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The authors assess incidence and mortality rates of two cancer types in relation to trihalomethane pollutant concentrations in drinking water.

The authors looked at non-natural factors that influenced the spread rate of fire ants in multiple cities in China.

Here the authors present an autonomous drone air filtration system that uses a novel algorithm, the gradient ascent ML particle filter (GA/MLPF), to efficiently locate and mitigate outdoor air pollution. They demonstrate that their GA/MLPF algorithm is significantly more efficient than the conventional gradient ascent algorithm, reducing both the time and number of waypoints needed to find the source of pollution.

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

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 the ability to predict future stock prices using various machine learning models.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

The authors looked at the impact of calcium on iron absorption in mealworms, hypothesizing that calcium would inhibit iron absorption.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

In this study the authors looked at the ability to navigate planes in space between randomly placed planets. They used machine and reinforcement learning to run simulations and found that they were able to identify optimal paths for travel. In the future these techniques may allow for safer travel in unknown spaces.