Browse Articles

The authors investigate whether air quality impacts solar production.

The authors looked at egg laying rates in chickens at two different small farms. They found when the chickens were provided homemade, colorful toys in their environment that their egg production rates increased.

Wind turbines are a valuable source of renewable energy, but face challenges related to unpredictable wind speed. The turbine must be able to control its angle to catch enough wind to generate electricity, while avoiding excess wind that may damage the turbine. Zhou and Wang explore different types of smart turbine controllers to see which appears optimal for electricity generation.

Hydrogels are commonly used in medicine, pharmaceuticals, and agriculture. Hydrogels absorb water by swelling and re-release this water by diffusion. This study sought to synthesize a biodegradable, cellulose-based hydrogel that is more effective at absorbing and re-releasing water than those produced by current methods. We tested the compressive strength of both the dry and swollen gels and the tensile strength of the swollen gels to elucidate the gel structure.

This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.

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.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

The authors looked at different models of semantic segmentation to determine which may be best used in the future for segmentation of CT scans to help diagnose certain conditions.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

The authors measure changes in the viscosity of glycerol with increasing temperature using the falling ball approach.