diff --git a/.gitignore b/.gitignore index 48a85dc..771393f 100644 --- a/.gitignore +++ b/.gitignore @@ -17,6 +17,7 @@ __pycache__/ # Fortran extensions *.o *.mod +*.f # Distribution / packaging .Python diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..877bd85 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,9 @@ +[submodule "dnad"] + path = dnad + url = https://github.com/joddlehod/dnad +[submodule "fson"] + path = fson + url = https://github.com/josephalevin/fson +[submodule "json-fortran"] + path = json-fortran + url = https://github.com/jacobwilliams/json-fortran diff --git a/Makefile b/Makefile index 0b5d7ce..b857cff 100644 --- a/Makefile +++ b/Makefile @@ -11,33 +11,58 @@ OPTIONDEFS = -DWRITEOUTFILE DNAD_OBJ = dnad/dnad.o +# If using JSON output, compile with json-fortran library +ifeq ($(findstring USEJSONOUT,$(OPTIONDEFS)),USEJSONOUT) +JSON_DYL = -ljsonfortran -lgfortran +JSON_DIR = /Users/jml1/GitHub/microCOSM/json-fortran/jsonfortran-gnu-8.3.0/lib/ +JSON_LIB = -L$(JSON_DIR) +JSON_INC = -I$(JSON_DIR) +FFLAGS += -Wl,-rpath,/opt/local/lib/gcc-devel -Wl,-rpath,$(JSON_DIR) +else +JSON_DYL = +JSON_LIB = +JSON_INC = +endif + MODULE_OBJ = mod_precision.o mod_dimensions.o \ mod_common.o mod_chemconst.o \ mod_chemspeciation.o mod_phsolvers.o \ - mod_carbonchem.o mod_modelmain.o + mod_carbonchem.o mod_modelio.o \ + mod_modelmain.o MODEL_OBJ = microcosm_model.o -# C preprocessing +# C preprocessing and replacing the _d in constants: +ifeq ($(findstring USEDUALNUMAD,$(OPTIONDEFS)),USEDUALNUMAD) +CPPCMD = cat $< | sed -e "s/REAL(KIND=wp)/TYPE(DUAL)/g" \ + -e "s/TYPE(DUAL), PARAMETER/REAL(KIND=wp), PARAMETER/g" \ + | $(FC) $(OPTIONDEFS) -cpp -P -E $(FFLAGS) +MODULE_OBJ += $(DNAD_OBJ) +else CPPCMD = cat $< | $(FC) $(OPTIONDEFS) -cpp -P -E $(FFLAGS) +endif -model: $(MODULE_OBJ) $(MODEL_OBJ) - $(FC) $(OPTIONDEFS) $(FFLAGS) $(MODULE_OBJ) $(MODEL_OBJ) -o microCOSM +model: $(DNAD_OBJ) $(MODULE_OBJ) $(MODEL_OBJ) + $(FC) $(JSON_LIB) $(JSON_INC) $(OPTIONDEFS) $(FFLAGS) $(MODULE_OBJ) $(MODEL_OBJ) -o microCOSM $(JSON_DYL) # Requires seperate pre-processing for some reason -pymodel: $(MODULE_OBJ:.o=.f) $(MODEL_OBJ:.o=.f) - $(PC) $(OPTIONDEFS) --opt=$(FFLAGS) --f90flags=-ffree-form -m microCOSM -c $(MODULE_OBJ:.o=.f) $(MODEL_OBJ:.o=.f) +pymodel: $(DNAD_OBJ) $(MODULE_OBJ:.o=.f) + $(PC) $(OPTIONDEFS) --opt=$(FFLAGS) --f90flags=-ffree-form -m microCOSM -c $(MODULE_OBJ:.o=.f) #%.o: %.F90 # $(FC) $(OPTIONDEFS) -c $(FFLAGS) $< -o $@ %.f: %.F90 - $(CPPCMD) -o $@ - + $(CPPCMD) -o $@ $(JSON_DYL) - %.o: %.f - $(FC) $(OPTIONDEFS) -c $(FFLAGS) -ffree-form $< -o $@ + $(FC) $(JSON_LIB) $(JSON_INC) $(OPTIONDEFS) -c $(FFLAGS) -ffree-form -o $@ $< $(JSON_DYL) + +.PHONY : dnadmod +dnadmod: + $(FC) $(OPTIONDEFS) -c $(FFLAGS) -ffree-form $(DNAD_OBJ:.o=.F90) .PHONY : clean clean: $(RM) $(MODULE_OBJ) $(DNAD_OBJ) $(MODEL_OBJ) - $(RM) $(MODULE_OBJ:.o=.mod) $(DNAD_OBJ:.o=.mod) $(MODEL_OBJ:.o=.mod) + $(RM) $(MODULE_OBJ:.o=.mod) $(DNAD_OBJ:.o=.mod) $(MODEL_OBJ:.o=.mod) dnadmod.mod $(RM) $(MODULE_OBJ:.o=.f) $(DNAD_OBJ:.o=.f) $(MODEL_OBJ:.o=.f) diff --git a/dnad b/dnad new file mode 160000 index 0000000..ca93275 --- /dev/null +++ b/dnad @@ -0,0 +1 @@ +Subproject commit ca93275a845137cf082bdff1d5a05a4b39de129e diff --git a/fson b/fson new file mode 160000 index 0000000..16731d9 --- /dev/null +++ b/fson @@ -0,0 +1 @@ +Subproject commit 16731d9b510b94a4e074a1fa7e35706f6045a85f diff --git a/json-fortran b/json-fortran new file mode 160000 index 0000000..280ae0e --- /dev/null +++ b/json-fortran @@ -0,0 +1 @@ +Subproject commit 280ae0ee17ba723a442bacbc9eff5188a13156ce diff --git a/microcosm_model.F90 b/microcosm_model.F90 index f13e5c3..763b86a 100644 --- a/microcosm_model.F90 +++ b/microcosm_model.F90 @@ -21,6 +21,9 @@ !======================================================================= PROGRAM MICROCOSM_MODEL !======================================================================= +#if defined(USEDUALNUMAD) + USE DNADMOD +#endif USE MOD_PRECISION USE MOD_BOXES USE MOD_MODELMAIN @@ -32,36 +35,41 @@ PROGRAM MICROCOSM_MODEL REAL(KIND=wp) :: & maxyears, & outputyears, & + m2deg, & gaovla_opt, & - gamma_Fe, & - lt_lifetime, & + gamma_in, & + lt_lifein, & alpha_yr, & - psi, & - atpco2in + atpco2in, & + psi_in, & + dif_in -! Input arrays (nbox dimensions) +! Input arrays (nbox dimensionesix) REAL(KIND=wp), dimension (nbox) :: & dx, & dy, & dz, & - thin, & - sain, & - cain, & - alin, & - phin, & - niin, & - fein, & - liin, & + depth, & + latitude, & + thin, & + sain, & + cain, & + alin, & + phin, & + niin, & + fein, & + liin, & fe_input, & - dlambdadz, & - wind, & - fopen + dldz_in, & + wind_in, & + fopen_in REAL(KIND=wp), dimension (nbox, nbox) :: & - K, & - R + Kin, & + Rin, & + Pin -! Output arrays (nbox, by timestep dimensions) +! Output arrays (nbox, by timestep dimensionesix) REAL(KIND=wp), dimension (:,:), allocatable :: & thout, & sout, & @@ -79,9 +87,36 @@ PROGRAM MICROCOSM_MODEL psout, & atpco2out - INTEGER, dimension (:), allocatable :: & +#if defined(USEDUALNUMAD) + REAL(KIND=wp), dimension (:,:,:), allocatable :: & + thdxout, & + sdxout, & + cdxout, & + adxout, & + pdxout, & + ndxout, & + fdxout, & + ldxout, & + expdxout, & + pco2dxout + + REAL(KIND=wp), dimension (:,:), allocatable :: & + tdxout, & + psdxout, & + atpco2dxout +#endif + INTEGER(KIND=ip), dimension (:), allocatable :: & nlout - + +! Local variable definitions +#if defined (FOURTEENBOX) + REAL(KIND=wp) :: & + onesix, twosix, thrsix, forsix, fifsix, sixsix, & + b, z0, r01, r06, r11, r16, r21, r26, r31, r41, & + rsf, rit, rde, rbo, rna, rbt, rup, rlp, rbp, & + rus, rls, rbs, rua, rla, rba +#endif + ! Input some initial parameters maxyears = 1.e4_wp outputyears= 1.e2_wp @@ -103,119 +138,420 @@ PROGRAM MICROCOSM_MODEL allocate ( expout (outstepmax,nbox) ) allocate ( pco2out (outstepmax,nbox) ) - ! Geometry and array inputs - dx = [ 17.0e6_wp, 17.0e6_wp, 17.0e6_wp ] - dy = [ 4.0e6_wp, 12.0e6_wp, 16.0e6_wp ] - dz = [ 50.0_wp , 50.0_wp , 5050.0_wp ] - - ! define array of mixing rates - K = RESHAPE( [ 0.e6_wp, 1.e6_wp, 1.e6_wp, & - 1.e6_wp, 0.e6_wp, 1.e6_wp, & - 1.e6_wp, 1.e6_wp, 0.e6_wp ], & - [ nbox, nbox ] ) - - ! define array of remineralization coefficients (Columnwise) - ! -1 indicates all of export is lost from cell, while - ! +1 indicates all of export is remineralized (gained) by cell - ! Thus [-1.0, 0.0, 1.0, & - ! 0.0,-1.0, 1.0, & - ! 0.0, 0.0, 0.0 ], - ! indicates the first box (column one) loses export from box 1, - ! the second box (col two) loses export from box 2, - ! and the third box (col three) gains export from boxes 1 and 2 - R = RESHAPE([ -1._wp, 0._wp, 1._wp, & - 0._wp,-1._wp, 1._wp, & - 0._wp, 0._wp, 0._wp ], & - [ nbox, nbox ] ) - - ! Initialize input arguements, and set some reasonable values +#if defined(USEDUALNUMAD) + ! allocate memory + allocate ( tdxout (outstepmax,ndv) ) + allocate ( psdxout (outstepmax,ndv) ) + allocate ( atpco2dxout (outstepmax,ndv) ) + allocate ( thdxout (outstepmax,nbox,ndv) ) + allocate ( sdxout (outstepmax,nbox,ndv) ) + allocate ( cdxout (outstepmax,nbox,ndv) ) + allocate ( adxout (outstepmax,nbox,ndv) ) + allocate ( pdxout (outstepmax,nbox,ndv) ) + allocate ( ndxout (outstepmax,nbox,ndv) ) + allocate ( fdxout (outstepmax,nbox,ndv) ) + allocate ( ldxout (outstepmax,nbox,ndv) ) + allocate ( expdxout (outstepmax,nbox,ndv) ) + allocate ( pco2dxout (outstepmax,nbox,ndv) ) +#endif + +! Initialize input arguements thin = 0._wp sain = 34._wp cain = 2150._wp alin = 2350._wp - phin = 1._wp - niin = 16._wp +! phin = 1._wp + phin = 2._wp +! niin = 16._wp + niin = 36._wp fein = 0._wp - liin = 0._wp + liin = 2._wp + atpco2in = 280._wp + +! Overturning and mixing rates (m3/s) +! psi_in = 20.e6_wp + dif_in = 1.e6_wp + +! Wind speed (m/s)for CO2 gas fluxes + wind_in = 0._wp + +! Open surface fraction in contact with atmoshpere +! 1 => fully open, <1 => flux impeded (e.g. by sea ice) + fopen_in = 0._wp + +! Gamma over lambda for ligands "optimum" value (Lauderdale et al 2020) +! gaovla_opt = 0._wp !4398._wp + gaovla_opt = 4398._wp + +! Gamma ligand production rate (in phosphate, not carbon, units) +! gamma_in = 0._wp !5.e-5_wp*106._wp + gamma_in = 5.e-5_wp*106._wp + +! Lambda ligand lifetime (s) +! lt_lifein = 0._wp ! 1._wp/((gamma_in/106._wp)/gaovla_opt) + lt_lifein = 1._wp/((gamma_in/106._wp)/gaovla_opt) + +! Dust deposition in g Fe m-2 year-1 + fe_input = 0._wp + +! Biological production maximum rate (mol P/yr) + alpha_yr = 6.e-6_wp + +! Deep ocean box lifetime modifier to capture the gradient due to +! photodegradation near the surface and slower loss in the deep + dldz_in = 0._wp + +! File number identifier + id = 1 + +! Array inputs +#if defined(FOURBOX) +! For a 20SV AMOC, psi_in (i.e. Southern Ocean upwelling) needs to be 2x + psi_in= 40.e6_wp + + dx = [17.0e6_wp, 17.0e6_wp, 17.0e6_wp, 17.0e6_wp] + dy = [ 1.0e6_wp, 3.0e6_wp, 12.0e6_wp, 16.0e6_wp] + dz = [50._wp, 50._wp, 50._wp, 5050._wp] + depth = [ dz(1)/2._wp, & + dz(2)/2._wp, & + dz(3)/2._wp, & + dz(1)+dz(4)/2._wp] + + m2deg = 180._wp/dy(4) + + latitude = [( +(dy(1)/2._wp)), & + (dy(1) +(dy(2)/2._wp)), & + (dy(1)+dy(2)+(dy(3)/2._wp)), & + ( +(dy(4)/2._wp)) & + ] + latitude = -90._wp+(latitude*m2deg) + +! define arrays (nbox*nbox long) of box connectivity for mixing and overturning (by rows) +! Box 1 mixes with box 2 and 4; +! Box 2 mixes with box 1, 3 and 4; +! Box 3 mixes with box 2 and 4; +! Box 4 mixes with box 1, 2, and 3. +! Box1 Box2 Box3 Box4 + Kin = RESHAPE([ 0.0_wp, 1.0_wp, 0.0_wp, 1.0_wp, & ! Box1 + 1.0_wp, 0.0_wp, 1.0_wp, 1.0_wp, & ! Box2 + 0.0_wp, 1.0_wp, 0.0_wp, 1.0_wp, & ! Box3 + 1.0_wp, 1.0_wp, 1.0_wp, 0.0_wp & ! Box4 + ], [ nbox, nbox ] ) + +! Box 1 is upstream of box 4 (ie AABW downwelling); +! Box 2 is upstream of box 1 and 3 (ie Antarctic divergence); +! Box 3 is upstream of box 4 (ie NADW downwelling); +! Box 4 is upstream of box 2 (ie SO upwelling). +! Box1 Box2 Box3 Box4 + Pin = RESHAPE([ 0.0_wp, 0.0_wp, 0.0_wp, 0.5_wp, & ! Box1 + 0.5_wp, 0.0_wp, 0.5_wp, 0.0_wp, & ! Box2 + 0.0_wp, 0.0_wp, 0.0_wp, 0.5_wp, & ! Box3 + 0.0_wp, 1.0_wp, 0.0_wp, 0.0_wp & ! Box4 + ], [ nbox, nbox ] ) + +! define array of remineralization coefficients (by rows) +! -1 indicates all of export is lost from cell, while +! +1 indicates all of export is remineralized (gained) by cell +! Box 1 loses export from Box 1, which is completely remineralized in Box 3 (Box 2 is adjacent) +! Box 2 loses export from Box 2, which is also completely remineralized in Box 3 (Box 1 is adjacent) +! Box1 Box2 Box3 Box4 + Rin = RESHAPE([-1.0_wp, 0.0_wp, 0.0_wp, 1.0_wp, & ! Box1 + 0.0_wp,-1.0_wp, 0.0_wp, 1.0_wp, & ! Box2 + 0.0_wp, 0.0_wp,-1.0_wp, 1.0_wp, & ! Box3 + 0.0_wp, 0.0_wp, 0.0_wp, 0.0_wp & ! Box4 + ], [ nbox, nbox ] ) + +! Initial conditions + thin(1:4)= [ -1._wp, 2._wp, 20._wp , 4._wp ] + sain(1:4)= [ 35._wp, 34._wp, 35.50_wp, 34.75_wp ] + cain(1:4)= [ 2100._wp, 2100._wp, 2100._wp , 2400._wp ] + alin(1:4)= [ 2350._wp, 2300._wp, 2300._wp , 2400._wp ] + phin(1:4)= [ 2._wp, 2._wp, 0.0_wp , 2.5_wp ] + niin(1:4)= [ 32._wp, 32._wp, 0._wp , 36._wp ] + +! Wind speed (m/s)for CO2 gas fluxes + wind_in(1:4) = [ 10._wp, 10._wp, 5._wp, 0._wp ] + +! Open surface fraction in contact with atmoshpere +! 1 => fully open, <1 => flux impeded (e.g. by sea ice) + fopen_in(1:4)= [ 0.5_wp, 1._wp, 1._wp, 0._wp ] + +! Dust deposition in g Fe m-2 year-1 + fe_input(1:4)= [ 1.5e-3_wp, 1.5e-3_wp, 1.5e-1_wp, & +! Hydrothermal vent input of 1 Gmol/yr (Tagliabue et al., 2010) +! mol Fe/yr * g/mol * 1/area == g Fe m-2 year-1.... +!divide by 2.5e-3 because fe_sol is multiplied again within model. +#if defined(USEDUALNUMAD) + (1.e9_wp*56._wp)/(2.5e-3_wp*dy(4)%x*dx(4)%x) ] +#else + (1.e9_wp*56._wp)/(2.5e-3_wp*dx(4)*dy(4)) ] +#endif + +! Deep ocean box lifetime modifier to capture the gradient due to +! photodegradation near the surface and slower loss in the deep + dldz_in(1:4) = [ 1._wp, 1._wp, 1._wp, 1.e-2_wp ] +#elif defined(FOURTEENBOX) + psi_in = 30.e6_wp +! Geometry + dx = [ 25.0e6_wp, 25.0e6_wp, 25.0e6_wp, 5.0e6_wp, 5.0e6_wp, & + 5.0e6_wp, 20.0e6_wp, 20.0e6_wp, 20.0e6_wp, 25.0e6_wp, & + 25.0e6_wp, 5.0e6_wp, 20.0e6_wp, 25.0e6_wp ] + dy = [ 2.0e6_wp, 2.0e6_wp, 2.0e6_wp, 8.0e6_wp, 8.0e6_wp, & + 6.0e6_wp, 14.0e6_wp, 8.0e6_wp, 6.0e6_wp, 6.0e6_wp, & + 6.0e6_wp, 14.0e6_wp, 14.0e6_wp, 20.0e6_wp ] + dz = [ 1.0e2_wp, 1.0e2_wp, 1.0e2_wp, 1.0e3_wp, 1.0e2_wp, & + 1.1e3_wp, 1.0e3_wp, 1.0e2_wp, 1.0e2_wp, 1.5e3_wp, & + 1.5e3_wp, 2.0e3_wp, 2.0e3_wp, 1.0e3_wp ] + + depth = [0.5e2_wp, 0.5e2_wp, 0.5e2_wp, 0.6e3_wp, 0.5e2_wp, & + 5.5e2_wp, 0.6e3_wp, 0.5e2_wp, 0.5e2_wp, 1.1e3_wp, & + 2.1e3_wp, 2.1e3_wp, 1.85e3_wp, 3.6e3_wp] + + m2deg = 180._wp/dy(14) + latitude = [( +(dy( 1)/2._wp)), & ! AA + (dy(1) +(dy( 2)/2._wp)), & ! SO + (dy(1)+dy(2) +(dy( 3)/2._wp)), & ! PF + (dy(1)+dy(2)+dy(3) +(dy( 4)/2._wp)), & ! AAIW + (dy(1)+dy(2)+dy(3) +(dy( 5)/2._wp)), & ! STAT + (dy(1)+dy(2)+dy(3)+dy(4)+(dy( 6)/2._wp)), & ! NOAT + (dy(1)+dy(2)+dy(3) +(dy( 7)/2._wp)), & ! PAIW + (dy(1)+dy(2)+dy(3) +(dy( 8)/2._wp)), & ! STPA + (dy(1)+dy(2)+dy(3)+dy(8)+(dy( 9)/2._wp)), & ! NOPA + (dy(1)+dy(2) ), & ! UCDW + (dy(1) ), & ! LCDW + (dy(1)+dy(2)+dy(3) +(dy(12)/2._wp)), & ! NADW + (dy(1)+dy(2)+dy(3) +(dy(13)/2._wp)), & ! PADW + ( +(dy(14)/2._wp)) & ! AABW + ] + latitude = -90._wp+(latitude*m2deg) + +! Mixing mask array + Kin = RESHAPE([ & + 0._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 1._wp, 0._wp, 0._wp, 1._wp, & + 1._wp, 0._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 1._wp, 1._wp, 0._wp, 0._wp, 0._wp, & + 0._wp, 1._wp, 0._wp, 1._wp, 1._wp, 0._wp, 1._wp, 1._wp, 0._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, & + 0._wp, 0._wp, 1._wp, 0._wp, 1._wp, 1._wp, 0._wp, 0._wp, 0._wp, 1._wp, 0._wp, 1._wp, 0._wp, 0._wp, & + 0._wp, 0._wp, 1._wp, 1._wp, 0._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & + 0._wp, 0._wp, 0._wp, 1._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 1._wp, 0._wp, 0._wp, & + 0._wp, 0._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 1._wp, 1._wp, 1._wp, 0._wp, 0._wp, 1._wp, 0._wp, & + 0._wp, 0._wp, 1._wp, 0._wp, 0._wp, 0._wp, 1._wp, 0._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & + 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 1._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & + 0._wp, 1._wp, 1._wp, 1._wp, 0._wp, 0._wp, 1._wp, 0._wp, 0._wp, 0._wp, 1._wp, 0._wp, 1._wp, 0._wp, & + 1._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 1._wp, 0._wp, 1._wp, 1._wp, 1._wp, & + 0._wp, 0._wp, 0._wp, 1._wp, 0._wp, 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 1._wp, 0._wp, 0._wp, 1._wp, & + 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 1._wp, 0._wp, 0._wp, 1._wp, 1._wp, 0._wp, 0._wp, 1._wp, & + 1._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 1._wp, 1._wp, 1._wp, 0._wp],& + [ nbox, nbox ] ) + +! Overturning mask array + onesix = 1._wp/6._wp + twosix = 2._wp/6._wp + thrsix = 3._wp/6._wp + forsix = 4._wp/6._wp + fifsix = 5._wp/6._wp + sixsix = 6._wp/6._wp + + Pin = RESHAPE([ & + 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , sixsix, & + sixsix, 0._wp , twosix, 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , & + 0._wp , 0._wp , 0._wp , thrsix, 0._wp , 0._wp , onesix, 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , & + 0._wp , 0._wp , 0._wp , 0._wp , thrsix, 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , & + 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , thrsix, 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , & + 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , thrsix, 0._wp , 0._wp , & + 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , twosix, 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , & + 0._wp , 0._wp , twosix, 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , & + 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , & + 0._wp , fifsix, 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , & + 0._wp , thrsix, 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , onesix, 0._wp , & + 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , forsix, 0._wp , 0._wp , 0._wp , & + 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , onesix, 0._wp , 0._wp , fifsix, 0._wp , 0._wp , 0._wp , 0._wp , & + 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , 0._wp , onesix, fifsix, 0._wp], & + [ nbox, nbox ] ) + +! Remineralization mask array + b = 0.84_wp + z0= 50._wp + +! Evaluate function at box interfaces + r01 = -( 100._wp/z0)**(-b) + r06 = -( 600._wp/z0)**(-b) + r11 = -(1100._wp/z0)**(-b) + r16 = -(1600._wp/z0)**(-b) + r21 = -(2100._wp/z0)**(-b) + r26 = -(2600._wp/z0)**(-b) + r31 = -(3100._wp/z0)**(-b) + r41 = -(4100._wp/z0)**(-b) + +! Calculate remineralization within the box depths (whatever reaches the bottom is remineralized) +! Four box columns in suptropics/North Pacific + rsf = r01 + rit = abs(r11-r01) + rde = abs(r31-r11) + rbo = abs(rsf+rit+rde) !np.abs(0.0-r3100) + +! Three box columns for the North Atlantic + rna = r11 + rbt = abs(rna+rde) + +! Four box columns in the Southern Ocean (AA/SO/PF), depending on slope of UCDW/LCDW boundary + rup = abs(r26-r01) + rlp = abs(r31-r26) + rbp = abs(rsf+rup+rlp) !np.abs(0.0-r3100) + + rus = abs(r16-r01) + rls = abs(r31-r16) + rbs = abs(rsf+rus+rls) !np.abs(0.0-r3100) + + rua = abs(r06-r01) + rla = abs(r31-r06) + rba = abs(rsf+rua+rla) !np.abs(0.0-r3100) + + Rin = TRANSPOSE(RESHAPE([ & + rsf , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! AA + 0._wp, rsf , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! SO + 0._wp, 0._wp, rsf , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! PF + 0._wp, 0._wp, 0._wp, 0._wp, rit , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! AAIW + 0._wp, 0._wp, 0._wp, 0._wp, rsf , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! STAT + 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, rna , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! NOAT + 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, rit , rit , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! PAIW + 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, rsf , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! STPA + 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, rsf , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! NOPA + rua , rus , rup , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! UCDW + rla , rls , rlp , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! LCDW + 0._wp, 0._wp, 0._wp, 0._wp, rde , rde , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! NADW + 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, rde , rde , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp, & ! PADW + rba , rbs , rbp , 0._wp, rbo , rbo , 0._wp, rbo , rbo , 0._wp, 0._wp, 0._wp, 0._wp, 0._wp],& ! AABW + [ nbox, nbox ] )) + +! Initial conditions + thin(1:14)= [ -1.80_wp, 0.00_wp, 2.00_wp, 5.00_wp, 20.00_wp, & + 5.00_wp, 5.00_wp, 20.00_wp, 5.00_wp, 5.00_wp, & + 5.00_wp, 5.00_wp, 5.00_wp, 0.00_wp ] + + sain(1:14)= [ 34.00_wp, 34.00_wp, 34.00_wp, 34.50_wp, 36.00_wp, & + 35.50_wp, 34.50_wp, 36.00_wp, 34.00_wp, 34.75_wp, & + 34.75_wp, 35.00_wp, 34.75_wp, 34.75_wp ] + +! Wind speed (m/s)for CO2 gas fluxes + wind_in(1:14) = [ 10.0_wp, 10.0_wp, 10.0_wp, 0.0_wp, 5.0_wp, & + 5.0_wp, 0.0_wp, 5.0_wp, 5.0_wp, 0.0_wp, & + 0.0_wp, 0.0_wp, 0.0_wp, 0.0_wp ] + +! Open surface fraction in contact with atmoshpere +! 1 => fully open, <1 => flux impeded (e.g. by sea ice) + fopen_in(1:14)= [ 0.5_wp, 1.0_wp, 1.0_wp, 0.0_wp, 1.0_wp, & + 1.0_wp, 0.0_wp, 1.0_wp, 1.0_wp, 0.0_wp, & + 0.0_wp, 0.0_wp, 0.0_wp, 0.0_wp ] + +! Dust deposition in g Fe m-2 year-1 + fe_input(1:14) = [1.5e-3_wp, 1.5e-3_wp, 1.5e-3_wp, 0.0_wp, & + 1.5e-1_wp, 1.5e-3_wp, 0.0_wp, 1.5e-3_wp, & + 1.5e-3_wp, 0.0_wp, 0.0_wp, 0.0_wp, 0.0_wp, & +! Hydrothermal vent input of 1 Gmol/yr (Tagliabue et al., 2010) +! mol Fe/yr * g/mol * 1/area == g Fe m-2 year-1.... +!divide by 2.5e-3 because fe_sol is multiplied again within model. +#if defined(USEDUALNUMAD) + (1.e9_wp*56._wp)/(2.5e-3_wp*dx(14)%x*dy(14)%x) ] +#else + (1.e9_wp*56._wp)/(2.5e-3_wp*dx(14)*dy(14)) ] +#endif +! Deep ocean box lifetime modifier to capture the gradient due to +! photodegradation near the surface and slower loss in the deep + dldz_in(1:14) = [5.52539622e-01_wp, 6.64231710e-01_wp, 8.15004116e-01_wp, & + 3.76404167e-02_wp, 5.13714214e+00_wp, 4.23725552e-02_wp, 3.76404167e-02_wp, & + 5.13714214e+00_wp, 1.10769475e+00_wp, 1.64961589e-02_wp, 6.84191721e-03_wp, & + 6.84191721e-03_wp, 8.13013117e-03_wp, 1.97010819e-03_wp] +#else + +! Default to the three box model + psi_in = 20.e6_wp + + dx = [ 17.0e6_wp, 17.0e6_wp, 17.0e6_wp ] + dy = [ 4.0e6_wp, 12.0e6_wp, 16.0e6_wp ] + dz = [ 50.0_wp , 50.0_wp , 5050.0_wp ] + + depth = [ dz(1)/2._wp, dz(2)/2._wp, dz(1)+dz(3)/2._wp] + + m2deg = 180._wp/dy(3) + latitude = [(dy(1) /2._wp) , & + (dy(1)+(dy(2)/2._wp)), & + (dy(3) /2._wp) & + ] + latitude = -90._wp+(latitude*m2deg) + +! Mixing mask array + Kin = RESHAPE( [ 0._wp, 1._wp, 1._wp, & + 1._wp, 0._wp, 1._wp, & + 1._wp, 1._wp, 0._wp ], & + [ nbox, nbox ] ) + +! Overturning mask array + Pin = RESHAPE([ 0._wp, 1._wp, 0._wp, & + 0._wp, 0._wp, 1._wp, & + 1._wp, 0._wp, 0._wp ], & + [ nbox, nbox ] ) + +! Remineralization coefficients +! -1 indicates all of export is lost from cell, while +! +1 indicates all of export is remineralized (gained) by cell +! the first box (column one) loses export from box 1, +! the second box (col two) loses export from box 2, +! and the third box (col three) gains export from boxes 1 and 2 + Rin = RESHAPE([ -1._wp, 0._wp, 1._wp, & + 0._wp,-1._wp, 1._wp, & + 0._wp, 0._wp, 0._wp ], & + [ nbox, nbox ] ) + +! Initial conditions thin(1:3)= [ 2._wp, 20._wp , 4._wp ] sain(1:3)= [ 34._wp, 35.50_wp, 34.75_wp ] - ! Initial concentrations in (u/n)mol/kg - ! Make sure to compile with -DFIXATMPCO2 first - cain(1:3)= [ 2100._wp, 2100._wp , 2350._wp ] - alin(1:3)= [ 2300._wp, 2300._wp , 2400._wp ] + cain(1:3)= [ 2200._wp, 2100._wp , 2400._wp ] + alin(1:3)= [ 2350._wp, 2350._wp , 2400._wp ] phin(1:3)= [ 2._wp, 0._wp , 2.5_wp ] - niin(1:3)= [ 32._wp, 0._wp , 36._wp ] + niin(1:3)= [ 25._wp, 0._wp , 35._wp ] - atpco2in = 280._wp +! Initial concentrationesix in (u/n)mol/kg +! Here are some equilibrated values run for 100,000 yrs (round-off error notwithstanding) +! Make sure to compile without -DFIXATMPCO2 +! cain(1:3)= [ 2263.27105_wp, 2104.02729_wp, 2358.11830_wp ] +! alin(1:3)= [ 2396.24755_wp, 2388.24068_wp, 2399.60156_wp ] +! phin(1:3)= [ 1.85304_wp , 0.31325_wp , 2.49804_wp ] +! niin(1:3)= [ 24.68043_wp , 0.04392_wp , 35.00046_wp ] +! fein(1:3)= [ 0.01007_wp , 0.37382_wp , 0.55782_wp ] +! liin(1:3)= [ 1.62217_wp , 1.58451_wp , 1.57992_wp ] - ! Initial concentrations in (u/n)mol/kg - ! Here are some equilibrated values run for 100,000 yrs (round-off error notwithstanding) - ! Make sure to compile without -DFIXATMPCO2 - !c1in = 2262.29678_wp - !c2in = 2102.93797_wp - !c3in = 2363.90418_wp - !a1in = 2395.63558_wp - !a2in = 2387.42536_wp - !a3in = 2399.11408_wp - !p1in = 1.82962_wp - !p2in = 0.25073_wp - !p3in = 2.49856_wp - !n1in = 25.31309_wp - !n2in = 0.05089_wp - !n3in = 36.01617_wp - !f1in = 0.00273_wp - !f2in = 0.32988_wp - !f3in = 0.57430_wp - !l1in = 1.70537_wp - !l2in = 1.62990_wp - !l3in = 1.62589_wp - !atpco2in= 280.00000_wp - - ! Input some example parameters - ! Wind speed (m/s)for CO2 gas fluxes - wind = 0._wp - wind(1:3) = [ 10._wp, 5._wp, 0._wp ] - - ! Open surface fraction in contact with atmoshpere - ! 1 => fully open, <1 => flux impeded (e.g. by sea ice) - fopen = 0._wp - fopen(1:3) = [ 1._wp, 1._wp, 0._wp ] - - ! Gamma over lambda for ligands "optimum" value (Lauderdale et al 2020) - gaovla_opt = 4398._wp - ! Gamma ligand production rate (in phosphate, not carbon, units) - gamma_Fe = 5.e-5_wp*106._wp - ! Lambda ligand lifetime (s) - lt_lifetime = 1._wp/((gamma_Fe/106._wp)/gaovla_opt) - ! Dust deposition in g Fe m-2 year-1 - fe_input = 0._wp +! Wind speed (m/s)for CO2 gas fluxes + wind_in(1:3) = [ 10._wp, 5._wp, 0._wp ] + +! Open surface fraction in contact with atmoshpere +! 1 => fully open, <1 => flux impeded (e.g. by sea ice) + fopen_in(1:3)= [ 1._wp, 1._wp, 0._wp ] + +! Dust deposition in g Fe m-2 year-1 fe_input(1:3)= [ 1.5e-3_wp, 1.5e-1_wp, & - ! Hydrothermal vent input of 1 Gmol/yr (Tagliabue et al., 2010) - ! mol Fe/yr * g/mol * 1/area == g Fe m-2 year-1.... - !divide by 2.5e-3 because fe_sol is multiplied again within model. - (1.e9_wp*56._wp)/(17.e6_wp*16.e6_wp*2.5e-3_wp) ] +! Hydrothermal vent input of 1 Gmol/yr (Tagliabue et al., 2010) +! mol Fe/yr * g/mol * 1/area == g Fe m-2 year-1.... +!divide by 2.5e-3 because fe_sol is multiplied again within model. +#if defined(USEDUALNUMAD) + (1.e9_wp*56._wp)/(2.5e-3_wp*dx(3)%x*dy(3)%x) ] +#else + (1.e9_wp*56._wp)/(2.5e-3_wp*dx(3)*dy(3)) ] +#endif +! Deep ocean box lifetime modifier to capture the gradient due to +! photodegradation near the surface and slower loss in the deep + dldz_in(1:3) = [ 1._wp, 1._wp, 1.e-2_wp ] +#endif - ! Biological production maximum rate (mol P/yr) - alpha_yr = 6.e-6_wp - ! Deep ocean box lifetime modifier to capture the gradient due to - ! photodegradation near the surface and slower loss in the deep - dlambdadz = 1._wp - dlambdadz(1:3)= [ 1._wp, 1._wp, 1.e-2_wp ] - ! Overturning rate (m3/s) - psi = 20.0e6_wp - ! File number identifier - id = 1 - call model(id, maxyears, outputyears, outstepmax, & - dx, dy, dz, & - K, R, & - psi, alpha_yr, & - gamma_Fe, lt_lifetime, & - dlambdadz, & + dx, dy, dz, depth, latitude, & + Kin, Rin, Pin, & + psi_in, dif_in, & + alpha_yr, gamma_in, lt_lifein, & + dldz_in, & fe_input, & - wind, & - fopen, & + wind_in, & + fopen_in, & thin, & sain, & cain, & @@ -238,7 +574,22 @@ PROGRAM MICROCOSM_MODEL nlout, & psout, & pco2out, & - atpco2out & + atpco2out & +#if defined(USEDUALNUMAD) + ,tdxout, & + thdxout, & + sdxout, & + cdxout, & + adxout, & + pdxout, & + ndxout, & + fdxout, & + ldxout, & + expdxout, & + psdxout, & + pco2dxout, & + atpco2dxout & +#endif ) !======================================================================= diff --git a/mod_carbonchem.F90 b/mod_carbonchem.F90 index 9840367..da03685 100644 --- a/mod_carbonchem.F90 +++ b/mod_carbonchem.F90 @@ -17,8 +17,12 @@ MODULE MOD_CARBONCHEM ! You should have received a copy of the GNU Lesser General Public License ! along with SolveSAPHE. If not, see . ! +#if defined(USEDUALNUMAD) + USE DNADMOD +#endif USE MOD_PRECISION - USE MOD_COMMON, ONLY : one, permil + USE MOD_COMMON, ONLY : one, permil, convmolkgmolm3, & + convcmhrms, Kwexch_av USE MOD_CHEMCONST USE MOD_PHSOLVERS USE MOD_CHEMSPECIATION @@ -255,29 +259,28 @@ SUBROUTINE CARBON_FLUXES(theta,salt,dic,alk,po4,si, & REAL(KIND=wp), INTENT(IN) :: pco2atmos ! Local variables - REAL(KIND=wp), DIMENSION(nbox) :: scadic, Kwexch + REAL(KIND=wp), DIMENSION(nbox) :: schmidtDIC, Kwexch REAL(KIND=wp), DIMENSION(nbox) :: co3, hco3, co2aq - INTEGER :: i ! Initialize pco2ocean = 0._wp fluxCO2 = 0._wp - scadic = 0._wp + schmidtDIC= 0._wp Kwexch = 0._wp co3 = 0._wp hco3 = 0._wp co2aq = 0._wp - + ! calculate SCHMIDT NO. for CO2 (4th order, Wanninkhof 2014) - scadic = 2116.8_wp & - - 136.25_wp * theta & - + 4.7353_wp * theta * theta & - - 0.092307_wp * theta * theta * theta & - + 7.555e-4_wp * theta * theta * theta * theta - - Kwexch = (0.337_wp * wind**two/3.6e5_wp) * fopen & - / sqrt(scadic/660._wp) + schmidtDIC = 2116.8_wp & + - 136.25_wp * theta & + + 4.7353_wp * theta * theta & + - 0.092307_wp * theta * theta * theta & + + 7.555e-4_wp * theta * theta * theta * theta + + Kwexch = (Kwexch_av * convcmhrms * fopen * wind*wind ) & + / sqrt(schmidtDIC/660._wp) do i = 1,nbox ! calculate surface coefficients @@ -298,19 +301,15 @@ SUBROUTINE CARBON_FLUXES(theta,salt,dic,alk,po4,si, & co3(i), & hco3(i), & co2aq(i)) - -!Flux = kw*rho*(ff*pCO2atm-k0*FugFac*pCO2ocean) + fluxCO2(i) = Kwexch(i)*( & pco2atmos * apiff_atm - & pco2ocean(i) * apiff_oce & * api0_dic ) - end do -! convert flux (mol kg-1 m s-1) to (mol m-2 s-1) - fluxCO2 = fluxCO2/permil - - RETURN + fluxCO2 = fluxCO2 * convmolkgmolm3 +RETURN END SUBROUTINE CARBON_FLUXES !======================================================================= diff --git a/mod_chemconst.F90 b/mod_chemconst.F90 index 9da4d32..04fae39 100644 --- a/mod_chemconst.F90 +++ b/mod_chemconst.F90 @@ -23,6 +23,10 @@ MODULE MOD_CHEMCONST +#if defined(USEDUALNUMAD) + USE DNADMOD +#endif + USE MOD_PRECISION USE MOD_COMMON, ONLY: one, two, ten, hundred @@ -469,7 +473,7 @@ FUNCTION AK_CARB_1_LUEK00(t_k, s, p_bar) zlog10_kc1_p0 = 61.2172_wp & - 3633.86_wp/t_k & - 9.67770_wp*LOG(t_k) & - + s*(0.011555 - s*0.0001152_wp) + + s*(0.011555_wp - s*0.0001152_wp) ! Pressure correction @@ -628,9 +632,9 @@ FUNCTION AK_CARB_1_ROYE93(t_k, s, p_bar) zcvt_to_kgsw = ACVT_KGH2O_O_KGSW(s) zln_kc1_p0 = -2307.1255_wp/t_k + 2.83655_wp - 1.5529413_wp*LOG(t_k) & - + (-4.0484_wp/t_k - 0.20760841)*zsqrts & - + 0.08468345*s & - - 0.00654208*zsqrts*s + + (-4.0484_wp/t_k - 0.20760841_wp)*zsqrts & + + 0.08468345_wp*s & + - 0.00654208_wp*zsqrts*s ! Pressure correction @@ -711,7 +715,7 @@ FUNCTION AK_CARB_2_ROYE93(t_k, s, p_bar) zln_kc2_p0 = -3351.6106_wp/t_k - 9.226508_wp - 0.2005743_wp*LOG(t_k) & + ( -23.9722_wp/t_k - 0.106901773_wp)*zsqrts & - + 0.1130822*s - 0.00846934_wp*zsqrts*s + + 0.1130822_wp*s - 0.00846934_wp*zsqrts*s ! Pressure correction @@ -950,7 +954,7 @@ FUNCTION AK_PHOS_1_MILL95(t_k, s, p_bar) zt_degc = t_k - t_k_zerodegc zrt = gasconst_bar_cm3_o_mol_k * t_k -zdvi = -14.51_wp + 0.1211_wp*zt_degc - 0.321E-03*zt_degc*zt_degc +zdvi = -14.51_wp + 0.1211_wp*zt_degc - 0.321E-03_wp*zt_degc*zt_degc zdki = ( -2.67_wp + 0.0427_wp*zt_degc)*1.0E-03_wp zln_kp1_pp = (-zdvi + zdki*p_bar/two)*p_bar/zrt @@ -1096,7 +1100,7 @@ FUNCTION AK_PHOS_3_MILL95(t_k, s, p_bar) zt_degc = t_k - t_k_zerodegc zrt = gasconst_bar_cm3_o_mol_k * t_k -zdvi = -26.57_wp + 0.2020_wp*zt_degc -3.042E-03*zt_degc*zt_degc +zdvi = -26.57_wp + 0.2020_wp*zt_degc -3.042E-03_wp*zt_degc*zt_degc zdki = ( -4.08_wp + 0.0714_wp*zt_degc)*1.0E-03_wp zln_kp3_pp = (-zdvi + zdki*p_bar/two)*p_bar/zrt @@ -1696,7 +1700,7 @@ FUNCTION ASP_CALC_MUCC83(t_k, s, p_bar) zlog10_kspcalc_p0 = & -171.9065_wp - 0.077993_wp*t_k & + 2839.319_wp/t_k + 71.595_wp*LOG10(t_k) & - + ( -0.77712_wp + 0.0028426*t_k + 178.34_wp/t_k)*zsqrts & + + ( -0.77712_wp + 0.0028426_wp*t_k + 178.34_wp/t_k)*zsqrts & - 0.07711_wp*s + 0.0041249_wp*s*zsqrts diff --git a/mod_chemspeciation.F90 b/mod_chemspeciation.F90 index df4657a..3d18b9a 100644 --- a/mod_chemspeciation.F90 +++ b/mod_chemspeciation.F90 @@ -23,6 +23,9 @@ MODULE MOD_CHEMSPECIATION +#if defined(USEDUALNUMAD) + USE DNADMOD +#endif USE MOD_PRECISION diff --git a/mod_common.F90 b/mod_common.F90 index 7169c41..9b71c14 100644 --- a/mod_common.F90 +++ b/mod_common.F90 @@ -1,6 +1,9 @@ ! -*- f90 -*- MODULE MOD_COMMON !variable declarations etc for microCOSM model +#if defined(USEDUALNUMAD) + USE DNADMOD +#endif USE MOD_PRECISION USE MOD_BOXES IMPLICIT NONE @@ -13,10 +16,12 @@ MODULE MOD_COMMON REAL(KIND=wp), PARAMETER :: speryr = 31536000._wp ! Conversion factors -REAL(KIND=wp), PARAMETER :: conv = 1024.5_wp -REAL(KIND=wp), PARAMETER :: permil = 1._wp/conv -REAL(KIND=wp), PARAMETER :: umolkg2molm3 = conv * 1.e-6_wp -REAL(KIND=wp), PARAMETER :: nmolkg2molm3 = conv * 1.e-9_wp +REAL(KIND=wp), PARAMETER :: convmolkgmolm3 = 1024.5_wp +REAL(KIND=wp), PARAMETER :: convcmhrms = 1._wp/3.6e5_wp + +REAL(KIND=wp), PARAMETER :: permil = 1._wp/convmolkgmolm3 +REAL(KIND=wp), PARAMETER :: umolkg2molm3 = convmolkgmolm3 * 1.e-6_wp +REAL(KIND=wp), PARAMETER :: nmolkg2molm3 = convmolkgmolm3 * 1.e-9_wp REAL(KIND=wp), PARAMETER :: uatm2atm = 1.e-6_wp REAL(KIND=wp), PARAMETER :: molps2gtcyr = 106._wp * 12._wp * speryr * 1.e-15_wp @@ -24,6 +29,10 @@ MODULE MOD_COMMON six, seven, eight, nine REAL(KIND=wp) :: ten, hundred, thousand +REAL(KIND=wp), DIMENSION(nbox, nbox) :: K, R, P + +REAL(KIND=wp) :: psi, dif + ! biogeochemical tracers internal REAL(KIND=wp), DIMENSION(nbox) :: theta, salt, dic, alk, po4, no3, fet, lt @@ -34,27 +43,32 @@ MODULE MOD_COMMON ! some arrays for average accumulators REAL(KIND=wp), DIMENSION(nbox) :: thetaM, saltM, exportM, pco2M, & dicM, alkM, po4M, no3M, fetM, ltM, sitM -REAL(KIND=wp) :: timeM, pco2A, pstarM +REAL(KIND=wp) :: timeM, pco2A, pstarM ! time derivatives REAL(KIND=wp), DIMENSION(nbox) :: dthetadt, dsaltdt REAL(KIND=wp), DIMENSION(nbox) :: ddicdt, dalkdt, dpo4dt, dno3dt, & - dfetdt, dltdt, dsitdt + dfetdt, dltdt, dsitdt ! Redfield ratios REAL(KIND=wp) :: rCP, rNP, rPO2, rCN, rCO2, rCFe, rSIP, rCACO3 REAL(KIND=wp), DIMENSION(nbox) :: pco2ocean, fluxCO2 REAL(KIND=wp) :: netco2flux, atmos_moles, atmos_carbon, & - pco2atmos + pco2atmos + +! DIC gas exchange piston velocity coefficient +REAL(KIND=wp) :: Kwexch_av +REAL(KIND=wp), DIMENSION(nbox) :: wind, fopen ! Iron cycle parameters ! atomic weight of iron = 56 REAL(KIND=wp) :: weight_fe, fe_sol, beta, Kscav, relaxfe ! iron input rate -REAL(KIND=wp), DIMENSION(nbox) :: fe_depo +REAL(KIND=wp), DIMENSION(nbox) :: fe_depo, fe_pptmask ! Dynamic Ligand variables -REAL(KIND=wp), DIMENSION(nbox) :: lambda +REAL(KIND=wp) :: gamma_Fe, lt_lifetime +REAL(KIND=wp), DIMENSION(nbox) :: dlambdadz, lambda ! export related ! half saturation constants @@ -62,8 +76,7 @@ MODULE MOD_COMMON REAL(KIND=wp), DIMENSION(nbox) :: export REAL(KIND=wp) :: alpha REAL(KIND=wp), DIMENSION(nbox) :: light, ilimit, plimit, nlimit, flimit -! nutrient limitation codes -INTEGER :: lim +INTEGER(kind=ip) lim CONTAINS @@ -94,22 +107,29 @@ SUBROUTINE COMMON_ASSIGNMENTS() rSIP = 15._wp rCACO3 = 10.e-2_wp + ph = eight + ! half saturation constants - kfe = 0.1e-9_wp*conv - kpo4 = 0.1e-6_wp*conv - kno3 = 0.1e-6_wp*conv*rNP + kfe = 0.1e-9_wp*convmolkgmolm3 + kpo4 = 0.1e-6_wp*convmolkgmolm3 + kno3 = 0.1e-6_wp*convmolkgmolm3*rNP klight = 30._wp ! Iron cycle parameters weight_fe = 56._wp !solubility of iron: - fe_sol = 0.0025_wp + fe_sol = 2.5e-3_wp ! conditional stability FeL: (mol kg-1)-1 beta = 1.0e9_wp ! Free Fe scavenging rate: (s-1) Kscav = 1.0e-7_wp ! relaxfe (s) relaxfe = 0.01_wp * speryr +! Iron precipitation mask + fe_pptmask=0.0_wp +! DIC gas exchange piston velocity coefficient + Kwexch_av = 0.337_wp + RETURN END SUBROUTINE COMMON_ASSIGNMENTS !======================================================================= diff --git a/mod_dimensions.F90 b/mod_dimensions.F90 index 91f9f59..f1c9166 100644 --- a/mod_dimensions.F90 +++ b/mod_dimensions.F90 @@ -1,19 +1,30 @@ ! -*- f90 -*- MODULE MOD_BOXES IMPLICIT NONE +#if defined(FOURBOX) + INTEGER, PARAMETER :: nbox = 4 +#elif defined(FOURTEENBOX) + INTEGER, PARAMETER :: nbox = 14 +#else +! default to three box model INTEGER, PARAMETER :: nbox = 3 +#endif END MODULE MOD_BOXES MODULE MOD_DIMENSIONS +#if defined(USEDUALNUMAD) + USE DNADMOD +#endif + USE MOD_PRECISION USE MOD_BOXES IMPLICIT NONE ! geometry -!REAL(KIND=wp), DIMENSION(nbox) :: dx, dy, dz, lat -REAL(KIND=wp), DIMENSION(nbox) :: lat -REAL(KIND=wp), DIMENSION(nbox) :: area, vol, invol, depth, pressure +!REAL(KIND=wp), DIMENSION(nbox) :: dx, dy, dz, latitude +!REAL(KIND=wp), DIMENSION(nbox) :: latitude, depth +REAL(KIND=wp), DIMENSION(nbox) :: area, vol, invol, pressure !REAL(KIND=wp), DIMENSION(nbox,nbox) :: K, R CONTAINS @@ -28,33 +39,19 @@ MODULE MOD_DIMENSIONS !======================================================================= -SUBROUTINE ESTABLISH_DIMENSIONS(dx,dy,dz,lat,area,vol,invol, & - depth,pressure) +SUBROUTINE ESTABLISH_DIMENSIONS(dx,dy,dz,lat,depth,area,vol,invol, & + pressure) USE MOD_BOXES IMPLICIT NONE -REAL(KIND=wp), DIMENSION(nbox), intent(in) :: dx, dy, dz - -REAL(KIND=wp), DIMENSION(nbox), intent(out) :: & - lat, area, vol, invol,& - depth, pressure -!REAL(KIND=wp), DIMENSION(nbox,nbox), intent(out) :: K, R -REAL(KIND=wp) :: m2deg +REAL(KIND=wp), DIMENSION(nbox), intent(in) :: dx, dy, dz, lat, & + depth -!dx = [ 17.0e6_wp, 17.0e6_wp, 17.0e6_wp ] -!dy = [ 4.0e6_wp, 12.0e6_wp, 16.0e6_wp ] -!dz = [ 50.0_wp, 50.0_wp, 5050.0_wp ] +REAL(KIND=wp), DIMENSION(nbox), intent(out) :: area, vol, invol, & + pressure -! depth in m or decibars -depth = [ 25.0_wp, 25.0_wp, 2575.0_wp ] ! applied pressure in bars for carbon system coefficients pressure = (depth/10._wp) - 1._wp -m2deg = 180._wp/(dy(1)+dy(2)) -lat = [ -90._wp+(dy(1) /2._wp) *m2deg, & - -90._wp+(dy(1)+(dy(2)/2._wp))*m2deg, & - -90._wp+(dy(3) /2._wp) *m2deg & - ] - area = dx * dy vol = area * dz invol = 1._wp / vol @@ -63,62 +60,27 @@ SUBROUTINE ESTABLISH_DIMENSIONS(dx,dy,dz,lat,area,vol,invol, & END SUBROUTINE ESTABLISH_DIMENSIONS !======================================================================= -!======================================================================= -FUNCTION TRANSPORT(x, kappa, psi, invol) -! atmosphere-3-box-ocean carbon cycle model -! evaluate rates of change due to transport -! mick follows, march 2015/ june 2016 -USE MOD_BOXES -IMPLICIT NONE -REAL(KIND=wp), DIMENSION(nbox) :: TRANSPORT -REAL(KIND=wp), intent(in), DIMENSION(nbox) :: x -REAL(KIND=wp), intent(in), DIMENSION(nbox,nbox) :: kappa -REAL(KIND=wp), intent(in) :: psi -REAL(KIND=wp), intent(in), DIMENSION(nbox) :: invol -! -TRANSPORT(1) = invol(1) * ( & - psi*(x(3)-x(1)) & - + kappa(3,1)*(x(3)-x(1)) & - + kappa(2,1)*(x(2)-x(1)) & - ) -TRANSPORT(2) = invol(2) * ( & - psi*(x(1)-x(2)) & - + kappa(1,2)*(x(1)-x(2)) & - + kappa(3,2)*(x(3)-x(2)) & - ) -TRANSPORT(3) = invol(3) * ( & - psi*(x(2)-x(3)) & - + kappa(2,3)*(x(2)-x(3)) & - + kappa(1,3)*(x(1)-x(3)) & - ) - - RETURN - END FUNCTION TRANSPORT -!======================================================================= - -! !======================================================================= -! FUNCTION TRANSPORT(x, kappa, psi, invol) -! !atmosphere-5-box-ocean carbon cycle model -! ! a la Follows, Ito and Marotzke, 2002 GBC -! !evaluate rates of change due to transport -! +!!======================================================================= +!FUNCTION TRANSPORT(x, kappa, psi, invol) +!!atmosphere-3-box-ocean carbon cycle model +!!evaluate rates of change due to transport +!!mick follows, march 2015/ june 2016 ! USE MOD_BOXES ! IMPLICIT NONE ! REAL(KIND=wp), DIMENSION(nbox) :: TRANSPORT ! REAL(KIND=wp), intent(in), DIMENSION(nbox) :: x -! REAL(KIND=wp), intent(in), DIMENSION(nbox,nbox) :: kappa -! REAL(KIND=wp), intent(in) :: psi +! REAL(KIND=wp), intent(in), DIMENSION(nbox,nbox) :: kmask, pmask +!! REAL(KIND=wp), intent(in), DIMENSION(nbox,nbox) :: kappa +! REAL(KIND=wp), intent(in) :: psi, kappa ! REAL(KIND=wp), intent(in), DIMENSION(nbox) :: invol -! ! +! ! TRANSPORT(1) = invol(1) * ( & -! psi(1)*(x(5)-x(1)) & -! + kappa(5,1)*(x(5)-x(1)) & +! psi*(x(3)-x(1)) & +! + kappa(3,1)*(x(3)-x(1)) & ! + kappa(2,1)*(x(2)-x(1)) & -! + kappa(3,1)*(x(3)-x(1))) & ! ) ! TRANSPORT(2) = invol(2) * ( & -! psi(1)*(x(1)-x(2)) & -! + psi(2)*(x(4)-x(2)) +! psi*(x(1)-x(2)) & ! + kappa(1,2)*(x(1)-x(2)) & ! + kappa(3,2)*(x(3)-x(2)) & ! ) @@ -130,7 +92,7 @@ END FUNCTION TRANSPORT ! ! RETURN ! END FUNCTION TRANSPORT -! !======================================================================= +!!======================================================================= !======================================================================= FUNCTION CALC_PSTAR(nutrient) @@ -143,7 +105,13 @@ FUNCTION CALC_PSTAR(nutrient) REAL(KIND=wp), DIMENSION(nbox), intent(in) :: nutrient -CALC_PSTAR = (nutrient(3) - nutrient(1)) / nutrient(3) +#if defined(FOURBOX) + CALC_PSTAR = (nutrient(4) - nutrient(1)) / nutrient(4) +#elif defined(FOURTEENBOX) + CALC_PSTAR = (nutrient(14) - nutrient(2)) / nutrient(14) +#else + CALC_PSTAR = (nutrient(3) - nutrient(1)) / nutrient(3) +#endif RETURN END FUNCTION CALC_PSTAR diff --git a/mod_modelmain.F90 b/mod_modelmain.F90 index a2997cb..a21a80e 100644 --- a/mod_modelmain.F90 +++ b/mod_modelmain.F90 @@ -1,11 +1,15 @@ ! -*- f90 -*- MODULE MOD_MODELMAIN #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION +#if defined(USEDUALNUMAD) + USE DNADMOD +#endif USE MOD_PRECISION USE MOD_BOXES USE MOD_DIMENSIONS - use MOD_COMMON + USE MOD_COMMON USE MOD_CARBONCHEM + USE MOD_MODELIO IMPLICIT NONE ! -------------------------------------------------------- ! List of (PRIVATE) routines/functions @@ -26,24 +30,28 @@ SUBROUTINE MODEL( & dx, & dy, & dz, & - K, & - R, & - psi, & + depth, & + latitude, & + Kin, & + Rin, & + Pin, & + psi_in, & + dif_in, & alpha_yr, & - gamma_Fe, & - lt_lifetime, & - dlambdadz, & + gamma_in, & + lt_lifein, & + dldz_in, & fe_input, & - wind, & - fopen, & + wind_in, & + foin, & thin, & sain, & - cin, & - ain, & - pin, & - nin, & - fin, & - lin, & + cain, & + alin, & + phin, & + niin, & + fein, & + ltin, & atpco2in, & tout, & thout, & @@ -58,7 +66,22 @@ SUBROUTINE MODEL( & nlout, & psout, & ocpco2out, & - atpco2out & + atpco2out & +#if defined(USEDUALNUMAD) + ,tdxout, & + thdxout, & + sdxout, & + cdxout, & + adxout, & + pdxout, & + ndxout, & + fdxout, & + ldxout, & + expdxout, & + psdxout, & + ocpco2dxout, & + atpco2dxout & +#endif ) !----------------------------------------------------------------------- @@ -68,29 +91,30 @@ SUBROUTINE MODEL( & REAL(KIND=wp), intent(in) :: & maxyears, & outputyears, & - psi, & + psi_in, & + dif_in, & alpha_yr, & - gamma_Fe, & - lt_lifetime, & + gamma_in, & + lt_lifein, & atpco2in REAL(KIND=wp), intent(in), dimension (nbox) :: & - dx, dy, dz, & + dx, dy, dz, depth, latitude, & thin, & sain, & - cin, & - ain, & - pin, & - nin, & - fin, & - lin, & + cain, & + alin, & + phin, & + niin, & + fein, & + ltin, & fe_input, & - dlambdadz, & - wind, & - fopen + dldz_in, & + wind_in, & + foin REAL(KIND=wp), intent(in), dimension (nbox, nbox) :: & - K, R + Kin, Rin, Pin REAL(KIND=wp), intent(out), dimension (outstepmax,nbox) :: & thout, & @@ -103,24 +127,54 @@ SUBROUTINE MODEL( & lout, & expout, & ocpco2out - + REAL(KIND=wp), intent(out), dimension (outstepmax) :: & tout, & psout, & atpco2out - INTEGER, intent(out), dimension (outstepmax) :: & + INTEGER(KIND=ip), intent(out), dimension (outstepmax) :: & nlout +#if defined(USEDUALNUMAD) + REAL(KIND=wp), intent(out), dimension (outstepmax,ndv,nbox) :: & + thdxout, & + sdxout, & + cdxout, & + adxout, & + pdxout, & + ndxout, & + fdxout, & + ldxout, & + expdxout, & + ocpco2dxout + + REAL(KIND=wp), intent(out), dimension (outstepmax,ndv) :: & + tdxout, & + psdxout, & + atpco2dxout + + REAL(KIND=wp), dimension (ndv,ndv) :: dxvec + INTEGER :: idv +#endif + ! local variables ! include "comdeck.h" INTEGER :: nstep, outstep REAL(KIND=wp) :: time - CHARACTER*64 :: fmt, inifmt, varfmt, frep, filename + CHARACTER*64 :: filename_avg +#if defined(USEDUALNUMAD) + CHARACTER*64 :: filename_dnad +#endif !----------------------------------------------------------------------- CALL common_assignments() + write (filename_avg , '(a,I0.6,a)') 'microCOSM_' ,id,'_output' +#if defined(USEDUALNUMAD) + write (filename_dnad, '(a,I0.6,a)') 'microCOSMDNAD_',id,'_output' +#endif + ! set some parameters nstepmax = int(maxyears*(speryr/dt)) ! initialize outstep @@ -128,21 +182,30 @@ SUBROUTINE MODEL( & ! initial time time = 0._wp - CALL establish_dimensions(dx,dy,dz,lat,area,vol,invol, & - depth,pressure) + CALL establish_dimensions(dx,dy,dz,latitude,depth,area, & + vol,invol,pressure) +! Set model variables from input values theta = thin salt = sain ! convert from u/nmol kg-1 to moles m-3 - dic = cin * umolkg2molm3 - alk = ain * umolkg2molm3 - po4 = pin * umolkg2molm3 - no3 = nin * umolkg2molm3 - fet = fin * nmolkg2molm3 - lt = lin * nmolkg2molm3 - sit = pin * umolkg2molm3 * rSIP - - ph = eight + dic = cain * umolkg2molm3 + alk = alin * umolkg2molm3 + po4 = phin * umolkg2molm3 + no3 = niin * umolkg2molm3 + fet = fein * nmolkg2molm3 + lt = ltin * nmolkg2molm3 + sit = phin * umolkg2molm3 * rSIP + +! More config/forcing variables + K = Kin + R = Rin + P = Pin + psi = psi_in + dif = dif_in + wind= wind_in + fopen=foin + ! initialize tracer rates of change ! temp, salt, and si are passive for now, just for co2 solubility dthetadt = zero @@ -155,23 +218,10 @@ SUBROUTINE MODEL( & dltdt = zero dsitdt = zero -!! Iron cycle parameters ......... -! Iron external source -! convert to mol Fe m-2 s-1 - fe_depo = fe_input / (weight_fe*speryr) - -! ligand parameters -! longer lifetime in deep ocean (Ye et al, 2009; Yamaguchi et al, 2002) - if (lt_lifetime.LE.zero) then - lambda = zero - else - lambda = dlambdadz/lt_lifetime - endif - ! Export production parameters (Parekh et al, 2005): ! max export prodution rate: (again, phosphorus units, mol P m-3 s-1) ! alpha = 0.5d-6 * conv / (30.0*86400.0) ! Recover with alpha_yr=6e-6 - alpha = alpha_yr * conv / (speryr) + alpha = alpha_yr * convmolkgmolm3 / (speryr) ! Initial export production and nutrient limitation code light = zero @@ -182,8 +232,65 @@ SUBROUTINE MODEL( & export = zero lim = 0 +!! Iron cycle parameters ......... +! Iron external source +! convert to mol Fe m-2 s-1 + fe_depo = fe_input / (weight_fe*speryr) + +! ligand parameters + gamma_Fe = gamma_in + dlambdadz = dldz_in + lt_lifetime= lt_lifein + +#if defined(USEDUALNUMAD) +! Set the Dual number sensitivities + dxvec = 0.0_wp + dxvec(1,1) = alpha%x + dxvec(2,2) = kno3%x + dxvec(3,3) = kpo4%x + dxvec(4,4) = kfe%x + dxvec(5,5) = klight%x + dxvec(6,6) = psi%x + dxvec(7,7) = dif%x + dxvec(8,8) = gamma_Fe%x + dxvec(9,9) = lt_lifetime%x + + ! max export prodution rate: phosphorus units! (mol P m-3 s-1) + alpha = dual(alpha%x , dxvec(1,:)%x) + ! half saturation for nitrate limitation (mol m-3) + kno3 = dual(kno3%x , dxvec(2,:)%x) + ! half saturation for phosphate limitation (mol m-3) + kpo4 = dual(kpo4%x , dxvec(3,:)%x) + ! half saturation for iron limitation (mol m-3) + kfe = dual(kfe%x , dxvec(4,:)%x) + ! half saturation for light limitation (W m-2) + klight = dual(klight%x , dxvec(5,:)%x) + ! MOC (Sv) + psi = dual(psi%x , dxvec(6,:)%x) + ! Mixing (Sv) + dif = dual(dif%x , dxvec(7,:)%x) + ! Ligand production rate + gamma_Fe = dual(gamma_Fe%x , dxvec(8,:)%x) + ! Ligand lifetime + lt_lifetime = dual(lt_lifetime%x, dxvec(9,:)%x) + + !! longer lifetime in deep ocean (Ye et al, 2009; Yamaguchi et al, 2002) + if (lt_lifetime.LE.zero) then + lambda = zero + else + lambda = dlambdadz/lt_lifetime + endif +#else +!! longer lifetime in deep ocean (Ye et al, 2009; Yamaguchi et al, 2002) + if (lt_lifetime.LE.zero) then + lambda = zero + else + lambda = dlambdadz/lt_lifetime + endif +#endif + ! evaluate pstar, consistent with Harvardton Bears SO sensitivity - pstar = calc_pstar(po4) + pstar = MAX(calc_pstar(po4), calc_pstar(no3)) ! Initialize atmospheric carbon content ! Mass dry atmosphere = (5.1352+/-0.0003)d18 kg (Trenberth & Smith, @@ -196,7 +303,6 @@ SUBROUTINE MODEL( & * (area(3)/(5.10082e8_wp * 1.e6_wp)) pco2atmos = atpco2in * uatm2atm atmos_carbon = pco2atmos * atmos_moles - sitM = (po4 * rSIP) / umolkg2molm3 !! Find out initial conditions of the carbon system for given input values call carbon_fluxes(theta, & @@ -229,92 +335,66 @@ SUBROUTINE MODEL( & pco2M = pco2ocean / uatm2atm pco2A = pco2atmos / uatm2atm -#if defined(WRITEOUTFILE) -! open an output file and write initial values to file - write (filename, '(a,I0.6,a)') 'microCOSM',id,'.dat' - open(14,file=filename,status='unknown') - -! write column header output - write(14,*)' t(yr) Limits ', & - repeat(' THETA ',nbox), & - repeat(' SALT ',nbox), & - repeat('DIC ',nbox), & - repeat('ALK ',nbox), & - repeat(' PO4 ',nbox), & - repeat(' NO3 ',nbox), & - repeat(' FET ',nbox), & - repeat(' LIG ',nbox), & - repeat(' EXPORT ',nbox), & - ' P* ', & - repeat(' OCPCO2 ',nbox), & - ' ATPCO2 ' - -! Construct fortran format string -! Output the time and nutrient limitation code - inifmt='1x, i10.1, 1x, i10.0,' -! Each variable then is a space and a 10 position float with 5 decimal places - varfmt='1x, f10.5' -! This is the number of repeats (10 variables of nbox dimensions plus pstar and atmpco2) - write(frep ,'(I4)') 10*nbox+2 -! Combine everything together -! fmt='('//trim(fmt)//trim(frep)//'('//trim(varfmt)//'))' - write(fmt,'(6A)') '(',trim(inifmt),trim(frep),'(',trim(varfmt),'))' - - -! Write initial conditions to file - write(14,fmt) int(timeM), & - lim, & - thetaM, & - saltM, & - dicM, & - alkM, & - po4M, & - no3M, & - fetM, & - ltM, & - exportM, & - pstarM, & - pco2M, & - pco2A +! Do Model Io For Initial Condition + call modelio_output(filename_avg , & +#if defined(USEDUALNUMAD) + filename_dnad, & #endif + outstep, & + outstepmax, & + timeM, & + lim, & + thetaM, & + saltM, & + dicM, & + alkM, & + po4M, & + no3M, & + fetM, & + ltM, & + exportM, & + pstarM, & + pco2M, & + pco2A, & + thout, & + sout, & + cout, & + aout, & + pout, & + nout, & + fout, & + lout, & + expout, & + ocpco2out, & + tout, & + nlout, & + psout, & + atpco2out & +#if defined(USEDUALNUMAD) + , thdxout, & + sdxout, & + cdxout, & + adxout, & + pdxout, & + ndxout, & + fdxout, & + ldxout, & + expdxout, & + ocpco2dxout, & + tdxout, & + psdxout, & + atpco2dxout & +#endif + ) -! output to array - thout (outstep,1:nbox) = thetaM - sout (outstep,1:nbox) = saltM - cout (outstep,1:nbox) = dicM - aout (outstep,1:nbox) = alkM - pout (outstep,1:nbox) = po4M - nout (outstep,1:nbox) = no3M - fout (outstep,1:nbox) = fetM - lout (outstep,1:nbox) = ltM - expout (outstep,1:nbox) = exportM - ocpco2out (outstep,1:nbox) = pco2M - tout (outstep) = timeM - nlout (outstep) = lim - psout (outstep) = pstarM - atpco2out (outstep) = pco2A -! Increment outstep - outstep=outstep+1 - ! timestepping ......................................... do 200 nstep = 1,nstepmax -! evaluate rates of change due to transport -! dthetadt = transport(nbox, theta, K, psi, invol) -! dsaltdt = transport(nbox, salt, K, psi, invol) - ddicdt = TRANSPORT(dic, K, psi, invol) - dalkdt = TRANSPORT(alk, K, psi, invol) - dpo4dt = TRANSPORT(po4, K, psi, invol) - dno3dt = TRANSPORT(no3, K, psi, invol) - dfetdt = TRANSPORT(fet, K, psi, invol) - dltdt = TRANSPORT(lt, K, psi, invol) - +! Calculate surface air-sea gas exchange of CO2 +! Diagnostically update silicate concentration linked to phosphate time=nstep*dt / (speryr) -! evaluate biogeochemical rates of change -! Surface boxes... - netco2flux = zero - -! Calculate surface air-sea gas exchange of CO2 + sit = po4 * rSIP + call carbon_fluxes(theta, & salt, & dic, & @@ -328,34 +408,51 @@ SUBROUTINE MODEL( & pressure, & pco2ocean, & fluxCO2) - - netco2flux = netco2flux + sum(fluxCO2 * area) - -! Make sure subsurface boxes are masked by fopen = 0 + + netco2flux = sum(fluxCO2 * area) + +#ifndef FIXATMPCO2 +! Update atmospheric CO2 (but only if you want to) + netco2flux=netco2flux*dt + call calc_atmos_pco2(atmos_moles, & + atmos_carbon, & + netco2flux, & + pco2atmos) +#endif + +! evaluate rates of change due to transport +! dthetadt = transport(nbox, theta, K, psi, invol) +! dsaltdt = transport(nbox, salt, K, psi, invol) + ddicdt = TRANSPORT(dic, P, psi, K, dif, invol) + dalkdt = TRANSPORT(alk, P, psi, K, dif, invol) + dpo4dt = TRANSPORT(po4, P, psi, K, dif, invol) + dno3dt = TRANSPORT(no3, P, psi, K, dif, invol) + dfetdt = TRANSPORT(fet, P, psi, K, dif, invol) + dltdt = TRANSPORT(lt , P, psi, K, dif, invol) + +! evaluate biogeochemical rates of change ddicdt = ddicdt + fluxCO2 / dz - + ! biological terms - light = INSOL(time * speryr, lat) - - ilimit = light / (light + klight) - plimit = po4 / (po4 + kpo4 ) - nlimit = no3 / (no3 + kno3 ) - flimit = fet / (fet + kfe ) + light = INSOL(time * speryr, latitude) * fopen + ilimit = light / ( light + klight ) + plimit = po4 / ( po4 + kpo4 ) + nlimit = no3 / ( no3 + kno3 ) + flimit = fet / ( fet + kfe ) -! -ve export is uptake by phytoplankton, +ve export is net remineralization bioP = CALC_PRODUCTION(nlimit, plimit, flimit, ilimit, alpha) lim = NUTRIENT_LIMIT_CODE(plimit, nlimit, flimit, ilimit) ! scale rate of nutrient export with rate of phosphorus export -! R matrix determines export flux and remineralization locations -! Spread broadcasts export and volume arrays to matrices +! R matrix determines export flux and remineralization locations, -ve export is +! uptake by phytoplankton, +ve export is net remineralization ! Each is volume weighted (technically for a single box, this is not necessary, ! but it works for accumulation of several boxes too.) - export = CALC_EXPORT(R, bioP, vol, invol) + export = CALC_EXPORT(R, bioP, vol, invol) ! carbonate flux depends on rain ratio - carb = export * rCP * rCACO3 + carb = export * rCP * rCACO3 dpo4dt = dpo4dt + export dno3dt = dno3dt + export * (rCP/rCN) @@ -371,33 +468,24 @@ SUBROUTINE MODEL( & ! Dynamic ligand production is based on exudation in the surface layers depending on ! production and release during remineralization in the ocean interior - dltdt = dltdt + (abs(export) * gamma_Fe) - (lambda * lt) - -! end of surface boxes loop - + dltdt = dltdt + (abs(export) * gamma_Fe - lambda * lt) +!500 format(1x, e15.8, 1x, e15.8, 1x, e15.8) +! write(6,500) export ! input of iron (can include (vent source)/fe_sol) - dfetdt = dfetdt + fe_sol * fe_depo / dz + dfetdt = dfetdt + (fe_sol * fe_depo) / dz ! scavenging and complexation of iron ! evaluate local feprime from fet and lt ! determine scavenging rate and add to total tendency - feprime=FE_EQUIL(fet, lt, beta) + feprime = FE_EQUIL(fet, lt, beta) - dfetdt = dfetdt - Kscav*feprime + dfetdt = dfetdt - Kscav*feprime ! if FeT > LT, then all excess iron is Fe-prime and precipitates out quickly ! Liu and Millero indicate very small "soluble" free iron - WHERE (fet > lt ) dfetdt = dfetdt - (one/relaxfe)*(fet - lt) - -#ifndef FIXATMPCO2 -! Update atmospheric CO2 (but only if you want to) - - netco2flux=netco2flux*dt - call calc_atmos_pco2(atmos_moles, & - atmos_carbon, & - netco2flux, & - pco2atmos) -#endif + fe_pptmask = 0._wp + WHERE (fet > lt ) fe_pptmask = 1._wp + dfetdt = dfetdt - fe_pptmask * ((one/relaxfe)*(fet-lt)) ! Euler forward step concentrations theta = theta + dthetadt * dt @@ -410,7 +498,7 @@ SUBROUTINE MODEL( & lt = lt + dltdt * dt ! evaluate pstar - pstar = calc_pstar(po4) + pstar = MAX(calc_pstar(po4), calc_pstar(no3)) time = nstep*dt / speryr ! Increment the average accumulators @@ -430,94 +518,140 @@ SUBROUTINE MODEL( & exportM= (exportM+export*vol*molps2gtcyr) - ! if an output time, write some output to screen and file - if (mod(time,outputyears) .eq. 0)then +#if defined(USEDUALNUMAD) + if (mod(time%x,outputyears%x).eq.zero) then + outstep=int(time%x/outputyears%x)+1 +#else + if (mod(time,outputyears).eq.zero) then + outstep=int(time/outputyears)+1 +#endif ! For output, work out what the average is - timeM = timeM /(outputyears*speryr/dt) - thetaM = thetaM /(outputyears*speryr/dt) - saltM = saltM /(outputyears*speryr/dt) - - dicM = dicM /(outputyears*speryr/dt) - alkM = alkM /(outputyears*speryr/dt) - po4M = po4M /(outputyears*speryr/dt) - no3M = no3M /(outputyears*speryr/dt) - fetM = fetM /(outputyears*speryr/dt) - ltM = ltM /(outputyears*speryr/dt) - pstarM = pstarM /(outputyears*speryr/dt) - pco2M = pco2M /(outputyears*speryr/dt) - pco2A = pco2A /(outputyears*speryr/dt) + timeM = timeM / (outputyears*speryr/dt) + thetaM = thetaM / (outputyears*speryr/dt) + saltM = saltM / (outputyears*speryr/dt) + + dicM = dicM / (outputyears*speryr/dt) + alkM = alkM / (outputyears*speryr/dt) + po4M = po4M / (outputyears*speryr/dt) + no3M = no3M / (outputyears*speryr/dt) + fetM = fetM / (outputyears*speryr/dt) + ltM = ltM / (outputyears*speryr/dt) + pstarM = pstarM / (outputyears*speryr/dt) + pco2M = pco2M / (outputyears*speryr/dt) + pco2A = pco2A / (outputyears*speryr/dt) - exportM= exportM/(outputyears*speryr/dt) - -#if defined(WRITEOUTFILE) -! Write model state to file - write(14,fmt) int(timeM), & - lim, & - thetaM, & - saltM, & - dicM, & - alkM, & - po4M, & - no3M, & - fetM, & - ltM, & - -exportM, & - pstarM, & - pco2M, & - pco2A -#endif - -! output to array - thout (outstep,1:nbox) = theta - sout (outstep,1:nbox) = salt - cout (outstep,1:nbox) = dicM - aout (outstep,1:nbox) = alkM - pout (outstep,1:nbox) = po4M - nout (outstep,1:nbox) = no3M - fout (outstep,1:nbox) = fetM - lout (outstep,1:nbox) = ltM - expout (outstep,1:nbox) =-exportM - ocpco2out (outstep,1:nbox) = pco2M - tout (outstep) = timeM - nlout (outstep) = lim - psout (outstep) = pstarM - atpco2out (outstep) = pco2A + exportM= exportM/ (outputyears*speryr/dt) +! Do Model Io For Averages + call modelio_output(filename_avg , & +#if defined(USEDUALNUMAD) + filename_dnad, & +#endif + outstep, & + outstepmax, & + timeM, & + lim, & + thetaM, & + saltM, & + dicM, & + alkM, & + po4M, & + no3M, & + fetM, & + ltM, & + exportM, & + pstarM, & + pco2M, & + pco2A, & + thout, & + sout, & + cout, & + aout, & + pout, & + nout, & + fout, & + lout, & + expout, & + ocpco2out, & + tout, & + nlout, & + psout, & + atpco2out & +#if defined(USEDUALNUMAD) + , thdxout, & + sdxout, & + cdxout, & + adxout, & + pdxout, & + ndxout, & + fdxout, & + ldxout, & + expdxout, & + ocpco2dxout, & + tdxout, & + psdxout, & + atpco2dxout & +#endif + ) ! Reset the average accumulators - timeM = 0._wp - thetaM = 0._wp - saltM = 0._wp - - dicM = 0._wp - alkM = 0._wp - po4M = 0._wp - no3M = 0._wp - fetM = 0._wp - ltM = 0._wp - pstarM = 0._wp - pco2M = 0._wp - pco2A = 0._wp - - exportM= 0._wp - -! Increment outstep - outstep=outstep+1 + timeM = 0._wp + thetaM = 0._wp + saltM = 0._wp + + dicM = 0._wp + alkM = 0._wp + po4M = 0._wp + no3M = 0._wp + fetM = 0._wp + ltM = 0._wp + pstarM = 0._wp + pco2M = 0._wp + pco2A = 0._wp + exportM= 0._wp endif + ! end timestepping loop 200 enddo -#if defined(WRITEOUTFILE) -! close the output file - close(14) -#endif RETURN END SUBROUTINE MODEL !======================================================================= !======================================================================= -! find light as function of date and latitude -! based on paltridge and parson +! evaluate rates of change due to transport +FUNCTION TRANSPORT(conc, pmask, psi, kmask, kappa, invol) + +USE MOD_BOXES +IMPLICIT NONE +REAL(KIND=wp), DIMENSION(nbox) :: TRANSPORT +REAL(KIND=wp), intent(in), DIMENSION(nbox) :: conc, invol +REAL(KIND=wp), intent(in), DIMENSION(nbox,nbox) :: pmask, kmask +REAL(KIND=wp), intent(in) :: psi, kappa +REAL(KIND=wp), DIMENSION(nbox,nbox) :: dconc +#if defined(USEDUALNUMAD) +INTEGER :: i +#endif + +dconc = spread(conc,1,nbox) - transpose(spread(conc,1,nbox)) + +#if defined(USEDUALNUMAD) +! DNAD has trouble with sum matrices, but it works on arrays + DO i=1,nbox + TRANSPORT(i) = invol(i) * sum( & + ( psi*pmask(i,:) + kappa*kmask(i,:) ) & + * dconc(i,:) ) + ENDDO +#else + TRANSPORT = invol * sum( ( psi*pmask + kappa*kmask ) * dconc, 2 ) +#endif + RETURN + END FUNCTION TRANSPORT +!======================================================================= + +!======================================================================= +!find light as function of date and latitude +!based on paltridge and parson FUNCTION INSOL(boxtime,boxlat) USE MOD_BOXES @@ -525,7 +659,7 @@ FUNCTION INSOL(boxtime,boxlat) REAL(KIND=wp), DIMENSION(nbox) :: INSOL REAL(KIND=wp), intent(in), DIMENSION(nbox) :: boxlat REAL(KIND=wp), intent(in) :: boxtime -! Local variables +!Local variables REAL(KIND=wp), DIMENSION(nbox) :: dayfrac REAL(KIND=wp), DIMENSION(nbox) :: yday REAL(KIND=wp), DIMENSION(nbox) :: delta @@ -544,21 +678,26 @@ FUNCTION INSOL(boxtime,boxlat) !planetary albedo REAL(KIND=wp), PARAMETER :: albedo = 0.60_wp REAL(KIND=wp), PARAMETER :: minsun =-0.999_wp - REAL(KIND=wp), PARAMETER :: mincosz= 0.005_wp - REAL(KIND=wp), PARAMETER :: mininso= 0.00001_wp - -! find day (****NOTE for year starting in winter*****) + REAL(KIND=wp), PARAMETER :: mincosz= 5.e-3_wp + REAL(KIND=wp), PARAMETER :: mininso= 1.e-5_wp + +!find day (****NOTE for year starting in winter*****) +#if defined(USEDUALNUMAD) +! Mod not written for dual, need to explicit + dayfrac=mod(boxtime%x,speryr)/(speryr) !fraction of year +#else dayfrac=mod(boxtime ,speryr)/(speryr) !fraction of year - yday = two*pi*dayfrac !convert to radians - delta = (0.006918_wp & - -(0.399912_wp*cos(yday)) & - +(0.070257_wp*sin(yday)) & - -(0.006758_wp*cos(two*yday)) & - +(0.000907_wp*sin(two*yday)) & - -(0.002697_wp*cos(three*yday)) & +#endif +yday = two*pi*dayfrac !convert to radians + delta = (0.006918_wp & + -(0.399912_wp*cos(yday)) & + +(0.070257_wp*sin(yday)) & + -(0.006758_wp*cos(two*yday)) & + +(0.000907_wp*sin(two*yday)) & + -(0.002697_wp*cos(three*yday)) & +(0.001480_wp*sin(three*yday))) -! latitude in radians +!latitude in radians latrad = boxlat*deg2rad sun = -sin(delta)/cos(delta) * sin(latrad)/cos(latrad) @@ -570,7 +709,7 @@ FUNCTION INSOL(boxtime,boxlat) end where dayhrs = abs(acos(sun)) -! average zenith angle +! average zenith angle cosz = ( sin(delta)*sin(latrad)+ & ( cos(delta)*cos(latrad)*sin(dayhrs)/dayhrs) ) @@ -580,17 +719,16 @@ FUNCTION INSOL(boxtime,boxlat) frac = dayhrs/pi !fraction of daylight in day -! daily average photosynthetically active solar radiation just below surface +!daily average photosynthetically active solar radiation just below surface INSOL = solar*(one-albedo)*cosz*frac*parfrac - where ( INSOL .LT. mininso ) - INSOL = mininso - end where - + where ( INSOL .LT. mininso ) INSOL = mininso + RETURN END FUNCTION INSOL !======================================================================= + !======================================================================= ! Calculate surface primary production given macro/micronutrient/light limitation FUNCTION CALC_PRODUCTION(nlimit, plimit, flimit, ilimit, alpha) @@ -605,6 +743,18 @@ FUNCTION CALC_PRODUCTION(nlimit, plimit, flimit, ilimit, alpha) REAL(KIND=wp), intent(in) , DIMENSION(nbox):: ilimit REAL(KIND=wp), intent(in) :: alpha +#if defined(USEDUALNUMAD) +! Local variables + INTEGER :: i + REAL(KIND=wp), DIMENSION(nbox, 3) :: leibig +! Stack the nutrient uptake rates and find the smallest that limits production + leibig = RESHAPE([ plimit, nlimit, flimit ],[ nbox, 3 ]) + +! DNAD has trouble with minval matrices, but it works on arrays + DO i=1,nbox + CALC_PRODUCTION(i) = alpha * ilimit(i) * minval(leibig(i,:)) + ENDDO +#else ! Non-linear model can use array operations ! minval accepts an array of values and then finds the minimum along dim arguement ! need to reshape the concatenated nutrient arrays here to stack them by box @@ -612,7 +762,8 @@ FUNCTION CALC_PRODUCTION(nlimit, plimit, flimit, ilimit, alpha) CALC_PRODUCTION = alpha * ilimit * minval( & RESHAPE([ plimit, nlimit, flimit ],[ nbox, 3 ]) & ,2) - RETURN +#endif +RETURN END FUNCTION CALC_PRODUCTION !======================================================================= @@ -631,6 +782,13 @@ FUNCTION CALC_EXPORT(R, bioP, vol, invol) ! scale rate of nutrient export with rate of phosphorus export ! R matrix determines export flux and remineralization locations +#if defined(USEDUALNUMAD) +! DNAD has trouble with sum matrices, but it works on arrays + INTEGER :: i + DO i=1,nbox + CALC_EXPORT(i) = sum( R(i,:) * bioP * vol) * invol(i) + ENDDO +#else ! Spread broadcasts export and volume arrays to matrices ! Each is volume weighted (technically for a single box, this is not necessary, ! but it works for accumulation of several boxes too.) @@ -639,6 +797,7 @@ FUNCTION CALC_EXPORT(R, bioP, vol, invol) * SPREAD(vol ,1,nbox) & ,2) & * invol +#endif RETURN END FUNCTION CALC_EXPORT !======================================================================= @@ -676,13 +835,12 @@ END FUNCTION FE_EQUIL !======================================================================= !======================================================================= -! solve quadratic for iron speciation -! mick follows, March 2015 +! Produce a code for nutrient limitation in each box FUNCTION NUTRIENT_LIMIT_CODE(plimit, nlimit, flimit, ilimit) USE MOD_BOXES IMPLICIT NONE - INTEGER :: NUTRIENT_LIMIT_CODE + INTEGER(KIND=ip) :: NUTRIENT_LIMIT_CODE REAL(KIND=wp), intent(in) , DIMENSION(nbox) :: plimit REAL(KIND=wp), intent(in) , DIMENSION(nbox) :: nlimit @@ -690,9 +848,10 @@ FUNCTION NUTRIENT_LIMIT_CODE(plimit, nlimit, flimit, ilimit) REAL(KIND=wp), intent(in) , DIMENSION(nbox) :: ilimit REAL(KIND=wp), DIMENSION(nbox, 4) :: leibig - INTEGER, DIMENSION(nbox) :: lim + INTEGER, DIMENSION(nbox) :: lim - INTEGER :: i, limout + INTEGER :: i + INTEGER(KIND=ip) :: limout CHARACTER(nbox*2) :: clim CHARACTER(2) :: tmp @@ -705,7 +864,13 @@ FUNCTION NUTRIENT_LIMIT_CODE(plimit, nlimit, flimit, ilimit) ! 4 = light leibig = RESHAPE([ plimit,nlimit,flimit,ilimit ],[ nbox, 4 ]) +#if defined(USEDUALNUMAD) + do i = 1,nbox + lim(i)=minloc(leibig(i,:)%x, dim=1) + end do +#else lim=minloc(leibig,2) +#endif ! write out array integers and concatenate as a string write(clim,'(I0)') lim(1) diff --git a/mod_phsolvers.F90 b/mod_phsolvers.F90 index eb80d56..df5764b 100644 --- a/mod_phsolvers.F90 +++ b/mod_phsolvers.F90 @@ -40,6 +40,10 @@ MODULE MOD_PHSOLVERS +#if defined(USEDUALNUMAD) + USE DNADMOD +#endif + USE MOD_PRECISION USE MOD_COMMON, ONLY: zero, one, two, three, four, nine, ten, hundred @@ -1751,7 +1755,7 @@ FUNCTION SOLVE_AT_OCMIP( p_alktot, p_dictot, p_bortot, & zh_prev = zh IF( ( ((zh-zh_low)*zdeqndh-zeqn)*((zh-zh_high)*zdeqndh-zeqn) > zero ) & - .OR. ( ABS(2.0*zeqn) > ABS(zh_delta_prev*zdeqndh)) ) THEN + .OR. ( ABS(2.0_wp*zeqn) > ABS(zh_delta_prev*zdeqndh)) ) THEN zh_delta_prev = zh_delta zh_delta = 0.5_wp*(zh_high - zh_low) diff --git a/run_microCOSM.ipynb b/run_microCOSM.ipynb index c77ff74..b2ddf55 100644 --- a/run_microCOSM.ipynb +++ b/run_microCOSM.ipynb @@ -19,7 +19,6 @@ "mp.rcParams[\"xtick.labelsize\"] = 14\n", "mp.rcParams[\"ytick.labelsize\"] = 14\n", "\n", - "\n", "def compile_microcosm(options=None):\n", " # Function to help compile the microCOSM box model\n", " # Supply compile options as a list of the following:\n", @@ -37,6 +36,7 @@ "\n", " mac_ver = \".\".join(platform.mac_ver()[0].split(\".\")[:2])\n", " env = dict(os.environ, **{\"MACOSX_DEPLOYMENT_TARGET\": mac_ver})\n", + " print(\"setting MACOSX_DEPLOYMENT_TARGET to: \" + mac_ver)\n", " else:\n", " env = os.environ\n", "\n", @@ -44,7 +44,17 @@ " if options is not None:\n", " optdefs = optdefs + \" \".join([\"-D\" + item for item in options])\n", " print(\"Compiling with options: \" + optdefs)\n", - "\n", + " # Clean environment of previous model files\n", + " runmake = subprocess.Popen(\n", + " [\"make\", \"clean\"],\n", + " stdin=subprocess.PIPE,\n", + " stdout=subprocess.PIPE,\n", + " stderr=subprocess.PIPE,\n", + " text=True,\n", + " env=env,\n", + " )\n", + " runmake.wait()\n", + " # Compile \n", " runmake = subprocess.Popen(\n", " [\"make\", \"pymodel\", optdefs],\n", " stdin=subprocess.PIPE,\n", @@ -60,7 +70,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## 1. Model parameters and initial conditions\n", "\n", @@ -80,7 +92,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Compiling with options: OPTIONDEFS = -DFIXATMPCO2\n", + "setting MACOSX_DEPLOYMENT_TARGET to: 12.5\n", + "Compiling with options: OPTIONDEFS = -DFIXATMPCO2 -DFOURBOX\n", "The exit code for compilations was: 0\n" ] } @@ -106,28 +119,37 @@ "outputs": [], "source": [ "# Run for 100kyrs, with 100 year output\n", - "maxyears = 1e4\n", + "maxyears = 1e5\n", "outputyears = 1e2\n", "outstepmax = (maxyears/outputyears)+1 # this is the length of the output vectors/arrays\n", "\n", "# Geometry: Box dimensions (m), areas, volumes\n", "dx = np.array([17.0e6, 17.0e6, 17.0e6])\n", - "dy = np.array([ 4.0e6, 12.0e6, 16.0e6])\n", + "dy = np.array([ 3.0e6, 13.0e6, 16.0e6])\n", "dz = np.array([50.0, 50.0, 5050.0 ])\n", "\n", "area = dx * dy\n", "vol = area * dz\n", "invol = 1.0 / vol\n", "\n", - "# Rate of overturning circulation (Sv)\n", + "# Rates of overturning circulation and mixing (Sv)\n", "psi = np.array((20.0e6,)) \n", - "\n", - "# define array (nbox*nbox long) of mixing rates (Sv)\n", - "# Box1 Box2. Box3 \n", - "Kmix = np.array([[ 0.0e6, 1.0e6, 1.0e6], # Box1\n", - " [ 1.0e6, 0.0e6, 1.0e6], # Box2\n", - " [ 1.0e6, 1.0e6, 0.0e6], # Box3\n", - " ])" + "dif = np.array(( 1.0e6,))\n", + "\n", + "# define array (nbox*nbox long) of box connectivity for mixing and overturning (by columns)\n", + "# Box1 Box2 Box3 \n", + "Kmix = np.array([[ 0.0, 1.0, 1.0], # Box1\n", + " [ 1.0, 0.0, 1.0], # Box2\n", + " [ 1.0, 1.0, 0.0], # Box3\n", + " ])\n", + "# Box 1 mixes with box 2 and 3; box 2 mixes with box 1 and 3; box 3 mixes with box 1 and 2\n", + "\n", + "# Box1 Box2 Box3 \n", + "Pcir = np.array([[ 0.0, 0.0, 1.0], # Box1\n", + " [ 1.0, 0.0, 0.0], # Box2\n", + " [ 0.0, 1.0, 0.0], # Box3\n", + " ])\n", + "# Box 1 is downstream of box 3; box 2 is downstream of box 1; box 3 is downstream of box 2\n" ] }, { @@ -158,7 +180,7 @@ "# Biological production maximum rate per year (mol/kg/yr converted to mol/m3/s within the model)\n", "alpha_yr = np.array((6e-6,))\n", "\n", - "# define array of remineralization coefficients (Columnwise)\n", + "# define array of remineralization coefficients (by column)\n", "# -1 indicates all of export is lost from cell, while \n", "# +1 indicates all of export is remineralized (gained) by cell\n", "# Box1 Box2 Box3\n", @@ -178,12 +200,12 @@ "# Hydrothermal vent input of 1 Gmol/yr (Tagliabue et al., 2010)\n", "# mol Fe/yr * g/mol * 1/area == g Fe m-2 year-1....\n", "# divide by 2.5e-3 because fe_sol=2.5e-3 is multiplied again within the box model.\n", - "fe_input = np.array([1.5e-3, 1.5e-1, (1e9 * 56) / (area[2] * 0.0025)])\n", + "fe_input = np.array([1.5e-3, 1.5e-1, (1e9 * 56) / (area[2] * 2.5e-3)])\n", "\n", "# Fixed value for uniform ligand control experiment\n", - "fixedligconc = 2.0\n", + "fixedligconc = 0.0\n", "\n", - "if fixedligconc == 0:\n", + "if fixedligconc == 0.0:\n", " # Assume this means prognostic ligand required\n", " fadd = fadd + \"variablelt\"\n", " # Gamma over lambda for ligands \"optimum\" value (Lauderdale et al 2020)\n", @@ -210,16 +232,23 @@ "# Typical concentrations, etc\n", "theta = np.array((2.0, 20.0, 4.0))\n", "salt = np.array((34.00, 35.50, 34.75))\n", + "\n", "if SPUNUP is True:\n", " # Here are some equilibrated initial concentrations in (u/n)mol/kg\n", " # run for 100,000 yrs (round-off error notwithstanding)\n", " # Make sure to compile without -DFIXATMPCO2\n", - " carbon = np.array((2264.67564, 2103.48757, 2364.66971))\n", - " alkalinity = np.array((2395.54471, 2387.42965, 2399.11941))\n", - " phosphate = np.array(( 1.81089, 0.25031, 2.49834))\n", - " nitrate = np.array(( 25.01353, 0.04412, 36.01262))\n", - " iron = np.array(( 0.00377, 0.49776, 0.58847))\n", - " ligand = np.array(( 2.08548, 1.56387, 1.62029))\n", + " carbon = np.array((2262.975612, 2102.979876, 2363.978019))\n", + " alkalinity = np.array((2395.675115, 2387.423546, 2399.113996))\n", + " phosphate = np.array(( 1.837220, 0.250380 , 2.498544))\n", + " nitrate = np.array(( 25.434742, 0.045298 , 36.015914))\n", + " iron = np.array(( 0.010337, 0.329535 , 0.574288))\n", + " ligand = np.array(( 1.664667, 1.631715 , 1.625804))\n", + "# carbon = np.array((2264.67564, 2103.48757, 2364.66971))\n", + "# alkalinity = np.array((2395.54471, 2387.42965, 2399.11941))\n", + "# phosphate = np.array(( 1.81089, 0.25031, 2.49834))\n", + "# nitrate = np.array(( 25.01353, 0.04412, 36.01262))\n", + "# iron = np.array(( 0.00377, 0.49776, 0.58847))\n", + "# ligand = np.array(( 2.08548, 1.56387, 1.62029))\n", " atmpco2 = np.array(( 280.00000,))\n", "else:\n", " # Here are some typical initial concentrations in (u/n)mol/kg\n", @@ -235,7 +264,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### Create a consolidated Pandas dataframe with the input values to feed into the model" ] @@ -275,7 +306,7 @@ " dz\n", " Kmix\n", " Rremin\n", - " psi\n", + " Pcir\n", " ...\n", " fopen\n", " theta\n", @@ -293,15 +324,15 @@ " \n", " 0\n", " 1\n", - " 10000.0\n", + " 100000.0\n", " 100.0\n", - " 101\n", + " 1001\n", " [17000000.0, 17000000.0, 17000000.0]\n", - " [4000000.0, 12000000.0, 16000000.0]\n", + " [3000000.0, 13000000.0, 16000000.0]\n", " [50.0, 50.0, 5050.0]\n", - " [[0.0, 1000000.0, 1000000.0], [1000000.0, 0.0,...\n", + " [[0.0, 1.0, 1.0], [1.0, 0.0, 1.0], [1.0, 1.0, ...\n", " [[-1.0, 0.0, 0.0], [0.0, -1.0, 0.0], [1.0, 1.0...\n", - " 20000000.0\n", + " [[0.0, 0.0, 1.0], [1.0, 0.0, 0.0], [0.0, 1.0, ...\n", " ...\n", " [1.0, 1.0, 0.0]\n", " [2.0, 20.0, 4.0]\n", @@ -311,37 +342,40 @@ " [2.0, 0.0, 2.5]\n", " [32.0, 0.0, 36.0]\n", " [0.0, 0.0, 0.0]\n", - " [2.0, 2.0, 2.0]\n", + " [0.0, 0.0, 0.0]\n", " 280.0\n", " \n", " \n", "\n", - "

1 rows × 26 columns

\n", + "

1 rows × 28 columns

\n", "" ], "text/plain": [ - " niter nyrs tout nout dx \\\n", - "0 1 10000.0 100.0 101 [17000000.0, 17000000.0, 17000000.0] \n", + " niter nyrs tout nout dx \\\n", + "0 1 100000.0 100.0 1001 [17000000.0, 17000000.0, 17000000.0] \n", "\n", " dy dz \\\n", - "0 [4000000.0, 12000000.0, 16000000.0] [50.0, 50.0, 5050.0] \n", + "0 [3000000.0, 13000000.0, 16000000.0] [50.0, 50.0, 5050.0] \n", "\n", " Kmix \\\n", - "0 [[0.0, 1000000.0, 1000000.0], [1000000.0, 0.0,... \n", + "0 [[0.0, 1.0, 1.0], [1.0, 0.0, 1.0], [1.0, 1.0, ... \n", "\n", - " Rremin psi ... \\\n", - "0 [[-1.0, 0.0, 0.0], [0.0, -1.0, 0.0], [1.0, 1.0... 20000000.0 ... \n", + " Rremin \\\n", + "0 [[-1.0, 0.0, 0.0], [0.0, -1.0, 0.0], [1.0, 1.0... \n", "\n", - " fopen theta salt \\\n", - "0 [1.0, 1.0, 0.0] [2.0, 20.0, 4.0] [34.0, 35.5, 34.75] \n", + " Pcir ... fopen \\\n", + "0 [[0.0, 0.0, 1.0], [1.0, 0.0, 0.0], [0.0, 1.0, ... ... [1.0, 1.0, 0.0] \n", "\n", - " carbon alkalinity phosphate \\\n", - "0 [2100.0, 2100.0, 2350.0] [2300.0, 2300.0, 2400.0] [2.0, 0.0, 2.5] \n", + " theta salt carbon \\\n", + "0 [2.0, 20.0, 4.0] [34.0, 35.5, 34.75] [2100.0, 2100.0, 2350.0] \n", "\n", - " nitrate iron ligand atmpco2 \n", - "0 [32.0, 0.0, 36.0] [0.0, 0.0, 0.0] [2.0, 2.0, 2.0] 280.0 \n", + " alkalinity phosphate nitrate \\\n", + "0 [2300.0, 2300.0, 2400.0] [2.0, 0.0, 2.5] [32.0, 0.0, 36.0] \n", "\n", - "[1 rows x 26 columns]" + " iron ligand atmpco2 \n", + "0 [0.0, 0.0, 0.0] [0.0, 0.0, 0.0] 280.0 \n", + "\n", + "[1 rows x 28 columns]" ] }, "execution_count": 5, @@ -363,7 +397,9 @@ " \"dz\" : [dz],\n", " \"Kmix\" : [Kmix],\n", " \"Rremin\" : [Rremin],\n", + " \"Pcir\" : [Pcir],\n", " \"psi\" : psi,\n", + " \"dif\" : dif,\n", " \"alphabio\" : alpha_yr,\n", " \"gamma\" : lig_gamma,\n", " \"lambda\" : lig_lambda,\n", @@ -410,7 +446,7 @@ "\u001b[0;31mType:\u001b[0m fortran\n", "\u001b[0;31mString form:\u001b[0m \n", "\u001b[0;31mDocstring:\u001b[0m \n", - "tout,thout,sout,cout,aout,pout,nout,fout,lout,expout,nlout,psout,ocpco2out,atpco2out = model(id,maxyears,outputyears,outstepmax,dx,dy,dz,k,r,psi,alpha_yr,gamma_fe,lt_lifetime,dlambdadz,fe_input,wind,fopen,thin,sain,cin,ain,pin,nin,fin,lin,atpco2in)\n", + "tout,thout,sout,cout,aout,pout,nout,fout,lout,expout,nlout,psout,ocpco2out,atpco2out = model(id,maxyears,outputyears,outstepmax,dx,dy,dz,kin,rin,pin,psi_in,dif_in,alpha_yr,gamma_in,lt_lifein,dldz_in,fe_input,wind_in,foin,thin,sain,cain,alin,phin,niin,fein,ltin,atpco2in)\n", "\n", "Wrapper for ``model``.\n", "\n", @@ -423,24 +459,26 @@ "dx : input rank-1 array('d') with bounds (3)\n", "dy : input rank-1 array('d') with bounds (3)\n", "dz : input rank-1 array('d') with bounds (3)\n", - "k : input rank-2 array('d') with bounds (3,3)\n", - "r : input rank-2 array('d') with bounds (3,3)\n", - "psi : input float\n", + "kin : input rank-2 array('d') with bounds (3,3)\n", + "rin : input rank-2 array('d') with bounds (3,3)\n", + "pin : input rank-2 array('d') with bounds (3,3)\n", + "psi_in : input float\n", + "dif_in : input float\n", "alpha_yr : input float\n", - "gamma_fe : input float\n", - "lt_lifetime : input float\n", - "dlambdadz : input rank-1 array('d') with bounds (3)\n", + "gamma_in : input float\n", + "lt_lifein : input float\n", + "dldz_in : input rank-1 array('d') with bounds (3)\n", "fe_input : input rank-1 array('d') with bounds (3)\n", - "wind : input rank-1 array('d') with bounds (3)\n", - "fopen : input rank-1 array('d') with bounds (3)\n", + "wind_in : input rank-1 array('d') with bounds (3)\n", + "foin : input rank-1 array('d') with bounds (3)\n", "thin : input rank-1 array('d') with bounds (3)\n", "sain : input rank-1 array('d') with bounds (3)\n", - "cin : input rank-1 array('d') with bounds (3)\n", - "ain : input rank-1 array('d') with bounds (3)\n", - "pin : input rank-1 array('d') with bounds (3)\n", - "nin : input rank-1 array('d') with bounds (3)\n", - "fin : input rank-1 array('d') with bounds (3)\n", - "lin : input rank-1 array('d') with bounds (3)\n", + "cain : input rank-1 array('d') with bounds (3)\n", + "alin : input rank-1 array('d') with bounds (3)\n", + "phin : input rank-1 array('d') with bounds (3)\n", + "niin : input rank-1 array('d') with bounds (3)\n", + "fein : input rank-1 array('d') with bounds (3)\n", + "ltin : input rank-1 array('d') with bounds (3)\n", "atpco2in : input float\n", "\n", "Returns\n", @@ -472,7 +510,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "timeseries = pd.DataFrame( \n", @@ -555,7 +595,7 @@ "outputs": [ { "data": { - "image/png": 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oasdGU6t+JL7+GjirCtcNmA9sBASYDKwQkebGmETXiiJyC/Av4JCbduYA12MF1wnAbOBzEbnEGJN3FeodoAFwNWCAhcCbQJ+K/UiWjNRMPpj3DWITRASbSP5r12eb6zZObkPynsFmszn3AREbUnibzeZ8Fmu7YLWZfwybs52T7br2q/C2Iv2j6Lb8ffKO76znts+2vD6L9Zqifc7rX4Hji1TGH41S6hyiwbVSSp1B7Ll2UpPTSU1KJzUpjRNJ1uuUxFSSE06QknDy+fixFJLiUkhOSK22/tpsQs36kcScV4uYJrWod15tYprUIua8WkTHROg0bFWljDFXur4XkbuAZOAy4DOX7Q2BucAVwFeF9gkD7gUGGWOWu7Szz1l/mYhcCFwFdDbG/OisMxRYIyLNjDF/VPRnS0/N5K1nP63oZs857i4I5F8YcAnCPQf/RfdxG/xT6OJEgfacwb+bCxFF+ufmQkSR/rm04/lChM3Zdw8XT1w+W8GLMR6+qwq4uJNX1+Zlw8vLhs3Llt+nvG1is8ptXoJNnM/Oura8Mluh93nbXPfNK7edPE6lqqTFMKp7lY0zjc3Lhl+Ab4W2qcH1OWLnpt1sXvW79cZ5hdn58uSV2rx/VPPKJO8/5P+D61rv5HZx0671D2ThfcX5D3xe+273LeZYeY1KofrlP5a7eiX4nB6+oyJ9wl394o7l+vlPcawi37X7fhZ7LE/fvdvvpezffZFj4f5zVsyxKn8EwhhDbo4de46dnJxc6zk7l9wcu/XIziU3x/k+26qTnZFNVkY2WZk5ZKXnvc4mKz2b7MwcsjKyyUzLIj01k/QTGWSkZpKRmkX6iQzSUzPJSq/a0eWSCosKIaZxTeqdbwXP9c6vTb3zalMnNlpHoNXpLASwAcfzNoiIN/Au8IwxZoebf0suAXyAb/I2GGMOiMgO4FJgGdAJSAV+dNnvByDNWafCg2tTiWu+n0scDgMYHLoSn1JnvUuvu5gn33yoQtvU4PocsWPjLt6c9kl1d0OpalGiiyFQ4mDebndgdwbQ55LQiGDqNIqmbuOa1G1ck5jGtYhpUpOY82oTHBZY3d1TqizmAj8D61y2TQISjDEvedinNmAH4gttP+osy6sTZ1yGkYwxRkSOudQpQESGAEMAGjRoULpPgY5YKaXU6UCD63OEnnTVuSzv77/1pP8veOIf5GclEKsfSa0GUdRqEEmdRtHUbhhNnYZRBGkArc4iIjIb6Iw1ddvu3NYVGAi0KUuTFPwHxt0/NoXrnKxszKvAqwDt2rUr9T9UOnKtlFLVT4Prc4TG1kqd20IjggmvGUpk3XAia4cRUasGUXXDiY4JJ7peBNExEYSEB1XJdHqlqpuIPA/cAXQ3xux2KeoO1AEOu/y/4AVMF5FHjDH1gCPObVFAnMu+NYHVztdHgJoiInmj12I1GI01wl3hAkMDuHP0dRgDxuHIf3Y4jPXa5G0zOBwOMM4p0MY461iPAtvytrs8O4ybbS77YJx1CuwLDocj/3V+/4zVl4J9dumf8dyXAtuM+74U6Gd+/zz3RSmlykuDa6WUOoOICEGhAQTXCHQ+ggiuEUhIjSDCokIIiwwmNCKYsMgQwqKCCa8ZRo3oELx99J97pQBEZC5WYN3NGLOzUPF84P1C25Zh3YO9wPl+M5AD9MLKCI6I1AMu5OQ91uuAYKx7r/O2dQKCKHgfdoUJDgvk7nE3VEbT54STQbqHQN81kHeWF3chougFhpPBv/ugv7gLG+Awbi4IONxcnDAeLp64XJSAYi6enOKCRZH2TDEXLMpzccfu/I7tDhx2B3Zn+3nvHXYHDpP33vmcV+5wFHxvd2CMwW534LDnkJubgcOehd2ejcOei8MuGCM47ILDAcb5nkq51mzlD7LS/eS9lvztuGwv8xH0InmJVcZynvpr6xxxYbvGJ69o512dzTuJFHpvvTz5GkzBabUur09uN/kT3Qrsawru63pl2HVfT/VLfSzcHOsU+3r6/AWOjbs+Fe63a7vuju3hWBTuUwUcC/f75n3+U32XRT5/Zf654eHvSDn7WVVsXjZ8fL3x9vGyHr7eePt44+3rhbe39d613NffF79AX/z8ffAL8MU3wBe/AOf7QF/8nOWBIQEEBPsTGOJPYLB//nv/IF8rk6xSqtREZB5wF3ADcFxE8u5/TjXGpBpjjgHHCu2TAxzJy/BtjEkWkdeAmc57qPOW4voFWOGss0NEvgZeEZH7sX4pvwJ8XhmZwlX5FUgIqqsYnDVycv8hPWsdGVnrSc9aT07unuruUonZbDXwskXgZYvA2ysSL1skXrZwvGzhzrK816F42UKx2UKxSTAi+ve3OmlwfY5o3uE8mnc4r7q7oVSVKclFgLIG8ojg42cFzBroKnVGedD5vLLQ9knAxFK08yiQCywFApzt3W1OrnEN0B94gZNZxT8Fhpeyv0qpEjLGkJO7xxlM/0R61jpy7Qeru1tl5nAk4XAkkcPuU1fOJ4gEYpNgbLYgbBKEzRaITQIRCUDED5v4Ifgh4ouIL4g3gjfWQgleCF4gXgg2cD6sgD1/OZlCr/OP7LY/pzNv73oE+Xeu2DYrtDWllDpNVOWSXEqpM4MxptT/IBhjYt1sywRGOB+e9ksEBpT2eEqpkjHGkJ37J+mZeSPTP2F3lD+lgY93YwJ8L8aYHOwmGYcjGbsjGYcjCbsjGXCUv/OVxmBMGnaThv107uZpIjjgag2ulVJKKaWUUucWY+xk5WzPH5XOyPoJuyOx3O36ejcj0L8jAX4dCfTriLdXrWL6YHCYVGfAfdwZdCdjdyTlB+F2RyJ2RwJ2u/Wc60jE4The7n6qM4MG10oppZRSSqnTijE5ZGb/mn+/dEbWBhwmpZytCn4+FxHo14kAv44E+LXH2yuy5HuL4CUheNlC8KFeifczJtcKxu0J2B2J5Drina/jsdud7x3x2O3x5DoScDiSyvDZ1OlAg2ullFJKKaXUaSE9cx2JJ+aRnrUeY9LL2ZoX/r6tCfTr6Aym/4WXLaxC+lkaIt54e0Xj7RVdovqFg/H8EXD7ceyOJOeo+XHniPkJHCYFuyO5Ar4vVV4aXCullFJKKaWqlTGGxBP/JT55WpnbEHzx97vYOTLdgQDfdthsQRXYy6pR2mA8jzG5OEwaDkeq8zkdY9JxmHSMycRhsjAmE2OyMWRbzyYXyMEYO4ZcMA7AjsGOdQ+3g5P3mZtCj7ytblZrqYYVXErL37dlhbepwbVSSimllFKq2hiTzZHjY0hJW1qq/UT8CfBtR4BfJwL9OuLvdzE28a+kXp7+RLzxkrBqGZ1XFg2ulVJKKaWUUtXCbk/kYML9ZGStO2VdmwQT4Nc+P/mYv28razkppU4TGlwrpZRSSimlqlx2zi7+ib+HnFz3aznbbOEE+rXPH5n282nuXI9ZqdOT/u1USimllFJKVan0zB84mHC/x8zY4SFDiQ6bgIhX1XZMqXLQ4FoppZRSSilVZZJT3+XI8TFArptSb2qFT6NGcP+q7pZS5abBtVJKKaWUUqrSGeMgPnkqiSfmuy23SSh1oxYQ5H95FfdMqYqhwbVSSimllFKqUjkc6RxOHEFqxlduy328Y4mJeh0/n/OruGcF/XbsKH8kxCOAt82Gj5cXPjYb3jYvvG02fL2s54JlNnxsXvh42bCJrdx9kPJ/DFUCPl5ehPr5VWibGlwrpZRSSimlKk2u/Qj/xN1DVs6vbssD/DpQN3Ih3l6RVdyzk+wOB5O+X8Vbv26rtj6oqtW7yXm8fO31FdqmBtdKKaWUUkqpSpGZ/SsH4weSaz/stjw08FZqRczAJhU7glgambk5PPL1l3yz++9q64M6O2hwrZRSSimllKpwqRnLOJTwIMZkuC2PCnuciJARiFTfROjkzEzu//xjNh06WG19UGcPDa6VUkoppZRSFcYYw/ETrxCX/DRgipSL+FMnYi4hgX2qvnMuDp84waBPPuDPxIRq7Yc6e2hwrZRSSimllKoQxuRw9Pg4ktPedlvuZYsmJmoJAX4XV3HPCvorIYGBn3zA4dQTRcoEuLLJ+XjbbGQ77OTaHeQ6HOQ4HOTkv7eT7bCec+0ny4wpejGhNMq5uyqFEN+KvxVBg2ullFJKKaVUudkdSRyKH0J61lq35b4+F1Iv6nV8vOtVcc8K2nz4IPd9+jHJWZlFynxtXjzX+2qubdqsGnqmznQaXCullFJKKaXKJTt3Lwfj7iI7d5fb8iD/K6gbOR+bLbiKe1bQit1/M+KrL8iy5xYpC/bx5eXrrufS+g2qoWfqbKDBtVJKKaWUUqrM0rN+4lD8YOyO427Lw4PvI7rGU4h4VXHPClr62y+M/3YFDjdzr6MDg1h8/U00j65ZDT1TZwsNrpVSSimllFJlkpz2HkcSRwE5bkq9qBn+DOHB91R1twowxjBv40/MXv+D2/LYGuG8fv3N1A8Lq+KeqbONBtdKKaWUUkqpUjHGQXzKTBJT5rott0kIdSNfISigW9V2rBC7w8Gk71fx1q/b3Ja3qlWb1/rcSGRgYBX3TJ2NbNXdgdOJiIwVkY0ikiIicSLymYi0KFTnaRHZKSJpInJcRFaKyKWF6viJyIsiEu+s96mI1CtUJ1xE3hSRZOfjTRGpUQUfUymllFJKqTJzONI5lDDMY2Dt41WfBrU+rfbAOis3l+Fffe4xsO7SIJa3b7xVA2tVYTS4LqgbMB+4FOgB5AIrRCTCpc4fwENAS6AzsAf4WkRqudSZA9wM9AMuB0KBz6XgjSbvAG2Bq4GrnK/frPBPpJRSSimgQi+i13ZeFD/irLdNRPoXqqMX0dVZKdd+jANxt5Ca8bnbcn/fS2hQ6wv8fKo323ZKVib3fPwBy3b95bb8xguas6DPDQT5+lZxz9TZTKeFuzDGXOn6XkTuApKBy4DPnHXeKlTnP8C9QBtgmYiEOd8PMsYsd2lnH3CFs86FWAF1Z2PMj846Q4E1ItLMGPNHpX1IpZRS6tzVDesi+kaspWwnY11Eb26MSXTWybuIvgcIAB7Fuoh+vjHmqLPOG0AEcD0QB9wIvCkiB4wxq5113gEaYF1EN8BCrIvofSr1EypViTKzt3Mw/m5y7YfclocE3kjtiOewiX8V96ygI6knGPTJh/yREO+2fEjbdjx2WRdsIlXcM3W2q7LgWkT8gYeBnkBNCo2aG2NaVVVfSiEEq59uUx+KiC8wBEgBfnZuvgTwAb7Jq2eMOSAiO7BGxJcBnYBU4EeX5n4A0px1NLhWSimlKlhFXER3br4UGGGM+cn5/jkR+TfQHlitF9HV2Sg1YwWHEh7AmDS35ZGho4gMfRSp5oB1V2IC93zyAYdOnHBbPv7ybtx78SVV3Ct1rqjKkev5WFd238MKKovmwD/9zMUKmte5bhSR64D/AYHAYaCXy9Xs2oAdKHyp7KizLK9OnDEn1wEwxhgROeZSpwARGYIVyNOgga69p5RSSlWAslxEB1gL3CYinzr37QNEAyuc5aW+iK7neXW6MsaQlPoax5ImAo4i5YIftSOfJzTwhqruWhFbDx/i3s8+Iikzs0iZj83GzF5X0bfZhdXQM3WuqMrg+gbgVmPMilNVPB2IyGyse6o7G2PshYq/xbqCHQXcD/yfiHQyxhwurkkKXlBwd3GhcJ2TlY15FXgVoF27dmfChQmllFLqdFeWi+gAtznL47Hys2QB/YwxPzvLS30RXc/z6nRkTC7Hjj9BUtrrbsu9bJHERC0mwK9dFfesqFV7djP8q8/IzM0tUhbk48P8a/tyeYPYqu+YOqdUZUKzdOBAFR6vzETkeaxkZD2MMbsLlxtj0owxfxtj1htj7sVa2O8+Z/ERwAsr8HZVE2v0Oq9OTXGZN+N8He1SRymllFKVxOUi+s3FXES/FPga6yJ6HZfyZ7DO81cA7YCZwBsi0tqlTqkuoit1urE7Uvgn/i6PgbWvdzMa1vrytAis39v+G0M//9htYB0ZEMg7N9+ugbWqElUZXM8A/iMip3WGchGZC9yJFVjvLOFuNsDP+XozVrDdy6XNesCFnJwetg4Ixpo2lqcTEETBKWRKKaWUqmDluYguIk2AEcD9xpiVxphtxphJWEnSRjib0Ivo6oyWnbuf/Uf7kp75vdvyQP9uNKj1CT7e9au4ZwUZY5i38SfGrFiG3RS9btUwrAbv39qPljVrudlbqYpXldPCe2EtS3WViGzHOlHlM8b0rcK+uCUi84C7sKawHxeRvKlbqcaYVBEJBR7DSnpyGOsk+RBQD/g/AGNMsoi8Bsx0Tv9KAGYDv+C8F8sYs0NEvgZeEZH7sa5kvwJ8rklOlFJKqcrjvIh+B9CtjBfR8xbELTzabefkoIXrRfS8i+Z6EV2dETKyNnIwfjB2R4Lb8hrBA6lZYzIi1bvokN3h4OnV3/LGLz+7LW8RXZPXrr+J6MCgqu2YOqdV5f8V8cBHVXi8snjQ+byy0PZJwESs+6ouAgYDkViB80agizHmF5f6jzrrLsVaxmMlcHehaWf9gRc4mVX8U2B4RX0QpZRSShVUERfRgZ3A38B8ERmF9VvgBqxBhOtBL6KrM1dK2occSRyJIctNqY2aNSYRHnJvlfersKzcXEZ+8xVf/v2n2/LL6jfgpWuvJ1jXsFZVrMqCa2PMoPK2ISI3lWG3r4wxGSWpaIwpdu0AY0w6VsbzU7WTiTU1bEQxdRKBASXpl1JKKXUuqOzzPBVwEd0YkyMi1wDPYgXhwVjB9iBjzGcubepFdHXGMMaQkPIcCSmz3ZaLBFE38mWCA3pWcc+KSsnKYtjnn7D+oPtUTn2aXsDMXlfh6+VVxT1TqmpHrgEQkcZAc6yEHjvc3etUjPdLeTgDnA+U5hhKKaWUqh6Vep6vwIvofwE3n6KOXkRXZwSHyeRI4n84kf6x23JvrxjqRb2Bn2/1L2F1LC2VQZ98yI74OLflg9tcwrjLu2Kr5rW21bmryoJr51Sr17BORo6Tm+UD4F5jjPuV3ouqbYw5VsJjlrRNpZRSSp0e9DyvVBXJtcdzMH4Qmdmb3Zb7+15MTNRivL1qVnHPitp9PJGBn3zAPykpbsvHdu7C/W3/VcW9UqqgqszcPRdoBXTHug85AOjp3DanhG28DpR06hfAW4D7/wOVUkopdbrR87xSVSQreyf7j17rMbAOCehD/ej3T4vAetuRw9z23v/cBtbeNhvP9bpaA2t1WqjKaeF9gRuMMWtctn0nIkOwEp2dMjtCae/bNsY8ULouKqWUUqq66HleqaqRlvEthxKG4jCpbssjQx8hMnQUp8MKut/t3cNDX35Khps1rAN9fJh3dR+6xjaqhp4pVVRV/h8TgJUYpLBEwL+sjYpIhzL3SCmllFKnNT3PK1Wxjp9YzD/xd7kNrAVfake8QFTYY6dFYP3hjt8Z8vnHbgPrCP8A3r7pNg2s1WmlKv+v+QF4WkTy1odERIKwMnSWZ83H98rbMaWUUkqdtvQ8r1QFMCaXo8cncCxpPCfTH53kZQunXs2lhAXdUvWdK8QYwyubNzBq+dfkOor2tX5oGO/d1o/WtWq72Vup6lOV08IfBb4GDorIL1gZPlsDacCVxe0oIv/nqQiIqMhOKqWUUqpq6Xleqcpld5zgcMIDpGWuclvu630eMdFv4OsdW7Udc8NhDFPWfMfin7e4LW8eFc3i628mOiioajumVAlU5TrXv4nI+VjLUlyAdcJ8C3i7BOtTXgHcBRSevyJAl4ruq1JKKaWqlJ7nlaokObn/8E/83WTn7HRbHujXmbpRC/CyhVVxz4pKzEhnwqoVfL3rL7flneo14OVr+xLi51fFPVOqZKp0nWtnEL2gDLuuAVKNMd8XLnCOgiullFKqGolIG2PMz2XcXc/zSlWCjKwtHIwfhN3hfl3osKAB1AqfgohPFfesIGMMn/yxk2dWf0tipvsxt2vPb8qsXlfj512l4YtSpVKpfztF5CbgM2NMjvO1R8aYD4spu76Ysl7l6KJSSimlKsYWEdkKLATeMcYkl3RHPc8rVfFS0j/lSOIjGJPpplSIrvEk4cFDEJEq75urgykpTPh2Od/v2+uxzsDWFzOhS3ds1dxXpU6lsi/9vA/UBo45X3tiAK+SNioiMcaYg+Xsm1JKKaUqTjNgMDAeeE5EPgReM8Z8W9qG9DyvVNkZY0hMmUt8ygy35SKB1I2cR3BAsSmPKp3d4eCNX37muXVrSc/J8Vhv9KWdGXZJ+2q/CKBUSVRqtnBjjM0Yc8zltadHiQNrp68qobtKFbBkyRKCg4OruxvqNGKMA7sjGbs9EYcjA2NMdXdJqdOGMeYvY8xYoAFwK9Yym1+LyC4RGS8i9UrRnJ7nlSoDh8niSOLDHgNrb686NKj5cbUH1n8kxHPre//j6dXfegysw/z8mXvVtTzQroMG1uqMUWU3LYhIF+BHY0xuoe1ewGXGmNWlaa5CO6eq1cCBA4mNjWXixImICHv27CE2NhaAjz/+mOnTp7Njxw5yc3OpV68enTt3ZuHChRXah9jYWIYPH86oUaMqtN3K8NNPPzFlyhTWrl1LWloajRo1ol+/fowZMwZ//zIvGV8p9u7dS6NGjTDGMHHiRPbu3cuSJUuqu1sAGJON3XEcu/249Zz/SDz52m69duSXJVF4+RKRAGwS6HwOsJ5tgSdfSwAizvc2q65NgrHZgpzPzvf524Kw2YIR8a2W70Wp8jLGOIAvRGQl8AAwDXgaeMo5mj2yBKPSep5XqpRy7Qkcir+XjOwNbsv9fFpRL3oJ3l7Vt3xVVm4u8zf9xEubNrhdYivPtec35ckuPTQjuDrjVGVGgG+BOlhTxF3VcJaVZvRah4vOAStXruTWW29l0qRJLF68GC8vL3bu3MnHH39c3V0rl+zsbHx9yxY4ffrpp9xyyy3079+fFStWEBkZyY8//sioUaNYuXIlK1asKHPbZypjDA6TejIIthcKkF0f9pPbjUmroONnYD/lggdl4YPNFpwfbJ98znsdVOBZJNAlqA90Cfj9EPFHxB+b+CPih4gmg3FVcAaC62vR0ZIyEJH2WNPDbwdSgGeBRVi/AZ4GPgb+dYpm9DyvVClk5fzFwbi7ybHvc1seHHANdSJewGYLrOKenbTx0D+MW7mcXccTPdapHRTM5O49uaLxeVXYM6UqTlX+whLcnywjsda6VhXsjwN1q7sLNKt/qMz7fvbZZ3To0IFx48blbzv//PPp06dPgXoffvghTz31FH/++Sc1a9Zk2LBhjBs3Lv9HsbtR6W7dutGiRQv++9//0q1bN/bt28fo0aMZPXo0UPDH9sqVK3n44YfZs2cP7du3Z9GiRTRq1KhAPydOnMjvv/9OnTp1uPPOO3nqqafyg9zY2FgGDhzI/v37+fDDD+nVqxfXXnstw4cP55NPPim2bVfp6ence++9XHPNNSxevDh/e8OGDWnWrBnt2rVj7ty5+Z8hJSWFMWPG8PHHH3P8+HEaNWrExIkTuf322wH48ccfGTt2LBs3biQ8PJy+ffsyffp0QkNDAfj666+ZMmUKv/32GyLCv/71L+bMmcOFF14InByVfv/993n55Zf54YcfiI2NZe7cufTqVTn5hxyONJJS3yAt81ty7XH5gTJ4vlfrzJWDw3EcB8fBXtFt2xDxRfBxZoj1QcQLwRvEG8EG2EC8EASKPPIYl4f13uS9NwZrlN+4bHc4txtMfpk1cmENdua9dzjrOI9hCrfj8jBujl2gb7h5Lpk6EfMIDbqxVPucy0TkP1hB9fnAF0B/4GvnSDbAfhF5EHC/HpBSqkzSMldzKH4IDpPitjwi5CGiwsYiUql3g3p0IiuLGT+u4e1ftxVbb0DL1oy+9HJdZkud0So9uBaRT50vDfCWiGS5FHsBLYAfK7sf6sxTu3Ztdu7cybZt22jdurXbOps3b+bWW29lwoQJ9O/fn40bNzJ06FBCQ0MZMWJEiY7z4Ycf0rp1awYPHswDDzxQoCwrK4tp06axaNEi/P39ueeeexg2bBjLli0DYNmyZfTv35+5c+fSpUsX9u/fz7Bhw8jKymLWrFn57cyePZsJEyawadMmjDH88MMPp2y7sGXLlhEfH89jjz1WpKxt27b07NmTd955h9GjR2OM4eqrr+b48eMsXryYpk2b8scff5CZaWUM/fXXX+nduzeTJk1i4cKFJCYm8sgjjzB48GDef9/KPZiWlsYjjzxCq1atyMjI4JlnnqFPnz5s3769wOj4+PHjmTlzJvPnz+eZZ57hjjvuYN++fRV6v7oxuSSnLSU+eSZ2R+HJL6r0HBiTiSFTxwdVRXoAeA1YbIw56qHOfuDequuSUme3pNS3OHp8LO6vwnpTO3wGYcF3VHW38q3Y/TdPfruSI2mFl7A/qUl4BFN79uJfdUuTlkGp01Opg2sRqQvUpFAyNGPMFg+7JOTtChwHXOdPZgNrKf3a1zpP7yzieg+u64jxiBEjWLNmDW3atKFevXp06NCBK664ggEDBuQHbrNnz6Zr165MmjQJgKZNm/LXX38xffr0EgfXEREReHl5ERISQu3aBe9Dys3NZd68eTRr1gyAUaNGMWjQIBwOBzabjSlTpjB69GgGDRoEQJMmTZg+fToDBgxg5syZ+aPnXbt2LRAU//DDD6dsu7A///wTIH/kuLDmzZuzYIH1v9KKFStYt24dv//+e379xo0b59edOXMmt99+OyNHjszf9tJLL3HxxRdz7Ngxatasyc0331yg/cWLFxMaGsqGDRvo3Llz/vZHH300fzbB1KlTeeONN/j555/p3LkzsbGx+X+mEydOdNvv4hhjSMtcRVzSM2Tn/lHq/SuDSBAiPhhHBoasU++g1LmjF7DfZaQaALH+IaxvjNlvjMkGXi9BW3qeV6oYxtiJS3qG46mvuC232WoQE7mQQP9Lq7hnlri0NCZ9v4ov//7TYx0fm41h7drzYLsOuna1OmuU+G+yiFwMvAVcQNGTnseltIwxg5z77wVmmQq40dEY07K8bajTX1BQEF988QW7du3i22+/Zf369YwdO5Zp06axYcMGatWqxY4dO7j22msL7Ne5c2cmTZpESkpK/hTnsvLz88sPfgHq1q1LTk4OSUlJREREsHnzZjZs2MD06dPz6zgcDjIyMjhy5Ah16tQBoF27dqVuu7SMMfnB/NatW6lTp47HQHzz5s38/fffLF26tMD+ALt27aJmzZrs2rWLJ554gp9++om4uDgcDgcOh4P9+/cXaKtVq1YFPgPAsWPlH13OzP6VuKSnSc9aW+623LPhZQvDyxaBzRaOV97DKxwvW8TJ9y7bbbZwbHJyupoxdozJxGHScZgMjMnA4Ui3nk0GxrndYTIwjnRnvXQcjjSMScNhUnE40nCYNOdzqrP8BJUwF1ypyrYL97lVIoA9lCK3ip7nlfLM4UjjUMKDpGUud1vu492YelGv4+vTpIp7Zv2WeG/7b0xd+z0pWZ4vQLepVYdpV/SmWWRUFfZOqcpXmstErwIHgPuBQ5RyMqExZlJp6iuVp0mTJjRp0oT77ruP8ePH07RpU1566SUmTpxYIKAsLG+7zWYrsmRSTjHrKbryLnQlNa9NhzPDpcPh4KmnnuLWW28tsm90dHT+6yA32S5P1XZhTZs2BWD79u1cdtllRcp37NjB+eefDxRO0FSUw+Hgvvvu49FHHy1SFhMTA0CfPn2IiYnhlVdeISYmBm9vb5o3b052dnaB+j4+PiX+DCWRk3uQ+OTppKR/QEn/mRHxdwmErcDYlh8ou3/YbGHlvv9MxAuRIGxUbDZTYwyGLByOVGcAnuoMwFPzA3IrcE8rEJznBfUOk+4sz7Smf5tMl9fZFM54rgrTQdMy8pRbJRjIrOK+KHVWysk9yMH4e8jK2e62PMDvUmIiF+DlFV7FPYO9SccZv2oF6/7Z77FOoI8Pozp15q5WbfByM0tPqTNdaYLr5sDFxhjP8ztOQUQGAf2w1sAskNLYGNPY7U6e21rkochgncT/BpYaY8qeUesMV55kYqer2NhYAgMDSU217t1p3rw5a9cWHNlcu3Yt9erVIyQkBLCC3MOHD+eXZ2ZmsnPnTi6++OL8bb6+vtjtpR8pbNu2LTt37uS88yo/q2Xv3r2JjIxk5syZRYLrLVu2sHLlyvwR9LZt23L48GF27NjhdvS6bdu2/P777x77nZCQwI4dO5g3bx7du3fPP0Zubq7b+hXB7kghMeW/HE9diDHF/w4PDriOiJCheHvVdgbK1Zf9tDKICII/Ni9/oOKv6huTizHZGHLyX4Mdgx1MjpUYzDjfuyQdcx83SaFnGyAnE6FJoffY3GyztlsXZ2z5beCslVffemdzOY7zuHKyDyfbxKVtd/1EM4FXEBF5wfnSANNEJN2l2AtoD/xchnb1PK+Ui8zsbfwTNxC7w31Kg7CgO6gV/myVL+WY63CwaOtmnl//I1l2z78TujZsxDPdryCmnLMKlTqdlSa4/hWoDZQpuBaR0cBY4BWgCzAfOM/5elYxu3oSDVyO9avvN+e2Fli/nDYDNwGTReRyY8zPZemzql4TJ04kPT2da665hoYNG5KUlMQLL7xAamoqffv2BWDkyJH861//YuLEidx5551s3LiR5557jqlTp+a306NHDxYtWkTfvn2Jjo5mypQpRUauY2NjWbNmDQMGDMDPz4+oqJIFNE8++STXXXcdDRs25LbbbsPb25vffvuNDRs2MGPGjIr7MrBGvxcsWMBtt93G4MGDGTFiRIGluDp37szDDz8MQM+ePenQoQM333wzzz//PE2bNuXvv/8mLS2NG264gTFjxtCxY0eGDRvG0KFDCQkJYefOnXz22We88sorhIeHExUVxYIFC6hfvz4HDx5k9OjRRUbbK4IxOSSlvklCymzsDs/LcwD4+7ajZo0nCfArOs1elZyIty7HpSpS3hRuAS7EyqeSJxvYgp7nlSqXE+lfcjhxuIeLz0JU2DgiQh6s8ouGvx07ytiV3/B7nOfbwSL8A3iia3f6Nr1AL2qqs15pfl2NA2aIyASsQLtAdGKMKf5XsTWdfIgx5n0RGQ781xizW0SeABqWptNOPwCpwL3GmHQAEQnESo62DbgGeAN4DuhZhvZVNevatSvz58/nnnvu4ejRo4SGhnLRRRfx6aef0qVLF8AagX3vvfd46qmnmDp1KrVq1eLxxx9n+PDh+e2MHTuWvXv3cv311xMcHMz48eM5dKjgQMfkyZMZOnQoTZo0ISsr65TTqvNceeWVfPHFFzz99NPMmjULb29vmjZtysCBAyvse3B14403snr1aqZMmUKPHj1IT08nNjaW++67j8cffzw/i7fNZuOrr75i9OjRDBgwgBMnTtC4ceP8pGKtWrVi9erVTJgwga5du2K322ncuDE33nhj/v5Lly7l3//+Ny1atOC8887jueeeK5LkrDyMMaRmfE1c8hRycncXW9fHuxHRYeMIDrhGT8xKnWaMMd0BRGQx8LAxHtYDKj09z6tznjGGxBP/JT55mttyEX/qRMwjJPDqKu1XRk4Oczes47Utm7AX85vpxguaM/7yrkQEnF0zzJTyREoaRIiI6016rjsJYIwxxSYqcU4Tu8AYs19EjgG9jTE/i8h5wAZjTKkyOInIYaCHMWZHoe3NgZXGmDrOJGwrjDGRpWn7dNeuXTuzadOm6u6GUmWWkbWFuKRJZGRvLLaely2cyND/UCP4riqf5qZUWYnIZmOMTq8op8o4z4vIWKwR72ZAFrAeGGuM+c2lztPArUB9To68P2GM+bFQW+2BKUAnrN9FvwJ9jTHxzvJw4AWgr3OXT4ERxpikU/VTz/MKwJhsjhwfQ0raUrflXrZa1It+HX/fVm7LK8uPB/YzftVy9iUneawTExLKlB696NIwtsr6pVRV8nSuL83Idfdy9uEI1s2D+4F9WCejn7GmhpdlpdVgrKykOwptr+0sA0ihCtbyVkqVTHbuPuKTpnIi47Ni6wl+hIfcR0TocLxsYVXUO6VUaYnIp8AAY0yK87VHxpi+xZW7URnn+W5Yt6VtxBocmAysEJHmLjPw/gAewspwHgA8CnwtIufnrd8tIh2AZcBMZ3k21pR111l972DlmLka63fOQuBNoE8p+qvOUXZ7IgcT7icja53bcj+fi4iJeh0f77pV1qfkzEymrv2e97b/5rGOAAPbtOU/HS8jyFcviqtzT4lPSMaY78t5rFVYV2+3AK8Bz4vIbUBb4P/K0N5HwGsi8hjWSdJgJU2ZAXzorNOeMt4jrpSqOHb7cRJS5nI8dTGF7igpIjTwZqLCxuDjXa9qOqeUKo8ETl4gT6jgtiv8PG+MudL1vYjcBSQDlwGfOeu8VajOf4B7gTZYATXA88A8Y8wUl6p/uuxzIXAV0DlvxFtEhgJrRKSZMeaPkvZZnXuyc3bxT/w9Hm+ZCvbvTZ3IedhsFbtShSfGGL76+08mfr+K+PR0j/WaRUYxrWdv2tSuUyX9Uup0VKpRXRGphXU1tznWSe534KW8K7mnMARneldjzMsichzrZPYBVpKz0hoGzMZaezvvc+QCi4BRzvc7sO71VkpVA4fJJOnEYhJSXsBhkoutG+h3GdE1nqjy6W1KqbIzxgxy97qCVMV5PgTrt8lxd4Vi3Y8yBGuE/GfntppYs+/eEZG1wPlYgfVEY8xK566dsO4Xd51K/gOQBlyKNTquVBHpmT9yMOE+HI4kt+XhIUOJDpuASImXjS+XwydO8NR3K1mxZ5fHOr42L0Z06Mj9bf+Fr1fV9Eup01WJg2sRuQz4GjgK5M1RGQD8R0SuNMa4n7di7euDdV/SPKwp4RhjlgLubyIpAWdyk2EiMhJogjUT5W9jTJpLnZ/L2r5SquyMcXAi/RPik58lx36g2Lq+3k2JrjGBIP+emqxMKZWvis7zc7GC5gK/YUTkOuB/QCBwGOjlMpCQt3ToJGA0sBXrHu1lInKJMWYb1tT1OOOS2MYYY5w5Z2q764iIDMEK5GnQoEE5P5Y6EyWnvsuR42OwriEV5k2t8GnUCO5fJX1xGMM7v25jxg9rSM3J9ljvX3VjmNqjF00izqr0RkqVWWlGrmcB7wLDjDEOABGxAS9jZeq81NOOxpgcEXkQ6z6nCuU8yf5S0e0qpcomPXM9ccmTycz+udh6XrZoosJGExZ0hy4LpdRZQET8gYexMnfX5ORi5AAYY8o0LaWyzvMiMhvojDV1216o+FusaeBRWCPj/ycinYwxhzn5uV4xxuStxb1VRLphjbY/kNd1d4f1sB1jzKvAq2AlNCvDR1JnKGMcxCdPJfGE+5/JNgmlbtQCgvwvr5L+7EpMYOyq5Ww6dNBjnWBfXx6/rAt3tGiFTS+MK5WvNL9o2wAD8wJrAGOMw3ly2lqC/ZcBPbCmc5WbiNxvjFngoexlY8ywijiOUqpksnL+Ij5pCqmZ3xRbTySAiJBhRIQ8WGX3iymlqsR84EbgPazp0OUKECvzPC8izwN3AN2NMUVubHUG9H87H+tF5C/gPuBprJFsgO2FdtuBlcAMrCSuNUVE8kavxZqaE401A1ApAByOdA4njiA14yu35T5eDYmJfgM/n/MrvS/Zdjuvbt7IfzesJ9tR+HrTSb0aN2FSt57UDg6p9D4pdaYpTXCdDDSi6H1CjYCkEuy/EpgqIq2AzVj3HeUzxnzodi/PZohIojHmA9eNIvIqcKWHfZRSFSzXHk9C8nMkpb0FeD4Zg42woDuIChuFt5fbWZFKqTPbDcCtxpgVFdRepZznRWQuVmDdzRizs4S72QA/5+u9wCGs5bxcNcVajgusaebBWPde59133QkIouB92Ooclms/wj9xA8nKcT8xI8C3PXWjXsPbq/KnXP985DCPr/yGPxPiPdaJDgxiYrceXNXkfL2NSykPShNc/4+TWTvzrkh3Bp7Fmi5+Kv91Pv/bTZkBSpsB4RbgQxFJyksg4jzhXoW11IZSqhI5HOkcT11AYso8HCa12LpB/j2IDpuAn+8FVdQ7pVQ1SAeKT7JQOhV+nheRecBdWBcCjotI3pW+VGNMqoiEAo9hZQ4/jDXS/BBQD+fKJs57p2cCk0TkF6zZe7cBHYHhzjo7RORr4BURuR9rOvgrwOeaKVwBZGb/ysH4geTaD7stDw28hVoRM7GJn9vyipKWnc1z63/g9Z+3FDvV5LbmLRjbuSth/v6V2h+lznSlCa4fwzo5LHLuJ1jrOr4EPH6qnY0xtlPVKQ1jzEoRGQy8LyJXYU3X6o11Jdr92gVKqXIzxk5K+vvEJ8/w+KMgj5/PRUTXeIIg/y5V1DulVDWagZXk9AHXW8jKqpLO8w86n1cW2j4JmIiVSeoiYDAQibW82EagizEmf3jRGDPHmUn8OWe934GrncnM8vQHXgDy7pX5FGfwrc5tqRnLOJTwIMZkuC2PCnuciJARlT46/P3ePYz/djmHTpzwWKdhWA2m9uhFp/qaZE+pkijNOtfZwMMiMpaCWTs9L3jnQkTuBpYaY7IKbfcF7jDGvFHybuf36QMRiQBWY11h7mqM2VvadtTZ57vvvqN79+7ExcURFRV11hyruqVlfk9c0tNk5RS+1bAgb686RIU9TmjgzVh5D5VS54BewOXAVSKynUKL2htj+pa2wYo+zxtjio1WnL9pbixhWzOwLih4Kk/EWlVFKcBaL/r4iVeIS34adykJRPypEzGXkMA+ldqPhPR0nlnzHZ/8scNjHS8R7mvbjoc7dMLf26dS+6PU2aQsv3qN8+GgdMlKFgNhbraHOMtOSUReKPwAWmIlB/kV64p53nZ1hhg4cCATJ04EQETYu3dvkTp9+/bFy8uL5cuXFymLjY1l1qxZldxLz8e69NJLOXz4MJGR5bsn6p133qFTp04EBwcTFBREhw4deOutt8rVZkXJzN7Ogbg7+SeuH1k52/nwvXTuui0BgLtuS+DD96xrbDYJJipsLI1qryUs6FYNrJU6t8QDHwGrsBJ6JRR6nJKe59XZypgcjh5/jLjkybj7+exli6Z+9AeVGlgbY/h453Z6v7W42MC6RXRNPr69P2Mu66KBtVKlVJp1rv2A6cBQwBdr5DrLef/TGGNM5qmawH0w3gArWVpJtPSwfRdW4pC8cl3C4ixy+PBhVq5cyaOPPsrChQvp1atXdXepAF9fX2rXLl+CrjFjxjBnzhwmTZrEkiVLEBE++ugj7r33Xn777TeeffbZCupt6eTkHiYhZSbJaUsp/n8rGzWCBxIZ+h+8vc7u0XullHvGmEEV0Iye59VZx+5I4lD8ENKz1rot9/W5kHpRr+PjXa/S+vBPSjITVq1g9f69Huv4e3vzaMdLGdTmErxtenFcqbIozT3XL2Hd63QfVhZMsDJfTsMafR7sbicR+ZWTo93fi0iuS7EX0BD4siQdMMZ0L0V/z3mNX3iuurvA7n+PLHcbS5Ys4aqrruLf//43zZo1IyEhIX+UuFu3buzbt4/Ro0czevRowLoyW1hCQgLDhw9nzZo1JCQk0LhxY0aNGsWgQSd/C3br1o3mzZtTo0YNXn31VWw2G3fffTczZszAZrN5PJa7aeHr169n3Lhx/PTTT3h7e9OuXTvefPNN6tatW6RvGzZsYMaMGTz//PM88sgj+dvHjBmDn58fjz76KDfddBPt27cHYOfOnTz22GN8//332O12WrZsyauvvkrLltZvzsWLFzNz5kx2795NgwYNeOCBB3j44YexOU+Us2fPZsmSJezatYsaNWpw9dVXM2vWLGrUqJH/fQ8fPpw33rmTx0cv4Z8DubRs48PUmWHUa1D0nwwvWwTRYY9QK3xMqf5clVKqMD3Pq7NNdu5eDsbdRXbuLrflQf5XUDdyPjZbcKUc3+5w8Pq2rTy3bi0Zubke611avwFTuveiofO3gFKqbEpzWepWYJAx5m1jzG7n423gXqyMnp68D3yANXL9hfN13uMt4H70niTlgTGGRYsWMWDAABo0aECHDh14880388s//PBD6tWrx5NPPsnhw4c5fNh9gq3MzEzatm3L559/zu+//87DDz/M0KFDWbmyYE6bt99+G29vb3788Uf++9//MmfOHJYuXVqqY23bto3u3btz3nnn8cMPP7B+/Xpuu+02cj2c1N5++22Cg4N58MEHi5Q98MADBAUF8e67VkL+Q4cO0blzZ0SE5cuXs2XLFh566CHsdmsJrAULFjBu3DgmT57Mjh07eO6555g+fTrz58/Pb9NmszFnzhx+//133nnnHTZs2MCIESOc33cu6Zk/kJWVxqwZrzNlVij/+ziSEykOnhpXcIKJTYKpX/Mj/Hya4e1Vy+1nU0qd3UTkFxEJd77+1fne7aO6+6pUVUvP+on9R6/1GFiHB99HTNTiSgusd8bHcct77/LMmu88BtZhfv7MuOJK3rzhFg2slaoApRm5TgMOutl+EHCf7hAwxkwCEJG9WAnNTjV9XJ1jlixZkv+68Kjzd999R2JiItdeey0Ad999d4ER3oiICLy8vAgJCSl2anZMTEz+aDPAkCFDWLVqFe+++y49e/bM3968eXMmT54MQNOmTVmwYAErV66kX79+JT7WjBkzaN26Na+++mr+tgsvvNBj/T///JPGjRvj6+tbpMzPz48mTZrwxx/Wyi3z5s0jKCiI9957L79+06ZN8+s//fTTzJgxg1tusa53NWrUiMcff5z58+czfLiVpNZ1dDw2NpYZM2Zw/fXXM+/l/iSkTCM5fRu5ufDEM2E0bmL9EzF4SDDjRiXhcBj8fBry0LCxPP5oX0SE7777zuNnU0qd9T4A8hKVvl+dHVHqdJKc9h5HEkdRKK+fkxc1w58hPPieSjl2Vm4u8zb+xMubN5Dr8Jy4/7rzm/FE1+5EBwZVSj+UOheVJrh+EXhKRAYa59oBIhIAPOEsK5Yx5vW81yJSg0Kj5s6smkoV8Nprr3HbbbflB5K33HILw4cP56effqJDhw4lbsdut/Pss8+ydOlSDh48SFZWFtnZ2XTr1q1AvVatWhV4X7duXY4dO1aqPm/dupUbbyxRstl8xS23YYzJL9+6dSudO3d2G4jHxcVx4MABhg4dygMPPJC/PTc3t8BFi1WrVjFt2jR27NhBcnIydnsu2dnZ/Lz9HmrVtpab9/UjP7AGiK5lIycHfBlNbJ2HKn3dTaXUmSHvAnrh10qdq4xxEJ8yk8SUuW7LbRJC3chXCAroVinH33DwH8at+obdx497rFMnOJjJ3a6gZ+MmldIHpc5lxQbXIvJpoU3dgIMu07taOts45SUvEWkIvAx0B1xTD+YlOvMqWZfVuSIpKYkPPviA7OxsFixYkL/dbrezcOHCUgXXs2bN4rnnnmPu3Lm0bNmS4OBgxo0bVyRw9vEpmBVTRHAUc9XXHXf3fBenadOmrFmzhqysLPz8CgatWVlZ7N69mx49epyy7bx+vvzyy1x66aVu6+zbt49rr72W+++/nyeffAh832Pjpi8ZOTyTnJyTbXt7nQz2BV9CA3sDrxMWdLcG1koppZQbDkcGRxIf4UTGZ27LfbzqExP9Bn4+zSr82ClZWcz4cQ3v/Lqt2Hp3tWrDqE6dCfHTc7lSleFUI9eFl874oND7PaU41mKgBlbis0Nops9KVxHJxKrT22+/TXR0NF9+WTDf3bp16xg5ciRz5swhKCgIX1/f/HuOPVm7di19+vThrrvuAqwg9c8//8xP4lVSJTlW27ZtWbVqVYnb7NevHy+88AIvvfRSgSnbAPPnzyctLY0777wzv+233nqL7OzsIqPXtWrVIiYmhl27dnH33Xe7PdamTZvIzs5m7FORnEh/FEMWx456/jwhgdcTFTaWQ7t2A697rKeUOje5JC09JWNMq1PXUurMlGs/xsH4QWRmb3Vb7u97CTFRiytlRY3lu/7mye9WcjQt1WOdJuERTOvZm3Z1Yyr8+Eqpk4oNrvOW1RBrsdoLgP3GGM//5xavPdDRGPNbGfd3N5LukTGmb1mPo04Pr732GrfccgstWrQosL1p06aMHj2apUuXMnjwYGJjY1mzZg0DBgzAz88vP2N34X2WLl3K2rVriYqK4sUXX2TPnj1cfPHFpepTSY41evRoOnbsyJAhQ3jooYfw9/dnzZo19O7dmwYNGhSp37FjR0aOHMmYMWPIysrixhtvzF+K64knnmDMmDH5mcIffPBBXn75ZW677TbGjx9PeHg4Gzdu5MILL6RNmzZMnDiRESNGUKNGDa655hpycnLYsmULBw8e5PHHR1Kr7q84HA7mzHmO3lf58/PWHN54Lc3NJ7XRoOYXBPjlfT+7S/U9KaXOGRV6n7We59WZKDN7Owfj7yHX7i41EYQE3kDtiNnYxL9CjxuXlsbE71fx1d9/eqzjY7MxrF17HmzXAT/v0twNqpQqi5L+X2aAn4HmwN9lPNYeoLxzUAqPpKuz1JYtW9i6dSv//e9/i5T5+vrSt29fFi5cyODBg5k8eTJDhw6lSZMmZGVluZ06PWHCBPbs2cPVV19NQEAAAwcOpH///mzfvr1U/SrJsdq0acOKFSsYN24cHTt2xM/Pj3bt2uUnZXNn1qxZtG7dmnnz5jFpknXbYsuWLVm4cGH+aDtYidlWr17N6NGj6d69OyKSvxQXwH333UdQUBAzZ85k7NixBAQEcNFFF3Hv/Zey50g3ouvtZdzEUBa+lMrcmSe4+BJfHhsfyqMPJQHg492Y8OAOiLziElgrpZR7lXCftZ7n1RklNWMlhxKGYYy7C9UQGTqSyND/FJtbpbSMMby3/Temrv2elKwsj/Xa1KrDtCt60yyy4kfLlVLuSUnvD3VO/RpijFl3ysru9+8BPA48aIwpa4CugHbt2plNmzZVdzfUGSAjayPHkiaTmb252HpetkgiQ0dSI7g/Ij7F1lVKFU9ENhtj2lV3P9SZS8/zpz9jDEmpr3EsaSJQNDeL4EftiNmEBpUuwemp7E06zvhVy1n3zwGPdQJ9fBh9aWcGtGyDl600q+4qpUrK07m+NPNDHgNmishwYJspbdYm+ARr5PoPEckCCiy4Z4wJLWV7iEgt4CGsEXUD/A7MN8aULr2zUmeZ7Jw9xCVPJTXji2LrifgTHjyEiNCH8LKFVFHvlFJnKxEZBPQDGgAFEkMYYxqXoT09z6vTjjG5HEt6gqRU97lIvGyRxEQtIsDvXxV2zFyHg9e2bmLO+nVk2d2vWQ3QtWEjnulxBTEhpf5ZrZSqAKUJrv8P8Ac2A7nOADlfCYLj4aXsW7FE5DLga+AokDeaPgD4j4hcWdYRdqXOZLn2BBJS5jhP+J5PviCEBt5KVNhofLw1uYlSqvxEZDQwFngF6ALMB85zvp5Vhvb0PK9OO3ZHCocShpKe+b3bcl/vZtSLfgMf7/oVdszfjh1l7Mpv+D3O8zWlCP8AnuzanT5NL6jQKehKqdIpTXBdruDYdZ3rCjILeBcYZoxxQH7itZeB5wD3axEpdRZymEyOn3iNxJQXcJgTxdYN9Luc6BpP4O/both6SilVSvdj3T72vnOW23+NMbtF5AmgYRna0/O8Oq1k5+7nYNzdZOe6TyAW6N+NupEv42WrmFHjjJwc5v70I69t3Yy9mAmjN17QnPGXdyUiILBCjquUKrsSB9cVERw7p3fdBTQBnjDGxDuvTB8yxpRmWS+ANsDAvBOus48OEZkNuF8HQamzjDEOUtI/Ij75WY9ZSvP4+lxAzbAnCPTvple1lVKVoR6wwfk6A8iLMN51br+/lO21Qc/z6jSRkbWRg/GDsTvc59yrETyQmjUmI1IxGbl/OLCP8SuXsz8l2WOdmJBQpvToRZeGsRVyTKVU+ZXpXwARqU3Re6n2n2KfS4CVWFnDLwJmAvFAL6ApcGcpu5EMNAL+KLS9EZBUyraUOuOkZ/7AsaTJZOX8Wmw9L1stosJGExZ0OyJeVdQ7pdQ56AgQBewH9gGdsFYaOY8SroVdiJ7n1WkhJe1DjiSOxOAuM7eNmjUmER5yb4UcKykzg6lrv+f97b97rGMTYWDrtjza8VKCfH091lNKVb0SB9ciEga8ANxGocDa6VS/2mcBc40xT4mI67zVZcCgkvbDxf+A10TkMeBHrBN3Z+BZrKvkSp2VsnL+JC7pGdIyVxRbTySQiJAHiQgZhs2mU8WUUpVuFdAX2AK8BjwvIrcBbbHytpSWnudVtTLGkJDyHAkps92WiwRRN/JlggN6VsixvvzrTyZ+v4qEjHSP9ZpFRvFsz960rl2n3MdUSlW80oxczwJaAzcAHwKDgRjgYWBkCfa/BHB3We8wUKsU/cjzGCDAIk5+jhzgJawlv0pNRMYCNwHNgCxgPTDWGPObs9wHeAa4GmtqewrwLfC468i9iPhhfV/9gACsEfsHjTH/uNQJx7pY0de56VNghDEmqSx9V2e/XPsx4pNnkZz2Du6W/TjJRlhQf6LCRuLtVbOquqeUUkMAG4Ax5mUROQ5cBnyAleSstCr8PK9USTlMJkcS/8OJ9I/dlnt7xVAv6g38fC8s97EOnzjBk9+tYOWe3R7r+Hp5MaJ9J4a0bYePl85CU+p0VZrg+mqgnzFmjYjYgc3GmKUichgYCrx/iv0zgHA32y8ASr2khjEmG3jYGRA3wToB/22M8Xy579S6YWU33ehsbzKwQkSaG2MSgUCsK/BTsKa6hWElVflaRFoZY/LSM88BrscKrhOA2cDnInKJMcburPMO1lIlV2NdjV8IvAn0KUf/1VnI4Ugn8cTLJJ6Yz6n+egf5X0F0jQn4+TStot4ppVS+RsaYXXlvjDFLgaUAItIT60JziVXSeV6pU8q1x3MwfhCZ2Zvdlvv7XkxM1OJyX8B2GMM7v25jxg9rSM3J9livfd16TO3Zi8bhEeU6nlKq8pUmuK6BdQ8VWPdBRQJ/Yy2PsbAE+38CPCUitzrfGxGJBaZjXdUuE+dJtvibTkve1pWu70XkLqzPehnwmTEmGesecdc6Q7HW3bwQ+NU5ff5eYJAxZrlLO/uAK4BlInIhcBXQ2Rjzo0s7a0SkmTGm8P1l6hxkjJ3ktKXEJ8/E7jhabF0/n5bUrPEkgf6XVVHvlFKqiG9E5DJjzBHXjSJyBfAREFKWRivyPK/UqWTl/MHBuLvJsR9wWx4S0IfaEXOw2QLKdZy/ExMYu/IbNh8+5LFOsK8vYzt35faLWmLTRKRKnRFKE1zvAhpjJSrZAdwhIhuwplEnlmD/UcCXQBzWCPBarOngPwATStGPfM7EapcCNXFORctjjJlfljYLCXG2e7yYOnnZUPPqXAL4AN+49OWAiOxw9nUZVpKXVKx7yPL8AKQ562hwfQ4zxpCW+S1xyc+QnbOz2LreXjFEh40lJPAGrBVqlFKq2nwNLBeRzs6L0YhIL+Bj4D9labAKzvNK5UvL+I5DCUM9LmkZGfoIkaGjynW+zbbbeWXzBuZt+Ilsh91jvd6Nz2Nitx7UDi7TNSmlVDUpTXC9BGgFfIeVTORzrLWvvYB/n2pnY0wK0FlEemBNrbYBW4wxxWdl8kBEBmCNmAtWYOuaidRgTe8ur7lY07/XeeiDL9a08M9c7qeuDdixMqG7Ouosy6sTZ8zJRQuNMUZEjrnUKXysIVj3s9GgQYOyfJbT1sCBA3n9dWulN29vb8LDw7nooou45ZZbGDJkCD4+PtXcw5PeeecdXnzxRX799VeMMbRo0YIRI0YwYMCACmk/M/s34pKeJj1rTbH1bBJKROgIwkPuxSb+FXJspZQqp+FYtzx96Ryt7owVWD9qjHm1tI1Vxnn+VLlVnHWeBm4F6gPZWAnansibaVaoPQG+Aq4EbjXGvO9SprlVziDHTyzhWNITWD/hChJ8qRUxi7CgW8p1jK2HDzF25Tf8meh+OS+A6MAgJnXryVXnnV+uYymlqkdp1rl+3uX1KhG5AGgH/Ol6UipBO6uwMoqW1xRgBjDZ5V7nCuNcR7Mz1tTtIv/SirWQ4VtY0+X7Fi531yRFfxicqs7JytYPk1cB2rVrV5YlTU5rV1xxBW+++SZ2u524uDhWrVrFU089xZtvvsnKlSsJCgqq7i4yZswY5syZw6RJk1iyZAkiwkcffcS9997Lb7/9xrPPPlvmtnNyDxGfPIOU9PcofsUab2oE30Nk6CN4e0WW+XhKKVXRnBeJ7wI+w0r22QJ4xBizoIxNVsZ5vhvF51YBa/bYQ1hLhwYAj2LlVjnfGFP4Hp2RuIvGLJpb5QxgjJ1jSRNJSn3NbbmXLZy6UYsI9OtQ5mOkZWfz3Lq1vL5ta7Fn+DsuasmYy7oQ5q8XzZU6U50yuBaRq7Eyc7bOm+YF1rrWIpIMbBORIcaYbzw2YrWzCPjdGPNcoe3/AZobY+4rZd9DgSWVFFg/D9wBdDfGFEnd6Ays3wVaAt2MMa6XII9gjeZHYU2Bz1MTWO1Sp6aISN7otfPqdzTWCHeFuCq8tF9pxfv6eEluxwc/Pz9q17YG7WNiYmjTpg29e/embdu2zJgxg0mTJgGQnZ3NE088wdtvv83x48dp3rw5zzzzDFdeefJ2+e3btzN69GhWr15NQEAAPXv25Pnnn89vf+DAgcTHx9OxY0defPFF0tLSuPXWW5k/fz4BAe7vodqwYQMzZszg+eef55FHHsnfPmbMGPz8/Hj00Ue56aabaN++PQA7d+7kscce4/vvv8dut9OyZUteffVVWrZsCcDixYuZOXMmu3fvJqZeMLf3N9w12BebzbqnavGCVD56L4MD++yEhApduvsx6ZlbOa/BRHx9GrFkyRKGDx/OJ598wsMPP8yePXto3749ixYtolGjRqX4E1JKqbITkbZuNk8F3gbeADbn1THGbCll8xV+nj9VbhVnnbcK1fkPVi6VNli3duVtb4e1YsolFDp3a26VM4PDkcqhhAdIy3Sfa8/X+zxiot/A1zu2zMf4bu8eJny7nEMn3E81B2gYVoOpPXrRqf7ZNTNRqXNRSW4aGQ7MdA2s8zi3Tcc6uZzKNbgfsV7lLCutt4Fry7BfsURkLnAn0MMYU+SGV+dyXEuxpsh3L5y4BdiMtVRIL5d96mElPMubUrYOCMa69zpPJyCIgvdhn9NatGjBVVddxQcfnMx3N2jQIL7//nveeecdfv31V+655x769OnDtm3bADh8+DBdunShRYsWbNiwgRUrVpCamkrfvn1xOE4uX/X999+zbds2Vq5cyQcffMA333zDmDFjPPbl7bffJjg4mAcffLBI2QMPPEBQUBDvvmstu3ro0CE6d+6MiLB8+XK2bNnCQw89hN1uDW4sWLCAcePG8djY7nz1bUNGjXOw4KUk3nnjZAJcmwjjngrlsxVRvPBSB3b+FsO0iQ58fU4GzllZWUybNo1Fixaxbt06kpKSGDZsWBm/baWUKpNNWKPAm1we3wH1gGEuZRvL0HalnOcLKTa3ivP2ryFYS2/+7LI9BOsi+1BjjLsVT06VW0VVs5zcf9h37HqPgXWgX2ca1Pq0zIF1Qno6jy77ksGffugxsPYSYdgl7fmq/90aWCt1lijJtPBWFJ+IZBUwvgTt1MA60RSWBpRlbYH/AB87l/f4FSugzWeMmVzaBkVkHnAX1lrex52JVABSjTGpzhHr94B/YU3rMi51ko0xGcaYZBF5DZjpvIc6bymuX4AVzr7tEJGvgVdE5H6sqWmvAJ/r1eyCmjdvzooV1m35u3bt4t1332Xv3r35950PHz6cFStW8MorrzB//nxeeuklWrduzfTp0/PbeOONN4iIiGDTpk35I8teXl4sXryY4OBgWrRowfTp07n33nuZNm2a2ynof/75J40bN8bX17dImZ+fH02aNOGPP6w/unnz5hEUFMR7772XX79pU2tpLGMMkyePZ9S4GnTq9hEAdev5c2CfnXffSGfAQOvY99wXhI93LNFh4wgOuJawgGVcf/31vP7669hs1jWx3Nxc5s2bR7NmzQAYNWoUgwYNwuFw5NdRSqlKVplTZSr8PO+G29wqInId8D+sBKyHgV6FpoS/DHxtjPnSQ7uaW+U0lpG1hYPxg7A74tyWhwUNoFb4FKzxlNIxxvDJHzt4evW3HM/M9FivRc1aPNuzN82jy7ecl1Lq9FKS4DoacBRTbrCW5TqVP7FGqOcW2n4t1pJepTUUa8pVPHAeRe9nLstJN29YsvBlzEnARKwr8dc7txVe/HAQVtI3sO7PysUa4Q5wtnd3oXu3+2MlOsmbTv8p1iwB5cIYgziXn9iyZQvGGJo3b16gTlZWFj169ABg8+bNrF69muDg4CJt7dq1Kz+4btWqVYE6nTp1Ijs7m127dtGqVSu3fZFilsFw7efWrVvp3LlzkUA8I2sLO3dN4J9/4pjwWBxPPn6yvVy7Ie8nmM0WzvatV/HfuT+zY8cwkpOTsdvtZGdnc+TIEerWrQtYQX1eYA1Qt25dcnJySEpKIiJC18JUSlU+Y8y+U9cqs8o4z+c7RW6Vb7GmgUcB9wP/JyKdjDGHnVPJW2PlnSmO5lY5DaWkf8qRxEcwxl3gK0TXeJLw4CHFnvM9+SclmQmrVrB6/16Pdfy9vflPx8sY2KYt3nohXKmzTkmC63+wRq//8lDeCjhYgnaeA14WkZqcnB7eE3gEK3FIaT0BjHRNtFZexphi/yU1xuzFOjGeqp1MYITz4alOIlAxKabPYtu3b6dx48YAOBwORISNGzcWySCed6+0w+Hg2muvZdasWUXaqlWrVpn70bRpU9asWUNWVhZ+fn4FyrKysti9e3d+gO8yUAFAdu5+4pOmciLjU9Izrd9vE6eGcXG7gsG34EtEyDBOHL+eO25tz/3338/kyc8QGRnJli1b6NevH9nZ2fn1vb0L/u+b90PAdfq7UkpVJhG5qaR1jTEflrL5Cj/P5zlVbhVjTBrWhf+/gfUi8hdwH/A01m+X5kBqoQBsqYisM8Z0popyq6iSM8aQmDKX+JQZbstFAqkbOY/ggCvdlhfH7nDw+ratPLduLRm5nlMEXFa/AVN69KJBWI1SH0MpdWYoSXD9BfC0iHxpjMlwLRCRQKwrx1+cqhFjzOsi4o+1pvVY5+aDwH+MMYtL123AShr2aRn2O2eUNJnY6eq3337j66+/ZsIEaxn0iy++GGMMR44coXv37m73adu2Lf/3f/9Hw4YNi13C69dffyUtLS1/Cvj69evx9fWlSZMmbuv369ePF154gZdeeqlAQjOA+fPnk5aWxp133pnfh7feeouMjKOcyHyZpNTFGKygOCrai1q1bezfZ+eGW07+7xcSeBPRYY/j412P1Ss/IDs7m+effx4vLy8APv/88xJ8Y0opVeXeP3UVwBqt9Spl25VynnfmVrkDKyFpkdwqHtiAvCur44HCV3B/BUYBnzjfu+ZWybvvWnOrVBOHyeJo4mhS0t3/dfX2qkNM1Ov4+7Yodds74uMYu/IbfjlaOAXPSWF+/kzo0o2bLmhephFxpdSZoyTzUaYAYcBfIjJGRK53Ph7HmuodhpUZ9JSMMa8YY+oDtYDaxpj6xpiXy9j3xVhTq9VZICsriyNHjnDo0CG2bdvG7Nmz6datG5dccgmjRo0CrNHj/v37M3DgQN5//312797Npk2bmDVrFh9+aA2IPPTQQyQnJ3P77bfz008/sXv3blasWMGQIUM44ZJQJDc3l8GDB/P777+zfPlyHn/8ce6//36PS3517NiRkSNHMmbMGKZPn86ff/7JX3/9xYwZM3j88ccZM2ZM/pTzYQ/cy4kTCfS58TxW//Aie/em8/knGez43bpdcPijIbz2cipLFqZy6EBLUuNfZMUX7Zk1800Azj//fBwOB3PmzGHPnj28++67zJkzp7K+eqWUKjNjjO1UD6wg+aoyNF/h53lnbpVBQD+cuVWcj2BneaiIPCMiHUSkgYhc4lztpB7wfwDGmIPGmN9cH87mD+SNghtjdgB5uVU6ikgnNLdKtci1J/DPsds9BtZ+Pq1oWOuLUgfWWbm5PLduLdf/761iA+vrzm/GN3cN5OYLL9LAWqlzwClHro0xx0TkUqzluKZyclq0wVqS4kE36z6eqk33GSRKJxC4T0SuxEoWVjjRyb8r4BiqiqxYsYI6derg5eVFjRo1aNGiBU899RRDhw4tcO/y4sWLmTJlCo899hj//PMPERERtG/fPn8ku27duvzwww+MHTuWq666iszMTBo0aEDv3r0LTOfu2rUrF110Ed27dyc9PZ2bb76ZGTPcTxXLM2vWLFq3bs28efPylwZr2bIlCxcu5K677sIYw4mMT8mxTeON/wtg5tQT3HN7Ggg0vcCbyc+GAXBrv0BCgmuz+FU7z0//noCADVx00UUMH27dct+qVSvmzp3L9OnTmTBhApdeeimzZs3i9ttvr9DvXCmlKpOIxGAFsoOBhpR+5LoyzvOnyq2SC1yE1edIrKSkG4EuxphfSnksza1SzbJy/uJg3N3k2N2nBggOuIY6ES9gswWWqt0NB/9h7Mpv2JPkNsk8AHWCg5nc7Qp6NnY/I06p0spbhjU11V1+aHW6kML3hxZbWSQcK6mIAH8ZYzz/q1J03wisUfCeWGs+Fxg1N8aElrgjVnvfFlNsjDE9StPemaRdu3Zm06ZN1d2NM1beOtcVOdU6Pesn4pImk5m9tdh6XrZoosJGERbUDyv5vFLqbCMim40xp0p2dVYSES+gL1YSsF5YQfFS4D1jzJ5StqXneVVmaZmrORQ/BIdJcVseEfIQUWFjESl5UrGUrCym/7Cad3/zfJ1FgAGt2jCqU2dCCuVoUWeGgQMHEhsby8SJExER9uzZQ2xsLAAff/wx06dPZ8eOHeTm5lKvXj06d+7MwoUVeytmbGwsw4cPz5+9CadvcP3TTz8xZcoU1q5dS1paGo0aNaJfv36MGTMGf3//6u5eAXv37qVRo0YYY5g4cSJ79+5lyZIlZWrL07m+VL/uncF0WdaqBHgNuBgrE+YhPGTLLEVf3N90q1QVys75m7jkqaRmfF1sPRF/IkKGERHyIDZb0UzmSil1JhORZlgJv+7GWmLzHazg+i5jzPaytKnneVVWSalvcfT4WKBwEngAb2qHzyAs+I5StfnNrr946rtVHE3zHNicFx7B1J69aVc3pnQdVmeElStXcuuttzJp0iQWL16Ml5cXO3fu5OOPP67urpVLdna226VmS+LTTz/llltuoX///qxYsYLIyEh+/PFHRo0axcqVK1mxYkWZ2z5TVeUaAD2B240xU40xS4wxr7s+StuYiLR0XiFXqsrl2hM4enwce450P0VgLYQF3UGj2j8QFfaYBtZKqbOOiKwB1gM1gNuMMY2NMRMqoF09z6tSMcbOseOTOHr8MdwF1jZbDepH/69UgfWxtFQe+vIzhn3xqcfA2sdm49/tO/FZv7s0sD6LffbZZ3To0IFx48ZxwQUXcP7559OnTx9ee+21AvU+/PBDWrZsiZ+fH/Xr12fKlCkFVpKJjY0tsqpNt27d8m8P7NatG/v27WP06NGISJF79VeuXEmLFi0ICgqie/fu7NlTcFLQZ599xiWXXIK/vz+NGjVi/PjxBVabyRuVHzx4MDVq1KB///4sWbKE4ODgU7btKj09nXvvvZdrrrmGxYsX07ZtWxo2bEi/fv347LPPWLt2LXPnnlyBOSUlhQceeIA6derg7+/PhRdeyNKlS/PLf/zxR7p27UpgYCAxMTE88MADpKScnHny9ddfc/nllxMeHk5ERARXXnklO3bsyC/fu3cvIsIHH3xAr169CAwMpHnz5ixfvtzjZ6gMVTkv9RhQkfMYtgFZIrId+Nnl4QeMMMZc73FPdU4r6/QPAIcjneOpC0lM+S8OU/xf5yD/7kSHTcDP98IyH08ppc4AnYB5wAKX5F4VQc/zqsQcjjQOJzxEauY3bst9vBtTL+p1fH1Kdg+0MYb/2/4b09Z+T0pWlsd6F9euw7SevWkaGVWmfp9L/jhQt7q7QLP6h8q8b+3atdm5cyfbtm2jdevWbuts3ryZW2+9lQkTJtC/f382btzI0KFDCQ0NZcQIjyv0FvDhhx/SunVrBg8ezAMPPFCgLCsri2nTprFo0SL8/f255557GDZsGMuWLQNg2bJl9O/fn7lz59KlSxf279/PsGHDyMrKKhDQz549mwkTJrBp0yaMMfzwww+nbLuwZcuWER8fz2OPPVakrG3btvTs2ZN33nmH0aNHY4zh6quv5vjx4yxevJimTZvyxx9/kJlprTf/66+/0rt3byZNmsTChQtJTEzkkUceYfDgwbz/vpWMMC0tjUceeYRWrVqRkZHBM888Q58+fdi+fXuB0fHx48czc+ZM5s+fzzPPPMMdd9zBvn37CA6umgGuqgyuxwOTReQeY04RlZRMBNY084uBtlhrZTfFmm5e4nvBlSoJY+ykpH9AfPJ0cu2Hi63r59Oc6BpPEOTftYp6p5RS1aod1j3Wa0RkL/AG8G4FtKvneVUiObmHOBh/D1k5v7stD/DrREzkQry8wkvU3t6k44xftZx1/xzwWCfIx4fRl15O/5at8bJV5URQVdlcB2FcR5xHjBjBmjVraNOmDfXq1aNDhw5cccUVDBgwID9wmz17Nl27ds1PfNu0aVP++usvpk+fXuLgOiIiAi8vL0JCQqhdu3aBstzcXObNm0ezZs0AGDVqFIMGDcLhcGCz2ZgyZQqjR49m0KBBADRp0oTp06czYMAAZs6cmT8K3rVr1wJB8Q8//HDKtgv7888/AbjwQveDSM2bN2fBggWAlbh43bp1/P777/n1GzdunF935syZ3H777YwcOTJ/20svvcTFF1/MsWPHqFmzJjfffHOB9hcvXkxoaCgbNmygc+fO+dsfffRR+vTpA8DUqVN54403+Pnnn+ncuTOxsbH5f6YTJ0502+/yqsrgegIQCxwTkX0UzfrZqjSNGWOSgG+dD8CaQoZ1Qh/rYTelSi0tczVxSZPJyin+tkFvrzpEhY0hNPBmdCajUupcYYz5GXhIREYCtwL3AjOwbj27VkQOlyYBqku7Seh5Xp1CZvYv/BN3D3aH+4VrQoNup3b4dEROfd9njt3Oa1s3M/endWTZcz3W6xbbiKe7X0FMSKly8aozXFBQEF988QW7du3i22+/Zf369YwdO5Zp06axYcMGatWqxY4dO7j22msL7Ne5c2cmTZpESkoKoaHl+zvj5+eXH/yCtUpOTk4OSUlJREREsHnzZjZs2MD06dPz6zgcDjIyMjhy5Ah16tQBoF27ojk3T9V2aRlj8oP5rVu3UqdOHY+B+ObNm/n7778LTBPPC4J37dpFzZo12bVrF0888QQ//fQTcXFxOBwOHA4H+/fvL9BWq1YnQ8q6da3ZEseOHSt1/8uqKoNr9wsMViBjzK8i8igwC/isso+nzm5Z2TuIS36GtMziEtaCTYKJCB1OePB9pV7OQymlzhbGmEzgTeBNETkPK8HZo8AzIrLKGHN1BRxDz/Mq34n0LzmcOBzrr15hQlTYOCJCHizR+tK/HjvK2BXL2B7vebXYyIAAnujSnT5NL9A1q89hTZo0oUmTJtx3332MHz+epk2b8tJLLzFx4sQCAWVhedttNluBEXGAnJwcd7sU4e1dMHTLa9PhcOQ/P/XUU9x6661F9o2Ojs5/HRQUVOq2C2vatCkA27dv57LLLitSvmPHDs4//3yAIp+3MIfDwX333cejjz5apCwmxspj0KdPH2JiYnjllVeIiYnB29ub5s2bF7ifHMDHx6fEn6EyVFlwbYyZVJHtiUigMSbdTdEeoJmb7UqVSK79CPHJM0lOWwoU9z+jFzWCBxAZOhJvL73XSiml8hhj/gYeF5HxwHVY60aXip7nlSfGGBJPzCM+earbchF/6kTMIyTw1NdzMnJymPPTj7y2dTOOYgKAmy5ozvjLuxEeEFDmfquzT2xsLIGBgfnLYzVv3py1a9cWqLN27Vrq1atHSEgIYAW5hw+fvMUwMzOTnTt3cvHFF+dv8/X1xW53l+2+eG3btmXnzp2cd955Zfk4pdK7d28iIyOZOXNmkeB6y5YtrFy5Mn8EvW3bthw+fJgdO3a4Hb1u27Ytv//+u8d+JyQksGPHDubNm0f37t3zj5Gb63mGSXWp8oV2RaQH0BzrnqnfjTHflbGpEyLyJ7AF2Op8PgQMB4pfF0kpNxyONBJPzCfxxMsYk1Fs3eCAK4kKG4efz/lV1DullDrzGGPswCfOR2npeV4VYUw2R46PISVtqdtyL1st6kW/jr/vqe82XLt/HxNWLWd/SrLHOvVCQ5nSvReXN4wta5eVU3mSiZ0OJk6cSHp6Otdccw0NGzYkKSmJF154gdTUVPr27QvAyJEj+de//sXEiRO588472bhxI8899xxTp568ENSjRw8WLVpE3759iY6OZsqUKUVGrmNjY1mzZg0DBgzAz8+PqKiSDeI8+eSTXHfddTRs2JDbbrsNb29vfvvtNzZs2MCMGTMq7svAGv1esGABt912G4MHD2bEiBEFluLq3LkzDz/8MAA9e/akQ4cO3HzzzTz//PM0bdqUv//+m7S0NG644QbGjBlDx44dGTZsGEOHDiUkJISdO3fy2Wef8corrxAeHk5UVBQLFiygfv36HDx4kNGjRxcZbT8dVFkGBhGJEZENwHJgDPA4sFJEfhKRsqQP7AzMBU4AtwCfAtuBBwF/EZkiIreLiKZqVsUyJpek1LfYffhSElKeLzaw9vdtQ/3oD4mJWqyBtVJKVS49z6sC7PZEDsT18xhY+/lcRMNaX5wysE7KzOCxFV9z98fvewysbSLce/ElfN1/oAbWCrCSgO3Zs4d77rmHCy+8kCuvvJK9e/fy6aef0qVLF8AagX3vvff44IMPaNGiBY8//jiPP/54/jJbAGPHjqVHjx5cf/319O7dm86dO9O2bdsCx5o8eTIHDhygSZMmBaZzn8qVV17JF198wbfffkv79u1p3749zz77LA0aNKiYL6GQG2+8kdWrV3Ps2DF69OjB+eefz1NPPcV9993HN998k5/F22az8dVXX3HZZZcxYMAALrzwQh5++OH8Kd2tWrVi9erV7N27l65du9K6dWvGjh1LrVq18vdfunQpv/zyCy1atOChhx7i6aefxs/Pr1I+V3nIqebAV9iBRD4A6gJ3GmP2OLc1Bt4CDhljbiln+4I1TayNy6M1UNMYc1Zll2rXrp3ZtGlTdXfjjGeMIS1zBXFJz5Cd+1exdX286hNVYywhAX0R0aygSinPRGSzMaZothhVLnqeP7dl5+zin/h7yMnd7bY82L83dSLnYbMVvZc0jzGGL//6k4nfryIhw90dB5YLoqKZ1rM3rWvV9lhHKXVu83Sur8qx9F5At7zAGsAYs1tE/g2sLEkDItIe2OycZlaAsa4S7HQ+/ues3w7YX7iuUpnZvxCX9DTpWT8UW88mYUSGPkyNkEHY5PS7OqaUUmcTPc8rd9Izf+Rgwn04HEluy8NDhhIdNqHYlToOnzjBk9+tYOUe98E5gK+XF/9u34n727bDx+usul6jlKoip8NE9dKkb1sH1AY8p3IsaBXWle2qy7+uTms5uf8Ql/wsJ9I/PEVNH8KDBxEZ+nCJ18VUSilVbnqeVwUkp77LkeNjAHeJi7ypFT6NGsH9Pe7vMIa3f93GzB/WkJqT7bFe+7r1mNqzF43DS7/kkFJK5anK4Hol8IKI9DPGHAAQkQZY91OVaOQaEGCaiHiey1PQqRc1VOcEuyOZxJQXOX7iNQxZxdYNCehLVI2x+Ho3rKLeKaWUctLzvALAGAfxyVNJPDHfbblNQqkbtYAg/8s9tvF3YgJjV37D5sOeE2mF+PoxtnMXbruoJTZdXkspVU5VGVz/Gytb6G4ROYSVLTwG+MVZVhKrgSalOOY6oPi0z+qsZkw2SalvkJDyPHbH8WLrBvi2J7rGkwT4tS22nlJKqUqj53mFw5HO4cQRpGZ85bbcx6shMdFveEwsmm238/KmDczf+BPZDs/LGfVuch6TuvakVnBwhfRbKaWqcp3rA0BbEekFXIB1dXq7MWZFKdroVkndU2cZYwypGV8SlzyVnNw9xdb18W5MdNh4ggOuyl9sXimlVNXT87zKtR/hn7iBZOX84rY8wLc9daNew9sr0m35lsOHGLfyG/5MTPB4jOjAICZ168lV5+mqH0qpilXpwbWIXA28BLQ2xiQbY5ZjLceFiISJyF5giDHmm8ruizo3ZGRt4ljSZDKzi8+06mWLIDJ0JDWCByDiU0W9U0oppZQ7mdm/cjB+ILn2w27LQwNvoVbETLcJRlOzs3lu3Vre2LaV4tbBueOiljzeuQuhfv4V1GullDqpKkauhwMzjTFFFhI0xiSLyHTgYUCDa1Uu2Tl7iEueRmrG58XWE/EnPPh+IkIfwssWWkW9U0oppZQnqRnLOJTwEMa4v90+KmwMESH/djvD7Nu9u3ni2xUcOnHCY/uxNcKZ2qMXHevVr7A+K6VUYVURXLcC/lNM+SpgfBX0Q52l7PZEElLmcDz1dSCnmJpCaOAtRIU9ho93TFV1TymllFIeGGM4fuIV4pKfBjdjziL+1I6YQ2hg3yJlCenpTF79LZ/9udNj+14iDLnkX4xo3xF/b52lppSqXFURXEdT/HJbBnB/44xSxXCYTJJOLCIh5QUcJqXYuoF+nYmu8QT+vi2rqHdKKaWUKo4xORw9Pp7ktLfclnvZoomJWlwk0agxho937uCZNd9yPDPTY/statZies/eXBhds0L7rZRSnlRFcP0P1uj1Xx7KWwEHq6Af6ixhjIMT6R8Tl/wsufZ/iq3r692M6BpPEOTfXZOVKaWUUqcJuyOJQ/FDSM9a67bc1+dC6kW9jo93vQLbDyQnM+Hb5azZv89j2/7e3vyn42UMbNMWb5utQvutVFl89913dO/enbi4OKKios6aY6miquJfnC+Ap0UkoHCBiAQCk511lDql9Mwf2Xf0Gg4nDi82sPay1aRW+Exiay8nOKCHBtZKKaXUaSI7dy/7j/b1GFgH+V9Bw5qfFAis7Q4Hr23dzFVvLyk2sO5cvyFf97+H+9q208BalcnAgQOZOHEiACLC3r17i9Tp27cvXl5eLF++vEhZbGwss2bNquReej7WpZdeyuHDh4mMLN/E4HfeeYdOnToRHBxMUFAQHTp04K233M8yqW5LliyhW7duAHTr1o0lS5ZUW1+qYuR6CnAL8JeIvAjk3RhzIVayMwGmVkE/1BksK+cv4pKeJi2z+JXbRAKJCHmAiJBh2GxBVdQ7pZRSSpVEetZPHIofjN1x3G15ePB9RNd4ChGv/G074uMYu/Ibfjl6xGO7YX7+TOjSjZsuaK4X1FWlOnz4MCtXruTRRx9l4cKF9OrVq7q7VICvry+1a9cuVxtjxoxhzpw5TJo0iSVLliAifPTRR9x777389ttvPPvssxXU27NPpV/SM8YcAy4FfsEKoj9yPqY4t11mjDla2f1QZ6ZcexxHEsew90iPUwTWNsKCBtC4zg9EhY3UwFoppVQRIjJWRDaKSIqIxInIZyLSolCdp0Vkp4ikichxEVkpIpe6lEeIyIvOOhkickBEXhKRyELthIvImyKS7Hy8KSI1quijnpaS097jwLHbPATWXtQMn0bN8Mn5gXVWbi6zflzL9f97q9jA+rqmzfjmroHcfOFFGlirSrdkyRKuuuoq/v3vf/Ppp5+SkHByTfVu3bqxb98+Ro8ejYh4/PuYkJBAv379qFevHgEBAVx00UUsXry4QJ1u3brx4IMPMm7cOKKioqhZsyajRo3C4XAUe6zvvvsOESE+Pj6/rfXr19OjRw+CgoIICwujZ8+eHDp0yG3fNmzYwIwZM5g+fTqPP/44zZo1o2nTpowZM4bp06czffp0NmzYkF9/586d9O3bl7CwMIKDg+nUqRO//vprfvnixYtp3rw5/v7+NG3alOeffz7/MwDMnj2bVq1aERQURExMDPfddx9JSUkFvu/g4GBWrlxJixYtCAoKonv37uzZs+dUf1TVoipGrjHG7AOuEZFw4Dys0eq/jDHuL1uqc57DkU7iiVdIPDEfY9KKrRvkfwXRNcbj59OsinqnlFLqDNUNmA9sxPotMhlYISLNjTGJzjp/AA8Be4AA4FHgaxE53zkYUBeIAR4DtjtfzwfeBXq7HOsdoAFwNVby1oXAm0CfSvx8pyVjHMSnzCQxZa7bcpuEUDfyFYICuuVv++mfA4xbtZw9SZ5/KtYJDmZy9yvo2ahJRXdZVYLGLzxX3V1g979Hlmt/YwyLFi1ixowZNGjQgA4dOvDmm2/yyCOPAPDhhx/SunVrBg8ezAMPPOCxnczMTNq2bcuYMWMIDQ1lxYoVDB06lAYNGtCzZ8/8em+//TYPP/wwP/74Iz///DN33nknl1xyCf369SvxsbZt20b37t256667mD17Nn5+fqxevZrc3Fy39d9++22Cg4N58MEHi5Q98MADTJgwgXfffZf27dtz6NAhOnfuzGWXXcby5cupUaMGGzZswG63A7BgwQKefPJJXnzxRS655BJ+++037r//fnx8fBg+fDgANpuNOXPm0LhxY/bt28eIESMYMWIEb775Zv5xs7KymDZtGosWLcLf35977rmHYcOGsWzZMs9/WNVEjCm67IE6vbVr185s2rSpurtRKYyxk5L2f8SnzCTX7vkqNYCfTwuiazxJkH/nKuqdUkqdmohsNsa0q+5+qFMTkWAgGbjBGPOZhzqhzjpXGWPc/pITkWuAz4EaxpgUEbkQK/DubIz5wVmnM7AGuMAY80dx/TqbzvMORwZHEh/hRIbbrxcfr/rERL+Rf4E8JSuL6T+s5t3ffvHYpgB3tWrDqEsvJ9jXtzK6rSrB2RBcf/vtt9xyyy0cPnwYX19fFi1axPPPP19gpDY2Npbhw4czatSo/G0lSTJ2xx13EBwczMKFCwFrZDorK4t169bl1+nVqxcNGzbMr1OSY/Xv359du3axfv36En3Gq6++mkOHDrFt2za35a1btyYmJoYvv/yS8ePH89Zbb/HXX3/h6+b/xQYNGjBlyhTuuuuu/G1z5szh1VdfZfv27W7b//rrr7n++uvJyMjAZrOxZMkSBg0axM6dO2nWzPp34u2332bQoEFkZmZiq6bcCp7O9VUycq1USaRlfMex5KfJztlRbD1vr7pEhT1OaOBNiGiyEqWUUmUWgnWLnNvhURHxBYYAKcDPxbQTCmQB6c73nYBU4EeXOj8AaVi3yhUbXJ8tcu3HOBg/iMzsrW7L/X0vISZqMd5eVrDxza6/ePK7lRxL8zxj7bzwCKZd0ZtL6sRUSp+VKs5rr73Gbbfdlh9I3nLLLQwfPpyffvqJDh06lLgdu93Os88+y9KlSzl48CBZWVlkZ2fnJ+XK06pVqwLv69aty7Fjx0rV561bt3LjjTeWap/ibq8wxuSXb926lc6dO7sNrOPi4jhw4ABDhw4tMLKem5uL6+DuqlWrmDZtGjt27CA5ORm73U52djZHjhyhbt26APj5+eUH1mB9Dzk5OSQlJREREVGqz1bZNLhW1S4z+3fikp4mPWt1sfVsEkJE6AjCg+/FZiuSfF4ppZQqrblYQfM6140ich3wPyAQOAz08pQfxnkf9dPAAmNM3jzL2kCccfkFaYwxInLMWeaunSFYgTwNGjQo+yc6TWRmb+dg/D3k2t2vthoSeAO1I2ZjE3+OpaUy8btVfL3L06qt4GOz8eC/OjDskvb4eevPV1X1kpKS+OCDD8jOzmbBggX52+12OwsXLixVcD1r1iyee+455s6dS8uWLQkODmbcuHFFAmcfH58C70WkwP3KJVHaWcpNmzZlzZo1ZGVl4efnV6AsKyuL3bt306NHj1O2ndfPl19+mUsvvdRtnX379nHttddy//33M3nyZCIjI9myZQv9+vUjOzs7v553of/n84L70n4XVUH/dVLVJif3EPHJM0hJfw/rdjRPvKkRfDeRoY/i7VW+ZQWUUkopABGZDXTGmrptL1T8LdAGiALuB/5PRDoZYw4XaiMI+Aw4iHUPtit3JzbxsB1jzKvAq2BNCy/VhznNpGas5FDCMI85UyJDRxIZ+h8Alv72C1PXruZEdpbH9trWrsPUnr1pGqlr9qrq8/bbbxMdHc2XX35ZYPu6desYOXIkc+bMISgoCF9f3/x7jj1Zu3Ytffr0yZ8ubYzhzz//pEaNGqXqU0mO1bZtW1atWlXiNvv168cLL7zASy+9lH8veZ758+eTlpbGnXfemd/2W2+9RXZ2dpHR61q1ahETE8OuXbu4++673R5r06ZNZGdn8/zzz+PlZSUy/Pzzz0vc19ORBteqytkdJ0g8MZ/jJ17BmMxi6wYHXEN02Dh8fRpXUe+UUkqd7UTkeeAOoLsxZnfhcmNFhX87H+tF5C/gPqwR6rw2goG8X9nXmYIntCNATRGRvNFrsYZaooGzdoUUYwxJqa9xLGkiUHRESfCjdsRsQoNuZE/SccavXM76gwc8thfk48PoSy9nQKs22DQL+BmvvPc7V7fXXnuNW265hRYtCiwwQNOmTRk9ejRLly5l8ODBxMbGsmbNGgYMGICfn5/be6ybNm3K0qVLWbt2LVFRUbz44ovs2bOHiy++uFR9KsmxRo8eTceOHRkyZAgPPfQQ/v7+rFmzht69e7udJdOxY0dGjhzJmDFjyMrK4sYbb8xfiuuJJ55gzJgxtG/fHoAHH3yQl19+mdtuu43x48cTHh7Oxo0bufDCC2nTpg0TJ05kxIgR1KhRg2uuuYacnBy2bNnCwYMHGTt2LOeffz4Oh4M5c+Zw0003sX79eubMmVOq7+B0ozesqipjTA7HTyxhz+FLSUyZW2xg7e97CfVrfkxM1EINrJVSSlUYEZkL3An0MMbsLOFuNiB/fqSIhABfA17ANcaY1EL11wHBWPde5+kEBFHwPuyzhjG5HEsax7GkJ3EXWHvZIqlf8/8I8O/LS5t+4uq3Xy82sO4e25hlAwZyd+uLNbBW1W7Lli1s3bqVW265pUiZr68vffv2zU8yNnnyZA4cOECTJk2Ijo52296ECRNo3749V199NV26dCEoKIj+/fuXul8lOVabNm1YsWIFO3fupGPHjnTo0IH//e9/Raacu5o1axYLFy7ko48+ok2bNrRu3ZoPPviAhQsXFljjOiYmhtWrV5OdnU337t25+OKLefHFF/Oncd93330sWrSIN998k9atW3P55Zfz6quv0qhRI8C6p3zu3LnMnj2b5s2bs3DhQmbNmlXq7+F0otnCz0BnWhZRYwypmcuIT5pCdu6uYuv6eDUkusY4ggOu07UqlVJnJM0WfvoSkXnAXcANWNm886QaY1KdmcEfw5rqfRhrpPkhoD/wL2PML87A+husJGY3ACdc2kk0xmQ7j/UVUA9rWrlgTfnea4w55VJcZ9p53u5I4VDCMNIzv3Nb7uvdlJjoN/gj0Y+xK5axPT7OY1uRAQE82bUH153fTH8HKKVOW5otXFWLjKyfiUueTEZW8en/bf/P3n3HR1WlDRz/PZPeICT0XgQFERQQRFGKYl276666CvZedi3o6irqKoINdbEX7K+rrrvq7lpAXSsq2EBBlCYCoSS01ElmnvePOzNMJjPpmclMnu/nc83MvefeOfdK8sxzz7nnuDqQ3+4KcrPPwCVptZY1xhhjGsk/cev8kPU3A9OBKmBP4CwgHyjEmRP7IFX1zw01EtjP93p5yHEmAh/4Xp8G3I+TiAO8DlzS1BNobdxVv7Bu8xm4q0IvhSMzfTwd2s3h7gWLeeLrRXhradQ5cfCe/HnceDpk2KClxpj4ZMm1aRHuql/Ysn0GO0v/VWs5IZXcnLPJb3cpSa7c6FTOGGNMm6SqtTaFqmopUOucNar6AU5LdF2fVQT8oSH1izdlFQtZt+VMPN7CsNtzs6fw045z+cO//8HaHdsjHqdXu/b8ddIhHNi7bwvV1BhjosOSa9OsPN5tFO64j207n0Jx11o2J/N4OrW/lpTkXlGqnTHGGGOaw46S1ygo+hNKuFG+XaSm38RdC3vx6tLXIh7DJcJZe4/giv0OILOW5z+NMSZeWHJtmoVXK9hW/DSFO2bj9W6rtWxG2lg6595Ieurw6FTOGGOMMc1CVSnccQ+FO+6OUCKLxdtnMPOtzRSWfR/xOIM7dmLGwYcyrEvYab+NMSYuWXJtmkRV2Vn2Blu23U6l55day6Ym70an3BvISp9sg5QYY4wxccar5RQU/Ymdpf8Mu72oYiCPfX8OH6xZE/EYqUlJXD5mLOfsM4oU37y2xhiTKCy5No1WWvE5m7fdSrn7q1rLJbk60rH9VbTPOhUR+ydnjDHGxJsqzxbWbTmTcveiGtu8Cm//8hueWLwnJZWbIh5jTI+e3DZpMv075LVkVY0xJmYs0zEN5q5cwebtt1Nc9t9ay4mkk5dzAXk5F+FyZUepdsYYY4xpThWVP7Ju8xlUemrOS71mRx5zvv09i7dk4Ay2XlNOahrXjTuIk/fcy+asNsYkNEuuTb1VeQop3HEP24qfJVIAdQjtsk6mY7trSEnuFq3qGWOMMaaZlZR9wPrC8/Hqzmrr3Z4kXl4+mpeWj6XSGzlhPmzAQKaPn0SXbLvJboxJfJZcmzp5vWVsLX6coh1/qxFcQ2WmT6BT+xtITx0SpdoZY4wxpiVs3TmXTdv+AniqrV9a2I3ZXx/GLzvzI+7bOSuL6eMP5vDdBrZwLY0xpvWw5NpEpOplR+mrbNk+kyrP+lrLpqUM8Q1WNiE6lTPGGGNMi1D1sGnbdLYVP1FtfWllCk//MI43Vu6D1jLV9ylDhzHtgANpl5be0lU1xphWxRXrCpjWqaT8I9ZsPJyCostrTayTk7rSNe9e+nR52xJrY4wxJs55vcWs2zK1RmL9RUE/Lpg/lddXjoiYWPfN7cCLJ5zMbZMmW2Jt4tbUqVMREUSElJQUOnfuzMSJE5kzZw6VlZWxrl41L7zwAmPHjiU7O5usrCzGjBnDc889F+tqtWnWcm2qqXAvY/P2v1JS/l6t5USyyG93CR2yz8XlyoxS7YwxxhjTUiqrfuXXLVNwVy4NrNtWkcEj303kg18HR9wv2eXivBH7csnoMaQnp0Sjqsa0qEMOOYRnn30Wj8fD5s2bee+997jpppt49tlnmT9/PllZWbGuItOmTWP27NncfPPNzJ07FxHhtdde4+yzz2bJkiXccccdsa5im2Qt1waAKs9GCoquYvXGQ+pIrJPIzZpC/26fkt/uckusjTHGmARQVvE1azYeFUisVWHeL0M4790za02s9+rchX/97jSu2n+cJdYmYaSlpdG1a1d69OjB3nvvzZ/+9Cc++OADvvrqK2bNmhUo53a7mTZtGj179iQrK4t9992Xt99+u9qxfvjhB4466ihycnLo3Lkzp5xyCgUFBYHtU6dO5Te/+Q1//etf6dKlC9nZ2Zx55pmUlZVFrN8XX3zBrFmzmDlzJtdeey277747gwYNYtq0acycOZOZM2fyxRdfBMovW7aMY445hvbt25Odnc3YsWNZvHhxYPtTTz3FkCFDSE9PZ9CgQdx77714vd7A9nvuuYdhw4aRlZVFjx49OOecc9i2bVtg+9y5c8nOzmb+/PkMHTqUrKwsJk6cyKpVqxp1/eOZtVy3cV5vCUU7H6Jo50OoRv4lBshOP5SOudeTlmKDkxiTiDxeL1VeL5Veb+C1R714vIpXFY968arzWlVR2PUewP8TAtv9qr3W4HdB20LWh+4Xaf/aylbfXleJ6vq270B+pt1ANIlvZ+kbbCi6HNVyAApK2nH/N5P5elPfiPtkJCfzx/0OYOreI0h2WVuNqdvhHc6JdRV4a+vjjd536NChHH744bz66qvcfPPNAJx55pmsWLGCF154gZ49e/Kf//yHo48+mi+//JLhw4ezYcMGDjroIM4++2zuuusuKisruf766znmmGNYsGABLt/vzv/+9z8yMjKYP38+69at46yzzmLatGncf//9Yevy/PPPk52dzUUXXVRj24UXXsgNN9zAiy++yOjRo1m/fj3jxo3jgAMO4N133yU3N5cvvvgCj8cZqPCxxx7jxhtv5IEHHmDkyJEsWbKEc889l5SUFC655BIAXC4Xs2fPpn///qxZs4ZLL72USy+9lGeffTbwuRUVFcyYMYMnn3yS9PR0pkyZwgUXXFDjZkOis+S6jVKtYnvJS2zZfice76Zay6anDKdT7o1kpo+NUu2MMeGoKuVVVRS73ex0V1T7WeJ2Oz8r3ZS4KymtdFNSWUlZVSVllZWUVlZRUVVJeVUV5Z4qyquqcHs8gaXS48ETIWltq2YfdiTH7B65xc6YeKeqFO28ny3bZwLgUeFfP4/gmaUHUOGJ3Ao9rlcfbps0mV7t20erqsa0CkOGDGHevHkArFixghdffJHVq1fTu3dvAC655BLmzZvHI488woMPPshDDz3E8OHDmTlzZuAYzzzzDHl5eSxcuJDRo0cDkJSUxFNPPUV2djZDhw5l5syZnH322cyYMSNsF/Tly5fTv39/UlNTa2xLS0tjwIAB/PjjjwDMmTOHrKwsXn755UD5QYMGBcrfeuutzJo1i5NOOgmAfv36ce211/Lggw8GkusrrrgiUL5v377MmjWLY489lqeffjpwg6Cqqoo5c+aw++67A3DVVVdx5pln4vV6A2XaAkuug4jIdcAJwO5ABbAAuE5VlwSVOQE4HxgBdAQmquoHIcdJA+4CTgEygPnARar6a1CZDsD9wDG+Va8Dl6rqtpY4Nz9VpaT8PTZvuxV31fJayyYn9aRT++vIyTwWkbbzS2FMc1LVQAJb7qkKJMHO4k+Qg15X7Eqad1bUTKKrgrppGWNMY3m1go1F17Cj9GUAVm7vxH1fT2b51m4R98lNT+eGAydw/B5DEIk8WrgxiUpVA//2v/rqK1SVIUOqTz9bUVHBpEmTAFi0aBEffvgh2WHmeV+xYkUguR42bFi1MmPHjsXtdrNixQqGDRsWti61/Q4G1/Prr79m3LhxYRPxzZs3s3btWs4//3wuvPDCwPqqqqpqvcTee+89ZsyYwdKlS9m+fTsejwe3201BQQHdu3cHnKTen1gDdO/encrKSrZt20ZeXl7EuiYaS66rmwA8CHwJCHALME9Ehqhqka9MFvAp8BzwTITjzAaOxUmuC4F7gDdFZKSq+ieLfAHoDRyB05vxceBZ4OjmPSXHR2tWc9/n80lmNSmuTWQk9yM9qRfpyW7SkyvJSKoMvM5MTqFzu6Pp3P43VJVns71qJ5kpKWSmpJCenGwB1bRJm0qK+ezXtSxcv47C0lIqPB7cnirfTw8VvpbgCo/vZ9Wu18YY05pUeQpZX3gOZRWf4/Yk8cKy/Xjlp33xaFLEfY4etAd/OWgiHe1RCdOG/fDDD/Tv3x8Ar9eLiPDll1+SklK9p0dGRkagzFFHHcVdd91V41hdunRpdD0GDRrERx99REVFBWlpadW2VVRUsHLlykCCH+lRKn/9AB5++GH233//sGXWrFnDUUcdxbnnnsstt9xCfn4+X331FaeccgputztQLjm5elrpzxe8baxRwJLrIKp6WPB7ETkd2A4cALzhK/Osb1vHcMcQkfbA2cCZqvpu0HHWAIcAb4vIYOBwYJyqfuorcz7wkYjsrqo/Nud5VXkKWVrwKF8V5AO5vqUuFcCrNdYKkJWSSoYv2fYv/nVZKSkhP1Orlau5znmdYUm7aWUKS0v5fN1aPvt1LZ/9+gsrt26NdZWMMabJKip/Yt2WKVRWrea7LT25/+vJrCuO3KrULTuHWycewqR+/aNYS2NanyVLlvDWW29xww03ALDPPvugqhQUFDBx4sSw+4wYMYK///3v9OnTp0YCHmzx4sWUlJQEuoAvWLCA1NRUBgwYELb8Kaecwv33389DDz1Urcs2wIMPPkhJSQmnnnpqoA7PPfccbre7Rut1ly5d6NGjBytWrOCMM84I+1kLFy7E7XZz7733kpTk3IB78803I55LW2fJde1ycEZUb8i36pFACvCOf4WqrhWRpcD+wNvAWKAYpwXc7xOgxFemWZNrkRS2l/8E5Df5WAoUV7oprnTXWbYhBKol3/4EPdK6QGKenEJmasqunyHrMlJScFnSbuphe3k5n69by4JfnYT6x8Itsa5STKS6kkhOcpHscpEsLlwuIUlcJLkElzivAVwiiECSuJwZb0UQnDvV/t84/3uC3u96U/33MrBPmN/XcL/BEmGe3bp+3Rvy16CDr+XBmERRUv4R67ecx46Kcp78/hD+u3p4xLICnDF8H64cO47sMN1JjWmopgwmFm0VFRUUFBTg9XrZvHkz8+fP5/bbb2fkyJFcddVVgNN6fNpppzF16lTuvvtuRowYQVFRER988AH9+/fnhBNO4OKLL+axxx7jd7/7HdOmTaNTp06sXLmSv//979x9993k5OQATjfss846ixtvvJH169dz7bXXcu6550ac8mu//fbjyiuvZNq0aVRUVHD88ccHpuL6y1/+wrRp0wJdzi+66CIefvhhTj75ZK6//no6dOjAl19+yeDBg9l7772ZPn06l156Kbm5uRx55JFUVlby1VdfsW7dOq677joGDhyI1+tl9uzZnHDCCSxYsIDZs2dH5f9DPLLkunb3Ad8AnzVgn66ABwj9Zr7Rt81fZrMG9dNQVRWRTUFlqhGR84DzgMCgCfWV5GqHK2kCUN6g/aJJgZLKSkoqK9lCabMe22kZr96qnhnUeh5unT95D9cS72+1t6Q9vu2sqODL9etY8OsvLPh1Ld9v3tTAsaRjI9WVRE5aKtmpaWSnppLj+5nlW7L9/3ZTUwP/9jNTksnwvU5PTiY9OZm0pGTSkpNIS0omNSmJlKQkkkSsB4kxCWpb8XNs3Hodn6zvx4PfHkxRec1nQP0G5uUz4+BDGdGtexRraEzrMW/ePLp160ZSUhK5ubkMHTqUm266ifPPP79a6+9TTz3FbbfdxjXXXMOvv/5KXl4eo0ePDrRkd+/enU8++YTrrruOww8/nPLycnr37s2hhx5arTv3+PHj2XPPPZk4cSKlpaWceOKJ1ab8Cueuu+5i+PDhzJkzJzB6+V577cXjjz/O6aefHijXo0cPPvzwQ66++momTpyIiLDXXnvx6KOPAnDOOeeQlZXFnXfeyXXXXUdGRgZ77rlnYDCzYcOGcd999zFz5kxuuOEG9t9/f+666y5+97vfNc/FTjBSWz/8tkxE7gF+j9N1e2WY7R2BzYQMaCYip+I8i50SnDyLyPvAj6p6gYj8GThbVQeEHHMV8Iiq1jrr+6hRo3ThwoUNOp87P/kfDy1q2D6mdhnJyYEkPGyrekjX+eB1TpKeHNIin0pGcjJJbWhExWgqq6xk4YZ1fLbWaZ1evKkgaqNjp7hcpCYlkZqUFEiKnWXX65zQ92lp5KSmkZO2K4nOSU0lLdnuibZ2IrJIVUfFuh4mfjUmzkei6mHztr/y8+bneOi7SXyyflDEsikuFxfvux/nj9zX/tYYEyVTp05ly5Yt1tU6zkSK9faXMwwRuRcnsZ4YLrGuQwGQhDOS+Oag9Z2BD4PKdBYR8Sfg4jQXdcJp4W52pw7bh1FdN7Nh62MkpR6HJI2lrMpLiduZqqekspJSt5vSqkpKw62rdBYbnGmXsqoqyqqqKCyrfX7whkpPTq7e3T0ltXrX92R/y6S/VbL2lnb/9raWtFdUVfF1wQY++/UXPvt1Ld8WbKCyCYNqCDC4YyfG9urNXp27kJWSSmpSEmnJTstvWrXXvp/JSaQmJVsvB2NMTHi9JazfcjGvLNvAE99PpaQyPWLZkd26c/ukQxmY3/RHyIwxpq2y5DqEiNyHk1hPUNVljTjEIqASmIwzIjgi0hMYzK5nrD8DsnGevfavG8uukcibXY+cdnTPPhGvHkaSK6fRx6n0eCgLSrb9S0mlmzJft+6ywPsqSirdQeXclFZW+X5WX1fhqWrGs41v5VXOHMRF5c2btKclJZOZklwtCXda05NDEvPwreqRWuKTW0nS7vZ4+G5jAZ/5unkv2rC+yTeDBuXls1/PXozt1ZsxPXqSm27P4Bpj4kNl1XoWrLqQu77sx+Ite0Usl5WSwtX7H8gfhu1tNwKNMaaJLLkOIiJzgNOB44CtIuJ//rlYVYt9ZfJwptDK9W3bTUS2AQWqWqCq20XkCeBO3zPU/qm4vgPmAajqUhF5C3hERM7FaRR7BHizuUcKDzk/kqTxiTVAiu/ZzHZpke9+N0aV10tptcTc13JeLQl3XpdVVVLi9q3ztbQHfoasK6+ypN2vwuPcxNha3rzP3qcmJYUfNd7X0h66LivMwHOZqalkJidXa5FPSYo8JQw4/2a+37QxMJr3wvXrKGvi/+9+uR2cZLpnL8b07EWnzPADiRhj4pOIXAecAOyOMy3GAuA6VV0SVOZW4LdAL8ANfAX8xT+7h69MGnAXzpSbGcB84CJV/TWoTAfgfuAY36rXgUtVdVtLnZ/fzrJvmP3pXTy3dH8qvZG/6k3q259bJh5M95x2LV0lY0wEc+fOjXUVTDOy5Lq6i3w/54esvxmY7nt9DPBU0LbHwpT5I1AFvMSuoHtG0BzXAKfhBF3/qOKvA5c0qfZxLNnlol1aGu1C5uprKo8/aa+q3qoeSMjDJO/h1vn39a9rahKXSNy+eZ63NXfS7koKDB4XOqCcx+vlq4L1FLubNmp9z3btGNuzN2N79mK/nr3omt20m0/GmFZvAvAg8CXOje1bgHkiMkRVi3xlfgQuBlbhxPA/Am+JyEBV9T+6NRs4Fie59t9Ef1NERgbF+hdwbsYfgTNu5+PAs8DRLXmCC9a8yg0fLGTl9n0ilsnLyGD6+EkcNXB3G8TQGGOakQ1oFoeac6AT0zger9d55roeLe3+97sS9JrrSnw3AEorK2N9agmta1Z2oJv32J696NmufayrZBKQDWgWP0QkG9gOHKeqb0Qo085X5nBVfVtE2uOMqXKmqj7vK9MLWAMc4SszGPgBZ1DUT3xlxgEfAXvU1UutMXG+xO3mjo8e5MXvK/ES+XGdEwfvyZ/Hjbep5owxpglsQDNjmlGSyxUY1bkTzdd12KvqJODB3d2DW9qD14W0qvuT/HDrStpo0p6fkRlolR7bqzd92+daK40xJlgO4AK2htsoIqk402DuwJmaE2AkkMKunmeo6loRWQrsD7yNM45KMdXHUfkEKPGVadZHwDzeSk586W6Wb031nU5NPXMyuP3goxjXu09zfrQxxpggllwb04q4RALzFZPZfMf1qlJeVRWxVT1cl/hqrer+7vRBZfzrWlPfl9z0dMb06BVIqAfm5VsybYypzX04SfNnwStF5DfA/+H8Jd4ATA7qEt4V8ABbQo610bfNX2Zz8JScqqq+sVi6EoaInIeTyNO7d+8GnYRLkjlu0A5mfd4xzDZl6vDB/GnsoWSmpDTouMYYYxrGkmtj2gCXSGDQsebM2rVa0u5vXd81qJy/u3uJ211tereyqqoa60qrqo8k763HIyvZqamM7t4z0M17j46dbLRbY0y9iMg9wDicrtuhUwu8D+yNM63mucDfRWSsqm6o7ZBQ7X5juD9ioWV2FVZ9FHgUnG7h9TmHwEFFOGffG3l75e18uzk3sH633DLunHwaw7v1a8jhjDHGNJIl18aYRhMRMnyDjTXnzKiqSoWnKkyLutOqXlHloWf79uzZqXOrmQrMGBM/RORenGk3J6rqytDtqloC/OxbFojIT8A5wK1AAZCEk3hvDtqtM/Ch73UB0FlExN96LU43mk44LdzNLtmVwaxDz+Q3L74CKOcO93DZ2GtITU5tiY8zxhgThiXXxphWR0RIT04hPTmFPBtzxxjTjETkPpzEeoKqLqvnbi7AP53FIqASmIwzIjgi0hMYzK5nrD8DsnGevfavGwtkUf057GY1ML8/Mybty27t1zKsxxkt9THGGGMisOTaGGOMMW2CiMwBTgeOA7aKiP/552JVLfaNDH4N8AbOs9adcKbl6gn8HUBVt4vIE8Cdvmeo/VNxfQfM85VZKiJvAY+IyLk43cEfAd6sa6TwpjphyMSWPLwxxphaWH9KY4wxxrQVF+GMED4fJ3n2L1f5tlcBewKvAT/hJNn5wEGq+l3Qcf4I/AN4CWcU8GLg6JBnt08DvsUZVfxt3+vTW+SsjDHGtArWcm2MMcaYNkFVax3xUFVLgePrcZxy4FLfEqlMEfCHhtbRGGNM/LKWa2OMMcYYY4wxpolE6zHdjWldRGQzsKYJh+hIzfk52yK7Dg67DnYN/Ow6OJp6HfqoaqfmqoxpeyzONxu7Dg67Dg67Dg67Ds1zDcLGekuu2yARWaiqo2Jdj1iz6+Cw62DXwM+ug8Oug4l39m/YYdfBYdfBYdfBYdehZa+BdQs3xhhjjDHGGGOayJJrY4wxxhhjjDGmiSy5bpsejXUFWgm7Dg67DnYN/Ow6OOw6mHhn/4Yddh0cdh0cdh0cdh1a8BrYM9fGGGOMMcYYY0wTWcu1McYYY4wxxhjTRJZcG2OMMcYYY4wxTWTJtTHGGGOMMcYY00SWXCcgETlBRN4Wkc0ioiIyoZ77jReRRSJSLiIrReSClq1pyxKRi0Rkle98FonIgXWU30tE/iciZSKyTkRuFBGJVn2bm4gcJCKv+85FRWRqPfZJtGtwnYh8KSI7fL8Pb4jI0Hrsl2jX4WIR+c53HXaIyGciclQd+yTUNQglIn/2/V78rY5yCX0dTHyyOO+wOG9xHizWg8X5SGIR6y25TkxZwKfAn+q7g4j0A/7j228fYAbwgIic2CI1bGEi8jvgPuB2nPP5FPiviPSOUL4d8C6wEdgXuAy4mgZcw1YoG1gCXA6U1VU4Qa/BBOBBYH9gElAFzBORvEg7JOh1+BWYBowARgHvAf8UkWHhCifoNQgQkf2Ac4Hv6iiX0NfBxDWL8xbnweK83wQs1lucDxGzWK+qtiToAnQEFJhQj7IzgZ9C1j0OfBbr82jkuX8OPBay7idgRoTyFwI7gIygdTcA6/CNqh/PC1AMTK2jTEJfA9/5ZAMe4Oi2fB1851QEnN/WrgHQHliB8wXsA+Bvbf3fgi3xu1ictzgfdC4W53edk8V6bbtx3ncuMYv11nJt/MYC74SsexsYJSIpMahPo4lIKjCSmufzDs5dzXDGAh+pavCd37eB7kDf5q5jK9UWrkEOTo+drbWUSejrICJJIvJ7nC8fn0YolsjX4FHgFVV9rx5lE/k6mLbH4rz9PreVa9CmY73FeSCGsd6Sa+PXFac7RLCNQDLOnfF40hFIIvz5dI2wT6Tz929rC9rCNbgP+Ab4rJYyCXkdfM8TFQMVwMPA8aq6OELxRL0G5wK7AX+p5y4JeR1Mm2Vx3n6f28o1aJOx3uK8I9ax3pLrOCcip4lIcdBS62AeddDQw0dYHy/CnU9t55Jo598YCXsNROQeYBxwoqp66iieiNfhR2BvYD/gIeDpOgZ8SahrICK74zybeZqquhuwa0JdBxN/LM7XyuJ8wyX0NWjjsb5Nx3loHbE+uaE7mFbndZznjvzWNfI4BdS8O9MZZ1CIwkYeM1a24DxrE+58Qu9M+UU6f2rZJ9Ek7DUQkXuB3wMTVXVlHcUT8jr4gszPvrcLRWRf4I/A2WGKJ+I1GIvT2rUkaADQJOAg34jJWapaEbJPIl4HE38sztdkcb5xEvoatPVYb3EeaAWx3lqu45yq7lTVn4OWOkeLjOAz4JCQdZOBhapa2bRaRpfvj8sinPoHm0zkZ08+Aw4UkfSQ8uuB1c1dx1YqIa+BiNwHnApMUtVl9dglIa9DGC4gLcK2RLwG/wT2wrmr718WAv/nex3uDnciXgcTZyzO12RxvtES9hpYrA+rrcV5aA2xPtajudnS/AuQ5/sHNAGnO8M5vvddg8o8AzwT9L4fUALMBgb79nHjdKuJ+Tk14hr8zlf/c3zncx/OSJp9fNtnAPODyrfHuXP1f8BQ4ASckQOvjPW5NOEaZLPrD0spcKPvde82dA3m+M5hEs5dSf+SHVSmLVyHO4ADcQbm2Mt3zl7giLZyDSJclw8IGkG0rV4HW+JvsThvcd53Tm0+zvvOq83HeovztV6bqMb6mJ+wLc2/AFN9wTZ0mR5U5gPgg5D9xgNf4QyEsAq4INbn0sTrcBHOHacKnDvcBwVtmwusDim/F/AhUA5sAG4ijqcjYNeXrtBlbhu6BuHOP/R3oS1ch7nAGt/vwiZgHnBYW7oGEa5LaMBtk9fBlvhbLM4HzsfifBuP875zavOx3uJ8rdcmqrFefAc0xhhjjDHGGGNMI9kz18YYY4wxxhhjTBNZcm2MMcYYY4wxxjSRJdfGGGOMMcYYY0wTWXJtjDHGGGOMMcY0kSXXxhhjjDHGGGNME1lybYwxxhhjjDHGNJEl18YYY4wxxhhjTBNZcm2MMcYYY4wxxjSRJdfGGGOMMcYYY0wTWXJtjDHGGGOMMcY0kSXXxhhjjDHGGGNME1lybYwxxhhjjDHGNJEl18YYY4wxxhhjTBNZcm2MMcYYY4wxxjSRJdctTET+LCIqIn8LWiciMl1E1otImYh8ICJ7xrKexhhjjDHGGGMaz5LrFiQi+wHnAt+FbLoGuBK4FNgX2AS8KyI50a2hMcYYY4wxxpjmYMl1CxGR9sDzwNnA1qD1AlwB3KGqr6rqEmAKkAOcGoOqGmOMMcYYY4xpIkuuW86jwCuq+l7I+n5AV+Ad/wpVLQM+BPaPXvWMMcYYY4wxxjSX5FhXIBGJyLnAbsDpYTZ39f3cGLJ+I9CjlmOeB5wHkJWVNXKPPfZohpoaY4xpbosWLdqiqp1iXQ8Tvzp27Kh9+/aNdTWMMcZEECnWW3LdzERkd+B24EBVdddSVEN3DbNuV2HVR3Fawxk1apQuXLiwqVU1xhjTAkRkTazrYOJb3759sThvjDGtV6RYb93Cm99YoCOwRESqRKQKGA9c5Htd6CvXNWS/ztRszTbGGGOMMcYYEwcsuW5+/wT2AvYOWhYC/+d7vRwoACb7dxCRdOBA4NMo1tMYY4wxxhhjTDOx5LqZqeo2VV0SvAAlQJHvvQKzgWtF5AQRGQrMBYqBF2JWcWOMMcaEJSLXiciXIrJDRDaLyBu++F3bPn1FRMMsh0er3sYYY6LLnrmOjVlABjAH6AB8DhyqqjtjWitjjDHGhDMBeBD4EmeMlFuAeSIyRFWL6tj3cODboPd1lTetwOdvfUthwTYAnFlUfXwvg9f5XwdWVdtWs7z/INUOGzhGXZ8Vsq6uz6pPfcPUqfoxml5fwpSvd33DXK/a6lR7fcMdI3J9w+3rr08w9Q2bpEGjJ2n1N5G3B60M3a7qrKz+2vk8dV7s2uY/Zshn7fqc6u81tE6R1gftrGFGh9JwKyNoSNlY6NS9A7uP7N+kY1hyHQWqOiHkvQLTfYsxxhhjWjFVPSz4vYicDmwHDgDeqGP3QlUtaKm6mZbx6px3+O7jH2NdDWNMFE04cTTXPn5ek45h3cKNMcYYYxomB+c71NZ6lP2HiGwSkU9E5KQWrpcxxpgYsuTaGGOMMaZh7gO+AT6rpUwxcBVwMnAkMB94SUT+EK6wiJwnIgtFZOHmzZububqmoVp791VjTOtk3cKNMcYYY+pJRO4BxgHjVNUTqZyqbgHuDlq1UEQ6AtcAz4Up/yjwKMCoUaMsszPGmDhkybUxxhhjTD2IyL3A74GJqrqyEYf4HDizeWtlWsKYw4bTc7eu1Vqwww70VNsgULUMYlV9sCtqrKsxyBQ1t9V9jOoDWUWqZ+jAWvUddCtcnajteoU7RjPUN2yd6rjm9apv0JtI9a1zoLg6B3CrZWA2/2txBlETcQqKOK8D24LW+Q8WaeC70EHZIg04V5+B3YJJuJWRNKBotA0a0bfJx7Dk2hhjjDGmDiJyH05iPUFVlzXyMHsDG5qtUqbFnHTpYXUXMsaYEJZcG2OMMcbUQkTmAKcDxwFbRaSrb1Oxqhb7yswARqvqwb73U4BK4GvACxwNXAxMi27tjTHGRIsl18YYY4wxtbvI93N+yPqb2TWtZjdgQMj2G4A+gAdYDpylqjWetzbGGJMYLLluIzxVHjweb61l6npeoj6PU9T5zEUTP6PuOrbiBzlM3Al9TkuVoOevFNXqz4g560LfBx0r8Oxczc+o7Vm0+jzfF/qsWuhItzVGvo0wXFJzjZCbCAPtts/PJj0rLdbVMK2AqtYZXFR1asj7p4GnW6pOxhhjWh9LrtuIfz06n0ev/3usqxEXmnyToYk3AJrnJkbT9m/q8ev3GU3bH6onmJGSSyeprVl+17bIg5WYtm3aY+cy8aQxsa6GMcYYY+KEJdfGhKgruao797LkzBhjjDHGmLbGFesKGGOMMcYYY4wx8c6Sa2OMMcYYY4ypJ493B5VVG1CtinVVTCtj3cLbCFeSi5TUyP+7m94Vuh7PqjbxM+xZWBNL/mfAA4+CiyAiiAQ9H+57XfN98DEk6JjVjx38LPuuz6tZnpC67No/wnrCv6/rXJsq3scXTM9MjXUVjDHGxIiqh0rPWtyVK3BX/ez7uQJ35c94vJsBcEl7sjMOIyfzaLLSD0TE4kZbZ8l1G3Hc+Ydw3PmHxLoaMVf3TYQm3mRo6ePX4xh1PfLd1GtQn0fKm3qe9bpRE5J8hia51bdJmHVB7yMkpTb6vDHGGJPYPN7t1RJn5+cKKqtWobhr3der29lR+nd2lP49KNH+DVnpB1mi3UZZcm3aFJvKyxhjjDGmbVGtorJqrZM4hyTR/lbopqqeaLcLatG2RLstseTaGGOMMcYYE/c83m0RWqFX19kK3Zy8uoMdpS+zo/TloET7N2SmH4RL0qJWDxN9llwbY4wxxhhj4oLTCv1LIHF2VwW3Qm+JUi2SEElBtbzOkjUT7UN9ifZ4S7QTkCXXxhhjjDHGmFbF49ka1I07eFCx1UBlVOrgcnUgNbk/qSm7kZo8wFlSdiM1uQ+qVZSUf8DOsjcoLnsX1dI6j+ck2q+wo/QVXJLjS7SPtkQ7gVhybYwxxhhjjIk6pxV6TVACvas7t8dbGKVaJJGS3CcocR5AasoAUpN3IzkpP+JeIqnkZB5JTuaReL1lIYl2SZ2f6tWd7Ch9lR2lrwYl2v4W7fTmPEETRZZcG2OMMcYYY1qM0wodNJ1V4PUaotUKneTqQEryANJSdiPFl0CnJe9GSnIfRFKadGyXK4OczCPIyTwiKNF+k+KydxqRaGeT5Uu0s9Mn2WBoccaSa2NMq+FMwVWFagVerUDVjeL7qRW+xY3iITW5HynJPWNdZWOMMcYAqpW+Z6F/DtMKXRSlWiSTGtIKnZLidOeurRW6OVVLtLXcSbRL36Sk7B28Wlzn/l4tZmfpP9hZ+g9SkvrQNX82mWljolBz0xwsuTbGoFrlS2DLUdx4Q5NZrfAluc57r7+sulFqlvUGkmL/uqDyWoG3xj67ytdrIm2f1JQ9yE4/hKyMQ8hIHYlIUstdJGOMMcbg8RRVH4078HoNUBWVOiS58qp13071JdDN0QrdnFySTk7G4eRkHI5Xyykt/x87S50Wba/urHP/Ss8a1m46kbycS+jY/spWdW4mPEuujYkRVY8vaXWHJKYVQUlreVByWrFre7jk1JcA10iMw7T8hh4DvLG+HI3irlxGUeUyinb+jSRXB7LSJ5GVcQhZ6RNIcrWPdfWMMcaYuOS0QjvPQldU/kxl0KBiHu/WKNUimdTkvoHEOTiJTkrKi1Idmo9L0snOOIzsjMPwaoUv0X6jHom2l6Kd91Na8SHd8h4gNWVA1OpsGs6Sa9OmqHqDEtpwCWdwIhq0Hd92Ddke0jrr9bX81ujGHOYY0bq721Z4vFsDzytBEhlpY8hOP4TsjENISR6AiMS6isYYY0yrUuUprNYKXRl4JjqardD5EVqheydsS61L0sjOOJTsjEODEu03KS57O2KiXe7+htUbD6Vz7i20zzrVvte0UpZcmxanqjVaZ3clp76ENKT1tUZLbS0tubuS1prHDj1GtAbNMLHmoaziU8oqPmXz9ltISe4X6D6emTbGBgcxxhjTZqhW4q5aXb0rty+J9katFTqleit00NRWSUkdolSH1qlmov0hhTseoNy9sEZZ1TI2br2akvL5dOlwZ9SeIzf1Z8l1G+Hx7sDjLarZmqpuvLU8G1uzxXbXc7k1ujGH7a7srDOm/lyIpDkLabgkFZHUXeskFa93JxWVP9T7iJVVq9ha/Bhbix/DJdlkpk8gO+MQstIPtsBkjDEm7qkqHm9R0GBiuwYVq6xaA3iiUo8kV8ew3bidVmhLO+riJNqTyUqfSNGOv7Flx92E+39XXPYWZRVf0S1vNlkZE6JeTxOZ/StvI7bufIzCHXfHuhqm1ZJAMrsrkXV+uiQNwZ/gpvt+pgYlv2lB63zb2ZUMu4KPR1q1Ywe2V1tfvz9LVZ4CisvmU1I+j5LyD1Etq9d+Xi2muOxNisveBIT01BG+QHYIaSmDrZuVMcaYVkvVjbtqTVALtH9QsZVRboXuV7MVOmUASa7cKNUhsYkkk9/+CjLTD2JD0aVUVq2qUcbj3cSvW04lN/tsOuVeb3NjtxKWXLcR1g22daqeVFZPTl0SmohWT0JdwduonrD6j+ciJBmudpxUXL7Pg+S4SyqTk7qSm30audmn4dVyyso/o7j8XYrL3qXKs66eR1HK3Ysody9iy/Y7SE7qTnb6ZF/38f1xuTJa9ByMMcaYUE4rdPCz0P5W6JUxaIXe1X07MLVVci9rhY6SjLQR9O3yDpu23cj2khfDltlW/ASlFZ/QLe9vpKcOiXINTSj7zWgjRNJiXYVWY1crbLjkdFfCGb6l1bedMAlroIU3rXriG6bl1yVpQErcJbStlUvSycqYSFbGRDrn3oa78keKy9+lpGweZe5F1Hc09CrPeraVPM22kqcRSScz7UBfq/bBpCR3a9mTMMYY06Y4rdCra3Tjdp6F3haVOgippERshbZZN1oDlyuLrnl3k5V+CAVbrwrbQ8FduYxfNh5F1/x7aZd5XPQraQIsuW4jYp9cp/iSSl+CGSE5dVGz9XVX8hvcYutvga3ZVdkVlOTWbLFNRcQV42thWpKIkJa6B2mpe5Df7lKqPIWUlH9ASdm7lJR/gFd31Os4quWUlL9LSfm7AKSlDPU9pz2Z9NTh9u/IGGNMnQKt0IHu28HPQv9C9FqhO1VLnP3PQ6ck9UIkKSp1ME2Tk3kEGWn7sKHwCkorPqyxXalgQ+FFVFatIS/nMmvAiRFLrtuIJFcuKUm9a302tlpX4zDPxjrdjMN1P04L6mYckuwGjmOJiImN5KR82medSPusE1GtpKziC4rL5lFc/i6VVSvrfZyKyiVUVC6hcMdsklyd6ZBzFh2yz7Wu48YYY/BqhTMvdLXRuJ1E2qvbo1KHXa3QoUl0f2uFThDJSV3p2ekFthY/wZZtt4UdNHjL9pm4q1bTtcNMeyw0BkRVY10H00CjRo3ShQtrDs9vjGkYd+UKisvnU1I2j9KKBTR0Ts/kpO50av9ncjKPtzvEJkBEFqnqqFjXwzQfEbkOOAHYHagAFgDXqeqSOvbbC/gbMBooAh4BbtU6vnxZnG+dnFboLTUT6EArdP0eQWqqJFfnkATa6c6dktTTWqHbkHL3D2wougR35bKw2zPTxtG942N2Y6WFRIr11nJtjGmzUlMGkJcygLyc8/B4d1Ba/j+Ky+ZRUj4fj7eozv2rPOvZUHQJW4ufpHPudDLSLJ8yJkFNAB4EvgQEuAWYJyJDVDXsHwsRaQe8C3wI7IuTmM8FSgCbvqMVU/XirvopQit0/R4taiohjZSUftWms9rVCt0uKnUwrVt66hD6dP43G4ou982AUl1pxcf8svEYenZ6jpTkXjGoYdtkLdctQEQuBs4H+vpWfQ/8VVX/7dsuwE3AeUAH4HPgYlX9vj7HtzvaxrQsVQ/l7q99ifa8es+pnZNxDJ1ybyAluWcL19C0ZtZynfhEJBvYDhynqm9EKHMhMBPoor65AkXkBuBCoGdtrdcW52PLqxX89OtuRON56CRXl2rPQO9qhe5hrdCmXlS9bNl+O0U7Hwy7PcnVkR4dnyYjbZ8o1yyxWct1dP0KTAN+AlzAFOCfIjJSVb8DrgGuBKYCPwI3Au+KyO6qujM2VTbG+IkkkZE2ioy0UXTiWiqrfg10Hy8p/4BIX7h2lr1OcdnbdMg5j7x2l5DkyolqvY0xUZODE99rm1h4LPCRP7H2eRu4Fefme82Ja02r4JI0UpJ7h51buDFE0p1noZMHkJayGymBqa0GWJwwTSbi8t3Y78vGrdcR+h3F493C2s0n0i1vDjmZR8Smkm2IJdctQFX/FbLqet8d7LEishi4ArhDVV8FEJEpwCbgVJznsYwxrUhKck86ZE+hQ/YUKip/YvO2Wykpnxe2rFJB0c4H2F7yIh3bT6N91u+t9cGYxHMf8A3wWS1luuLcbA+2MWhbtcxNRM7D6dFG7969m6WSpvFSkwc0OLlOTuoa1H3bSaBTUgZYK7SJitzsP5CS1JP1hefh1eJq21TLWV94Ll28t5ObfUaMatg2WHLdwsT5a/pbIBv4FOiHE1Tf8ZdR1TIR+RDYH0uujWnV0lIG0rPTM5SU/49N227BXbk0bDmPdwsbt17tex77JrLSD4pyTY0xLUFE7gHGAeNUta5+w6FdvyXCelT1UeBRcLqFN7WepmlSkwdQQs2bqCLppCb3rzmlVXJ/a4U2MZeVMYHenf/Fr1tOp8qzPmSrl41br6XKs5H8dlfZQKwtxJLrFuIbIfQzIB0oBo5X1cUisr+vyMaQXTYCPWo5nt3RNqYVyUofT98u77C95P/Ysn0mHu+WsOXclUv5dfPvyUo/hM65N5KasluUa2qMaS4ici/we2CiqtY1l18Bzs30YJ19P0O/A5hWJj1tOJmV42ok0clJPWx6UdOqpaUOpk+XN/l18xQqKhfX2F64416qPJvo0mEGIpYKNjf769ByfgT2BvYDHgKeFpGhQdvD3c2OeKdaVR9V1VGqOqpTp07NXVdjTCOIJJGbfRr9un1CXs6lCGkRy5aUz2NVwSQ2br0Bjzc6o80aY5qPiNyH8/jWJFUNP/dNdZ8BB4pIetC6ycB6YHXz19A0p3aZx9Gr89/pkjeDDjnnkJUxgZTkXpZYm7iQnNSV3p1fIyv94LDbt5c8z/ot5+D1lka5ZonP/kK0EFV1q+rPqrpQVa/DeTbrjzh3siH83Wy7k21MHEpy5dAp9zr6dfuQnIxjailZxbbiJ1ldcDCl5Z9ErX7GmKYRkTnAmcApwFYR6epbsoPKzBCR+UG7vQCUAnNFZKiInABcC9xT1zzXxhjTVC5XJj06Pkm7zJPDbi8uf4e1m39HlacwyjVLbHGXXItIuohME5F3ROQbEfkueIl1/WrhAtJwBjApwLl7DTjnBByI80y2MSZOpST3onvHh+nd+XXSU0dELFflWcfazb9l09bpeL1lEcsZ0xbESVy/CGeE8PnAhqDlqqAy3YAB/jequh0n1ncHFgJzcOa3vic6VTbGtHUiKXTNu5e8nMvCbi93L+KXTUfjrvw5yjVLXPHY0f5B4HjgZZxktNXd/RWRO4B/A2txgvGpwATgKFVVEZmNM4L4MmA5cAPOc9kvxKTCxphmlZE2it6dX2dn6T/ZvP32MIOKOLYWP0pJ+ft0y7+f9NThUa6lMa1Gq4/rqlrnyD+qOjXMusWAjWZojIkZEaFT7rUkJ3Vi07YbCf0TW1m1mjWbjqFH/uNkpu8f/iCm3uIxuT4O+K2qhp8Hp3XoCjzn+7kd+A44QlXf9m2fBWTg3MXuAHwOHGpzXBuTOERctMs6geyMI9ha/CiFOx5AteazTe6qn1iz8Tfkt7uC/HaXIZISg9oaE1PH0frjujHGxLUOOWeTnNSZDYWXorirbfN6t7F28yl0zbuL9lm/jVENE0PcdQvHeX5pbawrURtVnaqqfVQ1TVU7q+ohQYk16piuqt1UNV1Vx6vqkljW2RjTMlyuDPLbXU7fru+RkTYmQikPhTvu5pdNx1JR+VNU62dMK9Dq47oxxiSCnMyj6dnp/3C5csNsraSg6HI2bb0J1cpoVy1hxGNyPQv4k9hwjcaYOJKa3JtenV6hU/sbEVLDlil3f8OajYexdefjqHqjXENjYsbiujHGRElm+n706fw6Kcl9w27fWvwYv2w6kcqqDdGtWIKIx0A2GfgdsEpE/isirwcvsa6cMcZEIpJEXrsL6NPlLdJShoYto1rOpm038uvm31tgM22FxXVjjImi1JTd6N35DTJS9w27vdy9kDUbD6Wk/H9Rrln8i8fkegvwGvAezqjbhSGLMca0ammpe9Cny5vkt7sCSApbprTiY1ZvPISdpf+Nat2MiQGL68YYE2XJSfn07PwSOZnHhd3u8Rby6+ZTKCiahsdrw0LVV9wNaKaqZzb1GL65Jhvqv6pqc+YYY5qFSCod219DVvohbCi6jMqqlTXKeL1bWV94Nu3LT6dz7k24XJkxqKkxLcviujHGxIZL0umWN4f0lGFs3n4b4KlRZnvJs5SUz6NLhxlkpU9GpM7JE9q0uEuu/USkPzAEZzz5papa85tpZK808OMUGAg05DOMMaZOGWkj6NvlHTZvv41txU+FLbO95FnKKhbQLf9B0lP3jHINjYkOi+vGGBN9IkJeuwtIT9uH9VsuwOPdWKNMlWcD67ZMJSNtLJ3aX09G2ogY1DQ+xF23cBFpJyIvAz8D/wT+BfwkIn8XkZwGHKqrqrrqs+CMZGqMMS3C5cqkS4fb6NnpRZJcXcKWcVf9xC8bj/INdtbqpgE2ptEsrhtjTOxlpo2hb9d3yEwbF7FMWcVn/LLpN6zd9DuKy+bZ4KthxF1yDdwHDAMm4swVnQEc7Fs3u57HeBpoSFew54AdDShvjDENlpU+nr5d55GdfmjY7YqbTdtuZN2WP1DlqXln2Zg4ZXHdGGNageSkTvTs9H90zr0NkciPopVWfMS6LWewcsO+bNp6M6UVn9v0XT4Sby0gIlIIHKeqH4WsPwh4TVXzY1Oz6Bk1apQuXLgw1tUwxrQQVWVbyTNs3nYzquVhy7hcHejSYQbtMo+Jcu1MXURkkaqOinU94oXF9ZoszhtjYq2yai0FRVdTWvFhvcqLZJKROoK01L1ISx5ISko/UpK6k5TUCZekt3Btoy9SrI/HZ64zCD96aBHQ6P9zIjJGVT9vdK2MMaaZiAgdsqeQmbYf6wsvwl25tEYZr3crGwovoLjsv3TJvZ2kpA4xqKkxzcLiujHGtDIpyb3o2elFisveYsv2Gbirfq61vGoppRUfU1rxcY1tIpkkudohkolL0hFJQ0gBSUZwAS4QAQTB+enbs7lPq1YZafuR3+7iJh0jHpPrT4BbReR0VS0FEJEs4Gbg0yYc92WgdzPUzxhjmkVayu706fJvNm+7jW3FT4Qts7P0X5SWL6Br3t1kZ0yKcg2NcYjIRcDFQD9gqKquFJFrgZWq+vc6dre4bowxrZCIkJN5BNkZk9le8hJFOx8KO7tJXVRLqfK0/qEuXK6GDPMRXjwm138E3gLWich3OCN+DgdKgMNq21FEIgV4AfKas5LGGNMcXJJOlw63kpU+gYKiP+LxbqlRxuPdyLotf6B91ql0an+9tWKbqBKRK4BrgJnAHUGb1gGXAHUl1xbXjTGmFRNJJjf7NNpnnUJJ+ftsK55LSfn/gKpYV63VibvkWlWXiMhA4A/AHjgB9Dng+XrMV3kIcDpQHLJegIOau67GGNNcsjMOpm/X99m49VqKy/4dtsz2khcoLnubTrl/oV3mb20uShMtFwDnquq/ReSvQeu/AuqcO87iujHGxAcRF9kZB5OdcTAeTxE7y/5LSfkHlFZ8gte7LdbVaxXiLrkG8AXbxxqx60dAsar+L3SD7265Mca0WslJ+XTPf5Sdpa+xcev1eHV7jTIebyEFRVewveT/6NJhBmkpu8egpqaN6QMsCbO+Eud56jpZXDfGmPiSlJRHbvZp5GafhqoHd9VKyt3f4q78EXfVSiqr1lHl2YDHWwR4Yl3dqImL5FpETgDeUNVK3+uIVPUftWw7tpZtk5tQRWOMiQoRoV3WCWSk7UdB0Z8ijuJZVrGA1QWTyc2eQn67y0lO6hjlmpo2ZCUwAlgTsv5I4IdwO1hcN8aYxCGSRFrKQNJSBtbYpurFqyV4vdvxahmqZai6Ua1CqQI8oAooivPTt2cUz8CRnNSl6cdohnpEwytAV2CT73UkCiTV96Ai0kNV1zWxbiZBzZ07l0suuYTi4tDehsbEXkpyd3p2epFtJc+wZdtteDXcv9MqthU/wfaSF+mQcy55OReQ5Gof9bqahHcX8DdxJkUVYKyInI7zHPZZEfaxuG5aBYv1xrQsERdJkkNSMwwWFg9csa5AfaiqS1U3Bb2OtNQ7APv8twWqaxpo6tSpTJ8+HXBa5VavXh3Y9s9//pOxY8eSm5tLdnY2e+yxB+ecc06z16Fv377cddddzX7clvD5559zzDHHkJeXR1paGnvssQc333wz5eXh50OOpdWrVwee+50+fTpTp06NbYUSjH/Krn7dPiQnI/J816qlFO24j5Xr92PL9llUeQqiWEuT6FT1KWA6cDuQCTwLnANcpqovRdjH4nobY7G+YSzWGxOf4iK5DiYiB4lIjRZ3EUkSkYYOXmKj/bRi8+fP57e//S1HH300CxYs4Ouvv+bOO+9ENfrdRJqT2+1u9L6vv/46Bx54IPn5+cybN4/ly5dz00038eijj3LooYc26dgmfiUndaV7x4fp2ekFUpL7Rizn1e0U7pjNivWjWV94MWUVC+P+98m0Dqr6mKr2AToDXVW1l6qGnz8uhMX1ts1ifU0W642JX3GXXAPvE356jVzftoaI77/cCe6NN95gzJgx/PnPf2aPPfZg4MCBHH300TzxRPXva//4xz/Ya6+9SEtLo1evXtx2223VgnK4O9UTJkzgkksuCbxes2YNV199NSJSY4Tl+fPnM3ToULKyspg4cSKrVq2qUc+RI0eSnp5Ov379uP7666sFvr59+zJ9+nTOOusscnNzOe2005g7dy7Z2dl1HjtYaWkpZ599NkceeSRPPfUUI0aMoE+fPpxyyim88cYbfPzxx9x3332B8jt27ODCCy+kW7dupKenM3jwYF56aVcj0qeffsr48ePJzMykR48eXHjhhezYsSOw/a233uLAAw+kQ4cO5OXlcdhhh7F06dLAdv+d6ldffZXJkyeTmZnJkCFDePfddyOeg2lZWekT6Nv1PfLbXYnTQzeSKnaWvsYvm45h1Yb92bztDsrdP8T9l1kTGyLynojkAqjqFn+LtIi0E5H36nEIi+ttmMX66izWGxPf4uWZ62BC+OCZjzMnpvH5cW33WFeB3Xutb/S+Xbt2ZdmyZXz77bcMHz48bJlFixbx29/+lhtuuIHTTjuNL7/8kvPPP5927dpx6aWX1utz/vGPfzB8+HDOOussLrzwwmrbKioqmDFjBk8++STp6elMmTKFCy64gLfffhuAt99+m9NOO4377ruPgw46iF9++YULLriAioqKakH+nnvu4YYbbmDhQqel8JNPPqnz2KHefvtttmzZwjXXXFNj24gRIzj44IN54YUXuPrqq1FVjjjiCLZu3cpTTz3FoEGD+PHHHwPdyRYvXsyhhx7KzTffzOOPP05RURFXXHEFZ511Fq+84jz+WFJSwhVXXMGwYcMoKyvjr3/9K0cffTQ//PADqampgc++/vrrufPOO3nwwQf561//yu9//3vWrFlDdnZ2va6/aV4uSadj+yvJzZ5C0Y4H2Fb8NErkVo5KzxqKdt5P0c77SXJ1ISv9IDLT9yc9dQSpyQMQicd7sCbKJgCpYdanAwfWY3+L601gsd5ivcV6Y1oRVY2LBXjdt3iAt4Pevw78G2eU0rcaeMzvYn1ejVlGjhyp9bHsl24xX5qiuLhYjzzySAW0Z8+eeuKJJ+pDDz2kO3fuDJQ59dRTdeLEidX2u+mmm7RHjx6B93369NE777yzWpnx48frxRdfXGuZp556SgFdtmxZYN1zzz2nKSkp6vF4VFX1wAMP1FtuuaXafq+99ppmZWWp1+sNHPs3v/lNg48d6o477lBAi4qKwm6/7LLLNCMjQ1VV33nnHRUR/eGHH8KWPf300/Wss86qtu7rr79WQDdu3Bh2n+LiYnW5XPrRRx+pquqqVasU0IcffjhQ5tdff1UgUMbEnrtyrW4o/JMu+6VXg39/l68dqKsLjtL1Wy7XLdtn67biV7Sk7BMtdy/Xyqot6vVWxvr0WiVgobaCWNHSC84I4SMAL8580yOCln2BG4DVtexvcb2JcV7VYr2fxfqaLNYb03Iixfp4arku9P0UYCtQFrTNDXxMw+fItGezWrGsrCz+/e9/s2LFCt5//30WLFjAddddx4wZM/jiiy/o0qULS5cu5aijjqq237hx47j55pvZsWMH7dq1a1Id0tLS2H33XfMEd+/encrKSrZt20ZeXh6LFi3iiy++YObMmYEyXq+XsrIyCgoK6NatGwCjRo1q8LEbSlUD3dy+/vprunXrxuDBg8OWXbRoET///HO1rmPO3wlYsWIFnTt3ZsWKFfzlL3/h888/Z/PmzXi9XrxeL7/88ku1Yw0bNqzaOQBs2rSpwfU3LSMluSdd8+6mY/ur2Vb8DNuKn8XjLax7R8CrxZS7v6Lc/VXEMiKZuCQblysTkXSEVERSnIUkwAWShCDO68CfXfH9V4IP1qhzbCkdss8nM32/WFejNVsIgXlT3gmzvQyorVnR4rqxWN9AFuuNad3iJrlW1TMBRGQ1cJeqNrmrmKru1dRjmJY3YMAABgwYwDnnnMP111/PoEGDeOihh5g+fXq1IBPKv97lcgWCiV9lZWW9Pjs5ufqviP+YXq838POmm27it7/9bY19O3XqFHidlZXV4GOHGjRoEAA//PADBxxwQI3tS5cuZeBAZ37B0PMN5fV6Oeecc/jjH/9YY1uPHj0AOProo+nRowePPPIIPXr0IDk5mSFDhtQYSCUlJaXe52BiJzmpKx3bX0Neu8vYWfo6O0peobTiU5xGx8ZTLcWjpXgS8H95bSOwGwD64SSzK4HRwOagbW5gk6p6Iu0cT3HdN7DaVcBIoDtwpqrOraV8XyDcg7VHqOpbLVHHeGex3mGx3pj4FpPkWkT2As4HBgBnqeoGETkOWKOqX9e2r6reHIUqmlaqb9++ZGZmBuajHDJkCB9//HG1Mh9//DE9e/YkJ8eZT69Tp05s2LAhsL28vJxly5axzz77BNalpqbi8UT8DhjRiBEjWLZsGbvttltjTqdBDj30UPLz87nzzjtrBNyvvvqK+fPnB+6qjxgxgg0bNrB06dKwd7RHjBjB999/H7HehYWFLF26lDlz5jBx4sTAZ1RVVTXzWZloc0k67bNOpn3WyVR5NrGz9HWKy96hrOKLWp/NNiaUqq7xvWzSg/lxEtezgSXAM76lvg4Hvg16X9SclUpUFust1hsTr6KeXIvIoTjPU/0XmARk+DYNAKYCx9XjGGcCpwC9CRlERVX7N7A+T0bYpEA58DPwkqo2frSOGGnKACOtwfTp0yktLeXII4+kT58+bNu2jfvvv5/i4mKOOcZpUbryyivZd999mT59Oqeeeipffvkld999N7fffnvgOJMmTeLJJ5/kmGOOoVOnTtx222017mb37duXjz76iD/84Q+kpaXRsWPHetXxxhtv5De/+Q19+vTh5JNPJjk5mSVLlvDFF18wa9as5rsYOHfEH3vsMU4++WTOOussLr30UvLz8/n000+56qqrGDduHJdffjkABx98MGPGjOHEE0/k3nvvZdCgQfz888+UlJRw3HHHMW3aNPbbbz8uuOACzj//fHJycli2bBlvvPEGjzzyCB06dKBjx4489thj9OrVi3Xr1nH11VfXuANv4ltyUmc65JxDh5xz8HpLKa1YQFnFZ5S7v6Hc/S1eLY51FU2c8E2lNZrwcbnOZLS1x3VV/Q/wH9/x5zagOoWq2qITy1usd1ist1hvTGsQi2FgbwX+pKrHQ7Vmkg9wAnOtRORq4G5gEdAX+CfO3eQ8IFJArU0n4AScpH4333Kcb93uwDXAjyKydyOObZpg/PjxrFq1iilTpjB48GAOO+wwVq9ezeuvv85BBzlTn44YMYKXX36ZV199laFDh3Lttddy7bXXBqbeALjuuuuYNGkSxx57LIceeijjxo1jxIgR1T7rlltuYe3atQwYMKBaF6+6HHbYYfz73//m/fffZ/To0YwePZo77riD3r17N89FCHH88cfz4YcfsmnTJiZNmsTAgQO56aabOOecc3jnnXcCI3u6XC7++9//csABB/CHP/yBwYMHc/nllwe6eQ0bNowPP/yQ1atXM378eIYPH851111Hly5dAvu/9NJLfPfddwwdOpSLL76YW2+9lbS0tBY5LxN7Llcm2RmT6JR7Pb06v8xuPZbRv9vn9Oj4LJ1yp9Mh+xyyM44gPXUkKUm9cUnTnnE0iUNE9gCWAh8CzwOPA3Nxnpf+Wz32T+S4/g8R2SQin4jISVH4vLhjsb4mi/XGxC+p63mNZv9AkWJgqKquFpGdwHBVXSki/YClqppex/7LgT+r6ish+/8F6K2q5zawPtcCw4GzVbXUty4T50vBt8BsnC5gnVT14IadbcsYNWqULly4MNbVMMa0caoevFqC11uMajleLUfVDVSiWoVSBXhQ9eI8261BC2jw7EtRjkX1kZ46nJTkhk9zJCKLVLXmyEYJSkTeArYBZwMFwN5Ae+Ah4AZVrXVC3HiL677vMZfU8cx1R2AK8AlQBRwDXA9MUdXnIuxzHnAeQO/evUeuWbMmXDFjjDGtQKRYH4t+H1uBHsDqkPUjgF/rsX9P4Avf6zLA33zyom99g4IwcDkwyR+AAVS1VERuA+ar6iwRmQnMa+BxjTEmoYkkkSTtSHJZK3Ybty8wXlVLRMQLJKvqVyJyDfAAMKz23RMvrqvqFpzWeL+FvoT7GiBscq2qjwKPgnMTvaXqZowxpuXEolv4C8CdItITp/kiWUTGA3dRv0FCCgD/QzJrgLG+17sBjQlG2UC3MOu7+rYB7CCORlY3xhhjokgAfyK7GecGOjg3zOszAlRbieufAwOj/JnGGGOiKBbJ9Q0401OswQlyPwDv4cxneVs99n8Pp3sVwBPAPSLyPvAS8I9G1Oc14AkR+a2I9BWRPiLyW9+x/ccbDSxvxLGNMcaYRLcEpxs2OC3N03w3zW/GGTysLm0lru8NbKirkDHGmPgV9dZYVa0ETvM9SzUCJ8H/WlV/quchzvPtg6o+LCJbgQOAV4FHGlGlC4B7cLpp+a9HFc4gKlf53i+l4d3SjDHGmLbgNsA/we8NwJvA+8AW4OR67N/q47qIZLOrFd4F9PYNiFakqr+IyAxgtP8ZbhGZAlQCX+MMOHA0cDEwrRHnY4wxJk7EYkCzG4G7gp+F8q3PAK5W1Vtq2TcFJ4jPCZpfs7nqlYUzHZgAP6tqSXMevznZgGbGGNN6tbUBzcIRkTxgq9bxJSNe4rqITMC5YRDqaVWd6puea4Kq9vWVn4KTSPcBPDit5LMjDWYWyuK8Mca0bpFifSySaw/QTVU3hazPBzapalId+wdGG2+5WrZuFnSNMab1suS6YSyu12Rx3hhjWrdIsT4Wz1wL4Qco2Qcoqsf+bwOTmq0yIhG7hYnIw831OcYYY0wiEpF0EZkmIu+IyDci8l3wUo9DWFw3xhiTEKL2zLVv7kr/BKcrRSQ4wU4C0oH6BL35wO0iMgxYBFTr5qWqDR38ZJaIFKnqqyH1fRQ4rIHHMsYYY9qaB4HjgZeBT2n4CN8W140xxiSEaA5odglOq/WTwPXA9qBtbmC1qn5Wj+P8zffzsjDbFCdRb4iTgH+IyDZVnQ+BAHw4MKGBx8K3/3XACcDuQAWwALhOVZcElRHgJpyBXDrgTNFxsap+35jPNMYYY2LkOOC3qtrYeaNbfVw3xhhj6iNq3cJV9WlVnQtMBB7yvfcvL9YzsUZVXbUsDQ3A+ALvWcArIjJGRB7DubM9QVVXNvR4PhNw7uTvj9PVrQqY5xvgxe8a4ErgUmBfYBPwrojkNPIzTZAPPvgAEWHLli0J9VnGGNMKlQJrG7tznMR10wpZrDfGtDZRf+ZaVf/nm44LEekqIr2Dl7r2F5EzRCQtzPpUETmjkXV6FSfZ/RCYDIxvSgBW1cNU9SlVXaKqi4HTgU44U4v4W62vAO5Q1Vd9LdpTgBzg1MZ+bryaOnUq06dPB0BEWL16dY0yxxxzDElJSbz77rs1tvXt25e77rqrhWsZ+bP2339/NmzYQH5+fpOO/cILLzB27Fiys7PJyspizJgxPPdcvQaWjbq5c+cyYcIEACZMmMDcuXNjWh9jTEzNAv4kIo36ThEPcd00ncV6h8V6YxJb1Oe5FpF2wAM4c1+mhilS113qp4C3cFp6g+X4tj1TjzrcH2HTRmAxzpcEAFQ1XDe1hsrBuZGx1fe+H9AVeMdfQFXLRORDnNbuxszrmbA2bNjA/Pnz+eMf/8jjjz/O5MmTY12lalJTU+natWuTjjFt2jRmz57NzTffzNy5cxERXnvtNc4++2yWLFnCHXfc0Uy1NcaYphOR10NWHQQcLiI/4MzvHKCqx9RxuHiM66aZWay3WG9MIojFaOF3A8NxntEqx2mpvRr4FfhdPfaPNNp4b6o/x12bvSIsK4DsoPdD63m8utwHfAP4u777/zpvDCm3MWhbNSJynogsFJGFmzdvbqZqxYe5c+dy+OGHc9lll/H6669TWFgY2DZhwgTWrFnD1VdfjYjg//IUqrCwkFNOOYWePXuSkZHBnnvuyVNPPVWtzIQJE7jooov485//TMeOHencuTNXXXUVXq+31s8K11VswYIFTJo0iaysLNq3b8/BBx/M+vXrw9btiy++YNasWcycOZNrr72W3XffnUGDBjFt2jRmzpzJzJkz+eKLLwLlly1bxjHHHEP79u3Jzs5m7NixLF68OLD9qaeeYsiQIaSnpzNo0CDuvffewDkA3HPPPQwbNoysrCx69OjBOeecw7Zt26pd7+zsbObPn8/QoUPJyspi4sSJrFq1qq7/VcaYtqMwZHkNeA8oCLOtLvEY100zs1hvsd6YRBD1lmvgCOAUVf3IN+f1IlV9SUQ2AOcDr4TbSUQWs2u08f+JSFXQ5iSgD/Cf+lRAVSc25QQaQkTuAcYB41TVE1qV0OJh1jkFVR8FHgVn/sv6fHb/++9uWGVbwMrLrmzS/qrKk08+yaxZs+jduzdjxozh2Wef5YorrgDgH//4B8OHD+ess87iwgsvjHic8vJyRowYwbRp02jXrh3z5s3j/PPPp3fv3hx88MGBcs8//zyXX345n376Kd988w2nnnoqI0eO5JRTTqn3Z3377bdMnDiR008/nXvuuYe0tDQ+/PBDqqqqwpZ//vnnyc7O5qKLLqqx7cILL+SGG27gxRdfZPTo0axfv55x48ZxwAEH8O6775Kbm8sXX3yBx+P803rssce48cYbeeCBBxg5ciRLlizh3HPPJSUlhUsuuQQAl8vF7Nmz6d+/P2vWrOHSSy/l0ksv5dlnnw18bkVFBTNmzODJJ58kPT2dKVOmcMEFF/D2229H/p9ljGkzVPXMph4jXuN6a2OxfheL9RbrjYm1WCTXucAa3+vtQD7wM06r7uO17OdPuocC/waKg7a5gdXAq7QiInIv8HtgYsizXgW+n12pPghMZ2q2Zie84Gd4VKvfN/jggw8oKiriqKOOAuCMM87g3nvvDQTcvLw8kpKSyMnJqbW7Vo8ePbj66qsD78877zzee+89XnzxxWoBd8iQIdxyyy0ADBo0iMcee4z58+dzyimn1PuzZs2axfDhw3n00UcD6wYPHhyx/PLly+nfvz+pqTWfkkhLS2PAgAH8+OOPAMyZM4esrCxefvnlQPlBgwYFyt96663MmjWLk046CYB+/fpx7bXX8uCDDwYCrv/agfNc2axZszj22GN5+umncbmczixVVVXMmTOH3XffHYCrrrqKM888E6/Xi8vlYurUqUydOhVw/h8ZY4yIDAD8f+x+qMczznEX103jWay3WG9MWxCL5HoF0B/4BVgK/F5EvsCZuqoo0k6qejOAiKwGXlLV8pavauOJyH04ifUEVV0WsnkVToI9GfjSVz4dOBCni7zxeeKJJzj55JMDweWkk07ikksu4fPPP2fMmDH1Po7H4+GOO+7gpZdeYt26dVRUVOB2uwMDdfgNGzas2vvu3buzaVPoY4C1+/rrrzn++OMbtE+kLm7gfAnxb//6668ZN25c2OC8efNm1q5dy/nnn1/tbntVVVW1LzLvvfceM2bMYOnSpWzfvh2Px4Pb7aagoIDu3bsDTqD3B1twrkNlZSXbtm0jLy940HtjTFsnIvnAE8AxgHfXankTOEtVw3YNj7e4blqOxXqL9cYkilg8cz0X8P9VuwOnK7gbuBOYWdfOvqm7ygFEJFdE8oKXFqpzg4jIHOBM4BRgq29U9K4ikg2gzl+/2cC1InKCiAzFuS7FwAuxqXXrs23bNl599VUeffRRkpOTSU5OJi8vj7KyMh5/vLZODjXddddd3H333Vx99dXMnz+fb775huOOOw63212tXEpKSrX3IlLtGab6CL0jX5dBgwbx888/U1FRUWNbRUUFK1euZODAgXUe21/Phx9+mG+++SawLFmyhO+/d6ZPX7NmDUcddRSDBw/m5ZdfZtGiRTz55JMA1a5FcnL1+27+gN/Qa2GMaRMeB3bDuUGc7lsOwhm887G6do6HuG5ajsV6i/XGJJKot1yr6r1Br98TkT2AUcBPvmmraiUifYCHcebLDv7r6H9eucFzYrYA/wM180PW3wxM972eBWQAc4AOwOfAoaq6MxoVjAfPP/88nTp14j//qf7I3WeffcaVV17J7NmzycrKIjU1NfAcUiQff/wxRx99NKeffjrgBK7ly5eTm5vboDrV57NGjBjBe++9V+9jnnLKKdx///089NBD1bpxATz44IOUlJRw6qmnBo793HPP4Xa7a9zR7tKlCz169GDFihWccUb42WsWLlyI2+3m3nvvJSnJ+VV58803611XY4wJ4zDgYFX9LGjdJyJyPjCvrp3jJK6bFmKx3mK9MYkkqsm1iKQAHwNnqOqPAKr6C04X8fp6Cue57bOA9UQYACyWVDVyv59dZRQn0Z7eUvVo6gAjsfbEE09w0kknMXRo9cFdBw0axNVXX81LL73EWWedRd++ffnoo4/4wx/+QFpaGh07dqxxrEGDBvHSSy/x8ccf07FjRx544AFWrVrFPvvs06A61eezrr76avbbbz/OO+88Lr74YtLT0/noo4849NBD6d275lTu++23H1deeSXTpk2joqKC448/PjA9x1/+8hemTZvG6NGjAbjooot4+OGHOfnkk7n++uvp0KEDX375JYMHD2bvvfdm+vTpXHrppeTm5nLkkUdSWVnJV199xbp167juuusYOHAgXq+X2bNnc8IJJ7BgwQJmz57doGtgjDEhNgMlYdaXUr/Rwlt9XG/NLNZX38divcV6Y2JKVaO64MxjOagJ+xcDQ5tYh9fru0T7+tRnGTlypCa6RYsWKaCffPJJ2O2nn366jh07VlVVP/vsMx02bJimpaWp809a9f3331dAN2/erKqqRUVFevzxx2t2drZ26tRJr776ar3wwgt1/PjxgWOOHz9eL7744mqfM2XKFD3qqKMC7+vzWaqqH330kR544IGanp6u7du314MPPljXr19f6zk/88wzOmbMGM3IyNCMjAwdPXq0PvPMMzXKLVmyRI844gjNysrS7OxsHTt2rC5evDiw/YUXXtB99tlH09LSNDc3Vw844AB98cUXA9vvu+8+7d69u6anp+ukSZP0pZdeUkBXrVqlqqpPPfWUZmVlVfvMcOdojAkPWKitIFZEawHOxpmGq0fQuh44rdbn1GP/Nh/XQ5e2EOdVLdZbrDcmfkWK9eJsix4RuRPnL1WjBu7yTd0xVVUXNaEOT9VdyqHNMN1Icxs1apQuXLgw1tUwxhgThogsUtVRsa5HtPjicl+cZ63X+Vb3AMpxBvAMUNXqI0lhcT0ci/PGGNO6RYr1sRgtPAs4TUQmA4sI6UqmqpfVsf/lwAwRuUhVf25MBeIhsBpjjDFx4pW6i9TK4roxxpiEEIvkejDwle91/0bs/y8gDfhRRCqAquCNqtquoQcUkS7AxcAQnGe9vgceVNWGzctgjDHGtDHqm1KrCSyuG2OMSQixGC18YhMPcUmzVMRHRA4A3gI2Av6RTv8A/ElEDtPqo58aY4wxJoiIdAJQ1c2+93sBvwO+V9UX63EIi+vGGGMSQtSTaxF5ErhcQ6acEpEs4AFVPau2/VX16Wau0l3Ai8AFqur11cWFMy3I3cD+zfx5xhhjTCL5O/As8KSIdAQ+xBn1+1IR6a6qd9e2s8V1Y4wxicIVg8+cgjO/c6gMIPyEfSFEpIuIXCUiD/kCOSJygIj0a0R99gbu9gdgAN/re4CGzd1gjDHGtD3DgAW+1ycBP6vqnjgx/fz6HMDiujHGmEQQteRaRPJEJB8QoIPvvX/pBPwGpwtXXccZCfwInIYz/Yf/WazJwG2NqNp2IFzw7gdsa8TxjDHGmLYkA2c6LYBDcKa8Amd8lV517Wxx3RhjTKKIZsv1Fpw5rhX4AdgctBQAjwMP1uM4dwH3qeo+QEXQ+reBAxpRr/8DnhCR00Skn4j0FZE/AI/hdCszxhhjTGQ/ASeISC/gUOAd3/ou1C+ZtbhujDEmIUTzmeuJOK3W7wEnAkVB29zAGlVdX4/jjMS5sx1qA04gb6hrfPV6kl3XoxJ4CLi2Ecczxhhj2pKbcZLWu4H5qvq5b/1hwNf12N/iujHGmIQQteRaVf8H4Ht+6hdV1UYeqgzoEGb9Hjgt4w2tlxu4XESuAwbgBOSfVbW0kfUzxhhj2gxV/YeI9Aa6A98GbZoHvFqPQ7T6uC4iBwFX4dwI6A6cqapz69hnL+BvwGicBoVHgFub8P3HGGNMKxeV5FpERgDf+AYUyQfyRSRsWVX9KuyGXf4F3CQiv/XvIiJ9gZnUL4hH+txSYHFj9zfGGGPaKlXdCGwUkWwRUVUtCWrBrks8xPVsYAnwjG+plYi0A97FGTl9X2B3YC5QgtPCb4wxJgFF65nrhUDHoNdf+n6GLl/W41hXAXk4z2pnAh8DP+M813VDYyonIl1F5AQRuUBELgpeGnM80zBTp05FRBARUlJS6Ny5MxMnTmTOnDlUVlbGunrVvPDCC4wdO5bs7GyysrIYM2YMzz33XKyrZYwxMSUiF4vILziDie0QkTUNiKGtPq6r6n9U9c+q+grgrXMHZ3C2TGCKqi5R1Vdxbhb8SSK1LiQ4i/XGmLYgWt3C++EMaOZ/3WiqugMYJyKTgBE4Nwi+UtV5jTmeb5CTx3G6jW3FGXAt8HHUb5A100SHHHIIzz77LB6Ph82bN/Pee+9x00038eyzzzJ//nyysrJiXUWmTZvG7Nmzufnmm5k7dy4iwmuvvcbZZ5/NkiVLuOOOO2JdRWOMiToR+TNwHc7AZB/7Vh8I3CEi7VS11j+OCRrXxwIfqWpZ0Lq3gVuBvsCqKNSh1bFYb4xJeKoalQXnrnSdS7TqE1SvNcAtQHK0P7uxy8iRIzWRTJkyRY866qga6xcvXqwpKSl64403BtZVVFToNddcoz169NDMzEwdNWqUvvXWW9X2+/777/XII4/U7Oxs7dSpk/7+97/XDRs21Pi8W2+9VTt37qxZWVk6depULS0tjVjHzz//XAG99957a2y79957FdDPP/88sG7p0qV69NFHa7t27TQrK0v3228//e677wLbn3zySR08eLCmpaXpwIED9Z577lGPxxPYfvfdd+tee+2lmZmZ2r17dz377LN169atge1PPfWUZmVl6bx583TPPffUzMxMnTBhgq5cuTLiORhjogNYqK0gVkRrAX4BTgmz/jScwUqjXZ8Wjes4045NraPMO8CTIet64yT3YyPscx6+nny9e/fWRGOx3mK9MYkkUqyP5mjhW6h+9zgcpY7WdBF5EvheVe8OWf8nYIiqntPAerUD5qpqVQP3a/UO79DQS9H83tr6eKP3HTp0KIcffjivvvoqN998MwBnnnkmK1as4IUXXqBnz5785z//4eijj+bLL79k+PDhbNiwgYMOOoizzz6bu+66i8rKSq6//nqOOeYYFixYgMvlPAnxv//9j4yMDObPn8+6des466yzmDZtGvfff3/Yujz//PNkZ2dz0UU1exReeOGF3HDDDbz44ouMHj2a9evXM27cOA444ADeffddcnNz+eKLL/B4PAA89thj3HjjjTzwwAOMHDmSJUuWcO6555KSksIll1wCgMvlYvbs2fTv3581a9Zw6aWXcumll/Lss88GPreiooIZM2bw5JNPkp6ezpQpU7jgggt4++23G33NjTGmEToT/rGuL6jHaN8JHNdDv/NIhPXOStVHgUcBRo0aVe9BzyzWW6w3xrQe0Z6KK5LDgcuB+gTCI4EHwqx/D+e5rYZ6HjgqwjFNjA0ZMoR585yegStWrODFF19k9erV9O7dG4BLLrmEefPm8cgjj/Dggw/y0EMPMXz4cGbOnBk4xjPPPENeXh4LFy5k9OjRACQlJfHUU0+RnZ3N0KFDmTlzJmeffTYzZswI2y1t+fLl9O/fn9TU1Brb0tLSGDBgAD/++CMAc+bMISsri5dffjlQftCgQYHyt956K7NmzeKkk04CoF+/flx77bU8+OCDgYB7xRVXBMr37duXWbNmceyxx/L0008HvjRUVVUxZ84cdt99dwCuuuoqzjzzTLxeb6CMMcZEwXLgVJzW4mCnAj/WY/9EjOsFQNeQdZ19PzdGuS6tnsV6i/XGJIqoT8UVzDeK+EzgIHxTVNTjULk4XbJCleB0LW+oPwH/FJGDcUYVrTaqhqqGflkwUaSq+Md++eqrr1BVhgwZUq1MRUUFkyZNAmDRokV8+OGHZGdn1zjWihUrAgF32LBh1cqMHTsWt9vNihUrGDZsWNi61DYGTXA9v/76a8aNGxc2OG/evJm1a9dy/vnnc+GFFwbWV1VV+bsFAvDee+8xY8YMli5dyvbt2/F4PLjdbgoKCujevTvgBHp/sAXo3r07lZWVbNu2jby8xvwqGGNMo0wH/u6bruoTnJbZccB44Le17OeXS+LF9c+AmSKSrqrlvnWTgfXA6ih8flyxWG+x3phEEc2W6wDfXNe34QTdf+B0+1pRz92X49zlvi9k/VE4o4s21Pk4LedbgN2oOfCJJdcx9MMPP9C/f38AvF4vIsKXX35JSkpKtXIZGRmBMkcddRR33XVXjWN16VJn78SIBg0axEcffURFRQVpaWnVtlVUVLBy5cpA0A8OnKG8XmeQ2Ycffpj9998/bJk1a9Zw1FFHce6553LLLbeQn5/PV199xSmnnILb7Q6US06u/uvrD/j+zzDGmGhQZ57rMcAfgd/gdH/+ARitql/X4xCtPq6LSLbvWOAMuNZbRPYGilT1FxGZgXO+B/vKvADcBMwVkb8Cg4BrgZu1tiDRRlmst1hvTKKIanItIvnAjcAFOHe3x6rqwgYe5m7gYRHpjNNlDOBg4Arg4kZU6y/Alap6byP2NS1oyZIlvPXWW9xwgzMTyz777IOqUlBQwMSJ4Z8yGDFiBH//+9/p06dPjaAcbPHixZSUlAS6hS1YsIDU1FQGDBgQtvwpp5zC/fffz0MPPVStGxfAgw8+SElJCaeeemqgDs899xxut7vGHe0uXbrQo0cPVqxYwRlnnBH2sxYuXIjb7ebee+8lKSkJgDfffDPiuRhjTKyp6iLgD43cPR7i+ijg/aD3N/uWp4GpQDcgEEBUdbuITAbm4AxSthXnPO9pxjolBIv1FuuNSSjhRjlriQX4M86cld8AhzfxWOcDa3HmmvT6Xl/QyGMVAgOidR2aY0nE0cIPOeQQ3bBhg65bt06/+eYbvfvuuzU/P1/HjBmjxcXFgbKnnXaa9u7dW19++WVdsWKFfvnll3rnnXfqq6++qqqq69at006dOunxxx+vCxYs0BUrVui7776r5557ru7YsSPwednZ2XryySfrkiVL9J133tGePXvqxRdfXGs9r7zySk1NTdU77rhDf/zxR12+fLnOnDlTU1NTddq0aYFyv/76q+bl5emxxx6rX3zxhf7000/6wgsv6Ndff62qqo899pimp6frPffco8uWLdPFixfr008/rbfffruqqn777bcK6F133aUrV67UF154QXv16qWArlq1SlV3jSAa7P3331dAN2/e3KT/H8aYpqGNjRbuX4DuwN4402kFlnru26bjeuiSaHFe1WK9xXpjEkukWB/NoOvFeX7qTeD1SEsDj9kJ6NzEet0F3Bit69AcS6IF3SlTpihOVz1NSkrS/Px8HT9+vN5///1aUVFRrazb7dabbrpJ+/XrpykpKdqlSxc9+uijdeHChYEyy5cv1xNPPFFzc3M1PT1dBw0apJdcckngWP7pOW6++Wbt1KmTZmVl6RlnnKElJSV11vWZZ57RMWPGaEZGhmZkZOjo0aP1mWeeqVFuyZIlesQRR2hWVpZmZ2fr2LFjdfHixYHtL7zwgu6zzz6alpamubm5esABB+iLL74Y2H7fffdp9+7dNT09XSdNmqQvvfSSBVxj4kRbS66BfYDvAU9QcuxfPA08VpuM66FLosV5VYv1FuuNSSyRYr0421qeiMyl7qm4UNUzW742u4jIgzgjmn4PfEfNgU8ui2Z96mPUqFG6cGFDe9Mbv6lTp7JlyxbrfmWMaREiskhVR8W6HtEiIl/itBbfgjNgV7VYr6prolyfuIvroSzON53FemNMS4oU66M5WvjU5jiOiOThDIZ2MM60FtXmIVDVdg085GDAP+DKHiHbbNARY4wxpnZDgH1UdXljdra4bowxJlHEZLTwJnoCpwvao4S5Q95Qqlrb/NvGGGOMqd1inDmdG5VcY3HdGGNMgojH5PpgYLKqft4cBxORvYAfVNXTHMczrd/cuXNjXQVjjIlrvtZmvz8Ds0TkBsLPK11Ux+EsrptmZ7HeGBML8ZhcbwKKm/F43wIVIvIDzkjm/iUNuFRVj23GzzLGGGMSwRaqtzAL8E6YdQok1XEsi+vGGGMSQjwm19cDt4jIFFVtjmCch9MdbR+caUMuBgbhfCHY2gzHN8YYYxJNc3a9trhujDEmIcRjcn0D0BfYJCJrqNn9bFhDDqaq24D3fQsQ6FL2InBdE+tqjDHGJBxV/Z+IZAJ3AscBKcA84DJV3dLAw1lcN8YYkxDiMbl+paU/QFUXi8gfcebKfKOlP88YY4yJQzcDU4HngTKc6a8eAn7bwONYXDfGGJMQ4i65VtWbm/N4IpKpqqVhNq0Cdm/OzzLGGGMSyAnA2ar6fwAi8jzwiYgkNWQwMYvrxhhjEkXcJdd+IjIJZ25NBb5X1Q8aeaidIrIc+ApnXsyvcKYCuQR4qxmqaowxxiSiXsBH/jeq+oWIVAHdgbUNPZjFdWOMMfEu7pJrEekBvAaMxAmWAN1FZCFwvKquj7hzeOOA4cDewEnAdCDTt+0dEbkN+A74TlWXNq32xhhjTMJIAtwh66po4HcLi+vGGGMSRdwl18D9gAfYTVVXAYhIf+A537aTGnIwVf0M+Mz/XkQEp9vY3r5lJHAW0Jm6pxMxxhhj2goBnhORiqB16cBjIhLolq2qx9RxHIvrxhhjEkI8JteTgQn+AAygqitF5DJgfn0OICKjgUXhnglTVQWW+Rb/c2SjgF/qW0EROQi4CieAdwfOVNW5QdsFuAk4D+gAfA5crKrf1/czjDHGmBh7Osy65xpxnFYf140xxpj6iMfkOhJvA8p+BnQFNtez/Hs4d7s31bN8NrAEeMa3hLoGuBJnlNUfgRuBd0Vkd1XdWc/PMMYYY2JGVc9s4Y9oTXHdGGOMqVM8JtfzgftF5BRVXQsgIr2B+6jnHW6crmwzgrut1SG1IRVU1f8A//HVbW61D3Zara8A7lDVV33rpuAE+FOBRxryWcYYY0yca/Vx3RhjjKmPeEyuLwP+BawUkfU4o4r2wBmc5LJ6HuNDYEADPvMznDk8m0M/nLvr7/hXqGqZiHwI7E+E5FpEzsPpRk7v3r2bqSrGGGNMzMV7XDfGGGOAOEyufXe1R4jIZGAPnLvVP6jqvAYcY0ILVa8+uvp+bgxZvxHny0RYqvoo8CjAqFGjtGWqZowxxkRXAsR1Y4wxBgBXrCtQXyJyhIisFpH2AKr6rqo+oKr3A1/6th0a42o2RGiCLGHWGWOMMQkpAeO6McaYNi5ukmvgEuBOVd0eusG3biZwedRr1XAFvp9dQ9Z3pmZrtjHGGJOoEiWuG2OMMUB8JdfDgNq6iL0HDI9SXZpiFU6CPdm/QkTSgQOBT2NVKWOMMSbKEiWuG2OMMUB8PXPdidqn5VAgP0p1qZWIZAO7+d66gN4isjdQpKq/iMhs4HoRWQYsB24AioEXYlBdY4wxJhbiJq4bY4wx9RFPLde/4tzljmQYsC5KdanLKOBr35IB3Ox7fYtv+yzgHmAOsBDoBhxqc1wbY4xpQ+IprhtjjDF1iqfk+t/ArSKSEbpBRDJxEtd/R71WYajqB6oqYZapvu2qqtNVtZuqpqvqeFVdEuNqG2OMMdEUN3HdT0QuEpFVIlIuIotE5MBayvYVEQ2zHB7NOhtjjImeeOoWfhtwEvCTiDwALPOtH4wzKIoAt8eobsYYY4xpmLiK6yLyO+A+4CLgY9/P/4rIEFX9pZZdDwe+DXpf1HK1NMYYE0txk1yr6iYR2R94CCfYin8T8DZwkaraaNvGGGNMHIjDuP4nYK6qPuZ7f6mvFfpC4Lpa9itU1YJathtjjEkQcZNcA6jqGuBIEemAM2CYAD+p6tbY1swYY4wxDRUvcV1EUoGRwF0hm94B9q9j93/4ZgX5CbhXVV9pgSoaY4xpBeIqufbzBd0vY10PY4wxxjRdHMT1jkASENqSvhE4JMI+xcBVwCdAFXAM8JKITFHV50ILi8h5wHkAvXv3bqZqG2OMiaa4TK6NMcYYY2JAQ95LmHVOQdUtwN1BqxaKSEfgGqBGcq2qjwKPAowaNSrsMY0xxrRu8TRauDHGGGNMLGwBPEDXkPWdqdmaXZvPgYHNVSljjDGtiyXXxhhjjDG1UFU3sAiYHLJpMvBpAw61N7ChmapljDGmlbFu4cYYY4wxdbsHeFZEvsB5jvoCoDvwMICIzABGq+rBvvdTgErga8ALHA1cDEyLftWNMcZEgyXXxhhjjDF1UNWXRCQfuAHoBiwBjvSNeI5v3YCQ3W4A+uB0KV8OnBVuMDNjjDGJwZJrY4wxxph6UNUHgQcjbJsa8v5p4OkoVMsYY0wrYc9cG2OMMcYYY4wxTWQt18YYY4wxxhgTgVeVtdu381PRFn4sLOSnoi1sKS0lPyOTXu3a06t9e+dnu/Z0y8kh2WXtl22VJdfGGGOMMcaYNk9V2VC8k+W+BHp5YSHLC7fwc1EhZVVV9TpGkgjdc9rRs117erdvT8927enVrh292+fSs1178jMyEJEWPhMTK5ZcG2OMMcYYY9oMVWVLWSnLC50E+qfCLc7rokKK3e4mHdujytod21m7Yzuf/Vpze2ZKSiDh7tU+19fivet1ZkpKkz7fxJYl18YYY4wxxpiEtK28LNACvbxwCz8VOa+3lpfHpD6llZWBuoSTn5FBr3a59M5tz4iu3dmvZy8G5uVba3ecsOTaGGOMMcYYE9d2VlTwc1Ehy4uqJ9KbSkpiXbUGKSwro7CsjG82buD1H5cBkJ+RyX49ezK2Z2/269mLfrkdLNlupSy5NsYYY4wxxsSF8qpKfi4qCnTj9ifS63fubNHPzUlNY1B+PoPyOzIwL59e7dqzubQk0AV87fbt/LpjO4VlZc3+2YVlpfz7p+X8+6flAHTJyma/nr3Yr2cv9u/Zm17t2zf7Z5rGseTaGGOMMcYY06q4PR5Wbi0KdOP+qbCQHwu38Mv2bWgLfm5GcjIDfQn07r6fg/I70jU7u16txSVuN2t3OIn2L76E2598r92xvd4Do9VmY0kx//pxKf/6cSkAXbOyyUpNBSC4hv76Vqt10DmEnk1o+XDHqla+tkrGYcv6Qb37ctX+45p0DEuujTHGGGOMMTFR5fXyy/Zt/OhLoP3duVdt20qV19tin5ualMSADnmBlujd8zsyMD+fnu3a42pCYpiVmsoeHTuxR8dONbapKoVlZazdvq1ai/faHTtYu2MbG3buxKMNv3VQUFIM8dX7vVXql9uhycew5NoYY4wxxhjToryq/Lpje6AF2t8ivWJrEW6Pp8U+N0mE/h3yAi3QA/OdRLp3+9yoz0ctInTMzKRjZib7dOteY3ulx0NBcTFrtm/j240b+HTtWr7asJ4KT9Nbu010WHJtjDHGGGOMaRaqSkFxcaAF2p9I/1S4pVm6REciQO/2uYEWaH+LdL/cDqQlx0fKk5KURK/27enVvj3jevfh4n33o6Kqim8KNvDZr2v57Ndf+KZgA5Ut2KJvmiY+/qUZY4wxxhhjWpUtpaU1prhaXljITndFi35u95wcBuV3dBZfi/SADnlkJOAc0WnJyYzp2YsxPXtxBftTVlnJog3rWeBLtr/bWNCoruSmZVhybYwxxhhjjIloe3k5y4u2BOaL9j8bXVTe/CNjB+uclRXozu1PonfLyycnLa1FP7c1y0hJYVzvPozr3QdwBlArKN6JAsE5tvqGfQtOuzWoQI10XKuXr7VshHXhPiee5KanN/kYllwbY4wxxhhjKHa7nbmiC4MS6aJCNpYUt+jndkhPD3Tj9rdID8zLp0NGRot+biLISk1lQF5+rKthfCy5Nsa0Sh6vl2K3m2K3m53uCna6K5zXFRWUV1XRLSeH4V260a4N3702xhhjGqO8qpIVRUVOAh3UIr1u544W/dzs1NRAC/TA/I6BeaM7ZmTWa5orY1o7S66NMc1KVSmprKQ4KBnelSBXfx9InkPW7XRXUFpZWednCTCgQx57d+vG3l26sU/XbgzM7xj10T+NMcaY1sjt8bBq21Z+8rdE+xLpX7Zvw9uCXXfTk5ODWqHzGZjn/OyWnWNJtElollwbYwAnKa7wVFVLgKslvGES4J0Vbop9SXOx731JpbtFA3a1OgM/by3i561FvPLD9wBkJCczrEtXhnd1ku29u3SjS3Z2VOpjjDHGxILH62XN9m0sLyzkp6ItgeeiV7b0XNGuJPrn5QUS6N19P3u1b9pc0cbEK0uujUkAlR5P2NbgnYF1NRPhcAlzIkztUFZVxefrfuXzdb8G1nXLzmFvX7I9vGtXhnbqkpAjihpjjElsXlXW79zBj4Vbqg0stmLr1hadCzlJhL65Haq1RO+en0+f3A7WW8yYIJZcGxNDHq+XksrKGq3Doa3B/vfBrcXBSXF5C84bmQg2FO9kw887+e/PywFIdrnYI78je3ftFlj65XawrmrGGGNaBVVlY0kxPxU680QvL3IS6Z+KCuv12FRj+eeKrtalO78j/eNormhjYsl+S4xpBFWlrKrKlwzXnvjW9oxxcaU71qfSqmWnpJKdmkpOWprzM9X56RLh+82bWLVta6OOW+X1smTzJpZs3sRzi78FoH1aOnt37crwLv6Euyu56TZKqTHGmJZV6J8ruqj6CN07Klp2ruhu2TmBBNo/QvduCTpXtDHRYsm1aXMqqqp2tQq73RRXVFRPfCMMvBX8HHKxuwJPnM7hFw3pycnVkuHg5DgnNZXs1DRy0nw/I7zPSkkhqY6uZtvKy/i2oICvCzbw7cYNfFNQwPaK8kbVeXtFOf9bs5r/rVkdWNc3t4PTlbxLV/bp1p098juSkpTUqOMbY4xp23ZUlFdLnpf7unYXlrXsXNGdMrMYmF9zrmibbcOY5mfJtYkbVV5vtWeFd0ZIfCO3GDsty26vJ9an0molu1zkREiKg987SbCTCIeWyU5NjVoCmpuewfi+/Rjftx/g9ChYtW0r3xRsCCzLCrc0ejCX1du2snrbVl5b9gMAaUnJDO3cmb27dmNYl67s1bkLfdrnWndyY4wxASX+uaJ9CbT/ueiCFp4rOjc9nUF5Hasl0gPz88nLyGzRzzXG7GLJtWlxXlVK6jPydJhnjIPfl9lzxRG5RAKJbWhrcLjW4nAJc7vUNFKTkuI6URQR+nfIo3+HPE4YvCcAZZWVLNm8MdDC/U3BBjYU72zU8Ss8VSzasJ5FG9YH1rVLS2No5y4M69yVvbo4P7vn2FQjxhiT6CqqqlixtcjXAr2rRXrtju0t+rnZKamBBDr42ehOmVkWe4yJMUuuY0xELgKuBroB3wNXqOpHsa2VQ1Upr6qqR1fpyM8Y73S7KXG7sQ7UkWWlpFRLiGtrMY7UqpyZkmIBNYKMlBT27d6Tfbv3DKzbWFzMNxs3ON3JCzbw3caCRt+82VFRwadrf+HTtb8E1uWlZ7BXly7s1bkrw3w/bTowY+JfQ2O2iOwF/A0YDRQBjwC3qtpzRfGkMjBXdGG156LXRGGu6N3y8gNduf0JdXebK9qYVsuS6xgSkd8B9wEXAR/7fv5XRIao6i+17txAK7cWsXJrETsrqrcSB5LiiuqtyP4u1i05N2K8S0tKrlfiW707tb8l2WlFzkpJrfO5YtP8umRnc1j2QA4bMBBwHjn4qXCL07K9cQPfbNjAz1uLGn38ovKyGs9vd87KYminLnTI2DVIWuh3I0GCXvvL1L4uHP/2ur56BW+v7xe1tvR17oTBezKsS9dYV8O0Eg2N2SLSDngX+BDYF9gdmAuUAHdHqdqmEcoqK3ni60W+QcYKWbW1qEWnqkxxuejfIW/X4GK+rt292rW37wjGxBlLrmPrT8BcVX3M9/5SETkcuBC4rjk/6O/fL+bRrxY25yHjVpIIOWlpuxJf/7PCaak11rWr8Tzxrq7VqTawVcJIdrkY3Kkzgzt15tS9hgPOwDPfbizYNWBawQaKyhs/6MymkhLeK1nZXFU2UTCiW3dLrk2whsbs04BMYIqqlgFLRGQw8CcRucdar1uvlKQk/vbFgmYfo8UlQt/cXAbldaw2Snef9rk2WKYxCcKS6xgRkVRgJHBXyKZ3gP2b+/OyU+N/REgBsoJGnA43kJazLvwI1P5y6cnJ1p3K1KldWjoH9u7Lgb37As5jEr9s3843GzeweONGvttUwPebNtpYAMa0AY2M2WOBj3yJtd/bwK1AX2BVM1fTNJNkl4sBeXks3bK50cfo3a79roHFfIOL9e+QZ3NFG5Pg7Dc8djoCScDGkPUbgUNCC4vIecB5AL17927wh+WkpTa8hs0oIzm5Zlfp1LQarcW7kuKaiXOWb35jY2JBROiTm0uf3FyO3X0wAB6vlxVbi1i8aSOLNxaweNNGvt+8CbfHRqQ3JsE0KGb7dAV+DVPev61act3UOG+a16D8jvVKrrtlZ4cMLOZMc5Vpc0Ub0yZZch17od3CJMw6VPVR4FGAUaNGNbgrWWNbrlOTknYlvHXMTxz6jLG/tTgrNZVke2bIJKAklyvwZepE3+jklR4PPxUVsnhjAd9t2sjiTRv5ccvmFn1ezxgTNfWK2XWUD7e+yXHeNK+BefnV3nfMzGRgXkd2z89noK9L98C8fNqlpceohsaY1siS69jZAnhw7l4H60zNO+NN1qtdeyb27V9jsK2coEG2wg3EZd2XjGmYlKQkhnTqzJBOnfmdb11FVRU/Fm5hRVERVeok2aGPW1Z759umYbZFekxTQ7bX9c28MY97ahsb939o5y6xroJpPRoTswsilKeWfUwrMbFvP7JTUwOt0vmZNle0MaZuljnFiKq6RWQRMBl4OWjTZODV5v680T16MrpHz7oLGmOaXVpyMsO6dLXBsYyJU42M2Z8BM0UkXVXLg8qvB1a3VF1N8/APcmmMMQ1hfXVj6x5gqoicIyKDReQ+oDvwcIzrZYwxxpjqao3ZIjJDROYHlX8BKAXmishQETkBuBawkcKNMSZBWct1DKnqSyKSD9wAdAOWAEeq6prY1swYY4wxweoRs7sBA4LKbxeRycAcYCGwFWd+63uiWnFjjDFRY8l1jKnqg8CDsa6HMcYYY2pXW8xW1alh1i0GDmrhahljjGklrFu4McYYY4wxxhjTRGKP/cQfEdkMNKXreEeckU/bOrsODrsOdg387Do4mnod+qhqp+aqjGl7RGQn8GOs69EMEuVvip1H65II55EI5wBt+zzCxnpLrtsgEVmoqqNiXY9Ys+vgsOtg18DProPDroOJtUT5N2jn0brYebQeiXAOYOcRjnULN8YYY4wxxhhjmsiSa2OMMcYYY4wxpoksuW6bHo11BVoJuw4Ouw52DfzsOjjsOphYS5R/g3YerYudR+uRCOcAdh412DPXxhhjjDHGGGNME1nLtTHGGGOMMcYY00SWXBtjjDHGGGOMMU1kybUxxhhjjDHGGNNEllwnIBE5QUTeFpHNIqIiMqGe+40XkUUiUi4iK0XkgpatacsSkYtEZJXvfBaJyIF1lN9LRP4nImUisk5EbhQRiVZ9m5uIHCQir/vORUVkaj32SbRrcJ2IfCkiO3y/D2+IyNB67Jdo1+FiEfnOdx12iMhnInJUHfsk1DUIJSJ/9v1e/K2Ocgl9HUxsiMitIrJMREpEOPy0NgABAABJREFUZKuIzBeR/euxX6uJ0yKSIiIzfX9bSkRkg4i8ICK967FvqzkPX33i/ntTInznSYTvLYnyvSNRvzdEI/Zbcp2YsoBPgT/VdwcR6Qf8x7ffPsAM4AERObFFatjCROR3wH3A7Tjn8ynw30hBX0TaAe8CG4F9gcuAq2nANWyFsoElwOVAWV2FE/QaTAAeBPYHJgFVwDwRyYu0Q4Jeh1+BacAIYBTwHvBPERkWrnCCXoMAEdkPOBf4ro5yCX0dTEz9CFwM7AWMA1YBb4lIl0g7tMI4nYnzN+U2389jgV4455EcaadWeB4Q59+bEug7TyJ8b5lAYnzvSLjvDVGL/apqS4IuQEdAgQn1KDsT+Clk3ePAZ7E+j0ae++fAYyHrfgJmRCh/IbADyAhadwOwDt+o+vG8AMXA1DrKJPQ18J1PNuABjm7L18F3TkXA+W3tGgDtgRU4X3o+AP7W1v8t2BL7BWjni9eH1VKm1cdpYIjvPPaKx/OI1+9NifidJ1G+tyTS9454/t4QzdhvLdfGbyzwTsi6t4FRIpISg/o0moikAiOpeT7v4NxJDGcs8JGqBt8pfRvoDvRt7jq2Um3hGuTg9NjZWkuZhL4OIpIkIr/HCfifRiiWyNfgUeAVVX2vHmUT+TqYVsIXs87D+TL3TS1F4yFOt/P9rOtvbGs/j/poFefRxr/zxMN5xP33jgT53hC12G/JtfHritP9IdhGIBnnTm486QgkEf58ukbYJ9L5+7e1BW3hGtyH8+X1s1rKJOR18D0/VAxUAA8Dx6vq4gjFE/UanAvsBvylnrsk5HUwrYOI/Mb3O1kO/BGYrKqh/96Cteo47Uvy7gbeUNVfaynaqs+jAVrLebTl7zzxcB5x+70jUb43RDv2W3Id50TkNBEpDlpqHcCiDhp6+Ajr40W486ntXBLt/BsjYa+BiNyD82zjiarqqaN4Il6HH4G9gf2Ah4Cn6xhkJaGugYjsjvM84mmq6m7Argl1HUz01RKn38f5ndwfeAv4u4h0q+NwMfv3WNv3Dd8z1s8BucCZ9ThcqzyPRmhNfx/a6neeVnseCfC9I+6/N8Qi9kcccMLEjddxnrXxW9fI4xRQ825MZ5yBGAobecxY2YLzfEu484nUKhDp/Klln0STsNdARO4Ffg9MVNWVdRRPyOvgCyo/+94uFJF9cVrLzg5TPBGvwVicFp4lQQN+JgEHiTPCb5aqVoTsk4jXwURf2DitqiU4v5M/AwtE5CfgHODWCMeJdZwOex6+xPpFnMHZJqhqXXVplefRCLE+D7+2/J2n1Z5HInzvSJDvDVGP/dZyHedUdaeq/hy01Dm6YgSfAYeErJsMLFTVyqbVMrp8fwwW4dQ/2GQiPyvyGXCgiKSHlF8PrG7uOrZSCXkNROQ+4FRgkqouq8cuCXkdwnABaRG2JeI1+CfOl/+9g5aFwP/5Xoe7o52I18FEWQPidG2/kxDjOB3uPHzPFr8EDMNJIgrqcahWdx6NPFSr+N7Uxr/ztMrzSODvHfH4veGfRDv2x3r0NluafwHyfP9gJuB0XzjH975rUJlngGeC3vcDSoDZwGDfPm6criwxP6dGXIPf+ep/ju987sMZebKPb/sMYH5Q+fY4d6r+DxgKnIAzuMyVsT6XJlyD7KA/JKXAjb7XvdvQNZjjO4dJOHch/Ut2UJm2cB3uAA7EGYhjL985e4Ej2so1iHBdPiBoxNC2eh1sie6CM+jXX4ExQG+cwaiexHmucVhQuVYdp3F6P/4Tp+V3RMjf2OBRdlv1efjqFNffm0iQ7zwkwPcWEuR7Bwn8vYEWjv0xP0FbWuQfzVRfcAhdpgeV+QD4IGS/8cBXOAF+FXBBrM+lidfhIpw7TBU4d3UPCto2F1gdUn4v+H/27js+imr94/jnSSABEjpIUwQREKRIkSKgVLGiYgeRIkoRxYaAeAW5V7EgggUVEVCK8lPsXVGagEiVqlJVQKQjLSHJ+f2xm5hkN41sspvk+76vvdk9c2bm2cHkzDNz5hwW4BlcZjcwkhCYPiAb379tGv8dTCtAx8Df90/9u1AQjsM0YIf3d+Fv4FuSTflTEI5BGscldQNbII+DXrn7wjM/9Ad47oLEeH9+BDRPVS+k22k8J91p/Y3tlVe+hzeeXploK0L6e5APznnIB+ct6fxOJP9vKS98j2nk0/MGcrjtN+8GREREREREROQ06ZlrERERERERkWxSci0iIiIiIiKSTUquRURERERERLJJybWIiIiIiIhINim5FhEREREREckmJdciIiIiIiIi2aTkWkRERERERCSblFyLiIiIiIiIZJOSaxEREREREZFsUnItIiIiIiIikk1KrkVERERERESyScm1iIiIiIiISDYpuRYRERERERHJJiXXIiIiIiIiItmk5DoAzOxiM/vYzHaamTOzXplYp76ZzTezE971HjMzy4VwRUREJAvM7G4z+9nMjnhfS8zsygzWUTsvIlLAKLkOjGhgHTAYOJFRZTMrAXwD7AEuBO4FhgAP5GCMIiIicnr+BIYCjYGmwHfAh2bWwF9ltfMiIgWTOeeCHUO+YmZHgUHOuWnp1BkAPA1UcM6d8JY9CgwAznT6RxEREQlpZnYAGO6ce83PMrXzIiIFUKFgB1BAtQQWJja4Xl8B/wWqAdtSr2BmdwF3AURFRTU577zzciFMERHJqhUrVuxzzpUPdhySM8wsHLgRT6+1xWlUUzsvIpKPpdXWK7kOjop4upgltyfZMp9G1zk3CZgE0LRpU7d8+fIcDVBERE6Pme0IdgwSeGZWH1gCFAGOAtc559amUV3tvIhIPpZWW69nroMndZcwS6NcREREgu8X4AKgBfAK8KaZ1Uunvtp5EZECRneug+MvPFeukzvD+3MPIiIiElKcc7HAZu/H5WZ2IXA/cIef6mrnRUQKIN25Do4lQBszK5KsrBOwC9gelIhEREQkK8KAyDSWqZ0XESmAlFwHgJlFm9kFZnYBnmNa1fu5qnf5GDObm2yVWcBxYJqZ1TOzrsAwYJxGEBUREQktZvaUmbUxs2re+avHAG2Bmd7laudFRETJdYA0BVZ5X0WBx73vR3uXVwJqJFZ2zh3GcwW7MrAceBl4DhiXeyGLiIhIJlUEZuB57nounrmrL3fOfeFdrnZeRET0zHUgOOfm8e9AJf6W9/JTtha4OOeiEhERkUDw145ntFztvIhIwaM71yIiIiIiIiLZpORaREREREREJJuUXIuIiIiIiIhkk5JrERERERERkWxSci0iIiIiIiKSTUquRURERERERLJJybVIGqZNm0Z0dHSwwxARERGRAk7npXmDkmsJul69ejFq1CgAzIzt27cnLfvwww9p2bIlpUqVIjo6mvPOO4++ffsGPIZq1aoxduzYgG83J/z444906dKFMmXKEBkZyXnnncfjjz/OyZMngx2aj+3bt2PmmQJ+1KhR9OrVK7gBiYiIiKRD56VZo/PSlJRcS8iaO3cuN954I1dffTVLly5l1apVPPvsszjngh1atsTGxp72uh9//DFt2rShbNmyfPvtt/z666+MHDmSSZMmcemll2Zr2yIiIiLin85Lfem81JeS63zslz8qB/2VHZ988gnNmzfnkUce4bzzzqNmzZpcffXVvPHGGynqvf/++9SvX5/IyEjOOussnnjiiRR/6Pxd/Wvbti2DBg1Ker9jxw6GDBmCmSVd0Uo0d+5c6tWrR1RUFO3atWPbtm0+cTZp0oQiRYpQvXp1RowYkeKPSbVq1Rg1ahR9+vShVKlSdO/ePalrT0bbTu748ePccccdXHHFFUydOpXGjRtz9tlnc+utt/LJJ5+waNEiJkyYkFT/yJEjDBgwgEqVKlGkSBHq1KnD7Nmzk5YvXryYSy65hGLFilGlShUGDBjAkSNHkpZ/+eWXtGnThtKlS1OmTBk6d+7Mxo0bk5YnXv2bM2cOnTp1olixYtStW5dvvvkmze8gIiIiBVOwz0l1Xvrv/nVemnOUXEvIqlixIps2bWLNmjVp1lmxYgU33ngjXbt2Ze3atTz11FOMGTOGl156KdP7ef/99znzzDN57LHH2L17N7t3705aFhMTw5gxY5gyZQpLlizh0KFD9O/fP2n5V199Rffu3Rk0aBDr169nypQpvPfeezzyyCMp9jFu3DjOO+88li9fzpNPPpmpbaf21VdfsW/fPh5++GGfZY0bN6ZDhw7MmjULAOccl19+OfPnz2fq1Kls2LCBcePGERERAcDatWu59NJL6dKlC2vWrOH9999n9erV9OnTJ2mbx44d47777mPZsmXMmzePkiVLcvXVV/tchRwxYgT33nsva9as4cILL+SWW27h6NGjmT38IiIiIiFP56Up6bw0Dc45vfLYq0mTJi4zNv1eKeiv7Dh69Ki74oorHODOPPNMd/3117tXXnnF/fPPP0l1unXr5tq1a5divZEjR7oqVaokfT777LPds88+m6LOJZdc4u6+++5060ydOtUBbtOmTUllM2bMcIULF3bx8fHOOefatGnjRo8enWK9Dz74wEVFRbmEhISkbV911VVZ3nZqTz31lAPcgQMH/C6/9957XdGiRZ1zzn399dfOzNyGDRv81u3Ro4fr06dPirJVq1Y5wO3Zs8fvOkePHnVhYWFu4cKFzjnntm3b5gD36quvJtX5888/HZBUR6QgApa7EGgr9Mq7r8y28yJ5SbDPSXVeqvPSQEqrrdedawlZUVFRfPbZZ2zevJmRI0dSqlQphg8fzvnnn8+ePXsA2LhxI61atUqxXuvWrdm5c2eKriSnKzIyktq1ayd9rly5MqdOneLQoUOA5wrlE088QXR0dNKrW7duHDt2jL/++itpvaZNm2Z521nlnEvqOrRq1SoqVapEnTp1/NZdsWIFM2bMSBF34nHcsmVL0s9u3bpRo0YNSpQoQYUKFUhISOD3339Psa0GDRqk+A4Af//992l9BxEREZFQpPPSrCmo56WFcm1PIqepRo0a1KhRg759+zJixAhq1arFK6+8wqhRo1L84qaWWB4WFobnAtO/Tp06lal9FyqU8lckcZsJCQlJP0eOHMmNN97os2758uWT3kdFRWV526nVqlULgA0bNvj84QbPH/SaNWsC+Hzf1BISEujbty/333+/z7IqVaoAcPXVV1OlShVee+01qlSpQqFChahbt65P95vChQtn+juIiIiI5GU6L/XQeal/Sq7zsdpn7Qp2CAFXrVo1ihUrlvTsRN26dVm0aFGKOosWLeLMM8+kePHigOePSfLnVU6ePMmmTZto1KhRUllERATx8fFZjqdx48Zs2rSJc88993S+TpZceumllC1blmeffdbnj9jKlSuZO3cuTz/9dFJcu3fvZuPGjX6vEjZu3Jj169enGff+/fvZuHEjL7/8Mu3atUvaR1xcXIC/lYiIiBQEOi/VeWlBOC9Vt3AJWaNGjeLhhx9m3rx5bNu2jVWrVtGnTx+OHj1Kly5dAHjwwQeZP38+o0aN4tdff2XmzJk899xzKQZXaN++PTNnzmTevHmsX7+ePn36+FwhrFatGgsXLmTnzp3s27cv0zE+9thjzJo1i8cee4x169axadMm3nvvPb+DO2RXVFQUr7/+Op999hl9+vRh1apV/P7777zzzjt06dKF1q1bM3jwYAA6dOhA8+bNuf766/nqq6/Ytm0b33zzDR9++CEAQ4cOZdmyZfTv359Vq1axefNmPv30U/r16wdA6dKlKVeuHK+//jqbN29m/vz59O/f3+eqpoiIiEhBoPPSlHRe6p+SawlZl1xyCdu2baNnz57UqVOHzp07s337dj7++GMuvvhiwHOl691332XOnDnUq1ePYcOGMWzYsKTpDACGDx9O+/btueaaa7j00ktp3bo1jRs3TrGv0aNH88cff1CjRo0U3WYy0rlzZz777DO+//57mjVrRrNmzXjqqaeoWrVqYA5CKtdddx0LFizg77//pn379tSsWZORI0fSt29fvv7666RRF8PCwvjiiy9o1aoVt912G3Xq1GHw4MFJXWcaNGjAggUL2L59O5dccgkNGzZk+PDhVKhQIWn92bNn8/PPP1OvXj3uvvtu/vvf/xIZGZkj30tEREQklOm81JfOS31ZRn3gJfQ0bdrULV++PNhhiIiIH2a2wjnnO1qMSCapnRcRCW1ptfW6cy0iIiIiIiKSTUquRURERERERLJJybWIiIiIiIhINim5FhEREREREckmJdciIiIiIiIi2aTkWkRERERERCSblFyLiIiIiIiIZJOS6wAxs4Fmts3MTprZCjNrk0H9zma2xMz+MbN9ZvaRmdXKrXhFREREREQkcJRcB4CZ3QxMAJ4EGgGLgS/MrGoa9asDHwELvfU7AkWBz3MlYBEREck0MxtuZj+Z2REz22tmn5hZvUyspwvpIiIFiJLrwHgAmOace905t9E5dw+wGxiQRv0mQGFguHNus3NuNTAGqGFm5XIl4nxu3rx5mBn79u3LV/sSEZGgaAtMBC4C2gNxwLdmViatFXQhXUQS6by04FBynU1mFoEnWf461aKv8TTC/iwHTgF9zSzczIoDPYGfnHN+fxPM7C4zW25my/fu3Rug6ENDr169GDVqFABmxvbt233qdOnShfDwcL755hufZdWqVWPs2LE5HGXa+7rooovYvXs3ZcuWzda2Z82aRcuWLYmOjiYqKormzZszY8aMbG0zp0ybNo22bdsC0LZtW6ZNmxbUeEREcpJzrrNzbqpzbp1zbi3QAygPtEpnNV1IF8mDdF7qofPS06PkOvvKAeHAnlTle4CK/lZwzm0HOgGPAzHAYaA+cFVaO3HOTXLONXXONS1fvnwAws47du/ezdy5c7n//vuZPHlysMPxERERQcWKFTGz097G0KFD6d27N9dccw0rVqxg1apVdO3alTvuuINhw4YFMFoREQmA4njOoQ6mUyfLF9JFJPTpvFTnpekpFOwA8hGX6rP5KfMsMKsIvAG8BbyNp5EeDfyfmbV3ziUEIqBzXnguEJvJlq33PpjtbUybNo3LLruMe++9l9q1a7N///6kq3Ft27Zlx44dDBkyhCFDhgDgnO9h379/P4MGDWLhwoXs37+fc845h4ceeojevXsn1Wnbti1169alVKlSTJo0ibCwMG6//XaeeeYZwsLC0tzXvHnzaNeuHXv37qVcOc/NiKVLl/LII4/w448/UqhQIZo2bcr06dOpXLmyT2zLli3jmWee4fnnn+e+++5LKh86dCiRkZHcf//9dO3alWbNmgGwadMmHn74YebPn098fDz169dn0qRJ1K9fH4CpU6fy7LPPsnXrVqpWrcqAAQMYPHgwYWGea2njxo1j2rRpbNmyhVKlSnH55ZczduxYSpUqlXS8Bw0axEcffcTgwYPZtm0bzZo1Y8qUKVSvXj07/5QiIvnFBGA1sCStCs657WbWCXgXeBlPMr4KuNxffTO7C7gLoGpVv0O2iORpOi/9l85L8+95qe5cZ98+IB7fu9Rn4Hs3O9HdwDHn3MPOuVXOuQXAbcAlpN2VvEByzjFlyhRuu+02qlatSvPmzZk+fXrS8vfff58zzzyTxx57jN27d7N7926/2zl58iSNGzfm008/Zf369QwePJh+/foxd+7cFPVmzpxJoUKFWLx4MS+99BLjx49n9uzZWdrXmjVraNeuHeeeey4//PADS5cu5aabbiIuLs5v/ZkzZxIdHc3AgQN9lg0YMICoqCjefvttAHbt2kXr1q0xM7755htWrlzJ3XffTXx8PACvv/46jzzyCKNHj2bjxo0899xzPP3000ycODFpm2FhYYwfP57169cza9Ysli1bxj333JNivzExMYwZM4YpU6awZMkSDh06RP/+/f3GLyJSkJjZOKA1cL1zLj6deskvpF+I57ntf/BcSPc5/yrIPdRE8gqdl+q8NCPm72qKZI2Z/Qiscc7dlazsV2COc264n/rPAZc455omK6sE7PKWL0hvf02bNnXLly/PMK78cIXw+++/54YbbmD37t1EREQwZcoUnn/+edauXZtUp1q1agwaNIiHHnooqczfVbvUbrnlFqKjo5O69LRt25aYmBiWLPn3RkSnTp04++yzk+pkZl/du3dny5YtLF26NFPf8fLLL2fXrl2sWbPG7/KGDRtSpUoVPv/8c0aMGMGMGTP47bffiIiI8KlbtWpVnnjiCXr06JFUNn78eCZNmsSGDRv8bv/LL7/kmmuu4cSJE4SFhTFt2jR69+7Npk2bqF27NuD5Q9u7d29OnjyZdKVRRPwzsxXJ/75L/mFmzwO3AO2cc5syqPtf4CrnXKNkZWcCfwBtnHOL0lo3s+28SF6i81Kdl0L+OS9Nq63XWXJgjAN6mVlfM6tjZhOAysCrAGY2xsySX4r6DGhsZiPNrKaZNQam4mlwV+R28KHsjTfe4Kabbkr6hb3hhhvYsmULP/74Y5a2Ex8fzxNPPEGDBg0oW7Ys0dHRvP/++/z+++8p6jVo0CDF58qVK/P3339naV+rVq2iQ4cOWVonvedinHNJy1etWkXr1q39/gHbu3cvf/zxB/369SM6OjrpNWzYMLZs2ZJU77vvvqNTp06ceeaZFC9enK5duxIbG8tff/2VVCcyMjLpDxh4jsOpU6c4dOhQlr6XiEh+4W3buwHtM0qsvYrh6dmWXOJnnX+J5EE6L9V5aUb0xz0AnHOzgfuAR/E8g9UauMI5t8NbpRJQI1n97/A00Nfgef7qKzyDnlzmnDuWa4GHuEOHDjFnzhwmTZpEoUKFKFSoEGXKlOHEiRNZHkBi7NixPPfccwwZMoS5c+eyevVqrr32WmJjY1PUK1y4cIrPZkZCQtYegc9qb5BatWqxefNmYmJifJbFxMSwdetWatasmeG2E+N89dVXWb16ddJr3bp1rF+/HoAdO3Zw5ZVXUqdOHd59911WrFjBlClTAFIci0KFUg7HkPhHNKvHQkQkPzCzl4HewK3AQTOr6H1FJ6ujC+ki+ZjOS3Vemhka0CxAnHMT8cyB6W9ZLz9l7wDv5GRMgRi0IZhmzpxJ+fLl+fzzlFOCLlmyhAcffJDx48cTFRVFRERE0rMdaVm0aBFXX311UrcU5xy//vpr0mAJmZWZfTVu3Jjvvvsu09u89dZbeeGFF3jllVdSDBwBMHHiRI4dO0a3bt2Stj1jxgxiY2N9rhJWqFCBKlWqsGXLFm6//Xa/+1q+fDmxsbE8//zzhIeHA/Dpp59mOlYRkQIq8eHDuanKHwdGed/7XEg3s27Aw8AQ4ASwFF1IlwJK56X/0nmpR348L9WdawlZb7zxBjfccAP16tVL8erZsydhYWFJAzpUq1aNhQsXsnPnTvbt8z+7Sa1atZg7dy6LFi1i06ZNDBo0iG3btmU5pszsa8iQIaxatYq77rqLNWvW8MsvvzB58mSfrj6JWrRowYMPPsjQoUN5+umn+fXXX/ntt9945plnGDZsGEOHDk0akXHgwIEcPXqUm266iZ9++onNmzfz9ttvs3r1agBGjRqVNMLjL7/8wrp163jrrbcYM2YMADVr1iQhIYHx48ezbds23n77bcaPH5/l4yAiUpA45yyN16hkdXo556qlWu8d51xj51y0c668c+5q55z/Bw1FJKTpvFTnpZmh5FpC0sqVK1m1ahU33HCDz7KIiAi6dOmS1AVn9OjR/PHHH9SoUYO0Rlh99NFHadasGZdffjkXX3wxUVFRdO/ePctxZWZfF1xwAd9++y2bNm2iRYsWNG/enHfeecena09yY8eOZfLkyXzwwQdccMEFNGzYkDlz5jB58mSeeuqppHpVqlRhwYIFxMbG0q5dOxo1asSLL76Y1F2mb9++TJkyhenTp9OwYUPatGnDpEmTkqYqaNCgARMmTGDcuHHUrVuXyZMnM3bs2CwfBxEREZGCQuelOi/NLI0WngdpFFERkdCl0cIlu9TOi4iENo0WLiIiIiIiIpJDQnpAMzMrAgwGOgBnkOpigHOugb/1REREJG9QWy8iIvlFSCfXeEbfvg54F1gMqA+7iIhI/qK2XkRE8oVQT66vBW50zn0b7EBERCTrnHPEx8UTdyqe+LgEnHMkxHt+Ogd4x/3wN/5HsIcEiSpRlMiiERlXlOy6FrX1IiKSD4R6cn0c+CPYQYiIyL9Oxcaxc/Mefv9lF7u372Xvnwc48PdhDu87ytFDxzh25AQnj8UQG3OK2JOngh3uaRv6+p20u6F5sMMoCNTWi4hIvhDqyfUzwANmNsA5lxDsYERECqK4U3GsXfwby79dx/off2Pz6h3EnYoPdliSf6itFxGRfCHUk+tOQBvgMjPbAKS4BeKc6xKUqERE8jnnHL+u3MZnU+ez+NNVHD18PNghSf6ltl5ERPKFUE+u9wEfBDsIEZGCwjnHks9X8/bYT/lt9Y5ghyMFg9p6ERHJF0I6uXbO9c7uNsys62ms9oVz7kR29y0ikpesW/wrk0e9x6aftgZ0u2HhYRQqHE5YeBjh4WFYmBEW5pltySzx/5J+pGD+CnNJRJHCQdt3QaK2XkRE8ouQTq4Tmdk5QF0803NsdM5l5czvvSzuzgE1gcCeXYqIhKiTx2KY/Ni7fDplXqbXKVW+ONXPP5Mzz61IharlKFe5NKXKF6dE6WiKlSxK0ahIIotGUDiyMOHhYRlvUAo8tfUiIpLXhXRybWYlgDeA64GEf4ttDnCHc+6fTG6qonPu70zuM7PblADp1asXb775JgCFChWidOnSnH/++dxwww3cddddFC4cOnePZs2axYsvvsjatWtxzlGvXj3uuecebrvttmCHJnJaNi3fyrP932Dnlj3p1isaHUnTjvVp3rkB9S6qRYWzygb1rrLkH2rrRSSU6LxUsiPUbydMABoA7YCi3lcHb9n4TG7jTSAr3b5mAEeyUF8CoGPHjuzevZvt27fz9ddfc/XVVzNy5EjatGnDsWPHgh0eAEOHDqV3795cc801rFixglWrVtG1a1fuuOMOhg0bFuzwRLLs65mLePDyp9NNrGs1rsYDL/fm7V/GMWJqfzrechEVq5ZTYi2BpLZeREKKzkvldJlzLtgxpMnM9gPXOucWpiq/GPjAOVc2OJEFV9OmTd3y5cszrHdZ6b65EE36vjw4OcM6vXr1Yt++fXz66acpytetW0fjxo0ZPnw4jz/+OACxsbH85z//YebMmRw8eJC6devyv//9j86dOyett2HDBoYMGcKCBQsoWrQoHTp04Pnnn6dixYop9teiRQtefPFFjh07xo033sjEiRMpWrSo3xiXLVtG8+bNef7557nvvvtSLBs/fjz3338/P/74I82aNQNg06ZNPPzww8yfP5/4+Hjq16/PpEmTqF+/PgBTp07l2WefZevWrVStWpUBAwYwePDgpOdQx40bx7Rp09iyZQulSpXi8ssvZ+zYsZQqVQqAadOmMWjQID766CMGDx7Mtm3baNasGVOmTKF69eoZHnMp2JxzzHj6Y2Y+/Umadaqffya9H+vKhZ3qK5HOIjNb4ZxrGuw48gq19b4y286L5CU6L9V5aX6SVlsf6neuiwL7/ZQfAIqc7kbNrPlpRyS5pl69elx22WXMmTMnqax3797Mnz+fWbNmsXbtWnr27MnVV1/NmjVrANi9ezcXX3wx9erVY9myZXz77bccPXqULl26kJDw7/Sp8+fPZ82aNcydO5c5c+bw9ddfM3To0DRjmTlzJtHR0QwcONBn2YABA4iKiuLtt98GYNeuXbRu3Roz45tvvmHlypXcfffdxMd75gV+/fXXeeSRRxg9ejQbN27kueee4+mnn2bixIlJ2wwLC2P8+PGsX7+eWbNmsWzZMu65554U+42JiWHMmDFMmTKFJUuWcOjQIfr3738aR1oKkrhTcTx/z7Q0E+vIohEMePpWXl7wGM0ubaDEWnKD2noRCXk6L9V5aWaE+p3rb/B02+rhnDvuLYsC3gJKOOc6neZ2f3fOVQ1cpLmroNy5Bhg2bBgvvPACx48fZ8uWLdSsWZPt27dTteq//3zXXnstlStXZuLEiTz22GP88MMPzJ07N2n5wYMHKVOmTNIVvF69evHhhx/y559/Eh0dDcCMGTO44447OHDgAFFRUT5xXH755ezatSvpj2VqDRs2pEqVKnz++eeMGDGCGTNm8NtvvxEREeFTt2rVqjzxxBP06NEjqWz8+PFMmjSJDRs2+N3+l19+yTXXXMOJEycICwtj2rRp9O7dm02bNlG7dm3A84e2d+/enDx5MulKo0hyCQkJPHPXZObNWeZ3ec1G1Xj41Ts4q1alXI4sf9Gd66xRW+9Ld64lP9J5qYfOS/OHtNr6kB7QDLgf+BLYaWY/4xndsyFwDOic3opm9n9pLQLKBDJIyTnOuaQ7ZytXrsQ5R926dVPUiYmJoX379gCsWLGCBQsWJP1xSm7Lli1J3WMaNGiQok7Lli2JjY1ly5YtNGjQwG8s6d3BSx7nqlWraN26td8/YHv37uWPP/6gX79+DBgwIKk8Li6O5Be6vvvuO8aMGcPGjRs5fPgw8fHxxMbG8tdff1G5cmUAIiMjk/6AAVSuXJlTp05x6NAhypTRf+KSknOOV4e/k2Zi3e7G5tz/Yi8iIkNnoBYpMNTWi0ieoPNSnZdmJKSTa+fcOjOrCdwGnIensZwBzMzE3JQdgR7A0VTlBlwc6FglZ2zYsIFzzjkH8Nx1MzN++uknn5EaE59JSUhI4Morr2Ts2LE+26pQocJpx1GrVi0WLlxITEwMkZGRKZbFxMSwdevWpD+k6fUGSewC9Oqrr3LRRRf5rbNjxw6uvPJK7rzzTkaPHk3ZsmVZuXIlt956K7GxsUn1ChVK+eub+Ec0eTcjkUTvPPcZH0/6zu+ym++/gp6PXpsvryxL6FNbLyJ5hc5LdV6akZBOrgG8Devrp7HqQuCoc25+6gXeK+P5Xma6voSydevW8eWXX/Loo48C0KhRI5xz/PXXX7Rr187vOo0bN+b//u//OPvss9OdKmHt2rUcO3YsqavN0qVLiYiIoEaNGn7r33rrrbzwwgu88sorPgNHTJw4kWPHjtGtW7ekGGbMmEFsbKzPVcIKFSpQpUoVtmzZwu233+53X8uXLyc2Npbnn3+e8PBwAL9dk0Qy6/Np83nziQ/9Lhv4dDe63NU+dwMSSUVtvUj+p/NSnZcWBCGXXJtZV+AT59wp7/s0OefeT2fZNeksO63ntyTnxMTE8Ndff5GQkMDevXuZO3cuTz75JE2aNOGhhx4CPFfpunfvTq9evXjuuedo3LgxBw4cYN68eZxzzjl07dqVu+++m9dff52bb76ZoUOHUr58ebZu3cr//d//8dxzz1G8eHHA092lT58+PPbYY+zatYthw4Zx5513+n2uBaBFixY8+OCDDB06lJiYGK677jrMjA8++ID//Oc/DB06NKlrz8CBA3n11Ve56aabGDFiBKVLl+ann36iTp06XHDBBYwaNYp77rmHUqVKccUVV3Dq1ClWrlzJzp07GT58ODVr1iQhIYHx48fTtWtXli5dyvjx43Pl30HynzULN/HSgzP8Lusz8nol1hIUautFJJTpvFTnpafNORdSLyABOCPZ+7Re8VncbpVgf7dAvZo0aeLyk549ezo8z9i58PBwV7ZsWXfJJZe4F154wcXExKSoGxsb60aOHOmqV6/uChcu7CpUqOCuvvpqt3z58qQ6v/76q7v++utdqVKlXJEiRVytWrXcoEGDkrbVs2dPd+WVV7rHH3/clS9f3kVFRbnbb7/dHTt2LMNY33rrLde8eXNXtGhRV7RoUdesWTP31ltv+dRbt26du/zyy11UVJSLjo52LVu2dGvXrk1aPmvWLNeoUSMXGRnpSpUq5Vq1auXefvvtpOUTJkxwlStXdkWKFHHt27d3s2fPdoDbtm2bc865qVOnuqioqBT7/P777x3g9u7dm/FBlwJh/1+H3C217nedS93h83r1kXdcQkJCsEPMl4DlLgTailB+qa1P/5Xf2nmRvETnpTovzYy02vqQHi08kMzsZ+ec/xEB8hiNIpo96Y0CKZJfxMcnMKLrOFYv2OSzrP1NLXjolT56xjqHaLTw4Mkvbb3aeZGCQ+eleVOenOfazC42M5+u62YWbmZZHahEk7WKSIEx69lP/CbW9VvV4oGXeimxlpChtl5ERPKLUD+7+h7/U2mU8i7Lihy9RW9mA81sm5mdNLMVZtYmg/pmZveZ2SYzizGz3Wb2VE7GKCIFw6r5G5n1jO8V8FLlizNs8l0UKhxyw21IwZZn2noREZH0hPoZluG/oSyLZ/7LkGBmNwMTgIHAIu/PL8ysrnPu9zRWew64ChgCrAVKApVyIdwCb9q0acEOQSTHHNhzmKfvnETqR37MjIcn3UnZiqWCE5hI2vJEWy8ikhN0Xpq/hGRybWYfe986YIaZxSRbHA7UAxbnemBpewCY5pxLnEbkHjO7DBgADE9d2cxqA/cADZxzG5MtWpXjkYpIvjZx6CwO7f3Hp7zbkKto3LZuECIS8S8vtfVmNhzoCtQGYoClwHDn3LoM1jNgMNAfqA4cAN50zg3L2YhFRCQYQjK5BvZ7fxpwEDiRbFksnrvDWZ0PM0eewzKzCKAJkHp2+K8B/7OxwzXAVuAyM/sMT/f8+cAQ59zfORGniOR/iz9bxaKPVviUN2xzHt0evjoIEYmkK8+09UBbYCLwk3cfo4FvvT3UDqSznnqpiYgUICGZXDvnegOY2XZgrHMu293CnHP1s7uNNJTDc4V9T6ryPUDHNNY5BzgbuAXoheeq/VjgEzNr6ZxLSL2Cmd0F3AVQtWrVgAQuIvnH0cPHeekh3/msS5aNZujrdxIeHupDbEhBk5faeudc5+SfzawHcBhoBXzibx31UhMRKXhC+mzLOfd4IBrbXJL6ebG0niEDz3GPBHo45xY45xYCPYBmwIV+N+7cJOdcU+dc0/LlywcqZhHJJ6aMmsOBvw77lPd/6lbKVCgZhIhEMiePtfWJiuNpyw+mUyd5L7WtZrbdzN40szNyJUIREcl1IXnnOjkz6w3cClQFIpIvc86dE5SgUtoHxAMVU5Wfge/d7ES7gTjn3K/Jyn4D4vB8zx8DHaSI5F8///ALn0+b71N+Yaf6tL2+WRAikvzOzL4nkyNzO+faZ2J7od7WpzYBWA0sSadOlnqpqYeaiEjeF9LJtZkNwTMg2GvAxXiedzrX+z71M86Z2d6UNBY54CSwGZjtnNuV2W0652LNbAXQCXg32aJOwJw0VvsBKGRmNZxzW7xl5+D599iR2X2LiMSePMWEwW/5lBeNjuSe527DM56SSMAlH8grHOgO/MW/F4eb4Xm22PdZhVTyQlufavvjgNZAa+dcfDpVk/dS+9W7bg/gFzy91FJcSHfOTQImATRt2lRTiomI5EEhnVwDdwJ3OefeM7NBwEvOua1m9h88V4OzqjzQBkjg3xODeni6cK/AMxLoaDNr45xbnYXtjgOmm9kyPIlzf6Ay8CqAmY0BmjnnOnjrfwusBKaY2X3esvF4Gtrlp/G9RKSAmvnMJ+zc4ttJpvd/rueMs8oGISIpCJxz9yS+N7PngTeBwS7ZHHBmNp7MDTCWV9r6xO96C9DOObc1g+rqpSYiUsCE9DPXwJnAMu/7E0AJ7/u3getPY3s/AF8AZzrnLnbOXezdx+d4Rvc+G/gMz+iemeacmw3cBzyKp5tYa+AK51ziXehKQI1k9RPwjB76N7AA+Ar4E7jG32BmIiL+bN+4k3df+NKnvM6FNbiqb9vcD0gKqtvxJMSp77ZOxDOeSEbyRFtvZhOAbkB759ymTMZRyMxqJCtTLzURkXws1JPrv/CMxg2ehqil9/25ZPJZr1QGA6Odc8cTC7zvnwDud87FAk8DF2R1w865ic65as65SOdcE+fcgmTLejnnqqWqv9s5d6Nzrrhz7gznXHfnXFrPaIuIpOCcY9KI2STEp7weVziiEPe90JOwsFD/8y75iAH+RunO7MjdId/Wm9nLQOJz4QfNrKL3FZ2szhgzm5tsteS91BqZWSNgCuqlJiKSb4X62dd3QBfv+zeAcd5BVGYD75/G9qLxP79kRe8ygCOEfnd5ESngfvxyDSu/3+BTfvMDV3D2eZWDEJEUYFOAyWY2zMzael/D8MxRPTUT6+eFtn4gnhHC5+Lp7p34eihZHfVSExEp4EI9ibwL7wUA59yrZnYQz5ySc/AMfJJVHwBvmNnDwE94rog3A57h3wa8GfCr/9VFRILvVGwcr//nXZ/yimeX46bBlwchIingHsaTQA4GnvSW7QaeInNdr0O+rXfOZfjsuHOul5+y3cCNmd2PiIjkbSGbXJtZYTxduF7G+2yS99nm2dnYbH88g4/N4N/vHofnqnvi1eeNeAZXEREJSR9Pmut3ELO+/72RiCKFgxCRFGTeu7DPAM+YWQlv2ZHMrKu2XkRE8pOQTa6dc6fMbCCeAVECtc3jQH8zexBP1y0DNjvnjiWrszpQ+xMRCbRDe48w85lPfcobtK5Nq6saByEikX9lNqlOVl9tvYiI5Buh/sz1V0D7QG3MzO4EcM4dc8797Jxbk9jYmtmrgdqPiEhOefOJDzn+z4kUZWFhRv8nb9Gc1hIUZlbGzF4xs1/N7JCZHUn+ysQm1NaLiEi+kON3rs2sCJ7nsDoAZ5AqoXfONUhn9bnAk2bWAM/clMeSL3TOZXWgk2fM7IBzbk6qGCcBnbO4LRGRXLV17R98NX2hT3nnHm04p/5ZQYhIBPAMQtYImATsIusjfKutFxGRfCE3uoVPBK4D3gUWk7VG9yXvz3v9LHNAeBZjuQF438wOOefmQlJjexnQNovbEhHJNc45Xn3kHRISUv4JLVa8KD1HXBucoEQ8OgCdnHM/nub6autFRCRfyI3k+lrgRufct1ld0TkX0G7rzrm5ZtYHeM/MLgP6ApcCbZ1zWwO5LxGRQPrh05X8vOgXn/LuQ6+mVPkSQYhIJMnfwNHTXVltvYiI5Be58cz1ceCP01nRzG43s0g/5RFmdvvpbNPbTexhPHNOdgIuUWMrIqEsNuYUbzz2nk95lXMr0OXOgD2qKnK6RgCjzSw6w5p+qK0XEZH8IjfuXD8DPGBmA7zTdWTFVOBLPFfFkyvuXfZWRhswsxfSWLQHWOuNDQDnnL8uaSIiQfXZlHns3r7Xp/yu/95E4YiQnfRBCo5HgWrA32a2AziVfGEGY6uA2noREckncuOsrBPQBrjMzDbg2+h2SWddw/8z2lWBw5ncf/00yrcA0cmWZ3UAFhGRHHf08HFmPes79Vbj9ufTrHNGOYtIrvDtVpE1autFRCRfyI3keh/wQVZWMLO1eBpAB8w3s7hki8OBs4HPM7Mt51y7rOxbRCSU/N/zn/PPwRSDJ2Nm9H38Bk29JSHBOff46ayntl5ERPKbHE+unXO9T2O1xKvg9YDPSDlQSiywHZiDiEg+9vcf+/ngVd+xIDve2pJz6mnqLcnz1NaLiEi+kmsP65nZOUBdPFeoN6Y3sEjiVXAz2w7Mds6dzJUgRURCyFtPfsipmLgUZRFFCnP7I9cGJyARP8wsAs+gZrfi6cpdOPly55zfqbTU1ouISH6T48m1mZUA3gCuBxL+LbY5wB3OuX/SWtc592ay7ZQi1ejmzrkDAQ9YRCQEbFn7O3NnL/Upv25AJ8pXKROEiETS9F/gZmAM8DwwBM8AZ7cA/8loZbX1IiKSX+TGnesJQAOgHbDYW9YKeBUYD9yR1opmdra3XjtSXglPHPzE79VwEZG87o1Rc3Au5dhLJcpEc9Pgy4IUUdriExLYf+I4+0+c4PDJk/wTE8OxU6eIiTtFTHw8sfHxxCUkEO8SiE9wJDiHw+EcuGTjS7kQG2rqypq1qFP+jGCHkRfcBPR3zn1pZmOBj5xzW8xsI55BTV9Lb2W19SIikl/kRnLdBbjWObcwWdk8M7sLz0BnaSbXeKbgKAX0AXahUT5FpABY8d16Vn633qe828NXEVWyWBAi8nDOsfnAAVbu3smm/fv4df8+fj98mN1H/yEh1DLjAKhVtqyS68ypAGzwvj+Kp90Gz/RaT2difbX1IiKSL+RGcl0U2O+n/ABQJIN1mwEtnHPrTnfnZvZxZutmMC2YiEiOS0hI4I2RvjMbVapWnit7t831eE6cOsWC37fz1ebfmL9jGwdP6rFY8fE7UNn7czPQGVgBtAROZGJ9tfUiIpIv5EZy/QPwXzPr4Zw7DmBmUcDj/NtNPC3bgMhs7t9fYi8iEpK++78f2bruD5/y3o91pXBEro1ByfZDB5nx8xre3bCOf2Jjcm2/kid9AHQAluJ5FOxtM7sTqAI8m4n11daLiEi+kBtnag8AXwA7zexnPN29GgLH8FzdTs9gYIyZDXTObT6dnZ/mVGAiIrku9uQp3vzfBz7ltRpXo821TXMlhp3/HOG5xYv46JeN6psrmeKcG57s/Xtm9idwEfCrc+7TTGxCbb2IiOQLuTHP9VozqwncBpyHZ4CSGcBM51xG3cU+wnM1+xcziwFSzEnjnCuRAyGLiATFR699y96dvgMj3zn6JswsR/d9Mu4ULy5byhurVhAbH5/l9UtGFqFcsWKUKlKEEpFFiIooTNFChYkMD6dweDiFw8IIDwsj3MIwgzAzDMMMjH+/Ww5/zSw5r1z5YIeQJznnluK5i51ZautFRCRfyNHk2swKA38AHZxzr5/GJgYFOCTMrAJwN//Oub0BmOic2xPofYmIZNbh/f/wzrjPfcpbXH4B9VvVytF9b9q3l/u++pxf9+/LsG6RQoVoVLESjStVpk658tQoU5azSpSkWOHCGa4r+ZOZ3Z7GIgecBDY751alswm19SIiki/kaHLtnDtlZqc4zZE/k899GQhm1grP6KV7gCXe4u7A/WbW2Tm3JM2VRURy0IynPubYkZSdecLCjD4ju+bYPp1zvLlmFU/9sCDdu9VFChXiypq1ufzcWrQ6qyqRhXLv2W/JE14GIvBMo5XgLQsDTnnfFzazVcBlzrm9qVdWWy8iIvlFbpwhvQgMN7Pezrm4DGun4r363AOoAfzHObfP23Ducs5ty+LmxgJv45mPM8G7/TA882s+h+cZMRGRXLV9404+mzrfp7xzjzZUrV05R/YZn5DAY/Pm8va6n9OsU6ZIUe5qciE3n1+fkkUymtxBCrCbgJHA/cBP3rIL8bSr/wN24pluaxye9tyH2noREckPciO5bgNcgmdAs3V4BjJLkt6UGGbWBJiLZyTR8/GMOroP6ATUArplMZYLgF6Jja13/wlmNg5Ir8uaiEiOcM4xacRsEuITUpQXK16E2x+5Jkf2GRMXx+CvPuPrLf7HjooIC+eOxk3o16QZJSKzO4izFADj8LStPyYrW2JmDwBTnXN1zOxBYLq/ldXWi4hIfhGWC/vYB8wBPsczB+b+VK/0jAUmOOcaAcnngvkKaHUasRwGqvsprw4cOo3tJTGzgWa2zcxOmtkKM2uTyfVqmtk/ZnY0O/sXkbxp2ddrWfn9Bp/yWx64ktJnlAz4/o7ExHD7h++lmVjXKlOWD2/pzpCL2iixlsyqBhz3U37cuww8iXPpNNbPM229iIhIenJjtPDsTI/RBLjDT/luoMJpbO8d4A0zexjPHNsOaA08hacL2Wkxs5vxzO05EFjk/fmFmdV1zv2eznoR3pgW4Lm7LyIFSNypOCY9OtunvOLZ5bi2f8eA7y8mLo47P/mAn3bt9Lv8tvoNeaTNJRQppMHJJEuWAePMrIdz7i8AM6uIJ2lOvJtdE/gzjfXzRFsvIiKSkRxLrs3s40xUc8659Po9nsD/le7zgL9PI6yH8UwFNgXPdzcgFngFGHYa20v0ADAt2Yjo95jZZcAAYHjaq/E08DMwHyXXIgXOJ5O/Z+dm38GL+46+kYgigU1wE5zjwa+/SDOxfrRNW/o0ahLQfUqB0Rf4EPjdzHbhSWarAL8C13rrROF5/tqfvNLWi4iIpCsn71xn1OU7Mz4CRprZjd7Pzsyq4UlK52R1Y865WGCwmQ3HM2iK4ZkixF93tkzx3n1ugucKfXJfk86gKWZ2JXAV0Bi4/nT3LyJ50+H9/zDz6U98yuu3qkWrqxsHdF/OOf63cB6fb/7VZ1nhsDCe7XQZXWrXCeg+peBwzv1mZvWAS4HaeNrWjcA3zjnnrfNhOpvIC239cKArnu8Xg2ce7+HOuXWZXL8msBIw51z06cYhIiKhLceS62x2B0/0EJ5ntfcCxfB0ua4A/AA8ejob9HZVuwg4A88z563NDADn3MTT2GQ5IBzPlB/J7QH89us0s0rA60BX59w/ifvPIO67gLsAqlatehphikgomfHUxxw9nPJc38zo9+QtZOZvQla8sWoF01av9CkvFBbGpKuu5ZJq/h5PFck8bxL9lfeVVXmhrW8LTMQzGroBo4FvvY9/HcggFj0CJiJSQIT0ZKXOuSN4GsT2eO7whgErnXPfns72zOw2YDKehvEgKeffdngaztMON/Xu/JQlmgG84pxbmumNOzcJmATQtGnT05o3XERCQ1pTb116WyvObRDYi2cLdmznyUW++wJ4pmNnJdZyWrwjgU90zp30vk+Tc25cBstDvq13znVOtY8eeAZOawX4dkFJSY+AiYgUECGdXCdyzn0HfBeATT0BPAOMPp05t9OwD4gHKqYqPwPfu9mJ2gOXmNlI72cDwswsDhjoTaRFJB9yzjHp0f/zO/VWzxHXBXRffx87yoNff+532cMXteHa8+oGdH9SoNwDvAmc9L5Pi8MzVVeGQrytT604nosAB9OrpEfAREQKlpBOrs1sCrDeOfdcqvIHgLrOub5Z3GQJPAOPBayxdc7FmtkKPPNxvptsUSfSflasfqrP1wAjgGaA/9GGRCRfWPb1WlZ+t96n/JYHrqRMhcBNvZXgHA98/QX7T5zwWXZ7gwvo1+TCgO1LCh7nXHV/75PzPjf9TEbbygttvR8TgNXAkrQqZPURMD3+JSKS9+XGPNfZcQX+r2J/512WVTOBK7MVkX/jgF5m1tfM6pjZBKAy8CqAmY0xs7mJlZ1z65K/8CTUCd7P6V4FF5G8Kzen3np1+TIW/+E7E2Cbqmfzn4vbBfy5bhE/SpK5u7V5pa0HwMzG4Zna63rnXHw6VbP0CJhzbpJzrqlzrmn58uUDEaqIiOSykL5zDZQCjvopPwaUOY3tPQB8aGYdgLXAqeQLnXOjT2ObOOdmm1lZPAOvVALWAVc453Z4q1TCM2KpiBRgn74xL1em3lqxeyfPL/3Bp7xcsWKMvfRywsNC/bqqFDClyANtPYCZPQ/cArRzzm3NoLoeARMRKWBCPbn+Fc9V6wmpyq8ENp/G9voBl+F5TvpcfAc5Oe0G1zv6qN9BUpxzvTJYdxow7XT3LSKh7/D+f5jx1Mc+5fUvCuzUW0diTnLfl58T73zHPXzu0sspXywqYPsSCZA80dZ7e6XdArR1zm3KxCp6BExEpIAJ9eT6OeBVMzuDf7uMdQDuA+4+je39B3jQOfd8YMITEcmcNKfeGhPYqbeeWrSAnf8c8Snv36QZbapWC9h+RAIo5Nt6M3sZ6AFcCxz0TvUFcNQ5d9RbZwzQzDnXATyPgKXaRlO8j4AFKi4REQktIZ1cO+feNLMieLpbD/cW7wQecM5NPY1NhgO+t45ERHJQbk29tWznn7yzfq1PeaOKlbi/xUUB248IgJll1J6WyMx28khbP9D7c26q8seBUd73egRMRKSAC+nkGsA59xrwmpmVB8w593c2NjcV6E42un+LiGRFfHwCLz04w2fqraLRkQGdeismLo5Hv/vGpzy6cAQTLruSwuHhAduXiNf+TCzflpkNhXpb75zLsHuJHgETEZGQT64TOef2BmAzxYC+ZtYZ+BnfQU7uDcA+RESSvP/y16xb8ptPeaCn3np95XI2HzzgU/7QRa05s0Tg9iOSyDnXOwe2qbZeRETyrJBOrs2sDPAEnmevziDV1GHOuUx1OUumDrDK+/68VMt8R/8REcmGrev+4K0nPvQpr3h2Oa4b0Clw+zl4gJd+8p3t54IKlehev2HA9iOSE9TWi4hIfhHSyTXwBtAImATsIpuNonOuXSCCEhHJSGzMKZ7pN5lTsXEpys2M+1/qFbCpt5xz/Of7b4mNTzndbrgZT7TvqGm3JC9QWy8iIvlCqCfXHYBOzrkfgx2IiEhWTH/yI7Zv8J1t57qBHWnYOvXNtNP3waYNLPnzD5/yOxo1oU75MwK2H5EcpLZeRETyhVBPrv8GjgZyg2Z2M2l3PesSyH2JSMG09odfee/Fr3zKzz6vMr0e7Rqw/Rw6eYInFs7zKT+zRAnuba7RwSXPUFsvIiL5Qqj3FxwBjDaz6EBszMyeBWYA1YBDeEYyTf4SEcmWY0dOMHbgFJxL2bO1UOFwHp7UN2DdwQFeWLaUgydP+pSPbtuRYoUDtx+RHKa2XkRE8oVQv3P9KJ7G8W8z24HviJ8Nsri924FbnXPvBSY8EZGUXnvkHfb8vs+nvMfwa6hRP3BzWm89eIAZP6/2Kb+qZm3aVqsesP2I5AK19SIiki+EenId6IYxDFgd4G2KiACw+LNVfD3zB5/yus3P5YZ7Lwvovp7+YQFxCSnnzi5WuDAj2rQN6H5EcoHaehERyRdCOrl2zj0e4E1OAm4DRgV4uyJSwB38+zATBr/pU140OpIhr95BeHjgnsJZ8sfvfLN1i095vyYXUiE6ID1rRXKN2noREckvQjq5TmRm7YG6eKbnWO+cm3eamyoFdDOzTsDP+HY9uzcbYYpIAeWcY/zgtzi833dMprueuJlK1coHbF8JzvHkovk+5RWjounbqGnA9iOS29TWi4hIXhfSybWZVQE+AJrgmfsSoLKZLQeuc87tSnNl/+ryb1ex1HPhZGteTREpuL6asYgfv1zjU978soZc1qNNQPf1waYNrN/7t0/5kIvaUFSDmEkepLZeRETyi5BOroEXgHjgXOfcNgAzOwfPKKAvADdkZWPOuXYBj1BECrTd2/fy2iPv+JSXLBvNfRNux8wCtq/jp04xdvEin/J6Z1TgmvPqBGw/IrlMbb2IiOQLoT4VVyfg7sTGFsA5txW417ssQ2bWzMzCM7tDM2tiZrr9IyIZio9P4Nn+b3DiaIzPssETelL6jJIB3d/rK39izzHfruePtmlLWACTeJFcprZeRETyhVBPrtOSkHGVJEuAMlmo/z1wVtbCEZGC6L0XvmTDj5t9yi/t3oqLrmwU0H39dfQfJq34yXdfNc6lWZUzA7ovkRChtl5ERPKUUO8WPhd4wcxudc79AWBmVYEJ3mWZYcAYMzueyfoRWQ9TRAqaLWt/Z/qYj3zKK1QtR78nbwn4/p5b8gMn4uJSlBUOC2Noq4sDvi+RXKa2XkRE8oVQT67vBT4CtprZLjwDkVTBM/pnZkf7XADUyMI+lwAnshKkiBQssSdP8cxdk4k7FZ+i3Mx4aGIfokoUDej+Nu7by/sb1/uU92jQiOqlSgd0XyJBoLZeRETyhZBOrr1XsBt7p9M4D8+V6Q3OuW+zsI22ORSeiBRQbz7xATs2+Q5gfP2gS6nfqlbA9/fs4oU+QxyXKlKEe5q1CPi+RHKb2noREckvQvKZazO73My2m1lJAOfcN865F51zLwA/eZddGuQwRaQAWrNoE++//I1PebW6Vbh9xLUB39+Pf/7BvO3bfMrvvrAFJYsUCfj+RHKL2noREclvQjK5BgYBzzrnDqde4C17Ghic61GJSIF27PBxnhswBedS3kcuHFGIh1/rS0RkYAcfds7x9A8LfcqrFC/BbfUbBnRfIkGgtl5ERPKVUE2uGwDpdQf7DtCZpYjkGuccE4e+zd9/HvBZdvsj13BOvcAPPPz11s2s3rPbp/yBFq2ILBTST/WIZIbaehERyVdC9eysPOlPweGAsrkUi4gUcDEnYhl/75t8/96PPsvqtaxJ10GdA77PuIQEnl3se9f6vHLl6VL7vIDvTyQI1NaLiEi+Eqp3rv/Ec0U7LQ2AnbkUi4gUYHt3HuChK5/xm1gXjY7koVf6EB4e+D+lczasY+vBgz7lQy5qTXhYqP7pFskStfUiIpKvhOoZ2mfAf83MZz4bMysGjPbWCRlmNtDMtpnZSTNbYWZt0qnb1sw+MrPdZnbczH42sz65Ga+IZGzjT1u4t8MT/LZqu9/l/cfcSsWzywd8vydOnWL8j0t8yptXOZO2Z1cP+P5EgiTPtfUiIiLpCdVu4U8ANwC/mdmLwCZveR08A6AY8GSQYvNhZjcDE4CBwCLvzy/MrK5z7nc/q1wErAWeAXYDnYFJZnbSOTcrl8IWkXR8PesHXrx/Oqdi4/wuv+WBK7i0e6sc2feba1ax59hRn/KHL2qDmeXIPkWCIE+19SIiIhkJyeTaOfe3mV0EvIKnYU08m3TAV8BA59yeYMXnxwPANOfc697P95jZZcAAYHjqys651CcLr5hZO+B6QMm1SBDFx8UzeeR7fDDRd7otgMKRhRg8vicdb2mZI/s/dPIEryxf5lPeuUZNGlWqnCP7FAmGPNjWi4iIpCskk2sA59wO4AozKw2ci6fR/c055/sQYhCZWQTQBBibatHXeO5QZ1YJPM+fiUiQ/HPoGGP6vMbK7zf4XV6mYklGzrib2k3OybEYXl2+jH9iY1KUhZnxUMucuUsuEkx5pa0XERHJjJBNrhN5G9ifgh1HOsoB4UDqq+t7gI6Z2YCZXQV0ANI8ezazu4C7AKpWrXpagYpI2n7/ZRejur3Erq1/+11eu0l1Hpt+N2UrlcqxGHb9c4Rpa1b5lN9Utx41ymjQZMm/8kBbLyIikqFQHdAsL3KpPpufMh9m1gpPV/B7nXO+fUETN+7cJOdcU+dc0/LlAz+AkkhBtuzrn7mv05g0E+sON7fk2U8fztHEGmDCj0uIjY9PURYZXoh7m+dMF3QRyRwzG25mP5nZETPba2afmFm9DNbR4KUiIgVMyN+5zgP2AfFAxVTlZ+B7NzsFM2sNfA485px7JWfCE5G0OOd4d8KXTB39Ps75XgsLCzPuePwGut59aY4PJPbL/n3M2bjep7z3BY2pGF08R/ctIhlqC0zEc3fd8Ixk/q134NIDaayjwUtFRAoYJdfZ5JyLNbMVQCfg3WSLOgFz0lrPzC7GM8XIKOfc+BwNUkR8xJyIZfy9b/qdvxogqkRRhr/Rj6Yd0705FTBjFs4nIVWCXzKyCP2bXpgr+xeRtDnnOif/bGY9gMN4Huf6JI11NHipiEgBo+Q6MMYB081sGfAD0B+oDLwKYGZjgGbOuQ7ez23xJNYTgZlmlnjXO945tzd3QxcpePbuPMDoHhPTnL+6yrkVGDXrHs6qmbpDSs6Yv30bC373jWXghc0oEVkkV2IQkSwpjufRuqwOvKbBS0VE8jEl1wHgnJttZmWBR4FKwDrgCu8oqHjLaiRbpRdQDHjI+0q0A6iW0/GKFGQbf9rC6B4TObjnsN/lTTvUY9gbdxFdsliuxBOXkMCTi+b7lJ9VoiS3N2iUKzGISJZNAFYDSzK7QkaDl2rgUhGRvE/JdYA45ybiuRPtb1kvP597+asrIjnn61k/8OL90zkVG+d3+fWDOtNn1PWEh+feWI+z16/ltwP7fcqHtmpDZCH9iRYJNWY2DmgNtHbOxWdU37tOhoOXOucmAZMAmjZtmuGAqCIiEnp05iYi+V58XDyTR77HBxO/8bu8cGQhBo/vScdbcndU7iMxMYxf+oNPeZNKlbn83Fq5GouIZMzMngduAdo557Zmch0NXioiUkAouRaRfO2fQ8cYc8ckVn7nOxI3QJmKJRk5425qNzknlyODV5b/yP4TJ3zKR7Rpm+Ojk4tI1pjZBDyJdVvn3KZMrqPBS0VEChAl1yKSb/3+yy4e7/4yO7f4nxWvdpPqPDb97hyfv9qfPw4fZuqqlT7lXWqfxwUVK+V6PCKSNjN7GegBXAscTDYQ6VHn3FFvHQ1eKiJSwOXeg4UiIrlo2dc/c1+nMWkm1h1ubsmznz4clMQa4NnFC4lNSPm4ZmR4IYZc1CYo8YhIugbiGSF8Lp45qxNfyQclTW/w0uTr/JTz4YqISDDozrWI5CvOOd6d8CVTR7+Pc75jAoWFGX1G3cD1gy4NWtfrlbt38elvv/iU39GoCVWKlwhCRCKSHudchn8sNHipiIgouRaRfCPmRCzj732T79/70e/yqBJFGTb5Li7sVD+XI/uXc47/LZjnU162aDH6N22W+wGJiIiISEAouRaRfGHvzgOM7jGR31Zt97u8yrkVGDXrHs6qWdHv8tzy6W+/sHrPbp/yB1u2IjoiIggRiYiIiEggKLkWkTxv409bGN1jIgf3HPa7vGmHegx74y6iSxbL5chSOhl3iqd/WOBTXrtsOW6sWy8IEYmIiIhIoCi5FpE87etZP/Di/dM5FRvnd/n1gzrTZ9T1hIcHf/zGqatXsuuff3zKH2lzCeFhwY9PRERERE6fkmsRyZPi4+KZPPI9Ppj4jd/lhSMLMXh8Tzre0jKXI/Nv7/FjvPLTMp/yttWq06ZqtdwPSEREREQCSsm1iOQ5/xw6xpg7JrHyu/V+l5epWJKRM+6mdpNzcjmytI1fupijp2JTlIWbMbzVJUGKSEREREQCScm1iOQJJ4/HcGT/Uf76fR8TBr+V5vzVtRpXY+SMQUGbv9qfX/bvY/b6tT7lt9ZrQM2yZYMQkYiIiIgEmpJrEcl1zjmOHj7Okf1HObzvHw7v/4dD+/7xfN7/D4f3ecsP/Ls85nhshtttf1MLBo+/nciioTPq9p9HDtPvkw9JSDXndnREBIObXxSkqEREREQk0JRci0i2xcfFc3j/0WTJsTdZTpYcH97nXeatFx8XH7D9h4UZfUbdwPWDLsXMArbd7Np26CC3vf8uu4/6DmI26MIWlC0W3NHLRURERCRwlFyLiI+Tx2M4vO8oRw54kuJD3gQ5efKc/M7y0UPHgxZrVImiDJt8Fxd2qh+0GPz5df8+enzwHnuPH/NZVrVESXo2bBSEqEREREQkpyi5FsnnErtgH07W7frQvn9SJM9Jd5e9yzPTBTsUVDm3AqNm3cNZNSsGO5QU1v+9h54fzuHAyRM+y0oXKcIrV3YhspD+/IqIiIjkJzq7E8ljMt0F23tnOdBdsIMlvFA4JcpGU7JsNKXKl6B55wZc2r01USWKBju0FFb/tZteH83hSEyMz7JyxYox47obqVW2XBAiExEREZGcpORaJMgSu2Andrs+lJQgJ0uYQ6QLdiBFFougZNnilCxXnFJloylRtjgly0UnlZUsm+x9ueJElSgaUs9T+7Ns55/c8fH7HDt1ymdZpehopl93I+eULhOEyEREREQkpym5Fgmg5F2wE5PjxC7YSc8sJ0ueDx84mme6YGckulQxb1JcnFLlinvvMidLmJO9L1E2miLFIoMdckAt/H07/T79iJNxcT7LzipRkpldb+TMEiWDEJmIiIiI5AYl1yLpSOyCnfwusu/gXv8mzEcOHMt/XbC9CXOJstFJyXPKO8vRlCgTTXih8GCHHTRzt27h7s8/ITbB99/+nNKlmXHdjVSMLh6EyEREREQktyi5lgIl012wvYlzfumCXSQqkhJlopO6YJdMcWfZmzAnJs95pAt2qPj8t1+476vPiUtI8FlWu2w53rruBsoXiwpCZCIiIiKSm5RcS55VkLtgFy8dlZQcl/ImxJ7k2feZ5fzYBTtUfLhpAw998yUJzvksq3dGBd685npKFw2tAddEREREJGcouZaQEXcqjiMHjv3bxTrFnWX/00YlxPveLcxrwguFJ3WtLpWs23WKO8vloilZRl2wQ8nb637m0e++wTethiaVKvNGl66UiNRFDREREZGCQsm15JjkXbCTd7VOnjwnle/7h6OH808X7MQ7xqWSdbVO7Jad4s6yumDnSVNXr+S/C773u6zlmWcx6apriYqIyOWoMs+5BByx4OJxxOFcHBCHc/FAPI4EcPFAAg4HJIBL9h6X7OXpReJnL7nzZXJQRKGzCQ/X6O4iIiKSOUquJVMSEhI4duSEzzPJyZPnI/tTJtIxJ/JPF+zE5DixC3bJstGp7iwXp6Q3eY4sGrpJlWTfK8t/5NnFi/wuu+TsarxyZReKFCqc7jaci8O5GBLcSZw7mex9jPd1kgR3wrvsJAneMudOkkAMLiHx/b/re7YRk2wbMTgXiyPW89PF4jiFc6eAvD/oXm6oVOZlSkRdF+wwREREJI9Qch0gZjYQGAJUAtYD9znnFqZTvz7wEtAMOAC8BvzX+b8FFHCpu2D7S5hTPLOcb7pghxFdJpriZaOILu35WbxMFNFlookuU4zo0tFElY0iukwU0aWjKFaqKOGFwnHOkeA89+08d/28nMPhOOVgL7H8/c8+3JHEeinv7CXe90ss9/xLe346HM79u9y7VtK6/5Ynq5/GcpK/T9r2vz/h3+/ijcC3XrL3iaGkiDH5cp9l/x6XpO/oKUoj5n/j8ClPsX7K7xnvHPEJ8cQlxBOfkOD56RKIS0hI9tkRl/BvmWedBG954jZSvo93eD+T9Dk+AeK8P2Pi/fcyaH3mQUY0W8qe/dNw7lTKpDbpsyfpVXIrIiIikv8ouQ4AM7sZmAAMBBZ5f35hZnWdc7/7qV8C+AZYAFwI1AamAceA53IixmEj/svyz3ZjxxKwYwmEncj7iTJAQoQRX6wQ8VHhxBcLJy7Z+6TyKO/7YuEkFAmDFF2wY7yvA57ervu9LxEAMtdd/5IzN/FQky+Ij0tQ2iwiIiJSQCm5DowHgGnOude9n+8xs8uAAcBwP/W7A8WAns65E8A6M6sDPGBm43Li7vXhPccptCP0u2nHF/Ukw3HFwkkoFk5clCcpjk/6GZ7isyscFuyQpYDrVHUdgxt/Tbjl/WeMRUREROT0KbnOJjOLAJoAY1Mt+hq4KI3VWgILvYl1oq+A/wLVgG0BDpOIErn/T+3CSbpjHJfs7nHSneRUiXJ80XAI18BekndcVX01AxrOJSxP/mdbGLNCGIXBwjEKYRYOhGEUAgvzvg/Dcwff0+vD8xnPZ4zEu/vm7y5/Hh+oLzysdLBDEBERkTxEyXX2lQPCgT2pyvcAHdNYpyLwp5/6ict8kmszuwu4C6Bq1apZDjKyVPb/qZN3wY4rlkaCnCxxTohM3QVbJH8oXvgEN9T6iRtr/nSa/4mHYVaEMIvErIj3FUkYkVhYEYxIwrxlZkW9dZPVS+O953Nk0nuzSIzC3vcR3gQ6AgjXCPUiIiIiAabkOnBS9wk1P2UZ1fdX7il0bhIwCaBp06ZZ7n8a6efOdfIu2EnJceL7pOeWc7YLtuEwHFjie8Bc0sEwXFLy8u97z3LzdsP1LE4sS2e5dx946yW9T7ZPn1hSlKWx3DKzf8/nf+/zJcaTuPDfZcnX87y3NONOUdf75t/jmFjXkvadOo7E7fuPP+UTxyniT348ktf1OU4pY0gea4p/VyzFMfh33WQxmqde4n8H4YmvMCPcoFCYER5mFDLPzzDvz0JhYUllhSyc8HA8Py2MQuHm+RkWRnhYuHe5t15YOIXCPPXCwwp5thNWiEJh4RQOK0NY2DmY9fAkrRaBEeHzPswiIPEnhZMlvumPJi4iIiIieY+S6+zbh2fo34qpys/A9252or/SqE8662RLv5tup03tXyhRphjFyxYjulQxwgsldvM0zCAs2fukrp6JZYBZWLKkyJNoW/I6lvKzZ7l5P4OFhSUtT5W2pfqZuoup7/KUkpXb6Wwrg1h0h09EpEAzs+FAVzwDkMYAS4Hhzrl1GawX1JlBREQkdym5zibnXKyZrQA6Ae8mW9QJmJPGakuAp82siHPuZLL6u4DtORHnmZXP4szKZ+XEpkVERPK7tsBE4Cc8V19HA996ZwU54G+F3J4ZZNeeP3l02IRAb1ZS0XWRvCPbN0d0byXfO6fOGTw6ZEhAt6nkOjDGAdPNbBnwA9AfqAy8CmBmY4BmzrkO3vqzgJHANDP7H1ALGAY8rqvZIiIiocU51zn5ZzPrARwGWgGfpLFars4Msv/gPnZ9eDCQmxQRydf27zgBgc2tlVwHgnNutpmVBR4FKgHrgCucczu8VSoBNZLVP2xmnYCXgeXAQTxXscflauAiIiJyOorjeT4qvWw212cGERGR4DLdKM17zGwvsCPDimkrh+dZ8YJOx8FDx0HHIJGOg0d2j8PZzrnygQpGQo+Z/R9QE2jqnItPo87XwJ/OuT7Jyqriab8vcs4tSVU/aVYQPF3If8lGiPpd9tBx8NBx8NBx8NBxCMwx8NvW6851HpTdkzYzW+6caxqoePIqHQcPHQcdg0Q6Dh46DpIeMxsHtAZap5VYJ5PpmUGSzwqSXfpv2EPHwUPHwUPHwUPHIWePgZJrERERkUwws+eBW4B2zrmtGVTP9ZlBREQkuAI/cbGIiIhIPmNmE4BuQHvn3KZMrLIEaGNmRZKV5ejMICIiElxKrgumgHQ7ywd0HDx0HHQMEuk4eOg4SApm9jLQG7gVOGhmFb2v6GR1xpjZ3GSrzQKO45kZpJ6ZdcUzM0jARwr3Q/8Ne+g4eOg4eOg4eOg45OAx0IBmIiIiIukws7ROlh53zo3y1pkGtHXOVUu2Xn08M4M0wzOy+KvAaE27KSKSPym5FhEREREREckmdQsXERERERERySYl1yIiIiIiIiLZpOQ6HzKzrmb2lZntNTNnZm0zud4lZrbCzE6a2VYz65+zkeYsMxtoZtu832eFmbXJoH59M5tvZifMbKeZPWZmlt46oczMLjazj73fxZlZr0ysk9+OwXAz+8nMjnh/Hz4xs3qZWC+/HYe7zexn73E4YmZLzOzKDNbJV8cgNTN7xPt78VIG9fL1cZC8Se28h9p5tfOgth7UzqclGG29kuv8KQpYDDyQ2RXMrDrwuXe9RsAY4EUzuz5HIsxhZnYzMAF4Es/3WQx8YWZV06hfAvgGz9yjFwL3AkPIwjEMQdHAOmAwcCKjyvn0GLQFJgIXAe2BOOBbMyuT1gr59Dj8CQwFGgNNge+AD82sgb/K+fQYJDGzFsCdwM8Z1MvXx0HyNLXzaudB7XyitqitVzufStDaeuecXvn0BZQDHJ7RSzOq+zTwW6qyycCSYH+P0/zuPwKvpyr7DRiTRv0BwBGgaLKyR4GdeAf+y8sv4CjQK4M6+foYeL9PNBAPXF2Qj4P3Ox0A+hW0YwCUBLbgOQGbB7xU0P9b0CvvvtTOq51P9l3Uzv/7ndTWu4Lbznu/S9Daet25lkQtga9TlX0FNDWzwkGI57SZWQTQBN/v8zWeq5r+tAQWOueSX/n9CqgMVAt0jCGqIByD4nh67BxMp06+Pg5mFm5mt+A5+VicRrX8fAwmAe85577LRN38fByk4FE7r9/ngnIMCnRbr3YeCGJbr+RaElXE0x0iuT1AITxXxvOSckA4/r9PxTTWSev7Jy4rCArCMZgArAaWpFMnXx4H7/NER4EYPHPtXuecW5tG9fx6DO4EzgX+k8lV8uVxkAJL7bx+nwvKMSiQbb3aeY9gt/VKrvM4M+tuZkeTvdIdzCMDqSc9tzTK8wp/3ye975Lfvv/pyLfHwMzGAa2B651z8RlUz4/H4RfgAqAF8ArwZgYDvuSrY2BmtfE8m9ndORebhVXz1XGQvEftfLrUzmddvj4GBbytL9DtPIRGW18oqytIyPkYz3NHiXae5nb+wvfqzBl4BoXYf5rbDJZ9eJ618fd9Ul+ZSpTW9yeddfKbfHsMzOx54BagnXNuawbV8+Vx8DYym70fl5vZhcD9wB1+qufHY9ASz92udckGAA0HLvaOmBzlnItJtU5+PA6S96id96V2/vTk62NQ0Nt6tfNACLT1unOdxznn/nHObU72ynC0yDQsATqmKusELHfOncpelLnL+8dlBZ74k+tE2s+eLAHamFmRVPV3AdsDHWOIypfHwMwmAN2A9s65TZlYJV8eBz/CgMg0luXHY/AhUB/PVf3E13LgHe97f1e48+NxkDxG7bwvtfOnLd8eA7X1fhW0dh5Coa0P9mhuegX+BZTx/gfUFk93hr7ezxWT1XkLeCvZ5+rAMWA8UMe7TiyebjVB/06ncQxu9sbf1/t9JuAZSfNs7/IxwNxk9UviuXL1DlAP6Ipn5MAHg/1dsnEMovn3D8tx4DHv+6oF6Bi87P0O7fFclUx8RSerUxCOw1NAGzwDc9T3fucE4PKCcgzSOC7zSDaCaEE9DnrlvZfaebXz3u9U4Nt57/cq8G292vl0j02utvVB/8J6Bf4F9PI2tqlfo5LVmQfMS7XeJcBKPAMhbAP6B/u7ZPM4DMRzxSkGzxXui5MtmwZsT1W/PrAAOAnsBkaSh6cj4N+TrtSvaQXoGPj7/ql/FwrCcZgG7PD+LvwNfAt0LkjHII3jkrrBLZDHQa+891I7n/R91M4X8Hbe+50KfFuvdj7dY5Orbb15NygiIiIiIiIip0nPXIuIiIiIiIhkk5JrERERERERkWxSci0iIiIiIiKSTUquRURERERERLJJybWIiIiIiIhINim5FhEREREREckmJdciIiIiIiIi2aTkWkRERERERCSblFyLiIiIiIiIZJOSaxEREREREZFsUnItIiIiIiIikk1KrkVERERERESyScm1iIiIiIiISDYpuc4mM7vbzH42syPe1xIzuzKDdeqb2XwzO2FmO83sMTOz3IpZREREREREAqtQsAPIB/4EhgK/4blY0RP40MyaOOd+Tl3ZzEoA3wALgAuB2sA04BjwXC7FLCIiIiIiIgFkzrlgx5DvmNkBYLhz7jU/ywYATwMVnHMnvGWPAgOAM53+QURERERERPIcdQsPIDMLN7NbgGhgcRrVWgILExNrr6+AykC1nI1QREREREREcoK6hQeAmdUHlgBFgKPAdc65tWlUr4inK3lye5It25bGPu4C7vJ+bFKsWLFsxSwiIjnj+PHjzjmni9dy2sqVK+eqVasW7DBERCQNK1as2OecK5+6XMl1YPwCXACUAq4H3jSzts65dWnUT93129Io/3cF5yYBkwCioqLcsWPHshOviIjkEDM7kXEtkbRVq1aN5cuXBzsMERFJg5nt8Feu5DoAnHOxwGbvx+VmdiFwP3CHn+p/4blDndwZ3p97EBERERERkTxH3dZyRhgQmcayJUAbMyuSrKwTsAvYnsNxiYiIiIiISA5Qcp1NZvaUmbUxs2re+avHAG2Bmd7lY8xsbrJVZgHHgWlmVs/MugLDgHEaKVxERERERCRvUrfw7KsIzPD+PAz8DFzunPvKu7wSUCOxsnPusJl1Al4GlgMH8cxvPS43gxYREZH858SpUxQtXDjYYYiIFEhKrrPJOdcrq8u9I4lfnEMhiYjkKodjN7vZznb+5E/2spf97OcIRzjKUU5wgpOc5BSniPP+L554HI4EEnDesRxd2mM6BsUIRtCBDsEOQyTTDp08QafpU7myZm3ubtaC8sWigh2SiEiBouRaRESy5CAH+Y7vmMc8lrOctazlGPlvBoM7uTPYIUgIMLNHgCeAl51zg7xlBozEM0VmaeBH4G7n3Ppk60UCY4FbgaLAXGCgcy71dJwB8+qKn9h/4gRv/bya9zau545GTbijUVNKRKY1DIyIiASSkmsREcnQMY7xPu8znenMZS4JJAQ7JJEcZ2YtgDvxPPKV3MPAg0AvPNNxPgZ8Y2a1nXP/eOuMB67Bk1zvx/P416dm1sQ5Fx/oWP86+g9vrl6V9Pn4qVO8uGwpM35ezYCmzenR4AIiC+m0T0QkJ2lAMxERSdNhDvMkT3I2Z3M7t/MN3yixlgLBzEriGZz0DjzjoySWG3Af8JRzbo5zbh3QEygOdEu27h3AEOfcN865lUAPoAHQMSfifeHHJcTEx/mUHzx5kicXzaf9W1N4d8M64hL0+ysiklOUXIuIiA+H403e5BzOYQQj2M/+YIckktsmAe85575LVV4dzyCmXycWOOdOAAuAi7xFTYDCqer8AWxMViegqpQoSVQ6A5ntPvoPQ7/9iitmvslXW35DE5SIiASe+geJiEgKv/M7fenLN3yT6XWiiOJczqUqValIRcpRjlKUIppoilKUIhQhgggKef8XTjiGEUYY5v0fkPQzFJzP+cEOQYLEzO4EzsVztzm1it6fe1KV7wGqJKsTD+zzU6cifpjZXXie4aZq1apZjvnuC5tzy/n1mbj8R2b+vIbYBP89zzcfPMCAzz7mggqVeLhVG1qceVaW9yUiIv4puRYRkSQ/8APXci37fHKClM7mbK7gCtrRjqY0pRrVQioxFjldZlYbeBJo45yLTadq6lu/5qfMZ/Np1XHOTcJzt5ymTZue1m3lssWK8Z+L29HngiaM/3ExH2zaQEIad6hX79lNt/f/j4urVuOhi1pT74wKp7NLERFJRt3CRUQEgOlMpz3t00yswwmnBz1YxCK2sY2JTORGbqQ61ZVYS37SEigHrDOzODOLAy4BBnrfJz4jkfoO9Bn8ezf7LyDcu5206uSYKiVK8Gyny/i82+10OqdGunUX/L6dLu/M4N4vPmX7oYPp1hWR4Jk2bRrR0dHBDkMyoORaRESYwARu53Zi8X+j7hZu4Vd+5S3eohWtlExLfvYhUB+4INlrOfCO9/2veJLnTokrmFkRoA2w2Fu0AjiVqs6ZQJ1kdXJcrbLleO2qa3nvxltpVvnMdOt++tsvXDpjGo9+/y17jh7NpQhFQk+vXr0YNWoUAGbG9u3bk5Z9+OGHtGzZklKlShEdHc15551H3759Ax5DtWrVGDt2bMC3mxN+/PFHunTpQpkyZYiMjOS8887j8ccf5+TJk8EOzcf27dvxjEkJo0aNolevXgHfh5JrEZEC7i3e4j7u87usClX4lE95m7c5h3NyNzCRIHDOHXLOrUv+Ao4BB7yfHZ5ptoaZWVczqwdMA44Cs7zbOAy8ATxrZh3NrBEwHc+UXt/m9ndqXKkyb19/E1O6dKVOufJp1otLSGDW2jW0e+sNnvlhIUdiQu/kWCRY5s6dy4033sjVV1/N0qVLWbVqFc8++2yeHxwwNja9p1/S9/HHH9OmTRvKli3Lt99+y6+//srIkSOZNGkSl156aba2nVcpuRYRKcA+4RP60Mfvsha0YAUruJIrczmqgsHMBprZNjM7aWYrzKxNOnXrmtn3ZrbHW3+rmT1pZhGp6l3i3VZinf45/00KpGfwzFv9Mp672pWAS5PNcQ1wP/A+MBv4AU/yfXVOzHGdGWZG22rV+eTWHozvfAVVS5RMs+7JuDheXbGMS6a9wWsrlnHi1KlcjFQkNH3yySc0b96cRx55hPPOO4+aNWty9dVX88Ybb6So9/7771O/fn0iIyM566yzeOKJJ1Ik4P7uSrdt25ZBgwYlvd+xYwdDhgzBzJLutCaaO3cu9erVIyoqinbt2rFt2zafOJs0aUKRIkWoXr06I0aMSJHkVqtWjVGjRtGnTx9KlSpF9+7dk7qcZ7Tt5I4fP84dd9zBFVdcwdSpU2ncuDFnn302t956K5988gmLFi1iwoQJSfWPHDnCgAEDqFSpEkWKFKFOnTrMnj07afnixYu55JJLKFasGFWqVGHAgAEcOXIkafmXX35JmzZtKF26NGXKlKFz585s3LgxaXniXek5c+bQqVMnihUrRt26dfnmm8wPzhoIGtBMRKSAWsYybuIm4vE917+FW5jKVIpQJAiR5X9mdjMwARgILPL+/MLM6jrnfvezSizwJrAKOAQ0BF7H044/7N1mdeBzYApwG9AamGhme51zc3L0C+Vzzrm2qT47YJT3ldY6J4F7vK+QEWZGl9p1uOzcWsxev5YXly1h3/HjfusejjnJ0z8sZNrqVdzbvCU31q1HoTDdl5HT88sflYMdArXP2nXa61asWJFNmzaxZs0aGjZs6LfOihUruPHGG3n00Ufp3r07P/30E/369aNEiRLcc0/m/hS8//77NGzYkD59+jBgwIAUy2JiYhgzZgxTpkyhSJEi9OzZk/79+/PVV18B8NVXX9G9e3cmTJjAxRdfzO+//07//v2JiYlJkdCPGzeORx99lOXLl+Oc44cffshw26l99dVX7Nu3j4cffthnWePGjenQoQOzZs1iyJAhOOe4/PLLOXjwIFOnTqVWrVr88ssvSV3H165dy6WXXsrjjz/O5MmTOXDgAPfddx99+vThvffeA+DYsWPcd999NGjQgBMnTvC///2Pq6++mg0bNhAR8e915hEjRvDss88yceJE/ve//3HLLbewY8eOXHte3fJ6V4aCKCoqyh07dizYYYhIHnaYwzSiEdvwvSp9AzfwDu8QTngQIsv7zOy4cy4qgzo/Aj875+5MVvYbnnmVh2dyP+OAls65lt7PTwNdnXM1k9WZDJyfWEfyhqZNm7rly5fnyr6OnzrF1NUreW3FMo5m0IWzeqnSPNiyFZedW4sw07gLkjV5Pbk+duwYN910E59//jlnnnkmzZs3p2PHjtx2221JiVv37t3ZvXs33333XdJ6o0aNYvLkyfz555+A587xoEGDeOihh5LqtG3blnr16vHSSy+lWWfatGn07t2bTZs2Ubt2bQBmzpxJ7969OXnyJGFhYVx88cV06tSJ//znP0nrffjhh9x22238888/mBnVqlWjfv36fPLJJ1nadmpPP/00w4YN48CBA5QuXdpn+eDBg3n99dc5fvw433zzDZ07d2b9+vXUqVPHp+7tt99O4cKFU/QCWL16NY0aNWLPnj2cccYZfv89SpQowfz582ndujXbt2+nevXqvPrqq/Tr1w+AnTt3cuaZZ7Jw4UJat27ts43sMLMVzrmmqct1+VFCQq9evZK6viR/tWjRItih0atXL6666qpghyESMA7HAAb4Taw70pEZzFBinYO8XbmbAF+nWvQ1cFEmt3EucBkwP1lxSz/b/ApoamaFTy9aye+KFS7M3Rc2Z37PvtzZuCkR4Wn/7m87dJBBX3zKtbNnsvD37bkXpEgIiIqK4rPPPmPz5s2MHDmSUqVKMXz4cM4//3z27PFMArBx40ZatWqVYr3WrVuzc+fOFF2cT1dkZGRS8gtQuXJlTp06xaFDhwDPnfMnnniC6OjopFe3bt04duwYf/31V9J6TZv65IQZbjurnHNJXdpXrVpFpUqV/CbWiXHPmDEjRdyJx3HLli1JP7t160aNGjUoUaIEFSpUICEhgd9/T9nZq0GDBim+A8Dff/99Wt/hdKhbuISMjh07Mn369BRlybt55LaEhIQ8P0iFiD9v8RZv87ZPeWMa8z7vE0lkEKLKVwqZWfLbjpO8cxgnKodnmqbUUzLtATqmt2EzWww0BiLxdAt/JNniivgOlrUHT1tfDtid2S8gBU/pokUZ3voSejVszAvLlvDuhnVpzpG97u899PxwDhedVZUhF7WhYYXUs5KJ5F81atSgRo0a9O3blxEjRlCrVi1eeeUVRo0alSKhTC2xPCwszOf88lQmxzUoVChl6pa4zYSEhKSfI0eO5MYbb/RZt3z5fwczjIry7VyV0bZTq1WrFgAbNmzwuaAAngsNNWt6OlJldD6dkJBA3759uf/++32WValSBYCrr76aKlWq8Nprr1GlShUKFSpE3bp1fQZNK1z432vJGX2HnKA71xIyIiMjqVixYopXmTJlmD9/PoULF2bevHlJdV999VVKlCjB1q1bAU93mv79+zN48GBKly5N6dKlGTJkSIpfpoMHD9KzZ09Kly5N0aJF6dixI+vXr09anjiYw+eff069evWIiIjg5ptv5s033+Szzz5LupuePA6RvOZXfuVu7vYpL05x3uVdilM8CFHlO3HOuabJXpPSqJf6bMP8lKV2M57kuhtwBTA0E9v0Vy7iV6XixRnT4VK+6t6Ty2rUTLfu4j9+57rZMxn42cdsObA/3boi+VG1atUoVqwYR73T19WtW5dFixalqLNo0SLOPPNMihf3tK/ly5dn9+5/r3WePHmSTZs2pVgnIiKC+Pisj33YuHFjNm3axLnnnuvzSp08Z9ell15K2bJlefbZZ32WrVy5krlz59K9e/ekuHbv3p1iALLUca9fv95v3EWLFmX//v1s3LiRRx55hI4dO1KnTh3++ecf4uLiAvqdAkF3rvOxT2rUCHYIXO3typEdl1xyCUOGDKFHjx6sWbOGv//+mwcffJCJEydyzjn/Tg00c+ZMevXqxZIlS/j555+58847qVSpEg888ADg6d79yy+/8NFHH1G6dGlGjBjBZZddxq+//krRokUBzx+4//3vf7z22muUL1+eSpUqER4ezoEDB5LuqpcpUybb30kkGByOO7iDY/iO2fAKr2iqrdyzD4jHc6c5uTPwvZudgnPuD+/bDWYWDkw2s2edc3F45l72t804QJmPZEmNMmWZeGUX1uz5i2d+WMiSP/2Ns+fx5Zbf+HrrZm6ocz73Nm9J5eIlcjFSySuy87xzKBg1ahTHjx/niiuu4Oyzz+bQoUO88MILHD16lC5dugDw4IMPcuGFFzJq1Ci6devGTz/9xHPPPceTTz6ZtJ327dszZcoUunTpQvny5XniiSd87lxXq1aNhQsXcttttxEZGUm5cuUyFeNjjz3GVVddxdlnn81NN91EoUKFWLduHcuWLeOZZ54J3MHAc/f79ddf56abbqJPnz7cc889lC1blsWLF/PQQw/RunVrBg8eDECHDh1o3rw5119/Pc8//zy1atVi8+bNHDt2jGuvvZahQ4fSokUL+vfvT79+/ShevDibNm3ik08+4bXXXqN06dKUK1eO119/nbPOOoudO3cyZMiQgF8wCATduZaQ8eWXX6Z41iI6OpqhQz03ZR5//HEqVqxI37596datG1dddRU9e/ZMsX6lSpV44YUXOO+887jpppsYMmQI48aNA+C3337j448/ZtKkSVx88cXUr1+f6dOnc+TIEWbOnJm0jfj4eF588UVatWpFrVq1KF68OEWLFk1xVz2YXdVFsuNt3mYRi3zKe9CD7nQPQkQFk3MuFlgBdEq1qBOwOAubCsNzkTzxIdkl+HYr7wQsd85pLiU5LQ0rVGRm1xt569obqHdGhTTrJTjH/21YR/u3pvDkwnkcPHEi94IUyQWXXHIJ27Zto2fPntSpU4fOnTuzfft2Pv74Yy6++GLAcwf23XffZc6cOdSrV49hw4YxbNiwpGm2AIYPH0779u255ppruPTSS2ndujWNGzdOsa/Ro0fzxx9/UKNGjRTduTPSuXNnPvvsM77//nuaNWtGs2bNeOqpp6hatWpgDkIq1113HQsWLODvv/+mffv21KxZk5EjR9K3b1++/vrrpHPmsLAwvvjiC1q1asVtt91GnTp1GDx4cFKX7gYNGrBgwQK2b9/OJZdcQsOGDRk+fDgVKlRIWn/27Nn8/PPP1KtXj7vvvpv//ve/REaG3mNsGi08D8rsaOF56c51r169+P3335k0KWXvyVKlSiVdrfvtt984//zzOeOMM1i3bh2lSpVKqte2bVuqVq3KW2+9lVQ2d+5cOnbsyOHDh5k3bx7XXXcdJ0+eTPEsRuvWrWnWrBnjxo1j2rRp3HnnnZw8eZLwZAO69OrVi3379vHpp5+eziEQCQnHOEZtarOTnSnKa1CDVaxSd/AAyuRo4TcD0/FMwfUD0B+4A8/I3jvMbAzQzDnXwVu/B3ASWItnWq6mwPPAQufcLd461YF1eJ7Ffg1oBUwEbtVUXHlLbo4WnhUJzvHl5l8Zu+QHth86mG7d6IgI+jW5kN4XNKFYYY2nJyL5S1qjhYfevXQpsIoVK8a5556b5vKlS5eSkJDAoUOH2Lt3b4rkOiPpXURKPvBEZGRkisRaJL94iqd8EmuAl3lZiXUQOOdmm1lZ4FGgEp6k+Arn3A5vlUpA8iukccBwoCae56h3AC/jSbATt7nNzK7wlg0AdgH3KrGWQAkz44qatel0zrnM2bieCT8uYc+xo37rHo2N5bklP/DmmlUMurAFt9RrkO5I5CIi+YG6hUuesH37dgYNGsTLL79Mp06d6N69u88gBj/++GOKJHrp0qVUrlyZEiVKULduXRISEliyZEnS8iNHjrB27Vrq1q2b7r5Pd1AJkVCxjW08i++AI1dzNZ3pHISIBMA5N9E5V805F+mca+KcW5BsWS/nXLVkn992zjV2zhV3zkU75853zj3pnDuRapvzvfUinXPVnXOv5uJXkgKicHg4t9RrwHe392FoqzaUSKdr5r7jxxk1/zsunT6Vj37ZmOYI5CIi+YHuXOdjgRhMLDfFxMSkmIMPIDw8nDJlynDbbbdxySWX0K9fP2644Qbq16/P448/zn//+9+kurt27eK+++5j4MCBrF27lmeffZZHH30UgJo1a3LNNdfQr18/Jk2aRKlSpRgxYgQlSpSgW7du6cZVrVo1vvjiC3755RfKli1LyZIlU3QtFwl1D/EQMcSkKIsggnGMC1JEIpIfFC1cmH5NmnHL+Q2YtPInpq5eyck0Ru/9/chh7v/qcyat+ImHLmpN27OrpzllkYhIXqXkWkLGt99+S6VKlVKUValShX79+rF582bWrl0LQNmyZXnzzTe54oor6Ny5M61btwage/fuxMfH07x5c8yMO+64I8V8eVOnTuW+++6jS5cunDx5klatWvHll18mjRSeljvvvJN58+bRtGlTjh49yvfff0/btm0D++VFcsgCFvA+7/uU38/9nEvaj2GIiGRWySJFGHJRG25v0IgXf1rK7HU/E5/GHeqN+/Zyx8cf0KzymQxp1ZomlarkcrQiIjlHA5rlQZkd0Kwgadu2LfXq1eOll14KdigiIaUjHZnL3BRllajEL/yiZ61zSGYGNCvIzOz2zNZ1zr2Vca38J1QHNMusbYcO8vzSH/j0118yrNuxeg0evKg1tctmbqohEZFQoAHNREQKmB/50SexBhjDGCXWEkwvp/ocARQGEryfw4BTQAxQIJPrvK56qdK8cNlV3NX4QsYuXsSC37enWffbbVuYu20LXeucz33NL6JKCc2RLSJ5lwY0ExHJp57kSZ+yWtTiNm4LQjQiHt5B2Yo754oDtwA/A22AIt5XG2A1kP6AGBLy6p1RgWnXXs/M627kggqV0qzngDkb19PhrSmMXvA9+48fz70gRUQCSN3C8yB1CxeRjKxlLQ1o4FM+hSn0pncQIio41C0888xsI9DHObckVXlLYJpzrnZwIguuvN4t3B/nHF9v3cxzixex+eCBdOtGFS5M38ZNuaNRU6IjInIpQhGRzMvT3cLNrDJwBqnutDvnVgYnIhGR0ObvrnVVququtYSaaoC/q8XHgaq5G4rkJDOjc42adKhegw82bWD80sXsPvqP37rHTp1iwo9LmL5mNXc3a0G3eg2ILJQnTllFpIAL6TvXZtYImAGcB6Ser8E558JzP6rg051rEUnPb/zGeZxHQtIjrB4v8RJ3c3eQoio4dOc688xsnvdtd+fcTm9ZFWA6nnOUdums2/U0dvlF6rnBQ1F+vHOdWkxcHNN/Xs0ry3/k4MmT6datUrwE97e4iGtq1yE8TE80St4zb9482rVrx969eylXLmcH78vNfRVkad25DvW/UJOAP/A8f3UOUD3Z65wgxiUiErKe4imfxLoCFehDnyBFJJKmO4CywHYz225m24HteHqr3ZnBuu9l8fUukPaDv5KrIgsVom/jpnzfsy/3NGtBscKF06y7858jPPTNl1w56y2+3bqZUL4xJHlfr169GDVqFODpcbF9+3afOl26dCE8PJxvvvnGZ1m1atUYO3ZsDkeZ9r4uuugidu/eTdmyZbO17VmzZtGyZUuio6OJioqiefPmzJgxI1vbzCnTpk1Lmia3bdu2TJs2LWixhHpyXRe41zm32Dm33Tm3I/kr2MGJiISa3/mdt/wMsPwAD1CU9Od0F8ltzrktQAPgSmAc8DxwBVDfObc5E5uo6JwLy8wLT1dzCTElIiO5v0Urvu95Bz0bNqJwOnemfz2wn7s+/Yib3nuHZTv/zMUoRf61e/du5s6dy/3338/kyZODHY6PiIgIKlasiFnqTr+ZN3ToUHr37s0111zDihUrWLVqFV27duWOO+5g2LBhAYw2/wn15HotUDHYQYiI5BUv8AJxxKUoK01pBjAgSBGJpM95fO2ce8E5N8E5943L3K3JN4GsdPGeARw5vSglp5UvFsXIS9rzbY8+XHdeXZ9nAZNbsXsXt8yZTZ+P32fj3r9zLUYR8Nwlveyyy7j33nv5+OOP2b9/f9Kytm3bsmPHDoYMGYKZpZng7t+/n1tvvZUzzzyTokWLcv755zN16tQUddq2bcvAgQN55JFHKFeuHGeccQYPPfQQCQkJ6e5r3rx5mBn79u1L2tbSpUtp3749UVFRlCxZkg4dOrBr1y6/sS1btoxnnnmGp59+mmHDhlG7dm1q1arF0KFDefrpp3n66adZtmxZUv1NmzbRpUsXSpYsSXR0NC1btmTt2rVJy6dOnUrdunUpUqQItWrV4vnnn0/6DgDjxo2jQYMGREVFUaVKFfr27cuhQ4dSHO/o6Gjmzp1LvXr1iIqKol27dmzbti2jf6qgCPXRIR4BnjGzR/Ek2qeSL3TOpT/cpIhIAXKCE0xlqk/5vdyrea0lZJlZc6AD/gcuvTet9ZxzWRr23jmnK0x5wFklS/LcpZfTt3FTnlu8iO+2b02z7rzt25i/fRtX1z6PB1q0omrJUrkXqGTZOS88F+wQ2Hrvg9la3znHlClTeOaZZ6hatSrNmzdn+vTp3HfffQC8//77NGzYkD59+jBgQNp/ck6ePEnjxo0ZOnQoJUqU4Ntvv6Vfv35UrVqVDh06JNWbOXMmgwcPZvHixaxevZpu3brRpEkTbr311kzva82aNbRr144ePXowbtw4IiMjWbBgAXFxcX7rz5w5k+joaAYOHOizbMCAATz66KO8/fbbNGvWjF27dtG6dWtatWrFN998Q6lSpVi2bBnx8fEAvP766zz22GO8+OKLNGnShHXr1nHnnXdSuHBhBg0aBEBYWBjjx4/nnHPOYceOHdxzzz3cc889TJ8+PWm/MTExjBkzhilTplCkSBF69uxJ//79+eqrr9L+xwqSUB/QLPlDg8kDNTSgWbDDEJEQ8xZv0ZOeKcoiieRP/qQcGtQkt2hAs8wzs4eAZ4DNwC5StvXOOdc+KIEFWUEY0Cyzftr1J8/8sJAVu/3fZUtUKCyMW+s1YNCFLSgfpV+/UJQfkuvvv/+eG264gd27dxMREcGUKVN4/vnnU9yprVatGoMGDeKhhx5KKsvMIGO33HIL0dHRSV3N27ZtS0xMDEuW/DtTYadOnTj77LOT6mRmX927d2fLli0sXbo0U9/x8ssvZ9euXaxZs8bv8oYNG1KlShU+//xzRowYwYwZM/jtt9+I8DNtXtWqVXniiSfo0aNHUtn48eOZNGkSGzZs8Lv9L7/8kmuuuYYTJ04QFhbGtGnT6N27N5s2baJ2bc/sjDNnzqR3796cPHmSsCANcphXp+JKc5RQyV969erFm2++CUChQoU466yz6Nq1K48//jhR3kYycXCCXr16BSlKkdD2Cq/4lN3ETUqsJZQNxjO2ykvZ3ZCZVQQuwv8d8InZ3b4Ex4WVz+T/briF77Zv5dnFi/h1/z6/9eISEpj+82re27COPo2acGfjCykRGZnL0Up+98Ybb3DTTTclJZI33HADgwYN4scff6R58+aZ3k58fDxPPfUUs2fPZufOncTExBAbG5s0KFeiBg0apPhcuXJl/v47a49CrFq1iuuuuy5L66T3vLZzLmn5qlWraN26td/Eeu/evfzxxx/069cvxZ31uLi4FIMSfvfdd4wZM4aNGzdy+PBh4uPjiY2N5a+//qJy5coAREZGJiXW4DkOp06d4tChQ5QpUyZL3y2nhXRy7ZybH+wYJPd07NiR6dOnc+rUKRYuXEjfvn05duwYNWvW5K677kqqd/ToUSZNmsQDDzwQxGhFQstqVrMU36vSetZaQlwJ4PPsbsTMbgMm4+nZdpBUd8ABJdd5mJnRoXoN2p5dnU9+3cS4pT/w5xH/j8+fiIvj5Z9+ZObaNQxs2pweDS7QHNkSEIcOHWLOnDnExsby+uuvJ5XHx8czefLkLCXXY8eO5bnnnmPChAnUr1+f6OhoHnnkEZ/EuXCqUfTNLMXzypmR1V7KtWrVYuHChcTExBCZ6gJVTEwMW7dupX379hluOzHOV199lYsuushvnR07dnDllVdy5513Mnr0aMqWLcvKlSu59dZbiY2NTapXKNXvcGJyn9VjkRtCfUAzzKyCmY02s/fM7F0zG2VmFYIdV15gIfC/rIiMjKRixYqcddZZdOvWje7du/Phhx9SqlQpOnbsyKJFi1i0aBEdOnSgVKlSOXPQRPKoV3nVp6whDWlBiyBEI5JpbwOXBWA7T+DpXh7lnKvonKuU7FU5Kxsys7vN7GczO+J9LTGzK5Mtn2ZmLtVraaptRJrZi2a2z8yOmdnHZnZmAL5ngRYeFsa159Xlm9t6M/KSdpQtmvYMCIdOnuTJRfNp/9YbzF6/lrgQPAmXvGXmzJmUL1+eNWvWsHr16qTXpEmTmD17NomPbEZERCQ9c5yWRYsWcfXVV9OjRw8uuOACatSowa+//prlmDKzr8aNG/Pdd99lepu33norx44d45VXfHvDTZw4kWPHjtGtW7ekbS9atChFIpyoQoUKVKlShS1btnDuuef6vACWL19ObGwszz//PC1btqRWrVppDrSWV4T0pTwzawV8CewBEh84uA14wMw6O+eWpLmy5HlFixbl1KlT9OnTh44dO9KsWTPAM4ph1apVgxydSOg4whFm4Dv35AAGZPkil0gu+wN43Nve/4zvwKXjMrmdEsA055z/EXqy5k9gKPAbnpsQPYEPzayJc+5nb51vgR7J1kl9ZjkeuAa4FdiPZ5qxT73bSP9MWDIUWagQPRs25vo69Zi6egWvr1jO0VO+J/cAu48eZfjcr5m8cjkPtmxN5xrnZmuKIjl92X3eOdjeeOMNbrjhBurVq5eivFatWgwZMoTZs2fTp08fqlWrxsKFC7ntttuIjIz0+4x1rVq1mD17NosWLaJcuXK8+OKLbNu2jUaNGmUppszsa8iQIbRo0YK77rqLu+++myJFirBw4UIuvfRSv+fTLVq04MEHH2To0KHExMRw3XXXYWZ88MEH/Oc//2Ho0KFJ5+QDBw7k1Vdf5aabbmLEiBGULl2an376iTp16nDBBRcwatQo7rnnHkqVKsUVV1zBqVOnWLlyJTt37mT48OHUrFmThIQExo8fT9euXVm6dCnjx4/P0jEINaF+53osnqvatZxzPZxzPYBawDtA8EdFkByzbNkyZs2aRYcOHZg+fTo333wzV111FVdddRU333xzihEERQq6GczgGCkHOSxOcbrTPUgRiWRaX+Aonmel+wP3JHsNysJ2ZuKZKzvbnHMfOee+cM5tds796pwbAfwDtExWLcY591eyV9LsJWZWErgDGOKdVmwlnkS8AdAxEDGKR3REBPc0a8m8XnfQ54ImRISlPc7tloMHGPj5x3T9v1ks/uP3XIxS8oOVK1eyatUqbrjhBp9lERERdOnSJWmQsdGjR/PHH39Qo0YNypcv73d7jz76KM2aNePyyy/n4osvJioqiu7ds95mZ2ZfF1xwAd9++y2bNm2iRYsWNG/enHfeeceny3lyY8eOZfLkyXzwwQdccMEFNGzYkDlz5jB58mSeeuqppHpVqlRhwYIFxMbG0q5dOxo1asSLL76Y1I27b9++TJkyhenTp9OwYUPatGnDpEmTqF69OuB5pnzChAmMGzeOunXrMnnyZMaOHZvl4xBKQn208BPABc65X1KVnwescs6l3R8oH8vsaOGhcMfKkbn/vnr16sWMGTMoUqQIcXFxnDp1imuuuYZXX32V6dOn069fP9577z3AM3jEa6+9xoMP5u0roCKB4HA0oAHrWJeifCADeZmXgxRVwabRwnOfmUUAH+K5g+xv6s7Rp7ndcOBG4C2giXNurZlNA6717usQMB8Y4Zz727tOe2AucIZzbm+yba0H3nPOjcxovxot/PTs/OcIE35czPsbN5CQwfltm6pn89BFbah/hp40FJGsy6ujhR8GqgO/pCqvjqdBk3zk4osvZtKkSRQuXJjKlSsnXVFLnURHR0crsRbx+oEffBJr0EBmUuD0w/Ps9j7gXHwHNMtScm1m9fE8jlYEz53165xziXPtfAm8D2wDqgH/A77zdvmOASoC8d5YktvjXZbWPu8C7gL06NNpqlK8BM90vIw7G13Ic0sW8fXWzWnWXfj7Dhb+voMra9bi/hatOKd0aI04LCJ5U6gn1+8Ab5jZw8BiPA1ka+ApPN3FJR2ZvWscKooVK5Y0wIE/moLr/9u77/goqi2A47+T3ikBBJQmoIKAKDYUFQv67PpsWFAsWLAAKjYEKVZEBSwoomDBJ/au2DsWEAVEEaR3CCWF9D3vj9nAZrMlIcmW5Hz97CfZO3dm7g7I7Jl777nGVORr+a1e9KILXXzUNsYhIruz3MIUVfWdorl6bdkHOBdoDZRbz0VVr6jkYYYDt6jqYzXUrEVAd6AhcA7wgoj0VtUFqvqqR735IjIHWIEzLP2tAMcU8H9jVtXJwGRweq6r1fp6rmNmJk+fdiZz161l7I/f8fOa1X7rfrj4Hz5Zspjz9+/KTYf2ZI+0tBC21BhT10R6cH0bzs3oeXa1tRiYBNwRrkYZY0wk2MQm3uCNCuXWa20qYRxO4q7KJtdqhTPsukaDa3cW7jeBuUAP4FegPZAIfFeFQ8UC79VUu1S1CCjr9pwtIocAQ3DmUnvXXSsiq4GO7qL17vY0ATZ5VG0GfFtTbTTBHdiiJa/893y+W7mCsT98y8LNm3zWK1Xlfwvm8fbfC7nsgAO5tsehNEhKCnFrjTF1QUQnNFPVIlUdBDTCeYJ8INBYVYe4b3zGGFNvTWc6RV5JipvSlHM4J0wtMlHmYFVtV5kXkF9LbRgNjFLVnkAhTuKvtjjZuL+uwnGmQq1m8IvBCfgrEJEmwJ7AOnfRHJyOgD4edfYCOuGMwjMhJCIc3aYt713Yjwn/OZU2DRr6rVtQUsIzc37lmBem8PTsX8gvLvZb1xhjfIn0nmsAVHUHToISU0dNmzYt3E0wJuq8RMWs+ZdzOYm+YwBjPI3CmUtcWfcDW4LWqrp9gRnu34uBFFUtEJHRwIc4S1hVRgpwlYichO8lvW6qbINE5EH3uVcB6cBFQG/gVBFJA0bi9Lavw3kQ8ACwEXjbfa7tIvIc8LCIbGTXUlzzcB4amDCIEeH0ffbjP+078trCBUz8eRabdvhODptdWMjYH79j2h+/cdOhPTmvcxfiY/1nIjfGmDIRF1yLyHvAJaqa7f7dL1U9I0TNMsaYiLKABfzGbxXK+9M/9I0xUUdVR1Wx/gO11JQcnKRh4ASrHYAFON9PGlXhOJ1whpYD7Oe1rarzl5sDL7t/bscJik9W1Zkikgx0BS7FmY+9DvgKOF9VczyOMQQowXlwkIyTPfxSW+M6/OJjY7m46wGcvV9npv3+G8/M+ZWcokKfdTfm5XH3V58zZe4cbjn8SE7uuA8xtka2MSaAiAuucZ7wlt0It1D1m6IxxtR5vnqtD+EQOtEpDK0xu0NEBgJDgRbAn8BgVfU5z1hEeuMEbIcCDXDmA49X1ee96nzlY/dOqvp3JdvUBDgMZ87wL6q6vnKfZrf9jJOodCFOb/EjInIAcDZOtu7KugxYraouz0IREZz54pWmqv0DbMsHTqrEMQrYtV63iUAp8fEMPOQwLurajafn/MoLv8+lsLTEZ93l27Zy4ycf0GVOM4YecRS9WrdBLMg2xvgQ0etcG98qu861MaZuKqWU1rRmLWvLlT/O49zADWFqlSlTmXWuReQCnN7RgcD37p+XA51VdaWP+nfhDH3+GKe39CTgcZze0FfcdXrjBNf7U34I96bK9JiKyJnANGAxTtbufYCrVfXlYPvuLhHZG0hT1XkikgI8AhwJ/APc7Ota+DlOKdCibK1pj/JMYKOqRtWYXlvnOvTW5eTw+C+zeH3hAkqDfDfuuVcrhh5xFN2btwhR64wxkcbfOtcRHVyLyPPAIK+hVohIKvB4FZboqFMsuDamfvuMzziRE8uVxRHHOtbRhCZhapUpU8ng+mdgnqoO8ChbDLyhqndW8jyvAbGqeo77fW+c4Lqpqnqvsexr/0T3usxl7+cBfVV1ofv9mcCTqrpXZdoTTiLiApr7CK7bAAuD/XlEGguuw2fp1i08OusHPlryT9C6J7bvwK09e9GhcWYIWmaMiST+guuIzhaOM8wr2Ud5Ms58J2OMqXde5MUKZadyqgXWUUJEEnCWnfrUa9OnwBFVOFQGsNVH+WwRWSciX4jIsQH2/8Nru+LMEy4T8fODRWSiiEzEafv9Ze/dryeBN4Dfw9pIE1X2btSYJ045nbcvuJgjW7UOWPfTf5fwn+kvcPvnM1mbU+NLwBtTI0aOHEmXLl3C3Yx6IyKDaxFp7B7KJUAj9/uyV1PgNGBDeFtpjDGhl0MOb/FWhfJL7XljJIkTkdker6u9tjfBmdPsfR/bgJNEKygROQ04HpjsUbwOuA44B/gvsAj4QkSO9nOYC4GxIvK8iDQCRgA/i8jPIvIbTjKuSvWih1FX90twkpp19Xh1AH4Dy/Jnqu6APZrz0tnn8eJZ59K12R5+67lUeX3hAo578Xnu++5rtubX1qp1JtTmzp1LbGwsRx55ZLnyadOmkZaWFqZWmUgXiQnNADbjPIVWnCQn3hS4J6Qt8kNE7sT5ErMvzhqdPwF3quqCIPt1BZ7ASU6zBXgGGKORPE7fGBN2b/EWO9hRrqwRjTiVU8PUIuNDia+hYj54/3svPsoqEJEjgVeAm1T1l50HU12EE1CXmSUibYFbgW8rnFx1rogchpMobTZwF84868NxHr7/qqprvfeLJKp6LICITMWZRmbdh6ZG9WrdhiNbtebjJYt5ZNb3LNvma7AIFJWW8tzcOcz4cz5XH3QIl3c/iNSEhBC31tSkZ599loEDB/Liiy/y119/0amTJQz1VFRURIL9Ha8gInuugWNxnsgLcC5wnMerF9BaVe8LX/PK6Q08hTOU7zicIXWfi0hjfzuISAbwGU4vxSHATTgZY2+u7cYaY6KbryHhfelra1tHl804Q669e6mbEWRUloj0wklqNkJVJ1XiXD8DHf1tVFWXqj6Cc8/tD7wA/KGq70Z6YO1JVS+3wNrUFhHhlI77MPOS/jxwXB+ap/rvtcwtKuLRn36g9wvP8eIfcykqjfjZFcaH/Px8XnnlFQYMGMC5557Lc889B8DXX3/N5ZdfTl5eHiKCiDBy5EgA2rZty+jRo+nfvz/p6em0atWKGTNmsG3bNvr27UtaWhodO3bk00/Lzwj69ttvOeyww0hKSmKPPfZgyJAhFBUVldt++OGHk5aWRoMGDTjssMNYsMDpwyvrRX///ffZZ599SEpK4thjj2Xp0qUVPtOrr75K+/btSU9P56yzzmLz5vKpOaZOnUrnzp1JSkpin3324bHHHsPl2rUAg4jw5JNP8t///pfU1FTuuusuAN5//3169OhBUlIS7dq1Y9iwYeXaX99EZM+1qn4DICLtgJWR3JurquWW5BCRfjjrYh4JvO9nt4txsr5e5l7WY4GIdAJuFpFHI/nzGmPCQ0tLWV78L18lfuU8dvRw7toTyNmxBC0pwVVSgpaWomU/PV8uV7nfcbmcMlXnd/dPVJ3fPX7u/N1n46pYHiWaHHkkae3a1fhxVbVIROYAfYDXPTb1Ad70t597ePeHwEhVHV/J03XHGS7u75j746wLPV9VTxaRS4CvRWQCMDGa7kciEoczGqw1TrbznVS14lMpY6ooLiaGC7p048z9OvHiH78zafYvbC8s8Fk3K38HI7/5kufmzmHI4Udyxr771fs1sv/T6KpwN4FPtk6pVL033niDNm3a0K1bN/r168f555/PAw88wBFHHMH48eO56667+PfffwHKDREfP3489957L8OGDePpp5/msssu47jjjqNv377ce++9PPDAA1xyySWsXLmSpKQk1qxZw8knn0y/fv2YNm0a//77L1dddRUxMTE88sgjlJSUcOaZZ3LllVcyffp0iouL+e2334iN3bUAQmFhIaNGjWLq1KmkpKQwaNAgzj77bH7//fedS8YtX76cGTNm8Pbbb5OXl0ffvn0ZNmwYzzzzDOD00o8YMYLHH3+cHj16sGDBAgYMGEB8fDw33LBrFZJRo0Zx//33M27cOESEmTNncvHFFzNhwgSOPvpoVq5cybXXXkthYSHjxo2r9p9XNIr0bOE3ANu8lwFx3/wzVPWp8LTMPxFpAawFjlLV7/3UeRHIVNVTPcoOAX4B9lbVZT72uRq4GiAhIaFHYWGhdxVjTARQVYq2bqVgwwYK1q93Xhs2OK9Nm3AVFeEqLkaLi51AuCwgLntfXOxsLysvKcFVXAyqvH7tVl4aWn5IYstl8Uw6YS/EO+I21XbQY4+x5xlnVHm/KizF9RLOElw/ANcCVwL7q+oKEXkAOFRVj3fX740TWD+Fs1xVmVJV3eSuMxhYjrNmdgJwCXAHcI6qVpioLyI3A/cC83B6t+9Q1WfdOU8ewZnDPEBV51X5IlSBe2j68Tg99+VG1KnqTZU8xn44D7Tb4Tx+KsXpQCgGClU1oybbXNssW3h0yC4sYPKc2Tz/+xwKSnyvkV1mvyZNubVnL45t267erpEdTcH1Mcccw+mnn86tt96KqtKuXTseeeQRzjnnHKZNm8YNN9xAbm5uuX3atm1Lz549+d///gdAbm4u6enp3HjjjUycOBFwgtx27drx66+/cvDBBzNs2DBmzJjBP//8Q0yM88/ftGnTuOaaa9i6dSsFBQVkZmby9ddfc8wxx1Ro57Rp07j88sv5/vvvd84NX7FiBXvvvTczZ87khBNOYOTIkTz44INs2LCBBg0aAHDfffcxdepUlixZAkDr1q2577776Nev385jjx8/nsmTJ7NwoTNDV0S44YYbePzxx3fWOfroo+nTpw/Dhw/fWfbOO+9wySWXkJOTU6f/rvvLFh6RPdceBuN82fC2HJiK8yUj0kzAyUw6K0Cd5sBqr7INHtsqBNeqOhl34prU1NTIfSJiTB1XuHkzeStXVgie8z3eu2phOJSifHV2boXyY99Os8A6CqnqDHcQezfQAlgAnKKqK9xVWgDtPXbpjzPi6Vb3q8wKoK379wRgHLAnkI8TZJ+qqh/5acZt7u1fuZes+gR4VlWzgP4icgJOUrNam2goIrcCY4ElOA+mPe9vVbnXjQfm4PTUr3f/bABMwrnGxtS4jMQkbj2iF5ce0J3Hf/mJGX/Op8RjGK2nvzdv4qr33+aQlnsy9IijOLjlniFuramsJUuW8MMPP+wMkkWEiy++mClTpnDOOecE3Ldbt247f09LSyMlJYWuXbvuLNtjDyc53saNzqqBf/31Fz179twZWAP06tWLoqIilixZQrdu3ejfvz8nnXQSxx9/PMcffzznnXcerVq12lk/JiaGQw89dOf7Nm3a0LJlSxYuXMgJJ5yws6wssAZo2bLlzjZs2rSJVatWcc0113DdddftrFNSUlJhxNrBB5ePJefMmcMvv/zCQw89tLPM5XKRn5/P+vXradGi/q0FH+nB9V44Xxy8rXZviygi8ijOnPBeqhpsko2vRDa+yo0xYaKq5C5dypbZs9ny669smT2bHatWhaUtS7oWsrpDcYXy3u9YxtJo5R595fMhsar29/G+v6+6HnXG4gSqVVEWCZTiNeFAVT8XkQOreLyqGoSTmO2Jah7nEOAYVc1zr3kdp6q/ichtwONAt8C7G7P7mqWmMebYE7jywB489tOPvP/P337r/rp2Dee/8SrHt9ubW3r2Yr8mTUPYUlMZU6ZMobS0lNatdy3FVhZkrgryHSA+Pr7cexEpV1bWk1s2l1lV/fbulpVPnTqVwYMH88knn/Dee+8xbNgw3nnnHU466SSf+1W2XWVtKPv59NNPc8QRgVeDTE0tPyjL5XJxzz33cN5551Wo27Rp/fy7HenBddnT5+Ve5QfhJISJGCLyGNAXOFZVK2YRKG89vhPZgC0xZkzYuIqLyf7rL7LcgfSW2bMp2rIl3M0C4Mv/Vuy17vJTEnusifdR25hKGQd8JCJ/4GQJv8u7gqr6nlBaczIAfz3rVSGwM43+Jpze+0U4D+M71MDxjQmqbcNGTPjPqVx90MGMm/U936xY7rfuF8uW8uWypZy9X2cGH34Ee2U08FvXhE5JSQkvvPACDzzwAKeddlq5bf369WPq1Kl06NCB0hpKVNe5c2dee+01XC7Xzt7r77//noSEBNq33zV46YADDuCAAw7g9ttv5+STT+aFF17YGVy7XC5+/fXXnYHxypUrWbt2baWzm++xxx7sueee/Pvvv1x6adWW9TzooIP4+++/6dDB/pktE+nB9SvARBHJA752lx2LM/xrepjaVIE78UtfoLeq+n9cucss4CERSfL44tIHZ0jc8tpppTHGW8mOHWz7/Xey3D3TW3//ndIdO4LvGGLF8cq3p1UMrk/8Yk+SW7ZA4uKQuDhi3D8lNtb5PTa2/CsmBomNhZiYnb9LTAyIOOXiHmAeE+M8MXe/dj5VLyuDik/a/c2riuL5Vqlt24a7CbVKVceJyEx2JTSrzP2rpv0P+A/Vn+a1ADgAWIqTv+R2ESkFBuAMOTcmZPZvtgdTzzyHn1av4uEfv2Puet85BRV46++FvP/P31zc9QAGHnI4TVJSQtvYEKrsfOdw+vDDD9m8eTMDBgwgMzOz3La+ffsyadIkXnnlFQoKCvjss8848MADSUlJIWU3/9wGDhzI+PHjGThwIIMGDWLp0qXccccd3HDDDaSkpLBs2TKeeeYZzjjjDPbcc0+WLl3KvHnzyg3fjouLY/DgwUyYMIHk5GSGDBnC/vvvv3NIeGWMHDmSG2+8kYYNG3LKKafsTJy2Zs0a7rzzTr/7jRgxgtNOO402bdpw/vnnExcXx4IFC/jll18YO7aqA6nqhkgPru/BSU4yE2fIGjjJTl4HhvvbKZRE5EmgH3AWsFVEynqkc1U1112nXGIanIcG9wDTRORenB6DO4BR0ZSZ1ZhoU7hly84e6S2zZ7P9zz/RIElodkdscjJJzZuT3Lw5SXvsQVLz5s6rWTPiUlN3BcLx8TsD4pj4+ArlMfHxSHw8H8fNJCf2zHLnSCKJe4b9RsawqMrTZCKMqs4H5oexCauAUe61u+fhJCDbSVUfreRx7gPKxiveDXwAfIUzyu38mmmqMVVz+F6teOO8C/l86b+Mm/U9i7dk+axX7HIx7Y+5vL5wAVceeDBXHtiD9ERbXjEcnnvuOY499tgKgTXAeeedxx133EFubi7XXnstF154IVlZWdxzzz07l+Oqqj333JOPP/6YoUOH0r17dxo2bMhFF13E/fffD0BKSgr//PMP5513Hps3b2aPPfbg4osv5vbbb995jMTERIYNG8all17KypUrOfzww3nrrbeqlEzsqquuIjU1lYcffpg777yT5ORk9t9//3KZwn056aST+PDDDxkzZgzjxo0jLi6OffbZh/79++/W9agLIjpbeBkR6YgzPFyA31S10k+hReS/u3HKj91LZFXm+P4u4ChVHemuMw2nV7utx35dgSdxlg3ZCjwNjK5McJ2amqp5eXmVaZ4x9Zaqkr969c5e6S1z5pC7pAY6sGJiSO/QgeQ99yRpjz2cANodRJf9HpeeXqMZMi/mYl7hlXJlF3ABr/JqjZ3D1JzKZAsPt7K5yFW41w0Gnq/ptaRFpEICTw+qqntX49iNga3R+NDasoXXPaUuF2//vZDHfvqRdbk5Aes2Tkpm4CGHcXHXA0iMi/R+MBNO/jKXm9rnL1t4VATX1eFObFIVCnSsxLzpsLHg2piK1OUi559/ys2XLli/vtrHjUlMpFH37jQ++GAaH3IIjbp3Jz49vQZaXDl55LEHe5BH+f/n3+VdzqDqy0SZ2hclwXUp0LxsGa9K1M8GukfqvdH9wHphJZKJRgULruuuwpISps//gyd//YmtBYFTGrRMT2fI4Udy1r6diI2JCVjX1E8WXIdPVC7FJSITA22v7PqXOF8gNlbynIEfJxpjIkJpYSHb58/fNV/6t98ozq5+p1p8gwZOIN2jB40POYSGXboQk5BQAy3ePe/zfoXAuiENOYnKZwk1xgcBvhGRys6LSK7NxtSAP4BCEVmIsxxm2SsRuFFVz/S7pzEhlBgXxxUH9uC8zl2YMnc2z82dw47iiitBAKzNyWHoZ58wec6v3NqzFyfs3b5OrxtsTF0Q0cE10NXrfTxO4pU44LdKHuMFnPU+K+tloEaHvRljaoarqIgVM2aw9sMP2fbHHzWynnRyy5Y7e6UzDz6YtA4dnCRfEcJ7ODjAuZxLIjYfz1TLqN3Yp1ZS54vIPsC5QGuctbp3UtUrKnmYxsCB7tdBwPU4+UwUZ+qVMRElPTGRIYcfSb9uB/LUrz8xff4fFPtZI3vxliyu+fBdDmregtuOPJpD94y41WhNmPTv379ez2+ORFE3LFxEkoDngO9U9elwtyccbFi4qY+yfvmFeSNGkLt4cbWOk96xI40POWRnQJ3SsmUNtbDmbWELzWlOcfkcT3zJlxzLsWFqlQkmGoaFRwoRORV4E5gL9AB+Bdrj9Dh/p6q7PffBPVT8f8Cdqvp+DTQ3ZGxYeP2zavt2xv/8I+/8vZBg38yPadOOoUf0onPTZkFqGmNqS52acy0inYGZqtpqN/c/TFV/ruFmhYwF16Y+KczKYuGDD7L6rbeqvK/Ex9OwS5edgXTjHj1IaNiw5htZS57lWa7m6nJlLWjBKlYRS2yYWmWCseC68kRkDvCGqj7gnpZ1AM6ylC8Bs6qQLdzf8fsA41T1gOq3NnQsuK6//t68iUdmfc8Xy4KnNzh9n/24+fAjaRNF9zVj6oqonHMdQFMgrRr7v44z/MwYE6HU5WLljBn89fDDFG/fXql9YlNTaXzQQU4wffDBNDzgAOKSI32qqH//438VyvrS1wJrU5fsC8xw/14MpKhqgYiMBj4EKhVci0iKqvpapH6Z+xzGRIX9mjTl2dPPZvbaNYz98Ttmr13jt+77//zNx0v+oe/+Xbnx0J40TbVnesaEW0QH1yJys3cR0AK4GPgoyL6v+duEMzfLGBOhtv/1F/OHD2fr3LkB6yU2abIzkG58yCFk7LcfMXVk2ZI1rOFrvq5QfiEXhr4xxtSeHCDJ/fs6oAOwAOf7SaOqHEdE/sHJxzLX/XMtcAPwSY211pgQObjlnsw45wK+Wr6Mh3/8jkVZm33WK3G5eHn+H7z5159c3r0HV/c4hAxbI9uYsIn0b6E3er13AZuAqcADQfY9AegHeOemF+DoGmmdMaZGleTmsmjCBJa98AJa6n9FnYbdu9NlxAgadutWZzOnvsZrqNfMuw504GAqjEAyJpr9DPQCFuL0VD8iIgcAZwOzqnCcXjhDyrvjJEcbCaS4t30qIvcB84B5qvpXjbTcmFomIhzXbm96t23He4v+5rGffmBVtu+RXPklJTw1+2deWfAH1x18KP26dScpLj7ELTbGRHRwrartqrH7d0Cuqn7jvUFE5lXjuMaYGqaqrJs5kz/HjAm4NnV8Rgb7DR1Km759Iyqjd23wlSX8Qi5EqJsPE0z4iEh3Vf09TKe/mV3TvEYC6cA5wD/ubZWiqrPwCMbFeeq2L06w3R0nWdoVQDOweRUmusSIcNZ+nTil4z68umAej//yE1n5vmZBwLaCAh74/lumzv2NQYf15JzOXYir4/dLYyJJVCY0q+8soZmpS/JWrmTByJFs/KbCc7By9jr7bDrfcQeJTZqEqGXhs5jF7MM+FcoXspBOdApDi0xVRFtCMxFx4QylngK8oqqVS3IQZiJyKDBHVf0Pcylf/2BgpapurN2WVZ8lNDOB5BUVMfX335g851dyiwMvSbl3o0bc0rMX/2nfsc6O9DImHKImW7iIPF/ZulVY/xIR2VNV/WeFiCIWXJu6oLSwkH+ffZbFTz2Fq7DQb7209u3pOno0TQ4/PIStC6/RjOYe7ilXdiAH8hu/halFpiqiMLjuiNOr2w8nJ8lbwHOq+lWIzp8EnIazBNczqrpNRNoDW1XV79raIlIKNFfVTZU8TzbQXVUDpmEWkeuBa4C27qI/gXtV9UP3dgHuAa7GmRf+M3C9qv7pcYxEYBxwIZAMfAEMVNXVlWmrBdemMrbk72DS7F94ad7vFAWYSgXQbY/mDD2iF0e2ahOi1hlTt0VTcO29FuXROHOt57vfdwFigG+rsv6liMxT1W4108rwsuDaRLvNs2Yxb8QI8pb6/44bk5jIPjfeSPsrryQmISGErQsvRelEJxaxqFz5WMYylKFhapWpimgLrsuISAxwMnA5cDqwGngeeKGyQeFunLMD8DnO0PCGwD6qulRExgENVfWqAPu63O3zPT62oquBzpUIrs8EioDFON83LgNuA3qo6jwRuR24G+gPLAJG4Mz53ldVc9zHmASc6d43CyfreUP3MYL2tFtwbapiTU42E3+exZt//YkryPf6Xq3aMPTIo+jabI8Qtc6YuilqgmtPInIncCBwuarmuctSgeeA+ap6XxWONV9Vu9ZOS0PLgmsTrQo3b+bP++9nzbvvBqzX7Nhj6XrPPaS02q2l7KPaXOZyEAdVKF/JSlpR/65HNIrW4LqMuyf5OpzEoQlACU5v9i01PQJMRD7Ayep9HbANOMAdXB8NTFXV9gH2/Rqo6peYi1R13W60cwtwJzDZ3d4nyr6DiEgysBG4VVWfEZEGOMlXL1fV6e46rYAVwMmqOjPY+Sy4NrtjyZYsxs36nk//XRK07ikd9uHmnkeydyNbQMeY3RGt61zfBBxfFlgDqGqeiIzBGWJV6eCaqt+AjTE1REtLWfHqq/z18MOU5OT4rZfUvDldRoyg+Ykn1tu5Yb4SmR3FURZYm1rnnsN8BXABkA08iNMz3AIYA7wDHFLDpz0COFxVS73+n18JtAy0o6r2ruG2VCAiscB5OD3rPwLtgObApx7tyBeRb3E+yzM4ydPiveqsEpG/3HWCBtfG7I4OjTN5+tQz+X39Osb+8B0/rVnlt+5HS/5h5r+LOX//rtx46OE0T0sPYUuNqbsiPbhOw7m5LvQqb8GuJTaMMRFs24IFzB8+nG3z/Cfpl9hY2vXvz76DBhGXGrUdftXmwsWrvFqh/CIuCkNrTH0hIjfjBNUdcZbDuhj4RFVd7iorRWQg8HctNcHXekGtgbAlVhORrjjZx5NwlvQ8W1Xni8gR7iobvHbZAOzp/r05UAp4L0y8wb3N3zmvxhm6TuvWravVflO/dW/egun/PY/vV65g7I/f8ecm3zn8SlX534J5vPXXQi7rfiDX9jiEhknJIW6tMXVLpAfXbwJTRWQo8JO77HDgIZwhasaYCFWck8Oixx5j2Usvgcvlt16jgw6i25gxZOy3XwhbF5m+53tWU35qaxxxnMu5YWqRqSeuw5luNVVVvYPGMiuBK2vh3J/iLLlVdmwVkQxgFE6gHy6LcJbwaoizNNgLItLbY7v3aDjxUeYtYB1VnYwz7JyDDz7YRtuZahERjmrTliNbt+GjxYt4ZNYPrNi+zWfdwtISJs/5lf/Nn8c1PQ6hf/eDSIm3NbKN2R2RHlxfBzwCTGPXk+0SnC8Bt1bxWPVzjKkxIaaqrP3wQ/687z4KN/pf8Sa+YUM63347rc49t86vWV1Z/+N/FcpO5ESaUPeXHzPho6odK1GnCHihFk5/M/CViCzC6SWeAXTA6eU9vxbOVynuz1s2cXW2iBwCDGHXdLTmgOeY22bs6s1ej7OWdhOcudeedb6trTYb40uMCKftsx8nte/I6wsXMPGXWWz0k7cnp6iQcbO+54U/5nLTYT05v3MX4mNtWXhjqiKiE5qVcScxa48TIC/xnINdH1lCMxOpcpctY8GoUWz67ruA9Vqdey6dbr+dxMaWSKVMMcW0oAVZZJUrf5mXuZiLw9QqszuiLaGZiNwAbFPVl73KLwEyVPWpWj5/Ms6SVQfhZOf+DZiuqvm1ed6qEJEvcRKZ9XP/fFxV73dvS8JJaDbUK6FZf1V9xV1nL5zef0toZsIqv7iYaX/8xtOzfyWnyP8ymABtGjTk5p5HcmrHfYmpp3lQjPEnKrOFl3HfuDrgDKf6V1ULwtyksLLg2kSa0sJCljzzDEsmTcJVVOS3XnrHjnQdM4bMQ2o6J1L0+4APOJ3Ty5Ulk8xGNpJGWphaZXZHFAbXS4ArVfUbr/JeOEPFg/Zs1yUi8iDOkPRVQDpwEXA7cKqqfuxeimsYzlJc/+Asy3U0FZfiOoPyS3E1wpbiMhFiW0E+z8z5lWm/z6WwtCRg3f2bNmPoEUdxVOs29TbZqDHe/AXXET0WU0TiRORhYCvwB85a11tFZKyIVHkyiIi8IyKnudfyNMbUgE3ff883p5zCPxMm+A2sY5OT6XTbbRz9/vsWWPvxIi9WKDud0y2wNqGwF84yUd5Wu7fVGhG5T0Su9VF+rXtlkHBoDryMM+/6C5wM6Ser6sfu7WNxguUngdk4SVZPLAus3Ybg5IaZAfyAkxTt9MoE1saEQsOkZG4/8mi+uuwKLuzSjdgAQfOfmzbS/903ufit15m7bm0IW2lM9In0IHMscAlwLbAPTibT63CGZT2wG8fLw7nRrRaR+0WkXj2NzyefHewIdzNMHVGwcSNzBg3ip8suI2/5cr/19jjhBHrPnEmHa64hxhKk+LSNbbzHexXK+9EvDK0xoSIiA0VkmYgUiMgcETkqQN3eIvKuiKwTkR0iMk9ErvBR7xj3sQpEZKmvwNWH9TjJu7wdRMWM1zWtHzDXR/kc4NLdOaCIJLmHYXuX71+Z/VW1v6q2UdVEVW2mqid4DuVWx0hVbaGqSap6jKou8DpGgareqKqZqpqiqqerqv91kYwJk+Zp6dx3XB9mXtKfUzvuE7DuT2tWcc7r/+PaD95lcVZWwLrG1FeRHlxfhDNU7QVV/df9mgZcBVWfhKiqF7Nrvc4TgEUi8q2IXOqe81WnTWIS7WjHWMaSS264m2OilJaWsuzFF/mqTx/WfvCB33rJLVtyyDPPcOgzz5Cy555+6xl4ndcppPzct6Y05SROClOLTG0TkQuACcD9wIE4ayh/LCL+1mA6Amf01rlAF2ASMFlEdq7TJiLtgI/cxzoQ5yH04yJyTpDmvAJMFJE+IhLvfp0IjAem7+ZHrKxmlE/6VSYL2KOqBxORs3GGan8oIn+KyGEem1/avSYaU/ft3agxj598Ou9ccDG9WrUJWPfTpUs4+ZUXuO3zT1iTkx2iFhoTHSJ6zrWI5APdVXWRV/l+wFxVrVZA7H6KfRVOz3gR8CowXlX/qs5xa9vuzLnOJ592tGODO5lpJpnczM3cwA1kkFEbzTR10LZ585g3fDjbFyzwW0fi4mh/5ZV0vOEG4lJsOfrKOIqj+J7vy5XdxE1MYEKYWmSqozJzrkXkZ2Ceqg7wKFsMvKGqd1byPK8Bsap6jvv9Q8B/PedIi8gUYH9V7RngOPHAi8AFOOszg/Pw/XWgn6oWV6Y9u0NE/gHuU9UXvMr7A3eraocqHm8uzhDtTSJyME6G8/tU9RURmauqB9ZU22uTzbk24fbDqhU8/OP3zNuwPmC9hJhYLunWnYGHHErj5Jq55xeXrCY3/3NKXRsREkDiEUlAiEfKfpd4hET3+3iEhF3bKKvjlIHnPgnY7FBTE/zNuY70pbj+AG4CrvcqHwT8Xp0Di0hL4EzgNJzlvd4AWgHzROROVR1XneNHmilM2RlYA2SRxTCG8TAPM4Qh3MRNNKRh+BpoIlpxdjZ/P/IIy6dPhwAP5BofcgjdRo8mfZ/AQ8vMLktZWiGwBrh090bEmiggIglAD8D7PvMpTg91ZWVAuYXRe7qP4WkmcJmIxPsLkt3lF4rICJweb4DfVHWJr/o17BngMfc1+dJddjxOr/tDu3G8BFXdBKCqs0XkaOAtESlLimqMqYQjW7XhiPNb88m/i3lk1vcs3brVZ70iVynP/z6H1/6cz4AeB3NF9x6kJiTs1jmLipexJedxtue9gfPVvLbEeQXkXsG5JCAkEBOTTkxMGjHi/IyVDOd9TLq7zPkZG5NGTEwGMZKGSLIlfavnIr3n+micIW5rgVk4N8aeQEuc5CIVv5EGPl48TkB9BdAHZ57Xs8D/VDXXXed8YLKqNqyhj1HjqtpzXUAB7WnPWvwnocggg5u4icEMJpPMmmimqQNUlTXvvcfC+++ncLP/qZcJjRvT+fbb2eucc+ymUkWjGc093FOurDOdWcACBLuW0UhEinCGcJeZrKqTPba3BNYAx6jqtx7lI4CLVXXfSpzjNOBt4EhV/cVd9g/wsqqO9qh3NPAN0FJV11Xvk9UOEXkAGAyUfSMvAiao6h27cayvgEGqOs+jLAGnB/s8VY30TgXAeq5NZClxuXjzrz+Z8NOPrM8LPK0wMzmFGw49jAu7HEBCJdfILiz+h6zsieTseAdwVb/BYRVLTEwGsTENiY1pQExMA+d3abjr95gGxMQ0dP/e0P17AwvMo0xU9lyr6rcisg9Oz/V+OOtcvw48paq7k65wnfsYrwB3eN58PXyGk528zsgll+M4jld4BZeff7SyyeZe7mU847me67mFW2hK0xC31ESS3KVLmT9iBJtnzQpYr/X559PptttIaNQoRC2rOxTlJR/TQPvRzwLr6Fbi64brg/fTbfFRVoGIHIlzH7upLLAOckxf5d7HvACnx7gZXvlYVPWMYG2qDlW9U0TuBTrjtHdh2QPv3dAPry4vVS3C6Zl/onotjWzbNmVzz4WPh7sZpg7rocrmHTvYmJdLict/EPw8f/JybCwt0tJpmJTkN2B0uXZQUroBl2ubu+Twmm90dUhNdEAWARvdr2BiEInF6VmPRYgFYhGJc/90vycWJBYhZtdPYoBYQCI2QHc6dMte+Pjd+ane29S77q766vXee3uA1gDQ9Yi2XD3mukq1v7IiNrh29zJ/D1yqqsNq6LBDgNcDrZOtqluBdjV0vojQhCa8xEuMYAT3cz8v8RKl+F4NJJdcHuIhHudxruM6buVWmtM8xC024VRaUMDiSZP4d/LkgGtWZ+y3H11Hj6Zxjx4hbF3d8hM/sYTyI28F4eKq52s00WUzztxm739cm4HH/B0f3GtPfwSMUNVJXpvX+zlmCU6CMH/HfBin5/grnJFiIR3SJiINcFYDcQH/7m5g7T5Oc0BFpEBVt3luV9UfqtvWSFZSXMqiOcvC3QxTD8S7X8FsJK9SISU2LTEABYrdL1PT0jNrPut9xM7od88Ba0cN3uRV9aVAgXVd15GOTGUq//APAxhAXIBnKzvYwSM8QjvaMYhBrGFNCFtqwmXjN9/w9ckns/iJJ/yvWZ2SQue77uKod9+1wLqafK1tfSzH0opWYWiNCRV3T+ocnOlJnvrgZPr2yT3E+2NglKqO91FlFs5KGN7HnB0kKdmlwIWqeqJ7GarLPV9BPs5uE5HWIvI+TuD/M/ALsFlE3hORwOmKa+E4xhhjTHVFbHDt9gIwIGitIETkZBFZ7n6q7b2tgXvbidU9T7TYm72ZzGSWsITruI4E/CeeKKCAiUxkb/bmeq5nJStD2FITKvnr1zP7hhv4+Yor2LHS/59x85NO4tiZM2l/5ZXExEXswJeoUEghM5hRodwSmdUbjwL9ReQqEekkIhNw8ok8Dc48ZBH5oqyyiPTGCayfBqaLSHP3y3P+ztPAXiIy3n3Mq4D+VEyc5i2GaiYJrSoR2RP4CSeB2gjgHJxlxu7BSfb2o3tuekiOY4wxpj6q+YFakZ7Q7Cmc9ayX4TzlL5fFS1VvquRxPgQ+UtUn/Wy/DjhNVU+tXotDY3eW4gpkNasZy1ie5VkKCNyxH088l3M5d3AH7erW6Pl6yVVSwvKXXuLvxx6jNMDfqZRWrehyzz3sceyxIWxd3fYWb3EO5ZcfTiaZDWwgnfQwtcrUhMosxeWuNxC4DWgBLACGlCU4E5FpQG9Vbevx/jIfh1lRVsdd7xjgMWB/nCHeD6nq00HacR9QrKojg7W5pojIZJw2nqCq+V7bUnCynv+pqteE4jiRZncSmm1eu5VL9h9aSy0yxpi656A+yv2vPbdb+/pLaBbpwfVXATarqh5XyeOswrnxLvKzfV/gC1XdazeaGXI1HVyXWcc6xjGOSUwin/yAdWOJ5VIu5S7uogNVWobURIitc+cyb8QIshcu9FtH4uNpf9VVdLz+euKSq7WsvPFyNmfzDu+UK7uYi3mZl8PTIFNjKhtcRwoReRK4CFgIzMNrcl9lH2RX8ZyrcTKjf+Nne2+czOcB78s1dZxIszvBdXFRCf/Os9FlJrxUXfy29iteWbCYZdszAtaNpZQj91rDuZ270rJBH2Jk95bwqg2hjo9UFbSQUs3B5crFpXm4NHfn7+rKo1RzUc3H5drhlGk+Lt2BagEuLURd+VRnCbNSBJdLcKngQlCNoVQFVUEVXAgg7vxisjNFsnOpxP17+WRqurO87H2sk7BVYmFnIjbn5aw/HuM+luc23MncnPOoiruueLwAEXYNyvZO6ia7fmpZXWjRrD0nHHVXFa9U2emiMLiuKSJSAHRV1cV+tu8D/KGqURE91FZwXWYjG3mER3iSJ8kj8HliiOEiLmIYw9iP/WqtTabmFG3bxl8PP8zKGTMCrlmdedhhdB09mvQO9vCkpm1mMy1pSbFXgpKZzORE6s0MlTorCoPrGnmQXcVzFgLtVXW1n+174SQ3SwzFcSKNLcVloo1qKTn575OVPZGi4r9RhZ/Xt2fan71YkdMk4L4p8fFceWAPrjrwYNITo+p/1bBRVXKKithWkM+2ggK2FxSwtSCf7QU7yC7cQXbRDnIKC8grKmRHcRF5xcXkF5eQX1JKYWkpBSUuiktdFJa6KHYpJS7FVfdDwgpObN+Bp089c7f2jcqluMqISDLQ3v32X++hX5WwGugG+Ayu3dssY5dbM5rxEA8xlKGMZzwTmUgOOT7runDxMi8znelcwAXczd3sz/4hbrGpDFVl9dtvs/CBByjassVvvYTGjel8113sddZZEbucQ7SbwYwKgXULWnA8x4epRaY+U9VwzPfYCHTAuT/70pHKrV1TU8cxxuwG1WKyd7xNVvZEikuW7iwXgcNb/MshzZfy5cpOvPzXkWzM992TvaO4mMd/+YmX5/3OwEMO55KuB5BYD/O6uFTZmp/Pxh15bN6Rx+a8HWzOz2NTXh6bd+xg844dbMnfQVZ+Plvz8yly+V75x4RXRPdci0gi8BBwDZCA06dfCEwGbq9s5m93opg+QA8/c7JmA5+p6qAabH6tqe2ea29b2cpEJjKe8WxjW9D653AOwxnOARxQ+40zlZKzZAnzR4wg6+ef/VcSoc2FF7LfrbeS0KBC7j9Tgw7ncH6m/J/FrdzKwzwcphaZmhRtPddlRKQJzoPs31W1sJbPNRlnXevjvc8lIknA51R+znW1jxNprOfaRDqXFpKd9xpbsp+guHRV0PoS04Uv1/Rjyu85bCkI3EfWMj2dwYcdwdn7dSY2JtJzLwemquQVF7OpLGDesYNNeXlk5e9gY54TOG/ckcfmvDw25+8IuH64qXm10XMd6cH188CJwO04S4wA9AQeAD5X1SsqeZxmwFycUfaPA3+7N3UCbsAJ2g9S1YDri0aKUAfXZbaznSd4gkd5lC347/kscyZnMpzh9MCWawqXkvx8Fj/5JP9OmYIW+1+JJ6NzZ7qNGUOj7t1D17h6ahGLfE6h+IM/6Ea3MLTI1LRoC65FJB14HifTtgIdVXWpiDwNrK+NRGfuDN6zcdb7fgLnvqw4yckG4oysO1hVA44qq6njRBoLrk2kcrny2Z73CltynqKkdF3Q+kkJB5KZMZjUpBMQEXIKC3lu7hyemzubvADfSwD2ysigRVo6ibFxJMbFun/GkRgbS1JcHAnu3z3LE+PiSPJT7uwXT0LZsWJjSYiNDTpKT1XJLykhp7CQ3KJCcouKyHb/LHuVbcspKmJrfr47mN7Bph15FJTs/jxoU7vqY3CdA/xXVT/zKu8DvKmqgTMllN+nDTAJOIlds9oVmAkMVNXlNdLoEAhXcF0mhxye4ike4RE2sSlo/VM4heEM53AOD0HrTJkNX37J/FGjyF/tb7QkxKWlse/gwbTt18+W1gqR4QznXu4tV3YAB/B7aFdCMrUoCoPrp4ADgOuB74Fu7uD6NOA+Va2VYUgi0hZ4Ct/35RtUdamfXb2P0w54srrHiSQWXJtI43LlsS33RbbkPE2pK/h3v+TEw8jMGExK4tE+g9fNO3bw1OyfmT7vd4rD3FtbFrw7QbkTdMfExOwMpHOLinBFcLxUk+JjYoiNiSFOYoiJEWJFiJEYYsT53fmjdH7GiDjJyXCnFXP/Oe/86T5m2R+/Z112bpPyqcc8/q541ytXzU/7KzuZsex4h+/VimFH9a7kXhWOEZXB9XrgOFVd6FXeGfhKVfeo4vFaA7k4w94EWKyqW8W5wq1UNSrSbIY7uC6TRx7P8AxjGcsGgnf6n8iJDGc4vegVgtbVX/lr17JgzBjWf/ppwHotTzmFzsOGkdy8eYhaZly42Ju9WcGKcuXjGMct3BKmVpmaFoXB9WrgbFX91f1Q+wB3cF02RLxW14YTkUY4c6PBfV8O53EigQXXJlKUurLZljuVrTmTKXUF/18qJfEoMjOGkJJUuQ6V1dnbGf/Tj7z998JaWHG4bkuKi6NRUjKNkpJokJRMw6QkGiQmkpGYSHpiIukJiaQmJJAan0BqfDzJ8fEkl/Xsx+3q1U+IjSUuJpa4KB+CH2rRGlwPw0k21r9srrQ7udnzOHOo7g20v4/jlQItVHWjV3kmsFFVY33vGVkiJbguk08+U5jCgzzIWtYGrX8sxzKCERzDMUilnzGZYFzFxSybNo1FEydSumOH33oprVvTddQomh19dAhbZwC+4Rt607tcWQwxrGY1LWgRnkaZGheFwXUezooaS72C6+7A16raMKwNDEBETsYZlXaAqm732tYA+AO4WlUDP22MMBZcm3ArLd3C1twpbM15HpdmB62fmnQCmRmDSE7cvamAi7I288iP3/P5sn93a/+6Ij0hkaapKTRNSaVJivMzMyWVpikpNElJJTM5mcyUFBonp5ASHx/u5tZr0Zot/HDgGGCNiMxzl3XFaXeqiLxXVlFVz6jE8QR8PhhLAyqVHM1UlEwyN3IjAxjAVKbyAA+wCv/JLb5y/3cURzGCERzP8RZkV9OW2bOZN2IEOYt8LuUOQExCAh2uuYYO115LbFJSCFtnyrzESxXKTuREC6xNuP0KnAGMd78vu09eA/wYjgZVwQ3Aw96BNYCqbheRh4BBQFQF18aES0npZrbmPM3W3BdQDd6Rk5Z8CpkZg0hK6Fqt8+6b2YTJp5/FnHVrGPfj9/y8xv+UtmiTEBu7M1Bu4g6Sy943TU2lWWoqzVLSaJKSQrIFzFEv0nuup1a2rqpeHuA4E92/Xg9MBTy79WKBQ4EiVT1yd9oZapHWc+2tiCJe5EXu536WsSxo/cM5nBGM4D/8x4LsKiraupW/xo5l5WuvBazX5Igj6Dp6NGnt2oWoZcZbPvk0pznZlO8BmM50LuKiMLXK1IYo7Lk+Amd+8qvAJcAUnIRghwJHq+pvYWxeQCKyCjhBVX0+WRSRfYEvVHWv0Laseqzn2oRacck6tuZMYlveywRfjCeG9JQzyEy/icSEigk6a8LGvFzW5uRQWFJCYWmp++eu3wvK3peUlisvCrLdKdtVXtn53klxcaQlJJCWkEhaQgLpCYmkJyQ4vycmurc55Q0Sk2iaWhZIp5CekGhLm9ZBUTksvKaIyFfuX4/ByTpe5LG5CFgOjFNVf+tgR5RID67LFFPMdKZzH/exhCVB6x/MwQxnOKdzugXZQajLxaq33mLhgw9SvNX/HKjEpk3Z/667aHn66fYPe5jNYAZ96VuuLI00NrCBFFLC1CpTG6ItuAYQka7ArUAPIAb4DXhIVeeHtWFBiEgBzpB2n/dvEdkH+ENVk0Pbsuqx4NqESnHJKrKynyQ771W03NdjX2LJSDmHzIwbSYhvH5L21bZSl8sJyD0C8YKSEkpdrp2BdGpCAgmxUTFz1IRQtA4LrxGqeizs7AkfpFqJySOm2uKJpz/9uYRLmMEM7uVe/t65ClpFs5nNmZxJd7oznOGcxVnEYMkVvGUvWsT8ESPYEuiLlwhtL7mE/W65hfj0Ws1FZCrpRV6sUHYu51pgbSKCO4i+LNzt2A2rcXKz+Hs43g2IqmW4jAmFouKlZOU8Tnbem0CwpaLiaZDal8YZ15MQ1zoUzQuZ2JgYkmNibDi2qTH1oue6romWnmtvpZTyBm8whjH8yZ9B63ehC3dzN+dyLrHYE8OSHTv4Z+JElk6digZYM7FBly50GzOGht1szeRIsYEN7MmelFJarvxLvuRYjg1Tq0xtiYaeaxFprKpbyn4PVLesXi22pSvO/O72wBWquk5EzgJWqOrcIPtOAPoAPcoSn3psS8FZA/szVR1UK42vJdZzbWpLYfEisrInkrPjXSDwkGiRJBqkXkzj9OuIj2sZmgYaEyXq+7DwVOB0VX3V/f4pwDOjUykwWCuTuSECRGtwXcaFi3d4h9GM5g/+CFp/P/bjbu7mAi4grn4Mtqhg/WefsWD0aPLX+s/GHpeWxn633krbiy5CbPhSRBnPeIYwpFxZK1qxnOU2OqMOipLgeufqGSLiwneyTwG0NlfSEJETgfeAj4FTgE7uTOW3AEep6llB9m8GzMVp/+Owc3hUJ5xkZwIcpKrB14uMIBZcm5pWUDSfrOyJ5OZ/GLSuSAoN0y6jcfo1xMU2C0HrjIk+9T24vgFnvez/ut/nAL+wK7HZAcATqjo2TE2skmgPrssoyvu8z2hGM4c5Qet3pCPDGMZFXEQ89WP4zo41a1gwahQbvvgiYL09Tz+dznfdRVIzuwlGGkXpRjcWsKBc+Z3cyf3cH6ZWmdoUJcH1McAPqlri/t0vVf2mFtvxM/CCqj7ltQxYD+B9VQ3aXSYibXCW4zoJdibsUJwkbQNVdXnttL72WHBtakp+4Vyysh8jr+DzoHVjJJ1G6VfSMO1K4mIzQ9A6Y6JXVAbXInI08KOqlniVxwFHqOq3lTzO9zgJy95xv995A3e/vxCn5/qwmmx/bakrwXUZRfmETxjFKH7m56D129GOu7iLS7mUBBJC0MLQcxUVsXTqVBZNnIirwH/WztS2bek6ejRNj4yKRPf10ud8Th/6VChfyEI60SkMLTK1LRqC60ghIrlAF1Vd7hVctwP+UtVKrxsoIo2ADjgB9mJV9Z/tMfBx7gT+C+wLFAI/AXeq6gKPOtOoOEf9Z1U93KNOIjAOuBBIBr7ACfaDrjFkwbWprh2FP5O1/TF2FAb/qhwT04jGaQNomH45sTENQtA6Y6Kfv+A60scjfgX4mgvWwL2tsjoC/3i83wblJj/OBvuWGy6CcDInM4tZfMqn9KJXwPrLWMYABtCRjkxiEoUUhqiloZH1yy98c8YZ/DV2rN/AOiYhgX0HD+aYjz6ywDrCPcZjFcqO5EgLrE3EEJHzRORMH+Vnisi5tXz6rcCePsoPwklWVmmqulVVfwUWAsXVaFNv4CngCOA4nGxPn/uYm/450MLjdYrX9vHAOTjB9VFABvCBiNi8HVMrVJW8gm9ZufG/rNp4dtDAOjamCU0b3E37Fj+T2WCwBdbG1IBID64F3/PAMoGqdN1m4JG1QVVbqeoKj+1xUE/GGUcwQehDH77lW77iq6CJnlaykoEMpD3teZzHySc/YP1IV5iVxe+33caPF15I7mL/q8I1Peooen/8MfvceCOxiYkhbKGpqr/4i4/4qEL5zdwchtYY49dIwNeTvDz3ttr0CvCwiOyFc7+Pcw9THwc+UuwHICKDRWQlsB3YLiKrRGSIVHEdQlU9SVWnquoCdxb1fkBTwPtJZqGqrvd47Uz8JiINgCuBoar6mXut8H442ctPqEp7jAlGVcnN/5yVG09n9aa+5Bf+FLB+XGwLmjUczd4tfqJxxkBiYtJC1FJj6r6IzA4lIu+5f1XgZRHx7JqMBboAP1bhkKuAruB3HagD3HVMBBCE3u7/vud7xjCGT/nUb/01rOEmbuJ+7mcoQ7mGa0glekZkqsvFytde46+xYynevt1vvcRmzegyfDgtTj7Z1qyOEuMZX6GsHe04kwqdhMaE097AIh/lS9zbatPdwDRgBc4D9YXun68A91X2ICIyFrgaeBiY5S7uCYzA6VW+rRptTMfpjPAeZt5LRDbijIb7Bhimqhvd23rgPLTfefNS1VUi8hdOj/jMarTHGABUXeTmf0JW9ngKixcErR8XuxeZGTeSkXo+MWIP542pDRE559q9HjU485leg3JdkkXAcuBZVd1cyeM9BpyIs1RHgde2VJxh4Z+o6hBf+0eaujbnujJ+4ifGMMZnL6C3pjTlVm5lIANJI7Kfxm7/6y/mDx/O1rkBVpuJiaHdpZey7+DBtmZ1FNnMZlrRigKvDsHxjGcQUbUqkKmiaJtzLSJrgctU9TOv8hOBl1R1jxC0YW+coeAxwFxV9T98x/f+W4CrVfUNr/JzgWdUdbezM4nIazjTyw5W1VJ3WV+cpKjLgLbAvTgP/3uoaqGIXITT8x6vHl+0RORLnPng1/g4z9U4Dwho3bp1jxUrVnhXMQYA1VJydrxHVvZEikp8PRcrLz5ubyeoTvkvIjZQ05iaEHUJzUQkBpgB9K/uElkeS3WUAE+wa/71fjhLdcQAB3o8ca7q8Y8GbsV5Ut0SuFxVpwXZp6u7LYcCW4BngDFaiT+Q+hhcl5nNbO7lXt7l3aB1M8lkCEO4gRtoQGTNIyrJzWXRxIksmzYNLS31W6/hAQfQbcwYGuy/fwhbZ2rCvdzLcIaXK8sgg9WsJh17SFKXRWFwPQlnTvB/VfUfd9m+wJs4GcUrBII1eO4EIMbHg+8kwKWqRZU8zhbg8LL2e5Tvg5NorNFutu9RoC/QqywJqp96LXF63y9Q1bcCBNdfAYtU9dpA57WEZsYX1WKyd7xFVvbjFJf4/eu4U0LcPmRmDCI95Qxsqr8xNSsaE5opcCbOcK7qHcgJmo/AGW72IPC2+3U/sADnprlbgbVbmvs4gyD4xF8RyQA+AzYAhwA3AUPBJmIGczAH8w7vMJe5nMM5AetmkcXd3E1b2jKKUWytMKIv9FSVtZ98wlcnncTS557zG1jHZ2TQdcwYer3+ugXWUaiQQp7kyQrlAxhggbWJRLfhzFNe6J6nvAr4E8jGuTfVpteBgT7Kr8UZuVZZLwLX+yi/DnhpN9pVNurtQpylPANGMqq6FicBW0d30XqcnuwmXlWb4dz7jak0lxayLfcllq3rxfotQ4IG1onx+9My81naNv+SjNSzLbA2JoQitucaQETm4wzzmhW0cuWP2YhdN78lnglIauj4ucANgXquReQ64CFgD1XNd5fdjfMlYK9gvdf1uefa2wIWcB/3MYMZqM/cd7tkkMGN3MgQhpBJ6NdvzFu5kgWjRrHx668D1tvrrLPofOedJDbx/k5mosULvEB/+pcriyWWf/mXNrQJT6NMyERbz3UZEekDdMeZ8/wb8EVlRlNV85ybgd6ey1y5y/cHvlLVZpU8ziTgImAdztJZAIfhjCabjjNyDQBVvakSx5uA02PdW1X/qkT9JsBa4CpVfdGd0GwTzui7V9x19gJWAierasA519ZzbQBcrh1sz3uFLTmTKCldF7R+UsJBZGYMJjXpeMvNYkwti7ph4QAicjIwDGfo9h+7c5MXkUOBOWXzpCpRvwcwT1V3axmPSgbXLwKZqnqqR9khwC/A3qq6LNA5LLiu6G/+5j7u4xVewbUrMbxPaaRxPddzMzfTjEp9b6uW0sJC/p0yhcVPPomr0P+yYWnt29N11Cia9OxZ620ytUdRutOdecwrV34+5zODGWFqlQmlaA2uw0FEdgAHqerfXuWdgN9UNbmSx6ns8pyqqscFOdaTOJm9z8IZ8VYmV1VzRSQNJ4v6mzjBfFvgAaAV0ElVc9zHmQScgZM/Jgt4FGiEMy874HcSC67rN5crj225L7Al52lKXcHTCyUnHk5mxmBSEo+yoNqYEInW4DoHSMIZvl4C5Rc0VtWMShyjFGiuqpsqec5soHuwIWAB9q9McP0psFpVr/Aoa40zX+sIXz31nolOEhISehQGCNLqs8Us5gEe4EVepJTAz1NSSOFarmUoQ2lO81ppz+ZZs5g3YgR5S/3/dYpJTGSfG26g/VVXEZOQUCvtMKHzJV9yPMdXKP+JnziMw8LQIhNq0RBci8jNwFOqWuD+3S9VfbQW2/ETMFNV7/EqHwP8R1UPqa1zB2iTvy9Go1R1pIgkA+8ABwINcQLsr4Dhqrpz5RH3vPGHcXrUk4EvgIGedfyx4Lp+KnVlsy3nebbkPovLFXwqW0ri0U5QnXR4CFpnjPEUrcH1ZYG2q+oLlTiGC3geJ6tnZVwNdA5BcL1KVa/0KGuDkwW9p6oGXKDQeq6DW8pSHuRBpjGNYgIPQkgiiau5mtu4jT3Zs0bOX7h5M38+8ABr3nknYL1mvXvT5Z57SG3dukbOa8LvNE7jQz4sV9aTnvxYpdUDTTSLkuB6GU726yz37/6oqtbaclwicipOoPoa8KW7+HjgPOBsVf2gts4dySy4rl9KS7ewNXcKW3Oex6XZQeunJp1AZsYgkhN7hKB1xhhfojK4rgki8jUEmYxb0UWqGnxyi+/z2bDwCLKSlTzEQ0xhCkUETjqbQAJXciV3cAet2b1gV0tLWfHqq/w9bhzF2f5vkEnNm9NlxAian3iiDeGqQxaxiP3Yr0L567zOuZwbhhaZcKhscC0iA3EShrXASSA2WFW/81M3CXgaZ7mqTjhZvHt71emN04PqrZP3sOtIIiL/wVnv+kB30VzgPlX9OHytCi8LruuHktJNbMl5mm25L6AavA8oLflUMjNuIimhawhaZ4wJxF9wHReOxlSFiCQCFwOdcYLkP4H/qWqlxkV7f/mIELOAh0QkyWP5kT44yVCWh61VdVBrWvMkT3IXdzGWsUxmcoV1h8sUUcQkJjGFKfSnP3dyJ+1oV+lzbf/zT+YNH862P/7wW0diY2nXvz/73nQTcWmRvQa3qbrxjK9Q1pa2nMVZIW+LiWwicgEwASdT9vfunx+LSGdVXeljl1igAGcJx1NwhiP7sz/OEo9lKkyLck+ZaqGqG0XkeWBQ2VzhUFPVT4BPwnFuY8KhuGQdW3KeYnvedLxWofMhhvSUM8nMuInE+H1D0j5jzO6L6J5rEemMc8PNAOa7i7viLBnyn8pk8AwFd3KTDu63P+Is9/UesEVVV4rIA8Chqnq8u34DYBHwNXAvsA8wDWc+1yPBzmc917tvPesZxzgmMYkdQWYKxBJLP/pxF3fRcWeC+YqKc3JY9NhjLHvpJXD5T6bW6MAD6XbvvWTsV7Fn00S/LLJoRSvyvVbje5RHGcKQMLXKhENleq5F5Gec5JkDPMoWA2+o6p1B9n0C6BKg57qpqgbMguQeZdVNVZdWNTeJqX3Wc103FZesIiv7CbLzZqBBRtNBHBmp55CZfgMJ8e1D0j5jTOVF5bBwEfkMZ650P1VnEop7jeiXgURVPSmc7SsTYCjeC6raX0Sm4Szn0dZjn67Ak8ChwFac4X6jK5MR3YLr6tvIRh7lUZ7gCfIIfC1jiOEiLmIYw8oN+VVV1n30EQvuvZfCjf6XSY9v0IBOt99O6/POQ2IieWl5Ux33cz/DGFauLJ10VrOaDILmXjR1SLDgWkQScO5tF6rq6x7lT+IEzccEOX6w4HoFkIiT6fpeVa1wf3Ln/mgOzMHJZj0DvJ4MuXkm36xp7msxDGc96dZAvNe56+UCvRZc1y1FxUvJyp5I9o43IUiyVSGBjNS+ZGZcT3xcq9A00BhTZdE6LPxI4JCywBpAVbNFZBi71rEMO1X9GmddUH/b+/somw8cXXutMoE0oxkP8iBDGcp4xjORiWTje460Cxcv8zLTmc75nM/d3E275WnMHzmSTd/5nB65U6tzzqHT7beTmBn6dbVN6BRRxBM8UaF8AAMssK6f4kTEMzKarKqTPd43wRnmvcFrvw3ACdU47zrgOuBXIAFnOakvRKS3qn7rVbcfcCu7Rl1l4rUiR4iMAS7AWcrqMZw56G1x1pgeXpUDiUhz4AigGc4qIzup6lM10FZjqqSweBFZ2RPI2fEeBFkmVCSJhqmX0Cj9WuLjWoamgcaYGhfpwXUBvueVNXBvM6ZaMslkDGO4mZuZyETGM55tbPNZV1FmuP874p80zt/cgL1J9Fk3vWNHuo4ZQ+YhIV9FxoTBDGawjvI5EGOI4UZuDFOLTJiV+Hqa7YP3SCXxUVZpqroIZ8pRmVki0hYniP7Wq+4GnEC2LHP4haqatbvnrobzgWtV9RMRGQe8q6r/ishfOLlInqnMQUTkEmAKzjXcSvnrqIAF1yZkCormk5U9gdz8j4LWFUmhYdplNE6/lrjYpiFonTGmNkX6GNX3gWdF5EgRiXW/euHcbN8Lc9tMHdKIRtzDPSxnOfdxH41pHLD+jyfmMviDNdz79HoWd93V2RObnEyn227j6Pfft8C6nlCUR6m4DPA5nENb2oa+QSYabMYZG9rcq7wZFXuzq+tnqJg0QkRKRaSZ++1XEHQCaG3ZA2f4OkAuux6ofwKcWIXj3AeMBVJVtbmqtvB4WTegCYn8wt9YvelSVmw4KWhgHSMZZGYMpn2LX2jWcLgF1sbUEZEeXA8CFgPf4fRUFwDfAP8Ag8PXLFNXNaABd3EXy1nOQzxEUwLf7H7ps4Nb3lnD6Cnr2DigK71nzqTDNdcQEx8fcD9Td3zN1/zO7xXKLYmZ8UdVi3DmOvfx2tQHanxB9O6Ar6Ul84GyJQsuA5Jq+LyVtRIoC36XAGW5VHriZw64HxnANFUtqcG2GVMpOwp+YtXGC1i58TTyCj4PWDcmphFNGtzO3i1/pkmD24iNDfww3xgTXSJ6WLiqbgPOFJGOwH44w70WquqSsDbM1HnppDO09BZOndGICWvu4Y3LNrK1mf8kJLOPzeeqY99lBjsYwQh60SuErTXh9BiPVSg7nMPpSc8wtMZEkUeBl0TkF+AH4FqcIPNpAO9VJtxlnXHmUjcB0kSkO4Cq/u7ePhhnOcc/3fUuAc4CzvFx/h+Bd0RkDs69daKIhDyhGfA2cDxOHpUJwP9EZACwJ/BwFY4zHTgVeLzGW2iMD6rKjsJvycoeT37hz0Hrx8Y0oXH6tTRMu4yYmICLCRhjolhEB9dlVHWxiKxz/54b7vaYum/bvHnMGz6c7QsWcDrJnDi1FZ9dkMObV28jq4X/IPsz93+96c0IRtCb3oj/XHcmyv3DP7zP+xXKb+bmMLTGRBNVnSEimcDdQAtgAXCKqq5wV2kBeK+/8xHQxuP9XPfPsn9kEoBxOIFpPk6Qfaqq+hqf6pnQTAlTQjPPZcdU9Q0RWYWTzPQfVf2gCoe6GedhwfE4S3cWe51ndE201xhVJa/gc7KyJ1BQ9FvQ+nGxLWicPpAGqRcRE5McghYaY8Ipopfigp1P4m/G+bIAsBbnif/4yixbVRfZUly1pzg7m78ffZTlL78MPv56FScon5+Tw1uDdrChaeB1sgF60YsRjOAETrAguw4ayEAmMalcWRvasIQlxEXHs0tTCyqzznUkcSc0OzhUCc1EZCnOSiBZIjICGKeqwf9BDXzMG3F6vjcDG/FKaKaq3apz/FCzpbgij6qL3PyPycoeT2Hxn0Hrx8e2onHGjWSknkeM+E5+aoyJXv6W4oroOdciMhYYiZPArI/79TQwAngofC0zdY2qsvq99/iqTx+Wv/SSz8AaICW1MXd0n8SKzCye5Vna0S7gcb/ne07kRHrSk4/4CN39RMAmwmxhC9OYVqH8Jm6ywNpEFVVtF+JM4S2AFPfv97Br7nd1DAduUdVmqtpFVbt6vKIqsDaRRbWE7Ly3WL7+ONZmDQgaWMfH7U3zxo/RrsX3NEy7xAJrY+qZiO65FpEtwNWq+oZX+bnAM6paLxcPtp7rmpW7bBnzR4xg84+B8wi1Pv98Ot12GwmNGu0sK6aYV3iF+7iPxSwOeq4e9GAEIzid060nO8o9wAPcxV3lytJJZxWraECDMLXKRIJo6bkWkR9xhqJvc79/AHhYVbe43zcBflPV1rVw3jzge5zgehxOpvAKKjucW0SycOao/1tT7Qwn67kOP9VisvPeJCvncYpLlgWtnxC3L5kZN5GecgYisSFooTEmnPz1XEdDcH24qv7jVb4P8LOqNvK9Z91mwXXNKC0oYPGkSfw7eTKuIv+r0KTvuy/dRo+m8cH+l60toYQZzOBe7uVv/g567gM4gOEM52zOJiayB5AYH4oooh3tWMvacuWDGewzwZmpX6IouHYBzVV1o/t9NtBdVZe63+8BrFXVGo0URGRf4F6c+d7dcFYA8ZXlu9LDud1rZGfXlbnVFlyHj0sLyc6bQVb2E5SUrg5aPzG+C5kZg0lL/g8idj83pr7wF1xH+tjFF4HrcZbk8nQd8FLom2Pqio3ffMP8kSPZsXKl3zqxKSnsO2gQ7S67LOjSWnHEcTEX05e+vMmbjGEMC1jgt/4f/MG5nMv+7M/d3M15nEcs9qQ7WrzGaxUC6xhiuImbwtQiY2pESIbTqOoi4DzYGeAfUxbgV0MKcJWInATMo2JCM/uf0wTkcu1ge94rbMl5ipLS9UHrJyUcRGbGYFKTjkfERqIZYxyRHlwnAhe5b5Y/ucsOw1muZLqITCyraDdOUxn569fz5333se4jX8lzd2l+4ol0GT6c5JYtA9bzFkss53M+53Iu7/AOYxjjcw3kMn/yJxdyISMZyd3cTV/62nzdCKcoj/JohfKzOTvoHHxjzC4iEg+8AaTjJCGrjk7syp6+n9e2yB2iZ8LO5cpla+4LbM15hlLX5qD1kxN7kpkxiJTEoyyoNsZUEOnDwr+qZFVV1eNqtTERxIaFV52rpITlL73E3489RmmAa5e81150HTmSPY49tkbOqygf8AGjGc1sgg/x60AHhjGMi7mYeAL3lpvw+IZv6E3vCuU/8ANHcEToG2QiThQNCy/FGRa+yf0+B+imqsvc72tlWLhXG7YCPcqGohuHDQuvfaWu7WzNeZ6tuVNwubYGrZ+SdAyZGYNJSTwsBK0zxkS6qJxzbXyz4Lpqtv7+O/OGDyd74UK/dSQ+nvZXXUXH668nLrnm16FUlJnMZBSj+GnnIAz/2tKWu7iLy7iMBBJqvD1m953JmbzHe+XKDuMwZjHLktQZIKqCaxfwGbvWtz4Z+AYoWxYrETihloPr54C/VHVcNY/TGljla4lOEWmtqv7nAEUgC65rT0lpFltzp7At53lcmhO0fmpSHzIzBpGceFAIWmeMiRZRGVyLSFtVXe5n2xGqGji9cx1lwXXlFG3fzt8PP8yKV1/1u7QWQOZhh9F19GjSO3So9TYpyhd8wWhG8x3fBa3filbcyZ1cwRUkYst5hNtiFrMv+1ZYUu1VXuUCLghTq0ykiaLgempl6qnq5bXYhnuAIThB/WycLOKe5644B8P3cUqBFt5zt0UkE9hYmw8IaoMF1zWvpHQjW3KeZlvui1RmWfW05FPJzBhEUkKXELTOGBNtojW43g5cr6ove5TF4Kx9fbuq1stow4LrwFSV1e+8w8L776doyxa/9RIaN6bzXXex11lnhWXe1Dd8w2hG8yVfBq3bkpbczu0MYADJ1HzPuqmcG7iBJ3myXFlrWvMv/9pcebNTtATXkUBEAq1xpKq6dyWP4wL2KBvi7lHeBlgYbX8eFlzXnOKStWzJeYrtea+gWhCkdgzpKWeRmXEjifH7hqR9xpjoFK3Zwm8HnhaRU4BrgabAK8CewKnhbJiJTDlLljB/xAiyfv7ZfyUR2vTty35Dh5LQIHzrER/DMXzBF3zP94xhDJ/yqd+6a1nLIAZxP/czlKFcy7WkElXfFaPeBjYwlYodfTdxkwXWxuwmVa1WFkCPxKYKPCAinl2SscChECCrpKmzikpWsiX7CbbnzcArebwPcTRIPZfG6TeQEF+p5znGGONTRPdcA4jIfsB0nMC6Ic78sAGq6r9Lso6znuuKSvLzWfzkk/w7ZQpa7P8mmtG5M93GjKFR9+6ha1wl/czPjGEMH/Jh0LpNaMKt3MpABpJOeghaV79tYxvHcRxzdyYjdqSRxmpW04DwPaQxkcd6rkPHI/HpMcAsoMhjcxGwHBinqotD3LRqsZ7r3VdU/C9Z2Y+TveNNoDRgXSGBjNS+ZGZcT3xcq9A00BhTJ0RrzzXAepybYxcgBvikPgfWpqINX33F/JEjyV+92m+d2NRU9hsyhLb9+hETF5l/7Q/jMD7gA+YwhzGM4V3e9Vt3M5u5gzsYy1hu5mZu4AYL8GpJHnmcyqkVAmuAK7nSrrsx1eC5pKYvwZbZVNVj3ceZCgxS1Wwf5zgBiKrg2lRdYdHfZOVMIGfH+4ArYF2RJBqm9qNR+rXEx7UITQONMfVCRPdci8jRwMvAOuBioCfwOPAVcJWqZoWxeWFjPdeO/LVrWTBmDOs/9T+cGqDFKaew/7BhJDdvHqKW1Yw/+IN7uZc3eCNo3YY0ZJD7v0Y0CkHr6ocCCjid0/mczytsa0QjFrCAllRtLXRT91nPdeX5WHIzHmed6jjgt91dZlNE9gQuB64A2lhCs7qroGgeWdkTyM3/OGhdkVQapV1Go/RriIttGoLWGWPqKn891zHhaEwVfA68CBypqktU9SWgO84Q8fnhbJgJH1dxMf9OmcJXJ50UMLBOad2aw55/noMffzzqAmuAAziA13md+cynL30DLvO0jW2MYhRtaMMwhrGZzSFsad1UTDF96eszsE4hhQ/4wAJrY6pJVY/1evUC9gI+BF6ryrFEJFZEzhaRj3BGvJ0NPA1UaSkIEblTRH4VkWwR2SQi74tIF686IiIjRWStiOSLyNcisr9XnUQReVxENotInoi8JyJ7VaUtxr/8wjms3nQpKzb8J2hgHSMZZGYMpn2Ln2na8G4LrI0xtSbie65V9Vsf5THAXap6bxiaFXb1ued6y5w5zBs+nJxFi/zWiUlIoP3VV9PxuuuITUoKYetq19/8zf3cz3Sm4woy5C2VVK7nem7hFprRLEQtrDtcuLiUS5nO9ArbEkjgQz7kBE4IQ8tMNLCe6+oTkc7ATFUNOhFWRPYFrgIuxVnK6xWchKgHqOrC3Tj3TOBV4FdAgNE4I+c6l01LE5HbgbuB/sAiYATQC9hX1Vk8WUQmAWcClwFZwKM4uWN6qGrAycDWc+3fjoJZZGU/xo7C74PWjY1pRKP0q2mYdjmxMRkhaJ0xpr6IyqW4jG/1Mbgu2rqVv8aOZeVrgTsymhxxBF1HjSJt77qb7XMJS7if+3mRFykNkqwlmWSu5VqGMpQW2LyyylCUgQzkaZ6usC2WWN7kTc7kzDC0zEQLC66rT0SOAd5R1YDzXETkO5ycLG8AL6vqN+7yYnYzuPZxjjRgO3CWqr4vztqNa4EnVPU+d51kYCNwq6o+IyINgE3A5ao63V2nFbACOFlVZwY6pwXX5akqOwq/ISt7AvmFAVYDcYuNaUrj9GtpmHYpMTH2v6IxpuZF1bBwEflRRBp6vH9ARBp7vG8iIivD0jgTUqrKyjfe4Ms+fQIG1olNmnDgY49x+Isv1unAGqADHXie51nMYq7mauKJ91s3n3we4zHa0Y6buInV+E/6ZpzA+nZu9xlYC8ILvGCBtTE1SERu9nrdIiLjcHqOP6rEIXriTB+bUBZY14J0nO9LW93v2wHNYdf6iaqaD3wLHOEu6oEzf9yzzirgL486JghVJTf/U1ZuPI3Vmy4KGljHxbagWcMx7N3iJxpnXGeBtTEm5CIyuAYOBxI83l+PM5SqTCzOnCxTh2UvWsSPffvyx+23U7x1q+9KIrTt149jP/uMvc44A6dDoX5oRzue4RmWsITruZ6Ecv/LlFdIIY/zOO1pz0AGsoIVIWxp9Lif+3mYh31um8QkLubiELfImDrvRq/XQJzh1VOBayux/8E4yc++E5G5IjJERGo6ycYEnLWyZ7nflx1/g1e9DR7bmuOsA+WdAMOzTjkicrWIzBaR2Zs2bapum6OaqoucHR+wYkMf1mzuT0FRxdUaPMXHtmKPRmNp1+JHGqVfSUxMcohaaowx5UVqcO3NV8Rk49nrqJIdO1j40EN8e8YZbAkwLK5Bly4c9dZbdB05kviM+juXqjWteYInWMpSBjGIJPzPMy+iiElMogMdGMAAlrI0hC2NbBOZyN3c7XPbWMZyDdeEuEXG1H2q2s7r1V5VD1fVu8rmLgfZ/3dVvR5ogTOn+UxgFc73m1NFpFrLJ4jIozjB/jk+5kl7fw8RH2UVDumvjqpOVtWDVfXgpk3rZ8It1RKy895i+fpjWZt1NYXFgUf1x8ftTfPG42nX4nsapl1CjCSGqKXGGONbtATXpp5Y//nnfH3SSfw7eTJaUuKzTlxaGl3uuYej3nqLht26hbiFkWtP9mQ841nGMm7hFlJI8Vu3hBKmMIV92If+9Ocf/glhSyPPVKYyiEE+t93N3QxlaIhbZEz9JCJx7jnOVaKqBar6kqr2BjoBDwNDgPUiEnyNJt9teQy4EDhOVT2fRK53//TugW7Grt7s9Tij7JoEqGPcVIvZnvs/lq0/hnVbbqCoJPCy5Alx+9Ii8ynaNf+GBqnnI+J/epQxxoRSpAbXSsUnu9ZTXYftWLOGX665hl+vuYb8tWv91mt52mkc+9lntLv0UiQ2qpYtDZnmNGcc41jOcu7gDtLw/z21lFJe4AU60YlLuIS/+CuELY0Mr/M6V3GVz203cROjGR3iFhlT94nI8SJyvlfZHUAusE1EPvHMvVIV7qU77wBaAecDRbvRvgnARTiB9d9em5fhBM99POonAUcBP7qL5gDFXnX2wgn8f8QA4NICtua+wNJ1R7B+6y0UlywLWD8xvistM5+jbfMvyEg5CxH7HmCMiSwRmS1cRFzAZ0Chu+hk4Btgh/t9InCCqtbLf1XrUrZwV3ExS59/nn8ef5zS/Hy/9VLbtqXrqFE07dUrhK2rG7LIYjzjmchEsskOWFcQzuM87uZuutI1RC0Mn4/4iLM4i2KKK2y7nMuZwhRiIvYZpIlUli08OBH5DPhYVR91vz8U+Al4Difp11Cc7N8hHzYiIk8C/YCzAM9xybmqmuuuczswDGcprn9wluU6mopLcZ1B+aW4GmFLceFy7WBb3nS25kyipHR90PpJCT3IzBhMatJx9Sq3ijEmckXVUlwiMrUy9VT18tpuSySqK8F11i+/MG/ECHIX+x/+FZOQQMeBA2l/9dXEJtpcqurYxjYmMpHHeIxtbAta/7/8l+EMpzvda71t4fAN3/Af/kMBBRW2ncd5/I//EUu9fH5nqsmC6+BEZD1wqqrOcb9/GOipqr3c788D7lXVfcPQNn9fjEap6kh3HQHuAa7BCZh/Bq5X1QUex0nCGZ5+EZAMfAEMdGcND6iuBtcuVy5bc6exNecZSl1ZQesnJ/YkM2MwKYm9LKg2xkSUqAquTWDRHlwXZmXx10MPserNNwPWa3rUUXQdOZLUtm1D07B6IptsnuRJHuERsgj+5eZ0Tmc4wzmEQ0LQutD4lV85nuPJoWK+pJM5mXd4J2D2dWMCseA6OBEpADqWBZoi8iPwkare637fFligqlWef10X1LXgutS1na05z7E1dwou17ag9VOSepOZMYiUxMNqv3HGGLMbomqda1M3qcvFihkz+OrEEwMG1onNmtFj4kQOmzrVAutakEEGd3Iny1nOWMbSlMBZad/nfQ7lUE7hFGbtXIkmei1gAf/hPz4D62M4hjd50wJrExIiMlBElolIgYjMEZGjAtRNEpFpIjJPRIpF5Gs/9Y5xH6tARJaKSGWWswqHdUB7ABFJBA6Ecv/ApLNrapiJUiWlWWza9iBL1x5KVva4oIF1WtKJtG72Ia2avmKBtTEmKllwbUIi+++/+eH885l3110Ub9vmu1JMDO0uu4xjP/2UlqeeakPAalkaaQxlKMtZzqM8SnPfS6/u9DEfcwRH0Ic+fMd3IWplzVrCEvrQhy1sqbDtEA7hPd4jGVsf1dQ+EbkAZ/3k+3ECyx+Bj0WktZ9dYoEC4AngQz/HbAd85D7WgcADwOMick7Ntr5GfAyMFZHjgIeAPCj3D0s3YEk4Gmaqr6R0Ixu3jWLpukPZkjMRV8BV1YS05NNos8en7Nl0GsmJB4asncYYU9NsWHgUiqZh4SV5eSyaMIFl06ahpf7ztzQ84AC6jh5Nwy5dQtg64ymffJ7jOR7kQdawJmj93vRmBCPoTW/E51L0kWUVqziKo1jBigrbutCFr/maTDLD0DJT11RmWLiI/AzMU9UBHmWLgTdU9c4g+z4BdHEvO+VZ/hDwX1Xt6FE2BdhfVXtW/ZPUHhFpAryFs4Z0LnCZqr7tsf0LYJaq+l58vo6L1mHhxSVr2JIzie15r6BaMZ9FeTGkp5xFZsZNJMbvE5L2GWNMTfE3LDwuHI0xdZ+qsv7TT1kwejQF6/1nAo1LT6fTbbfR5oILbGmtMEsmmRu4gQEMYBrTuJ/7WclKv/W/dv93JEcyghH0oU/EBtkb2cgJnOAzsO5ABz7lUwusTciISALQAxjntelT4IhqHLqn+xieZgKXiUi8qlZMix8mqroZOFpEGuBk4fZ++noeTtBtokBRyQq2ZD/B9rzXwMfqC+XF0SD1XBqn30hCfLtQNM8YY0LGhoWbGrdj1Sp+ueoqZg8cGDCw3vOsszju889pe9FFFlhHkEQSuYZrWMxipjCFvdk7YP0f+IGTOIme9ORDPkQjbEn6rWzlRE7kH/6psG0v9uJzPqcFLcLQMlOHxYnIbI/X1V7bm+AM897gVb4BgszPCKy5n2PGuc8ZcVR1u69lqVR1i6pWeX1qE1pFxUtYlzWYZet6sT1vOoECayGBhqmXsXeLH2ne+FELrI0xdZL1XJsa4yoq4t8pU/jniSdwFfrPQ5O69950GzWKJkdUp4PG1LYEEriSK7mMy3iFV7iXe1mM/2XTfuZnTuM0DuIgRjCCMzgj7D3ZueRyCqfwB39U2NaUpnzO57ShTRhaZuq4El9DxXzwfhIlPsqqytcxfZUbs9sKi/4iK2ciOTveI9hfLZEkGqb2o3HGdcTFVufZkTHGRD7ruTY1YvNPP/HNaafx9yOP+A2sYxIT2ffmmznmgw8ssI4iccRxKZfyF38xnel0olPA+r/xG2dxFgdyIG/wBi5cIWppeQUUcCZn8hM/VdjWkIZ8xmfsS8iX0DUGYDNQSsVe6mZU7HmuivV+jlkClVh3z5ggCormsWbzFSzfcDw5O94lUGAtkkrj9OvZu8UvNGs0ygJrY0y9YMG1qZbCzZuZe8stzLr4YnL//ddvvWbHHEPvTz5hn+uvJzYxMYQtNDUlllgu4iLmM58ZzKArXQPW/4M/OI/z6EY3XuVVSvGf0K6mFVPM+ZzPl3xZYVsqqXzERxzAASFrjzGe3MOd5wB9vDb1wcn0vbtmASf4OObsSJpvbaJPfuFsVm/qx4oN/yE3/5OAdWMkg8yMIbRv8QtNGw4jLjYiZyQYY0ytsODa7BZ1uVj+yit81acPq995x2+9pObN6fHkkxz63HOktva3woyJJrHEcj7n8zu/8xZvcSCBl035kz+5kAvZn/15mZcpoaRW21dKKZdxGe/zfoVtiSTyLu/Sk4hKnGzqp0eB/iJylYh0EpEJQEvgaQARecCdMXsnEeksIt1x5k+niUh39/syTwN7ich49zGvAvpTMXGaMUGpKjsKfmTVxvNZufEM8gq+CFg/NqYRTRrcwd4tf6FJg6HExjYKUUuNMSZy2FJcUSjcS3Ft//NP5g0fzrY/Ks5jLSOxsbTr3599b7qJuLS0ELbOhJqifMiHjGY0v/Jr0Prtac8whnEJlxBPfI235Rqu4VmerbAtllje5m1O5/QaPacx3iqzFJe73kDgNqAFsAAYoqrfurdNA3qraluP+suhYpIAVRWPOscAjwH7A2uBh1T16Wp8HBMG4VyKywmqvyErezz5Rb8ErR8b05TG6dfRMK0fMTFB/9obY0yd4G8pLguuo1C4guvinBwWjR/PshdfBJf/ebSNDjyQrmPG0KBT4Lm5pm5RlJnMZDSjmcWsoPXb0pY7uZP+9CeBhBo5/63cyqM8WmGbIExnOhdyYbXPY0wwlQ2ujfEnHMG1qpJX8ClZ2RMoKPo9aP242BY0Tr+eBqkXEhOTXPsNNMaYCGLBdR0S6uBaVVn30Uf8ed99FGzwn2snvkEDOt12G63PPx+JsRkH9ZWifMmXjGIU3/Fd0PqtaMUd3MEVXEESSbt93tGM5h7u8bltMpMZwIDdPrYxVWHBtamuUAbXqi5y8z8kK3sChcULg9aPj21N44wbaZB6Hs6S7cYYU/9YcF2HhDK4zlu+nPkjR7Lpu8BB0l7//S+d77iDxMzMkLTLRIdv+IbRjPaZWMxbS1pyG7dxNVeTTNV6QR7jMW7mZp/bHuERv9uMqQ0WXJvqCkVwrVpC9o532ZI9kaIS/8sslkmIa0/jjJvISDkLkZqd0mOMMdHGgus6JBTBdWlhIf9Onszip57CVVTkt15ax450Gz2azEMPrdX2mOj2Az8whjHMZGbQunuwB0MZyrVcSyrB45PneI6ruMrntnu4h5GMrGpzjakWC65NddVmcK1axPa8N9iS8wTFJcuD1k+I34/MjEGkJ5+GSGyttMkYY6KNBdd1SG0H15t++IH599xD3rJlfuvEJCWx7003sffllxOTYMPCTOX8wi+MYQwf8EHQuk1owi3cwvVcTzrpPuvMYAYXciHqY63VIQzhER5BEB97GlN7LLg21VUbwbVLC9ie+ypbcp6kpHRN0PqJ8V3JzBhMWvJJiNhUL2OM8WTBdR1SW8F1waZNLLzvPta8X3EJI097HH88XUaMIGWvvWq8DaZ+mMMc7uVe3uGdoHUb05ghDOFGbqQBDXaWf8iHnMVZPpf2uoqrmMxkC6xNWFhwbaqrJoNrl2sH2/JeZkv2JEpd/vOmlElK6EFmxhBSk45FxP4NNcYYXyy4rkNqOrjW0lKWT5/O3488Qklurt96SS1a0PWee2jep0+NndvUb3/wB/dyL2/yps/eZ08NaMAg939/8AcnczKFFFaodwEXMJ3pxGLDF014WHBtqqsmgmuXK5etuVPZmjOZUldW0PrJiUeQmTGYlMQjLag2xpggLLiuQ2oyuN42bx7zhg9n+4IFfutIXBx7X345+9x4I3Gp9n3R1Lw/+ZP7uI9XeTVokJ1OOi5c5FHx/4FTOZW3ebvG1882piosuDbVVZ3gutS1ja05z7M1dwou17ag9VOSepOZMYiUxMN263zGGFMf+Quu48LRGBN+xTk5/P3IIyx/+WUI8ICl8cEH03X0aDL23TeErTP1zf7szyu8wj3cw/3cz3SmU0qpz7o55Pgs701vXud1C6yNMfVSSWkWW3Mmsy13Ki71PwqtTFrSiTTOGExyYvfab5wxxtQTlqGihojIQBFZJiIFIjJHRI4KUr+riHwjIvkiskZERkgIxmGpKmvee4+v+vRh+Usv+Q2s4xs14oAHH+SI//3PAmsTMvuyLy/wAn/zN1dwBXGVfP53KIfyHu9VeQkvY4ypK9ZmDWBLzuNBAmshPfl02uzxGXs2nWaBtTHG1DALrmuAiFwATADuBw4EfgQ+FpHWfupnAJ8BG4BDgJuAoVC7i/HmLlvGT5deym9DhlC4aZPfeq3PP5/jPvuM1uedh8TYXxETeh3owHM8x2IWcw3XBOyN7kpXPuZjvxnFjTGmPmiUNiDA1hgyUs6hbfOvadnkGZIS9g9Zu4wxpj6xOdc1QER+Buap6gCPssXAG6p6p4/61wEPAXuoar677G7gOmAvDfKHUtU516WFhSyZNIklzzwTcM3q9H32oduYMTQ+uML0AWPCahWreIiHeJZnKWLX3+GOdORbvqU5zcPYOmPKsznXprp2Z861qovlG06gqPhvj9I4GqSeR+P0G0iIb1ezjTTGmHrM35xr65asJhFJAHoAn3pt+hQ4ws9uPYHvygJrt5lAS6BtTbexYMMGlkye7Dewjk1JofMdd3D0e+9ZYG0iUita8QRPsIxl3M7tHMmRXM3V/MAPFlgbYwwgEkNmxiDndxJpmHYZe7f4keaNH7HA2hhjQsQSmlVfEyAWZ4i3pw3ACX72aQ6s9lG/bNsy7x1E5GrgaoCEhIQqNTC1dWs6DhzIosceq9iQE0+ky/DhJLdsWaVjGhMOLWnJgzwY7mYYY0xESk8+jeKMZTRI60tcrD14NMaYULOe65rjPZRbfJQFq++r3ClUnayqB6vqwXFxVX8m0n7AAFL33nvn++Q99+TQZ5/lkEmTLLA2xhhjPIjI0SLynjvhqIpIf6/t09zlnq+fvOokisjjIrJZRPLcx9urdtsdS2aDwRZYG2NMmFhwXX2bgVKoMDa1GRV7s8us91OfAPtUS2xiIt1GjULi4+lw3XX0njmTPY47rjZOZYwxxkS7NGABMAjI91Pnc6CFx+sUr+3jgXOAC4GjgAzgAxGJrYX2GmOMiQA2LLyaVLVIROYAfYDXPTb1Ad70s9ss4CERSVLVAo/6a4HltdXWJkccwfFff01yc3uibYwxxvijqh8BH4HTS+2nWqGqrve1QUQaAFcCl6vqZ+6yfsAKnCljM2u6zcYYY8LPeq5rxqNAfxG5SkQ6icgEnORkTwOIyAMi8oVH/VeAHcA0EekiIv8F7gAeDZYpvLossDbGGGNqRC8R2Sgi/4jIsyLSzGNbDyAej2SnqroK+Av/yU6NMcZEOeu5rgGqOkNEMoG7cYaGLQBOUdUV7iotgPYe9beLSB/gSWA2sBV4BCdIN8YYY0xk+wR4CycBaVvgXuBLEemhqoU4U79KcaaOedpAxWlhQPnEpa1bt66dVhtjjKlVts51FBIRF/7ngFVGHFBSQ82JZnYdHHYd7BqUsevgqO51SFZVGxlWR4hILnCDqk4LUKclzpDvC1T1LRG5CHgRiPcckSYiXwGLVPXaIOfc5D7e7mpCxcC+PrLr4LDr4LDr4LDrUDPXoI2qNvUutJ7rKFTdL20iMtvXouf1jV0Hh10HuwZl7Do47DqYqlLVtSKyGujoLlqPs0xnE2CTR9VmwLeVOF6FL2xVYX+HHXYdHHYdHHYdHHYdavca2JN1Y4wxxphqEJEmwJ7AOnfRHKAYJ1lpWZ29gE7AjyFvoDHGmJCwnmtjjDHGGA8ikgZ0cL+NAVqLSHdgi/s1EmdFkHU4c64fADYCb8PO3CrPAQ+LyEYgCyevyjycJbyMMcbUQdZzXT9NDncDIoRdB4ddB7sGZew6OOw6mIOBue5XMjDK/ftonERlXYF3gX+AF4BFQE9VzfE4xhCcpGczgB+AXOB0VS0NQfvt77DDroPDroPDroPDrkMtXgNLaGaMMcYYY4wxxlST9VwbY4wxxhhjjDHVZMG1McYYY4wxxhhTTRZc10Ei8l8RmSkim0RERaR3Jfc7RkTmiEiBiCwVkYDrcEY6ERkoIsvcn2eOiBwVpH5XEflGRPJFZI2IjBARCVV7a5qIHC0i77k/i4pI/0rsU9euwZ0i8quIZLv/f3hfRLpUYr+6dh2uF5F57uuQLSKzROTUIPvUqWvgTUTucv9/8USQenX6OpjoZPd5h93n7T4Pdq8Hu8/7E457vQXXdVMqzlIfN1d2BxFpB3zk3u9AnMynj4vIObXSwlomIhcAE4D7cT7Pj8DHItLaT/0M4DNgA3AIcBMwlCpcwwiUBiwABgH5wSrX0WvQG3gKOAI4DigBPheRxv52qKPXYTVwO3AQTqKmL4F3RKSbr8p19BrsJCKHAwNwMjcHqlenr4OJanaft/s82H2+TG/sXm/3eS9hu9erqr3q6AtoAijQuxJ1HwIWe5VNAWaF+3Ps5mf/GXjWq2wx8ICf+tcB2UCyR9ndwBrcif+i+YWTpbZ/kDp1+hq4P08aTqbf0+vzdXB/pi3ANfXtGgANgH9xvoB9DTxR3/8u2Ct6X3aft/u8x2ex+/yuz2T3eq2/93n3Zwnbvd56rk2ZnsCnXmUzgYNFJD4M7dltIpIA9KDi5/kU56mmLz2B71TV88nvTKAlzhqm9UF9uAbpOCN2tgaoU6evg4jEikhfnC8fP/qpVpevwWTgDVX9shJ16/J1MPWP3eft/+f6cg3q9b3e7vNAGO/1FlybMs1xhkN42gDE4TwZjyZNgFh8f57mfvbx9/nLttUH9eEaTAB+B2YFqFMnr4N7PlEuUAg8DZytqvP9VK+r12AA0AEYXsld6uR1MPWW3eft/+f6cg3q5b3e7vOOcN/rLbiOciJysYjkerwCJvMIwnvRc/FTHi18fZ5An6Wuff7dUWevgYg8CvQCzlHV0iDV6+J1WAR0Bw4HJgEvBEn4UqeugYjsizM382JVLarCrnXqOpjoY/f5gOw+X3V1+hrU83t9vb7PQ2Tc6+OquoOJOO/hzDsqs2Y3j7Oeik9nmuEkhcjazWOGy2acuTa+Po/3k6ky/j4/Afapa+rsNRCRx4C+wLGqujRI9Tp5Hdw3mSXut7NF5BBgCHClj+p18Rr0xOntWuCRADQWONqdMTlVVQu99qmL18FEH7vPV2T3+d1Tp69Bfb/X230eiIB7vfVcRzlVzVHVJR6voNki/ZgFnOBV1geYrarF1WtlaLn/cZmD035PffA/92QWcJSIJHnVXwssr+k2Rqg6eQ1EZAJwEXCcqv5diV3q5HXwIQZI9LOtLl6Dd4CuOE/1y16zgVfdv/t6wl0Xr4OJMnafr8ju87utzl4Du9f7VN/u8xAJ9/pwZ3OzV82/gMbuv0C9cYYzXOV+39yjzovAix7v2wF5wHigk3ufIpxhNWH/TLtxDS5wt/8q9+eZgJNJs417+wPAFx71G+A8uXoV6AL8Fydz4C3h/izVuAZp7PqHZQcwwv1763p0DZ50f4bjcJ5Klr3SPOrUh+vwIHAUTmKOru7P7AJOri/XwM91+RqPDKL19TrYK/pedp+3+7z7M9X7+7z7c9X7e73d5wNem5De68P+ge1V8y+gv/tm6/0a6VHna+Brr/2OAX7DSYSwDLg23J+lmtdhIM4Tp0KcJ9xHe2ybBiz3qt8V+BYoANYB9xDFyxGw60uX92taPboGvj6/9/8L9eE6TANWuP9f2Ah8DpxUn66Bn+vifcOtl9fBXtH3svv8zs9j9/l6fp93f6Z6f6+3+3zAaxPSe724D2iMMcYYY4wxxpjdZHOujTHGGF/7UvMAAAg+SURBVGOMMcaYarLg2hhjjDHGGGOMqSYLro0xxhhjjDHGmGqy4NoYY4wxxhhjjKkmC66NMcYYY4wxxphqsuDaGGOMMcYYY4ypJguujTHGGGOMMcaYarLg2hhjjDHGGGOMqSYLro2JIiLygYhMq+I+00Tkg9rep6aIyNciou7X4ZHQJm8i0khENohI+1o+zxsicrNX2TSP63NubZ7fGGNMaNl93u7zdp+PbhZcG1NDPP4xnOJj21j3toi4aXhy3+Se8CoeBFwSjva4TQVaAHPC2IZA7gI+UtV/a/k8o4C7RaSBR9kgnGtjjDEmhOw+X6PsPu+w+3wdY8G1MTVrFXCBiKSWFYhIHNAPWBm2VlWRqm5X1W1hbMIOVV2vqsVhbINPIpICXAU8V83jJASro6rzgaV4fAFy/9msr865jTHG7Da7z9cMu89j9/m6yIJrY2rWPGAxcL5H2alAAfC1Z0URSRSR8e5hRwUi8pOI9PLYnuJ+Sp7rrnOX98nEcZuI/Csi+SIyX0Qq/STaPfTsGOB6jyFIbb2HZrmfek8SkUdEZIuIbBKRQe7P8KSIbBORlSLSrybbF6DdAa+du06qiLzocf3u3J3hdj6cAriAH9znuVREskQk0ev800XkPY/3ZddwnIhsAn4QkaPdbc8Vke0i8rOIdPE633vAhdVsszHGmJph9/kabF+Adtt93kQlC66NqXnPAVd4vL8CZ/iTetUbC1zg3n4gMB/4RETKhgKNA/oA5wDHu+sc7XWMe4ErgeuBzsADwDMicmol2zoImMWu4VktcJ7K+3IxkAMcBjwIjAfeAf4BDgZeAKaISMsabJ8/wa4dwCM4XyjOBo4DDgCOquZ5cR9jjqqW/Xm+jvNv6ZllFcQZ3nU2FZ96XwKI+xhXAO8C37vbdhgwASj12ucX4FARSa6BthtjjKk+u8/XXPv8sfu8iU6qai972asGXsA04AOgEZAPdASaA4VA67Lt7rqpQBFwqcf+scC/ODeqNPd+F3tsTwO2AdM8jpEPHOXVjvE484TKtStAu78GnvD1WbzqzPJ4L8Am4D2Psnj3Zzq3Ku2rTHu8rm/Aa+dxrYqAvh51UoGtZdfPXfaeu+wNr3OdAizC6Z0Y6LXtHeAFr7IngE883l8HrAfivD7XPI/3jXG+iB0T5O9VN3e99l7lWnat7WUve9nLXrX/svu83ec93tt93l4+X3EYY2qUqm4VkbdxnlhuA75W1ZUi4lmtPc5N6geP/UpFZBbOk9/2QALO0+ay7bkiMt/jGJ2BJJwnuZ5Py+OB5TX5mdzmebRFRWQjzpPksrJiEdkKNKvl9gW7dp51fvGokyciC7yO9RjwLHBZWYE4c+cm4DwFzwJmi8jbqrrOXSUZ2OB1nGeB30RkL1VdjfNn/4KqlnjV25m4RVW3uIeuzRSRL4AvgNdV1btHId/jvMYYY8LM7vN2n8fu88YPC66NqR3P4wyfygVG+Nhedgf2HkJWViY+yr2VTes4nYpJVGojQYj3MdVPWVm7aqt9wa5dsDq7Kqt+JSK9vYoPBRaW3fzcX6BOw7mxAmzG6bXwPM4fIvIb0F9E3sEZPudrzlme136Xi8h44D/AGcB9InKWqs70qNbY/XNToM9ijDEmpOw+b/d5u8+bCmzOtTG14wuc4UpNcIYXeVvi3u6Z2CQW6AksdG8vBjzXf0wFPJNgLMQZUtZGVZd4vVZUoa1FOMOtalpNtc9bsGtXVqcY5wZaVieF8tfPn5aUn4+2GtjT4/1cdj059/Qs0B8nw+gPqrqoEudCVf9Q1YdUtTfOkLLLvKp0AdaqqvdTdGOMMeFj93m7z9t93lRgPdfG1AL3cKpugKhqoY/teSIyCXhQRDYDy4AhwB7AU+6hYc8BD7kzTq7FeTIe63GMHBEZB4wTZyzatzhzkA4HXKo6uZLNXY6TSKMtzhP4Lbv1oSt+xppqn/dxA147d51cEXke5/ptBtYBd+M8UAz4lBvfvQme+8x0HzdTVbM8yv8HPIozD+vaYJ9DRNoB1+DMB1sD7I0z72qSV9WjgE+CHc8YY0zo2H3e7vPBPofd5+snC66NqSWqmhOkyu3un1OBhjhPSv/jMefnVpzkHG8DO4DH3e89DceZF3Qrzj/W2cDvOFk2K2scztC2hTjzfdpVYd9gaqJ9vgS7drDr+r2H82XiMZwbc0GQY68BWnm83wuPuWOqOl9EfgH6Ak96lOeIyGvAecBrlfgMO4B9cLKQNsG5TtOBh8oqiEgSTjbSkypxPGOMMSFk9/kaa58vdp83UUlUgz3cMcaY0BGRr4EFqnpDDR83EVgBPKyqj3iU9wZuUNVz3e/jgL+BY3HmXc0BTlDVtR77/AcnGUpnVS31KP8YWK2qA2qozdcDZ6rqiT62KXCeqr5RE+cyxhhjQsHu8+XabPf5OsbmXBtjItHVIpIrIofs7gFE5EARuUhEOojIgThP7dOBGR51Psd5onyKiKwWkZ7uzJ9DcObTzQcmed5wAVT1E5yn2Xu5j9NYRM4HTsS5GdeUYuBGr8/1tIjk1uA5jDHGmFCz+7zD7vN1jPVcG2Miiojsya7lKFb5mstWyeMciJN8ZF+gBGeY2q2qOifQfrt5ruU42T7vU9WHglSv7rmaARnut+tUNS9QfWOMMSaS2H0+6LnsPh/FLLg2xhhjjDHGGGOqyYaFG2OMMcYYY4wx1WTBtTHGGGOMMcYYU00WXBtjjDHGGGOMMdVkwbUxxhhjjDHGGFNNFlwbY4wxxhhjjDHVZMG1McYYY4wxxhhTTRZcG2OMMcYYY4wx1WTBtTHGGGOMMcYYU03/B4UkX+5yRMSSAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -703,7 +743,7 @@ ")\n", "f1ax[1, 1].legend(frameon=False, fontsize=14)\n", "# f1ax2.set_ylim(top=3)\n", - "f1ax[1, 1].set_xlim(left=-3)\n", + "f1ax[1, 1].set_xlim(left=np.min(f1ax[1, 0].get_xlim()))\n", "f1ax[1, 1].xaxis.set_major_formatter(FormatStrFormatter(\"%.1f\"))\n", "f1ax[1, 1].set_ylabel(\"Phosphate\\nConcentration\\n[$\\mu$mol kg$^{-1}$]\", fontsize=14)\n", "\n", @@ -778,7 +818,22 @@ " linewidth=5,\n", " label=\"Export\",\n", ")\n", - "\n", + "f1ax[3, 0].plot(\n", + " np.log10(timeseries[\"time\"]),\n", + " timeseries[\"exportso\"],\n", + " color=\"firebrick\",\n", + " linestyle=\"--\",\n", + " linewidth=2,\n", + " label=\"S.O. Export\",\n", + ")\n", + "f1ax[3, 0].plot(\n", + " np.log10(timeseries[\"time\"]),\n", + " timeseries[\"exportna\"],\n", + " color=\"firebrick\",\n", + " linestyle=\":\",\n", + " linewidth=2,\n", + " label=\"N.A. Export\",\n", + ")\n", "# PS\n", "f1ax30b = f1ax[3, 0].twinx()\n", "f1ax30b.plot(\n",