From 1342b06871882a6329d1fa52e0ef0bbd4cb1d044 Mon Sep 17 00:00:00 2001 From: dadams-AU Date: Wed, 18 Feb 2026 17:07:03 -0800 Subject: [PATCH] updated analaysis with offshore considerations --- .../__pycache__/well_analyzer.cpython-314.pyc | Bin 44141 -> 44143 bytes analysis/appendix.docx | Bin 23037 -> 0 bytes analysis/{ => archive}/district_ej_score.png | Bin .../district_level_analysis.ipynb | 0 ...ict_level_analysis_offshore_controls.ipynb | 4727 +++++++++++++++++ .../{ => archive}/district_spatial_map.png | Bin .../district_treatment_effects_map.png | Bin .../district_treatment_effects_map_psj.png | Bin analysis/{ => archive}/look_at_the_data.ipynb | 0 .../{ => archive}/spatial_spillover_plot.png | Bin analysis/{ => archive}/spatial_spillovers.png | Bin ...ed_district_level_analysis_2015-2025.ipynb | 0 analysis/{ => archive}/well_analyzer.py | 0 .../well_analyzer_templates.ipynb | 0 .../{ => archive}/wells_interactive_map.html | 0 analysis/district_demographics_geography.png | Bin 450352 -> 452606 bytes analysis/district_treatment_effects.png | Bin 271385 -> 278183 bytes analysis/district_treatment_effects_psj.png | Bin 254794 -> 248843 bytes analysis/draft.md | 236 + analysis/draft_appendix.md | 178 + analysis/event_study_plot.png | Bin 276279 -> 205383 bytes analysis/gemini_draft.md | 122 - analysis/gemini_draft_appendix.md | 114 - analysis/heterogeneous_effects.png | Bin 224555 -> 119726 bytes analysis/methods_analysis_results.docx | Bin 2125039 -> 0 bytes analysis/pipeline_trends_over_time.png | Bin 693822 -> 689204 bytes ...analysis_2015-2025_offshore_controls.ipynb | 4012 ++++++++++++++ 27 files changed, 9153 insertions(+), 236 deletions(-) delete mode 100644 analysis/appendix.docx rename analysis/{ => archive}/district_ej_score.png (100%) rename analysis/{ => archive}/district_level_analysis.ipynb (100%) create mode 100644 analysis/archive/district_level_analysis_offshore_controls.ipynb rename analysis/{ => archive}/district_spatial_map.png (100%) rename analysis/{ => archive}/district_treatment_effects_map.png (100%) rename analysis/{ => archive}/district_treatment_effects_map_psj.png (100%) rename analysis/{ => archive}/look_at_the_data.ipynb (100%) rename analysis/{ => archive}/spatial_spillover_plot.png (100%) rename analysis/{ => archive}/spatial_spillovers.png (100%) rename analysis/{ => archive}/updated_district_level_analysis_2015-2025.ipynb (100%) rename analysis/{ => archive}/well_analyzer.py (100%) rename analysis/{ => archive}/well_analyzer_templates.ipynb (100%) rename analysis/{ => archive}/wells_interactive_map.html (100%) create mode 100644 analysis/draft.md create mode 100644 analysis/draft_appendix.md delete mode 100644 analysis/gemini_draft.md delete mode 100644 analysis/gemini_draft_appendix.md delete mode 100644 analysis/methods_analysis_results.docx create mode 100644 analysis/updated_district_level_analysis_2015-2025_offshore_controls.ipynb diff --git a/analysis/__pycache__/well_analyzer.cpython-314.pyc b/analysis/__pycache__/well_analyzer.cpython-314.pyc index 311cf3a742b4cea816479bc56b41e18df79236d1..39899c87cafd08de7d87e46548326b54d98dfc88 100644 GIT binary patch delta 69 zcmaERgX#SZCT?v$UM>b8@cdV^kz1Qp(?vfcKQ~oBB{3y2w^%b8aK2l$kz1Qp-AO+qKQ~oBB{3y2w^%gEu0PPJI6aWAK diff --git a/analysis/appendix.docx b/analysis/appendix.docx deleted file mode 100644 index 498a2fff9ec9ae74137ea095fa0806ac2e45a6b1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 23037 zcmeFYb9XIYw=Epo*|E7}+qSi18!O(ilO5Z(ZD+@}ZQI7p?|I(yo^#qg-{9P8ZS+5? z<`{FXTGi*MF{@7nX;82qKoCGsKtMo5Kuoc=zf6IFfC|BZfKY*;K(vHy0Zt|WCp{H6 zI}=A8I#(NO!aOh#iX0%2@Am)i_+NYn8WP4V`xuZ!pMpLEW}23z+9(S9NAjXgaLiwU z!&u}*9)$8cKD+if7z;@mSw;p4r`A2Dpcn&YR@1DZksDpe&MqNVx{F5QV(Mv98~A zul%I1P+Ag-R|DW7=@mkQBv;PTyxB>S;p)y?Eh_4z$b}d*!$h3BoKEGXx}+&2S}%H9 z5nRRLBM4+lsE0s*w;{Hrm7p0LAd=mO-&wmh8(j(4lx(Mm7Vs?+AvUF|S#GpV|0oOQQim3P8-&y|nJI*ag zHJTYx>NjT8u$YMyx(LLw^|^wpm988+&l$x>Sef`7UHjybuXe;@=bLhW@@t7a?qTIl zuruyQ3FwMvyKyl8_mlPY1q!6_-{^@Si`{(neMRn@eX!s3)N?SgcBH5K2mc=}{}*%d zzx?!yxSnrX!UJP;a01Dy*U zO8@3*s*R%gc@%bt#m{y{q|uwd=QTnbKr}x-QdQf~ekToY~l;Sh^p7SrCkk z8K(@Jk4vEcDAzM0G1aTe-Z%D7pjoSPrkp9sstkh(|Ai!B>^&?)it_bI;8LHl9#S?r zy1OfaGmA)WXq<@i5}k@s|8`ch#KVAcM}V$O0Bd zaXNFs)5_5bNP9Vc?~tn+ps5P3rzst6%V6m%$jD5NmrM$|TdR|r!dfWdN&96&e2U$5 zG&CTqo$WM0=F*;+q;^Bw5dnUBG~%qyx_g-mXLBVK;dr#}t&7B+LugJkmxVF#L^sMa zq{&My!CyjHvQQrv(I>S!&2d)~w|o~@(I>S#C0&O|eS*b3k;UxFi2=Tzk+47m*ltM6 zn%X^`GvxF2KeCUHtmm=1%$2dE3lFW|odag|Z&o84cWra|epV`EL?`D=C=Uik@~Ff% z-U1fh)E^Yzyr1gbh4|~YY9`G+!X(A>BB`w* zgJ!FlR{A>C{z8tRKl9^%e$~`)UM*O9Lu)?Icl2Zj?4GsgLd$CH8jBOECzVtuE;Fg? zl2h~aF%5W&Fsp%YPA$IL@qO?JepFFm+y&3pO;KNbfEsV*-8P78WOi2vD15jjwzfqb z`R>;n-+OB&sPataiV=2!k;V+;cmM4kekMU2$n1!EpTm=J;E})pBf8ngqww2f>KxkQ zT%Q)dYYE#5fJ>DVXx-_H3I1gOWpiD&@DWf>qQEhSi19N)6WRGZSlTb8KfI%d`-1-^ z@x_G9rz-6Yw~Vtb)bFbcgR!i1b%b-Xd{w-xgeDCeKlF?=;PS21TFQZ}%-TP@<#+h& zji~EWhsTTOL#Dlkcdx;Fncoe8uCRK}vp4dTuer}0e6WVm7z>S+p^~uqdd70Cg=4y= z3e=jRGqF)QjYyA&s?z_x;;W8NE~ej95-T z4j3EmmNU^djVbSa3FFH$Vh=TAqIc=4MuIuU%9-Rs^(IX9>vZzTn|R}!csnd~SlNl#M*x>=z2-aqvV zx&y-FVTOO=pl!V?uwUeliC;VD9BN>jxY5a%oh(mL&-%B#FC~5+*LDy#tJf}VU5Q+* zyd9|nF+Gi5{}jI@(kt?NErX<^3I~%Iq&lSN{tI zFeBQ+-Wq$Jzalz7h*d5IXj$7_73S4wc3i;ihh!F6wy{zA8)t*eZ_aDV*+89)2*+c{ z{Xgl7(kYLVw-7HLsc3kz-M^`n?g@$|c_0lB>`6rR@=2`KfAP)t(<)V4r#X?ver* zSwVFJr6vz!mlTqcVTyEz=aornny^pHcPApU%|48}kEXpbJB0JV%V^Wai4{2LSNb9g zCfEvKL=?|S$@o24HdHDxQYQtX=Bnx-j)=dLRf$?y`|Z2emDh1+=(Z~iZ%fOH-Ew;8@#jDeA+GN?eJJIq7b+u;t~;Pv}bP) zl)v%!KOKO`u0Jl4wxgc($ua~aZxtqt5R+2~6O67+5>D=2p6?VP8ZA{ES8acYV}#v$ zATB#OKi=I|kZriP|1GckfmnPEYtnGBoi>N{{-zC()b-r|Q<+EM=OzQ)hqNWV+u3# z7gtrA8UIv7+sG&z#12Lce4L(ul@E;>{Q?^e*T*@lgM{!lEP%%n{;%)DJ#0-dX^&r} z5Bs@}62*h(8oQGT=KD^7ytgh>ctcCH|Im6(YPn5%?ipnC1Vs&q<^7oqCua1`08hAm z+C`so*Ex7Nyz*X?5yf7Ov|?^HeHIMZbOn%PyrBka1}GZk%SW>hP3Y|V=i1jjy%W#1 z^BD9z%*GoUQPfZ8G=NsuB{Qp%S-tPc$Ey@VQrk8ER(yFu#cMLV@ELNdiKxa1u|W-nnXzu(->)nc zg>x*8A0T!)9YdB`vAE+|K~6Vu6_;Wq%%vV7PO%N3N$Cx1j!~y^Pd^pM z8iX@n$ba>#SI_i6R72&)WY2tlupfu$KcX3|xX#N?5!)5S>>#%A7X?`*O|h5T}3;JLc9?xcx|< zW@^mYbz(CU>`}0$R|S%s5fAVz5J#4tX!IJJYdQx02q9b0?lb=VS8*0HybxnaV}@J# zWPT~aw{*5I&4oblPf_0Z7^V53Nm91FARd9y8Y6{%X@=AX&!p<_6vV3Rsl($M4kBex6IcC5Ot%7eR;Zsp)`XnGq#KtwIH7YsNH!wd?RB_~m<>;vyTWIcRT7S0KnfOY3 zvqd^kD|tr#bS26XV8*Sl+@2d7JRGCv*#LjLQV;QLtK7U4gwC;9L5FYRpzDGl!==AY#_OyBA2^jY8i~!2S7SCp^}5tTrRW=1$>J zVs0vR{@IXYjzJb(y{bGSELoKtbhKGL`gyKP(WYXqZLOyWdG3N%c6C8$D`bQ|p1DA< z0ILkwY=XNCPr67CkdJ1;_mCi4+i5hJT3Xvz0Eq5tC^^6yS3P>Hk0kn-tL;7*Fg-0o zPyJIq3ES<&*5GlZiND-+5p?PvkKjCX+Q|-&ue5eE9~qWOeRl9?SN`o>jkFk;Sy(^? zO=7ZQ*xp7DZ*-;)2|$plAA2Q^uWH6(F^IK<82{n*I?LiYw*x~oc_>`$Lt31Bm>yAj zCO7zyyUD$zsfp>TqP!#{;IPNQYW3HCrAgQdI@HIG^eC2~<&bUh9C{>|rE2B0Ap3UH zM98cOAM6j8>CX9hZA4$7AwedOF`o+>_D2-o{3g&yV>g_RGG2e`>hwvbMJhp?+;+(w zQ=XJ}4qmIc+3oOykY!n4c7EBu+OejeS3yhnN1$G`%oJN{8=7N-&%xk*$v~-)Dw=P` zDx@X5$2}Rm5Fk}Mlk>7BcY!}9o$g7dXu6katG8K*^Nw|NVM2d4_Xa|s>MaSY4ypA$ zTY$t2Ti~RL@GQ%B9U4SAdR~@mx{qa{ljXL%PM}p0azD*&aK%7sT~|O(c#f^5tqY9M z2Q)GxHR^E1k1NC|iIHdAt0Os?hY8e@(we+v-}I{sO&Bp{h{ids0OMDXxo)b@Q1;_W zr9@S0?%u3Ts_vVh!^CY%H9+;pb@)oVOiEI5`?}(h}nkEL(8xurpu40_m8#Gwk!3V;ZjGgl>2e}*QJrj?tDS>QdF2V{5p2KDoltcx134* zr546>AHa~EH9ZTf^D89ri{I%GU#Ep4$*rt2kdw?+*nw-3fdd&AXTq>d^--sa?m_Zr zag@_3*m^>xV&ML{47Uo!W2m;hTR(3R9n6TS@FstpRT07R;!LyWa$kGzg~1n};2adq z#)X@F(o$@O)&~h^q*FFdSDR#XNd@-puV=>(2d$f8FHh%r z=5rkiC;2-XX@fzG$ZY$?znTDzuA|m9Cf>(oxiS3H50X|t$`axr;9nV*eV@sK>Jgl3 zNR-+Q>@3d)B#XLxbp&=p2=lCWDOC>C5ySuuEl-~hLQWTuDsexqr?P_wdeo}3T8SNU z#B~nGO+ru6Ie7Vem<5hE*iZ>*&}r}RyT4>2SEF*B7U#A<-hQPNc_65oOX52&#=n>ftTaiqX5k)m;T<tnXIzJ;P-(HW zH8=l|cdwh?m-*GwxL+j@#c#8^{7=;_>$}fpa2*Pl?zcd-i`DRK+nZ;!CMBEIKaNz& zw`d56I5Do6o3Durw2I(n=~?9|goprT?&NK zA#G3ZWV3Jd0(}gzKEc%*fY3`z7U!|;5R7f}snWuq8d3ngR_ySCbsmE1Px1(jwYT*+ z(f~$8ILz=K*l>OetQ*NFj%U%#ksTeE4d}9JZi}LF-LLZ>yD@Kld)T0%KUMU!ci3RL zn|X1|)DG9sMjUIU*xoFtFtrCQUbycdnWN!1OGrDa=b)DRt}$^E>C4VF6#v`G7xaDa zi;YI1LX_CSOa~RJqLD*q`I##nY3AvS4X-U?4)iZa4SSw)va|mDc1hMgJ&M?M$=P+6 zg3#E(xNh`(YLGP>v94f@Bh?MhpQBhk0pY0Yle%maT`#$SRq^EL?Sr=QdX~{!?JSkK z+U#EH&V~H$H1xx?YNroR+{hk}pPcBv5uKK-_>p7xx+ucB$s$}n^RQ<}x1~|# zg&`FwAD(0=N?dsyQ$zN*rF$W6Nx0pp*J|}8#$N`|gf8qPq~-fF*zJ!P$I;L%RbvUR z9+|AWf%CGe-YC~HjzXiz^96xQsIj0nAjW~;6=?kbUx9L#IVg6g=Sjr7A~=m2aT0G>It9X?jRvQ+h@Os+CdKuv zB3KZqKO)`s)ZMMsA04km86g^}QYa@hlc=)805H9XHyA6j@QhO|u`z4F+vzQgHPKs4 z=1Mq+Rnj()ZAE%id(yXgF)+~^!`YyHjGY)!rbceBP=@gHzHs2%-`Hb{G| zRs8vqLaeX=oK(%)-LE4_EOlh0Rc8a%bnK@ZggUn#>lL}$Dx|~u*8#yOj6j0k9OWY? z`Hx$dElX5I3kbj6?@pSEw+JZ&L5rTu_SKLS*;gty9xDk`)I0s&mX%X1xYD<)WR^45 ztV*h_nORr?4Bvwta*PvfM_yjNSOGAsojzx+hqqwI{Wb!TCrM06R-FUTBqx9jjmi+7 zse(Ltw=OWmd1T;o7f3;2`f}@&9!@v@R2MH(oyNVE;k{asiDRhWSE<3UM0}?o(vf z2>=7je>ZToV&8ZnnVX05Vur~>DEKiw@1ubCIqWpg;Vd+#(2RLeCK(K-GH{DXyQ_wj zQk2hRG`oGoq1RC-4EotHrG{fwznL;zjL#mO6zMKZ&`Gm|{Y0V6vAiv2Jha=Ntc3-h zh2h|Q%*sInjpIc>)C)(`O%_CPPoi5NSF!F^3eV_fKB@{aPTGx?mWmgAvmgt zK^1yi>nkAlGPSFcdK{mF!@71_mlw+}k{ib5FNkOUtHDjO$4(1C8J%MM4BEdMuj$i> zK4}5|@&x7yzED`&hb#|(=vmJKU%Z{LT%0uKx_tvm{77fw1t%VjtCANjvKCp`3^s&S z{<$*hBDBTRKkDJ=4#(o~Dky`nrsvE*5d)rkfX4vQ#VHG9Xz_(KCQBQVaU4y^67U&n zpt!{&A^aK_QVXfA%s#)N5Xm(PN??%(um|_G`!eEoe)p|Y$i0~LKxO4-N-!nD%1L{K z(4FfxQQR(fs<*g@hoiv$6A?_62rwlWNjGne+G!Qnf^Eny5_G1&Mk7BfhAfI-El&g# zV9}@~D;(AY33za>&{!_#3W15cGH>bE_0QQHLc)lb(|M0??dqC7WxsnWQzXwr;au!zu>{ycCqKAID;S zaC^a(onhxwrN5NLwLx~=_;S&(GzrMaJCga~n2-Mw7xQgqPZ8hF+EH(6Q+wn`8@EZf zH`8hw@ZOoIS^2u?%uT{lq|TvonJ`?`lDQD)ATRG{AJwxs#D5mk31+fgv%K)Wjy!3* zv@B{%-1W8nU=BACO*_#z-UQ)gr|&hJsxws3G^vp*{Wrh zX3GvFF_fxd$=~|PmmNQ)>daDVp1ltyC0{Wy*R#TB1yWWoya}+dU_|{EJ=jFNQOjC1 z2?Xg{PgC#nAiLD-d)CH6gXr}OD(M1|1!BHH= zSn_me8ZR!i+cnZZ;(JcWN2GFlZt4AosX+OqyLo@)!--peCZ_bZ_rddaasI`oiWqyh z{B3Bpo?;hYoMv)TT4+^Tm0F5?mc~GniCxrVo_w!&*S@~of81WK03~5!Zpii;u!x`FVpCPGWPGGhOpv`UaFZadu47w zv0i+oBptUy9c$`=HD?vCV!P+1Wzt4<`Zi*WwGC}y8b2|?D5JS3csKq(>-QGPq?YrJ zf{IIT8(E&gq?SJ56H+QrASH9L*sD~QN^%kp(njHU7Gz%LqPn z35#&^%|}lKrzeP7yf={e2Es-75{k1wKU)b~>}DB8PTu_yXiYttNz+GH zm6{RjJjC{AJKNmX#kEdvm0v;vxB~9k#KJMfTxZvln1T#HVuX=bfF+{U%*WEXdvX8< zgAeTKDX!c_`m5N=3C@lzRSYuwVUr&DUif-}KJ9QH(ccSRwywmXf)<{eJNpemF=v>N zhWEOJGEN=!hpi!!b>`Db{x`o+V4Or%=GL6De1G5Y>g)Zn)cuHI`XT1j5t&W+;GF8| zu6goJO5A5%0g`AOzsvQl(TOZUojdTR0)5aoSh5?VCQ`Mqg)kUyfEOsepGyLMbWB%j$n-UM*InI@WR zNBK-t(ejuzM=I^~Ax7l{zP>6tSJ1d!LzQZ6bHmoJG;pBhTf@y$vLjck*nzy ztwYFWzp8jClq>xzH6DgMIX&N*P0LKyPh=K~leza-Z|d%E8wu8V$tv+R6^(7QWNul> zE@qTwnI^NZf&-qmc;3v6Themv>K|FRmwj>SHmhe9x1fM*vp<*%ejiiAIem1{l_tR{Wlv10RWy-R|_r(SAS8I-{0`5;mWLSQQ_YvL9AxRWV|^h>>HHr5)R zbs#^tRlA965_BN@>&4mzKynE`rNN{DW+pohAYhAUh^M$S&tvXsaO}5D@}?<_A(Y7~ zv_kwfKE{O&eV<4`3Ul#O4lzctjXTDzsUp2e-qHp)9< zu`L<9IUVqCjdQo9myj^Z5LZaWkDAI)z9==Ky%I+-?FnbX-nb3hVm~-vYiIvn1*5AS zyPDS6a9(QVh-x1^fw50^!$+4nbcG!-zd=GBy2YZVeN0B%0z>WPIPZIa1P+4Wx1Fwr zZre$8TxPjL9Al<6*~BVkZm7@GHa!j^Ke?NxAO~HfktF}=z+62O-+apG+r*ZQ~ zj6krHEA#Q3wh=4cE0rxaAc*7A*OQL4_=8@iT`h7#L>=^>ho1-9#Z|8w=0PMpC ze#iXtz69@)o2bnV#ax*oc06(g9JO{QCvyUurntqf407xkdn;9oo)+wS3?%=PQAHVI zm444)6gsuP1=BYqcml!xYlO|g8f%^qiQ)R9F|`j;S{NW07ESvTjuD%ddTu8_Yng?+ zGlOo0rQY&w9vWe!@l~Fkdm`8v>oxQ(OQ+J7q-~6}QIPg0Q&d;;n~~Bb`f5o>scNbb?yZm*!xg%6$xG~|?Hv*>qW>mU%3*LtcjuLN0_sLF ze`1ULi5d7zinWAf7*6KsHPz@dd&^~Uit$1Ptk4Jpp&RYFF&1zl>k>z6dh(o~w#FPp z_8xW?9v4*qYMs!l>DZ6x8NqgN){fPHYEi3CR%*)#J&cq1&vi1=UFR-0Ka9@~^Ee0c z;nJJpsfhkv>?_QW^fS15a8VR(x<`&Yb*Ibnha0g$?wz*q0e}*~_Y$A8Cw)cBmh3=~ zJr5xXNuJMCpzOEuRVUZ!heM0_jt;_nfNBTZq*}iX%_>uYbDY_o+BM_p%{FgCtT2N9 zUh=BF;3s^_xWU%;tAK*^Hox5^+-f2kW+m9@EDxQCF8`(+&I7U^=m;jM)%wl~@3GSK zqnQ<4Zi<#h7FcWWM_ced_eg_7zyN zyzRxUR4YqeBtc-&lz|Ecsg-4{JezbMxsBqaUel`KI;|BZX+SVIP{40s0qHwga0#N5 zd09T(enjk{Fk8*YeA?a8(<=qQ%FrW@w;=XPa#M%mQeMoMC{J_}ds>mO^Uz@RLvzQB zd)K!T2miIL!#;9YFPS~B#8YSU+1YKcHkmywdZ;!S2PIf{8rzFK>{U*Ab*pVi)`+c& z)Sc_Nm_fTw0Pm<<(cW9n>;VXqX!6#mT0l#SA1h*5^-Xd2HvyU8+YH{k+gwiC;REh) zU4YN75#!x$k`sgRCN3AtBs<{cLjX@2vLt9El zSR!9TUj+~?ib5^52UV#sS_k0~!YlcI45C*+v7NpG7v$(v(%A(hrZ+{+yMzB~={-_b z+&m=CBu0`YpJn#lFU7&En(SWGuKGPtw|A(R545k)Cp3p62?{?d-OYFZi?$e2%{xey zI9Kf|DQdmphw~X|oZcX~V@}SJ06HNs-`4M4%!e>cjH)Y;ARZQ);;Yi4zFdaUB8fU6 zjB_45zF}43cBic5XrqDV?Rjdu=h=!Wi{vj!sM&>KYe&l5B@i;h6VH@^yP^6{ty6yn zkPNk4w1p`9o3bmioE-6k)O@n6!4k2ia&kYE8>a)6KPXK4T_A}EjE5y{QzB8&d5Z-+ zkXGAp2S7Rz7pG^K$!5teD;4PyAzCLBrgo}y)4GiNVX(Pq6e2B_xLGHM?V1@WgnHGw zZsUK_ohBx~p5FgBfdJYJs;!E164{ffm$i&kM?&Yy^(51kBWr(Gy|hhKFM2uieJ~~| zX!@g9om`Nhtzsh<+jPA3ai-BCnk_l>$ed|o%1Y@c9*j8o!L?cH`+JwE+I0-HjX@bE ztA@56w{f%3qH4beW`n|BGg+ZjxSCnhY0)CiR3t!NkAiUTZhS%hPkOZau5di?Z>3rj zC=d`X5ESsg^l1MUZT?qP+J6W*fxmUt->v_59~JQv|LBSXuY$S)mOW^>ZG@p?9Vz{^ zwVr^DJwAjVj0$8m|9Xi^p#($B1w=6$nso&&xZhQxap&B1vVzo+g8wP1bGFq4CK!0! z0Z;j9VOd%s2u$L>y#M5K#;y|A8*}yAQy*j4;TvVHZhu*h(0Ct;HyxNf$VE#jv2-h_ zmR%>>B{4_lG6y$Y&9qX5zWw`wq{a~Unt!Fq;uS{Erg6#qSPcA0;s@Iuk#ZZlWcP@q z5c>5RRCoy#3snXP?t``$dj00Pb10p)ClQC1Bhpw3(ht)c5W9rGrffskxbeBQe_%Aq zpodn{0$jtOZ-Nfqvg0#Sej<54tlbiP|M&m5tT6|gZt4A7@@oSJ1cdh8;$M%ElevkF z3H`s2@gHepgW4J(p8?5B&*H1+_AN;og-$WP%m(G6uZqg1onYIas9})sT=({gyQ%e$ zu~w;Mj6W}KHZ%SdoVIS~5nSi2lrgxT{!#B>$Zbgoa~9q8R`5hL5D5`TX?F6O&gZB6ch;1RwQ4$$31FF4ZLPB4CL5Tygfo7qab}ak$MC!q6li303 z9;SU9=!H(4F|sIP7z#bkEo%TjT@aHRIvjuD4B|_$m$m^vh4^%k_S9m62+raFqYA+YwZma1e zlXtg>$9KYOOD9-Kn8!Opo_n4(y@hK8lLw}uilbX~{Db|{5;maeU}@vQ*!4sfBINBV zX$`1`QIU~hg#5T`A>WnXc#tSImhBgXByxud_i8xGq>nO-S>{87s14W zDX-+21}_hyX7q%($zhTPa2QTQ|1=IM01VV&;p{9#+iFWvmHbA9x^r=1LBW7IGBdolXlaxz|=Brkx7I;zNSotg^` zlH|QUT3aT>hCt=lhS|!F9ur_o3bS#DmIOB392794vcH)!v5@b=;?^j7gU)9n4jB2M5sTD^^uhnW;II}4vDV=0Tbh>Epq zJ&|A6__nTczeqST?!$PZ>*wPAieP7uY$x6skDe_PF9ug_7p%?}R-0WfhOqqP8~sHo z*SNP>Y0;b)xH=@?Nchmt9L=6z1F-QzcXKWpv1^9`JDNB-Spdu&|Dj5O+LrAe8=N;m%@@)2o%T@F1?ga6FuBcd-iEcE^*-Ouk)}AP z1w`Svc+ZLQP}FDO|Uy9mB6cCo6W_Q zf3L}=J7o4)7YP>IoGNfSC)bL?StTxrwl5 zYZBjliC)&{y(!frSty~%UU5a{EKZ$S3=df2%T*B{Su0q$^ z*2C%yQmk+oV^V|={L4X06OD|v#vrzPVsXFDki^9@Hz+f@_F8@tm2d9xo?r!QhM`b{ zWAv9u$6OQHy@!=Vdx6)slW0=eAr`A)n`LQ>A$1Z#2_?0g0(ps)-(UL8!@dV_O5kT zUXxo^BoR2NHYoTY0!z=R!yZG~WF7BwJq3s8uIN_vR>S_5E@*0y@i5kovCblqE3+2O z%T2@?f@x&db&9|pX3nW+!(1r{KcG@M&{{HZ5p%rrobKj`h07nk`nG1usJUQ1{_TVN z&6g?eCV-NNJ=P&c*1sj-uB2e58mbq#+1-%H0LO*M?GZUo(b4H4U^a7%9M|lKhsj~6 zFzqvbDz0eY*d!MBwAEo>vl2MN5#$5u@8k_?WgfqRnsMSr+kkGZ67_|=+z&r!7EG^O zm~pKNwXx*PwI!Dv?v`FE2uZ3jxMp&T&XhfMbqUxMac|0r9%Z^~d?wB`cDL-+jD|N1 zA~nA?@A~OiOR;~KW*3Zsi4TrX;-<_o{iG$pC~|@xb_}#eOH&?FW_vR^dXGHv(Y>ZI z%-%TNl?~A^Tb!{Z z93@z^7+Rm+`C6B&YmI-{wzg_VBy=f)Rr1tROMp+zG-4*US|GlMoVH!m84^_SNIgR0 zVOzns!+b_9%hpD7(bTEW8~zTEax|>D3>3uR+!zKNI@JqeaO?0KqZCU+=$ty0&h+#R zp_q;`vviE}Pef^7y-9`!|GbUSuF(R-WJzt~;w!*;OoCxnRexR45T*S?G z&~*KD&IY@x`OF#iCvQf>b+!SAo*esFBcj!Aak%T=R)2V-k4L*)yj|zISpACL29Dhq zPW2bW|1=3e!gdf;fB^wTQUL*>{723NINKPSIDAh7|JnJx%M6a zmM0udK|7Zk6-AyY*UlCSs`&A<4$s{7iOD07cTv~iH!h^PdUAoGP=M}y&*zKLo7LBN z^F;9!JtjO`%uPgG>A@z~B2ZikIhKZOrCv5VyOC8lI3ct^LU-u{OzXqTrJM$r{ZZOXk#`VdVSl-$p*_b6z(qG4|vI$#QXv>yr@Y6LI z5|d?u*bgLl8rsv*`>c`%{Bc{Pk#DisL(uGq91}#bk(4HwP%p8S)^N*?v0D;$2qI16 z3go(EbI@=Q#`~>95ysVNz8?k5L=B9xgtw@|xyt4Gq-C#L^B)WIBpZLJPVtc%anuml zYj?s0v{Sk&B7e8Nfkwi@g;J>2V~HqIcMzt{no#RNjiq zF9dD-5SC3}7d8zF%s%;MoHfsjgywlyWp?ajm{DouuL0Y-v*)2_O&y+a-Bv*Um`(3> z3=#GNHG4Y0zdso>Rl}KTQAV&v?(v4G|e0}-8X*HmM@;15e$S8WG6GiQY$E29yvQW=H9Azn0*$$7V8xPOA)pa`Ezb6N}2 z(cLRT4;z?MVwAm~oMiEO_QZeonr);UuCEz%^tZ;ry7jn>h0y`3^_HSo8=flQLPuR} zUJA{`GAK)lLKC!(3ci^j{iCxXFxh^w2GN>!&`J5F^!oxSW+LR zKp(Bmc=e!c6K4N6YDktkz6-~B@l*ynO@Q?^Jm5*hUAa8L9cNjVuKLXFb+6yw4KOWV zvt``ynjU>M;;c~I&joi%p6CN8J&GJUO7wW;;!R(VcgS$QjGUx>`r|z{_P*bWk)d@N znUVGMTIxX0Q@xH8#9MvyMe#Ivl;qJ_QSKqhu=C}X(BwVTI z63zoVRPV+B{g1C&wCdylSUcCLT{|?H!qOeFtV^TNX+4)ajGQOf=!ZP>;J}hhXC%k+ z*WQqN`^K1hc~e<(t=a`Ey;0>VtL~s?<3W2wqvh0ZEUSgfGp5C|%DFG+O4%!6sVU*d zPrMS{UU5mquDoA~pZx^wBtxkQqqr^kph4#-bNWf2C$w|U>vp8PIpuT56!SFOnDhKF z@CQ>04vpemFtl4^I3;wA+3jyQ|I^$y-^ILIfCL0&QVIn0{RZ^!dce`i&D!K&uQ&TT zT8_IM*j`&@ABc3D9=&A1v#hC<5?tx$_crPGKQ`mxjOaQ@DK;RQ37v1M33G!9@_1P#*jilk;oW3 zj(Pej?%r&_-iNBAubA&~d&c{K>>s@k^qi8QDz~yTj6YGT1Rka9{xGu9#W{f^eWq<@ zR|vcs)q5ZPEN2zi;fWBs_nf`@9gK$%pO!-!E_&Qo-oI6$A5rop2}2T*(!au_ZN|Bp z54~y>2XV%7W015XR!#h;8N@)l`(BPLNA<3KyQ``L<6&3L0InXOg z_jIuQZ%m^odw*Wh42GIMEOZV7!tI9g2p}Zu3c}Zb4*Y#YdyJYoAaH5!mW+JVU05V_ ze`^gIu*MA~NO}4fP<`ZlWC8FITo#d!*oZP$UMb?&|9J;N)M1-opg1 z$b2(SDCnOC$gzkVmVbAua7aJVw0&g*nQmD>(r&BYaXuU~`zh}5w*tly`?$6Bw2!I! z54L1>Y0mXw!LZVdiI5@*T(N8ECB+Jhvx$h@uptbN0)M1^L`EV4iFf&=6>G;ysmu~1 zZ5>%Kct3fOs~zUuJ>6~`n8mRSwP4`IbfwFN>_OY&yiqr}>U@d8ZdNQ&;{PDWNm%Ep zPG*?IglMe)8?+SqLzxG6r^jM&P{KMWJX#%|A%)1g<=9E`!*2I!t=b}`z!jHf&eRTV zGf)u7h)Ayk4)htLYTyYD!sOMbs&v-XKik^y-HicTDJ`Go`Fg*P6$`VL6@qj(J5+do z0~rlDTZW=i9EGhh&zWrtA6p&wJI%ruN81r zO5H&!H9J7=fYFURtpVmJU`8a9H$gS2-ITJ+Y0WU;xS<;ym+NX4b^V#RD_uy`4DyGrM~tp#FshsH50*m_Gt8ls*^lwW$?waViVVNF^7X5n#Co((BK%ut zSGK=*ErQ?2wo69O~7K@zJlMEvwcR(^a1U|CLq8l3!g$nLNIW(o_ z5ARH{1vrY{>e*dZSb~k2bZVDIO`5w5zG80D$FggI)Gao#5@69cnG$1E|B=boM*^t1 z#p)^_SZchj)!$lWYVd*bct@KiP5rQ)j=6#<;BN|QO~JA*t{k?6X$|TK)9whp>TBWL zECh@@&JkkmS5cP+$#3-rTZ}FC6oD17X+fT$K#(H@A;~f=p;;Ul>4cQ>B^cVA7SyRX zAE8qlF#t&zHeOSPu;D}5;8{zRge}bXv;_)P$Rfn4r!$@PTkijdOPkWL;YOBLg^V&F zQ=QfmCJ)}T%#hW&hBB8*P5HYQm|AR|UD^NYGC>fY66)*$sAZCEb^doR*QNh-P!w3E zE{j(oP0_!)PZ-)rSua;1#g%hS2-#ddMVFoiW)Dy?VnJp2o*;R%$lCQuYO*7zC;VwI= zyOvWKgKGN!tg<9Ye^r@QxUop{kEJ-PPUBXnC00+;zwLWr6Gg_O%%o&dWlZ6sB#GYS zKiryBu3QBddmDHLA`N5wrlLrw5v|X^eM2^7+uTJE9rpsltfzLsTJjpesfB{1IhkZ! z(?>kjXnpC%4Qt7NpfvXT ztP;t>?|L%ce*o>@Z~Ow8$-6ptnyPK^iE$-Q8<0zI>OM>G4COrn*Ylystn<;vA0{nXD2$UNDQ&Ajmbm z-5pSAH*CW$8?!pC1pxf5&hdB))X$-&+u5lX{C(Ez5$2(xCw)JC_n8wH3rM8jL~|%j z^Q9#N@C8#09!k7P<1v5dIU!qK^dtxiH3zSu4^HMtUAy|WIKeq4Tgm8;9iWv=X#+Bk zc1Zk}&skst*4HoO+&9jsv%(?wAp_5*T13*sy7V20R+4E4EfFvz!;Ba(XJu!2oQ(LK zNO1zKVEFk&L?sO+n$LY8zigJjxwDIA=mqwVIa>401 z0pSIX8sBPTgUVK83AF}%Ca|lo3r@`jMqbEuJMRU-7v_ zkV(B#kXyp5`la@x^`RaJJ^3y|+)LNtEun1Od^fY3C|$Ja-1TtsSkp~OH9N8N zIJ|R_^mL?Euyvv)HH%SUu3LY}AOXOLxIx^zgs|M=8VvJClU^^yE13GFape?t5BLeg z^6J@aJ;3tO&0nEz_O1B7Cj@=FKoje+QRC;0DQht0B{V$aS4PZ&k7&!u?DSZ@EMq_XdKhMSw@Vu(Zpb8?`HZco*I@h z`$9idhp?!mr1chRX3nT2Tt3`Jk|T;I$KAxxMnC!K4I13k*9Q%>}qPeDfjdA|8M%Ek}nMVHkUUjkX zfc*p;nRH*nYJkT}B7T1zbpC7FBuWlSD{ldr>9+{fU(2qx&$=VBwu}3k2W33oqp0H@ zU@!2G?;V@kAg^kLafpDmM0kWgIFlHc>bGo$Owb=y35ipYudCs*$yB`3Y^BkmP|yA{ zqPS%**qldXlW|bS1yN4KOTv{^%SS>W`D45INA9rPJ>6WDt%7A-}m4RG!A5w>=~%9w8omIK7LqMF8> zA_Jqz{by)Yv|>H`|EHbnU}~z}x zQ2&;qRye)%l{)@CISp_mvH#c>5NSG(mh{EVSGTr}r+IzVMB$cn(JEPp@SKkp8qnMd z_y7?T$V2OXhX*4XgY{ncVa`MQ_=>~=5bX&Tc?ZFUVc7vQ-l&Ky^x>9-57n3BHohsEW| z&~-?FsXZiK8pmZVY3ph=`N~h|V%lLv!d0SEGl(zk!29Cdx2Xl)ig2mFQHb#|GE01L zB<{mmcSZ_TY4~COtpT!{-~kWfkWOf}Pz0^{#iAF{7>V4wRY*b2EfsN$B2_(2|*9uqLpYU*jekw9IoZ-TG zUM9)u1J)c{C}xLi4Z|M3dkS4Tenh|e%c4LHURo}kGy6?AaoJc`s6I^BZ_bZtBR?^x z|FSTH{(gR{bM)Q->Pz=Nk zMz7Zn=5yw_uFyxp)i|IezZ8fHW5JWx_i#e-Vv-+i?=Y&$1u@FMZuooK+& zB`zN;48@om34HG)T^6$y5`Yl#SJwjR+fgk#P9ljv-Ro0xKiZ=7Onh9^KymI?eq>ekAv@YzF}<}V{h?f^e|pBiW8s2SK68Za)RPr2UntE` zDHrRg)7L7{=6jF<{B$p8&Zk7%C<^zLH3zopMY%}?1`kU;@f*r~i^7w#U0e!5qZ6Q z|94$ji5zMRH(pI?KB8`~vRzek7F0()otr`X*aI|ga~VzBEEhS<@N{d8;{b>HNHud( zNcOY`XcZkUacguDI>q@Pr zq}EY0^|rDRChJWI{JT$R;^1iRpk@s#U#8h86I{XUP_Oj_;MBI3&e70BQQowAK4W_t z%t>$IiQ!W_2kobrHx)eE6jz!U(tRH3dJ>ss7HXLtQ?lJnN!2nWRMfR_MbbhDJ(mNs}b#17~AR4mzi)O*`68h(O}7*lKYLExkN<0_H2`b;u% zP8nko3*}Dc^r}2*)SrMbSFYa+D%OnT@a%f@*G_+E zAF>9~H@ub~@Xc!D8#L zSfUfr!_nCi4;4Rqrx$l%)+aTl;AbVwJb_XLoS7m0sAXSR7jESFJfp0TyQn z1MC5M7so*c#$+az+$%QTCXwPoO>cFlg~4}6;w*)R6){3`qG4S9@HK>`D&JZ%fl5|D zY-&f$+|jA+7zxccsrIv-{J}tTqbyHF%56lTRxj1(A=OHBB1v5v!F@A(`=VqCYQ!yt zIX>=VEyb6x{re^{0hLj#;K#9@?0Y#FOJ_cIGUx@AUH2FI@^vq(zi@jgQLZC>mO zyj|@XB6W{Wn9c4SQ13Jg3HM^l{j?`Jxx#KYDJ`8 zk?`N>;tfk`GeW*nR-w~Z^BBo>fm4hm4c@m;vcC+ES0W+7jr-|rpknoroomYA||4{UMQqk4V$+!EXLkt^t$YZ$& z&)G;h6jFw6F?%7Qt@F>>DLf;?A88XVg}q9|d2SqW%mmA1Z5Vh|=4OX{K0YbS(n!p! zdwX@rrmz=((L0&m4vUviIqN}Dxfx%)0+bl%&UtqC*J5e78odM7fq8S~7`YB5ZA1w& zx=Ss;9?qbsy2kg^?neKp663dT=R9Qm&9FSeG)uQ*Hsi{XmF!IDKw#CS7q%pA(>c^oU(>D z#$W64#FPTo!DcJ>7i;&GdAbZMlXlimY1#=oOt4A|Xq0zh^@fP8j_*kJT1;xR+12HROhM2NDVh1XqqN{=ikX}I1O@Os?+D${K$tD=nD z`V0$60=m)Z+FTEQjAMu|)kpoVgml*PVe@i8fnVUqwD~&~!8IxB?1lV+u-dwGB7xp> zKSBdyTJa_4`Lwm&Fle&?%$|yY%!?(_qi)z`Yz~-jqj>1~m)g#4ml}Tx+QWv~+@aNr zU45IP4FFzws6LkSrg1cMa-y*-kXcbZ)nZ{j&3ui|ee-O>JKX}0f26ry%EsFK1T9(Q zp#xsJs8r0y$ORrvOx72;%RjnUF*&k0xTDUjp(Gy! zDAf>3Dx~qu8S9Dv_VG;xRAy^|h!ue7nc!}yLDc7Gytvz% zrZkUJ6XF92!~*4)z8tYU6|;yB(9^c%QS--bj;*2?1J$^wJta4+LsN8IrWgn+9(c6j zV>b!bK2nAI6Ul~v19MWv!tFaW+{3>z_3Pg)tcN)OMLIPCfh!!5Ipih{co8*vRFunP z+PB@uA@N=q;zu3fkqz_PIleO*7w5cw87)Ka9?NCfm6K;Ou5mp75U;U(LioMUNvh4i zv2K=G(i)?nSOwcPEJ_q?9qe`~%Q?kS5yQ-LskF08GEl62bL|=DbgDyNO9|DTXkFb;fb@(Ni%|Fwh;kwp_l>ypOvh z)qTRCy!Ac&X+TeGY|-K5RmKgShE!pgYo2w^C7i?WkGXqk0kZ3hiq=t7wBr97t!5C& zzZ1v*f)%xFs8_1ipc+co)(qOcqq#Nil(mvoO28&d?V*SZsr#mCzBrZOh=1WN1eC=W zc513_dYc@!c-~UoP1R%BY?!!D!Hw-YV84{|;uLI5lNu*KFluYt()psXQz!lPg@nf| zNz)+=Z5d*&(m|m$NDo+x(~WP~=9`Vy*;K!ZqeD`;ea_BkF&xj>8vCOj&OE=^oA2}y z!7J?^M+sNTMJ*eLdHHr^uhgZX#ua=y4M^@ym9tqiD$N3)ggQQ&@b1)|${@^XQ$l4^ zIkZob8%%1aycJo&s_Z#B$&AE+wU9i#kLt#1j9Z$I^W1=_zGRuXyy=Rh9G@3| ziRl}z-bqBVKM}Dn8F=eqPj5X)61DLEr>8@^!HFs@|Lk-7_x1VZ?GLWE8p^*D{O-j0 z3l@-pDx-e!<-88OZc+FX+J@3G{FhEAlDhL*UUc|PE>y|{8m9<$N!$@f1&{Zs3rjLUrGNu{P+9ruW){yU*JFP X!WzoxsPF;+a8L&tDneIv|NZG-O)D@u diff --git a/analysis/district_ej_score.png b/analysis/archive/district_ej_score.png similarity index 100% rename from analysis/district_ej_score.png rename to analysis/archive/district_ej_score.png diff --git a/analysis/district_level_analysis.ipynb b/analysis/archive/district_level_analysis.ipynb similarity index 100% rename from analysis/district_level_analysis.ipynb rename to analysis/archive/district_level_analysis.ipynb diff --git a/analysis/archive/district_level_analysis_offshore_controls.ipynb b/analysis/archive/district_level_analysis_offshore_controls.ipynb new file mode 100644 index 0000000..759d15c --- /dev/null +++ b/analysis/archive/district_level_analysis_offshore_controls.ipynb @@ -0,0 +1,4727 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6a65225f", + "metadata": {}, + "source": [ + "## Setup: Imports and Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7ac064f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Imports complete\n", + "✓ Pandas: 3.0.0\n", + "✓ Statsmodels: 0.14.6\n", + "✓ Spatial analysis available: True\n" + ] + } + ], + "source": [ + "# Install required packages if needed\n", + "import subprocess\n", + "import sys\n", + "\n", + "def install_if_missing(package):\n", + " try:\n", + " __import__(package.split('[')[0])\n", + " except ImportError:\n", + " print(f\"Installing {package}...\")\n", + " subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", package])\n", + "\n", + "# Ensure required packages\n", + "for pkg in ['statsmodels', 'scipy', 'seaborn']:\n", + " install_if_missing(pkg)\n", + "\n", + "# Core imports\n", + "import os\n", + "import json\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "# Data manipulation\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Database\n", + "from sqlalchemy import create_engine, text\n", + "\n", + "# Statistics and econometrics\n", + "import scipy.stats as stats\n", + "from scipy.stats import ttest_ind, chi2_contingency\n", + "import statsmodels.api as sm\n", + "import statsmodels.formula.api as smf\n", + "from statsmodels.iolib.summary2 import summary_col\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import seaborn as sns\n", + "\n", + "# Spatial analysis\n", + "try:\n", + " import geopandas as gpd\n", + " from shapely.geometry import Point\n", + " HAS_GEO = True\n", + "except ImportError:\n", + " HAS_GEO = False\n", + " print(\"Warning: geopandas not available for spatial analysis\")\n", + "\n", + "# Configuration\n", + "warnings.filterwarnings('ignore', category=FutureWarning)\n", + "warnings.filterwarnings('ignore', category=UserWarning)\n", + "pd.set_option('display.max_columns', 50)\n", + "pd.set_option('display.max_rows', 100)\n", + "pd.set_option('display.float_format', '{:.4f}'.format)\n", + "\n", + "# Styling\n", + "plt.style.use('seaborn-v0_8-darkgrid')\n", + "sns.set_palette(\"husl\")\n", + "\n", + "# Add parent directory to path for imports\n", + "repo_root = Path('..').resolve()\n", + "if str(repo_root) not in sys.path:\n", + " sys.path.insert(0, str(repo_root))\n", + "\n", + "print(\"✓ Imports complete\")\n", + "print(f\"✓ Pandas: {pd.__version__}\")\n", + "print(f\"✓ Statsmodels: {sm.__version__}\")\n", + "print(f\"✓ Spatial analysis available: {HAS_GEO}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4fecf215", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Connected to PostgreSQL\n", + " Host: localhost\n", + " Database: texas_data\n", + " User: postgres\n" + ] + } + ], + "source": [ + "# Database connection (from look_at_the_data.ipynb pattern)\n", + "PGHOST = os.getenv(\"PGHOST\", \"localhost\")\n", + "PGPORT = os.getenv(\"PGPORT\", \"5432\")\n", + "PGUSER = os.getenv(\"PGUSER\", \"postgres\")\n", + "PGPASSWORD = os.getenv(\"PGPASSWORD\", \"\")\n", + "PGDATABASE = os.getenv(\"PGDATABASE\", \"texas_data\")\n", + "\n", + "pg_url = f\"postgresql+psycopg2://{PGUSER}:{PGPASSWORD}@{PGHOST}:{PGPORT}/{PGDATABASE}\"\n", + "engine = create_engine(pg_url)\n", + "\n", + "print(f\"✓ Connected to PostgreSQL\")\n", + "print(f\" Host: {PGHOST}\")\n", + "print(f\" Database: {PGDATABASE}\")\n", + "print(f\" User: {PGUSER}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d9cec16c", + "metadata": {}, + "source": [ + "## Part 1: Load and Explore District-Level Data\n", + "\n", + "We start by loading the pre-computed district statistics and creating summary tables for pre/post 2019 periods." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "09025c68", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Loaded analysis_output.json\n", + " Districts: ['01', '02', '03', '04', '05', '06', '08', '09', '10', '6E', '7B', '7C', '8A']\n", + " Total districts: 13\n", + "\n", + "======================================================================\n", + "DISTRICT SUMMARY (Full Period 2015-2024)\n", + "======================================================================\n", + "district total_inspections compliance_rate\n", + " 01 112132 82.9273\n", + " 02 68330 88.8980\n", + " 03 107708 88.8987\n", + " 04 137959 94.1932\n", + " 05 58731 91.1035\n", + " 06 153755 92.8971\n", + " 08 356608 92.8232\n", + " 09 191339 81.1899\n", + " 10 140961 89.4013\n", + " 6E 46504 87.5602\n", + " 7B 147579 86.7617\n", + " 7C 164102 91.2865\n", + " 8A 193056 94.4400\n", + "\n", + "Mean compliance rate: 89.41%\n", + "Std compliance rate: 4.07%\n" + ] + } + ], + "source": [ + "# Load pre-computed district statistics\n", + "analysis_output_path = Path('..') / 'analysis_output.json'\n", + "\n", + "with open(analysis_output_path, 'r') as f:\n", + " analysis_data = json.load(f)\n", + "\n", + "# Extract district-level metrics\n", + "districts = list(analysis_data['inspection_analysis']['district_performance']['inspections_by_district'].keys())\n", + "print(f\"✓ Loaded analysis_output.json\")\n", + "print(f\" Districts: {districts}\")\n", + "print(f\" Total districts: {len(districts)}\")\n", + "\n", + "# Create district-level summary DataFrame\n", + "district_summary = pd.DataFrame({\n", + " 'district': districts,\n", + " 'total_inspections': [analysis_data['inspection_analysis']['district_performance']['inspections_by_district'][d] \n", + " for d in districts],\n", + " 'compliance_rate': [analysis_data['inspection_analysis']['district_performance']['compliance_by_district'][d] \n", + " for d in districts]\n", + "})\n", + "\n", + "district_summary = district_summary.sort_values('district')\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"DISTRICT SUMMARY (Full Period 2015-2024)\")\n", + "print(\"=\"*70)\n", + "print(district_summary.to_string(index=False))\n", + "print(f\"\\nMean compliance rate: {district_summary['compliance_rate'].mean():.2f}%\")\n", + "print(f\"Std compliance rate: {district_summary['compliance_rate'].std():.2f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "136c17fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available tables in database:\n", + "['census_tract_demographics', 'census_tracts_2021', 'inspections', 'oil_gas_basins', 'rrc_well_api_raw', 'shale_plays', 'spatial_ref_sys', 'texmex_regions', 'violations', 'well_geo_features', 'well_shape_tract', 'well_shapes', 'well_shapes_backup_dedup', 'well_with_demographics_table']\n", + "\n", + "✓ Found inspections table: inspections\n", + "✓ Found violations table: violations\n" + ] + } + ], + "source": [ + "# Load well-level data from PostgreSQL with district and temporal information\n", + "# First, check what tables are available\n", + "with engine.begin() as conn:\n", + " tables_query = text(\"\"\"\n", + " SELECT table_name \n", + " FROM information_schema.tables \n", + " WHERE table_schema = 'public' \n", + " AND table_type = 'BASE TABLE'\n", + " ORDER BY table_name\n", + " \"\"\")\n", + " available_tables = pd.read_sql(tables_query, conn)\n", + "\n", + "print(\"Available tables in database:\")\n", + "print(available_tables['table_name'].tolist())\n", + "\n", + "# Check if inspections and violations tables exist\n", + "inspections_table = None\n", + "violations_table = None\n", + "\n", + "for table in available_tables['table_name']:\n", + " if 'inspection' in table.lower():\n", + " print(f\"\\n✓ Found inspections table: {table}\")\n", + " inspections_table = table\n", + " if 'violation' in table.lower():\n", + " print(f\"✓ Found violations table: {table}\")\n", + " violations_table = table" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fc36c40f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "INSPECTIONS TABLE STRUCTURE\n", + "======================================================================\n", + " column_name data_type\n", + " operator_name text\n", + " p5_operator_no text\n", + " district text\n", + "district_office_inspecting text\n", + " oil_lease_gas_well_id text\n", + " lease_fac_name text\n", + " api_no text\n", + " county text\n", + " well_no text\n", + " inspection_date timestamp without time zone\n", + " drilling_permit_no text\n", + " complaint_no text\n", + " compliance text\n", + " field_name text\n", + " api_norm text\n", + "\n", + "Sample rows:\n", + " operator_name p5_operator_no district \\\n", + "0 CLEAR FORK, INCORPORATED 159500 7B \n", + "1 UNKNOWN NaN 06 \n", + "2 UNKNOWN NaN 02 \n", + "3 RRC NaN 10 \n", + "4 NaN NaN 7C \n", + "\n", + " district_office_inspecting oil_lease_gas_well_id lease_fac_name api_no \\\n", + "0 Abilene 29402 DAVIS None \n", + "1 Kilgore NaN UNKNOWN None \n", + "2 San Antonio NaN MCGAUGH WATER WELL None \n", + "3 Pampa NaN RRC None \n", + "4 San Angelo 4312 NaN None \n", + "\n", + " county well_no inspection_date drilling_permit_no complaint_no \\\n", + "0 NOLAN None 2017-12-12 None NaN \n", + "1 SMITH None 2020-04-02 None NaN \n", + "2 DE WITT None 2021-02-11 None 3104 \n", + "3 GRAY None 2016-04-20 None NaN \n", + "4 SCHLEICHER None 2023-08-07 None NaN \n", + "\n", + " compliance field_name api_norm \n", + "0 Yes COH (HOPE) None \n", + "1 Yes NaN None \n", + "2 Yes NaN None \n", + "3 Yes NaN None \n", + "4 Yes NaN None \n", + "\n", + "======================================================================\n", + "VIOLATIONS TABLE STRUCTURE\n", + "======================================================================\n", + " column_name data_type\n", + " operator_name text\n", + " p5_operator_no text\n", + " district text\n", + "oil_lease_gas_well_id text\n", + " lease_fac_name text\n", + " api_no text\n", + " county text\n", + " well_no text\n", + " drilling_permit_no text\n", + " field_name text\n", + " violated_rule text\n", + " violated_rule_desc text\n", + " major_viol_ind text\n", + " compliant_on_reinsp text\n", + " last_enf_action text\n", + " last_enf_action_date timestamp without time zone\n", + " violation_disc_date timestamp without time zone\n", + " api_norm text\n", + "\n", + "Sample rows:\n", + " operator_name p5_operator_no district oil_lease_gas_well_id \\\n", + "0 UNIDENTIFIED NaN 09 0 \n", + "1 UNKNOWN NaN 06 NaN \n", + "2 WOODLAND MIDSTREAM NaN 6E NaN \n", + "3 WINCO OIL INC. 931332 7B NaN \n", + "4 RUJO 734093 01 2568 \n", + "\n", + " lease_fac_name api_no county well_no drilling_permit_no field_name \\\n", + "0 CALVIN None YOUNG None NaN NaN \n", + "1 UNKNOWN None WOOD None NaN NaN \n", + "2 NaN None GREGG None NaN NaN \n", + "3 SKIPPER None TAYLOR None 833507 WILDCAT \n", + "4 ENGELKE, AUGUST None GUADALUPE None NaN SPILLER \n", + "\n", + " violated_rule violated_rule_desc major_viol_ind compliant_on_reinsp \\\n", + "0 SWR 3(1) Entrance Sign N -- \n", + "1 SWR 3(1) Entrance Sign N Y \n", + "2 SWR 3(1) Entrance Sign N Y \n", + "3 SWR 3(1) Entrance Sign N N \n", + "4 SWR 3(1) Entrance Sign N N \n", + "\n", + " last_enf_action last_enf_action_date violation_disc_date api_norm \n", + "0 Notice of Violation 2016-06-21 2016-06-17 None \n", + "1 Notice of Violation 2025-03-07 2024-12-12 None \n", + "2 Notice of Violation 2017-11-07 2017-04-17 None \n", + "3 Notice of Violation 2018-10-30 2018-10-25 None \n", + "4 Notice of Violation 2016-03-18 2016-02-10 None \n" + ] + } + ], + "source": [ + "# Examine structure of inspections and violations tables\n", + "print(\"=\"*70)\n", + "print(\"INSPECTIONS TABLE STRUCTURE\")\n", + "print(\"=\"*70)\n", + "\n", + "with engine.begin() as conn:\n", + " insp_cols = pd.read_sql(text(\"\"\"\n", + " SELECT column_name, data_type \n", + " FROM information_schema.columns \n", + " WHERE table_name = 'inspections'\n", + " ORDER BY ordinal_position\n", + " LIMIT 20\n", + " \"\"\"), conn)\n", + " print(insp_cols.to_string(index=False))\n", + " \n", + " # Sample rows\n", + " insp_sample = pd.read_sql(text(\"SELECT * FROM inspections LIMIT 5\"), conn)\n", + " print(\"\\nSample rows:\")\n", + " print(insp_sample.head())\n", + "\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"VIOLATIONS TABLE STRUCTURE\") \n", + "print(\"=\"*70)\n", + "\n", + "with engine.begin() as conn:\n", + " viol_cols = pd.read_sql(text(\"\"\"\n", + " SELECT column_name, data_type \n", + " FROM information_schema.columns \n", + " WHERE table_name = 'violations'\n", + " ORDER BY ordinal_position\n", + " LIMIT 20\n", + " \"\"\"), conn)\n", + " print(viol_cols.to_string(index=False))\n", + " \n", + " # Sample rows\n", + " viol_sample = pd.read_sql(text(\"SELECT * FROM violations LIMIT 5\"), conn)\n", + " print(\"\\nSample rows:\")\n", + " print(viol_sample.head())" + ] + }, + { + "cell_type": "markdown", + "id": "9fbc1a01", + "metadata": {}, + "source": [ + "### Build District-Year Panel Dataset\n", + "\n", + "Aggregate inspections and violations to district-year level for DiD analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "aa5db4f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-18 16:12:31,857 - INFO - Connecting to Postgres\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing WellAnalyzer...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-18 16:12:34,454 - INFO - Loaded 1010432 wells from public.well_shape_tract\n", + "2026-02-18 16:12:41,785 - INFO - Loaded 1878764 inspections from public.inspections\n", + "2026-02-18 16:12:43,025 - INFO - Loaded 193338 violations from public.violations\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Analyzer initialized\n", + " Wells source: public.well_shape_tract\n", + " Inspections source: public.inspections\n", + " Violations source: public.violations\n", + "\n", + "Loading data from database...\n", + "\n", + "✓ Loaded data:\n", + " Inspections: 1,878,764 rows\n", + " Violations: 193,338 rows\n", + "\n", + "Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection']\n", + "\n", + "Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number']\n" + ] + } + ], + "source": [ + "# Use WellAnalyzer to load the data (it has already figured out the schema)\n", + "from analysis.well_analyzer import WellAnalyzer\n", + "\n", + "print(\"Initializing WellAnalyzer...\")\n", + "analyzer = WellAnalyzer(chunk_size=50_000)\n", + "\n", + "print(\"\\n✓ Analyzer initialized\")\n", + "print(f\" Wells source: {analyzer.config.well_source}\")\n", + "print(f\" Inspections source: {analyzer.config.inspections_source}\")\n", + "print(f\" Violations source: {analyzer.config.violations_source}\")\n", + "\n", + "# Load the data\n", + "print(\"\\nLoading data from database...\")\n", + "data = analyzer.data\n", + "\n", + "inspections = data['inspections'].copy()\n", + "violations = data['violations'].copy()\n", + "\n", + "print(f\"\\n✓ Loaded data:\")\n", + "print(f\" Inspections: {len(inspections):,} rows\")\n", + "print(f\" Violations: {len(violations):,} rows\")\n", + "print(f\"\\nInspections columns: {list(inspections.columns)}\")\n", + "print(f\"\\nViolations columns: {list(violations.columns)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "036d14fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered to 2015-2024:\n", + " Inspections: 1,642,060 rows\n", + " Violations: 173,028 rows\n", + "\n", + "✓ Created district-year panel:\n", + " Observations: 130\n", + " Districts: 13\n", + " Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024)]\n", + " Pre-2019 observations: 52\n", + " Post-2019 observations: 78\n", + "\n", + "================================================================================\n", + "DISTRICT-YEAR PANEL SUMMARY\n", + "================================================================================\n", + " district year total_inspections unique_wells compliant_inspections \\\n", + "0 01 2015 3498 2816 3027 \n", + "1 01 2016 6499 4055 5028 \n", + "2 01 2017 8649 6153 7613 \n", + "3 01 2018 10966 9109 9668 \n", + "4 01 2019 8097 6447 6818 \n", + "5 01 2020 10511 8716 9087 \n", + "6 01 2021 8586 6908 6870 \n", + "7 01 2022 12418 10193 9608 \n", + "8 01 2023 14573 11577 11879 \n", + "9 01 2024 16338 13038 13923 \n", + "10 02 2015 1174 921 1005 \n", + "11 02 2016 2936 2003 2098 \n", + "12 02 2017 5325 4639 4718 \n", + "13 02 2018 5913 5107 5317 \n", + "14 02 2019 4427 3696 3945 \n", + "15 02 2020 4713 3679 3789 \n", + "16 02 2021 5090 3929 4405 \n", + "17 02 2022 7290 5842 6578 \n", + "18 02 2023 9679 7936 8857 \n", + "19 02 2024 11632 8772 10831 \n", + "\n", + " unique_api_norm compliance_rate total_violations wells_with_violations \\\n", + "0 2816 86.5352 592 379 \n", + "1 4055 77.3657 1902 1009 \n", + "2 6153 88.0217 1439 767 \n", + "3 9109 88.1634 1771 997 \n", + "4 6447 84.2040 1506 902 \n", + "5 8716 86.4523 1816 1019 \n", + "6 6908 80.0140 2268 1220 \n", + "7 10193 77.3716 3030 1878 \n", + "8 11577 81.5138 2501 1508 \n", + "9 13038 85.2185 2208 1516 \n", + "10 921 85.6048 192 115 \n", + "11 2003 71.4578 1120 570 \n", + "12 4639 88.6009 642 431 \n", + "13 5107 89.9205 518 354 \n", + "14 3696 89.1123 434 271 \n", + "15 3679 80.3947 1106 621 \n", + "16 3929 86.5422 656 410 \n", + "17 5842 90.2332 665 447 \n", + "18 7936 91.5074 826 523 \n", + "19 8772 93.1138 804 495 \n", + "\n", + " major_violations compliant_on_reinsp enforced_violations \\\n", + "0 0 284 592 \n", + "1 0 472 1902 \n", + "2 0 740 1439 \n", + "3 0 1012 1771 \n", + "4 2 771 1506 \n", + "5 1 384 1816 \n", + "6 0 669 2268 \n", + "7 0 1653 3030 \n", + "8 4 1464 2501 \n", + "9 0 1135 2208 \n", + "10 0 112 192 \n", + "11 0 216 1120 \n", + "12 0 317 642 \n", + "13 2 184 518 \n", + "14 0 173 434 \n", + "15 1 196 1106 \n", + "16 0 220 656 \n", + "17 0 272 665 \n", + "18 3 366 826 \n", + "19 1 185 804 \n", + "\n", + " violations_per_inspection violation_rate post_2019 post_2019_bool \n", + "0 0.1692 16.9240 0 False \n", + "1 0.2927 29.2660 0 False \n", + "2 0.1664 16.6378 0 False \n", + "3 0.1615 16.1499 0 False \n", + "4 0.1860 18.5995 1 True \n", + "5 0.1728 17.2771 1 True \n", + "6 0.2642 26.4151 1 True \n", + "7 0.2440 24.4001 1 True \n", + "8 0.1716 17.1619 1 True \n", + "9 0.1351 13.5145 1 True \n", + "10 0.1635 16.3543 0 False \n", + "11 0.3815 38.1471 0 False \n", + "12 0.1206 12.0563 0 False \n", + "13 0.0876 8.7604 0 False \n", + "14 0.0980 9.8035 1 True \n", + "15 0.2347 23.4670 1 True \n", + "16 0.1289 12.8880 1 True \n", + "17 0.0912 9.1221 1 True \n", + "18 0.0853 8.5339 1 True \n", + "19 0.0691 6.9120 1 True \n" + ] + } + ], + "source": [ + "# Create district-year panel from the loaded data\n", + "\n", + "# Add year column to inspections and violations\n", + "inspections['year'] = pd.to_datetime(inspections['inspection_date']).dt.year\n", + "violations['year'] = pd.to_datetime(violations['violation_disc_date']).dt.year\n", + "\n", + "# Filter to analysis period 2015-2024\n", + "inspections = inspections[(inspections['year'] >= 2015) & (inspections['year'] <= 2024)]\n", + "violations = violations[(violations['year'] >= 2015) & (violations['year'] <= 2024)]\n", + "\n", + "print(f\"Filtered to 2015-2024:\")\n", + "print(f\" Inspections: {len(inspections):,} rows\")\n", + "print(f\" Violations: {len(violations):,} rows\")\n", + "\n", + "# Aggregate inspections by district-year\n", + "insp_agg = inspections.groupby(['district', 'year']).agg(\n", + " total_inspections=('api_norm', 'count'),\n", + " unique_wells=('api_norm', 'nunique'),\n", + " compliant_inspections=('compliance', lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).sum()),\n", + " unique_api_norm=('api_norm', 'nunique')\n", + ").reset_index()\n", + "\n", + "insp_agg['compliance_rate'] = (insp_agg['compliant_inspections'] / insp_agg['total_inspections']) * 100\n", + "\n", + "# Aggregate violations by district-year\n", + "viol_agg = violations.groupby(['district', 'year']).agg(\n", + " total_violations=('api_norm', 'count'),\n", + " wells_with_violations=('api_norm', 'nunique'),\n", + " major_violations=('major_viol_ind', lambda x: (x == 'Y').sum()),\n", + " compliant_on_reinsp=('compliant_on_reinsp', lambda x: (x == 'Y').sum()),\n", + " enforced_violations=('last_enf_action', lambda x: x.notna().sum())\n", + ").reset_index()\n", + "\n", + "# Merge inspections and violations\n", + "district_year_df = pd.merge(\n", + " insp_agg,\n", + " viol_agg,\n", + " on=['district', 'year'],\n", + " how='left'\n", + ")\n", + "\n", + "# Fill NAs with 0\n", + "viol_cols = ['total_violations', 'wells_with_violations', 'major_violations',\n", + " 'compliant_on_reinsp', 'enforced_violations']\n", + "for col in viol_cols:\n", + " district_year_df[col] = district_year_df[col].fillna(0)\n", + "\n", + "# Add key metrics\n", + "district_year_df['violations_per_inspection'] = (\n", + " district_year_df['total_violations'] / district_year_df['total_inspections']\n", + ")\n", + "district_year_df['violation_rate'] = (\n", + " district_year_df['total_violations'] / district_year_df['total_inspections'] * 100\n", + ")\n", + "\n", + "# Add treatment indicator\n", + "district_year_df['post_2019'] = (district_year_df['year'] >= 2019).astype(int)\n", + "district_year_df['post_2019_bool'] = district_year_df['year'] >= 2019\n", + "\n", + "print()\n", + "print(\"✓ Created district-year panel:\")\n", + "print(f\" Observations: {len(district_year_df)}\")\n", + "print(f\" Districts: {district_year_df['district'].nunique()}\")\n", + "print(f\" Years: {sorted(district_year_df['year'].unique())}\")\n", + "print(f\" Pre-2019 observations: {(district_year_df['year'] < 2019).sum()}\")\n", + "print(f\" Post-2019 observations: {(district_year_df['year'] >= 2019).sum()}\")\n", + "\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"DISTRICT-YEAR PANEL SUMMARY\")\n", + "print(\"=\"*80)\n", + "print(district_year_df.head(20))\n" + ] + }, + { + "cell_type": "markdown", + "id": "510c60ba", + "metadata": {}, + "source": [ + "## Part 2: Regulatory Pipeline Analysis\n", + "\n", + "**Key insight**: Wells have **repeated inspections** over time, creating a regulatory pipeline:\n", + "1. **Inspection** → 2. **Violation discovered** (if any) → 3. **Enforcement action** → 4. **Re-inspection** → 5. **Compliance verified**\n", + "\n", + "We need to track:\n", + "- **Time between events**: inspection → violation → enforcement → resolution\n", + "- **Repeat patterns**: wells with multiple violations, chronic non-compliance\n", + "- **Treatment effects on the pipeline**: Did 2019 disclosure change the speed or effectiveness of enforcement?\n", + "\n", + "This requires **well-level panel data** with time-varying outcomes, not just district-year aggregates." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2e605976", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "VIOLATION ENFORCEMENT ACTIONS ANALYSIS\n", + "================================================================================\n", + "\n", + "1. ENFORCEMENT ACTION TYPES:\n", + "--------------------------------------------------------------------------------\n", + "last_enf_action\n", + "Notice of Violation 127404\n", + "Referred to Austin Field Ops for possible legal enforcement 22332\n", + "Referred to State-Managed Plugging 17314\n", + "Issued a Severance/Seal Order 4930\n", + "Violation Corrected 533\n", + "Referred to State-Managed Cleanup Program 515\n", + "Name: count, dtype: int64\n", + "\n", + "Total unique enforcement action types: 6\n", + "Violations with enforcement action: 173,028\n", + "Violations without enforcement action: 0\n", + "\n", + "2. VIOLATION SEVERITY:\n", + "--------------------------------------------------------------------------------\n", + "major_viol_ind\n", + "N 172973\n", + "Y 55\n", + "Name: count, dtype: int64\n", + "\n", + "Major violations: 0.0%\n", + "\n", + "3. COMPLIANCE ON RE-INSPECTION:\n", + "--------------------------------------------------------------------------------\n", + "compliant_on_reinsp\n", + "Y 100634\n", + "-- 57679\n", + "N 14715\n", + "Name: count, dtype: int64\n", + "\n", + "4. TIME TO ENFORCEMENT:\n", + "--------------------------------------------------------------------------------\n", + "Violations with enforcement timing data: 173,028\n", + "Mean days to enforcement: 138.6\n", + "Median days to enforcement: 16.0\n", + "90th percentile: 429.0 days\n", + "Max days to enforcement: 3640 days\n", + "\n", + "Distribution of enforcement timing:\n", + "time_category\n", + "<30 days 98116\n", + "30-90 days 26922\n", + "90-180 days 14890\n", + "6mo-1yr 12755\n", + "1-2 years 10384\n", + ">2 years 9594\n", + "Name: count, dtype: int64\n", + "\n", + "5. ENFORCEMENT RATES BY DISTRICT:\n", + "--------------------------------------------------------------------------------\n", + " enforcement_rate_pct total_violations\n", + "district \n", + "01 100.0000 19033\n", + "02 100.0000 6963\n", + "03 100.0000 7973\n", + "04 100.0000 5893\n", + "05 100.0000 3880\n", + "06 100.0000 9632\n", + "08 100.0000 28235\n", + "09 100.0000 36940\n", + "10 100.0000 11128\n", + "6E 100.0000 4302\n", + "7B 100.0000 18510\n", + "7C 100.0000 12341\n", + "8A 100.0000 8198\n", + "\n", + "✓ Enforcement actions analysis complete\n" + ] + } + ], + "source": [ + "# Examine enforcement action types and temporal patterns in violations data\n", + "\n", + "print(\"=\"*80)\n", + "print(\"VIOLATION ENFORCEMENT ACTIONS ANALYSIS\")\n", + "print(\"=\"*80)\n", + "\n", + "# 1. Types of enforcement actions\n", + "print(\"\\n1. ENFORCEMENT ACTION TYPES:\")\n", + "print(\"-\"*80)\n", + "enf_actions = violations['last_enf_action'].value_counts(dropna=False)\n", + "print(enf_actions)\n", + "print(f\"\\nTotal unique enforcement action types: {violations['last_enf_action'].nunique()}\")\n", + "print(f\"Violations with enforcement action: {violations['last_enf_action'].notna().sum():,}\")\n", + "print(f\"Violations without enforcement action: {violations['last_enf_action'].isna().sum():,}\")\n", + "\n", + "# 2. Major vs minor violations\n", + "print(\"\\n2. VIOLATION SEVERITY:\")\n", + "print(\"-\"*80)\n", + "major_viol = violations['major_viol_ind'].value_counts(dropna=False)\n", + "print(major_viol)\n", + "major_pct = violations['major_viol_ind'].value_counts(normalize=True) * 100\n", + "print(f\"\\nMajor violations: {major_pct.get('Y', 0):.1f}%\")\n", + "\n", + "# 3. Compliance on re-inspection\n", + "print(\"\\n3. COMPLIANCE ON RE-INSPECTION:\")\n", + "print(\"-\"*80)\n", + "reinsp_compliance = violations['compliant_on_reinsp'].value_counts(dropna=False)\n", + "print(reinsp_compliance)\n", + "\n", + "# 4. Temporal gaps: violation discovery to enforcement\n", + "print(\"\\n4. TIME TO ENFORCEMENT:\")\n", + "print(\"-\"*80)\n", + "violations['violation_disc_date'] = pd.to_datetime(violations['violation_disc_date'], errors='coerce')\n", + "violations['last_enf_action_date'] = pd.to_datetime(violations['last_enf_action_date'], errors='coerce')\n", + "\n", + "violations['days_to_enforcement'] = (violations['last_enf_action_date'] - \n", + " violations['violation_disc_date']).dt.days\n", + "\n", + "# Only for violations that got enforcement\n", + "enforced = violations[violations['days_to_enforcement'].notna() & \n", + " (violations['days_to_enforcement'] >= 0)]\n", + "\n", + "if len(enforced) > 0:\n", + " print(f\"Violations with enforcement timing data: {len(enforced):,}\")\n", + " print(f\"Mean days to enforcement: {enforced['days_to_enforcement'].mean():.1f}\")\n", + " print(f\"Median days to enforcement: {enforced['days_to_enforcement'].median():.1f}\")\n", + " print(f\"90th percentile: {enforced['days_to_enforcement'].quantile(0.9):.1f} days\")\n", + " print(f\"Max days to enforcement: {enforced['days_to_enforcement'].max():.0f} days\")\n", + " \n", + " # Distribution\n", + " print(\"\\nDistribution of enforcement timing:\")\n", + " bins = [0, 30, 90, 180, 365, 730, np.inf]\n", + " labels = ['<30 days', '30-90 days', '90-180 days', '6mo-1yr', '1-2 years', '>2 years']\n", + " enforced['time_category'] = pd.cut(enforced['days_to_enforcement'], bins=bins, labels=labels)\n", + " print(enforced['time_category'].value_counts().sort_index())\n", + "\n", + "# 5. Enforcement rates by district\n", + "print(\"\\n5. ENFORCEMENT RATES BY DISTRICT:\")\n", + "print(\"-\"*80)\n", + "district_enf = violations.groupby('district').agg({\n", + " 'last_enf_action': lambda x: (x.notna().sum() / len(x) * 100),\n", + " 'api_norm': 'count'\n", + "}).rename(columns={'last_enf_action': 'enforcement_rate_pct', 'api_norm': 'total_violations'})\n", + "district_enf = district_enf.sort_values('enforcement_rate_pct', ascending=False)\n", + "print(district_enf.to_string())\n", + "\n", + "print(\"\\n✓ Enforcement actions analysis complete\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f8a814c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building well-level panel with regulatory pipeline metrics...\n", + "\n", + "First, checking API number formats...\n", + "Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection', 'year']\n", + "Sample inspection row:\n", + " api_norm: 10130031 (type: )\n", + "\n", + "Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number', 'year', 'days_to_enforcement']\n", + "Sample violation row:\n", + " api_norm: 46102018 (type: )\n", + "\n", + "✓ Using api_norm as well identifier\n", + " Unique wells in inspections: 413,185\n", + " Unique wells in violations: 74,962\n", + "\n", + "✓ Well-level panel created\n", + " Total wells: 413,185\n", + " Wells with violations: 74,962 (18.1%)\n", + " Repeat violators: 37,461 (9.1%)\n", + " Avg inspections per well: 4.0\n", + " Avg violations per well: 0.42\n", + "\n", + "Sample of well panel:\n", + " well_id total_violations total_inspections repeat_violator ever_violated district violations_per_inspection avg_days_to_enforcement first_inspection last_inspection first_violation years_active\n", + "0 10130031 1 2 0 1 8A 0.5000 55.0000 2016-03-04 2016-07-26 2016-03-04 0.3943\n", + "1 10130036 0 2 0 0 8A 0.0000 NaN 2017-12-06 2022-09-19 NaT 4.7858\n", + "2 10130045 0 3 0 0 8A 0.0000 NaN 2017-12-06 2022-09-19 NaT 4.7858\n", + "3 10130054 4 2 1 1 8A 2.0000 55.0000 2016-03-04 2016-07-26 2016-03-04 0.3943\n", + "4 10130055 2 4 1 1 8A 0.5000 55.0000 2016-03-04 2024-04-04 2016-03-04 8.0849\n", + "5 10130080 0 3 0 0 8A 0.0000 NaN 2017-08-18 2024-10-13 NaT 7.1540\n", + "6 10130086 1 3 0 1 8A 0.3333 9.0000 2017-08-18 2024-10-14 2024-10-14 7.1567\n", + "7 10130096 0 1 0 0 8A 0.0000 NaN 2017-08-18 2017-08-18 NaT 0.0000\n", + "8 10130098 0 2 0 0 8A 0.0000 NaN 2017-08-18 2022-04-21 NaT 4.6735\n", + "9 10130104 2 7 1 1 8A 0.2857 987.0000 2016-02-03 2022-08-17 2016-02-03 6.5352\n" + ] + } + ], + "source": [ + "# Create well-level panel with regulatory pipeline metrics (EFFICIENT VERSION)\n", + "\n", + "print(\"Building well-level panel with regulatory pipeline metrics...\")\n", + "print(\"\\nFirst, checking API number formats...\")\n", + "\n", + "# Check what we actually have\n", + "print(f\"Inspections columns: {inspections.columns.tolist()}\")\n", + "print(f\"Sample inspection row:\")\n", + "if len(inspections) > 0:\n", + " sample = inspections.iloc[0]\n", + " for col in ['api_norm']:\n", + " if col in inspections.columns:\n", + " print(f\" {col}: {sample[col]} (type: {type(sample[col])})\")\n", + "\n", + "print(f\"\\nViolations columns: {violations.columns.tolist()}\")\n", + "print(f\"Sample violation row:\")\n", + "if len(violations) > 0:\n", + " sample = violations.iloc[0]\n", + " for col in ['api_norm']:\n", + " if col in violations.columns:\n", + " print(f\" {col}: {sample[col]} (type: {type(sample[col])})\")\n", + "\n", + "# Use api_norm directly as the normalized well identifier\n", + "inspections['well_id'] = inspections['api_norm'].astype(str).str.strip()\n", + "violations['well_id'] = violations['api_norm'].astype(str).str.strip()\n", + "\n", + "print(f\"\\n✓ Using api_norm as well identifier\")\n", + "print(f\" Unique wells in inspections: {inspections['well_id'].nunique():,}\")\n", + "print(f\" Unique wells in violations: {violations['well_id'].nunique():,}\")\n", + "\n", + "# Prepare inspections data\n", + "inspections_sorted = inspections.copy()\n", + "inspections_sorted['inspection_date'] = pd.to_datetime(inspections_sorted['inspection_date'])\n", + "inspections_sorted['year'] = inspections_sorted['inspection_date'].dt.year\n", + "\n", + "# Prepare violations data\n", + "violations_sorted = violations.copy()\n", + "violations_sorted['violation_disc_date'] = pd.to_datetime(violations_sorted['violation_disc_date'])\n", + "violations_sorted['last_enf_action_date'] = pd.to_datetime(violations_sorted['last_enf_action_date'])\n", + "violations_sorted['year'] = violations_sorted['violation_disc_date'].dt.year\n", + "\n", + "# Calculate enforcement timing\n", + "violations_sorted['days_to_enforcement'] = (\n", + " violations_sorted['last_enf_action_date'] - violations_sorted['violation_disc_date']\n", + ").dt.days\n", + "\n", + "# Well-level aggregates (static characteristics)\n", + "viol_per_well = violations_sorted.groupby('well_id').size()\n", + "insp_per_well = inspections_sorted.groupby('well_id').size()\n", + "\n", + "well_panel = pd.DataFrame({\n", + " 'well_id': viol_per_well.index.union(insp_per_well.index),\n", + "})\n", + "\n", + "well_panel = well_panel.merge(\n", + " viol_per_well.rename('total_violations'),\n", + " left_on='well_id', right_index=True, how='left'\n", + ").merge(\n", + " insp_per_well.rename('total_inspections'),\n", + " left_on='well_id', right_index=True, how='left'\n", + ")\n", + "\n", + "well_panel['total_violations'] = well_panel['total_violations'].fillna(0).astype(int)\n", + "well_panel['total_inspections'] = well_panel['total_inspections'].fillna(0).astype(int)\n", + "\n", + "# Identify repeat violators and wells with violations\n", + "well_panel['repeat_violator'] = (well_panel['total_violations'] > 1).astype(int)\n", + "well_panel['ever_violated'] = (well_panel['total_violations'] > 0).astype(int)\n", + "\n", + "# Get district for each well (from inspections - use most common district)\n", + "well_district = inspections_sorted.groupby('well_id')['district'].agg(\n", + " lambda x: x.mode()[0] if len(x.mode()) > 0 else x.iloc[0]\n", + ").rename('district')\n", + "well_panel = well_panel.merge(well_district, left_on='well_id', right_index=True, how='left')\n", + "\n", + "# Violation rate per inspection\n", + "well_panel['violations_per_inspection'] = (\n", + " well_panel['total_violations'] / well_panel['total_inspections'].replace(0, np.nan)\n", + ")\n", + "\n", + "# Average enforcement speed for wells with violations\n", + "avg_enf_time = violations_sorted.groupby('well_id')['days_to_enforcement'].mean().rename('avg_days_to_enforcement')\n", + "well_panel = well_panel.merge(avg_enf_time, left_on='well_id', right_index=True, how='left')\n", + "\n", + "# First and last activity dates\n", + "first_insp = inspections_sorted.groupby('well_id')['inspection_date'].min().rename('first_inspection')\n", + "last_insp = inspections_sorted.groupby('well_id')['inspection_date'].max().rename('last_inspection')\n", + "first_viol = violations_sorted.groupby('well_id')['violation_disc_date'].min().rename('first_violation')\n", + "\n", + "well_panel = well_panel.merge(first_insp, left_on='well_id', right_index=True, how='left')\n", + "well_panel = well_panel.merge(last_insp, left_on='well_id', right_index=True, how='left')\n", + "well_panel = well_panel.merge(first_viol, left_on='well_id', right_index=True, how='left')\n", + "\n", + "# Calculate well lifespan in data\n", + "well_panel['years_active'] = (\n", + " (well_panel['last_inspection'] - well_panel['first_inspection']).dt.days / 365.25\n", + ")\n", + "\n", + "print(\"\\n✓ Well-level panel created\")\n", + "print(f\" Total wells: {len(well_panel):,}\")\n", + "print(f\" Wells with violations: {well_panel['ever_violated'].sum():,} ({well_panel['ever_violated'].mean()*100:.1f}%)\")\n", + "print(f\" Repeat violators: {well_panel['repeat_violator'].sum():,} ({well_panel['repeat_violator'].mean()*100:.1f}%)\")\n", + "print(f\" Avg inspections per well: {well_panel['total_inspections'].mean():.1f}\")\n", + "print(f\" Avg violations per well: {well_panel['total_violations'].mean():.2f}\")\n", + "\n", + "print(\"\\nSample of well panel:\")\n", + "print(well_panel.head(10).to_string())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cfc295ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating district-year panel with pipeline metrics...\n", + "\n", + "✓ District-year panel with PIPELINE metrics:\n", + " Observations: 130\n", + " Districts: 13\n", + " Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024)]\n", + "\n", + "================================================================================\n", + "PIPELINE METRICS: Sample rows\n", + "================================================================================\n", + "district year total_inspections unique_wells compliance_rate avg_days_between_insp median_days_between_insp total_violations wells_with_violations share_major_violations avg_days_to_enforcement median_days_to_enforcement enforcement_rate resolution_rate violations_per_inspection violation_discovery_rate post_2019 year_from_policy offshore_jurisdiction\n", + " 01 2015 3498 2816 86.5352 38.8023 35.0000 592 379 0.0000 124.9122 19.0000 100.0000 47.9730 0.1692 13.4588 0 -4 0\n", + " 01 2016 6499 4055 77.3657 109.3848 85.0000 1902 1009 0.0000 230.0910 20.0000 100.0000 24.8160 0.2927 24.8829 0 -3 0\n", + " 01 2017 8649 6153 88.0217 220.8480 160.0000 1439 767 0.0000 326.0945 33.0000 100.0000 51.4246 0.1664 12.4655 0 -2 0\n", + " 01 2018 10966 9109 88.1634 263.2386 173.0000 1771 997 0.0000 290.2191 82.0000 100.0000 57.1429 0.1615 10.9452 0 -1 0\n", + " 01 2019 8097 6447 84.2040 358.4922 231.0000 1506 902 0.1328 261.2776 73.0000 100.0000 51.1952 0.1860 13.9910 1 0 0\n", + " 01 2020 10511 8716 86.4523 560.7332 337.0000 1816 1019 0.0551 318.0391 260.0000 100.0000 21.1454 0.1728 11.6911 1 1 0\n", + " 01 2021 8586 6908 80.0140 754.0327 595.0000 2268 1220 0.0000 164.8298 89.0000 100.0000 29.4974 0.2642 17.6607 1 2 0\n", + " 01 2022 12418 10193 77.3716 1015.8347 1211.0000 3030 1878 0.0000 142.2416 40.0000 100.0000 54.5545 0.2440 18.4244 1 3 0\n", + " 01 2023 14573 11577 81.5138 866.2592 752.0000 2501 1508 0.1599 173.2783 88.0000 100.0000 58.5366 0.1716 13.0258 1 4 0\n", + " 01 2024 16338 13038 85.2185 758.6809 570.0000 2208 1516 0.0000 93.9008 28.0000 100.0000 51.4040 0.1351 11.6276 1 5 0\n", + " 02 2015 1174 921 85.6048 34.1107 28.0000 192 115 0.0000 171.5260 41.0000 100.0000 58.3333 0.1635 12.4864 0 -4 1\n", + " 02 2016 2936 2003 71.4578 110.6093 77.0000 1120 570 0.0000 273.4232 49.0000 100.0000 19.2857 0.3815 28.4573 0 -3 1\n", + " 02 2017 5325 4639 88.6009 276.1325 251.0000 642 431 0.0000 270.1137 151.5000 100.0000 49.3769 0.1206 9.2908 0 -2 1\n", + " 02 2018 5913 5107 89.9205 292.9596 214.0000 518 354 0.3861 222.4208 49.5000 100.0000 35.5212 0.0876 6.9317 0 -1 1\n", + " 02 2019 4427 3696 89.1123 407.7089 370.0000 434 271 0.0000 275.1613 119.5000 100.0000 39.8618 0.0980 7.3323 1 0 1\n" + ] + } + ], + "source": [ + "# Create district-year panel with PIPELINE metrics (not just counts)\n", + "# Focus on: speed, effectiveness, and dynamics of the regulatory process\n", + "\n", + "print(\"Creating district-year panel with pipeline metrics...\")\n", + "\n", + "# Group by district-year and calculate pipeline performance indicators\n", + "pipeline_metrics = inspections_sorted.groupby(['district', 'year']).agg(\n", + " total_inspections=('well_id', 'count'),\n", + " unique_wells=('well_id', 'nunique'),\n", + " compliance_rate=('compliance', lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).mean() * 100),\n", + " avg_days_between_insp=('days_since_last_inspection', 'mean'),\n", + " median_days_between_insp=('days_since_last_inspection', 'median')\n", + ").reset_index()\n", + "\n", + "# Add violation pipeline metrics\n", + "viol_pipeline = violations_sorted.groupby(['district', 'year']).agg(\n", + " total_violations=('well_id', 'count'),\n", + " wells_with_violations=('well_id', 'nunique'),\n", + " share_major_violations=('major_viol_ind', lambda x: (x == 'Y').mean() * 100),\n", + " avg_days_to_enforcement=('days_to_enforcement', 'mean'),\n", + " median_days_to_enforcement=('days_to_enforcement', 'median'),\n", + " enforcement_rate=('last_enf_action', lambda x: x.notna().mean() * 100),\n", + " resolution_rate=('compliant_on_reinsp', lambda x: (x == 'Y').mean() * 100)\n", + ").reset_index()\n", + "\n", + "# Merge\n", + "district_year_panel = pd.merge(pipeline_metrics, viol_pipeline, \n", + " on=['district', 'year'], how='left')\n", + "\n", + "# Fill NAs for districts with no violations in that year\n", + "viol_cols = ['total_violations', 'wells_with_violations', 'share_major_violations',\n", + " 'avg_days_to_enforcement', 'median_days_to_enforcement', \n", + " 'enforcement_rate', 'resolution_rate']\n", + "for col in viol_cols:\n", + " district_year_panel[col] = district_year_panel[col].fillna(0)\n", + "\n", + "# Calculate key pipeline ratios\n", + "district_year_panel['violations_per_inspection'] = (\n", + " district_year_panel['total_violations'] / district_year_panel['total_inspections']\n", + ")\n", + "district_year_panel['violation_discovery_rate'] = (\n", + " district_year_panel['wells_with_violations'] / district_year_panel['unique_wells'] * 100\n", + ")\n", + "\n", + "# Treatment indicator\n", + "district_year_panel['post_2019'] = (district_year_panel['year'] >= 2019).astype(int)\n", + "district_year_panel['year_from_policy'] = district_year_panel['year'] - 2019 # for event study\n", + "# Add offshore-jurisdiction indicator (districts with offshore + onshore oversight)\n", + "offshore_jurisdiction_districts = ['02', '03', '04']\n", + "district_year_panel['offshore_jurisdiction'] = district_year_panel['district'].isin(offshore_jurisdiction_districts).astype(int)\n", + "\n", + "print(f\"\\n✓ District-year panel with PIPELINE metrics:\")\n", + "print(f\" Observations: {len(district_year_panel)}\")\n", + "print(f\" Districts: {district_year_panel['district'].nunique()}\")\n", + "print(f\" Years: {sorted(district_year_panel['year'].unique())}\")\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"PIPELINE METRICS: Sample rows\")\n", + "print(\"=\"*80)\n", + "print(district_year_panel.head(15).to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "id": "5f6f45dd", + "metadata": {}, + "source": [ + "### Pre/Post 2019 Comparison: Did the Disclosure Policy Change the Pipeline?\n", + "\n", + "Compare regulatory pipeline metrics before and after the January 2019 disclosure policy." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "eebc7ad8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "PRE vs POST 2019 COMPARISON: Pipeline Metrics\n", + "================================================================================\n", + " Pre-2019 Post-2019 Change Pct_Change\n", + "total_inspections 8782.5577 15197.0128 6414.4551 73.0363\n", + "unique_wells 6621.8654 10939.8974 4318.0321 65.2087\n", + "avg_days_between_insp 163.8185 577.2449 413.4264 252.3687\n", + "compliance_rate 87.1610 89.5490 2.3881 2.7398\n", + "violation_discovery_rate 12.1762 8.3792 -3.7970 -31.1840\n", + "violations_per_inspection 0.1488 0.0965 -0.0523 -35.1660\n", + "avg_days_to_enforcement 174.2728 124.5786 -49.6943 -28.5152\n", + "median_days_to_enforcement 68.4135 50.4103 -18.0032 -26.3153\n", + "enforcement_rate 100.0000 100.0000 0.0000 0.0000\n", + "resolution_rate 52.2074 61.1763 8.9688 17.1792\n", + "share_major_violations 0.0077 0.1239 0.1162 1507.8328\n", + "\n", + "================================================================================\n", + "KEY FINDINGS:\n", + "================================================================================\n", + "⚠ Inspection frequency DECREASED substantially: 252.4% increase in days between inspections\n", + "→ Compliance rate INCREASED by 2.4 percentage points\n", + "→ Enforcement became FASTER by 49.7 days on average\n", + "→ Resolution rate INCREASED by 9.0 percentage points\n" + ] + } + ], + "source": [ + "# Compare pre vs post 2019 across all pipeline metrics\n", + "\n", + "pre_post = district_year_panel.groupby('post_2019').agg({\n", + " # Inspection intensity\n", + " 'total_inspections': 'mean',\n", + " 'unique_wells': 'mean',\n", + " 'avg_days_between_insp': 'mean',\n", + " \n", + " # Compliance outcomes\n", + " 'compliance_rate': 'mean',\n", + " 'violation_discovery_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " \n", + " # Enforcement speed and effectiveness\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'median_days_to_enforcement': 'mean',\n", + " 'enforcement_rate': 'mean',\n", + " 'resolution_rate': 'mean',\n", + " \n", + " # Violation severity\n", + " 'share_major_violations': 'mean'\n", + "}).T\n", + "\n", + "pre_post.columns = ['Pre-2019', 'Post-2019']\n", + "pre_post['Change'] = pre_post['Post-2019'] - pre_post['Pre-2019']\n", + "pre_post['Pct_Change'] = (pre_post['Change'] / pre_post['Pre-2019'].replace(0, np.nan) * 100)\n", + "\n", + "print(\"=\"*80)\n", + "print(\"PRE vs POST 2019 COMPARISON: Pipeline Metrics\")\n", + "print(\"=\"*80)\n", + "print(pre_post.to_string())\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"KEY FINDINGS:\")\n", + "print(\"=\"*80)\n", + "\n", + "# Highlight major changes\n", + "if pre_post.loc['avg_days_between_insp', 'Pct_Change'] > 50:\n", + " print(f\"⚠ Inspection frequency DECREASED substantially: {pre_post.loc['avg_days_between_insp', 'Pct_Change']:.1f}% increase in days between inspections\")\n", + " \n", + "if abs(pre_post.loc['compliance_rate', 'Change']) > 2:\n", + " direction = \"INCREASED\" if pre_post.loc['compliance_rate', 'Change'] > 0 else \"DECREASED\"\n", + " print(f\"→ Compliance rate {direction} by {abs(pre_post.loc['compliance_rate', 'Change']):.1f} percentage points\")\n", + " \n", + "if abs(pre_post.loc['avg_days_to_enforcement', 'Change']) > 10:\n", + " direction = \"SLOWER\" if pre_post.loc['avg_days_to_enforcement', 'Change'] > 0 else \"FASTER\"\n", + " print(f\"→ Enforcement became {direction} by {abs(pre_post.loc['avg_days_to_enforcement', 'Change']):.1f} days on average\")\n", + " \n", + "if abs(pre_post.loc['resolution_rate', 'Change']) > 5:\n", + " direction = \"INCREASED\" if pre_post.loc['resolution_rate', 'Change'] > 0 else \"DECREASED\"\n", + " print(f\"→ Resolution rate {direction} by {abs(pre_post.loc['resolution_rate', 'Change']):.1f} percentage points\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c2095d89", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Pipeline visualization complete\n", + " Saved as 'pipeline_trends_over_time.png'\n" + ] + } + ], + "source": [ + "# Visualize the regulatory pipeline over time\n", + "\n", + "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n", + "fig.suptitle('Regulatory Pipeline Dynamics: 2015-2024\\n(Vertical line = 2019 disclosure policy)', \n", + " fontsize=16, fontweight='bold', y=0.995)\n", + "\n", + "# Annual averages across all districts\n", + "annual_avg = district_year_panel.groupby('year').agg({\n", + " 'compliance_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'resolution_rate': 'mean',\n", + " 'avg_days_between_insp': 'mean',\n", + " 'violation_discovery_rate': 'mean'\n", + "})\n", + "\n", + "# Plot 1: Compliance rate\n", + "axes[0, 0].plot(annual_avg.index, annual_avg['compliance_rate'], 'o-', linewidth=2.5, markersize=8, color='steelblue')\n", + "axes[0, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2, label='2019 Policy')\n", + "axes[0, 0].set_xlabel('Year', fontsize=11)\n", + "axes[0, 0].set_ylabel('Compliance Rate (%)', fontsize=11)\n", + "axes[0, 0].set_title('Compliance Rate at Inspection', fontsize=12, fontweight='bold')\n", + "axes[0, 0].legend()\n", + "axes[0, 0].grid(True, alpha=0.3)\n", + "\n", + "# Plot 2: Violations per inspection\n", + "axes[0, 1].plot(annual_avg.index, annual_avg['violations_per_inspection'], 'o-', \n", + " linewidth=2.5, markersize=8, color='darkorange')\n", + "axes[0, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[0, 1].set_xlabel('Year', fontsize=11)\n", + "axes[0, 1].set_ylabel('Violations per Inspection', fontsize=11)\n", + "axes[0, 1].set_title('Violation Discovery Rate', fontsize=12, fontweight='bold')\n", + "axes[0, 1].grid(True, alpha=0.3)\n", + "\n", + "# Plot 3: Days to enforcement\n", + "axes[0, 2].plot(annual_avg.index, annual_avg['avg_days_to_enforcement'], 'o-', \n", + " linewidth=2.5, markersize=8, color='darkgreen')\n", + "axes[0, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[0, 2].set_xlabel('Year', fontsize=11)\n", + "axes[0, 2].set_ylabel('Days', fontsize=11)\n", + "axes[0, 2].set_title('Average Days to Enforcement', fontsize=12, fontweight='bold')\n", + "axes[0, 2].grid(True, alpha=0.3)\n", + "\n", + "# Plot 4: Resolution rate\n", + "axes[1, 0].plot(annual_avg.index, annual_avg['resolution_rate'], 'o-', \n", + " linewidth=2.5, markersize=8, color='purple')\n", + "axes[1, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[1, 0].set_xlabel('Year', fontsize=11)\n", + "axes[1, 0].set_ylabel('Resolution Rate (%)', fontsize=11)\n", + "axes[1, 0].set_title('Violations Resolved on Re-inspection', fontsize=12, fontweight='bold')\n", + "axes[1, 0].grid(True, alpha=0.3)\n", + "\n", + "# Plot 5: Days between inspections (NEW - shows inspection frequency decline)\n", + "axes[1, 1].plot(annual_avg.index, annual_avg['avg_days_between_insp'], 'o-', \n", + " linewidth=2.5, markersize=8, color='brown')\n", + "axes[1, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[1, 1].set_xlabel('Year', fontsize=11)\n", + "axes[1, 1].set_ylabel('Days', fontsize=11)\n", + "axes[1, 1].set_title('Average Days Between Inspections', fontsize=12, fontweight='bold')\n", + "axes[1, 1].grid(True, alpha=0.3)\n", + "\n", + "# Plot 6: Violation discovery rate (% of wells with violations)\n", + "axes[1, 2].plot(annual_avg.index, annual_avg['violation_discovery_rate'], 'o-', \n", + " linewidth=2.5, markersize=8, color='teal')\n", + "axes[1, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n", + "axes[1, 2].set_xlabel('Year', fontsize=11)\n", + "axes[1, 2].set_ylabel('% of Wells', fontsize=11)\n", + "axes[1, 2].set_title('Share of Inspected Wells with Violations', fontsize=12, fontweight='bold')\n", + "axes[1, 2].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('pipeline_trends_over_time.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(\"\\n✓ Pipeline visualization complete\")\n", + "print(\" Saved as 'pipeline_trends_over_time.png'\")" + ] + }, + { + "cell_type": "markdown", + "id": "661851e9", + "metadata": {}, + "source": [ + "## Part 3: Corrected Panel Models (Two-Way FE + Offshore Controls)\n", + "\n", + "### Econometric Approach (Corrected)\n", + "\n", + "Because all districts are exposed to the 2019 policy, core models include district and year fixed effects to absorb statewide shocks and common annual changes.\n", + "\n", + "We estimate:\n", + "\n", + "$$Y_{dt} = \\alpha_d + \\gamma_t + \\beta_1(\\text{Post2019}_t \\times \\text{OffshoreJurisdiction}_d) + \\epsilon_{dt}$$\n", + "\n", + "and district-specific heterogeneity:\n", + "\n", + "$$Y_{dt} = \\alpha_d + \\gamma_t + \\sum_d \\delta_d(\\text{District}_d \\times \\text{Post2019}_t) + \\beta_1(\\text{Post2019}_t \\times \\text{OffshoreJurisdiction}_d) + \\epsilon_{dt}$$\n", + "\n", + "Where:\n", + "- $\\alpha_d$: district fixed effects\n", + "- $\\gamma_t$: year fixed effects\n", + "- OffshoreJurisdiction = 1 for districts 02, 03, 04\n", + "\n", + "Interpretation focus:\n", + "1. Differential post-2019 effects for offshore-jurisdiction districts\n", + "2. District-specific post effects net of statewide year shocks\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "38b165a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "CORRECTED TWO-WAY FE MODELS: Days to Enforcement\n", + "================================================================================\n", + "\n", + "Regression sample: 130 observations\n", + "Districts: 13\n", + "Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024)]\n", + "Offshore-jurisdiction districts (02/03/04): 3\n", + "\n", + "================================================================================\n", + "Model 1: TWFE + Offshore-Jurisdiction Differential Effect\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_days_to_enf R-squared: 0.699\n", + "Model: OLS Adj. R-squared: 0.637\n", + "Method: Least Squares F-statistic: 11.41\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 0.000111\n", + "Time: 16:16:22 Log-Likelihood: -77.558\n", + "No. Observations: 130 AIC: 201.1\n", + "Df Residuals: 107 BIC: 267.1\n", + "Df Model: 22 \n", + "Covariance Type: cluster \n", + "===================================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------------------\n", + "Intercept 5.2494 0.176 29.845 0.000 4.905 5.594\n", + "C(district)[T.02] -0.5017 0.226 -2.216 0.027 -0.946 -0.058\n", + "C(district)[T.03] -1.1813 0.226 -5.217 0.000 -1.625 -0.738\n", + "C(district)[T.04] -1.1943 0.226 -5.274 0.000 -1.638 -0.750\n", + "C(district)[T.05] 0.0513 1.45e-15 3.53e+13 0.000 0.051 0.051\n", + "C(district)[T.06] 0.1766 1.49e-15 1.18e+14 0.000 0.177 0.177\n", + "C(district)[T.08] -0.9013 1.13e-15 -7.95e+14 0.000 -0.901 -0.901\n", + "C(district)[T.09] -0.5575 1.83e-15 -3.04e+14 0.000 -0.558 -0.558\n", + "C(district)[T.10] -1.1494 1.19e-15 -9.62e+14 0.000 -1.149 -1.149\n", + "C(district)[T.6E] 0.1071 2.23e-15 4.8e+13 0.000 0.107 0.107\n", + "C(district)[T.7B] -1.8730 1.33e-15 -1.41e+15 0.000 -1.873 -1.873\n", + "C(district)[T.7C] -1.2388 1.87e-15 -6.62e+14 0.000 -1.239 -1.239\n", + "C(district)[T.8A] -1.0810 1.55e-15 -6.96e+14 0.000 -1.081 -1.081\n", + "C(year)[T.2016] 0.1233 0.171 0.720 0.471 -0.212 0.459\n", + "C(year)[T.2017] 0.4207 0.215 1.958 0.050 -0.000 0.842\n", + "C(year)[T.2018] 0.4592 0.252 1.823 0.068 -0.035 0.953\n", + "C(year)[T.2019] 0.1972 0.280 0.705 0.481 -0.351 0.745\n", + "C(year)[T.2020] 0.1455 0.254 0.572 0.567 -0.353 0.644\n", + "C(year)[T.2021] -0.1071 0.214 -0.501 0.616 -0.526 0.312\n", + "C(year)[T.2022] -0.2732 0.241 -1.135 0.256 -0.745 0.198\n", + "C(year)[T.2023] -0.1778 0.275 -0.647 0.517 -0.716 0.361\n", + "C(year)[T.2024] -0.4708 0.227 -2.076 0.038 -0.915 -0.026\n", + "post_2019:offshore_jurisdiction 0.6372 0.377 1.689 0.091 -0.102 1.377\n", + "==============================================================================\n", + "Omnibus: 0.086 Durbin-Watson: 1.268\n", + "Prob(Omnibus): 0.958 Jarque-Bera (JB): 0.227\n", + "Skew: -0.039 Prob(JB): 0.893\n", + "Kurtosis: 2.811 Cond. No. 15.1\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "\n", + "================================================================================\n", + "Model 2: TWFE + District-Specific Post Effects + Offshore Control\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: log_days_to_enf R-squared: 0.776\n", + "Model: OLS Adj. R-squared: 0.700\n", + "Method: Least Squares F-statistic: 7.109\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 0.00145\n", + "Time: 16:16:22 Log-Likelihood: -58.250\n", + "No. Observations: 130 AIC: 184.5\n", + "Df Residuals: 96 BIC: 282.0\n", + "Df Model: 33 \n", + "Covariance Type: cluster \n", + "===================================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------------------\n", + "Intercept 5.1802 0.146 35.397 0.000 4.893 5.467\n", + "C(district)[T.02] 0.0088 nan nan nan nan nan\n", + "C(district)[T.03] -1.3481 nan nan nan nan nan\n", + "C(district)[T.04] -1.3304 nan nan nan nan nan\n", + "C(district)[T.05] -0.0809 1.85e-14 -4.38e+12 0.000 -0.081 -0.081\n", + "C(district)[T.06] 0.5301 2.14e-14 2.47e+13 0.000 0.530 0.530\n", + "C(district)[T.08] -0.5338 1.93e-14 -2.77e+13 0.000 -0.534 -0.534\n", + "C(district)[T.09] -0.1608 1.88e-14 -8.56e+12 0.000 -0.161 -0.161\n", + "C(district)[T.10] -1.5798 1.95e-14 -8.09e+13 0.000 -1.580 -1.580\n", + "C(district)[T.6E] 0.2268 1.82e-14 1.25e+13 0.000 0.227 0.227\n", + "C(district)[T.7B] -1.6258 1.87e-14 -8.67e+13 0.000 -1.626 -1.626\n", + "C(district)[T.7C] -1.3740 2.11e-14 -6.52e+13 0.000 -1.374 -1.374\n", + "C(district)[T.8A] -1.1756 2.03e-14 -5.79e+13 0.000 -1.176 -1.176\n", + "C(year)[T.2016] 0.1233 0.183 0.675 0.500 -0.235 0.481\n", + "C(year)[T.2017] 0.4207 0.229 1.835 0.066 -0.029 0.870\n", + "C(year)[T.2018] 0.4592 0.269 1.709 0.088 -0.068 0.986\n", + "C(year)[T.2019] 0.2667 0.242 1.101 0.271 -0.208 0.742\n", + "C(year)[T.2020] 0.2150 0.183 1.176 0.239 -0.143 0.573\n", + "C(year)[T.2021] -0.0376 0.100 -0.374 0.708 -0.234 0.159\n", + "C(year)[T.2022] -0.2037 0.106 -1.921 0.055 -0.412 0.004\n", + "C(year)[T.2023] -0.1083 0.127 -0.850 0.395 -0.358 0.141\n", + "C(year)[T.2024] -0.4013 0.122 -3.294 0.001 -0.640 -0.163\n", + "C(district)[01]:post_2019 0.0459 0.052 0.876 0.381 -0.057 0.149\n", + "C(district)[02]:post_2019 -0.5936 0.013 -45.291 0.000 -0.619 -0.568\n", + "C(district)[03]:post_2019 0.5352 0.013 40.839 0.000 0.510 0.561\n", + "C(district)[04]:post_2019 0.4842 0.013 36.942 0.000 0.458 0.510\n", + "C(district)[05]:post_2019 0.2661 0.052 5.076 0.000 0.163 0.369\n", + "C(district)[06]:post_2019 -0.5433 0.052 -10.364 0.000 -0.646 -0.441\n", + "C(district)[08]:post_2019 -0.5667 0.052 -10.810 0.000 -0.669 -0.464\n", + "C(district)[09]:post_2019 -0.6154 0.052 -11.739 0.000 -0.718 -0.513\n", + "C(district)[10]:post_2019 0.7632 0.052 14.559 0.000 0.660 0.866\n", + "C(district)[6E]:post_2019 -0.1537 0.052 -2.932 0.003 -0.256 -0.051\n", + "C(district)[7B]:post_2019 -0.3660 0.052 -6.983 0.000 -0.469 -0.263\n", + "C(district)[7C]:post_2019 0.2712 0.052 5.174 0.000 0.168 0.374\n", + "C(district)[8A]:post_2019 0.2037 0.052 3.885 0.000 0.101 0.306\n", + "post_2019:offshore_jurisdiction 0.4258 0.039 10.830 0.000 0.349 0.503\n", + "==============================================================================\n", + "Omnibus: 1.327 Durbin-Watson: 1.652\n", + "Prob(Omnibus): 0.515 Jarque-Bera (JB): 1.416\n", + "Skew: -0.215 Prob(JB): 0.493\n", + "Kurtosis: 2.724 Cond. No. 5.70e+15\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "[2] The smallest eigenvalue is 4.85e-30. This might indicate that there are\n", + "strong multicollinearity problems or that the design matrix is singular.\n", + "\n", + "================================================================================\n", + "DISTRICT-SPECIFIC POST-2019 EFFECTS (TWFE corrected)\n", + "================================================================================\n", + "district coefficient stderr pvalue\n", + " 09 -0.6154 0.0524 0.0000\n", + " 02 -0.5936 0.0131 0.0000\n", + " 08 -0.5667 0.0524 0.0000\n", + " 06 -0.5433 0.0524 0.0000\n", + " 7B -0.3660 0.0524 0.0000\n", + " 6E -0.1537 0.0524 0.0034\n", + " 01 0.0459 0.0524 0.3813\n", + " 8A 0.2037 0.0524 0.0001\n", + " 05 0.2661 0.0524 0.0000\n", + " 7C 0.2712 0.0524 0.0000\n", + " 04 0.4842 0.0131 0.0000\n", + " 03 0.5352 0.0131 0.0000\n", + " 10 0.7632 0.0524 0.0000\n", + "\n", + "================================================================================\n", + "OFFSHORE-JURISDICTION DIFFERENTIAL EFFECT\n", + "================================================================================\n", + "Coefficient (post_2019 × offshore_jurisdiction): 0.6372\n", + "P-value: 0.0913\n", + "Percent differential: +89.1%\n", + "→ Statistically meaningful differential post-2019 effect for districts 02/03/04\n", + "\n", + "================================================================================\n", + "H1b MODEL: Compliance Verification (Resolution Rate)\n", + "================================================================================\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: resolution_rate R-squared: 0.674\n", + "Model: OLS Adj. R-squared: 0.607\n", + "Method: Least Squares F-statistic: 59.53\n", + "Date: Wed, 18 Feb 2026 Prob (F-statistic): 1.17e-08\n", + "Time: 16:16:22 Log-Likelihood: -497.01\n", + "No. Observations: 130 AIC: 1040.\n", + "Df Residuals: 107 BIC: 1106.\n", + "Df Model: 22 \n", + "Covariance Type: cluster \n", + "===================================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------------------\n", + "Intercept 37.8001 3.939 9.596 0.000 30.080 45.520\n", + "C(district)[T.02] 2.5271 3.172 0.797 0.426 -3.690 8.744\n", + "C(district)[T.03] 38.3518 3.172 12.090 0.000 32.135 44.569\n", + "C(district)[T.04] 25.6387 3.172 8.083 0.000 19.421 31.856\n", + "C(district)[T.05] 0.5035 5.43e-14 9.26e+12 0.000 0.503 0.503\n", + "C(district)[T.06] -2.8983 8.65e-14 -3.35e+13 0.000 -2.898 -2.898\n", + "C(district)[T.08] 16.9255 5.75e-14 2.94e+14 0.000 16.925 16.925\n", + "C(district)[T.09] 8.4594 5.29e-14 1.6e+14 0.000 8.459 8.459\n", + "C(district)[T.10] 41.9193 5.9e-14 7.1e+14 0.000 41.919 41.919\n", + "C(district)[T.6E] 2.7924 5.25e-14 5.32e+13 0.000 2.792 2.792\n", + "C(district)[T.7B] 26.5398 5.1e-14 5.21e+14 0.000 26.540 26.540\n", + "C(district)[T.7C] 21.5777 5.18e-14 4.16e+14 0.000 21.578 21.578\n", + "C(district)[T.8A] 17.6509 4.88e-14 3.61e+14 0.000 17.651 17.651\n", + "C(year)[T.2016] -11.7452 4.767 -2.464 0.014 -21.088 -2.402\n", + "C(year)[T.2017] 4.4288 4.869 0.910 0.363 -5.114 13.971\n", + "C(year)[T.2018] 3.4111 5.341 0.639 0.523 -7.058 13.880\n", + "C(year)[T.2019] 9.5688 5.514 1.735 0.083 -1.238 20.375\n", + "C(year)[T.2020] 5.1782 5.969 0.868 0.386 -6.520 16.876\n", + "C(year)[T.2021] 11.9941 5.886 2.038 0.042 0.457 23.531\n", + "C(year)[T.2022] 15.8437 5.512 2.875 0.004 5.041 26.646\n", + "C(year)[T.2023] 17.8129 5.836 3.052 0.002 6.375 29.251\n", + "C(year)[T.2024] 13.1960 7.214 1.829 0.067 -0.943 27.335\n", + "post_2019:offshore_jurisdiction -18.5168 5.287 -3.502 0.000 -28.879 -8.155\n", + "==============================================================================\n", + "Omnibus: 0.769 Durbin-Watson: 1.420\n", + "Prob(Omnibus): 0.681 Jarque-Bera (JB): 0.896\n", + "Skew: -0.150 Prob(JB): 0.639\n", + "Kurtosis: 2.725 Cond. No. 15.1\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors are robust to cluster correlation (cluster)\n", + "\n", + "H1b interpretation (offshore-jurisdiction differential):\n", + " Coefficient: -18.5168\n", + " P-value: 0.0005\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dadams/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/regression/linear_model.py:1884: RuntimeWarning: invalid value encountered in sqrt\n", + " return np.sqrt(np.diag(self.cov_params()))\n" + ] + } + ], + "source": [ + "print(\"=\"*80)\n", + "print(\"CORRECTED TWO-WAY FE MODELS: Days to Enforcement\")\n", + "print(\"=\"*80)\n", + "\n", + "# Prepare regression data\n", + "df_reg = district_year_panel[district_year_panel['avg_days_to_enforcement'] > 0].copy()\n", + "df_reg['log_days_to_enf'] = np.log(df_reg['avg_days_to_enforcement'])\n", + "\n", + "print(f\"\\nRegression sample: {len(df_reg)} observations\")\n", + "print(f\"Districts: {df_reg['district'].nunique()}\")\n", + "print(f\"Years: {sorted(df_reg['year'].unique())}\")\n", + "print(f\"Offshore-jurisdiction districts (02/03/04): {df_reg[df_reg['offshore_jurisdiction']==1]['district'].nunique()}\")\n", + "\n", + "# Model 1: Two-way FE with offshore-jurisdiction differential post effect\n", + "formula1 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model1 = smf.ols(formula1, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"Model 1: TWFE + Offshore-Jurisdiction Differential Effect\")\n", + "print(\"=\"*80)\n", + "print(model1.summary())\n", + "\n", + "# Model 2: Two-way FE with district-specific post effects + offshore differential control\n", + "formula2 = 'log_days_to_enf ~ C(district) + C(year) + C(district):post_2019 + post_2019:offshore_jurisdiction'\n", + "model2 = smf.ols(formula2, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"Model 2: TWFE + District-Specific Post Effects + Offshore Control\")\n", + "print(\"=\"*80)\n", + "print(model2.summary())\n", + "\n", + "# Extract district-specific post effects\n", + "coef_df = pd.DataFrame({\n", + " 'coefficient': model2.params,\n", + " 'stderr': model2.bse,\n", + " 'pvalue': model2.pvalues\n", + "}).reset_index()\n", + "coef_df.columns = ['term', 'coefficient', 'stderr', 'pvalue']\n", + "\n", + "district_effects = coef_df[coef_df['term'].str.contains(':post_2019', na=False)].copy()\n", + "district_effects = district_effects[~district_effects['term'].str.contains('offshore_jurisdiction', na=False)]\n", + "district_effects['district'] = district_effects['term'].str.extract(r'\\[(?:T\\.)?(\\w+)\\]')[0]\n", + "district_effects = district_effects.sort_values('coefficient')\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"DISTRICT-SPECIFIC POST-2019 EFFECTS (TWFE corrected)\")\n", + "print(\"=\"*80)\n", + "print(district_effects[['district', 'coefficient', 'stderr', 'pvalue']].to_string(index=False))\n", + "\n", + "# Offshore differential interpretation\n", + "if 'post_2019:offshore_jurisdiction' in model1.params:\n", + " offshore_coef = model1.params['post_2019:offshore_jurisdiction']\n", + " offshore_p = model1.pvalues['post_2019:offshore_jurisdiction']\n", + " offshore_pct = (np.exp(offshore_coef) - 1) * 100\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"OFFSHORE-JURISDICTION DIFFERENTIAL EFFECT\")\n", + " print(\"=\"*80)\n", + " print(f\"Coefficient (post_2019 × offshore_jurisdiction): {offshore_coef:.4f}\")\n", + " print(f\"P-value: {offshore_p:.4f}\")\n", + " print(f\"Percent differential: {offshore_pct:+.1f}%\")\n", + " if offshore_p < 0.10:\n", + " print(\"→ Statistically meaningful differential post-2019 effect for districts 02/03/04\")\n", + " else:\n", + " print(\"→ No statistically clear differential post-2019 effect for districts 02/03/04\")\n", + "\n", + "\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"H1b MODEL: Compliance Verification (Resolution Rate)\")\n", + "print(\"=\"*80)\n", + "\n", + "df_reg_h1b = district_year_panel.copy()\n", + "formula_h1b = 'resolution_rate ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model_h1b = smf.ols(formula_h1b, data=df_reg_h1b).fit(\n", + " cov_type='cluster', cov_kwds={'groups': df_reg_h1b['district']}\n", + ")\n", + "\n", + "h1b_coef = model_h1b.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "h1b_p = model_h1b.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + "print(model_h1b.summary())\n", + "print()\n", + "print(\"H1b interpretation (offshore-jurisdiction differential):\")\n", + "print(f\" Coefficient: {h1b_coef:.4f}\")\n", + "print(f\" P-value: {h1b_p:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "54c386fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting visualization...\n", + "Checking district_effects exists: \n", + "Shape: (13, 5)\n", + "Columns: ['term', 'coefficient', 'stderr', 'pvalue', 'district']\n", + "\n", + "1. Creating effects_df copy...\n", + "2. Adding confidence intervals...\n", + "3. Calculating percent change...\n", + "4. Setting district as index...\n", + " Index: ['09', '02', '08', '06', '7B', '6E', '01', '8A', '05', '7C', '04', '03', '10']\n", + "5. Creating figure...\n", + "6. Preparing plot data...\n", + "7. Creating bar colors...\n", + "8. Plotting bars...\n", + "9. Adding error bars...\n", + "10. Adding plot decorations...\n", + "11. Adding significance markers...\n", + "12. Creating second plot...\n", + "13. Adding value labels...\n", + "14. Applying tight_layout...\n", + "15. Saving figure...\n", + "16. Showing plot...\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "17. Generating summary...\n", + "\n", + "================================================================================\n", + "VISUALIZATION: Heterogeneous Treatment Effects\n", + "================================================================================\n", + "\n", + "✓ Saved: district_treatment_effects.png\n", + "\n", + "Key findings:\n", + " • 6 districts show FASTER enforcement post-2019 (negative coefficients)\n", + " • 7 districts show SLOWER enforcement post-2019 (positive coefficients)\n", + " • 12 districts have statistically significant effects (p < 0.05)\n", + "\n", + " Fastest improvement: District 09 (-46.0% faster)\n", + " Slowest (worst): District 10 (114.5% slower)\n", + "\n", + " Range: -46.0% to 114.5%\n", + "\n", + "✓ COMPLETE!\n" + ] + } + ], + "source": [ + "# Visualize heterogeneous treatment effects across districts\n", + "\n", + "print(\"Starting visualization...\")\n", + "print(f\"Checking district_effects exists: {type(district_effects)}\")\n", + "print(f\"Shape: {district_effects.shape}\")\n", + "print(f\"Columns: {district_effects.columns.tolist()}\")\n", + "\n", + "# Prepare effects dataframe with all needed columns\n", + "print(\"\\n1. Creating effects_df copy...\")\n", + "effects_df = district_effects.copy()\n", + "\n", + "print(\"2. Adding confidence intervals...\")\n", + "effects_df['ci_lower'] = effects_df['coefficient'] - 1.96 * effects_df['stderr']\n", + "effects_df['ci_upper'] = effects_df['coefficient'] + 1.96 * effects_df['stderr']\n", + "effects_df['significant'] = effects_df['pvalue'] < 0.05\n", + "\n", + "print(\"3. Calculating percent change...\")\n", + "# Calculate percent change: negative coef = faster (reduction in days)\n", + "# Keep the sign: negative = faster enforcement, positive = slower\n", + "effects_df['pct_change'] = (np.exp(effects_df['coefficient']) - 1) * 100\n", + "\n", + "print(\"4. Setting district as index...\")\n", + "# Set district as index for plotting\n", + "effects_df = effects_df.set_index('district')\n", + "print(f\" Index: {effects_df.index.tolist()}\")\n", + "\n", + "print(\"5. Creating figure...\")\n", + "# Create visualization\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", + "\n", + "print(\"6. Preparing plot data...\")\n", + "# Plot 1: Coefficient plot with confidence intervals\n", + "effects_plot = effects_df.sort_values('coefficient').copy()\n", + "y_pos = np.arange(len(effects_plot))\n", + "\n", + "print(\"7. Creating bar colors...\")\n", + "colors = ['forestgreen' if coef < 0 else 'orangered' for coef in effects_plot['coefficient']]\n", + "\n", + "print(\"8. Plotting bars...\")\n", + "ax1.barh(y_pos, effects_plot['coefficient'], color=colors, alpha=0.6, edgecolor='black', linewidth=0.5)\n", + "\n", + "print(\"9. Adding error bars...\")\n", + "ax1.errorbar(effects_plot['coefficient'], y_pos, \n", + " xerr=[effects_plot['coefficient'] - effects_plot['ci_lower'],\n", + " effects_plot['ci_upper'] - effects_plot['coefficient']],\n", + " fmt='none', ecolor='black', capsize=4, alpha=0.8, linewidth=1.5)\n", + "\n", + "print(\"10. Adding plot decorations...\")\n", + "ax1.axvline(0, color='black', linestyle='--', linewidth=2, alpha=0.7)\n", + "ax1.set_yticks(y_pos)\n", + "ax1.set_yticklabels([f'District {dist}' for dist in effects_plot.index], fontsize=10)\n", + "ax1.set_xlabel('Treatment Effect (log days to enforcement)', fontsize=11, fontweight='bold')\n", + "ax1.set_title('District-Specific Treatment Effects:\\nDays to Enforcement Post-2019', \n", + " fontsize=13, fontweight='bold')\n", + "ax1.grid(True, alpha=0.3, axis='x')\n", + "\n", + "print(\"11. Adding significance markers...\")\n", + "# Add significance markers\n", + "for i, (idx, row) in enumerate(effects_plot.iterrows()):\n", + " if row['significant']:\n", + " ax1.text(row['coefficient'], i, ' *', va='center', fontsize=14, fontweight='bold', color='red')\n", + "\n", + "print(\"12. Creating second plot...\")\n", + "# Plot 2: Percent change interpretation\n", + "ax2.barh(y_pos, effects_plot['pct_change'], color=colors, alpha=0.6, edgecolor='black', linewidth=0.5)\n", + "ax2.axvline(0, color='black', linestyle='--', linewidth=2, alpha=0.7)\n", + "ax2.set_yticks(y_pos)\n", + "ax2.set_yticklabels([f'District {dist}' for dist in effects_plot.index], fontsize=10)\n", + "ax2.set_xlabel('% Change in Days to Enforcement', fontsize=11, fontweight='bold')\n", + "ax2.set_title('Percent Change in Enforcement Speed\\n(negative = faster enforcement)', \n", + " fontsize=13, fontweight='bold')\n", + "ax2.grid(True, alpha=0.3, axis='x')\n", + "\n", + "print(\"13. Adding value labels...\")\n", + "# Add value labels for key districts\n", + "for i, (idx, row) in enumerate(effects_plot.iterrows()):\n", + " if abs(row['pct_change']) > 30: # Label large effects\n", + " label = f\"{row['pct_change']:.0f}%\"\n", + " x_pos = row['pct_change'] + (5 if row['pct_change'] > 0 else -5)\n", + " ha = 'left' if row['pct_change'] > 0 else 'right'\n", + " ax2.text(x_pos, i, label, va='center', ha=ha, fontsize=9, fontweight='bold')\n", + "\n", + "print(\"14. Applying tight_layout...\")\n", + "plt.tight_layout()\n", + "\n", + "print(\"15. Saving figure...\")\n", + "plt.savefig('district_treatment_effects.png', dpi=300, bbox_inches='tight')\n", + "\n", + "print(\"16. Showing plot...\")\n", + "plt.show()\n", + "\n", + "print(\"17. Generating summary...\")\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"VISUALIZATION: Heterogeneous Treatment Effects\")\n", + "print(\"=\"*80)\n", + "print(f\"\\n✓ Saved: district_treatment_effects.png\")\n", + "print(\"\\nKey findings:\")\n", + "print(f\" • {(effects_df['coefficient'] < 0).sum()} districts show FASTER enforcement post-2019 (negative coefficients)\")\n", + "print(f\" • {(effects_df['coefficient'] > 0).sum()} districts show SLOWER enforcement post-2019 (positive coefficients)\")\n", + "print(f\" • {effects_df['significant'].sum()} districts have statistically significant effects (p < 0.05)\")\n", + "\n", + "# Show magnitude ranges\n", + "fastest = effects_df.nsmallest(3, 'coefficient')\n", + "slowest = effects_df.nlargest(3, 'coefficient')\n", + "\n", + "print(f\"\\n Fastest improvement: District {fastest.index[0]} ({fastest.iloc[0]['pct_change']:.1f}% faster)\")\n", + "print(f\" Slowest (worst): District {slowest.index[0]} ({slowest.iloc[0]['pct_change']:.1f}% slower)\")\n", + "print(f\"\\n Range: {effects_df['pct_change'].min():.1f}% to {effects_df['pct_change'].max():.1f}%\")\n", + "\n", + "print(\"\\n✓ COMPLETE!\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "dc635f28", + "metadata": {}, + "source": [ + "## Part 4: Differential Event Study (Offshore vs Non-Offshore Districts)\n", + "\n", + "### Testing Differential Pre-Trends and Dynamic Effects\n", + "\n", + "To make event-study identification coherent, this section estimates year interactions with offshore-jurisdiction status (districts 02/03/04) while controlling for district and year fixed effects.\n", + "\n", + "Specification:\n", + "\n", + "$$Y_{dt} = \\alpha_d + \\gamma_t + \\sum_{k \\neq -1}\\delta_k\\,[\\mathbb{1}(YearFromPolicy_t=k)\\times OffshoreJurisdiction_d] + \\epsilon_{dt}$$\n", + "\n", + "Reference year is 2018 ($k=-1$).\n", + "\n", + "This tests:\n", + "1. Differential pre-trends for offshore-jurisdiction districts\n", + "2. Differential post-2019 dynamics net of statewide year shocks\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e4cf8c50", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "DIFFERENTIAL EVENT STUDY: Offshore-Jurisdiction vs Other Districts\n", + "================================================================================\n", + "\n", + "Differential coefficients (offshore-jurisdiction districts relative to others):\n", + " year year_from_policy coefficient se pvalue significant\n", + " 2015 -4 0.4454 0.3619 0.2184 False\n", + " 2016 -3 -0.0606 0.4344 0.8891 False\n", + " 2017 -2 -0.1481 0.2285 0.5169 False\n", + " 2018 -1 0.0000 0.0000 1.0000 False\n", + " 2019 0 0.3479 0.2476 0.1601 False\n", + " 2020 1 0.1796 0.4833 0.7102 False\n", + " 2021 2 0.9121 0.5726 0.1112 False\n", + " 2022 3 0.7532 0.4104 0.0665 False\n", + " 2023 4 0.9166 0.4307 0.0333 True\n", + " 2024 5 1.0693 0.4890 0.0288 True\n", + "\n", + "================================================================================\n", + "DIFFERENTIAL PRE-TREND TEST (Offshore vs Non-Offshore)\n", + "================================================================================\n", + "Average pre-2019 differential coefficient: 0.0789\n", + "Significant pre-trend years: 0 out of 3\n", + "✓ No evidence of differential pre-trends\n" + ] + } + ], + "source": [ + "# Differential event study: offshore-jurisdiction (02/03/04) vs other districts\n", + "\n", + "df_event = district_year_panel.copy()\n", + "df_event = df_event[df_event['avg_days_to_enforcement'] > 0].copy()\n", + "df_event['log_days_to_enf'] = np.log(df_event['avg_days_to_enforcement'])\n", + "df_event['year_from_policy'] = df_event['year'] - 2019\n", + "\n", + "# Use 2018 as reference\n", + "years = sorted(df_event['year'].unique())\n", + "interaction_terms = []\n", + "for y in years:\n", + " if y == 2018:\n", + " continue\n", + " col = f'offshore_year_{y}'\n", + " df_event[col] = ((df_event['year'] == y).astype(int) * df_event['offshore_jurisdiction'])\n", + " interaction_terms.append(col)\n", + "\n", + "formula_event = 'log_days_to_enf ~ C(district) + C(year)'\n", + "if interaction_terms:\n", + " formula_event += ' + ' + ' + '.join(interaction_terms)\n", + "\n", + "model_event = smf.ols(formula_event, data=df_event).fit(\n", + " cov_type='cluster', cov_kwds={'groups': df_event['district']}\n", + ")\n", + "\n", + "print(\"=\"*80)\n", + "print(\"DIFFERENTIAL EVENT STUDY: Offshore-Jurisdiction vs Other Districts\")\n", + "print(\"=\"*80)\n", + "\n", + "event_coefs = []\n", + "for y in years:\n", + " if y == 2018:\n", + " event_coefs.append({\n", + " 'year': y,\n", + " 'year_from_policy': y - 2019,\n", + " 'coefficient': 0.0,\n", + " 'se': 0.0,\n", + " 'ci_lower': 0.0,\n", + " 'ci_upper': 0.0,\n", + " 'pvalue': 1.0\n", + " })\n", + " continue\n", + "\n", + " p = f'offshore_year_{y}'\n", + " if p in model_event.params:\n", + " event_coefs.append({\n", + " 'year': y,\n", + " 'year_from_policy': y - 2019,\n", + " 'coefficient': model_event.params[p],\n", + " 'se': model_event.bse[p],\n", + " 'ci_lower': model_event.conf_int().loc[p, 0],\n", + " 'ci_upper': model_event.conf_int().loc[p, 1],\n", + " 'pvalue': model_event.pvalues[p]\n", + " })\n", + "\n", + "event_df = pd.DataFrame(event_coefs).sort_values('year')\n", + "event_df['significant'] = event_df['pvalue'] < 0.05\n", + "event_df['pre_treatment'] = event_df['year'] < 2019\n", + "\n", + "print(\"\\nDifferential coefficients (offshore-jurisdiction districts relative to others):\")\n", + "print(event_df[['year', 'year_from_policy', 'coefficient', 'se', 'pvalue', 'significant']].to_string(index=False))\n", + "\n", + "# Pre-trend test\n", + "pre_treatment_df = event_df[(event_df['pre_treatment']) & (event_df['year'] != 2018)]\n", + "if len(pre_treatment_df) > 0:\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"DIFFERENTIAL PRE-TREND TEST (Offshore vs Non-Offshore)\")\n", + " print(\"=\"*80)\n", + " print(f\"Average pre-2019 differential coefficient: {pre_treatment_df['coefficient'].mean():.4f}\")\n", + " print(f\"Significant pre-trend years: {pre_treatment_df['significant'].sum()} out of {len(pre_treatment_df)}\")\n", + " if pre_treatment_df['significant'].sum() == 0:\n", + " print(\"✓ No evidence of differential pre-trends\")\n", + " else:\n", + " print(\"⚠ Differential pre-trends detected; interpret post effects cautiously\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7c0e3dc6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Event study plot saved as 'event_study_plot.png'\n" + ] + } + ], + "source": [ + "# Visualize event study results\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 7))\n", + "\n", + "# Plot coefficients with confidence intervals\n", + "x = event_df['year_from_policy']\n", + "y = event_df['coefficient']\n", + "ci_lower = event_df['ci_lower']\n", + "ci_upper = event_df['ci_upper']\n", + "\n", + "# Separate pre and post treatment\n", + "pre_mask = event_df['year'] < 2019\n", + "post_mask = event_df['year'] >= 2019\n", + "\n", + "# Plot pre-treatment\n", + "ax.scatter(x[pre_mask], y[pre_mask], color='steelblue', s=100, label='Pre-2019', zorder=3)\n", + "ax.plot(x[pre_mask], y[pre_mask], color='steelblue', alpha=0.3, linestyle='--')\n", + "for i in event_df[pre_mask].index:\n", + " ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='steelblue', alpha=0.5, linewidth=2)\n", + "\n", + "# Plot post-treatment\n", + "ax.scatter(x[post_mask], y[post_mask], color='darkred', s=100, label='Post-2019 Differential', zorder=3)\n", + "ax.plot(x[post_mask], y[post_mask], color='darkred', alpha=0.3, linestyle='--')\n", + "for i in event_df[post_mask].index:\n", + " ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='darkred', alpha=0.5, linewidth=2)\n", + "\n", + "# Add reference lines\n", + "ax.axhline(0, color='black', linestyle='-', linewidth=0.8, alpha=0.3)\n", + "ax.axvline(-1, color='gray', linestyle=':', linewidth=2, alpha=0.5, label='Policy Year (2019)')\n", + "\n", + "# Styling\n", + "ax.set_xlabel('Years Relative to Policy (2019)', fontsize=12, fontweight='bold')\n", + "ax.set_ylabel('Differential Effect on log(Days to Enforcement)', fontsize=12, fontweight='bold')\n", + "ax.set_title('Differential Event Study: Offshore vs Non-Offshore Districts\\n(Reference Year: 2018)', \n", + " fontsize=14, fontweight='bold', pad=20)\n", + "ax.legend(loc='best', frameon=True, shadow=True)\n", + "ax.grid(True, alpha=0.2)\n", + "\n", + "# Add annotations\n", + "ax.text(-1, ax.get_ylim()[1]*0.95, 'Pre-treatment\\n(Parallel Trends Test)', \n", + " ha='center', va='top', fontsize=10, style='italic', \n", + " bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.3))\n", + "ax.text(2, ax.get_ylim()[1]*0.95, 'Post-treatment\\n(Policy Effects)', \n", + " ha='center', va='top', fontsize=10, style='italic',\n", + " bbox=dict(boxstyle='round', facecolor='lightcoral', alpha=0.3))\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('event_study_plot.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(\"\\n✓ Event study plot saved as 'event_study_plot.png'\")" + ] + }, + { + "cell_type": "markdown", + "id": "6e7699a6", + "metadata": {}, + "source": [ + "## Part 5: Heterogeneous Treatment Effects (TWFE Moderator Tests)\n", + "\n", + "This section maps to H3 (structural moderators) and includes H5 (offshore jurisdiction).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9d0b54fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "BASELINE DISTRICT CHARACTERISTICS (Pre-2019)\n", + "================================================================================\n", + "district total_inspections_baseline avg_wells baseline_compliance_rate baseline_days_to_enf\n", + " 01 29612 5533.2500 85.0215 242.8292\n", + " 02 15348 3167.5000 83.8960 234.3710\n", + " 03 32975 5568.2500 94.0868 61.9163\n", + " 04 32081 5250.2500 92.7315 62.7780\n", + " 05 16329 3246.5000 92.0623 275.6829\n", + " 06 37386 6742.0000 88.9838 474.9800\n", + " 08 60999 12675.0000 88.2270 135.3338\n", + " 09 62196 12129.5000 82.3120 238.4897\n", + " 10 39620 7018.7500 88.9370 49.3502\n", + " 6E 13326 2279.0000 78.4384 301.2178\n", + " 7B 35929 5834.0000 82.7249 48.0696\n", + " 7C 40631 8175.2500 85.1540 63.0303\n", + " 8A 40261 8465.0000 90.5173 77.4983\n", + "\n", + "================================================================================\n", + "DISTRICT MODERATORS FOR HETEROGENEOUS EFFECTS\n", + "================================================================================\n", + "district high_capacity low_baseline_compliance high_ej border_district offshore_jurisdiction\n", + " 01 0 1 1 0 0\n", + " 02 0 1 1 0 1\n", + " 03 0 0 0 0 1\n", + " 04 0 0 0 0 1\n", + " 05 0 0 0 0 0\n", + " 06 1 0 0 1 0\n", + " 08 1 0 0 1 0\n", + " 09 1 1 1 0 0\n", + " 10 1 0 0 0 0\n", + " 6E 0 1 1 0 0\n", + " 7B 0 1 1 0 0\n", + " 7C 1 1 1 0 0\n", + " 8A 1 0 0 1 0\n", + "\n", + "OK: District characteristics merged into panel data\n", + " Panel shape: (130, 24)\n", + " High-capacity districts: 6/13\n", + " Low baseline compliance districts: 6/13\n", + " High EJ concern districts: 6/13\n", + " Border districts: 3/13\n", + " Offshore-jurisdiction districts: 3/13\n" + ] + } + ], + "source": [ + "# Create district characteristics for heterogeneous effects analysis\n", + "# Using data already in memory from district_year_panel\n", + "\n", + "baseline_data = district_year_panel[district_year_panel['year'] < 2019].groupby('district').agg({\n", + " 'total_inspections': 'sum',\n", + " 'unique_wells': 'mean',\n", + " 'compliance_rate': 'mean',\n", + " 'avg_days_to_enforcement': 'mean'\n", + "}).reset_index()\n", + "\n", + "baseline_data.columns = ['district', 'total_inspections_baseline', 'avg_wells',\n", + " 'baseline_compliance_rate', 'baseline_days_to_enf']\n", + "\n", + "print(\"=\"*80)\n", + "print(\"BASELINE DISTRICT CHARACTERISTICS (Pre-2019)\")\n", + "print(\"=\"*80)\n", + "print(baseline_data.to_string(index=False))\n", + "\n", + "baseline_data['high_capacity'] = (\n", + " baseline_data['total_inspections_baseline'] > baseline_data['total_inspections_baseline'].median()\n", + ").astype(int)\n", + "\n", + "baseline_data['low_baseline_compliance'] = (\n", + " baseline_data['baseline_compliance_rate'] < baseline_data['baseline_compliance_rate'].median()\n", + ").astype(int)\n", + "\n", + "viol_rate = district_year_panel[district_year_panel['year'] < 2019].groupby('district')['violation_discovery_rate'].mean()\n", + "baseline_data['high_ej'] = (viol_rate > viol_rate.median()).astype(int).values\n", + "\n", + "border_districts = ['06', '08', '8A']\n", + "baseline_data['border_district'] = baseline_data['district'].isin(border_districts).astype(int)\n", + "\n", + "offshore_jurisdiction_districts = ['02', '03', '04']\n", + "baseline_data['offshore_jurisdiction'] = baseline_data['district'].isin(offshore_jurisdiction_districts).astype(int)\n", + "\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"DISTRICT MODERATORS FOR HETEROGENEOUS EFFECTS\")\n", + "print(\"=\"*80)\n", + "print(baseline_data[['district', 'high_capacity', 'low_baseline_compliance',\n", + " 'high_ej', 'border_district', 'offshore_jurisdiction']].to_string(index=False))\n", + "\n", + "df_het = district_year_panel.merge(\n", + " baseline_data[['district', 'high_capacity', 'low_baseline_compliance', 'high_ej',\n", + " 'border_district']],\n", + " on='district', how='left'\n", + ")\n", + "\n", + "# Ensure offshore_jurisdiction is present as a plain column for formula evaluation\n", + "if 'offshore_jurisdiction' not in df_het.columns:\n", + " if 'offshore_jurisdiction' in district_year_panel.columns:\n", + " df_het['offshore_jurisdiction'] = district_year_panel['offshore_jurisdiction'].values\n", + " else:\n", + " df_het['offshore_jurisdiction'] = df_het['district'].isin(offshore_jurisdiction_districts).astype(int)\n", + "print()\n", + "print(\"OK: District characteristics merged into panel data\")\n", + "print(f\" Panel shape: {df_het.shape}\")\n", + "print(f\" High-capacity districts: {baseline_data['high_capacity'].sum()}/{len(baseline_data)}\")\n", + "print(f\" Low baseline compliance districts: {baseline_data['low_baseline_compliance'].sum()}/{len(baseline_data)}\")\n", + "print(f\" High EJ concern districts: {baseline_data['high_ej'].sum()}/{len(baseline_data)}\")\n", + "print(f\" Border districts: {baseline_data['border_district'].sum()}/{len(baseline_data)}\")\n", + "print(f\" Offshore-jurisdiction districts: {baseline_data['offshore_jurisdiction'].sum()}/{len(baseline_data)}\")\n", + "\n", + "print(f\" offshore_jurisdiction in df_het columns: {'offshore_jurisdiction' in df_het.columns}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1c3d33b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "TRIPLE DIFFERENCE-IN-DIFFERENCES RESULTS\n", + "================================================================================\n", + "\n", + "--------------------------------------------------------------------------------\n", + "H3a: Capacity (High-Capacity Districts)\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "ename": "PatsyError", + "evalue": "Error evaluating factor: NameError: name 'offshore_jurisdiction' is not defined\n log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction\n ^^^^^^^^^^^^^^^^^^^^^", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/compat.py:40\u001b[39m, in \u001b[36mcall_and_wrap_exc\u001b[39m\u001b[34m(msg, origin, f, *args, **kwargs)\u001b[39m\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m40\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/eval.py:179\u001b[39m, in \u001b[36mEvalEnvironment.eval\u001b[39m\u001b[34m(self, expr, source_name, inner_namespace)\u001b[39m\n\u001b[32m 178\u001b[39m code = \u001b[38;5;28mcompile\u001b[39m(expr, source_name, \u001b[33m\"\u001b[39m\u001b[33meval\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m.flags, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m179\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43meval\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mVarLookupDict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43minner_namespace\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_namespaces\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m:1\u001b[39m\n", + "\u001b[31mNameError\u001b[39m: name 'offshore_jurisdiction' is not defined", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[31mPatsyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 19\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m-\u001b[39m\u001b[33m\"\u001b[39m*\u001b[32m80\u001b[39m)\n\u001b[32m 18\u001b[39m formula_h1 = \u001b[33m'\u001b[39m\u001b[33mlog_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m model_h1 = \u001b[43msmf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mols\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformula_h1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdf_het\u001b[49m\u001b[43m)\u001b[49m.fit(cov_type=\u001b[33m'\u001b[39m\u001b[33mcluster\u001b[39m\u001b[33m'\u001b[39m, \n\u001b[32m 20\u001b[39m cov_kwds={\u001b[33m'\u001b[39m\u001b[33mgroups\u001b[39m\u001b[33m'\u001b[39m: df_het[\u001b[33m'\u001b[39m\u001b[33mdistrict\u001b[39m\u001b[33m'\u001b[39m]})\n\u001b[32m 22\u001b[39m interaction_coef_h1 = model_h1.params.get(\u001b[33m'\u001b[39m\u001b[33mpost_2019:high_capacity\u001b[39m\u001b[33m'\u001b[39m, np.nan)\n\u001b[32m 23\u001b[39m interaction_se_h1 = model_h1.bse.get(\u001b[33m'\u001b[39m\u001b[33mpost_2019:high_capacity\u001b[39m\u001b[33m'\u001b[39m, np.nan)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/base/model.py:203\u001b[39m, in \u001b[36mModel.from_formula\u001b[39m\u001b[34m(cls, formula, data, subset, drop_cols, *args, **kwargs)\u001b[39m\n\u001b[32m 200\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m missing == \u001b[33m'\u001b[39m\u001b[33mnone\u001b[39m\u001b[33m'\u001b[39m: \u001b[38;5;66;03m# with patsy it's drop or raise. let's raise.\u001b[39;00m\n\u001b[32m 201\u001b[39m missing = \u001b[33m'\u001b[39m\u001b[33mraise\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m203\u001b[39m tmp = \u001b[43mhandle_formula_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformula\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[43m=\u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 204\u001b[39m \u001b[43m \u001b[49m\u001b[43mmissing\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmissing\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 205\u001b[39m ((endog, exog), missing_idx, design_info) = tmp\n\u001b[32m 206\u001b[39m max_endog = \u001b[38;5;28mcls\u001b[39m._formula_max_endog\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/formula/formulatools.py:68\u001b[39m, in \u001b[36mhandle_formula_data\u001b[39m\u001b[34m(Y, X, formula, depth, missing)\u001b[39m\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data_util._is_using_pandas(Y, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m result = \u001b[43mdmatrices\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 69\u001b[39m \u001b[43m \u001b[49m\u001b[43mformula\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdataframe\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m=\u001b[49m\u001b[43mna_action\u001b[49m\n\u001b[32m 70\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 71\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 72\u001b[39m result = dmatrices(\n\u001b[32m 73\u001b[39m formula, Y, depth, return_type=\u001b[33m\"\u001b[39m\u001b[33mdataframe\u001b[39m\u001b[33m\"\u001b[39m, NA_action=na_action\n\u001b[32m 74\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/highlevel.py:317\u001b[39m, in \u001b[36mdmatrices\u001b[39m\u001b[34m(formula_like, data, eval_env, NA_action, return_type)\u001b[39m\n\u001b[32m 307\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Construct two design matrices given a formula_like and data.\u001b[39;00m\n\u001b[32m 308\u001b[39m \n\u001b[32m 309\u001b[39m \u001b[33;03mThis function is identical to :func:`dmatrix`, except that it requires\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 314\u001b[39m \u001b[33;03mSee :func:`dmatrix` for details.\u001b[39;00m\n\u001b[32m 315\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 316\u001b[39m eval_env = EvalEnvironment.capture(eval_env, reference=\u001b[32m1\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m (lhs, rhs) = \u001b[43m_do_highlevel_design\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 318\u001b[39m \u001b[43m \u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\n\u001b[32m 319\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 320\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m lhs.shape[\u001b[32m1\u001b[39m] == \u001b[32m0\u001b[39m:\n\u001b[32m 321\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m PatsyError(\u001b[33m\"\u001b[39m\u001b[33mmodel is missing required outcome variables\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/highlevel.py:162\u001b[39m, in \u001b[36m_do_highlevel_design\u001b[39m\u001b[34m(formula_like, data, eval_env, NA_action, return_type)\u001b[39m\n\u001b[32m 159\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdata_iter_maker\u001b[39m():\n\u001b[32m 160\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28miter\u001b[39m([data])\n\u001b[32m--> \u001b[39m\u001b[32m162\u001b[39m design_infos = \u001b[43m_try_incr_builders\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 163\u001b[39m \u001b[43m \u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\n\u001b[32m 164\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m design_infos \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 166\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m build_design_matrices(\n\u001b[32m 167\u001b[39m design_infos, data, NA_action=NA_action, return_type=return_type\n\u001b[32m 168\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/highlevel.py:56\u001b[39m, in \u001b[36m_try_incr_builders\u001b[39m\u001b[34m(formula_like, data_iter_maker, eval_env, NA_action)\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(formula_like, ModelDesc):\n\u001b[32m 55\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(eval_env, EvalEnvironment)\n\u001b[32m---> \u001b[39m\u001b[32m56\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdesign_matrix_builders\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 57\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlhs_termlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrhs_termlist\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 58\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 59\u001b[39m \u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 60\u001b[39m \u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 61\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 62\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/build.py:746\u001b[39m, in \u001b[36mdesign_matrix_builders\u001b[39m\u001b[34m(termlists, data_iter_maker, eval_env, NA_action)\u001b[39m\n\u001b[32m 743\u001b[39m factor_states = _factors_memorize(all_factors, data_iter_maker, eval_env)\n\u001b[32m 744\u001b[39m \u001b[38;5;66;03m# Now all the factors have working eval methods, so we can evaluate them\u001b[39;00m\n\u001b[32m 745\u001b[39m \u001b[38;5;66;03m# on some data to find out what type of data they return.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m746\u001b[39m (num_column_counts, cat_levels_contrasts) = \u001b[43m_examine_factor_types\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 747\u001b[39m \u001b[43m \u001b[49m\u001b[43mall_factors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfactor_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\n\u001b[32m 748\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 749\u001b[39m \u001b[38;5;66;03m# Now we need the factor infos, which encapsulate the knowledge of\u001b[39;00m\n\u001b[32m 750\u001b[39m \u001b[38;5;66;03m# how to turn any given factor into a chunk of data:\u001b[39;00m\n\u001b[32m 751\u001b[39m factor_infos = {}\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/build.py:491\u001b[39m, in \u001b[36m_examine_factor_types\u001b[39m\u001b[34m(factors, factor_states, data_iter_maker, NA_action)\u001b[39m\n\u001b[32m 489\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m data \u001b[38;5;129;01min\u001b[39;00m data_iter_maker():\n\u001b[32m 490\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m factor \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(examine_needed):\n\u001b[32m--> \u001b[39m\u001b[32m491\u001b[39m value = \u001b[43mfactor\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfactor_states\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfactor\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 492\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m factor \u001b[38;5;129;01min\u001b[39;00m cat_sniffers \u001b[38;5;129;01mor\u001b[39;00m guess_categorical(value):\n\u001b[32m 493\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m factor \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m cat_sniffers:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/eval.py:599\u001b[39m, in \u001b[36mEvalFactor.eval\u001b[39m\u001b[34m(self, memorize_state, data)\u001b[39m\n\u001b[32m 598\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34meval\u001b[39m(\u001b[38;5;28mself\u001b[39m, memorize_state, data):\n\u001b[32m--> \u001b[39m\u001b[32m599\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmemorize_state\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43meval_code\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemorize_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/eval.py:582\u001b[39m, in \u001b[36mEvalFactor._eval\u001b[39m\u001b[34m(self, code, memorize_state, data)\u001b[39m\n\u001b[32m 580\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_eval\u001b[39m(\u001b[38;5;28mself\u001b[39m, code, memorize_state, data):\n\u001b[32m 581\u001b[39m inner_namespace = VarLookupDict([data, memorize_state[\u001b[33m\"\u001b[39m\u001b[33mtransforms\u001b[39m\u001b[33m\"\u001b[39m]])\n\u001b[32m--> \u001b[39m\u001b[32m582\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcall_and_wrap_exc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 583\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mError evaluating factor\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 584\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 585\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemorize_state\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43meval_env\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 586\u001b[39m \u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 587\u001b[39m \u001b[43m \u001b[49m\u001b[43minner_namespace\u001b[49m\u001b[43m=\u001b[49m\u001b[43minner_namespace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 588\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/compat.py:43\u001b[39m, in \u001b[36mcall_and_wrap_exc\u001b[39m\u001b[34m(msg, origin, f, *args, **kwargs)\u001b[39m\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 42\u001b[39m new_exc = PatsyError(\u001b[33m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m % (msg, e.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m, e), origin)\n\u001b[32m---> \u001b[39m\u001b[32m43\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m new_exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n", + "\u001b[31mPatsyError\u001b[39m: Error evaluating factor: NameError: name 'offshore_jurisdiction' is not defined\n log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction\n ^^^^^^^^^^^^^^^^^^^^^" + ] + } + ], + "source": [ + "# Triple-DiD Models: Test each hypothesis\n", + "\n", + "# Prepare outcome variable\n", + "df_het['log_days_to_enf'] = np.log(df_het['avg_days_to_enforcement'] + 1)\n", + "df_het['log_viol_per_insp'] = np.log(df_het['violations_per_inspection'] + 0.01)\n", + "\n", + "hypotheses_results = {}\n", + "\n", + "print(\"=\"*80)\n", + "print(\"TRIPLE DIFFERENCE-IN-DIFFERENCES RESULTS\")\n", + "print(\"=\"*80)\n", + "\n", + "# H3a: Capacity moderator\n", + "print(\"\\n\" + \"-\"*80) \n", + "print(\"H3a: Capacity (High-Capacity Districts)\")\n", + "print(\"-\"*80)\n", + "\n", + "formula_h1 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction'\n", + "model_h1 = smf.ols(formula_h1, data=df_het).fit(cov_type='cluster', \n", + " cov_kwds={'groups': df_het['district']})\n", + "\n", + "interaction_coef_h1 = model_h1.params.get('post_2019:high_capacity', np.nan)\n", + "interaction_se_h1 = model_h1.bse.get('post_2019:high_capacity', np.nan)\n", + "interaction_p_h1 = model_h1.pvalues.get('post_2019:high_capacity', np.nan)\n", + "\n", + "print(f\"Triple-DiD coefficient (post_2019 × high_capacity): {interaction_coef_h1:.4f}\")\n", + "print(f\"Standard error: {interaction_se_h1:.4f}\")\n", + "print(f\"P-value: {interaction_p_h1:.4f}\")\n", + "print(f\"Significant at 5%: {'Yes' if interaction_p_h1 < 0.05 else 'No'}\")\n", + "\n", + "if interaction_coef_h1 < 0:\n", + " print(\"→ High-capacity districts show SMALLER increases in enforcement delays\")\n", + "elif interaction_coef_h1 > 0:\n", + " print(\"→ High-capacity districts show LARGER increases in enforcement delays\")\n", + " \n", + "hypotheses_results['H3a'] = {\n", + " 'coefficient': interaction_coef_h1,\n", + " 'se': interaction_se_h1,\n", + " 'pvalue': interaction_p_h1,\n", + " 'hypothesis': 'Capacity moderates responsiveness',\n", + " 'supported': (interaction_coef_h1 < 0) and (interaction_p_h1 < 0.05)\n", + "}\n", + "\n", + "# H3b: Baseline performance moderator\n", + "print(\"\\n\" + \"-\"*80)\n", + "print(\"H3b: Baseline Performance (Low Baseline Compliance)\")\n", + "print(\"-\"*80)\n", + "\n", + "formula_h2 = 'log_viol_per_insp ~ C(district) + C(year) + post_2019:low_baseline_compliance + post_2019:offshore_jurisdiction'\n", + "model_h2 = smf.ols(formula_h2, data=df_het).fit(cov_type='cluster', \n", + " cov_kwds={'groups': df_het['district']})\n", + "\n", + "interaction_coef_h2 = model_h2.params.get('post_2019:low_baseline_compliance', np.nan)\n", + "interaction_se_h2 = model_h2.bse.get('post_2019:low_baseline_compliance', np.nan)\n", + "interaction_p_h2 = model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan)\n", + "\n", + "print(f\"Triple-DiD coefficient (post_2019 × low_baseline_compliance): {interaction_coef_h2:.4f}\")\n", + "print(f\"Standard error: {interaction_se_h2:.4f}\")\n", + "print(f\"P-value: {interaction_p_h2:.4f}\")\n", + "print(f\"Significant at 5%: {'Yes' if interaction_p_h2 < 0.05 else 'No'}\")\n", + "\n", + "if interaction_coef_h2 < 0:\n", + " print(\"→ Low compliance districts show LARGER reductions in violations\")\n", + "elif interaction_coef_h2 > 0:\n", + " print(\"→ Low compliance districts show SMALLER reductions in violations\")\n", + " \n", + "hypotheses_results['H3b'] = {\n", + " 'coefficient': interaction_coef_h2,\n", + " 'se': interaction_se_h2,\n", + " 'pvalue': interaction_p_h2,\n", + " 'hypothesis': 'Low compliance districts show larger improvements',\n", + " 'supported': (interaction_coef_h2 < 0) and (interaction_p_h2 < 0.05)\n", + "}\n", + "\n", + "# H3e: Border proximity moderator\n", + "print(\"\\n\" + \"-\"*80)\n", + "print(\"H3e: Border Proximity\")\n", + "print(\"-\"*80)\n", + "\n", + "formula_h4 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:border_district + post_2019:offshore_jurisdiction'\n", + "model_h4 = smf.ols(formula_h4, data=df_het).fit(cov_type='cluster', \n", + " cov_kwds={'groups': df_het['district']})\n", + "\n", + "interaction_coef_h4 = model_h4.params.get('post_2019:border_district', np.nan)\n", + "interaction_se_h4 = model_h4.bse.get('post_2019:border_district', np.nan)\n", + "interaction_p_h4 = model_h4.pvalues.get('post_2019:border_district', np.nan)\n", + "\n", + "print(f\"Triple-DiD coefficient (post_2019 × border_district): {interaction_coef_h4:.4f}\")\n", + "print(f\"Standard error: {interaction_se_h4:.4f}\")\n", + "print(f\"P-value: {interaction_p_h4:.4f}\")\n", + "print(f\"Significant at 5%: {'Yes' if interaction_p_h4 < 0.05 else 'No'}\")\n", + "\n", + "if abs(interaction_coef_h4) > 0 and interaction_p_h4 < 0.05:\n", + " print(\"→ Border districts show DIFFERENT response to disclosure policy\")\n", + "else:\n", + " print(\"→ No significant difference for border districts\")\n", + " \n", + "hypotheses_results['H3e'] = {\n", + " 'coefficient': interaction_coef_h4,\n", + " 'se': interaction_se_h4,\n", + " 'pvalue': interaction_p_h4,\n", + " 'hypothesis': 'Border districts behave differently',\n", + " 'supported': (abs(interaction_coef_h4) > 0) and (interaction_p_h4 < 0.05)\n", + "}\n", + "\n", + "# H5: Offshore jurisdiction moderator\n", + "print(\"\\n\" + \"-\"*80)\n", + "print(\"H5: Offshore-Jurisdiction Districts (02/03/04)\")\n", + "print(\"-\"*80)\n", + "\n", + "formula_h5 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model_h5 = smf.ols(formula_h5, data=df_het).fit(cov_type='cluster', \n", + " cov_kwds={'groups': df_het['district']})\n", + "\n", + "interaction_coef_h5 = model_h5.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "interaction_se_h5 = model_h5.bse.get('post_2019:offshore_jurisdiction', np.nan)\n", + "interaction_p_h5 = model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + "print(f\"Differential coefficient (post_2019 × offshore_jurisdiction): {interaction_coef_h5:.4f}\")\n", + "print(f\"Standard error: {interaction_se_h5:.4f}\")\n", + "print(f\"P-value: {interaction_p_h5:.4f}\")\n", + "print(f\"Significant at 5%: {'Yes' if interaction_p_h5 < 0.05 else 'No'}\")\n", + "\n", + "hypotheses_results['H5'] = {\n", + " 'coefficient': interaction_coef_h5,\n", + " 'se': interaction_se_h5,\n", + " 'pvalue': interaction_p_h5,\n", + " 'hypothesis': 'Offshore-jurisdiction districts behave differently',\n", + " 'supported': (abs(interaction_coef_h5) > 0) and (interaction_p_h5 < 0.05)\n", + "}\n", + "\n", + "# Summary\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"HYPOTHESIS TESTING SUMMARY\")\n", + "print(\"=\"*80)\n", + "for h_name, h_result in hypotheses_results.items():\n", + " status = \"✓ SUPPORTED\" if h_result['supported'] else \"✗ NOT SUPPORTED\"\n", + " print(f\"{h_name}: {status}\")\n", + " print(f\" {h_result['hypothesis']}\")\n", + " print(f\" Coefficient: {h_result['coefficient']:.4f} (p={h_result['pvalue']:.4f})\")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "90104c18", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Heterogeneous effects plot saved as 'heterogeneous_effects.png'\n" + ] + } + ], + "source": [ + "# Visualize differential interaction effects (TWFE-consistent)\n", + "\n", + "interaction_rows = [\n", + " {\n", + " 'Hypothesis': 'H3a: High Capacity',\n", + " 'coef': model_h1.params.get('post_2019:high_capacity', np.nan),\n", + " 'se': model_h1.bse.get('post_2019:high_capacity', np.nan),\n", + " 'pvalue': model_h1.pvalues.get('post_2019:high_capacity', np.nan),\n", + " },\n", + " {\n", + " 'Hypothesis': 'H3b: Low Baseline Compliance',\n", + " 'coef': model_h2.params.get('post_2019:low_baseline_compliance', np.nan),\n", + " 'se': model_h2.bse.get('post_2019:low_baseline_compliance', np.nan),\n", + " 'pvalue': model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan),\n", + " },\n", + " {\n", + " 'Hypothesis': 'H3e: Border District',\n", + " 'coef': model_h4.params.get('post_2019:border_district', np.nan),\n", + " 'se': model_h4.bse.get('post_2019:border_district', np.nan),\n", + " 'pvalue': model_h4.pvalues.get('post_2019:border_district', np.nan),\n", + " },\n", + " {\n", + " 'Hypothesis': 'H5: Offshore Jurisdiction',\n", + " 'coef': model_h5.params.get('post_2019:offshore_jurisdiction', np.nan),\n", + " 'se': model_h5.bse.get('post_2019:offshore_jurisdiction', np.nan),\n", + " 'pvalue': model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan),\n", + " }\n", + "]\n", + "\n", + "int_df = pd.DataFrame(interaction_rows)\n", + "int_df['ci_low'] = int_df['coef'] - 1.96 * int_df['se']\n", + "int_df['ci_high'] = int_df['coef'] + 1.96 * int_df['se']\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "y = np.arange(len(int_df))\n", + "colors = ['darkred' if (c > 0) else 'steelblue' for c in int_df['coef']]\n", + "\n", + "ax.barh(y, int_df['coef'], color=colors, alpha=0.75)\n", + "ax.errorbar(\n", + " int_df['coef'], y,\n", + " xerr=[int_df['coef'] - int_df['ci_low'], int_df['ci_high'] - int_df['coef']],\n", + " fmt='none', ecolor='black', capsize=4\n", + ")\n", + "ax.axvline(0, color='black', linestyle='--', linewidth=1)\n", + "ax.set_yticks(y)\n", + "ax.set_yticklabels(int_df['Hypothesis'])\n", + "ax.set_xlabel('Differential effect on log(days to enforcement)')\n", + "ax.set_title('TWFE Interaction Effects (95% CI)')\n", + "ax.grid(True, axis='x', alpha=0.3)\n", + "\n", + "for i, p in enumerate(int_df['pvalue']):\n", + " if pd.notna(p) and p < 0.05:\n", + " ax.text(int_df.loc[i, 'coef'], i, ' *', va='center', fontsize=14, color='black')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('heterogeneous_effects.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print()\n", + "print(\"OK: Heterogeneous effects plot saved as 'heterogeneous_effects.png'\")\n", + "print(\"Interaction estimates:\")\n", + "print(int_df[['Hypothesis', 'coef', 'se', 'pvalue']].to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "fabc134e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "DISTRICT PERFORMANCE ANALYSIS\n", + "================================================================================\n", + "\n", + "High Performers (enforcement improved >40%): ['09', '08', '06', '7B', '6E', '02']\n", + "Low Performers (enforcement worsened >30%): ['10', '04', '03']\n", + "\n", + "================================================================================\n", + "COMPARING HIGH vs LOW PERFORMERS\n", + "================================================================================\n", + "\n", + "--- HIGH PERFORMERS ---\n", + "district coefficient pct_change total_inspections_baseline baseline_compliance_rate baseline_days_to_enf\n", + " 09 -0.9239 -60.3014 79906 80.5994 211.0839\n", + " 08 -0.8820 -58.6034 73122 89.0789 130.9169\n", + " 06 -0.7906 -54.6442 42132 89.0666 451.1938\n", + " 7B -0.6348 -46.9978 46919 80.6755 51.0413\n", + " 6E -0.4534 -36.4538 13327 78.4406 301.2178\n", + " 02 -0.4513 -36.3227 17288 83.0064 228.0713\n", + "\n", + "--- LOW PERFORMERS ---\n", + "district coefficient pct_change total_inspections_baseline baseline_compliance_rate baseline_days_to_enf\n", + " 10 0.3948 48.4118 43570 88.9002 48.6891\n", + " 04 0.6408 89.7959 34783 92.9634 61.5493\n", + " 03 0.6638 94.2124 43477 94.3861 59.0730\n", + "\n", + "================================================================================\n", + "AVERAGE CHARACTERISTICS BY PERFORMANCE\n", + "================================================================================\n", + "\n", + "High Performers (n=6):\n", + " Avg baseline inspections: 45,449\n", + " Avg baseline compliance: 83.5%\n", + " Avg baseline days to enforcement: 228.9\n", + " Avg wells: 8,519\n", + "\n", + "Low Performers (n=3):\n", + " Avg baseline inspections: 40,610\n", + " Avg baseline compliance: 92.1%\n", + " Avg baseline days to enforcement: 56.4\n", + " Avg wells: 6,913\n", + "\n", + "================================================================================\n", + "PRE/POST COMPARISON BY PERFORMANCE GROUP\n", + "================================================================================\n", + "\n", + "High Performers:\n", + " Days to enforcement: 228.9 → 122.8 (-46.3%)\n", + " Compliance rate: 83.5% → 88.0% (+4.6pp)\n", + " Violations per inspection: 0.196 → 0.116 (-41.1%)\n", + " Violation discovery rate: 15.7% → 10.1% (-5.6pp)\n", + " Total inspections: 272,694 → 754,109 (+176.5%)\n", + "\n", + "Low Performers:\n", + " Days to enforcement: 56.4 → 103.7 (+83.7%)\n", + " Compliance rate: 92.1% → 91.1% (-1.0pp)\n", + " Violations per inspection: 0.077 → 0.073 (-6.0%)\n", + " Violation discovery rate: 7.7% → 7.2% (-0.5pp)\n", + " Total inspections: 121,830 → 271,373 (+122.7%)\n", + "\n", + "================================================================================\n", + "KEY INSIGHTS\n", + "================================================================================\n", + "\n", + "High performers improved enforcement speed dramatically, while:\n", + " • Maintaining or improving compliance\n", + " • Potentially reducing inspection intensity (may be more targeted)\n", + " • Finding fewer violations (better deterrence?)\n", + "\n", + "Low performers got slower at enforcement, while:\n", + " • May have experienced increased workload\n", + " • Different regulatory priorities\n", + " • Resource constraints\n", + "\n", + "✓ District performance comparison complete\n" + ] + } + ], + "source": [ + "# Deep dive: What distinguishes high vs low performing districts?\n", + "\n", + "print(\"=\"*80)\n", + "print(\"DISTRICT PERFORMANCE ANALYSIS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Get treatment effects from model 2\n", + "district_treatment_effects = effects_df.copy()\n", + "district_treatment_effects = district_treatment_effects.reset_index()\n", + "\n", + "# Classify districts by performance\n", + "district_treatment_effects['performance'] = 'Middle'\n", + "district_treatment_effects.loc[district_treatment_effects['coefficient'] < -0.4, 'performance'] = 'High Performers'\n", + "district_treatment_effects.loc[district_treatment_effects['coefficient'] > 0.3, 'performance'] = 'Low Performers'\n", + "\n", + "high_performers = district_treatment_effects[district_treatment_effects['performance'] == 'High Performers']['district'].tolist()\n", + "low_performers = district_treatment_effects[district_treatment_effects['performance'] == 'Low Performers']['district'].tolist()\n", + "\n", + "print(f\"\\nHigh Performers (enforcement improved >40%): {high_performers}\")\n", + "print(f\"Low Performers (enforcement worsened >30%): {low_performers}\")\n", + "\n", + "# Compare characteristics\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"COMPARING HIGH vs LOW PERFORMERS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Merge treatment effects with baseline characteristics\n", + "comparison_df = district_treatment_effects.merge(baseline_data, on='district', how='left')\n", + "\n", + "# Compare high vs low performers\n", + "high_perf_stats = comparison_df[comparison_df['performance'] == 'High Performers']\n", + "low_perf_stats = comparison_df[comparison_df['performance'] == 'Low Performers']\n", + "\n", + "print(\"\\n--- HIGH PERFORMERS ---\")\n", + "print(high_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n", + " 'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n", + "\n", + "print(\"\\n--- LOW PERFORMERS ---\")\n", + "print(low_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n", + " 'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n", + "\n", + "# Statistical comparison\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"AVERAGE CHARACTERISTICS BY PERFORMANCE\")\n", + "print(\"=\"*80)\n", + "\n", + "for group_name, group_data in [('High Performers', high_perf_stats), \n", + " ('Low Performers', low_perf_stats)]:\n", + " print(f\"\\n{group_name} (n={len(group_data)}):\")\n", + " print(f\" Avg baseline inspections: {group_data['total_inspections_baseline'].mean():,.0f}\")\n", + " print(f\" Avg baseline compliance: {group_data['baseline_compliance_rate'].mean():.1f}%\")\n", + " print(f\" Avg baseline days to enforcement: {group_data['baseline_days_to_enf'].mean():.1f}\")\n", + " print(f\" Avg wells: {group_data['avg_wells'].mean():,.0f}\")\n", + "\n", + "# Look at pre/post trends for these districts\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"PRE/POST COMPARISON BY PERFORMANCE GROUP\")\n", + "print(\"=\"*80)\n", + "\n", + "for group_name, districts in [('High Performers', high_performers), \n", + " ('Low Performers', low_performers)]:\n", + " group_data = district_year_panel[district_year_panel['district'].isin(districts)]\n", + " \n", + " pre = group_data[group_data['year'] < 2019].agg({\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'compliance_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " 'total_inspections': 'sum',\n", + " 'violation_discovery_rate': 'mean'\n", + " })\n", + " \n", + " post = group_data[group_data['year'] >= 2019].agg({\n", + " 'avg_days_to_enforcement': 'mean',\n", + " 'compliance_rate': 'mean',\n", + " 'violations_per_inspection': 'mean',\n", + " 'total_inspections': 'sum',\n", + " 'violation_discovery_rate': 'mean'\n", + " })\n", + " \n", + " print(f\"\\n{group_name}:\")\n", + " print(f\" Days to enforcement: {pre['avg_days_to_enforcement']:.1f} → {post['avg_days_to_enforcement']:.1f} \"\n", + " f\"({((post['avg_days_to_enforcement']/pre['avg_days_to_enforcement'])-1)*100:+.1f}%)\")\n", + " print(f\" Compliance rate: {pre['compliance_rate']:.1f}% → {post['compliance_rate']:.1f}% \"\n", + " f\"({post['compliance_rate']-pre['compliance_rate']:+.1f}pp)\")\n", + " print(f\" Violations per inspection: {pre['violations_per_inspection']:.3f} → {post['violations_per_inspection']:.3f} \"\n", + " f\"({((post['violations_per_inspection']/pre['violations_per_inspection'])-1)*100:+.1f}%)\")\n", + " print(f\" Violation discovery rate: {pre['violation_discovery_rate']:.1f}% → {post['violation_discovery_rate']:.1f}% \"\n", + " f\"({post['violation_discovery_rate']-pre['violation_discovery_rate']:+.1f}pp)\")\n", + " print(f\" Total inspections: {pre['total_inspections']:,.0f} → {post['total_inspections']:,.0f} \"\n", + " f\"({((post['total_inspections']/pre['total_inspections'])-1)*100:+.1f}%)\")\n", + "\n", + "# Key differences summary\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"KEY INSIGHTS\")\n", + "print(\"=\"*80)\n", + "\n", + "print(\"\\nHigh performers improved enforcement speed dramatically, while:\")\n", + "print(\" • Maintaining or improving compliance\")\n", + "print(\" • Potentially reducing inspection intensity (may be more targeted)\")\n", + "print(\" • Finding fewer violations (better deterrence?)\")\n", + "\n", + "print(\"\\nLow performers got slower at enforcement, while:\")\n", + "print(\" • May have experienced increased workload\")\n", + "print(\" • Different regulatory priorities\")\n", + "print(\" • Resource constraints\")\n", + "\n", + "print(\"\\n✓ District performance comparison complete\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "33e621d0", + "metadata": {}, + "source": [ + "# District-Level Analysis: Heterogeneous Effects of 2019 RRC Disclosure Policy\n", + "\n", + "## Research Question\n", + "How did the January 2019 policy change (making well-specific violation data publicly searchable) affect enforcement patterns across RRC districts with different characteristics?\n", + "\n", + "## Key Hypotheses\n", + "- **H1**: High-capacity districts show smaller disclosure effects\n", + "- **H2**: Districts with poor baseline compliance show larger improvements \n", + "- **H3**: Environmental justice concerns moderate disclosure impacts\n", + "- **H4**: Border districts behave differently due to competition\n", + "\n", + "## Analytical Approach\n", + "1. **Descriptive Analysis**: District-level summary statistics pre/post 2019\n", + "2. **Difference-in-Differences**: Basic DiD with district×post2019 interactions\n", + "3. **Event Study**: Test parallel trends and estimate dynamic treatment effects\n", + "4. **Triple Difference**: Test moderators (EJ, capacity, baseline compliance, border proximity)\n", + "5. **Spatial Analysis**: Map heterogeneous treatment effects and test for spillovers\n", + "6. **Robustness**: Alternative specifications, placebo tests, sensitivity checks\n", + "\n", + "## Data Sources\n", + "- PostgreSQL database: well locations, violations (2015-2024), inspections, enforcement actions\n", + "- analysis_output.json: pre-computed district-level statistics\n", + "- Spatial layers: district boundaries, demographics, EJ indicators" + ] + }, + { + "cell_type": "markdown", + "id": "c7b729db", + "metadata": {}, + "source": [ + "## Part 6: Spatial Analysis\n", + "\n", + "### Geographic Patterns in Treatment Effects\n", + "\n", + "Now that we've identified massive heterogeneity in how districts responded to the 2019 policy, let's examine **spatial patterns**:\n", + "\n", + "1. **Spatial autocorrelation**: Do neighboring districts have similar treatment effects?\n", + "2. **Geographic clusters**: Are high/low performers geographically clustered?\n", + "3. **Spillover effects**: Does one district's response affect its neighbors?\n", + "\n", + "This helps answer: Is the heterogeneity random or geographically structured?" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "510085f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "SPATIAL MAPPING OF TREATMENT EFFECTS\n", + "================================================================================\n", + "\n", + "District Treatment Effects Summary:\n", + "district coefficient pct_change effect_category\n", + " 09 -0.9239 -60.3014 Strong Improvement\n", + " 08 -0.8820 -58.6034 Strong Improvement\n", + " 06 -0.7906 -54.6442 Strong Improvement\n", + " 7B -0.6348 -46.9978 Strong Improvement\n", + " 6E -0.4534 -36.4538 Moderate Improvement\n", + " 02 -0.4513 -36.3227 Moderate Improvement\n", + " 7C -0.1121 -10.6071 Moderate Improvement\n", + " 8A -0.0547 -5.3278 No Change\n", + " 05 -0.0520 -5.0693 No Change\n", + " 01 0.0335 3.4088 No Change\n", + " 10 0.3948 48.4118 Strong Decline\n", + " 04 0.6408 89.7959 Strong Decline\n", + " 03 0.6638 94.2124 Strong Decline\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Spatial map saved as 'district_spatial_map.png'\n", + "\n", + "================================================================================\n", + "GEOGRAPHIC PATTERNS\n", + "================================================================================\n", + "\n", + "West Texas / Permian Basin (Districts 7B, 7C, 6E):\n", + " Average treatment effect: -31.4%\n", + "\n", + "South Texas / Border (Districts 03, 04, 08, 8A):\n", + " Average treatment effect: 30.0%\n", + "\n", + "Panhandle (Districts 09, 10):\n", + " Average treatment effect: -5.9%\n", + "\n", + "East Texas (Districts 01, 02, 05):\n", + " Average treatment effect: -12.7%\n", + "\n", + "✓ Spatial analysis complete\n" + ] + } + ], + "source": [ + "# Spatial map of district-specific treatment effects\n", + "\n", + "print(\"=\"*80)\n", + "print(\"SPATIAL MAPPING OF TREATMENT EFFECTS\")\n", + "print(\"=\"*80)\n", + "\n", + "# Prepare data for mapping\n", + "map_data = effects_df.reset_index()\n", + "map_data['treatment_effect_pct'] = map_data['pct_change']\n", + "map_data['effect_category'] = pd.cut(map_data['pct_change'], \n", + " bins=[-100, -40, -10, 10, 40, 200],\n", + " labels=['Strong Improvement', 'Moderate Improvement', \n", + " 'No Change', 'Moderate Decline', 'Strong Decline'])\n", + "\n", + "print(\"\\nDistrict Treatment Effects Summary:\")\n", + "print(map_data[['district', 'coefficient', 'pct_change', 'effect_category']].sort_values('pct_change').to_string(index=False))\n", + "\n", + "# Create a simple visualization showing district locations and effects\n", + "# Texas RRC district rough geographic positions (approximate)\n", + "district_positions = {\n", + " '01': (32.7, -96.8), # East Texas (Tyler area)\n", + " '02': (30.2, -94.0), # Southeast (Beaumont/Houston area)\n", + " '03': (29.5, -99.0), # South Texas (San Antonio area)\n", + " '04': (29.3, -100.9), # South Texas (Eagle Pass area)\n", + " '05': (31.5, -99.5), # Central Texas (Brady area)\n", + " '06': (31.8, -106.4), # West Texas (El Paso area)\n", + " '7B': (31.8, -102.4), # Permian Basin (Midland area)\n", + " '7C': (32.0, -101.5), # Permian Basin (Big Spring area)\n", + " '08': (28.0, -99.0), # South Texas (Laredo area)\n", + " '8A': (26.2, -98.2), # South Texas (McAllen area)\n", + " '09': (33.5, -101.9), # Panhandle (Lubbock area)\n", + " '10': (35.2, -101.8), # Panhandle (Amarillo area)\n", + " '6E': (31.0, -103.5), # West Texas (Pecos area)\n", + "}\n", + "\n", + "# Add coordinates to map data\n", + "map_data['lat'] = map_data['district'].map(lambda x: district_positions.get(x, (0, 0))[0])\n", + "map_data['lon'] = map_data['district'].map(lambda x: district_positions.get(x, (0, 0))[1])\n", + "\n", + "# Create spatial visualization\n", + "fig, ax = plt.subplots(figsize=(16, 12))\n", + "\n", + "# Color by performance\n", + "colors = []\n", + "for pct in map_data['pct_change']:\n", + " if pct < -40:\n", + " colors.append('darkgreen')\n", + " elif pct < -10:\n", + " colors.append('lightgreen')\n", + " elif pct < 10:\n", + " colors.append('gray')\n", + " elif pct < 40:\n", + " colors.append('orange')\n", + " else:\n", + " colors.append('darkred')\n", + "\n", + "# Plot districts\n", + "scatter = ax.scatter(map_data['lon'], map_data['lat'], \n", + " c=colors, s=1000, alpha=0.7, edgecolors='black', linewidth=2, zorder=3)\n", + "\n", + "# Add district labels and values\n", + "for _, row in map_data.iterrows():\n", + " ax.text(row['lon'], row['lat'], f\"{row['district']}\\n{row['pct_change']:.0f}%\", \n", + " ha='center', va='center', fontsize=11, fontweight='bold', zorder=4)\n", + "\n", + "# Styling\n", + "ax.set_xlabel('Longitude', fontsize=13, fontweight='bold')\n", + "ax.set_ylabel('Latitude', fontsize=13, fontweight='bold')\n", + "ax.set_title('Geographic Distribution of District Treatment Effects\\n2019 Disclosure Policy Impact on Enforcement Speed', \n", + " fontsize=15, fontweight='bold', pad=20)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Add legend\n", + "from matplotlib.patches import Patch\n", + "legend_elements = [\n", + " Patch(facecolor='darkgreen', label='Strong Improvement (< -40%)'),\n", + " Patch(facecolor='lightgreen', label='Moderate Improvement (-40% to -10%)'),\n", + " Patch(facecolor='gray', label='No Change (-10% to +10%)'),\n", + " Patch(facecolor='orange', label='Moderate Decline (+10% to +40%)'),\n", + " Patch(facecolor='darkred', label='Strong Decline (> +40%)')\n", + "]\n", + "ax.legend(handles=legend_elements, loc='upper left', fontsize=11, frameon=True, shadow=True)\n", + "\n", + "# Add Texas outline note\n", + "ax.text(0.02, 0.02, 'Note: District positions are approximate center points', \n", + " transform=ax.transAxes, fontsize=9, style='italic', alpha=0.7)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('district_spatial_map.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(\"\\n✓ Spatial map saved as 'district_spatial_map.png'\")\n", + "\n", + "# Geographic patterns analysis\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"GEOGRAPHIC PATTERNS\")\n", + "print(\"=\"*80)\n", + "\n", + "print(\"\\nWest Texas / Permian Basin (Districts 7B, 7C, 6E):\")\n", + "west_districts = map_data[map_data['district'].isin(['7B', '7C', '6E'])]\n", + "print(f\" Average treatment effect: {west_districts['pct_change'].mean():.1f}%\")\n", + "\n", + "print(\"\\nSouth Texas / Border (Districts 03, 04, 08, 8A):\")\n", + "south_districts = map_data[map_data['district'].isin(['03', '04', '08', '8A'])]\n", + "print(f\" Average treatment effect: {south_districts['pct_change'].mean():.1f}%\")\n", + "\n", + "print(\"\\nPanhandle (Districts 09, 10):\")\n", + "panhandle_districts = map_data[map_data['district'].isin(['09', '10'])]\n", + "print(f\" Average treatment effect: {panhandle_districts['pct_change'].mean():.1f}%\")\n", + "\n", + "print(\"\\nEast Texas (Districts 01, 02, 05):\")\n", + "east_districts = map_data[map_data['district'].isin(['01', '02', '05'])]\n", + "print(f\" Average treatment effect: {east_districts['pct_change'].mean():.1f}%\")\n", + "\n", + "print(\"\\n✓ Spatial analysis complete\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "fa8946f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "SPATIAL SPILLOVER ANALYSIS\n", + "================================================================================\n", + "\n", + "District own effects vs. neighbor averages:\n", + "district own_effect neighbor_avg n_neighbors\n", + " 09 -60.3014 -3.0643 3\n", + " 08 -58.6034 42.2340 2\n", + " 06 -54.6442 26.6710 2\n", + " 7B -46.9978 -35.7874 3\n", + " 6E -36.4538 -29.3296 4\n", + " 02 -36.3227 -0.8303 2\n", + " 7C -10.6071 -47.9177 4\n", + " 8A -5.3278 17.8045 2\n", + " 05 -5.0693 2.8475 5\n", + " 01 3.4088 -20.6960 2\n", + " 10 48.4118 -60.3014 1\n", + " 04 89.7959 -6.3450 3\n", + " 03 94.2124 26.4663 3\n", + "\n", + "✓ Correlation between own and neighbor effects: -0.110\n", + " → Weak spatial autocorrelation: effects appear geographically random\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Spatial spillover analysis complete\n", + "✓ Saved: spatial_spillovers.png\n" + ] + } + ], + "source": [ + "# Spatial dynamics: Moran's I for district treatment effects\n", + "\n", + "print(\"=\"*80)\n", + "print(\"SPATIAL DYNAMICS: MORAN'S I\")\n", + "print(\"=\"*80)\n", + "\n", + "neighbors = {\n", + " '01': ['02', '05'],\n", + " '02': ['01', '05'],\n", + " '03': ['04', '05', '8A'],\n", + " '04': ['03', '06', '08'],\n", + " '05': ['01', '02', '03', '6E', '7C'],\n", + " '06': ['04', '6E'],\n", + " '6E': ['05', '06', '7B', '7C'],\n", + " '7B': ['6E', '7C', '09'],\n", + " '7C': ['05', '6E', '7B', '09'],\n", + " '08': ['04', '8A'],\n", + " '8A': ['03', '08'],\n", + " '09': ['7B', '7C', '10'],\n", + " '10': ['09']\n", + "}\n", + "\n", + "valid = map_data[['district', 'pct_change']].dropna().copy().sort_values('district')\n", + "districts = valid['district'].tolist()\n", + "y = valid['pct_change'].astype(float).to_numpy()\n", + "n = len(districts)\n", + "\n", + "d_idx = {d: i for i, d in enumerate(districts)}\n", + "W = np.zeros((n, n), dtype=float)\n", + "for d in districts:\n", + " i = d_idx[d]\n", + " nbrs = [k for k in neighbors.get(d, []) if k in d_idx]\n", + " if len(nbrs) == 0:\n", + " continue\n", + " w = 1.0 / len(nbrs)\n", + " for k in nbrs:\n", + " W[i, d_idx[k]] = w\n", + "\n", + "S0 = W.sum()\n", + "z = y - y.mean()\n", + "\n", + "if S0 > 0 and np.dot(z, z) > 0:\n", + " moran_i = (n / S0) * ((z @ W @ z) / (z @ z))\n", + "else:\n", + " moran_i = np.nan\n", + "\n", + "rng = np.random.default_rng(42)\n", + "B = 5000\n", + "if pd.notna(moran_i):\n", + " perm_stats = []\n", + " for _ in range(B):\n", + " zp = rng.permutation(z)\n", + " ip = (n / S0) * ((zp @ W @ zp) / (zp @ zp))\n", + " perm_stats.append(ip)\n", + " perm_stats = np.array(perm_stats)\n", + " p_two_sided = (np.sum(np.abs(perm_stats) >= abs(moran_i)) + 1) / (B + 1)\n", + "else:\n", + " p_two_sided = np.nan\n", + "\n", + "print(f\"N districts: {n}\")\n", + "print(f\"Moran's I: {moran_i:.4f}\")\n", + "print(f\"Permutation p-value (two-sided, B={B}): {p_two_sided:.4f}\")\n", + "\n", + "if pd.notna(p_two_sided):\n", + " if p_two_sided < 0.05 and moran_i > 0:\n", + " print(\"Interpretation: positive spatial autocorrelation (supports H4)\")\n", + " elif p_two_sided < 0.05 and moran_i < 0:\n", + " print(\"Interpretation: significant negative spatial autocorrelation\")\n", + " else:\n", + " print(\"Interpretation: no statistically significant spatial autocorrelation\")\n", + "\n", + "try:\n", + " import libpysal\n", + " from esda.moran import Moran\n", + "\n", + " w_obj = {d: [k for k in neighbors.get(d, []) if k in d_idx] for d in districts}\n", + " w_ps = libpysal.weights.W(w_obj)\n", + " w_ps.transform = 'r'\n", + " moran_pkg = Moran(y, w_ps, permutations=999)\n", + "\n", + " print()\n", + " print(\"Package check (esda/libpysal):\")\n", + " print(f\" Moran's I: {moran_pkg.I:.4f}\")\n", + " print(f\" Permutation p-value (two-sided): {moran_pkg.p_sim:.4f}\")\n", + "except Exception:\n", + " print()\n", + " print(\"Package check skipped (esda/libpysal unavailable).\")\n", + "\n", + "lag_y = W @ y\n", + "fig, ax = plt.subplots(figsize=(9, 7))\n", + "ax.scatter(y, lag_y, s=140, edgecolor='black', alpha=0.75)\n", + "for d, xv, yv in zip(districts, y, lag_y):\n", + " ax.text(xv, yv, f\" {d}\", fontsize=9)\n", + "\n", + "coef = np.polyfit(y, lag_y, 1)\n", + "poly = np.poly1d(coef)\n", + "xs = np.linspace(y.min(), y.max(), 100)\n", + "ax.plot(xs, poly(xs), '--', linewidth=2)\n", + "\n", + "ax.axhline(0, color='gray', linestyle='--', linewidth=1)\n", + "ax.axvline(0, color='gray', linestyle='--', linewidth=1)\n", + "ax.set_xlabel('District treatment effect (%)')\n", + "ax.set_ylabel('Spatial lag of treatment effect')\n", + "ax.set_title(\"Moran Scatterplot for District Treatment Effects\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.savefig('spatial_spillovers.png', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print()\n", + "print(\"Saved: spatial_spillovers.png\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cf9c7ade", + "metadata": {}, + "source": [ + "# Part 7: Robustness Checks and Sensitivity Analysis\n", + "\n", + "Now that we've established the heterogeneous treatment effects, let's validate our findings through several robustness tests:\n", + "\n", + "1. **Placebo tests**: Run DiD with fake policy dates (should show no effect)\n", + "2. **Alternative outcomes**: Test with other measures (compliance rate, violations per inspection)\n", + "3. **Sample restrictions**: Exclude outlier districts and re-run\n", + "4. **Time sensitivity**: Test different time windows" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "ce410119", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ROBUSTNESS TEST 1: PLACEBO TESTS\n", + "================================================================================\n", + "Testing fake policy years for offshore-jurisdiction differential effects\n", + "\n", + "============================================================\n", + "Placebo Test: Fake policy in 2017\n", + "============================================================\n", + "Differential placebo effect (offshore-jurisdiction districts):\n", + " Coefficient: 0.3114\n", + " Std Error: 0.3428\n", + " P-value: 0.3637\n", + " 95% CI: [-0.3605, 0.9832]\n", + " Percent change: +36.5%\n", + "\n", + "============================================================\n", + "Placebo Test: Fake policy in 2021\n", + "============================================================\n", + "Differential placebo effect (offshore-jurisdiction districts):\n", + " Coefficient: 0.7854\n", + " Std Error: 0.3533\n", + " P-value: 0.0262\n", + " 95% CI: [0.0930, 1.4778]\n", + " Percent change: +119.3%\n", + "\n", + "================================================================================\n", + "PLACEBO TEST SUMMARY\n", + "================================================================================\n", + " placebo_year coefficient std_error pvalue pct_change significant\n", + " 2017 0.3114 0.3428 0.3637 36.5311 False\n", + " 2021 0.7854 0.3533 0.0262 119.3319 True\n" + ] + } + ], + "source": [ + "## Robustness Test 1: Placebo Tests with Fake Policy Dates\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"ROBUSTNESS TEST 1: PLACEBO TESTS\")\n", + "print(\"=\" * 80)\n", + "print(\"Testing fake policy years for offshore-jurisdiction differential effects\")\n", + "\n", + "placebo_years = [2017, 2021]\n", + "placebo_results = []\n", + "\n", + "for placebo_year in placebo_years:\n", + " print()\n", + " print(\"=\"*60)\n", + " print(f\"Placebo Test: Fake policy in {placebo_year}\")\n", + " print(\"=\"*60)\n", + "\n", + " df_placebo = df_reg.copy()\n", + " df_placebo['post_placebo'] = (df_placebo['year'] >= placebo_year).astype(int)\n", + "\n", + " formula = 'log_days_to_enf ~ C(district) + C(year) + post_placebo:offshore_jurisdiction'\n", + " model_placebo = smf.ols(formula, data=df_placebo).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_placebo['district']}\n", + " )\n", + "\n", + " term = 'post_placebo:offshore_jurisdiction'\n", + " coef = model_placebo.params.get(term, np.nan)\n", + " se = model_placebo.bse.get(term, np.nan)\n", + " pval = model_placebo.pvalues.get(term, np.nan)\n", + "\n", + " if term in model_placebo.params.index:\n", + " ci_lower = model_placebo.conf_int().loc[term, 0]\n", + " ci_upper = model_placebo.conf_int().loc[term, 1]\n", + " else:\n", + " ci_lower, ci_upper = np.nan, np.nan\n", + "\n", + " pct_change = (np.exp(coef) - 1) * 100 if pd.notna(coef) else np.nan\n", + "\n", + " print(\"Differential placebo effect (offshore-jurisdiction districts):\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" Std Error: {se:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + " print(f\" 95% CI: [{ci_lower:.4f}, {ci_upper:.4f}]\")\n", + " print(f\" Percent change: {pct_change:+.1f}%\")\n", + "\n", + " placebo_results.append({\n", + " 'placebo_year': placebo_year,\n", + " 'coefficient': coef,\n", + " 'std_error': se,\n", + " 'pvalue': pval,\n", + " 'pct_change': pct_change,\n", + " 'significant': (pval < 0.05) if pd.notna(pval) else False\n", + " })\n", + "\n", + "placebo_df = pd.DataFrame(placebo_results)\n", + "print()\n", + "print(\"=\"*80)\n", + "print(\"PLACEBO TEST SUMMARY\")\n", + "print(\"=\"*80)\n", + "print(placebo_df.to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5c9294c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "ROBUSTNESS TEST 2: ALTERNATIVE OUTCOME MEASURES\n", + "================================================================================\n", + "Testing offshore-jurisdiction differential effects across outcomes\n", + "\n", + "Outcome: Compliance Rate (pp)\n", + " Coefficient: -2.7970\n", + " P-value: 0.4370\n", + "\n", + "Outcome: Violations per Inspection\n", + " Coefficient: 0.0401\n", + " P-value: 0.2865\n", + "\n", + "Outcome: Log(Total Violations)\n", + " Coefficient: 0.3713\n", + " P-value: 0.2843\n", + "\n", + "================================================================================\n", + "ALTERNATIVE OUTCOMES SUMMARY\n", + "================================================================================\n", + " outcome coefficient pvalue significant\n", + " Compliance Rate (pp) -2.7970 0.4370 False\n", + "Violations per Inspection 0.0401 0.2865 False\n", + " Log(Total Violations) 0.3713 0.2843 False\n" + ] + } + ], + "source": [ + "## Robustness Test 2: Alternative Outcome Measures\n", + "\n", + "print()\n", + "print(\"=\" * 80)\n", + "print(\"ROBUSTNESS TEST 2: ALTERNATIVE OUTCOME MEASURES\")\n", + "print(\"=\" * 80)\n", + "print(\"Testing offshore-jurisdiction differential effects across outcomes\")\n", + "\n", + "# Prepare alternative outcomes\n", + "df_alt = district_year_panel.copy()\n", + "df_alt['post_2019'] = (df_alt['year'] >= 2019).astype(int)\n", + "\n", + "alternative_outcomes = []\n", + "\n", + "# Outcome 1: Measured compliance rate (percentage points)\n", + "if 'compliance_rate' in df_alt.columns:\n", + " df_alt['compliance_rate_pp'] = df_alt['compliance_rate']\n", + "\n", + " formula = 'compliance_rate_pp ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + " model_alt1 = smf.ols(formula, data=df_alt).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_alt['district']}\n", + " )\n", + "\n", + " coef = model_alt1.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " pval = model_alt1.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + " print()\n", + " print(\"Outcome: Compliance Rate (pp)\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + "\n", + " alternative_outcomes.append({\n", + " 'outcome': 'Compliance Rate (pp)',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'significant': pval < 0.05\n", + " })\n", + "\n", + "# Outcome 2: Violations per inspection\n", + "if 'total_violations' in df_alt.columns and 'total_inspections' in df_alt.columns:\n", + " df_alt['violations_per_inspection'] = (\n", + " df_alt['total_violations'] / df_alt['total_inspections']\n", + " ).replace([np.inf, -np.inf], np.nan)\n", + "\n", + " df_vpi = df_alt.dropna(subset=['violations_per_inspection'])\n", + "\n", + " formula = 'violations_per_inspection ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + " model_alt2 = smf.ols(formula, data=df_vpi).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_vpi['district']}\n", + " )\n", + "\n", + " coef = model_alt2.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " pval = model_alt2.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + " print()\n", + " print(\"Outcome: Violations per Inspection\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + "\n", + " alternative_outcomes.append({\n", + " 'outcome': 'Violations per Inspection',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'significant': pval < 0.05\n", + " })\n", + "\n", + "# Outcome 3: Log of total violations\n", + "if 'total_violations' in df_alt.columns:\n", + " df_alt['log_violations'] = np.log(df_alt['total_violations'].replace(0, 0.1))\n", + "\n", + " formula = 'log_violations ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + " model_alt3 = smf.ols(formula, data=df_alt).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_alt['district']}\n", + " )\n", + "\n", + " coef = model_alt3.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " pval = model_alt3.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + " print()\n", + " print(\"Outcome: Log(Total Violations)\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + "\n", + " alternative_outcomes.append({\n", + " 'outcome': 'Log(Total Violations)',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'significant': pval < 0.05\n", + " })\n", + "\n", + "if alternative_outcomes:\n", + " alt_df = pd.DataFrame(alternative_outcomes)\n", + " print()\n", + " print(\"=\"*80)\n", + " print(\"ALTERNATIVE OUTCOMES SUMMARY\")\n", + " print(\"=\"*80)\n", + " print(alt_df.to_string(index=False))\n", + "else:\n", + " print(\"WARN: Could not compute alternative outcomes (missing data)\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "63a2ea9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS\n", + "================================================================================\n", + "Testing offshore-jurisdiction differential effect sensitivity\n", + "\n", + "Restriction: Exclude Extreme Outliers (['09', '02', '03', '10'])\n", + " Coefficient: 1.0153; p-value: 0.0000\n", + "\n", + "Restriction: Exclude 2015-2016\n", + " Coefficient: 0.7705; p-value: 0.0545\n", + "\n", + "Restriction: Exclude Pandemic Years (2020-2021)\n", + " Coefficient: 0.7126; p-value: 0.0249\n", + "\n", + "================================================================================\n", + "SAMPLE RESTRICTIONS SUMMARY\n", + "================================================================================\n", + " restriction n_districts coefficient pvalue\n", + "Exclude Extreme Outliers 9 1.0153 0.0000\n", + " Exclude 2015-2016 13 0.7705 0.0545\n", + " Exclude Pandemic Years 13 0.7126 0.0249\n" + ] + } + ], + "source": [ + "## Robustness Test 3: Sample Restrictions\n", + "\n", + "print()\n", + "print(\"=\" * 80)\n", + "print(\"ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS\")\n", + "print(\"=\" * 80)\n", + "print(\"Testing offshore-jurisdiction differential effect sensitivity\")\n", + "\n", + "sample_restrictions = []\n", + "\n", + "def fit_offshore_model(df_in):\n", + " formula = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + " m = smf.ols(formula, data=df_in).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_in['district']}\n", + " )\n", + " coef = m.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " pval = m.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + " return m, coef, pval\n", + "\n", + "# Restriction 1: Exclude extreme districts\n", + "extreme_districts = []\n", + "if 'district_effects' in locals() and len(district_effects) >= 4:\n", + " sorted_effects = district_effects.sort_values('coefficient')\n", + " extreme_districts = sorted_effects.head(2)['district'].tolist() + sorted_effects.tail(2)['district'].tolist()\n", + "\n", + "if extreme_districts:\n", + " df_restricted = df_reg[~df_reg['district'].isin(extreme_districts)].copy()\n", + " _, coef, pval = fit_offshore_model(df_restricted)\n", + " print()\n", + " print(f\"Restriction: Exclude Extreme Outliers ({extreme_districts})\")\n", + " print(f\" Coefficient: {coef:.4f}; p-value: {pval:.4f}\")\n", + " sample_restrictions.append({'restriction': 'Exclude Extreme Outliers', 'n_districts': df_restricted['district'].nunique(), 'coefficient': coef, 'pvalue': pval})\n", + "\n", + "# Restriction 2: Exclude 2015-2016\n", + "df_recent = df_reg[df_reg['year'] >= 2017].copy()\n", + "_, coef, pval = fit_offshore_model(df_recent)\n", + "print()\n", + "print(\"Restriction: Exclude 2015-2016\")\n", + "print(f\" Coefficient: {coef:.4f}; p-value: {pval:.4f}\")\n", + "sample_restrictions.append({'restriction': 'Exclude 2015-2016', 'n_districts': df_recent['district'].nunique(), 'coefficient': coef, 'pvalue': pval})\n", + "\n", + "# Restriction 3: Exclude pandemic years\n", + "df_no_pandemic = df_reg[~df_reg['year'].isin([2020, 2021])].copy()\n", + "_, coef, pval = fit_offshore_model(df_no_pandemic)\n", + "print()\n", + "print(\"Restriction: Exclude Pandemic Years (2020-2021)\")\n", + "print(f\" Coefficient: {coef:.4f}; p-value: {pval:.4f}\")\n", + "sample_restrictions.append({'restriction': 'Exclude Pandemic Years', 'n_districts': df_no_pandemic['district'].nunique(), 'coefficient': coef, 'pvalue': pval})\n", + "\n", + "if sample_restrictions:\n", + " restrict_df = pd.DataFrame(sample_restrictions)\n", + " print()\n", + " print(\"=\"*80)\n", + " print(\"SAMPLE RESTRICTIONS SUMMARY\")\n", + " print(\"=\"*80)\n", + " print(restrict_df.to_string(index=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1bb308ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\n", + "================================================================================\n", + "\n", + "Testing alternative functional forms and control strategies...\n", + "\n", + "============================================================\n", + "Specification 1: Linear Days (no log transformation)\n", + "============================================================\n", + "\n", + "Linear effect on days to enforcement:\n", + " Coefficient: 80.14 days\n", + " P-value: 0.1227\n", + " Interpretation: 80.1 days slower enforcement\n", + "\n", + "============================================================\n", + "Specification 2: Year Fixed Effects (not just post dummy)\n", + "============================================================\n", + "\n", + "Average effect across post-2019 years:\n", + " Mean coefficient: -0.1144\n", + " Percent change: -10.8%\n", + " Years included: 6\n", + "\n", + "============================================================\n", + "Specification 3: District-Specific Time Trends\n", + "============================================================\n", + "\n", + "Pooled effect with district-specific trends:\n", + " Coefficient: 0.1667\n", + " P-value: 0.6063\n", + " Percent change: +18.1%\n", + " Controls for: pre-existing district-specific trends\n", + "\n", + "============================================================\n", + "Specification 4: Winsorized Outcome (top/bottom 5%)\n", + "============================================================\n", + "\n", + "Pooled effect with winsorized outcome:\n", + " Coefficient: 0.5889\n", + " P-value: 0.1137\n", + " Percent change: +80.2%\n", + " Robust to: extreme outliers\n", + "\n", + "================================================================================\n", + "SPECIFICATION SENSITIVITY SUMMARY\n", + "================================================================================\n", + " specification coefficient pvalue interpretation\n", + " Linear (no log) 80.1362 0.1227 80.1 days\n", + "Year FE (average) -0.1144 NaN -10.8%\n", + " District Trends 0.1667 0.6063 +18.1%\n", + " Winsorized 0.5889 0.1137 +80.2%\n", + "\n", + "Baseline specification: +89.1%\n", + "⚠️ Some specification sensitivity detected (range: 0.422)\n" + ] + } + ], + "source": [ + "## Robustness Test 4: Sensitivity to Specification\n", + "\n", + "# Test different model specifications to ensure results aren't driven by \n", + "# specific functional form choices\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\")\n", + "print(\"=\" * 80)\n", + "print(\"\\nTesting alternative functional forms and control strategies...\\n\")\n", + "\n", + "specification_tests = []\n", + "\n", + "# Specification 1: Linear (non-log) outcome\n", + "print(\"=\"*60)\n", + "print(\"Specification 1: Linear Days (no log transformation)\")\n", + "print(\"=\"*60)\n", + "\n", + "df_linear = df_reg.copy()\n", + "\n", + "formula = 'avg_days_to_enforcement ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model_linear = smf.ols(formula, data=df_linear).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_linear['district']}\n", + ")\n", + "\n", + "coef = model_linear.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pval = model_linear.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "\n", + "print(f\"\\nLinear effect on days to enforcement:\")\n", + "print(f\" Coefficient: {coef:.2f} days\")\n", + "print(f\" P-value: {pval:.4f}\")\n", + "print(f\" Interpretation: {abs(coef):.1f} days {'faster' if coef < 0 else 'slower'} enforcement\")\n", + "\n", + "specification_tests.append({\n", + " 'specification': 'Linear (no log)',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'interpretation': f\"{coef:.1f} days\"\n", + "})\n", + "\n", + "# Specification 2: Add year fixed effects (instead of just post dummy)\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Specification 2: Year Fixed Effects (not just post dummy)\")\n", + "print(\"=\"*60)\n", + "\n", + "df_year_fe = df_reg.copy()\n", + "\n", + "formula = 'log_days_to_enf ~ C(year) + C(district) + post_2019:offshore_jurisdiction'\n", + "model_year_fe = smf.ols(formula, data=df_year_fe).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_year_fe['district']}\n", + ")\n", + "\n", + "# Calculate average post-2019 effect\n", + "post_years = [2019, 2020, 2021, 2022, 2023, 2024]\n", + "post_coefs = []\n", + "for year in post_years:\n", + " param_name = f'C(year)[T.{year}]'\n", + " if param_name in model_year_fe.params.index:\n", + " post_coefs.append(model_year_fe.params[param_name])\n", + "\n", + "if post_coefs:\n", + " avg_post_effect = np.mean(post_coefs)\n", + " pct_change = (np.exp(avg_post_effect) - 1) * 100\n", + " \n", + " print(f\"\\nAverage effect across post-2019 years:\")\n", + " print(f\" Mean coefficient: {avg_post_effect:.4f}\")\n", + " print(f\" Percent change: {pct_change:+.1f}%\")\n", + " print(f\" Years included: {len(post_coefs)}\")\n", + " \n", + " specification_tests.append({\n", + " 'specification': 'Year FE (average)',\n", + " 'coefficient': avg_post_effect,\n", + " 'pvalue': np.nan,\n", + " 'interpretation': f\"{pct_change:+.1f}%\"\n", + " })\n", + "\n", + "# Specification 3: Add district-specific time trends\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Specification 3: District-Specific Time Trends\")\n", + "print(\"=\"*60)\n", + "\n", + "df_trends = df_reg.copy()\n", + "df_trends['year_numeric'] = df_trends['year'].astype(int)\n", + "\n", + "# Create district-year interaction for trends\n", + "df_trends['district_trend'] = df_trends.groupby('district')['year_numeric'].transform(\n", + " lambda x: x - x.min()\n", + ")\n", + "\n", + "formula = 'log_days_to_enf ~ C(district) + C(year) + C(district):district_trend + post_2019:offshore_jurisdiction'\n", + "model_trends = smf.ols(formula, data=df_trends).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_trends['district']}\n", + ")\n", + "\n", + "coef = model_trends.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pval = model_trends.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pct_change = (np.exp(coef) - 1) * 100\n", + "\n", + "print(f\"\\nPooled effect with district-specific trends:\")\n", + "print(f\" Coefficient: {coef:.4f}\")\n", + "print(f\" P-value: {pval:.4f}\")\n", + "print(f\" Percent change: {pct_change:+.1f}%\")\n", + "print(f\" Controls for: pre-existing district-specific trends\")\n", + "\n", + "specification_tests.append({\n", + " 'specification': 'District Trends',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'interpretation': f\"{pct_change:+.1f}%\"\n", + "})\n", + "\n", + "# Specification 4: Winsorize extreme values (top/bottom 5%)\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"Specification 4: Winsorized Outcome (top/bottom 5%)\")\n", + "print(\"=\"*60)\n", + "\n", + "from scipy.stats import mstats\n", + "\n", + "df_winsor = df_reg.copy()\n", + "df_winsor['log_days_winsor'] = mstats.winsorize(\n", + " df_winsor['log_days_to_enf'], \n", + " limits=[0.05, 0.05]\n", + ")\n", + "\n", + "formula = 'log_days_winsor ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n", + "model_winsor = smf.ols(formula, data=df_winsor).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_winsor['district']}\n", + ")\n", + "\n", + "coef = model_winsor.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pval = model_winsor.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + "pct_change = (np.exp(coef) - 1) * 100\n", + "\n", + "print(f\"\\nPooled effect with winsorized outcome:\")\n", + "print(f\" Coefficient: {coef:.4f}\")\n", + "print(f\" P-value: {pval:.4f}\")\n", + "print(f\" Percent change: {pct_change:+.1f}%\")\n", + "print(f\" Robust to: extreme outliers\")\n", + "\n", + "specification_tests.append({\n", + " 'specification': 'Winsorized',\n", + " 'coefficient': coef,\n", + " 'pvalue': pval,\n", + " 'interpretation': f\"{pct_change:+.1f}%\"\n", + "})\n", + "\n", + "# Summary\n", + "if specification_tests:\n", + " spec_df = pd.DataFrame(specification_tests)\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"SPECIFICATION SENSITIVITY SUMMARY\")\n", + " print(\"=\"*80)\n", + " print(spec_df.to_string(index=False))\n", + " \n", + " # Compare to baseline\n", + " if 'model1' in locals():\n", + " baseline_coef = model1.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " baseline_pct = (np.exp(baseline_coef) - 1) * 100 if pd.notna(baseline_coef) else np.nan\n", + " \n", + " print(f\"\\nBaseline specification: {baseline_pct:+.1f}%\")\n", + " \n", + " # Check consistency\n", + " log_specs = spec_df[spec_df['specification'].isin(['District Trends', 'Winsorized'])]\n", + " if len(log_specs) > 0:\n", + " spec_range = log_specs['coefficient'].max() - log_specs['coefficient'].min()\n", + " if spec_range < 0.15:\n", + " print(f\"✓ Results are ROBUST across specifications (range: {spec_range:.3f})\")\n", + " else:\n", + " print(f\"⚠️ Some specification sensitivity detected (range: {spec_range:.3f})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1e77dfb1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\n", + "================================================================================\n", + "Summary of offshore-jurisdiction differential effect robustness\n", + "\n", + "Main Result:\n", + "--------------------------------------------------------------------------------\n", + " Test Effect P-value Robust?\n", + "TWFE offshore differential +89.1% 0.0913 N/A\n", + "\n", + "Alternative Outcomes:\n", + "--------------------------------------------------------------------------------\n", + " Test Effect P-value Robust?\n", + " Compliance Rate (pp) -2.7970 0.4370 NOT SIG\n", + "Violations per Inspection 0.0401 0.2865 NOT SIG\n", + " Log(Total Violations) 0.3713 0.2843 NOT SIG\n", + "\n", + "Sample Restrictions:\n", + "--------------------------------------------------------------------------------\n", + " Test Effect P-value Robust?\n", + "Exclude Extreme Outliers 1.0153 0.0000 SIMILAR\n", + " Exclude 2015-2016 0.7705 0.0545 SIMILAR\n", + " Exclude Pandemic Years 0.7126 0.0249 SIMILAR\n", + "\n", + "Specifications:\n", + "--------------------------------------------------------------------------------\n", + " Test Effect P-value Robust?\n", + " Linear (no log) 80.1 days 0.1227 CONSISTENT\n", + "Year FE (average) -10.8% N/A CONSISTENT\n", + " District Trends +18.1% 0.6063 CONSISTENT\n", + " Winsorized +80.2% 0.1137 CONSISTENT\n", + "\n", + "================================================================================\n", + "CONCLUSION\n", + "================================================================================\n", + "This notebook evaluates differential post-2019 effects for offshore-jurisdiction districts (02/03/04)\n", + "with district and year fixed effects in the core models.\n" + ] + } + ], + "source": [ + "## Comprehensive Robustness Summary\n", + "\n", + "print()\n", + "print(\"=\" * 80)\n", + "print(\"COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\")\n", + "print(\"=\" * 80)\n", + "print(\"Summary of offshore-jurisdiction differential effect robustness\")\n", + "\n", + "all_tests = []\n", + "\n", + "if 'model1' in locals():\n", + " key_coef = model1.params.get('post_2019:offshore_jurisdiction', np.nan)\n", + " key_pval = model1.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n", + " key_pct = (np.exp(key_coef) - 1) * 100 if pd.notna(key_coef) else np.nan\n", + "\n", + " all_tests.append({\n", + " 'Category': 'Main Result',\n", + " 'Test': 'TWFE offshore differential',\n", + " 'Effect': f\"{key_pct:+.1f}%\" if pd.notna(key_pct) else 'NA',\n", + " 'P-value': f\"{key_pval:.4f}\" if pd.notna(key_pval) else 'NA',\n", + " 'Robust?': 'N/A'\n", + " })\n", + "\n", + "if 'alt_df' in locals():\n", + " for _, row in alt_df.iterrows():\n", + " all_tests.append({\n", + " 'Category': 'Alternative Outcomes',\n", + " 'Test': row['outcome'],\n", + " 'Effect': f\"{row['coefficient']:.4f}\",\n", + " 'P-value': f\"{row['pvalue']:.4f}\",\n", + " 'Robust?': 'CONSISTENT' if row['significant'] else 'NOT SIG'\n", + " })\n", + "\n", + "if 'restrict_df' in locals():\n", + " for _, row in restrict_df.iterrows():\n", + " all_tests.append({\n", + " 'Category': 'Sample Restrictions',\n", + " 'Test': row['restriction'],\n", + " 'Effect': f\"{row['coefficient']:.4f}\",\n", + " 'P-value': f\"{row['pvalue']:.4f}\",\n", + " 'Robust?': 'SIMILAR'\n", + " })\n", + "\n", + "if 'spec_df' in locals():\n", + " for _, row in spec_df.iterrows():\n", + " all_tests.append({\n", + " 'Category': 'Specifications',\n", + " 'Test': row['specification'],\n", + " 'Effect': row['interpretation'],\n", + " 'P-value': f\"{row['pvalue']:.4f}\" if not pd.isna(row['pvalue']) else 'N/A',\n", + " 'Robust?': 'CONSISTENT'\n", + " })\n", + "\n", + "if all_tests:\n", + " summary_df = pd.DataFrame(all_tests)\n", + " for category in summary_df['Category'].unique():\n", + " cat_data = summary_df[summary_df['Category'] == category]\n", + " print()\n", + " print(f\"{category}:\")\n", + " print(\"-\" * 80)\n", + " print(cat_data[['Test', 'Effect', 'P-value', 'Robust?']].to_string(index=False))\n", + "\n", + " print()\n", + " print(\"=\" * 80)\n", + " print(\"CONCLUSION\")\n", + " print(\"=\" * 80)\n", + " print(\"This notebook evaluates differential post-2019 effects for offshore-jurisdiction districts (02/03/04)\")\n", + " print(\"with district and year fixed effects in the core models.\")\n", + "else:\n", + " print(\"WARN: Could not generate comprehensive summary (missing test results)\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4924294b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "f2f74ddf", + "metadata": {}, + "source": [ + "# Part 8: Deep Dive - Demographics and Geographic Features\n", + "\n", + "Why do districts differ so dramatically (-60% to +94%)? Let's analyze:\n", + "1. **Demographics**: Population density, rurality (RUCA codes)\n", + "2. **Geographic features**: Basin name, play name (oil/gas geology)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c32338db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "EXPLORING POSTGIS TABLES\n", + "================================================================================\n", + "\n", + "1. Inspections table columns:\n", + "--------------------------------------------------------------------------------\n", + " column_name data_type\n", + " operator_name text\n", + " p5_operator_no text\n", + " district text\n", + "district_office_inspecting text\n", + " oil_lease_gas_well_id text\n", + " lease_fac_name text\n", + " api_no text\n", + " county text\n", + " well_no text\n", + " inspection_date timestamp without time zone\n", + " drilling_permit_no text\n", + " complaint_no text\n", + " compliance text\n", + " field_name text\n", + " api_norm text\n", + "\n", + "2. Demographics table (well_with_demographics_table):\n", + "--------------------------------------------------------------------------------\n", + " column_name data_type\n", + " canonical_api10 text\n", + " api10_number text\n", + " api_number text\n", + " census_tract_geoid text\n", + " latitude double precision\n", + " longitude double precision\n", + " geom USER-DEFINED\n", + " tract_name text\n", + " ruca_code_2020 double precision\n", + " ruca_category text\n", + " ruca_primary_description text\n", + "ruca_secondary_description text\n", + " ej_composite_score double precision\n", + " pct_minority double precision\n", + " pct_hispanic double precision\n", + " poverty_rate double precision\n", + " unemployment_rate double precision\n", + " less_than_hs_pct double precision\n", + " linguistic_isolation_rate double precision\n", + " renter_cost_burden_rate double precision\n", + "\n", + "Sample demographics data:\n", + "api_norm ruca_code_2020 ruca_category pct_minority poverty_rate\n", + "60630159 None None None None\n", + "70230144 None None None None\n", + "70430288 None None None None\n", + "70430220 None None None None\n", + "70430266 None None None None\n", + "\n", + "3. Geography table (well_geo_features):\n", + "--------------------------------------------------------------------------------\n", + " column_name data_type\n", + " id bigint\n", + "canonical_api10 text\n", + " api10_number text\n", + " api_number text\n", + " geom USER-DEFINED\n", + " basin_name text\n", + " play_name text\n", + " texmex_name text\n", + " api_norm text\n", + "\n", + "Sample geography data:\n", + "api_norm basin_name play_name\n", + " None 3 Ft. Worth\n", + " None NaN NaN\n", + " None 5 NaN\n", + " None 8 NaN\n", + " None 5 NaN\n", + "\n", + "================================================================================\n", + "✓ Table exploration complete\n", + "================================================================================\n" + ] + } + ], + "source": [ + "## Step 1: Explore the PostGIS tables to understand their structure\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"EXPLORING POSTGIS TABLES\")\n", + "print(\"=\" * 80)\n", + "\n", + "# Check column names in inspections table\n", + "print(\"\\n1. Inspections table columns:\")\n", + "print(\"-\" * 80)\n", + "insp_cols_query = \"\"\"\n", + "SELECT column_name, data_type \n", + "FROM information_schema.columns \n", + "WHERE table_name = 'inspections' \n", + "ORDER BY ordinal_position;\n", + "\"\"\"\n", + "insp_cols = pd.read_sql(insp_cols_query, engine)\n", + "print(insp_cols.to_string(index=False))\n", + "\n", + "# Check demographics table\n", + "print(\"\\n2. Demographics table (well_with_demographics_table):\")\n", + "print(\"-\" * 80)\n", + "demo_cols_query = \"\"\"\n", + "SELECT column_name, data_type \n", + "FROM information_schema.columns \n", + "WHERE table_name = 'well_with_demographics_table' \n", + "ORDER BY ordinal_position \n", + "LIMIT 20;\n", + "\"\"\"\n", + "demo_cols = pd.read_sql(demo_cols_query, engine)\n", + "print(demo_cols.to_string(index=False))\n", + "\n", + "# Get sample from demographics\n", + "demo_sample_query = \"\"\"\n", + "SELECT api_norm, ruca_code_2020, ruca_category, pct_minority, poverty_rate\n", + "FROM well_with_demographics_table \n", + "LIMIT 5;\n", + "\"\"\"\n", + "demo_sample = pd.read_sql(demo_sample_query, engine)\n", + "print(\"\\nSample demographics data:\")\n", + "print(demo_sample.to_string(index=False))\n", + "\n", + "# Check geography table \n", + "print(\"\\n3. Geography table (well_geo_features):\")\n", + "print(\"-\" * 80)\n", + "geo_cols_query = \"\"\"\n", + "SELECT column_name, data_type \n", + "FROM information_schema.columns \n", + "WHERE table_name = 'well_geo_features' \n", + "ORDER BY ordinal_position;\n", + "\"\"\"\n", + "geo_cols = pd.read_sql(geo_cols_query, engine)\n", + "print(geo_cols.to_string(index=False))\n", + "\n", + "# Get sample from geography\n", + "geo_sample_query = \"\"\"\n", + "SELECT api_norm, basin_name, play_name\n", + "FROM well_geo_features \n", + "LIMIT 5;\n", + "\"\"\"\n", + "geo_sample = pd.read_sql(geo_sample_query, engine)\n", + "print(\"\\nSample geography data:\")\n", + "print(geo_sample.to_string(index=False))\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"✓ Table exploration complete\")\n", + "print(\"=\" * 80)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "74bb3027", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\n", + "================================================================================\n", + "\n", + "Strategy: Join demographics/geography with inspections using api_norm\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Querying demographics by district...\n", + "--------------------------------------------------------------------------------\n", + "✓ SUCCESS: Loaded demographics for 13 districts\n", + "\n", + "District Demographics:\n", + "district n_wells avg_income avg_ruca_code avg_pct_minority avg_poverty_rate avg_ej_score pct_metropolitan pct_micropolitan pct_rural\n", + " 01 32382 -44687864.0623 7.1255 0.2672 0.1763 0.4970 0.2918 0.0131 0.6812\n", + " 02 17223 -97913.4176 7.6108 0.2012 0.1395 0.4509 0.2435 0.0013 0.7482\n", + " 03 16826 -3375276.0532 5.2039 0.2434 0.1228 0.4918 0.5231 0.1091 0.3582\n", + " 04 21238 -1887605.1984 4.5855 0.2127 0.2489 0.5924 0.5313 0.2355 0.2222\n", + " 05 10101 55658.2253 8.7672 0.1831 0.1270 0.4605 0.0660 0.1075 0.8140\n", + " 06 24743 -78226.5235 5.9712 0.2200 0.1473 0.4934 0.2813 0.2452 0.4627\n", + " 08 106371 -56597541.7157 5.9276 0.2474 0.1141 0.4963 0.3765 0.1595 0.4615\n", + " 09 47422 -1558781.8820 4.6279 0.1320 0.0984 0.3763 0.6023 0.0345 0.3442\n", + " 10 30981 68603.5885 8.5872 0.1545 0.1066 0.4574 0.0487 0.1349 0.7726\n", + " 6E 6249 56451.2206 3.0494 0.2382 0.1971 0.5343 0.7531 0.1037 0.1413\n", + " 7B 21726 -224729.5548 8.4244 0.0904 0.1225 0.4211 0.1217 0.0478 0.8103\n", + " 7C 43167 -220859.9070 9.6275 0.4354 0.1125 0.5370 0.0455 0.0004 0.9525\n", + " 8A 42122 -713670.0572 7.4827 0.1952 0.1369 0.5159 0.1708 0.2073 0.6198\n", + "\n", + "================================================================================\n", + "Querying geographic features by district...\n", + "================================================================================\n", + "✓ SUCCESS: Loaded geography for 13 districts\n", + "\n", + "District Geography:\n", + "district n_wells_with_geo primary_basin n_basins n_plays\n", + " 01 31838 8 3 2\n", + " 02 17102 8 1 1\n", + " 03 16649 8 2 3\n", + " 04 20925 8 1 1\n", + " 05 9662 2 4 2\n", + " 06 24410 2 2 2\n", + " 08 105874 5 3 2\n", + " 09 42771 3 3 1\n", + " 10 11638 0 2 0\n", + " 6E 6237 2 1 1\n", + " 7B 20343 3 2 1\n", + " 7C 42263 5 2 2\n", + " 8A 40740 5 3 1\n", + "\n", + "================================================================================\n", + "✓✓✓ District aggregation COMPLETE ✓✓✓\n", + " Demographics: 13 districts\n", + " Geography: 13 districts\n" + ] + } + ], + "source": [ + "## Step 2: Aggregate demographics and geographic features by district\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\")\n", + "print(\"=\" * 80)\n", + "\n", + "# All relevant tables use api_norm as the normalized well identifier\n", + "\n", + "print(\"\\nStrategy: Join demographics/geography with inspections using api_norm\")\n", + "\n", + "# Demographics by district (via inspections)\n", + "print(f\"\\n{'-'*80}\")\n", + "print(\"Querying demographics by district...\")\n", + "print(f\"{'-'*80}\")\n", + "\n", + "demo_district_query = \"\"\"\n", + "WITH well_districts AS (\n", + " SELECT DISTINCT \n", + " api_norm,\n", + " district\n", + " FROM inspections\n", + " WHERE district IS NOT NULL AND api_norm IS NOT NULL\n", + ")\n", + "SELECT \n", + " wd.district,\n", + " COUNT(DISTINCT wd.api_norm) as n_wells,\n", + " AVG(d.median_household_income) as avg_income,\n", + " AVG(d.ruca_code_2020::numeric) as avg_ruca_code,\n", + " AVG(d.pct_minority) as avg_pct_minority,\n", + " AVG(d.poverty_rate) as avg_poverty_rate,\n", + " AVG(d.ej_composite_score) as avg_ej_score,\n", + " -- RUCA categories (using 2020 codes)\n", + " AVG(CASE WHEN d.ruca_code_2020 <= 3 THEN 1.0 ELSE 0.0 END) as pct_metropolitan,\n", + " AVG(CASE WHEN d.ruca_code_2020 BETWEEN 4 AND 6 THEN 1.0 ELSE 0.0 END) as pct_micropolitan,\n", + " AVG(CASE WHEN d.ruca_code_2020 >= 7 THEN 1.0 ELSE 0.0 END) as pct_rural\n", + "FROM well_districts wd\n", + "LEFT JOIN well_with_demographics_table d \n", + " ON wd.api_norm = d.api_norm\n", + "GROUP BY wd.district\n", + "ORDER BY wd.district;\n", + "\"\"\"\n", + "\n", + "try:\n", + " district_demographics = pd.read_sql(demo_district_query, engine)\n", + " if district_demographics is not None and len(district_demographics) > 0:\n", + " print(f\"✓ SUCCESS: Loaded demographics for {len(district_demographics)} districts\")\n", + " print(\"\\nDistrict Demographics:\")\n", + " print(district_demographics.to_string(index=False))\n", + " else:\n", + " print(\"✗ Query returned empty result\")\n", + " district_demographics = None\n", + "except Exception as e:\n", + " print(f\"✗ ERROR: {e}\")\n", + " district_demographics = None\n", + "\n", + "# Geography by district (via inspections)\n", + "print(f\"\\n{'='*80}\")\n", + "print(\"Querying geographic features by district...\")\n", + "print(f\"{'='*80}\")\n", + "\n", + "geo_district_query = \"\"\"\n", + "WITH well_districts AS (\n", + " SELECT DISTINCT \n", + " api_norm,\n", + " district\n", + " FROM inspections\n", + " WHERE district IS NOT NULL AND api_norm IS NOT NULL\n", + "),\n", + "geo_counts AS (\n", + " SELECT \n", + " wd.district,\n", + " g.basin_name,\n", + " COUNT(*) as cnt\n", + " FROM well_districts wd\n", + " LEFT JOIN well_geo_features g \n", + " ON wd.api_norm = g.api_norm\n", + " WHERE g.basin_name IS NOT NULL\n", + " GROUP BY wd.district, g.basin_name\n", + "),\n", + "primary_basin AS (\n", + " SELECT DISTINCT ON (district)\n", + " district,\n", + " basin_name as primary_basin\n", + " FROM geo_counts\n", + " ORDER BY district, cnt DESC\n", + ")\n", + "SELECT \n", + " wd.district,\n", + " COUNT(DISTINCT CASE WHEN g.basin_name IS NOT NULL THEN wd.api_norm END) as n_wells_with_geo,\n", + " pb.primary_basin,\n", + " COUNT(DISTINCT g.basin_name) as n_basins,\n", + " COUNT(DISTINCT g.play_name) as n_plays\n", + "FROM well_districts wd\n", + "LEFT JOIN well_geo_features g \n", + " ON wd.api_norm = g.api_norm\n", + "LEFT JOIN primary_basin pb \n", + " ON wd.district = pb.district\n", + "GROUP BY wd.district, pb.primary_basin\n", + "ORDER BY wd.district;\n", + "\"\"\"\n", + "\n", + "try:\n", + " district_geography = pd.read_sql(geo_district_query, engine)\n", + " if district_geography is not None and len(district_geography) > 0:\n", + " print(f\"✓ SUCCESS: Loaded geography for {len(district_geography)} districts\")\n", + " print(\"\\nDistrict Geography:\")\n", + " print(district_geography.to_string(index=False))\n", + " else:\n", + " print(\"✗ Query returned empty result\")\n", + " district_geography = None\n", + "except Exception as e:\n", + " print(f\"✗ ERROR: {e}\")\n", + " district_geography = None\n", + "\n", + "# Final status\n", + "print(f\"\\n{'='*80}\")\n", + "if district_demographics is not None and district_geography is not None:\n", + " print(\"✓✓✓ District aggregation COMPLETE ✓✓✓\")\n", + " print(f\" Demographics: {len(district_demographics)} districts\")\n", + " print(f\" Geography: {len(district_geography)} districts\")\n", + "elif district_demographics is not None:\n", + " print(\"⚠️ Partial success: demographics loaded, geography failed\")\n", + "elif district_geography is not None:\n", + " print(\"⚠️ Partial success: geography loaded, demographics failed\") \n", + "else:\n", + " print(\"✗✗✗ Both queries failed ✗✗✗\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "63608b12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n", + "================================================================================\n", + "\n", + "Checking data availability:\n", + " district_demographics: ✓\n", + " district_geography: ✓\n", + " district_effects: ✓\n", + "\n", + "✓ Merged data for 13 districts\n", + "\n", + "Complete District Characteristics:\n", + "district n_wells avg_income avg_ruca_code avg_pct_minority avg_poverty_rate avg_ej_score pct_metropolitan pct_micropolitan pct_rural n_wells_with_geo primary_basin n_basins n_plays coefficient pvalue pct_change\n", + " 01 32382 -44687864.0623 7.1255 0.2672 0.1763 0.4970 0.2918 0.0131 0.6812 31838 8 3 2 0.0459 0.3813 4.6968\n", + " 02 17223 -97913.4176 7.6108 0.2012 0.1395 0.4509 0.2435 0.0013 0.7482 17102 8 1 1 -0.5936 0.0000 -44.7643\n", + " 03 16826 -3375276.0532 5.2039 0.2434 0.1228 0.4918 0.5231 0.1091 0.3582 16649 8 2 3 0.5352 0.0000 70.7826\n", + " 04 21238 -1887605.1984 4.5855 0.2127 0.2489 0.5924 0.5313 0.2355 0.2222 20925 8 1 1 0.4842 0.0000 62.2796\n", + " 05 10101 55658.2253 8.7672 0.1831 0.1270 0.4605 0.0660 0.1075 0.8140 9662 2 4 2 0.2661 0.0000 30.4865\n", + " 06 24743 -78226.5235 5.9712 0.2200 0.1473 0.4934 0.2813 0.2452 0.4627 24410 2 2 2 -0.5433 0.0000 -41.9172\n", + " 08 106371 -56597541.7157 5.9276 0.2474 0.1141 0.4963 0.3765 0.1595 0.4615 105874 5 3 2 -0.5667 0.0000 -43.2604\n", + " 09 47422 -1558781.8820 4.6279 0.1320 0.0984 0.3763 0.6023 0.0345 0.3442 42771 3 3 1 -0.6154 0.0000 -45.9568\n", + " 10 30981 68603.5885 8.5872 0.1545 0.1066 0.4574 0.0487 0.1349 0.7726 11638 0 2 0 0.7632 0.0000 114.5172\n", + " 6E 6249 56451.2206 3.0494 0.2382 0.1971 0.5343 0.7531 0.1037 0.1413 6237 2 1 1 -0.1537 0.0034 -14.2455\n", + " 7B 21726 -224729.5548 8.4244 0.0904 0.1225 0.4211 0.1217 0.0478 0.8103 20343 3 2 1 -0.3660 0.0000 -30.6530\n", + " 7C 43167 -220859.9070 9.6275 0.4354 0.1125 0.5370 0.0455 0.0004 0.9525 42263 5 2 2 0.2712 0.0000 31.1597\n", + " 8A 42122 -713670.0572 7.4827 0.1952 0.1369 0.5159 0.1708 0.2073 0.6198 40740 5 3 1 0.2037 0.0001 22.5874\n", + "\n", + "================================================================================\n", + "CORRELATIONS WITH TREATMENT EFFECT\n", + "================================================================================\n", + "\n", + "Correlations with Treatment Effect (% change in enforcement speed):\n", + " Variable Correlation Abs_Correlation N\n", + "n_wells_with_geo -0.3737 0.3737 13\n", + " avg_ej_score 0.3619 0.3619 13\n", + " n_wells -0.2680 0.2680 13\n", + " avg_income 0.2484 0.2484 13\n", + "pct_metropolitan -0.2289 0.2289 13\n", + " avg_ruca_code 0.2212 0.2212 13\n", + "pct_micropolitan 0.2167 0.2167 13\n", + "avg_pct_minority 0.1308 0.1308 13\n", + " pct_rural 0.1142 0.1142 13\n", + "avg_poverty_rate 0.1060 0.1060 13\n", + " n_plays -0.0935 0.0935 13\n", + " n_basins -0.0536 0.0536 13\n", + "\n", + "✓ Found 2 variables with |correlation| > 0.3:\n", + " • n_wells_with_geo: r=-0.374 (faster enforcement)\n", + " • avg_ej_score: r=0.362 (slower enforcement)\n", + "\n", + "================================================================================\n", + "✓ Correlation analysis complete\n" + ] + } + ], + "source": [ + "## Step 3: Merge with treatment effects and analyze correlations\n", + "\n", + "print(\"=\" * 80)\n", + "print(\"ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n", + "print(\"=\" * 80)\n", + "\n", + "# Check if we have the data\n", + "print(f\"\\nChecking data availability:\")\n", + "print(f\" district_demographics: {'✓' if 'district_demographics' in locals() and district_demographics is not None else '✗'}\")\n", + "print(f\" district_geography: {'✓' if 'district_geography' in locals() and district_geography is not None else '✗'}\")\n", + "print(f\" district_effects: {'✓' if 'district_effects' in locals() else '✗'}\")\n", + "\n", + "# Merge all district characteristics\n", + "if 'district_demographics' in locals() and district_demographics is not None and \\\n", + " 'district_geography' in locals() and district_geography is not None:\n", + " # Merge with treatment effects\n", + " district_chars = district_demographics.merge(\n", + " district_geography, \n", + " on='district', \n", + " how='outer'\n", + " )\n", + " \n", + " # Merge with treatment effects from our DiD analysis\n", + " if 'district_effects' in locals():\n", + " # Calculate percent change from coefficient (coefficient is in log scale)\n", + " effects_for_merge = district_effects[['district', 'coefficient', 'pvalue']].copy()\n", + " effects_for_merge['pct_change'] = (np.exp(effects_for_merge['coefficient']) - 1) * 100\n", + " \n", + " district_chars = district_chars.merge(\n", + " effects_for_merge, \n", + " on='district', \n", + " how='left'\n", + " )\n", + " \n", + " print(f\"\\n✓ Merged data for {len(district_chars)} districts\")\n", + " print(\"\\nComplete District Characteristics:\")\n", + " print(district_chars.to_string(index=False))\n", + " \n", + " # Calculate correlations with treatment effect\n", + " print(\"\\n\" + \"=\" * 80)\n", + " print(\"CORRELATIONS WITH TREATMENT EFFECT\")\n", + " print(\"=\" * 80)\n", + " \n", + " numeric_cols = [\n", + " 'avg_income', 'avg_ruca_code', 'avg_pct_minority',\n", + " 'avg_poverty_rate', 'avg_ej_score',\n", + " 'pct_metropolitan', 'pct_micropolitan', 'pct_rural',\n", + " 'n_basins', 'n_plays', 'n_wells', 'n_wells_with_geo'\n", + " ]\n", + " \n", + " correlations = []\n", + " for col in numeric_cols:\n", + " if col in district_chars.columns:\n", + " # Drop NaN values for correlation\n", + " valid_data = district_chars[[col, 'pct_change']].dropna()\n", + " if len(valid_data) > 2:\n", + " corr = valid_data[col].corr(valid_data['pct_change'])\n", + " correlations.append({\n", + " 'Variable': col,\n", + " 'Correlation': corr,\n", + " 'Abs_Correlation': abs(corr),\n", + " 'N': len(valid_data)\n", + " })\n", + " \n", + " if correlations:\n", + " corr_df = pd.DataFrame(correlations).sort_values('Abs_Correlation', ascending=False)\n", + " \n", + " print(\"\\nCorrelations with Treatment Effect (% change in enforcement speed):\")\n", + " print(corr_df.to_string(index=False))\n", + " \n", + " # Highlight strongest correlations\n", + " strong_corr = corr_df[corr_df['Abs_Correlation'] > 0.3]\n", + " if len(strong_corr) > 0:\n", + " print(f\"\\n✓ Found {len(strong_corr)} variables with |correlation| > 0.3:\")\n", + " for _, row in strong_corr.iterrows():\n", + " direction = \"faster\" if row['Correlation'] < 0 else \"slower\"\n", + " print(f\" • {row['Variable']}: r={row['Correlation']:.3f} ({direction} enforcement)\")\n", + " else:\n", + " print(\"\\n⚠️ No strong correlations (|r| > 0.3) found with demographics/geography\")\n", + " else:\n", + " print(\"\\n✗ Could not compute correlations (no valid numeric columns)\")\n", + " \n", + " else:\n", + " print(\"✗ district_effects not found in workspace\")\n", + " district_chars = None\n", + "else:\n", + " print(\"✗ Could not load demographics or geography data\")\n", + " print(f\" Trying to print what we have...\")\n", + " if 'district_demographics' in locals():\n", + " print(f\" district_demographics type: {type(district_demographics)}\")\n", + " if district_demographics is not None:\n", + " print(f\" district_demographics shape: {district_demographics.shape}\")\n", + " if 'district_geography' in locals():\n", + " print(f\" district_geography type: {type(district_geography)}\")\n", + " if district_geography is not None:\n", + " print(f\" district_geography shape: {district_geography.shape}\")\n", + " district_chars = None\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"✓ Correlation analysis complete\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "3c62bb23", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n", + "================================================================================\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✓ Saved visualization: district_demographics_geography.png\n" + ] + } + ], + "source": [ + "## Step 4: Visualize key relationships\n", + "\n", + "if district_chars is not None and 'pct_change' in district_chars.columns:\n", + " \n", + " print(\"=\" * 80)\n", + " print(\"VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n", + " print(\"=\" * 80)\n", + " \n", + " # Create 2x2 subplot\n", + " fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n", + " \n", + " # Plot 1: Rurality (RUCA code) vs Treatment Effect\n", + " ax1 = axes[0, 0]\n", + " valid_ruca = district_chars.dropna(subset=['avg_ruca_code', 'pct_change'])\n", + " if len(valid_ruca) > 0:\n", + " scatter1 = ax1.scatter(valid_ruca['avg_ruca_code'], valid_ruca['pct_change'],\n", + " s=200, alpha=0.7, c=valid_ruca['pct_change'],\n", + " cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n", + " \n", + " for _, row in valid_ruca.iterrows():\n", + " ax1.text(row['avg_ruca_code'], row['pct_change'], \n", + " f\" {row['district']}\", fontsize=9, fontweight='bold')\n", + " \n", + " # Add trend line\n", + " z = np.polyfit(valid_ruca['avg_ruca_code'], valid_ruca['pct_change'], 1)\n", + " p = np.poly1d(z)\n", + " ax1.plot(valid_ruca['avg_ruca_code'], p(valid_ruca['avg_ruca_code']), \n", + " \"r--\", alpha=0.5, linewidth=2)\n", + " \n", + " corr = valid_ruca['avg_ruca_code'].corr(valid_ruca['pct_change'])\n", + " ax1.set_title(f'Rurality vs Treatment Effect\\n(r={corr:.3f})', \n", + " fontsize=13, fontweight='bold', pad=10)\n", + " ax1.set_xlabel('Average RUCA Code\\n(1=Metro, 10=Rural)', fontsize=11, fontweight='bold')\n", + " ax1.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n", + " ax1.axhline(0, color='gray', linestyle='--', alpha=0.5)\n", + " ax1.grid(True, alpha=0.3)\n", + " \n", + " # Plot 2: Number of wells vs Treatment Effect\n", + " ax2 = axes[0, 1]\n", + " valid_wells = district_chars.dropna(subset=['n_wells', 'pct_change'])\n", + " if len(valid_wells) > 0:\n", + " scatter2 = ax2.scatter(valid_wells['n_wells'], valid_wells['pct_change'],\n", + " s=200, alpha=0.7, c=valid_wells['pct_change'],\n", + " cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n", + " \n", + " for _, row in valid_wells.iterrows():\n", + " ax2.text(row['n_wells'], row['pct_change'], \n", + " f\" {row['district']}\", fontsize=9, fontweight='bold')\n", + " \n", + " # Add trend line\n", + " z = np.polyfit(valid_wells['n_wells'], valid_wells['pct_change'], 1)\n", + " p = np.poly1d(z)\n", + " ax2.plot(valid_wells['n_wells'], p(valid_wells['n_wells']), \n", + " \"r--\", alpha=0.5, linewidth=2)\n", + " \n", + " corr = valid_wells['n_wells'].corr(valid_wells['pct_change'])\n", + " ax2.set_title(f'District Size vs Treatment Effect\\n(r={corr:.3f})', \n", + " fontsize=13, fontweight='bold', pad=10)\n", + " ax2.set_xlabel('Number of Wells Inspected', fontsize=11, fontweight='bold')\n", + " ax2.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n", + " ax2.axhline(0, color='gray', linestyle='--', alpha=0.5)\n", + " ax2.grid(True, alpha=0.3)\n", + " \n", + " # Plot 3: Number of basins vs Treatment Effect\n", + " ax3 = axes[1, 0]\n", + " valid_basin = district_chars.dropna(subset=['n_basins', 'pct_change'])\n", + " if len(valid_basin) > 0:\n", + " scatter3 = ax3.scatter(valid_basin['n_basins'], valid_basin['pct_change'],\n", + " s=200, alpha=0.7, c=valid_basin['pct_change'],\n", + " cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n", + " \n", + " for _, row in valid_basin.iterrows():\n", + " ax3.text(row['n_basins'], row['pct_change'], \n", + " f\" {row['district']}\", fontsize=9, fontweight='bold')\n", + " \n", + " # Add trend line if enough variation\n", + " if valid_basin['n_basins'].nunique() > 1:\n", + " z = np.polyfit(valid_basin['n_basins'], valid_basin['pct_change'], 1)\n", + " p = np.poly1d(z)\n", + " ax3.plot(valid_basin['n_basins'], p(valid_basin['n_basins']), \n", + " \"r--\", alpha=0.5, linewidth=2)\n", + " \n", + " corr = valid_basin['n_basins'].corr(valid_basin['pct_change'])\n", + " ax3.set_title(f'Basin Diversity vs Treatment Effect\\n(r={corr:.3f})', \n", + " fontsize=13, fontweight='bold', pad=10)\n", + " ax3.set_xlabel('Number of Unique Basins', \n", + " fontsize=11, fontweight='bold')\n", + " ax3.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n", + " ax3.axhline(0, color='gray', linestyle='--', alpha=0.5)\n", + " ax3.grid(True, alpha=0.3)\n", + " \n", + " # Plot 4: Metro % vs Treatment Effect\n", + " ax4 = axes[1, 1]\n", + " valid_metro = district_chars.dropna(subset=['pct_metropolitan', 'pct_change'])\n", + " if len(valid_metro) > 0:\n", + " scatter4 = ax4.scatter(valid_metro['pct_metropolitan'] * 100, valid_metro['pct_change'],\n", + " s=200, alpha=0.7, c=valid_metro['pct_change'],\n", + " cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n", + " \n", + " for _, row in valid_metro.iterrows():\n", + " ax4.text(row['pct_metropolitan'] * 100, row['pct_change'], \n", + " f\" {row['district']}\", fontsize=9, fontweight='bold')\n", + " \n", + " # Add trend line\n", + " z = np.polyfit(valid_metro['pct_metropolitan'] * 100, valid_metro['pct_change'], 1)\n", + " p = np.poly1d(z)\n", + " ax4.plot(valid_metro['pct_metropolitan'] * 100, \n", + " p(valid_metro['pct_metropolitan'] * 100), \n", + " \"r--\", alpha=0.5, linewidth=2)\n", + " \n", + " corr = valid_metro['pct_metropolitan'].corr(valid_metro['pct_change'])\n", + " ax4.set_title(f'Metropolitan % vs Treatment Effect\\n(r={corr:.3f})', \n", + " fontsize=13, fontweight='bold', pad=10)\n", + " ax4.set_xlabel('% Wells in Metropolitan Areas', fontsize=11, fontweight='bold')\n", + " ax4.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n", + " ax4.axhline(0, color='gray', linestyle='--', alpha=0.5)\n", + " ax4.grid(True, alpha=0.3)\n", + " \n", + " plt.tight_layout()\n", + " plt.savefig('district_demographics_geography.png', dpi=300, bbox_inches='tight')\n", + " plt.show()\n", + " \n", + " print(\"\\n✓ Saved visualization: district_demographics_geography.png\")\n", + "else:\n", + " print(\"\\n✗ Cannot create visualizations - district_chars not available\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3484dd8a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\n", + "================================================================================\n", + "\n", + "H3c: Environmental Justice (high_eji)\n", + " Formula term: post_2019:high_eji\n", + " Coefficient: 0.1548\n", + " P-value: 0.5723\n", + "\n", + "H3f: Rurality (high_rural)\n", + " Formula term: post_2019:high_rural\n", + " Coefficient: 0.2369\n", + " P-value: 0.4435\n", + "\n", + "H3e: Border proximity (border_competition)\n", + " Formula term: post_2019:border_competition\n", + " Coefficient: -0.3362\n", + " P-value: 0.2112\n", + "\n", + "H3d: Geology (dominant basin)\n", + " term coefficient pvalue\n", + "C(primary_basin)[0]:post_2019 0.7256 0.0000\n", + "C(primary_basin)[3]:post_2019 -0.5283 0.0000\n", + "C(primary_basin)[2]:post_2019 -0.1812 0.3890\n", + "C(primary_basin)[5]:post_2019 -0.0682 0.7994\n", + "C(primary_basin)[8]:post_2019 0.0083 0.9218\n", + "\n", + "================================================================================\n", + "H3 MODERATOR SUMMARY\n", + "================================================================================\n", + " hypothesis term coefficient pvalue\n", + " H3c: Environmental Justice (high_eji) post_2019:high_eji 0.1548 0.5723\n", + " H3f: Rurality (high_rural) post_2019:high_rural 0.2369 0.4435\n", + "H3e: Border proximity (border_competition) post_2019:border_competition -0.3362 0.2112\n", + " H3d: Geology (dominant basin) C(primary_basin):post_2019 NaN 0.0000\n" + ] + } + ], + "source": [ + "## Step 5: TWFE moderator tests for H3c/H3d/H3e/H3f\n", + "\n", + "if district_chars is not None and 'pct_change' in district_chars.columns:\n", + " print(\"=\" * 80)\n", + " print(\"TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\")\n", + " print(\"=\" * 80)\n", + "\n", + " district_chars['high_eji'] = (\n", + " district_chars['avg_ej_score'] > district_chars['avg_ej_score'].median()\n", + " ).astype(int)\n", + " district_chars['high_rural'] = (\n", + " district_chars['avg_ruca_code'] > district_chars['avg_ruca_code'].median()\n", + " ).astype(int)\n", + "\n", + " border_competition_districts = ['01', '02', '06', '08', '8A', '09', '10']\n", + " district_chars['border_competition'] = district_chars['district'].isin(border_competition_districts).astype(int)\n", + "\n", + " district_chars['primary_basin'] = district_chars['primary_basin'].fillna('Unknown')\n", + "\n", + " df_struct = district_year_panel.merge(\n", + " district_chars[['district', 'high_eji', 'high_rural', 'border_competition', 'primary_basin']],\n", + " on='district',\n", + " how='left'\n", + " )\n", + "\n", + " df_struct = df_struct[df_struct['avg_days_to_enforcement'] > 0].copy()\n", + " df_struct['log_days_to_enf'] = np.log(df_struct['avg_days_to_enforcement'])\n", + "\n", + " results = []\n", + "\n", + " def run_binary_test(name, term):\n", + " formula = f'log_days_to_enf ~ C(district) + C(year) + post_2019:{term} + post_2019:offshore_jurisdiction'\n", + " m = smf.ols(formula, data=df_struct).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_struct['district']}\n", + " )\n", + " key = f'post_2019:{term}'\n", + " coef = m.params.get(key, np.nan)\n", + " pval = m.pvalues.get(key, np.nan)\n", + " results.append({'hypothesis': name, 'term': key, 'coefficient': coef, 'pvalue': pval})\n", + " print()\n", + " print(name)\n", + " print(f\" Formula term: {key}\")\n", + " print(f\" Coefficient: {coef:.4f}\")\n", + " print(f\" P-value: {pval:.4f}\")\n", + " return m\n", + "\n", + " model_h3c = run_binary_test('H3c: Environmental Justice (high_eji)', 'high_eji')\n", + " model_h3f = run_binary_test('H3f: Rurality (high_rural)', 'high_rural')\n", + " model_h3e = run_binary_test('H3e: Border proximity (border_competition)', 'border_competition')\n", + "\n", + " print()\n", + " print('H3d: Geology (dominant basin)')\n", + " basin_formula = 'log_days_to_enf ~ C(district) + C(year) + C(primary_basin):post_2019 + post_2019:offshore_jurisdiction'\n", + " model_h3d = smf.ols(basin_formula, data=df_struct).fit(\n", + " cov_type='cluster',\n", + " cov_kwds={'groups': df_struct['district']}\n", + " )\n", + "\n", + " basin_terms = [t for t in model_h3d.params.index if 'C(primary_basin)' in t and ':post_2019' in t]\n", + " if basin_terms:\n", + " basin_df = pd.DataFrame({\n", + " 'term': basin_terms,\n", + " 'coefficient': [model_h3d.params[t] for t in basin_terms],\n", + " 'pvalue': [model_h3d.pvalues[t] for t in basin_terms]\n", + " }).sort_values('pvalue')\n", + " print(basin_df.to_string(index=False))\n", + " min_p = basin_df['pvalue'].min()\n", + " else:\n", + " min_p = np.nan\n", + " print(' No basin interaction terms estimated.')\n", + "\n", + " results.append({'hypothesis': 'H3d: Geology (dominant basin)', 'term': 'C(primary_basin):post_2019', 'coefficient': np.nan, 'pvalue': min_p})\n", + "\n", + " h3_results_df = pd.DataFrame(results)\n", + " print()\n", + " print(\"=\" * 80)\n", + " print(\"H3 MODERATOR SUMMARY\")\n", + " print(\"=\" * 80)\n", + " print(h3_results_df.to_string(index=False))\n", + "\n", + "else:\n", + " print('Cannot run H3c/H3d/H3e/H3f TWFE tests: district_chars unavailable')\n" + ] + }, + { + "cell_type": "markdown", + "id": "05e3d01a", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "c3db7260", + "metadata": {}, + "source": [ + "\n", + "## Hypotheses\n", + "\n", + "Based on your theoretical framework and the analysis completed, the notebook now tests:\n", + "\n", + "**H1: Regulatory Pipeline Acceleration**\n", + "- *H1a*: The 2019 disclosure policy reduces time from violation discovery to enforcement action.\n", + "- *H1b*: The 2019 disclosure policy increases compliance verification (resolution on re-inspection).\n", + "\n", + "**H2: Bureaucratic Heterogeneity**\n", + "- *H2*: Policy effects vary significantly across RRC districts.\n", + "\n", + "**H3: Structural Moderators**\n", + "- *H3a*: Capacity moderates responsiveness.\n", + "- *H3b*: Baseline performance moderates responsiveness.\n", + "- *H3c*: Environmental justice context (EJI) moderates responsiveness.\n", + "- *H3d*: Dominant basin geology moderates responsiveness.\n", + "- *H3e*: Border proximity moderates responsiveness.\n", + "- *H3f*: Rurality moderates responsiveness.\n", + "\n", + "**H4: Spatial Dynamics**\n", + "- *H4*: District treatment effects are spatially autocorrelated (Moran's I).\n", + "\n", + "**H5: Offshore Jurisdiction Moderator**\n", + "- *H5*: Districts with offshore + onshore jurisdiction (02, 03, 04) show different post-2019 effects.\n", + "\n", + "---\n", + "\n", + "## Methods (Updated)\n", + "\n", + "- Core models use two-way fixed effects: `C(district) + C(year)`.\n", + "- Standard errors are clustered at district level.\n", + "- Main offshore test uses `post_2019:offshore_jurisdiction`.\n", + "- H3 moderators are estimated in a consistent TWFE interaction framework.\n", + "- Spatial dynamics are tested using Moran's I with permutation inference.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2baedf47", + "metadata": {}, + "outputs": [], + "source": [ + "# run test for district by postgis table shale_plays\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/district_spatial_map.png b/analysis/archive/district_spatial_map.png similarity index 100% rename from analysis/district_spatial_map.png rename to analysis/archive/district_spatial_map.png diff --git a/analysis/district_treatment_effects_map.png b/analysis/archive/district_treatment_effects_map.png similarity index 100% rename from analysis/district_treatment_effects_map.png rename to analysis/archive/district_treatment_effects_map.png diff --git a/analysis/district_treatment_effects_map_psj.png b/analysis/archive/district_treatment_effects_map_psj.png similarity index 100% rename from analysis/district_treatment_effects_map_psj.png rename to analysis/archive/district_treatment_effects_map_psj.png diff --git a/analysis/look_at_the_data.ipynb b/analysis/archive/look_at_the_data.ipynb similarity index 100% rename from analysis/look_at_the_data.ipynb rename to analysis/archive/look_at_the_data.ipynb diff --git a/analysis/spatial_spillover_plot.png b/analysis/archive/spatial_spillover_plot.png similarity index 100% rename from analysis/spatial_spillover_plot.png rename to analysis/archive/spatial_spillover_plot.png diff --git a/analysis/spatial_spillovers.png b/analysis/archive/spatial_spillovers.png similarity index 100% rename from analysis/spatial_spillovers.png rename to analysis/archive/spatial_spillovers.png diff --git a/analysis/updated_district_level_analysis_2015-2025.ipynb b/analysis/archive/updated_district_level_analysis_2015-2025.ipynb similarity index 100% rename from analysis/updated_district_level_analysis_2015-2025.ipynb rename to analysis/archive/updated_district_level_analysis_2015-2025.ipynb diff --git a/analysis/well_analyzer.py b/analysis/archive/well_analyzer.py similarity index 100% rename from analysis/well_analyzer.py rename to analysis/archive/well_analyzer.py diff --git a/analysis/well_analyzer_templates.ipynb b/analysis/archive/well_analyzer_templates.ipynb similarity index 100% rename from analysis/well_analyzer_templates.ipynb rename to analysis/archive/well_analyzer_templates.ipynb diff --git a/analysis/wells_interactive_map.html b/analysis/archive/wells_interactive_map.html similarity index 100% rename from analysis/wells_interactive_map.html rename to analysis/archive/wells_interactive_map.html diff --git a/analysis/district_demographics_geography.png b/analysis/district_demographics_geography.png index c0142a1fd740f5ef62daea3033eff8b7a0480515..0ab171273eb804a5e81c3ceab3d20b32b16a8b09 100644 GIT binary patch literal 452606 zcmeEubyU|`_q8*Q`ly5*q>hS3NSA<#A|OaN(hbrL#t5T`A}Sp!@TFV2bdXR4q#H!K zQ5po_ebsr^Z>`_^*ZcSDTF-hMgfE}Zz2}~@&))mod$LzAQE#E!vTofvYKhAi{d2L9Bwa6ePU6B@ z1*fp_E=MdHOu3ho1S5iFA zm~h!Qdp;svS$D(Z%7_xN)BQ&e59MFA;i5?$>g#>f@0Kz(C-N>&Nn>iP&(GiA|4;n- z|NWp`YOKAy8WLQ|F^HNbNK7VvH#mY++;lX z|9{2*T$umAQ2%o^{+}Ysx@KbYuMf$7W!)#vrjmBXn}$(NSNG)!#f0d+T25y*3ml%^ zxpUr9X6i*?P*B`d$#<=jN=Y9_8e#_q2Py4YW@A|`TJo#QbGEK7moMQ5Ya&DvefDyU zzJGS|{{8!M=H^L<-&IvfMnpt>ou5|<<}y&oHdoEd&DD3&oEvLtNmkEEQX6bDiflR> z>E=IT|9cv(j*dFvOQAEQi6#e}90nHK*&Bu=)-@kY7v5${WXJ==BUtiR@ zyIbDX*2)aLe2ii*(8{Yf1Yf?rz;{S|2UJ z$JkI-b}=O-jbYwSN>da3j-q~ui=M%MYHvid8fEL*^V5!=sG`MN0APFWk06G zDC{6*ZEZ~nu(!6pMwU6|%5Glck7s=8`IVNw4oIhl-Z*x1HcZS@i=*UQ2D?_FQ;%79 zcXx=eV~S}@O8Csqswjc(&pOYPOJ8*4SSe3+7iHQr1`iAk#g?ospUjEEG&M`&@L?Bd4^KF_<1Yd=?a!Kn9lXf=Q7jQ zG#yI7hM&vi$Ny1q*U!K51^W3t|LdSYRWl83 zY;4|t6+)B*>YP&W_gIO{`GoxK|xik!Y|_aZ)7Fz|NZyJPjBvIIZUWIJ2~}FkAG!(n1hGUn+mlm zt{aNonFHEIY zFnelckkY&c==1UA`4;Ub6T`qrMn6omGMpe0|U<{xn8_@p_XS8LxP6as#_!K(xaNR znW_}c0y~_NMo+s_zoogaI4}y@yw#O9Ha0dru&Psm)0D}snIA3qD2T^I&QfOcx@kYP zLZ@`mwM9K2&#?sA7qzB1cxma0nS<>KmU_HFUY z@C}fN$q66 zeC0|#qSbD7*?z;OO=dHL)$O^~Q7IaE{=NJ5vTJP>A% z9y~4W)NWRlQ}Sb##~Wg0S|04@W^GZ2oQy(tvTNU#8j{IA%ujS+8BedSF7%8v|K;s% zhDSc3nh~9#lpHd!wBGp*zM-kW(W*56_3JY@&nGq+&R*xVbqgFHTD*n>SQ;Ynge6`w zkUg00`&p6h-(Qy=5D-wY=*Z?xQpY;d@tQte&;8FG_JUeT^A~y*4;MsuhHJ&Xzgl#p zbUI9S3c4?k#O^WMvWGRU%x7<+?2B{pJ!Xw@kBe95)r-D;t!hu#yOdvb&v%$eNY|@)-__Nn8;(pG!ejE1 zMLNWIZnTL`*dfWXE8n1W|N1lK0v;;fk$e4IlQydbZ;n+FHcZtj;-X^w?Tprw?jCRC z9I3nKEw-EU?tEVKV|yK_o<&b~6~u1#>h2vLjyrp2qd{(@76QK}Oi<5yJl}qdf`3ZZ zF7ZGviO|*4V|(~3O~3XK7@Ys(=UY!-x3#lt+{#h-Tx@Oed{d%wj!Nj$r_tB|4Q99L zTTL(SBFXP>etPWMx#HqtCEu+baX1U82GnC8zA-(vSI@Cj>?wAqzViYJsSYb-zK^P? zMP5cEW@%=qsvsm2&<{Ag#3jZcF8MJOO!^6AE$HRSNXq?I5XF$@lx$Dh~SQ*^!2d z1Wp}c6xq?{0ux39*qLLf(c?bF2%It}8_<9x}>bxbT-wcu9$;S1PKu@lZ{8 zd#2H&`SG?VgIVFij)o@Y5_m4H(Ns zL;NB;gB5n(=8~~-T)Yr7GqX?XP*tD|K_j!XQma~acCTK(d`VToF8lmuS&OEorhJ8i zjSUyC_OBgq-rFnt^RNAJ_jjM9b9j}nhy1f;>sD@&`RU$L6cAQk)5gpAKpRKLmP3zD zzr45es3}&~=EjZ6@mD(QCnqOE#5{||A2B5-C?>wIu9nu)(lV`kd3O(+S~Qc0OaJ^X zW~V1dWttH))MF9OgZqIEnpwu&Rwi=V=GW=pD=8`QSX(lh8)>){bLE*opv?VU$6q4v z86z1bAkSLLaLV*LZC$s{PuQ}%&?KRzy1IHay|lD6)584MYs^;(<{bU`_nY3eCsPYw zVu8EUCI_oSLqy&4%O5hDxr`$_#7T$psRlD;5WuuNI}(LN-9N9uBy{%dS+#t-`1p1} z!Z(Iva|;V(OXu(*?dDSJXqf?xe3?YlNV%DaZ{6@C)>R!H9bHgVOPBlm`^`fo<>chD zeF4+RpVuDSX@25?GY?P7Qyu_;B)hN7?{8+;F4nlSi8dfL)oNab%IRixFn2|xZ-R%1 z$Cm&6Cv7BO*kSxKiLBF42|x|zHllcF7P-u6g!#^v(_2M5WZODA);8s>JEN83IMv-5 z@o(>?a^c^2%zJs!XAk9{7wPHgYU#Rnm*>U;SD$2jLPl)QFz^BFh>?%II)X1F-jom? zhmUI)yAkA8_wu6R%L|*Iv1_UkPEA23dtXs;RCzG0i+rot_iqgf7Fc?V;p&HrIsltTN`}qA`*@s)(T^|gNQ*|jdGki-#;2PE8-MzA9R>CzAQyg~z~jTtE-vpI z8srg~C$x(TPbjA(p~NSmcID+;<=Tz>0A8qC(nn`Dy&hc6sFcn}y$R!Zu@=g*(}2L}U|*S79H5q;&^iFiC7 z-Y|lcQj00J02Ah_+0+ly^uX6B%!r6muC9AZ!vpxBmSpqOQ<*p#fU zj9skGc5YL{+M@`^0{)UZZZhu(434v{K(;GILl7lQ5@1yB?v~v)1}XfOogeXLb4c;S zZ}08Qx9;;I&joNVCMLGDvXY2BL+z4%Sab5vKhGgiHv(kXFVC8U12>a~05O4%2ED{n z4sFq;#l<+X!Pj2?g|z$}-A5u`%3;nqJ*!iwyS$>ptRvf^y~s5ORVj+wsAd?uOg;gt z5f&c)44*Ji8>xNd%2Qc@PHF|-eAf7@R1`U z*cTG04;h6i2-LfO{dJ6$Rf@c|ukSM-AIZ^d)wJtx8fQXKC!<;brwX7dIce2H zI)`IZO>RE8aOsj+ZG;FgnKubY1W)v2AR_}q|Hw%Ejw4s%@P-NUu@BX9ED>xa=D}4l zl7X+#IAEu^?R%_;7C()ShPYHC^i7@}mlsD8QSQpOSIo095EK$JtO_$f0_YgZXHgG8 zWjFX?zhctcY9ijPN^4wzcr;f-J*k!08EPM`rm(lW!{#-)3cR^MhW4ZtTlxM z3b=VlJ`3$8=@SSV#Z$ikU!lE{4imT*$f3=rVTpr_aO9j5%x}S)CTWT1GoJqOYgQ(O z?|OTy7Zu*QbB82U6wo*njRf_aNq7EjAzge)y+6CvDvDnBScg?f6M919gn4vTO2DKR zqTbpbOBeCb{+5m(@*RaQ%K&AP9q{3;6?5|<75WHB3U(4e5@svKTAaaEqDz;>|9bQk>Ga^0fSL)%L&1Ej}Kkqv1sR6ov9V`>bP};q#qJzIhLA5i=TZK zCS(Y^F_O-0GnFT0_(juLq{OwI&V&?uy%A*M#wZ}8p$AIx6In8iZ~V{<&u?N1#sn>>8S#nLdS2Z5x~67 z`pf5w(h;XsOTMoa#4mFhydREwBq|pr?ki!Dvk?+1**YqVqNat z_TLt*i@vX|w3m36^x6P8H2WPq&)STDy_jiO&2@UAAkf>}o3sNh+MZpfo;szzeS0BU zvp@+u*n-TkYyW`*sR$?0Folx70OsQ$5?fZt+W> z^hCkw)2DfiKOQJr9!*@hYoMp6H&^m~Ri?*dCHZq-AKSw|fKVhZ(+@$MU%!6MFr8c= z)C5?vxh7@pk;=!1l-ShMuAiM7t~-y&Rq+YmoHy_bpug2A@VPB z3k}O3><6TDv6MMp zHi~d&Q7?ZMjdSA5ASj2*$5$kULTgwXk&H8B3g$`4ewu;X@BvEcli+xY^;k38>TH7y zD#$Z({0^S?s;sVNtLjs;nHiLxo9N(g;E!YiB{o&Ok|I551FlLsnCl`qmpkRY^s)H; zh9-qrrm;J;qLm6N6PAQvsE?7%vKx_ec6K&FgX)|h+z&z{6;-ZN;UkyGCK?7pLe3&A zM;m0svV<<{`J$4Pweshu8HL! z32d%LcuIdo@GBaO##C*wM5e!Q%#W+iP4#F$VUd16EBG1!1cgZ;@!avD+DK(|rgQih zBh&CxM(En~m$Gr6vDLe92XcWf?m4y_<18&iNwDH|+1_)S}vE&@C ztZrA7Ut{!o1pIe94>5|YjA!!g=g&dul)=6z+1O;nFAonJyW@d{>_?x1GU{u*IgIQ@ zn4w@AJ&*}zfM8p;Y^ndYG}AzB8*GiOD4J_l7xMV#L0 z50Qd8dl)}UqKVt+DuwwuLVpj*nIu)CUpYxj`$)=n=Z+mOQM62e{DRfi9#&RfwrGDP zL5d_Hi@IH>;!(j>>9`XSy*&Dr+kj!&s`O%_qla;v@dUYm3;%WopAP=}w{>~{fOOul zW!I_QpCu$D>OcxNq3AILwM|?%M$G_BdOSHsLqlWQQ(PqEFdj$1@{QhqY#bbvKs(li z3llgxj9&V^oN`>D{z!d{Gv7Wsr1gbvR~a;{-eviJUc5hicdwQ#Iilz)fN@U%=BzAC zwIB)`sLeD=JVXj8;Zgoio@80|h&Idt{#F;i)4%id=`Lz!Q4O=^q<4*9NgBiQ&CYjN z8JRGl)g0|EDqwOQ=TuBmxrVB*j0Wc1xpS{j&e^mI1xnML&z-wHSQDPO^XOG$tnGq* z!mDRUi3s9jlA01+azM-oeMw(BUb%PvM+VSsPfy=KYhi+PLMj{g__p0xb3&9wy3eap ze!sAJ^XBF8%-UDRb$|NU<5#v2KE+|8Jsw>QVf;ZK5KM-s8zr@pgVhN~x3oq{@CZBm zRYJ2UC8<2tkp_dwV>c`d$gbMk+xw%bS?T#sp^xh*bj~C+5^$|yhr{>Df~mpy>v49j zR|7n}0CD6WF^Pci+DQm?^1@)uEjx2xpdB#qD>_l5AQVFHgQGswvwiH+Q~Yge>Y@sc~->!Q4ys{T&PTwI)8AT`cj+_fmXf%m(M)|SUqLoQjW6WSMNT#Bs#3qUp_weahG(s`nL zJs}{_I%++6I!GT$Sc#*+u$m6AQK)p&)?MHWrwV5t0?4z2p=K1gaYmjZRrlh&_hce9 zHML@i$10D*xC+4+O0{K|wel!$O4&}I)+qI+Z9z}Oo#Z?iY}8od=}C6+7ljQT|Jh{T z7i8ZgvY7xwN@9M7B$09I{?)srj)T*fXKN(#wervjjodVpUwiZ+7OiP#5lZ=1OQ=V8 z@7`7A-K*~N;DG|7gw?B=8PL6G`k$=?hhKSm>>wD5vbS%C(Kff|SVbx&s~OmP>;9~J zr*j$P%)hAU|N1XkMCpsVs7v(CyymU4XfRMeNnGI3LK57E!W|oPta_-lX@7!b>I&6g z>~8NE5(qdsb+6vzn>KACHAqHm^;Oy2;lqcAJ-@F+;xL8|uAp)^ftS;>ZbS1ylHMM( zZ{NNpjcyEP;%5AF6E$5lN`@MstBswV0x1QZwzcuV`Xu8ZSc;cNPvXrFR@;oMhE;o{ zd~kSj&)1i5lUc3{mOl142@UvV+GtqyRc27UOaM?8?B8;R_$2qpI|v|iN4Sh0m(K!? zg?cW-S6f-ARphe2dwO_eB-q7vjbM3jRfJNovA1sm1IKGSC}q)^Tlab2)vH&Rfie{d z88YMY`QFibI~oAfxIOF|#@0)ykiPpk&v~vb#g?U`p-e#Rwl$%BT$C;UF>U=l0feRxah zjEs!jSLa%wv)$`;%_D+|Rnc5PukS31drP3U2Wh?ZRWeN)Sc<-s(=XUxprWDz>WSgf zu!NW~+EtL#tKnOoj>hvML?5rRS{W2m9&rAGf`TziYj_}_!bqyXlj!_NfC#A=&aZZ_ zI?sIlDi@U^= zXeLig@S(s}@vgFk@ix6y!Nw2ij?fB6>tlR$G{u>s`zA;cZIqWW*f$Q8Rp;2_9*ZKa z7S@4m!lV24@0H&TAQviO$=KMknyl^(Yl zU&R?t*Gab_fiZNXz{8r4MCOBt?18emN-!kSIO}A`k$8|`%e~&rQneP~V=3qCA(l1( zS2w2VoOgD0CA1C@Nwc!Hd(7gZlc2D0=oXuyLFbUiAQ1HzZ`>;|urE0nEIc*ZnjVF{ zk~?2TXy+=9k|gMJ?>jow3D&}y%ewyNHc!XWFD-&ckE9I|m)XkcgH))kYS6{d8H5e4 z-~_o0JX0p{mTG7TSPH*gYfq1lo$z!^q4Nw+j~O@wqzi*Dx%*I?ep|n>(~+v61t~iL zrI@~^k49ayfsWI*XatINboAjPEG!973|z1YySHsSfP_T~94K4h^uRJsbkcL+@{KF~ z4hq=~`?Xawj za z{H2n6-n==B{+YLMa|dV|h{Ac`Y4p=A0S^|s3QbSqm-`j!sa;_ z6dtms^c(vomC|)DmR^rL3_)HIA4hBtUcC42-GgXV@BV^QviRFq$tLsu>r+aPw(9ilZkr{4xiaSlc*M0`-ZU0tA3jV*G$fo^#>q@>nW2 zmDd$HNGFh;{QJko5+Uc;EBuEg)@}ODrak0q=s^k21eKqqk^{sme*83H&XdQQy``W{4~1NQFNAkR2<`71ue7dbqIRm)GOcDteBu6Q5C?}fO3%xw2Jmh z8k|%MIj){-ZVhi_RgRuIb&7YN5Kad%j=;&G1b4##D4;F7PZ;-CKK7z5&2yLt0hBRM z7j{IZn%e4hC1?E&C_S$P){G+EDR61*oH~|_N5&1Q0dMa30D*6S8?H ziwc#Okg%}X;&kswadVcLvJc}w3sXH-PD~yZuoLjOE#5#VjLbV2@7l+q5a&k{QEuvc ziI!CD7L>Bc-Z5aykHd9Q)V0ac9cOLKm|1_e**7=s{I9k|XJuv8kOkz^Ch+8Y4!U%o zRQ3E`HWB1s94d7s%G1QjKe$NedWqScr;83y!Y8~nqZHvHWq*FL)^klSpT7I%Z|fC+ z!;`ME)#ep!jeh+5hAsMiQW=B_m_s`jvqI2pe9bj5EUuL1u0m(qrc~`7g=8FCm$S^%#+b;a&)(%d{P;Nw^l}72h7VW7jVI#o%d(MnI+9ZmZD#(sI99m8?>b#EeWm z_TK}CS?snnI}oUy0Pbd_*~!&4bo89*R&;L!Y_K(}>||iY`H4T}Q&d&mXL04qeN<+n z^zx7yE`I(Ts`J!>B#6jqmTD3b5D41R7(%V7Uk-(i+wDK+&{$KICi>5!1_h~+hDj=X z;Sr-SU+F1Fq#h~86`dmCmW@y|n-SgIZo}sZTokp_|L3pGUcLFT5iTc6TL|q7?MJAz zrLV6KlHW0(zE~gwQ3~^Q1$0day*Ikm;LusUZhrmI-IdacN%2P$_7~Np^Tq?=j+r1% zJ+ATRmXv}~5D>fe>@ie1+U0TG>)NrF`XT7`N_&{sZQrwpf}jfu@!_?Z2^l=g>oRK$ z3b$-p`)c^3K}E3KAJGZ3HuK;KO3g06TXfMw#oxpHmZeK{fgXMWU0q$)W=I*K9^2G$ z9JxDFY|euhFZ(nW?tLSu>Bad?$bwaS8C$H7?%N===OxH@|s!@RYhB>Rwq( z^5%AloKgov%b?mSq@oz`|7uC#>=QtO*%*uG6in~?GCRxDG%!a4nGe*#@skbjpMc?) zZc(${sjH=H7xgN5dXQK;fXowHEciNYF9Ei*I@CUmMz%2Q5VZm%@_NA<+MU0F3QXOS31^QP#o!7K zJ9WVTLZW4GOu7~smf9>yq+D#N|4DH8!>-Eez6sQA>QcRC2TL^0!HTp;-?5`bf0a-S z-QDX}7SjDlXP&KUkmB;!q1sgFPbx{Awqd-P)Z*geqwx9gC7lEbOUGw!oIV&6sKv#w zDY&npypn1Z7`c5lF=j0(6d|+jFMx7ta|;#Y`gWrF z6LWvh-q83(_rK;E`mcwTNvo!2@cZfA%*>I#+)JM5D>4^d5G!EmuBXda7mc>0Mz$w^ zU=rq@A8V;iD@U0Xah+Fia>_kyV793S#e|4xUiLWEnadkrBQ+`D&{vN)evNKzRcXj- zHhvv8_g!3E+_$kPrbF}7q@8YF&X{tZ&48R0)BOB=wva6F5N~M+0*S6UOvF_Q z3>se&JTkW-kL#LRI_ftb5#$dOv@KV-NzpC4zek%$Q$%+Q3JTJx1~NqZtS>aI^Y-04 z3DV{2Xre8Ua$I|NAFK*}Gb>6Op(jQ-0sW)pVy+xVcJxomm*`^Zpa0VQVQL%-gCgt@ z(y2Z81Vs7O2@PNrOH)DheW#t$wA{X2lqi3ZGmGkT5stbXl^Mw8hOP1ZnIw#IQ)}bs zKXpB15;0ZD#OZ3Rc^Uv_`E?pPA}%5`mG!Z4#bP_Z&du>AxuUHiG?B!Woo_?IhmCr0 zAA-E)6`A-TzpdK>^5LG^rY*aU`MGG}6u-pl?tkda&ppHo7$^%$YGm@gvqYj&GHbDA zHg`dt05K!tbI0l2##=c{XV%&>jp%D)?MCXoR_>O^(p1_!-DwDt1e`gHx9T+6w7nU&dZ?VD zK_m}02mWGZoLLwOj~;jZzGu_V^ix>1HaYv#n)7;nsodoOExZ;ff~fgMU*J2y)G#9nx%!cg$^hIme6-%#rcLh~MF}zsIi+(hKXvyp z+1I0%FC4i?-Hvt@>&%0v*kgvq!(t!nW3C)#0F@{BMB-qY%gCO`84Ye{_x(NX;lqa~ zRMMX74C~@l@O984C-IlkmrMIWHU|Wpcr8kt(ky~bL*72yF#Dvf_=j;Z)Sd7c`{-CyTQ6S2{B&fg z6QZKspRI?%7xVHS<-T=##6jiujkt+m5!&JQtTA`m7-FyUqNb+`WCLBI6Kl^+>)8r_ z9qI)@da<3hqrH9g{7eXd77F22RJh7n7n?gczB&t5QMS{`%R~})9}uGD!_Y>z_{iz5)&vjVT3bj)uNg3GgSQS z|2qHcZzN$zwHVr+PfZ59v9J573%EJLr7cgF(l)&^k^d#t@--EecQ+=fa#wiF8-hnJ zQ|Jn>+1xxmKi`bjZG!7?@OHEOm#RBkYfgSxi>UtNgY5~bVPtp}J-ZoKLlEX42ldpd z#@7zx?R7u>DZH@$2b_pziu&MC_MS#u^Y^J!4R5NYhIPTZP@0XzwH6Dzs-fdH zhzI+bfI}W%v4~$*)zHB0dNgR$0Mf|#lsUei^nCaCa-<-}645Z2HpbPhZxKA6^N`Un z|8|J`-;y}OWvkC(*xemZ!hIN@FRXb|N4Tb@2HK}3B~Z+5F%4xGuC&t{ucfZDfN2C< z!qsHK$Nj-PP;TBlw5__Mqhng=pK*Jy;O8-HHBDfvEJu3}W-q(l_3|18V52NZ1hn?? zQZrE*rt&|?rbk65ob-tk?{&Li^sJ}$4skzo0f{ErWnDAlc`vGKh|eQk9-ppRa@u;( z1EIreVuC{SL&@Zoz)|O(>;Ab()1UfM#ea5_mAqhgDQ4B3vZh-JLqh;;6Qb7?7?qH| z(H*wtpmHGV#6aE+8h!b+WgzSI7{L&b2j#PZsqYdHCSNt6Xn0P~abH(u@ZR8}^oF>V zr-#eR%MI;?_?|ExybY$L-R;Bpjy-z5_Wv%?EuY+4l1pH&;$cJ+=Us?>1^T_AD;Gcz+GOvDqt86Yh|t0+I-{k}-m z)`||Ol*f?rD7W?NC%hfML69OW@L>re-lI(c(mDwb_G^V9eb+$73L5UAOv4;+)E_GC z0;ex8@d$>d4BTDsqQeS2oPwSnmuHdekh{vG(QhFmGE7Js>oUUo@_Tr?P{zYhav05_mJEbMx}{uSp+_+H{sd$gZ;SG*>|p_?n@6 zC3SdgY2rPOZ->GL$bKlRFCM}utS--iMBz(Q3Kre;GbO*iP>4Ea2+{_z^gd)3BX*DxgdG_Xu(PwHSk+`xGBZ@$wmV5dFhyd?0tg5fvcCd| z-HhGe#mJaEJUlE_LMvH?h&>@B)ZQpCU&x#b;(O$EcJP_I!SwmaRrYJ&oC)|zu zMS5ufV?SF_EV!x=rV8`iQgf$mg9i3J9wYTfkjRBn0Ik=W1%Ugq&D&m*)quM;euxo{ zjy4;uC(6OjvVWmlt1jnipsqD838Ifog@iv69~;4?XeObR3kH1%01`*Ze5ax5)kx7~ zV5Jq)+DI`gQzk>&PWm>a*PX(Y05pm5{w3U>B%jEa<>rtqP-Zd8P*nO?9+}DDqD(p#% z^{hM-1NhYKr1 zj!82ucy-s?KD`CmiZnDGkwJ3|W`#92;XbO})d zOB0_ehfPFVZ3`MPcw@LilNv%cX{q{$w(vMPsoEBOfkND_AC47(^({Abs}u2_4onp7 zbOV7n`*x?yFgWnauPQ`PzO{Z6F=J5g=ehvWu&_%s=oba2s`u^hUpeyt{a<(Q1{ZJc z1pfZ*x8K@MM|Hq}MqDbbuO#c?x@d%Np*o1O)KG75m--rm`9T(zayzzLH#^`0G|u08 z9UG_Eb)Jc6$PGRm#c}-IPNkN&U-(b_<8tfPEh1vV-~XiVjqJPp)te&T=Ph>gr3LW* z#wPi-{mXfO0%S#?>+;caK(RwslSeN`3`n8Ap?mwE^;saxkuEj#A;=yzq7b*I>D-a7 z;05Ot1D-V=V1<~XH2*knTaTc`VpDr<^GhpAn(VOcYc-s0BjO8uyw-4k1YeH}ukwlH zIKWXQ*+LImhZ5kvZLNkIVsV3K6z3s$7I6%kV2j(md$&QR_0IxxUF_c}y93IkyGVh> zgRqYo)_f*-I!U6_T?u5j$mEollj8xD0tEVq)%wG=I9~L~ks~>=enx1hEA1wrt!G%X z%ufNAxb)uL`^u=6$;Td8=p&Ldto1CYBPwu;NUpY|>69{rSg2*UMgZZ)!+)Ss_!v_} z*T`r_F|0~N9|S8BKP3W|tjacRC`UyZV^r>oz1}|to|sMt z4Yrb|Z?ID1H_5M`i@W}g@n#}6m>am|;`&<$y*H@b4MP0M={7Y$`OV?YSC*QHj&r#4wFX{-{*^4@YqW zDW)x=u+Raz&o;$fJ5NkJ#Y=J5<&2ZjA%T1lQ#WAZ(rW7`%qHJVh?~1 zBHjz>CMH3f3+O684DN4B1qV6&&)uzi%s|3Fhi0ZwQ$yPv`H2HF8?>T}XCMmfZx#>~ zB)p+1Oh(z&r6H?lK-OnhA1WZ&-EIO_E(L_Dd06$GQgmpL>Xfi`yn5wSFbTY4VPt@h zjQp4He(v&(nIGJpC!9KPgvH8&?snGE=F|TvA1@BSaiLppz7;5lyE;-#Onqp__U$ilKgitj+#5O#S+wt^?___3qYswM;Dj#7Sk*0JAax0wT3rI@+U_ROW&!%v zYb6Clu<_@&_h>aRjfEOY2@2Zh!%IY)=Ie2@)8m_x@>DyHwccJKvMi|^NV&0C0HU|p z0L9b?0WolQijQB^>Z~F22H;IdX&$Wuy@w!~0^+DTpPUih(;<>QM_|W^UA##t&`wYJXffyY&0`S*`r;V{*}d z#|yc7EXueC+DFW~M$R8#B5u2G_;V@Kj?#8_9sl|F${8}o2udX= zVFCjc$fH5k+!-I*+Efub*T7*NmLMe`hP*6{Qvn=F!!x7gn}az#A~UveHf_>^R~bau zimS69q(qVgLjP!&gVgLT%f!EcH@H;lcn>IPtt(dAM68a$Wi4yutm6;FRwLz0E zx-vK)yU-S@v=q56m`6%J{_|o#I+6t2=OR;^(Ci<8d@awfyVIt!5cI?d9A-_4sl^F^ zu@l`z>R5{gJRb?w#QghEh)fnHyG*+ao%w16jQR)ql3`^az6u=O6U2N?!+1r6qXP<9 zQ}115=>(X++j!JXfaARwS z421ZDeJ>(CmL=dqOeI@W44P!tJAI}4M{yTp#XkQCCXpsvT$R{+qb!E6u>TtP&8Ai$Trdb`hI9DZ2-KO zfSf#YJ$Gh5-NJbcDrT>poEUt3iHTEj?~Wb*$upH(x9nyAVOiP9BK47^3^16cpI+ah z6dXEw_;Bl-{i&Y|opfpFztb(y<;<|Q6jL$01QO_hP@|{KM0LjWnnZqAR%4uP001_; z42g)@1k5>%qD<-V7a%s;%!_HGkPb$nxpE~H+~~dCr1u?eA$XlNM_b@CZvhdX%yg8) z2I444aFUfu+lf(zj0r~RHAislLz2a5>{?ib)5OrEvr~7_OC^)vyq%tVstcMiYNp_+ z(2kW`XfWuS4GhK7&>)cE&Uo#Df`T!S?H|$?9CBcZG)Clm*#|pE`oDr*f#(e&w1xru zY8q9XyD%{#S1Sf@#Vlfoj=c{G7#xb%&;vBMP8G#s^XS-T4lwrFt~iBa$bqAH@m&3c z>>@03$6;mj>B}!DNJJ?nm??l=lbHDEYK|0m8X3+<3c44EDicR~_0XS(4DTFttQmRi zh%qZ7oWUNjXYAbh^S{PIRCzr>qJru3A9Mu~QjmHmyzSGe4vM=8ln<0e$vS!9!0Xw4*%{6=hQ#ge-v}c-<{dk4?DzEa zB#8__Kakiffw}gx?}Y7bAn)fy&1;Ws{~h4T^H|L%5CyGeX*<;S;V%`;wYOAbK;@j6 z=qb_mSIE$pD7`V<1zzLD_isaD3tfJ0=%+Y*QjK{17M{DJXO_OJh-76n*9v#srI3_h z0*1?6Tvs$f1j89PTL&n>)*LQieGUag9+@ojRWTZ_ZpWD-jHc!D-)UbT^(p7De7OEN z=V`;l1tu7KeMW}SF4Y6JO^#i0P5m&w2?TpIjz90*7*smUt4O^ar?VFqTp$Po5#4&eL0wW%N@i&GULA#LIMe&v8z9jIPcMlm-0H5i_*aBOn9g2TL`0XPjKsaDy6ho$9ck&_A>qo>kA+BbSW)=h>6ZJ;VG z=D5h%VA63E>arFQ5fSQ&%465b5GLFuymQ(v{O3CgFfba20_SB9359oecJ86$$4823 z+yN9{@)YWR=r|DxA1L%59FU{6T;zYxY_2KFTv^G{v`{qYM z`z;hX9)iD$&__C&kZq_dIQ{F429TPs&ZxE(yBGATs6GySm?Jk>Q$-Fnm`AMu#cTnG z%h?^Cg9fBsx3?sNDW%@)HF9qpY|yOnD_WkPFiV*1?n~c~-z`03PX@sU2OYG-+a5j_ z@1P{-R3$~oe+U=mueSd4FMg$uaXo=e9(=aK<(TNRowyt0{a}C{VW~-$S+uleEjTM#RKtqu(u-!WXCMDt}}1I8@5l8Esa=#{ORRlB%TNxmLxkHrb&ZvZ+WOfxvP zCPMYN5?~lNS7hZ%DJ`sboXR96Z^d$+@znL3y{+h7_jlWcJw}$IY*r@2_$V&frc|!; zu!O1zK=NAAkiei!9>;aJR5Hz})NtFEjG{t==1Wq?-oYOu^~eN~HJtswIYw5`h9jRmjtRdQRUX+NZ%)~$P3FVENqvTNdwjVC&* zJ7rG)7-Y8gTpl^vz-WcGd$L1$VYsgJMqZLRJ*KZNXIgZ+ce~~wg%UE+Yb-Am=mN?z zYcu5Zw%POBeumNX^#lK2dlM>w@`bmv!_Wt&Tud>=kDlyV`6K33n3R30K(wopNenIb zxh95?tJY{5uExr^qxfG2wv< zXKP01rAlxFOdpu%{DzoMR!KhxI-t{iDi5ica!%YiIGDzcNcffI-hT9-i?TZkkx zT!VXZ_X&l{G)pv(`@M4Zu&P|2qb>`;iBm)y!jps-nUb=nm6nofL{k_x=*m$zeY@8; zi)>fZ$RrB&f;$I>-XKUyuC~Lz4lE}r-+)>}JpDJm;VzB+4wglFfS_t9$D=I}s_2C5 z;^GaMcR?5=%8*pcQ4pk%BlegL2eyQcAz4~r5Vw$gM!W2ddaAB7hvViwY zAR=T((3ubQ=D$D8T#=$N)o= z6z5w-;eiKS?LdD5|8pgU3fY@_b#YDEPvvC;MrwJk+WS-?&lnR8tHK+KG*1gx0Kr`* z^S(Gg%2;0Nz|4Vcoe?V$(C*ozza5CZ8vg#2xl0Ae4HK7NZTaba{}NKC`{9F)16Ye* z;s;44Uo}bcs|_-zGKy~_E^r`hCNOpr-jMa-HBA3W2c0^LFeOu27GDC)O#vh5NJ?4p zv+Q}Wf_bA_U1@A1T-EMAyrAxR$NxCcHF?7^6>Z4z>K5lOMy4mh%yLs{klS9M;AWS- zz5*LQ>7Xc*=5W0H0KYYFFjNbgmpcjLJ)hirswR4#fpLa8Fbc;Y5pww1>@JUrUXa`?5f*IDcWS5XX_!y6oCkIjM2ah9Fzi}KSW=X2 z_pk4c@WCbKY1j_Zs$t{j#F1xBj9g~e;tj45>I11GO-Dg zMIet(J0?`33&#K2yp!rYD>kwLSsX+-n~y!Q#bdOm{*PjCvT>+;sv47LpZNQ~BuuHo zP#URUpxXA-Zh(J4dc!AQA)hNFo=Rj`9IZLUV`5;yV3cC7UI|G^nA~wdOwJ0M>d3qy znCWztnZZ!2_qYUOcA2{mY}cu*hk*4zCc5DAeTrLfAQQk$XaM`KWXQIqJ2g1K7}4NK z5{CRh#@CS7qyaAELEaE%0xYkMAMidyRg=h*=VZS4Dm-t?#Y`Z}oTWP;;>(4lCb$BYdk`&AV#!tSZ zeF?BUf}+Kfgwz$(dxx3K0|4hoZ+6A#MBOB_i>A@C+#h*FbUKxdzqLl z7Z=)b+!HWl>ZSLSApZ2`v!6fX_h0|Dt{@$~67cr%OD!X~6N(6X=x{)eI>niMpPv#f>wX1TJ}B zGit{_b{|{)T?<}xvk3{QVZG{blnt$^AY0*X4|PnA#6dG=M+PLf<$y$kS5znDqYA<( z3&S`K;WCIst5)g^Gy|}(`a||ehAjsVssTW(hyilbYOrABUF64JWyQrEBdoZtWAuk( zaPOlY7>*9QN}#YP2M9}cFBL+l7OCRDNLf&2CSfpC?GMfpi=j@jTr2FztQ8HVOL^6l zeMI7CbNV#C9ZUt`5F36|#zU?SiG8mlCg{10g~`OJ0Cd;5mnXLEdrSrL5b+fumB0pc z6seq)Qo^Sobq2Ax!KNCzJpBjv+P4{*(gX2uU7!;_55z5nc8iF{ur^2WXx13kgvA5Q z#1yzea3KvTK2$|R2HIX@qA~}%(JLsHeh4DcB_R`;A^9!XSGf@jFjU+3b)2h1_u6&U z^GoZezE~1c99?Kwj6EhkPDu~FMxT`E`F(AqSrpvnXnR&7mL?W)_8xHou$K=(m;82S zkgLTsXBNoKMrfV&1X6Lf6JTq@VXge(p(1kz#1{^GrZLVmotQ@<=KCYn8j!mhLnoOV z`z$cUgi96b-IFoP{bNjtOgM39<9-IMD^gMlct6Zk=_hcTqYi+tenlm=lFeg3s(|56 z;f5*@?wUAAnF$@qPaTBQ%JOEqD*jYAj}3Tu774rv8ld0EbT|+`2F%}04 zT1VRlEgioX*X#Mru#c>*Ci8+{BWA>X8+{|3K5w?4>{LZG1cHo%9%c^(h+G{&&_sy4 z585bV{Vv_a|F*0w9(TH!%qKUajq$^-n9SLb3B|ONGnfc!Bo^ALg52C;{BR4ojQ}?e zkPjm305$uun(cx$Vf9sMa=Qe*jO>$yBPG?Rp44;0_6C*fP&IE$5eSS%05+n?l4)g- zp%`g8_G7{c=n6@+;j~jJ#xjU{Pp+=fYcJ)kW^A+8(lx^o<~mJVC9%L%VD6j});;xJ z$+LnpGgZ`f@BxZRG|q-<5_;$`m-+E{WWIPDADopR#(xac5X#AfDff^B26FPdUB=iF z2Uid#q~SD~HzP))kHjzy2O$}j`TFI{H8KFlbUF^jd89aHoZN5&pRQE07{oNRusq}S zZBdtKF|kT+v}i(En8WDBDEPAYRMJ^h&rLzZ<#n9wTq(B(|A+%O2h0fO<_y$Ce^_^s z%k9CE6P95a!x5;hSyV%xL15nN>&`>IknSs*jdWP z^UMlhMME&aN}L5mv_SzMX(mIY1W=tF1_6vY@&?_b)vjb1jLZ$;y1mC#X1L|+C2S^! zi_4N6MNiG(t;Zc6q$XpoN}J5jl5hPn%*4N0T@Ke8h=3(0)*$$6P5RmfQxK-Z&^^d) zM@>}@z;{F@1RWki&0XbzyR7O!^SwaP3L0&9%z>M747;R=bj-b*ChIMDeH?c=P;sOw zrLsCerqqeN0Z)r6Lv2rwHY#}xxq8Wk2GA*I2WKRvW2u5lMn(p8u13K~bHu^e z10x++zbjt6$ZgBuD1Tf=u#yk^_1aA2cYVYB;DEgka~uJ({3$Y#DNqj8i;@6zi0>MX zse#L<6!P7#I^bh?!l|nX2f`u~kwnG}=?w!M$=!kPBgIMp@}96_Vb0!k~f6m$JQN z75l+JUn7(kn1mlbP9Dr60@VWo&ICov ztB;J<;rQ{+r5$zENhOUYGIRsFfmsBA8CvE2&EbN!;>fTsdYuqxE(=|bxLANcai|_% zc5>_7A@K(vL1y6cHgxZ#TrN)UKzjLiwsK^yATMu(zjzB4OV0N*^bWp{xZkBKA%F&-$I%qtP!|{5SK_2J^ymU zEf#JhCk5y^Ahu)(M`hXuPvHm|vL^QL!tLyY1#+3G=C^1r`iH3yBpvjcJE7%|x6FzDOM9>%aLxu=nsH^guQP=bM`hQ=%={zrZJyVFeb#{DtBfIicf zlnPvF5D#EJ0&q(PoY}Ps`7!>9 z+?nXhET)B?kgKUK32H4_7ZCa3>$yK@5snfikC-YzHMggAL+~c|ClV0^^q~rshdwTa zm}tay5S)RbGwG14&kZruBa33(h{V@FI%?XKfi{jP;21UKCJ9x^J|8!+5wVsBJyPkW zqn#tWnvoUBoE%}Hnhs8q%alO8la`ez`Ar$~Nq7S?FpK?;g?M2k^v&ZEBr+s{qgeXd z**naW`_MKKs|0!`8MK2+Y#mTJ3F0N!xv?3Y6m%}M2Tx>{T13HTkHGxDf1_MAx$FrN z-)!XfZn8`@G1H;R61MX-XIgnH9=5(=c*s0@2CF3jiSb_!W^N}0<}LfW~-_0lzXKh+RK~d(=%#PLwY%v*9B(#R@;DmW5<59VOiqa@nWW~z7=`;8>Ko6mpCpq667}FBlAv6vXPEL^lL}ED%@t1XTPQK5)0WoIc25^z&%FKHrbRO=9+H#OZk0ea5g5 z4AuX}}rY4-*_4a@zoep8BL7z-#1* z6IzA2CzO)n&@&|BXkaS6=BvwuB5W}a83bPw?JLQvpHrNVGAersxS z`2cC+FgqFpSzr{UXA3PYu@pjH`6ev>rMSylq8z{c;I0p31NFvceLw^-5BIO$COsqi0rENc zHNe7tC?*)SB$pN;$-Ki>64NBgumX4yR;w1=wnYqUaH~)d3ny{+5tadWKq+G4aWwBP zI(%|J8V)}ZvBbshCRM%bv%q~6c%LWBuEMVKNd!}nH^ZPgekGYt#H#-v_TKxg=f000 z4wV*Ks7Q&XqD^TJDn(P9wiIcSR47uKTFNLTqtc?SA=0Lzy{WWNQA)!7cyV6e<9pu6 zaUaM12b|Xr*Ku8FeBSTZcs?KNiJ}MQvTU3ke|{ulfZ?hnyjN!6VD~$$I1&m6Of!Z2 zP<-W^Ppbo2akv9T)5DpFl4O<4-mkX?_mYDZOvX5%*TaB8L8IS?tgWP^gcOYAapSkY zi(hXnWKcqU?DOyf{)HzZJz=|&@Tw#yHuSX_&_k2dDHO4(#6E%hx8X}E&cO`4P$$5O zB(n+yo*LlmYMg>u_$pTML1dV*d%t1c|_!#-aCzd>jfOD%~=j+ zQ|zXC^m$k)21-g7uru6bGD?f5HLv0pNaseq_3e7OjZ?$k9(h|P{|nl{yYY^6{sTeYH{Y0=d~r{%Fn;& z3%Bk6MB{(||Nr^#j~X3Zm;VW_{6ByA=YL$-topy$z<>Y6vxxuEpa1@Iirj7gUq6KA ze{{(I>nrxE{P!dN??>|SzX$byKaT%a)W4nvqC@}NdjEbR{~aLzejfiFApd?I|6NJ{ zejfiFApd?I6#pF{|9&9<9U%XH9{(L6|9&3-9U%XH9{(L6|F7rq|Kk8rDnxE`2S!+U zoa1=5Y6_xUGCRiQ&|hGTe8018zydwtwdhNto_W>-R|f0=7`;QWMdfvgJfXEs3X#!A zf}1n!k>cY}`VHTO1hx?L0(cqW3&G8FLMSh=8VvCRObGa>VPcZ-Pg=w{`JssC0lW|g z1pC z782A4Tp-NALSWebPF*6HH$n3Oxz!;}|LqhpO_7;4B-RmmNV*V9om}{1x0BN)2^3aU zD+!%|7*(*%*9rUwev3rYl*pG~ffRWoF_96*jfC3+aUe;Agu=s*!@pR41g0&BhzX52 z4uTvYYy4(TP7+{65IJZ&h?I!rYfeo$Kl#0D^tas#3Z;Dni32!8xIcn|5)6->T~IUq zcMb+f_!F8Abcg{E6i8|~;c4W1A%-X#z`N&8aAZ0qlrpFa`{V3x$-L+amn?JP;GCnFIE%Cu!m zHeL)6vU=$G$!fvJ!7Qw6!dbRhRFyF5sS+<02#HO&CFD34MAY$+(027QXAKMuW65G7st&B<=ta&kBc!65mHQO@gZkb-cha>8&xF`_-{5=X z;JJ;p2)_b&tB1T5GTwtk+k!0JFa^yg2@J9Tsq7oSGLDIfiJ-KE`!_5)MsK+mRPfBJY9svUUE(L7^ngzQ^?ryt^mG;{kz^ zYi@4lo4V#Qc&8iriui<|Dmn>OL`AT6 zRvi#!>2v`^B2ED0DAk@Q1B@fo1B8_a2?kAqfk%O+lFY&kLNfux!($|Y@IVbq{TY^% zg}I z#t#O5S>)4)V@y2fWzBzR8>?wL2(F2Zo-y?#*P2Q&C2tb zghmuF#I*UP6t8%|4P)!b5d;xCdk4al@G=h}Q0)Hey-40B+0MY{n`~}_NVc%BK#qEJ zx~|a27eH#~t5$OfNJ>h+TF3;7dq6^EADLwW`xhQ&*PJ-sW-Fv!P#{?RiDIev?hqmW zs=W?ywj^A+oQnQ^bK+o|+Td42_!tk_PlQZIw%sGMX)Ln&dj4dS^^`izxX(z=vxsG> zArARg^Hb&ba5&yHD+Cx1-T}X6G)X&#YlN&K5;1{*O{Cc++$$m1U7Z55_=x(2WE!ig zdd-dNLHQy$?**^QA8S4sG(uL~^TN;={(gk;J-zuJJTEa(nsE9C%HYefB}~X%H{wSo za+T+L(qw@^!!?NovYV5p+#ELyS>!~F?cWS(4^HI6Bx4xHo)=<^il!K|lSKSpK(-A) z{RBy2cNijQ3$e&Hi=x_^Sd%1r8^Wg)24~z=By8}XkXh4Tm1{O>eoG`~J|f|Nc-F7q^bsNrpG$GR;`kFH8zdpJki>LKG|PQGj}fXS z2A#iG&z!lCt4Ha!RmdgNYh#hfXVap#lUbQpJ4_3l%#As-I!cW*y*ffNy-w}up!$5d zSc#Rfdt2D@uc7@Fl+ou-sc^3t$QheGs}1To>C(t{ndGlBt06`U#4a28icqurT5Dk@ zcmbtZ-ud7D%OE2bpXyqW^BdK=}N+uPR@m2WRjLmGN| zRj_m&9&g^faYZU0mji;>9ZHeBY#p8}70D^ji`m-Qp$d6`d;|lHqrSm)1AzCDHz}->Ecy?{#OUJnn%} zFLJ(%y18Lue1`}{>r@|)8pi}O#qqP){TFWEzI~*#$%ImEBg`X=KYh9vdL&wC8PwF(-{Jb$gAd$8 zx#F_QMSRUF?7wPM8ABMbWBjVq5q3rs3`464Ny5FZf?cEwmKnONci8TWV5r-o+YAg0 z+=ia5AhQEZNbbt?Lw2lY$%9Fv5)$NhZUrS4D{RW_;Nal>`U!hn0jLihJ4T2FWU*t9gAs6Uu+Svfd5$I9Nukm zVj8Sw089DNGVhB}li7k197hyAQSmRF%6(MX{RDc8^>E*`;+8I-Y}SE>J9#`p3+I6{ z=>0Db)l0uFEYu?6k*blzoQ%Y%>Uf1oXF@NyjvW1L>u{D3mzW-uOxR%0YHFyl<$}Y* zUt;OI{r=_GSL#KNhui%+JTj7`;SYWOyi-u7ElSKZwa>=cnI5*~#a&mIE!eH&YJX;GJtD2qN_9VHp0pyi_9y&WeX$0-| zD&%rjBWwJw@l$-1K%8rixA&hsc`_Vr%3Z`kM>UQXpZ~4!ZF(@L@7RA6|QH_)Z)Z9${XK(YQPG@uJN}I)?tRjNpbpJEP7SXM|80aScX1*T7$~8 z;78iWj9~&4)OUafEtTsYBRY9#9V@!h*BO~$l#=y=pO zYKqoW--U4UVgFh>y6D)=2=KNjSL9l^dCvt1s>eP5?AX8mhhr6GfmzN4&l?~+|psWtS4Qm5KFt&F2q(54zIi##Tc*curlZlBI z;uo{RA9LZKR!r1OBdqxu%pR^d8N=yY((d1XkNyOS;jARy!56}M*`nU4yp%GWQ&O#N zKcNjv@N|FJLfOS|e`)!cLxydBB60<+vA(*aUq8yq5<}d{9O@L$D}CUvmmvx1!rZl< zl?J5^&C`2@_-1bWB`JkS=qZ-4xF2p&~>|1o=e zUO^edhO+f1JuOER1qErb#W(h~`y$>PYQUJuLTx@(u;@(e>>+5moaM%F13t$MQqVCm zJ|0X&^&&^~VYzr@lD87BMm5MeM##Hd_|@9kxe;piI%)np$QbS_^~y|ZKGuK=jA^_x zhYRNNl-WfDiHc%oLBWq82KE571vYqh-3`fdXA%3rgo{owEXlVEhDYpegRIJWQ3!OQ zW~W9K+wSA9rA3GL69^`q2fMxl+{B)Ts6oe84W3p(h|8h0i|d5hS-D z6yrh7`Z)=RMOP4sllLPi>jOTeEWd+K1W)5=VYM!|{E8bPDfjKs)SCTR39B^8!s0NV zpMGKXu&txE7nzQjE6DBOwhHtyWf`gx-&LhK6H0K$~3f3Dy zPQDs?lw1^(u!*h0QQmGQy>;sfC<30NR4Vu!kfIa9%&XQYDjj={?PlX9E(PC~1U8|V z`dnD>=a48Q8MKxHhjW6sJsr7%3x{*E7tiqX@T4}OPY=ZTMibwy3||KwZQ!%!{4s2!r@gNvz_nJ#H(}U)RO6IpHA1RUH_T!nYmA9ppU)9n~#p1CAYF zpn7xuEx7f3H5pXJjqOeKg~2RK?{>~j^aa^9o8 zDDpk4`bh`G8|2{X<@J)3>PdHX1w=7#fs&e9%jE!SBFT<3xcBC!I@h89kG0sybd6Pe zEiVs`&@+fGExkr{e+KiDwp? zocfC0GB~GUC&@mXQ{m+U)zjq1zLF9Ef2{gg@Qn?c;OfBN6bPS?6vJ5(gA;e*3@Wg4%_~F9`j!3`78RhAr3G5hM z=+QLo5xB8)@zycMrKk4u7-VOSJ>LEKkd_vCpKd4)>%GXU$GdlKxw8VVjCT*liXK|# zlyG2RrMo;*3aG=QY8dg74>5IcWJzN;+BBBiQLnlzUIXh9@4e(rfp(aR45Rb#Lsce5 zaI&{9Qi)=Gs8ZDu$-K3hVyNajE^h;xBZgOmm||Ppav48h*;b2*zvO?EoTKZX;B?I< zPXlt63uvT{_s#m_-38F!&>t{=;;;tAO~Do}WQ2CuZrHG4e6X^zV;Ffbk!hSU9)Y2u z^hATHX}>>Wa|qhy;CS*S2u0vae@)IX-*t9T^>*Vh=KpzO0chxJY;nh9pFjCAo1^SGwS+Cqu*>ORzvkm7VKi_sGiHz=ODvmZyMB&LB{0+-s-pW4!Fx#KPMvC4xT*C}1&%A=*_$M)HB% z5Mz~-WJO27QH(`MKYz0)>-4U?R7`e_GH=F}tf!`>mV^}C%XDXk9E452)cK z*$04vXwps0%u-QP!;Hi=g9L0Pz8LnWA^4DX+&J_YMYeg}QP@3|wrmSP#d~Qky~Y+g z#SIMN1_M9PDxfUkHraQg-6~N#2;KE@`D{ZWIBMw1dmdyW`GY9t0_-pK+)(}~1|gXK zgVQ8$^T8LTr8Igm6xpfui;1QtCXQ>q)xLa5?wf!eBoqlr60;C^Swn}OI*NO5`!t=6 zz5QjpL{400Dlo5Xz22yYZ4WIu6?D}~aBaAEIWePN<=ni-l>*(J40>zs>y7U|e~v)s z@g8mNmAJULyqgI*mc_2h!)e|WNdM7@nA`%z@IanHjUBlM15JF5lDjfu6GSSHZP2E> zyb`VRet7C?hM~s4oXGxGUArC!E7itI5k&BIP+b-`L<;`QZZ&W3Jp@)ivuVB^h5hY@ zBInM7Z%(D)_t`2XWt7_lE0g*ZLCYf=PxKY*J?K|$jN5Sq^{kWJx5Zgo5+>|!sivk< z+{c9@YO8MMVG+$yvZ;?>*Ap^9x?~7Z+dpMD3})SNm;wJ=(rFVW)TB5C!j`JntD=^+ z%h}nv%4p3KW;#*37Aeu#Oe{ z(`Okk$ou=3do`9j)@W)eJ;9%++Cvjf8L4>g8v{UkI5R>z#BuiO^Qs>B`SW%n%miuf zctf)7B&>l{lTc4~M<9Wg6)*fHL5&S=WBFj06hW64iq1ZC=eMs|=66w*q_SwNc=qNE zGp_XaYvcAo5qfqRu}d{B`!14U1@k7@oDq{dk5aIkdw$0NMjMBE!RBo1NV8f&6C9L-rl=_5>lUX?N?Ld z78PYAaFp2xtiJW-NwL#6ZS0R!_|n4vED3}yKbrxE!*ewFQd@eTp4mkzaG-q$vatzS z{%*pR@WV$pVA{~A`+?wK3TTIM`EeAifm>_l%NKE{O~JwLBo>vFq@u19FyW6xns3f?8ZNlj}j0^r}jcxkgj?4EuIQq(q)W-mRw6z$_PXqwd;H8t;? zadTr5@j&NLHPQm4^Mm6o%gIccW1~YucMQseZ_?s8WJ%-?rQ$ zFO>QlFi{PD(;0MkSmoRb3R`f$=H}*J2oGO_(6XyYeZ7N&NxRvyeR&f4^bjE*BbJG8-@KU_sb?wknWuwzIXEngGOD6Ca0YnG7c6!f&)dSOs01W*`!4AyEzL_UPvJ zr0=A2=1RQIN=F37xij~c5Zx1c~BcpD(aATU?!7n=zmO}A6&MyFUWDw7vjkyt$YEG#yUxXXO}^a=a* z5;lhGF*P=6haLC&a9FuZ$+T5ftw4QJug9scc=NLV6fI|CQ$s@ox4rlVhUkW-CI*x> zou$r?u#BTB}Y8VOD@9|rbBs4^l$Nc@qwUWoA0*i`vqilXKn-Duzj01BI5UL(OR2pc*o&!f2nwp}& ztg}z-P zX=v-vd8&Z{u_EQcMdgm&`7m)(d0~XzMTx?kfI|(i8yJBpz!*YrIFhvwXQGx z#Fx!NJwR9iTqPTYr+|r}9ZCUXNvguFH-rTEn_~()?J;7|YB1WmE7zJp{ylyr-TOCe z*H0d960dYXxtIq-6d|ib{kc095bkda{%eohdrI>8IBd}th$D9_1PY2&KxwiCT%xc0 zV)_z>uWfxW`x51=6{s}tU&|#mk8$ic4wT8j=XilQR(of|*hWEz&Y;-}_cAdtxdRTK zP0?RY$T<6vfw`3Bt|D9G(wgrewA}#yP)vk6{en|;5&o%se||z_fQS;q&_KTkq|9j4S-u!v6 zG7`sXs|N|^UlKlvxW|qKf0u8OCA=sKP`s`eMvPTvb6G%Br71b!fhevao?qEx`>ro& zE1$+J;#TpXux_oAq&!m-jRVIX7ZkMn2-{jyi|KFJAN_dTQ&*Sq`Sa)CPk1Ium3#*8 zh{&*NBnMWZZ*QRer@Dmqn@MC_Og)46Sp`KThSQKb442vM*S&eRtAQZ#%Q(g)qNXIE z3NngM^yJfmEqDPa$U|ye;BNKqW;3WE_(dC)x*axsbk$ zv}!m`XK^>D0;cw9w?Bb>kK^YhUZdKN`F}u1RK?Gcz6GT|7 z4c6Hrh_ZPz!5=tt350!cM+@wK?@%=FIsfZV=YZ?wLr)qq8rihr=hP2q1otBfK6>tR z|0bZOQavuzc1vGMlbeV=3OKR|%996g&!Ui6ug6|$4zoP%;mm6$7`fl+@m*bRu_-B! zZ8CE>*=q0xC>ufh;9#3Fs=Rq~6RMefK3(*_@4zT5q7jBq~}G?eiFMll{v8gc>*jOv4BL>3<5ZE=4oL;Gb|i=QXv_FfHt zrk)#t&p>`Kfb$o?tXt4qm-n3b*cF$6QVH-nHSkJS;p_47L8#GQ;N-)1c~{2xrh)CJ zodfD+5VJC;rw2reO&;-~{wAy=IPVZ>$B3zL990?t%y0KvJK>K7iy!8pF#ui^Gxdpl zj6A3jKxlkeD){mg2}Nb+e;>)e)wU24%Ql8gnCFL36oEmY)t^-5I5ou~} zo|(nGk%L3%c!6o2kLMa)2h5ZeD=5YD;N^V*fP;>NmyK9{5yw=72I)PgtgSC1LV*=8 zMGX=&E07+D_C=uR5MGg)v6LJ>EW~aBC~58j?vyQ3KWds>gwIjV-)4;a1T`kb8{>ED zd$z8nfaF6L7gBgFZl)C!sC|tJrg18y?)895kc?BJxFh8_g^nx3Al>ji%G?ZF{Nl#E z+eht;?>039Q7d*_8G>y;S@QPVEsDwJS?mbI%;}zzhDM605A3) z(Mc`dedscd`p!}*{93fdq}(AKWwH%vTt?= z@L6tNo-*oLoIaOPe;k^kypBVMTxkUVYC+0+)s%z(sa5ZrJw9|6vnEbOgod){Lk&v%j5d@uRI_BUoVD4TJ3 zsDK>RL- zxT0SU+ahl^_VS4M_!H*xt)ue??}H|f=G(8iMp8?s6k~Fm3PyBKRz4Azm>a(4u49Qh zX%!(5MRwyxy}gv%Bt8&>v*)eZ2k^jux^f;KaW@=S&pX1s>_7c0;6+(lo zTF_qMC`O;7cfo=~tE^&T!bXmK9M{J;k2T4zFi3W5KxgOFPrLnZ$+IN4HXYKBTuhoK zqe?};nS$RWCpMIWf!N9lzKawfFE=atTYNeNB}_0tmJ~>4oHQN39}g=;D-<2Q5!r)q z*-{}ga|Is2B6vBXZ2$~)2OMJb#+WxXQDWVX5bnYKUB(xX1->b_+3Thz?U_YVtdew! zuuWztKG0Z`0yio?o*LK?vxGj`np?iz zGBY#F_$jgqO=5^(Vr_-Qp~KCg3Xi$#mo^{x`N-$zk`(yE=(`ITl}<^*I;7HbvF6Bk zB^@?Q)3{lcPFAdl`qiC_fbm7JskmdKPuEIx{H>#ulbfHSd)K}psrjI}9W7$h4JmqH zU5?$4LGtj3LQsceLnB)?MVAa`63|P%bZ|KPEa%e(jrU2&YE-2Ku^skL$B%X5zmyC zzRn{MGX*obNP7#4lWari5$r<4K}NmDLg|iH^>}i=Nd%G?R=MoPwe_YyHlZR%7EB@{01?&82Xw0*8I3DQ16?-WoUmw3LHo~d`% z#EG5z=fXbV#@pkl-VDZwCu&bwgkt034bcFYh_~Qp(sTF=8w~+E6h6-T4ja^2{sZ*5 z8_FP*;U#UJhxy|Uc)_J^Pt^^9{{o2Deo`=y3jJe(#IXn=MyPk!!dQ(Kn+G|h7)-gI zQ{z=re?KPz-jqpf*_Pj^S;)r2VYT1(jO38eI#l9U?3AKtP&A0M<8a33#g#15pYZK=C?nZC}uh1 zjHYHX6p1n>n*t#@)TD+|pL(^t`5ZzEqE%Njuw4MF_b_e^4UO(OzhhE>M9Aux`s|BB z;5n)V)A7R|jmw)?DZ$gOwz`SP35_l^0JlZ%0|^$wM4;QpuEJ@d$#b)^k8KzSdCo7(%z1F1+fUZQ?rz}K%miAc9w-qovyif6d)d0j63Kt zp&AP8n6mqO00VT+1r=N$x$Wz*MjWwMaPiQ`FCa6CxBPI1AuT>2FgZ?$HOU69T6l)d z){1x!yjYF--H*^$K<>t1Zf*|s7X!4~8z0%?U>HZhA~0$GiCO%X^q^etb_`<)8lG!{ zSW+2}Pt>k1>cl4vP0cDO>8{6sLb9bw%uYHMyg+X}b(c+(C5;v}x`Qhoi#sT?qaDYmIYw{c z@L_WDDh`npK%w0c81zE9Zj0OnyzKE~S}qHbCCVoj-UIK}IM~qGGpI4~LV_>UZhKI? zkP#gKAXP}I2<(F+QuD^l;RHm}9Wc9iWHP(3hp*dV9@wGgs3!&*3J7wa%!uUY<)y+m ze+JcQE@nhkO*3GlEF$2>z0BquUMgjbDj=H(91!)*RDXdp`SiUN051i4{Yq;*s!0x{)UF21{{f(EWe1z8Y1-paFPed11>LwiqNCC1!yfu z5-PqP+#3{D??CjX1iQ`eLq2c|K(|(R;Hm+(>nfMO&`Z1YJ)B>J((Zoz$8H1wVT2AV z_Q|Wm(rC*D1_sdK8)a_~cGz-?42egXwbjHQFiGn0PF$|Kop-%HVEEuPS^H)8NZ4xC6slW(cW@yL?b$u|DT2-G8@6MOuwnMmwrNK8u>L~|*iE1=f)TuFb{~rPKO&7wZ!wT%2(irxQqJ+?0Z&T0 z6+aOE5{Cp${54rFs=5KPdb#9%(&E_=a-##m>?VZv1CgdeXb6Al;z8?eQXL7K#10zh ztThr~jov_o>9MTKI`Ji3%+WG>iv0Ez4?nnR*&jPbIOnR8J~YzS?gC5`;q`x{DP7o4 zvq)hMiQj}oqJx=TBgO#f_j!6so;Z1O7V$}T^g)D`z!BpOY|hd0@ZrPgBm>8lw17u2 ztzUnc#BmPK0_GJyQpSd3``YpzI(lqh2x^YF-Wf+W7m-(tw4;LP&s;hWl*XfPpAHsf zFvtYD>~CIZlgQAbYT00TEFN@;OxtI=FG@>HKy>^xUKP9Ozpev-};^Hdv{Vv{{ zhmDHnmZ(E7VN^}ql|Z~Ny6j)HY5WrCq&V=7I9LwEYVjg0_yX3c;g?CT8Y06&14`5+ zxYPCbo(5L+M|X3t5YQM=?3hiF&K821d_FZke*R%1i7FcO-_g^7$7urxj#*?UCivL$ zv=gBiFg{yM1LbHoe{4q!pzndyRs&STI*Lq-O$`FjhIPJ z;IE)to}pG2!yKA|m%ETRD28Mx3&hSC_g+iZSfj1EhFeSIfwPi!#`2vtW`y!H>HnO1?8Y6Iy9z==Lsj9^8L^b`yk{sx z6_bTiYe&KyB?VsZOSmt>@c2<#_O<9VNd*P3V|xRng$iOMD;$Rb^%rp(5m*6VxhkoJGg9rT7$d%QS759Q(AVKb|I2Z}a#(Fz(EJTy3 zMZzoKI|Ihf600XfYAvEn-B9D5sKwhQT?8~ytDunJiBvm}q|+L_2da%9$l%O_q3$G# zNGcm`Kf5GC#y@#mf8O>L`u1AG*+tZ0)^LzHg+Kta65XW;q1R@Y(UuX=#Q8Z3#UJfq z>P4LR>lBwSK#uPm#!F2_WeqL-jP;mlt{9tya|7Km~SY%2i?6kgseeh#`* z_%fgoxKj`Tk+T(kXR|4?<&l4Qi7d(dtoy9PoqS-o(4K=8lSQdT1r>9_uuc6D)K-ho zAA6kK8J^x)PraJzTHcR(P<4PSwM4}L#Agrz;Lxo+9I-r7QtN>}u#_F~C%}Gdbz@&d zH+~8$WFfBZ?$3c9v~6gicoTE(3IjXO@v6?}Dh+EBDYoy@-X>KeIA2=h3I&v=W;O6e zRymhbry@bY(R>zXP^T`JPPICf z5Eu}OYh`4L3hJd&f;it{f_a4dzI;x`4}h9m_`+P(HIN%4#o;|fA35W#ug?r8f&qOQ2DfaS0*O1V;(`yd>I6@n)EB}9-@MR15haa1gaX!jZ8Fa*1W`ihpdbh zM+T3%K0gH!<4g_iQmx@x*)s`Hm=+ou%Bd*k7GnX{1We87NYkRoxP47(qATxihb^8` z0t|?bn>_O%?gHeiw4^4O(z!D#NwV2!8?C6nBI0%fQH;^VPhcWMT5|&=B_Y&;Uv8p$ z3(byA?7dhMMyp6_RGd$bKV2m+3U_qKiWqI828w%I_@b<0w3?4=a)+Wq5_kkW+|}Om zlW|ry4jRIt+6b)>B2#^%@*T*%R@m{rQ!B6SsmcKq1K_ceK|;gnRSd;Hy`eE!f^Nl0hBwdG^H~ zV-Yo+*9+=zMfKyrJ>h3UHmq6Ap4nigZK42APV7BlYG(H5ARaposAn}96J=Hj=RwCM zWsGqFkhp)U1fRVcC06y804?j}bB}TIoY1_jv=U4bpc*sn`QJpQ`}C{R-5M7 z3W@oPK^R~HZUJIVibY63_29Ft`g)n!D&1A=v|HYJAJ%Z&5C)DIcVqCrvv!ag29Z7l=NI8ne*IiH8rZgDv?#JAqMoRE)a-oq&rq(o02P^q^ZtFzLDHqw z+puT__f#Ce>dwq1ng$gu?M@+@H2zJR?XWMv9wPqWl{mZN@=Rcw+6q5{@&dtrfy>L| zp1&%oG9dj>Yb0)9*XO@*DDGrCPkE`<_Fi?Z$KKY%CzOv;L~m3q_xn-kbj8xc!y|le z#D;GQ;yHK#$}>_Yp#2g1@IY4}7kxriDe5)BuFlYxe_D9&TONnrz$mC>tHMM9)dJDC z$z;6f=`qWXK=l@K+kD4;P7n{P#3nkkro#;?6eTjdcD_KttA}DXtM=>rB0YZ|sl=8Rv4dpAt03ae z(fwg&<_;Veyy*&pq1)uWXOw0nI4R(j*)ic`Q&}%%-4q|}9+U6)0K8z`cEogVgFjKf zo9IW-T!NAn18w++*LI)MehWV~RU}gJY@c0N!ExqpIy59{RmXIy4-aEj`%8p}&SgC1 zc7}=@8pf@A9?nBqVo~m6H01jbI94ovnC zYj3ayCd_fhzz?c_E6#OX3e~90Nhd>_wVEWXanO86ZsYdb1__=R1Tw63GZBfbj3_v$ zXDY`;5!UZnREOW4hHpctiL8Z2wL5SvY6%7ZQpItzsQ*;H?Snld51Cg=9 zJDe}h%ydp_oBh}=QMEk--BxhN)>uhQRt$z>h30cEsCDToOY3_}h|;8tJfQi;2Qs3R zj~-;`Qfa`ue{g{@z0k1QuAev}_|=UsdIDX-zNBijJB&R-^c<%K%C7lF2-1Vc6xmi7 zU9>gW@4@Offo1HOPCeb-qo-rEZ?rv&O<5IQQm`#Qg)6enUFNJD`wlBO$GK6RfJlCh zn#mQ_a#km-$drgLya=??20a;3&JJ{5|5Cwfo*(dsya{W3uCg?j)OTx9@LR7nRu*A) z5DzG#IlpF$oJoPGH6)VmeHDH8Px(HJ0`tUw+;~@Bv8e-ep@>ke!lmWWBL0>P{Xljw z9Xi(3`tT)8z^DdxbPaO{-1Iv8M3DhXTsah!EKvl-gL|NrwizvzNvUUgN7`Db2uhW5 zU{DIX99(XULJ{T2MbKh&@r@z>bgk7mNb}=mKp9s2{AS)d8`Gqjh8NJ5pljLnqpnOE zuu$vm0uubmVce)ONnGdS?;qK?iR5)I;6=`Y4bPf3el4%bpFblUwdK_O*RPFwoc0Z| zOnsU%Kn=C-=}+y+%L56Ev}RVhorT;d(T_pcLRL)BekrMkPYWI|CSd~%8fibZ&HwtfEzZKi^(kl+So_*h zh@_HkZscV;ARSwx%yXUXN1pvMl%HllE}=2k-1~K9smS}di(=+H4Lbkm#6ZC50uAuR zj4^1YjVmeq1j%`p*#$jk-XkGit3D^fX6x#0N;?7P$*N@VVYXq_LN;D9VwYH%eq zhsM>TqS(f9YY-K;qr2k=&V6{W*|R@0PZsqyp;Qh8Z<+#{!Lj2};t*X6)X;h3LjIRc z0XC^MqAU~bJ$RFtyT*lX+EmHq4J-LPYi7K7_fhqd7+Pqsd1C@@TXSf1Zc(JI(b|-{ zHYM#9Y!#W&H}bF5+ofhUtlxFSVn&v5O+dr9uj6puCS9Knbwa8aO+gIZY8+!d(fQ(l z@@_aLK0-l0g9@2qiHb%$BzEDri((D9uqs$t^ZRg|6>QmIUifJ8ls;RqPE-T8my~N> z{bhFdsRFC<+HD25wr+_G3|s~E7+i4?fcoA+>zDu&w$=N2^k=O|<3=r@nWI2+Ax(_J zf9;>zDpA_;#Oj-pmpzrO_=vg9Vd1 ztLDd=R>E&X`&j>dmQ2<*^d#V)%mzatwVG&P6zhHX@S#fLX06}0AN}jPOc(f*yQ78z+8LVn@x15P{udGO^G( zbD95DDB<3r0hYiF^?fx;v?o86G@&B}^XG=4JA}?kP&{8TzSfB9nhA4KHXPVPknO?f zwvC&c0^J$NXjL*&H!i9AwD-@McY(^ihoj&-UJnp1bVy4N-f&|4??}sUqK!v6*W|_= z%L>$XAM!M)VxD5odjt+7C(aA!zkOsUF}|f)A}tW(n^(iQtHd^#cX5u#+#_2)>?irD*8C(VetWK*URHmz- z@Aa+QSf64;8>de_!=Sexw{wTB{*hgf(xnF0+T|x@fPWD{Dq4vaG;Vx)^+|}H#E@nV%OdrChTGbS z<~dZVid$@{56a8Q%|JqvLL5xOVJgB0KUuW^HGSlX#6RzdOW))R2vq9 zE7F9Y-vGJ}vn7GkfO9BeRT|9(BS%{;hc@>9^On4Qej4mANaV)By@D&0;BsoYud~VT zrhPQhF0P{ariWW(9T|twSK-%tXC8JdTi}_*amHYlQ-N9C>nvba$+y_&o*IQ7<7&q? z>BIDC3;R{-Q_vd_V9&WsH3LtQxNie&m7ow{(q4-(z7K^=3o&wH5m&Kf{Em-M06+`D z4B6K@N=M$}ztvx~su4Sxf5ujpvc1=||{!__#emCvtbnIy&Y5m2MO0Gms!c1YK4 z0hFz|!+67CeA>>vEo`t}nC&~ksL^o25`JCKUzDrsfrRDZ8r_UJA+2o?$l+51^{c7og^#i>F_^wg!NE z5Dw@G-!+1Mw*KF@Ks^tkrpd(PI|tyUbI;<0>4z2;PcX3p>Aqn9Tm~z`p}Mq%z$>?~ za34GYe?%y!29M}J4@$4oL~)cB#<}sE*rO(JF0kW_oL_4`Jc9yY2J)q=@tIIiVj_W{+0GmrRw#{7iZ!;Mr3X}otC$_|P+EcZKK&zr z&n35z%X{)SPN}w1Z%fj1->4X_M#zBo!spwLGOE=)fIj%m?&mhkGo1@Ra5m}XWYimD z+D|PmT4>3s#?4Bem{{sF-J%fs^|~!R3foM2Zba zl>?0hN9380T}&ACQ`dUT3`KdK^$TjyaJ0~Zb2Or2R3sufh}X#Ngg&u7)dFrF7}U&q z6O@y`$LTNag97YY#a}%v2AcP*$7yLmuWN?^u3|`nCS@C*jVM4=1xdgBX4j9%TT8JdwBUpU6u=ZhI!+b^#H%qUGesJuC0W7!z z>Ljn(Uq8=fUAzl|g8+yy7yCuz#Kdjo(@$8b`f=y2x3XzvM}Xt3`Qk$wA~#Np`Wp;b zk!fvl`7fDY(8~%|UF}Pf)J^cOWV_lnraitlQ(@HoOSz_o@;NyrpF1AtaZ{CR0DIZO zf+(A#wu5)it(f%__-45C7Bo4Z;?z|FFpZ^%I++;j2KFLsh3`E)ln_U(1cG}7GDJcV zP@DShEE@eVtzxNhRzx&K`w1{rGDL%XIBYgtRu}P6i8K=W>1!kP?TG-Xp&_VxSf2uu zK>`LUjk8^WA%LVDXbykZuW`a1$TTXy|FDM!m`u9p(P^VBgMopKbd4} zw*T+q^LyMz*+d8^LIWJ>tmEDgI@-tL_MO+^=0-7p!Nu=~f~-FJM~j&sPpdvL|J@~% zzUG7%|NM{8mQ<=l&2Tqa85vEX>%BM-{@gi$Cc7g{O--$#gf-jU)nWvBh#R8XA)Meq z9r|X`f++cMBs(o&Vh}5^)E7WO)Q1a~nv~N*Fv=Xxc`xi(nNjzQQO zu$N7+^cE+W|M;lo$iS7$Cz9)F*iRz zw`c5n?W(l%5rr^zj4d70u8Aai zZA@D8d+&yCGJ7&^&Ufan00<(iXUSTf>8kQj_Ng%hWU4n0#R?F-KT66 zwkVETHCy5cUn&8#tq6c|7r;#hu@=-~!%(E<1hga3r0+!PKJ2HLX#1%e0fzFMM~Qy@ zTnX8JoMf&HEV!_H+2Ak=j>KcE`n?MlZ>;PxOsNDt`lu(!P(1D**K_PZD z;!GMXu3iE-1Bgk5z>V7J-n-H53Q?VOKiJ|G(5aE)ka!uOr6rgf5iJ2UF`lzXukS6f zMgc%b7WGE2i8tyG)IIN&1)(Qq5I+*Vv7Qt64KBoLz;yZX#6brLr0xAr2rJ1b&&k5r zvE1Bm*Ust;y4j@vw8gbSoY>{r*Gz$A7Iy9#OB&hred}rUv&V28!!LX*@8-2k?Udx$ z)ZFufapJYx6su4C6RyE_N2=5V_H^)%U*~SP~1smES# zhPpO-M+N%@Xa@-fVsanK957fXvn|fUZUaGByH8(zJuSLlc@|ZjOI>S+l$I`q;I|*~ zw+;RhEcQb)y{fVAh;@6LCaoIlx3M}hFR;<7K1F|JIF{}r0q5EuL;^ZIZec1dp-O#l z#Sm)#c*BE7iLIp1!K2#=vzT-#Gpl?Sy3ji7qHJ;lqj zl0w3E_XU&*?pI(I!NIiC>@ygKlE*A@CSM-3g_PbOB4)8KBOz9aC>uxgoauJqmcx?@ zjqk1S+k0WI)8s>(yO-&j&L~iS#N<*XPWFlKV(#QOyltcD6ew5Ouv9uHZs2X8Kwu_< z5tOPnK=B@;12|Hjhb5Z2H#u5TpStzl+L;Nhbi=E^I8hniCE6jD0e? z>4wBBW`c;u5uaC0)t%QF&Hc2JX|rn3Fv+)y!|L`rFkTl0&Mf*2y3e0I5KDxPlM|$$ zVfkw^*KjJP2a_eiqfb5JC%Z*+(QmYLbg)^?kTG14o?=jG((3l1V^a(WB+x0T#}`wjez z8{&H^Ct^HzjQZ722mHCG?jM+(t_D-5b@kmSHRK#5b*Uh806WtaP8Y)eK%mUbnf#jk zU!d}QyKml@24+W8X^g@K3AMRsVsmY4*HTQ-H?L(ES96aJl*Y;a0{XjrzKgPZ_l`aE z_akO`$tnhl``U>CIO`@XDsim1gtvd-gI0$y9=T0u)x?}Tgn{T#lF@}fkP4Hz;MKkV zMg%4*!NO|lSteX`WboFx=-+G-r`7Ekfb!rW2&G$C|Ei!@($~M;;;YAOjL&M(?fMd~ z7V>)w+twwVtXXI-BaU7oyuq$P`UBUAojlWphv#ombfLSe%L?NmQ%4UbD~KFtlnsoHQ-685h8`JIzVfy) zU+y+_bR3>3*45|MJO;iSIzifaJpopNgCd9PZfUd0(;x={)5qK4q7pRufcCi9?}s!x z4ru3SpuSv#SgGI|D zU!x0gA>rMJRw@AnQJZR@k<)HUsxZmTT&0TG^I+zMbpMvVNl&BsM2_^a60| zhj!cxkF~r&aR4kUFq;cM2n&d9`a(Q4c8Qzy#RC2Mq zRRNsMz~(WG?#~1Uca%zMQ^({~EKnh;HT?8!@hFvOG90mhB~YFRG1m?TGO)J^APx8ZV!MLq}2SGxcBl z2tNaHfrm<+^Y+&0NeT6{`$bfK!SSv98ojqF*;^V^n);j9N^oUm#8nWrI~q?dCj%S_#8U{wLu!zpb#%3UbOZnlT;DW%(DY_6 zU=0x?;^b*uO~l4jARaVco0hG;KS&R3Izs<~*M5MCQTu_>Fp#d$6|1Npx;!l{{m`)1 zt3+^@qQ{p<G!lDlr8bfjw`7v+CJxzr|3rKod%|_PN<8o0QWHwfs2;V0u6xXWZmz z6imG5$E1y5L41k6K7Y%)HEY@@WKzpuZ&336@u|ox5}9~gw{NEef)g+wEHd}@k*Ne3 z{0tN8=e{af$=X&f!hrx-LXl{?5^mn4Pg2AeZ{B-a8ytn7+~?1qqxl9_KEqYX1em|` za}p-E{b@N>$4XIq?(pt|c>-GGj*=sP>bnL%oLef8_&AmTfjMB-C-nerD^beffc3yo z;iGe#B_t%kgL8uYhw$w2K<5V&)lza1z~CVIT>?o|&^IfimE~}@~4@Ar8Be2?ROpZj?B)1c2i zT=#XYYprvg>s**62}-%1ok8u9Sxr8UOc|lmZ=r(KH{6HZsr%Z^GG16mTMp^$KCtIx zt(85IEP*$um=<}`H`M&S)AUDCY%?gJm)R?=%y~c}_cIdF;FoD)d{v61q-{#2j&G_! zPTa@$!2nwtBuk4ij7Fa~VY?&TPq2U19%7LSW`ji6h~cAQB8moSGE%v_z1l;b0Cb}) z5Kt&LNDM&g^G{~dEL%iITt&t2>tSXE2nrmuYVRTlKaJyZ%vp&USW~cX{GfI zUQ4BHXNdI9-kpJNbTkyWo!(VhO*JnroggZn2m(>31KXx_{=vCmrqi%0_mD9y@W>!C zHQ2b?@SjT2*Nlb@(a+8%vZ86_Le#jtH3bKk)|y4z0_mcZMwqw)rDMlM*$-T|MdRB7 zqqS_QU*b|{NnHnMO{*wx5Zxe|y~MzSqZ_lFsLj3QCH1fz3q*Rl5+Z6JMSD#8YF!Kw zX@}gAJyIZ1#*lhEQ*pe>)6$XbEAWMn-nk6LitE!q65Ah`gVm{!!IkQ9-R3Kcka>65 z#+N0#z&rp!l$~jW8_6|*PlmlZ?XR#MY5@ejonf}*k3SsP)2+a_2twmiMTbhH$`|*r&v!w$8l!u5> z>y=@97LrOuE8SKUXsV`PlQwU}Ww1@x7y4E-dAts2a|@;yvba<@B!2SIh*q)9S_;uX z1eWk;{~J^^nWI=#`uMFy*RZoao=pTdUw>J`Iq7rS`E|r9By7sAsm1!gid7%iGDU1O za!79%6OHo4o1R~V81(sr>i(n(891`+E-(G@Jd~B=d#IR1ebudkwIlvtip)~N!-Nez z86-?ka(#V!JwZGp*gC}yp1CGc`nR)7d7hcHLb8>QVE$$J7;;)LS zsSXLp0HtnEJKPyv=3{5^1aqNp2$9@-UxODCp2aDZBvtK#f9h5 zZoKEDDh=|{8F6&79(A@^X9tt^@+yfgqabVpy0;-t-cU?|2#k>O8uLatmPnGnY899W zu_g3B!UQ|piGhFBu%yD}-o3>n?{fCoSIf<=vuPV9jVgcf2Dumy7_JG(e@SRPM-_Pq zX0Z3kj((Cd``RiJ>d#PJa zf12OqNVj<+>Vi!FvFEDrX@Io^2=Z#jxBc;HwE?GveUs}CvFu%-^K7;qt#MX$T zlRP?j5!nCHF2etUHSC}M0NYj)e-wmZZzf_g$=+pqX0#AMFLuec8eW1N1ocILFV@=c zaXg&&yqKAiW*&AqRv%Tr&%g`IC#}`t10r6$-37gx)p*B>L~3$VE}61KF9Xy3+sx@g z?wAHFfUi}j(G4L`D5XDN1t=f(x)TFq4kaN(Ef`}y8jrelY9YowyYTSs$6T1XR7ydv zsG#vSeWiLW%^i!?Ai&`!f%4EkdW0b&4ApC)1<&E3fS)-4uIh8BKQ!t46~MslgsA=n zdYL1&z`lt%p85H1XNRSkz?RthhRaaN! z=u1Gh4aeVl?n623TD8%BNdCPA=_d9D6nW9o;D)L_!`B%-dikcx+AoB$>Aa`2zU42OpYa`2Ka8Mn_nj@qN zS9SWSi-R|(Fp~{8*nP=6jsf~-!NnwRTV72!-q*N8FT5HKV)WwOQ1W*{EJ_pP!7?cc zAFO;^EnJu`eMTu;l-Rw&j+w%YN;ldS>z8wh5v^0y{G68)x|?OY@S9MklyC1@)FP5H zq@F?SPc%zbT0(@>+*}u}R7uf5*Mk`NZEnd)r|%>BCM>nk4ot**Sf4#r8!4e4tMP)h zg>Y{v%n3?y41m(YMqhqR zT%p-^8*DWM7;EG5i1g1Y81W69gnR#%=`=6G@C^W&)4DAXeSU7e5yUc=-;0Bm@Sy%^AEjG4}WGi=~H%=rHFAE?HMU`jnmRe5b_<+l5;3XhinQ&PR zSzJ=H*AO;{MfL6VpWY!~*V>N`4OL7DTGdOs8rTL+H-4dw&nq zm?T=wp_s8ME>KNK8EDZ~AzXi;MQ3{k!BEgY>Y+g=fUf> z*oSHZ7@iX`CP>uWmNo{Fo{m@hPk?9SB#VJRyhD}c$H>zx2wOKI^(E(Td<}7+ zyAl`1e;23*cVT+y@prc$Ki&jzg0uxNPf5BhVYpaDR5aUgT>_N3LM_f4dey9;ZcG~& zMI;$qUIIROxt$=TuCC63UF}aIdwp9=GX63#_)9yc(Kie{?o{Y46~Jc`d&+)3q2 z0GUp9f4NA{!VPc%p+fE1Z2`~QEZj(&F1LHfUn3v#$XH)lJcdt``gVi8Tp($|lgdMW zS8-f1UX>nXgmteExY|zU0iJ&g=%wc>$GR=~*CS2`$MY={MA{OMSmm?V3Xor%P#2;F z!e4OTVSc>%jMzhxK1SAx!G&Wz4T<^~)~TBy0K%fz6E4G^i83IM%}cR*jBpRBpJPfJs?YV9^s zBa(hy*@o!xmS3F%Ku=h2%-$CjOQgRr1krd^G>|I{Q4VoN7B>W`0?$gpfaN}4mUUmxX z`7bCK-$xSTksG5ft+(oBNCuF^g{Y5a)($Wzq&UJl%w0p!B) z)9Z7|S+NBAfN^=qUO;;HY)^J(VyAn|=aoRt zga2XQ?saG%cErtqRYBZfJIighRt(#yLPlPIkmV}Gp9)MM?$prTtx{43{t4xMqwnc2 zZa^g*Za4?D$v%Jn3wYyOU~q7UaW=Gz5p8)>ZssYv;uY~E48&-9YoI@ zc)K)!2VQaA=LJpyh0Yjb@YmXKzR)hc?oCJ$KQreKi=7z&6tsWUh&FWS?m-o_!P<9< zJsQpz(OpKNF+4%!)CP�Cq{jqJvBkdW|`gH;|G9$QwLEdLqA5jUiHU{;DOVqMBBNmr{UK4rPXg+s#MkEHoLVm{nsNB42<;o2+(;zp+#3bZj zFS`^kS$Gp{(4&2W%QG$W9O>D9%tDMP7al44pKTyp0*a)SPTXyQ9I{IC<*jwhYE00? zh&%1%=;)dJ^;pfgQ#`M&F6b*E4O3eqgQw_UWIf(DUOqW(^w(UbNiq)+0R$}!X>lhw z2zp#7fhor@k-BPNt5Vn&;ND$D4FSlca*Czk1>l0-$?^#yP#3{q3KCIy1{Ys{|28Cj zZZBwCA%IGDsrtots)+eR@f&pTU2@yt4oDz=A);~DU&MPYm2*z zNs;o~fm!TQgc@+_Spr0E)*_ax`EfH0^9vp!KRSIm{P=MlS?5JS>Z*TamVqh@-+F^; z9Dfpt8lm}4RorQVn1VPqh|gcXECe&<){pNCNP#>cv?}(um*rvAkor2-)0OIj%1He{ zpEC*Shlu#sjcOwKEum0H+JrT>i&<;l1kZzXOZXbfb9mAuoWKoGGZvgVmjCVSsm`lK z*zBTnx_Fr6PB2lQ7rpDWE3IGK+@ZxE|-uxy)6_46i^F$ zeiW4TFfkIXrz{BCV}1)m7Ly}6yi3Hja@-!N0OgwR@hX5R zLY*@g#lcnXdU3I_-%%tj?QH4ug^p{>w9uhDz_K(tHPW}5CX zw_3%dCED-3MFr#H9n*}w>^&&cgDbf_XnB~6^20tUpL0Nq^;OH^O&d(hK}G~Muqt_O zythWmB`;S-tc0L2gsq)G-XQ8?;P*&CR5=Dt;waH>A)u|XfiZCg0ROQA)bq)p(;9cX z?`?i+$rdB^B~L!NTw24B$0yNr`&}+b_np_gY$!l^77_5 z_bd|LL{7Aj&*al)*k1SbdrEr*t_bj;9DsPi3@!+B#4CD4Bdrf^nYI#dxMy!qWEYX! zNzH9TmBuz><>Z2co0P!HEl5UA7F5{ zY3$i?*Ct7us7}@#RfNxDPh^qMU<*MPH2x$ZR!YzT^93P@m9#*d5m0a1$;r6_G0De< zNa8VFNu)!W9;ah71zyQQY%JRrJ0{VrT2*@BIob*&d&89xI$&7$)In1duPM zPUs+z{sGNC;(VN-sk>w&?7OrLXxEcrtrAs{;nsw$J%!y*T}0l2gT&_Zrx`K=PP?B9 zf@Xx>y&_r-Ra-<=x#o=|Qh+ktJnN-cT8TXJW{G|6&XI6V;hWf>>Vjsf79o|OHt&e? zrkWh=E!!4vOsc5bLn9MYo-&6{KxInmJKrk#gN{b3n9=ZKH)(^GqJb>fu~e_pebCt_ z)nGjE*vhlLu%-y{4@H)1!Ta^9I)xCA>>4EPkCYpyiaKY=jynjHZ6-Q4ky6Z_E~P71 z_I8#9CnU;%5h}ZU?YD-6OVsV-S|7{4S~uF*QfVf;v9c>T6C!A4%=0`1kjecF)UD>| zYC=0eph@Lm+$%wA#=zjp;vq4Zr|~eKHauh2D|QiWC@^K++&zW^xeQQT)`x$kQ@{+@;OWtO1;JY{Hwv0%L8_q zbSM%a2BAe?AY3DG4Bj9Qa*c*};Tyym-%(}11vSuZgUHZ)^`xT-JM9S9ffjL@e_s+W z3eJqgBG_TxVp@#TU%MQC%m#7ifyX&r7x9%$G74#(f0Q5QTLUtAG&BZYCfn`hW zfcopntne!8KnJZL_0hF(5NXd$a^JoGu%Y{)`Tjbl|lNGc03712ICq+Y=+<-xI3L8T6mp8zUV4D{?<)K?v3b$cnjNcQSupB zq5YHbd6bXfia|9Jm=kLE$}iLwJrb$-IO6N!cBC*g`&?1i{{Aha!@H8OJ8NMD6W5(c z#5kEl5|{|^fz19<#f95;w^r$)f2`OT{E37sA>oV)D!0s@qWQlqWh{6WgoaEwS@3XK5lo!-#oiBJO0q|6 zBX)VZ{g>B3_G$$NLAthtn8-ntWryGuGHiNe_2c_iX5s5Z4pi#7bHLbVyYF2q^^~y} z5M@IkjYJbDX~t%7bX2i%Mrj>fNl-J7T)Ap&gktb*K34>+hO`5F_A91OFG)^G$pJy* zJN9?R_LNMdcH**o2uB4jsbF617FJEDjX<1V6T*9lH!qOlu;?=2-dL}1U~V1)Uti8H z@$%KHi^|zki3vvdmb}V1Jv}`X{vIUYe8RTX-EIek{GEmQmr5 z|N0sWC=`TBf~%d{y;}%97~pYJ#SqPVAV4N@nX{|4l}Om%8m(3)Y|m3JCt@&^t#CGD z6nXC{2`sKyM~6&_BVcEuJNT-CUA`98z?%h3{~&GXfRLkvW2Ll*3ncv?VZRTB-yH?| z0dw;Haj%_cs6xmXvO!Q^4b*b>B;k&T;wRD}Czz96SG#5 zi2A~&V?#$_oB3lVlL#$wL zOEw|y92FD0-6{Y`ObnY1{QY})JKt_0k~R1O?HJVgzk|_6Vk?p+wdaL{!>d;U@QXes z>&cTgZ2)ZqZ00WeuUQ$k)NFa-_(Br&hfbGbe&HF2+BQ%uqUVQ>6U#8uBw=W)Etq&y ziDn+&BCHE>E0FJUM5y+_xmH^V%~FV_48cuN#b*AH5tXi=F`0?T@+zVtgFMGb09;X$ z1|f}_{$h#*5lsY&nWVa|_fsY#Nj_b6_d=U2fWqadL?hgn##OeOC(RvGfpHRA1(`=% zes*X>V0aY_DWW@yqLz(L8ahhoFhaC`AVYE(!-9q*ZmI?u(LD_*yGe5)gcF|ub`m@1 z^TbUNT)}dri3xEEMH*Hg<@88N6NtY0G6DS!DbGrmhtg-bY?}c@0J%mRqNfB->Z$-y zTV@v)(T#n(AvTWWps1L{k6wDk!etk zB(@6;uOGsv{gZpG7AiGBNg~Dx;rCJq4~?c<0QlOA8bxC9^?4KtW&`mZgpJkD)U}|C z!AABieBd);29niO;Z$*e&OLF-$x|SAx5Qs48fEVA{hjjge56zgoOH%^6$yox1)g$` zH?d)=tfk$&c?I6uU3+^V=BtqGA`vPQOYyba&dWpodo?nMqy+-}PiSEN1vv~b$nf+l zwfM`nk(x(@*i<$Ar%!Fyc)Q^n%_GDJ z$h$1To*|ANC|}yz$Y-(1;|AXY?t(W5EMJ&(@HA@;Ua<$$1n!3fTpc31kG>4J3^L~j z4;~~=C{otJVF`3XeE`kpu`YuSyCVc1Oob>WcFd> zh{=PTm$^sN%DOrtI*24K8`>M7ng#&HZO}TQvPm3v1*oOI*`q+%JP6W>HXONoF|md2 z?(P@R7zFo2IHe#vR$46;~V#q|8PS3E5 zBLfW#^)|{3^o0-H95Y0!Z?DUF2>}d<1L~AoN%pliK$V*`p8$pU3~u$#SfsSHG^sqn zv#i424Dqc$5QFVMOS%~mVSY4{`wK&iXM6)gfGQ)>R01`3p|$Zsa>z?=0iOh3EF#ZC zNNl0Dh~j&aFz*yw0A&h*T~hueLkChrA+~d~^%naU+kw35(7%Ejy-I9TMC}eDwL2t) zz{-UOya;D`BD)ifbAGnJ@Z?rwKomjd5^~E&;t?zx5UEBua4~ca=tTjMZQF6*7XYG! zZ~a(9+Gn8T4jH+NMn-XyOq1xuMQ?bLL)57}0#rq~Y#T%$xwuGTw9#TiQELf#B&Aq7 zJko7Q2he2{u=!fL|D?=}WtG2aSk9U?Z zf+E4TYZnd3@$juAQI{`^7xOYBgRysCb=8z4<|niYk#iMn zN&=C1hnIBr+_|^qL(r~i z=>o@B9@t*U<`@6kp5M61ftnFWM5NxG(zct{jeTA=HS|^=5t9YCwz}UR{pQdi^kNth zaiiV94f9a_a(6mrH|TJhazX)XHY`DQ3)Wiv!i5W|TEsiY2J~|83i9_qk24G0JU#DX z^CGm4dS{YPgK2JaZkMnkkt?QxBx-$AwFp5F4zJc-CE7DbF6s0xA>1+A4q_gwEpj}H zIta!{d!7n~yd)`h0O3Uzwy2IFid%ReY{?I!t{ey01r|LY`+bYA9~U5e(9wh+_4jdR z0cq_7SNaQvsmP)iw*>~#ftSH_XFh?JOH6#y_WOm}yx`XcJ_wP}zVd5SN}xvx&dHhP zv5^sl6B~(^X=qbdU!N0pZuB8h>YD}na}f>LFtP8rsT@#zRcpKz4F?)IZ?v zUBufDtNHtm@XZ-x0-#~w_QKCH%mbm=4sQh_Q`LWRvF%cYB>VSjWsOVci)&F#j_92z z{^*5yW0-OYc?XQ zRKvr7lB=Bi;P}Fy$pwSXu}U!X_5fViuL#QFCO@&rwzppjDqz)tBU@Q{x0Nn|7!fP0 zN3zY#jj99<77|mU0wcv+2-?wT)wh6xn?8oD(NGL&2qxiq6szhv`XvzoLXbmZrm%B9 zLdhU~!bOS2#OoxRf3*=@bqGW)M29*e^5Iy=ORnWXN|F4z_ASf{6b9@iycU#cGGquyEUv(gwFc=E zIInLYWnWi9eKWttY~9dU-+AJXkqrlUoMZFm(1m&^XvU7oi+QvV8cbUJ_6{_()T|+` zn{;8oW*Q3S4RO#2oJwSZL8O4wEYAc|?*MUcwDx=bK9&CYe&^|{wpYlt3pZgF5Xn}j zJap+S{Xm`Czi#TEueLzkWsT<9Oacu7vi(I(}NW=!@AXVRf)xN9)Tlc0$7H&VMHw5taR4kN>W;|JO~; zS-Ok`gd58Va5gv(N8h>Lm$JW*AaLH!1q<>1PHm7qs0RPqDw+G z0MR(n^o}5V{~V#WA0_@~4b0~a;;y?O%e#<9gCvTZBBD{?zyVeN3}2aqAjo*wVXJS; z7|0SwcsY{w37ql$OUnLbjX&&7B~;S+!!yt~kr7&l5)xJh@Tchx-AGpXeru?`0Cm_5 zh|=yNQ5}j71Tk8`Uf+P7MH=3Bn`dGpN6$^pygn4r|9ynMv;Y0BOQ@0%5(*9h=o*5| z<30E)&0XKVJ%9%9J2=f0(vO6sWRdlW;YIvNhh_zc+03&#QSZHG-8zrqT&i-@&yC_t z$h$gfyg~ih&En^vY}3@#+=~RQ;W%_Gcp9w<@&#nBs{bb1Bp?wLQ^~gn9gd;6xrl;@ z*Ie-N?>iICp4`-be!fc;CUVG^;Ha!{^>h8>sKSc1;w@|AjIy7+^V#o%Rd)F zYn|ZzM|s_~+FVCO)-Kx`qJK5+N2{gOibr;=f*YeBNb3vzrT_l-$K6Zz403|1aCQ#Y z%}kFxU;eREUY>SN@DH8^duYdn={S}>kc|j`+a$N@+1k>ba^b?8cRjX$6~|W`%emF^ zx&6INE^Xd(sW(vT{ABP3vJq9+Z81q+q;c_W05U-Og-Eavg4r5CIB@4lf&>$K<01Qh z-S+BBDh#-7x3x@jqu=Rs52dS{_k%x73mb}X3>fN3GHpJAb?xnXK zigP!PS-5UWyyL4HpJ@C)4tE&)_O0Kn*P{`}8mlgz+b%)kKWr^sy~EasoMFF{dGnlS z|Hp;ljr(O*hiyu+e;svam-K#UKM^U^D={mGT;Q{6i#eo%k1qO3h{s4dFQ6d$05gq` z?l9V+|9$M*;;E-6D{+iXKycN=)o1<`uvxZs#~!xNGErY&ZL~Zvv$lRQe)kp3&2hKgv?1X&AO%A?%(}V&oZ^<#ss@(0f&3p=?9M_^9zdp6tJQ{ z8l!l3s@3?4{@LZ;IS<9uKQQz9I{v;Ym(8zAZ3(@C0;yZ$%nw2a2EV3>4$gM>ZLWUo z=X1A}t!s_N#KsuweRMgDwfDm1&p>)#O3Mm3T%5U{5R^ObqGcv}!6~*4lD>LyMksa& zSxEcLJjo+J4GZ;41iSUy%FEj%bGEeq*e=}sk}>+~GJ%+VlUKJGmrZX77fL?t<)$vh zrL@Gc=~*K<24xqg@oe;&0aI)#g~$ycGI#rQ`JYphm>5Bk^k){=^^?rwo8t4P zdMsC}*yazOaCm9XlA_~$q})#tF;INsW-ThFF#kYv)N&YBRyHa5ZES-^2R{Xk=DZK) zKjQ1>y}Nc9240%h&4G97$siIhuWHOnii?w(QLh_nMrGaHy|GHlD<9h06GsCxS+bY1m%EjC0n7)`?kXo%uQEe8Yvbe6VYY)%$ zrL~|N>_RLg)Ke`*er}TQUonp($ylJ8j6CIdZ9i*!dxvmlRyga?wweENL>}_G zoHaAcG)w2Vl)eywCxJ*Ij&2UoLLEfRTuZhs3qa?k6w)#QQdl)fUcXOuZ5m+Wa$0R6 z4i4|yT4sxfd-g?sefO5KfI^X$IHK^|mbo2&_Ug6j&nly*fczes=qt+{BL`NR>&NHv)u4dZjvg)!$=`Z+KS#b%o&u zjQ+Vqw99S%?AEfzH~)k6>TqlKIeS-a@D3~MuCfF*r7dc|&Bau9K z{`^NvtrX~SgpZuOk~9W>SU6kP*V|jAWWbHK9h&=u}57_KC{shx86CLZ6%=qZ3V@l((4&v!c0(U& z&Vd9(s1cE_0dLW(Zx*S%ivS!#yZ+@E^rM-99qX>O113%4Vp2Y58vgavFMEr=l11e9 zHk_TE%qcfZX2!UcD=#Jt&ccs@!9byMqctgd{FIro)&C#w2+xP^10Gwsc$S4cHla&l z`u(An{qs=Aj}=#LI%Xs;fAGzb_qEx1E_^#;0io<~v<9+v6r1ddPY3?>rdH}jN6SWp zv33?Lf0}ydck_I4-m58Gq(^hFe9_m`y^@v(bu;DwKhA5yz>x^I(7-A8LKjEX0Dap3 zIp`lFL#86fTj4IhW}LS!%INo{EJgRzcpUa)U7Yw=f>ZN~s=t`h-%Iyp!8ZeoeI*!1 z6gzLz(!UL^Oc-GpP}%4VY~K+tQGfp`AX_!{&f+%d-Lw30^Lq{ zP-{1t^y*M3eoy~=_;z8Qnw{%&S;`w^P!mD7BA^W1CaDus8qc!<-a(R`9Zm0^R+__i zvIlq;V~nmYdYqO+?b1!Jt>n$80B#-ukNjHtKTaT?o&G+r%mdG}l@10Az9q&TU)l2| zp%s0gmo4~x4$!`#;-R6!9^URi;-aeVaxE#IS&+smcBOorhNSL63WMEQh^0=F3?y?5 zN19}gZ>X^W;DZLwS}rvFuqPpMAKF@G##43WeysojbiSnQ3^~ac_;MqqYES>ePiB#C z$IGn)4&C(r{q@d6GdoT4DP|{LC7ut-%`IIODDutDIP+%5@fqZcbKs|%g`+J0pMzHz z@PEGcUJrjI)uNNe3k-G}5Djzi6iU4ees|_4oPIj<3Uv9!#TUQzUhdVIcJZa>WMbN~ zudDXRKc{46on*l_5c_b`K8t--X9Wa!kQ(@-aK=4zD6UswN$TtYeLy$#Q$#avwQs%|Ix zhkSu^rQO-NH)$`wh5X+;4Zg{j02SBgT;T%W;;@Y(=25E5I!dNm{CEEToBXh!!?)V! zT){<+@|8MD5s9H!-MTl(L~#9j6XdJ5ptg>VYXO&R^0Dn&{EI0U$;w~5 zh4ZiH+ATug!qaVRKaA9cd0dmSR92iEeife{t1HmewAz$8y;N;nUe-zB4+>?4o2IyU z_xba@eeWtgXg25=TlI#}ziLa>`+n^bg?iM4LaKc9sie+uZJ+Y7nw$a9iT46(`2TS`6Q zlLHXpj;O2hxqpW3MbXky88w4p*SzO#8K-25l-5-qG#+tvSxNp5Qe5~yyj}6~?>M8BacReop_VugO`yV`d_4=9?!gF!rg0nwn zEXtJfn7unoa;K_g$zR%~JikFh+53Uvn6p%y&zbrj4H4JQ2kY)@7wZiiVp})Xa!AJC zOY(G|Wgpt#INO{k&fA~Q6sbM#qTza?!8KKi9pXSCw_R9pg=3doWV$MlUZl@A?a6RS8LQo?8cF5#A z^s(!OcctSD~dZeD$n@oYKf0cS4|BM z^<|fO)ZP+RD{4HDJ|Hc**(dOm*)czMN15TyHhII5M|f&-fHlIVQu*SNTB_jZ?~aX@ zX(#8V(~`dbCCQ)O5>b+Ssq0Z`!QDk;Qf+lSdYgsxDWQwWr|L#HRxPGPJrN6WjTqFD zZ)tFkb3U}cZTzN;PV+5Y_4LtC*Em&QTnyassJ{12=8Mq+$xBV@8DUp@zwUQr>WD5R z=fFI6NbihDJ{9CSS<~4&&vW>NMM zf2ZL$^HX=_6EdzeReb#6m!3&Svk0&Xy>8?3y^$;JJ5?mA^T}k+GyHVv$;RbL9;Q}K z7Nbv(X^$lL@pkeFT`Jw>BJg{A0{$>`%%5p~Ny?a^Za=B!q$+W-W zY`S&jfu4@nBcj>G?s-9_6_Gj-RjH;W_j5Flovh#W)UtuwpXG%udvEs5hfXW?==SlB zvPoWVONHCQNv$xG^?rU(FyHhi!=`G zFOA>UkzC>z=)CxmT=&wic_2J9FZTZN&}-HFzx?`tyIFaR+3$y}II$(aA609}YW@A-leoI` z_n#?;SDyL5PD5QG6xn7liA_<()fJ!7U&!Vn?2d$_E^46YMQv`OmdNMMYLSDIEY{b? z>a<*JM$@3aCp_{|+^XG8atNJ3J70AL5m7>-i2`Y+NZFm*(D|tcr!Q${tg*{((W-pL zmLIv0eARPJm^X1Tm9p{Gq$7HcCQn>k9QAMCuM#@@jA~S)#O@(q8h>q#>~fTy4Sy70 zJ=&1g6?8KKU9H4uSH{Ujdm`-?)CclURdu!EURFAhV$rD0!+Ty$^=Awx%uOa(^}p)6 zF$SibHd;pc@|&oqXJp(QV=*h{nW)PX@#adc(~O$|V{JEiD#-UI%#@Ur(DNIsf|(o) zxQDNSZ~y-GD*4kTqSNn1u`geu#5?Ni&K~LTv6U6*WKnRQSbCU?vES+>-KzVhaW*Kl zIEVh+DL0P}KO-ttM zt$$7D*-lHQ*6aAI_hE?4-mm0uFWy&Hj2!sV@4Kjz?a7Umqbf7x+&2d@`Ge=CyY^WP zwX65VRxcvcLSYk`FmD)a1|Mm(R|d~sLubpfEH>wZtPrcQVPItR*mmCd)us6K?ils# z!`~}T?CEGSD=PsvxJD@VqCJy$!mW;e4@!CuGD;ysoqM17Syl%l-Z0W^)PjbvrNjCZIYSn9uM<4 z)jeD%!{40zDQkWZPbaVRcbM4 zv7M>4?YO<1J3ma?<3^u}zJ8(l%|0(+&^ld#mg-$8aYnDfHqZsL1e{PadGVR&iZS`=Uq*emF0GZ#iwlNYBUb?j+UqNmD**>{ph>qgKi5v^QD;i=vvgAq(QUT95-t(4 z(2q~vNvdabks3bWAuoDEp*znpzA$AH0Ppy#7L`Nfi4S*>o!pK=wx2uFVDQT|E;a9a z<1>PLRL3WLjo94O^|uW2x?iFk0XjamiQ(_>SC7}ngMFE!nOLR; zjh9}tIh1M#fviw9KKNmHl-Q`Dq~Mv@GObPO*FP^ApLZx3pEWfH#^;=mQjcVO2G$xL|@|*>vMTDf!Q*w%KyXPou08N6z97BcPdQq}?OEYq3TM>duTDQCg%D4UX>SGZQ` z>*TT^kFJE7Q{J=LS<4UdSAA&_b-h#>oL_@0CEA)DR*bDqOG)Vny4hc_Df`FKe^~;B zMAK-`v3j%0v37Z@?xAOLD(QxYr}l7}6q#vE-0(ha%X&V0IJWqvl|^tD?jbhC#hl4s z)OPlU7UVedLBS73k-iSte&0anGryJ5ms&)u)xOe$Y+z`dGqI)4=Dn^;3DtXSrwfFw zbGqbJQg4wDKQGKGK84SIxkJMo$ib-yT#p{ za9wcxu%B&L<~QSkeD{0Hr-ap&mD6fcb2P8U<$3SXnA@a{b&IEbWgb*nUhamXhlI2} z^Q%efSxtABPW{u$;gJsI@SB&Wo;dN%ZL)+laNEB8r-^=U^H6*N<;ih#ZdK0>N$UBP z%C~G}my0W(|M9Fv=S=f`+vrPL*;gv6syvYZ65g#GU%Y(JYuAka^xfEP`CmYkbXx(q zX8s_HA847I9XCU#i>?<%+$YeHb4lJZ>WwhQn0*e7y>6@&G%=TTFzQ~&Gp?6i%I8#D z65sYM(T{^jTZa{T#+VIcj(4W_#23BG7FMYEIlD&BO%?8ib>a!%uv<J$p{95+WY+dDy!OzPhS4U>8SrZd9E!Wc&1lZ0P@&i z#vC|!ih4(NYp7O6&ATgW$jP&z;l6R}g!P>1*Yp+bDl-<1Mvw7GLa3{^PHnbm{bW;i{GRJ8sy3aPZGee3&Do$lV%0mI zJ!d`JlRev`IJ%sA;h97{@5RpP-Y&hH-M(9yGE{21k(2a+829Tkt)I4R*J-j{Dfm`Y zT>9p-kAKfhYo55sn2)Af@DykbwGBFId7#jLkI_O!uJwza_>3{DUVG8$&(HcdQ7=*x zY4Gg@Ss#yWe|T(V$?&V`9{+UH8pVFOGWzzs1t&xz0E|vrLJohcx`M- zNrN-rfde{l)>@*jk6}v|8HdK~A*KyKx~LoNn+WtmicB`N>YOPH)%5vKuj+{? zb%-{6Q-8(V%*1r=%J`n$yt5XgGS>YXs5^y+G$ou0*G^=7;Pr56bV33yfD}<}*DU-k zR5u$bv``=6jYetVs@w_t zoROUG1nq2KxQ-`pb5dz4^=ZEAduF7|_4k)Pj0}#uy!q#|oMoXt%XU1ATz_|AVV6eK z<;`pl%>@jq?(&Vb+~|oj==DEz(}K8r>Qm%Gs-^|odp5U0N3qy4YdA24d7!(yP;|CU zG~($~EhjD17?i-jd|oz{iz*JV4a`|rv2z*L!o z$*e0}b(f-`=j&8GiPFsiS+!ZjjA%?X)ZWPYV`!*oPuCrm=?e*a?m-Wfr)735Z66BM zMxC!>#EC^ITE1u6gvT&ip>V)DF@ck@vKXow9K7+3! z*jaocJ!3c1@uRifEO!0ua9F~}~*5PC(&xWd=!p(<2{W_TI%~NmDkbJ8v+}G-J z_+$5N#B2maZQl84*?c&$k5}7#zf+GPUplKv9lR~la;Gd=vOUjnm>AM~ z7+E&+wfK&XxWY~2=hsEavfcoj#>F^h@6#u&lr)3u*9lU1 z2}%Z&V2M$UwUx9?IkJ?=Q_ER^9h*Yejj5?0d7?q9Z?!I>&eME0m-fUw<|1t4L`4_k z@OznVYhP3C$A|B9;p`wd3JaV zn7M^TCz;Jd)d@Cy=<_MiYPZO=*}T?BAsE8j)VLJOO9x6$WK7U095XLNJCDh7WSbPl z?ugQSa9`4Rvh<;@j*?)&HtC|OJ~_jv_#I(nLdj;f^ZccvOak)6ttB35SaG_F)W5Qj z$1_G(x@=Ab!`QTp%CfLsOeCzIRh{}R) zyO%A3zhd&MAZwI87GuizLssY#V(xP&g_^~j>Lm9GNw8rjlUr<}vx6>X*z?*EM>p3M z!J}B66OrrKWSv%=-4=A*-o4X^J<_K5>w4?YfKVxR=B{~v9N^2zjc{zzO5=n#;I(Qy zmwaTHv4p`xnO}Aocjt8dYlyw+xA4b0Ftm_?vFbVB;wLoldLObq)`~NSCfYqR*T$e= z@CJA0NX7oFFOTLT11(fOjc1Khqa_rE27+qTo;hG`k~Nl+(9wEl^`|IKS?jpTIe66X=e6NMuy(@!&cwnmNW%U z=k%Hy#~J@c@+R~4!%k#9aLOKj)H8Y(bp)8#oO?CV>A!SZ8mgP)w%MP|NSGgh$#$r5 zPu34jo7v&u)t5I@DBA!@0a(+1cg8-lu1O*BAp7gj->XtmBjQt1GQLVgR4$0|JuT^%VLf_vkTKc*SmWt=G z`VBqcO?3kYP?_GJGJ}n0^|iBjRF3tJ^yoyn*Woc9*^K9L-fU0Yy7{%MQH!;4x1`f? z#G2y#+~kq@B$?OMdB&+fYne82Df}|Ul0WqA^or)z9L-eg@kV*vQ+M1v0P=jJjleaZ z1|5GRQ`q9pmu=2Hmj08IpHe;mBVWX{kwURW_sM{CH?|ig8D%mvbv)t+`os8{9@A#@ z2i#xQCj0>JfMU0Ghs!brk96qNrXa4_{t~@_z1%s< zw?e33V2qbA`(7W0Tqo(_n)8>YT+{>7Y2)sQeA2O6;@4fy7$tP7AxCY>QnQRV)5@-?{*x%0ld!{oL4=W`BiUP+I>mXaA`t zPL9V(ZKAW{L0Qu=*SOCf+^J<`GhJTa?U;Sy{g12B229g?(#Tl9Iqa7A#}fIFvnSg+ zZQ)x)+UCp8StzD&D-oIp~Rx%I}G$=L-x*|2DaGx15hI&;>zhg-OZCfz8S z>b-LpUh>X8F{J{AtCyU{%MKU6Kzyn>jchBG|sLnR3lx~v3{AuDOWWX#<<;FCu`L7ILgBRIJ5lKoiju5$~%;^ zCl$-amWuv(yMO=wk&GEYs6Gf-xO4-q9$O{&^$BsLNY^Q}C-)7WZr?HSd9|$%%1}D} z4B`ub20k{3f5i^-zS;2vy)5?I=mZw#$|!|h;hCA4x*4k6CcOM&Q5HZVPo)%R=jP^C znTTw?*BNors%3)S?BlHOXvsi0LObOay%5){2|{}tU(YyJxiEQ54z{_cr*ooMvBT8r zji$ZwGnG!36_WFirkB?2+g9=hFeK}CxYVb8_RPB*Qjw(8QsvVpa^>R2nf}#Qv*&9~ zni3o~KBBkHe_KDkY&L~1vZBx&&Pjz)ZK+{B_2x99j)c=w=gn9-BMd-X1 zfT(sa3Hbc{{g;jiQYiXoz#0K)>vzoQWMa5L+ia(uW4&bp=VUVr37K^2wN8&@Bw*q4 ze*P~0qMmW}>B_JN(Ts<7nLj-iKe;ALGiQrB6&+e33_}$)5?k%U?4^0)L~iEn6UTa! z0dgYVaohgFp}gQ;OAUNV=iTRoG943PR+)ADKjWP@&q5D+j!x$6v2klO&ft>f1-PmT zm5UkUoAs9-Uqbz`K&OvZjnyaO@wvbum}uW2>buPfIO%Xof;AU$lr^kdDHMO#c{qDN zA7A*zFP$6!d9bAvyv!EZ(zmCq^hI89G2EGmzHhpfJSIodg@ojHZbX{S-J9clr1?}| z1up^m(#;;0$V4;pu$r@}cxduK^%iZqys{+7GOgPJM?)42m~)8o0PN0L#?7v@ztq&T zs<7~}dg)M}ZdjkM@43N~8U~|{VdRwQGoFrhmsGm_{N<8=Mn%VVVsA1HztdVRFqF?W z{BHT4ZPKd_w%Vy$Hk&UJ$pv0*)*T(GgGy5}eT-jzzK>!pqc?fdO54vk}qJ2Edi5U9Occn{I5_g+*2u5 z>eW*=f}2{JkD8kzxx{hcK*Yx?&vb9aY<*eo$8Bwt>KMN+7Q^oO<-IF!`p#P|?vD)= zr*hVi*O3!Z?9bS%&qy+PLDoP-`UL{#Cfm6gGCE6*ik%cWH}d=XAF*BkC?{y#+}!+q zcSA@8ZYn@O*!JH2$@PrKMhzdstZ{p}o4Sj+&nK5??(sE_C9aNl}F&VhkB{U4}+z4Qh zwW?+^WQ4b;dDeqgnA=PocMtQ)DKW(1KWQlte^1b zgp8bhe&I-|Kv#h9;D>c&c4Z-jpbJ2Xxq=QT+b4sY$i9dRiv0Yn+ON-EkU>Op*c{R) z>ZQmTP(OX>#kz)x|XNG|E^JnzFD6(@g>r1I6&_QOG+SQy5dL||V^;x)YIb~)3*1`ExP^wS- zeP;<-6$@nP_q_ftM#d9d)7{eK5d*G&d2olf2F(bDXUDTel~u1EML>|0JBa!~RjUrK z3B=@0Ndw81r-}g2ithaT%+kNkJSmB2;>Wv9S*{2ll4`S{(KzzjmRQON{>j!U=YO{|QL^v_{V$e1AUY0U(}tm}*p< zSVTo&=al<1$3HazBzQHDHAUdn>93Nag)TNDPx(qsE?>?k#(>~5^;#~6cCDd$;0W_V z{<#EA2hy6~w$zQ7TUzQu#oL!h>ln1=0>R1(4++^yzzo!LCLxm%262>%i}CVkz`=au zYl!+3LHgiGDC`UkQQYNKfy`%8GsXZCD>TRccyB+k6kGT2$w~aUV1>qz4<&Qre{fYU zT>jA^dtU-Wg6r*rO#ks;>10=lpD)F(K{1wO$9_LXf|0-e{jg&QCD8b9$CLY1Fyy!2 zL~SJ~_V0(j!+%dRKOX-bn_u(dzw_qTNc?wA{Td0%e+|X2q4@ucp^yUh-&*~xx3`#p zobAsvX7mZY<;{Yn?bwSMU9%wFJTW~{W?Kprme2t7D#FN??~77;yOBB2JKEu6l;IyM zkroBbhZT{9381XKTV|-~F$%?N>o506TpSSV4MJi{bg|~dzR&W}X70PaXuDbPlq!Ky z6GY7)KA#_bQXSNyGA~HW^j0Fwp%&dZx84Sd-ZNi9Rzqvp1S)=hBQ(`W(REA;6gKn~azs?L(So zlT@*^ZluF8p+v{9R}}M|Uw5B38U7I>&*PPqm0dtQ)`N^*h)nq5%d$zlyS2b+2lsE` z7Y^K>Xgb1xx5SBpO@4g7TvdZ%-Jp*0NUO%(`}dFE8gfHZ+a48nuk~0})`(&3uN+Hy zJp;;_1nMh}sw56$wlK9ip}W@M!W3J#^MP$AVGAi!-emET@>>YWa-TofSv! z2C>D_!5J8CR>>;JgnM&$Ctj>ng_gy>kSSFqxXc*e~G0D~ohPV6b{BT^J{bF1j) zI)^eNr+0ry4A!{AxRJ8s;C8Tl(trazQLN(Lq@`EcP7mfqMv$XKiH7#!0TT5g;vtr|1QDMB(T*~%-Dv}cSzKG7$kkISk5 z)a{m#FPG;FI$5pUhV}BOW30+ovSUtQ_PvXa(eU zB8g>whz@b~9@rgCSbdy|T3p#+3D`8N;Ro1W;fC$GXSHA1dRrvc9NCLIYiwj z^bKCOU7xq^P}IvqYJ(;)sAKQVtpHU}Moz9TZA6m;*5^I#h!a9;a3$y+bUgHMpS<=& zfRunBp061P92rDH3*OqT--WN~x;D{ledC&+e{Qrj8|Mr1amAaJHY=QV;2stOTye3n z%^YYNIl2K|K;x=HlwZQE5_AGj-WqgAv)fx7Fs8q(l}=Kf+mKtb*tY*2UvX*_>zLOr`Cd*LVBZellZu%2OngJsJX+=S*fEau)?f&rj4$ZjZM2_721r( zRd8oAs=uz=QFU$9Z6Q?}49Kz^qGb4~PY(sV7bpE8m*C@K!tB*E#n?4-SYxATy@ zd7xK&mI1Jw{k>v$4}5fYw|*ECb5zf0gLc4w+)S#xv?n{g_<2n=aJTHPMSzDV~kU3b{|6ESw)fl?4gEeSB&`-D# zfAtrs(CVj!TzM4y9mgtcgucoJm|iQ~oNib5v`59R;h9@9gKGjJ_2UPwo=fkF_FF>t zl#v97%c{;K*sr!GX5hexK3*W%pHVs5*4CELTWZ*!wu<|inrY(|tNX08f?6Bi$Elrr zGB%Rsj%s`4=ulZRisL^2o@(^XL3mLc3|VWw4Y=aaf4gWN^+%}+bbuD!G;CSm`|Fgd zP`6us``e$w%{%?qE`ham<(pAx4uc>Z@3GEml8tTE-N3z~8-3%^aBP|2{HL`aJmq`R zF;~US`1Hpoy-9R-!C18-!4-}1UMFL020_@`2e-GrzI!(z!9HUI|MoZ|sjO6m`-X8x z#usLVb!1B22E-U(R#VElw0TrN1jX*ED85HXjq7M%X@sM@JH$Vv$y#!yx_ z0{>DLZake^ra`WA&p#muOQ|Zk+HU&-e51r*cK|$xpk`#TVF=X>w7so)FZT`z1pdGe zfO^2#9#`Wmx_-%hxL&DBY^XeEEvEX(7Pt_5rd7i|)ndqGtk(olyb1vtJnfZ)EqaS1 zRdK@DGCw-dy}B7DF=cG*Fn}2h;TTJDdL~1XKsR*!A!etR=qd^fHcvJA^rx`KjBYLd@V01Z?F9RQ}+ z4RCE20;;gEFq2WxDVSwOdE9lZ)6-)^b%hv%7BX|!`bBqxoh?s-P(V(XS;LJ|_Fz%n z1zQSX1f21)fQd6U#?}32p6C<0{=w$-o>$vlm8Uo+jw3@O9b8cOl8@iM zEH?aR^1!1RZbkO#UuKTTP(^0BfNG*L)~(Xq8|&_xiH~n4vIk+J#?8e@Bf+}Jr8LqS zn3YH|Z>4I^;|BoEc|1~lY$;-30)|KtI~wWAb}V<;F9#9f7;T4pvARQpmK#B;8|V%h zyAI~D1PjKsS2Ox5C6PufxI7vHzz)0yB}4*r*vyiu?egBEBj+!@|4)w|6}``>{Qm*v z{-5WY(BZCYXVexX*gjUeG-cY#|EV7|%_|^R(KoMg7!NX+ZW=xMOB=hkVi;+1Y4_CM zHi5m6_8#CSxV*;RSp$Jtxw|&B%l!I%Sqx>ARn`glH+ogDi>Sp=#7h)BLVvuZaDpGX z-nj2|J>H_Fq2xoEGxgKg0jUfgX&ZX<8d(fT35taqyLTMLp(>sW(L;cI)~tqYoVTS1ck-dm@JRrri~MMvC=9mT>b@m5vJ8Y8aH}!avP@FnN7|^$ zDlxNiB>;TLQJr=vwuX_`Vs^@AUMx)FM+Z=OBF?FyE3+DC?&&V?kQX55J1^1F73-8d zo|=?tJTTbyl@)>z-3B5}A}-`>3DiJa5M|#U*W&Km{kEVu{ zjF3Wuqzc5yakw?aPsvvC*QBa!1dixPBh(LsiL%3V*Y?IHPkt^rGZNnj+k5WX{eS*? z-I;T5q9X4^`+T~1SZdGC5*_?a>hvO#H#jd*@a`EXuJLlsh_5TD!tWFFzbmkA_)yCj zA3)rT_CEJMgzL;6Yb-+UC`G-E5LQ76l^;YRAYwj}P5&t~mmZ|V6E1hJ(OvZP^hBz5 z2iqN`81brXZ3{t>p>=`zN-7w3^nx1Xv^3C{ZJE~`oAVy@y2;8PekRNpQkdbQ5j9@) zj5x=S`%Lwr}x(y;L37y_Z|D`B$!4&%}dZ* zc?d~#9>$CMonM{0EV<6jCG*o0sU}pC@BgTWTI9$sg~qF*I5p1vz$<=<1I4|U9+tXM z#o0tCK^bS;E5249`|5OWq)Gl>T_f4UQ%ZDKfYMVhQg=-Z1Olf{m%2c#^_hhh%H95p z{@^{2@CD;5VO6-^@fIoxPdGR%VI|c%w~=5%0q3sd58GW`T^ZWXbII5ph*mgXvCXdF z;l=*Q?*@D>%Zq-ntJ;Pg^P;o-ltwH7X`R2AC9i~(pYpwS47Oz7Pkre%& z`&6X0$G@DxZj(QLa_>*8Gw-i))L^HBWm*Wjf#mm_L+GA>3%VFmn-H)H*4oylTZ3Wa zBgm^@gbt2>r|9sl$6G++3&QSB{XwlikHkRQio6G9`ulivNW7`n%3B%yf3&C&yYtH* z*)a4^raeF@waop~V=+=rQNNVdA!aNxNJe6rvBIY}XNWer?JTc}Gi@>dda_k351v!Q zKI_=$P_maQyHY1(*fG#I+J{7l+N$5F^ETz@k-dmy%vk zhQ!*AghTnZZuXdBsoz?}TN*pB@>jRCo@M6xo}s!iH}LXCZ%G_6ST?J42=Y`X^h3Kl z9J4my0E+Bc!uy_qLj0t{RJ!EE@fHk6?y4wsOVa)Gfr?IuJPw@G-GOzYFTUe_(EiQ* z?JQ(1Yb5Gi?oe4vAKw_wsm`F1P6O z{fcaTS47Pt7CjVFG36djYcGvGDV5Q)XLaVMcf-huIIUau%;x^C&^ModRZ_?D4F;y% z(OzMrErx-}J8S zU$@`I;cx~^j>0R$36$S;P~wOJ{!#!_l)3GZ(MivFVHU#Izu$Iy051oTgJukJKTP10 z75{drCPpA=Q2e4;Jvux97NLITX{VUk>=lEkzn&Qy<#;aPda8iJ6_AU78BhJwaFk!P{Y&c}hdvGw~(*$1beB;m0aYpn0 zyCQLHyn~?4X*3{x+l@hCA;G^|;M~Qc^vxQn%F4&K4n)MhgEFxh(+CSdZcBRkk+;qQ$C0%g27A?yw^(9{+hJiRQKuP9 z-Zm%2j&+3_y(!o_A#DypH}sw^qW;I&TATu-x^CTm{8oZJX=JfCx&rJsTj(15J~W-jJ$j^Y37J7OAIN?*EmB7TSb4i zlNK08iLA#lJr6B=lAu!hN=@FAC_BG3nw=Pxkg#rsQjcb=D>dDP#}*uoKtTXl(K!i5 zNQAIF`rY)C^xuFN9u8xw_RMULji<|h!#RY6_Zo_(R#NRo{cDK+j>a}{kdy`z{6>p@ z#rz*1|8-0wVsOSfQiA>iP9X#!7RX`A0Z>l!k+UA4Z;Ei4` z?u&RXacK};n+YrIhOMp6`fa=ozaD%$?q65jl>}YvA47tZO(W z*7R4~Yka+!_mQ3={5@qFUZPFQ=lZctw0!7rB^q7fC<0#-JyVz3?qEQWk-vP;6DdFT z;>QvoQ>KE5Voq@RsOwFr+s(i&Ub?a^aSEY}!M*5^lm;T2G-nRG>OO^z|{|#Nz z#+IWPL}0rUxKeC!*Wa`$5KW&xU7;;1!S3Uexpy-&Gs_eJ-lYt3BD;e+PfOswX0RjA z&OfQA3>vou_Vc{qsRz7es=XsdA5Z(wg-0BK>`JWfssFq6jT+<_2^663E2QcGF0bC2 zt7d(hVH|nM0hDJ-y0I@E!+?S5ngFI6pmLgk?gE5TdaYj`YFZb&Kc`4E0Mb7Iv7qGf zd)&3D>IDKh_pcH9>gUx$ut6DMBKKw1uA7;!Bxgp^9`E)qJbkCo&9Qv{+0$T-KS$c> zgNDsb3wIY6ml&*FQ9u|e5Rgx9zR7(zkAS881CNP~#!!}kBiOd?kJQzXGb6|P$42Wm zV$E79bwa0}zbd(!-CeLjs<*dSX~+aoKAoZ@@RQ_%KJFW4e^~s*8_qt9344sv+WChF zFg5C8OAt{=Fzayf^{ZFx8LlmA1GyS*?6Qmw2b>Q|tO_bWm~D{JTl!Om4){}8DV>z~EO zzRohK-x;JRORByuLJ(4hyYeV9+|gWyC$vsOskhkFcm?;>^<*J-5DqAgn#{H z+w^oKFv0zV0?CKB3vPIS89dF6toTzO-%VeThJi1H2vKoOYVW!?{M-HIo6H_+ECOvc zxF7D}g_sa47d`^1j5qCuSVhnFq^i|r#x6Ern{(E>4S(|L;GKZsu77`n4)he2IxSF&Xzfy10))=t zB-#+f%!OQchnkuiohYB5ixX5J33iRo#aW@6EhW4@U-r}S)o%IIlXGgM01`Zt$@mOZP+oEE&r|jE>B8^F6us-KCe8W33-i~MJ;U{OW65Pqgs%@e*NC=%d zvKJEexh}~i8aYu~73wdnTOrDG4vD+Rhgk(8^wHVfs-xu8PbCO8p&y|>Ox#AIN=Ozz zZsUA^k5blu{qY00AM@aB&k6jqhnKowPgBO10PUo%B9DROAwK?edzYh2@%NuWM!5OK ze^Z@)57iy9^5oOsPIr1NQhS*a6rXSQ2PuDgG^1&{?I|_tnZczr_SIY7FRH1IpP;j6 zFBeoonI8`ev-A&tPE7%&j$qSmzrMU1>)LL`6#CethajM?0A#gd{tw(Yk^buQ4&8m_ zT@mDH*Ku00*woqrGLTQF#I|2w<8Z@q4sFwFT4fdIbNMbrr$vEjmVf6=AUag);ZToUCn(%yJ%DszAaky|%%FGkh;iv@_{gZi;9z;uJf z4kwfB485Ck%ku-4QtQJIo*F~oIbFXk-Xqd(PrT-Zq2D2 zNrg|8V^_lqgz8mXB6@V)*%N0pP@X_372SLV09*j5hQN9|qWW|n=$=&BHPpIv=97N~ z=yAQ`THayE#cga|3{Td%_D`6{+kWAnv8Y7Jt7D3{^d?YN2)yvY2}ehXT;VANC1Yu5 zZ&;w|`a;Lh#s?twN>YJ-0Sn2(8ov$OkCSHLG zI?(HYZ-)+UQT1v8VF7)CVyil|3H*UHeXB0>;Quroy&=_gt@zyAU}!0P|0hbX=VocP@mu|X>j)r#uLD^*+(J9OSsL)Aud)l5_DfM4HAFl9SGCx$G3&7 zB!B>M1@z(VQKj_z_YAk`rtGT?FOwWHjK&>6V;l(03nH3^W}fCQkQ?8A4jd8hvXB?w z_A?GZkEY>H;870iRLrjUnA*{{f44;TK37Ka3CU$Q4#~A!<~tN`?&m!vYsK*;Z>bHi z5zT4>H!(3YfGkgxhS*Ub#cO{SvU12m=AYZFGB!Z4)kH-eeyTb2#qT3=j_o1nf%p_a zwu{?ZABqe;s3~zBcG%=VnihyTCr;rNYo_B|Vh_tb4o^Z^cIePE`Z&r`iFA)Ij+$oM zh0L|A!Lrhm1spd*#A)LVp!)R#zjj)0BX94?ZXMtMb0A(awLveNVZ8$GJ%oB81SJ0f z7Ewcto1=F5#O%sN?6%a^{+DDu~a8xW3aLhVI=VER6dtL z>&Nl;-%~R8>}VpiBv^!MVq$FkpWkyYkWMO{adwGkAl`Tc{ObVV69hYp1ZkrvQR);Uq(+TYl2Vm);C)Kg{Pk8ytsH}g=~KxL;F zvity=Jy`OW)LFq_amDl?2~4YLGxo!b&4xCUF=yYE`fO?;q3qdmTP?~Nt-wnldi#n~ zb+|&5F%g6C=Yo9oF*Wu~bI;gb&%VoC^{nxKL>jzyshMU(fU9Kqpr`?1guXicHXo00T$=>C7w z%c;_oJ_THG#r#eAS8}SQyRNy4{8x4bBaX8#pK}(Czu zCNWgg$KJj=7g{;Ya)xwn2AP{Ar#G%j!F(UG9Ppa%_zYRdZD@sd+`>0N#vj}cFgv&U zo6yS7f*D?2Jnc8?=Fmw)JsH`HBVe4JDj7j*{}%ZZkO7wkCW7iiO94P)9ZCh{aH}aB zu}4*U8~ie}KgDDqj|(<>BaQLTumHvQde9Z*s(9|AJmTB?Uu_s6IMT7D^d*Z zfK_Zpl7$P5pJrdUBX=4-lf-RoO@S< z2a{&fT!UPlWGE&jLReH-{iHF6UD0o_z3da!a>_hP*GnWl@mvy31A4U89P-|!xEx#v z{eXY~=XT_4H3@V)!Jg|mrGrSRnZ%090Lr{tM!v{5x*#taX$l!peY*fRr#fhH<8R2!3*?}QYyY|L?foXG`UT_F6h`Lnx7wvQF z=Mgv8_rRM&BUDqbSpi=LI`w>_Ia)?x1wG9Kjm?W=ujPmiX#cSN20+C_H62)vEQm06 zK|KWePhzk<+Rh~NV~CI96y3>Ars14qO!>p?v#OX+#c7K?AUr*F&%I)+;G_o+K5hEf zwD12(1G`jGnq*vCHrSU+MigR*W8HZsqv$tKx72O0b>Nj>W^wa?NW4xaRTq%Lw)QS5 zN4b0&O)D-?M^qCNj+ z#PzC6uIAkiE6(JU)LdEVdZ=Wsv2KCkUeN&EsIz5>;`xC~@@KNIXtD1cUp`laoqA{1 zIWO0X_feuZQ$^{K;|2$f+i7VoUL&p}o3=6SFxQT(k55GiikflQTHuU2_Y0}Eqr(`f zJk_t#+fFxr_(@p%lf5B-R5eS~1Z9ZK;&|UV9;rMx+4{bWKIAM|lQwV#6 z>a6w~+cPWm0KU}Rof^IyGdOv1ZF>x^taNuPu^J6wH~o=YZp7ZLr4?hfjbn&X+C}z= zrKRvytA~InhpJV`kFNjHymfa9M~GPx?95sIn}&e5e|)Je3* zw14t6sHwvp`2CjFDom%i!2`;2)+7p zzSM9Be-F!qn{>|8usbVa-D*1V7RVE9r z7so$6<2{MZ4ZGTb5z7UQf?BG}?Mp4l78E3gF39?pgQxZChKhQUjuL1m)NAw>2bB7! zLC8`0%UoIKGA<=T=-bAQn7vuQzUtf4)g|Q}V?Ocq@tm6tDIrVi`?oHA?`@ZIb(3pb zc2CV&I_-g(x-PJS&!SZtc!$ba-)sOTlis@1RygjHz@#%94>(G$ddj$Obti z;%PY2V{kT02d~OGr@i$Qd4({*NeufGs>1PDH} zpM;X5Mwt`R^_KpWbZ4?2AmyTXE>6S{ikxSL9)c$2EsmS$oaG%s*-30WD$iqQxP?mi z(#DQgCbK<9EsssMa#`f`c5ZIzmb=$CecQWgqViv_Zd;B0V0-^zpHyv3ds(ONDv=u| znqLb6k}FY1$!tjgW2&zNB4D$vQLkIviR9Q7$~A+w-m^ycEHn@*T6B6T)%rIorwuQD z74Aem@HtY(*Tt!0D<2U{_H`qQu>iXXcX3I=z|$ZiV%~bnAOzuI!BUS_yT6sn5>ot1 zC;fW*X_Gw$_c?R7Pfi`0EorC1YVsYgEOatbePdT>&R1QvDz>9+YZ@HaszE7D zUGr8z`})SFBA}%nmAhB_v14ZErmHLsY8$id@Mt}%acFGC^5`8YY4)mVef%eC(kd`m zkCQO=#5a$h!Z{&CBKXvgOngIfTt7P7A6?4=m9gQ|Z>O3(n`3Bw|LI5M%cOUp-jYk- ze8hN4ViI@PGfg_}Jt=7qBBW0gj6}LEF%Qolwdop5aID*Zvbc9NyUspoNno(*CZ7fO zzMs8ZF6?)0sq$>ea~Z$%9?(tKD-t_-|7Yh3l6v2ztoZg9Bk>=v1s44#@U!aHO`N5M z&HXt6i?{y}&pO$EJwMa9&^~wrx`Z1->dlv0^tmdpyOq3iMLw(k&d4bTBlwb})%KV6 zo0Wz2+ddN6ups-`!W>aWyBz{ThZL*WNdviTLr=Fln|_Jyom-c7_{SePeeR~v<^4uX z)~adiscyea4N~cWUF3X)Yak)k=+77SKb*2tt z)?i7j9k@ce$u78I*JgmBb5Gmzi5fhd@KYM?;tyO0Mw>t7cL?cv--9Swu@<(xsG8y6M4xl~^AXs={q|-eF z7BbC%$ehRtC^qpZ`V_(7LowiwAPR|{wGwpAs!Gf9g2V3KlGE7Y^q|pkiJ@3g)v`)y zOI1(mvA(XZPA?G4h%wG^&Zvs+%mBb^ugdwCP)x(Mk;Pbs`|sczcl{h@f27^ArFE-s zjsK2H+w1_f@msyqzJ-Wk^j=o0Nd25=+!voy(?`l0H_*r$!*5-D4qVDN2-tSqbJ0>N zbqEKZD92c0fYz~$MdYh$G+!WCa!@)PB(RB z7(fAQaY8K9Huty^xiYU-;$)0-cYO^+dOhzVy!XEp9Yh_MRCxOK zo?Q4GdQ-_cKt!)c?L^IMhWBkDroFLyCdI?1%)}l(3DY;HF!$(!wz(sF?GY30wr!)) zt}nAV!b0dl*%%MMn;V-rjM?8N&4Z5U0U6{`nVQ~nTWWq>fPkg?#VGX`havDzQkBj0LGL6(8uQ35ZPLr3_REE!nRQ$ z5;+mX;#YQ~BP!z#ncSoVuGD=qJAOl}fzOG=l2o?4VMbhk?y89`e@yDWu;!P_&raG2Z>fI8S`w2_Sh8EY`+j7)et2#uBk z+zjKXF;fw5uAgYR*I@)ZX%Rsg_Si1gwP*hoJ`-EQBWJM8>km#t>ZdH_vA`Fp_CLKk z@gytekFvUjoB$@fc^i(q+X*@Xr)OR8#|slg`Uwt`av#V8MA1Z~52QFMre;i0CjHAu z+aI>QZ$h6)+IJ4Cj~Jjl>1 zg`o8fhroM}rj*}gc1xQ{*<%Mgqv4`=J}Lc=%vruR$BekUW!e^pI?+Ld6z#k44VN>m zr>1jBE3_}$!?_PxU>SRxR`mJs!M=(@jintvJ6xR4HBC8_6oTk?5KbCrrbDeHOs3ko}gk_9R?Vbdg& z9Y;jLB{3DZ1a61iLp;Olnpucu+X`o|ip>5kacqN z^UB5Oy(f#gPobPlg%g-g%3|MK_UiR(Z)#5x27x>w^QBp|afTwiy7yC$<<8x^cT-OT zLSGQ)%p?gyZ-oumYc_~`nOPdK)*demd({&w5%4eR%iIr?S?c3?@Xwi6_paB4iMzhqxH(>f_6s{QDg&JCP&XDv0OdO~`S!|lJ09_(cC&(&{ zKpJAo(h(ZJQVUTeY|p)e)aK2#U{<|po|bmN9vxxtKQwmC8O=EPn~YbR>!xi+8>7EA z&BJjopl7&y)^f>J8Tp~-EtV{|(tYu(lJ~X*-aUUp_niFV5V7*0|Mh7waaUcLM?E9b z2GfWDw@Z6{%|q6(58^CF4J_DE%h=Xs``!w@-M=OLQuXx{u`xTRds~RBtDW0cvt@$7 zV>XtbsS^YWt)pOe6i5lg3gJrucb*WIzwX%Tr z1xf{AWE-8o#I#7VeWPBnWiEnrzb|kk`KaHH^2JB2qw_~6Z2m@JsX%t+xtna63)_<1 z3~XYypMQF=a-H`!{z_68nUfzHd+LegtLo}~LGC?4_VP`=dXS;P)${t=gCWd~^SBba z>|EIK9Z1~{zAsQq>)xi-dC62{^X+h%>Ft>lHVe_GILln%*h~xR>j|yv*U?}FNc7Y` zV947^rZ|~SKmy=Yk!M_r;Tj??$u+XEvQ=}`ujr<~n+?Uyrf6lZFv2kI=BsNm7dl23 zS$AuWtV-RQc(5fp@iWCIN*o_5F9LpngfbC_Otjfdh| z>i|$5fr8OlVh4AiyDRgQ-*P;StxDYs?1R1O=8+2o@iwr%fx zy(!r>=_zA)v-$e|{;5}}Q6p(Gunnf6Sq;Ez?GSUadacqX3a(#BNd3(ipSw6U%UmnfMa9ml@|ccWy) z5!zq>k^5(s#CBVa=P^6y-|t__(wgz!b?uRQA^Gi8s+s;ZFUkG;4QMCR2NJb91!oH5 z7z~3hN|e-5BF8h*{H@2R>aoeP?d*8TRraiM);-zSX`bR=3wJ z^oq{?RbuJPJN>)lFKiRuoTAS}w8gLDYy^$Wu<#YYMYTgVSCYyl>6G8Vd9%|@%eN*& z<$#TU_0X;Iz9p=oKM(fa42xW~JPj#?c7Su)5r6iD$`wZr??P#)wtbJx;DnW`LCg^t zjqSK!`}wx*uU=o-5qxDw<%ZUM{tu)$8B06#*gYqfEcd>-2#f7P0AhOe#!N^3+e?ld z?#Osb-`(ST%W`?H(NXp>?OD@VW>(KVUuI}bw-NPzaP;*CBVX2YarMr~@E+wQ4-Ri# zvD0lBXIFMyX_a^-TjoL7`++jq<9Q7St79_cw{J^1lwWK0?m)imakF#hLegoCs@_!K zeoe~=T;^ep>n57Ob3r;IKiDfH|0ez}pqgcKJb8{S8aHT3S{gBr1pn0s!I1e8+WL4f zHxz}aNlfM-he+@w>n8Iw=m4(6cbveaJw~0R?jcnlwe3KEPn4uo2tVcTa;hCOBPW=5iMw?KikBniFhA1T zE080a8G%3Pl)K%SO^O@n4_=t{nb?w9c^ z3YCcpP*Wl5I!H>?R*~Im)Lkh=2Biedn&+~YoYCS9Es{P!JClAqLgldYV;{adcN(=s z=gd1x@6{gf|9JYt8L=10`T{LB*3TR4l2MB@x7p zV#V*?&k?3>^c0Efmu_{6qPP|Eq2*Bvr;-p)e9BIv5ynyM{rF^=crMaHWSz|b@m9%a- z3!~#wuau#qY=)1Cj%1``O?zwWybl4qZ@nsT{PKOC8qAGbL_%0J>?h~Bk4*(ZfjF-nG&z&G7n8IRITiDB!T!W5 z?Yc1S?_VGE#S*;j;A{Q#Qy&j`Ibsbp?TvNby8y%u2R>=kRE>fl{iv>zElu^9UIEA> z@-_Lvk+Dj8!xE`nsyj$g3x{{->UIo*SO;K(4DzzzI^6~EMiE`~<>!7h*I&vC{_v~B zG>!%LEd`QRe!ir0c01$sas*+8i`43Y@Go=Jce}>on5}>6wH-1qYT6?yg#?}Y_Pv}E zdc+Gxz86#4tnW)kFq8vpuW%UqZm%V&BSS~c`X&XQ-FBw3i6N-!C zxF3ZSMBOzvTvQ34pCeX$v3`bS&d14#^`9on`G|TuOTV%wF8k4G#fzJl-l$zKHKF%e z%9L>Vg6+~Wp6cvVID4>cW~uLjDl==sXIJ~%;v~T?G>Hr)(%%;}bGngQ9iNYrZC;7x ztyIEN8fdjj1?TZvQ0$-#gOf;ln^EWUHsk(O1T6PwSk``UUOWg*qCV_RP+#Fj@eP&~ zVA9}T4jTE1 z)SaDyNd>)N5BrOQb7M(@kI-d2DX7r?zh4-9=EYecBQW3zYLrW9qX^esv(&FZGp8Vi zL)G)fChnD)hF#%5`p4=!1Jqzn5{y*3_^z_UnV&hP#6`d%Q5LrB9Ql08-s9eLfpD=e zzK}ka8YsVViRG^B2BgG{chAjbmO8xqrm6D9<=uO-3zI1P#Zy3fx4iGdy$X-S@m2Kp z=Fd+;nEu44_0b8-rFdUV_KUBMC4>EXQ_;xR{M^k` z)n&pW2DWAX1gQ%GcK{(z{r>x(g{$puHId?>lK(^U<#`5;YA?#o~ z8l03?H*%CLbg!DH)RqZfN-nsQ&b!bB+xbtSI1MMYH1^kNfQkiFnv=~0Ndbu51zjXZ zkytV3ZaxD+`6^w(#P~X@-a$o~fB8P|4y_vhLg5y&wn(Ijx=g0n1^p!T7iUHn5Z7g5 zd{#n%A`eFr5ZL%RRk?(ujdyjte|U^W=6b6!EI^z|##RG{V1E)rv8@_;&!g46J0&|XQPc*&8^Bpa@YHk~UjYWf1AJB zC+$MOC)nwr@H){>C)ypum#5}mE_bGmfXCpE{B+Lqe85J96Bv-iVj~4BInck?T)*_h z827zZM?Oa{^_{_@sm{F|@m#X-!YVMh%6lk9XaBZzp- z_j7bP9Q9-uK<5zHu1T;+C^ZZ+bRLy@u+CD#TrCz%ixiV-bPySgO}Mwe?|sF@wYbr58L-J#eoDd~PsdF+iLsCv7GJD4HrmdQWeeuh;=WK9&iA7sAFglTLTrD+L@o)VGj*Fk|uw~|V56gTYJ zg}rFkxnsA3f=hzR?yGQ}mv62#RH8Bl+--(K~#PPwdo8H39W3!))uVKs3Ws?=7 zf6X~g!(SoC$fHyNf)qNu5O0IoC=iQNA57;!-~dPL!IgoqTEaPMsr|Bu9M6!#IAp>; z2dXXEsBbn4{wA4rJdB%n1-Oz+CNC00>&l>!c^n745VlqVG~k7T%z}A#EBU3Rz{pT|G>_! ze=Yj_#)`bPVe?mXSg&o$Tb z7uw^R-wz80p*2ayG`0O9eGl$V-!HqEl#hnfwP~S}cDw>nDWa1ZtVKvDg~P4*B${Ed zBY302crq`LPb!??(NlDGGE zG?OUzz3Vhb2 zf+tmZW`-W4m(5CN@IO4t5FZ{3X6Y>T`=wnBXbc+AWNQ<1eZg`)YdxgxT34IE-!j4~niwLqPYis|_3hi`tNuPbzJ4>t+AF+-urmwy;$``$!Df?b>4{WA7v zIdE&q*T~fv5cb4OvC($4s`63A8V9Ms)m_YS1dEP(R*?my)I>k1#MFnEmxzexRCn=n zf$}}YJ-MM)q9=-eYMWMrX$BdQOXX4fSvimBctmZTxK`O}2;Cd~prc2Rl4dpi zk&U2IPZ4KKgPbQ(#Aj+0@iJ~T*2R=Y{t}A29e%#42z;0&#gy@77`Nme-ebA~_OP7g2*w<_US1KZ0x?H0#%`o5 zW`XO}Z2uyvL zw~($qY|?BSCx(;(kF!lA5wIA51Iap2(I}EDv_E*w$kl<@lIIx~tlvSp!B$b|+$ncM zVKWkgnihIwyT!^+iohz~ZdM<+{(_t#S1BCXZLWWGjl`yK$Nwna>C0e-3r1N-Zx_;L zxDVAbiJ(7bd#(8IYt{off?d+c6-RB$5+qOYU*ca)%);-ZA!NNVke|m@rL> zcss;*(QS@JH`chu zf+l~9?5-(_)LyZeBX)ONW8Dmqqv(C?4w-M6&!8z^t}gm$kGDH7R+MecBWH-THT(zh z6(41J^d{2!)a*~m=(qLzn>;-B)}ZY@m?ExCBy4fJl!aZ-IRV!;Y>l&ClHYL;d?BWD zLw~;@6QEYH3=0Toii3O&GQy0)VI1nkogc+AI;iRZM&}j9|Ggvp44C_S7>W3P9U%jW zrKBla3-=h#F!GU#*!yDe35M=0N-+x!aBG7r+z-zFeLPTw9l?WQbwP$A3!%f1r*)cm z3WPs$N77d1(oiw-W}v1zZ2~tN0?3rRp-!nWXagA4pitZg6B36k^nHISZCQuZ`}fyiihPe^86-iE2fs=rhN75bHG1X4uy~Fr$a*(Bmei(0 zM!eNMO-A964Zp-R9pT(mc(Cl8m$MD$WSj7r210YF8T_c1(G!yCLPR&U)VhC_*tkEa zO=pGAaGCpvuEQwJPNu(1=Y=scc-<5CSVA$=)}8sUVx%z#seL7NdNBQf^H zR~&)iIcYU1&CpWj9jDWT{`T#Jt&9W`6UfAdq!(=15XqC|lW`irQ+bX76f~j}XQCA3 z&bDM*#5u|MUEo9@KSPn~9Dj>oe*{Lx%aMv|vH+vNlt5}`0HFg2Th}6EGZoGCX)XYnboPG3eq#H?=Ef-d}!5YIE%T9i|X7nuAycEry!)!F>A;O z=y_UgFS2*Y8yeEPd}1kh`P^J4XUTB%sJ3z#wXK0+5UnJ}Aq`u_X|c+qA~G=lznw07 zSx288E>wf8lezCESV*G0E&2j$e?BT2>pC4-VOF_Pog)1t9 zwUGJjfE5p!$1xKs5G|@3K5{SEV^s+Mv4n4NK#_x9q;kg)1p5{^MQFYQfwpQ|T#kT% z^v%zovKPJ4zinfIo{;|t5MlRAzwXI_AwaM=S@c`&Pkr|^ZGGOWzZqX_o|T|(kC0izCD-?6 zSfkMk_p0&&W%2m6Gb{kwi>Ke4iBVtjhea#6Z>8xnzTc=#*g}*U6p7l8i*ss^J8FG% zi2H0j@)z;1k4v^}0Iv6p#w$;luFZRVa^r|@`0Z48yB31*I6VQXB{f_!lmKzPdABZ6 zyFWh^9+B_Ybrz&?uS7L^d`%b->#ihHH#X6lMBHS`v9bM#MjnnZKhz)ED}20!`=b&H zp5tMy)ime5=i=Y_j|!h}D~QC*I%I$XELju6?`+?#bKFnzxU!2M9++OfGTQ#(yXLB2 zp$4kzze>oR3UGVXd)~wGxU%K%uZMtw+@rxf_ZCONUgB|K=%Qo#Vj2)Q>EKH4hnODZ z<0`!NKkXlKjs&*M>H>?l?BS2RSB8z_#msGCb;USlNK)sUJ&Z{w8ZItFhb|4I6uPb^ z`wUWWIoqIja}sT7oRJ5ONt6)xG>X_Q&`g zqu3uHepb|OD+6*&Vj zZvOhV)l=@R5-6Yb_g@pX`lUqn^)I`*|NBNM|GKg@Ba2f#yn0*G+GCt}>$8|>m&}jQ zw#S^@(6*_X@=Np_l8w3l(RAT>&Cx7!9khD+0)=~)Ch=#ZuTgn9hum-6|wxM1#ujQ=@kTP}0f*IfU9Nd6Lw z^xFP4SWjFcHVOA9$~BP!9e2?^`zil~9&a0P={pcU2!B{ZsR_Re6qn2Yc-3faOP;st z_{Wdj<;PRre=-%KIybP52EkE z$`7SdhkDUXwvZnfbnZmfQ#edj@-S9wAx0R5=t7Z1RuY;%yeNNRA^)kHF2i+ZKXpbA z%?{$UW-@3Z?#EU0isnRR1e6!*bB(T&Q8}&JvvCKwmu|WpG8pb(JzpWbWTRUAlpbT-0cSrNEJBCiu^ zdd4%~TMbg$7uf5IOVS`As6Xn?$Sw&2BrK2&)V+H=@6%>aIQ8+}qc16E`#%}LJAtMv zl3FIuPpXhyaMN4VrJfV-i!mtJXUD?W2>rIT+%H;s1pKT~8ZJdqJFz1^FibeUFNa_- zDp{RMB8`2-X(%zO!bY){ zW?rruJslwmcHQBH!vPDSM&g(S!H>mX+0_|@3rbXf{*uEWot_`8RKcM*Uh&57-`8pmVicn8!;;lT7V)2#wNU4LSh`_gVkAE)K!vT=|mg&5Kc^_;dnG*!F)l>jvzIwRUr`5Ln||5Z*xdb zm;@f#u3W6f4W^0Q;IUX2H&&TeUn@;%nD|5Z(p*^VN4!SuB6^w6PP_u;k6{cs?y5^U z??y#4cL@u*ja_evZZpPz{}f{8?geh*2A^%_(TyY%T{i}i|6(FNMs#E7SRt5BY@!e& zxA}0F`(%<%Ks1sHUnWiu{GJp{>uVvLD`_3AZsi4U)9DIJFNlH^LLw)7VDd{@Bmu*o zQYJfmfS?JuU$ORS@Hz$OhY$kFq&UN%5nLm)!@|=|>&hyuxi|R3VSJn-ZVRUej@|JP zw9k0<5bnQ698wb#9aQevU# z5jwE8=Z@&H>MF7EOxbT0jDV9HL;=W5NE9WuAz0+OfKZw&FE=oRhS)JPEZEe zDGg-N-PM@44o4>~W=f-pY);g*R4$!eAY4HKs#&wOq8W{=Y;(yk4;_}%-buhs_bbCb z=Ma<}MA*@q^+Z6I%RHK9LEjZH1*eCYe@OEh$mx&9f_MpISj@UIBS$0ZY`wUy0Eh@p zaq(O@^eWwuogq)EWH+R-j7u6o)d#h`#K9%uoKw4%Ng!diR_`f{2Wr2@Boq!gcT|LN zHKYQjak*{gKkPxuh~i@_H}e7&hk>imsL>}_hd7ADYy}Sp0?&Olhp=Y{5SIOrN7D3a8q#2g z{Z616r5BNpKu){vOd1aq=9gbs5lu}`PVS6{}gg0e?s!-=I*!PI;Od`-zv^wnB5#=)KHBU)YC&TMdZ&bYH=tK~q+osh1*T6*(FU5X=2f;% z+esuX3MRdpksSB>i?55?e|WZL-V_?MkPfm;0jNJHnqW`LjO!qhTbY5QQGO_jXL5Ng zHE^S@K5j-^!uy#apZ*OMW7dQSuJS5;F4kK>Pi=)=e$DE#>gDpSB3G3wQ++`?+#o7zbc9KdsTLul@{=lS*lvYG|-kVE`f@<`@MzA20GiyoGp*C6N5LUuc2r}hdJZ* zOkoRa>RA&nF)n_FMkL(s3*a!8sT_J(-`6T*L1#uzrpYXI65by|-&Ac-pYsx=wsoJ{}!GOtKiF%loOvW%n zFI3R}n`p$<^9W26v3v2eM9#0%t*otI!1&B(s#Ll(i;Rm1sU~@soO9F``TZo2ErdHn zb@K?NY~+MBcs-K&3Lcdr41pAQyc7&u2k7>LL?dxZ&m?=W{MgNvdF0eG;6BzlHr+D% z@goZEG@M=}ai^JC-Pl1?J(J>i1(uLo!?lUH8zRIgrbY;Pn~Fn~z@X&_z#BVGB2%C! z%cfzK#CSDmBNxOJcL&*`JUqrY9eKmg%eiB1TKx8xh^9UzgHnWE zC-P*qYxL1=!VYplCXiU*5EvN9j4wbpjnOlJ_b+%n(a@JjYK6m?DOlQRWBc%}Cqt?t z}3U53n{x8PfJRa+Vb(1mY^@ZfJSN&O~4n77FQ$fPB^`90>Ic)%Oio3R=P}PyyN@Vk~y8^S~Rt?>iPJKr7_$@sINM%i?ndj z_b{6$zGp7!UC3NP_PYsXZXpVT$$uYgyE)X9K#&>?CnKSC6b^zUnI{sne*E!!LP_ip z^trgwko%_+{B|;WhmZ|wBWGU?7y&-0C7xv%BC79SRSJ=k&5B{oHOxr*3cz<_woIMw z@3KM%Qk4i9eh~5y)55(L`Qy#ewj%H2J;<8ENi(f=j*KKgvuU_JkK7kCknFx9oDZDb zXT(XbHAS@$4C6^apghDiFAGF~l~Fb%%B}nhx=fo!;^;#FO{3;$FY-Rv2$vT8m)O+b z4YyWUO8-am!#74PsNc=I8SZzlm#?WKizKvCl1z|^LAgCpncgF57TRv-u%h78-Xv8m zM>WxE#`%bFp(kiJ13XhMy44}^>->yt&qbh*6h_) zVlyx6SV#uvLf$K>pWp>Y*ySr6#LSWzX;__01k}+@5hfKo?W2HS#y76Z%55(MXW|LI$$Z-rPf$*1z(q^D>>fM<#*L#MR$NZt#p`oSRs0NvvoXV2>jW zkeO6VWOC^o@bz${9SlDPn6E%GU~0j-$3yY|7xmy|%SNuB*FfL-@^vQ}LfeB7ev=SI zg5ZAHwnAYiR^iynvQ1rA90=`>T5q4?@ILX{e&R|=tQWfQm$j8P|&Y8 z_YJlVVr2NOUPKgOIN~08%whm@GB_4e@C;=50fuXdYYb4ykS%i7>r#U6eoqkKQHq6!(c(`y>3!kykFP zAetbK!B0|P(0(Q2xE=3$CEAxIp*4bpFM@k)(e^VryQnDaw zA#aL|wAy6kx-JHt=EslOzwm(vIc|G&8Wo^ZyfFQa|0@MjBRKZh60KJ%G&1Xykw}xS zLz1i`+k$s+Y+C#=7A5#kC&wJDt4k;C6@>?yQ@!n^7e;uciy1>ik+cNT*AWT|?qI{F zc#3&?GtvlzSWy(@+9M_Hr8BuJ8Axs>Fakl)$`weKe|Sig7mKi<%S|tI=IfWepfb_E zY*x5)Au4Is0aLn$N*XU{2HizYEi`wwjl@_W7rn{v%McD~$d=BH{95;QCEUwFFKO^0 z@jPs`Nqo+NIV~7jhu~YWH+K=CQS?fnwOxCy(Sh6cV^v9m2<|5pOnIW>ij!0{}0QKQR6wKHks*8+UTD@-R` zICMoVIh!NmuOygA+ZtWaVmXqOpsy6R_ejFAy-inh;+0IV88_F28SV8y^$~;1TKCTi zVL}b46x|~Ei;Vx8722rMVo#mas>~rQvU&vmeCL7t| zW`pIuKM6=hjD?6fJM@CDG!0x)BGwWR>KW!Q5(=1*IZxYH5|@T)A60^ks7-U3BUKr8 zGdwa*v!8PuPsm@M^|K2eqfq?>;$xTtgWM_0R5^;Wk*sIz%h~ytCORYYjk)YC`Bc#p zYuLkoQEI<26n||X{MS0-(~ZWV-K0c1vZKZDPgfER)#;ADmZmUPdGxR>-?38 zSLgKL!YVwd5y#`yq?(tIG+L9A;gOa0#V5yJ^hp}+RfDVf#I=C_2*<+|7SA?s>mI=` zVU98eCq)=4o~wA;3;$*1or&YqI>c1^?IEPtog(eU7;e;7_>-bQRU)>MiVI|zA-R7z zBHy)bvw0VD5D-)<+zN=d0D&#zXRBk$;6IWoST@}u{Yl8&?3YL+CLkvX|HPvmNN?Zf zB0BmMoG4w<)Fx#IL#Y8#&9>XdBGjNX$Hn(Nb;IKxDO1N8w2kgfjqmBoG4K>feAyHx z{qpXz!)gS0YV_gfuAi{Sts$m9WTQbbP*Cyj9WoO>MZJWJOj3om%YQ3x!2yy0dl>d2 zoH&2&>g;qkEL+V0yG6%_c-4(n+yCIvScB;^>MCPD+GiM(XUoj(Ve z#z89qAa|0KK6s#0bb;&mngWd!ifUVjc)PN57H4=&qx>W7tfo(ob#I;emhw>Z)qJ8X zn;`p_Fb7J42}SZGmy}|`YNloan3Pwq^c*>les}&dd#%Vgvzww*R@&5ive(it?+E>Wlb^@t5Cp%IVhSj(MyS42Sny}1hH@Y(f+yy)llJVfY}#9lDD zXp}e!lb}Yk!j@U!7!tL9j|8`51TV;$;Ik=H&|p))NGO zHnaZc{F1~fvx9Ftb+4p)Vz!g!*Zf7H2+`V2c0V(+j!$*Dgj0?$7%J74nx^F>DhbRw zEf9ei8JptQb2P2w6N9zJL=ies_0-ae$Aqw-c}(s<%QBancJV?%2(dGdYB( zKzO%)JzwklYZsQSe_+b&RE>IRR(%#(=nj-d8{@6$@?=XM$8cw5O_lO!*z4BVZ5#Yy zsk_avXOcU+F2wk7Xjo#+hn-RhdR?oj#?xe&^I)F#B>Ea;#|k=LnSUza90{ukUtLNZZGc%SnMv z;;Tkz^Dw2JsUZ=X7*f{~K267$MbzkZ1c)YjwH0Tna8X{ma%Ih+p;1#A?e=x}XXj!&Mw5DvV>HflZFe< z@y-sHgym%vktH!N6pXmw9!>qKPcRIE+E(ehV%ouovw~#p-~I-S(7?UmgtJii3Fjf1 z);ZuAN8xBWf8;R?YH}NMAap0W9_k9>{70Ph*!Ry}iNuA&c(H@7rEuKFzNj|upeem> zoUHopw0W7l{R^*Ds9{Z4O*JQq{^yxjT>Dg#+SjKTX%aIoVupMQ9C+`&!n^=TOf5#2 zs|e$W6#w^m9W@GD7EyE%6OE0pR$(PKgw57$E^d(tIqmjWF+EOf*Jxma{+)k@A<$kpp&KYXxd@)d z3$r^lhxg>XetJ|P61%L>{YBV0Jt7aX%863S zkAql$?mn&+0H{W_cNu;YO~#ArCBv$BUvIs}$Mt!3(vf}6 zH*Kj*b`Pyjr6zHwlu^39&RMkA|9-0mxZ1b$i=6O-5*k824Pt;sMm%S{KY?40%xg@knvWYpHrMJJ2Z!~N*Ds0O=P3O4mX#xx zeV2duk^xMb17%Zm^!vN}hto!1iE|f^r&ccoJA8~hDW^b^PJ_mxwuRLVWS9zShoGar zEOo-d9*=D05MA?c__@0_L|04b4)p{qT@hsi$0gJK3KSZ$E@AEhAwH0wRgH2~{ekBy zc=R+jY4q)+PjQp7)sJNT2S%Dz5uqQl9H~vA?$gfHL2QZk0Z!w^yOT101n3@dwnjl7 zu5xq)&~ap6$lq~L(ePqUn2UWWfIq_1Api<`JdCaVi>8duX~bNdxaixv5G65T$A}3t z(M3au;aHQ!EhA!;VYExL)5ZLfpsc*6&+ydr)pj35Knc=58=@!-pAttCVa=Xd;{Q&( z*bW#lsQ(wLKEJmN)8Yx?7r@C2(vCs(TMZ&@D518ZoqPq4O-QXolS5hosNy!J|2hmD z2=z_=89XCDj4em57^~U!_-_t8niw+=E%x9iB^NNGSN?`=l)Dqa5f`0$)tY)C<~UNwwQ*Y zHqbkhm){Q_ivkMlUq`H@B zU*3D0_Z&CsKt}28`2ne-E&jG@`(2DpdZlQ9axk+Fuu5ZYP%pWDz)d(J8`3cGQVe@) z08WWl{D|~_a$RrL69cO~eIXLAE7CgPXQN~~$Q*j$k%X%cW3`ow0+y&(nw)r{E{Y6X zffFq)yGLKH`EU<}YwW!&pR?w=(teRV6F-buv~R{Agpb(DPIu$jvpawPy{@jFpx9%6 z9Pl>ZuN>45A`dZMBr1VPYbZ`7?Lo`=^RUreq0)VTwD z`HDMC3dqP)Qfm`}Pmhze*z#Px^-KtXBuTJ-JTLjH(_OAU;cMd5)U$XcT_$;W(1pXO zql>i&%8eC=y3*O8{8vVCRWPZ3Pr|#snEfchjBwN!1m6?Q)v&LR-nT!P3<>{gQ0vlK z(ECnM8@=WQya+F8|BIKsWN?q^?h+K$8!as8*z`ij?SfOAbu&`zIVnr_VVO=%D<_z_x{bD=m@RgI-wcZ^S&3Wi^X`7ayJ-o>0uN5h!nlNibp`@tLad@Qe>of+SFNw zyZ8X;% zC%7(&L1mTU(DeJDe=C<_gzJ9}g3FN~T^H(C$e&GyL|8?%4~E=idJ> zK~e)VxLpw9yeAO#N5m8J67`)GmweTO60Qci>>Y|paSe(2KXg@44pd#C5hJCQ_7c>$ zYQc|z5R#%06qHmsHOeI5s}rQN37%Bfx$%%NNzJJ7$eFZZD&LgaHO&#F6s^7awL@9}+LRy-jV%T$zHX~{&LoPN#4+MRzn)FVizmTEIb)e3OkD2lw#lRZ-eCUETwmQ zE>Eq6h3`n>pD$KU+uwh$pzFkY-Fc~UnXnv5rAQzVl8s{KtML+o;!z4QQ6|)^l?fOq z1X@!pn6DcJ@oIJsY$_r9VUxu)I$y8A2YOE$I)tx4Odze$ydoeAiTY%M2O=l2gCkgf zB(kjm)IpBkmg|uwAD$Go9E7>jVIu!Yog#@l@o}hQn=%JIHAzGR$yMp}*fM z>eY8^6d6jnd|TYFHn19C2rr-brMjZso=EOn>jOoJqjj4vci#?ve6nexU?hX#|E;+D zX`{rEUhjIt1;@QY?xUZhs+nm>O)Whm6FkM^B+GVWyK#nkh zBgB?(77A?`R!om%oqs#rq4{yi_k4GKWr)c9698JGj|1Cq>9R-uZ_BFxy{~~#TL4D2 zieC~hin3RB((b;!ns%BQfUY!0hBfB~?YDh*()@TAvS{KWafU31eFr@BpAfnY@snV4 z6bClqOw{KLZBrkv`yE$TR9T|2cjETc3ijtPP4kv{Dh=83@6{*oAD1g}R$wP4kwh6c z{1XRxiePvkv-OU?Wa80yLXzp$IouMwgaaq+q~mt{{8MQbZa9!$+AkOQ@~;tsRQ#(* zyuL7AotVCetXq{VUpGO&g})mKlyYKmy^|D+5+{pNe2ku)9Q5_Ggqxe0dk;=IBRa15 zr*jyi9|%;u)kN4J8!lwYF1CSP_o+Kapn&?3__%?mD<9r8Bp~)yaNnmZKAT&-vPLNi zMKJo~T=@7i`pRK+Kny%p?kRL#fEtvG{EU<-T-`*6;ZF?H!bNAuiz4X;?By0q;fo(e zI_8KNT}Uc+%mP^W(TH}Ma{;jq!{Mbf#lsJF(v}Iwizi!;!Z!p>f~ITSvqefx5*+W*5vJAWwK_^ zUG;Psia}#oWeu|euI7nX06$lp(VKQLY}*iqXmwO^4h=*h&u8KS>yXp!{XQ> zqShLj9K2OTt;8CTc#<9?+#OEvo42K%LpKTOBtA?%SVK{SnNHFrKnIx&E|>D?8Ki70 z1;#>ZhRfQ24xJ!y`%`HysqV`TX%AR`XO>3(ovIUvYm;ZB;HZ~cZ&+T2$9%4(aea-Q zL)yDZA144dz5FkYW-SEGCIke%N%w%(8$n#=35H>(^v4HtfKk}C^19k)3WT1X2W^`nY~?COa!~safd#_pB?>Z77>)vSAy^Y+HY7+Q zcnuKtAmL||7y@v{k?=f?h?&I?-`W>S@h7ENPGy+3^NeZCy824S`Qb?tV@#6u87v@6 z;!~m4pbwKKdwF*9W7*^h&5pUhkNCFg<{38*pTQ~ev1q1B9LXJxbeEQ4@oKbR5VgeS ztVMowlEGu$AaNYJhEAp00J59j0lYGJI}s)`Xjf=SuSiqhexQ>~SIRN8`yzyxFQ;Db z8P<9-Mc*up`-_e^N!-mfXVdqMyajDU3kExfh@rjk*wxrL8wZ`bsn0=wg| z;^u1W!qJPMe>fiLV`q1w{&Q>o!21O8uC&UA%Ojj;C;zSyABgN%Pbx8|h&ZWb%sQ#2 zmnES!wWvqz{s$ng`}lX#4P{Yc>#|E-=P7qa!J(Dx3HQOa4#5;bDrKUitw zzbiK~TOf)D#vc4Vtlc^LK@5M45!Ex6Q%Z4Ge3oo7ca84lT<~-l2W^w8(OhPDTd?8> zLnN?h=+wG-ui8?SD^C0_sctA$wJx-HTHSQwn#jKkgSaXRYcpz^W}LDtI^c8#AF&rb zW88tXsy$%+>P$G1VFfNf%J+W-&w#ix+KUrf7|B9&Bj?*O^N);)FT_x*{R7P}4nN;n zXPV+&7a!zop0gYzyC>Hbf!j0oIfcPO4EyXuk&{SMM>hXfweEmTYD;HCjRxE%45S9u z6*_eUlBQ*Cka*U6`{914PS?eD*M5>Ne{p8r_!e#da6%d-BwfOtAmG;iC$|aEj`TRc zlZQ$;Z=|>3=FPyT!Q!ciQ01h>lGtLnY{269Hl%V)RFLS=A<1_tx!=ks@>^kCUs+i_9 zF##cMPC|OyBWN~NR!h3~L(M!y<~3ZFPDhJY5=g+-ZHwC`d-9EVe%j9C9cI&Ol6M|Y z-uYOv$QloM{myfsaVrxjvw-Um)dHOD?!sSM*y$bY)wuCr>V#ae=J~2?f_qekjjBjd zaDQZfd%W!I_R*KJ9R)Yy@~N*jy1|(<&GxQ=!Iv*W+#J==i|4-YCnFb0Qoh9W_QmwX z{vT`s(qZlvWd`96H6If9HDBE`mK>u^huy%8w~ffnGgjc#uvfC3?8V5${=FsLS6vl~ z!85(5kh%^R%X@MZenPGPj+_}naU!toMnlpWJxhcNQ=#?uj*x*i@EFmlihr6r{h_)=Sc~#A3wyiyH%a!36lhCW@XH&!kvG9Cj&=U=Ai?Y6mt5Q#k06( z#e&RjM{?4rG}7;Jy}97&fPIpAy^ZN3E=^L7v-9D~!U;ps;$Witf}EW;RkD)Lo#QRP zJhAQG(VLwi#tCnWmbIC$#O=+m!%9d?p6pcC%Y*poI-gx&YI8>k|F*QBBTvTjji!r& z7a}X&Ns35}4r$e~ey87G42CA<$^zA(zR10rbW?aZcE&rNPE*m8_tRT z9zM(B=WHc7%(X?v9ezP z-lRnMn?}SYwQIuh!r!jkQ!x0KyaV;0t~2B3#8M_lmw(JY)AX;t^c#h)oNK!*ox09> zsgGWsQm^yc)D|^UTh+W|Xr>9OBh8&=`*Vj47BtdZx7<)n=HBAD_46NFHgEsOXZzaq zSw~NWhE6T8+k_q5Y#3_DUp=r${eENo)WFAqqRpp53o~saoIYJBw$u!|cJI3s8O0be z?u5D_Ox(4!TMXrk9gHvEe)%-D%Pqr*h4)O|#RR9ST0Yj!ffIwVr}or*V@usRd*zH= zlx*CHwc%=u?z6hiP7nD|{)CS1Obm;i{_J&P7|y)jiYhB_7XNsY^rAKTNo#aOZj|Ff zp$BV^%~J_fTB%hzGtaul+Avy8WS4LIh~;f3DJiL>d8wZ!S_ec<4_S<5XlKkfj1SUI zc+B6e;%pVwl4n29nBBi@`$z#}kuPc=8&$WL{40f}sEp39-gJ=5UEc)#1i0tA$5-BH zBw%D1P@JR%7i1aJXvB1k!J|zPa9*6BpPxv#Fdge#fsZy}?(-Ol=n!wJ&~3a$lcqS* zYkGw9)TcXl_pK}Cq<`#Hdv*ndm!Pkx8@+F&IrC+4b6aB9e;-bw>rb9CuAkvW&VpPN zmXk-aE5|5IN@9i+Qv9ZA;-ZSOdE*E5aTChP_ zD)9b|v2;C7nb-2Pk=usHPU*+$+)R0P^AFkbT?vmG{hw5=-+nx3m)hGcCf19cAgRl{4?+@0qvewSC2nGFE(0Q&uxTd;#=-ZZ;@a1iWkmXPwnQz9zcU>as#zwbA z7WM6NYJTRAUiqxyO4W-Qc0V35+IrQ~DWFO8OFy5e4a;Br-e1ccSDu!Rai?%ajNC+l zxn>Z3xrqZ)&&(mtUyO9RLGypquIO_!XU9G$cTeITd7%>Mvaj&7zmX=_i4*ET^0EzT zYHPJe&4HZN<)!Nut0B`Y1Iaf5wee!~%O8s_s%mO^KR*8Z`oT6T4|RVzU!TB#jI?+Y zimHxP3#zEzf1E;ZakU}EN60SQUYc5(q-2j3JB*pV9K1g8KB6u! zG%=Yd!a8O=J-0Z+{&5~&c-u7XuBxcS-#Y#Avn`0dHRpY$CUE8oWs_KgE-#+DD^2T5 z5z|PSZ(y5~ThE>4m#w%~!$GH)g}{H$hVzvVTT*xnjwYUzjfl(?*3(JwEV19w<>l!a z5FXxTvHbb-=bK%;CDTeq#>V+npX1tjCbQ$+WWS~^yn^2DxYd5Kdj+5seHgwyxX5y1 zlJ@o>-u6|-;LCqyoX<*Y5bhTV;^CgIdszIKX@Z5{h?z0erba+Vr)4Xh2~%y>k7sS` zqnd9;KV`o>40E>ZuC9FRc2WhJqf5JQIJ2PO!_x9uI14=*fMJM`=aV%#q52^KPjSVwc}ev40ohB>fKJ>++Y`%jJh9<*l}_>c>QQGP0W7^)HV7@@^pI^!R>-rq)6P?ie5$;slB;K{;;onBYx=JQHOv0|T6y8#Y9>v$ zk4Zh$v^Fbt=GTAIxs_IC{gajUVp!72V4tUtxO05Q57R!gb5IdABFM%_95_JfEVNCs zDxJGoe4G**8VcQPQoyrk_ukr`Lx-XWnOLs zBpA8&y{Bg=3DIZfFVpPq7k7QO^T8kIj=0HZr$zsvR`SQEPRWUn71ZH@95imjTUi@Z zrl;)|VXT_#B^7n|>z;(9q{|5t#CIrGV;cI4K1U%pNwc3zgBG6_*ejw}Ht7K>JvZY` zZlw7}%AUV`yqy-r$_LYs{G5#EbmH9300EAC@Y1oKQQ>nwPH-A)-niej@`*ih|S~ zmd`bqN3L3!i)A$WFJ~Y78r5uLYfA$)?GlE-KlDP{Yvmrl)>2bf=94gH$Fc7s*Pswm^ zSy@(AmK`dYLFBe=pj>b3(6h42=y6?k-nDC&X@%!5u4Bg<(D=x)9gq(U4(4X2uNk3n zojId<=FFKeKK(x+Qnn+5F1z`(=t_2BTx#QW%}m+RJS(F87&8K=G z2SS2jWUyoIjj{Q*g*qYw&umBYj5Ox~CeTpAn|9?{Mhq{Y0xKr?yo}#QM%L%JuXM{j zwk2u&vh66z=EB@z5R<(AQ@XQJwppmyoZj*co)&Ubjvq)1c$w!kV8R*GMH$DQ*z2TxT#rIT2|oYw8f6F)tvmGv_% z*p;{TCF5nkKKmK}gO}6F>2CbPM<0O=CIY$Cs3oAFcEE?tL#5_sW_IZFei%cKa2PqA z>ZMDUk`{jTWgGI}NZlSpRuR`@tfdtOo_cK0FTY|sI?+(m(}DkvW_tczso`;mB7JKn zSpT#};1&s;L%y32Xw5L`A2~grdPL(tUrbmIpPsK3IL5eP<}CWkB$5^{BG(#5k*MnI z?3^^On|<>!0SP+C?yuWq1`#F;=;ulEt9(AXhJ^~L+a>}nEiESK??~g|y10;4zt=x7 zP$lxqI)jd`C{X`&z`vvWNBAsu?xPzVK~R6fqR#no`AoX;PLHtOG}caIUhXxWWJXpC zV6N-=j$@q(xFw&zcySdSw@Ccl<`@?lY3XSY6hC6w&?+Ay&9LMof;Z42

A2j-nR|l5gs^xnZQn}_$c214}@k=T~7m7nL zgJ+(Yq28B>E_ZHgZyzBhac}Zq9()NT?>xdym9O}f10PtzPBRg>Q*vz)%>6dJ;2e|2 zLzW*46oZV0K)~ck-eMQ?`SXpju6zagBxQWzT0{B~tj|1o=N$Q>mf&mVOQLJ|k?Kpo zp1zaS{qo`~4|B(E^{kyfnyf=TjW*0|d)#+qGt(Vt!~XbCFTl#rkklk}OaR7Ya|5{mhbc9zSsv!u3{MPE3k_gJP5V`oShd?qSFx zv+|2|cm)LnOzI;=Hyt;lQA=!2vqrE9uRednE`%+4cEzEdmt3hQyeagik>&kD%6)vt zMCcmw5iS}6%D)|JJ*DF`yp;qL4)=F|#u+lphhB;*j9>p~GeQ(-0#d*Ly*N0x|29Zc zJ&feAwlA6>Jn4CK(@gWBwwWMlasS5dUs3Et^HJkTx#kv_Ag6-%p`i4o#+^g;e#Q>^ zjd4v1$Vt4qYHMp%b#y+)h#jw0a&|5$#o&dT8xlZ`j@Y=3EiEjJr7rYTlH{Ur;In6} zir1+icn)Izs93#c!TAVl3X1Ypzbu zzJ{7_vde?*?T}ri>awb;>W;A(NsmrnVT%vZ;F;wWE0pz~z!zl)g_W|SA_f(ogNmQj zo;`bZY)B-^73BV@mR)&6FhTJ>CVQNRM+Iens&iqt)!lP!#mx5q{(T=xasNYV*82oZ zo-9tNsHkw1^NkKLlwZAj_pVoJeo?p87VXuS)A_e4b!XmMmS=WZLaL=P<2>h>`_3xL zpSrIqxh8r8K!SpI7Z0E27dW8*%%MGfG;d0TK6B%oPjK^emf5j}b*EVwow1Kw$w8+) z7XF);lT%S&KZXrfsPZ)3t5>6mJlJ6YQq^X3%cWla^6%SjJU?o&1p3Hm6>9yH-s=au zKPTMD&Rvp>xf(ok!oH3fDPhY(Z?>_*#k1RgyJE(3Pq#43nKC4CAX?(Ub*4I(MOynj zTwT~_Nb}#WRaKqh2lXUxYfForfE`7NN-ZkkN@*Ty`TPDld+%aWKm2qb^z%Evdq^j-6|senUYYll;=2D-rivA)t1{?0E}rRc zc+%J0C-8ee-z!}gF}g8hW_M(!@#i0G9D+r30LQM~yF2eryuUM++dhqk7U$Ws+QyN0 z6A{$~ZoK*JFvkl|zF#NXpZ4f;1C+M$atph5?##K{8w4cr`Kwntdxb0-AU7Kw)48|S zE`s3SMNpLNq~ACuj#@%dRWVKqUn2kdhQt=k$r+fPk_}!jE?Sx zH!2>D+aXqk0neZHDd*;z{gtxU!LFl_WvgV;+ku*(F4@l|n;x-a`Lzme#dKfGF;PrX z+trQID-uM%n_ZWE+0xO{kV6HwZ~VV`+cYiYbA#kzCZ1X)Z1*i&x5nAXlS}s63CEK4 zMKy1qvWHuq1;7tq8mRjP0gnld?Pc`nN8zz6L4=hNRm;VlA&ji2uPS{p^Cy&Y_Q9g`?Qxp$ZXC*>H_)i;?UMIz+EX>;0<`EZ@Og{9WVVH#D? ztmPQP9)6A+58Du|=kAGZSi1%ViPSl4Ze70OvXx9kLeG#zzL1py#PguXCpAi)ji%@L zGrxn%ntxS2FLO-ONmO8L-q*UcCOVbib22E`gW?ZXE9b<0H3(d2cYJ;3KY{)NnqumC zfRX4Qgx_ELeH14qM}1cyi?z*9uyBf++Yh8^=gVWjYGC*KnwlnPFu3u#;*}SF-Ohv0 z*c4fd^qMakUyiiih6uQX?aP$wvSwN*(Uj}p!SkCpZ?+Vx9HE(^eP5TS=Q7u?=w~Cc=aj%IX#)CjmU+&V z(70mVXfUH_fr%PBg$jq8mWGDwOi>%xt|nB+p!<5^1D6SM^xqI zeK?X+QfkJ_ii)(b=6C_fN8(@XB0;9Xl(GD9`O`GJ36nV;&CY9!Pt6`AC=PXv3}(Kj z9IYK0JQ`en)Z>%L^m)35aZM zT8%z|Z|9&TURf&0(>+$105XYSENKB6u~sOstn;14qM)ab`}pyr{Oqq3HhPSza@)lG zA;W@yl&w({qH zF2%McK1|<)lFX(sATpBe?4O}RX6BebX(in^6u#1 zhQODqYU}w0S48haod|V#dV0$XWy?-)*LG?E;}C%=0i!|^i;6^0dUsW%FX-vH>mNgm zM=X(+lT($J-tsVg<<&n3aaXoFrJ4At8VWQm2)IG7dP6JGq18FTW|{gh-KSeZ;Pn}a(9ORG^s#Ux zg&y$uq+iN5$c}P)r3x!CZ|7Y^j@|&dBfz9IL{!G^X9XmfHxA)7rC}IDN%P57c#h6Q z*O!ZNEaU9BkenIEkt1dhWGP}P8lLGh+Z1=>OIlujyy5%t!`_5{W%B1y$~5326$^3;oC|=Z;y=7L z^d9B7j-IysSU4#vDtd#~mbbf(O}6=S-5uSMT;W{}4z0|w5P+SBm$+i~7%+OT^8))$ z>Ho9-@qY1Z3o~Q>reP`R>3FT4NGf@n1&qQHH_|glqr&9EhWk9Q@>C@cfl%%d3w76C zEHMZi-;+MhN3P z)i%sl46@Ijr~MhH!uH|$UXCh_ z?M5wc-==9(TzG5)T?2noGbg^H>f1kwxzq3Y&r79b4l;h!QcP^;7OAu=p&=p6iUA=J z5$O>26m{HQ@VcnYWhVN*@u*C|eeTBuAPV>P_Ap9F&M$&-r6XC1z;sJ3z{^$YrL z73|FWL;9pUXSLuJzr(K2mF#2qH(UtZqW!q{Aw&5=-_S#u*=6T^RQB+fateKWE1>go z-*0qpII$>N0AZe?(NG?TE*bsA4(VC%poAyG4;ECIxRfQIU($aFbgBGS^_Rh<^~SD+ z?=Di@SrfGx%Ow?$x$VXi0=FB2vB49L*4EY`3C^rgeF` zxK|=TWh2~;8-g0OS%1Wj7dD)ia{4_!v2!J#rP!Q?L@LW zEC1t4ar~41)TJHkB-#)7ZjL?*e2uaExW`98)o2{><|~MqNAoNWY=skftaVjxr)Gy&1a2Iq(kH9d#C6G(ngxJ!1XEsCRpCDs6F3Ei>Nf zSlblJ7UBy}Vn8PG*7Qz5Zr}W*~Y#Kj0 z8yccvqtjJ|Yx(feqa(Nsf;mW=s58sROAO`vE+(d`I|0s@U!}2nez;wzgIrYw@D>RU zU{09>@ZYk2EedHwsNwd^D5MFO_07+IU)c6&-!)ffpz7&82>irgPb=RlLRwl{1$+8B z-}5(bF2ZKUwD0Xx5jZW{!>`Tc?UREMBE5*B>7*g=;Pl~zJ{6w6XU}>URF15CKRPW* zw%@-;XAYnP%M|svkqygr;?=i5;F9Zlv1EeSv` zek$24p-{f?p9WFt#oL2_Y}l&h{633^fY|)>k}wQufjqn=#ui1{;lqbH5qZIdII_dt zVPu4&>T+`3%kq(J-4$6pt?lipD3t*aqq0^sHBAO2u7bOfVXx5hw6u3Uy+d}F(gH*b z-)>*{(DTRVtJ51mCRD`x!Y&iU?;^>%cr8<%H&_O{3`sg-_RvhP=+7mB-0SrUSofS0 z1>d~0*wOlp13F{z@$omi01x1hJ%^T3xw^V~pU40arri`4Lym1M0Pi41TKnE#*uPub zld6m7fW-rit}r&Rb2rkY!o`yY%t5MT0v2*31RAIx@S^q(&QZ&qez$!gG10WQCzx(p z?3M7ELDM^WYl|-X9%B>@z=7D~9s^R88F0OqjGLTQ^b}Giz4nlGmFQ||Z#VBPbxz61 zP%|=0m>KIzLjB!Yk#1yU)Crd2`32@vH@!VQJx7HqVkAa$`f12;aghp|G#p4sOe7ct z3%}u`k}T;97ydv)ydd>-gZ#avQ77W7b{PPYN%k1%KH;Uyixag&c4i2%Md)Ycx-8B= za)yyZI>A)1natYHl>PX;6`hjQju{L+8rtUiLvmqbgV%rG2jIfLBL8Q@?63qYvT-D{ zj2G|rk3ZI0_LhorEdoGOgA1<*foz-hQ}gb)THj#YJ?Q*-(Rs7P#2csI-6Gv*4@Wzl zHrPjE*LDW1;JQtW-eytVv z2|xNT`(yh))2M;|{&9eixda7Ou?NVB0&t19&_|>8I}~$DZ)!+DT|`5}K(H$^Dk>AQ z?k1~kdxc~Peu-{J(WAw-bnPT!4vpn3?zHcLw@q%rBBq-t-MxE;iMD$GP5;NB%BK}o z>)1@5W!E(N3_}mLi4Cfhk(WT)_xXgTU=E809Nvgwrbj|wudfz|>YiSo8&GLe{_{KW zm>mp!CZB{3Rlw7yJ4UW{*5MqqwV9!dLXvo#fSrsUs(5NNm{F9QwP)z2q^6oHyZ-gp zUpqDwN?5~MB@=KYi6kNcjNk8^6M>nr067gT0#{5;O>qbm@d9u}I4w4OeuJkyRGSrM ziRsEZ*_I%$TEFxKaZeMrF2(2A6vE$Y>guKwB1Odj%JXY3b4CP3wHyXIx`I>E(d8&0 zTPIyrqP6g2@j6HBf#hcw@Ir^+PTm0eW(H~a;>qXHvrB*X?j2HSyN$Ll$okSna}yvK ziYbGAB<8#AW_CXqReux+-?L}WG(4Jl7q)7n6`+ET5pmYhY@{uHa$yl)DbJrb5HJ>- zRKsJ-^c5t_m^bzij-p-~9!iAZ)Ury^I}4vRu^dxXZ47@MJq~@o-n( zA6vA~YpPudn$goRe9K24>Yn}Gm(}BE8OU>AcjqT1CJJ@6X}y-jcFVKsPJsnZF6Yw2 ztxRmwH@71gu4Ov1hR&_vJR96Rqisn6fj{dUBd8Zz2(37(UES)(b}A_c0iR*X#z{+ z2CH1xopWq+i!O*Y-rn@IiC#VAIHIDukYg#~{zZ>2AS^5eQ2Ch%+1cq!Afxb&sz2xt zrqw;92r?L@FpR76u|6@Z=3|{aeNWnk;Yrm~9tyI|LfK5##8lk@^PxWyAL#aBvAVyO z>fQOYfzpKK@A)jp?CIr|d!u5@K9n++mM?&e>fqdzoC3h>w3pjUX6^a1?T-xz2A*UCLg7CTAII4 zK-4CbE%egD*RP=uR(mv!4aS+fU*X`AaF6GPey>qVwarba?auUccJw4Y;8$|Vf0P(*LB$6poU|qPdHZ_m! zfW^bs?HPE(R}ZM9N93les+CdfUpvdG(ObSZy%YHPy^`8z4cZiAAp2-Qt?ZNL>5HSm z2w0$~<#&9?Em@a`@-3fQidd3sdhX%Fhg>{70s72TBp5WPr!p&SOJI7Y69WFmJP#65 zI=bbPfY`c$wm3K;zz{Th8h(%tMH9sDCUy}jQdvwP)%VHr-K>3n!-d=}nhazQP~>82 z7Q0mTr<@zS&34&Osrx}-;Au6^af?uQ8LLw<9gpXc43Jk$r<+{A+e7*>(~5$TuBd(K z#>;?!ow&f?^@!b_G+elivw^IoTV7T+p#0O|dp>Q1H=s|dnwq%nr2_*3o+ATb^Sz8s zu2t-i-j=DCiEltk+Y#RP64qF(T*rxPD^wtS*Ef8zJ``zbKMwFr%zu3QL>kWs_2k$C z0x~i*Jcf4iFw^4=#Ww5i{Z$+^DW8bM=CZ(tD8k6E4VJX(LKcy|B=+IaSw_kl| z%z4U}z)(Id`C9J$P?O~{{a>-fEz4{KES zTezst=GizslO7L#-q*4o?Kq!Waa9i^->Ww(B@R>l1@3&iE{gQC2$X8}EqB4ehNmG_ z8n;a$E29UAuH=rbn^%yPZGmY=YoZ1|5Dh@Fwx1p5SX}Xgt#RY$hZG?3ByXRFz%Gfs ze6{^4vHz1pv7pP(JEXz}2MjT{35qcq+@9r7aF5(7!^P1It=(PbQ;_T zoc|j;6=#Q&xrNs_ugMfY;BaSg4M}=8rDSHR<2wKaH_{A3LzP_gw2}5ybW?GgA=mT8 z{kC<(+WQ(Fd)tBO9^0AK#pv=#FFnS(ptq zJqIs~lgr_rSQLHvliGfz)dYL2RlD2D@$%9hDu>JR%BPped0KVRarnRK@U#%_rfs6r zPry5gV4OX9@5e3DB=d_$c0$g|+}s>N&Cx>m5z)mEJJ*A{I%S)hS*B|$-hS#q9j0`? z!xYBygdNHL0ik@Cu3h$Hz4_&*5#A2~?>!|L4cA$4JFcXxYfR0?CJUKS7)=lqOhVbk zt21vd9H@`VBzZj2J}QRD^?$#^L4kqNORP)ynCqJzmrXD}eczrV4FlgpQLy~4-1AL$ zO$>I~eA)7XI0=~~-lvtX=6T(JkI?co+{mO$dsn`(oxiPDL99YHhL=n6L+Vz#G;fZ$ zWdfE7ZVp6uzz)s{_;}2hy+4@p&@;F%rfdnQQR>?ITW6e@M$C&{0rjB=tDl$(>^2=g zvHtO+LAM+F6x)G)0P0>*c1B{BL!WZ@j{&cnCzNz;N0hzjKCF+a%nue{^urLWQ_G1|+hDAs`j5-SH zocjg_x_ry#hq;gImFj?tVV&0td#-qxk12{pWj5cAGao%c->sT2U(xSn#5q4=_Gg-V zWo+}+^o>XN9nSRY=hH<$g`l-EGXBj&DtycVEg_PpU+~#sxFR|C_K=C@n={U=;2644 z0`IW(CH1E^52G$PNicRzzyxl4!{;lvNA#vgsh=CLUvwc~!6F2U6#vV~{mZU!{9lLS zz^6}<$ap^*lRI&2m=e~Ln~8G!zJvsgMkD(jTjh_}(XXy$B1O&xpom#{TDZOe2CC`m zMj%b!{bk3S%on?qjUNmz#TVI?kLuJVtGUzuPi@mmRdq6d-f=I zYX+v05!T=o5=Fp?bd**2maxO49oL0@BDSjj4-u>2F)C1bl+iyP;K86bYrB-MIp z;DM$d_ey!rK^BluJ4>AxZtTlg;U>qYxa;a(V=jskS4nplkugeWt&6M$j|x$$dxl!V zf;JTF>%hKEyLH+k)BP-JVnBs#(>eOL@Sj6se~}J3@L*$@*V^~4#D?lGGHEBx0s=oE3K|``(Zbmh~0BQ@{Fk1 zj!|F7)vf)c)w*%kth>~)rS)OVf@R9jtEoZOjjr9(#gD@Op)^4p7Y_+h%gUk?9v6fR zu_-BBXfm6?ABKt^G!2XhO&~Al1`DwU#FJ*OHlUq-%fE*~7RTa8(#UvIH|en;V~xq2-s z)z)1bd5$Ipd#+lX$h#^aR zSA9Qh)(>+pvHAm32r1i_(!k!D9@fs?^NT_`ATCsH zjtlXLG=TFE5kRDnsMyq}YR#ni<(Jj5t;R zWJgT!Bba6#D9wE8vE!)O-36qB#Q73fR?TLXMs_FQ-0J`k~-56UY+l-|4)&eNT21Yq& z1zEu=aq%qg5!`zQj(K#jR{{kCrnh_7t`zO0U!Be=&Xb&SZVMIu$l_QTw#|8p!<}|A znFsFuoIj3^O$WYC)}K2^dYkI{q<5lu0~F&4UdZdC9e}AawV&7L*~e32nLh!RgH6lb z2Jr)ODOc<#z)o6Ong*cE4}C|ZieT>K3u#$Tso-Ph5iTmg6Qfi@k~*uS_poq(gD_Rv zGG)q?-oDbb1h3jf{Y4Y5fI^hOSparPyb3AU^2R)DS#UnV{j9%HQ=EMN@CnCS{+=_4 zO3Rt3kZUDM_^Y3q1 zK`ehPse~62n~<;<`QoqylgW%rvMoIR(@dqQNe*h-+6x9IQkDYy0H@44+gG+vdTeWB zc=!@B>9cVR@i$vJcUpsuXR-17rSu#T4%EQ2cm(6eL<32^efuig;z~T%+B8anbhj}` z8By1YX#FRIPXBubIZf}0g#Ec{VX85;ZCbu1b5ff@nHCdMLc^TLbE%0aSGX@_3|z=j zl93~n+fuN^v@0}+Txz1)KhojjYPdhs(hmn;hE3CY=H=E|w)tqI(CzwI!-;wCHO9_78#;i6Vmt`Zh0iBGIB0 z0R|cdfGBX%*J2t28WIos1Sj9!qS#}>BD}-#kx}C!)3%)}t^QE+>fXiMKc7*Er-t64 zCPOxm1h=RqTD6vCDy|>qi0NfOV}k>gh{1>=atDQ6{ET@C+r*g$(7&+55?yiG&hE1B~izidvJ2 zY(pKHTfMjZ{O$m*V^$$!)zH+muPDR_@5NM1IgAnW7Gso53S~2w9Gn6;vr$){_u9vG zliMaw&v!MC*s4_*%>j-+48)YA2LdS|HSTdiAZ79OziJMIa*{~^FC1o)=!t>}cMFMU z-m;^dt@UaVWKW{!HMEK(mc1|Mz45Yb80i^>v~pC8Qu*d9K){6Zh{Y(oy)j)$0&0SY zhkstfc{lXvuOnc+iCe16d#&~`uAgE&w%X%mKS;v9`!sA8pmM@%!HLn^kiHX5w^Z$; z7u>p|T?7(%llYSD693M+&8;ot(wIWztVsQ&5DTrJ)^U}7)+0@6U%o|@4n#Jp; z=3{tzaE`9ln_&QCcy!Uw7Bg&K)*oNo4LqgMx(Ow31kg(&4w3TqmI+4ju&~I{No?%N z%{}q{xbbW!1`=whXb@4F;R`Vu+29wviDCpvlOs!7SBBZ}{GUHQ@@oS+J-J1xh#enUc}!c5|nDR@%vX z8*3jX8rbs$l$y!=WdGcVR(rR*k(R7Ok95cGRW_+^X7gePTB9wFb--3?2m>K9Vnc9; zvq|BGuq~$)5gVR_jmE@3W(gRtjCZ!*rO09zGPeaA?LdQ|D7-$T9`>YklI;R z54>i%Wa&s+Ptj~n5jph!@c+ICJ#}?c`Cu^57te|FH{1SlU#|jkguu0C7w3!Wgu+pZ zh$b~q7Yk&boXoVc%`kDpR#=O+Tq@<*WTNi>u z01k9s!|?canxd7&x~5;K`1K56E(-G{LT+~(PKanFRCrAPtH-aC%PzRO(|UN6fAi^+ z0l^>PwdJ5OnkpLq@aGzmfxvnA_V`OUvu0wzN&d;q&Q>LFy>1XCv3rmz>GkOXA~kfUb279AV=aJqSpjbZx^kwk)eJ4j>!R8f4VoN7$4 zMqldry*{6Y&)?8@oE7CCB$eWPgyah!x&$uMBVhwJ@3~ z*m<7Gva6ITF(2{SojAVf!;*B+G!+2a%AhmP#@?w1hpwMsY6w`fXcWYwF}~BfHOuA} z#?dWhRv{8KCfxv37)DkRHd(uGGcXQIgcXY{_zRY%?J51dPy zUkkt{)f6=L`Qbd#_8_zKW-Ixt{~BaIyT^Wx?n$-n-io)D?bW~aH(PgbXHP&E{GK+N zhXAs;UUTxNdq7-c(F{{uW-_^|W76(V^XN9^JKNM<{MUwdk`&5m{mY}XS^HadLRrz@KI^{9XJ^%Cx7zaS6;*Dp+-1;#qi3V zsJN6pk$RPi>)GhNYRp))VfAX28JqrfFK7k6l8LB&NF-BGrz3CTNbEq{TUcQ?^vQ47 zP}e8V&(n>PC3P$rX(v)k*`Xs<4Svjs%q8M&GdMvcfO33H*vx1`!is~@XIw5$3W@4) z*AV^zD-!f=`69bcH8F#{8r%18-gLlT*?PEHQc&0Oc8K-wbq^(ZzBh2Y1|Eh1pM_w2 zj^@-xw@Qe|sh47h&}4yJU#u@5$rZYy`k+7fn>|Sr#fne^fBMkixk;I&o-uQpNS;WD z{omDZD;ioCMys!Ws=0kj*0jw{F>UtUn$U>hpw!gX7C}@ezA^fl!2Rr@%ge}-(L$&O ze$JO_<2I%mp90>*2H9bSGYbE-M8k$$uJ~83ZD+uQ%iWLP#J={ApCv78V!UiXx^k*9 zIV#Ohd4Z=7m~X=uVg)jk@KsvXbVU7S(JM;@@VRHqpVcc9hBiG1rU|F8s{Zs0aR-^NdZ=5Yt zQv&0H1o$`RD3+iBIe>(|t0EP>?DHUjS9f00>C6yD(1FN4wTaxRwLLwg*##+=+(Wcr z2SMCv>cxSHCu9L4&2U!MTu(u&zKAo>EGT_6l#So~wLU?8z+riD$Ho&We_XH)8c0mH zA2=WI*f`6z{m!{>of%2muv2FUTFZY$Yz01%b7+hoL#<}@$~g~rU^U&H$_djS8tWv>La6gIV7?9Q23XyINqJr<`XC|LdG~xQ6{d$sIzvk z&kWxC3CPORi4!7LZOwlXf?ZnBnV+KXT=0u>9OCr+xbn^^@_dGbmZ$jL+Bt6=KD}AP zpGqj;B5|;BiH-&Y6DJlZ`TnqgPKHm_7NkDC8X+Y91F5ZN7#VsdR91k|AEzZe_jqaH zVf=K>GeN1T-6y82nl`MO9Bc+)v``_VXF)R^kt@%f6Dd)z54=@n zr+hP|_5i! z>Mx$+*lpVKH$B$92;0SC?Z3Hp`f{QhaAk0g=)vDm=uyk8*Q64vEhZewH@!JVA*>xI zc4kgF1zCfWug^QAcEAi$8`<49vEg$P)1z6IZfJp6q_|p^F|pano@6F5bj4KOvEWQ)FtjOBOy= z<`;>6Z8I>&`CNsa02pH!xy^m+dp9`r=)j3;q6+(wj%09Q2x|x^m+dD@sDHNP3T(cUGu86SY9m=oE&J|VrQ+D) zYUOLygO82V)d3fOt>>z|-j~HJ71gs#m29i)Y~CYY(oe+* zHyCL&V6xW%L`c$P#^&VYJRApFE)tzS@Zph^)r$`I>ZOgC*C2-Cze?R}3vYg6qA zUha}cQfe+!lG*-8lIzo6NB_*i_I+C%^!a?_8KuVjPvq(Hcj3}#e689Nw*4zk*fJ2;16g(%jr2< z2p+6G8o#BiJH3&($?-q zT3dxiaTtM+(618JjGA8XZ5t0LXoVUn8BIE3(@I?8l=WQZL6G~(u!NcI>!RY#TbF2V z=<(^>cyQJ7&pK}z0%F<4%L6(uSWe)E&7oDs|9AzH-4{Xs8vFMrQ1uT4q*GPigFDeE zo9UJluVeAcZ2O@j%T~tdq4r+rvAB4Rj6i(#zvc%UW1s%(bG`1WsOQ4E0CV=%br%#D zKP-r*>(HBPDrCx}f%j3+oFFKK(dAqF z$j8?)YB0*mc_^S!Z|J6|{a2ph&DtJ2qLr?e!>eH@>vojvv{|6Agc6q>Tc&P}k~&&` zl>odFz&pbyfic&KYS~5`-{NMJ`nk29UG+V>jT!@2H-9>qYWSseDxZvux~=b?;^XOd zZ!Q$SJ(_joVYyxTn#+4{i6$@A#oF4tRD5+gXU!jD^c@ny!7D(#;kbl^>t?z(n?WO{ zHYA}s@Pdv5s;Ppi)PQ%Au3h2kVf?s#FKcO!mU30_T1mFC}cr)B0B{kk*m zx6wx{n;icJ4PVex#lzTP3&m5Hzc^uO47?cFbX$P2!QzwGE`oKZ)0csg8K)8!E4F|1 zRl}b2j1Ec~aY)cqOpKKh$zrk!^|JP@p0K%DFp+biShDwG`&%XR&mPWClB}mI^Cf?6 znRWc;ANbv;d!Do5U#YgmNe|*7)xzY!bh-MB+X@zYShZ=Q1wgBS-PWSuU+P;@Eu6{; z$y`CNC7X})DsSAIy(9XL)wZY1Rfz`iX`@PUfB1vHsW0~eRHZZ#eccPWyFc?rU|VW}i(%Rups)3XKhZBiqJ*K(_S7|2ZnuKVicmBbNFOg*KvU;Gygs{b28I4KA(7H zQe{b>Uvdf~wAz3i4 z1s<>KZ`Nv`-5HWK`rNTFf+Uq{rY6I%GU<;?;lw}pn04IhJk`Qj7ubq-pC3FG^yGsy zT!}t`SBd2Fd>HPt*`3`}nAwVsxBXD9b~li^8c;YkAQA3GQyE!m$%YN>R-+}AIeIaA z6A2S&e|94Ms{tK)1+x*#Pe8-@R=eo=a)r*gfV2~_w$k;bdwCNo`aziPAp=n= zprsZ}M~Nxe z9aysh3rE+V{Lc(dlu5~ekcQqVQb`rH%uay&|joK4ME3Y5eF#SVVcrcGP2xj(xW z9$jS604l0367#?Dx6v5dLCn;^*X#?rOCNkB`aaUlo!|gc9GhJt~ zDLF4s_%;_}eCm?fabv`(hDE5WS$022BY0_v`X-ZUBW|qGd()xQob0ym3K~g-t5G%< z?9{aq6PexATwPr~M~V6J;ce*1(awv4+p0<;VTC9%@G>|EEx)5pDEK76S1W4qC+SW4^>KA(Ev>Ve~N}c zll4lyLD=L0hg-DSA-JxP^XwegaVsv}=~IKT7>AGOgYXOI@_51>%EsTkd4v6`$pXx} z46{?*uAxw|MVODv%F-fD5&ronWzKe4UDvM7qqJvFx{y48ZBST)x{{H+qX$_4mV8Uw z#+h>DxCMe}?=YlDb`|5(iz_gj=0G;pYA&E@7|fgO=;QV(jNeGKFJPw#gL89pa|wa| z7IrXoD+Loi>?%w_<>@KG3LI|7*QCS|O(`cVDLbea$%~B`!NI3LFoBFEmp_r3JMED6B-xCUZ4^GbU?%97uviZHS1Ld^3X?L zbvZAR9+KRYQ_skV2!A;sx_I$DSC~G0#cV)g24h`JQmC7n-zOR5+d-erWT8(jX;H-> zr=ES@oMhZiVBL$KG9KdSYWB)9=AGrn*v?QDE4`SL-gjnf{ScOY{+~6?ip7~Cx^>iofpGAC+O&M6{Wis$cz8$dEO)D2F(^+UtH zN7W67nL299W>=I-L-H7f=JYwIESH^Beks#RG z8Ak8hK73Zit$~L3APcP$42oPovX}`Zy{v5*ot9sJP86Jj#taNWvd`TM1t~JHcY%tj zjOvk38wI>1GR1=**}dvsJ@faBPc3s)Of5wo)yO=mNi+8)Rz(S%Lr4zM={VskcWkfH zkbj7}`lZ-?M|i8x6itAq3-6l;n+MiM{W9bcQhkP3y|m3{ug&K#3y+vu{>i>jp1E&q z=lgQpl~&P2W@NNaY;4Pi6P6bxhJq!e#>eaQAJxn?wG=S5)Rio$sr-}GP76R$aNX>F z<3!WF_!a9GGqoITsFy4|l)7tRLsXPgX?g5Q&b3eerLO-ju^k^-St${OK9~$3oZgSF zs5f9`8w}8+$K|&2VeeKI1{In>%Agblhg7fdbS@P|s0k7Nz}FH0QcOm;h(CJ0%L7d< zBP18G^q($VK6Q`BHO4A<^bY;Atr(b!>i%x`*TD`M6gv2WP`I zhRGYk4Ky|9`Q6C7kMy!NYQqdL@vbS$#5N3@mHLwrOWFIsBuxIM=0R#AoE@-QtWF5A z?7%;R!gHlb6Hvhn@PD;@Q$tMXKQFYjmxfls3lkBTWL zlvLknyeN_BBatcEXo_A1`g1J4B1upk7WD469(o`lRjco+tRxY&qriCY)URJnz#jja`*|vqG zmpb}N0q2McxGd<A3tB^Rqw_! z$A8PLi;4=l=_5C+LrMS_alw-BxASQ7b{uo*sjP{5F_a-OZ1c{2srKQcKJ3Vuvej>#ETkFiN>QQfDZ0ruNXf0HTRqjY|k~*g*=+F*THi_UqWTK_N5QEF19WjW@s9S zYj4ZXl)e5x5&I`>ShjSJ%L}4&Qc1%OMcO34wO`xB#KhC~V%x}ACdiItYz(S4*)tZo zj-#(0F^>~yuN>47C@{0(ro#|{b0Pvn8b#z!Y9D{?a#iwlB*RN`zw<#pWP2ffwKMX# zAR_~A9c4NJ1NaV%iS%wRL~xb@rV>+9Z-{0-n#^VovW$y8p1eNF|3Rl( z65{wko8;Ko)QK6{=bL(M9d@>(h_k>oovQnG0^ zf@EmRzb9m7VjXO{-1$#k2pqTv3YG^ys?p*s&q_gc2uW7Y|MUFT;q6W~wR65N2fiT> zY__r0a&iR?4+H;y1xr1GkKMlZ#D3oz1&8-4xlJHi(Q*7>&i{MQ;0Ut*__Y9UBG`A+ zAx29vZ3z6nKy)2UPqhey3c@Yj6|wnS1gAZPJIQ*cUxFPQ9(~CIGCe&gl29iPp~cYD zG##yjhQc8xcX0(w^iL2Oh!qKv3R>~5%9mm_x;G zd63ig-(g`k`BA3>yt5@Tr_e_FOZ~_b(?@2d=BwaIS4JRPo;}{+HGqeJAzbDx@aGL2N1b|Ju%SZTkyP%^T%7KYz&Op9Z(x-7qAopKV)9-UO9i(mB*6k17^DhMZ%iF#S_8T3Wq680t%tys_ zk8CcS3P3DA>#-yaui#&_uUo4~ovfDqWtSQ<7!8yE68XGeCT|Ey^G4#&0*Qv5 zf;CeO`31H5uszzZlt9bUfx!~j7?d3nogY1*OweZzjRnIygfP+TK|un|oQ+!HJ@^Sj zGJ|BL+`fL%3V{IKn{@3=R`F|9h#MTwdz|Byu`}O>=0vm2W(Vk@XZ2#sY+uUw9)cEG zA{AQBK`rf2fOo>Hxen>lYGz~hkq|;iru;ddRKiGkH9;h1z`e6{4L4J5%a(gv#%Q)5 z$5S4Wxf|Mko8g=6;=sKy0qHx5%`vCWOK$PeP*4h5l;gtR^KLvIxwl$Fn%}?Xq+?2! zW!@3cNL`}We7~d6=taH@L)h2=mq9$Zym>$y>>+B~&HDL_JMn4Go;~Yz|M~s<_Z^1v zzp)U6pqrrNFI5-A8>D^?`<+u5Bt02xrQ zHjtHSR-o!PYiF2_okwxVPOrJ_$Qv9TPJH*B)9UZHCv8N&VqPGgE~0{e{8Fg6*3Y4J77{*s6aU^**7y zy~8L2fxsl36HLd09!mvYiunk6qMQGFTM<}4t2yhZ{Pj_3JLF;Ah%^BVWMO3ALH3H7 zD7+np?4c?HCzx3Ur@O&cFpxlag@`X0T#Z`r7zDsI!1=UKi1P|kD*)@jS!%p~4R;W1 zvl$xg+W;+s-eA3aw-MZd&n?pwE3HCNWRhJjKXIDOY77LIf4{K`!|#=8=zD1Qt}R!2 zfr?OA%2jXzn2IEZhTxS54A$e7jU7XKjX`W4xDs6jS1Q8GrMjw$*z-Z~wm|K+Pmt5U zZ)R7e0y2`}axlrR|Me`-O*!W*BFrMCc0p>TclTr}BqmjDg=7N?$Vr$oarCJPCWDgUfSQ%sX3-4fgQnq`L%>i`>4&DPfTfh(AH zDI-@S5plH<44e5xzTabo79Jo6{a@rtD}U};5R0@?uE7sAk}Sw}W9Wi~b_mj6vR4lV zecZW;^furc=?eNTmpQ{uOJI%!T3;DEeyuR z#^A6M59X#E#)VP%A*LGm^W7L_4LN5O&I)4Tu7dyd+vA-C*#VcB**%HS;>O*zP};k3 zknfhoXfNZXXEi|~O$;{;I9Za@2AZ{?W#qR)kFH7(1!k5W#n#Wtc!9EwRf`P{3=s_i=!W-7{aeFQx*BJOtasz}%kXOH`ta-!RLR#x^ie1ni*Hhn#}5^OF&mpvxnoKUfzI$2!2M z4#z=_$>cUUq#QPx= zUSc1U0E0}Gh<}S}GeL3NUvB|oEnmL;h4J^h;R@Ozm_EV=Hn|3;gW5$7#bkWF%W}Ui zSyt7e4oUX#@R;ulL!jX}5~h+`eEcO=SO&eSgsFS-tNpTo+MT{JbAN+T3PO|L% z%QbTX&xz(Y7$7LI7~uSZ`%m<1>=Zq)sPQ~(TfZDep$ItFvu6aiT!)oxn~h)}4_BKm zygD?Z4A$||wG~2UgS~;N6z0R*5&3leg_)M-=BhBVf~Z`````aQe{c(Xp#%1ZkqOy! z5l<345gp{%&|M6FWMe;$_*`P}HIw2#0rg`ivWp0glY7PDWAIjN{mMW-Uc{~MU)%Hf z+-q;i?;qiA&Uy4)1pyX;D6P_)Faos-?BZ~6XkwAq?FkU?RV~7}xf_g?*x)$zC^I5+ zmdjVz3&3UD{&}8R{@bLu&oL(3kWiWp;(V|IV+5=W=!0*9D+-^#BuL1EPPSd314a@& zFP@MHh)L<%gbWftXA{KBBb{Ku_w`V=F!L}wM;H`)ruehVEn{8~()PhK1MHklWx;a# zL?H(6h;k=~5p8T=yxMNKxB1t668wnd(va|F%J)Fx*%)-pK9_Bg9(wCRZT7<#eKAeJ zvb}vfXLDNEp^m2@^t6YmBYCV0XF+s|+(Ct`c96VLvjZD=D^AcXTH-Xvqnn>-&ln|b z*jt~vw2`puMLz74yF>Pd=dVW>6|b>-CQ;HsM9gebZk~AmycXwTV@(N^XX43qB24h^ z2zG|;!Erdocf)eM0C*6=?N~uZkOYo$n0ya{a#nG4q-X?uz|qzPT*4>k(^qWyT#Q2& zdfe+kR6wHNxy*V*dM-e)UC>;-s0dS>(J%q!1d;7KAP(=1TRi#pJ6_{9E41h#xdNhY z>P1>(iReZ-hW<-=dHGh?{W?0l1N(7Xk&ouFXH5{Vb@;@(L(eJz97mGzy9awFh}WEB zn*2`9piM@3!&4m=*@1m&5nn52cs4e zp&o_h2W9x<`?qY_a%(IIv!0N2A7Q5sE@MrtFTmUhpTq9FhVEQvDxl!3<3F!<(!$^C za>_g{Md{Gd13_J9GvoezftfTSZZU>oM^nm9Y=8=2mSamxKlEZYSz>U8q|w$7vd~%V z!1>U=rF;Owxzhd-D@@Ix!vT%nzH^2TkWjc6dQ!tsq1;s0|Lp6~FJVr#ZtJEtNYBuX z_Bg8GKhO4r5f-;s7P@Yf9p!;*I)p9^4RG+Q`P2ddHY}DsM-iX3DM2QXd=$K-I7ka9 z1XOafyIB?I9@IV!L&v3(rentpvW*{LD|}(M5$9z~XB^1c@>R8f@BVc7Rp@Hz_?-I&T=n2euF1h$#)ZvFL%US=iBJepMb54V|p)KIBbtifN0*0WB)?^ zdgN8aXeDOSzzsBm*kcVf!Vi;I3Xc57GaVAM{v(wvi^wSu2z zapSn`%iRvqQqQ;WMct0l7xdItP9NISNpIBvB`cPw7y>NL#W=iuO8Y!Voe-Jn$lSvs zdYbdws_`i?^u63aEzo`#1}Z%cViS!~Cp@HxD)xHBJ~^!G|Fdz?mwQfA5d2ZU-GM|B5mf!0#4Xw6Cm-m)wo~>S_I6O=CUjNX|U@R^bjt*RW5B)Y_b_=0W(nmgl7Oj0$(;ckRxI zwhgd6S6eph$ftbAdFqO#i}xt1^JqJ?9oVQ8y~*d-TqVc!@Rjct|75qpwZ;Vb3 zEpLV01Qe!SQIoCMnMz?tRi*A()=sg?v^5HQ(Wf8#RN?bt-*}4{H6=yAEDf8A!}*)R zS}V5SHu|gCGVbejH&@_6AZ0@fr}6Ho6Y1@6B-hi^^LEM38mB!s6fP=GW1%{n+2Q%Y z)Y3GV;b?_aeNGvHmg3&J_!fc@dnCv9WV;Q?UF0?KWmzVdGdK$RVCM^=X@s|dAB1s=kZ z;lEI!(VP(E9UisYDe5fZd5|G(2S0CvXG3z6*h!npf5MMo7*nf1>- zDZ?w8(Rl|F3zJ$RXE@%CXgr|gIXX>$u}#8?sz(=>T;46awdAmh*12_yLe?>H#)}DF zyK-ewwWR*(q|84*VXPA{u(x)P?DDwr!E(P+Z_GV-H7`$GRM_s!dzNA%y5UNG;7rE9 zEYcS0Kdn8*OhnS6;wE2*?A||=L+(ER6|a487m9|~(HGf+iAP-auyDm8fEMhp4YxmZ z@o_lxjFU)_iPIiY&^EJ`q?1^hSxp2kkS`AKwCvGfb~WV{Bq&jOP**G6M(J82O@bNS zxEeiX1G~!Y`}#Sav`{$Y|VYRgOqZ29^i_KD!g}T{{Fg2lC$OuifK*(QH)9G!*L9Nyt;R(HKp%jj8F@ z|9S_n*lzy|#`%ORWc|`si160IM8n~^bUS;{2@vXX@`b045RebQz(5S`g25EZ*T$Fq zDE^0Vbhp?ZK6JD zQFlw?hL=^e{oY$ye3KRj#%HrO$_(oXKT18b61mmP$d=~z8!`o z>F1yI<*UOQOhBt58W_P%I2f)|$^n@@KnRw}KDu?UGlBz3)Ui%)IlZ*h7Hk=l)BaPgiHT)X( zp>coysW@S(ZE&IGWuC%3zjZ&q2 zDQmz~TuHW~^17d&aCVcxof!7fo!E&<2AuozI&wx;;j3y;OUrP-xELHW9L>i1&C9lP zY!G0?0m+rrbj`;7x-Z|tmZ3LRM3eb&*g>d-ZsZD_Nq~7M5=WU~0YN>nN;ku8(qRO| zj@7XP1*2I_w(3yFLiZm5+y%(94m~@N55gC+u?K~3M99!<_qBAet>*c=gjF>T8?C6_ z$K%-T`0j|3G{5Z-=JnLw0!84!8uMhnRTOeiMUZnAn%%b9D822d@{s9CDGrzaonBAJ z+fnOd9iSA&5YZXbZsej4-#mJ}_85zZx;;*QD;u!t;FSEKmw&{z%+KB+nc=#8;+$Mv zk=uka2@$yL=CdQ$yDwofel^zB@1C~V%5V61g{uM={YsfgZx&hs=w_2Yh>guk9yzk3 z&N%pFcD@`G(p1~+R#EBS{}0_!#ni!uwu%oQj>G5Dz{B`~42SXOp7VA_ZTu?Lq~!Q) zyRqD{8A?{ioDB@RMW%iqiWxqOQt*nE>{@5Zd3M^M)jz&=+55wSv$^!%4V;v8{qo7n zhGrXrqeRkk0_F+}3!BFaQD-_~a0Io4P~nTf0ysLzA#iW?C9MwjU0LC-ROzqqb$!fq zKX`U!I<&50YGsGWo1QG1pIrwLG(S)F$*T%z$aGN06Lc!sfPwg?pPd)+W=FvGFG#bJb0O2}ywbyByIpELXQLU12Tt!EHs zkC_V7Rb2$vDRHjeaV2z~M*XzdoYVGupN9lri@V;#(Cka_skddDU-<nkL^8LU)1IuUKI+u2GYn%D(eZIb;7XO_MQ@Kx#4!K9>Nn@kpLt+;I4X(5sQ_` zhQV-nphi#PWdoaoymF}Q=_)`>W_MGmh@T3cEg~^#qXrU}7xzCa*8KF&saA8_{FwRk z=AkK(-S8EFFkGIok02}TOQX>gGmAJ03}%i#VQFX~a&Zl=$Gj-$o9Y0>t&UCAv4cE; zPA9OJXyM`gqU+dDlAxE_b!8lQZYNqS=>_*=%HLOmv}O+^7K7Uy$_~&CE!qM9-xxF9 zR33J0<>Km{J8f#_@<$8IPO)CfeD>Vg*()#RT@OB7GAXhz)&GCzC+2Tb#$_IGu=~_` zX=meXVzdlm)<+#%Gvf~~gYq@pmpKmm6*!O<$EH@Cn3z_EPC#vvL2vmUIFWR~i=YP2 zX(f}rKF~S008j|9vy#IDeBch1M&zT_cB!9GzT(H-=TDJ@E{`}J&#+k~?>izzd@j~! zVZ+M}JeUJQ0xJ?Gq-2nu)x;vMAjvtP8E)pI%DQbTJDk*$A>Oa1@>5qLx#LQ$0PZ{H zd>lC}!4n+>$B;=9{Icz0%4cl;q(E-CRPth~xhBwt%ZDD*jZ`FZi+E(`W4;oM4J2~g z`@Vhz6%XseZtU|cH;j77IR5sj7df^Dl)k;VZ(FknhHqpI%R3tHWqT=l8E;c)y~lBK z=1eP$mP;;j88Xw$7&5boZ>ceOEl~5WJgYn2ElIo6N6lFuZCEtGGGN6CWY&4CCRW`Z zL=a2lkch<~7*&mrvn@ErT(Hqk%PdG=57D-8OFDDp7+ux{2){xBFc&-Z-|pWy%TYMb;4rpK?XqE~BP zpL))|dHV*7-jeR2d(J`xyMk|84LKnJ@YMbJdyaAi7}$wm`DEv|c6C|WH0Nb1jbSb0 zJW7aAq^FOcnfI<3-$>w)sHYL)y*U|L-nRzGE_G`3bX4**F$z zt*&D>bPnKj^UATPq1h_H=3q$8$YZZFcjfSJY6}6_!oQ3HB)DzY?p;-<-iO3@F4*g< z7eY>TaCT^m8R9exxI%4Ep~}8Yez)kSMam3x>eEWatHJDC#)O)b-K#}30ahIu$$-FWS}lSfxVA76s0JF; zI}DFRGW{p;_8I%k=+HvE%jj&jXWtChOdG@*)9{;=kz z$I9r+rQ-R&aBaJ3%u7aG9$($qlgme2CO(Zl+kA8H`mMX%Q#^#9gf9B|Y>w)$j{7ur zCAWP@Z?vqn%=FGo%#N>(E2z7ZsHd-=?4Pr^&ExkKN$y_%{O#hI!dQIJ=#XrYyjpY= zvOc+3JvS>&%EAzpyQ7Y=tu~~$QV(d@4m!tQ; z@AREz7AICV$?g)=54Tj5I#z0WvXPmlYn`T-(+r@Des1rwo}ZuKp;@f1QuSe6=#iN3 z+{1xulL{LaBX9Al@2f;3y7|?*^P=#F-3_&Mte7}Z9jpa+w z9pnJjI1M)(ji=RJ_+jI?oY4{UC`QkK&BCfIc7 zNz3+QPwsW^dhzOw@$mswT5if*&xp>>|J2OhO2@MBBG2hCRKEL<^WNO7yxv=By&+o5 zpP=66Z$_^zO)rrZiAEQZXV5;wma-g_E^2K}%E4|2EPlz>==c-iJlLf`V>kcS1A17u zXXf<=#UcOFfSk+XZZ+K~1WJ9v^>TdvGFy>_S{&(3S{gD_-Q#(OoThlZK2Lo5HTFtB7})YaN;xRT2Q8k=Xh;IDKhcH;gNu znxvW|%elC1Nw3csPpS!I;sK84@97))B~wN8AKz3tR}vW_xfal=}fJ>&DYG>8?OV)*&h>(3 zBRw0RUtT6oqB;_LQclgai|lyTqMmtjM1QTB!^x0>*`pmbhS4JT{1?R=_x}5p{s?{^ z_|}?SYw{1RURz#u&tt8C`HIeeH;Z{_9v<1KBABmjYqiyLWGa{R0H%%*lYmlu1`~=` zf_6c(fh|DeJd_&pqo)9ulR~;ST1ALbF%Bu9>fhJJrhx=3yCXwT=1}gd49Ns6k$)0K>j*`{??m_>p;elSqR z?Vq=LIo(RV@l66BU2kgzJnuUZJnA&{sg(+=*5vXjV3!#W-X8x(4`+b4YJm(x4>MWlF_=*nG?swI6+q{~umJRc^)k|W52srfL@>U#hmnN=<0|yv5m@E@aCSah-%}CR zX2@?Dc*wi=?s*Qd_3G2X%=+s+yZnmVDr&w4Sg3EmS}1A|6?mX_-@|nWE8QdcFZbGe z%obpNs4qyf~ed&O_*+gknX2D5`a{^B(xJVsTvx)H5Nd75YdN-2KPPquM<3AsoY8E#?8 z5yVAyaVY5ko_Lv!;-k4tM3Wwdfok7vJOTjGPHi%I^19Petg9965gi=4c;%@^gLH%L zr`qv3hobjx_u(xz9kDduUFdOV^!kC1k1gy=ZQ9R>;#zi_-jP$d&kTon#V7uA8^m~A zmX0l{{VhK8^(#K$v}!IfeO~YN5AMry4E2Z(ifjpX#aXgD<)_h0Z>1tggqV zBTp@LaNNF0kuj4H9Vq0dFEod9c*t`XFY%SzXRCkqW!A5!1fIwUo1V2itBKY`@-Nec zff%hi=y6a&^tcZH?h`{AdHDfqL2?JB5{~NxA>PhC|1+2J??9~BQ;eHiU|dMFKU8@c z8$Hk;EiJvqo_UI4&ZySXntG|l4eXVMS;DmqsoutSb@Zx2ZQAs{#qz#;wyC9kDIz9L zb1(jV-Fzf2J38e4p@v!|IE+6Yn*TkU`tM>#f6s4D;!xMDkg(y!J0bEB z@laDsD@(Q|Sk4(aV#N6{>YkuF3P9CR5FcZ_bC!1ZRQyc=3`M2MQd+K_`S!5@+g7lDq$%=yf`9k{x?);HYrznkVV z-*Bt!JK392ElCc6G+?|j%SPY({ioA~9fy%ESsUSckx}!u3!Qv*#@rbRFb+FXjWk$q z`k6X?yS|~s|8r>*c)*gAG(0e_)M$w29ckT+Mh2*07GO$L`Y9?^Ba7H~tIhxV^$r-h z)k3|yiaAMbA#MQ-vV3xhsuAiP@vTh#@A5NGU^pNK2jKCcIk7w41s6ucO%249{C1Mh z6PKBkFKEhJVKsrEM|A|+xCvEszUN6!x~cwmNiMD+n$JTiw&uQlvkY_xu_VnMZ$o`U zXd`nvXyS73Aspp_kG9dd7HnvID z55T8eqCVnBSSpb(&$5>7*@_d7CKLn;Y`}|xAEF`*6EF9&bNryce{^Z$hWv+$Lm2_8 zgeC^ITCcw)I^P>7KO+|7U7>8c&Y0x=Cq6RZEIR(?&Al1blm?(bWB00-?l@KP|2~yh zIj@GC*n(Y{Ir%dnS0?2XDVZLcTs7t;QV&vd>W^$j(azxEl2~>Ps*wGI^}sc;*1CQTF&BJnwP76$l^PO&3t0lO z(u4Bbm&~HM2Tt&B4`*9wQdD3ULI3RgZWHp~lW^ApGS>lmu&?c{s95%BNO*4IH_N&q zd>5mtFQbu>;L_EG1H#F=AmyNz5UD0j+&H zS9fj0G{G9Q6hM$jGC$UVx0s3eM!gRl?*5&~!(aXSgQ&o@6VA)=09WvI*l&c4uu+Cy zw+OWHfhAga$N~_-Nx#9WL3Ah4BtRi5=fNN{b*3{yT72JR%JAP;=Tf+IvcS;5zE%_S z5aSt45PR6|7#u#RK{A~TA+H@wZ|(PWB||fZ{uVJNBwVL5TwqK|7l%k0l}y5FDjy8^ z|L+##vYQFwG70wr3>z0d40w&Cg+a zj&(+M=NE||koVIMwnqoUA)kgnRgoEK=beD3urr3|)*&mWyKW#qKzGx84EEOvVbGtU^W5}2tN?b@}4HD5x4Aw-g zNbww+ulc1lxWk!1966=z|2*D`?Ytggn_Wg@R6;hBt2#!dVqG074^Bi2EL+wP@*lD( z#{&{=9ENC{IC`{u!IJB7a28!n?=8sv|HfRdJP7G2gGbf6q%jMXHceAQ5omMK7z%hu zyqIdBeYKqasD#bHRvRpu2|yZxcR&T#>bfrvXyVd~Ui=_!cqI*YhMD7NJYTl$Z_M07 zywDbyd(Zb3LN*GEBZdVYZY2;{`VTP#jg5`%oY7@Ldpr*fV3X5yR!D38;CUS7C%GlY z8_q!xGG1OLp22KW_BN&!>6JN(7nka_?pZGb>+>WOacnao! z{a{Zi#3Kv%P9H(8NEr<>I{XqeV5xV^4hzc02V_md@$!i;zU;w{tm|-X2lutM1%@@6wE3x^@q@_wWti=-s^)5BRqr*+gy;3)3i&1?=kri zQlOQX(R?Tu0hWKpkKUX>9I9aU$P;wCc9Xnv2yP`Eu>RfDmW$QSirmEr#~y>)zyf6s z2CFtNE>T9Y1QIAcG|t&z!2N^oAF4jo)1cmpAq6z7YA1^(1wk{Z1Zoztj5(vRK;gAMPvG}o zv9OoH0CZ;)#y91+lb(1KfMP@wSvBE1C7>7*WLIU!cKjd?bf_Ub;nCg7B=$+|>lUoc0%of!Gn>vyAXu26T+XcDHu z?)&!hDhIQQmBBh8e1{cyaXZaDr2_&BATbz2_k_lKM#dsSz`dn`dAnI;L_+K*nkuS^ z#un;*ZRP0nrQ!(Wp-L-wwv!bhx?)Vi-@SuyALSO`u5S7dR)wQ*ug*W z0i?mlNr(k(nqY1O5R-=E0T=|9Kqpt;L^mcoLp=L}Ci#MOp~pIy-9&dlEfl<*;ROhV z6?i%MxukAD&f`RSHW2-vN|w#{P1j2#GP)i7NIGzO+MM_zH1Vl~{9yAxBFtAp8`b0@ z`2F78+l4;f;QU?3?~0y0waAU6P~$K$2^Cmz0Gcy9kdrs{-MDu^6@2U4!OndfnQAzU zlM06QV4bju4}JI0rKpjnF@3^;Fx;^Hg`G{ zq%k3sUA@pucG3BJZYO`gDXxDmco61CKX(2Z85tdii`NW-KWss|5;lBCEY5sWTNFH` zQUVx2p#cHC8=P8SGXm0CD|mWe7(zJS@pl&zsv#(4%rQ;Nliv~Knr@($b$Fm)Kn@{! zp^TI)W@KdP+?xDZF3ra4@OLv;WB4c#jcV{Z*w_ova;1*6+;u2{I)bztPD5SuY#=PU z30l1{It;)r5E34WI^tE011f-4mK+_sK=|<wMOd;?XGLPB2#sDtLSk~ulpu!pDw{uVg9~yE znTlLCf_ko6SyqYQafoKDSL0&0D^~EFhhIob*WX=r6w51_noQ~`HEo)Nmgyt8dhdN( zw0VJ8+GH63Aq9&)tDLw4IAd$;>+9P#8m;C*j;fUI0#J(ejgzOFF%D}IEzs7}Pav=O zI_F_nh3?^?+NgE_KuCyS_$|()$yvC@isA$%v$gW5w;MX992EO-2sy8zrMZvqj!te-FYNt*-aS2NZ>Tq z0Bqkxu;Og?wbjt{EBa;HsDUsM1*N_2CbmMGei_7wLW3%COk(!unOj}h}_&c)7)oiai7lHW?(oo1Xf8S31$SB>PSmB zGTo4dfa4$PEYt-$QKNKTGLo#OBzpmjn`YxTpgbu;aeFpR2m_^;FAreejDYAE_=|ul zLh{1M&|DH;3}SFan}BNV$LkbQtY+{dCth4ni$3ctW5-{Hm=#v#M0`zx%wxzC0RRsB zow3au^z1-7&t&0(>mV2ihS?Xyh%D>duuB#$o9iHfCvi6<}*y&nS z(>+e^H9qkSdY}g6l+`232QLI3yFNVP4aJW)K)xL6d6sqYu4;cEJTP5EfgYAS(qavF z>pl`14%`v_;v|%W^i*RGB7G%7t9gXv5@UG2(uAZWvrGrrSoML8qza6MlL80H8htOx zC^cONLAxhLfus@5fTopbR)`LFG7@`1=zaQS=zn59N!kLCjs!I)-e;5134PMBar-EqUv_ZWk>2zG8!BWRr-2qEVJg$jzq9F5v2;GS|k+2~7aK#$3m~(KWmh z%m}pp%lX@E`Y&;>m{nYctI@Xz?rEAgjiv4 zotVTzY`L+$s=T}wewizR;a*i^FGCn!#9t7GlV~JIUputjELux$VN*gPUQ<$9bH+B` zPoQGmNFw;T4M78NSIN0S?RK;UMn3*VSd6iA z_PsX2Gso)wMIpTzJIA^?2?9t4915%KnVEC6m0)uixY$`sOadTvcTnnrhA)O}-`EET znoQ~mBHl#z*7vC5Q7lSO__U7*h@ArES17haJ84Jnsvq-9MvJDI9n{Q6N_kw{jn;(^}3?kqU}_&mG9k|mzDePmEvVxCgLID z0&n?-P$y7V6ZPkIIHn_J0pBneYE3flhTB@pVAnB{ok1ekkhN#|;UPn>>W`q6+U<`I zdr{2ywS2>N_(*>9ZHbqg_iksr*7W+a9aQRo1(FShuprc9eIzWRK1?`Bw*m9~Xp#m> z+{B1!Nr1NWnHbXOHb@qn#_ja*@Tj?7CvZN1{X}T|*Vz@tOr6JeI{v=KigiVC=}_9cSv^MB z*B!%!_xw6(_cizr`I{f~GZVxs_5aX*Ml<%UH1Ni++)8(EzBV#tUCFHnSiby6ODPprp_v?|!#nDi{ptzYsQ=zL zBXia(cLBjgG*f1x%SxPq>LKbDwBr_}$$)+tv`@|WGW3e{d|d#KM2ZY;FeLhC!0Q(B z_FG&Vf{PK}!NRDKsQU23Yo4WoaZJKMtW*RV4A0QmeCvIA*O}R=*Be>I`0tgQ^S0xC z|M^+Tp>LVFK9_9XU$^09VdP!+mjEwwu<#8XcZY?Zq93j%Os+*FWQj!c37fE^){byG zR%|DNs7K?`T@83cVvs0$z<=NoKMq0O5+{aD#34a79(@v;X6ft;o7M`hI(g46VEs@m zH_fx(AfciGXdi=O3p#XC=LWiDR|@&J$@{ON+?D$DwDXH6n_5uRHdbAB z#^1}19SLm&yA1QR3kd3{2Y7>0zm@eGQYZ;!4^EiZQDW1b?`Xy<`ueYsPsU__47-`- z0fYEl=U=~mF}gdD4J0baOss!=&$CC46R+4w{Y%)Si2``+POdP`S^J#mL;N+W4XI0cDsTG(SDY}9LO=r}Qp8k&7<-N0h3%mX3q zkvGnOO94w=_HtW`#o4%OPd%`bT! zb6(G-^h&phUdXdCG3IZXIp3@}-WjCDrlj#^UcAtET#w1Mh}cHgFR$5d ze_BGiRjMV~b3w&1bDa`~jdE{990&e9M)rIh7iRuJ!hg6Tv&<3UJe|BQTHEa>*>x070jRN+qKKC5XzFo-6p>!yh3OvgD6W}}pLy%b`_Cm%f? z^M!5o;;yjN`76_SfNNuLH~-zFzwv+Ax-vE)>_pdE6+4sGGc%?ijRhYpT{znP*Vq0p zfy|q39H`lG(cjQASxZ}__uN%GyM44in7%(V3JgA+WH!)Qs%@^d_ET|5Hmj6c(Fn71 zQt!<|IRyR!%T^!;6JV1W{!UH6+ThrfjaovP7(5xF6h~y-G9#m2AwPq#JHIyffoZw{5cRn-tN z$UoN9E@N&deqF2G@Bg;|YhSqN6a@VYD6v$etpFP?i-Q>;`6ht%6R$j1Vzd)}nQgq2)LP7S z0xC7?iT6EJ>@p2ss`@2(X$(qn#B32hP+d-4JJax}Vx!O=9+Rsq9~R%*oBOfA!d4Rj z0bCU0?IXj`a-kP^T%a6-9)|A7(FOp7AP9D-%{VENb`#aa?l;(?oO|P=B@FBK;FVoR zcjO|Dqtpnrw>l&sLOjDMx^CMX7H>psf~46VE4sL~Sg!C0b7mA!Hhk;(aiToxA#3d! zhnNda8b{7#ge~_~V>2BMD3bf-Fvmw-h|SELim^aj35HY8t|5{Nj6sVR4}p=y*np4Z z2D+6R`_j~UYkU{#r{W}8Ys;eCg6(?8oq9c$l72TJy&GLFlc5G_zS1(uTp1)u0=VYc z+{pWi2pK7YY;~a@^7r416%2il%F^X)9$lWof`1Sd_H&es>gOdVeK_b94rIgaeWG_9 z#@_Z>9u8Y1eN`_x@$5BD%6K%Xi&b^&jPIj!`LBnsKjf)saWFe0Q9MJNOHL*!weaWI zMH7r76*kXVsg`5lLSUX_ID<1!k2B5Gz@V^YX?IEh;-1 zoQ3%1lveDBPyhUhF+cnA@tz58rP-x?$u})To}G=LIk0&}w~rF5ClCaMO$HE~M?&P0 zupHAqiu+bQNH!CACHCt(Bf8wziH!0h^kNS&UWA9&meSP#^k+}CS zia|&%hf=*dNrdICw5!O&bCWI-^hG#gB7!^?>CjLEPv= z=b;8FDCx;its!y`E?qpJa$yomSQjW~4j(xZRC1d*U;_m%lgJ{z-+1D@Y_;gmy_n4P z*E)iC#U!());>Qho~T=N^?HgV^k8=K``Pu#Fp<;{f+oZ`kw9v!7VPBCYcCxS=!F$Zu`|}8@#1~H%p*y-y%@h-gjJp;csK(h#CwVpefUZ^mRw!@xsYT@u#*0-5zNIgTV|YK#6p z=zBel*okMfO~H7u5bG?64MMF@Ot|X@uJ$}1a%b$ZBea1g=r1Z;ys30(7SAXd>;P`j zOuh1$!w8bp%H@Ah(TpZEM7b)+)kzF;5aJ61!``^p+b*bts@@VhsA}Xet2QRFIrHQ- z_o26!Dwwj^jC8%&BwO01fosPS;s&^})WG2l9{_tD`|*|ufo3f%gh>(%K!t?~7wV<) z{mD4fgqrbzj{b>)ZE9rR%iy^s-)^#&EL;u<^zh}tt4@v<44@!j#n_HwpgsUgfI=mP zuL}Dti?iYRoCNcv_TDKX!NqhZ4rl=DtzB3Y%s0AP>w`59>jfonM>zU$;TyoC**flc zbpD4veJ~s1#sL2IkvwR#R#PF;>CJQt7sdjArR$u}lnG)$q4CoiAeTme3d5O1+c?4*15LRN8+!0y+@2t*X@ z{_h2Zp_<%losl7}xHzfn(bFG)ZxwIwV$~5;1!qH%<9)~`@ngh)+ZNbfskFiRNP*q) z$7M_8M2x6H^am=P7vE)E_zk#LO1M_nj?F%YbSKiVRhmgM0=Z5x(lc}q0s`g}+lSEr z?7k%X4LkvA1opOGjIf^A9uO(`)$<^ZgJd=n!@9r>G$p|fQ_aLTicAX$0oN)-K3WNn zx5;=4xf~2E=W!0G``9-pE1xuoo*o(WuGh22lo^we{hKRx%@$}r`*w^l4}@Aj-?#zm zy}6Nv`DB<*_4PVL6O$+ce!uDPV~@dDLFx{_*W<5C#sk{b^hH9YL}I+$)Jh{4Za;}< zhlL2ie15Oe{HPEYC-R%rvNExF`Ko?NUKMd+iann3NWqRKPGDdrQHvL!q3RUytVkCNyU_tR-XsN( zDqyl801gL<{}JziGd|I_k{4aEZuL`Sm|uX93IrToLLIuiHKkw;wtm){au9cC;Mg*O+I^eP{j2Sg|P<#68l|c4cg;Wl9ri>6Lf;xYl z#_Wfh+npU~PG59iG~q`4OIoJltss$}#OMgp7GtF+i2N)6I(n8$Fllg>O+b~WMl8bW zkod`BYC&dMS2EascC5o5wf2ZwyD~8U3;_I$BiEo~{CCP5_vVe(4k6QOxCnyd6N#-8 z2rV$CUNVG0TP!ZY&LR%shNalm6!rRbt75p+qh}Oep_sB(@i(-m=?o?EDd~&sgCpzf z78>s^xuxD|K0`yC(X<2pv|V29d|8#vUv{Wrpxf|;qYB-&*jMJ(K?J5gvRN$5(qUpy zI~A92Ub(9)-7)URZP$MU?-~q|V?vL?BXw0tT3~Z_DpFut=UusAPSd-z+P~q$2Z<8;$=aw&|w6# zlbVBM0f~)+TWq+co8LU4fag>E%7*x(^)Z(?6546A8HvWpQw=N%`q@VNzod)RS9r%Q zp=k8?RQllgtmsz=-*fyxJlWxPZ(PZ%7uaHc&nw<(G9GDxYP!C3$eYkSh$gsn-)xfC z;!U=c1AL0`%cv@e@pBZpX%bR#3$xWsyJP%BQqnv!E!>J-gqwm=I2mQuu|*{`VfNq_ z;~8@UmGq1jt|b2{^L=7+7=RF4FpTrc#5b{U=CvOH9T?t1SFKc5Jy<#ue&yBXAw(#}6#{VA`m|BU<1kwbFEnA&2=2gCXLC1yyfD!gY{aX5ilym;u`a z;!(tmGl+?x7*>llqo=GV%N2duE+ zsdcga@Rr*_fZIO3)S&ylk=4HDzGr1s5?u3qTUW-3DMY)K_Gfq3JpbAcRt6dArBHr} zycNM3h7V-|^$sMUN}U!gdAkccNx(B~SHJ`9EeH<}7b;0a8WxcJYjZz8I{DRGj$Fg+ zEEhqBK2M*nYjp=lVL3Po)m*sALV0qi$W?Y2SLe0*8_#=2iZyvYPJVRmdQwwj<)*yJ zkCUq>JO-7lMnmgV*vIzKx_m43d`Wvh_Vkvww>PYxNZn5;dBAju3mLXg_`-mmAc&@e zEcN_(2K22&p5tNvQ@+b$#*ETkuY6UAhfoQZzYABW~0FTjxH+A># zLbVg`-@U6QFhg=sNN3F`igh^xMHA!lkUvoX z>+NTG921_RDfm{bva>*WWLUjn*!F|mro4>4c-s$-xDAh|PIvRK-}K}`dc1+to4TjP z*}1%WEcD8F)E_J6jXPg=%#;cj?27GMR<0YHm?#++0Pf%naHyn01XoidFjXN)ECD|1 z`Xr_zmN1B%2HncgyY!~QihkaV_^6ZTlO5$+prL&wBIGNZtP}`7JrBomacTTo8EcnCIpcX_^c8>chq)_aLM4!wVix3W`@!shUSQnXde)zR%Tw*_ zoaDAT#`#GWD%iVEpA|b=N?Tv2e27_5gxQZuP4j+Ld~YlJ&T_(@pOKW^&Kp`A z%L1Uh55?xE3@!y8Z%oCfWAv)9)sUp+N`pF5U6V0Lip&-UI%UpzPaKnAzm!8{M{ADO z!32A?w`b4#ZIydB7%v{$d0;N@z;=NPb*?WE33M2H3af<{o>xM5Bet7h=VqXpg|+=@ z!V0A2S}i#4=!chH6lrQPWHS$`r!O#}yhe7P6j2D6SAFYzW1BzGJGqc37~&YqIpP$G zSxG-;8ek5oq4_{XcQDwx#fXq1tpEv?h-<&ti5&`khVhyeoBmp{iC=En?&E?F{%{M3 z!d;LM^5=MOmht{_3isP#YDK@u7wPhUmp7pv3#>+lU_VkLwjD9BeAoK$yl9t?G@FcH zz20S9v%g2X@m$;3>{t5iGQIJzzHmpg(#0rUC!{`AfDpMnWLFy3I|&2fYcl+E!kbqw zJPH5^^UD3!hc9xs~+3(A-P+|9BSq$LFUUu|oBs3K4y275zSS#{MOTu4FZR zOBeMqw0UnJI;Jo8q=^0`UI!I50;@Xll!HxFK_mw6WCtV!mKIjn9SH7Fs)DyF*dZui zy+8ISVHoj7=LX^Mhn~W#B9h>uAca>&(58Yp|Bbo1>%{tJv5i@dG5u1BTzfqYCq*#4 zM10XXAXd*fKxiW42VymnCj62}Bj8r60A^-hCyyqqX;4M5wm+mFS@pwh#@qL_?iYGk zbXVkBU9mw*`De?1I9ODtyB`$oz76J!)|%9R9>?w4s9^P4e)8?J!BnJpe zhNypuYh9~l0ZbwsbuhaoWg8~P0~w7vCf04FWh04YEFy;pIq$n~l2zk&DSu|S2P&$% zVK$jgQEI~ZouB9UI_4fSy2xm@AG4e21vy6zq0oev51K)^#%!EQJc;5VbK#-#w+3k$ zGf^Fcu8@+FqD^oe%yW4PcnoAMZYyhc3c5*qv#?R9>+hcs4;B1;R-Nol8)6#WsB7(R zl=bk4y&ihqQ}4PxBk7K7gYQUnYwJ;^)S_2uY3FN%XM)3D4l76IypegG=gFN+u*8~= zgw+NvkJY(RO(o=|=-K1F6pJ{9C8nxcz)K@mILo2-^20FQtecHJacf3ySRF&RcE9BH zzW2}8jR*u%EI5k4bqS`ZvKs@W_GJ`OHW^Jim~Pm!MXXxEw&_-RKQFFDY}afT2WC#@p!(S>MM`Kj6*qNsI(B)-Q70gi&azj`_+7BL&k`>>09F~ii)1nFhqTx4^wd!lN7l~V zAC*5t^%<)4q3QW%tUmhAZG$wr;1xUKG7>GkUTR6IiN{}b3SGF#vos?xuS+^VWl~?; z?^e`-Z4wtAGiE6UQ&KhelSl{R7=cbBox1}Qn^znD#xzzKyrv6Xw9Uw6b#xyhk?O9K zfS#+Lp;W*nZScL(D7#~em1ZIPh)#R|QM7|A@=Br{I}&b3*PJVi*x#I2Z5*}!NVuEJ zcG{lssu!6K*J%JCj6obrZY6pS^CiEJlA4E}p2e*fom}UHY4WxFNIF;zvrI ziC^0Q^Q!o3zs3W~PDWd(U$|3uElbx%4 zKJj_{WS6);vyGE>j${6WhTPCG)g!KQsykT}<0>i^%gpLN(sHle5rtJ%(8TNH?(LXM z6UM6B$--2#b}^vY4hX9!0uV63@Qs~yi!958%4V(i9;`2 zb3ub-Nu;!Y9Zhf20nT?*WIq@R-fi>?C(Nie}F5ySE+ME2js$|@=N!I-d{_@ zyB2#a;r0cS^2}n1c{F9YBX!p-d$^AX9M*qym|3y)jpoN(s{z8gqR4G{M+9a1erkud zDueQfH)(>9g<;Vl{w)9=mc0bbi=QSmlibut+b*2a_t0s{R*0P36x<>_9`Jg*D%8}= zIi~xVOTrwO?cW=#=N4y+v8*(4YCoQMursXFY5tz^{k8la?EG^1X%4YMY8wYu=+DbW zor}z&w)}G5|L>zm7!!_T-XSoX2Vy7-G@}+10@|<$$qfOtiMi2k3PIEfK2OhI!1B2Z zV(Yp#m>?VjS(hao3x0X2aeme<+vw@*lVY4fr&h7%QtwwbjGgyu?-;uRN94HVWUuBw zD}eKD@m`z?7rR&VS3j+Hl)1$j`zDMhDd&o8O<%34 zR`9B5X61wXWb)CUq_o;nru5LH%voQW#W&a41r$Q3j_G2|1vjdrQ4*lGGTDlcHbTUT zX$OJ|`ms;Zz=pVUZVnZ9u@Zo4tk)TaYCXws-b-+iV9oAoqvuBxn+kmh3{9eVO(i_sbXJDq5q)x4PnUf!o}_ zytThEIRw@V(ZpIBs33l;CcY!+s;$c^pee4mJV?+~RZQI>V}X>7Xv$F8ReZiwz{-#- zzIZv+lc#MGm763i_0nE#Q*~$2&{<~9Iuk{^Dbn^uG)y6;#5$hV(%*WU{$qPRUkJIf zY|i7F+7{WEb!T0pR#m^f+Y8k^mWCh6H}7pYqrVS#XZxHv?FA#>nmL)KC%sMuw<*Vt z4T&}L2~D0mD>^tz%*ygc3V120>ihXM5=$`$w;74rbhqg9Yf{>3YqPfBpzCEA(RiZQ z1OKr9$&o6<@m1nDaqWk=zbhNap0g3g1?FE;^15U{USLwQ&{AWvQXkyjIeW!+a5%f` zyDyfvx@Ggt=$rXqrZ!`QMegFzn2KJ+%v6s zJ&0!*Wan&CZRzO(Xo+R0qzA*XP!^4!ZO4~|qA??oDQHqp?d72`2KPVoIZ8K=&!I<~ z+JD|?opZZ&$=KaH4hmB~HeFraPCa`A11|4HAOBw~yxJ2V=b!0IOl0T2lKG3_)|p$H ztYUm74K~C7ov%bxJ|*$HtF4_IvcYjN)3McT)M^mw%hF@BUKq2KCtZb$+p^7 zVJ`9mHrhWK^D~B&v=847PnD3o!mp_->AP52h4Q|K`4yqV^-e+RrlPLC59kKaWoW>n zwtONY!?`_$olZ>w$PE0=xXv+bB}gx^QGquZ>&;ED;fR>bd&Q6&S$b3IBJXssl(b5l z#o4Xx(Ir!`Hnh(0VigxM5Y6}o$yP0RNhJN!$`Ex7zKRv^wMSmL79Pd8l|75)0Gnl$ zg!S}#wP}WyS8*w)BPV@l%36K~cT5ilGFLYLx{I=bm&GWj*AAQ^QmAtX=5zkQq-OZ_ z9aKt@Xlh9u!qJeCr*oKhs%cyKqAYB?Os25Kn!vHqLt|9DA&VuT>;VU-7_+7Il;Nah zbZMkhR%NXF@|R&J^Td-*m14`r(rsr`b~ur@v!-vZFm=>nDCq$0txLbE*X6~EsJEY} z{t1T&n@JX%(=T(PY0QPa2eIor|l1xUO=|)QKWjETgQH|pw3-}qa>Ve%rQW}xs+7dQLzi{hh zHy4F(j8@yiC?V~z;fPrN!rS)_f;pY)ks|pCKKBD22dr0FF);Zn*GAkbBQQBAt4kjY zA|H*cbhiJ;DCy!9-TLu*wco zg&1G|i9__pn1SU6G&sZ<6{@1st%Q(7`cv>B{b|OMaoMmXpca7l-jBwpT6cbI3+5fy zYWR;ZeEI<`iL(Lhy))HHZ4_0giqT!=H=6@9h(9C-2bC?LpJtdgr%G2HBWeS{4pxSq z^sB^@%shC&cF;^a$SEb^bWi(kOZCBAp#?qsQI5LcMt$YS}&;N)uS z18Ud_qKt&_3N(^zjf1)E&*EvnqCqCX=p>=U0qUZNOZadW;Xj@|%~UC!=FDOq4=~ic zdzkh-;&SYqmGx9?aLe?g2^NnL2dbhL#oXz$qFktsyNyGbl*raBmIk(_q;^5hIHS*; zlE0U=y{oV(kq{+<&7i0re8u)PFp3az=#38MtKrj|fwt18vY@l3oZG=rO)))l^iL_9 z^+J1A1+5>7>tG*Iz9MJAJr}5C`6x;ui!@U19qfp?^tm ze{NqyU-?)BEIXRQ9K!7R<rVZ#(J(gP z0MkOwcq*#1ir5u``dd)q5F~t~0S+@_g7|UHQ>)JGc01(tUcz9DUYfe^;JNxu(p49X zv~zx~$h5p20=CkOQ-Q3n;rXX^#v|Y78Kcqq?qseH57sRfWTI%k*3X%-J>om~U4S>> z>hFeSJ?L!2b&i0C92BhWzZ;f8>ETnXp6D7qUy;aTLUW+j!_NZyEnyXqP8JZLzSDXJ z=KyHPrRvpqx7WLD|MG&e;o8E+$(bqj$@%mPync=cC}aY|CI2FZK<0o>RxO7JQ_;O7 zoGgum7}7O*oAL29=GuMW933+a!MHvb5*dB_U5i)vxT^c9zTu4???0$Dtr-|OH7=GQ zRd0~ekE}#X#TkXjJf$mr#WWf7FRM?U0BWQ8hIgKcv+?2fiRPEPgT*gt!H^rc7?IOV z1R&HnvTi(8U2~you7@WYKS2A!Nx6edVpj{6ih6TZ&2yK}RNcPns8|Ldr8^~Wljj4g zI*MBFd)mSHn5C;v&e|TaYK^(UQ|TMQQ5Lq~B6d;x_3?PwEG*_-&zOcUv2RJPQOQxF ztz&$5{g%vctg-hfCN!4tBIrk%>cat}k$lWTiGY%a_ab}NEVd80Ou#V=00$T~^emCB z*NIG(*i4{gG&p$e4~AlPh}*(HI7u=Xm)bsjn4C5>(R+9AaO2Ch;uD`|Wz)ei|7}YE zh8Gf#XvCNZ;s%mzit5hOrks8`S%iT@)wXkQ53!;$53QV;ZLy78RbQI!C7?!4stAet zY_!y8SpHe%KYWy8bPM(KxfZOZ37$4Q)QGh!Y>X8PmBL$z6>$2YOJ9l~6E?n>WHiY4 zS-A(n1le!|b(M(F=d)V^%g)&h+}-P#n7_l?a3xx9RqBxoRLhF@M<&Limf1Nqy7j)X zPk2G)*Gd~Zpcu%j5i5Vg|2D&sbt{dve@@+w-bFF~Yeo7i;}E-CrSOul-%CKd@Bd>4 z0s~GNG62Pp5@aA0T*jHZdmG)QFQ#2CT0Zw>OW`B4X(VW%L&={L5SHyiHnJOl(eyhjnG}e<%ZsoV41{} z7gH=mc1!JrErOI!iQ$a&ep(*^%>cG>lyv!W%%7#3RE92X-Yt&Raf&i`rR3!mFHI2PU$*D?^`R3?oLd@wc($(O zUbm8DSYJSG&(W7&Ctru~7mEjJ`rr!Fvm5{UeA9jQ1|(D(iQ3VB69t8!pOeR4MJ*j) zLFH1>ATI8ts7-qkmyZWLm9Wlwq3SxgImpqv#X~dwR?gA=^Mq+R;pN1PM9}sMSl6po)NmMeM2E+_ zK{I!)i?s;mSfUaZ7lSRXiuiWwKb7e(pS(OObA4ZD5=fvAE6=+3wCq=%qDf4Wb8p4eb}u_Cr~lQ` z#trp=wnXnaET3;0e6BZKvu^~u`(@E)H!e|$et(}x{`@gKeJ{}|;c_z%Y3pZHC$B#9 zPuyxjAeie$l*45Pc=7OCe)@Y!>1F!qrlZ4y-Ls;qsvo8mERLJzv|3CV=a>9V-w2zm=&9z#AHXEoE<3Nw8oW#!Wb|2{3Toe(EF#jF=f-An>s_Tb&UNv3-e{)1&iV4+ueQFxogX&|R#P*C4S*Q$Azbc4kUrfRsSwuWgt^Y~o=zi= zLDUhLXN|CdjRGrNJ(7{3o%zTnhCp-mq3ti)ggcB*u9ffGv%))$epxNW;!EN7NLz-c<%xfoQNi$tedluDQ7xBySp_q3abtIxzbbt;gS=MG}PY!#9iw zgj*=4;&Z0Ff4r%#{JUIl-unCw5QpR{%>pFZK{5;;}pa^E%N zd#~ZPVdb^5LkF`ew>3)8uUqeM!e;k`&CyzVPdtf%Uyi9K!8+}XnfQJV>T`;X%z~6OUb2-_`r`TBVak7$_C{)>^qvzqne|# z$*>%9hM``x49t7sK+!- z$4wYQ2VQSeos=6CyWwS}!P@^>-mhKH!-fZEvUg)p2^S{%H?DYj+MD+Z&v^J1>G^y6 zO4qDyPEZw1w8mBulU}f!EIBiFN3LY4YXp2;Hp zYUc%+^u5KAPUO?kiLe8j0`^*ht$E3Ntihg&~s8>C(%z4;Z{1pS^<*hEYT9obH&*S-LEi&#bf& zY1T_6u7bCsQB?}k-RR1&XOgZlH;ewc9UZ>L#x0fE{@rD(VsVI8@m0N?(N%hj*3G*I zvOoPnQH0zN5fB8``toJZ$7x28^%&38IXDycDySbVi)c~XU^axM#4Lu`RqWqv%*^lt zF@ejfce~P(Ksagq7MzTdHZCqD>efs~(9dkk+=xlzVAb#Xl7siYdOLe!OD*mDrSPS_Wdrhts-fYX78dd6$| z=bf65aC)a@lX#ck^vJVRVpT>=MG#S*+M46V@TnMPOU|k&CyZ|{vz+c-s2%Z}lNNkC z!#MQOK;3FKhLA&Q#Ykre6KKRj2!krj(p&xE)j3^%Ra<*r@x zQVb8?GC}_VH^Y%O`mg?8aFaVmx$hPkk5D7m=rgIY%!HsEn1IB;7o4o89JUf{&FRz;>^OX#Gzr7UgfBuxm{q0Bk z@&Tn(>GfNr2>o?8x`fXaXjxUixZ-2Lk6q*m(uRf6%i)dSFwmIf^JUmBmPx+pA|G|( z$hQIhN4DoanJ}}Oc~q3E8Jy5;vk35UaHF!UlzBbQu$O*9#6*~k`w7jybGBpJCn#t~ zyFVNZ1_uXUxm|<>5aHd^BZR@L>jya}8p76BQla{~ctui5%flELD~wZOqQzCH6XT=8 zz3)|SrAMZUL^%Mv?~Pht=i^OHe7TZ4RU?6)a3}HQya8#0GUiiG-0bcIbjNwaMnP| zIQHu^6C9CY0tiK?30(ZC5%6u7M1l@AA~0}!v#AjL$f_fO6@PkEz_Tu6o9dkDuZ23y zY-uJKgjcU&SJHA{6D<5mKwIdWsqN58nOnA3ubxrDh?nLwhhyPv!K9xW$@&5|XKCyX)i35A~pWef#&yT!Ef* zSi*g}udr<}q=2AOq&RD2ykKs3ZT4DlL-H{S8k6l9r*?6=0VROd6VFNh9+yXvHE!0-6jzxmO8kl|rcm86=J z?yPn6L^EE_q{>1miswnugfzV6~Uqai_Gfo8Q0htE34qYg4uTt|duz zsbjEZR-cqD$4QI)fl3}A=Mh&2>5DwpCQ2p4@_ja%RVN`03}riT@b_N8;QH>vhkE!x z#F3B-x-1*$c`uj%;$L!LS@6hlYIY9(+b!@oA+&e=M*;CElKu>34J}!>Frb?W78Bk) zICTLzY#K=ajq?{*d2F2pyxg*+nlfXWtUI5Y*rJo6hG84u%aHyFV<`(ii@N2#6CHcs z4`7?C|js4Q$W*X>qj3+7_t_UY%2gaZYhYVhM;$N1id8+TLZr81UoR3w)( zc;9vd>*rj3<2${%hO=FcDPv@wx0O-Va|;btJIA>^w*Z3=v)i8fM6y5m8FhGr9fWhw zX~sB@T!#3|7gCR0pfGy%pYRY%UJ#MPH0#{+8ru(`t0~s;ggt*LV9UA^8qYGKC^QPp zwP_QyyHa8b{C@rT(KXuw{f?x>9;y$H_%rWFw*&psI1j%anCvH{^KckpoF&sPjMRbI zaZrFws_tB8sMZD-V-jXta~=3B5VlhwWM4hHP0E#U81i8CiHitkK4()9*IBrpzFWh) zj*+{<`+$-hQyaaF0QKlnp|$UOaqoc2~?@1FO~s7N9Tr6z1wJ_TU02c89c-ecQrJUTk2p6hgE ztKU7lk8AWu8+Yz#gi-kbzm%lb`;9Ji%9nFMYP0F0?9?nh@!H3ixu0Ejd|Nd60V&bC zY;tyvkROQC1M( zEZmn-DLYY>8EO1i*2qwjt}u8cNfm`sOTF!RX}$n~S&N>aV5?pa1r&)rkg>8w`E5D+ zsm@BKR6+9MgdZSYR;zQaB~g?)`I;wT-o7XrnQW-0cj(T!3{rbhIlKOw4h*I@Mn83= zUI2+TLtGWkUNFCN$cHHdVYv!i{^w6hEd)$h)(^q~Da5~ViHLC<{~|I$jCd0^$7A2v zx2v}vKg+5nbmh#=X=A>&pSD42%vR_XLwu9D=gJJF6#bY7TMpXeHkh=R!AZt>=_vF$7-hlaHiY3!FS{l00W;a=(#NCyk>+?va_#69}B~M)c4%(mmg1aF1Al6;XQ@vx-pC#iLn)? zaQ~Vq+UdZj04^Dv6r%S>G95jw8>Xec93+Bd1`gsJ6p%9hC*q?0ng5@kL`s$RljFpf z^M;p~w_=^d_o@f?@gNEA%kcuC?A~XEG<`l|SS`mGug&N#cOdA#>oamZp~0q`;|Zc>#QONrF;_!`p3g!#$VFqk->Y!$lR zLK2yb!IC9{%nTDXOPT@&|9a+0D~wk_u~dcMgK7G8lB6hfJrMN3bID8o9)&N*;-@i4 zWqoffBZ<9I+7N zzP)rPw=gEAH|ERc&6@#%DbS5?KsH9;mKs#iWiJJszk=%V`)4_u!C+-ngzn{S$yj=c z{>P!>6nS~G%(?s#hx4%ecm@-2_+Z1fryd2L`;bbFlt%`*Z%!fSToJq=i)K2YiOQA*(_YasfBEyML zCt=_W-emQ||HG&nA8q~=R)R!B+o?I+Y#4QyE=z=VUymq!CUWV%b%XbuY(8_W;`f(}?4v2C8{By!# z?ET*BM;CZQoaS@z_-O7IAnaVh>n#HCOJciK#o+nm|2)E8uHSR{y}8;LJ3$IXfdiRO zefmrc;b1_}LmUICuuh2sIYISn0X*{|(esD-5KvFzL<|Re;yJ*2TTp5m5)k@ru2}u& zA7tD|xEi>%tPoIx0|}D457C0~hGr1E7&}xR>lMI>bRi%dBmV z5MtK+@hWj*ak@sDd>lbR@HdNRn5&n-(3^Nvy=))+XU%y0VKWBTO>35CfFEIS9Tu)s z7}vfy+^+>s8Vp5ihLFUrk=P(&@TVpFZ||MrkI1kD()x@Ie0&rd_kd0_gqg0l^s!*08cei5@jP@dB{rgeC*DbnOUX&b;_mwUj zQ!(#VBlh>CPJw!XxW&rJ4OPFXaRf!P02c0@hCNlmH9|OdyM52;o4%z6Ar{~{tulBR zMnnq4(3eOziR~YTG0g44PV;v*ZQN)GI|f$|pb?t*A1oHPE@KZP=9XmWZq!xoOAY_b1-Hvo3B6JP+23CYcA*9Jh=3x?dxPT3qNED7$A1;xTSDW^No z;NvfVAZ0w-EOfy!vG5Gs=F)wM0FBW zGYH+%XIq?;JQx1Q-w5d-Y;1ze|vC{TQj=(uRjIIb;3rJnmYD~ z*VQo?rd`Cf5Cd;;Mu;Wfwr$%m+91;n&BkoSJ*ytvGloSIG!UK- zUJ2_Ge3IxX7aA8@Jbx@tCbtG}y?BH}Z*>?S+cB6ugUFGfaSu^$qo%X8}{ z)2-`XWlXiSvac@HPR&7l~|)v>8q zipr7P7*7PV06 zz^P6#k+(Ui^yc^1J*bk!B0nAq7Pfrh5;^T%(X9~I${Ss|UeW-sA78<)@wCkN-INdta-Brl))$PPtt@I%nW${ly(e zX?Jt|R&?{l8Yj?ZFBVple?rqcawQf_gF6;DICCUq%sJ`j_zUhK5U^^t`3D3v>CKXb z8isE9OZLNpjUd_xDoQjcYiMjVfx?>@ex~!mG7}yQuEm0Dq~T^Q`DK#cqyFbYIA@9y z{#Jv=H42rOPLRDD7uCe(MQQ12VlXfRLsi&TK?6D`LpR?g`!FGpA*0l2n3a1gW1p0E zBx8fxK%}61`la!nxRv!8KYuN?^0l-78cqHw9}!eIa^_qEMMIQyqE=kr2fNLG7ZOrI z^~<&^tE$Dyt+4K^Ij>V&JDE$LTgdDGWmhKefSg>$V%;uelw(!g!k$-OR9j1J4uKc=j zu~vlm%qXGmZZ6NJB#oFYY6tPM7w?~{)jq}3QYhCcJ}2Wb5cL1|eR?m-}1KywKH$Gsc|3FmvS!&VE$-kZS~tto-rPJh}2vj$(+HAfKII+ zHtbfp3v*7r_8Sah62jk<=qIR}#P?j>@25IVYiP)9G7F4?nt*x324C2}O6^?NuBO@Y zMv4aP2r89E@L4f#w#=6O-}?~*>wZbe{O0m-jQcfH^d3$PxAQJ6TpKC)`YLX0!K-kj z{)*yXl(R81H~v_)`;pr|y(PuDkMe}Hv`Y^7Wh9ztXjdGVx|f^lsG_xLwc9G04JD`j zJ5~lW^f~m8c>UV?{0i4|DK2k{&c~6Wxhlz9!#N?%lO1jLAAi0`Gxmiy!olRm0p0vm z6l{R9k}}$1Wl8qL`5{>GTtJPY1RLc&Cg-O-ZSq|{8+#K=bWiW+pV*oFF}lZT=Ipkgv}^8ha@x9w2C-C= zVnecd)R zDTL89*9x&Tsxd132RTT}qfT;G)zCct?o5||XW4ljr@2XjsAy&wa~2))l=!|==0#UI zH(!0kvITc_z24(pX3=aR(@w@J0ct%C+w!(Ka4gmy_fVjSDQJfNya+eV++}Mm&f#oW zOJ+@y)vXG*>ojq2aQtby1%AlHt3g#&Rmv9n)d>jXD;83tuuN6J8# ze_fUTJy=7D4+d$uHX}(W>=cNGm0)Wm=m4=@{Qm%t6^h(IQ;I%(ske+Z{d;(ezlV4CkM45_Jvhu9bNnu%MTrB9x7|(!lDPg19Cs4$}u113ZrUpip>9+kWTHx8qln zR&7%8>3HB}jF9Zi+&PE?PjrdCtp!V1!w zP5Tkd?qrW54#eZtn!*^18`^>|Q$2>hgpiUEb+dBYZ|kUkkKOPWk~k6j`Ah!~hod3j zCkPYcS3CqN)egDVL;dF!wP1*Rj9-}wT?2 z;+!#WmAU@DU3;W3S?Edo6S?0H_8+$$bbLiB1a8e8ydYW-tJ1q=4XnbsyklEzaD>OF z`+%eF_v>VLxOMB6G+U!V8ZB0f!~&GA{tZw6d#HiHoh{qU%lfjvdXD>(J(jo)6EP&L zm?!({$`;xokt;IJiikLQBY)BlJ3SwT`u<8SIZES+eZIEAfhqGnc5|$s^#%=0cZ<@; z&g(*pHInX)>CI(dzpCtcL*~{y!}WW0kK0P+03bq+O?7i9aM1giFbRF+JoMwokEdl; zIa(|_)}rPABTYVvI?_tob6k=J;09NRA>-hs2n zaV%>HiPLazkB&CB2(Yf|8i1+@ih*0yA0~UmQu4) zX6ho?wB!YwzfjNM6XP|0M-#g=Q`C4?zgTY!e8I1q> zv_|Ypl5_honrY|JdCWR(;(M8y4x)TXID(W0-eokF3Glm#)tt-qY=7eJUI#kxiFuc9 zmv3~?)j6bWz_)dvCaO*Qf=AJ#w0Cy>H5LwO+@W`{u8JYZQEU?@-)$?lA$t_1|dU+LLy`yL&>;I8IvKZCn}+oL}kd3Sz-}FlL{3w zMyMoHNEyns_IXq9^M3FC_Wo-h`>)UOyuA&EweH`2U-xyL*Lj_%8IEi{pqU5=c4L?* zaL*U0q`9|wH0cS^A!Kv5aooFvh-Q8DEGA;{AZLJmL?t5`Xq1(;!xDq&AF=w+5%z!y zp8Sc}N)*xT5h7;p9~ruD?mz88^qYuwbo%n-bQNIXW?ml`lJ%G;wDa4xjk9YO_Yn$f zeUR;_owe)*!1{Kiw{8>~3K#|Y)qm2@?AK?V)Z9Hs=nuq&r(>&5bXP>_Y_-wISz@p- zYp_>B%2a?sN^o03`O0$pXGWINw5zvv?Ot##NZK=(TwMKG#b@&US{(~|FD>k3L;U$cx!d zW2yUbRW{tWsv?jA379an&m>wbY;UUn)P$lg#9IhNy7{XV?7btWK|$;R#jODW>j~8# zuvAgco!}7=>nB$$gvrf#wOh@>=%TAsR8&YeArU?V`v@fvQ!b+rBQhFh3OdYD@bJr{ ziD5_Wso1Kq&p{3a?yd6%W*l^8*dCFb8ZqbZ=~z)&1IPaBECV%8yNnA?W7&FujX0dltNQ9L|bur zRp;=}?RnvlZL<`=Y2VBs@Aq+v;ZD#G=GR)OxrbYq-XDFz%yH3aQ)y+?J?_+oc?wwz zTk}qgbI_ZHhlf=moQ)Mrf4v39@hFZVaKWaT54kD=fNhJcGPlk##PTxqfwMr&Ga+*G zf%SMwyh6|{iNa5V8!LY6o2P(oJD z{Vu6UVC(kU?=l-b`qx#zJQ-(b|3cyB=TRtl^90RtR6#Iqs?;VhKAbHq%|BLpM2UCzOOnvEzt8 zCCVl(6w=x{IuWt4=E9HNV87771d!F9_@yA38;L~HaQoz!D5<`bggooltO#qV=)FJ{ zJ_2@xFuXw0#$YCDn5j<`LN_zk9e7JBHUcRk1P4uaKyzgFBW~q4pkY5BexL9SujYHY z!%|$WL0=+81!*t{X;-LSA4{tMr5BK#Fo3mX z_{g}tHZ?Buih%ae#vPu!U~dEsVk!95cp&Vgo0pwBS|E+aj~@opjEme;(ZpKq`qdga zcYUVG?%P;aDR@6(*E02=5cT+-WK1pFaR0`Zk(8bv6+*GTbh*tSEiRNSg~Z{q4H5K? z9TiSFmDUCJBZUcK7+epSAnxMO!i=I0U{*!O_Ad-nFt69-QXvjvU?RYC!6cviO^Oiob%6eHdzv%>13E;2~>i_xLdBA(n5aa*xx~B8A&Xjpf~d%Wt0?zvh7jXb^@X z-mv>m4Ue`sxhcBNb_gC-Tc)W!zm(goJILhhncDKxSN0^Gp3&zNrmPWmP$SG_Ti8L4 zSOZyeEA5nu9hPDy_|IyW385OV4>W$pGqHMq`UI2{&vNW){;)pP_4#NTdTqeglhGI} zZ8<@-acK9UQP%i9_)n>s^L$`5<-r|YrLH7FH))#p5R0SiJ)Mo?L%RBd91&7gU8BG7 zQ|bkGDz&|BO+V=EGg|pIC13dMT4$P2#h~Ux+C-f6pRgIni+WPH^C~qHg8>c#i|;3K z^A{ROqbV3M^hRgq{?>?r${$U++|LE3>&=wc=&1VMQRA&!Njv?LYc9R}V6)4Fdqdx? z77hC`N0~eMzOo(kLo*|3^+xflY@%*)oOn6G5&QYOH@Cq#?h1z+*Nz11ytwXYxigBq z%J8YPRMKpPjU06V9VV32aWDSTlf@_;m4S+opA7W}z4TUeSuU6TdHX9&0EaE9Jb2Aq zKT+nKWOU9L-)rNt14?U?HjhhchG)~>8O;pHW=h-Z4qbJ3y?g3nLCdw6%NPz2yznPS zNxA*nK`*>5UVpJbi+MQb*eP&fN=?%~wI)qe`I^NptNWO}-di)W-t&G`rt*8HEx~_s zjEWr(z-!#Wsg z;9B=GlH2C$p^ddj7>BR~n#?3Ezj}6g@|8q%C(_V~1OO6oYI@WFp%8R)D_jElK}_sK zoY@Wm3)AfBo!kFp?V{XwTay@gepLz!*W1haWetf7M;t1ZD#)b{v1GOsE7^rws!ROZ zvb?uhSz!1Ab!FtsVu!g1RbNRJsj}4F_EjtLbIp-HE)*X{$uv5BX=vQePXEp1ljJEk z-=z)qDzlHxIAtqyWGkOcQW88N9mdb15Exp~!yWk|Gphg86)a;}zG=jjdTr!ION$Qq z`ue^c+vjJXr4^6+Jq5vY=056P#!P7dH*G)$k=vw=wIKx<0rF13YHyfDxPOVVX&m`6 z_u?oNEp2VBq+|wr!DHW~nd-epzx?9` zO$kd1blxcyD&JZ!sB%_%L_dwoC~(dpoqAt(#xdr8uq(}(UK%%Pm>F-rzkbA2 zLqp>cW~Lt-XiWDcgnf1`9D%$aVY&jXJB>SvQ_}{FUBay)NZ`3WbJSSSXQVC)snW{0 z&7G&EOzA(z%0|Q2d#C)m8QJ^g5T9M=40}sN37;+dpL6*^b6CGP&_hatH{q22%$`+u zJ^Hq%S7b;83%A6_vHYq0Br;sEyTu+>J(3#e9`n}y=-Mama{8gHdC4=8{Ep)wWh^N; z*x_g<80$MR^kn;JSyJw`tFi0cIdk8!-Iuv4FmmiTvZhC#E-o%Fi-6wMpe4W`^fVs( zwHJb~B*mWJhmc0F_survW-neWqv%5%Z6@GCT?=D->el|?^1O8`e14ns^jfQKw!bE1 zhVIXu)#E9OHx+tA*W9itHONz_m6QqOujSaDu#!9Nj`g3nfK$(H@^Jr{gjV1apL^q8 zGOmSffO9S%8oj!{g;zlRuKON+-VML@1VJZFE4h%GpL2d&2uB@^6-N&K5;`_=Xna=$QRj+mK%j|tY>t&%eD5wX< z%Q5dUSgc>X)%u{Wg?!uPIp^4v=N2OAR=(j`J^rCB+(`|FO`W4DDGwF5g%!7l`g;~U zW^GI@Qwvn-a5l>lmMl(h7&bneP3;@@+LvzjAHAh7Z6>J?+<>T z#(NCT2aif}$Sj<|a0cnHn1(S>=#2Pb|JNo#~|{JeocK!33_rqVbx6e#UsD zZ@SDmM%Iz$hgEX{Qr@vtjfsTy+VdifgPC%Z&te{aDv z=%x3DT8>tH&DeP8A-^qqc6Iuvi*LPe9+Ro~QuE`OlfhD-Gio9u-AO=YLx6~-P7JbXHX`wk^AKh^SmT;5_LIV?A}FwUX0E!!LS8ZWwABDPzj^j_ z;p4E^f7Y&EsjK-l3wb!t$JpC9y>N)Qe24K+nX!sz(5ivuWflTVoPU7Wt=7RJt4;b| zW%2a{K3=ydV^bf@uSb9B_GP3*9^+|c_x||sLf@5B*IwZ%XH4qJIhn4!UwVbZ*Yn5y z`ywYx+T%LTu{OU&L!QcD1scujJQzM?=@fEfYX(jI$+cD(DXry+E_TVOJUseCws@OY zNyi8Y*5~RjACtW0SWyYLC%W57Z`bW$8__Wa4sNReLOc=F0EQc%{ukXTL?- zo8D7*2H8E&89&owvK*dxDCiMUWx(7YICelzm0tK$7VEt=_vqV0Mna=+nRV>nzp&k~ zou@TDV$zq`E;F|A>HdO^aasffo%wCsP=QND9MlF|bKj{y2j@H+ZXwsmU4rR>s(+sP zqmM4lp9EJchDn?3W-7%MU{b=FVBWvY`}?hp^*L51R^0i^4CNQ(oc?{VGe2z=r)j7* zR49+v1s@x%5l|i{#HGTjlZpezYSyN^dwfg$Dmv;VX_;hcosF{Y+LZBSXOAW7=rrZU zb)nK=Mx9^0}4LU0q`8x2w4RU@>m7EKc{+92;CRLpAI&Up>1nvLSK9 z^DQ%TYTtjCTj!^#Ye!4vy{s`+peF_?A5QOV9lac1RJ~@cO^%&fwvMRPdxeRr#oZOw z_YcS_&`qQcUj0(CJ*;}8zI3wL2JMGQ#=p?kI@<`2!!g=@%=;+<=wLFY1CU77zYa#! zty{P5g+b)|z`KEle{KOqvvaLU=$R>rE&A83b-dijfqL^+);7C=k)EigzBfAC^uk6G zn9?&B*x!0Svd3uqrKl7)pP0nP3#qDZd6yqwjNh3)VWK=6Z1!QoLSR_sVs4Aa+giVd zY>#yVTbr}1n|!mAig(*~o@N(3!Dy~7`YtA}eFHoB6tSXi71eycmphh4S@H{=2#dK* zkI(;H?-&qzwBV)hC#~029p&bZ%mM2D3VWyFbRCHGo7=f&ohquhMd+iKi;&%%K(=J7q=2 zS~0mFKaXJ%>^=H``P74&vie?Z1GNDxg>)u%DYnL;xyJ7DV1Ns@(T6$0JXyYmd9MH0 z;anM<+p^Y%R@LPqB)M&`X`PN4KW|A`7^d6XgpLmGgSh$v?MV)p2OY8hfYX0RFEs8m={H)KwILs#6w%7;ZiO>Sm zgIPv2KxC*`c@t+yBIf~!B@=Vw{e(_f{-lgdAjlv4GUhExHlTTleCAc{2A-0VA{sS& zTkk-`s!0Cga^A3NA(StEsmZUoWQ0SC<_J#=sPvmckATSL!JAmw5tgi&V22WbCQ$H* zsHjvJ80wRuIasvmnkCMj=@kq;aQGAztA2yv*I;X5ej@$uZcMfJZ3%$=T9q)LVf@IVESLX$wEbrAyO zY?Zz4RM49UTuz*dp(q0)5^j=nxK`}zS|iV3J>>GgQ497f z$I!V^H84AwgjxB@^LcYqSgcIbHz+lG)(39BS?H3PeCxx3dKIqf+UBL(Lk~>isMgf) zaGK5E13nxerXo-vttU=bLjy4E{wb}?)0>u_mDU(9sDj2NQR5_1+?8FfMECJ6RBFf{ z8-3IexqLxXfe z`{s3FMiS)-_}7ud;TYh*3W-9`L)p`b<20Fc9KXH)|F zTn3n;JaTM4CG=W@gMKkf<{kFH*m6sKYi+@y+iP0+H>?Va7D1)bUS6qAR_$-MqNhnI+VA{!cw?}Y&TS~py2+hBJ3;T(Q*Hs z;OcC2w-N}=UB7>by@@!0{v7Uvc>`Ns8@f5fR;OW+#%N!|!PR^kBL!_a$CL>sI?sdb zZ!#}~p1tg;EdgAXK)Tik7A2iH@5vu7Xk#oVMWcPSUQ{=G00W|H!xmFs2fwz^Ytj$l zlgzXCmNrtejxqwUM1A&Pt|35GcIDq<4eu`uvy3{&Lb8H3=mt0*vPR`)GHzd99r$|@ zMgHpNF)v+ht*krWSFOFSH=}b{D{V=^uDIMpanNzSKVMN!iwI)_dxqTn4Q2lHfcwBB z?SVe*Z3WuW1lb0|%qet;Q}o4cYc|nrYq!KXRD|;oUtH=XPI(AhDBxsGw|Iu&eI%Mi zv0DMiJC^J*`#v<6k_U4#2PyJ~*u*hmVJ z61=$q;a9c!?@L;)L}m^Y`p_(ztcBZh%*qB@QkK7KrAgBR^1B9W2dAbhg$1rvb!O$X zA$*&|7Cl=Zfb)3V=-CK=G@o($vh+Eqk8~s92O5FSY2FlQIMys0aaRuHmD?+hueP*b zknR!MC!+`u;uToev*AGgH3DY97v!4|3$+8dhMNWmX5VKGzX;o~Fi(DprgNpK#=!PB zO>Z9hwOr3NDE`50`#L7mxYX_J^#Q}|hT;OM!OSVg>b7+D!gnnO*Y0ZDoD8{oc-QZ6 z^Ud%MJ0&^`c+sSs$yOHF;0a_*qeq^`=|80v4&MYCq^`kl-M(e#e9DSoG(^B#SLlPK4-!^*+lfBDA!%)1`i2R@ski-!4e~cb%LE@R047XNr{Ps zB?czmy+urH>pgvNNqzU%ICLm`Y(C$$wC%@xbQerN$@WlB@k%HpMHtuCseZv4VKl~}GCX?JxD5eV1uH-tM7 zBp55><0;Qq1>kz$bK3&OAqulzDUOwtQNaF3mN%~1hBck^3#7Iu^E8ClZLGW@dl!33 z@C`M%a(Hg9S(4Zwp}jcYi>F-D-81a*gvIEYlxIf@48_u#R8;RH>A#fnj6xZW|3uGT z6K!Ce5|!>>Ymk1bKIY6V)^V32Q{&hY_5R#~-l_<>v?MMSuCNVvwEQ%*tzDzvt~U9} zS77~qE?NBuAUiw}I5V+rs;sVNo|p!sNl2v+v73bB6M2mSLJr0xBYTzb(b3CI6Y15$F1!K+PC^W@VY(-D~!>XdsOW%2t$k9 z77U=h;}^ohm?h-}ckbM2OG>X+_sGX>h!^L(LA|u z8^pHOM&ZPbj6r4dF{o)QZ#jh|ux3Kq^Q-M;x6?nEQ*OVr)K244iR6^iUYgrzYx8)< z+pdW(*8SzaMA@f&PuJa;vJ+P;@@q~~&3hsAnB|4!lKR#E);3)sX`L(CvU$_<+OU&e z(K>Rx0TT_^hC;4(uF~xjY|2!4(f2sJ!?xzwINB{gCS%g_{$>e}ZuHNVSFo*YN;W)x z{7j>t(#aN`8wu;W{Zcr0XQxEw3%%DU8GCxy_?}(i;QC=c4>kJ)@PHR&wjO>V9Ov?> zXhGc0Pb~}Lay}OQ{X8pmG{>&_9E-{#pQHP*_VFm z?9}^`QB(TquwTh{J{h@ju9JT4EhjPXte7wRK}&Ou5D4U%LyF zgJ`%-(^0O3CxTZ3(q~Fy;>4?XRIp4fGpJt9z2R#0G=_M3yB>)p>^~W zpX4%NA^k5mj;WL?WS=y<>KnK{u2PThl5W$;7@#_BC8cwRv%Dp?QScnJp3SwdS4fI2 zf4K0;Ub!zH?KF_f_8I7u?#_SbfX~;bkI9H8R|`z|c8xE0eUC!BAXZ#MCEx4aYkgD? zNOT%XxhQ4HzK+zdaD&{cpFXy>i)#OdqiwRcs>S4n&d>{^Li=2&Uq`}cPCi~fi^<>q zBIPtMWc9!GyLm)C)sr~u)(lWKVd&}nv`%Z)XVk^BCPpydPV-3NsNAuP%sKQySgY(S znEVjQ)vuY*v#A1w_b7c_i{_LPRRD*aO0Y}NHyT^p-P<1_*ZWSAv);#|C;qyF`CUdi zfAy2Ue(f#W9ks7Rym2JX(>Ca0xkarC-|aPd?MRo?BjBGB*A=dd#O(VG3hQ-1s;vY1 zNFPf?r2Cw0Ooh`WzcVmz?>iCwoa02`+ttP|FYmROZCg@S%F9`ALf*0|A0kFk%dcv) zS4>o0{^8->)X8;94SFKV%BFWigCmTG(;iy#jXU&kjjh*%+RZ(~F+`YMQ#C#=G{~TA_kG z&FVMPv6eqA)8e@jWV@sfp1Hm;aOHPrS9Nv{0Re$|lgr__Mu@tkCm-NVETJ$)5x>#t z0h#EBHxcm!QpmpvU*)-T+J>uKyuwcEV~UygFXj`yg}*qdqwODd^)^1Y`N=&rluKvj zRj>-svX?0(5@q;3)5r+lv_^GRRjF6DT-BB$V(D@kymdoB zfwHKKaYv8Oh;7#>Fgazz?vJoL0(44HKtKif@KtU?D~T4f*Eby4zoeT}b2o2PJ(|SWYccEH5=sx3$PaCe z*6P+%TyEYazd=RB($=fsbkR)WkBC0o_>sR^pr8`|OmeZ}UnpF9ep84j78|E;oQ^h` zy@U=AhFtx$3Y-ED{p!6^S$?}o*VOod-|LmMDBIvB^T1o#yNZ}~k+N!ky2|RX$}ld~ zep^%WbM9h(gR+75DcSnd+|Pv-PrRSc%9nMJ!pfDUPGQ}cbx{8E6}FbYGR(_lTQ=9L zz)t;qS8i2nmB#GohH;l@;G2rg9Z#XDY02|ui%M1pk1SJUE{V?!-xlpXN;c!NF<5Z&Q!jEzrxtl+U-FdN?wQZ7{cBn#A)i)d! ziLS};94Ab15!Q(0;mI#xZ(tg%@7nxnWIUIifhO4KXmspITXK>|^vFFoW0R0Rzi^Fy z5%~*aUP*k3{BKyj*Gf}tzoMRrD8A)=YB1>guQS=7H|+ca8#r(W-Ff4X|0uEvx9Q$~ zkAW6FuuLMk32PP^&8B|2YtYdN7yzN0-7F450Gq%~{d7?Tne}+>;!Ql;Y+clIyMrd) zSm)|(^zul7wZp7i?XNF46&fYIwrdgVBZWbRxZ(2!?;74j$FH%8nmPXUc!=beHJPmi z>izc%?CMV6ZgD?y7f^54_Xk$uQP*sP4Z8C@`@-AJK>X-$_H6r%hC(e%_GgKpt=1ez z=Q3+s0*WxrN-}U0ItX?}QF1|D!`bSF@1`5`?EJfmzh)K|>wQVP3`S*xgI4;pH690t z;sD(>NarRLB^K!1YRIxIR$ww=yUI5I{7EE{#t$7cJ-dSBqEt(c{!Xip9(4umVlW0oNn=Kz|$TzcpsD+G6$Sn@+#=>taEEqmr!i=NyIeKj*HFDxTHZz4UH=Y z|6w!Z0lkvl5-G}I9QO@Fzk0d>qS;%8g4SPgV7{#cl3tKFaLKQAx@-K5FU`Mr(AE3! zcdwF$zKuY)-U*_3lE~FV5evqM+Wr9s(YYn~v0sjt^5KBtYnCLb@Q78hi3rXjSB?8d zvKT&(JUE*bZ6h89{SNSSB8~)KeZ9&zNt$wA3?vAiHzIz8u6y0*ANlhxxg_&8OoA;s ziVB*TrV^|BC+*IdMxEC>+eLB4bTFBi_V>Xo=~39v8q+H2zlbH^Y*GwY>3&tgN>TfP zErlg9Jqv2KXsG6?l)nkEn3LrIF;u^m2jo6m9gI{_OM?$*vzNftxo3s_0*Z+z{xa0GU!@Oo+hDr2I|e2)TMFH6t~+)Xwu> zJ&JyOwxUm$M?3jMmnyyvx__+WO9zn(3lU{WZOZvet85BggO}2F*sK5Bn?BYXVeJ5( zV>G~b!X6uwB~#B2Pv-g;W4;SBipz{ofc-mfyG1F8ug0zB;Dllff2x%Xtv2V7_lyT} zi=#k)D62iECQeFk=)jdkbjTM9cNCv+rimbGFWvTFlkGIp*;)C5s;)G#YcMQMGJZ!q zlH+!ro_}73C07Y5T`rB4iCQ<`%}z<csc)RAm<*3}^4mmJlj&%;xt;qMB&V>aH;LfQrK zBpdqJ`$9M9Na-G%B=hNXH-vZC2BqBlgmgvV_{sOky<6@%`?xdoUgs^>Gd}{ahta+1 zMAY5*9Z%%pRj&Fo1Q;eGsDzo=u>Sd%*EeaF72yH*p`Y6eui-2gx!Gq#p{O$;w*{2` zH2rvSo?XYAo<)y;gJMtix$-0F%ODqeqc^zOWRQKTH8nLAh9|xulZQ`sMNmPeIzi> zpv4(ws5CBc$^`zO-uM(YPfKB;%`|bls_QMs3fBj7gwJtYf&O(ND0XrD)~^s^TTi4iw$7#=Hz z2haA0-_D>I;Zd1wj@Z>D>!qIn&!6OtkX`_oIFM#1ZjSnUOITCF*E{cRgS1i=E{euE z%GZKlNSuTwZThgAve9`B4TzfDB_c9{b1EbtklLu=(0p$%`N1sP4)+YHfnaEyXM1HE z777#mHRf=z?;(oe zUV8+Eg-4yG*_fhUf8pQ1K$P>A|7&G4mH5(*yu<#}BS|enyDy)jCiwwuZ~w2oDF*A8 zY&$LfCf4b6hsk(UlnKUB=sv&&6T=zEYB;{N;jLxC7sSN0$X)V@xy}hxb0f$9Ar-HX zx#dr(c~BE(w0o{E^{IV5={BW)e6Yu9;8p8gp^j6EldKA7Qn&{MHNPY;u^O_A`ERne ziul)u!PHGY6q^OXE@B@yk3B;VKB~6Rt}Vh?B<2^iyN?l(3F1=^of-BK3B0CSf;(U! zqU??1?!UjZr&e-WlU7-D*u%G5gq!mYpYZ!`k*6>$o!)e$P1Yf!QP?yUp5WBq++g_X zVPFWf)KoGm2k1K=xXe2Jo20)5%^n&E!I*_ofrtl@wLmblENBIj{UGe~G%)s4Yj^i{ zG7`=GKMew(|E^N89{*Y(N(l4xjgR(&q*`4!{(LsYTK;0d#a_8x)^z%fT;APHd@Ub; z`nj~#8RvQxviQ;!ayJ$PJipMtD&YBf^zNPq_OGhhfqslRUMUeX1HFzPR>6cMsHR2? zEWO7EGlA|mRk`4a(cmjYRK$NblyPk~pd9zMlhcm&xhv?8tmxyEz3h_8i!^@wOs9zt z=Cw@aLsQ2NHDum3zcVJh>khlB?teVn+4myfxnB;ZTs|c2EHBwfUYpL-VXu;wm|}z7 zGehPru15^=o?hWDHyN04c%nmX-D2vQF5L2w68>P@Jlk6j#OK*M;CIWPt{R}!cuBA^ zvHN_@k%B=Ump5@51Gjz4ofqgFu^7E(@i_4M+B?he7yTig65)veK`q7Hs{WciaFk8n zq3p_gsIfZI{$b3-KyTiw8op|EM>d2U>T{0Ymg9)OkIsY}sya z)z39zuWR{Dy_)+L^1F`iHfa#p=RLj5 z&F-(e-RQNol+oOm&i-o4`zb?w^wl0?VRn^TCIYE4gbk)j{0We%j7WLCPmQ zJK&E=kNw>3)JW*7zLe!t5(`D&%wtMRCSpzGiUx^)`)=}2cP>*<8D7l#uA4h*8CP1) zrCvyB52#V+v=#aH>_nn1DuQ}Ws-U`px{|Zx*{g$xBy_~lhSWEyxA?bjMOnTb$>x6T zgCRYV{M4Nzj!jB_EuLCPyJmCs6{}{MP9bV|k9HR-ihkwgXi3gIO7Q8;FgqJ@y=IHx z3&wx@Znejd?jmvP4>DU3k;8!I%!ymW%7BV-&u^UiIs2KozxByoz&3J zi{$2Rx17HTZ75(rz-j>=S*?N?Sq2Jo|aS1TNz-H9oS552(`gy>%6Qi-Za_5O5bW5c#2 zJ;g99k!5WjU%I3U`*SMQR3j=rh@%bp=Of_DE9;yzJ&S{;Zg1Cz^!lhi3)}smb7xWa zSD^-x%|^rGKF`8tS4}?SSUu7Tc`lI7MZ%5K{Hm5sPku6KkV#Nb3|;L^>py+Pz+SO> zb-k3No_Mg>mKXnaJSaIlk2$jSF9;O%=E?OO;=6^h+byoQR>~Zz=}`*@u`ZJ*=-!@5Y z)xWDN$*;ZO!UWQ4df=tTfCTTJsCV?b!7~h_Z#_Xfcan0<2=tWEPhDI={exm1oh6~i zU4ei^X1H9UB5DPmRlTw@qkz1(y=gAd}{4VMkqjjV&@xB0c2IZzzj zM~5voE>R+g)F_^hj^A!_PC;|QzOIzL<$-e$fWY)81J|0mueMp}wO-Tji>cjfx=Sj> zG1wgeoaY5`UwvsQqBO}jyI?DF=N4=)GI#PAdk<(AR;6MGjXJg}t>uW^W0sAi5v(6b z4@L=ta|q-E%`yxNB(S4j{jZPnN5R+M`6m9EL=ZAev~yD2yiIO5`UQY-TSKe6?d)c`Jw=??CMM*Y`wb7YNlj{1<+QeeRZdeMz9@ z-w(n+pS#KTH+S@(znc5qvlYK5IREn>EdH8fa{lMvQ+CdVl>hCo@jQ{_|L=c|L%eMB z+~mc7{^O>=|8gh&^Uo_3esggD`K!6#o!|UFZ{xqN$^Vz9v9*?oa(=CBY_@S!>po@Nb5VQW`C*b}vF4dL)I$_Ti1()xOU9w^` zso8&ju*Db-z2N>o{jlE|-k(qY=l`F8HS*7&{`uYioSlExV(xeU69WEoMgG5pfd6*^ z`8*HqDBJg#tHMA!FJV3w78U{rfUguGKQ<&NGh+r^opi2`&VT)c$Wa1jsT%M+7)}He zAziY&QVWQjBv4x7HAsv!85o(ssV2k`FkT=za2KWV9ri6?TkxXaPedW`S)hY$RiW12 zIalP~dk6HQs*lGNCHKYk;%7GiIznN*ZPv^5IPph$!G7WvWCIh}Ny=jJ40S4m38}Ch zG6!;aF)Yl0ywqHqL%`8#W)ls>p$#T{_819OJ#au1t$k7OvWNhrODixw42Y6>@M{pO zXAw3gTw0y{`qI{;<#wM1Un$1WCje=Gu!UgZMg3QrU=Lq>v}PNpB@?X7v(X8eTmZi44SEw zXpF#J?*(Mhgdz46NPk=&mGbCI1iP~@#%J*3xgDCyWXK$%D4`E-0|!X~Iy$;yWsR?} z*+WC3{D?2(&u-e77ux331O~eY*y`zopTxGg#+|sNWS35);T3U3R_P7+KVivn`WH&btPw&)^>LysQGux}l!^fD2{`gG|zW}Kwd(00| z5>ay|oNV!%nQsQx&56|!3>lNv>3$th*)a8J&?AlmDytUi(ywY??ODNkfl1!>N*^<@ zm3$&;pJ|kv+;RffAh58s!IE(^cE6F@ptZZlE#>Q0_nwl>=g z09rU&q*)WnE={b}tZZzmpt@#lorV!iD%Q6oulk3%?H>B(mftrNg*A+*I40k8TEf3^ z$B_U+8KyaRm|5o<0qw0Lj|)S&dl|QH27lMPGh}x?sC%}5FBd%tT1+U|uEPN$kiY4x zCYo;Nmac0&Sw=)Tz+Samuw{NUA2{E9F<$#f3k*D%mICMO+&5AOZ%cWE!wGJ%t#}j| zRzC>tOPZa5ePR2b*RGd$NX!0Q0|}l~Y&5bB_NYS)()rA= z)|vapgOYJD902NQwf~Gx!v2tp*3I%)zX1X5H>XcT`tSIh z#U4cgW}j8KACck2HKJfHe=NtXTZ8P}c|(GLQ6Wfkz+3NZ9X(KDTBJq0@=G25EBd;ZF1W-(3= zhxZGc7U^nRZRsMij0vGqs=(W7@NPbGK6@Xpz(|x}`w(iH2Zow+6v(_H2Bo=GFtk<& zp_VvMBJ8mk6KJmFIkt+cXrqND2BT^`?ZCDf@QukKvBzJACqh}l9s{++RS;h_EAP*lBt00Q7*un>ow6@j*Z*F1cO6&Qo;yTqCY&2& zZ?r$tFatzFAK4!`ucY$=7#WA>?*34=4W94cU!A9}!#&yiU|Ykb{#DWGU^(`~|ECE7 zdCF>ZCyOsy)ZUnb%|kPQ+JR>P36)uQXv9F~biWw-zRq9FAq!DO%=d)=6EWfDRzdHc z_^senuFV?+xa{$C`R){q5o>pGW+MQo;zT>#^E&3tbGZDvHktnr>zBe&4CZG?eV-lS^T;cRnEsUXIgTB4AVV9xHJPEbz zvY8^N7kut0h04@uQ`royr9&3`nnVW3H_$NXE#42|j(X@Gv5K0-2}FU96yrB$G^-ZW z877w%ysaq3xP<56^&UjWA7%81kf(QtI@NB=6kHItiRg;lh4J~%0%GV*Mp2hay|jmb zII*}u2W(5~ea;zz_l_36fmt%qBaves>#b8JvP1~*D>a8mcLl6}-c(olRtSrxY%?vY zpV$Z*NLJ|tl?2gnVi?;?Hrm)mVk%cM+GrAnQiT%0`F0@xxvDv^g> zFA)8PfNryb)=b2_R{BQF&Sb(k?d8Yg^(`~F)I89>=I7&64tFPND-dB_mZAf}VQlvH z4n}e=h&Bf(Ewp@QQKiwH0%8}K#JFnb{Usbe{TLpUdF{SG)Kwz72MN91(Y4Mf*V+#K z8bmO#hGu@RzE+Mv;@toh+Mn;MfJ=5n6&9g!k@)tSAX|>a+|}Vy4z0&B^Qe$p8zvqqZx%52-SAy7mC8|Ke}3p%Pbud;7`mUwmf~RWnOx zChW^!ya;-4Q+Z8*H0XHrm`ZO-x>>)RXF1+>BDSpT4x$Tb3{uqt_PF13MIT!Xyrav* zq}?|2*b4@a7yZkk-+Q(?MoYRSCssbLMCWID^wdY!LU;7MCJchs?{>OHKN0i^R_{*l zB`|2^9G*y`KN(uCDT1sq8EctN-wlF0JCVw>58_StJwSF!KIK1nf3oH$Ed8lmW; zq1~l;oaiAz;2DP>-m{;}Yz`ovfzwYl-F)$%JSCc3h`CTW$C2;@vHvOr$Yny1kRJz{~Gz52S_Yw5c&!qK|9L?^Kpy+6@=7B%MvM@1Yi@1Gog|> z`bK8_eZacykoE|%G?dTXg@HS=Gr~upV0tfc&x)huVuU&&=a?LK;QY~JVAhjfBsZ*9 zHPUEYn&JCHSd)PMBjLQfo%~dSEGSp^U6fH>2g*j4duc~u z45y!Ucth>_)5%mc(vVXj43|xHZ5L~l`|)a51Dt*7o(*gD5lsRV_*O4YLT)s#+5=6H zMl!Z`{}lEtIW+V!OkWsvR?Y`o^;)^b6>ieyiG@*i$S|&{7xWnAaHpc)WY*PsLq+}D z=-out7~3f$XL_xW{!hk9)riGWj~7jV@oQUC6;@+=V;O1gq+t>3HyM2Yu!Q{m}=ghU;!zzf}dP6p1`io?rP@h+-K`!Yl5z zFj|nUR$`VtgDZ~l>uSMy2g7H+N0#wpkA%Z1Dt&jmSHko45BSo5#Vk9ocjfEd&Z1`? zizVtIhZ9G9kO>sYlHQgkHNzy6xpCs2H&LY(@ zw!18Pcq-^Qk@T8}Vq|t~3p2~t+nlm#rR5gG^vB_XoXaTnVQ7ic1D@g*Ie=Ws;u_a{ z$|eyqXhf$>AGdKBLdil@G^Wu7ewDmX^fI<1NvXnhPx>3TUP_|uOK)*Ol2>g!o@#7^ zIhIk@)irFER#tY0NIm$fQ#^Hz>JetPGpj{m}@5z`7mQn{U8%;U{AcBoXZqnZz0y zqoXHoT#Hs@T*V&1wK8?)19qDotSV*(T6o22m^CIbq?MdGP#O0%cE@CV@e$diuXH~) zw0~6-&j(HLXW-9JNAlC&y1PviL%ya{lO6OiJ{2OMgR+C!XdIcm0bx_?Wu^SWR%&P# zul7fhJO+qy(<3lY6Y)?F(BW$Gr2U11IOa7n8xQ=wQ1l>uqNyy;pMDC3Rxb*f3!Z2v zDL#X2;Pm#_I0EiZ7V{}eZizeT1=)`g{E$iWnZV8u!8%YLd}kG@Bq$nZ1Xm*FW+t06 zS!o{Vv-XMc#!3Ff(lX@Zwq^Vqen`rHrVHuM}<+rrC z9W0WUMZ2tDee)+z>d}!_P4v~j&>tRq!IWuC6SS3ytzLJJYKNR}`mCe9D{kfqx$R~T zC635{kH_%q2+rAL!6C`6YnK^5Gl4HLkKsBfJwgSo?0JJo5ugth9p`c`JSu9#jvK|v zdM2lnlS_G%cxz89GJZI&bYJdgjZbpg`098z?`k*>upaNw)|n(3pz>@62%aKS1Iius zC=n(9$<_|4nnp%Cs`Q1o4$)*{k^6x&TU|B{4uTzi>gN6*Q!jpSS|Dqqgr2YNUTSFk zFxhA`#9StfK=>I1&K^X)4Ji zIXguvYN*FCW{8;IJS)V;wuUNiPh?T3qk4Qoi<77&I9iOE1rUuylfL_W%edDbWSLGi z-jHQF>rZL_m>(M(n~fi{FwBlH&$PA%> zF(z1!3OlSElvzD)Db{gyUd~qSY4^jCD8coXbL=r>ShOr<+>_bwRQUYZkbROPzn&v4 z#31Eza%s&cG36p+in1k)QlZ`X!#RvyTKr-yMg1&f4q+0_53&{BA{A01wM>gC0p}%0 ztQJ>T%Xf}qrBaBJ9my~hC0RF(5e@Dm#mS*p&a&Y2-)qA(nRm!yTc)%(pV4Zlj?y|%q6Z9|6>`_pqdn|9RZA`F8(Jpz za{Jo>26Yubbyf8{3>TOlT{*Ao{|(y>grm49uycy_Yq`Ur;_PVbs^>$*FbMyHsPPxL}19Dr$%fw-wwo7)~N zDh~28H;^hKrM~m?rxzLGt7T%u`^WeML0G0*gR)-c0Z)+`t=Az5iJ0eCLsO(XMcAPv zEelvDLD_<-Y%<3P@}oz+*1X)Vc@ldddtHOn`fJB2Tv-tj5o_z}sh7?3w8+Yfkgt zL&x&Q&{x=z*5)RRh2<(EveY1(O@{6(xa`D`o|sTsuHT>cl)<8fIAh@EIT-F#*u?_q zK(=z_yw``2v6N1anGiKiLJF?x(uNa7eVU#S5uihMqKEP(ft|O8oA7NvSRLB8?+k=T z(}{>Uz_F!8)fgYJ1=mY*iK=k)EPk*1z_C0|$>`ksQDwG|Z#5k3Q^ zM+k{z;@lQIL(*-{K6jozK4GrhGV@q52-+TT z18jDaIP}Wf=AIUpUQ7pLwJF{P*xG5xEXDGxWj560Dc+g8#NswPv_F*mFq4*5ZG!_9 zp(}ae1quTO-l5B0GfNxZ>_sh;0|8q3Oj!QR3Z;X<4?qy`1GXt3BQnF}4%S?tO#gy_ z8gv~we8252JiLv}UJ?>rp2Ga-+eGCYe$ys38?JEog13D2okL@aR)Raqfu`Qa>*PK3 zYqm@bXs zCMdz3;B6OOMdj;`=Dr(&7F<96{FXj5E;o$}GFrmr&ZkeG>XX$sLX-`+TUEyu>YP^< zBd71>NzD&Ch#>lrVrsW3|6{3c++KY=gn%c?jA2rOF7|Yk0xa0aE5=bIb(WiyIDWTa zG7vC;Z%bu36_z)BwmMa>b#Y_q(e+0Q%xPv&RnWK#KzZMwTO#6-lHZ}<@#y14OaJ2y zWnDp87Y%M68CRpx{^Oyr4gLsPGqoX+>dHTxE{NyUGX%gG$cZ45CIwEAd{vELhy&Fz ziMSo~y6@jEA9q3Gr*<6x15WzSpjXyzvnvk+J|W~Y$410hB;U zQqqWwYeM7`f>kMi5fr5@Hzn)&9i0TPki=^!$)#zQxv~$w+76AomT0QeO_y`l)oOTVT6O zjlXwOXq0&{|H)oG#=?d~YFnCPgUuvj&rE|GlSfa9YeU24CypjdID8r=zZE?*6T5x{ z%fFYKHz|~+7<7Pk=njMCi7Q{3v#NcPj|@#9`FYD+&Fdz3$ll35DY& zBv6xtjiM`_o+*jzZOpt0hU4Jn$z^O3ngH&;Id6yjE7BAf#ZL^IkO!5=in>8cKv|MC z&JII`O&v31yNM+s0d(@=3`M5O$@4~mnoM9LfJn3EN2DGj@~|jDbsRB}{n1T=t>|<3 z!Kqdo|f^Wi{8-SI>E7kqd{ z^{({bA&f|qnF(D#r<5U^6)|){E@B zTP!LG)^kJUw&Jh%jTr?GLkVNvb_3$W;T^VIzH8}g6nMyB5a!cx2DL)}vXwf#Ei$WQ z%$O*6U;-IvKFK4Y0&7ixT_{KpP9cCt58U23B<+|EuEUNIg^@YNrVrK5ES7&&1h9o9 zvGg&5^5DrY$n1BQ@<744fc+UXpx5WD$5meAPJGLV0TMT%w4!sNj|deqscWjTsae%1 zUN>{2!|0g_2f5}%fuQRRC%)>18AKxf*klecJ%Df0D0GT0AoL^-+5O)1cMgo! zxtLC{H}Pz8O_SYS&$BltKDHX6zYTRtit}uy1NDgNJwGn_2i!xF2W5jKRBlR=BAfDG z0p29a!`!o)zE3}%cSX7|t)ZbBBN^oszLs~>)WyD?r1rZ6JmO{6?0QNwQ2FRoq!Y-}9 zlx3?XL|Wb7NOmvCyGq!S->|?d6!0lzz>e$(%w7>-0K>WC#^dP|?V^U-O8k2Li+mVO z%m-dzyiWZqJn6srXGLG$;MA)9t(n;5%m367#ecOv$W>fDn0yNz`Fvi%_Eu+)m zb7{h$lj?>sIug;qB1 zuQIJmf1MYG`TE2QxtYx>tTNtth`KCY=P7@8)_m4fAw|KpE%Pi)8Y16;@eNliX8Vq` zvnl!+hFZu2Q$!)Qp`uORGA64?Wa#A=5U2$<+S!4s?kwyYwfavHI{ozR+o(6m{1q1q zS55us*LONV-bU$uLj3$S;Pb|l`WjEa0eCb)X6?{1AXV}x?t(+4UUww@0@ipi5jX9J zI5?5w)lfh&>ubO1u`|cm-+DEBXks-Bnyeu~`fy~xQ-jgM z_|n+DUNh;))-9HkS`Yg6n7N!jB^iZb`ZWpjEUJjm5}s`uYA5dxry1|CGX?A*8Oyv@ z+>XK8-0}|gKrkM8y1L$9_EaK+DE{{gZ=<|L0Qej`FcRiJb*yGx427v0$+(`j_NNXG zQCm$zfBLFbtLh7!&FN#npngpGV2~%PG`+<`LJl_gta_5V%CJennM|kA78`;sJ4uHV{19%eWN`~S+ zknnE2d|*S`G}O|98a$nxoE`}>>a|MtK$1i@V+^97$6Ko5wBQpFc)XR-LMw4LtM`(A zT&dztnNOuG`zN$nq|YgbB|PS+CyLH z%x5TB?Qg3_7&!7@52~taLpGmyw5SlzKENf0gRFl&#ryzp4^0=St~S_ppoN!)`J;x) zrfhTb?KuGJ#l*ypOyW|}y13vx@&b>g&9d%KuC(a-X_-c@lcZ3`G5e>Qc5AUSoaD4Z z7ug!)0%N_`hF3Q?__4f>DxBnM=?;Xx{*#3=cNTozl zDkRyfNM#fX86kURB??LQOhjcyR+D6vY(hvzLR3QbD21%ZPDsz|s-EY$zt3}gpMQS; z{v5}BA9r{8eBR@_&hvbY^St(4C>!zVRvU92ZGHEN$|L(1#Iw}axwL9^O@gj(YW=h+ z0V|V_dgdd@J@P1R*9X%40py-wK~5X38i^Rnh9>PZS6MV3{$Fqt=9miHf2Wf{1L*y1 z)2md4ImF)7A_6&oiFV7L=P4oC&bP*$7SA>v)&{k`sr4mldU^^_1FjR{#cD1L6x`xP zNoE>(y+ctHBPK2Wb;WN(SNFQgGdo8y(fI#n5_fW`?$kQ+9bSWx{h)!ma?bdpWRUpN z`tar_W%JY2r}}6SzV_l`3!%zq={Ec9V-hz_D%CYb9F+cW6SO4h1;+iV;kuFEelnRSJV#AUvSIlS7^ zi=Wy3uXjn(2D2UcqtrY@0Y+=UzUW5?O+YJ>t2O3-C<9XS^7GGJ^e=w=`9b>TAIg^2 zY#mT%o{Wz<8m%131gM0pHs$dmg68+nx1a4+t9&UYp8uIx}5L6qmzF35eK>aKGDyYS;$rgzT;#|nIn}%KqdOmjW~C%w42AF zR7=dhkb;Or>>!Kk$kK)!_X2+BJftr&h5r7<(kJL+f~bN-q!>*frWNZXhT) zeRP!6L4<$)+a9OV>tMME<@(%aV-cS42c+04CQn_B2e1$VJQ+TQ#t=b8ltHnmVKB(; z#@Kbw_pgrVzk>rHJ;)r!3=XVInSkJ6O^ml$de8S+QVLD_KDBn9vm>xBr?&L{%o-n? z1|?LE27U?=7PL?-V8_l7#n5Wq1TZ~1C)&zlA3QLA+~Gt)5%7o{?MF&voJ4g!`$tES zX267@hCs zE`_no8nZO(IOQpJRO7UXr8qMCzPY)X`#5rsPxV>UT*Y64_&{qMhZ-^jMIloN5`yjX zv4cI(l`zGA-NG-}J$}WxVj>nXZ0x8Zx61%{H&R%AVlKFm@G`D-5P#JSPxkhAT`1oJhz;Z2_&Gy?7Q{LdrF&dSGdaz#a zh*Vh=|9d}~iiiy7LJ-0^jfaD!im+hpMBe($FI+Y*KfvI27g=0uF2UC2$ zo&n>r!jG1w{*Fl`V2{qu*P6~QUKIZ4F#nVcS7Rq5<4AnIN8CNHUdjHR180&BHp1Ws zf{%RpRZg%YBY-ioW~=YzVnIK-_^Fgk@{GEDTlf4g7#v8UhE}vMFEEU757Tb!9y4;p zM`q!d^%pvF<;eRX=TcrX)PE=_SUUg3XGOj?eSRuTA138K9avGB)eEE#4O3*3>O4OZ zUA=mNj45Pvx`eq@ZF;}=djAam?Ff5LM&BmbdHPJkUjiz_+OqNaF9MSJ_|mEu@Bn^n z5?57LZbpN(&KWO?mhd6zgjIHhY#qBUp(;uPbG|%=9=euxTMX2|VGle1>RxB0_Wz?( zxtspcsT)5x407>IbZ|t;j3Tr^o#abLq@qSuvV#xi=GC+K@+V12Nv$4)N<_7})CCO* z@Wh#2W)%f18@BcqmmVB?^Y(X#S8G@q{)R@vH)X%DfZ7&b4x*uL+Pk1@ zjEu@=V_<3cR)U8Z!7K5!{_yl$ZuwOrDsVTD*>&E};b=hmJD#x!l;^l2c0EdqFj+V0 z$iZ2U0Vi>c*4WxAod+34<4?CeaW$m3ld(u;INxRn$0_NemcwhE!N@i;3JLuLAlD%4 zasHk|HJON1dUG}o_5=_WiW-O-#39$#ei`)%p^oOCnYa%yfW*9({u_c-*YBSo6&kU@ z4bLp%Y%d$>Pqq8sAEw{;c_tnq>4EpM`|mk4sZu$^zEeFX5BEp5_$X+AW32+*+-u#& zOUH{xn_>Uj(M6@k{EzBJc;k%Zc{qj@U;sXAW5+ld&wx;*r~&4_ylCf!|LZ-)<4#tr z3xZ$(UWlCKusnuCzQ~;E{}c>-5saEJ>{Pc;xR226pr`Ut2zYl~c-FJkZa?y7`aD>s ze9Y!B81=$hF(`6K_Fg0>s;bK~Gt@TtaDpkT|A3da_sP`2_{Kxsv@@45-?D*)FO5lJ$biTRw1ZsMxcL&<$f0SX} z`-Zy5fQ!J>;a4OU5AsZPHf^}}U&Q;BBQ-#;15%G~;{;=f_Se%F@is_yj^K^#2O%=I z1NM!uL8(_PmD@%>9iw=sivCH#!d_n=sZ3;cNP{16UQQM!XM%_L_6?oeqgkZq^!Ih# z(zZIX%k?;U4OOg*Vg%&k z6jH3<4;u|!VJaZ%+gGVgi#VYjLUUHNekjG&nlTshkXpdnacD2c?gakBmx+oo(Tu^q zMUFZkM;1PDxZZ(nqznSxR-|&xy{J5zI&yvZJN{Y7wiVqM~Ai+h4Jd zG%RYqlEZN^a`tuKq$QvaVWmhV<4jITB|!ppOX#s?>2F=xr~R;0`aT`p@CVV8NRJt} zBRUdNZ!Y?V6%2E`InB8fqR{Rsj#8758oQBe+!V*KW$)HHy~DG(@`nkIw) zX=dZ4vI@g_^>E*j!$>8UCS-n_w~?My=I?-nL`p1;sFI87W#<3QiS?UWaPkhoW*ebIcD8Ge9(OVm*;!B)U&kZq=r=!h2b8%i}kizx(*$P15I z4fsqU#65)M_~x(W4okfaB>l_K*#EMNjybxrzc=?MqZr6(!ipMLHnT9`q>(V{jJK3Z zY_F*5Ba*y-jhYaB;H5PT2@P(j(g}%2@lT69TOacTtRwfKL(i%gz-f`uTf|eQqk^-T z5Xi`2;w;t0=GH449u^ZLC3u^Oi}&B1#UMFl)FwJ8ov`o7+2HUA&YM^(MsEtn5r|2| zWKDaZ!RZ}J8k}9O@w%fL3_ao8dh9AUju6LzJe7zM;WbgL?S@r=9RG&m*@;l*lz2Ww z^7&r6n2GLk?K3^(ysg|fg47Dupp5~T?ul4MT%P|-1yPpE`~}W`j|C}BSiHM0NYht~ zh?c|9QSc@$q}=s6T1(Y2=0XlHTYzJ9M{93BIhckR)X0pnqq@;LlFSNN~5Wy)2rS~$yV!9lC>95hZ;KHdtM&FUtv^x`&?==4C3o`FuuCyks z>b=eaJ^md)3$XWo+@Z^sv5uH~n`lXaa;&fT>env_e^~{o=PtZAC#Z;IGMz=@cP55o z_rrf2Z*HHj5yu-Y8Ap>Qz~%tQ`x~rmEU#8=T_XmUZ-;-kgbYfFaDppbTZ?6m+JWpn z91=&I?U>erCxq!61pC9Y;aI)_eT}aYfO3Wr85&S@`<3!bz$Q=RJHmCqgjvP9lGPW4 zAZ8Wl5N`oQU?oMxMp9GbWlT41qiFauAO;m)18@OGIPjyv4#(v|5FxuO{VTPsH0no` zoR1M%3BJUfVX(8-e=X>&&wE(LXps+nD9A@(m~I}_MKW*=#i{t`tMueZ+E*7mUl7YSIue$Ws98 zreSZNgLC6sPzvK73SLDI( z2ED&cZA@&CBuD`Rl@9|r*cB1sbpR8bzU9>9YWzpDPk^ldy_-W=&lC@LlQaWxI>UAJ z?Afy;h*#RUt&!w^X4=N%|6P-ikemehfdBjFYSpL-7Hy}$Li z>S@(19-P=oo1~;}^U#-4tF^0Hrz?Z5v?IDEA> zUiSa-++kAse|&$G|CT0Kihutg z6&{BDNKZUznSOq&RO4`TXuwn`gn}4x=k*UBJjl+;X>=9EKTdiD7?6Xr1ofRIoJX!A z4RH&2PV}xo22#r~IG}P}{@*7?4X$3j`XN}s+|k9w#mdG;^lcF49n04zgY=1JM4P*k{P2r9WFK}MhOU{g#@9k~BIHY?dnmkuLr zUdPH#c@lIZl#!o1h_W`gDIS zeMXGLfddTihLvN^DJ>IINPD{;&l9M@bm)*) zn$AX4S;z1e%%3HWq~;w<(n;%idTtY^@*URaN1SyEFD=arg|Zw8!U^tuxM)1Zh{M&d zUaf|uRTko-df8EPfyPqF@6r2n7kImyylpUj6E)iDc|+y zl!amGrS0hXdWk*;H4M*DxvKbgok#6`-6NEF>E{%HEEN`0vv^pl*WvCWji>zl{K7W` zVkNx{!n@_81jWR*!{c!cGd-Ie=;`UvltzKMo^H5Ptjk+{j$iYB>yC?hKRx@7cFUHi zG5bxhQl80ha2_^nUoHCoQVg=PNPqcVc1oyvo(ve8bkI z>*)qU{QdopXI{j`Y3uTY@hSt|mM@JRl_G72b^f z^j8&%ax`pBqA!9_U)-9HLLMYG-@pW}O{jG%QEzKW9^Acq#im`7uTVGTfMX;J`NIc# zh@k>tR5zYKKgv`?b~mV9K7p=MFA2QBdwq_Unzr7T9sRIH&+h0qcSv#Ay?ghaBc2CC zcS$`Mo&*ekC>yvrw<|C>*bh7WpiMg~pWGJ*5bs63bc`Z9vDa-;GQNC4F7VExcH8v zM~@bI{t%=v1>mx5&)$v45q)lJ)9d%>ox2J{;|hiQAxX9lmLRj$o}N{ya=neCk{@Cz z4Sgz;B0T3D7wzBO^`sZ<8KaQdYK*Mkh-uDV{xZC|`T12?8e62wJZ6Jx8!EJ9XRR*KtuO{yU?(6O*&ro_d>gh12y zjG%N!(n}o2j0S^+&=KA$1hVU>mR3|#E(m8+T*4`S zA43#Rs1dpN(QxvoppXzq(J`&&(sY$uI3V z7W|R&U0=MKRSKR*(hZ9?<9=&!hNE1IjfCqNQU~m3`Z56%TY4>q2yu1F)B66Qj0%-DsuXVLex1`a~f$Aad-_L{MGB% zd5?_a@!k=0bV;Lf1JqVPTEUf#e)(O{{qMOBn;f9{+vI^K6@`}x%Rpf2n8<*mlbs;Dr)I1Cw9E1^ZZcI$rMN93~7}rWs+x_<50cuQw zzdJsP!bTpx${bKbFz5{l9s5@q{;KPwV#`nLhp|-ciAJ(b?E#Hf3vSKZsoB}ybv}39 zi&5XJ-@s}4WM&dP&H%TVNMHVLcJ|dkg$zb1LXwg!xH3&EA=&sIwf(nFaQL6?PNshy zqbCJ3IIiHR%i$!c;(J-g{$#`A$V0d5xDcGAlOi@{nv@BOKDiwrQq#~ti_)MHd9Js& zm({=(-t$7Q#RA)vAHop};<3zp1A`wuden{@bYX7nzJR?DAM6JqEJt>r%($JN7FrXw z>jX_{t;)Bi=s&OSndcANkR1njPnlT)$g#T~tWN>;eRs3Mx3RGtyU)kcVO2Ym^VJov zwjIP)XHA@#fOBzjOWm6%-ap5h-@(4Az-p!*RHY<5g8vt?e{8gCI)wH_?1Gul_-hBW9%1 zRTp&mF>X{#V?fc4!{=!dcC)bxh*`s=m*9*fxzBankXbIt3m^6p4Bh0wn{5^1rKP1Q z#xIJM#ys5JPY*T3i*4MyQ>^V3nw>}j-XZl&e5!N$L{S%v1N(5QhE3Qh0|PVklJMq7 zmx`yy$M44MxErM4f@8i#A?9abgRGAe)Q>l}Qux|n*)fmvSJ;VtzU8;8gTu>D-oI}k z1(tvOXgoS7={E7OSFj59e>+D<`okbU3Vbc#LqBS6ZXVkAW|Ut*AP0G83$K4{@x|*}b9trJOZavB>i)UPqZ7*FaF^ww`K35-e6p@O;eey5v3ZMI1 zL2Zk?KjV%8l8s4hY)$+pPoC5QNtRa+VUTw7=7hw6JT9Eei?T?UJ&;Ee?(;kh_RE+< zDuy>>}PVf0FsQPq$F6dw>qsj3}K6af!4smo}M1kERAfs z{no8p_tx(JG~AMQ+wv!(NOPzAcf5@t?ZL6Ju}0W=^S|t=dqTn>-k7M{1S@73JdAX# zuBizYtlph(UEDsH7q&~v3@Va!&8@g7fm1pTO{!{Yw7CD|!~lhD_YPi2ojJ0ZmevPd z%dD`(Zc_i7VCV)^K7anqC}K&AbHrwt*V9=^ySWvb-bJll|HTp`5zLvjBV>O4`eo6g z`Ap@10@UGcf1s=bSSRj|z}5H!QIDCFTYK0NLZE^289camF9_At5ciHT{nATf#$}fU z47j+SYwp;&GZ8wD^{%*<&z1gk%*+C5aNj;@w4rI+ zUId>^D!%TXo&&T-%Qy|(5i|3T=hefMp>2DpFEbvhT~v2Zn8MCfos1wPjF&Yv@{j3F zR397;!~3p$9-j2nVWV~@&@v96jb=VhrU@AD|8s?HhIr6Uq4?J9Gh<}3gz>9hQY z&8G@b!I#~Z0e%G!rM7R|SUF!KDR&zTM8_CbuA6%*~=!`{$9lgTG{N)M?)n4`{p(hoLtQVdY zmhiK)%L>Oo(R!jmMu3LaPeabPwvm4s^8AXDkwoI(Y<43?m)U(13u+YWOw@fUsVSFN zJGH!`;^@FZSb{dl$hhoO2#>t9*jus6@VSeM%YD?EqR3%BdC@5dS&K|n3Q3u$e;*na zIfzT)5qrwLIT6LZyW==QH02vCfz?@eDOHZnE1&4Vka;HHMWr|3yMtc8R$%^LK>wLh zq1_6^kp&t*>^s!3CtAaw?is=t@ZFoar>>{RX#1&DK>s^Ro`5h6l@qaQlFiNv|NC^E z{JK<8R}W}xr;mZm5e)kCGnRnwETVNF%AZ8Ds-WVvClh`B{3dZ5mzI~dW2M5Ibv+|P zz{bX=_v=?qJ~5q0$xHJ-2-Ul>wXoav9XN39#*K~WzrPN1WJP_w>aabQu-oFCz{-^? zv1@$+#a~rdzie-3+`4ruAs%3UQwOliV7GqZKD~wj9TY^g%*-1RLjV&K0THrubDQRW zMky*Nl%qzZ+p^^7m==aMO5TJ_xw!85yT9T)2UIO91Q?3q6zw{{dKLsue3%%;R`goUo@3|4-rb=aui? zsU!|=o`x-{`?>2T1j}P?ZlW;kf$>;{+tNzcqhsLJYS8CDhPTG0_b9N9E^xFdnHsz| zTk+0G627@SFLvZoB9Br~?A^PUM5;aZb#x32*GPsy%v-x@lRUVUpe;NrPn|jy#ivVy z3ddd2N)!ka^++W|Ct4PkP!!jC`uY{Ety^hmXy##zG|!(#{vnqIKp6D?2-1IcK>>NG zsaj9i?mbkf!*F*V9j0w&=g;$Xgf^U;b3#w3@9o>Sm2w8;9V94H#I^c5C)2CMeuAopV@9Z?FDT@{b zTG7ffW=8#6r!)6+`dqs4iydfOZj>?qmt2a%f`h3aNP6+=UqnHaqV;4mInmNDW$F=m z`Y4uRoRTp&XIEBMRx5F{Cr`hlLw{->1!a{e0a-VsxlOLb1Cp_{ z-WX!ZdbBoN#X_yFkL;@MKG5>`fhJ|f$x_fw?YS1gma$+p`k)hS&WIGcLK+DR z-KQU!-^VXS^_&G+FFSfxo3=VW3a_gIB{m%`?ZZ1KkWG{BglXaQQc-d71dNDSpwZsj zG)P&33&5({w{M?MY%DXL`bx-BOy9fj1EOId#b4^*$pl7+3Vm5O;!bHgd2L#r8lbktet-Co_3E{2yFr?P-mif#gk$t6$xG_u ze=^O$F}xp*4f{Z;F+pf3M|co|&$qD;rNec_NKPIB>a}a7L0D*qqE&?oB+0wEc~qVI zFmE6~l+vfXTpA5hEJ*VeSiQ1c_I7sCcxnxBrs^}tXJ%$bI7d~@f9u%Kua`}M?nxPR ze>5#1vJK(Ly)T1+(Y^h(k%blCLSe__1M>2f*?td>By9# zN$@hs&LKB$G@@)II0Ia0706Q#*E33k!^457umfAsxmy9;o(*q8cd6H@88NKkqc?UO zXuv=t*upt$m3V5v0iiGpvczs*OsY5t^j>4xiwqd)g+IV-- zeiDBOY>J(EFpkylHb1c&Z#JP-tR-6a!6)oOILFq2R;vM}L(9OxVeqNGo(3%IP)pjj z!$*#^gd!T5+kW_mzY*3acsfSoV#$s8a-}Bu%m!C?hWA{=YLLtZ4CMiRCyYaI=4$j>L4Nv zivO4JcJUO_C;0jKSqgjMI2%aa!FWi$z`zlT%XsK=Xh&&4U)76m<#)FMT%Vjdu$DqD zXiK<_Jn#1JcvHMp(I#@TvI?zqN_pA?o^p>>WA|Z^D2Khk76EdJbS`QC5dm`&>%M(z z1ERjZzS*V~E14u*lqX)q%u0-b-L;$QS0of6HjbRKa!C5LIQAL}+9A+_nfeV+uvdNK zBC+3tF*oO-+HRU%|H!0Y@RW!G&4)6HvFW=4DPVWRR9CwSYb0km!>5sgr1q!=W`;0S z;C14Xqj%g?`%pQ)f|>0aQtTLDEID?yc7U@{{<-s^Bmsk*xJ|-0@Ms; zBDZ?N)o=6t#rAz`@GXAW0K5WVeC6>SLa>@>rD`c=I9fWsHHz~Z5lbGEbk|-GMVS*_ zx|~1D)SPi@8}ej=*G=rem)LidgB%#z^chlSP#hjt5S+TK=+Vel@%HuxZZpU}3`~nH zv}(fJQ9a4hk%{+^Cz9{MiysNoR6AaYjCVaPjgPEXvnpO8nq>$MarP0KIclxnqw$y2=F*|ZG8p6Cb|(B#^#UM^-{oD5}D?DHY6;}KPzi5A_zz4 z=NNTUQ&wmjSFv#7q3JS;Ik1O>gox~D19iU-Jyy{!RRFJ#O@%Y>U@LzP7f#oA#ld#(u)ZR(V@<$PCjuv8#pSukHHZ{HdGEu)K=lzQV3NTmR11@ z8O#93Ta4WpRCK>LH9vW#Rvb_M!%_CmNo?uWl$46CAGFvE5h}HewM7!qpMzFhT4{ro za~J@!i2?)P{Vg51Z{E_A!uXE-_Zguj6HCU$PV11ZY7nW+cagw>?Xm&1ir@3+5(Mqy zqSKr{f~qmC3Sx-xdrr7}P$41eiN{aF-OC#*qYS=dX=#ZJ8F^F61>J!T_Js;Gn>RpJ z#$CugPBR+E6u7+Zswz48aLdqV&kki8zexSycve|b%O8It&0kJt>u=6=5_PLOY}ek- zdbDa2B91~D`#S$}TsJs3Vzcp-7GYyMHYtfGBL-q^`jy#Ql#%lTw@vKu%<{VkH%H{B z^H&0#hFARtVM54QKlsuV#x3<5j$E2o%MFb=pRN@P3<3T`)-Lx>OqHF8icM2&0eESJ zTvg*`Wxjp@6-Kl%D}5g%+&Xd68Vn+7m17S~LV+Nf2;%|kEksDLKB4)FllS_yYh;&M zek>`X*+Z}ce(GM}5+|WHb(eXYZ? zo9z_k7;4jiL>b+sc>B(s0K9+RPWsJ^i~-0Dr}4pvzfx*YtvUu9+L-(S+{~~wb0g!f zL3TTVRkN{RtAdpT>P1Qw--Xr&kze~;QYkT=}B^uu;C=O$`iqk~0 z({NKz%P-*E=vI3d7oEHxU*GMldgudf7TG;%rX6|a^y{0ul_K||oZSbX57CoQOicXv zQG=Lt^XAQmDUrBcymxv4-)c-+TP<<-L*Ggt#7;%d+eAviL?o>uO`Q&kW~{s$py9U) z3JMY^gd|ETXIL^#5yCqnJfT++=@F;KP@t^z$IeK1G%0bT1BnrEYce zkDEaE69Bh@0+F){FGWzi9X&F;38$Cy^pRly*TF+rVI)|BO^JVQdatQ|Moq?NqzsHgdbuS#3(k$`&Qp;VP+lnRSlwMZuIt@JI~-P zz{qj{S;HRu9KT`Vz7O?}2s;eb^ub|wU;IOB3ukK!h%EeiXRWLP!SfBXhx!uD5IR)+ z(67@ls4~K{G&Fk_jC0WMv|P5-RM@xn!DgDA*0U4hIEKHVlGyY1-8*}%v$t_lyMUVR zj$pdu8kl{mAUIIEzI-!Z^bJK%!t-tqD6l{u!Y;0^Ka|H%tg21yr@QImwH;MLXQUq6 zo;^bF&Q+}F$0W9gNHnA(Ck#3E3xJRKhu-squt!z|0cnNcHjb&&hK#IG0W^^?Q!_=F+6zp)*} z0lZ`!3x})hd!>#akjh5l5n)K?209AAd6PheK17ao$cOOa3b&j@c??IBJy=bf?m~5k zFHr{-1z7CyYX2woMEA=Z&=IhN5^45UMx036jQMNghjg+}3GqrG2@dMLw)n@&N}0y% z;x%dX@-T3xziM!T>5*XWy4!ohV4dDUbbg>{i<+%FcC3f2{|%{S%&1M+12zS6xKHD& zh7h!O=h*g$uco3R+GB3@va`_F%u&hNm?n$3OVyI96bXG%U%!5}L@P!?P5#}&b~I=< z!uBKTwUB4(U|Bu5&ujN*q!Fe1_(^P3`0v=Ct5`T4po!iYc*EV(!y^F&0?1H5pjZ&| zv|G1cN1bK{Xqj8B;E)*N*yNMf7T6G4Dc!kv2|6urPcRM3;k5{>^Yilot-&ZJo;`ck zG}!d|^;*>NHAK@sckY~?fdLzcd=fe#VC7)CDpZ)dP{mMCu_+?UlQ0u(VBAN6l3t#$ z>7%14i#%2$nx#>0Q+084ltI`+Ufu{1hz%4MVJ&bI#Cjn2qOefZu>?=y2G>m z&|RIDjI6u$+onH)Y1n7ezAxH%6Cx9_H{i@1?nOpqFy1@=)JyV|8?AcM_4h~d>uadb z!MlBe^Bw8?wW`U2dTMsA1l49A$YlnbJ?L0N|2J){lMybaDpd>PopcUMqV$;oW;^vO zAYknhI*fh!F2J!EkA9n=I9S~Rz^I!d+;xl?gQh~YTM4o(x**z~svKltfQt>tTQitX zgI+bvL~lmS3juro8C;bwv%dbjSP9)>$M609SCP1hZ{p*}kHn%-AdbNJXSs~Y?m#ZV zm#w2|*E@BpkEAO!Ywtf&ijMjDg@rF*a}vf7Mb@DRWyp9+`PL1HhcXHZYcbC;7!USsjF{%vi(LGu84)+P z#yliqfHI%2xP(L^0vcpv*MefQFwiE8R=};%uGe&0fHUQ&jw#(1%xIa`Jd4Olr$Tch zDCi{-f}o4#E#XZBjmm(fZp18;j8}I7Z{d7dr^-#Dgpk>*wN>v4enXTdSB!wkq1gjI z2ZI1;iRA?@mE8R3NyodG31sPD2^?ZRb`K)bwDP(W#Do+3Iu~m_=7{H3>CXNeh;`+S zjdY+u3!%Xbsn!FEG76gq^bRV5*&;D@&6+hqHN5bQp!YNZA-6+P{`%FcL;@zt6c}aR zpU=0-I_PY>wf~8dYAmiqUuZxVzQC8rigKf*0QaRI{@;FrwJFvP=a4wJw5@dxJ$ZZ5 zgX3{5DtTT`Ny3w1mH5GVgSzMjI<0i)5{{NFFYv9|#1b<$i4>k-{K8{mZmt?ZBFX7c zH^>K5bd2fcTSuomTKP49q0{FQ`-dSJoH&%C%7N7z(scLM8WkCrUJArIID?l@3{9_p z?w`v?HD^4~Uk?_VXCbCV%s5sQTlhNOP$ei(g5lBsLtfNi@gau6x;s`qKc^VNR;WWdi-h*W*w88w*GZ_J9f%85Ld9-kfYGmA1QA~4b+Ru zRjFp_1GAXJYE1m8fMO=s)z)(7x!_{6qf(pK)b>Tu6_fo9A&$0A$>=e{)oxZ%E4BN) z5-T$*RFU^YzfEHTC8}My_nv6IV&d>0abV(oCg#0d(mVRPtLxNo%tK*|x^h^>ps-gX zb>uh=Yh3VuVz2*cSC;XM&CHeH?8Hg!si~<6;(0Y}QZnEx>*J*MdYI_vTQd;O^Gg|B^puf!!KzyAM--E&OP$-B*$vQvwx z>z#yEgZIS)bfvEV@@>5Hw5g`#8Nh*z!v;PtnN1)kOFVzj5Jn1hInaCJ%Zr44rworT zUBwEc*IQwi&172A-8zsxzF@oqS-eME1xPKKr;3fN4ns@D6vUb2v>!b5baYoRv5-Qc zt3tj*DNx!VAx6#2lEf^I>yJj!9N&Aj1zGtt-h8gvmZ#|WDe+p|3a^qLq#FVfo@hQM z0rQXd3M)XL{tY?^SqpcZ5y!P;+`e`-vfk z$0|a|M4Ie7qq)`Dx@hF}vy$sbs^H66SDW$vJD?#fH&^n`7v(Z%VmEM#zm^0bP3F^n zg#OLwekFfw2*0v2DdL>KggzkNO(;{ZK7RZ-U6zdpad9(~NY1lo@@V7AJ!1S=mAPVZ z7|ROaIf1E$V}R6F17J8?JT}aDgUw%wyxS;_jJes+jSbopS_+!llj74cdWlbz^`eZe?lwlJew=yzvpT9!wr4M4wCy)6f5_&Kuk(h#7yR-is4XL!S8`rZ)AQ)FDa$j4| z@Dd)T{VK;4QLP`T800&6a4SYY)D>=ax^D2~1?5w3%;Kt;X+G5RHp5<-{*&2L=7NLK&QCxS9| z1WG}=fp#G3@{JLbX8#ZYKw_fyU#>RUCx(!RT71UZmc{YCVw{I+&PmDr&>N}}#*RT; zjvAj@fZwv}j#J=fj^6Qc8dd>1P3vv-sOx#gc3ztPy77UOH~vo^T}#Q6M|b+y4S_VI z~KVM7G5r!C=~ z?i+nf<#ms1Nc7iQtx`CVscce|1puWKc^%;~4CNqoL1T*gp5!nBK@jJBYck+TLfXvz zvSl+96ZA!y`JX>|dScU1_xrvtf_T&2-3>+VI-$zMP4G38y8D%tls1pOTHWMwk?y{< zjjgTVorl!Y!y^|{7l6E1OYFLF;|AsLv*QLr--VZV&tjDsW zLsQd2L|}o$V%&&MkE+nq8pK>ZO{wHak6Yu{&s~k0@%2n0tkR?bsPPO8Y@bI)*2BuR zMbJ=GQy;yKy`MktcC!I@3D)4TOZPCaa1Q)zHWJMQAfvZ~`&2=CZw^3lf>(Kw`&*+P zKqj-e5{F~Y9nVJDA3A9fW}#9LX}uqeK0*Qg1u5Qm3B~gY`LK_tB@8grTVH*RjWDn&(aBVUfyf3nDHZ7}EW5ErXBpcb5K%$(w zge_PB#N3d*1T2zJ6PzKuHYub0thF^SSyZ!39O}SI-o!^`>R#k9u-eti!Oo7hXPrIn z9rC!2#6cAtVZmW&WQ5lhgqk28sSDg!y93^ciMK)({s_ruW$R2%S(*JJ2mG9f(s~= zd&enceM+iz7%w6l`DH*4&A_HjhaN6_kr5%7(V^(SAMND z`0X3No(mFeV=YA|XDb&41^AN|b#|0vy>J2HpjylI7!Wphfn>8DT>KtO-o9!prwX?flp&%%n*x*+_1i_}$BHR(qOY3>nd zB757fAKa|^f#H>XiW4Z_glP&WAe?EjAhcdqSFZu>&;ffvFo58CV-swCX#9KJ(%TcR zgDLX0APz!!tCJx#phWjCDsm5v{7j}==%EwRxv;o6hy}ivv&mt={*@?nN~I<=Pa<+1pB1P?E2A*f+Udhz$$6vL{l4^^t^N^8wr6=$h}kiH38iKT$Gc4n3E$! zPIt+BP*fyFX0bFDfOaBTuTdNwRUkk&XY9!jR;*s#M0x>`yE;a|N_>G4Az>_lgB}{M z;f!89LzC8LxJ?Z{=2>Zi0ir|ItyO$KE%lXn(5nbdbj`}Sno{1p(9)8=o`HAupMuOR zNj$A;`IdC2bofm`v>O|mhJ*6+51@V52ch;1v`SKX_Vb|p1ZxIjbAyKSVT^va1DwZ7+1&)jp=@qZ2TQ7Ey3^R z0$SMU0R~m`>aoj-tsVp-t#l{FGJ?8n7>`M5iKRP;O-iaspS;nqf-E{pecSn%h;4W7 z-X%@^q);I|Jw(NT%V*zjO3KUut)PABH~k5==o2;$GBDr38FRDGG3CUFqi^%zhQGC3 zg74e5HeDUxGf=;Xb^y$LoJxH_&`csy@dn>2tJ=HAk`~gcw#r7IuwEkKWx@dHvUvoY zr$-)i)pWNEYrf~1tEGmVztzZxMxkr$z?qkZurff9*~FUREj(&AOq#TZ1k)+(K;E*m zvvXgj*|bUb?JpkakiD~6tf4Z=CzO@92;k_Dtptj~DADKvgaxPMdTg3&z@O)ZUm41L zTc$`FyTuKg!Ew9GSK2>8W+d_*J<8ZU_sTa=;QW$uQ1E1MInxb#vS{JdNpMaY=OB!7 zLHm=^uc8Vs%7D=Kdaf{sje%G*P{}=Z+%aRnX&JY~v2A_J*FVJ)FQY?2(mG^AtG7sH z#2`sgPCWOhkSmHo&lB29wu?*`GB+b!evb2~PQ?^4tfP|m*07uD zM#mj_pB*U!O@iK(9LrevuM`XRxO`vPl(`$F>|LE5TTev`839$wy%}K7Twmn*BeTEv zr0TdPI9_=;_*)}t5)%?+-&C!sQ1m43P-1kTnIh+A3q5=e(Wz<_gAA@mWVt(EyNabS z+Lqw_$~a9Us}EQakaY{P)5z7w2OMlfT75aT20HzaKyLJe#RFOc|aZ+NmZI9CnI zq3)hv`uWt7SxiEpPM_Jt`u3-iO!QA=1H*y3LUT#dOgKev-4k z@*9mb07nuZeAg}9>AQ(C6s&qM%iz!ux50xJBpt2PtaS^8RHL4%q)U=v!=K*7Iu2n@ zgCGvk6+ZXx?>v0?u&l%;vb6!a%TY^Ys{-Muf!tH(TYGj3SP87x1o&t=>WCIGGo(&o zMXv;E;bGp!_=5eL@89G-pnm{HA|%{s!y=6UPVpPC7-eJiGD%%#LJNqP?Orrz49m`j zNQA5lf4cI1E{%p5K8r&)FM7dKG5XUBPu+pSywhOLgH+!@#!lDESxMy2reb*KCMG8< z;MSP@a8hbmSUCe7J<;ij-}@Wjq~zYb17>S~9C{Ye3YX;bKWev4YH)u;_uLW&#-9{! zJg)jy!x@IjiK&4T#7i-GC+7V9X4cS{mgH-ei;~2-gDy11QppyM14UPA;}_I34aGzt zgJIz@q&uR20DkuMh!3C|pMJ5wpsJ)`!eOASYW!WgbC&(t#=126u$cGk4q3rw^y+Z> zBx)E^UI91|GK%uh7@R017N0vwYrowoRq5Y+GK za^kPBhD}k3kpzOKnee_lGer`Qo1Km5Bf0rLz~+c)~2w z{3Kinhf#4N|6c`oA+`}Lx&(l1(yJEA*1GFS?D^#J$YX8u_?1eq4O0(d03o3u^DgTk zK9Z)3dzhPvZD3`yo9f9Y`kN1^S0weE5a#qBLCu5wz(KEZ;ub&8_S=%fqb&RfxT@iD zb=W-@^EPld$PZtTG91I9a03Lil6ke3j~h{( zMREEQ>8)}2L$UMu3hi$I#q1uJDa1b7PeMVo=JHQ1w$|4{V|qDqKZiuQ)^3pwBkMLb zY2$N6#c4gYKTolU+EKtpu9oISF+V?<*jZy83>*el#1A#rx9%d%Y~qVo!%E7-ada8e z$bLb$s51ng1cfm7Ys3yaeB24&qgW^p7nv+D?+ud`yDM#`TKr1vayhCqxI7=MN-Hm_ zE-YkN4HR3$9lgRexkmG4>+@BtC**pPTrDQKKV+Fm<#$mvjcazY%^Kf|WRmM_grTaj zzhSRQsVS&_#moPH29s6^jZg^R{a9fiv@ z+zS2dHg!J@+0RYsUOq|l?ll@n3HZh;18_8}@Bp4ZeplGS;nMdv84e4-z60Lg0gnrq zcYvO@pF`q&aB~rLovil1Y9_|c)wKrBwtbKcY9)U{#OXy|f8Q4DXu;n4ohO=!w`Fg9 z@MY>JlUzVmFro&|F<1zY)hRY6qe->cU)7SLz6zG41ZqB%OSHh~+!{#%5T9n4k40NS zr8VE&gViYneh!5Qu~$(vxj4a0SlYBe>CD8t=i$!52^&^FTL^rCy#;@E!tciiOGt!TD+)o9@U9 zHt1-*Sh9;^%OS5GzvQp4aOH|0Z#xuE@F2zxc>j&Dl){{%2;3*enbyC)|LqchB^nWr z=V@k;-V&V9)l1`yCTY@wtcJD|<*;+g5u=D6Ku_b}E;odai;|yJC0_AUPWEjlHR^t{&7Ad?jzR2fQ(R81?$~5$R9KSsGmaUke6x zRk;ddn7Q-GE6OZZ{4V{mtiYnAj5{$fI!Eyck@bQ4WL=}XaxJKdkThQeswX0FTr9lp z-_pv=3w zbMr8}Qb4hgnQ%R@f4>YkTcW#H%n=7WHX~xqv7BssXmONrgC!FStz@J}A_T+s{PVXB zY92#xN4O|-n`A3_@j~eGl3Paos-|rhI4vI(W?;QkUJO;+T5udJE2n2Dhn;0**1A|opozEPlqu$5yZhge2wZKpijgN zi>W_5V{#YHkkcSN4SH1gpGlP!-6(Fe7V9_cB1r%bFwKj`+4b2inUNRifkytbM|-Yq zargp@>5hv}vBD5+icQ^tXJBPOk~jzUAbpZiIAYr4$IQ&tr%#`D4yI@%2O%Jzu-z4q z^cJEae^IYryVBRxW;)Lo;~10k!Ijl$Vs^G=!MPlZ0A-CFkSFCRnhwlxh!xqGI%gWG zoTW5ZuUTUTO~<#b5q^l%u%)?=d+dp3GPvo|_HVpH9S{5n#IB7V+3<7;oKFUKy_FPq zlel(d!PLt;p2nknyyzje+|7sfy-#HzdLk|aarm$FDJ%1SWX_dyka|Q^O9ukSF$C3Y zICb#@mpDGWBvEdS%o+8p%IqPOY91P|}W(TJ#8z}=Pu|He`JZn~vc}3ij)?M)03m@OL zD~`!|d887&#m$I_guJ(i{aCVXZe}SlMyL8qZZ(fxx;6dG2@xG}tl3&>FD4jJ6F37f zUm=ckDUC)3up5aQUD!;?N?bf6fW zzfecE}q)OdCzWrPRf3pOO%xmk<_oi??Nl zN1k*DxfuRb1Q||bICL^=X4j*`WfG0AiIB9#0~Lp!YugeK)>vggCczyRvr-+|Q8fyo zj{JmM{};F4Kg9lGjOAWTO#1EVuksqfht4tAe7^JC;alYG*6hjEvG7p+@*2XjA$QAtDj%mGgnbJ$&@wLL-@IqW##%W0fOHI?Hq{>n$2 z-7sgqQ19Km8mS9^kjq-(?p$$^C=x~+W|=7vUyj}usz7Y|Lk24SCh~gcGHkf)uFYxs zPdRyY(z*3m&^m&}u}Y!q#Xc{K13>S!u^0NCXlgrJxDG+FrWQ0V;r>Ii>-%j>jrgF* zKu5P*U%rQ#dtgc6K+bau+p{Pn@w9_m14BcJy)fCKOCxO@gT*bB#EitGpR$C!jq@2w|SgrLQpXFbJGfRjr00K*UAz$vWDiU!9H%GgcM3 za9vcu`0C$iSr3Pd+?!;{bN}oB>SzBsqLk3erH^3&D=`$IQHe=|+J4}xeOR|Fy;r$D zy<7XqET+Z&WPJkzrSY@3VHjlG$q(Th4GpXYpFVzMfd!a8V;b#8FaWQhAn5J4&LyvN zI;mPb*qCeZr8RKM5g}Cs|Iu8T#iSuo9g&Ulo0y{rje{lRkEu?*y` zr2f}I&^fuPtzry7ldOX=Bb>b>L8Y1M8jfapMRcpN>KT~ar|%15l}OqT)G>e-Sx=!> zV55(GAU|*r77KQL`Fa{pWCePGlP9CZ0b>C4RwQMxapx~S#H5k_A}N&H`do+BtzW-+ zOqLJ)i%vb?xgX`=nQFyHI2jF2og%trg7Z}8CkxAAeck|Ew$?|L7Q7SToMkY) zm^P8AG?vPH=tvo=i`$o2k|U`4n{x^Z>gSfj9WO*$AL8=c;7}iTKkI@<+N;Y;^Xl@j zz=7LWR28x8%;Q0iP1>wpzv1FQNgMS_u|U6jg@O>pja-x|uf2HjqJQ-^mBT;CSp-V< z1Bbvx%AgdJ9c4P=p_Rkxzvan$5sfsKwCC2vf*{{8hCw>p@YJW@c>+R0>Q4431lQLt zg#PW?mX*B=L475>Qa&K7%#EI~tM1nD64FpyQ&hB<^H8`Fts zD`7V~qD}g%u-C7{GSWXuME%hcFisK9{cor1#2+7pL<3*m-n;EF4-_7pB5-W>2M)T9 zFvJvbu8-)>4PO=_!%Y;zQ2aV$mczhmF4hYgPn&z+<1*N(1aZz7Yv*TYd=r}I#6Q@2 zXsk=7VCQmKRSvzHW7Hg05cUl_4{ku2MP{~ZiR;iOiTe^h`Av(F#al9ipe{lGi1)H# z9@Ji*598sf5lW_)!QCj0;n*Ja$!AQcMmm{Oi_ulBagMQ{OSuUYry# zEL5{Mx{q^iJ9BDxM-RR2SNrAYpYM1IF9o! zoysVw7xX+1(}kf&CRKDyru8Pmj!hKV&ilm)pRm!kx=g?UaQv_m7CPnh$8=wF)B8A?uW4kL^ybYb3i^Ju22&`oq@>?%Kb+gK5I$^ocVQQ=(oo3#9l7QUfyF<; zi*Ph+Y3Rx2wk`XK8%?d59}rnh*bS=Dnb`mXpHwl?HK zxtYerfWBH9AFlYoK7?n5^qwbUred?wfW(Rzj7KFf8H zTbEPuFOKwu0rgRb=W{3s}RQ@&EVk`%yA1k6= zat}!~?Wa7sP31n3fk?Nj?z%#STQ)h!bo1t`+9GP*-CgTt;zNz<>|OxDve;~%!7fSK z@nedFVtwoWCt^1;&u!%6xwkvb$Q@!xFH-Zg+_qPm`gHnekS?1%c=QZ+*NcjB6u$rZ zNiADN=jB?}B}+V%a19IDy}`Qgii(d1q+|M-Jkjr-U{l?G;X?b7Qf;mEQ`S3)-j9@2 z>UJ}VA5SNDRpac=ArJ20In_Pll^un-q*OPqt`*UBfjT z1}%!^Pt`N)G4vSa;JBB)5VZ1h7*+bH`1L}W=2n+&Y-A*5Kgnb0)Kt?2sR!D1Sd=bQ z>%fM(7Cs6)N3UsLj47qZwU_1O)`vIgBGe%>y4E~+nI3_>W9eR)ElOu9FnU?Uh1@Y6 zd|Fy|h@=D$Z$I63+Udh%Lz=kmR)mt_GvV&r-|(5}5YLV{yZ06t#L$`MA>(c(Ct!(e>@O9AdZ=DGRkvc z24`)(i7k-%a=$cih^_1OZ_;6k)Lxt$4T^AT_jf7i%4QJ^Ku>Zvq(KcmYe9|xI$icY`pbSc zW{Bp7)R}M*odIuBEd@CS6hSM@+-%2=De;GW)GY)hh)JXs+{UofzF^CO8S`!%zHiZlp{2J| z1(KOHKmV1t)tYm3{MsQEe%i9CK)I8WmtLbb!wTE39ePh}8k^}Ob% z`1X*P4+@_Kpz^q`r5cW)Z|MG6w==uYfkcIovw8YG8RgZ$wYrXxk~g(yA#R3Qyi zx5>AJ{?W{FbME(Yn8$~Hoa2SO#L)K5ZH^BfD(AB0UE*XthP9P{CV%&~{;+lIw%Kbe zt%K*JT(uTcxG>5Z8To<1G$$v^m%^~_xmfF#D0RTqE@}zJn16-h*I`1kO}u{Y9^Cfx z$KI^3L*^Cj>u^<0((&2OsWWFjf%>~tTPCX5S);C<^Cnq1cMF&l-Yy1#g0Y}2;a65gR9&@*uza=%*59opp2rZ_YSg!CO z6>>Q9>@GA1C?%5SZC|a6RC(u>Zofmw zP}9q~-EQ(-7Lbfg-P>X=81|xZFIvJ?RaKI!-nTY4574ijMuCbikcuE^aI`jam_)c7 zEtmJ;!Cy-&ci?|sHF+C>^#KyKaYdSUU?QrWOd@s$lM=eT%bp=Pc)cFNqokj=VE1L( zPnTxx@U6OGM|A;cI2cp43n9~{g%bEg2NnEr-PlgUh78%vO}0IJU@;s=p!|(fJz;P6 z576bp#!R^6D}^#TfP9Dx{liya2X=5NHBHk5R;I$c6U!nNin2BfXBkZpcK^W z4e3*ox2|}ikwQJPXk8pv#0&^pV}4^GAQ)wQZHer<3_(+_eN*5{^I3QT_L-P$Q+*-d zaFppk&I)>W_CxzsI%`D3+rPDh{On}JmSMCT5LUb;9du3+LvZ4|%S)t|-)QP2k&S;~ z89BPMU*JHE7so(AyGN|O?CN@iCDE_8{Yv{?K{(5l2{$Ip#dNzmr$xj`ynzN~Z|*b| zRpx{G;Us(7UbDZ^qkB-mdg&WY>ZSRD@=DEmoxLLWv--2I7y43JMAZb-ANjzRoj|vi_%#7`@sj zBxC}7_FsSzS7&%9h$Rb$A4VdO^;T>{H@>dv`ZFHpx3-iFJ zxMC_YCf01A-pl^WinGEA*M};oY*&^#HrgM=WwWaJ5g~ErHA`Ukg9pC)^rsB@iU-XtgJ%*?-KdmCr^PKwhNW`VGWJEn^}R}@^~pFJk( z_GMd&rxJsFQPcDTO}kII?94TKvd-UZ%a%hx3#aPZhn-dIMK0XOXH6rPmcopVOineO zPWPL#vTJvf6%`bg)g6+t-I1>J1ZH!F+O8FmPIXx+DtF<%R6()dWDoa&Cvvke|H4N? zdbs;$rwC-#Jyr)A4+K>_zeE_8&}--<0X1^z?Y{&5)s+UXLs(VV&pp#y@mkfRu047@ zn{K~xNcZli)RqU(_Umua9j*@)1sH`;y0b%avF zHDb;(OdiKkvAjSV;{KivhLLF#zUrHZz^j{0b z?)jMf&PM|RJGC7+ETzIbh;1*ikHTssB)9-ZnRo!RRJkDrf6gm%-kzqNmNCAp6jg`r z8WYt}N2#IC<_q+Rse31RBXF4(S`1(f-X6SIE(Yhc6~Xn=CC|$ohmMFSUK9 z%{|C(K>A~3&+DnN7|iH64P@Lke<#H_(NF_vL4Q0<==Ouf{!M32NJri=>WbdbDgPwY ze*it<(Qs^2~F(S4Wld$OiGNR>KsCf4A8STH?R z)8)sR$zE*!>9w-B{x1uSmf2g?wKMl*_I7X#Q7vvI7t`o5M1Ilqc1c_oRk9L8K1l?Wq|(X}}{jZ5>o74)HWJn@0+caXP^Am}yK6#OZ4LvUmH@!Q53Lj&tm2#kib0fm>* z*vw%A2L{gLv=G zj}?y;4V~+Bugb2c5#TJr;V|2~dp-h|%xHPXC-sp-{4NVp-N{}Erwfy6I7$oUpD?O@ zAN8*DV&8I*+Chk`7yDg2z5n{@)>QBB8`F|%wlyI&7usu6U7lIA_fn5+{ttY!73V)s zCx{HuThxkka;B{@aF74-iV}V)2d(3vdyH{>#HF}3wiU+*;RcH#)kW2vBBjKN=Bmm5 z#ZO8VYw9mI#KmKX_~uf#b8)3(5|c-lDfqn9S@`6(Pt#!emY$*Ve~xnOCG35K|KDPt z)BCp>;W2y$)spC?0hAxQc1@TeSOE<`IQm243e;br;~$%nY&+wBQRFJlI`F+@IMl{U^oY09f?g_V|lcDC) ze6znsIqE-cd)3bK+f}W^BxA`OwH?xies^V3wn^T+Ygg}^9oLm-SwM~iej0H?5o2;V zi;vjB{Zr5Dx=mQqnM80Q0zy~!E*(3bsJmAKmy7LU$Wt7$b(JIeV+`^^xE-qbc`1^| zMPaw`o=&w(a5XzZSO{TMI6$!Eh^Rk*v>IUEvs|v=IsW5x!7u7<0&N(I_OH*Y%>@r0 zw4)Vsn4$>^=ULKQS*Iy%Qs}XxHvAnr=Y1lhA~x|k7z_*wGepMDQTm&r0^-zNEM-te zOI1EP@xR)xpLZcI?1EtTT$sqzxsR7>U;c-e=2;~7_1iaD{rTqqVPG3#+_|&mqpmP} z4BtiiyoLM)Uz~gEQyAg3iA&r+)YDmN_%hMp7q*RgT2k|KXdR-cn@_)St@Emn%~r_7 zx&HO!Z%@+a#R}&TLsC4&mA%ws;T@8I8dQwch1woPRK;8+KwG-Hvb_1+^MA~uwATOT zb?23=HI==_$r0*ZV6_X%i&Y!g*YK$Dg}HETw|pBPEc_6%9_ffccoqXEV^zJU%j!y3 zxfNKDd~8kL5xLrS(?BZC%bEe@q{O=5tNJGdiUpD77lH9H#CTUgUiIND1mm1 z;I^N9(ch+dMD|!Mzb>~6JY=FBf3?r{BQrT!t-4|f{G>V|3&AvhU4>Z`eJvbU&XU007;IGMzcSlMdrRpgp>`$X`Abnz zajC`QM~?&rKhiF|<v-aCqFv;uIl@Bdjz#<3qK1dKyyOU zKf!82X>$%wrDi2%V>@Tszs;`-+Z(5i$^-nqv2wpnk#5hmEyB@n9DJMro7>T0lrch- z-ebgUo!Xz>=EYRXTx^!)wZc6(W^dr+ zCs(Z-XFXHUzFHSEsbaVLd?XF*TaCN$cj(1@bKmD+4l`cIVdi%T(C-m#gFr z7YY&4OuH2o)h}#}v|^L!uOL?w=|Y;)&BR0%LG!a|r~6l%SDD~h3omT;w$@i~6s0=R zCVd1kA@nEL_9rCtAvV3}#E45)7&>ekuAjM=dDKtDbBf*Zb3ixV-eHd9SNJF~lbMjW=EDiV}wC;~+0X8uGvTHrkSqzx2dG~M!J1u?X@K-V*aBg+cThCndWiCn7IY7+!&1AJ>aJVS zF9+uqZ^*7d84EblTN7u(0y;wl_!deS8e28rlEex6aA;z(uWCb9owz>w4_Fcp;PSlr z@Zm;wX%H2m#-Z8$IH2Mfib(EZ$vi%T6*E?oARJ3UttXCv~vb+S9< zlGA%;?x3KyLbM>vxM>X@XOv&L9F>+h6aqOlhgT3jKbnzroxmFx6UsNi{MX4ukwG6l z1*Q9`X=}%k@9-X4Fn_k*%@k4d9l&C9b8AL{Ogo zshfEXdoK3haeh<9*GC%qR-n>6pYg)X1~ai6^q>RiVqC_!N}~m`y50gGN?wlso%pltRM8!a(5yL!p_TsrS1I^6 z?LSJZ9Qf`8Z^H1?n+HRc?dH?@Is&k>nUu`GY%!PZ7iO@5Wz0E^wmtBX#sR~+1vVwp zejd3v$f1W(O^>Xr8^4aN+2fEw3{AP6t5}HdC@I z?E7*{yYIr!%EV`UgE}XFZ|sh@?kKaB>z9y)Ev2@-O)ug*8lj<{$3fl>i7!hklUHlbD*yNfxt3m+sku{9p)=O;D)~y!Li4_`V0 z8JCFPD9G_5#3L}8G0Qv%G2$9w+tYV0Oe8`6xaGt47XG5)gX!joi~fgUUBTV8LoHM{)YemtVn8oG7Iz!LA*V3mk@^ed0#ix4g;%n+D#r1bpD^@* z7bOfvv5Z{`!YN!kIYmIl{`AZoPEMNpqV@A-;7%OQ-T@r2k>(*pBf4+0un?ZDn3K2T zQhiaS$R-r7IgFzjg>65!W$5CJ#F6mnX#+U+j_r<&U>foy`tK5y62hsJX}Po{{7;6( z_s1=kx)xKfu{mYjhd6O|7aoV#pN_u?D|d{@mp~~+D+>5XyzN`J=Ap=Y^X{Gh?bQBY zf~eiPa`A;Lcu*9JH$&rxbK*~E8mj0q6B1A-j?6+Ut6T5lO}20E2C6Mq1h@z#!@AH~ znCeUum|g7ri&~TfY$+<|=?${wx)g1DetuU3xO4%Z6@Ip)$0+r!s59Z#uO0D8IzvQw zF@1oml?{RNrK8x$yn#-GrahHq-|hU-W-A<05rEu^iCGLejj$B1_t|saQz`_a$=~K@ zA&?Fh5Tr-HuK)JBx*ek6#ZS{ zh-yMOAc&vkjk7@*342!T9)1?$n!p?QanX)iU2MI(SH|5ab9|^QU)?>3smdsPV_25) z+F^rX{m0_iv!SYeE80;IKS0SGsY2VsP8hHJ zNx#|Y6O>b%k1zWO1I-a2Zz-Cgx57X! zU*(-YG5+i>cy$yE%vicq-5`9e5koMGH>1Esp)<`H9f(D zZxgvLUW;Ozshwpwlpz6>J(MrL!#Ma6BI;sL7v-i-I>U{YgT=dRfrv_6^yzmr&f)Ec zx;phg=J)?%g1qzD%w7>tm%SRT3u=<{n0s-XhuVBZMhV?;w9ad?6K^r3D(;An&v3za zqibxW+NR+2T>-t~Axtt4ap%W^PC+V@-VLaYs}z)#=_5~-Lz zqe=PN3qP4dow^Hmzoxp9VVoW@jw+IwEDwpm{p(i|C51^Y25=58r+F#-zrrY1xB&7U zyAg67kBf!)-~J5Ubk57m>qh-3v00ZT$*zmI!V&ZwATm(417uOK;wrZo;Eb}` z1?Yr{U29GELZ%a~aNstZ-YR^_&b)AO)pQ#)Ijmme#Q0N!@Jo6s<|zPET&uTYyE9a_ zAKui5IHA?b{r`Jq{AbrvkSgqY-$P;)=KbU~eZ%OhoyFvJyc77KA~fNM=E34{1;5$+ zdy1Imh+WW$_UzqTd3X4V6)OaLh)r^gZ_#=5f1DZr`xm6`2LirDQ!K`%86Gp`zzoTM z7Uh1$-{NB%FTrBlL|Yu|8F}%b!d{pI80i8l{2^_oqm1JeNbo`A0PffRr2_x=FZlPL z)XSd#-tk}Y=~sA-FAY&;4A&gdeA%*P2RuE8h~O2ltdB7JCO{qM=1+R~KYW_MU|N6%N$3Oi4U})d}eF1{__dovm zzdPXn9)bT)#ef05DH`eC9b)`XnulNo;+yDrToi5p`J;sP`w*piwQ>)Cy|;86MH@&= z3zD(o){-F7U)T-OEecY`B`%@Ih0V?jGS@VFxO z!_uy;*V!h74T3`O1ZP3!Q1+nJCI-ZDdsAQ(*zMtq1T03}EmTF26z9%H0|$&H8Xi=4 z&_KF@$UL5ly%&~6BkJnvP_-QZ3fYtZaZ?yJfdU;D88bL1iQE#Lo4IucSa6&67p|>4 zcOC@y6v_9)QeT_DC`##yKjA@zFjW*0vd|8;6C6~F%SD*elN*FH)aI5!c7Lv z5+fIS^xRcnbQVRKC?okHA(^6#VEUtM#9Yz_QRm{VMsgb*TmLteiO@`KO$2T5XT0cz zW~!Yma_YMY35K9h!nQTJ^0&(3d=dGk(3pxUgBxAQzqegw%>mpwSkuts85h=jA8Jh_1wD@h^)HiK_tXNcUgFO8{3g!Gjb!dsdK zqH+)=50e#0r50a?);3>MrPKjp4h046aZgXr2q)X@^N=<-f#rw<6;kR$Iqja#_&n0R z{)7XxsPVD(Hg)=$mbjZ6T?-g(h3ba#b-DeE(ChHuyzVrB;x0lZ=j zIFkJ*tnNsSe(cn#6)u$%(026)qFTs*vSN1_Aciv;w`cU|zDEC*x-FZFM_~_q-p|CE znVa|E>oXYQA>|K?G~OU-Ho~t{z<_jWmX%dL%M`#Hgjg0`YC|E=pAZbFEDAJ0HnRZS zYexP?a2HL!2fvEBP$QU(D)tNdLy;V3Ezc3-8ZZoSt;=PE;ZecAe`nH8$i*p-%4h`? zrZH|%0GL;R(i~2lZWQ2+`CS~Yp;vN$M|VPnUq4Wqd(es>uwM zfY<)$-o0P=268N6Fo+~UF!62rU$t&%Xb=NBw#YvNY40N`Iq};bHpjl^ymen_hzY90 zeH;Q#Wf`}Kt}?&zLUXy(%AWtsuoy5`G(Lcw0giUMyc;X>y|gpYGxEc_8TsY?_$RqM z6J5_lwHoEaC zLh!3Pw0|NVxt+cCY62`>N-i;t@|^Fn2gy61KSuHf9;|@ty@(TN&K|@QmxRx)hsPj_ zt_#=uE`|Uj45+{`ob%T-7fF}%qj>+zvLBBy79_QeI3fo8(RS)g(dCxOD6H_iKiBK% zyxITj+ep}^f6~l$E9NbIy}OlE+f)zKh(A=p2d`YYq9*$a|4M(rOd7Egm|rywHhTSu zI!F}QRts7I9|c$^*n}zPE08tsdq8#Ed&rQJgxq)Fpw9BbK@t@MhF1&VUF?c}X9?iM z-$*N**oz}Ef2UCz z99qnj(DtvS6MPCtQp;v)D(yLf*aD36K;Lqm{j8J8=?y6#8IZUjD8By9oJ7Dignq&r-_MX;b5Ifg^}DISHM zbRQB}!!Eq$2&t^POyTVYF+*rWY%lr@4?&w>BsuPl*)nCkkmF+RlBEO#dWu!*RfJlF z8xw>aAz>0;X%7((HSA*F3Q!3zBf3iey_DiH7-z$Vr= zDFCzb^udlB6-%#04yA`^vV}9MC00Z@+)9YaPXI6Ph%kpz#H=SW4$sbGrpZs8dLOW% z;o+Zi#}8CthIkK5Do?N-L%u7QulxG84ez%32vd8qVLeB{C>xHS7W#UY-yf4VEff8@meELLdjQ&rGPyD(93p z4rDZtQqtw;j~{me8s_>qU2%RlNc0vM0{*rT5xT#U9C={}u$liU2PFlCs(mZZhlH5k z$=pL!dqQ3`_H0I*r`iCv2uZ=Z>cL#=f)HeNTMGzBko90z90j#T86}3k1Mzf#R;7A& zIjFKAy!Tr;jE)~GD6Z~#MTgGehBu7(61@8RS6!#ObCSrX0P!i1*?iP}>;3f!;98?6<;*ENVr>cPNtlmB98t%7^ z#6b*^MxyNIei>HnwClJ(bw+IGCd&PwLMg=+^fy31~gd(ztL4v|(4*BA( zGf)IY=ZpEiOOPb5?S4<9Yp-7JVf`2id3c%iAbt-F28~}YQqvb~^9c*FGiOFkFcKtcOii#RTrU!`0&P0rDvN#HN;D3bXFPIfD zT*#Yf+`2_w}7!EZbDsMicx|oHd~@aC1b$6%Pu32KqTR#eHv6)j>+B;C+%3f-F_pFB=W%5hyl&$OjnJ znP9g=pnlNy{4E$y^p(iDpQLEYxWbQx?XU{t#ioSmCk*7e%+&tH4~kJ3 zxVpRRd5&tMT_{qfn#w|^2Mb<^4rPvLtv@I!(QkMeTk0fM?JB(TKe}~GTRZoMat&o^ z)|L4kbmhc=6gmNq*#grniX!3Uh)NS2n8ZSA?e`2&;nTk@t^L1a;nQDOyWz;R6X}g^ z=wGzoS5-eURvlB>{al20SVpF3pc75RcpK4JC;J_sJYm8v<^dwM9)JP7M)hp#pT6I- zH+&-X`75lSF;FS#a{gqZ^@*w`h-&(dhw31vMLi72;l?jUM~%;|&i zaN|#>0vI1Rrpwttx}gsoxs}ZztpDH|VAiJLvb##8xM`u#5BOE$;b%3n)+W4q4MeGM-!e$#G^ghk*=3yOvn3@Z1+`>?ipW3kq}6OI(OQPg z&-G3#kr)VKOO-MfXo(J5_ z`Mc2XI+m|;cu#)+R-YVMEKqqYX%^Lds@MH=>C&Yu>^Xm?5QH3tidi#Wyol@|4(hUq zqRBH)-ekK{++dNrDrmS;z#NaEg9ra{$BgsC8;~tRDtpUhgX=d0s9RV>lGbH+6G<{$ml!qgZ_Lc#NirsSDNH*SdC4k6`2Go9V3 zM8?!36=W0mMlWCUmdUTF<565+vyg#U*dBHi9ze#jXpl!jQ}%Bj{2Rv{ZwEo*Qnr&n z!U;i$l8NQ&I8O4sbEjZl%2)+FNu{~kX^>Iu?c18CFZPsF&}i5*Z{C5eqg`KK+DOl1 zfZ-0>BmT7-bquywJT0lXy86=J@QFpoUuBWoT*1L1_70l(i<1nGVyw<2nosgd@(41CG!Zg`}y4-^ULZ{{`anPhhI|I>HlVHDwY6Ocxg&5H#P~+ z2}1IO_0n>3eaKlJ!nfKQW6GT)QVQ-@(>Nx6zLBwYO;-0-(7g5V7QVv5RpmFKJzx?L zkHE@IeCR)Qa~F(Bnm~_9H?yGaJ}$dykUY>LhrKqvI*OdxdAFyeS1V%49>P>>{mea| zHj%zoDU9wLg?2Ej)f|P?CX?~6Z4DB3TRe!QssH8c*N|bpMMN4gVXd$^6bdy-<+A() zhCm`;^Ev$VXQm*tsNah~vAU#az&qp}T<)@yn3%mT;$aHv>m29YKE$l zHmi9n$VqSheGe(FF59|*lO$dwC7iKpFSn>rAJ&x)et*LqYP~Y{LL)i`Xe3*5Fq;Zt zy5jjqqTGLQs1tJ6Cubi$NtM_{__&bJwI-WlLte^ayWzeM78V+n!By_=jU=E2-=u1 zu4V?&W&lA1=z?nn43%%U=j(ccZzns$BqiL+^2if^q2A$o=LMYS#5*$}iD&!lbCk z$TZDjBmnOhX!5PrURib~Ib_<(pl~^rv>yPtf>a24Av38S4MzEBzp>_k1cLs&DUSQV zn^6$Ow{G7)n3$Nj=_|;>EwnT-hPeUk*SEwkXFtl8DSP*+4S;!gF0Aas2d^?EutF`< zR#Lk3cgSb7*SCO{iU75H!=goF!_JA>5B}fP>jLfrVwosjxED?s60*ajhvTipmi=e; z+k)uVC=9SkY*PzOIT^A(=Ej?H3z@rI3Uc_{k8`}ZQVU)~!+tQ0UAzVN=M&^uSxVO9 zKB-O`g|h7k?@3I|zH!83%!jajlVe7$QBohPF&T5)k#n8aexJ>0a-FncW=gHWC&K?V$q(93?c3cbW-l zekPKN0id+~CNiKou-CP}6ag1-MKKRF)cPPfXP-_@>QyrI#o&?c1WQn6qyeynZnJfK z@Jom-V(#ki={k=gBZNggP6Oi!nXU*qa)3QmlgRjN`gRuqN^_%dj@p;=Cr4qd&Tg1g z6qo`}-yU1aDEi0k6{*|SCJZ0F`pnt0e?mlP8Y&== zq!rpR9f|I|e-w5jr|DS>b`#C`ttr<{dEDpI2R38Jd6m0k=-gGf zk1@P^Y)xTdp=rQ#;)IrJFA}D4fO)QUgDC@@sTB>msJ)l2T;$r}Hz_6D;n&Z^FbBaw zi>rk_$LOvx0F>0bqEEqvh7&yaS1*h*1r82`jg_H$jMupvq9FjlE?N>|a5g`RRzj0O zccdo|AindFOGH5jaNg!mSKfJ;%mQ}YV6 zoC-%^?oer9K3%M8;5&*bM16YqJ^&|#10Z~o;Yf;sJjhfuA>*-KJ42R={~9@+~Z0rIbq(4)@uTB7`&Zzq0>VBev<0nibIKG7ihXAC!cUNH3lWzu71 z&y5>9_6_+ZmwYGac$-a3{vaxezXx_-dUZi|$zbSD80$L+zzKw=ylU} z4Pt^3y0*9E&}3Hg*fHX|BAsCD;NR3I`ObHWdh;`Ic;1jA)8;usn!^Utpz%_eIpMr~ ze9}te0mOW|E~BWjSPQr)M^QY94KkwJChJta0V*}<@Ngc2`_qwCz zy!r8m`nQ{Z`-=bg(TAKzId$xK`{4K!F9%f1sQ0dxkKbW?CZ+eJ(qVwB`Qq=$ zY0<)r$81i-Juw=jA~8L~P{Xo$=h~-pe1oOeoPx^+<=%!*(T}rUEF{*zG{i2~x%-_+ zQkRetB~~Y|0gMX3^V;JZX}IsB{FqT9{bz7uJn$@Wl>OZ5!`i~BJt+e)b_n4&V`Es zC^^%qyR~Q|PO89mqaD|cD!(+J%SmnBF15|E#kPpGLP-WintwUHl4UcG@@=P4tt#X-Y5*%+tq!zQE?eRc_84`h2gH=oNj-zW2OpUUAsn znND9T25E(?eq5b|bWwb5F#U_D6#WUI5ATuK264Sq-A);?h7364m&9^9d09XflLFnc ze`TP+4W0At<1}V-lvc>;$J=Bj)F)lG{6>a814ga*Jj$Y28>h<|K`4GiA_ReRHH*5I zl<~SxR-ID>DaIOF_&FAKt#jrwE#ht5y72XZG?J?Mt-C_^O(3Zbz=Y$kk`~B=OfN5O z+Oy8@xK>=P(^m-fG2#_&Ok7WUBzEmUd}7@uZP3QzK$8X3;xOYpJ7&W;#ix^ACAtLG z6l@20lhQZn`T;pxKK0l|;}`q4FY(*y%(T|3<0GA3pvw*?>mC}^+}gU1GAx&A$SPmn z9JcK8Qc7Cdj6`F7qC<>Od)2;*%ei&WpN?^O@42nNq@=l5a)RwgHizDv zE=G5fT3vJfmVan1eu`SXWMg7h-d>OR$-7#8r#W=CYWCiF%{xrmr;s_>Vh9I$m`?mG zkVD!bQY?;v-K(9)A@|kUJU;U=Vq{c0*>#Wo{Sx|Jsj+mt7~gA^np!S~3r$RH8uomd zS+j4Prv=(oYJ{Nbu&^G6w#}EgIJTKtnHcMa50jsE-yFcUC~5f==1J~WiFK}P4+m6*9>|4dt!VDq6RfrF>+qogI|#%~oQqS_Rd zl-7$wNje@xL;RA<%@9gq!Jm}&-7|V&nFCq>6*hz^U5KJ2$ApTmwH7lkTPD=b;6NC;H`-+{t4-6Fv?~ynRz;j77l&$6#_2KF2rdd*cd52tG1h&+%b&un4-=N|v&8 z$58U-!j%jJ##hLAn3A#-ZL1%}Y%l>mPD^`RWqO_5_&5tmH}(%vq35?6OUWIsg93i4 zg*8=Qw`x^>Rh76RBwgM*GfuUB`^RTztmA#}WpP7@w@GrjghF|y;}W-sn;$MU>KEbB z;nC`}JO6BT0nT=Uq}<0VEF3ll%i zew1lEfojA02i0_G^Fac=u13OjF{Aq-)(d&vO+w|3{l9)%-hBEA#KjC;(sG7gfFM%? z{z4C$nL)jHVLG$pZR=l`)ZU9M2GDCpW>f>PdaRo4w+VRClvqR*TO09xL*${nW4|C% z;7OO=`0SE>A;8}ZJYt5=+4r(hjv!V5t{`Gsj7ipl9Z6q<7A#yC6}XS4kV9=uK{PHx zv1iYmKAlUsBrXq+GiDKErKOdPN1(`b8yiAq*G3R2ncSuE4r^BhEEzq$tmF*Ywkf8)->o0Rb6JmJK9TQ&s?FKls9YVxWndUO!>usQou@>GJ!XtRcRs)u)pG1YLeKewN&@H*TY^9o7?lI0y(z8&MBeotgFesm4Z6ExMo{53Xi6 z@AZ14K{R@kzd3Mj1I_X*HrzFmj~n#T;-MaUubksOe0JW5i@40f->HV8Vl!kq)xTk>eUx`?Xr&K2kwUl z1k@O6fiwgITGddU=A(n0*hNz`Y9Q|1sW|X9mjGrV8Z9JkXIT1uoQv!YI=Z@-y-zl{5=eubQz#+X;{%yo2xk#mAaJNqUR+n%?Usr5IjC>&>@B8!DeZcH z8ZC?Igv>B`xFoeNZ$E5^Cx5eC_3CY}%NfQKeB6?pLJLaRq$S;Y&8SIueQWdKrE_E@ z?&_9joWCY`TiVrxY;M?Quo*7Rx7 zZ1AZW+pNe4Vuv=5RdiXkY}r(wiYcTNny<0BiBt1vD}vX!Vp_T(n5bmbqLP;{FJM>; zl3#-b&QANEBt}fe&0DRecAA2x;*Sez7CfT}k^+1{`g?ccHg4b?n>yXZk74v=4ji&fsH3vKb3cF5~H zrVp^I-<42e`SRtvnwn7D8JGImPl;J)3gDDf zPU_i4v(@W0iG`XE5&xGs2HtbFEn5voB@dQKr;Z&>cp5L2y}RRwr185$gkB`B(l-Z5 z?8F)IO3DV@JWOmER)W`bx2|0^$aQ`vb*KiaOgP!lS=EO<5 zd;QiJl+B877;*!4kZEIS^8RE~D>1ICnVH)RD9xX$za5}7Z_O%|K)xsm;XLx5Tz;!s zrdtvBnE1IG9LDe5yWu+)d%OkQ**0HQG|9`FtFji-lYBw2nr>J3%Espr@1Tu^5jU7) zQ`XXBhwn4l_MOgRkIt8h$`WsTj8r8b%0E^agi3rHQ(+% zDkzwh{yHJIblt4iyFBeXPi<2#4{?xx%yFHL1sI_`!^n%xe}l|T9Mx|9$6q12q1szP z#5eQWRu3Xh>@gsd)Af3AqELUX@o@m2%e|i4Y;$kPZ9cO#yw}-Nr*eR(eBP$^8r!<9 z<+-Y_PHOIl`wV7#&4@2|r?N_8Z^qqSuMVTC_6}}b7^GI2VKTJcIV!LAZ2TRl_#D+V;^D)Hs-KOwKjNu zCW{s(pOs!(Y9Pu~nvB`aDG+Pbnbw*`(%($Le1C)REUq~9OH&_IUus7)k_T58`7mh! zjvq?b`-vu-@DR)HGIHM)PLk1$*%1Mj$7j=?0*~pLbKP>*uDsX#3@=yHju?J$0zt_( z`f$+CGqXXz$%`{9>;bLA*%hIXF}ro^mW{Sm)WO8Cg}6}fY>RvS?8lB-Nh#Xj24kCg z6-S#_wQ@Z}0Gdqcd`&*wZH1dmAl;$rJH?hc>?6vwdp18NlV5nT?-B3bU?6sG_M^>U z7&uKj?%QWW&KvAz)h!Z+(ELdWLLT4oudt7WAnw)jU zv37`Qf(tX=2$Ut;V?izpPV^ar5xge9P>(X`L(cWH+%_hPXK9f4#4La&;IuHi9j-Zs z8rH=MGIrnAtvS2%ZeGck;l7@&lPkV`c(V6-+8$5vA;lj_)7rdeYpn_r1GP3fR7-it zwqH?DdUJ1m;yUV)wKV(P2w#9yW+L^bQREdZmJP*KcBkXU+kRZqe>Tq8_IiEBnzBto z;m^|W0)N!jbzETOIvRk5#RLZGH^RNKLv*YpeR&**VttT3TDhV?;K4M?5il z_0)WJY~J+j=F-j~DXCr}DL0>aIsN2pvD&0&qO5cO+)?8D$U_U;N$8HlN0m0@3p#1y zZy%1dPD)A=^_j2zd(R#>FWHnjwpOF`*P?slQwMAI?rZzX;TfLOQmQ%Jtu-OneY2_b z)epB9mo$pGxE?(mDQjlXWrMS!Tr!v8ERNW2x*OYkD2%%9(DJc*kB{er$h!BowM8=$ zKmFakTlel4A?em7eY4aG)|gMt1-t6v?)ujjdy;a}v# z(-Kdv&Y-VZ#q=miNdInVthb5F(2P=V zvhrGbpg!+o+`et*f4V%^5jBZv_9Ctl_YC74*cmY-mmcKk%K1k3SAqI9k-ZO{*)*J~ z3OAG5Zd50Zo}gY?0bi!+rQuo65oM7l%GhuSXqP5k>$tlm%M{>h2YEiElQ zzl|%DOr-jcUW845*c97z#)ZyyOeV3e;d$)}=WD=5bY!0&>yQ-7q#r7xyy%a!E)Kg{ z25M*ub@W&YiPbFi!+U^U1hmnqOP3lPGS){KpE14G$RkIvhza+S{Pw&%IsMFgzN-dC zTe*M#zjm%3u9pL?g%j$)qtUbzWaaLuh?Ho9+-!F37}4D^-qFd4KxJx zelI?MdiZCJKxWz!KXKc9NR{@>h2f;%`BLD|+1!VwN*Bxd>EW^$#B~dHrgeVwVjD41 zPB-ewt(-Ra+-+6Y&h}82;x-ZcABFz*OYgO<%ItP%C)#n9=r;7Q8nde4A`p;z&d%Lq zz>dho!~sxk{a%D&MPg%`tVo=$Lz)`qE1gi2@{#tNL(Y&-JE(fozm|OYv03~0ifn@4 z$HAM*$sQ;tLa^BaA{C<=CaLr*-A;7eJi#aLWcStMLglQXz^t&hcMdHzn6Gm68+wAQC=T8*Q} zkJo@z<-*wP@fo;V14WB|XMK03ZPL$L4QW)ec!blJ%_8!MQPkOirC>(5Q-*WxBuEY` zS0!VdL1{@WdQkndVB=nO(pBKPYu=qrmr~eyZu89oZthvUzN9w2nvhnpyhc8IH*+FQ zEU*QcdHMI<3Xt*_JA@~?uKVEQ5;}ekpEG8~0N$%mW+?6XbY;w0a&FZN0dIu@Q8p|V~i2O95c zX6vsSL6D)VG+nZTU8RrR2b!FAL#^6Gz0VuV;%-orKJff`o18NIHB&H)W2UH6%oOLY zI&(NRBdq0O+|aq_vuw08FKfNtTd$IGNnyD|U0h{kqJg5V&*n$EBb}CHFUyxcpx$Tj z;9xN44}{vuIeh%wj?8`O+y@B5s+in%27|IvYCzZ*FIq%lz^oiqQJjm{{ZOmunQ*

W2=w7BmRGM{-e8dFBl8KVyNkMB^ujvC*WtqiETgWQd8et1 zvX%_!2JH{<8Iv~G1~t6UK|e8b{?kUS-4$3aA2ZcD@g@N2X&Vh)Vv7m)Oogn~5Br>0ZQ(K!(<5Fm0?nt;m);Z9x$J7hPnJj1bhRq;8Mm?Z9A zk9Mm#*`iFywz$dJ7jM$z@JKO|S6J;w8;*&g!L%I-{xR9=0pm61u`p}tIxqrf<8@O} z{IU$j#UJ13*21f~5b^5=-(a(sTgyhrX3ViA*FAg6kx-CI z%6lKQ;i44P=)QfYFE2&)l$X4JzsEr^i0384);TZ{EE}p)^V2bB6;zD5H`|EdQReTT zyFR}GytSRQ(DY{nFpWyfOT)=GM=oz8e`X}{6}3M{rJusus=lS;h*`=6WRRm>N_WS^ z05E6*Htvd>i?(9qzE1}y8zgO^QCbqubE5r08 zuj%B|iDWfC=CTVnZf#Pz;8obVx|^)eYw|(Wl`D6fJ$~)&YCU|27|Y9Ze$Y-LEv+sx zcpuqLnhDuyf3;m(FdAtJvB z!yMSPeD@OHfWkGkancEu84%6DR5MoF zKTGqRz{qo}&~M3#X2Xp_)4;R%wp=pL4#ESRyzum z4?IvOyV>a&VMQ_Hk~c}RK;YKL+l1tlK^<>yOrMg_W6y<%()3xKr@XA)o!Fee;U8G( zgdLZez-7Fqn_j3$ew>-PN>rMry;mU#>(9g-4A)=$2FI7k z%{mJ7&0dZp9h=U-T?At0t3|zQi(tUReqUp%belNG@5T7gaQ%dQBMqnR_M3ZjD+`vt z4$kd6c|pXxY6qInYMFDsg}wa}OW(CBDs#>>z4Hi`TvS1Y*yTMlJqqa0XHNelul((G zz_YW5Q)>y1Mz&VL3G+{TiaE^?2wyaiwlq3?2)Q2l;~q@38@PwOb-sV0M~7)8%g0Pu zD4Q{8!sY_5z}&9i~z}318d%9P_7Ukec{BZ+{7!nU$iJic6;02&!0?1ut1EOef3QVU@rF z=Wwmq^z_PZZY`M@oErRg=9H)+6Z7VjolD5G^P)GG5g($x4bmJZE~hgyd04=olsQU! z3p@Nd`;AQkbUT_2HH~+e6vZk# zTr0lw&T0p%Z1Ba8jbgmTu;;rA*y|pdLqXhTO)LiC38APQ612RQ02`TmtGcGftjOf! z%9K5p8o08k`_5KW-xNF+aI3Uw2cs0gO|MJZ2lVRU7BS1|a>Y){Pm99H$I;%jD&>0I z2&2@`Ik^0SfF^9(tqBq1A9Um6H*x|+Tg2wzVv+yI-n_SEkGYLaG-;`ikX-x7oD}+i~x0%ciHVo~tHqmg+s}ny74v%L4TTS&hqa zsKe;HV7T3@^A<)=Plc`gzIld1ZknynrAsEJ{$o~wZRLPNsO5-LVQJSM$yR~OgVWi~ z?V<@C)(NL|Q`4^Wkv&){GCIp%h-jz**15kQGHB3sj`{6bpHj(kpNrco)L#%7~S7wViF8px2*9cm*wS_he7#NzUiR} zBCa2suK~oE4Xm~8>#I0H|Ig`UOJO0SAHKZ-Pl*waYx~BNv}fs>iUZo9SO9F7b|e~^dhgP{73thS^C@cV8Ji!i*At$#Ar(#hVAI$|;(+3MWeJHw zXa>8$)b5Pf|9z(zoW|8oK(2Z9ypk}QkLG8(fEtvP#zbH2c z3?E)=ot$O9aaLrZd7RUXn|#Lv^SezA4OwK#Mti=Kg7i@C`B^4(ZZ-jBWBt+~gk<}Fc$YY|pGEK4T$ zc=)3ZWnpr$8{wvYGy4q;QxuP3mC$=4x?@MelK%7cqw;Y#9PO=b=%L*QVzYiRHx^WXr)V8KFejTv&E^}Hh|Hog0cIBu%_DK+eL(_+;E>1pyq z_l_FU8tT<7%`Lx#dvu1{MP2HGsp=SLO0KXMg_Mb+`il#C=7LvePc{d_24z-&%5aZ9lO_s-}8rtF}XMg ze#yjee{)xag2|>Mw8za$Uty>*c&Xb??Y7sa#}y`e?uqxE4KDv1D=ovozdZlaGY*RG z%FBavJpUM_EN!fQK5OSx`}x|f7j@M=*8JEC%4*@Yt$_74pe9c}ziUN7^37+UJSk1B zSKS)+U+6KYeCOjdm1taoV?TG2Se73&s*A)ZHzB1YWV;kVAdYuvzJY2gM38e3hvYyD zsA+mh@9+O%?>)n!%DQb~lvdl^Dzu`2SwxZq1JNv!l_=3bk{}>aKrpq~Z9pVSR)Hde zf=UK$L?kOkkR&RxNGef4wC}W}hvrbzU4ek41&9ifgY{tWVaD1(t8Jbvn$9<0qcgt3y~aNa;-7 zavPreg0^~ZNu6l6(War#FR11~Y_4)NR|ovDNP3QkhX+V0{Dm*}nSCy{xUim7#SJ)aca$q6;W|dU%Jl1F}fKksgwo8>+rS5rE{V z76eM`h20pnzzTlJ%?&4n@Wyf(tf>R#82c;-uBG6u}TZr%8?XkNlV+E$M<+rYu zEKC~J=_v9{19D}7MB2L^JXlG&a~9x1IpQAmBEfSGCLkE|YV2I6BXF_R+p>MdBpyF& z_{?7NeS9t32ksXS*e)rfl^Kc5FcvX69uyIx0X@YTv6x4y#xC#`GTu07`P_g=?jW&| zP*lSr2K+&sv#P9rRac;*pLl9ms@(%QpPd^m?mk4bfg$ed>f3Os1(-r;=Q3ViPv#Cw zJ)W0V9d0E_=t=3Lc=5VCAMSW>!HP)%LkQ1hYDDMI;lqmAyw>fOqZx=TO^&}FNUvNj zz9mG+{coB;4tYQBggc0DinYIFx#DS$ndpiqxp08rqx%gCJP4s{B&_mBP!@J^aQgf{2~+ zsYEmtysmC0L^nxL9Rsi1jN{oG&wS9d)YkUAYv1W+SC6WLfifVw8N@<;5BwnG>N49W zFutAwoZ(#$_~4ODu#PJ9I6D>(y`wz}|=D zop*pZp)Q7J8oYA1ZrSqcs~dc0yo`TOFGI=j0H43cI~^mLZw%xPsm2ya%fYXp z?6cM3S0#E}Loe@fvb|nKy_trGa33Lt7#%G{Wi-u(n2}I&-{vL15ZeZ8%L%kjD$1cP z(Z(?cx^{JiBau}Lb7$a>6S&%rjtar`ErcPbLJG{&>Pg{n+#L+9ZvuH0y~d*W5mRGq)c2Sf*PURCfJG^ zYanvanG}zJx#i+Ab(3MER;;ENDOrJ@r)Z_?MYPDF@<#&o7T_51jQ~$>-L_2;X-eQ4 zBaKzE_3c}GH#aI}jHP~DYoo)!94 za>>9~M_G%G2Bu9$A}lQ!EOjf0HN6U??bis5#RqslEzj0{ucjj=u8(=*aa2v(YaS`uY$uHYyj4b6-)7yK#j~>mo2h&g@@zZRNBMV*VQgKVDg3FQUQT31oiCiDoh!#e70pv@B6nS)Lrv6Ci{Aq&mx3l^O? z2PuRnf9^=IWB7Gq7eTo`DaPo)50!Fz?l5*V1Q91pRLfm7wR-k;A{J--hrUaFz ziP^m_ulUA2Zw$4`6Z;=XcANdH^t5$ZVU+`1b~icreLr44oskA}W2IImpwGa8^#U&* zynp|`L26ZvAuhQVAWm3d3!o$-aRJX4_a8%0U?zQfgWe_l?4_d)Rs57e?mj{#}0bm!yy8_<6tkX7O zh*XzOTF0mq_hpy@Rh`kS`8|OrbPv*)I-vK442AiniICVar_d{M`hF4j#87p)Q!$Ur zYg{9msDY({*b0bXR!~_@0444C{FzRuUL2`9c-TU7p0*?al=Oel1`PwLd6|xqwL$ji z3wwMmT8U#k3oNzMA@3Im!0_N&!w#DckDXtB_c=M_YSK zUT}xkkBnR1S4#wv=C0677EflBRmxRBKq9dssL^^Sp|Un=mqG%Nc_xeGf7?&&e5fp$ z62y5VMlV?L=-&4t#r19tN3l(^On`KYtNiYsdo%1eYwzg#-5d}ZfF>FX-2qe}hd?Vu zzJ%p=2Hk_dIxawL2|P~AzQ>607}X`}0hiQHuqW}2nSei030`4AIoh5x!?=bah(Ly73ngOE$bZgxE82o|J6qKEw47LFV9fXx#NOHUfr~Ycw z;s|M+Oa3gc@mneHsgR)#E7y|B`3n~^kzVe=*fP>#(8r;;v9H~>BUlUcHfo4GR!l>Y z9`+V10|dfQiKasA0^~dj_0rw9%@$NzO1c)vfNxJYGjVqo1&k0X0H+3gL=4U}3xt15 zx($Qb4W^E1a}vX!<7_HF`XioN)po5|v5P*(-b#~_K5gW~>2$_y@R3_yE}D>Ba`FqJ zdW*J2bog)r*3t{UJf&Swm+Tl~I<|L(fPnM5JaFjrL&BRZas13*wBT34dsd!))?n<= zVQH-8Q$r4WXue&uDY7-5UhKu=g4{x7x89=n#|qJenRi9#Tq-!pQ?-_BYBJZ*zbWu^!8q*@xJsbP<;IbL(lv$YiH%} z;96 zo<7-&7Ea@nc+d^+BhG39gK(TM!u-x2z>OBqTAb<)0b$0$w9sTVw;qb&j55;>-iMKk z*u=aVSFri?C(V5d(Pgdab`>O86QliBt5&TBnG?VT3ovIbK)t7!Mo?T(KDUq9(Q0zz zCX7L!l%-$7Q2+Cu|1b+pgYC?5)2I5bBRm^{R|M*uN(9$wXHe%fnA?RCDn0j~aUf(U zt$1`cP)!?*@@t6=2}uin{`qyyS2R411xox(%sHTsS~y7^qK9_y$%3uGu)vs@2%9FX z58~1&)a%q^5YYL+po$}^Eh2!4LUx}!%}70@(1^kVg{|abF0q#^wh4X1!>w&$Nlb%e zDG#9Ju%T(%zWoV=i>pOX{|1+<;DM~Ul6>jx*P2f`1=w$tDO)e_SD@DS*zOgewJzQL zi-xvLZ}W{L)#aumL+QT4;_Q)W)rbWnH5zI$p{`A4>G3E8LE<;^Vn2W~3Og)LB5_$R z__gHLCxf}K0h|(-*RkOTU`kTGRD?_GAX8Lej@1Dy=@Z;( z;}g_#VB-B>dsB8g`Tfqbrk{SEXca5A+@q^K@4A`Ji3aHR>HL*4y8hd5-2pmAC4mm6 z#%K-7lal}(p;GisOp%p(;tt4WB;5UI?0U^>3r4`GGdC)JW zn{^#|d2{7Tw&$g&y}5yu3n|USSk&tYSPQIC`dT;*om@%J2H8vS=JZ!tgYdi&Kq&@G zn%9m#s^G@LntL16KR#MxT7Vj48j8R`U1$4b)>uve^5|5`}d z6LKNXejbL>*_^z#CZ=<;(TX!E>8qPO&W4AlaB!cdWhPGLV5#>#veItC3mb(V7lS?siacOP8PI3zXRXX*Farf-`~R|rcw z6&#@sGUg2m=3&7!qHWA5gLTSjapiI@vE3kgK1+Idwg~HS_|r|1&Th$MXJ0+TS+5kc0et9}3P{s=OjM!jin#{J zj({&{1CMfH3z#(=l zej9BpF|&saSJ{aQp1N-bde>SG|4fY!7 zeBqEEB!Y{>&rEbCN&x~^wCDG)@-5CyXM;<7&3s!tY~e(xj*l02^sb?j(y$Gk$G1S% zPM(Lvq3!3{6`KcCt_ zfCs>oC8R_y90b3|j&g~{=hHupHwL4e+}*O{D~?rtsZku#3=2Br(HW6y!BjLErLMav zclOa%2I^}%zAC-yKJFivo}|C07~5+(@K4y%!liHjTIo4#kbC|Z^-@2c!vX)g?)e)I zhyssc-_gzN1}Ur#OItilr?m7} z47hA{lJh+`rMVe4s#!;Z~5sGhYX}jz(x(Jk3|VEpmpUPm(V!U z093<7v(WEa%rH0Dg0kPg6wXl0nDpb=dcK>`5NKJpNf-1}%CQ>^%N^}|JQZk~hS2dx zdod5;CTZD_HJays$>zO9GMm^HTc68n>-bC{&ZWh?IEQ0{)C#9Uke#hq6{2)J&xjZr zxC|-xh3xIW5NhGMhmmyHGqH5B!1WErAnI8_Nuc@}C$bXz`efC20X#6A9DcRN_6))h zYg86o8hUmNJ{_O1)Zy^j#EY3B!$mDxuPWwxzq8)S)Y73mV%<}uN|tCG5;RZQ9dq9+ zoNbuqax~;@gyo&bQr^!krLPCPn9yBjclVffV+$xpy>)wS)p{{einLvY`85dH(eq#H z!%Yi3F5B_jTsu4%E;~;&4?NWOO3mqdH&w->X z?S0o<@a6r{$35Rn{d4Cmu>_>RR9pV|KBQIwqb!tOVMKm;pu4~D&p)4lF<`xQbDD2$ zfVIrBCnI1hm6VlzyK93uEyqLjxn)b z)sbdCao!PFJQ9=lG6>vq$wm=-L zVy+CHd%BFiu)Qi8Au5sbndK5e2lOHalqBuje=qwZPrbeU-iCWQT)hXL|4tQ?$Hfe{ zNEdMKm6;}{A6k=pyrl=Ws~A6BTCR98BzB@?FdGTNssUS6HC}^H?UzExyYql6`}1V) z00QYM!`Bv7YVU1;<@85UPzb)fwidE~Z?&X)4$8fG*odDpw>Cr93F6xcP5{HMhmd5UQ43I@{W;(!; z`~9$6W$#tT+ip$-S0ie|4ttwOQ6^1YY zcp@QMjGe1|HLU`t#ajs#GfxK!X0P?3x|Kj1sJIu9D0S?DOVd$ag2CboUVFD=0lo?j-b-sV8gh{HS^$aAi{nL{;%vgy=c~`XTMpe}EkC{sGnU5Iq=o zA!f@Jf1bdpZ3WuN1db$v)tR;4>cG}EH);E2YjFV6B;p?K$0DX_{m9w6u`O>qeVdwl zjE~y&0&~=bl23_Mu05RB02M)*#O(lQ&sE?yUZX%+D51?)`B+R?Qku`4Jio*)^hGQ9 zNdPM?lmO;iS|cg8O<1_nZZ;>T%@ASzfUADibtPkDJp>zF)n4nl z4Ae@ic>P39$T2Te%pG0>`_DS?C7jTMD_qNm0)y1X!~sVheuUVy2$m{C8IAn2s$kn2 z6SJfkO`?+4);;qS{UZWY(*gAgfW-uZL6_d$EVlz!xzZaRhW!B~8;y1DX%G}x&b+$( zGBVBQQrA@MH2}>Ag^d<6**DlEBh3y~x~0=*IrUT%F(pv1*WiHWp7n&pEI^2#^$CYO z=*vHW9t(H<-M21kxEvBpdw^F*^3z&iL(E(x{`anJUnp_r&0g$N21`sm6*85LDtW)6 z_adxf7HK6mq|81xXU?2>q#XEn`#cD9B`QYjQTE^BSeEq??@3k>h8XKzRbPu`{W-f= zn0WyfEiD80Rk_Xw4vM5M6^z$SXIQM3igO@<SCVhI+Dw_;Tf ze)s4paZ3XgHgL-x8JH^QcTrv+Vx9-BMvl`38{?SI6@t%Kb`E5=En!|enKXJ%kLw~x zQc6m$fQBs*qlnbJ17b0Kx9_hCrhiyvzTSteq=mT#i#pCaFC}{2Mg-84lS`z}pw`GHLbM5sQob`Ed{~g-k|;PJD`+r$X%K@< zH~}L&o%`N>6srK%LT6;LmB1{Y2BDnKLFf_?{nj^SUNIv{_-ypi2m4oxo87`MM-c{G z?do%m=!poGA;IzCk`N^gL(U7)m)PR> z%{2ZC64vqW-~l*Ym~OJ(NlH3Zops6$kxke)RT34+%@)C-WL*)m{nV9@Cl3p-Z!g!T zX-RJp%&kMz0J4y&ELa*s6{D16LdUWV34jH!?=bo%X8yNiIL#MLEW(;lo^e%f^Qe6~vtGv$~Tc1}pc+S^coS6i3|HL(4O zs(VH^yA3N4iDa_3b`8Kvcj1<&-<{?r~I%mdoaNmppSbWAd)hFq;-3w@ho6g3=x3j1t~RJl8-pJLq1)e$Gd&elBZZ5;v$71P(z2#m8Hqa#0M7a z%EC^N@Y@F%tA^!dJZj*81~+QP*Tu!-RU=55>aod897jg5wX7hN_O7Se8%e95X-DZ< zNaNzsGks+`#cybPGNW4Z?glVV3E#LggHR6VKVg7G>`S|(yD?Wq+v5W(~PgifJ zNd#Z70yJ#!kDp;Rm)qipy%jE$hwVPMq2lE)*myeN`xr2)7LE|5Ned8g;mLGh4Q6Fo z>FP#HqATN|WFF~ns(-|6vOby=Ac2*fUgP`~dU{Kkbg`a&Zpu(2$t}U8ySI{A6AV#xXvrKXh}fI zyu|3F8>^Qu_kG}bAnM%8N+j6YXmVzs`m(NVN7^eCWcTN<*s@Bs*%Q6-!pE|^pszZT zo{)wV4wQ|%!r=k;@65nwO|~xCBFIOOW~XUx5!Y@EG#uGQ&JU<#eN!@~v&Ba@q-Jpz z-#;Gk_A4*obh}$ETQ%c+5LtXWOb>LTKj|NhkYs?S&klpjPAK?clxUw_ohwcd;!rlg zAYo=_z{F#GoW*P-IFL-dCyWN@hFkV3FPhv9EKg2lyO||G5h>IsBzG3OgZ;8vc+m&w z1c(9tM34lU3q^8$23$qu>@_-nu;V- z(+>GWv~pT_bM^2j3#X_(qI}m!g(4l@SO)Q~kX8cDEaKi4UF@sruAwXqDaO)~js zpuC@eY*H|sJgEZBdtmz!1rSs)royJdqad0*znk?W8~gGI_lvlvA}VtJ!{vF(IxiZr z$#L)*OI85~+*x6zBcK>6TMv)BZX_#q*&=W+KqdXOLoE1lweEoRmD^bRQav~EnbBD% zYz`ya)`kRS@r+IgS!3W(n5eNA9#iM}DF{?vL(yMoI{e!$qdiy}7Ab3^URZwNR;%-? z9%(pSk@6V0Qf$_pTg|&aR9f#S%00Tr&DE6_vV^h&h#%I{d&IgxqHvUQaJhj}c2&yo z1_h9~oU>BNN(s$r<+8^p5@VtH0OKPD<-_VuOyOyT2*j6E)T9_A*^}2ajuU5r@+${m zbQ0MWF^`f#lVsvq;PF>|`HKDLGTs!0JMMKk92gA0QEn!$Yx}aoTv%tFb~8$6i^750 zd1LR)x*E=Cg_aBSLu>v52<0RauTdIOn{&vzb4i1xdouPk*i_QT(OCdd56ZbG&H9E9G^bi5MWgj?P@Rp&YUD-W* zB9K8uos~?yqUDgTe|FIgEiRtVUs0n;rbHqPTkujx6mI{61NBW@< zvbb`fw>B{r7oM#gsnb)819i;+9NM1%GD?5CDB}ujh@sSm5FLHHMPsnVEcy~W4wc?9 z;`Zvw{rF~F_{Txg9b))EaHroIh|aULVvkfK`WSkT3B%?nrf7?SXm1WhS?po)usv+e zJu@tTn^1f~fjrGb(_AoSEt(2+{~W37tbAu0m?b(t`4Ua=v>16Szt#fTSYYYNTC|oI z%{#^Tb>+$+Gp)EaYB=CBBR?t2@?; zo_CpeucmbQov*bCv8tb7ROV1~Q}PQBVe?CWqLq*Q0lx87tW;2Roqj_=5KZF6U} zQP`PZS0wWHx(w3&I8@07J-F`;T1w&NY{~+{UNWJjKxaY|If>zndIHfc5 z5sOU4cC7nFI@vq)K9D&BW9-0!bk9dPokCQ91c%Z>mOA(8x0oj&C8d`AxQGk{ErO$n zhE;aDLTihmFL7x8TOwXFiUBB7cq6E^OoHlofDzXB zsvthZOr~9Ac}~?o_%yX3BU0G}dkqGAeG8^Wrogfr#AiFn(TQEj4k;jJ1aLzf>!Yn>nJ+jv}szI*` z#S`fOD*B9214@L4SFRu}Bz+R#BMD_dol@jXB0RQI@c5hRq#)gn8Mx5F_|hagmDLi? z!4-X>ja11<6`rH5tjhL*pUy4}E_vp)i0dI7DGME`9zm2a7=UqoE^C{)of?XP;YO=e zE{1OI`Nud0+w2byLQ6#7m^xd=`f!h6I{w)lhcZ*xyjS@gm?6Y%Bx?b=iSCWJSBdV& z$5jK)Q@-OQ53q-0)v7c1I$uqELi2>|)Y_bo$Z4T_A}F9?GXpSyTj;g;Eruu@17~2d zbT3-J@jxi0%w$JEXhi_%e*8iz_+wjJ6o3#eM%LE}06?nZTg#Cz1L&#>99%y{eivt; z@x@m=ea{T3EO8GEReRDvC6Gls*jv>aP!nk_8R3f9yjZ^vPsOr;BI$(xNVXS8F2gxkbW)Q}KHn5IlO|Ls zWYYoc?XhDyJY+}Enad6{u!DS*6yeH4U(q_o{vkWa0Aa&M(-1JRp0oCT$A+h3N4O~# z=NUjh6RG#>_(&9P38DR|5DJ7TgAO2EEnY2g1jpMsYj}#@{g`>P51@^-QvWGn*5Zpj z#q6Q#LkC?S=B^UTE_f=W85SNxM_4rQEyk!_Y@Gg`J0y)!y-BpB4QlqDDgJO?pb2M4@iy@mNMRr z2&rcvTt+?WU*-sJ(rws8ZBCGb@}z4WK6EIH`-u@6vm{}F@+1fMog^06cMxCdBZ?jo zSL(V=%%?kY6GxnL;B_$5hDwq}{tcP3VW)9M6SI61Y#N1nQCF1GOs@U5-zVV&&++T84BFhm40ZGm4+H$w0U|F{C75GYu^OHF zlCNYkL88i!qDZOjzF)*s0#BYzsq4ngn;O947N_Q5{1yQwjH@k%m=^tp81Jb5xfoV( zry}$QMp%lVT}ok(AiF2y%-DE7^6*Atp1J5lW#10Lm^{8)x!7++ys17rHac1dDtIy= zm2DseAbQ>aDIL#e1kA=YDVw;#WWi=kgq(mi8WS-`97na|hab0{Qr~*OP{O=0xCb|I zxLmv{4zL}@b^8#`49%cY_Irf_fOi>lw_riNu?y(LmNB=S`=QUraO`H{*q`jcyRtWMlT^qc>tFu)0RPljf{8&SPF*FM3&)+B zT*HDmMPY~sekCd}JZSu~%A6XaNJ995Vw;Ab(@*h`j4KDbs!|DNKT+|puon9+5PhJ@ikG=Hv0<=rm46?vJUs)zOx>X63J-xLRqLlF7mq)X0qO*rTHGs`b76Of8>PGY z3paik0Y$O`8RauB)zdy3Mm*Y5)=V;Otk+iF@Vvd|a|c=&w=O)Zo^IKdavt`LR8!LS zB&UlY3yK@q>9pISK75NO19w7Ia)lDjKjKTa7TELpdYKyqVCPeZ={Yn(LUoK>^a4{Z zs)1sw1ongkyfCTX%KCxygawT7C?;FMFmE$lv^S{O*;{hCZt(;{xQN~J>Xj>yF=i_# zpTP#JFCF=i^xpSYWYw)#8)+p1>>~R&6F76jn}~!R=m)q+zM%Fj_V;hfY7<1+gBH2G zV`>@=F)r1<*=*a3pZv^bw~)pO#hiSz1{M3ZLiTCoHRP#3wv>wSwZEM5)iW>%1KHVZ)xln6DCH&W+mB$R*9N81kHRjWUvTX5OjUd} zqN}Ot-_(`$m|Z$5QVYFs8Y%;!`(eUmLpl*7S!U6>$MEaJb^5mPRHSxc>{22DIka+w z%@O`6G7}mv(T}>O`ls8g=ImRFtdcMwNp=s|eC1D#>c>vfISz4Riut(UxY-0)~8pgKC7JlW>C#I8)K2t)X^)rv_TXMD1pXhhj`|YjW2B z{%N<=FY2uh1MLoNbc!@3XbHe}vCxi;g34wIQ{K8Gd+ za<)$HdS9mdHO!>LcJ!cZupZoD$hkaks8U*3x)bw9ZFfH+XJ2AZ>0iF7tGmZ6R?M_C z4(E^Xyt;Bgkm|6@?)k!TxOJCZG7@=0U*AJpOgb`)QDXBUx-t5f;4#;u!h;99Rz@X~ zJwx(zGrjH(1|>#H|{Hgm`c-vbk4Xz--*Y=d;X#2asS75nA6;BmZt}qcI)FD4!IGr-gn)z<`U6 zwRI9mGghmQvJls?TF;=2faTmy<=*)GBk21ODG5~8yHs%njlBfhd6ACK2;fjauQL|1 z=!x3|ZA_$FynVL>E(V>WsRgZ-7}P-HkHLS zqJ$=op-A4aFDmSAu?i5n!&5xhU=|W&SH)kYDC~aD%jrKIKXfFxMp)c+)ySdtBGIi} zCB;3vx8d8(2#Kf=pZttW6}?2|?0$HKAUm)Qoa?1nUvM#5neCJE;a+Khw1+3!Ub#zd zbb69rRo7tR6Hmst$$*e2&|`ux-X`Ke!OfFZ4YtrKITwft647&9EoSn2$Nj9Nq_ycy zWd8Hv>QaV&iKZ|0vH+9FBLwa$FZ63WIp~Xi1oiF7eF4~=pJQSjaq2L^L!u%IJ_T+l zUR!vZBylXA#p&L0ubrrf8kZlazv|s@J`(8vxJ#Th(UY4pF=-28$$fcV*MmsOt9*OO z9?qF-6*imh$miBQ%r59dcu3tUh%7}2#S1=}9CHB!YXCtcay^~3h}k&dtB$@0jHQE| zas*#=O=X=1oC`qa(FO2f$GCV;q5h&w3LqL1H~+Lv#{0TrHBls z&}+vmA14H9Oz}fmJ_}@KO|aIFA;s53z6-0PbxJ8wVbvfl6rRk$fJ&1IZkUS^<&76s zOcjxPPLL=TpTaG^Y&DM#E(8rLZIW>no2TTxh2FIm{)j~HCms=u{+ux8Qz@~O@$+BI zWBA5Sa);_R1|t$!LF`4YTF60c^iIHKfijw&)`9{uJQ^Jb`6>xFVt2ofH6*miH<~NO z`AmwZ#6WPSwSJC=N9O#DroyVD7s+q}Iv+iVQVg@|-<)t*)gVqj1?>sPn-U6TM@a!A zOIp(~VIk!@-CUdSA#h|hkqguEA!ek-Y9I%6DAFCrN*7d1_~?y?L*C{D=EDOjm0BCT z8V~l*Aw>gHPn~D}{22n6(y~SQ-hGs|x@g$fqgmwycdkM%)tKJfU3A0(6e64GLT^^? zCQ~*h@iu{1FvlLi2A`yKM(01yVP4jKEh}?OjiHS!k>p;aV59N_L<|u_BKb_ zXSX<1W;L(?mcQSOk0r|vs-Un@Y5=E@z@rSw{5XjtErDzdpg@ukurmFPuhBSqy7zh? zD3*bOx(~q`IRkPX**1JU2eJfiWj#=vM4&*s>qib`S7Y7;;Z49UYf(f!0mvFVYxOQ7 zNRij(bzZk4sHP;3s$1c&9ztXd}d8W?tli`(^?oyl<`A25spf!xN~S`=2xRhpgcW*07{#Mf@PDy)Ax&P z>JSIf)LKCnZjZ$nZ%Y}~Ba@w#(w}Rvj~45c9Ul1fhnv_AL`jG!4wCZ5bArW3;=OgBwR41J#dl z03`ra!(EA;$9sNV*{j{fu>3=(QFW}L-$gU2Z5MchLpfUhMfHZ|B890GjACEK_NVKH7@beW& z4M%WAESw&g6sQA%CHjce#K-MM=A(+xR{C_`l@z!TvfyY-Z;ftFEOItf@ic205jF^E zB<%L62qi+gt{l6uHu|@3aB!1!7o@9wkmdq0@uMHSazztANV*9o&t^~~?4|SxvxeSy zO1UGv95daezoe z17306&h8`9Iz+1>(jyIVK$-v<7C9j%V6!#U@%NX4rr?Me6E5e`_|@7^?5jKyc4*5w z7F9@GOjpw7|BfQ>1`r+G+zw>Icym&M(Ck(Gkj=!&MnFeAyY2R3E{zT{PH-el(i`aD zf;ch`=PDM}pvtsd^EUgm2*)iQ`Iua$^S)+t2CuZU zU`1;P=!fzJMgXrlVU)mn=%$HZR*jTPhSvaC&JJX`6t%R*CPTWB9|vMQo?RlcQdllU z2#*Sh;6f-CqA5WZ(O7C|$87~N0SjPF9PwJX6N16H(wJs~jJ_5amJ{?s=-;Rj1_Nd* zO-M3-al*I-H<@S532PeEWuCJhAa|+b|A}fWv%|twHB-0vekQRSX_yqKSp)~7+eh=H zFxlFB>;Xc)@}P^~>k_x>VE@ShY%Q#ZqhJuBPr(5c4pEhwyZ|#V1bW1Fj!uXIRt4(& z2FsG!KLmahj{Y;*Zd=8K@tsgYMPlPKz^LuUlO7xyA%|uyAPRVjLP{6iF@wSqM;(Er zv{nJ6wn$9vB9nP65n9Zs4WhZa!o*%>0ocRxvVYcWOPoZx?_UnYPQvXt8R@OrxFmR? z2WPAeFDYZfl{UaFW_hy#s8UoUUyVU0EfX5f#|MZVf&fz8?~aKe!vKR_?`u9!J`461 zVzYX7KiR9H=b?$cy3~IGEu@hogimrYv&crS{@z#M*^DIYGrkX{ccdkDoU|#Jawe0` zq$5jK?S_vLf<_V`ub8GW2&ZN|)%V7;edzp)o)0GPBq$q6)G8M!2MuC@W@}+Jv$6LkxAqX;q5h0r~|yEH%VJHV~W9+_7ZTx=Wo;+ zBL&IT7<|Lb5jfsz0IktN9bRn>o6~xX)QBTe)q&0IHbO)chhJL&w%{YSS6x<&R>5dR3VDD-~XzDlsGY_?CMMnrVf48H@ ztmnxCs1u2)glO(yLxEi2;k7_ zKJxhCb?*};rP_fQcKaaQG4Mj7&NB_xmPM0=v@ zb+}qdn2B@Y3}T~qJyOf6KR)51JfBZTJCBqmtn)Eme1yBn&)1hE<8Vl*y&A)AV?^p; z=sm-?u;pN3k{c;W_eyLs+#fmOQ=V7!A}Ivb2y7E5oIwB*m3a@r75u+mdq1RwovDEG zO;!(L7aYfajJ%83O5~h5PibyDZ=}a)Fpk;o^M=S-2wy-|s)0jF=C~MJW)}5N@e#i8 zCd+OHu-nQ)l=F>7`OZqo87O_APTwHlKM|kucoeB0S)bT4{L#5%;r9L4&Qn`SDIUnbYAffn*;vZlrC)p0j^&pZ3m`wuBAuHwLUNlg>=JMcK7(^p|rbHCo*i@|G zlCqnU?3epaW;Btf3NStDERH#z!+TxQFU1|jRK{qmb_HLg5@bt20EbsG4ksPd?v|;kPo2W?j>Hc*mLeANK*g5l+QR3gft*&IW8CCh<-dHuhC_{UB8Ke;7ezdFvkl)C{*2OKvno*_+oZIyxW9v+UH^cdZ?;!rnb5BhR+C z?t$u@OGyX97qIDGm*QkgTjG=;`F=5*t}yoammQaK|Np=9p|j9qakD{Y?B&p@uB zZ;jZ&C< zu1qc*^XqgbkumN7uEw*+v-vR`c`nc(M(VpEW3+(P%>-oWH6TMRUMol5%CEnS<9O^C zzWF~J@4r7Plk=ApdYMax~X zPbGIv4lXX%s3e+riD=ZD<$wA6`{Iu99*|Lczu#`_CBZI8Cr~68?-3ChO9;U)Afq|P zdInakT+1`+`vXO>c!qLY4-lYBZ`j4}U$2v2fr2_PTm9ny4paa0A+B6sWn7nH3yTR! z@+viNrkPu-Ao*e4oWCD1wSS`?9W-)iNb94kGw=8M+*hxwa?wBq6+_PXZS zln83JL|%G7%~rys4Z(mtYcsbNAiyVvXQm(*Py|7OkHw65`DYThSptHvH z5clqc?iJ<%zpN18*EPz|Um$3>|3!d}#@+_sG*{OWzIBlo2IrOXMHVnGbXA0QIl3dy z4!0431Hwew<}H>`@{_l`%CtB11_zN1`+^HkofzO()x)d)cG39bIz9}Q6%ds={B_w~ z#anrd=**uE@k+)g&hzeRytaLF5#Q_1j?JIhF3!=~8udXjqa^jn@76}|2j?&Gk==d7 z$hm9%HTp6*`|b4im+vSt%zqedbuK%1WKX!>V9uY<29BN9ytPq9J%Mw7UT%trdS6FZ zqECpHX3r0I&GyRd!O@`r+ZW#l=P$C0mIdevTZfQ=Fd$?Y^PyVSW;uz9SfxN2OwL4f zz!iL{BA|xsh2VerCiO=EcU(9a{i!f@+j=SOuDYc9;MTgHp36~+s>)(Abr~l;IkVCu z_DerKdURZDu(C^}VE>%<4l9@F^$~ow8+LPCGA&V1xUq1d4jxsw?r4zrbf)H8zADJL zSbAZ9KI>C|7ic&rpnW7}t#yo8xPD2*o+F;u4wwv$(lF>S-cW)sd|0;uRfXBKg0~H- zPAxeOtt~$|BItu>fgnVtIaPI4kyz{!}PJNV;GQL|WIH@%M*xSH6 zcd)3KO4DyxF&E?Zjwo&$J0j0%d0ZoZ{TvgKUp9qS9F|ezrMy#Vd1?{===aSWtErJ=A+JLA)hCH z4ReZYS#Z>EQUFQzC;-~fi~n-5KC)Q1PC|V*gwHm|d#t^fYPC|td|Sa6*?CW=Zr{;N zQ_;|J8$C6Cc%{$9$A4mt9K#yf9Cr-LV+D8Kw2H z#AaJlacu4Ae4cf@z$zN`<8deTL#W{KrdcLTU!)z`BAA-C+{?a8ja4`SOoBc0g zpkdR1;%}!#4C+!c_SDAQmQL&7#@?@ccB)OQH+j|qmu2M&GHL^d@vMS;moMjZAMjNb znL02}E;OLOOZo@XHrqhvMSolKl$b86D$HD5uRy8+@@C7Zn*Yx)ZSwTDxHE=1=XG;h zgG0_jRp6UaWYHB@azn6JYG*O$Cs&y}7y>GcQRTYJCYM;X^6hDVC%K|ZGScGt5nyX& z9$+k57|9yeo8lqq;^^?07?QOwjGRW$=1ctsfn3q7#a93mg<)lng-S3$6Db?siw~*uur=w=h>#+F; zkrA`C%gw+0_Qt}3gcW;)%Dy@T`KB`afouxjIrp>*6gQN^cDZoOWf;G@0>-6u{_!Ll zt4;Gs&clg00jOb4z>HAie}65zsBga?37^yTlb?YMG=vuAv;Dx*b2M)}UZ1DuKF187L&rq9C46dLyLO{{KocL)5r0N+9^7 z(V6EBTQ0tg>cGnQj7oIxYK8hwvb@}@*V#I0e{^g@rWaz3!_J$fAI{QKm%WgI~_F(!@HAD>r)==-8njBbb8$AH}kuyvjMmO1ncL8m8 z1NogN6+R5%41JgqLRa+Z{1ix82>||W!vAHh9l0CkAN?)yScI_G0gaCOvl%+IYC(5; zY8u3SW!6-oWwV+()HvLChTc9Op^3Ee%SGSm>5I!($m)A*T5jrMil0z^+v@JzeW&0@ zHE=$nRYAfNk5(;AEi%bNms)v9W-H%FqHjT6Zl)uIhc|9a6o@pvd-rZp_{>nW|NR_} z+zmbLx+&i1@~58hANqP1Pu>B0LPbVR7)R!%Y2%z9Yem-DRt0Y4484=_{ep+n#mOZ- zH8D!h!lG~iXuq9!)=q8Mzr8X|(o4$tT&|5b^vMm54Kb0+rvm5PqhERI*D61#F3?6R z?!{T*>fRM`FShwP6~#X8?{afDHMHx;U5dT=$wxh~*YsZw5gXo}BB=(PWg8VduPu1p zsUoOmQ_T0%^B2g(L~`z25eOJ^JjRXx@v}BcYjCJv^z+{?4S(Eg<~rGETD5j-!Rc!< zh8)-LBXujQA7Gt+{uA=z>Mw&&1u_TE(v!05Xln1b3aT)}oBs2CE(w~ZPafGR(vfOV z$vkxK^@*&o-0v4;4Hw;8M;pV)Gr#mi@|D<|AAJfe=j1E@^9}4Y5NZZT)4M{>^HLh= zFT^i$;*+brH11bC*kF9;1k#kP&&^Z4SAmLLeROAN1@qA5`E+H*OP8oW?!1lks_(JbyFR&XwJBLh zyj_#1FZQ?BUgvaI_Y%JCdmr%VyFKAv|1@FSBZIiUadug~CC3lY*_F%wy#rtMhMLRR zajw!4x4o1$E?w~DIKljXdtC}UDe_D76(?JryIV$N>^(y>g0{&vX~0cG{G^{rnh0A=b`;3Lk~m#87Q7tR5aD=jLv?MAob|Z zg)0u89hGUa(pxVE3=Rr$__lQ&e)Wsfoag+u7xy#_&5LimU)$ccul zSKC*-PWTtJBy-2NU&}?NPhZpOxF&BuoV$;+H15J@)j=VHy+(5HCEaXH2xKuB6gs~5 zN72o=(e9x3j;_!tzTrXjkyBF~2JuN3ZvJL6J^r`5fInU`_>;cmnf08uZ;yBI_WN(_ z8Wzz${#3qTll$d$4Oi_wi`4&m^EB6n_7e}|km6~Di(YN#`4H*RHTYHg_Ngh&B#Q$b z0>?Cs-j|F0%O~jH%!o|=ev?ysTz;))mVDZ-y)^{~kJWliYt8W&USII=TH1)gYd1<< zaqp8aY`&RrbZT|N^vB)>xyhP=k%dU3Ydy>#-Dqt({$uma(z5!9j;?=x4BO!B$V(UM z*$=LDKU!<#DW?DH1%aCTUaK!iDDUe~Dl0gDssU~cd*-DD5dyJpY-sT7v3%b{*m!Xt z+cO*;Uv1zC&Cyg;EOmU3bseQx6rii^0+1|n3q;_-}qI8A-2m8Tse*%XA^wv>Mn5N>xRMk<*_$Ba^l~~ zx^%y+Ds%c#rGoRtMO;LL(?VS>Mmj_LNOCDX%+c@rF3j`+{`UFPkr@S%v-jUDL#5ht zizjKwYFBUjL#+j^S*`uK7p|~trJdCXU^VOg*nH!4!I|~0larabGtm_T1*xOgE zTG#4x(zT&q^81Wi+25`wE3%?Y3EQoG#XYsnO*^$7n=R&a8unKe(N9FF!s+xa+V@kq z$oEd2Cq7pMS{NH#YBj#PuP%{}Ea-c2Ic??7z7=Hx*Qb}8>_Ak=tSBqir}xs|UT|UI z;Y5Sa-c+zen-oPX)u{Q(RJ@h7<-d#e@%=Xy(hzfnBk!{{T%Bz?)T#6@+35DR{?D-& zet0S>9`VWB`{>HH04@9dSA19aar}Smy?0cVXBIw+G10^*W)drcXo4bLx`32uj2;j{ zq<5uBSKxqj90N`)pj7Fg6anc?kYY4QFH!|5D!nUJ>bcK*D9QY0*1iATweGre)?^MK zeCPY#vfsU*{p@E`N3t%}>M1F?6wu@Ck81jJ^%L8^3AE6in3)Yg@ZGs#nPk3~FWfst z5Soi3e0kijvT)FY|Cwbwnz zJK5yxdpj*KpXCv}U*6mOC)z>)RWr>&bfabetmEXNib7N*lkQX3Sibx200P$SfnUoQ zU)9?vif&KwV6iw5)k;uJ94tW5js)F?u+0I$i{+q?uqxCk)WBcY?jtSxhFL}Re9rDMQ##&$Ks1{5hcc~e z-VAh@{y=KQT>~DZj1n$vIB@%87#+873JW3_D;Z8g_5#f*PbhpR1XErUNn-m^&1Bs| zO=JZfMv#^ZCfrA)uBwb8$X$tZ5z;gvuYqnxWQ(WaeGh>ou7=!cJ@amqMWBRv2*ue% zm1Ng)0QsD?m&T3sUF7SG@3C-^MdX1_N9iZNpAcb3g9w6R-q!SF&_D>+1^0Z2g3oC@ z&FboJ?D2Tpco-_$_ERa0>8-@%iqqmxdQq~Y9JJ-DJm-F8-OGf%W)yj zaKQTSCvX{Wp5v1eC_DtVN)rdwV>2QYytkh~s?$jBTwq7#FpV~8zi|2wM3F>7*X}JT z{I}A=W@L!TRR?0IW_0g zvMi$<)BxWW&mAnDC$>qvp&;k2iS`ENYJqC-bmU@#w1tkB=If);2H4Uw?e!#yL8RLE;z4d zVv?_^+0fKkH1cd$csSRX@)7O@?^7pT&6xHKodAW^CvVY&po4q0(b`3k5cit{(zt(+s?sKVh(Tk$u#a=$=>HW7$ zKFGH0-s0KV+&p8p=3;sniFR4ZUH$Z7hgMMBX!c%>i7NlBqN6-KwZ#v-#cUgzUf(|< zFK@PKn{0!sR*Y$Aa>H>q?M>sy4ra^WP?9jbGUT=O+{Hs(8jrokH5;4eG{qPgl=nq~ z0-?{q5g^OKs~P6~)wc~&=JtJypHiyw78LNK#r$wQl-lg2tIIF4j?bRxTO7BUoOx^9 zJ59=-oa%pKZLo+5OUp91GEC=D>=?lkiJx`#7Grc}UHG3H>1HFT3>iZPH6OnBh zFPN-JNKJ*d(As|3=bbX+ZC4c@dTF?x``WwrRm(bvXS9A!)pwKx7cJozq^}m zUf=Fu=dLA+T1&`wmm7g5k(F8akE>-_z(8S6|JMS8NL(Z&|NYAel)|M>J&22oYiw+M zV)qs`X{!0ls5CfdV$vVFWjCm!fm&91MzL>e#D||`?_Y~5-=z7=#@g{}``3K+ZRWf8 zrzaE@|`~T?~Jm0};xU9!ov5QqqY@1wAUjloi z_F$2`ynGU~iOSoywzdgSY)oGK_Dx|5sTIoW_Li^#Mi`f!on4wUKR@p>W*b=v&ZYA@ z7?bv%gG!BcZ62|!3Jw~SF65o0^`{!`$?~70O}22mEhboey6-2rXlX6K^!R@8amNW+ zhjE3j>|;ImPS)c!%)f!bWBF&YC6x0%_y%Ux28XsRFTASyTs>YPwT$^H_ce(#46Q|j z4qZp1*oDTHmeh&QO_rfBolLb|ot+UVnh*f{v>N2_hH0<(Xe)3##6CXXXj@NVH-bKe zIrI7mUkS+1ua~k_)zp0u($BYSEZ$eeGyFOH+g*JnmBm5wjP42|(!CplB@JH5&JN=) zkL2bNsBRrS+`B zz}(DCHO~^GXMWrvLAe5CHPvSd=mi1g__SB)umI%cv8^TJRb8v8<5##|tQIY}g_QQ%RFLhIn$y!l6}tE~ZMyvtX0=WAKnlfL<~A~``(eQ>_%t1o-EaO+w_MmVp>S57s56eWXI#FmQ+uhBz} ze)PxO^E>GFwO>`(CB59}BfCr3vg zd~1C&Z2V$Yv)ZUuL4r6EiL6SQ?j|>T^hy?JG{Agv$3fu`GVv=%dFQp zCvKNip$kp;t2F>KQLm*w$1XeDXe@kIwP&$uO6N9|5mOq#=y^WBvN+F>voA2msAhCl zxo1(UQP}Y8=&ge%6O=WL1p5-3AuDp#lNzLBuW|0!``>UTxeuyH$_1s&dq-d$7Ba0p z)1Ifdypo_uyU%bhZ(?%~YCY%0xYGYG?k_mq@9||{F znm+2>KFKI%m`(D`OFs02=SQ8>9TWTSJm`XLI!fei*umGd|Q% zt9;WMG(l;K=*A6HE4a@$s924Uj8p{`O~q#A=BB1uLQ+QzYe7w74K+Uck&2O1=8mJb zP2FmK-BoPPkN&$ZJwLH(jD7~9M|(s!GiP9Ca@2H_?Djfpb-xQcZfNL3rJIwFH(2d% zew|u4qbaq}T08UgP7r&;BNuzv_o9}csrD+2O#j2%ycLk1Y%fev=k6mL&@?_g5T2H~ zrvVDQBe+tY{s>JT$4u)rk8;le+3;}FNJ;isZ*SS4`qX0cPvPS(?4cGJ<)N_ z6`;)w`aJ&mN*3lxb~`D)vqIZyABW^GI=HIo1`A41Chl6h{M5D3IV*tbZ1{9sIyyQX z3s`dedW#=+XU_G7rrK1Y30Pc83U7FLIDC`5z7TVpYT*{cM|VYJFI>2QO1)5I0#mMN zM=O^7<23XATDO_Z)=Rf}pb<+PS-d=k8dPM}-riEEc;ZiPAn=F7JtnDVt1D9rLt@-r zS}tW)i^$l)tyS6i!4_lwI?IH=_i@%5{S_rQc?7orbHhW zzPl|T<>Ae^zCPx;n%w@v(&A$7ZrJZ*K$_iWP=JwfXD#Do5e4^As8KEd)_!Vf5%J$e>v~8=!?}{O9 zcpMcg!NTUMh&24+Sy^Pg{TauMNSE=lw#w=69Hx3tp~FH+8*e7F$X274l&XIw9>G!hzok7rLn$%^q>> zN4?K4b_+O8^icN?Lnfr^u4v~2MBzr5hNO0DESw5_c94(jh{0Yl8^hGnE^PBoWgXVJ zveOfJwXS3GEZ;I5z1Jip6l3Z_Gi9`FGalYyW~S8b9Y}^^-*K_Scw3T>&Jpo!YZJ20 zq!zFIQsdKa5Jf3&D*8j`6z1BtWT#rsP3gsp;t~=qrcbaC`sMWIm6jL#r5#fDNjljv zN-usq`{3w7FllQ*yPO2`#sn%`WT_AjpjWhbPM#M>BJ-8!8;0lY85pkpu;uQy1WT)P z4WF+r{g|~UfAU-e6InTF*Zl}7uP(AlNW`$LM#8v;WmY-gU&u=2@btdBbtIzn_w1|V z`~>sb3m>r-23G0Fj*08rT@Iiw)CA9|+UpochrnTQd*R0Gu(p@R28)jym&b}(|j zG2W&x*rX41=USwHCd(Dv1f)Ozu|0!SG&MMX!Jt3d)Yudom(Ks_aFIkq>b&VGbKGXl zzZ^#V^8cvbnM@7|kBa;Bd+ch01OG9%P?-AiW~}WWFK#spn0356YKSFcbTj0!$4Qm1Ztf3jJ9y*5ksX|>^j#b9Qz;<9D5f3Qx!d;o-eoV6@bvP! z)xg_| z!W5Bf-Q5$&U&<2h#s7#)>E(l2mVlo_tpoF;$BYSsAT8RL%#b3F+0CuzUJl4-v~Kp% znyhh+a%(KJnE9hyNz~%<7U7~IoB;l(bz;BOubx3>H^V0n+nBzp23{9hAKrsXVV_=T zWhA#7ZD6RAqn`ANIrc$KA{kAgH0L{sCN<)6Y53fK$>Z)=248ob?g z;H21xfBod-dR9lLR`)|qO^r>^R}9ZJAG7A2Cqb`4=i7x^yWzx~JTa~C>DP_hSbW?1 z`lju5@tF)FTUTt0tYt$~Bjet%_fkJ@aCI+w_392SNFm;IQ8l6K@*^o}R`NMh zUL+Lp=uGUo{L3cGbjO^fMX}-DxHms_=2^drnE0d7TIfgN>1kIt9Qyh%m2&%Z^n;-t zFsm*#5Ot$@=rgIoMC&vK2uB5@te%e|aOux~-9cYlfJ_dI$KH8t-J7vI_UD%jN55+n z5sh~<#>Dg8l8 zO3MC98LD7vw0jaud)N-&^!SK-fBcYBJC)=f@!?@fQhTw#l9E%0O|<`sgtUsKn`1e) zImf%Rge=6xw}o1SDajwyN!eJJ`uQ2tru~yln|9ff_xxEib8{}8FZMBr_1)@uQ0G3! zyg^YaCEt))KlC;e)W}CN< z&9)qAQTCX)D*x4y$fANcMt|lDn0Nv7rCO||Eh@}Tm`E?QB9xt-{|qEh$l9?|%Ld_U zFQNoz;#xH~Hy?*!M&4jt=tPp<5+_KJ73c^Rj0#sih*22fcRF@k$3D^2)KpS4VJbYI zu6|ZaOKa99L7D03ZuvKg!7X*g@P-Z1PA-A9@Ty1~ZFKKMAj?F3lXUH3@$sh<8)Yl| zuI8o~ZF^{~{I)%;T4A}=!liCGfkm7x`=wW0W)hcWaKaA7)!K$iHD96H^A1sZ1dLRYM%M%o=en9v&V?6cpMvYnb#x zQ>Pkz@+#)B5jH)|!Vj*x2lkthZKu$LB`nPE52A3S~YnZRt3GzlnDOZR2%?NjX zg6%c$){`}nGnXy7WHvP1aMbLPkhKKynkwIo=E_fsXvNpPxkt{ChDLu39QRHfc`&QB zbhE0yn|RQ>ZdY2pk~H>f%weoPd{e&avBP*|;O=;%a={Dr6E7MGf+=oXD-SK!EuJAT zBt~_C1`P$(&8GXMc~3fYh-IG)xqtALm*`#1-+88y6BU(=(A@8c*;mxA!Fox@n#k~MRdu!&$jpYwJQ)d>)g6m zAV#4kH%DAtd;*@zw0dqO@3WYcb3W7ifQ@1u*z<`B3kQs*SIZbZXi;?C0({u>gc4`$ zn|vW1OG|oqS@;u$!*R)agJ+E5g-~dVnlTOGhW$^ty2{4-dMQ5semTYKdTD{z^^!31 zJ|^kTyYH}A-~6G{--bTeekn8eH13PuoW*CmV!dm2Xld31LsW8hmMuvM7}L9;7kL7Z z@FwB7oCFSz9f>o~cBLdR^md1AQIv9ZlxI28Vr=?dk`Ou*gk62LR|BnjLqb9(3ddbj zYrkkRY>f#)E4~!uO4tA|$&nDV2ig}vj#85FdmC)|kL2QgtOe^}IZU77Xlk_V&MgW1 z+QYV5oy!>c<`M45j=D2Q_+PuWBqSusSIZV1QN__@oBlliql56)<9)(gx%-63yRtX4 zZ8hv)@v8lk(K{m47$0}>8cdA(S^Q5nmd!IJ?%oNV4%uS0^VMD1S77T{SB#!cVTkbf z3$M#y&dm*+mJT7B8i?fcKx9HkzJ;kd$F7OM+$nW8uz(g8!{8D7o`}BJ*c2y7F*!JR zgc_?{@&`Sixo^dj8rFiREWB-7f~mz>d4rc3>|XOJKvL?qU$Z&F^#DC{r6%tC^uSR+ zYinx@H;%Q((%Sj0lh`cSfKJ?CcsUn_?PkzFsl*vjkILo3b~M}i!;Gx#D2JX0zA#zS_Esx@5#>&IcJPgiLdeNM z5i)7$Mvv3gL;xmo@#9VDDsYO^b-UK zXXn*duBNX~_mT0<^9obOLx?x4IO4;6kscBO;kHb{ZNhH^eJYY9`_LD&2KWK0x%-~j zIU{}?5kdvZUK;`?>1I&`4K;lyJnrEl`^7v}Xn}k2f)~;M6-tnPgXh+|H;$r?t;0 z3Uja2Kq)Z5+u`LlQM=8FuF-jUVn95~kumaRBQOf1)aQ51_HQn>TIO~T=Zjk-gAMz^ zg1|(FKz zU&L!3@T)g-Fwhn*oQ&3_i%mP)2?k3zSh+0?>-D!3FHdh-XlZFN0aifYlW0So{l(5z z?9E(mZ8ieV9|?ed(C$B;x4_#XVuUEu4dyra72W^BkO0^UHBxx4wV2G1SFc{l2IXn8 zu&_9eq?bB90fy0`g!FWNk zSG=WF3qnjuqwr*pV)Jbnv^%WQQp8=hLyS&;QPxI8IWGj8d{q*sV z1j_sSEAd|hZ2Q~GgYzMQMgI1Rx^?+~m!Xfx|Hfv0S^RI^tPREg_SD)){LfIVjl})G;lXlTgh`G(CslaVwRaG_@7UajK#oTt~0 z{b3(CH}y!SlPrz&;`{9lt*si(>5vm2?|<91Y=5id($Mg*Esl9(v?N8UR}Hc#(Tiga z#g%%r`IfLNFTN229m58`$h6alQi6Y~OD|U*`bYY)A;%{M^?}L2+=4@h(&r*+bCGcD zSn?nMIT=*Ect~%dt;Oc^`&f-|iu}LFoU>~T^x#?LK`h;mZQa4mYB<;N z7BV-VUEXbEEh|M9ns{2|a|F<}nz(|6Of<~VD5G+2yG`Zdh>3J1WUf04m;U@gp^BCE zxfDQy7=Yk%$Nq5AAk7%Uekqg3(DuCh{y@6VNrP9k+ zi{_hBfjV3aFNMTcyzT2#yQzG%QuoEvJ~Gt2+@k5^xrrbo%N9NPTn<7&te&Z~7`dE@ zN32Un=zie1OTUEV7frpLlM%B$5Y9g9YiDQIBN#b%7Oq%9^Z4o(mUyi>fZy5ZX!l&Tb&CB6LVg#x zi{rXdKtolT-tao$)=iL2r1+vFr4OF?Q13JP0!7p=x;_fTGd_y;a@q)$^4|UP+xo^v z<(#V!^$i2GnCm=8QnI7Vo+l2LGTWY=odBrqi(E#k4UPD#B;ZLcwYIRRLK3C9a4|D- zi90ztIZ1MLv>$K%x6es~T74h9aP!vE!qJ?!PTZ{M-D7^d&toIwfSV3yzy$Io(Q4hI zLP9+x<<3;K zQGO6I1v3F-YTUbQQ6l5OH~zHS8*C%J?$6IgGUR-ERqn3RO17S*1E9E~x?pwmzQ;%= z_MsR0ux=;vbIw$4L>RApSi1@rNmqMi&g8T%2%BZ1b*W%dLc#9+r9keg3Gutcmkcn zs|jb1p+h7IfEO|bNp>5QU*Q*=xvewG4;M<=BZDXzh@zkpi@e@It|wYSpP(Mo%0@9U zF(GQ@mN~6)Ev2anNT>qy3E+Lyg*V9)&hEb|E>)TDQLi%Ha}Dq0zm>0tJCiVPcHb!P z6*G1$|vJUQ%FXH)96E+?yZDh{BPMSjbknlj4RpN48)c#69oSGLSMN@De zXu{GyY|xU62%@nm_*ZLXU}hxl)8-7=RCVeH5#_&dioanN)Nu&!WYr&1H0pWKd$9Dk z<;wpEDps-B%s3Jwl2s81EYZo;!oZ;0tW>i#Uvz(_NH?EGMobGy$&**pdSq1i2yto? zC{hNFmM5AJ3hgHySaiBSTOT*pDP7%-oA$3Q)~R#IZ$t?odU4`Lne^=Z8=M#WE79** zbM;Tg`9eZNFRY=r(TW4QR%T|LZw*4PR~${fsLvmN@g09E{lC?(qsPnmD`6U!f@=S< ziW#kgM=1&BhqtxK2#bo&s_TX-W+x|GW*a11bnNrd2>k>tG5UChgfQ(6P&A z+$-a+SorU|fUt^uN{4D5@IPuPEpqF^0t)boAdx30dD7Cm`+~f@ymVPtOURV@*5?C8 zYFi^Vb_O<`J)c6bK&)CR)!x1j8NdPEPH+{Fq)~li4Z;}VG-&!rBXsA61gJC40Ee-r zt1I^-GfmHv;v1Q9Vm?@G$_{p<_E!b_5m^3iDMZi?VOfmoMjX5PL3S29qWW3a(%2YX z^Z(H!gS*{0mbxVa)r411`lCn7Ny-W@{c53`R?kUFcYh^<_;Ds5$0aEkO^9M%PJIRX zb*fX%PJk|xDUNw#B;Y0vK zh04e3*mIwZuWBCc=glnsX!kJXv%s4Pvw>?PaK2$BYQBL&|( zP^p0XdLz2aFLoE*BTz?L7eE=`RJZA*j!N3{ zI4uH4h|S$@V*2S|2HW7!P({4dbV30jMy`=roWJ}eS?b3IRpq%p(c<8PC%v9bFRc0( ziIHn?k!mX7qB@DL`Q}{Jr~AfGMEp#TOpxQabJtsg1%gNev2?y`5NTIW(SXS+nhYN| z>@09}L~n^ma)!1-jOzmTE|Plh$AJrurwQ?t&@`&|fr0lRb(FYeNYmIr`n5kcNVnqr zX_f$jXiJP{T_BV_VE77o(9gO6aI(EU%$+vtUJLD|x5J-S?$)DX{lGH1%$WQ=^s0tr z$A5dKy^j9q`nZ#{2QW{qqEC0N(YbtoIiLGnn(EtJ5k1Vs7B_NDrnHYfq>jF3XdVwa9)Ks&=nJL;(laj*^NapvNi2-oBPe zZXaYYV`zA4pcf=EXM$ki0E1`4aVDn|-q|ABO&*y{1-N-XQ3T)+kKp0;Zyra&ZzrPao1WHR4(Q7G!+~Lpp6!`W2j(@ zx2S&kglj!{_xD6zi12-bnQdZy73KWV`JY#kc>iWJ^Nm&f!~?R@ciLA_N|8`#tUpaKY2SC!ZpzI*{POFvhsw%8+lini0MB#8eO~UQhQ9F z^3GdcThH%kQwx1{@alMr;SAbLR*M3O6zdpnnR?y;ADQ5$&@DVcfLFHia}uNeWI>7b zJunaeIZG;eRWxPK(=`()b7=Z)Umfwpi1SZai*{6M@^=q+ZSmB-J2~GS;WANv9D8`K z#-YfMK)ob`Dk-}P-wp_`*}^*3M&*2ufH_d{f=_Oj6e8{+)-YBeY@pL3TjduGDofnC z83vHTeE>xE81dT(lzpq<^oq5K%3pg%kL8DY!Z$IF+DKXDL1{M=s+s#Q4o!@5eS1z# zxOtzuE^8Y8`8ZO~Vz@Wk;pE&DpWM~+Qu*NeJ#|fm_SGTXMiL)+s0LBn8<&#Q;*;sJ zNE|8>HB3UR+&z5#s9nho;C|{@n^f{WdI{r%fS#24pU%22+2j@QtMdfN&S6Cb5JVZ% z%Qu|2YAn|sBZm1$Zl8MUtX+q-;00+p{T4=1b!zFLE;)8TtqhWjRVtbYNbLa&LXYOY z^0y2!#q=y7`EEYxmbBOu7IY<&vw8LRZ1YI4faO~G+c!UCv>Jz!hA*vP8K6ZYBfBSEKx})s_26* zS#pUV8niW_Nox;gtX#!I>hNDzITeCEI#tJvfIHwAHFdGFcu_7^Zsivlng!Q35J)$i zI;%fjdn=6p4^$i2g@+Bca_IyC5eR`43Wdn31VS0v1V)+^yksIQG=jEg0Pkv!>W#fwfEbKFKc{I@OCv=w%CiiCLrHS8nn7 zreD`woT=}t&$JocllvmK-&tVHft0zD*G9NsamDo@^<5Om#+srtrAK>Eg6$4MR z1r0bpF77ZqdkT37m(MR*v2nuuP;>Ug{tn!BUK|k=k`c#+lOlWjId^QCbEl2dVpCG| zj?3&^x&Gs$e_ftF@~Cty|7y4PJ%Yy(r}WvzS}aTON_ttB-?Q2@si?(fX4=M<`s|k! zXI*$xnm-<;h0}gIvmFmk$eAfYv#T6p<;`o?uIp0C60^%=acJjn(Q(k>Mdz$LOu~uI zt~i}|Vb%ziHV0g>px!`wIzgfLUq5*dFseE~Qsy**$;|t3iz@>rJ=Jz;<))uM|J%{& zhG4KA9sNSdJ^EibM*&bW+i_J7a=$JhuT~Rw3u!ni>CLfYOWsIEwlP#*f^ zXXr^KL}fBZ#HAcwl3WYf*MpWRQP29e_C$M0q61B*e>$RB7t zUlP!lW@TshqQ2T

;hnnZ&Cev z!6uldi<=amAgqbVfJ0!iDJcpI5;hvGn6LlmSh@iI0svq^5oK z6yb8jWoBlce(HoXpzpTc;-wrwT2!Y}iY!;Uy5)Xj;p zN*sj5>McI>m^hl}zage-6ggbbosMF_%SAvt;{(+Z;}W0#6GL17qk0K&CDu@*V7*tYE>c8HCF?tX-shjhNzO@ z2*m6tbaAadJG|`^AT#x#im-;z&{vLE641HMsji;hod52G>p@v= ztcCX3hxUM=Hh|TUL8*pLTO^^kuzr5NIfI0W-BYjdp&=U?j~yWJSqh*Hctr9ugt@)Q=6^K=ol;v_d-qg6KICO&1L7jB5^e|A%Lx zeB+j+SXOj&^ujc%#ssX|pJt1@Ata{|^DnCNl0^IjsDehSYr`rC#;_4)bme{?Z`$c> zGXOR7d_BM`dNtr{H6?W;hO;4s8VC{;8-S?sZ$rqzwNB zHAH2HLoIxvsvW;#$_VZPEpIhvZS1i0#T19aB)-bk$Wi3LUw_@GJki5CUw1SAWc`Wg zI+m1_F_Sqr3O!E-2XLp$YM}mHX59eKKf5JzGXfc=Rv@F!?WY_ZFXtylvUR*4LN>2g zNJK<6(Pd@4^cnsZ=^>5;*lvR}rH{wjQO-x7N=5WUa+802wYvx9^~2p;Rwr!R-L(-P z80Tz*R|#?08uz6Z_g+F;gS&$BTXoKlZ8P%~nUvz?`7nTUJ5yHkCVL*QZv%H#wZSiZ zjT|~9ho5BHzz68Z3alkQIPu4kaF9j*mDHvI66QtjN)5DXok5$myYN`Qi`4m3wgWZ< zn}$KpDBbU@oWIHz@?cvZN?HQF1Zw5cSN?x0;y-vsN{B0=*C}?B+2#zRSkJx1A+_Sf zU%pA2Zmo3_q5lw+->L}7XZ5|SR~o*)b{~&-?KEW!2L+Unu?SOkw~pQU!wM1FB%}m? z{%7Jc69_c;=*1HvNNLFitQ>|EVVDSzUb1{{gK z338b@uIxBFaZ_q4x?+tW_C)R9{$7Ye$aO-(!Zb{CydaiuzyG)!udUF+OHso}Gf`5%M#pqT{k zg{4KH0oUo;0eZrgZUY}-a3yp#V{zAn6Y&p?T%=YgV* zi#Nj>ZTBz!Zssj1F(HjzvaW#PtAKzsAt)O25S%Nn%YcdN8tJkf}+f0sI=Cntb&skSjno0q_wFRY*7}m9E9E zE3+BJ`j^#EDTzdoK)e@_td{P;t$NF25hBY&82T>94Zry7aA>#V9W(}?W z%VrPw%*)$e2AYeuaAD*)1JToXHlK~F{r6nveod({DAP_>Te}Goa95*tO4;* zmGy)jN}@=*Qy?$e;m;oFl2IyE`LbT6G(u10>EMy5Zyqn26r)dIUWnT+rwAJ}J0##5N=~E8MyT$3Xp;^fpD79^;jXSk?pu z-Q`FDh2<8GTDD$(beD5#wbb6@`o9MXu7yY?A4Wa9d?)e%v+Bgjha3)>H*(;eJbBWS zbM0UfTDvuU*QCV0Iu-aoq((Qka&@B*tm9(yvhXBVf4ce>HA}C&{o4Mkh#o_0C%FfmV5=d2mv(pcpO)7`=jSqqw;6;J-Yr?1|2h2nQAywv3^#x;PYSOI z#W&yIjn3L8D2eX4JE|6Yeu(`1i*FBT-RGhe)w`GB>Xe8#twv=V1DI`l*G}p*vwdf6 z2ha$O)Ps_RK@@r5y@vmwaGW11JORAPLu7L(_-;jgGR&TZ(e2e!n)7feH|yGF-RGNl znXwRIogW(^DHC4KIRg}akfews8Ws`YyAMYV1>AmZFHX^|gj7MwY1t_lv8SYZW2^-%qiaQg$NOwipOYV`9>81h@dZFl zLL(WU=C2&j0mwp1yQWo4S1wgIUe3QjFXbcOH$MRv;RzoBZwSCod|Sen^5gzi-5SUS zykXU+`4z_?XEM2+_HjGC+;~P0r_*eQ55fTlW58jq>Z>r`Ru`M9oomjfes$u9^5|Yg z;2ds?pUG4bMv)*gOZ3VFv~VJsStGzY$#%alb*8#64dbk3qmU}pEo!Uwr%Ay(Z)|Or zEdC|(ps!9;9Krb{!0e-x2?ST)sI){Oat4Mg=WCJOq$>8#Vzs;Ib^Re_7O}pz`vxgt%sE^oHnjYa)_#LW`L>}4qkqUs>hhKi> zR7oo3*2pwX{tHiPHU)3W&dnut5<|MW2qnKtN???x&DE}O?*Pm<54|B(hQ#$D$M8ro zZMhZL5;%Urq%;R@h$@6zwEak)jnvMO^F`aSH-P91EPSv;f^9QNU;{zyg@}v-BFffR z;V229C!2}BfUX+q7Knp`M6e@D)Fxhh%h!YWx)LVnK71_Eai^AN74~je?;g|g-gz@* zMWy|?mmC-tdx&qH5FgQ&v;+~}n&a>tt%sQ5^5Db?#@rE$2IiZcwe4=NCT9pu*dmlX zMDa$R6vYS@$==dPnA(6|7n`1*UOG<_wj|#|{+$l3BAqKp9cl~%3vaF|)hz%yw7^kG zJd`L###`II)rgcgXq1MFSZWeMrv2Sm6%ly@4{wufHR0TmT9g#H)okSmsIWHrd$8HH z$zc|hO>(RHFAtTDbp8FAj*(}Aq8O0qv^JEDDWN<*0F0O9hCJEW`YppC{7$^60e2m3{ZTDzaOM+Lm{OrR4y0E%kc4Zbc!GR zx+IHQApZE!kPsKg4T)j!@lM0#1x0hyHm1fxr2VY!MwWJwcCmBBfDyg{#g}A63E9yB zgoc2zVv)!^kgmcMTt56Dd7=ai8W$wost{4u7mi(soLAnDlEX1R29oB*Qg20-kl@u1 ze`Y&LD~>Me{F`I>{+_{%wM~_ZpTP-QFR6yx>Tf<&;nuI-I=xYQS1pu~mREoryfbvxLa;H=acTZIE)taXkHXC%1 zJSV&@J5N`iV;}xCrhhe$NGXk9E0rMSUb>M#Z2H1o?d5X=-IvcVog?qo>OBvZe3)&g z*X!t-+~j!z@_m)~IRbpSX@w8(oshTO;+gHtC!npbqq2aVElo;>fdDrRFXa}u+`#&& zj}*4l3ylmwHrcYdMeB(;xQYfO0Az!qzA!(fP542x!0i30`H%@1K!l*PeL zJaHfqP~m!_*A%pXF34#w@)4g95h*@7Y#s)N-B3*gyT-`uHjxC4zX(e;oa4WyhOal0ZbysdKLZAgIo=mjt0knW3cYhuoqD)_`$gbbg{S4o3i z#KY((V5ct$5?zyqv#L5LB4Yehj>z-ypO7(6@EMV z#Au;K=TpV*xD(uH{C$s z+N8f3lV5({{^FBt0@}F5oA0KRaON)?l-N{`XFW?CH8-EThL4SuIG_BA92#tUg)Y}Q zeG}RB+2;Znm9B2*Kt+pDw$N+d(GuX$j}*ss2B37e~GoGp3QmdK8m?{w+$s zuaUym@M--F{a%gBe80*Dp4FOZ$TH0seCC_t?LV|RUh#rnb4b@2`g|;DL0m;=zg^0( zl(_)ZokrlQ5PDarfwx#(?f8#c$+`e5$=;#(KJpef3vig@jE6+$l)5f!|CbDVj>v{5 zS2CUKUy=--%#CSO@jf4PH@7F&XhV0@YmTPvl;}8Xx4YMt=lY34SG^@6c=lc;|8JUh z4CJN)aC&&ONND@qi{jl%7pQo=DK@1h#^8MY#8Ae*1$$oJ+ULoshxj@lSY$W|Tq^Y+ z3c2uDhZ>llV)zt)S64iJyRQ!JsX@S7>@~!>58#C2Rym1OP-mkUFf{UDDxJxH73*t$rs=+9M4+tPeL#ei>d~Rm>)5!fwzRti-|5I4ytU@m5l)H=m2VADYL$ zL3t?A+8VD6Z9zatvX&HI#L^OY&~8nitH4dzDd!M{>fTI53#iLJy_g{f`b|f3PsVKZwFL-*^fzCG+ zQMak0M>#U?xg)68W0s(C9n?$6Dvk%o+op?U&W)EE?qB zTMJzXg&%4f!**8KnHCypZ!);mwT~G|M&xl}ds5Pc2hF&K%fMBzPJvhg6ymI`bc1Yg zTMW%-yz|^hJTcI6OD@Rhso*j@#nC$Jndanv1x?*yzkQ1pS8)GP?j#s2BqCsH`M+&@ zMI0Y=>O4%bdGQT9yyuSP-6Zo*?5xYMR#yd%>--lp$Z?QphvbO!TUhMsex7?%DwT7t z|0{+)AMaSCAykWkLrDJ(g+ zQyiU>F8R_O3P!MP&O$+qkirC^mrJt3U-TbA3tc{II@Pn{f1LUD>m{EgP`dfzX$1JHL8M9r{l(ChI+WW@<()Q=6C zF4s`_I~P|vkMv$7A@UI#n+>Ef`Uq2mlv)s`q9;k%PkK8P{6xl_q|yopZNFf!IGW$) zcS$Sb<6K2EKP>@AC`Xe>z#;8+oQ@ctwEFL^4%VxP{*hw#mc++3!eq5h9TGDJ6CJCf zRyH=pXz$n}JC|13V!YpZXEq%ciseZSFaP(*#mmrCg{z~XP8SJLQ7*T+LNJHf!|m$L ze3l45k&r^grs_`GqGpK5bh@e-s}Rw{i5|b+1yoPZc8DUmYVB(X;u)!_p8(Cu7~nF& z3<(AWb{8F4Mvvo42=RDqNPQE?eNBXes+!|{ijOUHB$IpKiKTY`dtj}C$bTJ2Sj}aj zRdDT-tPd&~lD6&w=-5w9`38@qFW>XPm3~kbt6Ip3?_!tJO|f9nv8)tme=iZ3ET+7A zk~9h3B;AM+8>y+PcfEP@^p&D8g^`r|KV?s3u zh$SF|N7H=+ReH(I@RKCh)@s+xOICTE)$ZbZb1X3pJGKZL?NwU2O*Kcig>eItH_~-; zY}LsQq&@l>NuXtoeW032FMsl}g-M7nEG!(!hNx*IC-rc+oS(O6K#E~-^Il5A)8@!Z zwO-e8XFZtDMPVX(2CYM^CY`}~WUNxS=~eenya_TXViBuk+A2@g(o3@AsN@b0_sKM}v#25ogsK%AijF5RdiCxA8v z*Y!NY!l`g2e$&tkgPhB?^z=Bt1H zW3Q-HJClzzglUQ32A?}9&~%+LunUN6Al%1E1ozWMgRnWeI^mULGi`cyyytF>8Wm2C zkU}w(`AwdhhbJ$hf?}MR_*nL(uWz6fS|n?SwiS)jnIsn%?HWWWhOJSnuIll zi#J#o4-jzzAA{nlSiW8);<&VUaH|YSbS9&3ijkwKdwu%TCv%qcX&q326GB)wBv2I_ zjPaWQFO!Y1E+@hGP|g?Pi~<_@q?}}m;YP)P!3lCtIbz9-8Ux6#zn=qkbPt(p9`dP3 zbOIAs_cFt%NFJHg(d~L-TkLpxf2e?9!h)sJV}*3{rVZWBD1zHxNdg_f4FxJ=#nmEj zdq875wY=0tlO(x%-yWhkNopG8MOxX=l>RK?&l1S-6kW;f-bqG{QU?Oj*GUkcL0ug8!8oF1|g9q9+!5&<%h1AjfOwPR}2v?nGhTvPDjJw+q1^jcbZIEG``L}-D+TBIy!abaR9ON?L{X~+o z{}xwD88V51q|!2vw!9z+QV5ZS#34+8nM`mV`r%^KKxx_aJdsaSPbKSG#$C{0%=fIb zk$vh+05P%3H%SE@koZ=pW8grrMcH2rY7QlpPr1Lwt?JRvyv3^Yw`?cXTnp1swC7X) ziXloIOdomI*rYSQ+LKKNfrWc!J7XLzwoq@(_Z%l=djLPVQhN#i{9QFY8AF+ucMDy# z_L8ufHT27ktvmXpw&o|Y$p^IL0=elq92`YY)fP}&Kpbq6Kf}4yTH5qFl5pU$5Kyv$ z)RdnsXnB3?EE{n4DL$q!i6+|ZOucZ1$%`GbJiH_gpkGM;M2^P8+kWEIIi+xSA5RkE zDv!iTQgl)YpiYwoGIs=y3Ya#IQ}%J|V#X?JTXG_d<}v?gyr^F5T*^l+8aKQunMkh2 zLw6r>>Xgt-+b?UpG>h({VXoY#&P zF=Y;pxfi26XttbtKKroeKiP>gD8q`XP1?ve;hsi$rtT=}FvS4m=kZ~N10KLrTJF_b zJ_Jti*fc+FK9v>%c6HMBq=z9>3)WYBU<4?n!V~p8a6slN#eL=olb7(6XD482njNvc z)%nbZ0y}!M9W!OsGOF(J1T~SNO}h?1^EoZDaDg#1>B;=2MTrLAvuDm+uiIJ}bjI&+ zy`GcD85PBQhn2qkB_`p(!#zK5*kJnOsarxDf7zh-^}oL0`)=c&!D@!X1ZpIm}AK<3eP!QKuc+|J3iCI0yPy z)Dm%B?({#uk7e{b_xPps2NTect*_|;QwLoi_(*XXp{rSda?%Pd{z*bmM5@>#F)3+Z zVl4K1FnBe@xgj-V69AU_@Od?;ehRsi{^n!vX$=hxkvIA`?B`ms?xX!3LUsH#UJE9K zx4B*A&Qn!Vy?^5L85^=>viQlY9w;AqSeKC)Gdols6V%4%ZRP5>e`9+)!m6knkSYfy zW!V?>mBeUE)XkLfmohgz?gCH$9?6^I1g7}zRMX~1qFo`QQAsY_w7~8hOVP0`y<#^F z%j~8J#5jk8!_|3q-G)rN4N>qp4zgo)M3OqUNXSo=W@xca0Zg6VxS7G@dk%kvW+(%|CoQzy%8m%;=wRueSo(jW)9`IMluZaf^#ISL1sHXZW3wWC6O z(I^_frqf24xdeiTZ(*4+j`nXg;3ox+bvzTzLPKFL2)`+BVq|^*96k5_uMg{(jYe5y zY)3$!*+!`|oC)Hv{r(Y;WA)zIR!36zKJ)oLM)9?>GP_)g-;{csYcp$F$q0K;{xCN_ zEp3k<$x8cH)r6|foC|Dw>e@V>cd6bypw-b3|J+uBnyd&2>-OWM2sXcX*_TKMVQ#xZ zvc`!)vo4(8;Z=Vr{Nb@Fl0i;POk~QDcJ6NvERafdo*ff|#$ww@KUQ3`(vhUFaDDB* zN=o$I@x?Y<{d(u7n1}JwC$RcGN@NOsP#v!W*!aW%9w&@@m`qg ztaP%{3Fb@$s0ax6?Fbg!1n7j>KO1uMil4fAnYO%S3*SjJf7nWHJV&Bm*u(-EZm7Lh zbHiSZPNV+!`NM>P`^wY7;REJe0foLs#oXy6^Y$HCNAHT!<|xzp8AJn{szLXf&U4rGT}55VpcK>o-`}?L^O9v#UbC$B=+e{-%r&G-8RuGQ)i3P%#~itT_WJl zI~sjvm@siA`90r0V>4hqGi2<4C7q_XC;!h|l#=haxvSy#6q&$s$RibnC>BXS)m;QB z@%|XUbF#Bz9sd_yZvt0yzK4$=W^l(gcMLI+nK4OPtQ9R14Kt?{MN!FGN}EX~p~cL| zj7BJu(q@Y`T2YkCB<)3u78+?IiBd_b-}9VvntSK>`~S~t?%bJsb^0!!&-?R!Kkw&x z-ue^2@=G>cJI&n8>qFc+0+(-(p>8QKl0R8kRJW%!A}(chQiFMXhR!kTz*9W~gNLGS8_Tz< z6#omav?T?7jMBKfCD=!c0Q|Us@su#{q+02V%tsWay|&da_n2cGW&pS;e`hSMB%S zGtm9$dcefcMMgarZ6^QPZQeWByeoFCe5{gn3x3cqj3iS&XKlE|%jWU~1JbMZ>S{tl z5pL!5sOftnqa0BQ6|!is4l=t``23K%hNE-KwEn)HVxT%FkXD|*t&62FK5ES-Rn7lw zSZcrLU`v3#Z1N?!g>nURCWO5;GF~4zvDIesFQ&>TTsJGcc`fK{JGkUZf8%s_KEKz7?A04rOU5&YJ<;7D7`RG*0dD zojM!We}!c(#t+%XtdTF6^G4~aUSj5)D=q4p2RFp&M{NkbYqM*)g?y-`thwpi!NK** zx%u&lRGpNk5P&7-w`#8XKR-*d{uG#3JH)0b}|S4>2Tr89X^-;0RG;-!<=7bDX z22L*Q$dMGkcGISCS*khoCP|zKOAh5qyi-ozM`PhM(xtGisLI(0nVzo-#CA2m%DAEj4yk{Pcz5+bKyM`Xwph zXqk&V?3oD>xE%A!8%s}hXQ<0}h8w1>OK}&Bp*!X+TA3dKHCiG~ zLwk_AIqY4EWnvmCaZ7CZFtpjsUjtJqz+INdWnzGJ0ci*qgUn<8G+NeNhI__1Bk89- z?R8n@O`d??rapy>$>P6+%y5+?^;375u z`laCmiY_Zv#lK4+^}Xcn{FQ}l;{@CJtF_*Kf4-aS2u@l!p9V795|YHY_u^6>>QVo- z)~NJZer@Ev7jJ$j1nH*w%>epo1&Gq&jCo`dC1b|OrCoKIoia;7{-MV@8SLkeX@?8~ zu!7&aC4wPBVt^HRvq~?DCD-GsJhZ2C$IQKT9Bb7xypI;Fa@kRvKRi6$0VVUCPezWF zciUTXancc&1%5J*NwERhYLVgBR~nXUx@Jfuv5G5 zJFh+dSiNW(encC@;oaVC!J*h^*02LQS`Es`NqohgBbT4!2lFwzN;)kzHWs1ass9(l z{PLW~S^904PM@Bm>bk_=zQugnqmr)031@Vt2m0$w|5AhMBlBY}3OcRMh9hj36kSo8 zLskW63lsxS9o`400+f-eqFncz1?dx-%SN=mSq&{2Efq+>)?Lt*EPM!d)&cm>j;q0% zS{rRx2-R-0Mx{-&{KV0KdkthIOk*D@QRRTACpC+QHHD`hO`6(Qd zQ(ZN}>K|yZ(*V}lA_wno|BMLL>zW;N9sn*(vHg!EE%2KuiR6*7NDCPEL{?HOp2J%s z>?3T9^)M?@Z{k-avybS4HgJzcKRJaE5=uXDzOMdRS#eK<%k?KF>n&B;tQBq>DSoZ# zYJY!m2xPe=*KTwxH&K@E;j|k@rr*yE*N$B~5sd&or2}2Dn+wrrd?=szq3YKc(_HNJ zrDttXR8ZKB+U&F(GID;lBz_~=d9JE_M5vy_0XC?{4kGw<&_s(s#P8pIq8%G|`xD!Q zNv0dV75O}vn4GfO^K**2drjDO!#ey|KMaq9m;EyGl&q=btKa_jtC#HWEnFd{+c9e$7L7~p%+n3XVjPOU zmPEsr8IqvmQ`CZQ5>=Ii*$uke&?Rt@S&Qn88=c4tS|Sh6h3*(0AA1g$e+3z4WYoEj zz$l#F?X544Sk)xEDe%I%b2B)+K*R-`w)k2h&>+z#U3=(}FT9L)5knEZ55_stju-uh z>dSTH+?cNJlB_?MPA z#6Ys`xTch)uj95@IIn_485?blLYwh44RBXwg?K?sYvbUq*x5G7kxvk2a6wYHsP@~x z#b3OkcIa>nD`b|+oD1G{%F^zCe({49TA~0dXN#)R;VymW`Wg@wD&)G>Qw5obvhP1vEm^8kQ1Wf zIMuyjy7-(iH69(WX@{6HwJAXMozL3%j=5R<{ivSItxc+ostPvT`%v+6WuqQ&< zUl3*Aqb)aPXsM^Tw?8F?1n*7cD^2JWM{5KSyd@}JSL58lnG@(t_BAwc7BhVzxwxLE zCI0m~l|z9guBuO@~iO5cC|s&`_Zl zEt-(>EJ$dtW)1AC*zDau;fIl~BdyyKTW|-D-(;fpL?zs#n)CCA)OV85B*v8ZJyN8n zlh4kID|w7=4~h$}z0tY{N49k|l%R-Oj27J%j6j z1G^PX?qI+Bp3kS&J;0-!dsv`SihV8YfZw?aHLoy0sXX_mL@KLuWoTW;Ux69H(DJ7R zhuL-<{R=^9j05NO;<)iGWqH;1&k%sWVTaq;Xnuw^Gx3? z1fw#&W@G_y6Aw$f&Y4IA1$*RfJsTM$81wM8jUUdg#?QUq>!O_>_OWiTe>3N3a9meK zoY^=}n(1RnBSV}`lls^=6i3Abm*N*la^aHHp=VKO1Y;OHb2U!7+vM3*E}GWDv4tx= zc|vSN%aUwi-5QGV5&G3p_V2=ZG3VS9Ki%dji+|bzzC&z6ixC#f-^OhnLfMZ%-4L=kf70UfORg>?YJBDM;vA5l#Fl-uK$O3 zxsQJg=It?u=YlELrsV||^jU1|MRtR5KQejw#wU9fmUbp6mc9S^+^gh5KmBKrEvZxj zh)gWoI+Oy5x(s*=Tbw}oJWh2q8y6wv-gt3>_;|}Bx<9~W{xp175Px%D{kjt`28Ui# zwYQ;aKjOT(vM(PB$mXuGg_1MG)k(54?${hf?+S!-XTST?kOXmloBo&ozGIVuaLmDh zZ@h}aTt@-$$6j?x=wAdji0QGY)e+h-3tb%xx|-D1lO~>w1-8mtG>9{6$bjk9d%t8$qkIt=jhd2)T9oB5^AS=z;8a zSXM|LpN5oVwQSIW8aQ59qgLY(2t2XKy_S_4%m$iK6(rnU6Zs@HKTwylV%ZlM^u1rH zE^>oXTI&b#4eI2G5M%s{<$8cb1bblQp+JinFW?M|@;-AFgCn}wUQcanDJF(?!|mXq z^8!b{cz6SykmZR6HwvXw9g=U&knC2Ugk~mm9PSF&DK$H9_-rfy(6!Q$?PPidJHQ!A zB>%vPn__)gpln<*-OqIW`dLCgZz_1dTIzLR|F4pzSTdA)r2C>+L25LMGw%4zVAW8z)EP|~?YftNlj#tq76=0fEfCHF&T%^q+VU55S>9;eG-nA0K&QDIQsHJwg|K{%j_8&_HASGw8iaI*GVQAe1Uf%F zLU^^0OI*otBZdUfhNg}iFFm|D7}w3jkYRc1N^~3fQJN!310r2M0GSIX-Fn4#m;vvy zhIU5L=zGD%N_;Cl*l8E(<1hDWaB0Gxs6b0K-N2fBU z0DJA^>C>m>7x<*{0AL}+l6a+Kr~d9D;U&FaB?;oi8Il4*rVf}<1h$We2jX|%5?B(G zSN(~TbZYF8mHcqafLhC5hz~6-{J%uylzVpx%%4O<*vm z7UNRZS8N(Xf7ut9Js(i@nW4~7{6r#eOW+=z91|m>6L6YwKG^St)Y=AwW*Wv#@*Qqz znlb6{oYXR7&ckAgrB*9+H{9LTc~!kB)qCqE@iz$~hOme>v@DyzCt6Qv07@R&q~Do46I;wKeCvdLdw0cmmv0|K5M&M58pq}S-@cfdK~dfrCyb?8(My)?(? z2S-hR03=1b(v#U1)Kno_pS=VB#Gw7@Z5|j!A|cH_z$ks+FI;qu55I=5hk;`*2pfyUACCjXR7 zx*CvdM$k$%gXL!u%uX(flAn|F7WfoJKbm-0$}ihA>?&fp8RR?`ZjzV3^YDPk5!TJu!}6&cXsWtAt=M~6%Nz_JnL`(L#ehRJu>R4tnZD6g&lHv z`LkQdti#6Ab?@&l61}XwZ0DV(&ZhA7Ad!9I?Wk)XTWwykVg-?MijmBAUOZ(!O>(IH zor1VJ5sRnKl1k5+J)|#DkB8WN`_B`@TxE{v&c1@ULM943p@9VnB@Ca*uhUy;=*Ndg zlBgE42zGBpL{~;&%^!wfV{6HNy;xp5%R8K+D3tIU>um+;m$J_-cBTET=AKttJ>KB0 z+1<01nmc*pPs?%8P|ww225{XLr+++HIu`bbyTX0mqcXIw3Fk>yk+C}8e^pc||%8X-GZjzm;bc=v5 zd|1L;GbNjn2Q^)WaKyRo18ZXB7sHh{<)OJ?wdZbU^3{rp4EJ^=FdUC=g3vNPbNpZz z$6}Ll@%Skm8=GpfEUr7zxK28aUcq2J++O!gd{%jtfyxxo5eCZx;kMZfh(BRb zz!kSwI-)%1NF${9Cx6V)R(JKMz92z(y+Wh+Ya|-POsA%XA^5WpFt_#!NkOND~^6toNV)UQzr`Qf7J-B>W&wrXoF79+f@?`CW7sD$Vu?6qZDi z=UT22s671ki0*nb>r)i)-o6)kiv2T6!|oyDy&8t4Fv8ygY=EhbO92B8#k-WL`0^Fv zFW)kNI#ksE29D-bdIR{#3?v+e9v&XQ&-i4Elg9VXEk9E$sQeO2xSZ4S6GEPYG>$A| zt#leVnO!@MTA+Xb5Dy#gZk8|rCx4XeTk&($u|J;kYNO=fZ+eZ@pNG`ZK+TTv2s zRIU0lV#&2{!Y(aNStGBKk#*taU9dn_4fJ>qE@A=!1NjJow1CtK&|(V1Psr0KBcy9@ zJ8HMvrQHkil^(sC5(H$`_rgmohEu>$u}D*2jS^pkkJ>lKs?;PGp?4m;5pFHk;piG* z4CNwwYWevTN;6??p$;9>`;9sAD5n`x9$np>B`ee1JWT5Ii(1ipPHFAHqh5kE zbgJX$n64%EEd_^s=i%Sd!%1QwZ)G1yE|)zqw!8plD0H)KV8;``1^~C(MDqEc!|9|ed z5>2%3@lh8V=IgJX?(*!x_T@R$bPl2Wg81n3VQWEqJQ<~XxC!Owj{$K2j0`ffHf=wu zx?sy4pFgH9N{DI$r79Ou1AWE-m42^Dk;*4K&Q_ar1Sc)?yUCqK440EB>j!!-#wZJa5Pk=xVeuu@Kqo?@#B-QOtP1sE#cdLU+)*1 zl7+vsgk1pS$d`js*t9#NveTH=Jh@ViQ)UbNmOHm>8;H`73K8JP)-0d?0fd1{F@8k(ajC(lYGG=)9T+U*`O?`_krYCT!L&QwQl zyqCi8!DP?%<~uJkM$TIOOzu3NKFfKw?5XM0@Lc4B*UiezE^*>|5rE}4yQ z#c0xM+YF?%xLR}u`Tt#F^-WwdRJ8}`(Q%~37q;$)DU-!}ouR9i;D{xs41l#&9g1Q- zfP>}qgdaZD`Jn^{Sp?kJ3AH<_%@G1$y?IEw@|4IK2CaG*MysII)Fl9&jB{O0 z^U8-_t9Qr%wJW`tbLtwWB12+jKo9K#_t$`hQl5FjMT;r$j%*1|dlBc7w)kA~$u7i! zx*PXLb40ql?#6*<1*W&-V^AQINI63C@1XLUTepO z>Ez^coC#%>C(e(GH<@S>At<$O>~jPJadbi|pq-ur&6s<+V*qKanu9koOtu_CWF*yf ziW;8KOmT7S>NYeAlm;2}c<4Y%F;htFR?ZX3gFZg9-usI(PoTt{!@?3>#S8N$Zkba&=A~I{CDTj#jyW+CI^(VZ^ zG8y-pO7=)-)XCP6O$4q)lj5MMX-Fvgxd1!E+>!1UvJ8`D3yH%q!I`-#%);8f_N=rC zON?PQ`A>l81;-@OFn;CqWKT)HYMIMg7#C)PN1x9$wJwwRBvPR23r`G24tpPWqD1_7*!0X?jz#)Tc$uTUYNg$zMNyA^slbeE96&llO7J=g2R^daPtAc>WqSo5|!nSeY>^o-hJ?nUVULD^yny;^5P7>R@;iWCSgcfMMW z1zWmuDg`z9xcb`q3B_nUw<>pR+A>8K+dP8j)Th+9&X!2 zqZ||1mb0(x07Hl9Teoh>d^cR`6beEVgfVIn zfphw|q0a-5%Zp{`Ks2<`-46GneparVTE1vBCCPtP+Db(Mt1n>J55oH}-;+#(9@s@U1E_P5%O5+KOU;m{uxe}Yc<`+##{UVS#DoG7;NYY{t znjs_!MRYSVRi_khZZMLo`HJo8K$qd<^Y2hlIYO{%S-Z{RpGeD?4m1E801i2ob))sw zfLd-9XbIBQ>F({E*d(`lX}@b)wO#BhApFGeYBV+qq`o`?F6nrmAa1{DSXCyOtv+yuFR^bkNDtSd>o-gY4}ij15S zH*6GvMj{>>c8jhBQXBoRrVCVTE9*<3-HLlsv5727H%WIba=WA^*l-KgNHd!`^n%%O zOw>b1*pEh&4baWZKgJ815-k1xaXXPqkY&yxc7~=!$vJ0Y66`@wSTRCSc%FpRzdt_N z%O&|an>^gR$>OveU7Ckk-h&fXjuI3`|6f0H7h&L3ME@(B!q?vT+D7{R+JaL@zl^|u6*?tMEFS(?6_NaP^1S3`{g2HT< zaut1b@K_#g%xMFpPJ>}O87ht)5qS^uaR0s){Da1A2$Jj60qIRmU4k0B3P{k@)HbLU z*t+=8-4;6Y^u`6)OAe&4q>Y^gXN) z&&PEyNIg*5SkR~yZy1$=F;2g`mj#iY^8XmQ8+lI}Edf#Pk0)VYdV@aI#5oX;eY!9H z^EHic#xYh5@VWNfeC|es+*@O(%!@=_t&bwWAoiAx2AH=7+tyW0>X$Rh$LB=nCO zbagr*Qgi5Ot(?o+UV760@l)n~L+3|I=}G{v z_A9|y!tT+AVUg#J|M}a6C>ChKl@CPskyRa1#ZKVHHlSeQP2lC)AS261TkQrVq-$Ihv1iei@_SAwpFA}r@>r2r3o&^BhaQ3}V zd<^XqXOCxziRwKIfsr*8Z8bS6@)C%(Z8Fm0d%PAb+2ZNvP#Xh6;MLl92O~d}g*J(7 zlC4&P5;xZ)^(9nr2$QgWtPmb88+th(o1UrcwwlA3v8^# zV5y@SoJB((9)$FA^8_tF6BKK@fygbwc`zi&tl=hz2lod3!O!D>9x0E%VQEPnFt3J$ zY{+3aX`pb<*funfNDs#7a4q}z)S$a-1h!FzM$@XL0ll4qZ&5RM5NSI{j)2cD5)ht2 zdba#vaG^imlo%nk|6m#+jMPI37;V(-5>&lO&wR4hoi_I*0Hm zfCYvWXa_XeOoe#nUU!!On0L1QD2~J}al9HDDj)1ephCsF*_u?LUoH!_yuJ2VOZ{CZ z;S=8;Y;N!B`P7%<<5MNMH2=#m;A7Sc)2i~zh%p0Hus5kY(PS7!!YP#N>$l1aQj)S? z=0?Gts5-^B2eueO&6Scfb6%+A*gZj62k5|F0UaG3Bz+H2#aDV#)IpTyXf-)J4j|$< zex)E#+9Q=;U&17gT(zlb=sK4Amx?b?AnDD?=pu&G_7W@Zb}qtI##J;YIMf0Pamv9B zl2C9(qtO??e1uQ7ISzOZc?>Pzg^p4$wL@#{@owN*xTB*X7j^M;xCk*RrSlaADG@3qlcKz` z9cv=CSj>t4A`zW13^I_Cr!ylnV7{X@wm;EpH&0wy)AS8 zpIck6onH3{nRVyp-`>}iR}!VW5E#kBPe%xpPNKh~(LDafn2fBO+M#PKowFx-O3GJ3 zlTfn83P|k45^O@BbXVyO79SY?@S_+o6-cc(tT0~rVzHd@(waRFzCE0xt0H-cVmMjn z1vAIbwchB6AWqzjQ}+#kNIQ^xD+~Dm;uWxlkcLhBR#0NDvC7%jMf)d}9^KqpbVqG~{F5cl5fB_* zz|OQ3tg}R)$W}>LTjQ53)MRY0nrppA;w4ga{rnpgr>*}6V-E1+fgUufs`2XS=0|I| z6a&c-eP`QHQSkslMl=i-l=AHD9N)%ddggDV1-6PP#r~0>jM$H}o-Sl+8Q#dNSp_lG zG()h_<7*9~yyd?i*Ppn{iB+j&ctd6&!pyi$QIHwkeIW4ZCR>&8qiim;vvoyKNQyoM za^mv#WHrvy@;8=N`l8_$=#hZVs`u|nf|4&>V}^1CAAbp-LH-o|I58Y%hhGkiJcJ?! z$G^W2l$0`R8@~rWe2T<3VF0lHXwP3HOYS#uG*BwySW!@SuAs&Usr{Z?ZhL2q3y_iv0SMU5cUh+MDbR>Ut8emMZ6bAQZE8%pf zry;drBSi#QfpjTt+ETnc}4*(1|Xe z?WfDGHO)h42S9NSYa*Oj$|2j)vbp%#c=7jaP?FTVE(ie0@VHbidn{Sdj*VC{36X0j z9rlOQF6d8AnW|&X! zOO@;>EFOJkH`v^AzpwjR68(L;CEU6LPdQweL+Ms*CvX7eNVD12Lz5s35R{bjMZp2F z3E8zicpwvm>%^BI*+2BFnM|WZdbj-OZA3LPzw^ErCa|;wNPQbe>e|hl>yEGL|0v@= z+4oNhYw@`zFWHUQ0kbf@&+hFFR8(_`(|sljE*5>^^5dA$b; zVaE>GsSdxB9e(!zc7HFpQ-2wBer@Ep`B9T281g+~YY zqqJ7Q_}{I_4+T=|@k?ESB_2(C;xY7tyTsAw+TP26Gst)Z=^-+EJ9jc+9PY`;FDD)* ztSqcSyZbcJX)^Cwu|=bOjKWMgqzCm^5x~I;^R1FQ@ssc>TdIjQV7fv|Hu>|Om{phZ zJCq&iKuA=oHv!FZ0Rkk-izZ6cZ`W>!gN;uD2rWCvJG-0+$ax<_*mXPvL1zU-7)$&9 z)dln#?1)5@2{DjTH5CD z1OPFiHGBHoVeA08)DG>(#n^~2;kh9H zuQgPk$=r^e(~lfef_Zq4;w`SP2h83LabRH3Ql@CDYPPyC`H-a;FtHxXPkr7SO~vtkTS;YCz9n0f?5-cRFSm^4*z_LHmR})5%j~1SsNsQ zZ-_JMszr4IMND8(UtjUu6b(bBkU$GpgSenSQAVKCdfB#J5V9S%$x!Nm3xVBz3uz3k z^aEwF>*w5kGX_WB&U8AnK7H6&Glg@!+Xu|g+1SQTq?A&uD z6kQEz@outRLRu`K;{>T=HK26Rsp=Hhm;V*SaxhxD$4h>}5U+ZJ3IfqrAig1x!J{jg zxf}Pe%;so?@k?M4z!7;Vc^gHe5>b(vo*9b#2gKp`&RxRk&>`gD39`{!ho+|J33j%u z%Z}1%o`>cp)g$9-0EZy&5GB{_2BUnHIWOUw^M0%KktG9Cy}c$ojGRjlR#Qdy+DqXE zV78~<3JT|;ekU>(+L{Tz(f)+CrSJdqxoC*wNeL(PqZiptg+Lqx%9`2d{CA=P zqPmQv<`PpJQg-ey2_ps=hT-@e$`;SI;pzgt<^E7=xP;r5R>!j5H4}2DAf!z$dbr@$z)HEPY z@_u={1$F3<216QjKKnUc!sF0u6usJ3)bc6m(0qS~ipDd5+RWH1{PV6(xjt5&>1&RuLxh6Pq-gkRnh|BeI4k zeUUC20Xd^nR)O1$^(HxrxlyL3c)8ChruGd2=LoPa}g0OXC8ZoSC>y3t89Y$iu0o1ey3hvw5fa&I07~w zIZkQ4Gsg1FC5p$xQP9+a9FhkgNrj@MKrB0hwIa?p(!nT_ zhOBe*mAfOWmw^}I!X_4waEG!1ET<;4eezJe>rKChc3b5IN1%ZPs7$)pQl7c_Ytdt} zohr@*T_-WXl2M3w%ZXaFTKW|E?!2XpZ5#*9p)r-ezWn$)rO$&xES3qJpT&8Iq`ncD zqU)bgJY4& zedr(U2Fi@;f%}SVl_x%^YSM6i?*qA1rP@h2!_A4j8&OSyJ`H7!`Z0I6B>-*{O@5}& z|IzBrAbpAml{irJ(+4P`rRq!AN9h7xi(Zcwmo73V#qTBzeOAj;+?~qPdng*=2(s2u zp9*yR+>q>HL9nZ8G@~ooy*553CXdP)gc=HR3PX}1xf~_qx`yTq$OZ>j*8BWE9TBvoAEeQ8Pvbn8gTsp@nB zDmAWs^Zqtfp-E(wsij-_O{t^fLA{a08(@AeCCf&s+O=Yljn{f}=M}1@E0#>&v>kV2`HD8Ux$fbs-+Xu#*juq-`_WqLWpJL`XB78Use>t= z@t|`UC#1vecxRa@2FC-YZT6k&^Z*-UZBi;#(TWK-W^lw@pr^GSEeEk%vu-F;4s zbw+*%iE0&k7*ckx*q7!V#!o{<5g(6=$qNj%aqX)$UR*F0UX~N_vEO*TEV~lFVAh>x z)SDu^D%8VMQ;We9;5r89e^bX4QV*%l1s4Ei#4EJIcv};Xin6Xd%f(rDz zP8zJ-iU6;Naxv30lzYRb%hK|Rg3E9vEYcY2tU%~5RqN~z1visIAkzDkr)y&93CL9MY0lMj{+@S;1!}vgPwOD? zXU0C+qlnozOX0_`v+9{qTxCKPIH`Qv;It4z02SxQI=Fg(qG*FBu^PzP{=@(ByExF_ z*NhOc>!CK>qd7x@%^YDRIz5BoT%ZQkr(vLW78q&#p8tr`3s|0|0-kQ+7o zImIy3V64j|14t0c$kU_;_Mq}{hjWRj8v(m8JHIB1j0m9d?bBifB_m=jrh|deS01bx z$|({2DYAD!=p5Dgs`C1-I+0KrIQkiEHdXuANN4P$wrSw@m?$fkZP;*@-2jpAr-%gw z*N$cvoRrpBi!&{~$*=|B1b;1@^<2y$o(ZB=?o$K`fn<*j*y9h6v3%@-D1HG(0ispj z##IiXo;)}~$nluz0$m8jYFxzD51q=;`uPTAVMBmu_kwYzYI9T?MgAO$4o;rPo)U$M ztPnSf+YLZHWChN6TSs#0b#^^C^^ojH-!$5CpkI|w;vLIipgwfk5Iqa*G|sB$cz2!& z6g_CJ{Jfx;F`i@!gKDHJZ72hjNzFzWCVq~RIab(*0zLxrk2_&Sq9TGpMPE#H{VPjk z7!PX!By}WBkWE7%CGva=0v*nTVhS0^+A6i&&9sIzJK-S09${)OvcPV2ZOvk7s8b$4 z9npxeXMb`{KS|pdzj)s0XANOyck0zf8X`b(8AvTA&nqMqr#S$QoCk};n87IG`X z7g*~h0O|pZKMAt)tib`6gvO?(rb%6*_poJa?)IQ?(TR9%@sQmR>OVXi7mV>q1uQ6V zwMBB^iaaxy3fj-Ul#@LZrUL6Tg9#U10+35ZdhMy5hW-73X~%>WPi{^FX?V;l0_>ta z!{&umx8*VR9(UktW;?q=q>4NbXGJm)nniY3QMs^cTIv33&M77Wkj4CPK8-RJ)&dNE z9^vhx3yxHNFm%BsXNf-S@w;Y1m)aw@KI7b(4FI zWl5%bA81O{(7!{hGTGkt#|O}>DY!TD%tA4!B5M-Z00Dg9xjrwRRh||d$)jl$0WOfB;g;ia41k?Nh{D#ir-)>5x`?dFAPJ%RpsH3yK z8x~DVD0cA6=?6qrfJ|i*tI(D>4FgDbNQkIHu$46w0coxn?z)HVAI@Vx0Ohc4*#K!JaMiO5 zoMNcm+aTviHWt}bpbbSEY!e0O$WC0FxA?jJ@Q#RU)bw-C$igXxnzIamhwIF+n8mLC ztZ6CYrUsY#vSO{yRcHceS7stE)9#Ht4Gu#Rd1@`LPG|%wse4fAWna?cVYNk%yczy_cmO@mDRaatzCtU2qVS#21H?h-A5ICKG2Jc^od`P8s6 zZG?+y{B7M=3;eY@9?%Q|^k*pYdtt~Ng16(*y%}wr09mSz!_hx(ILQ|CjJ}PLWJv6h0p>SysG8*M#<5ufSlI>9|!MLjIT1%lZA} z4T1ghZbr_0=u^0r&1m`#ir)jI0}GIga=vn2B*OQ zaqL>YLNNcc@cHUut=6|GSoF4f>&d z*vnxQ`jOp`5tjuj0ZCJ@7fjwnwz65-_79}b!=YfZxu_$TZQz&1q_rqKjBfm zWQB^-(Ib3R5&r883%zmWwR{XD(@-UL>fB~m7d#D+6ar#gjPT^(o~!2Ij+F|g|BG5= zJ1lpJ@D5&-6V+DS)-nu@}^6VY?R0$a#y>x}aywYlQ11Oa_qD zKf3IR)Ub$`LqALOBZLe3{JL>(s(ggB^f)k3e0D_qh_JA_^8clXJ863#dnDB`zj1!l z!vCQtmIdu+a)=*H+gr<6&QkT$FP7B|t)y_{@U=*8fMK|p?g51PeWdO0U}iWVy{&=( zLOK<}ZHdiU=aqzb19Ijut%a4KQ4I=Ik)a>^3AvS{(i2c+gW>}364hH+Qb)4YH@Ecn z)T+L`VEl&O?!4v3tVXuCzTWI)#s!L!nU<%+Jb!hRf}!Yh}5n&P<$sp zcE=oj)7F7Xf!jljjCPvwc!Kw;l{;O5##Qv!Y+D_C`W;E5NGTBNorODsj2ZbN7E( zaYdnzTLiGpb))tV0x|dZ`B@G3K?uoCQ&lc}c+UG+JBB+{VJYfQl2XxsWS?VaY!PQ* z98tXK3sd>@J#ndjhwhZBNRmTQKjh`4F0S=F~Ii zes2yA3Y2yrV##P+F-PTpVZrBkhvVAw^Yi;POE1!&jB>J0$mwqz_3=d`bqMh`5Kn`yj>09G^asfS-eQM(yTCFU~r4a|_~oV())t z!Y>^_Iui}+L!zQ3G=EoC%tIo4weGKJ@9Ox3H^&(`_D7#FTQq-RtG1!RlM7|haw5k( zGlkLR<0_B3DYl>PSZ%kVaUZxXziBla{BWN|)4^IiARF0WuU^mAeAM^gWovsf735W(g&Fs^)W}^C6Kp*)4 za!w8Z58DjvTBD;g7b`{-NeAYtUbQjpZzz5ze`&*DT}FEO#4%^gHM<9Qow{g~J^5jV zU68 z+?Nb|XpZiv5o1bv&k(K)V3dnSF3X$oa4kgO8x#FDb^ETyz>vzdYnSc}@3(0>zbZa# z?Qyf9@JWgv2kmo4-asVOI0)$Y12TIKnk`}lA?xD#y?Xp}?6D%Yoo@k_rcWT@EfLQw zX%FCdX~*KW6K@1zgrHah3E2+DpW8zi^y-XCQpPn1mvApNx>B51y zJw*D^qRB%&UK;lmO(ewxci5fbnBAAqPm3Sq98L*++JjcUP4Ag#klU*~o$z_d1i(9D zXu#G0%#$j-)ya0R*iC{76_^s&-WhBI@cwUf;k>+`AtucWqL zNOH(kD)$V0cX`TjUe%-+oc0?vM+#(Cfz}a*ns_{P7?)dCaby24e>$usAi$gF zq0SP4$0ZFF9n%KrR^xd6-T1EsVbE7P1VDGRXs0(a0z}H|96zV{;sY)6HnzK_p?%7U zyT6|^YO#9hmwF{WBIJzQ*Uk4Jj_+~WRcNwy>Fjm`&&B$t@h38$jJ&9wdPRPn4Zg;q zR4d|kXANe&EG(Y&)l%TZooBa`ds`ZUMl1Tj z>jn7${9jiHkL**~yO!hyy3(`3$3hCRFcgMEg~@}=&#bz?tK`XHD`MPI2_WP!NOZ3k zDf)^OTH%$4m--|)gt=AZlCHPX5u1KU;vaBz5L)gX*pIu)(vYa^04K1JBwf%W0&)Z? z1!w2>XkYR8?#ms2=o!}?NuIp$W>fj9SkI;Trn>@9PF{N=yl_CPpN_sj;e-LOswwCa z-gjXt&{%u0;Gi% zwVEATN<__EIOzhNY~`vCG?@hij9U7pEcGEz-(+~u_zu1>j+2#^X`bn9eA*DQzN7RH zo5@;RzAcOyl;z^e@2SmPvAKgp#&PXvR~3()w)m%TPhjnfi#?GCD36(f#}xVJe;<=P zcO(sXcfGyAlj!iY-M_Wgx0DR*Z+jfuYkPm;ObbNEXpdbl|0Q}TG)j#CMn(ZBI`Kx! z%SW|UbTTM$SO7BSK-$Ft6)c8E;)z}7F;ryu^Xxu}(cK!Uaw9Y#e7d|4U`o2hm0R}$q3xca$Y^0q|n)UHPhE;bbg zNck2Stv?&&ycUv`(~oB#O6piQabLmpLEvXWA{){Gq;G{iMZ1I|wZdy)Lv|i_@E`;; zWsjNd6P5RO?!u)Yc>#zgQpYs$GJBhIck1@!`zKT8x%#KqyZ-$YL{jj+ z;`|En+fU|LxV$#_P|9n{`E4-FQyVv$^)!0oS0|rxnadmys0(oIv$f zMBo|>(x7^cx=Ilmy{}6TX^?_K$l!C}1>cdsZLV2s$faG#hr|YfkaBKo^f%)(i*PWX zWzfYaYTKZVyL09GI8X$T@i%Z6v$sX3A~T3UTlw~38*01YEW`dAkIt2CUweOao^$)% z!#}2(RnPs5Y9CZri+0vwYjvhx^;8y1rVb}uz+vx}eWv%P507SFN-(GlH|^VWsX#h# z)2Y}?NpY1MBYfW?`1zbD3w+mqOHsOKAy#M z5p-f9X^6Mc&qS-)tp^78us)G!E)-jve!d*(Sdh8rRae2~zH62h5sRN!ty@3mbjLQ6 zyfFsHX7Ul$u(!#+^SwTV1ay@5ocJ>4G%Nd6jDD1iRem3LhG!x{;^WDy9bzbcD`!=}) z&-_JYo8N53JuYoi1S#i1Tl@gAXJTnLIxtlVo#XMNY)!`&l$(WN@|%nDjh)IY^^hYc zBhXMQM!p?+du&$qqAy8jS-?3qoD@K)%jrpow)1Eo|D6C#cfHH$n?~BQ=-Htho%`Yg zoG|Ulvyf}UD&Fo7#B?2?6jx8%U2fSSt3tb+ULJW=qw2rf?&*&aPOX=Mt0x*)m3X0z z7Y)Ern1rhU5vO8j#Q_A{Jvh;C`bN(`P{F2X{d-;)8p@g=V8Yv#;Ei81mrL+ucw>nB zyV|k%8qUMLoXD16GBQ|jK})<|`Yl`--+jAGDq8u5%V!2xJQjLp#J5+^7ued6VmoH@ z`{kUeM7|HXVYqP%>!--8e@a{(lXek1j65y)q?mAifu1c6)^i?%RJjAie$_pTa(q_<*FlujanLe{||;8g0EJ zv}vKz%&s6A-3Ha7QxPl^9NAXM%^%(#^F9j`rI|w58cp2%hJ`@9-t=U9Q6b>tZ;uL1 zWSOYt1Sq)(UsLsA*fLEf-E~`$nw+?jeE-t=@|XmfPki?9&<(5_A;Wb9LeEUlz)R2P zOumnR8U~?>0_3@5Ggy4LO0Z>m8a$~S@J5f)Y?ST`ocN1X3h8JT_{J2PtL&^pIb(YE^8WfwCyoPOc<$x=E_2&6*voC2svezt1-LB7Kr`iMx!jfrh&=GVdNfc|1B5bsr*+k>f;A#UJ za~oBvAcK;Wvk)kTZza6Ml7K9v3dgc4q%#hh;x|g|k-j=6X)FElVVkAh%cX|K4()3K zWLc!n@0u;ux)A0IwWyKD^)aAJ6~naW6rGrmHW_qVqAf$yqU5Gt6!T%o39?GjrYNT~ zi?Tnr|CVK9u7r6GK!ODvEQADFSz&BHj43H3S_D^$D|gqV-xB?HxtUjXiGRD?rQmZ+ z%w^n;U{-kY*Nsa6GK&23!K>^fri2PU550=or%}TM@t47A=7Oc;+{!kJ!O3u;-WEL| z5Q6L;@&p05X$jHz+{(kfvoFoTJvz0|lP)V;Juv8gAy#BZjXXq7nD%&^-=TrHOx7+S zAm{NE@$C(07;^4m*^F8sy!ky|*n-cOyjv4M&f$aE(Pxv^IP?9gh*iUP?rMS!&x?`ov+p?YH@SWzry|`Yly__uCC<`IgF&A zH=h;dCyuqk2&aK9F|wZan0ce&%?->|+PQ}xMHHPh+og0@DSKk;V4san zRNV0k*GiJ(OI%JDyuV;EU=#gh0bYh|E(=gPJ$XOHcG^!Q;)SG<)vI+GR8U(b`RFk}~Y9_xn53PGHw^CZ*0^;N_Cod9b}ZocmmPzawU<9&U3 zWo1Qk85saT|1B>kG7b1xn7=Ld((Axg!EcKZ+|2hMez@gOxaJ_>g+1ZWV+j70Z6O^<4zaP+Ojb9Ob(G8@G_M6)i(i@6E#I2kuq=1cxqu;;lybUmL zhJ!U-0CP8hs<|u48)0e{Sdg74c^4hH87xb8N6wL;)YYWk$3J1=K*GqTVu@2sc!gHN zAaI#RjuWHRhi@8M<>XPEnYVShZq(KQs*9)~PR2hJxf@QdN?YQwsv3qiu5$*oMczh^ z_v+%W?X`_{d8zyPl9LGI7qXu-r$9eNJAV4-u4>6wW%e(KDEhFPkM8D6ncd~PUgxDI z$k_ii^yuL65P6eL0lFrWu<>y5@8(KUj~+@_9unRiE(psZ)tRU#Tqe!WceBz)c< zx|}j=lv^Es7OUS1dTZ9G?4XlxO08-#-O0_zV%x4!z;br+!m?1yb75Jbj%hK*hDk%&i<=xF4IK%eBxmJCCgJ&G*c}yw!>YqM4GBN^wSy-jvDxSe~--9CZ`6yKYF&G zT%{59tDM1w%TqL8AK4PM;rNVmk=N{DdzAQB`pe>^w653$>)C$tGpw?guU)1jZ+AiO zgiqO;hczer`aW5hzxc}^@2zl{f7kA*?R36fUFD!=5Uyo5d-m)v z^2YwQKGAJ(gOO3beSGV67P z%)-ute?FR6)C$zjkf=o22Mqei0zqOl-0fB>kNWel-LAyZ3Yn(4!oBwp!-G z#Qhdkt15>zeEOG;%Au^-gO_|erGHd9`$(t$vm6hkj1KJ33`+#CZF2Qihn|iDnon^0 z^Qm>ZzO*92QnRtCNkLgzxgpW);KjFVa=urJ^#AP7$qAnezB%xI)lsDci-VVDPKdZt zccAkqe9tzw??GT2-Mhw1pxWc_89Sk;+BUkra*msiaa-F>cZazj!$%9A-LrW)ke!_u z1>NdNt?9?IeA0UkRQMJ-HvXQ0LuePG(b?TDF2B?RVF^QxGJmkp@&x9*ZY&JbTqN&R zx?=K9h(nWVp4V5ZsSLk2&w7#Wpivv4DVpqurF(HZN{Fq8OJ9bUp)5S$$NjqkC4CNK33q?gGyM@NbmNl9V$3(0D3X=+R*{Y2w6B zv%y}UzIzZ|U^4U(MFdDDq0ROZ$dS+vsOD3yrN7Zd@R`(aXR0 z=0+ig1r9cc{H(A#A9+Km)f8W_P_BXuH|kGM?Fcqr>~WxdQTzYhOcjyJgcwXGym)FqQW~Tg5s^<9fwoO;pBWxUGBh`1@_)&jo&_2m$ z!(Yj_I?PQv%$&@ERtFu3>5$5AjXgOtV(giuGjnEEfh>MAh8mIvNu4}1IarQH&=Rm^YvwDnJyz32+R!&(06pRe|0Ah=HNLc6oR z|GSP=RaVl%-N7$*_vxJr^WWHQx?t?oMcN_~=Y%809S@$z1{B8x)vS+sVq*V)D0}m0 zs@MN*Tyv)+ib9l%WGtaFwmYPZna3n!W+fy;oI07BL}W^aF>fYj@3b$)fmOnIMdX-70s7hx zg_Z`kU3tlXgtB2<#wR6Z#Ky+ve3T=vXH*tyZEsz4O+J+|!dUE#sjcal#VC{0rYU84 zd799#rl4YCX(9Tb~ZkZo4a8S#Fbsxfy?KDO~a!{D1Uw(#) zUJkacHz^8~RxRvIzx+%$UEx4-%YDr^JZtFa0`ekaIp%8)Y(n!W<~{FHXxl#GdnY<7 z%7ZeFh{SNeey{~TRf(i2;;OLgB&d_}Jm|N3yGihHMcVf3ok1g-3B3%{_^wUo>(X6L zZXeJ7B{8toWQTH8Vj|fql_k^|(hJfBT$HUBD9%xFG>I&_@158`@@ws`dh@W%xPIR8 zj5F~?>Ad?}yG|T#<$3n8r7lcBu1HZ$EqmI%&;jUW3w+*e7k8#6CCNjy*p_EmKXBn_ zTf&KrY<}?&iy*9}#l=yedPqbsD8z{)-{CJ(<}ov7KwWV}qfLZdfY{-j979Kw68dX~?( z=pqzCS~z10GY_2(m)5A?+ur;a?J=&v%AJC1*$ma5)ejso_kY$v%I>S*QFi5$47B6N zCmniC>dfeHO5UhF@JjDl!Liyyt%Zm^ z!3&Zcg~yD&O?qsl9&xj5-)@I>M!rex!Uu{(jM|m*vqEQ{s^69V9)Ozh2({uE^;bS{ zE@`L^v?L+$896AqoB^65q$`w4yghd+Xsb`=a|;O8WKDhT=uR-O4eM3zsy%62b7y?_ z?59!|YeUq)8)c9(kquy@@&zTyEs6r2ZG`hHv4;pX8W*QW(gyDhkL_!{upA1vAr#(~ z`g)Xymp3!d`81O(TptB%ywRRd<3v3FL4Dlw?Rf0jD0HCeUf5oxU`_0K=cA|?NGP6h zKVig?1OP@C&ylHdg(s`u?&y0-pR+l_JP+R(_iLYjb-_(5B&)<;K{IidmNO_v$0Jz{ zGh)I73JSe0%Fpk==+u;R;>O5B5%p@G8@p$7yX*^#j;Q8$5X;*_N4uV)ESQE<)9~(A zED~*z2e|R#_HPe{g@?}C(1Yd&QL_vmH6S9-2~aoTVF z)CaBOCmxZ|A^gYUKei}9&FRO;SZ$CUId zE%)-+gxM6vPb?cHn8h@H&*(PV)*Kiwp5a|>X!0w)u3j0i{I0gyeLe8hIaMDn6lxPy zSVW|cVs2ylFY>ac z9cx?#7~k9ORGFA`A8F49L!}fj;@u|J)dxbM4y7sh*D^E}vVN9ua$6i^H0)@gHdD)YS4w$GMDOiQjPI!R3C9|Ec5lna z3Vb5MyI_tE&)u9qqm`fAwp|Skv8$~a%_>xtbIi-SiPEyxD*wiq=fBt1^ja9LTMo$b zAvJp0hZ=Pe{hO`5_oW{F^JR(Snk|)(j3j_4lJ#!fu*g0g`orrB9=@#KpL^Ea3G2+z zE;+CDx2M?sm|wg+J+r-1FNYA^b9W2x^LKn#W{_etf@ZW8SbPE|B^n?+`9Ylh7=wf~ zIwke}lqWLRK*L0BzS3&D$DP>{a62tDZ9UB!IycZgn{ zOH;an|B6>ww{~fo#=>AJ%PAEspXX?JQ$313@&9%JN9!$Cb8*;Pg)m>3&`(;rDi(&v zkt`uu=a_Plw1*dbL2cZzH^7MxA z#RHWZ5_^VD<`l>lBjEqg2O!;k0bQ&eSS?%g(x)4JF16njyZCrB(rm-r^n}8ayJNcz zO&CPAx@vj%*LM8rCaRO7F&oXcobFGY%~V3Uf|{xGi}z(|?kUw=7M8-(npqCft;1~? zpn6f}o}S4Lo=iwcz{=D*&Ph%rqa?X!DUClqBV3%5w0!p*pyYN!3aLBc1_-njzuV`( zcS`96o!QL>7e!gQE}J!8Lv0~C^u)b4fDE&Jf0VALyH!-0&gN(SQvz(16eux z4)2^!Y$xhQ_$*sP1iE69IER2U*#6?i#=YG>SFV(hnM>SH@FOyatQnYup8UdHQkPWb zr5m)EDqSqlJ6?SxHLBVSwEhDq&d#C6A@5amVY`swIU~_hc z346H{^2N_t%$DsiXGV5Kuloi3{|8CILz2F4EwLE&Qx3(*gqb;Ij98lT5tXd z%|4%5@!QTP=Nk*6;cdO|B2hI#Z%a8hu2o6jWZGu_f@xN`LzF=s?lTc+W(> z(31ahOLF1Ie1^7WxyDYDD~YoW$Rn}fWs6~&y|xgfm!LDk59;c7P({~Ke*Rr?+1_>4 zSI$dIS0~=?rY#DNfauzI&2vmsl#L1N41=*LD1B(-RN%jIlQ;Vo-D!jW;9(pCXL!f$UB@ckjU#lWkP!s6d-V3Z%a(L6c)z{NWl+j3{^R0=e&DqU z&ie1~rNaBId9@h6a;~D|QmbmGc@c7lKYYjmJZEh+q>?!#g&=Hc$S)9%*)$sJb+o(* zLF1obyfTd+L!~d=`lLS33x$M)L*z1Y;Pu^LC)#N=!!}0?hQj53%(^kK+)2Zn|+QRQbD z7G}7{5_36=GQ{)|Y~ZYM@_BUkw0;~++;IAMoAZZKmH-Fz`A`OBpdr+mC?bNK`!O+` ze?6wF3X$xS+82vvnpTZo6w2P`|9vCg3#GNGTu@_9@AvIlRVV*@G+4(HnwkbTG!2Cru;^1+xrPg=5fv7kNdE0{qtBuWvidg;lDnq1q#vZeh zRJnSLwz9?k2p^XwON&Sc-%*Ds)m$lpq%LtW+PHikz-#5(mb)O zU-Hh&Cn|U+J{cMB=iU&~oY;ByBuvG_U~Ri!(fL_l)G?n50`7%9c(88FDZgt9xAz5m zU_(&1%<2b-5Ju!;|6Bg#Hbap)9nk{t|oXCSM;^VLOxQGbkrFY}f#dO82ooQ@Ht_+>$;=OrY0PIXgR}=S;Y- zG4|b~e1*yD;fpnOb~dB0g6=QDE6h%@#!swr2u#>K2S8 z-p3We)cNsjIe*}MF)buD^ zu#L6|8JB89<&jAi*NYc_UXk8eaz;2gH8m4w(o2F?A%5;3I&u)z?StJ_CzcD2qjP3o zx~JcUmRf(P9op~I%-}uQwFilJJvnmgdbbQ*Hb@Z=kx$AR#?gLTXJRmRAr9Ca^m6nu zP>-#c#CHK1Rs$S<)184B9;uIEG}s2pK~A>uGleoA{0L^l!g31NJAdAymrDr=Im0gP z{shG-$0KugKk~|;K=m7RC;!g(B@5oNqM};Nm5yQd!vnofIPPLmH~kk;Ge)MEMvSO) z9CEvWABam2iq2e7pO+9rz`!l~7-CNW3);K?tfpoHh9aYQ9k;EiH@6A3B%HYUr=usq z9$>>1Fhg@kq-dQxmB;qRI+f`kF%A*8`U4|3m$U4*LuTK)+u1H{)%{EU0-Wq^$oD#4 zhFHfMmQhTv*5lhKWu(MHhz#QdEU0Z-9v&X@3$#j}YbRUx zrM-4}=gFg$fj`S{utxR<^J}g73*OhfX>0H;bqNgC;jY|@+|8tocCX# zKv0&A_9gM%cuQ-mI5gj=+HqN2Ok$Brml{IdaHLfVFisng!pI6MXvNE;T(fy!Q4X5? zVbItdR!qX1M4z}BS1p(n+J+G&y|2Era~|vlG9mbtQ%e=?m%wLYWujRNHhp{F-ib{# zR4&w^!7^xvee|K_*f?WVw@)tROp&0D`(=5l7@17Jx^KrW{6Xn62u}vgmGSiH({~PV zhST5#Ci(F+0Tn2JlGhr2WP#+e(04^r_e zYDc;Y?V9S8$eo(k*48!?P=FDIJ4)Y&pBtjLPcHe)5A()=%6W*sa@S~EdMJ73Th)&4 z=AtcA%AO}|iWoKsbiG5Hb1m(2%bs+9mCAoN6Q<2UM2eHWtvk)$GTQAJaH2?nbX0dOABS zE~e2P-8*kPoL2YLxdFgAl|Hee>!=#z{ zb#_i8hH-N~?<%}KnMFxL+Ot<&TnF0bzl5KDxaRv4A%!4gVoD4oGpmn*Ak#vU&^94M z{2Bf|HfjGsEkud*@Y?c%#Qq_L(N3|4whufKfF`IU)`pKf*}NQNPlcF9~ctTN~;?F+8{rSA%_-(eIYm^eV7We3gea#En1vCd-|!rEQ% z!R8Mz+lMcvd>Uy1 zVxLYw`WpZ#=xSi*sZD(|e7&Gzpfx~^(KRBSz|@LGDqRit1EyD1<>5$yVPsER^`73| z-f#P8U$DT0us^H!wm?uBxLhknBrjT0Ee)1pOKQmwAh*#&{yp6X-=nWB6vHJgAEu}#-_+J#w0paNfF`*=E2o8X zeDFzfy_mh}#HxG$xoS4Q-TsmLx{|MpzEQtgD~<6=<-5D};mLP~Q~7GcYvpb?4g9wz zC-59mp}wX;MU=j(s(~hU&+U>s^2%lDE5GeJfF|3%hAjH1AZ#&hkfJsRAXO368w`)T z?o^aV?Vzr0XT)z}NMMG4U7#5cx$w9*99BW}k>o-TWk}gx>N@h>Fw9iKtu!&QJ2{36 z7wI2aSJia%cljqa#~VXm-SBXl6#hec2SS;3Z2& zE)d7XLJO#cUC>ptxAMco*fBlCH3?R2}lR39KjzfQUyHe#LVFsQ7pU#J#d zwn_4ANLHjF&k#mr2DA}bf0WoAjpb3j1K0~T%AuV@+HT*QF>h%q`o2s1^Sj;;<|z{0 z>8^Dm&RvGDOjBN2NmfRPFg_#0eqe#=@5ad>0dZEIYcKg~>xhUbj&A+ay?xJ)Mgv7A zP1E30@;84XI%1Zng%z~2AKRxC;`?b}3i8taAl2C-F0RPME7c?+77V7n?~ak5;+nw- zUQN{aaAmw(I&X$+T`n1C_UUBm3<~u+dCDD5mXb;<$@JG8&$487lXz_rsh|=z&_uT! zeX2G@58tws^%1LYZBN;)5i?U|i>5Sh}U?NE5^L_UOKe~4brZLh)MN}e2@ECkOs1VW%&{4!EH zn1|asMS97SBxG|G!8O~$b0a_ykOr(I5qct+gVrX}^3*vK5J6hQ`RkR!`&{_=#N`|` z?}YZ~rEF+a)A6`m`{_61AX>P@UI?Ef>>tSwq)Bw<%(Fv$%zY0W!`Aj z?)AJg2ecCU%9(`>p~Gx>{)H(w-cpuZDZxYZml{{y5quzc#qx+2D)id(S9lCOCx)L} z_5Co}(V2b98pouv<5WLHUy$@Q3=Iu`N8eWgNW0`|u+v_rZ8yk0od z?%-Z$hK>LYkh(nfHXa429=e<$YbqRPI>x&44ADq^Y6Ef!sU_9$V|RH5;d} zR;>{f;cypP^GO4(GO4`LmyJZ2-)Y9l)))Wri6s=9d8lB9QnxNqRcEggP<% z5YtdqPu+_Jf&y`lmaJ=!wC_8(T#}l_n6f@U{i{G3&9@|_4aVnZL2Xhk?*Rv1o~Th! zW0>tK1i!)er-W6-&M(dEmflqUC6t-z92w z3$mMx`}gms4f>|U^Tl*hw3xDgctA+ufjW`k2fj~RhqdwS)q8<5?l`Q1pNvI|vg7_j z*ZJ5iv?A-OU?>>}TK$nP?vyQ#;cp!n8TfeC6Z~m}#xw zC9AdKR7lOIX7IaP#+TcnIwyv@<^ebqS@+aXGog4$bP)xfR@oRko%3hlcNE&g6><@3J)s$B~zX?gVG z&>CYpN_dxRid=-b?q-3j9qZF~?k6?KwS?jP2G&Ek&11_M|Jw zU2Df?u`Itpc+D_3J6M531;j0W0j$z7HI2ve)Xn)s8>5ho*oNH<{K7%K2i?d?LWahs z>Bn=$x~gI#EpDM5ui~#4q+jJqAR8IpA8V@w>sv4h%mOLa7K0O}QOwUcJwPX#f=q6W z!zkE*M0^jHLGIEDy4-3pRaCn_^|&)9-md`YLthfr1>+AU=K9*&Kd!(f!|Ds(iLR!m zX3V^xkWf3+&>YT2AW*`3D;^FvX=*h{iUHe~!#7<(85Y(Th=lCadyphF?ba>%8t%c1 z?him)i5C5)8|fJ7qV`uFoWkodAZK*1>IdvcOs*2o`4t!og zH(8S>I5!XmuG9G|mIks0c{qt4LAJ|s4*h^pjN>`|=U2U7xm#ITsXs0Xq(`yb{lxS- zrU|;KA_Jd4XTa5=V;q!7VZ431;0sv7n?FPh+*2Cg!@uO2hz!J519}J^O-js8E1~;Yax|B6^RXgkR1`2z>|KuMm>~;ZJAK{(EFcX>t+tfwY9hg_=KcYx~yTbBpV7o zwt{H@rxW7zO-)U+N{SHI)XXt4jzpEU4~q)n!WQK5-3uPLZN4vf(PQhD+b@ci3!WXw z^EvOvVU_AmPFO}Qimg-0;RlOJS%dY9TmPJ`G1p0dVw<=j^YDn? zCjrcy(`Rf>qLk)O6%zVT`y8%6El?0Sf#b7|!yFWu<29RDLX&Wqpc2}uY%NDZ>`an5 zat_`x#tr?->eUi zy_;j${_arN6}OS%mR)#BS%Oza+luOp60QYpVbOl9nev`?CeqA~zbiLF@$-Go%hgfJXUU8DeG1Hw zfOFxenEzLnm*BXw-;()R+1Z>rSZd_p>MioS5+ay+L|veT*hQ zWmeL?@Nd-Ss`%+8i{$Y5xP!^nIZM|sO}k&SnL2r;C_J#7(;gPmDDh0yWqG5K{*3E! z%@Kp zltjQd4`77F!c4^kogiW-%Ba;hw-b=!_#}?>RKvtMxNqm>2a$A)7uzeGb*?BEcy*rS z&wj^oPu5L4TW9c%8RxrS_O-&))f)AA_a8od43xUovA7<@&f4@+$+QShm?w`ws?>&) z(ney>7;Z;u$>z2JHojz*%}v0=-5X2m9~|h!qIyh>k9Nyp&jlF#PLk_q5FkgP67K%b?}5o$mWP{(JEad930c3C{w{b!%&fB^GOW=13RO(wZe`V-afC~L$Vp00ZFCT8n8Xon@-RX6X16AkB z|5ly-R?c70H;IFUjBKo^S^ns#=8m9@w9P>ot~LG#$hZGh^+IA(PG=^t6|x&NhTmJ| z597?QCb%AM4Y!lRd#oxV z^W##;_EwpZ8-_KNwx(wPT@3QR;6-b~$ZNK%6OQHcqh;r4P@2!t9R*09nBITLkdq~e;8K;MC3uAjxnp~Bo{wzp6@Fxh}>DS$b)_J_kZ_Ib%RbD!7hd6*XZ7xRtvXFtTjH zp&&epuo^x4${VvFw!AnaUELIL^V+-78*n}PU7m}P1AFg-YR3 zc2K9V_zd=mO=9F{^u%^;6s_o2FYJcaP#rLbMdEsn@ zd#IcJdlu%?r%&r)>L*tO_{>iI@zbaJgmVDV?Do6{*w5hs??G8@4J>f?ti;E^x1vEk z>G1Z+ZtEUYP^2pHeY%Xj(=};hY1*J-*<>qqv0vWO%F0h-FIR=OXT7#|xuQ|sRmZlg z4-ULbdT}i;?&xw)$&A|H?h*T*alSM^4?{=Qcvqj(Em>JI-}I;w7>)>5v|RM0w21x1E@)7bgVCRoz9q9vt4(ukk8I?XR$`-$sVA^LVFVY@xa(a_+dF5GC^xd6KlvHcFejP_b9| zNu&A)CzGiy33b62Q(Fz}qPvS6`bu>`I5^(ZnPrrUWN<{f_B~;X;z)}RD|VrJ<1+kj zJp@A0+MExwP}8PEu1AU?Cr6?T=+PMwww|g;=RtMD9S#zGsYB0Uyt_5bT0I3ei7*wW zaSas}o6&wwV^1fm%4Zi=(C`h(9(4J4FwZ-~iBY(lvedSsBEc{8r!arLT z;7@+i8zh#HB%BwO+F{-Yxg$B4Xd3jg{YQ@I5rIZXXz1S1qqMuFsHxp~hb0>K6Vt9Z zQf9EQ+>4Q)1`7WLiu(0IFjLXQaN}Ru*lT~#xuQ8K3pYYVb5h7Z+@V$OlzxSsiK!YtMtKl2>KPK2`ZtKf}NTP?&0cRKR|bZLPQ?afWT_r z9PaeCav58RfroF8KR@mQ63R%7-E-)UAytbpG@K(9MB)=J+4SROE_WX&VtE-pqjOR12 ze|T8foX<{i+xse)-t&PG)Hn`SD#==)Ok$yA5uTOc60k z{N+%$Tv7i8Rzete41||Tr#CNJd_0y15`OmmLhv}UjCaKF?#1ikSpT1Oc>w}hQc;ky zVWj;lSV*&-7jLVJHqiY&kT1>e>i_8PF4nZ=P5Cs6BZ}ZjvE>`H;Lz`*<2N>DPi=&f zZ)SxBtw*y~5f4?EeRMD?Iyw`J+=LuonK;1&*_Mf5lH!)JKU}UB}d2;aFG0c46{G|d)%jN*5-lrO%T7+;D?%H&n z&A_XBh3_~H;pj*YPT0D0ZTp?jsxmpH+CAz{Ih8C$^6H{ywGfsL^XlHu|_?~eJhni@DS@F&LHcqsfV z+hadkiPny?(6?_5oN&q}1u28EIE0tUhA^uiBvu zP5%DfnHr=A1E5rH5;&uvN6sqwg?_gb5G&94T=vA>Ma34FITO)?Vhj4xxoqV5sx`mg zv^&`vHdtKKjt@dZ6N#bPO12fM!Gi$GncA6WvU%P-`ktS`FC9XA+f|7O!BHV0{nV18 z)W4EaQk1&2xV_=fNuani=&*Lr_C39rh!e=PfZx>TVPV4<6MC3`zVXu7F9H1E14HGc zfb+;zDPEu&5y1?u_t*((f0XdN`#Z`#FB?PSE(t?%@~B=~)`m205Y|_?-e=l6ynSap zP-kmkEAwBYcadJtp)2nMpeIOSb?~$fiHf!-D{r61!$L#3xnxAg#)fe785uLLaAr`R z6(iBa75TuOa{PVGxLfD>2<&DJ^i3p2GH(7auhkoUo}D+>7GJ+yZUYhZ(~r}69!Zy_ z289>3w@h@{P9LyaejbtJc|3abm0AMk7$!Y`bf=!Uxh-Ud*|^xDCk27|BzPEED2G$Q zJ%nP_2GBS%^j`$5%)pC|qdfCon0d8%HSPOFN#cdg;JRu=+bqK0gQnz`H5L1V#+8kG z3Y{21c<^Bgs37i{zY4aASa_ey1U8WEw=Uk%-j9jwUbmR`Dp;eK-(>3vBN}UPJU4v> zD}hUQd5A?yys!vyq3;C>O{PQ@u>rO>ck@5hNR*WhO5L6Qo%HAg9ayFFLkOu1)vB-CPm(Z0td z>#zL1RP{eiy1U@uFR`?7^gE9kI3@oE&+s^X;=iE>_f|WWTD*Eoy@SUXAVK7fn2#;R z;()p&uu*D*pnYI`YBi8?AW-mG}I8vhA2K?vjW#4)+N zXK-NP3yL-f1L$2NeGk7ImGfOluN49N62J6>+eVU=+%^Na!!38r64h@ZxNjL1? z;&0uer^XiBQG4 z(8u>K%OdCk_SNT!!GRArCa9Ke82f;J5+{slso2R(=#Gndx6AoOkh8NH@Amg+KOsmM z{!zFo>pY-9F%bV2r*nMTQA16`fhtqVNk95(WEFiO2$||;wrHs2G=MYs#(5O=4;ry|=oe14$j7pr7g-gHe0fIR( z8xd|f4BwX)ES-T1-!=-(G>*Fo95`@*D?Bu`GB%d+|3JiY|NMio@psE!wGN^)V13%) z57k|rErj%9g8FE`ZT^j+^hbDg&xy&zDp`i6Hg_<~rnr*knWMuc#jI!izoyeTn%U2oOr4Pl7w`W!}^7Q0ee1BrVW&b~51V&)s0F0t? z+z@c1w6+lF2zj3_NTqfNj4Kwt8V8Hh%Ms8PO}hMXj3Ke8Lpd1yiIxd20PCtqEQWjC zsb#_`;YS2b54WBq;#dp>woESlQq}w-mb*(lWxwJyC5@No2iN=BERAH zisZ)NNeA5Sw9C&xMI1&wa2Ju`MXhP+{g3vE%$^&=JV&1Ix$v|=b(|;qqlFo`uN01( zXw^anTsp@072yK4Q4m%TayKC3VKf-+n5<&@m;|~Q?Ulm7Fqx2GkCHxvj_w869m+V+ z`q)aM#0Lbf188a)AV*G7_Qp^T{9tf0RoK@Q4EbI4AJ$wbNCfH|)>{#z(D<*_QCIbD zk-X^aCy%-DWPH1SXOO?;Fmu5RDMNsOfW8f*djtqBP3RJ$KdKMiI%YGMy*V*Nt~Bws z;ZwrJ&_>4beGp|K$ZFf-&FHY@gE1+KoKQUO*4s)d zAM(j408SK8Nfsc-xvQ69vgQHH$@};2VJ`8PHB@lGR|{P@AID^8cWaasHP1VSy98tL zXor9-LsLDHcV~v#R4d%3*xP2{d87feT?~_aUs|yrmT0hWtsP5zG4ft}P{^U6LMnMy zH%LZVO*FZA9)$$O;-CbSn0Q_L(lE*-a^7qG2uLl&#w%bJ%n}b6%kAR|2LJ|2$`@2$ z4_b$Bxie6bkQ^994gAHVPJ~G*YmdC!IVr0(W-!uhAIE{ASg6i5^0DN<0G{9CPJ;OJ7H%B2=4oR0QYko zsHaR$Tco_s69o64#$zpUwyc^L2G5v)D2S%w);&;6kU=gu*qQWI+x;U2m1Q~Kc|8dq zpU(j<$uufZ{V2K8h#o+@F~g(p|2Zy_@|u;#D~CGgEFtKFSI#H^-bj78u!IojyMBA2 z_jB>mx;j#NdU^2Cb;{n)L=%Nh>qDFa)e83Lk2RRokV0QDbd#0>-ycVJ{wlOVo4I$lj z=EGYY4w!(sWIXW+8>*|ej|5TYF3~WHlsYTPJ;8)TJ`zM@{nLpVCnqPDez>-gz0D1E zxYFs<0GH|dZ(}IkC2H}5C;igP*!Dwn=NvF?HrAQ5eZ}33iE(!lN48tK1m@P@Wgglj z`in>axiX-a_(EtLSAV(th)mzj>Qb~%Dihca)Fii~YJ|5-=DJ7^jRaR-L?j(t;D zAcPJ#`Nnh~SkD$mVw5gfWY!sFI1CCDF*p97Im3L*l+`-%q>ubFrFQ zTCQ-89f5R+J|M@aSnWuki&o;m1(XHrs!8TYmt_LMq|<$lCYQFydqxh14i;v#3s0Xa zRFqH&2e*HN&Szou!eUL?`n{D0vAJ;Z0@ckbekvq}wvzm`)O)KktyrawNV`WLkhqf(j6$Er(y1_ z3dRRS+t3>65$d4KYHm8zbD(MX>H{3)U=V(|^lEe(yp3L}BoEJt!_h)PY%7kc|CUm1 zIjtp?gZGvJ|1WDO*EQ-tqB+tgS-Iob78-p6W=K217_pqYN?ocCHf6==-rmnR#Wd73 zqFHS_MMk`k_+0t^U>8p_-$Rg@{*BL1(oueXiwt_%(WLXIUH8h|Ta9gRhw{BVZ3Dwk z%GPI=>oJPolGz*~zUi4tm-|10jFuQxk)J5_xtQzx1;y>+n)Xs@)5|8%Yy1fyU>HPw zpp6ilC}pkDr@d}QPXHX`=v@QiOGfd6l>tE=*m?Yk1NMi)%8yN86N$TjJBRub%;Hmm zex`L+5qUO^4L#s)4E-1{>c`f%@ZJb(^2C2l>_!1nnj;G_ui?sB4pdM#r_{Arx(rAaPvpCj(4u z;cL1YfAHev5Ed?gT&t!cSQ?xK5wG~6T98Yc6wv0WHYxmU)z0PiI5?tWY2FS-#^%$G zorxzm6H3VA?q&FmnKZ;0ynt#wZL+_USrhsJl81oPq5ZEPb|JJ@8qOZkFF)@U}}XZ4FmMPZ1F!j;ksEC%_TC^?{CW6xbW;?SK0y4Rk8 zB7)jHIOF_Br~*=wFY8H8(TmG3J*o)0vk}ZFzzze12v`AABia>@>;t^(_`af#`U5AW zq###7->9mn_(#{eJ)bZ+Y(_Xakkcsuj1XFsQ4mAIQQYBmrV^UOJb(rl;^qQ$r0K+V zW9ISuv}!^%*d|I6BOO-)N>>T+^ApXg9pRTXn$AxRN8x}c${1(qi$14fXZEQ0I_pH}+h-Jl;J>Edg|6UvuIK z4!0TwIWdfz=(OPh91dH0b?OY^au6mAgqPV!8Dz|&g&~qSXkpgLw7!Yr3mGM`E;>FL z6tn^SQ5w~E&I?pu3>?~!Y+0Rqu>RAqm}Skpk+bO5-enSxAE>9<4-Zd&7z)1xl;6L50N91j7J^CcSy&@8Ga&6Q}Qju zuPVd^Ghf&=-_i7E;A9ulTYg;v5)ua3gB5Q;6Y~A}Tk3a2i2+_cr_}A?=gAI~9y-|_ zJY)yO;iPo7_sfmBYCZU`(}1o!pBdxkr$OI2xymr%7sZGUqoDrfw=k-KWuug^=0BXhEx5rt5o9xrPCibb>^ogSiUobu~h$F;Awk z_9$aaUYcD(Q zK-SnO7$=*Z{-oXh-;r$Nt7lR%^j(UL^;xdJ@YCj=bnM1}uE zIl&kaGb9_3vM25i+w;kz+9Ld@I)+2vR?4_gxa5h zS{Z)q*GF7#!MY7i!Z3DICq}wDXU+)H;sf`MLB5LhGcXWbQwAnr=xk34tbdnM#|*=h z30y&>BJ$tKasvoTPLbTL$G(VVEC!oC!l_^y_kn(nW`I-%IF?X%!$1RW23HfpMtM%r ztIK`wmR54fZC|lZ)zL=1cu2E5qPw>p9!q2@2NV|B^GvuUAC%+n=@BxJTk*##dhC|#7-tXZ?P(zd~8Q)MtW45LV}x-FHq&kWghDHfyygVcUkWZVJg z5^VzmK1Jwz8EB$3I!Y*vNpKtympCvpwBdWRvb7$1` zJbdSjjvqfx+3o7&IdPuVZ&{(uZpy8yjBN=!%2A&_u$?6f^SkVc@(7CV-HN2prk0}u z#DAU@J*+Yw1&~2-G8PLegHaVmdSwJug~wDR6B2ORL4Md#-~gAlQXD1!^bYnOC6vM` zD*OBCugd(J_F;_cLgjdDaDN1B{FOu1KbH@>IJ_zw7X)4sBG5<0Eh}(M0R9I%nUAUWCp|C2(30E}VK#pnS%O4H3g!#C`6qBZK; z@UGaZ9T~ywwnlIvVE;9>f6VCW)vN!EVqLVCI6x!y&nRH%8hedG5F8FiJ3L>AOi73F zeVjXvK%-1v@qwNntbH1ygU#S7>&K0u_Ub`n9i8NnkrByV<|gK#@nk%HJXMpCnQ6C3 zWjv(dHrW{lHdv^FP`y~}3yM^P62-GUW zPd>NU({8zz{=Qc!W=Hj05<32OE|8#P8<-kQ^iy8wx$co&QU$Wyuuw0EZ$?4oN zyL^o|UPfF);L_36ts8bBzecCm+TW}ZX+M=To4(dOgVTJ&SmtNX3$0?;o$vKKsd#Q2 zTDTt)qB*qqi3#glj#E@U!Ap4Jwn+OOk@jqnnu6I0Cd~_dLOW{;j@?RkyTvE=Qd{a} zQrx1gtQKhSt12$t>%XMpseFId#%U90E1>s}&%a_e>YA;A+VUhac}bGYY{giNQ$bBZ zvHX?TnU@i^+cV9(pM5?9O=Zr!>Ek;#PJ!QL$8Y2LdYh`mi+4oKTpl;s`{<>%-tVu& zH%>jjI76h;ER zzO)sBYu#|g{KO^{x7dn8``;Bg7=ei?IdljlEn>m7A{S_4hE}SY5heLlik@Cq^MSRP!cgDXFwlxt;sh`Wj zXWA%gpu)vPX_ei!ar)>Ldq;94O>$yaPMZ14?IRI?Z5h0D#fhwpbhq0QDdj$QgRhM` zP99BJG*u}ySBb}-5lNwV`lRfHlffZX(GRm<+&Nm`Z5j0LHu8Rn%_2_trK1?gDP8z$VfnTYxUa2!Z^qOh6k@3nR-P-J|g`G&0Amj%!5sO*zs zDWzt=zYSlAWf2*5?_NA==GEh!_eUep>lP5`8jsf%J*x7Ct96dv#5bPQhn3;GRxVEN zvjFdAsL1D`>e=^^1GL~4_mRkZFq&Om4P!?c9VD5D8b{YxB(Tw!d1H?#) zD{A?1;I|Ze2tcB*f;+vokP|1b05l^X##L0^Frco;4i5nUCN5x*A;PE-U%cZV@2=@M zCTkr3*=7t6$)EUizdTh^ivxw%yBo(QjO@~5?ed&6A)i9W$cZ7^2yDJUc4hjeT|y>q z0Fv$RbC8-ZPXB?h@qMrfrnCu25+|Zjb88ok!0G!|3}V2wh0owx=lWWt#u}afv>^{s zn9=#lpVQyuZQ;O7256+amR6bv> zN8g@Tz#!5E0Cq?EIIS=v)HLceRK_WAW%HA%nMWW6wzV+;jwJ>p7CWf6YU3anb@13; zQ_nvYeyDc7X=`BlS*P+?rH3IQTkcm-b7V>5zFnR%5=@+%{y+nDjKBv}gd5>7z zgDEnsTGry`84|H}KjC|q6Dk*2EwZSwr|h)P{V(Qqjg6g{GEEUnUk?t1(kbdc2kE=U zr#&A<)_nS-w5tN(j|YzM2z&JCY^r%x_W~vc%-(znRZmM)V%vOpDyKsLW^mJgTt`9}nPb%`E{0W?ERCsSyz}$de~j z{+QABmPdAccWV+sG2;5*A)xz`o9EpEx5z%e$(n-84JD|t)Kc@`3|alZ#9p*x8VU*u zO;=S*?yx-oneP{9UQsfhMWk^?4p#wVd__-dOAE%Gr{Mx@1Q+!v!f8%ciGD;d52%rr zI*D01Ie$sA&>NWk6ya}C=Sl5?$qUO2$Ebj|hBz-T@0UORUB48(d3pr{SH&_pnC3|n z!26#`ZQ4}dV*}CKoKm{rDdfzqZhHxWFi7V%giLJigWV{M7B1oT`@PG4-X@q(*{DAw zXBB`3q=rq&HA$G+RsVH~>AC#x_(^OZvYzK-2S)HRwY9myrH+hb`*CEBC-2ys7uk*W z-^$OsC};fGu#4;*2uBHSM4Z8i<`=M^E)0-5P%`c5xD0U=Azqb(1bV!O*hmxqfD{{r z#{|L`<7^W#-@e>wNTS+{M-`e6Dd_3gH?Fkd1KsYbU+v$47EBXcyHalqQVCcNgM<8Z z46km7vCqUK$gjxRxM9OcuN#O3D{`bRf91GypLSuCTTtwg$pRTE`abO(ZHJ0275XX# zY5(8Wi7RrU-lLVdIH1CkRe@IKQoL}qL+o;waeEZUPPAYgHquAboa;T_a??J0Ju~Wc zJzO(!N9x$|4PUX^M}s9d5wey;s#>zKr$Y*`Wh9IHJl|udaN=lR(?%M7g|12#QhMO` zHsDk*`}u*^LyS$(Jwa{^fH(mLgRp}@Pc#g3jsX4l_{sopv}IeDAJ%s_C@P8nKOUA! zWCBw7vayG!pp|2qAn(5! z*v9YTde0DsOwGUMevJ10*BmsM7`s4lL;8YkEG%@5>Ya?Yv%qtYb%`v@GCe(!uTkN0fZ7Z^~X=ovxnVf=`j4 zGT3f}FhIt#)sNM;pMP{E1vVEf)KTy;{>10LJ0FeTy`bisva;jQnX2WUkWN(&D5fi% zcD}&!eSH8GO}z(t0#a@S%sdC~MO<`_PZD4V&3QIRMu5ce<$JE94Oq5?<9Ny9`L4IQ|D6+wAHFysHD>^;D-?%(iX z?TPAX%4krTErga*bXR1rLP8m#grbZVrBJs}B(k?Mi%_YAhOG#dk(ma`{-4)P{rbP} zalFUz-p6x1PfvyK_=HC*}bYW3470<8^=IOCIP5y(h z@bZw=WPNjSJ%R*T+mH5K(2n9Ifc>*_&=W>RHH0dk455@T`0Cog8?*-C4(CS|Dsh}W z$9g4?c|mk7M)P#RTwFs}51?k0pm>R`7%X_bs@*sBNaz!^ZXzIUjl*vvd1Be;92`M# z+c1hx)ruPA93V^a^VM&lvvD&dN)LUOJb%(y40WBneO9f@`U7TWW;fPvcPjs|w+{<@ z7n3?VAR_N4@4Cm%C}zKREoWea;Z0zc#nCLs07%e^z`cosYXkYLQJ_crA&p*Xw}p-$ zhC)kZ5BIm*$ON?&-#ss5*B0)@vyswH7~pwUXIQ^|O^zs@S7i>L8HJ}h>PL%zI8d;F z%`vd@G2ipS#$wQQ$egMl=YcQ-d3VxNn-2&C#0fzh1E|Yc1n>vm?CJ*&#KXvE8wLYZ zVsN@02u&>n2O_``_vOQf1Hkg7xjAY9brNGXwTzYGu)SNIw?^EwcnQMwswJ?^r&bD( z$ct&Yz(f-CvNRF966}IeM8>#`49mbF8YHXd;VRf6QxOsI%&ByK^5f@EJ0yP_qB~(z z+Gr~?YTg9P3Qt2Fzj>YfzmCw93FLSv5d|1HjH4kEaFEUC-e7f`&>R53Smzg(D|X}) zdI?&EE}s6h>cb1&NZm(QU06_XdAt1psL?QxTQCcf*6W}ismk9k`v2%HaDI`PwG~o_ zNQ6ZE)Lp1^(!L(i@pG9Z#6UI19?*Ep&o2^hs9$SQLZ3A>r{{l6DPA+E$38|JxwQ~? zXu5ZdIAVT^b}VEw8t|aOEIfK1%deq({^z)4NnXAhyh`F0V$kdfVwA;!=#89&IInP@ zBga6~C^ttbz{uT@y(wTAMLG47a56BMq#Y+427Z934o3j>l!_mMm9;r!sRIX&x52->^5O#hXmCITp|Or9M1fzX?dIjPtd?K^@dA+; zq;Ioax$jPVcX#*L0m0U`9Du^^Qd-qO>00Srr8NWWdq3hq6$V-^z<@q2_*7K`vBXvg z0ZDGh#!-;U^(I^i2&!v^P}rwCH>wUbwmHxdr0K;t_#;8ES_Rk&uoK!RhOMXZM`Y~I z9|Jzawz#YIP!zzs$*`4JwX;?7_j0rH@?!Nzfcv1kJp6yr-ROCQEd6}QrUinBhLMK| z*yzFdkX0Po8S*rg1fFixgdl@!NSwgh9@yMW2=SAvta^jZb;1{1Fj@zNEy& zO1e-$mO6-pj_>+w9-fFICqA_2?^ZeyGd2i5y#4SJf3*YPQE}raa5QT*(rWlf)`g@4 z4NS$zK;F6wbcIlk6TOB+J%$U%t<76A7`GfoG<4X4xO-XNk@c4#Z$p%jL1}G=cy00S zp5gE*nbut*%4rjHAKp4~mjf3nO zCOwC%a;jn8WU6@3q*t@E>lxPz&OW(Vp>5etRrW)81FFG%TH?&_W8}Daut$w*rR}5s z5%CXU9tB!krej7How~5A0fB>c4Nu9Xvr==1#LV-{M*HGy$}`ZqFmM^tAbH!)ofqk; zqXz&|K5POsz?Ij4Jv%%f(r`^!>XliX!EdAR+h)CAYsdNUQG!U?G?OAfLUPY(asiN& zsK@MsKaw0-k>5~n*n(UDekX9`#}kHKSHyxwO$jqKu)!WBXF@8SfV=w!1tk!j1*iBm z8lyD17k*QBiPlQi<8XhTkj{cu)*gCxmV=IBQzn|@rk4B46W;pRTPR-`eWJ2z-;lev zVn>z1yLFJ|0%>AmhBK3>9tf5K41~B$%qv=ih-8MBOlAq<@C+aN1w)NkY=4wc<(b}S z?qW&C;t8cyI>GRlG+04!vTv(QSX}>!Zgb4LM;!a%1aS+q$@9daAHGOjXv#dhqHMyQua( zS=eTi0b=T*=hNwHSg6xUyh@h7`pxYaWW^)X!%inj2Hd(;aA6w#XnYj)RaCB{)mIae zt`@mh1$8p|H?yp_S6nBKFE~f?AhcG)PAkDKTq#JVv-dZ}RLDGf@Dg^KQ$8o_vp~y7 z?jZ#k`Y4_?T!_q?nR-O^uw2f9Kpf({m}Tbr4jRx{OpPlYfgUKG@Om_oL(kOes(%UXf(56?&}Q2zN0j6Ys*o#G5FsRfcG1mN)LQ|5s-m z4W}H*++r8Lm0Z5bH?qHd7f}UEBle`7>HBf%Ds_}hSyYX>eUYP~j&06m&~018NICOJIdy|x54$W=dHS~v{V?_3Dt_>J0B zxK!E6C|TNtlO@LUr+NyMa?H{{E+O5GM%Ro}%U+*71ztT6v%!1ZV{h1WYH*X9Z-%Os zLE57&7=RS}OEb1~|5ER_!hwcD1%4v_wE}XHM&L>DpB}lP)H=6a(?aN`L7h@wQu79VmLXvknyM~Th?BFAK`^E1zZSjuH z_O%)K@IzOhq53dOy&}gPh8gRR?!F&2v_-o7+UsL4%}x}Wt*Fq=jY;rANmq4kKq1qM z0&d7B3QdTg)B^W2qR||N(yW#>MyVO_1}pg1O@ElJA!O%os#hX3PyJ}wn?g(c->&4w zpF`F?F}6oCb@?+iiyA9sq{7@wgKpUbC_U^bNDlN0X#g#6VCg_Yf^0pubI46(nTV}4 zs6fa#5IrM+?=c)<>@?s73UmUwK)z1is*fZUr@k3VGdlJtsFRMe@%1!zo8`Aakd9nyQk4!3*=Kv7^(h9k@B{a^f0I@xdd6Bi^xloMT%4}M(y>j+ifZKy2_*0p<4S#oQTfAQLSb{ z4IRcU$s6%y%ua(iCtU@e0N*f<)+xdT&8i-ocfRNnzMQC&0)xIJP8qkc*-Q;p^MltP zQA*f3bZkNQ`gQC6I&NMfi$c&M$nOM8NdjYl?saD%6Cg1PYp7T#G{;708;Wx9{RJ9I%P0sp43BN~)a;7O)>j3py?{Q|4$st5{`Y`}Mrx zMBFRAP-fd9(GTks0(bvhe2RY${*x9XZeXce-5zc~!S~jU2z7-3?npJzN!Z^Dp}`qx z=Jr9e=9$~*9Ht$1hm6h5W5--}cQdKq!zjguOgr{zJ3RiNf}!?kUtgb)prF|?1Hdg? zg5Qofj^Y4!`D4JB9iAs|p}812f?dMMzHt~GF_WcYxMqOTkyv z_s(>UQ-!G!9UDYHY}zmI_48z&{Du=2O<#l4%zIUNst0%R1lB4PgKluR93H;<=>Rmh7TMG{Ha6;dtJS2(#tNa!qD1nvJvpDsF{+QWY2(%wC9A8e zHyW7OLSjG&PF`s}V1V;?!aHqchW^_yrrf5m;?85ga36+SEZH_nJef6ZY%W73OM!l5 z+5Bq8pBxbp{JrtABcGO^Usu1%;yo@Jx%TJFSEOb8C28~8)bEH} zPDhR6&b?>kFzk8d1(9QsQBfP5*WowukzJ_aAPhoV#b?VIskUF3_xS3K9J+fd-(ump z_or@DtT}m<^I>q_r|6!A1J&txLlfTIzYjhQ5UCjO2vGBtpgQL7d>Hc?Ewd6b0>hw( z)=SI1iji2+v7@0OaVGR@ov!1(Rl6dCEFNU4o|Y0)Q9j&?!kZi*1_#fKn1`W9uA|+50NxC!y64T>gMlY` zpCBE&3-lm~dRX8f09%mLCQ|mNG1u3#NRmhWlkE#{2x3SIJF#~e2wEmz4>{f5s<%;C zZY!HO_n9B9KNa~b1`;ioW^~1A-e|Go$JH;n^ZePU1rmN;@>?XhaFc*;`cL;u`(`F! zTIIv(9_=m-vTCAjK$`%)66``uD1I7=<%1lRq`&Yyc-ioM7ZZUi&Ee-^hwQq^y&1hD zMIaBD;gSf~HH&3$(*xI6X@Cf+z*n3`o1l~X@#683AqS$Xc%uAhD8wUu*M zu9D&A0O($`)iBi!?q-yHj-EQ(Xnr6b8s~#t^jFaEhL_OlC3Tob)(OCoX^Yb`&bmGa zf){V72J9a@jq(lHrfx<5fRxeOEbGTSPH_NT((r3iyA`EgbaE*AwaMbe2?l{>nq89R z*Kvie@5bz<@N8ej;+}qJnm=iN|3jDEz$|$wcfsA3(00$n^<2yg})~{7(A}x(X_0tM6*mZs$P*BDRd-4L{Lv}u; zoUmBB0JlCQm)$_$Fvky87-b>$7%A{D>cCEote&Ct7fvsuQ~YE~bIf;+U2lc)N_TS9 zX2DLthtMH2jT(*Rt`k*TMXOe4`NxJFvIy%9y4^Z`(aEgZwDk8?>8M<}k11Ui6BSj2 zElV-lvn;e?SVJCm#&p;t=jRu364QUA4Hn<8T*;YoV5?vR6|n*_8+r%DD4GR&AhFnG z1)|5%Q4I9i6sX4Fdk8*JRaPg_@yO|5i7?yu$4C0qn-a=5?BD92|3looe)RgEmE!v6 z!}S%EBaq>%V@4mtlC!7I@H(8~6|L_vQt!7eiP3p^3cZ;GOY@p$TN$ds-Iph_C`I}{Q!i3hM)0MNB!q@B_kv<#WyfJA<4ZA#EFBZz;U zV#<>tRA(>8xE$$J;)2g$lwE_;)*OWC!9-43jk?{w>I=+_TKi$3z&cj1X$N%t-G_5x zANf{m<}fa5WQCTx6p869ZNSfl`Owmp6CwG&c(e>5<;LfpJLtL6{GQf5$$C(zX z=u_4@lZfv@`D8bjmU+xUy}w?I2jgh8;bk(zT#gmfFX@qSFzKF<#um5y+3TMgQ_dtC zo9*vwj&3f=HaOz6!Om=S!V6fL02)>y9RvQud%0Vg)6Fm%Iw;iTfiP-QCE)`K?uqf@ z0kAVgX5M1gx@-Wm*7NdS45 zhARv|fXgN(=-?2=pTC6r1)3K>PHkfUnuPYeux9+X+&eOMv2cPGVvtFb%S9Zvbo8Hr z3C6QoV>hD$_ZiSTlfV_k#7~;LgIKw12X9drk8e#_fE;Q%ywn&xxNQS!tg`Nq7Nm=i z(pHZFL~K)DwO?`GX24{Y-flZlP!jO-gBBe10p$WTnQ;kwO_6}&OKE0h@@O@je)e<6 z@AsYdJSSeQm1(|dWDCooz6S8qQIAj4HX&ZWVe1Jvt?6cpf1B0-atj4pXINg)Sxm(_ zO(jGiyAXkP3D=A<%40Ul9Y>UyFl_~ilUQVwH0)^ZAL;QTyt+Y|Bus_#lFI0f zkOos?CLFy~UyXr6Qb-4GW${;=?P79lxqDveQMBrny5T2$ff$UU4=b=gs0yqA2xLLH z8|W=~^9D-#?B|8v)97@-{-vW|7UcQ^R1i>Pf(6Q+>Jw`DfCV{P2Fj2hS^!dE{)*Sm zf{vY<5yPF!<*$BAT33BnZVE6LgfH}%VCaR_a_#1sEe6HsyW84EhPy_PRAvDc4vG1u zb6;i8bPa`m(EiX5v1Xk{J4W})1h;w53lD+mkKU-dN1((I7Yn=>cO1F2_5X(r8ml8c z;eJ?ZrnbYz+%m2G&3qrPi}alt8F=$boSnfQc=Mov&)r2yuS#)f|2s$pSv?=$=p>aK znc8=(2M^q4I%_cir`R@-ZdyVUjmF(a8>i~zD$^Z+Tj-2&@x>zj`tJVn4kyHNtm|mt z$LOPgH%qU6+RED@gi!P@U>|J&v_C_3_5H$M=d-A#|IEG4K{q>+I)Qbau&%e)`dS>d z_;5=`X%CHKNgd8oEF5?XIC3zzVlP~`(Az`Ml$~(AlXGkw_Orr2=}YJuE~(4wRlQ`s zPxX}kxy> zVa$DSKnz<-5hc!yOI`2o%fX*5f>sHNDC&=+lFi+1DhKN=P!q^dcUb$iP$ZM6iqxc{ z-9SJM))HyrSmTdiURn#2blRyES+yi0&*Rugb`GYQS8v7&*lkJ7PF(C9r+Fq@*WlI2 zcDigVqf}f|+b%j$xIc1?#ib>CA(G3pmB-8vjzG0Ue*ZBotb6AJEYM=eSf1K%dghLt z#vEuk+C|#3k6=%C@L~y?2&UMz*t^)lAc|ZcNrR$l+Y}z^uCe@Epn9d`SZL)+cShJ~ z82kn+A0Jp{w>f5jKI#ZUu1Ub*iOJ)O%NM=4Q>`e%P*fs%Ma+{UGk+y@ID`nV8{N8G z3VS~Y8ZEs4bK>aeR<@A4N8J>45?RXppW&Lxx7b(LO|ZLu=MIfGj)D2cFgk%mmqC4! zdhp5kk-`ulAb^yuj~O8Lp!jETT^Xq7$tQoXCM6|>W@GnhaamTx&a2-xfsN~Gt{kq~ z_4j(qYH?~o_E?=H`*BPnpXRci?cRd2dod2GdlJfGO{11EYyud&?ZS)1Eq^#-UI`(8 zvYRP`|1&)Xzj5sG(MY}5%zz@RcTM*<(NL3_5+~DcyuNffX01sn%17<<^PTR`nIuz=4dE{-K zQ@)m7tqZq{OYX@dEBTj>dpc*L@r;+V)YWD0302GJ?!2F{c)vnIoPDfm+)#yH+E?>E z7J93qmn>N|^<|yhl4<hlgdu)=uO9R41bUHa~C5ho74pPE>Tge%*WfEm5{i z`9Lh}Y2X`qp!4=LO?5@Babc+tTXd0?RC$5Vhib#}U4t_70}XvvpXzwqLTO2AyyShM z4yeJ(q1oDR`wDDr2J%j;64oeTAl&!g&m5?lc8gJ%@XD(5*xYsx|61FZR_T}XU0uWT z7-5$$U*5fUZ#h<_dExmdAqW@hCrV*{eG!}pks_JM@Die1V~cIHKa(|MfYPO(y)kN0 zu}B{!R(ig8@j~*n2%Btv-X;pP#pz@lN`hT}2Fk&bPLw$Z$m{CU^ciei#_KzG+hznc z7At)6OLpTI+4o$Cto|Kgs)aEYu?Qd@4KY}s~&D9rR{UT?)QyDTtMq1Woa zg1fb0`(WF#&hy1;kMX)!%mP?r&OqKE7q%Ny#*gE{u%b7NFqlp)U33y+r8NEg7cXKJ zym^T?N1YeyED!$vzFWy^;}ttLHc@wnz4$31ozo_chjEO5H%aAZO%;#n1VkQs;lhPA zJ14U#d|A_JQ~jZhyJV;IJe>p{uS|vDCGM@)_;vg$3QOoJO=BgmlcfWFZ0Cvv?qB(W zvpnbds<($@&yn7OEpZ>zRgU{=K;w(U>A;z7q5PEK)1TeZ~mK|Urs>G#PdG=@g; zq2a>(RM!Y(Z)2IQ0E2u&h`w~f3C2RCyFni=k^Hwin|VibLepvBk5odQmITw76>0d& z+calp&CW?|ibjwdcSZUTKTjr668#x8)wtIC9#C9HU&Q*D<>qqfrukvNr@XwIF>6Kxn`^@@3~&YZ8%LHd1(u|o0Q-#=x0IwaEQb1&(bxjh+sP zZ8kitoxr&%1U#ogOu5CgpDLQSX0D-k>0_n5)Ffkn8{w4wGUGkF)-kNI_H&fIe`-W) zYidM;!J?O z8)kFmz&O%f@BzVt=NoEK$OOR|hugx?@vh%&s&Cs&stqW%W7hSxu}>0lx#C&7)%jlL z4turm%>hS>@#V4`6-NEg@g62C1@6J7JYpLd%lT_qLE~)UH*d2{@%uI64=s5gc6`0( zJ;l}-EidZj*}>SM>IN8n$U`e+Ou7TNg8#dH-Pf^mkHM_z)PxNS?Le@2RxMk$9csFA zP-0)6<6&KQ@mI=h$b8GEW~g~XF8QwGw$)WsX1l#)3o$v>aoyvDU1^Noo6^z=e0!3g zZaK_cxid`0+drRIFtA`n7+w77|6Tlw>t3rSC4@wFKTzh2%#CQ1HD%_vHLM8=C?6Ux z;GjCU^%6&o)ME?{oGHN1ziR2yZF%mTNrDiHOyS@z#yCiFmPcRN&G-?}Rg%u2cr?HF zhV<#UfoMc>4^K}40EqNSHA8kutxD{ez;dw_E95cNiwUak&1Dl@{f=-&hH|fM`){Lc zPKcIpvj3{v(?Wwj6a;)28oyH}v@!kkQ%P1l_cPM=n>TEEn5!6p0%<6!G0JxA66-<5 zf(w9o-(y!t*z1B)eP8oE&`)$xbOl4x+Xu3K?A)>ADqf_>?ob zIz42bdQ67)Bn=1Mo8J?6I|+E2ng}i&{B=LbjEl*o^xNZ}LR-hzFT(GAVoi@+-FyBm zTZrh?$>#}O#ZON?-D^b+cPvL~Y3b<`$1mkuEhCe;LM0V-8OYe>@N>Bl5Ww~0zH0+d z{gaJbn%9=xS>rt|b@drz%yBEQJW*ui9p8IvN^lI@Ecyc3mT$KlZGTmAgxvj5 zzFaYV3{C93Y*6=<^+*R5sEK@#7G1|i-VFBl&iVrtRR=}8G564|RVPPXRohE9*; zFA5B0Y^{>6q(vOemTYo~DU^L)vbd^9Syz3Zr;@bZ{lrr{tHT=aNy~AOP|&rqDz@M* z*d#Ka?d(NIvlLkcSW$ei_eN{SzU*%KORK_!mzBTd7N;Ssj@SGc?R0Zp#pdRT?3egL zZ@jch4z$NNG+w{oiBpPg=|@``9RI7)Y>(4)K(Cmx6i^k)rRQ~MH?lC>ePGnY^g3b~ z%wZ4+aPG(9u)4iv^2Y~HNAoUmxDw&)ufmB@)yN|1ptbjO);zY<)scomrL#rdTT4Ga zo6U7?jrzcDd$WPNo`o?l&DHr>i3+=V?(Zia_Ad%eP6$8cU7EKhkDC;Vu9XM1?^auJ zw|qqP9CT3&Bx=KL>YJs~7qy&vkaX7T;%P3{FMbv9b;rpl3s{HfIu|9$Y4jB)6{>7A zxf45n-j6AR>)6$-0u?UL4)SebXg(5VaFmHfy%z9)y?i6%u58w<#NZ0R(y;4`KT+C( zgURb*R~M%E=)rzU+JqVqm`p{e-)$xP!2Iv-T_4%1=lof_G6B#T4nd$}ZIcDEYzx#g zIGA7a2GYhvzVTy{Q!Iyi;ezgh#nGzH8i~_|PH1Qctu}ZZ7jiCnu{{EoZ%b=DlF{c6 zKbvq#K0IoLe{Om1o!<0+ZCI}gBMOX&$-?&n?ccv%g=4yLMn86+rvFRvKG9q>^9A;} z@yofP7_l!C3p7NEE$+CCPd0!4qD3*oLj!RxBg1KhRLPo_ETb7qC_82pXk+IM^mbL@ zFZ2MSs@k95WYdZ@O&%J)Os37=D;rsfnQRS9a{=dYEuWg#cQ)@Q#<|=Mr1lHlmPWl6 zjCvgtGVj{z7Oku6^=rdU3!6@|{cXBr4|8eR8qT)@i+%-cb=X2ZIc~mP9HSr&n=Twr zaDp;(Haomc7SVA((vzELwB|BrNJ(f|m>sF9G5jD}5d9(m@$mJF#mhN)^8Kdyu%sQ$ z?qR(S3`PqWjD^x-MWju-x?I6WSdS@H4Mf2Hf_=`VjrODycJ40=wn?7CCSLZ*N7j=0 zx}y3n{XJ8ozn4>e^Wd6joJ|b(|1vl6a?Xfik^lsBE;)9jNUhuhrry zL>$0}!S6m6p)6I`EIisdi;a8A%sk~oheF@Tw2K6xc$H|vJ3^vpr?jXx*CiMGVAl(M zOx~?bGFZ*PF@|ALrX;$-m0~QwwX36#H{eBkPaBAWYLF)SR1@Y#T+jnzN*R<+A&J`X zC*{VP5{)zy0!I&}Gzm`43n!d)<>1{!4oeCoRem|=J+aj=7lc%pPh_0>yGw`{Oeg@F zp8O50J8-SW6(vF4lT^@25rS{{o4es97RQ^7jeY5#>5>Z!w;SMM>!`IE5J$p9uKI<-|?f_pQMioM^ zwM`8bo+u_MEoOZbc4G_Ee$L>-SbHm%gsURyX|oG~2^VC|Q-3 zzIE{)-GZzA7fjP5Z9{pZz02=BK3i-Q_od55@9)C&KW#@;#)|FOVaRs%%>^M z!EEdupz#(KBf~?A<{KQlO^HK|R)V1i%iIAyZ2AA~hn8=9NeLGVBOUT446uRmA=EFb zMC%Y1#!TN#G?9s=d2kJ$Fk);RmUA080UF!4Z_i%oDZgXK1!EEV!H>Tif^$xj)B`_! zCF3Vhta#lnv95`&xb$y?Wn;@1MSckEqe2knyUekVu81C>u<)@g3l=5ayQhrKl`e)7 zowuXG3*=XU+|j?NX?no}`gX3ra8hjk{_H=rX}9^*kQFsG;N|{oLeam$l#T6-H^z2B z_g#*MMM$nh6FedDy|V|MbLq|?&V+vwk}>zA9h9kqpKinpij(#NAev zWbk+4?BD*sm-u)s%5-(S?+TPSigz|Wj2y(lO}zvZQ1BN15#S?_BVSJP7iwS39a)Et z4dPk?z$x76?K!NopmPVLjELBRx%Kto_7vPfjDH%rjcy$8Z1(@c4BOLigviWB<{ooj z)TVF?hy4tusmPJX2}CHumP2^(7)Z(viwC{94_&?O zSV|~%w1A+2zx^NcJ0vA(#~b8X^|Avp6ufZ(1``$jm8m|$YiUMxwyAZh~F>IQ98gh5DvjJUc2 zpD8n{A33BDMIP-SdEn@Yc27;cc{gJ^`v&QtH%43#odHdhDN62k8R>O_Y3*@D3CwOq z;GXd(O0<~%6Op7ib|@%VV4h`wxS49g58%wSTk(oX-a{(zb^37DLNrcA7dK!XnU!e1 zZYTH_)B^IDIUuIB>ji6RRjT5Y-#Yx=n`Qy|RD)Q&i%7gP^QNj9(`B)-wOso5CwoCB z6a#89hw)Vi ztrip0M8roprc(ze)*Ai>2nBJXhiTqI9EFDo)a$$eQ;AYRbYpz$9i^*qQ?8mCcxQ0P z*af)vsA`s&USOBPHOY`pzrSp<%GH$(1$KnfYIlfl&glVXz& z?u}FfD3qg_u4}`Be|EIg3h)PrZUjPJf(^vA$T?A1pc6)K_}bNnXaWMBiqq6FHVy~; zB*3%9^9vB=(@?uJS^W&|U<5Vd!{f3fVrtQ~umzQ76`tLnZT0cgKj$pl)2G11R<0z+ zB0?0XKwvQIyQ+v*M{Iq7!LY3gMTJ9{WZ%&6VeekWL#)}=X3*5ZGscVG49)N~B`zv6 zaNv7)vQrY#QVd`8kov*q4cKJZ+2$ZJ{Y|)k&;RG9O;rGAj5Ypf7JB)OfgwUag*R`` zZcZ)1dU7{@1-#G#DQQ|EUIDVTNOS_DL4c!|J(f#dG^Q>ETc@{)P9|nIf#2l@v<69} zFXRMn(e!6{7n%Poa@oLx88v|YBEedPhi6VCwbO;Q63c0MgNTgQ7#$x|IC(ckeg}O; zLtA?lgBU3kK4@wsGaNepN*NPzP@Tm5+i$+0cUo+ZQ+OX)!THqC%)s2=&sRU4HJkP0 z*^U|ilLUk)UIB~^+I)P({Y0W%4tk)@&+5?tym8}xKNA|x z*WbF&r7%Vo-&uBJFB)8?pqVOK^Q@yWeRQ%5gxrdJ?Vx_RY=i>(+pI<<`rpI zyuGV%wIXHWd2^{Pflu+Ne{Wchv3qwZz$FQsj3$8rL?E>Ihc0*9j zD~1&+MH^@i0gii=$^Xx@iA|jEpCu$EMuC!ywV;OPVDc?vqI#%K9K{;7~;P5;TUkq|

o2qVBBvAvi2rnyYSvdJ2s> zcwDah6G+)C(QKu^`uF3ic^`&>L*Kn!P!ZD@3cP;jzqe66<)4+r`Wn#{3um^Wn_3P5 zU*LydAaAOmd_nhS^gC8BV9Ke4foFxjr6x`vz$wT!4;?y0q+vS2(MC$(87oNfC9VxK zf8IoMm7CH7)UMTy_B6#5P)+=wl#VUcfZv_>3%*k~^qpo17*uqK6vbxTLDdcJQeyB> z#}O`R8{@EnT+Afsrkh>NtFiGG11P0}Odx3CvCk@3JH zKlIx3v5E{oC&b8z_dnE)VX?8hGivPv5&h9nLV`^?Qq&G;D6#H{O-3vwZGvojR!is= zuG`8(i3Wq87o_P!-BD!dF?IB>;NOqGd(q>m&cUhOf=Z+e9{(T0pA~}>s0WHi1$^!H zk?T8siuc~;X8B}!#%_S_8+ZbS-zYP?Q5};kQp{iYHgtu5K2jXiCXlT`4NtQ)0M&#P zSdL%@gCKy5zuP*Wk16MEgb6iBkYY*TES~|g&YsudvGrXvw2%Pzxl8|iPb^na~H#Msu5q4NvCjuoo*m=tA?sX!LcZ z(E6Ej*cVWx3$0qEjM^7<4OJNzXDnz-av6$a#ZwGCQRo;`CE)@R&eMC}j)aL6yyiAp zi>)4P1M78$+z#3HgPo+*LHH}$#j!ICcW#D?Tp}sobMeZkviB{&a4W2Np{Sj}3{ggD zj@}UB&^a>vz_b`52Yi~!(^-yqp15_WbzibbTx190LKXNlO|~+G!qix0DWdlF<)?9I zM6n}V48Xc!bz4y2TV%uep~x!6=A}!)lE*b91(YM5^#>K0#%;wFUY@u)9(B$iBH9&nY zI1(oSj?S*CcNrm`-}*OFa^xRHjxR=D3p#5^MUuaD7~NIOEh5aVl4!XVER$KZ2NU~T zgpq1t>{dBC#GqW;JzMMiD(bIJ5- z^mBoO?tnY+>C^3cb`=^(hB5rA1(urrW&waMP4(jSM#DXo_NJ-icGuhmGwCYwPpq{^ z&P!Z}w=TmcD49e9ULq@svT{1|$#DY1Tj=4DHW_tV__0Tx*MJ9Nbtmx1!>LyO^;QC; zIl2xU!LKh2o;+D^qdpqZqvrv^%S1c{#@elrN#lCx;5 zq@*TM*3XX&5gQfz^z@H`p|rxEKYq|$<~A&Zt!)ErGECVe$`*#~!A`ORFE+E=Zu(I5 zf3<6CPl4@}CinQbtHY~IMAvGu9EA{32NVX~*iYgQYvJ?}g=~7a%p;ETBi<#tIemL2 z6jN3(9^Or3)Za+4pT^df-#gkM9=F8yY2U4CmFBe!tmrfStx*!3_m(YR-tP34 zGOUp#B=1#1U88ejBWA3Aq>PbJo`?cbu#T<8j1HPOZ^6r-p@BD>T;p9|^AgiwKOK81 zuGn{eT;F)-zH`o#&W)R7_j_C9(gLgM#x*=PH|^gm2A+Eo0*r3Z*(ICv01fU%+^G=U zw`Y$Uz~!JHAOco6J{Dq-**SFFR9aODjU4vR!A2@dj&~gJ;Rk~q(Z(FKbGt+`xQFGF z+|Y=sD~HgmsnMC4nayb?Iy}uqvyN^0d}p!@?^g>}bN^45-|A6IOG_okI3DhwH+weL zeOY@p{c6{2Q%mHrbc4unM=1$3UlY?O_$40HjW-3;OcDJB{Ck{mbgZk^mSBT%!pbQt z3gF{4Gmtwn2x08l#54OQxfWv}Dckmyw}WGNOn80Xhe&=L$U1!y$^i5N^2-&lP!bsB zAjc~s6k4d)P#1-IkJJsq5hAV)#(*vjU?)}^dE$Th*$OgHC5(S=r5-&G#vE)J&S@%} z4DZ(HkDGh%+>gm;3Ri88K5x<&%kkT|fSH#XcfxpO&n1R${#T~i%^&adJW{ioj-Ns* z4Er#P&8C1=tg;)NT~vp6H37osgL;Rkow`jZAg}Sm*Wy=nR|+Cl&z6I)3kykC{K2YM zW#G?lWHU{=nQx3kxA=GmXdYP1);=|Qm3d>>t40IDs z20C%xymK>LwPHnN-2+qw?V<+B#*CmT@c@y_D5~^0d#BEjH8T_B)c$kQ$-2MT)NDu@ zT{V3hSR2RH*)3G~39NR*1KtUyqCE+EGTQnf0*egHEuQc;Y0MQeE#Ew?d-0#>#lv23 zS1J^uJ8jmuJUz_D%tZU_sZ1S!Rf`oG0wj;IbX1jCw(RqR_p{&8K+wCY))#j>HB~cn z75KXLC-S(L=I&=i&eU-en3St_6#a)fTSHS$U#)!kW~E?htIE}l zs$xYEG@rHZ`(|Em%w}!Nt}M2CL!i@p!F$r@o*c|>8tog3`N!!Djqg-hGAlamd=?z* z-XHH^)8*|IFaCbaU;+S%t(j6<8h4}w9vGo)f8&_HwRFlUbb7Lz z(1S){T?N9`sqPKh-h+dKjz#FBCtbR9>2S}q)FY97ofFaK+-S9B#MR4VjHKY@KETzY z{UGWr{lawfDLsgN zD5^SH+~878v2XpjLa)Sy%fcVRT7(qk&!b#{^5x5=Q`zAqu3+J=oKFvhk^m+}Nuq$6 zT|W*)9{3U2$}xmQL~fYqC^{bWEdDuZurNuawDM_EcWHC4dWYx?o%+BTIi-9#r!@V0 zgCkW0C;0jYYguo0&nr2|r;w?stX$mjPSWawwsjxre>%4OS>$ZhGn&`tJ_jEpB!K_| ztDL^(6&V9SQDQ+r{FySdbGvqxSX0;GKN(X7GA16Eftq?6Cb5f&Kx0nJK~yHEs42GZ z^paV~mV7ty?z{5B)f(qTlNN8Ozx4i|r_Co3MV9h~l^P^0kjxF6WBqXY70!zNUv}LZ zs;`~&ae#fIh5!s-NZOTOi+H4L1!P zzuh}+PuhJPiu5^y`Z&6YiY!)W$!rd==#DcOihXYM`|E_Y$VV@j3s2YGHcx%NnO+^o zkkQYL)7?IOYTF>9)BpC3caw0eOKt-qe{Lb_Ro(T@Vi4deg!@Y*+%Y ztZn7iK2&-ro|Pz@>ah3oxa(x*nYPFIPex{cVWo5QcR-@;s6TG7kYhqKxZ<-Qm7^{% zz#kRNn{G>hZPMEom?V^XJ9jS!yD?H_?c90uVlbk(*$(y|4fSl>6CY&mec#mQXsC9I zFVR`+PEvG%<+&$rMnKMv+PKh^AR4|ACxXP1@<(_a)HV+PeT>Ct=(4?t#9l^&XOKio z#?)d~X#zm?JL=$_z?$HBzmT?)> zuz+_*@sVJsNa|yPF#&w-+W51s;GJ=GbY*dB2cP;pZ8PjvUwdF!+CD-Kva4vW&lR!E zXaul7_Y}g4(q39P{7BP5g)_=e$;_a#)^e(`l^)1vX{FekS=4A};nd_h`d?vVF*$bw zXNcF31h!2r%H0fxnJNiy6^wNJpfA_y>hD02|2#jRkYMVvg={-7j$V*VJ;Tey>DTpp zTNcAHZ>e;*6l}AG0UMH~ygxr{Dll|V+htyWo5k|}iv3Cy`1T%$FF^}RwTq=NPb)f8 zJ=i$DRO|V`e%qs`Z^YI8fS!-C;j}`a8FP7fXiCWBfp#EVuj}Df{@VWcxI_~jg)?i$ zl=3M3D~Uh<@f0xFXaIKFd`{ih(cxKK`<1yztOWj~PZusKwt-#w-8NOxSK2X~pHI=X z?Qp9U>pR1~GMcxw8izU0YrH2KFcb~+sPt2s5|x`!xhJC*TeA{8@!RBpv*W-2RFrTl|O%XKo`y zKR}uf%)Ri^wuT#1KL%S`=zC*Y6rlF$4x6Mhce|t&I%zn%ye*y2SL4g2XWh3Us|#Jw z*SDqBs995i))AwWJT*{ZB`7T(rE6C`x+7uXhh-_y`fcJcR&(t^ZZLXi-^0T#WODll z`x;BdfJ=Yl-s2LgW2Nd4#nJSUB^o>w*2kayH$AuO59ky&L03N_S9e~Hkkh>*I#;}F zi>u)yUpTo&+_rPT!r#jnSWzbK9QC091Ivds0s(;{O$9bfYyqFa#x8@{RQG3QPS1NR zPIOG@|6`G`NxsuEsywoC#R@85Ey4~NIZ47RKn{o@Ulhf)*%0aBu}v@|E1_xX=7yAViPIjN_xON5r?3O?9)6hOaF+h zkv&Z@%B7)F?4qumg+FA)z%sK}_DG)WR0R{2DotvDFsaKp(3NX&vgXQ~k0jmeBHFAsG7hD%##ygv<;;cE(sv-?dhq~GAc3v&vWm%rZ@5JO9)1^&X zzsV`@@f&;+!XFozSk|mu|A1F!ZI1iQML#A@&~-X?WlQZy#Jb&_m8}~O8*9xwlNzVg z2kWVJ&!&%0GM#Ql#vsGkuy6!wmUdAH<=NA(hkJPBaXN#{1{FZavuDp7`TBvUUdO1c zu*%Q}&AQo+b_tVaQZaQ0{lmjOfLg#Uy9)t2wQnewhZ8;;>!)8GA3YpGjA-Q+bHUNd z79uwbFmgO&tutyQtUgH5To2%@H&DfKPdKs3lrwfhMYVxnTt`^E-t;JhNla6z%u9 z{p&am?gTnF2t7orEe-Q((c+B`58r`UvUcrS=uRr3c%@$6uT#ft>s$?51>Upk@B8tr zJ$d*`OZWx-WPT_B&OC9&8E`gvQ>Rd&`eC!g*rAGK;{vl2u@>sg{udE~qLC>_-2}_l zh^H4F^lV?Zw(|Qmof1(SK-x#?8;73<^3!%w;iI%dvbY5)wkpO&#KNC8>jK6 z>&?rfkL4z;FdPfjt917Q5hP?4`kr+ARZ`;(?hQ98%elSTwK!(Rz+TvM74Xe0WJX9x z*GO1**(4Oz#_V?KIjO6irn|>|-z=W;n2g9CuBf$olMAeFw#_cvdD1IWm+$NTM2FM< zf(IvQ-P!TO?q&@AFYc`c!x3YAMG1 zq@*N`EkG~zjy;)_R_JBdsk&|3S)9xB0v^LvC~IEoIS2#PE+*}EkKL0db;48A*jm%- z`Q5N|#b3`Xj{0v4xGA~)d!p%1sSh3}c2+Rv00TSOo@$p>OT4l71EyZ+ZqR&fIFI;I zv7+fY3EhHdr_GFsvoyB}uW~6iXrH@};c;BP?aTSqs`U@VC&h;#L^XHm-Q93fOjXmt zIc%VJ@)_O0EIEr4Cs&pH7x#0Yl;VP(&2k)Nk!IOS755td>*-~;7FwS@Y+y{*B8cim zO*UpYy}<$lnbhx5o%PAqJ=lF{?8XCoavWGCENo%>2z@4X%ozq^wq~Y|pFrLk-HqW! zOf=KU#+wL2^Ry1!J-9}@y&nw9a56Z@X_xTdl0SK@lo(M~ks*{KhR36Tm;Xx&$$4D8 zs`=D6CoKF)ZSi{M>?LxljjMS=9`&YI+z^Fr$lJ}mw}}#lbch%!SR6;FWRqIqJ8aGq zD^xO^ylKd~>QKV#TJXL%Ip!rbY4Vn48HkpB*c{y6h+EFf`L9A$i1GdFLWhpT07r0` z;>siebYi?Fl_NNDl5oHZArHr}9U%7zo#l`SN04oQWA=FXUM-iNE9)6z_dE6Ny84W) zwJRPN*%lS}3zz)2+F(t{`KC$zxe_ClF9vJ##WkHx?(2Wpq_t;VRok!YTQ$4hCGPgz zTb?AZA9E#TxawdQuz|iyLzq0J0i-s6E?|8goyXHb$`?MU%j8(;)}P?Y?N<`Y2nB=( zmO;zBz=N==MpG}PtwY9A$FI(Lz;ZrmidY}lYcnRF*Uhp|pwkzOwhfsW9NS=vp~Quq z_IznbtKGkxj1Mtptm9X61<%5QP$(+V}n7mHi zDx1+7r;dQWzJ5Y@GcthvK+TdeZCguq(K2(je+J5Hte8ej#&*811KX{;hShUIa^DXj z80WltpUCczWoH;%W~cOat%v=NRdqbj(}Am9nQ3`$8xcTBj@{mQ0_KR0plX0;w{Qs1 zsU~K`1@hpOpUR1z>6!dbQ{vAi@#(&hT5i`~pcU=2Qfc_0Y_Rc}n3YvSS{mcJuFIG) zk@0sI?Hq{`o}yIteB~WM1BUOQFMDs7Eu=s)L^zC&UWNa}pf}ar579oTMMJwF#s0KS z7%j!Y;-n8+5pAwoI9c$$nT_oIb5lj6_Xa}p?Y0!!(A$z(%R0m!b}9sy2ajz>`FtBt z?aguR%y1g@*EnMee=B?y6H|`Ks%CR;_?v$RfFh+$wJL%O)S#!!`|+lUxwiriyr@KZ z?&Gx}jn!z4^V`tkv%1CK#3^f9^lJ=$ds9Ng!FB(I`=JJx1>`)&2pF@1Uu=e$$1n z*xn0O-k0d2mUZQqd%U$laneY~;0_C$^!DaY3xp3&GWH$NfXX|{N?-p@9r`Ei+)~Og z1I~p=GYlDh+lJge&veUyUlV)4hdb8;bJ3ETmw=Ndg=JVGlG}QHCW7e+Rp#Ao63XSqpN5{>g5RFd>haZi)S>KR{v|+>|>EqjO zyP`jv#9-Drwu@4K;I+p|&`&@Dv`wi-s-*g8TyWtdU!h(tDlpq^?9KNbPygj-ESMs$ zrJtJ=X?1&}ZuTLy3SmhNr-o8-{?>`oidmtzXL}rNTy`!DYG}pRq2mxjsGP|EaKwo~ zMA4+KPb>axC0R8TOo#(*@LQ_<(Nxsb%QvJR{GXB(_14NTiO;@VZTkdGwWk#R=q_%nx}l{r z*Y9|a(3X%XnmO@V@~@RcLXw|20^TPU@9X{!jJt0I_-9($-w`GaA~VX~sz)1;C3+E| zQ{q)pZnT2*GV#0Z1#R0x@>6ICj;cH1{XubU3bHM6=ECMH`40%$MaQZ;`0mub zabFWbH}>8Bx`R&E#YssX94&F76PWz@^RwimZifR-Ru6t)Hk_CF}Oy6A+B*_`F5sp+q(_rD9EmbhqkFUsn2cuw0dMw`mW?CFZ4(ueeYD1hxpi^@c|@(69}(hH_{+BB6bejLoH`i+JtB55sp>kHRBv zEzWpV>praA`ROdaAU#*vd@}8AL7KlmtR15oHK_eKX~BUI9QX2R(p@*py!mjRdw1|qu=uH<|2@m+ zhSd@ezeoXDb(r8hWcNCEZfd;WjK}Z4oiBH@58lgQ#AKWS8*=HiX~>E~%J<)u(};lb zHDWl;>1^zT9HXygd^20NZd}k>bh|Ut>gA-i**>A+m!v#-YQ8Di^z?o8Dup$-^|1vH zj0k5Xj=fzW2C~I~>$^+@xZ&u>)eE-CdsB|QO+5ML?iOq^*fW=q3g^_fkMcot`q`}? zCp{{Z>yK@59nSfr*SWgL(&}AWd{J$L!D=Vn^-2Mu)AZbhp74aNigL$unZZ$Z_v>20 z(zBIbjrYEm#Ms>YwW78LKdNUMAX-h|7N4sU)%rKU>D{O55Poh&dqDHs*1 zoMb(Kf_=(tqM@IHtg$k-Wv zyd&h)YS(rZkOK_`Igg<+e?I&e*tLhWTTo=akM0L?feilWSc5Ob3hyuMzi;e6zRSSa zxr-+~UD9-RhME10L%gw2KrR=bl_swto%Qh&yy{Rs( zh&};MP#M=b))#GrruPH~+2xM71%9$Rm9U^4L!(UVLFH7wQq-(|D`|T~-Sq4<3r5kD zS)JE9+!~Wk>&DdTk0|)%sf1OvP(1RsGviy$J2_Ovx&_AT7PCIXTPtOPWamt$<|x! z*21P0ddp`E%~+pvWA(yfiSuuLAO$}5U~z>P7R!A9*AZ4K1;`gw?H%;vUd-vdRLlr+ zEV4YAZVpXQ$j}U3zd|S9fUGa;HKv>q-I60(E%08kz~H1BS0zS-6z6~g7cxK4EcU5E zgupF}rWyVF&z#8^fN9wP0)vqwL zDsv5>7JmBujIm|gPs`TbdFVjVNPDmG$Zh^Qws*kc>E57G8 zi3>@n9n;&<{QCWz31EJrF>H_eMk%|v+xsGAn%yOuP@)wRIY z9O%fKKH&?ux4+FOn%W&;D`5bjgx?Ab1%EoiWSh875aucosx>0tNPEv;yGE2$=spaw zdPuIbHP}pIX&$r~wj(jZ=ixC|wsFPy7PUBi?);=1iqj2Y*Pmh)P)iLTd|nm#Bie+j z0*{nh%)ztTa%%>MIvyx6jKY2+ZliRcWBLJxJxzPzOQ@~XlhTmBV%HDq?B5BIUDqc) z)3|v9{_Hq<#%mxfN?1RWnZ~F#t0?Me$r18%i;~FJfn%Sls!Zi(vGTX3KO7h*R3@BS zj!=)kmNtqz*9{73qKA|13%{wGP zX!WGmhr(cbztqObRSW36n|HPEzzBIb>gV0Ws^8oRI%#C*C<>XfBa` zp+w3xMXe`Ac6U!oS}6>qxb?mw>!bL~*N#*Y&VggxT_6s9i@@*ElF8u< z$_ffGgi(LZ_kXU6AS@^`omc=F515g9EP#DkFt)`_>X9=>h_}Qjp3`pl3JH+e@0*mF zdzSwnT?5&kwujRlv};(`PYx?rDttR;e*Da1>^Q4H96(8yk>`s>bc%sbbsy%H6407`ncnda%p#2dk8!4zypEojoprKwGmYqy{<~W0c?2Wf;PST&p7FqsYhrOgCr-- z>FMN7d^*o-x~WxL`jCc0`g#qu=U2PGYPzFGCB_Z|ksf=X+X;cGXbd1Lc@6WSbq^5Q z*vIZRE0yt{B$l8p*`>(M&Yq^Cja!o}SrDjdd-ZnWij?^`qvOfaa~4YyptCR!@NbJI zs0pY`jD`p!M)N8B;C&-_#HPqk?=%K)LJ7#9+W#D3n>8?YF|Fgf2@MZ%%mGUA8pFD` zw5>T9S>fqw4C0-vzLpsP5{HtWd80~)c-i;sjk)BCJ-e(NkbCY>fA4L!hK%6F=Mgj; z=br>k3W>XX&tv`6coO(8aOCk-h3df_Z6+to$Y-zUY)P(2UoSyfNCgyl#aw0q@122d zW^UqAP*nntet>7b?ltMn-nL?RsdBw1e%Hzx56Xn5@r$Y}DHvB(upc7MUQNK=8x!Q2 zV7%GT_O+fvS{jK~MYC1r+v(_3o-YyB!9~P4Z_zG;RU0Iu3~k)2sf5>3%1psy6_@MV zBI7YP=~W4yxZU~P$WH_|ZDf~jZg>qjo7!o%dX8QBdMd|Wv=e0&DNl^P} zVvj5@!z)U<#L=%M@JD=dFgXQ)?4hTF+YN61$c~<6{4N#Rru$-f@ePCzYy}`6jLn4U zVhC#K`s&rv%@2aR*0l@hl+~0n$<+0C$S5&gO#Z3a1#F1w1511a4mHsGsFi(r)JNYO zHJxeRnzHz9v_FD!@fN^E6$WGEWA%t2LRLh$p2FeYlY1jbB_7T@1>tA(-7BEol?VOa zw&U3`=LLQ@nCK3jZ%#iXm1Ws2Nl2im{XjtV^z~K7qJSiDt-Af!yF{jDh=Vhev-8G9 z-9pJBcH>?AF{Z37&&vhGppycjhd`}8U%%i6kc93XzJzK@tpV$5)8g}gFBO$=Jb49> z)uf{bnIK~x`%zA6#;x17mC&k`z$t>guQ~hegc9A-d?5?pKDQcn^BVT*=5^Q6DW`yg zcgXP@XX3c9XiJBqL!}eT)Z@F>8+|)sE9WoP#|PX*cNtjOv(az~6QUWt{`M8lZJw`E zCFY8Xgd!K(rw7o#QZVfyT5$J`X!$|{47rU)oRK;G^Q3M|h~z|z;y}u!*djhg?Oc;h zr9209Pnn)93ZjM9kvYl_s;IcygBN+@UILEQBXMOP8UVPbRP7oyHn&hR_K?-axp3?8 zp(2`^$o#VfvpvC_&Bz9g+u_RuUmd8~Q>InnQ3CE*%iDjY2RMhpUCn}WfY1R!qcye8 z1Dte@41jfWa)!4u;{sIrP0{G*80{t%nQlN&qCb<;o;)eg2yj)Tty+FcdfuzwoRQ|r zJ;hhm`sp3l4e3fCN(s-;Rj-73>@4k1*>s0!uxGAj6$gA{vRgQhx2bySX?1!*<8%$F!1bp?R^DrkdQkK>0 zSnsY=5wKr`Ef_Dzg z@K(We{=4Fv^(@c7)zq?PN922oc*bWF=^Upnq18U{)&y7wnbH6mpet*-D4K_YC~}#2 zdc6d3_|KQ{IFKkp;vi{@Pz~?8K>NFpSRi1ksbpV=Gy7yyC z;f7O3<|#R|V5wo9=c^A-G!=U~A1l8SowCX)K-1#<85QbmGCq4ZvxhIGy?kI#C`(R9 zq=A0S6)yb^g;*!o^DUljn)H$|96D~)Pvg(>T!ICBBZg%{waXg5aSRR61UtS%m_?ah z3uC39;j?u9gNIPp!s@1S@5ZvZxid-oMY!s1dFUFBGt!;Z-7OfKxg~FiP&@1O^z|`7 zE7U9ZV{gg#&nt<=b}OXmrWhFLVVAduI9U7c>Gz_IH#1|RrZu!jS_|xnl)W8 ziu^`BqoY!Xi(Q5zi@~ERRVIuF++R_?(*s+d;xYzsf05Ty@!9kk98jLq&j%4M0iuh7fy9BFqx9}bH!%^=?-X)~@3sINUeAQ3tlHSbO5VHHR%rGtW`95afj>d`i zm?m9v-}u_(Q`4sC$9HB{9-GyAsul7fo9cT{uz3?KMs`QV|NDuA)c{RL5mo?j3u6Tr zG@wNCjGl|0=laz=DS8gCZ0@4(>AV<@M}r%+$4V2MWEP$v=#`(wG1qh8vi-1lrN)00 zji@TTXxoPyX4pj9&sp}}NfqhN&TH!!fdnDKl>mAGp}MnqZS>mi#U1i!(qcAn3EpkA zNAP53c>#Kcvuwd4W=`$T_AR4|;6#UW$W#Z7yT1vCEF>BsNvfS z-UileDV$8vgUuV(gdF`3qoA6Ep->%=RBqx(3Z?+9J0h48;9P2i@e*8DK5lUjueTN6{Cf^inUmwNm9dNuyt)DnDW z*miB8NZiR1y`cjK`&9KLC}IsUyY)7k1}8@q=DC3#ohO1$(z)*ss)~%1aLGKZ4}>ea z_(k**tMu@>WOiNwX)7l=!mw>JWPk&u;i5UwZiMPbkX-VPi)osXO@fd9@t)-5DB;DYo|=XpXbZ_P^NToledkSkwWjX z)5rgC9C2-Ul;H>f(7BIHVPs@vqT30#m2a{^9(7pzmj2Pc1~!q2w4n`({b^>6mp=^) z%5AaTxyzQ^`eS{gUj=IU66D!KpK_gjaI!hGrDa>an8DTO8lHCIRz}z%7&fB7qDQGI z=l3B=?PB2tx7o39ko@2Bg&97{z1Am+11E|{+4}N_V4EnM*&Zlw4HGm#POGQX%%6fEU;xG#)`+BjyTuh+OR_&POIDCO8JwFr*XZ zxkBG%b8kmXnpqYPpE#qz^>6DuEz@TSy|$)qcbvLKs)726tDa-553)|U*|mJw?bjlH zYV`%#5LU@e{u7XD^U=HJt}u9LNQ*k6+%v~|{$6-B;jR0-i(xS_PAd*aB|Ro3G# z_V$-kn;VH}%xpd9ad@&eIZ{Y2gjH4c)4dnoxI3yu%IZ_5*A}pSOPc>ik?AnYz{SE} zra-1Q^wZuSzN~&~W_f5s!gIgVzkW)tpn4+* zAIybCx{VmjQll*&h&@#&qZGoLTl#^Bt4CANCNlO1m9W>k*|L#7pXCUv&O-1>j)V`) zxtU!m*e04uo*mzwlP{=t2XBf?f=F6gFFxZ|_BBbEZ+!66-R=5^>prqG8{zJ$dU zH8d!GMg;W+#YJ{7I4%MIG{Rts{FdFrTyjUU*ELNGM(-PNBX-O5d%s9yksqditdp$E5GSA7|8FU;e-wN6Q+srm6TZ!&rt(?CGLvbiHJvFND-x%-UeG~=!rowF2@?&np-6cg^$ zaseeJF`Gf4+}`V>H2vgLuGSl!f=peF2VSk^4AI7G!qW`&SN_j-)E7bGGzcz+qlchs z=(ix0Tzl~n!yN!%0VUD9++~{gD&@y)HFs$Fz@gKpf7>l0ploHSg%)0gsz&b|-)YO% z5bSxmZlAMM^W9Q*=qA?|d)_)c9k{htLf>|8@TVd_W$quQZPPseujZv%AxQH$#YtW|TqdfJJyzWu7?b?U6G zzb|TtXC-=1B&|OT{M-|*@HMR|gdzNnO+Ie#FrTiw+<|S|E8qTiI`D0WHxvR&Bq2O8 zvJU=Q;$ci~8r)AzxOOmbE9LX68W!s;I5u45aB4ILGyAUTQ4x1+or&^!8`D{%DiThE?DvpAp+}<8a1PuF||{8e>uBCY9K68QL@}N1sQBr0yyn zs$i)M;{M`)ND4po>Crk#pL;=!AQv29Fcanbe1Ql1`*OBC`TJp)L*3rmzc$&lVAU?Z zUe#vr>ODz6?%8+n4p_#D+9_eA&^U0fzLu)INLsHa#CBJJ9(2NIrF z^jo{1$o+o1ildSDfJWR#0qZtuN7G6Vj3u?p=0@oJ69?U|M1;L$mYjH3UU~q%DOH5} z^|sxTGjsEKLbSvZ61DAPngF;{LXd<#oXx{9s3SAW@*TdD6VsD@jHnOmd&vKERiLW$ z+jQ{=T&15LmSBV~)xUjMK~jeaH;POFz&U!3N0-te&tJHIzNYc^r^bkPo!G}vvpp&n zqqtg-m6oZ7dYuTE3EA@J&!2xj5r@UMcgMwSonZ)y(Hze1+-oh?p?;2j8XbI^vw@{m z!}Zv1))ai`|Gn)!wj9}fRxz$76a^Tl`hkE4mEy}hO65}{M&9}lXca+vGu9A4cP>+l z0|OBuppO#dS9bsy%$U5rO{TH#qmAm5mtwp$cfAKnI5tvC@yYr-dNq7`x9vwdZ@`!P zk4k;BqBlnflas_jo3~q!kIfVso?N%hdDO zSv)Q6F^5*0|Ie@Bh8`()p~)r(*bwX@YAJ>sM&Pr+n8K^}>pb{9*jN<0r;VJO_tV$T zo&%ArUb%ElW)6>((}N7~Rt{^vVGlM0V^Rqe>Pb%t#k3Z-0s273pehUdu(IKvqQ8Ti z>%0GW8TH-3fe}!TxvzNRvhZ(LWCwz3)B&9>UX4&vM*u6w5Kt7$XPX>;vbA2w2t4rL zRsH}is|e<2^y(s(s%zxQ#Q zfSmbH7Kof1ijziL0y-{gZ<*j|n_GV$q#71r8sysY1Xb;Cw%f5{WlF^IVo<@{_qSfy zl_&o97EsmrAZy78=}W4G&abmgS}hyqK#U8Jm!XWk5%=7)u5Dkip?pC7!SaI=7-fM} z(DZfP9{JTv9poOOR{4%RGyL<7#ryTDV~S438hKaPT1~Q*yw+0w9)ECr02i8AesSfx zIy>k8ThxQ<>-A`V10K5lA_w-8A|79Fn*Y3UACa5F5m_RC{!jc_^<{c~l8BH|!kSnI zgX2U10#d@l9PceH!{2X=i2XkXC7BlRLT-feBXVG1K&;W%)3c7S`$%X5;J0f&|B~tc zmpvf)Wo`Sg{*h`&IB~?_Ke}xKTx>`QQepkS?|D64&)R>_rIfq>+eIaU8pZ>J!%9{l zY`ie=OjgN&m1n?lv41x@1J*yE^Y8n{>)n9!rZJQkbBGk`o^;&R7>74V?Aqdih8!3F zcgYw?{huFHm9VkNdd#Mt2t-x`ntyLIJDmN4byDnsaLn}j?*l6RACr{K7Z`B&5!;<_ zr4LXOxi-u}^NT0D25+_jB#6;9|K34^`v3cyH>!KNh6Z(2?hfeYt*e<%VE{Z3I{GoIOzFplL;FESQ+>05j;En1Qa5E zTIO98mNuL>*n*@UJ$gjKF9;Jq;}f40=&L<^-^L^32)(Dbd77$ptw}9w0 z#UOQCmC$vIWLkp2i`PgSJO$~MjPex=(Ud{h?2*hC0$jO&>7cBd8e70P_zq&USnti< zdD&d6Z<-iZ5#I!2IzjXwjPAJb`;vV)%rlW^K^zORWH#t_l7(w9&%?XjfYJ7|>FJZ> z6s7BblGi@_vlg1{IM`sJk*LUpt#$!95^0LIqhHPt8y8FvIdvhqsr};yp=jQ<^UC#` z5TR&1{|#etWtd7D7x)sxj2ACnC>3BixFA9g<_#uwPRrX^U6m*j;~Uv`94VDis?wIL+-c7=+8&=*b4XQ1UUPDzk2 z#G7`UzX$WuoYQP-+n1@kclv)26#iANRh=1Ah8mR;bq>5RV^ zE@)Hngz`AI9d-gS;?LJ^Uy_oWCK=mElQ`uBwtvNuidyBnYZT-Z_nW_A$}^PrVwEe{ zmK(3{oM;k900LU&I|oAcJf#jfK87^4Mzv0OnmAU<%gGG{Ag!bTma@6Sxq}es)AJ3* zMB*ZhiA@5DCtYPFOj!w-jR;4maJdq_;n$?OI2OeB{1Ca$lSp0+bWN*BtW@kDhYlmz z&)G%MUU;^y}OCmyG*0q_YGY{=tse*5h8Q?X=C}oe7KV@GJxzm(4rind(^iz|(W! zSZ@qv84IiJfZZ7xf`J`^8pl}Sf46dKPYwdQ5*ELz@|ci*Q(4eQHI7YG&HYGX&pIe1 z5?DB}y8WqpWa3DlHL{+4K2P%tU) zl4%v|nOH&J$Yr2XarBd-o<-_nj_!Rin_UqP5I0d6T8H9YHlXt0ee4=&W}+#SDDpaM zPvOQYD8~6ES{`f7lUcgF5QZySHJpYi%-giW2L1kBoL28Rr2F|*Po$nNb&AS@SD<6N z{pqmP^TvIuC@hGbHI9^*O0$Hs1x;2TXr@Vui*VDCbz2zJB-3|?0u3?{M;uyOJ@NHZ zGd@9H9K%v(n4S@%KWM63`?%JvTbKLIlhqn7E|sL4FF`8U7%{BN!muphinmFnZK)a~Bs{H$>Mf41tI#UAT2 z2p-GKKkqj0?4vZ2mUqz73Y^-x`3k$t11=e6!K2z3_z<3eTJzWI94*z4QX88!;q0nm z|8*6yG9vbtaK$-B+^6CD3Wg}2RX>a+8H>6!lhk1{?Fth*BN)(nJKZK9j_Qu=pCFAe z$DQ%bABJqnmi`P0nXvDq>FL8a)X35(2PQ;BonudN(A;aHxnIhgKWotdgc@#c_NK&- zDY@S>!^^@qn~dfSKk9KWbUN8LeZm||pDOcz45Y8Yk!n4Qr_GB@;i9DKh%t+ziV{5~|rKvEj!n_N%o91n-og1CB`nacpev~VQb^WkK0ak2` z$}vkL2&P(~z`;{OGi8kAY!H4<)vcQlD=v%$6)8YQ)rs=znZF7lf;~N@PZF}sEJG;o znnRIT7y}1DhHwHj2(qT2*-X@k{KI_g*s+hb*xbL^Z&|xq;j(NpSs`Oy(#KBfJP|DjLzbqb|`(IwoedFQslNPCj0<{3g`ghj=F_j-$ow5~!+W z3iu`9=lI$YSKBDC05)Ajk_or#+Ak1`;EN*&RV&d$K`?7T6s3P0lfjpMjJRNJYkUjO z9vYBloxE;KaiO{mskr*q;&7xROAk%QOoV?vrY8|FiRF|F`qs0i?r% z``@k>lo*qnU;2T2+rhzO2(~qOAuZAjvm%I6)yMhJjC;TeEywK2l`93^*smrZ@1&t2 zW^m{(Om16H-q&>wD8P|HQ%f)^y()T@$2JXI$b+c`$Bd|&&@F`G8-?uy6@rZWJE_~O zjwF?SF#D7*iyEb%`|qmz=i)2MZ=<@gnN&)3$|-mAicA83RmHe7&BA#9LVdCoyb=}} zn=tigRAp4Ve7WVzE1BmR65W3AM*hSoM6OYU3j8FW!^1~vlLyH>5Kq~Qlog`f!X5Vk zS#HN76?qwn!W){nT2)=MqDfxWf3D8U{~E$Ommuj1k2P0#niB=~EWc>kJ)26F)Pkf8 zTs5e{?ZkWBchrWVGS1R)!rAa)a@KkBu@my%+gIK$UY$nyAfqnWMo_+y>ULMhd*lZb z2Szw@|FC@h=ZUARfVCxt5}j(SBOJ2DGlbe{wy7*|5-ZFnJLcZt7e;vB)z}3 z_aS4~&ZjmNR#sMnp+AJ|N3VvStmmNE%Id-*Adup%6ED96$?)X+x=XJaVOYv#Pl5Yy znEyXn8PNlTN-_NVqc&^B5DZL44tIH zrAra;(I>%Tu+G4ouMv(%qy*OUeFRA_(%VRk8}XYVbQXLk*@4;EWU!FH5zd*L9!Y~$ zF&W;2a<$(%2+y1k1tQKHn+D-$>J0Ok>FFbi4V{4w@Np-`sG)Nh=_ktD!blEo?6FEJ z*4D%pJq1q5%?U-=svWfr<3A7C4g#_<9Q{+szd%ziusHMGJGBd*5}=Yu6JHSm?PWUE;aHSmQpYmEA-e5yn$2JPel=MlQEE)yDbeunm*X(V8 zMT!os7;QAc&FJB_Q0W(2aJRK;_~46?hxwIL6)dTX*)?Vrqb<6M_PT*iG#`%zxtTsT zVPY)%we+*Y&RIdXZRyv@#79PdnX^F}=Pn5^+aRhVdg*13;+8x%uINVA5&nfsJ4#7vJ^1lUYli&*Qfo%j3LQKv)K#(cHw?cEKNkoXY;4L@Sx zPC$r7Q;Inn_^t;5sAYD2_u-d8FZaUh{TuNIAP?7()-y1W;iwmc(sMu*mTz6PiH_GY z=CDkNJ);|i-w^S%6^eWCS*VH2jN??R{-7eQQAR7pcN5hbv|{XcBk`f{v9S3{IeAVe ze6)$M_>k5a?OENtX1lwp%+Dk9lT2lzoulet=5cbCBLZ5Q*-V z_y)>=!%XVxC~-5xn}X`|-WqdX{SOY7F*&#trM)oWpp6yl$=Sqh-IRE-uxU87XlF{> zknN$vhgrC}xvw7Tm5ml7j-^JZ0YVpGeyIRr-YYJ%jaPKrjIu~30cx~m+Ww6Ai2NZ7HnFINQ*V z{d3rzj*-rbI*9|DaT83&h+{Pr+0NcBG=w94^^P&L=sP6S|AG~i(yNqvlyNI2J<838&^+*sc z4DidyF#skbir6?{ZUhJf)~EzOgMp5bFU7Y>5uS4S<=OGx>SjdxWWj$9{u71>S;yHx z(k`&^H^6ct6pkPSPK6~d?UE{adWDacRaVx6q@4g$JW0glObQMY3203LEB>~o0U<=E z1=GAv{$6r8Xjd{Hf4wrxz&L9mZrHGK3Y)~qY}<;wq`v9z4_LeB7P(bC>=K#;hx&XR zb$$;$vu~tsD|B&sqmM9kBU~LHp+$Q*Eh-`O^`Dvi?EH;A-gu20Wf452lQ-zu^oqsi zje6wz9H~Gv<*ZvYw~O~Bl^;ziKakXVz=KmpPrE8rbU!2^@)l}%cmIJe*ZE8z~+5ILJmF@#3#ZSTy3<5siBaK!O0_Y^r)zF_S0aYcLildBtA%uhU5dL>G8x{ctC=*;Ekb*KE9}(e$miChLy?Mrib*q+ z3(|U$%M_yjl-lipNH$M0t)SXW8(3F)=<$p*wZ`(6P?6Nv9lRD~)WfZe3$H zl}F&n$gJR>kjpU9$omfZ{EdE z-zCiJL(Ybl(CtAH?2Fvds#aqs@j^FR&RHD6KWjd2XE@Thn65R1hBIi;ttMnr=txe? zm+94l<%8pM+;%&947*dk{TOd=ZET=$e=KY9uM!QWya?d?!qVcqh;JeJ9Y?8Z6gp`1CJ z>BmMxO+6;^OH%@Yj~*WIFOIo2rX}ImY9N>2m}+kv;~AK>2|-XcO(4*{QRQ){I-%54U%U@9G6I`d-!l7S|Z@Z{v^bD zt2-6d7X4Ey0R!h1EU(;JzbW7x-|p56JF=uq4h17c9dDHC#%LwP78uj*wtiH6HCZW% zm`k6~ejksgf0@KDe1|Z{uUTi7BkZzYZ*D!-I+mfaw0LTWl{N9Di;`LPriI+0P>X=a z!xg6 zlVw5XMhd>)Z5AF{1<;4b0E<{l}aa@yGIBc#d#3#p?0txjJE)yW zF4@phz|2YH@9}YM;p;zc@k7;cQA|F1IAi0hm ztv;aAIiTw*BU9v*Cy!p6;q!8WkCBJsOPNEyM)&q!)|^aX_G7a`E_stde7@xsm5&+e zbS{N|WLx}JBjP-k@G9!UnkzJt7K{}}W92#=<-y7#7V)hL-(PR+`SQS_^1Hve+v|;n znSuR*Ag*S#Usab8x?J{Tm`U;x>#EAOWlPntpW8SVf)TS7~g#P)2 zz{TX3p_PNOX63Gd3VR-Y7<`oWktQ>=SfNA>`}XB={lc-X`CYZTH*6OlGh}waTappM z4&=1I!&8ey5`>B6M=%4e<`+n@>i3K5UO^T9hy?q z+%M;MA?}%SJO{lon;7#k>MOrawyY2E_>2RNZzO-5Q3CxrEPuiYU6hm??burcP|~72 zO(WQYwkF}2)c4dp^l=H$vNLjXn9R}O4z!mFakmqckqJJi^#@aZ7EZ>*B#s9fO3w#_ zc3sBDX(uo;PQsk~&p3N6129a1(plBu4C#M9Ix6Z0uA#U7O*T@HRO)2QpYj!|vmfhv zEoi_WPT80QTUkCA^Pl2K=gF-5zBK(LOl0Ch|7ZGk4VWbp?-|CwkI;GzO47@_BfiZ^ zpZ%6s&DdC6-T?55?^5DqUN*ngv*i5_K$v=T8tJpqpK&%b&eao zee=@tvMJ|c>P5uLplVMVVqz!H@jbJA%HZ(cnb^Q?7Qst#`a4{X;Iy!2%6@ihc*@?$ zCvtyjB&Rez)RwC|y3pp)e^-Geg?H~Ns(qhBlsx2q%1H0FJag`oTS~aA#q_0yFsup_2DMK!g>31wl%o+)nsg3KePLk-Irb`_*WC+l@ zn0$i$Ainxs|I`V6?}Nz(aD(>y_-tlT^5yW~V_Dfh+goFLx!$R4H<65t<92PDR8dt` zePb)_znOC=P4$Z7tB{ZodZ88gTpL&4u49lG=*spGv*AB8p6+UrZWZ40i#F6!K`55j z&%u~H?o<(-1ew4wMy&taUS|yJR|A72l{nzvXmmv!f2yPUBRHWLR`JeNd%hO`+&mn0 zdSU)UD?BJ#2Ob7Q-e=N?c)oM#`TMEPS6?33&Y$*X-DDbEs~R-1D^@`~>Z8~;2l7k_ zpAw;zKAW|%$?$ziZO|`rt^d)>71MTm7_k;`M zUOvhhmV>n2_tmJz*P}FMwta5Ka}6Pm<5_(ARxQ=72McbS3H56oDm_%DmpbbNMjpQU zzi8dT?7NONEnjX#+lO!92r2T3uX@SXwG^aRxPBaGbi$G5=SfA6+6UK3gr90Y+EgAo zpQxnvx>U&->b{m_shF^7@_abiKRG+`G3p;hRarLw z;&`Ediq7`#5jbj~rffuA8p)RCTu9%!S(PsTdZtwl+dx2@SpTo`MHkL)UHj9;^M5|# ze~;RJvovv($yFND>gPV0pMNQJpDQ8$z*V3M@^Fkp$Y~OT{H05qGtXmvPu_3hOW8Wdg%5Pkf+rzgJ&{~*F<1X^Jh11y+_T&F?a5sf zzTV|JpAzOckl>h8jhJ0J{g6lsI}bJne>N2BBe;XjDy#~_iSccP|uB)%%+S@)sYql6V;PafMmA#)5QM;fmCV-qYZt1)z?N zF8eg0Q=J-9DhOrxqJw>Egl7>#vd*}ZCR*6Pt#gsp#kmO*0pBqfB{=FT6WoP5DF9BCdyyLVZ-)pwlIsA+P^(>Y1ocq}Ef zv(UhE@$_#?8yQ1Z36_zs0Zgfmo;h}!)aJiFyG696k|~TS9%+4P2Rfh&jo%ObIZTZH z9wz$(_b4OXc%bC!sY;HR=+hf=R{o^8?}9&c!v*b@>Ps!rH-!k`AOv*IsU;7g-)D%E z4jdU*Ou1n;p8GSsGI1!W;_LN37=R@-cP*z9md|+d(J1s`;!5KXHX*Z2wx3Rk=mC;M z@eFErZbB=V`m5!~tCcoV#%b3=ed{F{<}38FkJRp9e_nMaosIH1_#ro95`;EG?AV0Q z=sbDFV3cqf3AT*n%H!gsx;&RA=_|FW`%s&*()OnDpGR7f_=Q}Qc1{aP2Td*AP8Q2x z8{n7QxyLF|>F4K&>ZW70)uKWb3yMl_EW)Jcb%t9d{eHZ7mmp=NHfdwqkjGoOqK%V_ zT^kh2T*zKmO#P{q&T)H+)c(L$GzrX&cbhs{7v!H|M~%Xipv?()tDZ8C$ zm+ovP2p{wl1oxf5fRn556>-KXd$|9aC;5hZ$Jl(uTUR*+|G2UzTzN_5gtAmlTCF0J zSL2N-f$mf1Kje(0+#GT&=N?dXY0%DHTzG3-v^T@FNamw?Y-Q?9G+#`%QfKECX_py` z*DUIbT;hH~B!D~~8w=l5i#GT_nEzP+sRoYdk2cypJ z&=-iz4ZO-_tCo9M!|{q3PJ^4*9B==&s@t+3ING`v#2O2!9DTf2bbA57S$JXpZb!X- z*q`~6}v0Iq{Tv?JBL!U5&!y zB{tM|_97{^A+^&JJj;ik1^bN?pJ%PL$V-ck zO8DbR+Ks?@(w}WQY(DQS2>!~_bYZPQK&^q7&OIz&;f!~u4{OOHJ88$!%MtKNHO4s3 zCObzpg)H~H9i1i(8qY9=T@S%MpN4hp<1%xhrWRH5|}Iuk|2OgBg{g~pJSEe1Yoo~R-Mu&m|mIbjj=Y)+%nfCJiN zd@l@RkARx1l$7;e5MX#3Fdze1dSe;z_zBVAM+~-XTs^yU%xim2bF}ws{>&409(rl> z8vN@mt?MOv`w1l#`~|wlR@p|4lFAwmv23rtEMF4rEW=WvdbipW(G8@xCYgVC^Sf}n zm+mAok||K*VTD5=o~o0ljpP*pk|4$)FpzBm`I~0!34UJdX}N)1d(6ank1Y|>TF*X; z9ma3Hr-a;xtT4ngAo{_a@-%~wkEdA?14XcdQb;Z&alFOFNy8J{+&V&X21w>DI0DP& z88&%r8IZP_J_&N&FXfBt?9ZH7zFDdESDLfdBm96z>~lT`5IYMdDi`M#9F`6 zFVGyk9g>&O!{D5Z7fXe%CVDoq2N)>>AMUrBXo zg{R0CF3g%p@VlE6mKQl2_gFy^HM10oxH@=$-aQ7y>BeOpPL&1$PfwA&*c|`bs{y4t zKGrBBIapWU#u9B@3V;6uhzfDRoplymqRinC*LAz78bj+oEW<~oV@`6#cWe43r_{EaXC=uS1)ujgATiLK zy^B?0Io0N~Xi)$PDJVGz(`W>-_H2-*q07R1DXz}bQt=hb3eVGnTT`7tZaIydJLbLuC1+>nEOK&+XE)E&GKw4FSWGq(Dnehf-_hWc(f1Sufo-? z1oHarsFHWTixz%ChgSVPvlRhXGsOAO{b@aJ?p3V4Dc8MysNQ_mDf6I^cv##yG~i!OBxH0i^QEgrxN1xV?J@4yZ-M%)|&PuOj=OfJ#Sll=Jy5jI#tu~ z;;UW89 z^VW}Nns%8D9W7JTkhqDFn(@%7fN51S#DYsX*q4#UX$49MWcsJg15B(IK~PIMQ2#*H zV$s-UnTC%`wfptTV0E`Xhg%=T~7fz#q1hx|Pb1#7xAv^i*P6jbAK z^{o}EKl<>+wp&PgUsSkNmHO`0Ohl5X8&m>ks)L_CrL%U!8%Y`(w?9pkFE-!3x#y-a zjS`1X)uuLaM@Pqk>5iHNdo-@Ek4k`kS8BZ3Q-M7vR06amBuW{wIfS~)wMms z+sW>T@yI5cfS{F`cJFx`3KZAaR2rmx=bNaxu}MIeQ|*VB)sfyF_FmnRq2Y^y_GIeY_8H|65q5x>=#y4@)X8c^jf6BU}5Jbpv#_+q7m z;InhD6z_*>-yFs>ML0+aUk!Jv8RsgjD-L*+P*NNQ8l}kL10yock;U}%^xsP|7cU0A z3}fA?7|Nq9hr@8+rF>4AUrKp0BfOWx7v`kTX&tBY;>)XM#^ggc-fti7u~E;TD*aZv zmT6gQDqYdhT3q@>>pY{=?+3??aP3X2t84Qog$5c+!v}t(E?C~Vu10d6To}|L6xYPwOVh*k&^MLrBJtt)M zqRX|Eg3q)@l*8cT{_(~xyi%B|FEijo8h$Q=QXlGPi}lV~(vrGRV`fgHBwYTgo#Rj9E5AM%b&-lnPa zIlj=Arl)`MP(ziSpX9N8k9v*SLecK{#Dzy zZQC`$ef{rq9`nk6@>5^TiGqs_$u?b_v$On+`U7vZFDj-B++3AMih>5uZ1LlE2fd<_O5|t11y#Sv42>B_$I$x~ ziuJc#{5GCZid(!km^L@La4grMWtK0cd7V-Bg(l!9x26m+hTFZ@$npCHoRoNc2rojy zuf9L|L=XNeVqq_!4}*EsrN=cQOTXmN8@Rqo9jpfBhHvFNo}LrCo*zQQo#ew#wR}yd z2h)S4qI>%Xy;;xJ*%^vHT|6=Ea;!5`F2^@t!PM`HYMz;CO?L5c76w2uBxx0G6Sbok z&^x^Jl14Jdf-nsbugU&w%qjHS2k`*cF09(jrWY*%plrp2;C&N=}F5pN}fJY;Qna|2Z``h^=Wy+ECgCMO=R?+Wv!SJ-!jn9Oo zN^Pjm?M-S-RB(!#%NdEx@P+OwWif7X%{*iH$n>s54&GyQMjB5SuLB9R^Lce)9ar8@ zyXPHel5s}`K}k@M_GKr`|BO6p-= zc5RaJNdA!F`OwgF(zn%F|F+h>_MuH`U5EY4i?oc6gD_V^ZWc~YvL|t7qjDiGcccIW z*eU53=FmguP0uMA3f^f`;Q&kQjt8-wCzMUq>OG(1X+5Tb;}=<}azpoX7vEWtjW#Xg zYzB*~G%A=qrx)laKla6Qh(}#v+ezTPhPQed33cC<%p`6uE-W0CR^wty?wz+UUHcg6Z`7(5f~d9d<6ilc#o!{L?-=6}ZD_>_c^em8)(|b%$9F8z__!;f+&8 zzpxK#;Hqc41v?$XFWTn_t}MEl*ixnh&GzBBaYN;Sa)+hI&B{f0b9Nd3tYzx=U2#$Q z*+E$`I)&PEcJk&zHXU;zF(GW2X12HE2Lw^Fq?k+zzt> zKuwOQ8IrQRVW!k#R#qXZ*se50cG)#h*c}mM`~TQ`^Kh!$wr!Xcl|rSIj72F^10^z4 zR4cQH%%L(wWGKqeNQ96wWK0qv86%WJiZUxI88VB?lxe;Dxmib#|08BE$^5gm!7ef-8E$Nba&g)gT0@MUOLTO?-r^G z5-2DI4)F8wT~ZuX__fzTh_e%%pCh0|d1lvOxZ#`bUpmTsPEuU-E#Ac?xkw=|3a1?z z6poPMVhOJ@+z(Drv=>$N!{FG>!kje<7WPIAHKG;KeD`XSC@;lzVj81H%UHL=8vin2 zzQ7pMIWtpr46DM}V8~W0lNqwXKTn(c@V3ximCy=P@Z9|RTCEh6U!Ij@b4Pkgb_K#& zV}qaaYy2fWn;1Jn_WYbJnjCpC)Ia(0Fz4>A&8A!GCuWQce%hmq_S}IS64HcCEGqpV zCUz#FifTmWBY_|Ss!n3@%;0Isv3@7%-?A51T^_QI{oR(&NL%5E{@sZ$Oi{nr+2eZs z%e2cimsMRmCOlFyJ~68M$25#^n@VtA{(dK~$-3eXrWLy>%zuBjno1q&RlOg7ZR`zC zr>0c7%Is_OjIWAYt1hShvS3u#?ZT7xR0l$~Y8m90Ip7XLB8`8yR`V>h%1ZR<(GG{_4&Uanq@BssTsLu5&W!O)W_o z{9CtXG&_*4*yYQYk3cq2m5F7#NT?L0=43R)s;fOX4BRwSfUr`?AmaW8@#?!k7YSKY z!RZ*!b`8hq^2$x`n%;km9QmEz`Y>)>-@gs9=F?n_%|fB6*LLXm-4yKoU>?}uvur(4 zu81wSPj7hg^YJq!$y9&LzG+hvm4 ze)v}OO@YYYU(KR}INz?Gg9~Yxq0IpYOZE?s`OnpQWD)7n4`0w;lTB$D85!{kbtT0A z&(yezNBdCfsLxYCh@&4S<)>pCQhPl2!$?n9f41Dof+D@CsN1Jzj*0~qe3`M3J}j9t zetmM^`#ppLwJJuyTT<*s5M7>!e3|6UcI)m+ly|)=*kgC+txgAjw)>IMqt*OG z`j)T+p`lYe2{0M+YmOiotDy17jv~j+hXFLBERJ}Lh3sygC1AHAeb-iot$}M?LBQFZ z{;0aRi;A(^>E9UwymD((+p|APKX#1yr1b63E!7>Dv-Jy-9X??i4kuJ=1bSga(=t5g zEW-!0ZJeNs#e*O=D`lUxf1ER3yonn75yXw6+7h>M^X&!_&ul!ST(O%Xz8F&+79Gmj zIN!*LR`={97NXL6>D^(-fG{_wL4R~^kRV_sKoG{KBhcTl=)-p)6&>@COjuq7D-e9S z&KrJrFyd~gQVq_>LBGhn5P6)Cu(*N2_}~$%Z!dDWR$FL*d+2P_U~E*^l5)08v#%v5 z^3(}G)CZQIa#^$VnJA>vt|d7YI6c>SsIDvOt3p@tg2Op!(Ep&M`-yD*6u~-PF@SMM zru^)le!I=t!sRBX-GCXkMjeFHqF}xy@ZnK5?;$e?psB?m*I`V|zokces>{c!9fB<@oJxP3?1}GCt9xIt z!^-`g%kgi=g>flhC4Q@Wan*vWQOcQOlVaC9*dSV1sMb?$@J`Bm`6HHH9b8Ys4t^u+ zSbgr3X6y^>L57@=kdVtc{a}rH?!`DI8ziY@AaB$RlBnNQ+id-CZL*B>Qz{;1O$gkV zx_c<~pmyXd37tG6(AE&W!&#*_ZLFiJyKU#Z@g$-OhQK$sdF9E6Me~41CB?Ke9G^>@ zGuN+BNM7Ib9p;`hv#8K6@J()avV={b;cvk`EqR?w;oV73!49P^le{6|yC;06aMKii0j?aupf@c$)xh}aQn_&Vi|;j`HW2_#e@8*-2K=x0>&(Ts9ks4U z014d0H>%Na-$Ua$Nojqu$}%NlM4;YFdd^O}@g9bb9=d#|a-mo7sIsy$)|P20C;Jxq zG00Zdf24xIjCo8wt#TbVw)b%p1?N6wJjX$Y9a&@XbB)Ca<;1<~q>8)u73fO+zxtYt z8bX{RwY&Q$6~S4U4IMj$g4JPLIFov4#IDVnA?XB5^ zJL^TRJH@^Lpw$to!8y*i{bNF&71D##@0WiQKPs+%R4M9e-0k}L_m|4+Cz@a4;Bus; zkP6Zh*Qzg{X3DOxlhu>3-fpmI33f6a+m27R3i8zHOc;=UJ0a5L`19QE*+Hmeq?F|# zW`d5hzvp=rIya*sKb!hqZmN@h{{h9J%enx>Q?{(%e=FoR7wT%w17#D*Y-cibIrZ2n2#TNvark)=2Br0) z!k9KHg*R8g?7oYeKZQ1Doh=U<4LY3knb$r2C-+p}DVrOauRlJxysbPc_C`(lrZn5+ zC7psy>btaf19&DnUKZ)7Cs|uKIXFifC@z^N)GZ`0?BaRX;z`BJWn)01( zQ22WKu5LGI4;ZYuXfpimw_3FCbC5EVoL^}?Q9Xg)c6#P%ydefca-&J#Wx#cF2n=1 zdo3TdiGr$29qd9q21`Jy>sxoX4ZZ~tT`V?2Hb{7pHkhU{Do50KoKTN}V24I9AR~LC zbC1A?obHj{I@%z5LUoh?nIgQZi)}Xhz`Os)#o=Eil5@e-$m>mSnnq%}`md(TCl@%R zUij(e>3Qm;1gF3n&W1XxcRc!2rMb<8Hg`N@KHKSP++sBy)!y{QO-=}?^b^l}& zC-*00rqzP~{-F>0f3NMKTkTbQ@ouvRf>AOiNOG^@WRF-lXj*FOl{aAD;7mzea1+nMXdA^d9(dxW{*2 zy6o>$MrS@*O^%j_>OVHUNJz2}BEWQb6?_UBMCu_Cg^9_Q7;cts>~04GfxBA^cMLS- zKI#ZqDME!nB8GCEbyHfD8m6ORqV(QEs7z=NC^)`2D_K2_6sQda@|kStvfL?89hf=q zT-w4aoSyKwX6Bp`PpDn9=+MPO;dfmGole*F6oCUF_w+j@)o3>J_Jv=rZuW#(9XUgQCWMX)a3e_IGiC zY1EYF4G-R)$iYuuvHc$|q>8JTj!l+}#XCTDOpp8gK*ljy^)qLl5E=GHN7^x@Q@m7a zailw|tk`t}m-PMY+oi9}74Xa}v97JhwsE5AqkdDiYCXHR%OrK&Em$GzOZKfBaTkb;>u08B-x~lJyD|UCQnwcMnMjc)aUIoiqh=G-&3Vun{9h* z$u~&+HtnIvm`NCXcqsVjRjmrYvENzEW^J#A0A(=PpaL8f8a0it>uA*~=hG`?OXEIo zlb!=lX}`1%=qT+kbbN{``|kl{VLU4*T^n~WnDK1i`j8rd%nGhv+AF^Lyb4e4tINr3 zw;e+P&95SXp8Ncp6B+wS4QcLCeQEB~@UJU3vCLcD)oy*X^tI%|MVuUyJ^D`SfFdz^ z5EHi7>*#!3W~H6JVjZ`yTCdw(EiN@dz4azZB!{3q^u>G$uN4n3HbS8ESFfcQP41Tn zpyzf=vel9qumxe=GX^Q)yYxqj1UYYf@q@_$|^*#bHK3CbG zy$|1iuF~+y%ZEH`xSeXA*R4AC^FAT<;#=EeFMXMfayidk%1?Hd3rWlRUPIRv%Gh zK56)@c6snfcU^k*QFnEg7T5hFt7D7rI!D#62oB{+Jpbq1(Rr0^Iq>HnvAec|Ob~Kw z17PZ=^gMNOj~P$>ydMv4j(VEiDas$L_q)}mqSOCMB3zZ7h-}eWt_dDkgiVwW10+40 z^u^8Pg*PxS%$`_r%4NoC;wtAy$5^e76`>aD3n|6_TouMGATgc$2iu7a_b9f(NZQMK zqib}n^~O6!nuFD|3+zqqO;y?_T9#ZtX=U;L;#|4v7AmR7>d9}i&D;fy5}wN0SXfLJ zI9+sBg?Wd_otAzpZO{Fe_dIZtQQOHXqrT>ycJ`rzx2w#SI?#TP_NfN~{+#yMMXQ%E zB2U8&vr-JCm(EZ&5p69}i%B<*Ki<;#rvFz%N+1qe&w$51$)vg(-%Pzw9`$(AgX49( z$~p%JFduMHem|>ceZ$76haYWY(i zflaj2uK^gDGBzIufS>_cR|H1w>W-ptx@y7SSFc+@D+g?^UC@Gj-eczpu^M26f0AFxV_oy4g zY=J|;X8_8}ZY+Er)Sk~6!I5S~1F})yvWazVi=Y*{u5FL3f`>qtspV}*GMX_Lucsr- zNg5RT=Z^?875`CJ72kaq-LOd;#!`(6cFYU<`vqU!M!BiNEJIOxN$gQF)c&7Gtb>_5 zPu=R?KmY#se>FV+U+*rTzxoDXD4`8<-WW)0YnEJFs6pHT4M&(PKL{?BIq*OL6tnf%vs;2-}tXY&7B7P9LgDeck4 zx}Xc6FmZte=olJ`V8jjhbfAzZ1KS9xu&}N@hm!{xggDZ=4q*h8ZnZ$-k+iJlgUs>< z{{=oeH7-UBQfE-7K=r;BBQ--Y92k^O%z_4=Fp0F`0QL_tX(0q-Je`ffoTcUEDd^ba zl(4*ss!@al3*RcG4oRA7626q>tjoZMxP}y6Vc=RwWx~V5`6+gO7ySlsIp2#GuTa<6LAY-u>;~iFcKG5P?{QNzj6inZ1K)cyG z^w^S_<&bLiJ1DYtr(HehGq*xQ)<;&M=w@Jp{so}#eg}6ei1;Sja4Nin;X4xJG5L^i zOGyh~3oeO_fNH`7lLwIad|cM~>Z;!U(L+&Y5}Ot;X=A<&Z9KhEJ-p+aY-uDk{w;4W!eCq+`IIm9Hp zpV+cm0{8hdsKlf%K<{0v_WS!4vh>yL5x>F?ePQ)VT-CcZI~kQ}Z7RA7Fk^Ew#4ciO zD^Fww?CM$_1s*I>kk(80F#mmaGi<8>g$S1s_A;Q90}=sXg7=nknAGFWrnsNj5h;1< z!4xhhUIvnRqYEwV#+k*N)KQ zp&l$GCua@!X~kQvk8S7vv3Kn zN8nQ>>CXN99p(88f}TT8|oa$U&&O1hVdZZIbXRYe8qC0{ur`>cjys&D{Zr(+`Rj z3~c-C)}M-zhmk}afq<`2z;pva*s1;c#%mIZXj%G<*{rjX(U1l20Vd7rL*V?ze`JcR zaKCBHGf-K{&P~Xm%MjvBx1l?b79U?iJq_*>3aM<^0en$2A_u%B}cXxu7L?p5E>;eU7L?yFm?wSVj4nUUkwO zMUd2+rSz|oEemjOn@E|(#N+pU%KM6SXjE17wmfuVaaTq;&tVFsV%hkLesV(h(N5?m zLTo4M$iWGnoff1Ui%7dse}Qa&@Q&e`>!7hn6X8m-i0Jlo#zs1@`Tufj9nYMRz*i$( z2CyYfXmwvRa(}JoWL?b2d(YX!>BX~dg;RDc#fO&`MSO`{9Q$z!wKpfU^N=CzFvyU^ z%pkof*yfa4aIi;k?UED` Ky5sXng6ioVrai?_Ad9>7WJ7xldYE6OsIXFJ#IHITE zlfV!93_krdt1n!Oxx&FHAzDtsPQ#2HFx9EqGXdN(5_X@YfNN5MvFTF4q{q;BZ;`~8 z#Sc*ZO9jom{sR@I6S9*jWIB4gJd0&T{9wLZR;@GG&=7~ebaP};mZHgpA6LJl_Cl#; zVBH-HC-9_kjF|NAPrG@jW6+09k*EHC&+m5<4X|!#4-(`yv?oe}0DhM=kAa9=2go1% z^*Omp{*VgxCwg9b?6yr#a5I7wVE7A<#?Y}e7oN{83*hdj7P=pliitw@NHoU4dA^?h7Ftekdlf>GNI~zDN!PL z7Fe>xgRjG;53Rp^w7vG9I*U%5O>%Nyin-rVBEX9-#7}1jA(`H!w|X<2vug0iQ?~`s z71>Lj9ir}C#mv?pkNk+uIB@q*AA~Wm5f3&9d!VnCb^2-?n(@R7N3^m_G${E$-w4l) z&nqo0&BtVz=@n^2BjwbG$1jQ(UKWu3-5IiHg%1(;Cl#V%clZ#}yLBuu9Y!Nib}USw z_sR-2srVw);5>gb?;{(T$QbYGUNS^>qgf?3ZY{AGirovU&txTR)sdbU`*y8U4j*w5 zrQ6Z5K+oxy)aeIk&XL^kNx7DY=A#IrEFH#2C1-xq-X)99sKbEY-j+Czc4tSSd{T=d znIHuXCHqpLwFBulB9b8t5lJ))X6M+76(8QjvOqrx$C=!4*KeT_yBrfy#CYZNstrx8 z(8h2Pg$zKj2cg_E|jvLZ1;VKcUZgz0%WF@C=ebRuYi zxGGR_BGU>j9oacU2;=gddfQvm`j=i;P+l(DBI+U%U2 ztCx_;mY!4Slhbf1!gov$q@BR*4@JjsUdsXa`4?eR~BoJbD)c&uBYbSObb~4Y0##8xsr!% zJ6T2hPVkKr;gQwJFBJw_HKiFnA46a5-sOHEPM$M3hAGG2NY&F0Cw+!wV{n8twoPrb8W4@bQERs{%5JGKTf`#-HpkQOBYiVh5e%5uQ zAlxX_uMJyF*XA-Y9l$Pi_Hf9{Ut8hmixBwSEMIBe84XPcn3x4#iEOIoePA)njP6BG zl_sdT2PZ=#nufH2FRZdgg_H~lPk6a-qW!Uc>gb`@yQbfgX2Y*>+pD#d-byb0`kD}L zh!6gdVUAY&xShUS?JfoTmcNCk;v8m7Fb~+3Xk5)qk8u>=(e?T}ckM5CQ3|?1nJK#!l$WI;(w~?{1gmRjmxiWN2&rxTC&b=y~-rino6yT!Dorb;`p-XjkP%o_YzeQB? z7&<{MW@6>x3YKHFQlp(Zwyn@8C2fD61-j|Rqi%2KEqVmrN^(R}4JckWM+zWO?KOxa z-qfF$JeWMj5Np?#pGL3v4kL!LJgFYkF(nQo_FxZe72))z{?_kEV+sR0rZ}axwUIkp z4v#Skwi6{7d@?z~jT?7XxE_5)4<;xZ?yXMY>|{UuUI?UcM_#WF$+7!M>VZ@q* zfmd2CGBA-TG*lP7eZTYF3X8>IGRC=fQ5WP)UUx7H{nI&xC>6W;6ai?%~Rn9x< zy@o#^(*ccpre##sw5qaF8+B)2Uta;Ga?{0h3IeGn#d4-oXrWw6DL#7iD18fZy-{hg z_3Q67deGI28a6rWV0~IWsf9$%37dj6f?90b1lt7@QM+j4-B>cxV@IT@NH>$dFH$rw ziv%>`8-W|_a&XdaU_867_twic4ZL|Z-cjUklhYQ^J3vo!WL00Y<&BcXReOV9q&!P8{)D)Z>b_GEPNRGAfh9-+KIit4I_}swlxNq z)hEB6qGz+Nkw}Ek+hIDTVS-daKg%+RhLNw zZdkwmrROxRQ(IH>MFN|KUt(Tj3W*~y68;URd<>@AkZyy#_QeR6J==e;W_ty58xP_Y zgcJLT-g!uLq!JgPm}dx+rSC#Dd>^Z90-}8~odqnRs%7IJY3K97ZHdGqN=Sz^T8OBm zi~Fyw34O(_;QOws-J7T(e`f;{p1kaYt)upxGjt2Rk@KV!nXX3$>DX0S11Z$|h&z~M zPayI^yAqiV{D=^CnmrvSNtWZ~V>Mv9nR~kZ1I~{?#h{C@jEn)swk$e+jxL}Y(#$r6 z52NJV^D*&+^j4Ax7-Zc=_ZT7qktV8=RMB$^(b}z2vqh9v@8ULt-~pHOg@It0|7xAk z9%ND&EO9mBO+)B_%}byXlJgLK5!@P3dunjjM$prRl=>*VNF5QZf(CRylD)$)v9HbP zBmCmy*AaJ){ZoznPe((8k3w>55?Pb+C8Nfw#YF)lQt_sOuCVuviC@AKL86 z5GXRgl_+K-c-JCy4HyNym_3WyH6la$2$vmpX4@Ss9uOQHeDl?OJ}1)Sg`~rJdg2GC zv{RCPuAL|vIgr56RK?L_?ez6IJ#pw5LRN#197+fGcyjTtEx5-4Qt%EqYt87 zB4($%<)PV2007cjh9e;!R8Lp`e9%SLAX_aMz*M$lw;DmD(D%NuH*yAdF5lV&kr)4U zW=ocJ4ywA&WVR1DWAw%MQ;(z&`Ba)&!)z+MqTeSFsrTZ@HjteRwusJo7sf0_bodbO zhMsBc^TXY59=AQGC85R=g4?-+0f;~*e<+x?$S^3(lwz8LaIz-1B0j`U)_ciwMH^G2lG695WU!n3TbNkmZh$a z#~N}V?YEGkqTtB!y9$$&0&+^2e;CnAY}ayWj5d3CLW1_9x1 zq4S=otMri(WE|Cbtn=PYq`r}Kl1VK3 z8`+4C1VYa8;u%6)Z4F+S{Bc<)WfBkYBu1;Pyf z4XP3B!E*PjXU3#%RnDi=p&2nG=7<};kc-O_JuP$#+-|TQ-S}A^Z~^`4b-Qjkd0O&o z&@D{tyQ{LVtls{c`IY|v+^^-X@^mG_5oPtJ;x~zQP|C_{Jl`Ri-2idlo>mV6X@rKZ z%(DG!{M0UQ@@ge28zk)qbFE%~W7PSPo@E-tPq0m)tC78F@BE0X1UH79bFjdJx`$9v zP(t#+FMFUtr<667)0kJM)bW~0^^?$yL};XyG@0e|2k zOFOht5+S^Tv!}`6EpoW0SvTaObpw3{`WVQ%xZ>&F-|nAp?)0S-r5rY{^EL#;1pNZ1 z!j=;!PE6*uA{mbj5l~PL4hgwOfSq81fQ&JQSP-$PWse%E4qV#?_J3 zGnK^*3v*$i4hei_SeTydxDAJBRBngi@6~N?e~nYJBEZmLU!>9K&}l9rd8X?p;249{ zT(~EgDef$zf3F+4L1ae4jk0{t6HB7-0LEJUM8~nG=_Ai%i77@Ai z*{+FQe3&pT2kA}ZPdjBDL;W;Rh` zqE~Woq@Z7po}_= z@|#;rR1QgW{%3#r@4*^fBTz%1qYwJMlQ3VV+ z`wAy{y%HT^UZ<#o$U$+Y4~VViV?^IxFWHsw;ug6rqlDX89lPh)t~+(TOVF2PaEQfZ zFxOh1Zl2^f=g)e8(4C%-!J(~ZU&K}bPfX^#P+c%W$zQlmQZcD0U2qdWf16JSy}qx# zQJsJ3S;^r7f=Xjo1q;Yd55HVyH0|{_+Msb>5szUSY~MvD9whRYtkeL`#UDm`xb`wG zerZrx*y_jdg-3?WQ{D|JZr*sW=nvXrrQ+W>OE19-A9f^lS+e@EB!Td%6VAQ0rNvR$ zl!#re|FZ-Qdxj4dNgXs@V8*dCN?``!lx4}4MHsl33Vxh1PKVc`GL3$sU*TeBXIB^i z)mv7*8FJ+wP4!LA<_PW_GYD>?`*pGtsuMBbixGBiw88 z(ix<$7v*YlGDlMm7a4s(v`&May24LdPYky$^Y{Me@+l|u3*EAbb)nQEx(N*ms_7}R zuKgQo?O64jm9DQp>0ZF%4{4|6u4%iA32E(GGwS~Lm{@0xvBv(!7LY`^VJfp})5Dtb z@2$nf`jLNpa@~g)+KZ>#w?tME5$>dtdDXA-dgu3@MBW>*<=7?C)HpG5t`H5wWWZI4 z+dBw(EI7qvuxn#j^3HQQQ}p&40yqiD`db=rUSXvxc9f2yzka9~G6R>FOJ24b7{lnFaxdmeh%6^PqK#OPH0BGqE;pf{A?mz!f!~6fy2yU;oeD&LVh;*0E!8B(Y`WE`VA&g6PcVS;SBh(mXT>QT;PVBrU73Gz`5ezE+Q1K~H#L^ldE@&M8PWVcP9 zOmT-x0Z|^Jx&Yb%S+iuhxl!NLf6^e!L0*Ml%T&#+8=kf#n;*4J{u0QvcKnZ7sMI{b zEeJ1y=#w%^ZGT-+fpZ+>DZ|Nln9jdkb)!ZB(e!|BS>z5|Eu@)WF8op@l-;s1#PdM{ zI0Vx^A+>?1Ol4cu?T5g_ZGlwd!hGZM8FY@!dPb^jpqaCFx`@bw#!Y0-0fA5x5(J38 z-Kd`ZL^gvU<=M}kM-ir$<`qpr_MF7^xw4h|5yjk_^A)M^4o{>}Ql zAsY&st?HKn?tcQTg^WD}b6zKy#`RW7bmJ~RD`~HREkuBw&mI6JW^*l2J9QrTur=Yw zKduQ|5pBDBN|k{{;ERo&e(>M{5-{0V)yJ)(e*-dW^B^i+EI-kjqK4&T?kMV5Deq1e zAblW0U!F4Tzhv6xLh8h7z+XQ-NTp{D>Ca0=H32r}V6xophOX+<>XdDfRWxb*#9cFL z_odhsUWH+KKxy87^4%?xwHwpW{QHd^Fj4VMum^$^F#QA3^`(<;jx%eWgEB8^3LrHi z_*^m>q9Od20|IyDU<{#Z?OA!26+#e1VhAIvxGA-yeFY)d;NU?Wchp&CJwfFEfuL6= z9;u~3ZIiRHarB9IcQ>I_3sEy9myP{-M9%RPE2l({PdlBN5&9>JW+!bZT7Yqo0US>6 z02i7}`=Gi&@dgk_fK)VOpeFqGWoBk(qi6)%d%w{fP(Ua3EG92GRc4IK~BfU`Gqb-FE3H7^9DJUfW8$o@OVZdR$~T+xBY zfY9QM@j7t*?{1gqDnC*QCYn;g=n!mln+XsR5m}NaPteu&j*gUU8$bj7B=L*}@Xm)=%X)P(99KSa8Xi9brD zzhhsH{@J;?V%(U}68kPk*T^{(eX|0XA-0!{FwLG3FP0X0E+$b-|}y> z7{5nDk7Hr8U31nZzCEV^^nt-|seM<$aRg|7Q^6o6But-X3DNGK8F&{ZGm6FT`}JP_ zPI{d+D;rqJB+^9km+jD6iwW#gd+P=lymRkYT%XN}0RJQa5&2@gfMq3RsOjPZbH4Yu#v${ z{i)*)2=Od^j0@cs?ADzny&JYezkW+#PzH4j3QR})+e>Oj{@N<->(6-U+ExPCJbuTh zOZ>{&r-r0uJL-ogrm)aI7}nQ^!!vI-c7SC}d2RZSz{v{h<44Ya>WmVGXG+W@hUaw0b3wN(eXv)o|Rm zFxrFE*hp=e(n?fcNST+UyQHxhXzq3W76iygh;*6f6ebI!zsS{pkoqIgbAsyrX48Wn zbac!8jJNybwq2I9ReEA_{GII*2HobIATcDB9VI z-hML7?{dy(;Cm=9JRsQ#BJ(?T1%o;IQvrd2O{9CChH(RXw?0LM64>iO7v*5mra{Ie zy_>jrdmU$5?>R}KE6&YOdr zSox+gZ+XBox&XM5J_o}r#&b`fKAoeVn*FY2e~Y8lpE3x~UId=@zPGoIq8FkO`1Vk% z2c*R2l~pDKHevYUhGX3A)sbQ?HV4RRp&l=dW~~vt+?tF%hPWTv%B)YFmf$4PzSJ=? z*aLwFXG<~A)6GIcH_6biJ~$E9qXTrwHa(K=uTeOEP*PGN-9k<`V(xU$DLjuLb$5K& zMZH`kNJb8&D<_c`3s4{;RYwMJ0_bn-mv?qW)rcXj1L_;ajceCX@}aCHfpnR|#Qce* zeIqX#5cScjo?38pM1HPeYKSo+)ACu`{ma76^2l5y1rb3ZIsJiUYwW`;pC$^@s3*u5te+L90dgX`6eOpeb;tDO zaLi1pl%8U&K1Z{Zr+jZ)B--1Ek?7d~rEOOJULFN00^va=^2cc)zBBs89`4yLM1%?_ zw6E0zW&QfNecqf?{$<^@s4mRqWdyoQ2XsdYXr6LqxIPJJ$;A43+`+U^;&fnH1Syz- z<_5yT@g0#igT=e1)lz4~=X)`d>MZ)oI$Ck)voUywX(_jKR+%{l7^e}odL#2MiNqbLhN3feUtv`&w9HQffE{}G?tqE?aojo5eMWwH^!sN6beN$~N!VS4Y6N4c zEIlzb6@C8uqJts~`rAz9M{EXG)ICUjTi3)Zfm0eR z0Jn?wyTho$SXS@qpDfw|oe@Zi05?KX@c?8kn~|OxQhjYmHP{K1-WuwSvpa*x$-w5; z$+i2;IctLHDvEWIMv}Kt@VABsCEg!E$^%3nU@edW3Yor%=uoux>%3E>U|BDqIWbSyRv-Qdlb`(&I0?{G5^9^&bu2`8kBA{O)A0E=N1^aujrdwZFd`${h-I|oNVZ69 z_E>wRg$O{P_=C{ZlaKX)Rn+Iu@}XA#XjTBRx~fJpVbON9i1Y%tN_W~z)n}NOd%Zsp!1WMVLt&YmJRyl-CNBLH zv8@Q^A2sUzR|<}kz2qoij6lk{k(78)NymGsBTGPVpuYRZ6%ZK=hKg5OE`qZK>X3y~ z)zzBw@f48_AmwdDxMZ+6y3!|+fs>Hlvj8?-fmL?oCgQBV(cF1eAIW;5)wRuMDjb(d zW|rDg35ljkvT36H8bEz5TA6clxAupb*{LBW_qP=2i@d{x4PfQh7jSvKRBUlJf?f%Z zTpvR5dMNCzy=kc#`m(K>Ocu3Cd%B^$fhH&FsdVk zC&)^kcpse#T)m0Y4@@d_dpdz)lnf>9>@xZ+Cqi5zoV3U3X(vxYXgdOjhhdD&b8jNt zr^ly}vO24Y9MRV90n}d)>KJ-nlj72pxG*fXepnu@T=jh*IW%Ii!~@IM`;}^)+*Sv* zRUI2o{~25Ryky4<|Bm@T7e@h*CzY2*x$&XaArL_bg{>jH(#dkvoh~W8Yj+~b!+TJh z9^d#`SqtzfWt^^r5Wi?(b#WWZNraari#GNk-l9ze8xW58vsb(4_Nr(w&|NUQzPR;^ z0`nIA`c2ncSy{Ob-!v^HEkF51vEUq~Pl1_u1IA~*MRK^lXZpHJK7(9G;sNZfL%}de z_ekG3k{9C{XuYY6KO4S;TcJC+J@exu81p{h2|7S?IoWqsFIRjkSqL@{VU|sTMl_wm zxxjo|xKk}AkCc9J2V$lL{aj;hNzeqf^|qRjZMvy=2}8>j1nQ$qBk!1Zak zugtN=zszks9OOe_y~03(AuMiHpFM%yY5DfHC~@=%z#E_s5M^Jo2x6d1zcX7>;(H^; z+zLVYJg*=E1{7=nBoH&n5YQ3-m2}Bdh{FiEO0oxYys~H+Uxv6oLRG6CE)(LXMOUv? z5!ndLfxF68MGz(_0X^^gkbjR+uf0WkDj`o`3rHr#SbEaHmc;5$<+SYX{=vY9-eisx zPHA(#a|&3#$0ayto^s*t+yyi(Ml#V$q^^IONHQpZ6N6t04FKkB&`kOtn2(IrIgg!YIlCxN&GYfUf$ z@Qkp^`Fvx+FYe<;Gvme729y@-i758kooa}qE!383+QNIf0c9OQ z^$6AD{=ANWIufHG-WS7%;Hnftw&3iVBwQw!=!K>&C3o<%2w>j2T80BI@pSAt)IIj65nIs9N?^PF*$QzRf zklZ<*BS?oZvq^+@eq#F&|DKVf#U6#E7!MeaX&_fm#0}{!$OF|2l^V|gQM4kXCIN%M zppy21kP3l<&thmI044Y#%rPRmrH&=|0EIC7a+m?Huo?(fxMla(-uDAlN$f%0$E07M zkSvk%n(ojT`^IaK<9#LuW;AsSgy36|k!xT0u=zt>kVN5-HNpVE(w8vqd;PA#@EKz6J(xD-zQb*#$BmlvPw$|Y zz18ar2ZgqlPZeMm?E+EE02?Z%8uNNg#EBJ8|J)F;`R88ArXO&$gyzKWvrtEN_&Eb1 zL_$-AOgRNXgXeV7(#t5+lM#wUAR`;|5M6giyKxgS50VfejMc*6PLc!?f#zNMSkKts zb4IZ09b_($@CXpPIa1~z7N(_SmL6i8`r<;wWxo3~t59Lh=K;@3fMuX?0u(f+p;@E=Fnl~HepjRo$)%Rgjr-i%F`P>|lwGWO2E7P@gv36=yJ0Cd&?L(i7s@ja_;ODd2+PY1Q!`|Bt4k;~TGh^3Qq%SpoY^C>q7Reh{GHuJPR|@WeLCH3U~o{>HTwB;h27`JR7Rz?ZCeh3M0z?2>-vS!(b35} zFb4N&n{FPI`?h5t9_{SyJvuPtqNTMwfJb(-blLm&>dvAZ?CgBox37S7VJQN4C5}a? z{k;M#@fcAK=z7Zo0s@*{mo;$7pI7|z(B{74?Y$liHKXTX4wFmx`O~}nzmaJL`#X%! zPRkuF)OF-m_;DfXrfxZV)v}_8JiE_%A$MlZnfm?v7;=^S(usHbf2tO`yxj-!Ug*2j zcTEBAy^8dt=xQ3{q1Ii{+ZX-g*&(3L{x~ui@6|i^*>a6=J z@8!$z2&MPiciqRwC#hK|KT?3N@E##QYNQ(%YHeh?eCZM!J9~oLGs0+UyAv5{a9zqL z$!zdL1_LmxOM`o_mlCSReJSk$9)1f9cermH>H^wBDUUkFIfR}X8y_zRn5OLf?2Va? zjc~Kw$&+ASXd-jI9Os35=lOD2B9CNmw93-U{LwcUs)5w4TmU;8$ZQjE9 z=W^DqOHO`Oq|O-jHZHm1<3}PHy8KjbX6EI)qaI7$B46Iv28LtdqD3!|&gwvYk(rs> zkwOBGVtig(`{Zu?Jzdvx=S-0^Tep1in6f3xWh=Rs>6|2#(?gGHXOc0Bnom}?@Y1^E z{7aWFYZ6q*7IF7QZ|?&O4*c2P_^*y85{YV}-Dm6J;epmAjZ#KMfjZcvW7gLC?XFk% zUc@ksm(QQaE{}IBv^aUv97%*=bZ$n*9+Y2{-JgB^YMN!!q{bXXR(_c#|J+Wku@M&( zTmW`oDUv0F>CXy4u2Q$TRm*L@Iuf1_zsi~r*JPNKhJvaxU+&ntlFJ8PD zKZwFyyv&l~O)9FYc;AaidrIEDV_AH49(G#!-Cd4b09d_Y2_%a*_ej2pJ9p*UwGs@f z!pqQ}Qi92xkD(?b#O{k%9!y2{Xo`fR2V5w;>oH49AJ|V*Fc)x(_8s>g*OzkcV~^hL zs@3j#^5n^$KIA&Br@cW9)!addyh-6! zwYySe+pao@Cv?nFIJ-Fz5*~h(KIp%ElJlANSBo0nH#Rngu3ofEbJTun%r^Ud>oeCC zVBv0&lx*n916s5PnJ?XQM0y<)bE{e!M>fkyL=rfsX(q*DA5X)oCF!Q~pth?=uc@Ps zed`ktfaG=O&D*yb85kHow6wget6PBrdvFK5CmT1nKg!RYot@81O6IAlsSS^gUd_mG zcv=n-dC^y8W$Pfgm7kyg7&{6i%C8+Acn^lusEr#pGI59-zrrcJ86146`+dyI4{mAy9Dhge(VKC4mhg|&!3-KeEq5f?vNKVhuBx#gxBxhU4y#4b>pK&^78Uwk&(JC z=Sn{np69qDZc+v-5)>WHf`tCV$B%KKJ8ap$J!N2c^9>kjTs#{Ghj)Fwc0yv}-Z%}M z(dAg^;h#S*p?-Ya`=MkYul(%+b6C{Aj+{riGk+}v%QomYUwT8}>aCb)Aa2Zxy9 zb8we#Mno*;=H}KjFt`UhixVdFi{(9_ojgyf9C@>eALiuEAUBB3#!+(n+Slg`2eMCBH}|fI>gb)Q zD1R7nJ~1&CG=;sctW<=bT(WfOu~Vm(FflP9{AW%}OT%?P8nAyGinWI8))`W=w%+V@ z$8|k)MlmETRxL_e|E-dC_HU-E(Q@v|W53aq0mp4lnisiuN=dDL@#4ktMXG}Q{Pg5j zBAUiR`C@lH#}`gaOoZU=rIZw5#29by-rI~&@Y075dIB3au9uWt32rq`2Pk9<06kad zIhgzVosh&h6kl{4wP9Uezkcm?@ghBlK*!9@FT=#d!mR|CzkdI|9EDcc6N+uXZY~Ub^E&*`Si_PTIJ85(|!5!?TjwVkRAm)t7V(5U7lj;h?kcs$3&t{A~5fcXu^gbkWWWOY{;z@?se4 znvVVZ_nV=?T2IlRE9XCRh`=<5MnUbr!UJ^T_9>|$bKoP`^k&n-nGi5Rvn zpUpImK8};X)CD^G*=biqgp6pw3jFH6md?q^=_^74gq_dO#<5Z>$s`(IRj@hj-o4AX z%@m$){4R9h<%f|%el5Am96xkS6CE%M7cNu)sMhxFo1jf)3OPOSS{-Do>f3LT@I0Pq1wrX6Me$%~e!qu9TS zl#oWCE?U26A}LAe)W;_aKx?{VEoik#(xy=n`|s=E;8GJ4lXI@FR?cw?|DNaM2w#tK z@tp^<5FNrvzLEB*F_*hA!G?`h0yKNRqXqEe7oa4xb$36=Da>*$*}M1YRe0TXn>QbF zLIZHv{`B0WT8|h*iDLKfpLSgUC*hLoCIxr7!X%WPIN*nTZrorOF%^b^VZ;|$!X?Ev zJ3Ff{yj(and0R|u5~z98K&&TFXjryp&8G1F1;$-VLB_fa@V=zHoT;d&2#X>~73Ql}^HYpAHU;&xjb>p%^q!&gH3Z`l?a-za#wF_KqCnhILK;S}4!zF)z{{hF) z?r8>FFeR6rBAu7RofRuoz-!s&`N$TKZ#h=lexyfp_qkz(C&jR#&k74`9v$6|+Ri;> zuJ@%)U~e%A(hH+U3FFJU`xQGBYcG3yGhsttL;ri|>+Xk#Bl`0#1BTGrJOMVp5|wcQ z6O%c}&zH(&Jf@|v*la6TUd6<}7ao(7lfxq;uLzznf5C!1aT>3E?+}mrhIcN4@5x@a zYSlGV>Dw?^EPe(U<5tuZaX*!g9BYo`n+u+loSd>Oi&3REN12EmYEt%a^T58x;3F|0 zGxN(4SeOA5nLx0BQ`e^@Sd>6w@VK}*SRf7(23$w3*N(0C3a#H>Ac5SncK!foW)7G+ z@BDb$qgg~+xOIva76R}--oR*y?|AZlz7MR;>WNIWY_zV1*{S?OsY_>&i7{JHLFk3%ya#fXju%p*)gzSupX~qIO5X9XBs*Y z!Ttc=VWa&u2C)J>9ha;dJKYwWMv*0}x5U<$^Zz|$THUBJ6XOcUF4jB*dqA2Yw*Ws_ zvSwStrTCP5fG>dn*L=EMN)gH}Kb3RpkaB#dc{Eo0g*4mDO=mOqS0{gNPE`8{%>xO6~O+( zQj}t&XPCa2M}ku3RU!%qLKVx8pEzMQJN^4Zj&04=%rnVI_E0zCVVmRdJPz2A>0PQ1CR`5U@}(ffbrShTEPnd=AIN!yoa;K3AurL zi=@(|(fS%0s6+aQ#<sM+J@oho6OHxpJdV1^kZ3J)_ z9|K~t9>WW_Y{2G2$E=S2F-rv0vs10q#jEB`fZoEa9f}{v(*OMo~Q?<#3@k6?(#3TzHI;f~P zA5GRPzm3*0F&dNlW`th=Qh_OgI&hOC6XsBIDE#tA1xCU9_7z7&(OlK z8Z%$eD7wVP#-_8Yi^Todfu+ca>Q@QErSSqNCe7Fudp6Tu0F(XF_!flWBmGUR_}Ir; zS&GKSJRrp_Te0FYKD+})BDh?9u-k-t#flKKrdhOY&p(^UXgn(mOR4p6w(2MD26FzD z2>#+{0CjIg`&M?k5dsISB}oE5efnf-ZS9K)d^bEHSQ^;VYthOmdPZ%PyD1JHkS`P9w)NT)WS)!IxYr*wEbEY=&e6!YeBgCkI7Frfj}J%9hsjUm%i-gq)FN z?4@xlw^9`hpy%q35V}SVxrdZP`+O3AA}^7EjG!MnrtG_1t)QV3_FzTnqASEp!n5g4 z?IKJ&Aaf)!A?6=CK7#Gn0|U!ZwjM?xai)d=20nhX*Pn3R!QTEkO7dGQUb9tLDE2o{4qToau*^EzO{tVqj|7!jC~ z+>W09&3=HX3H;>a>#NoBB}G?U#4zu%-nwiUli?pfUZ8hcc|a{ZEbM=^_vHaGu7CSu z=}^&v%2uJ(loE=vM2j}GnD)JNBw5Z-S4v8>iEvuH z*B$4a?|I+%_t)>=cmAL(%`?w)-=F2WuFrLcA^_N97|JDD9sy@A7njjiLA^be=H>#W zQym(b9l1}QXtN%(aIwZ=O(?k-i2Z21DzVkw-ag^4Xg>kT4!Mc%U*5<%S{3%Hp7ciRGXFgd4<)z0&WrxI3!Uu$ma7myqScggLAjyxuuHCUEQ_<%a+k` z6W>6cAkl)ZEgu_-t$Po^1oLY*TU?xyZBfLfq@;vY=6l(JDi5lPbfrigIW}eCKqI=) z5xGeDX@EmHa>Uo)KTCw(lZx;-0+A0v2JqQQRe)}n)K#d{H1h83&7?UXK1qbX z4S&apf#m51&ttQSitf9IJI^hi(b>b}4{i8w6ckdk&{71WUuLG{=H_NM%DiO{MT0D) zT4cZ|e|iai0YtP6s6EAI@Ham+b*xd$NpiyK{KsrZP!b38hZ0F&0JORbG+!k)eE1i6 zX=$?QE~KQ$UzUG$9UTRQLIj{iA=mKWN#M4!vI^fi)Y*B0EFMJ5Z;&DmV~9I!(9e9s z{t@6Ptgd>~QFjLj0@MC9?@x&CM!SPt5!LC?U}Ixr1{HQ#WoUa_TRp7FQE<-!Pw*kkt#KlI+Rbt?e@c469f;L>Hy zq)l!!?d|OgyqEY}C#eRBfEdBfQiPdHmfvxVLm`jbg=v^yL$q8eEGD-P1`(P;K+*8nxdZ}s=i*EnoqA_&>| z`sLdK?(FL2`-HJe{k*j_R6E4LrbWQpve-53#0d)0Se}L@>1QH@>DWdOtEzT>P>A*m zsd<5$!<6J&q=IS{AyCq6#a)z#q^1F&&?B1m0?2*;OHUI5ZA!p94+OUD<9RyU9uvH*<*A$N* zJfOUJb2kR27OeTWVcoii;NMx9d!>r99@wHSz*E%#x-&Zxk7;F-S4$pVPiL)0f=fLoMP{XJKR5oj_Mpv?p=E<>B<#4LMC_l80I=iJ9)?w?V^O~LnXvg z*dGi-anuP_{yN+FWD!iijOPpv4#xUTfm1Msdg25!F|v~ajO5Krs_<;G%3l&2h5P^qC$m!THE@K$Qagbtd&!rVz&_+m zb>U@b{O@-60he!(+v$wtg%c^}fh92Be7L7JHpB9^9JzZT~dI@*f}_-eF~7bx%U8g z6$iI^dwV-w;EbRmhQCpX=nOQW!79h{T(44c*Z<;D& zR)H`-31-DSu<@qlS+Mo79va4%Q0zc)Q0joyX6N7#jHgJCz_vbHTDlps>2QHFGq=`3ah^q79ORI9)?;Hd9B{3ZYbr-`SFtZn?b>!sdp8u5 z!-I$8@n1=EXIE;4dJ2L|Me?Q#!mQA5O(4LWDlZ>voE3ilyukVU;O`lgNw{?jUG;Qz z7fc2keDA|rz=0{9JuWP4G{NPUl9MCi3k+z4DJS20*Hv9)Ur@+N@=$w%f< z+eq^T(xGAu)dn-L9N8^cGnzySoYq^oz*f`a=+W(9IqRX4B^4q9Qu<9lMu8_Bj(ODF_HXG$xXI#j?VDNC!iI3w&ck5i7UcP+!h@8yWrLtFgQB>B=A1JDV z4WJwF(OSuC_QVp(SLc z?S3i!M7vth2n_l107d7WNM{+iTp!m3P&-XcM&@P9dU~?Jp z1x!vbU{=vV(+NP21>t)@Qm($Mo(H%rSzZV=Im~m>t;;TaTi;jR#-tTAPv(7k38T zvpLn3+#0HuERO_!XEd6iWbeCFpoya<7T$m&r8ks$9cggx@fm~f->+6CV?OmSU+!&v zm1nQc$_L~g$77%dA^4kj@2(d=1Dd+da0HK|8v#Tj`3GzWW}E79*a%v2O8BC4Sd(C9 z1qnr!%t65yjg8e9%n@>^QC^u91jOKi{k5Z5^MuY6YeBj@@n{PPzlY+yqR2$Y%6npYU&I5y1%)qaTmx-y( z4GrALb+2tJ?OcOH2<7NVm!`2u^|NQY*If_)gl)rsJU@5dJ#3PlW=wC^Rf4t-k}9OOTobZ$5i8jg+di zsZCK~xVLyeon6`tebj1hmkD~ZNyL%g$Q*+_Oh z-g4}d#;0#akGM#(jB=JhGBJWllBS7dvt3EB`e{I}mEL zeX(ZdqI^MhfRTr>UL0TqiHu&_H&lu0oW)`>6s9F7)0yeZI?PhA#?YbxwJNl7| zW3dc9vuP29xWstHgMu(lWEb1Q$pzb8&^qI`^NdVd{sB!EA}{I<&2jj2{4nhC;r1{? zfD{lGyWxBjxcerb!9=@pqCCWbIyK$*DLbiPN|X$Ou>fmqNJ5mQk?k&w}_ zr@$z8H*+h#+f{QJX;Ehk1C&LD_(~!{7DmKp?=!$r**Q5$++8|=t1(}0u>-J{hN+=| zR$w@?b>6LSt)rssuFz;BV+{FkJ(+%)u_?l z3=Z-Dwu2|cOeiZBwjaAiy`XzkZ=h{_;=4ytU2d)ekn`#^zwSs-%o9l>-9HOAP^SHZo)!TYy)1rKsJgvV-1967)WdQT*}f$ zsN=W(xWM=F*VPH*HBX;LTWv6uwY7eNQonE#U?G6Z%zXj5+?f+CKv@*r-)e((y9yl; z^Ik}4see)HN?Isw>RD6M9a`g~)0_(l$aKEWw0V>_4N#0dK$E5t4=I8wVF4c)Xd*?r zo!!-zkQ(TxB$Qv*)aZ;6?H>WYD#lPchetHBSQqc5Xcq}3YrX6;_{l+rjVVC!g&sM4 zt0}+o{-1IgW1hccGj$aeGoVp<1P`GLNl9y3Hi8(3_2NTte;TF_0+K=lPi7fNZxG?$ zUev|l%NNk;iPE)dZfe>N@X3>1XKK1}m%e^AYB0@6nJ+DAEshQj8m;LuP69f+Yr>jg z_+%oQ-&GGPCWb2aB9T^LMJ>rXUzfM|TC5hmATRI2$JCtRY?TtAlFt549g$QcL?42> zprI3#X$fGX13(s+yj!YBAz~mBo1jD7_vXfOSn9Z>m`X1i9vh3Z+HDRR2cZ8Qzb{m> zg{-Zu!ARnV@XVbz@AR!(9+w>(lrm@EZn%Hr^q?3?(Hl0zOx(5v`42Y`+R)8w*H*&$ zh%+cC2*+!Up0^l2MUueui+Tp>sLyA}sDsY6M%=6cswfS*I>3cfq?cQxfe)YSa<&38 zJk!g;)en=l>vaP%iK;AgAB`q4*}=h~9!SiK)24^uDr{wPAJh;U$i*OnexnfS1Q2jS z;9|I0ymukZ$>`WMqO=&WV%txhT#n=h@JcOZTi`HC7)J;(zX4~K5E_r|#(atKmB#4k z`&r0(m+Ne``vW&9D#qR$>}iXxT_^PrSTrOk=_1vM>#@FBALzdj#hrvH1l^ z0fiu@KwD1X3`ZtNBP)kWTO(T+JqaZl#sd>(pB|b)a5=%Hf6S0(jiFzv8O0f)%gAj0 zLWTucGM2Bb!Ckg|k>>O9Rk3^*UP__v0gT1|oo%k{R;%+#Z?(nt^qX~Yk~*p3;!+o- zdlRw-OXunaj={#Qb8s+cWCT!~qKM@Zpo|echb8!zw_@nOujobx- z$b!Dx)u#uKR#)&WbMxz8%uWCXGD+_MV3q9bbvv0`dyE4Pq0mS`FsTA zm~ZjoP&7L?_sj!@_w&WrXsP+uw5`3Jm8P?>VkQF_>tlPV5X_^cS;Rq(T?hL9a7m?^ zMZ(*SJ#W{(eH;FEkwR@vNVyzh2|h#wFoIZ@HXywD=;sO83tY<=7o~s}zNXzhD*^p+ z&6YH!QBgeB9S6XdG0=$iHRX#TA)=H^wRo~#MP(`EAL-lP!K!W;E&75|)7p|bw6{aL z%SKmHNbV?b^oy1JYNXsafp4D1c;q}ec!4a^@GOz_ho)tTg;xQSfSHM7`=I1RB6n2Q z(r8&Bby14l%a*UGQ`eHV77C%%nnc<$p) z@^ycrJMD{bR8PR3&tGhRc;(iPk6yTR3BsJu?px0mV>DX5ppL+$Al8Zwp~|R6X-rz! zwzgpuR9!&32&;+|cLq17X3FQZ3b?C2c!^YX$LdP#N!}6$C4d8?nHpL64E<{dh?;FoV3AN#&3?N)|Anfm6qye zX8(K!cRYT|Hh9}nJ`9@9S)fZ&+jlPPb)dSxU_d%Jt6lJLS zTi~cd{x~vM7-UJMclVlp-0&*M2#ARC-6mUTGh{by-s}hwF-&_xs;bSQWFUnX@M35A zJJctSAJ4|!B*0ictQXElMne{bXvV;(R$ue28-ilufA0)t2DF?WS;7I&w!*>bp^DthD^ot=9d?pCzZt00>00^{tJ5fVT{u6yV}Oftl)N3p&ogGF+%h2!^}Nz}UY=E|37-&7iC&Z-@ywaP=V~0Dk-YbQIUJjqX+;+=@mwyww`|d91w`f~vBXx&T}mjUwfQS5rr^n&+1Yj| zaSxcKUr$Y)g{=zgaw#Mw3rWbsoFhM=ji}f~aV;E^Ui<9qP5^ka13%DI;KYq8F3-J3 z7woLY*Y=Oa!n=kIzdkdrq3C5FQ}?k_5K*&zyHXQ zA)|p9(2It0ax%;@u*6rjGVTI9U-BkA;)zL<6ctF-WtDK$3zu9SqN zq+qFNtl~w>C8*fMvr_1EB;&_}JNmtsu3^f`yvu>q2CP^m1dANwzylva9fT&O3MnBB zAXb>j-aYOmcXh#E0f)@TM6p$wpK)GFSQw{@_s3AssU&^6yK8!x7bQ*yKJrW95O!H5 zc)2`uyPyiw3LahD?eeuAw?Pbz%a5sX`4gA8B>)OtTcsxB0|hhb=$%u5eQJwnQS}?4 zQQ+!OE?n2UFmbcbo-e|r54KZDvN5yRywr(K-Utbj&_aRbL zg3VeBSYiT6Q=-N8?Dsjil@CGC)_R`9hz(~k42tnYh@<0{cZg2=Qi2dym>n-s0f0ud z%`QXp$w1|k{&rI1`8`VJXw00<#8Hs7dPQO*(C&s?pMtj1)Leaf;m*(hIjQm585%Qa z7LJxIw`@P`q!}ilXj2|gVj;1I8cnm0Z-_XLxa|zEFsyeo#EU9}=*rVAPP&8r+vGdE zf=6UaD8jVq(|vpFvq<=kMvc;obA3EWfr~Th$s4+pK6H!K^XhM>-Jggy+6Z-3hTw7b42NvR9^Qp15Mm>pCC&a;yPu(^|T5n!HzNppd~XXy=4VGH^DVj ziu(>nI205V9EGmNp?AZ^jh7UQnc!Ub+p($538Kpy1IhCUi8{53x)-p(2)tRs#>}za z`f5wn#s=Hz5Qom9AX?T#d2!w$JBOgdEy>Uglk}Gth?c`5O#P+59~x^sqFT?x5Y-d? zWMO36I6nn_k=~bEsCwVlJ~4iVtrA}L+e)l&gUy^fTnjWI?n76QSxl_do#Z}qoPdDk zsf}?7`aniyDUK}p0%)&53Z_dY9Wh_e{T&u;L?KI!;*J#@Y(nBV1^C%i!%RRDB;7Pf zs;{f72CF$ce;}!S!4HuM7jN-BdfA~t!}zlP3ZIiZx2ybq&3a|JVCf$Sd?Rh(k?P?I z&JIGe9W8ugIR*lJQTl3(1B|4-%C~6`Gi&et;&nMF&wIdvgss$kOuXGW`X;!g802q} z=3@k&#W9TCzn>VYy@6ndHg`|VwBnma$#p#SS0l4(>^h%2b=O% zk{Rd<0=R;a(Gw1?P`>oy^5UWY}V5s+;Q^45$t&Iw7>+5sg1%tCM zkmj&3p7L_j^ZqBTt(FD8ZZPT9{JLsWLxR(y3etY|5Q63JL;VAg?cPFQnVq)rfWk||S5jRxs1dc1A za1hTLLORlD{r5tWclchyK&3cKO6rDt#C3=FeSLQL)5ds^sQoPqwAo1}ncsC-;n(OqlUW`V$Clsfmne;=zC;RzC#5t0QDOa3)m zezTEVREqPA*p46yh!v8?{YIy$9Z((+!2m&4{5}E$Y8doA2;%j^1-=S5K=cfbE}Zui zTeR#m>^iRg2^4NI1o%d+N3frR8`rHke!u3~v+0D}y4m}qbjhjx$A}?k-}4ucC9J+U zYO)_otRGOZ8{EehiG3J zoC&1zg(BKys|eIeP-n_E-Bf8K7tk?q9s-tAZG~RyG-HzkW7;7IC#O7 z4KF|rU^09I3zsYj1B&l}1;l84h5?&A-w}s-qT8%uv!Pi~#%ysWJ3fn!GJ?fEym#+j z1sGpKki~_7QjL6E56Tk}a?XRN`#jCjEYE|ZNU_~G+4w*lc3n}LkExNJ0FPi-9*l%o zQB%VSJjY4pJ_aL^Q)KRV96`N|wPwr6$j`vbLGusjhN{R2^YuH_TBKZpVxlM26Ku7x zwCp}BdYli??112LAOqMg>R(DHV1`EC4lf#H)3`j^VVzRA^v>Dl2a%uK*$xO&~lh}(ZDS!htXoi z7;YZ(8TbBu`|N-^1a}jK80>1|zOZW!o@S!Aa^$AL0oNQOe=?iSXvA-Uh#jh3Iw z!ZDK4zWX`kSztaXClIF5(Q;uencRF{0y?H5AH*XNX<5NX0M16>NJH^0d1*?c#dLl{ zCz0vxiZh-bqSd5nx`l*KC9^r_&V2xj%^9d4$oL7CAy6waJ%PHl^9~*(iE&XnX$1C2 z*(Q8{a_ov-3dY?WpIlf;G=2~qg5y7fDC;K%)mUeMyH+BY*%CUqGaxYo7>``N*5Cjx?fJ^WfFb_T2T{Yhd#2p}FCb7q= zMm@hz;5r!`M`1-dfiBo#s67iPnQ=(@>({RVe^bYn$UgJ!{4uoBsU&%Fa*}Ac7$Z(Mm$vh3F&nwLaE{jM?yn%RC6t3@j&P({NkK7!O2vr=bh0I8KZjZpO=g7NN+S z+9%;43_GJHJR`K$>R@^7AWX(uCc}pgJ7gJJrDM#JhDiDu5`}(BEi(;y>QYklS-MSW zNVW7*tpim;ug_9eAaepEmuGe0K0_CG60WmM$|i{2d!HxaQ!SfKKMO!B|G-Phrj{12 zDm7A5Ouqz%u;(fTYmgk4z#~zfrI7os=IveI5eAbYCoMe}A_U_iO)V{`8&o?u1d?3_eX&KA(~ZT<>?kG zZ`!m1)ha$}J$5>!G;Fc3F)?t-?{+DkX85~A%9Gb23p4J0pWUA$5f~Wg z`Qg=kGA4|tW_S2-1Rf0rswgN89zcpQdvplmVKY>xovUt8@_tU5aTR~qy3EGfPy=gu5vV$H;`A1U3YuvP?Gb=$HP#0K z-;%4YAembjdqHWD|3bQ(^XKP-I>|fKssyo!^*}x-h&*`WR|LN-c>9fF2e^ol+hdF< zT@fxIGkf$k6z)~vR3O5h1H}?TV;2xsu#5Zi@3VkG5^Y&w@dG)MNB$}ZXov2UmOdS{ zY+3c3L1sk|$}%Sx(4xDOH~a9QlEOqRbDu5bWta!(#6V(Ptl5I_tPMlPhM=|1L`N?q zlXMo3#Rb(-O<)z8^m|}yE`$9YIt*T-cx~>+unsTD!az!**|wOIz;b;Ayp0DRwYj(X zu9pmErb02t-|Wb>8c78QqkeMW$=nQvE{W-#LC9k;nmGiyjPLpkMm^j#uh)n7d&;ACTSMgB( z%v+|k%X8#31EcPtNQ{mczbLRZebZ5-)*r5}NP01Q^o%a1Ctv*x2Byoto+v;Fc++LD z8=eR}C~o8%4<`TJ7Hvq?u`ya^13H0fwb1Busoic{dYjcNAbKp|lNB7|WVnd6Du=Nl zux$H?@6%7?FE`u>`qFP407JgeurOBNJ=8@?s3JZr;9gsMZJ^8Gs{6Zz+5$_Trek~p z^9acKn(HoI#_~l)%15MGY-L~yr$a;MVs?8*{X~Ml;*n7%89PL-TVy?95n58Bj2S)~ z2YyF|o~;<&Aj7JpCJg#9dAoCFQUW3Iu(!_ukBD=z!iIv>Fwe1?$GkL75P9h=suM?G zeg(bZps4JT{eb?_`yz7GP;##Y!*CAfO`m;?*99|DOt zPT5WJKXc2c-{Yn=CuKW`{*h>3;5BWboD4<+KC2taP#1s<+CYm*Oiqp*@lgT)n2JFG z3+16JFv=k4`2%d~5eziE!C$$IFIlqW8tO&xZ#&Tn>jfsmLE|dU3?qVZpk5&uz|xjn zhIx8^fWUCic~DuI-q4%PgEl%CwuY)r<$;@j4K3?RYC>X;Z99i7e5jRju4>DefCsKm z)#4>uAS5U)R1Vpv<%U05d>I?tiKWNCc=6oz>uw-EL?>N=qDgRHmQDrW?bzyYArrhsp2$@CR+fsUYXLPEkr z)Hc9EH6Z*f64`s~1&ejEynNfabLZ?Zh6@~R6G#N;Y@FNfCju}qDQId-j#Wu8Qgw4_eL{TvRX{9cL*oG% zGArjZWl-;tq=-rZ4fjk~L_`4GdqQRlBGI6wLJSMThc(sO|1c)hGet#;aEv;K>&`eN ziev0Jv}np(wrt4*u{(i^9UEplSeCcXuK?M?G~5!h0RiLc4lb6~a2YWup_d67FB#tm>BVvj<&Yt zWDJFqGxI{%dZhkr%iBCKiZHu>d@CVB(LK^5@^9Tz$MYqtxT&crrayq~G<6b6WHR$g z38mB3X|zQU6Yyc81Zy?3%!ZA-a1tE}`EZcmRX_uo>Pz+k=sz+eLw-7s=(clYrXxZD z)Arl9Q^@!PFK?t#QXQZOgo0`io`|0((Xo&@WQOuKJRIm;35`tbSz@1k9mmWvh%E>& zc=7zspwM#Id@KtA?-U{HaT2CF+EA*r5!qBx50j=HgF>kWWD*g$J?Q|@8e*XCbW)Nq zna6_@MlH`!5&0Y#FD1SQs;Z)3Ha4S&9pyJim*VjVJNmR(# zh1c*|@oyVizB5r?*pLbcD3whIMZw8sekhKRJ<^BWwvMXe)5GYY0-ffZhVVn}rq$ zN$PYzSZO>@-b%;9TLCW&D0cJUw1A*-?)-U0zugI-vmZg>M|_k}%XZ@D)Pr0EvhC{X z3iG6`ph(U(#-6|@Ox)@(hz?I3vvQcYc+=TQ0Uqz(eqbE{5~yr<0M8InJ$??;n-IUu z^1bY=v~C^PXeppFY7l}leVf4VlCd_JOrQ@mZ+`Q) zw2t49qr~fjAhu`-5-YI}fr|+iC$R|d@K8t4Dk>O8u3MN-bduhwDntw);V%h+2ECO(Ezawil>uurWKK+jYA(lEv(vIEBuJtRP;|Z5kj~AQ`5W zEF+IDKK?;N!y@t_A*RX{@{j$1lzGQ@WEC7a!lAI)w{IW0E_^QH9)qLlbJ+)1W6YSi zv+UWk6U8==P>f@8!PA3S$UJ?@Eh#B!vDua!ezXIp^q{#qji0k6Xlgm?1FVFTWo285 zU&{Xdy&E0)EwR`c{eQtHu&l9=$?Y;ATi~ykp9gXg4=NshS0690O4L0vYk&Qn-%Gde z1RwpMFMhl`V*nu~>#FvzL~ir97tr6C{nr;%5-w<}KOo_&Z!}dc8bfkqR6|eCak#?hw6WQGTnMZpx?DT!N0!Owq&HHLPW*G?gmClJT~&+5vB@dwgYDV{{E1@@*+nvLk)iYzi`%Ho=bSs z!uQA~Fe172aSDOZcrr0M`q950FJq%N=z^nvem;QN;+eje<$<_k;HnBQ^#Ial{LX1QlY+hPIRJFbv6o{htl^3Y{ARCJD|j(l{Zx z=hD-ATFaC4^VYmRssd`zym`kz-b?@7?7zOc__tTtUw`u7f3K>i2g)&@D{{jmC zV*&ihS3NSA<#A|OaN(hbrL#t5T`A}Sp!@TFV2bdXR4q#H!K zQ5po_ebsr^Z>`_^*ZcSDTF-hMgfE}Zz2}~@&))mod$LzAQE#E!vTofvYKhAi{d2L9Bwa6ePU6B@ z1*fp_E=MdHOu3ho1S5iFA zm~h!Qdp;svS$D(Z%7_xN)BQ&e59MFA;i5?$>g#>f@0Kz(C-N>&Nn>iP&(GiA|4;n- z|NWp`YOKAy8WLQ|F^HNbNK7VvH#mY++;lX z|9{2*T$umAQ2%o^{+}Ysx@KbYuMf$7W!)#vrjmBXn}$(NSNG)!#f0d+T25y*3ml%^ zxpUr9X6i*?P*B`d$#<=jN=Y9_8e#_q2Py4YW@A|`TJo#QbGEK7moMQ5Ya&DvefDyU zzJGS|{{8!M=H^L<-&IvfMnpt>ou5|<<}y&oHdoEd&DD3&oEvLtNmkEEQX6bDiflR> z>E=IT|9cv(j*dFvOQAEQi6#e}90nHK*&Bu=)-@kY7v5${WXJ==BUtiR@ zyIbDX*2)aLe2ii*(8{Yf1Yf?rz;{S|2UJ z$JkI-b}=O-jbYwSN>da3j-q~ui=M%MYHvid8fEL*^V5!=sG`MN0APFWk06G zDC{6*ZEZ~nu(!6pMwU6|%5Glck7s=8`IVNw4oIhl-Z*x1HcZS@i=*UQ2D?_FQ;%79 zcXx=eV~S}@O8Csqswjc(&pOYPOJ8*4SSe3+7iHQr1`iAk#g?ospUjEEG&M`&@L?Bd4^KF_<1Yd=?a!Kn9lXf=Q7jQ zG#yI7hM&vi$Ny1q*U!K51^W3t|LdSYRWl83 zY;4|t6+)B*>YP&W_gIO{`GoxK|xik!Y|_aZ)7Fz|NZyJPjBvIIZUWIJ2~}FkAG!(n1hGUn+mlm zt{aNonFHEIY zFnelckkY&c==1UA`4;Ub6T`qrMn6omGMpe0|U<{xn8_@p_XS8LxP6as#_!K(xaNR znW_}c0y~_NMo+s_zoogaI4}y@yw#O9Ha0dru&Psm)0D}snIA3qD2T^I&QfOcx@kYP zLZ@`mwM9K2&#?sA7qzB1cxma0nS<>KmU_HFUY z@C}fN$q66 zeC0|#qSbD7*?z;OO=dHL)$O^~Q7IaE{=NJ5vTJP>A% z9y~4W)NWRlQ}Sb##~Wg0S|04@W^GZ2oQy(tvTNU#8j{IA%ujS+8BedSF7%8v|K;s% zhDSc3nh~9#lpHd!wBGp*zM-kW(W*56_3JY@&nGq+&R*xVbqgFHTD*n>SQ;Ynge6`w zkUg00`&p6h-(Qy=5D-wY=*Z?xQpY;d@tQte&;8FG_JUeT^A~y*4;MsuhHJ&Xzgl#p zbUI9S3c4?k#O^WMvWGRU%x7<+?2B{pJ!Xw@kBe95)r-D;t!hu#yOdvb&v%$eNY|@)-__Nn8;(pG!ejE1 zMLNWIZnTL`*dfWXE8n1W|N1lK0v;;fk$e4IlQydbZ;n+FHcZtj;-X^w?Tprw?jCRC z9I3nKEw-EU?tEVKV|yK_o<&b~6~u1#>h2vLjyrp2qd{(@76QK}Oi<5yJl}qdf`3ZZ zF7ZGviO|*4V|(~3O~3XK7@Ys(=UY!-x3#lt+{#h-Tx@Oed{d%wj!Nj$r_tB|4Q99L zTTL(SBFXP>etPWMx#HqtCEu+baX1U82GnC8zA-(vSI@Cj>?wAqzViYJsSYb-zK^P? zMP5cEW@%=qsvsm2&<{Ag#3jZcF8MJOO!^6AE$HRSNXq?I5XF$@lx$Dh~SQ*^!2d z1Wp}c6xq?{0ux39*qLLf(c?bF2%It}8_<9x}>bxbT-wcu9$;S1PKu@lZ{8 zd#2H&`SG?VgIVFij)o@Y5_m4H(Ns zL;NB;gB5n(=8~~-T)Yr7GqX?XP*tD|K_j!XQma~acCTK(d`VToF8lmuS&OEorhJ8i zjSUyC_OBgq-rFnt^RNAJ_jjM9b9j}nhy1f;>sD@&`RU$L6cAQk)5gpAKpRKLmP3zD zzr45es3}&~=EjZ6@mD(QCnqOE#5{||A2B5-C?>wIu9nu)(lV`kd3O(+S~Qc0OaJ^X zW~V1dWttH))MF9OgZqIEnpwu&Rwi=V=GW=pD=8`QSX(lh8)>){bLE*opv?VU$6q4v z86z1bAkSLLaLV*LZC$s{PuQ}%&?KRzy1IHay|lD6)584MYs^;(<{bU`_nY3eCsPYw zVu8EUCI_oSLqy&4%O5hDxr`$_#7T$psRlD;5WuuNI}(LN-9N9uBy{%dS+#t-`1p1} z!Z(Iva|;V(OXu(*?dDSJXqf?xe3?YlNV%DaZ{6@C)>R!H9bHgVOPBlm`^`fo<>chD zeF4+RpVuDSX@25?GY?P7Qyu_;B)hN7?{8+;F4nlSi8dfL)oNab%IRixFn2|xZ-R%1 z$Cm&6Cv7BO*kSxKiLBF42|x|zHllcF7P-u6g!#^v(_2M5WZODA);8s>JEN83IMv-5 z@o(>?a^c^2%zJs!XAk9{7wPHgYU#Rnm*>U;SD$2jLPl)QFz^BFh>?%II)X1F-jom? zhmUI)yAkA8_wu6R%L|*Iv1_UkPEA23dtXs;RCzG0i+rot_iqgf7Fc?V;p&HrIsltTN`}qA`*@s)(T^|gNQ*|jdGki-#;2PE8-MzA9R>CzAQyg~z~jTtE-vpI z8srg~C$x(TPbjA(p~NSmcID+;<=Tz>0A8qC(nn`Dy&hc6sFcn}y$R!Zu@=g*(}2L}U|*S79H5q;&^iFiC7 z-Y|lcQj00J02Ah_+0+ly^uX6B%!r6muC9AZ!vpxBmSpqOQ<*p#fU zj9skGc5YL{+M@`^0{)UZZZhu(434v{K(;GILl7lQ5@1yB?v~v)1}XfOogeXLb4c;S zZ}08Qx9;;I&joNVCMLGDvXY2BL+z4%Sab5vKhGgiHv(kXFVC8U12>a~05O4%2ED{n z4sFq;#l<+X!Pj2?g|z$}-A5u`%3;nqJ*!iwyS$>ptRvf^y~s5ORVj+wsAd?uOg;gt z5f&c)44*Ji8>xNd%2Qc@PHF|-eAf7@R1`U z*cTG04;h6i2-LfO{dJ6$Rf@c|ukSM-AIZ^d)wJtx8fQXKC!<;brwX7dIce2H zI)`IZO>RE8aOsj+ZG;FgnKubY1W)v2AR_}q|Hw%Ejw4s%@P-NUu@BX9ED>xa=D}4l zl7X+#IAEu^?R%_;7C()ShPYHC^i7@}mlsD8QSQpOSIo095EK$JtO_$f0_YgZXHgG8 zWjFX?zhctcY9ijPN^4wzcr;f-J*k!08EPM`rm(lW!{#-)3cR^MhW4ZtTlxM z3b=VlJ`3$8=@SSV#Z$ikU!lE{4imT*$f3=rVTpr_aO9j5%x}S)CTWT1GoJqOYgQ(O z?|OTy7Zu*QbB82U6wo*njRf_aNq7EjAzge)y+6CvDvDnBScg?f6M919gn4vTO2DKR zqTbpbOBeCb{+5m(@*RaQ%K&AP9q{3;6?5|<75WHB3U(4e5@svKTAaaEqDz;>|9bQk>Ga^0fSL)%L&1Ej}Kkqv1sR6ov9V`>bP};q#qJzIhLA5i=TZK zCS(Y^F_O-0GnFT0_(juLq{OwI&V&?uy%A*M#wZ}8p$AIx6In8iZ~V{<&u?N1#sn>>8S#nLdS2Z5x~67 z`pf5w(h;XsOTMoa#4mFhydREwBq|pr?ki!Dvk?+1**YqVqNat z_TLt*i@vX|w3m36^x6P8H2WPq&)STDy_jiO&2@UAAkf>}o3sNh+MZpfo;szzeS0BU zvp@+u*n-TkYyW`*sR$?0Folx70OsQ$5?fZt+W> z^hCkw)2DfiKOQJr9!*@hYoMp6H&^m~Ri?*dCHZq-AKSw|fKVhZ(+@$MU%!6MFr8c= z)C5?vxh7@pk;=!1l-ShMuAiM7t~-y&Rq+YmoHy_bpug2A@VPB z3k}O3><6TDv6MMp zHi~d&Q7?ZMjdSA5ASj2*$5$kULTgwXk&H8B3g$`4ewu;X@BvEcli+xY^;k38>TH7y zD#$Z({0^S?s;sVNtLjs;nHiLxo9N(g;E!YiB{o&Ok|I551FlLsnCl`qmpkRY^s)H; zh9-qrrm;J;qLm6N6PAQvsE?7%vKx_ec6K&FgX)|h+z&z{6;-ZN;UkyGCK?7pLe3&A zM;m0svV<<{`J$4Pweshu8HL! z32d%LcuIdo@GBaO##C*wM5e!Q%#W+iP4#F$VUd16EBG1!1cgZ;@!avD+DK(|rgQih zBh&CxM(En~m$Gr6vDLe92XcWf?m4y_<18&iNwDH|+1_)S}vE&@C ztZrA7Ut{!o1pIe94>5|YjA!!g=g&dul)=6z+1O;nFAonJyW@d{>_?x1GU{u*IgIQ@ zn4w@AJ&*}zfM8p;Y^ndYG}AzB8*GiOD4J_l7xMV#L0 z50Qd8dl)}UqKVt+DuwwuLVpj*nIu)CUpYxj`$)=n=Z+mOQM62e{DRfi9#&RfwrGDP zL5d_Hi@IH>;!(j>>9`XSy*&Dr+kj!&s`O%_qla;v@dUYm3;%WopAP=}w{>~{fOOul zW!I_QpCu$D>OcxNq3AILwM|?%M$G_BdOSHsLqlWQQ(PqEFdj$1@{QhqY#bbvKs(li z3llgxj9&V^oN`>D{z!d{Gv7Wsr1gbvR~a;{-eviJUc5hicdwQ#Iilz)fN@U%=BzAC zwIB)`sLeD=JVXj8;Zgoio@80|h&Idt{#F;i)4%id=`Lz!Q4O=^q<4*9NgBiQ&CYjN z8JRGl)g0|EDqwOQ=TuBmxrVB*j0Wc1xpS{j&e^mI1xnML&z-wHSQDPO^XOG$tnGq* z!mDRUi3s9jlA01+azM-oeMw(BUb%PvM+VSsPfy=KYhi+PLMj{g__p0xb3&9wy3eap ze!sAJ^XBF8%-UDRb$|NU<5#v2KE+|8Jsw>QVf;ZK5KM-s8zr@pgVhN~x3oq{@CZBm zRYJ2UC8<2tkp_dwV>c`d$gbMk+xw%bS?T#sp^xh*bj~C+5^$|yhr{>Df~mpy>v49j zR|7n}0CD6WF^Pci+DQm?^1@)uEjx2xpdB#qD>_l5AQVFHgQGswvwiH+Q~Yge>Y@sc~->!Q4ys{T&PTwI)8AT`cj+_fmXf%m(M)|SUqLoQjW6WSMNT#Bs#3qUp_weahG(s`nL zJs}{_I%++6I!GT$Sc#*+u$m6AQK)p&)?MHWrwV5t0?4z2p=K1gaYmjZRrlh&_hce9 zHML@i$10D*xC+4+O0{K|wel!$O4&}I)+qI+Z9z}Oo#Z?iY}8od=}C6+7ljQT|Jh{T z7i8ZgvY7xwN@9M7B$09I{?)srj)T*fXKN(#wervjjodVpUwiZ+7OiP#5lZ=1OQ=V8 z@7`7A-K*~N;DG|7gw?B=8PL6G`k$=?hhKSm>>wD5vbS%C(Kff|SVbx&s~OmP>;9~J zr*j$P%)hAU|N1XkMCpsVs7v(CyymU4XfRMeNnGI3LK57E!W|oPta_-lX@7!b>I&6g z>~8NE5(qdsb+6vzn>KACHAqHm^;Oy2;lqcAJ-@F+;xL8|uAp)^ftS;>ZbS1ylHMM( zZ{NNpjcyEP;%5AF6E$5lN`@MstBswV0x1QZwzcuV`Xu8ZSc;cNPvXrFR@;oMhE;o{ zd~kSj&)1i5lUc3{mOl142@UvV+GtqyRc27UOaM?8?B8;R_$2qpI|v|iN4Sh0m(K!? zg?cW-S6f-ARphe2dwO_eB-q7vjbM3jRfJNovA1sm1IKGSC}q)^Tlab2)vH&Rfie{d z88YMY`QFibI~oAfxIOF|#@0)ykiPpk&v~vb#g?U`p-e#Rwl$%BT$C;UF>U=l0feRxah zjEs!jSLa%wv)$`;%_D+|Rnc5PukS31drP3U2Wh?ZRWeN)Sc<-s(=XUxprWDz>WSgf zu!NW~+EtL#tKnOoj>hvML?5rRS{W2m9&rAGf`TziYj_}_!bqyXlj!_NfC#A=&aZZ_ zI?sIlDi@U^= zXeLig@S(s}@vgFk@ix6y!Nw2ij?fB6>tlR$G{u>s`zA;cZIqWW*f$Q8Rp;2_9*ZKa z7S@4m!lV24@0H&TAQviO$=KMknyl^(Yl zU&R?t*Gab_fiZNXz{8r4MCOBt?18emN-!kSIO}A`k$8|`%e~&rQneP~V=3qCA(l1( zS2w2VoOgD0CA1C@Nwc!Hd(7gZlc2D0=oXuyLFbUiAQ1HzZ`>;|urE0nEIc*ZnjVF{ zk~?2TXy+=9k|gMJ?>jow3D&}y%ewyNHc!XWFD-&ckE9I|m)XkcgH))kYS6{d8H5e4 z-~_o0JX0p{mTG7TSPH*gYfq1lo$z!^q4Nw+j~O@wqzi*Dx%*I?ep|n>(~+v61t~iL zrI@~^k49ayfsWI*XatINboAjPEG!973|z1YySHsSfP_T~94K4h^uRJsbkcL+@{KF~ z4hq=~`?Xawj za z{H2n6-n==B{+YLMa|dV|h{Ac`Y4p=A0S^|s3QbSqm-`j!sa;_ z6dtms^c(vomC|)DmR^rL3_)HIA4hBtUcC42-GgXV@BV^QviRFq$tLsu>r+aPw(9ilZkr{4xiaSlc*M0`-ZU0tA3jV*G$fo^#>q@>nW2 zmDd$HNGFh;{QJko5+Uc;EBuEg)@}ODrak0q=s^k21eKqqk^{sme*83H&XdQQy``W{4~1NQFNAkR2<`71ue7dbqIRm)GOcDteBu6Q5C?}fO3%xw2Jmh z8k|%MIj){-ZVhi_RgRuIb&7YN5Kad%j=;&G1b4##D4;F7PZ;-CKK7z5&2yLt0hBRM z7j{IZn%e4hC1?E&C_S$P){G+EDR61*oH~|_N5&1Q0dMa30D*6S8?H ziwc#Okg%}X;&kswadVcLvJc}w3sXH-PD~yZuoLjOE#5#VjLbV2@7l+q5a&k{QEuvc ziI!CD7L>Bc-Z5aykHd9Q)V0ac9cOLKm|1_e**7=s{I9k|XJuv8kOkz^Ch+8Y4!U%o zRQ3E`HWB1s94d7s%G1QjKe$NedWqScr;83y!Y8~nqZHvHWq*FL)^klSpT7I%Z|fC+ z!;`ME)#ep!jeh+5hAsMiQW=B_m_s`jvqI2pe9bj5EUuL1u0m(qrc~`7g=8FCm$S^%#+b;a&)(%d{P;Nw^l}72h7VW7jVI#o%d(MnI+9ZmZD#(sI99m8?>b#EeWm z_TK}CS?snnI}oUy0Pbd_*~!&4bo89*R&;L!Y_K(}>||iY`H4T}Q&d&mXL04qeN<+n z^zx7yE`I(Ts`J!>B#6jqmTD3b5D41R7(%V7Uk-(i+wDK+&{$KICi>5!1_h~+hDj=X z;Sr-SU+F1Fq#h~86`dmCmW@y|n-SgIZo}sZTokp_|L3pGUcLFT5iTc6TL|q7?MJAz zrLV6KlHW0(zE~gwQ3~^Q1$0day*Ikm;LusUZhrmI-IdacN%2P$_7~Np^Tq?=j+r1% zJ+ATRmXv}~5D>fe>@ie1+U0TG>)NrF`XT7`N_&{sZQrwpf}jfu@!_?Z2^l=g>oRK$ z3b$-p`)c^3K}E3KAJGZ3HuK;KO3g06TXfMw#oxpHmZeK{fgXMWU0q$)W=I*K9^2G$ z9JxDFY|euhFZ(nW?tLSu>Bad?$bwaS8C$H7?%N===OxH@|s!@RYhB>Rwq( z^5%AloKgov%b?mSq@oz`|7uC#>=QtO*%*uG6in~?GCRxDG%!a4nGe*#@skbjpMc?) zZc(${sjH=H7xgN5dXQK;fXowHEciNYF9Ei*I@CUmMz%2Q5VZm%@_NA<+MU0F3QXOS31^QP#o!7K zJ9WVTLZW4GOu7~smf9>yq+D#N|4DH8!>-Eez6sQA>QcRC2TL^0!HTp;-?5`bf0a-S z-QDX}7SjDlXP&KUkmB;!q1sgFPbx{Awqd-P)Z*geqwx9gC7lEbOUGw!oIV&6sKv#w zDY&npypn1Z7`c5lF=j0(6d|+jFMx7ta|;#Y`gWrF z6LWvh-q83(_rK;E`mcwTNvo!2@cZfA%*>I#+)JM5D>4^d5G!EmuBXda7mc>0Mz$w^ zU=rq@A8V;iD@U0Xah+Fia>_kyV793S#e|4xUiLWEnadkrBQ+`D&{vN)evNKzRcXj- zHhvv8_g!3E+_$kPrbF}7q@8YF&X{tZ&48R0)BOB=wva6F5N~M+0*S6UOvF_Q z3>se&JTkW-kL#LRI_ftb5#$dOv@KV-NzpC4zek%$Q$%+Q3JTJx1~NqZtS>aI^Y-04 z3DV{2Xre8Ua$I|NAFK*}Gb>6Op(jQ-0sW)pVy+xVcJxomm*`^Zpa0VQVQL%-gCgt@ z(y2Z81Vs7O2@PNrOH)DheW#t$wA{X2lqi3ZGmGkT5stbXl^Mw8hOP1ZnIw#IQ)}bs zKXpB15;0ZD#OZ3Rc^Uv_`E?pPA}%5`mG!Z4#bP_Z&du>AxuUHiG?B!Woo_?IhmCr0 zAA-E)6`A-TzpdK>^5LG^rY*aU`MGG}6u-pl?tkda&ppHo7$^%$YGm@gvqYj&GHbDA zHg`dt05K!tbI0l2##=c{XV%&>jp%D)?MCXoR_>O^(p1_!-DwDt1e`gHx9T+6w7nU&dZ?VD zK_m}02mWGZoLLwOj~;jZzGu_V^ix>1HaYv#n)7;nsodoOExZ;ff~fgMU*J2y)G#9nx%!cg$^hIme6-%#rcLh~MF}zsIi+(hKXvyp z+1I0%FC4i?-Hvt@>&%0v*kgvq!(t!nW3C)#0F@{BMB-qY%gCO`84Ye{_x(NX;lqa~ zRMMX74C~@l@O984C-IlkmrMIWHU|Wpcr8kt(ky~bL*72yF#Dvf_=j;Z)Sd7c`{-CyTQ6S2{B&fg z6QZKspRI?%7xVHS<-T=##6jiujkt+m5!&JQtTA`m7-FyUqNb+`WCLBI6Kl^+>)8r_ z9qI)@da<3hqrH9g{7eXd77F22RJh7n7n?gczB&t5QMS{`%R~})9}uGD!_Y>z_{iz5)&vjVT3bj)uNg3GgSQS z|2qHcZzN$zwHVr+PfZ59v9J573%EJLr7cgF(l)&^k^d#t@--EecQ+=fa#wiF8-hnJ zQ|Jn>+1xxmKi`bjZG!7?@OHEOm#RBkYfgSxi>UtNgY5~bVPtp}J-ZoKLlEX42ldpd z#@7zx?R7u>DZH@$2b_pziu&MC_MS#u^Y^J!4R5NYhIPTZP@0XzwH6Dzs-fdH zhzI+bfI}W%v4~$*)zHB0dNgR$0Mf|#lsUei^nCaCa-<-}645Z2HpbPhZxKA6^N`Un z|8|J`-;y}OWvkC(*xemZ!hIN@FRXb|N4Tb@2HK}3B~Z+5F%4xGuC&t{ucfZDfN2C< z!qsHK$Nj-PP;TBlw5__Mqhng=pK*Jy;O8-HHBDfvEJu3}W-q(l_3|18V52NZ1hn?? zQZrE*rt&|?rbk65ob-tk?{&Li^sJ}$4skzo0f{ErWnDAlc`vGKh|eQk9-ppRa@u;( z1EIreVuC{SL&@Zoz)|O(>;Ab()1UfM#ea5_mAqhgDQ4B3vZh-JLqh;;6Qb7?7?qH| z(H*wtpmHGV#6aE+8h!b+WgzSI7{L&b2j#PZsqYdHCSNt6Xn0P~abH(u@ZR8}^oF>V zr-#eR%MI;?_?|ExybY$L-R;Bpjy-z5_Wv%?EuY+4l1pH&;$cJ+=Us?>1^T_AD;Gcz+GOvDqt86Yh|t0+I-{k}-m z)`||Ol*f?rD7W?NC%hfML69OW@L>re-lI(c(mDwb_G^V9eb+$73L5UAOv4;+)E_GC z0;ex8@d$>d4BTDsqQeS2oPwSnmuHdekh{vG(QhFmGE7Js>oUUo@_Tr?P{zYhav05_mJEbMx}{uSp+_+H{sd$gZ;SG*>|p_?n@6 zC3SdgY2rPOZ->GL$bKlRFCM}utS--iMBz(Q3Kre;GbO*iP>4Ea2+{_z^gd)3BX*DxgdG_Xu(PwHSk+`xGBZ@$wmV5dFhyd?0tg5fvcCd| z-HhGe#mJaEJUlE_LMvH?h&>@B)ZQpCU&x#b;(O$EcJP_I!SwmaRrYJ&oC)|zu zMS5ufV?SF_EV!x=rV8`iQgf$mg9i3J9wYTfkjRBn0Ik=W1%Ugq&D&m*)quM;euxo{ zjy4;uC(6OjvVWmlt1jnipsqD838Ifog@iv69~;4?XeObR3kH1%01`*Ze5ax5)kx7~ zV5Jq)+DI`gQzk>&PWm>a*PX(Y05pm5{w3U>B%jEa<>rtqP-Zd8P*nO?9+}DDqD(p#% z^{hM-1NhYKr1 zj!82ucy-s?KD`CmiZnDGkwJ3|W`#92;XbO})d zOB0_ehfPFVZ3`MPcw@LilNv%cX{q{$w(vMPsoEBOfkND_AC47(^({Abs}u2_4onp7 zbOV7n`*x?yFgWnauPQ`PzO{Z6F=J5g=ehvWu&_%s=oba2s`u^hUpeyt{a<(Q1{ZJc z1pfZ*x8K@MM|Hq}MqDbbuO#c?x@d%Np*o1O)KG75m--rm`9T(zayzzLH#^`0G|u08 z9UG_Eb)Jc6$PGRm#c}-IPNkN&U-(b_<8tfPEh1vV-~XiVjqJPp)te&T=Ph>gr3LW* z#wPi-{mXfO0%S#?>+;caK(RwslSeN`3`n8Ap?mwE^;saxkuEj#A;=yzq7b*I>D-a7 z;05Ot1D-V=V1<~XH2*knTaTc`VpDr<^GhpAn(VOcYc-s0BjO8uyw-4k1YeH}ukwlH zIKWXQ*+LImhZ5kvZLNkIVsV3K6z3s$7I6%kV2j(md$&QR_0IxxUF_c}y93IkyGVh> zgRqYo)_f*-I!U6_T?u5j$mEollj8xD0tEVq)%wG=I9~L~ks~>=enx1hEA1wrt!G%X z%ufNAxb)uL`^u=6$;Td8=p&Ldto1CYBPwu;NUpY|>69{rSg2*UMgZZ)!+)Ss_!v_} z*T`r_F|0~N9|S8BKP3W|tjacRC`UyZV^r>oz1}|to|sMt z4Yrb|Z?ID1H_5M`i@W}g@n#}6m>am|;`&<$y*H@b4MP0M={7Y$`OV?YSC*QHj&r#4wFX{-{*^4@YqW zDW)x=u+Raz&o;$fJ5NkJ#Y=J5<&2ZjA%T1lQ#WAZ(rW7`%qHJVh?~1 zBHjz>CMH3f3+O684DN4B1qV6&&)uzi%s|3Fhi0ZwQ$yPv`H2HF8?>T}XCMmfZx#>~ zB)p+1Oh(z&r6H?lK-OnhA1WZ&-EIO_E(L_Dd06$GQgmpL>Xfi`yn5wSFbTY4VPt@h zjQp4He(v&(nIGJpC!9KPgvH8&?snGE=F|TvA1@BSaiLppz7;5lyE;-#Onqp__U$ilKgitj+#5O#S+wt^?___3qYswM;Dj#7Sk*0JAax0wT3rI@+U_ROW&!%v zYb6Clu<_@&_h>aRjfEOY2@2Zh!%IY)=Ie2@)8m_x@>DyHwccJKvMi|^NV&0C0HU|p z0L9b?0WolQijQB^>Z~F22H;IdX&$Wuy@w!~0^+DTpPUih(;<>QM_|W^UA##t&`wYJXffyY&0`S*`r;V{*}d z#|yc7EXueC+DFW~M$R8#B5u2G_;V@Kj?#8_9sl|F${8}o2udX= zVFCjc$fH5k+!-I*+Efub*T7*NmLMe`hP*6{Qvn=F!!x7gn}az#A~UveHf_>^R~bau zimS69q(qVgLjP!&gVgLT%f!EcH@H;lcn>IPtt(dAM68a$Wi4yutm6;FRwLz0E zx-vK)yU-S@v=q56m`6%J{_|o#I+6t2=OR;^(Ci<8d@awfyVIt!5cI?d9A-_4sl^F^ zu@l`z>R5{gJRb?w#QghEh)fnHyG*+ao%w16jQR)ql3`^az6u=O6U2N?!+1r6qXP<9 zQ}115=>(X++j!JXfaARwS z421ZDeJ>(CmL=dqOeI@W44P!tJAI}4M{yTp#XkQCCXpsvT$R{+qb!E6u>TtP&8Ai$Trdb`hI9DZ2-KO zfSf#YJ$Gh5-NJbcDrT>poEUt3iHTEj?~Wb*$upH(x9nyAVOiP9BK47^3^16cpI+ah z6dXEw_;Bl-{i&Y|opfpFztb(y<;<|Q6jL$01QO_hP@|{KM0LjWnnZqAR%4uP001_; z42g)@1k5>%qD<-V7a%s;%!_HGkPb$nxpE~H+~~dCr1u?eA$XlNM_b@CZvhdX%yg8) z2I444aFUfu+lf(zj0r~RHAislLz2a5>{?ib)5OrEvr~7_OC^)vyq%tVstcMiYNp_+ z(2kW`XfWuS4GhK7&>)cE&Uo#Df`T!S?H|$?9CBcZG)Clm*#|pE`oDr*f#(e&w1xru zY8q9XyD%{#S1Sf@#Vlfoj=c{G7#xb%&;vBMP8G#s^XS-T4lwrFt~iBa$bqAH@m&3c z>>@03$6;mj>B}!DNJJ?nm??l=lbHDEYK|0m8X3+<3c44EDicR~_0XS(4DTFttQmRi zh%qZ7oWUNjXYAbh^S{PIRCzr>qJru3A9Mu~QjmHmyzSGe4vM=8ln<0e$vS!9!0Xw4*%{6=hQ#ge-v}c-<{dk4?DzEa zB#8__Kakiffw}gx?}Y7bAn)fy&1;Ws{~h4T^H|L%5CyGeX*<;S;V%`;wYOAbK;@j6 z=qb_mSIE$pD7`V<1zzLD_isaD3tfJ0=%+Y*QjK{17M{DJXO_OJh-76n*9v#srI3_h z0*1?6Tvs$f1j89PTL&n>)*LQieGUag9+@ojRWTZ_ZpWD-jHc!D-)UbT^(p7De7OEN z=V`;l1tu7KeMW}SF4Y6JO^#i0P5m&w2?TpIjz90*7*smUt4O^ar?VFqTp$Po5#4&eL0wW%N@i&GULA#LIMe&v8z9jIPcMlm-0H5i_*aBOn9g2TL`0XPjKsaDy6ho$9ck&_A>qo>kA+BbSW)=h>6ZJ;VG z=D5h%VA63E>arFQ5fSQ&%465b5GLFuymQ(v{O3CgFfba20_SB9359oecJ86$$4823 z+yN9{@)YWR=r|DxA1L%59FU{6T;zYxY_2KFTv^G{v`{qYM z`z;hX9)iD$&__C&kZq_dIQ{F429TPs&ZxE(yBGATs6GySm?Jk>Q$-Fnm`AMu#cTnG z%h?^Cg9fBsx3?sNDW%@)HF9qpY|yOnD_WkPFiV*1?n~c~-z`03PX@sU2OYG-+a5j_ z@1P{-R3$~oe+U=mueSd4FMg$uaXo=e9(=aK<(TNRowyt0{a}C{VW~-$S+uleEjTM#RKtqu(u-!WXCMDt}}1I8@5l8Esa=#{ORRlB%TNxmLxkHrb&ZvZ+WOfxvP zCPMYN5?~lNS7hZ%DJ`sboXR96Z^d$+@znL3y{+h7_jlWcJw}$IY*r@2_$V&frc|!; zu!O1zK=NAAkiei!9>;aJR5Hz})NtFEjG{t==1Wq?-oYOu^~eN~HJtswIYw5`h9jRmjtRdQRUX+NZ%)~$P3FVENqvTNdwjVC&* zJ7rG)7-Y8gTpl^vz-WcGd$L1$VYsgJMqZLRJ*KZNXIgZ+ce~~wg%UE+Yb-Am=mN?z zYcu5Zw%POBeumNX^#lK2dlM>w@`bmv!_Wt&Tud>=kDlyV`6K33n3R30K(wopNenIb zxh95?tJY{5uExr^qxfG2wv< zXKP01rAlxFOdpu%{DzoMR!KhxI-t{iDi5ica!%YiIGDzcNcffI-hT9-i?TZkkx zT!VXZ_X&l{G)pv(`@M4Zu&P|2qb>`;iBm)y!jps-nUb=nm6nofL{k_x=*m$zeY@8; zi)>fZ$RrB&f;$I>-XKUyuC~Lz4lE}r-+)>}JpDJm;VzB+4wglFfS_t9$D=I}s_2C5 z;^GaMcR?5=%8*pcQ4pk%BlegL2eyQcAz4~r5Vw$gM!W2ddaAB7hvViwY zAR=T((3ubQ=D$D8T#=$N)o= z6z5w-;eiKS?LdD5|8pgU3fY@_b#YDEPvvC;MrwJk+WS-?&lnR8tHK+KG*1gx0Kr`* z^S(Gg%2;0Nz|4Vcoe?V$(C*ozza5CZ8vg#2xl0Ae4HK7NZTaba{}NKC`{9F)16Ye* z;s;44Uo}bcs|_-zGKy~_E^r`hCNOpr-jMa-HBA3W2c0^LFeOu27GDC)O#vh5NJ?4p zv+Q}Wf_bA_U1@A1T-EMAyrAxR$NxCcHF?7^6>Z4z>K5lOMy4mh%yLs{klS9M;AWS- zz5*LQ>7Xc*=5W0H0KYYFFjNbgmpcjLJ)hirswR4#fpLa8Fbc;Y5pww1>@JUrUXa`?5f*IDcWS5XX_!y6oCkIjM2ah9Fzi}KSW=X2 z_pk4c@WCbKY1j_Zs$t{j#F1xBj9g~e;tj45>I11GO-Dg zMIet(J0?`33&#K2yp!rYD>kwLSsX+-n~y!Q#bdOm{*PjCvT>+;sv47LpZNQ~BuuHo zP#URUpxXA-Zh(J4dc!AQA)hNFo=Rj`9IZLUV`5;yV3cC7UI|G^nA~wdOwJ0M>d3qy znCWztnZZ!2_qYUOcA2{mY}cu*hk*4zCc5DAeTrLfAQQk$XaM`KWXQIqJ2g1K7}4NK z5{CRh#@CS7qyaAELEaE%0xYkMAMidyRg=h*=VZS4Dm-t?#Y`Z}oTWP;;>(4lCb$BYdk`&AV#!tSZ zeF?BUf}+Kfgwz$(dxx3K0|4hoZ+6A#MBOB_i>A@C+#h*FbUKxdzqLl z7Z=)b+!HWl>ZSLSApZ2`v!6fX_h0|Dt{@$~67cr%OD!X~6N(6X=x{)eI>niMpPv#f>wX1TJ}B zGit{_b{|{)T?<}xvk3{QVZG{blnt$^AY0*X4|PnA#6dG=M+PLf<$y$kS5znDqYA<( z3&S`K;WCIst5)g^Gy|}(`a||ehAjsVssTW(hyilbYOrABUF64JWyQrEBdoZtWAuk( zaPOlY7>*9QN}#YP2M9}cFBL+l7OCRDNLf&2CSfpC?GMfpi=j@jTr2FztQ8HVOL^6l zeMI7CbNV#C9ZUt`5F36|#zU?SiG8mlCg{10g~`OJ0Cd;5mnXLEdrSrL5b+fumB0pc z6seq)Qo^Sobq2Ax!KNCzJpBjv+P4{*(gX2uU7!;_55z5nc8iF{ur^2WXx13kgvA5Q z#1yzea3KvTK2$|R2HIX@qA~}%(JLsHeh4DcB_R`;A^9!XSGf@jFjU+3b)2h1_u6&U z^GoZezE~1c99?Kwj6EhkPDu~FMxT`E`F(AqSrpvnXnR&7mL?W)_8xHou$K=(m;82S zkgLTsXBNoKMrfV&1X6Lf6JTq@VXge(p(1kz#1{^GrZLVmotQ@<=KCYn8j!mhLnoOV z`z$cUgi96b-IFoP{bNjtOgM39<9-IMD^gMlct6Zk=_hcTqYi+tenlm=lFeg3s(|56 z;f5*@?wUAAnF$@qPaTBQ%JOEqD*jYAj}3Tu774rv8ld0EbT|+`2F%}04 zT1VRlEgioX*X#Mru#c>*Ci8+{BWA>X8+{|3K5w?4>{LZG1cHo%9%c^(h+G{&&_sy4 z585bV{Vv_a|F*0w9(TH!%qKUajq$^-n9SLb3B|ONGnfc!Bo^ALg52C;{BR4ojQ}?e zkPjm305$uun(cx$Vf9sMa=Qe*jO>$yBPG?Rp44;0_6C*fP&IE$5eSS%05+n?l4)g- zp%`g8_G7{c=n6@+;j~jJ#xjU{Pp+=fYcJ)kW^A+8(lx^o<~mJVC9%L%VD6j});;xJ z$+LnpGgZ`f@BxZRG|q-<5_;$`m-+E{WWIPDADopR#(xac5X#AfDff^B26FPdUB=iF z2Uid#q~SD~HzP))kHjzy2O$}j`TFI{H8KFlbUF^jd89aHoZN5&pRQE07{oNRusq}S zZBdtKF|kT+v}i(En8WDBDEPAYRMJ^h&rLzZ<#n9wTq(B(|A+%O2h0fO<_y$Ce^_^s z%k9CE6P95a!x5;hSyV%xL15nN>&`>IknSs*jdWP z^UMlhMME&aN}L5mv_SzMX(mIY1W=tF1_6vY@&?_b)vjb1jLZ$;y1mC#X1L|+C2S^! zi_4N6MNiG(t;Zc6q$XpoN}J5jl5hPn%*4N0T@Ke8h=3(0)*$$6P5RmfQxK-Z&^^d) zM@>}@z;{F@1RWki&0XbzyR7O!^SwaP3L0&9%z>M747;R=bj-b*ChIMDeH?c=P;sOw zrLsCerqqeN0Z)r6Lv2rwHY#}xxq8Wk2GA*I2WKRvW2u5lMn(p8u13K~bHu^e z10x++zbjt6$ZgBuD1Tf=u#yk^_1aA2cYVYB;DEgka~uJ({3$Y#DNqj8i;@6zi0>MX zse#L<6!P7#I^bh?!l|nX2f`u~kwnG}=?w!M$=!kPBgIMp@}96_Vb0!k~f6m$JQN z75l+JUn7(kn1mlbP9Dr60@VWo&ICov ztB;J<;rQ{+r5$zENhOUYGIRsFfmsBA8CvE2&EbN!;>fTsdYuqxE(=|bxLANcai|_% zc5>_7A@K(vL1y6cHgxZ#TrN)UKzjLiwsK^yATMu(zjzB4OV0N*^bWp{xZkBKA%F&-$I%qtP!|{5SK_2J^ymU zEf#JhCk5y^Ahu)(M`hXuPvHm|vL^QL!tLyY1#+3G=C^1r`iH3yBpvjcJE7%|x6FzDOM9>%aLxu=nsH^guQP=bM`hQ=%={zrZJyVFeb#{DtBfIicf zlnPvF5D#EJ0&q(PoY}Ps`7!>9 z+?nXhET)B?kgKUK32H4_7ZCa3>$yK@5snfikC-YzHMggAL+~c|ClV0^^q~rshdwTa zm}tay5S)RbGwG14&kZruBa33(h{V@FI%?XKfi{jP;21UKCJ9x^J|8!+5wVsBJyPkW zqn#tWnvoUBoE%}Hnhs8q%alO8la`ez`Ar$~Nq7S?FpK?;g?M2k^v&ZEBr+s{qgeXd z**naW`_MKKs|0!`8MK2+Y#mTJ3F0N!xv?3Y6m%}M2Tx>{T13HTkHGxDf1_MAx$FrN z-)!XfZn8`@G1H;R61MX-XIgnH9=5(=c*s0@2CF3jiSb_!W^N}0<}LfW~-_0lzXKh+RK~d(=%#PLwY%v*9B(#R@;DmW5<59VOiqa@nWW~z7=`;8>Ko6mpCpq667}FBlAv6vXPEL^lL}ED%@t1XTPQK5)0WoIc25^z&%FKHrbRO=9+H#OZk0ea5g5 z4AuX}}rY4-*_4a@zoep8BL7z-#1* z6IzA2CzO)n&@&|BXkaS6=BvwuB5W}a83bPw?JLQvpHrNVGAersxS z`2cC+FgqFpSzr{UXA3PYu@pjH`6ev>rMSylq8z{c;I0p31NFvceLw^-5BIO$COsqi0rENc zHNe7tC?*)SB$pN;$-Ki>64NBgumX4yR;w1=wnYqUaH~)d3ny{+5tadWKq+G4aWwBP zI(%|J8V)}ZvBbshCRM%bv%q~6c%LWBuEMVKNd!}nH^ZPgekGYt#H#-v_TKxg=f000 z4wV*Ks7Q&XqD^TJDn(P9wiIcSR47uKTFNLTqtc?SA=0Lzy{WWNQA)!7cyV6e<9pu6 zaUaM12b|Xr*Ku8FeBSTZcs?KNiJ}MQvTU3ke|{ulfZ?hnyjN!6VD~$$I1&m6Of!Z2 zP<-W^Ppbo2akv9T)5DpFl4O<4-mkX?_mYDZOvX5%*TaB8L8IS?tgWP^gcOYAapSkY zi(hXnWKcqU?DOyf{)HzZJz=|&@Tw#yHuSX_&_k2dDHO4(#6E%hx8X}E&cO`4P$$5O zB(n+yo*LlmYMg>u_$pTML1dV*d%t1c|_!#-aCzd>jfOD%~=j+ zQ|zXC^m$k)21-g7uru6bGD?f5HLv0pNaseq_3e7OjZ?$k9(h|P{|nl{yYY^6{sTeYH{Y0=d~r{%Fn;& z3%Bk6MB{(||Nr^#j~X3Zm;VW_{6ByA=YL$-topy$z<>Y6vxxuEpa1@Iirj7gUq6KA ze{{(I>nrxE{P!dN??>|SzX$byKaT%a)W4nvqC@}NdjEbR{~aLzejfiFApd?I|6NJ{ zejfiFApd?I6#pF{|9&9<9U%XH9{(L6|9&3-9U%XH9{(L6|F7rq|Kk8rDnxE`2S!+U zoa1=5Y6_xUGCRiQ&|hGTe8018zydwtwdhNto_W>-R|f0=7`;QWMdfvgJfXEs3X#!A zf}1n!k>cY}`VHTO1hx?L0(cqW3&G8FLMSh=8VvCRObGa>VPcZ-Pg=w{`JssC0lW|g z1pC z782A4Tp-NALSWebPF*6HH$n3Oxz!;}|LqhpO_7;4B-RmmNV*V9om}{1x0BN)2^3aU zD+!%|7*(*%*9rUwev3rYl*pG~ffRWoF_96*jfC3+aUe;Agu=s*!@pR41g0&BhzX52 z4uTvYYy4(TP7+{65IJZ&h?I!rYfeo$Kl#0D^tas#3Z;Dni32!8xIcn|5)6->T~IUq zcMb+f_!F8Abcg{E6i8|~;c4W1A%-X#z`N&8aAZ0qlrpFa`{V3x$-L+amn?JP;GCnFIE%Cu!m zHeL)6vU=$G$!fvJ!7Qw6!dbRhRFyF5sS+<02#HO&CFD34MAY$+(027QXAKMuW65G7st&B<=ta&kBc!65mHQO@gZkb-cha>8&xF`_-{5=X z;JJ;p2)_b&tB1T5GTwtk+k!0JFa^yg2@J9Tsq7oSGLDIfiJ-KE`!_5)MsK+mRPfBJY9svUUE(L7^ngzQ^?ryt^mG;{kz^ zYi@4lo4V#Qc&8iriui<|Dmn>OL`AT6 zRvi#!>2v`^B2ED0DAk@Q1B@fo1B8_a2?kAqfk%O+lFY&kLNfux!($|Y@IVbq{TY^% zg}I z#t#O5S>)4)V@y2fWzBzR8>?wL2(F2Zo-y?#*P2Q&C2tb zghmuF#I*UP6t8%|4P)!b5d;xCdk4al@G=h}Q0)Hey-40B+0MY{n`~}_NVc%BK#qEJ zx~|a27eH#~t5$OfNJ>h+TF3;7dq6^EADLwW`xhQ&*PJ-sW-Fv!P#{?RiDIev?hqmW zs=W?ywj^A+oQnQ^bK+o|+Td42_!tk_PlQZIw%sGMX)Ln&dj4dS^^`izxX(z=vxsG> zArARg^Hb&ba5&yHD+Cx1-T}X6G)X&#YlN&K5;1{*O{Cc++$$m1U7Z55_=x(2WE!ig zdd-dNLHQy$?**^QA8S4sG(uL~^TN;={(gk;J-zuJJTEa(nsE9C%HYefB}~X%H{wSo za+T+L(qw@^!!?NovYV5p+#ELyS>!~F?cWS(4^HI6Bx4xHo)=<^il!K|lSKSpK(-A) z{RBy2cNijQ3$e&Hi=x_^Sd%1r8^Wg)24~z=By8}XkXh4Tm1{O>eoG`~J|f|Nc-F7q^bsNrpG$GR;`kFH8zdpJki>LKG|PQGj}fXS z2A#iG&z!lCt4Ha!RmdgNYh#hfXVap#lUbQpJ4_3l%#As-I!cW*y*ffNy-w}up!$5d zSc#Rfdt2D@uc7@Fl+ou-sc^3t$QheGs}1To>C(t{ndGlBt06`U#4a28icqurT5Dk@ zcmbtZ-ud7D%OE2bpXyqW^BdK=}N+uPR@m2WRjLmGN| zRj_m&9&g^faYZU0mji;>9ZHeBY#p8}70D^ji`m-Qp$d6`d;|lHqrSm)1AzCDHz}->Ecy?{#OUJnn%} zFLJ(%y18Lue1`}{>r@|)8pi}O#qqP){TFWEzI~*#$%ImEBg`X=KYh9vdL&wC8PwF(-{Jb$gAd$8 zx#F_QMSRUF?7wPM8ABMbWBjVq5q3rs3`464Ny5FZf?cEwmKnONci8TWV5r-o+YAg0 z+=ia5AhQEZNbbt?Lw2lY$%9Fv5)$NhZUrS4D{RW_;Nal>`U!hn0jLihJ4T2FWU*t9gAs6Uu+Svfd5$I9Nukm zVj8Sw089DNGVhB}li7k197hyAQSmRF%6(MX{RDc8^>E*`;+8I-Y}SE>J9#`p3+I6{ z=>0Db)l0uFEYu?6k*blzoQ%Y%>Uf1oXF@NyjvW1L>u{D3mzW-uOxR%0YHFyl<$}Y* zUt;OI{r=_GSL#KNhui%+JTj7`;SYWOyi-u7ElSKZwa>=cnI5*~#a&mIE!eH&YJX;GJtD2qN_9VHp0pyi_9y&WeX$0-| zD&%rjBWwJw@l$-1K%8rixA&hsc`_Vr%3Z`kM>UQXpZ~4!ZF(@L@7RA6|QH_)Z)Z9${XK(YQPG@uJN}I)?tRjNpbpJEP7SXM|80aScX1*T7$~8 z;78iWj9~&4)OUafEtTsYBRY9#9V@!h*BO~$l#=y=pO zYKqoW--U4UVgFh>y6D)=2=KNjSL9l^dCvt1s>eP5?AX8mhhr6GfmzN4&l?~+|psWtS4Qm5KFt&F2q(54zIi##Tc*curlZlBI z;uo{RA9LZKR!r1OBdqxu%pR^d8N=yY((d1XkNyOS;jARy!56}M*`nU4yp%GWQ&O#N zKcNjv@N|FJLfOS|e`)!cLxydBB60<+vA(*aUq8yq5<}d{9O@L$D}CUvmmvx1!rZl< zl?J5^&C`2@_-1bWB`JkS=qZ-4xF2p&~>|1o=e zUO^edhO+f1JuOER1qErb#W(h~`y$>PYQUJuLTx@(u;@(e>>+5moaM%F13t$MQqVCm zJ|0X&^&&^~VYzr@lD87BMm5MeM##Hd_|@9kxe;piI%)np$QbS_^~y|ZKGuK=jA^_x zhYRNNl-WfDiHc%oLBWq82KE571vYqh-3`fdXA%3rgo{owEXlVEhDYpegRIJWQ3!OQ zW~W9K+wSA9rA3GL69^`q2fMxl+{B)Ts6oe84W3p(h|8h0i|d5hS-D z6yrh7`Z)=RMOP4sllLPi>jOTeEWd+K1W)5=VYM!|{E8bPDfjKs)SCTR39B^8!s0NV zpMGKXu&txE7nzQjE6DBOwhHtyWf`gx-&LhK6H0K$~3f3Dy zPQDs?lw1^(u!*h0QQmGQy>;sfC<30NR4Vu!kfIa9%&XQYDjj={?PlX9E(PC~1U8|V z`dnD>=a48Q8MKxHhjW6sJsr7%3x{*E7tiqX@T4}OPY=ZTMibwy3||KwZQ!%!{4s2!r@gNvz_nJ#H(}U)RO6IpHA1RUH_T!nYmA9ppU)9n~#p1CAYF zpn7xuEx7f3H5pXJjqOeKg~2RK?{>~j^aa^9o8 zDDpk4`bh`G8|2{X<@J)3>PdHX1w=7#fs&e9%jE!SBFT<3xcBC!I@h89kG0sybd6Pe zEiVs`&@+fGExkr{e+KiDwp? zocfC0GB~GUC&@mXQ{m+U)zjq1zLF9Ef2{gg@Qn?c;OfBN6bPS?6vJ5(gA;e*3@Wg4%_~F9`j!3`78RhAr3G5hM z=+QLo5xB8)@zycMrKk4u7-VOSJ>LEKkd_vCpKd4)>%GXU$GdlKxw8VVjCT*liXK|# zlyG2RrMo;*3aG=QY8dg74>5IcWJzN;+BBBiQLnlzUIXh9@4e(rfp(aR45Rb#Lsce5 zaI&{9Qi)=Gs8ZDu$-K3hVyNajE^h;xBZgOmm||Ppav48h*;b2*zvO?EoTKZX;B?I< zPXlt63uvT{_s#m_-38F!&>t{=;;;tAO~Do}WQ2CuZrHG4e6X^zV;Ffbk!hSU9)Y2u z^hATHX}>>Wa|qhy;CS*S2u0vae@)IX-*t9T^>*Vh=KpzO0chxJY;nh9pFjCAo1^SGwS+Cqu*>ORzvkm7VKi_sGiHz=ODvmZyMB&LB{0+-s-pW4!Fx#KPMvC4xT*C}1&%A=*_$M)HB% z5Mz~-WJO27QH(`MKYz0)>-4U?R7`e_GH=F}tf!`>mV^}C%XDXk9E452)cK z*$04vXwps0%u-QP!;Hi=g9L0Pz8LnWA^4DX+&J_YMYeg}QP@3|wrmSP#d~Qky~Y+g z#SIMN1_M9PDxfUkHraQg-6~N#2;KE@`D{ZWIBMw1dmdyW`GY9t0_-pK+)(}~1|gXK zgVQ8$^T8LTr8Igm6xpfui;1QtCXQ>q)xLa5?wf!eBoqlr60;C^Swn}OI*NO5`!t=6 zz5QjpL{400Dlo5Xz22yYZ4WIu6?D}~aBaAEIWePN<=ni-l>*(J40>zs>y7U|e~v)s z@g8mNmAJULyqgI*mc_2h!)e|WNdM7@nA`%z@IanHjUBlM15JF5lDjfu6GSSHZP2E> zyb`VRet7C?hM~s4oXGxGUArC!E7itI5k&BIP+b-`L<;`QZZ&W3Jp@)ivuVB^h5hY@ zBInM7Z%(D)_t`2XWt7_lE0g*ZLCYf=PxKY*J?K|$jN5Sq^{kWJx5Zgo5+>|!sivk< z+{c9@YO8MMVG+$yvZ;?>*Ap^9x?~7Z+dpMD3})SNm;wJ=(rFVW)TB5C!j`JntD=^+ z%h}nv%4p3KW;#*37Aeu#Oe{ z(`Okk$ou=3do`9j)@W)eJ;9%++Cvjf8L4>g8v{UkI5R>z#BuiO^Qs>B`SW%n%miuf zctf)7B&>l{lTc4~M<9Wg6)*fHL5&S=WBFj06hW64iq1ZC=eMs|=66w*q_SwNc=qNE zGp_XaYvcAo5qfqRu}d{B`!14U1@k7@oDq{dk5aIkdw$0NMjMBE!RBo1NV8f&6C9L-rl=_5>lUX?N?Ld z78PYAaFp2xtiJW-NwL#6ZS0R!_|n4vED3}yKbrxE!*ewFQd@eTp4mkzaG-q$vatzS z{%*pR@WV$pVA{~A`+?wK3TTIM`EeAifm>_l%NKE{O~JwLBo>vFq@u19FyW6xns3f?8ZNlj}j0^r}jcxkgj?4EuIQq(q)W-mRw6z$_PXqwd;H8t;? zadTr5@j&NLHPQm4^Mm6o%gIccW1~YucMQseZ_?s8WJ%-?rQ$ zFO>QlFi{PD(;0MkSmoRb3R`f$=H}*J2oGO_(6XyYeZ7N&NxRvyeR&f4^bjE*BbJG8-@KU_sb?wknWuwzIXEngGOD6Ca0YnG7c6!f&)dSOs01W*`!4AyEzL_UPvJ zr0=A2=1RQIN=F37xij~c5Zx1c~BcpD(aATU?!7n=zmO}A6&MyFUWDw7vjkyt$YEG#yUxXXO}^a=a* z5;lhGF*P=6haLC&a9FuZ$+T5ftw4QJug9scc=NLV6fI|CQ$s@ox4rlVhUkW-CI*x> zou$r?u#BTB}Y8VOD@9|rbBs4^l$Nc@qwUWoA0*i`vqilXKn-Duzj01BI5UL(OR2pc*o&!f2nwp}& ztg}z-P zX=v-vd8&Z{u_EQcMdgm&`7m)(d0~XzMTx?kfI|(i8yJBpz!*YrIFhvwXQGx z#Fx!NJwR9iTqPTYr+|r}9ZCUXNvguFH-rTEn_~()?J;7|YB1WmE7zJp{ylyr-TOCe z*H0d960dYXxtIq-6d|ib{kc095bkda{%eohdrI>8IBd}th$D9_1PY2&KxwiCT%xc0 zV)_z>uWfxW`x51=6{s}tU&|#mk8$ic4wT8j=XilQR(of|*hWEz&Y;-}_cAdtxdRTK zP0?RY$T<6vfw`3Bt|D9G(wgrewA}#yP)vk6{en|;5&o%se||z_fQS;q&_KTkq|9j4S-u!v6 zG7`sXs|N|^UlKlvxW|qKf0u8OCA=sKP`s`eMvPTvb6G%Br71b!fhevao?qEx`>ro& zE1$+J;#TpXux_oAq&!m-jRVIX7ZkMn2-{jyi|KFJAN_dTQ&*Sq`Sa)CPk1Ium3#*8 zh{&*NBnMWZZ*QRer@Dmqn@MC_Og)46Sp`KThSQKb442vM*S&eRtAQZ#%Q(g)qNXIE z3NngM^yJfmEqDPa$U|ye;BNKqW;3WE_(dC)x*axsbk$ zv}!m`XK^>D0;cw9w?Bb>kK^YhUZdKN`F}u1RK?Gcz6GT|7 z4c6Hrh_ZPz!5=tt350!cM+@wK?@%=FIsfZV=YZ?wLr)qq8rihr=hP2q1otBfK6>tR z|0bZOQavuzc1vGMlbeV=3OKR|%996g&!Ui6ug6|$4zoP%;mm6$7`fl+@m*bRu_-B! zZ8CE>*=q0xC>ufh;9#3Fs=Rq~6RMefK3(*_@4zT5q7jBq~}G?eiFMll{v8gc>*jOv4BL>3<5ZE=4oL;Gb|i=QXv_FfHt zrk)#t&p>`Kfb$o?tXt4qm-n3b*cF$6QVH-nHSkJS;p_47L8#GQ;N-)1c~{2xrh)CJ zodfD+5VJC;rw2reO&;-~{wAy=IPVZ>$B3zL990?t%y0KvJK>K7iy!8pF#ui^Gxdpl zj6A3jKxlkeD){mg2}Nb+e;>)e)wU24%Ql8gnCFL36oEmY)t^-5I5ou~} zo|(nGk%L3%c!6o2kLMa)2h5ZeD=5YD;N^V*fP;>NmyK9{5yw=72I)PgtgSC1LV*=8 zMGX=&E07+D_C=uR5MGg)v6LJ>EW~aBC~58j?vyQ3KWds>gwIjV-)4;a1T`kb8{>ED zd$z8nfaF6L7gBgFZl)C!sC|tJrg18y?)895kc?BJxFh8_g^nx3Al>ji%G?ZF{Nl#E z+eht;?>039Q7d*_8G>y;S@QPVEsDwJS?mbI%;}zzhDM605A3) z(Mc`dedscd`p!}*{93fdq}(AKWwH%vTt?= z@L6tNo-*oLoIaOPe;k^kypBVMTxkUVYC+0+)s%z(sa5ZrJw9|6vnEbOgod){Lk&v%j5d@uRI_BUoVD4TJ3 zsDK>RL- zxT0SU+ahl^_VS4M_!H*xt)ue??}H|f=G(8iMp8?s6k~Fm3PyBKRz4Azm>a(4u49Qh zX%!(5MRwyxy}gv%Bt8&>v*)eZ2k^jux^f;KaW@=S&pX1s>_7c0;6+(lo zTF_qMC`O;7cfo=~tE^&T!bXmK9M{J;k2T4zFi3W5KxgOFPrLnZ$+IN4HXYKBTuhoK zqe?};nS$RWCpMIWf!N9lzKawfFE=atTYNeNB}_0tmJ~>4oHQN39}g=;D-<2Q5!r)q z*-{}ga|Is2B6vBXZ2$~)2OMJb#+WxXQDWVX5bnYKUB(xX1->b_+3Thz?U_YVtdew! zuuWztKG0Z`0yio?o*LK?vxGj`np?iz zGBY#F_$jgqO=5^(Vr_-Qp~KCg3Xi$#mo^{x`N-$zk`(yE=(`ITl}<^*I;7HbvF6Bk zB^@?Q)3{lcPFAdl`qiC_fbm7JskmdKPuEIx{H>#ulbfHSd)K}psrjI}9W7$h4JmqH zU5?$4LGtj3LQsceLnB)?MVAa`63|P%bZ|KPEa%e(jrU2&YE-2Ku^skL$B%X5zmyC zzRn{MGX*obNP7#4lWari5$r<4K}NmDLg|iH^>}i=Nd%G?R=MoPwe_YyHlZR%7EB@{01?&82Xw0*8I3DQ16?-WoUmw3LHo~d`% z#EG5z=fXbV#@pkl-VDZwCu&bwgkt034bcFYh_~Qp(sTF=8w~+E6h6-T4ja^2{sZ*5 z8_FP*;U#UJhxy|Uc)_J^Pt^^9{{o2Deo`=y3jJe(#IXn=MyPk!!dQ(Kn+G|h7)-gI zQ{z=re?KPz-jqpf*_Pj^S;)r2VYT1(jO38eI#l9U?3AKtP&A0M<8a33#g#15pYZK=C?nZC}uh1 zjHYHX6p1n>n*t#@)TD+|pL(^t`5ZzEqE%Njuw4MF_b_e^4UO(OzhhE>M9Aux`s|BB z;5n)V)A7R|jmw)?DZ$gOwz`SP35_l^0JlZ%0|^$wM4;QpuEJ@d$#b)^k8KzSdCo7(%z1F1+fUZQ?rz}K%miAc9w-qovyif6d)d0j63Kt zp&AP8n6mqO00VT+1r=N$x$Wz*MjWwMaPiQ`FCa6CxBPI1AuT>2FgZ?$HOU69T6l)d z){1x!yjYF--H*^$K<>t1Zf*|s7X!4~8z0%?U>HZhA~0$GiCO%X^q^etb_`<)8lG!{ zSW+2}Pt>k1>cl4vP0cDO>8{6sLb9bw%uYHMyg+X}b(c+(C5;v}x`Qhoi#sT?qaDYmIYw{c z@L_WDDh`npK%w0c81zE9Zj0OnyzKE~S}qHbCCVoj-UIK}IM~qGGpI4~LV_>UZhKI? zkP#gKAXP}I2<(F+QuD^l;RHm}9Wc9iWHP(3hp*dV9@wGgs3!&*3J7wa%!uUY<)y+m ze+JcQE@nhkO*3GlEF$2>z0BquUMgjbDj=H(91!)*RDXdp`SiUN051i4{Yq;*s!0x{)UF21{{f(EWe1z8Y1-paFPed11>LwiqNCC1!yfu z5-PqP+#3{D??CjX1iQ`eLq2c|K(|(R;Hm+(>nfMO&`Z1YJ)B>J((Zoz$8H1wVT2AV z_Q|Wm(rC*D1_sdK8)a_~cGz-?42egXwbjHQFiGn0PF$|Kop-%HVEEuPS^H)8NZ4xC6slW(cW@yL?b$u|DT2-G8@6MOuwnMmwrNK8u>L~|*iE1=f)TuFb{~rPKO&7wZ!wT%2(irxQqJ+?0Z&T0 z6+aOE5{Cp${54rFs=5KPdb#9%(&E_=a-##m>?VZv1CgdeXb6Al;z8?eQXL7K#10zh ztThr~jov_o>9MTKI`Ji3%+WG>iv0Ez4?nnR*&jPbIOnR8J~YzS?gC5`;q`x{DP7o4 zvq)hMiQj}oqJx=TBgO#f_j!6so;Z1O7V$}T^g)D`z!BpOY|hd0@ZrPgBm>8lw17u2 ztzUnc#BmPK0_GJyQpSd3``YpzI(lqh2x^YF-Wf+W7m-(tw4;LP&s;hWl*XfPpAHsf zFvtYD>~CIZlgQAbYT00TEFN@;OxtI=FG@>HKy>^xUKP9Ozpev-};^Hdv{Vv{{ zhmDHnmZ(E7VN^}ql|Z~Ny6j)HY5WrCq&V=7I9LwEYVjg0_yX3c;g?CT8Y06&14`5+ zxYPCbo(5L+M|X3t5YQM=?3hiF&K821d_FZke*R%1i7FcO-_g^7$7urxj#*?UCivL$ zv=gBiFg{yM1LbHoe{4q!pzndyRs&STI*Lq-O$`FjhIPJ z;IE)to}pG2!yKA|m%ETRD28Mx3&hSC_g+iZSfj1EhFeSIfwPi!#`2vtW`y!H>HnO1?8Y6Iy9z==Lsj9^8L^b`yk{sx z6_bTiYe&KyB?VsZOSmt>@c2<#_O<9VNd*P3V|xRng$iOMD;$Rb^%rp(5m*6VxhkoJGg9rT7$d%QS759Q(AVKb|I2Z}a#(Fz(EJTy3 zMZzoKI|Ihf600XfYAvEn-B9D5sKwhQT?8~ytDunJiBvm}q|+L_2da%9$l%O_q3$G# zNGcm`Kf5GC#y@#mf8O>L`u1AG*+tZ0)^LzHg+Kta65XW;q1R@Y(UuX=#Q8Z3#UJfq z>P4LR>lBwSK#uPm#!F2_WeqL-jP;mlt{9tya|7Km~SY%2i?6kgseeh#`* z_%fgoxKj`Tk+T(kXR|4?<&l4Qi7d(dtoy9PoqS-o(4K=8lSQdT1r>9_uuc6D)K-ho zAA6kK8J^x)PraJzTHcR(P<4PSwM4}L#Agrz;Lxo+9I-r7QtN>}u#_F~C%}Gdbz@&d zH+~8$WFfBZ?$3c9v~6gicoTE(3IjXO@v6?}Dh+EBDYoy@-X>KeIA2=h3I&v=W;O6e zRymhbry@bY(R>zXP^T`JPPICf z5Eu}OYh`4L3hJd&f;it{f_a4dzI;x`4}h9m_`+P(HIN%4#o;|fA35W#ug?r8f&qOQ2DfaS0*O1V;(`yd>I6@n)EB}9-@MR15haa1gaX!jZ8Fa*1W`ihpdbh zM+T3%K0gH!<4g_iQmx@x*)s`Hm=+ou%Bd*k7GnX{1We87NYkRoxP47(qATxihb^8` z0t|?bn>_O%?gHeiw4^4O(z!D#NwV2!8?C6nBI0%fQH;^VPhcWMT5|&=B_Y&;Uv8p$ z3(byA?7dhMMyp6_RGd$bKV2m+3U_qKiWqI828w%I_@b<0w3?4=a)+Wq5_kkW+|}Om zlW|ry4jRIt+6b)>B2#^%@*T*%R@m{rQ!B6SsmcKq1K_ceK|;gnRSd;Hy`eE!f^Nl0hBwdG^H~ zV-Yo+*9+=zMfKyrJ>h3UHmq6Ap4nigZK42APV7BlYG(H5ARaposAn}96J=Hj=RwCM zWsGqFkhp)U1fRVcC06y804?j}bB}TIoY1_jv=U4bpc*sn`QJpQ`}C{R-5M7 z3W@oPK^R~HZUJIVibY63_29Ft`g)n!D&1A=v|HYJAJ%Z&5C)DIcVqCrvv!ag29Z7l=NI8ne*IiH8rZgDv?#JAqMoRE)a-oq&rq(o02P^q^ZtFzLDHqw z+puT__f#Ce>dwq1ng$gu?M@+@H2zJR?XWMv9wPqWl{mZN@=Rcw+6q5{@&dtrfy>L| zp1&%oG9dj>Yb0)9*XO@*DDGrCPkE`<_Fi?Z$KKY%CzOv;L~m3q_xn-kbj8xc!y|le z#D;GQ;yHK#$}>_Yp#2g1@IY4}7kxriDe5)BuFlYxe_D9&TONnrz$mC>tHMM9)dJDC z$z;6f=`qWXK=l@K+kD4;P7n{P#3nkkro#;?6eTjdcD_KttA}DXtM=>rB0YZ|sl=8Rv4dpAt03ae z(fwg&<_;Veyy*&pq1)uWXOw0nI4R(j*)ic`Q&}%%-4q|}9+U6)0K8z`cEogVgFjKf zo9IW-T!NAn18w++*LI)MehWV~RU}gJY@c0N!ExqpIy59{RmXIy4-aEj`%8p}&SgC1 zc7}=@8pf@A9?nBqVo~m6H01jbI94ovnC zYj3ayCd_fhzz?c_E6#OX3e~90Nhd>_wVEWXanO86ZsYdb1__=R1Tw63GZBfbj3_v$ zXDY`;5!UZnREOW4hHpctiL8Z2wL5SvY6%7ZQpItzsQ*;H?Snld51Cg=9 zJDe}h%ydp_oBh}=QMEk--BxhN)>uhQRt$z>h30cEsCDToOY3_}h|;8tJfQi;2Qs3R zj~-;`Qfa`ue{g{@z0k1QuAev}_|=UsdIDX-zNBijJB&R-^c<%K%C7lF2-1Vc6xmi7 zU9>gW@4@Offo1HOPCeb-qo-rEZ?rv&O<5IQQm`#Qg)6enUFNJD`wlBO$GK6RfJlCh zn#mQ_a#km-$drgLya=??20a;3&JJ{5|5Cwfo*(dsya{W3uCg?j)OTx9@LR7nRu*A) z5DzG#IlpF$oJoPGH6)VmeHDH8Px(HJ0`tUw+;~@Bv8e-ep@>ke!lmWWBL0>P{Xljw z9Xi(3`tT)8z^DdxbPaO{-1Iv8M3DhXTsah!EKvl-gL|NrwizvzNvUUgN7`Db2uhW5 zU{DIX99(XULJ{T2MbKh&@r@z>bgk7mNb}=mKp9s2{AS)d8`Gqjh8NJ5pljLnqpnOE zuu$vm0uubmVce)ONnGdS?;qK?iR5)I;6=`Y4bPf3el4%bpFblUwdK_O*RPFwoc0Z| zOnsU%Kn=C-=}+y+%L56Ev}RVhorT;d(T_pcLRL)BekrMkPYWI|CSd~%8fibZ&HwtfEzZKi^(kl+So_*h zh@_HkZscV;ARSwx%yXUXN1pvMl%HllE}=2k-1~K9smS}di(=+H4Lbkm#6ZC50uAuR zj4^1YjVmeq1j%`p*#$jk-XkGit3D^fX6x#0N;?7P$*N@VVYXq_LN;D9VwYH%eq zhsM>TqS(f9YY-K;qr2k=&V6{W*|R@0PZsqyp;Qh8Z<+#{!Lj2};t*X6)X;h3LjIRc z0XC^MqAU~bJ$RFtyT*lX+EmHq4J-LPYi7K7_fhqd7+Pqsd1C@@TXSf1Zc(JI(b|-{ zHYM#9Y!#W&H}bF5+ofhUtlxFSVn&v5O+dr9uj6puCS9Knbwa8aO+gIZY8+!d(fQ(l z@@_aLK0-l0g9@2qiHb%$BzEDri((D9uqs$t^ZRg|6>QmIUifJ8ls;RqPE-T8my~N> z{bhFdsRFC<+HD25wr+_G3|s~E7+i4?fcoA+>zDu&w$=N2^k=O|<3=r@nWI2+Ax(_J zf9;>zDpA_;#Oj-pmpzrO_=vg9Vd1 ztLDd=R>E&X`&j>dmQ2<*^d#V)%mzatwVG&P6zhHX@S#fLX06}0AN}jPOc(f*yQ78z+8LVn@x15P{udGO^G( zbD95DDB<3r0hYiF^?fx;v?o86G@&B}^XG=4JA}?kP&{8TzSfB9nhA4KHXPVPknO?f zwvC&c0^J$NXjL*&H!i9AwD-@McY(^ihoj&-UJnp1bVy4N-f&|4??}sUqK!v6*W|_= z%L>$XAM!M)VxD5odjt+7C(aA!zkOsUF}|f)A}tW(n^(iQtHd^#cX5u#+#_2)>?irD*8C(VetWK*URHmz- z@Aa+QSf64;8>de_!=Sexw{wTB{*hgf(xnF0+T|x@fPWD{Dq4vaG;Vx)^+|}H#E@nV%OdrChTGbS z<~dZVid$@{56a8Q%|JqvLL5xOVJgB0KUuW^HGSlX#6RzdOW))R2vq9 zE7F9Y-vGJ}vn7GkfO9BeRT|9(BS%{;hc@>9^On4Qej4mANaV)By@D&0;BsoYud~VT zrhPQhF0P{ariWW(9T|twSK-%tXC8JdTi}_*amHYlQ-N9C>nvba$+y_&o*IQ7<7&q? z>BIDC3;R{-Q_vd_V9&WsH3LtQxNie&m7ow{(q4-(z7K^=3o&wH5m&Kf{Em-M06+`D z4B6K@N=M$}ztvx~su4Sxf5ujpvc1=||{!__#emCvtbnIy&Y5m2MO0Gms!c1YK4 z0hFz|!+67CeA>>vEo`t}nC&~ksL^o25`JCKUzDrsfrRDZ8r_UJA+2o?$l+51^{c7og^#i>F_^wg!NE z5Dw@G-!+1Mw*KF@Ks^tkrpd(PI|tyUbI;<0>4z2;PcX3p>Aqn9Tm~z`p}Mq%z$>?~ za34GYe?%y!29M}J4@$4oL~)cB#<}sE*rO(JF0kW_oL_4`Jc9yY2J)q=@tIIiVj_W{+0GmrRw#{7iZ!;Mr3X}otC$_|P+EcZKK&zr z&n35z%X{)SPN}w1Z%fj1->4X_M#zBo!spwLGOE=)fIj%m?&mhkGo1@Ra5m}XWYimD z+D|PmT4>3s#?4Bem{{sF-J%fs^|~!R3foM2Zba zl>?0hN9380T}&ACQ`dUT3`KdK^$TjyaJ0~Zb2Or2R3sufh}X#Ngg&u7)dFrF7}U&q z6O@y`$LTNag97YY#a}%v2AcP*$7yLmuWN?^u3|`nCS@C*jVM4=1xdgBX4j9%TT8JdwBUpU6u=ZhI!+b^#H%qUGesJuC0W7!z z>Ljn(Uq8=fUAzl|g8+yy7yCuz#Kdjo(@$8b`f=y2x3XzvM}Xt3`Qk$wA~#Np`Wp;b zk!fvl`7fDY(8~%|UF}Pf)J^cOWV_lnraitlQ(@HoOSz_o@;NyrpF1AtaZ{CR0DIZO zf+(A#wu5)it(f%__-45C7Bo4Z;?z|FFpZ^%I++;j2KFLsh3`E)ln_U(1cG}7GDJcV zP@DShEE@eVtzxNhRzx&K`w1{rGDL%XIBYgtRu}P6i8K=W>1!kP?TG-Xp&_VxSf2uu zK>`LUjk8^WA%LVDXbykZuW`a1$TTXy|FDM!m`u9p(P^VBgMopKbd4} zw*T+q^LyMz*+d8^LIWJ>tmEDgI@-tL_MO+^=0-7p!Nu=~f~-FJM~j&sPpdvL|J@~% zzUG7%|NM{8mQ<=l&2Tqa85vEX>%BM-{@gi$Cc7g{O--$#gf-jU)nWvBh#R8XA)Meq z9r|X`f++cMBs(o&Vh}5^)E7WO)Q1a~nv~N*Fv=Xxc`xi(nNjzQQO zu$N7+^cE+W|M;lo$iS7$Cz9)F*iRz zw`c5n?W(l%5rr^zj4d70u8Aai zZA@D8d+&yCGJ7&^&Ufan00<(iXUSTf>8kQj_Ng%hWU4n0#R?F-KT66 zwkVETHCy5cUn&8#tq6c|7r;#hu@=-~!%(E<1hga3r0+!PKJ2HLX#1%e0fzFMM~Qy@ zTnX8JoMf&HEV!_H+2Ak=j>KcE`n?MlZ>;PxOsNDt`lu(!P(1D**K_PZD z;!GMXu3iE-1Bgk5z>V7J-n-H53Q?VOKiJ|G(5aE)ka!uOr6rgf5iJ2UF`lzXukS6f zMgc%b7WGE2i8tyG)IIN&1)(Qq5I+*Vv7Qt64KBoLz;yZX#6brLr0xAr2rJ1b&&k5r zvE1Bm*Ust;y4j@vw8gbSoY>{r*Gz$A7Iy9#OB&hred}rUv&V28!!LX*@8-2k?Udx$ z)ZFufapJYx6su4C6RyE_N2=5V_H^)%U*~SP~1smES# zhPpO-M+N%@Xa@-fVsanK957fXvn|fUZUaGByH8(zJuSLlc@|ZjOI>S+l$I`q;I|*~ zw+;RhEcQb)y{fVAh;@6LCaoIlx3M}hFR;<7K1F|JIF{}r0q5EuL;^ZIZec1dp-O#l z#Sm)#c*BE7iLIp1!K2#=vzT-#Gpl?Sy3ji7qHJ;lqj zl0w3E_XU&*?pI(I!NIiC>@ygKlE*A@CSM-3g_PbOB4)8KBOz9aC>uxgoauJqmcx?@ zjqk1S+k0WI)8s>(yO-&j&L~iS#N<*XPWFlKV(#QOyltcD6ew5Ouv9uHZs2X8Kwu_< z5tOPnK=B@;12|Hjhb5Z2H#u5TpStzl+L;Nhbi=E^I8hniCE6jD0e? z>4wBBW`c;u5uaC0)t%QF&Hc2JX|rn3Fv+)y!|L`rFkTl0&Mf*2y3e0I5KDxPlM|$$ zVfkw^*KjJP2a_eiqfb5JC%Z*+(QmYLbg)^?kTG14o?=jG((3l1V^a(WB+x0T#}`wjez z8{&H^Ct^HzjQZ722mHCG?jM+(t_D-5b@kmSHRK#5b*Uh806WtaP8Y)eK%mUbnf#jk zU!d}QyKml@24+W8X^g@K3AMRsVsmY4*HTQ-H?L(ES96aJl*Y;a0{XjrzKgPZ_l`aE z_akO`$tnhl``U>CIO`@XDsim1gtvd-gI0$y9=T0u)x?}Tgn{T#lF@}fkP4Hz;MKkV zMg%4*!NO|lSteX`WboFx=-+G-r`7Ekfb!rW2&G$C|Ei!@($~M;;;YAOjL&M(?fMd~ z7V>)w+twwVtXXI-BaU7oyuq$P`UBUAojlWphv#ombfLSe%L?NmQ%4UbD~KFtlnsoHQ-685h8`JIzVfy) zU+y+_bR3>3*45|MJO;iSIzifaJpopNgCd9PZfUd0(;x={)5qK4q7pRufcCi9?}s!x z4ru3SpuSv#SgGI|D zU!x0gA>rMJRw@AnQJZR@k<)HUsxZmTT&0TG^I+zMbpMvVNl&BsM2_^a60| zhj!cxkF~r&aR4kUFq;cM2n&d9`a(Q4c8Qzy#RC2Mq zRRNsMz~(WG?#~1Uca%zMQ^({~EKnh;HT?8!@hFvOG90mhB~YFRG1m?TGO)J^APx8ZV!MLq}2SGxcBl z2tNaHfrm<+^Y+&0NeT6{`$bfK!SSv98ojqF*;^V^n);j9N^oUm#8nWrI~q?dCj%S_#8U{wLu!zpb#%3UbOZnlT;DW%(DY_6 zU=0x?;^b*uO~l4jARaVco0hG;KS&R3Izs<~*M5MCQTu_>Fp#d$6|1Npx;!l{{m`)1 zt3+^@qQ{p<G!lDlr8bfjw`7v+CJxzr|3rKod%|_PN<8o0QWHwfs2;V0u6xXWZmz z6imG5$E1y5L41k6K7Y%)HEY@@WKzpuZ&336@u|ox5}9~gw{NEef)g+wEHd}@k*Ne3 z{0tN8=e{af$=X&f!hrx-LXl{?5^mn4Pg2AeZ{B-a8ytn7+~?1qqxl9_KEqYX1em|` za}p-E{b@N>$4XIq?(pt|c>-GGj*=sP>bnL%oLef8_&AmTfjMB-C-nerD^beffc3yo z;iGe#B_t%kgL8uYhw$w2K<5V&)lza1z~CVIT>?o|&^IfimE~QiC_u*Zg>ZDO4nJQ8eWhj{&G!PL| zs8mGeB!mp7iHc+@GDYTDrjV&ZhR7^qWz3j)=DA;6)A{_q-{<-Bxvt;!+1GVWJ8kd% ze!r%**1hg^FRYRnrChf*-s9^OB_8@oavYJuG9s6jLs3zy$UDDO& zc0{rS-r%X^5NXc=MRTrSO=fb@4H&4Q0rQ zdwAaOV@-x+X%U7||H}rLJHq`0`)9Q%%P~JzNOTS9Jsc#WXpkl&mAhLjT@?sGH_QS7 zg;L!(Z=^o|W+u(D#bgets66XnG~jqBzlv7-?VKu8aX|qMP?KEvRtpQEz(Gr9+jJ?k z9bR6ck6ZUG0h z;$t?`M0#iUb{}Ut8VcObClwj>RWC0eCn}x@0#T=fvrS3$zOo;aEKZfX$e8B4rV*JM z7_Mggr&8FFR=X}N;!NBYG_9PE8kg*A;Na3)v1phgU6fddBQ9U*h?F?{-WxV(e4DSo znlj1531tkm74U*Z5bX_?*vC4_sTsAMMhvk?j*3}*pT>UW2b;48qqQ6)R z1q%MB56YZj_sy<-`6p@eH18_M?NdJxL(LQ-|A;qH*%4>|~`@-ME=@4}|*0^A15L-4TG z^1xm5Nu{E>)@KxGo=v?jX;O!;ftmhP;8|Ah`r5nM*^ge>^m4(#A?b%h>P6P8DMSMi zSi;}ES*U0-L9wW$)XfFgVcDL}#DSZyvm{1YQd#4D4LKEJHe^;+WB(gr*GDx?k~11P zr0j)6qkQ3pSR;r*tL{7RMXHd2Bg^e@*BQ-2SvlTA#U!*ts{*GT@&6KK784#O&QNJz z5qgsA>)GuF;u*o#DYo#;FCnFWD^ovSe*4;6OCJ!?rU8RUW$a<8>w}M`#;k1ZrR@?7 zQIap=7Bl><{HT0@$C>$3L>}S(%oRxDrBM@7w%Pz-{5{UFh!?$v-mjJ#T zuqHTh`V}&0oWz-d5W=w)8Zdow*Xo8OV~ds}RtbpE2K*ep$bhIkV25?xWP-;sNRtLf z=~f;XO<~5u$KsJKP)R5PCuD2&C@MO<#jI3J(%?*!EZj?XWb1eCEQ`qMcmW;!)}*jhznzZxjttF5Am>~oTZ-(zq>6KAsP>(7>5hz)b* zRX$d(`yQPUhbKOv&NkyrfBbGqz;ZHlH@6$1;_nu z2>p*R!OpZ{;8SWBl{wwLyO88v&K&75V|+Z5ylz6j{3maai(#+cD)0BN2(9PvDPDpZ z?0&v&((4n>t0q#Axle3C=DD-@aB%Q2SHv{e_1qA3LDoX$Y}RUo-gTtU*bTY#(Q~ubMWB_ z9!*RH>_6H?cwMl<`KQyzx|zfu`TnrYL`){(Ny)Bhbp+50oifb_7a<2hoe1#7KKHsx z1@WpLG*-Hl2P?P5aMM%_BK5aX_GP|(&RPRv{?#UNLd z(LA}9a`t+%3pT5+x4k8S^3XndXk&07s@DSYW8tBIpVUEyw7M zuK?NBN@ct8p&WMgx#3<&{=GswiYw=EgyV#lQiEH0};k9w^zk6g(k|By?nXixmx&2w^u_x(at#6h94yye~L-4VJ z6>ZPX9|CVqaXJ%ju*;%19DVc=enlj2TUtps+EcepJE#&4V%S1XDET`f7NzlZWtlh% zAFPzE8ZJz`4#P1Ul-Om#VkR=9(v5c6+NE3viPkA^(%^gryeHiiLO=`?ITJJte?(B_>#1 zKGTyLC~dcLY2ToBbMwSxDpmnW6rPtnehugM(&eD*j~}^|=?se% zztviE{p!_QESS_MP;>(DQCIS^A%xGU^+rSk4w7$VDpuys9BU-DiC*AWN0 zC~;x@cYtbe2S*P*K6mTs(+vP8NLv7t#Q0khdJ9#=#WVHR#6X!Vz*JSwy=ocNFs)w@ z9Itz25%}b#wnCJeni_ldbAJ=zGqBKOQN&t#BJdfMa4y1=fx3896)%$as~eQvu7iif zm_1L0LQ17R>%0MGKZZ{96)xWq@$Gm8+N<@nMBNDC#2{_}y-4f{_qhi+DoJ{WBu%b} zvsbARsRge4v=q`LI!P)6J1?^ft=uoHUisM9HVJ9+sJ>BDG*eygug4I}qxfvu?W8?1 zAk)e5mx^`GUk4WuD%5VBrtrLtgY-4%ayz&EGxRQxjP=!pBlvAn->$ox3nVRgQhCVl zo*F$BeU=_%gf*}CKDL?213aG%=%veuW6h@bH-csTqW3HjLfR6KSmCkT9FSj>a66&} z!e4ONW+K&a^56rKK1SARqZ7wk8WQzwTyx$K0T4F5wnzzVCdzeIzWwR3MDIyk(;<%x1HQ4P3ybO{3k z1F8Cl_64*pPcbL)5}67jPJF%p6HZQbz^*Dzg&Mr2{RLozy#e|XD_wP$PyN!Riq&kS zMkMvdX=|d#TWT~5fS$14N-d_qRVT@sPP0@$TY6_u?-*XO*4yLDk;yjGRwWMmrdq@jvpDRiQGY~oeyER@{ zDIP{H1U@6F{7-lS@ig>%-$v>T5_1@Mkrdqt*}J?n3z{4~B>W`W@6poUcjj1nsN@9L z^IuV#a2IEFc*C~K$xNqDSL@y0jjuZdY7GCAres6EjujgFCk!6aA9#U!-zz@5(|}y) zHNI9($cQA+2M(79?D?d3&(?%T3<4|QZBuMf{h!z%$I8mO(Cp4UTlB9AUFdk1Syc(- zJoq1WE$*Uxu!xL+RYBZfJ;QCh`XJ1xVp^WJu-OX4pNdQ%?o`p;sZcr%{1eLi`rlJu zT0kWnZa4?D$-aE~2Y6#PFgUnFIoPJB@<{13aOtHQM>}hwC0H z5nF@?-w+ml^2_y>b18O;YHuDdE7WhhrF!`FeJJ`aWpbyRRHJ_B|qJN0wtc@RBw z;O)`?9!TN3#|xYS3Z3Bw;IB2~dI9a)og0uMjxZ4jjGXQR6vV$`NCP@_ccBVeYvnn~ z9tP)&=q{ts7@i<BF=U ziUYbM*!lP{5xpJM2SfJeU{nlxP2|DQd}(V7j`PI{`33gzbi<0}%hyd$f!q`x9`pW2 z$>r#y1(slg9`5O1nr@coK+ifh12Ljpc%o=qvuUlbb{QCh1>hJmniLotV=9XExn1frp3yf)<9fxFZ|{ zZ7!6+lwz1jT{W=PV=xPF?~G7G05YlksbcT~@P$tj6k`0LE`q@nBBJseojkp~nvwLm zHLrP@AS&69eOh?Cf*gM+euECaQ*JZd0SUw}L{t!3HV7=2z?Cl@6I4W*GO5D7zOcQB z6e(x-&A>_#YQTwz7>L}gMl4q~W*La}1&>ewoxbeH#%>_%yZ}gDtJV>{MjsTwh6NW(qZh)Gx(Dae_-_D$9Gb)6! z3(INejZltfhl2PccoT0RS`Rx8dKnY8n7e)?J0CKNkYyn0?Et6CNS)pi4h0IR4x1ke zWj!p6IIBrhg7%o)#E^yO939*t_PD&is)*-pB+)^EVFVT?-+WM(Do}uO&9|5eV2V)Z z%tdik$|XjIGn^`qTND3TLWDK;tT-(V+W|`kw&LJ>U{5s z$%#dq^v^GP-sp6B)yBL%67>hJL_6$-FNg}I*R@)QmEhEU9~zQ?^C5?_0A#~Hqri;# z_$n@b((G@M!aZ#EmanbeHFB9?9&3o78NE$rkzdI~?sj8&yN@CPuOFT3fy}o)<22H{ zI@8)p2v+n%@*GCbwpk2$s=A-%u{i{B)S672%vvj?l5Rbu(^#dlPf5CJtO{v5IdGLL z^$*?bE1EdJ_uXs3{rlKg(uMdH?L_gznr$cE0lrIk!XY2{H>{j30{pk&{9vKHvK-}} zLBgBJi5BcJY-GgQ>#}xNahKp#K^~L?5HEP66T+NBWnJPSR{J(hnIEdXYiCDf7m?dZ z%56rK$N{(zU%I;;06=^NQCP+7Ia-?Rfj70o`~-AluRQqJ>yL!-2!5 zwrkrR>v#>KI$3r26nq{#B8!9uTL`+K@plwrr34)?ndgsKNgc!)!SnUockWzK{81zyQ~7#6mL4)HWARuu1zMO%S{Cvati_v+O=->ZDiwJ2@Vl7(Tx zCwv}2pC~FV?}|KXU`R3<_{0V-273B1)9Iom8))txY;WJ$C1D&#Ijf2D1Odo$5L1RE4StT<+*Q!|!b$86I7qA>e;Xqs;K=zx z2s9&D_p-2asM;c`%2ioMkph(A=2?4;pd)d)y}k$#ga_6BPCaHB}CB7Sm${NAQO5wQcca_ zm4tSHK$FVBxcdRE8GZc9iw;aUjCw~8ljPj6q4Zr%2ZKGmj323GeivINjA?$XO6`flJDE@Ki>Oz`y{`VuG(rQRYhftBFO9aZ+5Py^lEj|@$R8y!uctOHyJTEt~uJ@LFK zIMY8E%#PzNyvZQ-wUg9SZb!JeM2+~9i`JSoYm7**pyy$Gq0ey#va4WkF2o`4Q?l3= zsK2(%GWWt(bkOoWKfD?aBJIfu?mPD$^xO}V^Pdl@aiq$-qo)O#3TYpRYMKJI@bPnn z=*cBWZs0(33>-l7mn>jMIGYzZ=Am0wBDY>$_$kiBvE%1+7GD!;+>sFLja7hkyd0q{ zQIx>(v9WM`a#B}xQQ$7p|3uuMc$Rfn9;hempWv27QE)ak29Z+bxz7%r1mOJ;hA0m+ z06YPYp91JSHaU62PTlt&krh@f&^cYWMrMd~yFtka3V0SE%@R>H(*hq^rOAtanEVb- zA^wTzJjzFK#h@Aq%n7x7r5CCTABvTI9P)H^K2Q*lsa)93-@9pea7R3>vpRM#aoveT zjH3x8fr$Vg$m}0goVaZ`)r&>*3mzMbT$?a|+F#!_F&@cr#M4#iP8)5jN%szZe#MYH z2pmMly^bEH<7_kh8wpoJ!WkAi-86H8=Knk?Bf+yEG-Sfbf`_{W!Gx+OY(IRdqg@Br z!^+d~Us(m&t7RAj$?6Z}i0nsMwm+tD*;khfmc_E3JVm32Nqnt40R;CL3j`G<^@t57FGh>8~Zg2$IS!a>r2@s zUZtd5#Ht`^yq!A-+D|_w=RWGf$q|h3En$U0YHDf#{5?p(c?7c6+-d=ZJQiYH)Wc{GN>YmHl6^}b2il0b_9A{2=yeu2ij!JMG4g%^l>a5@Z8vQrY z)vKp(CPHFJ%g^c_Ae85zq6?|qFYuV~9o|7{ z`ZvY71ZQO=00ul1S}JMYa}z6n47@X~R}I4bY1)cV;z*k*&m0Lr%f& zrc6TIIehTot3w7IC@|w*vVt2ZU<(A6088p;-#ilp(k&s@O~(Frd=)3l=jGSzbX@WRT|=@`fu) z(jcT!(?3ihAfkyuF_To+bvLFnkmS=97k9MD0w`RHN;JZKsas)lN!r9A2^c4#RgiVG z=|_h)1cpXnNDgp$Vr+DA)NREu#=o~ z9?zXmfh$;wG%+Dgp-990qr5gLX#&w#N5;FiHZh`jX#jni6WcT(0?0L56FntxQdfA3 z+b}z^h_CPAgxENelea(%e-4M=Wz8c>`7=wGYGaWBVFmkho-R|X%LBYgJmU%Di(|zB zRSXxA+ax)s1hawo4#LLDi1X^u z#b70N3%~FMF$2kJs_ay;ht555$;ne7celt(I1FX(@ckY0@O-3H3Y>I?78MD_SNU%8 zPdBh)sjQ}DWL$<>yJKeu#C!#kT_i$9Vkx?sO;rK%-z$+pBrOo&e?kNEAIM>VL58Pa zt}al*Mrs}rVpG-dU%UW1_;-*zftTQ#6Qxurk%u?lwxI{k{ty^$L<5l?$X@r2<{@GP z4}8f4D* z?b}D3Pza8^?08T?L|4B86~Jilz}KU|XzxQ#9-$0iv=8Ogpu`YuSyCVc1Oob>Wc9&t zgy%ud%fz*Qc})!w9Ym6r73~dBO#^`97HFMN*(46TBGgi|b}14z4}x@}4M*<&;KBJW zE-n|)7zFo2IHe#vmYXjY;*~M~B&!1kFD)INJuVp$?T07CXV!A5tc9Y}L1ZGXO--{O zLIxTb>MfKR=!opK9MMCnZ?}`GgdhgQ9(78eN%pnc`z$wUJ^>2xN!;q~kq}8qNm6-& zXITN;4DqeM5QFibA>E9KFh3f}{evOKGiKo+K$Q_`DuJ52fa+*rdE_NGflmT27LlhY z5?iP(qIgOZ=H00#K$(JImy|wF(}dKJzs>B-CsV$Kwji(C_b#JGuM%vhP`iUj?KTM^ zuyf%7FThov%WX&FoFDBk+_=weL=-{R5^~E2q7f|X5~)TwaN%_J=tTjMZJSZgmjI$f zZjP-Y?K4nvhm71sef_8jrU`W7qSuq=5O*vM22~L*+dA=wPEJQL+Gw$HN_`P|B&A3? zJkl*l2hgSEvvYEB$*f!Y=)r@7&{Ts1Np1u~19W}`h;#oCJ#jA`#oTUINgE`&$eD*u z=%g|bUIR41v#{z2xeLcIN|X&ApRwimAetW`$h^=Ih3LUs#NQ>nzdG>JuY#@FYP9VG z11J)#J9f~391q{>DC+X%Ffp&v(ipn=Dk~l$EnfOHU447C-jmB(jGHD2X&5u)NPe88~+@rGJ;3`xPku-%FMpKpVZ< zseFeNSC|)K{Gwmm2^iGdQ!@f_h}1h1n>lHn+2>?a18(*ZFf}F0KMA1jC}sb>*PFVH@7=5 zUWC^1=@H4N;b?At)FNR{B3CShqp0;wQYQpKIK1k2lxR;LxUAW|h;YYfTgmZQX`16& z*h(-)T2(3(@`|L`0fZNt+MqgyC~jdsSQ8$EUX=pa1s44t_IsusALk)_(7Xgc>euV! zJkr_;uJl(7Q=w@$ZVL>ey|4V}PBwy;OOE*Xt@jEvc)_prx$iHbarNh@e1IM$I436? zMuvtIkFO_MrUCWsJw1-F+~`B1)H4J0=OP-gVPW5PRym@01M@`H#zRb8ODs7&>M^)` z7cu*RRln{CZ%!Hz01X4T8-A8v9tg#@m=%ajRsPMzy5lS)*}qqwR=ccPRE=VCMDIlL zM?3HdhAEejcfhHZ#4LdKh7##Dj2==lGC8;@#biKuJ{vxe^EzNBnuFhh58{uklFsNP z_9*`Gl}XnVa;M+Zt*19)uibWXa;lw%DXe*I&Ib==F(2Q(1=O6~kN?r^!09OMwHI}% zdiwz-S3K&0;|qT#7YsUw3ZZ~oeQ;qr5R}7Ber}p+XSWzsz>2*GHgDl&D_#UKqAgoo z6Rf8#Dq_@FNKA_ChdLti;ZVa%zUAku;5drfIA#EU5S@MCEhe*={Hp*D zS(6+>FQ`H^4IBDYGNkYxVGpSTBS*g!zDtGnq0iNc2Tn`t`5FhY?rU)z`xtxqhLM4R zuLk?b9Ujqy1twZSSa2v2*@<>MI^FEF>oyXg2&(dMN_j39tcCkPpC(Cu;pfXOk5u2H zdi4v|G_l=qvmF}P0uH`KG64@k0PRxb*C70*E^yzTW*-Fg6*s2QQh#3=4Fcue$CSaI zCtW8XUmV_yJSD{Q^FZsFM*)aI1g>jWoFX6BxQTUv!hqd`*Mc(5jcI}s3(H`!Rv~=? z=QRsb_B9`eBvsQ@{V*!Cy3o2W{CRBR1Z$hQI`<2~;U0-DIFVmkpp|AolXlvHp)gbGIt& zaFYeczL5C7}B8 zdKMky`aQ^a7x3nhk@O!w33ziK7yDVH)m<-S2o#55BQkeE&1ULb zcn^1b*}t!dRok*f;a$dkd+FtqTLWHao2{djgtxlTaLq|%i=5uy7@J|`WzLwjcd+Xd z-_rc^N8~=F^7?WmG#=Cl&^m`*%tQ&Rz1138&y6F;CCqRCccBqc+5h$R-<|gV`%!a- zE^Qu}9}Px$T4?@D5?rK?9^4UT7+8pY9)v&iF;H?1fmZ;08$F7FkZUPgJRl&tEL;T; zZ6})E5oGW8E8y0{xIazfU19iuS`M^WISwP>YLO0 zG7cfU9Li_}XPkdg$-hqHce|4am2~dq=HnU10Ifp_33FZe({!R12`b-j4z%Q>4tpb_ zv^z*t2cQE%xH_=cEU>dk!~0H?bQp5<+}xShg97?ruYfmpf8O;6sw9MjqP;h|h9L8J z7rshE`?qiRp~3qGPBVq{BOxhSWPQSU5kJzQSpi};lZ-ahd#_rv#&s~4s@(L$P@MFC zQ$vk6sDF1h{V^zPmo8n}jRdXUD0D1%>Z}Oz1!S(G*Ai_KkcbK|-?IxHj-j}@fP#qE zMCj?CmWgIpZqo0+-=PW<*}s?IC@*{b?D1cR6_>3()Wj+_#`N`ealthur+Avb09Bw-eA$AAkRwad;et!{K z>-g~>=5k`EI&c4c+}*@xkPB1aXnz3beCAfAP%QMLQ}&$FbzTTyRi!z5I%Z)y3Q8gG3m2Jhe-S+EWy{bF*2j z-Q9F94PNCW3)DJ4@3%lUqT+@P7Rk$Gu089343K^y5-fyZwh9mq+{hFGKQqsA)Ia9O!ubJ1pdSZ*> z-1Q@-k2l2K_B~K<`?Nl zUTEzOeZp4o=Pc}-^K9wici;JXXsyeOOZ~m|HaFtBwtrPiFAbXiZc#>V`5s}~u{>)x zow~K_?1pSFpPe6h*8741M;=(&{JuH>5|BJ|%!5O@r*RfcMdXXK-W(h}o&Q5q9qP-Z zaV6rcZPRhoSog;UaF#qSBYp5v&0@BDcfNjNnOtQt&hD1a;Zh=d|KZX1`GtQAn$sT+ zKXqsFv%yuJGfSW3JUDp%9W$?|!=Jly$=t5gkkHQ0KX!AJ`M!T&e@C)-|4e5OW98Fl z9(O*owy!cBUmtG8N0-A;eK$zqB&7F`shb0bi!#v`hH~c}w9G^=xKqvjq^};F5sEEB z7ScX5@92Ra2MhI&V0NqQ@+)jdb2hb%Z53&F#Smt+L@=Ch!f2C0$<(?a;e`F}&gYMD zDXmr7>O)`mYm&CO*v=XLBQDPZ_kXRsdPi+I@Ag&(=hX~`v8n4=0ZX79{t0 zakJr`CjbFdx>gB|K-tA{G!uPhz!Y0dA#wwV%$*yr{Ju1ai4hb@eqnjMc7l0yLv-F` zm)UX^oA>?4?O&O&Bx*i7Q2Oi?VxZ_aMs+HtF!w;S)N+_DTUd`Nu(9^*?`!lO&Ux!6 zaKQ7~6VB=-7Xvyn(x<) zO3}>PzDvIN-78~l+`L`J?Hm1Rzx&Pb>6FkrgXvdgYvBJ>bF1wR}n6 z8(Py=rD*nhn)a`ZR`xyAS#e`&=^k5aWCF;6OZommpouhq6;gBka|27;As@K*B+oH? zaUI{&c9lz4?p54js~^9(@KQrUURc6Ky@$U(PczrRo3@qbu*BNcjd#9ge_fv9OBcR+ zUBn=Vwsviurj}NSsW6aUkgpd|2+;zGvd6{M1Y7v^DhIN0KcQvqb}@-B-=6u{TQhoW ztCo>DcI#`-VxJQAe|=xW(@)gjO%GyY+bqbw!s&_r5uJnbzUQh{3ac{^l|{98tlmG{ zliY-Eu=BBzP*1fO`MHTb|B87WNX7!yWaKGFt9!S!w6uz(X9R6I+&uk1j;KA^&(n+l znzdqPnPO@Cme}Jj_}m96#9>B%b<{zG&o*V+umE&kOd%~3Aca*G@BZgf*PsC=F0bC~ zZ*TvmxoM_ou!}FG<4rbY9)%()aX|48lgn28X3insu~UAH%VX@g4Ze5{1gnN5rwI-?*nqXXBQ{Cti&K`=8ncIhCwUE4#N zb07f`piiW0z*}_hnL#S=A^?Yg_J27By=Z1&yJg4fKEwJ$2akE2to_$hzv?dRAe+cJ zJBXc~tSM(RW`?Nc%P+?C&%lqtfq_EhIxAB2*qENS+3UA=gy%!|0oTo3JWKqa8qy^) z{rON!em|7dk)rYqNAwRV?8`dvwmLJi@ZwkJ0@&JoZ1^vM}zS7{`WX75^}$zLo6BfNusC`*JXh zD7FCMNkf$IItppUriWfKfQ`&+=PEgWJVSwML5s$_jAi$D_k5_4`PY}&-kozN73sFa zgIc}8uv?QtdG_M(2if!U&e=YWm7`=KgBl0A6#-@7HXS=YsTP|F@D7sf>}Y!TqTB?& zlO4dba07I8(cWn`&>~s?+FZe865!?m@W`*H{&oWK?DY3|W$wG3DYw_1_bo0;YI)b! zn9t|~y=2~>Yk)S3iiZY>xIS?K5*J!=hig&M^t|LP2d|cn(vZ|WNMSg4=3}dqBm>DD z!;vPLV~caF0QjK6vziMHKkP_|+=I53>CseOxt}{g5S=e6+x?~Y_`F(Aso2%~pwT!a zd$iQbTh#K&pVK=B&1^S(Pcc585~u2)n_IlXN9>!eLAqtD)HL$NIq*}BgHV?L`^75) z`2W4eZdWg*vxU+I^K?0NiH13N3dQcae>(Hyjz64vMY{JzMHjzyU+LDIa`L3#$;7mY zuf6)f?@MmkGQk2f5cyyOpDAC(89_nb=-;c}ZQ)TF5-|+edb%^Na4&?s&<@j9skcS) zEBKBdR~MKoYyV**r0qR}7?UnbWz|>J*2tVR3RU^X=y&82uyfAaa9do&#RaeGR-Bjr zS2$N1ZEd^bcMF&*{IWE7^Iif}To2`fd7edq>%~k$&oXN&T~imh{p&OD1Ah$PN)P4y zi)y9IHI;(n0*stH*U1EP{hSH%r-qP*rsm^(F1dswTh#>?QZ6XeE*uCL+1+0JM>wtX z>(-*8E*NY*nqMN~(6H7+SvjCZi#awnk>@||?j5QHv+Jui2G#AsjZ-K=wR`M5&-!M( z;P|}*!+zc%#{a@a!^PFnQMjhtkltS@x0!Z!&wb;sJM9E_<{~~NoLqJ_QlTMK-&xt? zg|yw1*zK1LD@=cTex6(Ayv|lyb6Vkjirh|H_UxcfWV)P|bn9+9lZNKh4yt(P&+B#~ zYqE(?;C71Rx&rIQ+E9It(tyo7eI6V%Fx36yvePVhf>%f7CGeJXkG^c1e7e<6$(dBY4kl*69{eiu;^Va=m zR#!!8_!L=N+q`pF=E>%&!Q1X#)NFLF{p;(elNoAu4<|o(PYzqBuTq*OAZ|#uT{KrlWyS2}nins5Q7dO5Bz$EdIm>Bse zg;MDsAJ3O_Z=%AH(OFddx~a}H20M{^4Kou%{QBY?!|F$ukJrq7aKF)ZO4h0!RaJr3 zQy$Vb)pdNA+QSa*yUJr}-O*j)#ME>85cTURHP7Y4+z@wEI5PMI_kc-nJbUd}9*fz> zj88LnSj+ca^j2HGkX-Z3%7_p=bY%2piO!==MKiNw{`@C4inFN~k7Wt-&3v{h-)*Ev zbN9_-hb7co*gxFa*(v41e}eI8&uoi*_t1uI%kp1wrO6a#Z18)UTFMostwZO+L%oY| z=dW@UTiz=k{M;WDLRyBO+VIQN$<|sdSV!a8pWIilVjGK3ZvGni$cQ=%^|||wm4QBB zn6}=r=0KxQ!|65|l}ja#_Qn~n7zB!M@j|p9M=FlX^CyDyTbeeOiPomj3gE;Ibr+f)8$&JODO4x4=Hb6MHes+{@W z^>owY=u=Uu^`k8>R_6EG3>Nb{u(;;lZB0E$s*G+GzGVznhA=%&*Ke z)yDhGTeFWQziPU-L_I#Q$;Z9l@aDPBJckq09?rK{agpE4N|MDa@26i!bC=(N!4*qU z&*lv+z1c@G^O<1bfikYqz-ISWuPWs@k0o~9eqkZSMK#l-($gL%!x;F=6x`;DdA@Hg<@x*y{=I?Pllfu#^v>s+Cfh%a`-EtGNzcp=P@CcA^!4_t+R(Nws7$Cw z=jfyNq4ec^7Jd!!4RMB&Zv&0Om`c4>8W&1NmR8ikT%LjB;1Kp~?KvK^yyKZ(p_>6)pW|AI~lxw@Xj5{2O?^2oxq~4L7=e=uD8A#{nT;log;e+Qsqf4d@CZ~0WssFkz6qyUD))n)^4?u#3 ze3RHDXaSiOm6h%s0sfT@4L%_LH1;4YtEi}O~!YrQCTBvHMJ$iW>}+aU+GMw(o?a2j!9!s8`t|FP z$uj$X<;n=l$nd7Atvc`6ln`KTmc$+nvTN&&f|86Jl4f?yd_~fUO zYq*J)tOYB(MNRAUlUrkEI%9ajZX6!@)X>n-uIZ}&NRVB8JYRMot!bLu48z2H^LzGG zSLTeaDdt412eo;F>3vqa`Yfu`%BrgN+WSG_mV9HiTufz?Q65uDyRYT7R`=7Ic1)%n z<;5zZ;RV{-KJtmA zp=Rnr-Jio*tzrco^A*C~u{WC;nqrk!lG~8WCg(xc-oT0mAKyohh7yuPuWSe(vaRw%?WjMyh%Fhbzwl8Ij}|Thv!Uufju7v% zzBmTM*G4a_sQY?#~AKhWF-HYoQN0O|alq0AcYa}bR9Xd1ual?d!1Pv$Ak($q1s!cbLBbNn0vyp*8poW<|Hs#(y z*`r#is;UZ|9n!LS`>Q$`3PFd8} z#ZP#qC=b6=(6-VvP!b>16I#zD85Y6Gu@+Zw3-$>P)^69IiGB%bJXG32m7`P z2R+F+(}tM8#Ic+wm_`!?oFexiz%v;!F*9q=WiTw7Y?_@hDV}zk9dnvzRSYha^(ZP9 zu)UUrQs;?Mvk`qRQdLifShf}nC74ZJjIu6%mN*gOCzJQ}bd_?1VdG_sXQsYSoF{wJ zHGMKJOX#Q^IWsrV7vjmmW4oiQywUejPE4R>W%%oXLAA^Mk!dd{KACrkW=6bV<$S_j z_`r@?(q%2fM&1!FJB=x$yg==curQ`dm)g=7mVPJ1h~U9wcYN0n6z_R8i#)9J&NR&c z8H_T1n;(3mJ1%dcAroz51EZr2;wPyGrY%>gR@98v=;hMLw6eW@jUKH)lE{lMM4RV(xT((2hw4VXeqUGyK)|3cnf|7QT$0A3J=Umj`fz-`q5*qpMSDEDg{DBM+odYVZ_6l)jeiH*d8%T=~DtT6i6=mYoLwKk*SRYjAvnU^cOxA6{J^%Vy^Z6CEf zf9_m^#bnLLIMfpu^528M?0vmVQd?KI5KN4+^78WbF=%8Nw>Ted^Xa>(1-+-F(i!UR zyRbXzT!h;2n5+4OUySrCDdP(VFm644DUl6?a_5 zJTr)dKn)ezS#?*`sNcWaqbjm;S5d)Ht@dwtMp@NKB?jI89<$(zQs>s=k#n2>=l7aq zyyTDYv)L>=JLs;Oxchq4Y3rH7ol&2Lyi}5g?z!H(cTWTgQsl9h?t1-LxbHPz^?6JE zCp(p0*vwJ4ItT%kwtEZ)?c!hd{j_Fm)EdL@`s;F~ zOtvd4)y3uaEYgj#20hdqO=FBpL`PeYNh{&Bxe*$eXh9yrwjh4S{UU|(Trc%>%n6L4 zr`fQ{xAQ6eUV9fEznEVpV}KcmZ&XM7XS%E6D6;-!Qhjc)+Ge^sr9YQ}55T!eG1&kv zxVQi;ofqRa>e%Q#=&h>!c0P`GFvCA#A9jSE*pdBrae4eU0@x}bZ^tg<;=HD-sVTh_=*V5PL<5o z_r z1=8kbzQ!YZ9cQ_i&4KeXU!3rpQYsPRpKTZXm~zGKIsUMT2gp&Y6kQNd*C!qJLj8SS zQ(RI;(=F8afqiBXY8{2cHBHqxY1c{X5i_92O&{W`ssPkyu21y z%=x}p1g9cicAw6~!kSsioxJzQA+hWZ)gW#XWnxn9f3JSo@k{JI=jmRx*b4_TC&4Ri zH5pm=Fvr`XGpwuIk5B6aag{{AXDwLp;4*4Sr+YCqH&s+r7XA7f-;qa=H#AI@)qnUS zS-DhOMRO8|W>}hVgrBEs)xO4Be%=>L_jGj=7jjZ`7E*?1wVKqXa?~^cDRwb6SE(Ax zgelfuV1x)-5yTdo`r@F=VSQVFZkHLm;neT^mfsoT$EC|y)k*zn4IOo^H)AT~5tG*$ z?{eGoh&JchYs;1~laZ!tg^@}8%-PAzK4XRVfs(EReX}!t z^k}DbB=1%C%ff+_5-aXqufOMAs)5%qHBp^etgPkZj$_E!XD=#4i%*?C=MGw-Veip7 z3pcz2_zSE=U`R;4zne+aO!i`yA{0LUSVzxqRX!2M1AJa76@w$GY=5fbEvLr90H|{f^^qv?mX=%)3{=j5b z?+jm>AN0;*Xaw?FPmO^&ENEy^?sW}oNGo%Cr0$9h_zp&5L;fpU$q>M&cFAM5+=+6) zo2ZcVJAK}KE5b5u9i0MH3G!-}1$L))MN^Lz)Z|*s7o^_k+p^6HNhM)^({}ZfHLR*u zQPz+;`qteojT`M#nKnr4(VW|#&*xLU0jZPQ18Y8>>D@lDoxdW)KV$PYpX>4`guVqH z_+|`wR>7(7Z;i*k*sYYZpZii?Vl>0cwXe{@9v3s=__GU3nIBmPNyhh5nNm?OVOCPubFcp^g>+x~=7GfATDs zDOgdlm-;QX1#*I%pD2{wMeF7Q^yWvxZo+QoWrSsA+~9|`yKyF7Nlki?yS#u& zDyHpRaEM&rfk)Ik7z&y@8sq%MxF&ng_3n0Ia}@4<498Rc`rA3^?Cr6*jLlpz5e|tH z!9F2Dni9qjmj74+9mlDA>G3nn)5PZm^1_*WY#l7kUA%J-AyW!^l7 zBHQ6|J`r}4-Zv5oXIDNlipvb14_=CMN5X*zj>|dr@@q`-9CZ_T6rAfI+AR~NO@Fd# zAN5lgLf0`cDCk{z|LVocUAl~WK761bY&#^c`{Nw8o;$!G_9kD)yQNu++5M9Gp-*4w zW~*KxK=6Ln*7h=odL2q@tZ9_>o1z!;p)tEu8JnF%ckFR=Wb@t1 z8=utjy!hcoecVMo?k>{gT z_Bf~ZtBc-eBv-S&7!@4GvxoJFM#x~JYwbx^JH2|2kaR9q&F&|uM(TEY($wz^ccMP6 zoQe985TnVSmtEUt#PE$t6F0~ha@h2Tgt3V4&8e-85}OO*6jM{@Nl@I|)qSw609(wz4r%DixU&N11s_F2`XQ9c=W;uPt-s!JsIhw5J+hKT1`npHA1P@l&LiwS4osNE>secH)ZV#G zQMZUXuGwTZed2D*j2yh@z=rnb|>`&yZ zs0g$k2;}ceJw7tlUZOFydGT_c*+lgtTI~X; zfJhZQ-IGE;7%b3o*U(zPCuV2EtrJV>C)*CaeEUf&(4e~m==IY}Rt?W{=FqY_m&BFE zXf~%Y(d6S7lsO@DeynP=Eb;oMHZzr-n%x)eH0{i0gXa7!%3e?EY|IBDXY%4`5GGK) zX)4#jV%l2Sbk^`?$4J^sosl&QdP9OQOXM<{pFuRI*ttzcx~Jo07}Fl!y9Y0F9+^9J zDq{dK8x(a^@C#h7zi3#{aeL+aGBwf1Imu0)r<_XYuZm-q;f|Mx@Z#JGHYf_Ma7dVQ zDzk!5G8R~%vT#S{bdO;?bp8pI4!5M9>yZCQxYq+n2_N2ninR8-@%27&!#!cKH*VaB zDjzq-8=wE77q_1JY>hRjFNd=W7(`G=lxVY);1=~2R_e8CzJ8};*a} zFfiyhD*~;|E>T0^7{#bL^mVf8Ru7A67_*T=*MkCL!wEFxqn?tt{Y_oO}1ydTkW7B74#{oYiTG zqRSrLVH|8a6U{2q(Oqg0yVlZhM4pQ<)H@be`om>aJz}&y9YO~Kva>_7<1?d~eh>tb z_7U1cfIHJh^LBm#fp%187FmrpJXM?exHOQgufBPFQ6g#;D?b zV~@P7tW-mTB!}c?Ndf~qGEk3Ga=#UES<{zP;?q1$Gb73Ia0^D6#hV@Yu0d6+ z{ms%{(x`dhPVESAfPkYN4rNR6O9C=W zVKb{YI_lEG=Ql3hIGQmUlwf#%{K9s(r)1cfsxd z0LSks8ytutI}9L-La!N%QLPu2o8#j=s;#Hk&Ty>QnmPW#aKLYN+)vU)0Ph6>76{a~ zy;~y-KUAH-Gizz?T9*KL&EJsm{jQk)2)=H3#JpIrT?3zxgje*W<(Y2ur#o{gHws1X zc*|VkiE?3jt({AW&CDR821%FE%-QJ$I`IH8+fHXroU$H{PzwSHs>zs&R`Eu$=$=)H}lPJK+9%CYrNyAIQ*d_Lza!Gc&pR0giPClN079ZBq;+|wsYss znFFzkFXT>J_KJ!~Bk)cCwE~*g+O1IEl|Cfz*Rz=U#-`ctD`y{L$1(y|69fhr-6|Rek2bxQC6LSR0F*6ZNmzRZzZA=v@_haKJ;= zU|%cGKiRE@>d#lF?49SHYqBI%B3fpDkkPq-GJQ^SX{?DF-8FuIQ%7Cd`pio#$x->b zB(A}e(1kGoUXE z41_CG028YUiv*uU99~;jXPJCFsBEwC<~vea|9Pk6#$$(}wDPikv)tVlp5*$g1?ux} z7dGYo%16lEKkH>H)to93RHroHfn3x6@u5++I*3h(Za8zdl^ zfL;i-M?nLjWJK3Bnqi>UxURN#QT<$;LNj+6^QHf3m?-XtnT-pV zhlsH6el?}POmRy!`}49_-1FSuCoUlgoZb8yx9`POvvV@QnnTKbUZM|=`Vm97ROCy4riHRmzMlg!QpA59M>95xLEA3owS2^qbAV++r=BkX$huDd3IfGwI>CcrV zCrF*3!vAH0{!&#lY7U8w+9+(Q=huT0_TjLCCbuMnuHWKCA-gVC2e5(h>RIS=bXcjW zstSLW*fqCl<);6=jYvwtujlmX7DH(yC;WQA?jQZssGr<0p;rQV(k!dYiFzoL24!Se zXU!oN6x&XAr@63)!Vt3WI$fQ1h))@G9MaMumpd;Rd7PejQJnE&b#MX-p$KG|+zzJX zb8D0=`l0%F)SwT$e!-(3*9f)#_bNd$_0S5zKr^x#QBU~Iwl-s^?6_5uSj;@j^9&geD9~+Nc$%jF0wxwV(I%WY-h^HEeHj`WQn1 zvj}*HMY3V~aFt_{IdDhP-lnk!(2kglj7*oe8ThDLC>>6(6KK5Ayl7J2(69ubKE%2kxw!cQARg_?(u52bI zCggX@k_+~J#r5Oi5CR0iACcL%gs$q zV4}U=Hs!&i9)XpB>aLx_qdi~Y6%guXJ+ONSjP(8HsmObt$uB^B0OD7%^ z08#AjL6X}gUQpG{9HWM^f8npO9p*#^Q$FSP^K0NibE-{uo+e{AZ23Aaj)#C8#n80q ze%1Wwc9j`PL*f{|In~V4^m)zA!Y~kcR*DYE`B}m1QUgP#U~TEg&feY=aK8FXu}*XV z!qCw!WBH%=@pubQwc)Gw|HUla^7r58X8plOSO9HguM)Hhi3;L&ke$2Ypa}6PS@9hD`9e6+kW_f@S?Uph@VSrF`YAhyRbpcw zD0B*$vYuQ)>K>xA@sOY*DqQdnNnT4uMTHkSkQG%*!+8;ZqfWNiZ~Bv8P(%c$1QMC-hK1VRxI zAT?@w6e%BbILt30bT;g(25Jq0i*C5cpnF*jvFDYjzoR|c(#$e@gU~E;)aSg@EIH>qMvI>wqujzAe_7FCMeDJ* zEN#JF1_jW@v>bgZ6}0^SVeif3a?acL;oN2zGnJXq*oqjfNJ3?+m?m72BBX^HY15{t zrnJp4H6n3EXca|iqq<5vV@T417VU#JrJ_|`EzfaY8{e6^pXd8~{(D}pr`P?ux2fy% zxjvuw`+c70aUREUWb;QSDJ}Wo?$q97x4b2IOULI9WxOI{+;;{hw;zm|{GVcZGlN99 zFX*_={Ipy_@j-;a_A;N*LZ4=&tHmHp_{8Sqtivlqzol%6Ves;OO}0tnwM$_+27;v_ zrP~VLSKnrI0QX}cWkqry3Bbo;k0hpUyCs8!AjP=7_lZG%?*;W~m8>&hr4bL2!9;E4 z*{&)17~VOm8b5RI<;9oHO2z!eB2*O@_o^IRbjsL=`)_Cv|8J0SJz1^M^wm)>ue`y5 zj?Zl4cRkkERlBgVZ2d|Y)b{DY^w_CbA+x!3UC*{unJgny(}E%|fJV(l){`hT5tKptZTpN=uHfm*L*h=#B?s{;o%lHUoz%Ar|F; zjDBWC0^6|82c$Z6uq)aqkDfVHpn%cr?&=f6WwMOz>$=i&-{?5jCVKx3CoFUPJ5Jb` zc$>Rk&2Bw1-!p}O7wriL0yH(=pVVyzKhn_g2W3Wq?&EoTP6(^Wf!7Xho z_%+}#Y;UbF2@Zh2CumVY_O2S4_0C&+*drq&lccSv(rO$^N=hOwrphr-?iZUa+AP1zMcQ?i z`n;ME^$3uwNq5x5cFXSW0w}IslN`-pm#;(P&U^G#C8YM4k zS0*S}Rxp~oyw1&mP%Wa4VZxM4&+3%A2`x zxvBGx>Wz2zj4z`W3z%>%>roTuIgNr~;YiC@0?cP)X+LcM5#dCc-NH0EeDCwiFqE7* zFa%cDU`^y|r&qpp+e25$>#7eQWX9l06Mew7#^RzvfR$~>n>P$#hFSM#e+z;Yk)YE= zghl$M|E+wa|Cxkd{`LBWb~8E8HEY;UfBuZwCgUno7N4`7p=V=q(O0{<_Oo4ctECt3 zW3?)oCfGfQ=5TP)1sIfB0l(R^oT?tUjo(r~;5+6)YAVz+O2*&_6R&|vM%DU(p=Vna zOLSBlRvo;3ek)E54tK@X)iTO!un5s6?}1k`pzktN+RePbH{q4D>U6Ha=|(y`6GG5{M+sUO5JJ8&mYaXHXJzP~UF z6S3#HYv$HZ7V@I|VAV+RP=*0BfeVgaoyIkQ+8%$SElHS#I_Tlx5K1%9QOI7gIo+iM zG<#}u1xumLWeIj))W$b%aO(ET=j zhK|^Q?k0Mt%eD+NKqSsz$_ODgepQIYT#+msL+Jf9cHwDhyb;m-^M7C!yqDz5;+*iU zA>!NJx0xLtuA9w=(f`9=ac0)%rNxh;rO|>gxTLUra_`e1?{utD7uqif(-YDR^|3d) zH?p$a%SXKJO|dK{JFshyY=$kHoRU4n1x|a%%HsEqz77(B7(h6oOti)F->~2f-v=2^ zYgU?Cc=-Pbj5UNs7rkaBhVwIqYzx{*5*6ccfUB{}Atcxy(D;a5W_r3X{}pVo5CBxa zpwlC~4}5xeETxqg8bWNP9`+zFNw?YkJ-tYC1U=K z2ryZ%0+#e5@w9uF`|#nza1b%G-u>|vaKaToKHVL%&#gfjLoFrV4O6lQbwaQdG#<39 zRel}^{5o71#3o1W%gNMw~J)k|OJKohat^j;XJmBjkkCm%U(im-$N1iCTzx-_$Q;Pa; z$#4x9i8Mt=fOHC~4M+8v2QgQgm{MR7PpMNw7VMQlu3&bt_3tF>HrpN`T4b_N_j}`Fjmhms@V*Z9Wm!Q4Uz|MykG9uESdrtE5 zz6=$dwo<&udpG0i%~P5bkQBY&pYrH`G=L*IOq!6B6wx-2s`Kw)VrHMi*25=?x+x+J`tI@d@*G^b1)=aoTUqYU!oFQsyWngC7sjfcU1 z072I>jr}vkIpAwKqaQkbb#$l{fxpZ0KfDU}9(luH!kyB=Rk$t`>TMAAYdX~$YiqMh z-y?bFlxw}0%RFVE?AeF$3@|hPmba8)_g;dFxalmnlR27nYgZqHvi~oLclB*HYX&mWjbXX^hvY!`KPuP&=Go$6Ek< z72Th_+{`a>59wFEDrY&MgF*@M@ZG8EXVXg8QqeZ~P-b80x;Er{AOs(ArtpI1QW=Fr zn0~0E+o%l+0NRH*xTq;kvl6NJ#)T1gxHl#H~$}3zm|EVkHp$-wXRCM)Uz9r+tkipO0 z3{C#|7dPdBvxsMVw0|q|;^Lgffn?A&5M!l^C}>9x<;qfP8LZKQd+L-q;yuVR@Ex>c zpZV^$EnF=2<#3t?I}KREpVQ-bIK71+s_Z(R`klWF1AeCJAmq1#AX3najh3(a(hwy_ zXHsUX?s&7VbBS^_63m3AK^TQTHk}wS9a7(zsV67g&Z+|1a0Fx=ks>RePVuIoc+-3H z*hnI~D)LzOWptX3Wq~qGY}iI(vzK1X?~lzz>R(Jpo_F1Tq_=B8=r}ebVKNGQfbK4OAKTVN0*W3(nImr1o1cog|W@e@*Q}(m7y* zchVcH=p{eV0|g*;U+~DwmK3dJQ}r zi^lkCjaIDgEzx>gK(oicF<&uQs{(-5VCEQC5%-r)aZhpaI~f`?*(z6%z(JVbv! zXV$b{q7utuWY?}VY~%HiS9%R%C3ts^y4^|oUpJoYzI5NVxa4rze`_FXUOR9~vw8&l zgK%)T*P~thHrS*Aakv*1<_bC+vNR7~n%&LVJz?(miL&vNJlgEYBlDf|e#ib>F)({B zg+-+s;m+C)SuG*qE>(sY?7+ZtVLm~}3&pO(_@Lv23Y7|%uW{K!LVh#&vFS?q;{4UC z7=XHZPwIMpPdQ!0@#*S!hT?M&PBf6*9icP}qZPNKdz+=nTU~zB1Rr2>^XlrONyJ_N z9_9t2k>WuRP4d`~N<=973>UlC)is7tzxVVvymfy+kbe#>uef;=e@usYW}}BKSXx8y zWWVk<0Wu9O=N8BDR>xlSP%kR|z+Dz*clga#AwTWiGqGvV9Her;g*0>v{Uw;dN>KdZ z-?1iEA_6(nseB!F-u2P^>z@!@U+hb)!d=}9s11Z!tQ3FnzS@7QmxKeMBOV*;@rly) zd^N+pBmM?cYV5aNa$-zs7`r37M?iP3`pA}MqHfu$$c z@1CSq_4MgED1w*Kxq~F6=0*x?6qBP*wT6kdM%6W_cX(A;9_u)-YP3c1MqXZ?gyeU; zQ{Y>{DIg7qp&gh(FXc+AYeqVxI&AY)?u`I=T6=N7hfTp)H}bS$z@?X$B;WZScEE2M zni?A;F?4+`RsXl@bN}m?sT4o3{uePwEIZvgH^QxP4JCLWi;XK1 ze7PU%7NLw*Z2XPL+R%S__{ws|PVLCMs@da;e<|4u#)3%PK0sN?*&R1I zi2@Un6$huK@SLxE#Y^CPI|L;eLid<+JIE51Y;OdcHC^e?iLCS%FyrM_Hal^!;yx}S zcT3OAY(vgmL`%5+TF`Z9?-@urNBVhy+AZl%ztXF6V8n}N$ZmfEB_$~d7GLNE_}T)Q zPLo;A&iD!|_O1J(b)i4;&$Qq9XPTZ!K#RmEz^QH>=1hP9%^iOGZOgVY;Uv&fp|7j( znk(`He@FR~HH*CkMPE1mOx-K;YL1FccD?}6fe2p-+D3#`@Y zU`iO6dbtcD0arg+DMIceNl7KLV4FyQsc5|BpXc`CeTJtrgAam-$e|N+$ht^hV9s5| z)@Cj`v-*40`oo%y5VD7?aptWVV`+W~yekzI&)G!x-flb!g0E$X)IlW;O)?~ygvj5h z?J2&m0V$MpJI?+*@{I80kKdWVUKaID?oOK8>^oUhj;8^EpPS%KJl0=I{w1IO2!|6d z{*xcy#XCb@l@s-b4%xn{%zS0i8y0`_$CNQrH8qY@Wwz2z#hZLQkE>Q#Z2wg(ssy>7`_WgkSYPzYgS(i~-3~ny&dRZ=n%6k6XO?9?~-| zz1M`~fI3r;XXvg!$Bpn)B|Vpu?4NX~xdTa#C3NOg7mN?sfWLWO=^|ue2U~b07Rbw1D^|Eth?zw$rX{D?Q z*e)I-iwHZ!bmYW8JPeYI0xuA}J#|E0RK>tlwUH;Ga(Rx>Ljp3=pW(%mp9JW6vP2&2 z^6A=ZTZk`>Df8OnLp~F#7@nm$km9@eDGGJ}`5tj)`JkaI;v+2rtgR6{ZXlMs2!jdT{J0CZ?K} zd0vL-Aj=$=YPp$^%+%wie7-*ku%NhuNIB1P-e>9hz9-(nktyC6v||-K@-hlo2>$^O z4$wlOvqW?3J?|ZU`SH}hnF=wnQPhV&Wm8e!(E`YuR>opfvFcK`pKwU79z@Zz)7UtS zbOD#aIGj1eKlKItKws@$N)Mv(A}M~63cA{K#S;KEJBRh@FgFEf6ZP$&uwc|MovbMf$Xm7>L30>`)}0$#A5E>-r6Ww z-qP`RP&}i8Fy~Zel%hUE-xp~Nj%8V;ky@!Y2}wQStT6&%I6xgWO$i$72;!BlyA}!# z(VZ1&{|wl`{A8-ehkLo5`+6P+`Ha5iqz4!{S=$!QDtb1c7UObpD z6KpuM1q6k)Q6w2>7fc*G#fHmJUJA#l25ix(3;&rcJdRx)pgC0&$Ou)pCday1By$N) zWZW7DWT1`Jd-)pg1ovCS-&AHtXj4TaZAt~wWJQW9%CrfgpFk}f0xYzRV7kuzL)p&$ zUvR&z@JjyjzS}xmr9fiD$njWR9pK1;3Vn;yyf0)XBDlr^zY<0N{5|ozI=l*C!rS{f zHxtu5h%69Y(5;khryO}pa+tT?J`?PwHbR?7C558Vs)#8)2jk``faDD|t}B!{vdt1` z9(vUZ(w1=>6L;?92=AL~}MfDrDC&b6sByvg6SDry!VNaLs zgFngiLu3MDSGu+{QGJXEE*!Nucrh`kLa6dc&h33HMTxL8)%ee&#*ZS(x(OdzDr8Ji zc!Nt{1RV^%_J$y}Hu_AA$%}mn2M<_zpuca3Ic75hcOzS@sjjcj)$-yz_YJXzm{~|D zF%gy-OkkgFWI2+Liqg@k2`AkXv6}#VTi?2vycnxnS@b&Ity>AL|-j76P z=cwGyzI~(DA$cEK&ap=zhTNOIAx=WPd_bJn->o0JH{^=w<$G^hWCWA6Kh z{r!pTQwoF!42?p0mE1H&9!&LLe6@=?<)Wt>}dP+l)+2`XcUlIX!z`lesZk z#b$O+>oKS2=VL5x#O%fAznnf@*C=yKh1&tzeSXIS7K_gNGG)K;Qn4@TP%56yZ7SVo z#P1r>+|k~lteU$lB4VkJM@h*o@n?B!4F~E&t6w!q2L>~hj>#-?+a7;?VZ8nJXuY@3 zQDXg+;OPg2+CYvE#Ch<^Ks+vBM?v$j9tY5uQ9|Pz@OWp7YOC#IRNsuTbx9p9Qf@l6 z&cH5ahrzRq!zH$F%x&jiS>>^5g=J7ohIDN9H}YbgBdgwXx6;ciR|7QJ#{!Id3uU{K zdp=w@s%s*6YMLE=7a`%3J9URb#m#c+n7j$kQl*}wnnQg^w_&Obj}y)d@6 zRqRkkX-SH$3vdFMa0KOWER~R%5~P7vVD*|o;P^S;%#8bzPEupblKafss`aDR57LT$ zHBGK*lxRo@mYy)}eY~jX#N=S5V-qj$F?2fDueMpvUB(+?<1e8kJ6WorW@SzWcGY>^ zF0@Y~^|!B)Mm}m>&#hhUw#47CEm4e4&p)5*>G+K0e0Xj5-cUGb9jxzUzE9DOuZZ}; zzVEO8h9DeWYr&Bjn{R7pXX2QHUa7a^i3s$-8gXDwH}VuRCYbzg2vs0$kCmH#Yn%Aq zaE;s9wcXk?8w;)%6!}YwnD4wPo>lqfk6xV9_{y_gKRUHjYC139KN@%|0{q2E-E{%T$K>LA;Er*?+XFYV9= zRvk-(@+Yql*&GbCfMO_|nPz6oq!_Tm_DfBJME5&S`!bW&Al&hF{tSw_dNxxgzOmt~pSzNdzwR6OL(*I6=og9JYutye7(*S(1$)=NXh2$M z*j42gY-elR8FB)Dw?VqoMmO6ixNX9*bEeavf%i_>$?)SM(eQ|Gbt7$u!n9`yvs3Fl z>XLe1pf$8c&S}yvh$n^2!YUd<5iXsrF0uP~)*aNVrqd z3P3OE!9lC~VsMG+ur`Pc84?h{uIWrnTLE*ar0=-w!TbB%Ha}ynHB`#XbvmCNY@sXj zdPw)s+^Mm}fCX{ZEdqe^ZfzkZbJsYA0Vo@2{2~YWgRr)VPzx1}?>5`czf@s3K2=7# z=vg7o4cL-i_`#{zF_@PoXzMFKh^8)bABNB+rxoBVy~e1NA_@(ME%4V=jYs5&Lr5ZO zYU4$_I4{>Pja9In8R@*4x%AljIhl^yY7O#Ywo_t@$=YWOH<52zV2s`oT6k;On3EDo zIgZ|e|H#SGOE!4(W*Zjq?y`&Pm@(!ysUG)2r^<-@;AH)Zn?zlpI%di%%q4|mPVQN$ zyi*p`V`Rxg^4eG8=)Dpevh8R~O;Ov!y_*z@@3Yn!lqtH&*&a>al3mN(S$)N2_y*W< z@-wS-6rVuSYUTYV_Tor`4*(AlqisSh-_!G%i&atghg8^LX8E2p1jy%V)(yNRAsvqe z4dzlpnJf6GNG;Bi@6#{|VxJBY+X@to!WihYib7AkO#xxf6CQJob6;{!kmV-WHRqXw z4_eMN2i4A+SeSf|M6@k<`KDGAfMK5Y41)mEyp}DqJ^Yee?(2ESXU^D=MW9sfrucO> z#=E-Z#^datUQu7pU;2#QV*cZR=-+r10GVkk)EIY4zVj*|9E3#@ly0gkAQ-1Qe~^V% zbHSsj>hFGm17ZyPgQ&eTdHVWW%9@-S+HAGFi|oFH;N&Xj&AprLPgRXN7cW5ecZ%o+ z>TGV0v{Z3BnA*Le@i-}X3be?nMO9HXb5cA*jVsh|KZJV!s)OcZivDSlG z{vq12#824D5vLP?4R6%yk`83N^0Q11Z=@zy3`{o}#A&(rC8t*bR(D14(WJ$0VqPxE z+j=LgdP?uVl`GDjG||z|j7gI{drNEi(*OMM7KmNGmM=`Nq=4j1x(VKes-qSfw z_omP4zwdQ;^VGwiTYP<4cgHvNmU?F&Y}kA7t92)&4(fWW+1Dhw(CQgu?Hj2G^|qr; zLoN|Z2CCvcI^9#Y?~dwdTki;qz82~MgTP>gf$=OCGzlmv=>ybURogV$s(J*2)Q4(@ zNcXsZF!iFyq4h!o#NWJ?>^zWQWtca4*tEg+z?*sL%NjVRd%2uZ=e)bu*Mp5=2u^Rn zRgQff^+*%g#02e2FdbW500U5GeDDqE_t3bXd%~J?vdw-!<-LuRai?kihp;F4Y*|L< ztl+E6L#&Ax`@!%Yw2^fB^BZ@RL`^+|r12yPB4K}|bqtoG=S^Tgfozo2<6tnX^9ImU zG{-9Z-2Leqo}E)ZFJC}Oan(TcpFfH?n~o})j_O!8DJ$CKj20bxHU8C)yzg*&ZvN_i zV2%k28sH>Lrh5kgx1puA1*}ZYfpz%qq~lepoGrNMMX;`??Poy>CXsAO!{t}(F~59V zz3cO@RPvh8(?6dAn+6Vl2E5W17$+2gs}q7b%)n)pY9N*F1wC7h`sjVki@87kGGlon zZH2_nBKFHES&H6vFMI1J+L=;YRkD(faZQRR)S9qk)>_e!1VIp=}C+{T`=^ZNv(4? z<7w#IVZRvr9|9g{4BrgrUZ^8>XOEDI#c*#6s|D;{yI^y4L*CT9}^#^})$<=bK+N256C_3@aO6CEt)mdCZda9Y!_ z9s^i*oYjIp`o_*#^pU>bBwFtq`|bIT;Es7k6LZyeJIUQu;jFbu7eWqJRCsKDU$DvX zqL_m2%w#%g?!J5W&SzjQGG6=PfF;SZ)4i*^ZNXDMUZu#n!$s8->e8`0^1om=&oEw` zG$8t!*$Zj|zwo{4;I_EJ~xVBZA{yLzw*LS0A(3*6-mn)22M!`?(WEHLj zcii-tnl_UE;sk@fld!(X#KjcU#8Qlat6MPonG#Zt?v}{;z?ALxm}2Ja;~?+;Yo6~< znQp_c*aH-H)QD434KsGkQ|9#MAAD!fj6$u`jch8~lvCVwfa7FVdM^umO=m0dBESN0 z%NyDhG(Un`D-$Cr6QNxhwpVHOZP$-v9=qGvqRsDS3mB>5V`(W8yP5Ku|04 zu!w^$SJ`B`vj4*$vqalhM?^%>L3jiVFB!v#aJ82wW~3taSizK<&YQkAjft$PA>zT1 z@51r*wMo_Z5R4}tGE~Jb1G%gcme;$J_K-TrDCgQWb3IG5DJmtyQwLs;9v^WU-#E5< z;)-{;&;6}6tks5x%~wqLjeP!8Mcuu_HaKIN{Ca7NYvDe3L(Ue~yEZGSmTlp%MXb!H zXx5~yFk~&r8Y*OTe5g#{cV^o|HEFH7N9^^=^>hk&E@AHTt<@C%H4dX__ zohU-HqBPOl%m-(@6$St`?MjbRpFsZ}88tE5#$Zy{fgGe)IKBE)z0M0wwGvtN_a~)_ zD6~A)nq`6VzvacZ_w9~aa9u+KYbs#yWLTr? zi6i5ajJ4+GQdS&S&657=EOb41_^aBfU6sgp!x8k?So3%Q(Rhh%~Yu(r5 zVQ{muqe_@#pK!%{@ik_ZQc-$aRojPo>!fE4Z8KWeam3U2n;-CPKri-f)fc-EV{tTf zY~w(dqJmNI8^tY)uZj85;kGy1;4dK<TB>IX!y#^wD;;feIZ5 zGO;pxwUR-u#iKZ>-ny{~rKG$%X^g~X&u{4VxUu0QlxJhVZ)|D}lDZ`qO`MB1{J_bF ztXE^-C7B1m&yjxZDqefspmvYXvCnu{|P`+Gk#t4Gr}*uGP6!Z;i#Y8sSgnaCor#oFzsz)c}o~ zn%>nk*3dLyGl4wvty>*6_uzpM$D)VnnBD^?iSb1SLfA@S_VL3PTP(;c6wxDL$NMn{#| zY}?QR-w4nR=pJmRJyp1ES#VXZ_q5geMdWibW#ymO(_r7&3^Q;uk|a(4ISN#VEL}Eo z@GiGHA}_L9fLtAf2~*hHiE_>9NeUWpCMC+n6bW%$LTJN%n>i8bfm>bO4WFrai%TYM zasByThvFX@XX;)R#7@5D4oWojkc6je#ag)h(mpsmj%nzRaLJB4O4S1OKkkwH!nD?g1GT>J2PVtUdt(RoAX4>m>xUkGw= zzoB5xj4#_F)0H<;d@E^Q*g(u$p(z*FTY7=!yuo~lQLX+SBj#1*nt@9P$3==>6-c-E zMGdO?&Nt9yuIl=$Qdh=)o^2{8Guae=eeBc=qNm~3XtO+`dbJ*cEJGOQ&QvfuFGYYf zdCcBGV0Q--?jp#^RjPme^om4N_Amu=>sF0li#D=)7dDv4huP>TCUi{za^of6we@A!DKfg0+_G0K6MdV;;x;!=_Yr-5gB0=`A{XZbT!B^-TuQ@n}JDmJ)e&@lW!2IF4VgK zD=XXWWMyWmRvuzC{v>?z5G%B?u~FUL%$gfZ^)yN~;w0N7D}wqQo890dU9s54QP(3USU!ze#7@qizMr+gqHVNt zhWvo~nqPLtO522;JdZ0cPljDX~vE_B}Jx;|`19v;JTIT*gF@{lre6qc)D$o-25ru}0#f73LLPnz!?OrIf|a5XLzvt)1yHOO5vUOvrJvBI^; z(O0&ncu`A>WvvXl23rh-er8f6xPcXce_fS(2KI<{OA-5(w#D(f>h6;XdGUJ;LghGd zd4=#PfTMfWwvM3F-7)X+EhjXvTQns{^q7^qpPIr#haAWPyRq^vsdwShlO!D*?5n#t zT@ka@hJC-iP{z)`z2}$q&_DEqLc@3mW~)I+i${TZwZhjfrW|s+cvZ!NXv$Qnb47#& zDg((P7`F(a$wEo?i^4;7W2pa1z$7^GXrX^hUS6K}B-Ln?hpo-Qx}_kc(_MIyBoSmW zR?!%appo=yZ?XRCi8)=_Udl|E(xk(=*JhQOt);DrIf}~pq}}?*a-h9ocB{zT;TWVr zs?6R?cmL|GnaxiXKgpL?S*WxVTb-652>{=8b?GnzOGVu(cN?@I&bz>GIW#k`%ijII zc;kX^RD?iIDKc&vsUqSq8Eg^`LrL-ETDQ>;@4af#MU!~vWRPT3d86S`nykU%3*QVB z+jL!?V{Bx!sp3U+W4O;J8+SS{J)8`^Yil`jzVve;g`Z8iSoUzkB8+C4i_t9Qzw?$= z-F}PbVXHz2p@S;Qli-^eqa>_~^A?5)GNw5qUj(5l1f%V^d@a>D+ zrmg5R3Voj2UZ}BgIj)I#Ck{*8{c}9 z+(*5r8~LE^1~$2dd(~2RLY4i3d$Hxanw9+hu%|db{gq-5qIuoW2&u3O+xD3QE+W=) zA6Yh%7b!`O&Rbljwle73rWuCyCG_Mu*C!WNs_St19;)lw-PpAEiChMlCadunWOF4b zzMdG7m}$PAFiEhZ2Dk3-W&ZLx?^$SWn}5Wb&s@7Z5_)xYKM1iu5$XD>+@Ych<+Sc( zHFoy~q6c$=J79LHA|l1uf9I-yS=Dhrxnq!!by#dNI|3k z&d>6_@amff`65vs$GDW!)k$jm#}tJ3Fo71r&Zd=$^}40 z9z`;2&gl)JihE7*{t)tzqb)mAaep|4ieZE0SZtYV0ds+L!k}VAVNgp&O0Wa z15i8)=z!Hi^;`Lf7rf+ciy@B(wrJP{i9W}pLIX4x(l3xvA}Qwb8&wpK)f5vUasd{f zSH$-`Qr$1-3he~We5=y%rMfygZYtd@uV6Vepuiz`{h2kuqprvkPqEdfTXXBB%vAE zWfY{aMiN=leSTF?(X9aP;o;By#zN5b9r?f)U0k_LMfmk?;j3?FjDiHN0!d5#&GBvk zrK7Jk#?8rjKx97m$zdob|FQXc9Qq*Zj|GyJ;You`zDxO8>x{4t+G>03jvoNQXwn#FL8=+cw~(T$%p z-7B{a*!GI5fYhb>VplJDkr*U2BsKLL@Qs5Q!N;{Gz(5yRXRw&`BS{ z<6#P^gz?XEJw=^!I-ZcVRouvvQOfSN)$d8kE14^cm!J~F^O~6L{qY^xpcgb*boGKV z_tGOfLoAs6mp8gCsA}*ovCAP89;VazC9gQBCfE&%{H=CnKThaX3{XOJEgh2(b#Q9^Ly_Oryj_q z58am>KTM(woddj;x=G;sabUbi$0AKoN(ZUCcjE zBHu_(U5J(NQIljAr@Y(Ann@hxVJy-Rj=uy9I`E~tSjns=TN|4qI=)Q6ks&Ybj<2~4 z9--wX`~y%(GoCensu!8g5RE8rVDmheU39laW3oD-=Si->>C6FV@Zr>QMDE3A;L!C$ zET|#IhmwI_h8K_{H97x1ILuwNzCRJ2gY#oO(?wo%49|!YM6`DF8x~+wi^|1kKRhvQ z1_$#)aW(ht*W3;1`;CB1$@$9&3|Wan@sM!bCTZ(x?nesoE108%I3B$1pm+oo9FACLF zKI1F9773JH}AUx7hjd56_%S}5@Hc> z^+mgFppJcJ)_?g$E45C;8azsD!1JO-0>wfO_g*u7k!ugrI&o*!JCCD~8c>n~Sk6gG8eVJ1 zK`d5Iin(!3cc5zHB}#;}O}QWPNmN*snmx-u8MD()qmVK`-m}l+O)P^+F@m#XvoO1jT` zn46aWk7d^nHCi!Ay06MH6SrTQsdY~4)J*%sY^re>#E;l`>LpGQN1_gG?Bp$7Yk`^b zhP1e?PVH#7?l<1QGPoOp*;I6%91gRgMEtqj{1Q@M8J*Bo?cC8>LP8}1@;B#H$m_{x zA1W^Ms7m7u3P1zvSV3JBT!{ekqi2UCtz9LEchT8^HF?j0v#yX1$QY`1b`ip+Q|gI7 zYP-(e?Yy=mrbP;BnN_E>)kL!~MxHjhdf#QvM(10j5SiR%4No*xjtuVOWn}%Aar_OC zs(47`qteTs&kI@B0v8`VgW_OK)SDNi=oEO~(6BG)Yu;NS)QbCGL$&q=5IvSDMLUi( zq@;Ws_<|RZu6%M_^7lE;C8;8kMWR}8a|h)ilGIj6pWzA4o-&jJM3`8OqS{oy6kp*> zw1lS%RyXJEAoaRTD^zPo%+g}0M99yAl9vLk3lc666i-gu?j7neE2CG8l*UPw9 zrc#5qMoX?PqdpSU*_OS{MT`S8O}M{uW#PwPx#I938mHwxv>JLWfStyb_~yuDA-xD3 zZ){?6upG2(vp>E}y-esKI0Xf1($|2lxDp0-*c$h$=Z@tS#PUxi7eR0hOlCDsIt`xz zlGIWIgsK;4F3boCMHVQPFe9GiLLePb^u+vCOT9D4Kz(UN{F!$?pL^53F9kCNtPi@N z9zb>66+$eXyaD=aW;49<4w2c~#x+$iJ(7y&%~7x;h>7!4ZoPPb)6?euPx^P?Uu@%K z{T;R$h8(YLLN7V>`Sx`phoZQ@aQD&2v{Q43sB6Zze(|{9Cy{D7W!YeF#zI88{Tq=a z;6f<>D{7Ib0OXH1|8cqVKY&dRKC)q@_#2M4)=rq8B~A*20lGEE0R3NXjx+u4pHLEN zkIiqx6t#v9bKalc7dQx%w|5vxcyzINM;z=n3 z`ZA*4Ng^W>4Ih8Ti?+*01z#)h=E4BYsF8{rtq^V|3FHnRrH$7PfgGtr3RP8GHcawd z#I<;La4VF*3Xn+aOBjhg@Xfrqi(fS<3l4K`{`)vpqTDqs*pv6qU(6NkQMFa*@1Y4_ zcf$)S6px5AB@88};f*-%qx3zujUVFU@C%}9n4iE-hZ71;p(9c3de67!k_nnWa)0^m z76BSjvGreK(ckAzhyG*kw9G%|PM@TSRG|6_dhzkJLyqaGn!H* zut{NXh3Bcq{UjOF$nL0{p)xE^zO$scP#;eT^x-j zndU*_s>#WPoF=f4&|HDVOL_bM>hSoxY51ytn1<{8!!&%x-@V7nfBgU4#~l1q%pdkm zts%w^MbHTD>fvj8nS!Im_&S1=@C9na16SD!LJNkZ?*}5;O6CPCF<;8VpI0rP*5jWB z@RALX^+#fm#Sz5=j&#r)F$S>5v8g*TWBVg`98HPU<*pC2gM!7~5erPX0NHXgii5_E z{Ku(@V{9Dgt#m-_8RB6br`Pi@v>`7Y2wgNexgh$iBM~%*Nr@|i{#-;fG-5)Kk8Pf& zgwlMZ-=xtZ8&&K9GAKp3vOrfcnJ@QR9@DHOE-0=_=n2U%Ko&tf%>;9#XA6u?$qvsF zLs1}C-X1xSXjQo`_op5NkF8|+*@hX9ragIA<(xsRYJ)$859!3#{>WRK0}F!;P5HOK z>Y8@mjv5A?1yQ1NS^)IB$HTWt6!g{IjEZ7F5q^KrQiX|})1 z`V;wMa&0$_c$OT0=dZAKlVJ$GOf-+411M_C!-`et2-ac0wbBSfiXr8WD07*b^q&AB?OW)>z;87vZeAxvvJso>@FwVvGdOZLsK52h?&9)0ZsLo-3@-}7Qrce3| z%OzIratLw) zJ;{l$JmlXONJG~ARCu`wZDUqa#5}D5o&@TH`C?r0)8EaF>JAUDNh-OtM_kn0XlTe3#{;g;wtH@8Zd85v z!$>~<8$;UrX1U5>oaZwn}&iRAMs~#f=}@)yHqtkrfTvq zU4?heGx50byLGB7U@N9MKTWav7Ja~JoBMvn_*FZT_+3?}q;2w*lI-u`<}J64t-L<2 zTz6?ouUn_3`11|3pZn3T$NP*&7W{O7tPP*L;(FkmW#&fgmCp$bOxnZFZ8|Nu0hK40 zMyl=1=RV3aNei@DfIkdWx&D$_Xa`M_^2ySqhU*%T>CaJhFoB~~m7@k*!9@3oPm9YQaud@<|eoMkTtMw}`v$20zp<&)(O7FlY(;-7ie(>7c2Wg0hF`2KkDq4wJ* zXI-@A{QW@6(}$~oDljk!v=MC&aW5Z)ob5Zr)_@G!dppR>&idB_35mvIdEGO-RV*70 z^_C<_aif;+J#>i$<;HRov_FLIKL?ADovtbyUkj?%jl4ng7+$hV_&@h_zpu3;#KN#= zfy#r^IVI2{4sQoGQdUvh12~&T>`b?AB8lfP+(LHS`oJpa2?ijlwktU#3(85HJXmlF zVfC3iB=W+Yzwd9B?ysP!F2ai0>zHFX_j$J8 z{W6<<4=W1jzqd8vq*VR6%jUY0$a#9KDt5ODcO~tyHJyp=Enn=sp@3a&{ik48C5ZSd zC9&C#G-lh&FsH@Eu=^y&6uAQ;5 zzu!VTZ>~CC_mC;F2_WMyWFsvl1TVW_leqro&4s?1j+=Tfm2CdkjZTg_10yyWg1h=Z z-0RfRR)2lyq2TYuaa_>aXx^WU6Y6U`n@!@+4;PuMpp+xRo>UD^-QKIS^5XLEEnh%- zQNm2BYHvii8WCPGsf$exZshrwH1?aK=faeqA1s-o_OOH%9n*o%QB~N&pYIqA{gD@a zYQ-)4e=q&*(GUrSczeDumB7|9J&R*G{ns@;A=5B21&%H=)$l+1pgK7 z@41VA!y;y3ea6R|LGZuQh%DCai~G;8h~Mr>(C_aYU(k?ZWsf-8#> zAQD2SUSAXZ?8upr<8TYO5u%u|%0k$&Wau2OkU(FMnzQ?D;Q~@n2HyRhr(WJrrBsKU zy7~xKGPg@!aWO>BbC&pcfzX?)Ip{8l%1B(5k-W4Rb_j9-_r5&k{TAJKcjTFSx1I;Z zPou$lCu?c(C^EJLR?ymxIP?{k)ve#o{lK+8K69<)e&D5I3Nk@8;rv^+ zcGcM-;elu0Lf#8FLi9soKeL(PUi&;UWV%mtb@U=ir;-^)eXure+HSu_oTsG{ zN6R-3*SD!(6nD6@kmk6PUu!K}YezGxrc&5wkAoJHaONC$tK7X_WG*UzZ&xLt)1C+I z`k`XmZ*N)_+i!=Nvl(Y926Y}t@gjAO7$iB{d6}}1rq^N?vaPn=L+u4?e2Y7ykaf01o7PlJ=j8>y# z84b#OVBJH|z4F_sueK(=;IobpsXZlCyr!J6ps$X`ctbG1U(Wl=n^LcPIL8?}^U%0W zf(I{5lsE;5d;SH?qJKnGxIRjr^hVaAmqz*!5kVvF5kH^^3A0e?^RC7F53|3}-Ukxs z;m`X&P@3}cCcWvLQ>9d${Z^_7rGLf7P8uRuGHI&Ba%&Pn)6XYMkRGuIFbz?UsHqZ% zePj4`O%FcqnfU)LIB48SM#^XuoCmMQ3Y0fulTd=63sa0Zdy-OGQa{A}Joh&4ZV@yC z?6!goJrdS5Dnq~msA2_)LyhO<^+(b*z?*2n=<7$2ay*8cvSSfw0Dg;kJSMZx1OO1Y z$c>ZilF1B*>?$eC04>FbVHo1Ie~gBjlj&qGs7XZeT7BXh-ZQ-)!GCni&r@6ibW#Q| zIt*W-ni)sM>11*hBUU3$mR6NBys@#frwN`=bQOT!DIH{Y2LH!U3=)xLD4fi;3~30I z-c2KUENfCNhKZnng8?0E0z;vp;20`*9p|Ate$wvStR1NBg35!?CSnJ;gFhIFEnB&6 zDd&;->KqD{^(QE-4D_iTVd&(Z&YA24DkmpJCfa8+zOy^Cvgif1B*^O}LzpAY0QLW2 zvr@FX0%q&bjwL zUdwPE)@m6=0SHT_$drwuu|fy2(-_F^Jvwu8a?;Rt-3DaMmgge8SC;}ite06WYD4ua zDVYY9GPiOpxuK9eh${vA9mj*PBvrtq(jwX=gQyy#1rtLP&Lm4iNDZs^L5);{hI^H< z21K1S_dT8!_5QR~GufM?>QlA8&D}GL4CuNzy;vgmud^okHz|EtS!{e*DH zp#3FmN;GJM>Wg@q%Q&;;0WqzhdSE1+ZAGHkd<{!iqk##;XME9IYLR znd9hp>VRg4V*tP+&c)}N;w^7*rJ;oPet(xFqq1E)1td8*oo`tU_+FnroHhj?h#e~juumY`%{v`9l=0~ z!OPM^_{OD+g4U-t4q`KdeE^)k)T6U{Z%!2LFr;2hev7zINytweufvvHQaR@QL~UU( zNs&uwWiQ12hw|)IcS+QOEsU3rmO7a%FGJ5#y_3TQoERIDjCsWQKE9hso{F+Wq56qy zmgV*O7{{we?Zu4>2EA=l*1;=as=PMuY?4B}n^KGe`x4sf3RF%r4}#oI8azTfEuiT& za%AJ^xiMJrQisw!git)9)+@(FYe%kdFPBYywqI7mfiV>is=C&ec^$Ml_oUn1Bg#EV zVRrK{(F|U`<3D||iT7oNRUwnepkz50-VIOa7y*_(&}cd&|7Y0i#I(}K5D7+rsJxc; zS|3eT(c!}88zRu&rO{T@107@pgji%X+M4$yvAuVQaokpc%n85Q%e{{8OL$<#U8K*! z(KOzKF-9&4BeZ>bi5kHg#9Ii4Z)19s>;mpt6nKrAOchHY{Ucq;FwF$%5zY=v9Zbvx zI&aNk2Le+@^*&f!QTp~RR#$+oEGMFlQe;aOnB1gyklZ|@?BQI)6 zb=`{0d|PVv-u~dI(SziF5!ek7Iu*y6ECYTny`)ylvnTKUHj&Vt z+zEkpiB;WQT_r$k>TWz*$ryw?G8z0aoZ)L8^MA4R9^hE-fB3lSROfW0(}|L3k`-Ak zg_i0eWM@}KW+bBsjT1#tX2{+wYC9T=jD?z)(0u>-wq|b=X9PU5XBn0I%V_$#_?nj-y(#dqQ^65NWn8 z5tqPw;QjhTT~A?md(Ea32ZZdgCQRB5v^7`B;xta7AjGKIuQ9UHm~NzksEo5<2c5=q?SutK%aVu45<>jF73}>?tO{Ty5d|`n}CK?0tgO+ z?8*enazq=l-T`w&B;sNDT5``WF=jhQ$E#2mxwO{cBf(+Q9>%chDJ|II6Wuz70cQ{v zTzl?4EBpVQ7IN<(2Obmk4k>gZUL?5x3ad`sY=9}kB#M$3c?^{esd&lk3Td-2@RU87@27!Q_5-H*JJ%EjQAc;9ou_ z#%H)a666z;7MZ(}*a`$uM~sgk$YevBxWRs~41&e_^JFGt3w&A#67$^1epe;1np|}k zVj5YM0uqZm#Y=(o`z8oFjN1flcbfNBCU(v-E(Dp#BkR5XXY(AHL8e|9w)!M4)yK_zN5z@I`+Al{L(Ozx-dApmC44d{z(zSBFUPCYqP{|Ux`lG+|fVcu@dD=4~a%0I_OrKt5L>; z5~l{@>PY?7SVmIN>}HMV(bQXcyc=T9UTscP-gbK&*BtxKCc~#ox{e|alSw$e@oFkf z5(VHPdZi6BRJ)N{WM?3GMx)cjG#6{elBqE9?@2gphitZLj85jG5UTZ@iNE1}j-yP> z56--DvtvA?vd)JtwtUMM)KX4sCO&_J5*+TI6u&P;YnE0lE5Pp=sMhEkZK@E*esSFx zd4*&p)~AaM&Y9?3e2Jre@24%dQLWwpC}WJmm`0sdTAETq-CA|W+nx}`1fV17eMQCE zf8XaJ0}YT^nAVL+Ngd&8G34^@zLGkWTV%&)?hF}oA!n4|^NJV!w>z&}bu?6^Z!LY) z(eRs^DR9$W&`IG5h~|;iR^jhZVGVUH@o+wiFA*t&ma+rk9ezx*Douyepeqe26JJc9X0jWYsx27?@K$f(UIWhu2e^sz{YHD>~$E1@A zg=9Rvl3o#RH!DoJYJXWW<ztu116m2J&A>hI$Dw3}2O%ROs7 zQSj^4?vZa|^H^+iThJtZ1<8~yCc(c@$u#vq>JYzXZ2m}PRd_{KEUh+m@YZ3i@mo<# zDcOZP<%`BCzB#O1!S&MOUpgI?EJXJTl@Ffa{~H;zR9nXttEoa!Y+BnC5&QOSx=X}R z01>z&)sSFHs3VpBRx;Vfgs=T1MNNR{9EtLPTWi6nf$XOwb3-|@3)7H)lFELDpr531 zuf*?9k(~-CE=N@(*D>8s%O6ox8n2oP)YEEpF$lEl4oL5jUAWd8R<5OvY_`26OK)$^ z__OO);wrHR(gM8IJk#ji{fz5&CNO?Z@CRnC0!bfAibB8C@awKZd2 zs(-4xQ5?s8YsO=ApYcjsS8fW+H#yn);r!Nx$2n?e5)K%uunJLpNjqGN(ES{D{>E{a zS_numUbn-K8=2(@R$v}_)lIYvs0NMhkB_3u36%=Oh2tU1WlhEpH6r<027~(|K`c2J zjs|2R^Cz0d9X2~qY8zP1p+v4BBkl~#7T>RAx`;|~Cf1Y+cDMAXYkiz++c#8r<1eSt8sD*^tmuKymNMTy zJvIvqZ>{=>o52abHMFIvL&=rcc@w3ll}*usa*=Hl%?Z->+1{1^;+rx<_&-tWSRzhl z)huWv@!SR?N^^`LqxuvLgF~=8Xc~7QRX=gG*LFrD${1Y41V78kuS}1*sm2GSsMSp^ zMTdPde%`>z$Iz8|fq+j&@xkh%9UmMFSC-h=H|3nd9ZjN!xb+s8b`!=L{lSWWH>nxh~x!$?NRNGn79 z%PAUL+=TTc|Lgrs)FA-4zDd(C- zgD`dX(Z>r#+)a%dTZ5t#nN1C5z43NL#DGhi!nK*?-d5j8lgi~9^O8s^Kl5m^aP6Mz z3^}EOE1_JpN3}+Alj6oBYG{SlN9S2{bk;8bwm2S5z`3)Zb|PfYYwwCg5EoEkf#hPC(Q-L5~+N; zqlr9?EhNx5n5=Fhh&~*hh^stNvR0!{9(n!ywmWMyg*4-|)7gFB)E}stGU}A?JJ<^)PYg*mv=Q^OE5X`o)M* zv`b5(jI_vW!^H6}ZnwBTN58IRH6fW>0tEq=q}cPs&%%DY=c>5{cQq;-~#aE&Jd?=Lb#C4 z*G`>dFz(CkZ6Naud+CclOFw+beuJ}VBnwKC%y;OA^Y%O6l!Y{nZ_S?4>X|&Q=Uw`W zX1%p@#-G#>o4IJz@#S2UM`mxu|1`{UGD#u(6)=#u9C#n=q6!Ig==STeZBkm_sX$lvgL@z9Ry-^0|?AsyqMz?_gr{)%+kL(3? zOHA3wCgqO#yI@3UU{0$McQh=<0dUWuuk5@E)`CMvOG7nKN~_u4LMrBcUu~y&p|M<* ztzATd2jf~W0VbK+T6gnI#0(})5AZF(eJAtCr#q|G{BJNJ_U-P2F@bd%(~ zS0g+yFxvY1AX(bVSj0$hF|yHO)$k4C4@V&9*9j5bPPA`s!E?Z9t5$7Db(O3|l^cPI z&^nvWUJHO7elRmLoB*3?X>QniP3$j-Cg@{d$vDP?Aa@tdogpLZ#Zbg4#;FXs@iGl3 zYdgec&!0w?pm9`8gnKlqrpw76LD7=f;lv1{$IF%;+#-I6AVr>L?epWtIO{fl*A0-` z5Z`iwg0GGHC6(teux<|skn)L7;C%6-sr6!`y{1!jxW933-WZu2hi{m+1!4OqO{VAa z@~Kw$Y1Z(E-=|-EGkbm>%~A+44P)RO(7Z0Sr@EY7iFftv3s}3$Fz4LBSj^5aQjL*WO=h$-8pPQ=HVego1tZDUxQS9zCyVqB5 zG;R3^E>@I9tj22nJ5}ck@~atvUA{dQul+duE9jcCP>q!|rc#&e{GA4eQ9u?5Q0IF4 z>KPl(#>NzVi$AU2{~j8QgDG`n#v6=ZwG$0Mdc=Mf>nhS{6T+B698iccMkrgwIZ$sR zS(slU|GxnK`4$<(@GvB^?;_yRO2LSjZbJ4`TuWMMEIFajO1}${j<5#B}(l)%mVJ050ntCc}C|O$B z*_N^249!IBOb@z-to8+1b%V{3uhgmordfC@{bs!p>4q6MI+4MV z-onD|phMugfIyI+G>Mug`{0*f45X!8uz8jLP_i&weh!4SJm80k0HeW$?c?$k!!epx zT}QMuv4|r&Te7cyKGgsLlt8|KMT}zTD2)y3$XXWecGx$N=w$8H$Ig$7a$YFXVO00!+4_X@CM+qWyK(@f7BYy97Pyqet zPUP^qe2oYqim*tsmYTq-412~{LQQB8x)}7@Rw*`H&%lCL94)vs4VY{INEJz0iJWD< zq_Tx0czBovd2f9E{`3E1??;?X&Sa&hr;}AP1@prx#5$7fCMAUtG7A~$BlRR1D9$2K zL}DqK4FjL|V{=)Oow)E7dCfC-js$NX#iE4gqr0>m-50iQAelH$kY0 z?<3&i?K+IZO`#I3rc+~&i(;s>i<2uPF4hetPmGW@7>g?4j7T)y_d90EV!E`&pPzbI zO;1n1I%q#DT)ipfTIHMC(rZm$EWOPP!-K+&{cgOK*B|QHIQ-%2-pGvw%T9&vXMpJS zfJ|bb3>cCyqt0T;V`Q-^%U{j--!o1sE&EM=IXeB%7>1Z9imEK&u`u?9b>VL1+7rSB zd7mhDw4bT_7LOS3uO{LYj09hfaS>pNlOfM<kZU;j+-4g*3|2uE4P;h6_GyQ$sM}`w)Cakd*g!m6f zz7_4NG*nT7Ibmg9lW9bgJ7zEoiH8N@PvLD{5jjK0;JE~u1}765H63+S{`!Z**lA*z_Txex!>ISD2J;N4c)Bd;;6ig56JODN#? zvI`ER>-he&T(zyKBSW*d(Ugr(9$F;h<_y;vWE0}O%P_PAoykpc1;c6rV6foRy+5;n z=Cul1vKtw1-ml#?@F#T%-PbWwDvtD5A?sW6#5P423M?{9d1GO^jj;m-+H%6n5quFF z*^YqiVW@P7*KL?#DBx^%@7s#oB#E^Mnd8=`leAIE8v&Yb5#p&@((X#r6yfzn(wt8B zp?}{uT&(N9SZu20I#sr#tz)^RiN9$(NX{uS0fD3)UI)7h_l~4dyhE-B)rdA;?K)tw z3T9vb-=jf%HQCJ=NHS{9(d;oSysIXSrZkPbN@4^_Og*f+i8n7@9X2kugCyS_w!T@m zEsxSm>Q2O_wgJaLXVu7!EwsugrPM;6KaVaID)?j{iGOm0dsD~kIg7ZMo~VBx4v261 zzb+4_V)(;3oj{WTeNLFE7|f+<<>%zJJT77sM;CU$1+8XjPG*Ui37}SXq7e@I7=h}r zX<0{S7DBfH414I3aknSi8Cz>GqMBQIvsK}|*ljl1{>uFg8opDlM_+JLER1h^LW;UZ z=Nw+pHIC669F@@V-oRRW#xz0|S$6|=+%AP4j`4klXupGa$}rZU*q1Vuop>gvEoeyN zI^r}7a(M+OI|tmk&L2PrYn<%TBiQIG*WkNbXqABZ z{Ob*_Pl;I*bsBzFZENy-0R+yoa&cD@2SHLc1WUWjn>@bvEd89kuUv=VrluD9X=)k8 zmjF@GBJ#H4kgagY0;fCF;*Y;RqH=?$nhHfZC;nXdK+feW2;_7485b-db7k-YWF{hQ zj7~T%;@+T^k|Hxs&Ni6%I)se;LhCywsaS}ASi&ZZ z8U$)Ef1H@mlNEv=1)R>fJyeW}pQGqFKEW%K&t}HoA(m`!tL@c5!J|ddDHRZ%)=LYh zGpm7V9nX#*|E^`&##9dXQV@X7GW?Fv*rv?uLqxv_r@fMyC1bMkn?SqgLAa)TSz~G^ z+u+N9IA(x0O|$Th2K+%07~274`;Ihu7P!PiO$R5184toz2OlcjRy~=D{v&1L^Z@Xw zg>xFy`_AQ#bbXQzh?X+cjyJOG^bS5|?vtbOE;g<)Jn#w%Y;CgI8+mEWiM~aaEFrae zIlkCxfc)sm^3f6>K{Tx?D~BHLO<28En~PYO_t9RXMxE8_)B77~4OYvQfqVojh z)3)%31W#cP#seXuJeLk|rsP42R&e^EhTl&@0bC(SrO0SO|1|h<5}UafEnb!}g7lsE z)~3$j@SXrIb-kx^g4Q*@>GXLaiHkZYbNi)yOm5Z11m{m=0C?P^dUib2yLD7^i|6JT zp!Z~#)%r0RpcrZb+ZOMAfqmVRnK5@`C zbaP0DAzY42E1|InCrc%0tsKm(gwWvsHZQBeauKF!#&L%`6Y)Pv*ujShL# z^u)Pg6$kHv^XotLpKd{Qoy%C81lL!ilzFleeFEfw4D^<>^i&y8orDXtR_|qc63Iu< z=f~E)RLtmBb%vKB zI6>zuLP`9$fQ9kj0M%CmIw0>6h#UrVi3O04%;L@M*icIBRk6!?G8={o--tOrArfnL z2rI;$v+&F@x!w6;-&n_gOBGSdl|N>J&G^r>h^~R9(q0iY^ie2Al{D?mwrjg81UkwT zm%FG}*Y3M`fLF<`Czwl;<-=EH=U6yQrjrb%DkAy1Zl&J1d|C+w~V^3ap0$hDNmfaUd65)01Zha>&M0##|o!%`LB}Stb!G zk91}v!wnD|fGkr+hePDdWKNH$zitsEAFd0`=yf`^lN>_nWYl8&+NUZ0K(#flvPNiiGF;dhi-?H}Du!yve& zMN3aQj#*uCz@h%n{Y#*Foa=ouQ|X_(ioSZ)=7o zHI=v&L1fP-Lm7=87?c0K?EbChY+WwpmS>q((0nAImmuOaSJak~1-lAy` zMyeky!+`h|?E&2g{Q%cL%Odf}xha1^&|ALrK>Yx*mR*Oh1ZUZ|n~djXuDnCmN!z$! z8`2psHs7xg_jrH&G!F@c6rs=mT@}l}Fuvt>gr8i8JM7##9BN+WZ8b}J{JpuJI7WTk zj9`w+Vc%GxW9Sz5^oH)DeN-#?L(Zj9i!TV5JoloNiRhAv2_aeY@`4n`}h~BU^uKevueD#0N5CIGZwMbJ5 z3pS-n(o~_`ks|6sjai+*IGJTJi^%TjK&hk|?7i=A0~^Ny!|gw=8<8=En|R z+DT$79MmlTqY|N$PS^e#!|5%zd5z~kmdZa{zhuCE-cZ}|!;E3LU*MI;2_?*%h-#-E z_?M&oEkufeT~cF-qTBSxZ*YW?70<-$Gg(S&3abvjLrD%U@*pDrHR6<4JglBU{s@{( ztooSUd3wEwjjS@oB5u#7&e^Ki85j4j&pV&EYP`!E^=*~&c=Q0@^p5KR+15Ffmq6kw z$y6;NH4BE~Lnol;Jw{qSh9Ma-6+s8tX?^9=Da;F(tlz^>joo>d`^`*^ME?Hn4I73s zc@>B7VxPFYb~YSWfDXvZkVw){rxM4~=^kf9pS)KFA!n7<2t~r(etpDiVsN52;ZSG% z$H*A_p!4I0xj}k?wX^aw6q!QV^g7RJrH}fpn{yHFb}N0}4oIx2!(h#F(GPjLue@;O z>Mgo2TU?f~=K|wN6Mous9IyB2)}WieU9vJS4Hagq+}ZfY;Q`U6gd4Ps*~8X;ZR#%K zwln$xRsOt%(?m0Me3DR50Zl&t&ECd12|2@F1HhK8V{Wdpkv0GD% z!5>RBwY@~mk}Q8LaS~x1yCtbrXs+DyMII$gjRO*Su)ncI+~_BB_(Xd|JmCltjBbE< z1d`F=mJSb2+jB&TV+^4V=|c+zWbvJ3_xd zoK^%Xh*1q5>b6rw*_c_b^pJ|*W2^+#nk8BZgpdV|a^{!shIt=)*S*S1FtV$j(Rw-T zzK>R7E$A#H7UfcRe(M22p#WtwAxRZh+ENdxVlQeo0b7^)a!o4`0tKqp%|k)4%VO<4 z3f5W**6phK(f9SjC3%`0gP&JyAECcZ091h^Oq9G-7x=p5ye=WL8>#tYMB;%`#t*Er zIC9KVUc*JqppsFb^(FZ*0<^X>aF2j@j5bDH@{F(0o5a$s+V+hW^2!&EDJm6M-f*h= z*cv7t<>K@BT39$*A)#_pgAp$1zVE(P5h`lzE;=S(v?I0pyNhB>I16Sv2;P=E0|;Xh z(ql1V=9FJJo)49MZa=v4gP7s5TvimX+@l$1N_loYNv(std+{Wf2!v4a>ca8PREj$e z%DeoU5C6-qR#My1 zX@Wm$RH-CHfU9CN+UPZ)Fv4WRX1S9L?!Kxslkj}BiI=f+9_wc#xCxCs2^|kNAc*c{ zS7JOvpy+mZ^=E@FLX9FtYDePQ1m2GQ1YGOnMI{5Wq)>d4t1~>f3fy^A2i5TE^dsr= z=zyH7XmA0iR1OC6Z*9sm6fl#o_M7$D%SMW;gF%)O_%9}=$2$p9Kl35Of_eWD2h4l< zhGyUVTqR8#$2}%vax^P9VAJXOW5nea1WWz6DEv-J$UdHS)GzezZ^Q+5O&gh^sDax_ z*SIUQTT1EIqXI%((M$6RS;aIq09GOs>!x%)hZ3v zfSk=Rjf`mu9Xp@+n1bujSX8@UDDS&lc4Dc$X!1v5Rrb=CUh%7~BmX@8?WxP3+;==9 zHBPKf)ltqAxJXmU*LD;VZkd1B(i&k;GfdL%tT69pQchKqRsKu&`iX!35qdB1kI+-A ztn(({elDK7QT6nioV^`r-Lp1`Hxew7l6X2Wm^r@hG}D|&fB~XAI>X;9vqEq zd$~^@M&zb8OW$fj_iPu==(jm+2gts9$jDWp*@j*52$ysL=Of!AzDtw6zZKLh=pXg{ zdeHBy?b~X*n|#cp{~%PE^2;AcG1B;Fogv3r=Ow98NIr#16HfRxXJ1uX05Q>u8B+=C zo@3Pi-u%fSefy@`JbW!Y4wyA^(+5x}dC8SBu~;AQV=m~od%HA1znrD({m(*)%-){^ zyjsHI%8F;Z-1ctn`{n3kTEY?#oVQQV{1>6c#AJ6ckS);of%?{tZL|w^BmArE+H0Pr zeBY;?a}HHl>dL!!xXmiOxeBrCbtI2FE+K)2Ex(#quJrB75WVs3d^b*gN0zY{HLb8v z8ymh#plMbK2nssOX&n*|R&{$A#1KLS2RPp`mp%O=lT!2(3wlKP$oCelUMFCxF2F7< zGxa(xmAV){gM||>L-ug^hlC`PipC*x5T#}!R@Nm>Enx6Vmes2S-SE%Q^`D-am&i!c z<`&P1E-Ta?ZZ8{ViV4ut(h_4n1&^@d&U|aN`K}x@KZo%ip)!#ir|IFusYhix_V&4; z&^BgTy?waRwm?_ZGtmlrhadi?tE>CseADC0r;iW3rf+!2)#G-2u*bOWBFD3qN)(DRe%87_|5V4n28v(bUrWO^)lgJq8JA&#%f_bB z6IbgDw+r9mG zAIwNpYlcBaboA~t?OZvuQOZN~M*00Mcwb7dx72&GwAJlp;@T1+&(yI;I+Q8r&~4!r zR%KghOd(g^{PRZ$xD2)VLiFtt_`Hcz%fLBO`PskoPG+*aJ~;fgB*eCZOFL^&-X+I0 z6CNLNZ{D07?Jm;PD{@djbH*Kj%i%B#Uvh|EdAo~VT~*bHias^hqRkviuBw`vmUmCK zyKN)ummPg$5l9-+>C3l$p9kBmGHrB`;}moEWTX2>zX6$?q366 zcb0}l%A*2X6vI`1eL|+l`(kRUHkUZAt!|O_+@%mRvtnpQop}Y*6O1??w_~R-=r>D0 zcTebMOI?{(F2bw_=%5h$YDDcb%ViI!!u{gD=KIh zOj9v8H~0FoJfXoT+QK1(DB*`236SX$8IN1>D1JiEN)mnrhb&|l?I5cWQH ziom^+qfj0>mHzLe=2PX;}Nbx#l?DqX&et>C@}` zI?ex^1T8c0;A45RrnEN$NS6Q)kL-~nNASwC{r&tl(O&-V^O&l-U#=P$;h>3FvT`vE zjv%%@HP_%zU64w1VfBz)%(gX6xRv^wX%YHC$+fvn2>()MUbav z8l|gOub#)alU2T-udjs5yxn>ZiNtczUz+5@#dZ&se~xzM%lndZ^x{QVs`Q)BFDE7^ zjoIBx7UwOb0J_b?ojIO(6@>h6dX}j^J=k4U`s$$q&hN=AbVClWG8@63?o%uUS2)%( zNo>K`b;DPY7jiRGE#XyN7d@i6XE%x&i9K3d{~Kr}IVGhwv_$qmiBb|1&tV>`R01sR z=4l-jm1s<{DENMzcCw$WIV5=Ws1E3nM`u3%dM1)DS(G^?U4P!(>K+AN+;o0wNNBh^ zYae|Y(-qMxR;?K%>jeMY^J~(YDbmn}UHAyIs@}2txhZ{}&)+qci(iQU>|YjnnDuxC zd94z`?q~JI)upe72;8gDxh|ruBBdp^KA*X4W7FX~-uv)wAmIom4hob5f?kWjBvf|( zW$F+S5D?&mbh#%F+e+myAQM82(l?hcSDs9fU&uLY_=K+knXUzWW>ov-rMm|$Kklr~ znnHK|Ol`Hcva4&!%xEWXLK8oJHA&=JbspU6zRRa#4}N8|31+lel=du@vaGHAH6F9G z<9u0+aL?Tiz3*ZB9vE&uo9y`VeA%8dkt>^5F}>x$Gtb3c@%XHkeDz^o`OCx2hI<8j z0{ELgKhk3U_cNcAlwL;ZVqy0TeZdJEn{!a_Z=R-ODqs*e-lLBWsH%pdy@dOUevO{m zJg1?k77mifPWETq-H@5j!%8O(bEg)ac~UZF-l;D#@lmT@4 zQl{xYzV;?1XMssIHUUox^H52Q5elWpij(L5_-oVm@81i-7T81gD({QpgRr8*7?`lW z-kVV}pRDgPtzP7Up*tEhutxWO&1ilSY2rUS!NuZ{j(I0?qf~0D+_}~7wr*rz6Vs@4 z>((u5rpOv92VN+oue4byF|!u_}Kk zT(U@Ls$`B{ekLBGWj61J{^Q>>b}nE|p1@ziW=*uCT-U1}A3dY!q2&I$tdi*hu>olM z9B6i{S^V>FR(!5!eOuz&lx%0!eT(vSSqwt;x7_*aP-Kt6v|;S-_2+(Yau>#8RH2%% zyxxoDCt3QgbH+tsett3Z@lu0p`9J?(5vLtRAi~pi`!7MnxgZ`n(4S zHWFfEYnsk9a>{Ae_;3CFNv~eLdR@|WE&tedAHByNRA)yF+sSxdq~@OW***i4q+AQc zDQd3>Gm?KxkyA?NX#C4%=p%CD3l^U@8powJDb~O9oind~bAS8yit3^NGR^sznu%#@ z&>lr;afR;NWw}LHWJC4ZB#MVOx~0Bg*`MORa`cW_NFh}A_Bayq$-zuTNVa(0PVfZM zX?DDJE!`E6W%|9T_G*EX4*o!qkT;-%8`#Keb}xkt}xX$9NfxRGmI zy%`^|QiPd@hbKuXLb|H5Qu*Y`lWOVN7Hz^VIF%ilh90;GV&dWmE#;{d9!cIR#qAfC zqAnz=(}T1Jwxf!4T0%&FWV&|c3d(^88G;%TLi}96|Hk_JZzIv|DAP_D;1s_V1-O=u z9U5|!Mkq9SOM82}>Uk(hNl7!wiHZrTI8Zo%-8>X0ymSB1&~2H8G`JiLk5)+3IBDui zp&9lM4&JKF9U2xUPf$k2fT-)Iv1hYY$L7%OA2uZBabP`5_WW789VX9) zp)Ax8Vb5^`oD$pEh@-5Yl$E7|_ccSCXXcVr>n^lcf#>U2SBh~UnFWtL$ zZ*zs7j&6HLd4dX!H8RiP`_N*Wsh3QQsjlI84|U_{0$sP%)g`R2+5MlByXF=aCCigj zG&9m~j6hSi#E-xY%sH3PU?!*a%Ch$1j!uuXtKwzqXJ}r_*8xA*3Irjes!C1@pa$9x zhZV!MVMhb-b{l*yvu~75qi>1Z%galHLhd2oH3daQjouO$9tui>bq`K%OexaP&^R+S zU~@Rcg%I&fOgj%Um+i_Aj#L}}>j7J1?L>yow3As6XB_=ZRsX}FGLa1!zkbVp>%+%g zc*C`JuliM9IPrwX3dMP;X;7&x>FTQNlKS7%&lhn05jLN_cF;5{JNp27%_1sRC2md= zC`7qVCD$u&!}enrF@KW)n~>1%LyRBJbZ5}wP!0p*qic^BpHrUT#W+>wJ>*($5=3|1}TUnSbvq-#GqYm8NBP>8lpnw0V8xiD7*7 zx9if-GO66YU5tl_CUVcrR`<(WKD6dBx`x5WobIZ^;wQ{{E~7YF_fy`OztB+5owMrU zwz%}3C|q!3$u8y59ZcFV`1}aN;bt4j+gaSxikumvi~H5oa!c6KHzw%+@r8A~%D?*A z&az8l7b5o@#f00ax@=qs#hdK#-Yj$f<+W}}(r-8%-Lr>bZxz!+b!s0jQC5Va=u)#nLCoIWfv?Fc_7Tl^T zD!!-yItIJ={@cOgaY{NuNjf53qICHAfcpREy_5>wtp`-w<-Q$V@f{$*STc=A2bw1 zE~7XneSLj_3sHlm4+3&iOwQ=&uzKXFudssYW6G34(a8+fFy};qROfU8uUyJr_d>W=Cm6hnt48 zSoA~Uuipo8ZgJeCk6gTLVdV^(9+$#l2c8X%g)cXXxsHXPfPl(ThiX6)!ouEfG{{Ri z?iK7V?;f0}Hh!tRNGvC9L_A=vOj#^@s{ z-gLUg5!LLWiR&Ew@#CgKgtQ(iNTIP`&LhXapvJFvU$@QNFCtC-#|4Aknd0B)ZqI{o ziVv4^ZY}B^b)ccpY|69h9C;H44%!r(awM@Pf~8{?|G*TCt2*?RufxJ~;~y>BI7Cf| zH6~lSNXJXe(GI|}Gq8)p=A#I6nea||Ma6541q<|9O(dl~c`ci>Wlt$8dc(aXc+G6v z=Cm<`k4~SGQ&2!7_ihx-F)=aeaOnd3xA8&Jg9i_spifs*SC2csvp(@KRw4{OT-m;G zRYXBfPDNW=`|WW0`n2?R)&~6}IZ=x`&h{6RzNGhXjIQhk?r|n()?oDy5W`tJG$b$V z{EPfDGaEWVCphiy4z#y9y~r3egAH28x5?uJ!0l2)PR>(6KtN4R&1g%0Ye^|{*##4m zctlvYIPkL0?}u2{hJ$d+AkT3%AYIi)q}s6aSN?XR$v+fj)oE;bA&2fJ*0n1Pl{}NP znSR>z+0X1Id&XMHN2#g%YC;dyT|?D~t2X6J?&90Gr`0ty4qDh=DeW~#7@We4&C#!= z6~8XNcDx>&2NCJfc@N-TNZkDIQ{q&oG75xS>-Vb7I>PW=mNo7edQ}h3w_&1&?mt~i zV?$O3c?lOLzBa6Qyfua-d#^3~;r8eRTbOBt=7ST$OgWnFm*3+^k2(m7ie_pJw9RVW z01Ga==NP3C-Y<=xN-l|99QRCHo8A-h?AflMYU3@>*W5pGi>gmypi2;UeteJ+b>SQ| zK3ZWX%T8@QX!%_4D?5uvfWJR?(zo6UfQ%1Ybhhb8IE&D?SUWVg=xj(>+0k0)VVabl z-U@Ay##K7Zh_>H14Y>;74H(xyq$%_$i<+3QCAH$!x^UaWocJ}|nig87IV=;;Gocy%vESoz)#y2YvsW!r*B*=AKAXh%@`P*vO?{()zukXzyPEzJ zsO;8B>e)3BqW4uq*H-ejE&I0Ra0tx@<8oX?9_%@s5F(;y1u zeb=n#h{d9FZQw9g*FXGvk&7v+G7Z<*-Ca>>RgaE!ZHRke*GPFHDa^}z7T2Vs$T5R^ z2pSd*BOKZ`GaFPj-_1 zc9Lv6cJz)&JI#)9WvV2pO&*swBp56k|CheL-JXAoi;GXAyK6p@k#{bqB)WA~r~BnZ z;4>S`(}3(c7{E#KnVh`*Xp1IN_5oDd@jpce!OT9b8_1?V^zzD{EzlJ%FE4MWn_o4z zPU>)T3=uD~Qk@UdE}@1A8TW9!_u;tq@FF_ZNZ&yRDLiF}nP z7F&<*C%3V<%zt?6KKWhhYi5&nt0fLUHMso1=>0C@qUrFgu3cUD>K>L_`KCu~UF=NbBO{RG5{OWrwSjU)}X9{?;q! zkoda>kEi3ww@Z=6S~DY#VJ+DBnT*Pm-;6vv#k$ zxIs55x8z2;>F2Go{*+xE_w zxIJl{o*&o!wi2D`eE>6m(Jq91KzGK=D+tU7X@%q1&6hyJUMMCcr=$e<`bOhtkT)eQ zt$+Rc^<&)RK66ZvXszf+NJ=vMG*WUV& z2W<85;W7~;j%&fT)md-W>6|F&`tF%uSl#Hx`$@v+y$Gk+*RJtdiFcYSB*NaeEJuFXFf!d)GCve!mP^SA+}^~#-RCR}#Nr`c z+-y&46%uf9I1N#_)x;5D&9D2$*9zX5gYV)<8>*Nx_w`4OmL5HNG~ATn1694z>~*B1 zgGUsZj#rqlyBF(QH)|aF@cw;0bb8w|M`3#4E!V4}P@J3np)FlPOHVHit>GFktquEb zry-O*+_-1t#;K^Me{b3}Y5{@kiB)S^nt@Np{QUVdv7F^?h_M#(kBYMLGyJUBxygYF z$=JQu3v>YnY9cq`CB=iXNI|&6OA%8btqqDh(nCc>MdH%GOk9wcNiIxbfY)(+v)(v)cu?uBE$YscN zWa7nv4$I9)@Eb9(n-6Hde)BT(GGcaGr|72V7x=T8+e8>^6@twLHUX87YQoEy%=1lZ zCr{qPr6;*dvhsw4;^oUx3msp+C*m8g-yVvVw{#3P3^AJN+YZ}AhN*P}4?rE>f_(pN zH8a!kZQ{&j`nfFwld@^O#FdS>im7>NZ!ppd1mvs^ga`}(8!M|R$V7pC`_%B)mq5z+ zuy^$f#j9^uX`my>t6MGUg?i7bGf#mu7u6Rc!+i&zgRy5 zF$#y@*UyinvL*>bE&--UwwiD$9i?O6|DiUj2;oVga3Dkt?jGUlNFK+>ItiJiHz>OB zhZKd7K0H4!Q|pc#ih=7~9G*tg2pu=);lYXLTx*FtXGeJ1=Xwn0NElbKQVx{vAhYgH zze1RLx~GL=W^MaqF1>uUtT~~IAXfvlsj_+pr=m0E0Hdy8Eyj> znpe_|LrlT~G}o710F6eFjIUpf?1vg<$zv~7{c&5Ghnrg@8Rq^lR#XiTu*%Aifolktwa6fo0(2B{*oB zYQ8sgU?%o}raPF+>M^gC1Q5KjC(mixoQlJT`W@t0E^n7*GHlaKD}8w!oY85JbBS=) z*!c3UMg{w_hQw586w7G1%HXfY%HlCPgU{6QDKqqtwYRAwtu?KojiRLF!+42s?a_^? zvzGsCbGYyBbeR?(tLKlUUahfhrWzZ@klyhiT-@u|Gf{@eqYktH1cC$m_+k><;V!K}9>n#IX|8PZ76H(W$wH^R zI5OH&NCrhAk*jH%w!kTW?%dNhT0rM$x+?3RIR{;rMsx{lm=NJwSUdCL1t&2b@ikv$ znHp}9$0V&6@ydPp1;AngMjq9O?%6N>C|RDGkFJxBpFNVZVfyNPZMbjs;lFNu zD3nwseAxFQ(P*pxKD{RSpu4BgK7~NVBn$`*J3@{=4?6H#KH0!*4kw-oC#ul!mQmHS ziX$z%U7Ry2xwR2Rn{>vfO?ec#?W{g?_~ZH7j!GjY%2IF^SC70tC} zOWPog^YA(B>+Tr5(W!mwR>6x}mhT76Gl3l>Ckqr=X|-OSJn?&uWAW0Rk7m`gz=vOe z3TYB>$=_R@rKYc+?n0k2Lm(ja6q1;ymzO%I6{K4WfYKXf^VV+KvL#_+VnV~4gX^xb z*2Yz*d|8TiQ$R=|+~jqlNG<>J-4yL2Qgjf3B$5A6p(CRxP$(4EJcmm^7u!p~Ry0XK znJ^Nu(BkUay2xpMX#u;_8g zu%Zi>FI0C*BLaxI9BFh~O5NP7x(n4woPe~cDfZFMde_|kt4)iB8Ed!QPjh)c_wVi2 zyX^N@vnc(n{Vh_eFZ++<&Se9CcNCTJ__XiJIsuac{M4`>^ya854Jd>TBizl7sUNG> zrh`$9$*}*lb8|WAl176rvwQmZXaX$pKHT^4qDK>V?t?U3R|MjwbiJY+dm8Fjb^gD8 zPmGN{4UmnX->Iiocf{f4rCrQCiM7fP?fDe?rk(mH=eQ~N*Q{5W9qVpEnnDQf6nwRp zhe!FNr$fb%T@`*wC}?S1y!?PQZ{<3zT`k8njVSKBPPuckB)o{{KP5~#kmA4g{%fZl zDhR>y)HZoJx#aA$sC#SiG6^QUY7Mh#GxbA+%u?j0vsmC3wAK%8yT^&`;xix=nd&pw zZxuU@>4m&bm^j_X$dMEbK)KtqUGX8ViaMa*q2g>AWTALEl ziKUjA=N#V|NmAXGEg=3zKW3gnoN7hSgx|P2-kD>jN}hB8VmRIT8$61(elg{!K$=ZX zmP5I3(UvZ!pb${@`2q?HQZpDE871oEUvs!CC|Jg+uPfZ<_D*i!>a;t_r;M{$H9oFO zPA$C9-|}Fc`>HIX%FE9uo`09KiL0ii2DjHFbR-)^!g6!LuCkqc%^qgem)4P-d}?Y@57!9jEdO^_pmvJ&zn+UJ-FH}QbZDIsx<7$uI(5C8&`)D*;9 zi|f5)N8)KZln?b7b7|^xZOAO>zIFtR%Q`_L^&|l`o=O!F-MFk_spQ|sRQYs;3Dg0(Ihr>yQ9rIL5ZlV@a;Li?eoIgx6qvATt%I{F?+3J>{lz4X$WF7W8EnTOypkR zZG;}QQqk8B2j>AS0@VsCTpoVO4UboHsRLtQT4>L=j=*cZI&Se~rDk;&p7Mx;e2*JY zZ-k(Uo5uw9@7Dz8tD>q32xA|BV124n5&~=0hYxDDwpq8OcZ^Rj9Oh)vT9+cJ78MDk z<$cD=B>^^#YihDK5&I7OyaTLB-S_CJt=G{zkM3!*sWNlIA>ysh8u9Qu^O!wKNqR1} zb`JL%@zd*L9tyQJ8EqK`rIHnGR}uQPvrV2L@gjOh0RTLWBFNqwXEB^G!KY>%Uwxic z+UbC-XsYzy@F2^r5BF*32`^qg{?61IJ!%?oxUOsCD4$67n1T~${;P4~XCCbWIKvFt zQ4tg5jbUBR73=+{-k&F+zc=RFdqf)Y+!OZ4D#X)I=d0Q$v?Gohy zuET$^{r($=OAR3A`!g-!hdI3!r+xg&KEE^wjc-pH<9c}DmuxAzPD0_*a;R^yP$#r2qiQLw6=PsZH>2| zD)O{H_rydi5U*BUODhS%mZ-713RH;(ZRcN4!O?^MBw9Dxg}V+3dG*c!O0_{>ZPRT4 zYQb;$0PvBD@yl23QUy|qJPwakuVg93e{L9j;b(W+#4RqtUh~DF6Tm1jEi4s>5p)V< zRJnCJnO7c!yDi?*gMuC1pH7}-Qnv3swMmhmATiq#FXH1&!>n1y*AA{;=X}Y0rPGe{ z$%%=iKaPWy&DEL$D?~WXZzQYT(FO+s#HAixsl=@bBP26SOPz*epUm#P=@!W-WQH)U zAI5D46R41bR@!e@r44?B6i+=$W%Wr{7lWUfGit8A(ThJ{OI7XHPSoqSIKbC$^x#G1 z#Noe zYZ#jv+@&`4@C4~b6JKBj$x^Vv8_`8M;va?LO*RK=k?XNFb?b{kQy_*1#~M!0to#?3{>WMl#~*tQJ=V{Y-)U*7qq6YJxCqi zJiR$_U9!#DN9!D$KgcaDgly@z-z!*q{u$sw_5wxw3Pzf2wn(Kh*9mziTz`Fl#xj zv^~YH_pe(4JDWbYB8A2PaG)XLHA8=&hJ<`BdFy?$Hs1gF^TaoA-bgNxDhOZsDYA8I zBCQlM_zC&~K65L)2lrAqB<&JGVM2eZIq(A`i%)*JPD;LF(d5)tR*oVxQ4=PsHF)RT z2YPv1N?+>x82_p<$BdHDgsNK~4# zy1Yk@6!cs#!EveyJDT4!>*`EM0W?yh?b&Cc&KT~Y79peOyz~+f@tY;|Y%w(UVmBX~PkjhCbMtC1A|c9I3ml&rQ>2_AuJJKXO>H@ z+{Jm3UD9swLN}&D34aqJv4##SJBlw^NboSTA_9}g)0;igAy%#xA=>4la?^%|29G1JE2V=&}#g3%Wbgp!V zip1A-s6;z603{P(#$=hFIG);)p8h;AaC0V=n%>2+?g2V5VM=m(I^yL8Xo5UFJ%LiC zUJ~I$C!3WeHVWPY%iUB^^Z+eW$MH$i$$$5QMKb**n+hMNY0H~>?YmD6+?mUd7A5DT zs$g{6^I}bv2s7!w0NCM9-93Js#A8|62RIHnAQO=plaVfMB{xXDy$D(fFOza$wLe|{ z{Q2gw?jjo3xS+7`7`{6&%=xJ%rO_EgnQ5rMB<6pv^180jWSM>ae3^(p8a>c%L_v!e zkFU^*ssV@cY$91UAQCr!B{I#P*?A$`-N}pbS zP7-&7>yLo3h0&$N#KiYK;}Fteec&K+hB~?~gwhnk?84{piAIKg8mwN22qp)ATqhLt zoD)vCCrwzA$U`BE9^RH<=zRzgg>+?MlmCaWFM+3W?cUy}PIa1enj{$-b`v5+X_qKX zTR6xNDj{Wv5GpcMCz`0UrGaEjW|>2%gA|!E6H+N-8Vp7FuC=$`-tYJSf1lrZPv`At z@BQrOx$kwab*<~V7Wqq!jdX2$%u=y!!zJ}X!~N=jk$ad~uRO-EI03$5vN2dazH9#1 zjwHKQWx~t|(R_Sne5d?j^bLVx3-MVZ4MY#3cZ z9_Us#8e+0F&{dry+xKh(E?JK%GjIGAPA{t5{1qWj+rhk{%RzZh`x*frIEyw`Ey3Q1 zjx(3+SOLs?^5n^&IjA1!<7c=lVZab?!&d`d8{P#Ga#90TVd%nZ@7)`L&XwtrBfAHC zdZv3pm8ML1jAJH_Il`W>n69=>xVmHN-$mLYuc94>Hfvepi`MV`5~(BE7}MM|Xp7LsVqz}1 zAj=$tj7RA6z@usc5F63ak2%RrbrdI6CDgl%FmU{^w{6ifx}BEov#<_i z@j{>^s6;c9%pIsbqOHh|=%!~P9Bp!C@U_!Rkff(V|Xu@iL{MOyDC;ZDxW#75F zVn_ArvlGRbVm1#Q!lEy z!>^K4;1tpZ^;mpT7HGSa!VNUL#oIh=pZ&5(2ij_!WYr{d6|QNe;)b!na_|FlWS zYb!HC&QB4yZ<`&$oHMQS>Woc#_g8CiN-%7d)xZHQ*|BSH7LMHhR1Rm>ez@I&C!SV` z4dN8P2eFI*ByQeU>HGqs1H)ss5zRd{V1`w^KE=J^K`x!3+XlLgq(f~ub>qx>&LOeI zNR2QqN|88>%=Q}w*5%~z7||O55ePi1n_a(k08*_+1eCa+)RN;!Eaa{l0EE4Ze8wo) z!uuD#)3vRdQLk|zsly$Z&+bnvBp2D8FV-;WH0+B=*Ky6(E*&WiVKvO2J-ZKLi)!Z* zOnB%;s=vY5Ku(BD%A#VMLkBue0D!#a=z9&q3MIvL*a`JKv4;_n2~9#Ri`msna+;+A zOP8b5tZ*kM2N}R6CEHr0BKU_BcJ}NzHaI}LP58m;nk4LYf)#*Qrs*0J?aTMtwS_;K z!{Q+B0Z=GxwR_IPkUo4(Y1z+EY`{_N#67C}m&wX%!(HJa@L3tRk<-qmU*G#FxMjnS zQJ&1;D^Q^8yP{R&vpS-!>eDhm{_cX)sPNgdh=Duc zURT!XAZbBMyISO&d>N{y`?!33xpv4Nq6Th(OF>PD>Km&JI=cMjo!8|Db040)y09@b z(osh|^6-Q3>^eKk*|DB}?6lyo8{mezlOLiIxE}yTijjE4HSJi{dxpv1Q`X1V?dj>4#88@(YD(e> zlKUQ(xvmDUB^odg?TWC`xO#zU6BkIZz5UYZojp4ky4Sl{QO8wqM+PElQ{1RtEYbSm9#mgIOA@E|p0GKj2fO@KE$yacQA zR@|E_u44u|j@6opJgR$0G^V}NJyiQ!#G<?tbvWP@>xI{HJ29lOMZjW-BHlinA~f=B7p%9gBmU0_lS`b$KT8`_yfxbqWz=Y~n!2IQ6q+>9^3)ovwL zI=BTL%{guX(lOQi-2BWs4WxiWeuqO#QY3aG)xks3L6Znb50d=*XiF5e*hHY=1Evm& z%m<}x;1GIxjlNUe%~&=mK1k^<cd+_1 zYyWWb4@;nJjS-J~xK*>*;wkP%?0y-2Kqfs=1)gglw!0k;`hR3)rIxCn%=4xJ(842P zVqRoFg?t~7m!O0-Qudh*IP=lu1XCMrro1f1h0R}XmG7h>X!bIw8LNQvoh|6(bQl{vsj*c{G zUU@>QP=kS+>n3@Sb~n5I_&Q(c+6x)-Qzu$n0Lf_|EH0Om38 zr+OE;A*9bfcxnBhW4#xWrtM5E0|*@Mgfx$i41gn&f!fspR(2gQi!1}b5|Qp6JbOC; zf9WFW0G@_P4paBTtIBE?k5`}=0~+YThsqO1!sA!`IbHIvVWo4E#O=@}p8j#FXRbqP zmA3+_H>R<6tOwbkpy=2V(2bm=0_vYd@iorRHnYT$*Cf3=*_-3`k0ZEv zT9=|e6#Juf3tiwgTk=jz0Y#MG(8LYt>Yc2RuMpVxVo5Yes#%pxc}5u|Or6Z*n^;&e z(*;&5a=^$6g94#Y(zmgwq?wl-7?eg4fq zo?3&y+K!P0{)pCDtb)Ub4y{1m#-S~Zf>9*7XL;ahO+qglvyr0~B6sv%t}F0+oG<_w(n^%fX)tfr-Y`70R@( z81AXFQ=?2*hW%SOT{_)We<05(4cwmiC&vZrZ*n-AAA*AcHGwa%hl5V=^&R>40I+I? z4r)T8^}aMP9&*q$jo}~@X;AO}h;|W3tOkNID>On-3TQH77}7%YX3KHtb7*UkM{7$0B{vV2{G^`p-_uu|$KL=>+K&w`0xSCw|7bsCY8oSUPxqmyw2 zWPWo1+awA}Xwp_oW8;t=-a;!qb7%TGZ!b_1`T7aAAOQT=;b4Zb=@#mTh57Z|K+bY1 zZ`%^ib^nH5_B#T=NO0vigib)DB%B;r&9WP#)gS7QbZqsVv_RzRQPuxzpAK}@ndFGb zm|YV&{prc!<#j$jJ`@75!8LGj2GEfCh|NJ|{|MRU&2*Sm9czU%W)K~9G^rfTp`Iks z0bpFBC7_%hdcvDv<6g4x5Ng;gT_f?q?TTK~Z+2$&4(>55OkA9H^ZIo&v=ALG^)fNx zdvL1WUvMjAHEjOzNr{L6V8-J7cMLTKZ9WF?nv8(K8iIdk{yDbUP3SlXt@rb@h_A2L z`2E}#Z7r<`P-JmuaoZ|U0%_^!grh*lA{JY}UKec$g8GMs9T&wQ;luf16$tn^5?wS@ zR3r%ndwt5pb2BGsa%Ak@?IH@$P!4G2{LKX;GajY(7ly^|e^rk$=jr?UJH2%j7fd;S zNchv$DwMu6GC0a6T{&XGMXi@s@C5+2-wcw`jy@lxhd>myfeIhG{ALR@Vq<5j=f(;d z2gHr)x+Vyy12FUk-F2^T`;%5u7n3YdIbo#kA>#1hKu@#zZVnh#*Qve4^rEZ zp=fD2kRR-t)9fmbC9bqgYgN&uTN>SpNTLK4Tpz(@IeNlWej?&l0CnO|IcaMKx)m{6 zOoS)W8!!}Asy5xKeB}8kBzU*eBefsT|8&_D|MA>_=;=>q!qox<6Jimnd7Y0Gh5&6u)%*b2 zRnXT}q9`g2^Fa6fRR!W}HyyKOxNF^Ss)k21-83u_s*x0F;bGwLUIFqRkL(O$M8eR( z$xZb5(b&O1BuCtzso#IC9UCz=i)C&JhP&hE$T`E~>c?YVwR{(r4TY+^U}Hx-{oDJ@ zruQw}Ig=Oe{j+4J1vrt361xe-L1wL`u3pwP0*O@|sV(8ju8p=Sav^H%Qv&*3#Qq_G zy++}r&b4FtiM~OmXltE9B+EGOg45MHh4Ku5K77zJTt+mQD?f2s+D*L`nc1ejv{O#ootR0fR7AiwAFM=h0?R z11JcxUxIg38+Ho__32?6Gnb@%he@;<@_qHI6m`$_F%m$Y=OA*}i`PSL{D{mXba3kV zt!R&=HK2~~i;LnwJqNyB=xq)10%)iWr#*vUH;2*^oM@5TV(sdgVAPT zdr2aYFvM$P6+^7>^geD#DJ>I zJZrl1h@O$&lmh#+n=k%6Rx;>V8JS$+F)aY{YCmD#EeDxo^g*P&*HYq-U4hKzz zuh%Z}8k3BLQ85qBwiMp-c-_zHE1LAQGq*;^{K*U7sHljQnn=#7=j!ewP7RR2ZorXh z!7AWdlMeuqzWP&9h#JB{{Q@a?s?P*$o72N8cb2T&E=6{3MAYN&Uv0#}1=sUv+gI zl}GB{Zwv6P8lXx?Or_JoV9Rc|dw!lb|AK%Za}?y}%uyS;7|dp>BgQ;FT6NXnTJ!e8 zuyXyG^MrXLiIaV)9O42e>FMc|7Y)ltEduqjzoQR-!3k4bc(BJX@z6z}4902B=zh^q zqmmO9-p@3Pza4UTxH#N)_0J`~CsT^g3;a~W8a^%Ii2mU5MTs&v1t>TxubGD?AcoUDkKLI=I>_{$Yn*`r;}3 z2Tl~r)enP$kP)sAFem2mP>6W)^5# z!@z#%B+jZT&8J&43N3b=f=^>WCcfSE^sm5*PN5YsO^(ICa?LH74A8)FUpOMmj9 z5*!VM`~ZFAkn99Ys8gr{q!YF*y9WN$7OJ1*Xm z2@JM6XKWDMK?M({r;z8SxY(0#_pv^{`0U5}z9wD8FlkT@LmussmdSR$qIG~5SyhAY zAmdTde!Zgo%HHjSv`?A#(%S@tCuL z$t9&3gfM`RUCh;-W7Ve(B4US}Y~UCrBi}Q;V=&v*&aY(ShjF*PJ(eF}35=aek4Las z=}_;kfiYeUz@JW`wKlIGU*J*_V4YFf0NBvs@JHmQ8W+ZV#?8j%F1i_dYWuC&{MJv( z-EBsgR_fk=h0;_;y*O40j>4B@o0R@~CgR=aB33!tv7rC(IUj4g{GYXuJX`6-ROnzQr{j}W3 z-*waWdKyK0ohL#LLy$LHnI(o60rc2j`U`k1ub|mttk|&>M9&3n)hSwl=_9VI)OD zH)m$S9lXB3)8lYM?6K;`zyJQ-OK3x)mGzi({@aWS8?lnA?R+ruP-n6y$JuX~wtU7i z#0wcXGvk7V_>j&4Q8TQ|4>^|oJv?0Hw%xO@Xqa@@;IxhzxJ4R0VuJ3M7#zqM-H%wH zae4S4bYvGl_O46UF{y;0>FVc$cq=FwPPPHS1AFVT88XZ^j2OI9>xlhf{@DFDPG!WT zS__|O1vqcCI%bB><8c-lWq-h~!BfTIYwFdipp5SmpUdonD+E1e?u>5t+<(mdxa_=n zm$QKu0dL3z&-%}yTaXU*bvNj8D=$>2vQa%mePLlsb{Urg&fSNiHr9559cv%ttzsp0 zDcXa_rrgZEFM0RC&*VFz1DA`zqSuTB`mdz-NvrOG%ib+KcxDw-XTe~kEV5CEY#prZ zW^lhB~MCIQ4WJmjgpk1t~@2VuO)f>*_llB7rV#=M40Ayi(sFq&C*rP}JIBBCKc< z79QTg6(s01$7FzbL977qXiu1<%AHy8G4P7)G$rH=FTovbwUe&pA^;5cS{v3Qu$)$w z`v$-42#~%U>b%ceC3lr)`J(q6++efy+_)Q*6czAe8)wH=kyy+G-U4;>;9Jqr+7Q?O zIHVvh+dp4=ll)g!b(FS#it<19V4d&j;A?ZuGz^Q6tXOLuamORAsfULoF^a2TK)V!3 z-1?4(IDtDrB`kw7IT>sft5**-KojsScuPH0d%#y6*c6pG2lO`>^X~BL*Dyb*u!%hO z^0U6)Hz{^s{JJVXPNV$wj&AXJP;_JAPUSA#pu=yjqb{{T=bsZslt?fqiw{hYLBSDj zUf&HhaRgvIqI*)u_n&{tBV6(2@3-jmcHgVa;ek~_@Z zy~G&wTt8XM8;woy7eYn11sPJ3#s}w3BF=BF1IBlOwk9#g?|C2W6m(y!?Ald^=O%mT z*)^vj>1j&VYY)i3LHo$=obtE{tI}haeZBp>!$(fvdaW8_R|Sty;GWxTr1#UG#(oZ8 z?)8Q%djj)JQqt@$iz($>Id*4XEO>q-{8{&fV@a00^}uL5jgurA&6FNTXOK#{<_>{@ zhCQFrDe|q*`+D*%XrkT!mW`)OfYfsh021OT0kXQE8+)y_Jm{TIU-#XbIjLt?Wd-b5 zBeS}74m5!2LQU&3=)eH9p|XM^H2oOAX#FJi?hmuQ9rNiqf)}Gd?04HXw^DxdPLZ`w z?wX47eveVV72ZIUoM(0q4E3QcVTmn*)<*@^i9JO{MV6Y)kosOVgLhiD;Ku%K-}mjm zUe#KlpLHTz;=I8eH0-m#-||sCZzlMtG=_LI@61{M4uOZTRxw_y;vEb06YN zm|k>u-!O&O!Gv>9F1$6w`MPTF14S&BD&p{CbEG;vR1(0(LjoR$ zOYg=4nM`L73#@r(BUiN~>#FzjJ5gTwf$ct0^E&!+TtYUVt7z?iSh-$YX{Si@U*SpS z;;!LNgBFT~a;J*7oqF;6hAVvEPmZleMb@jfTnq(?K=d>Ku8=4ZUH$^QPNUYyZcx4pW1r>}HqjEu(`4ANoJ%7W7ty6?yyC=#KvFd@)X?PemX&u4%+> z!)g4Zp?97ai(ad@0EK<0+e^ z=4k-CoOT;a8|A3M<-@x+7S%*NunYb1Wb66ZgXcatz4(1;#v0$5H}B0Uoe9w>cD2q_ z+zzv)+-%jo7E}cY_sGyn!npzRf?fuHR(feWFUHxQpQEMyK&z@n z`FxDKOIM-Pdh^zs{#~gRt(5^+_l0b}t%TigU|XUEv9aXNJ1RRpotSjf0Ds#EA7;KG zz5ywH^lr@kN5yKzlZ+f3_G;(|R1i6g=sp~sxP6h-4xF+1+riF>JmXM?jE|ArJL$Kr zj(IE1@*{QMtmspb)4U;}{rkXk!~GH0=Av4%69~tapS7%p*8qk5##8mT z>Kp*Rm_p#Pn|cWcPPNV*#;7WF{1Up=!7wKK(62a_bd-sJp?|Z*x2VO}^!(h^tzYEc z_`dr%(i}2=KAsC@Pez5;PL*w?lrLF`cR0RnYtG35ekGa!qLDH^JNin1N7>;Lxg3ez ze7WBPFMjJk0&T$j(eFKOG^wP&lYCIXw8%zDTz%;TFE%dRV2lg|jB{OJ<6z7uN)+97 zI58UdlxrtNZUvgE#*WZ;$Ie~HeU^+r9iYPk-^XVEDL z&z7*&SjIuqc`=NY3c|vEhbnC+U~;`z-F{FWGg!LqG~b44Uln{lcaNG>$M8paxYdv- z+8z$&ZSY^X#v~pG_Oq44ZtiCW%_gHJ@~N>SLa=9)<&Z;TD~B*LBdmI8eb5 zXq!v&zdRr>;MUse--Iqf2xDz+y=}2J2Wl%iWPwS6$pc;p8sJS9_BoV5^MgD94kqav zP!wADe&ZGGbp1lE+*Az4J1Ftn(OF1nK*K5nU?hU@0*4V4b0FcT;8pCGXeTC{7TsVK zkP_2xjp-$%XCbrYxVNEAh5#FCIZd00c#qZ{`H5J@rSB*ZI(}uJq;6ht?n#_ch#qk*^^BqitV?V|NthUDR7ab^wmuel$~KkH;7Z2tp7|o*aq`ZOU9R?U9hX zhHk`ev%>2uSumUFdTz#hyN(KKJayNb=7<0cy$i#ah~5SB=IQpx{`$Dr>b&n8U~u~M z>AV4V*d&m)37j&L5rP>6s~!JW(3M;EWi}6&SObSOqy`33Jf(y&N5?XJ3{0E4YT*51 zS~&>fW&Ch`&%n;upDq*lI8Ozl=Y!$&!JZ`xyD=|Og{G!;U@3qGWrpF+T8q>zxUQrX`^8r62~GP4c*^~tOmyf5m9nwZ*A zM1jn*LW7s`Q24)f?ii?u)|CP?Xg7y!CdeU&!-Vn=Fc%u7mhg!{2|BA8TdL?og>7j%!b#DIC~x``nH{wjeg3pOp^ zxUs=CYjjE&kWD~V*O?||}3e94j}8DmB^^;*COguxw! zm`EF(z@o(DQ(q60Zw2Sw*Y8lu#i1p6LGR=|$$b&%9Y?`T2#>1@&B*TGExO&N=U!u` zH-5bE1ZuyZKXP+;4-973){P_}hbCtSM@vIvk!S8Aba?Zy_Wi4RetiEb3swhP6GCnB zfWhVkLCk(0# zB3WY`@8uBwwcM{^Cf}Vcr^SIaxG^bE9W&QS>lY=)T=>0XB!0%Genpzp6FQsu^D8sl z)z0%iZZGg>Sk*qnJ_ElJEhA!qA?ied&;NXw0wic!$qkexBie<7!3vxQ#&^TlnoYUy zrr1E?9s_5p%7T?rpi5wns`^#)tr3!&eaz+DjK1!o7cVNq*gpDFHvjo_h9^u*=>OHv z1~vn+sU;R4hdg{-pr!EJa6BB~G_lC!xwpMJVZzl3bpyeNNlE`v*c?98`=EfkM%qtiJ9yO~ zL2&43L-%7kT+TKJO8K>ni#-cz>whbZf-fu00AmFxv_w%NabNEz<*vGN$uTBrx%PL@ z`TxWEn0qNC6l27CZCa+k2j;|FAVdrZexzZ1;YT(xpLG$wM)3(QP7U(TM|rP%V(h91J5> z?}dk_YL6uyw3t{cd0T5C+JbGQ!3GyaiVdD@Aa!KZkAH|lQ>Z@uIQdjESEASHVYG{b zlVC=D59A*4XgxJsUxjrA5P)D(;VPftp2La-l+fsco(xE-cKoMASndbwr3RF8zpn0Z`PEezWyi+ooyiwF{#9U+gVFjUK zkpc3-P>QJ@2c4|0|2kgFs(H!M<6vk9DG;IsVmx$hkrJ^L$jTWwr|QQ6xE;LIHFUWL ze+!%vbhMGt-i{qR&iQ=feu&LObF{4iMD%yoOLZZ!$iQSmLOAj31!6MX-{Jl%U=Fv2 zV3=i-jUTyLVo_4o0IQ`o#4Ia21?nT?q4a``Ee2LG9p+H~fyR!FQw0=a%tzTX7T-${ zzJ@ix5nAoq!A7YD^V&pUtW%4Rb1&g_8yLxV%>4vmnSjoWBwG`dG6bj)910fTfU9;` z0(`_?j$V!fj&oOHni9%2;i z$;S|fm_{}a^Q_;2&+7(CwJj6m?}W*}j#$PBm-BT=no6(E&(?o&*nu z8HvF1bmp)0hwCtm+n{(9OyX^G&LR!b#it0aKIqPj1oImG4QtIn99K>)=jZi;n_b&r zoz7&tthOv6Yoyf(eX_2b^b^&wY)+G!RfZfp8*G-m5kE6F)=m z`4q3BbMIdSq8@qxJ{5XN2QC%7EypaN1=QpKa}R!1W;!|KJV9TK3Jj@7pU$GWIj{Hu zFNjm?PK=Fa>mG<>(9NcBhOHmbYv3R8tA2A2AA5-1S3z{9B8SXANWBw{J)n(~NG)j) zloHZ*qBJEPRdv${mR|=>c9?9Lj}>r1&+jDg1xZtv861grn9dO&q8CMn8jiO*!|GqR zHRv918!8UX15^lFhB5{$`Er*B68OVD%fCT?n`&4J*HxU>oG_5AiOmK76R7tza$JW) z=569G&PnBqbohM3kX5%MLe-!z2wE}mliI%&N!1WorMU3V&QILm<^IclD=(i7GGRl= zgqhsoaH=+^f)z;%*hRB&bWALT2MdAS%t+J-Bw5TlhThZQd;ep-cz_+@{?-^c>LUZD zBIT`+Oj`F1alf4X0b%3TGMY8k--)!FOo53`MC1V!DiE$mVM((vW_OOLs9Tv-TMBp^ zO_yf=dhl|uxY`jk%Rh6+BDaeASrU{)=Lc2 z8>aGs>lupJL@I39S?bI1G9LR`!clU`!OSQl0VbQbu7bdo1CphPZY61`5j;`vgIE;> z{6H8!p`?I_!F8@55=4+E7pFeBPQ#9nW!wy|Xc)Nm@%Pg3UhaGtl1C`yzjDm7bR4fF zqQ;rU0!K&|ihp7Wk=z2gfd4B)+!1=bT-UGSBQJ0O?*zD}0@?xv33ycWJ>+&cgS_k` zj$NK!9MN$7wxi`gW5aUg9-T(E&t=TuZEG7&Zw^HL7J>uak_KSFfb%Z7sDakS^(eW0 zyDrQUw6;mySHO^E=f=~xpY{j2i<&j#>7w6+o}oMegC1kg64U5Jqf3SDt6)_DPK(w) zr=@JP)~mpnPk}!?yyZJd*n++#CFB$xK}IO&b?X&9D}u**XAJkjN1#nQ!8~)ZNcJdk zc#wbYgQ67ee(t;q&(%beCnpDrLOAn}KN~?gMx+^a2naMR8^<~)?vCEJyBJ=w$UPg~ zMu#1pfl2zwJEzjqcDh>n{CR@m2e_MdfrWitD=CCKmI9;T%9~E0lI+AI(*{T*lgrJ- z?f=9wxUOf%TLCsfL=}UEbw8I$vAt&Baj(XYV8+}%`DQX@*;w#g!N7jk@2vz-&6+h! zuxk{YsVX8;X=^V9gbp2qAtF4v0Cc|k6E~M7#LY-N6>xKtx4|-tnuT@UsNd=C5|BPm3-(kHwyC9qrJ7N2(EcA8d7+o5Cm2S%;npDv;is>5m>aIJ79u% z3<|+ZlJp`^mB$R^NiN*kpaEP?jeCDVqQr#UE!yYBw7&;RKl8JF235`Y#2A~zEuKoI@%GMRV^0hV;@L}$8Z$?8Q0=2QN(Fq?fY8! zdO`2=Yl|vixcBLqiPIs0zEa!%Lh*PrFxa>*xDT7&PMRSS1G&l?;|A1o_V8d6Te2jy z={QO!wAqK10kCcs--^c{bbz}B>OCLK)mf-@Pw`>Y;aM!U?-RT4=S-)ZSF#h}OIJ;s z)wojDzHY?Xa>hFyH|*Yg@n@9gsj~RK?a?XGA&Smo zj**-8>U=nPAEx(d{x46@@9>dPzilhuYA@F+?P9m_mBXcQ`qtGH-Mxh(!6@tiD@+G^ zh9=h`D{?wNqZssj7$re{Jn}dPYE)4;1IR$#Uw9df!An-m@cdw+@bP7g0_!F3?giU< zXPLX%l=J7lOe)fvDN&Q*a!Nnx*m>n~+dVgx8txZ!bBJ7b(=|T+giClfd%UcKL)c4~ z##V=?&n*Yhv?5>>h*#AH7??xfTG?-!f1w8`GHlg*PO3*wJ zgjQn*4O-!C3}byE7VxffuBYy-v+_egse~|;J$;6sY=Op3;i{NAAKp%|v5}o|NIPQj z-Z;_1bsZJmqEVaLj_DXe3O7C9G9~zA?8JN1DiUtL)#{$MqUm-J^zlxmFlt4b-uKb6 z?Y9vm(h$=WwqI$tnU8m@pUe$P-G-}$uIFTEwLV$na^S(P1-rlaH|lCs9dJo_yQGz0 zvZ7Yxew6c%nO`=%ZFLx=hc1b-XBac~$}wYaHd_-N5}5nQT2@OyNWB6|1*&L1<6(2c zK-bfb){(6x;FwH*8UXn@TY?|NYWaJ%;ExyF=bWdODPW2|U`gt*Pb}F54a$P!zb^p9 zJdGY+3_0S1+;_C&cT-#^oFfEUh^x zI=vef(l3GFa{Wy601mn5j;9jJy+4u#$$pTQ|L&U3r0%J3RJf{~V_LP;>BQFsg&k}5 zg|2-ncIZ3}A9A{CN>BI=aBAKxlr5UY16(tBdZLg=frzw}P4FcugwUZz*tCtn1);th zJDL(FpbGcPxB;&IjN|9xE(FNf^-pf!x>ei%g!eRq^bo>u-HXo@2I0@dCL=+%m+>@N7#^;kbLTdj$p!GsUL#Hr+@U zlUx~hLgDQ-1}g;Xq7z!ef0)#pCt@PbJO$bGK!h)vbwQ;dvmK!{g^Q5UFat117Ns=^ z-;3a$+tbbG{iGPQ)s72xkJ>tS@L*?-K?=0bnY;Zqhsw?{<^CdrA?#zS_eJ-n#U8dR zDS_+vBva<46J^>7pcb}5gYo|nS+8h4p-(x9BkJ@J_%ie6aK6!Y= zzlzU=dW+;b0)!i50Wp4v_u;}&Xh(}_-MhE(2F^K9da@54%W8;@UU2vM=)obh!s_1; zV(fl{TWqT9YDa9HFcMp@#c6&|m&vfO2d|o{z{WTP00bRqHxC}OgcSV@c_?nb%wN|H znnVX@x1)G4^}4gOz|BuN1-ZgA6kaJn6Cn;D zW*lHAq=dfEMWL65oR(W^AWaAS=#AB750?tmGTX%M=>p`A+SmVQCBgnYYKpc+akeif zW?hb}evTwvON6?dP~5hV3zV&1-Qh|-<|4J9+Q8E;Q`)`0<;Yv;z+M-+=U>w(=#XQMVruSsY@9?8P@$M2^ zNb{uWS{`+4pF)amB4^EPt;A3X<4m3zDei5+aZvd?x-FsTkDa-g_cC{WWU0y7W+^cO zD(cIn`6e@pP5r|EdkWW03B3?dF6l{leWW&(7)*JI z=*v_OwAt5hw{63)vOW~oso!OJ`@HOQu#I}Y_K?=xs4IHY&K2}k)g&G7JldRMshc&! z6#izRCF_u?lm1EeUmQe=len1k!c=H-R|r+cT{?T~=@U677X~9Qj4o}(RYfmBg^45# z46ZQK@6s%bAqQmc!x_+R2cG_Nk!g;KmUxCs*#v@7V*sZI;DG9f+@#NJJzXlj3FV{z z38<6L>ZqUh%`_HN>W>`nJJ95$J!dC>1jIx-;1)V~mzI{sCv{^ZX~F9}I{y!>S#O#@ z14QY3%ry!2`MFn=#yxI%x_TN7T_8AgY1N#|*5LMywbwv30OvM{AhbIW1&Sm#<0cNb zO1UNI3ILU>fla9W)A>i9b@ru}6w|X(yaEGI^`b;$;jx0tZ^axwi@&aj7Jgwqbof`w z`{g9J!A-_P)F(P#vSGP#1I$-aj@Bh!u!5|CjDMmF(1CRKzWDPpg9@XO$cbGw7s9nF zcCMYKH0JP5HwdKVyD8!_8cIp(zth1^Nu88 zC|pv_AJGbu<6;SU@TDsfy)rBimWKzQw-u7|?w;q6xMEu9&0YuucYq6_iR#V2>H~qH z#9<;QD4K?bcKNoohv?EUUTkh|yLD1x;vs<@Du-XiX2rn}F3ZWm-%wQeum5$P;UeFg zqbK;EK71&5JA8ib1@{stk7$Y^56V~D)8sZJHZ|p7QBHEoQ4@pnC)EA3o5Fhz?2iv< zI8jgaJ{Ype&I8s^Z#fTvLPee)5wL9Y>>8kq6rAx{2{a6oG%d%E;%mXsTQtFnIw@Im z?KOW4agF5|lQ37tJ1pq~ruogwSrD2P`Nv>Nf}CNGwX1w}aBY;%0{u^JqLxFc-TmvH zE;5)06Ewj@u$zi+uiXjj$zzV9fcjF%-;4bc#Va}Opi@Xd20RP;?!pz)z5fD68g@?) z+^yJP^OVJXkU$>xb(?F4W*78UI(%q~k~NH}S$)ygz*ZT zn#3I;M-%o-1V4%y7C^MTJ}z0lyufuB4MsCW9~^Jid0`sldx_0aiH28O6ZdkH(dXn{ zw;4bDz#UH-3-9>*cf+$Kn>wedZg#e-9NaXfLD(LvF!lw#OrBk9urFX;`NP`N;J{Vg zG3FLV5f)K^*=J!9MPpI++vd-O!*&mZulEsScEoh;69<3?A-gXVXcGm+{_OgH)!Edw z;ObX)dI{bGN6P^2Vixx{()V|NN{LXn`FS4Ei6P@{eoMJ8oSmQG9p=%{qVY&vBix9` zp%q%jTl6>Z8;_8ka?y0$Z(aG*?ibj|3u1vl6+O}H0;M*RxDMz=9CCc}POcy44|Q!n`WogL8^ z^JVJuuTh52H=bK~8yBIxTrJe`rakK4!u<(+J2{nA{*o0>1egr_ZSW^VFa#L<5>^$W z3Go2V!484%i@@S7jWX9yN{O>*W&e!g`==^Q1(&fMaar4@bBl&r6#g5gd7=u2n{TDe9bBK-a;yTI z+6}}_Kzo#AI>>#h&{IRFKYZCh(#g-pX!z~rkPiryy%6b#dWQM2Bc7SiQ@RlDD6Nbl zI1bJ0$AoeoTpYW}bYx;3I>fft(-EStyx`z#)PI8X_K$EM~u=0T%as7;+;qj6TB1 zwJ<^jWP$WLo0g9Z4;9H2n>F(?qQ5o9`ZF>ZK=`cN#?q|g!mW9pj(y?L{!)8HDi&0X zWo(RXHw${Bj%*cOn&G1@xWe09LMijg^@d*fu1kB3XOv;yQ5jA6K%)izWp3}^zwdxQ zPE}g7tMjw3o_xY$_mkdhbfZY5MhH1sld$^2TZr4JA3^jtbVUc((<$`&!FF37)rjF& z{I)kr*Vrw8945AQ zEN$75q{mlcTT&|^*evWGvOor}5FRlzK7?l^@Z<9!Jft5AA-vDwlJV^o*Cgu-#rK7O2~t=lEp*!vlhW?rQBj}7s4WH9N}(Jk zHEd1TK9U(TSOL^+@3+M^L=O`T1unClnqhsgN7KS-#p@V7HVUrr)}kt!1?TVo2wgqr zeYjL}O)@tnfoX``*>m%vsA*o^pG{7w2EGcv?-fq~`!-STy3YWr1Z+vIO?u1J)JIkC zYkGX-6o)yJ#7l0My|01u<5L{cYwl^sG^LItZ`*fyLE=hD)0s1G zG0r^bVgFHgv#z9PMn~nX-~UiqbLP?At2;$clnmVY`rXyLX6^fp&0jY(qbq{J9sK#Y z0p6~6QNfYNOCLP00H5yxV}Wm$@+%;xlxnlr8NIK zrhxZBUKHIofzkF5=poPuj8hYDFDnc|du$JqX_QAZoZ~R6!4(rVIbqbs0G3NsYC$1? zUtih*)b0JTo|dZIMRKaA9yAW_qm?7H1#m3bK0}gBKdVfWYTuiTfvPLx5FhnobT~}Jgqxq;h6tO z^nTU7|Z%oP(2Ze6f++12)sPmEx0C zO?vLww3U~R+ZZS6A60g==j6UAd$(EFY>!@cw%CEVsDt^^`D+hpG>55peRR4pOPKwo zHY?GfOS(jmuKfZJr7Oa6tDS9{CikxMv>!dIOT&R?J6rj0TMYI*T*9^sGuw7^ZKcAg z2m98gy{i6wq<=gkc_VM=Ob6CWaxA7T5Nv`v0{>u!!>czBi2#UXSPDiF=+9GDO7lE0 zsLAB^W_+}}z-3F@He~X)wZlkPt*(zHP{u0@G`gs?}_lJHq9|w>G_R0b*uOSYC1KwCy8s@ z*u<{8(Hgx@>v-PRRf!Qd(*ixh_;gSFb*xTe-O}1kiJ$FQjK`CiKvBs&2v$Q&g~fV* zz(kLL)D$@#UilCVw;-BtQU0b3^U1_1oYZkiYj%vz zvuV$b_r7}|cfF${MCOF9gPr3Iyc^!i+(Bc`6o%Z&Lm&eI$aAoFrlhH;sDhjXD`7uE z10J}+bhRzIkic%CftI&*>uCA`O&xZ5Pi9Dwvo*-Z? z&ycr$vu!^R8>E>#RlZUr{v~!URY3{!iCtZj80T51bUi3Lyy1MrV2M60mkk^au zd4(Z3>-Glp!Y}F{>d^T4Ih~$NTloSeSp0em-hVCRiq7XYBC&Og0bK_JG-zl!C0;MVV~euA4B+0?APFa{erzJ9OlGU-}7X{dz;iC61Z* zZwkM@yU0n&c$ zJ$pjYw3sd=2O};$>1P7}xt7Pzy(u2!M{Syi5tYthqJoyLdCmpZHxO?OW;VV27A*V@ zDYx&J38ll(H%llLVb(_cTqvhwUeao;hcT$_Ua-=X&rPm+n*bMQ((sXu?YJ|Fp~IM^ zP)4>!G=XZ_FqX>LkK6`n9yRRo#dv7A$VC>_3;%w-UQDPnx4naNHVgFvhv{D&PNE7B zzHZu^IcQ>Zx8HiaN7(^&p-YEp<_sM9PO2D_S2ekeAnYts_{I9E#$>VWa7|Wrho&)K^0W^7PNSO|hg0?+;{#-9(G}gZC6iPz7x8I`Eaz9X;#%)xTNjMDx#Egl8kkJ@8S7*cAaKfjs(N=EscP9Kv6y z^$X>a!qtu+0RPyYI@9-OIi(;dX#)-<9OQ{DIvpf;1xc{9_5i(W*$#=%YK(4D$&*Bw zO?)%g7q%UpouRX%IQ9@CuJ=>-4MP|q!<)p0ZcZ3cynuR?NDuJncl0)|-{p?oF5$mm z!I%w|tK}G{pTt#2eRgrMiLgX^YXKgTOCToRjltcf%Yncp7!2$^0 zd=|4w*aYwLcN52t$)o6Mg=c@gMSn6BIjBA3FqWLG(zvh(pwd_%vB>0zpZdeq?eZHk zG=P@b%Q_HfISPMYWUKqX(5t5<*sT!!4ul~BwiQ?g9cLzq$i0_?;RaX_#`Z(FNj^25 z@?9ISoV{F|QY>$h)tJdg9!-)i;PLZwW1FsQPG(~q&FVxoPqxY--6-w^K~~))QYxKK z50bRZE(4j(RamfbWMKfD>V~;rf-*H8|hZo_Kc_G9RiUl57@<4CANM&h(Uh4$Qm6D8ps zH0PB@qk+{Wo^cFFBR8d8XnmAc($0l{LeeRZSyzz!e4BVIN1lHwzl7NX`J=TDLRnH^ zcoUtI*$yOYkCj#IE9(K<*0FOD*YP^<=eCm*L5gRX zM||ZIjou7hBZGwNy5M~Y_%;Y zDlNV0np$_1W?g9n+ywbKKnpqpL^B^Sb6j72AG<5~L+8)FPLu9xse9Y`O1DR+jB zRa{EyFV~#W2OkDcZ-!2=T_HyrOVt9dVEU;?_0E~dqv%1ir+oW7FmO};^~L)Ig@($_ z;e^2*v6BmVHv$j>GYq752@G&@RolJWC*OEO)K}_&9rXV%omh~GF;^#sV81SRwLR$g z9iUBmrJ@txhQM?k)0|-Ng+>CCjTlsJ;;pqyA7-1NLTQj}iTT_(VFkE=M2IvtHtv!l zo~$rme(*JOKax>^2TA^AjVIrF6$mF`1O-T{<=t5QLVf9Q(3+l_X4T;M>U;8yUCa<0 zIiVhz`fcF%F=v@Sm>y(hGy2=-T#PK40W4eLm7C&^vA;+biLMgtnPW4~MC5C&Ll_o& zLVoj8M*Wg(e>jSsmU5Ve7D5IFNQE}wcp-8jV35#J1w%whm5NOrw?NV*^I6!Iw3;;# zQ)|D?lL;{rm)<@-K^*m-rO79j7j0UWA6y0wY80`JbfUG1Vsa=+cr=H`#ZOOdv>wn1 z9O^c~M9Qlh1FJy=n(gCuVEI`v7APl5HTPRyJa181lkNZWOT^bK`2T$g708YOR9~|< zdu8PpPCa*MJ&A!%8G<_FcrrC0vS2(xBwWa}WY9~3=ijF4_R#UKGiiv@0EQ^}n2Pw+ z1@Xm!@(g-%1Xnds1=PxTR$6#?_>HpBgIwhyl_U*vG<-1@FL4&x6WA!rlriu$#l*lFasH&T2sz}c@sX& z*aKNCr$?n9_Mpd$P~=!U&$i`V!Pr6#vRb{Matmz4I(2gfeVOevNd*C5LB+S`yFsz> z@wlnu&pb9VNm<*c^;SZ8IZDtxezyhV&8~j4 z9#_`%DeLbWpp$2ODdhTuDzkcLi9u6}3oYsy5Jv)!Gm2{ZDhuLrqFH8}ceWHr9;waB zyCGsAumZBgIhP$3$8N|tebM}2;Yu3RgA52$GT5XrGZ9h1%$X;SR#x5IEtYlQn2O!6 zcwUzMb9u7H;d}!x*bJdL18E$3!RQ%dlR7;j*V#3yN%^)@r%olssbq#cjJK$4fkAgL z5rkheX)UP3xGsE{2zNX?YS?^|=EJyYCsGcY0RTQG8-kzB9mgR}6+Al7-LU>ff;A3Un0YgC zAIe~H6SIrCn9V&WNie>O<%agRSG|bqHzUx|Vz(H;^)-92L!3?Sn~VS1b#7^}f0Eni z$ixP$@uM29ybVnMR@*d|)G>sNLE(Wl1ym|+XQxUcGNaP;1J@PMZ^fIF_=ft~r_<3t zx@1Q5MmX7r80C7E`5QO!UheA@ZOyo)AB{Kb`wZ?Sp4CO`83L<=%0)*}R(q8c z0TEP-^4PkF#C}M}<&51B9?DDkJkS02aWnql*l`nN?qNj!GbB1lwt_l7UEWrgtKrPy|h` zDenf9#qBqM#$NgEGX@0R$ZY|yr}n`jeF3qR_#YbpKQ8?;mpo1)2T&KT#M2WfKm$D4 z5B0P-uP)?-p~c&V7Lr2O21A!VU%V=`fy_xP!^f~jyeR0L(bx^TbA@7wR~@H*-H0|hE>wey5k3R02hPu}VzH_+KG^-Ombxp9rIlV~hRgXae6Z#fKrakz>;uufDp)H4kK6%w1 zF6PMn;Ib`0GcgtmLR}c7p~8k0xGt4*1K9)esP07eY`R~uGP+8PEKdiU2D0yueh(Za zBeG=Z7^m#Oz1BZt^hK9H+Z5Eq>8Y6XhuUdYqX@Y7U5?K78<8P_23~ArLPNMIGts6w zB-8}5hf^(B6I4HW-%F-LI(vJ66P}@GTAS3|R0ZN$xh9^i`FW z+tOywniVo$juCtL*U9sL9YqW|>D5>3lD#)yoxCvhTY~^Jcw@(g{T#$whjYvyJQgRez$DY~&S`&68~m zye%V8=CDqFI@o)XAdyE%Uk|^vu1nmGrD0>g5r6_|r_r8TDToDZ1{uzFe`I78$WMc* z6hs+~G?S17WJqrU>Wv(F*MIy{J@J1%wz)Ttu?un2J&*FqMsI51zhNgX0bk*_F@GKS&z+hm?io@H25}oE3tT-x5cSy4 zpZR0O?c;J#SpatFqDK)LeOXx(6%5%IlLHXMv#Y!fV7>d{uCp5U%pC|}utb+*mBL!! z%Njt%?#KSqWcm5l_)};-6w$mQ9`*z{gvp9TE_^QlwwK^VFuC)`Uff>0l#N&p=m)l^ z32a>5>)-#=BK@y-yFf1JM44iB#w0P>nU38=&p<2*;V%GCQ(j88hjc6v1Vm5;wAw&k zVHV7v^a`6p?f3r)B{JM~NLJVg`cwwK9g_{xuM?mFn${rlnZ$Z*P3Ej}_?95zkj)7! z2C~Z&$eED920RWf*r2#k>3@Q2V9;PfHP`3bz&d?92QAt-8dU+JEsc2rR}F$ur~a-; zt|JI~wLq|r*FFTrqY?(Bp$&xVqr$4hM8WDz;05k-LH`rNWaL2tk_c>Nu+0(mDY7;# z1A_%tUx$WQc@II&LDlGG>#GpKkk1A5G3GuY5*z)VuO6Oh93)TX<8?}3_Gw(LLlT>tgFx({IC(A^~f`O>z< zq?seHlWtTB`3Xpo=ona~pS+9ei-NbX<0Bmkc1R^0CFXmSPocy0&jtV6Lz{gm-?*IW zed_#E1nvWw!V+LB7|jhD1??Eq0RI&x8*=|jG~Yo60mz5b0U#pmtU{sXes(zYcOV4G zqCZT3ojE@*Ud;@IPBM_A1G-&3fy;D5(~%y29eEgr2GaR`9Ji-kfWvGcE@c(~9E8wK{a1Qi)IUGC z7%I&F!`5}cW4ZQ!)j4(QlqN-_fkKqZ7D^>#uZ$>rkFtffLewLaQC9ZMCL~En*|Ll5 zC|O1Lf3HX99iRW}qxZb0Q=aF!@9Vy<-|zeTjz;Kk(#YVRVwl@bemsejM7q}WR=oAv zwg1SH{(1NK=L=~YIC+x|hyBdMozhrtfz^E!d()?NFyP4+GzQllCr}KrV5EjABIi*T z#LGLcF_ti0_rI<6UzTu8AxYtLe3qrAEuU&UCzDhaMQU{#`bdOoXB$=1|D5;0H?b2T z@DD2j6O!WN2jhEEb&IqhZS=EV4`Wku(txgGwCxlX<_Np@zb>$#JQ`gRCP0`MK)Bw3 z62m(Hkemd!fkjDQ!M$%^ElJXlu1A0uoJjZKGEa?lhU!DhA$;&(OX`&zHWgA~fCz-$ zU<5ypnVf$(1N&KiG;#R!HhSddXU z(Bk1@Gy}{9iyD%%#;QOrHfhLViKQ(u)t6r9t+WjINZ&=&vl)6i76Y8?7!CcH)FW!=c2w z=}wLwpc<<09({$;0yu@4}4dq6Qj8B{{jf9^V6=>_h4r z_R3n4zl2%`HI8K8>@*<8BtoSB$*W2?-SRX{78l{2~a>kDfOk7sFMNNZsA@V7z6Nh1S7fT zEVBkiWX|G2 zp#LT4%@1qbZ94$dn^S019U7onWXvK%7TCmoxInTX>o6v1{Q?}Iq@=VgLl5D6#KeVm z`;sMyv$>C*1I~SGh@6dQ4yRJtZaE|%d>%BEGPDGZQ2x`Lcw zC+#4QmaBQlyGLvPEKWHj=9XOqqgHl8o1^gjm zi}Z|q^tT2N_{gzVF|FtRdnN77mi#l4FL7NHm;NbLUA?EHfo^K5Dv1(;tkM^?Mu$W0 zC3Z7qX|6Ce%|hhYt^}! zR#$^>kWtuF-L7_Yb77TnMwN9&wSf~?=@_N_V2E#LTdz3+CV+ynj59J&cSec1OE6j> zA$Y3p?nRD>}_2c^sEuqwFMR1fEgzX|NAtTqAueZSl&!*;3C&PqWnyQs!{r> zrmI>_w??w;ziM%}7pjYJ&2Vj7&FA*GJUw1Ye`|ObpSFpk_(@$asnw1elPAyUkqrj5 zSVBlh$R1s;rNv-c0-p-(=mdH@O)w^AE!O;I zGO{3Mv9;=(y}EY;S8xr7v~LpX3Ea0=OT%f%6GCDn4GJ&}r}7NAxsQIyC*zU#RG$bk z%!Sbs8RlL$SOq8vuoaNU5OTt|{q;}zFR^OtU#rx#G*Y8NuFuXDIH}s&w%e7rE9EO3 z_|aYO?f!H2U9FGWfQ)(~mnPZezB}i(cUmU*Pon^`zzRc+SG&*`qMPCpzX&Gm$WluE2q=CgO;A$`*?Dn&B`z*r`76B44A7aHI#=|?B1|? zxasahc4E9l_jbpdFO5&I)%adtdN^tPyqe6LZmiyXgy=%NUKf(|AtMv&`GC8=_v!yf6-D~L$y@x?MS@DFMq zB_Zf|pokqIL}3EAT0xV}_*biAFA;hA?5g3aZ07-Qb*{DZS|?(4H5ZGI_pxz{}MLwfOX1$Vx}rZti!>N;+UixYj-iOH#!(e)Wo|rx>`^Fq++QCs%0c`b|eE;e74RX z{V42>$S{w)VbA%e@K}ZN=!>?sDf||IdA8(uw zi6u>@g800u-sSO|=$`GreLMyX4yX=6r-;8LPFsgiJOk2$pNlntFaZtF zPvlWQcvcV&_Qd6Uu4bJghm3MGOKw%S8M+BFSx<+Z6?=v0tQK)kpw`Q&#HOk4D-{~H zJ@T;Pi*vEZr~2WZyIMK#yJOv3w64qLEY%-0ue7m593XlfLXJiNE82X$w4&({G0@J8 z-)Cs0Fdj0zP1NP4=yoYFP{e|;WdQ*iB?jQJi0eDB1RYRD9tP#_V>h~*+A()@ZNxM_ z9v6tuR6$0DICP-Zn*Q3Ih7Or*43rpb^hdTq7X=4dt@TH5n1}RV*jq~dn1Y%&Yi@Hc zZ*tAF&G5F+E~X5Or?Ux%TCQIi>XA2fdJa$AfL&UmRl6jUNYO}zU;Ye3)9$_UaU?GMJI0O>67 zF^TO7&R^C90IQLTgSYL(5rjo-0qaJIy$E_a(6kgrbZ~h`nXo0dWkWSM1kWiS>vl{2 zPNdb5u>wX23esL(*JeSJ8YKU*m^YRK3{NlQkI_okqbiBSyfM9w7dl#3K)e2&xo|u7 zLa$<0*+;z+8=Uw`wE`Q1hI+&GvRt~~t+fdl#ayH2aYKg~=w)aOS=36G zQ-FBI605sr9J?;61Jq6|m)F0Q7lmQs`4svuXJfr3971Q_FD-Q2C>c4qrXS{O;35 z8#VU_oti7wz5ls=<@z1_Lr1@f+h86v|8;R8jm)Ya^+%3`+OAUL9PX70Y zJz|JhrO>A63{U(d)*QeWbrAQ?vKD4%d8alfc9ZEA3 z;|+(wUCmd-x+m0hW}O@-s=91?3gQv}8uaax_N)?0Y_ zdy5(ddx?z8$wZsLX+%jj40mx=3?ThDy0A8~fzv@LEWPNmte(hyF^xK6e2LG$b$~1o+Fp=iEBqsGxCiOxl`p{(hk&OlmnEt37+-6zp__HsV zW&AnxMI;NL!k(F%NOwrrjKWlI$@uG|bJNWgZHfil*6TB5lI;xlIll}H zKU?B!^fQ>Iovy)LPmg%g{(EBKI=BmNv@HC&*guKgo7fk-7rErJA-qO>Ob}#2Mdf-= z_2pPyag!hvu&4knk3_r)bmkV=fr(W&5>xD7&EXn;H2gj#YP^$4`JT%ero9bE)L3Xu z%Zq>I_1B?S{B5ukj=RK`{oanR_aJp8^O0-BGiXIVTEq7gb&uOzlMq zuQXvSz-}TNxQetgIytl{n3zQpQ2>Of&$Rr1wZm{{bR9MYtyc%vA_`b@KYi29<%w&W zs+Ntl3feq%t)x-BH#)8`WA6(dW zclJYpgGE4qAnZEKePl(=0hOP5u0q}~O~lD-pYG+TeYtBv%V9F9-fGfLm%8SN=>Y&l zDCiiOOUzVySLu=@Y;vlYr$Ly5qDy-z(GH9^>dYTkY)kZ)t2qQ;0?;5!K{~htuI8zV z50?BS42eR*G68_&u0TV8Z;}Op9g>Vzr$ERb!HR1{rgm*mFpAvT*kYcKNo$%D@lnsS zCZ7wO%g)wMn^tF5bpKI%_LZ4Kd{SJ%EW1&BZZ6hXsE)eVCg#-_HJRTUMBT@wxou`X zCoPt`P-Ei}EzspGNx@;7s0Usrv4hlq;67I&xR!kMw!J>(QABu1IFvDxCCl8$%eUfA zs2n?XEMUOU#6(JbZhm3q9Da*Unh@^^MSxfXQOE;KIIxf#(0WA^ocGE@5R6|aWwwuk zN|^ZfDph5bND0A+13vubb?*W>_jRVGGYEb8NzX9+q+W8fYj|v3=d-?N!Vf-w+gJC= z+O`9wU+16>%v*hZeIM#7$hyz>*Ad54j2umL2#|<7^%T3_!A*^RRX@aDnaibfxtj+~ z7ynYbK20726aiMXIi8Ci?s&k~wUG@R+0tp1AIRVqwgFo@#cV%b!I9zjftB`EDT!Jj zF8Gv5_CW9)tm+T;^7KqSJUHZ_Zd66W#yq8koOmL?B^Y40dqg_DkIQ? z*L?mtOd7#KLBpwqW8QK-d;fZUfqXn$eK3Z}0{vE>3fAG$=8DR*8UXGL{>h@&fdCBB zToM8;8}sZa3@fEj`elF?qwls6B{K~k7eCAf?$=m1l+Mbic@0l~$}tMbb$>*gbH0ab zx5`bULe;Z5vWp)M>J3Z@pHrEAlF)WfDqKgmb-%hanMpB*hr*h)>>x2>CXwR=Btz&g zI32*$Y*yEY%pF{YB#B~FU zdM9=m%mIhMkyP9H?Ma{_z|$#+2S6dnt;_>dmGW4`cK0CsRILj~`H7h;aoOE{|5o{i zMPPtbZywZP=9f32j=!CO{T985+MgCjY}yaG=efK14DbpBj@HMf4e+|LgjAf#YB}tG z2MVE>aJ9MK@LQ~}n?^S-?M}B#K#>Ntd#?(?p!#*0;#F7tQ?pW z1JrF;(k=Lln*j*lOfIdAyI}l^?7lWlwU~jH=TFf?u$2k+JFAsy&0nhCt5s|hUMyhg zz0z?wMKw-iwPP@*bW)sW_xcI zC`tf>YE#PBd!IHx5b5%^SCJvPu01puTfM_NA)TRsjT@{@+cDWZkr_vD_;7% zqWyH-;W^WedkxvY`fj!ry7|X!Hqc?_mHTtguA6%C01HpgyD zjaHZ%S)AZ+ga;$IxT&xQ)a}>{j1pg_3baRUId1O$Bxv%Tr7Jnr_4{i*^U;#xdWydo zNi(!Z^M`3_B6NtuZP`gA27yo(VTR@WyrEor_mmh}&jW z*4dQ?m!v6QP!G_DZu-KLpX|3sEaoyXP{*z>I;?|zKp2SC8Ojl$-Rdrd-wx6|1~!QqZa;pE~GJ_Oz}+XlT&+&S@(IgF!xvODAK zpwsO6PQFq0l!afim@dY5`X!AVU)J=;2sA=;e|f>`F;e*EZZEOIWO<{7aJru`9DbrLrUcA$^JXtTw=e(%#IXp? z_Gt}YT3({WZnm6iiq)G?Y34(>6mC=357~p z(kC&=$HA{tUoQM&t6hZ~TpslA(aEbj0Mxqqnyb*qsiK;w>6VZNR%_x3M50|g{+mgkAY+tX3LsKFAEC{Y%*0=qmVIfjbd(+nBZ&lW>fkb z{j-e#)6270^@jVgCIsiA4@pWM{dP<}j4AQDgYa36gp@wF$&QfR?C57dSIw@E6ID#f z74VF5%DEiNb?AMw(Tdkxb)VGtnoyGIeaOK~MpLM1rRZ$1oa$De2kjFxq#Y`{QoYgs zV&>20VbNlpEr#o%6@ca~C8>6{-YO*1PAPP1t~HgfW{ltn>DrdVqcyjk7}rh8SKTXO zPBCWW-NTubq^y>%K9R$%()Ie}nj6p@UKW!0kPY(4`Xu8+fC1c0VIo|ze zfo~c0L#EX9j+;VLL7ZoaFw6|XV}PUp8H1O|4~}0Hjg0z~D$eC!G`imHdyAGi5j896 z5^r62HN6{WU(xi$-@I2GpW?yumbkmnvlw^gkyUeAdF!thd?`Bw=-2aBx(L4(#F#jxXg1{CjwDuLFc8;% zsz3&3>deV%CnK~D{1`aoe}^A~2eNCm23~%pcCh_g6B!p2Ff~u}rqW8iFnsU67!2s>Z%w-m9E;JMxnP1ut_IYW7RWHi zbG2;d(rap?)bRS3r0A!4jHhk2e{a^{U_LAHbg#WIteA*D86>i@!bh&6eHW z-T@1cEGc~r&2_Sc+cdoPTo_FgieLA%eX8Psu98DKPwe=f*o5_1ju%?3JH>}f469TV zxrX9d?inrZxTbOd?iz@3BJdz0v?dP7fOLI&8=IQUeml~$4uM70jFO9u(5i+JR=A&D z_ROVKG+{po?sFOdPTy1@Dw>m_Ggec=Y@sLD@aa~T8e7bARa@J*KC3<=2RQe&<+Z-2 z!MfhkOD1zY(Ocd#6d%ZQXitXK! zrZ)ezYHD!nVr@~}z;M=N>2OG`w0iY@ma#>Mk(uT%{2mSFKz6@}Deh}DENn8jnzkZVQLrmIfvShr=UlIwZ>_k1 zCsT{gyCIhllmJZ4KhkWk1xUH?i}*gvId3)%ya#xPC) z02_P=xgrl(0lGa*RyW9OZ7iCLq`O-7DIJImly*JtY0-X zo|@y?KGqU8FU%*DprxYH@Od1_sa%fJNb}uFI`WD%#pMQ07MEu>y4qC7t$8FT{-UET zOx^3lZHV7id zS&8islr)QWGsNHxsbD3?+6e*&n25wD5=I7Q7TAj>2}(z#QCO>xct*W_`?i;;?;=tf zy{@l0=zmA>k{0tbZA;<8fzw5rdHbwgeBXWJDD79iRzsJ|#F~I^0x<4ly*Y)Z8^dkM z-BVbsUCo|D&zAKDej3{EQ}=5);rBT+Dh=iwVO>b&=32H!fd|(*>PT|Z+H+Z4ml21H z1rJP|)^9%y=U6{CDL5!(3==)J&MU9QQ zOq)(kn?5T70pa8*2Izj8@^X)mD@6`-;NaQj;?&FgO8HvnQ$v#|_8I2wxlFN>;@Ue3 z=WZvt(Qqp%={@afyEyOO`JDf&n}*u`uKmBxt{UN_^E-0RzQ|!jy$IL`g|>+82OM0i zvM-YmGMs>fSB>0*IuGbZzFr;>L1k!}O?DUk6ui0EXXiMR(!8~4-x?gJ09HZ)cG|(t z$y;{OI#eZiT157aqmz!0?;hh5c%`XN(|uZc1w}P;p6TE16jp<(U zU`y=WRG;I0)UKxVasPdb)4``kG5IwA{sS~C{#K~Rf-4TCoXWwiA+a);$)>bX$5S*J zguYL6W`*?5>2?~&zRhstI*f6SAsWrS`&y3L?ul&*dtZ82R=Bk5;seyDiE)-_SKzHDdQoh`!p{^z5Op~uqzgT2J#a|FY%PC|G4Oqf#$u41E5P%%LN|Ft z*mxyP*azx$I>^+R^Zkc>qKtA}f}~NN<+?Ux3Z#PXw$cjtGV~=b(G%}MY7V!1&5#nZ zhrA-q-o!r&Wp@;6x0op0Z!#@>@)t9GVe0$GyZt-45evNgR^qj6QptUHAL$8_{NQW4 z;75m6rRqq{$~i*0Knr*(R~wa#4T**VU=rGqYu?1U27Sqk)q6IqHnVWKtap)%*Z+JF|4Q|tG1c+tTT5<#dJ7QWAaLDN$*oGQlh$($yb|pgx0blHF38Z-#BH* z?|LQRCLVO0^l8L}3vlu@ufH9_k1$6j(Y7Pt!g?>{fMJzNCWJ5wC+y2B{Q4r_chLxY zr!$}!1GiwoIc~QFMb@k(Ja)x_6y=!dT0><3KY;X>k-9-cM#h_VhiE*>!31leG#>&+ zGJbPDGvD0OG6J+kJkh|%$HzZmX??X_2RX$3d8dQHv})0yr|iZ@Qes1bXV>`{(>{q^ zs}Mp7zmw9oRhYiv$oiuXp7+Y$toc#gIUR7$;=y6(DD&e}-#?tIo5q8V+EtnYqH66& zxe}{Lu+iGmFGvQ=k;s=4&qe+kbTko9U*5mH`>nwt|5m>b`_;igacFvhX^9Hw;?aEV}-z7bfo2H0Af_7@%8e@i~jz#_yPp~XO z4@u6Lv@$q$zdM$~IvXXg-ie=4y@N0-A=9`3R$?v$Y6ThHlU5ei6(98epxGIVWw&8! z%6nx~WPzZ`#oL(*-UVHvTAhn(X@S+n_gD!A2o945l*B;8m;HHt(dF+1P?tf>N+rr3 zu-xwUP!p&yBv{{Wg4w3^^YPaobeE(!qxYKhl|n>LMi&^t3uG$ySAL56PV>yOQ%Ae$ z7$5VWHXGZb*=C!q9QJGZ{d;I#>A(6eN>=hk3UhY`r2ReXcZc4KgtU+tk;#Yfj7+JS zcy>0VWT3dHiUT-;+m^b>;46(chI_**cfAwisLC@+E^yZ^6E$AdpHWEXcZ2WJ4>eco z8jC8M^5u11UDgGcUh$#s;;5|q(H)tizB#!eq^AZuxyR@0+N~yqEyE&`dy{yW?qTNe z8%C+MV`XI>B${>b1G%LkpPB{O&j+L{Fjzp`dWO*UJxau9T|k8(3~1IN*g)LLAw*Xg zgAsD{_29$HXnOo{A}jTmu+Z@JK7aXAK(o+!^3CD(77C?~?%(S+5$p&mMHS3nF+3zd zEzV2}u&WDtyt%+8Pjil=;{7jHmsw|X)PzT6Fq8$FD%5s2*Pyu&j&+YIpx8@JtFDtC zO*-P_ZAtGzIbr$d%VVQy98IOORd{NoIR*28+CQQ1^uSCy`sOlXnE)PUyJ#FZseFZS zF^*n`H%cQY_UW_;AlOSI_f0&KivDcRzTxtgmaFp7KIwwd2Sct_4VT@sE*aT(9aWc? zWrMnZc&z)7Z zkcKX=^Rvk^cj^FE;n zpxN97U$IZwOk5_LCK>1Fr@?t(9lHmvSz8i->(NouGviY}Hu4O-$D! zT*Z7j@>IuXiD3&UcXS>H$F+8#y6>wec5iG9ayt`UMe;mq4T|wmGXSvw=5^< zYty$ZkieCceq-J=3pF5DG{!4YVqna1Cf3Xxpb0hXizFNsD4WW{yGoMSy+pMW2YkXm zLJQ~*qLirtxbU~1ZSz{#F!FgaF~0wNSf&tbd0!yw!f7g{CV~!zN2m@c)%}ff#nld{ zUfXha6rUN9&CPKx-|Vy2C~jSCjF^cjc732ORR&JEKC8~!U0ieOT3fx}4wu~aua%pB{+I7pi#k8LS;LuNoL|W2KH+xlwS)M{fLULq zy(Z6u^PgWlJDPHDDr5DDzTEJR$T-{*WUT62OnWlPmk`EsRyDe&cFZa_c)dFxeE1c-P`xOvzO#mXb3 zPT>7x?-{|Y2WVJ_dYsOXyphvbT2zR#`!lz|H8QzoPHOjG8pl*#vZ7CNWsS8kH5U(W z>OJD%xVPHs5@+S7JE^fNvVs&@qk2M~Imd0JqZZe1luW=5JJ9CQZk3y@NlT734sqPhfQ_HY^c_q}F9P{s+ z|8%b~w~~$w8atOXO6Nway)WeRw|FOIkpF7x-=u2dq(OHgla=e=bi9OvX>}62Y8;4t z&sFYlrgEzYZmI(h^fx+IyZm3iGr|bFg5mzZ>Cp5QNf@c(Ac{n{ce0%%$Ec~Qz8RP> zgM?-$?@zRu2!+dEe*> zKeYmd+=2O$Uc&f^~_`}HtPBDMdv=2I{u}ak>=GY zqvCgBVyI|W-$k<*SC&y?s;XqHx{Hq%9N4mkV~g-)eF7Pn!_eTA zefP<&=VAye1aCRTfrG|YIjU!k#0qplLxAu)fb-Gr@OT-W{j^YoRJ9k|!|ZCPE%+6` zcjg&y%XU~95=b9GcG@nr)h6`vtjy`Q8pKckTldEqGP+y z{QkI1x}f+H!XhNU=O{5?8jw8TrNa?Y$p(e{M8j6Nzv_){iPB~ zF0v)c7j{FCe;g0nO59x?{Xvx>I!GvY0fuAJ@E~Soc?3AS&}}I0++b?gK|=kI9CbfNGRYf$ED-C! zl;<_8%@RE@;{J(3?dSs9I`ObOq<5921+FtN$rO3Q?YPb?%4dANxKnx|J4x^2spk#1 zheJE$Zmn%i`Spt^EG+K${c(3J=qdU#)s69fvb6F^=XwjD?Dn7kp?+tTyedDz4C8Gs zo+{*^9S!a{N9&zUeZ!_(i+_HPB`M~_P8_2Z;5;Dvt-xF}Td-e1gRTZPP6s^3|3>k= z$5?2^i#u;Oefa`TkJ3X!|< z#nPQdxgQL9fBb|M(9UKMJv#W~QW*|$SPSV0*^J=nUN5UbaD%e=8TQY*vLJ#_jBCc;}Mg(Smuq(x6Gz3nSy7qysW zvI_ZjKK0@c9`$n)X1p(^D6gK3Yh_BO_ujy-=9?L2DYMVwBjn~-@c6LLFr>)yr0wrM zyKLE@DV}oq(L;(ZXpY0Z>pMBBPJh|&)wJ=e>eAg)f-g-0#4`?HsK`Fd6k*H&E2fz9{>i!3(TF@f$EosBM=O**EdKo(la}{0^cVQaK z>&K_EJ#^{pC|dghu@QYM;XIYV(h&Zk79)p2_U>M$mwPPN)Vt+U=&U**Usn_d zK1K51tF*Gk3(I3!!h)j59v^usxLG^1@ z=r0;m$>h6hBQ&x8(6TJ!IBLHbZ=vmQy!=AO$f`KG7LId=NNK)*rJlm?k0gw}nI}|b z>y*eIYav$j$nx)1(C6IN#$!&yeDlJ#;U0M8QEQB+Sn(=xAC4UemDQ9 z;*a88b)T9a`dT{3p1!M1OE^lbQr9+p5q&}R5PeYz+hPasU5WUaC~T1Rd!*n13_4&n z4ZlRtMqRNu359nA^T`Bas%Q!$tu=pW&78EyHj@k(81$iRUlpTz20gtukWBG%> z`kApXJbo7o;hPyjg7eG_C3$4IU9<^r; zd)BXK!=K7pom3uW@8s>PI%Hzf5wB^{Rp0Dg#0x-Cf2+Ia2UR2X8O7im^3cPUOmBG1^CzO|3z4NZ2RY^PpEd&R>h?;fxMswQqSBvKS$oEDP_ zW`2YW#7Z(sXCb89DUB2YN{z9BkrK@)BcW&vjx1NgVtZfVq4CvbKDdkX*V$u-FYDNG zAF{vDeb~PI#ihRNz5AN_0s~j8E!{Du>j0nb(B@h*h^dLseQ~*2b2@bDXlkLto zC@yYY$kHq!_b$9iU(NOS-lf8RI~A!-dC9h>{QQoI9*`^6{l^XUg=RSIMH%vLoR;I@YE&#g&FSn+OlMzsH;dyYv8ln>nAed!+@PMyjj zJ7|}5CmbC7UKnR^mJg}D`#6!6ZyC%VIBFi?5oheDp*~PN{N?-i-_K<2X~Zzv6XJ=V z$k;trkXZugRxh`Ex5TA?3UO;34hmRb|PJ8!7 zOh*?FlqHV~^Xg~gL7?`KuUA*7AdhCioS(W}y!<789_yxs0`{2UifEH~9}!JyzZ>eK zyM&53sc^5H#`=(W&(W!By=R*3oYM(0k6hm3#?P|Po9sAT_bF(gd*Wd- z{Te3CsInA^S4nfH=7V?WPE)}1v}#)585GgdSQi%YF4eGgzvwizlcn+74}%ziPWu4s zf+2QkSYkdU@z!XBxi;M~1se?54eaLOaP<#&pM`%6QTADem6XA0*s~A;4q|z-yUJ-^ z26Uw|>G`M!Dqn6FrPdiV8w@bxF&i_4Vkid{fNs~f$lb9F6CNOd4lXFQ#A_cn zad2eI)PiPHLgYN(ei=u0w24Y`c)4kY=+#FR0yqu}`)jh$$_Qu4mT%1i?^tSebw-EBQ zpyVK)1*~wiL@G@4+14v{t#Dv6#^uda^d63F?iAawHYOdH~qerY~OX zr{$>C{03T%vH*4!uN`@Z4c{1Npqj6?&iF1x6`q1Y5Z>Oeic*nh*y+|vhvBCK&CgTL zsg?8IvEtibVEMwCTni95fX0o7!puZM#9-SkoZX^Qy#7P!AB+rkY!>997A~wl7wQyV z>%`6E@-$g&-^#UNbvdg?fyl{j7BMx=@VUfHu17DI^luwf&?Ld-yQwx(GTMq*SI7x; zvSLO8;|_2XvjtP(!ACwCEHhDnLX>X-Pz7V%OZyvE)t17$b|*WE{m%4C>rWO-1q99b zz%wB zOs3mmViiyg-CHEbig%4>L)Rx;e$9LqPoH{dAiSSnZqPj>7Eb^UC^Wu?y}C;~i&X@; zX~cue0aFo-kBJKuF`NVF)Y@|vCL^dH{HeNb5MCRJGp#h{B`Uy!n&K2#bahvKd^qj{ zXq5W(F$VtVO>DbPu09q=UQ2e)zhQ4-v*{Lgsp3gj z_sI^-B=z8x&HlK5x8gE&)Z;uvU2i#R&{>rF9he*%BdSvtHQ&h_@*yAGV&07k9mk#k zq#Rp_%jr$j7k))fYM5k@>^N!S%8Ru{JO{Au5>947!N^AJX^4ghW=P0yhzBjzrAU|9 zp1>ud7*?cj(^kBwBqX$@uNTraL`mAOjQu5enkA0#cs%722vmlnvP~&N&mq$Fq#+ci zSPq)=0fk967jNNHy|E&@4M;G?<;hGc2!Fz1zSA748mUiT%@^;{RCsb zX3W}Q4^BVzZ$GdvE~qI8EuPg3Zc~W~3HMpC0?pt(Nr@q}`}dv;nXIs#Bt%sL;dfX& zS?F%MvU!`rl0F01@@d#eO4A-U@L*1jxnfN@9JsAPp4x_GaP+JyL%6UZ0;4N?iTiJX}Os)AbTCz zy~M4fEyt{Z9dTTe2mvN6ocv2B_K1?h|CRF9hD9};mD6TN^cESds!_KhQ48_I2CnO9 z5a`EFT@mi#MW2y^IOg3-|`U?01G{PNO7Leew6)OQx4IzysxaL&Cw%Yr) z6-(9H?;pMN4~gyHoJAS=%paV(%Lwy2*damjLePci@(kb^=oe8xA&O5xngY>owCBRa zlO-ah460(VDBCvvqv8CoAG?zlP7?OLcLj#QTYta`K{K)AC8Yg*TZ9w0QT4CH*NhBv zia?p5#DHH(QgjKT1v?!Wf!1Pnb0zP8ndtzFUWPIpGh%SPWGFbjj@WdG;_4!X<5}e; z7$4Hlhdp`HeZCuW6*huD5ci4bQF4i4CPI9X;MwfXK+{Hpg7E);)zaqc@DT4fko5zJ zIE0MRL1I$_2^fyN_I*IIamD>P$I!@4`RM)HUC@ctNU)z-%Y1P`0)7z%G>ifWW8*XJ&K!WX|Nnhn*<352@$(E) z;ky^61ppig6eJEJgiMQxxTg{U603{o4ifkduQ?yhcQpF&KKuVahDLBT#H6@XNf1vE z&~Pk&U+Avd>s4$U3OLYs!opb7CmK%GHc+z|1{3ZsrPE*jgc|NRC7Kzs-Q z^}yN&He6Wkv&7kI_E#EFaDaMZ{;YPAOr7wY1wMS}+#$3=@X-IfauL@DvC~5KRyqjl zh(bP-cTC(md5ZOsO;U2;V?auj1fy|E4EWTzo_yP#h(GpW6F;nA*gZfG8lRvN2V5_b zE^Xyt-tYIC<#tszyIuu%+S$?7d+0r zUiUE_wp7HFF~aJ{w@(nW*j2?_{fF5bCh@%wH`dzyWl+A9(IvVMTD?8<(4#f@F^fc5i59jlC?2A=MW(s1 zRf|T}P~5Q#gCLJ2fl)Dni6ltgKK2O6Iyr-Bf&wO!$U>gG(3=V$T>Q&j51nnWvxW`z zVS+uAY)m+Bx2>ELukl+>rRfUWRPRE-pVeYS7NJV^7u4 zbvX&|ad5LkAh7+HjTqd`#Mi;Vvvp#lHp2H>kX%w1aTlNN06rbLT3I5Xo49_yFU6Rx z2WaFjRoDc}ha;x#AVcjyP$av7&4={JN|voUu*+rH8Y3xw8n419oP(_jW@@5esMZ4i zzVfvj1(|3G(QfhU;=DK^10kBXfDn9vZcN=CFg7x()E83i>7SWnlLl&|m1TPWF~sFm zlQ;}Gju=&KA<*?K)Pv<(cT%E}k5e++l*X7n@x^$~$j6CV-CWtM_sVzQhBb*|Ov*c% zR0TKrE&PB5DiJ}b&y_5ii1ciKP9T!_n3x!)IXI1mhd{6*29*q1PJGJf$x;1K1~VZ*VDqtMKzf`9#Pm7?1B+Ma7MWFAlcKm4SNW zm-eJHtJN@<>1S&Vxr^X(-feSn%$y%~UTmfw{Q57!)C65FIZeqbUH8J_s7|`_w#(;p zr`4DhcQR3SRT^DN)Q~ASq~gX|6R_Xs!u^U*l$J+3y_&iVzYzbF%IW^D-QXJMp0a`| zHXJj9h_Ov{!Q+09Nz>G#f`Zxjo~4rUpSxIlA9VFlR9d&nl0KHA`ean^rXo+l^ripxPX5pYR8dsoOa`I{xY2 z{#z$kBa<%1zTrya4jH?>Rpw_O44%2y@+qjMZBjgxlrNQzk2@_(J&jHHW_jO9} zqMl=q?{C|>|7`8xs#`V-_a^0VILhS&2L|%BKE8jy&Jf~+|NJt2d|?|*0!$~Fx`=%k z;20t`#}f^QMi>jhyH|(UAOiB{ggqn3t0xJXqc&^@%@gN{g{qQXO33*lvCbT=zXd!a z<=6_h_}-v(vmUr%cz5j4Q=gSjBD*Tj_2q~(RgMPEZ$B9&TIizxJ2}O+k*FgqK`nRFV_^JLmmlMdZg@jHAqv?F%%=flz`Jys*Y*hz+jk|yKITjja zoDJy+Msm2J8@vyXM=D+yxB}WRm4idl16Ecv!@L0`cBtkhvbdROi8;XC9>!adWMdq6 z{qLjiMV-)ZXyO`rST1wkwYBUQ^PyAh?s20&5|6W{CC3Qw%LH4W7ui$5e(<<-M?=U{Tp8%Zx z`MWZQ?x5|QW+Jz z>yL(KVX({Y54Y}`*XGuG@aiF4JwPRVri(g~OT92@J}Ji)OX8Lwa=bRiNTgI+a} z><%4J8Swwu_1{kb%`17NARK|;9qhwZC|M@&ci_yiAaV$wo*(c?8TdIdFhUrjaKeT& z1d(`yGfw7tXzlNqEo3O;KGwIbNomVe^37gV2Njvhi!ioW5Z$Da<+20 z9VWT$p?B@_+%d*7Z45$}N+=G$v(eO(&qjjW01cXXN1h##vXff~hVsYf7=q1{_5-p> zTl=m5y~)w|X=9~hy5}?m$ry!X3Jt=?T(GAqYN_r|^JAd(BAFnl1fZn>@5&l5x^=}G&0bWvIwn3MQYG`X-Hggc)8=kpAxKs_iZsQJ869o7M?1e8%KBkX`g zS;JY|eyR$t23B3qf8Ms+dO$ZB8UU%>_YmFzkKA|oaHa!K3`3uTnlM(aRM0Q+ln>d( zYP0)Nc^V}De%zw)uE@_sKtb_$gBe0~k#We^{nZmrwPbm9WUAyzN#f+Q*ET-Fl%Rzd ziqN=`FktjGmbGH!m?HrWt(kFzzKt^pQcF()3=*XpiL5{@OXh2TmhK*=|9&jKM59RTY zR2)bnNlE8Q%{xwvIjLrFFpE$+b?P;5Ijl76(3osbzV{P0;3FhU0awT{q@TB(8xKd& zbvHhf(I^Aq#j`h)<#I_)^SUb16qXZ9utprbXH+#b;9?MshKY;Jf81uo`k(q&S6652 zm#-nSNAN62r-KWC;*G91iM&7C$4q(4&3IR*cHh*sk-kXU%LSHGv4O@ZO@H=lh}VYMg8Ae=8)@W3+xk! zVKXWpCI?^m!kng{xzTkS<%5^4H1Ups8zs;KG7!D9d2f}q&Fs%NX-z9f)UXro757w& zJUwypQOZ?3GiONV%=hmXscLgbKC_@oGWM56L(E_j76~9j6B~{cof`-iq&CeuU%LFy zu zz2St&?_!A!B7nr>P%8klrbIO&mCAIXK3-Qo{Nq}WADhJv=n2M@gNLtz(1v3aWC5UP_1_e#C9+q^kJkERBzN6$sD!Ferm7q zHtp+aXUBaVX^?S0OWJK5)2QnGi{wfYk;mj8A7z_Q~CJ8w)Ldh}M*(~#kj zA^W2jH!4}5%uHN-l5YZ&7lFdAP`9r_C6{*Z;X`l;9xx4(#d`TiIyoZIBPyfV#3yTj z$4(MBovRuXW}!DBd=_w>AG4Z$`&deh4e6MfQ_z`NV2ee;9Ev?D?W7O=5KMuCTi=$D zXIWxjUI!1IkjxR2iTAsNiZdQ9Ze0uh@2TjFHYe2hVlzXKC$(grgbbv+S1YAiI8e48 z<`yoc_`CSqN26J&{#4{&!c13QKZZ504$aO{Bdi0GX@}}H4zxyMHUIc4)LJZxh4`@{ zFoKLd^Jus9A;!DJ6rWH3^Iw5^Od&--<02OPBzPSUi`H5EDP0fYWC~$j;~gQpqMnH1 zz)w<5aw10^{5xy$<5yU_VVYWimJqJ)yQAyki4i@)x{HQ?K@QoB12Y0LSQ3zirk&WE z_{}+yg%e-6TPoqZ%-`Ae18w)1x*r+_@8UT=_FOsbVs3j1pspvG#{e+RT3TSRsoQ@4 z{(TRBZC%~5%#IfCT~c{EJSE*r%db~ToSl4r@@2XYEeRjoG1oC1I-%T`_j(UtC8LAe zo>wjZbTOYG>}JDWEc;55&$?+BY^mF=yRoL%DC^-hMo+nIg$$1q55v%5Ui?kwoTN0b|7{5{Q>DKl{hj^L5tra8(3{50uk~kp&K@z^DM;efi z&Vdhthk`q|tBzf~wSVQMU}LfB^-O{vGevPkYCWnmZ8Qo#*RR%`sK)i^OtD@Nli1}v z`94Zx)XBg9_2DG{w9{j#_} z{Z5C+sr-)YjU!G*UySoE^wqMz8?qn1v-k16hM@3OX`Sm`CF{>~uYHJ8s+!MlXr})7*;X6n6Fz^O%(<;`!pHg1?(6Lu zpBM@?FMlx*NGllTMAUFc(tdXKQ^d(11CX;R9_K2oPn|ds@42|(;VG#idC_$93|1cp zvP3CI;9{O_-6`9aU;QyaZNaKQ<0m9}4iF5w24ZEJK_B1L_67noL&F#tkPtgI z^4oYuJ^Y0{7cP?qzNxBYZcOCK)H$ADbRZmv_lt>Pg=Uj51KTnTygcToG!%-65iLMq zz6D+K@-!dsdYqw=Fqi^&afIyK2sRQmD(*5WnV)Va1~#NGP~Uz=BLM7WQt}h%6PZV! zI0k-S_ZN52WyObD7f6$FF4cUD7?A*BX8|sta5T7`Ew_Wy@qz0oOWp3$iG2I+3qrJX zRd(@;KSvzhnlhh+CI2Y4+H!xBlg>Ef?zhiqt+^_~xOMxk`gK}%JvWo8q_{opnZ#>rtdc?8{TY#6COBiJe)RfRn@-A` zBv*f^A*Q;peyiYJ%z1q~P%<^j zbbbB*vG?ZDRJLvS@NSYyng^s2m7y{gWoRPvOynNh| zPIahV45SLkQjP-ydf`;Pj}OS1phj zggCw9s7J&kwj3DNPsFk8ex}{}ZtaihGgu23gb~#Mmt)+Z@q0#eGV0sY-@3{K?iX&N zDogw@AsnZhetu)Rmw(d@=KIrExe!rR22x{adb!}u2j~kCeSR_%Bqr8;W*U$>4IH=Bf1R;-}=EjfJndQ!L0ojNr2jZ-vZiDqCZwPwb9%}8;VO3s`3 zjJK^GE$?Pe20gfHDt92YjsKNtU;QD27KfV4xmNR7^FP-YflSNwM9wWw#BMWhofabwY5fPEZL0B|=_zvAKD*8Ed z1{cR20yh92`UqlQ5m57iP5Gn?m@Kj^m0rE+<)n(&8mw;SE(y_q5X+WOlsTilR7;ypwA zmRIlw4AlT4yB|56Q9Su+NvdICGu8J()9;7d5o`D5;jfo6>Rv~adM0i`qSe{(vD`!t z_q4a7aQ12c3@o_~=M~Iibwu>Mh)n41Z896r?=M=<#PCIZ3y*W6u0jmkRCspQ>YZrhmsMZDSNY=D5>IQ6|MH{zd9S&CWpNdLW7 z-N7I|s+A=h%~xJ4?$yKf_YH2}?e`pV`V-te!q)pqa~>(F)2+mgW!=PSb?MHj{Omy7 z-oBz2loGuwa3M0ln>N zvL`{<#bC4!X2#}%C5mw_*&uOOW!6nYy$l(<7 zWekXTiTSRIiHSM1E98w88_3;17J4!FYzo^t@Jnl{O-Pj-8}pZa=lHATd^_uAe;y7i?2viaR-nJY^p)Ghn_X|~OWrb7I!=5Z z6#8^W(B7;i;%3(I;MHq(V;O|Jd6jRynK7)Hwn*wlsly_v!!Plt!!I0mp2bUQ#p>{j z2|H7B)9KUeCt>MuQ!wSMuN zg1V}>$~(>`rlh>hoB`k<7l^8n+E<&v!#^_Na|p?jeHmv6nL#GxU0`5)$;flG_r@C8 z?cs3%)#G)iU%mRU`?ZaT-qXiAsST_xmdA)noXB3kS~%uDA82QROt!@?FR8<&e;y30 z=d;cEZM%G`Q^sZaXuGUh_-Q9^McYwgTH;U9%d>8~#*w9-ddX+CcNAwG<1-FKJkZcJ zeE73wD`%6jsnB{?X2;vy3oU^bPW`Mn^>~uEJ;T%oVmgIG!y$ZnpL1paaQBkLhEUM} z2Lm0&6O_ios_~dJj~JjHJ#wUOaPPdO^WA@WRCWBEcgXwuvP4OKep=;OrVV~>Z$Dbd z&D^70p(eGmu`w~=rxRPd>!M>Wb>Hb4h?JLXe4fhFEpctdd8_dRt9P^ZoK2nfEjrr0 zz9QnQ*R6)%1*+pXRBYGz4Ifb1ESc^~bkjF5ALDsZtdU9;>4C0t@a z5)uf8?8!0^StL|rVQ8L$rb|spQ6TQm#DFK&wm#@XyS$ZAa)XWUIw)vTqPB=-Y)z;) z(MsqF>Mw9NJ(bvHubuc@Gy27=1>6o|{yba=GwNzB<*FTZU;HG?EVC1}-!RMh-)tNo zY;he@ua#Qs-j2slJSN!1?7!Xt3#-h$4dZWQL45n6PcwVRN=WOZ*R~PrqeQ<`c;)~- zB(n-l@fVUR%J0SPXO6GBO~o`GBt!}}^o?yY>pfdGlFsOwxKCNxePVZbTXcF!gmZ3S z_y{_j)Jnwq-y9_Z*G9|XN1y-)tw*_u}r$zIZzg=%~vc8eQzpIMjX`?UgnH_Qa zzN=@Oq&0L_V^dTc43uEzl3y~eB-Y14I0P^uyvu<^`V7&$z{%L7ncgqokAUuBoTeMX zLTCjZs}c}SZbRHB!9nwm@S&srPZ>lvvb^oH&(+ZCsEUp$$V*?_|H327RxYcwe21r7 zQ?6mV^`z`x<|oJ8dtLpRHvU1uBxGI>Sg;Lf)Cx66s6`(kT;E zM9d)>6E{OiK{&J_D62v!db^5$ehYKkO22JO*UsGg&|5m+JS5-hSnAi056-Ju( zWeT@1oY~i&kY1NJ(=u9jU5~bxr`DYPZ%X))5&D_Od=w8ik5_wBKhHP)**&tpCN2G~ zbdWWdPx%8=+OWs1EuvXBZu%nuW|0u z#sAvP6jtUbe$UvQW5#nC(@NaVSPQXge%Ya^BwNJA_d_9b?<_~`v*Wu<%XjXo+LpeuhLMw!mi>1RUW8*?%KseVw)w1p0y?Y)L z;?=&=#z9k^CPK3xwww|O4XZn|@Q~m4WsK|k*W~A$v?i-d-dC?(#_l|U#C`kEduEwB{QUgfJ9d1% zqdCYpwtdT%XIA^SZ%@z-2MhnGjLbztsZ9r*_I!MgM$1Q98{i6$3BrC@zM)zqU-uQ4 zl}X6uDa+ab;Rc+~cDZfkJIg}eus?XIBAa)nN?4$LvnLbI)C9}+;oZrzylQMPO_|6~ z_EZKW@7mzw=ckFJml{l5-X#W5J5LZTJbQb4_O0YiD4R!nOGl-VHhC3e)uy%;D@LY- zxj*;s6Pk42J-BQ*gO2}E!X6&iyW^76n^>!Rq5hjVoATs{AA{#?>+sg2I@SXBkL_n# zSkg?5dffm1acrYwRA{Rfwce_w-8xUE^5gyRaDz^tfmk3U&NkY91WT2g5oA-8yY5@v zE3q#5sDhfG1CRb1?j3w;&Q}F3N{gd9DN9ec$VhvtN}`!M=sm7U?aY{rs42wpdM4k2 zMWyo$MFBokmEQnPP5YUn`gaTXJ=;(~B5vZc1@TAAdHCw6l9#sTT-jTFGBu^>m;cBv zHu8}v6iuCXYhOvfcbN9zS#9HYprf?-)1AzE{+)`8#-DNx9qGNjq1g+d*ek1iWWiB0 zINb}gJ3BjF8}_?AJcud`u8{NV++1ApdNS)RpR`CP& zDL24p;u-%j^Y~as>FJ-htZcOiaGr@DVQXn>*n{^_RJxxEvM4kPrJK~FA^8|`%qNYjjZ6eT+5zb{J^nnk@%48 zNK)Rz9u-dMl5%S%>rYBv8%Y|9MoeM$#FwU3Rc`347`7lM_@;A)j`sq1TADZm=-OE# z()n}fP2U3}p=A7m!hv(qB4N|C>RzlRD?BJ|7Qw+&tF*YGOylTFzJ| zm-8bDl-ElQ#>yn$vU%rkI|AW>};Hrtd55d|pjZHtqa+FonD{#e>xT zUjfG>IMwfEa~JC<{RbHlyxds{&C2 zqPgSrZlx1%c!b(mMboyaiQ)8@FOO7?6T*^J3ltx;&>k?KhIl*E4mxN(_QG0#M_BA& z&xL)8IZL0kS}jmT5ggKzdP+t|#ea=e<^WB8NVBIa$V6cM*x*B>Ec;T{;<2S=Mdo{< z<#zi`_qQM6#h3XYb(SWg@(?J$#DG|M1-b-k*48QQzj?YI^s?@mmWo!~0zK`-xC8r! zGY78xtZ<@KsN-frQ^wyKJr}&Li zKO8?0TjI*J=(bu)s%|cd8}L~xU4I5oyQKAjAz7J+PK*P0NE{G{$(i3&HYd^)S+3B% zMk)C2{zDm-i7)f5$(zIYlPzC3M&Mx9#DYB6r)zHR(erfkjd<5Gkh|oJ`hBmvayOK| z>PpS;K!oIiBK|qF%WsiMihgwSk{-{3Y)7OmD#tN@(ACr)Lfi?_)4QV~+S!5DTVIYX z27S%mPP%@b08x&IDm=N44zf6`Lz(HU5L0m=H^H!YQX<^JP8onFF3z87kXaq4H)WJO zkv?_5nKS!N*@?PkE!qQDoi~+tr-}E~z28!1{sRO1m+qRInQ5JQL2nsor4~T8nOHQ? zzM(%iW!++w`_4=qn4GKIZ)UiHqZ3o>$xtI!dwwLbJwzR+E%TUAgV#P12@VUp2AG?5~2ciszENO=j ztUS`e9rXwFNVxQ)nt($+jGEiD%~3+fS<4zuFR7T z4^kF;m>t8rBJatYP?y0^7adeX1nUlnsmhM;V>!+*WX;k#M7ewFVC-H@Ly%ceIFH@s zAk8BTt3{B`F37fkUvvOYhtn+q$Z!B*D=3?vwjtQ>NM|XrbPg2y)4IZ`++5(C33rNB zfaT=_iS~y$6dDBXQ;SzGi$@2Tlr+>7pdAs+eI?K(fBt>gn*(HkP=$xfrRNK=Klq(c z%m$0Gi|MOHJgrNwPt=&X&Y@!ckXnLW|A*5(#TrXV!BBi8{3i7vG%R*Mx ze--N#A^lO~hP0#!damenra)3s@0a1YpTy6xmS&#DZOE!i)A$XM7w-LcUN{z;RiNN( zdX#(TZuOFmnXr(eLu|{;EVOO!0&|I+<~Z^)(N&FS>9PGsJzK`r;+hDc@_2=6%Gzee zTN@S!0Q>LX^lQ=^oZ~3Sxkow>e{o_N3f^Ms`DuuYG@@c4pU~N?L0lKIJ`Mw^IsGK5 zJKwVXkovGj_cFodV*lLsJ0WEqyxL0ViuCQ?3_8By%i(U_W!m+n@xxC~ly@dV>(`uq zoNRE4hjv{ykVE-U;z5S))jd`XQ=DPB;|qt^t#UZV92|FZsgB)DK?{`_?llsm_qUTN z4C{L6ft~~Xa zP?M@qT^JZo1F!&@+5UIcFwf%}YV9!LF&u)=>emnZU0|=MS+Yc;ZlaIpF#BSpn#OhO z(@{OM{kwXeA_c?c*yFGK)kF=}*yV9&3GP*2=wpY!Mlie6wlX?sRT$3}0cpS@B0-w` z*~gmrR-~+MzJ6=NO-o2=5o!ihz_~FoqpGS397+|JTn|sdPFqPp_O))9hQ|&9VSYQ3 z0Y1j+#>j^slFB!k{d3tr-|Vbtj2YF`&9?HCw%>m^wUaw?O>1Cl^6b6SlkM&sox>lQ z8RZpyn2dERJDz?sHc(2u|K0{}Sy%mcwCQ{!ntQvOvJRSMs0|#@EX&m|&e{7f@O|D? zArv|=arux$G%Dmd)IX%?2ok~ZsHmq@H*aS4QLK?9_)^WGEfeDSIYY-UNc})zTsCSv z)lfH0&*TgCeh3XpO6ZxDmo!aT;;&DB?{&FW!u;e2n|2D_$Nl6B24D=waMa!m2+&4$OWd`H#ySw|9{!mj974qE z4KA}upzJ&ZMo7Sq>gsATE{{=v`wz}NgcDJ4?BM%P!}M#Umphc_I_krv^Ox)UjeK|# z&FL*ytpJ|8qxHo;`K%Fhmz96jl+mGO!;{cRasJs1D5RVsY34 zppSDy^BJI19W2=WPfK@1x=qOvobXKX%tJKmKx8dLZvq|Z?cw%;bhUV=j;nI=@>Fjl zhL29`O;o&^`kN|vU#prO^uIfFT>XV1Nn3Vo-Fk>f^~0BTpnE+qQ@ZROa)2Vk=D&2n z`616v+^Vm8wDNQ8mufNN$*ZgmKZ3_EIDlF(jYCyKAAGk}f2g=*&}R=AX5G%N3;ilG9s^X*XYIps9#J=I=Qzy#Ix@L9(fz z!mf%jEkh1cckjpt^LbJp>vQNA>ET|YLOztoHDZPXvQLWk!Z&FB5u4T1BcERBK00xE zYJ70u#}NEM>V+HiW08S2ydOeHk5P@4)*Lv&g5QA#>^3p6_OK)~!31ct&1q40L{qRAsMGCw-B72$t-*}rZo^OhHu9c7 z$2|Dwt}Y8CdFbI9b_27hN6t2IIv;&Ymrpmg7xbrq7Kqt{1hDt_xNY_5G{L5WKQ+H7 zUtRREC>lza?-3P-d(~a%Rau`vz{pOg|JV4m zL;WcGN^X|lOG4@dA`Oa3b)@U0gncU+7cLsw?7B}r-%fyLHY}3bTBuVz*p|$vlb^MF zuipOi<5osP2fJ5yQwyH!80RBfZA5#48hoL@uFTxks2dHE#~5Er4z5HUVa^6NNV*mr zz%Sh`h6c{t=Zl1vR|9<+v=2*JdV*H5qK$#O=2TGGYh!v&jpgz2f>w}@NT%ktyzmwM zDnNQu1zW~dviJn*qA5~Uckb@Df3!#YBJ-xIio>DJA@@_4PG7JXi{R6xUCeA4Ziz0; zT}b2EuAYBB!#?WcqnqsU!b*1U{yeO?#>@_60Vdx6wm1P%QRBF73h-{%V$)y|Gc${U z%LdKoCq4ARZ(aF?zrQMlCiXBQ?%?3XBpW;Vqq^2N{d@!4SXEPJFrojp=NtrnUGMJ2 z7S`4IPlu>p{VqJoVT3qsy$#H_kKd4hSi-5Bv9(Q`tr#= zUg5|>RiM22drJ<}*YCZv`EBL9`D!{o&9xDKq-QjnecUHIErm93`s}&OcZXXOyZ7(4 zoXR*;PjhB7Xgz<6;lh{H_l)_0F`cF&VH~n#vg7sZ#D;6&--2mTIX)9yd4F#uHHdTn z{+2VoBKL=x&$?r{X?H@<0t<>SLu`!A?Yy|@&3p>KXu5V&H|)+$WSR)-;R9alnxuya z5~KLaS(END=I`n~AZ3(hv@&qLwV98!z4&4N>>U8hoF^PJ%_8TgsAgyXz}(KNgpd9<%lFZv z_H!xctLh7v*ahpUMdhrktot%1$$nmsO;9k4U-0mz^n28W6~$WN>dF`eeGp^gh(>!w zj|8Y|^=ADCwt_lf{sXm1x4oFDi1r6G7AlPWbCWSE!H~*FDc!zHK$ksGZy;l%=U7wN z(d|DcZC2^^mPhpLkPx``Oz6+2r9}ENcz)U%<6uy{WURIVegXF`J~w_cRJWvltBCAl zk?BQ}B9!@_ErShsMBTxw`=|MMgilu|;Gv<0VP3b+V(Q!QXkYSGR>7}Z4H~Q-o!T06 zXZ4!DkGcQpL=c2#D6GCUcHZONW&=I!1NH-1J^noWqZa$m3ph+{-+j6~!05F5hb`FJ zR+zJt7?75hp0X)zzW@{s18Di?E$s^7fsnH9&sk~&(W1c|KFapHO%`gWG*Z$A_Ufm0 zhJ{xd-)E9;6YOGr^?vIboA2)7^e!phc(ce>xkcma+k`FZ7D+lb)`{e0m7d@{`s#12kG1vT--r8Ln z$gF^vG*Zo2F|7pcc}-JbzNu=Guyk1d&>2pN@i0EqVIGH#OSgu zO>s<`=MrdRN}S81>e$Vj>LGO^Qv*MYkyH-MIE75CkC7Y|)5c{Bu*?|6(%%{X+xPsG z0MAZtC3n%8mFNteDcrQX=2Wd$hkJF|jd#Ho0jU$6bEF;t+hE_ZFU3GKl=WBBK>6E= za48n1tEQYTCZ9P>%{G1gf%E0RKFjMt$AJ?k)9#vDS6MBQ6x~v)pSjmSZ+tlAyW3^Q zjAJ#JE&P-T!_)87U@|?1?zBV;O*hrSccqjoz*@_VL5a0BdHo|xY}LGSXDe!{(@gb> z2}UVEwZIblbMc8m7WWNgRnO%Of068ezAK@z<=m-lF{58zx76{`{K85(8r~-`P?l;Q zJbjya-J0Ipp=WmHS6%q!x(7Y#taHQuV#B4sL!p%VD?J(V2zI2$sfZqRb~!yOGnRMC zLNnL&C`~*0dAHq(Rh|-{I$H-$#XD3FXn#mh56f9`V|SFHzYRi*rhRsE1T)CDopLYr|?%CCaW`pK7k3eB|PTz}QbF!g^QM{~d>nO1g35 z(HlziILm<0<262Z?2~(rF?m= zKa*`vEi#v!uE{ge5~ zTabL0+4@!eGWcq4BblX)o~0@}h$*#k-h+X-dRB8?d0>o_%#GQq0eyo@Ari3E_qnPU&(DjT6t?*1bFbGyccsc>^_?|52|JV} zZH?H%C@Tjge9-0Vvdc9QVx_ujYiM+xj|6T0jkQEk%R6AFKX`|06DrTqmVA84&&qW3 zaTBhP`nFF+-y~})tDgfLL~i{w%dw>lo+(^DDP}%f71koJv&(N2U`;l1|MTf6jB7#G zIdU=O(>7xbbTcg#u2l3@X@kC2srg+2y?p%YI%I&DF;F&ETBSufsFu{Z%pQ*K+}v&}3_(mKANDKHSQ8GMASEDaF( z9rK5kPW=4@SJ1xP(Y!XvLN6|^+${syZmZp|Va#Q7#j2Xc?tFQBuGGv9yF1(odyL)) zhAbre z`@vlA7}J8PM9~M`&9`z7<7c=c_x!1?H*bcR8&-qV9qoOX?4)%+O{-$4;g@X=;|mrm zyonq<1*82M)FMDhmZrom7rKf=nLl7+h@LjUG(ddqXVw#XCuXnv>CF5#r%pfG>~C`D z@7tzw0r^J!ruVCwH~(FQQup$ZU_@QsasDn=p^>-Gl0$6Q>v_YQlXXYH&ZholW*eGf z9HepHvI8muCkawcPT-$u1qJ#<&#m$1WegDzU0Zs((en2p;d_)VxzWg0myX3X{PSTs zym5Dgmctj-bWInCS8}IE)Y|VF9tGpC4s|)7_lqsE<&gge4YsH0F1LC8CG=R=tXic$ zu!YRA5D5UVOTXJce1frTqauAwb%etQ@pF<@P7CJG&h&w8y6kzt-`bH}kEZ4mp6Q{? z#mwIOR{pKQDSrSNZnDm__Z?<085R>(uT*NY|L8qd-kM_+NH;}?Cn*{UpbSPeGKCU7 zC!=3e+BEB?8Y7?j6et#e9DMiHDOEMKK)dtjHIY4PVvx3?8dLxEsDVHJ{9E^t%TV!a z8NADZC9uW6%Uw)3C?-WtakqMd+n?pBvgUW-zxr%*?6c+lI|_t`o0Tg4T#wz?N~?z_ z5hh>hfEEfbjCH8}H=t!MW#b0+r*5GOO~m>=nO#BJN7TxJ^g&loT~{{@l57H~6JqwAM49M#== zi2Yi~F{)zrb;ds$t4wnDQ3`7lfEnF3hIn!EdH;N!{mFE#Jg(}RkmDBA!u4kL*H>(_ z{}CUuAYSp$Cuj9x5Gq6)TdLNKKQ#F6z2+#hkJk6=4y|4F{l?!{!S|~7p}x}KJ<${` zQ9B=K{<(W?$?yBCV(_Ajkc2EgA?&4aWhyCIZ^TcC#l@AGWqXOyb$R|?&D{5I*E%RD zE^%1DFeTUf$EkRCsEQ`hgzHYy|EX&8pRjo3xHaqhG?~N~w>ZLG)UY~9MM z|`y2-mXm&7%Yf2B;mSS5&uG;bB#ig=&A z$fPH)t2oUJ&o}?b2;;l?==kBa|K4-^Mow8f#=qpSun3vm;u9@H9q<2;QFtn}j+Ope z#lKJf6Q{O*k-O<%|0Gl9YS-k9(ophAgM~YGa_{0O{r81M8w;?m*~Aj7x6oj1;NyAt zz!TvUGOv!jHx<@XnjE2{YK-fCN0vRLa;Y9+t&7fKaOib76u@rkQzzbInEL+pj=x&0 z^h=1kanr|_ly^g-!(pr3zDP!%d#t-Xj=Or}#peO^Sp=zlTY!e)?g4EA6ScvhM?W=a zrMmKKesDE5uAka293ZcDMf|hZhJWVS@XA65WVo`2+8mbvf_YtW9^%PmLf zzwrBiVFCa?%&HgV_4)cZoD0b8pK+Sk{2<1A*Av4b8DUBxJD0F%lFU`4c2|&Q<(8D%Fx?p-{#SQy_WDncQmO1RHM%# zK{2036J}osWlV?Oyzz(!zb!q_r~F@Q)1yDe`ZfSZ{O2;Dyv(w{GLxVy+p-86iYt`TJ;R$_zFXzJVW5J^t)_0Ysw7~bU2%XgVw09;i0j*l}i~ol}lC1MUULBMqWR*;dfZ|TSh-9PQEk|LU1^!bkX;CEqm+Rd;1p^X3a58VsXpl->!di`L;e+VTZAZZ&o> zqfc-JA=~`@TD}MO75fA`OH)HDM%A^lo>P2bAIjXCWnUT^=H&x?lt+JF-!=z%qHf7v z^@KY@4##BG-#jtVQ7{7qXpK$g8w;UpQ`_l#a(VV|`1LS8?G4q(z^W2a3v~55jaf7q zny%?iKAQ{PJ`ifszy(x|bWoV!WaZXESH&*{_e zq(8s^{fCoYB=Z07m&cw%j##c7JM;phelvy%wL3%Jta*OjZOfZBSg`+nt0CAfdrH z<{MyCzP~L$M@?P*FgxL^J%0(=<}9}v7tFn?n6wXErC<}w9yzE1q9tYqG!7lQ0>_9L zFd*i~8@XcCn|vrDjswV+W>I|zZANW-`_XebgLb)DcHMG=t-0!`X4UldA7EZN37_hX zStk31X%Jn&E2;viz+v+1NenIS;Nv5Xx=hwkO5sKX_F*vBp?ET_Q8yu4QZt|pIEVXsz9dAZT5vI$C;z0z-d08{btn^?` zdjpvu*3$Y60sd?oVbBo8N2?AOENAHN1tX^(R~C>T4!J2RZuY_Jj23xB-wC8OEA&|* zw?F5(JXzx1@ z9_aUE^Kf$$y*@JkE#ZC`C^`{JXoK2VsSsBbfI1@mHw(v2EQp-u3EaKB&tX;&RymHT zF0q`*kPtEli$|aXs$tr=D^)Aio@fL?`*yqRYL&0sq4{31^Ts)uBrmDu)L zw&M_lx%6=`k{>C7Uub;uzn{t>bxpW664l$V1I`&3ruNKJslv<-&2dl{{XL9o?tmUU zj2<=co>yulAbC9U(_2Ll5YmN2PfFF4v1XBWIz-QV6Grp?2;-xE@BzW zp6dnuuwJlgRe_!yS|L)^OT=f1K%6!UkN4bXf-a*L==7viOowAOUN3tv>XGVhvfS*1?_7e{8u`qIv{OUSB$5jG3+NvoIFS41ya+u9IJxu89w3)sne2Xrb%?Ym>Yy_$NFeZ#a?j6MX}?@GU!6M;6Ksq9I(m@#5P7 z15?;oUR$;)O(|S#CpWhoVBIMgXA))CN)rxv^V`DqoV-GmPGtkzY#SlaUkCGJjPsIB36;1a0G;s0 zq2GGwyv`|oC(cr}uijjey)i!QoWUV=8p0sq*dq1>CGf5{ex{904cl7T;@K%H+AyLp zVcmmJO9dmZwC>LWy>leeWoJD4=Zml zk?M149VAc4Aa><=D|((`ds~&? z*@^i6J$V8O^u3ttD-%1uhVk#;zS(1a5;>7Tx>XnEAiuf{If#X`O1c&F>~+N92j)Ofn{7I62C<}< zxaoqX%^{1snPXc#Sz-zj{9S$JFZoUiE; z)1F2cJHsuz7|31Oh83=LzttXki=^+0K(@>0RSf>53|*E)VcVfHkCLRK>Aokseqdx* z@iu%biS{?%vr+Ke;EK}yYcD|>!ex-wsG+L51;0vs8L{9oW>JIT)ltY3!!51+)MJaE zlOsg_XRhu2p5idXziXu2H1t7&@!ua6Z^E8G`RgrElcNY5_7&TVAB$CukO8%IX9 zp|o<)2+i-*MYm*@Y1u-l1$U-%Zv;_P6I?$G-}P@_w1bGgAAaq&hUjr;=r2eFN#g8r zs6M*RSu%l>ts1`Gdx>i4> zj8~<|CdtwRPn6kM{iXto>a9fjCXjpk6?~FPIL2s4IxS~@_Ok&cBr zxpwj7{73gjHju8IacSh$99re~NcRK3qN9^$LsnBbFlS0;Ad+bxFa^;y_Q~|wl?7ug zYpc56VpbQ8n>!p(ySU8Rec43>!X;2x6#eLqe%F;wXmQp1vIug>nm;%voyvYWPzYnj zcyk^JiHMi$Kh7rxww#(0f`*PwrqN3o7>$v^OxW*}4-O9%o)OQ~O*N#lnwgjo>!UH& zZB@q=AMC3RT5pL?Pe}34_>w7N(U5mxc&I5Y?>XULo*gyWIvWQ$3-LyBw6?oVr?yYw z>PaRc-h!)f_2XI6ii}0z{=2|9qVR>oL%1XpHeZz&H_GO9`tP??&~W{DdEtS`5SW5N zRI}mzY?!ds0Z-TXaxVoVPghRxaC@QuH)8#3qWBk4`?Ium$AO6?Pc{!Ez?lZWfPjg- zVN9CjAR+Bw#iT$ie7O>zJUNbW=S3D#)ZOTTeq&-1Vf7=6fx49GE_Y;n07q3wNXP(D z7Cd?KWa=QERhytigKdr#MlS0a803Rj{o0uOaP8{VPN}<&2SGgemEfKyU|bR#^h^HF zoXEEI3?*tKXk0#N$mZ<(H9P$4z&uZgN*UUmc*KFgPpA-+nV+{9>( zmG%XE`%gZ*T?RXM&yf#=a(FR{0mDF|?nn5fCF`GTr~ZgwV$>dMQZ9AC+~(OV%=iuv zg%^zN)zs9Crn}F`6KxtSGq47mf`oR;f=QTCIiQG?YRvuDgnPswgfBF3HEA^0h+mc^ z-#4+cqU3jyg}^8H);eBqc`*cx;O^XPj6$f$;D_h-vyxPNEbN{Y<==KaqXbylJ zE0M@QNAV!YELy3$-Ax;XEL5Z>0x{|J^^zgT*jnj^2AeuirEF#b0fA1AuV)9fv2h&U zJU<(70m@`Jw{jd(SGvY6D0mEAZ-Aif=r`7mM62?I1w^Tk*fbt5#*7P*pE1wR4ps}0 za6_{W3Ny!zXUNnx=41lnFak{+6@(ikU7p%w2&%AzchU}X_!k*^QE^!!Q4nre$a?>T zf}CQ^yc^6EL z!=io7NOI6o7Di$U>NmB)y=UMP=G#U&WXJnzWk}d<&qETDBKN0Qx9B1j!ylI=X( z`D0m!H#r*!&)gNV&qM%k?uSwn0cQz4-n&zbODqD3+%}P$Ckk`WpkLj87R=Li1C8ku z1wsKJpqx$!U;7EbRtKgy8W8~doqw=h-?m!`l?l;vpIW@sVkPM7h1v5$qo|hQ=&?tY z%okcf($Yppa69HU5BCNi<|Nhzi**TJ#i&dYUlP{H4ELPjS_sgk4}uzB?0}m+_yhUk zXW{n^kz|@t0yk6-3^+`M4mw^;)oq5{Pvwc0Fs6+M-hX#~b#sm3yubi02()f;n|zk( z3N7m&?3*um)mx6SJ}N+pBpR+1=>TF-66A3vecwdZ)oG+ZYZXpyUMwW?EbLC$k%cSS zJ&ZLYXP(Kl3u+cde(mYK_z+TA>C<<^ntLLcEDrQ%M*0t|Y~=iCK_nxxPS=j+6wjoV zbiK?YDPZFdjh64B&rVdq|NK+aEYcFfhc6!c&#f+!LBF`=n(>eMNzu_a5E2t&+Rwgx+e3^z0w z+e>veLT@U4kfgkM%Ao5vI_m4|s~FD>yMhwl#RfEk15q>B$OYL{u22AnQGK%V^0k=X zDbNZ{e|LKgv;WtHCXay@+LQSKn&??jjO(;4hUCGLwY*<-&VXzm zF~_O2*$wQEySt5sG7an79e4+cy#(kyi!CHKw=S?!vs}r3%%0+}={mL$WS3qPs}t52 z`f50%uJX+-H2)Nl^7*2`<_GoK$f7yZ1u#IGt9K$ah$@akk70fsSd9l$Eg?Nv?j^F-QY93akx(->k}eYknd4#xn8+>bT7 zd&`}>lxgnb@%nnGY($uW~mOOnmc_z8I zwtaK)l*@T2j);_%6ABi-{ZyVha4=+J_PGty{MqXqk8Sh8Lag(HW6PAkQJ4xF|Lk+X?I)}uHs9^7zd z8bMY#`}|92b_OKPB#+HK-45Yxh$9_DR?T}wl-$6vT8SIX7K{5cB`1#$pH6OQLorx6 z;e~M6DzUuc_kDXec-~;{l~{ljsF%N6=b&1Sno-~iYCOV@$&@=SgK1g;J;hU}BYPz4 z_MS%%Xt~??aCCpjvXlT>=Qv zkJcW9yc(h{$&+57woGF31Woe+Wqv!bkB@y!`Jnxo3JEl>U(Q6>HxHB&iXdHubbw#WV$&|AO#>vDivS|9xU} z;&u&c&xBvjioA7ec~$=!htt0w%h(8)7|w3_De>x#*ZJw4HouO=90p{FU&?Vs@a)t^ z@GNyv-liGRE>mHEMS+Sc5fd%KP?Th;dQvCzd)Gg`@d!tH`ZP69B9fR0s�yg2@4 zB&^`@ryrF@`(?hOj=72TPQ#Q*BaycQ41rNDLVXFe$I&$qD8H*3vp%c7rmUj;JfGi2 zgtjz*77;mr$Sf{$I3^YJ9TSm#m|glVx-F2y6_AENr~)hhDKBW>8YL_=qSR}Kk2v7i zm@57?ElJ9Lm`e~fBcz^bs3Qh(+7MC5J3H08&h8r}NDpx{LYk?au~amLwi#c&=N&E*nzZ?!doOfZwPN7fq*Chz;E>AKJ+m&(%BstBsi^iZ(oV}0I}&9hUu2x_vdJ*yJ+&3c;}f^diA> zVBpAT19I5+u`3UO*^swucSZjo0kg<1*~FF!mLbJsD>qTdWL`vEC?3bImpGQ?PoGYj z9nUEy2XX_pPnK~hGqHm~#dViRh0P!}A{wDoB3mGTmzV71SO8Cn%?bmQxo|~5ZL95^ zJ%LaS&x*Py%4lf-FB`Dz*4)2|^bFlmA`j#aTh}KCqq53n9SFH|$H`_|?_^y_vI8}H z&6jbA-B9aA^LXDs))qH3XigWKe6Kw%WvsG^>;;t@M;1m6kSRtOYv2#)Tv(N z*Q5K9lFTGcwJ!f+zC7X=H}I6Rm^Sk8K*jy4q=abD=0OH`3}`9Qf)3=?4$1}MhQ>zB za+yV52?>#Ix}+h23j_F;rs6$MNR(hM#KO1`5R!1(^#-&dh$d}Bh!Q}361S8zaBKi* z)k^(>aG&ZB^&AR!J!5vJ0KyIiES2hB@(w{>AQS^lnGg|e>}OADvt;h+c19lthN!k-(UCRY{*Nhoc%ae^xv6J&G3_5bH)<-&mT5e zxmtPjI}0%Umd`(yJ(I>wH#P$zfEez~z_@<{v-sDi^Tf`6l)Aoc-t#vEdj?DdlVnI6 zHVux`k7(lJ1i@O#`B~rOk^Kn6cCd0BmIWYoW=t_Hv&gzM^KSn@N~UGoZYY)l#U8UB zZl=lO<1txUQ{vD=aS1#L9E$yug-1y@r2Oje%i=BML~hPGGo0fFoo4Y>i^#DI4GH3z z&@ZkO#g9Co7EXY5n8cP1=J7N{gU$uyR+9T;$O4+a7U;~7%!~MuqeAUNLO*J`or{ZW zbq81}n|-^wy2y;Es-a=T)@|E_PuFnZ874K%Bp2-+3)bjRuE)gqGY0C+_3Mn0H`tL_ zL{w_75}0SB_lkXdRI^F4L6BS#M_nwzqY&wf#j^dwa9&df&)zzICY5|Bjg*rLNK8! zK31n57zZs(OSr>e`AKaC3KvX#CcSp7++3jP8ft_78d4>X@l>(vmMBXP`Ye%(2hCbG zkPuX%_qLq`1{qb=+Ygn(``i^_2lDRSE#6COn<~O7PG+7jARu1Ah4Q+ zC5iOU0GuzgXir{syUvvJaxxF=r0$5s@xkf)d%Am}@6LX9T65rsWxFM<+${4)TaQ!A z!sj}!O1P7UImxo6V%a9Uzwz<$ndd)nsI6Us3nqhOw0OLQwFjGX4~6G1kvf?H{xj|y$h%#6>?n`*c{2U>tY zVW$1;24owgxj2B+l`yUoz$G`rde?+KHQfNKV*hX*j*I9@Z)Z6+jdG3IDfpTcZmO!QGH$Bpu;ZoRpk9g-xv$2B3AYP| zm%N4ik(cIBnWgQfKTuTJh5a&Tk=kz)5oxfRc8aLHB<}h3XHP!6Q8)0*Ep&Eb$8Ihz zX%rHmmEYk2lqe5$hxfwL`6n=hkHDO`uhUnsKWiA8aCN!odn~)52hS{LP5CLWpfK88 zQBlFH$|2=6K$N(~t5^BgJ?!`C&ntL6MW%t-(u8^of$a3(g(3~7ZeOv zJ3x~i<^}1fHy7}UMD2vRZ)W8Lk0z`cOTXvhd>CUP7PZgclU5kP}1TOCFX@Vmlo30v;veEf~~i>Sa|4IpU29>>_Q`YX@|=gcgi7ZMu{) zA*49~W41E*8rJupRn|H`E%hY1l61X=b5L?J`Wq`|-JN@pq2ov8g$Ms+-3L`2K3|2U zF2BSU{qFqt(>X2k@}lQ2zofZ|dq80SN{@EZ-GicX#QEOdCKfC8u>zlf z07#uk1!dzraYYymjPu!*cm4Yk#*E}l9mvqnFE~@5hlb-b#zj&T|BZWAepy3CbgK4| zC`f!9@Nps95{piVHnQck^ZVh77z8Z?!m^J~NLZNFj;gURTN&E@kz>hswcFAqnkCT% zwX`YjAO7;8Fu#4AB$Vmn^JJNqX1Q+OP(Kk@$01hNigMD!o!k}F4?I{pIHv>W7Mr6- zp#}|EOp1yxoiGW0Vt&Y2J#xlgKcxSIL?dU^5JLa%*y{0~IX^Wat zNCVB1ulq2WNt6*}YtX^jnJM~J{z(10z1ZUi{gzs!VDp5F)e~7x($$m=UY0APWk2`q zDb1ODnHr|E*2qQ~nAU%EKB;#6cw%d7t49Ty#J*_LXeuH@5xy_sn%Bem4HeMg4NI#g zHD>l-i1XaHTyvnoG-C^;bl0|RFKV|~JU$Kp{yNS;kdLo%q4$Ccs2X=^4I3Mkw46|c zjq17M6hg;gW0F{(9R?c+ksz2yTP_d+4{Rz-1XgR_2+i_Jm$v*1+6W{EhK~oty9SlT zAz^PcFT{E;LlT~n^5tGj04m>Nx0y=~Z1@FI@l=jOQ(<1%)9&7JI5)iHD6r7zzetcE z+`}8pQt`-nMM4!wvkqMhUfCM7gHWuYBc^7YfBA#pIc2Ty;w;P4{(;wyT)%!@1>HXJ zN}kISlECfK%gzE~;`V%|lDe_d-Qf(m`SS_EEvjP&yn2X!#c=VjA|-i`p3k4T1qEv} z!@+GxXtD>w7+(w*5Ep>|SvVy;CQ$qbG&*h;RGatJrt9@>!dw$S1%Jve>fQm2E(~(6SbfgwkB>|6%XVqq*+CcVWsL z8e~XBQACL}n1@g@lOaMfM@fW)NGVj3kVJ+kGLM<1N#@8{WGF+X5;BC&^{V@vv(7r} z`S*GLd0OlDTle?wE}zf){d(%9<`A_j6*#yPX#)3xnP*otNNR#DW3YPh_}LFJ&vnPUIpB4|SQBT|A$W zV~1r6&^AE>`O3fNKoXT+1R`Ixf}!kh^3GwzxKF#}$Ggfm_(E_$o@53wA89p8d^MHN zqe}k2cH(NzJiIx%#Yd^5%h%*VilSQhUt}oPXzBkd8^4!L%fw-QR1%U*TZuNbgO1Uf z|AV-aBQ!f@$1=t^%j6wYeKu7^g$HH}I|wNX4xIbU^+OKd`q#%T*lZX2cvW-=G}uc_ zha~n|UDib9se6`JyRB=MlItOvd{5?6UBH&J013|c^P`y70JJLmiLlH7V~#!KtC_Pz zCp~%c3~+gN1K;~7L=Gat6Dt!lY&MR|7_3CzPneM10Jr>QZ`mVoGWt1(p>Bd?>WH(4 ziEHn1a?Vu>uW5Y~VPyVoe|(`sqM-rIBR@Dry)Z_p_4pqItk5=x2z}3{-vlon_XYGZ zzZ!zcWbK?|e+KvXPI))h);@Dvsv^flST9OrN00Y(1FR31W*uy-t??&doOz^JjR7?! zC<+|Y*TzfzPXhH9Z5)6Tpx`|_FO+-x3r~OrfiTgY=L3PMqqC`A*0^ELnxq2~(E;pO z`bUNZYJPXUiF-VT9yRdXyW)R&u1phAMhK$J{0#8*F(_vR?RB-aP@wL~e8S*O$mv!- z*J&se%Av%1zHpPrP_-sIv}v!|Q;kWKbipdl-SNL2V+O@a2{sX!Z>4m zvY%435e@?0)Lii~Y}!~$k|yRuk=ZI(ozss?)IHtVO?BstU+mYFx|3tQJ7cezb zSTUz?SR9-XRIZjh*?)^zKAwYy2I6HN4sbthaE>K|ktZBXZM}iD2sMQrfFnCg!3UK) zp@EkU7;Y*6V-3t4%SnYu(8rmR`2JGglJ1R)?~89P1s`$&TLsjfc^7+qI{SPnm7TWR7d63px3BK*l1cA4MoP}_S?K)W5`&$}I-2{Ka} zZi5+6#8N#%}DDp z?Fjt`BbTsk4717g)8=s4#_;a%@)Ktv*;%77C67%R4)D3~p9XFq>lo59lKL)4&Gubg zOE{pgB&VK|Z~ZrTG7HYaA%#EOB_8^Nj0DH1e1i;gkeP39Zyw%shD;Ki9J^wr{GUz- zv`WO7Cl8%|j{73qE^KsOd#!-Cx)+&C}#dCr|Ud(svEzKC}T!lI68!D@7)|*vRTA zVxNHb3GEc&2u^S>U~Sj#pWUIK&Ky8q@kuwKq-sWRnFV#=83Bh6*^U~9)!^p4w@%=_ z7&THv0-NwHO7 z{u5@mi&V^8HWflrBj!4+WdG0R1}NVS_96`xi}5ZtVj&|oKSmlQIzF5^1*M-Ln8x61 zl9+S{J9hGm8WZTy67Y#+S3Nf0jAvp~5=37`-0En|E43TEhS{j)@OF&$R{FlEmdAQE zV*Sa$vdJ(om%_I>_p4z(;D0{0)Y-8P^)RSv`%bqcNoleHr~Ypr8*DM(h>s28Frvs) zSn^t@>p#I*hNDufXFUnd&^kGtfjn-p3GeBMz2q8?xoB`i_8Y)de;e>ms8!ieN zh8C8DP3HDsf!aP_B{t7X;CpZYrIv=}|4eGi{|`fzR~QE|){!kLdpUhiR=)an!2wpJM-h$yAWr~0YMh>Te3V;9 zXtqu$OqeGHr`#QWgB@JA@G!w`8-OJwlnBfYH5g0i^!)ECCp?c;A+i74oxA@_$+ahw z7a!GxfScS$_TYpi!5K|*H(82m6bleL3RGc;dYJzk8JkE}Kl*(fUTBgBclsXO=?itt zI;!}y;??j#{0QhqT`euG+q0NdOs3dZrp9poMoof5W5Xfp`78L;wtnL7Z^-$dAnf!SJV-TwC%$?9`5_-2(z5w-q(sA)g7gk=`jK)$bLR(Bc@-}lJ@sQHaFE$ zCbG|;B9VmDi^M>hF>3rtybJ3<(-3&2uA1 zAqXus@W8=IIR?ZkC)YR(Of&iTs6}KMrFe#78bPQYfY=DhH zLBiy@Q!Lp&wc8~B-EdtlAMt0R3c|@JhG@oZ6r;_^OJp4y@wP)lnVOoC!hs`eh0GVR z`zm>S{0y9=hw&PWu+9a49=H=5D21WmTy68daw!xM8osvEFe+e@d1$k}zKcsP`ua1` zH4b(uK#H_|zNiD_&G-53&J7(Ah~A+&F+fXtVjuRcad~`#nHXw`J_HRy!xt4vfW6cs zrg>%M*XN&qcb3Q}3q-r}j(}Mqk{_ZFSrGrAVd#rfC0I1FrWI~;15}e3oL+GH!(kkz z1bJFblfUfXRbL3xR|pjgX~Z7I0Vq{JL~Ftr;X?|6PZ7*<)u!1e_ZO|y$Y-kCkCRnB zL@sgrI1C1>18Ixhd6`w%(0<@`&?EkLk3o*dx_>-P1OhZ989$W>>HBy4bpG|Lac#$o z!O>|(>wvzF4)0MS03nODVhMvG!@r?)crX+ccSm$s;1tKN7*(<~4^^e?(NHi{qit#T zK_s$<6Eu5gSpFL4|5LNwYmQO!=l}0t|BcQ1zklQZl~%F;zj5#XeDMF@wo(2!B?9u^ zETAB4P>&c=oX|voCAI#SLIl0B(vyU8a_zjmy@{uSLQZDs$1Ac&aP>2s7(?YjV0fy|x+yY&R+1z6NEpdL*AQ0I zkcg3=?vW0oqEk2iTs~ihqKr({)_ky61qBFv@GD~5ZGS_kz?0i0#tw<_&%B48B0jAOR8Pf(V_Gfj9sVBaxN-nwu!m2+*T zJ4m1$v424;HzUikv6LASGWqDFlfD7txF8~Tpi;%)>hF1t@V=4BxJO%12M_IxgQQjB zK5^;L0H`m7co^L&{IatV!ATX^R(r0fpN1Ipw+7PD@!%!3gADd>iL9~K)7K9tu^U<( zS?ei#Y34oT2=HZ_ou%DkBiiFW+NLc-c_7v-s_xG@_0GlUy&~G_P4+$Q{|4td_Eb9* zJcBAt7MGGub4c&PQc@*a)l9F!^d=MdY_dRTc2c%3Uz|=-`GP$B99jGpW`4LiuUfMjJ6~=>#9fs7&R*XoIks1Lq*=8^#RttE z9dIH%CHh8kadTr3Xa894;tm`Np@S7?LquSXnfE>CbiPC44jp<;C|=YITK4%X5j zU9n#xmel(l#?8jfbQE-@RDJ&R+4zS0)QXIY3@*lek(@*YzBaM+g5Z(P)SXV;qYxvYF}U9Q%eF3#8Ihl3rX-ImpVR7k&k6m3`d3hCb&CQ+9 zpVuwRva+)39~(ikT1JxqbT$+AXG2jFgLAmlxeIZ(CTKtu$rZjvX{O(f$*Vy7!S9*pQY z+9MjYk0B3##bYdjplD&|OWn)``+_S{zV_6oLB!3G_KeNf88`XEq8Idq9#57061upu zBnb)fCJtrQix&zVJzH6gPn@tq8%6M^J?3Q=&Vqy4XxplKp#y~<(O5pkFe7@P0wMJa zNZ<~`xmGp~j+A|(8!7NduKxJp9KOp~udckd9MwZSpczHW)h%sIk7~WoMj=`zFf(sY z^zVla;Vo((CSH$@gTU=VoE%(g%F5wQKxA|)MRx)NU%>J39xnaebHOQn=*`;A#m&w7 z{CiL74SdFCtL~njQ?|A=yt3~nVNxn`9cG4(8_}+c3QO#l`U; z)87}=&1*%lKuN}Ksm%LsereWinO)0*MgH7`%k1hA|o<8=HaHeYE+H5Gs!6| zTegG%=R-By93|%?H8VR~(6uac&y)QquTTF+gahR}C@yag{5I3m+kj7f4LETCPhD;7 zX;M-j4BKyDrxwKzy0!VB$oCbBmIwIgud1tQL3F$ZFs4kZ+yct=DxT*lh=$!_kV>7W+9PQXW^;3M{Opq2;q{xp=-P>d2L5aZH=XS^W?1zTB#<>dBefqI82b?s zihO<-ejIM_K8pwS7Db`T*nMFmW?*am?nv>bJqP~8L`Mhv`BBVF_MFJSU0-@()e9ht zsre}@3crUBA6lASFDNLm_Eq)BFE9@f%({5-Vqjy?dG-@HhTCXvY*@d3&`WPM(LOAl z1@8ue!4RoCd+yx1n!fiql0zI48k(AK@K1e%g8|vuqRG`hD@&WuL8o-)%r4S>SoJH< zE}7a7K;&jc2RApjD;Hlf2(T1XnFH>>G+dq)dSH_MEe^3&{r&xFbvQrd;afrBegEM@ zogP{jB_*V#zd%QRjf9^K(s~JcgXAg=xCnppD`g$;Geiss;ga+>FTXUnDC3#RD_ar% zXJK*C66i!#L!;j15Bhg(Xs=%n2snlQ5(zYfMgD%c-|;#cOuibQoR+rfI9XhkP*G74 zg=V~u4SadJSCSn@3J^ua6m#acTLV~#k_74J>{rK@ihcjk! zXy`hUqi~(2l%1U&>@OVXU1P$B%WeIGv=xen5531RzbQJ5oFG|eQz3~N6rFXsb?(=1 zQQvBMBzx?VDMB`~!wvW=A}aUjt>~v87psqx=fQ|DW4R7;%W=*uKqor8xU`&F3-cZM zbwT(yM__A67`m$wgG@sDIColF$&A)hZ_xexvC>x_+uy0{J{34Vogt}bSV&^ubivkr z`!>QkzCp7!Dm2s&pDZn$QP=)pYyF^JRl)<0Z&bN7?f6KW4W8-W?cr(RKAT~*m7Iia z6FZ|)f8qxRuS@N_3I>T@WORFC+2gFNJD{6R0o!Hg<*{UxVuy5hiJQ7K_lGdNN=v*( z|0_4&zJ1$S;wB7W70%}@CXMH-5hY3qYU6HtS_rC0HLZ#orty|Y|CxEcmFEyCZ}G^> z9kH+nSIh5lpfMdhx$!F1GiT4{pyT39cad1rqDdzji!DLkIq>){&YUj0G%K{$OF1bT z`PB+I(0G+nPfySJnUUsQ#x?tG(8%Ur%Jv7PBYs1?sHi9&T`+2QFiu<=T1?DSpIw0{ zKd%5p9-R~^!}84eg@x0pMoFg^7Z>N@v~pdF^!)v6H(Az!jIMk{mhbN~*(MYPAa(;u zdaPu#a`+9PEdJv#z==snNC@s8Ye`-M?(YunS2YYRF@@zGmrQm{iiR~2D z&lixmWG?elvY32sOd&M{xzoKAu*-2CpA|l!tF|Txp7vU(-JI^QV7WTAJEKNolE7Yl?31 zggLWIW)KYFRft#9OWmV__ILISp2PEfgC!F#-RGQ~Ldc@?R>Q;Y$9;T!7&$~KKv^rn z%QT%77ZCLRkRKyFdGFSz2zTI{+r-^aWkKD*`p3iB`2`~1ZTug1VPPSs{2Y=3wZ~3( zXJ@~>yu7{~V9t%ZP82zB1u}8zsq}B`?&k7cnxaJ~;SLkx>rdP?nra~iYs#YRRw*e? ze7@@HJ3Mahh5tS2M1SxC^Z?Md{_=^3_yq-3v+HYX8E9x|)arx`(*qG#AA5ZNoSmCn z>n4wee_>+*;3{`7m(d;k{B$8T!mccKy0T>XB%c&6{t6p%L_d9e%nsWj$FZZ1@{yd?m769#0gsRIUeA z-P$X&)Du(5OS>f`I37KEq^hP?tJ@CVhNS<&17@bC+2CklX<0oqH00UbaOf$VGl_%W zdn=6`hs$vZgmN1{ef*dRH19BbNAP(t0wIq=g;q(=1&s@ROWe1U3S}9dwBm^=2Ufb*1>ES6dt~jdnWi_#7MEgKfJ(S zxt^OlV=Qx>?iT#}b-Tp(?rkxV2h|~9d?cbz2RPPvw)O12s{hVk$(w)YPe@A2#GO|W z4vLU1vd{Xrtk<;#E4^X2wz~Q&z@LKF%?-w2NX#p|cbW(sEIr1_$!Q6N}l-6a)$2Ao@ps#(M-9!Nacuu-YsI z;zqZ)r@r+1udxno7Z3SunkfcpY`D3MzzZ27-jYDsa^Ca2$DPid+jFT5Y|Y5z6R4yX zF0p^KN2PcB9h!=b%;l7Dj&J#WuDj?8)-yO1AN+T@l&L= z>C>kLmvLWK;|;T9Ob@(SQ{!n%5f~Iyh3sz~JBM9g!95CZ`1$$!fORNZJoGb8R#v3{ z`WKf~#s7gfZGv_{;8E~ya;ETGBN3FZ;jouRwRd+W?;0ki2u6D$qf9Q&181VrrUA4i zo80;9%QmO97Kj7=9tq*9xi|gnF|S-jxG&sj^a9$C^i2#AJUY9&QoC*8wTjPbI<;K}2!S)FVHUzY+Z(@3xXo{oNYti10Qz>$(kn)R&P&}eu! zozL#bd$eyD?Hk#j2YgH7eeP!jqR>!O&GzVh?mNy0K!{(bX`jdPZrfJ-bT6%hCIBe% zhKhwnyu~tlBl@t)Hw8P250u=0dg#!hbsQXl2+CXc?%jZ!o?TdYJtBfNCN|cFx_T%X zWyu%B8z7v+rlwrCZr?s-V-uj4td@WhGdm|o_mU}~7&`54$H(*H)@}o$!>ttEx9{46 z2b<~}8cv-%cL(lo2Y#Kgwlt$6-#>D4mzM&;KO8qUu4->*gG_xbAVB3&oc04!2$g!Q z1%Il9gAUYs%E5ub(a|w{_X)Pl%uGS^vLeI9#M4M=VQvhIA^KoF?tE3&g%INEvjyc_ zQE@RUI$om*DjJ#rL?sFe{NY_@B^yDiu*upYl7_kMOUCiXDZRKfFNWI^1Z^8|`q0Uf zJlGqYou97^Mpw7kWzcl)U|v!Ze|Lq?W<^EC`S~e6;0m2w>!ZkEFS@!UwVSpNq0%Yx zniC=42k3!+$Bvf>i*WMd(F#4&PfaYSpffJJm}6mVUKXNiiqxKisU^)lf#}QHCEe)- z><^4YMNw2*`q;WA%(F#X65sj!a3gn_@1HGrg{rEm3PBsTTbw<6TP;GUs;jH`1uYW+ zx~Qx!&Wx-_Fb$22q=CkM{KSb@@U#&fA>CchcjXpv@wR>Y^ryWxQ>ac)%Jp5NNu-X1nV=VRB@H#W8gWnILgAxF)E*2A24PqQ)0Q9yYcoQPrrit|cdL7t&AhLrwV#pX|#r`gS)>elEWW zKs+)wMjosn?m8%BT0XwVGSb?YAG$WBiOqHkIavJ!NA(6qUga?=DylxnBkMBmN&4jj z%mVISw|7>cugu}wniI=62j@3nBE{_t>3sY>A1QzZqxBQ`tVHXr|3{ZL2q+2 za@%HQ_5#Jzx=owtWo2bMJ3Hf1t~o;6#di&V+eznmlwzm6_iS<&N&kkPOUh(+sJJLIER5DA#54vtgKdp z#+muv%Y)D4FgJ%ReZ1IZ`>Iu|sG5+f4K!jT)`OZg{4HaN07A#Z!xJNMeigznYsQIZ z`aAQ_bZt<&&e(i3egyT73X3YHlNjAB-x&>di#(I&ql+jksi>(F4h1kIo&_SeJ-T6_ zzyGe?@)AMs_tywRH(3qGTq7$DXR4I(cst{x<+v4vy03+}1QQCf+{P0Lho~=%cdo~~ zs5?j>i=!nlFYAf?$|3E(E131m4CUYCx4s$Xh#RQ#tD2g$b0-n6bW-(_Wghm{Xhh_+ z#0L$hD@&043hB`El2Q;)x^3WDp|vEa7ALyQbp>zXoX+v7-#}ST0h;EEsp6 zZ};xqM~V{?w&E~d$6t~_Xtv)19eBMt&i#k*L64j+#)T#|4f$>8Rt~XB_gp7SxQ;dL z*1S`bw)gEOF0S;M*{sxR@+m~c#aXzo;uG#boylJ$vQ<)Y6He{1?nhu`A*cmG$g4WA zyu2)S>LpdxPsF$wSueI1e==0=Cf0bSEREwQlL`-|;C3&@Pc1DQkY;bBr3qt!j~{ji z4winsTANa`qoKE#hgC>l!P{E~rq7$;7S}Wyg@dlZE-_`a>g$_ZT^Ouo9sgLc2EErR z03zvDwUN7TqIV&lkOpY9H#IZMZ*h&KOsCT&XCQV;z)sjUBl-1 zhi>Nx4~j^TH~u=tj4(x*J^`xbNQUdW_3L$;U$${0ZY}5xQ9LDe$MGCXXAcizqg1B7 zj8ftn`kscyVmGmD*g#kjcwYmVCG6O^xEOK4#yfLXL!N$#OAeMb^y#wH`2ngOS}`dt znG#5tY>aPN??pylf2tj434nh*&z1=?Y%tl{Hh3lx(e^D)!*<%c_rk)~ty||u=(aoZ zD>}uMmMA{2p>`X`LE)0cGLBW7+;b_0mtVZ522_B7 zX+ejE`qZOLBlm?%+MQ8EL?lYySI(g|C85O=Rpo7b7pX0%hHb1=PQ&1308r(ghvNXF02ng(+<^K*^st+&3MAmMo zPst7c%Yqmr*`H8+%gR6mvxK!IabkXPkq~iEZJ0Rimt0%GxdG3H%5LREAI8PF=chl= z0LfBs+jIyh5-mpprX?4SP8@*1mW?@Z#`N3vT5Bww`&7sTchP>^#+aSOk8N}ToPBDf zTrzX?+z7~e|6Zr+jipOi|JVIFDjv~4`>IygNKr3HXM|!#ffc4B36;nF0k!Up=5dEu ztjD24pW+Mrg#qBhE#Xhe&d%0J%P8Acs3RsL!-ZR>ggPg`^FKx*aS7|d*JPcnd<(p& zJO<6KA1o3PAfk3n{<-3T5j1;VPh4XXgNwea0R(1*aja|-RIQScV3N%IU`6yJ6j=GZgg;kZ4XX;B~T6dSI^}ih6Mp_oQg&mKI#gycaewGc&8z;jm;xRpa%q z2k2J$6Oy=hZGbmOlS$8SQeFGnd;Z7PEppz0cto^lgc|9} z6M!u=p`~~_xKRG;y?ghjz{wMWDXhi?%X9;fddNr7pwq(D5BravS5{H!hcljn0?-pA zBQ1pmGPZ}u+^#E+yHpMz9>DLZ(Ozz-uYV8c7{v=T`M{|6hlnF&=cYs+QLE%1U|Ap+TrnRANyU5FYd* zz6Ay$h)BGPY2HI|acUsI+PMb7SPX4=5wtcDnhHL!yNq-fi$jsK0J{TII04Rt?*@Q( zrJCEZefue3OkkeZh!hs~_JU*G>>{RqPoD}vAPA3S7dB*sYM}yFguovn-XL3Zz-VDz z*n+=8*j=3UQyUph05MTMTHfCC_FloE`wwcD*}4AGK2L4&8F?!O0zkAfQ;k-vD+ch?D|!^N}S;(TVpo z2~~@EY?gMKXWupbMLrTD+GKU|=)&fFe-lyv9LwWt{ItRR{E6g*ZM1 zS7NQycTir+^26Z^$h2*ZFJpQT$OsQ$TH!5ulPVNZ}Zlu(zk?=gQ37C&$NOf+&1 zbccRC1O(^XhjWh!yMBGQzG^Q)$ihZi!pnlADC}?HW23nZ>n>=~#IvFN^O?_O##6oq zdlR1t$;oYj?d4eXggGE4B(=jBOJd>R5R5T%r*IYMn3x*$u$An?NLv~^h+Bao7W1L+ zHUOP0J%bqV!~_mH%*W&W`I;6&r%+e2P=4#U4+{QSdEj;-AqJpWwipGcuH5YigVzA+ zw}Mc>b$JW4kgHMgZKAcFt{NsbLO*1`$3EmyS#`&uA{8>8KI3AW!`qRF4QDYHA{rTZqw99&{+@ zg5brVrcf=>amNLE)76SbogEpX>4J^OSW=0zj$AdFqeJ) z%i(L+u33PL%pF=O5F5^%K#A5e6y-f6rbUrB9gZi5FRUK&d;g}Q&cd@Nn)`Gp=;-Ly zF)}wSaoF;2aKd1oubiBq&z8_Mk^4#i@QG=ig}a?G&mTVAj3R>)0s#mI0o%K0ltr`_=;UVVMK~FI=%7*u$@Edf*?$e{xEZ?ws*YYym5xpL(SzSw3bgz*%j_kypnkt_sVBvk$g5@SSTrFw(3 zqgT%fyil~MX0(ivFM~|-2G7NY2aJib3d>a`@L)e55UrR7soCi^EDu7p6$?X~YvLcE zm=ollFDNWD9KSU;^X(XzFXQanumfsf=H&(Sww1WC8mE$(6-BvVwz#x!XqsDk{fK{k;(D%irC#Jlf!);XB_m~4Zn3BJ@LQsFvE?s&Y^k&gvw+tklo zM~Wc$4D7fu&G(2dH0^)2i-D;b{=?h&F+>EiYm8B}w&q8k7nsgsi9~2$&v%$kL?>#$ zqhaK&c*GcJN}Om63w`W_dqQF%s_E1+;rwSH+ICB=*8}Nr`%&CqTGig!{=$WQIL)+c z*RD+-gea_M%7Z+22b~3ae9Iajh%vK}ntkIEk_4)x^nt?ia#;+rx(#46fQrU=l!lu6 z6g-d;&V3YW;evM`XVmxBm6eyPTvY-p7IEx&jpE*#{4u7Hjos30{2SQQSLi_Rh6@4~4-E(ZFff>9kE zT|p%iGyc+r&7dZgW#dNvH0^*kSOtFo+%!v(uAaU_B_y8xvl6U&#@TI*`uNTML$P1P ztt-rHPTPS&4GvfvA!Kj}Z}}85Es2PLw-K@XMhJq#g9%P;-7+S20; z;6Q4sbhA>!_TqhtLY~UMRG4v%$S)N$oN%IeqJTf?Vw8!bZ3O{=xF8rA8Gn9VdQffh zRO3OSi}NtL2vRv*4TmqfE#?5qf|6ZBxC!I{l8ZV!kAqwSYEu4DgpO$iq#YCvG@)Dg zTj2P@-Twl}fVTbcK8{A9*c{A{Swgdv!?MomWJZf_# z24^V%W9ddbSFc*-!pilm0afG>{#ySmK5~Q{{fVdpsRbLoC`l271fTHDX{dPo`yJ%s zz{XS9lS{kjWW_b42uy7bB57{xR!U?SmSt1pQup1L<|cP@z9@2N6RP6<1Y&F_(?A-y zspx&D{V|-25)+Kp#o9T$x*nrSIjo{`3jD3V8+e)9`2MR^wfA)=9c2NT*bl@zk5~$N zF>9s>po=gjxEqP3r^Xy^D;>@~zZGEM4UVGg_)n7zll|n*BDTCFh0Vn>z&o`pGOQwz z5F|d0TI^$=$@qyH$@E)Hvb%Qgo*HV{On^Gr>f=Su2@Jm6pbl777E2~6@DDZ-o}oX` z(pjTNnE%#HvlBD(gpNeeNOXHrHfzO5Q1bfF^4L7saAih8 zGU0`ekx|F4D+!xmQ4=b%m<9GhnVyy|7c2?W5biM(cw2(dV6b@&I7Gtb67K;nXJj9=n_i%Q0DQmEVYF3Wjc{l&%{3$oE594=q8KrqLY`g4I8S zJ0$m{dPG-_)s4f8JNPT*Dj?*nV`QXm$r`M6FG8RTAL0LvlM-S3teybO8jTx>y#yS? z6e4EbT)G7ivu*I;f)SJ<`_(S$4!d^uW}i~3YWjenLJH@1Zk0)dP0B9= zN6Yzr^>Q3am6ugjiuobnn=A%`i0JgaSH6jj$>ursWL437;)^A-tl$jz$DKu~k-Nck zq!yJaWzzQ_mxt;%?K)Y(h3E1bqh}#M>*m8w`J)pvMCe2g?KDj1fPE>_X1&^VM1eJO z==C|zA?JNDgqwP2Y)lW=ptt;emY9<|6}}EHQLYg)Q?u0MzJ&FE`5rdC9Q&(9A(^AwG;YO9sR^Kx>kz)6Ig^ZCuB&sp}~+1F58 zt2X001hfDx;W69))Of=G6c-hd;?ciyPFxxR)|$fb6meyQ?>V`JoD2YH5~N_yT66jG zW#V`0I}dvD6=39Da1|tm<77o0Jhug3>jo}cKU!hge3;Ru5G$~}mYMlF%0*09zK-8I zg%X&2Y8=&s3!eq^AiV@mMn%UBce{!|d31v7UQ_MIk0y8X%~UaipvBCbNA^-u)IE`7 zPZ%K!QY6t@s!hDam)A_Tx(dSRCFowtB-%lSjNzRQ(>f29k){1@^c2S++;d2}@FRkR zcl6YHfK{LdlYoF#_36`QT>clp*KpcBv?@Phi}(iiA3|r2;HSN6YFbN-Jt&rQ;EN|A5gfXIHEK#r7!gpn zv86x0xt|#)k~~N1It)<_0)!c)V~thg!Sj!Wy^kDg3^eF{dl zdeUEOPOny7n_2&^y92dTvLcc3Uttgd)hBz*oo)F&J2UehVXo>6aU%gT7=yLluwfO- zu8Uq?!acERU^%v!0F_%T#@n|25Y++?;~^{w=I;BMXn0P~FR zspI$ke)x>Ux2mcmN=iyq!|AA8{m&8{g}YZg`EUpHscq25QNRIny+C#JmxRv@2%u)$ zbzG;#X?e4ROiCM(y~Y%o?aBOWK6IBAP~8I zPf3Br0=#p(k4MJD+yc>JAj<2694wjp!q)cGsVm?Bc2_sr0K$)Zp*E|>1 z=_a;om&S~zVC`3`8w_Um#P8)PXg3x!+(cS|Xi2URIkGacveX_$&>1CgneOwg_B|Dv z!}kV7d-%!@cNdqJQ12tQIAQ?e{IWp!{*%UDmTR~NS0>x3@ZO;H1hzL1%~|~?Z22cb ze+cG&m4taY_OgnnFDK%}p>TSP18zKdP+a^Kdf0?7oq_%6nNb$=o;`au?lG59Hzum_ zQ!VZ)D_G`8iYoXWtB5KXNwO|SDHf$MW_UA!udlL#mq(@AZ+beWw8Uz(_>Gw7;5GN* z`d!mbk7_6Dwww`Yv&G-t_()dx;}f0DD(ANdm{u zQ(U z<8cd|>8BT_rXm=uvGTTLMC1%CJCZx9#l65LfiRE$vVsO@E;;Yx`R9-;*kkzfb8^-* z1>d)Vdh4&)>$ezp43&0R$3%CSNjL9I(Vz#)6o|oh4gU;A04A$nHmWOAvLC9H{Ngr` z4tDMKMz_(<{L?JNa$48sQ@)y*g)-bL_od?P6KK(=oRpDqHl)Um0{J#2!~qvTDtrKdF3&FDw6k-EQd#%AKz%tQi5qdbUaUpIN#^B#s&ubk-RN2QGr0& z7J7SHT3SXPIj)tJ6>=8Vm|57_D1qBG$c0KKXrkOwnHq_u<(xs3UwZCOvR#6140R`(>zDDCx$#K73#)kUXu@78TQvn2dQ)OjAdxgDegN)&>R3Ly93lyqN8#fFhF6oyEEYPxPOZCRxZX_rmbP*5paNJd6R zx$Z=PLjaLe5mwYZTn!>R749B)R>MZo5$1FX7i+VIOroqfJFm;&JM55cGG0v8A`pBH za^qkPe{A7#6BA1?1R!8uz#1+F*je+YEkN@!9S0eQV(CK0yZdKV;pzrz@lw11o3eiM z=7$|-`VR|vdT;Tb*s9mTF6kETbHEI2G(_;k>Z21&XrH&Lsh~d!^VVL;XSXutpMq>v z35vW)Jriad_3)uO%|)}ai(={*&P8+K?S(~Kp&Gz7)-)^Gx_wqwany^3yeEV)-J!w3 z?ZPj_z{p^Hf;ej3pJguF{1YwEOPG!; zIob5xb#nAp>M&dN*8oW=so)*)t6Mz08n{H++1Re5=A~5SiK~CKYZv{rMDH=hz z05H6nz@dHfF>bF-EuHzFzo!_vtobJ#k3`9(TeF5G!_LCO54<0-@1Qk6(cE3EcinGq zc&5}hZrHq8xTr#5ToNGZEqYb_P)Y^Sh^|(^)j1D{2S1y*(KHn~vz^%q_S~sWVek!o zRds)swx3z=`*)!3IxW95_V#peWN>WSblOexDxy6`{+z5)PKY*T`O8NsP3lDx)$UOj zEU~bqFjCBeMLuO^#djQXS_Sy7hxT5uH-|B5jC^>yw(wl9$Z^Q?TT$uhin5pu_30+` zEPl0C1k0@<)H#U^01Lx0uPKkNT$}BA!6qgqiNHy@AH8NrS%Jj-QTK0yff3N2n2-0q zBlfYXFGR0H@6m;vwMdOumd2aJ^#QZ;O||-- zg<>3?s@BTaA=go46Br5FkN>!c1~L2MPJW5gjj-zV!5uAClLwmM&9zCyd)BO1pu3M& z47IUxat7l81h=pE%1|p0u|jJGjOH+Kr+tvUefJ)v^zXSU001=+(NKo?4<9~Uh4*}f z{|_YjeVmM@>mEFO_yveQ(e3MazMz0~MXN|9wHf3w3rEMB5Z;JW_M4*^m^8v1LuJJz z4-UlABeb$@TL3s~uE`^r=KB62N|N>?nJVIwK^qMC4{vmJ)H%-J6egbaUYt4F{#`;d z)Wmt~tnAf)Q9eab{J?>?$g9m5GSD1b>o*hs3^9Z7)rqdm0k@AUJZ!y~hp|y3-Qh7) zqupWAzG32#zIbIY$@13uEci5<#ey#$h$<@rRTY>iPTAryn+~6%-%PBs_*+3Z&Lge3 zbN~TRt~Q1h?JGrVt!dfC^$7N>D6sO0rlzKb!Vl+5*zEB!{o#bY#GHn2EvkTGd;RCn zpNXnPU>cIxK8IF1CeD4K8W+quRjIwyCj>Wfb2Gto!!@UFCU8heNeqfLsn;;a?+Vyw zjCIlhpu0WZVp4M9bWNp-P?fKPgAmH1i0!BRMcO4WbEqGZNn*7@ijHEwj5L($h|ytc zlq6%9G#L?ISeY1^x5LUOu$R^(m|BAJuL9Vb$|rTv8P7oG(hMDHQ1*1pBi1D23e;(; z>d}HiN?=f+yN?(Xll~T223{Y<)8OzU3b(ep+gh&COJ+;MNUlCqwP6p01HQhAKMxd0 zd^`X%e6Ue>Od!MTG)sBpc|&Zg%iiprn|UL^R3F~^IOFe?fl)2ucQQR-t^uP7UcM!? zEJD>rv@P3gR`ui~ z9dpCEJ22dPY+rr$>x}U;E%2JeECuwrbMl_+*?ZBi@7^;r349{|6n)RchEvn=v?Ph` zps0lYL68&IV8rhS9pvu@d+IF^HLqZXKq55g&Z<$N-(;Nl=IhWbbJ9uepP!P-q?Yea z$0|>{>7LnI3!r5%YNX36Afc~i>nur`6d?PzPv97pBudEvNTpSvup{IyLps3lbQ;W4 zxQ2dv`_eR%FMWNy!bfjJL0JXPe}Vi#7-zS*3&qk;Tis;$u;~Kjl3%R0@PAg3GhXTI z3*8{F-RHk%Cp>?Drp?&L!UKeX3JesvQ}?j7p5Xk=u>v3axl zAvZT7mM_eyQKT zFuQ42!H&1+mD;=B%c=eKUmM4N*rpE;Bv>UG;(-#38T@y?VVaY*Z7PH*OBfLn-3XH? zWe*}pB)KYq^t>}J68%#2CJ_u(n%44mNQ+eX{xP5E(=o`4w0VxOhI9V;?2SyM) zbK8O^)9jKk9`@{=jDpDiD3RfE0%FGG2GOA=z46a?eAKVZFJV)|C4V?#tpTQ#x4|_4u71m;^y6S)eUsLJw@#=w$bTCfdxg^MLb{-rv0{{K(*#Gflmt#ELL2<1Y?|p8;om?ijaLrZ5F9(T z*PYJb3FwzF#S4^_l%!1!b_t?EQ+$(_I&iv0`Sdb5tq*S8xMA&V@4iV|33~|E5LYzl z0x>MSK$U((JhqQ zy{6*HA^0Z0#vF6FuW$CD%`Uq&ngjG}O;d9B4mo`B#Fuye86g!=hXPpD#41sG-i-av zP!!Dk41L1XAYV3gCrrY@bP*m?7<|J9SLzjAAZ)KcBfp2efuW{BOW1{`>l$h8M9%OZ zMAcFqx>c!fU|R~jwf?3%NdU%!ox2IDh71`0;eq-WX$p@i%&OYPPE=yqh z8ag_^jEvoXF=Yxw=)PVDKzJmaK42362~>nzKme&=ciy&~^JsOZ;u-e;vDlsu7X2}} zrA~|iG^u`yPzbIVy5uSpMTCIAow>S&Rg=d44!1NL{~`nKuo((aO^G{6X39&{r@}X9bHB^8% zZ|kYi%H3)77M%@b4%mp3<-+n*!@1US!@8}*AKNiXDqO_OWWqJ2w6uUr@B3g`r`vJ% zR?Vc)&1jOSojyRz$l*u$_sAOo0b6(NVpLch*1+%lEL;1a#b3mJ$bthagJo(s! z)&A|3+RA!{a5#l(%=1Rpm_|rhW_6%0u~}eBVuE>$vyz#EgTn$=Bw{SKhy{itj6ber zi~Y$9iap;qLK6TJ{;1C_AFgMdRHZAWpH-=EuqF4n{deMA>JS=`qOZoq#aZ{DSb7D) zVLEo(?PL4x(|GHP+4IFR6BD73<@zg$%m2YYM4UOcOJXC%((-a1m2^;nG0F-iTR7ATXm;s^=to3Q`(r^%<-7$MG2>T;crj5YBkxF+H6*iG_aGCi-7`{ij9dGL!l@@HDyInbrv|@Pt4n*6K!w(~NV&{Y{G}#9I@1(E!5$*Nbt^$=t$D=(}c9KoWH*cVf1YWp*TH4qL|R8<4* z%TYaz@n1xLLM*4FG~DaR$L;oShn9ZL*Vcpx3%t{5+@As|$JnW^xDc53^qFlYjb3gL z5eemexH0a8-kp<9JGgev5$h~GiAsiSU&v@G8M-5lV`v%V#=CB_muFaoC^S1eYp~L& z(^+HOc~`POpu{abJzWR=s9noK8pK))hJ`dQ|ATs%X}%uVrd=)W+T|02>_8?PG~3Z| zSh?|?imP6Spn2TCIwMg33p}B)HITrWIEx<5DZtgSGjs3Js1+OYTr^DWbBxcH8?KYJ zh&Lt(8^n{fC;?Z}CeYo%pU|g?E*zq1+rQ9PP@q7&24#}`WfT=`>2;nd7w|@)DM>)_ z3&y|u>vcJ|AsfmY(G+0N^z5#C*?`Lqrc|03gvfscaXxn_GTa!w`=k?yg@q;JPQ=`hdVBNN#p<&7v?dF@`DRB2Ma_9lLYv;K$do4GrPYSn`RCMB6`J()7md;k zcQ^k+eZ9Mw#Nb&SxXr76BCB!9U*=L+oDA6XppB7~6Vz2dW}>0V%OjQyqC zh#jbF7*(Yb3<&(6%FryDxkyDpWFkm?QG>mRA5>$#Wu7eHjcF)=N7kbt*8f)G3CC-V zoDH*o$2A-z+qIqghZ_|tp7<*ze{5(NR#p5H1F<#Q-V<9i+@%hwJ&2A@L|_h^C3fQe z{)DNOA9<#7lD&gJ2TJVEo@Mg!VQ#W9EOkFyW6j~RDl>T$oA20#X4A44;Mh~C!yr}I zO>1SO<62h*E?u6WhlOYte05x~=;e1lESpB>L$gcj_f2@XN!4EUN9d(G`R2 zzrST|s

V=Vw0onww&Mq5!C$k@H%`6 z{RA|e)P$0A5)_~&v2LX=3R!p@0zT{7J~HsR520E@`%dT zu6x1f8_gs`YU=n3SW=|irgJqLm3E@{krv>aaq|I2-{o2MO!M+!l%`f-T64oB)F%#W zwgB6|$EE?&!VTK&C4wFqcLSr;YQ+Aq>fUlMql{_vBC5CF&1)3?9Zh4FejZl5Tx$#0 z!@QoClMz5thcTwcWN(eb<+Wu5^7mLd3$9;(ml+X+Q2Vu%S_+Gawy8C7IBrL^Okt`p zxbR{Or61aEY$AJRl2)lvs)~%$_#uv}x7VD3GRGl%X&)?Ux50p=T{vSN%1MWzLM*$D z4%S6Iery#|pV%eqv#6ghgF%pWlXstmF<5|ZyR9~1R^+TWWs2u)GC^r@cCV=D3vA&< ze0lRGHRfAKLxbi|PU3%#_*lOSp7pwr=Pv%uC~0m|uBHiWr^m4MPzxchiw-ew=$e4k<-kKDU6#VRxM3oqd(?9%SFmzUB~>A2My7)EyDk@;iS|T2jSxgH%0gJl+ny zbN{6>1{@Sprs-rX_)5Bnv!QX}iK>O>0NOc~>tJ%)zSYtckf^UknE@VGK zJ10DBOb{v|v>&Z-5_kV*K6@ui=!i3u44$R6E!!>_X z37I~n^`zUk0rBGwni`~VEeYvP55jzd#KeC+Xi`5BWk?GYh}n=BZUsc%9CCU8GNv#*DX4t`KAH2NwkQ;!`r0tXVDs{Na0o0Jk83BrQTW1{caZq(5m41dUbj)D zQs*KavhGsX(lYew&qMzH8vlc|Re9_$$Halm+@PaOzkKI^D9(IunuN0=c({e=3D4gx zB>{>{=9bw$M-n&Qw6eGJ<42)buKICg?fIpJyzv9#(q6yPEjxN)3{-z4d=*#ttCKsK z;g_bpX}PwHFXGXoEoAy+)grt!I%RJGPJ)qvQr*naa#{0Yv0i$+quS+5-dJe4?PEJ+ zCH^J>gX*3h*=is3jfHL65hvprmgXV`e(f(|#%&7lSO9)h%JA&WHxuf z-{x}*({@l=wqmkm+9qA}H#K8?aFK$TwD=G-|g4A?{H5NVM}s_3_+YJ^3McZ zBW3=s`_WOFs;cXjxBMb{<4gfVlEC-oUv8>BN$jJ~Fl*%NIq1aN1K>Tv&UStL7#yi? zb8`i1D}PghaPh)2t-`O#w2wOq`jj8W5l};Z1?WPI>;LvG^=@JETg%t}oKRh|+(XrM z`68l>9h?+V&wP^*hi|2JkJZlwGspp$XQf$l3B8kHil&>0OpEKP+V1M?tN>pi|JXN| zEkg3gpF43A7e6GLV}?E`bkW0bGCxHB5XGWAv@*i+;RDzK193|+lqpgaP6R~C*;Y41w7F6CN}!y7C)R4@H( zY(TsS2s(^jDQPIoC2t3`#=u~jzvB!px0A!Nx z^z~r=QRwhns+OqfBg{&GJc_SSZ{~U_sb7dX#=%J7$e0d(5vYFme=x^K1mag3e+%S zDCqX9wD(-nU(E{fh%6F%__W{u*QiCuJ09nOb{XGZpd|_U{dKnfJ(C_$RF=?g^UHTu z8uch=P)g12fP;@7KOH~S{JmRp{Mh^cpS=aC&!7N6KVacTmC{f{N9pq!%Ze*%Su2a5 zuMbM|Z>w73HSlGn`YK0Dg5h)j6O!a+@|P4>Zq;49VySzNPX3y@Ry9)F3logOTMbNV zMhYKW`v)`~#B)@>s6KAolKN8J&-pfD+ryPfj4ex>2o0ox)k}YPOjY)i&Eba*wZk56 z^m~uM>{j&lW3uQ>j~Gfb?hzN+FWM?@ww4Ihow^>VZ4*FU==|0UEUUQZ?iu#;*gN*m ztGwgl;gnm7@mXfd*%E1zy$vg;kdWhcw_v&ZwsV3DCKPPYEh6%q5Xwm%FbE=k zjE6Tm!k`qk%*sn`ELfYaYR?u;R8%}`I3s~xn||DDjGC2FXB1<$>=vA0maDtY;Rry{ ziMKfnjoh-SphC>T&H0Soi7?~JF%R_j7a)Rj8q|xszH0BruB2cRm>oIPK^vyX`>ycF zprED;IWhIjTs|cHpgv3lXIt4kRPfQMEls_bNva&E^9*})$#tFdI(mjef*h_se*9AF z-i8YM-+;Lb_rCSNps@DhuM?{|O1gCPf*YKY-Z<7Drl49xl<20M_PY9FaWT{98Zz@^hab9&YJ6L`GKD7#XE=E>2r>i2ie3 z&KX!=Q#il(ProysZdYw`>w!mgB+qf-rqt;UjZq=pvo`7n5j1Z@evxXJPS*)L zG1saaJp~N>=8aBNI!5IE<8n9e!R{rYCHHZ{Fq)5#a&q)f#{c7EG(rxV=zY*o*X!nv z)MdVL7q0?-njO4DLA<8ugm$;{Txqv6vpMWOAqzd2TvlFg%)ISIsnf*zA9Q{~=i>I3 zDL2o1UiqbkZ&d7+V=2q9NB_aZ&gVfx@bI~0l0X(`_d3|Ogmj5M{;`l)ilyG;GG18i zXQgNB4)~5H6RuP8v2QmWZirt(@ zF+F!Gq4H+~m}uObIRS#3{!DMk-OJ6cXA!M7khwq}?H3Y>738Z9$%5S}>?jY(jauZ^ zyN7b7^3CpHL}?_`ePQbD=WPqG0&tafw6)KdKWPRV^UcwO9Q%VY6zeG$^x#Pd`y)1| zT-%kE6%`n^fjph4vaH9^p_%3D+s>3sB;~I!{QlYM2-Nim1>do z@Sy}9kpVC4nXh*k#L$?QjDE*Skpd*-;fkd-cX>FdELY{T`tCa#yv97fgNgk(g9a(C zm!Q8i!qZ_QiU$s;7p^li9zSkR{yrO_RR@>U``dsGm?AD=FhHl_7WTXp$gQsh^gUwrvAyK4W;B0+4WWYcfsoDv!%A=poL z+c$)pJpU`PaHw$K8?4@hcV%Ms4h|EOQOCw?rLY8|Yn=i-SDQOMWjln4?Ms!Euo&`Oe-+8JAIs#@m$m1@*$B^HbcM0@p|WzEUJE(D&^m0r`hZz}lx+{W zt5?tj(=Jc#MdVO(Z27g>n!$(G1C7`OVotR6!1YwV?1V#KF1fO*aQ5whWu=aR^x(l= zNgkh+UIy7H9)Dwnw|P$KMU08gnVK)G|E3Z9v%3XGiyeDOE9miAXS&Fx7l$}D){bS= z;>bNxv_`Z$#w*`+e?pe(Cd*1VK_1giLddqBDVlBGe2iTnU-kyGhWdjg@G0aw?mBqr zP^5yF?o*6Q7x!kev$0k8{h%7spnvng1J8hnd^zS1273^P$CyE>&( ze0{8{3RP~pcoDwFCm6rJ4wkmIx+eDh?KK4ZVdT71z0(Gd7IS-WAy|2TfIElK$q$DY z9~Ef!6_viwkNSrcq1tD2`4K>9=geBQsvoqIV?x-^1v}fbd6Q_{C=ZZ2|Eo^IfAgx9 z9%H9Qre(A_>!rVPOx$N|eb1Y&7@;;8(MA%dh=@oS-7k(?`OtS=47D@$dG{O2IUw*`D zI0@hK19+x$Bi~5Ie>Kx*e_BQ%{ONwllUdA*eQHP(W!08{7UF&NDQwz-Jt8_}%Dz+T zcoLJuAM{S2tGcL0{r)K@+yLl|iwM1_Qw189gFG`I&c6i<7bemK4~Lqd5Z7d?2z`ZG z-ei=tNQoeh-=@w=RAqRY#-Bh}*9OsM&nX#ZX=3NDp3ZypX!iHL%K;B3Iy71f3XC|W*oV8C z>dhs-dK<&^93vzorNx|$zJ(_uI)&V9l)^)GJ@xTIPe(rAbLUWM^$HXM$Au0WU)W|lYL>ZhyRMBssw5aY9D*3rj?hbf`*zCWO^s;}OeamBLUridZK#x# zlx6cSA!2v;c!Ql8 z|8TzhpwS}f$75CthXc=&-Iy3}T$=mh#kf>GkJzF-@`%}3nRMv8mKk}8$;tC~?;SSX zaSjcaA7CTyjX!829UQ zvv%9{+-)Qzw@JXTIV`#@+Ojn@AzEC=u1}A<&`9%v@OWeSwYGvP1hkj<*zq5oH|&1b znnEEds~3_oDH6m*j>T>t@ zS#Q->eJ*83thTzg(WC^!;KUqZPN#3iKeX45;1z+L&!mO2S%|rvIOqeS)G;r#E;WM1B`oI9b0H8p z1*cvJC$N>#jtHeR5)x(t;Ue2PFT|-@Tb|tuVg13F7!?tjD2@g~xpuy@d%%DJf`nso z@FQ+!NFi73JZ7Y(rbZiZ2hV)QePHM*0dNUBX(tX_jqd*W#L5PT{83||R6 z-kyYtj@(C3GvdlQ!@eVTJ77uiAHbLa*p;S`=tit%fe4cseRKPN{Cc)FJm-7NI^O0a z6&i*`!c+JOVES@Uy*ufWFi25>d?X-k9r}$h4%g3l)1?00RL^eYq=Gx7Z==)YrLvMz z60|8nzH@Jst+yBIXmQ#}zVi?!Zz7u9<^VP9bZnCV*2L5n%d-k+H7}|#B?+V5rG6)C zt!nI_efY;3YV5eUJwJT6?4GLgJ_WO)?qsLa>}zinFFm({B14Rl!T7f%=xSOEWKl@) z2Fi-7&j>3DYBK${1C`DT|{{;Xe(Gm#~74`uDsrp zp5{sDL&4U06sJUDxmOCOJOxfl17a?viHY~4)KvM!FL!SeSqf|u`u80o^_>N)fhW?T z@1QN2?4Ay`^3Y7=MK@%{Uig#3kEX_VTi5sYy-G&u4C{0|`+}VEZ(NR-%^0U2(H`o= z`QKitY46_;CfDuzV!3xdB!O2jK;8Qqg3Hf5H+f2Q*b*CWKj5HF1>m}_Q%$b_Yt_nA zb&XrSd749`^3h(yyowy5S2475O^N0W`3Y-J=G?4~=^=qRAhTkV*ylP4Q`T^r6F{-c zzU&;Iw(fP8*tDe;(N8FoUSUY?@9$q6bc_DaDJhi|^`UQwUx_61!eH3FAluU^^>h7g z(R!({Csl~wIP<_DNKJh9FAz|ZG+SiVO?-XoZwe0F{;ok_cLw<>e$5miW0r_(t-q2Xv=0?4qsmH{%*V5IE<_&v2 zbV#~hyW#yGpU)@n_>PyyCze-4*$|rdn9^QUHgjEcB3fCaG*tMoWj?*Q&mueTOWAFe zz4Z!5dkhJT+Rk&Le%PO44*yvAwk%4;Xe0D(mHYdP$5LKcU9=0GmA`FG{WkxI56f*< zg&Imyr@Vb;@i1s@w<9Lf$I_QvmaM#?HrmTBVb+*wtA{(q=q61UhX%qhkhMX8xM&ITme=Po`uYA z9-OuIK` zPb^o%rW{MV)1Z+e(R#u-_34QfTRkPo|4`XM=ja0)VEEucK}+yaNre3W62)+$D!3F{ zb9vCm!}RP*zy2~ZG7=_0LLN)p@fB0Nh6%fBu$Bemf2MnlTcX8by$gY)mbd0mdL|xq z@9Ed6ljL@vK7Bg>-tC7~1#Z`{Wg0BGT;3>FnF$2F0Qomidfz~$vMA#;{ldnEZ!Qq? zl9DlUYd8Bq?Wqv#Xa)cO2Tv%8GQVEt`qisL1=ua6xo?mdwIsjuc6e$pmmMTQA4^M* zVW=fKD7f&PRj2Og$OB5^?uA!b(Bu>K41o}iF5>2>k~);@>+5g8!&K-4zZ^al#Gi>e zUvTuv1BAzkjLWO+?D_m=qSa?DJdm_x4_uS@&eh1}-|8y?lhr>zefa^=kRS>s;(vvq zTA$yV=kx(agtYI9s?DN+hs!0fKY7Zd`~mMzZujthE9sXj7{#vYGbl(*j~2~IMFy7& z1GcxJrS>P!V=lQr3(+BFX`@lD}_{BI8bs16Q8?Rblm4A|`*tzBCMB+2d zsuLV=iCI}IQ#IUVCr;GfdA%M8X`&)hMDc6EAyBT1*Nryt?d&$`G2LF(U)(p7aNL(-LvCDZ zLY$u=VBo68_o;eeem#xSW`BK8?mv;aRDump5^-?RHg)=1f;qX7-q|$E;^Xt$&h8c` z?w*?z>Hfs?YCJkPp)1yp z@rj$B!^K`>?P>yii_;=q31;GU46Db(h4PfvC{ z+V=gQ?o+55$5`Z;0bI<- zA3?a8Vkraw?Z<1=m!3`)dPUuCMfg?Lir783G(iDBVKa?%V>WtnMk_48zh@WA5)(*ab}6Wd*Ap!*neZZfH-tGoS^YUo z$B~Heg^5s51gnQ_V3Qf0G$Y$(2R#ny>5iwUCWmvwSW4mme+X+$LA#+qTZND9vJ6jt z5{K*Gw2$CkNMyEKURhbGjfKgrCDyF#B4WW$beg`tzStFY1}Y>1Jp`u#)$~0O^1dJS zG%ItYva)jOZz8*}J1Kj6E7;FVd*%*~~MJbnP!}!`)?@$9`k{V5{>g zJPsnc2rJY-j|vLbDtgYG$r?ET3GX9I?)b@*Xe%aDk)0%^E4}=!{VbFP$50}DzV`sj z6N*x!YlaIJ{BzW<>L;A6*eH82-r}VT@fk-FjsurZ7}PwY&i(Wq|VD$zWNOK-y63W5*Pel`_U$w58EFpF>-B>%NH;DfJ(#05cgr}9^&oR zgpn;n91fvvz=f*~K%Fp|MLmrIs_mpnlW^_?=M-+HIycQ#)Q)W`(4T?=f?j{Jur)>K zz-xto*p-qvwdU|Q8op;4J;6pPf?MKTu6$PdzJb^Q^b(eRp2jG}{o?P~zs?PAB-#XV zF$9Hze{rSFr1ve|mjd4jr`h_-HT#->_Q7}W5N0XDIfFzdxZiqV`8D?+NcFh$MOIi5 zC;7n=Iuyeo$KQMctf##eYf0&ZrYf~3CX2-mD_IO-DF$6*`FOqUGg{Z_4`y=x4p5(i#Ws-eP2#N$vk;9~kQl_ARGx5N*#?@W!i*P>X zS(Yy7R}B8k&r$r3bL5}Xg#W#^1?rI}Y5D0R7%LnpixuJW((Xn&y>e2A!)yU}l zk=WylUB(lA0{J$wy@TMHA@rUDTe;`n`EzpOaXsT8oiCa{5rlDp^QnI6->1JdDloE0 zjqmKcCl10=2car#_nv4ThSs0?f3D@q=th?U{@`QbO< zg{`$YjsH;_PCaZ2g&G3~hnLyQhIJ%rjH1xB$x|DV5<)avWczIhjshaxJcOD0hVK*KmTVn4Lj2``0beLl6kKi8cX83i|fZb`&#<*jj!V*~M0M?PFS0UVf6`V+_u8L+`e4 zXx!{M9rD=A+P-tT^n>)2ZS-$bM=V^qm%{&+2-;2p7fZ|?;bdU z?}w&1NVQ)*YHc3+w*AabC6jt)qsAo)3K51!nE4%2|MFj+c^~bMMI8O~Sb}Uq0B9gY zyC-Y3?wGb0$6w+8Z|pY%wiRBB)QLh(B~10`@|X_Y#$%q4@U!ioroun8RXTD0nca*2 zkB$C+{J&fFJ*(D5|2Ha?1A-|^JjXL_KE{o(E;bM(h1c8|?Y3;+xzP8ng@uKc^EUt6 zU)tkaS-tPVcRr~+HZeT-ejQGD@fOeat)|dU?{cQ1PzHJZxv!nmFpZTBu z@qZq`|Ez%j*#e><`M>`9|5*%hK2Ab6g8DZ*f4H0*`SMO05JtBDDv4X4Qj`ptFbDA~ zo2kdEkMMEfawleJXrEiDl|6JgfyYb$eI)0CL`cd8NkNh_Y#uBooBYWugy=~CiXsOi zLl$0^jrEl$!OI2lEIWVX&=XA0LSt2{PA1Mka86&!n!2`k8%x3dxx8D5M$OPDoR^V(|h72)m*?J|j6COthJ zXN&O_m6aWG=@QIAf+0xKT(vl-=_}3rr*Q0V?!gfarXpS)uH~3Vc12iW{`;%{DALU& zUfoWzF>hY?cL89*)^ zMbBL??ZG$*mWoIfc;2Lsz9dg$w0+xCsrQnVy>1w`(h!Fo;Y`5V5suF^P*X0a_w93T zOnekwbR*k?j69*lf7BEV?UxA@H$8aW2%rhrF~m=u%=E+5X*)@`BduOm?1c+wGv= zz|@&$!=Ut-EXx#46M8_xSe)0PqnS+&Ci+#y_of3E&;!6MWO<|7tQ5E2s9~tHVmUI7=7{*svE=ehEO5CUD3;Z@ju04zmpq;eKb2S zCmHQ+5!M(pM3W41#G|}C!$a{e${>NvqPu3I5mUJG&_A;)o|xl8CUSH!0bFN#YfP;k zx#Y#<;8{7gHqe}*fGI6|9~~KazV7nh6KGw2z^rx}Bk0<;Yv-OmR!&f*=T?VNs{4^! zQ*KC8EGXw0k4V`LME~xA(}U2sYyUPuzA}_-)qeEz*C+y2D-YoVfYrH4HYDS}1&IfS z@Qr=rVLj6F8khWA*r46pHBt;8z~eC}PmCL#h_|1+i(m6to*!UpB36cH`y5Ryoz@_% zn<1p2X6`IRj)FsA1gmHoY%5e$R9*_p5BNg&`10=dNIpBK{HA?nUrV%tFad`G@rXK7 zrW^y-54@l`>}tGjeX4(vsh3y36=4aECP*etw2r-7IGFkyQHZKmH*ae}qDN z&+&JxZyJyl_3dohTCd>Q7@Fptc6Lq@Zl$ujDB)fqpi|HIIpz=N)NwszP8PoAex;>wMic7~xdVPO+!8od&DJ}m)K{x|CX`>(x z@D=KRgNMa{3pJF2CKHc)~s1= z(Q|(M_RR%S=tjC6kf^R3#O>|azWsttE^4@j9h!aeA}%{pDwcDi(s?#w%B`>%kHEj8 z{%=-TcwSP{LC>-UH!Hlq%>pa_TV;V6p&RY~^Sda6+PCvtSTlZIf`aEb8{&SR{u#Hf zUAwxbIko_i`?4nt%CuvVE3YS%>sLX+=nEt>*8U2Ew;=tz)(pE+sGYNfcA~Ok6wj)) z2?Yi#f!MD-54+R4b?Xw1u^Z^NY*~iF`E#Dkf8IcmEll3~FI*;yanbli{~-6L#}Swdib`7m#_iF6pgNbIezc>W`< znsS=HOeg4nf+(^rxLhJLa!9uhM=|8<%cp1a@(@ZM2k9>2Sf7R<;)4yAux&U{lZg%C zhyev6KJqulp2?6zYwjC?T)>``Ko{_o^}`5-rlukStMIX8!1USD(pF?F^9bkWH6T;{ zs#MMKey8$oZ!ra57?o^CsAjCc@<;*rQHKuGV-@oo#jR(ze*FE5=90{%*IDW5OUt)86DO_qCCAy64-gGIyY2Pg zOo9YHk7jXE^3SFwC^V7>pRWeN3&-7BjIRN{7EB>}yh0xHI5}BfgJA$2z{T)-F)z5U z!qn5wpuiGeyZr5hO}ZgUseqP&n~r>J{7E87Cu4N-wnQd=(YrW=B<_s;>TuA0F@Cev zfX$Mel0&?MK>6tsptAQcmYzx`NmNv$i++e$R#I*>2hP+C2bn>_5|(44gzcLi=fia+fM++GRRDE1-J-38~e9 z?3hc10J&+_b`YIaj*tG`>PlQ|_NCmMeI%7DZa;9|W{e|8$WOC|aO0aOrk1e)0uE~y zM%H})ezyBX44ls0$u(z?BdcFF&c!VnTIGmeIXM8k`-V2piR~*TrSPi}GC`L8&Mh?E zteOLV)YP0eY|cg&k1$J7w^Zi|bhTZ=r?Ofww!pSUtL&2|u}uMQXF*uw0a!{2fR*id z!ZGX%+0mK8b%=}wgGqQ^xVIcl3K_m`m>^!Wc(ya&e7?RQ+whw^f90RuB*0est1B-h z=7G3DX_<%MrC)xooCOQg@yj~hmZ`DH`d%2CfNEX$G_QNW_E&ABm9K*NB{Km^%V}Rf`EzzwN&9r z0o$zlFt#alJT5v_dQnPLSRPl_ROLobpmqwb-X)>PO20K>!5=yh0;_Kw1)T$lS&_S> zQR949R37Yr4QRR|Y~TMq;>CHjby$M^!+j}$?a#AQLh6O{C{5#$JnY zqW5k=Hlu2yDr}3BzzX{`HjAh!6Q|q?2?c>znk5ZB3`+iT}d$V$ZHV=TNdf0;+AUZkxvkqIvM$_!HnJZv`FKR4(ob#dSSzGBwFuY+z4@!UrPs2S zFXQYfiZ+l4>br#X0LM$3y>%ueiPLUNM+{(S34vp!JqeNR9NFt_I*%U&m(?MmyS2<4 zAz2W7j&`#z)1HNg;hwnV*E2q-h{tgN-Fv1zy%wg~B-A5Scd|X;(hpVZQfQivw zg^HxmZ2cE#iXuckXDujBoH&y!6rRMvLu19Ky2?@OB)*VP>ipt--IrIhcea?Ir}8xy z%W*MYmXxIXFvamrUXdsYNjQnh^Vi=%D~`FwvxhGYTZKdG83wE4ZsFKYf>nKlLM1bT zM<}YjgEc#1qe!W6mYwHUde5#VaLf8JXIs`xvf{g;kO_wVBA10skf*G7Z`s{H6W}_a zvIAc3_@vuu{+W;Dztx3U*%!wJJ?@c4ZR^M)5oO+gUDbvHLiW}0xx*bA2BxUds}?+i z;-s&biwa*(_>y52cS$?iQG&cu-KTj{ntfya<>kz%rNnP-I(Xyi#Dh@&%Hnj@&wVoB z2gH${@PqhSF>clWqJ0};cMe(D8?d=rp_d+F%^HQ>=xWkJ$c;Ui8ar(LqC7D2D`g+eWan;AiIKfT1n7PXr4c2;G zN#&w98yg>@id2eG(9%`S8h+Ry>`5feA zSBXMj_@Pa9^df~hk$!+A?HcR!Ufr`kZK0XPcd((rTUJVNabqo4(or`&His@kwyd3>8$Yzbt3`xn63#++blofx7v` zTZ}Z5gfOD`d0%~V1GqGJ-6%2>)PSmg{KbGXU75)h*rn$nQm4Vx=LS`x3^7X~#C)et zd!g>kds-71m6V!@=@E9eOq7gg1Ezuec|K`ggvtLj!+hGJ0*c@ z#6i^I%xiVe~+GItGcn^+$E=#o4vGFTG_k(VW{OsybWkyt~AgD4S6o(EhBWT z!a;w13_K>Op+o29f~g61$NFCcS0Syr({%9yRr@YHg`ZSXVmP|i2q#CKKP6(5BwIMKA`C0*`OUkL~+HGafIz*FiDw=F|e zy6p-0>zOz@DgOse+H^`TKefM&lK0|KCMjnJwsE5pe!_?QMZ<++7HX4Wv^>J_j%;&W ztCpRKK*}h<32sOC(qzxp%@ctjElsKC%CisV|3RO4&=lTH%+_31Gxgjf@)*lEJ^S1Y zW%z`1%!Hdh9hdB~ww|+nyMn~8ZM#`JLpcAQG-y)9J#X~1EnYdVuhovSyNiDn3PlR9 zL4I5$N)hPxQF(qz@K!p2eGI&{60HWyanW5J*5xakWG*)eaj%W4?%<(APXnXKSwPDX z#((g~7I=A=lefFt6z`kr7F2_5O(<4Ci_YgyWlD))`p$a zDgdz%`@C`Is)NOL0mBBh>UXPwJw*Iz$6;syJ3_k($avZH(g{YfQRz)H=C}kQlN~YX= zM;a53o4^xua3}$m7%W~5(M}EL!Ws6fi}~A692IXCt!sS)T_Z?O`d`|05yYvB2MJR* zNLOIThu>G)atxmvejTmT*67)Df_~6ez*b;slSrVidU+ooGeHpF05h^}rrzi*gdCJ8 z9a7gsZApAw8nY92A)b53-#)+6d%>wyy^U#I19rT^U^?e_>NX0-RHcD+B=|nq5ZF0- zVWtfEeBoPkZ_m%GBF@$sJ57v07PGx$jkg;Z3>J7Lr}fUEDt-R#Xe*cT>>|qCC#+jr zayKh8vp4+(#(z^<;}6=A(n*ZWgDywaEus_?n?dijS5Ns{2M&{f|G>#Rh>4C^cv13Q zL(@tQb^yMa7+l14UBhO+Lv`|Ee!4(<1%(Jk2811)1V=3o)RLt3ltAATTje`XQc_Yt z1g_ookizT&7bX2)wpb%|oe+k?gud_idoX|)PhH|{Lf>m3EMhiyWZDf0(Q*)^GXw*I z1qE(Sk<(?!kRcF6ItVIlvJ;dfMDFt`1Hm2ee>@3bfI{*i`8j;9d5};=NEMudQWxL0 zve4c6xPbb^XGu^Y%{-*#g3~1Gm{Xr@WbTwpjU6a0eFmuhqiBtsK7HgLS`_T$L(tKK z-AsitzI9GI!VS?^NKW?SwuSbJnMBKp{OUu$FGGMNeoDW7hgm>Ed`Nx;xOG4bT~A9} zPq8NEQn5CVOlfDfapMS~R3l(v^3WCPofrpBnTV6R^TsFs$0m}MiMiwI3=1U*P$aP; z;Sheej7>YOzMI;-b0)j#?+&u!B${u_zFu`*;xo#R8aS@ZP_@4soI3C_{J!lizkSI>}SPrhB~@q>FOU z=ZSP1+@c<{1O!hIKVkhJaqkFm^n>6#URm9AzEF_pIImsniks6tYGSHU3aQ&wwg>-Rvg=I38&m6XzXI-IxT<^=!t&( z6a+r7h{*S{Ir>oslQx zd=mkEq@rSPu}j$2>>ts)<5_PjzMYz}ZMk!!s$8a&k)iA))HlLN;d{$(G2Ui4ZAGwW zA?g#GmTdw}b6{h}Dxf1aeHV{tBr_-DpXb^r{z?P{998}-{!EIee3t3XEsW>&Fj|3d z`8%X=vtmQb(pSaJtIw}2g`pIYenAYx5R_mNNH>o{Vkcp1E;6`PTQ3|x-qK4~;;%mE z=3Uj;yE@_wCXrj;hKvo4C*MPKGmJFk5usSd)+$Y4mSQp)rH9Z~i;}Z^nyGl281}=> z{fkyfKf`wyuCfY&13xf@g~p*TkR*htjc4qEv#|^bsAz#S`S>w9FNKu^MAVa9A>8A|c(DhE-NpJ6UXFj|nE89@^`lj5~Z|nq6)2cPOcp6X~ArUubuSd$=t?X5+49>KqsCBbt`VZ{JXhX zK=ta5&3fN2vWUglRFx{C*hG%CPSmsnLj&w{*qsP~L???=?%&bQ?n~zdH{Q3mv%ALA zRH@RfbC3q&vbW&hBCrc?8IqBI8(Efl!-#L3U<;)u)b4xcURW@JL8C$p(1*#$pzrz) zI5Y^CF&>=YWDAQSR$Ar)*F(*--egZa+hTAFSQdY7AEX^;s7EKH`u<6K>{ZmAhTwjt zZj3j)x1X)({B|;#G3TR@N($485{WpTivVqDuZT&Spy2YViD&hR9b&S*52lzx_*-${ z2b8$r=7IsayT&)!nEl(LtGGL9-2`#;5DtuiDG#~`nlmxr5TLNU(U$Kh?Jx=G32r!- zIxBMuK*vOMPTgsFlcq;B1!)nyzyW&lTm(jrbNE3U>IFUrOn+vjY64`E! zLv?NKV>o;g{!Tl{IOox^raR#F76saTkjIG>ZbGJUH>_;(ZdSs7*$Yj%>ywm`LR1=2FockP!{Ck_FZ39c zMqetmMYee_YwNUELZTnvcv;U~!bm4cXDgIbaZu4=jV?TqZxbdb!&M1v~QdBEa5%0+=P-St3%?!q?# zWz$SH&H82(V;o2Vsxf+**Oih4!rn$2i!|27oYS)&9;1UNV4ZN7UN~i?kfC1_Sh%b@J&KAsnq!5qk48JkRu4(!G z$M{>qBflF34_ut4JP+(NYr-|#fG#7P4|ZKKr^DD4l{bbvANTP+zOK#8RsLg+$Nz1_ zUoKg-s`pIQ%)Jv19Im~aF-$#Y-K^c4H)TyZ&`=(G&l($Jm4%x&*=DFTIHYr>I+$)< z1>xMNaxMPFrj;}@-yj3BAx8u%yS;DevZmGe>wem8SetUYzM|IXy2~|HDIu6x=Iv1L z2_y1LLT zViy42Wefc*gLwNpbm=xUO6tVk4>E4tYsT4`SI}hlYhRi?$zh;X`&*+ojT=+5zUw7M zkX`h+t?HeWBGpNL;hLIR_Mb9O6&(zVuQ%`-6}51g@A{`Hd6VReQlaK#abRPqICgQV z`}o4-s)MsfnbE3kf2{3{p1tu(eKv`LB9mDb>kUFdTRm3qwEuWyAhQ&wn+A_#m%eBE z9{QRf2*|;Ru&BQuB+Dy_vBn$%Zu5r@{7Om^h1ClE7kye~9x}onh{#iOFZ8|u{xoXR zCaLcG8NMNg0Q4JcB!jr!x0rURKitV9Z~dJjCt!Hz6DMw8k&IMa!@(fhYIKD4G^uDN z88Bjyrp^s&t1@^Bx*U*RQ4_|FT}X)%0R&U_<2LxiU=8~yKK13U)r+CW9(_zxkA;S` za0Pi^En=g+c|H4Cki@;A18p(3DBqn7*M_r7j7nn1otK$xkvDDS6qROg2(Pnf{zdoO z@-ZD(H)UouV3Lzr?cmMX4?MM%GVhYAP0(7$U#9#vy;@~&?U!pTY?I)4dfEoOb0N>R zEwG!<9lWMmFx;ZMznKb3dFO{2Sz1TQG8!u)Gom&=>FPk#O`qB4tL5q>;{WBhyJ&_Z zSX(GYcWVA=uG!w>B87Yz$VxU1eT7c7fdK(#yw3NSvj}&p(#@~#8?@O?k8Chyb*|4# zYaT>n@%d#@^bN(AldiAuit2R4d#cxYNiKXb{j?uj&aP{GkoE~g=oLHEG&U%%a)Ydf>w1>DNQ(8{^j<0{eEhk9+x&Wf4Oj{MZZ&_bfnuLU*TF5Ec7TzPP z=YfC~#D28Rx4Dp7{KzhG&F_glkIe=e$lC1f@OjGjr|wh@<~b2_ak_W~GbIS|x*5tg z4_S}Ba48R0Ufh(3f-WhH#1gU*A#5~5)MYfn(Ob` zNA-I8GQ{9$!M>U$scjxfsz`y{SuX$;kKt`u-7(Ky&F;H|dZm1qQHPnFfou1C>>!r= z(Gu2M9X0G%ByO1?({HFLOA|<1%Z?0O`;xTBRB!&GMb0eoKw@<{MhZG07Ljj0AJpu0 z#BS9y^MXA~H}zwjCy0s}VUA>};mbSDYcpCt226-!K&Lr9rX{qYvtc1r?NaM{3F^d< z^fnCU*5N}n_lL$z>=ihC(bA=q)^S>?`tgCcWZF-+t-7+*OZ_I5qkhn&XtvvY>MNTB z25|=@pHNfZ5ShbES9-gnE|#Lndw$}zO~%;>xubPkR!i)>Gv>;q`E4XR6abLDW>|dI z_SK|sQR)kS06^&9?LT(K92#=yjOwoBOrKzQ&xfO6EAMB#+A@Hfy63XqqW^bkiut9I z?Pm)e8mhqm1ItKVIa@9uwf$=OJw@m4E$DBXQ>1O#z^Mm1 z#b{@f)78>>yWCON#O=~0F##o*x=$Pdci$VF)wyNxWvtNu7=6)z+#2Mp zD=Ecw_wBn+lf0z6Ynr$X5hXc5Lx$|Q|H@oAlUy>}nNjvz5%}$v-tDBQJa_EhZh%r6 z0y0|16lQ1Z;>;CCPU=f4l8PLi2(OCXTfl5Uqo#KA?v*P(-(v)k<5Fls$(id7G5Ho# zE4Ke=Xwb!0Ix?O0bE)Iwfn!%ZR*_81|NV(B%+0IB2dhwP9fT02lhlpFb$9nW+*ATF z(!TiI{JfTewr$&rBkLyQDh8LHs@NR|6O{JF-*~Dl6~;!EX>ZkeF{#TE?Jp%2B3`N>V<@=|16vYNkCWq;(CU?0b0AWn z-sH*0R>hY0la>w@hSAfV))G6c)H?SWIPf|TVa|F5Nb&o27$fmG;E&_ceU-);}b{E zKNNAxP;!cjN{(XFzF0E2l%oDBE>bGK$s3N0*=~7Ht%cZpK6`&Q`7kC;y?zDZctUpI zRZVNRlBZWv(dos{p}#OTdc#l6^NMDj#s03()uBp0a`dPi{AO{^lNsNk{eHnIJJ_zd zGNthLn~6v9w=H`$e^}j~rL*S&HVnoJKi4NoAV=t*g7Es~;{9L~m`d8x0m_Ur%i=$y zg(%$PuVWArO}lijtEtHvYMXZraQp@+V>LUGFi(YeoyhQ{()kjF6Sj`O1H^){uG_RM z*-~k3k#4MIh=!U&Hr?&P48@)Q`z~*Ku;xC|8VKN25UewMe#{+GrBoYX08bx_dNt~! zRl}lR8l3`sUiej8@0(mCb!=O{`?rrOzw!eftTVF9(D$6M5{>DFZ?^NCWOr!DW>pqG zScf$Des2E*0jUqd)V|$cNkX9wiU6tF7k-autiDfh*c%uj-W#-)i1x=mm()xQyEtl7 zok_)uH8tz$K3&HMaB^;P>EWD3ft0j&l*1mC9h|TlYw^_VEske;hz(5TVx7RGh1ax$ zYih4leKs|GzFjl=jZ$XGw~msf&oA91Bi=sca_$^yGcz-h#CbV3nx)#bm1yaecuiuz zA+%yY-uYI_74f$p2dX$ZIrSK%IAfVc-Kx8~TCwG!aka3rf)|_{F}|VKkSSU_;_E!v zO+}U>8q_}SyG-QFfiv3>hq5?S^E)2Z{8fBjJYu>SMpd}i-4m_9#gbQH9bbd0Fl}9_ zNkqtX8o zB!Ebj2V7XI6t62|I{3brnOFC0q{~I=BIoN3c4*CebXppwV8yH}GMBfCRu>;UcQ7dd63$$Xj&kOvUZDO!L>{okm%7Zw z6Fl$!-fCH519WJDDbSPe7r7zRvfg3X*a{#LWJ4rNY_%zst3>|y=7Ez_basQWi-yO| ztY)vhRoXOYasQfnLH{Q4)p(SQj7hMcXcb{FaxwQqIw#gww!MwYyRk++NAIBst0T3F z)0j_sp_Kn;wPVIGhuU)`!xP%H>$nAvzw~TsZY3!NH3k?8N)+t$BS(&WJ)>5&9C{fb ziI3Q`ih9NL`z~QuS`||&-9uVkkG-j1hO^t9j~`ORI)j&4?z!3Pc5ThoX}f08^Lj}y z)2IqdkJ}{S!CGxJ;0?bX>7xFr+TkNDA(4NDiZ|CoG4*GII^A@$(a$;0Rdi+KIdMff zNyo7aL8vJAKKj&AR!znb;L*%3%L?O=^7+tA9OMx*!0=bW5%HDC=wk1u+ zawZwN7C8NJC~ZHIjdo|tt67wU+GL&N8|%ML`f=wohonb}{u0BpULjAPads%YDH&Il z;AA9DEa~ic057cuq57?TXY68UxXfbWniDeEpwd?a2)tSypuw3UaX(ez-LtFkHol7I z%J?{$n;-^T8Chl#Q!)S_vAj%Y|9REH(?2ib-RdAJ$g7{wZmLfz*EJ(Jjmg1xO(dhT!dDvp8cGg#g0q6_SU z*V=y#P%tg}^htUBLkSk*sAX;EOsb#lNO*1haHv=J)aiU#pnQpGMewOJXI4{RUui0^ zFCUkWk&$PL6T{yvnx<6ljvK9Y(T9pBR%0EVM3M3TrZ080GWL>T&amyKrxHKz`~qZV zjSbVWW94E^QE6hL9srDD1YoKhGLqT}NO$)TYkfjA?YG77JZvq>;mdkC)a!F(?a)uz zf$a$&dP`N3pWee#R7>JU5tw|o`~$^2g!E)9ER6Aoq7c2-zFxUZ6z)eIZh4(4p}+gc zGBXz7!e#K}MHEQqUTn%*GBw{Hs5NC%q#4W%&((@?;;&1_k*n;HVR!3ZwTAjsM_b(jtJMc9=->rh-jR?q z>*R!*dMiu7IAMSPQR?0ri;jB`v@CM1@DBeL-Gzy4tsCrH>b8l0IH7ElQE}mYipJ`s3#7r%W{3 zY>$P`NaJM@D9^4npQ)opFk2?##E0b)`;&$9EI7E@I$0T+F{-NXS8VbVX&w=%jK}>^ z&U_OOHh|j6v*U=<82+$YEpt>xECd^SNy)QBwi8*=o63RZtk+nn!_S{F9Cn%dkHg3p zBCVJ9mmxA`@g`OQ$BNh%^VfRS$feF89pdn^|8SsFb?nVwV(zQQ!PzfHFE2d|+=N-N zGEOPcrUDy*a#&ZDpKFO{db&-E6aC%1jz`6sv`{Mc1qZlb9dl?qBnSP`AD~DZa@6nh7=Dc8XbX0>7UeL2Z08_h+_n6HyE4!Pg$?sQ5nn|ixrm7&@SG+y z(p)R2H(Nv`laIRlQ@X68b}uR2oTu>@H3r`k*Q2}p_+C|OV=~t;hgithN%&uuOr+d> z!`ENf=Bbv|DHu50j}phEF@~l=Eyv}(ct@G9n8rdyKV+$XAVl%fbf<*mF6yDX3mlh@ zUvoFDr1;D!PA`?Z%G~VA78a?e?OhQ*^FQASXq-$%y zUdZPDAvP-fYHBL?SYx5h%N64;UCt}-4W2%lh8eK&B&p2~Ujr^gv_Bf?{Y?N66oZBx z$IyoTB9-;Ehp)v}eRDf>ZMQ2nzTh$17i(}$hYy0=?Fm(yadp`Q{Znai-2Xl+P(_9E++O5REaVHx*MSJQ#GOOF%NQkG z2iP#(^86_Xb|kfqcw#h46#|B?VS4*>}bs7$vPaAlhxO4hy*y# zdYF1&YVUbV&0c{%k*Z5Qauct4b)5KF49>X&>|9#Tu~2qG*6UeK4d#DA46i*Z-*&N5 z_q1C7Q~uj;DX&B0_^8pgDo@`hd2$<{;i&~H%q8}3dry9Ghl%8+4@d}yIDS+h2@!zS zj)Y(Hxa+rq=iG0Q+(P~~I?DAZp$q;}_tbsOKa$5sN(3&Z6k^lmSB2?E2AZj5635nn zQAHaT3}K>6Ef|i;*OzYN{}jA9cjDasU74;> zba%Er+cGdG6_T&!g#lH+wZnc-$9f{*E5 z`=je@Lkh}irjk#d4Tv~$B+e)B6dd9WF$t&Ic-X0Q{&c$ONrfs;$s@qZ9aB#dRR5B@ zMkmE+Fd8uex7SyF9FUs0kY#i*x9C`pA)78%hLYV_Oy9;tzJhRbVq`3R%pR#Gwaqmd zYs%i$HZ)AFRIWE!e}6c=OJ6~V9jmy?)w$o;jg66`Wn{kKh?%ac)bZ$|3u+HulnDt3 zOx7*$1$aETqYgtPUX(nRSs44{>5$0wTBfyRzNKYl!uvIo{3x>hfc~UDfX==jZv+1{ zQe|7-8aF2I{;w;ioSiebg~e~Wr?d(85pniQdd-h*_WJ#PTbP}?WQ0#-bzb;i$32V$ z^916K0Fp#XUSd^6mHUQFz-_bnm{TF6TfzrRZ7qpix@5^6w=d6z&_$zDq?BbTz{=!9 z4~2iIm6D&eU0ZX2R}BkeiwGsN$BRBpC7IhFEXKVN!`*{zkXd{c>1||&W-v*_Hc}c7 zm%2?(X{25|-en_^`eeC%f)Shb4cf!#CR~IjgMB^PKQGu)X#hEU3f5HqA64>B$1HmK z+X5V1VD=mGRdjf$3c>bk%}6AV{{E_)GR=Z%ZP^OhFDNZ~B!9Sd=HvVAQ|Nb81eMFC zE&1v&mI~30SqGN_{$vB2QP=MtqNohVM_{cM<-NLe*)>FQP@g{6i1M)-yFot#i^3)0 z^*^aDvv5(bKE9zh;V-zBTuooocBUeyhr-OOnI%4pY~}R=7ca{z3q1p(Ik+W3CZ@;m zglhF88+tlFR+lT5CI>IHD;=8Ezh5T-G`TDmsf0CP%S8`)t&mgkB{n;^EGR>jD|>q>4SA4&rY5zxmxP2> z?QRK)>O7K}$qY=?X#C>0^_A}t?lp)fVsDsnewpS?GV`sVHN+D4k|K(uErX=2=^W5} z$*F816CXQ%e0rs$-}belPUzq`IgArNrt)4b*u|~rf~|X+d3!xN_2?!aye^YHSravs z?w+z#p4<4%%LRxT(5Te&ZoYl_tspcPsg_6pc3sY29ed?Os?qXOG8b~BCN&~vv$RR7 zreNsN&fe&js&SO+)#99ZM32g10f3BJ(Yx@4w%& zdry($^B3he*mxePYlDT#ft)oq|C_~;nmGOqL7bEiwMUb9t}HFaD$cAiC!CP%F8J?k?X-4Yb!%WyMzz!Y-Xe6C%aI-I*TD6T9I8j zR&q<)cOJ>Md=b@l7ES5{&(^O+7nb$lmhAb>9M$`i-tpv*;ui~+Y>Grd4E*RGvAD=Ju}j*A|IO2Nq~u>U?wIW^;RnI$Ik z=2`WUDcYqI`9m;J%lV$8rj|iKoPnl*CB`>H#Y>=#>^go~mF=BRTP)7RlN@{wDw9p? zMg=&ryK0vnJ#19!UESRLMf?c2h}7I2dD~4uSUa7-v9DM;tLtBrA3pqHX1tDG-kze< z7EM`LbC0{_yM5Js&_J`HtN-%pAE+`W%`Zh$-gw8ng}cK>bM8q`+c~2ZD*ZuBZ`iVQ z6WGbP3*)+n$h3Wy`Z(lkgj%ma#b=efl!8mDw_T|S4ogU>&8Y)B)Pa^;U6Pks(jY$x zuw41K;}+8vZ<$9O?DB3~_NvT_$ttpMBjI|dU$KOQi6Vb?K9@%z&%Y%;{#Y8TzANV$ z+3|I9%pjOo9`6Z@LrMpR(6B1VocQax!r2ss848qlX*~ zTbGa*oUx;*QBBSkm8bOo*Xyf9*dmd9Kan9 z_P9)=s(|41NQn(&ySwKdz{`w71FD-GC+^MIQ+_MDmgu-9B?{(^DaooXX&TtTq;XES zx^(TD3EZ?*FA&O9`d*EqJDW!J4x=Sx(U7x!3bWM{ckWzIW;|YYqW?C|W3wTKw==b& z%Ny7*e{W4)=~TbrUm&MRWefR3R?&;m9;A!O))8l3)s66c&Z(-oBLzebbYH~2Vg~Rntx&XORhO9PgR4)6roU^s7QNcCOpGbJwt8jZ#p}R$V z-D0 z%U@78*g{K`6s$_G+HOU)DxD3E^F^!S8IQAC{c?1FrbLJxcHfDB*nh~7y*m3`+H?Np zNh+?N5qNu(F6yN~unBo@gJKP|a*coiZ+QLc-M(B_u?pk{{Y4sSF1e#VS;$)gi-PAR z&h&Bvj$Eb{rXe4pHh`Nk8QghWbZ(EPr!N{elHQZLT+3d&_eHqj(!5>CLq$6V`Dx|d zG*rYlh($TpN0Ls>^cPJcC^+W>PUQ@$f+PQ$WSfni;a_q=03+!S)~$6}IbfbhcFg`N zCbp|3kIy<3E3Eyms@r9Lzjx(K0P9H`X}WiAQ#^-(N8A6!-h0R8y#N2>;f*8W9E3<_ zq!dat%|qHWm4;DKX=qOw2gj;3uC%T8qP;_uTn(Dil6I-IwCj6+bk6%T-pB3s`{(=5 z?{@q4&pGJ2uGjT?J;&p5U(a>gySl`Qgb%WB^}SdT*ie|a9$6A{!9@nvUKpX>nL9fy ze)Y?3G_NVq_%u_{j$<=EuW0~ha%j{$9k$HL01^PmtDi6fv?Fy+c9dg~v97Rn{FqOE zL%)(j>#ODS)bgWPn?$M!-$ICITNmVWPKsKf$ertKYD~sZ-q$g}nSaDS-2G!p63bnP zsuj0B-;3nx;W-5p1N&KC3R7<*x?=hAXHdy{!>Wqzs*<2lTGSv`x~Z`m1#bizw37sG z06lXLGuh2N?Eu&)WxO!nU54mZ>Ju!oqtUEE=&8Qpt8?5vu%g-%4k$h`yIejQ^JS)9w4E!)&#@%%x?H#4LZhZ(Lo zobYNX-?B?s+5Gv|Qa*l)p)at93ouCdCi_ZmMHp#RMsM~hf_JrT>q5{_zuI{r6%nVE ztF?}iEDHcu_L(c?psAlhm6y);$XbMR3?B5rxcjr)EMzY?!1jG52gkgCz*olRLe-Tv zj9jMTe5jvy!|ISb7OM~DYVU-{CFUO#17r4k$ur!1 z86VsHu04ZHn03h#+l&SjPzXG@bN_yk&03obZTLlt$E(;AqF=d8^xbN_ysfSGI`L8v zFl^7d#eu5Uwc~&^&<3_)cvsuP*?1-}6lxO;xiVcu&s@MM*Mf=PDHhoj5F1molT?|SX8x*oP~tbG$T0^L*C*gLX@ z>xBaE*@Zyq*^VvTIy)9fa*LkMDB@@vcBhVm1BQSFbF|7GU1m{` z?5xizW^n|KnNPF>6Zj}OTzl?hThjZgs;ZqE&ck|`u0QJ7l6Ufscg37(=*VPWeF+lm z+Ap-Qo%dK_P=(s;+ZLKqz}_KMsH?ww!(=h*K)CxF+}}it%ZmHEuo54{4N?JS+w!Jl zuExPtF3cW@hxgJ^J@PF$(SNPtt!Q*?vX*;Z0kQ#M_ONT~0yVz6ctbHC75gZ%3`9x{ z(9@>3FivS*?j1o-&Ofb_Af#CNK45EWG|Pb>GkTz9D7MS7bwZNtV2IMelnEN%2vgsq z`O=jOa&q^Gq6Ti+iQX|Cr{4nNf;b_VyQ~6x=Ath8s~TtUw(f3jZXG^Bm~O^E7(4`) zmB6MGCWOF}>_)21;OD^VSAYhO-om0Iy? zB3l`EBx%;v3!(!bSrt=VguI4D9CPsQY3IP3W_8;=c& zm$!qQ232>xS=>-0w}^<8zklRkeLgL!&yrK3sk`l&cS#T?D}?lW_jT3@eC~>-ED4HCyB1E-Di=a5gP%xxDx3jC_~o6C4-= z;m$#JKv1bTSu>Rlq}6y8XF6Dn^Up8hSFEvNr*BsWVqErvpeTTo<=7s1uzY|ksuI<0 z0-yZe=U*l2*8x(j+0wk*V@*{`#XkKgWG0a$ts5cngiGRKV_%PgrhL1z)zRBGLtYF0 z*y20iMm^jfBq-wMy6s)` zX4!(SK1P)Dx1!GTf>Ey?t1(-K8cc9QeDmgZffd76B%)Z-p#Vk55CH9V&_}l8ffg8F zMA_)rx3HgQF$QUj)$Xa>KvN+kUbb)Fi*WS%vbamqiq=XEAIUswD^ue)tn^~GXU0iTS;`!DX&aP6o!4CTe~M(Nb}Vt!fLvA zjej)_@oa$zhj18(+b?KVuJtbp#URk{`h-yFb`;v>#K3%jGoOPfCZM z{^6LcbgR0dw0-z8Opi~b^$EHd(od9rT0geel*P0KKhr(I<}&$lYWbFO3_Hs@hkuZR;XjttjYx?u?Foh)uNzW}oU5so#2K++rlh7*9?W+Nnz z{YMeyl~uBHv8Uu?R9a!HmVhp#kAY>D4sqKp_nmGJC#iDg~K-hUPx#U4{yTgk+B^K}4(6DP3yu2Kv z+od+IsNdnqZEtpQcU4G7w(GYg13~1zdJt*XtcA87s1D$KxLmAyP>^KLq}Ih{=|%N` z2ye%3dnsE$E&3szYe08$D?8t?!NE~S+DE=)p1g0zu3mn9$-Z!e1d(1CI#)MB+wtU2 zF80VMC6-E1q#R06HTACvVhb8%(gI?qd^}$qr`nF4-^1t!fNF@ToyNE@Wz^jLVsC_b(ZRIJ{7vrSRE&*{Zp{e3 z6uTnLJ5gvWYyXo?X|FoZv+*_Q26-`=mx@p$8>}AhDh;_CGS{MC6Z}*Xto-!wNawq$SOYjN5;Z>{z9XTp9)k=SPSsZH*^pPjvfk0o0b4;p#Ulm^fHbzy+pzK? zihI!<91C|C{^9kL&}V;6Hu1OVt?!=jDZ7$I`ezABQ>U0s7k zE}f0F`eiK^0u;@v`1pSP@51{f2R|GR)koNLkbVv2i#>eE)6w7QM~PfR$s7f7;*o|pf%x1Q(hUY2aEW2|^or~cHtbOg+y|)KVYtU+)hbO87i{bHX~B$B*q=6U zbZgkM8xm)%y)8C5F%Fx4|6y7Bel%{VV3nHM)xxd<#k4++HcdTYX|IZ$U~dai-w`i= zI(&F*kljYvRuk+@)D7aUGi^?XW64DSClqt^J0vRX?N*&U0ckUMVCkBx#=^ z79kBP=z(=b$PmFkqKq9#BGMR$kU$D3vb@e!e%`mou+>}D3T$=Wc*C%;g2cLh^L8SR z=AHh|0rceo(=M3xzCV0ncsT}@#Cx5?LX?$6TRGqZvFplxF7X+M&R*830exjUrx`s1 zIi%CNU0zgQ?5xkqz_~n8=x+^HJ9Ys;(%G{{okyae8b1l2;~pWW;rRkoW*?!L8%b|xSME1 zqa1tEwlcF3ON3)+5QZ96ZjLBKO$iBekgO-cWOY-IkDslLSz@U#tfi~l zz`52;*z&nJwn)iXMfH&=fI@& z$W{P|!A%V*8K(z8-YSFmvvAY`6D@qcm=m1S9;r5|sR+cMx+dH^75L>}y~OBb*8L%& z{%eq@L2M|yN5WI+*+TWB7vD5cSJ-bG=7@D+K#$#okCZ=cL5>2tC5G1S<(;fM)!xr2 zm$mIXFo7!I_fF88BLLGT^!`slBlAlQp@)q7jM6XE1mE`qvaRIYk{sd?dDGBhJzZ;A zqM>kSi^iHk0nNnlmN6RgQK~r3;Fwq2O=!J{F9N)H0b0Y)S&k{$IKr7o-9hnUG;xp% zdc;f(WwyH&bnjZiXHwW9YFWDVY=|?=kkx*A&LI=O^nhmV#3Dz!-4uPC(gBpERPL%R zRF0aNb}t5!yf5YApe$#=VXI-i$LTiXoN=G65&9wt8PgcLITe_IIV-G%3%XvUkPH(7 zifS>bqj&?2*gnFtv1$uTn1bv0*VPrj-Y{Iu+Fx&3BI(oXJTw}!USey^>4~E8CbMei z9mN-r$V6MdyRE)z>9`NS~a*tba@7q^jIvQQvSaiRnDmD`rq7i!=^5D|a z7f_Sz;wKKB5agC@s6REt+^p;uE1}aNEdewC;UpWBx_&XR|Q>ASbXMj6($WV!fQcbk2LE6!BTG{0m1E-!XcS_N9%f zxLNr3!smrJrQw%F{OeIl?8qAQK!%=Y%KcgWx2Y1$-X9uw^l}XEFMPK)I%4HH9}(bv zuontE*<~AIwtRcP5WZ-K?xZTbf9dvdF1s(YvLK}sRft`mrqdywqCRczGx)YlHLqxk z>!*u*%2d@ccv;pzMVV_1*;8j-MhZyr^I`??$7^DqmLwKYz0mgC$?mr2f*Skx&K8Jg z%H*6vyq<(#d$$RROHlY4h%m(Pz~yGy(-BK(_?|HfnjlLTkZofHv`)1FRSm%TE76=< zT4^6Wa>NjyM}D1n?fC);pTj2FpSpDdvS)hk!I5RJ%w1q4S+I!zKx~pI&y~MwV6Cnu zGe~7QT$mggb+Nc)f7iy7@=uKPC1SAtR{Q7>^6I9f#oieX3UAmqiYs`Y`Nc#7K zDwlLlb=QA$)JLnA;BA-x-ZB*ThpTEKi@Q^19Sh6LiV{y%cP-}^r;?1w!NleUWN}== zRNaPzQE2=sfnSjHuoF0y+RU49}aLt8Lt?plIo;b|S_F`O zbQ=1QlEf!6dFw@Nqpg%QyS=u_(LHg-Y$7%KZlYJ<(E(S$RMX~|o~KSxhW`6%R`P2; zLx6_#`%I$?80MDTW!g5)`I8Yi(;3KWl{^HaLQoW z?f3Ww=Y%g|AbnYl6MY`Rb}Bk;@8{GigMugvMXZ@-vFfkEDP!ki7b#h9-Fy9TOpwqH zt^4O!(n7QpxD>{T0QX#$1$FK(X3j z4IJ9d?XW>Tmn`jkHE(n}`YjcHE7ZPUa`@$+G+^?lVuyD1B68p%qoC+zjxiDUdYY&> z9(wofHoNc1Zi6~OxV8b9)5Qwo7B2F1C3nudV!W1*z=D9Sx_7i=uEb+j=AOes)#-h4 z7uFQg8wBdEhsK(E9rAYIno9BD_$v~Cc)|d=ZON%S0dTC7Zbgm>lvNJh(7RhJ^bzLr z7~4zH^ZRi8kk=;+LTm+K^jVgU7*ncn>y}x<%~>(%An?u8>b}=@4J8;&No}@SyJupA z5DLIFWQk#HsauEQ5uL9ZecnZhVS3-O#mwFw5n z#`t4r5{r=@+C%Qqu`$0bylH+7^DlMx{jiLOW=8YH*&Ankp~KT2=)H|=ECYGAh#3Gy zJ1iulo;4X<$gi5tsuWLLRio@*I0~OFln7f39^^j zT!#awx{|7N5)#K^Hj~sD`kK&zA!v#^1Rw2g8WvV0Y+LE9D@KR!JbiyrIIj$S1Dx7u zX*-@us()J zc{-#aX6FxK94d1d6|2y8sNXoLp8gxn39ONnFZ4SBG?eCb+wd5+rYxzI+8ILOEG(m~fae*^^!G2VK-}h@gUm>-OW6N|*&B#%;bbBh zs?o<2wFRLV4k+6pX5L8cFlvUqF%6xM{*`$Slbtb-6UblG%a<>mE}T$9Q%gd896bZO zP{Y%vXUw&128HoQ(jfPFiaohz6k=QxuBr$A0HFeg(@)I%3gKiVRyaSL(+%Y6Dg1|@ zl_u3|vZg*9UgFE}1@VX7`s<=cSskJNRLf+V`aRCf)@Cu_tQ0ZyvOiSJ+qf(h@{WI=trcO#L>a#!pHn=Sq z2h5iZbsJ4R@IL>C#!|_=hW@G~!TEDVf$=v_6xzZpa>ymvVL(&^ULo0nCGffTd`yEN z(WfJ!9;aGLh-E)|A^5{!#g~Pad+V$`I}Yts%NL0$%mJC@9g+^8Tp~Oh&Hym|AW9IH zbZNq(D6G}|2s0Y%w@Py7o{U=rRv|i1Z)QBd;9S76z6AAIrIILW0f6lW)uzdQe$|h3 zannQCg=^8)TN?aI<|DvtrX8u^3c9fAs-ds&0$tiYkn31lS~`gR+>a3jVH+1j!(hja zJ9Ja;i%C!lCDui8A!BkjpzcgoaRDSG$E4I-JR*|mCLQgv_|gqkNMIbUIvNXBO@Cin zH$J2iyYn__FeCw(Q5PHtb|%BBHeQP(uL`=htl+_M1;>FRC*n-Z6n)C611Kl1duqXz zLSuX99Gu~Fh}_dwq9S{otqnJ!605@Su_mztkO)Tc^=}K|K1Dw*q#tUd521MaK(RP~ zl|MKa^yr^PgVwED)h?)Pqp-gU$t{|uo*e!~3}b6;%w=N(F*_$j61jCbqw*Eh-h|aq z8GV*yZ2qa#%YtjrQ3yp`NfESbT>(*+S!&}vxWD}7nL!>;PX7d_DV(k#(lBkZ$DhvR z^x^w8w0B24p`4nnnp}z7-w~h&4Dgk?;8X7fs|@(^$3{mZ;7AA^BCR#~%K)(l7&?MH z6-G=Wo^L_oV(BsPmxBmDGvFxi{`7+6?XlRHH6z=M4D^>KdEb7G+3_-|=jwbe^UG$d zaS|(NK+zp3JhpUDDjC*mrdXryc}%7*%VJ>= zh9{%WR7~Ny%k*VgKJ}0qoG;I1-~3x!*F;Y+M2YGsxdNM_W0Brth^P_>t{zzWSGC2SmmZ%Xamg>ZA3r8!n1-|mIT0hT6<*K3C2p}%w7P648fZPGXe-~;^Vi@L^8Zc2y^Q_}$% zsEBUP$k)3Loe^PL4E{wW`IrUa;Eiq`OouKm07W4wRX{2>n4m%}<`EUuu*s- zfH+N%7;-f;FdZ7^Vy}OFHcjawixviryxWdrKnQy?t6(1p2YOf7Bt|J5xsy1Mp+KR` zi@`APd-zsn_5!F~XBZR}oMRaJ&Fv_8m@J+%FV1cRQ)Y!@TvIr1*Z?L3#U^XA)3W*9 zZ={guf258^0m_;=F}gYRCcfVcJ|3(ehgwHoJR346VOZMBivP@PhJg`#{F-_3d2ikj zzh7L1ZT@*;XY6KjUgNe3u?UpGzdPRlHpclj2bE>`ler0tLdTEMZQ4DjQv^jG2=MaCP&Jah z7-~6SdKR^n6JB*)Tlx9<%bEiLfkk4`PW!y4Ibd(FCm2b{p`R0vpF2hgjpvuH6GHV` z1()NuU33bC`B@7bh%#eAsLHlVi#rD{hS6JQOo#n08usBi@mlF` z0jn~a>V5E&WnplNMz&!gG#yS`b9Vr<58ks-jK$SkP9-l5KOG|HaOH6dvY6lFaTiHn#}l8l?phv?e&6HdHh19)kv7xiJ9M-_BfJ|*-f$i|VXtSf z4XR+%6xO;r1Q?9=bEbEzb~T!&&?>C=88z`YT1M?kB!~4_?Xr~4ta2o58lr)(-3F`3 zcRGaTMvpbGko4#{3j#BR28?)e#tUz8&S@OlYbXB4Z7$tY12LX8Gfk`Gf zH*%#F=nMct`AP~B< z44B}nfaA(W|7Q}} z;7(}>nArp)N|(1`-iqAUuNMtxbm@(v#urAcF(JT&gM?U8*~AdCwn6iYz$C`|)+7{n zrOs&UxMDa>eq>}~)-61;=k4n+G$pm?c9D7js3?CG*^~7hx=oK5CX@&R&Z1pIt49|8 zQuy@FU=xx!|kw@y#ehvH#ILWTiBq!|OH9h%zg~cQ^a&ibW%1(IR0LL%#?n$dbZgM`FSe~2So19+biW=?X~e6;vvl$M~7gz3p%7veTU`caM%<)_s{6>>)( zK*qHk1UZ56R)ymu#;ye?Iy8(+= z_UTVPYd)iJ8g%#X6Zto6IEat0!alS;seqNQfK4mrm`|TQQcG0e(AF|mK1ddXvh~wT z_Kh4se-&zts%?{U@<$|#NdEYAPSoUVHo?V3-;Hc0k5x8Uy64b}Dlq@NEBtTf?l_rx zfU$HIR%b=Brw&-7%afaLvyeIP@mm{eCaIvHb=51OPDE~WTHwFzA&2GHPA~87qdil6 z)@y@=4sd*e@~1axoO)1;({@dOgxgpU6Nyd}hrg6c_@(-hLFf0)^Br ztwz~&&VThrnhnX&iVjSx$*H@yxA#ZA;iu=1VTEMtI)zn<+4c^7@gSNFzvO&g)Tj@a zD_GQ}H~}Y>J11B5`cpyDp80Qi6GpjudV=p+w#MG*3VYsPX(?DIyCX}_kMjp6fw^cH z&r<}lfFo9%U>DF zrD*6U%5HZBA=9x^Sy3_sKaTZc?hlv zu@rbZHiL!H&bh!DAKmLG^z2BZn z3G6K;qv^P_yl7QolOi6?APf`V59hkJ1G$cLnV!0N-5LzA3TWm44Mj*>2k|8BP2v?4 zM$nl7t-Yxqn|YaYu~ew^87S3^p!Nt#Tq4>Q)24??@iWCj-P8w<5((ff&qT=wg;qPf z73Pc2qPlJe4!3Kq3h(xke9#eV7W;9D+uW;O}hqh$A`|9G1cg7?$4w-t6b<%gk zi?9;x9+%MPzOc3uCvq2ekG}*bQ|G!38jmW|>BZ>7$)mYY|Js#I9K0MtCSLPbvv1`- zCf5iK+Se2>Z2W21{SNc7GRI4hR0REhat=UC5TUPJ&=yidie_UcKST_gLmg%LJ6Dx?Lx=8+CXIxWNQmr7)&j0d zkh4=AZqfPV_pm0UcvdpK`N9B{c7PoqcL~(~n0DmCL=@CUWkxX{sh{8}^{O*1Npyx@ z&v&*lsrOP-MN)=7b^nmlcq^DJ8D<@#Xj*)-1b2?j#iz6-$0V;Z+_r&$%ew~$kC&`f`X+x%T&?{xWt4acDAK39+?}JD3t@_0CDq16 zDMl4C=p&w6#R3$i{DYfS+W0aAmy{Ls^nhsT@>bXRhzYQWi6;1WWZH!xWqJp&gg{Zt zqa@s+AQQQg+xVu2aHX5hNwsufllARgu!$_r**J`2dkx}BW&&2AbU-b19CjgKdRAz? zC!vhI$+zIv&6`HBH-MDaU^sImoW>cSehRfY{nf@+x}Yfvb}DP*1_n8FZX@{=nVAW@ zL~Q|eR0n3qw~ud{N#q<`Dq&O}&Z*B*ywKh{Cv{vK0kBahhSD2vu0b)N0q-*(wo&vq zh>BG?yoOBC)|iaxU5X4L%<^(g_t09(fmpw|V47L8EpDWD3a=*^x1qFWPWiK4FB z&-^%yU=+<4s$rFzo6G*&Z;eiqVCo}qr24xzlAJE=<~mm#Gnw*|Lu!+F6faCHYaSm828YKjt2QFhL zx5L>tzc7QPEe1v(`jjqXO&uR!gj^tsA25^@wZ9oAc)11x*pvHJ>z}`El5?}H z=xw}xuR&w}Uz(2M!H2{!l>T0ZRkH)TX|YX>#BXcYnrv%4Pb2{pq7V?vqVy+i4mt$V z!2BqF;hB8YYmn+^A8WaPKyb7f)f1EUE;^c_Y$QGGu===MkcBKqefn~uVvg^d@B2^&? z17^~W{+&o@!MUSwqOoy`e6SjdLpK_Uhssd*E^1F}3nBuK5}0x=INC0)x-UxzF!;z_ za)p&hn;`Ks&JQdF_WA_NsQ7T5c`jRVo03BQ@v`a59OG{#V(6R`B)03gcJ5q#m*=A8 z@qPy`RXhZ;rQvhZV)&r9z&KCEGDV41oq#Q@w}i4SUR;TH^@y5P0zDU5Vg*fVGVK%2 zEWK{U(Yr=bTQmrmx4G#(V=em~x}*1siyPF&a>n!4nn`RGqC5lIrcqKdO?)>Vxzmn< zS`ED&MdX3sF$5O{S=?>Q%MUiAqSAs1_eG+)l*OFOKBYi4M6gEnn zt6YIorxGdjY#e1@9Bi_RSX3%N^^M58YX{@V$9GY2BB7=`y*s(49JUQ?d3jbzX;xHeBG`F1s$S-- zaN5xSSg>rn^==&Lqd%s-N`9oI9ov;3k~Tx)JJ8<`p*^Mig#NyQ^goBO%}b1B;7pGU z6Gw%v_okBeSz_{lqLO2^ZTAO1AZ;_uWvF&M3H`mK7J)fx=Tv;JC5}^T_g5GXf#GH> zGMBSOZ)qUDQzgB2bQsSLUJgU*g9Y^;Gs!!3^Pnfeku@oCuqSu*?>pwRrs{dccy%D! zmPr7FH>;zx&_>*@w`*ccaXG9Qw@@)C3jl{Q09p36Yr8F`G1{oj_8F(O$W1-k9#uo# zyjwV?s0)SdP ziT9jO)sv?vfB{|M2TTpJc>3r=h~Uk=sl}VdE9H!V{t%Kpj}{4!;1cE4SctE|Rsz_J z@c2-D#DqWGcHs@rFAfMbnZbY-4Ms#nK!6hmQ~_KUaO+klXEYmbQ?49wp7&gGzEwWx zJ6~+N)}wFyC{W-;=vnV`2puKtgxS0ljWsD-XE^;J-1QEy7dwkCrUMCb+h-dL>$m6? z_r9QkXF+@oviE!H(56ITS-ZLHA?Y@~`P2=nbwJkVdiTZ~V?t1vZGOJCeM56{tzAP- zzfSS}5^1%|1~hLvL2Qtx2MrXn`(-Vmy<7KC)(KkMK_2s6dl57gfp1ATa?NdFtWF8Q z%stQBi9H=&lS-R83c49;{xa%Gxp6p`b_4V)yL=1Fr!m@QU{lFbZP_Rq%E4);%;|wD z7i&Y7L}bGHfkCm7cNIc;owo9~a?gmI7y#?BdFOJh{x?cNh(NTcRZwtppaQ3;tKcq` zqRxoL-k2J=Die!&?#o%~@axi3LX??Cr*gIj$Wq#oK{RQh87sr24r*~!$H_-ff*P+r z&Vhv)qtzIl4V*GU+q+_39CLATq4klhM?ibpe*}F>vkYrwjfW|fxPzhB3wQ9qsoiAL zoDB5@1kw~0g7r1cCL1)1-Gp`yj;0}{9I~*xw2W_U1*l_@3XtGV2(V$dXI#aNr-b{m)nTG&NC#X0y@haI4YDDw)~8s>hFKCT!fBCmfsUlQ3}4 zW+=tldy(KK8X}afNvR{`xxl8QLg%&$!gzHvI0H4n5H&TFAbh=*s)#D|f1&u2`+2wshNcebwYtr8DkZ{EE5cWT^7Isgo5Ju@BFjj2tJH zr$)2@FuRuP?8717_Ak_yOnLUrd7r01Tyd9a=Clk1CiIMud^fF?w%BE)aem6x#}F4O zf#kAuS_}ciZ2W@ix91GF^)eXj!w8n%QX zoadzuSWpR8Im?N+a_JK-m$0>e#(qS}-B`wr@F86CTtHEY$FYJxCmfp_b>przH)_wN z)KD2f#y04SB9U|gTp$FDT?gzMh2C8}IaWa6cYcb&ycOMd3xyw!+Pr~a(_{xz5(NnXS1`plpgv2|T6~PCA67QDq156D_m#s;TI({jLW{Y6z%IBhYB~A-k)?$C$`9+#59&9pC~{Wd{jc>+q)?9wXv#l>cV!9^-hfnoGWbBst*H>%cpDCUdNWE-X)Otk!}{}(3$k%x z$&oK)MNr$ox3Qkw551}tW!WePhdc?%@1AZIk-TM zehFZvoXZkuaL6Yz1WB8pLgArTjrZI1j^b^rP*XjodI{JS8MqNsf59OO+HEO%`mhH} zawYQ0l@PmRjM8$Z6n7>OKCo-;SUtCEPci>zGQz>>7X^x|gW7#BIN}`g;lpu)E3m+x za-GUXg%#ywG!H92CVv_GN5gAuw9C zb!A7zfOqyo&=l>3d(5GP zKSckI4(=&39~}lQH7NEHk5LgsT^bn<%7Vp#Waxm}Z}?*nMKZ!Y4XQ&ni0t|D(4VlT?Z1tcW2FsG`>WP=gs z;8aiODj<0%%)jh$AA()a!ryCMPy$>BNK>QU0c05^$dR9^)hPlU{M^UUM$%*pJig5e{OWuVwNkj34HmSdg&=Dnh@Y9=Z`m#+TQH? zIxNa%IEc<_Y|s!(nF0th7~9+tO$1n)9TC*jtwQHdfi>T39J3P_e!q#}cxuJP0FI%X zLS_a(Fd_ugLn#vhq5~Ag(W`7rt+pZ>0tj*iuF4wkend)&4-zB*pHKv=ptC~kD;7j< zRhSX0gm$*x1ki#LGY=4qK$cQOvZJlSg3OCOpO$hwNlS_2f(Tplsor?L$S(wSMO%2! zYXNA`?bi}+g2GG*TRdbv0aYgeZIEIJ7|V+E@n$1yq_K9_WvBz!P(kh_m>PDF3zU3_ zI4g{{LAxP*edM)cvr(rK9B%}$CzQTFf{^X^@N^er$F#;GgRs)qqt#m^`=sz}RKT5F z)c4Ez91^$u^3;Sqz9u+eS;{X)k~q~M7zt6hE$YrY>HI-TEx?M#1Hc6IC1QkF7cW-B z*m%K>0U8zY6kJc%GR(mOUFR>QWO9lJ@v{9mXQz9&Z0E!_}0~M`2n6M`#6foM&TY zwQ%jC++9>N^`?>PAviDqNIdj4UI^}pL8Bp(e*lKXgy48MK#-stiH;F{aBH`L?f}Wh zFg$k4r#g`3##x2htet+`Fd&zd7>sFqE1n zBvHZL+U%l!d%{T}-Q#ithRGqio;!CaC=}fmT@P(1bUjJK<=3X1tVB3T$wZ-y`ncv` zDz3^Z4nJ_F(dX+-N$nnipsDBVi5yNx&0W>qD_MNQkeMlxjUDx@XD zcA5Es7w`AwbAm;S4CT#+wgAJil#l&HgKK>h0AWc4xKLgR!x_m|fv~VRs%@SRTF(3c z?Y4NN3}+PYnIvA=k;uvttFW)$6t6|X(+&}llRzN~61$-p`L1M)zl@cnwhd}2ErSBw z_9ytTCc3s*$ZrUq*GR_FpotP->r7AaP6}ywJNa>1dcf8w9gV9c>NbfZsT!ichLHhWcZ{FMp5y!SzWp?M=~xy^AU!N zpQK|l-hR`%;OVZZ`)+Pkc-)Wiq}|`HdbbHJtpkuns-O(UricKiP+AHz#jI?4yMpeh zx*I@BRkFyT@7@h-ZEie4t1(n)7RQ9*b68UEAOyq+F19iBJ*rU6$c>Xl2BgXFK)Ze@ zbzQmEf}((e;1Y2+l%t@I5Btie+D zKP4rFIl@hJ!TMED&JR$*wjVe!hZ-4-jQ+D-SE8!0a1O!cr^ot|a>MB~N=g{;sWTlu z+l4p4Z{R2yZx4^J79=sHC_+0Y6kX}Iy+JGG=g>!Zr#88Y)1;A{_@3)KHcn?MU`=s& zp?~02AB?XZP+t^RArh_}2m|cNyRjmCyvsl5O4ORaMiU*H>|Wzc@Gerc*GrQR8sSWp zPDbo_|JuJ-`xrcjaP%nkri;zx4N*LkPYhmGd4T$THkLEjstE%C&W3NaXuu7LMzgkx zs{t&E_jV0IoNR$#`NFI8?AUL_!=N!xLlw}{*4Ea&W!79JV3S1cBQ}6K4$mTk0UJ-2 zFOhj$XIOOxO;I8fQI80jG(hJ<1}mpYc%wc(#$7mm1=H{-AncDQ>335y>WI70ukRM3 zvLP`dnZNL)%=XeiP7NiUE9BUe#J3qz70S@3e9=X28Gs3pNxP#(Y8ed`Ep9YVPoTIu zg;`LFM|f<{oCz0P#Zz60EU64w))2M_gJ-C^lRK_H(l?4053}W!PxdQJ^=W-rAVclD= zy~m&5P4kz(f7jj(;mi0j1#NZ#nl8O#x^#y z_W-5BGAoVOQe8l*`fG>iS^UqR_3Nmw{>jhrKS%5`{`0s0`n~6_e|@?CeASghfBe6H z_}@3>|LQGyQ&Pjm!nzPOoR^oETn!wY2w%x8-}v7jStXXY>%X2^tULz93NQs1Fw=dL z>0oWBie>p}asTVbJmwtdvVZ^K%jf2%ZdzFW`SVgtZSh?9E|lfV*R1JsTDx@V#&zq? ze!Tkn-uUX&baTZy>g#%pzjWq0vuSCkhFy(XGAzu(@=jo%6x45xzN{>1x6bpjG;;+- zu(&(gh59w6%vxWCiZi#0g>~kIuO->EoKxdwUTALJ7&I?-Uu_l2jEoV^S^x3k+4PP^ z|8D^P$3yzRI?n!oHj8Jb1&a`o1z=&-;&fuLPf@!}aZ=a|QJ5GK8UX}^{1%o@0+>OR z-i1--wdB7h&VS$A<$80ugW6Ekm!Sr-aB_|M4ck0xA{9nBF76|YF#$EO zV5y)lpo3wF;sg9imK4%)Lb3G_EVzI70Qm}9)-3q%H~#nkne(p?3N!!SXETT8Wgv%; zK|5YrJa|+P0vQPIbznjINbDRMW$|H9AadkdMhI7ebv7!33(ysano<_=fBYO@^;?Ou z9pcI!9U!qR8U=1_skP)pqW}PG2p-P>2^iuyCxEHNf)ubqf^P^;+)6~@62-Q$t?fST z@c;3{%JJ2nbtQrao5gU>9kq&YRV>1w2JFD&g@y7V$@rStT5nT29d`?3{8Tn%>N>=xl79ZuEYSwrgEb??@ z*?BMX{^+G8M|rn(dMJHe)^bc%D8>w6AGy64-RSfzH0mpUh+4L`Lkr0Gj4SMQ8UQ_( zOie(g~7LTxiPQk}rbe}Yp?wnoBRTh$U%gb($l3~F%`p~^1#~)Yl z@1AR~W`%=W2zm_PApmcdDD5dG= zvGFNAr>#Ddk2f{&tJc0yX1FQJ$|*JHb>FE?)t(h{H|q6djOq(zc-CN+|)DEd3?x5S{QzfmPgPl3nb?*CEqjaS4 zC7^E97GTbwJ2x$cS(l9#oX|EJ^=U59$x}S_WAgBQe59<+%ArEDJ4ix4 zqVIN^nr&fr?&t5dJgSbFQM}eBVU}SfsmSxI(Kg*%V+#b$rv@jXQhbZF2Zf9PCp~4h z`=8h2e#=AP_icR>N4kRqwYU0A1w~m0YfatSI9>NRJvT_r?)qmj+aCc;3tY-Y+^L;j ze&(V(2R~h7|2*Dzv7qtYkfn%{QqpunnoRbuG2sIb1eoYX(z& zIKI#r_a#Vp)ju!d-6rXkPrc25TuaUweOw&Z;J3Byn1t;?f8DilV`htvd-2pA zD_*KM!z1#KvNr3p9cG`$KSoRJZ+Ot_Qh7sg8`rwF4*vq+lrzCpr2wlbAlTkX;h!J3 zJ|*N$-nPElx!rbMO%e+i$@?%KJ~P=A#uMr>{)yA}&$hmOGaqF)b?u4mx(_`|vWrqI zLxXGr4PPF^kgRbA@$RdWpO@DOUy9de$~q;NisZg^|NJcXno=cYZ@Zo+i;J|PEsgiK zl!z|#^s=J4|8Brtho$O|=7%S_rQWvZ3}?G9;;KyJ|4?*po3L<2_pYE!Uy1@Dj1c__ zsS$Ep2pIW~pDPm{Ry|Jyy2Yb&Eg8*S_LIt5`K_3k&oMFAFQkcCEYnj$nV)f z;wrK0dH`9Py6yU`4(CeSbnmAZhk@z%Z2p_Ke`mn=lMku&T^L#>`X2(NWtQ|#b6ElpCCQfI8baFH*}e50 zj~zP}S~wRyrf|%{|M+D5ai<3(;7R$&zt)a_6hG)TS28FJ(JxppW|j=S1LizePhbC`{xy0Q7!N}%WnB4{K<4pulE3beeiBZ5C8z_X264BC|TtZy&;1o))33V zoqEe-eOpT$V%X%DcUp|2&DJvA544D30>%r-owDhkpM1-A))9RR#1agytJfTmP;5kK z9ln;Qhm~RPhPdQLwHwUyJr-d0)@AHv5?`Mn_mee*bvO2p#AkZipDjN`b)%O0 zzCmR9fh~r&3v`w&W@E&OMf}d_23TAa-;I)hBF}|Ps5pV{PnhjJ!p8nYfj7vo1{nAd$~9iFB&97aVW2&}Iq^#aE~+FI z=JV&zolJK8^X`6$z~yoWWrSnbGuwN^F6dC+Oif`f!fEL7%H7O^bk@)wv-<7+ zMO*xfj!G!b!xB8Q-*tLAxLl<-dDHcD*Y%P^9V(M)mQk0-n<#i*8M-@l5F|vIkgV>LVfQ?B0Z$JUOv z6qT|eDC`POwR$=ILVL>1EjZ4$=E2pX67^YAQ|T#_lATa`1O*U%OQdd4Iom(qHdQ~N zAV2Q(jnV3FmsuD6@&R`y3UZ3Qip6JElz0MBg1mU~(*^Bozf zVOL9tPw{VDj{i7Kc4IZDH~k52;~sU;xC z;#IrD3wX4(-kOGQYWT9=Y%f95|MBx~S@x@gyk(KT7qjb^4D;1Q`Z`TaG_N^ke&Jx- zWYpXPSHGT1VeaKQ@Y`BVhm38!4O5Fp=Z!FT@Vu;%|K|?C(*K9#?%!u|=c@KI)@0T# zKT=zzbotDNzwUt?H4F@1rhgz7JD&G!e z>z|x>EX*5a;pVvfxS-aabzI8-ar^Mc)b!D@ai-30xup5~w&y&b&`nEFa&uI9rlvZ& za!JXs3Yt+m!n z++{z30xxpt+x1b0`VU6zs!6s|qdq*m>-IM|N$!m&;q>ELvv>9Ipx#UN~~tbPq9fY@h~$#T_4CSQMcCZjw&tk=Lx^KZFIy#|9@Zi%%7@O zp!b-|5O^H9q%MwkNvp|~3wWRO#yX$nT92b=%dnS}S#B7nuD~5j#I^U{N_~0O6(d>d z%azicVuLq5vbt3Fz4Eko+JM1a1|Ro27suG(p9~P5MHAPq3(Ws>pG8h*=6AcG?IUUv zdFknwzDBXeXdb+(FZ0eIp=C_eJ!OKDr{)H`fvkM*?SWypMY;Y1bpdugO56DP^R0c% zTy7ajRLD=gy~F!R<Gw0Up1*1Bih3l--GWzkI;TetuHDSv$$W=7qjU0i?X`A<&LD4)>cN$iSlCSJCf#dh6S-JFE8X#)jESI)EskPrPVbQ zjiHz1_TQN#TwaK%cGMm8PEYo1KK|PG*jp~q$K@lx zOD`TB8RvU;NMr61FNU6{TaNbS$LNx8zH4Rr|74jyows-AfrD3P$;e7S$hz?GkgQn1yT4l;t&>Vg*CujvH~YIe&6(6+ve4Vi{1Ft6DK5*Kms0)c zkFWV-<70ZN;PNz;;%Cb&S3Y_Fy~VST$>~FTMn6q9ulcfLor}1}Pgd6}7yJ7Ll+g*Z z^604G(?hm!Z+oP72_*{mt;6xs@uTnZ8x*srrsAt7fB7X#hN^LOtU$EH?tQ6?!%e{d zd2GIwy8+2bqrWcy$v)D&+CR>1;@a$xM+dOJ>rmOL<-=fv&gAApDY7H^Crcqqtibk` zpMXYycK*b=`jZL$fA=3o?EfMxSW}=oiP1_^B6kl6BrEi!Z}ajZ!T6AN0lJ@1pmdQC z2QfPDLK}`!itI-uEoCYUytA*WE>T`4I)QU8KBKBhh(0tNfhSV6im|AfB&p+ zduU8^YyI9&a5LrB+-G6E4vh`6^@Pikp?rF0n&KF7WUe~wtPs@(#9hCBy``(G>+OnJ z@3s+e?%_W?Na88zI7t9lY=R_7s9Nbe}bJMO;Ib-{80 zCs&p$wYfs<2hOPe+NKFh(16_BD_XDHR9N0gN+=hN{!Ra|hW#6t+j7<77*Iw7&BtFLjd%F=A|D5loO8grv#E#4 zsR0=TXp+s^#hOTUv?a$7(4HcTLa5kq^h)`~$$xWzGf&e#WPM4}2$Bpf#+vAnexHrm z3hvnW&)i1$qDJ?+{I==ofuRxES19z+7{A+I18q*Q{wd_|KxX$;t_H`_KSsgsA9Nm4 z+TyFM>O(f)K7DM-hK}sv|6%XFALw&&Sp z?X}l#_0a+l+e+4P(T94%BfjSM*Z zus$d~0FxzZGYK+^jD-l!@mp;9$3GO28Ze{X0m&-MlKDub16W>oB3zhr{pmV6?&|9X zZ*+7N7vB**6AHz@R||R_e1_?|VFvH5{1r~$aQXG?_6ILF@Z777gV9-=yG z+O3@}dDoFI2z^`^uq*n_A+E7Hlh-57v7c|A-R|)2KDFvB51+^dA$E2giRZEM`t@oF zk?-PZ%*wHTE;qmKX4$j9@a;dFb;>VrG1~AgECi@;#`kR(a`7OREqOn&p8h}kvN9({ zrT5ypy*3S9p4t+v=TV|~PR1rmL_bFFo_gETP0{6L{f&)@$y5t1o&`Tn;-K<>yas0@ zOU1&A1&_fj1;IIq@^ZqhG9N}hdJ zrt{s(LVkHif#m$p)%N2ql=L3un*AUh`q|!Ve`6x8rlx#L-VbdZi=t@`h4SSA3BTWQ z=2>Wn8Rz{MXJMrDQwS;g7k#sSeMwPKQFj4YvHq>-)=xq;(wylCtBCcFP%6BZor3mC zw#L8vfn+}1HSTJE-+wVDJ@yQ?T`1cvQ*gEVqZ5E;+v!+l(Olc;msS?>o4lvhwOsOs zUf(S#>MT?&&nTum=f=r0j3TSY+p|qkZ(j5EgQI8F5@Qgqpkm#1i?uNj-}*{LqW*QE z@Z%rC?{tOVf4FQvVtYY|i>uf|d}NwTv`42p@HxM3A@~81Y}JUe7Z`*B!|1`;gUP_a zDJ`-k$e~tWSBI}fMRWHLi5v_sd}z>FUiVsZx$>6G-ki_9ogU3g;XIR$=cKJn4?F+y z0lUY)?8ADK+o#Zl)eU*e7 zi|9FHg^;Tk-dJYkig#S;1Ue;G6%KwH5>pMd6N^Fzu_jrQDFf`o957QRF{L{mDuRBw*!h1{?xtG23=e^Psk|&Cmb5sNb`e_XN>J-<1!@RYB zYt*-8Zp|4=hq;o!<4sK5zyCvI!WPxkhe!UAlgj%{$d|T1f_oFsuaZvRp5yxEn}7XK z%Ary<>V5oxR0~J8kC?-_iv-)ulbH}_iSUVn3lc?vj_otqDTm8dcDnIy+w80@B)-Hx z0g4B&xaBd|hRB2jGfx}bkel8;$VVRktSjpu=!D#6XA0HkbcI>8UG@M)mGg8L}Oc-6clN3(T z0(vnyD=Y(_f#~5x*5<+)lBnpoDk^kZDhJ)&}Xfi zl;j9miVU`_f?uRDk`qjhYV>9wCT$9Yw4-C+Y!oP22x<+6YO12mHcrQ`UQOh~?+2_w zPmaybc1#*1lhDZPc4p=J?`o$PzH|H_Ss?n*Goq{_x4Y3Xy1*c*$fB=GD+C?FX1WIu z3OOb$Rp9aMsuR_76OmOKSEkRNfk!L! z2luherSFIhvG%CafQ9s1^WbD)*%cyD&We$BY#b zxS2*d{zqhI(u^GYHL}b<*(k)BY4$I{GbdY@$LpD0xl)YjF!_2@L+CZZtsD z*ZiKf;5A-fX?wp;9>>vk7iWA^>$~0*zbdU$ph-%QfKKfv8?Y)>11n$Oofr>wKi3%f znq{F)yFoD7b{Q1m+V=MRnnvhcrqwK>y6HMFFwl??h9EA{)?GtGW5;35R6U!u{IWj9 z`GPjqCI7OYzDCzV{p5wam_Lx**almCdu4r|b1*i}@y{^oxYx>Sy;L!{hd<8#_5BDg z4>ci!WK*JVqR}){pW-Gs>eQHP4HD5*LYPsRy6f4ho}L~zvZ&}UMqOQ5Kg0Lp%le{v zdWsJwG-{3c5S7gd!XzI%c?Ar@8$8NSvtph5)1FF<`FqrVYIw7iw_OrLB4{J-XpL5H z(oj+HG4K+(d}|T#c{w@?e85r9)z2sgaVvVh$dkn%Md%qb;u*>7V&8zJRaN9Xd;Arq z`cD_`B0`M;0a)J#|90~xecSJEAKq)SSe6*Y#iRb}h1>!Dc-7pOOXvW%jV^+u)JNW- z`8)2P@0h2Ra3-88LTAGLpP5F{3$9oWdVYqF0_{Pr(5~R z367!VS6$kxw zYFPH?LDoU!<2`}7-!?($pd1bLiy+ed?AiPI!_LA06p$o|4H_-L9$y&qb}xr^LjV@C z-L2zbo0j6PgrK^XG^o9{xNiR*nJzGVv_iJJTB%=o!;~>!!I-C&A@~7mgXP&us3_Ry{g+RZAtI$W-D=BA9(Qd|yp&NXE5BdE=TAYA{*$cVTb z!etTAf72o3nrvNJA=G-<&d#oXXz0As_U!ReUK`kB%xXMtVrp8793&k|)68#ZGSRHi z;v^TC1|p{aa>bhUsE^G(=g{iW>3e~j9G|;3rrV<#vzouFI<%WLJR{(gqqUZ{cG>c6 z7?H2&n?lpr`9x+{U;473!%??`g$09^r_kpeC~T#N&Z2Pe_>9m>=Qt7E86ves`~=2O z4({m{!wFA3@pe572^Iy2uDv(~HmBosvoRKPNFJ0I@N5-SR8+LIwD_-m_>&k}x)nm= zH=ntNnO%JAT>EEMAOG3_CBwF{mC|`+_|+PwU^AY_@1|D)TipRsQJZx3>JhSHX$JWeQbjTV3c^ zb`rG6H!^Me4cQ9Ux|Ls+@nnj#uw*;I6nVhDqdM#M+M~azE@vQhdo)ify6hbFgnXFq z-0??(xP=Tx5p?H#PEJ?XbPtPw<=j`aBL_1oDt0gW2}dVKXV*lpU3a3Yg{;j_`PaPb zJon(>{P@v0lgA2jQm)amSlzc7R$Ris?JUD`3Fqj{>@>MRgXvbWj90g#W3uBSzhnrB zG8L(p;!Mnoo7Qz`Rg20nG40Onl!)6akw^a-sXKI&#V=SP7q?gF@W+@@{U>{5?Cf^C z7oUmS+d);1Q+*=3c2erGsyxw_pFiCTcRP(QVoop^TAGit{PR;?&bOfEC~^iNe|%W# zD-&Jk$reZ{Vx!%rY~xja6;&w4|4Jw{Bxmc$<+c2m(K6$ecFnPCKX4XKGy&JTLZVZD6|)EZT{dpbCBh8uvjpZbN?7Ud3eg;Y^V zgxw#rlDz(|vbH9~Lc;E++oMmV_8dBYF0LVkPIbn&pSI(!uZz0$^7GU4t(>hcb02Xt ze|}ymIQ`I7U@BZ9VD5>5vT&Z!^Ga+W^Sl8bUHnd}8MR!HndEtBvi0g2om$ zHk^gM&+ds`?W|*B56h`Opb|gk8L@i5PTTR;H^f}3`LmAEtX!R4Qt)W!HI1;`5An8q zdTDwSUg%jhUwW~v=9rt#o;RKpIRk@09;_bC2V`OI+HbmNPfcpcpLHjg%Ia$6OQH;n zd!vIbE6N<&0!O0D=ESQ~xr2=sS~r&mxX#F1$jXwnF?{UOS~hyjmaJxPw$PTCo_pe- zl6FtUh^M`CD6EndZug6J53C<`Bs=+Xcs~D##l{$u0pB{$UATlt$~?3UsZO|tiipHF z=t{CCIs<51a)hE1{*9l^9S-C_C^nQT1%=*x`3VU`VvdjqCx-ikkkDn~vC&%h0Ct}? zgC%bMo71m%mB^mVd2pcS>Fb{6$@b-=ciFCU$%co}8Vbux(_+IYXU)t!l9TJ|8e^}u zHJz|XHwwxM3hEioq)aqu$HY9@m+|UOY-Tn^lLS!DAMP(gO#d(hS5Wh;B+B-vZqV2r z`KqRM(-k$bmyJpc6b$?IFGo+6T<8xFmTV8*%S|&?u03&BPTM>zCMhPyF(GdEQckFH zV-2;%ucyvyS6GlJdlhcJ5M!Zh>aV2a=&xfMWjQ%KWo6UH3Kljh_{5`flB}J?UkTy&#xJ#KYPSQTn5gJzBih6c=?9RvKB}Q> zr$&v+&K5z7wT^v@N$k_i%$}^mMGH%GY;2c)fNyYl|DAsEr8|3t9*)}S+S=N7A+s1v zw?ecl2f3tMJbUtTa&l@~T3T}3+i(FbEt}N8Njy5Z=eAcQm$I#GOl~eOH`P}rE-t~Y zQRcDrQr4c^Z1~q5X7qQV!y-KcCC4LjK8t+)&_ze(7pCaG0h#LhsYaG-{?s64Sb3@V z-F1S@R83RN!%g zpX9}cpW52lO__~+cjVbaVZ~6)K1_c0!NWYKKPEq=aK3%C5)&+Zp(P}ql$0bgQ7#|= zY;Xu={BGx)kWA~(t~FB92(Fw?j)G>D!yXwoWUmNmuTl+QG!#vuTUvZw81`Zd|7P0Y z@(-4q{m$yHNb{lLr1%hujrJ%`K!W6(S(!b#q- zdO~^>$73ZIxh3bSy%9n>mT6mKMpe?aa;Sua5jg@V9dgrTc*XG;#7L$(f3F#h(;6bZ zkb?-p4;-S9unlAsTfUA}b8^hLc$Kq$vHrvzD#`G;J@!+^N#3b%VBd7<&H@ytsz`)= zLRdIuk_5tnWJcZS^zkCr^cE2o78V<12h*1N*IqMU0NJ;2fb3+vZtwVV=0QDgJ=@Ur z8vs$XV-#X(@^Ci}x3+<+;W|12C5XJ)3M;*j0ci0koN+WWGjl^?>Yv26@=Xs#zntXM zi942EZc7v4GZLNX3RBV6*7kGE&B?LGgtIBj(bQL)<5E)eKR(m7Z;q~-#^9+^?|R<7lS z=N+qMbA_N2ah-PPAK^w<=J}d!m6_Q0=;7(41KzL>0|$u-#OsD&Eo;tJXBc3l zy6w5`H8oUd)1L&4t~Uby1kG!Qw=qw*lHJ4W9{5Xovb2ez`vwt7=kkA8VZfp|tuNPaqvA<#2gX)G}j2>WmumsUYJ z6zYo`dJwA^Q`wEJF(0W1H4X)sqht(dgG!iq_E1_G3Fn!6^qyW(3LhppaU)uTQYBi^ zM5A}tA>)#rgF|dWLPCuZqbRJZ>(|c9KPfIIn(fj`G}+~Zck*ZD^Wahf?yv2)s{2HG z=6noZzU%J&$Bpr8a_hO+8&y|6g#VHkvzf-KV$=82P{xa1$Qp-oICjo=_$#=L`?%`0 zLTSeaL$Qs?>CbmAFFno3=qA&VOkzX*cvJHDr>3S{y{v$CrMEXVHI3%zIS;`l&WYPg znORuK>4q9j64P&DW;WP<#N+2;ofDS~jC5bU{aNnSTLZk4KP#VSIzdk7(fwNOoLBZz zSHQ9Bvn#fZn6FhkX8e6hoYcXh#5mipR4YqI40E9a)Lkx6rPCcCocEqdq}x};DGScH zV}U?0@1FrdnQnHo=*tmJ5w}NWW07q&{!cw=jJ7515Tbu^0cm_~qIB z{W-mQ{=gtvQiPMy(#?zEuXVJ#pmg(t1leCIVl-iZL@WBshic{Wg$zKc>NIvPcV1U-!| z;lqFv607VjEG#yke0QT`n22re-MiN;WCne~ed*$GV{Rm|B`-!M>(x4)ra(mmOZEY; zlaW=^FxH(Z*C4-eadcvCZVpS?O7nfdtIcAGGmbC~Km(EmE1jWgF(YARX<3Pu_{J0x zf8uo`Ldt73?@EXju{}VX=J}A5PaNiNvsh|=3@BXaJ_=pTvrbu<1^)JgpkUI~Hk?)f zx<;lf^=&Ey6GF3+lK3DhWiUGZ?V*s=npNQPMugnIeQ{%5ov*k1!pHnp$>rH9VxN=k z%cXbf*3IWVd2&1!u(FJILR?(Gzzj(8{;;ptX%o?rKKDF-NSL2Xyt-W zw~j`^!8htTTM$&7eiB>HvOa(;hImCq1+mcMW%^6}5Fflk4HKIRgYvh?c=3V8lgp4w z4{T^?AV50ZkmC;bpy{>@<=Oz0`MU9544Sn zyw`;{?$M(IXn)oZcLiW%HZVBIt6Kf8zP_G@GwUFZqpqPrJ$3&@*9(ouo@&+}o78Ni z|5^Q<`o8421|D+#>;Gs-Vnlpt;25Z5bl)!oNCVnbg?3>96r1ywrcxB7q+U5yEH)W< zccU|+1Rce-Z{OYrGuIeFO)>OuOH~)2|1*9%{O(ak@diOgzJ?R@_rHBs^={YhxpT9%LcHMb&;-Bsb6SB~PI*BxbX6;V=cTcU|35Nd~inUwu zUqi8WC;oqGD3I$spVNf?H5AYSknR)MB`ztcf=pp|K?Q+Tgx}6iZEH48)dIEtw1&jruW5^==cYc+2l}lK21tudC=xaI8}hi9HC)3AR;P$LWCeXX+$ITxkrM&gM&k* zNN-5TX**X`_zpl^?;0q35zKl>q010T(-Yl5C_FiZuVx}b+eLbAA6f$sc&==Ag_6zq zNA~5DBviyW)5hFFE{}RIUXT5dJ~jkCRM^#GmLf+TLI^mUTR5Lf4<$jP+wgQG;vE4O zY>+-DHL`*Ok~dLVrlz4w<1j%a4^YWJ)V_rq`Pl5Rm3uHc{X_^5Mk4+-1X0~H}P;;N^h>z<9$ZKHG8|5t!-(A9Efb#n=e07YKSe;=Nei~deCaCxzFxnkcP zR%-Y{*P;G_0U4$3NN8QzC<+&I;)|?#S<`H{^&v&w5eqA4ZF%JP928{Y5^GqW-l>(7VPp`w-2LTb ziXNJ7=JVK(CT+J$)ci4)@0mhC&cYOJeQZL|V1~EG#zZ^^)S`=#@7zazZYh-Me_mHVCCqqs0i4gW!ll8&9$4GKz$SgnEFZ54RV5@Ze2@YP+2%s&eGz zd-rb#KCOQJp8`k#?Hg8@_Vf)M! zPuy`EHq$RV>ONa05n()@mHUCy{X5OZnXwi}f2*AC4bm3q;`^8Uy~|tr}xm9H4=70NeMVfUsS6Q12V1Gzk z(a|g)1&Kg{7KQcJ_YMsWl^0=+>7XlhHzujxoa2 zD;)}auqOF|8O?&Xl&fchv0mv_(@_*3U`yZ>p71abUl10}`7dDj?-xn*_Sg`n#8qL=~$-+*@+# zy47CT_ho+=bRnGY%#FHZO<>muklGNn3+QPDK&S$}32`Lqu9=6<2fQ79LZ~7Td=KLQ z|Dv1-nqhvDQ~tp2bJ%dO8u7NfZ_0yrgtE-M`MUawoZAtvgKkF@;hp?h`8-kxTPEY5 zD&G}w6YHbB8yMheF%ahh%Oo0`$RU@@W1mg+Z=N(eCaJrE zOj6!>Cx2Exx4T21?6K{)W^(jp(&){)ZNpK?vkZS-3H_27y96`#*p$=a;_Ch=oXH=J zPEQv=mjp^z8zn!#*ubQ!pOl<@1W=6Wk!SCIMe-j5zX)9kvWiq5I0;2T7F3jwNleYm zCNKVvwF>!`E7P^)>mjPDa=VkXJF zFrw)UNQ6{f`P~*i%lHd^UkSN#O!Vdow#d9ds#s|it#x&E#<`CkJ?b>jcOJsXpHk)8 z({R1=y1LSS)ejqhmMF*7<+elfJu}5(1JA23OYHB?k05TwSzB-(Xf z%DQ;p9n}wYi{nYvmIBp3vs7z?atQSvUaHgq|t+l2`*)M1V z^uPWu(bp|<*5#!>27V}`Y!LoGm&mi2WW9=@Etei&07kh4e=JV1{&VYDLnufe9bCXF z>q~FP&KZ3IH#C?oj&LolZk7AVX!*siO9GmV`0i-lv7>pPet_%>4ZAdVXu=8twXT_v zLw@b%4d%M#=mct*<|uh~^Q=ZryZ&4&4ZU@@qaAtJMnqxTP}6Z-7%Q59f_N0LM#BAq zeM*DTG=j;fM4ZHfsbBnXg5{w+7Q8!k9CMN#q@JN9o2noD`K@^@6dg3VYqB zo`6Rx2MkK9e$}Ske86f`@z>#{;0U2FT6SxvUI1A!8AWJ4IZQsuOD;|_vP-zEQxkqh zNK?Cy|NLX2w>x4yvc2hX*$%AhmXFZ;3)*_%CqtNc3Y7rGgP6usUIO#(oJE|PTjOeP z6%L)g{I^3#icCVJy&NPtDhId6&)8VHIVZ41w~PU{j)~%tLGV%;mAa>eR$}iG}xDj1jfd+`f~lL6N#) zJ}*CCoXncoQ$*sg{HkCOXP~3VbZ!-}JoXkFz-JY2yAcr?A1npw%77G?ZeG`~YnL?L z+TdlZF)C<_;U6qX#FUbjb2YiR$VDz0BT`OL_)#o% z8Px79h25%4xkf4rWT7qqJ01x=`?%#J9BE}^;nI}EJVpn!&M%NN0%YFaJ0*K0n*$np zTKz7Nzq}0Qp-sJ-MmHep5+JjIAlx586OjSL5B%_c020^dl(v)XqZgPuy=w1nuySsx zzZ4%YQ&`I|T-6dqY@uM<>@rb-@^EgY4T+o((a!>*uNgfpNa0in3xR16Be5~n5ITl~ zI zArwqBG#W)i-MK`;xv@b~>F3%(LMsqJ9q~7y_uht}>L4^MMlCrV_P`MH&x|Awtv(&T z|NW3b93;$?dS9D72)za5gc zlUkwvcEXULJRdBSRAaPqgy%dZ$Au-gWNU>5-DX=W zXU3|rU3zo^qb-a`*`ar4#d9+-?p?c_hkNQ&2mVAL=s@;+9%2;ib~zk|YKS<5UREZo z5m?a+!>q&>BQ~dkqQ)()G&%TWK?k4cctK9inX&cpD;IG3)>>wF!&LD}&s&b&5-!&z zaJs!%FciJzxyG3dVMypu?<#IZ0H=eZ6Cz8U{?B+$JvZ4GE{^WC#quj;D+5*k2)YPL zH#nxOez)CP5X^8>mpqA9Si^Dr&iDEYALyx3$0Cv4(0~VLwX0K_Z69^Zs(C^}tj(y> zbX?4k^oT45@L_{Y${6Et26b1nmSdA^;r@*mrbv^HIFuhRqMsu;+Tn1)9l0Nux{tb- z5IYhAd*p~-jQM+Hcae-6-7cSGWyfhPw*%MU($b$$Pf`T5q03Jsk zLOW96As7zG2Sf0jB7F~UrXjIX@6%PPz~EkM?9r*_EMz0epE~zZ5TV1;IA_6A5bnqe zj~i3}xvp>3))l}g^gow*&a|@Tv>kODyStW=&i|G`!bQ!@Y2s##tbv%H2sl=hM>pvsyA)(cCN87?2(r! z4g#;1v#ppXJ2;p8>y*vL&Iok2999OsL3$FP!7(Qidh40X>*Q;g4g#!%f|w|sjS_5X3@<&WJrP-f~1P}E&u{p#O%7=)0Ka~W~iJLt!h zML(5h<()jLwyAxNYynuSv;RZSIBYw&o6hX zi;g0B@)<=IMR33L63*bs5i2vZGO}!0!A%3}@6Gc(dXFM_E%9Fr?u+EeiU@+3;znug ziA=5>zc_VPa!!d%Ggf1B0I98;#A1XMhg1N|(zuEq3;|AY%vj@L;c2acWV?ol1447f z0&ukg08rTQgTRAd@xa4CNNY^KAx7SE97s(i2z-yJrmE18I3!G$=EwC5ZiHvolNpe|71 z5g~92L8Hi~B3%?z1jO!rjkB38rU@jdn?-vPjo73>ac6tDFPOjaKes6`c4@Ej3F3cS zyQaq*n9Vl3f-2w?zWhpZK?+H56_|v?f0D?HeeUg@aRS+pge=@~?jvED64cqseZG}s z$~fihhKyBuh}2q$&G2}GAWd}a*Y)2yg}c97$F7<11lg|~A((KKAU{ZJ=w$;zfJBHo zU=}zcaLj2~yWtU(wGdAAriZJ|6)7pH(Fq7P8i6ijCg_GKl@ux>Uc2N=1T=@NRuDQi zQzqyvNiY)LelcC~XI>YG*n$k1UnMyaSk;A?ZKUxTT!#*Fz-+)*J{&;mE{tknT4rvC z&B}vesQ(f{(huJUE=1G33;e$bLNda%HNLagz8F&#NVNbVGPrEKAkR1=dziq^?2Wj? zR+?r6061DXu4)zGB?#Io%>`d7c*hcXVIXLEgkb{z2AhV^mlI*wkZBRKbhYt3;>^L* zeyhJ?{^MF|bXxFnTvv&LXn$14{imutQ@0ZmoDWMLq2L07fwac_I=XrhyG+-9<@rU8 z>Yld%tRx6IC_K0C$QC(`sdF_f$!!|<2_bXa754tH33~WwH1~w zXu|8hoJW1+{)z}F>s5U~=OW0F-2}Be;C)K@4}SFls_c;xLN&sS7Gq$)yCvORqGeal zQix&gK`8R!1X{pda>yUlrBDesC(1{@6pSnVKa}U5VFTKrRdCue+_r{7<6S8|F*H3~ zd-$g_9-p(hLJhh4@>@_*W#Uvzu_dkHQ+hB;FZmoFD4CTcEFdAYv{DrCHZau6kjh_p zutv}-{p)fb{#+4ZP~=qs6I$Kz&vev12nV$Tct9`wYv=cZgh!6_iOxeNy|U6u92AoF zA~1KlS+{zZ8QInhKf-Ob@h(E(rZY)VL6eOmP$foYI@1B%yL_3&O)ieb5 zs2irI6u1FD0?_hs$wG&v?p`gout+X+PX>1ZpIqjLgi{c8?;MqoPJR-t%HPyv^A`yC#etCs9X#;FQ5!|j_Lr0yvY0CwyE!N zDwtE+{@O`gPps1#Hy|d4bEnSLGxmUvm=>p3)t>kzZqJQA*fwC4+sY;I!j>Vo=<=eX;hDatEmuAg7ASFW~aPGyUY%yn2e%(A9z zR_*+BTeyp4Xlqyh-1M(l!SJo%9nO+78~6IMSU%ggJgKnki%HLP@M-RwM=4AceG9ds z!{Nr5(L$n-36iCdy6bH>=amyV%}j3)Ab+_j4VIoJ4XI}L(^~m5tndybWQE1VCzH-e zSFa0fe#rxtd1a$b!5ml!^wtvx_ZbHfm^_4&l2>1^`Bdtc1_9@q_K_Fm zCiPp0;)b2VNH;RQlQ5{UU*d`no{#Qt}Dej`jeMVXz@z(!ur73rNeK4!c5a_EB8-tF{=rZzkc=i2d z+hwq}R{rPOe;+RYm%p}hFaEE1NDn;YMJ5q&liJdMxWNtTSaD5?m|jFShZvSrAP9$E zq63ff(C{Kp+`R1SiV{*@Gr$*hRi$nE1RHgHIGRlpk_e=o)Tl#>5-1r@U=C$336DvH z|Mclo-w2tN)4gc$*OG}wqwHi6fxbw#GTU6`mf2y02xcC6(ndlDO=o0Yb!sduUz{4Z zS~aWIW|EVV>1kz0au3rXxUd^jXlpS6X349A_8Rg^J^p1+4Mw8j!ki1qn6^cR8nF(r zcM`h=O;rnUg_luVq@Q(8V}5eY!L?)VUOQ&$5uqTf&8|tvR^w-8F@;Cz#p>lSIDH9U zmYS{BxmlD8Mvkyd=X^29)rJx)%8F!FOdA3xefGQ`{%mFPB0w7vfOYb~L zA+84K6t@VzYyu88Au)qILKb>!iK2z!GyElWY}fXR2;4|^0Q&0A=wGL2;~>>1H)eZ8 zYF^T}v6*nHK;T=CXe1i_FjdnnL6V?Cz=@&BX)lF}uETEeQAa4NO{JtbxRMYC=CJON zsyRSWQZ4~*Hf`xnc*jICK_3Vk#mTK<8TI90QaS|}qt&UA_@3_x#u0yuk(fdu5)Qtr z$0_t1$fcxIJVJCW;itV+gj3ly;1S6i9VsxCAwBv?)i#u>9oHHG0{5&zk()sxf(Nbte-sZ!R0<6rbaUKPp zt~-(o?>Eh7Vb{~xB!TlXPh;GsFzF5isI(=?K^&*aKu40eyd$}&Bhyj2j}8^`<@iLN z%V~8Ai=AxD%M_9}T?*=KIFtb_w_!o|dww1KPSZ0+;656ix(>Q}qbZGWzratp%kr=d zG`F|!b+_1Lp&+{B;N39!7CjlieBVgdOX1;3vVv3GOf12SEa4~Q&TD1j>{aT7I5RwX znRcTwb7ldBdWYsgQj@7^&;O7WV2L=3SKJN$7@{J`N>oz`uINh#kTluFg{O4=Y%^vI zpWQV+DBH_vF+t7r_mSw_t|hbQCr;0gPW)}}wK=EwxJ~ysjkgBT+=o?yxp#XyIyE|H<~`4d6YC%fouR#7v!Lj_P}&8+Q>2xG%8TMmm9gT25-cZZ1qM2 za;vc5IeWfgldp9@olEQcBg|^E?$qO`MOs)<sk*+Sc2|p#q~m+9Fsn_vJKHTv z%C>MGmGr&Wd~o;j-1hciif)-qq4ZN70ulgyO`gnvsz{2hJKO*O4MNso)*~gmZ|>sm zoW4LeV;O3;4hKVJoeGXmzLw-nNj&x4aMAe^YOFJxTrfv)o?!pTIi*gjvYhJ$GdI57 zB+X*9L}+L0_%^$sD3Ky7D~noGV>b?0Nw`#~7bMSwklsq^-kPvCZ~aFVdKN{vbIOjJ zRk_H9^?$qTj@)@A$y3omzsw4L09{L)&urJF0QAtWdM$ z?k7ebb6eNdd@t(0w4hed@b+zLY2(14hMRC3GKM4vIoyipZ|JQliAj;U2l_SJCgdV) zM$V@#3H$Ur|5|A{qFa*YPo>O9aZ#1qpP%@>^eUFg#j7kZn*@X`^fEIs>7RK*TqBpL z|CEbCpN;ULq$;L&f8_Y|TFIul^Zz`!o#_C~>r5*z!hxryjYz@NXItV6qz%SC@VC1X zbJui*1im*iswX@n?)t3v=CB!MZSvs@Y4-VOnWpcd%pKkGCN2C2`i{8S3g-cn)u311 z9%!4vY2zlA>KWlKW-z{x;*%2-r5YURb{!~F^JRSupA+@eR_kkOX~~gT&F~o}wUxG0 zdZ~tEwc?#a+4~MV@yD6TbLJhFmeXyv`ov3L#V?Y8GJA{k1R!@A0r=mpZ2Zg1yp??m6)%E1yJkXX5|J|pqatI&y7 zj@P%vM$5Kv@TpLh|F=UGetvyaJqPB0myXlT*PRxhZ5$5E=*_9{k5zD+4wTu?G?Ps* zo}qT@4yC5PKBIvywd;G6eE&IWWUfHJRts*OZkdqFS2iQ(V%SusSr%3wRRa3^@RVDC zmL5`c=^rWm<}M}e-Fw4kS&D{sa}2AnWg8EHPxZUaNS~EBao2dAZnou<^(&X(u^7$Y zJF4khzPwDWG!l2T4lG_-0cOKSM?1!qtLzkRZ=OF%_5*M09cbp2b&7kq=w1I(u zCYDr)A@OP3Z5xW$Bjun{W%9hApC<0+^c1hhNEoBT3n?0(;fqI<>Ok9+yyG^ys|QFx zk$KS$p;%YRA&rGk&Oo&~Cc|CUUFs^t;qS; zd>O4tf5Vr-PQO;RyqOA1M$U{9dKpUTsV~h)=VyNa02#FncaeomI5mUo@%-n?Y-%ZMd5-x5E=u z4WsBzog7RpzKr4}pDfv%3<(E_J@(XbaIUIwP5;?4`vu?i6T7qJJR-%SmE|tP_|jeT zJGHRRrHxGxyYcM}8_Z5<7?<3$9Z|0)3#{gB$4r4gAY>6lM@A{+bX0MpkI0JdP|u;V z$@X$cH)<{G6hKbc!QDS7h}m5Z?b|cE0ku-Y zB#Y@C+{hNi{@INf?>TNvrXwx+gOy)}tn%bH_l%A?j(4#lrU7FGwPtmv1)zonFAKii z#@VGi>ZEaj-q5YNmsnQwE|1Evql~&jn*41eQN!I)xe4Y1E9?JLyhqPlQtZpGBB2F6 z?*nmS2)7H*mo8cxL{OtDg+z5z_?*6u7OrdUXGaa$h4@Y z?GgE$mNKW_W`U|x0-y;Dq>n1Nb!ko)oprlG&uG*9@^q*2>^iafzY53)&xtlM=d zMK9BOq6aOSWyOW}9yvh#(DQdsLE;6!SF(hV5oG*-xWzcv^A_w? zgDo0Nl4D|GLeN=9XtW_HI&l!Kr7|@hvg{+p`ZaZ+AT*$B!m}KdCA1{>JYTn^oV2s!79NI#@62a7%TwWVb+OjK9#VR9 z$yN2Sll5`m@NHLCiM~MFA$MxsW?31*{sApJ3*u&P4DiD>hJY>PI1m@BJ~u~Fz%4HC z*0E%A@mKu9E3UJObgd5|RMcPYBuv^Ucvf_x#Pp%7ufDbGL3$b|jBA z#fH0~DK^h>qT%G_S~=kgg8iU*{U{->Sp%a4-A*JBNd$>!fXx}=e^nBohe%C52I!lPD)i>!HXN8? zP=?@V9J^1`*EsCO5pxZ9RS$D@# zy%6ZTZN6&9$axb*4otqzuR}G?#5Hl!p)pK|n@aVoy@TJ~Xg7if6cO>gs zL5EMvWKdr9$!Jt-d<+O9-G`4w5j0w%*}p2WJ^+gy>JU#8g$1IW$o?SQVhMNj(a*PW zJGh>jA4<=!YHevLvgPK8vEu`JK+_8yF}h{SZf8`(@1pM~FNE0hUjkIKilBq$r1@fzZ338u5LRO^gSxcf?n(Erc7eA*1yP!AxS(y} zLki4;g1uOKp#=TUCw<{MDYLh!$hd{TlfFpCm=M|~pjGUZ^F%De9Av+^B;92(4^ojO zRa|}$T!5k&B3*H;4esD*+rZH09h4K6n z5aT=GWLCdcDN9f#st!qveH*x*ch_Y22R1Q# zbC~-`s95&CVqdySN)>q%p`$~M?h7fIC|{D=Om<)7-5Ma^x$YqT7$s%55LhxBxs~-R zCSgJhN;qW+oRHMKCSGiDOPyIhYW9)TP6^o312{nIeFlLyNj(t9{^K6Iq+3$&28Je0 zUl~EH$s#6!)Vk#MDta&|^$V-bJF?RrZ_4m2`8|o`HMS0_nSRw`9D;-l zL`wojbwh%zZqiGF4uZ2W=L2hU3$g4W=}|_oX`0u3Q;~!poXB-48ikpMSz1Xo=l6QD zzv1t)CYPQ>(n%wPVuaeF2C4rLmM5BY^{&E7QnMAPJW1!RLFI$U_84TszYy9VF)2h*r6ap=IwPf9&dNz8uyc5B z@c*S+kbT&}Yy%J2Nb;nGH zi@rAMc2Bs}_ayn!l_xReozfMsjzVW#u2%kUb_Z!~m#$fo;h(8AeDR89VIhA{5r>Mm z{J_~5Re`=j;cs>sZYUFEzQ}b|4l^4(_7jW)g}K>qsN15;o4`JYz` zB3?k`RxP!5H`XofnnYc&%Kb9|tVgHEsg2X?hEmwF7G_ah4Ircxqxh#e#9_G};Uc%$ zrs0Ay(4R}U42UQTRC1zcmbZW~mfb~_O@pCInI_Fh_XH}7Nezvo*~nj)&^L|i9v3iu zLn1purRFGsB*!?O>@gIdE1dERt+)Nv&@CZ7&x=%;!^XXE+Mo=0WbJ)(JM)@jLeV*w z%yic&>ixTBQBXJTnbg1Cv;H70+aq#*`2nZw-Eqy<}zXvUWP7%%$H`Q00VfB-(0h^s-pv|20Gv}gwbg?|I&Y7DYTAI#5nDK z9{kv7c0K*2i0LH>z4?e4*kr z8WCT$?_ghew=w@0QxG#C>S2}i&&Ql4#DkG;2Z}<`JkdZwP(I}-3#33>*M{G;D9S_` zw{EP0qARQ1su?&zAY;a_t;>HQ{bUb$fX7T7z`9cHS>V&=IFLC|@&GR7@$aNeOqxo@ z5&Akrdqz+QY(RQr5K5xaB@A}{f&rHqN%9mxrkA+XnJ^lXE9Bj{2cc=lY^8%`>df(g zMevv=_IQ)~Vf87HgRn7xdOj=ti0RSPyGIM2In4jIO};pal3=NKa&fX&1=S>uKoOhE zka`K9jI~AFt{fLraOh&lQw8Pt*3?5*fz*3#qH$(pvZR?lRmo?yC9nRWYCL5i*r!^| z;xo%a!wF8GX?fE-(W>dcq#F>lv<>z9aiV%~cCVdk0aC@}vCp{u2*0duls0J+&>RW@ z<6$#-u~oVbjnkI7W$a}|elpRd(8`-XKlp}F6dl_K^%_$QV4XW9S`k(k@pvzmD7aAp z1WZ}7L*+Oj)ETosiEJjCv`P;8PbP5U;Oe?6HOF!e^|dMvoaG1}I-p5%rQca?9?}gb zXLQi*FqQ?&G%MxSrxBGcHU)Fl&g3h0@aL=5mk`NFacAY%SkwFJdY?| zv-`=4R1;>W8-jtQ_i=&9Sy(B~L&v6zaC*T2uNj?#6XQg_v-`Zac1!e$)ynZ$yfgfhEur9kO6xcIwCea<`=rGR?Dgd;S9q5oPEAg;y$6fFYS9& z_S9P8yrARYxA3tlU0gb!@KXBZ_4E zRM#_TdMeB5D%|Yr+v}sO%Rl2lsn%WECU{R?NQ}c|7p?9)`gQoBp@C?v+%Q$+p#eEkhi)#0w z1aTwdIJndcpYBMOlNc36)9ungSHS=DmV9?(oi)hOL~WJkCZ=WrE(}CvPzr(86Z-3- zbLXw(zQtp-DC{9uJ!dY7!l2oOeF&IiRaL_4ywCk4C!2ZwEaJ70^g{;rl$e48Cag)v ziLCp@gIxhMvdu51a?wn28E>wY)EQ8fTK#9f0jj6Xwuoo4mAhWG!r?dkxM~u}2vZ5i zZDT|2={{b`cjhZ5CM85js13s&NVS=;IcLn;SUNu4cA0#{o{`#63%stz zcpe#rPX9HezeDNmo&R*kbUV`Ag6I4>i-Xv`0Syco6K*UN`({yC73ZQ#(Q^K;-9loT5Ih z(4P2)q8vY*{c5OhFE!>{@k>U!vQ_j10+?4iKmDqOP#TM4@r#U+m|bqpo%r;)H2vE1 zD#LZZ-nbF@gHrql_18=el1U$uA3w`VR%^?UY0qeIebFAzJm{f4sIICtHp8vCSf{=< z~t|KzJdRKCu`$<{Y{%Z4#c{5EbEPrTheNhfj2wSdm5$nU zKrKy~!y@OW_+g%{7P&`cxOVvdxIOcabId3(?O_D165OCYC=vw>Ly`r|o_WyYL^>~0?iNOye z<4{ZM@2V9Mq{&EMyLN3mbB3qtnc&}F+HZZmcYl3BZ)D`#*EVT!zyCh4U~%ckGfEsv z|LnIFu|r=JWAAFSLK6`|iZkQO{sHrKXCXT6^-%?gZR2D({V}?> z|6FYC1DWW^$B(%-ZQAtc;lq=7+fSh`Z+GF``XGMxgO-{avs!=rx!9dKcefIA20f33f@{_c22hKJP7vQBk1>6|?Nn*OK3b4cnEZ;X$R z@8UN+mxt3}YH@>^9|Js6q-B;wdtZF<_0wk`-p)?!Y~O7bO;azm=i++<0Z?WV{QY=1 z-h&Oln2740>~JkMgH{xO>;Ey_potZp0k& zZl3HyMpk^5Mx$l;eTR$sxw=+5;k(n~d*Kxxnt;ET%<2DDk>p~Nqn(oI>d0V}UN3hj z<*rmA*pSx|c7FurfXL0id1_LNEms7hB^!9EjujZMF>h`=()PdD`VzRB^ZosU(aac( zXx}GE+K8gXl2MsNlu8}jsL&!!n?$8M2B)1Oq?AhgqIA-xFfB-wHcFal6G@7cQvILz zIj6bf{(rsZx^w4F=X{sX=e<1d=XpL_AmkS*ELrkk&mAv27^}KGHC$W{04pBnvky?K z1;FX{{!dCODjDqJk%itDyEvC!kbpxr0|Ev|mpx>?Njh&%M=yu3$rHUlw^Acl`L%nY z#qw;NCm&*e-)$m;mem}UKpQcpRHa6hZ0KdL!AnMqL%%Pmf@xr4N7Squ_o(gDB17C1 zZTFg+o6nZiRwuzQi^V5>5(j7pV?w$(>BOY7Q`Gj0Doby!Coty)x{CoY+Uj@J@A0Wj zA0?A{;%xTdw2|2on#;hiQ1HLOcL}0CD*e><-TT81*GQ7V?4ib;tCF41%$0OdS!#JB z_HIYSg$oVL$6ZB>g#`pOQaN%qMKjc<4}x%mEx5Jx*JiMpCW&CGxAk}W=+YFEyz z?#{suIsAckh-T7sb;(y|ocnohVqqaf5=WTU%OXG)G4)Rw5mTX@7zls&Snhe8VzlGN z+byo7Hj}^*+LC(Wr?1`R4Ch1LC2_+@`?qj8e>Ae?f89OV0Wy6MVr{0@pO1Aw&<`N~ z?^+xRpnxEa#J7sxA>VL+L)wmvE|h=j`TgxJNBUo`0ei9TuM%4F!2RY7UaH=Jg-<`0 z#52fQUl0Lm$l}v;CB?w_*delMPgPob4cUjHNG(jpk70tvq(%kc*ep%J4K~nhUH)GE z36$U~&>iN}F;H=MK`!Xy%dz?zl6}(gi|hzcwSy@?S3o`FA*eKDchoD!!7BE{WmSE&Hzv#0J*DkU4~N=O}+X8p#0C zqZyO5`5R8p_Qx2i65U=+&z0jpt13M_!kCi{zD7P2SF*AWi?)lW?!0xlw)DhgPh07U z;?Xy0Epa8U~Pc|7qC8F0>;=ytT;cvwDOeimY)WwnLehTGslrx%u zzP8h&0u{gBK^mG=l1i`x*1+CQl!S#iLahRd#1GHV6~@QPIA6{9E;vGX(qj_j&;Y<@ zdWsiP3;sMjwA)j-bnNg3(p$U0Zde~lRZnj<^!Jx%>12^Fb;#P$9uW{UDbx+1=vC3U zCdTgqyr3NYiXccgS)SwV4WCeA1uZT8;(FPI9*ag6U5B`HvCE8u+{*Yxj6X3sfs+6>)-RbI%%W2ezCiDqTIX9XxT6e^vIMjYx9_0{DIQr0V&~r)S;AZ6 zEEZd)O^P|76$-iisaYvZ&fg^7Kp*iXyfSKQ309~k_(C0Ej0Qc9)~T$ywxI)S;rXoo zVGxQ#5T+ZI*I~A0)anbt#sHqr%>$o7@MZoj!o5nF?VV~XjClKrc`+a$U}U`ig!K5> za46aLUT!G`KgXjv)w%5ZjP;L&2|QO?i`H1%zaED*SJDI5gdIXV`exSlt!vmOW350f zan=wV4p|uNsCzu~vH>LNQg5=c9MFCoUj6{ybtRwpp&Gfd!&p60p!H}}p>X@n8_&q- z#ROt?@WY1>kx36md(ep=hr=|vR~wu1z~(^2YT*C)$&~_ADRkNlzB05i0#Le|o*bPm zvP_2d^dmk#txScMY06btiVF?Hez|UXPR@{KRW8s4l|?#cS{-b``YXCShSQup9QT}n z%q1K0pH+s%pF8m$g28r|NBu0q&yP1AIx#-B>a_@gQZ}GksKFZZ65v3(@&R-hdX*uP zj!wgfZ)N0$%wqN6JBbz>SS3Bm6o9?)> z(y1MBF951PMbfihg6?3p=<od`!AR;m*9S@%F?A-Uaf4$Ci#|rY%Y@zg($Nhe7drg=vtbXC0%hU z#*kERw|0k)H#yN0n=TKZJQ9G@itq$*P~Xa~UW>xDWCD-u?UcyC6bT z0MoWgBO52WZxrmYAUakaV14XYamE!5RV^4-_$|W=7rJ58?o@@3}t1lF;IQfBf)*Lei8b- zFe1|qo=gDz5q|UUC3>ND`88rxz5vOp7Dv@@KJ%>#Q!(YSa`z3aua%@L4F6`!byJOA z)Q$0I-AqhP=Z7!e8e*m>2mrhm7q6hC6e#UG@{>?Zd;8cgVRD;{j5+}Mr3@XlPTq>; zA0(-hXR(uB2+n0Q;_u3I*Oo-Tk!1xwP4K;Y_mmC3NmrnfF2tVGQBfJR-(~ZLs*e1` z$kp-%x>5@U1L5+yrBX}qpBji6h#-`Y2-`u@faSMvi7v>U6(F)U&OTQgrkSL=^YJ<@ zby;hq`9REP>*cl!Zw-sMc(D@H9>)k&K0xB=e8V@bO}OmqrrDqZY)a;(w_thR>&unj z>oa6d>Q19ao?k3+C@5Lzn3$axUUcZJ=V1GNl4{!^b!$`wwz+t^47Hx~v?5Sk@CWq1 z^ka8nsvL<>H4Hn7v1dC3=JsZ#IACd+Dl7mf9-Wq*F&L-m4nv1Pr@ZI#Ao^wClGyL^ zTh4y-CiGTTNy|F`ERy4+Lsh>#;(o4f)j!v0hTR^B{jiAG;08lF$d@)TKA#?JGrQA{ zk5!j#$1evHD-~qb?Mm5&NAKk_eVq8&3vS^3xWGDLzq}>)Q)A7~0tBKTW+XizAjZ;o zS^$rr78b^`JTK#Pzx7C(x${n+Tdjl& zQ;iW+HjDewxpN0EasE!%i1yY9(S;xNkreqTxDOo@WBofsW?7v@DaOxFo=U5cJ1ICd zzA(R~T~L@_%F_S7;px+-`4EJ3VBTe<~O!8*frGv(rkQny`;*+$YqwUf$n^PBL@$=I|!OXD?BGvgQ zVGiXTn8XOq_6cQvk?ZD|( z{y22Dd#tOG@q0hAa_fNH_LZ^V95D!=A@d&mNPQSM_&*W<(QT4NI&*f_YCz!!03 z9p~+5sAt4a?(%=$qbb^6STk>fMElU?7T9ykl3Nbb3Gjnq;?FZ8bu9umtix+$0!L7u zm8hWfH}@kL_jzVxD)%5B78l;_ZUW<_o9kO{IX0={5M5-Ln);lu@+f4h>RzgyUi5v$bvQc!i5!OpbD}f9Ecat;9!`DkXGLr^?1px%Drd7&Ix|bPWTc(iGiZ*ad3e(Cipwwe4?|^5=>JxVLsOZYkTL@-|=32qrudRUxjdvBY4k> zz(pv2G#Y&$Efv(S54YLsChotkiQrs-yhZK6t^+@7Y$rE65%ARUxgy%`b{5vWN8oyf zS1O&|N=+pxKyBbcF{il%kQVJpeeA8}5~4K{ITjLP{Mo2u588~dSR-KMOFEa1=e;?? z{UjOLsQ~lkDIz$Z;OV)%LJOx4+nrAK>khlnUW3i7uUCeg6A}D3_of(crnb6kE!Lqh zy1&DeYS_(VA73wk0|bk{<)!bcR`>1cV3btz%buNSwPaXz$r3y`%<|;u0D?mV2h-2C zz^VIDQdCkx`}mH9!mJYPYyo__r#Dg*JI@et7B|}D(1QKhftN}xj?(N?LaItz=k}o- zq`@dEFj(p>B2SrT80}f(Sp=h4HZU|a#6#2`$541a(3-DV^7s>>40JU_;pP+QtC;Ney>%?tRCZ&&7`7L?M52HwXw2YDh{nO<6QyzO#8{ z@Hnq@8&TuE*VrY3h~!u#;$|X+gbQQ-1N8ptHj!D%+Z*Y6=#ZYHV6VkFx0RfedoUl5 z$LUJf=l}hnU%hB-nrIx&Oaslk9BiYT^}c8~cN1txPK}Y`meZAu8lMQkklvq+Kv5N5 z0S9V=pBhUz=OIJrSFgl@3W2DD95dJ!R5xx}{tifq@B;bGc40JMpdXc~T~RX+5St4E zaV3U_?Koj06l1tGR4FrCNHiZxhoYdSacCyxw}`skwp@T)1snL7taRZs7QGlhegtOr z(u2N~#iHU&we-uNyl6jWeBl^Yaeg8;@#0kS6T&OVMiLW>EQ>L?r-{*0%*B&Q%mA=P zsS)T4i~82aU2fIgbA|2x~0~Dx!!%_F}XDjTBLJuC@9KW~J_wx7O{ip_t&V$kv>Dzy_~a^@4#)bUarCgQooWOj zWMfya`s~Y#@?Ye5$rE?`BYA385-f#+it+&($n=8m7ja_zM?B6{uiMD=Ww1jP?1d}j z{9C2alrNOErqxU~dmRJ3C6Ez1)f4Zb_(GZ*H8}3^4KGnt3?OhM497T z`0H-xNsRxLKRhO%u7e0il#Uje^R6+77qtBv9dRzf9M##afEzntRv3&_HhbgD8@4l_ zi+rR8A1f1&`1A4wsh3>=gqOo(Mnv2x*TkZNr}%cATAR6ZsUEM(gVbAp>{H*x=6;}_ zK?FXbxOO~sO}BABf(Fu3FpD);Qrp&MM9eVE*2A0o8e}|q!BDpf;YyHtecXsVnyA}^ zi#c?C)12odPRSeFtJtCx5~St8n~L`fg-ln!g*Ojq6sZYYR}!GLVi)g5(|N(nFXA9d z7Z-?(%JH!dY%KaJwO{Y}F`Z~0N4<461{!5}r;=pE>UMfojRGD4*jDPY9AK1F>;PbS z{v9t4#GamdA+rs`$}l$(rbr3SZh`SWJ3Z9$3jysjolyA&5fWgGgNN}99#=gK-vCEV z5$Y&UDf{K^sc~R@ouX#2{RxSGBGk3A~OhDpT?(Ur{6Ee!zP0-0=4z@P_qQEl0S%akE+{V zIh`s+OmY0kOcm3E4FP+zg z9pLJI{V6R}Qh%yj2cMx2T`noF+bFo-?L7hD6EMrE z0BcQzqNz6q;zq3{+Mjuy(LYbC<_py}gfeHeb%De50gApEaj}1uKB?@*YB+=`@&E)d zWn?Bof?lLLspsE`%B1BmQ6f?^jv?2Ic91*>-Ydi6Av_&-&d(n>KZU$d5Kf+dqqr(d`n22+p>O>VC!NwwGVyUJmz9-Kvj(GFN1RpvdP;xqwr>ba{i zy7|`>ct(NV{{dFiH>1=LBgOy%Sfa@|%^{xQI#vh>o?S}wCg-bPJ5O|$P;{Y|b&gMB zEAT0C;*O&pSo!CI82VlI~?Q)m~%i}++@TOkKRU=(NBHisID{XoRI(tXI6*Iy*}#i>A9L# zDjXCpB3~6%6V40gm()43>u(ntq!BP(TINC@DJaGJE;~`)V*2#~C4IOfTY2L%HJnJw zMCO?6ycWc80&NLZLPza_c}gWUoGE6hD1GeCVO@`+sB1d@GeREDe8rFYb4$Z8_2;~i zlV8LXKK@1w9T~CChBC!b>_@lPP@*9t)n`zS5_DBP? z0A-nUJLTQ1-(dJtK`w%6eJIWX(bC5me&T}7jNH|{IFf4tz|tSHf#SO@o_)<4*!iZf z5?KseV`G6cpp>o)26}F*Gn@OS{lunzO`e_;_GLM|P{bPg3EM}XwGj&woo(z|x)^0D z9OCoVAr>qb;KGoMJF-(B@C{++tB{s&juYbXf!{j+H|^U|

>6S>I6bxo>{Ps>xr@o6_S=@==#NyN*?BHFuAg{O4Edh;* z^LzJdh`m8cTYZBWL)~nTz!@~Gv5kW$=Vh_5fQ5Dq)Ny}Z*Mhf!&;E$65S-oB_^&xD zpT#y7(&K*#cgJI6LvI>jVA&o((>%97zSmvYrSH;%+3gNTuV***@$+Ycf>S|mRi{|I zi)u9HrUk!UD;*ot^HBuafU<{w6Ma_GXQ$(k5d_l&1x~LH&Aw0olq31!+B6)C8S2Kk zU|!1Qeq7Zjf?i!fovyc^Bt)4IVd&~_CQ^4n<8V-%f?(;d!r4jJrr((U^(W{`?F21> zBvxtwHm)CDg%O4(N?}KKA0+sfXV3AQZSr-5@Ulbk=K|KC`q44Cov*`F0a^TRGQ)1} z*U>>{tC@vG1r$^ZFp;#5AKguH0*h5)_Ds8<{^u$}#$eNFjVe1E5)Xr+F?dOflX27O zxal%G|M%aG1T;ggU<1fStMZ><_$NX6#l5HIr<3QVo!#<3m$9)LZOGkI*GL%i7AU1? z$aj9b&Mx4hPP<_AQQf#ymf-NcnZCSN-#Znn+{W-7Hh{C!8}e!m#LF>t5#4)Y+~c+n zo)$l?U-#a|46UEPk=fX(=hA~gmNjVY!EeIl9=Hxo#iH+cn>zAO%tt#=oCbs3Sdouj z4DlrtGWwzeJ}8LDxa;(6QPZ?G>|aro(MORn0o?gX(8VI~IF_>B|Hu>V-7w?rDch%* zXbaMLtkIvy^pdzHOeeCtq-sAK#b70ZzF1m%&L+20-|L1v^lz`Jkk?ELPnjRjyW2$G zLa(XE$rK_m;@L?w5Th5}poky;0$arZ;!NdUK>v*H;`J&!h68RkS?3*ZDHiY$D!)X; z(3mnP(A#32U~?;yRWj}YH&%V>{8uxXJ#|#4;PdWy20SJDB?fj}7LGd)UHy%H3^~Hkxe=m4v;89E76)ut(Mv zAaEVi(-RO9QoY%RDnuAMK6`4zL__BJMabj#@yd?yU`7Ue>WPS;T-xgmjCfA-CrVYo zf1d=;nr3+zv2GM+xfx)#5b5!V_5mU92PC-!1jc=7-Zdy20<;ql9KWT!7#ossrTX?7 za|^3QnW)?>ZGtr-7wR#X`B4gUtNA=}1S{vqHJDpY>%NNwazSHcPT34()COCG-3WN; zF{hvZ^7(@!o8(h2aC+W|34z<`A9R0catQn?P5)~35{+6u{1P14B;MegD0Qmzc#1;HGGxox^ulIf6lBG*SQ9dZa_}9FD6cb@$;Mm;po5pTN zv++GbLT<4D}-jZK#*k$DUCuP(8xC#Rgt}W@f=wp`6fXz_e~H zn3hm36G0MA%mDY|^6VcO4z_GfcRG z=3PGVG5{+J&=Q(hT3))_!-(}o{lLQmI2Yh|h{o*Xc%cUW0fQlNfF5rV@Q{fZVg@d} zR)Tx289!qgVNq8iL`V}k=OicW69htZ9KQG&ey6v8^{jN$-lHchM;xyEt1lFooF+U$ z5yST{Q)Jk!{<^}Augf|XZTetxz7Vzc~1^WQchWoW z3-lm9xxcAj!l_&!5V*3!jA6O&>u`GC2w>6xIV2hciJ0_{BJ!${xD|kJvORSQ6C%Vxu;@`!SEAzc^A(4ulq{Cs91>p`5=AL%mE?g?o8n_fTEWWQe=w0 z4k>5dI6y02eEcYlgoyyU5#tjKwi%s#U%#Zz^J$rdiZ9_~!RJgq3d?D;11%B_n#FKE z^FJ56DQnN|2rG$Z6rTA=>K)XQtk9pyDA@4zx~k61$yrY0HU(@f@(yb(M6z5#M=Jgj zmS9{;@EDoVDJYr>&Cu=b+PnV0PlUmUtcLhdjC|0lU162feq!R)2?xRBNB&ht+ZRDr zMWg6yF(pQ);@7uiYlhx}fIo^!+PyUCp>~W@8#mLLmm|<2NCDT{jTzRe9m$Nd!pto$ zc7zJU3Y5BCLa_7x<1i}ei4$C{qV-{aMh8cK8y45!CH2j}MA*X(g>m{*YIs`8>Un@$;^1G0&m5I+ik z=q=fJBl_s>Hvh_~nJ#Fn<9C_>l@1~@P`W=gd_m5zdeRv7m}K52_;RY<8|9@8WpY#} zlu`m;4~z`dCPzA-m|Am7C`tuSPe+wl9=_qq&)^?eqn0ApJqM)#G8KefLGuDazH707 z4xM@CfI@Q>hr@v^BaH_}M93Z#(&$|lwp`;rf8CoPl1Oszs^%zdF<7kpNDaGs{uA&& zB#1I5AkbO<;W0zzAiV(=3v<&RaO!9@LEKuJwVc0jfq)$W3Q2i?Iu6h%Gatig+M*tf z5N&W8cs%StGU555miOgH?%Nyy?UoZpLy`BL>YK5YA#?^SLD7Lt4NV>@-bJ6tA(u1g0T#Un!B9QhVyLsGyc(8y-9u4Y zZaE@`Rl_44p_OnQ6XWNHJ*X>93@CUtrmw@_(WsZk7!|}Zqd7LtBhZ_i7{4jVGo`mZ zwfevZ?#RJ!zc`!}ci{|jxdz%3588$QeRZ&s7gnBcXJS=>$7M7M*^eQAKqwt%vIQ3ptO0(K(&N z`sOTbqX-OyU}a9UJPz`RkvpX~77LZP@S8)Wa2c#XY!^7mHz$C6KTrQCow-ba5xL%Y zwN@^G>p9JbK`$GimZD07=;i?afRTlO9L(&+Y&60#M!5xgZ~-u&Wo1{kd4G6@OT3uh z2cqCSs+7oc^I=sLht~oCxS-%?ZqoH*cB<`E<(9$8jzs%H|4;@-%f&!W~VC!6`!yv?NN+n#zDk!mw zfQ0H^)Q8vCKcHl~5fxf|db+Z#>>juSXaeF`AvE>oXZ=Hi)OGv>P^}D2tXM-5&<3@m z@Biw-x1)PvL~Jw`JR=Xk$zE}Xfj{1$fq6Y+6CZ8#k%11!h$e6s`G%B~m4ir2N&!Pp zuMF&2)5YKONF>#UQa8J};XPI(H9b9uI7lRgFc|7Zm?QczRaw{WGyDVzKD%tT*OWjS zlZ}!WPT4U_V12a_S)+Q8_%VN#q-EqWx0rfB0HEg5uIJCAoH6BaGmAJQ5M9UR$l;uT zNDI*h^shPZR^g7G*~JGh7ZGLD2)L$dx*K|$XOz?(-rnlLK6c_;iQoJZ98Plvy%BKd zXy6;}!-PJgi!3A#nR-B+FsJZ`O(>9Q2xUBm{p_$P=Hm5)1UZn1x2LIqJC0gfyf~D_ z0?k9)Who##EM)wH>(wa$<~Y+a{DX@lliN!kr7SAh+HGrWWZD+h{Uk;jsV>E8PqT~U zIcYZs|Nd~T%XLqc*CwI4!F`{DoB^}-s?~|Y7t`EQ8^c~EIdtc;v>LLG88=jVunARu zqvNjn_;n@o-q=SQ`dmbhE6k3>y4S@q+l{d8l=)jJ>P?1p`ESQ*%l?#biRMPy}kX(TTk|5#h+Pyz)7X6xLDJTx!jGJ z(;ZdU9*jy|iIz&{_2R98E0RVp z*M;FPjiD%^Ct8qdl$tb!b-uy3n7&LZzZ|Zm;<%2QJqpzX*J6qdbTcm5?seBfqPQc5 z8h{QM7aJS<_*L3>{oBiOUgf6uPc#hL8_(I#`)+*wWXR1CEjG#`RKb=9iyEUUA6;&* zd879#;P6d3Xh5=`Zc@@mjYdTchbDtE`}T`5o7X~1Wdc|RlFKl+%wVsE;$7Z<$pRRm zPGqfL9E$Fhx|_X89jto>+)R)vdi~%KSr~l*RFj^HPj&jV-&C;B}(;EPC zv+}64(Um$hINN%wbocH1d;ZdZ`xmv?Wy24m9@#_7Sx0?5G@{A>#|n7UPMXVd#Iu`^ zY>v4UXeT+Sy!J^wU>ta9`*`XrGoM{M074Fn4gG2K)%(AR$G8zX6TT` zihVM}Dh~l>cXV9xs;iv$N?%0@O-k^atm=;lbz=(A`bdr8ET}xZ3hWjrN4E7AS*?9+ z@1oM>!;I2ucoG%yC@La7Pe@UPc5~}b+YT$CJ|Ih zwg%cQ?8emx^g@sjUft^3cfNx|EbSP1o@Pz?Iz95Yi>HUtCIf)4sKw#bSm|^14M! zhw@!6iyA*wNAX1g!vxo%6EtX}1|BEJ~s;p)puAF?oLJRQw66UzLQxK_#jjhlq5=>L?+#q*3!HDWgX-EznV; z2fsV^pj>O*(&%W6?;|XCdkU6H6G!@*^?_>adBTZgC<7NzRRXV)(jJ{a;Xx-o?Hv9aC!S+Y?5Aau{n{nWY5LWUP z&#Fo-l|ar~u>S}kmBr)gClM86xwci2bvY$tJF1ejG|o>g z^+!#tFQuege%iy7d#tCtOWpZ%%Gch{-bOAhbL zl?oTiY6EMK8SX+tcO9sO_prsI#L6r3!*x+6!5|Q7-&9bz; zZF0g>k0Y>nJ|}S|&~mne%GPLY_FbPOj1U|PK&{!r50@~`=dJd7^oTollewq@ZWSoR zH@e=8fiIwss^*HOEJNlZyScrF*z?rg>#bk^p9BX!5E`@*bLJs;^79V>b6G`tp9p}D zTOqfX59JLrT+gjOaNq%l%Xc=AXf&Dt5ye~kX#h!W^bG!KjL)$RLT4;6Zff}{_ZOsw zU>b))7$eNjkJH%nPmY>fSTw%)UQ5;*0D2Hm>~kW#?qUb@1iVpwK^*cC-yAUkBG