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The authors looked at different factors that influence specific gravity in beverages, including sweetener used, caffeine, carbonation, and sodium.

The authors looked at public knowledge regarding suggested daily limits for sugar intake and then looked at how sugar levels vary in the same drink obtained from different sources and across different days.

The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

The authors looked at the ability to use audio clips to analyze the progression of Parkinson's disease.

The authors looked into eco-friendly alternatives for insulating material. They ultimately found that a polyurethane derived from eggshells was an effective insulator and further research into it is warranted.

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

The authors looked at the impact of different harvest and feeding treatments on Heterandria formosa over three generations as a model for changes in marine ecosystems.

The authors looked at beach nourishment (a way to combat erosion on coasts) and resulting grain size distribution. Their work is important to understand the dynamics of erosion and it's relation to wave action and the implications this has for efforts to mitigate coastal erosion.

Using machine learning to predict the risk of algae bloom