Ischemic stroke remains one of the leading causes of mortality and disability, and accurate segmentation of the affected areas on CT brain images plays a crucial role in timely diagnosis and clinical decision-making. This study proposes an ensemble approach based on the combination of the transformer-based models SE-UNETR and Swin UNETR using a weighted voting strategy. Its performance was evaluated using the Dice similarity coefficient, which quantifies the overlap between the predicted lesion regions and the ground-truth annotations. In this study, three-dimensional CT scans of the brain from 98 patients with a confirmed diagnosis of acute ischemic stroke were used. The data were provided by the International Tomography Center, SB RAS. The experimental results demonstrated that the ensemble based on transformer models significantly outperforms each individual model, providing more stable and accurate predictions. The final Dice coefficient reached 0.7983, indicating the high effectiveness of the proposed approach for ischemic lesion segmentation in CT images. The analysis showed more precise delineation of ischemic lesion boundaries and a reduction in segmentation errors. The proposed method can serve as an effective tool in automated stroke diagnosis systems and other applications requiring high-accuracy medical image analysis.

According to the WHO, the secondary form of hematopoietic-depressive status increases the risk of death in people with oncological, infectious, and hormonal diseases. The choice of drugs that stimulate the hematopoietic activity of B-lymphopoiesis is limited. The current leucopoiesis drugs have a number of side effects: thymic preparations stimulate the production of PGE2, which causes chronic inflammation and various autoimmune diseases through the differentiation of T helper 1 (Th1) cells, the proliferation of Th17 cells, and the production of IL-22 from Th22 cells through EP2 and EP4 receptors; cytokine preparations can cause uncontrolled immune reactions and impaired contractility of smooth and cardiac muscles; drugs based on nucleic acids can stimulate the division of all cells, including bacterial and cancerous ones. The use of oligonucleotides such as ribozymes and antisense oligodeoxynucleotides (AS-ODNs) shows promise as therapeutic moieties, but faces a number of challenges such as nuclease sensitivity, off-target effects, and efficient delivery. The search for substances that stimulate B-lymphopoiesis among ionic compounds was motivated by the discovery of the unique properties of lidocaine docusate, one of the first ionic liquid forms of the known drugs. The lidocaine docusate (protonated form of lidocaine (2-(diethylamino)-N-(2,6-dimethylphenyl) acetamide + docusate-anion (dioctylsulfosuccinate))) suppresses the division of pheochromocytoma cells and activates immunity in rats. The trimecaine-based ionic compound (TIC) demonstrates high B-lymphopoiesis-stimulating activity. The TIC compound stimulates an increase in the volume of transitional B cells, which play an important role for further differentiation and formation of a sufficient number of mature B1 cells and mature B2 cells, where mature B2 cells make up the bulk of the functional population of B lymphocytes. The TIC compound most strongly stimulated the restoration of the number of marginal zone B cells, follicular B cells, and activated germinal center B cells after the cytotoxic emptying of the follicular centers of the spleen induced cyclophosphamide. It significantly exceeds the activity of the comparison drug methyluracil. The TIC compound does not affect the level of pro-B, pre-B-I, or pre-B-II bone marrow cells, which prevents the risk of the formation of immature functionally defective cells.

One of the new areas of cryptography considered-homomorphic cryptography. The article presents the main areas of application of homomorphic encryption. An analysis of existing developments in the field of homomorphic encryption carried out. The analysis showed that existing library implementations only allow processing bits or arrays of bits and do not support division and subtraction operations. However, to solve applied problems, support for performing integer operations are necessary. Because of the analysis, the need to implement the homomorphic division and subtraction operations identified, as well as the relevance of developing our own implementation of a homomorphic encryption library over integers. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the areas of application of homomorphic encryption. A homomorphic division and subtraction methods proposed that allows the division operation performed on homomorphically encrypted data. An architecture for a library of fully homomorphic operations on integers is proposed. The library supports basic homomorphic operations on integers, as well as homomorphic division method. The article also provides measurements of the time required to perform certain operations on encrypted data and analyzes the efficiency of the developed implementation of the library.

High-frequency induction logging is a crucial technique in subsurface exploration, particularly in the oil and gas industry. It involves transmitting electromagnetic signals into the ground and analyzing their responses to determine the geological structure, lithology, and fluid content of the formation. However, one of the major technical challenges in processing digital sounding signals is the complexity of wave propagation paths, which can be influenced by multiple factors such as subsurface heterogeneity, noise interference, and signal attenuation. Traditional signal processing techniques often struggle to accurately interpret low-amplitude, high-frequency signals that contain valuable subsurface information. These signals can be obscured by noise or exhibit complex, non-stationary behavior, making them difficult to detect and analyze using conventional Fourier-based methods. Moreover, standard regression-based approaches for interpreting induction logging data may fail to capture intricate signal variations, limiting their effectiveness in real-time decision-making. To address these challenges, this research explores the application of machine learning and wavelet transform techniques for improving the accuracy and efficiency of digital signal processing in high-frequency induction logging. The wavelet transform, particularly the Daubechies, Morlet, and Galerkin wavelets, is employed to decompose signals into different frequency components while preserving time-domain information. This enables better identification of subtle variations and anomalies in the recorded data. Furthermore, machine learning models, including standard regression algorithms and neural networks, are leveraged to enhance signal interpretation and predictive capabilities. A key innovation of this study is the development of an algorithm that processes low-amplitude high-frequency signals, which are often difficult to detect with conventional methods. Additionally, a neural network-based forecasting algorithm utilizing the Daubechies wavelet transform is introduced to improve the predictive accuracy of subsurface characteristics. By integrating wavelet-based feature extraction with machine learning-driven analysis, this approach enhances the ability to detect complex wave propagation patterns, leading to more precise subsurface modeling. This advancement supports the implementation of smart field technology in Kazakhstan’s oil and gas industry, ultimately improving decision-making speed, operational efficiency, and resource optimization The results were based on a set of three drilled oil wells, namely, West Kazakhstan region, Mangystau, publicly available well log dataset. The evaluation performed using R-square and root mean square error to validate the proposed approach revealed values of 0.887 and 0.0091. This implementation demonstrated the effectiveness of the proposed model, significantly outperforming traditional methods.

An integrated circuit (IC) that can be programmed to perform a series of functions to control a range of electronic devices is a microcontroller. What makes the microcontroller special is that it is programmable. In this article, we're going to try to rely on the mbed platform, the most common open source microcontroller development platform; we use completely homomorphic encryption in a binary number ring to ensure the data protection feature. Let us compare the time it takes to perform encryption and decryption on a Visual Studio C ++ and a Bluetooth Low Energy (BLE) Nano kit microcontroller. Experimental results show that the device can complete a fully homomorphic encryption in a binary number ring in 64.2 microseconds, which is reasonable in a real application scenario and illustrates the feasibility of implementing a more complex cryptographic system using a microcontroller.