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Determination of Optimal Relevant Joint Angles for Vertical Jump Height Across Teenagers with Differing Amounts of Jumping Experience

Bahng et al. | May 09, 2021

Determination of Optimal Relevant Joint Angles for Vertical Jump Height Across Teenagers with Differing Amounts of Jumping Experience

Reaching one’s maximum jump height requires optimizing one’s jump techniques. In order to find this optimal jump technique, three high school participants with varying vertical jump (VJ) abilities recorded videos of themselves with varying degrees of maximum/minimum shoulder, knee, and hip angles—with or without respect to the horizontal—at the isometric phase of a regular countermovement (CM) VJ or countermovement jump (CMJ). Results showed that the shoulder angle without respect to the horizontal (SA), knee angle with respect to the horizontal (KAH), and the hip angle with respect to the horizontal (HAH) possessed a more consistent correlation with VJ height across the subjects compared to the same respective angles with opposite relations to the horizontal.

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Assaying the Formation of Beneficial Biofilms by Lactic Acid Bacteria and the Effect of Ayurvedic Plant Extracts on Their Enhancement

Rajpal et al. | Oct 12, 2017

Assaying the Formation of Beneficial Biofilms by Lactic Acid Bacteria and the Effect of Ayurvedic Plant Extracts on Their Enhancement

This study aimed to obtain an optimal non-antibiotic method to suppress the growth of pathogenic bacteria within the body. The two-fold purpose of this project was to determine which combination of bacteria would result in the most biofilm formation and then to assess the effect of ayurvedic plant extracts on the biofilm. The results show that the addition of a plant extract can affect the biofilm growth of a bacteria combination. The applications of this study can be used to design probiotic supplements with added beneficial plant extracts.

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Evaluating machine learning algorithms to classify forest tree species through satellite imagery

Gupta et al. | Mar 18, 2023

Evaluating machine learning algorithms to classify forest tree species through satellite imagery
Image credit: Sergei A

Here, seeking to identify an optimal method to classify tree species through remote sensing, the authors used a few machine learning algorithms to classify forest tree species through multispectral satellite imagery. They found the Random Forest algorithm to most accurately classify tree species, with the potential to improve model training and inference based on the inclusion of other tree properties.

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Reinforcement learning in 2-D space with varying gravitational fields

Rousseau et al. | Jun 07, 2025

Reinforcement learning in 2-D space with varying gravitational fields
Image credit: NASA

In this study the authors looked at the ability to navigate planes in space between randomly placed planets. They used machine and reinforcement learning to run simulations and found that they were able to identify optimal paths for travel. In the future these techniques may allow for safer travel in unknown spaces.

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