Browse Articles

Estimating Paleoenvironments Utilizing Foraminiferal Fossils from the Toyohama Formation, Aichi Prefecture, Central Japan

Kimitsuki et al. | Dec 11, 2017

Estimating Paleoenvironments Utilizing Foraminiferal Fossils from the Toyohama Formation, Aichi Prefecture, Central Japan

Foraminifera are a diverse phylum of marine protists that produce elaborate shells. Because of their abundance and morphological diversity, foraminiferal fossil assemblages are used for biostratigraphy, to accurately date sedimentary rocks and to characterize past ocean environments. In this paper, authors collected fossils within the Morozaki Group in central Honshu, Japan, to assess past marine environments and species diversity.

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Do Initial Strategies or Choice of Piece Color Lead to Advantages in Chess Games?

Ponnaluri et al. | Feb 07, 2017

Do Initial Strategies or Choice of Piece Color Lead to Advantages in Chess Games?

White pieces make the first move in chess games, and there are several opening strategies and consequent defense strategies that white and black pieces, respectively, can take . The author of this paper investigated whether taking a specific opening and defense strategy, as well as playing as white vs. black, can increase the chances of winning the game, by playing against various human and computer opponents.

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Redesigning an Experiment to Determine the Coefficient of Friction

Hu et al. | Jun 27, 2016

Redesigning an Experiment to Determine the Coefficient of Friction

In a common high school experiment to measure friction coefficients, a weighted mass attached to a spring scale is dragged across a surface at a constant velocity. While the constant velocity is necessary for an accurate measurement, it can be difficult to maintain and this can lead to large errors. Here, the authors designed a new experiment to measure friction coefficients in the classroom using only static force and show that their method has a lower standard deviation than the traditional experiment.

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The Protective Effects of Panax notoginseng Saponin on the Blood-Brain Barrier via the Nrf2/ARE Pathway in bEnd3 Cells

Yang et al. | Apr 06, 2016

The Protective Effects of <i>Panax notoginseng</i> Saponin on the Blood-Brain Barrier via the Nrf2/ARE Pathway in bEnd3 Cells

Disruption of the blood-brain barrier (BBB) is related to many neurological disorders, and can be caused by oxidative stress to cerebral microvascular endothelial cells (CMECs) composing the BBB. The authors of the paper investigated the protective effects of the total saponins in the leaves of Panax notoginseng (LPNS) on oxidative-stress-induced damage in a mouse cerebral microvascular endothelial cell line.

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Understanding the impossibility of machine learning fairness with data examples

Pillutla et al. | Sep 04, 2026

Understanding the impossibility of machine learning fairness with data examples

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.

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Study of PINN sensor layout in evaluating WSS with application to patient-specific carotid flow

Nie et al. | Sep 04, 2026

Study of PINN sensor layout in evaluating WSS with application to patient-specific carotid flow

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.

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Retinal biomarkers for the detection of neurological disease using deep learning

Kim et al. | Sep 04, 2026

Retinal biomarkers for the detection of neurological disease using deep learning

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.

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Global trends in total cholesterol by gender (1980-2018)

Qi et al. | Aug 24, 2026

Global trends in total cholesterol by gender (1980-2018)

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.

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Evaluating the impact of prompting styles on LLM accuracy for AIME math questions

Ganesa et al. | Jul 26, 2026

Evaluating the impact of prompting styles on LLM accuracy for AIME math questions

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.

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