diff --git a/README.md b/README.md
index bac0bf373..6a6e165d7 100644
--- a/README.md
+++ b/README.md
@@ -39,29 +39,30 @@
 - 代码导向，提供精简、可运行的算法代码；
 - 讨论学习，提问一般能在三日内得到回复；
 
+如果感觉本书对你有所帮助，请点个 Star :star: 支持一下，谢谢！
+
+## 推荐语
+
+> “一本通俗易懂的数据结构与算法入门书，引导读者手脑并用地学习，强烈推荐算法初学者阅读。”
+>
+> **—— 邓俊辉，清华大学计算机系教授**
+
+## 贡献
+
 我们正在加速更新本书，期待您来[一起参与创作](https://www.hello-algo.com/chapter_preface/contribution/)，以帮助其他读者获取更优质的学习内容：
 
 - 如果发现笔误、无效链接、内容缺失、文字歧义、解释不清晰等问题，烦请您帮忙修正；
-- 欢迎您通过提交 Pull Request 来增添新内容，包括重写章节、新增章节、翻译代码至不同语言等；
+- [代码翻译](https://github.com/krahets/hello-algo/issues/15) C++, Python, Go, JavaScript, TypeScript 正在进行中，期望您前来挑大梁；
+- 欢迎您通过提交 Pull Request 来增添新内容，包括重写章节、新增章节等；
 
 > 有任何问题请与我联系 WeChat: krahets-jyd
 
-如果感觉本书对你有所帮助，请点个 Star :star: 支持一下，谢谢！
-
-## 致谢
-
 感谢本开源书的每一位撰稿人，是他们的无私奉献让这本书变得更好，他们是：
 
 <a href="https://github.com/krahets/hello-algo/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=krahets/hello-algo" />
 </a>
 
-## To-Dos
-
-- [x] [代码翻译](https://github.com/krahets/hello-algo/issues/15)：Java, C++, Python, Go, JavaScript 正在进行中，其他语言请求大佬挑大梁
-- [ ] 数据结构：散列表、堆（优先队列）、图
-- [ ] 算法：搜索与回溯、选择 / 堆排序、动态规划、贪心、分治
-
 ## License
 
 The texts, codes, images, photos, and videos in this repository are licensed under [CC BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
diff --git a/codes/go/chapter_searching/binary_search.go b/codes/go/chapter_searching/binary_search.go
new file mode 100644
index 000000000..cbaa85162
--- /dev/null
+++ b/codes/go/chapter_searching/binary_search.go
@@ -0,0 +1,43 @@
+// File: binary_search.go
+// Created Time: 2022-12-05
+// Author: Slone123c (274325721@qq.com)
+
+package chapter_searching
+
+/* 二分查找（双闭区间） */
+func binarySearch(nums []int, target int) int {
+	// 初始化双闭区间 [0, n-1] ，即 i, j 分别指向数组首元素、尾元素
+	i, j := 0, len(nums)-1
+	// 循环，当搜索区间为空时跳出（当 i > j 时为空）
+	for i <= j {
+		m := (i + j) / 2      // 计算中点索引 m
+		if nums[m] < target { // 此情况说明 target 在区间 [m+1, j] 中
+			i = m + 1
+		} else if nums[m] > target { // 此情况说明 target 在区间 [i, m-1] 中
+			j = m - 1
+		} else { // 找到目标元素，返回其索引
+			return m
+		}
+	}
+	// 未找到目标元素，返回 -1
+	return -1
+}
+
+/* 二分查找（左闭右开） */
+func binarySearch1(nums []int, target int) int {
+	// 初始化左闭右开 [0, n) ，即 i, j 分别指向数组首元素、尾元素+1
+	i, j := 0, len(nums)
+	// 循环，当搜索区间为空时跳出（当 i = j 时为空）
+	for i < j {
+		m := (i + j) / 2      // 计算中点索引 m
+		if nums[m] < target { // 此情况说明 target 在区间 [m+1, j) 中
+			i = m + 1
+		} else if nums[m] > target { // 此情况说明 target 在区间 [i, m) 中
+			j = m
+		} else { // 找到目标元素，返回其索引
+			return m
+		}
+	}
+	// 未找到目标元素，返回 -1
+	return -1
+}
diff --git a/codes/go/chapter_searching/binary_search_test.go b/codes/go/chapter_searching/binary_search_test.go
new file mode 100644
index 000000000..3dada9593
--- /dev/null
+++ b/codes/go/chapter_searching/binary_search_test.go
@@ -0,0 +1,24 @@
+// File: binary_search_test.go
+// Created Time: 2022-12-05
+// Author: Slone123c (274325721@qq.com)
+
+package chapter_searching
+
+import (
+	"fmt"
+	"testing"
+)
+
+func TestBinarySearch(t *testing.T) {
+	var (
+		target   = 3
+		nums     = []int{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
+		expected = 2
+	)
+	// 在数组中执行二分查找
+	actual := binarySearch(nums, target)
+	fmt.Println("目标元素 3 的索引 =", actual)
+	if actual != expected {
+		t.Errorf("目标元素 3 的索引 = %d, 应该为 %d", actual, expected)
+	}
+}
diff --git a/codes/go/chapter_sorting/bubble_sort/bubble_sort.go b/codes/go/chapter_sorting/bubble_sort/bubble_sort.go
new file mode 100644
index 000000000..a51d19925
--- /dev/null
+++ b/codes/go/chapter_sorting/bubble_sort/bubble_sort.go
@@ -0,0 +1,38 @@
+// File: bubble_sort.go
+// Created Time: 2022-12-06
+// Author: Slone123c (274325721@qq.com)
+
+package bubble_sort
+
+/* 冒泡排序 */
+func bubbleSort(nums []int) {
+	// 外循环：待排序元素数量为 n-1, n-2, ..., 1
+	for i := len(nums) - 1; i > 0; i-- {
+		// 内循环：冒泡操作
+		for j := 0; j < i; j++ {
+			if nums[j] > nums[j+1] {
+				// 交换 nums[j] 与 nums[j + 1]
+				nums[j], nums[j+1] = nums[j+1], nums[j]
+			}
+		}
+	}
+}
+
+/* 冒泡排序（标志优化）*/
+func bubbleSortWithFlag(nums []int) {
+	// 外循环：待排序元素数量为 n-1, n-2, ..., 1
+	for i := len(nums) - 1; i > 0; i-- {
+		flag := false // 初始化标志位
+		// 内循环：冒泡操作
+		for j := 0; j < i; j++ {
+			if nums[j] > nums[j+1] {
+				// 交换 nums[j] 与 nums[j + 1]
+				nums[j], nums[j+1] = nums[j+1], nums[j]
+				flag = true // 记录交换元素
+			}
+		}
+		if flag == false { // 此轮冒泡未交换任何元素，直接跳出
+			break
+		}
+	}
+}
diff --git a/codes/go/chapter_sorting/bubble_sort/bubble_sort_test.go b/codes/go/chapter_sorting/bubble_sort/bubble_sort_test.go
new file mode 100644
index 000000000..bab85139e
--- /dev/null
+++ b/codes/go/chapter_sorting/bubble_sort/bubble_sort_test.go
@@ -0,0 +1,20 @@
+// File: bubble_sort_test.go
+// Created Time: 2022-12-06
+// Author: Slone123c (274325721@qq.com)
+
+package bubble_sort
+
+import (
+	"fmt"
+	"testing"
+)
+
+func TestBubbleSort(t *testing.T) {
+	nums := []int{4, 1, 3, 1, 5, 2}
+	bubbleSort(nums)
+	fmt.Println("冒泡排序完成后 nums = ", nums)
+
+	nums1 := []int{4, 1, 3, 1, 5, 2}
+	bubbleSortWithFlag(nums1)
+	fmt.Println("冒泡排序完成后 nums1 = ", nums)
+}
diff --git a/codes/java/chapter_hashing/array_hash_map.java b/codes/java/chapter_hashing/array_hash_map.java
index 5fad6f3ea..89082c2f9 100644
--- a/codes/java/chapter_hashing/array_hash_map.java
+++ b/codes/java/chapter_hashing/array_hash_map.java
@@ -22,15 +22,15 @@ class ArrayHashMap {
     private List<Entry> bucket;
     public ArrayHashMap() {
         // 初始化一个长度为 10 的桶（数组）
-        bucket = new ArrayList<>(10);
-        for (int i = 0; i < 10; i++) {
+        bucket = new ArrayList<>();
+        for (int i = 0; i < 100; i++) {
             bucket.add(null);
         }
     }
 
     /* 哈希函数 */
     private int hashFunc(int key) {
-        int index = key % 10000;
+        int index = key % 100;
         return index;
     }
 
@@ -102,23 +102,23 @@ public class array_hash_map {
 
         /* 添加操作 */
         // 在哈希表中添加键值对 (key, value)
-        map.put(10001, "小哈");   
-        map.put(10002, "小啰");   
-        map.put(10003, "小算");   
-        map.put(10004, "小法");
-        map.put(10005, "小哇");
+        map.put(12836, "小哈");   
+        map.put(15937, "小啰");   
+        map.put(16750, "小算");   
+        map.put(13276, "小法");
+        map.put(10583, "小鸭");
         System.out.println("\n添加完成后，哈希表为\nKey -> Value");
         map.print();
 
         /* 查询操作 */
         // 向哈希表输入键 key ，得到值 value
-        String name = map.get(10002);
-        System.out.println("\n输入学号 10002 ，查询到姓名 " + name);
+        String name = map.get(15937);
+        System.out.println("\n输入学号 15937 ，查询到姓名 " + name);
 
         /* 删除操作 */
         // 在哈希表中删除键值对 (key, value)
-        map.remove(10005);
-        System.out.println("\n删除 10005 后，哈希表为\nKey -> Value");
+        map.remove(10583);
+        System.out.println("\n删除 10583 后，哈希表为\nKey -> Value");
         map.print();
 
         /* 遍历哈希表 */
diff --git a/codes/java/chapter_hashing/hash_map.java b/codes/java/chapter_hashing/hash_map.java
index bd145b76e..bb72a4b0f 100644
--- a/codes/java/chapter_hashing/hash_map.java
+++ b/codes/java/chapter_hashing/hash_map.java
@@ -15,23 +15,23 @@ public class hash_map {
 
         /* 添加操作 */
         // 在哈希表中添加键值对 (key, value)
-        map.put(10001, "小哈");   
-        map.put(10002, "小啰");   
-        map.put(10003, "小算");   
-        map.put(10004, "小法");
-        map.put(10005, "小哇");
+        map.put(12836, "小哈");   
+        map.put(15937, "小啰");   
+        map.put(16750, "小算");   
+        map.put(13276, "小法");
+        map.put(10583, "小鸭");
         System.out.println("\n添加完成后，哈希表为\nKey -> Value");
         PrintUtil.printHashMap(map);
 
         /* 查询操作 */
         // 向哈希表输入键 key ，得到值 value
-        String name = map.get(10002);
-        System.out.println("\n输入学号 10002 ，查询到姓名 " + name);
+        String name = map.get(15937);
+        System.out.println("\n输入学号 15937 ，查询到姓名 " + name);
 
         /* 删除操作 */
         // 在哈希表中删除键值对 (key, value)
-        map.remove(10005);
-        System.out.println("\n删除 10005 后，哈希表为\nKey -> Value");
+        map.remove(10583);
+        System.out.println("\n删除 10583 后，哈希表为\nKey -> Value");
         PrintUtil.printHashMap(map);
 
         /* 遍历哈希表 */
diff --git a/codes/java/chapter_tree/avl_tree.java b/codes/java/chapter_tree/avl_tree.java
new file mode 100644
index 000000000..5666b5d1d
--- /dev/null
+++ b/codes/java/chapter_tree/avl_tree.java
@@ -0,0 +1,218 @@
+/*
+ * File: avl_tree.java
+ * Created Time: 2022-12-10
+ * Author: Krahets (krahets@163.com)
+ */
+
+ package chapter_tree;
+
+ import include.*;
+ 
+ // Tree class
+ class AVLTree {
+     TreeNode root;  // 根节点
+ 
+     /* 获取结点高度 */ 
+     public int height(TreeNode node) {
+         // 空结点高度为 -1 ，叶结点高度为 0
+         return node == null ? -1 : node.height;
+     }
+ 
+     /* 更新结点高度 */
+     private void updateHeight(TreeNode node) {
+         node.height = Math.max(height(node.left), height(node.right)) + 1;  
+     }
+ 
+     /* 获取平衡因子 */ 
+     public int balanceFactor(TreeNode node) {
+         if (node == null)
+             return 0;
+         return height(node.left) - height(node.right);
+     }
+ 
+     /* 右旋操作 */ 
+     private TreeNode rightRotate(TreeNode node) {
+         TreeNode child = node.left;
+         TreeNode grandChild = child.right;
+         child.right = node;
+         node.left = grandChild;
+         updateHeight(node);
+         updateHeight(child);
+         return child;
+     }
+ 
+     /* 左旋操作 */ 
+     private TreeNode leftRotate(TreeNode node) {
+         TreeNode child = node.right;
+         TreeNode grandChild = child.left;
+         child.left = node;
+         node.right = grandChild;
+         updateHeight(node);
+         updateHeight(child);
+         return child;
+     }
+ 
+     /* 执行旋转操作，使该子树重新恢复平衡 */
+     private TreeNode rotate(TreeNode node) {
+         int balanceFactor = balanceFactor(node);
+         // 根据失衡情况分为四种操作：右旋、左旋、先左后右、先右后左
+         if (balanceFactor > 1) {
+             if (balanceFactor(node.left) >= 0) {
+                 // 右旋
+                 return rightRotate(node);
+             } else {
+                 // 先左旋后右旋
+                 node.left = leftRotate(node.left);
+                 return rightRotate(node);
+             }
+         }
+         if (balanceFactor < -1) {
+             if (balanceFactor(node.right) <= 0) {
+                 // 左旋
+                 return leftRotate(node);
+             } else {
+                 // 先右旋后左旋
+                 node.right = rightRotate(node.right);
+                 return leftRotate(node);
+             }
+         }
+         return node;
+     }
+ 
+     /* 插入结点 */
+     public TreeNode insert(int val) {
+         root = insertHelper(root, val);
+         return root;
+     }
+ 
+     /* 递归插入结点 */
+     private TreeNode insertHelper(TreeNode node, int val) {
+         // 1. 查找插入位置，并插入结点
+         if (node == null)
+             return new TreeNode(val);
+         if (val < node.val)
+             node.left = insertHelper(node.left, val);
+         else if (val > node.val)
+             node.right = insertHelper(node.right, val);
+         else
+             return node;  // 重复结点则直接返回
+         // 2. 更新结点高度
+         updateHeight(node);
+         // 3. 执行旋转操作，使该子树重新恢复平衡
+         node = rotate(node);
+         // 返回该子树的根节点
+         return node;
+     }
+ 
+     /* 删除结点 */
+     public TreeNode remove(int val) {
+         root = removeHelper(root, val);
+         return root;
+     }
+ 
+     /* 递归删除结点 */
+     private TreeNode removeHelper(TreeNode node, int val) {
+         // 1. 查找结点，并删除之
+         if (node == null)
+             return null;
+         if (val < node.val)
+             node.left = removeHelper(node.left, val);
+         else if (val > node.val)
+             node.right = removeHelper(node.right, val);
+         else {
+             if (node.left == null || node.right == null) {
+                 TreeNode child = node.left != null ? node.left : node.right;
+                 // 子结点数量 = 0 ，直接删除 node 并返回
+                 if (child == null)
+                     return null;
+                 // 子结点数量 = 1 ，直接删除 node
+                 else
+                     node = child;
+             } else {
+                 // 子结点数量 = 2 ，则将中序遍历的下个结点删除，并用该结点替换当前结点
+                 TreeNode temp = minNode(node.right);
+                 node.right = removeHelper(node.right, temp.val);
+                 node.val = temp.val;
+             }
+         }
+         // 2. 更新结点高度
+         updateHeight(node);
+         // 3. 执行旋转操作，使该子树重新恢复平衡
+         node = rotate(node);
+         // 返回该子树的根节点
+         return node;
+     }
+ 
+     /* 获取最小结点 */
+     private TreeNode minNode(TreeNode node) {
+         if (node == null) return node;
+         // 循环访问左子结点，直到叶结点时为最小结点，跳出
+         while (node.left != null) {
+             node = node.left;
+         }
+         return node;
+     }
+ 
+     /* 查找结点 */
+     public TreeNode search(int val) {
+         TreeNode cur = root;
+         // 循环查找，越过叶结点后跳出
+         while (cur != null) {
+             // 目标结点在 root 的右子树中
+             if (cur.val < val) cur = cur.right;
+             // 目标结点在 root 的左子树中
+             else if (cur.val > val) cur = cur.left;
+             // 找到目标结点，跳出循环
+             else break;
+         }
+         // 返回目标结点
+         return cur;
+     }
+ }
+ 
+ 
+ public class avl_tree {
+     static void testInsert(AVLTree tree, int val) {
+         tree.insert(val);
+         System.out.println("\n插入结点 " + val + " 后，AVL 树为");
+         PrintUtil.printTree(tree.root);
+     }
+ 
+     static void testRemove(AVLTree tree, int val) {
+         tree.remove(val);
+         System.out.println("\n删除结点 " + val + " 后，AVL 树为");
+         PrintUtil.printTree(tree.root);
+     }
+ 
+     public static void main(String[] args) {
+         /* 初始化空 AVL 树 */
+         AVLTree avlTree = new AVLTree();
+ 
+         /* 插入结点 */
+         // 请关注插入结点后，AVL 树是如何保持平衡的
+         testInsert(avlTree, 1);
+         testInsert(avlTree, 2);
+         testInsert(avlTree, 3);
+         testInsert(avlTree, 4);
+         testInsert(avlTree, 5);
+         testInsert(avlTree, 8);
+         testInsert(avlTree, 7);
+         testInsert(avlTree, 9);
+         testInsert(avlTree, 10);
+         testInsert(avlTree, 6);
+ 
+         /* 插入重复结点 */
+         testInsert(avlTree, 7);
+ 
+         /* 删除结点 */
+         // 请关注删除结点后，AVL 树是如何保持平衡的
+         testRemove(avlTree, 8);  // 删除度为 0 的结点
+         testRemove(avlTree, 5);  // 删除度为 1 的结点
+         testRemove(avlTree, 4);  // 删除度为 2 的结点
+ 
+         /* 查询结点 */
+         TreeNode node = avlTree.search(7);
+         System.out.println("\n查找到的结点对象为 " + node + "，结点值 = " + node.val);
+     }
+ }
+ 
\ No newline at end of file
diff --git a/codes/java/include/TreeNode.java b/codes/java/include/TreeNode.java
old mode 100755
new mode 100644
index 69aafd500..98b9f826e
--- a/codes/java/include/TreeNode.java
+++ b/codes/java/include/TreeNode.java
@@ -12,10 +12,10 @@ import java.util.*;
  * Definition for a binary tree node.
  */
 public class TreeNode {
-    public int val;
-    public int height;
-    public TreeNode left;
-    public TreeNode right;
+    public int val;        // 结点值
+    public int height;     // 结点高度
+    public TreeNode left;  // 左子结点引用
+    public TreeNode right; // 右子结点引用
 
     public TreeNode(int x) {
         val = x;
diff --git a/codes/javascript/chapter_stack_and_queue/array_stack.js b/codes/javascript/chapter_stack_and_queue/array_stack.js
new file mode 100644
index 000000000..a83d16f0e
--- /dev/null
+++ b/codes/javascript/chapter_stack_and_queue/array_stack.js
@@ -0,0 +1,84 @@
+/**
+ * File: array_stack.js
+ * Created Time: 2022-12-09
+ * Author: S-N-O-R-L-A-X (snorlax.xu@outlook.com)
+ */
+
+
+/* 基于数组实现的栈 */
+class ArrayStack {
+    stack;
+    constructor() {
+        this.stack = [];
+    }
+    /* 获取栈的长度 */
+    get size() {
+        return this.stack.length;
+    }
+
+    /* 判断栈是否为空 */
+    empty() {
+        return this.stack.length === 0;
+    }
+
+    /* 入栈 */
+    push(num) {
+        this.stack.push(num);
+    }
+
+    /* 出栈 */
+    pop() {
+        return this.stack.pop();
+    }
+
+    /* 访问栈顶元素 */
+    top() {
+        return this.stack[this.stack.length - 1];
+    }
+
+    /* 访问索引 index 处元素 */
+    get(index) {
+        return this.stack[index];
+    }
+
+    /* 返回 Array */
+    toArray() {
+        return this.stack;
+    }
+};
+
+
+/* Driver Code */
+
+/* 初始化栈 */
+const stack = new ArrayStack();
+
+/* 元素入栈 */
+stack.push(1);
+stack.push(3);
+stack.push(2);
+stack.push(5);
+stack.push(4);
+console.log("栈 stack = ");
+console.log(stack.toArray());
+
+/* 访问栈顶元素 */
+const top = stack.top();
+console.log("栈顶元素 top = " + top);
+
+/* 访问索引 index 处元素 */
+const num = stack.get(3);
+console.log("栈索引 3 处的元素为 num = " + num);
+
+/* 元素出栈 */
+const pop = stack.pop();
+console.log("出栈元素 pop = " + pop + "，出栈后 stack = ");
+console.log(stack.toArray());
+
+/* 获取栈的长度 */
+const size = stack.size;
+console.log("栈的长度 size = " + size);
+
+/* 判断是否为空 */
+const empty = stack.empty();
+console.log("栈是否为空 = " + empty);
diff --git a/codes/javascript/chapter_stack_and_queue/queue.js b/codes/javascript/chapter_stack_and_queue/queue.js
new file mode 100644
index 000000000..5d26727f1
--- /dev/null
+++ b/codes/javascript/chapter_stack_and_queue/queue.js
@@ -0,0 +1,30 @@
+/**
+ * File: queue.js
+ * Created Time: 2022-12-05
+ * Author: S-N-O-R-L-A-X (snorlax.xu@outlook.com)
+ */
+
+/* 初始化队列 */
+// JavaScript 没有内置的队列，可以把 Array 当作队列来使用 
+// 注意：由于是数组，所以 shift() 的时间复杂度是 O(n)
+const queue = [];
+
+/* 元素入队 */
+queue.push(1);
+queue.push(3);
+queue.push(2);
+queue.push(5);
+queue.push(4);
+
+/* 访问队首元素 */
+const peek = queue[0];
+
+/* 元素出队 */
+// O(n)
+const poll = queue.shift();
+
+/* 获取队列的长度 */
+const size = queue.length;
+
+/* 判断队列是否为空 */
+const empty = queue.length === 0;
diff --git a/codes/python/chapter_stack_and_queue/queue.py b/codes/python/chapter_stack_and_queue/queue.py
index 93729a868..740d9b1e1 100644
--- a/codes/python/chapter_stack_and_queue/queue.py
+++ b/codes/python/chapter_stack_and_queue/queue.py
@@ -1,5 +1,5 @@
 '''
-File: que.py
+File: queue.py
 Created Time: 2022-11-29
 Author: Peng Chen (pengchzn@gmail.com)
 '''
diff --git a/codes/typescript/chapter_stack_and_queue/array_stack.ts b/codes/typescript/chapter_stack_and_queue/array_stack.ts
new file mode 100644
index 000000000..5a57b6cf7
--- /dev/null
+++ b/codes/typescript/chapter_stack_and_queue/array_stack.ts
@@ -0,0 +1,86 @@
+/**
+ * File: array_stack.ts
+ * Created Time: 2022-12-08
+ * Author: S-N-O-R-L-A-X (snorlax.xu@outlook.com)
+ */
+
+
+/* 基于数组实现的栈 */
+class ArrayStack {
+    private stack: number[];
+    constructor() {
+        this.stack = [];
+    }
+    /* 获取栈的长度 */
+    get size(): number {
+        return this.stack.length;
+    }
+
+    /* 判断栈是否为空 */
+    empty(): boolean {
+        return this.stack.length === 0;
+    }
+
+    /* 入栈 */
+    push(num: number): void {
+        this.stack.push(num);
+    }
+
+    /* 出栈 */
+    pop(): number | undefined {
+        return this.stack.pop();
+    }
+
+    /* 访问栈顶元素 */
+    top(): number | undefined {
+        return this.stack[this.stack.length - 1];
+    }
+
+    /* 访问索引 index 处元素 */
+    get(index: number): number | undefined {
+        return this.stack[index];
+    }
+
+    /* 返回 Array */
+    toArray() {
+        return this.stack;
+    }
+};
+
+
+/* Driver Code */
+
+/* 初始化栈 */
+const stack = new ArrayStack();
+
+/* 元素入栈 */
+stack.push(1);
+stack.push(3);
+stack.push(2);
+stack.push(5);
+stack.push(4);
+console.log("栈 stack = ");
+console.log(stack.toArray());
+
+/* 访问栈顶元素 */
+const top = stack.top();
+console.log("栈顶元素 top = " + top);
+
+/* 访问索引 index 处元素 */
+const num = stack.get(3);
+console.log("栈索引 3 处的元素为 num = " + num);
+
+/* 元素出栈 */
+const pop = stack.pop();
+console.log("出栈元素 pop = " + pop + "，出栈后 stack = ");
+console.log(stack.toArray());
+
+/* 获取栈的长度 */
+const size = stack.size;
+console.log("栈的长度 size = " + size);
+
+/* 判断是否为空 */
+const empty = stack.empty();
+console.log("栈是否为空 = " + empty);
+
+export { };
\ No newline at end of file
diff --git a/codes/typescript/chapter_stack_and_queue/queue.ts b/codes/typescript/chapter_stack_and_queue/queue.ts
new file mode 100644
index 000000000..0701a9af5
--- /dev/null
+++ b/codes/typescript/chapter_stack_and_queue/queue.ts
@@ -0,0 +1,32 @@
+/**
+ * File: queue.ts
+ * Created Time: 2022-12-05
+ * Author: S-N-O-R-L-A-X (snorlax.xu@outlook.com)
+ */
+
+/* 初始化队列 */
+// TypeScript 没有内置的队列，可以把 Array 当作队列来使用 
+// 注意：由于是数组，所以 shift() 的时间复杂度是 O(n)
+const queue: number[] = [];
+
+/* 元素入队 */
+queue.push(1);
+queue.push(3);
+queue.push(2);
+queue.push(5);
+queue.push(4);
+
+/* 访问队首元素 */
+const peek = queue[0];
+
+/* 元素出队 */
+// O(n)
+const poll = queue.shift();
+
+/* 获取队列的长度 */
+const size = queue.length;
+
+/* 判断队列是否为空 */
+const empty = queue.length === 0;
+
+export { };
diff --git a/docs/chapter_array_and_linkedlist/array.assets/array_definition.png b/docs/chapter_array_and_linkedlist/array.assets/array_definition.png
index 401b06691..f5cb45aa0 100644
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diff --git a/docs/chapter_array_and_linkedlist/array.assets/array_insert_remove_element.png b/docs/chapter_array_and_linkedlist/array.assets/array_insert_remove_element.png
index a35f52076..6e02d129b 100644
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diff --git a/docs/chapter_array_and_linkedlist/array.assets/array_memory_location_calculation.png b/docs/chapter_array_and_linkedlist/array.assets/array_memory_location_calculation.png
index 3e1a08d75..6565145f8 100644
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diff --git a/docs/chapter_array_and_linkedlist/array.md b/docs/chapter_array_and_linkedlist/array.md
index bba71f06b..c6f2feca7 100644
--- a/docs/chapter_array_and_linkedlist/array.md
+++ b/docs/chapter_array_and_linkedlist/array.md
@@ -65,13 +65,13 @@ comments: true
 === "C"
 
     ```c title="array.c"
-
+    
     ```
 
 === "C#"
 
     ```csharp title="array.cs"
-
+    
     ```
 
 ## 数组优点
@@ -131,7 +131,7 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "Go"
 
     ```go title="array.go"
-
+    
     ```
 
 === "JavaScript"
@@ -163,13 +163,13 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "C"
 
     ```c title="array.c"
-
+    
     ```
 
 === "C#"
 
     ```csharp title="array.cs"
-
+    
     ```
 
 ## 数组缺点
@@ -227,7 +227,7 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "Go"
 
     ```go title="array.go"
-
+    
     ```
 
 === "JavaScript"
@@ -265,19 +265,19 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "C"
 
     ```c title="array.c"
-
+    
     ```
 
 === "C#"
 
     ```csharp title="array.cs"
-
+    
     ```
 
 **数组中插入或删除元素效率低下。** 假设我们想要在数组中间某位置插入一个元素，由于数组元素在内存中是 “紧挨着的” ，它们之间没有空间再放任何数据。因此，我们不得不将此索引之后的所有元素都向后移动一位，然后再把元素赋值给该索引。删除元素也是类似，需要把此索引之后的元素都向前移动一位。总体看有以下缺点：
 
 - **时间复杂度高：** 数组的插入和删除的平均时间复杂度均为 $O(N)$ ，其中 $N$ 为数组长度。
-- **丢失元素或：** 由于数组的长度不可变，因此在插入元素后，数组原来的末尾元素会丢失。
+- **丢失元素：** 由于数组的长度不可变，因此在插入元素后，超出数组长度范围的元素会被丢失。
 - **内存浪费：** 我们一般会初始化一个比较长的数组，只用前面一部分，这样在插入数据时，丢失的末尾元素都是我们不关心的，但这样做同时也会造成内存空间的浪费。
 
 ![array_insert_remove_element](array.assets/array_insert_remove_element.png)
@@ -318,7 +318,7 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
         // 将 num 赋给 index 处元素
         nums[index] = num;
     }
-
+    
     /* 删除索引 index 处元素 */
     void remove(int* nums, int size, int index) {
         // 把索引 index 之后的所有元素向前移动一位
@@ -338,7 +338,7 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
             nums[i] = nums[i - 1]
         # 将 num 赋给 index 处元素
         nums[index] = num
-
+    
     """ 删除索引 index 处元素 """
     def remove(nums, index):
         # 把索引 index 之后的所有元素向前移动一位
@@ -349,7 +349,7 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "Go"
 
     ```go title="array.go"
-
+    
     ```
 
 === "JavaScript"
@@ -364,7 +364,7 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
         // 将 num 赋给 index 处元素
         nums[index] = num;
     }
-
+    
     /* 删除索引 index 处元素 */
     function remove(nums, index){
         // 把索引 index 之后的所有元素向前移动一位
@@ -386,7 +386,7 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
         // 将 num 赋给 index 处元素
         nums[index] = num
     }
-
+    
     /* 删除索引 index 处元素 */
     function remove(nums: number[], index: number): void {
         // 把索引 index 之后的所有元素向前移动一位
@@ -399,13 +399,13 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "C"
 
     ```c title="array.c"
-
+    
     ```
 
 === "C#"
 
     ```csharp title="array.cs"
-
+    
     ```
 
 ## 数组常用操作
@@ -459,7 +459,7 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "Go"
 
     ```go title="array.go"
-
+    
     ```
 
 === "JavaScript"
@@ -499,13 +499,13 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "C"
 
     ```c title="array.c"
-
+    
     ```
 
 === "C#"
 
     ```csharp title="array.cs"
-
+    
     ```
 
 **数组查找。** 通过遍历数组，查找数组内的指定元素，并输出对应索引。
@@ -550,7 +550,7 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "Go"
 
     ```go title="array.go"
-
+    
     ```
 
 === "JavaScript"
@@ -583,13 +583,13 @@ elementAddr = firtstElementAddr + elementLength * elementIndex
 === "C"
 
     ```c title="array.c"
-
+    
     ```
 
 === "C#"
 
     ```csharp title="array.cs"
-
+    
     ```
 
 ## 数组典型应用
diff --git a/docs/chapter_array_and_linkedlist/linked_list.assets/linkedlist_common_types.png b/docs/chapter_array_and_linkedlist/linked_list.assets/linkedlist_common_types.png
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index 73120e956..66d82b4f2 100644
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index 3fcda1f63..3ab4da38c 100644
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diff --git a/docs/chapter_computational_complexity/performance_evaluation.md b/docs/chapter_computational_complexity/performance_evaluation.md
index 84c3dc80d..45031b2f8 100644
--- a/docs/chapter_computational_complexity/performance_evaluation.md
+++ b/docs/chapter_computational_complexity/performance_evaluation.md
@@ -30,7 +30,7 @@ comments: true
 
 ### 理论估算
 
-既然实际测试具有很大的局限性，那么我们是否可以仅通过一些计算，就获知算法的效率水平呢？答案是肯定的，我们将此估算方法称为「复杂度分析 Complexity Analysis」或「渐进复杂度分析 Asymptotic Complexity Analysis」。
+既然实际测试具有很大的局限性，那么我们是否可以仅通过一些计算，就获知算法的效率水平呢？答案是肯定的，我们将此估算方法称为「复杂度分析 Complexity Analysis」或「渐近复杂度分析 Asymptotic Complexity Analysis」。
 
 **复杂度分析评估随着输入数据量的增长，算法的运行时间和占用空间的增长趋势** 。根据时间和空间两方面，复杂度可分为「时间复杂度 Time Complexity」和「空间复杂度 Space Complexity」。
 
diff --git a/docs/chapter_computational_complexity/space_complexity.assets/space_complexity_common_types.png b/docs/chapter_computational_complexity/space_complexity.assets/space_complexity_common_types.png
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index 01826bc0c..951a4f4b5 100644
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diff --git a/docs/chapter_computational_complexity/summary.md b/docs/chapter_computational_complexity/summary.md
index 6550e776c..7e76d2c15 100644
--- a/docs/chapter_computational_complexity/summary.md
+++ b/docs/chapter_computational_complexity/summary.md
@@ -13,8 +13,8 @@ comments: true
 ### 时间复杂度
 
 - 「时间复杂度」统计算法运行时间随着数据量变大时的增长趋势，可以有效评估算法效率，但在某些情况下可能失效，比如在输入数据量较小或时间复杂度相同时，无法精确对比算法效率的优劣性。
-- 「最差时间复杂度」使用大 $O$ 符号表示，即函数渐进上界，其反映当 $n$ 趋于正无穷时，$T(n)$ 处于何种增长级别。
-- 推算时间复杂度分为两步，首先统计计算操作数量，再判断渐进上界。
+- 「最差时间复杂度」使用大 $O$ 符号表示，即函数渐近上界，其反映当 $n$ 趋于正无穷时，$T(n)$ 处于何种增长级别。
+- 推算时间复杂度分为两步，首先统计计算操作数量，再判断渐近上界。
 - 常见时间复杂度从小到大排列有 $O(1)$ , $O(\log n)$ , $O(n)$ , $O(n \log n)$ , $O(n^2)$ , $O(2^n)$ , $O(n!)$ 。
 - 某些算法的时间复杂度不是恒定的，而是与输入数据的分布有关。时间复杂度分为「最差时间复杂度」和「最佳时间复杂度」，后者几乎不用，因为输入数据需要满足苛刻的条件才能达到最佳情况。
 - 「平均时间复杂度」可以反映在随机数据输入下的算法效率，最贴合实际使用情况下的算法性能。计算平均时间复杂度需要统计输入数据的分布，以及综合后的数学期望。
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diff --git a/docs/chapter_computational_complexity/time_complexity.assets/time_complexity_logarithmic_linear.png b/docs/chapter_computational_complexity/time_complexity.assets/time_complexity_logarithmic_linear.png
index 9f4f51539..6f336f4b5 100644
Binary files a/docs/chapter_computational_complexity/time_complexity.assets/time_complexity_logarithmic_linear.png and b/docs/chapter_computational_complexity/time_complexity.assets/time_complexity_logarithmic_linear.png differ
diff --git a/docs/chapter_computational_complexity/time_complexity.md b/docs/chapter_computational_complexity/time_complexity.md
index 9ee13d8db..ab55268c5 100644
--- a/docs/chapter_computational_complexity/time_complexity.md
+++ b/docs/chapter_computational_complexity/time_complexity.md
@@ -203,7 +203,7 @@ $$
 
 **时间复杂度也存在一定的局限性。** 比如，虽然算法 `A` 和 `C` 的时间复杂度相同，但是实际的运行时间有非常大的差别。再比如，虽然算法 `B` 比 `C` 的时间复杂度要更高，但在输入数据大小 $n$ 比较小时，算法 `B` 是要明显优于算法 `C` 的。即使存在这些问题，计算复杂度仍然是评判算法效率的最有效、最常用方法。
 
-## 函数渐进上界
+## 函数渐近上界
 
 设算法「计算操作数量」为 $T(n)$  ，其是一个关于输入数据大小 $n$ 的函数。例如，以下算法的操作数量为
 
@@ -284,34 +284,34 @@ $$
 
 $T(n)$ 是个一次函数，说明时间增长趋势是线性的，因此易得时间复杂度是线性阶。
 
-我们将线性阶的时间复杂度记为 $O(n)$ ，这个数学符号被称为「大 $O$ 记号 Big-$O$ Notation」，代表函数 $T(n)$ 的「渐进上界 asymptotic upper bound」。
+我们将线性阶的时间复杂度记为 $O(n)$ ，这个数学符号被称为「大 $O$ 记号 Big-$O$ Notation」，代表函数 $T(n)$ 的「渐近上界 asymptotic upper bound」。
 
-我们要推算时间复杂度，本质上是在计算「操作数量函数 $T(n)$ 」的渐进上界。下面我们先来看看函数渐进上界的数学定义。
+我们要推算时间复杂度，本质上是在计算「操作数量函数 $T(n)$ 」的渐近上界。下面我们先来看看函数渐近上界的数学定义。
 
-!!! abstract "函数渐进上界"
+!!! abstract "函数渐近上界"
 
     若存在正实数 $c$ 和实数 $n_0$ ，使得对于所有的 $n > n_0$ ，均有
     $$
     T(n) \leq c \cdot f(n)
     $$
-    则可认为 $f(n)$ 给出了 $T(n)$ 的一个渐进上界，记为 
+    则可认为 $f(n)$ 给出了 $T(n)$ 的一个渐近上界，记为 
     $$
     T(n) = O(f(n))
     $$
 
 ![asymptotic_upper_bound](time_complexity.assets/asymptotic_upper_bound.png)
 
-<p align="center"> Fig. 函数的渐进上界 </p>
+<p align="center"> Fig. 函数的渐近上界 </p>
 
-本质上看，计算渐进上界就是在找一个函数 $f(n)$ ，**使得在 $n$ 趋向于无穷大时，$T(n)$ 和 $f(n)$ 处于相同的增长级别（仅相差一个常数项 $c$ 的倍数）**。
+本质上看，计算渐近上界就是在找一个函数 $f(n)$ ，**使得在 $n$ 趋向于无穷大时，$T(n)$ 和 $f(n)$ 处于相同的增长级别（仅相差一个常数项 $c$ 的倍数）**。
 
 !!! tip
 
-    渐进上界的数学味儿有点重，如果你感觉没有完全理解，无需担心，因为在实际使用中我们只需要会推算即可，数学意义可以慢慢领悟。
+    渐近上界的数学味儿有点重，如果你感觉没有完全理解，无需担心，因为在实际使用中我们只需要会推算即可，数学意义可以慢慢领悟。
 
 ## 推算方法
 
-推算出 $f(n)$ 后，我们就得到时间复杂度 $O(f(n))$ 。那么，如何来确定渐进上界 $f(n)$ 呢？总体分为两步，首先「统计操作数量」，然后「判断渐进上界」。
+推算出 $f(n)$ 后，我们就得到时间复杂度 $O(f(n))$ 。那么，如何来确定渐近上界 $f(n)$ 呢？总体分为两步，首先「统计操作数量」，然后「判断渐近上界」。
 
 ### 1. 统计操作数量
 
@@ -416,7 +416,7 @@ $$
 
     ```
 
-### 2. 判断渐进上界
+### 2. 判断渐近上界
 
 **时间复杂度由多项式 $T(n)$ 中最高阶的项来决定**。这是因为在 $n$ 趋于无穷大时，最高阶的项将处于主导作用，其它项的影响都可以被忽略。
 
@@ -1330,7 +1330,7 @@ $$
 - 当 `nums = [?, ?, ..., 1]`，即当末尾元素是 $1$ 时，则需完整遍历数组，此时达到 **最差时间复杂度 $O(n)$** ；
 - 当 `nums = [1, ?, ?, ...]` ，即当首个数字为 $1$ 时，无论数组多长都不需要继续遍历，此时达到 **最佳时间复杂度 $\Omega(1)$** ；
 
-「函数渐进上界」使用大 $O$ 记号表示，代表「最差时间复杂度」。与之对应，「函数渐进下界」用 $\Omega$ 记号（Omega Notation）来表示，代表「最佳时间复杂度」。
+「函数渐近上界」使用大 $O$ 记号表示，代表「最差时间复杂度」。与之对应，「函数渐近下界」用 $\Omega$ 记号（Omega Notation）来表示，代表「最佳时间复杂度」。
 
 === "Java"
 
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diff --git a/docs/chapter_hashing/hash_map.assets/hash_map.png b/docs/chapter_hashing/hash_map.assets/hash_map.png
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diff --git a/docs/chapter_hashing/hash_map.md b/docs/chapter_hashing/hash_map.md
index 62725faae..77bc82321 100644
--- a/docs/chapter_hashing/hash_map.md
+++ b/docs/chapter_hashing/hash_map.md
@@ -4,9 +4,34 @@ comments: true
 
 # 哈希表
 
-哈希表通过建立「键 Key」和「值 Value」之间的映射，实现高效的元素查找。具体地，查询操作（给定一个 Key 查询得到 Value）的时间复杂度为 $O(1)$ 。
+哈希表通过建立「键 Key」和「值 Value」之间的映射，实现高效的元素查找。具体地，输入一个 Key ，在哈希表中查询并获取 Value ，时间复杂度为 $O(1)$ 。
 
-（图）
+例如，给定一个包含 $n$ 个学生的数据库，每个学生有 "姓名 `name` ” 和 “学号 `id` ” 两项数据，希望实现一个查询功能：**输入一个学号，返回对应的姓名**，则可以使用哈希表实现。
+
+![hash_map](hash_map.assets/hash_map.png)
+
+<p align="center"> Fig. 哈希表抽象表示 </p>
+
+## 哈希表优势
+
+除了哈希表之外，还可以使用以下数据结构来实现上述查询功能：
+
+- **无序数组：** 每个元素为  `[学号, 姓名]` ；
+- **有序数组：** 将 `1.` 中的数组按照学号从小到大排序；
+- **链表：** 每个结点的值为 `[学号, 姓名]` ；
+- **二叉搜索树：** 每个结点的值为 `[学号, 姓名]` ，根据学号大小来构建树；
+
+使用上述方法，各项操作的时间复杂度如下表所示（在此不做赘述，详解可见 [二叉搜索树章节](https://www.hello-algo.com/chapter_tree/binary_search_tree/#_6)）。无论是查找元素、还是增删元素，哈希表的时间复杂度都是 $O(1)$ ，全面胜出！
+
+<div class="center-table" markdown>
+
+|          | 无序数组 | 有序数组    | 链表   | 二叉搜索树  | 哈希表 |
+| -------- | -------- | ----------- | ------ | ----------- | ------ |
+| 查找元素 | $O(n)$   | $O(\log n)$ | $O(n)$ | $O(\log n)$ | $O(1)$ |
+| 插入元素 | $O(1)$   | $O(n)$      | $O(1)$ | $O(\log n)$ | $O(1)$ |
+| 删除元素 | $O(n)$   | $O(n)$      | $O(n)$ | $O(\log n)$ | $O(1)$ |
+
+</div>
 
 ## 哈希表常用操作
 
@@ -18,19 +43,19 @@ Map<Integer, String> map = new HashMap<>();
 
 /* 添加操作 */
 // 在哈希表中添加键值对 (key, value)
-map.put(10001, "小哈");   
-map.put(10002, "小啰");   
-map.put(10003, "小算");   
-map.put(10004, "小法");
-map.put(10005, "小哇");
+map.put(12836, "小哈");   
+map.put(15937, "小啰");   
+map.put(16750, "小算");   
+map.put(13276, "小法");
+map.put(10583, "小鸭");
 
 /* 查询操作 */
 // 向哈希表输入键 key ，得到值 value
-String name = map.get(10002);
+String name = map.get(15937);
 
 /* 删除操作 */
 // 在哈希表中删除键值对 (key, value)
-map.remove(10005);
+map.remove(10583);
 ```
 
 遍历哈希表有三种方式，即 **遍历键值对、遍历键、遍历值**。
@@ -51,28 +76,6 @@ for (String val: map.values()) {
 }
 ```
 
-## 哈希表优势
-
-给定一个包含 $n$ 个学生的数据库，每个学生有 "姓名 `name` ” 和 “学号 `id` ” 两项数据，希望实现一个查询功能，即 **输入一个学号，返回对应的姓名**，那么可以使用哪些数据结构来存储呢？
-
-- **无序数组：** 每个元素为  `[学号, 姓名]` ；
-- **有序数组：** 将 `1.` 中的数组按照学号从小到大排序；
-- **链表：** 每个结点的值为 `[学号, 姓名]` ；
-- **二叉搜索树：** 每个结点的值为 `[学号, 姓名]` ，根据学号大小来构建树；
-- **哈希表：** 以学号为 Key 、姓名为 Value 。
-
-使用上述方法，各项操作的时间复杂度如下表所示（在此不做赘述，详解可见 [二叉搜索树章节](https://www.hello-algo.com/chapter_tree/binary_search_tree/#_6)），**哈希表全面胜出！**
-
-<div class="center-table" markdown>
-
-|              | 无序数组 | 有序数组    | 链表   | 二叉搜索树  | 哈希表 |
-| ------------ | -------- | ----------- | ------ | ----------- | ------ |
-| 查找指定元素 | $O(n)$   | $O(\log n)$ | $O(n)$ | $O(\log n)$ | $O(1)$ |
-| 插入元素     | $O(1)$   | $O(n)$      | $O(1)$ | $O(\log n)$ | $O(1)$ |
-| 删除元素     | $O(n)$   | $O(n)$      | $O(n)$ | $O(\log n)$ | $O(1)$ |
-
-</div>
-
 ## 哈希函数
 
 哈希表中存储元素的数据结构被称为「桶 Bucket」，底层实现可能是数组、链表、二叉树（红黑树），或是它们的组合。
@@ -87,10 +90,12 @@ for (String val: map.values()) {
 以上述学生数据 `Key 学号 -> Value 姓名` 为例，我们可以将「哈希函数」设计为 
 
 $$
-f(x) = x \% 10000
+f(x) = x \% 100
 $$
 
-（图）
+![hash_function](hash_map.assets/hash_function.png)
+
+<p align="center"> Fig. 哈希函数 </p>
 
 ```java title="array_hash_map.java"
 /* 键值对 int->String */
@@ -107,16 +112,16 @@ class Entry {
 class ArrayHashMap {
     private List<Entry> bucket;
     public ArrayHashMap() {
-        // 初始化一个长度为 10 的桶（数组）
+        // 初始化一个长度为 100 的桶（数组）
         bucket = new ArrayList<>();
-        for (int i = 0; i < 10; i++) {
+        for (int i = 0; i < 100; i++) {
             bucket.add(null);
         }
     }
 
     /* 哈希函数 */
     private int hashFunc(int key) {
-        int index = key % 10000;
+        int index = key % 100;
         return index;
     }
 
@@ -146,8 +151,18 @@ class ArrayHashMap {
 
 ## 哈希冲突
 
-细心的同学可能会发现，哈希函数 $f(x) = x \% 10000$ 会在某些情况下失效。例如，当输入的 Key 为 10001, 20001, 30001, ... 时，哈希函数的计算结果都是 1 ，指向同一个 Value ，表明不同学号指向了同一个人，这明显是不对的。
+细心的同学可能会发现，**哈希函数 $f(x) = x \% 100$ 会在某些情况下失效**。具体地，当输入的 Key 后两位相同时，哈希函数的计算结果也相同，指向同一个 Value 。例如，分别查询两个学号 12836 和 20336 ，则有
+$$
+f(12836) = f(20336) = 36
+$$
+导致两个学号指向了同一个姓名，这明显是不对的。我们将这种现象称为「哈希冲突 Hash Collision」，其会严重影响查询的正确性，我们将如何避免哈希冲突的问题留在下章讨论。
+
+![hash_collision](hash_map.assets/hash_collision.png)
+
+<p align="center"> Fig. 哈希冲突 </p>
 
-上述现象被称为「哈希冲突 Hash Collision」，其会严重影响查询的正确性，我们将如何避免哈希冲突的问题留在下章讨论。
+综上所述，一个优秀的「哈希函数」应该具备以下特性：
 
-（图）
+- 尽量少地发生哈希冲突；
+- 时间复杂度 $O(1)$ ，计算尽可能高效；
+- 空间使用率高，即 “键值对占用空间 / 哈希表总占用空间” 尽可能大；
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diff --git a/docs/chapter_preface/about_me.md b/docs/chapter_preface/about_me.md
index 1b6dc2a18..29bd0bdb9 100644
--- a/docs/chapter_preface/about_me.md
+++ b/docs/chapter_preface/about_me.md
@@ -10,4 +10,4 @@ comments: true
 
 <p align="center"> 力扣（LeetCode）全网阅读量最高博主 </p>
 <p align="center"> 分享近百道算法题解，累积回复数千读者的评论问题 </p>
-<p align="center"> 创作 LeetBook《图解算法数据结构》，已免费售出 21 万本 </p>
+<p align="center"> 创作 LeetBook《图解算法数据结构》，已免费售出 22 万本 </p>
diff --git a/docs/chapter_preface/index.md b/docs/chapter_preface/index.md
index 0defc4a99..896f6e0c5 100644
--- a/docs/chapter_preface/index.md
+++ b/docs/chapter_preface/index.md
@@ -44,7 +44,7 @@ comments: true
 
 首先介绍数据结构与算法的评价维度、算法效率的评估方法，引出了计算复杂度概念。
 
-接下来，从 **函数渐进上界** 入手，分别介绍了 **时间复杂度** 和 **空间复杂度** ，包括推算方法、常见类型、示例等。同时，剖析了 **最差、最佳、平均** 时间复杂度的联系与区别。
+接下来，从 **函数渐近上界** 入手，分别介绍了 **时间复杂度** 和 **空间复杂度** ，包括推算方法、常见类型、示例等。同时，剖析了 **最差、最佳、平均** 时间复杂度的联系与区别。
 
 ### 数据结构
 
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diff --git a/docs/chapter_searching/binary_search.assets/binary_search_step5.png b/docs/chapter_searching/binary_search.assets/binary_search_step5.png
index 8cd4c64b7..1710f2433 100644
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diff --git a/docs/chapter_searching/binary_search.assets/binary_search_step6.png b/docs/chapter_searching/binary_search.assets/binary_search_step6.png
index a82323c25..b0f6ab1b8 100644
Binary files a/docs/chapter_searching/binary_search.assets/binary_search_step6.png and b/docs/chapter_searching/binary_search.assets/binary_search_step6.png differ
diff --git a/docs/chapter_searching/binary_search.assets/binary_search_step7.png b/docs/chapter_searching/binary_search.assets/binary_search_step7.png
index f34257e38..20bbcec0f 100644
Binary files a/docs/chapter_searching/binary_search.assets/binary_search_step7.png and b/docs/chapter_searching/binary_search.assets/binary_search_step7.png differ
diff --git a/docs/chapter_searching/binary_search.md b/docs/chapter_searching/binary_search.md
index d0e4a6db9..0a407ff91 100644
--- a/docs/chapter_searching/binary_search.md
+++ b/docs/chapter_searching/binary_search.md
@@ -123,7 +123,24 @@ $$
 === "Go"
 
     ```go title="binary_search.go"
-
+    /* 二分查找（左闭右开） */
+    func binarySearch1(nums []int, target int) int {
+        // 初始化左闭右开 [0, n) ，即 i, j 分别指向数组首元素、尾元素+1
+        i, j := 0, len(nums)
+        // 循环，当搜索区间为空时跳出（当 i = j 时为空）
+        for i < j {
+            m := (i + j) / 2                // 计算中点索引 m
+            if nums[m] < target {           // 此情况说明 target 在区间 [m+1, j) 中
+                i = m + 1
+            } else if nums[m] > target {    // 此情况说明 target 在区间 [i, m) 中
+                j = m
+            } else {                        // 找到目标元素，返回其索引
+                return m
+            }
+        }
+        // 未找到目标元素，返回 -1
+        return -1
+    }
     ```
 
 === "JavaScript"
@@ -220,7 +237,24 @@ $$
 === "Go"
 
     ```go title="binary_search.go"
-
+    /* 二分查找（左闭右开） */
+    func binarySearch1(nums []int, target int) int {
+        // 初始化左闭右开 [0, n) ，即 i, j 分别指向数组首元素、尾元素+1
+        i, j := 0, len(nums)
+        // 循环，当搜索区间为空时跳出（当 i = j 时为空）
+        for i < j {
+            m := (i + j) / 2             // 计算中点索引 m
+            if nums[m] < target {        // 此情况说明 target 在区间 [m+1, j) 中
+                i = m + 1
+            } else if nums[m] > target { // 此情况说明 target 在区间 [i, m) 中
+                j = m
+            } else {                     // 找到目标元素，返回其索引
+                return m
+            }
+        }
+        // 未找到目标元素，返回 -1
+        return -1
+    }
     ```
 
 === "JavaScript"
@@ -294,7 +328,10 @@ $$
 === "Go"
 
     ```go title=""
-
+    // (i + j) 有可能超出 int 的取值范围
+    m := (i + j) / 2
+    // 更换为此写法则不会越界
+    m := i + (j - i) / 2
     ```
 
 === "JavaScript"
diff --git a/docs/chapter_searching/hashing_search.assets/hash_search_index.png b/docs/chapter_searching/hashing_search.assets/hash_search_index.png
index 51dcebc6c..ab9f0b6b4 100644
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diff --git a/docs/chapter_searching/hashing_search.assets/hash_search_listnode.png b/docs/chapter_searching/hashing_search.assets/hash_search_listnode.png
index 26f3c0ba7..dd1ed8fab 100644
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diff --git a/docs/chapter_searching/linear_search.assets/linear_search.png b/docs/chapter_searching/linear_search.assets/linear_search.png
index 722904e3c..01c26cbea 100644
Binary files a/docs/chapter_searching/linear_search.assets/linear_search.png and b/docs/chapter_searching/linear_search.assets/linear_search.png differ
diff --git a/docs/chapter_sorting/bubble_sort.assets/bubble_operation_step1.png b/docs/chapter_sorting/bubble_sort.assets/bubble_operation_step1.png
index f1790fc4c..9b72415ed 100644
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index 653f78047..b4d9588d2 100644
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index 3a1bfb57c..bc44269ce 100644
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index 5d1a6abf2..126f24db7 100644
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index c0d00d9c0..0d4335767 100644
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index 94d187634..265b063f5 100644
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diff --git a/docs/chapter_sorting/bubble_sort.assets/bubble_operation_step7.png b/docs/chapter_sorting/bubble_sort.assets/bubble_operation_step7.png
index bc565e742..3899ca716 100644
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diff --git a/docs/chapter_sorting/bubble_sort.assets/bubble_sort.png b/docs/chapter_sorting/bubble_sort.assets/bubble_sort.png
index b6bd51f92..693d915a2 100644
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diff --git a/docs/chapter_sorting/bubble_sort.md b/docs/chapter_sorting/bubble_sort.md
index 05ef478b0..488c57561 100644
--- a/docs/chapter_sorting/bubble_sort.md
+++ b/docs/chapter_sorting/bubble_sort.md
@@ -6,7 +6,7 @@ comments: true
 
 「冒泡排序 Bubble Sort」是一种最基础的排序算法，非常适合作为第一个学习的排序算法。顾名思义，「冒泡」是该算法的核心操作。
 
-!!! tip "为什么叫 “冒泡”"
+!!! question "为什么叫 “冒泡”"
 
     在水中，越大的泡泡浮力越大，所以最大的泡泡会最先浮到水面。
 
@@ -112,7 +112,19 @@ comments: true
 === "Go"
 
     ```go title="bubble_sort.go"
-
+    /* 冒泡排序 */
+    func bubbleSort(nums []int) {
+        // 外循环：待排序元素数量为 n-1, n-2, ..., 1
+        for i := len(nums) - 1; i > 0; i-- {
+            // 内循环：冒泡操作
+            for j := 0; j < i; j++ {
+                if nums[j] > nums[j+1] {
+                    // 交换 nums[j] 与 nums[j + 1]
+                    nums[j], nums[j+1] = nums[j+1], nums[j]
+                }
+            }
+        }
+    }
     ```
 
 === "JavaScript"
@@ -239,7 +251,24 @@ comments: true
 === "Go"
 
     ```go title="bubble_sort.go"
-
+    /* 冒泡排序（标志优化）*/
+    func bubbleSortWithFlag(nums []int) {
+        // 外循环：待排序元素数量为 n-1, n-2, ..., 1
+        for i := len(nums) - 1; i > 0; i-- {
+            flag := false // 初始化标志位
+            // 内循环：冒泡操作
+            for j := 0; j < i; j++ {
+                if nums[j] > nums[j+1] {
+                    // 交换 nums[j] 与 nums[j + 1]
+                    nums[j], nums[j+1] = nums[j+1], nums[j]
+                    flag = true // 记录交换元素
+                }
+            }
+            if flag == false { // 此轮冒泡未交换任何元素，直接跳出
+                break
+            }
+        }
+    }
     ```
 
 === "JavaScript"
diff --git a/docs/chapter_sorting/insertion_sort.assets/insertion_operation.png b/docs/chapter_sorting/insertion_sort.assets/insertion_operation.png
index 75c6e47a3..cdaaa4e1a 100644
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diff --git a/docs/chapter_sorting/insertion_sort.assets/insertion_sort.png b/docs/chapter_sorting/insertion_sort.assets/insertion_sort.png
index 6e5ca0c08..61ee43dfe 100644
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diff --git a/docs/chapter_sorting/quick_sort.assets/pivot_division_step8.png b/docs/chapter_sorting/quick_sort.assets/pivot_division_step8.png
index 418b17cb4..acb723b77 100644
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diff --git a/docs/chapter_sorting/quick_sort.assets/pivot_division_step9.png b/docs/chapter_sorting/quick_sort.assets/pivot_division_step9.png
index 3e423596a..c07b45749 100644
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diff --git a/docs/chapter_stack_and_queue/deque.assets/deque_operations.png b/docs/chapter_stack_and_queue/deque.assets/deque_operations.png
index 94ede7b2b..242ea98c5 100644
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diff --git a/docs/chapter_stack_and_queue/queue.assets/queue_operations.png b/docs/chapter_stack_and_queue/queue.assets/queue_operations.png
index 61e433c5c..b165cd10d 100644
Binary files a/docs/chapter_stack_and_queue/queue.assets/queue_operations.png and b/docs/chapter_stack_and_queue/queue.assets/queue_operations.png differ
diff --git a/docs/chapter_stack_and_queue/queue.md b/docs/chapter_stack_and_queue/queue.md
index a663b9745..c27058519 100644
--- a/docs/chapter_stack_and_queue/queue.md
+++ b/docs/chapter_stack_and_queue/queue.md
@@ -20,13 +20,13 @@ comments: true
 
 <div class="center-table" markdown>
 
-| 方法      | 描述                      |
-| --------- | ------------------------ |
-| offer()   | 元素入队，即将元素添加至队尾  |
-| poll()    | 队首元素出队               |
-| front()   | 访问队首元素               |
-| size()    | 获取队列的长度             |
-| isEmpty() | 判断队列是否为空           |
+| 方法      | 描述                         |
+| --------- | ---------------------------- |
+| offer()   | 元素入队，即将元素添加至队尾 |
+| poll()    | 队首元素出队                 |
+| front()   | 访问队首元素                 |
+| size()    | 获取队列的长度               |
+| isEmpty() | 判断队列是否为空             |
 
 </div>
 
@@ -143,13 +143,59 @@ comments: true
 === "JavaScript"
 
     ```js title="queue.js"
+    /* 初始化队列 */
+    // JavaScript 没有内置的队列，可以把 Array 当作队列来使用 
+    // 注意：由于是数组，所以 shift() 的时间复杂度是 O(n)
+    const queue = [];
+
+    /* 元素入队 */
+    queue.push(1);
+    queue.push(3);
+    queue.push(2);
+    queue.push(5);
+    queue.push(4);
 
+    /* 访问队首元素 */
+    const peek = queue[0];
+
+    /* 元素出队 */
+    // O(n)
+    const poll = queue.shift();
+
+    /* 获取队列的长度 */
+    const size = queue.length;
+
+    /* 判断队列是否为空 */
+    const empty = queue.length === 0;
     ```
 
 === "TypeScript"
 
     ```typescript title="queue.ts"
+    /* 初始化队列 */
+    // TypeScript 没有内置的队列，可以把 Array 当作队列来使用 
+    // 注意：由于是数组，所以 shift() 的时间复杂度是 O(n)
+    const queue: number[] = [];
+
+    /* 元素入队 */
+    queue.push(1);
+    queue.push(3);
+    queue.push(2);
+    queue.push(5);
+    queue.push(4);
 
+    /* 访问队首元素 */
+    const peek = queue[0];
+
+    /* 元素出队 */
+    // O(n)
+    const poll = queue.shift();
+
+    /* 获取队列的长度 */
+    const size = queue.length;
+
+    /* 判断队列是否为空 */
+    const empty = queue.length === 0;
     ```
 
 === "C"
diff --git a/docs/chapter_stack_and_queue/stack.assets/stack_operations.png b/docs/chapter_stack_and_queue/stack.assets/stack_operations.png
index 3c42ee229..59acdacb9 100644
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diff --git a/docs/chapter_stack_and_queue/stack.md b/docs/chapter_stack_and_queue/stack.md
index eed703353..d988bdfe7 100644
--- a/docs/chapter_stack_and_queue/stack.md
+++ b/docs/chapter_stack_and_queue/stack.md
@@ -397,13 +397,13 @@ comments: true
 === "JavaScript"
 
     ```js title="linkedlist_stack.js"
-
+    
     ```
 
 === "TypeScript"
 
     ```typescript title="linkedlist_stack.ts"
-
+    
     ```
 
 === "C"
@@ -587,13 +587,93 @@ comments: true
 === "JavaScript"
 
     ```js title="array_stack.js"
+    /* 基于数组实现的栈 */
+    class ArrayStack {
+        stack;
+        constructor() {
+            this.stack = [];
+        }
+        /* 获取栈的长度 */
+        get size() {
+            return this.stack.length;
+        }
+
+        /* 判断栈是否为空 */
+        empty() {
+            return this.stack.length === 0;
+        }
 
+        /* 入栈 */
+        push(num) {
+            this.stack.push(num);
+        }
+
+        /* 出栈 */
+        pop() {
+            return this.stack.pop();
+        }
+
+        /* 访问栈顶元素 */
+        top() {
+            return this.stack[this.stack.length - 1];
+        }
+
+        /* 访问索引 index 处元素 */
+        get(index) {
+            return this.stack[index];
+        }
+
+        /* 返回 Array */
+        toArray() {
+            return this.stack;
+        }
+    };
     ```
 
 === "TypeScript"
 
     ```typescript title="array_stack.ts"
+    /* 基于数组实现的栈 */
+    class ArrayStack {
+        private stack: number[];
+        constructor() {
+            this.stack = [];
+        }
+        /* 获取栈的长度 */
+        get size(): number {
+            return this.stack.length;
+        }
+
+        /* 判断栈是否为空 */
+        empty(): boolean {
+            return this.stack.length === 0;
+        }
+
+        /* 入栈 */
+        push(num: number): void {
+            this.stack.push(num);
+        }
 
+        /* 出栈 */
+        pop(): number | undefined {
+            return this.stack.pop();
+        }
+
+        /* 访问栈顶元素 */
+        top(): number | undefined {
+            return this.stack[this.stack.length - 1];
+        }
+
+        /* 访问索引 index 处元素 */
+        get(index: number): number | undefined {
+            return this.stack[index];
+        }
+
+        /* 返回 Array */
+        toArray() {
+            return this.stack;
+        }
+    };
     ```
 
 === "C"
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diff --git a/docs/index.md b/docs/index.md
index 7ea936c19..b0a533086 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -56,6 +56,16 @@ hide:
 
 ---
 
+## 推荐语
+
+!!! quote
+
+    “一本通俗易懂的数据结构与算法入门书，引导读者手脑并用地学习，强烈推荐算法初学者阅读。”
+
+    **—— 邓俊辉，清华大学计算机系教授**
+
+## 致谢
+
 感谢本开源书的每一位撰稿人，是他们的无私奉献让这本书变得更好，他们是：
 
 <a href="https://github.com/krahets/hello-algo/graphs/contributors">
diff --git a/docs/log.md b/docs/log.md
deleted file mode 100644
index 3a1d4d8ed..000000000
--- a/docs/log.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# 更新日志
-
-| 更新内容    | 日期     |
-| ------------ | ---------- |
-| 新增：算法无处不在  | 2022-10-10 |
-| 新增：数组与链表   | 2022-10-15 |
-| 新增：数据结构简介 | 2022-10-20 |
-| 新增：前言        | 2022-10-23 |
-| 新增：计算复杂度   | 2022-11-03 |
-| 更新：配图 | 2022-11-04 |
-| 新增：数据与内存 | 2022-11-05 |
-| 更新：各章节 Java 代码 | 2022-11-06 |
-| 更新：列表 Java 代码、配图 | 2022-11-07 |
-| 新增：栈与队列 | 2022-11-09 |
-| 新增：树     | 2022-11-12 |
-| 新增：二叉搜索树</br>更新：二叉树、表格居中 | 2022-11-13 |
-| 更新：二叉搜索树 | 2022-11-14 |
-| 更新：首页介绍 | 2022-11-15 |
-| 更新：关于本书</br>新增：如何使用本书</br>新增：一起参与创作 | 2022-11-16 |
-| 新增：查找算法 | 2022-11-19 |
-| 更新：Markdown Stylesheet</br>新增：冒泡排序、插入排序 | 2022-11-21 |
-| 新增：快速排序 | 2022-11-22 |
-| 新增：归并排序，更新：快速排序、关于本书 | 2022-11-23 |