answer:To implement this functionality, you can follow these steps: 1. Import necessary libraries and read the CSV file. 2. Define a function to filter rows based on the given criteria. 3. Define a function to create a new table with the 'Address' and 'Phone' columns for the filtered rows. 4. Display the first 10 entries of the filtered table by default. Here's a sample script implementing these steps: ```python import csv def read_csv(file_path): with open(file_path, newline='') as csvfile: reader = csv.DictReader(csvfile) data = row for row in reader return data def filter_rows(data): filtered_data = row for row in data if row'Name'.startswith('ABC') and int(row'Age') > 30 and int(row'Income') >= 50000 and int(row'Income') <= 80000 return filtered_data def create_new_table(filtered_data, limit=10): name_to_address_phone = {row'Name': (row'Address', row'Phone') for row in filtered_data} unique_name_address_phone = {k: v for k, v in name_to_address_phone.items() if name_to_address_phone.count(v) == 1} new_table = unique_name_address_phonename for name in list(unique_name_address_phone.keys()):limit return new_table if __name__ == "__main__": # Replace 'employee_data.csv' with the path to your CSV file data = read_csv('employee_data.csv') filtered_data = filter_rows(data) new_table = create_new_table(filtered_data) print(new_table) ``` Replace `'employee_data.csv'` with the path to your CSV file. You can set the `limit` parameter when calling `create_new_table()` to change the number of entries displayed. For example, `create_new_table(filtered_data, limit=20)` will display the first 20 entries.,To implement the filter, you can use the `csv` module to read the CSV file and then loop through the rows to apply the filter criteria. For the custom function, you can use a dictionary to track unique names and then create a new table with only the selected columns. Here's an example implementation: ```python import csv def filter_table(filename, limit=10): # Initialize dictionary to track unique names names = {} # Read the CSV file with open(filename) as file: reader = csv.DictReader(file) # Create a new table with only the selected columns filtered_table = for row in reader: if row'Name'.startswith('ABC') and int(row'Age') > 30 and 50000 <= int(row'Income') <= 80000: # Check for duplicates and add to filtered table if unique if row'Name' not in names: namesrow'Name' = True filtered_table.append({'Address': row'Address', 'Phone': row'Phone'}) if len(filtered_table) == limit: break # Return the filtered table return filtered_table ``` To use the function, call it with the CSV filename and an optional limit parameter: ```python filtered = filter_table('employees.csv', limit=20) ``` This will return a list of dictionaries with the 'Address' and 'Phone' columns for the filtered rows. If there are more than 10 entries, only the first 10 will be displayed by default. To set a custom limit, pass a different value for the `limit` parameter.,To accomplish this task, you would need to use the `csv` module in Python. Here is a step-by-step guide: 1. First, you need to import the `csv` module. ```python import csv ``` 2. Next, you need to open the CSV file. ```python with open('employee_data.csv', 'r') as csvfile: reader = csv.DictReader(csvfile) ``` 3. Then, create your filter function: ```python def get_filtered_rows(reader, duplicates, limit): filtered_rows = for row in reader: if row'Name'.startswith('ABC') and float(row'Age') > 30 and 50000 <= float(row'Income') <= 80000: filtered_rows.append(row) # Remove duplicates filtered_rows = list(set(filtered_rows)) if duplicates == False: new_table = pd.DataFrame(row.get('Address'), row.get('Phone') for row in filtered_rows) else: new_table = pd.DataFrame(row.get('Address'), row.get('Phone') for i, row in enumerate(filtered_rows) if i == 0) # Display first 10 entries if limit > len(new_table): new_table = new_table.reindex(columns='Address', 'Phone').iloc:10 elif limit < len(new_table): new_table = new_table.iloc:limit print(new_table) # Call function get_filtered_rows(reader, False, 10) ``` This script will print the filtered data table with only the 'Address' and 'Phone' columns. The default limit is 10 entries, but the user now has the option to set their own limit. Note: In these scripts, I've assumed that you have a CSV file named 'employee_data.csv' containing 'Address', 'Phone', 'Name', 'Age', and 'Income' columns. Also, the age and income columns are expected to be numerical values.

answer:Here is a potential design for the Tampermonkey extension: 1. The extension will start by detecting the login form on the page and capturing the relevant login credentials (e.g. username and password). 2. The extension will then gather information about the device components by using the `navigator` object in JavaScript. This information may include the user agent, operating system, browser version, and screen resolution. 3. The extension will send the login credentials and device component data to the backend for verification. This can be accomplished using an AJAX request. 4. The backend will use the device component data to help determine the validity of the login attempt. For example, if the user is attempting to login from a device with an unfamiliar user agent, the backend may require additional authentication steps to verify the user's identity. 5. If the backend approves the login attempt, the extension will complete the login process by submitting the login form on the page. If not, the extension will display an error message to the user. To ensure that the extension can identify and adapt to all types of login pages and forms, the extension can use various techniques: 1. The extension can search for common HTML attributes (e.g. `type="password"` or `name="login"`) to identify login forms. 2. If the login form cannot be identified by attributes, the extension can search for common login form elements (e.g. `<input>` elements with labels containing the word "username" or "password"). 3. If the login form still cannot be identified, the extension can prompt the user to manually select the login form on the page. To maintain security, the extension can use several measures: 1. The extension will only send login credentials and device component data to a trusted backend server via HTTPS. 2. The extension will not store any login credentials or device component data locally. 3. The extension will not modify the contents of the login form or any other page elements., Creating a Tampermonkey extension for automatic login that requires multiple-step reasoning can be quite complex. Here's a rough outline of how you might design such an extension: 1. **Create a new Tampermonkey script**. To get started, create a new Tampermonkey script and give it a descriptive name. 2. **Identify login forms**. To identify login forms on a page, you can use the `querySelectorAll` method to search for input fields with names like "username" and "password". For example: ```javascript var loginForms = document.querySelectorAll("inputname='username', inputname='password'"); ``` 3. **Extract device components**. Before submitting the login information, you'll need to extract the device components. This can be done using JavaScript's `navigator` object. For example: ```javascript var deviceComponents = { browser: navigator.appCodeName, platform: navigator.platform, userAgent: navigator.userAgent, language: navigator.language }; ``` 4. **Send device components to backend**. To send the device components to the backend, you can use the `fetch` API to make a POST request. First, serialize the device components into a JSON string: ```javascript var deviceComponentsJson = JSON.stringify(deviceComponents); ``` Then, make a POST request to your backend: ```javascript fetch("/api/device-components", { method: "POST", headers: { "Content-Type": "application/json" }, body: deviceComponentsJson }).then(function(response) { return response.json(); }).then(function(data) { // Handle the response from the backend }).catch(function(error) { // Handle any errors }); ``` 5. **Adapt to different login forms**. To adapt to different login forms, you can use a combination of CSS selectors and conditional logic. For example, if a login form uses a different class name or ID than the one you're expecting, you can use a more general selector to find the form and then check if it has the expected input fields. If it does, then you can proceed with the login process. ```javascript if (loginForms.length > 0) { // Extract the login information and submit the form } else { var loginForm = document.querySelector("form.login"); if (loginForm) { var usernameInput = loginForm.querySelector("inputname='username'"); var passwordInput = loginForm.querySelector("inputname='password'"); if (usernameInput && passwordInput) { // Extract the login information and submit the form } } } ``` 6. **Submit the login form**. Once you've extracted the login information and received a response from the backend, you can submit the login form using JavaScript. For example: ```javascript var loginForm = document.querySelector("form.login"); if (loginForm) { var usernameInput = loginForm.querySelector("inputname='username'"); var passwordInput = loginForm.querySelector("inputname='password'"); if (usernameInput && passwordInput) { usernameInput.value = "my-username"; passwordInput.value = "my-password"; loginForm.submit(); } } ``` 7. **Security considerations**. To ensure security, make sure that the Tampermonkey script is only active on trusted websites. You can use the `@match` directive in the Tampermonkey script to specify which websites the script should run on. Additionally, consider using HTTPS for all communication with the backend to ensure that the device components and login information are transmitted securely. Note that this is just a rough outline of how you might design a Tampermonkey extension for automatic login with multiple-step reasoning. Depending on your specific use case, you may need to modify this code or add additional functionality to meet your needs.,Designing a Tampermonkey extension for automatic login that requires multiple-step reasoning involves the following general steps: 1. **General Approach:** The extension will analyze the login page content to determine the login form elements. Utilize Headless Browser for this task. 2. **Extracting Login Form Elements:** The Headless Browser extracts the login form elements such as username, password, and submit button. 3. **Detecting Device Components:** Utilizing the JavaScript navigator object, the extension determines the device's information, including the device model, operating system (OS), screen resolution, and browser type. 4. **Sending Detected Device Components to Backend:** The extension sends a POST request containing the device component information to a secure backend server. 5. **Receiving Confirmation from Backend:** Upon receiving confirmation from the backend server, the extension submits the user's credentials on the login page form. 6. **Checking for Dynamically Changing Login Forms:** Dynamically changing login pages require HTML parsing to retrieve login form elements. You can use a library like Cheerio or JQuery to parse HTML. 7. **Login Functionality Improvement:** Utilize artificial intelligence (AI) algorithms to enhance the extension's abilities to recognize and adapt to various login pages. Remember that the developed extension needs to be continually updated as websites change their login requirements and formats. Always prioritize security when developing the extension.

answer:How can you optimize the control of error messages generated by the browser console in PHP code? To optimize control of error messages generated by the browser console in PHP code, you can follow these steps: 1. Enabling Error Reporting: Use error_reporting() and display_errors to enable the logging of PHP error messages. - Set error reporting: ``` error_reporting(E_ALL); // Include all available error levels ``` - Set display errors: ``` ini_set('display_errors', 1); // Enable display of error messages in browser 2. Capturing Error Messages: Capture error messages using a custom error handler function. This allows for more control over error handling. ``` function custom_error_handler(errno, errstr, errfile, errline) { // Log error messages // You can use any file or database-based logging method } set_error_handler('custom_error_handler'); ``` Now, whenever an error occurs, it will be logged using the custom_error_handler function. 3. Implementing Exception Handling: To catch errors that don't trigger PHP's built-in error reporting, such as uncaught exceptions, you can use try/catch blocks. ``` try { // Your PHP code } catch (Exception e) { // Handle exception and log if necessary } ``` How can you modify the PHP code to redirect error logs to a remote server rather than storing them in a log file? To redirect error logs to a remote server, follow these steps: 1. Connect to a Remote Server: Use the fsockopen() function to open a connection to the remote server. ``` sock = fsockopen('www.example.com', 25, errno, errstr, 10); ``` Replace `'www.example.com'` with the IP address or domain name of your remote server and `25` with the TCP port number (in this case, SMTP). 2. Prepare Data for Transmission: Define an email address that will receive the error logs, and prepare an email header. ``` to = '[email protected]'; header = "From: [email protected]"; header .= "Content-Type: text/plain; charset=utf-8"; header .= "Subject: Error Reportnn"; // This is the email subject 3. Write Error Log to Remote Server: Use fwrite() function to send the log message to the remote server. ``` if (is_resource(sock)) { if (@fwrite(sock, header.log) === FALSE) { // Handle transmission failure // You may log the failure and/or close the connection } else { // Transmission success // You may log the success and/or close the connection } } ``` Replace `log` with your error log data. 4. Cleanup: Close the connection and close the file. ``` if (is_resource(sock)) { fclose(sock); // You can use file_put_contents() to store log files locally file_put_contents('/path/to/error.log', log); } ``` Replace `/path/to/error.log` with your desired local path to store the logs. To ensure higher security, can you suggest ways to encrypt logs before transmission? For higher security, you can use encryption to protect your error logs during transmission. Here is a possible approach: 1. Generate a unique secret key: ``` secret_key = openssl_random_pseudo_bytes(16); ``` Store this key securely so unauthorized users cannot access it. 2. Encrypt error messages before transmission: ``` encrypted = openssl_encrypt(log, 'AES-128-CBC', secret_key, 0, iv); ``` Replace `log` with your error message data. The resulting encrypted `encrypted` string can now be safely transmitted to your remote server for safe storage. 3. Store the secret key: Store the secret key securely on your local server. Although the encrypted error logs are sent over the network, an attacker would need the matching secret key to decrypt them. 4. Decrypt logs on remote server: Decrypt the error logs on the remote server using the same secret key. ``` openssl_decrypt(encrypted_log, 'AES-128-CBC', secret_key, 0, iv); ``` This will give you encrypted logs before transmission, improving the security of your error logs. Remember to replace example strings with your custom configurations and manage passwords/keys securely.,Optimizing the control of error messages generated by the browser console in PHP code can be achieved by setting the error_reporting value in PHP.ini file to E_ALL & ~E_NOTICE. This setting will display all errors except notices. This way, only errors that require attention will be displayed, and the browser console will not be overwhelmed with unnecessary notices. To redirect error logs to a remote server instead of storing them in a log file, you can modify the PHP code as follows: Step 1: Open the PHP file that contains the error log code. Step 2: Add the following code at the beginning of the file to define the remote server details: ```php remote_host = "your_remote_server_hostname"; remote_port = "your_remote_server_port_number"; remote_user = "your_remote_server_username"; remote_pass = "your_remote_server_password"; ``` Step 3: Replace the following code: ```php error_log(error, 3, "/path/to/error_log_file"); ``` with the following code: ```php connection = ssh2_connect(remote_host, remote_port); ssh2_auth_password(connection, remote_user, remote_pass); stream = ssh2_exec(connection, 'echo ' . escapeshellarg(error) . ' >> /path/to/error_log_file'); stream_set_blocking(stream, true); fclose(stream); ``` This code establishes an SSH connection to the remote server and appends the error message to the end of the specified log file. To encrypt the error logs before transmission for higher security, you can modify the code as follows: Step 1: Add the following code at the beginning of the file to define the encryption algorithm and key: ```php encryption_algorithm = "AES-256-CBC"; encryption_key = "your_encryption_key_here"; ``` Step 2: Replace the following code: ```php stream = ssh2_exec(connection, 'echo ' . escapeshellarg(error) . ' >> /path/to/error_log_file'); ``` with the following code: ```php iv_length = openssl_cipher_iv_length(encryption_algorithm); iv = openssl_random_pseudo_bytes(iv_length); encrypted_error = openssl_encrypt(error, encryption_algorithm, encryption_key, 0, iv); stream = ssh2_exec(connection, 'echo ' . escapeshellarg(base64_encode(encrypted_error)) . ' >> /path/to/error_log_file'); ``` This code generates a random initialization vector, encrypts the error message using the specified encryption algorithm and key, and then encodes the encrypted message in Base64 format before sending it to the remote server. This way, even if someone intercepts the error logs during transmission, they will not be able to decipher the contents without the encryption key and initialization vector. Overall, the above steps will help you optimize the control of error messages in PHP, redirect them to a remote server, and encrypt the logs for higher security., To optimize the control of error messages generated by the browser console in PHP code, you can follow these best practices: 1. Use try-catch blocks to handle exceptions and custom errors. 2. Implement a logging mechanism to store errors, warnings, and notices. 3. Set appropriate error reporting levels based on the development stage (e.g., use `error_reporting(E_ALL);` during development and `error_reporting(0);` in production). 4. Use the `set_exception_handler()` and `set_error_handler()` functions to centrally manage exceptions and errors. To modify the PHP code to redirect error logs to a remote server, follow these steps: 1. Install and configure a remote logging service, such as Papertrail, Loggly, or a self-hosted solution like Elasticsearch or Graylog. 2. Set up a secure connection between your PHP server and the remote logging service. This can be done using various methods, such as SFTP, FTPS, or HTTPS. 3. Implement a custom error handler following these steps: a. Create a PHP function that will handle errors, e.g., `handleError()`. b. Register the custom error handler using `set_error_handler('handleError');`. c. In the `handleError()` function, format the error message as required by the remote logging service. d. Use a library like `phpseclib` to securely transmit the log data to the remote logging service using the secure connection established in step 2. e. Encrypt the log data before transmission for higher security. Here's a sample implementation of `handleError()` using OpenSSL for encryption: ```php function handleError(level, message, file, line, context) { global errorLog; // Format error message error = "" . date('Y-m-d H:i:s') . " level: message in file:linen"; // Generate a unique initialization vector for each error message iv = random_bytes(openssl_cipher_iv_length('aes-256-cbc')); // Encrypt the error message encryptedError = openssl_encrypt(error, 'aes-256-cbc', 'your_secret_key', 0, iv); // Prepare data for sending data = 'iv' => bin2hex(iv), 'data' => base64_encode(encryptedError), ; // Send data to the remote logging service // You can use cURL, phpseclib, or another secure method // ... } // Register the custom error handler set_error_handler('handleError'); ``` Remember to replace `'your_secret_key'` with a strong encryption key that is unique for your application. Using these guidelines, you can optimize error handling in PHP and securely transmit encrypted logs to a remote server.

answer:Sure, I'd be happy to help! First, let's modify the Mongoose schema to include the new field for the order of categories. Let's assume the existing schema looks something like this: ```typescript import mongoose, { Document, Schema } from 'mongoose'; interface ICategory extends Document { name: string; parentCategoryId?: string; } const CategorySchema: Schema = new Schema({ name: { type: String, required: true }, parentCategoryId: { type: Schema.Types.ObjectId, ref: 'Category' }, }); const Category = mongoose.model<ICategory>('Category', CategorySchema); export default Category; ``` We can modify the schema to include a new field called `order` that will store the order of categories based on their size and position: ```typescript import mongoose, { Document, Schema } from 'mongoose'; interface ICategory extends Document { name: string; parentCategoryId?: string; size: number; order: number; } const CategorySchema: Schema = new Schema({ name: { type: String, required: true }, parentCategoryId: { type: Schema.Types.ObjectId, ref: 'Category' }, size: { type: Number, required: true }, order: { type: Number, required: true }, }); const Category = mongoose.model<ICategory>('Category', CategorySchema); export default Category; ``` Next, let's write a custom sort function that will query the categories in the correct order based on size and position. We can use the `sort()` method provided by Mongoose to sort the categories. The sort function will first sort the categories by size in descending order, and then sort them by position within their parent category in ascending order. We will also handle the edge case of equal-sized categories by sorting them by their `_id` field in ascending order: ```typescript import Category, { ICategory } from './category.model'; interface ISortOptions { parentCategoryId?: string; } async function sortCategories(options: ISortOptions): Promise<ICategory> { let categories: ICategory; if (options.parentCategoryId) { categories = await Category.find({ parentCategoryId: options.parentCategoryId }) .sort({ size: -1, order: 1, _id: 1 }) .exec(); } else { categories = await Category.find({}) .sort({ size: -1, order: 1, _id: 1 }) .exec(); } return categories; } ``` Finally, we can update the code that queries for categories to use the `sortCategories()` function: ```typescript // Query for all categories under a parent category, sorted by order const parentCategoryId = 'someParentCategoryId'; const categories = await sortCategories({ parentCategoryId }); // Query for all categories, sorted by order const allCategories = await sortCategories({}); ``` That's it! We have successfully modified the schema to include a new field for the order of categories and written a custom sort function to query the categories in the correct order based on size and position. We have also handled potential edge cases such as equal-sized categories.,Certainly! Let's create a custom sort function that determines the category order based on size and position. Here's a solution for this problem: 1. First, we'll modify the existing Schema by adding a new field `size` to the Category model, which will store the size of each category. ```typescript const mongoose = require('mongoose'); const Schema = mongoose.Schema; const categorySchema = new Schema({ name: { type: String, required: true }, parent: { type: Schema.Types.ObjectId, ref: 'Category' }, children: { type: Schema.Types.ObjectId, ref: 'Category' }, size: Number, }); module.exports = mongoose.model('Category', categorySchema); ``` 2. In the order-categories function, we'll query the entire Category collection and use the `reduce` method to sort the categories in the correct order. We will iterate over each category in the collection and execute a nested forEach loop, accessing any sibling categories within the same level (i.e., with the same parent category). ```typescript function orderCategories(categories: Category): Category { return categories.reduce( (sortedCategories, category) => { // Find the level of the category with the given position in the collected data const levelCategories = sortedCategories.filter((c) => c.path === category.path); if (levelCategories.length > 0) { const categoryIndex = sortedCategories.indexOf(levelCategories0); // If the category has a lower size than the sibling, update its index to be lower levelCategories.forEach((siblingCategory) => { if (siblingCategory.size > category.size) { if (siblingCategory.index > categoryIndex) { const siblingIndex = sortedCategories.indexOf(siblingCategory); for (let i = categoryIndex + 1; i <= siblingIndex; i++) { sortedCategoriesi - 1 = sortedCategoriesi; } sortedCategoriessiblingIndex = category; } } }); // Otherwise, continue to the next level return sortedCategories; } // If the category is a root category (i.e., its parent is null), set its index to 0 if (category.parent == null) { category.index = 0; return ...sortedCategories, category; } // Otherwise, find the index of its parent in the sorted categories, and set its index let parentIndex = sortedCategories.findIndex((parentCategory) => parentCategory.path === category.path); if (parentIndex >= 0) { category.index = parentIndex + 1; return ...sortedCategories, category; } // If the parent doesn't exist in the collection, return the unsorted categories console.error('Parent category %s not found in the collection!', category.parent.toString()); throw new Error('Parent category not found in the collection!'); }, ); } ``` 3. The `orderCategories` function takes the entire Category collection, adds a custom sort function, and queries the data in the correct order based on size and position. 4. Handling potential edge cases, such as equal-sized categories: - When two or more categories have equal sizes, we should maintain their initial order within a group of siblings. To achieve this, we maintain the `index` property in the `category` objects, which is the order in which the categories were sorted. 5. Finally, you can use the following code to bring everything together: ```typescript const connection = mongoose.createConnection('mongodb://localhost:27017/db_name', { useNewUrlParser: true, useUnifiedTopology: true, }); const categoryModel = connection.model('category', categorySchema); // Import all existing categories categoryModel.find().then((existingCategories) => { const allCategories = existingCategories.map((category) => { if (category.children && category.children.length > 0) { category.children = category.children.map((child) => connection.Types.ObjectId(child)); } return category; }); // Function to sort the categories based on size and position const sortedCategories = orderCategories(allCategories); // Update the existing categories with the new `size` field and modified `index` field const sortedResults = sortedCategories.map((category) => { category.size = countCategoryNodes(category); category.index = category.size; return category; }); // Update all existing categories with the new data categoryModel.create(sortedResults, (error: any) => { if (error) console.error(`Error creating new category: {error}`); else console.log('Updated categories based on size and position successfully!'); }); }); // Helper function to count the number of nodes in a given category function countCategoryNodes(category: Category) { let nodeCount = 0; // Count both top-level children and all nested children for (const child of category.children) { nodeCount += countCategoryNodes(child); } return nodeCount + 1; } ``` Remember to import the necessary dependencies and handle any potential errors and edge cases.,Sure, I can help you with that! To begin, let's start by modifying the existing schema to include the new field for storing the order of categories. We can add a new field called `order` of type `Number` to the schema definition. Here's an example: ```typescript import { Schema, Document, model } from 'mongoose'; interface Category extends Document { name: string; parent: Category'_id' | null; order: number; } const categorySchema = new Schema<Category>({ name: { type: String, required: true }, parent: { type: Schema.Types.ObjectId, ref: 'Category', default: null }, order: { type: Number, default: 0 }, }); const CategoryModel = model<Category>('Category', categorySchema); export default CategoryModel; ``` Now that we have added the `order` field to the schema, we can write a custom sort function to query the categories in the correct order based on both their position within their parent category and their size. Here's an example of what the query might look like: ```typescript const categories = await CategoryModel.find({ parent: parentId }).sort({ order: -1, name: 1, }).exec(); ``` This query sorts the categories in descending order by their `order` field (which is used to store the size), followed by ascending order by their `name` field (which is used to sort categories with the same size). This ensures that larger categories are listed first, followed by smaller ones, and that within each size group, categories are listed in alphabetical order. To handle the edge case of categories with the same size, we can assign them a unique value for their `order` field based on their position within their parent category. For example, we could assign the `order` value based on the number of ancestors the category has, so that categories closer to the root (i.e. with fewer ancestors) have lower order values than those further down the tree. Here's an example of how we might implement this: ```typescript const calculateOrder = async (categoryId: Category'_id'): Promise<number> => { const category = await CategoryModel.findById(categoryId); if (!category) { throw new Error(`Category with id {categoryId} not found`); } let order = category.order || 0; // Increase order value based on number of ancestors let ancestor = category.parent; while (ancestor) { const ancestorCategory = await CategoryModel.findById(ancestor); if (!ancestorCategory) { throw new Error(`Ancestor category with id {ancestor} not found`); } order += ancestorCategory.order || 0; ancestor = ancestorCategory.parent; } return order; }; const saveCategory = async (category: Category): Promise<Category> => { if (!category.order) { category.order = await calculateOrder(category._id); } return category.save(); }; // Example usage const newCategory = new CategoryModel({ name: 'New Category', parent: parentId, }); await saveCategory(newCategory); const categories = await CategoryModel.find({ parent: parentId }).sort({ order: -1, name: 1, }).exec(); ``` In this example, we've added a `calculateOrder` function that takes a category ID and calculates its `order` value based on the number of ancestors it has. We then call this function in the `saveCategory` function to set the `order` value if it hasn't already been set. This ensures that categories are always assigned a unique `order` value based on their position within the parent category, even if they have the same size. I hope this helps! Let me know if you have any questions.