Currently, the issue of modernization of outdated automatic control systems at strategically important infrastructure facilities of the city, such as water supply and water treatment, is urgent. Using modern automation equipment allows for improving the environmental situation and increasing the reliability of technological processes. This article discusses the development of a new automated system for the biological treatment facility of the Almaty Su sewage treatment plant to replace the outdated current automation. A control system for air conditioning machines, based on Schneider Electric equipment, has been developed using EcoStructure Control Expert software to manage the Modicon M340 controller. Additionally, using EcoStructure Machine Expert, a control system has been created for air injection machines during the biological cleaning phase, which uses the Modicon M241 controller.A process control mnemonic circuit was designed to switch between Modicon M340 and M241 controllers. Remote I/O architectures were developed using the Architecture Builder to create network configurations. An emergency protection system was implemented on Honeywell equipment at Kazakh-British Technical University (KBTU) JSC, using Safety Controller and Manager tools. Security levels at SIL enterprises were calculated.

This paper examines the impact of gamification and external motivation on the engagement and completion rates of Massive Open Online Courses (MOOCs), with a focus on underprivileged groups in emerging regions. The research centres around the “LEVEL UP” course, a gamified MOOC designed to enhance STEM skills among young women in Kazakhstan, developed by GameLab KBTU in collaboration with UNICEF. Utilizing a combination of quantitative data analysis and literature review, the study investigates the efficacy of gamification strategies in increasing course completion rates, which are traditionally low in MOOCs. The findings indicate that the inclusion of gamification and external motivational elements, such as competitive elements and rewards, can improve completion rates. The LEVEL UP course, for example, achieved a completion rate of 10%, which is higher than the average completion rate of MOOC courses, which 5–8%. However, the study also highlights the complexity of balancing external and internal motivational factors to sustain long-term engagement and deep learning. Limitations encountered, including technical issues and platform constraints, underline the challenges of implementing such strategies effectively. Recommendations for further research include exploring the long-term impact of gamification, the optimal balance of motivational elements, and the customization of gamification to individual learner needs. This paper contributes to the growing body of evidence supporting the use of gamification in education, emphasizing the need for nuanced approaches that enhance both engagement and learning outcomes, particularly in the context of democratizing education for learners worldwide.

Nowadays, decision-making systems that rely on images are becoming increasingly crucial, especially in the medical field. Images have become a fundamental tool for clinical research and diagnosing illnesses. In the case of glaucoma, a disease that can damage the optic nerve head and result in irreversible vision loss, a new Fuzzy Expert System has been developed for early diagnosis. Original ONH images are preprocessed with filters to remove noise, followed by using the Canny detector algorithm to detect contours. Key parameters are then extracted by identifying elliptical forms of the optic disc and excavation using the Randomized Hough Transform. A classification algorithm based on fuzzy logic is used to assess patients' conditions, taking into account both instrumental parameters and risk factors such as age, race, and family history. The system is tested on a dataset of ophthalmologic images, showing a significant improvement in predictions compared to existing methods, with over 96% accuracy in identifying cases suspected to have glaucoma.

This research presents a comprehensive investigation into the application of machine learning techniques for addressing the pervasive security challenges within Internet of Things (IoT) networks. With the exponential growth of interconnected devices, ensuring the integrity and confidentiality of data transmissions has become increasingly critical. In this study, we deploy and evaluate seven distinct machine learning methods tailored to the IoT network intrusion detection problem. Leveraging the rich and diverse UNSW-NB15 dataset, encompassing real-world network traffic scenarios, our analysis encompasses a thorough examination of both traditional and state-of-the-art algorithms. Through rigorous experimentation and performance evaluation, we assess the efficacy of these methods in accurately detecting and classifying various forms of network intrusions. Our findings provide valuable insights into the strengths and limitations of different machine learning approaches for enhancing the security posture of IoT environments, thereby facilitating informed decision-making for network administrators and cybersecurity practitioners.

Combinatorial hyperdeterminant DET – is the homogeneous polynomial in the entries of a hypermatrix of even number of indices, which is also a unique SL-invariant of minimal degree. It was first studied by Cayley in the middle of 19-th century. Given its fundamental nature, the computation of this polynomial is an important task. For fixed d and a cubical hypermatrix X of length n  Barvinok introduced an algorithm of computing hyperdeterminant in 0(2^nd n^d-1) . Since the problem of deciding whether for the given hypermatrix X the hyperdeterminant DET(X) is equal to zero is NP-hard, it is essential to develop efficient algorithm for computing hyperdeterminant, as the size of problem grows exponentially. We provide enhanced algorithm of computing hyperdeterminant that requires  0(2 ^n(d-1) n^d-1) arithmetic operations.

One of the most pervasive processes of modernity is undoubtedly digitalization, which has encompassed all key spheres of human life. The development of information technology has contributed to large-scale changes not only in the everyday aspect of life, but also more globally, automating complex business processes in the field of entrepreneurship, economics, and healthcare. The transition to digital data and documentation has provided greater accessibility to necessary information and has also enhanced the efficiency of its analysis and processing. Due to this fact, optical character recognition (OCR) technology has gained significant importance, enabling the identification and extraction of textual data from images. OCR systems play a pivotal role in the digital transformation of society as they eliminate the need for manual handling of textual information in images and are applicable in automating the majority of business processes associated with paper-based data processing, such as gathering statistical data from paper forms, reflecting paper documents in electronic document management systems, converting textual information into audio files, and so on. This paper is dedicated to describing optical character recognition technology, as well as providing an overview of machine learning techniques that are actively used in the context of its modern implementation, in order to enhance the quality of the obtained results. In addition, the paper presents the principles of operation of the described approaches, their capabilities, as well as some limitations that may be encountered when using them in various scenarios.

Alzheimer’s Disease (AD) poses a significant challenge in contemporary medicine, necessitating early and accurate diagnostic methods to manage its progression effectively. This study explores the development and application of the Robo-pen, an innovative diagnostic tool designed to detect early signs of cognitive decline through detailed handwriting analysis. The Robo-pen, equipped with an MPU-9250 sensor, captures three-dimensional coordinates, velocity, and acceleration of handwriting movements, crucial for assessing spatial control, movement consistency, speed variations, and the ability to modulate movement speed and force–parameters often disrupted in cognitive impairments like AD. Participants included 20 patients diagnosed with AD and 18 healthy controls, matched in age and educational levels. Data collection involved tasks such as sentence rewriting, figure redrawing, and digit rewriting, processed using CoolTerm software at a sampling rate of 18 Hz. Descriptive statistics revealed that the AD group exhibited lower mean values for gyroscope and acceleration data, indicating slower and less variable movements compared to the control group. T-tests confirmed significant differences (p < 0.001) across all measured parameters between the AD and control groups. The results support the potential of the Robo-pen as a non-invasive, cost-effective diagnostic tool for early detection of AD. By capturing subtle neuromotor changes, the Robo-pen facilitates earlier diagnosis and timely intervention, potentially altering the disease trajectory and improving patient outcomes. This study marks a significant advancement in the early detection of AD, highlighting the Robo-pen’s promise as a transformative tool in neurodegenerative disease diagnosis and management. 

With the sophisticated technology that modern industrial organizations are equipped with, state prediction and diagnostics are essential duties. The current research aims to develop a more accurate modified artificial intelligence system for industrial equipment diagnostics in the oil and gas industry. Researching faulty signals and processing methods utilized by equipment in the oil and gas industry, as well as assessing the advantages and disadvantages of different signal extraction strategies, are the first steps in the process. The second is the application of artificial intelligence to decision-making and equipment defect detection. This method widely used by the oil and gas sectors to lower equipment failure rates. The recommended diagnostic system helps organizations reduce the financial risks associated with equipment defects by increasing production dependability, enabling for maintenance planning, predicting probable failures, and expediting equipment repairs. The article is devoted to the study of the data sampling influence on the classifier’s predictive ability in diagnosing of the industrial equipment. Various types of data samples were considered, such as: simple random sample, cluster sample, systematic sample. According to the results of listed data samples were built classifiers based on particle swarm optimization and ensemble models (bagging and voting type). The best results were achieved using the systematic sampled dataset and an ensemble modeling strategy with voting, which combines forecasting based on a neural net, gradient boosted trees and naive Bayes models: accuracy 93.6%; classification error 8%; recall 94.32%; precision 93.87%. The resulting best strategy for diagnosing equipment based on data sampling and an ensemble model was used for implementation in FMEA (Failure Mode and Effects Analysis) technology in order to obtain an improved version, which is adapted for working with big data.

In the recycling industry, there is an urgent need for high-quality sorted material. The problems of sorting centers related to the difficulties of sorting and cleaning plastic leads to the accumulation of waste in landfills instead of recycling, emphasizing the need to develop effective automated sorting methods. This study proposes an intelligent plastic classification model developed on the basis of a convolutional neural network (CNN) using architectures such as MobileNet, ResNet and EfficientNet. The models were trained on a dataset of more than 4,000 images distributed across five categories of plastic. Among the tested architectures, proposed EfficientNet-SED demonstrated the highest classification accuracy – 99.1%, which corresponds to the results of previous research in this area. These findings highlight the potential of using advanced CNN architectures to improve the efficiency of plastic recycling processes.

Understanding the complexity of entangled states within the context of SLOCC (stochastic local operations and classical communications) involving several number qubits is essential for advancing our knowledge of quantum systems. This complexity is often analyzed by classifying the states via local symmetry groups. Practically, tthe resulting classes can be distinguished using invariant polynomials, but the size of these polynomials grows rapidly. Hence, it is crucial to obtain the smallest possible invariants. In this short note, we compute the basis of invariant polynomials of 7 qubits of degree 4, which are the smallest degree invariants. We obtain these polynomials using the representation theory and algebraic combinatorics. 

This article presents a comprehensive investigation into the classification of quadratic irrationals with period two in their continued fraction representations. Building upon foundational results in Number Theory, particularly in the context of continued fractions and Pell's equation, the study reveals intricate relationships between quadratic irrationals and their periodic structures. The main object of study is √N and properties of its continued fractions. While it is well-known that continued fractions of √N is periodic with periodic part being palindrome, the distribution of the lengths of the periodic parts are far from being complete. Our main goal will be to focus on the period two case and provide a complete characterization. The research's proved theorems clarify the conditions under which the period length is exactly two and give an insight into the underlying algebraic features. Additionally, it delves deeper by offering numerical analysis and illustrations demonstrating the distribution of period lengths among quadratic irrationals. This research opens up new paths for future studies on quadratic irrationals and how they're shown as continued fractions.

In this note we obtain Hardy and critical Hardy inequalities with any homogeneous quasi-norm in unified way. Actually, we show a sharp remainder formula for these results. In particular, our identity implies corresponding Hardy and critical Hardy inequalities with any homogeneous quasi-norm for the radial derivative operator, thus yielding improved versions of corresponding classical counterparts. Moreover, we discuss extensions of these results in the setting of Folland and Stein’s homogeneous Lie groups. Such a more general setting is convenient for the distillation of those results of harmonic analysis depending only on the group and dilation structures, which is one of our motivations working in the setting. Our approach based on the factorization method of differential operators introduced by Gesztesy and Littlejohn. As an application, we show Caffarelli-Kohn-Nirenberg type inequalities with more general weight. Because of the freedom in the choice of any homogeneous quasi-norm, our results give new insights already in both anisotropic ℝⁿ and isotropic ℝⁿ . 

In physics, nonlinear equations are applіed to characterize the varied phenomena. Usually, the nonlinear equations are presented by nonlinear partial differential equations, that can be received as conditions for the compatibility of two linear differentіal equations, named the Lax pairs. The presence of the Lax pair determines integrability for the nonlinear partial differentіal equation. Linked to this development was the realization that certаіn coherent structures, known as solіtons, which play a fundamental role in nonlinear phenomena as lattice dynamics, nonlinear optіcs, and fluіd mechanics. One of the famous equations is the nonlinear Schrödinger equation which is associated with various physical phenomena in nonlinear optics and Bose-Einstein condensates. This equation allows the Lax pair thus it is integrable. This work investigates nonlocal nonlinear Schrödinger-type equations with PT symmetry. Nonlocal nonlinear equations arise in various physical contexts as fluid dynamics, condensed matter physics, optics, and so on. We introduce the Lax pair formulation for the nonlocal nonlinear Schrödinger-type equations. The method of the Darboux transformation is applied to receive analytical solutions.

A linear boundary value problem for a parabolic equation is considered in a closed domain. Based on the broken line method, the boundary value problem for a parabolic equation is replaced by a two-point boundary value problem for a system of linear ordinary differential equations by discretizing the unknown function u(t,x) with respect to the variable x. The obtained two-point boundary value problem is investigated by the parameterization method of Professor Dzhumabaev. Based on this method, an algorithm for finding a numerical solution to the two-point boundary value problem for a system of linear ordinary differential equations is constructed. The constructed algorithm is realized by applying known numerical methods. The constructiveness and efficiency of the parameterization method also allows us to construct a numerical solution of the considered linear boundary value problem for the parabolic equation. One numerical example is given to verify and illustrate the proposed algorithm.

In the article by M.O. Reade (Duke Math. Journal, 1956) using the condition |arg (f'(z)/ g'(z)) | ≤ γπ/2, where g(z) is a convex function,0≤γ≤1 , a class of functions close-to-convex (almost convex) of order γ is introduced. In our paper, we introduce a subclass of the class of close-to-convex (almost convex) order γ functions satisfying the condition |arg [(1-λzn ) f'(z)]| ≤ γπ/2, which, for different parameter values, gives a number of well-known subclasses of univalent (schlicht) functions. Based on this subclass, a class of close-to-starlike (almost star-shaped) functions is constructed, containing a number of subclasses that have been actively studied by many authors in recent years, as well as a classical class of typically real functions. For this classes exact theorems of distortion (growth) and radii of convexity (starlikeness) are obtained, generalizing previously known results. The case is also considered when the functions of the introduced classes have missing members in the power series expansion. The results obtained are accurate and not only generalize previously known results, but also reveal the properties of a number of new subclasses of univalent (schlicht) functions.

The investigation focuses on a boundary value problem for a high-order hyperbolic equation with impulse discrete memory in a rectangular domain. By introducing new functions, the problem is transformed into a set of boundary value problems for a first-order differential equation with impulse discrete memory, which depends on unknown functions and integral relations. D.S. Dzhumabaev's parametrization method is applied to this equivalent problem. The domain is divided according to the time variable, and functional parameters representing discrete memory values are introduced within the interior domains. As a result, the family of boundary value problems for the first-order differential equation with impulse discrete memory and unknown functions is converted into an equivalent family of integral-multipoint boundary value problems involving functional parameters and unknown functions. These equivalent problems include initial value problems for first-order differential equations related to the new functions. The solutions to the initial value problems are expressed using Volterra integral equations. By substituting these solutions into the boundary and impulse conditions, a system of linear functional equations concerning the functional parameters is derived. An algorithm is developed to solve the equivalent problem, and sufficient conditions for the unique solvability of the family of integral-multipoint boundary value problems with functional parameters and unknown functions are provided. Additionally, sufficient conditions for the unique solvability of the original boundary value problem for the high-order hyperbolic equation with impulse discrete memory are established based on the initial data.

A linearly ordered structure is said to be o-stable if each of its Dedekind cut has a “small” number of extensions to complete 1-types. This concept, which was introduced by B.S. Baizhanov and V.V. Verbovsky, generalizes such widely known concepts among specialists in model theory as weak o-minimality, (weak) quasi-o-minimality and dp-minimality of ordered structures. It is based on a combination of the concepts of o-minimality and stability. As we know, the elementary theory of any pure linear order is o-superstable. Indeed, this follows from the fact, which Rubin proved in the late 70s of the 20th century, that any type of one variable is determined by its cut and definable subsets, distinguished by unary predicates or formulas with one free variable. In this paper, we explore the question of what happens if a pure linear order is expanded with a unary function. Two examples were constructed when o-stability is violated; in addition, sufficient conditions for preserving o-stability with such language expansion were found. Research work on this topic is not yet finished, ideally, it would be good to find a criterion for preserving ordered stability when enriching a structure with pure linear order with a new function of one variable.

The microwave electromagnetic properties of ferromagnetic-paramagnetic and ferromagnetic-diamagnetic composites can be changed by varying the concentration of diamagnetic (paramagnetic) and ferromagnetic components. To implement the task of introducing such composites into production, research is required to find effective and simple synthesis technologies that make it possible to vary the content of components with different magnetic characteristics. This work demonstrates a simple method for the synthesis of ferromagnetic ((NiZn)Fe₂ O₄ )- diamagnetic (ZnO) composites by modified chemical deposition followed by annealing. Also, a comprehensive study of the structural and electromagnetic characteristics of experimental samples was carried out. Using the powder X-ray diffraction method, it was revealed that the phase composition of the final samples is represented exclusively by diamagnetic and ferromagnetic phases. Using scanning electron microscopy, it was found that after thermal annealing the powders have submicron sizes with an average size of 100–137 nm. Using vibration magnetometry, magnetic hysteresis loops were measured, the analysis of which showed that an increase in the concentration of the diamagnetic phase leads to an increase in the coercive force of the composites. The measured microwave spectra of complex magnetic permeability show that by changing the ratio between the ferromagnetic and paramagnetic phases, it is possible to realize a frequency shift of natural ferromagnetic resonance. Also, through the calculation of the reflection coefficient on a metal plate, it is shown that the resulting composites can be used as the basis for new radio-absorbing materials. In addition, the synthesized powders can also be used to create microwave devices and microwave antennas.

This article presents a comprehensive survey-based analysis of the current research landscape within Kazakhstan’s academic community. Drawing from responses collected from 265 researchers and academics across various universities, the study unveils insightful demographic profiles and research attributes across four distinct subject areas. While encompassing a broad spectrum of disciplines, particular attention is directed towards the characteristics and trends within the physical sciences domain. The survey explores Kazakhstan’s physical sciences research landscape, revealing a focus on international collaborations, particularly with Western academics, to enhance research visibility. Challenges include limited emphasis on partnerships with governmental or nongovernmental organizations, adverse working conditions, and disparities in resource access, notably in highperformance computing facilities. This research provides a detailed understanding of the current state of academic research within the country and offers a nuanced perspective that can inform strategic planning, policy formulation, and future research endeavors.

This study investigates the electrical properties of a graphene oxide (GO) and nanocellulose (NC) composite using impedance spectroscopy, complemented by thorough characterization through Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and scanning electron microscopy (SEM). FTIR analysis revealed characteristic peaks corresponding to functional groups present in both GO and NC, providing insights into their chemical composition. XPS spectra exhibited distinctive peaks indicative of carbon and oxygen bonding states, elucidating the surface chemistry of the materials. Raman spectroscopy provided information on the structural order and defects within the samples, particularly highlighting the graphitic structure of GO. SEM images revealed the morphological features of the composite membrane, showcasing the distribution of NC particles and structural modifications induced by their incorporation. Impedance spectroscopy was utilized to investigate the electrical conductivity of the GO-NC composite. Results indicated a temperaturedependent behavior, with an increase in conductance observed as the temperature rose within the operational range of fuel cells. Remarkably, the addition of NC did not significantly alter the conductive behavior of the composite, suggesting compatibility and stability. In summary, this comprehensive characterization using multiple analytical techniques offers valuable insights into the electrical properties of the GO-NC composite. The findings suggest its potential for various applications requiring enhanced electrical conductivity, particularly in fuel cell technology. 

The development of devices combining optical and electrical functions based on silicon-containing materials is one of the challenges in microelectronics. By plasma enhanced chemical vapor deposition synthesis and subsequent annealing, silicon nitride samples with both excess silicon and excess nitrogen were formed. The high concentration of Si-H and N-H bonds was determined by Raman spectroscopy in samples before annealing. By the transmission electron microscopy, it was determined that in addition to silicon nitride, silicon clusters were formed in the sample’s matrix. The photoluminescence spectra changed significantly for both types of samples during annealing in different gas atmospheres. Heat treatment of samples at 1100 °C after synthesis led to the disappearance of the PL spectrum, and after annealing at 800 °C, photoluminescence increases. It is noted that the highest intensity of photoluminescence was detected after annealing in the air atmosphere and the lowest in the nitrogen. The participation of N centers in recombination processes was confirmed by the method of electron paramagnetic resonance. The different mechanisms of particle interaction leading to photoluminescence and charge storage are considered. Thus, the conditions for the synthesis and annealing of silicon nitride layers are selected to obtain controlled luminescent properties in various spectral ranges.

In recent years, the emerging field of astronomy focused on the history of galaxy formation, known as Galactic Archaeology, has been gaining popularity. Globular clusters have been involved in many key processes occurring in the Milky Way, making their study, particularly the reconstruction of their orbits, significantly important. The Gaia DR3 catalog provides parameters for 165 globular clusters, such as proper motions, radial velocity, and heliocentric distance, with certain accuracy. Therefore, it is important to examine the influence of measurement errors in these parameters on the initial data when converting to the Galactocentric coordinate system and, consequently, on the shape of the orbits. We integrated the orbits of globular clusters 10 billion years lookback. For physical justification during the integration, we used the external dynamic potential with the individual number 411321 from the cosmological simulation database IllustrisTNG-100, which best reproduces the potential of the Milky Way. The integration was performed using the parallel N-body code φ-GPU, based on a fourth-order Hermite scheme with hierarchical individual block timesteps. A total of 1,000 randomizations of the initial data were created considering a normal distribution of errors, and the influence of errors on the scatter of initial velocities and on the shape of the orbits was examined. The parameters with the largest relative errors are proper motions and radial velocity, while the smallest errors are in heliocentric distance. It was found that 85% of the globular clusters have relative errors in all parameters of no more than 10%, and 5.4% have errors of no more than 1%. Investigating the influence of measurement errors for clusters with different magnitudes of relative errors, we concluded that for most globular clusters, the influence of measurement errors on the shape of the orbits is not significant. Consequently, it is possible to reconstruct the orbits with high accuracy for these clusters. Since the reconstruction of globular cluster orbits involves cosmological timescales, accounting for measurement errors is an important aspect of the preparatory procedure before the main integration.

The paper considers two independent methods for measuring the velocity of the plasma flow generated in the PV-7 pulsed plasma accelerator: a method based on observation and evaluation of the Doppler shift of spectral lines, and a method of high-speed visualization of plasma motion. To record the plasma flow radiation spectrum, a monochromator M833 was used. High-speed video recording was carried out at 640,000 fps using a Phantom VEO710S CMOS camera. The results of measurements of the average flow velocity obtained at a working gas pressure of 2⋅10^-2 Torr, capacitance and voltage of the capacitor bank of 400 μF and 4 kV are presented. The results obtained by two independent methods were compared with each other. Argon was used as the working gas in the experiments. It is shown that the value of the plasma flow velocity estimated by the first method is 12.5 m/s, and the value of the plasma flow velocity estimated by the second method is 16.7 m/s. From these data the measured flow velocity values have a small discrepancy. Thus, it has been established that high-speed video recording and Doppler shift methods make it possible to obtain comparable estimates of flow velocity within the measurement errors. Determining the magnitude of the plasma flow velocity is of great practical importance.

This research presents a comparative analysis of two solar still configurations utilizing the ANSYS 2023R2 software package for computational fluid dynamics (CFD) simulations. The study employs the Volume of Fluid (VoF) model to simulate phase transitions between liquid and vapor, specifically focusing on vaporization processes. It is important to note that the VoF model used in this study primarily serves to visualize vaporization, with its numerical results aligning with theoretical expectations rather than providing practical applications. The relevance of this research is underscored by the global drinking water crisis, which drives the need to enhance the efficiency of desalination systems. Solar distillation is recognized as one of the most environmentally sustainable methods for producing clean water, making it an appropriate focus for this investigation. The primary objective of this work is to conduct a numerical analysis of the solar still, compare the performance of two different configurations, and evaluate potential modifications to improve the system's efficiency. The study simulates heat transfer processes within the distiller, the distribution of vapor volume fractions, and temperature variations over time. The findings indicate that the dual-slope configuration outperforms the single-slope configuration in terms of efficiency and productivity. Additionally, the research provides insights into the physical processes occurring within the distiller and identifies potential areas for further refinement of the system's modeling in ANSYS.

The films of titanium nitride were deposited by direct current magnetron sputtering on the surface of singlecrystalline silicon samples in an Ar-N₂  atmosphere for use as a diffusion barrier. The thickness and density of films were measured by X-ray reflectometry. The design of the MAGNA TM-200-01 installation has been changed to increase the supply of nitrogen into the chamber. The influences of sputtering conditions, including the flow rate of nitrogen and argon gases and their N₂ /Ar ratios in the range of 1–60 in the chamber, magnetron power of 690–1400 W on the formation of TiN_x  films, their density and stoichiometric composition, were studied. It is shown that the value of x is affected not only by the N₂ /Ar gas flow rate ratio, but also by the magnetron power. At the sputtering parameters 1200 W, N₂ /Ar = 30, 0.8 Pa, 320 s and 100°C, a maximum density of 5.247 g/cm³  of a film was achieved, which corresponds to the composition TiN_0.786 = Ti₅₆N₄₄. The presence of nanocrystalline film of titanium nitride and the absence of a nanocrystalline titanium phase were confirmed by photographic X-ray diffraction. It was found that for the synthesis of titanium nitride as close as possible to the stoichiometric composition TiN_0.770 - TiN_0.786, it is necessary to use magnetron power in the range of 900–1200 W, nitrogen rate of 30 cm³ /min with low argon flows of 1–5 cm³ /min.

This article deals with the solution of scientific problems, including the development of holistic conceptual models for presenting, processing and analyzing geographic information, and comprehensively assesses the extent of the impact of regional systems on many dangerous natural and artificial processes. This makes it possible to resolve the contradiction between the need to analyze the state of various territorial systems in order to prevent the occurrence of emergencies due to the influence of natural and man-made processes.On the one hand, providing information and support in decision-making, and on the other, ensuring the provision of cartographic models of transport systems risk assessment of dangerous natural and man-made processes and their mapping. Integrated environmental assessment and modeling of industrial systems. A method for mapping payment systems and assessing the health of the population. Since the city of Almaty is a large metropolis, the ecological situation is very poor. This directly affects the ecology of the City, Public Health. We manage, process data by processing GIS data. In solving upcoming environmental problems, in developing new solutions, a special data card will come to the rescue. This will lead to solving problems from this point of view. It is possible to create monitoring maps by combining space surveys and statistical data. One of the most effective aspects of GIS data is that it allows you to optimally consider problems and work in any area. From this point of view, the GIS industry is developing rapidly. In the country, this industry is developing rapidly and is gaining a lot of demand.

The purpose of the article is to solve the problems of drawing up a map, its presentation, processing, analysis in order to comprehensively assess the regional system of natural and artificial threats of the Almaty region. By solving these problems, you can consider ways to easily resolve the upcoming conflicts and problems. It can be prevented by making predictions about natural and man-made problems. The root of natural and man-made catastrophic conditions of Almaty region are natural disasters characteristic of mountainous regions. We will take into account the conditions of seasonal mudslides and earthquakes. Since Almaty region is a region located in a mountainous zone, there are a lot of earthquake foci. Tectonic movements often occur. In order to prevent major natural disasters, it is necessary to carry out special monitoring work. From this point of view, the compilation of Geodynamic maps, the study of the Earth’s crust, the identification and differentiation of earthquake foci come to the rescue. With this, major disasters can be prevented. The article compiled a map of natural and man-made conditions of the Almaty region. In the process of creating a map, using GIS data, we created a map in the ArcGIS application. We received data from the GIS through a special platform, processing space surveys using a program. We received the data on the basis of SRTM space survey. By studying the dynamics of changes for each year, by comparing maps, you can make conclusive forecasts. Collecting data through free platforms helps to create rational solutions. It is possible to monitor, update,store,and edit the database of information using maps. In the article, a complete study of the region’s region was carried out, the importance was given to the rational, effective use of GIS data.

The article provides a detailed overview of the history, development and applications of frequency converters, with particular emphasis on their use in the oil and gas industry. It traces the evolution of frequency converters from their inception in the late 19th and early 20th centuries to their modern applications, which use microprocessors and digital signal processing to precisely control the output frequency. In the oil and gas industry, frequency converters are critical to efficient and accurate induction heating of pipelines. They convert a fixed frequency and voltage power supply into a variable frequency and variable voltage output, controlling the speed of the induction motors used in the heating process. The article also covers the design and modeling of frequency converters, discussing the process of characterizing them, creating mathematical models, and using modeling software tools such as MatLab. It presents equations for inductive energy, capacitor energy, resonance conditions and power factor, which are necessary in the mathematical modeling of frequency converters. The article concludes by highlighting the impact of frequency converters on the efficiency and economics of induction heating systems. It emphasizes the need for careful design and modeling to ensure optimal performance and safety.

In Kazakhstan, options and regions for hydrogen production are being considered, including in Western Kazakhstan, the Mangystau region, using the water of the Caspian Lake. The current advanced hydrogen production technologies, including green hydrogen, are very expensive, requiring billions of dollars of investment and potentially creating difficulties for the local population and nature, ecology, flora and fauna. At the same time, geological studies of alternative hydrogen production options in Kazakhstan are poorly developed. Kazakhstan needs and is currently very important to study serpentinization of ophiolite rocks using natural «white» hydrogen. In many countries, there is practically a «gold rush» now, hydrogeologists are conducting research on the search for natural «white» hydrogen. Hydrogeologists have now discovered numerous sources of natural hydrogen in geological environments from Mali to Switzerland, France, Germany, the USA, Canada, and Australia. More than 50 companies have sprung up to discover and mine it. A natural hydrogen well operates in Mali to provide energy to a local city with a population of about 4,000 people for free and forever. Unlike other geological fuels, which are formed over millions of years, natural hydrogen is generated by mixing water and iron particles, serpentinization of ophiolite rocks, and is renewable on the scale of human time, and not in geological epochs. Alternative options for the production and use of hydrogen are presented in this review, including various types of hydrogen, black, gray, green, blue, turquoise, pink and yellow, depending on the method of its extraction and production. Kazakhstan, with the richest natural ore reserves, including coal and polymetals, where ophiolite rocks may be located, may well have deposits of natural «white» hydrogen, such geological studies in Kazakhstan are poorly developed.

Water flooding is a commonly used development method in oilfields, and the water cut of the output fluid of oil wells gradually increases with the increase of injected water in water wells. Due to the presence of natural surfactants such as gum, asphaltene and organic acid in crude oil, it is easy to form a high strength viscoelastic oilwater interfacial film and a stable emulsion. To facilitate the storage and transportation of crude oil, it is necessary to demulsify the crude oil emulsion. In this paper, the CQ crude oil was taken as the research object, and the effects of demulsifier type, concentration, demulsifier temperature and oil-water properties on the viscoelasticity of oil-water interfacial film were investigated by using rheometer and then the demulsification mechanism was explored. The results show that the lower the interfacial film strength, the better the demulsifier. In addition, with the increase of demulsifier concentration and temperature, the oil-water interfacial viscoelasticity decreases and the interfacial film strength further decreases. In general, the demulsification effect will be significantly improved with the increase of demulsifier concentration and temperature, and the dehydration effect will be better. The research results can provide theoretical guidance for the field application of demulsification.

In today’s environment, managing employees’ career development is becoming a key factor for the success and sustainable development of organizations, especially small and medium-sized enterprises (SMEs). The purpose of this article is to investigate current approaches to career management in SMEs and provide practical recommendations based on theoretical research and empirical evidence. The novelty of the study lies in the integrated approach to analyzing career management practices using both quantitative and qualitative methods. This allows for a deeper understanding of the impact of career development on employee motivation and retention in a resource-constrained and highly competitive environment. The research methodology includes reviewing existing literature and analyzing case studies from different industries to identify best practices and success stories. The use of mixed research methods, such as surveys and in-depth interviews, provides a more comprehensive view of current practices and their impact on employees. The results of the study show that career development management is critical to improve employee motivation and retention, which is particularly relevant for SMEs. Pay attention to aspects such as training and development, performance appraisal systems, reward systems and their impact on employee motivation. Training and development help to improve employees’ skills and adaptability to changes in the market, while appraisal systems such as 360-degree feedback and regular evaluations help to identify achievements and areas for improvement, forming individual development plans. A fair and competitive compensation system motivates employees and promotes employee retention. The author’s personal contribution is the design and implementation of an integrated research methodology that combines quantitative and qualitative methods, which provided comprehensive data and offered practical recommendations for SMEs. The article also discusses the factors that influence employee retention and the creation of a positive corporate culture, which contributes to their long-term professional growth and enhances companies’ competitiveness in the market. In an environment of rapid change and globalization, SMEs must pay special attention to human capital development to maintain competitiveness and achieve sustainable growth. The article provides recommendations for the implementation of effective career management programs and examples of successful practices from different sectors.

Effective management of financial resources in projects is crucial for project success. Often, difficulties with financial resources, such as budget overruns, lead to unfavorable consequences that directly impact the successful completion of the project, the quality of the outcome, and stakeholder satisfaction. Therefore, identifying and developing tools for the effective management of financial resources, is of paramount importance. The purpose of this study is to apply non-traditional earned value management (EVM) models based on machine learning to predict project costs. To achieve the research objectives, previous literature on the topic was analyzed, a dataset of past projects was prepared, and a machine learning model was applied. The study found that non-traditional models, such as the regression algorithm AdaBoost, produced results close to the actual costs. The research indicates that the developed model could become an indispensable tool for project management and business decision-making, as it demonstrates the ability to adapt to various conditions and make accurate forecasts.

The purpose of this article is to determine the contribution of digital marketing technologies to improving the quality of high-tech products in Kazakhstan, as well as to identify prospects for improving quality management practices in high-tech projects in Kazakhstan using a digital approach to marketing. Based on international statistics from IMD and WIPO for 2013–2022, using the correlation analysis method, the results of implementing high-tech projects in quality management using alternative approaches (digital and pre-digital) to marketing in Kazakhstan are compared. The key conclusion is that in Kazakhstan, quality management in high-tech projects using digital marketing technologies is preferable. The key ones are: personnel marketing, product marketing and process marketing. The theoretical significance of the obtained results is that they allowed us to rethink quality management processes in the implementation of high-tech projects in the digital economy of Kazakhstan, and also revealed promising digital marketing technologies based on AI, IoT, Big Data, the Internet, mobile devices, chatbots, machine vision, smart consultants and virtual assistants (VR / AR). The practical significance is due to the fact that the developed digital approach to marketing allows us to improve the practice of quality management in high-tech projects in Kazakhstan. The author's approach includes a marketing mix of quality management in high-tech projects using digital marketing technologies in Kazakhstan. The approach also reveals the organizational and technological aspects of quality management in high-tech projects in Kazakhstan with a digital approach to marketing. 

To analyze the demand shock effect, we concentrated on the 2008 Financial Crisis, relying on the data from the European Bank for Reconstruction and Development’s “Life in Transition Survey” conducted in 2010. This survey offers detailed information on how households reacted two years after the crisis. Regression models were developed to analyze the measures that households took during the economic decline and their implications for consumption. Such measures entailed alterations in spending patterns, saving practices, and other mechanisms of survival. The empirical investigation of the paper gives an understanding of the effects of demand shock such as the 2008 Financial Crisis on households’ consumption behavior and their ability to cope with the shocks. The results show that Financial Crisis affect negatively the labor market, which had a negative impact on consumption. Moreover, we explored how government tried to help households, what they used, etc.