Browse Articles

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

This study investigates PFAS contamination in Santa Clarita groundwater, focusing on potential sources. The study employs statistical analysis to assess data quality and trends which allowed them to identified domestic waste, fire extinguisher materials, and food packaging as the most likely sources of contamination.

The authors demonstrate computationally that two types of nanoplastics can bind to estrogen receptor beta and potentially disrupt endocrine systems.

The authors looked at non-natural factors that influenced the spread rate of fire ants in multiple cities in China.

Black goji berries have attracted interest for their high levels of anthocyanin pigment, which believed to have health-boosting effects. Yu and Zhu research a method for measuring goji berry quality by detecting anthocyanin content under different conditions.

Air pollution is thought to increase the prevalence of health conditions like chronic obstructive pulmonary disease (COPD). Ganeshwaran and Ropiak investigate this relationship by determining whether there is a correlation between between one type of air pollution (fine particulate matter concentrations) and COPD hospitalization rates in Massachusetts.

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 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.