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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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A Juxtaposition of Airborne Microplastics and Fiber Contamination in Various Environments

Truong-Phan et al. | Dec 04, 2020

A Juxtaposition of Airborne Microplastics and Fiber Contamination in Various Environments

Microplastics can have detrimental effects on various wildlife, as well as pollute aquatic and atmospheric environments. This study focused on air samples collected from five locations to investigate microplastic concentrations in atmospheric fallout from indoor and outdoor settings, through a process utilizing a hand-held vacuum pump and a rotameter. The authors found that the difference between the average number of microplastic fragments and fibers collected from all locations was not large enough to be statistically significant. The results collected in this study will contribute to knowledge of the prevalence of airborne microplastics.

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Antibacterial properties of household spices and toothpaste against oral bacteria

Toliver et al. | Apr 24, 2023

Antibacterial properties of household spices and toothpaste against oral bacteria

Bacteria cause tooth decay, plaque, bad breath, and other diseases. Despite being cleaned with water and toothpaste, oral bacteria live on our toothbrushes. Bacterial growth has been shown to be inhibited by different toothpastes and common household spices. This study tested how different toothpastes and common household spices, including cinnamon, cumin, nutmeg, and ground white pepper, can inhibit bacteria from growing on toothbrushes

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Discovery of the Heart in Mathematics: Modeling the Chaotic Behaviors of Quantized Periods in the Mandelbrot Set

Golla et al. | Dec 14, 2020

Discovery of the Heart in Mathematics: Modeling the Chaotic Behaviors of Quantized Periods in the Mandelbrot Set

This study aimed to predict and explain chaotic behavior in the Mandelbrot Set, one of the world’s most popular models of fractals and exhibitors of Chaos Theory. The authors hypothesized that repeatedly iterating the Mandelbrot Set’s characteristic function would give rise to a more intricate layout of the fractal and elliptical models that predict and highlight “hotspots” of chaos through their overlaps. The positive and negative results from this study may provide a new perspective on fractals and their chaotic nature, helping to solve problems involving chaotic phenomena.

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The Prevalence of Brain-Eating Roundworm Baylisascaris procyonis in Merrick County, Nebraska

Reeves et al. | Sep 20, 2018

The Prevalence of Brain-Eating Roundworm <i>Baylisascaris procyonis</i> in Merrick County, Nebraska

The authors investigated an important parasite-host relationship between the raccoon roundworm and the raccoon to understand how parasite prevalence is affected by location. They found that the parasite infection was more prevalent in raccoons found closer to human dwellings, though the number of roundworm eggs was not significantly different. These results are important human health, since roundworm infection is lethal to humans and can be transmitted from raccoons to humans - the authors suggest that more research into this parasite and awareness of its prevalence is needed to prevent disease.

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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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Recognition of animal body parts via supervised learning

Kreiman et al. | Oct 28, 2023

Recognition of animal body parts via supervised learning
Image credit: Kreiman et al. 2023

The application of machine learning techniques has facilitated the automatic annotation of behavior in video sequences, offering a promising approach for ethological studies by reducing the manual effort required for annotating each video frame. Nevertheless, before solely relying on machine-generated annotations, it is essential to evaluate the accuracy of these annotations to ensure their reliability and applicability. While it is conventionally accepted that there cannot be a perfect annotation, the degree of error associated with machine-generated annotations should be commensurate with the error between different human annotators. We hypothesized that machine learning supervised with adequate human annotations would be able to accurately predict body parts from video sequences. Here, we conducted a comparative analysis of the quality of annotations generated by humans and machines for the body parts of sheep during treadmill walking. For human annotation, two annotators manually labeled six body parts of sheep in 300 frames. To generate machine annotations, we employed the state-of-the-art pose-estimating library, DeepLabCut, which was trained using the frames annotated by human annotators. As expected, the human annotations demonstrated high consistency between annotators. Notably, the machine learning algorithm also generated accurate predictions, with errors comparable to those between humans. We also observed that abnormal annotations with a high error could be revised by introducing Kalman Filtering, which interpolates the trajectory of body parts over the time series, enhancing robustness. Our results suggest that conventional transfer learning methods can generate behavior annotations as accurate as those made by humans, presenting great potential for further research.

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The journey to Proxima Centauri b

Ramaswamy et al. | Apr 01, 2024

The journey to Proxima Centauri b
Image credit: The authors

Someday, rockets from Earth may be launched towards worlds beyond our solar system. But will these rockets be able to reach their destination within a human lifetime? Ramaswamy and Giovinazzi simulate rocket launches to an Earth-like exoplanet to uncover whether it's physically possible to complete the journey within a lifetime.

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