### Install Dependencies (npm)

Source: https://github.com/bettinasosa/portfolio/blob/master/README.md

Installs project dependencies using npm. This is a prerequisite for running the development server or building the project.

```Shell
npm install
```

--------------------------------

### Start Development Server (npm)

Source: https://github.com/bettinasosa/portfolio/blob/master/README.md

Starts the Next.js application in development mode. Opens http://localhost:3000 in the browser for live preview and hot-reloading.

```Shell
npm run dev
```

--------------------------------

### Install Dependencies (yarn)

Source: https://github.com/bettinasosa/portfolio/blob/master/README.md

Installs project dependencies using yarn. This is an alternative to npm for managing project dependencies.

```Shell
yarn install
```

--------------------------------

### Start Development Server (yarn)

Source: https://github.com/bettinasosa/portfolio/blob/master/README.md

Starts the Next.js application in development mode using yarn. Provides a development environment with hot-reloading.

```Shell
yarn run dev
```

--------------------------------

### Solidworks FEA Simulation Setup

Source: https://github.com/bettinasosa/portfolio/blob/master/OldHtmlFiles/bike.html

This snippet outlines the setup for Finite Element Analysis (FEA) simulations in Solidworks for a bicycle frame. It details boundary conditions, loading, and material properties used in static and fatigue studies.

```Solidworks
Boundary Conditions:
- Rear wheel bearings: Hinges (rotation on y-axis, no translation on x, y, z).
- Fork shell: Fixed geometry (0 degrees of freedom).

Loading:
- User weight: Two vertical downwards forces of 981 N applied at seat points.
- Gravity: Applied to simulations.
- Pedal force: Remote load of 1000 N on crank shell (static simulations).

Material Properties & Analysis:
- Materials: Magnesium and Aluminium.
- Stress Analysis: Von Mises criterion for ductile materials.
- Fatigue Study: Stress ratio R = 0 (alternating force 0N to 1000N), Gerber mean stress correction.
- Frequency Study: Neglects user weight on seats, uses other fixtures and forces.
```

--------------------------------

### Build for Production (npm)

Source: https://github.com/bettinasosa/portfolio/blob/master/README.md

Builds the Next.js application for production deployment. Optimizes the app and outputs static files to the './dist/' folder.

```Shell
npm run build
```

--------------------------------

### Build for Production (yarn)

Source: https://github.com/bettinasosa/portfolio/blob/master/README.md

Builds the Next.js application for production using yarn. Generates an optimized build for deployment.

```Shell
yarn build
```

--------------------------------

### VCP Webapp Development with HTML, CSS, and JavaScript

Source: https://github.com/bettinasosa/portfolio/blob/master/OldHtmlFiles/vcp.html

This snippet covers the core technologies used to build the VCP educational platform. It highlights the implementation of various web elements and features based on design prototypes.

```HTML
html5
```

```CSS
CSS3
```

```JavaScript
javascript
```

--------------------------------

### Initialize Data Layer and Google Analytics

Source: https://github.com/bettinasosa/portfolio/blob/master/OldHtmlFiles/daftbot.html

This snippet initializes the data layer for Google Analytics and configures the tracking ID. It ensures that the gtag function is available and sets up the initial configuration for tracking website activity.

```JavaScript
window.dataLayer = window.dataLayer || [];
function gtag(){
  dataLayer.push(arguments);
}

gtag('js', new Date());
gtag('config', 'UA-152965829-1');
```

--------------------------------

### Python Algorithm for Tetriling Reassembly

Source: https://github.com/bettinasosa/portfolio/blob/master/OldHtmlFiles/tetris.html

This snippet details the Python implementation for solving the Tetriling Reassembly problem. It focuses on designing an efficient algorithm to tile polyomino regions with Tetris pieces, minimizing uncovered and extra squares. The project involves analyzing algorithm running time and implementing scalable data structures.

```Python
def solve_tetriling_reassembly(target_region, tetris_pieces):
    """Solves the tetriling reassembly problem.

    Args:
        target_region: The polyomino region to be tiled.
        tetris_pieces: The set of Tetris pieces to use for tiling.

    Returns:
        A perfect tiling solution or None if no solution exists.
    """
    # Algorithm implementation details would go here.
    # This includes exploring tiling strategies, backtracking, and optimization.
    pass

# Example usage (conceptual):
# target_shape = ...
# available_pieces = ...
# solution = solve_tetriling_reassembly(target_shape, available_pieces)
# if solution:
#     print("Tiling successful!")
# else:
#     print("No tiling solution found.")
```

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