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The authors studied the relationship between air quality and weather conditions (wind speed, precipitation, and temperature) in the Washington D.C. area.

Autonomous drone imaging combined with machine learning offers a promising approach for early detection of invasive species. In this study, students built an autonomous drone and compared three models: CNN, SGDC, and XGBoost, to identify Brassica nigra from aerial footage. Their results show that CNNs most effectively recognize key visual features, demonstrating strong potential for supporting conservation and invasive plant management.

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

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.

The authors looked at how injuries sustained during water tubing, that require treatment at an emergency department, differ between males and females.

The authors looked at how vocabulary is impacted in Alzheimer's disease and whether it could be used a predictor of disease onset.

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

The authors looked at the correlation between C reactive protein levels and neurological deficits in patients who had suffered an ischemic stroke.

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.