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Investigating the functionality of an Eco-Cooler made of plastic bottles

Tong et al. | Jul 22, 2026

Investigating the functionality of an Eco-Cooler made of plastic bottles
Image credit: Tong, Tong, and Migenes

In the study, the authors looked at an environmentally friendly alternative to A/C systems/cooling. They wanted to test the effectiveness of this cooling system called the Eco-Cooler as well as see if they could improve it. They found that the Eco-Cooler had minimal cooling, while their altered Eco-Cooler system had some cooling. This results shows the use of water helps cool the environment a lot more.

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A model for angle evolution in conical piles formed using the fixed funnel method

Capaldi et al. | Jul 20, 2026

A model for angle evolution in conical piles formed using the fixed funnel method

When granular materials are poured onto a surface, they form conical piles whose slopes increase before reaching a stable angle of repose. We found that this angle evolution follows a previously unrecognized two-phase exponential growth pattern that is conserved across granular materials with diverse particle properties. The parameters of this model correlate with particle friction and are influenced by deposition conditions, providing a quantitative framework for describing pile formation.

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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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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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