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The authors looked at the ability of a convolutional neural network (CNN) to sort contaminated recycling, with varying levels of contamination. They found as contamination levels increased, the CNN faced more difficulty correctly classifying items.

The authors examined the relationships between S&P 500 Technology and Financials equity sectors and their corresponding investment-grade fixed-income markets from 1995 to 2025.

The authors looked at fertilizer derived from coffee ground tea leaves, measuring the effectiveness by measuring the height, weight, and number of leaves on basil plants.

The authors looked at the ability of Shewanella oneidensis to generate energy in a microbial fuel cell under varying conditions. They found that the S. Onedensis biofilm was able to produce energy in microgravity and that one of the biggest factors that limited energy production was a decrease in growth medium present.

Understanding and predicting cognitive decline in Alzheimer's disease

The authors looked factors that could lead to earlier diagnosis of cardiovascular disease thereby improving patient outcomes. They found that advances in imaging and electrocardiography contribute to earlier detection of cardiovascular disease.

Asthma is one of the most prevalent chronic conditions in the United States. But not all people experience asthma equally, with factors like healthcare access and environmental pollution impacting whether children are likely to be hospitalized for asthma's effects. Li, Li, and Ruffolo investigate what demographic and environmental factors are predictive of childhood asthma hospitalization rates across Pennsylvania.

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.

This study collected samples from water bodies near hog farms and an aquatic environment not near a hog farm. It was hypothesized that water bodies near the hog farms would have lower water quality with higher turbidity, total dissolved solids (TDS), and pH than the water body not in proximity to a hog farm because of water contamination with hog waste. Results showed that the turbidity was 4–6 times higher, TDS was 1.5–2 times higher, and pH was 3 units higher in the 2 experimental locations compared to the control location. This study and its findings are important for understanding the impact of hog farming on the proximal water bodies.