Browse Articles

The authors assess the relationship between supportive family environments and internalized stigma in adolescents.

Here the authors investigated the impact of player ability versus starting positions in the game of Nim under imperfect play, hypothesizing that player skill becomes the primary determinant of outcomes as pile sizes grow. Through computational simulations and a mathematical model, they demonstrated that favorable starting positions lose their advantage over time and provide key insights to help improve reinforcement learning algorithms in abstract, complex decision-making environments.

In this study, the authors examine the relationship between ESG (Environmental, Social, and Governance) factors on financial performance of Vietnamese media companies, as measured by ROA and ROE. They find that ESG is positively correlated with financial performance, as is sales growth. They also find that financial leverage is negatively correlated with ROA. Overall, this is a quality study, although the methods section requires more detail.

This article explored the question of when do people decide to stop gambling and further tries to extrapolate why people stop gambling at that point. Their study showed that people tend to quit gambling after 4 consecutive losses, significantly more than 1-3 consecutive losses or a win previous to quitting. They also found that participants commonly quit at a point value approximately 5 points greater than or less than their starting balance. The authors concluded that these results may be important in understanding how to cut down on excessive gambling or in creating policies that make it easier for people to disengage from gambling.

The authors use computational methods to compare tau acetylation to the better studied tau phosphorylation in Alzheimer's disease and then design and computationally test a new drug to prevent abnormal post-translational modifications of tau.

The authors test the antioxidant content of Taiwanese high mountain teas grown in or processed under differing conditions.

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

This study investigates how the hyperparameters epochs and batch size affect the classification accuracy of a convolutional neural network (CNN) trained on pulsar candidate data. Our results reveal that accuracy improves with increasing number of epochs and smaller batch sizes, suggesting that with optimized hyperparameters, high accuracy may be achievable with minimal training. These findings offer insights that could help create more efficient machine learning classification models for pulsar signal detection, with the potential of accelerating pulsar discovery and advancing astrophysical research.

The authors studied the ability of deep learning models to predict droughts in the midwestern United States.

The authors studied the response of various construction materials to UV solar radiation and heat.