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

Agricultural fertilizer application is a key innovation in providing enough food to feed the world. Fertilizers come in various types and farmers must choose which fertilizer is the best for their applications. To learn more about the effectiveness of various fertilizers, Wilson and Rasmus studied the effects of natural and chemical fertilizers on growth of basil plants.

Default opt-ins and social proof tags effect on decision making in an e-commerce context

The authors investigate various methods to decrease heart rate after drinking caffeine.

This study contributes to our understanding of how urban energy systems respond to climate variability and inform strategies for enhancing power grid resilience. The findings can help inform urban planners and infrastructure developers by identifying the factors that make regions within a power grid more vulnerable.

The authors looked at ways that seizures in patients with glioblastoma could be treated using C. elegans as a model system.

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.

Sodium dodecyl sulfate (SDS), a detergent component, can harm plant growth when it contaminates soil and waterways. Authors explored the potential of lactic acid bacteria (LAB) to mitigate SDS-induced stress on plants.

The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

The authors looked at whether youth use of marijuana related to later high-risk drug use. Using survey data from 2010-2019 they found that youth marijuana use did correlate to an increased risk of high-risk drug use.