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High-resolution hemodynamic estimation from ultrafast ultrasound image velocimetry using a physics-informed neural network.
Phys Med Biol
January 2025
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of life Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China.
Estimating the high-resolution (HR) blood flow velocity and pressure fields for the diagnosis and treatment of vascular diseases remains challenging.. In this study, a physics-informed neural network (PINN) with a refined mapping capability was combined with ultrafast ultrasound image velocimetry (u-UIV) to predict HR hemodynamic parameters.
View Article and Find Full Text PDFFeasibility and Safety of Distal Radial Artery Access with Recanalization of a Chronic Radial Artery Occlusion for Subsequent Coronary Angiography and Intervention.
Acta Cardiol Sin
January 2025
Department of Internal Medicine, Division of Cardiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Aims: This study aims to verify the feasibility and safety of percutaneous coronary intervention (PCI) after a distal transradial approach (dTRA) with radial artery occlusion (RAO) recanalization.
Methods: Between July 2018 and January 2022, 30 patients underwent PCI following attempted RAO recanalization via dTRA. Among these cases, the target radial arteries could not be recanalized in five patients, necessitating alternative vascular access.
Numerical simulation of coal particle motion characteristics in the envelope region of spiral drum based on discrete element method.
Sci Rep
January 2025
College of Mechanical and Electrical Engineering, Zhoukou Normal University, Zhoukou, 466000, China.
In order to study the movement characteristics of coal particles in the coal loading process of spiral drums, the spiral drum of a certain type of shearer was taken as the research object, and the intrinsic parameters of the materials were calibrated through the determination results of coal sample properties, the relevant parameters of coal particle adhesion were determined, and a discrete element model of spiral drum coal loading was established. The distribution of coal particle movement subsequent to the fracture of the coal wall was derived through simulation. By spatially dividing the envelope region of the spiral drum along the radial and axial directions, the number and velocity distribution of coal particles in different envelope regions were obtained.
View Article and Find Full Text PDFApplying Resin Radial Injection for Manufacturing Fiber-Reinforced Polymer Composite: Advanced Mathematical Modeling and Simulation.
Polymers (Basel)
December 2024
Department of Mechanical Engineering, Federal University of Campina Grande, Campina Grande 58429-900, PB, Brazil.
Recently, the liquid composite molding technique (LCM) has been used for producing fiber-reinforced polymer composites, since it allows the molding of complex parts, presenting good surface finishing and control of the mechanical properties of the product at the end of the process. Studies in this area have been focused on resin transfer molding (RTM), specifically on the resin rectilinear infiltration through the porous preform inserted in the closed cavity neglecting the sorption effect of the polymeric fluid by the reinforcement. Thus, the objective of this work is to predict resin radial flow in porous media (fibrous preform), including the effect of resin sorption by fibers considering a one-dimensional approach.
View Article and Find Full Text PDFEstimation of Central Aortic Pressure Waveforms by Combination of a Meta-Learning Neural Network and a Physics-Driven Method.
Int J Numer Method Biomed Eng
January 2025
College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular system diseases. But the accuracy and practicality of current estimation methods need to be improved. Our study combines a meta-learning neural network and a physics-driven method to accurately estimate CAPW based on personalized physiological indicators.
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