Applied Sciences

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2076-3417

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Multidisciplinary Digital Publishing Institute

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Advanced Neural Netwo...

Rock Mechanics and Mo...

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Integrated Approach to Aerodynamic Optimization of Darrieus Wind Turbine Based on the Taguchi Method and Computational Fluid Dynamics (CFD)
(Multidisciplinary Digital Publishing Institute, 2025-05-20)
Nurdaulet Kalassov
;
Zhandos Baizhuma
;
Rustem Manatbayev
;
A.K. Yershina
;
Muhtar Isataev
This paper presents a numerical study of the optimization of the geometric parameters of a four-bladed Darrieus vertical-axis wind turbine (VAWT) with a NACA 0021 aerodynamic profile. The aim of the study was to increase the aerodynamic efficiency of the turbine by selecting optimal values of the rotor diameter and blade chord length. The Taguchi method using an orthogonal array was used as an optimization method, which reduced the number of necessary calculations from 77 to 20 while maintaining the reliability of the analysis. CFD modelling was performed in the ANSYS 2022 R2 Fluent software environment based on a two-dimensional non-stationary model, including a full rotor revolution and an analysis of the steady-state mode for the twentieth cycle. As a result of the analysis, the optimal parameters were determined: rotor diameter D = 3 m and chord length c = 0.4 m. Additionally, for the selected configuration, the numerical model was validated by constructing the dependence of the power coefficient Cp on the tip speed ratio λ in the range from 0.2 to 2.8. The maximum value of Cp was 0.35 at λ = 2.2, which is an increase of ~64% compared to the least efficient rotation mode in the considered range of λ. The obtained results allow us to conclude that the Taguchi method can be used in combination with CFD modelling for fast and accurate optimization of the aerodynamic parameters of low-power wind turbines.
Цитирования (OpenAlex) 9Цитирования (OpenCitations) 9
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Pyrotechnic Oxidizer Chemistry: Study-Normalized Performance Comparison and Bibliometric Mapping
(Multidisciplinary Digital Publishing Institute, 2026-08-18)
Kaster Kamunur
;
Dinara Muktaly
;
G. S. Beisenova
;
Aigerim Akhinzhanova
;
Tolganay Atamanova
Modern pyrotechnic materials are evolving from conventional fuel–oxidizer mixtures toward function-oriented energetic systems designed for controlled ignition, light emission, gas generation, pressure output and thermal response. This review analyzes recent progress in pyrotechnic materials through oxidizer chemistry, thermal and kinetic behavior, study-normalized performance comparison and bibliometric mapping. Representative oxidizer families, including nitrates, perchlorates, chlorates, metal oxides, hybrid oxide–salt systems, high-nitrogen salts, halogen-oxo oxidizers and coordination nitrate complexes, are compared in terms of chemical role and functional output. Thermal data show that decomposition temperature, heat release and activation energy must be interpreted together, since lower activation energy can indicate either enhanced reactivity or degradation during aging. A study-normalized relative performance factor (RPF) was used to compare performance changes within individual publications. The strongest relative improvements were associated with oxide selection, particle-size reduction, hybrid oxidizer design and coupling of nanothermites with gas-generating components. Bibliometric mapping confirmed a shift toward metal-based energetic materials, nanothermites, gas generators, combustion diagnostics, color systems and pyrotechnic devices. These trends show that oxidizer chemistry, particle architecture and additive function are increasingly selected for defined pressure, gas, light or thermal outputs.
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Advanced Techniques for Thorium Recovery from Mineral Deposits: A Comprehensive Review
(Multidisciplinary Digital Publishing Institute, 2025-10-24)
Tolganay Atamanova
;
Bakhytzhan T. Lesbayev
;
Сандугаш Кудайбергеновна Танирбергенова
;
Zhanna Alsar
;
Aisultan Kalybay
Thorium has emerged as a promising alternative to uranium in nuclear energy systems due to its higher natural abundance, favorable conversion to fissile 233U, and reduced generation of long-lived transuranic waste. This review provides a comprehensive overview of advanced techniques for thorium recovery from primary ores and secondary resources. The main mineralogical carriers—including monazite, thorianite, thorite, and cheralite as well as industrial by-products such as rare-earth processing tailings—are critically examined with respect to their occurrence and processing potential. Physical enrichment methods (gravity, magnetic, and electrostatic separation) and hydrometallurgical approaches (acidic and alkaline leaching) are analyzed in detail, highlighting their efficiencies, limitations, and environmental implications. Particular emphasis is placed on modern separation strategies such as solvent extraction with organophosphorus reagents, diglycolamides, and ionic liquids, as well as extraction chromatography, nanocomposite sorbents, ion-imprinted polymers, and electrosorption on carbon-based electrodes. These techniques demonstrate significant progress in enhancing selectivity, reducing reagent consumption, and enabling recovery from low-grade and secondary feedstocks. Environmental and radiological aspects, including waste minimization, immobilization, and regulatory frameworks, are discussed as integral components of sustainable thorium management. Finally, perspectives on hybrid technologies, digital process optimization, and economic feasibility are outlined, underscoring the need for interdisciplinary approaches that combine chemistry, materials science, and environmental engineering. Collectively, the analysis highlights the transition from conventional practices to integrated, scalable, and environmentally responsible technologies for thorium recovery.
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Electrocardiographic Signatures of Dysglycaemia: Mechanistic Foundations, Digital Biomarkers, and Artificial Intelligence for Non-Invasive Diabetes Risk Stratification
(Multidisciplinary Digital Publishing Institute, 2026-03-18)
Chingiz A. Alimbayev
;
Zhadyra N. Alimbayeva
;
Kassymbek Ozhikenov
;
Kairat Karibayev
;
Zhansila Orynbay
Diabetes mellitus is projected to affect more than 1.3 billion people worldwide by 2050, with millions remaining undiagnosed or in a prediabetic state. Cardiovascular complications account for nearly half of diabetes-related deaths, highlighting the need for scalable tools capable of identifying metabolic dysregulation before irreversible cardiac damage develops. This review synthesizes current mechanistic, clinical, and computational evidence linking diabetes to cardiac electrophysiological remodeling and examines electrocardiography (ECG) as a non-invasive modality for early detection of dysglycaemia. Chronic hyperglycaemia, insulin resistance, oxidative stress, microvascular dysfunction, and cardiac autonomic neuropathy collectively contribute to measurable ECG alterations, including QT/QTc prolongation, increased QT dispersion, changes in Tp–e indices, and reduced heart rate variability. These changes often precede overt cardiovascular disease and correlate with glycaemic burden and diabetes duration. Recent advances in signal processing and artificial intelligence have expanded the diagnostic potential of ECG. Both classical machine learning approaches and large-scale deep learning models demonstrate that ECG contains latent signatures associated with incident type 2 diabetes and glycaemic status. Despite promising results, heterogeneity in study design, limited representation of prediabetes, and lack of standardized validation frameworks remain major barriers to clinical translation. Prospective, multi-ethnic studies are needed to establish ECG-based screening as a reliable component of early diabetes detection strategies.
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1-Carboxy-2-phenylethan-1-aminium Iodide 2-Azaniumyl-3-phenylpropanoate Crystals: Properties and Its Biochar-Based Application for Iodine Enrichment of Parsley
(Multidisciplinary Digital Publishing Institute, 2025-10-06)
Aitugan Sabitov
;
Seitzhan Turganbay
;
Almagul Kerimkulova
;
Yerlan Doszhanov
;
Karina Saurykova
Iodine deficiency remains a significant nutritional problem, which stimulates the search for sustainable approaches to biofortification of vegetable crops. The aim of the work was to develop a "smart" bio-iodine fertilizer based on the organoiodide complex 1-carboxy-2-phenylethan-1-aminium iodide 2-azaniumyl-3-phenylpropanoate (PPI) and highly porous biochar from agro-waste, assessing its efficiency on the parsley model. PPI was synthesized and characterized (IR/UV spectroscopy, thermal analysis), biochar was obtained by KOH activation and studied by low-temperature nitrogen adsorption (S_BET) methods, as well as standard physicochemical characterization. The granulated composition PPI + biochar (BIOF) was tested in pot experiments in comparison with KI and control; The biomass of leaves and roots, iodine and organic nitrogen content, and antioxidant indices (ascorbic acid, total polyphenols, antioxidant activity) were assessed. BIOF provided a significant increase in yield and nutrition: leaf mass reached 86.55 g/plant versus 77.72 g/plant with KI and 65.04 g/plant in the control; root mass — up to 8.25 g/plant (p<0.05). Iodine content in leaves and roots increased to 11.86 and 13.23 mg/kg (d. w.), respectively, while organic nitrogen levels increased simultaneously (57.37 and 36.63 mg/kg). A significant increase in the antioxidant status was noted (ascorbic acid 36.46 mg/100 g dry weight; antioxidant activity 44.48 mg GA/g; polyphenols 23.79 mg GA/g). The presented data show that the combination of PPI with activated biochar forms an effective platform for controlled supply of iodine to plants, increasing the yield and functional qualities of products; the prospects for implementation are associated with field trials and dosage optimization.
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Physicochemical Characteristics and Prospects of Carbon Nanomaterials and Composites for Gas Sorption
(Multidisciplinary Digital Publishing Institute, 2026-02-04)
Nazym Asanbek
;
Almagul Kerimkulova
;
Leticia Fernández Velasco
;
Aitugan Sabitov
;
Meiram K. Atamanov
This review presents a modern comprehensive analysis of the physicochemical characteristics of carbon materials for the sorption of gases. The main classes of carbon sorbents are considered as follows: nanotubes, activated carbon, graphene, fullerene, composite materials, and organic vapors. Modern methods of modification of carbon materials are systematized. Particular attention is paid to the effect of particle size, morphology, and porous structure on the kinetics and equilibrium characteristics of adsorption. The results of experimental and theoretical studies of the adsorption of the gases (CO2, SO2, NOx, H2S, NH3, and CO) are analyzed. A comparative economic analysis of carbon materials is carried out, taking into account the cost of production and estimated costs of modification. Modern areas of application of carbon sorbents are analyzed as follows: industrial gas purification, automotive filters, air conditioning systems, personal protective equipment, and gas sensors. Particular attention is paid to the study of the prospects and future of materials. Prospective development directions are considered, including the creation of hierarchically porous structures, the development of self-healing materials, and integration with artificial intelligence to optimize adsorption processes. The cost of graphene and nanotube production is predicted to decrease by 50–70% by 2030, which will lead to an expansion of their commercial application.
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Fostering Teachers’ Digital Competence in AI-Supported Learning Environments: Implications for Interactive Teaching and Student Achievement
(Multidisciplinary Digital Publishing Institute, 2025-11-27)
Gaukhar Aimicheva
;
Aigul Shaikhanova
;
Kainizhamal Iklassova
;
Parassat Tazabekova
;
Aizhan Nazyrova
In this article, the development of in-service teachers’ digital competence is examined within an AI-supported learning environment designed to enhance professional modeling skills and the creation of interactive AR-based instructional content. The study investigates how such an environment supports teachers in developing contextual digital skills that enable not only the use of emerging technologies but also their meaningful adaptation to pedagogical goals and instructional needs. A training program involving 916 in-service teachers from Kazakhstani secondary schools was implemented, and survey data were collected to assess changes in digital competence and readiness to integrate AI and AR tools into teaching practices. The findings demonstrate high levels of interest and engagement: 96% of participants expressed readiness for further learning, 86% reported satisfaction with the course content, and 84% showed contextual maturity in applying newly acquired technologies in their instructional processes. These results highlight the potential of AI-supported professional development to strengthen teachers’ capacity to design interactive learning environments, promote equity and quality in digital education, and enhance student engagement.
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Use of Urea-Modified Activated Carbon Sorbents Derived from Plant Residues for Gas Sorption
(Multidisciplinary Digital Publishing Institute, 2026-07-07)
Almagul Kerimkulova
;
Yersultan Yermoldanov
;
Aitugan Sabitov
;
Leticia Fernández Velasco
;
Nazym Asanbek
The growing demand for efficient and sustainable materials for air purification has stimulated interest in activated carbons derived from renewable biomass resources. In this study, activated carbons were prepared from rice husk, wheat straw, sawdust, and walnut shells and systematically investigated as sorbents for toxic gases and volatile organic compounds. The materials were characterized using nitrogen and water vapour sorption isotherms, scanning electron microscopy, thermogravimetric analysis, Fourier-transform infrared spectroscopy, and energy-dispersive X-ray analysis to evaluate their textural properties, morphology, thermal stability, and surface chemistry. The results showed that the precursor type strongly influences the pore structure and functional group composition of the activated carbons. Wheat straw and Rice husk–derived activated carbons exhibited the highest total pore volume and a well-developed porous structure, together with a high content of oxygen- and silicon-containing elements. Gas breakthrough experiments with different probes showed that wheat straw–derived activated carbon excels in VOC removal due to its highly microporous structure. In contrast, rice husk–derived activated carbon displays strong affinity toward inorganic gases such as NH₃ and, after urea modification, achieves enhanced performance for SO₂. These results underscore the versatility and practical applicability of carbon materials obtained from plant residues.
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Temperature and Moisture in Pavements and Soil Bases in the Southern Metropolises of Kazakhstan
(Multidisciplinary Digital Publishing Institute, 2026-08-28)
Bagdat Burkhanbaiuly Teltayev
;
Giuseppe Loprencipe
;
Umir S. Kalybayev
;
Aizhan Muta
;
Yerbol Aitbayev
In this paper an experimental study and comparative analysis of temperature and moisture in pavements and their soil bases on experimental road sections located in two southern metropolises of Kazakhstan—in the cities of Almaty and Shymkent—were conducted. The temperature and moisture values were measured by systems for long-term continuous monitoring of temperature and moisture in structural elements of roads. Sensors were installed at ten locations within the pavements and soil bases at depths ranging from 2.5 cm to 270–275 cm below the pavement surfaces. The temperature and moisture monitoring period extends from 21 October 2024 to 31 December 2025. Temperature and moisture readings were recorded hourly. It has been established that the temperature conditions of pavements and their soil bases in the two cities are qualitatively the same, and the quantitative differences are small: air temperatures and soil temperatures at depths of 2.5 cm and 10 cm in Shymkent are higher than in Almaty by an average of 4.75 °C, 5.69 °C and 2.12 °C, respectively. These facts make it possible to use a single methodological approach to account for temperature changes in the design of new roads and in the operation of existing roads. It is recommended to pay attention to ensuring the resistance to rutting and fatigue cracking of asphalt concrete layers of pavement. The moisture regime at the experimental section in Almaty up to 130–140 cm depth is unstable due to seepage of precipitation and surface water through the stone mastic and the two coarse-grained porous asphalt concrete layers of the pavement; in Shymkent the pavement and the soil base have a stable moisture regime throughout the year; these features should be taken into account when calculating accumulations of plastic strains (increasing of rut depth) and fatigue damage in asphalt concrete layers of pavements while also accounting for temperature changes in them and transport facility loads in different time periods.
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A Structured Critical Review of Machine Learning Approaches for ECG-Based Detection of Dysglycemia and Their Translational Readiness
(Multidisciplinary Digital Publishing Institute, 2026-05-27)
Zhadyra N. Alimbayeva
;
Chingiz A. Alimbayev
;
Kassymbek Ozhikenov
;
Aiman K. Ozhikenova
;
Ussen Shylmyrza
This structured critical review provides a comparative and analytically grounded overview of machine learning (ML) approaches for electrocardiography (ECG)-based detection of dysglycemia, with a specific focus on translational readiness for clinical screening. A structured literature search across PubMed, Scopus, Web of Science, and IEEE Xplore identified 183 records, of which 17 studies were included following predefined screening criteria and PRISMA-guided selection principles. The included studies demonstrate substantial heterogeneity in dataset size (ranging from 25,000 subjects), ECG acquisition modalities (single-lead, 12-lead, wearable), feature representations (raw signals, heart rate variability, engineered features), and ML strategies (classical algorithms, deep learning, and multimodal models). Reported model performance is generally high, with accuracy values frequently exceeding 0.85 and area under the curve (AUC) ranging from 0.78 to 0.99. Smaller experimental studies often report inflated performance (up to 96–99% accuracy), whereas large-scale population-based investigations demonstrate more moderate but clinically plausible results (AUC ≈ 0.80–0.85). External validation, a key requirement for clinical applicability, was performed in only a limited subset of studies (approximately 12%). From a physiological perspective, ML models exploit ECG alterations associated with dysglycemia, including reduced heart rate variability, QT interval prolongation, and changes in ventricular depolarization and repolarization dynamics. However, the relationship between metabolic dysfunction and ECG signals remains indirect. A key finding of this review is the mismatch between reported predictive performance and translational readiness. The majority of studies (≈65–70%) are classified as early-stage (Level 1–2 or 2–3), relying on small, single-center datasets and internal validation. Only a minority of studies achieve near-translational maturity (Level 4), characterized by large-scale datasets and external validation. ECG-based dysglycemia detection represents a promising non-invasive and scalable screening paradigm. However, its clinical translation is constrained by the lack of standardized ECG acquisition protocols, limited dataset diversity, insufficient external validation, and fragmented methodological approaches. Future research should prioritize large multi-center datasets, standardized feature extraction pipelines, hybrid interpretable models, and prospective validation to enable robust, generalizable, and clinically deployable screening systems.

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