Understanding the groundwater chemical regime on irrigated lands is relevant for assessing hydrogeological and reclamation processes. Water chemistry, natural and climatic conditions, and drainage characteristics define the complex hydrochemical connections which are formed between irrigation waters, soil, and water of the unsaturated zone and groundwater. This research paper presents the results of studies of seasonal changes in the chemical composition of groundwater in the Karatal irrigated massif. A detailed analysis of the salt regime and chemical composition of groundwater have been performed based on a large number of samples for 2019. Besides, data related to the Karatal River and drainage waters quality inside the basin have also been analyzed. Results demonstrate that the salinization processes in the massif are determined by hydrogeological conditions and drainage availability. In the north-eastern part of the massif, a high risk of soil salinization has been observed as a consequence of all the salinization processes which are determined by its hydrogeological conditions and drainage availability. Data analysis over soil samples and irrigation and drainage waters show that the hydrochemical regime of groundwater on Karatal irrigation massif is influenced by the presence of saline soil-forming rocks, the groundwater depth, the volume, and the quality of irrigation waters, the characteristics of the natural drainage, and evaporation processes.

The known theory and experience of energy-saving architecture allow solving the article’s task and ensuring sufficient insolation, passive solar heating, and occupants contact with nature through semi-basement windows. The goal is a full-fledged use of the semi-basement space achieved for seismically active regions with a moderate, cold, and hot climate by ensuring the normative seismic-resistance, energy-efficiency, and microclimate of the building and preventing influence of thermal bridges and mold growth. The set of recommendations also covers the provision of the required sanitary-hygienic conditions in the semi-basement rooms. The multidisciplinary problem is solved by integrating the methodologies of various fields of science. By means of numerical investigations, it established that the soil adjacent to the semi-basement foundation wall increases the thermal mass and building envelope heat-protection capacity. The isotherms and the intensity of heat fluxes made it possible to eliminate the effect of thermal bridges that interact with the soil and outside air. The expedient thickness and width of the additional layer of thermal insulation of thermal bridge zones in excess of the normative layer of the enclosure’s thermal insulation were established. The graphical dependence of the wall's inner corner temperature from this width allows selecting the microclimate level. A multilateral contribution to building improvement is derived from a single-family home example: comfortable microclimate conditions have been created in the semi-basement for placing main rooms there; energy savings for heating this space is 16-20%; saving of monolithic frame and foundation concrete is 10-12% ensuring the higher than normative building seismic-resistance. © 2022 by authors, all rights reserved.

As in many regions across the world, southeastern Kazakhstan is subjected to ongoing water-resource depletion. The livestock sector is already a major natural water resource consumer, with groundwater use becoming its water supply strategy. Remote pasture infrastructure development requires new water resources to allow pasture area circulation. The distant pastures in the middle reach of the Aksu River consist of three pastures, with a total area of 32,450 ha and a permissible number of 3245 livestock heads. The HYDRUS-1D water transport model and MODFLOW groundwater-flow model simulated complex water infrastructure prospect hydrogeological scenarios to allow the consumption of 302.4 m3 per day for livestock keeping on pasturelands. During pumping for livestock watering, projected production well drawdowns were quantitatively evaluated. The findings show that the projected pumping flow rate equals 288 m3/day during the water consumption season and 95 m3/day outside the water consumption season. While the production wells on pastures No. 2 and No. 3 can be considered reliable, on pasture No. 1, an additional production well is needed. To maintain the production wells’ drawdowns to less than 6 m, a projected pumping flow rate reduction to 216 m3/day and 70 m3/day, respectively, is required.

Aramide- epoxy-filled composites are widely used for manufacturing in the structures of modern aerospace vehicles. Not only do they have excellent mechanical properties, but they are also radio-transparent materials for wave transmission. In this work, an epoxy-filled composite and a fibreglass were made by vacuum infusion for a comparative study on radio transparency and dielectric permittivity. The radio transparency of the materials analyzed has been evaluated by measuring in free space in the frequency ranges of 1-6 GHz. According to the results of radio transparency, aramide- epoxy-filled composite suffers less electromagnetic wave losses than in fibreglass. When measuring the dielectric permittivity of the aramide- epoxy-filled composites, a low average value of 2.874 has been set, whereas for the fibreglass is defined at 4. © 2023 E.A.Buketov Karaganda State University Publish House. All rights reserved.

One of the most extensive natural plague centers, or foci, is located in Central Asia, in particular, the Zhambyl region in Southern Kazakhstan. Here, we conducted plague surveillance from 2000 to 2020 in the Zhambyl region in Kazakhstan and confirmed 3,072 cases of infected wild animals. We used Species Distribution Modeling by employing MaxEnt, and identified that the natural plague foci are primarily located in the Moiynqum, Betpaqdala, and Tauqum Deserts. The Zhambyl region's central part, including the Moiynqum and Sarysu districts, has a high potential risk of plague outbreak for the rural towns and villages. Since the phenomenon of climate change has been identified as a determinant that affects the rodent populations, thereby elevating the likelihood of an outbreak of plague, we investigated the potential dissemination routes of the disease under the changing climate conditions, thus creating Species Distribution Forecasts for the rodent species in southern part of Kazakhstan for the year 2100. By 2100, in case of increasing temperatures, the range of host species is likely to expand, leading to a higher risk of plague outbreaks. The highest risk of disease transmission can be expected at the outer limits of the modeled total distribution range, where infection rates are high, but antibody presence is low, making many species susceptible to the pathogen. To mitigate the risk of a potential plague outbreak, it is necessary to implement appropriate sanitary-epidemiological measures and climate mitigation policies.

Widespread adoption of internet of things (IoT) for automation, monitoring and control in various engineering, business and industrial applications poses serious challenges of data security and cyber-attacks to IoT networks. Blockchain enabled IoT networks, due to their immutability, transparency, and accountability, are commonly employed to ensure safe and secure implementation of the IoT networks. However, blockchain technology is prone to degradation in performance and efficiency in the presence of large number of IoT devices and massive data generated by these networks and does not scale well with the growing size of the networks. This research proposes a Scalable EdgeIoT Blockchain (SEB) framework using EOSIO to enhance the performance and efficiency of the blockchain enabled IoT networks. The proposed framework leverages upon the concepts of sharding for parallel execution of smart contracts, Delegated Proof of Stake (DPoS) for consensus in the network with large number of devices and Interplanetary File System (IPFS) for the data storage and management of massive data in the IoT networks. The proposed framework is implemented in the EOSIO blockchain. This study experiments show significant improvement in the throughput, latency and resource utilization compared to the state-of-the-art solutions in the blockchain enabled IoT networks. © 2024 The Authors.

One of the main issues of environmental protection is the quality of atmospheric air. These problems are especially acute in industrialized regions, where the level of anthropogenic impact is increasing; in Kazakhstan, Central Kazakhstan belongs to such regions. The purpose of this study is to study the relationship between diseases of the population and air pollutants from industrial sources. The research methodology was the use of ArcGIS tools and the construction of a correlation between two parameters: pollution and morbidity in the region. Analysis of mortality rates of the population by main classes of causes of death for 2017–2020 in the regional context in the Republic of Kazakhstan revealed that the mortality rate in 2020 increased by 20.2%. When analyzing the causes of death of the population, diseases associated with the negative impact of the environment were selected. It was noted that, in general, in the Republic of Kazakhstan from 2017 to 2020, there was a downward trend, but in the Karaganda region, in 2020, it increased by 8.7%. In Astana, this indicator also tended to decrease, but as a result, a very strong correlation was found between the incidence of malignant neoplasms in Astana and nitrogen dioxide pollution (Pearson index 0.95). © 2023 by the authors.

Digitalization has affected all spheres of life, including education. Modern didactics and methods of digital education are designed to solve problems rеlаtеd tо the use of dіgіtаl tесhnоlоgіеs, tооls and resources іn the education, uрbrіngіng and dеvеlорmеnt of children with dіsаbіlіtіеs. Тhе аіm of the study wаs tо dеvеlор a model of the components of dіgіtаl lіtеrасу and in practice to assess the level of development of digital literacy of studеnts with hеаrіng іmраіrmеnt. The process of forming and improving the components of digital literacy of hearing impaired students was carried out on the basis of the scientific substantiation of the content of the special course in addition to computer science. The study was conducted in Kazakhstan between the years 2019 and 2021 among 127 students of special (correctional) schools. We have proposed the author's two-component model of digital literacy of hearing impaired students: (1) digital user component and (2) digital correction-intellectual component. In the first component students with hearing impairments will able to know the basic Engineering training. For example, installing, starting, removing and updating software; installing the operating system; increasing the speed of a computer; working with drivers, peripheral devices and etc. On the basis of the collected data, we have noticed the following: digital user component of digital literacy is an important for hearing impaired students because the respondеnts possess the lowest lеvеl of knowledge in the аrеа of engineering training and have the ability to create digital content. This led to create the second component as Digital correction-intellectual component. According to this component, students will be able to improve their cognitive, logical, critical, creative, systems thinking, memory, attention, speech, communication skills through learning adopted additional course. Thus, facilitating the development of Digital literacy of students with hearing impairments has bеcоmе оnе of the kеу challenges fасеd by special (correctional) sсhооls tоday © 2022. by Cherkas Global University All rights reserved

Heart disease is a leading cause of mortality worldwide. Electrocardiograms (ECG) play a crucial role in diagnosing heart disease. However, interpreting ECG signals necessitates specialized knowledge and training. The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis. This research paper proposes a 3D Convolutional Long Short-Term Memory (Conv-LSTM) model for detecting heart disease using ECG signals. The proposed model combines the advantages of both convolutional neural networks (CNN) and long short-term memory (LSTM) networks. By considering both the spatial and temporal dependencies of ECG, the 3D Conv-LSTM model enables the detection of subtle changes in the signal over time. The model is trained on a dataset of ECG recordings from patients with various heart conditions, including arrhythmia, myocardial infarction, and heart failure. Experimental results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease, achieving an accuracy of 88% in the classification of five classes. Furthermore, the model outperforms the other state-of-the-art deep learning models for ECG-based heart disease detection. Moreover, the proposed Conv-LSTM network yields highly accurate outcomes in identifying abnormalities in specific ECG leads. The proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and diagnosis. This study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease detection. It highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.

Suicide rates are increasing, particularly among adolescents. This constitutes a significant concern for public health globally. A multitude of social and emotional factors complicate the identification of individuals at risk. This study employs sophisticated AI methods to forecast suicide risks among adolescents through machine learning techniques. We will implement multiple measures to ensure success. Initially, we cleanse the data and extract essential features. We employ techniques such as TF-IDF and BOW for this purpose. Subsequently, we implemented other machine learning models, including Random Forest, Support Vector Machine (SVM), and XGBoost. We additionally employ deep learning models, such as LSTM and CNN. We evaluated our models using actual data and implemented all components in Python. The findings indicate that our method is proficient at identifying individuals who may be at elevated risk. Our method demonstrates superior risk assessment and preventive strategies compared to existing models. We examine variables such as accuracy, precision, and reliability to substantiate this claim.