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We offer the dissipative stochastic dynamic model of the language sign evolution, satisfying to the principle of the least action, one of fundamental variational principles of the Nature. The model conjectures the Poisson nature of the birth flow of language signs and the exponential distribution of their associative-semantic potential (ASP). The model works with stochastic difference equations of the special type for dissipative processes. The equation for momentary polysemy distribution and frequency-rank distribution drawn from our model do not differs significantly (by Kolmogorov-Smirnov’s test) from empirical distributions, got from main Russian and English explanatory dictionaries as well as frequency dictionaries of them.

A new grid and cloud services simulation for NICA accelerator complex data storage and processing system are described. This system is focused on improving the efficiency of the grid-cloud systems development by using work quality indicators of some real system to design and predict its evolution. For these purpose the simulation program are combined with real monitoring system of the grid-cloud service through a special database. An example of the program usage to simulate a sufficiently general cloud structure, which can be used for more common purposes, is given.

This paper considers the integrated approach to modeling the dynamics of genetic structure and the number of natural population. A set of dynamic models with different types of natural selection is used to describe a possible mechanism for the fixing of a genetic diversity in size of the litter in coastal, continental and farmed populations of arctic fox (Alopex lagopus, Canidae, Carnivora) observed now. The most interesting results have been obtained with the model of population consisting of two stages of development. At that with the frame of this model a dynamics of population genetic structure on genotypes was analyzed to consider different reproductive abilities and fitnesses of pups on the early stage of lifecycle which defined by the single diallelic gene. This model allows to receive a monomorphism for coastal populations of arctic fox, where food resources are practically constant. As well the model allows polymorphism with cyclical fluctuations in the number and frequency of the gene in the continental populations due to regular fluctuating of rodent number, the major component of its food. In farmed populations by selective selection carried out by farmers to increase the reproductive success, this gene is a pleiotropic one (i. e., determining the survival rate of individuals both early and late stages of their life cycle); so an application of appropriate model (with the selection of pleiotropic gene) allows to get an adequate rate of elimination for small litters allele.

In the last decades, universal scenarios of the transition to chaos in dynamic systems have been well studied. The scenario of the transition to chaos is defined as a sequence of bifurcations that occur in the system under the variation one of the governing parameters and lead to a qualitative change in dynamics, starting from the regular mode and ending with chaotic behavior. Typical scenarios include a cascade of period doubling bifurcations (Feigenbaum scenario), the breakup of a low-dimensional torus (Ruelle–Takens scenario), and the transition to chaos through the intermittency (Pomeau–Manneville scenario). In more complicated spatially distributed dynamic systems, the complexity of dynamic behavior growing with a parameter change is closely intertwined with the formation of spatial structures. However, the question of whether the spatial and temporal axes could completely exchange roles in some scenario still remains open. In this paper, for the first time, we propose a mathematical model of convection–diffusion–reaction, in which a spatial transition to chaos through the breakup of the quasi–periodic regime is realized in the framework of the Ruelle–Takens scenario. The physical system under consideration consists of two aqueous solutions of acid (A) and base (B), initially separated in space and placed in a vertically oriented Hele–Shaw cell subject to the gravity field. When the solutions are brought into contact, the frontal neutralization reaction of the second order A + B $\to$ C begins, which is accompanied by the production of salt (C). The process is characterized by a strong dependence of the diffusion coefficients of the reagents on their concentration, which leads to the appearance of two local zones of reduced density, in which chemoconvective fluid motions develop independently. Although the layers, in which convection develops, all the time remain separated by the interlayer of motionless fluid, they can influence each other via a diffusion of reagents through this interlayer. The emerging chemoconvective structure is the modulated standing wave that gradually breaks down over time, repeating the sequence of the bifurcation chain of the Ruelle–Takens scenario. We show that during the evolution of the system one of the spatial axes, directed along the reaction front, plays the role of time, and time itself starts to play the role of a control parameter.

The article provides approaches to evolutionary modelling of synthesis of organised systems and analyses methodological problems of evolutionary computations of this kind. Based on the analysis of works on evolutionary cybernetics, evolutionary theory, systems theory and synergetics, we conclude that there are open problems in formalising the synthesis of organised systems and modelling their evolution. The article emphasises that the theoretical basis for the practice of evolutionary modelling is the principles of the modern synthetic theory of evolution. Our software project uses a virtual computing environment for machine synthesis of problem solving algorithms. In the process of modelling, we obtained the results on the basis of which we conclude that there are a number of conditions that fundamentally limit the applicability of genetic programming methods in the tasks of synthesis of functional structures. The main limitations are the need for the fitness function to track the step-by-step approach to the solution of the problem and the inapplicability of this approach to the problems of synthesis of hierarchically organised systems. We note that the results obtained in the practice of evolutionary modelling in general for the whole time of its existence, confirm the conclusion the possibilities of genetic programming are fundamentally limited in solving problems of synthesizing the structure of organized systems. As sources of fundamental difficulties for machine synthesis of system structures the article points out the absence of directions for gradient descent in structural synthesis and the absence of regularity of random appearance of new organised structures. The considered problems are relevant for the theory of biological evolution. The article substantiates the statement about the biological specificity of practically possible ways of synthesis of the structure of organised systems. As a theoretical interpretation of the discussed problem, we propose to consider the system-evolutionary concept of P.K.Anokhin. The process of synthesis of functional structures in this context is an adaptive response of organisms to external conditions based on their ability to integrative synthesis of memory, needs and information about current conditions. The results of actual studies are in favour of this interpretation. We note that the physical basis of biological integrativity may be related to the phenomena of non-locality and non-separability characteristic of quantum systems. The problems considered in this paper are closely related to the problem of creating strong artificial intelligence.

This paper presents algorithm for modeling set of trajectories of non-stationary time series, based on a numerical scheme for approximating the sample density of the distribution function in a problem with fixed ends, when the initial distribution for a given number of steps transforms into a certain final distribution, so that at each step the semigroup property of solving the Liouville equation is satisfied. The model makes it possible to numerically construct evolving densities of distribution functions during random switching of states of the system generating the original time series.

The main problem is related to the fact that with the numerical implementation of the left-hand differential derivative in time, the solution becomes unstable, but such approach corresponds to the modeling of evolution. An integrative approach is used while choosing implicit stable schemes with “going into the future”, this does not match the semigroup property at each step. If, on the other hand, some real process is being modeled, in which goal-setting presumably takes place, then it is desirable to use schemes that generate a model of the transition process. Such model is used in the future in order to build a predictor of the disorder, which will allow you to determine exactly what state the process under study is going into, before the process really went into it. The model described in the article can be used as a tool for modeling real non-stationary time series.

Steps of the modeling scheme are described further. Fragments corresponding to certain states are selected from a given time series, for example, trends with specified slope angles and variances. Reference distributions of states are compiled from these fragments. Then the empirical distributions of the duration of the system’s stay in the specified states and the duration of the transition time from state to state are determined. In accordance with these empirical distributions, a probabilistic model of the disorder is constructed and the corresponding trajectories of the time series are modeled.

Equations of motion in Newtonian and Hamiltonian forms are used for classical molecular dynamics simulation of particle system time evolution. When Newton equations of motion are used for finding of particle coordinates and velocities in $N$-particle system it takes to solve $3N$ ordinary differential equations of second order at every time step. Traditionally numerical schemes of Verlet method are used for solving Newtonian equations of motion of molecular dynamics. A step of integration is necessary to decrease for Verlet numerical schemes steadiness conservation on sufficiently large time intervals. It leads to a significant increase of the volume of calculations. Numerical schemes of Verlet method with Hamiltonian conservation control (the energy of the system) at every time moment are used in the most software packages of molecular dynamics for numerical integration of equations of motion. It can be used two complement each other approaches to decrease of computational time in molecular dynamics calculations. The first of these approaches is based on enhancement and software optimization of existing software packages of molecular dynamics by using of vectorization, parallelization and special processor construction. The second one is based on the elaboration of efficient methods for numerical integration for equations of motion. A procedure for constructing of explicit, implicit and symmetric symplectic numerical schemes with given approximation accuracy in relation to integration step for solving of molecular dynamic equations of motion in Hamiltonian form is proposed in this work. The approach for construction of proposed in this work procedure is based on the following points: Hamiltonian formulation of equations of motion; usage of Taylor expansion of exact solution; usage of generating functions, for geometrical properties of exact solution conservation, in derivation of numerical schemes. Numerical experiments show that obtained in this work symmetric symplectic third-order accuracy scheme conserves basic properties of the exact solution in the approximate solution. It is more stable for approximation step and conserves Hamiltonian of the system with more accuracy at a large integration interval then second order Verlet numerical schemes.

In present paper we reexamine the properties of CABARET numerical scheme formulated for a weakly compressible fluid flow basing the results of free shear layer modeling. Kelvin–Helmholtz instability and successive generation of two-dimensional turbulence provide a wide field for a scheme analysis including temporal evolution of the integral energy and enstrophy curves, the vorticity patterns and energy spectra, as well as the dispersion relation for the instability increment. The most part of calculations is performed for Reynolds number $\text{Re} = 4 \times 10^5$ for square grids sequentially refined in the range of $128^2-2048^2$ nodes. An attention is paid to the problem of underresolved layers generating a spurious vortex during the vorticity layers roll-up. This phenomenon takes place only on a coarse grid with $128^2$ nodes, while the fully regularized evolution pattern of vorticity appears only when approaching $1024^2$-node grid. We also discuss the vorticity resolution properties of grids used with respect to dimensional estimates for the eddies at the borders of the inertial interval, showing that the available range of grids appears to be sufficient for a good resolution of small–scale vorticity patches. Nevertheless, we claim for the convergence achieved for the domains occupied by large-scale structures.

The generated turbulence evolution is consistent with theoretical concepts imposing the emergence of large vortices, which collect all the kinetic energy of motion, and solitary small-scale eddies. The latter resemble the coherent structures surviving in the filamentation process and almost noninteracting with other scales. The dissipative characteristics of numerical method employed are discussed in terms of kinetic energy dissipation rate calculated directly and basing theoretical laws for incompressible (via enstrophy curves) and compressible (with respect to the strain rate tensor and dilatation) fluid models. The asymptotic behavior of the kinetic energy and enstrophy cascades comply with two-dimensional turbulence laws $E(k) \propto k^{−3}, \omega^2(k) \propto k^{−1}$. Considering the instability increment as a function of dimensionless wave number shows a good agreement with other papers, however, commonly used method of instability growth rate calculation is not always accurate, so some modification is proposed. Thus, the implemented CABARET scheme possessing remarkably small numerical dissipation and good vorticity resolution is quite competitive approach compared to other high-order accuracy methods

The study completes a series of the author’s works devoted to the spread of particles population in supercritical catalytic branching random walk (CBRW) on a multidimensional lattice. The CBRW model describes the evolution of a system of particles combining their random movement with branching (reproduction and death) which only occurs at fixed points of the lattice. The set of such catalytic points is assumed to be finite and arbitrary. In the supercritical regime the size of population, initiated by a parent particle, increases exponentially with positive probability. The rate of the spread depends essentially on the distribution tails of the random walk jump. If the jump distribution has “light tails”, the “population front”, formed by the particles most distant from the origin, moves linearly in time and the limiting shape of the front is a convex surface. When the random walk jump has independent coordinates with a semiexponential distribution, the population spreads with a power rate in time and the limiting shape of the front is a star-shape nonconvex surface. So far, for regularly varying tails (“heavy” tails), we have considered the problem of scaled front propagation assuming independence of components of the random walk jump. Now, without this hypothesis, we examine an “isotropic” case, when the rate of decay of the jumps distribution in different directions is given by the same regularly varying function. We specify the probability that, for time going to infinity, the limiting random set formed by appropriately scaled positions of population particles belongs to a set $B$ containing the origin with its neighborhood, in $\mathbb{R}^d$. In contrast to the previous results, the random cloud of particles with normalized positions in the time limit will not concentrate on coordinate axes with probability one.

A new type of cosmological models for the Universe that has no Beginning and evolves from the infinitely distant past is considered.

These models are alternative to the cosmological models based on the Big Bang theory according to which the Universe has a finite age and was formed from an initial singularity.

In our opinion, there are certain problems in the Big Bang theory that our cosmological models do not have.

In our cosmological models, the Universe evolves by compression from the infinitely distant past tending a finite minimum of distances between objects of the order of the Compton wavelength $\lambda_C$ of hadrons and the maximum density of matter corresponding to the hadron era of the Universe. Then it expands progressing through all the stages of evolution established by astronomical observations up to the era of inflation.

The material basis that sets the fundamental nature of the evolution of the Universe in the our cosmological models is a nonlinear Dirac spinor field $\psi(x^k)$ with nonlinearity in the Lagrangian of the field of type $\beta(\bar{\psi}\psi)^n$ ($\beta = const$, $n$ is a rational number), where $\psi(x^k)$ is the 4-component Dirac spinor, and $\psi$ is the conjugate spinor.

In addition to the spinor field $\psi$ in cosmological models, we have other components of matter in the form of an ideal liquid with the equation of state $p = w\varepsilon$ $(w = const)$ at different values of the coefficient $w (−1 < w < 1)$. Additional components affect the evolution of the Universe and all stages of evolution occur in accordance with established observation data. Here $p$ is the pressure, $\varepsilon = \rho c^2$ is the energy density, $\rho$ is the mass density, and $c$ is the speed of light in a vacuum.

We have shown that cosmological models with a nonlinear spinor field with a nonlinearity coefficient $n = 2$ are the closest to reality.

In this case, the nonlinear spinor field is described by the Dirac equation with cubic nonlinearity.

But this is the Ivanenko–Heisenberg nonlinear spinor equation which W.Heisenberg used to construct a unified spinor theory of matter.

It is an amazing coincidence that the same nonlinear spinor equation can be the basis for constructing a theory of two different fundamental objects of nature — the evolving Universe and physical matter.

The developments of the cosmological models are supplemented by their computer researches the results of which are presented graphically in the work.