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This study investigates the effect of water saturation in a two-layer fluidization behavior of an overlying dry sand bed. Building on prior work showing that moisture inhibits fluidization in granular media, the authors address an underexplored configuration involving layered dry and wet sand. The experiments demonstrate that even minimal water saturation in the sub-layer prevents uniform fluidization of the dry upper layer, leading to the conclusion that two-layer fluidized beds with moisture are not viable under the conditions tested.

This study examines the possibility of utilizing proteins from Drosophila melanogaster in early drug discovery. Structural and sequence analysis of five diverse Drosophila proteins and its human homologs were conducted. Drosophila proteins with high conservation of amino acids at functionally significant positions are better candidates for use as human surrogate in drug discovery efforts.

Machine learning systems are often expected to make fair decisions, yet the most widely used fairness criteria—independence, separation, and sufficiency—cannot generally be satisfied at the same time. In this study, students tested these criteria using a logistic regression model on a real-world student performance dataset and found that each criterion was met only at different prediction thresholds, with no threshold satisfying all three simultaneously. These results illustrate the inherent trade-offs in algorithmic fairness and highlight why achieving perfectly fair machine learning models is often impossible in practice.

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 looked at whether retinal features, specifically lens clarity, optic nerve cupping, and hyperreflective foci, can serve as biomarkers for neuro-ophthalmological diseases, which share pathological mechanisms with neurodegeneration and may indicate broader neurological risk. Using multimodal clinical data and machine learning, they investigated the potential of retinal imaging as a complementary diagnostic tool for more accurate and accessible detection of neurological disorders.

This study explores global trends in average total cholesterol levels among adults from 1980 to 2018, finding a significant decline in both men and women, with a sharper decrease among men. While these improvements likely reflect advances in public health and medical treatment, the persistent gender gap highlights the need for more targeted approaches to cardiovascular disease prevention.

Large language models are increasingly used to solve math problems, but their ability to handle multi-step reasoning remains uncertain. In this study, students tested whether different prompting styles could improve LLM accuracy on challenging AIME math questions and found that detailed step-by-step solutions did not significantly outperform simpler prompts. These results suggest that improving LLM mathematical reasoning may require deeper model-level advances rather than changes in prompting style alone.

Here the authors propose integrating magnetic iron (II) oxide nanoparticles into the high-density polyethylene linings of disposable paper cups to create a waterproof, magnetically responsive composite liner. Their findings demonstrate that these nanoparticles successfully bond with the plastic layer without disrupting its structural integrity, offering a viable method to improve plastic recovery through magnetic recycling and mitigate global microplastic pollution.

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.

Cancer cells rely heavily on glycolysis, but how they respond when glucose is reintroduced after acute starvation is not well understood. Using fluorescence lifetime imaging microscopy, students tracked metabolic changes in HeLa cells and found a rapid shift toward glycolysis within 20 minutes of glucose reintroduction, followed by heterogeneous recovery toward oxidative phosphorylation. These results highlight metabolic flexibility and variability in cancer cells, offering insights relevant to treatment resistance and therapeutic design.