diff --git a/.gitignore b/.gitignore
index d0841a54..dfb17803 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@
 *.jl.cov
 *.jl.mem
 Manifest.toml
+.vscode
diff --git a/test/data/center.png b/test/data/center.png
index 7691ad72..8fcfa89f 100644
Binary files a/test/data/center.png and b/test/data/center.png differ
diff --git a/test/data/eigenanalysis-1.png b/test/data/eigenanalysis-1.png
index 5b11ccb3..37215cc3 100644
Binary files a/test/data/eigenanalysis-1.png and b/test/data/eigenanalysis-1.png differ
diff --git a/test/data/eigenanalysis-2.png b/test/data/eigenanalysis-2.png
index 2f633e53..de9e9865 100644
Binary files a/test/data/eigenanalysis-2.png and b/test/data/eigenanalysis-2.png differ
diff --git a/test/data/scale.png b/test/data/scale.png
index 8c511ead..acd91782 100644
Binary files a/test/data/scale.png and b/test/data/scale.png differ
diff --git a/test/data/zscore.png b/test/data/zscore.png
index 42f7d3b1..8f66e933 100644
Binary files a/test/data/zscore.png and b/test/data/zscore.png differ
diff --git a/test/transforms.jl b/test/transforms.jl
index f40d9a0a..1fcbef15 100644
--- a/test/transforms.jl
+++ b/test/transforms.jl
@@ -1,4 +1,6 @@
 @testset "Transforms" begin
+  # using MersenneTwister for compatibility between Julia versions
+  rng = MersenneTwister(42)
   @testset "Select" begin
     a = rand(4000)
     b = rand(4000)
@@ -196,9 +198,9 @@
   end
 
   @testset "Center" begin
-    Random.seed!(42) # to reproduce the results
-    x = rand(Normal(2,1), 4000)
-    y = rand(Normal(5,1), 4000)
+    # using rng for reproducible results
+    x = rand(rng, Normal(2, 1), 4000)
+    y = rand(rng, Normal(5, 1), 4000)
     t = Table(; x, y)
     T = Center()
     n, c = apply(T, t)
@@ -214,7 +216,7 @@
       p₂ = scatter(n.x, n.y, label="Center")
       p = plot(p₁, p₂, layout=(1,2))
 
-      @test_reference joinpath(datadir,  "center.png") p
+      @test_reference joinpath(datadir, "center.png") p
     end
   end
 
@@ -228,18 +230,18 @@
     @test n.x == x
     @test n.y != y
     tₒ = revert(T, n, c)
-    @test tₒ == t
+    @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
-    Random.seed!(42) # to reproduce the results
-    x = rand(Normal(4,3), 4000)
-    y = rand(Normal(7,5), 4000)
+    # using rng for reproducible results
+    x = rand(rng, Normal(4, 3), 4000)
+    y = rand(rng, Normal(7, 5), 4000)
     t = Table(; x, y)
     T = Scale(low=0, high=1)
     n, c = apply(T, t)
-    @test all(x -> x <= 1, n.x)
-    @test all(x -> x >= 0, n.x)
-    @test all(y -> y <= 1, n.y)
-    @test all(y -> y >= 0, n.y)
+    @test all(≤(1), n.x)
+    @test all(≥(0), n.x)
+    @test all(≤(1), n.y)
+    @test all(≥(0), n.y)
     tₒ = revert(T, n, c)
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
@@ -249,14 +251,14 @@
       p₂ = scatter(n.x, n.y, label="Scale")
       p = plot(p₁, p₂, layout=(1,2))
 
-      @test_reference joinpath(datadir,"scale.png") p
+      @test_reference joinpath(datadir, "scale.png") p
     end
   end
 
   @testset "ZScore" begin
-    Random.seed!(42) # to reproduce the results
-    x = rand(Normal(7,10), 4000)
-    y = rand(Normal(15,2), 4000)
+    # using rng for reproducible results
+    x = rand(rng, Normal(7, 10), 4000)
+    y = rand(rng, Normal(15, 2), 4000)
     t = Table(; x, y)
     T = ZScore()
     n, c = apply(T, t)
@@ -275,7 +277,7 @@
       p₂ = scatter(n.x, n.y, label="ZScore")
       p = plot(p₁, p₂, layout=(1,2))
 
-      @test_reference joinpath(datadir,"zscore.png") p
+      @test_reference joinpath(datadir, "zscore.png") p
     end
   end
 
@@ -304,8 +306,8 @@
     t = Table(; x, y)
     T = Functional(cos)
     n, c = apply(T, t)
-    @test all(x -> -1 <= x <= 1, n.x)
-    @test all(y -> -1 <= y <= 1, n.y)
+    @test all(x -> -1 ≤ x ≤ 1, n.x)
+    @test all(y -> -1 ≤ y ≤ 1, n.y)
     tₒ = revert(T, n, c)
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
@@ -314,8 +316,8 @@
     t = Table(; x, y)
     T = Functional(acos)
     n, c = apply(T, t)
-    @test all(x -> 0 <= x <= π, n.x)
-    @test all(y -> 0 <= y <= π, n.y)
+    @test all(x -> 0 ≤ x ≤ π, n.x)
+    @test all(y -> 0 ≤ y ≤ π, n.y)
     tₒ = revert(T, n, c)
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
@@ -324,8 +326,8 @@
     t = Table(; x, y)
     T = Functional(sin)
     n, c = apply(T, t)
-    @test all(x -> -1 <= x <= 1, n.x)
-    @test all(y -> -1 <= y <= 1, n.y)
+    @test all(x -> -1 ≤ x ≤ 1, n.x)
+    @test all(y -> -1 ≤ y ≤ 1, n.y)
     tₒ = revert(T, n, c)
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
@@ -334,8 +336,8 @@
     t = Table(; x, y)
     T = Functional(asin)
     n, c = apply(T, t)
-    @test all(x -> -π/2 <= x <= π/2, n.x)
-    @test all(y -> -π/2 <= y <= π/2, n.y)
+    @test all(x -> -π/2 ≤ x ≤ π/2, n.x)
+    @test all(y -> -π/2 ≤ y ≤ π/2, n.y)
     tₒ = revert(T, n, c)
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
@@ -344,8 +346,8 @@
     t = Table(; x, y)
     T = Functional(exp)
     n, c = apply(T, t)
-    @test all(x -> x > 0, n.x)
-    @test all(y -> y > 0, n.y)
+    @test all(>(0), n.x)
+    @test all(>(0), n.y)
     tₒ = revert(T, n, c)
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
@@ -360,8 +362,8 @@
 
   @testset "EigenAnalysis" begin
     # PCA test
-    x = rand(Normal(0,10), 1500)
-    y = x + rand(Normal(0,2), 1500)
+    x = rand(Normal(0, 10), 1500)
+    y = x + rand(Normal(0, 2), 1500)
     t = Table(; x, y)
     T = EigenAnalysis(:V)
     n, c = apply(T, t)
@@ -374,8 +376,8 @@
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
     # DRS test
-    x = rand(Normal(0,10), 1500)
-    y = x + rand(Normal(0,2), 1500)
+    x = rand(Normal(0, 10), 1500)
+    y = x + rand(Normal(0, 2), 1500)
     t = Table(; x, y)
     T = EigenAnalysis(:VD)
     n, c = apply(T, t)
@@ -388,8 +390,8 @@
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
     # SDS test
-    x = rand(Normal(0,10), 1500)
-    y = x + rand(Normal(0,2), 1500)
+    x = rand(Normal(0, 10), 1500)
+    y = x + rand(Normal(0, 2), 1500)
     t = Table(; x, y)
     T = EigenAnalysis(:VDV)
     n, c = apply(T, t)
@@ -401,9 +403,9 @@
     tₒ = revert(T, n, c)
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
-    Random.seed!(42) # to reproduce the results
-    x = rand(Normal(0,10), 4000)
-    y = x + rand(Normal(0,2), 4000)
+    # using rng for reproducible results
+    x = rand(rng, Normal(0, 10), 4000)
+    y = x + rand(rng, Normal(0, 2), 4000)
     t₁ = Table(; x, y)
     t₂, c₂ = apply(EigenAnalysis(:V), t₁)
     t₃, c₃ = apply(EigenAnalysis(:VD), t₁)
@@ -424,24 +426,24 @@
       p = plot(p₁, p₂, p₃, p₄, layout=(2,2))
       q = plot(p₂, p₃, p₄, p₅, p₆, p₇, layout=(2,3))
 
-      @test_reference joinpath(datadir,"eigenanalysis-1.png") p
-      @test_reference joinpath(datadir,"eigenanalysis-2.png") q
+      @test_reference joinpath(datadir, "eigenanalysis-1.png") p
+      @test_reference joinpath(datadir, "eigenanalysis-2.png") q
     end
   end
 
   @testset "Sequential" begin
-    x = rand(Normal(0,10), 1500)
-    y = x + rand(Normal(0,2), 1500)
-    z = y + rand(Normal(0,5), 1500)
+    x = rand(Normal(0, 10), 1500)
+    y = x + rand(Normal(0, 2), 1500)
+    z = y + rand(Normal(0, 5), 1500)
     t = Table(; x, y, z)
     T = Scale(low=0.2, high=0.8) → EigenAnalysis(:VDV)
     n, c = apply(T, t)
     tₒ = revert(T, n, c)
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
-    x = rand(Normal(0,10), 1500)
-    y = x + rand(Normal(0,2), 1500)
-    z = y + rand(Normal(0,5), 1500)
+    x = rand(Normal(0, 10), 1500)
+    y = x + rand(Normal(0, 2), 1500)
+    z = y + rand(Normal(0, 5), 1500)
     t = Table(; x, y, z)
     T = Select(:x, :z) → ZScore() → EigenAnalysis(:V) → Scale(low=0, high=1)
     n, c = apply(T, t)
@@ -457,9 +459,9 @@
   end
 
   @testset "Parallel" begin
-    x = rand(Normal(0,10), 1500)
-    y = x + rand(Normal(0,2), 1500)
-    z = y + rand(Normal(0,5), 1500)
+    x = rand(Normal(0, 10), 1500)
+    y = x + rand(Normal(0, 2), 1500)
+    z = y + rand(Normal(0, 5), 1500)
     t = Table(; x, y, z)
     T = Scale(low=0.3, high=0.6) ⊔ EigenAnalysis(:VDV)
     n, c = apply(T, t)
@@ -467,24 +469,24 @@
     @test Tables.matrix(t) ≈ Tables.matrix(tₒ)
 
     # check cardinality of Parallel
-    x = rand(Normal(0,10), 1500)
-    y = x + rand(Normal(0,2), 1500)
-    z = y + rand(Normal(0,5), 1500)
+    x = rand(Normal(0, 10), 1500)
+    y = x + rand(Normal(0, 2), 1500)
+    z = y + rand(Normal(0, 5), 1500)
     t = Table(; x, y, z)
     T = ZScore() ⊔ EigenAnalysis(:V)
     n = T(t)
     @test length(Tables.columnnames(n)) == 6
 
     # distributivity with respect to Sequential
-    x = rand(Normal(0,10), 1500)
-    y = x + rand(Normal(0,2), 1500)
-    z = y + rand(Normal(0,5), 1500)
+    x = rand(Normal(0, 10), 1500)
+    y = x + rand(Normal(0, 2), 1500)
+    z = y + rand(Normal(0, 5), 1500)
     t = Table(; x, y, z)
     T₁ = Center()
     T₂ = Scale(low=0.2, high=0.8)
     T₃ = EigenAnalysis(:VD)
     P₁ = T₁ → (T₂ ⊔ T₃)
-    P₂ = (T₁ → T₂) ⊔ (T₁ →T₃)
+    P₂ = (T₁ → T₂) ⊔ (T₁ → T₃)
     n₁ = P₁(t)
     n₂ = P₂(t)
     @test Tables.matrix(n₁) ≈ Tables.matrix(n₂)
@@ -508,4 +510,4 @@
     @test isapprox(mean(r.x), mean(t.x), atol=1e-8)
     @test isapprox(std(r.x), std(t.x), atol=1e-8)
   end
-end
\ No newline at end of file
+end