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This study examined whether findings from laboratory studies of baseball cognition also apply during real games. Using 2024 MLB data from 30 players, researchers analyzed batting performance under different levels of feedback and anxiety.

This study shows the efficacy of leveraging transfer learning, specifically from residual networks, to detect CVDs and possible signs of CVDs. The findings indicate that leveraging transfer learning from residual networks alongside medical professionals is a highly promising approach for CVD detection and diagnosis, warranting further investigation.

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

Elevated acetylcholinesterase (AChE) activity contributes to cognitive decline and neurodegenerative diseases such as Alzheimer’s, motivating the search for more effective inhibitors with better bioavailability. This study used computational methods to design novel, non-toxic AChE inhibitors.

The authors looked at how the presence of difference antibiotic resistance genes would influence antibiotic resistance in Staphylococcus aureus.

The authors studied the impact of AI face-tracking technology on the immersiveness of videogames as an alternative to virtual reality gaming.

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

Studying cut-resistant socks for hockey players

The authors developed a quantum inspired model for stock market fluctuations.

The authors looked at energy efficient ways to generate small nitrogenous compounds.