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The escalating crisis of climate change, driven by the accumulation of greenhouse gases from human activities, demands urgent and innovative solutions to curb rising global temperatures. Plasma-based methane (CH₄) and carbon dioxide (CO₂) reforming offers a promising pathway for carbon capture and the sustainable production of hydrogen fuel and syngas components. To advance this technology, particularly in terms of energy efficiency and selectivity, it is essential to enhance the conversion efficiencies of CO₂ and CH₄.

Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.

The authors look at using publicly available data and machine learning to see if they can develop a criminal activity index for counties within the state of California.

The authors looked at the primary structure of lignin peroxidase in an attempt to identify mutations that would improve both the stability and solubility of the peroxidase protein. The goal is to engineer peroxidase enzymes that are stable to help break down polymers, such as PVC, into monomers that can be reused instead of going to landfills.

Your choice of font can impact more than style. Here the authors demonstrate that font choice can affect the amount of ink a given print-out requires. The authors estimate that a switch to Garamond font, size 12, by all teachers in his school district would save almost $21,000 annually.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

Improving the classifier of the difficulty level of Sudoku.

To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.

The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.

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.