![A spatiotemporal analysis of OECD member countries on sugar consumption and labor force participation](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBb3NPIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--8fd988341b02a557ced8be3b8862a00c9a660d45/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJY0c1bkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--33b2b080106a274a4ca568f8742d366d42f20c14/Figure%201.png)
In this article the authors look at sugar consumption and the relationship to productivity in the work/labor force.
Read More...A spatiotemporal analysis of OECD member countries on sugar consumption and labor force participation
In this article the authors look at sugar consumption and the relationship to productivity in the work/labor force.
Read More...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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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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Read More...Artificial Intelligence-Based Smart Solution to Reduce Respiratory Problems Caused by Air Pollution
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Read More...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.
Read More...Comparing the Effects of Different Natural Products on Reducing Tumor Growth in a Drosophila Model
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