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Impact of study partner status and group membership on commitment device effectiveness among college students

Gupta et al. | Jun 03, 2022

Impact of study partner status and group membership on commitment device effectiveness among college students

Here seeking to identify a possible solution to procrastination among college students, the authors used an online experiment that involved the random assignment of study partners that they shared their study time goal with. These partners were classified by status and group membership. The authors found that status and group membership did not significantly affect the likelihood of college students achieving their committed goals, and also suggest the potential of soft commitment devices that take advantage of social relationships to reduce procrastination.

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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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Beeing sustainable: Honey as a bioindicator for pollution

Donnellan et al. | Oct 06, 2021

Beeing sustainable: Honey as a bioindicator for pollution

In this study, Donnellan and colleagues investigated how environmental pollution may be affecting honey samples from Chicago apiaries. They found no significant correlation between heavy metal concentration in honey to distance from local industries, suggesting a minimal effect of proximity to industrial pollution on honey contamination.

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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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Carbonated liquids and carbonation level

Irina et al. | Jan 21, 2024

Carbonated liquids and carbonation level

In our work we followed the formation of gas bubbles on the surface of the vessel walls in different carbonated liquids, over different time intervals, at different temperatures and in vessels made of different materials. Our results made it possible to identify patterns affecting the process of formation and disappearance of carbon dioxide bubbles.

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Differences in Reliability and Predictability of Harvested Energy from Battery-less Intermittently Powered Systems

Sampath et al. | Apr 29, 2020

Differences in Reliability and Predictability of Harvested Energy from Battery-less Intermittently Powered Systems

Solar and radio frequency harvesters serve as a viable alternative energy source to batteries in many cases where the battery cannot be easily replaced. Using specifically designed circuit models, the authors quantify the reliability of different harvested energy sources to identify the most practical and efficient forms of renewable energy.

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