Atyrau State University named of Kh.Dosmukhamedova

Nonlinear single-input single-output (SISO) systems operating under parametric uncertainty often exhibit bifurcations, multistability, and deterministic chaos, which significantly limit the effectiveness of classical linear, adaptive, and switching control methods. This paper proposes a novel synthesis framework for self-organizing control systems based on catastrophe theory, specifically within the class of elliptic catastrophes. Unlike conventional approaches that stabilize a predefined system structure, the proposed method embeds the control law directly into a structurally stable catastrophe model, enabling autonomous bifurcation-driven transitions between stable equilibria. The synthesis procedure is formulated using a Lyapunov vector-function gradient–velocity method, which guarantees aperiodic robust stability under parametric uncertainty. The definiteness of the Lyapunov functions is established using Morse’s lemma, providing a rigorous stability foundation. To support practical implementation, a data-driven parameter tuning mechanism based on self-organizing maps (SOM) is integrated, allowing adaptive adjustment of controller coefficients while preserving Lyapunov stability conditions. Simulation results demonstrate suppression of chaotic regimes, smooth bifurcation-induced transitions between stable operating modes, and improved transient performance compared to benchmark adaptive control schemes. The proposed framework provides a structurally robust alternative for controlling nonlinear systems in uncertain and dynamically changing environments.

Экономика мен әлеуметтік құрылымның серпінді өзгеруінің қазіргі жағдайында озық кадрлармен қамтамасыз етудің маңыздылығы барған сайын айқын бола түсуде. Адами капиталды дамытуға бағытталған стратегиялар мен құралдарды кешенді зерттеу жекелеген аймақтардың бәсекеге қабілеттілігін арттырып қана қоймай, тұтастай алғанда елдің тұрақты әлеуметтік-экономикалық дамуына ықпал етеді. Осы мақаланың мақсаты Атырау облысындағы кадрларды даярлау нарығының ағымдағы жай-күйін және адами капиталдың даму динамикасын талдау болып табылды, бұл қазіргі проблемалар мен мүмкіндіктерді анықтауға мүмкіндік берді. Зерттеуде форсайт әдісі қолданылды. Rapid Foresight әдісі жылдам болжам жасауға және икемді стратегияларды қалыптастыруға арналған. Ол тез бейімделу және өзгеретін жағдайларда қолданылды. Бұл әдіс сарапшылардың сауалнамасына негізделген және болжам бойынша консенсусқа қол жеткізеді. Белгісіздік деңгейі жоғары стратегиялық мәселелер үшін тиімді. Озық кадрлармен қамтамасыз ету және өңірлік даму құралдарын кешенді зерттеу кадрларды даярлаудың тиімді жүйесін құру өзгермелі жағдайларға жауап ретінде жүйелі тәсілді, барлық мүдделі тараптардың өзара іс-қимылын және икемділікті талап ететінін көрсетті. Rapid Ғoresight әдістемесін пайдалана отырып, форсайт-сессияларды өткізу сарапшылар мен мүдделі тараптардың пікірлерін жинауға, трендтер мен ықтимал тәуекелдерді анықтауға, сондай-ақ өңірдегі жағдайды жақсарту үшін ұсынымдар әзірлеуге мүмкіндік берді. In today's dynamic changing economy and social structure, the importance of providing advanced personnel is becoming more and more obvious. A comprehensive study of strategies and tools aimed at developing human capital not only increases the competitiveness of individual regions, but also contributes to the sustainable socio-economic development of the country as a whole. The purpose of this article was to analyze the current state of the personnel training market and the dynamics of human capital development in Atyrau region, which made it possible to identify existing problems and opportunities. The foresight method was used in the study. The Rapid Foresight method is designed for rapid forecasting and the development of flexible strategies. It was used in conditions of rapid adaptation and change. This method is based on a survey of experts and allows you to reach a consensus on forecasts. It is effective for strategic issues with a high level of uncertainty. A comprehensive study of advanced human resources and tools for regional development has shown that the creation of an effective personnel training system requires a systematic approach, interaction and flexibility of all stakeholders in response to changing conditions. Conducting foresight sessions using the Rapid foresight methodology allowed us to gather the opinions of experts and stakeholders, identify trends and possible risks, and develop recommendations for improving the situation in the region.

The object of the study is the adaptive machine learning systems that are able to process large amounts of rapidly changing streaming data in real time. The problem of maintaining prediction accuracy and computational efficiency in the presence of concept drift is treated. Concept drift refers to the overweighting of static models when stationary models are tried, and the nature of the underlying distributions changes. The adaptive architecture includes revision divergence-oriented concept drift detection, incremental model updating via hyper-dimensional statistical clustering of segments. Results from experiments using simulated and real-world datasets demonstrate that the adaptive architecture maintains predictive accuracy above 0.83 across abrupt, gradual, recurrent, and continuous drift scenarios. Compared with non-adaptive models, adaptation latency is reduced by approximately 2.6×, while unnecessary retraining operations are decreased by up to 40%. These results are possible due to the fact that proposed framework is able to retrain solutions if, and only if, distributional changes are determined to be statistically significant and meaningful to the model. This leads to the avoidance of processors being given redundant computations and providing a steady-state model during non-drift conditions. A principal contribution is that feature engineering is accomplished in a drift-aware manner, thresholding is made adaptive to the distributions indicated, and update mechanisms are employed which efficiently utilize resources in a unified high-performance streaming pipeline. The architecture performs well under abrupt, gradual, recurrent, and continuous drift and effective for real-time applications which include smart-city analytics, cyber security monitoring, roadways system works, and IoT for industrial systems

Abstract Precise forecasting of oil production rates is essential for effective reservoir management and economic decision-making in the petroleum industry. Traditional techniques such as Decline Curve Analysis and Numerical Reservoir Simulation are limited in capturing the complexity of unconventional reservoirs. This study presents a framework that applies two machine learning models: Random Forest (RF) and Long Short-Term Memory (LSTM). The models were tested on production data from a large oil field in Kazakhstan for the period 2003 to 2020. The RF model identified Water Cut as the main factor, while the LSTM model captured time-dependent patterns in the data. The R2 and MSE results for the training set were 0.98 and 281 for RF, 0.93 and 807 for LSTM, and 0.95 and 512 for the Hybrid model. When it comes to the testing dataset, the results for error metrics were 0.83 and 2769 for RF, 0.94 and 899 for LSTM, and 0.88 and 53 for the Hybrid model. These results were further supported by the model's selection criteria: the Hybrid was characterized by the minimum AIC and BIC, which demonstrates its better balance between forecasting accuracy and model parsimony. To the best of our knowledge, this is among the first attempts to utilize such a hybrid model for oil production predictions in Kazakhstan. The findings demonstrate the potential of this tool as a valuable instrument in reservoir management and production planning.

The following paper analyzes relationships between CO2 emissions, economic growth, total energy consumption, and employment across five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) over the period between 1992 and 2022, and uses panel econometric methods to do so. Annual World Bank data has been log-transformed to improve statistical properties and interpret coefficients as elasticities. The empirical strategy includes panel unit root tests (LLC, IPS, ADF-Fisher, PP-Fisher), Pedroni cointegration test, Pooled Mean Group-Autoregressive Distributed Lag (PMG-ARDL) estimation, Dumitrescu-Hurlin panel Granger causality tests, and Forecast Error Variance Decomposition (FEVD). The results have shown the variables to be first-order integrated and have a long-run equilibrium relationship. The short-term analysis has revealed an economic growth’s unidirectional effect on CO2 emissions, indicating a direct deterioration of the environmental situation against economic activity expansion. The long-term analysis has shown how economic growth, energy use, and employment jointly determine emission dynamics while adjustments occur based on the emissions indicator. The variance decomposition has revealed GDP and energy use to be the key long-term determinants and insignificance of employment’s effect. Recommendations include reducing energy intensity, developing low-carbon energy, diversifying the economy, creating green jobs, and strengthening regional cooperation.

The article draws attention to the fact that the interpretation of a civil contract in the courts of the Republic of Kazakhstan using the principle of "contra proferentem" is not regulated or is not considered by the current legal norms. There is an insufficient number of special studies in the domestic legal literature that take into account current trends in the development of the rules of contract interpretation, the principle of "contra proferentem" in the legal systems of countries with developed economies.The purpose of the article is a comprehensive analysis of the "contra proferentem" principle at the Institute for the interpretation of civil law contractual claims and to propose recommendations for improving its effectiveness. The article examines the legal basis for the application of this principle in the national and international legal order, its role in protecting the interests of the weak party to the contract, as well as the experience of application in court proceedings.The authors analyze the theoretical foundations of this principle, its development in the context of jurisprudence of various legal systems, as well as judicial practice reflecting its practical implementation. Particular attention is paid to the application of the Contra proferentem principle in relations between entities with unequal conditions, in particular, when concluding accession agreements, consumption agreements and insurance contracts. The conclusion is made about the importance of the principle as a mechanism for protecting weaknesses and ensuring fairness in contractual relations. The authors emphasize the importance of contra proferentem as a means of ensuring fairness and honesty in contractual obligations, and also offer recommendations for clearer formulation of contract terms to reduce the risks of legal disputes.

This article presents a comparative analysis of the capabilities of the Creo Parametric graphics system in comparison with other popular software products. Special attention is paid to the specifics of working with assemblies in the full-featured NX CAD system, as well as the process of creating a three-dimensional model of a part in SolidWorks. The application areas of graphics systems such as NX and CATIA are also considered. The principle of building a 3D model in the T-FLEX CAD system is investigated. In addition, the possibilities, approaches, and principles of operation of applications of the Creo Parametric graphics system are analyzed. To improve the performance and quality of work in graphics systems, it is recommended to use each of them in those areas of industry where they demonstrate clear advantages. In the engineering industry, the use of the Creo Parametric CAD system is especially advantageous, as it allows you to combine two approaches — parametric and direct modeling. Furthermore, it is convenient to work with small applications and supports the import of formats from computer-aided design (CAD) systems from other manufacturers.

This study analyzes the effect of artificial aging on the mechanical deformational and optical properties of various paper samples, which allows us to evaluate their durability and suitability for long-term storage. The methods of accelerated aging, measuring the breaking length, specific resistance, elongation, and fracture strength, were used, and the optical characteristics were estimated by the R457 and CIE whiteness indices, as well as opacity. Mechanical measurements (breaking length, specific resistance, elongation, and fracture strength) revealed that bleaching reduces residual lignin and strengthens interfiber bonds, boosting pine pulp strength by up to 8%. Optical properties initially improve slightly, then increase sharply after the second bleaching cycle and stabilize, while opacity decreases, providing greater light transmittance. After accelerated aging, the following deterioration is observed: for bleached samples, R457 whiteness changes; and for unbleached samples, CIE whiteness and opacity increase. After aging, aspen pulps and kraft papers retained over 90% of their initial strength and whiteness, whereas untreated and office papers lost up to 20–25%. These findings identify that aspen-based and kraft papers demonstrate better mechanical deformational and optical properties, which makes it possible to predict the operational characteristics of paper depending on the processing and aging methods used.

Abstract The purpose of the research work is to determine impact of the economic growth, digitization indicators and birth rate on female labor force in the Kazakhstan. For this purpose, Autoregressive Distributed Lag (ARDL) method was used. Data were obtained from the WDI website for period 1994–2022. Individuals using internet, Mobile cellular subscriptions (MCS), ICT service exports were taken as indicators of digitalization, and GDP growth was taken as indicator of economic growth. As a result of ARDL model, Internet users are negatively linked to the female labor force both in the short and in the long term. Mobile cellular subscriptions have a positive influence on the female labor force only in the short term, and however, showed its insignificance in the long term. ICT service export and GDP growth has a positive effect on the female labor force both in the short term and in the long term. Fertility rate has a positive effect on female labor force in the short and long term. Research work includes review of world literature and, based on this, an econometric model for Kazakhstan was created, and further presents proposals on how women’s employment can increase economic growth in Kazakhstan, also a comprehensive literature review and updated econometric model were developed. This research work carried can be used by other researchers for their regions of interest, and further recommendations can be considered in their case study.

The research investigates the thermal behavior of mixed systems based on natural and artificial cellulose fibers used as precursors for carbon nonwoven materials. Flax and hemp fibers were employed as natural components; they were first chemically treated to remove impurities and enriched with alpha-cellulose. The structure, chemical composition, and mechanical properties of both natural and viscose fibers were studied. It was shown that fiber properties depend on the fiber production process history; natural fibers are characterized by a high content of impurities and exhibit high strength characteristics, whereas viscose fibers have greater deformation properties. The thermal behavior of blended compositions was investigated using TGA and DSC methods across a wide range of component ratios. Carbon yield values at 1000 °C were found to be lower for blended systems containing 10-40% by weight of bast fibers, with carbon yield increasing as the quantity of natural fibers increased. Thus, the composition of the cellulose composite affects carbon yield and thermal processes in the system. Using the Kissinger method, data were obtained on the value of the activation energy of thermal decomposition for various cellulose and composite systems. It was found that natural fiber systems have three-times higher activation energy than viscose fiber systems, indicating their greater thermal stability. Blends of natural and artificial fibers combine the benefits of both precursors, enabling the deliberate regulation of thermal behavior and carbon material yield. This approach opens up prospects for the creation of functional carbon materials used in various high-tech areas, including thermal insulation.