Browse Articles

This study investigates the ability of Gymnema sylvestre tea to acutely suppress sweet taste perception across various food groups. Our findings demonstrate how this natural botanical can reduce sugar cravings, offering a potential complementary strategy for managing dietary intake in type 2 diabetes.

The authors studied optimized delivery routes to reduce travel times using different traveling salesman algorithms.

Recent observations by the “Extremely Metal-Poor Representatives Explored by the Subaru Survey” (EMPRESS) collaboration found normal deuterium levels but unexpectedly low helium-4, challenging current cosmological theories. This study used simulations with the PRyMordial package to test whether dark matter particles interacting with neutrinos in the early universe could explain the discrepancy.

The authors tested the impact of various additives on biopolymer degradation.

The authors looked at what conditions with host stars favor development of exoplanets.

The authors re-examine the galactic kinematics of relic galaxy NGC 1277, recently identified as dark matter deficient, by reproducing its rotation curve with data from the George and Cynthia Mitchell Spectrograph.

The authors looked how different working conditions impacted the microbiome of the human skin.

Toxic particulates in the atmosphere pose significant health risks, and while modern masks can help reduce inhalation of these pollutants, their availability may be limited during health crises. This study evaluated the effectiveness of household fabrics (cotton, fleece, wool, and rayon) as particulate filters, finding that cotton outperformed the others in filtration efficiency, while rayon was the least effective. The findings suggest that cotton is a preferable alternative for filtration purposes, while rayon should be avoided.

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.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.