Fiber-optic gyroscopes (FOGs) have become one of the most important elements of modern inertial navigation systems due to their high accuracy, reliability, and independence from external signals such as satellite navigation. This review analyzes and discusses the key FOG architectures: interferometric (IFOG), resonant (RFOG), digital (DFOG), and hybrid (HFOG). The concepts of their functioning, structural features, and the main advantages and limitations of each architecture are examined. Particular focus is placed on advanced signal-processing and error-compensation algorithms, including filtering techniques, noise suppression, mitigation of thermal and mechanical drifts, and emerging machine learning (ML) based approaches. The analysis of these architectures is carried out in terms of major parameters that determine accuracy, robustness, and miniaturization potential. Various applications of FOGs in space systems, ground platforms, marine and underwater navigation, aviation, and scientific research are also being considered. Finally, the latest development trends are summarized, with a particular focus on miniaturization, integration with additional sensors, and the introduction of digital and AI-driven solutions, aimed at achieving higher accuracy, long-term stability, and resilience to real-world disturbances.

The use of deep learning algorithms for the classification of crop diseases is one of the promising areas in agricultural technology. This is due to the need for rapid and accurate detection of plant diseases, which allows timely measures to be taken to treat them and prevent their spread. One of them is to increase productivity and maintain land quality through the timely detection of diseases and pests in agriculture and their elimination. Traditional classification methods in machine learning and algorithms in deep learning were compared to note the high accuracy in detecting pests and crop diseases. The advantages and disadvantages of each model considered during training were taken into account, and the Inception V3 algorithm was incorporated into the application. They can monitor the condition of crops on a daily basis with the help of new technology-applications on gadgets. Aerial photographs used by research institutes and agricultural grain centers do not show the changes that occur in agricultural grains, that is, diseases and pests. Therefore, the method proposed in this paper determines the types of diseases and pests of cereals through a mobile application and suggests ways to deal with them.

The object of this study is the municipal solid waste management system within a modern urban environment, where rapid urbanization and population growth pose significant challenges to ecological sustainability. The key problem addressed is the inefficiency of waste collection due to overflowing containers, poor route planning, and suboptimal resource allocation. To tackle these issues, an intelligent waste monitoring system has been developed that integrates Internet of Things (IoT) technologies, computer vision, data analytics, and a Regional Geographic Information System (RGIS). The system includes a computer vision model that analyzes images of waste containers to determine their fill level. Fine-tuning the model on locally collected image data, reflecting regional characteristics such as lighting, container types, and weather conditions, significantly improved detection accuracy and adaptability. Route optimization for waste collection is implemented using a mathematical formulation of the Traveling Salesman Problem (TSP), solved via Mixed Integer Linear Programming (MILP), which helped reduce fuel consumption, travel time, and staff workload. Integration with RGIS and GPS enables dynamic routing and real-time geospatial visualization of operational data. The proposed system forms a closed-loop control cycle that links automated detection, spatial analysis, and decision-making. Experimental results demonstrate high efficiency, adaptability to regional conditions, and scalability, confirming the system’s practical applicability to other urban areas. In the future, the system may be expanded to include environmental monitoring modules such as air quality, noise, and soil conditions and predictive modeling of waste generation, thereby supporting the sustainable development of smart city infrastructure

The unconventional proliferation of unmanned aerial vehicles (UAVs) has led to an urgent demand for advanced counter-unmanned aerial system (C-UAS) technologies capable of accurately detecting, classifying, and mitigating these threats. This paper offers a comprehensive overview of current detection methodologies, including radio frequency (RF) signal analysis, deep learning-based visual recognition (e.g., YOLOv5), thermal imaging, acoustic pattern classification using convolutional neural networks (CNNs), and integrated sensor systems utilizing attention mechanisms. A comparative analysis is conducted based on key performance indicators such as precision rates, mean average precision (mAP), operational range, response time, and robustness to environmental noise. These performance metrics are organized into a summarizing table for clarity. Additionally, several real-world C-UAS platforms such as DedroneTracker, AUDS, and Fortem DroneHunter are examined to illustrate approaches to full system integration. The discussion also encompasses the legal and ethical considerations, the implications of autonomous UAV swarms, and emerging trends in C-UAS strategies, especially those leveraging edge computing and cognitive modeling. The findings support the effectiveness of adaptable, modular, and interpretable counter-drone frameworks suited for dynamic and high-threat environments.

The object of this study is the deformation process in reinforced-concrete structural elements equipped with embedded fiber-optic sensors. The problem addressed corresponds to unresolved issues identified in previous studies – namely, the lack of standardized quantitative evaluation of accuracy and stability in fiber-optic deformation measurement. Despite high laboratory precision, existing methods show reduced long-term reliability, temperature-strain cross-sensitivity, and calibration inconsistency when applied to real structures. The main results show that fiber Bragg grating (FBG) and interferometric sensors achieved sub-micrometer deformation resolution with deviations below 2–3 με and long-term drift under 0.5%. Measurements remained stable under variable loading and temperature, confirming high reproducibility and electromagnetic immunity. These findings validate the hypothesis that optical wavelength shifts directly correspond to mechanical strain, ensuring reliable strain detection without recalibration. This effectiveness stems from the intrinsic photoelastic coupling and refractive-index sensitivity of the optical fiber, which provide nanometric resolution, corrosion resistance, and long-term operational stability. The proposed method is applicable for long-term monitoring of bridges, tunnels, and high-rise facilities exposed to environmental and cyclic stresses. Therefore, research on high-precision fiber-optic deformation measurement remains scientifically relevant for improving the safety and durability of modern civil engineering structures.

This study focuses on climate-dependent production processes, in particular grain crop yields, under conditions of climatic variability and uncertainty in Northern Kazakhstan. The problem addressed is the low effectiveness of deterministic risk monitoring approaches due to limited predictive power and the lack of formalized risk criteria, which leads to unreliable decision-making under uncertainty. The results include the development of a three-state hidden Markov model (S0–S2) and a TO-BE architecture for continuous risk monitoring and decision support. The model enabled the identification of latent climatic regimes and probabilistic assessment of risk states for 2025. The highest probability of an unfavorable regime was observed in Korgalzhyn (61.2%) and Ereymentau (58.8%), while Arshaly (42.9%) and Zhaksy (38.1%) showed moderate risk levels. The Brier score ranged from 0.106 to 0.199, confirming acceptable calibration of probabilistic estimates. The key feature of the approach is the representation of climate-dependent processes as transitions between latent probabilistic states, allowing the capture of temporal dependencies (climate memory) and the persistence of unfavorable conditions. Unlike deterministic models, the proposed framework enables dynamic risk tracking through continuously updated probability estimates integrated into a monitoring loop. The advantage of the approach lies in combining probabilistic modelling with an operational architecture, where risk probabilities serve as formalized decision-support signals. The results can be applied in early warning systems and digital monitoring platforms using remote sensing and IoT