HIL test results of curved road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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Comparison of average reward values in various simulation scenarios. by Honglei Pang (22693724)

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HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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Screening of the potential small-molecular compounds for the treatment of NAFLD and AIS by cMAP analysis. by Junjie Su (368807)

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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Data Sheet 1_Understanding acceptance and resistance toward generative AI technologies: a multi-theoretical framework integrating functional, risk, and sociolegal factors.docx by Priyanka Shrivastava (472059)

“…Additionally, the manuscript now highlights demographic diversity, including variations in age, gender, and academic discipline, as relevant to AI adoption patterns. Ethical concerns, including algorithmic bias, data ownership, and the labor market impact of AI, are addressed to offer a more holistic understanding of resistance behavior. …”

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…<div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. …”

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling by Shan He (121220)

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Python code for a rule-based NLP model for mapping circular economy indicators to SDGs by Zahir Barahmand (18008947)

“…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…”

Functional module diagram. by Fan Hongxia (21029673)

“…The intelligent system supports visual, intelligent, and scientific water-saving management, offering essential data and functional tools for urban water-using units. Future efforts should focus on further utilizing big data and artificial intelligence algorithms to simulate and identify water use characteristics, so as to assess the unreasonable water usage more scientifically and provide more effective support for water conservation work.…”

Exponentially attenuated sinusoidal function. by Hang Zhao (143592)

“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”

AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper by Patrick Ryan (532054)

“…Here, we present a label-free, longitudinal, and quantitative monitoring of mineralized collagen formation by osteoblasts and subsequent osteoclast-driven mineral resorption on DBP using brightfield microscopy. A Segment.ai machine learning algorithm is applied for time-lapse bright-field image analysis, enabling identification of osteoclast resorption areas and automated quantification of large image datasets over a three-week culture period. …”

AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper by Patrick Ryan (532054)

“…Here, we present a label-free, longitudinal, and quantitative monitoring of mineralized collagen formation by osteoblasts and subsequent osteoclast-driven mineral resorption on DBP using brightfield microscopy. A Segment.ai machine learning algorithm is applied for time-lapse bright-field image analysis, enabling identification of osteoclast resorption areas and automated quantification of large image datasets over a three-week culture period. …”

AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper by Patrick Ryan (532054)

“…Here, we present a label-free, longitudinal, and quantitative monitoring of mineralized collagen formation by osteoblasts and subsequent osteoclast-driven mineral resorption on DBP using brightfield microscopy. A Segment.ai machine learning algorithm is applied for time-lapse bright-field image analysis, enabling identification of osteoclast resorption areas and automated quantification of large image datasets over a three-week culture period. …”

AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper by Patrick Ryan (532054)

“…Here, we present a label-free, longitudinal, and quantitative monitoring of mineralized collagen formation by osteoblasts and subsequent osteoclast-driven mineral resorption on DBP using brightfield microscopy. A Segment.ai machine learning algorithm is applied for time-lapse bright-field image analysis, enabling identification of osteoclast resorption areas and automated quantification of large image datasets over a three-week culture period. …”

Convergence curve of the GL-SSL Model’s training loss function, illustrating the trend of loss reduction over 300 iterations, reflecting the model’s stability and rapid convergence... by Jiangjiang He (9676377)

Functional enrichment analysis. by Junjie Su (368807)