The paper investigates the numerical simulation of wave processes in multilayer thin films, which is relevant for understanding their physical properties and optimization for various applications. An integrated mathematical model has been developed that combines Maxwell’s equations, mechanical vibrations and thermal conductivity, taking into account the interaction of physical fields in structures with defects. Adaptive algorithms have been proposed for automatic mesh refinement depending on local gradients of physical parameters, which allows to increase the accuracy of modeling in critical zones. A hybrid approach to calculations using CPU and GPU has been implemented, which ensures efficient use of resources for large-scale problems. Software with a modular architecture has been developed that allows integrating numerical methods, optimization and visualization of results in real time. Experimental validation has confirmed the high accuracy and reliability of the model. The results obtained contribute to a deeper understanding of physical processes in thin films and are the basis for the creation of highly efficient multilayer structures in industrial and scientific applications.
The solution of the scientific and practical problem of determining the effect of factor influence on the result of the work of the information measuring system for the technological process of manufacturing processed cheese is considered through the use of a factor influence model that takes into account the simultaneous effect of five factors and their cross-interactions on the control indicator. The task of the study is to implement for use a simplified cross-classification model that makes it possible to estimate the amount of expected information about the levels of the control parameter when taking into account the levels of both influencing factors and their mutual interactions. An electrical schematic diagram of the control system has been developed and its practical implementation has been carried out, thanks to which statistical data on the main parameters of the technological process have been obtained. Conclusions have been drawn about the possibility of further use of the proposed cross-classification model for various information measuring systems regardless of their purpose.
The publication is devoted to analyzing electrocardiogram signals of artificial origin of realistic form with the possibility of controlling the duration, sampling frequency, noise level and pulse rate using Ingrid Daubechies wavelets. The synthesized signals were investigated using discrete wavelet analysis to study the influence of these parameters on the approximation and detail coefficients. The priority influence of noise on the detail coefficients and the dependence of the number of signal peaks on the given parameters were established. The article uses for the first time the method of packet discrete wavelet filtering of detail coefficients and approximation coefficients. This allowed to provide a high degree of signal restoration to the original form. Similar studies were conducted for continuous wavelet transformation with the generation of wavelet scalogram images, which provide additional diagnostically significant information. The results obtained in the form of an algorithm are promising for use in analyzing signals from radar systems. The developed model for generating realistic-shaped signals is more efficient and exceeds the average accuracy (96.2 %) compared to analogues (88.03 %). The effectiveness of the developed method is fully confirmed by the correlation matrix of functions of discrete spectra of arti-ficial ECG signals.
This article explores the issue of the application of Kolmogorov–Arnold Networks (KAN) for biological age estimation using a dataset of 344 male patients. The dataset includes biomarkers related to bone health and body composition. To enhance model performance, data preprocessing techniques such as polynomial interpolation for missing values and standardization were applied. Pearson and Spearman correlation analyses identified the most relevant biomarkers. Machine learning models were evaluated, along with neural networks and KANs. Experimental results demonstrate that KANs outperform traditional machine learning models and classical neural networks on small datasets. The optimal KAN architecture achieved a correlation coefficient of 0.93, a mean squared error of 18.81, and a mean absolute error of 2.8, surpassing the best-performing conventional models. These findings highlight the potential of KANs as a robust alternative for biological age estimation in resource-limited settings.
The important task has been solved in this scientific research related to specific development and verification of the fundamental suitability of the method for automatic synthesis of Petri nets based on the functioning of an artificial neural network. This allows to automate the generating process of logical control algorithms. It has been proposed to divide the artificial neural network into separate local networks for the subsequent stepwise training. Such stepwise training is necessary to form the logical control algorithm and to synthesize the appropriate Petri net. The proposed principles can be applied in the early stages of training for an artificial neural network while synthesizing a Petri net. It has been conducted the experiments linked to the training of local neural networks and to implement the formation of an algorithm for logical control of the laboratory machine for processing chemical solutions. As a result of the experiment, it has been determined the fundamental suitability for the stepwise training method of neural networks for generating Petri nets and the automated formation of logical control algorithms.
