Browse Articles

Increased survival of aquatic life through algal pretreatment of nitrate-contaminated runoff

Venkat et al. | Sep 12, 2026

Increased survival of aquatic life through algal pretreatment of nitrate-contaminated runoff
Image credit: Liz Harrell

Here the authors investigated the effectiveness of algal pretreatment in reducing nitrate toxicity and extending the survival of fish in contaminated river water. Their findings demonstrate that treating nitrate-polluted water with algae extended fish survival by up to 50%, suggesting that algae could serve as a valuable tool for mitigating the destructive impacts of agricultural and industrial runoff on aquatic ecosystems.

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Fundamental understanding on the dynamic reactions of liquid gallium with aluminum

Li et al. | Sep 11, 2026

Fundamental understanding on the dynamic reactions of liquid gallium with aluminum
Image credit: David Hofmann

This study investigates the dynamic reaction between liquid gallium and aluminum, which poses a challenge when using high-performance gallium-rich thermal interface materials for electronic cooling. Through the usage of in-situ microscopy, we show that while aluminum oxide coatings slow gallium-induced damage, nanometer-thick iridium coatings effectively prevent reaction and surface degradation at device-operating temperatures. These findings highlight a promising thermal-cooling architecture for extending the lifespan and reliability of high-power electronic heat dissipation systems.

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Comparative analysis of player ability, game size, and ideal starting positions in Nim games

Sinha et al. | Aug 02, 2026

Comparative analysis of player ability, game size, and ideal starting positions in Nim games
Image credit: Immo Wegmann

Here the authors investigated the impact of player ability versus starting positions in the game of Nim under imperfect play, hypothesizing that player skill becomes the primary determinant of outcomes as pile sizes grow. Through computational simulations and a mathematical model, they demonstrated that favorable starting positions lose their advantage over time and provide key insights to help improve reinforcement learning algorithms in abstract, complex decision-making environments.

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Implications of various pure tones on Phaseolus vulgaris and Gaultheria shallon

Judit et al. | Jul 13, 2026

Implications of various pure tones on <i>Phaseolus vulgaris</i> and <i>Gaultheria shallon<i>

This study investigated how different sound frequencies (0, 1,000, 5,000, and 15,000 Hz) affect the growth of Phaseolus vulgaris and the transpiration rates of Gaultheria shallon. Although some differences in plant growth were observed, the authors found no consistent evidence that sound frequency enhanced overall growth or transpiration, highlighting the need for further research.

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Measuring effects of caffeine and melatonin on learning trends of Zebrafish juveniles

Wei et al. | Jun 28, 2026

Measuring effects of caffeine and melatonin on learning trends of Zebrafish juveniles

This study investigates how caffeine and melatonin affect learning in adolescent zebrafish, serving as a model for human teens. Using an automated system to track behavior, we found that melatonin slowed learning while caffeine caused erratic, inconsistent responses, suggesting both substances can negatively impact adolescent learning patterns. These findings highlight the need for further research into their physiological effects and potential implications for human adolescents.

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Mitigating skin color bias in dermatology AI using CycleGAN-based data augmentation

Kannan et al. | Jun 24, 2026

Mitigating skin color bias in dermatology AI using CycleGAN-based data augmentation
Image credit: Kannan and Ramasamy

This study investigates skin tone bias in artificial intelligence models used for dermatological disease classification and evaluates a CycleGAN-based data augmentation approach to improve diagnostic performance on darker skin types. We generated synthetic dark-skinned images to enhance dataset diversity and compared model performance before and after augmentation. The results demonstrate that augmentation with synthetic dermatological images can help reduce disparities in diagnostic performance across skin tones, highlighting a practical strategy for improving fairness in dermatology AI systems.

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