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The Effect of Music on Heart Rate

Agrawal et al. | Apr 25, 2013

The Effect of Music on Heart Rate

Different songs can seem to evoke different emotions. Here the authors demonstrate that different songs can have a significant effect on the heart rate of listeners. A slower song slows heart rate, and a faster song increases it.

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Music's Effect on Dogs' Heart Rates

Aubin et al. | Oct 03, 2017

Music's Effect on Dogs' Heart Rates

Music can affect the behavior of humans and other animals. In this study, the authors studied five types of music with different tempos and demonstrated how each one affected dogs' heart rates.

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Nitric Oxide Synthesis/Pathway Inhibitors in Daphnia magna Reverse Alcohol-Induced Heart Rate Decrease

Gunturi et al. | Sep 17, 2019

Nitric Oxide Synthesis/Pathway Inhibitors in Daphnia magna Reverse Alcohol-Induced Heart Rate Decrease

Chronic alcohol consumption can cause cardiac myopathy, which afflicts about 500,000 Americans annually. Gunturi et al. wanted to understand the effects of alcohol on heart rate and confirm the role of nitric oxide (NO) signaling in heart rate regulation. Using the model organism Daphnia magna, a water crustacean with a large, transparent heart, they found that the heart rate of Daphnia magna was reduced after treatment with alcohol. This depression could be reversed after treatment with inhibitors of NO synthesis and signaling. Their work has important implications for how we understand alcohol-induced effects on heart rate and potential treatments to reverse heart rate depression as a result of alcohol consumption.

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Predicting college retention rates from Google Street View images of campuses

Dileep et al. | Jan 02, 2024

Predicting college retention rates from Google Street View images of campuses
Image credit: Dileep et al. 2024

Every year, around 40% of undergraduate students in the United States discontinue their studies, resulting in a loss of valuable education for students and a loss of money for colleges. Even so, colleges across the nation struggle to discover the underlying causes of these high dropout rates. In this paper, the authors discuss the use of machine learning to find correlations between the built environment factors and the retention rates of colleges. They hypothesized that one way for colleges to improve their retention rates could be to improve the physical characteristics of their campus to be more pleasing. The authors used image classification techniques to look at images of colleges and correlate certain features like colors, cars, and people to higher or lower retention rates. With three possible options of high, medium, and low retention rates, the probability that their models reached the right conclusion if they simply chose randomly was 33%. After finding that this 33%, or 0.33 mark, always fell outside of the 99% confidence intervals built around their models’ accuracies, the authors concluded that their machine learning techniques can be used to find correlations between certain environmental factors and retention rates.

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