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The problem of accumulation and the statistical analysis of computer experiment results are solved. The main experiment program is considered as the data source. The results of main experiment are collected on specially prepared sheet Excel with pre-organized structure for the accumulation, statistical processing and visualization of the data. The created method and the program are used at efficiency research of the scientific researches which are carried out by authors.

The adequate complexity three-dimensional finite element model of biomechanical system with space, shell and beam-type elements was built. The model includes the Ilizarov fixator and tibial bone’s simulator with the regenerating tissue at the fracture location. The proposed model allows us to specify the orthotropic elastic properties of tibial bone model in cortical and trabecular zones. It is also possible to change the basic geometrical and mechanical characteristics of biomechanical system, change the finite element mash density and define the different external loads, such as pressure on the bone and compression or distraction between the repositioned rings of Ilizarov device.

By using special APDL ANSYS program macros the mode of deformation was calculated in the fracture zone for various static loads on the simulator bone, for compression or distraction between the repositioned rings and for various mechanical properties during different stages of the bone regenerate formation (gelatinous, cartilaginous, trabecular and cortical bone remodeling). The obtained results allow us to estimate the permissible values of the external pressure on the bone and of the displacements of the Ilizarov fixator rings for different stages of the bone regeneration, based on the admittance criterion for the maximum of the stresses in the callus. The presented data can be used in a clinical condition for planning, realization and monitoring of the power modes for transosseous osteosynthesis with the external Ilizarov fixator.

The paper presents a physico-mathematical model of the perturbed region formed in the lower D-layer of the ionosphere under the action of directed radio emission flux from a terrestrial stand of the megahertz frequency range, obtained as a result of comprehensive theoretical studies. The model is based on the consideration of a wide range of kinetic processes taking into account their nonequilibrium and in the two-temperature approximation for describing the transformation of the radio beam energy absorbed by electrons. The initial data on radio emission achieved by the most powerful radio-heating stands are taken in the paper. Their basic characteristics and principles of functioning, and features of the altitude distribution of the absorbed electromagnetic energy of the radio beam are briefly described. The paper presents the decisive role of the D-layer of the ionosphere in the absorption of the energy of the radio beam. On the basis of theoretical analysis, analytical expressions are obtained for the contribution of various inelastic processes to the distribution of the absorbed energy, which makes it possible to correctly describe the contribution of each of the processes considered. The work considers more than 60 components. The change of the component concentration describe about 160 reactions. All the reactions are divided into five groups according to their physical content: ionization-chemical block, excitation block of metastable electronic states, cluster block, excitation block of vibrational states and block of impurities. Blocks are interrelated and can be calculated both jointly and separately. The paper presents the behavior of the parameters of the perturbed region in daytime and nighttime conditions is significantly different at the same radio flux density: under day conditions, the maximum electron concentration and temperature are at an altitude of ~45–55 km; in night ~80 km, with the temperature of heavy particles rapidly increasing, which leads to the occurrence of a gas-dynamic flow. Therefore, a special numerical algorithm are developed to solve two basic problems: kinetic and gas dynamic. Based on the altitude and temporal behavior of concentrations and temperatures, the algorithm makes it possible to determine the ionization and emission of the ionosphere in the visible and infrared spectral range, which makes it possible to evaluate the influence of the perturbed region on radio engineering and optoelectronic devices used in space technology.

Automated urban traffic monitoring systems are widely used to solve various tasks in intelligent transport systems of different regions. They include video enforcement, video surveillance, traffic management system, etc. Effective traffic management and rapid response to traffic incidents require continuous monitoring and analysis of information from these complexes, as well as time series forecasting for further anomaly detection in traffic flow. To increase the forecasting quality, data fusion from different sources is needed. It will reduce the forecasting error, related to possible incorrect values and data gaps. We implemented the approach for short-term and middle-term forecasting of traffic flow (5, 10, 15 min) based on data fusion from video enforcement and video surveillance systems. We made forecasting using different recurrent neural network architectures: LSTM, GRU, and bidirectional LSTM with one and two layers. We investigated the forecasting quality of bidirectional LSTM with 64 and 128 neurons in hidden layers. The input window size (1, 4, 12, 24, 48) was investigated. The RMSE value was used as a forecasting error. We got minimum RMSE = 0.032405 for basic LSTM with 64 neurons in the hidden layer and window size = 24.

We describe an engineering-psychological experiment that continues the study of ways to adapt a person to the increasing complexity of logical problems by presenting a series of problems of increasing complexity, which is determined by the volume of initial data. Tasks require calculations in an associative or non-associative system of operations. By the nature of the change in the time of solving the problem, depending on the number of necessary operations, we can conclude that a purely sequential method of solving problems or connecting additional brain resources to the solution in parallel mode. In a previously published experimental work, a person in the process of solving an associative problem recognized color images with meaningful images. In the new study, a similar problem is solved for abstract monochrome geometric shapes. Analysis of the result showed that for the second case, the probability of the subject switching to a parallel method of processing visual information is significantly reduced. The research method is based on presenting a person with two types of tasks. One type of problem contains associative calculations and allows a parallel solution algorithm. Another type of problem is the control one, which contains problems in which calculations are not associative and parallel algorithms are ineffective. The task of recognizing and searching for a given object is associative. A parallel strategy significantly speeds up the solution with relatively small additional resources. As a control series of problems (to separate parallel work from the acceleration of a sequential algorithm), we use, as in the previous experiment, a non-associative comparison problem in cyclic arithmetic, presented in the visual form of the game “rock, paper, scissors”. In this problem, the parallel algorithm requires a large number of processors with a small efficiency coefficient. Therefore, the transition of a person to a parallel algorithm for solving this problem is almost impossible, and the acceleration of processing input information is possible only by increasing the speed. Comparing the dependence of the solution time on the volume of source data for two types of problems allows us to identify four types of strategies for adapting to the increasing complexity of the problem: uniform sequential, accelerated sequential, parallel computing (where possible), or undefined (for this method) strategy. The Reducing of the number of subjects, who switch to a parallel strategy when encoding input information with formal images, shows the effectiveness of codes that cause subject associations. They increase the speed of human perception and processing of information. The article contains a preliminary mathematical model that explains this phenomenon. It is based on the appearance of a second set of initial data, which occurs in a person as a result of recognizing the depicted objects.

In this work, we develop a model of the immune response to respiratory viral infections taking into account some particular properties of the SARS-CoV-2 infection. The model represents a system of ordinary differential equations for the concentrations of epithelial cells, immune cells, virus and inflammatory cytokines. Conventional analysis of the existence and stability of stationary points is completed by numerical simulations in order to study dynamics of solutions. Behavior of solutions is characterized by large peaks of virus concentration specific for acute respiratory viral infections.

At the first stage, we study the innate immune response based on the protective properties of interferon secreted by virus-infected cells. On the other hand, viral infection down-regulates interferon production. Their competition can lead to the bistability of the system with different regimes of infection progression with high or low intensity. In the case of infection outbreak, the incubation period and the maximal viral load depend on the initial viral load and the parameters of the immune response. In particular, increase of the initial viral load leads to shorter incubation period and higher maximal viral load.

In order to study the emergence and dynamics of cytokine storm, we consider proinflammatory cytokines produced by cells of the innate immune response. Depending on parameters of the model, the system can remain in the normal inflammatory state specific for viral infections or, due to positive feedback between inflammation and immune cells, pass to cytokine storm characterized by excessive production of proinflammatory cytokines. Furthermore, inflammatory cell death can stimulate transition to cytokine storm. However, it cannot sustain it by itself without the innate immune response. Assumptions of the model and obtained results are in qualitative agreement with the experimental and clinical data.

This paper presents a model mixture of probability distributions of signal and noise. Typically, when analyzing the data under conditions of uncertainty it is necessary to use nonparametric tests. However, such an analysis of nonstationary data in the presence of uncertainty on the mean of the distribution and its parameters may be ineffective. The model involves the implementation of a case of a priori non-parametric uncertainty in the processing of the signal at a time when the separation of signal and noise are related to different general population, is feasible.

Database to store, search and retrieve DNA physical properties profiles has been developed and its use for analysis of E. coli promoters has been demonstrated. Unique feature of the database is in its ability to handle whole profile as single internal object type in a way similar to integers or character strings. To demonstrate utility of such database it was populated with data of 1227 known promoters, their nucleotide sequence, profile of electrostatic potential, transcription factor binding sites. Each promoter is also connected to all genes, whose transcription is controlled by that promoter. Content of the database is available for search via web interface. Source code of profile datatype and library to work with it from R/Bioconductor are available from the internet, dump of the database is available from authors by request.

The number of papers addressing the forecasting of the infectious disease morbidity is rapidly growing due to accumulation of available statistical data. This article surveys the major approaches for the shortterm and the long-term morbidity forecasting. Their limitations and the practical application possibilities are pointed out. The paper presents the conventional time series analysis methods — regression and autoregressive models; machine learning-based approaches — Bayesian networks and artificial neural networks; case-based reasoning; filtration-based techniques. The most known mathematical models of infectious diseases are mentioned: classical equation-based models (deterministic and stochastic), modern simulation models (network and agent-based).

The article deals with the nonlinear model of population dynamics of different ages workers in the regional economy. The model is built on the principles underlying modeling in econophysics. The authors demonstrate the complex dynamics of the model regimes that impose fundamental limits on medium- and long-term forecast of employment in a region. By analogy with the biophysical approach the authors propose a classification of social interactions of the different age-group workers. The model analysis is given for the level of employment among age groups. The verification of the model performs on the statistical data of the Jewish Autonomous Region.