Browse Articles

The authors used developed a machine learning tool for studying social media discourse surrounding use of tobacco and cannabis.

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.

In this study, the authors conduct a survey of high school students to evaluate the effects of screen time and habits on motivation.

As fast fashion explodes in popularity, the fashion industry remains one of the most prominent industries responsible for pollution. This pollution includes a lack of treatment for textile dyes that remain toxic or carcinogenic as they persist in wastewater. To resolve this, the authors of this study set out to determine the efficacy of using edible white rot fungi for cell-based biodegradation of textile dyes into harmless chemicals. This method takes advantage of fungi found in excess from the fungi industry, decreasing food waste while addressing textile waste in tandem.

In this study, the authors investigate the effects of the COVID-19 pandemic on South Korean international school students' anxiety, well being and their learning habits.

In this study, the authors surveyed a number of students in Singapore to determine how their experiences changed after the implementation of home-based learning during the COVID-19 pandemic.

Household detergents have surfactants that can potentially harm the soil and broader ecosystems. In this study, the authors investigate whether eco-friendly and less-eco-friendly detergents affect soil pH, phosphorus, nitrogen, and potassium levels.

Clean drinking water is an essential component to maintaining public health. The authors of this study tested the bacterial load of water from three different disinfection and filtration systems in order to determine which system might be superior.

This study used machine learning models to examine which factors most influenced U.S. household energy consumption in 2020 using data from 18,496 households.

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.