In the mining industry, the work system relies on equipment that consumes large amounts of energy. In mining operations, diesel equipment is widely used due to its flexibility, load capacity and adaptability to various terrain conditions. However, it has high diesel consumption and high greenhouse gas emissions, mainly carbon monoxide. Using hydrogen as a fuel not only offers the opportunity to decarbonize transportation and the mining industry, but also significantly reduces local air pollution. The list of possible objects for using the proposed hydrogen technology includes: diesel power plants, high-voltage diesel distillation stations, mining excavators, drilling rigs, diesel-powered loading and hauling vehicles and others. The use of hydrogen as a fuel to drive engines of power plants and transport facilities in the mining industry requires the development of equipment to control the leakage of hydrogen from storage elements and fuel supply to the engines. The solution to the problem of monitoring hydrogen leaks from fuel tanks of mining vehicles and power equipment that ensures the operation of mining enterprises using Raman lidar is considered. It is shown that at a laser radiation wavelength of 532 nm it is possible to obtain the minimum time for measuring the hydrogen concentration in the air with a Raman lidar at a given sensing distance. It was found that lidar sensing from geostationary orbit at a laser radiation wavelength of 532 nm also provides the minimum measurement time for a given concentration of the hydrogen molecules under study.

In this article, composite binders based on industrial waste—phosphogypsum, granular phosphoric slag, and burnt barium carbonate tailings––are investigated. It was found that the optimal composition (65% slag, 20% phosphogypsum, 15% tailings) provides compressive strength up to 31.1 MPa after steaming, which corresponds to grade M300 cement. Replacing natural gypsum with phosphogypsum increases strength by 5–10%, and using waste reduces cost by 20–25% compared to traditional binders. This technology eliminates the need for high-temperature firing, reducing energy consumption by 40–50%. Neutralization of harmful impurities of phosphogypsum with oxides of MgO and CaO reduces the ecotoxicity of the material by 70–80%. It is shown that hydrothermal treatment accelerates hardening, providing 90% of brand strength in 28 days. The developed binders are promising for the production of building blocks, road surfaces, and land reclamation.

The rapid digitalisation of the medical field has heightened concerns over protecting patients’ personal information during the transmission of medical images. This study introduces a method for securely transmitting X-ray images that contain embedded patient data. The proposed steganographic approach ensures that the original image remains intact while the embedded data is securely hidden, a critical requirement in medical contexts. To guarantee reversibility, the Interpolation Near Pixels method was utilised, recognised as one of the most effective techniques within reversible data hiding (RDH) frameworks. Additionally, the method integrates a statistical property preservation technique, enhancing the scheme’s alignment with ideal steganographic characteristics. Specifically, the “forest fire” algorithm partitions the image into interconnected regions, where statistical analyses of low-order bits are performed, followed by arithmetic decoding to achieve a desired distribution. This process successfully maintains the original statistical features of the image. The effectiveness of the proposed method was validated through stegoanalysis on real-world medical images from previous studies. The results revealed high robustness, with minimal distortion of stegocontainers, as evidenced by high PSNR values ranging between 52 and 81 dB.

Due to their inherent variability, incorporating renewable energy sources into current power grids poses major challenges. This study aims to optimize renewable energy integration using Internet of Things (IoT) technology and machine learning (ML) algorithms. The study was conducted across 30 renewable energy sites in the United States over six months (April-September 2023), encompassing solar, wind, and hydroelectric installations. Three ML models (Random Forest, XGBoost, and Long Short-Term Memory networks) were developed and compared against a traditional persistence model for energy generation forecasting. The study also implemented a reinforcement learning-based grid optimization system. Results showed significant improvements in forecasting accuracy, with the LSTM model achieving a 59.1% reduction in Mean Absolute Percentage Error compared to the persistence model. Grid stability improved substantially, with a 64.2% reduction in supply-demand mismatches. Overall renewable energy utilization increased by 19.2%, with wind energy seeing the largest improvement (21.8%). The implemented system resulted in estimated monthly cost savings of $320,000. These findings demonstrate the potential of IoT-ML systems to enhance renewable energy integration, contributing to more efficient, reliable, and sustainable power grids.

This study provides a comprehensive assessment of the population structures, anatomical adaptations, and chloroplast genome organizations of three rare tree species—Fraxinus sogdiana Bunge, Celtis caucasica Willd., and Betula jarmolenkoana Golosk.—from the Northern Tien Shan region of Kazakhstan. Field surveys revealed species-specific demographic patterns, with F. sogdiana and B. jarmolenkoana populations displaying a complete age spectrum and signs of ongoing regeneration, while C. caucasica exhibited a lack of juvenile stages, indicating regeneration failure. Anatomical analysis of leaf and stem tissues highlighted adaptive features aligned with habitat conditions: F. sogdiana showed mesophytic traits suited for riparian environments, C. caucasica displayed xeromorphic structures reflecting drought tolerance, and B. jarmolenkoana demonstrated structural reinforcement adapted to high-altitude stressors. Whole chloroplast genome sequencing revealed conserved quadripartite architecture across species, with minor variations in gene content and inverted repeat boundaries suggesting lineage-specific evolution. The findings underscore the ecological sensitivity and conservation priority of these species and provide foundational data for future ecological monitoring, restoration efforts, and phylogenomic research in Central Asian montane ecosystems.

Industrial technologies for processing tungsten concentrates using soda roasting or autoclave leaching are based on the production of alkaline sodium tungstate solutions that contain impurities such as silicon, phosphorus, arsenic, and others. The purification of these solutions from impurities requires the neutralization of excess soda or alkali with inorganic acids, which leads to the formation of chloride and sulfate effluents that are subsequently discharged into waste repositories. An analysis was carried out on existing methods for the production and processing of sodium tungstate solutions using HNO3 and NH3, as well as extraction and sorption techniques involving anion exchange resins. Currently, processes such as nanofiltration, reverse osmosis, and electrodialysis are being applied for water purification and the treatment of sulfate and chloride effluents. These processes employ various types of industrially manufactured membranes. For the purpose of electrodialysis, a two-compartment electrodialyzer setup was employed using cation-exchange membranes of the MK-40 (Russia) and EDC1R (China) types. The composition and structure of sodium tungstate, used as the starting reagents, were analyzed. Based on experiments conducted on a laboratory-scale unit with continuous circulation of the catholyte and anolyte, dependencies of various parameters on current density and process duration were established. Stepwise changes in the anolyte pH were recorded, indirectly confirming changes in the composition of the Na2WO4 solution, including the formation of polytungstates of variable composition and the production of H2WO4 via electrodialysis at pH < 2. The resulting tungstic acid solutions were also analyzed. The conducted studies on the processing of sodium tungstate solutions using electrodialysis made it possible to obtain alkaline solutions and tungstic acid at a current density of 500–1500 A/m2, without the use of acid for neutralization. Yellow tungstic acid was obtained from the tungstic acid solution by evaporation. © 2025 by the authors.

This study presents a scenario-based assessment of the future sensitivity of minimal low-water runoff to climate change in Western Kazakhstan. An ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), combined with dynamically downscaled projections for Central Asia, was applied to estimate minimal monthly runoff during the summer–autumn and winter low-water periods for the rivers of the Zhaiyk–Caspian water management basin. The analysis covers three future time horizons: 2040 (2031–2050), 2060 (2051–2070), and 2080 (2071–2090), under two greenhouse gas concentration scenarios: SSP3-7.0 (moderately high emissions) and SSP5-8.5 (high emissions). The results reveal a pronounced seasonal contrast in the projected hydrological response. During the winter low-water period, a steady increase in minimal runoff is projected for all rivers, with the most significant changes observed for the Or, Zhem, Temir, and Shagan rivers. This increase is primarily driven by higher winter precipitation, increased thaw frequency, and enhanced infiltration recharge. Conversely, despite modest increases in summer–autumn precipitation, minimal runoff during the summer–autumn low-water period is projected to decline significantly, particularly in the southern basins, due to elevated evapotranspiration rates and soil moisture deficits associated with rising air temperatures. These findings emphasize the importance of developing seasonally differentiated, climate-resilient water management strategies to mitigate low-flow risks and ensure water security under future climate conditions in arid and semi-arid regions.

Modern air compressors used in the mining industry have a high margin of safety when operating in difficult conditions: This includes uneven terrain, high temperature differences and work underground. Scientific research, complex of theoretical and experimental studies, is carried out in order to obtain sound initial data, to find principles and ways to create a new generation of compressors for use in a mining field. Air compressors are used in deep mines, including supplying clean air, since there may not be enough air in the mine. The compressor can take in outside air, compress and filter it, then feed it into the tunnel. It is necessary to choose compressor equipment for use in mines very carefully; there are many potential risks and limitations. The basic problem, its relevance – in this paper, the issues related to the device, operation and the possibility of optimizing the design of a screw compressor, gas compressor station in the mining industry are considered. The methods used. The modeling of processes and the construction of flowcharts were carried out using programs MathCad, Drawio, MS PP. A simplified mathematical model of a screw compressor is obtained. Key hypotheses and conclusions. Of the work carried out, the optimal regulator capable of controlling the system with minimal energy consumption has been determined. Originality. The relevance of the topic is explained by the fact that in Kazakhstan and other developing countries special attention is paid to the issue of energy conservation. This requires an increase in the efficiency of gas compressor equipment in general and compressor stations in particular. The practical value is an optimal regulator capable of controlling the system with minimal energy consumption. © 2024, National Academy of Sciences of the Republic of Kazakhstan. All rights reserved.

This scientific research explores the possibility of remotely controlling an ozone generator ETRО-02, based on electric spark discharge, through the Internet of Things (IoT) technology. The presented ozonator is designed to sterilize and purify drinking and wastewater. The use of IoT technology can enhance the efficiency of the ozonator and allow for remote monitoring and control of its operation process. The aim of this research is to describe the architecture and working principles of the system developed for managing the operational modes of the ozonator through IoT technology. This system can automatically adjust the ozone level through sensors, controllers, and other electronic components, as well as provide the user with timely information about the device's status. The research also pays special attention to the security, reliability, and scalability of the system. As a result, this study contributes to the development of a remote control system for ozonators using IoT, aimed at improving their efficiency in water purification and agriculture sectors.

This chapter discusses current practices and new developments in the preparation of activated carbons for the adsorption of pollutants. There is widespread pollution such as pharmaceuticals and heavy metals in global surface water that needs to be mitigated and carbon sorbents made for it can be part of the solution. Preparing carbons for such challenge requires tailoring carbon's structure and surface chemistry to maximise interaction with low concentrations (part per billion level) of a variety of pollutants. The preparation of effective carbon sorbents constitutes a technical challenge. This chapter explores treatments and analytical approaches for the preparation and development of modified carbons to remove water pollutants. Effective carbons will help to control global contamination problems but should not be the source of more pollution: carbon dioxide emissions during the production and maintenance of carbon sorbents are a concern. © 2025 by IGI Global Scientific Publishing. All rights reserved.