Vol 32, No 4 (2024)

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Purpose. Investigation of spin-wave signal passage in a system of magnetic microwaves separated from each other by a one-dimensional array of holes. Using numerical and experimental methods to show controlled spatial-frequency selection of the signal in linear and nonlinear modes of operation. Methods. Micromagnetic modeling of the spatial intensity distributions of spin waves. Obtaining S-parameters of spin waves propagating in a tangentially magnetized structure using a vector circuit analyzer. Results. The spatially selective properties of the structure in linear and nonlinear modes are demonstrated using micromagnetic modeling. A mechanism for controlling the frequency range of the Bragg zone is revealed using a vector analyzer. Conclusion. The proposed structure can be used as a functional element in planar topologies of magnon networks and parallel signal processing devices based on them.

Purpose of this work is the comparative analysis of two approaches to the synthesis of two-dimensional binary identifiers of nucleotide sequences obtained using DNA sequencing of biological objects. Methods. One of the approaches is based on modeling the polarization-dependent diffraction of a coherent readout beam on a two-dimensional phase-modulating structure (phase screen) associated with the symbolic sequence obtained as a result of DNA sequencing. Another approach uses a two-dimensional representation of the symbolic sequence using a chaos game representation (CGR). To obtain a finite-element CGR mapping, it is fragmented into a given number of cells, ensuring acceptable sensitivity of the synthesized binary identifier to structural changes in the displayed sequence. Results. The comparative analysis was carried out using fragments of symbol sequences corresponding to various strains (Wuhan, Delta, Omicron) of the SarSCoV2 virus. In the course of the analysis, the correlation coefficients between the binary identifiers corresponding to various strains were obtained and compared with each other. Conclusion. It has been established that binary identifiers synthesized using the polarization encoding technique are characterized by significantly higher sensitivity to structural changes in the analyzed sequences and smaller sizes compared to CGR binary identifiers.

The purpose of this study is to develop and investigate a new short-term memory model based on an artificial neural network without short-term memory effect and a dynamic short-term memory model with astrocytic modulation. Methods. The artificial neural network is represented by a classical convolutional neural network that does not have short-term memory. Short-term memory is modeled in our hybrid model using the Tsodyks-Markram model, which is a system of third-order ordinary differential equations. Astrocyte dynamics is modeled by a mean field model of gliotransmitter concentration. Results. A new hybrid short-term memory model was developed and investigated using a convolutional neural network and a dynamic synapse model for an image recognition problem. Graphs of dependence of accuracy and error on the number of epochs for the presented model are given. The sensitivity metric of image recognition d-prime has been introduced. The developed model was compared with the recurrent neural network and the configuration of the new model without taking into account astrocytic modulation. A comparative table has been constructed showing the best recognition accuracy for the introduced model. Conclusion. As a result of the study, the possibility of combining an artificial neural network and a dynamic model that expands its functionality is shown. Comparison of the proposed model with short-term memory using a convolutional neural network and a dynamic synapse model with astrocytic modulation with a recurrent network showed the effectiveness of the proposed approach in simulating short-term memory.

The purpose of this paper is to comparatively analyse the efficiency of using artificial neural networks with different convolutional and recurrent architectures in the task of depression diagnosis based on electroencephalogram (EEG) data. Open datasets were chosen as objects of the study and own EEG data of real patients with depression were collected. Methods. To solve the problem of identifying biomarkers of depressive disorder from EEG data, we used convolutional neural networks using two-dimensional or one-dimensional convolution operation, as well as hybrid models of convolutional and recurrent neural networks. To test the developed models of artificial neural networks, we selected open data sets, performed an experiment to collect our own data from real depressed patients, and merged the prepared data sets. The result of this work is analysis and comparison of the performance of different classifiers based on convolutional and recurrent neural network models. Conclusion. We show that the average accuracy of classification of depressive disorder in a test sample using cross-validation was 0.68. The results are consistent with the known results from the literature for small patient-disaggregated datasets. Although the accuracy obtained in this task is insufficient for practical application of the model, it can be argued that further research to improve the efficiency of the model is promising, as well as the need to create a sufficiently large representative dataset of depressed patients, which is an important scientific task for further construction of biophysical models of depressive disorders.

The aim of the paper is to obtain a system of nonlinear evolution equations for two coaxial cylindrical shells containing viscous fluid between them and in the inner shell, as well as numerical modeling of the propagation processes for nonlinear solitary longitudinal strain waves in these shells. The case when the stress-strain coupling law for the shell material has a hardening combined nonlinearity in the form of a function with fractional exponent and a quadratic function is considered. Methods. To formulate the problem of shell hydroelasticity, the Lagrangian–Eulerian approach for recording the equations of dynamics and boundary conditions is used. The multiscale perturbation method is applied to analyze the formulated problem. As a result of asymptotic analysis, a system of two evolution equations, which are generalized Schamel– Korteweg– de Vries equations, is obtained, and it is shown that, in general, the system requires numerical investigation. The new difference scheme obtained using the Grobner basis technique is proposed to discretize the system of evolution equations. Results. The exact solution of the system of evolution equations for the special case of no fluid in the inner shell is found. Numerical modeling has shown that in the absence of fluid in the inner shell, the solitary deformation waves have supersonic velocity. In addition, for the above case, it was found that the strain waves in the shells retain their velocity and amplitude after interaction, i.e., they are solitons. On the other hand, calculations have shown that in the presence of a viscous fluid in the inner shell, attenuation of strain solitons is observed, and their propagation velocity becomes subsonic.

Purpose of this study is to formulate a working definition of information to meet the needs of computer science. There is currently no strict definition of this term. There is a methodological contradiction: the development and application of information technologies requires accuracy and rigor, but at the same time the development is based on a vague, intuitive concept. Materials and methods. The materials for the study are existing classical approaches to understanding information, and the main method is the analysis of these approaches. The proposed definition is constructed taking into account two mathematical transformations: the selection of a certain subset and the mapping between sets. To formalize the allocation procedure, it is used apparatus of fuzzy sets. Results. A definition of information is proposed as the result of a mapping in which the selection of a subset from a set of prototypes leads to the selection of a corresponding subset from a set of images. The selected subset can be understood as fuzzy, then an equivalent definition of information is acceptable as a result of mapping, in which an increase in the heterogeneity of the distribution of the presence indicator on the set of prototypes leads to an increase in the heterogeneity of the distribution of the corresponding indicator on the set of images. The essence of the new definition is demonstrated using models of population dynamics in discrete time. The significance of the proposed approach for information technology is revealed using the example of the numerical method of multi-extremal optimization. It is shown that the proposed definition makes it possible to formulate effective stopping conditions for the numerical method of stochastic optimization, which guarantees the receipt of a given amount of information. Conclusion. The proposed understanding of information allows us to overcome the shortcomings of previous approaches to understanding the essence of information, retains all the advantages of the classical approach and is consistent with other well-known approaches in the field of computer science. This definition can be used to improve numerical optimization methods, as well as other information technology tools.