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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.

Multi-perspective drone imagery (adding oblique views to top-down shots) raised crop classification accuracy from 35.73% to 52.93% across five species. The improvement scaled with canopy complexity, from +4.2% for simple rice to +38.1% for structurally complex sugarcane.

This paper utilizes multiple publicly available cancer datasets to explore the abundance of different genetic dependencies across various cancer types. They explore this by looking at four genes highly associated with cancer and their genetic and pathogenic relationships.

The authors assess the effect of ER stress on sleep quality in fruit flies.

When granular materials are poured onto a surface, they form conical piles whose slopes increase before reaching a stable angle of repose. We found that this angle evolution follows a previously unrecognized two-phase exponential growth pattern that is conserved across granular materials with diverse particle properties. The parameters of this model correlate with particle friction and are influenced by deposition conditions, providing a quantitative framework for describing pile formation.

This study investigates the ability of Gymnema sylvestre tea to acutely suppress sweet taste perception across various food groups. Our findings demonstrate how this natural botanical can reduce sugar cravings, offering a potential complementary strategy for managing dietary intake in type 2 diabetes.

This study investigated perceptions of the emerging workforce toward the use of artificial intelligence in hiring, specifically for assessing subjective "culture fit." Through a mixed-methods survey of 150 high school and early-college students in Nepal, we found a significant disconnect between organizational adoption of AI and the profound skepticism of young job candidates, who express deep concerns about fairness, transparency, and the potential for AI to perpetuate systemic discrimination.

The authors looked at the ability of a convolutional neural network (CNN) to sort contaminated recycling, with varying levels of contamination. They found as contamination levels increased, the CNN faced more difficulty correctly classifying items.

This study contributes to our understanding of how urban energy systems respond to climate variability and inform strategies for enhancing power grid resilience. The findings can help inform urban planners and infrastructure developers by identifying the factors that make regions within a power grid more vulnerable.

Textile waste from the fashion industry is a major environmental pollutant, but recycling waste into novel building material is a strategy to reduce the negative effects. This manuscript characterized five different binders that can be used to repurpose textile waste into bricks for construction purposes. Water-based glue, cement, white cement, plaster of Paris, and epoxy resin were mixed with shredded textile waste, and the mechanical characteristics and thermal insulation of each brick type were measured. Bricks with increased mechanical strength had the poorest thermal resistance, and the contrasting properties would suit different building purposes. This work provides a first step in generating recycled textile bricks for construction in a circular economy framework.