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The authors looked at if there was any correlation between the phase of the moon and admissions for psychiatric concerns.

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

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.

Using machine learning to predict the risk of algae bloom

This study investigated how well food labels from 280 different brands across multiple food and drink categories in India and the US adhered to recommended nutritional labeling standards as outlined by the Codex Alimentarius.

The authors looked at alternative production processes for PHB plastic in an effort to reduce environmental impact. They found that no alternative process was able to significantly decrease the environmental impact of PHB production, but that optimizing dewatering steps during production could lead to the largest improvement on environmental impact.

Here the authors aimed to understand factors influencing adolescent fentanyl exposure, hypothesizing a positive association between social media usage, socioeconomic factors, and fentanyl abuse among U.S. teens. Their analysis of the Monitoring the Future dataset revealed that a history of suspension and use of marijuana or alcohol were linked to higher fentanyl use, and while not statistically significant, a notable positive correlation between social media use and fentanyl frequency was observed.

The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.