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

The authors test whether coating stainless steel orthopedic screws in silver will promote calcium precipitation to improve orthopedic implant integration into bone.

The authors looked at ways to provide better forecasting on website traffic. They found that deep learning models performed better than statistical models.

The authors evaluated the effect of concentration and variant of N-Acetylcysteine in hydrolyzing mucus.

Due to a critical shortage of donor hearts, researchers are exploring cardiac xenotransplantation—transplanting animal hearts into humans—as a potential solution. This study synthesized nearly two decades of preclinical research to evaluate multiple factors affecting xenograft survival.

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.

In this study the authors looked at the ability of the Discrete Fourier Transform (DFT) to analyze different musical elements. They found that DFT is a powerful method to analyze recorded music.

Microplastic pollution is a pressing environmental issue, particularly in the context of its potential impacts on ecosystems and human health. In this study, we explored the ability of plants, specifically those cultivated for human consumption, to absorb microplastics from their growing medium. We found no evidence of microplastic absorption in both intact and mechanically damaged roots. This outcome suggests that microplastics larger than 10 μm may not be readily absorbed by the root systems of leafy crops such as lettuce (L. sativa).

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.