Volume 5.
Issue 08.

Reliability Centered Maintenance (RCM) is a widely used approach in the maintenance of complex systems, including ship systems. However, the traditional RCM process can be time-consuming and subjective, requiring expert judgment and extensive data analysis. In recent years, the Weighted Aggregated Sum Product Assessment (WASPAS) method has emerged as a decision-making tool that combines subjective and objective factors in a systematic and efficient manner. This study focuses on the application of WASPAS in enhancing Reliability Centered Maintenance for ship system maintenance. The objective is to demonstrate how WASPAS can improve the decision-making process by considering multiple criteria and providing a comprehensive ranking of maintenance strategies. The study utilizes case studies and simulations to assess the effectiveness and efficiency of applying WASPAS in ship system maintenance. The results highlight the advantages of using WASPAS, such as improved decision-making accuracy, reduced subjectivity, and enhanced overall maintenance performance. The findings contribute to the advancement of Reliability Centered Maintenance practices in the maritime industry.

The article presents studies of the interaction of the working bodies of agricultural machines with plants. The study of theoretical and applied issues of cutting desert-pasture stems. Theoretically proved the shape of the cutting element in the form of a parallelogram with an angle of oblique or sliding cutting with the optimal value of the cutting angle in the range of 30º- 35º.

Currently, it is necessary to assess agricultural land and crops based on remote sensing materials and conduct research on monitoring and improving land using drones. In particular, this article provides information on seed germination, development period, unmanned control of sown areas using drones.

Textile industry wastewater contains various pollutants that require effective treatment before discharge. Aerobic biogranules have shown promise as a sustainable and efficient solution for wastewater treatment. This study investigates the effect of a static mixer on aerobic biogranules formation for textile industry wastewater treatment. The static mixer is introduced in the wastewater treatment process to enhance mixing and promote the formation of aerobic biogranules, which are dense microbial aggregates capable of degrading organic pollutants. Laboratory-scale experiments are conducted to compare the performance of aerobic bioreactors with and without the static mixer. Parameters such as pollutant removal efficiency, biogranule characteristics, and system stability are evaluated. The results demonstrate that the use of a static mixer significantly enhances the formation of aerobic biogranules, leading to improved wastewater treatment performance. The findings of this study contribute to the understanding of the role of mixing in aerobic biogranule formation and provide insights for the optimization of textile industry wastewater treatment processes.

Background: The migration from monolithic systems to microservice architectures represents one of the most significant paradigm shifts in contemporary software engineering. While microservices promise scalability, resilience, and organizational alignment, the transformation of legacy monoliths remains complex, risk-laden, and methodologically fragmented. Foundational works on domain-driven design, refactoring, legacy code management, service decomposition, and evolutionary migration provide partial guidance, yet an integrated, systematic framework for service boundary identification and transformation remains underdeveloped.

Objective: This study proposes a comprehensive, publication-ready framework that integrates domain-driven design principles, systematic service decomposition methods, workload-based clustering, topic modeling, architectural meta-modeling, evolutionary refactoring strategies, and machine learning-assisted service boundary detection. The objective is to synthesize established theoretical foundations into a coherent transformation methodology suitable for industrial adoption.

Methods: Drawing strictly from established literature, the research develops a multi-phase transformation framework. The approach incorporates domain analysis, bounded context identification, service cutter decomposition, workload-based clustering, topic modeling of source code artifacts, architectural meta-modeling for granularity control, evolutionary migration patterns, resilience-oriented refactoring, and machine learning-assisted modularization. The framework is conceptually validated through theoretical alignment with prior empirical findings and architectural principles.

Results: The proposed framework demonstrates theoretical improvements in service cohesion, reduced coupling, granularity optimization, resilience enhancement, and legacy risk mitigation. By combining human-driven domain modeling with data-driven boundary detection, the approach balances conceptual rigor and empirical grounding. Evolutionary migration strategies reduce transformation risk, while refactoring and legacy isolation techniques enhance maintainability.

Conclusion: The study contributes a unified transformation methodology that bridges foundational architectural theory and contemporary machine learning techniques. It offers a structured pathway for organizations transitioning from monoliths to resilient microservice ecosystems while minimizing technical debt and architectural erosion.

This article discusses the issues of displaying a special useful model “Georeferencing Roads” on satellite images by installing them on the roadsides, increasing the accuracy of geospatial linking of space photographs, vectorizing space photographs by transforming them in the geodatabase, and including information related to the state cadastre of highways in the attributive data table.

Nanocarbon particles, such as carbon nanotubes and graphene, have gained significant attention due to their unique properties and potential applications in various fields, including thermal management and nanofluid-based heat transfer. Understanding the thermophysical properties of nanocarbon particles dispersed in different base fluids is crucial for optimizing their performance in thermal applications. This study presents a comparative analysis of the thermophysical properties of nanocarbon particles in ethylene glycol and deionized water. The properties investigated include thermal conductivity, viscosity, and specific heat capacity. Experimental measurements and computational simulations are performed to determine the effects of nanocarbon concentration and temperature on these properties. The results reveal significant enhancements in thermal conductivity for both nanocarbon-ethylene glycol and nanocarbon-deionized water suspensions, with higher enhancements observed in ethylene glycol. Viscosity and specific heat capacity also show distinct changes with the addition of nanocarbon particles. The findings provide valuable insights into the thermophysical behavior of nanocarbon-based nanofluids in different base fluids and contribute to the development of efficient thermal management systems.