The global water crisis, which intensifies each year, underscores the urgent need for accurate assessment and sustainable management of freshwater resources. Lakes, as significant components of surface water systems, are particularly vulnerable to human activities and climate change due to their slow renewal rates. Western Kazakhstan, a region affected by economic development and intensive mining activities, faces acute water scarcity. A reliable water supply is crucial to meeting the requirements of industrial operations and the needs of the region’s population. Understanding the dynamics of lake systems and implementing effective conservation and restoration strategies are essential to addressing this challenge. This study, employing cartographic and remote sensing techniques, identified 323 lakes in Western Kazakhstan, covering a total area of 1944 km2. Comparative analysis with historical data from the 1960s and 1970s revealed a 23% decrease in the number of lakes exceeding 1 km2. The most significant decline was observed in the Atyrau and Mangystau Regions, where the number of such lakes diminished by a factor of three. These findings highlight the pressing need for an integrated, interdisciplinary approach to water resource management in Western Kazakhstan. © 2024 by the authors.

The growing demands for sanitary regulations in medical facilities, particularly operating rooms, highlight the importance of ensuring high air quality and minimizing airborne hospital-acquired infections. Improperly designed ventilation systems may lead to contamination of up to 90–95% of patients, especially in light of evolving threats, such as COVID-19. This study focuses on enhancing the energy efficiency and performance of air conditioning and ventilation systems for cleanrooms, where air recirculation is not permissible. A novel energy-efficient direct-flow air treatment scheme is proposed, integrating a heat pump system with adjustable thermal output. A computational fluid dynamics CFD model of a clean operating room was developed to assess the impact of inlet air velocity on aerosol particle removal and airflow stabilization time. The model also considers the effect of personnel movement. The results supported optimized air distribution, reducing microbial contamination risks, with less than 10 CFU/m3, and improved thermal performance. The proposed system was evaluated for energy and cost efficiency compared to conventional setups. Findings can inform the design and operation of cleanroom ventilation in surgical environments and other high-tech applications. This research contributes to improving indoor air quality and reducing infection risks while enhancing sustainability in healthcare infrastructure.

The study is aimed at determining the social norm of the density of people on the beach and identifying the weaknesses of the organisation of beach-bathing tourism for the rational use of recreational resources and the development of sustainable tourism. Aerial photography of the coast and conducting a sociological survey of vacationers, statistical approaches and Importance-Performance Analysis were used. The total threshold density on the beaches is estimated at 14 627 vacationers (3 m2/person), acceptable density at 5 683 vacationers (8 m2/person). Safety issues in the water area, pollution of the coast and lack of awareness among tourists are recommended for priority solutions. The results will improve the quality and safety of tourists' recreation and generally lay the foundation for the rational use of recreational resources and development of sustainable tourism on Lake Alakol. © 2024 Editura Universitatii din Oradea. All rights reserved.

Breast cancer remains a leading cause of mortality among women, and early detection is crucial for improving patient outcomes. However, current detection methods, including mammography and BMI-based approaches, have limitations in terms of accuracy and reliability. This research proposes an innovative breast cancer detection using the Breast Cancer Detection and Risk Assessment (BCDRA) system. BCDRA integrates the features of convolutional neural networks (CNNs) and transfer learning, with an optimized feature selection process designed specifically for mammographic images and patient clinical data. The system follows a multi-stage pipeline: Data preprocessing and augmentation, Automated feature extraction using CNNs, Transfer learning for enhancing model accuracy, and Risk prediction using a hybrid classification-regression algorithm. This pipeline is supported by the Breast Cancer Prediction Algorithm (BCPA), which identifies early-stage anomalies and provides real-time risk assessment based on personalized risk factors such as age, family history, and genetic markers. By integrating a real-time risk assessment module and offering higher diagnostic precision, BCDRA aims to revolutionize breast cancer screening, allowing for earlier interventions and improved patient care, ultimately contributing to better survival rates. BCDRA implemented using both Python and $\mathbf{R}$ programming environments and validated across multiple public and clinical datasets, demonstrating significant improvements in accuracy, sensitivity, and specificity over traditional detection methods. © 2025 IEEE.

This paper presents a research study focused on analyzing the spatial aggregation patterns of urban population within Almaty City, Kazakhstan, using a heat map approach. With the emergence of the Smart City concept, understanding how populations aggregate within urban environments is crucial for effective urban planning and resource allocation. Leveraging geographic data collected from OpenStreetMap (OSM) and actual aggregated data obtained from the telecom operator, hourly loads on city quadrants measuring 500 by 500 meters were analyzed. The Python Folium library was employed to visualize these patterns, providing insights into the distribution of urban population density. By examining the heat map, this research sheds light on the spatial dynamics of population aggregation within Almaty City, offering valuable information for urban planners, policymakers, and researchers working towards the development of smarter and more sustainable cities. © 2024 IEEE.

Vanadium and molybdenum are critical metals widely used in steel production, alloys, and green energy technologies. With growing global demand, efficient recycling methods for industrial waste are essential. This study investigates the extraction of vanadium (3.44%), molybdenum (0.75%), and nickel (8.82%) from vanadium production waste using hydrometallurgical methods. Optimal leaching conditions were determined through experiments with sodium carbonate (Na2CO3) and sodium hydroxide (NaOH) solutions. The highest vanadium recovery (50.56%) was achieved with 1% Na2CO3 at 80°C, while molybdenum extraction reached 56.33% with 3% Na2CO3. Alkaline leaching with 0.5% NaOH and sodium hypochlorite (NaClO) at 25°C improved vanadium recovery to 88% and molybdenum to 67.7%. However, increasing the temperature to 85°C led to nickel vanadate (Ni3V2O8) formation, reducing vanadium extraction efficiency. X-ray phase analysis confirmed the presence of aluminium oxides (Al2O3), nickel compounds, and vanadium phases in the residues. A proposed processing scheme ensures high metal recovery while minimising environmental impact through closed-cycle operations. This study advances sustainable metal extraction from industrial waste, offering a cost-effective and eco-friendly solution for resource recovery.

The logistics industry is rapidly adopting machine learning (ML) and cognitive technologies to enhance operational efficiency and decision-making capabilities. This study aims to explore the impact of these technologies on logistics by reviewing recent advancements and case studies. The research focuses on the application of ML and cognitive systems in optimizing delivery routes, improving demand forecasting, and automating warehouse management. Results indicate that integrating ML and cognitive technologies significantly improves logistics processes by enabling real-time data processing, accurate forecasting, and automated decision-making. These advancements lead to reduced operational costs, improved service quality, and enhanced sustainability through optimized resource use. However, the implementation of these technologies requires substantial investment in technology, training, and data security measures. In conclusion, while the integration of ML and cognitive technologies presents challenges, it offers significant potential to transform the logistics sector, making it more efficient and responsive to dynamic market conditions. Further research is needed to refine these technologies and explore new applications to fully leverage their benefits in supply chain management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

This article presents the results of studies on the comprehensive assessment pertaining to the peculiarities of nutrition, accumulation, transit and discharge of groundwater in the adjacent territory of the Sorbulak reservoir. The information about the waste water storage was presented. The results of the analysis and decryption of satellite images, as well as the results of aerial photography of the territory of the Sorbulak were presented. The results of field route studies of the work area were presented to identify the external manifestations of directional filtration, areas of exits to the daytime surface of groundwater. The results of the calculation of the filtration flow controlled by the Sorbulak storage lake, unloading through the western side of the plateau into the Kurty River, were presented. © 2022, Journal of Ecological Engineering. All rights reserved.

Features of a constructive solution of coupling an external brick wall and reinforced concrete attic flooring of existing low-rise residential buildings forced with a reinforced concrete monolithic frame are studied. A photograph presented shows mold on the intersection zone of the inner surfaces of the external wall and the attic flooring. The calculation of the building frame carried out using the program LiraCAD 2013 on a seismic load intensity of 9 points on the MSK-64 scale. Two-dimensional temperature distributions in the cross section of the enclosures, in particular in the area of thermal bridges, presented as isotherms using the ArchiCAD 20 software package. The multidisciplinary task of ensuring the required seismic resistance, energy efficiency and microclimate of the building has been solved. An expedient constructive solution of the coupling external brick wall and reinforced concrete attic flooring for the reconstruction of existing and design of new buildings is proposed. The dimensions of the cross section of monolithic reinforced concrete columns and crossbars of the building frame and thickness of an additional layer of thermal insulation of the thermal bridge zones are determined. A new mounting unit is developed to attach the pitched roof’s Mauerlat to the anti-seismic belt. Practical recommendations are given that aim to reduce the negative temperature and thermal effects of the thermal bridges. The proposed constructive solutions made it possible to exclude the main causes of violations of sanitary conditions in the premises caused by mold growth on the surfaces of hygroscopic enclosures materials. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

In recent years, extensive research has been conducted to develop methods and algorithms for controlling the coordinated motion and interaction of autonomous unmanned aerial vehicles (UAVs). The issue of UAV control in real-world conditions, especially when controlling autonomous unmanned vehicles like airplanes, remains relevant. The complexity of such control is due to the high dynamics and input constraints of real-world autopilots. This paper presents an adaptive control strategy for unmanned aerial vehicle (UAV) group coordination, integrating integral control laws and fuzzy logic to improve dynamic formation stability and energy efficiency. A simulation environment was developed in MATLAB/Simulink to evaluate the proposed strategy. Simulation experiments demonstrated superior responsiveness, reduced trajectory deviation, and increased robustness against external disturbances compared to traditional methods. The results validate the viability of the proposed control scheme in real-time multi-agent UAV systems operating under dynamic and uncertain conditions. © 2025 IEEE.