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Analysis of Technology Usage of Teens: Correlating Social Media, Technology Use, Participation in Sports, and Popularity

Düzgezen et al. | Mar 27, 2020

Analysis of Technology Usage of Teens: Correlating Social Media, Technology Use, Participation in Sports, and Popularity

Social media usage is predicted to impact teen well-being and emotional status. This study sought to assess the impact of teen technology usage on their social lives. Surveys of 8th and 9th graders were used to assess compare technology usage between males and females as well as and how social media usage impacts the perception of social environment at school.

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Quantitative analysis and development of alopecia areata classification frameworks

Dubey et al. | Jun 03, 2024

Quantitative analysis and development of alopecia areata classification frameworks

This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.

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Prediction of molecular energy using Coulomb matrix and Graph Neural Network

Hazra et al. | Feb 01, 2022

Prediction of molecular energy using Coulomb matrix and Graph Neural Network

With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.

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The Effect of Wind Mitigation Devices on Gabled Roofs

Kaufman et al. | Feb 20, 2021

The Effect of Wind Mitigation Devices on Gabled Roofs

The purpose of this study was to test devices installed on a gabled roof to see which reduced the actual uplift forces best. Three gabled birdhouse roofs were each modified with different mitigation devices: a rounded edge, a barrier shape, or an airfoil. The barrier edge had no significant effect on the time for the roof to blow off. The addition of airfoil devices on roofs, specifically in areas that are prone to hurricanes such as Florida, could keep roofs in place during hurricanes, thus reducing insurance bills, overall damage costs, and the loss of lives.

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Simulations of Cheetah Roaming Demonstrate the Effect of Safety Corridors on Genetic Diversity and Human-Cheetah Conflict

Acton et al. | Apr 02, 2018

Simulations of Cheetah Roaming Demonstrate the Effect of Safety Corridors on Genetic Diversity and Human-Cheetah Conflict

Ecological corridors are geographic features designated to allow the movement of wildlife populations between habitats that have been fragmented by human landscapes. Corridors can be a pivotal aspect in wildlife conservation because they preserve a suitable habitat for isolated populations to live and intermingle. Here, two students simulate the effect of introducing a safety corridor for cheetahs, based on real tracking data on cheetahs in Namibia.

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A Novel Approach to Prevent and Restrict Early Stages of Cancer Cell Growth Using a Combination of Moringa and Sesame in a Drosophila Model

Ganesh et al. | Sep 28, 2020

A Novel Approach to Prevent and Restrict Early Stages of Cancer Cell Growth Using a Combination of Moringa and Sesame in a <em>Drosophila</em> Model

Sesame (Sesamum indicum) and moringa (Moringa oleifera) have natural antioxidants that could prevent cancer growth. Previously, this group found that sesame and moringa individually suppress eye tumor grown in the Drosophila melanogaster model. In the present study, combinations of sesame and moringa at different concentrations were included in the D. melanogaster diet. The impact on eye tumor development was assessed at different stages of growth.

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