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Development of a pH-sensing hydrogel wound dressing for early infection monitoring for all skin color types

L. Chong et al. | Sep 07, 2026

Development of a pH-sensing hydrogel wound dressing for early infection monitoring for all skin color types

This paper develops a gelatin-based hydrogel dressing infused with bromothymol blue dye that visibly changes from yellow to blue as wound pH rises from 5.0 to 8.0, signaling possible infection. The color shift was visually detectable and statistically significant across all six Fitzpatrick skin tones, suggesting a low-cost ($0.40/dressing) tool for early infection monitoring in settings with limited access to care.

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Understanding the impossibility of machine learning fairness with data examples

Pillutla et al. | Sep 04, 2026

Understanding the impossibility of machine learning fairness with data examples

Machine learning systems are often expected to make fair decisions, yet the most widely used fairness criteria—independence, separation, and sufficiency—cannot generally be satisfied at the same time. In this study, students tested these criteria using a logistic regression model on a real-world student performance dataset and found that each criterion was met only at different prediction thresholds, with no threshold satisfying all three simultaneously. These results illustrate the inherent trade-offs in algorithmic fairness and highlight why achieving perfectly fair machine learning models is often impossible in practice.

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Early detection of student burnout using data science: a study of behavioral and psychological indicators

Baber et al. | Jul 13, 2026

Early detection of student burnout using data science: a study of behavioral and psychological indicators

This study examined behavioral and psychological predictors of burnout among high school and university students in Pakistan using survey data and machine-learning models. Shorter sleep and greater mental fatigue—especially fatigue—were associated with higher burnout risk, while a Random Forest model successfully identified students at risk of burnout.

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Leveraging transfer learning with convolutional neural networks for cardiovascular disease detection

Chen et al. | May 25, 2026

Leveraging transfer learning with convolutional neural networks for cardiovascular disease detection
Image credit: Stephen Andrews

This study shows the efficacy of leveraging transfer learning, specifically from residual networks, to detect CVDs and possible signs of CVDs. The findings indicate that leveraging transfer learning from residual networks alongside medical professionals is a highly promising approach for CVD detection and diagnosis, warranting further investigation.

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Eye color, visual acuity and photophobia: How eye color affects light sensitivity

Spencer et al. | May 20, 2026

Eye color, visual acuity and photophobia: How eye color affects light sensitivity

This study examined whether eye color affects photophobia and vision in elementary school students and staff, finding no significant relationship between eye color, light sensitivity, or visual acuity. However, photophobia was common across age groups, highlighting the need for greater awareness of light sensitivity in learning environments.

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