This paper introduces a data-driven Distributionally Robust Optimization (DRO) framework to identify risk-averse Hamiltonian cycles in non-stationary stochastic networks subject to severe probabilistic uncertainty. Rather than relying on static nominal travel times, which suffer heavily from the optimizer's curse, we construct a distribution- free ambiguity set defined by a Type-1 Wasserstein metric ball centered on an empirical distribution of historical link latencies. The resulting minimax optimization model minimizes the worst-case expected routing cost over all probability distributions compatible with historical observations. To resolve the joint challenge of combinatorial hardness (NP-complete) and non-linear robust objectives, we synthesize this DRO formulation with an evolutionary computing engine governed by precise permutation set operators, deploying an Edge Recombination Crossover (ER) operator and an ergodic Swap Mutation mechanism. We evaluate our model on two structurally contrasting urban networks in Kazakhstan: the dense, topography-constrained grid of Almaty, and the expansive, bridge-dependent network of Astana. Our empirical findings demonstrate that the proposed framework prevents catastrophic out-of-sample routing delays by preserving edge adjacency maps and outperforming classical stochastic programming models.

The article is devoted to the testing of the possibilities of using geographic information systems (GIS), remote sensing materials for updating existing cartographic materials and creating electronic maps, databases of forests. The study was conducted using the example of the «Rovnoye» forestry DMSI (department of the municipal state institution) «Sergeyevskoye Forestry» of the North Kazakhstan region (NKR). Geoinformation mapping was performed using the ArcGIS 10.8 software package. A database of forests of the forestry department has been created, electronic maps (forestry measures, species, age groups of trees) have been developed on a scale 1:100 000, 1:150 000. The developed cartographic material and the collected digital data set can be used by forestry for effective planning of forest management, control of forest resources and development of strategies for sustainable forest management. It is shown that GIS technologies and remote sensing are an effective tool for updating existing and creating new detailed forest maps. In conclusion, the conclusion is made about the prospects of using the applied approach for mapping the forest fund.

Agriophyllum squarrosum (L.) Moq. is a drought-tolerant psammophytic species with high potential for domestication in arid regions; however, its agricultural use is limited by unfavorable traits such as small seed size and unstable productivity. This study aimed to develop an effective method for inducing genetic variability using ethyl methanesulfonate (EMS) and to evaluate resulting mutant forms. Seeds were treated with 0.3% EMS for 18 h, followed by washing, drying, and cultivation under controlled and field conditions. Phenotypic assessment was conducted in the M₂ generation. EMS treatment resulted in viable mutant populations with increased phenotypic diversity. Compared to wild forms, mutant lines showed significant increases in plant height and bush width. Seed traits were improved: seed length increased from 2.4 to 2.8 mm, seed width from 1.8 to 2.0 mm, and thousand seed weight from 1.83 to 2.17 g. Morphological variation was also observed in plant architecture and leaf traits. The results confirm that EMS mutagenesis is an effective tool for expanding genetic diversity in A. squarrosum. The identified mutant forms represent promising material for further selection and support the potential domestication of this species for cultivation in arid environments.

The emergence of zoonotic RNA viruses poses a critical threat to global public health, with approximately 75% of new infectious diseases originating from animal reservoirs. While global evolutionary trends are well- documented, the intersection of viral mutational dynamics and regional ecological factors - particularly in Central Asia remains insufficiently integrated, hindering effective biosafety surveillance. This review follows the PRISMA guidelines, synthesizing molecular-genetic data from PubMed, Web of Science, and Scopus to analyze the mutational plasticity of RNA viruses and their circulation in natural reservoirs. Our analysis characterizes the molecular drivers of interspecies barriers and demonstrates that Kazakhstan’s unique biogeographic landscape acts as a catalyst for region-specific viral evolution. We conclude that integrating «One Health» frameworks with metagenomic surveillance is essential to mitigate future zoonotic risks in the region.

The article presents the results of a study on the health status of university teachers. The research included an anonymous survey and an analysis of physiological indicators. A total of 186 individuals participated in the study, of whom 127 were affiliated with the Karaganda Buketov University and 59 with Altai State University. Data collection was carried out during the transition to distance learning amid the COVID-19 pandemic, as well as after its completion. Biological age was evaluated through a comprehensive analysis of body weight, blood pressure, static balance, and subjective self-assessment of health. The calculated individual scores were further interpreted by comparing them with appropriate age standards. It was found that in 22.58% of the examined teachers the aging process was slowed down, in 25% it corresponded to the population norm, whereas in 52.1% accelerated aging was observed. In addition, inter-university differences in the characteristics of aging rates among teachers were identified. Among teachers of the Karaganda Buketov University, the proportion of individuals with an accelerated aging process was 55.9%, whereas among those from Altai State University this indicator was lower, amounting to 44%. The observed differences may be explained by the fact that at Buketov University the study was conducted during the pandemic period. Given that hormonal mechanisms exert different effects on male and female organisms, the rates of aging may also differ substantially. In particular, among male teachers of Buketov University, 79.37% were classified as being in the risk group, while among women this figure was 32.78%. At Altai State University, the corresponding proportions for men and women were 66.67% and 31.58%, respectively. The findings indicate that the pandemic had a significant impact on the health status of university teachers. Morphofunctional differences between male and female organisms may also contribute to these outcomes.

The mean total nitrogen content in vermicomposts based on cow manure, a mixture of willow–poplar leaf litter, and mown grass amounted to 1.92, 1.64, and 2.15%, respectively. The increase in total nitrogen content during vermicomposting of cow manure and leaf litter was 39% and 50%, respectively. During vermicomposting of mown grass, the total nitrogen content decreased by 26%. The actual activity of Azotobacter bacteria was highest in vermicomposts based on mown grass (98%). For vermicomposts based on leaf litter, this indicator was 85%, while manure‑based vermicompost was 70%. Vermicomposts obtained from mown grass surpassed other variants in the content of ammonifiers (10¹⁰ CFU/g), nitrifiers (6×10⁶ cells/g), and inorganic nitrogen utilizers (1.4×10¹⁰ CFU/g). A very strong positive correlation (r = 0.9999) was established between the total nitrogen content and the content of ammonifiers in vermicomposts. The balance of total nitrogen content (percentage difference between nitrogen content in vermicomposts and initial mixtures) correlated negatively with the content of inorganic nitrogen utilizers in vermicomposts (r = –0.97).

This study was conducted to assess the biotechnological potential of the Bacillus subtilis A-12 strain for the development of probiotic feed additives. Lyophilisation was employed as the primary storage method to ensure the stability of the probiotic biomass. During the study, the lyophilisation tolerance of the B. subtilis A-12 strain was assessed using various cryoprotective medium compositions. Specifically, a gelatin-sucrose-milk medium, a sucrose medium, a milk medium, a gelatin-sucrose medium, and a gelatin medium were tested.

The results showed that the post-lyophilisation viability of B. subtilis A-12 cells was significantly dependent on the composition of the protective medium. Among the tested formulations, the gelatin- and sucrose-based medium provided the highest cell viability after lyophilisation and showed a statistically significant advantage compared with carbohydrate-free media (p < 0.05). Although the number of viable cells decreased by approximately 25-35% following lyophilisation, further losses during storage for up to six months at 4°C were only 2-5%. This demonstrates the long-term stability of the obtained lyophilised biomass.

Furthermore, the sporulating nature of the B. subtilis A-12 strain was found to enhance its resistance to dehydration and thermal stress, positively affecting the stability of the lyophilised preparation. As a result of the study, an experimental probiotic feed additive based on lyophilised B. subtilis A-12 biomass was developed. The obtained data indicate that the developed technology is promising for practical application and justify the need to assess its in vivo efficacy in animal models in the future.

At present, there is no modern annotated checklist of rare medicinal plant species of the North Kazakhstan Region (NKR). This study presents extended data profiles of these representatives of the flora, compiled based on the analysis of scientific publications, field research materials, and herbarium collections of university researchers. According to the compiled list, eleven species of rare medicinal plants of the North Kazakhstan Region are identified. Among them, four species belong to the family Ranunculaceae, while one species each belongs to the families Poaceae, Liliaceae, Lythraceae, Droseraceae, and Menyanthaceae; two species belong to the family Orchidaceae.

The taxonomic position, morphology, and ecological characteristics of each species are examined. Particular emphasis is placed on their pharmacological properties, confirming their value as sources of biologically active compounds.

Special attention is given to the rarity status of the plants and their distribution within the North Kazakhstan Region, reflecting the uniqueness of the forest-steppe and steppe ecosystems of the region. The results of the study highlight the importance of such research as a tool for inventory and legal regulation of plant protection, as well as the need for further investigation of the pharmacological potential of rare species.

The article presents a preliminary GC-MS profile and antifungal activity of a hydroethanol extract of Chelidonium majus L. collected in the high-altitude conditions of the Kungei Alatau range (Kazakhstan). Antifungal activity was evaluated in vitro against one Fusarium spp. isolated from potato tubers with symptoms of fusarium lesion.

The extracts were obtained by Soxhlet extraction using 70% ethanol. The mycelial growth area was determined from digital images using ImageJ software. The experiment was carried out in four repetitions (n = 4). In the treatment with hydroethanolic extract of C. majus No. 1, the mycelial growth area of Fusarium spp. was 60.25 ± 4.08%, compared with 72.50 ± 2.65% in the solvent control containing 70% ethanol. Inhibition of mycelial growth relative to the solvent control was 16.9%. GC-MS analysis tentatively identified 11 compounds in the extract, among which ethyl esters of unsaturated fatty acids accounted for a substantial proportion (43.4%), followed by phytol (13.8%) and the compound 4H-Bis[1,3]benzodioxolo[5,6-a:4’,5’-g]quinolizine, 6,7,12b,13-tetrahydro-, (±) - (30.2%), tentatively identified based on mass-spectral library matching. The results obtained indicate the inhibitory activity of the hydroethanol extract of C. majus against the mycelial growth of Fusarium spp. in vitro and justify the need for further research.

A cytogenetic study of Anopheles messeae Fall., 1926 natural populations was conducted in two biotopes of the Astana vicinity (Kuigenjar and International, Akmola Region). Polytene chromosomes were prepared from salivary glands of fourth-instar larvae using the lacto-aceto-orcein squash technique; 111 karyotypes were analysed. Pronounced inversion polymorphism was observed in chromosome arms XL, 3R, and 3L, whereas chromosome arm 2R was nearly monomorphic. The dominant sex-chromosome genotype was XL11; chromosome 3L showed the highest variability (3L₀₁ heterozygotes: 47.4% and 66.6%). Interpopulation differences were significant for 3R (χ²=4.37; p<0.05) and 3L (χ²=6.21; p<0.05); the International population deviated from Hardy- Weinberg equilibrium (χ²=6.87; p<0.05). The data confirm the adaptive nature of chromosomal polymorphism and local biotope differentiation within an urbanised landscape.

This article examines the scientific and methodological foundations for assessing and improving the level of physical fitness of 15-year-old secondary school students. During the study, pedagogical assessment tools aimed at determining physical fitness indicators were applied, and the obtained results were subjected to statistical analysis.

The research data were processed using descriptive statistical methods, and mean values and standard deviations were determined. The statistical analysis allowed for the assessment of the reliability and validity of the test indicators, as well as an objective characterization of the students' level of physical fitness.

The results of the study demonstrated a positive dynamic in the development of motor abilities among 15-year-old students, according to initial and final testing data. The use of pedagogical tests at the beginning and end of the academic period statistically confirmed an improvement in the quality of physical fitness, indicating a scientifically grounded and age-appropriate approach to organizing physical education lessons.

The article addresses the problem of preparing school students for the experimental round of chemistry Olympiads using the example of qualitative analysis of unknown substances. The relevance of the study is обусловлена тем, что despite relatively high performance in the theoretical round, students experience significant difficulties when completing experimental tasks. The paper demonstrates the need to develop a systematic methodological guide aimed not only at mastering individual qualitative reactions but also at fostering critical thinking, practical skills, and the ability to independently plan experiments, which are essential for successfully completing Olympiad tasks. The aim of the study is to provide a theoretical justification, develop, and test methodological recommendations for preparing students for the experimental round of chemistry Olympiads.

This article is devoted to the methodology of level-based education of 7th grade students in schools with a non-Kazakh language of instruction. The method of level-based learning provides an opportunity for effective learning of students, taking into account their level of knowledge, abilities and age characteristics. With the help of this teaching method, the issues of increasing students' interest in the subject, easy assimilation of educational material, as well as the development of creative and cognitive abilities are solved. Through this teaching method, the tasks of increasing students’ interest in the subject, easy assimilation of educational material, as well as the development of their creative and cognitive abilities are solved. The article discusses the theoretical foundations of level-based learning, the features of teaching non-Kazakh-speaking students, the methods and techniques used in the lesson, and specific practical recommendations. This article discusses the specifics of the method, methods, and specific recommendations for 7th grade students. Innovative approaches aimed at developing students' communication skills and increasing their interest in language are also presented, their role in improving the quality of education and the impact on teacher performance is described. The article aims to show ways to simplify the process of learning Kazakh as a second language.

This article addresses the problem of assessment through the authentic integration of interactive assessment techniques into school teaching and learning processes, using English language lessons as an example. The article draws on the materials and findings of an applied study conducted in two schools in the North Kazakhstan Region, which involved the experimental piloting of a framework for criterion-referenced assessment of reading, listening, speaking, and writing under conditions of self-organization and self-management of assessment activities. The materials and findings were conceptually interpreted from the perspectives of pedagogy and language didactics by faculty members of Manash Kozybayev North Kazakhstan University. The originality of the article lies in its focus on learners’ cognitive development and on expanding the resources and mechanisms of criterion-referenced assessment in response to the challenges posed by Kazakhstan’s updated educational content. The research findings demonstrate the effectiveness of organizing criterion-referenced assessment as an integrated process in English language teaching that promotes self-regulation in learning, elicits positive emotions, including academic buoyancy and enjoyment of learning a foreign language, and supports personalized learning. The findings are of practical significance for school teachers, university lecturers, particularly in terms of preparing pre-service teachers for criterion-referenced assessment, as well as educational methodologists and education experts.

This study is devoted to the topical issue of developing soft skills in primary school students through the use of activity-based learning. The study was conducted at the First Gymnasium of Petropavlovsk, Republic of Kazakhstan, with the participation of 20 third-grade students. The novelty of the work lies in the comprehensive application of activity-based methods for the simultaneous development of communication skills, critical thinking, the ability to cooperate, and emotional intelligence in primary school children in the context of the Kazakhstani educational system. The methodological basis of the study was formed by the concepts of the activity approach in teaching, the theory of speech development by L.S. Vygotsky and A.A. Leontiev, as well as modern approaches to the formation of flexible competencies. The pedagogical experiment used role-playing games, project-based learning with an ethnocultural component, Think-Pair-Share methods, group projects, and interactive tasks. The results of the assessment showed a statistically significant increase in communication skills (from 35% to 75% of students with high and average levels), critical thinking (28% increase), teamwork skills (32% increase), and emotional regulation (24% increase). The structure of the study includes a theoretical analysis of the problem, a description of the experimental program, and a quantitative and qualitative analysis of the results. The conclusions confirm the effectiveness of Activity-Based Learning as a pedagogical technology for developing soft skills, which meets the requirements of the updated educational content of the Republic of Kazakhstan and international educational standards.

This article, based on archival documents from the National Archives of the Republic of Belarus and the State Archives of the Russian Federation, analyzes the experiences of Soviet prisoners of war in Nazi captivity during World War II. Documents, including orders, correspondence from camp commandants, circulars, and various directives from the occupation authorities, are used to analyze the organization and operation of the camps established by the German occupation authorities. The article also uses the memoirs of camp prisoners and recorded interrogation records of former guards, demonstrating the criminal activities of the German occupation authorities. The high mortality rate of Soviet prisoners of war, including soldiers from Kazakhstan, was due to inhumane conditions in the camps, exhaustion due to starvation, and unfavorable sanitary and epidemiological conditions. In conclusion, the conclusion is made: Soviet prisoners of war in the camps experienced the entire arsenal of Nazi Germany's criminal policy of merciless extermination through the prisoner-of-war regime they had created.

In 2016-2025, retail beef prices in Kazakhstan nearly tripled - from 1,245 to 3,515 tenge per kilogram (by about 28% in constant 2016 prices) - despite a twofold growth in the beef cattle herd.

The aim of the study is to identify and quantitatively assess the factors driving domestic beef prices in Kazakhstan. The study uses 2016-2025 data from the Bureau of National Statistics of the Republic of Kazakhstan on beef production, cattle population, retail prices, the consumer price index and foreign trade. Correlation, dynamic and structural analysis were applied; prices were examined in nominal and real terms.

Beef production per head of beef cattle more than halved - from 212 to 104 kg - while beef exports grew almost twentyfold. In nominal terms, both indicators are closely related to retail prices (r₃ = -0.95; r₁ = 0.96), but after removing inflation all pairwise relationships become statistically insignificant (|r'| < 0.57); for net exports the link is even weaker (r₄ = 0.80; r'₄ = 0.17). Correlation does not by itself prove causality.

The average export price (about 4 U SD/kg) is below the domestic retail price (about 7 U SD/kg), although premium shipments are exported at up to 14 USD/kg.

About three quarters of nominal price growth is attributable to inflation, while herd growth does not translate into a proportional increase in supply on the organized market, likely due to herd rejuvenation, live cattle exports and on-farm consumption in household farms. The role of beef exports is limited: Kazakhstan was a net importer of beef until 2022, and in 2025 net exports amounted to only 3.7% of production.

This article summarizes the scientific, technological, and practical aspects of applying the microbiological fertilizer Azotobacterin, developed on the basis of the free-living soil bacterium Azotobacter chroococcum. It is shown that bacteria of the genus Azotobacter are involved in the biological fixation of atmospheric nitrogen, the synthesis of physiologically active substances, the stimulation of rhizosphere microflora, and the reduction of chemical load on the soil. Laboratory and field studies demonstrated that the application of an activated Azotobacter strain to grain legumes increased crop yield by 8-16% compared with the control, while potato yield under the soil conditions of Central Tajikistan reached 252 centners per hectare (25.2 t/ha). The paper also discusses the technological requirements for the production of the biofertilizer, quality control parameters, and the prospects for its implementation in the agricultural sector of the Republic of Tajikistan.

The article presents the results of field studies conducted in 2025, aimed at a comprehensive investigation of the effects of mineral fertilizers and chemical plant protection products (PPPs) in combination with a biostimulant on the yield and individual yield components of the ‘Vekhovskaya’ lentil variety. The relevance of the study is determined by the need to improve the efficiency of grain legume cultivation technologies under conditions of unstable and uneven moisture availability, as well as varying degrees of weed infestation in agrocenoses, which are characteristic of Northern Kazakhstan and adjacent regions.

The experimental part of the study was conducted in the Zerendinsky District of Akmola Region. Field experiments were established on two contrasting agrocenotic backgrounds: a weed-free site and a site with a pronounced weed- infested preceding background. This experimental design made it possible to more comprehensively assess the crop response to competitive pressure from the weed component. The agrotechnological practices included the application of starter mineral fertilizers at a rate of N12P52, as well as an integrated plant protection system involving chemical agents in combination with the growth regulator ‘Fulvik’. During the study, systematic phenological observations were conducted, morphometric characteristics of the plants were analyzed, yield components were assessed, including pod formation and seed productivity, and final biological yield was determined.

The results demonstrated that the combined application of mineral nutrition, PPPs, and the biostimulant had a pronounced positive effect on the growth processes and productivity of lentils. More synchronized and accelerated progression through the phenological phases was observed, with crop maturity occurring 2-5 days earlier compared with the control treatments. An increase in stem growth, improvement in plant architecture, expansion of the photosynthetic apparatus, and more effective formation of generative organs were also recorded. These changes were accompanied by a reduction in the abortion of reproductive organs and an increase in overall crop productivity.

The maximum biological yield of the ‘Vekhovskaya’ variety reached 28.0 c/ha under the weed-free agro background and 28.8 c/ha under the weed-infested background. The minor difference between the treatments indicates the high plasticity and competitive ability of the variety. The obtained results confirm the high technological responsiveness of the crop to intensive cultivation practices and demonstrate the stability of yield formation under different agroecological conditions. The findings can be used in the development and improvement of resource-saving, adaptive, and environmentally oriented cultivation technologies.

In Kazakhstan, protection of apple orchards against tetranychid and fruit mites is of considerable agricultural importance because intensive pesticide use contributes to resistant phytophagous populations, reduces beneficial acarofauna, and disturbs the ecological balance of orchard agrocenoses. The aim of this study was to evaluate chemical and biological approaches to spider mite control on apple trees and to substantiate the inclusion of predatory phytoseiid mites in ecologized orchard protection systems in Kazakhstan. The paper summarizes literature and experimental data on acaricides and entomoacariphages, including field trials from Central Asia and the experience of Amblyseius andersoni introduction in apple orchards of the Almaty region. The analysis showed that chemical treatments provide high initial biological effectiveness, but their protective effect decreases by 14-21 days after application. The most stable effect against phytophagous mites was obtained with A. andersoni: at a predator: prey ratio of 1:2, the number of spider mites decreased to zero by day 14. Safe release of Phytoseiulus persimilis after Biowert and Metarhizium robertsii treatments is possible after 3 days, whereas after Fitoverm and BTB it should be delayed for at least 5 days. The scientific novelty of the work is the systematization of data on predatory mites in Central Asian orchard agrocenoses and the substantiation of A. andersoni as a promising element of integrated apple protection in southeastern Kazakhstan.

This paper presents methods of digital processing of electroencephalographic (EEG) signals and machine learning algorithms for assessing human cognitive states based on an open-access EEG dataset. Electroencephalography is a widely used non-invasive technique for studying brain functional activity due to its high temporal resolution. However, EEG signal analysis is challenged by low signal-to-noise ratio, inter-subject variability, and the presence of artifacts, which necessitates the use of advanced digital signal processing methods.

The study employs an open EEG dataset recorded during the performance of cognitive tasks associated with creative thinking. The proposed processing pipeline includes band-pass and notch filtering, artifact suppression, spectral analysis in standard frequency bands, and functional connectivity analysis based on phase synchronization metrics. Spectral and connectivity features are combined to form an informative feature space for machine learning-based classification of cognitive states.

Experimental results demonstrate that the joint use of spectral characteristics and functional connectivity measures improves classification accuracy compared to the use of spectral features alone. The obtained results confirm the effectiveness of digital EEG processing and machine learning methods for automated assessment of cognitive states and highlight their potential for further development of real-time cognitive monitoring systems.

This paper examines the operating principles of a fiber cement panel lamination production line, as well as the main technological parameters affecting production efficiency and final product quality, and approaches to their optimization. An industrial PURETE automated lamination line was selected as the research object, and a comprehensive analysis of the technological process was carried out based on actual production conditions. The effects of laminator temperature, pressing pressure, and conveying speed on the adhesion, uniformity, and defect-free quality of the surface layer of fiber cement panels were experimentally evaluated. During the study, a multi factor Taguchi experimental design method was applied to determine the interaction of technological parameters and their influence on product quality. Based on signal-to-noise ratio indicators, an optimal combination of technological parameters aimed at improving product quality was proposed. The results demonstrated the potential to ensure lamination process stability, reduce the proportion of defective products, and achieve savings in energy and material resources. The feasibility of implementing the proposed optimization methods under industrial conditions was substantiated, confirming their practical significance for fiber cement panel production.

The article presents the results of the pilot implementation of the predictive equipment maintenance system at the SaryarkaAvtoProm LLP automotive industry enterprise. The system is based on an ensemble of machine learning algorithms trained on multichannel sensor data (vibration, temperature, pressure, flow rate of process fluids) and failure logs. The prototype of the online monitoring system was integrated with two priority assembly lines of the machine assembly shop (engine and transmission units).

For two months, the system analyzed sensor data streams in real time and generated warnings when the failure probability threshold of 0.7 was exceeded. During this period, 14 alarm notifications were generated, nine of which corresponded to the actual pre-failure conditions of the equipment, and in five cases pronounced signs of wear were detected, eliminated as part of scheduled repairs. It is shown that the introduction of a predictive maintenance system has reduced total maintenance costs from 1,000,000 to 720,000 conventional units (savings of about 28%) and reduced the duration of unplanned downtime from 340 to 200 hours (a decrease of ≈41%). The economic impact factors are analyzed: advance planning of repairs, reduction of the number of emergency situations, optimization of stocks and reduction of the volume of emergency purchases.

The pilot implemented sensor-data acquisition and preprocessing, ensemble-based risk estimation, an alert interface, and feature-contribution explanations. Digital twins and federated learning were not part of the experimental system and are considered only as directions for future scaling.

Selecting and deploying an appropriate wireless sensor network (WSN) is an important challenge in IoT system design. Evaluating different WSN configurations through physical deployment can be costly and time-consuming. This study proposes a simulation-driven framework that integrates a simulated WSN with an MQTT-based IoT communication infrastructure to enable network-aware performance evaluation prior to physical deployment. An SDN-WISE-based WSN was implemented using the Cooja simulator, and the generated sensor traffic was forwarded to a Mosquitto-based MQTT cluster via a socket-based integration mechanism. Unlike conventional approaches that use independent traffic generators, the MQTT workload in the proposed framework originates directly from the simulated WSN. Experiments with 24-, 36-, and 48-node scenarios showed that average end-to- end delay increased from 37.51 ms to 138.01 ms, while total application throughput increased from 142.51 bps to 281.12 bps. Packet delivery remained effectively near 100% across all scenarios. The proposed approach provides a controlled, repeatable environment for evaluating various WSN configurations and can reduce the need for multiple physical deployments in the early stages of IoT system design.

This paper investigates the performance of ensemble machine learning algorithms - Random Forest, Gradient Boosting, and XGBoost - for solving the dual-classification problem of credit scoring and fraud detection within financial decision support systems. The study utilizes an event-driven architecture for real-time data processing, evaluated on retail lending application data through cross-validation and hyperparameter optimization. Experimental results demonstrate that Random Forest and XGBoost achieve high predictive accuracy for credit scoring, while boosting models effectively handle fraud detection. Furthermore, cost-sensitive threshold calibration optimizes risk management, and feature importance analysis reveals that behavioral and data consistency indicators outweigh traditional credit scores. The findings offer a robust methodological and architectural contribution to real-time financial decision support systems, enhancing automated risk assessment and fraud mitigation in digital lending.

Real-time interpretation of seismic waveform data is essential for monitoring signal dynamics and detecting abnormal activity. However, forecasting seismic amplitudes remains challenging because seismic signals are highly variable, noisy, and non-stationary. This study presents an automated, gap-aware real-time forecasting pipeline for minute-level seismic signal amplitudes built on IRIS FDSN waveform data, together with a rigorous, statistically grounded comparison of seven forecasting models: Naive persistence, ARIMA, Ridge regression, HistGradientBoosting, a standard Gated Recurrent Unit (GRU), an LSTM, and a physics-inspired regularized GRU (PI-GRU) that adds temporal smoothness and amplitude-growth constraints to the training loss. Models were evaluated on 81 independently sampled, non-overlapping forecasting windows drawn from a continuous 91-day observation period across four horizons (5, 15, 30, and 60 minutes), using paired non-parametric significance testing, a quantitative ablation study, and noise- and missing-data robustness testing, and the main findings were additionally verified under an alternative peak-priority signal aggregation. Classical models, particularly Ridge regression and HistGradientBoosting, achieved significantly lower forecasting error than every neural model tested (Friedman and Holm-corrected Wilcoxon tests, p<0.0001) and were robust to synthetic noise and missing data, while the proposed PI-GRU was, contrary to expectation, the weakest-performing model overall and did not produce smoother forecast trajectories than an unregularized baseline GRU. We report this as an honest negative result: as currently configured, the physics-inspired regularization examined here does not demonstrate an accuracy or stability advantage over classical machine learning baselines for this task. The principal contributions of this work are the automated real-time forecasting pipeline itself and a reusable, rigorous evaluation methodology, together with specific, testable hypotheses - concerning hyperparameter tuning, training budget, and calibration of the physics-inspired constraint - for why the neural models underperformed and how this might be addressed in future work.

In this paper, vibration monitoring is considered as a tool for detecting hidden defects in electric motors. The results of the development help reduce the risk of emergency shutdowns, thereby increasing the overall service life of the equipment. The product is presented as an experimental vibration monitoring system, in which the IIS3DWB three-axis digital accelerometer serves as a key component. The proposed system includes a data acquisition module (based on the ESP32), a DSP module (digital signal processing), an interface component (Grafana), and a database (InfluxDB). A key feature of the methodology is the sensor attachment to the electric motor housing with the function of recording three vibration components, which are respectively determined in three mutually perpendicular planes. During the experimental studies, the accuracy and performance indicators of the monitoring system were established, including the relative error of RMS acceleration (< 6%), measurement repeatability, wireless data transmission latency (< 15 ms), system response time (< 50 ms), and packet loss during transmission (0%). The use of a broadband sensor made it possible to identify low- and high-frequency defects characteristic of the initial stages of bearing wear with minimal data loss and system response time.

Accurate measurement and control of wet and sour gas flow remain critical challenges in modern oil and gas production, transportation, and processing systems. In operating conditions, natural and associated gases rarely exist as single-phase media and commonly contain liquid components, primarily water and hydrocarbon condensates, forming multiphase mixtures. Even insignificant amounts of liquid significantly affect flow hydrodynamics and introduce substantial uncertainties in conventional gas flow measurement methods. This review analyzes the importance of flow rate measurement in binary gas-water systems and examines the evolution of wet gas metering technologies from traditional approaches toward intelligent measurement concepts. The study summarizes the physical characteristics of wet gas flows, including phase slip, flow regime variability, and the influence of liquid loading on measurement accuracy. The limitations of classical metering techniques are discussed from a system perspective, emphasizing that measurement errors originate mainly from the dynamic structure of multiphase flow rather than from individual sensor inaccuracies. Particular attention is given to operational challenges associated with sour gas environments, where corrosion, sensor degradation, and changing thermophysical properties further complicate reliable measurements. Based on the analyzed literature, current development trends indicate a transition from single-instrument solutions to hybrid and multisensory measurement systems capable of simultaneously estimating flow rate and phase composition. The review highlights emerging directions involving data-driven modeling, artificial intelligence, and digital twin technologies for real-time identification of flow conditions. These approaches enable adaptive measurement strategies that reduce dependence on empirical correlations and improve robustness under variable operating regimes. The review demonstrates that future wet gas metering systems will function as intelligent monitoring platforms integrated into digital oilfield infrastructures. The combination of multiphysical sensing, advanced signal processing, and machine learning provides a pathway toward reliable inline measurement without phase separation. The presented analysis outlines key technological challenges and identifies research directions necessary for developing next-generation intelligent measurement systems for complex multiphase gas flows.

The article examines the transformation of mythological motifs in contemporary Kazakhstani literature. The relevance of the study is determined by Kazakhstani writers’ sustained engagement with mythological models as a means of artistic interpretation of historical memory, national identity, and ecological and civilizational crises. The aim of the study is to identify the principal mechanisms through which traditional mythological motifs are transformed in contemporary Kazakhstani prose and to determine their artistic functions. The research material includes works by Abdizhamil Nurpeisov, Aslan Zhaksylykov, and Nikolai Verevochkin, as well as selected texts of contemporary Kazakh prose examined within the context of the national mythological tradition. The methodological framework comprises mythopoetic, comparative-historical, structural-semantic, historical-genetic, and intertextual approaches. The findings demonstrate that mythological motifs in contemporary literature are not reproduced in an unchanged form; rather, they undergo modernization, symbolization, psychologization, historicization, ecologization, intertextualization, and neomythologization. The study establishes that the most productive mythologemes are those of water, earth, stone, the journey, the boundary, death and rebirth, the animal as mediator, and sacred space. An analysis of A. Nurpeisov’s The Last Duty reveals the transformation of the mythological model of disrupted cosmic equilibrium into an ecological myth; A. Zhaksylykov’s Singing Stones demonstrates the psychologization of the motif of transition and the mythologization of inner crisis; while N. Verevochkin’s The Mammoth’s Tooth (Chronicle of a Dead City) reveals the historicization of the end-of-the-world motif and the formation of a myth concerning the disintegration of civilizational space. The study concludes that contemporary Kazakhstani neomythologism constitutes a form of cultural self-reflection that combines the national cultural code with universal mythological models.

The article examines the Eastern poetry cycle of Daniil Andreev, specifically the works "The Prophet's Silver Night" and "Palestinian Melody" from the collection Voices of the Ages. The study focuses on the mechanisms by which the Russian poet assimilates the Muslim religious and cultural tradition and transforms it into an individual authorial artistic system. The relevance of the work is determined by the need to reassess imagological approaches in literary studies and to identify the role of poetry as an instrument of civilizational dialogue within Russia's multicultural space.

In contrast to previous scholarship, which has concentrated on biographical or general religious contexts, this study offers a systemic-conceptual analysis of Andreev's Oriental texts. This approach allows them to be interpreted not as exotic Orientalist stylizations, but as the outcome of a profound metaphysical fusion of the "native" and the "alien" material. The novelty lies in identifying the specific devices of poetic adaptation of Qur'anic meanings, whereby borrowed imagery ceases to be external and becomes an organic component of the author's worldview. The article analyses how Andreev overcomes the external "otherness" of the Eastern source through his handling of rhythmic-syntactic verse structures, systems of repetition, and caesuras, which produce an effect of confessionalism and epic grandeur. Particular attention is paid to the means of conveying Qur'anic meanings, as well as to the reconstruction of the Eastern worldview at the level of figurative imagery and spatio-temporal relations. The analysis highlights the key metaphysical constituents of Andreev's literary text, examines the specific embodiment of Oriental and Qur'anic motifs in his poetry, and emphasizes the value of the poet's engagement with the core meanings of the Muslim Holy Book.

The material is presented sequentially: from the formulation of the imagological problem and methodological foundations (grounded in the theory of the systemic-conceptual approach) to a detailed poetic analysis of the two key texts, and thence to a synthesis of the identified mechanisms of the metaphysical chronotope, which Andreev constructs at the intersection of Russian and Eastern cultural traditions.

The findings demonstrate that Andreev's poetic interpretation of the Qur'an does not reduce to thematic quotation but instead generates an independent artistic model that expands the boundaries of space and time. The resulting synthesis of Eastern and Russian culture possesses significant heuristic value for understanding intercultural dialogue, and the revealed principles of adapting "alien" material may be applied to the analysis of other instances of Oriental poetry. Further study of Andreev's creative dynamics in this respect appears both scientifically and practically warranted.