1,314 research outputs found
Swarm behavior of self-propelled rods and swimming flagella
Systems of self-propelled particles are known for their tendency to aggregate
and to display swarm behavior. We investigate two model systems, self-propelled
rods interacting via volume exclusion, and sinusoidally-beating flagella
embedded in a fluid with hydrodynamic interactions. In the flagella system,
beating frequencies are Gaussian distributed with a non-zero average. These
systems are studied by Brownian-dynamics simulations and by mesoscale
hydrodynamics simulations, respectively. The clustering behavior is analyzed as
the particle density and the environmental or internal noise are varied. By
distinguishing three types of cluster-size probability density functions, we
obtain a phase diagram of different swarm behaviors. The properties of
clusters, such as their configuration, lifetime and average size are analyzed.
We find that the swarm behavior of the two systems, characterized by several
effective power laws, is very similar. However, a more careful analysis reveals
several differences. Clusters of self-propelled rods form due to partially
blocked forward motion, and are therefore typically wedge-shaped. At higher rod
density and low noise, a giant mobile cluster appears, in which most rods are
mostly oriented towards the center. In contrast, flagella become
hydrodynamically synchronized and attract each other; their clusters are
therefore more elongated. Furthermore, the lifetime of flagella clusters decays
more quickly with cluster size than of rod clusters
Exact solution of bond percolation on small arbitrary graphs
We introduce a set of iterative equations that exactly solves the size
distribution of components on small arbitrary graphs after the random removal
of edges. We also demonstrate how these equations can be used to predict the
distribution of the node partitions (i.e., the constrained distribution of the
size of each component) in undirected graphs. Besides opening the way to the
theoretical prediction of percolation on arbitrary graphs of large but finite
size, we show how our results find application in graph theory, epidemiology,
percolation and fragmentation theory.Comment: 5 pages and 3 figure
Adaptive networks: coevolution of disease and topology
Adaptive networks have been recently introduced in the context of disease
propagation on complex networks. They account for the mutual interaction
between the network topology and the states of the nodes. Until now, existing
models have been analyzed using low-complexity analytic formalisms, revealing
nevertheless some novel dynamical features. However, current methods have
failed to reproduce with accuracy the simultaneous time evolution of the
disease and the underlying network topology. In the framework of the adaptive
SIS model of Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)], we introduce an
improved compartmental formalism able to handle this coevolutionary task
successfully. With this approach, we analyze the interplay and outcomes of both
dynamical elements, process and structure, on adaptive networks featuring
different degree distributions at the initial stage.Comment: 11 pages, 8 figures, 1 appendix. To be published in Physical Review
Modeling the dynamical interaction between epidemics on overlay networks
Epidemics seldom occur as isolated phenomena. Typically, two or more viral
agents spread within the same host population and may interact dynamically with
each other. We present a general model where two viral agents interact via an
immunity mechanism as they propagate simultaneously on two networks connecting
the same set of nodes. Exploiting a correspondence between the propagation
dynamics and a dynamical process performing progressive network generation, we
develop an analytic approach that accurately captures the dynamical interaction
between epidemics on overlay networks. The formalism allows for overlay
networks with arbitrary joint degree distribution and overlap. To illustrate
the versatility of our approach, we consider a hypothetical delayed
intervention scenario in which an immunizing agent is disseminated in a host
population to hinder the propagation of an undesirable agent (e.g. the spread
of preventive information in the context of an emerging infectious disease).Comment: Accepted for publication in Phys. Rev. E. 15 pages, 7 figure
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Does bicycling contribute to the risk of erectile dysfunction? Results from the Massachusetts Male Aging Study (MMAS)
An association between bicycling and erectile dysfunction (ED) has been described previously, but there are limited data examining this association in a random population of men. Such data would incorporate bicyclists with varied types of riding and other factors. Data from the Massachusetts Male Aging Study (MMAS) were utilized to examine the association between bicycling and ED. Logistic regression was used to test for an association, controlling for age, energy expenditure, smoking, depression and chronic illness. Bicycling less than 3 h per week was not associated with ED and may be somewhat protective. Bicycling 3 h or more per week may be associated with ED. Data revealed that there may be a reduced probability of ED in those who ride less than 3 h per week and ED may be more likely in bikers who ride more than 3 h per week. More population-based research is needed to better define this relationship
Propagation dynamics on networks featuring complex topologies
Analytical description of propagation phenomena on random networks has
flourished in recent years, yet more complex systems have mainly been studied
through numerical means. In this paper, a mean-field description is used to
coherently couple the dynamics of the network elements (nodes, vertices,
individuals...) on the one hand and their recurrent topological patterns
(subgraphs, groups...) on the other hand. In a SIS model of epidemic spread on
social networks with community structure, this approach yields a set of ODEs
for the time evolution of the system, as well as analytical solutions for the
epidemic threshold and equilibria. The results obtained are in good agreement
with numerical simulations and reproduce random networks behavior in the
appropriate limits which highlights the influence of topology on the processes.
Finally, it is demonstrated that our model predicts higher epidemic thresholds
for clustered structures than for equivalent random topologies in the case of
networks with zero degree correlation.Comment: 10 pages, 5 figures, 1 Appendix. Published in Phys. Rev. E (mistakes
in the PRE version are corrected here
Propagation on networks: an exact alternative perspective
By generating the specifics of a network structure only when needed
(on-the-fly), we derive a simple stochastic process that exactly models the
time evolution of susceptible-infectious dynamics on finite-size networks. The
small number of dynamical variables of this birth-death Markov process greatly
simplifies analytical calculations. We show how a dual analytical description,
treating large scale epidemics with a Gaussian approximations and small
outbreaks with a branching process, provides an accurate approximation of the
distribution even for rather small networks. The approach also offers important
computational advantages and generalizes to a vast class of systems.Comment: 8 pages, 4 figure
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