scholarly journals A Comparison of Classical and Aggregation-Based Algebraic Multigrid Preconditioners for High-Fidelity Simulation of Wind Turbine Incompressible Flows

2019 ◽  
Vol 41 (5) ◽  
pp. S196-S219 ◽  
Author(s):  
S. J. Thomas ◽  
S. Ananthan ◽  
S. Yellapantula ◽  
J. J. Hu ◽  
M. Lawson ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Incompressible Flows ◽  
Algebraic Multigrid ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Multigrid Preconditioners

The Perceptions of Nurse Educators regarding the use of High Fidelity Simulation in Nursing Education

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Viola Janse van Vuuren ◽  
Eunice Seekoe ◽  
Daniel Ter Goon
Keyword(s):  
Nursing Education ◽  
South African ◽  
Clinical Training ◽  
Nurse Educators ◽  
Teaching Staff ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Simulation Based ◽  
Nursing College ◽  
Faculty Shortages

Although nurse educators are aware of the advantages of simulation-based training, some still feel uncomfortable to use technology or lack the motivation to learn how to use the technology. The aging population of nurse educators causes frustration and anxiety. They struggle with how to include these tools particularly in the light of faculty shortages. Nursing education programmes are increasingly adopting simulation in both undergraduate and graduate curricula. The aim of this study was to determine the perceptions of nurse educators regarding the use of high fidelity simulation (HFS) in nursing education at a South African private nursing college. A national survey of nurse educators and clinical training specialists was completed with 118 participants; however, only 79 completed the survey. The findings indicate that everyone is at the same level as far as technology readiness is concerned, however, it does not play a significant role in the use of HFS. These findings support the educators’ need for training to adequately prepare them to use simulation equipment. There is a need for further research to determine what other factors play a role in the use of HFS; and if the benefits of HFS are superior to other teaching strategies warranting the time and financial commitment. The findings of this study can be used as guidelines for other institutions to prepare their teaching staff in the use of HFS.

scholarly journals The Learning Pathways Grid

2018 ◽  
Vol 17 (1) ◽  
pp. 160940691879160 ◽  
Author(s):  
Andrew Stuart Lane ◽  
Chris Roberts
Keyword(s):  
Qualitative Research ◽  
Data Collection ◽  
Data Gathering ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Cognitive Frames ◽  
Open Disclosure ◽  
Learning Pathways ◽  
Social Constructivist Approach ◽  
Insight Into

The interview is an important data-gathering tool in qualitative research, since it allows researchers to gain insight into a person’s knowledge, understandings, perceptions, interpretations, and experiences. There are many definitions of reflexivity in qualitative research, one such definition being “Reflexivity is an attitude of attending systematically to the context of knowledge construction, especially to the effect of the researcher, at every step of the research processes.” The learning pathways grid (LPG) is a visual template used to assist analysis and interpretation of conversations, allowing educators, learners, and researchers, to discover links from cognition to action, usually in a retrospective manner. It is often used in simulation educational research, with a focus on understanding how learners access their cognitive frames and underlying beliefs. In this article, we describe the use of the LPG as a prospective adjunct to data collection for interviews and focus groups. We contextualize it within a study among medical interns and medical students who were engaged in high-fidelity simulation exploring open disclosure after a medication error. The LPG allowed future optimization of data collection and interpretation by ensuring reflexivity within the researchers, a vital part of research conduct. We conclude by suggesting the use of the LPG has a reasonable fit when taking a social constructivist approach and using qualitative analysis methods that make reflexivity explicit and visible, therefore ensuring it is truly considered, understood, and demonstrated by researchers.

scholarly journals Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Erik Buhmann ◽  
Sascha Diefenbacher ◽  
Engin Eren ◽  
Frank Gaede ◽  
Gregor Kasieczka ◽  
...  
Keyword(s):  
Particle Physics ◽  
High Speed ◽  
Deep Neural Networks ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Physical Processes ◽  
Electromagnetic Showers ◽  
Information Bottleneck ◽  
First Time ◽  
Processing Network

AbstractAccurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new architecture—the Bounded Information Bottleneck Autoencoder—for modelling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. Combined with a novel second post-processing network, this approach achieves an accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full Geant4 simulation for a high-granularity calorimeter with 27k simulated channels. The results are validated by comparing to established architectures. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy.

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