The article discusses the method and results of processing statistical data from an experimental study of vibrations in marine diesel engines caused by the operation of cylinder-piston groups. The results of the application of a ranking method for identifying factors that influence vibration in marine diesel engines are presented to determine the most significant ones. A series of experiments were conducted according to special plans to actively implement the random balance method. This helped to establish the correctness of selecting the most significant factors from a variety of factors that influence the process under study. The article presents a mathematical model that enables the calculation of current values and prediction of changes in the most significant indicators, with the clearance between the piston and the cylinder liner being the most important.

Diesel engines remain the foundation for obtaining mechanical energy in sectors where autonomy and reliability are required; however, predictive diagnostics under real-world conditions remain challenging. The purpose of this scoping review is the investigation and systematization of published scientific data on the application of vibration methods for monitoring the technical condition of diesel engines in industrial or controlled laboratory conditions. Based on numerous results of publication analysis, sensor configurations, diagnosed components, signal analysis methods, and their application for assessing engine technical condition are considered. As methods for determining vibration parameters, time-domain and frequency-domain analysis, adaptive decompositions, and machine and deep learning algorithms predominate; high accuracy is more often achieved under controlled conditions, while confirmations of robustness on industrial installations are still insufficient. Key limitations for the application of vibration monitoring methods include the multicomponent and non-stationary nature of signals, a high level of noise, requirements for sensor placement, communication channel limitations, and the need for on-site processing; meanwhile, the assessment of torsional vibrations remains technically challenging. It is concluded that field validations of vibroacoustic data, the use of multimodal sensor platforms, noise-immune algorithms, and model adaptation to the specific environment are necessary, taking into account fuel quality, transient conditions, and climatic factors.

Currently, one of the most pressing global issues is ensuring that human activities have access to water resources that meet essential quality standards. This challenge is addressed by implementing a series of organizational and technical measures aimed at preserving the ecology of water basins and reducing the level of harmful industrial emissions and other pollutants in the aquatic environment. To guarantee the necessary quality of water resources, monitoring is conducted based on selected parameters using various methods and means of technical quality control. From these results, suitable measures are formulated and applied to maintain water quality. Various scientific works extensively discuss different approaches to water quality management and compliance with specified requirements. Modern strategies for developing water monitoring systems leverage the capabilities of information systems that collect, process, store, and transmit information, enabling the resolution of issues in geographically distributed water bodies in real time. This paper proposes an approach that employs mathematical methods to identify the most significant factors determining water quality and to assess their interrelations using methods of a priori ranking, multivariate correlation regression analysis, and integral quantitative assessment. A hardware and software solution for the development of a unified integrated information and analytical system is proposed. This system enables continuous monitoring and assessment of water bodies based on a set of key parameters, addressing a range of critical tasks. This paper provides a detailed description of the software product, presents a demonstration using real-world data, and discusses the anticipated benefits of implementing such an information and analytical system.

The optimization of transportation logistics is a critical challenge in industries with complex supply chain frameworks, such as the oil and gas petroleum sector. This study applies Linear Goal Programming (LGP) to address the multidimensional transportation planning challenges associated with the South Pars Gas Field Development (SPGFD) in Iran. The proposed model incorporates conflicting objectives, including minimizing transportation costs, maximizing refinery capacity utilization, and fulfilling depot demands under logistical and operational constraints. A multi-objective optimization framework was developed to balance cost efficiency, supply chain reliability, and operational goals. Furthermore, sensitivity analyses were conducted to evaluate the model's robustness under fluctuating transportation costs, varying refinery outputs, and dynamic demand conditions.The results demonstrate the LGP model's effectiveness, achieving a 16% reduction in transportation costs compared to traditional methods, while ensuring full compliance with refinery output capacities and depot demands. In crisis-mode simulations, characterized by reduced refinery outputs and increased costs, the model maintained 98% demand fulfillment and achieved a 9% cost saving, underscoring its resilience and adaptability. The study highlights the potential of LGP to optimize supply chain operations in dynamic and uncertain environments, offering a scalable framework applicable to other industries.

This study presents the development of a conceptual model for an autonomous underwater vehicle (AUV) information and control system (ICS) tailored for the mineral and raw materials complex (MRMC). To address the challenges of underwater mineral exploration, such as harsh conditions, high costs, and personnel risks, a comprehensive model was designed. This model was built using correlation analysis and expert evaluations to identify critical parameters affecting AUV efficiency and reliability. Key elements, including pressure resistance, communication stability, energy efficiency, and maneuverability, were prioritized. The results indicate that enhancing these elements can significantly improve AUV performance in deep-sea environments. The proposed model optimizes the ICS, providing a foundation for designing advanced AUVs capable of efficiently executing complex underwater tasks. By integrating these innovations, the model aims to boost operational productivity, ensure safety, and open new avenues for mineral resource exploration. This study’s findings highlight the importance of focusing on critical AUV parameters for developing effective and reliable solutions, thus addressing the pressing needs of the MRMC while promoting sustainable resource management.

Centrifugal compressors (CCs) are key components of marine power plants (MPPs), supporting engine boosting, boil-off gas (BOG) handling on liquefied natural gas (LNG) carriers, and auxiliary services such as heating, ventilation, and air conditioning (HVAC). However, recent publications are often fragmented by domain (aerodynamics, mechanical design, standards, and digitalization), complicating cross-domain engineering decisions for marine duty cycles. This structured review follows an explicit protocol to synthesize peer-reviewed studies (2015–2025) retrieved from Scopus and Web of Science and organizes the evidence by application class: turbocharger-integrated stages for marine diesel and gas-turbine engines, LNG/BOG compression trains, and auxiliary onboard services. The synthesis consolidates (i) aerodynamic KPIs (pressure ratio, efficiency, surge and stall margins, and operating range), (ii) mechanical and lifecycle enablers (seals, bearings, and rotordynamics), and (iii) quantified impacts of digital methods (control, diagnostics, and digital twins). Reported trends include single-stage pressure ratios of ~5.4–5.7, multistage overall pressure ratios exceeding 10, and surge-margin improvements of ~40–44% associated with advanced diffusers as well as casing and endwall treatments. Industrial case studies (non-marine) report downtime reductions of ~25–35% and maintenance-cost reductions of ~25%, while evaluated diagnostic datasets show high accuracy. Key gaps remain in marine-specific validation datasets and harmonized testing and data standards.