Biomechanics of diastolic dysfunction: a one-dimensional computational modeling approach

2020 ◽  
Vol 319 (4) ◽  
pp. H882-H892
Author(s):  
Karim Kadry ◽  
Stamatia Pagoulatou ◽  
Quentin Mercier ◽  
Georgios Rovas ◽  
Vasiliki Bikia ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Computational Model ◽  
Diastolic Dysfunction ◽  
Cardiovascular System ◽  
Active Relaxation ◽  
One Dimensional ◽  
Modeling Approach ◽  
Diastolic Stiffness ◽  
Biomechanical Effect

This study uses a complete and validated computational model of the cardiovascular system to simulate the two main pathologies involved in diastolic dysfunction (DD), i.e., abnormal active relaxation and increased ventricular diastolic stiffness. The three phenotypes of DD were successfully replicated according to literature data. We elucidate the biomechanical effect of the relaxation pathologies involved and how these pathologies interact to create the various phenotypes of DD.

scholarly journals How Computational Modeling Can Force Theory Building in Psychological Science

2021 ◽  
pp. 174569162097058
Author(s):  
Olivia Guest ◽  
Andrea E. Martin
Keyword(s):  
Computational Modeling ◽  
Computational Model ◽  
Process Modeling ◽  
Theory Building ◽  
Formal Modeling ◽  
Inference Process ◽  
Experimental Practice ◽  
Psychological Science ◽  
Scientific Inference ◽  
And Behaviors

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.

scholarly journals SUBLINEARLY SPACE BOUNDED ITERATIVE ARRAYS

2010 ◽  
Vol 21 (05) ◽  
pp. 843-858 ◽  
Author(s):  
ANDREAS MALCHER ◽  
CARLO MEREGHETTI ◽  
BEATRICE PALANO
Keyword(s):  
Lower Bound ◽  
Computational Model ◽  
Regular Language ◽  
Finite Automata ◽  
Complexity Classes ◽  
Language Recognition ◽  
Deterministic Finite Automata ◽  
One Dimensional ◽  
Sequential Processing ◽  
Trade Offs

Iterative arrays (IAs) are a parallel computational model with a sequential processing of the input. They are one-dimensional arrays of interacting identical deterministic finite automata. In this paper, realtime-IAs with sublinear space bounds are used to recognize formal languages. The existence of an infinite proper hierarchy of space complexity classes between logarithmic and linear space bounds is proved. Some decidability questions on logarithmically space bounded realtime-IAs are investigated, and an optimal logarithmic space lower bound for non-regular language recognition on realtime-IAs is shown. Finally, some non-recursive trade-offs between space bounded realtime-IAs are emphasized.

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