Kazakh-British Technical University

Kazakhstan’s economy remains dependent on the extractive sector, which poses risks of instability due to fluctuations in global commodity prices. In the context of globalization, the integration of the manufacturing industry into global value chains (hereinafter referred to as GVCs) is becoming an urgent task that will increase the competitiveness of the national economy. The purpose of this study is to quantify the potential of the manufacturing sector and assess the relationship between indicators of GVCs and main indicators of economic growth. Statistical data from the Bureau of National Statistics of Kazakhstan and international rating organizations like the OECD, Asian Development Bank, and Islamic Development Bank were used for this study. Regression modeling, reliability analysis using Cronbach’s alpha, and analysis of variance were used to analyze quantitative data. The results showed that the volume of non-primary exports had a statistically significant impact on GDP (p<0.05) and labor productivity (p< 0.01), but the share of manufacturing in the economy remained low and the process of export diversification and integration into global supply chains was slow. This study highlights the need for active government policies in the development of the manufacturing sector, attracting investment in non-resource industries, and deepening participation in GVCs. The findings can be used to formulate industrial policy strategies to reduce dependence on raw materials and create sustainable conditions for economic growth. Promising areas for further research include the analysis of factors affecting investments in non-primary products, the study of structural reforms in the manufacturing industry, and the assessment of government support’s impact on the development of the non-primary sector.

Relevance: The growing need for highly qualified medical personnel, especially in oncology, makes staff development essential in improving oncological care. The dynamically developing healthcare system of Kazakhstan requires adapting educational programs for medical professionals to modern requirements and challenges and ensuring their accessibility, regularity, and practical orientation. The study aimed to analyze the effectiveness of professional development programs for medical personnel of the Kazakh Institute of Oncology and Radiology and their impact on the quality of oncological care. Methods: The research utilized both quantitative and qualitative data collection methods. It included questioning employees of the Kazakh Institute of Oncology and Radiology medical institutions and analyzing statistical information. Results: Key issues related to the accessibility and regularity of educational programs, staff’s insufficient awareness of the possibilities of continuing education, and programs’ inconsistency with current professional needs and the needs of medical professionals have been identified. Conclusion: Increasing the effectiveness of professional development programs for medical personnel requires improving their accessibility, strengthening their practical orientation, and introducing modern training techniques. Implementing these recommendations will contribute to the growth of the professional level of medical workers, which, in turn, will improve the quality of oncological care in Kazakhstan.

This chapter examines the challenges and opportunities within the global healthcare sector, with a specific focus on Kazakhstan, incorporating the role of artificial intelligence (AI) in enhancing healthcare delivery. By utilizing surveys to estimate public perception and experiences within the healthcare system, this study identifies critical areas for improvement and potential strategies, including AI-driven solutions, to address key issues. Through qualitative and quantitative research methods, this chapter explores factors influencing the global healthcare sector, including technological advancements such as AI, demographic shifts, and healthcare policy reforms. This chapter provides a comprehensive analysis of Kazakhstan's healthcare landscape, integrating data from government reports, academic studies, and industry publications. Several challenges facing Kazakhstan's healthcare sector—such as inadequate infrastructure, limited access to healthcare services in rural areas, and healthcare workforce shortages—are examined in light of how AI can offer innovative solutions. For instance, AI can be leveraged to improve diagnostic accuracy, optimize resource allocation, and expand telemedicine services, especially in underserved regions. The study also highlights the transformative potential of AI in improving healthcare access and delivery, offering promising opportunities to overcome existing barriers. However, this research is limited by the availability and reliability of data, particularly concerning healthcare indicators and outcomes in Kazakhstan, as well as the complexities involved in integrating AI technologies into existing systems. By examining the problems and prospects of the global healthcare sector through the lens of Kazakhstan and incorporating AI-driven strategies, this study contributes to a deeper understanding of healthcare dynamics in both local and global contexts. The insights gained have significant implications for policymakers, healthcare professionals, and other stakeholders seeking to harness AI's potential to address challenges and capitalize on opportunities within Kazakhstan's healthcare sector.

Artificial intelligence (AI) has become a critical and foundational component across various fields, significantly enhancing efficiency, decision-making, and driving innovation. By automating routine tasks, analyzing vast datasets for insights, personalizing consumer experiences, and optimizing operations, AI delivers substantial benefits such as cost savings, improved outcomes, and more effective responses to complex challenges. In a country like India, with its large population and high demand for healthcare services, AI holds immense potential to address the shortage of healthcare professionals and enhance the overall healthcare system. The adoption of AI in healthcare is essential to streamline administrative tasks, allowing healthcare professionals to focus more on providing direct patient care. While AI is already making strides in multiple sectors across India, the healthcare industry still requires broader and more impactful AI applications to improve access to care and clinical outcomes. AI can play a transformative role in enhancing diagnostic accuracy, optimizing the allocation of resources, and streamlining patient management, which are crucial to meeting the healthcare demands of India’s vast population. However, the implementation of AI in Indian healthcare faces several challenges, including infrastructural limitations, data privacy concerns, and the need for upskilling the workforce to effectively use AI technologies. This chapter focuses on the adoption of AI in the Indian healthcare sector, exploring both the opportunities it offers and the obstacles that must be addressed to ensure successful integration. This chapter examines how AI can assist in overcoming critical challenges and help the healthcare system operate more efficiently, ultimately improving patient outcomes and healthcare access across India.

Public-Private Project (PPP) projects in highway infrastructure development are gaining popularity in emerging economies, optimizing public budgets through private investments, sharing risks between the public and private sectors, and benefiting from the multiplier effect. The goal of this study is to investigate the success of the first mega concession toll road project in Central Asia, the Big Almaty Ring Road (BARR/BAKAD). The project was analyzed from three perspectives, demonstrating the BAKAD project’s overall soundness. Both qualitative and quantitative analyses were conducted based on the publicly available data extracted from various studies and official reports. The methodology included valuation questions grouped into different key performance indicators’ groups and the scoring system. The results show that the weak point in the project is the payback period for the government due to low toll incomes, while the strong point is the traffic offloading and travel time reduction. The proposed evaluation system allows in future studies both scholars and practitioners to comprehensively assess the success of PPP projects in Central Asian countries.

This paper presents the synthesis of a composite anode material for lithium-ion batteries consisting of graphenelike carbon obtained from coffee waste and silicon. The carbon material was synthesized by microwave carbonation and physical activation using CO₂. This method yields a porous structure with an exceptional specific surface area of 1300 m2/g after physical activation. Such a porous structure is crucial for efficient lithium-ion adsorption, high charge transfer, and improved overall battery performance. The morphology and structure of the material were analyzed using SEM and Raman spectroscopy, which confirmed the formation of highly porous graphenelike carbon. The electrochemical characteristic demonstrated a specific capacity of 350 mAh/g for 160 cycles, indicating excellent long-term stability. Coulomb efficiency remained at 98–100%, demonstrating high reversibility of electrochemical reactions. Electrochemical impedance spectroscopy has revealed a moderate 550 ohm charge transfer resistance for the composite material, which highlights the efficient electron transfer between the material and the electrolyte. These results highlight the potential of microwave carbonation and physical activation of CO₂ to produce high-performance, cost-effective anode materials, paving the way for their application in next-generation lithium-ion batteries.

Climate change is transforming water systems worldwide, bringing more unpredictable weather patterns and challenging water management practices. Prolonged droughts, intensified storms, diminishing snowpacks, and shifting runoff dynamics complicate efforts to ensure water security. In Kazakhstan, attempts to mitigate flooding through dam construction have proven inadequate for managing urban stormwater runoff effectively. This study explores the implementation of Agricultural Managed Aquifer Recharge (AgMAR) in Kazakhstan, leveraging 3D visualizations created with the PyVista library to model soil layers, water flow dynamics, and the MAR principle. The findings highlight AgMAR as a promising solution for irrigation and rural water management, offering benefits such as groundwater stabilization, aquifer recharge during seasonal precipitation, purification of underground water sources, and increased freshwater availability.

In the present paper, we study strongly minimal partial orderings in the signature containing only the symbol of binary relation of partial order. We use for partial orderings such characteristics as the height of a structure that is the supremum of lengths of ordered chains, and the width of a structure that is the supremum of lengths of antichains, where an antichain is a set of pairwise incomparable elements. We also differ trivial width and non-trivial width. Recently, B.Sh. Kulpeshov, In.I. Pavlyuk and S.V. Sudoplatov described strongly minimal partial orderings having a finite non-trivial width. Here we study strongly minimal partial orderings having an infinite non-trivial width. The main result of the paper is a criterion for strong minimality of an infinite partial ordering of height two having an infinite non-trivial width.

This paper compares the finite difference and finite volume methods for solving time-fractional diffusion equations. These methods are widely known for diffusion equations with integer order, but their effectiveness for time-fractional diffusion equations has not been sufficiently studied. The definition of the Grunwald-Letnikov fractional derivative is used to approximate the equation. An explicit difference scheme for the finite difference method is obtained and a stability condition for the fractional time order difference scheme is derived, which is also a generalisation for parabolic and hyperbolic type equations, which was previously unknown for schemes with a fractional time order. An explicit discrete form for solving subdiffusion equations in two-dimensional space with fractional time order by the finite volume method is presented. Numerical results show that the finite difference method demonstrates high accuracy, while the finite volume method is better suited for complex geometries. These findings provide insights for future developments in anomalous diffusion modeling.

Commercializing Si/perovskite tandem solar cells requires high-performing and cost-effective materials. The reliance on high-cost indium-based transparent conductive oxides (TCOs) for top electrodes poses a challenge for large-scale production. This necessitates the development of more cost-effective alternatives. This study investigates MoOx-Au-MoOx dielectric-metal-dielectric (DMD) trilayers as top transparent electrodes for 2-terminal monolithic Si/perovskite tandem solar cells. The DMD trilayers exhibited optoelectrical characteristics comparable to established TCO layers. When the thickness of the top MoOx layer was adjusted to 30, 40, and 50 nm, the average light transmittance between 400 and 1100 nm was 70.6%, 71.1%, and 69.7%, respectively. Corresponding average power conversion efficiencies (PCEs) of the Si/perovskite tandem solar cells were 12.46%, 14.10%, and 15.97%, respectively. The observed increase in PCE with the thicker MoOx layer was attributed to increased current density, resulting from enhanced light absorption by the Si subcell in the near-infrared region. The figure of merit (FOM) of the DMD trilayers ranged from 1.45 × 10− 3 to 1.75 × 10− 3 □/Ω. Photostability investigations performed on single-junction perovskite solar cells revealed that DMD trilayers improve the stability of devices in comparison to the conventional opaque gold electrodes. These findings indicate that MoOx-Au-MoOx DMD trilayers are a promising alternative to TCO layers for the top transparent electrodes in Si/perovskite tandem solar cells.

This article presents the development of an automated control system for the process of amine purification of polluted mixtures, a critical industrial process for removing hydrogen sulfide and other acid gases from gas streams. To emphasize the relevance and significance of this study, a preliminary analysis was conducted utilizing databases on pollution levels in the city of Almaty. The analysis provided valuable insights into the current environmental conditions and underscored the necessity of implementing effective purification technologies. The mathematical modeling of the amine purification process was carried out using the Simou method, resulting in an accurate transition function for the system. The parameters of the mathematical model were determined, and an in-depth analysis was performed to evaluate the stability, controllability, and observability of the system. These analytical procedures were executed using MATLAB software. To enhance system performance, PI and PID regulators were synthesized and evaluated. The simulation results guided the practical implementation of the automation system, utilizing the Modicon M340 programmable logic controller from Schneider Electric and the Harmony 6400 control panel. A visualization system for the amine purification process was developed using a mnemonic circuit that includes a control panel, an indicator panel, and graphical representations of key process parameters. The EcoStruxure Control Expert and EcoStruxure Terminal Expert software were employed to design and optimize this visualization system, ensuring user-friendly and efficient monitoring and control. In addition to addressing industrial process needs, a Smart City concept was developed as part of the research. This concept leverages the ARIMA (Autoregressive Integrated Moving Average) artificial intelligence method to analyze the concentration of harmful substances in the air. By integrating this analysis, the study aims to contribute to broader urban environmental management efforts. The outcomes of this work highlight significant advancements in industrial gas purification technology and its applications in environmental management, contributing to the development of sustainable and efficient solutions for modern industry.

As we navigate through the digital era, the scope of biometric authentication has significantly broadened, establishing itself as a cornerstone of modern security systems. This study explores the sophisticated methodologies and leading-edge technologies that are at the forefront of biometric access systems’ evolution. The transition from elemental techniques to advanced systems integrating facial recognition, fingerprint scanning, iris tracking, and additional modalities – each enhanced by artificial intelligence (AI) and machine learning (ML) – is thoroughly examined. A special focus is given to how the convergence of accuracy, speed, and user experience plays a crucial role in the broad acceptance of these technologies. The paper also delves deeper into the implications of biometric data processing, discussing the critical issues of security and privacy, as well as the ethical and regulatory challenges faced in deploying these technologies. Moreover, this discussion extends to the potential for these biometric systems to adapt to dynamic security threats, highlighting their resilience and flexibility in a rapidly evolving digital landscape.

The rapid introduction of artificial intelligence into various areas completely reorganizes their components and accelerates all processes. Education, a key area of human development and the beginning of its beginnings, also feels the need for the rapid introduction of Artificial Intelligence, which will provide an opportunity to revolutionize educational processes, thereby significantly improving students’ literacy and academic performance. It should be noted that along with the undeniable advantages of using Artificial Intelligence in education, several problems need to be addressed when introducing and using Artificial Intelligence in higher education institutions. This article discusses significant challenges such as data security and privacy, digital inequality, ethical issues in the introduction and use of artificial intelligence, and the need to prepare and plan teacher internship programs. Understanding these problems reveals opportunities for creating a strategy for the introduction of Artificial Intelligence in higher education institutions. The strategy that will allow further successful implementation of it is due to thorough consideration of risks that might be faced.

A preorder R is linear whenever the corresponding quotient poset is linearly ordered. This article discusses computable reducibility on binary relations. We study the degree structure Celps of computably enumerable linear preorders under computable reducibility. Concatenation yields the ordered sum of two given linear preorders. We show that the elementary theory of Celps with concatenation is recursively isomorphic to first-order arithmetic. We also show that the theory of all countable linear preorders (under computable reducibility) with concatenation is recursively isomorphic to second-order arithmetic.

This study aims to predict the number of corruption crimes in Kazakhstan using machine learning methods. The research is based on official monthly crime statistics collected from the Legal Statistics Portal, specifically the Report Form No. 3-K, which records corruption-related offenses since 2016 [3]. Three regression models were applied: k-Nearest Neighbors (kNN), Extreme Gradient Boosting (XGBoost), and Linear Regression. Model performance was assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) score. The findings indicate that Linear Regression achieved the highest predictive accuracy (R² = 1.000), followed by XGBoost (R² = 0.9977) and kNN (R² = 0.9333). These results suggest that machine learning models can effectively forecast corruption crime trends. This study highlights the potential of machine learning in corruption crime prediction. Future research can explore additional predictive features, alternative machine learning models, and real-time data integration to enhance forecasting accuracy.

Investment risks in IT project development are heightened by uncertainty, incomplete information, and fluctuating projected cash flows. These challenges are exacerbated by the lack of robust statistical data, leaving stakeholders with limited tools for making informed decisions. This research addresses these issues by proposing a novel methodology for optimizing risk management in investment processes using advanced deep learning techniques. The study aims to develop and validate an algorithm that quantifies and mitigates investment risks through the integration of machine learning models and convolutional neural networks. A key component of this work is the Risk, Investment, and Compliance (RIC) method, which combines multiple financial indicators into a composite scoring system. The methodology was validated using five years of historical financial datasets from reputable sources, and applied to ten companies across diverse industries to analyse financial performance, market behaviour, and consumer sentiment. Key datasets include Kaggle’s Twitter Dataset, encompassing 1.5 million tweets to assess market sentiment, McKinsey’s dataset of 500 million consumer interactions, and daily updates from Yahoo Finance. The findings demonstrate that the RIC methodology effectively distinguishes between high-risk and secure investments. Companies scoring above 60% were identified as strong investment opportunities, while those below 30% were flagged as high-risk ventures. These results provides a robust framework for managing risks in IT investment projects, enabling more reliable decision-making under uncertainty and offering broad applications across industries.

Over the decades the increasing computational capability and development of new technologies in the field of artificial intelligence have given us the ability to translate sign language in real time. There exist two main approaches to sign language recognition, the hardware-based approach and the software-based approach. The hardware-based approach relies on using special gloves, Kinect-based devices, and different levels of sensors. On the other hand, one of the approaches to working with sign language is using neural networks, which is the softwarebased approach. In this work, I observed existing approaches and experimented with machine learning and neural network models for sign language recognition. I got the dataset of Azerbaijani Sign Language, then trained my models based on that dataset, and got the results and metrics. The dataset contained over thirteen thousand samples of signs, which can be used in Kazakh Sign Language. In the end, I discussed the probable opportunity of using the developed models.

The Sulfur Production Unit with Hydrogen Extraction (SPUHE) plays a critical role in oil refineries by converting hydrogen sulfide into high-quality sulfur and hydrogen. However, optimizing SPUHE operations is challenging due to the uncertainty in process parameters and qualitative assessments of sulfur properties. This study proposes a systematic modeling approach that integrates deterministic, statistical, and fuzzy logic methods to enhance process efficiency and accuracy. Mathematical models were developed for key SPUHE units, including the thermoreactor, Claus reactor, and Cold Bed Absorption reactors. The inclusion of fuzzy logic allows the incorporation of expert knowledge, enabling the assessment of non-measurable sulfur characteristics and improving model reliability. The proposed system accounts for interdependencies between process units, ensuring a comprehensive optimization framework. A comparative analysis with traditional deterministic models demonstrates that the proposed approach improves sulfur recovery efficiency by 11.94%, enhances hydrogen extraction, and reduces operational costs through energy-efficient process adjustments. The developed system provides a robust decision-support tool for refineries, contributing to environmental sustainability and energy optimization. This research offers significant implications for oil refining, hydrogen energy, and industrial process control, demonstrating the advantages of hybrid modeling in managing complex refinery operations under uncertain conditions.