![Determining the Effect of Chemical and Physical Pretreatments on the Yield and Energy Output of Cellulosic Ethanol from Panicum Virgatum](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBJZz09IiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--04578af70b4dc645d600fd27149dad3e4c56e59a/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJY0c1bkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--33b2b080106a274a4ca568f8742d366d42f20c14/pagadala.png)
Fossil fuels are a limited resource; thus, it is important to explore new sources of energy. The authors examine the ability of switchgrass to produce ethanol and test the effects of pretreatment and grinding on ethanol yield.
Read More...Determining the Effect of Chemical and Physical Pretreatments on the Yield and Energy Output of Cellulosic Ethanol from Panicum Virgatum
Fossil fuels are a limited resource; thus, it is important to explore new sources of energy. The authors examine the ability of switchgrass to produce ethanol and test the effects of pretreatment and grinding on ethanol yield.
Read More...Examining the Growth of Methanotrophic Bacteria Immersed in Extremely Low-Frequency Electromagnetic Fields
Scientist are investigating the use of methane-consuming bacteria to aid the growing problem of rising greenhouse gas emissions. While previous studies claim that low-frequency electromagnetic fields can accelerate the growth rate of these bacteria, Chu et al. demonstrate that this fundamental ideology is not on the same wavelength with their data.
Read More...Influence of socioeconomic status on academic performance in virtual classroom settings
In this study, the authors conduct a survey to evaluate the impact of household socioeconomic status on effectiveness of distance learning for students.
Read More...Low environmental pH inhibits phagosome formation and motility of Tetrahymena pyriformis
In this study, the authors look into some of the implications of rising carbon dioxide levels by studying the effects of acidic pH on the ability of T. pyriformis to feed by quantifying phagosome formation and motility.
Read More...Investigating Hydrogen as a Potential Alternative to Kerosene in Fueling Commercial Aircraft
Growing climate concerns have intensified research into zero-emission transportation fuels, notably hydrogen. Hydrogen is considered a clean fuel because its only major by-product is water. This project analyzes how hydrogen compares to kerosene as a commercial aircraft fuel with respect to cost, CO2 emissions, and flight range.
Read More...Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques
Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.
Read More...The knowledge and perception of opioid abuse and its long-term effects among high schoolers
Due to the susceptibility of adolescent age groups to opioid misuse, here the authors sought to determine if there was a difference in the perception and knowledge between 9th and 12th graders regarding the opioid crisis. An educational intervention trial was done with the 9th graders and surveys were used to identify its effects. Although the authors acknowledge a small sample size, their results suggest that their are gaps within the knowledge of adolescents in regards to opioid misuse and its long-term effects that could be addressed with further education.
Read More...A novel encoding technique to improve non-weather-based models for solar photovoltaic forecasting
Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.
Read More...Using machine learning to develop a global coral bleaching predictor
Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.
Read More...Integrated Ocean Cleanup System for Sustainable and Healthy Aquatic Ecosystems
Oil spills are one of the most devastating events for marine life. Finding ways to clean up oil spills without the need for harsh chemicals could help decrease the negative impact of such spills. Here the authors demonstrate that using a combination of several biodegradable substances can effectively adsorb oil in seawater in a laboratory setting. They suggest further exploring the potential of such a combination as a possible alternative to commonly-used non-biodegradable substances in oil spill management.
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