Browse Articles

This study examined behavioral and psychological predictors of burnout among high school and university students in Pakistan using survey data and machine-learning models. Shorter sleep and greater mental fatigue—especially fatigue—were associated with higher burnout risk, while a Random Forest model successfully identified students at risk of burnout.

This study examined whether eye color affects photophobia and vision in elementary school students and staff, finding no significant relationship between eye color, light sensitivity, or visual acuity. However, photophobia was common across age groups, highlighting the need for greater awareness of light sensitivity in learning environments.

The authors studied optimized delivery routes to reduce travel times using different traveling salesman algorithms.

This article investigates whether induction heating can speed up static recrystallization in AISI 4130 steel compared with traditional radiant heating. It was found that induction-heated samples recrystallized faster, softened more quickly, and showed earlier microstructural changes like grain nucleation and pearlite spheroidization, suggesting induction heating could be a more efficient alternative for industrial metal heat treatments.

The authors test the effect of petroleum-based and bio-based microplastics on the growth of two microalgae species.

This study evaluates the potential of natural language processing (NLP) models in an emotion-driven bibliotherapy framework to improve mental health challenges.

The authors studied the bioethical values of Generation Z.

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 looked at the feasibility to predict wind speeds that will have less reliance on using historical data.

The authors employ computational protein design to identify a mini-protein with the potential to enhance binding of the tight junction protein, claudin-5, at the blood-blood barrier with therapeutic potential for neurodegenerative diseases.