Volume 6.
Issue 12.

Methods of testing and quality control of materials in the construction of oil and gas industry facilities play a key role in ensuring the safety, reliability, and durability of structures. Both destructive and non-destructive methods are used in construction, each of which has its advantages and limitations. Destructive methods allow a deeper study of the mechanical properties of materials, but their use leads to damage to samples. At the same time, non-destructive methods such as ultrasound diagnostics and laser scanning ensure monitoring of the condition of objects without destroying their integrity. Hybrid and combined test methods allow for more accurate results, which is critically important for the oil and gas industry. The use of modern technologies in the field of quality control significantly reduces operational risks and increases the service life of facilities.

The most important task of hydrodynamic forecasts in connection with land reclamation is to predict changes in the groundwater regime and control the moisture transfer regime in the upper layers of the aeration zone. In this regard, for the development of hydraulic models of moisture transfer, it is necessary to take into account the physico-mechanical properties of soil, hydrophysical characteristics of soil type, conditions for moisture entry into soil [1,2].

It is known that the soil is a dispersed body, i.e. it consists of a large number of particles of different sizes, mostly small and very small. The consequence of this is the well-known fact that the soil is a porous body, i.e. permeated in all directions by a large number of interconnected gaps between particles. It is in these gaps-pores that the moisture that enters the soil or into the ground accumulates [3,4]. The article describes comprehensively study of the processes of forecasting changes in the groundwater regime and control of the moisture transfer regime in the upper layers of the aeration zone.

This article examines the capacity of yarns to absorb moisture under different environmental conditions. The study focuses on yarn structure, moisture absorption and retention properties, and the influence of environmental factors such as temperature, relative humidity, and pressure. The article delves into the mechanism of moisture absorption in yarns and explores ways to optimize these parameters. The findings are significant for enhancing productivity and improving product quality in the textile industry.

This study presents a comparative analysis of machine learning algorithms for customer segmentation in the banking sector, utilizing a comprehensive dataset that includes transactional, demographic, and engagement attributes. Various clustering models, including K-Means, Gaussian Mixture Models (GMM), Hierarchical Clustering, DBSCAN, and Spectral Clustering, were evaluated to identify the most effective approach in terms of segmentation accuracy, scalability, and interpretability. The results revealed that Spectral Clustering consistently outperformed other models, offering superior accuracy and valuable insights into customer interactions across multiple banking touchpoints. While K-Means delivered fast and scalable segmentation, it lacked the flexibility needed for non-spherical clusters. The study also highlighted the benefits of a multi-dimensional dataset approach, which provided deeper insights into customer behavior, engagement, and loyalty. Although limitations such as computational complexity and scalability challenges remain, future research should focus on real-time data integration and multi-channel interactions across banking operations. This research not only contributes to machine learning applications in banking but also offers actionable strategies for targeted marketing, personalized customer engagement, risk management, and overall service optimization.

This study investigates the application of machine learning models for real-time dynamic pricing strategies in the retail and e-commerce sectors. We employed three prominent supervised machine learning models—Linear Regression, Random Forest, and Gradient Boosting Machines (GBM)—to predict optimal prices using a dataset sourced from Kaggle. The models were trained and evaluated with a 70:30 train-test split, while hyperparameter tuning was performed using grid search and cross-validation. The results indicate that the Gradient Boosting Machines (GBM) model consistently outperformed the other models, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and demonstrating a higher R-squared (R²) value. The comparative analysis highlights GBM's ability to capture complex interactions in dynamic pricing data, making it a robust choice for accurate price forecasting. The Random Forest model also delivered satisfactory results, balancing accuracy and computational efficiency, whereas the Linear Regression model showed higher prediction errors due to its limitations in modeling non-linear relationships. Real-time testing in a simulated environment confirmed the models' adaptability and responsiveness in a dynamic marketplace. These findings provide actionable insights for retail and e-commerce businesses, emphasizing the importance of model selection, hyperparameter optimization, and system integration to implement efficient dynamic pricing strategies. Future work should explore more extensive datasets and real-world applications to address seasonal variations, regional preferences, and consumer behavior, ensuring a more comprehensive and practical deployment of machine learning-driven dynamic pricing models.

Asynchronous programming in Python is a powerful tool for improving application performance by effectively managing multitasking. The main focus is on the use of the async and await keywords, as well as the asyncio, ThreadPoolExecutor and ProcessPoolExecutor libraries, which play an important role in organizing multitasking processes. This is especially true for applications related to I/O operations, such as web servers and APIs. The use of asynchronous programming allows you to eliminate thread locks and optimize query processing, which makes applications more responsive and scalable. The article provides specific examples of using the asynchronous approach in practice, including parallel task execution and resource management. In addition, the study demonstrates that the introduction of asynchronous technologies helps to reduce infrastructure costs, ensuring high throughput and stable operation even under high load conditions. Asynchronous programming stands out for its flexibility and the ability to create high-performance systems that are capable of handling a large number of simultaneous I/O operations.

The landscape of software engineering is currently undergoing a fundamental paradigm shift, transitioning from manual, heuristic-driven development to an automated, cognitive-centric model powered by Large Language Models (LLMs) and Machine Learning (ML). This research article provides an extensive investigation into the integration of neural code comprehension and generative artificial intelligence across the software development lifecycle. By synthesizing contemporary advancements in architectural pattern detection, fault prediction, and automated code repair, this study elucidates how modern AI architectures-ranging from bilateral tree-based convolutional neural networks to transformer-based few-shot learners-are redefining the boundaries of software assurance and system design. We examine the transition from traditional source code metrics to learnable representations of code semantics, discussing the implications of neuro-symbolic program correctors and graph-based generative modeling. Furthermore, the paper addresses the emerging role of LLMs in identifying architectural smells, refactoring microservices, and maintaining consistency in low-code platforms. Through a rigorous analysis of existing empirical evidence and theoretical frameworks, this research identifies a critical "automation gap" in software architecting and proposes a trajectory for future autonomous systems. The findings suggest that while AI significantly enhances productivity and fault detection, issues regarding software fairness, carbon footprints, and the nuances of cross-language algorithm classification remain pivotal challenges for the next decade of academic and industrial pursuit.

The article discusses approaches to the development of systems that minimize the risks of unauthorized access, leaks, and data integrity violations. Special attention is paid to the introduction of security mechanisms at the design stage, the adaptation of architectural models to the conditions of the digital economy, and the use of modern technologies. Due to increased cyber threats and changes in the norms that determine the need to create secure data architectures in financial organizations. The work is based on the concepts of Privacy by Design and security by Design, as well as technological solutions, including blockchain, and adaptive authentication.

The approaches presented in the article include micro-segmentation of networks to isolate components, restriction of user privileges, and the use of cryptographic methods to protect information. These measures ensure compliance with the requirements of the digital environment. Scientific articles by foreign authors, as well as materials that are publicly available on the Internet, were used as sources.

The information presented in the article is intended for professionals involved in information security, system architecture development, and project management in the financial sector. They will also be useful to other scientific specialists. The focus of the work is on the need for an integrated approach, updating solutions in response to modern threats.

The article examines the problems and strategic applications of using Generative Artificial Intellegence (GenAI) in order to transform the development of digital products and digital sales in the banking sector. The intensive development of GenAI creates unprecedented opportunities for the transformation of this sector. The relevance of the research is due to the need to rethink traditional approaches in the context of digitalization. There are contradictions regarding the optimal pace of GenAI implementation: a number of researchers call for aggressive digital innovations, while others point to the need for a gradual transition based on financial institutions’ readiness and the maturity of the technologies themselves. The aim is to analyze the key areas of application of GenAI in the characterized area. The article systematizes the elements of the conceptual framework and the advantages of using generative artificial intelligence. The study proposes a novel strategic framework for assessing GenAI's impact and applications across key areas within the banking sector. Special attention is given to how GenAI affects the process of digital product development of financial products and its potential applications in digital sales, particularly through customer engagement, hyper-personalized communication, and chatbots. As a result of the study, it was found that the introduction of GenAI in the banking sector can significantly reduce the time to bring new products to market, enhance personalization in customer interactions, and drive revenue growth through innovative cross-selling strategies. The articles’ materials are of practical value for the heads of commercial and retail banks, specialists in digital transformation, and researchers in the field of financial technologies.

This article will rediscover the participation of synergistic competence in the Integrative teaching of technical and art disciplines, the importance of pedagogical synergetics and approaches.

Understanding the pattern of energy use at the household level becomes ever more urgent in light of growing concerns about climate change and resource sustainability in the USA. Energy use depends upon various factors, such as climate, household characteristics, and behavior. Of these, income, education, and size of the family are very vital socio-economic factors that depict energy consumption levels and their pattern. The utmost objective of this research project was to develop predictive models using machine learning techniques to analyze household energy consumption trends in the USA, integrating socioeconomic factors such as income, family size, and education. The dataset retrieved from Kaggle integrates detailed weather patterns with energy consumption data, putting into perspective the interaction between climatic variables and household energy use. It included key features such as temperature, humidity, wind speed, and precipitation, along with time-series data on energy consumption metrics like electricity and natural gas usage at the household level. It provided information on several geographic zones across extended periods, so seasonality and regional variations may be studied. It was complemented with metadata that included timestamps, energy pricing, and household attributes and should therefore be a rich resource for predictive modeling and extracting relationships between weather conditions and energy demand. For this research project, three models were selected: Logistic Regression, Random Forest, and Support Vector Machines, each possessing particular strengths for the nature of the problem. This study employed key performance metrics such as precision, recall, F1-Score, and accuracy. The Random Forest model had the highest value for accuracy, similarly, the highest AUC was for the Random Forest with the best AUC. As such, it was concluded that the Random Forest model provided the best trade-off between true positive rate and false positive rate and can be relied upon for this classification task. The machine learning models generate valuable predictions about household energy use. Particularly, Random Forest models, which are trained on socioeconomic and weather data to predict the likelihood of a given household having high energy usage. The predictions by such models can be used to help energy providers determine when to invoke tiered pricing or encourage energy-saving behavior.

The performance of flexible robotic systems, particularly robotic manipulators, is often compromised by their inherent elasticity and vibration dynamics. To address these challenges, this study explores the modeling and implementation of feed-forward control schemes to enhance the accuracy and efficiency of flexible robotic systems. The research develops a dynamic model of a flexible manipulator, incorporating both the rigid-body and flexible deformations, and then applies feed-forward control strategies to mitigate the effects of flexibility-induced errors. By predicting and compensating for these errors before they occur, feed-forward control can improve the system's response time and reduce vibration, resulting in smoother and more precise manipulations. This work includes the design of control algorithms, their implementation in a robotic system, and experimental validation. The results demonstrate significant improvements in the performance of the flexible manipulator, highlighting the effectiveness of feed-forward control in enhancing the precision of such systems. The findings provide insights into the practical application of feed-forward control schemes, offering a promising approach for future developments in flexible robotic systems.

The article presents a study on the development of technology for the modification of chromium-molybdenum steels in order to improve their mechanical and operational properties. The methods of introducing a modifier, the analysis of microstructural changes and their effect on strength characteristics are considered. Experimental data have demonstrated a significant increase in the strength, toughness and corrosion resistance of modified steels. Examples of the application of the developed technology in the energy, oil and gas and chemical industries are given. The developed technology opens up prospects for increasing the durability and reliability of structural materials that meet modern industrial requirements.

This paper introduces SwiftProtoReflect, a Swift library that implements Proto Reflection to enable dynamic interaction with gRPC services without the need for precompiled code from `.proto` files. Addressing the inherent limitations of Swift in handling Protocol Buffers—such as restricted dynamic code generation and limited reflection capabilities—the research employs theoretical analysis of these constraints and develops an innovative solution utilizing descriptors and dynamic message handling. The main results demonstrate that SwiftProtoReflect allows developers to define Protocol Buffers message structures dynamically, serialize and deserialize messages, and access message fields at runtime. This advancement overcomes existing barriers, offering enhanced flexibility, scalability, and performance comparable to statically generated code. Concluding, SwiftProtoReflect significantly contributes to the data engineering field by filling a critical gap in the Swift ecosystem, enabling efficient development of high-performance and adaptable applications. The novelty of this work lies in its original technological solution, expanding Swift's capabilities and providing practical benefits for high-load systems and microservice architectures.

This article briefly explains the integration of technical and artistic sciences with different disciplines in a synergistic competence, the methods of drawing the perspective of the interior room in pencil and color, and the method of drawing the interior of the auditorium.

The article examines the study of methods for implementing integrated identification continuity plans to counter cyber threats and outages with third parties. The study focuses on the analysis of various types of authentication failures, their implications for data security and business processes, as well as their impact on user experience. Approaches to the use of backup authentication and multi-factor authentication (MFA) providers, and continuous monitoring to increase the level of security are considered. The analysis demonstrates the need for regular testing of systems, the introduction of backup mechanisms to prevent failures, and risk assessment. The article also describes the importance of integrating modern solutions into an authentication system to minimize threats and maintain the continuous operation of key processes. The study highlights the importance of an integrated approach to ensuring data identification and protection in the context of modern cyber threats.

Personalized treatment recommendation systems have become a major focus of modern healthcare informatics because conventional clinical decision-making often relies on generalized treatment guidelines that cannot adequately accommodate individual patient heterogeneity. Variations in demographic characteristics, disease progression, genetic predisposition, comorbidities, treatment adherence, and longitudinal clinical responses necessitate intelligent decision-support systems capable of recommending adaptive therapeutic interventions. Reinforcement Learning (RL), a branch of artificial intelligence that learns optimal sequential decision policies through interactions with an environment, has emerged as a promising computational paradigm for personalized medicine. Unlike traditional predictive models that estimate clinical outcomes independently, RL continuously evaluates treatment consequences over time and identifies strategies that maximize cumulative patient benefit while minimizing adverse events.

The growing availability of electronic health records, observational clinical databases, standardized healthcare data models, and real-world evidence has accelerated research on RL-driven treatment optimization. However, practical implementation remains constrained by data quality, privacy protection, observational bias, model interpretability, and clinical acceptance. Furthermore, translating algorithmic recommendations into trustworthy medical decisions requires robust governance, transparent validation, and integration with established healthcare workflows.

This research and review article critically examines reinforcement learning for personalized treatment recommendation systems by synthesizing contemporary literature on health informatics, observational healthcare databases, technology acceptance, and clinical decision support. The study develops an integrated analytical framework connecting reinforcement learning methodologies with real-world healthcare infrastructures and technology adoption theories. Special attention is given to standardized observational data repositories, electronic health record integration, physician acceptance of intelligent decision-support technologies, cybersecurity considerations, and regulatory requirements. The analysis demonstrates that successful implementation depends not only on algorithmic performance but also on healthcare data quality, clinician trust, system usability, regulatory compliance, and secure digital infrastructure. The paper concludes by identifying current research limitations and proposing future directions toward clinically reliable, explainable, and patient-centered reinforcement learning systems capable of supporting precision medicine.

Blockchain technology offers secure, decentralized systems but faces increasing threats like double-spending and Sybil attacks. This study evaluates machine learning algorithms, including Random Forest, K-Means, and Deep Q-Networks, to enhance blockchain security. Experimental results show Deep Q-Networks and XGBoost outperform other models, achieving 97.8% accuracy and 0.99 AUC-ROC, demonstrating their effectiveness in real-time threat detection. This research highlights the potential of machine learning to safeguard blockchain systems and suggests future directions, such as federated learning for collaborative security and explainable AI for improved transparency.

This study aims to design and develop a bio-inspired soft robotic fish for underwater environment monitoring. The ocean is vast, covering more than 70% of earth’s surface and largely unexplored frontier having diverse ecosystems and vital resources. Monitoring underwater environment is important for understanding marine life and studying impacts of climate change. While traditional robots such as AUVs are precise and durable but due to their bulky structure struggle in complex conditions in ocean. Due to disadvantages such as less adaptable and potentially harmful to marine ecosystem of hard robots, the increasing demand for effective underwater environment monitoring has sparked interest in bio-inspired soft robotics. Soft robots are ideal for underwater monitoring due to their flexible and adaptable structure. They can navigate complex environments more easily, reducing the risk of damaging marine life and robot itself. This study presents the design and implementation of soft robotic fish inspired by manta rays known for their unique swimming pattern, efficient and agile locomotion. Our robot mimics real manta rays’ movements patterns by utilizing pectoral fins made from soft materials which generate thrusts using pneumatic actuation. The robot fins were designed by studying manta ray fin propulsion and simulating in ANSYS software where we observed same pattern of movement of real manta ray fish. The fins were fabricated using ecoflex0030 which is flexible soft material. The prototype was tested to observe the movement of fins and evaluate its performance which was close to real fish movements. This study helps in advancement of bio-inspired underwater robotics field by improving efficiency and capability of underwater monitoring systems. Future work will focus on refining the design, improving performance of robot, developing communication system and embodied sensing for data collection such as pressure, temperature of underwater environments.

This study investigates the kinetics of the pyrolysis process of a propane-butane fraction. The thermal decomposition reaction was conducted in a quartz reactor packed with 0.3–0.5 mm quartz chips (hereinafter referred to as "quartz") under an oxygen-free environment and elevated temperatures. The research focused on analyzing the decomposition process, which involves breaking C-C and C-H bonds in the absence of air at high temperatures. Before propane and butane undergo physical adsorption on the quartz surface, they first decompose into radicals. The subsequent thermal decomposition of the propane-butane fraction, primarily driven by C-C and C-H bond cleavage, is hypothesized to occur predominantly on the quartz surface within the reactor specifically designed for this process. At temperatures ranging from 500 to 800 °C, the catalytic decomposition of primary hydrocarbons on the quartz surface was examined under helium conditions, both with and without quartz. The results demonstrated the suppression of coke formation under these conditions, highlighting the significant catalytic role of the quartz surface in facilitating hydrocarbon decomposition.

Data centers form the cornerstone of modern digital infrastructure, enabling operations from e-commerce and streaming to cloud computing and artificial intelligence. The United States, at the forefront of technology, houses some of the world's most extensive and technologically advanced data centers as a part of its economic and technological framework. This study aimed to explore how different ML techniques can be used for optimizing resource utilization by data centers in the US, focusing on strategies to handle incomplete data and implications for business development. The dataset was retrieved from the GitHub repository, which provided a rich dataset of resource usage metrics and operational data from U.S. data centers. It contained complex and fine-grained information that was necessary to optimize data center performance and deal with incomplete data challenges. A detailed description of the dataset and its key attributes was provided. It was designed for analyzing resource usage patterns in data centers, putting much emphasis on energy efficiency, workload distribution, and operational reliability. This integration of time-series data with sensor readings and performance logs provided a comprehensive overview of resource consumption and environmental conditions in data center operations. This dataset was curated for the engagement of machine learning models in the study and optimization of resource consumption along with the challenges of missing data. Analysis of resource utilization in US data centers was accomplished using the application of various models for machine learning, most notably, Logistic Regression, Random Forest, and Support Vector Machines; Retrospectively, the Random Forest and SVM models seem to be robust and reliable, placing the Random Forest slightly above, given their performance is nearly perfect for training and testing. The application of machine learning techniques holds huge potential for the reformation of resource management in US data centers. These models analyze a pattern in historical data to predict future resource demands, thus allowing optimized resource allotment and minimizing operational costs.

This study investigates the thermal effects and pollutant dispersion patterns within urban street canyons using Computational Fluid Dynamics (CFD) modeling. Street canyons, formed by closely spaced buildings, often experience restricted airflow and elevated pollutant concentrations, impacting air quality and thermal comfort. CFD simulations were conducted to analyze airflow patterns, temperature distributions, and pollutant dispersion under varying meteorological and canyon geometry conditions. The model incorporates factors such as wind direction, building height, aspect ratios, and surface heating to capture realistic interactions between pollutants and thermal effects in street canyons. Results show that pollutant accumulation and thermal gradients are significantly influenced by canyon geometry and meteorological conditions, with increased heat retention in deeper canyons leading to higher pollutant concentrations. These findings underscore the importance of canyon design in urban planning to mitigate adverse thermal and air quality effects, promoting healthier and more sustainable urban environments.

In this article, students of the faculties of Architecture, Design, Engineering Graphics and Fine Arts will learn about the purpose, tasks and objectives of color science and painting, how to create works of art using various art materials, modern trends in fine art. and highlights the history and theory of painting, the life and work of great artists, sources about their famous works of art.