diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 329407265..9aa965e42 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -44,7 +44,7 @@ repos:
           - --py311-plus
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.254
+    rev: v0.0.255
     hooks:
       - id: ruff
         args:
@@ -69,7 +69,7 @@ repos:
           *flake8-plugins
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.0.1
+    rev: v1.1.1
     hooks:
       - id: mypy
         args:
@@ -79,11 +79,11 @@ repos:
         additional_dependencies: [types-requests]
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.2
+    rev: v2.2.4
     hooks:
       - id: codespell
         args:
-          - --ignore-words-list=ans,crate,damon,fo,followings,hist,iff,mater,secant,som,sur,tim,zar
+          - --ignore-words-list=3rt,ans,crate,damon,fo,followings,hist,iff,kwanza,mater,secant,som,sur,tim,zar
         exclude: |
           (?x)^(
               ciphers/prehistoric_men.txt |
diff --git a/DIRECTORY.md b/DIRECTORY.md
index f25b0c6ff..b2daaaa9c 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -334,6 +334,7 @@
 ## Electronics
   * [Builtin Voltage](electronics/builtin_voltage.py)
   * [Carrier Concentration](electronics/carrier_concentration.py)
+  * [Circular Convolution](electronics/circular_convolution.py)
   * [Coulombs Law](electronics/coulombs_law.py)
   * [Electric Conductivity](electronics/electric_conductivity.py)
   * [Electric Power](electronics/electric_power.py)
diff --git a/machine_learning/sequential_minimum_optimization.py b/machine_learning/sequential_minimum_optimization.py
index 37172c8e9..b68bd52f4 100644
--- a/machine_learning/sequential_minimum_optimization.py
+++ b/machine_learning/sequential_minimum_optimization.py
@@ -569,7 +569,7 @@ def plot_partition_boundary(
     """
     We can not get the optimum w of our kernel svm model which is different from linear
     svm.  For this reason, we generate randomly distributed points with high desity and
-    prediced values of these points are calculated by using our tained model. Then we
+    prediced values of these points are calculated by using our trained model. Then we
     could use this prediced values to draw contour map.
     And this contour map can represent svm's partition boundary.
     """
diff --git a/physics/lorentz_transformation_four_vector.py b/physics/lorentz_transformation_four_vector.py
index 64be97245..f4fda4dff 100644
--- a/physics/lorentz_transformation_four_vector.py
+++ b/physics/lorentz_transformation_four_vector.py
@@ -2,7 +2,7 @@
 Lorentz transformations describe the transition between two inertial reference
 frames F and F', each of which is moving in some direction with respect to the
 other. This code only calculates Lorentz transformations for movement in the x
-direction with no spacial rotation (i.e., a Lorentz boost in the x direction).
+direction with no spatial rotation (i.e., a Lorentz boost in the x direction).
 The Lorentz transformations are calculated here as linear transformations of
 four-vectors [ct, x, y, z] described by Minkowski space. Note that t (time) is
 multiplied by c (the speed of light) in the first entry of each four-vector.