Security management of the functioning of a multi-node mobile cyber-physical system with a distributed registry based on an automatic model

The problems and features of security management of the functioning of a multi-node mobile cyber-physical system with a distributed registry based on an automatic model are considered. The algorithm of the functioning of the system node allows for the possibility of increasing or decreasing its resources using various approaches. Models of stochastic automata with a variable structure are used to model such systems. The process of functioning of a node of a system with a distributed registry based on a chain of blocks in the form of a finite automaton with a variable structure and linear tactics is formalized, which ensures that the sequence of changing the variants of the node’s behaviour strategy depends on the conditions of the environment with which it interacts by constructing state matrices.

Cascade Products of Stochastic Automata

Stochastic Moore automata have in opposition to stochastic Mealy automata the same capabilities as general stochastic automata, but have the advantage that they are easier to access than their pure stochastic counterparts. Cascade decomposition of automata leads to a loop-free partitioning and in this way contributes to the analysis of automata. This paper shows that stochastic Moore automata can be decomposed into cascade products of stochastic Moore automata under mild conditions

Machine learning of stochastic automata and evolutionary games

A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices.

Fuzzy Stochastic Automation Model for Decision Support in the Process Inter-Budgetary Regulation

The purpose of this article is to study the theoretical foundations of the concept of fiscal decentralization, as the main path of self-development of the national economy of any country, and to develop mathematical tools that support decision-making in the aspect of “hard” budget constraints. The study of the problems of fiscal policy formation in foreign countries presented in modern scientific literature has revealed that the degree of application of the concepts of “soft” and “hard” budget restrictions is an actual topic in the theory of fiscal federalism. It has been substantiated that decision-making within the framework of “soft” budget constraints (financial assistance) leads to low tax autonomy of territories and limited liability of regional and municipal authorities for the results of their financial policy. As a research hypothesis, we put forward the thesis that it is necessary to create conditions for encouraging subnational authorities to support the territorial economy by granting them the possibility to use part of the taxes collected in the respective territories. The implementation of this thesis has given rise to the problem of quantifying decisions made regarding the establishment of standards for the distribution of tax revenues between budgets of different levels of the hierarchy of the country’s budget system. In terms of solving this problem, the author has constructed mathematical models based on the use of synthesis of mathematical apparatus of the theory of stochastic automata, fuzzy algebra, and simulation. In terms of solving this problem, the author proposed the use of mathematical modeling methods. The article presents the results of constructing economic and mathematical models to support decision-making in the vertical distribution of tax revenues between budgets. The models include stochastic automata, as mathematical abstractions, describing the expedient behavior of an economic agent when choosing management alternatives for territories of different levels of economic development. The transition functions of automaton models are formally described on the basis of the synthesis of mathematical apparatus of the theories of stochastic automata operating in random environments and fuzzy sets. The expediency property of the behavior of automaton models is justified by proving the corresponding theorems. The random environment in which stochastic automata are immersed is formed by a simulation model. The article demonstrates the results of experiments carried out on models, as well as a conceptual scheme of interaction between the automaton and simulation models.

Analysis of two mechanisms of telomere maintenance based on the theory of g-Networks and stochastic automata networks

* Background Telomeres, which are composed of repetitive nucleotide sequences at the end of chromosomes, behave as a division clock that measures replicative senescence. Under the normal physiological condition, telomeres shorten with each cell division, and cells use the telomere lengths to sense the number of divisions. Replicative senescence has been shown to occur at approximately 50–70 cell divisions, which is termed the Hayflick’s limit. However, in cancer cells telomere lengths are stabilized, thereby allowing continual cell replication by two known mechanisms: activation of telomerase and Alternative Lengthening of Telomeres (ALT). The connections between the two mechanisms are complicated and still poorly understood. * Results In this research, we propose that two different approaches, G-Networks and Stochastic Automata Networks, which are stochastic models motivated by queueing theory, are useful to identify a set of genes that play an important role in the state of interest and to infer their previously unknown correlation by obtaining both stationary and joint transient distributions of the given system. Our analysis using G-Network detects five statistically significant genes (CEBPA, FOXM1, E2F1, c-MYC, hTERT) with either mechanism, contrasted to normal cells. A new algorithm is introduced to show how the correlation between two genes of interest varies in the transient state according not only to each mechanism but also to each cell condition. * Conclusions This study expands our existing knowledge of genes associated with mechanisms of telomere maintenance and provides a platform to understand similarities and differences between telomerase and ALT in terms of the correlation between two genes in the system. This is particularly important because telomere dynamics plays a major role in many physiological and disease processes, including hematopoiesis.