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

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

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

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

Python code for hierarchical cluster analysis of detected R-strategies from rule-based NLP on 500 circular economy definitions by 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. by Honglei Pang (22693724)

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

Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’... by Enock Niyonkuru (21246879)

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Schematic of the approach: This schematic illustrates the entire workflow of the project. by Enock Niyonkuru (21246879)

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Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). by Enock Niyonkuru (21246879)

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Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

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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,... by Enock Niyonkuru (21246879)

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Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. by Enock Niyonkuru (21246879)

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Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... by Enock Niyonkuru (21246879)

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Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... by Enock Niyonkuru (21246879)

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Python implementation of a wildfire propagation example using m:n-CAk over Z and R. by 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. …”

Multi-Version PYZ Builder Script: A Universal Python Module Creation Tool by 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.…”

Code interpreter with LLM. by 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. by Joseph James Tharayil (21416715)

Python code for estimating parameters of an ALAAM model. by Alex Stivala (8356257)

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Datasets To EVAL. by 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. by 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. by 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. …”