Central Asia regions are characterized by active tectonics, high mountain chains with extreme topography with glaciers, and strong seasonal rainfall events. These key predisposing factors make large landslides a serious natu- ral threat in the area, causing several casualties every year. The mountain crests are divided by wide lenticular or nar- row, linear intermountain tectonic depressions, which are in- cised by many of the most important Central Asia rivers and are also subject to major seasonal river flood hazard. This multi-hazard combination is a source of potential damming scenarios, which can bring cascading effects with devastat- ing consequences for the surrounding settlements and popu- lation. Different hazards can only be managed with a multi- hazard approach coherent within the different countries, as suggested by the requirements of the Sendai Framework for Disaster Risk Reduction.

The great interest in nanostructured magnetic composites is due to their great prospects for use as a basis for the development of catalysts for the adsorption of manganese in wastewater. Interest in magnetic nanocomposites in this direction is primarily due to the possibility of extracting them from water media using ordinary magnets, which allows them to be used again. Additionally, it is worthwhile to note interest in research related to increasing the efficiency of adsorption, as well as an increase in the number of repeated cycles of operation. In this regard, the main goal of this study is to study the prospects for applying the method of mechanochemical synthesis for the creation of iron-containing nanocomposites doped by rare-earth elements Gd, Ce, Y, and Nd in order to obtain optimal catalysts for cleaning water media. During the studies, structural properties and phase composition of synthesized nanocomposites were established, as well as ultra-thin parameters of the magnetic field. It has been established that the kinetic curves of the adsorption process can be described by a pseudo-first-order model, and the process of manganese adsorption itself is associated with the cationic interaction of manganese ions with the surface of nanocomposites. The kinetic curves of degradation were determined, as well as the influence of the number of cyclic tests on the adsorption of manganese for synthesized nanocomposites, depending on the type of dopant and phase composition, respectively. Iron-containing nanocomposites doped with gadolinium and neodymium have been found to have the highest adsorption efficiency and corrosion resistance. Particular attention is paid to the study of the stability of storage of nanocomposites for a long time, as well as the preservation of their adsorbent properties in the purification of aqueous media. It has been determined that the modification of nanostructures with the help of rare earth compounds leads to an increase in resistance to degradation, as well as to the preservation of the efficiency of adsorption for 5–7 cycles in comparison with Fe2O3 nanoparticles, for which low resistance to degradation was observed.

The aim of the study is to substantiate the parameters of technology and equipment for the manufacture of building products using alumina sludge from stone processing as an alumina aggregate. The standard methods of setting up an experiment for laboratory washing of granular sludge from stone processing and studying its physical and mechanical characteristics, manufacturing laboratory samples of polystyrene concrete blocks with further study of their strength characteristics were used. The physical and mechanical characteristics of granular man-made material - sludge waste from the stone processing industry - were experimentally determined. The possibility of using man-made sludge in the manufacture of polystyrene concrete blocks was substantiated by its particle size distribution and mineralogical composition. Average samples of granular material were deslagged using the TurboWash unit. The particle size distribution of the material after washing was analysed. According to the standard technology, recipe and raw materials for D300 polystyrene concrete, polystyrene concrete blocks of standard sizes of three grades were produced. Primary sludge without preliminary processing, sand and dusty loess obtained from the processing of primary sludge were used as an aggregate. The numerical values of the axial compression resistance for the studied grades of polystyrene concrete blocks were determined. © Published under licence by IOP Publishing Ltd.

The paper is devoted to the development of new equipment for the production of metal-polymer thread. 3D printing with metal-polymer thread is one of the advanced directions in the technology of manufacturing metal parts of complex shape. The proposed technology is an alternative to the currently existing metal injection molding (MIM) technology and selective laser melting printing technology. An important step in this work was to conduct computational experiments to determine the effect of screw rotation on the process pressure parameter and the design of the main assembly of the screw extruder. As a result of the research, the pressures on the metal-polymer composition were determined depending on the rotation speed of the screw. With a rotation of 30 rpm, the pressure reached 0.05 Pa and the maximum pressure was 0.18 MPa. The experiments were carried out in the CradelSFlow program. The computer calculation showed a margin of the screw strength coefficient k=1.8, and a maximum deflection of 2.8∙10–4 m, which meets the condition of static rigidity. To determine the correct value of the gap δ between the screw ridge and the extruder walls, an analysis of the rotor dynamics was carried out. The result of this study is the critical extruder rotation speed of 60 rpm at which the phenomenon of precession may occur.

Hydrogen Obtaining From The System Activated Aluminum-Water

There are presented results of the accumulation of polychlorinated biphenyls (PCBs) – the most toxic compounds among persistent organic pollutants (POPS) in the snow cover (SC) study (2018 and 2020) in the Almaty agglomeration (AA). Protection of the natural environment and the population from the efects of POPs, including PCBs, is one of the most acute problems for Kazakhstan. Тhe territory of AA is experiencing a serious technogenic load, the concentration of a number of pollutants in its natural objects exceeds the permissible standards. Snow cover among natural objects, is one of the informative indicators of pollution of the natural environment, including the air basin, and reflects the main trends in the spread of pollutants in the region.

The article presents a study on overburden deposit rock in Southeastern Kazakhstan, focusing on the lithological composition and water-physical properties of cover sediments in infiltration basins for artificial groundwater replenishment. Analysis at pit No. 5 revealed sediment layers affecting dynamic infiltration: the uppermost layer (0–0.5 m) consists of fine-grained, micaceous sand; sandy loams (0.5–1.4 m) exhibit good permeability; dark brown impermeable clays (1.4–1.7 m) act as a barrier; a second sandy loam (1.7–2.4 m) and medium-grained sands (2.4–2.8 m) provide high permeability; and mixed-grain sands (2.8 m and below) show minimal capillarity and emerging water-bearing properties at 3.5 m. These findings confirm that surface infiltration basins can effectively enhance groundwater reserves with minimal surface runoff. The study's practical significance lies in its scientific basis for designing and optimizing groundwater replenishment systems, allowing precise site selection and contributing to predictive models for assessing infiltration rates. Overall, the results strengthen water management practices in arid and semi-arid regions, demonstrating the suitability of these basins for artificial groundwater replenishment in Southeastern Kazakhstan.

It has been recognized that Blockchain technology contributes to environmentally sustainable development goals (SDGs). It has emerged as a disruptive innovation capable of transforming various economic and social sectors significantly. This conceptual paper is driven by the need to explore how blockchain, specifically a consortium-based Ethereum architecture, can be integrated into higher education institutions to ensure data sovereignty, integrity, and verifiability while adhering to legal and ethical standards such as GDPR. We propose a multi-layered blockchain-based model for Kazakhstan’s Unified Platform of Higher Education (UPHE). This model employs hybrid on-chain/off-chain data storage, smart contract automation, and a Proof-of-Authority consensus mechanism to address system limitations, including data centralization and inadequate verification of academic credentials. Empirical simulations using Blockscout and Ethereum-compatible tools demonstrate the model’s feasibility and performance. This paper contributes to the growing discussion on educational blockchain applications by presenting a scalable, secure, and transparent architecture that aligns with institutional governance and Environmental, Social, and Governance (ESG) principles. It also supports the objectives of UN SDG 4 (i.e., Quality education) by fostering trust, transparency, and equitable access to verifiable educational credentials.

The study presents a comparative assessment of eight machine learning (ML) algorithms - Random Forest (RF), Lasso Regression (LASSO), AdaBoost (ADB), Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LGBM), and K-Nearest Neighbors (KNN) - for modeling daily river discharge at ten hydrological stations within the Zhaiyk - Caspian water management basin. Model performance was evaluated using mean absolute error (MAE), mean squared error (MSE), and symmetric mean absolute percentage error (SMAPE). The highest predictive accuracy (MAE ≈ 0.3) was achieved by ensemble tree-based methods (Random Forest, CatBoost, Gradient Boosting, LightGBM, XGBoost), while LASSO and AdaBoost exhibited the weakest performance (MAE ≈ 22). Identifying the most significant predictors enhanced both model interpretability and forecasting quality. The findings highlight the importance of tailoring ML approaches to the specific characteristics of river basins and suggest promising prospects for their integration with physically based hydrological models to improve river discharge forecasting and strengthen water resources management under climate change conditions.

One of the main problems with graphite anodes in LIBs is the limited charge/discharge rate, especially for the lithization process. In recent years, in order to solve the above-mentioned problems, more and more attention has been paid to the practical use of silicon oxide-based anode materials in LIBs, due to its large amount in the earth's crust, low discharge potential, and high initial irreversible and reversible capacity of 1961 mA*h*g-1. The study explores a novel approach to producing high-purity SiO2 from rice husk (RH) and enhancing its electrochemical performance by incorporating a carbon coating, aiming to develop an advanced anode material for lithium-ion batteries (LIBs). The methodology involved a multi-step process: initially, RH was subjected to hydrochloric acid pre-treatment to remove impurities, followed by calcination to yield SiO2. Pure SiO2 was further processed with sodium hydroxide and hydrochloric acid to achieve high purity. To improve the electrochemical properties, SiO2 was coated with carbon derived from sucrose.