diff --git a/code/simulations/simulations_inference_bernoulli.R b/code/simulations/simulations_inference_bernoulli.R
index 7e37362..a5db43e 100644
--- a/code/simulations/simulations_inference_bernoulli.R
+++ b/code/simulations/simulations_inference_bernoulli.R
@@ -12,42 +12,6 @@ suppressPackageStartupMessages(library(colSBM))
 
 set.seed(1234)
 
-#  Functions
-compute_mean_ARI <- function(model) {
-  # We compute the mean amongst the two networks and return values for
-  # rows and columns in a vector
-  # sapply ives a matrix with in row the axis ARIs
-  # and in columns the networks
-  #    1     2
-  # ax row1  row2
-  # ay col1  col2
-  rowMeans(sapply(seq.int(model$M), function(m) {
-    matrix(c(
-      aricode::ARI(model$Z[[m]][[1]], row_clusterings[[m]]),
-      aricode::ARI(model$Z[[m]][[2]], col_clusterings[[m]])
-    ), nrow = 2, ncol = 1)
-  }))
-}
-
-
-compute_double_ARI <- function(model) {
-  model_row_Z <- as.vector(sapply(
-    seq.int(model$M),
-    function(m) model$Z[[m]][[1]]
-  ))
-
-  model_col_Z <- as.vector(sapply(
-    seq.int(model$M),
-    function(m) model$Z[[m]][[2]]
-  ))
-
-  return(list(
-    aricode::ARI(model_row_Z, full_row_clustering),
-    aricode::ARI(model_col_Z, full_col_clustering)
-  ))
-}
-
-
 # Network param
 nr <- 120
 nc <- 120
@@ -148,8 +112,6 @@ results <- parallel::mclapply(conditions_rows, function(s) {
   Cpi1 <- matrix(c(current_pi1, pi2), byrow = TRUE, nrow = M) > 0
   Cpi2 <- matrix(c(rho1, current_rho2), byrow = TRUE, nrow = M) > 0
 
-  current_seed <- .Random.seed
-
   netlist_generated <- list(
     generate_bipartite_collection(
       nr, nc, conditions[s, ]$pi1, rho1,
@@ -194,7 +156,7 @@ results <- parallel::mclapply(conditions_rows, function(s) {
     global_opts = list(
       verbosity = 0,
       plot_details = 0,
-      nb_cores = parallel::detectCores() - 1
+      nb_cores = parallelly::availableCores(omit = 1)
     )
   )
 
@@ -210,7 +172,7 @@ results <- parallel::mclapply(conditions_rows, function(s) {
     global_opts = list(
       verbosity = 0,
       plot_details = 0,
-      nb_cores = parallel::detectCores() - 1
+      nb_cores = parallelly::availableCores(omit = 1)
     ),
     sep_BiSBM = sep_BiSBM
   )
@@ -223,7 +185,7 @@ results <- parallel::mclapply(conditions_rows, function(s) {
     global_opts = list(
       verbosity = 0,
       plot_details = 0,
-      nb_cores = parallel::detectCores() - 1
+      nb_cores = parallelly::availableCores(omit = 1)
     ),
     sep_BiSBM = sep_BiSBM
   )
@@ -236,7 +198,7 @@ results <- parallel::mclapply(conditions_rows, function(s) {
     global_opts = list(
       verbosity = 0,
       plot_details = 0,
-      nb_cores = parallel::detectCores() - 1
+      nb_cores = parallelly::availableCores(omit = 1)
     ),
     sep_BiSBM = sep_BiSBM
   )
@@ -253,6 +215,40 @@ results <- parallel::mclapply(conditions_rows, function(s) {
   BICLs <- c(sep_BICL, iid_BICL, pi_BICL, rho_BICL, pirho_BICL)
 
   # ARIs
+  #  Functions
+  compute_mean_ARI <- function(model) {
+    # We compute the mean amongst the two networks and return values for
+    # rows and columns in a vector
+    # sapply ives a matrix with in row the axis ARIs
+    # and in columns the networks
+    #    1     2
+    # ax row1  row2
+    # ay col1  col2
+    rowMeans(sapply(seq.int(model$M), function(m) {
+      matrix(c(
+        aricode::ARI(model$Z[[m]][[1]], row_clusterings[[m]]),
+        aricode::ARI(model$Z[[m]][[2]], col_clusterings[[m]])
+      ), nrow = 2, ncol = 1)
+    }))
+  }
+
+
+  compute_double_ARI <- function(model) {
+    model_row_Z <- as.vector(sapply(
+      seq.int(model$M),
+      function(m) model$Z[[m]][[1]]
+    ))
+
+    model_col_Z <- as.vector(sapply(
+      seq.int(model$M),
+      function(m) model$Z[[m]][[2]]
+    ))
+
+    return(list(
+      aricode::ARI(model_row_Z, full_row_clustering),
+      aricode::ARI(model_col_Z, full_col_clustering)
+    ))
+  }
 
   sep_mean_ARIs <- compute_mean_ARI(sep_BiSBM)
   iid_mean_ARIs <- compute_mean_ARI(fitted_bisbmpop_iid$best_fit)
@@ -345,15 +341,13 @@ results <- parallel::mclapply(conditions_rows, function(s) {
       alpha = current_alpha,
       pirho_double_row_ARI = pirho_double_ARIs[[1]],
       pirho_double_col_ARI = pirho_double_ARIs[[2]],
-      netlist = netlist_generated,
-      seed = current_seed
+      netlist = netlist_generated
     )
     saveRDS(object = inc_data, file = incorrect_filepath)
   }
 
   return(data_frame_output)
-},
-mc.cores = parallel::detectCores() - 1L
+}, mc.cores = parallelly::availableCores(omit = 1)
 )