scholarly journals Exact probabilities for the indeterminacy of complex networks as perceived through press perturbations

2016 ◽  
Author(s):  
David Koslicki ◽  
Mark Novak
Keyword(s):  
Complex Networks ◽  
Network Topology ◽  
Network Dynamics ◽  
Ecological Networks ◽  
Primary Result ◽  
Qualitative Modeling ◽  
Expected Number ◽  
Recent Advances ◽  
Modeling Techniques

AbstractWe consider the goal of predicting how complex networks respond to chronic (press) perturbations when characterizations of their network topology and interaction strengths are associated with uncertainty. Our primary result is the derivation of exact formulas for the expected number and probability of qualitatively incorrect predictions about a system’s responses under uncertainties drawn form arbitrary distributions of error. These formulas obviate the current use of simulations, algorithms, and qualitative modeling techniques. Additional indices provide new tools for identifying which links in a network are most qualitatively and quantitatively sensitive to error, and for determining the volume of errors within which predictions will remain qualitatively determinate (i.e. sign insensitive). Together with recent advances in the empirical characterization of uncertainty in ecological networks, these tools bridge a way towards probabilistic predictions of network dynamics.

scholarly journals Special Issue: Advances in Computational Electromagnetics

2021 ◽  
Vol 7 (6) ◽  
pp. 89
Author(s):  
Valerio De Santis
Keyword(s):  
Rare Earth ◽  
Magnetic Materials ◽  
Permanent Magnets ◽  
Computational Electromagnetics ◽  
Superconducting Materials ◽  
Special Issue ◽  
Full Characterization ◽  
Recent Advances

Recent advances in computational electromagnetics (CEMs) have made the full characterization of complex magnetic materials possible, such as superconducting materials, composite or nanomaterials, rare-earth free permanent magnets, etc [...]

scholarly journals Data-driven control of complex networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Baggio ◽  
Danielle S. Bassett ◽  
Fabio Pasqualetti
Keyword(s):  
Complex Networks ◽  
Network Dynamics ◽  
Noisy Data ◽  
Data Driven ◽  
Control Algorithms ◽  
Optimal Controls ◽  
Network Systems ◽  
The Past ◽  
Efficient Control ◽  
Finite Set

AbstractOur ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

Exploring wall shear stress spatiotemporal heterogeneity in coronary arteries combining correlation-based analysis and complex networks with computational hemodynamics

2020 ◽  
Vol 234 (11) ◽  
pp. 1209-1222 ◽  
Author(s):  
Karol Calò ◽  
Giuseppe De Nisco ◽  
Diego Gallo ◽  
Claudio Chiastra ◽  
Ayla Hoogendoorn ◽  
...  
Keyword(s):  
Shear Stress ◽  
Wall Shear Stress ◽  
Complex Networks ◽  
Coronary Arteries ◽  
Early Stage ◽  
Flow Direction ◽  
Computational Hemodynamics ◽  
Time Histories ◽  
Wall Shear

Atherosclerosis at the early stage in coronary arteries has been associated with low cycle-average wall shear stress magnitude. However, parallel to the identification of an established active role for low wall shear stress in the onset/progression of the atherosclerotic disease, a weak association between lesions localization and low/oscillatory wall shear stress has been observed. In the attempt to fully identify the wall shear stress phenotype triggering early atherosclerosis in coronary arteries, this exploratory study aims at enriching the characterization of wall shear stress emerging features combining correlation-based analysis and complex networks theory with computational hemodynamics. The final goal is the characterization of the spatiotemporal and topological heterogeneity of wall shear stress waveforms along the cardiac cycle. In detail, here time-histories of wall shear stress magnitude and wall shear stress projection along the main flow direction and orthogonal to it (a measure of wall shear stress multidirectionality) are analyzed in a representative dataset of 10 left anterior descending pig coronary artery computational hemodynamics models. Among the main findings, we report that the proposed analysis quantitatively demonstrates that the model-specific inlet flow-rate shapes wall shear stress time-histories. Moreover, it emerges that a combined effect of low wall shear stress magnitude and of the shape of the wall shear stress–based descriptors time-histories could trigger atherosclerosis at its earliest stage. The findings of this work suggest for new experiments to provide a clearer determination of the wall shear stress phenotype which is at the basis of the so-called arterial hemodynamic risk hypothesis in coronary arteries.

scholarly journals Characterization of the resting-state brain network topology in the 6-hydroxydopamine rat model of Parkinson’s disease

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0172394 ◽  
Author(s):  
Robert Westphal ◽  
Camilla Simmons ◽  
Michel B. Mesquita ◽  
Tobias C. Wood ◽  
Steve C. R. Williams ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Network Topology ◽  
Rat Model ◽  
Resting State ◽  
Brain Network ◽  
6 Hydroxydopamine
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