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A cluster method of mathematical modeling of interval-stochastic thermal processes in complex electronic systems (ES), is developed. In the cluster method, the construction of a complex ES is represented in the form of a thermal model, which is a system of clusters, each of which contains a core that combines the heat-generating elements falling into a given cluster, the cluster shell and a medium flow through the cluster. The state of the thermal process in each cluster and every moment of time is characterized by three interval-stochastic state variables, namely, the temperatures of the core, shell, and medium flow. The elements of each cluster, namely, the core, shell, and medium flow, are in thermal interaction between themselves and elements of neighboring clusters. In contrast to existing methods, the cluster method allows you to simulate thermal processes in complex ESs, taking into account the uneven distribution of temperature in the medium flow pumped into the ES, the conjugate nature of heat exchange between the medium flow in the ES, core and shells of clusters, and the intervalstochastic nature of thermal processes in the ES, caused by statistical technological variation in the manufacture and installation of electronic elements in ES and random fluctuations in the thermal parameters of the environment. The mathematical model describing the state of thermal processes in a cluster thermal model is a system of interval-stochastic matrix-block equations with matrix and vector blocks corresponding to the clusters of the thermal model. The solution to the interval-stochastic equations are statistical measures of the state variables of thermal processes in clusters - mathematical expectations, covariances between state variables and variance. The methodology for applying the cluster method is shown on the example of a real ES.

Theory of anomalous diffusion, which describe a vast number of transport processes with power law mean squared displacement, is actively advancing in recent years. Diffusion of liquids in porous media, carrier transport in amorphous semiconductors and molecular transport in viscous environments are widely known examples of anomalous deceleration of transport processes compared to the standard model.

Direct Monte Carlo simulation is a convenient tool for studying such processes. An efficient stochastic simulation algorithm is developed in the present paper. It is based on simple renewal process with interarrival times that have power law asymptotics. Analytical derivations show a deep connection between this class of random process and equations with fractional derivatives. The algorithm is further generalized by coupling it with chemical reaction simulation. It makes stochastic approach especially useful, because the exact form of integrodifferential evolution equations for reaction — subdiffusion systems is still a matter of debates.

Proposed algorithm relies on non-markovian random processes, hence one should carefully account for qualitatively new effects. The main question is how molecules leave the system during chemical reactions. An exact scheme which tracks all possible molecule combinations for every reaction channel is computationally infeasible because of the huge number of such combinations. It necessitates application of some simple heuristic procedures. Choosing one of these heuristics greatly affects obtained results, as illustrated by a series of numerical experiments.

The repressilator is the first genetic regulatory network in synthetic biology, which was artificially constructed in 2000. It is a closed network of three genetic elements $lacI$, $\lambda cI$ and $tetR$, which have a natural origin, but are not found in nature in such a combination. The promoter of each of the three genes controls the next cistron via the negative feedback, suppressing the expression of the neighboring gene. In our previous paper [Bratsun et al., 2018], we proposed a mathematical model of a delayed repressillator and studied its properties within the framework of a deterministic description. We assume that delay can be both natural, i.e. arises during the transcription / translation of genes due to the multistage nature of these processes, and artificial, i.e. specially to be introduced into the work of the regulatory network using gene engineering technologies. In this work, we apply the stochastic description of dynamic processes in a delayed repressilator, which is an important addition to deterministic analysis due to the small number of molecules involved in gene regulation. The stochastic study is carried out numerically using the Gillespie algorithm, which is modified for time delay systems. We present the description of the algorithm, its software implementation, and the results of benchmark simulations for a onegene delayed autorepressor. When studying the behavior of a repressilator, we show that a stochastic description in a number of cases gives new information about the behavior of a system, which does not reduce to deterministic dynamics even when averaged over a large number of realizations. We show that in the subcritical range of parameters, where deterministic analysis predicts the absolute stability of the system, quasi-regular oscillations may be excited due to the nonlinear interaction of noise and delay. Earlier, we have discovered within the framework of the deterministic description, that there exists a long-lived transient regime, which is represented in the phase space by a slow manifold. This mode reflects the process of long-term synchronization of protein pulsations in the work of the repressilator genes. In this work, we show that the transition to the cooperative mode of gene operation occurs a two order of magnitude faster, when the effect of the intrinsic noise is taken into account. We have obtained the probability distribution of moment when the phase trajectory leaves the slow manifold and have determined the most probable time for such a transition. The influence of the intrinsic noise of chemical reactions on the dynamic properties of the repressilator is discussed.

The article describes the examples of the analysis of the experimental data spectra for identifying typical structures of processes forming plasma turbulence. The method is based on the original algorithm which is close to the one-sample bootstrap. The base model for description of the fine structure of stochastic processes is finite local-scale normal mixtures. For finding the statistical estimates (maximum likelihood estimates) well known EM algorithm is used. The efficiency of the proposed research technique is demonstrated for a number of spectra’s set obtained in different modes of low-frequency plasma turbulence.

A critical analysis of existing approaches, methods and models to solve the problem of financial resources operational management has been carried out in the article. A number of significant shortcomings of the presented models were identified, limiting the scope of their effective usage. There are a static nature of the models, probabilistic nature of financial flows are not taken into account, daily amounts of receivables and payables that significantly affect the solvency and liquidity of the company are not identified. This necessitates the development of a new model that reflects the essential properties of the planning financial flows system — stochasticity, dynamism, non-stationarity.

The model for the financial flows distribution has been developed. It bases on the principles of optimal dynamic control and provides financial resources planning ensuring an adequate level of liquidity and solvency of a company and concern initial data uncertainty. The algorithm for designing the objective cash balance, based on principles of a companies’ financial stability ensuring under changing financial constraints, is proposed.

Characteristic of the proposed model is the presentation of the cash distribution process in the form of a discrete dynamic process, for which a plan for financial resources allocation is determined, ensuring the extremum of an optimality criterion. Designing of such plan is based on the coordination of payments (cash expenses) with the cash receipts. This approach allows to synthesize different plans that differ in combinations of financial outflows, and then to select the best one according to a given criterion. The minimum total costs associated with the payment of fines for non-timely financing of expenses were taken as the optimality criterion. Restrictions in the model are the requirement to ensure the minimum allowable cash balances for the subperiods of the planning period, as well as the obligation to make payments during the planning period, taking into account the maturity of these payments. The suggested model with a high degree of efficiency allows to solve the problem of financial resources distribution under uncertainty over time and receipts, coordination of funds inflows and outflows. The practical significance of the research is in developed model application, allowing to improve the financial planning quality, to increase the management efficiency and operational efficiency of a company.

Instrument of numerical-analytical modeling of characteristics of propagation of electromagnetic waves in chaotic space plasma with taking into account effects of gravitation is developed for interpretation of data of measurements of astrophysical precision instruments of new education. The task of propagation of waves in curved (Riemann’s) space is solved in Euclid’s space by introducing of the effective index of refraction of vacuum. The gravitational potential can be calculated for various model of distribution of mass of astrophysical objects and at solution of Poisson’s equation. As a result the effective index of refraction of vacuum can be evaluated. Approximate model of the effective index of refraction is suggested with condition that various objects additively contribute in total gravitational field. Calculation of the characteristics of electromagnetic waves in the gravitational field of astrophysical objects is performed by the approximation of geometrical optics with condition that spatial scales of index of refraction a lot more wavelength. Light differential equations in Euler’s form are formed the basis of numerical-analytical instrument of modeling of trajectory characteristic of waves. Chaotic inhomogeneities of space plasma are introduced by model of spatial correlation function of index of refraction. Calculations of refraction scattering of waves are performed by the approximation of geometrical optics. Integral equations for statistic moments of lateral deviations of beams in picture plane of observer are obtained. Integrals for moments are reduced to system of ordinary differential equations the firsts order with using analytical transformations for cooperative numerical calculation of arrange and meansquare deviations of light. Results of numerical-analytical modeling of trajectory picture of propagation of electromagnetic waves in interstellar space with taking into account impact of gravitational fields of space objects and refractive scattering of waves on inhomogeneities of index of refraction of surrounding plasma are shown. Based on the results of modeling quantitative estimation of conditions of stochastic blurring of the effect of gravitational lensing of electromagnetic waves at various frequency ranges is performed. It’s shown that operating frequencies of meter range of wavelengths represent conditional low-frequency limit for observational of the effect of gravitational lensing in stochastic space plasma. The offered instrument of numerical-analytical modeling can be used for analyze of structure of electromagnetic radiation of quasar propagating through group of galactic.

In the present article we explore the process of changing the level of approval of a political leader under the influence of the processes taking place in online platforms (social networks, forums, etc.). The driver of these changes is the interaction of users, through which they can exchange opinions with each other and formulate their position in relation to the political leader. In addition to interpersonal interaction, we will consider such factors as the information impact, expressed in the creation of an information flow with a given power and polarity (positive or negative, in the context of influencing the image of a political leader), as well as the presence of a group of agents (opinion leaders), supporting the leader, or, conversely, negatively affecting its representation in the media space.

The mathematical basis of the presented research is the Kirman model, which has its roots in biology and initially found its application in economics. Within the framework of this model it is considered that each user is in one of the two possible states, and a Markov jump process describing transitions between these states is given. For the problem under consideration, these states are 0 or 1, depending on whether a particular agent is a supporter of a political leader or not. For further research, we find its diffusional approximation, known as the Jacoby process. With the help of spectral decomposition for the infinitesimal operator of this process we have an opportunity to find an analytical representation for the transition probability density.

Analyzing the probabilities obtained in this way, we can assess the influence of individual factors of the model: the power and direction of the information flow, available to online users and relevant to the tasks of rating formation, as well as the number of supporters or opponents of the politician. Next, using the found eigenfunctions and eigenvalues, we derive expressions for the evaluation of conditional mathematical expectations of a politician’s rating, which can serve as a basis for building forecasts that are important for the formation of a strategy of representing a political leader in the online environment.

This article discusses the influence of the shadow, informal and household sectors on the dynamics of a stochastic model with heterogeneous (heterogeneous) agents. The study uses the integration of the general equilibrium approach to explain the behavior of demand, supply and prices in an economy with several interacting markets, and a multi-agent approach. The analyzed model describes an economy with aggregated uncertainty and with an infinite number of heterogeneous agents (households). The source of heterogeneity is the idiosyncratic income shocks of agents in the legal and shadow sectors of the economy. In the analysis, an algorithm is used to approximate the dynamics of the distribution function of the capital stocks of individual agents — the dynamics of its first and second moments. The synthesis of the agent approach and the general equilibrium approach is carried out using computer implementation of the recursive feedback between microagents and macroenvironment. The behavior of the impulse response functions of the main variables of the model confirms the positive influence of the shadow economy (below a certain limit) on minimizing the rate of decline in economic indicators during recessions, especially for developing economies. The scientific novelty of the study is the combination of a multi-agent approach and a general equilibrium approach for modeling macroeconomic processes at the regional and national levels. Further research prospects may be associated with the use of more detailed general equilibrium models, which allow, in particular, to describe the behavior of heterogeneous groups of agents in the entrepreneurial sector of the economy.

In this paper, we test the performance of some modern stochastic optimization methods and practices with respect to the digital pre-distortion problem, which is a valuable part of processing signal on base stations providing wireless communication. In the first part of our study, we focus on the search for the best performing method and its proper modifications. In the second part, we propose the new, quasi-online, testing framework that allows us to fit our modeling results with the behavior of real-life DPD prototype, retest some selected of practices considered in the previous section and approve the advantages of the method appearing to be the best under real-life conditions. For the used model, the maximum achieved improvement in depth is 7% in the standard regime and 5% in the online regime (metric itself is of logarithmic scale). We also achieve a halving of the working time preserving 3% and 6% improvement in depth for the standard and online regime, respectively. All comparisons are made to the Adam method, which was highlighted as the best stochastic method for DPD problem in [Pasechnyuk et al., 2021], and to the Adamax method, which is the best in the proposed online regime.

Distributed saddle point problems (SPPs) have numerous applications in optimization, matrix games and machine learning. For example, the training of generated adversarial networks is represented as a min-max optimization problem, and training regularized linear models can be reformulated as an SPP as well. This paper studies distributed nonsmooth SPPs with Lipschitz-continuous objective functions. The objective function is represented as a sum of several components that are distributed between groups of computational nodes. The nodes, or agents, exchange information through some communication network that may be centralized or decentralized. A centralized network has a universal information aggregator (a server, or master node) that directly communicates to each of the agents and therefore can coordinate the optimization process. In a decentralized network, all the nodes are equal, the server node is not present, and each agent only communicates to its immediate neighbors.

We assume that each of the nodes locally holds its objective and can compute its value at given points, i. e. has access to zero-order oracle. Zero-order information is used when the gradient of the function is costly, not possible to compute or when the function is not differentiable. For example, in reinforcement learning one needs to generate a trajectory to evaluate the current policy. This policy evaluation process can be interpreted as the computation of the function value. We propose an approach that uses a smoothing technique, i. e., applies a first-order method to the smoothed version of the initial function. It can be shown that the stochastic gradient of the smoothed function can be viewed as a random two-point gradient approximation of the initial function. Smoothing approaches have been studied for distributed zero-order minimization, and our paper generalizes the smoothing technique on SPPs.