Browse Articles

The authors investigate how well AI-detection machine learning models can detect real versus AI-generated art across different art styles.

In this study, the authors use aerosol optical depth data to determine if aerosol levels were lower in major metropolitan areas around the world during the COVID-19 pandemic.

In this study, the authors investigate the effects that flagella have on E. coli's ability to adhere to glass surfaces.

The authors looked at the ability of eggshells to slow ice melting. They found that eggshells were able to increase ice melting time when crushed showing that they were an effective thermal barrier.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

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.

This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.

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.

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.

The goal of this project was to see if the addition of wild orange essential oil to freshly squeezed orange juice would help to slow down the decay of ascorbic acid when exposed to various temperatures, allowing vital nutrients to be maintained and providing a natural alternative to the chemical additives in use in industry today. The authors hypothesized that the addition of wild orange essential oil to freshly squeezed orange juice would slow down the rate of oxidation when exposed to various temperatures, reducing ascorbic acid decay. On average, wild orange EO slowed down ascorbic acid decay in freshly squeezed orange juice by 15% at the three highest temperatures tested.