Introduction. Registration and analysis of the distribution of the values ??of the parameters of the magnetic field of the heart in the air above the chest (MCG – magnetocardiography) is performed using SQUID magnetometric systems (SQUID – Superconductive Quantum Interference Device) and is increasingly used in clinical practice for non-invasive research in cardiology. The purpose. In the spatial analysis of magnetic signals, both the spatial resolution of the magnetic field measurement data (resolution by “magnetic field”) and the spatial resolution of the biomagnetic signal sources found after solving the inverse magnetostatic problem are important. The correct solution of these interrelated problems affects the interpretation of measurement data and, to a large extent, is determined by the design parameters of the magnetic flux transformers of the measuring channels, the geometric dimensions of the measurement plane and the distances between observation points in this plane. Results. This paper examines both the selection of design parameters for magnetic flux transformers of SQUID gradiometers of measuring channels, and the dimensions of the measurement area and features of the created software for a magnetocardiograph for studying small animals. The results of experimental studies of rat magnetic signals (MCG and nanoparticles), which were performed at the V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine, are presented. Conclusions. An algorithm has been developed to optimize the design parameters of flux transformers of measuring channels, the size and number of points of the measurement grid, as well as taking into account the features of the research task in the algorithms for processing the magnetocardiogram signal. An important practical result of the research of the small animal groups is the logical conclusion about the design and use for these purposes of a multi-channel specialized magnetometric system, with the found design parameters of the magnetic flux transformers of the measuring channels, which would allow obtaining measurement data for each object in a given spatial area in 1–5 minutes. Keywords: magnetocardiography, spatial analysis, SQUID gradientometer.

Introduction. The achievements of the School of Modern Inter-Industrial Balance of the V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, founded by Vladimir.S. and Michael V. Mikhalevyches, are of interest both at the national and international levels. These developments can be interpreted as improvements in the methodology of two main approaches to innovative development, namely: "top-down" and "bottom-up" approaches .The top-down approach provides an econometric forecast of the characteristics of new technologies based on correlations between the volume of expenditures for new developments and the number of human and other resources employed in this, on the one hand, and the improvement of traditional and innovative, in particular "green", parameters, on the other hand. The bottom-up approach is more accurate and developed, and is based on the final results of specific developments of innovative technologies, for which the dynamic task of planning and forecasting their practical implementation is solved, as a balanced transitional process of replacing old technologies with new ones. The article proposes a problem statement oriented to the supply, which has an independent economic interest and is solved by an optimization model that covers the volume of consumption, the dynamics of replacing old technologies with new ones, etc. The purpose of the work is to develop an optimization model for finding the highest technologically permissible consumption to ensure a balanced transition process to new technologies, which provides for the highest possible welfare and calculate the optimal scenario, which may contain endogenous fluctuations caused by technological changes. Results. An optimization model was built that reflects the dynamics of technologically permissible consumption during the transition period. The properties of the model were studied and the cyclical nature of the transition process was revealed; an optimal transition scenario was built. The example shows how even in the absence of problems on the demand side, inevitable recessions can still occur as the initial phase of the transition process. Conclusions. The dynamics of the economy, driven by radical technological change, is a full business cycle with phases of initial temporary recession, recovery, growth, overheating and Kondratieff waves that decay to a stationary state or stagnation. The proposed modeling approach has the potential to increase the accuracy of planning and forecasting innovative and “green” development of all countries. For Ukraine – to increase the accuracy and detail of recovery planning, including “better than before” and “green recovery”. Keywords: dynamic input-output models, optimization, innovation, test calculations.

Introduction. The quadratic assignment problem is a well-established NP-hard problem in combinatorial optimization with applications in diverse fields like economics, archaeology, and chemistry. Due to its complexity, research on efficient solution methods remains active, including efforts for parallelization on multiprocessor computing systems. However, effective parallel algorithms are crucial to fully leverage these computational resources. In this context, algorithm unions (portfolios and teams) play a significant role in achieving parallel execution for solving such problems. Research objectives. This work investigates the application of portfolios constructed from modifications of the repeated iterated tabu search algorithm to the quadratic assignment problem. The effectiveness of these portfolios was evaluated through experimental computations. Results. The portfolios, derived from modifications of the repeated iterated tabu search algorithm, were applied to the quadratic assignment problem. For the most demanding test instances, the proposed algorithms were evaluated on the SCIT-4 supercomputer, alongside previously published results from the authors, confirming their competitive performance. Additionally, we assessed the parallel efficiency of these portfolios in solving instances of the quadratic assignment problem. The results demonstrate their ability to accelerate the optimization process (with speedup dependent on problem size and utilized processors), enabling the solution of large-scale problems. Conclusions. The conducted studies demonstrate that employing algorithm portfolios significantly accelerates the solution process for the quadratic assignment problem. Analysis of the results reveals a near-linear speedup factor achieved by the portfolio. For the challenging test instance tai100a, a new best solution value of 21040996 was obtained using a portfolio of 16 algorithms. Keywords: quadratic assignment problem, algorithm portfolios, experimental research, algorithm portfolios efficiency.

Introduction. The article is devoted to the development of the algorithm of classification of several groups of patients using the LDA method at certain stages of its work. The algorithm allows achieve high discrimination value, to work with a variety of parameters and to separate more than two groups of patients. It is used for the analysis of biomedical data, namely for the array of indicators of various nature described healthy persons and persons with the detected disease. The purpose of paper is to develop an algorithm to increase the power of discrimination of an array of medical data that may include hundreds of healthy people and patients with connective tissue diseases, and each person is described by hundreds of parameters. Results. The authors proposed an algorithm for increasing classification accuracy based on parameter selection using sequential multivariate LDA tests. An example of the application of the algorithm to the task of analyzing a fairly large array of data (501 individuals: 295 healthy, 206 patients, 240 parameters) and identifying informative parameters for diagnosing children with connective tissue diseases is given. As a result of the application of this algorithm, a discriminant function and a decision rule were obtained, which allows achieve an average accuracy of discrimination over the entire set of parameters of 85 %. In addition, the algorithm is applied for two separate groups of parameters - blood indicators and biochemical analysis, while the average accuracy of discrimination reaches 84 % and 90 %, respectively. Conclusions. The algorithm of classification groups of patients with the use of LDA has been developed, which allows achieve high accuracy of discrimination. The results of its application in solving the real data set are given. The results of its application to solving the task of classification of medical data show its ability to improve classification accuracy. Keywords: algorithm, classification, analysis, informative parameters, LDA.

Introduction. Varieties of the problem of packing of rectangular parallelepipeds have a wide practical application in various fields of activity, for example, in the optimal filling of containers, in the design and layout of a wide variety of technological objects and systems, in the creation of backup copies on removable media, in the optimization of storage, protection and transportation of goods, in additive manufacturing, etc. This work continues research on this topic and is devoted to the problem of balanced sparse packing of a given set of identically oriented rectangular parallelepipeds of different sizes into a rectangular parallelepiped of minimum volume. It presents a mathematical model for this packing problem and a parallel algorithm for solving it. This algorithm is based on the reduction of the original problem to an unconditional optimization problem using penalty functions, which is solved by the multistart method, in which r-algorithm is used to find local minima from the set of generated starting points. The purpose. Construction of a mathematical model and development of an algorithm for solving the problem of balanced sparse packing of a given set of identically oriented rectangular parallelepipeds into a rectangular parallelepiped of minimum volume. Results. A mathematical model and a parallel algorithm for balanced sparse packing of identically oriented rectangular parallelepipeds into a rectangular parallelepiped of minimum volume are presented. The algorithm is based on reducing the problem with the help of penalty functions to an unconditional nondifferentiable optimization problem, for finding the solution of which multistart method is used in combination with r-algorithm for finding local minima. The results of numerical experiments are given. Conclusions. The application of the algorithm described in the work based on non-smooth optimization methods allows to improve the results of balanced sparse packing of rectangular parallelepipeds in an acceptable time. Numerical experiments showed effectiveness of the algorithm in practice. Keywords: balanced sparse packing, multistart method, r-algorithm, penalty function, "Master-Slave" procedure, numerical experiments.

Introduction. The judicial system in today's world is faced with a large number of cases that require efficient and fair resolution. Thanks to the development of information technologies, the automation of the process of conducting court cases and the introduction of intelligent systems have become urgent tasks for improving judicial justice. The justice sector has been slower than other sectors to adopt artificial intelligence and information technology (IT) in general. However, in most countries the amount of digital information resulting from the use of IT in legal proceedings is increasing. In this regard, the possibility of using artificial intelligence (AI) in the work of judicial bodies, prosecutor's offices and other specialized judicial bodies around the world is increasingly being investigated. Jurisprudence includes a wide range of problems and, accordingly, different sources of data for their solution. Using legislation, materials of criminal and administrative cases, concluded contracts and other legal documents, relevant specialists make decisions. In turn, artificial intelligence technologies, according to examples in other domains, can become assistants for specialists or make decisions in an autonomous mode.. The purpose of the article. To review the existing approaches to the application of artificial intelligence in the judicial system, to find out the shortcomings, advantages and limitations of machine learning algorithms in the judicial system. Determine the goals and methodology for further research. Results. In this work, the application of machine learning methods for solving problems arising in the field of jurisprudence is considered, algorithms, data sets and goals for further work are defined. Keywords: recurrent neural networks, machine learning, vectorization, text classification.

The application of information and wireless technologies, multisensor and biosensor devices, as well as sensor networks based on them to control the parameters of technological processes in different industries makes it possible to greatly improve the quality of final products and reduce losses of raw materials at all stages of production and storage. This covers both the industrial production of consumer goods and the food and processing industry. The article describes the developing and testing of network of wireless smart biosensor devices for food industry, namely for controlling the quality of wine, wine materials and food products in the production process. It is considered the technological process of production of wine materials and wine, as well as parameters that are subject of controlling. The requirements for smart multisensor devices, which are reasonable to use in such technological productions, are formulated. Measuring nodes are built on the basis of amperometric sensors and wireless data transmission units. Structural, functional and principle schemas of measuring devices were developed. The design documentation was created for their production at contract factories. The integration of wireless data transmission units into measuring nodes made it possible to build wireless sensor networks based on them. Several structures of wireless networks were developed on the base of wireless nodes with different functionality and with using of different wireless data transmission protocols. Hardware and software for wireless network nodes were developed. It was fulfilled the testing of the multisensor measuring node in autonomous mode, as well as the whole network consisting of several wireless nodes. The test results showed both the weaknesses of the developed devices, which should be eliminated in further researches, and the reasonability of using smart multisensor devices and networks based on them to control the quality of wine materials and wine at different stages of their production and storage. The work was performed with the support and within the Complex program of scientific research of the National Academy of Sciences of Ukraine "Smart" sensor devices of the new generation based on modern materials and technologies." Keywords: biosensors, amperometric transducers, smart multisensors, wireless sensor network, wine and food quality control.

Introduction. Minimization of ravine convex functions, both smooth and non-smooth, arises in many problems of planning, control, stability analysis of dynamic systems, artificial intelligence, and machine learning. Therefore, the development of new and improvement of existing methods is an important task, taking into account the fact that more and more frequently the functions to be minimized depend on a large number of variables. Special attention should be paid to the methods using the operation of linear transformation of space, which allow to improve the properties of the objective function and significantly accelerate its minimization. Known methods of this type, in particular, ellipsoid methods and r-algorithms, require recalculation of the transformation matrix at each iteration. Therefore, the development of methods with a one-time transformation of space, which have a lower cost of iteration, is definitely an urgent task. The purpose of the article is to review modifications of subgradient method with Polyak’s step that use a scalar parameter m(1 and one-time space transformation operation. Supplement the justification of the convergence and convergence rate of the modifications described. Give recommendations regarding determination of parameter m and space transformation matrix B. Results. Subgradient method with Polyak’s step in transformed space and parameter m>1 is an effective method for minimizing smooth and non-smooth convex functions that have a ravine structure. Determination of an appropriate value of the parameter m(1 and space transformation matrix B allows to significantly accelerate this method and use it for minimization functions that depend on a large number of variables. Conclusions. The development of fast methods for minimization of non-smooth convex functions of many variables with a ravine structure makes it possible to effectively solve modern problems of artificial intelligence, in particular, the problems of machine learning, image recognition, big data analysis, etc. Keywords: subgradient method, space transformation, ravine functions.

Introduction. A rapid increase in the number of objects that simultaneously take part in combat operations in the air requires improvement of systems for recognizing military objects both in terms of qualitative and quantitative indicators. This requires the development of appropriate algorithms for identifying new-generation "friend-foe" objects. Such algorithms can be based on various methods of information security, in particular symmetric and asymmetric cryptographic algorithms and other methods of cryptography. The purpose of the article is to survey modern systems of state recognition of objects (SSRO), identify their shortcomings and provide recommendations for their elimination. Results. The requirements for SSRO as a system for processing, transmitting, securing information and identifying objects based on cryptography and computer security methods are defined. Common and distinctive features for civil and military identification systems are highlighted. The advantages and disadvantages of the existing SSRO are shown. Recommendations are formulated to address the shortcomings of the existing SSRO. An example of a stand and a hardware and software basis for studying SSRO algorithms with backup channels is given. Conclusions. Eliminating the shortcomings of the SSRO and improving the level of its reliability will require the implementation of the following organizational and technical measures. 1. Replacing the current state recognition system with a more modern one, which will support more modern cryptographic algorithms and a larger number of recognition objects. Adding support for radio tag recognition (RF tags). 2. Support for recognition lines in various directions, including "ground – UAV", "plane – tank", "plane – UAV" and others. Adding support for the NATO standard – STANAG 4579, adopted in 2001, and others. 3. Using of broad-spectrum signals to reduce the probability of detection and interception, as well as a number of signal-code structures and a working frequency grid. Keywords: Friend-or-Foe, object identification, cryptography, backup channels.