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A Taste of Sweetness in Bioplastics

Tsai et al. | Apr 05, 2019

A Taste of Sweetness in Bioplastics

Sweet potatoes are one of the most common starches in Taiwan, and sweet potato peels hold significant potential to make biodegradable plastics which can alleviate the environmental impact of conventional petroleum-based plastics. In this paper, Tsai et al created starch-based bioplastics derived from sweet potato peels and manipulated the amount of added glycerol to alter the plastic’s strength and flexibility properties. Their results indicated that higher concentrations of glycerol yield more malleable plastics, providing insights into how recycled agricultural waste material might be used to slow down the rate of pollution caused by widespread production of conventional plastics.

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How does light affect the distribution of Euglena sp. and Tetrahymena pyriformis

Singh et al. | Mar 03, 2022

How does light affect the distribution of <em>Euglena sp.</em> and <em>Tetrahymena pyriformis</em>

In this article, the authors explored the locomotory movement of Euglena sp. and Tetrahymena pyriformis in response to light. Such research bears relevance to the migration and distribution patterns of both T. pyriformis and Euglena as they differ in their method of finding sustenance in their native environments. With little previous research done on the exploration of a potential response to photostimulation enacted by T. pyriformis, the authors found that T. pyriformis do not bias in distribution towards areas of light - unlike Euglena, which displayed an increased prevalence in areas of light.

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Developing a Portable, Reusable, and Inexpensive Magnesium-Air Fuel Cell

Tota et al. | Mar 28, 2019

Developing a Portable, Reusable, and Inexpensive Magnesium-Air Fuel Cell

One of the greatest challenges we face today is the sustainable production, storage, and distribution of electrical power. One emerging technology with great promise in this area is that of metal-air fuel cells—a long-term and reusable electricity storage system made from a reactive metal anode and a saline solution. In this study the authors tested several different types of metal to determine which was the most suitable for this application. They found that a fuel cell with a magnesium anode was superior to fuel cells made from aluminum or zinc, producing a voltage and current sufficient for real-world applications such as charging a mobile phone.

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An explainable model for content moderation

Cao et al. | Aug 16, 2023

An explainable model for content moderation

The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.

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Characterization of antibacterial properties of common spices

Gehad et al. | Oct 03, 2020

Characterization of antibacterial properties of common spices

Bacterial infection is resurging as one of the most dangerous challenges facing the medical establishment. Americans spend about 55 to 70 billion dollars per year on antibiotics, yet these antibiotics are becoming increasingly ineffective as illness-causing bacteria gain resistance to the prescribed drugs. We tested if 11 commonly-used spices could inhibit growth of the gram-negative bacteria, E. coli, the main takeaway from these experiments is that certain spices and herbs have antibacterial effects that inhibit growth of E.coli , and these spices could show similarly promising activity towards other bacteria.

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Development of Two New Efficient Means of Wastewater Treatment

Bao et al. | Feb 06, 2014

Development of Two New Efficient Means of Wastewater Treatment

The water we use must be treated and cleaned before we release it back into the environment. Here, the authors investigate two new techniques for purifying dissolved impurities from waste water. Their findings may give rise to more cheaper and more efficient water treatment and help keep the planet greener.

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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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