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A land use regression model to predict emissions from oil and gas production using machine learning

Cao et al. | Mar 24, 2023

A land use regression model to predict emissions from oil and gas production using machine learning

Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO2), volatile organic compounds (VOCs), and ozone (O3) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO2, NOx, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.

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An efficient approach to automated geometry diagram parsing

Date et al. | Oct 02, 2022

An efficient approach to automated geometry diagram parsing

Here, beginning from an initial interest in the possibility to use a computer to automatically solve a geometry diagram parser, the authors developed their own Fast Geometry Diagram Parser (FastGDP) that uses clustering and corner information. They compared their own methods to a more widely available, method, GeoSolver, finding their own to be an order of magnitude faster in most cases that they considered.

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Health services in Iraq - A cross-sectional survey of adolescents in Basra

Al Saeedi et al. | Aug 12, 2022

Health services in Iraq - A cross-sectional survey of adolescents in Basra

This study is a cross-sectional survey of adolescents in Basra, Iraq, from November 2020 to March 2021 about types of adolescent problems, the individuals and institutions adolescents turn to, and the role of public health centers in dealing with their problems. The survey found that psychological problems represent the largest proportion of health problems, and most adolescents turn to their parents to discuss their problems. The work indicates that there is an urgent need to pay attention to public health centers and provide health and psychological support to adolescents.

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Artificial Intelligence-Based Smart Solution to Reduce Respiratory Problems Caused by Air Pollution

Bhardwaj et al. | Dec 14, 2021

Artificial Intelligence-Based Smart Solution to Reduce Respiratory Problems Caused by Air Pollution

In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.

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Reducing Crop Damage Caused by Folsomia candida by Providing an Alternate Food Source

Tamura et al. | May 28, 2018

Reducing Crop Damage Caused by Folsomia candida by Providing an Alternate Food Source

Tamura and Moché found that Folsomia candida, a common crop pest, prefers to consume yeast instead of lettuce seedlings. The authors confirmed that even with the availability of both lettuce seedlings and yeast in the same dish, Folsomia candida preferred to eat the yeast, thereby reducing the number of feeding injuries on the lettuce seedlings. The authors propose that using this preference for yeast may be a way to mitigate crop damage by this pest.

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Comparing the Effects of Different Natural Products on Reducing Tumor Growth in a Drosophila Model

Ganesh et al. | May 31, 2020

Comparing the Effects of Different Natural Products on Reducing Tumor Growth in a <i>Drosophila</i> Model

In this work, the authors compared the effects of common natural products, including sesame, cinnamon, garlic, moringa and turmeric on tumor growth in Drosophila eyes. The data showed that these natural products cannot be used to reduce tumor growth once it has completely formed. However, the data suggested that some natural products can reduce cancer cell growth when tumors are treated early.

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