Vol 25, No 2 (2024)

The method of recording electrocardiograms as a non-invasive research method is widely used in modern functional diagnostics. Spectral diagnostic methods based on Fourier transform and wavelet transform are being developed. For the purposes of identification of cardiac rhythm disorders, the method of research selected is spectral (frequency) analysis of short-term ECG recordings, up to one period of heartbeats. Fourier series decomposition of the cardiac signal (ECG) in EDF-format for one period was carried out. It is determined that the maximum accuracy of cardiac signal description is achieved at the number of harmonics equal to half of the number of sampling points of the cardiac signal during the period. The correctness of the script developed for spectral analysis was checked by reconstructing the cardiac signal from its spectrum and comparing it with the original signal. The correlation between the spectrum and the shape of the cardiac signal has been established. The conclusion is made about the applicability of the spectral analysis method for the identification of heart rhythm disorders, as well as about the possibility of using the spectrum of electrical signals of heart contractions as a multidimensional function of the heart state. The direction of further identification of regularities by means of statistical analysis with interpretation of results by specialized specialists is indicated. The theoretical and practical value of this study lies both in determining the areas of application of spectral analysis of the cardiac signal for diagnosis and treatment, and in the practical results obtained, which can be used in the development of an expert system or a specific technical device.

A method for determining the degree of loading of rolling elements in the working area of roller radial single-row bearings has been developed, according to this method the load distribution between rolling elements in the working area of these bearings depends on their location in this zone and the size of the contact pad between the maximally loaded rolling element and the outer ring of the bearing. Using the developed methodology, the radial force acting on the bearing is calculated for specific examples. As a result, the ratio between the radial force of the bearing and the force acting on the most loaded rolling body is obtained, which differs from the calculation of these bearings accepted in modern practice. It is also shown that this ratio is not constant, but depends on the magnitude of the force acting on the most loaded rolling body and the size of the rings and rolling bodies of the bearing. In this regard the following is proposed, in order to determine the maximum load on the rolling bodies-rollers in the working area of the bearing using the developed methodology, the method of iterations or successive approximations is suggested to be used, the essence of which consists in the initial approximate determination of the force acting on the most loaded rolling body, subsequent determination of the load on the bearing and comparing it with the actual force. By repeating this process many times, it is possible to obtain the maximum force acting on the rolling elements in the working area of the bearing with any degree of accuracy.

The work is devoted to solving the current problem of forecasting real estate prices by building a predictive model based on the generated database of real estate in Moscow, posted on the Move Real Estate website. Existing machine learning methods for solving the forecasting problem are considered and one of them is applied - multiple linear regression. A regression analysis of the obtained results of solving the forecasting problem was carried out. Eleven independent variables are considered as control parameters. The influence of the variables taken into account when constructing the model on the results of solving the problem of forecasting real estate prices was studied. It was determined which of the independent variables have the greatest impact on the results of the model. To improve the quality of the model, preprocessing and standardization of features were carried out. Identification of outliers and omissions of values was carried out during the formation of the database. The coefficients of the multiple linear regression model were determined using the least squares method. To assess the quality of the model, the following model parameters are analyzed: R-squared, adjusted R-squared, p-value. The result of constructing a predictive model is the resulting regression equation. The application of the resulting equation can be used to subsequently take into account specific characteristics when solving the problem of forecasting real estate prices. The work shows the advantages of using this method and the prospects for applying the obtained result.

The age of artificial intelligence emphasises the possibility of justification and the dialectical aspects of legal reasoning. The need for validity in legal reasoning mainly stems from the existence of exceptions to rules and conflicts between rules. Formal logic may well account for exceptions to rules and thus characterise cancellable reasoning. The presented article focuses on legal issues related to Artificial Intelligence (AI) that are being discussed in the scientific community because of their importance for understanding the mechanisms of law realisation. Some of the most pressing issues in the application of artificial intelligence include: transparency of algorithms, cybersecurity vulnerabilities, unfairness, bias and discrimination, lack of adversariality, legal personality issues, intellectual property issues, adverse effects on employees, privacy and data protection issues, liability for damages and lack of liability. Recognising the importance of artificial intelligence in the field of law, and acknowledging that the field requires constant reassessment and flexibility, this article develops a discussion that is important given the seriousness of the impact of artificial intelligence technologies on legal actors. on legal issues related to Artificial Intelligence (AI) that are being discussed in the scientific community because of their importance for understanding the mechanisms of law realisation. Some of the most pressing issues in the application of artificial intelligence include: transparency of algorithms, cybersecurity vulnerabilities, unfairness, bias and discrimination, lack of adversariality, legal personality issues, intellectual property issues, adverse effects on employees, privacy and data protection issues, liability for damages and lack of liability. Recognising the importance of artificial intelligence in the field of law, and acknowledging that the field requires constant reassessment and flexibility, this article develops a discussion that is important given the seriousness of the impact of artificial intelligence technologies on legal actors.