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Exponential regression analysis of the Canadian Zero Emission Vehicle market’s effects on climate emissions in 2030

Ajay et al. | Feb 25, 2023

Exponential regression analysis of the Canadian Zero Emission Vehicle market’s effects on climate emissions in 2030
Image credit: Andrew Roberts

Here, the authors explored how the sale and use of electric vehicles could reduce emissions from the transport industry in Canada. By fitting the sale of total of electric vehicles with an exponential model, the authors predicted the number of electric vehicle sales through 2030 and related that to the average emission for such vehicles. Ultimately, they found that the sale and use of electric vehicles alone would likely not meet the 45% reduction in emissions from the transport industry suggested by the Canadian government

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The Effect of Various Preparation Methods on the Spoilage Rate of Roma Tomatoes (Solanum lycopersicum)

Cataltepe et al. | Feb 22, 2018

The Effect of Various Preparation Methods on the Spoilage Rate of Roma Tomatoes (Solanum lycopersicum)

As levels of food waste continue to rise, it is essential to find improved techniques of prolonging the shelf life of produce. The authors aimed to find a simple, yet effective, method of slowing down spoilage in tomatoes. Linear regression analysis revealed that the tomatoes soaked salt water and not dried displayed the lowest correlation between time and spoilage, confirming that this preparation was the most effective.

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The most efficient position of magnets

Shin et al. | Mar 28, 2024

The most efficient position of magnets
Image credit: immo RENOVATION

Here, the authors investigated the most efficient way to position magnets to hold the most pieces of paper on the surface of a refrigerator. They used a regression model along with an artificial neural network to identify the most efficient positions of four magnets to be at the vertices of a rectangle.

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Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Sharma et al. | Apr 19, 2024

Groundwater prediction using artificial intelligence: Case study for Texas aquifers

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

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Impact of Population Density and Elevation on Tuberculosis Spread and Transmission in Maharashtra, India

Rao et al. | Nov 07, 2021

Impact of Population Density and Elevation on Tuberculosis Spread and Transmission in Maharashtra, India

India accounts for over 2.4 million recorded cases of tuberculosis, about 26% of the world’s cases. This research ascertained the bearing of both the population density and the average elevation above mean sea level (MSL) on the number of cases of TB recorded by the districts in Maharashtra, India. The results found a strong positive correlation between the number of TB cases per thousand people and the population density and a strong negative correlation between the number of TB cases per thousand people and the average elevation above MSL.

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The Effect of Poverty on Mosquito-borne Illness Across the United States

Kar et al. | Feb 25, 2021

The Effect of Poverty on Mosquito-borne Illness Across the United States

Mosquito-borne diseases are a major issue across the world, and the objective for this project was to determine the characteristics that make some communities more susceptible to these diseases than others. The authors identified and studied characteristics that make communities susceptible to mosquito-borne diseases, including water in square miles, average temperature, population, population density, and poverty rates per county. They found that the population of a county is the best indicator of the prevalence of mosquito-borne diseases.

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The Impact of Age on Post-Concussive Symptoms: A Comparative Study of Symptoms Related and Not Related to the Default Mode Network

Wurscher et al. | Mar 05, 2017

The Impact of Age on Post-Concussive Symptoms: A Comparative Study of Symptoms Related and Not Related to the Default Mode Network

The Default Mode Network (DMN) is a network of connected brain regions that are active when the brain is not focused on external tasks. Minor brain injuries, such as concussions, can affect this network and manifest symptoms. In this study, the authors examined correlations between DMN age and post-concussion symptoms in previously concussed individuals and healthy controls.

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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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