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The authors looked at how soil temperature changes with fire to develop a sensor system that could aid in earlier detection of fires.

The authors looked at non-natural factors that influenced the spread rate of fire ants in multiple cities in China.

The authors looked at the ability to use audio clips to analyze the progression of Parkinson's disease.

The authors computationally screened potential ALDH1 inhibitors, for use as potential cancer therapeutics.

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.

Building on previous work by earlier astronomers, the authors investigate the correlation between the density and size of celestial objects in the universe, including neutron stars, galaxies, and galaxy clusters.

AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.

Here the authors sought to find a more ethical and efficient way to temporary paralyze a cockroach by comparing the results of two methods. By comparing immobilization through immersion in cold water and exposure to a 100 % CO₂ environment, they found that cockroaches could be immobilized and recovered significantly faster when exposed to CO₂.

Here the authors performed a comparative analysis to investigate the viability of using PLAY® instead of fetal bovine serum (FBS) as a growth medium to culture cells with an enzyme-linked immunosorbent assay.

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.