Browse Articles

The cosmic microwave background (CMB) is faint electromagnetic radiation left over from early stages in the formation of the universe. In order to analyze the CMB, scientists need to remove from electromagnetic data foreground radiation that contaminates CMB datasets. In this study, students utilize extensive updated datasets to analyze the correlation between CMB maps and Faraday RM and WMAP sky maps.

The authors explore hearing students' misconceptions about the Deaf and Hard of Hearing (HoH) community. Results indicate that some misconceptions are more common than others, and that personal experience with individuals in the Deaf and HoH community reduces the frequency of such misconceptions.

Coronary heart disease (CHD) is a global disease that causes fatal buildup of plaque in the arteries. Currently stents are placed in the artery for many patients with CHD to support proper blood flow. Here, the authors build a system to explore how the shape of the stent affects blood flow rate, a finding that can help optimize stents for patients.

Numerous organisms, including the marine bacterium Aliivibrio fischeri, produce light. This bioluminescence is involved in many important symbioses and may one day be an important source of light for humans. In this study, the authors investigated ways to increase bioluminescence production from the model organism E. coli.

The authors use machine learning to analyze electroencephalogram data and identify slowing patterns that can indicate undetected disorders like epilepsy or dementia

Droughts kill over 45,000 people yearly and affect the livelihoods of 55 million others worldwide, with climate change likely to worsen these effects. However, unlike other natural disasters (hurricanes, etc.), there is no early detection system that can predict droughts far enough in advance to be useful. Bora, Caulkins, and Joycutty tackle this issue by creating a drought prediction model.

Athletes with performance anxiety may struggle to play their best and enjoy the game. Various factors may impact how much anxiety an athlete feels, including how much experience they have in the sport. Concha-Ortiz and Navins survey teenage club volleyball players to look for relationships between years of experience and performance anxiety symptoms.

The authors survey how emotional responses to music differ across cultures and the impact of hearing loss on these emotional responses.

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.

Many common respiratory illnesses like bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) lead to bronchial inflammation and, subsequently, a blockage. However, there are many difficulties in measuring the severity of the blockage. A numeric metric to determine the degree of the blockage severity is necessary. To tackle this demand, we aimed to develop a novel human respiratory model and design a deep-learning program that can constantly monitor and report bronchial blockage by recording breath sounds in a non-intrusive way.