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The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.

This paper presents findings on Carcharhinus leucas (bull shark) and Carcharhinus amblyrhynchos (grey reef shark) aggression towards humans at Beqa Adventure Divers in Shark Reef Marine Reserve, Fiji. We hypothesized that grey reef sharks would receive more prods than bull sharks because grey reef sharks are typically more aggressive than bull sharks. The results supported our hypothesis, as an individual grey reef shark received 2.44 prods on average per feed, while a bull shark had an average of 0.61. These findings are meaningful not only to the world’s general understanding of shark aggression, but also to human protection against grey reef sharks as well as public education on bull sharks and the conservation of the species.

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.

Here the authors evaluate the relationship between state-level abortion policies and maternal mortality across the United States from 2018 to 2021. Their findings reveal that states with restrictive abortion laws consistently experienced higher maternal mortality ratios than those with protective policies, culminating in a 72% increase in deaths within restrictive states by the end of the study period.