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Temperature and Precipitation Responses to a Stratospheric Aerosol Geoengineering Experiment Using the Community Climate System Model 4

Anderson et al. | Aug 19, 2014

Temperature and Precipitation Responses to a Stratospheric Aerosol Geoengineering Experiment Using the Community Climate System Model 4

We are changing our environment with steadily increasing carbon dioxide emissions, but we might be able to help. The authors here use a computer program called Community Climate System Model 4 to predict the effects of spraying small particles into the atmosphere to reflect away some of the sun's rays. The software predicts that this could reduce the amount of energy the Earth's atmosphere absorbs and may limit but will not completely counteract our carbon dioxide production.

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Testing Different Polymers and Boron Nitride Nanotube Properties in Fabrication of Ion-selective Membranes

Yi et al. | Sep 28, 2020

Testing Different Polymers and Boron Nitride Nanotube Properties in Fabrication of Ion-selective Membranes

One largely untapped source of clean energy is the use of osmotic gradients where freshwater and saltwater are mixed, for example at estuaries. To harness such energy, charge-selective membranes are needed to separate the anions and cations in saltwater, establishing an electric potential like a battery. The objective of this study was twofold: to investigate the creation of the polymer matrix and test the properties of boron nitride nanotubes, as both are essential in the creation of an ion-selective membrane. Out of three polymer samples tested in this study, the mixture known as Soltech 704 showed the best resistance to etching, as well as the highest UV cure rate.

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Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste

Singh et al. | Apr 24, 2023

Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste

About 25% of the food grown never reaches consumers due to spoilage, and 11.5 billion pounds of produce from gardens are wasted every year. Current solutions involve farmers manually looking for and treating diseased crops. These methods of tending crops are neither time-efficient nor feasible. I used a convolutional neural network to identify signs of plant disease on leaves for garden owners and farmers.

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Utilizing a Wastewater-Based Medium for Engineered Saccharomyces cerevisiae for the Biological Production of Fatty Alcohols and Carboxylic Acids to Replace Petrochemicals

Ramesh et al. | Oct 02, 2019

Utilizing a Wastewater-Based Medium for Engineered <em>Saccharomyces cerevisiae</em> for the Biological Production of Fatty Alcohols and Carboxylic Acids to Replace Petrochemicals

Saccharomyces cerevisiae yeast is used to produce bioethanol, an alternative to fossil fuels. In this study, authors take advantage of this well studied yeast by genetically engineering them to increase fatty acid biosynthesis and culturing in a cost-effective wastewater based medium; potentially providing a sustainable alternative to petrochemicals.

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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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