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Correlating inlet gas composition to conversion efficiency in plasma-assisted landfill gas reforming

Kim et al. | Jun 28, 2025

Correlating inlet gas composition to conversion efficiency in plasma-assisted landfill gas reforming

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.

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Determining viability of image processing models for forensic analysis of hair for related individuals

Wang et al. | Feb 04, 2025

Determining viability of image processing models for forensic analysis of hair for related individuals
Image credit: Taylor Smith

Here, the authors used machine learning to analyze microscopic images of hair, quantifying various features to distinguish individuals, even within families where traditional DNA analysis is limited. The Discriminant Analysis (DA) model achieved the highest accuracy (88.89%) in identifying individuals, demonstrating its potential to improve the reliability of hair evidence in forensic investigations.

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Using broad health-related survey questions to predict the presence of coronary heart disease

Chavda et al. | Aug 23, 2024

Using broad health-related survey questions to predict the presence of coronary heart disease

Coronary heart disease (CHD) is the leading cause of death in the U.S., responsible for nearly 700,000 deaths in 2021, and is marked by artery clogging that can lead to heart attacks. Traditional prediction methods require expensive clinical tests, but a new study explores using machine learning on demographic, clinical, and behavioral survey data to predict CHD.

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