An approach to obtain light-signal characteristics in graphoanalytical form is proposed to substantiate the operating modes of the emitting equipment of active optoelectronic complexes of remote sensing of the Earth. These complexes are used for shooting in conditions of insufficient natural illumination of the terrain due to the difficult terrain, geographical location of the area or the low position of the Sun above the plane of the local horizon. Using the presented model, calculations of the energy illumination of the Earth’s surface were carried out, dependencies were constructed that take into account the influence of the position of the Sun above the local horizon plane for specific dates and daily time on the distribution of the spectral density of the electromagnetic radiation flux. Light-signal characteristics have been obtained, which can be used to justify the operating modes of the emitting equipment of active optoelectronic complexes. Based on these characteristics, it is concluded that it is necessary to artificially enhance the spectral density of the radiation flux in a given range of the spectrum in order to achieve the required illumination of the photographed area of the Earth’s surface for a specific date and time. The amplification of the spectral density of the radiation flux makes it possible to create the exposure required for the formation of images with high visual properties. The simulation results are used in the problem of predicting the quality of images obtained using artificial sources of optical illumination. The proposed approach makes it possible to obtain images characterized by the high value of linear resolution on the ground, without resorting to increasing the charge accumulation time by the photodetector of the recording equipment. The application of this approach is particularly relevant in the conditions of conducting aerospace surveys.

The results of a study of the luminescent dynamics of combined aqueous-alcoholic solutions of extracts of red viburnum (Viburnum opulus L.) fruits with chitosan and gold nanoparticles at different oxygen concentrations were presented. The search for natural sources of photosensitizers is an urgent task. The sensitive analytical methods, in particular luminescence, with amplification of the analytical signal as a result of the generation of plasmons in nanoparticles of noble metals, have been used in this work. An additional studies under the conditions of plasmon energy generation showed a significant changes in the dynamics of optical spectra with variations in oxygen concentration in solutions. The spectral-temporal dynamics was investigated with complete oxidation of vitamin C in the studied system. The main method for recording the dynamics of interaction of Viburnum opulus L. flavonoids with oxygen molecules is the luminescent method. Luminescence spectra were measured by means of Fluorolog-3 optical system (Horiba, Japan). Methods of absorption analysis (Shimadzu spectrophotometer, Japan) were also used in the work. The nanosecond luminescence lifetimes of the extracts were measured in the multichannel photon counting mode using a picosecond NanoLED-405L nanoled by means of Fluorolog-3 spectral setup. Microsecond lifetimes were recorded when excited by a pulsed Xe lamp. For the synthesis of gold nanoparticles, the method of laser ablation of a metal plate of gold in distilled water was used. Laser ablation was performed at the LQ929 installation of Solar Laser System (Belarus). A plasmonic effect of amplification of the optical absorption density and luminescence intensity was detected. The kinetics of luminescence quenching of Viburnum opulus L. extract with chitosan under the influence of gold nanoparticles, close to diffusion, was studied. The oxygen concentration at which the flavonoids of the extract are oxidized was spectrally determined. Under the oxygen concentration changing, the dependences of changes in the luminescence intensity of the extract with chitosan at the wavelengths of registration of luminescence spectra were established. When oxygen was doped into all solutions, the spectral and kinetic features of luminescence attenuation with maxima at wavelengths of 480 and 580 nm were detected and investigated. It was established that the lifetime of luminescence at a registration wavelength of λ = 480 nm varies depending on the concentration of gold nanoparticles and the concentration of oxygen molecules and it is the nanosecond spectral region (3–4 ns). It was shown that luminescence at a wavelength of 580 nm is due to the oxidized form of quercetin which is a part of the Viburnum opulus L. flavonoids, appeared at a high oxygen concentrations. Long-lived chemiluminescence at a wavelength of 580 nm with time decay of 15 μs as a result of radical processes involving molecular oxygen and extract molecules was recorded. The spectral methods presented in this paper, as well as a method for determining of quercetin as a result of oxygen oxidation of flavonoids of red viburnum fruits, can be used in the field of biophysics, biotechnology and chemical analysis.

In this paper, we study the trajectory tracking problem of a three-wheeled omnidirectional mobile robot with full state constraints and actuator saturation. Firstly, we analyze a three-wheeled omnidirectional mobile robot and give control model with actuator saturation. By using tan-type Barrier Lyapunov Function and backstepping method, kinematic and dynamic controllers are built, which can ensure that the system full states will not violate the given constraints when the robot is performing trajectory tracking. Then, considering the differential explosion problem which occurs when solving the derivatives of the virtual control law, we use a second-order differential sliding mode surface to calculate it, so as to reduce the complexity of the operation. In addition, due to the output saturation problem of the robot drive motor, an auxiliary compensation system is adopted to compensate for the error generated by the saturation function. Finally, an experimental simulation is performed in MATLAB and the simulation results illustrate the effectiveness of the control algorithm proposed in this paper.

A method of dual-wavelength digital holographic interferometry for analyzing and controlling surface shape for technical applications, including surfaces exposed to high-temperature plasma in fusion reactors, is presented. The capability of applying the method both using miniature vertical-emitting diodes (VCSEL) and conventional wavelength-tunable lasers is shown. The research method is based on dual-wavelength digital holographic interferometry, in which the phases of wave fronts reflected from the object detected at different wavelengths are compared to provide information about the shape of the object. Moreover, the sensitivity of the method is determined by the value of synthetic wavelength, which depends on the difference of wavelengths used for acquisition of digital holograms. The method used following wavelengths 854.000–854.082 nm and 779.900–779.870 nm. Implementation of vertical-emitting diodes for dual-wave holographic interferometry methods is shown. It is found that such diodes have a coherence length of about 20 cm and similar to He-Ne laser. The dependence of the emission wavelength of such sources on the current has been examined and it is determined that the output wavelength deviates less than 1 % during 24 hours. The application of the holographic method has been demonstrated for measuring the shape of objects used in various technical applications (a car body part and a shielding element of the internal wall of the Tokamak fusion reactor). The results of the research illustrate the opportunity to apply the technique of dual-wavelength holographic interferometry to measure a shape of the technical objects surface of various types. It should be noted that other method such as fringe projection can also be used to solve such problems, but it does not work sufficiently on low-scattering surfaces, in our case the lacquered surface of the car body, or specular reflecting surfaces. In addition, the LIDAR technique requires scanning the surface over time, which can lead to measurement errors if the object is unstable due to mechanical movements or vibrations. In dual- wavelength holographic interferometry such drawbacks can be reduced by short camera exposure times of milliseconds/ microseconds, or by using pulse laser with pulse durations of 10 ns. However, the major disadvantage of the method is the dependence of the mutual correlation of speckle structures of holograms on the wavelength difference. In order to increase the sensitivity of the method, it is necessary to increase this wavelength difference, and this can significantly reduce the signal-to-noise ratio and decrease the accuracy of the obtained data.

The constituent parts of systems where radiation-catalytic processes occur usually differ in terms of mass and electron density, structural characteristics, electrophysical and chemical properties. Therefore, interaction between phases in any form has a sharp effect on the direction and parameters of the processes in individual components. In this work, X-ray diffraction patterns of nano-ZrO2 and nano-TiO2 samples were obtained before and after gamma irradiation. The crystal structures of these samples have been studied. The resulting X-ray diffraction pattern was mainly determined by the atomic plane (ε), the intensity of the obtained peaks, the corresponding syngony of the sample, the lattice size, density, lattice constants, and the distance between the phase groups. The X-ray diffraction data were processed using the Fullprof program. Full-profile processing of ZrO2 X-ray diffraction data showed that the initial sample has a monoclinic structure (space group P21/c) with the following lattice parameters: a = 5.1506 Å, b = 5.2080 Å, c = 5.3293 Å. Full- profile processing of X-ray diffraction analysis of ZrO2 after gamma irradiation showed a change in the structure from the monoclinic (space group P21/c) phase to the triclinic (space group P1). Full profile processing of TiO2 X-ray diffraction data showed that the sample has a tetragonal structure (space group P42/mnm) with the following lattice parameters: a = b = 4.5931 Å, c = 2.9592 Å and unit cell. As a result of calculations (BR = 1.27; RF = 1.98; χ2 = 2.68), it was found that the structure of the initial TiO2 sample is single-phase, tetragonal, and is described by the space group P42/mnm. Crystal structure of ZrO2 (monoclinic structures, space group P21/c). Crystal structure of TiO2 (tetragonal structure space group P42/mnm). The scientific component of the article is of interest because it touches upon the issues of structural transformations of zirconium oxide and titanium under the action of gamma radiation.

Silicon solar cells with functional coatings based on polyvinyl butyral with carbon quantum dots. The change in the parameters of solar cells, when these coatings are used on the front surface of solar cells, is studied. A know-how method has been developed, which consists in the formation of a thin film of polyvinyl butyral with carbon quantum dots on the surface of solar cells. The coating is formed when the solution (isopropyl alcohol with polyvinyl butyral and carbon quantum dots) is pumped out of the cuvette in such a way that the contact boundary of the solution with the surface of the solar cell moves from top to bottom, while the process is carried out without and with ultrasound. Using an SFL MDR-41 monochromator, the luminescence spectra of carbon quantum dots were obtained showing their strong fluorescence in the short-wavelength visible light region (350–450 nm). The coating thickness was measured by ellipsometry on a SE 800 instrument. Also, on the SolarLab 20-UST sunlight simulator, the main parameters of solar cells were measured before and after applying functional coatings. When applying functional coatings, the following pattern is observed: without exposure to ultrasound, an increase in efficiency is observed only at a concentration of carbon quantum dots in a solution equal to 119 ppm, and when exposed to ultrasound, a smooth increase in the efficiency of solar cells up to 2.34 % occurs at a maximum concentration of quantum dots of 463 ppm. In the short-wavelength region of light (365–470 nm), an increase in efficiency is observed for all concentrations of carbon quantum dots, which varies from 4.5 to 38 %. It is shown that functional coatings based on polyvinyl butyral with carbon quantum dots are promising and unparalleled coatings for solar cells, which also perform the additional function of a protective coating against ultraviolet radiation. This coating can also be used for other optoelectronic devices.

The results of developing an algorithm for searching for optimal initial concentrations of substances in a chemical reaction are presented. The algorithm combines a combination of optimization methods with the theoretical foundations of modeling chemical reactions in terms of constructing their kinetic models. A mathematical description of the dynamics of the concentrations of reactants over time is presented in the form of a system of ordinary differential equations the initial conditions of which are specified by the values of the initial concentrations of the reactants. The problem of determining the optimal composition of the reacting mixture is formulated in general terms. The problem contains restrictions imposed on the values of the initial concentrations of substances and on their initial total concentration. To solve the problem, the penalty method and the Hooke–Jeeves method were used. A penalty function is described that allows one to reduce the original problem to a problem without restrictions. A step-by-step algorithm for searching for optimal initial concentrations of a chemical reaction is formulated. A computational experiment was carried out for the catalytic reaction of aminomethylation of thiols using tetramethylmethanediamine. A kinetic model of the reaction is presented on the basis of which an optimization problem is formulated to find the values of the initial concentrations of reagents to obtain the highest yield of the target product at the end of the reaction. The optimal initial concentrations of the starting substances were calculated for different reaction durations and at different temperatures. The developed numerical algorithm for determining the optimal initial concentrations of reagents takes into account the physicochemical features of the problem and can be used in the study of complex chemical reactions containing a large number of initial and intermediate substances. Its use makes it possible to determine the patterns of a chemical reaction at the stage of a computer experiment, without resorting to laboratory experiments, which significantly saves the material and time costs of the researcher.

We report the ZnO/ZnS and ZnO/ZnSe nanocomposites synthesized using the solvothermal-microwave method. Raman analysis was thoroughly studied to explain phonon vibration mode in this paper. The strong intensity confirms the high- frequency phonon mode of hexagonal wurtzite ZnO. Also, the presence of Raman intensity of the cubic ZnS and ZnSe structures indicates the longitudinal optical phonon mode. In addition, we find several slight shifts in all ZnO modes for ZnO/ZnS and ZnO/ZnSe which demonstrate stress and strain in the crystal lattice. We investigate the change in particle size from confocal Raman microscopy. Therefore, the modifications to the material structure and particle size have enhanced its characteristics. Accordingly, the nanocomposite heterostructures by the simple chemical method are attractive materials suitable for optoelectronic devices.

Social media contains a huge amount of data that is used by various organizations to study people’s emotions, thoughts and opinions. Users often use emoticons and emojis in addition to words to express their opinions on a topic. Emotion identification from text is no exception, but research in this area is still in its infancy. There are not many emotion annotated corpora available today. The complexity of the annotation task and the resulting inconsistent human comments are a challenge in developing emotion annotated corpora. Numerous studies have been carried out to solve these problems. The proposed methods were unable to perform emotion classification in a simple and cost-effective manner. To solve these problems, an efficient classification of emotions in recordings based on clustering is proposed. A dataset of social media posts is pre-processed to remove unwanted elements and then clustered. Semantic and emotional features are selected to improve classification efficiency. To reduce computation time and increase the efficiency of the system for predicting the probability of emotions, the concept of data parallelism in the classifier is proposed. The proposed model is tested using MATLAB software. The proposed model achieves 92 % accuracy on the annotated dataset and 94 % accuracy on the WASSA-2017 dataset. Performance comparison with other existing methods, such as Parallel K-Nearest Neighboring and Parallel Naive Byes Model methods, is performed. The comparison results showed that the proposed model is most effective in predicting emotions compared to existing models.

The rapid increase in the volume of visual information on the internet stimulates the improvement and search for new approaches to solving the problem of image compression. One of the important characteristics in the field of image processing, in particular in matters of compression, is entropy. The work explores the possibility of using the method of image decomposition based on topological features to reduce entropy in order to further compress the image while maintaining high quality. Topological decomposition involves decomposing an image into components each of which reflects a separate element in the image. Topological decomposition allows us to group global structures and their details into separate matrices of special types. To reduce entropy, it is proposed to remove some detail components and restore the image. A distinctive feature of the proposed approach is that it does not distort the entire image, but only some areas. The proposed method is tested in a practical compression problem using the entropy-dependent RLE algorithm. The results showed that topological decomposition is good at reducing entropy, which will allow us to use the preprocessed image for compression. PSNR, SSIM, MSE, NRM indices are used to assess image quality. When compared with the wavelet transform, the proposed approach is competitive in terms of image quality assessment at a comparable compression ratio, and exceeds it for a certain class of images with slightly noisy long objects. The results open up opportunities for further study of topological decomposition in image compression with potentially greater efficiency and less distortion.

The paper analyzes the features of representing artificial neural networks in Simulink and SimInTech. Examples of visual schemes (models) built in these modeling environments using neural network blocks are given. The following　shortcomings of such representations are the lack of mechanisms: for carrying out structural optimization of neural networks, for combining them into ensembles, for training them synchronously with the simulation of the object model. It was noted that there are difficulties in using other tools, such as specialized Python libraries (Keras, PyTorch, etc.), the NeuroGenetic Optimizer (BioCompSystems) for building neural network control models. A method is shown to implement the representation of neural networks in the formalism of the method of multilevel component circuits, according to which the construction of models of an object and a control system is carried out in a visual language from ready-made blocks (components) with directional and non-directional connections. A technique has been developed for multilevel representation of neural network control models, which allows them to be combined with other tools of the component circuit method. Two options for representing neural networks are proposed: with an encapsulated structure and with a component structure. The first version of the representation is characterized by the compactness of the representation of the control model, the possibility of automated variation and optimization of the structure of the neural network, and the possibility of changing the structure of the network during the executing of the model within a computational experiment (scenario). The second option has the ability to perform detailed debugging and research of the network learning process, and the ability to construct a network of any structural complexity. The paper describes the main developed components with their connections: a neural network, a training block, an ensemble unit (bagging), a block for reading data from a file, a sampling block, a neural network layer (input, hidden, output). A multilevel computer model of the uncontrolled flight of a body (target) and the controlled flight of a projectile is presented as an example to illustrate the operation of the developed components to solve the problem of controlling a projectile to hit the target. The developed component libraries can be used as part of the MARS modeling environment to build multilevel control systems for objects of a multiphysics nature.

The problem of organizing multi-level data storage is discussed. Information loses its relevance over time and the cost of storing it on highly available media, such as solid-state drives, becomes impractical. Until now, the placement of new files in the data storage system is decided horizontally — without taking into account the multi-level organization of the system. The file migration between storage system tiers occurs over time as statistics on the frequency of requested files are accumulated. All files have metadata, such as type, size, creation date and others, from which some information about the importance of the information can be extracted and then the distribution by levels of the data storage system can be implemented at the system input. A modern data storage system represented by four levels is proposed. The first Hi-End level is intended for storing critical data with the requirements of maximum access speed and reliability. The second level, Upper Mid-Range, is intended for enterprise applications that require high access speeds. The third level, Mid-Range, is proposed to be used for organizing file storage, and the fourth, Entry Level, is proposed to be used for creating backup copies and archives. The proposed algorithm for arranging files across tiers of a data storage system takes into account metrics indicating storage requirements and selecting a level of a data storage system that meets the requirements. These metrics include availability (speed of information delivery), importance (cost of data loss due to hardware and software failures), retention period, and request frequency. Metrics are extracted from the metadata of saved files. A new solution based on the functions of a fuzzy logic controller is proposed. Its operation algorithm can be integrated into the data storage system before the process of writing a new file. The algorithm includes three main steps. At the first step, file metrics are analyzed to form the corresponding input fuzzy sets. At the second step, a logical model is used to form the final fuzzy set. At the final stage, the fuzzy output result is obtained and the file is placed at the appropriate level of the data storage system. An example of how the controller works for files with different values of metric characteristics is given. A fuzzy logic controller can be integrated into the operation of a multi-level data storage system.

Stress detection is an active area of research with important implications for personal, occupational, and social health. Most modern approaches use features computed from multiple sensor modalities, i.e., grouping different types of data from multiple sources for processing. These include electrocardiogram, electrodermal activity, electromyogram, skin temperature, respiration, accelerometer data, etc. Also, traditional machine learning algorithms (decision tree, discriminant analysis, support vector machine, etc.) or fully-connected neural networks are mostly used. Using these methods requires large amounts of data. Researchers are considering different approaches to personalization or generalization of models relative to subjects, namely subject-independent and subject-dependent (initially personal or adapted) models. The aim of the presented work is to develop a method for detecting stress based on heart rate variability data, taking into account the process of personalization of neural networks. The use of a convolutional neural network is proposed. The dependence of accuracy on the length of the input signal is studied. The dependence of accuracy on the data dimensionality reduction layer (one-dimensional convolutional layer, maximizing and averaging pooling) used in the network is also considered. The importance of personalizing models is demonstrated to significantly increase the accuracy of models of specific subjects. It is shown that the proposed method, based on 60 intervals between heartbeats, makes it possible to binary determine whether a person is under stress. Personalization allowed increasing the accuracy from 91.8 % to 98.9 ± 2.6 %. The F1-score value increased from 0.907 to 0.983 ± 0.038. The proposed personalized networks can be used in systems for monitoring the functional state of a person. They can also be used as part of a system that grants or restricts access to private resources based on whether a person is currently at rest.

For the first time, a simplified approach to constructing Bayesian neural networks is proposed, combining computational efficiency with the ability to analyze the learning process. The proposed approach is based on Bayesianization of a deterministic neural network by randomizing parameters only at the interface level, i.e., the formation of a Bayesian neural network based on a given network by replacing its parameters with probability distributions that have the parameters of the original model as the average value. Evaluations of the efficiency metrics of the neural network were obtained within the framework of the approach under consideration, and the Bayesian neural network constructed through variation inference were performed using topological data analysis methods. The Bayesianization procedure is implemented through graded variation of the randomization intensity. As an alternative, two neural networks with identical structure were used — deterministic and classical Bayesian networks. The input of the neural network was supplied with the original data of two datasets in versions without noise and with added Gaussian noise. The zero and first persistent homologies for the embeddings of the formed neural networks on each layer were calculated. To assess the quality of classification, the accuracy metric was used. It is shown that the barcodes for embeddings on each layer of the Bayesianized neural network in all four scenarios are between the corresponding barcodes of the deterministic and Bayesian neural networks for both zero and first persistent homologies. In this case, the deterministic neural network is the lower bound, and the Bayesian neural network is the upper bound. It is shown that the structure of data associations within a Bayesianized neural network is inherited from a deterministic model, but acquires the properties of a Bayesian one. It has been experimentally established that there is a relationship between the normalized persistent entropy calculated on neural network embeddings and the accuracy of the neural network. For predicting accuracy, the topology of embeddings on the middle layer of the neural network model turned out to be the most revealing. The proposed approach can be used to simplify the construction of a Bayesian neural network from an already trained deterministic neural network, which opens up the possibility of increasing the accuracy of an existing neural network without ensemble with additional classifiers. It becomes possible to proactively evaluate the effectiveness of the generated neural network on simplified data without running it on a real dataset, which reduces the resource intensity of its development.

The results of modeling deformation processes of uniaxially oriented polymer materials are presented. The discription of two-barrier model is given, according to which polymer macromolecules can be in three stable states. The constitutive equation of the oriented polymer material is obtained. The solution of this equation is shown for the case of a deformation mode with a constant load level. Based on the energy barriers theory, as a result of the transformation of the balance equations of the occupation numbers of steady states, the constitutive equation of the polymer material is obtained. This equation is a second-order differential equation in time. For the deformation process with a constant stress level, the constitutive equation takes the form of a linear inhomogeneous second-order differential equation with constant coefficients. A general solution of this equation is given in explicit form. The solution of the Cauchy problem gives a general solution of the constitutive equation for the considered case. The analysis and transformation of the general solution leads to dependencies that determine the deformation of the oriented polymer material for creep and recovery processes. The use of a two-barrier model with three steady states of macromolecules made it possible to obtain a constitutive equation which is a second-order differential equation in time. As an example, the application of the constitutive equation to the deformation mode with a constant stress level is considered and its general solution is obtained. A universal function has been introduced with the help of which it is possible to calculate the deformation of a polymer material in the creep and recovery mode. By combining the theoretical curve with the experimental creep curves of polyethylene terephthalate filaments, the applicability of the considered modeling method is shown. The obtained constitutive equation makes it possible to describe and predict both static and dynamic deformation modes. The applicability of the obtained model to the static mode of deformation is shown. It should be noted that the solution of the obtained constitutive equation in certain cases leads to an oscillatory relaxation mode.

This paper presents the design of a microstrip patch antenna based on a Square Split Ring Resonator (SSRR). Wireless technology is switching from 4G to 5G due to the need to overcome limitations, such as low throughput, high latency and path loss. To increase data transfer speeds, the next generation of wireless networks uses 5G terahertz technology. The use of microstrip patch antennas in wireless technologies has increased significantly due to their low cost and simplicity of design as well as the ease of printed circuit board fabrication. However, in some cases their use is limited by low bandwidth, low gain and low throughput. To solve these problems, the Fennec Fox optimization algorithm is used. The algorithm allows you to optimize the length of the microstrip patch antenna resulting in increased gain and reduced return loss. Bakelite is used as a substrate. The width of the patch antenna is set according to the most suitable length selected. To increase the bandwidth and Voltage Standing Wave Ratio (VSWR), a square split ring resonator (SSRR) is used as a metamaterial. An evaluation of the designed microstrip patch antenna model with existing patch antennas was performed. The estimated values of the parameters of the proposed model were the following values: return loss –72.54 dB, resonant frequency 1.11 THz, achieved gain 15.25 dB, VSWR value 1.5646. The estimated values of the developed model exceed those of existing samples. Thus, the developed microstrip patch antenna using Fennec Fox optimization and square split ring resonator metamaterial shows better results in the terahertz range.

The development trends of modern electronic equipment included in radar stations consist of a constant increase in the output radiated power. This leads to a significant increase in heat generation of power amplification units as the most heat-loaded ones. To reduce failures of these units associated with overheating, this work proposes an original algorithm for quickly maintaining the temperature regime. The algorithm is based on a thermal model, which allows, unlike the known ones, to calculate the temperature distribution in the block in real time, taking into account telemetry from temperature sensors installed inside the block. The novelty of the proposed algorithm lies in the real-time control of the cooling system based on the block temperature forecast obtained using a thermal model. The thermal model is based on the mathematical formalization of thermal processes using the anisotropic body method, which allows minimizing the computational costs of calculations by representing the power amplification unit as a quasi-homogeneous body. Simulation of the temperature distribution process in the power amplification unit was performed in the COMSOL. To evaluate the efficiency of the algorithm and the ability to operate in real time at the operational stage of the radar station, a computational experiment was performed using model data. The simulation results confirmed the possibility of calculating the temperature distribution in the block in real time. Unlike existing algorithms for maintaining the temperature regime of a block, based on the readings of temperature sensors that determine the temperature at the current moment in time, the developed algorithm implements a temperature forecast. This allows you to take measures to cool the unit before the onset of critical emergency situations.

The present study analyses the influence of magnetohydrodynamics on endwall heat transfer in turbine blades using computational fluid dynamics simulations. The simulations consider the three-dimensional geometry of the turbine blade, the magnetic intensity, and the boundary conditions. The outcome revealed the existence of a magnetic field can outstandingly increase the pitch-averaged film cooling effectiveness and endwall heat transfer, particularly near the edges of the turbine vane with an optimal magnetic field. This results in a more uniform distribution of heat transfer along the endwall and can help to reduce hot spots and prevent thermal damage to the blade. The research also highlights the importance of considering the magnetic intensity and its impact on the flow characteristics and heat transfer when designing turbine blades for high-speed applications. By optimizing the design of the turbine blades to take into account the magnetohydrodynamic effect, engineers can improve the overall performance and lifespan of these critical components. Numerical simulations had been utilized to forecast the impacts of contouring of endwalls efficiently, employing the secondary kinetic energy coefficient as the accomplished parameter demonstrated in the current investigation. A reduction in endwall heat load with enhanced net heat flux reduction and aerodynamic performance is reported for a non-axisymmetrically contoured endwall subjected to optimal magnetic field strength. The novelty of the present study is the establishment of the impact of vortices on endwall heat transfer with respect to the vane under the influence of magnetohydrodynamics to reduce the weight and cost of a turbine engine.

It is known that there is an information signal in power supply systems and networks. The presence of information signals in the power elements of power supply systems and networks (electrical signal) in combination with other information allows extracting secondary information from power supply systems and networks. In some cases, this kind of information is confidential, has a high level of significance, and power supply facilities may belong to critical information infrastructure facilities. Thus, auditing and ensuring information security of power supply systems and networks seem relevant. In this regard, the issues of identifying previously unaccounted for channels of possible leakage of confidential information, developing methods for contactless monitoring of information security of generation, transportation, transformation and electricity consumption facilities are important. A contactless method for recording and calculating spurious emissions in established operating modes and during transients in long lines is proposed by solving the inverse problem of calculating the currents of multi-wire long lines based on measuring their magnetic field, taking into account the principle of superposition. To implement the method in application to a Q-wire line, simultaneous measurement of the magnetic field at Q different points with known coordinates is required. It also requires knowledge of the coordinates of the wires with the length of the line. Geometric measurements are proposed to be implemented using laser rangefinders or scanners. When measuring the magnetic field of a long line, the quasi-constant component of the Earth’s magnetic field is taken into account. A method is proposed for determining the direction and delay of reflection of traveling waves in a long line, based on information from two magnetic field sensors located at a sufficient distance from each other along the line. Methods are proposed to ensure the audit and monitoring of the state of power supply systems and networks that are under the influence of threats to information security violations. Mathematical modeling of the proposed method of contactless current measurement in a long line and field experiments of current measurement in a long line and registration of traveling waves are performed. The experimental results show the accuracy of the proposed methods sufficient to solve the tasks. The work develops an idea of methods and means of ensuring audit and monitoring of information security of electrical systems and networks. The results of the work make it possible to identify new, previously unaccounted for channels of information leakage and to develop new contactless methods for registering information signals in power transmission lines.

The problem of estimating the parameters of non-salient synchronous motor with surface-mounted permanent magnets is considered. A parameterization of a nonlinear motor model is proposed, which allows obtaining a linear regressor equation using measured (estimated) values of current and voltage in the stator windings and the angular rotor position. Using the method of dynamic regressor extension and mixing, an algorithm for estimating the desired parameters in finite time is designed.

The problem of protecting the information contained in the internal memory of the Renesas RL78 Family Microcontrollers is considered. The vulnerability of these microcontrollers has been identified and investigated, which allows extracting data from the built-in flash memory using a programmer. A method of automated recovery of the contents of the entire memory area, based on specially developed software, has been tested. The results of the study indicate the insufficient effectiveness of the access restriction measures implemented by the manufacturer. A variant of changing the programmer’s control command, leading to an increase in data security, is described. A technique for complete recovery of flash memory data is presented, tested in a program developed in the LabVIEW environment.