PTPC-UHT bounce by David Parry (22169299)

“…<br>It contains the full Python implementation of the PTPC bounce model (<code>PTPC_UHT_bounce.py</code>) and representative outputs used to generate the figures in the paper. …”

A list of all facial muscle movements mentioned in the chimpFACS manual [5] and considered in our present study. by Aisha Mahmoud (20623732)

A list of all facial muscle movements mentioned in the catFACS manual [14] and considered in our present study. by Aisha Mahmoud (20623732)

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. …”

OpenBookQA experimental 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. …”

AI2_ARC experimental 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. …”

TQA experimental 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. …”

E-EVAL experimental 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. …”

TQA Accuracy Comparison Chart on different 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. …”

ScienceQA experimental 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. …”

Finites differences python code to solve CH equation with a source term and Comsol routine to solve Brusselator equation in radial domains. by Giulio Facchini (9031490)

“…<p dir="ltr"><b><i>* Cahn-Hilliard simulations *</i></b><br>Finite difference code implementing the modified Cahn Hilliard equation with a forward Euler scheme and the possibility to parallelize the solver using the numba python library.…”

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”

2D Orthogonal Planes Split: <b>Python</b> and <b>MATLAB</b> code | <b>Source Images</b> for Figures by Nektarios Valous (20715650)

“…The output files generated by the code include results from both Python and MATLAB implementations; these output images are provided as validation, demonstrating that both implementations produce matching results.…”

Correlation (a) and Mean-Absolute-Error (b) between frequencies at a reference cutoff, δ₀, and different frequencies, , at different cutoff-values, δ_scan ∊ [3... by Guillermo Pérez-Hernández (21156182)

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

Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission by Monika Salesh (15306281)

Subjects:

Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission by Monika Salesh (15306281)

Subjects:

Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission by Monika Salesh (15306281)

Subjects: