Search

The burden of mental disorders continues to grow and has a marked impact on health systems around the world. It has serious social and economic consequences. Unlike physical illnesses, mental health problems are often overlooked. It is very important to get a diagnosis and timely treatment before it can become serious. However, current diagnostic methods are based on subjective assessments by experts, making treatment difficult and costly. This article provides an overview of artificial intelligence (AI) technology and its applications in health care, a review of recent original AI research on mental health, and a discussion of how AI can complement the current distance learning model aimed at monitoring the emotional state of learners, as well as areas for additional research. Several studies were reviewed that used videos captured with various devices and pictures to predict and classify mental illnesses including depression, mood disorders (affective disorders) and others as well as different levels of stress. Collectively, these studies have shown high accuracy and have provided excellent examples of AI's potential in the mental health field. Most of these should be seen as early trial work demonstrating the potential of using machine learning algorithms to address mental health problems. However, caution is necessary in order to avoid misinterpreting preliminary results and not to violate ethical boundaries.

Education around the world is constantly developing and improving, teaching standards are changing, the content of education is changing, and the requirements for a modern teacher are increasing. The Federal State Educational Standard puts at the forefront new approaches and requirements for the educational system as a whole and for each individual subject in particular. The introduction of extracurricular activities, design technologies, an innovative approach poses new challenges for the entire education system and for the modern teacher, the school and the education system itself is becoming more and more innovative in development. The development of hardware and software, the increasing introduction of network communications into the educational process leads to the introduction of innovative technologies. The implementation of which is now the most optimal on the basis of actively developing and widespread information technologies. The teacher receives an excellent tool and teaching tool, which, in skillful hands, allows you to change the approaches to the transfer of the studied material, test instruments and change the emotional and psychological climate in the lesson. The article examines various innovative technologies and the possibilities of their adaptation to the use of modern teaching aids, highlights the main options for the digital configuration of the information educational environment for organizing the use of innovative technologies in physics lessons, offers methodological recommendations for conducting physics lessons and using innovative technologies to increase the motivation of learning. The greatest attention is paid to the technologies that were revealed in the results of the questionnaire survey of physics teachers and the use of which helps to increase the motivation of students to study physics, such as game technologies; portfolio technology; method of projects. Examples of innovative developments for physics lessons have been developed, aimed at increasing the motivation of teaching physics by means of innovative technologies and using information technologies.

Increasing the profitability of sales agents has been and continues to be given great attention. The article discusses various approaches to solving this problem, one of which is reinforcement learning, which is actively used to solve algorithmic trading problems. Increasing the efficiency (profitability) of algorithmic trading is possible in two ways, on the one hand, improving the algorithms, on the other hand, enriching the data that is transmitted to the input to the algorithms. The study confirmed the feasibility of using derived financial indicators for the tasks of applying trading algorithms based on reinforcement learning algorithms. The main idea of ​​the research implementation is aimed at obtaining the results of the sales agent's work based on Q-learning on technical indicators and on derived technical indicators (the agent is implemented in Python). The substantiation of the choice of the Q-learning method for solving the problem is carried out, the basics of decision-making, policy, strategy, and reinforcement learning are considered. The paper considers the issues of increasing the efficiency (profitability) of a sales agent based on the Q-learning algorithm by transferring derivative technical indicators to him, determined and substantiated derived technical indicators, verified the results of a sales agent's work on technical indicators and derived technical indicators. In the study presented in the article, an empirical test of the possibility of creating synthetic financial features to improve the efficiency of learning algorithms was carried out, in addition, the verification of obtaining the necessary results when using reinforcement learning algorithms was carried out. Empirical confirmation has been carried out that the use of derived financial indicators to increase the efficiency (profitability) of sales agents based on the Q-learning method with the use of reinforcement learning algorithms is expedient.

At the moment, there is a large volume of literature on exchange trading. Obviously, every year the mathematical base of work is becoming more complicated along with an increase in computing power, machines can process more metrics from year to year and produce more accurate solutions per unit of time. The use of deep learning has already proven itself well, as the application of this approach has given a quantum leap in algorithmic trading. The article presents an algorithm for trading long contracts with one asset in the financial market in the Python programming language using the LSTM neural network using the Keras library, which is used as a demo example in the Reinforcement Learning discipline. The formalized LSTM model solves the vanishing gradient problem, which can hold the gradient of the objective function relative to the state signal. As applied to our problem, such an improvement in the model allows us to collect data on certain patterns of price changes, that is, when predicting the price of the next step, we rely not only on the data of the previous step, but also on earlier data, when there was a similar state of the environment. Sharpe Ratio is used to determine the optimal strategy and make decisions at each time of application. The optimal minimum time period for the model operation has been determined; the signal transmission delay from the moment the market situation changes until the signal is received by the model, which will be infinitely small, and the computing power will be considered infinitely large. These assumptions give the right to say: when the market situation changes, the model is instantly ready to react and make a decision to sell, buy or hold an asset.

The quality of knowledge is the most important task of the learning system at any level and stage of education. The COVID-19 pandemic has made its own adjustments to the process of organizing education, requiring the transition from traditional to distance learning as soon as possible. In the new conditions, the use of adaptive knowledge control has become relevant, taking into account the individual level of knowledge of the trainees. The study is devoted to the analysis of the features of adaptive testing, the conditions of application and the possibilities of web technologies for its organization. The article presents the results of a study aimed at organizing and conducting adaptive knowledge control as one of the means of implementing an individual learning trajectory. The study presents algorithms for constructing an individual trajectory of adaptive testing for each user. The analysis of web technologies and learning management systems that are currently used in terms of their capabilities for the implementation of the adaptive learning module is carried out. An adaptive testing module has been developed for implementation into the LMS Moodle learning management system, built taking into account the modular organization of the system. The construction of the module is based on a Markov random process with discrete states, continuous and discrete time, which makes it possible to implement a condition for completing testing with the function of viewing detailed statistics of its passage. The problem of developing and implementing an adaptive testing module in an online learning system is considered. The results of experimental work confirming the effectiveness of the implementation of the adaptive testing module are presented. The study of technology by future teachers and its application in practice will contribute to an increasingly widespread implementation in practical activities.

Currently, a significant group of industrial facilities can be classified as chemically hazardous facilities (CHFs). To predict the spread of harmful impurities in the programs being developed, Gaussian and Lagrangian models are actively used, on the basis of which the complexes used both in the EMERCOM of Russia and in research organizations are being implemented. These complexes require the introduction of a large amount of information, including the characteristics of the wind field in the distribution of an emergency chemically hazardous substance, which limits their use. In systems, the formation of which is influenced by a large number of different random factors, spatial scaling (similarity) is often found, and one or another parameter can be described using the methods of fractal geometry, which in the past few decades has been actively and successfully applied to the description of various physical objects. The purpose of this study is to analyze the possibility of using the random-addition method for early prediction of the distribution of harmful impurities in the surface air layer during the short-term release of a substance on the surface as a result of an emergency.

Mathematical computer models of the permittivity of silicon-based nanostructures upon interaction with electromagnetic radiation in a wide frequency range have been developed. To implement computer models for studying the electrophysical properties of the structures under study, algorithms and a set of programs have been developed. The results of the study of materials will not only provide fundamental information about the physical effects occurring in composite nanostructures but will also be useful for solving problems related to calculations for given electrophysical problems. For a nanocomposite based on ceramics and semiconductor oxides of zinc grains, resonant bursts of permittivity are observed within a wavelength of 300–400 nm; it has been found that this is due to the presence of electronic polarization of the nanocomposite core. The paper presents the results of modeling the current-voltage characteristics of a nanocomposite based on ceramics and semiconductor grains of zinc oxide. The obtained results show that the geometrical parameters, such as the number of layers and sample width, affect the CVC of the nanocomposite, and the operating point of the CVC shifts. This may be of interest in the development of materials with desired electrical characteristics for the creation of varistors.

To predict the spread of the new coronavirus infection COVID-19, the critical values of spread indicators have been determined for deciding on the introduction of restrictive measures using the city of Moscow as an example. A model was developed using classical methods of mathematical modeling based on exponential regression, the accuracy of the forecast was estimated, and the shortcomings of mathematical methods for predicting the spread of infection for more than two weeks. As a solution to the problem of the accuracy of long-term forecasts for more than two weeks, two models based on machine learning methods are proposed: a recurrent neural network with two layers of long short-term memory (LSTM) blocks and a 1-D convolutional neural network with a description of the choice of an optimization algorithm. The forecast accuracy of ML models was evaluated in comparison with the exponential regression model and one another using the example of data on the number of COVID-19 cases in the city of Moscow.

The problem evaluated in this study is related to the optimization of a budget of an industrial enterprise using simulation methods of the production process. Our goal is to offer a universal and straightforward methodology for simulating a production budget at any level of complexity by presenting it in a specific form. The calculation of such production schemes, in most enterprises, is currently done manually, which significantly limits the possibilities for optimization. This article proposes a model based on the Monte Carlo method to automate the budgeting process. The application of this model is described using an example of a typical meat processing enterprise. Approbation of the model showed its high applicability and the ability to transform the process of making management decisions and the potential to increase the profits of the enterprise, which is unattainable using other methods. As a result of the study, we present a methodology for modeling industrial production that can significantly speed up the formation and optimization of an enterprise’s budget. In our demonstration case, the profit increased by over 30 percentage points.

The authors carried out modeling of the electrophysical properties of composite media. The frequency dependences of the dielectric constant on the type of inclusions in the composite are investigated. On the basis of the nanocomposite considered in the work, based on Si, B, and SiO2, the authors model a reflecting screen, the lattice elements of which have a layered hierarchically constructed structure similar to a fractal formation. The influence of the level of fractality on the optical properties of the object was also investigated, and it was found that the proposed structure makes it possible to increase the operating frequency range of the reflecting screen and the efficiency, in comparison with reflecting screens that have a lattice of traditional structure. The results obtained can be of practical interest for broadband and nonlinear radar devices, localization devices and mobile objects, microelectronics, as well as intelligent applications in the field of information security.

A universal solution to an applied problem related to the study of deviations occurring in the joints of manipulation robots, for example, due to elastic deformations or gaps in them, is proposed. A mathematical (dynamic) model obtained by the Lagrange–Euler method is presented, making it possible to investigate such deviations. Six generalized coordinates, three linear and three angulars, were used to describe the variations of each joint in the dynamic model. This made it possible to introduce into consideration joints with six degrees of freedom (6-DOF joints). In addition, mathematical models for external forces acting on the links of manipulation robots are presented. When composing matrices of coefficients of equations of motion, elements identically equal to zero were excluded, which significantly increased the computational efficiency of these equations. The dynamic model based on the obtained equations can be used in the computer simulation of manipulation robots.

An algorithm for parallel calculations in a dynamic model of manipulation robots obtained by the Lagrange–Euler method is developed. Independent components were identified in the structure of the dynamic model by its decomposition. Using the technology of object-oriented programming, classes corresponding to the structures of the selected components of the dynamic model were described. The algorithmization of parallel computing is based on the independence of the calculation of objects of individual classes and the sequence of matrix operations. The estimation of the execution time of parallel algorithms, the resulting acceleration, and the efficiency of using processors is given.

Recently, we have seen the rapidly growing popularity of unmanned aerial vehicles. This is due to some advantages, namely portability, the ability to fly over hard-to-reach areas without human intervention. They are also widely used for commercial purposes, agriculture, delivery, automation in warehouses. The potential of unmanned aerial vehicles is vast and demonstrates promising opportunities. However, when using these devices, the issue of safety is acute. This article presents a developed software application that is used to improve the efficiency of flight research of groups of unmanned aerial vehicles, based on a new method for assessing flight safety by comparing the complexity of specified air routes. A practical approach to modeling and evaluating the search for a safe way is proposed. A suitable method of research is computer and simulation modeling. It is suggested to use the spectrum of dynamic characteristics of the sequence as a formal attribute for analyzing routes. The method is illustrated by an example of comparing air trajectories according to the flight safety criterion. The software application is intended for use in the educational process when training specialists in transport security, robotics, and system analysis.

A model of a layered hierarchically constructed composite is presented, the structure of which demonstrates the properties of similarity at different scales. For the proposed model of the composite, fractal analysis was carried out, including an assessment of the permissible range of scales, calculation of fractal capacity, Hausdorff and Minkovsky dimensions, calculation of the Hurst exponent. The maximum and minimum sizes at which fractal properties are observed are investigated, and a quantitative assessment of the complexity of the proposed model is carried out. A software package is developed that allows calculating the fractal characteristics of hierarchically constructed composite media. A qualitative analysis of the calculated fractal characteristics is carried out.

Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.