How RAG work. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”

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.…”

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

“…</li><li><b>Cross-Platform Compatibility</b>: Designed with cross-platform functionality in mind, the script works seamlessly on Windows, macOS, Linux, Unix, and other operating systems where Python 3.6 or higher is installed.…”

Fig 3 - Using artificial intelligence tools to automate data extraction for living evidence syntheses by Evan Mitchell (20995743)

OLS regression results—Distances under 9 ft per image. by Matthew Martell (18536273)

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

Estimating dopamine effects on humans. by Domenico Guarino (20428000)

Dentate granule cells have polarised responses to neuromodulators switch spiking behaviour under neuromodulation. by Domenico Guarino (20428000)

Smart Reaction Templating: A Graph-Based Method for Automated Molecular Dynamics Input Generation by Julian Konrad (9609845)

“…Accurately modeling chemical reactions in molecular dynamics simulations requires detailed pre- and postreaction templates, often created through labor-intensive manual workflows. This work introduces a Python-based algorithm that automates the generation of reaction templates for the LAMMPS REACTION package, leveraging graph-theoretical principles and subgraph isomorphism techniques. …”

Datasets To EVAL. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”

Statistical significance test results. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”

OpenBookQA experimental results. by Jin Lu (428513)

“…Additionally, the integration of an LLM Code Interpreter enables the system to perform multi-step logical reasoning and execute Python code for precise calculations, significantly improving its ability to solve mathematical problems and handle complex queries. …”