Browse Articles

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.

Utilization of neural network to analyze Free Word Association to predict accurately age, gender, first language, and current country.

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.

The authors test the effect of test score comparison on the self-efficacy of adolescents versus adults.