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Hemani et al. tackled the problem of rampant hospital waste by implementing staff training to help inform hospital workers about proper waste disposal. The authors observed a significant increase in proper waste disposal after the training, showing that simple strategies, such as in-person classroom training and posters, can have a profound effect on limiting improper waste handling.

Antibiotics are oftentimes used to treat mastitis (infection of the mammary gland) in dairy cows. Regulations require that milk from these cows be discarded until the infection has cleared and antibiotic residues are no longer detectable in the cow's milk. These regulations are in place to protect consumers and to help prevent the rise of antibiotic resistant bacteria. In this study, the authors test milk samples from 10 milk suppliers in the Greensboro, NC to see if they contain detectable levels of antibiotic residues.

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.

The authors test symbiotic relationships among cyanobacteria species to generate more robust cultures for potential biofuel production.

The authors looked at what conditions with host stars favor development of exoplanets.

The authors looked at ways to provide better forecasting on website traffic. They found that deep learning models performed better than statistical models.

Wind turbines are a valuable source of renewable energy, but face challenges related to unpredictable wind speed. The turbine must be able to control its angle to catch enough wind to generate electricity, while avoiding excess wind that may damage the turbine. Zhou and Wang explore different types of smart turbine controllers to see which appears optimal for electricity generation.

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.

This study explores how different economic sectors, geographic regions, and greenhouse gas types might affect future global mean surface temperature (GMST) anomalies differently from historical patterns. Using the Finite Amplitude Impulse Response (FaIR) model and four Shared Socioeconomic Pathways (SSPs) — SSP126, SSP245, SSP370, and SSP585 — the research reveals that future contributions to GMST anomalies.

The authors investigate how well AI-detection machine learning models can detect real versus AI-generated art across different art styles.