The key objective of this study is to determine the effect of interphase boundaries, the formation of which is caused by the variation of Li2ZrO3/MgLi2ZrO4 phases in lithium-containing ceramics based on lithium metazirconate, on the resistance to near-surface layer destruction processes associated with irradiation with He2+ ions. During the observation of the deformation effects that have an adverse impact on the volumetric swelling of the near-surface layers of ceramics, the thermal expansion factor caused by high-temperature irradiation was considered, simulating conditions as close as possible to the operating conditions of these materials as blankets for tritium propagation. During the studies conducted, it was established that an elevation in the contribution of MgLi2ZrO4 in the composition of ceramics leads to a rise in resistance to deformation swelling caused by structural distortions of the crystal lattice, due to a decrease in the effect of thermal expansion, alongside the presence of interphase boundaries. The established dependencies of the change in the hardness of the near-surface layer of the studied ceramics made it possible to establish the kinetics of softening caused by the deformation distortion of the crystalline structure, as well as to determine the relationship between volumetric swelling and softening (change in hardness) and a decrease in crack resistance (change in the value of resistance to single compression).

Glacier retreat has caused the emergence of numerous moraine-dammed glacial lakes (MGL) over the last century which have become research foci in many mountain regions of the world. Outbursts of MGLs have caused destructive floods and debris flows, leading to numerous human casualties and significant material damage. The mountains of South-Eastern Kazakhstan have also become prone to lake outburst floods and related debris flows, specifically in the second half of the 20th century. This paper presents and reviews existing surveys and knowledge along with results of own investigations on the formation of MGLs and the characteristics of lake outburst floods and debris flows in the Kazakh part of Tien Shan. We suggest a workflow to identify the most dangerous types of lakes and provide information about their morphogenetic features and hazard criteria. The number of MGLs increased since the 1970s with more than 160 existing in 2018. Forty were identified as being dangerous. Forty-eight lake outbursts occurred since 1950 with all the documented events happened between end of June and end of August. The most dangerous outbursts were caused by ruptures in ice-cored moraine dams. Outbursts of nine MGLs caused disastrous debris flows, with some occurring repeatedly. The number of outbursts decreased since the year 2000 compared to 1970–2000. However, due to ongoing glacier retreat new lakes are forming at higher altitudes. Their greater potential energy makes possible future outbursts more dangerous. Re-evaluation of existing methods to calculate the water volume and peak discharge based on bathymetric measurements and observed outbursts revealed that the applied equations provide suitable approximations and allow supporting mitigation and prevention measures. Finally, the presentation of implemented measures to lower the water level using siphons or artificial flow channels shows that they can reduce the lake outburst hazards. However, they are associated with risks and financial costs and it needs to be carefully considered whether protection measures of the endangered areas are more cost effective. © 2022 The Authors

Glacial Lake Outburst Floods (GLOFs) have emerged as a critical threat to high-mountain communities and ecosystems, driven by accelerated glacier retreat and lake expansion under climate change. This review synthesizes advancements in remote sensing technologies and methodologies for GLOF monitoring, risk assessment, and mitigation. Through a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-guided systematic literature review and bibliometric analysis of studies from 2010 to 2025, we evaluate the transformative role of remote sensing in overcoming traditional field-based limitations. Central to this review is the exploration of multi-sensor data fusion for high-resolution lake dynamics mapping, machine learning algorithms for predictive risk modelling, and hydrodynamic simulations for flood propagation analysis. This review underscores the importance of these technologies in improving GLOF risk assessments and supporting early warning systems, which are crucial for safeguarding vulnerable high-mountain communities. It addresses existing challenges, such as data integration and model calibration, and advocates for collaborative efforts between scientists, policymakers, and local stakeholders to translate technological advancements into effective mitigation strategies, ensuring the sustainability of these at-risk regions. © 2025 by the authors.

The article discusses the analysis of the state of control of the processes of biogas production from animal waste by methane digestion. The article discusses the problems of synthesis of automated control systems of biotechnological processes under conditions of information uncertainty. The analysis of the current state of control of fermentation stage processes shows that insufficient attention is paid to the problem of synthesis of second-tier banks under conditions of information uncertainty. Construction of mathematical modeling of biosynthetic processes is a kinetic model, where experimental and analytical methods are used due to the difficulty of identifying patterns in microbiological processes. The article discusses the application of methods and algorithms for intellectualization of problem solving in ACS for the synthesis of complex biotechnological objects in conditions of lack of information, that they should be attributed to priority tasks. The results of research on the application of a neuro-fuzzy system for controlling fermentation processes under conditions of uncertainty and multimode of processes, as well as a forecasting algorithm using nonlinear sets and neural networks are presented.

Additive manufacturing technologies can offer a cost-effective alternative to the production of metal parts of complex geometric shape compared to traditional production or expensive methods of melting powder coatings. In this work, the starting materials were developed by adding 75 % by volume to 316 L stainless steel powder and 25 % by volume from LDPE materials as a binder. Methods of burning and sintering were carried out in a hydrogen atmosphere at a temperature of 1387°C. The resulting metal sample was described mechanically and microstructurally. After sintering the metal samples have a powder size of ~ 7 μm, a level of 250 MPa and a nanoindentation hardness of 4.87 GPa, which are the characteristic features of burnt steel.

The development of advanced industries of the Republic of Kazakhstan (RK), such as chemical, oil, geological exploration industries, etc. dictates the need to develop promising and resource-saving technologies of manufacturing parts and components of machines and technological equipment. The uninterrupted operation of the above industries directly depends on the quality of manufacturing machines and technological equipment. The carried our studies show that there is a problemof machining large-sized parts when manufacturing and repairing machines and technological equipment. The most problematic issue is machining stepped holes of large diameters. To solve this problem, the design of a special boring bar was developed. It allows simultaneous machining stepped holes of large parts of technological equipment. This article is aimed at studying the effect of the boring bar amplitude-frequency characteristics on the accuracy of machining a large-sized part. It is known that the quality of machining depends largely on durability and rigidity of the boring bar design. In this regard, in this work, by using modeling in the Ansys Workbench computer program, the effect of amplitude-frequency characteristics on the boring bar rigidity and durability was determined. For the amplitude-frequency study, graphs of the radial displacement amplitude and the phase angle dependence on frequencies were obtained. It was established that the radial movement in the boring bar cutter at the optimal frequency v = 20,83 Hz is 9.9 µm and at the resonant frequency Vp = 1167, 1 Hz 67.2 µm, which is almost 7 times more. The durability of the boring bar was also calculated in the Harmonic Response module using the additional Fatigue Tool. As a result, it was revealed that the obtained radial movements are within the permissible limit of the tolerance field and the boring bar durability corresponds to the tabulated data. © 2024, National Academy of Sciences of the Republic of Kazakhstan. All rights reserved.

This work examines concerns associated with traffic analysis applicable within a given region of Almaty City employing real data and modelling approaches. In light of analyzing traffic congestion and accidents, the study unveils the following caveats and research limitations: In order to overcome the above challenges, the study proposes the following ways: The study also has the following achievements: The solution suggested to address the problem is adopting an Adaptive Transport Control System to improve traffic management. By employing simulation models and real data the study aims to enhance traffic flow and the transport system in the Almaty City. The research utilizes SUMO an open-source traffic simulation software to perform simulation settings with consideration of parameters like signal lights, separation of lanes, and probability for appearance of vehicles. Also, in this study, parameters of BPR function are calibrated to improve the modeling of traffic flow in order to support the application of efficient traffic control measures. The analysis shows that even minor changes to the BPR parameters can lead to a dramatic increase in the model's accuracy, which will be useful for understanding the planning of cities. Subsequent work is to conduct studies on these models across diverse datasets and road types, as well as incorporating real-time data to the models, and covering more extensive facets relating to the traffic flow model so as to provide paramount support in enhancing the efficiency of urban transportation systems. © 2024 IEEE.

This paper investigates the application of convolutional neural networks (CNNs), particularly the UNet model architecture, to improve the accuracy of breast cancer tumor segmentation in ultrasound images. Accurate identification of breast cancer is essential for effective patient treatment. However, ultrasound images, often contain noise and artifacts, which can complicate the task of tumor segmentation. Therefore, to highlight the most robust architecture, modifications were made to the original set, including the addition of noise and fuzziness. In this study, a comparative study of five different variants of UNet models (UNet, Attention UNet, UNet++, Dense Inception UNet and Residual UNet) was conducted on a diverse set of ultrasound images with different breast tumors. Using consistent training methods and techniques of augmentation and adding noise to the data, an improvement in segmentation accuracy was highlighted when using the Dense Inception UNet architecture. The results have potential practical applications in the field of medical diagnosis and can assist medical professionals in automatic tumor segmentation in breast cancer ultrasound images. This study highlights the improvement of segmentation accuracy by introducing dense induction into the UNet architecture. Importantly, the Dice coefficient, a key segmentation metric, improved markedly, increasing from 0.973 to 0.976 after data augmentation. The results of the study offer promise to the medical community by offering a more accurate and reliable approach to segmenting breast cancer lesions on ultrasound images. The findings can be implemented in clinical practice to assist radiologists in early cancer diagnosis © 2023, Authors. This is an open access article under the Creative Commons CC BY license

The concept of the sustainable development of the world economy is currently aimed at achieving carbon neutrality, and this is due to the global warming of the planet. Energy and construction make a significant contribution to the release of carbon emissions into the environment and atmosphere. According to statistics, simply burning one ton of Portland cement clinker provokes the release of at least half a ton of carbon dioxide. In this study, the prepared samples were subjected to electron diffraction studies, as well as the X-ray phase analysis of the zone (XRF) using an ARLX’TRA diffractometer. Studies of macro- and microstructures were carried out using a Quanta 3D 200i scanning microscope. The obtained spectra were processed using EDAX TEAM software. The study of the microstructure of the samples showed that the bulk of the heterogeneous systems consisted of volumetric aggregates and intergrowths, i.e., small accumulations on their surfaces with pronounced cleavage, features of the microstructure indicating mineral formation processes. Therefore, the development of low-carbon construction models will make it possible to make a contribution and open an effective path to the implementation of climate policy through the rational use of natural resources and the involvement of industrial waste and nature-like technologies in the production process. In this regard, one of the options for solving the identified problems is to revise existing technologies and develop low-carbon, low-clinker binders using industrial waste and substandard raw materials. © 2025 by the authors.

The paper considers discrete and continuous models of the epidemic propagation with a limited time spent in compartments. It contains a comparative analysis carried out for the influence of process parameters on both models. The problem of system identification is solved. Namely, we first estimated the accuracy of the solution of the inverse problem on the model data. Then the system is identified based on real data on the spread of COVID-19 in Kazakhstan, after which a forecast is made for the propagation of the epidemiological situation.