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The problem of optimal control of traffic flow in an urban road network is considered. The control is carried out by varying the duration of the working phases of traffic lights at controlled intersections. A description of the control system developed is given. The control system enables the use of three types of control: open-loop, feedback and manual. In feedback control, road infrastructure detectors, video cameras, inductive loop and radar detectors are used to determine the quantitative characteristics of current traffic flow state. The quantitative characteristics of the traffic flows are fed into a mathematical model of the traffic flow, implemented in the computer environment of an automatic traffic flow control system, in order to determine the moments for switching the working phases of the traffic lights. The model is a system of finite-difference recurrent equations and describes the change in traffic flow on each road section at each time step, based on retrived data on traffic flow characteristics in the network, capacity of maneuvers and flow distribution through alternative maneuvers at intersections. The model has scaling and aggregation properties. The structure of the model depends on the structure of the graph of the controlled road network. The number of nodes in the graph is equal to the number of road sections in the considered network. The simulation of traffic flow changes in real time makes it possible to optimally determine the duration of traffic light operating phases and to provide traffic flow control with feedback based on its current state. The system of automatic collection and processing of input data for the model is presented. In order to model the states of traffic flow in the network and to solve the problem of optimal traffic flow control, the CTraf software package has been developed, a brief description of which is given in the paper. An example of the solution of the optimal control problem of traffic flows on the basis of real data in the road network of Moscow is given.

A model of the behavior of the active neuron, which was the development of the model described in Shamis A.L. [Shamis, 2006], is designed. Proposed topology is locally connected matrix of the active neural network and the structure integration of information from different sources. An example of the script behavior robot controlled by this neural network is described. The results of experiments with the software implementation of a neural network are presented.

The paper simulated the spread of certain ideas in a professional virtual group. We consider the propagation of excitation in an inhomogeneous excitable medium of high connectivity. It is assumed that the network elements form a complete graph. Parameters of the elements are normally distributed. The simulation showed that interest in the idea can fade or fluctuate depending on the settings in the virtual group. The presence of a permanent excited element with relatively high activity leads to chaos — the fraction of members of the community actively interested in an idea varies irregularly.

This paper targets programmers and developers interested in utilizing parallel programming techniques to enhance application performance. The Oracle Solaris Studio software provides state-of-the-art optimizing and parallelizing compilers for C, C++ and Fortran, an advanced debugger, and optimized mathematical and performance libraries. Also included are an extremely powerful performance analysis tool for profiling serial and parallel applications, a thread analysis tool to detect data races and deadlock in memory parallel programs, and an Integrated Development Environment (IDE). The Oracle Message Passing Toolkit software provides the high-performance MPI libraries and associated run-time environment needed for message passing applications that can run on a single system or across multiple compute systems connected with high performance networking, including Gigabit Ethernet, 10 Gigabit Ethernet, InfiniBand and Myrinet. Examples of OpenMP and MPI are provided throughout the paper, including their usage via the Oracle Solaris Studio and Oracle Message Passing Toolkit products for development and deployment of both serial and parallel applications on SPARC and x86/x64 based systems. Throughout this paper it is demonstrated how to develop and deploy an application parallelized with OpenMP and/or MPI.

The research is devoted to the problem of the use of social networks as a tool of the illegal activity and as a source of information that could be dangerous to society. The article presents the structure of the multiagent system with which a social network groups could be clustered according to the criteria uniquely defines a group as a destructive. The agents’ of the system clustering algorithm is described.

A crucial component of distributed computing systems is network infrastructure. While networking forms the backbone of such systems, it is often the invisible partner to storage and computing resources. We propose to integrate Network Elements directly into distributed systems through the workload management layer. There are many reasons for this approach. As the complexity and demand for distributed systems grow, it is important to use existing infrastructure efficiently. For example, one could use network performance measurements in the decision making mechanisms of workload management systems. New advanced technologies allow one to programmatically define network configuration, for example SDN — Software Defined Networks. We will describe how these methods are being used within the PanDA workload management system of the ATLAS collaboration.

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.

We consider a one-dimensional three velocity kinetic lattice Boltzmann model, which represents a secondorder difference scheme for hydrodynamic equations. In the framework of kinetic theory this system describes the propagation and interaction of three types of particles. It has been shown previously that the lattice Boltzmann model with external virtual force is equivalent at the hydrodynamic limit to the one-dimensional hemodynamic equations for elastic vessels, this equivalence can be achieved with use of the Chapman – Enskog expansion. The external force in the model is responsible for the ability to adjust the functional dependence between the lumen area of the vessel and the pressure applied to the wall of the vessel under consideration. Thus, the form of the external force allows to model various elastic properties of the vessels. In the present paper the physiological boundary conditions are considered at the inlets and outlets of the arterial network in terms of the lattice Boltzmann variables. We consider the following boundary conditions: for pressure and blood flow at the inlet of the vascular network, boundary conditions for pressure and blood flow for the vessel bifurcations, wave reflection conditions (correspond to complete occlusion of the vessel) and wave absorption at the ends of the vessels (these conditions correspond to the passage of the wave without distortion), as well as RCR-type conditions, which are similar to electrical circuits and consist of two resistors (corresponding to the impedance of the vessel, at the end of which the boundary conditions are set and the friction forces in microcirculatory bed) and one capacitor (describing the elastic properties of arterioles). The numerical simulations were performed: the propagation of blood in a network of three vessels was considered, the boundary conditions for the blood flow were set at the entrance of the network, RCR boundary conditions were stated at the ends of the network. The solutions to lattice Boltzmann model are compared with the benchmark solutions (based on numerical calculations for second-order McCormack difference scheme without viscous terms), it is shown that the both approaches give very similar results.

We found a predominance of α-rhythm patterns in the left hemisphere in healthy people compared to people with COVID-19 history. Moreover, we observe a significant decrease in the left hemisphere contribution to the speech center area in people who have undergone COVID-19 when performing speech tasks.

Our findings show that the signal in healthy subjects is more spatially localized and synchronized between hemispheres when performing tasks compared to people who recovered from COVID-19. We also observed a decrease in low frequencies in both hemispheres after COVID-19.

EEG-patterns of COVID-19 are detectable in an unusual frequency domain. What is usually considered noise in electroencephalographic (EEG) data carries information that can be used to determine whether or not a person has had COVID-19. These patterns can be interpreted as signs of hemispheric desynchronization, premature brain ageing, and more significant brain strain when performing simple tasks compared to people who did not have COVID-19.

In our work, we have shown the applicability of neural networks in helping to detect the long-term effects of COVID-19 on EEG-data. Furthermore, our data following other studies supported the hypothesis of the severity of the long-term effects of COVID-19 detected on the EEG-data of EEG-based BCI. The presented findings of functional activity of the brain– computer interface make it possible to use machine learning methods on simple, non-invasive brain–computer interfaces to detect post-COVID syndrome and develop progress in neurorehabilitation.

In some cases, information warfare results in almost whole population accepting one of two contesting points of view and rejecting the other. In other cases, however, the “majority party” gets only a small advantage over the “minority party”. The relevant question is which network characteristics of a population contribute to the minority being able to maintain some significant numbers. Given that some societies are more connected than others, in the sense that they have a higher density of social ties, this question is specified as follows: how does the density of social ties affect the chances of a minority to maintain a significant number? Does a higher density contribute to a landslide victory of majority, or to resistance of minority? To address this issue, we consider information warfare between two parties, called the Left and the Right, in the population, which is represented as a network, the nodes of which are individuals, and the connections correspond to their acquaintance and describe mutual influence. At each of the discrete points in time, each individual decides which party to support based on their attitude, i. e. predisposition to the Left or Right party and taking into account the influence of his network ties. The influence means here that each tie sends a cue with a certain probability to the individual in question in favor of the party that themselves currently support. If the tie switches their party affiliation, they begin to agitate the individual in question for their “new” party. Such processes create dynamics, i. e. the process of changing the partisanship of individuals. The duration of the warfare is exogenously set, with the final time point roughly associated with the election day. The described model is numerically implemented on a scale-free network. Numerical experiments have been carried out for various values of network density. Because of the presence of stochastic elements in the model, 200 runs were conducted for each density value, for each of which the final number of supporters of each of the parties was calculated. It is found that with higher density, the chances increase that the winner will cover almost the entire population. Conversely, low network density contributes to the chances of a minority to maintain significant numbers.