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Researching the research enthusiasts: examining their motivation and the impact of a successful role model

Jubair et al. | Sep 18, 2024

Researching the research enthusiasts: examining their motivation and the impact of a successful role model
Image credit: The authors

High school and university students have various motivations for participating in research, ranging from strengthening their applications for university to building skills for a research career. Jubair and Islam survey Bangladeshi high school and university students to uncover their motivations and inspirations for participating in research.

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Effects of urban traffic noise on the early growth and transcription of Arabidopsis thaliana

Kim et al. | Sep 18, 2024

Effects of urban traffic noise on the early growth and transcription of <i>Arabidopsis thaliana<i>

This article explores the largely unstudied impact of noise pollution on plant life. By exposing Arabidopsis thaliana seedlings to urban traffic noise, the study found a significant increase in seedling growth, alongside substantial changes in gene expression. This research reveals critical insights into how noise pollution affects plant physiology and contributes to a broader understanding of its ecological impacts, helping to guide future efforts in ecosystem conservation.

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A comparative study of dynamic scoring formulas for capture-the-flag competitions

Ho et al. | Aug 30, 2024

A comparative study of dynamic scoring formulas for capture-the-flag competitions

The use of gamification in cybersecurity education, particularly through capture-the-flag competitions, involves scoring challenges based on their difficulty and the number of teams that solve them. The study investigated how changing the scoring formulas affects competition outcomes, predicting that different formulas would alter score distributions.

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Optical anisotropy of crystallized vanillin thin film: the science behind the art

Wang et al. | Jul 09, 2024

Optical anisotropy of crystallized vanillin thin film: the science behind the art
Image credit: The authors

Microscopic beauty is hiding in common kitchen ingredients - even vanillin flavoring can be turned into mesmerizing artwork by crystallizing the vanillin and examining it under a polarizing microscope. Wang and Pang explore this hidden beauty by determining the optimal conditions to grow crystalline vanillin films and by creating computer simulations of chemical interactions between vanillin molecules.

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Public Perception of the Effects of Artificial Sweeteners on Diabetes Based on YouTube Comments

Kim et al. | Jun 25, 2024

Public Perception of the Effects of Artificial Sweeteners on Diabetes Based on YouTube Comments
Image credit: The authors

Artificial sweeteners are rising in popularity, in part due to the influence of social media platforms like YouTube. However, YouTube commenters often repeat information about artificial sweeteners that is not supported by scientific research. To investigate how misinformation about sweeteners spreads through social media, Kim and Yoo conduct a content analysis of YouTube comments to reveal how many comments repeat misinformation about artificial sweeteners' effects.

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Enhancing marine debris identification with convolutional neural networks

Wahlig et al. | Apr 03, 2024

Enhancing marine debris identification with convolutional neural networks
Image credit: The authors

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

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Determining degree of dissociation through conductivity

Dunn et al. | Jan 26, 2024

Determining degree of dissociation through conductivity

The authors looked at how molarity impacts the degree to which ionic compounds dissociate in solution. They found that lower molarities led to decreased conductivity of solutions in a manner that did not follow the theoretical predictions.

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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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