYi=K=ZZCF#&R?SOms?Uk^P;*+j(kkl-OD4WTJLOKgq#%n5srh$3 zIk&5C25(cnf784NZFfYrSNreMOs+0gTi5*Q?k#QJK7W2*{PW4sZOuQ_`~m;`BTjyU z)b)S9ga4PR`Wz%=Wb`eB7U79JC2jzl{D_LEgg5zNkF%}Tv~*X>hHXjbxgfcXfYv5~ zP*p8KyTVQ1ExWOzYy`?u{_ZuYT+}haHwa;i=waLZY9}tzT?EPg*Yf4v+-(c*H(q)+ zU39fDtFOE0g)G>hWq8PTXOEg+RBZ)=!NnTpHdwqP+0siLeOR}E1*Xe8ZacX@_{gCa zPV?+H=`6Z+KcT^^Y$N)cvOB{O;BbFh#M$%}H|oyzJ-2az9a!dDRzOwY>$mswIP|>Y z<{`0H6U%BY*{_5%tuFDyOWN$;mb?L;HW z|66!y=nmW-t|lj+9fo0rp_!QiH&lRwx4H`Z1{vDCMW@XMFI~1w2JlJCj}mDB{KE8I zcdE}`_53xKaYcX`+Yjz0Q>Us99xP8L8FlPR8Vj(KDY7NB6mN}*aSZ-(Cidgb{OJ}J zifCQ57N>digrz5i=FT^wK=}kQ-{4U7$@y=IVQ@M(VJT`0KRc{wt!22Uj$VeYbi`!9 zvj+iZ!rjKRhzI7>{T{;+@c-7O&(S?mXVl_f+bPZ+)$~d3(x@@p*6dz+u<%jeW3%%e zdPFS8@6yU-+bXp#@|`4o3vH?$BBK>WK^D^e z8t?%Df*>9?ZQdNV%=hq;bg#Y)L>)F_7N#6SjBAS3eD#@OlwV7&69b$H;`M=yQHp9$ zA7aQYY(DIn7)<83fT+3oC);6}qvI|f!in>S<~7L4MX_~W_FMo>*S3r$tW@u5J_d0<#xk!DCQg*oYUtKNns)C& z=B3d;F6w^m*DdXt^u_J#;NOy)s8vAV%1G2b)_$un9|qNcvLgdM(>JfoqHIj#*I|SU zm^zlP4J6kG;|&`&%yi@^4d+$ujCNWltmq6{jLKcaExVWG8folO`t1eJ$+&uds!nn3 z)t;^9b*v_~rGaF|gkHblpdk+~E}S|tOdUCpd!7r3nQje#ZpY@-W8#Kk8<>fZ)%k3@ zPMu8Hd%U~*ItN@|}hxPY&eX*+-;F(114Tc`us8S#|s{&B< znYf|7fNjVdkmLA4RG&L<_$>N-(N@hn4vX%r@P`d6#rK;2sh&awBrZViPvz!7W#hVA z_GVS%g?$E3+sI5<;{H)?_4A7=)IWwaS=TQUGY{~M8V|3zgI~-ZD4Roh|Gti##-Qij z{PR#K$iu(sNuq)*QV~WPc64%b8Zc;(J?ZztOMkV27=F9HEWYb=bb!Lft?y2fhnY5o z&ug6UYiiinOA@jVJf5=1_WfwyFG2dzr*{4$;^d8o4`a{Y8k`63CrMO0v`VLGZ$WEU zMN@zD@Gk{%%Cvv(ruEE@!Z9^O$7>%mym}Vi$W|!b-Ku>LA6PqkvNMK>Yb{;%;P^kp z=ldwf7q+f1Xnrk2((n0ghv_Or=jNz);1u z?nMwwHy%CeDCIyVxaaM-cFjFLbA4%6hx3cCb>Z-i7&XcOh0h3)^o$=L?L5dggB6I! zN(|zll>dA2$`wCiG$RZ;n3$M2E63TG4psK7oYh4^!AF2BL_8wnV#%H$-bBHsJay{S zR-yJ}&U6kb+Z-lJ93#Nmqgj@G;O_*DrV(d$B`GP3ftMqgWI_E|NG1-Baq5Pa#!Az+ zb*z}cf>B%x@KYwq$!v@jNnV3aiw+)tOr2slmg)xzJB=075V(0cl z(j#bl96EW#+E0aJh<+7I!nq@Rj9t|O^tPRnK`Cs(^Az{dsB(pvhbm~c_F_gtJ%OMTlRY(D1PcsRDoT=6 z>|Vp~(f1+Tp;|o?W_$7Ew+v{x_2fxsLEOd|&MO%8Zeznw7rJ4U1hYz7CsKNX&N8&- zg6n$9Ol<|0^%k7N6KL#EPkEJ#v@L!_cfYreaB!2)ASzsR11bsv8rhQ8n$gHx;Y;M1 zJZr+Ky+~i@5hMd-!e~r0Amv0cD)j@UvJMo_e50b&`H+!g{-x|1&x;GOK&O7XJ~1&v z=;QU_m_@=&Y%Yt5L*(bUq#)hf8^DK(K?QE-OUI>#MazJ%f|3VqvzRTAjtVga%J}H8 zq!SyY9{H_gI6|!%%i4m6=0^jVAYIt-cW820wq{LSflJJgEcTUf7hlK-r2KVXm(LzwTpu#E28guXy^YL5k9I85nyD?J}FLoMU{PCs;CIz*toB7Ji zvgG%Y%}ujjc*Wkf%MGWRA@!$75a*H_7#Jw^7cv1~1v{FvWqpJnmJ#?sPOAHYVhpXU z3sSj6x7_30?mvrhObY8HQ5hP5xr+vLYT*XhTU9(loUcc|rJZpUe|PGVuq7Mwc}D$~ zhM(~%%A{{H`UJ%8)-$QjC<&vbB1Sfo2MkX9Bw$o*d|_c>02$j^=aa)fZ&UnRFLTiv za=S?6@>cehG%4RGBX+--@vNKw{rw!{c2XqaR(15~%NYsa)JRl+P?Dn@OotdRyh@2w zUb^}C@tiZ?P`D`~pUB&kGBnh*iqGTt@)2x(rTb~N7BJjE-IL)JxUAuB>AQFD0>mgXr$VT~U6607A6Dg@ zn9}RVGe*xoiL;$7qc66$v$f~_!S0a230b&sX&%#cS3N^aJuCHxXpFiP#90)lA-1fb z+htrirT$)+*wLVVHvXZQ&RCw@)xe)?gY_bET0Mn_v_jq%TXu|DwS{8~?_Y4f7V)__ zUCY*xWb=K>QI$_-XPZErQ^&)Pq-4FgxNN(XP0P?1`^J))hd}_xxF-A;-kds5am;A}BM5eP0Mp{# zpmz0OH2J2VUsZS;g2Relwg12YU!G`$h|F$on2ok%d4p(PeFKrY2^WRV{rcIwty7+% zqik5ooUCQ|+IGTDegwRhkjGMnKoWe1aa8+`99gw?g1E$?N7J;Ow??N>9KHd$D@j1) z1rt_l$CyRPiVuMG%2MQc;)c@RyLtFySfeMG|DHp+R|)OpEiM|-DMD+42=oF3iK)dP z+DPJuM$z)W{>msQ=uSWeXEmD#2dwq5a_vQOUI)t3f$VKRnz>ep^R`JEx+e zRH#tGs)nauaYI?W2gdge<=!lQ!7>Mj9h~Ji7#Q4UevP<{(fC|2%3c$V!bJsP1;yII z_4#GHYe!($!PHMlc%2vZX~gzVKo6P?h(HFE*Iq-yP+7sLN=uaBkvsa5u&{#{T3drI z6^QG1ptAGc?R(V3j7#r^&cHxl>RGrQ5FZ8b{I zr3Bv*W5#3(;3lp^(k6@%Z@{dVIq5mG2e%^-69lWUyc9@Ph=?IXv(Q+NT$KK##kqNixcb5fOXAyXXvJPdI9RXgJ2-d{-A#EH3xv}4%9i~66oe4^F?YSyO;WNCBV_VAss zyw%NhA5YMv`H&%%{Z??w7nnQhKJoazI%Ud9@2K<>TdLH%gr_A0O6;2M7-4mhe37k? zc6Bvqm?*n2#v*`Y7V~Kh!Bf^J+ALxl>ax>?^=$vP(oNAe+oEYLBT#Fbr9n;}a67O>N$soKph6*b8pzAieJXL2b2 zC(U&PbZS!Fdu?16F#7nLv*R33&4$aoO{b=I*7rB^Jq+(xT}o&fS^b8~z;$B2o1&GS zU1Z8EGc&U`Ceghe<<jnzSN)^s9D{ee(wxJBoEuVuf>m()cUc6Xepg&ily zxRZ>&==ZSE9R74{FU_Xte{W8eS}3sI3yS>KUoh7Q)i}94@l)L3vVzkmI%m+ArbwXR z{z04%3hic4VH;jo`I@Pf7Dbx)k6~PjT&A~y_NOaUW@LSyQxv|Rj2&T9>)hFXjhbO!-piEc6YJxAo2B* z^}+V_!2Rlw9x{D;_0l-L8&A9;3~u!WX^2}nV$G7fk8?H*e&#J!83OwjuZXT^wEPMh2MJ`7o}ar=AadM*VpQ519qSpt#wBx zd-uzzxpvX!NC9)7j`cE`h8fLdN=rj*nRT65i(0#{)SpWN)!E9*$-h31PVB6~*v9JU zonre~+d%aUUH{jvv;ei!-EpmM38eAgz1tAE&HZE{(csiK_kiSEuepH}VPl^-+w_hS zdwY3swqBh>oZpMPX_9uZ|}yEAOaWX~d5b9a@%6y|&vxGNq8p6Qx? z$qfGGfI*)uRK`kg?mpLEju)O2l#NRJ9OZI1LEz>>S94;u5atllLrqpCITdu|GgF0b zfv`a)`dB!RL};xR&VqdYrcun)`Isak*=ic#1l*=Y#jHXvV6NAa6udF3uO$;QL<=L) zwH1YXGVttmN|n~(U83ghqJQG-@btB>Wk0`4ZHKNaal#~-3`C9u{Z)?b2rLn<42=>hs@_K+mKyuL9m<5SHRq?wZ9 z!83&FV|tzeLPJSPC2IXSzEs|F|RWBeOP+O>h$~tb}Lr~K$*p491sbIAS2ry({!tWNs}5Fyr&va}0V_eluog?O1YUvU{~0 zguF(1cK4ozt)eP35XdUJpY6JYR*V0wPHx>vNOCDBt*^}QfSVDRI^s^4MQd6_&>s!` z?zfn2HO;%MGTfFlCil>p{bSD04BQuTh&KNDr>CwcS(xTk@*O#nx6$a)qYY6>w>wg5 z@l;;9q%NoX-5o^$U$)zjXmLG%(@FWY($Kr@K$oXlL3^653~kT5aHtNGJLV z%{#HD=#nK}-r*4?~j-$t=6%7~i z=MVJEjvET`2)j+|ppXU7@@lpvI(uL8ffd(J2itjm|JYNslAbTiEt_cYLHlXh=$zV# zboC4=C4;}{`D+7giW((tScev0Q)=AEk9(O+dN=5yy;V-_!ewfOG*Yx(9QSym0|8h@ zzu)l2NKAf&!i#GI+v?#sinL)C%fdyAZrr^qFDMs1k4-Tild)hsdnBBO#oY*qL{XZH zTv>4A~UY+u~%*~O20KF5Xa-?PfR?|kDvQx?Azl!V)< zNWD{MUMB|5r^hk_7dngFcS3p>sJ8vo@9Anf0cvp}W`{>kkJE2_yMNBxf;QzpAgsGN zHbosGh`f9IR!*YhZSQRN1M@wn-?#rYK%)bNUO@_C7A4xwMERfAsGP%kNU&%Ml=<2a z`k8NuQd$}BvUaULeQE-n)OP3@(s1%uR81~=+!2h}OiU=8+B5n3^-@}bAf4=6u^+ zwoUPSUzvu9I`hK6$j8(W+vYm;n&qw(wTId?@{}8N!Aa7OFU5C+%%iUSxINHaWSGKr zYQ+P1t7nH#pK!~32w^3O;!9xV_HEm~qCAIGZcQ*)dGs*}l6pV;DW{%9ipL|^m3gh# znioYRecymcn`+I?=g+U^*x$+-8~w#?sY?&;jmDp)oz5@0-VH}1O_5t5>9F7iCzG2u z-MWy+DKjk5p5v{t<-g}VJ8#41h5a^ls@JWEGn3{OUJG3~(;=3I9P+f*qG};J!<-dQ z9zU)O>1uY(Rp>XOozpS6N_=#U>=}olDX+X;$}AP$o20U;?lz`N4*WG!E5z$TzP6Pn z{id`f4a;quFzHxC{=mCZ1(Bb}pQT|C4(3n2JjMrqyJ;tp*@~B8@y$vK=$bH>gg*5&GkHpjm zk*Gkp>_sw28`HTQ=gXHal`y0we_1{1Z=o~t`Oy4~)5j~BEJ?e|Px0tre39zz%r9_o z3Te7-xSzDWg(O~v*g(qHO`}qI(&hn=kDtx{yMGk@*SCrI&vXirUaF;!8V8G0WlT`W zAf4y#^yLN-{r?&lTUSCNBaP9wMNbv zrQLbOr(7InJ zIWhTb@4Is++H+Y^P)JH?t2$|tZBEL%!(t=2Zrs>f!imDjA@3fmC77sKwT1WX^>*g{ z!?UH?eGgAF0@dxxlfHycA>&oN)UT`|c9@kxm z8WgRNUWsL-4(<9*va(6V8V$h@221V4nLBrMj=0=;=-++zueYfOHd?;xFv+uCyH%%t zdtNTCbsWgJSbsiObnL{nB@&{9_L9!Sbd5tW?UW+ldW*|dfNJ0iXm=L`Yxm?%hS|19 z+Q($yM8kXomV>pa!1+%2vV;vBFhGv<({ju2#Bcw0#+%-y7`3MOYhMM zJIO&Jq03^kzP(~6JvKQ?jWj98wfh;`@B2y?d;J7dzzLiMj{-zR6DU-G8BbeVd;-D$K8HX9hY zAI<&aO#nMnaCkYFBNs4?EFw;^1K9CxC0QS;rPnArTE1xIL)E5>Q2j{#k=_|0g7!^J zOicNgL@HoR+_srVbu4F>zkff3+NYZMAJ|^w4E;sjB_m{8yY9TLs2D8yqe^#QglZFW zVFgJfo)o_ofs^4sY7sfn)XLR0|3!Gx)x;s6JpwMj5+)H39zJX-5vz>I3xyTM+YL1+ zqyM{|<8T-LyROH+M&P4`0HwEu%}jp8Sx2n2r2ZhF^`_}mcn}X^UqR;~0i=W2_Gs4z z6ijJc7ouQ5KaZIWO;YNp)Q0ux(??DcIr^ z6|~dG%!uhHM%%lk3o4z$h3haNRTpBlBL5MZK2M5%_6#z;%nc~sm8`rQTE5PdZCno% zh9^u#nMGt8gn{dTf|D?r<5Ac?a@$bzZkG5Abz4p$pBBu!c)B1H@yQc)MkMUvu`ifj zOlzZ<{!^Eni>QrUz;w2jXZQuHL{s%I^y=I#eu4TWk`)s~zCnydTk#4Q*RU*wi8CA~ z(kdXMFKhtqmk3iENdfrqTUC%xYG5UD*7ev?EK_Y9#-8eHT8q{6mb!yTSr0|ek3HPy4lM&J zoNG6zh&zn--nN3S!;~~CJ(2W6-lS|`6V|kOdU)xji;va+w29YUKhNQ}s!Y<}JA69X z*$$c&aKX&&HODK;GaUiSH{1WHY|OfiOBmO0(5@jxac$1c`EhoAgOz9Uf|)P8X1&~W z#6rWR;@kiopB)OKimNgCuB3o?!;ZbKI%ImP<|*Z&WQQ8f0)3M~QX~Yq9T*|=*n%md z9zc=t?4>!<8^~$b!`pP`?w&Z%`KT9!T35<<^Y7hZy=a%y_aHj;ny6j-Nz!cI|Go-> z6pw^QYt|uOClW0YEQqn@o$Or7Ea!Mi{t(OZLx&CxEzLj`Kl9KiV`%b|=MD3w6?a^! z;tTtKz5)yW!n(VN&fE7MJ2SS%Kwd?ZlQyPJ>pOzWG?^OmVfx`tSo!^ng3O4|XPGuA z>dSpB2290R0mpWg(rU`!SZQ=6>z?~!0o3npg4Hv-;n6Lg=TtkLT+{lZLVZD$X5S10 zc@|hjxU<(qL^OB{vP|H93`xz3K9fZY5x>U!?!&E4g$xAa5&Xd>8h-Kw?al%p##R_el`Fn=+J ztXq7ua68f7&=6|TS`1M-TVCAx#eSB{ZP@ACEI)8cAijox_hR*xfHgjR2C_I8Qm_jShdhIwJ6cAuQH~gb@i_o2x zvh;*dA3s08{Kq13B6Fd>CHWv$f0??aI~kzpNI!7@=HHKU#>7zw;+rb8&)9f)=!!zp zW2O8^<96=ph-CN-W67&y$E3Xpcn>9x{L}z4U`*Qg;nWVug0W}{ncV&T<+C&l68YaI zB;Ssmgp{a zQbb2RQh%#tN`c_K{`A5CHKHwILS|R3sG&UrS|{VijVX<@hel5#aUsQo`jDX*$CTSK zf$!O~yDKUxWP{E?)2cEGTzxtGvv?&qbBDbp7*FW}kC+9r6#~964V{4>(i(h~mw0`O z(}YVIXxmy*yXh+=-dYkC3M~9xUxM^qaziQ$$iTKzO6YF2VaW$09Gm(e*L_XRjg)>H zAvnuDzc$KgqDOq=I#*X=xamH{KUgXtY%rpKC; zum}^D*+N5rKh3H0x7^jm)8R9dMBd}DSDZglKvhG-)ZqnH z1CgvsqSGjgL#p3sF-0l1YMn-Zd zqo1U>*VwryR(mhnyje%gdqT$5nyWtA`idqw*o?$^O_r3k#*lk<`)TDhYm>NWRz~t`cH%UI|@dY)8pgtyVqPL9b5{RaMA*TG72E!&Z;ell&?vt9jwAqC&h~L>J4nY4Q}Wgm752<8T-q`gU{Nhb*7iih@r_rX2dtj%nMj zouRNLq&gysN9S+VJ$_TJ-=xlN6PYV8L>5!Ayz`5?qE6PRyciHD=)ZaM5WaUrq;aWZ zYwnG^4<3jsflZLD(}zl->7swd)BYXatYdyM^iAd?Tj}ns08huMvdRd|MA|HLP#Zt* zp&l3sLkLdm#iW+4g!OPYf8bS@?1vAHxhh1D2M3D)e5vE~K?QNPCLkt3e4uloAhP&C0#-0)Ff zUthxVccjC8HVj#J{HCfmm)c{OXrr2&Aj`eA^5kDQxG8k+?uS^23+8~iZz1O1H_4Mx z)rjJekUg^S%h10Gn;D``73D|)3a~332gPlZufqEfCv?J}5}ek^C9GYU%K&coQLnt4 zJTW5iW?*QiL~V40x4{9TuUvgbm@uTu`VD?tQs~lU8;Q%?vNB<_so|&v-vR`CURX$O z)d883l|3~{BeY8d=GPS!{^%-TK@86HU}3kR%@fK((B2d>tGC~T{*e+F>t~+0(vsQT zT~Lgi@uZ8fJD}mVS#V-X za1W#TUZh|eBfiiDCpM3PyzpWaEuxu=Z3^eRWIaLYHFwgx>e1iU4ZbT1^R!@vdNRad zDP;tfVhaB%OQ`plno(n~gJOn)y|A@}c>*fs*$Jg8@A!%ECph^FH~HO2iQh1lTeSVX zEh~R~+=TiPiA4)|X&Mqr*cBAx4XJUMaYR%L9%pL~9M}qdflgFt?gSm3u)n5lJ|uiM zTwQZ8e-yqOr%#{Os2oc7li(=$G&k9yLbxm(Sx6$Y1c0EeI`sS5dtgaw$h!&d5foEa zR-&+9i(lbi4i4g;WD{`#jk_$xMc%OLA1RQiu-xCcUsE)T3(g5K6|*hI z&;k1_mzP*ZiUU#=l{4+nf74D+VdNHsZLEtY&<#RJbbW#Kg(aL$X86H_{zyk?RuW-x zMNUawqG+?*_s>7=NHt^KqGqJhaNG&`5_pGFPs&Q1CgbR?9)~x~`hd{p_|Ujrj-9S_ z9pu)T_H1CN5#V#z+u+Vh!&c8;r*K0pqM-PpV@FjrjjKj>xdl{$T1sf9g>Gx^(n2cq z!dOi5$Nznbyo&HIY$=J*5L@fphS=5;gE9-OrIIVJM?UM@A*!|7@ z%_@{+2a-z-TvJ+7(w~%I+_(h~j!s-aS8a5&Fu;yIqVY?3GZ+i`ZLZa#MZ3sgV=^1; z+LQcSybuyNJQL>f7Jz{}sK{Bhhx{%`ek{uAC{sh7DH7iXt2;M#r4DQ zqDYaGgf=#?($LV5H}w8}OyVl`PII2LD-(`cOm^w@g8(uFzHTtd*AW}M8wue7Fas#j zh$)$KzlW@_R_|=$sJzbjcb|mmbLMmxhH-eSsSBy7)4yjlj|VFl;NLV0zI1-<9uz`$ z1fiAjDKxLk`gpudTk-SjYcvpCNB66R212p}p4;KOB2;%8bs*j8x*k8x;x@a7B3Igl zlDOh`cv#r{{D^j>u5KR=G%RY>vZd1DG-7(QEu@lxu2wn~cUtn^aLjvMpeVJQ(JOI%T@woXc`J@~=^VNs%HcmZA^>S4V2S^NS96!%uqUOjflN= ztyw`v!cj_$`dM)@h;zO~w6&0xKBvQ;%H^4)e>o;H5`}Lc^6Qiav;j9zG_{tZtr9ih zbX9yazS3F_bhXQdkfOAmmw#Z_tcuuF4wZbaBLNV7uKhJM+7d|pAmCAbFDWUv z=u>Omg&W`r?7K*bh*>*PH;X%f3{AK}z#KLc6-hnEtW*Su$w=(1?YIPU1gj8p39Cn| z8VxPya|7NC-fjOV=W#@T^j)_|tYst`0(>xq;alUm#9rd1p5ffCeh;5es8~?J3fDFP zZBQQQ25iJN;yG5@`N94>7hYYf!d-!SGXT`BqNmqSxKDE6a(Ji0tdhN$8CIOFs;T*? z?9ST|(iOHhvMKIh&%NfxjT0EdHXlxv& z#oSS49=#tTRsp}^;RvM}Y!esgJ)uLSsD)rAx5o^^s{;ByQa{0fgD0OjeOn{`klU8x z^$FN0W?hSJ!YcHCNOHSkSHB-qnY(nH4=tD>-rvP#hM?|Doq2c6KkQrZj2ONKl7JXo62OM89 zcKzEm?tR+4gnLBzgcwsN$~JjD@@Anx(y~TCE%g^$JWLP$6x3=PNp<|s;!8U*52m@E zJM*@naYmWa%wR6)B1wT$VD{p;wyZ+aWStTC{|=wy==&ZjIxnAc%&b*RCv|wi>5Oza zV1qRDVp38!F2px<*Y>g(l=l6*7r-27NCyV(gZ#aSdLyL3mhQ-Y>IZ56{&JZfcMY|X z#N143hlGEqR8$wk;+~F`eV$W4e44ol5;4JSp~S3CCW{vRQGuVzK}xbdBPh@bpZNR9 zgp7M9&29UrTY(##tiYd-&KJCu-cFeN_Yu@H0 zl$8Ncfp`lfVXLc(sjtm~yv`ZYZRE77@SkOF;OFl210n_|YhHihp9Pr3>=o?U6g3}g zN=}V+O&zwU*|37z>60Ozti#KGr_3L~qI8o4m#)3~Dc$Jsw=XUk+l2AyfWR%Kk?Y>g z3{5(WWwMy-D5Edf5ohmQc^gD4E{Oj<1I}GaC8XknZMH~Mn(r4oQ*)?xH8hbXBclU< z%S%F4YSD91A4SCh9Pj}Je>VoF5PkRmxU#9rF4U9K;&iWh_jVU4McG8zPhgg)6M8V4 zvBDUE+}C1|4;UoUHFmI?x9xyHxkny7=C%*+`HsgYop7<4JPVz%BoRQWM}Z1J>;lPc zs(!o6w4UC#{yI9n_;exV?^^iK9IFPG^d+UHBPjU7DAOldS*-x!&>=U$@32l$Bg2HJ z0b<~l*(e1hiJq7F)&&T0#YS^@fq$?-mM<{^gm1e2C!q@xA|nJbqzAWY^wZ9wck9fk zxv{WUR8RZ(l)KgA=W}k4!pc9EhceRDO;wRuo|+3UinKheiJN;5KPapeHX(2TdPTWh zZwNT)HRC1$)sg|o$W@79sL^6c^pn$_FP?5@JL4oYW4QU^v-t z+p5BZHqr=u#6s&Lx0b7Qa_d=kA!aodE`Rg_P=8C2mIxlNfcyS6Cw@Djl#Ft{&ffK6 zz>!DI!*Lqxwv1!SO~2u3-28Ugl@44o5pIghmyyf9ovPMEkOEaD z`svkw(<-B&pdjJ!5n6Zd_wV0TRNlFPgmZ$%$veWvk{y1M*oA*^TLSEutZF7}IUYH; zWC45KfRJYyHVZKcmeZ|#70_C5zws-KJ=qe}Ec6*d1sz(q5o{SKCieWRDY_nuuG~Q{ zZIO?fyFH{Yf-9H2q0Jdiu-?zMNL*DlMx(8geCUHantLWHO2+4Yr!%AkMtIF!MB#_P zwlik*2MVUgMS8{?mUGOdV;gmyi_`iV$V+B*ajX!pqQDf8+~AfH`Urh$eA-zU6M75H z6dUa}&p3z@;mv2yES(OCDl1Lh(Qf+h&b+rg4dgZe?>`Hh;XY=~tHq_2Dp9p_%MH9p zPomWHg>Xr@7zjNL+fNWwvGKCTam68KL>f_Ga&~7AOVI^>c~kV3W=Mg*dTX!Pm$f&s zIycF5)ZSd(ckO@oY3oYfA?5t6Us>Wli5u~Y)s=$z<8ntXMm(v_FLh2Ozh{C#Agfe@ z9hlLp!?NqZdwW8A0*kbY8yq_5z03tUk2bwom)tUUC-cYBbpIV9AAU z4tKVrDIPFn3I-)yAUmntr7YH&e-3sQIa$|?n$pot@fvPpsW|tgRcXQQD4v&2=J{Du z?)*Zn(Y7HiFLeEC_o|5V4enyP)5>hO3EtD zb6%eL2N2w-Ld|rcy@CB$h|kW;HuFhgVfs762OAJD$`S+@P3=G!6;M85tVxfK9XnPB zRiWU#y5)x^*;HQjld*qa(l$-FSbJD!JZ;tqkDd;8)7Ha_zqGy~z$#sRN^$+ZsX31S zw6n0j+2%h{{*)(BXh~`P&3E3<=8wb7b4S*I*!{WPT02+GKWowEKcYbo zj|T6vEf&u6+vLc~pZYSvGhI3M{Q0H$AZfv`3=lmFfFI$IPb1v~Nwg41dCK|w_F9UD z&Ckxx2}jd+>-f1YyK`N$*l@>?k1vG`5B2@E?cXy$B^;d$g?`%Q`kUwl>}S3_|D!pY z*XL+PSpth)o4{s9J;qj>y3C?QrJY37iM>A|1wTgtU`019mx3LU zNtnlglSRc8lEBJg^4u3}^9~y~{*bMKT{@jrw&UpJwlzX}RNlsL?{PBFz)vSG*7$t$ zT)4PM$t0B$5MS@NHD}f(^*BbNc$@e3cEPDIUtygB_^BQ`&fdQ!LNG{l9~ z!8o&a-}w3E6qZuJN?f-Ok_E9;fX&nElY%)n<8Pp^(^fFJGm6zh^ zbs}`s^g`pLbXp}z)XcI?p-eFz$dJ%SxhEYS>LK0&}X@=E{+RydW z?Abkg&YnH<{J}YT@q3>8zOVbbzSsBrz2Z-_0p{1#k|vLlOPLJ}{o|B#Y7L(SdW1J< z1gQ>{J+`0?7e6~8{=kLgoTf=Cl>vns95526?{Aw~nIr-uz*62-dp>Z2NejJg0$q_@TU*yU+}t_6PEgjmGj!a(|(hBm2phNv01S_TGcJfOt|coAc$ADDIhM# z04$XuY0*}2XS;FR$>Mcd3B*cts7<_Akf+)iqwbR+4p5fGJs(KvVfV-)$uTS}uOW1> zfkExLdfxt_ea|Us6*M)Sm61t?!UaLAoAXhR+Y5$a6qFdO2Tzf0NWp$B;*(;Ft(`C0 z+CV~z?|4^zH33f>x+lfg@tGm{hujnz9TW5AwXf+mEx%v3Wz1PCDTkWU_FSH_;&IC& z!~7w=3_)0V^kdw(hSW_`q}xb|2-sB4n%2-GzW5eB!X$EmYta*8W5Vrw;XOscC-mIZ z@r)=zBILUS2kNa5-F1{U2od4NHND3RDE{(S``6R>vPImfl!CDcBpLRjqOYgtL{A^3 zNFvCdsFD2Uf5)ZjLr7$-_9d3^6oW(A4*9be3{(hATfFlth+z-cN7#|`SvWr#{1T5R zGnhM}cSylcp>$Rj?JsDkuQvuHaHE!$e5z<6gqS$v^KA!sy2Q1z9^wn@{}gUqt3AOG!dV?6YO2 zRtibuCAfw-992^Aie0+mNV{6b6sWuM>Ay;31z-+gfTgk#lg=>Xt9D%GGL8YUixY=o z3L|4gh0Q*H59PhoqfCPkwJA}vreGFV?{0@nAm%|)QK`U@BE1C!N`^cS<3mRg<~1Cd zB+>N3v!k||3HmEiYC)HQz1ppo&Zmf-xx;v-7KY5^jz=Jh57O7)lMrJ%ef84p!jn24 z$Kqk6wh`;aj#H{~ap9$8G_$tWr*K!j^mpt+FAWDw3?&c1*_8?v0N=&u3}5qmxA%&+&#{;DVkk z7?K=brEBH)MJckuooEU&OCuophdxvqI+i+wXfIMd(Q@Jyp2Gx5=E|vth?Q~nhbxhLvnvzKZxUD z`5_GV@yxsv1vAL&uxcA~%fx1AcV_M_^00|@J1QE1ka5dGo z<(fJ0e!cIgW-R{meoSd9P{ z!nWftx1xf%7A=fTOln^G!U+@12ss->oY2g&AFO82W=0TL&PrhpPLz48?L@V00s|o@ z16#5VwRv{NcUSa7|C?0@4L?aR7^r7pFcNU)`*YQ#z(A;UWKq&XaTONW)VEF_LGM{;W4F!}7ZnX78>%e`40OZG`vEP0Ej=(l_QmFn z!6LLom zmo{=%0lo6RaXQdQ)hMc&bgx`WBNxPuw{9ND$dKs?Z>jfWnZ5WR1JI9yqC?}OoODx+ z2ubKe$R$w;YX%-Lo}*}C@C$SuI)EAJ)oz-E3QGeGK^;r-dmXc6s;WEkFP;HKv-iL6 zJma8OEsN411B~N;ksnblJ*U74Y<+)2&C5^Mz4{?wx)9gJo{VKQjrHYfvTg(GmU4~-}0K6N_Tj1Jo#qN%fqKoH6$?kQcAYWk?au~#fJ1_HcYZ{D1GjsFudo%as{)DaZ7Mi01 zgV5xlv+#SYc!mf#l#!7axdN+x@YP_B7-TqW&@;*4!N#=MJ>+U$nRSkJ&k)YOf?3zx z8Z>lhT%(nPqUfBM=j!A$6v~`F6?6qpkp^Hit&p@?Bjpn#M-W))m{Y9VGK`zCIk<=E z0?SqU6$(hfnoBlA?|&Sd)p-y{cC1geh6?aY<6%SRa#!I6_)Gxn;PTUMi;H((IA`(R zB$x-5pOxCUwAiGVaKOuG#G!0l``3w`g`G5Ro|6Ct6F^>(7hkhCK5;qKG!@;n3D8!1 z+&4a_=EZvJgoIAa1?rt_f@Rc+(841rNa9{j&^6D)M`gH%pLhsK5w|yLH zrq-EO6dlxJW3+Q58Mb%k-Qfy>QpyQ!rGWvmHw`}Z$N;DpN4}5{Z72mF_Uv+k?|VGD zp@Z2wL7Y0}Pd(!#k2+FRl25Mem5PE1PfK*`4qUW!m%RX zaUm1`oBaYDgvBA7TPzs#U0gY(RlupoGh!$ak?zJN^c3=)^6E?t5eu>#Rw*VIU0v%i zuDD0n>=j(^n%ZJSZJhCB=j_ReS5orM7vQfcswbYJI98+utn7?AHd2}yrO)hhDSKVD zBp{%S+np;r)MT(%KH$k@aq%Jb5ZiC!6O1TFvT@PyPfl-6IChXr!JPL%0?kj{NbrD0 zCyo-rO_01vn$(~>qHyFLu_{(i4f`%tHbP2~0W|K%zPsVE$@oD>b`;UV`?BV#Q-eM`)HhUCT@dj1!|k z|JrQBzQ;+o&-K>J^}g!GUUJRd_}?Gzg%yc?zqkKSJP-S*ZJNy}fBa_i!r%Cdx=qy+ WTlA9_+Nxv-`8+pI*OSgcoBs*O;mA4w literal 0 HcmV?d00001 diff --git a/docs/guide/serosurvey.md b/docs/guide/serosurvey.md new file mode 100644 index 0000000..5676287 --- /dev/null +++ b/docs/guide/serosurvey.md @@ -0,0 +1,176 @@ +# How much survey is enoughโ€”and whose uncertainty matters? + +This notebook is a 15-minute demonstration for a mixed audience at the +Johns Hopkins International Vaccine Access Center (IVAC). It compares +serosurvey designs using financial cost, overall estimation accuracy and +accuracy for an underserved group. All populations, prevalences, prices +and preference weights are hypothetical. + +Open the executed +[Jupyter notebook](https://github.com/jcm-sci/trade-study/blob/main/examples/serosurvey_study.ipynb) +to read the narrative, code, tables and saved figures. The accompanying +[Python script](https://github.com/jcm-sci/trade-study/blob/main/examples/serosurvey_study.py) +contains the simulator and plotting helpers and regenerates these figures. +Use both files from a checkout; the notebook imports the companion module. + +## Run or present the notebook + +From the repository root: + +```bash +uv run --extra notebook jupyter lab examples/serosurvey_study.ipynb +``` + +Choose the environment's Python kernel, then restart it and run all cells. +The `notebook` extra supplies JupyterLab, nbconvert, pandas, matplotlib +and Pareto analysis without requiring the other optional modeling backends. +Installation needs network access; the executed example uses only local +code and synthetic data. Run from a checkout of `main`: the notebook uses +the preference API added after the 0.3.0 release. + +Clean notebook executions took about six to eight seconds on the development +machine, including kernel startup and rendering. The companion script took +about two seconds for 19,800 evaluations and four figures. These measurements +exclude dependency installation; check your presentation machine before the talk. + +Saved notebook outputs provide a fallback without running code. Export them +to HTML for an additional presentation copy: + +```bash +uv run --extra notebook jupyter nbconvert --to html examples/serosurvey_study.ipynb +``` + +To verify execution in a fresh kernel without modifying the saved notebook: + +```bash +uv run --extra notebook jupyter nbconvert --execute --to notebook \ + --ExecutePreprocessor.timeout=60 --output serosurvey_executed.ipynb \ + --output-dir /tmp examples/serosurvey_study.ipynb +``` + +## The decision + +IVAC's [SISS project](https://publichealth.jhu.edu/ivac/our-work/strengthening-immunization-systems-through-serosurveillance-siss) +examined the design and use of serological surveillance. The +[serosurvey costing study by Carcelen, Patenaude, Moss and colleagues](https://pmc.ncbi.nlm.nih.gov/articles/PMC7561102/) +provides a concrete link between epidemiology and economic evaluation. +Its study-, cluster- and participant-level cost structure motivates this +example; we do not reproduce its study or use its historical prices. + +The fictional population consists of an 80% group and a 20% underserved +group, with antibody-status prevalences of 90% and 65% respectively. +These are assumed model inputs, not measured data or protection thresholds. + +![Hypothetical population assumptions](../assets/serosurvey_population.png) + +Compare 18 designs: + +| Factor | Levels | +|---|---| +| Participants | 300, 600, 1,200 | +| Communities | 10, 20, 40 | +| Allocation | Proportional 80:20, or oversampling 50:50 | + +Allocation applies to both participants and communities. Within each group, +participants are distributed as evenly as possible, keeping exact totals. +Independent community probabilities follow a Beta distribution centered on +the group mean, with illustrative within-community correlation 0.06; +participant counts then follow a binomial model. The estimand is the fixed +group mean, not the realized mean in the sampled communities. + +The overall prevalence estimator uses population weights of 80:20 for both +allocation strategies. Oversampling does not change the population composition. +Financial cost is fixed setup plus community visit costs plus participant costs. +Average per-community and per-participant costs are not added together as if +they were independent marginal costs. + +## Run, aggregate and refine + +The notebook visibly constructs a `Study` with two grid `Phase`s: 100 simulated +surveys per design, followed by 1,000 per design with an independent phase seed. +Both phases evaluate all 18 designs. Refinement increases simulation replication, +not the number of participants per survey. The refined estimates replace the +screening estimates; the two phases are not pooled. + +Each scorer call returns financial cost and absolute prevalence errors in +percentage points. `aggregate_replicates()` averages those errors, producing +mean absolute error (MAE), and retains their Monte Carlo variation. Averaging +signed errors before taking their absolute value would measure something different. + +## Inspect feasible alternatives + +A `Constraint` imposes an illustrative $40,000 financial budget. The Pareto +set minimizes cost, overall MAE and underserved-group MAE simultaneously. +Subgroup error is a narrow measure of information equity, not a comprehensive +measure of equity in health outcomes. + +![Cost, overall accuracy and subgroup accuracy](../assets/serosurvey_tradeoffs.png) + +Gray designs exceed the budget. Outlines show the feasible Pareto set computed +using all three objectives; the two panels are projections, not independently +computed two-objective fronts. Design labels identify the preference winners. +Vertical bars are approximately two Monte Carlo standard errors of estimated +MAE. They express simulation precision under this model, not uncertainty in a +real survey's prevalence or the model assumptions. They are marginal bars, +not simultaneous post-selection confidence guarantees. + +## Change priorities without rerunning simulations + +The notebook passes the raw results to `preference_sweep()` with three explicit +preference vectors and `normalization="reference"`. Fixed reference ranges are +$0โ€“70,000, 0โ€“4 percentage points overall MAE, and 0โ€“10 percentage points subgroup +MAE. These anchors scale preferences; they are not feasibility thresholds and +do not clip values. Scenario weights are hypothetical, not elicited stakeholder values. + +![Ranks under three preference scenarios](../assets/serosurvey_priorities.png) + +Rank 1 wins within a scenario. The displayed rows are feasible Pareto designs, +but ranks include all feasible designs. Choices depend on point estimates and +can change with further simulation or different assumptions. Any reported +selection fraction describes the supplied preference scenarios, not a probability +that a design is best. + +For a short live interaction, change the budget to $30,000 in the decision +cell and rerun that cell and the figures below it. Restore the budget and edit +the subgroup-priority weights to compare another preference. No simulation +rerun is needed. If the budget admits no alternative, the example reports no choice. + +The optional cost breakdown in the appendix supports an economics discussion: + +![Cost components for the selected designs](../assets/serosurvey_costs.png) + +## A 15-minute presentation + +| Minutes | Content | +|---|---| +| 0โ€“2 | The decision and the two population groups | +| 2โ€“4 | Design factors and competing objectives | +| 4โ€“6 | One visible `Study` definition and a live run | +| 6โ€“10 | Budget and Pareto plots | +| 10โ€“12 | Priorities and an optional budget change | +| 12โ€“13 | Assumptions a real project would replace | +| 13โ€“15 | Discussion | + +The notebook includes presenter notes and slideshow cell metadata. Leave the +model details and cost breakdown as appendices for the main talk. The saved +notebook and HTML export make a Beamer build unnecessary for the current +fast-running example. + +## What a real project would replace + +Replace the synthetic population with context-specific prevalence, clustering, +nonresponse and sampling-frame assumptions; add validated assay characteristics +and uncertainty; use local financial and economic costs; and define objectives +and practical constraints with stakeholders. The example holds assay effects +fixed and does not equate antibody-status prevalence with complete protection. +Communities are sampled without selection bias by construction. Real selection +and nonresponse can introduce bias that more simulation cannot remove. + +The source notebook links the IVAC projects and member profiles that informed +its scope. Those connections do not imply endorsement of the example. + +To regenerate documentation figures: + +```bash +uv run --extra notebook python examples/serosurvey_study.py +``` diff --git a/docs/index.md b/docs/index.md index 69fdffa..d1f5bf2 100644 --- a/docs/index.md +++ b/docs/index.md @@ -7,6 +7,9 @@ sensitivity analysis, and model stacking. For installation and quick-start examples, see the [README](https://github.com/jcm-sci/trade-study#readme). +For a short presentation with live code, tables and saved figures, see the +[serosurvey design notebook](guide/serosurvey.md), designed for a mixed IVAC audience. + ## Overview `trade-study` provides a structured workflow for multi-objective diff --git a/examples/serosurvey_study.ipynb b/examples/serosurvey_study.ipynb new file mode 100644 index 0000000..ac7096a --- /dev/null +++ b/examples/serosurvey_study.ipynb @@ -0,0 +1,1153 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c790ff9d", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# How much survey is enoughโ€”and whose uncertainty matters?\n", + "\n", + "### A 15-minute trade-study demonstration for IVAC\n", + "\n", + "**Decision:** choose a serosurvey design that balances financial cost,\n", + "overall estimation accuracy, and accuracy for an underserved group.\n", + "\n", + "Every population, prevalence, price and priority below is **hypothetical**.\n", + "We estimate antibody-status prevalence; this example does not equate it\n", + "with complete protection or prescribe a real survey." + ] + }, + { + "cell_type": "markdown", + "id": "69e09179", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "## Presenter route and setup\n", + "\n", + "| Minutes | Story |\n", + "|---|---|\n", + "| 0โ€“2 | One decision, two population groups |\n", + "| 2โ€“4 | What can we change, and what counts as success? |\n", + "| 4โ€“6 | Run 18 designs with repeated simulated surveys |\n", + "| 6โ€“10 | Inspect budget feasibility and Pareto alternatives |\n", + "| 10โ€“12 | Change priorities without rerunning the model |\n", + "| 12โ€“13 | What would a real project replace? |\n", + "| 13โ€“15 | Discussion |\n", + "\n", + "From a checkout of the repository, start with:\n", + "\n", + "```bash\n", + "uv run --extra notebook jupyter lab examples/serosurvey_study.ipynb\n", + "```\n", + "\n", + "Choose the environment's Python kernel, then **Restart Kernel and Run All Cells**.\n", + "The saved outputs also support a presentation without execution. Model and\n", + "plotting helpers live beside this notebook in `serosurvey_study.py`; the\n", + "trade-study orchestration is visible here. Installation needs network access;\n", + "execution after installation uses local code and synthetic data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "45c7efdc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:19.701247Z", + "iopub.status.busy": "2026-10-03T09:22:19.700748Z", + "iopub.status.idle": "2026-10-03T09:22:20.187824Z", + "shell.execute_reply": "2026-10-03T09:22:20.185881Z" + }, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import sys\n", + "from dataclasses import replace\n", + "from pathlib import Path\n", + "from time import perf_counter\n", + "\n", + "repo_root = next(\n", + " (\n", + " path\n", + " for path in (Path.cwd(), *Path.cwd().parents)\n", + " if (path / \"examples\" / \"serosurvey_study.py\").is_file()\n", + " ),\n", + " None,\n", + ")\n", + "if repo_root is None:\n", + " message = \"Open this notebook from a trade-study repository checkout.\"\n", + " raise RuntimeError(message)\n", + "sys.path.insert(0, str(repo_root))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2776e8c1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:20.193768Z", + "iopub.status.busy": "2026-10-03T09:22:20.193032Z", + "iopub.status.idle": "2026-10-03T09:22:20.476987Z", + "shell.execute_reply": "2026-10-03T09:22:20.475256Z" + }, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.display import display as show\n", + "\n", + "from examples.serosurvey_study import (\n", + " BUDGET,\n", + " GROUP_NAMES,\n", + " GROUP_WEIGHTS,\n", + " SurveyCosts,\n", + " SurveyScorer,\n", + " SurveyWorld,\n", + " plot_cost_components,\n", + " plot_population,\n", + " plot_priorities,\n", + " plot_tradeoffs,\n", + " survey_factors,\n", + " survey_observables,\n", + ")\n", + "from trade_study import (\n", + " Constraint,\n", + " Phase,\n", + " PreferencePolicy,\n", + " Study,\n", + " build_grid,\n", + " preference_sweep,\n", + ")\n", + "\n", + "plt.rcParams.update({\"font.size\": 12, \"figure.dpi\": 110})\n", + "world = SurveyWorld(seed=2026)\n", + "scorer = SurveyScorer()" + ] + }, + { + "cell_type": "markdown", + "id": "7be85b27", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 1. A familiar decision\n", + "\n", + "We need useful evidence about a population, but have a finite survey budget.\n", + "An overall estimate can be reasonably accurate while a smaller, underserved\n", + "group remains poorly measured.\n", + "\n", + "This problem connects to IVAC's [SISS work](https://publichealth.jhu.edu/ivac/our-work/strengthening-immunization-systems-through-serosurveillance-siss)\n", + "and the [serosurvey costing analysis by Carcelen, Patenaude, Moss and colleagues](https://pmc.ncbi.nlm.nih.gov/articles/PMC7561102/).\n", + "The paper motivates separating study, community and participant costs;\n", + "we use **invented coefficients**, not its historical prices.\n", + "\n", + "**Audience question:** Would you spend additional resources on more people,\n", + "more communities, or better representation of a smaller group?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f47b951a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:20.483635Z", + "iopub.status.busy": "2026-10-03T09:22:20.482846Z", + "iopub.status.idle": "2026-10-03T09:22:20.837872Z", + "shell.execute_reply": "2026-10-03T09:22:20.836251Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plot_population(world)\n", + "show(fig)\n", + "plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "id": "4339b184", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 2. Three choices, three objectives\n", + "\n", + "| Choice | Levels |\n", + "|---|---|\n", + "| Participants | 300, 600, 1,200 |\n", + "| Communities | 10, 20, 40 |\n", + "| Allocation | Proportional (80:20) or oversampling (50:50) |\n", + "\n", + "That gives **3 ร— 3 ร— 2 = 18 designs**. Allocation applies to both participants\n", + "and communities. Within each group, participants are spread as evenly as\n", + "possible across its communities, preserving the total exactly.\n", + "\n", + "We minimize **financial cost**, **overall mean absolute error**, and\n", + "**underserved-group mean absolute error**. Errors are in percentage points.\n", + "Subgroup error is a specific measure of *information equity*, rather than a\n", + "complete measure of equity in health outcomes." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "935208cc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:20.843552Z", + "iopub.status.busy": "2026-10-03T09:22:20.843296Z", + "iopub.status.idle": "2026-10-03T09:22:20.915820Z", + "shell.execute_reply": "2026-10-03T09:22:20.914160Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
First 6 of 18 designs
designparticipantscommunitiesallocation
D0130010proportional
D0230010oversample
D0330020proportional
D0430020oversample
D0530040proportional
D0630040oversample
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "factors = survey_factors()\n", + "observables = survey_observables()\n", + "grid = build_grid(factors, method=\"full\")\n", + "\n", + "designs = pd.DataFrame(grid)\n", + "designs.insert(0, \"design\", [f\"D{i + 1:02d}\" for i in range(len(grid))])\n", + "show(designs.head(6).style.hide(axis=\"index\").set_caption(\"First 6 of 18 designs\"))" + ] + }, + { + "cell_type": "markdown", + "id": "d2926809", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 3. The simulator and scorer do different jobs\n", + "\n", + "**Simulator:** draw community-level probabilities around the fixed group means,\n", + "then draw antibody-status counts within communities. The illustrative\n", + "within-community correlation is 0.06. Each design/replicate uses its own\n", + "reproducible random stream.\n", + "\n", + "**Scorer:** compute the absolute difference from known synthetic truth and attach\n", + "the financial cost. Average these absolute errors across repeated surveys to\n", + "estimate **mean absolute error (MAE)**.\n", + "\n", + "**Important:** use the population shares, 80:20, when combining group estimates.\n", + "Sampling 50:50 does not make the population 50:50." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3314ea20", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:20.920909Z", + "iopub.status.busy": "2026-10-03T09:22:20.920642Z", + "iopub.status.idle": "2026-10-03T09:22:20.934407Z", + "shell.execute_reply": "2026-10-03T09:22:20.932896Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
grouppopulation_shareparticipantscommunitiestrue_prevalenceone_survey_estimate
Other communities80%150590.0%80.0%
Underserved communities20%150565.0%72.0%
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Financial cost: $18,250\n", + "Overall absolute error: 6.6 percentage points\n", + "Subgroup absolute error: 7.0 percentage points\n" + ] + } + ], + "source": [ + "example_config = {\"participants\": 300, \"communities\": 10, \"allocation\": \"oversample\"}\n", + "truth, outcome = world.generate(example_config, rep=0)\n", + "show(\n", + " pd\n", + " .DataFrame({\n", + " \"group\": GROUP_NAMES,\n", + " \"population_share\": GROUP_WEIGHTS,\n", + " \"participants\": outcome.participants,\n", + " \"communities\": outcome.communities,\n", + " \"true_prevalence\": truth,\n", + " \"one_survey_estimate\": outcome.prevalence,\n", + " })\n", + " .style.hide(axis=\"index\")\n", + " .format({\n", + " \"population_share\": \"{:.0%}\",\n", + " \"true_prevalence\": \"{:.1%}\",\n", + " \"one_survey_estimate\": \"{:.1%}\",\n", + " })\n", + ")\n", + "scores = scorer.score(truth, outcome, example_config)\n", + "print(f\"Financial cost: ${scores['cost_usd']:,.0f}\")\n", + "print(f\"Overall absolute error: {scores['overall_error_pp']:.1f} percentage points\")\n", + "print(\n", + " f\"Subgroup absolute error: {scores['underserved_error_pp']:.1f} percentage points\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7526d6fa", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 4. Trade-study orchestrates the comparison\n", + "\n", + "First run 100 simulated surveys per design, then run 1,000 per design with an\n", + "**independent phase seed**. Both phases evaluate the same 18 designs. We keep\n", + "all of them because this model is cheap; premature elimination is unnecessary.\n", + "\n", + "Refinement increases the number of *simulated surveys*, not the participant\n", + "count in a survey. Its estimates replace the screening estimates; the two\n", + "phases are not pooled." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "134b7db6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:20.938174Z", + "iopub.status.busy": "2026-10-03T09:22:20.937971Z", + "iopub.status.idle": "2026-10-03T09:22:22.497878Z", + "shell.execute_reply": "2026-10-03T09:22:22.496474Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18 designs; 18,000 refinement evaluations\n", + "Both phases completed in 1.55 seconds on this machine.\n" + ] + } + ], + "source": [ + "SCREEN_REPS = 100\n", + "REFINE_REPS = 1000\n", + "\n", + "study = Study(\n", + " world=world,\n", + " scorer=scorer,\n", + " observables=observables,\n", + " factors=factors,\n", + " phases=[\n", + " Phase(\"screen\", grid=grid, n_reps=SCREEN_REPS),\n", + " Phase(\"refine\", grid=grid, n_reps=REFINE_REPS, world=replace(world, seed=2027)),\n", + " ],\n", + ")\n", + "started = perf_counter()\n", + "study.run()\n", + "study_seconds = perf_counter() - started\n", + "\n", + "raw = study.results(\"refine\")\n", + "means = raw.aggregate_replicates()\n", + "print(f\"{len(grid)} designs; {len(raw.configs):,} refinement evaluations\")\n", + "print(f\"Both phases completed in {study_seconds:.2f} seconds on this machine.\")" + ] + }, + { + "cell_type": "markdown", + "id": "28c5798d", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 5. Separate eligibility from preference\n", + "\n", + "A financial ceiling is a **hard constraint**. Within that ceiling, some designs\n", + "have lower cost, some have lower overall error, and some have lower subgroup error.\n", + "\n", + "A design is **Pareto dominated** if another eligible design is no worse on\n", + "all three objectives and strictly better on at least one. The feasible Pareto\n", + "set supplies alternatives; preferences select among them.\n", + "\n", + "The following three priorities are hypothetical scenarios, not estimates of\n", + "stakeholder values. Fixed reference ranges make dollars and percentage points\n", + "comparable. They are scaling anchors, not feasibility thresholds, and values\n", + "beyond them are not clipped." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8dc9279", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:22.502822Z", + "iopub.status.busy": "2026-10-03T09:22:22.502635Z", + "iopub.status.idle": "2026-10-03T09:22:22.530462Z", + "shell.execute_reply": "2026-10-03T09:22:22.528998Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Budget: $40,000 (illustrative USD)\n", + "Feasible designs: 11; Pareto alternatives: 9\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 cost_usdoverall_error_ppunderserved_error_pp
Cost first0.9500.0250.025
Overall accuracy0.1000.8000.100
Subgroup accuracy0.1000.1000.800
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Live-demo control: change the budget, then rerun this cell and the figures below.\n", + "budget = BUDGET\n", + "priorities = {\n", + " \"Cost first\": {\n", + " \"cost_usd\": 0.95,\n", + " \"overall_error_pp\": 0.025,\n", + " \"underserved_error_pp\": 0.025,\n", + " },\n", + " \"Overall accuracy\": {\n", + " \"cost_usd\": 0.1,\n", + " \"overall_error_pp\": 0.8,\n", + " \"underserved_error_pp\": 0.1,\n", + " },\n", + " \"Subgroup accuracy\": {\n", + " \"cost_usd\": 0.1,\n", + " \"overall_error_pp\": 0.1,\n", + " \"underserved_error_pp\": 0.8,\n", + " },\n", + "}\n", + "constraints = [Constraint(\"financial_budget\", \"cost_usd\", \"<=\", budget)]\n", + "policy = PreferencePolicy(\n", + " weights=list(priorities.values()),\n", + " normalization=\"reference\",\n", + " reference_bounds={\n", + " \"cost_usd\": (0, 70_000),\n", + " \"overall_error_pp\": (0, 4),\n", + " \"underserved_error_pp\": (0, 10),\n", + " },\n", + ")\n", + "\n", + "report = preference_sweep(raw, observables, policy=policy, constraints=constraints)\n", + "print(f\"Budget: ${budget:,.0f} (illustrative USD)\")\n", + "print(\n", + " f\"Feasible designs: {report.feasible.sum()}; \"\n", + " f\"Pareto alternatives: {report.pareto.sum()}\"\n", + ")\n", + "show(pd.DataFrame(priorities).T.style.format(\"{:.3f}\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cfadcaba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:22.533942Z", + "iopub.status.busy": "2026-10-03T09:22:22.533698Z", + "iopub.status.idle": "2026-10-03T09:22:22.901958Z", + "shell.execute_reply": "2026-10-03T09:22:22.900593Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plot_tradeoffs(report, budget)\n", + "show(fig)\n", + "plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "id": "192cd7ba", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "### Read the trade-offs\n", + "\n", + "- Blue circles use proportional allocation; orange diamonds oversample.\n", + "- Gray points exceed the financial ceiling; the dashed line shows that ceiling.\n", + "- Black outlines mark the Pareto set computed using **all three objectives**.\n", + " These panels are projections, not separate two-objective fronts.\n", + "- Labels identify the preference winners; they can change when inputs change.\n", + "- Vertical bars are approximately ยฑ2 **Monte Carlo standard errors of estimated\n", + " MAE**. They describe simulation precision under this model, not uncertainty\n", + " in a real survey's prevalence estimate or in the model assumptions. They are\n", + " marginal bars, not simultaneous confidence guarantees after selection.\n", + "\n", + "**Audience question:** Which alternative would you defend, and what information\n", + "would you need before committing resources?" + ] + }, + { + "cell_type": "markdown", + "id": "547ead3b", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 6. Same evidence, different priorities\n", + "\n", + "We can change the budget or preference weights using the already computed\n", + "results. This step calls `preference_sweep`, **not the simulator**.\n", + "\n", + "The full feasible Pareto set is shown below, sorted by cost. Rank 1 is best\n", + "within a priority scenario; ranks are calculated among all feasible designs.\n", + "Nearby point estimates can change order with additional simulation or different\n", + "assumptions, so a rank is not a statistical guarantee of superiority." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b3025dab", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:22.908045Z", + "iopub.status.busy": "2026-10-03T09:22:22.907813Z", + "iopub.status.idle": "2026-10-03T09:22:23.162750Z", + "shell.execute_reply": "2026-10-03T09:22:23.161308Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
prioritydesignparticipantscommunitiesallocationcost_usdoverall_mae_ppsubgroup_mae_pp
Cost firstD0130010proportional$16,0002.598.17
Overall accuracyD1160040proportional$35,8001.514.59
Subgroup accuracyD1060020oversample$33,5001.973.56
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plot_priorities(report)\n", + "show(fig)\n", + "plt.close(fig)\n", + "\n", + "winner_rows = []\n", + "for priority, ranks in zip(priorities, report.ranks, strict=True):\n", + " for i in np.flatnonzero(ranks == 1):\n", + " cfg = report.summary.configs[i]\n", + " cost, overall, subgroup = report.summary.scores[i]\n", + " winner_rows.append({\n", + " \"priority\": priority,\n", + " \"design\": f\"D{i + 1:02d}\",\n", + " **cfg,\n", + " \"cost_usd\": cost,\n", + " \"overall_mae_pp\": overall,\n", + " \"subgroup_mae_pp\": subgroup,\n", + " })\n", + "if winner_rows:\n", + " show(\n", + " pd\n", + " .DataFrame(winner_rows)\n", + " .style.hide(axis=\"index\")\n", + " .format({\n", + " \"cost_usd\": \"${:,.0f}\",\n", + " \"overall_mae_pp\": \"{:.2f}\",\n", + " \"subgroup_mae_pp\": \"{:.2f}\",\n", + " })\n", + " )\n", + "else:\n", + " print(\"No eligible choice: increase the budget or revisit the available designs.\")" + ] + }, + { + "cell_type": "markdown", + "id": "0a44a734", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "### Two safe live changes\n", + "\n", + "1. Set `budget = 30_000` in the decision cell. Rerun that cell and the figures:\n", + " which previously attractive designs are now excluded?\n", + "2. Restore the budget and edit the weights in `priorities[\"Subgroup accuracy\"]`\n", + " in the decision cell. Rerun that cell and the figures below it.\n", + "\n", + "The results table remains unchanged. These scenarios expose value judgments;\n", + "they do not establish that one set of stakeholder priorities is correct." + ] + }, + { + "cell_type": "markdown", + "id": "53f33ff0", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## 7. What would a real project replace?\n", + "\n", + "- **Population model:** locally relevant prevalence, clustering, nonresponse,\n", + " sampling frames, and uncertainty in these assumptions.\n", + "- **Measurement model:** validated assay characteristics and their uncertainty.\n", + "- **Cost model:** context-specific financial and economic costs, including\n", + " participant and staff burden where appropriate.\n", + "- **Decision rules:** stakeholder-defined objectives, acceptable error, budget,\n", + " and field-capacity constraints.\n", + "\n", + "Our target is the model's fixed group mean, not the realized mean of sampled\n", + "communities. Sampling communities is unbiased by construction; real selection\n", + "and nonresponse can introduce bias that more replication cannot remove.\n", + "\n", + "**Takeaway:** trade-study makes alternatives, evidence and priorities inspectable.\n", + "The domain model and the decision assumptions still need substantive expertise." + ] + }, + { + "cell_type": "markdown", + "id": "3b23c87a", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Discussion\n", + "\n", + "What is the closest decision in your current work?\n", + "\n", + "- Comparing designs for surveillance or program evaluation?\n", + "- Comparing delivery strategies under cost and capacity constraints?\n", + "- Explaining a recommendation when several objectives matter?\n", + "\n", + "Which objective or constraint is missing from this demonstration?" + ] + }, + { + "cell_type": "markdown", + "id": "6cf8e4ea", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Appendix: what drives financial cost?\n", + "\n", + "The illustrative coefficients are $3,000 setup, $250/$700 per community visit,\n", + "and $30/$40 per participant, for other/underserved communities respectively.\n", + "They represent a hypothetical access-cost difference, not observed local prices.\n", + "\n", + "Fixed, community and participant costs are additive. Average cost per participant\n", + "and average cost per community are **not** added together as if they were\n", + "independent marginal costs. Participant burden is not monetized in this version." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e2b2552e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:23.169890Z", + "iopub.status.busy": "2026-10-03T09:22:23.169299Z", + "iopub.status.idle": "2026-10-03T09:22:23.332303Z", + "shell.execute_reply": "2026-10-03T09:22:23.330632Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plot_cost_components(report, SurveyCosts())\n", + "show(fig)\n", + "plt.close(fig)" + ] + }, + { + "cell_type": "markdown", + "id": "a6812b59", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Appendix: inspect weighting and Monte Carlo precision\n", + "\n", + "For the same group estimates, a sample-weighted average changes when allocation\n", + "changes. The population-weighted estimate uses the fixed 80:20 composition.\n", + "The comparison below demonstrates the estimand, not an accuracy claim from one draw." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "80988a02", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-03T09:22:23.337273Z", + "iopub.status.busy": "2026-10-03T09:22:23.336997Z", + "iopub.status.idle": "2026-10-03T09:22:23.359237Z", + "shell.execute_reply": "2026-10-03T09:22:23.357199Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Population-weighted estimate: 0.784\n", + "Sample-weighted average: 0.760\n", + "Population target: 0.850\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
phasesurveys_per_designlargest_subgroup_mae_mcse_pp
screen1000.676
refine10000.200
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sample_weights = np.array(outcome.participants) / sum(outcome.participants)\n", + "print(f\"Population-weighted estimate: {np.dot(GROUP_WEIGHTS, outcome.prevalence):.3f}\")\n", + "print(f\"Sample-weighted average: {np.dot(sample_weights, outcome.prevalence):.3f}\")\n", + "print(f\"Population target: {np.dot(GROUP_WEIGHTS, truth):.3f}\")\n", + "\n", + "precision_rows = []\n", + "for phase in (\"screen\", \"refine\"):\n", + " summary = study.results(phase).aggregate_replicates()\n", + " errors = [\n", + " row[\"score_std\"][\"underserved_error_pp\"] / np.sqrt(row[\"n_reps\"] - 1)\n", + " for row in summary.metadata\n", + " ]\n", + " precision_rows.append({\n", + " \"phase\": phase,\n", + " \"surveys_per_design\": summary.metadata[0][\"n_reps\"],\n", + " \"largest_subgroup_mae_mcse_pp\": max(errors),\n", + " })\n", + "show(\n", + " pd\n", + " .DataFrame(precision_rows)\n", + " .style.hide(axis=\"index\")\n", + " .format({\"largest_subgroup_mae_mcse_pp\": \"{:.3f}\"})\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5f4e667b", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Appendix: export and present without execution\n", + "\n", + "The notebook stores tables and figures. Generate a standalone HTML presentation\n", + "from those saved outputs:\n", + "\n", + "```bash\n", + "uv run --extra notebook jupyter nbconvert --to html examples/serosurvey_study.ipynb\n", + "```\n", + "\n", + "For a fresh headless run, with a 60-second limit per cell:\n", + "\n", + "```bash\n", + "uv run --extra notebook jupyter nbconvert --execute --to notebook \\\n", + " --ExecutePreprocessor.timeout=60 --output serosurvey_executed.ipynb \\\n", + " --output-dir /tmp examples/serosurvey_study.ipynb\n", + "```\n", + "\n", + "Export results from a code cell if needed:\n", + "\n", + "```python\n", + "report.summary.to_dataframe(include_metadata=True).to_csv(\"serosurvey_designs.csv\", index=False)\n", + "```\n", + "\n", + "The companion script regenerates the documentation's PNG figures:\n", + "\n", + "```bash\n", + "uv run --extra notebook python examples/serosurvey_study.py\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "531c85ec", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Sources and audience connections\n", + "\n", + "- [IVAC's portfolio](https://publichealth.jhu.edu/ivac/projects): coverage/equity,\n", + " epidemiology, economics/finance, operations research and policy.\n", + "- [SISS](https://publichealth.jhu.edu/ivac/our-work/strengthening-immunization-systems-through-serosurveillance-siss)\n", + " and [Serosurvey Tools](https://serosurveytools.org/about/): design and use of serological surveillance.\n", + "- [Carcelen et al., 2020](https://pmc.ncbi.nlm.nih.gov/articles/PMC7561102/):\n", + " *How much does it cost to measure immunity?* This is motivation, not a reproduction.\n", + "- [Andrea Carcelen](https://publichealth.jhu.edu/faculty/4158/andrea-carcelen):\n", + " serosurveillance and reaching vulnerable populations.\n", + "- [Bryan Patenaude](https://publichealth.jhu.edu/faculty/3683/bryan-n-patenaude):\n", + " economic evaluation, financing and equity measurement.\n", + "- [Shaun Truelove](https://publichealth.jhu.edu/faculty/3998/shaun-truelove):\n", + " modeling to inform prevention and response.\n", + "- [Chizoba Wonodi](https://publichealth.jhu.edu/faculty/2206/chizoba-barbara-wonodi)\n", + " and [Molly Sauer](https://publichealth.jhu.edu/faculty/3466/molly-sauer):\n", + " immunization delivery, implementation and prioritization.\n", + "- [Svea Closser](https://publichealth.jhu.edu/faculty/3657/svea-closser):\n", + " health systems and the experiences of frontline workers.\n", + "\n", + "These connections informed the example's scope; they do not imply endorsement." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/serosurvey_study.py b/examples/serosurvey_study.py new file mode 100644 index 0000000..b0737cc --- /dev/null +++ b/examples/serosurvey_study.py @@ -0,0 +1,578 @@ +"""Synthetic serosurvey design for a short IVAC trade-study demonstration. + +All populations, prevalences and prices are invented. The model compares +estimation of antibody-status prevalence, not clinical protection. +Run: uv run --extra notebook python examples/serosurvey_study.py +""" + +from __future__ import annotations + +from dataclasses import dataclass, field, replace +from pathlib import Path +from time import perf_counter +from typing import TYPE_CHECKING, Any + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.lines import Line2D + +from trade_study import ( + Constraint, + Direction, + Factor, + FactorType, + Observable, + Phase, + PreferencePolicy, + Study, + build_grid, + preference_sweep, +) + +if TYPE_CHECKING: + from matplotlib.figure import Figure + from numpy.typing import NDArray + + from trade_study import PreferenceSweep, ResultsTable + +GROUP_WEIGHTS = (0.8, 0.2) +GROUP_NAMES = ("Other communities", "Underserved communities") +COLORS = {"proportional": "#0072B2", "oversample": "#D55E00"} +PRIORITIES = { + "Cost first": { + "cost_usd": 0.95, + "overall_error_pp": 0.025, + "underserved_error_pp": 0.025, + }, + "Overall accuracy": { + "cost_usd": 0.1, + "overall_error_pp": 0.8, + "underserved_error_pp": 0.1, + }, + "Subgroup accuracy": { + "cost_usd": 0.1, + "overall_error_pp": 0.1, + "underserved_error_pp": 0.8, + }, +} +BUDGET = 40_000.0 + + +def allocation_counts( + config: dict[str, Any], +) -> tuple[tuple[int, int], tuple[int, int]]: + """Allocate participants and communities to the two population groups. + + Args: + config: Participant/community totals and allocation label. Totals + must be positive multiples of ten, with at least one person + in each community. Proportional allocation is 80:20; the + oversampling allocation is 50:50. + + Returns: + Participant counts, then community counts, ordered by group. + + Raises: + ValueError: If totals or allocation are unsupported. + """ + n, k = config["participants"], config["communities"] + if any( + not isinstance(value, (int, np.integer)) or isinstance(value, bool) + for value in (n, k) + ): + msg = "Participant and community totals must be integers" + raise ValueError(msg) + if n < k or k < 10 or n % 10 or k % 10: + msg = "Use integer multiples of ten with participants >= communities >= 10" + raise ValueError(msg) + shares = {"proportional": GROUP_WEIGHTS, "oversample": (0.5, 0.5)} + if config["allocation"] not in shares: + msg = "Allocation must be 'proportional' or 'oversample'" + raise ValueError(msg) + fraction = shares[config["allocation"]][1] + second_n, second_k = round(n * fraction), round(k * fraction) + return (int(n - second_n), second_n), (int(k - second_k), second_k) + + +@dataclass(frozen=True) +class SurveyOutcome: + """Group estimates and exact field-work counts for one simulated survey. + + Attributes: + prevalence: Estimated antibody-status prevalence in each group. + participants: Participant counts ordered by group. + communities: Community counts ordered by group. + """ + + prevalence: NDArray[np.float64] + participants: tuple[int, int] + communities: tuple[int, int] + + +@dataclass(frozen=True) +class SurveyWorld: + """Independent clustered surveys with known hypothetical group means. + + Attributes: + prevalence: Target means for other/underserved communities. + correlation: Within-community intraclass correlation. Communities + are independent, with Beta-distributed probabilities. + seed: Phase-specific seed; configuration and replicate identify + independent, reproducible random streams. + """ + + prevalence: tuple[float, float] = (0.9, 0.65) + correlation: float = 0.06 + seed: int = 2026 + + def __post_init__(self) -> None: + """Validate the illustrative population and random seed. + + Raises: + ValueError: If probabilities, correlation or seed are invalid. + """ + if len(self.prevalence) != 2 or not all( + np.isfinite(p) and 0 <= p <= 1 for p in self.prevalence + ): + msg = "Require two finite probabilities between zero and one" + raise ValueError(msg) + if not np.isfinite(self.correlation) or not 0 <= self.correlation < 1: + msg = "Require finite 0 <= correlation < 1" + raise ValueError(msg) + if ( + not isinstance(self.seed, int) + or isinstance(self.seed, bool) + or self.seed < 0 + ): + msg = "Require a nonnegative integer seed" + raise ValueError(msg) + + def generate( + self, config: dict[str, Any], *, rep: int = 0 + ) -> tuple[NDArray[np.float64], SurveyOutcome]: + """Simulate clustered counts, preserving exact participant totals. + + Args: + config: Survey participant/community totals and allocation. + rep: Nonnegative replicate id. Configurations do not share + random streams; this example makes no paired-CRN claim. + + Returns: + Fixed target group means and one survey's group estimates. + + Raises: + ValueError: If the replicate id or configuration is invalid. + """ + if not isinstance(rep, int) or isinstance(rep, bool) or rep < 0: + msg = "Replicate id must be a nonnegative integer" + raise ValueError(msg) + people, communities = allocation_counts(config) + allocation_id = int(config["allocation"] == "oversample") + rng = np.random.default_rng( + np.random.SeedSequence([ + self.seed, + rep, + int(config["participants"]), + int(config["communities"]), + allocation_id, + ]) + ) + estimates = [] + for p, n, k in zip(self.prevalence, people, communities, strict=True): + sizes = np.full(k, n // k, dtype=int) + sizes[: n % k] += 1 + if self.correlation == 0 or p in {0, 1}: + probabilities = np.full(k, p) + else: + concentration = 1 / self.correlation - 1 + probabilities = rng.beta(p * concentration, (1 - p) * concentration, k) + positives = rng.binomial(sizes, probabilities) + estimates.append(float(positives.sum() / n)) + return np.array(self.prevalence), SurveyOutcome( + np.array(estimates), people, communities + ) + + +@dataclass(frozen=True) +class SurveyCosts: + """Invented financial costs in illustrative USD, without price calibration. + + Attributes: + setup: Fixed survey setup cost. + per_community: Community visit costs ordered by group. + per_participant: Participant costs ordered by group. + """ + + setup: float = 3000.0 + per_community: tuple[float, float] = (250.0, 700.0) + per_participant: tuple[float, float] = (30.0, 40.0) + + def __post_init__(self) -> None: + """Validate the nonnegative financial cost assumptions. + + Raises: + ValueError: If cost coefficients are invalid. + """ + if len(self.per_community) != 2 or len(self.per_participant) != 2: + msg = "Supply one cost per population group" + raise ValueError(msg) + values = (self.setup, *self.per_community, *self.per_participant) + if not all(np.isfinite(value) and value >= 0 for value in values): + msg = "Costs must be finite and nonnegative" + raise ValueError(msg) + + def components(self, config: dict[str, Any]) -> dict[str, float]: + """Compute additive fixed, community and participant costs. + + Args: + config: Survey participant/community totals and allocation. + + Returns: + Three separate financial cost components in illustrative USD. + """ + people, communities = allocation_counts(config) + return { + "Setup": self.setup, + "Community visits": float(np.dot(communities, self.per_community)), + "Participants": float(np.dot(people, self.per_participant)), + } + + +@dataclass(frozen=True) +class SurveyScorer: + """Financial cost and absolute prevalence error, with population weighting. + + Attributes: + costs: Illustrative financial cost coefficients. + """ + + costs: SurveyCosts = field(default_factory=SurveyCosts) + + def score( + self, + truth: NDArray[np.float64], + observations: SurveyOutcome, + config: dict[str, Any], + ) -> dict[str, float]: + """Score one survey; averaging errors gives mean absolute error. + + Args: + truth: Target antibody-status prevalences, ordered by group. + observations: Simulated group estimates and field-work counts. + config: Survey configuration used for financial cost. + + Returns: + Cost and overall/subgroup absolute errors in percentage points. + """ + overall = float(np.dot(observations.prevalence - truth, GROUP_WEIGHTS)) + return { + "cost_usd": sum(self.costs.components(config).values()), + "overall_error_pp": 100 * abs(overall), + "underserved_error_pp": 100 * abs(observations.prevalence[1] - truth[1]), + } + + +def survey_factors() -> list[Factor]: + """Return the factors for the 18-design demonstration. + + Returns: + Participant count, community count and allocation factors. + """ + return [ + Factor("participants", FactorType.DISCRETE, levels=[300, 600, 1200]), + Factor("communities", FactorType.DISCRETE, levels=[10, 20, 40]), + Factor( + "allocation", FactorType.CATEGORICAL, levels=["proportional", "oversample"] + ), + ] + + +def survey_observables() -> list[Observable]: + """Return the three objectives, all minimized. + + Returns: + Financial cost, overall MAE and underserved-group MAE observables. + """ + return [Observable(name, Direction.MINIMIZE) for name in PRIORITIES["Cost first"]] + + +def preference_policy() -> PreferencePolicy: + """Return three hypothetical priorities with fixed reference anchors. + + Returns: + An explicit policy; anchors are scaling choices, not eligibility limits. + """ + return PreferencePolicy( + weights=list(PRIORITIES.values()), + normalization="reference", + reference_bounds={ + "cost_usd": (0, 70_000), + "overall_error_pp": (0, 4), + "underserved_error_pp": (0, 10), + }, + ) + + +def plot_population(world: SurveyWorld) -> Figure: + """Draw the population assumptions, rather than estimated outcomes. + + Args: + world: Hypothetical group prevalences. + + Returns: + A two-panel population-share and antibody-status prevalence figure. + """ + fig, axes = plt.subplots(1, 2, figsize=(11, 4.2), layout="constrained") + for ax, values, title in zip( + axes, + (GROUP_WEIGHTS, world.prevalence), + ("Population composition", "Assumed antibody-status prevalence"), + strict=True, + ): + bars = ax.bar(GROUP_NAMES, 100 * np.array(values), color=list(COLORS.values())) + ax.bar_label(bars, fmt="%.0f%%", padding=4, fontsize=13) + ax.set(ylim=(0, 110), ylabel="Percent", title=title) + ax.spines[["top", "right"]].set_visible(False) + fig.suptitle("A fictional population: assumptions, not measured data", fontsize=16) + return fig + + +def _selected_indices(report: PreferenceSweep) -> NDArray[np.intp]: + return np.flatnonzero(np.any(report.ranks == 1, axis=0)) + + +def _mc_errors(results: ResultsTable, name: str) -> NDArray[np.float64]: + return np.array([ + meta["score_std"][name] / np.sqrt(meta["n_reps"] - 1) + for meta in results.metadata + ]) + + +def plot_tradeoffs(report: PreferenceSweep, budget: float) -> Figure: + """Project the three-objective feasible Pareto set into two panels. + + Args: + report: Preference report, including budget feasibility and Pareto flags. + budget: Financial ceiling in illustrative USD. + + Returns: + Cost versus overall/subgroup MAE, with approximate Monte Carlo bars. + """ + fig, axes = plt.subplots(1, 2, figsize=(12, 5.2), layout="constrained") + table = report.summary + costs = table.scores[:, 0] / 1000 + selected = _selected_indices(report) + for ax, name, title in zip( + axes, + ("overall_error_pp", "underserved_error_pp"), + ("Overall population", "Underserved group"), + strict=True, + ): + errors = table.scores[:, table.observable_names.index(name)] + ax.errorbar( + costs, + errors, + yerr=2 * _mc_errors(table, name), + fmt="none", + ecolor="#bbbbbb", + capsize=2, + zorder=1, + ) + for i, config in enumerate(table.configs): + ax.scatter( + costs[i], + errors[i], + s=75, + marker="o" if config["allocation"] == "proportional" else "D", + color=COLORS[config["allocation"]] if report.feasible[i] else "#cccccc", + edgecolors="black" if report.pareto[i] else "none", + linewidths=1.4, + zorder=3, + ) + for offset, i in enumerate(selected): + ax.annotate( + f"D{i + 1:02d}", + (costs[i], errors[i]), + xytext=(5, 10 + 10 * (offset % 2)), + textcoords="offset points", + fontsize=10, + fontweight="bold", + ) + ax.axvline(budget / 1000, color="#555555", linestyle="--") + ax.set( + xlabel="Financial cost (illustrative USD thousands)", + ylabel="Mean absolute error (percentage points)", + title=title, + ) + ax.spines[["top", "right"]].set_visible(False) + handles = [ + Line2D( + [], + [], + marker="o", + linestyle="", + color=COLORS["proportional"], + label="Proportional allocation", + ), + Line2D( + [], + [], + marker="D", + linestyle="", + color=COLORS["oversample"], + label="Oversample underserved group", + ), + Line2D( + [], + [], + marker="o", + linestyle="", + color="white", + markeredgecolor="black", + label="Feasible Pareto design (all 3 objectives)", + ), + Line2D([], [], marker="o", linestyle="", color="#cccccc", label="Over budget"), + ] + fig.legend(handles=handles, loc="outside lower center", ncol=2, fontsize=10) + fig.suptitle("Cost, overall accuracy and subgroup accuracy compete", fontsize=16) + return fig + + +def plot_priorities(report: PreferenceSweep) -> Figure: + """Show ranks among feasible Pareto candidates under three priorities. + + Args: + report: Results of the three named preference scenarios. + + Returns: + A rank heatmap; highlighted cells identify winners, including ties. + """ + indices = np.flatnonzero(report.pareto) + indices = indices[np.argsort(report.summary.scores[indices, 0])] + ranks = report.ranks[:, indices].T + fig, ax = plt.subplots( + figsize=(10, max(3.5, 0.42 * len(indices) + 1.4)), layout="constrained" + ) + if not len(indices): + ax.text(0.5, 0.5, "No design meets the budget", ha="center", va="center") + ax.axis("off") + return fig + ax.imshow( + ranks, cmap="Blues_r", vmin=1, vmax=max(2, float(ranks.max())), aspect="auto" + ) + labels = [] + for i in indices: + cfg = report.summary.configs[i] + strategy = "P" if cfg["allocation"] == "proportional" else "O" + labels.append( + f"D{i + 1:02d} ยท {cfg['participants']} people / " + f"{cfg['communities']} communities / {strategy}" + ) + ax.set_yticks(np.arange(len(indices)), labels, fontsize=10) + ax.set_xticks(np.arange(len(PRIORITIES)), list(PRIORITIES), fontsize=11) + for row, column in np.ndindex(ranks.shape): + rank = ranks[row, column] + ax.text( + column, + row, + f"{rank:.0f}", + ha="center", + va="center", + color="white" if rank < (ranks.max() + 1) / 2 else "black", + fontweight="bold" if rank == 1 else "normal", + ) + ax.set_title( + "Same evidence, different priorities\n" + "Rank 1 wins; ranks include all feasible designs", + fontsize=15, + pad=16, + ) + ax.set_xlabel("Allocation: P = proportional; O = oversample", labelpad=12) + return fig + + +def plot_cost_components(report: PreferenceSweep, costs: SurveyCosts) -> Figure: + """Show financial cost components for the preference winners. + + Args: + report: Preference report identifying selected candidates. + costs: Illustrative financial cost model. + + Returns: + A stacked cost figure, or a message if no design is feasible. + """ + indices = _selected_indices(report) + fig, ax = plt.subplots(figsize=(9, 4.5), layout="constrained") + if not len(indices): + ax.text(0.5, 0.5, "No design meets the budget", ha="center", va="center") + ax.axis("off") + return fig + components = [costs.components(report.summary.configs[i]) for i in indices] + bottom = np.zeros(len(indices)) + for name, color in zip( + components[0], ("#666666", "#009E73", "#E69F00"), strict=True + ): + values = np.array([row[name] for row in components]) / 1000 + ax.bar( + [f"D{i + 1:02d}" for i in indices], + values, + bottom=bottom, + label=name, + color=color, + ) + bottom += values + ax.set( + ylabel="Financial cost (illustrative USD thousands)", + title="Why do the selected designs cost different amounts?", + ) + ax.legend(loc="upper left", frameon=False) + ax.spines[["top", "right"]].set_visible(False) + return fig + + +def main() -> None: + """Run the notebook's study and export documentation figures. + + All 18 designs receive both screening and independent refinement. + """ + started = perf_counter() + factors = survey_factors() + observables = survey_observables() + grid = build_grid(factors, method="full") + world = SurveyWorld(seed=2026) + study = Study( + world=world, + scorer=SurveyScorer(), + observables=observables, + factors=factors, + phases=[ + Phase("screen", grid=grid, n_reps=100), + Phase("refine", grid=grid, n_reps=1000, world=replace(world, seed=2027)), + ], + ) + study.run() + report = preference_sweep( + study.results("refine"), + observables, + policy=preference_policy(), + constraints=[Constraint("financial_budget", "cost_usd", "<=", BUDGET)], + ) + asset_dir = Path(__file__).resolve().parents[1] / "docs" / "assets" + for name, figure in { + "population": plot_population(world), + "tradeoffs": plot_tradeoffs(report, BUDGET), + "priorities": plot_priorities(report), + "costs": plot_cost_components(report, SurveyCosts()), + }.items(): + figure.savefig(asset_dir / f"serosurvey_{name}.png", dpi=160) + plt.close(figure) + print( + f"18 designs, 19,800 evaluations, completed in {perf_counter() - started:.2f}s" + ) + for name, ranks in zip(PRIORITIES, report.ranks, strict=True): + selected = np.flatnonzero(ranks == 1) + print(f"{name}: " + ", ".join(f"D{i + 1:02d}" for i in selected)) + + +if __name__ == "__main__": + main() diff --git a/mkdocs.yml b/mkdocs.yml index c8ebb02..b1a47c7 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -65,6 +65,7 @@ nav: - Reactor Design (CSTR): guide/cstr.md - Hyperparameter Sweep (sklearn): guide/sklearn.md - Assay Costs and Annotations: guide/assay.md + - Serosurvey Design (IVAC Notebook): guide/serosurvey.md - Monitoring Station Design: guide/monitoring.md - Bayesian Model Criticism: guide/bayesian.md - API Reference: diff --git a/pyproject.toml b/pyproject.toml index 36ebe0e..b5ebefe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,6 +86,12 @@ examples = [ "scikit-learn>=1.3", "trade-study[all]", ] +notebook = [ + "jupyterlab>=4.0", + "nbconvert>=7.0", + "matplotlib>=3.7", + "trade-study[pareto,viz,dataframe]", +] test = [ "pytest>=8.1", "pytest-cov>=6.0", diff --git a/tests/test_serosurvey_example.py b/tests/test_serosurvey_example.py new file mode 100644 index 0000000..89fb921 --- /dev/null +++ b/tests/test_serosurvey_example.py @@ -0,0 +1,142 @@ +"""Statistical and decision invariants for the synthetic serosurvey demo.""" + +from __future__ import annotations + +from typing import Any + +import matplotlib.pyplot as plt +import numpy as np +import pytest + +from examples.serosurvey_study import ( + GROUP_WEIGHTS, + SurveyCosts, + SurveyOutcome, + SurveyScorer, + SurveyWorld, + allocation_counts, + plot_priorities, + preference_policy, + survey_factors, + survey_observables, +) +from trade_study import Constraint, build_grid, preference_sweep, run_grid + + +def test_allocations_preserve_exact_totals() -> None: + grid = build_grid(survey_factors(), method="full") + assert len(grid) == 18 + for config in grid: + people, communities = allocation_counts(config) + assert sum(people) == config["participants"] + assert sum(communities) == config["communities"] + expected_share = 0.2 if config["allocation"] == "proportional" else 0.5 + assert people[1] / sum(people) == pytest.approx(expected_share) + assert communities[1] / sum(communities) == pytest.approx(expected_share) + + +def test_score_uses_population_weights_when_oversampling() -> None: + config = {"participants": 300, "communities": 10, "allocation": "oversample"} + truth = np.array([0.9, 0.65]) + outcome = SurveyOutcome(np.array([0.9, 0.55]), (150, 150), (5, 5)) + scores = SurveyScorer().score(truth, outcome, config) + assert scores["overall_error_pp"] == pytest.approx(2.0) + assert scores["underserved_error_pp"] == pytest.approx(10.0) + assert scores["cost_usd"] == 18_250 + assert np.dot(GROUP_WEIGHTS, truth) == pytest.approx(0.85) + + +@pytest.mark.parametrize("correlation", [0.0, 0.3]) +def test_deterministic_population_has_zero_error(correlation: float) -> None: + world = SurveyWorld(prevalence=(1.0, 0.0), correlation=correlation) + config = {"participants": 300, "communities": 40, "allocation": "proportional"} + truth, outcome = world.generate(config, rep=7) + np.testing.assert_array_equal(outcome.prevalence, truth) + scores = SurveyScorer().score(truth, outcome, config) + assert scores["overall_error_pp"] == 0 + assert scores["underserved_error_pp"] == 0 + + +def test_clustered_estimator_matches_known_mean_and_variance() -> None: + world = SurveyWorld(prevalence=(0.9, 0.65), correlation=0.08) + config = {"participants": 300, "communities": 40, "allocation": "proportional"} + estimates = np.array([ + world.generate(config, rep=rep)[1].prevalence for rep in range(5000) + ]) + # Unequal cluster sizes are intentional: 60 people / 8 communities + # requires four clusters of size 8 and four of size 7. + n, sum_squares, p, rho = 60, 4 * 8**2 + 4 * 7**2, 0.65, 0.08 + variance = p * (1 - p) * ((1 - rho) / n + rho * sum_squares / n**2) + assert abs(estimates[:, 1].mean() - p) < 4 * np.sqrt(variance / len(estimates)) + assert estimates[:, 1].var(ddof=1) == pytest.approx(variance, rel=0.12) + + +def test_streams_replay_and_phase_seeds_are_distinct() -> None: + config = {"participants": 300, "communities": 10, "allocation": "proportional"} + first = SurveyWorld(seed=2026).generate(config, rep=3)[1].prevalence + replay = SurveyWorld(seed=2026).generate(config, rep=3)[1].prevalence + new_phase = SurveyWorld(seed=2027).generate(config, rep=3)[1].prevalence + new_replicate = SurveyWorld(seed=2026).generate(config, rep=4)[1].prevalence + np.testing.assert_array_equal(first, replay) + assert not np.array_equal(first, new_phase) + assert not np.array_equal(first, new_replicate) + + +@pytest.mark.parametrize( + "settings", + [ + {"prevalence": (0.9, np.nan)}, + {"prevalence": (0.9, 1.2)}, + {"correlation": -0.1}, + {"correlation": 1.0}, + {"seed": -1}, + ], +) +def test_invalid_model_assumptions_are_rejected(settings: dict[str, Any]) -> None: + with pytest.raises(ValueError, match="Require"): + SurveyWorld(**settings) + + +@pytest.mark.parametrize( + "config", + [ + {"participants": 300.0, "communities": 10, "allocation": "proportional"}, + {"participants": 300, "communities": 40, "allocation": "unknown"}, + {"participants": 30, "communities": 40, "allocation": "proportional"}, + {"participants": 300, "communities": 11, "allocation": "proportional"}, + ], +) +def test_invalid_designs_are_rejected(config: dict[str, Any]) -> None: + with pytest.raises(ValueError, match=r"integers|Allocation|Use integer"): + SurveyWorld().generate(config) + + +def test_budget_change_uses_existing_results_and_reports_no_choice() -> None: + grid = build_grid(survey_factors(), method="full")[:2] + observables = survey_observables() + raw = run_grid(SurveyWorld(), SurveyScorer(), grid, observables, n_reps=8) + original_scores = raw.scores.copy() + for budget, feasible in [(16_000, [True, False]), (1, [False, False])]: + report = preference_sweep( + raw, + observables, + policy=preference_policy(), + constraints=[Constraint("budget", "cost_usd", "<=", budget)], + ) + assert report.feasible.tolist() == feasible + if budget == 1: + assert np.all(np.isnan(report.ranks)) + assert not report.pareto.any() + figure = plot_priorities(report) + assert figure.axes[0].texts[0].get_text() == "No design meets the budget" + plt.close(figure) + np.testing.assert_array_equal(raw.scores, original_scores) + + +def test_financial_costs_are_additive_and_nonnegative() -> None: + config = {"participants": 300, "communities": 10, "allocation": "proportional"} + components = SurveyCosts().components(config) + assert components == {"Setup": 3000, "Community visits": 3400, "Participants": 9600} + assert sum(components.values()) == 16_000 + with pytest.raises(ValueError, match="finite and nonnegative"): + SurveyCosts(setup=-1)