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The network of 5823 cities of Mexico with a population more than 5000 inhabitants is studied. Our analysis is focused to the spectral properties of the adjacency matrix, the small-world properties of the network, the distribution of the clustering coefficients and the degree distribution of the vertices. The connection of these features with the spread of epidemics on this network is also discussed.
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Recently the A/H1N1-2009 virus pandemic appeared in Mexico and in other nations. We present a study of this pandemic in the Mexican case using the SIR model to describe epidemics. This model is one of the simplest models but it has been a successful description of some epidemics of closed populations. We consider the data for the Mexican case and use the SIR model to make some predictions. Then, we generalize the SIR model in order to describe the spatial dynamics of the disease. We make a study of the spatial and temporal spread of the infected population with model parameters that are consistent with temporal SIR model parameters obtained by fitting to the Mexican case.
We discuss several models of the dynamics of interacting populations. The models are constructed by nonlinear differential equations and have two sets of parameters: growth rates and coefficients of interaction between populations. We assume that the parameters depend on the densities of the populations. In addition the parameters can be influenced by different factors of the environment. This influence is modelled by noise terms in the equations for the growth rates and interaction coefficients. Thus the model differential equations become stochastic. In some particular cases these equations can be reduced to a Foker-Plancnk equation for the probability density function of the densities of the interacting populations.
We study the primacy in the Bulgarian urban system. Two groups of cities are studied: (i) the whole Bulgaria city system that contains about 250 cities and is studied in the time interval between 2004 and 2011; and (ii) A system of 33 cities, studied over the time interval 1887 till 2010. For these cities the 1946 population was over $10 000$ inhabitants. The notion of primacy in the two systems of cities is studied first from the global primacy index of Sheppard [$^1$]. Several (new) additional indices are introduced in order to compensate defects in the Sheppard index. Numerical illustrations are illuminating through the so called length ratio.
It is well known that life on Earth alters its environment over evolutionary and geological timescales. An important open question is whether this is a result of evolutionary optimization or a universal feature of life. In the latter case, the origin of life would be coincident with a shift in environmental conditions. Here we present a model for the emergence of life in which replicators are explicitly coupled to their environment through the recycling of a finite supply of resources. The model exhibits a dynamic, first-order phase transition from non-life to life, where the life phase is distinguished by selection on replicators. We show that environmental coupling plays an important role in the dynamics of the transition. The transition corresponds to a redistribution of matter in replicators and their environment, driven by selection on replicators, exhibiting an explosive growth in diversity as replicators are selected. The transition is accurately tracked by the mutual information shared between replicators and their environment. In the absence of successfully repartitioning system resources, the transition fails to complete, leading to the possibility of many frustrated trials before life first emerges. Often, the replicators that initiate the transition are not those that are ultimately selected. The results are consistent with the view that lifes propensity to shape its environment is indeed a universal feature of replicators, characteristic of the transition from non-life to life. We discuss the implications of these results for understanding lifes emergence and evolutionary transitions more broadly.
Complex networks provide us a new view for investigation of immune systems. In this paper we collect data through STRING database and present a model with cooperation network theory. The cytokine-protein network model we consider is constituted by two kinds of nodes, one is immune cytokine types which can act as acts, other one is protein type which can act as actors. From act degree distribution that can be well described by typical SPL -shifted power law functions, we find that HRAS.TNFRSF13C.S100A8.S100A1.MAPK8.S100A7.LIF.CCL4.CXCL13 are highly collaborated with other proteins. It reveals that these mediators are important in cytokine-protein network to regulate immune activity. Dyad act degree distribution is another important property to generalized collaboration network. Dyad is two proteins and they appear in one cytokine collaboration relationship. The dyad act degree distribution can be well described by typical SPL functions. The length of the average shortest path is 1.29. These results show that this model could describe the cytokine-protein collaboration preferably
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