Browse Articles

The authors looked at preferences of donors to donate between different animal-centered or humanitarian charities.

Here the authors introduce pressing filtration as a novel, efficient, and low-energy method for extracting dietary fiber from cabbage, which successfully retains heat-sensitive nutrients and achieves a high fiber yield. The study demonstrates the scalability and economic viability of this technique for commercial use, highlighting that the resulting high-fiber cabbage powder can be incorporated into familiar foods like hamburger buns and beef patties without compromising taste or sensory quality.

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

The authors looked at the ability of berberine in combination with melatonin to have anticancer effects when tested in an in vitro cell model.

Investigation of the largest rigid shape that can be moved through a circular hallway of unit width with an arbitrary turn angle

The authors looked at how students performed on standardized tests when they were taught material via memorization vs. conceptual based approaches.

The authors used Monte Carlo simulations to assess the impacts of various factors on neonatal seizure risk.

Here the authors investigated the use of an affordable gas sensor to detect volatile organic compound (VOC) emissions as an early indicator of tree disease, finding statistically significant differences in VOCs between diseased and non-diseased ash, beech, and maple trees. They suggest this sensor has potential for widespread early disease detection, but call for further research with larger sample sizes and diverse locations.

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

This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.