ENDOCRINOPATHY OR EARLY PUBERYNY: NUTRITIONAL AND CHEMICAL ASSESSMENT OF PACKAGED FOOD PRODUCTS IN CHILDREN

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ENDOCRINOPATHY OR EARLY PUBERYNY: NUTRITIONAL AND CHEMICAL ASSESSMENT OF PACKAGED FOOD PRODUCTS IN CHILDREN Abstract The global rise in early puberty in children is an important public health problem, which needs a multi-disciplinary, toxicological, nutritional and computational study. Packaged foods are most common foods consumed by children in today's diet and are a double-edged sword as hyper palatable foods containing excess amounts of sugar and caloric density, and simultaneously containing a hidden vector of exposure to endocrine disrupting chemicals (EDCs) via the synthetic packaging materials. This multi-faceted issue is addressed by a novel, comprehensive method that couples the use of AI-based dietary assessments with quantitative structure activity relationships (QSAR) toxicological modelling and an efficient Bayesian ordinal quantile regression model to dissect complex developmental endpoints. The framework allows for high fidelity exposure information as the packag...
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Build Your Own Modules and Master NumPy: Python Essentials for Power Users 🚀

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Python is a magician’s wand — but you are the wizard!

Today, we’ll unleash two powerful spells:

Ready to level up? Let’s dive in! 🎯

🧩 1. Creating Your Own Python Module

Imagine you’ve built some useful tools like:

Wouldn’t it be awesome to reuse them anytime without rewriting?
That's exactly why modules exist.

Visual: How Module Importing Works 🛠️

Generated image

How to Create a Module with SageMath Python 3

Step 1: Make a Python file called mymodule.py:

 

 

How to Use Your Module

Challenge 🔥:
Create your own module statsmodule.py with functions for:

  • Mean
  • Median
  • Standard Deviation

Drop your solutions in the comments! 🎯

🧠 2. NumPy: Superpowers for Scientific Computing

NumPy (Numerical Python) turns your simple calculations into industrial-grade processing.
No exaggeration: It’s the foundation of data science, AI, and scientific computing!

2.1 Installation

If you are in SageMath Terminal / Console

Just run directly:

2. If you are inside a SageMath Notebook (Jupyter Notebook style)

You need to add a ! at the beginning to tell it to run as a shell command:

The ! means "run this in the system shell" instead of trying to run it as Python.

🚨 Important:

Depending on your SageMath setup, you might need pip instead of conda, like:

because not all Sage installs have Conda properly inside the notebook environment.

⚡ Quick summary:

Situation

Correct command

Terminal / Command-line

conda install numpy

SageMath Notebook

!conda install numpy or !pip install numpy

2.1.1 Why NumPy Rocks 🚀

Feature

NumPy Brilliance

Memory efficient

✅

Lightning fast operations

✅

Superpowers: Linear Algebra, Fourier Transform, Random Numbers

✅

Alternative to MatLab + ready for AI/ML

✅

🛠 2.1.2 Python Lists vs NumPy Arrays

Generated image

Hands-On!

NumPy saves the day:

Element-wise magic! 🔮

📊 Array Creation Fun

🔥 Exploring Mathematical Functions

Built-in constants:

🌟 A Fancy Function!

Imagine analyzing signals, waves, or real-world data with functions like these! 🎵

📢 Community Challenge!

Your Turn:

Create a random NumPy array and:

Share your solutions in the comments! 🎉

🔮 What's Next?

Coming up next:

  • SciPy: Dive into optimization, integration, and advanced scientific computing!
  • Matplotlib: Bring your data to life with beautiful graphs and charts!
  • Pandas: Rule the world of data analysis!

👉 Stay tuned! Bookmark the blog and join our learning community!

 

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Unsure about something? Ask away—I’m here for you!

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