90,345 research outputs found
Phase transitions towards frequency entrainment in large oscillator lattices
We investigate phase transitions towards frequency entrainment in large,
locally coupled networks of limit cycle oscillators. Specifically, we simulate
two-dimensional lattices of pulse-coupled oscillators with random natural
frequencies, resembling pacemaker cells in the heart. As coupling increases,
the system seems to undergo two phasetransitions in the thermodynamic limit. At
the first, the largest cluster of frequency entrained oscillators becomes
macroscopic. At the second, global entrainment settles. Between the two
transitions, the system has features indicating self-organized criticality.Comment: 4 pages, 5 figures, submitted to PR
Using skewness and the first-digit phenomenon to identify dynamical transitions in cardiac models
Disruptions in the normal rhythmic functioning of the heart, termed as
arrhythmia, often result from qualitative changes in the excitation dynamics of
the organ. The transitions between different types of arrhythmia are
accompanied by alterations in the spatiotemporal pattern of electrical activity
that can be measured by observing the time-intervals between successive
excitations of different regions of the cardiac tissue. Using biophysically
detailed models of cardiac activity we show that the distribution of these
time-intervals exhibit a systematic change in their skewness during such
dynamical transitions. Further, the leading digits of the normalized intervals
appear to fit Benford's law better at these transition points. This raises the
possibility of using these observations to design a clinical indicator for
identifying changes in the nature of arrhythmia. More importantly, our results
reveal an intriguing relation between the changing skewness of a distribution
and its agreement with Benford's law, both of which have been independently
proposed earlier as indicators of regime shift in dynamical systems.Comment: 11 pages, 6 figures; incorporating changes as in the published
versio
The Earth as a living planet: human-type diseases in the earthquake preparation process
The new field of complex systems supports the view that a number of systems
arising from disciplines as diverse as physics, biology, engineering, and
economics may have certain quantitative features that are intriguingly similar.
The earth is a living planet where many complex systems run perfectly without
stopping at all. The earthquake generation is a fundamental sign that the earth
is a living planet. Recently, analyses have shown that human-brain-type disease
appears during the earthquake generation process. Herein, we show that
human-heart-type disease appears during the earthquake preparation of the
earthquake process. The investigation is mainly attempted by means of critical
phenomena, which have been proposed as the likely paradigm to explain the
origins of both heart electric fluctuations and fracture induced
electromagnetic fluctuations. We show that a time window of the damage
evolution within the heterogeneous Earth's crust and the healthy heart's
electrical action present the characteristic features of the critical point of
a thermal second order phase transition. A dramatic breakdown of critical
characteristics appears in the tail of the fracture process of heterogeneous
system and the injury heart's electrical action. Analyses by means of Hurst
exponent and wavelet decomposition further support the hypothesis that a
dynamical analogy exists between the geological and biological systems under
study
Network Physiology reveals relations between network topology and physiological function
The human organism is an integrated network where complex physiologic
systems, each with its own regulatory mechanisms, continuously interact, and
where failure of one system can trigger a breakdown of the entire network.
Identifying and quantifying dynamical networks of diverse systems with
different types of interactions is a challenge. Here, we develop a framework to
probe interactions among diverse systems, and we identify a physiologic
network. We find that each physiologic state is characterized by a specific
network structure, demonstrating a robust interplay between network topology
and function. Across physiologic states the network undergoes topological
transitions associated with fast reorganization of physiologic interactions on
time scales of a few minutes, indicating high network flexibility in response
to perturbations. The proposed system-wide integrative approach may facilitate
the development of a new field, Network Physiology.Comment: 12 pages, 9 figure
Dynamical mechanism of atrial fibrillation: a topological approach
While spiral wave breakup has been implicated in the emergence of atrial
fibrillation, its role in maintaining this complex type of cardiac arrhythmia
is less clear. We used the Karma model of cardiac excitation to investigate the
dynamical mechanisms that sustain atrial fibrillation once it has been
established. The results of our numerical study show that spatiotemporally
chaotic dynamics in this regime can be described as a dynamical equilibrium
between topologically distinct types of transitions that increase or decrease
the number of wavelets, in general agreement with the multiple wavelets
hypothesis. Surprisingly, we found that the process of continuous excitation
waves breaking up into discontinuous pieces plays no role whatsoever in
maintaining spatiotemporal complexity. Instead this complexity is maintained as
a dynamical balance between wave coalescence -- a unique, previously
unidentified, topological process that increases the number of wavelets -- and
wave collapse -- a different topological process that decreases their number.Comment: 15 pages, 14 figure
Non-Markov stochastic dynamics of real epidemic process of respiratory infections
The study of social networks and especially of the stochastic dynamics of the
diseases spread in human population has recently attracted considerable
attention in statistical physics. In this work we present a new statistical
method of analyzing the spread of epidemic processes of grippe and acute
respiratory track infections (ARTI) by means of the theory of discrete
non-Markov stochastic processes. We use the results of our last theory (Phys.
Rev. E 65, 046107 (2002)) to study statistical memory effects, long - range
correlation and discreteness in real data series, describing the epidemic
dynamics of human ARTI infections and grippe. We have carried out the
comparative analysis of the data of the two infections (grippe and ARTI) in one
of the industrial districts of Kazan, one of the largest cities of Russia. The
experimental data are analyzed by the power spectra of the initial time
correlation function and the memory functions of junior orders, the phase
portraits of the four first dynamic variables, the three first points of the
statistical non-Markov parameter and the locally averaged kinetic and
relaxation parameters. The received results give an opportunity to provide
strict quantitative description of the regular and stochastic components in
epidemic dynamics of social networks taking into account their time
discreteness and effects of statistical memory. They also allow to reveal the
degree of randomness and predictability of the real epidemic process in the
specific social network.Comment: 18 pages, 8figs, 1 table
- …