Python implementation of a wildfire propagation example using m:n-CAk over Z and R. حسب Pau Fonseca i Casas (9507338)

"…</p><p dir="ltr"><br></p><p dir="ltr">## Files in the Project</p><p dir="ltr"><br></p><p dir="ltr">### Python Scripts</p><p dir="ltr">- **Wildfire_on_m_n-CAk.py**: This file contains the main code for the fire cellular automaton. …"

Resolving Harvesting Errors in Institutional Repository Migration : Using Python Scripts with VS Code and LLM Integration. حسب satoshi hashimoto(橋本 郷史) (18851272)

"…Therefore, we decided to create a dedicated Python program using Large Language Model (LLM)-assisted coding.…"

Multi-Version PYZ Builder Script: A Universal Python Module Creation Tool حسب Pavel Izosimov (20096259)

"…This tool represents a significant advancement in the realm of <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank"><b>secure code sharing</b></a>, providing a robust solution for modern Python programming challenges.…"

Comparison of performance between our next reaction implementation and the Python library from Ref. [3]. حسب Samuel Cure (22250922)

"…<p>We simulate SIR epidemic processes on Watts-Strogatz networks with parameters <i>k</i>₀ = 5, <i>p</i> = 0.1 <b>(a)</b> and Barabási-Albert networks with parameter <i>m</i> = 5 <b>(b)</b> using the Python wrapper of our C++ implementation and compare its performance with the Python library from Ref. …"

City-level GDP estimates for China under alternative pathways from 2020 to 2100-python code حسب Jinjie Sun (11791715)

"…The dataset is complemented by processing code and raw input data in the "Python_Code" directory to ensure full reproducibility. …"

System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool حسب Pavel Izosimov (20096259)

"…This tool provides <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">code obfuscation in Python</a> and <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank">Python code encryption</a>, enabling developers to <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">protect Python code</a> effectively.…"

Fitting a feast/famine cycle to a glycolysis model. حسب Paul van Lent (16876977)

الموضوعات:

Python code for hierarchical cluster analysis of detected R-strategies from rule-based NLP on 500 circular economy definitions حسب Zahir Barahmand (18008947)

"…</p><p dir="ltr">This Python code was optimized and debugged using ChatGPT-4o to ensure implementation efficiency, accuracy, and clarity.…"

Code program. حسب Honglei Pang (22693724)

"…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …"

Python Implementation of HSGAdviser Chatbot: AI model for Sustainable Education حسب Suha Assayed (22454038)

"…<p dir="ltr">This repository contains the Python source code and model implementation for HSGAdviser, an AI speech assistant designed to provide personalized college and career guidance for high school students through conversational AI. …"

SCADET algorithm parameter configuration (Python implementation). حسب Xiaolong Zhang (470454)

Code interpreter with LLM. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , w... حسب Ziyan Jin (21021976)

الموضوعات:

Procedure for aligning the NeuroRat head model underlying the FEM simulations with the BBP circuit model. حسب Joseph James Tharayil (21416715)

Datasets To EVAL. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

Statistical significance test results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

How RAG work. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

OpenBookQA experimental results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

AI2_ARC experimental results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"

TQA experimental results. حسب Jin Lu (428513)

"…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …"