Search Results (793)

Maritime transportation plays a vital role in international trade and commerce, with ports serving as critical points of connection between land and sea transportation systems. The operational efficiency of port facilities is essential to ensure the uninterrupted flow of goods and services, making port safety a top priority for governments, authorities, and shipping companies. Due to the importance of Occupational Health and Safety (OHS) within port environments, it is crucial to develop a structured framework in order to collect and analyze port accidents data. Today there are several different national agencies, private organizations, and/or local regulatory bodies taking charge of these data over different areas, each with variations in how they document and classify the events; in addition these are frequently limited to only major disasters and/or summary statistics. This paper aims to create a general framework to collect and fuse open-source port accident data from different sources in a structured way and to analyze the safety conditions of port facilities by conducting a comparative evaluation based on design of experiment (DoE). Through this analysis, we identify common causes of accidents and injuries in port facilities, as well as any differences in safety conditions across regions, types of port facilities, and other relevant factors. This information can be used to inform policies and practices aimed at improving port safety, reducing accidents and injuries, and ultimately enhancing the efficiency and sustainability of maritime transportation systems. The motivation to develop this research relies on the necessity to define requirements for the development of innovative solutions to be developed by the authors using modeling and simulation (M&S) and XR (extended reality) in order to increase safety in these contexts. Full article

This study examines the determinants of financial sustainability in Turkish maritime industry by analyzing firm-level panel data from 190 ship and boat maintenance firms and 208 coastal shipping companies for the 2010–2022 period, comprising 5174 firm-year observations. Fixed-effects models with Driscoll–Kraay robust standard errors are employed to evaluate how asset structure, liquidity, and energy efficiency jointly affect firm profitability across subsectors, using the Operating Return on Assets (OROA) as the principal indicator of operational performance. The empirical results indicate substantial heterogeneity between maintenance and shipping firms. For maintenance firms, OROA shows a positive association with the Non-Current Assets to Total Assets ratio (NCATA) and the Economic Efficiency Ratio (EER) but a negative association with the Current Ratio (CR), suggesting that capital deepening and operational efficiency tend to correlate with stronger performance, whereas excess liquidity is associated with weaker outcomes. For shipping firms, OROA is positively associated with EER and Total Asset Turnover (TATR) but negatively associated with Fixed Asset Turnover (FATR) and CR, indicating relationships consistent with efficiency gains from energy management and asset utilization but linkages suggesting challenges from fleet aging and liquidity mismanagement. Overall, the findings suggest that the drivers of financial sustainability are associated with different structural conditions across maritime subsectors, highlighting the importance of targeted modernization, port efficiency, and energy-transition investment strategies. Full article

The maritime industry faces increasing pressure to improve energy efficiency, a challenge that extends to the luxury yacht sector. This study presents a comprehensive hydrodynamic assessment for retrofitting a bespoke Energy Saving Device (ESD) onto a 45 m motor yacht. A full-scale self-propulsion Computational Fluid Dynamics (CFD) model was developed and validated directly against dedicated sea trial data, ensuring high fidelity and bypassing traditional scaling uncertainties. The validated model was then utilized to design and optimize a custom pre-propeller duct system. A parametric study varying the duct’s angle of attack identified an optimal configuration of ${20}^{\circ }$, which achieves a definitive power saving of 4.7% at the vessel’s cruise speed of 12.3 knots. Analysis of the propulsive factors reveals that the gain is primarily driven by a substantial increase in the hull efficiency, ${\eta }_H$, achieved by conditioning the propeller inflow. This improvement successfully compensates for the corresponding decrease in the propeller’s open-water efficiency, ${\eta }_o$. This work demonstrates a successful end-to-end numerical workflow for designing and verifying an effective, retrofittable ESD, highlighting a practical solution for reducing fuel consumption in existing motor yachts. Full article

This study aims to evaluate and compare the performance of Autoencoders (AEs) and Sparse Autoencoders (SAEs) in forecasting the next-hour concentration levels of various air pollutants—specifically NO₂(t + 1), PM₁₀(t + 1), and SO₂(t + 1)—in the Bay of Algeciras, a highly complex region located in southern Spain. Hourly data related to air quality, meteorological conditions, and maritime traffic were collected from 2017 to 2019 across multiple monitoring stations distributed throughout the bay, enabling the analysis of diverse forecasting scenarios. The output variable was segmented into four distinct, non-overlapping quartiles (Q1–Q4) to capture different concentration ranges. AE models demonstrated greater accuracy in predicting moderate pollution levels (Q2 and Q3), whereas SAE models achieved comparable performance at the lower and upper extremes (Q1 and Q4). The results suggest that stacking AE layers with varying degrees of sparsity—culminating in a supervised output layer—can enhance the model’s ability to forecast pollutant concentration indices across all quartiles. Notably, Q4 predictions, representing peak concentrations, benefited from more complex SAE architectures, likely due to the increased difficulty associated with modelling extreme values. Full article

The relative wind is a significant but underexplored influencing factor on the tradeoff between propulsion efficiency and pollutant emissions for ships. In this study, full-scale measurements obtained from four voyages of the training ship of Baekkyung were used to quantify the effects of relative wind on ship propulsion efficiency and pollutant emissions. The collected navigational, engine performance, and emission data—including parameters such as shaft power, engine load, specific fuel oil consumption (SFOC), and NO_x and SO_x concentrations—were synchronized and then analyzed using statistical methods and a generalized additive model (GAM). Statistical correlation analysis and a GAM were applied to capture nonlinear relationships between variables. Compared with linear models, the GAM achieved higher predictive accuracy (R² = 0.98) and effectively identified threshold and interaction effects. The results showed that headwind conditions increased the engine load by ~12% and SFOC by 8.4 g/kWh while tailwind conditions reduced SFOC by up to 6.7 g/kWh. NO_x emissions peaked under headwind conditions and exhibited nonlinear escalation beyond a relative wind speed of 12 kn. An operational window was identified for simultaneous improvement of the propulsion efficiency and reduction in pollutant emissions under beam wind and tailwind conditions at moderate relative wind speeds of 6–10 kn and an engine load of 30–40%. These findings can serve as a guide for incorporating relative wind into operational strategies for maritime autonomous surface ships. Full article

Maritime accidents are low-probability, high-consequence events, making mechanism analysis crucial for risk mitigation. Existing studies often focus on single scenarios or factors and frequently mix pre-incident observational data with subjective unsafe behavior labels, limiting causal-chain construction for proactive risk prediction. To address these issues, this study proposes a Bow-Tie-based causal-chain Bayesian network, establishing a hierarchical inference chain of “observed parameters–unsafe causes–accident types” to capture causal interactions among multiple factor categories and enable inference from pre-incident data to potential unsafe causes and accident types. Applied to the Bohai Sea region, sensitivity analysis quantified the effects of risk factors under varying conditions on collision, sinking, and grounding probabilities. The results show that the method can infer accident types and unsafe causes using only pre-incident data, achieving over 70% accuracy and closely matching accident investigation findings. Moreover, it reveals layer-by-layer mechanisms of key contributing factors and provides targeted management interventions, supporting quantitative decision-making for maritime regulators and shipping companies, with significant practical applicability. Full article

Inverse Synthetic Aperture Radar (ISAR) plays a vital role in the high-resolution imaging of marine targets, particularly under non-cooperative scenarios. However, resolution degradation due to limited observation angles and marine clutter such as wave-induced disturbances remains a major challenge. In this work, we propose a novel deep learning-based framework to enhance ISAR resolution in the presence of marine clutter and additive Gaussian noise, which performs direct restoration in the ISAR image domain after an IFFT2 back projection. Under small aspect sweeps with coarse range alignment, the network implicitly compensates for residual defocus and cross-range blur, while suppressing clutter and noise, to recover high-resolution complex ISAR images. Our approach leverages a residual neural network trained to learn a non-linear mapping between low-resolution and high-resolution ISAR images. The network is designed to preserve both magnitude and phase components, thereby maintaining the physical integrity of radar returns. Extensive simulations on synthetic marine vessel data demonstrate significant improvements in cross-range, outperforming conventional sparsity-driven methods. The proposed method also exhibits robust performance under conditions of low signal-to-noise ratio (SNR) and signal-to-wave ratio (SWR), effectively recovering weak scatterers and suppressing false artifacts. This work establishes a promising direction for data-driven ISAR image enhancement in noisy and cluttered maritime environments with minimal pre-processing. Full article

This study aimed to validate a bias estimation framework for low-cost maritime IMUs by applying it to real-world shipborne data. Six estimation methods—including statistical (mean, median), model-based (least squares, cross-correlation), and signal-processing approaches (FFT, Butterworth filter)—were compared. The results demonstrated that the low-frequency Butterworth filter achieved the smallest residuals, with RMS residuals below 0.038 m/s² for accelerometers and 0.0035 deg/s for gyroscopes. In particular, AccX and AccZ residuals converged to 3.04 × 10⁻² m/s² and 2.30 × 10⁻² m/s², respectively, while GyroZ achieved 5.58 × 10⁻⁴ deg/s. Estimated accelerometer biases were 0.0405 m/s² (X-axis) and 0.1615 m/s² (Y-axis), and the optimization successfully converged with an objective function value of 9.314. The findings confirm that the previously proposed bias estimation method, originally validated in simulation, is effective under real-world maritime conditions. However, as ground truth bias values cannot be obtained in shipborne experiments, verification relied on residual statistics and cross-correlation analysis. This limitation has been explicitly stated in the conclusion, and future studies should incorporate sensitivity analyses and controlled experiments to further quantify error sources. Full article

Monitoring the condition of ropes aboard historic ships is crucial for both safety and preservation. This study introduces a portable, low-cost imaging device designed for deployment on the Italian training ship Amerigo Vespucci, enabling autonomous acquisition of high-quality images of onboard ropes. The device, built around a Raspberry Pi 3 and enclosed in a 3D-printed protective case, allows the crew to label the state of ropes using colored markers and capture standardized visual data. From 207 collected recordings, a curated and balanced dataset was created through frame extraction, blur filtering using Laplacian variance, and image preprocessing. This dataset was used to train and evaluate convolutional neural networks (CNNs) for binary classification of rope conditions. Both custom CNN architectures and pre-trained models (MobileNetV2 and EfficientNetB0) were tested. Results show that color images outperform grayscale in all cases, and that EfficientNetB0 achieved the best performance, with 97.74% accuracy and an F1-score of 0.9768. The study also compares model sizes and inference times, confirming the feasibility of real-time deployment on embedded hardware. These findings support the integration of deep learning techniques into field-deployable inspection tools for preventive maintenance in maritime environments. Full article

Maritime ferries increasingly operate under non-stationary hydro–meteorological conditions that complicate cost planning. This study investigates how short-term weather variability affects expenditures for a ferry on the Gdynia–Karlskrona route. We combine a state-based operational framework (18 discrete states) with a subsystem-level cost model covering navigation, propulsion/steering, loading/unloading, stability control, and mooring/anchoring. Direct and indirect costs are linked to subsystem activity and state duration, while weather is incorporated through hazard categories that scale hourly costs. Expert-elicited rates and observed monthly state durations provide the basis for baseline estimates and hazard scenario simulations. Results reveal a disproportionate cost structure: two open-sea states constitute over 97% of the baseline monthly cost (19,490.19 PLN). Weather hazards further amplify costs, with moderate (1st-degree) and severe (2nd-degree) scenarios producing increases of ~8% and ~20%, respectively, compared to normal conditions. By embedding weather as an endogenous factor in a probabilistic cost model based on a semi-Markov process, the approach enhances predictive fidelity and supports decision-making for climate-resilient planning. These findings suggest that adaptive routing, speed management, and targeted maintenance of the propulsion and steering subsystems during open-sea navigation offer the highest potential for cost resilience. The study provides operators and policymakers with a transparent framework for climate-resilient planning and investment in semi-enclosed maritime corridors. Full article

The meteo-tsunami as an atmospheric phenomenon is still being researched, and its effects beyond physical ones (destruction if they hit the shore) are yet to be fully classified. One such effect is an increase in false alarm occurrence in high frequency surface wave radar (HFSWR) systems used for vessel traffic surveillance and control. Unfortunately, this effect is characterized by high-dimensional and highly nonlinear functional dependencies that cannot be described by closed-form mathematical equations. Since an artificial neural network is a highly parallelized distributed architecture with a fast flow of signals from input to output designed not to execute a predefined set of commands, but to “learn” dependencies during the training process and to apply that knowledge to solve unknown, but similar problems, it is a natural solution to the presented problem. Hybrid empirical–neural model-based probabilistic neural networks (PNN) used in this research proved to be quite capable of recognizing when an increase in false alarms can be expected based on the monitoring of atmospheric conditions in the HFSWR network coverage area and to eliminate them from the system, thus increasing the safety and security of all the actors in maritime traffic. Full article

Efficient spectrum allocation and telecom traffic steering in densified heterogeneous maritime communication networks remains a critical challenge due to user mobility, dynamic interference, and congestion across terrestrial, aerial, and sea-based transmitters. This paper introduces the Moving Colorable Graph (MCG) framework, a dynamic graph-theoretical representation of interferences that extends conventional graph coloring to capture the spatiotemporal evolution of heterogeneous wireless links under varying channel and traffic conditions. The formulated spectrum allocation problem is inherently non-convex, as it involves discrete frequency assignments, mobility-induced dependencies, and interference coupling among multiple transmitters and users, thus requiring suboptimal yet computationally efficient solvers. The proposed approach models resource assignment as a time-dependent coloring problem, targeting to optimally support users’ diverse demands in dense port-area networks. Considering a realistic port-area network with coastal, sea and Unmanned Aerial Vehicle (UAV) radio coverage, we design and evaluate three MCG-based algorithm variants that dynamically update frequency assignments, highlighting their adaptability to fluctuating demands and heterogeneous coverage domains. Simulation results demonstrate that the selective reuse-enabled MCG scheme significantly decreases network outage and improves user demand satisfaction, compared with static, greedy and heuristic baselines. Overall, the MCG framework may act as a flexible scheme for mobility-aware and congestion-resilient resource management in densified and heterogeneous maritime networks. Full article

As a zero-carbon energy carrier, hydrogen is playing an increasingly vital role in the decarbonization of maritime transportation. The hydrogen pressure reducing valve (PRV) is a core component of ship-borne hydrogen storage systems, directly influencing the safety, efficiency, and reliability of hydrogen-powered vessels. However, the marine environment—characterized by persistent vibrations, salt spray corrosion, and temperature fluctuations—poses significant challenges to PRV performance, including material degradation, flow instability, and reduced operational lifespan. This review comprehensively summarizes and analyzes recent advances in the study of high-pressure hydrogen PRVs for marine applications, with a focus on transient flow dynamics, turbulence and compressible flow characteristics, multi-stage throttling strategies, and valve core geometric optimization. Through a systematic review of theoretical modeling, numerical simulations, and experimental studies, we identify key bottlenecks such as multi-physics coupling effects under extreme conditions and the lack of marine-adapted validation frameworks. Finally, we conducted a preliminary discussion on future research directions, covering aspects such as the construction of coupled multi-physics field models, the development of marine environment simulation experimental platforms, the research on new materials resistant to vibration and corrosion, and the establishment of a standardized testing system. This review aims to provide fundamental references and technical development ideas for the research and development of high-performance marine hydrogen pressure reducing valves, with the expectation of facilitating the safe and efficient application and promotion of hydrogen-powered shipping technology worldwide. Full article

Korean melon (K-melon, Cucumis melo L. var. makuwa) is a key horticultural crop in the Republic of Korea, but its short shelf life restricts long-distance export. This study evaluated the modified atmosphere (MA) films of varying oxygen transmission rates (OTR) at controlled atmosphere (CA) storage under real maritime export conditions to Vietnam. In the non-permeable OTR 0 (Control) treatment, internal O₂ rapidly declined below the anaerobic compensation point (1.67% at 10d and 0.47% at 10+3d) while CO₂ accumulated to 32–36%. This ultra-low oxygen environment induced anaerobic metabolism, evidenced by strong accumulation of fermentative metabolites such as lactic acid, acetoin, and 2,3-butanediol, along with glucose/fructose retention and increases in alanine and γ-Aminobutanoic acid (GABA). These changes disrupted glycolysis and the Tricarboxylic acid cycle (TCA), consistent with CA shock, and were accompanied by rind blackening, elevated weight loss, and hue angle shifts toward yellow-orange. By contrast, OTR 10,000 and OTR 30,000 films significantly suppressed weight loss and color changes. Partial least squares-discriminant analysis (PLS-DA) identified volatile organic compounds, namely acetoin, 2,3-butanediol, and hexanal, as key discriminant metabolites, with OTR 30,000 clearly separated from other treatments at 10+3d, indicating minimal fermentation and oxidative stress. Microbial assays revealed a dose-dependent reduction in bacterial counts with increasing OTR, while fungal growth was most strongly suppressed under OTR 10,000. Overall, OTR 30,000 maintained the lowest and most stable levels of stress-related metabolites, minimized microbial proliferation, and preserved metabolic stability throughout shipping. This study provides the first quantitative evidence of anaerobic metabolic transition and primary metabolite accumulation in K-melons under actual export trials. The findings demonstrate that optimizing MA film permeability, particularly OTR 30,000 films, offers a practical and cost-efficient strategy to extend shelf life, maintain quality stability, and enhance the global export potential of K-melons. Full article

Analysis of the impact of aging processes induced by environmental conditions, particularly aggressive ones, on the properties of polymeric materials and products made from them has been the subject of intensive research for many years. Developing materials characterized by high resistance to the specific external factors in which these materials are used is a key issue in the context of developing a sustainable economy aimed at minimizing waste and extending the service life of polymeric components. The main objective of this research was to assess and quantify the degradation mechanisms of polymeric materials manufactured using additive Fused Deposition Modeling (FDM) technology when exposed to aggressive marine environments. To achieve this, the study analyzed the influence of seawater corrosion conditions on the changes in mechanical and rheological properties of two polymeric materials: recycled polylactide (rPLA) and a wood–polymer composite (WPC) based on PLA reinforced with wood flour (MD). The results revealed that rPLA exhibited an approximately 16% decrease in average molecular weight after 9 months of seawater exposure, accompanied by a 37% reduction in tensile strength and a 24% decrease in elastic modulus. In the case of the WPC, the molecular weight decreased by about 20%, while tensile strength and elastic modulus dropped by 30% and 51%, respectively. The findings provide quantitative evidence of the susceptibility of additively manufactured biodegradable polymers to marine-induced degradation, highlighting the necessity of further optimization for maritime and coastal applications. Full article