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)

"…</li></ul><p dir="ltr"><b>Application Areas</b></p><p dir="ltr">The <b>Multi-Version PYZ Builder Script</b> can be effectively applied in various areas:</p><ul><li><b>Commercial Distribution</b>: Distribute protected Python code to clients or customers, implementing advanced <a href="https://xn--mxac.net/secure-python-code-manager.html" target="_blank"><b>source code protection</b></a> for sales or rentals.…"

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.…"

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. …"

Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... حسب Enock Niyonkuru (21246879)

الموضوعات:

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. …"

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. …"

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

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

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:

Python sript. حسب Kan Jia (6438599)

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. …"

E-EVAL 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. …"