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.

This study investigates the impact of industrial emissions on the concentration of toxic elements, such as barium, strontium, arsenic, thorium, and uranium, in the biological tissues of pregnant women residing in Kazakhstan's industrial regions. The study focuses on the potential health risks to both the mothers and their developing fetuses, given the ongoing environmental contamination due to rapid industrialization. 67 pregnant women from various districts in the Akmola region were selected for this cross-sectional study. Biological samples, including placenta and umbilical cord blood, were collected and analyzed using instrumental neutron activation analysis and scanning electron microscopy techniques. Data on environmental and occupational exposure were gathered through questionnaires. The barium, strontium, arsenic, thorium, and uranium concentrations were statistically analyzed using Microsoft Excel and STATISTICA to assess correlations with health outcomes. The findings showed elevated concentrations of barium and strontium in both the placenta and umbilical cord blood, indicating significant exposure through environmental contamination. Arsenic and uranium were also detected in smaller amounts, with localized variations across different regions. The study found a strong association between higher concentrations of these elements and adverse pregnancy outcomes, such as anemia, preeclampsia, and developmental anomalies in the fetus. This study highlights the critical environmental health risks of industrial emissions in Kazakhstan's rapidly developing regions. The transplacental transfer of toxic elements poses serious risks to maternal and fetal health, increasing the incidence of pregnancy-related complications. These findings emphasize the need for stricter environmental regulations and public health interventions to mitigate industrial pollution and safeguard vulnerable populations. © 2025 The authors.

The sustainable growth of the economic development of the mining industry directly depends on the level of implementation of innovative technologies in production. Rational use of subsoil in the development of deposits should be based on the maximum completeness and complexity of extraction of useful components. Today, the issue of introducing low-waste and non-waste environmentally friendly technologies is especially acute. The main direction of using waste from mining and metallurgical production is the filling of mined spaces. There is extensive experience in the use of production wastes in stowing operations, technologies for stowing operations have been developed, which are used in many mines at the mines of East Kazakhstan. One of the urgent tasks in these conditions is the development of resource-saving technologies for the extraction of minerals based on the diversification of the products of mining enterprises, which ultimately will reduce the cost of mining and increase the profitability of production and the competitiveness of the final products of mining companies. The article provides a substantiation of the main direction of using mining and metallurgical waste as a backfill of mined spaces, a resource-saving technology of mining based on the diversification of the output of mining enterprises, parameters of reducing the cost of mining and increasing the profitability of production and the competitiveness of the final products of mining companies and parameters of reliable artificial massifs in mining systems with backfilling of mined spaces are given [1]. It is recommended to use the technology at mining enterprises with complex mining and geological conditions. © 2022, National Academy of Sciences of the Republic of Kazakhstan. All rights reserved.

Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, and human impact. The primary concern revolves around water resources, which are influenced by the area’s trans boundary flow of major rivers. The low level of water and food security has pushed the development of new approaches based on remote sensing monitoring and geographic information systems (GIS) to provide solutions for soil salinity. The research aims to focus on utilizing high-resolution radar images. This data type is effective for cloudy weather and can be useful for continued monitoring of some areas. Machine learning methods can solve the problem of automatic mapping of agricultural land salinity in Kazakhstan’s southern regions. The precise mapping of the salinity area helps prevent or decrease salinity’s impact on agriculture. The experiment realized that complex models such as LightGBM do not have significant accuracy performance over simple models on a small dataset compared with Ridge regression. The results allow us to recommend an approach for further improvement with ground-based measurement data and other deep-learning methods for mapping the salinity of agricultural lands. © The Author(s) 2024.

This paper describes an approach to the detection and recognition of crops and weeds in an agricultural field using data obtained from unmanned aerial vehicles (UAVs) and the YOLO v4 neural network. The advantage of using YOLO v4 is the recognition of objects and the names of each object. The creation of a data set for training a neural network is described, its preprocessing, training of a neural network, and the results of recognition of crops and weeds in a soybean field are described. Neural network quality for object confidence threshold ≥0.25: precision = 0.81, recall = 0.82, F1-score = 0.82, average IoU = 58.40%, mAP = 31.81%. Also in the corresponding section, the proposed approach is discussed in comparison with the approach that does not require manual data annotation.

This paper is dedicated to the study of the importance and efficiency of developing and implementing ozone purification systems for disinfecting drinking water sources, water pipes, and wells. Ozone is a powerful oxidizer capable of effectively eliminating microorganisms, including bacteria, viruses, and protozoa in water pipes and wells. Such systems serve as alternatives to traditional chlorination methods and leave no polluting purification by-products in the environment. The research explores the technical parameters of applying ozone to various water sources and purification systems, as well as operational parameters like ozone concentration, treatment time, and water flow regime. It also covers issues related to the design, installation, and operation of ozone purification systems. The topic contributes to the development and improvement of efficient and ecologically sustainable water disinfection solutions by providing an overview of the working principles, technical specifications, and mobility capabilities of ozone purification systems. The introduction of ozone purification systems extends the possibilities for improving water quality and adhering to safety standards. This study also identifies key factors such as ozone solubility, reaction time, and its efficiency in dispersing through water, which can enhance the effectiveness of this method.