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The authors test electrocardiography biofeedback as a treatment for individuals with Postural Orthostatic Tachycardia Syndrome.

The authors identify disinfectant-resistant bacterial strains of infection-causing bacteria from samples collected at a hospital setting.

Poor air quality is associated with negative effects on human health but can be difficult to measure in an accurate and cost-effective manner. The authors design and test a monitor for measuring indoor air quality using low-cost components.

In this study, the authors address potential reasons why employees may voluntarily resign. This is in response to the currently observed economic trend The Great Resignation. Through analysis of federal and local government data along with survey results from Fairfax County, they concluded that adding additional benefits will help companies retain talented empolyees.

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

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.

In this study, the authors studied the ability for bacteria to develop antibiotic resistance over successive generations and modeled the trajectory to predict how antibiotic resistance is developed.

Pediatric asthma remains a significant health issue for Dallas students. This study examined the relationship between microclimatic parameters, vegetation, and pediatric asthma vulnerability (PAV) in urban schools.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

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