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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 explored sunscreen storage temperature affects the efficacy of sunscreen to block UV light.

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.

Excess sucrose consumption has been associated with several health problems, including inflammation and potential negative effects on immune function. However, the exact relationship between sucrose intake and immunity remains unclear, especially during bacterial infections. This study examined how sucrose intake affected the survival of fruit flies following oral infection with the bacterial pathogen Serratia marcescens.

The authors investigated adoption rates of solar photovoltaic power at K-12 schools in California.

α-amylase breaks down starch into glucose, which can cause blood sugar spikes and increase the risk of type II diabetes. This study tested natural extracts from cassia cinnamon and green tea as alternatives to synthetic inhibitors like acarbose, which can be costly and cause side effects.

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

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

The authors investigated the impact of light pollution on astrophotography.

The main goal of this study is to determine what demographics are related to tuberculosis incidence in the United States populations, particularly if changing demographics are related to differences in tuberculosis risk over two discrete time periods. The major finding is that in the two studied time periods, tuberculosis risk factors were somewhat consistent and may be influenced by things such as immigration, healthcare access, and race or ethnicity, although the top predictor did change.