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

The authors looked at which measurements from the NFL combine were the most predictive of success for defensemen in the NFL.

The authors use blood smears from individuals with sickle cell disease to correlate sickle cell frequency with the occurrence of vaso-occlusive crises.

The authors looked at how pharmacokinetics changed depending on the use of an in vitro or an in vivo model.

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

The authors looked at how activation of the sympathetic nervous system impacts short-term memory.

The authors looked at the ability of different machine learning algorithms to predict the level of financial corruption in different countries.

In this study the authors looked at the ability to navigate planes in space between randomly placed planets. They used machine and reinforcement learning to run simulations and found that they were able to identify optimal paths for travel. In the future these techniques may allow for safer travel in unknown spaces.

Domestic waste classification using convolutional neural network

The authors investigated whether the physician-patient relationship affected patient perceptions and treatment adherence.