According to the results of research in the Ustyurt region, the surface of the basement and Paleozoic rocks, as well as the bottom of Jurassic and Cretaceous sediments, are divided into three groups of geostructures by the nature of their correlation. The first group includes mobile fold systems and “mobile corners”, where correlation coefficients are weak or absent. The second group includes more inertial mobile elements with high correlation coefficients. The third group includes structures characterized by prolonged and inherited subsidence in the Phanerozoic and high correlation of the basement, Paleozoic surface, and Jurassic and Cretaceous sediments.

На основании проведенных исследований установлено, что в Северо-Устюртском регионе в ранней юре — байосе, начале келловея и оксфорда аккумулировались аллювиальные, озерные и болотные отложения, выносимые реками с Центрально-Кызылкумского и Аральского нагорий, из Карабогазгольского, Актумсуского, Карабаурского, Центрально-Устюртского и Южно-Эмбенского поднятий. К концу байоса, в батском веке, среднем-позднем келловее и киммеридже большая часть Северного Устюрта стала областью морского седиментогенеза с карбонатным режимом седиментации. Оживление тектонических движений и региональный размыв происходили на рубеже ранней и средней юры, в конце бата, келловея и титона.

The article discusses the results of interpretation of gravity survey data in the southern part of the Ustyurt region to identify zones and areas heterogeneous in terms of density properties of rocks. The study relates the distribution of anomalous gravity field with areas potentially productive for hydrocarbon accumulations. Gravity field transforms, Euler points, magnetic survey data, airborne gamma-ray spectrometry and CDP-2D seismic data were used to determine structural features and prospects of oil-and-gas bearing local structures at the Shakhpakhty step.

This article introduces the Energy-Efficient Theory of Inventive Problem Solving (EETRIZ) approach, designed to reduce electrical energy waste resulting from human mistakes in IoT-enabled environments. EETRIZ utilizes a novel integration of transfer learning and an activity-dependent environmental management algorithm to adjust settings dynamically according to real-time occupancy and activity data, hence improving energy efficiency. This system efficiently utilizes the advantages of the artificial intelligence-based adaptive gradient algorithm (AdaGrad) and root mean squared propagation (RMSProp) optimization methods to improve prediction accuracy through enhanced weight determination. EETRIZ is developed in the C programming language and is underpinned by comprehensive platforms and libraries, such as MPLAB, Nuvoton 8051 Series microcontroller unit (MCU) programming, GNU’s Not Unix multi-precision library (GMPLibrary)-GMP-5.1.1, and Miracle Library. Thorough hardware testing verifies that EETRIZ surpasses current solutions in energy efficiency, cost-effectiveness, accuracy, and user-friendliness. The system’s capacity to simultaneously control numerous IoT devices enhances its utility in various environments, including residences, workplaces, and educational facilities, providing a scalable solution to mitigate excessive energy consumption resulting from human mistakes.

The paper deals with the development of a model for real-time recognition and classification of UAVs and birds based on the training of the YOLOv10 neural network. The research area is considered relevant in connection with the problems of UAV detection in the context of security, given their growing use in various fields. A dataset consisting of 6,255 images collected from proprietary archives and public resources is trained to train the model. The process of data annotation, augmentation and distribution was implemented using Roboflow.com service. The model was trained on NVIDIA GeForce RTX 4080 GPU using Ultralytics framework. Test results showed high recognition accuracy with mAP50 and mAP50-95 metrics exceeding previous versions of YOLO. The model demonstrates the ability for efficient object segmentation and tracking, which makes it promising for optoelectronic surveillance applications. The results of the study can be useful for developers of UAV and bird detection and classification systems, as well as for improving safety in various fields. © 2025, Kazakh-British Technical University. All rights reserved.

This article explores the challenges of integrating two deep learning neural networks, YOLOv5 and RT-DETR, to enhance the recognition of unmanned aerial vehicles (UAVs) within the optical-electronic channels of Sensor Fusion systems. The authors conducted an experimental study to test YOLOv5 and Faster RT-DETR in order to identify the average accuracy of UAV recognition. A dataset in the form of images of two classes of objects, UAVs, and birds, was prepared in advance. The total number of images, including augmentation, amounted to 6337. The authors implemented training, verification, and testing of the neural networks exploiting PyCharm 2024 IDE. Inference testing was conducted using six videos with UAV flights. On all test videos, RT-DETR-R50 was more accurate by an average of 18.7% in terms of average classification accuracy (Pc). In terms of operating speed, YOLOv5 was 3.4 ms more efficient. It has been established that the use of RT-DETR as the only module for UAV classification in optical-electronic detection channels is not effective due to the large volumes of calculations, which is due to the relatively large number of parameters. Based on the obtained results, an algorithm for combining two neural networks is proposed, which allows for increasing the accuracy of UAV and bird classification without significant losses in speed.

This article investigates fraud detection in financial transaction networks using machine learning and graph-based typologies. The object of the study is financial transaction data, analyzed to improve the accuracy and efficiency of identifying fraudulent activities. The problem addressed is the limited generalizability and low recall of traditional fraud detection models in complex, real-world settings. To address this, a hybrid framework was developed that integrates Random Forests, neural networks, and graph-based typology indicators. Seven laundering typologies were extracted from a transaction graph – fan-in, fan-out, scatter-gather, gather-scatter, cycle, bipartite, and stacked bipartite – and used as additional features for classification. SMOTE was applied to correct class imbalance during training. Experimental results show that adding typology features significantly improves model performance. The best results were obtained with Random Forest: 98.5% accuracy, 79.1% precision, 56.3% recall, and an F1-score of 65.7%. Adding typology-based flags raised recall by 9–11 percentage points compared to models without them. Graph patterns like fan-in and fan-out were detected in 3.5–5.1% of transactions, while more complex structures such as cycle and scatter-gather appeared less frequently but correlated more strongly with known fraud. Unsupervised methods also showed promise: an autoencoder captured 60% of fraud cases among the top 2% anomalous transactions, while K-means identified 55% of fraud within flagged clusters. These methods proved useful for identifying emerging fraud types not yet labeled in training data. The model is suitable for integration into financial security systems with minimal input requirements – account IDs, timestamps, and transaction amounts – alongside basic graph analytics. Its robustness across datasets suggests strong applicability across diverse financial institutions. Copyright © 2025, Authors.

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.

In this article, we consider the roles of transcrustal magma- and fluid-conducting faults (TCMFCFs) in the formation of mineral deposits, showing the importance of deep sources of heat and hydrothermal solutions in the genesis and history of deposit formation. As a result of the impact on the lithosphere of mantle plumes rising along TCMFCFs, intense block deformations and tectonic movements are generated; rift systems, and volcanic-plutonic belts spatially combined with them, are formed; and intrusive bodies are introduced. These processes cause epithermal ore formation as a consequence of the impact of mantle plumes rising along TCMFCF to the lithosphere. At hydrocarbon fields, they play extremely important roles in conductive and convective heat, as well as in mass transfer to the area of hydrocarbon generation, determining the relationship between the processes of lithogenesis and tectogenesis, and activating the generation of hydrocarbons from oil and gas source rock. Detection of TCMFCFs was carried out using MMSS (the method of microseismic sounding) and MTSM (the magnetotelluric sounding method), in combination with other geological and geophysical data. Practical examples are provided for mineral deposits where subvertical transcrustal columns of increased permeability, traced to considerable depths, have been found; the nature of these unique structures is related to faults of pre-Paleozoic emplacement, which determined the fragmentation of the sub-crystalline structure of the Earth and later, while developing, inherited the conditions of volumetric fluid dynamics, where the residual forms of functioning of fluid-conducting thermohydrocolumns are granitoid batholiths and other magmatic bodies. Experimental modeling of deep processes allowed us to identify the quantum character of crystal structure interactions of minerals with "inert" gases under elevated thermobaric conditions. The roles of helium, nitrogen, and hydrogen in changing the physical properties of rocks, in accordance with their intrastructural diffusion, has been clarified; as a result of low-energy impact, stress fields are formed in the solid rock skeleton, the structures and textures of rocks are rearranged, and general porosity develops. As the pressure increases, energetic interactions intensify, leading to deformations, phase transitions, and the formation of chemical bonds under the conditions of an unstable geological environment, instability which grows with increasing gas saturation, pressure, and temperature. The processes of heat and mass transfer through TCMFCFs to the Earth's surface occur in stages, accompanied by a release of energy that can manifest as explosions on the surface, in coal and ore mines, and during earthquakes and volcanic eruptions.

Current data reveals about a hundred gold-bearing geological bodies have been identified in the Akmola region of Kazakhstan. Such ore-bodies are characterized by various quartz veins that occur often as stockworks, and new genetic types. The region looks promising in terms of the formation of gold-bearing rocks in deep horizons, since favorable factors of ore-forming geological structures have been identified. This assertion is supported by the known gold occurrences, and historic data from detailed exploration work, which prove the prospects for identifying economically viable gold deposits for development. Irrespective of the promising gold-rich terrain, the existence of complex geological structures, presence of extensive colluvium or loose sediments cover, coupled with poor geological to geophysical information, impedes exploration successes. This paper shows that considering the aforementioned challenges, a composite geophysical methods of aeromagnetic, and electrical techniques through unmanned aerial vehicle (UAV), marked by induced polarization in the modification of the median gradient can be effective. The results made it possible to solve the problems of detailing the geological and tectonic structure of the area, the spatial positions, and identification of deeply buried ore bodies. The adopted combined-approach projects subtle geological structure of the site, identifies promising geological bodies of interest for steppe-type gold mineralization, and demonstrated the applicability of geophysical methods in the search for gold-bearing zones