400 research outputs found
Dynamic Critical approach to Self-Organized Criticality
A dynamic scaling Ansatz for the approach to the Self-Organized Critical
(SOC) regime is proposed and tested by means of extensive simulations applied
to the Bak-Sneppen model (BS), which exhibits robust SOC behavior. Considering
the short-time scaling behavior of the density of sites () below the
critical value, it is shown that i) starting the dynamics with configurations
such that one observes an {\it initial increase} of the
density with exponent ; ii) using initial configurations with
, the density decays with exponent . It is
also shown that he temporal autocorrelation decays with exponent . Using these, dynamically determined, critical exponents and suitable
scaling relationships, all known exponents of the BS model can be obtained,
e.g. the dynamical exponent , the mass dimension exponent , and the exponent of all returns of the activity , in excellent agreement with values already accepted and obtained
within the SOC regime.Comment: Rapid Communication Physical Review E in press (4 pages, 5 figures
Clone size distributions in networks of genetic similarity
We build networks of genetic similarity in which the nodes are organisms
sampled from biological populations. The procedure is illustrated by
constructing networks from genetic data of a marine clonal plant. An important
feature in the networks is the presence of clone subgraphs, i.e. sets of
organisms with identical genotype forming clones. As a first step to understand
the dynamics that has shaped these networks, we point up a relationship between
a particular degree distribution and the clone size distribution in the
populations. We construct a dynamical model for the population dynamics,
focussing on the dynamics of the clones, and solve it for the required
distributions. Scale free and exponentially decaying forms are obtained
depending on parameter values, the first type being obtained when clonal growth
is the dominant process. Average distributions are dominated by the power law
behavior presented by the fastest replicating populations.Comment: 17 pages, 4 figures. One figure improved and other minor changes. To
appear in Physica
Explosive Percolation in the Human Protein Homology Network
We study the explosive character of the percolation transition in a
real-world network. We show that the emergence of a spanning cluster in the
Human Protein Homology Network (H-PHN) exhibits similar features to an
Achlioptas-type process and is markedly different from regular random
percolation. The underlying mechanism of this transition can be described by
slow-growing clusters that remain isolated until the later stages of the
process, when the addition of a small number of links leads to the rapid
interconnection of these modules into a giant cluster. Our results indicate
that the evolutionary-based process that shapes the topology of the H-PHN
through duplication-divergence events may occur in sudden steps, similarly to
what is seen in first-order phase transitions.Comment: 13 pages, 6 figure
Network analysis identifies weak and strong links in a metapopulation system
The identification of key populations shaping the structure and connectivity of metapopulation systems is a major challenge in population ecology. The use of molecular markers in the theoretical framework of population genetics has allowed great advances in this field, but the prime question of quantifying the role of each population in the system remains unresolved. Furthermore, the use and interpretation of classical methods are still bounded by the need for a priori information and underlying assumptions that are seldom respected in natural systems. Network theory was applied to map the genetic structure in a metapopulation system by using microsatellite data from populations of a threatened seagrass, Posidonia oceanica, across its whole geographical range. The network approach, free from a priori assumptions and from the usual underlying hypotheses required for the interpretation of classical analyses, allows both the straightforward characterization of hierarchical population structure and the detection of populations acting as hubs critical for relaying gene flow or sustaining the metapopulation system. This development opens perspectives in ecology and evolution in general, particularly in areas such as conservation biology and epidemiology, where targeting specific populations is crucial
Geographical Embedding of Scale-Free Networks
A method for embedding graphs in Euclidean space is suggested. The method
connects nodes to their geographically closest neighbors and economizes on the
total physical length of links. The topological and geometrical properties of
scale-free networks embedded by the suggested algorithm are studied both
analytically and through simulations. Our findings indicate dramatic changes in
the embedded networks, in comparison to their off-lattice counterparts, and
call into question the applicability of off-lattice scale-free models to
realistic, everyday-life networks
Evolutionary and Ecological Trees and Networks
Evolutionary relationships between species are usually represented in
phylogenies, i.e. evolutionary trees, which are a type of networks. The
terminal nodes of these trees represent species, which are made of individuals
and populations among which gene flow occurs. This flow can also be represented
as a network. In this paper we briefly show some properties of these complex
networks of evolutionary and ecological relationships. First, we characterize
large scale evolutionary relationships in the Tree of Life by a degree
distribution. Second, we represent genetic relationships between individuals of
a Mediterranean marine plant, Posidonia oceanica, in terms of a Minimum
Spanning Tree. Finally, relationships among plant shoots inside populations are
represented as networks of genetic similarity.Comment: 6 pages, 5 figures. To appear in Proceedings of the Medyfinol06
Conferenc
Stochastic synchronization in globally coupled phase oscillators
Cooperative effects of periodic force and noise in globally Cooperative
effects of periodic force and noise in globally coupled systems are studied
using a nonlinear diffusion equation for the number density. The amplitude of
the order parameter oscillation is enhanced in an intermediate range of noise
strength for a globally coupled bistable system, and the order parameter
oscillation is entrained to the external periodic force in an intermediate
range of noise strength. These enhancement phenomena of the response of the
order parameter in the deterministic equations are interpreted as stochastic
resonance and stochastic synchronization in globally coupled systems.Comment: 5 figure
A Hebbian approach to complex network generation
Through a redefinition of patterns in an Hopfield-like model, we introduce
and develop an approach to model discrete systems made up of many, interacting
components with inner degrees of freedom. Our approach clarifies the intrinsic
connection between the kind of interactions among components and the emergent
topology describing the system itself; also, it allows to effectively address
the statistical mechanics on the resulting networks. Indeed, a wide class of
analytically treatable, weighted random graphs with a tunable level of
correlation can be recovered and controlled. We especially focus on the case of
imitative couplings among components endowed with similar patterns (i.e.
attributes), which, as we show, naturally and without any a-priori assumption,
gives rise to small-world effects. We also solve the thermodynamics (at a
replica symmetric level) by extending the double stochastic stability
technique: free energy, self consistency relations and fluctuation analysis for
a picture of criticality are obtained
Living with diabetes: An exploratory study of illness representation and medication adherence in Ghana
Background: Compared to other chronic conditions, non-adherence to medication in diabetes patients is very high. This study explores the relationship between illness representation and medication adherence in diabetes patients in Ghana. Method: A total of 196 type 2 diabetes patients purposively and conveniently sampled from a tertiary hospital in Ghana responded to the Revised Illness Perception Questionnaire (IPQ-R) and the Medication Adherence Report Scale (MARS-5). The Pearson Moment Product correlation and the hierarchical multiple regression statistical tools were used to analyse the data. Results: Illness consequence and emotional representation were negatively related to medication adherence, while personal control positively accounted for significant variance in medication adherence. However, none of the selected key demographic variables (i.e. age, illness duration, gender, religion and education) independently accounted for any significant variance in medication adherence. Conclusion: Diabetes has a telling consequence on patients’ life; the patient can do something to control diabetes; and the negative emotional representations concerning the disease have a significant influence on the degree of medication adherence by the patients. This observation has implications for the management and treatment plan of diabetes
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