Publications by authors named "A Mostafavi"
Background And Objectives: This study evaluates the feasibility of virtual reality (VR) wayfinding training with aging adults and assesses the impact of the training on wayfinding performance.
Research Design And Methods: 49 participants were recruited using a convenience sample approach. Wayfinding tasks were conducted by 3 participant groups: active VR training, passive video training, and no training, assigned randomly.
The investigation of the relationship between vitamin D level and severity of diabetic ketoacidosis in new cases of type 1 diabetes in children referred to Hazrat-E- Ali-Asghar Hospital in 2021.
J Family Med Prim Care
November 2024
Background And Purpose: According to the role of 25-hydroxyvitamin D (25OHD) in glucose homeostasis and immune modulation, vitamin D deficiency may be associated with type 1 diabetes and diabetic ketoacidosis (DKA). Therefore, this study was conducted with the aim of investigation of the relationship between the vitamin D level and severity of diabetic ketoacidosis in new cases of type 1 diabetes in children referred to Hazrat-E-Ali-Asghar Hospital in 2021.
Methods: The present study is based on a cross-sectional study.
Background: Percutaneous coronary intervention (PCI) is an effective treatment for coronary artery disease. Previous studies have demonstrated the delayed effects of PCI on left ventricular diastolic and systolic function. However, the early impact on these parameters has not been systematically examined.
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- The Fraunhofer wind turbine dataset includes monitoring data from a 750 W wind turbine, capturing various metrics like vibrations, rotational velocity, and environmental conditions using accelerometers and tachometers.
- It explores different damage scenarios, such as mass and aerodynamic imbalances, as well as bearing damage, making the dataset valuable for machine learning and condition monitoring applications.
- Collected under real-world conditions, the dataset accounts for factors like rotor speed variability and environmental noise, which enhances its utility for uncertainty quantification and signal pre-processing tasks.
Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average of 0.
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