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A hierarchical method of mathematical and computer modeling of interval-stochastic thermal processes in complex electronic systems for various purposes is developed. The developed concept of hierarchical structuring reflects both the constructive hierarchy of a complex electronic system and the hierarchy of mathematical models of heat exchange processes. Thermal processes that take into account various physical phenomena in complex electronic systems are described by systems of stochastic, unsteady, and nonlinear partial differential equations and, therefore, their computer simulation encounters considerable computational difficulties even with the use of supercomputers. The hierarchical method avoids these difficulties. The hierarchical structure of the electronic system design, in general, is characterized by five levels: Level 1 — the active elements of the ES (microcircuits, electro-radio-elements); Level 2 — electronic module; Level 3 — a panel that combines a variety of electronic modules; Level 4 — a block of panels; Level 5 — stand installed in a stationary or mobile room. The hierarchy of models and modeling of stochastic thermal processes is constructed in the reverse order of the hierarchical structure of the electronic system design, while the modeling of interval-stochastic thermal processes is carried out by obtaining equations for statistical measures. The hierarchical method developed in the article allows to take into account the principal features of thermal processes, such as the stochastic nature of thermal, electrical and design factors in the production, assembly and installation of electronic systems, stochastic scatter of operating conditions and the environment, non-linear temperature dependencies of heat exchange factors, unsteady nature of thermal processes. The equations obtained in the article for statistical measures of stochastic thermal processes are a system of 14 non-stationary nonlinear differential equations of the first order in ordinary derivatives, whose solution is easily implemented on modern computers by existing numerical methods. The results of applying the method for computer simulation of stochastic thermal processes in electron systems are considered. The hierarchical method is applied in practice for the thermal design of real electronic systems and the creation of modern competitive devices.

Modern methods of complex planning the execution of task packages in multistage systems are characterized by the presence of restrictions on the dimension of the problem being solved, the impossibility of guaranteed obtaining effective solutions for various values of its input parameters, as well as the impossibility of registration the conditions for the formation of sets from the result and the restriction on the interval duration of time of the system operating. The decomposition of the generalized function of the system into a set of hierarchically interconnected subfunctions is implemented to solve the problem of scheduling the execution of task packages with generating sets of results and the restriction on the interval duration of time for the functioning of the system. The use of decomposition made it possible to employ the hierarchical approach for planning the execution of task packages in multistage systems, which provides the determination of decisions by the composition of task groups at the first level of the hierarchy decisions by the composition of task packages groups executed during time intervals of limited duration at the second level and schedules for executing packages at the third level the hierarchy. In order to evaluate decisions on the composition of packages, the results of their execution, obtained during the specified time intervals, are distributed among the packages. The apparatus of the theory of hierarchical games is used to determine complex solutions. A model of a hierarchical game for making decisions by the compositions of packages, groups of packages and schedules of executing packages is built, which is a system of hierarchically interconnected criteria for optimizing decisions. The model registers the condition for the formation of sets from the results of the execution of task packages and restriction on duration of time intervals of its operating. The problem of determining the compositions of task packages and groups of task packages is NP-hard; therefore, its solution requires the use of approximate optimization methods. In order to optimize groups of task packages, the construction of a method for formulating initial solutions by their compositions has been implemented, which are further optimized. Moreover, a algorithm for distributing the results of executing task packages obtained during time intervals of limited duration by sets is formulated. The method of local solutions optimization by composition of packages groups, in accordance with which packages are excluded from groups, the results of which are not included in sets, and packages, that aren’t included in any group, is proposed. The software implementation of the considered method of complex optimization of the compositions of task packages, groups of task packages, and schedules for executing task packages from groups (including the implementation of the method for optimizing the compositions of groups of task packages) has been performed. With its use, studies of the features of the considered planning task are carried out. Conclusion are formulated concerning the dependence of the efficiency of scheduling the execution of task packages in multistage system under the introduced conditions from the input parameters of the problem. The use of the method of local optimization of the compositions of groups of task packages allows to increase the number of formed sets from the results of task execution in packages from groups by 60% in comparison with fixed groups (which do not imply optimization).

Dynamic game theoretic models of the corporative innovative development are investigated. The proposed models are based on concordance of private and public interests of agents. It is supposed that the structure of interests of each agent includes both private (personal interests) and public (interests of the whole company connected with its innovative development first) components. The agents allocate their personal resources between these two directions. The system dynamics is described by a difference (not differential) equation. The proposed model of innovative development is studied by simulation and the method of enumeration of the domains of feasible controls with a constant step. The main contribution of the paper consists in comparative analysis of efficiency of the methods of hierarchical control (compulsion or impulsion) for information structures of Stackelberg or Germeier (four structures) by means of the indices of system compatibility. The proposed model is a universal one and can be used for a scientifically grounded support of the programs of innovative development of any economic firm. The features of a specific company are considered in the process of model identification (a determination of the specific classes of model functions and numerical values of its parameters) which forms a separate complex problem and requires an analysis of the statistical data and expert estimations. The following assumptions about information rules of the hierarchical game are accepted: all players use open-loop strategies; the leader chooses and reports to the followers some values of administrative (compulsion) or economic (impulsion) control variables which can be only functions of time (Stackelberg games) or depend also on the followers’ controls (Germeier games); given the leader’s strategies all followers simultaneously and independently choose their strategies that gives a Nash equilibrium in the followers’ game. For a finite number of iterations the proposed algorithm of simulation modeling allows to build an approximate solution of the model or to conclude that it doesn’t exist. A reliability and efficiency of the proposed algorithm follow from the properties of the scenario method and the method of a direct ordered enumeration with a constant step. Some comprehensive conclusions about the comparative efficiency of methods of hierarchical control of innovations are received.

The main statements and inferences of the Dynamic Theory Information (DTI) are considered. It is shown that DTI provides the possibility two reveal two essentially important types of information: objective (unconventional) and subjective (conventional) informtion. There are two ways of obtaining information: reception (perception of an already existing one) and generation (production of new) information. It is shown that the processes of generation and perception of information should proceed in two different subsystems of the same cognitive system. The main points of the Natural-Constructivist Approach to modeling the cognitive process are discussed. It is shown that any neuromorphic approach faces the problem of Explanatory Gap between the “Brain” and the “Mind”, i. e. the gap between objectively measurable information about the ensemble of neurons (“Brain”) and subjective information about the human consciousness (“Mind”). The Natural-Constructive Cognitive Architecture developed within the framework of this approach is discussed. It is a complex block-hierarchical combination of several neuroprocessors. The main constructive feature of this architecture is splitting the whole system into two linked subsystems, by analogy with the hemispheres of the human brain. One of the subsystems is processing the new information, learning, and creativity, i.e. for the generation of information. Another subsystem is responsible for processing already existing information, i.e. reception of information. It is shown that the lowest (zero) level of the hierarchy is represented by processors that should record images of real objects (distributed memory) as a response to sensory signals, which is objective information (and refers to the “Brain”). The next hierarchy levels are represented by processors containing symbols of the recorded images. It is shown that symbols represent subjective (conventional) information created by the system itself and providing its individuality. The highest hierarchy levels containing the symbols of abstract concepts provide the possibility to interpret the concepts of “consciousness”, “sub-consciousness”, “intuition”, referring to the field of “Mind”, in terms of the ensemble of neurons. Thus, DTI provides an opportunity to build a model that allows us to trace how the “Mind” could emerge basing on the “Brain”.

Mathematical models of gas dynamics and its computational industry, in our opinion, are far from perfect. We will look at this problem from the point of view of a clear probabilistic micro-model of a gas from hard spheres, relying on both the theory of random processes and the classical kinetic theory in terms of densities of distribution functions in phase space, namely, we will first construct a system of nonlinear stochastic differential equations (SDE), and then a generalized random and nonrandom integro-differential Boltzmann equation taking into account correlations and fluctuations. The key feature of the initial model is the random nature of the intensity of the jump measure and its dependence on the process itself.

Briefly recall the transition to increasingly coarse meso-macro approximations in accordance with a decrease in the dimensionalization parameter, the Knudsen number. We obtain stochastic and non-random equations, first in phase space (meso-model in terms of the Wiener — measure SDE and the Kolmogorov – Fokker – Planck equations), and then — in coordinate space (macro-equations that differ from the Navier – Stokes system of equations and quasi-gas dynamics systems). The main difference of this derivation is a more accurate averaging by velocity due to the analytical solution of stochastic differential equations with respect to the Wiener measure, in the form of which an intermediate meso-model in phase space is presented. This approach differs significantly from the traditional one, which uses not the random process itself, but its distribution function. The emphasis is placed on the transparency of assumptions during the transition from one level of detail to another, and not on numerical experiments, which contain additional approximation errors.

The theoretical power of the microscopic representation of macroscopic phenomena is also important as an ideological support for particle methods alternative to difference and finite element methods.

The article describes a mathematical model that simulates performance of a hierarchical organization during an early stage of a crisis. A distinguished feature of this stage of crisis is presence of so called early warning signals containing information on the approaching event. Employees are capable of catching the early warnings and of preparing the organization for the crisis based on the signals’ meaning. The efficiency of the preparation depends on both parameters of the organization and parameters of the crisis. The proposed simulation agentbased model is implemented on Java programming language and is used for conducting experiments via Monte- Carlo method. The goal of the experiments is to compare how centralized and decentralized organizational structures perform during sudden and smoldering crises. By centralized organizations we assume structures with high number of hierarchy levels and low number of direct reports of every manager, while decentralized organizations mean structures with low number of hierarchy levels and high number of direct reports of every manager. Sudden crises are distinguished by short early stage and low number of warning signals, while smoldering crises are defined as crises with long lasting early stage and high number of warning signals not necessary containing important information. Efficiency of the organizational performance during early stage of a crisis is measured by two parameters: percentage of early warnings which have been acted upon in order to prepare organization for the crisis, and time spent by top-manager on working with early warnings. As a result, we show that during early stage of smoldering crises centralized organizations process signals more efficiently than decentralized organizations, while decentralized organizations handle early warning signals more efficiently during early stage of sudden crises. However, occupation of top-managers during sudden crises is higher in decentralized organizations and it is higher in centralized organizations during smoldering crises. Thus, neither of the two classes of organizational structures is more efficient by the two parameters simultaneously. Finally, we conduct sensitivity analysis to verify the obtained results.

The Compact Muon Solenoid (CMS) is a high-performance general-purpose detector at the Large Hadron Collider (LHC) at CERN. A distributed data analysis system for processing and further analysis of CMS experimental data has been developed and this model foresees the obligatory usage of modern grid-technologies. The CMS Computing Model makes use of the hierarchy of computing centers (Tiers). The Joint Institute for Nuclear Research (JINR) takes an active part in the CMS experiment. In order to provide a proper computing infrastructure for the CMS experiment at JINR and for Russian institutes collaborating in CMS, Tier-1 center for the CMS experiment is constructing at JINR. The main tasks and services of the CMS Tier-1 at JINR are described. The status and perspectives of the Tier1 center for the CMS experiment at JINR are presented.

In this paper we consider a new modification of the discrete version of Mikhailov’s ‘power–society’ model, previously proposed by the author. This modification includes social-economical dynamics and corruption of the system similarly to continuous ‘power–society–economics–corruption’ model but is based on a stochastic cellular automaton describing the dynamics of power distribution in a hierarchy. This new version is founded on previously proposed ‘power–society’ system modeling cellular automaton, its cell state space enriched with variables corresponding to population, economic production, production assets volume and corruption level. The social-economical structure of the model is inherited from Solow and deterministic continuous ‘power–society–economics–corruption’ models. At the same time the new model is flexible, allowing to consider regional differentiation in all social and economical dynamics parameters, to use various production and demography models and to account for goods transit between the regions. A simulation system was built, including three power hierarchy levels, five regions and 100 municipalities. and a number of numerical experiments were carried out. This research yielded results showing specific changes of the dynamics in power distribution in hierarchy when corruption level increases. While corruption is zero (similar to the previous version of the model) the power distribution in hierarchy asymptotically tends to one of stationary states. If the corruption level increases substantially, volume of power in the system is subjected to irregular oscillations, and only much later tends to a stationary value. The meaning of these results can be interpreted as the fact that the stability of power hierarchy decreases when corruption level goes up.

In this paper we consider mathematical model to control water quality. We study a system with two-level hierarchy: one environmental organization (supervisor) at the top level and a few industrial enterprises (agents) at the lower level. The main goal of the supervisor is to keep water pollution level below certain value, while enterprises pollute water, as a side effect of the manufacturing process. Supervisor achieves its goal by charging a penalty for enterprises. On the other hand, enterprises choose how much to purify their wastewater to maximize their income.The fee increases the budget of the supervisor. Moreover, effulent fees are charged for the quantity and/or quality of the discharged pollution. Unfortunately, in practice, such charges are ineffective due to the insufficient tax size. The article solves the problem of determining the optimal size of the charge for pollution discharge, which allows maintaining the quality of river water in the rear range.

We describe system members goals with target functionals, and describe water pollution level and enterprises state as system of ordinary differential equations. We consider the problem from both supervisor and enterprises sides. From agents’ point a normal-form game arises, where we search for Nash equilibrium and for the supervisor, we search for Stackelberg equilibrium. We propose numerical algorithms for finding both Nash and Stackelberg equilibrium. When we construct Nash equilibrium, we solve optimal control problem using Pontryagin’s maximum principle. We construct Hamilton’s function and solve corresponding system of partial differential equations with shooting method and finite difference method. Numerical calculations show that the low penalty for enterprises results in increasing pollution level, when relatively high penalty can result in enterprises bankruptcy. This leads to the problem of choosing optimal penalty, which requires considering problem from the supervisor point. In that case we use the method of qualitatively representative scenarios for supervisor and Pontryagin’s maximum principle for agents to find optimal control for the system. At last, we compute system consistency ratio and test algorithms for different data. The results show that a hierarchical control is required to provide system stability.

The paper considers a method for studying panel data based on the use of agglomerative hierarchical clustering — grouping objects based on the similarities and differences in their features into a hierarchy of clusters nested into each other. We used 2 alternative methods for calculating Euclidean distances between objects — the distance between the values averaged over observation interval, and the distance using data for all considered years. Three alternative methods for calculating the distances between clusters were compared. In the first case, the distance between the nearest elements from two clusters is considered to be distance between these clusters, in the second — the average over pairs of elements, in the third — the distance between the most distant elements. The efficiency of using two clustering quality indices, the Dunn and Silhouette index, was studied to select the optimal number of clusters and evaluate the statistical significance of the obtained solutions. The method of assessing statistical reliability of cluster structure consisted in comparing the quality of clustering on a real sample with the quality of clustering on artificially generated samples of panel data with the same number of objects, features and lengths of time series. Generation was made from a fixed probability distribution. At the same time, simulation methods imitating Gaussian white noise and random walk were used. Calculations with the Silhouette index showed that a random walk is characterized not only by spurious regression, but also by “spurious clustering”. Clustering was considered reliable for a given number of selected clusters if the index value on the real sample turned out to be greater than the value of the 95% quantile for artificial data. A set of time series of indicators characterizing production in the regions of the Russian Federation was used as a sample of real data. For these data only Silhouette shows reliable clustering at the level p < 0.05. Calculations also showed that index values for real data are generally closer to values for random walks than for white noise, but it have significant differences from both. Since three-dimensional feature space is used, the quality of clustering was also evaluated visually. Visually, one can distinguish clusters of points located close to each other, also distinguished as clusters by the applied hierarchical clustering algorithm.