Search Results (599)

This study experimentally investigated the wave reflection characteristics of a vertical-type OWC installed by partially removing a section of an existing rubble mound breakwater under irregular wave conditions. Hydraulic model experiments were carried out for multiple water depths and irregular wave conditions representative of OWC operation. The results demonstrated that the OWC structure generally exhibited lower reflection coefficients compared with conventional vertical breakwaters, indicating a low-reflection behavior even in random seas. The influence of the non-dimensional amplitude of free-surface oscillations inside the chamber on the reflection coefficient was examined. In addition, an empirical formula for predicting the reflection coefficient under irregular waves was proposed based on key dimensionless parameters, and its accuracy was validated against experimental data. The findings of this study are expected to contribute to the design and performance evaluation of OWC devices and to provide useful input for harbor tranquility assessments in coastal and port engineering practice. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

Coastal flooding can result from multiple interacting drivers and can be a complex, challenging topic for learners to grasp. Interactive learning with apps offers new opportunities for improving comprehension and engagement. We present the Floodport app, an educational interactive tool that puts students in the role of coastal risk analysts exploring how natural hazards threaten port safety. Users have to adjust key parameters, including high tides, storm surges, terrestrial rainfall contribution, sea-level rise, and engineered features such as dock height. These forces, individually or jointly, result in water-level rises that may flood the app’s port. The app supports exploration of mitigation designs for the port. Developed in Excel and Python 3.11.4 and deployed as an R/Shiny application, Floodport was used as a classroom game by 153 students with no prior knowledge on coastal flooding concepts. Pre–post survey statistical analysis showed significant learning gains and positively correlation with willingness to engage further. Floodport was found to be a useful tool for basic introduction to flooding concepts. The results indicate strong pedagogical promise and potential for using the app beyond the classroom, in contexts such as stakeholder engagement and training. Full article

Coastal cities face escalating flood risk under sea-level rise, yet landscape-based adaptation strategies often remain speculative and weakly connected to the accessibility and economic constraints that shape sustainable urban development. This study developed a modeling-to-design framework that translates coupled climate and land-use projections into implementable landscape interventions, through planning-level spatial allocation, using Dalian, China as a case study under “middle of the road” (SSP2-4.5) climate conditions. The framework integrates the Land-use Evolution and Assessment Model (LEAM) with connected-bathtub flood modeling to evaluate whether strategic landscape design can redirect development away from flood-prone zones while accommodating projected growth and maintaining accessibility to employment and services. Interventions—protective wetland restoration (810 km²) and blue–green corridors (8 km²)—derived from a meta-synthesis of implemented coastal projects were operationalized as LEAM spatial constraints. Our results show that residential development can be redirected away from coastal risk with 100% demand satisfaction and elimination of moderate-risk allocations. Cropland demand was fully accommodated. In contrast, commercial development experienced 99.8% reduction under strict coastal protection, reflecting locational dependence on port-adjacent sites. This modeling-to-design framework offers a transferable approach to quantifying where landscape interventions succeed, where they face barriers, and where complementary measures are required, supporting decision-making that balances environmental protection, economic function, and social accessibility in sustainable coastal development. Full article

The Port of Gdynia is the largest Baltic Sea port handling agricultural products and has adopted green port policies focused on sustainable development. Despite these measures, minor, unavoidable losses occur at transhipment points. With monthly grain transhipments ranging from 62,000 to 96,000 tonnes, accidental losses provide a significant supplementary food source for birds. Four species benefit most: the mallard, herring gull, common gull, and black-headed gull. These birds congregate primarily at transhipment sites, forming one of the largest winter concentrations in Poland. Together, they account for 93–96% of all waterbirds present in the port during winter, with maximum counts of 6232 mallards, 5815 herring gulls, 4482 common gulls, and 1624 black-headed gulls. The abundance of the first three species even exceeds the average winter counts of the nearby Natura 2000 site “Puck Bay,” established for its significance for wintering waterbirds. The energy content of spilled grain is sufficient to meet the daily energy requirements of these species, supporting their high numbers. These findings suggest that, despite intensive shipping and human activity along the port’s quays, unintentional food availability at port transhipment sites can support high waterbird abundances during winter, highlighting the potential conservation value of managing incidental food resources in industrial port environments. Full article

This study analyzes wave data from Typhoons Hinnamnor and Muifa in 2022, improves the traditional one-dimensional wind–wave and swell separation method (PM method), and proposes a wind–wave and swell separation strategy suitable for the Dafeng sea area during typhoon events. Combining this with the WH enables high-precision separation of wind–wave and swell. A numerical model of MIKE21 SW waves was established based on the superposition of the Holland typhoon wind field and the ERA5 background wind field. Furthermore, the study conducts controlled variable experiments through numerical simulations to systematically quantify the differential effects of the maximum wind speed radius (RMW), translation speed, and track geometry. The mathematical model in this study couples MIKE 21 SW and MIKE 21 FM, importing hydrodynamic conditions through FM as key variables into the SW model. This enables real-time data exchange during the computational process, thereby yielding results that better align with physical reality. The results from factorial sensitivity experiments demonstrate that the significant wave height and average period of offshore waves, far from the typhoons, significantly increase with the expansion of the maximum wind speed radius, with wave heights at offshore points reaching a maximum of 7.5 m. Specifically, when the RMW increased by 50%, the wave height increased by 2.5 m. The wave characteristics of landing typhoons are more influenced by terrain effects and the location of typhoon landfall. Additionally, changes in typhoon translation speed lead to a first increase and then a decrease in significant wave height. The typhoon’s path significantly affects the propagation direction and energy distribution of waves. In the Dafeng area, distant typhoons often generate long-period swells, which continuously exert high loads on actual engineering foundations. These findings inform early warning systems and the design of shelf-aware port and coastal infrastructure in northern Jiangsu and similar regions. Full article

Test fires of a rotating detonation engine (RDE) annular combustor operating on a methane–oxygen mixture were conducted. Compared to the original RDE combustor previously tested, it was modified in terms of changing the layout of the water cooling system, the positions of ports for sensors, and the shape of the supersonic nozzle. The stable operation process with a single detonation wave continuously rotating in the annular gap with the velocity of ~1900 m/s (rotation frequency of ~6 kHz) was obtained in the wide range of flow rates of propellant components. This is an important distinguishing feature of the present RDE combustor compared to the analogs known from the literature, which usually exhibit an increase in the number of simultaneously rotating detonation waves with an increase in the flow rates of propellant components. Compared to the original RDE combustor, the maximum duration of operation and the attained sea-level specific impulse were increased from 1 to 30 s and from 250 to 277 s, respectively. The thermal states of all heat-stressed elements of the combustor were obtained. The maximum heat fluxes are registered in the water cooling jackets of the central body and the combustor outer wall. Heat losses in the water cooling system are shown to increase with the average pressure in the combustor. The maximum value of the average heat flux over 20 MW/m² is achieved on the combustor outer wall. The average heat flux into the combustor outer wall is approximately 20% higher than that into the central body. The average heat flux into the nozzle is several times lower than similar values for the combustor outer wall and central body. The total heat loss into the water-cooled walls of the combustor reach about 10% of the total thermal power of the combustor. Full article

Extratropical cyclones are the main drivers of high-energy wave events along the southern coast of Brazil, frequently producing hazardous coastal conditions. Between 2001 and 2020, we identified 51 high-impact coastal storms based on Marine Weather Warnings and ERA5 reanalysis. Events showed a clear seasonal pattern, with the highest occurrence in winter and autumn. Composite analyses revealed that these extreme events are consistently associated with strong meridional pressure gradients and southerly to southeasterly low-level winds, which establish long wind-fetch zones that favor the generation and shore-normal propagation of energetic waves. Significant wave heights typically exceeded 4 m along the entire coastline, with maxima south of 35° S. EOF analyses showed that the dominant mode of variability is a recurrent low-pressure system centered between 40 and 45° S over the southwestern Atlantic. In contrast, the second mode represents the dipole between continental high pressure and oceanic low pressure that intensifies storm-related wave generation. Case studies from 2008 and 2015 confirmed that these synoptic patterns result in prolonged hazardous sea states and coastal impacts, including bar closures at the Port of Rio Grande, totaling 355 h of inoperability. These findings provide a clear characterization of the meteoceanographic patterns associated with high-impact coastal storms in southern Brazil and offer a climatological basis for improving early warning, navigation safety, and coastal risk management. Full article

Ship positional data are widely used for route inference, yet most existing studies rely on automatic identification system data, which contain irregular transmission intervals and limit the ability to capture vessel-specific operational habits and subtle route choices. This study addresses these limitations by proposing a methodology to infer customary routes using periodic 3 s ship position data collected through the Korean e-Navigation system based on long-term evolution maritime communication. The method comprises three main steps: constructing a sea-area grid with an associated weight map, determining data-driven importance and updating weights, and performing pathfinding. Domestic waters are divided into 100 m grids, and navigable and non-navigable areas are binarized to establish a framework for route exploration. Ship positional data are processed to extract inter-port trajectories, which are then classified by ship size and tidal time zone to account for navigational differences arising from vessel characteristics and tide-dependent accessibility. These trajectories are combined with spatial grids and transformed into a document–word structure, enabling the calculation of movement importance between grid cells using a modified term frequency–inverse document frequency measure. The resulting weights are applied to a pathfinding graph to derive routes that reflect vessel size and tidal conditions. The effectiveness of the proposed method is evaluated by computing cosine similarity between the inferred routes and actual trajectories. Full article

Effective abnormal behavior detection in ship operations is essential for ensuring navigational safety and operational efficiency in marina ports. This study presents a hybrid method that integrates statistical analysis and neural network modeling to detect abnormal behavior based on data obtained through leisure boat sea trials. Detection criteria were established based on ship motion characteristics, operating area conditions, and the properties of the sea trial data. The method combines Rayda’s criterion and standard deviation thresholds to identify sudden changes in measured data, while a Long Short-Term Memory (LSTM) network is used to predict normal ship behavior. Deviations between predicted and measured values were evaluated using three thresholds (levels 1, 2, and 3), with level 3 effectively isolating the most significant abnormal data (representing 2–10% of the data). The proposed method is capable of successfully identifying sudden acceleration or deceleration, unusual course changes, extended stationary periods, deviations from expected routes, complex maneuvers, and track continuity issues. The results demonstrate that the proposed hybrid method can reliably distinguish abnormal ship behaviors based on real sea trial data. To separate true abnormalities from false alarms or sensor and environmental noise, its practical application on a real ship is planned as future work. This study provides a foundation for intelligent ship monitoring systems and supports the development of autonomous and semi-autonomous navigation technologies. Full article

With the rapid development of remote sensing technology and deep learning, the port ship detection based on a single-stage algorithm has achieved remarkable results in optical imagery. However, most of the existing methods are designed and verified in specific scenes, such as fixed viewing angle, uniform background, or open sea, which makes it difficult to deal with the problem of ship detection in complex environments, such as cloud occlusion, wave fluctuation, complex buildings in the harbor, and multi-ship aggregation. To this end, ES-YOLO framework is proposed to solve the limitations of ship detection. A novel edge perception channel, Spatial Attention Mechanism (EACSA), is proposed to enhance the extraction of edge information and improve the ability to capture feature details. A lightweight spatial–channel decoupled down-sampling module (LSCD) is designed to replace the down-sampling structure of the original network and reduce the complexity of the down-sampling stage. A new hierarchical scale structure is designed to balance the detection effect of different scale differences. In this paper, a remote sensing ship dataset, TJShip, is constructed based on Gaofen-2 images, which covers multi-scale targets from small fishing boats to large cargo ships. The TJShip dataset was adopted as the data source, and the ES-YOLO model was employed to conduct ablation and comparison experiments. The results show that the introduction of EACSA attention mechanism, LSCD, and multi-scale structure improves the mAP of ship detection by 0.83%, 0.54%, and 1.06%, respectively, compared with the baseline model, also performing well in precision, recall and F1. Compared with Faster R-CNN, RetinaNet, YOLOv5, YOLOv7, and YOLOv8 methods, the results show that the ES-YOLO model improves the mAP by 46.87%, 8.14%, 1.85%, 1.75%, and 0.86%, respectively, under the same experimental conditions, which provides research ideas for ship detection. Full article

Ports are vital components of global and regional supply chains, supporting trade, transport connectivity, and socio-economic development. However, their functionality is increasingly threatened by climatic hazards such as sea-level rise and heat stress, both of which are projected to intensify under future climate change. This study presents a comprehensive framework for assessing the criticality of ports within a national network, demonstrated through its application to the Greek port system, which encompasses a multitude of ports of all types from large international hubs to small island ones. The framework combines openly accessible geospatial and socio-economic data with projections of exposure to sea-level rise and extreme heat within a structured multi-criteria decision-making (MCDM) approach, enabling the identification of critical ports and the prioritization of adaptation needs. Results show that large mainland ports dominate in socio-economic importance and network centrality, while smaller island ports are vital locally due to limited redundancy and high exposure to climatic hazards. By 2100, nearly all ports are projected to experience freeboard reductions below operational thresholds and increased heat-related stress. These results highlight the need for targeted adaptation measures, including engineering interventions for mainland ports and redundancy-enhancing actions for island ports. The proposed framework provides a replicable, data-driven tool to guide evidence-based prioritization of adaptation investments and strengthen climate-resilient maritime transport and coastal management, thereby contributing to the achievement of Sustainable Development Goals (SDGs) 1.5, 9 and 13. Full article

The ascent of the low-altitude economy underscores the critical need for autonomous perception in Unmanned Aerial Vehicles (UAVs), particularly within complex environments such as urban ports. However, existing object detection models often perform poorly when dealing with land–sea mixed scenes, extreme scale variations, and dense object distributions from a UAV’s aerial perspective. To address this challenge, we propose AUP-DETR, a novel end-to-end object detection framework for UAVs. This framework, built upon an efficient DETR architecture, features the innovative Fusion with Streamlined Hybrid Core (Fusion-SHC) module. This module effectively fuses low-level spatial details with high-level semantics to strengthen the representation of small aerial objects. Additionally, a Synergistic Spatial Context Fusion (SSCF) module adaptively integrates multi-scale features to generate rich and unified representations for the detection head. Moreover, the proposed Spatial Agent Transformer (SAT) efficiently models global context and long-range dependencies to distinguish heterogeneous objects in complex scenes. To advance related research, we have constructed the Urban Coastal Aerial Detection (UCA-Det) dataset, which is specifically designed for urban port environments. Extensive experiments on our UCA-Det dataset show that AUP-DETR outperforms the YOLO series and other advanced DETR-based models. Our model achieves an mAP50 of 69.68%, representing a 4.41% improvement over the baseline. Furthermore, experiments on the public VisDrone dataset validate its excellent generalization capability and efficiency. This research delivers a robust solution and establishes a new dataset for precise UAV perception in low-altitude economy scenarios. Full article

Understanding past and present maritime activity patterns is critical for navigation safety, environmental assessment, and commercial operations. An increasing number of services now openly provide positioning data from the Automatic Identification System (AIS) via ground-based receivers. We show that coastal vessel activity can be reconstructed from open access data with high accuracy, even with limited data quality and incomplete receiver coverage. For three months of open AIS data in the Baltic Sea from August to October 2024, we present (i) cleansing and reconstruction methods to improve the data quality, and (ii) a journey model that converts AIS message data into vessel counts, traffic estimates, and spatially resolved vessel density at a resolution of ∼400 m. Vessel counts are provided, along with their uncertainties, for both moving and stationary activity. Vessel density maps also enable the identification of port locations, and we infer the most crowded and busiest coastal areas in the Baltic Sea. We find that on average, ≳4000 vessels simultaneously operate in the Baltic Sea, and more than 300 vessels enter or leave the area each day. Our results agree within 20% with previous studies relying on proprietary data. Full article

In this paper, we release a new large-scale dataset containing multiple categories of ships and floating objects at sea, which we call MASS-LSVD. It is used to train and validate target detection algorithms and future large models for ship autopiloting. The dataset was captured by a visible light camera installed aboard the world’s first intelligent research, teaching, and training ship, “Xinhongzhuan”. This MASS (maritime autonomous surface ship) was operated by Dalian Maritime University, China. We have collected more than 4000 h of video of the “Xinhongzhuan” vessel’s voyage in the Bohai Sea and other areas, which are carefully classified and filtered to cover as much as possible the various types of sample data in the marine environment, such as light intensity, weather, hull shading, data from ocean-going voyages, entering and exiting ports, etc. The dataset contains 64,263 1K-resolution images captured from video footage, covering four main ship types: Fishing Boat, Bulk Carrier, Cruise Ship, Container ship, and an ‘Other Ships’ class, for vessels that cannot be specifically classified. The dataset currently contains 64,263 pairs of 1K-resolution images covering four common ship types (fishing boat, bulk carrier, cruise ship, container, and other ship, where no specific ship type can be determined). All the images have been labeled with high-precision manual bounding boxes. In this paper, the MASS-LSVD dataset is used as the basis for training various target detection algorithms and comparing them with other datasets, which compensates for the lack of first-view images in the vessel target detection dataset, and MASS-LSVD is expected to be used to facilitate the research and application of autonomous ship navigation models in the framework of computer vision. Full article