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This article solves the problem of vehicles drivers intoxication functional statedetermining. Its solution is relevant in the transport security field during pre-trip medical examination. The problem solution is based on the papillomometry method application, which allows to evaluate the driver state by his pupillary reaction to illumination change. The problem is to determine the state of driver inebriation by the analysis of the papillogram parameters values — a time series characterizing the change in pupil dimensions upon exposure to a short-time light pulse. For the papillograms analysis it is proposed to use a neural network. A neural network model for determining the drivers intoxication functional state is developed. For its training, specially prepared data samples are used which are the values of the following parameters of pupillary reactions grouped into two classes of functional states of drivers: initial diameter, minimum diameter, half-constriction diameter, final diameter, narrowing amplitude, rate of constriction, expansion rate, latent reaction time, the contraction time, the expansion time, the half-contraction time, and the half-expansion time. An example of the initial data is given. Based on their analysis, a neural network model is constructed in the form of a single-layer perceptron consisting of twelve input neurons, twenty-five neurons of the hidden layer, and one output neuron. To increase the model adequacy using the method of ROC analysis, the optimal cut-off point for the classes of solutions at the output of the neural network is determined. A scheme for determining the drivers intoxication state is proposed, which includes the following steps: pupillary reaction video registration, papillogram construction, parameters values calculation, data analysis on the base of the neural network model, driver’s condition classification as “norm” or “rejection of the norm”, making decisions on the person being audited. A medical worker conducting driver examination is presented with a neural network assessment of his intoxication state. On the basis of this assessment, an opinion on the admission or removal of the driver from driving the vehicle is drawn. Thus, the neural network model solves the problem of increasing the efficiency of pre-trip medical examination by increasing the reliability of the decisions made.

The article deals with the problem of a distributed system operation reliability. The system core is an open integration platform that provides interaction of varied software for modeling gas transportation. Some of them provide an access through thin clients on the cloud technology “software as a service”. Mathematical models of operation, transmission and computing are to ensure the operation of an automated dispatching system for oil and gas transportation. The paper presents a system solution based on the theory of Markov random processes and considers the stable operation stage. The stationary operation mode of the Markov chain with continuous time and discrete states is described by a system of Chapman–Kolmogorov equations with respect to the average numbers (mathematical expectations) of the objects in certain states. The objects of research are both system elements that are present in a large number – thin clients and computing modules, and individual ones – a server, a network manager (message broker). Together, they are interacting Markov random processes. The interaction is determined by the fact that the transition probabilities in one group of elements depend on the average numbers of other elements groups.

The authors propose a multi-criteria dispersion model of risk assessment for such systems (both in the broad and narrow sense, in accordance with the IEC standard). The risk is the standard deviation of estimated object parameter from its average value. The dispersion risk model makes possible to define optimality criteria and whole system functioning risks. In particular, for a thin client, the following is calculated: the loss profit risk, the total risk of losses due to non-productive element states, and the total risk of all system states losses.

Finally the paper proposes compromise schemes for solving the multi-criteria problem of choosing the optimal operation strategy based on the selected set of compromise criteria.

This article solves the problem of constructing a neuro-fuzzy model of fuzzy rules formation and using them for objects state evaluation in conditions of uncertainty. Traditional mathematical statistics or simulation modeling methods do not allow building adequate models of objects in the specified conditions. Therefore, at present, the solution of many problems is based on the use of intelligent modeling technologies applying fuzzy logic methods. The traditional approach of fuzzy systems construction is associated with an expert attraction need to formulate fuzzy rules and specify the membership functions used in them. To eliminate this drawback, the automation of fuzzy rules formation, based on the machine learning methods and algorithms, is relevant. One of the approaches to solve this problem is to build a fuzzy neural network and train it on the data characterizing the object under study. This approach implementation required fuzzy rules type choice, taking into account the processed data specificity. In addition, it required logical inference algorithm development on the rules of the selected type. The algorithm steps determine the number and functionality of layers in the fuzzy neural network structure. The fuzzy neural network training algorithm developed. After network training the formation fuzzyproduction rules system is carried out. Based on developed mathematical tool, a software package has been implemented. On its basis, studies to assess the classifying ability of the fuzzy rules being formed have been conducted using the data analysis example from the UCI Machine Learning Repository. The research results showed that the formed fuzzy rules classifying ability is not inferior in accuracy to other classification methods. In addition, the logic inference algorithm on fuzzy rules allows successful classification in the absence of a part of the initial data. In order to test, to solve the problem of assessing oil industry water lines state fuzzy rules were generated. Based on the 303 water lines initial data, the base of 342 fuzzy rules was formed. Their practical approbation has shown high efficiency in solving the problem.

The proteins of the Major Histocompatibility Complex (MHC) play a key role in the functioning of the adaptive immune system, and the identification of peptides that bind to them is an important step in the development of vaccines and understanding the mechanisms of autoimmune diseases. Today, there are a number of methods for predicting the binding of a particular MHC allele to a peptide. One of the best such methods is NetMHCpan-4.0, which is based on an ensemble of artificial neural networks. This paper presents a methodology for qualitatively improving the underlying neural network underlying NetMHCpan-4.0. The proposed method uses the ensemble construction technique and adds as input an estimate of the Potts model taken from static mechanics, which is a generalization of the Ising model. In the general case, the model reflects the interaction of spins in the crystal lattice. Within the framework of the proposed method, the model is used to better represent the physical nature of the interaction of proteins included in the complex. To assess the interaction of the MHC + peptide complex, we use a two-dimensional Potts model with 20 states (corresponding to basic amino acids). Solving the inverse problem using data on experimentally confirmed interacting pairs, we obtain the values of the parameters of the Potts model, which we then use to evaluate a new pair of MHC + peptide, and supplement this value with the input data of the neural network. This approach, combined with the ensemble construction technique, allows for improved prediction accuracy, in terms of the positive predictive value (PPV) metric, compared to the baseline model.

This article solves the problem of developing a technology for collecting initial data for building models for assessing the functional state of a person. This condition is assessed by the pupil response of a person to a change in illumination based on the pupillometry method. This method involves the collection and analysis of initial data (pupillograms), presented in the form of time series characterizing the dynamics of changes in the human pupils to a light impulse effect. The drawbacks of the traditional approach to the collection of initial data using the methods of computer vision and smoothing of time series are analyzed. Attention is focused on the importance of the quality of the initial data for the construction of adequate mathematical models. The need for manual marking of the iris and pupil circles is updated to improve the accuracy and quality of the initial data. The stages of the proposed technology for collecting initial data are described. An example of the obtained pupillogram is given, which has a smooth shape and does not contain outliers, noise, anomalies and missing values. Based on the presented technology, a software and hardware complex has been developed, which is a collection of special software with two main modules, and hardware implemented on the basis of a Raspberry Pi 4 Model B microcomputer, with peripheral equipment that implements the specified functionality. To evaluate the effectiveness of the developed technology, models of a single-layer perspetron and a collective of neural networks are used, for the construction of which the initial data on the functional state of intoxication of a person were used. The studies have shown that the use of manual marking of the initial data (in comparison with automatic methods of computer vision) leads to a decrease in the number of errors of the 1st and 2nd years of the kind and, accordingly, to an increase in the accuracy of assessing the functional state of a person. Thus, the presented technology for collecting initial data can be effectively used to build adequate models for assessing the functional state of a person by pupillary response to changes in illumination. The use of such models is relevant in solving individual problems of ensuring transport security, in particular, monitoring the functional state of drivers.

The most important principle of military science and border security is the principle of concentrating the main efforts on the main directions and tasks. At the tactical level, there are many mathematical models for computing the optimal resource allocation by directions and objects, whereas at the state level there are no corresponding models. Using the statistical data on the results of the protection of the US border, an exponential type border production function parameter is calculated that reflects the organizational and technological capabilities of the border guard. The production function determines the dependence of the probability of detaining offenders from the density of border guards per kilometer of the border. Financial indicators in the production function are not taken into account, as the border maintenance budget and border equipment correlate with the number of border agents. The objective function of the border guards is defined — the total prevented damage from detained violators taking into account their expected danger for the state and society, which is to be maximized. Using Slater's condition, the solution of the problem was found — optimal density of border guard was calculated for the regions of the state. Having a model of resource allocation, the example of the three border regions of the United States has also solved the reverse problem — threats in the regions have been assessed based on the known allocation of resources. The expected danger from an individual offender on the US-Canada border is 2–5 times higher than from an offender on the US-Mexican border. The results of the calculations are consistent with the views of US security experts: illegal migrants are mostly detained on the US-Mexican border, while potential terrorists prefer to use other channels of penetration into the US (including the US-Canadian border), where the risks of being detained are minimal. Also, the results of the calculations are consistent with the established practice of border protection: in 2013 the number of border guards outside the checkpoints on the US-Mexican border increased by 2 times compared with 2001, while on the American-Canadian border — 4 times. The practice of border protection and the views of specialists give grounds for approval of the verification of the model.

The volume and structure of accumulated credit debt to the banking system depends on many factors, the most important of which is the level of interest rates. The correct assessment of borrowers’ reaction to the changes in the monetary policy allows to develop econometric models, representing the structure of the credit portfolio in the banking system by terms of lending. These models help to calculate indicators characterizing the level of interest rate risk in the whole system. In the study, we carried out the identification of four types of models: discrete linear model based on transfer functions; the state-space model; the classical econometric model ARMAX, and a nonlinear Hammerstein –Wiener model. To describe them, we employed the formal language of automatic control theory; to identify the model, we used the MATLAB software pack-age. The study revealed that the discrete linear state-space model is most suitable for short-term forecasting of both the volume and the structure of credit debt, which in turn allows to predict trends in the structure of accumulated credit debt on the forecasting horizon of 1 year. The model based on the real data has shown a high sensitivity of the structure of credit debt by pay back periods reaction to the changes in the Ñentral Bank monetary policy. Thus, a sharp increase in interest rates in response to external market shocks leads to shortening of credit terms by borrowers, at the same time the overall level of debt rises, primarily due to the increasing revaluation of nominal debt. During the stable falling trend of interest rates, the structure shifts toward long-term debts.

When person`s diseases is result of bacterial infection, various characteristics of the organism are used for observation the course of the disease. Currently, one of these indicators is dynamics of cytokine concentrations are produced, mainly by cells of the immune system. There are many types of these low molecular weight proteins in human body and many species of animals. The study of cytokines is important for the interpretation of functional disorders of the body's immune system, assessment of the severity, monitoring the effectiveness of therapy, predicting of the course and outcome of treatment. Cytokine response of the body indicating characteristics of course of disease. For research regularities of such indication, experiments were conducted on laboratory mice. Experimental data are analyzed on the development of pneumonia and treatment with several drugs for bacterial infection of mice. As drugs used immunomodulatory drugs “Roncoleukin”, “Leikinferon” and “Tinrostim”. The data are presented by two types cytokines` concentration in lung tissue and animal blood. Multy-sided statistical ana non statistical analysis of the data allowed us to find common patterns of changes in the “cytokine profile” of the body and to link them with the properties of therapeutic preparations. The studies cytokine “Interleukin-10” (IL-10) and “Interferon Gamma” (IFN$\gamma$) in infected mice deviate from the normal level of infact animals indicating the development of the disease. Changes in cytokine concentrations in groups of treated mice are compared with those in a group of healthy (not infected) mice and a group of infected untreated mice. The comparison is made for groups of individuals, since the concentrations of cytokines are individual and differ significantly in different individuals. Under these conditions, only groups of individuals can indicate the regularities of the processes of the course of the disease. These groups of mice were being observed for two weeks. The dynamics of cytokine concentrations indicates characteristics of the disease course and efficiency of used therapeutic drugs. The effect of a medicinal product on organisms is monitored by the location of these groups of individuals in the space of cytokine concentrations. The Hausdorff distance between the sets of vectors of cytokine concentrations of individuals is used in this space. This is based on the Euclidean distance between the elements of these sets. It was found that the drug “Roncoleukin” and “Leukinferon” have a generally similar and different from the drug “Tinrostim” effect on the course of the disease.

The paper considers the model of national security, which reflects the dichotomy of values development and conservation, to evaluate its options on the example of Russia (USSR), the United States, Germany and Ukraine. The calculations to assess the safety of the Roman Empire. It is shown that in 160 AD the conservation value of the function has reached a critically low values, which served as the impetus for modernization and reform.

A mass congestion of people always represents a potential danger and threat for their lives. In addition, every year in the world a very large number of people die because of the crush, the main cause of which is mass panic. Therefore, the study of the phenomenon of mass panic in view of her extreme social danger is an important scientific task. Available information, about the processes of her occurrence and spread refers to the category inaccurate. Therefore, the theory of fuzzy sets has been chosen as a tool for developing a mathematical model of the mechanism of transmitting panic state among people with various temperament species.

When developing an fuzzy model, it was assumed that panic, from the epicenter of the shocking stimulus, spreads among people according to the wave principle, passing at different frequencies through different environments (types of human temperament), and is determined by the speed and intensity of the circular reaction of the mechanism of transmitting panic state among people. Therefore, the developed fuzzy model, along with two inputs, has two outputs — the speed and intensity of the circular reaction. In the block «Fuzzyfication», the degrees of membership of the numerical values of the input parameters to fuzzy sets are calculated. The «Inference» block at the input receives degrees of belonging for each input parameter and at the output determines the resulting function of belonging the speed of the circular reaction and her derivative, which is a function of belonging for the intensity of the circular reaction. In the «Defuzzyfication» block, using the center of gravity method, a quantitative value is determined for each output parameter. The quality assessment of the developed fuzzy model, carried out by calculating of the determination coefficient, showed that the developed mathematical model belongs to the category of good quality models.

The result obtained in the form of quantitative assessments of the circular reaction makes it possible to improve the quality of understanding of the mental processes occurring during the transmission of the panic state among people. In addition, this makes it possible to improve existing and develop new models of chaotic humans behaviors. Which are designed to develop effective solutions in crisis situations, aimed at full or partial prevention of the spread of mass panic, leading to the emergence of panic flight and the appearance of human casualties.