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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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Unit-price anchoring affects consumer purchasing behavior

James et al. | Jan 15, 2025

Unit-price anchoring affects consumer purchasing behavior

This study examines how anchoring—providing numerical suggestions like "2 for $4"—can influence consumer purchasing decisions and increase revenue. The researchers tested three types of price anchors on 29 high school students shopping in a mock store.

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Genetic Bioaugmentation of Oryza sativa to Facilitate Self-Detoxification of Arsenic In-Situ

Bhat et al. | Dec 03, 2024

Genetic Bioaugmentation of Oryza sativa to Facilitate Self-Detoxification of Arsenic In-Situ

Arsenic contamination in rice, caused by the use of arsenic-laden groundwater for irrigation, is a growing global concern, affecting over 150 million people. To address this, researchers hypothesized that genetically modifying rice plants with arsenic-resistant genes could reduce arsenic uptake and allow the plants to detoxify arsenic, making them safer to consume.

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The use of computer vision to differentiate valley fever from lung cancer via CT scans of nodules

El Kereamy et al. | Nov 12, 2024

The use of computer vision to differentiate valley fever from lung cancer via CT scans of nodules

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.

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