Abstract Purpose. Quantitative assessment of the impact of the level of digital integration of participants in the raw materials logistics cluster of Kazakhstan on the stability and controllability of iron ore supply chains based on hybrid modelling. Methodology. The research methodology is based on hybrid simulation modelling, combining multi-agent modelling (MAS) and system dynamics (SD) to describe the functioning of a raw materials logistics cluster. The model with a discrete time step of one day simulates the behaviour of the mines, transport operator and metallurgical enterprise, as well as the dynamics of supplies, inventories and order formation based on the base-stock policy. The level of digital integration is set by a scalar parameter that affects information lags, correlation of failures and coordination of participants' decisions. Findings. It is shown that an increase in the level of digital integration in the considered operating modes of the system leads to a decrease in the standard deviation of total daily supplies and a decrease in the probability of shortages. A significant weakening of the bullwhip effect has been established: the ratio of supply variation to demand variation decreases by more than half - from 4.6 to 2.1. The nonlinear threshold nature of the influence of digital integration on the dynamic stability of the system has been revealed, which manifests itself in the dependence of the time of inventory restoration on the level of digitalization and the mode of operation of the logistics system. Originality. A hybrid SD + MAS model has been developed, calibrated on a real raw material logistics cluster in Kazakhstan, and the threshold nature of the impact of digital integration on the sustainability and controllability of raw material supply chains has been identified. Practical value. The developed model allows one to assess the consequences of the introduction of cluster digital platforms, considering the operating mode of the logistics system and the large-scale parameters of the raw materials cluster. When planning the digitalization of raw materials logistics clusters, it is necessary to consider not only the target level of information systems integration, but also the current operational state of the supply chain, including capacity utilization levels, inventory structure and system sensitivity to information lags. © Bekzhanova S. E., Imasheva G. M., Issina B. M., Mukhametzhanova A. V., Lytvyn V. V., 2026

Abstract The object of the study is the onboard diagnostic process in CubeSat nanosatellite systems, with particular emphasis on fault detection based on real-time analysis of multivariate telemetry data under real-time embedded operating conditions. The problem statement is the gap between the relatively low computational expenses and predictability of classical FDIR approaches and their limited ability to detect complex anomalies, and, conversely, the greater anomaly sensitivity and lower embedded performance of machine-learning models. A hybrid onboard diagnostic architecture that integrates telemetry acquisition, telemetry preprocessing, statistical feature extraction, a deterministic FDIR branch, a lightweight machine-learning branch, and decision fusion into one onboard diagnostics session has been elaborated. The proposed hybrid onboard diagnostic architecture has been evaluated based on hardware-in-the-loop testing using representative telemetry and fault cases for CubeSat systems. The evaluation metrics were the fault-detection accuracy, the detection latency, the CPU consumption, the memory consumption, and the resistance to telemetry noise. The hybrid onboard diagnostic architecture was compared to the conventional threshold-based FDIR system and standalone machine learning algorithms under the same HIL conditions. In the reported experiment setup, the proposed hybrid onboard diagnostic architecture demonstrated the fault-detection accuracy of 94%, while the classical FDIR method provided 71%, and the standalone machine-learning approach provided 88%. The average detection latency was decreased to 83 ms, in contrast to 120 ms and 95 ms, respectively. The embedded solution requires only 17.6% CPU and 58 KB of memory. Under the highest telemetry-noise level, the fault-detection accuracy of the proposed hybrid onboard diagnostic architecture decreases to 80%, whereas the standalone machine-learning and the classical FDIR baselines provide only 65% and 43% fault-detection accuracy, respectively. Copyright © 2026, Authors.

Bentonite materials are widely utilized across various industries due to their unique physicochemical properties. This study presents a qualitative and comparative analysis of mineral substances derived from ben tonite clay deposits in East Kazakhstan. The montmorillonite content in the samples ranges from 75.5% to 88%, with adsorption capacity (determined by the methylene blue method) varying between 119 and 204 mg/g. The pH of the samples lies within the range of 7 to 10. FTIR and XRF analyses confirmed the presence of major components such as silicon and aluminum oxides, while X-ray diffraction identified montmorillonite as the dominant crystalline phase. Textural characterization revealed specific surface areas of 94–104 m²/g, pore volumes of 0.03–0.05 cm³/g, and pore sizes between 0.8 and 1.04 nm. SEM analysis demonstrated a flaky, layered, and porous morphology typical of bentonite. Based on these properties, the bentonite samples exhibit strong potential for industrial use. They are applicable in oil production (as components of drilling fluids) and construction (as insulating and sealing agents). Additionally, their high sorption capacity makes them promising candidates for pharmaceutical applications, particularly as carriers in topical formulations such as ointments and pastes for wound healing and inflammatory skin conditions.

Abstract This paper presents a comprehensive study aimed at improving the efficiency of unmanned aerial vehicles (UAVs) through the enhancement of their aerodynamic and mechanical structures. The research is based on coupled computational fluid dynamics (CFD) and finite element analysis (FEA). The airflow around the UAV was modeled using the Navier–Stokes equations, while the structural behavior was described by the equations of linear elasticity. A UAV configuration with a wingspan of 1.8 m and a mass-optimized structure was investigated for flight speeds in the range of 10–35 m/s and angles of attack from −5° to +15°. The results of the aerodynamic optimization, including airfoil thickness variation and smoothing of the wing–fuselage junction, showed a reduction in the drag coefficient by 9–12% and an increase in the lift-to-drag ratio by up to 11% in the cruise regime. The structural optimization based on replacing aluminum with a carbon-fiber composite material led to a reduction in the structural mass by 13–16%, a reduction in the structural strength criterion value by 18–22%, as confirmed by the Tsai–Wu failure analysis, and a reduction in wing-tip deflection by 20–25% under 3 g and 5 g load cases, while satisfying strength and stiffness requirements. The obtained results demonstrate that the proposed integrated aerodynamic and structural optimization approach significantly improves the overall performance, efficiency, and operational reliability of UAV systems. © 2026 by the authors.

he impact of thermal treatment on the properties of bentonites from the Kalzhat and Orta Tentek deposits was examined. The samples were analyzed using XRD, XRF, TGA/DSC, and lowtemperature nitrogen adsorption (BET) before and after heating at temperatures ranging from 100 to 900°C. Moderate heating at 200–300°C removes physically adsorbed water and part of the interlayer water without affecting the layered structure of montmorillonite, which retains its specific surface area, water-holding capacity, and cation-exchange capacity. In the 400–600°C range, dehydroxylation and interlayer collapse occur, resulting in a decrease in specific surface area and the loss of exchange sites. At 700–900°C, high-temperature silica and aluminosilicate phases form, and the porosity approaches zero. The comparison of the two deposits reveals that the Orta Tentek bentonites undergo structural degradation earlier than the Kalzhat bentonites, with the latter exhibiting greater resistance to degradation during the initial stages of heating.; However, at elevated temperatures, both materials cease to perform their agronomic functions. To utilize them as soil conditioners, it is recommended to restrict the treatment to a temperature range of 200–300°C, with brief exposure times, followed by granulation. It is crucial to avoid temperatures exceeding 400–600°C and to completely exclude temperatures above 700–900°C. These conditions ensure a sanitary pretreatment without compromising the soil's moisture retention or cation exchange capacity. This approach is particularly suitable for sandy and sandy-loam soils in the arid regions of Kazakhstan.

This paper presents a large-scale empirical study aimed at identifying the optimal local deep learning model and data volume for deploying intrusion detection systems (IDS) on resource-constrained IoT devices using federated learning (FL). While previous studies on FL-based IDS for IoT have primarily focused on maximizing accuracy, they often overlook the computational limitations of IoT hardware and the feasibility of local model deployment. In this work, three deep learning architectures—a deep neural network (DNN), a convolutional neural network (CNN), and a hybrid CNN+BiLSTM—are trained using the CICIoT2023 dataset within a federated learning environment simulating up to 150 IoT devices. The study evaluates how detection accuracy, convergence speed, and inference costs (latency and model size) vary across different local data scales and model complexities. Results demonstrate that CNN achieves the best trade-off between detection performance and computational efficiency, reaching ~98% accuracy with low latency and a compact model footprint. The more complex CNN+BiLSTM architecture yields slightly higher accuracy (~99%) at a significantly greater computational cost. Deployment tests on Raspberry Pi 5 devices confirm that all three models can be effectively implemented on real-world IoT edge hardware. These findings offer practical guidance for researchers and practitioners in selecting scalable and lightweight IDS models suitable for real-world federated IoT deployments, supporting secure and efficient anomaly detection in urban IoT networks. © 2025 by the authors.

Bentonite materials are widely utilized across various industries due to their unique physicochemical properties. This study presents a qualitative and comparative analysis of mineral substances derived from ben tonite clay deposits in East Kazakhstan. The montmorillonite content in the samples ranges from 75.5% to 88%, with adsorption capacity (determined by the methylene blue method) varying between 119 and 204 mg/g. The pH of the samples lies within the range of 7 to 10. FTIR and XRF analyses confirmed the presence of major components such as silicon and aluminum oxides, while X-ray diffraction identified montmorillonite as the dominant crystalline phase. Textural characterization revealed specific surface areas of 94–104 m²/g, pore volumes of 0.03–0.05 cm³/g, and pore sizes between 0.8 and 1.04 nm. SEM analysis demonstrated a flaky, layered, and porous morphology typical of bentonite. Based on these properties, the bentonite samples exhibit strong potential for industrial use. They are applicable in oil production (as components of drilling fluids) and construction (as insulating and sealing agents). Additionally, their high sorption capacity makes them promising candidates for pharmaceutical applications, particularly as carriers in topical formulations such as ointments and pastes for wound healing and inflammatory skin conditions.

The object of this study is to robotize the technological operation of removing the oxide film from the surface of a magnesium melt poured into continuously moving molds of a casting conveyor for the production of commercial magnesium. To robotize this technological operation, it is proposed to use a two-armed manipulation robot with a spherical coordinate system, which has six degrees of mobility. Software trajectories have been developed according to the degrees of mobility of the manipulation robot in terms of position, speed, and acceleration to perform the technological operation of removing the oxide film from the surface of the magnesium melt poured into the moving molds of the foundry conveyor. Programmed trajectories are described by quadratic polynomials that satisfy restrictions on the values of the generalized coordinate, velocity, and acceleration. These limitations are determined by the design features and energy capabilities of the degrees of mobility drives of the manipulation robot. Programmed trajectories along the first and second degrees of freedom compensate for the continuous movement of the molds of the foundry conveyor. Programmed trajectories along the third and fourth degrees of mobility enable the collection of the oxide film from the surface of the magnesium melt. Programmed trajectories along the fifth and sixth degrees of freedom enable the discharge of the collected oxide film into a special container. The reliability of the developed programmed trajectories is confirmed by the simulation results using MATLAB version R2015b. Based on the results, a cyclogram for controlling a manipulation robot has been constructed to perform the technological operation of removing the oxide film in the production of commercial magnesium. The results could be used in the robotization of technological processes for removing the oxide film in the production of commercial magnesium or similar foundries. © 2024, Authors. This is an open access article under the Creative Commons CC BY license

This paper was performed using geometry and interactive motion (GIM) software, a learning and research software designed to facilitate the kinematic analysis and synthesis of planar mechanisms. This study presents an analytical and geometric approach to the synthesis of planar four-bar linkages for precise path generation using sixth order connecting coupler curves. Emphasis is placed on defining linkage configurations that enable a coupler point to pass through a set of three, four, or five user-defined precision points. The methodology integrates classical kinematic principles, Roberts' theorem, and focal circle theory to identify double points and optimize the trajectory of the coupler. Using complex number representations and algebraic formulations, the equations governing coupler motion are derived and applied to various linkage types, including crank-rocker, double-rocker, and limit mechanisms. Parallelogram constructions and focal circles are used to detect critical configurations and analyze the continuity and self-intersections of coupler paths. Computational simulations validate the synthesis approach across different configurations, highlighting trade-offs between accuracy and mechanical feasibility. The results demonstrate the applicability of the method in advanced motion control tasks such as robotic arms, dwell mechanisms, and function generation. This work contributes to the field of kinematic synthesis by offering a rigorous framework for linkage design with enhanced precision, reliability, and versatility. © The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits the use, sharing, adaptation, distribution and reproduction in any medium or format, as long as appropriate credit to the original author(s) and the source is given by providing a link to the Creative Commons license and changes need to be indicated if there are any. The images or other third-party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

In this work was investigated the methods of acid modification of bentonite clays from the Kalzhat and Orta Tentek deposits located in the Almaty region of Republic Kazakhstan with sulfuric and nitric acid in various concentrations and determined the effective concentration. In this study, the effective concentrations of 20% H2SO4 and 10% HNO3 were selected, because bentonite was modified with 10–98% H2SO4 and 10–65% HNO3 acids during the study. Among these concentrations, according to the results of various studies, 20% H2SO4 and 10% HNO3 acids were found to be effective, and the concentrations given in this work were considered effective. The obtained results were studied by the IR-spectroscopy FTIR, X-ray fluorescence, X-ray diffractometer and scanning electron microscope methods. Bentonite modification has a positive effect on opening the interlayer space of clay and improving its properties. After purification and modification, bentonite clay can be used in agriculture, manufacturing, medicine, and various fields such as drilling, construction and water purification.