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

The study explored converting Gracilaria seaweed waste—known for releasing toxic hydrogen sulfide when decomposed—into biochar as a sustainable solution for waste management and soil improvement.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.

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

The authors looked at the impact of vitamin A (retinol) on growth of S. epidermidis (most abundant bacterium on the skin) and E. coli (found in the gut microbiome, but not on the skin).

The authors looked at the correlation between being able to speak more than one language (multilingualism) and visual imagery. They found multilingual individuals had higher visual imagery as measured by the VVIQ.

The authors created a 1D model to diagnose hepatomegaly and cirrhosis via ultrasound of the liver.

In this study, the authors investigate the crystallization kinetics of vanillin thin films from a solution or a melt, as well as how a silane coating on the glass surface affects these properties.

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.

Current horticulture practices often rely on pesticides, causing environmental harm. To address this, authors explore the use of ultrasonic sound emissions to detect plant stress at an individual level.