The development and spread of digital and intelligent (IoT) technologies, their radical penetration into all sectors of the economy and society is gaining momentum, changing traditional business models, traditional industry structures and schemes of world markets. The scale of the changes is so great that it is safe to say that this is another technological revolution in which digitalization is almost synonymous with competitiveness. Despite the fact that the idea of the "Internet of Things" appeared about 20 years ago, the active development of technologies began only in recent years. The relevance of this direction is constantly growing. A smart environment is impossible without the interaction of its objects, and it is the concept of the Internet of Things (IoT) that takes this interaction to a new level. The Internet of Things allows you to quickly obtain factual information, quickly analyze it, and, accordingly, make informed decisions and measures. The paper explains the terms "IoT technologies in the energy sector", which define the unique content of the processes behind them. A conceptual vision of the development of the electric power industry based on the principles of IoT technologies and a development scenario are presented. The Internet of Things (IoT) is beginning to define the future of many industries and emerging markets. One of the IoT target markets is energy systems. IoT is the production, transmission and processing of information, so everything that is part of the system, including software and hardware, should be considered as a whole. This article presents the current state of IoT-based energy systems to review the latest activities for each IoT component in energy systems. Problems in this subject area are discussed, and then some solutions are presented.

The city of Turkestan, Kazakhstan is experiencing growth leading to an increased need for electricity. In order to meet this demand the city is upgrading its infrastructure specifically focusing on improving its 35/10 kV substations. Engineers are utilizing calculation software like RastrWin3 to design and analyze these substations. This software offers capabilities, for modeling substations. Using RastrWin3 the ability to import data from sources like drawings AutoCad, GIS maps and other relevant resources. This imported data serves as the foundation for constructing the substation model. Engineers can easily incorporate components such as transformers, feeders, circuit breakers and busbars into the model. Each element of the model can be assigned parameters like voltage, current, resistance and power to represent real world conditions. Additionally, load profiles can be generated for analysis purposes to capture fluctuations, in energy demands throughout the day and year. Numerical calculation software plays a role, in the design and analysis of substations. It provides engineers with a toolset to achieve the following objectives: 1. Construct models of substations. 2. Simulate the behavior of substations under operational conditions. 3. Resolve issues that may arise in electrical substations. 4. Enhance the design and optimization of substations. One notable software in this domain is RastrWin3 which offers capabilities for calculations and simulations related to electric substations. Engineers can utilize this program to evaluate power systems, in emergency and transient modes. Accounting for various factors such as non-linearity, power and reactive power losses, as well, as the influence of capacitive coupling. Various types of loads such, as consumer loads, substation auxiliary loads and loads from protection and automation devices are considered in the modeling process. The software RastrWin3 is utilized to design and analyze 35/10 kV substations, in Turkestan. This software assists in enhancing the precision of substation design reducing the time needed for designing and developing substations improving substation efficiency and lowering maintenance costs.

The theme “Energy of the Future,” presented by Kazakhstan at EXPO-2107, is one of the most relevant and globally significant for modernity, concerning the whole world—the sustainable use of energy. The issues of developing renewable non-traditional energy sources have largely been considered in scientific and technical programs of different countries. Particularly, such countries as the USA, Japan, Israel, and Germany work on these energy sources at the level of long-term national programs. As for the possibility of using solar energy in Kazakhstan, it is a country in Central Asia with great potential for solar energy. Solar energy resources in the country remain stable and suitable due to favorable climatic conditions. The research develops a method of logical planning of the database model of solar systems of Southern Kazakhstan based on a computer program. A logical model based on the system model obtained with the operation of the relational data model consists of relational model relationships. Table “Columns” represents the information necessary. A database computer program will make it possible to develop a solar power plant, which is planned to be built in the future.

В статье исследован фракционный состав железной руды и дана оценка ее гравитационной обогатимости на начальной стадии технологиче- ских исследований. Анализ распределения железа по классам крупности и плотностным фракциям позволил предварительно определить степень раскрытия минералов и перспективность гравитационных методов. Рентгенофазовый анализ установил, что основной ценный компонент представлен слабомагнитным гематитом. Ситовый анализ показал равномерное распределение железа по крупности, при этом до 94% железа сосредоточено в классе -50+2,5 мм. Обработка кривых гравитационной обогатимости выявила легкое выделение тяжелых фракций с содержанием Fe свыше 55% при плотности разделения 3400–3500 кг/м³. Рекомендуется применение отсадки для промышленного обогащения. Работа имеет практическое значение при проектировании схем переработки железных руд Казахстана.

В статье рассматриваются результаты исследования процесса бактериального выщелачивания медно-молибденовой руды сульфидного типа, добываемой на Актогайском месторождении. Целью работы является разработка эффективной технологии биовыщелачивания, способствующей повышению извлечения меди и молибдена с минимальными затратами и экологическим воздействием. В практической части проведены лабораторные опыты с применением штаммов Acidithiobacillus ferrooxidans и Acidithiobacillus thiooxidans. Получены данные, подтверждающие целесообразность применения данной технологии для переработки низкосортных руд.

We develop a complex technology of separate processing of fine dust including the procedure of preliminary firing of dust with removal of arsenic from the technological scheme with subsequent application of a hydrometallurgical scheme for the extraction of copper and rhenium: sulfuric-acid leaching of cinder with cementation separation of copper with iron, and extraction of rhenium from the solution. Complex analytical studies of the intermediate and final products were carried out by the methods of atomic-emission spectrometry with inductively coupled plasma and X-ray phase analysis. The compositions of liquid solutions were determined by the method of chemical analysis. The rhenium contents in the original solutions and extraction products were determined by the methods of colorimetric and chemical analyses. This guaranteed the possibility of step-by-step quality control of the obtained products in the course of realization of the technological cycle of processing. As a result of laboratory investigations, we established the following optimal parameters of copper cementation: a temperature of 333°K (60 °C), S:L = 1.5, and a duration of the process equal to 60 min for which we obtained powdered cement copper with the following composition (wt.%): 82.0 Cu, 09.27 Pb, 1.49 Zn, 0.19 As, 0.72 Fe, 0.03 Re, 3.43 O2; balance 11.89. The extraction of copper into a commercial product was as large as ~98%. We obtain new data on the extraction of rhenium from sulfuric-acid solutions of the process of cementation by using an extractant containing (wt. %): 10 trialkylamine (TAA), 80 kerosene, and 10 di-2-ethylhexanol. On the basis of the indicated components, it is possible to choose the composition of the extractant for the processes of extraction of dusts of different types and compositions. We obtained commercial ammonium perrhenate with a rhenium content of 69.17% corresponding to the AR‑0 grade. The end-to-end extraction of rhenium into ammonium perrhenate is as high as 93%.

first_pagesettingsOrder Article Reprints Open AccessArticle An Adaptive Task Difficulty Model for Personalized Reading Comprehension in AI-Based Learning Systems by Aray M. Kassenkhan 1,*ORCID,Mateus Mendes 2,3,*ORCID andAkbayan Bekarystankyzy 4,* 1 Department of Software Engineering, Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan 2 Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal 3 RCM2+, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal 4 School of Digital Technologies, Narxoz University, Almaty 050035, Kazakhstan * Authors to whom correspondence should be addressed. Algorithms 2026, 19(2), 100; https://doi.org/10.3390/a19020100 Submission received: 24 December 2025 / Revised: 7 January 2026 / Accepted: 21 January 2026 / Published: 27 January 2026 (This article belongs to the Section Algorithms for Multidisciplinary Applications) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract This article proposes an interpretable adaptive control model for dynamically regulating task difficulty in Artificial intelligence (AI)-augmented reading-comprehension learning systems. The model adjusts, on the fly, the level of task complexity associated with reading comprehension and post-text analytical tasks based on learner performance, with the objective of maintaining an optimal difficulty level. Grounded in adaptive control theory and learning theory, the proposed algorithm updates task difficulty according to the deviation between observed learner performance and a predefined target mastery rate, modulated by an adaptivity coefficient. A simulation study involving heterogeneous learner profiles demonstrates stable convergence behavior and a strong positive correlation between task difficulty and learning performance (r = 0.78). The results indicate that the model achieves a balanced trade-off between learner engagement and cognitive load while maintaining low computational complexity, making it suitable for real-time integration into intelligent learning environments. The proposed approach contributes to AI-supported education by offering a transparent, control-theoretic alternative to heuristic difficulty adjustment mechanisms commonly used in e-learning systems.

The article reports on a bilingual and interpretable book recommendation platform for schoolchildren. This platform uses a lightweight K-Nearest Neighbors algorithm combined with gamification and learning analytics. This application has been designed for a bilingual learning environment in Kazakhstan, supporting learning in Kazakh and Russian languages, and is intended to improve reading engagement through culturally adjusted personalization. The recommendation engine combines content and collaborative filtering in that it leverages structured book data (genres, target age ranges, authors, languages, and semantics) and learner attributes (language of instruction, preferences, and learner history). A hybrid ranking function combines the similarity to the user and the item similarity to produce top-N recommendations, whereas gamification elements (points, achievements, and reading challenges) are used to foster sustained activity.Teacher dashboards show learners’ overall reading activity and progress through real-time data visualization. The initial calibration of the model was carried out using an open-source book collection consisting of 5197 items. Thereafter, the model was modified for a curated bilingual collection of 600 books intended for use in educational institutions in the Kazakh and Russian languages. The validation experiment was carried out on a pilot test involving 156 children. The experimental outcome suggests a stable level of recommendation in terms of the Precision@10 and Recall@10 values of 0.71 and 0.63 respectively. The computational complexity remained low. Moreover, the bilingual normalization technique increased the relevance of recommendations of non-majority language items by 12.4%. In conclusion, the proposed approach presents a scalable and transparent framework for AI-assisted reading personalization in bilingual e-learning systems. Future research will focus on transparent recommendation interfaces and more adaptive learner modeling.

Международный проект в сфере профессионального технического образования (TVET), реализуемый совместно с организацией ASSIST INTERNATIONAL (США) и Purdue University. Проект направлен на развитие системы профессионального образования и обучения в Казахстане. Руководитель от университета — Муканов Р.Б., Институт экономики и менеджмента.

With the continuous rise in the popularity of mobile devices and the growing volume of data related to education, the protection of users’ personal information is becoming increasingly important. This study presents a behavioral authentication model that can be effectively utilized to enhance security when accessing intelligent educational resources, based on the processing of gyroscope and accelerometer data. The paper reviews existing authentication methods, highlights the advantages of behavioral authentication, and proposes a new model. The behavioral authentication model analyzes users’ behavioral patterns to identify and protect user accounts. This approach is particularly crucial in the context of intelligent educational resources, where users frequently interact with various types of content that require the safeguarding of personal data and the preservation of the educational process’s integrity.