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An Analysis on Exoplanets and How They are Affected by Different Factors in Their Star Systems

Selph et al. | Dec 06, 2018

An Analysis on Exoplanets and How They are Affected by Different Factors in Their Star Systems

In this article, the authors systematically study whether the type of a star is correlated with the number of planets it can support. Their study shows that medium-sized stars are likely to support more than one planet, just like the case in our solar system. They predict that, of the hundreds of planets beyond our solar system, 6% might be habitable. As humans work to travel further and further into space, some of those might truly be suited for human life.

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Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires

Bilwar et al. | Jan 15, 2024

Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires
Image credit: Pixabay

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.

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An Analysis of Soil Microhabitats in Revolutionary War, Civil War, and Modern Graveyards on Long Island, NY

Caputo et al. | May 05, 2019

An Analysis of Soil Microhabitats in Revolutionary War, Civil War, and Modern Graveyards on Long Island, NY

Previously established data indicate that cemeteries have contributed to groundwater and soil pollution, as embalming fluids can impact the microbiomes that exist in decomposing remains. In this study, Caputo et al hypothesized that microbial variation would be high between cemeteries from different eras due to dissimilarities between embalming techniques employed, and furthermore, that specific microbes would act as an indication for certain contaminants. Overall, they found that there is a variation in the microbiomes of the different eras’ cemeteries according to the concentrations of the phyla and their more specific taxa.

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Assessing the Efficacy of NOX Enzyme Inhibitors as Potential Treatments for Ischemic Stroke in silico

Vinay et al. | Sep 18, 2020

Assessing the Efficacy of NOX Enzyme Inhibitors as Potential Treatments for Ischemic Stroke <i>in silico</i>

Ischemic stroke occurs when blood flow to the brain is interrupted, causing brain damage. This study investigated the effectiveness of different NOX inhibitors as treatments for ischemic stroke in silico. The results help corroborate previous in vivo and in vitro studies in an in silico format, and can be used towards developing drugs to treat ischemic stroke.

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Artificial intelligence assisted violin performance learning

Zhang et al. | Aug 30, 2023

Artificial intelligence assisted violin performance learning
Image credit: Philip Myrtorp

In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.

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Machine learning on crowd-sourced data to highlight coral disease

Narayan et al. | Jul 26, 2021

Machine learning on crowd-sourced data to highlight coral disease

Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

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