Browse Articles

PVC is a widely used plastic that poses harmful health hazards when burned. In this study, the authors ask whether or not burned PVC (PVC char) affects bacterial transformation.

The authors looked at ways to provide better forecasting on website traffic. They found that deep learning models performed better than statistical models.

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.

Utilization of neural network to analyze Free Word Association to predict accurately age, gender, first language, and current country.

In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.

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.

The authors investigated the efficacy of functionalized graphene oxide nanoparticles for turning saltwater to freshwater.

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 shows the efficacy of leveraging transfer learning, specifically from residual networks, to detect CVDs and possible signs of CVDs. The findings indicate that leveraging transfer learning from residual networks alongside medical professionals is a highly promising approach for CVD detection and diagnosis, warranting further investigation.

Heavy metal contamination from transportation can damage soil and vegetation by increasing oxidative stress, disrupting enzymes, and impairing photosynthesis. This study investigated whether chondroitin sulfate (CS) could reduce the harmful effects of contaminated roadside soil on plant growth by binding with metal ions.