The development of a reliable model of recognition of Kazakh sign language is an important step towards the development of inclusive communication and assistance to people with hearing impairments. This paper describes in detail the process of collecting and annotating data in which gesture images were used. Special attention is paid to the preparation and preprocessing of data to ensure their compatibility with the model. The process of learning the model involves optimizing hyperparameters and using various techniques to improve recognition accuracy. We also conducted a comprehensive performance assessment of the model based on test data to ensure its effectiveness in real-world conditions. In addition to the main development phase, we are considering testing the YOLO-NAS model on the same dataset to explore potential improvements in accuracy and performance. In conclusion, the results of our research can be used to further develop technologies that facilitate the integration of people with hearing impairments into society, as well as to create educational and communication platforms based on the Kazakh sign language.

This article explores the methods of processing and analyzing big data in order to improve the accuracy and efficiency of machine learning (MO) models. The main focus is on classification problems, the effectiveness of algorithms such as XGBoost, the support vector machine (SVM), ensemble methods, as well as systems for working with big data, including Hadoop and Apache Spark. The key stages of working with data are described: cleaning, normalization, selection of features, which is critically important for building stable models on large amounts of data. Accuracy, completeness, F-measure, and AUC-ROC metrics were used to evaluate the effectiveness of the algorithms, which made it possible to conduct a comparative analysis and identify the most productive approaches. Special attention is paid to the application of MO in the context of organizational innovations, including the tasks of classification, forecasting the success of innovations and innovation portfolio management. Recommendations on the choice of technologies and algorithms for various data types and scales are presented, and prospects for integrating distributed computing platforms with MO algorithms to achieve scalable and efficient solutions are discussed.

This article explores the methodology for evaluating the quality of simplified texts in Kazakh using BLEU and SARI metrics. Text simplification is an important aspect for ensuring information accessibility and facilitating the learning process in Kazakh language. The BLEU metric, based on comparing n-grams of translation and reference, is widely used for evaluating the quality of machine translation, but it does not take the context of the input text into account. The SARI metric, specifically designed for evaluating text simplification, considers semantic changes and shows a higher correlation with human judgments. In this study, algorithms for replacing complex words with simple synonyms and for replacing or removing complex phrases were applied. The analysis results showed that the SARI metric is more sensitive to the changes made in simplified texts compared to BLEU. Therefore, the combined use of BLEU and SARI metrics provides a comprehensive and accurate evaluation of the quality of simplified texts in Kazakh.

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.

In the context of the aggravating environmental problems of the planet and the continuing to worsen global energy crisis, the role of optimizing systems increases, including by reducing energy consumption of production and, as a result, reducing the carbon load. This problem is also relevant for one of the flagships of the economy of Kazakhstan – the mining and processing sector. The purpose of the stated study is to study the possibility of optimizing one of the most energy-intensive stages of the beneficiation processing of ferrous ores – concentrate drying. In connection with economic aspects, the study was carried out on digital simulation models of the drying process of iron ore concentrate in BSA 3.5-27 drying drums developed by the authors in the Matlab visual modeling environment. The authors, based on the results of the conducted theoretical research and field experiments, constructed a model of the object under study with existing automation systems, proposed an adaptive optimizing process control system based on a neural network of radial basis functions. The value of gas consumption for obtaining a drying agent was chosen as a control criterion for the technological process. Based on the results of comparing the operation of digital models of the original concentrate drying system under study and the system with adaptive control, the quality of control was assessed. The obtained results can be applied to modernize the control systems for drying processes of beneficiation complexes of both iron ore mining and processing plants and other minerals using similar technologies.

The technology of intelligent detection of military vehicle objects has already become the foundation for tasks related to intelligence and tracking of weapons and equipment, which is essential for situational awareness in modern intelligent warfare. Over the past two decades, numerous datasets of military vehicle images have been collected, primarily focusing on the classification of various categories of military vehicles. However, almost all these datasets are not publicly available, and the publicly available ones suffer from annotation quality issues. To address the lack of datasets and the quality of existing public datasets, we propose a specialized dataset based on our own collection of images, as well as publicly available ones. To develop methods for automatic detection of various categories of military vehicles and distinguishing them from civilian vehicles, we created a new dataset for military vehicle detection and classification (DMVDC). It consists of 5,899 images of military vehicles collected using three different methods: automated scraping, manual selection of image search results, and data augmentation techniques. To the best of our knowledge, our DMVDC [1] dataset is the only publicly available dataset of military vehicles that consider hidden and partially visible objects. This dataset will contribute to the development of computer vision models for detecting military vehicles.

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.

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.

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.

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.

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.

Spatiotemporal analytics of population movement and density data plays a crucial role in building a «smart city», providing a basis for optimizing urban planning, improving transport systems, increasing public safety, environmental monitoring, developing digital services and urban analytics. This article presents the results of a study on spatiotemporal patterns of distribution and concentration of the population of Almaty using the method of dynamic heat maps. To build a complete picture of the movement, density and activity of the population, open geographic data from OpenStreetMap (OSM) and aggregated data from a mobile operator were used. Analysis of the load on urban quadrants of 500×500 meters based on OSM made it possible to assess the key patterns of change in population density depending on the time of day. Visualization of spatiotemporal data is implemented using the Python Folium library, which ensured the creation of clear interactive maps. The scientific novelty of the study lies in the study of urban processes in Almaty based on integrated data from different sources reflecting the spatiotemporal features of the dynamics of the urban population. The results obtained demonstrate clear patterns of population concentration that can be used to more accurately forecast and plan the allocation of resources and urban infrastructure.

The problem of constructing a normal solution to inhomogeneous systems of second-order partial differential equations using the Frobenus-Latysheva method in the neighborhood of an irregular singular point is considered. The compatibility conditions for the considered inhomogeneous system of partial differential equations are shown and an algorithm for constructing normal solutions in the vicinity of a point at infinity is created. A theorem on the structure of the general solution of inhomogeneous systems of second-order partial differential equations is proved and a “resonance” system is studied, which arises if the particular solution of the corresponding homogeneous system coincides on the right side of the inhomogeneous system. A specific example shows how to construct a particular solution to a non-homogeneous system of partial differential equations.

In this paper, we investigate a non-local boundary value problem for a second-order differential equation with an involutive transformation. The aim of this work is to apply the parametrization method developed by Professor D. Dzhumabayev to study the solvability of non-local boundary value problems in the context of differential equations with involutive transformations. It is known that the Cauchy problem for such equations may not have a unique solution. Therefore, parameters μ₁= y(½), μ₂= y′(½) are introduced, and variables y(x)=u(x)+μ₁+μ₂(x-1/2) are replaced. The parameter values are determined at the midpoint of the interval, which guarantees the existence of a unique solution for the Cauchy problem of the original equation. The performed variable substitution formally divides the problem into two components: the Cauchy problem for the initial equation and a system of linear equations with respect to the introduced parameters. By solving the Cauchy problem and substituting its solution into the boundary conditions, one can obtain a system of linear equations with respect to the parameters. If the matrix of this system is invertible, then the problem has a unique solution. In the case when the matrix is non-invertible, two scenarios are possible: either the boundary value problem is unsolvable, or it has multiple solutions. For the second case, the paper defines the eigenvalues and solvability conditions of the boundary value problem.

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.

Modern research emphasizes the need for an in-depth analysis of both numerical and experimental approaches to optimizing the shapes of building envelopes in order to improve their energy efficiency. The relevance of further research is explained by the complexity of numerical methods related to shape optimization aimed at this problem. This article seeks to fill the existing gaps in this area by creating and analyzing a suitable model. For this purpose, it is proposed to use a two-dimensional model of steady-state thermal conductivity, which describes the heat transfer processes in building facades of various configurations. At the outer boundary, the Neuman boundary condition (of the second kind) is introduced, considering the effect of incident short-wave solar radiation. Calculation of the latter includes factors such as illumination and shading of the wall surface, which are due to the surrounding urban environment. To numerically solve the problem while maintaining good precision, the boundary element method (BEM) is used, including discretization of the boundary of the study area into individual elements. During the study, two key optimization problems are defined: improving heat transfer and ensuring thermal insulation. Optimal façade shapes are designed with the constraint of the volume of material used not exceeding the volume required for a flat reference facade. The results obtained demonstrate a significant improvement in energy effectiveness of 13% in summer and 100% in winter compared to the flat wall facade option.

This paper deals with numerical modelling of supersonic flow of cone and sphere bodies using the penalty function method. The main objective of the study is to evaluate the effectiveness of the penalty function method, also known as the immersed boundary method, for solving compressible gas dynamics problems. We apply modified Navier-Stokes equations considering streamlined bodies and use the ENO scheme for the numerical solution. The simulation results demonstrate that the proposed approach successfully describes the physical processes occurring in the supersonic flow of a cone and sphere, including the formation of shock waves, pressure, temperature and density distributions. The obtained data are compared with experimental results, confirming the adequacy and accuracy of the developed numerical model. The presented work contributes to the development of methods for numerical modelling of compressible supersonic flows and demonstrates the promising use of the penalty function method for solving a wide class of gas dynamics problems.

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.

The paper investigates a second-order differential operator on a star graph. A special class of differential operators on a star graph with simple eigenvalues is chosen. The structure of the characteristic determinants of such operators is studied. In the case of the Sturm-Liouville operator with constant coefficients, the formula of the first regularized trace is written out. The main purpose of this work is to calculate the regularized trace of an operator on a star graph, which differs from calculations for similar operators on segments considered in other papers. The article also describes in detail the properties of the eigenvalues of an operator, including the theorem that the eigenvalues of an operator coincide with the zeros of an entire function, and the algebraic multiplicity of each eigenvalue is equal to the multiplicity of zero of the function. For clarity, the results of the work are presented through the characteristic determinant of the operator and numerical series that describe the behavior of the regularized trace. Using the methods of function theory and analytical series, the first regularized trace is calculated, which is an important step in studying the spectral characteristics of an operator on graphs. The article is of interest to specialists in the theory of spectral operators and differential equations on graphs, as well as to researchers involved in calculating operator traces and analyzing their asymptotic properties.

In this paper we consider a system of first order nonlinear Beltrami equations with singular point in the angular unbounded region of the complex plane. This system of equations is used in the theory of surfaces of positive infinitesimal curvature with a density point and for the construction of isometric nodal coordinates on surfaces of positive curvature with a density point. In this paper, for this system of equations we obtain sufficient condition for the solution of the Dirichlet type problem in the space of continuous functions. For this purpose, we will use the general solution of the system of corresponding linear elliptic differential equations with singular point. The proof of existence of continuous solutions of the Dirichlet problem is based on the Schauder fixed point principle.

The purpose of this work is to study logarithmic solutions of a system of second-order partial differential equations, as well as to establish the conditions of their existence and characterize their properties. Special attention is paid to finding such solutions using the Frobenius-Latysheva method in the vicinity of a regular singular point (0,0). A method has been developed for finding recurrence relations for existing logarithmic solutions when the simple roots of the defining equations differ by integers. A concrete example shows how to construct a logarithmic solution for a homogeneous system of partial differential equations of the second order.

In this paper, we consider an initial-boundary value problem for an unsteady flow of a viscous incompressible fluid in a bounded region, solved using a system of nonlinear Navier-Stokes equations. The equations describe the motion of the fluid considering viscosity, pressure, and mass force, as well as the solenoidality condition of the velocity field. In the general case, finding an analytical solution to the system of equations presents significant difficulties, and it has not yet been proven whether there is always a smooth solution for all possible conditions. In this regard, the fictitious domain method is used to solve the problem, which allows us to reduce the problem by solving a system of differential equations with appropriate boundary conditions. Particular attention is paid to introducing the concept of twice the mean curvature of the surface, which is necessary for the correct application of the fictitious domain method. For this purpose, the article provides a detailed calculation of the mean curvature using surface parameterization and matrices of the first and second forms. Proof of a lemma related to the calculation of twice the mean curvature is also given, which is of great importance for further numerical methods for solving the Navier-Stokes system of equations. The obtained results expand the scope of application of the fictitious domain method in solving hydrodynamic problems, especially in complex geometries, and can be used to develop more efficient numerical algorithms.

In this paper, thin films based on tin dioxide containing hierarchical structures were synthesized. The process of “growing” these structures was carried out by introducing ammonium hydroxide into the composition of the initial film-forming solutions. The shape and volume of the hierarchical structures were regulated by the amount of introduced ammonium hydroxide. The optical properties of the obtained samples were also studied for further use as transparent conductive coatings. The films were applied using spray pyrolysis. During mapping, it was found that the greatest accumulation of tin was observed along the contour of the structures. Whereas oxygen and chlorine were distributed relatively uniformly over the surface of the samples. Elemental analysis of the samples showed that in all samples the ratio of elements was as follows: Sn>O2>Cl2. The highest tin content was in the sample synthesized from the initial solution containing 0.8 ml of ammonium hydroxide. To determine the effect of the content of hierarchical structures on the optical properties, transmission spectra of the synthesized samples were recorded. The analysis of the results showed that with an increase in the addition of ammonium hydroxide in the composition of the initial solution, the transmittance of the samples decreases, but not critically. They still remain optically transparent.

Improving the mechanical properties of technical ceramics is an urgent task of modern ceramic materials science. There is a need to improve the mechanical properties of refractories based on MgAl₂O₄ spinel, since this spinel is characterized by impeccable resistance to high-temperature corrosion. The work is devoted to studying the microstructure, phase composition and mechanical properties of MgAl₂O₄-ZrO₂ ceramics. Experimental samples were synthesized by the solid-phase method from powder compacts. For a comprehensive study of the microstructure and phase composition, scanning electron microscopy and X-ray diffraction were used. The mechanical properties of the ceramic samples were studied by measuring the strength of tablets in biaxial bending. Reinforcement of magnesium aluminate with zirconium dioxide was carried out to improve the mechanical properties of composite ceramics. It was found that a change in the fraction of the press powder can affect the shrinkage, density and mechanical properties of MgAl₂O₄-ZrO₂ ceramic samples.

As the methods, instrumentation, and resolution of three-dimensional spatial analysis have improved over the past twenty years, it is now possible to image the internal microstructure of multiphase materials in detail in three dimensions. Three-dimensional X-ray microtomography offers a unique opportunity for high spatial resolution imaging that can be achieved in compact desktop systems using X-ray microfocus sources. Recently, a modern desktop cone-beam X-ray microtomography system for quantitative analysis of materials in three dimensions was installed at the Institute of Nuclear Physics of the Ministry of Energy of the Republic of Kazakhstan. This paper presents the design, description of the main components, and technical parameters of this X-ray microtomography system, called IMAX. This system is designed to acquire, process, store X-ray images, and reconstruct three-dimensional data from angular projections for the study of internal structures and non-destructive testing of materials. The system consists of a microfocus X-ray source providing an X-ray energy range from 35 to 80 keV, a flat panel scintillation detector system allowing high-resolution digital imaging, optomechanical platforms for sample positioning, and radiation shielding. The first results of test measurements using this X-ray system are presented.

The article presents the results of modeling a differential pressure flowmeter with a flow transducer in the form of an expanding device that measures the flow rate of a pulsating liquid. The article describes a method for obtaining basic modified equations for describing models and presents the structure of the flow transducer. A conical diffuser is used as an expanding flow transducer in operation. In this article, a model of such a flow meter is obtained and the factors influencing the process of measuring the pulsating flow rate of a liquid are investigated. An estimation of the uncertainty of measuring results of the pulsating flow rate using such a transducer is given. The factors influencing the accuracy of flow measurement are investigated.

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.

The design of technology for drilling directional and horizontal wells is an important stage in the development of oil and gas fields, especially in conditions of complex geological structure. This topic involves the study of methods and tools used to create wells with varying angles and directions. Effective design of such wells is necessary to increase hydrocarbon production, optimize production processes and reduce drilling costs. The scientific article discusses the main problems in the design of technology for drilling directional and horizontal wells, as well as ways to prevent them, including the selection of suitable formulations of drilling fluids. This article is intended to summarize the key aspects of the design of directional and horizontal well drilling technology, provide an understanding of the methods and tools used in this process, as well as identify the main advantages and challenges faced by drilling engineers when working in this field.

Low-permeability reservoir has low matrix permeability and small pore throat. Conventional enhanced oil recovery methods are difficult to effectively displace crude oil in low-permeability reservoirs. In this work, a new nano-imbibition oil displacement agent (NIAG) was developed by compounding nanoparticle (NI) and surfactant (APEG). The dispersion stability, interfacial tension and wettability were evaluated. Core imbibition and displacement experiments were carried out to study the imbibition and displacement effect of nano-imbibition oil displacement agent in low-permeability cores. The results show that the average particle size of NIAG is 70 nm. It reduces the oil-water interfacial tension to a certain extent, and the oil-water interfacial tension is 0.026 mN/m. It can change the wettability of rock wall, and the change degree of wetting angle can reach 70%. NIAG has good imbibition recovery effect for low-permeability cores. The spontaneous imbibition recovery rate can reach 21.5% at 60 °C, and the imbibition displacement efficiency can reach 37.5%. This work provides a theoretical basis and technical support for the efficient development of low-permeability reservoirs.

In the context of globalization of markets and increasing competition, supply chain management is becoming critical to ensuring the sustainability and efficiency of business. The relevance of this study is due to the need to develop new management models that can adapt to rapidly changing market conditions and customer needs. The purpose of the study is to develop and practically implement an open supply chain management model based on the planning mechanisms and modern optimization methods. The model is aimed at integrating various functional areas, such as demand, inventory, supply and production planning management, with an emphasis on increasing transparency and efficiency. The study used data analysis methods, including ARIMA models for demand forecasting, multi-criteria analysis (AHP) for supplier selection and linear programming for production planning optimization. Microservice architecture was also used to integrate all functional areas into a single supply chain management system. The novelty of the study lies in proposing an integrated model of open supply chain management that takes into account modern technologies and optimization methods, as well as in applying an integrated approach to managing logistics processes. The significance of the study is demonstrated by its practical applicability for companies engaged in the production and distribution of goods, which allows them to effectively adapt to changes in the market, minimize risks and increase customer satisfaction. The results of the study can be used for further developments in the field of supply chain management and business process optimization.

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.