Search Results (332)

Liquefied natural gas (LNG) is increasingly used in maritime propulsion systems to reduce atmospheric emissions. However, methane slip from dual-fuel engines remains a critical limitation due to the high global warming potential of methane. This study presents a comprehensive experimental assessment of greenhouse gas (GHG) emissions from a new-generation low-pressure four-stroke dual-fuel (LPDF 4-S) engine installed on a cruise vessel and operating on both LNG and marine gas oil (MGO). Measurements were carried out during full-scale sea trials under real navigation conditions. Results show that methane slip remains strongly dependent on engine load, with low and stable values at medium-to-high loads (1.95 g·kWh⁻¹ average over the 60–90% range) and a value of 5.6 g·kWh⁻¹ at 26% engine load. Compared with the previous engine generation (46DF), the 46TS-DF engine exhibits an approximate 18% reduction in methane slip above 60% load. On a well-to-wake basis, this results in an overall carbon dioxide CO₂-equivalent emission reduction of about 6%, of which 42% is attributable to methane slip reduction and the remainder to improved energy efficiency. In contrast, switching from MGO to LNG operation leads to a 21% decrease in CO₂-equivalent emissions. Black carbon (BC) emissions were measured and as expected despite the limited number of available studies, they were found to be significantly lower in LNG mode, with reductions exceeding 90% compared with MGO operation. Finally, an Engine Load Monitoring (ELM) analysis based on one year of operational data highlights the strong influence of vessel operating profiles on methane slip. The application of both International Maritime Organization (IMO) and FuelEU Maritime methodologies yields consistent methane slip coefficients (1.34% and 1.36%, respectively), significantly lower than current default values, noting that these estimates do not include crankcase emissions. These results demonstrate the importance of integrating real operational conditions into emission assessment frameworks for LNG-fuelled vessels. Full article

Accurate, large-scale maritime traffic monitoring supports marine spatial planning, fishery regulation, and ecological conservation. Medium-resolution optical satellite imagery, such as Sentinel-2A/B, provides a cost-effective complement to incomplete Automatic Identification System (AIS) data. Detecting small vessels in coastal waters remains challenging due to target size, complex backgrounds, and class imbalance. This study presents a robust end-to-end framework for small vessel detection and traffic density mapping using optical remote sensing imagery. A high-quality dataset of 8123 manually annotated vessels was constructed from 14 Sentinel-2 scenes across three marine environments in Fujian, China. An overlapping sliding-window cropping strategy, area truncation filtering, and negative sample preservation improved training efficiency and balance. Experiments compared top-of-atmosphere reflectance with surface reflectance (L2R) from the ACOLITE atmospheric correction (AC) algorithm, showing L2R mitigates aerosol scattering and nearly doubles vessel edge sharpness. YOLO-based detectors were evaluated, with YOLO26m achieving the best localization: F1-score 0.8461, mAP50 0.8979, mAP50–95 0.5536. Using this framework, 1 × 1 km traffic heatmaps for the Fujian coast in 2025 captured seasonal variations influenced by logistics and fishery moratoriums. Results demonstrate that integrating atmospherically corrected imagery with optimized deep learning strategies enhances sub-pixel ship detection, offering a scalable solution for intelligent maritime governance. Full article

Introduction: The Strait of Gibraltar, a narrow gateway between the Atlantic Ocean and the Mediterranean Sea, concentrates intense biotic exchange and functions as a global “ecological gate” for marine species. Despite its ecological relevance, sustained acoustic telemetry monitoring has long been hindered by oceanographic complexity, strong currents, intense maritime traffic, and geopolitical constraints. As its Atlantic approach, the Gulf of Cádiz is a highly productive area supporting diverse habitats, key spawning and nursery grounds, and major migration pathways, while underpinning important commercial fisheries. Objective: to deploy a system to enable continuous monitoring of animal movements across this key biogeographic boundary. Methodology: The EU-funded STRAITS project, a multidisciplinary consortium integrating physical oceanography and marine biotelemetry, has deployed an acoustic telemetry curtain across the Strait of Gibraltar, complemented by a coastal acoustic receiver array in the Gulf of Cádiz. Results: Over the past two years, a consistent pattern of frequent acoustic detections has demonstrated the system’s potential. Data reveal spatio-temporal migration patterns of commercially important species such as Atlantic bluefin tuna (Thunnus thynnus) and meagre (Argyrosomus regius), alongside conservation-relevant species like ocean sunfish (Mola mola). Conclusions: These results highlight the Strait of Gibraltar as a biodiversity hotspot and a strategic location for quantifying connectivity, migration timing, and species fluxes, supporting ecosystem-based management and blue policy frameworks. Full article

Foggy ship detection frequently suffers from image degradation, blurred object contours and a high missed detection rate. Moreover, most existing maritime datasets lack adequate real fog samples. To solve the above problems, in this paper, the Fog-SMD is constructed on the basis of atmospheric scattering principles and fractal theory in combination with a diffusion model to enrich samples covering various fog scenarios. On this basis, we develop an improved SF-YOLO model that takes YOLOv12 as the basic framework. By embedding the shallow–deep adaptive feature fusion module, scattering-guided refinement module and spatial-frequency dual feature attention module, the model can effectively alleviate feature loss resulting from image degradation in foggy environments. Weighted-EIoU loss is introduced to optimize the bounding box regression and reduce the localization deviation of slender ship targets. The experimental results show that SF-YOLO achieves mAP@50 and mAP@50:95 values of 79.3% and 61.6%, respectively, and outperforms mainstream detection algorithms; compared with YOLOv12n, it improves mAP@50:95 from 59.6% to 61.6%, with only a slight increase in parameters from 2.5 M to 2.8 M, providing a new solution for the practical deployment of detection systems and all-weather maritime monitoring in low-visibility foggy scenarios. Full article

Hydrogen is increasingly recognised as one of the leading pathways for decarbonising the maritime sector. Proton exchange membrane fuel cell (PEMFC) hybrid propulsion is emerging as a promising low-emission technology; however, its safe deployment depends on marine engineers being trained to interpret and manage coupled hydrogen, fuel cell, battery, and electric propulsion systems. However, a critical training gap remains. Alternative fuel guidance identifies hazards and safety barriers, but does not consistently translate hydrogen PEMFC–LFP operation into observable competence assessment evidence and implementation pathways. This paper develops a demonstrator-anchored and regulatory-grounded competency framework for marine engineers operating compressed hydrogen PEMFC-lithium iron phosphate (LFP) battery–electric propulsion systems. A structured purposive narrative synthesis combined prototype vessel testing evidence with regulatory safety training, and competency framework literature. The experimental operational data, including compressed hydrogen supply, pressure regulation, PEMFC charging, battery buffering, propulsion current demand, voltage sag, state-of-charge response, monitoring tasks, alarms, and emergency isolation, were used as operational anchors rather than calibrated performance validation evidence. The analysis identified six competency domains. Compared with IGF/LNG model course training, the largest hydrogen-specific competence gaps concerned compressed hydrogen handling, PEMFC purge and shutdown logic, battery-buffered propulsion monitoring, integrated emergency shutdown, and communication during abnormal operation. These findings were translated into assessable learning outcomes, a provisional 40 h training module, instructor prerequisites, practical assessment evidence, a proposed digital twin/VR supplement, and a staged implementation roadmap. The proposed framework provides a structured pilot pathway. It translates operational testing evidence into assessable maritime education and training. It also establishes a foundation for future competency development and certification for commercial vessels. Full article

The growing adoption of artificial intelligence and autonomous decision-making systems in maritime operations introduces significant challenges related to safety, accountability, and human oversight. Although Human-in-the-Loop approaches are commonly proposed to maintain human control over autonomous systems, continuous supervision is often impractical due to cognitive workload, operator fatigue, alert saturation, and scalability constraints. This study introduces an Adaptive Human Oversight framework for Maritime Agentic AI Systems, extending the Controlled Agentic AI Systems paradigm with a risk-aware, constraint-preserving, and auditable human governance layer. The framework employs a two-stage risk mechanism in which hard safety conditions, including critical loss of separation, boundary violations, infeasible actions, and excessive speed conditions, override the weighted composite risk score and trigger human oversight or fallback behavior independently of activation thresholds. Under elevated but non-critical conditions, a Composite Risk Assessment Module regulates human activation using interpretable indicators of separation, congestion, speed, and uncertainty. The framework also defines the behavioral semantics of human oversight actions, ensuring that approval, modification, override, and rejection remain compatible with governance constraints before execution. To reduce unnecessary supervisory burden, the framework incorporates hysteresis and trend-aware cooldown mechanisms that suppress redundant repeated requests while preserving responsiveness to critical safety events. Simulation experiments conducted using the MARIS-AI platform evaluated risk thresholds, CRAM weight sensitivity, cooldown diagnostics, operator reliability, intervention delay, and stress-test scenarios. Results show that threshold tuning primarily regulates non-critical elevated-risk states, while critical states are governed by hard safety overrides. Trend-aware cooldown reduced intervention frequency without suppressing critical safety events, whereas operator reliability and intervention timeliness strongly determined safety outcomes. The findings suggest that effective maritime human oversight should rely on hard safety constraints, interpretable risk assessment, timely human activation, workload-aware cooldown, and auditable decision traces rather than continuous monitoring alone. The proposed framework provides a pathway toward scalable, trustworthy, and accountable human–AI collaboration in maritime agentic AI systems. Full article

As climate change intensifies heat extremes, the Urban Heat Island (UHI) effect amplifies local thermal stress. Assessing the UHI using robust observational data, whether ground- and/or satellite-based, is essential for climate risk assessment and evidence-based urban adaptation. Therefore, this study aims to provide a comprehensive climatological assessment of air temperature patterns and UHI intensity across the Lisbon Metropolitan Area (LMA) over a 26-year period (2000–2025). The methodology employs a dense, high-quality integrated network of in-situ weather stations from the Portuguese Institute for Sea and Atmosphere (IPMA) and the National Water Resources Information System (SNIRH). To bridge critical gaps in traditional climate assessments, this research implements a dual-perspective approach that combines the high temporal resolution of MSG-SEVIRI and the spatial precision of MODIS Land Surface Temperature (LST). This framework accurately captures the lag effects between surface heating and atmospheric response. Validation results demonstrate that satellite-derived LST is a robust proxy for monitoring the nocturnal UHI, with differences generally below 1 °C compared with near-surface air temperature observations (T2m). However, daytime LST significantly overestimates atmospheric temperatures, with deviations of 2–8 °C due to solar radiation and urban geometry. The selection of rural reference stations constitutes a critical methodological factor, as a baseline shift can alter perceived UHI intensities by more than 3 °C. Despite these sensitivities, the results unequivocally confirm a persistent and spatially heterogeneous UHI effect in Lisbon, which intensifies during extreme heat events by up to an additional 4 °C. Analysis of the 2003 and 2018 heatwaves reveals surface LST anomalies exceeding 10 °C and urban–rural thermal differentials reaching up to 7 °C under conditions of suppressed maritime breezes. These nocturnal anomalies are particularly pronounced in densely built-up areas, limiting thermal dissipation and preventing physiological recovery. Integrating multi-sensor satellite data with in-situ validation provides a new benchmark for climate risk assessments, delivering the reliable, reproducible data required to strengthen long-term urban resilience under increasingly frequent extreme heat events. Full article

Accurate vessel trajectory prediction from Automatic Identification System (AIS) records is important for maritime traffic monitoring, route planning, and intelligent vessel traffic management. However, reliable prediction remains challenging for long forecasting horizons and turning maneuvers. To address this problem, this study proposes the Geometry-Consistent Trajectory Transformer (GCT-Former), a progressive and refinement-based framework for AIS-based vessel trajectory prediction. The proposed model integrates multi-scale historical trajectory encoding, progressive residual future-position generation, and global–local trajectory refinement to improve the stability and continuity of long-horizon trajectory prediction. The predicted trajectories are evaluated as geometric future-position estimates and can provide trajectory-level information for downstream maritime traffic monitoring and decision-support applications. Experiments are conducted on three real-world Danish maritime regions: Aarhus Bay, Great Belt, and Skagen. Compared with representative conventional and deep-learning trajectory prediction models, the proposed model shows its most consistent advantage in long-horizon prediction, particularly in terms of ADE and FDE. In the long-term setting, it achieves average displacement errors of 0.344, 0.546, and 0.218 km and final displacement errors of 0.774, 1.368, and 0.525 km on Aarhus Bay, Great Belt, and Skagen, respectively. The ablation analysis further shows that removing the multi-scale encoding module increases the long-term average displacement error by 7%, 4%, and 3%, while removing the progressive residual decoder leads to larger increases of 15%, 9%, and 8% on the three datasets. The turning-maneuver analysis also shows lower geometric prediction errors under mild-turning and sharp-turning scenarios. These results indicate that GCT-Former improves AIS-based vessel trajectory prediction, especially for long-horizon and maneuvering cases. Full article

The safe and uninterrupted operation of the ship’s main engine is critical for maritime transportation. The shutdown mechanism, part of the main engine protection systems, prevents serious damage by automatically stopping the engine in critical situations such as low lubrication oil pressure, overspeed, high bearing temperature, and cooling system failures. However, identifying the faults that trigger the shutdown system and evaluating their risk levels is crucial for improving system reliability. In this study, shutdown events that may occur in a two-stroke low-speed marine diesel main engine were investigated using Fuzzy Fault Tree Analysis (FFTA). The shutdown event was defined as the peak event, and a total of 34 baseline events were modelled under five main branches: low lubrication oil pressure, overspeed, high thrust bearing temperature, abnormal jacket coolant inlet condition, and crankcase/cylinder oil mist formation. Fuzzy assessments based on expert opinions were defuzzified and converted into probability values and used in fault tree calculations. The results showed that the shutdown risk is largely affected by failures originating from the jacket coolant system and the lubrication oil system. Specifically, lubrication oil filter clogging and contamination/blockage in the coolant line were identified as the most critical risk factors. The findings significantly contribute to prioritizing maintenance and condition-monitoring activities aimed at improving the ship’s main engine reliability through a risk-based approach. Full article

The transition towards sustainable, resilient and digitally integrated port ecosystems has increased the need for collaborative innovation approaches capable of supporting broader sustainability transitions. In this context, open and sustainable innovation (OSI) offers a strategic mechanism for integrating economic, environmental and social objectives within complex maritime ecosystems. Although previous studies have explored technological innovation and isolated sustainability initiatives in ports, limited empirical attention has been given to how stakeholders perceive OSI and how its implementation is operationalised across a national port system. This study addresses this gap by investigating the central research question: how do key stakeholders perceive and implement OSI practices within the Portuguese port system? Specifically, it analyses organisational culture, governance structures, stakeholder engagement mechanisms, institutional barriers and sustainability-oriented innovation practices. The research adopts a qualitative approach based on ten semi-structured interviews with representatives of five Portuguese port authorities occupying senior management and strategic positions. The findings show that OSI is widely recognised as important for competitiveness, sustainability performance and alignment with transition agendas, but its implementation remains uneven across ports. Organisational resistance, fragmented governance, regulatory complexity and limited monitoring mechanisms constrain the institutionalisation of OSI practices. Nevertheless, collaborative initiatives involving universities, innovation networks, public–private partnerships and digital platforms indicate a gradual shift towards more integrated and participatory governance models. The study concludes that OSI can support sustainability transitions in port ecosystems when enabled by coordinated governance, stakeholder collaboration and organisational capabilities. Full article

For stable operation of autonomous surface ships, real-time anomaly detection of engine conditions must be coupled with an operational framework that sustains model performance in dynamic maritime environments. This study proposes an autonomous maintenance system that combines a subsystem-level condition-based maintenance (CBM) model with a dedicated MLOps framework. The main engine is decomposed into multiple functional component units, each governed by an independent diagnostic pipeline that applies a hybrid algorithm combining an attention LSTM autoencoder with an isolation forest to capture subtle anomalies. Although this hybrid attains higher precision than conventional single models, it remains sensitive to operating environment shifts. To address this, we develop an onboard MLOps pipeline that monitors distributional shifts in real-time sensor data and executes an autonomous maintenance mechanism, retraining and redeploying models on local data when performance degradation is anticipated. A dual-monitoring rule set based on a standardized deviation score and its smoothed change rate is used to discriminate abrupt mechanical anomalies from gradual drift. Experiments on a fault simulation testbed indicate that, under data drift, the system can recover detection reliability and adapt to changing engine conditions, providing a technical basis for the self-sustaining reliability of autonomous surface ships. Full article

To address the demand for high-precision, high-efficiency, and standardized hydrographic information in intelligent shipping, this study systematically investigates key technologies for high-resolution hydrographic data parsing and intelligent information services. Focusing on the East China Sea, a space–air–ground integrated monitoring data access system is established. A hybrid data assimilation method combining four-dimensional variational (4D-Var) and ensemble Kalman filter is adopted to realize quality control, deep fusion, and optimal state estimation of multi-source heterogeneous hydrographic observations. A hybrid tidal harmonic response model is further developed to improve the refined forecasting accuracy of tide levels and ocean currents. A hierarchically decoupled system architecture is designed, and modules for data production, sharing, exchange, and visualization are developed in compliance with the international S-100 standard. By integrating hybrid spatiotemporal indexing, multi-level caching, and intelligent query optimization, the system achieves low-latency and high-concurrency service capabilities. Experimental results show that, compared with conventional models, the proposed framework reduces tidal forecast RMSE by approximately 15.8% under extreme weather, raises the continuity index of current vectors to 0.93, and cuts the S-100 product generation latency to less than 30 s. This research establishes a full-chain technical system from data parsing and product generation to intelligent services, providing a reliable technical support platform for ship intelligent navigation, dynamic route planning, and maritime safety assurance. Full article

The article presents the development of a multi-zone temperature control model in an Internet of Things (IoT) environment, designed for the cold chain of pharmaceutical products transported by sea. The model is based on the Elephant Herding Optimization (EHO) algorithm, which is used to regulate cooling modes in three independent temperature zones. The study is designed as a simulation-based proof-of-concept rather than as a full-scale experimental validation on an industrial refrigerated container. The proposed framework evaluates whether an EHO-based controller can generate spatially differentiated cooling decisions under synthetic but controlled disturbance scenarios. The variability of sensor readings reflects conditions typical of long-distance maritime transport. These include transitions across different climate zones, changes in solar exposure, and local differences in thermal load. The simulation results indicate that EHO maintains the temperature within the target range required for pharmaceutical cargo, i.e., 0–8 °C. The algorithm responds effectively to local disturbances and to asymmetry between zones. The proposed model provides a basis for further research on autonomous monitoring and control methods in IoT-based cold chain systems; however, validation using measurements from real refrigerated containers, physical heat-transfer modelling, refrigeration-unit response delays, and IoT communication disturbances remains necessary before operational deployment. Full article

Ship exhaust emissions have become an increasingly prominent global atmospheric environmental issue, triggering a series of ecological disturbances and adverse public health consequences. However, comprehensive analyses of the research progress and evolution trends in this field remain scarce. This study systematically retrieved 1346 scholarly publications in the ship exhaust emissions field for the period 2011–2025 from the Web of Science Core Collection and carried out a bibliometric analysis encompassing publication outputs, contributing countries/regions, and keyword characteristics. The findings reveal a sustained and robust growth trajectory in global research output, with annual publications increasing nearly fivefold over the 15-year study period. Notably, academic interest in this field has increased significantly since 2020 due to the implementation of the global sulfur cap regulation. Core thematic clusters (mean silhouette S = 0.7205) in this field include source apportionment, numerical modeling analysis, atmospheric criteria pollutants, and technological emission reduction strategies. The geographical distribution of research output shows a significant positive correlation with the importance of regional maritime economies. China, the United States, and Germany are the leading contributors in terms of publication outputs, while frequent research collaborations have been observed among European countries. Since 2021, the emergence of Automatic Identification System data as a keyword with high burst strength (intensity = 3.60) marks a paradigm shift toward a “big data-enabled refined management” framework. Concurrently, the sustained burst activity of keywords including nitrogen oxides, volatile organic compounds, and traffic-related emissions from 2023 to 2025 indicates rapidly growing scholarly attention to secondary aerosol precursors from shipping, and the critical need for coordinated multi-pollutant control strategies. Future research directions for ship exhaust emissions are expected to transition from fundamental characterization research to big data-driven monitoring and estimation methods, as well as advanced emission reduction technologies. The bibliometric insights derived from this study provide a valuable reference framework for subsequent in-depth studies on ship exhaust emissions. Full article

Maritime piracy poses an ongoing operational threat to commercial shipping in high-risk regions, where fast-approach attack scenarios leave vessel crews with critically limited reaction time. Automated threat detection technologies—including radar, electro-optical, and thermal imaging sensors—are increasingly integrated into maritime security architectures; however, their operational effectiveness has rarely been evaluated through quantitative engineering frameworks. This study presents a technical analysis of mechatronic detection systems, focusing on detection range, reaction time constraints, and classification reliability under representative piracy conditions. A kinematic time-to-contact model is introduced to quantify how detection distance directly governs the available defensive response window: extending reliable detection from 1 NM to 3 NM expands the reaction margin from approximately 171 s to over 440 s, a difference that may determine whether protective measures can be executed in time. Classification performance is assessed using standard metrics, with recall identified as the operationally critical indicator in asymmetric threat environments. Model-based simulations indicate that, under the assumed scenario parameters, automated detection systems can reduce operational risk by up to 45%, illustrating the sensitivity of survivability outcomes to early detection capability. The findings translate directly into design thresholds for sensor range, algorithmic sensitivity, and processing latency, providing actionable engineering recommendations for practitioners responsible for maritime security system design and vessel protection planning. Full article