With interest in camel milk growing due to its nutrients and biologically active compounds, research into methods of processing and drying it is crucial. In recent decades, extensive studies have explored its chemical composition and health benefits with a focus on drying techniques and their effects on its properties. This review systematically summarizes the available literature on camel milk drying processes and their effects on its chemical composition with a view to shortening the drying time. To achieve this goal, we meticulously reviewed numerous studies published between 2014 and 2024 to identify optimal drying methods that maximize the preservation of camel milk’s nutrient components and bioactive compounds. Our analysis revealed significant findings: freeze drying preserves nutrients better than spray drying, but is less efficient. Spray drying, while faster, tends to compromise some nutritional values.

Modern energy is at the crossroads of cutting-edge technologies that are revolutionizing the way power systems are controlled, managed and optimized. Leading the way in this process are IoT (Internet of Things), FPGAs (programmable gate arrays), and microcontrollers, including powerful devices such as ESP32. These technologies not only significantly improve the efficiency and reliability of energy systems, but also open up new prospects for creating sustainable and intelligent energy infrastructures. In the Republic of Kazakhstan, energy systems are actively monitored and optimized in order to ensure stable development and meet the growing energy needs of society. The use of IoT technologies allows you to quickly collect data on the operation of energy networks, analyze electricity consumption and predict changes based on information from sensors installed in various network nodes. The use of an FPGA provides high-speed processing of large amounts of data, which is necessary for real-time monitoring and control in conditions of rapidly changing load and dynamic energy processes.

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.

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.

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.

Gamification and artificial intelligence (AI) are transforming modern education by increasing student motivation, supporting personalized learning, and fostering the development of critical thinking. In the context of rapid digitalization, analytical skills and the ability to evaluate information have become essential competencies across all educational levels. This article presents a bibliometric analysis of 101 academic publications that explore the integration of game-based learning and AI technologies into educational environments. The study identifies dominant research themes, methodological trends, and the distribution of publications in educational sectors, including schools, universities, and corporate training. It also examines how these technologies are being used to improve cognitive engagement and support the development of independent decision-making. The results highlight the growing global interest in applying gamification and AI to improve learning outcomes. This work provides evidence-based information for the development of adaptive educational platforms and instructional strategies aimed at cultivating critical thinking and enhancing student engagement. The findings are particularly relevant for educators, policymakers, and developers working at the intersection of education and technology.

Поиск экономически эффективных методов переработки золотосодержащих руд неразрывно связан с рядом предварительных исследований, включая анализ состава руды, оценку форм залегания ценных компонентов и оценку влияния примесей и физических характеристик руды на извлечение золота. Флотация остается краеугольной технологией в золотодобывающей промышленности, необходимой для обогащения золотосодержащих руд. Ключевым моментом этого процесса является выбор флотационных реагентов, обеспечивающих эффективное отделение золота от пустой породы и сопутствующих минералов. Данное исследование посвящено флотации ранее неисследованной низкосульфидной золотосодержащей руды Актубского месторождения в Республике Казахстан. Минералогический и рентгенофазовый анализы выявили кварц, калиевый полевой шпат, кальцит, слюду, лимонит и сульфиды в качестве основных минералов руды. Фазовый анализ дополнительно показал, что 46,47 % золота находится в виде свободного цианируемого золота в открытых срастаниях, 37,61 % заключено в сульфидах, а 15,92 % мелкодисперсно распределено в пустой породе. Для оптимизации процесса флотации было систематически исследовано влияние критических параметров, таких как размер частиц, дозировка реагентов, время флотации и тип флотационной машины, на извлечение золота. Были проведены испытания замкнутого цикла с одной и двумя стадиями очистной флотации с использованием оптимизированного режима реагентов. Результаты показали, что одна стадия очистной флотации позволила достичь более высокого извлечения золота на 1,14 %, составив 89,84 %. Концентрат, полученный после очистной флотации, содержал 23,01 г/т золота, что делает его пригодным для последующей гидрометаллургической переработки.