scholarly journals Activity Scenario Modelling: an emerging method for examining human-artefact interaction

2020 ◽  
Author(s):  
Miguel Montiel ◽  
◽  
Ricardo Sosa ◽  
Darryl Hocking
Keyword(s):  
Scenario Modelling ◽  
Human Artefact

scholarly journals Scenario modelling to support industry strategic planning and decision making

2014 ◽  
Vol 55 ◽  
pp. 120-131 ◽  
Author(s):  
Romy Greiner ◽  
Javier Puig ◽  
Cindy Huchery ◽  
Neil Collier ◽  
Stephen T. Garnett
Keyword(s):  
Decision Making ◽  
Strategic Planning ◽  
Scenario Modelling

Scenario modelling for selective hedging strategies

2004 ◽  
Vol 28 (5) ◽  
pp. 955-974 ◽  
Author(s):  
Andrea Beltratti ◽  
Andrea Laurant ◽  
Stavros A. Zenios
Keyword(s):  
Hedging Strategies ◽  
Scenario Modelling

scholarly journals Scenario Modelling of the “Green” Economy in an Economic Space

Resources ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 29 ◽  
Author(s):  
Larisa Rudneva ◽  
Irina Pchelintseva ◽  
Maria Gureva
Keyword(s):  
Green Economy ◽  
Economic Space ◽  
Scenario Modelling

scholarly journals A Protocol for Automated a-posteriori Adaptive Meshing with SimVascular

2019 ◽  
Author(s):  
Akash Gupta ◽  
Ethan O. Kung
Keyword(s):  
Mesh Size ◽  
Patient Specific ◽  
A Posteriori ◽  
Adaptive Meshing ◽  
Considerable Effort ◽  
Error Metrics ◽  
Scenario Modelling ◽  
Order Of Magnitude ◽  
Novice Users ◽  
Specific Geometry

Abstract Objective: Operational details regarding the use of the adaptive meshing (AM) algorithm available in the SimVascular package are scarce despite its application in several studies. Lacking these details, novice users of the AM algorithm may experience undesirable outcomes post-adaptation such as increases in mesh error metrics, unpredictable increases in mesh size, and losses in geometric fidelity. Here we propose an iterative protocol that will help prevent these undesirable outcomes and enhance the utility of the AM algorithm. We present three trials (conservative, aggressive and moderate settings) of our proposed protocol applied to a scenario modelling a Fontan junction with a patient-specific geometry and physiologically realistic boundary conditions. Results: In all three trials, an overall reduction in mesh error metrics is observed (range 47%-86%). The increase in the number of elements through each adaptation never exceeded the mesh size of the pre-adaptation mesh by one order of magnitude. In all three trials, the protocol resulted in consistent, repeatable improvements in mesh error metrics, no losses of geometric fidelity and steady increments in the number of elements in the mesh. Our proposed protocol prevented the aforementioned undesirable outcomes and can potentially save new users considerable effort and computing resources.

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