Brain-Computer Interfaces (BCIs) leverage brain signals to facilitate communication and control, particularly benefiting individuals with motor impairments. Motor imagery (MI)-based BCIs, utilizing non-invasive electroencephalography (EEG), face challenges due to high signal variability, noise, and class overlap. Deep learning architectures, such as CNNs and LSTMs, have improved EEG classification but still struggle to fully capture discriminative features for overlapping motor imagery classes. This study introduces a hybrid deep neural architecture that integrates Convolutional Neural Networks, Long Short-Term Memory networks, and a novel SVM-enhanced attention mechanism. The proposed method embeds the margin maximization objective of Support Vector Machines directly into the self-attention computation to improve interclass separability during feature learning. We evaluate our model on four benchmark datasets: Physionet, Weibo, BCI Competition IV 2a, and 2b, using a Leave-One-Subject-Out (LOSO) protocol to ensure robustness and generalizability. Results demonstrate consistent improvements in classification accuracy, F1-score, and sensitivity compared to conventional attention mechanisms and baseline CNN-LSTM models. Additionally, the model significantly reduces computational cost, supporting real-time BCI applications. Our findings highlight the potential of SVM-enhanced attention to improve EEG decoding performance by enforcing feature relevance and geometric class separability simultaneously.

Motor imagery (MI) based electroencephalography (EEG) classification is central to brain-computer interface (BCI) research but practical deployment remains challenging due to poor generalization across subjects. Inter-individual variability in neural activity patterns significantly limits the development of subject-independent BCIs for healthcare and assistive technologies. To address this limitation, we present a transfer learning framework based on Convolutional Relational Networks (ConvoReleNet) designed to extract subject-invariant neural representations while minimizing the risk of catastrophic forgetting. The method integrates convolutional feature extraction, relational modeling, and lightweight recurrent processing, combined with pretraining on a diverse subject pool followed by conservative fine-tuning. Validation was conducted on two widely used benchmarks, BNCI IV-2a (four-class motor imagery) and BNCI IV-2b (binary motor imagery), to evaluate subject-independent classification performance. Results demonstrate clear improvements over training from scratch: accuracy on BNCI IV-2a increased from 72.22 (±20.49) to 79.44% (±11.09), while BNCI IV-2b improved from 75.10 (±17.17) to 83.85% (±10.30). The best-case performance reached 87.55% on BNCI IV-2a with Tanh activation and 83.85% on BNCI IV-2b with ELU activation, accompanied by reductions in inter-subject variance of 45.9 and 40.0%, respectively. These findings establish transfer learning as an effective strategy for subject-independent MI-EEG classification. By enhancing accuracy, reducing variability, and maintaining computational efficiency, the proposed framework strengthens the feasibility of robust and user-friendly BCIs for rehabilitation, clinical use, and assistive applications.

This scientific article presents an innovative approach to optimizing routes of high-altitude unmanned aerial vehicles (UAVs) for effective monitoring of smart cities. The main proposed method is based on the ant colony optimization (ACO) algorithm with the implementation of an inverse pheromone mechanism—a repulsion-based exploration strategy. Unlike standard pheromones that encourage exploitation of frequently visited paths, this approach promotes exploration of unvisited areas by repelling UAVs from undesirable route sections, allowing UAVs to adapt more efficiently to dynamic changes in the urban environment. The authors developed a simulation system in the Webots environment, taking into account numerous factors: atmospheric conditions at high altitudes, potential for improved energy efficiency, urban development features, and priority of observation zones. Simulation results demonstrate that the proposed algorithm using inverse pheromones provides more effective area coverage compared to traditional route-planning methods, which may contribute to reduced UAV energy consumption and optimizing the monitoring process in real time. The research makes a significant contribution to the development of smart city technologies, offering a solution that can be integrated with existing urban monitoring systems to improve the efficiency of urban infrastructure observation, enhance security, and optimize urban resource management.

One of the most important problems in swarm robotics is coordination of the movement. Several problems have to be solved simultaneously here, such as avoiding collisions with each other or with an obstacle, etc. One of the most common methods for solving such problems is to move while maintaining a certain geometric pattern, or, in short, to form control. If formation control is done properly, many benefits can be obtained, such as system cost reduction, increased system robustness and efficiency, while providing the reconfigurability and flexibility of the system structure. This paper presents an algorithm for controlling the formation of a swarm of autonomous mobile robots. Also within the framework of this work, using a software application written in the Python programming language, the motion of the swarm with nine autonomous robots was simulated. The results of the experimental research of the movement of this swarm of robots using the algorithm described in this paper are given.

Статья посвящена сбору доступной информации о водных объектах Восточно-Казахстанской области, полученной из открытых источников Национальной гидрометеорологической службы Республики Казахстан и Министерства по чрезвычайным ситуациям Республики Казахстан с целью формирования базы атрибутивных данных с пространственной привязкой в ГИС и формированию основы последующих исследований, направленных на разработку и внедрение моделей прогнозирования наводнений. Проведен обзор исследований, посвященных прогнозу наводнений в разных регионах Казахстана и в других странах, в результате чего сформированы критерии оценки данных и системы гидрологического мониторинга. Приводится характеристика гидрологического режима основных репрезентативных водных объектов Восточного Казахстана. На основе пространственного анализа сети гидрологических постов и известных зон затопления дана оценка пространственному охвату государственной системы наблюдений и определены рекомендации по ее расширению. Сделаны выводы о возможности применения собранных данных при создании прогнозных моделей.