Browse Articles

The gender gap in STEM at top U.S. Universities: change over time and relationship with ranking

Kruus et al. | Jun 25, 2024

The gender gap in STEM at top U.S. Universities: change over time and relationship with ranking

Authors address the gender disparity in STEM fields, examining changes in gender diversity across male-dominated undergraduate programs over 19 years at 24 top universities. Analyzing data from NCES IPEDS, it identifies STEM as persistently male-dominated but notes increasing gender diversity in many disciplines, particularly in recent years. Results indicate that higher-ranked universities in disciplines like computer science and mechanical engineering show a weak correlation with improved gender diversity, suggesting effective initiatives can mitigate the gender gap in STEM, despite ongoing challenges.

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Effects of caffeine on muscle signals measured with sEMG signals

Park et al. | Jun 20, 2022

Effects of caffeine on muscle signals measured with sEMG signals

Here, the authors used surface electromyography to measure the effects of caffeine intake on the resting activity of muscles. They found a significant increase in the measured amplitude suggesting that caffeine intake increased the number of activated muscle fibers during rest. While previous research has focused on caffeine's effect on the contraction signals of muscles, this research suggests that its effects extend to even when a muscle is at rest.

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Deep learning for pulsar detection: Investigating hyperparameter effects on TensorFlow classification accuracy

Upadhyay et al. | Jan 31, 2026

Deep learning for pulsar detection: Investigating hyperparameter effects on TensorFlow classification accuracy

This study investigates how the hyperparameters epochs and batch size affect the classification accuracy of a convolutional neural network (CNN) trained on pulsar candidate data. Our results reveal that accuracy improves with increasing number of epochs and smaller batch sizes, suggesting that with optimized hyperparameters, high accuracy may be achievable with minimal training. These findings offer insights that could help create more efficient machine learning classification models for pulsar signal detection, with the potential of accelerating pulsar discovery and advancing astrophysical research.

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Analysis of the catalytic efficiency of spent coffee grounds and titanium dioxide using UV-Vis spectroscopy

Jahng et al. | Dec 09, 2025

Analysis of the catalytic efficiency of spent coffee grounds and titanium dioxide using UV-Vis spectroscopy
Image credit: Jahng and Kim

This paper looks at using spent coffee grounds as a partial substitute for titanium dioxide (TiO2) as a catalyst for chemical reactions. Using UV-Vis spectrophotometry, they found that adding the coffee grounds to TiO2 in a 3:1 ratio, there is still meaningful catalytic activity. This offers a cheaper solution than just using pure TiO2.

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