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As it stands in the contemporary world, compliance with regulations concerning data protection such as GDPR is central to organizations. Another important issue analysis identified is the fact that compliance is hampered by the fact that legal documents are often complex and that regulations are ever changing. This paper aims to describe the ways in which NLP aids in keeping GDPR compliance effortless through automated scanning for compliance, evaluating privacy policies, and increasing the level of transparency. The work does not only limit to exploring the application of NLP for dealing with the privacy policies and facilitate better understanding of the third-party data sharing but also proceed to perform the preliminary studies to evaluate the difference of several NLP models. They implement and execute the models to distinguish the one that performs the best based on the efficiency and speed at which it automates the process of compliance verification and analyzing the privacy policy. Moreover, some of the topics discussed in the research deal with the possibility of using automatic tools and data analysis to GDPR, for instance, generation of the machine readable models that assist in evaluation of compliance. Among the evaluated models from our studies, SBERT performed best at the policy level with an accuracy of 0.57, precision of 0.78, recall of 0.83, and F1-score of 0.80. BERT showed the highest performance at the sentence level, achieving an accuracy of 0.63, precision of 0.70, recall of 0.50, and F1-score of 0.55. Therefore, this paper emphasizes the importance of NLP to help organizations overcome the difficulties of GDPR compliance, create a roadmap to a more client-oriented data protection regime. In this regard, by comparing preliminary studies done in the test and showing the performance of the better model, it helps enhance the measures taken in compliance and fosters the defense of individual rights in the cyberspace.

A simple non-linear model allowing to calculate daily and monthly GPP and NPP of forests using parameters characterizing the light-use efficiencies for GPP and NPP, and integral values of absorbed photosynthetically active radiation, obtained using field measurements and remotes sensing data was suggested. Daily and monthly GPP, NPP of the forest ecosystems were derived from the field measurements of the net ecosystem exchange of CO₂ in the spruce and tropical rain forests using a process-based Mixfor-SVAT model.

The results of determination of the risk of cardiovascular failure of young athletes and adolescents in stressful physical activity have been demonstrated. The method of screening diagnostics of the risk of developing heart failure has been described. The results of contactless measurement of the form of the pulse wave of the radial artery using semiconductor laser autodyne have been presented. In the measurements used laser diode type RLD-650 specifications: output power of 5 mW, emission wavelength 654 nm. The problem was solved by the reduced form of the reflector movement, which acts as the surface of the skin of the human artery, tested method of assessing the risk of cardiovascular disease during exercise and the analysis of the results of its application to assess the risk of cardiovascular failure reactions of young athletes. As analyzed parameters were selected the following indicators: the steepness of the rise in the systolic portion of the fast and slow phase, the rate of change in the pulse wave catacrota variability of cardio intervals as determined by the time intervals between the peaks of the pulse wave. It analyzed pulse wave form on its first and second derivative with respect to time. The zeros of the first derivative of the pulse wave allow to set aside time in systolic rise. A minimum of the second derivative corresponds to the end of the phase and the beginning of the slow pressure build-up in the systole. Using the first and second derivative of the pulse wave made it possible to separately analyze the pulse wave form phase of rapid and slow pressure increase phase during systolic expansion. It has been established that the presence of anomalies in the form of the pulse wave in combination with vagotonic nervous regulation of the cardiovascular system of a patient is a sign of danger collapse of circulation during physical exercise.

Industrial imaging or “machine vision” is currently a key technology in many industries as it can be used to optimize various processes. The purpose of this work is to create a software and hardware complex for measuring the overall and weight characteristics of cargo based on an intelligent system using neural network identification methods that allow one to overcome the technological limitations of similar complexes implemented on ultrasonic and infrared measuring sensors. The complex to be developed will measure cargo without restrictions on the volume and weight characteristics of cargo to be tariffed and sorted within the framework of the warehouse complexes. The system will include an intelligent computer program that determines the volume and weight characteristics of cargo using the machine vision technology and an experimental sample of the stand for measuring the volume and weight of cargo.

We analyzed the solutions to similar problems. We noted that the disadvantages of the studied methods are very high requirements for the location of the camera, as well as the need for manual operations when calculating the dimensions, which cannot be automated without significant modifications. In the course of the work, we investigated various methods of object recognition in images to carry out subject filtering by the presence of cargo and measure its overall dimensions. We obtained satisfactory results when using cameras that combine both an optical method of image capture and infrared sensors. As a result of the work, we developed a computer program allowing one to capture a continuous stream from Intel RealSense video cameras with subsequent extraction of a three-dimensional object from the designated area and to calculate the overall dimensions of the object. At this stage, we analyzed computer vision techniques; developed an algorithm to implement the task of automatic measurement of goods using special cameras and the software allowing one to obtain the overall dimensions of objects in automatic mode.

Upon completion of the work, this development can be used as a ready-made solution for transport companies, logistics centers, warehouses of large industrial and commercial enterprises.

The article presents the numerical modeling and study of the kinetic model of propane pyrolysis. The study of the reaction kinetics is a necessary stage in modeling the dynamics of the gas flow in the reactor.

The kinetic model of propane pyrolysis is a nonlinear system of ordinary differential equations of the first order with parameters, the role of which is played by the reaction rate constants. Math modeling of processes is based on the use of the mass conservation law. To solve an initial (forward) problem, implicit methods for solving stiff ordinary differential equation systems are used. The model contains 60 input kinetic parameters and 17 output parameters corresponding to the reaction substances, of which only 9 are observable. In the process of solving the problem of estimating parameters (inverse problem), there is a question of non-uniqueness of the set of parameters that satisfy the experimental data. Therefore, before solving the inverse problem, the possibility of determining the parameters of the model is analyzed (analysis of identifiability).

To analyze identifiability, we use the orthogonal method, which has proven itself well for analyzing models with a large number of parameters. The algorithm is based on the analysis of the sensitivity matrix by the methods of differential and linear algebra, which shows the degree of dependence of the unknown parameters of the models on the given measurements. The analysis of sensitivity and identifiability showed that the parameters of the model are stably determined from a given set of experimental data. The article presents a list of model parameters from most to least identifiable. Taking into account the analysis of the identifiability of the mathematical model, restrictions were introduced on the search for less identifiable parameters when solving the inverse problem.

The inverse problem of estimating the parameters was solved using a genetic algorithm. The article presents the found optimal values of the kinetic parameters. A comparison of the experimental and calculated dependences of the concentrations of propane, main and by-products of the reaction on temperature for different flow rates of the mixture is presented. The conclusion about the adequacy of the constructed mathematical model is made on the basis of the correspondence of the results obtained to physicochemical laws and experimental data.

Modeling banking systems using a network approach has received growing attention in recent years. One of the notable models is that developed by Iori et al, who proposed a banking system model for analyzing systemic risks in interbank networks. The model is built based on the simple dynamics of several bank balance sheet variables such as deposit, equity, loan, liquid asset, and interbank lending (or borrowing) in the form of difference equations. Each bank faces random shocks in deposits and loans. The balance sheet is updated at the beginning or end of each period. In the model, banks are grouped into either potential lenders or borrowers. The potential borrowers are those that have lack of liquidity and the potential lenders are those which have excess liquids after dividend payment and channeling new investment. The borrowers and the lenders are connected through the interbank market. Those borrowers have some percentage of linkage to random potential lenders for borrowing funds to maintain their safety net of the liquidity. If the demand for borrowing funds can meet the supply of excess liquids, then the borrower bank survives. If not, they are deemed to be in default and will be removed from the banking system. However, in their paper, most part of the interbank borrowing-lending mechanism is described qualitatively rather than by detailed mathematical or computational analysis. Therefore, in this paper, we enhance the mathematical parts of borrowing-lending in the interbank market and present an algorithm for simulating the model. We also perform some simulations to analyze the effects of the model’s parameters on banking stability using the number of surviving banks as the measure. We apply this technique to analyze the effects of a macroprudential policy called loan-to-deposit ratio based reserve requirement for banking stability.

In our research, we focus on the problem of classification for silent speech recognition to develop a brain– computer interface (BCI) based on electroencephalographic (EEG) data, which will be capable of assisting people with mental and physical disabilities and expanding human capabilities in everyday life. Our previous research has shown that the silent pronouncing of some words results in almost identical distributions of electroencephalographic signal data. Such a phenomenon has a suppressive impact on the quality of neural network model behavior. This paper proposes a data processing technique that distinguishes between statistically remote and inseparable classes in the dataset. Applying the proposed approach helps us reach the goal of maximizing the semantic load of the dictionary used in BCI.

Furthermore, we propose the existence of a statistical predictive criterion for the accuracy of binary classification of the words in a dictionary. Such a criterion aims to estimate the lower and the upper bounds of classifiers’ behavior only by measuring quantitative statistical properties of the data (in particular, using the Kolmogorov – Smirnov method). We show that higher levels of classification accuracy can be achieved by means of applying the proposed predictive criterion, making it possible to form an optimized dictionary in terms of semantic load for the EEG-based BCIs. Furthermore, using such a dictionary as a training dataset for classification problems grants the statistical remoteness of the classes by taking into account the semantic and phonetic properties of the corresponding words and improves the classification behavior of silent speech recognition models.

The numerical methods developed by the author recently for calculating the molecular system based on the direct solution of the Schrodinger equation by the Monte Carlo method have shown a huge uncertainty in the choice of solutions. On the one hand, it turned out to be possible to build many new solutions; on the other hand, the problem of their connection with reality has become sharply aggravated. In ab initio quantum mechanical calculations, the problem of choosing solutions is not so acute after the transition to the classical format of describing a molecular system in terms of potential energy, the method of molecular dynamics, etc. In this paper, we investigate the problem of choosing solutions in the classical format of describing a molecular system without taking into account quantum mechanical prerequisites. As it turned out, the problem of choosing solutions in the classical format of describing a molecular system is reduced to a specific marking of the configuration space in the form of a set of stationary points and reconstruction of the corresponding potential energy function. In this formulation, the solution of the choice problem is reduced to two possible physical and mathematical problems: to find all its stationary points for a given potential energy function (the direct problem of the choice problem), to reconstruct the potential energy function for a given set of stationary points (the inverse problem of the choice problem). In this paper, using a computational experiment, the direct problem of the choice problem is discussed using the example of a description of a monoatomic cluster. The number and shape of the locally equilibrium (saddle) configurations of the binary potential are numerically estimated. An appropriate measure is introduced to distinguish configurations in space. The format of constructing the entire chain of multiparticle contributions to the potential energy function is proposed: binary, threeparticle, etc., multiparticle potential of maximum partiality. An infinite number of locally equilibrium (saddle) configurations for the maximum multiparticle potential is discussed and illustrated. A method of variation of the number of stationary points by combining multiparticle contributions to the potential energy function is proposed. The results of the work listed above are aimed at reducing the huge arbitrariness of the choice of the form of potential that is currently taking place. Reducing the arbitrariness of choice is expressed in the fact that the available knowledge about the set of a very specific set of stationary points is consistent with the corresponding form of the potential energy function.

A crucial component of distributed computing systems is network infrastructure. While networking forms the backbone of such systems, it is often the invisible partner to storage and computing resources. We propose to integrate Network Elements directly into distributed systems through the workload management layer. There are many reasons for this approach. As the complexity and demand for distributed systems grow, it is important to use existing infrastructure efficiently. For example, one could use network performance measurements in the decision making mechanisms of workload management systems. New advanced technologies allow one to programmatically define network configuration, for example SDN — Software Defined Networks. We will describe how these methods are being used within the PanDA workload management system of the ATLAS collaboration.

Heart rate (HR) is the most affordable indicator for measuring. In order to control the individual response to physical exercises of different load types heart rate is measured when the athletes perform different types of muscular work (strength machines, various types of training and competitive exercises). The magnitude of heart rate and its dynamics during muscular work and recovery can be objectively judged on the functional status of the cardiovascular system of an athlete, the level of its individual physical performance, as well as an adaptive response to a particular exercise. However, the heart rate is not an independent determinant of the physical condition of an athlete. HR size is formed by the interaction of the basic physiological mechanisms underlying cardiac hemodynamic ejection mode. Heart rate depends on one hand, on contractility of the heart, the venous return, the volumes of the atria and ventricles of the heart and from vascular heart load, the main components of which are elastic and peripheral resistance of the arterial system on the other hand. The values of arterial system vascular resistances depend on the power of muscular work and its duration. HR sensitivity to changes in heart load and vascular contraction was determined in athletes by pair regression analysis simultaneously recorded heart rate data, and peripheral $(R)$ and elastic $(E_a)$ resistance (heart vascular load), and the power $(W)$ of heartbeats (cardiac contractility). The coefficients of sensitivity and pair correlation between heart rate indicators and vascular load and contractility of left ventricle of the heart were determined in athletes at rest and during the muscular work on the cycle ergometer. It is shown that increase in both ergometer power load and heart rate is accompanied by the increase of correlation coefficients and coefficients of the heart rate sensitivity to $R$, $E_a$ and $W$.