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The authors looked at ways that autonomous vehicles could be trained to better perform in adverse weather without the use of actual images taken during adverse weather events. They were able to improve performance, but models using actual images still perform the best.

The study examined whether high school boys’ water polo teams located near strong programs had similar season performances during the 2024–2025 season.

This study used single-cell RNA sequencing to examine whether B cells can predict response to pembrolizumab and radiation therapy in patients with triple-negative breast cancer.

Physics‑Informed Neural Networks (PINNs) offer a promising way to estimate blood‑flow behavior in arteries, especially near the vessel wall where traditional methods struggle. In this study, students tested how sensor density and placement affect PINN accuracy in modeling carotid artery flow and found that accuracy improves up to a moderate sensor density and depends strongly on how close sensors are placed to the arterial wall. Applying the optimized configuration to a patient‑specific carotid model produced velocity predictions closely matching computational fluid dynamics results, highlighting PINNs’ potential for future personalized cardiovascular assessment.

This study compares traditional and discovery-based instructional methods in online middle school mathematics education. Findings show that discovery-based learning significantly improves students’ academic performance and attitudes, highlighting the value of inquiry-driven instruction for enhancing both understanding and motivation.

The authors studied hippocampal gene expression in Alzheimer's disease.

The authors studied the relationship between air quality and weather conditions (wind speed, precipitation, and temperature) in the Washington D.C. area.

Autonomous drone imaging combined with machine learning offers a promising approach for early detection of invasive species. In this study, students built an autonomous drone and compared three models: CNN, SGDC, and XGBoost, to identify Brassica nigra from aerial footage. Their results show that CNNs most effectively recognize key visual features, demonstrating strong potential for supporting conservation and invasive plant management.

Iridescent materials reflect different colored depending on viewing angle. This specific effect can be achieved by biomimetic photonic materials. This project models the quantitative relationship between these material’s coloring and its nanostructure to facilitate personalized design of art materials.

The authors used developed a machine learning tool for studying social media discourse surrounding use of tobacco and cannabis.