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Entering the era of digitalization, the real estate industry unlocked the bricks-and-clicks model of operation. The virtual-and-real interaction has led to new demands for service quality from a new generation of customers, and e-service quality (ES-QUAL) is a response to this demand and has attracted the attention of scholars and practitioners specializing in the sales and service sectors of the real estate industry. The study investigated the relationships between the implementation of ES-QUAL, knowledge culture, and employees’ performance from an internal perspective, while emphasizing a specific Chinese research context. A total of 602 responses, received from brokers, front-line salespeople, and sub-branch managers, were collected through a web-based questionnaire survey from the Shanghai branch of the largest real estate brokerage company in China, called Lianjia. A multi-method approach of the structural equation modeling (SEM) and bootstrapping estimation was employed to test the hypotheses and evaluate the validly and reliability of the data. The results indicated that ES-QUAL and knowledge culture positively influenced employee performance, and the direct effect of ES-QUAL on knowledge culture was stronger than on employee performance. Additionally, knowledge culture was found to be partially mediating the relationship between ES-QUAL and employee performance. The study provided managerial insights and discussed strategies for implementing ES-QUAL to achieve desired organizational performance by fostering a learning-oriented culture.

Heuristic algorithms are often used as an alternative when solving problems of high computational complexity or lacking an exact solution, allowing to quickly obtain the desired result. Usually, they do not have a strict mathematical justification, but their application is justified in terms of practicality. Formally, algorithms that use approximate methods can be classified as heuristic. However, when applying them, the problem of determinism lack is often arises, which does not always allow one to evaluate the solution obtained accuracy. The paper considers a methodical approach to assessing the accuracy of heuristic algorithms designed to determine the useful signal shape and parameters on the strong noise component background. It is based on the method of analogy and consists in modeling an artificial signal with given parameters and a background noise interference similar in its characteristics to additive white Gaussian noise. In this case, the noise component is formed by software using a pseudo-random number sequence generator. Such generators are included in the packages of almost all high-level programming languages built-in functions. A comparative analysis of the real and artificial noise characteristics is presented, that shown the problem solving by numerical modeling possibility. The results of accuracy estimation in determining the artificial signal parameters, that is separated from the noise component using piecewise linear approximation and averaging heuristic algorithms, are obtained. The problem of empirical data smoothing with the discrete signal equivalent replacement by a quadratic functions whose parameters provide a piecewise parabolic approximation its shape is also considered. This procedure eliminates the residual signal bounce that inevitably occurs as a result of linearization and allows further recording at any sampling rate. Thus, the proposed approach allows us to quantify the accuracy of heuristic algorithms used in determining the expected signal parameters.

During the analysis of the most common architectures for building neural networks for image analysis – a direct convolutional layer, a recurrent neural network, a convolutional neural network – the authors determined that the last architecture option for the task is the most suitable. The solutions available on the market, although they allow us to recognize emotions, do not evaluate them, and this is an important result for a wide variety of tasks, because such an assessment allows us to more accurately predict the possible future behavior of both individuals and groups of people. The purpose of this article is to design an application software product (application) and develop a prototype that could perform the functions of analyzing emotions and evaluating them. The objectives of this study include the choice of the architecture of the designed application; the development of an algorithm for the operation of the application; the design of the user interface; a description of the neural network learning process and its results, the model of which was defined in the previous article; demonstration of the prototype (control example). The scientific novelty of the projected application lies in the formation of an assessment of the psycho-emotional state of those people whose images of their faces were evaluated. Such an assessment with recommendations can be widely demanded in various branches of human activity, since it is important to be able to express emotions not only to ordinary people, but also to those whose job is to transfer their emotional state to others: business coaches, journalists, actors, animators, dancers, etc. The presence of emotional intelligence is very important at the moment time.

The aim of the work is to develop a system for verifying subjects by face based on a neural network model that is executed in a protected mode. Protected mode means that the identity verification system is highly resistant to destructive influences, such as competitive attacks, and allows storing and processing biometric data without compromising it. The system is based on a biometrics to code converter trained according to GOST R 52633.5, which allows you to associate the subject’s facial biometrics image with its cryptographic key or long password, which can later be used for authentication, and deep convolutional neural networks. For face detection in the image, the MTCNN artificial neural network architecture was used, and several neural network architectures were tested for feature extraction: InceptionResnet, Facenet512, VGG-Face and OpenFace. The best results were shown by the InceptionResnet neural network. When evaluating the effectiveness and testing the reliability of the proposed system on a special dataset of faces collected under different lighting conditions in a room, it was possible to achieve a relatively low value of equal probability of errors of the first and second kind (EER = 0.0146 with a key length of 278 bits), which confirms the effectiveness of the considered approach to building face verification systems.

The article discusses the features of applying the precedent approach when managing complex energy-intensive systems in the context of the need to take into account various energy, technical, environmental and operational indicators, as well as the uncertainty of many internal and external factors influence. This leads to the presence of a large amount of semi-structured information that can be presented using various scales, which determines the prospects of using the precedent approach. The proposed fuzzy ontological model for supporting decision support based on precedents is described, characterized by the use of dynamic concepts, as well as concepts in the form of different scale numerical and linguistic variables. An algorithm for assessing the proximity of precedents based on an ontological model is proposed, which differs by taking into account the dynamic aspects of changes in the state of controlled systems. The developed algorithms for fuzzy inference for decision support based on precedents are presented, which allow the use of both linguistic and numerical variables as input characteristics of the fuzzy production model, as well as using various logical connections between the rules pre-requisites. The software that implements the developed model and algorithms is described. Particular attention is paid to the modified fuzzy inference component, implemented using Python 3.8.7 language tools. To implement the user interface of the specified component, the cross-platform graphic library Tkinter was used. The results of computational experiments using real data obtained during the operation of an energy-intensive system for processing fine ore raw materials, including a conveyor-type roasting machine, are presented. Minimization of specific total costs for thermal and electrical energy was considered as a criterion for the effectiveness of management decisions. The outcome obtained showed that the proposed model and software make it possible to obtain a result comparable to the one of using complex analytical dependencies, while ensuring a reduction in time and financial costs.

The work is aimed at improving the efficiency of cybersecurity management (­CS) of economic entities (­SED) by organizing effective ­CB monitoring, taking into account such features of its process as the heterogeneity of sources of initial ­CB monitoring data, their presentation in different data formats, their inaccuracy, and largely uncertainty and noisiness, as well as a large number of ­KB events processed by heterogeneous components of the ­ERMS ­KB monitoring system. In this paper, in contrast to existing methods, a complex two-stage method for fuzzy clustering of ­SI events is proposed, taking into account the assessment of the criticality of ­SI events and the functionality of the ­ES ­IS monitoring system. At the first stage, the ­KB event clustering model based on the fuzzy c-means method is used. This model allows splitting the set of ­CI events into several fuzzy clusters according to the a priori probability that the ­CI event is an incident. At the second stage, to refine the results of the clustering of ­SI events obtained at the first stage, the model of clustering of ­SI incidents based on the method of extracting α-kernels of fuzzy clusters is used. This model allows you to manually select the thresholds for the degree of belonging of ­SI incidents to fuzzy clusters, taking into account additional information and features of processing ­SI incidents in the ­SI monitoring system of a particular ­EDMS. The paper evaluates the effectiveness of the two-stage method of fuzzy clustering of ­KB incidents in the ­EDMS ­KB monitoring system. The proposed approach makes it possible to increase the efficiency of ­ERMS ­CM monitoring and reduce the period of time required to make a decision on the ­ERMS ­CM management due to the complex consideration of the features of ­CM event processing in the ­ERMS ­CM monitoring system.

The determination of indicators of the technical quality of electric motors, as well as their modeling, are in demand in the organization, final control of the technical quality of products, in the procedures for diagnosing and monitoring the technical condition of electrical equipment during operation. A large role in substantiating the criteria for the technical quality of electric motors and developing methods for their determination is given to computer modeling. Along with rotating electric motors, linear asynchronous electric motors (LAEM) are increasingly being used in technical systems. Criteria and test methods developed for rotating electrical machines are not adequate to the specifics of the LAEM. As applied to LAEM inductors, a method is proposed for assessing the technical quality indicators of three-phase inhomogeneous inductors with a non-uniform distribution of magnetic properties along the magnetic circuit. The method is based on the impulse effects of a voltage source on the LAEM inductor. A mathematical model, an algorithm and a program for modeling the technical quality indicators of LAEM inductors that implement the proposed method have been developed and presented. The program is written using its own interpreted programming language of the Maple software product and requires the user to enter the parameters of an electrical device. At the output, the user automatically receives an image of the Green’s matrix, oscillograms of phase currents based on the results of three experiments, and numerical values of quality indicators. The simulation results indicate that the proposed method can serve as a tool for the technical control of inductors, and also that the developed technical quality criteria, mathematical model, algorithm and program allow modeling and evaluating the technical qualities of LAEM inductors both during final tests and during monitoring the technical condition of the device during operation.

Mathematical modeling of an active exoskeleton in the form of an electromechanical model containing three movable, controlled links interconnected by hinges has been carried out. For the considered mathematical model of the active exoskeleton, differential equations of motion are proposed. Numerical methods are used to solve the inverse and direct problems of dynamics in the created software package in the environment of the universal system of computer mathematics. A comprehensive study has been carried out, considering the problems of exoskeleton control, in the form of solving inverse and direct problems of dynamics, in relation to the created mathematical model of three moving parts of the exoskeleton, taking into account electric drives, using modern methods of mathematical modeling. Analytically determined are the angles between the links that define the anthropoid movement. Solving the inverse problem of dynamics, the moments controlling the movement of the links are calculated for each electric drive. The found moments are approximated by stepwise piecewise-constant functions simulating the impulse control of the exoskeleton motion. Dependences of the angular coordinates describing the positions of the links of the active exoskeleton over time are found. A comparative analysis of the numerical solution of the Cauchy problem for the mathematical model of the exoskeleton in the form of differential equations with the initial, given movement of the links is carried out. A good agreement between the results of simulation with impulse control and the original motion is established. The total energy costs have been calculated. Modeling is carried out taking into account the presence of electric drives: dynamic equations for this model are compiled. The Cauchy problem for the system is numerically solved taking into account the presence of electric drives. As a result of applying qualitative, analytical and numerical methods for studying the created mathematical model of three moving parts of the exoskeleton, taking into account the presence of electric drives, the significance of the influence of electric drives on the dynamics of the mechanism was established. The implementation of the electromechanical model of the three links of the exoskeleton was carried out in the environment of the universal system of computer mathematics “Wolfram Mathematica 11.3”.

The study is devoted to improving the management system of a complex technological system for processing ore waste. Such waste accumulates in large volumes in the territories adjacent to the mining and processing plants, posing a great environmental threat to both the population and the environment due to dust formation and the penetration of harmful compounds into the soil and groundwater. Therefore, the task of improving the management systems for the processing of ore waste, as one of the priorities, is on the current agenda of the management of mining and processing plants. The complexity of the technological system is manifested in the presence of two processing lines that differ in the set of units, and the choice of line depends on the granulometric composition of ore waste. The scientific novelty of the research results is the proposed structure of the neural network controller based on the reference model for the technological system, which is used as deep recurrent neural networks. The general structure of the neuroregulator includes several local neurocontrollers for each of the units of the technological system. Recurrent neural networks make it possible to create high-precision digital copies of individual units of two processing lines and use them to simulate the response of control objects when setting up controllers. Approbation of the proposed structure of the neuroregulator was carried out in the MatLab-Simulik environment, neural networks were designed using the Deep Network Designer tool. The results of testing showed that the speed of the control system is increased compared to other architectures of neuroregulators available in the Simulik environment, which can positively affect the operation of the entire technological system in transient conditions, in particular, reduce technological losses.