This article presents the results of research work devoted to improving the characteristics of paint and varnish coatings based on aqueous dispersions of polyacrylates; it is proposed to modify them by introducing mineral raw materials as fillers and hydrated lime, with subsequent processing in a vortex layer apparatus. The introduction of activated diatomite does not cause the deterioration of covering power, adhesion or an increase in the porosity of the paint material. The modification of coatings contributes to an increase in their operational properties, which can be associated with a reduction in the free volume in the composite and the formation of polymer boundary layers with modified physical and chemical properties. The aim of this study is to obtain a water-dispersion paint and varnish composition containing modified diatomite on a polyacrylate basis and, subsequently, study its main physical and mechanical parameters. The work has been carried out by the following method: determination of porosity, adhesion, elasticity and covering power of the control composition; determination of porosity, adhesion, elasticity and covering power of the obtained composites using modified filler; investigation of the influence of radiation on the infrared spectrum of the paint coating surface using a FLIRB620 thermal imager. As a result of this research work, it was noticed that the modification of water dispersions with silica-activated diatomite helps to eliminate the main disadvantages of materials and coatings based on acrylate binders—low water resistance and low physical and mechanical characteristics. The introduction of modified diatomite into water-emulsion paint on an acrylate base does not lead to the deterioration of the main performance characteristics of paint coatings—porosity, adhesion, elasticity and covering.

This study focuses on the chemical modification of the polysaccharides chitosan and gellan gum to produce water-soluble amphoteric polyelectrolytes. The modified polymers were used to stabilize gold nanospheres (AuNSs) and gold nanorods (AuNRs), whose structural and physicochemical properties were thoroughly characterized. The results confirmed successful polymer modification and effective stabilization of gold nanoparticles with suitable sizes and shapes for biomedical applications.

The Shu-Sarysu Basin in central-southern Kazakhstan remains one of the underexplored gas-prone provinces, with 12 discovered gas fields including Amangeldy (884 Bcf) and Pridorozhnoye (225 Bcf). In the context of global energy transition, such basins require integrated geological assessment to constrain exploration potential. Historical studies within the region were spatially limited and prematurely discontinued, resulting in fragmented datasets and a lack of modern interpretation. This review reassesses published geological data within a petroleum systems framework, applying contemporary geodynamic and stratigraphic concepts. Analysis shows that tectonostratigraphic evolution of the basin during Devonian–Permian time (390–250 Ma) favored formation of mature, gas-prone systems within structurally compartmentalized troughs, with effective source, reservoir, and seal configurations. Building on these findings, a three-tier classification of exploration zones is proposed based on system maturity, trap integrity, and gas shows, reflecting geological success probability. This provides a basis for prioritizing future exploration despite limited seismic and drilling coverage in many areas. Recommended priorities include digitization of archival data, structural modeling, modern geochemical and diagenetic analysis, and focused evaluation of promising areas to support future exploration.

The Devonian–Permian succession of the Tasbulak Trough in the Shu–Sarysu Basin contains confirmed gas shows (wells 462, 1-P Izykyr, 1-P Sokyr-Tobe, and 1-P Kamenistaya) and a sedimentary cover exceeding 5500 m but still lacks a unified 3D structural interpretation capable of explaining the distribution of gas-prone intervals. This study addresses this gap by digitizing and integrating legacy well and 2D seismic datasets to construct horizon-consistent three-dimensional structural surfaces for eight target horizons. The resulting model reveals a low-deformation structural framework dominated by a previously undocumented element—the Central Tasbulak Ridge—which exerts first-order control on fault segmentation, trap geometry, and gas preservation. Structural surfaces were synthesized with stratigraphic intervals to define reservoir–seal–trap relationships, highlighting the late Visean–early Serpukhovian carbonate subformation as the primary target interval. Building on these relationships, a prospect evaluation matrix was developed to classify structural, stratigraphic (including intraformational), and combination trap types together with their corresponding sealing units. The results demonstrate long-term tectonic stability, multi-level evaporitic seals, and inheritance-guided trap evolution, providing a reference framework for assessing gas prospectivity in data-limited intracratonic basins and advancing understanding of petroleum-system architecture in stable continental settings

N,N-diallyl-N,N-dimethylammonium chloride (DADMAC) and vinyl ether of monoethanolamine (VEMEA) copolymer P(DADMAC-VEMEA) were synthesized using radical polymerization in water media using ammonium persulfate as an initiator. The synthesis of this copolymer was investigated over a wide composition range at low conversion levels. The viscosity of the copolymers was measured in aqueous 1 M NaCl. Flocculating action and antimicrobial activities of the copolymers P(DADMAC-VEMEA) against microbacteria and sulfate-reducing bacteria (SRB) were studied. Synthesized copolymers had flocculating effects and can be used to purify wastewater from particles of bentonite clays. Also, the copolymer with a molar composition [DADMAC]: [VEMEA] = (80÷20):(20÷80) retarded the population of both bacteria, Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) in the diffusion zone. Furthermore, the copolymer with a molar composition [DADMAC]:[VEMEA] = (90÷10):(20÷80) had bactericidal properties against SRB. Minimum inhibitory concentration (MIC) of the copolymer varied from 0.01% to 0.1% depending on the molar composition of the copolymer. © 2019 AOCS

Kazakh Sign Language (KSL) is a crucial communication tool for individuals with hearing and speech impairments. Deep learning, particularly Transformer models, offers a promising approach to improving accessibility in education and communication. This study analyzes the syntactic structure of KSL, identifying its unique grammatical features and deviations from spoken Kazakh. A custom parser was developed to convert Kazakh text into KSL glosses, enabling the creation of a large-scale parallel corpus. Using this resource, a Transformer-based machine translation model was trained, achieving high translation accuracy and demonstrating the feasibility of this approach for enhancing communication accessibility. The research highlights key challenges in sign language processing, such as the limited availability of annotated data. Future work directions include the integration of video data and the adoption of more comprehensive evaluation metrics. This paper presents a methodology for constructing a parallel corpus through gloss annotations, contributing to advancements in sign language translation technology.

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.

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.

The presence of natural organic matter (NOM) poses many challenges in the process of purifying water intended for drinking. The presence of NOM leads to high coloration due to natural conditions. During the coagulation and flocculation processes, NOM stabilizes dispersed and colloidal particles. Currently, there are many methods for removing natural organic matter. In Kazakhstan, coagulation is commonly used for treating most waters, which effectively reduces color and NOM. It is known that the efficiency of ozonation and coagulation is high when they are used together. The impact of ozonation on the properties of coagulation is significant because coagulation is the most widespread process for treating drinking water. The combination of ozonation with coagulation has been proposed as an improved method for reducing coloration and removing surfactants. The studies were conducted at the K.I. Satpayev Kazakh National Technical University, where a liquid phase ozonation laboratory system was developed. The aim of the research was to demonstrate the positive effect of combining ozonation with coagulation on the removal of color and surfactants in surface waters. Studies were conducted on model waters with different contents of humic substances. Positive results were obtained at low concentrations of ozone (0.1…0.8 mg/l) and low doses of coagulant. A low dose of ozone showed improvement for coagulation. The results of the study indicate that ozonation, combined with coagulation, has a positive effect on color removal, enhances the removal of NOM, and reduces the necessary dose of coagulant. © The Authors, published by EDP Sciences.