This study addresses the growing need for predictive maintenance in the maritime industry by proposing an optimized strategy for ship engine maintenance. The aim is to reduce unplanned failures that cause significant financial losses and disrupt global logistics flows. The methodology integrates Weibull reliability analysis, Markov chains, and Data Envelopment Analysis (DEA). A dataset of 512 diesel engine components from container ships was analysed, where the Weibull distribution (β = 1.8; α = 18,500 h) accurately modelled failure patterns, and Markov chains captured transitions between operational states (normal, degraded, failure). DEA was used to evaluate the efficiency of different maintenance strategies. Results indicate that targeting interventions in the degraded state significantly reduces downtime and improves component reliability, particularly for high-pressure fuel pumps and turbochargers. Optimizing maintenance extended the Mean Time to Failure (MTTF) up to 22,000 h and reduced the proportion of failures in critical components from 64.3% to 40%. These findings support a transition from reactive to proactive maintenance models, contributing to enhanced fleet availability, safety, and cost-effectiveness. The approach provides a quantitative foundation for predictive maintenance planning, with potential application in fleet management systems and smart ship platforms.

Work-related musculoskeletal disorders (WMSDs) are among the most prevalent occupational health issues, particularly affecting public transport drivers due to prolonged sitting, constrained postures, and poorly adaptable cabins. This study addresses the ergonomic risks associated with tram driving, aiming to evaluate biomechanical load and postural stress in relation to drivers’ anthropometric characteristics. A combined methodological approach was applied, integrating two standardized observational tools—RULA and REBA—with anthropometric modeling based on three representatives European morphotypes (SmallW, MidM, and TallM). ErgoFellow 3.0 software was used for digital posture evaluation, and lumbar moments at the L4/L5 vertebral level were calculated to estimate lumbar loading. The analysis was simulation-based, using digital human models, and no real subjects were involved. The results revealed uniform REBA (Rapid Entire Body Assessment) and RULA (Rapid Upper Limb Assessment) scores of 6 across all morphotypes, indicating moderate to high risk and a need for ergonomic intervention. Lumbar moments ranged from 51.35 Nm (SmallW) to 101.67 Nm (TallM), with the tallest model slightly exceeding the recommended ergonomic thresholds. These findings highlight a systemic mismatch between cabin design and user variability. In conclusion, ergonomic improvements such as adjustable seating, better control layout, and driver education are essential to reduce the risk of WMSDs. The study proposes a replicable methodology combining anthropometric, observational, and biomechanical tools for evaluating and improving transport workstation design.

This paper explores the application of advanced analytical methods and predictive maintenance technologies to optimise maintenance strategies for complex technical systems, with a specific focus on marine engines and high-pressure fuel pumps. Weibull analysis facilitated the identification of failure patterns and precise planning of maintenance intervals, while hidden Markov models (HMM) provided deeper insights into the dynamics of state transitions, such as degradation and failure. The results demonstrate that combining reliability theory with cost analysis ensures an optimal balance between maintenance costs and system reliability. HMM revealed how changes in pump performance directly impact engine functionality, whereas failure data alone often fail to provide a clear understanding of system conditions. Integrating HMM enabled better comprehension of underlying processes, which is crucial for aligning pump maintenance with reduced failure rates and improved operational efficiency. The application of these methods and technologies reduces maintenance costs, extends equipment lifespan and enhances overall efficiency. System safety is ensured, and the risk of unexpected failures is minimised, contributing to the stability and economic sustainability of complex technical systems. This integrated approach offers a framework for future maintenance strategies, uniting technical and financial aspects into a comprehensive solution for long-term reliability and effectiveness.

Floating Car Data (FCD), traffic information collected by vehicles acting as mobile sensors, are becoming a core input of smart urban traffic networks. This narrative review proposes a five-dimensional taxonomy of FCD technologies (probe vehicle type, data collection mechanism, communication architecture, sampling characteristics, and ownership and access) with multi-source fusion as a transversal layer. It analyses six performance dimensions, reviews six application families and two cross-cutting perspectives (sustainability and equity), and derives seven coupled research directions. Penetration requirements for live monitoring rise by roughly an order of magnitude from coarse highway monitoring (lower bound from one simulation) to high-quality monitoring of urban arterials. Smartphone and taxi positioning supports network-level estimation but not lane-level applications, and no technology class is favourable on all six dimensions. In this purposive sample, 18 of the 90 primary FCD studies use fusion, which outperformed the best single source only on motorways. Although most primary studies are urban, urban field evidence is limited to a few arterials, intersections, and cities, so transfer to dense urban networks requires field validation.