Thirty-one central neural blockade simulators have been implemented into clinical practice over the last thirty years either commercially or for research. This review aims to provide a detailed evaluation of why we need epidural and spinal simulators in the first instance and then draws comparisons between computer-based and manikin-based simulators. This review covers thirty-one simulators in total; sixteen of which are solely epidural simulators, nine are for epidural plus spinal or lumbar puncture simulation, and six, which are solely lumbar puncture simulators. All hardware and software components of simulators are discussed, including actuators, sensors, graphics, haptics, and virtual reality based simulators. The purpose of this comparative review is to identify the direction for future epidural simulation by outlining necessary improvements to create the ideal epidural simulator. The weaknesses of existing simulators are discussed and their strengths identified so that these can be carried forward. This review aims to provide a foundation for the future creation of advanced simulators to enhance the training of epiduralists, enabling them to comprehensively practice epidural insertion in vitro before training on patients and ultimately reducing the potential risk of harm.
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http://dx.doi.org/10.1016/j.medengphy.2013.03.003 | DOI Listing |
JMIR Serious Games
January 2025
School of Computing, Engineering and Mathematical Sciences, Optus Chair Digital Health, La Trobe University, Melbourne, Australia.
Background: This review explores virtual reality (VR) and exercise simulator-based interventions for individuals with attention-deficit/hyperactivity disorder (ADHD). Past research indicates that both VR and simulator-based interventions enhance cognitive functions, such as executive function and memory, though their impacts on attention vary.
Objective: This study aimed to contribute to the ongoing scientific discourse on integrating technology-driven interventions into the management and evaluation of ADHD.
Soft Matter
January 2025
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria.
In this work, a theoretical approach is developed to investigate the structural properties of ionic microgels induced by a circularly polarized (CP) electric field. Following a similar study on chain formation in the presence of linearly polarized fields [T. Colla , , 2018, , 4321-4337], we propose an effective potential between microgels which incorporates the field-induced interactions a static, time averaged polarizing charge at the particle surface.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Key Laboratory for Laser Plasmas and School of Physics and Astronomy, and Collaborative Innovation Center of IFSA, Shanghai Jiao Tong University, Shanghai 200240, China.
Time-dependent density functional theory (TDDFT) is widely used for understanding and predicting properties and behaviors of matter. As one of the fundamental theorems in TDDFT, Van Leeuwen theorem [Phys. Rev.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Quantinuum, 303 S. Technology Court, Broomfield, Colorado 80021, USA.
Although quantum mechanics underpins the microscopic behavior of all materials, its effects are often obscured at the macroscopic level by thermal fluctuations. A notable exception is a zero-temperature phase transition, where scaling laws emerge entirely due to quantum correlations over a diverging length scale. The accurate description of such transitions is challenging for classical simulation methods of quantum systems, and is a natural application space for quantum simulation.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Xi'an Jiaotong University, School of Microelectronics & State Key Laboratory for Mechanical Behavior of Materials, Xi'an 710049, China.
The bismuth monolayer has recently been experimentally identified as a novel platform for the investigation of two-dimensional single-element ferroelectric system. Here, we model the potential energy surface of a bismuth monolayer by employing a message-passing neural network and achieve an error smaller than 1.2 meV per atom.
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