Browse Articles

Effect of sub-layer water saturation on two-layer sand fluidization

Doan et al. | Oct 27, 2026

Effect of sub-layer water saturation on two-layer sand fluidization
Image credit: Doan, Damm, and Doan

This study investigates the effect of water saturation in a two-layer fluidization behavior of an overlying dry sand bed. Building on prior work showing that moisture inhibits fluidization in granular media, the authors address an underexplored configuration involving layered dry and wet sand. The experiments demonstrate that even minimal water saturation in the sub-layer prevents uniform fluidization of the dry upper layer, leading to the conclusion that two-layer fluidized beds with moisture are not viable under the conditions tested.

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Investigating the effects of PVC microplastics on the growth of Chlorella vulgaris

Beaubien et al. | Oct 13, 2026

Investigating the effects of PVC microplastics on the growth of <i>Chlorella vulgaris</i>

This study examined the effect of varying PVC microplastic concentrations on the growth of Chlorella vulgaris in freshwater, utilizing six PVC levels and identical culture conditions. Cell counts revealed that higher PVC concentrations consistently reduced algal growth, and a Spearman correlation test detected a significant negative monotonic association between PVC concentration and viable cell density. These results support the hypothesis that PVC microplastics impair algal growth, underscoring their ecological risk in freshwater environments.

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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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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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Examining the impact of consecutive losses on gambling: When do we decide to quit?

Kim et al. | Apr 28, 2026

Examining the impact of consecutive losses on gambling: When do we decide to quit?
Image credit: Kim, Cragun, and Kim

This article explored the question of when do people decide to stop gambling and further tries to extrapolate why people stop gambling at that point. Their study showed that people tend to quit gambling after 4 consecutive losses, significantly more than 1-3 consecutive losses or a win previous to quitting. They also found that participants commonly quit at a point value approximately 5 points greater than or less than their starting balance. The authors concluded that these results may be important in understanding how to cut down on excessive gambling or in creating policies that make it easier for people to disengage from gambling.

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Assessing machine learning model efficacy for brain tumor MRI classification: a multi-model approach

Dhingra et al. | Mar 14, 2026

Assessing machine learning model efficacy for brain tumor MRI classification: a multi-model approach
Image credit: Dhingra and Dhingra

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

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Feature extraction from peak detection algorithms for enhanced EMG-based hand gesture recognition models

Nathan et al. | Jan 10, 2026

Feature extraction from peak detection algorithms for enhanced EMG-based hand gesture recognition models
Image credit: Nathan and Raju

This manuscript evaluates peak detection algorithms for feature extraction in EMG-based hand gesture recognition using a random forest classifier. The study demonstrates that wavelet-based peak detection features achieve the highest classification accuracy (96.5%), outperforming other methods. The results highlight the potential of peak features to improve EMG-based prosthetic control systems.

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