Browse Articles

The authors studied the ability of deep learning models to predict droughts in the midwestern United States.

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 study explored converting Gracilaria seaweed waste—known for releasing toxic hydrogen sulfide when decomposed—into biochar as a sustainable solution for waste management and soil improvement.

The authors looked at if there was any correlation between the phase of the moon and admissions for psychiatric concerns.

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

Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.

The authors examined the impact of mutations to CDK2 on protein expression.

Analysis of the Sloan green and red photometry of the Type Ia supernova 2024neh

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

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.