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The authors looked at how acetaminophen may cause liver damage by looking at both serum level markers of liver damage as well as liver pathology.

Additive manufacturing (AM) is transforming the production of complex metal parts, but challenges like internal cracking can arise, particularly in critical sectors such as aerospace and automotive. Traditional methods to assess cracking susceptibility are costly and time-consuming, prompting the use of machine learning (ML) for more efficient predictions. This study developed a multi-model ML pipeline that predicts solidification cracking susceptibility (SCS) more accurately by considering secondary alloy properties alongside composition, with Random Forest models showing the best performance, highlighting a promising direction for future research into SCS quantification.

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

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.

This study evaluates the potential of natural language processing (NLP) models in an emotion-driven bibliotherapy framework to improve mental health challenges.