Python file of the computational framework. by Benoit Haut (9753029)

Analysis variance P-value table for wear rate regression model. by Hosamane Guruswamy Prashantha Kumar (21433265)

Table 1 - mdciao: Accessible Analysis and Visualization of Molecular Dynamics Simulation Data by Guillermo Pérez-Hernández (21156182)

Distribution of closest heavy-atom—heavy-atom distances over five different MD datasets (S1 Table). by Guillermo Pérez-Hernández (21156182)

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

“…</li></ul><h2>Integration with Other Tools</h2><p dir="ltr">The System Hardware ID Generator Script is part of the broader suite of tools offered by the <a href="https://xn--mxac.net/" target="_blank">Alpha Beta Network</a>, dedicated to enhancing security and performance in <a href="https://xn--mxac.net/" target="_blank">Python programming</a>.</p><ul><li>For advanced <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">Python code protection tools</a>, consider using the <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">Local Python Code Protector Script</a>. …”

Things to do after installing mdciao. by Guillermo Pérez-Hernández (21156182)

Catalogue of compact radio sources in Messier-82 from e-MERLIN observations by Sibongumusa Shungube (21197363)

“…</p><p dir="ltr"><b>Table 3.2: PyBDSF Source Catalogue</b></p><p dir="ltr">This table presents source parameters for the same 36 sources, as determined by the automated PyBDSF algorithm. …”

Overview of one by Guillermo Pérez-Hernández (21156182)

Example Jupyter notebook illustrating how mdciao can be used in API mode. by Guillermo Pérez-Hernández (21156182)

Example Jupyter notebook illustrating how mdciao is used in API mode. by Guillermo Pérez-Hernández (21156182)

Overview of the MD datasets used in S2 Fig. by Guillermo Pérez-Hernández (21156182)

“…<div><p>We present mdciao, an open-source command line tool and Python Application-Programming-Interface (API) for easy, one-shot analysis and representation of molecular dynamics (MD) simulation data. …”

Table 1_AgriLink: an innovative real-time data monitoring and connectivity platform for an orange orchard in Morocco.docx by Kamal Baraka (20778785)

Table 1_AI-enabled pipeline for virus detection, validation, and SNP discovery from next-generation sequencing data.xlsx by Abozar Ghorbani (5081753)

Table 1_Net2Brain: a toolbox to compare artificial vision models with human brain responses.pdf by Domenic Bersch (21248975)

Table 1_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.docx by A. L. Back (20719049)

Table 2_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.xlsx by A. L. Back (20719049)

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

“…The full implementation is detailed in the accompanying Python script (Imputation_Air_Pollutants_NABEL.py). …”

Bayesian Changepoint Detection via Logistic Regression and the Topological Analysis of Image Series by Andrew M. Thomas (712104)

“…The method also successfully recovers the location and nature of changes in more traditional changepoint tasks. An implementation of our method is available in the Python package bclr.…”

Trustworthy and Ethical AI for Intrusion Detection in Healthcare IoT (IoMT) Systems: An Agentic Decision Loop Framework by ibrahim adabara (22107287)

“…</p></blockquote><h2> How to Reproduce Results</h2><p dir="ltr">Run the full pipeline (training + evaluation):</p><pre><pre>python src/train_agent.py --config configs/agentic_ids.yaml<br></pre></pre><p dir="ltr">This script:</p><ul><li>Trains the supervised flow-based detector on CIC-IoMT 2024</li><li>Fine-tunes the DQN triage agent</li><li>Evaluates under domain-shift using CSE-CIC-IDS2018</li><li>Computes Ethical Compliance Rate (ECR), False Escalation Rate (FER), and CAS metrics</li></ul><h2> Key Metrics</h2><table><tr><th><p dir="ltr">Metric</p></th><th><p dir="ltr">Description</p></th></tr><tr><td><b>Accuracy</b></td><td><p dir="ltr">Correct classification rate across all flows</p></td></tr><tr><td><b>F1-Score (Weighted)</b></td><td><p dir="ltr">Balanced measure of precision and recall</p></td></tr><tr><td><b>Ethical Compliance Rate (ECR)</b></td><td><p dir="ltr">Percentage of actions consistent with governance rules</p></td></tr><tr><td><b>False Escalation Rate (FER)</b></td><td><p dir="ltr">Proportion of overreactions (false alarms)</p></td></tr><tr><td><b>Contextual Adaptation Score (CAS)</b></td><td><p dir="ltr">Robustness under domain-shift</p></td></tr></table><h2> Citation</h2><p dir="ltr">If you use this repository, please cite:</p><pre><pre>Adabara, I. …”

<b>Beyond absolute space: Modeling disease dispersion and reactive actions from a multi-spatialization perspective</b> by Shiran Zhong (14518376)

“…The following sections will guide you through the setup, data structure, code execution, expected output, and any additional notes necessary for reproducing the results presented in the manuscript.</p><p dir="ltr"><b>Table of Contents</b></p><p dir="ltr">· Requirements</p><p dir="ltr">· Data files</p><p dir="ltr">· Code structure</p><p dir="ltr">· Running the code</p><p dir="ltr">· Expected Output</p><p dir="ltr">· Troubleshooting</p><h3>==========================================================</h3><h3>Requirements</h3><p dir="ltr"><u>Operating system</u></p><p dir="ltr">· Windows 7 or higher (recommended)</p><p dir="ltr">· Ubuntu</p><p dir="ltr"><u>Software</u></p><p dir="ltr">· Python (version 2.7 or higher) or Jupyter Notebook</p><p dir="ltr">Required libraries: numpy, pandas, scipy, matplotlib, pgmpy</p><h3>Data files</h3><ul><li>Survey_data_processed_Anonymized.csv</li><li>ProtectiveAction_Anonymized.csv</li></ul><p dir="ltr">These two data files have been pre-processed from the raw survey data to support the Python code for generating Figures 3, 4, 5, and 6. …”