Nowadays, Wireless Mesh Networks (WMNs) are widely deployed in communication areas due to their ease of implementation, dynamic self-organization, and cost-effectiveness. The design of routing protocols is critical for ensuring the performance and reliability of WMNs. Although there have been numerous experimental works on WMNs in the past decade, only a few of them have been tested in real-world scenarios. This article presents a comparative analysis of three proactive routing protocols, OLSR, BATMAN, and Babel, using Raspberry Pi 4 devices. The evaluation, conducted at Al-Farabi Kazakh National University, covers both indoor and outdoor scenarios, focusing on key metrics such as bandwidth, Packet Delivery Ratio (PDR), and jitter. In outdoor scenarios, OLSR achieved the highest bandwidth at 2.9 Mbps, while BATMAN and Babel lagged. Indoor tests revealed that Babel initially outperformed with the highest bandwidth of 57.19 Mb/s but suffered from scalability issues, while BATMAN and OLSR exhibited significant declines in performance as network size increased. For PDR, BATMAN performed best with a decline from 100% to 42.8%, followed by OLSR with a moderate drop, and Babel with the greatest decrease. For jitter, OLSR showed the most stable performance, increasing from 0.281 ms to 2.58 ms at eleven nodes, BATMAN exhibited moderate increases, and Babel experienced the highest rise.

Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivated us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results showed the comparative performance of MEGA in relation to other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA’s effectiveness and usability in determining optimal locations for mesh routers.

Unmanned Aerial Vehicles (UAVs) are increasingly employed for real-time object detection in critical appli cations such as security surveillance, disaster response, and environmental monitoring. However, accurate detection in UAV imagery remains challenging due to small target sizes, cluttered backgrounds, and varying environmental conditions. This study evaluates the performance of YOLOv8n/v8s and YOLOv11n/11s models for human detection in UAV-captured imagery across diverse natural landscapes. To ensure practi cal deployment in resource-constrained environments, the models were implemented on a Raspberry Pi 5 using the OpenVINO framework. Experimental results show that both YOLO series achieve comparable detection accuracy in the range of 80–82%, with YOLOv8n and YOLOv11n demonstrating the highest processing speeds of 10.50 and 11.04 frames per second (FPS), respectively. These findings confirm the feasibility of using lightweight YOLO models for real-time human detection on embedded systems. The re sults highlight the potential of integrating edge AI and UAVs for autonomous, on-site monitoring in remote or complex terrains, offering scalable solutions for intelligent aerial surveillance. Key words: UAV, object detection, accuracy, YOLO models.

A hierarchical surface that includes objects with different sizes, as a result of creating local fields, initiates a large number of effects. Micropores in the composition of macropores, as well as nanoclusters of the substance, were detected by scanning electron and atomic force microscopies on the surface of ZnO/Por-Si samples. An identical fractal dimension for all levels of the hierarchy was determined for these structures, which is associated with the same response to external excitation. Photoluminescence studies have shown the presence of localized levels in the band gap, with the probability of capturing both electrons and holes, which ensures charge transitions between energy bands. Decomposition of the electron paramagnetic resonance (EPR) signal into components made it possible to determine the manifestations of various types of interaction between paramagnetic particles, including the hyperfine structure of the spectrum. The ordering of the structure of the substance as a result of sequential annealing in the range from 300 to 500 °C was revealed in the EPR spectrum. This fact, as well as photo- and gas sensitivity for all types of samples studied, confirms the prospects of using these structures as sensors.