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Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

Ramprasad et al. | Mar 18, 2020

Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

In this study, the authors test whether providing a larger dataset of glass transition temperatures (Tg) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and Tg is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's Tg, providing valuable improvements to this useful platform.

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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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Effects of Wi-Fi EMF on Drosophila melanogaster

Anand et al. | Jan 29, 2020

Effects of Wi-Fi EMF on <em>Drosophila melanogaster</em>

While increased access to Wi-Fi has been a great advancement, we have a limited understanding if there are any health effects on animals. In this study, Anand and Anand exposed fruit flies (Drosophila melanogaster) to different concentrations of Wi-Fi electromagnetic fields, and observed effects on their reproduction and survivability.

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The Development and Maximization of a Novel Photosynthetic Microbial Fuel Cell Using Rhodospirillum rubrum

Gomez et al. | Mar 02, 2014

The Development and Maximization of a Novel Photosynthetic Microbial Fuel Cell Using <em>Rhodospirillum rubrum</em>

Microbial fuel cells (MFCs) are bio-electrochemical systems that utilize bacteria and are promising forms of alternative energy. Similar to chemical fuel cells, MFCs employ both an anode (accepts electrons) and a cathode (donates electrons), but in these devices the live bacteria donate the electrons necessary for current. In this study, the authors assess the functionality of a photosynthetic MFC that utilizes a purple non-sulfur bacterium. The MFC prototype they constructed was found to function over a range of environmental conditions, suggesting its potential use in industrial models.

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Floating aquatic plants form groups faster through current

May et al. | Oct 16, 2023

Floating aquatic plants form groups faster through current
Image credit: N Band

Here, the authors sought to investigate the effects of water current on the growth of colonies of duckweed, a floating plant that forms colonies in silent ponds, marshes, lakes , and streams in North America. They found that current flow mediates the formation of colonies, disrupting and recreating the colonies which provides the opportunity for reorganizations that were identified as beneficial.

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The effect of viscous drag on damped simple harmonic motion

Michael Powell et al. | Sep 14, 2023

The effect of viscous drag on damped simple harmonic motion

Dynamic viscosity is a quantity that describes the magnitude of a fluid’s internal friction or thickness. Traditionally, scientists measure this quantity by either calculating the terminal velocity of a falling sphere or the time a liquid takes to flow through a capillary tube. However, they have yet to conduct much research on finding this quantity through viscous damped simple harmonic motion. The present study hypothesized that the relationship between the dynamic viscosity and the damping coefficient is positively correlated.

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Utilizing the Magnus effect to produce more downforce than a standard wing

Angiras et al. | Aug 15, 2022

Utilizing the Magnus effect to produce more downforce than a standard wing

Here, seeking a better solution to produce downforce that keeps a vehicle grounded at high speeds than wings which tend to result in degraded car performance due to increased air resistance, the authors considered using the Magnus effect as a replacement. The authors found that a spinning cylinder generated significantly more downforce through the Magnus effect than a standard wing at all wind speeds as simulated through the use of a leaf blower. They suggest that a cylinder could be a potential replacement for a wing when downforce is a priority.

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Expression of Anti-Neurodegeneration Genes in Mutant Caenorhabditis elegans Using CRISPR-Cas9 Improves Behavior Associated With Alzheimer’s Disease

Mishra et al. | Sep 14, 2019

Expression of Anti-Neurodegeneration Genes in Mutant <em>Caenorhabditis elegans</em> Using CRISPR-Cas9 Improves Behavior Associated With Alzheimer’s Disease

Alzheimer's disease is one of the leading causes of death in the United States and is characterized by neurodegeneration. Mishra et al. wanted to understand the role of two transport proteins, LRP1 and AQP4, in the neurodegeneration of Alzheimer's disease. They used a model organism for Alzheimer's disease, the nematode C. elegans, and genetic engineering to look at whether they would see a decrease in neurodegeneration if they increased the amount of these two transport proteins. They found that the best improvements were caused by increased expression of both transport proteins, with smaller improvements when just one of the proteins is overly expressed. Their work has important implications for how we understand neurodegeneration in Alzheimer's disease and what we can do to slow or prevent the progression of the disease.

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