Diffusion dynamics on multiplex networks

We study the time scales associated to diffusion processes that take place on multiplex networks, i.e. on a set of networks linked through interconnected layers. To this end, we propose the construction of a supra-Laplacian matrix, which consists of a dimensional lifting of the Laplacian matrix of each layer of the multiplex network. We use perturbative analysis to reveal analytically the structure of eigenvectors and eigenvalues of the complete network in terms of the spectral properties of the individual layers. The spectrum of the supra-Laplacian allows us to understand the physics of diffusion-like processes on top of multiplex networks.Comment: 6 Pages including supplemental material. To appear in Physical Review Letter

Assortativity Decreases the Robustness of Interdependent Networks

It was recently recognized that interdependencies among different networks can play a crucial role in triggering cascading failures and hence system-wide disasters. A recent model shows how pairs of interdependent networks can exhibit an abrupt percolation transition as failures accumulate. We report on the effects of topology on failure propagation for a model system consisting of two interdependent networks. We find that the internal node correlations in each of the two interdependent networks significantly changes the critical density of failures that triggers the total disruption of the two-network system. Specifically, we find that the assortativity (i.e. the likelihood of nodes with similar degree to be connected) within a single network decreases the robustness of the entire system. The results of this study on the influence of assortativity may provide insights into ways of improving the robustness of network architecture, and thus enhances the level of protection of critical infrastructures

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio

Patient-specific image-based computer simulation for theprediction of valve morphology and calcium displacement after TAVI with the Medtronic CoreValve and the Edwards SAPIEN valve

AIMS: Our aim was to validate patient-specific software integrating baseline anatomy and biomechanical properties of both the aortic root and valve for the prediction of valve morphology and aortic leaflet calcium displacement after TAVI. METHODS AND RESULTS: Finite element computer modelling was performed in 39 patients treated with a Medtronic CoreValve System (MCS; n=33) or an Edwards SAPIEN XT (ESV; n=6). Quantitative axial frame morphology at inflow (MCS, ESV) and nadir, coaptation and commissures (MCS) was compared between multislice computed tomography (MSCT) post TAVI and a computer model as well as displacement of the aortic leaflet calcifications, quantified by the distance between the coronary ostium and the closest calcium nodule. Bland-Altman analysis revealed a strong correlation between the observed (MSCT) and predicted frame dimensions, although small differences were detected for, e.g., Dmin at the inflow (mean±SD MSCT vs. MODEL: 21.6±2.4 mm vs. 22.0±2.4 mm; difference±SD: -0.4±1.3 mm, p<0.05) and Dmax (25.6±2.7 mm vs. 26.2±2.7 mm; difference±SD: -0.6±1.0 mm, p<0.01). The observed and predicted calcium displacements were highly correlated for the left and right coronary ostia (R2=0.67 and R2=0.71, respectively p<0.001). CONCLUSIONS: Dedicated software allows accurate prediction of frame morphology and calcium displacement after valve implantation, which may help to improve outcome

Self-Similar Bootstrap of Divergent Series

A method is developed for calculating effective sums of divergent series. This approach is a variant of the self-similar approximation theory. The novelty here is in using an algebraic transformation with a power providing the maximal stability of the self-similar renormalization procedure. The latter is to be repeated as many times as it is necessary in order to convert into closed self-similar expressions all sums from the series considered. This multiple and complete renormalization is called self-similar bootstrap. The method is illustrated by several examples from statistical physics.Comment: 1 file, 22 pages, RevTe

Towards designing robust coupled networks

Natural and technological interdependent systems have been shown to be highly vulnerable due to cascading failures and an abrupt collapse of global connectivity under initial failure. Mitigating the risk by partial disconnection endangers their functionality. Here we propose a systematic strategy of selecting a minimum number of autonomous nodes that guarantee a smooth transition in robustness. Our method which is based on betweenness is tested on various examples including the famous 2003 electrical blackout of Italy. We show that, with this strategy, the necessary number of autonomous nodes can be reduced by a factor of five compared to a random choice. We also find that the transition to abrupt collapse follows tricritical scaling characterized by a set of exponents which is independent on the protection strategy

Correlation between centrality metrics and their application to the opinion model

In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The m order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the B_{n}, the closeness, and the components of x_{1} are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between x_{1} and the 2nd-order degree mass is larger than that between x_{1} and a lower order degree mass. Finally, we investigate the effect of the inflexible antagonists selected based on different centrality metrics in helping one opinion to compete with another in the inflexible antagonists opinion model. Interestingly, we find that selecting the inflexible antagonists based on the leverage, the B_{n}, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the B_{n}, as well as a high centrality similarity between the leverage and the degree.Comment: 20 page

Discordant severity criteria in patients with moderate aortic stenosis: prognostic implications

Background The criteria to define the grade of aortic stenosis (AS)-aortic valve area (AVA) and mean gradient (MG) or peak jet velocity-do not always coincide into one grade. Although in severe AS, this discrepancy is well characterised, in moderate AS, the phenomenon of discordant grading has not been investigated and its prognostic implications are unknown.Objectives To investigate the occurrence of discordant grading in patients with moderate AS (defined by an AVA between 1.0 cm(2) and 1.5 cm(2) but with an MG = 20 mm Hg) in terms of clinical outcomes.Methods From an ongoing registry of patients with AS, patients with moderate AS based on AVA were selected and classified into discordant or concordant grading (MG = 20 mm Hg, respectively). The clinical endpoint was all-cause mortality.Results Of 790 patients with moderate AS, 150 (19.0%) had discordant grading, moderate AS. Patients with discordant grading were older, had higher prevalence of previous myocardial infarction and left ventricular (LV) hypertrophy, larger LV end-diastolic and end-systolic volume index, higher LV filling pressure and lower LV ejection fraction and stroke volume index as compared with their counterparts. After a median follow-up of 4.9 years (IQR 3.0-8.2), patients with discordant grading had lower aortic valve replacement rates (26.7% vs 44.1%, p<0.001) and higher mortality rates (60.0% vs 43.1%, p<0.001) as compared with patients with concordant grading. Discordant grading moderate AS, combined with low LV ejection fraction, presented the higher risk of mortality (HR 2.78 (2.00-3.87), p<0.001).Conclusion Discordant-grading moderate AS is not uncommon and, when combined with low LV ejection fraction, is associated with high risk of mortality.Cardiolog

Josephson Coupling, Phase Correlations, and Josephson Plasma Resonance in Vortex Liquid Phase

Josephson plasma resonance has been introduced recently as a powerful tool to probe interlayer Josephson coupling in different regions of the vortex phase diagram in layered superconductors. In the liquid phase, the high temperature expansion with respect to the Josephson coupling connects the Josephson plasma frequency with the phase correlation function. This function, in turn, is directly related to the pair distribution function of the liquid. We develop a recipe to extract the phase and density correlation functions from the dependencies of the plasma resonance frequency $\omega_p({\bf B})$ and the $c$ axis conductivity $\sigma_c({\bf B})$ on the {\it ab}-component of the magnetic field at fixed {\it c} -component. Using Langevin dynamic simulations of two-dimensional vortex arrays we calculate density and phase correlation functions at different temperatures. Calculated phase correlations describe very well the experimental angular dependence of the plasma resonance field. We also demonstrate that in the case of weak damping in the liquid phase, broadening of the JPR line is caused mainly by random Josephson coupling arising from the density fluctuations of pancake vortices. In this case the JPR line has a universal shape, which is determined only by parameters of the superconductors and temperature.Comment: 22 pages, 6 figures, to appear in Phys. Rev. B, December