Browse Articles

In this study, the authors use quantitative digit ratio measurements and a survey of personality traits to evaluate the potential relationship between sex and levels of conscientiousness.

This study investigates the presence of alkaloids in a variety of medicinal plants using the Marquis reagent. They reveal some surprising results and how useful the Marquis reagent is.

The authors looked at if there was any correlation between the phase of the moon and admissions for psychiatric concerns.

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 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.