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The effect of adverse childhood experiences on e-cigarette usage in people aged 18–30 in the US

Bloomer et al. | Oct 06, 2022

The effect of adverse childhood experiences on e-cigarette usage in people aged 18–30 in the US

Recently, e-cigarette usage has been increasing rapidly. Previous research has found that adverse
childhood experiences (ACEs) are correlated to cigarette usage. However, there is limited data exploring if ACEs affect vaping. Therefore, in this work, we investigated the effects of ACEs on e-cigarette usage and hypothesize that witnessing vaping in the house and facing ACEs would increase e-cigarette usage while education on the dangers of vaping would decrease e-cigarette usage. We found that different types of ACEs had different correlations with e-cigarette usage and that education on the dangers of vaping had no effect on e-cigarette usage.

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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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The Analysis of the Effects of Smoke and Water Vapor on Insect Pheromone Communication and Physical Condition: An Investigation of the Causes of Colony Collapse Disorder

Delatorre et al. | May 20, 2015

The Analysis of the Effects of Smoke and Water Vapor on Insect Pheromone Communication and Physical Condition: An Investigation of the Causes of Colony Collapse Disorder

The cause of insect colony collapse disorder (CCD) is still a mystery. In this study, the authors aimed to test the effects of two environmental factors, water vapor and smoke levels, on the social behavior and physical condition of insects. Their findings could help shed light on how changing environmental factors can contribute to CCD.

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Use of yogurt bacteria as a model surrogate to compare household cleaning solutions

Shukla et al. | May 07, 2023

Use of yogurt bacteria as a model surrogate to compare household cleaning solutions
Image credit: CDC

While resources on the safety of household cleaning products are plentiful, measures of efficacy of these cleaning chemicals against bacteria and viruses remain without standardization in the consumer market. The COVID pandemic has exasperated this knowledge gap, stoking the growth of misinformation and misuse surrounding household cleaning chemicals. Arriving at a time dire for sanitization standardization, the authors of this paper have created a quantifying framework for consumers by comparing a wide range of household cleaning products in their efficacy against bacteria generated by a safe and easily replicable yogurt model.

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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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Development of a novel machine learning platform to identify structural trends among NNRTI HIV-1 reverse transcriptase inhibitors

Ashok et al. | Jun 24, 2022

Development of a novel machine learning platform to identify structural trends among NNRTI HIV-1 reverse transcriptase inhibitors

With advancements in machine learning a large data scale, high throughput virtual screening has become a more attractive method for screening drug candidates. This study compared the accuracy of molecular descriptors from two cheminformatics Mordred and PaDEL, software libraries, in characterizing the chemo-structural composition of 53 compounds from the non-nucleoside reverse transcriptase inhibitors (NNRTI) class. The classification model built with the filtered set of descriptors from Mordred was superior to the model using PaDEL descriptors. This approach can accelerate the identification of hit compounds and improve the efficiency of the drug discovery pipeline.

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