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

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.…"

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. …"

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. …"

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’... حسب Enock Niyonkuru (21246879)

الموضوعات:

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

الموضوعات:

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

الموضوعات:

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

الموضوعات:

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:

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

الموضوعات:

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... حسب Enock Niyonkuru (21246879)

الموضوعات:

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... 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. …"

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.…"

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)

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

الموضوعات:

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. …"