Improving the balance between cost and accuracy of computational fluid dynamics solvers by local mesh adaptation has become a topic of increasing interest. Numerical error based adaptation sensors proved to be robust and converge faster than sensors simply based on features of the flow field.
The widespread availability of modern infrastructures able to process large amounts of data and run sophisticated models of complex phenomena, is making simulation-based research a usual technique among the scientific tools.
Short-term properties of atrial fibrillation (AF) frequency, f-wave morphology, and irregularity parameters have been thoroughly studied, but not long-term properties. In the present work, f-wave morphology is characterized by principal component analysis, introducing a novel temporal parameter defined by the cumulative normalized variance of the three largest principal components (r3).
A discrete framework for computing the global stability and sensitivity analysis to external perturbations for any set of partial differential equations is presented. In particular, a complex‐step approximation is used to achieve near analytical accuracy for the evaluation of the Jacobian matrix.
A discrete framework for computing the global stability and sensitivity analysis to external perturbations for any set of partial differential equations is presented. In particular, a complex‐step approximation is used to achieve near analytical accuracy for the evaluation of the Jacobian matrix.
New economic conditions have led to innovations in retail industries, such as more dynamic retail approaches based on flexible strategies. We propose and compare different approaches incorporating nonlinear methods for promotional decision-making using retail aggregated data registered at the point of the sale.