Sodium concentration distribution cloud map. by Chenglong Gong (20629836)

“…A random aggregate model was implemented in Python and imported into finite element software to simulate sodium diffusion using Fick’s second law. …”

Sodium binding coefficient R. by Chenglong Gong (20629836)

“…A random aggregate model was implemented in Python and imported into finite element software to simulate sodium diffusion using Fick’s second law. …”

Error reduction over time by the HOFA-SMC. by Asra Sarwat (22453794)

“…A detailed simulation study is conducted on a full hand model, comprising four 4-degree-of-freedom (DOF) fingers and a 3-DOF thumb, implemented in Python. The controllers are evaluated across three test scenarios: flexion, extension, and ball grasping. …”

Comparison of SMC techniques. by Asra Sarwat (22453794)

“…A detailed simulation study is conducted on a full hand model, comprising four 4-degree-of-freedom (DOF) fingers and a 3-DOF thumb, implemented in Python. The controllers are evaluated across three test scenarios: flexion, extension, and ball grasping. …”

Proposed HOFA-SMC with experimental validation. by Asra Sarwat (22453794)

“…A detailed simulation study is conducted on a full hand model, comprising four 4-degree-of-freedom (DOF) fingers and a 3-DOF thumb, implemented in Python. The controllers are evaluated across three test scenarios: flexion, extension, and ball grasping. …”

Computational performance analysis script. by Le Qi (8859521)

Statistical analysis script. by Le Qi (8859521)

The artifacts and data for the paper "DD4AV: Detecting Atomicity Violations in Interrupt-Driven Programs with Guided Concolic Execution and Filtering" (OOPSLA 2025) by zixuan yuan (17602152)

“…</li><li><ul><li><code><strong>DBDS</strong></code>: The code implements our proposed dynamic scheduling execution method, which systematically explores task interleaving for atomicity violation detection, enhanced by an effective prefix-directed strategy.…”

<b>Anthropogenic nutrient inputs cause excessive algal growth for nearly half the world’s population</b> by Richard McDowell (7311011)

“…<p dir="ltr">Contains</p><p dir="ltr">Final Analysis Output.xlsx: Current and reference concentrations of DRP, TP, NO3-N and TN along with pivot table analysis</p><p dir="ltr">Code: Python code used to implement the model in ArcGIS Pro.…”

Missing Value Imputation in Relational Data Using Variational Inference by Simon Fontaine (7046618)

“…Additional results, implementation details, a Python implementation, and the code reproducing the results are available online. …”

Void-Center Galaxies and the Gravity of Probability Framework: Pre-DESI Consistency with VGS 12 and NGC 6789 by Jordan Waters (21620558)

“…<br><br><br><b>ORCID ID: https://orcid.org/0009-0009-0793-8089</b><br></p><p dir="ltr"><b>Code Availability:</b></p><p dir="ltr"><b>All Python tools used for GoP simulations and predictions are available at:</b></p><p dir="ltr"><b>https://github.com/Jwaters290/GoP-Probabilistic-Curvature</b><br><br>The Gravity of Probability framework is implemented in this public Python codebase that reproduces all published GoP predictions from preexisting DESI data, using a single fixed set of global parameters. …”

Overview of generalized weighted averages. by Nobuhito Manome (8882084)

“…GWA-UCB1 outperformed G-UCB1, UCB1-Tuned, and Thompson sampling in most problem settings and can be useful in many situations. The code is available at <a href="https://github.com/manome/python-mab" target="_blank">https://github.com/manome/python-mab</a>.…”

Table 3_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…</p>Conclusions<p>This study demonstrates that integrating deep learning with multi-sequence breast MRI and clinical data provides a highly effective and reliable tool for predicting HER2 expression in breast cancer. …”

Table 2_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…</p>Conclusions<p>This study demonstrates that integrating deep learning with multi-sequence breast MRI and clinical data provides a highly effective and reliable tool for predicting HER2 expression in breast cancer. …”

Table 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…</p>Conclusions<p>This study demonstrates that integrating deep learning with multi-sequence breast MRI and clinical data provides a highly effective and reliable tool for predicting HER2 expression in breast cancer. …”

Data Sheet 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…</p>Conclusions<p>This study demonstrates that integrating deep learning with multi-sequence breast MRI and clinical data provides a highly effective and reliable tool for predicting HER2 expression in breast cancer. …”

Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds by Gloria Castañeda-Valencia (20758502)

“…The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. …”

Artifact for the IJCAI 2024 paper "Solving Long-run Average Reward Robust MDPs via Stochastic Games" by Krishnendu Chatterjee (15367413)

“…<br></pre></pre><h2>Structure and How to run</h2><p dir="ltr">There are four Python files in the repository.</p><pre><pre>(i) `StrategyIteration.py` is the backend code, containing the implementation of the RPPI algorithm described in the paper.…”

Probabilistic-QSR-GeoQA by Mohammad Kazemi (19442467)

“…</p><p><br></p><p><br></p><p dir="ltr"><b>Perquisites</b></p><p dir="ltr">Two spatial reasoning tools, SparQ for conventional reasoning and Probcog for probabilistic reasoning need to be installed:</p><p><br></p><p dir="ltr">- Probcog ( Follow the their github repo in https://github.com/opcode81/ProbCog)</p><p dir="ltr">- SparQ (Follow their manual in https://www.uni-bamberg.de/fileadmin/sme/SparQ/SparQ-Manual.pdf)</p><p><br></p><p><br></p><p dir="ltr"><b>Materials</b></p><p dir="ltr">This includes codes, data, evidence sets, and mln folders for two experiments:</p><p dir="ltr">- Code: This folder includes questionGenerator.py and answerExtraction.py for generating synthetic questions and post-processing of inferences from Probcog and SparQ reasoners. …”

A Hybrid Ensemble-Based Parallel Learning Framework for Multi-Omics Data Integration and Cancer Subtype Classification by Mohammed Nasser Al-Andoli (21431681)

“…<p dir="ltr">The code supports replication of results on TCGA Pan-cancer and BRCA datasets and includes data preprocessing, model training, and evaluation scripts:<br>Python scripts for data preprocessing and integration</p><ul><li>Autoencoder implementation for multimodal feature learning</li><li>Hybrid ensemble training code (DL/ML models and meta-learner)</li><li>PSO and backpropagation hybrid optimization code</li><li>Parallel execution scripts</li><li>Instructions for replicating results on TCGA Pan-cancer and BRCA datasets</li></ul><p></p>…”