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The authors studied the effect of different substrates on mycelium biocomposites.

This study explored using iron oxide nanoparticles embedded in liposomes and coated with antibacterial chitosan as a potential alternative to antibiotics for treating bacterial meningitis. The nanoparticles were tested for their ability to cross a model blood–brain barrier and kill E. coli, with and without magnetic guidance.

The authors investigated the efficacy of functionalized graphene oxide nanoparticles for turning saltwater to freshwater.

This study used machine learning models to examine which factors most influenced U.S. household energy consumption in 2020 using data from 18,496 households.

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

The authors created a 1D model to diagnose hepatomegaly and cirrhosis via ultrasound of the liver.

The authors explored the effects of the Mount Laurel Doctrine on housing affordability, unemployment rate, and civilian labor force in Burlington County, New Jersey compared to nearby counties.

Microplastic pollution is a pressing environmental issue, particularly in the context of its potential impacts on ecosystems and human health. In this study, we explored the ability of plants, specifically those cultivated for human consumption, to absorb microplastics from their growing medium. We found no evidence of microplastic absorption in both intact and mechanically damaged roots. This outcome suggests that microplastics larger than 10 μm may not be readily absorbed by the root systems of leafy crops such as lettuce (L. sativa).

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.

Here, recognizing the difficulty that visually impaired people may have differentiating United States currency, the authors sought to use artificial intelligence (AI) models to identify US currencies. With a one-stage AI they reported a test accuracy of 89%, finding that multi-level deep learning models did not provide any significant advantage over a single-level AI.