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In the centuries following Christopher Columbuss 1492 voyage to the Americas, transoceanic travel opened unprecedented pathways in global pathogen circulation. Yet no biological transfer is a single, discrete event. We use mathematical modeling to quantify historical risk of shipborne pathogen introduction, exploring the respective contributions of journey time, ship size, population susceptibility, transmission intensity, density dependence, and pathogen biology. We contextualize our results using port arrivals data from San Francisco, 1850 to 1852, and from a selection of historically significant voyages, 1492 to 1918. We offer numerical estimates of introduction risk across historically realistic ranges of journey time and ship population size, and show that both steam travel and shipping regimes that involved frequent, large-scale movement of people substantially increased risk of transoceanic pathogen circulation.

To understand the extent of anthropogenic noise in the ocean, it is essential to compare the differences between modern noise environments and their pre-industrial equivalents. The Santa Barbara Channel, off the coast of Southern California, is a corridor for the transportation of goods to and from the busiest shipping ports in the Western hemisphere. Commercial ships introduce high levels of underwater noise into the marine environment. To quantify the extent of noise in the region, we modeled pre-industrial ocean noise levels, driven by wind, and modern ocean noise levels, resulting from the presence of both ships and wind. By comparing pre-industrial and modern underwater noise levels, the low-frequency (50 Hz) acoustic environment was found to be degraded by more than 15 dB. These results can be used to identify regions for noise reduction efforts, as well as to model scenarios to identify those with the greatest potential to support marine conservation efforts.

Protected areas are typically managed as a network of sites exposed to varying anthropogenic conditions. Managing these networks benefits from monitoring of conditions across sites to help prioritize coordinated efforts. Monitoring marine vessel activity and related underwater radiated noise impacts across a network of protected areas, like the U.S. National Marine Sanctuary system, helps managers ensure the quality of habitats used by a wide range of marine species. Here, we use underwater acoustic detections of vessels to quantify different characteristics of vessel noise at 25 locations within eight marine sanctuaries including the Hawaiian Archipelago and the U.S. east and west coasts. Vessel noise metrics, including temporal presence and sound levels, were paired with Automatic Identification System (AIS) vessel tracking data to derive a suite of robust vessel noise indicators for use across the network of marine protected areas. Network-wide comparisons revealed a spectrum of vessel noise conditions that closely matched AIS vessel traffic composition. Shifts in vessel noise were correlated with the decrease in vessel activity early in the COVID-19 pandemic, and vessel speed reduction management initiatives. Improving our understanding of vessel noise conditions in these protected areas can help direct opportunities for reducing vessel noise, such as establishing and maintaining noise-free periods, enhancing port efficiency, engaging with regional and international vessel quieting initiatives, and leveraging co-benefits of management actions for reducing ocean noise.

ABSTRACT This paper proposes a robust discrete‐time integral fast terminal sliding mode predictive control (DIFTSMPC) scheme for DP ship trajectory tracking under the consideration of unmodeled dynamics, environmental disturbance, and input constraints. An improved single closed‐loop control strategy is designed by adopting the earth‐fixed reference velocity as the virtual velocity. The improved nonlinear PID discrete‐time integral fast terminal sliding mode is designed to achieve faster convergence. The unknown nonlinear dynamics and the environmental disturbance are combined as a lumped uncertainty term and estimated using the one‐step delay observation method. Then the corrected sliding model state is optimized to track the reference sliding reaching law in the prediction horizon. The lumped uncertainty estimation is integrated into the sliding mode prediction to guarantee the robustness of the control scheme. The stability of the proposed scheme has been verified. Comparison results demonstrate the effectiveness and superiority in chattering suppressing and constraint handling of the proposed scheme. [ABSTRACT FROM AUTHOR]

The Arctic region is undergoing significant changes in maritime accessibility. This study investigates observed ship trajectories from 2013 to 2020 to demonstrate the recent trends of Arctic traffic. A notable surge in maritime activities has been observed, particularly during summer months, driven by economic interests and the increasing popularity of existing routes. Unique patterns in the northern Barents Sea have been observed where ships favour different routes based on seasonal ice conditions. Another contribution from this work is the validation of the Arctic Traffic Accessibility Model (ATAM) using the observed ship traffic data. Results show that the ATAM model underestimates the accessibility and vessel travel speed. This is largely due to outdated model parameters. The predefined ice multipliers and calculation of ice numerals used in the ATAM may not accurately reflect real-world conditions. [ABSTRACT FROM AUTHOR]

This article examines the contributions made to Arctic knowledge by Kallihirua, a member of the Inughuit community of Northern Greenland who was abducted by the crew of the Assistance during the 1850–51 expedition in search of the missing ships Erebus and Terror. Unpacking this important moment of cultural encounter, the article explores the ways in which Kallihirua's presence on board the ship became embroiled in wider scholarly debates pertaining to the 'origins of the Inuit' and to the historical migrations of human beings around the world. Furthermore, it studies the ways in which this 'indigenous intermediary' became an important influence on the emergent scholarly disciplines of anthropology and geography which were undergoing a process of institutionalization and professionalization during this period. The article therefore contributes new insights into the fundamental, yet overlooked, roles that Arctic indigenous peoples have historically played in shaping non-indigenous knowledge about the region. [ABSTRACT FROM AUTHOR]

Many ships from Thonis-Heracleion, a submerged city on the Mediterranean coast of Egypt, show numerous parallels with the construction of the Nilotic freighter baris as described by Herodotus (Hist. 2.96). During the spring mission of 2022 four massive constructional pieces of the bow from these ships were excavated and studied. Bow pieces played an important role in the structure of a baris. Obtained information complements the documentation of a baris as described by Herodotus and serves as a new architectural signature for this type of construction. [ABSTRACT FROM AUTHOR]

Dynamic changes in ship maneuverability challenge the accuracy and effectiveness of ship maneuvering models. This paper proposes an online prediction method based on the adaptive weighted ensemble learning framework, which can adaptively update the model according to changes in maneuverability, especially for reoccurring changes. The method contains two main mechanisms: the change monitoring mechanism and the adaptive weighting mechanism. The former identifies the change in ship dynamics and decides when to incorporate a new base model; the latter adjusts the weights of the base models to align with current scenarios, thus ensuring the predictive accuracy. To assess the method's effectiveness under varying ship dynamics, the online prediction of ship maneuvering motions under speed-induced dynamic changes is investigated. Compared with the offline model, the result demonstrates the superiority of the adaptive weighted ensemble model. The proposed method can consistently provide accurate predictions in the scenarios with reoccurring changes, and can also enhance the model capability by adjusting weights to cope with some unencountered changes. [ABSTRACT FROM AUTHOR]

Industry 4.0 technology is promoted as improving manufacturing flexibility, and competitiveness; though Australia has been slow to adopt. The Australian Navy shipbuilding program provides opportunities for accelerating technology adoption, revitalising manufacturing productivity and competitiveness. Adopting a sociotechnical systems lens, our research sought to identify usability, workload, and user experience of an augmented reality head-mounted display (AR-HMD) deployed to complete multiple work tasks in a workflow (electrical assembly, collaborative robot (cobot) mediated inspection, and remote troubleshooting using video call). Usability was rated 'average' (System Usability Scale mean = 69.8) and workload 'acceptable' (NASA Task Load Index mean = 25.8) for the AR-HMD alone, with usability of the integrated work system (IWS) rated 'good' (SUS mean = 79.2). Results suggest software interfaces, tracking, and gesturing methods for the AR-HMD require improvement. This trial shows the AR-HMD provides a versatile platform for integrating multiple digital technologies without hindering effectiveness of end-user performance, potentially benefiting productivity and quality. PRACTITIONER SUMMARY: Using an augmented reality head-mounted display (AR-HMD) to reduce and correct errors in electrical assembly identified factors influencing technology adoption in shipbuilding. Mental workload, interface design, tracking, and gesturing most hindered successful performance. AR-HMDs can facilitate the use of more complex integrated technologies (i.e. cobot), improving usability and acceptance. [ABSTRACT FROM AUTHOR]

The detection of marine vessels for revenue control has many tracking deficiencies, which has resulted in losses of logistical resources, time, and money. However, digital cameras are not fully exploited since they capture images to recognize the vessels and give immediate notice to the control center. The analyzed images go through an incredibly detailed process, which, thanks to neural training, allows us to recognize vessels without false positives. To do this, we must understand the behavior of object detection; we must know critical issues such as neural training, image digitization, types of filters, and machine learning, among others. We present results by comparing two development environments with their corresponding algorithms, making the recognition of ships immediately under neural training. In conclusion, it is analyzed based on 100 images to measure the boat detection capability between both algorithms, the response time, and the effectiveness of an image obtained by a digital camera. The result obtained by YOLOv7 was 100% effective under the application of processing techniques based on neural networks in convolutional neural network (CNN) regions compared to MATLAB, which applies processing metrics based on morphological images, obtaining low results. [ABSTRACT FROM AUTHOR]

Ship detection over long distances is crucial for the visual perception of intelligent ships. Since traditional image processing-based methods are not robust, deep learning-based image recognition methods can automatically obtain the features of small ships. However, due to the limited pixels of ships over long distances, accurate features of such ships are difficult to obtain. To address this, a two-stage object detection method that combines the advantages of traditional and deep-learning methods is proposed. In the first stage, an object detection model for the sea-sky line (SSL) region is trained to select a potential region of ships. In the second stage, another object detection model for ships is trained using sliced patches containing ships. When testing, the SSL region is first detected using the trained 8th version of You Only Look Once (YOLOv8). Then, the SSL region detected is divided into several overlapping patches using the slicing technique, and another trained YOLOv8 is applied to detect ships. The experimental results showed that our method achieved 85% average precision when the intersection over union is 0.5 (AP50), and a detection speed of 75 ms per image with a pixel size of 1080×640. The code is available at https://github.com/gongyanfeng/PaperCode. [ABSTRACT FROM AUTHOR]

Abnormal behavior of crew members is an important cause of frequent ship safety accidents. The existing abnormal crew recognition algorithms are affected by complex ship environments and have low performance in real and open shipborne environments. This paper proposes an abnormal crew detection network for complex ship scenarios (ACD-Net), which uses a two-stage algorithm to detect and identify abnormal crew members in real-time. An improved YOLOv5s model based on a transformer and CBAM mechanism (YOLO-TRCA) is proposed with a C3-TransformerBlock module to enhance the feature extraction ability of crew members in complex scenes. The CBAM attention mechanism is introduced to reduce the interference of background features and improve the accuracy of real-time detection of crew abnormal behavior. The crew identification algorithm (CFA) tracks and detects abnormal crew members' faces in real-time in an open environment (CenterFace), continuously conducts face quality assessment (Filter), and selects high-quality facial images for identity recognition (ArcFace). The CFA effectively reduces system computational overhead and improves the success rate of identity recognition. Experimental results indicate that ACD-Net achieves 92.3% accuracy in detecting abnormal behavior and a 69.6% matching rate for identity recognition, with a processing time of under 39.5 ms per frame at a 1080P resolution. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imagery is widely utilized in military and civilian applications. Recent deep learning advancements have led to improved ship detection algorithms, enhancing accuracy and speed over traditional Constant False-Alarm Rate (CFAR) methods. However, challenges remain with complex backgrounds and multi-scale ship targets amidst significant interference. This paper introduces a novel method that features a context-based decoupled head, leveraging positioning and semantic information, and incorporates shuffle attention to enhance feature map interpretation. Additionally, we propose a new loss function with a dynamic non-monotonic focus mechanism to tackle these issues. Experimental results on the HRSID and SAR-Ship-Dataset demonstrate that our approach significantly improves detection performance over the original YOLOv5 algorithm and other existing methods. [ABSTRACT FROM AUTHOR]

Ensuring safety at sea has become a primary focus of marine monitoring, driving the increasing adoption of ship detection technology in the maritime industry. Detecting small ship targets in SAR images presents challenges, as they occupy only a small portion of the image and exhibit subtle features, reducing detection efficiency. To address these challenges, we propose the HCA-RFLA algorithm for ship detection in SAR remote sensing. To better capture small targets, we design a hierarchical collaborative attention (HCA) mechanism that enhances feature representation by integrating multi-level features with contextual information. Additionally, due to the scarcity of positive samples for small targets under IoU and center sampling strategies, we propose a label assignment strategy based on Gaussian receptive fields, known as RFLA. RFLA assigns positive samples to small targets based on the Gaussian distribution between feature points and ground truth, increasing the model's sensitivity to small samples. The HCA-RFLA was experimentally validated using the SSDD, HRSID, and SSD datasets. Compared to other state-of-the-art methods, HCA-RFLA improves detection accuracy by 6.2%, 4.4%, and 3.6%, respectively. These results demonstrate that HCA-RFLA outperforms existing algorithms in SAR remote sensing ship detection. [ABSTRACT FROM AUTHOR]

Currently, path planning for an unmanned ship is usually performed in a single domain on the water surface or underwater, and little research exists on path planning for a three-dimensional (3D) environment where the ship crosses the water’s surface and underwater. The existing 3D path planning research still has improvement possibilities in reserving a safe distance between the ship and the obstacles. In this paper, path planning for a submersible surface ship (SSS) in a 3D environment is carried out based on an ant colony algorithm. Path safety is enhanced by introducing constraints related to boat width to keep the path at a safe distance from obstacles. By extracting key points, the length of the path is further reduced. The algorithmic procedure with the objective of the shortest time was obtained by assigning different speeds to the SSS sailing at the same depth and across different depths. The rationality and superiority of the method given in this paper are verified in several sets of simulations and comparative experiments. The paper concludes that path selection is more inclined to choose the water surface as the depth of the obstacles increases and the density of the obstacles decreases. [ABSTRACT FROM AUTHOR]

Target identification of ship-radiated noise is a crucial area in underwater target recognition. However, there is currently a lack of multi-target ship datasets that accurately represent real-world underwater acoustic conditions. To tackle this issue, we conducted experimental data acquisition, resulting in the release of QiandaoEar22—a comprehensive underwater acoustic multi-target dataset. This dataset encompasses 9 h and 28 min of real-world ship-radiated noise data and 21 h and 58 min of background noise data. To demonstrate the availability of QiandaoEar22, we executed two experimental tasks. The first task focuses on assessing the presence of ship-radiated noise, while the second task involves identifying specific ships within the recognized targets in the multi-ship mixed data. In the latter task, we extracted eight features from the data and employed six deep learning networks for classification, aiming to evaluate and compare the performance of various features and networks. The experimental results reveal that ship-radiated noise can be identified from background noise in over 99% of cases. For the specific identification of individual ships, the optimal recognition accuracy achieves 99.56%. Finally, we found using spectrum and MFCC as feature inputs and DenseNet as classifier can achieve excellent recognition performance. Considering the computational efficiency, CRNN and ECAPA-TDNN are also good choices. Our work not only establishes a benchmark for algorithm evaluation but also inspires the development of innovative methods to enhance underwater acoustic target detection (UATD) and underwater acoustic target recognition (UATR). [ABSTRACT FROM AUTHOR]

Addresses the numerous drawbacks of cable mooring in terminals, and based on the relevant requirements forallowable wave height in Design Code of General Layout for Sea Ports, utilizing ANSYS/AQWA mooring analysis software,athree-dimensional mooring model of a shielded container terminal and a small barge is established as the research subject. Themotion responses of vessels under different operational wave heights and with/without cable mooring are simulated andcalculated, a comparative analysis of the allowable operational wave heights with/without cable mooring is conducted. Thesimulation results indicate that, compared to cable mooring, cableless mooring imposes greater constraints on ship lateral,longitudinal, yaw and turning movements under different operational wave heights, reduces the motion of moored vessels, andenhances the allowable operational wave height of the terminal. The feasibility and effectiveness of cableless mooring have beenpreliminarily validated, providing a simulation research foundation for the development of cableless mooring technology. [ABSTRACT FROM AUTHOR]

In view of the first use of an integrated ship for floating installation of immersed tubes, it is not possible to directlymeasure the force on the buttress at the ship-pipe connection, and there is no relevant engineering experience for reference.This paper uses numerical simulation analysis of buttress forces to determine stress characteristic points during immersed tubestransportation, establishes a relationship between vertical and horizontal pressures of buttresses and stress at characteristicpoints, and proposes a new method for measuring forces on ship-pipe connections. The study shows that during the process ofusing an integrated ship to transport immersed tubes, the vertical pressure on the bow buttresses gradually increases while thereis a slight decrease in vertical pressure on mid-ship buttresses; there is little change in vertical pressure on stern buttresses.Horizontal pressure on buttresses is positively correlated with speed; during high-speed travel, there is consistently greater forceexerted in the horizontal direction. When decelerating, inertia forces mainly affect the buttresses causing changes in forcedirection which require attention. [ABSTRACT FROM AUTHOR]

Marine engineering officers operate and maintain the ship's machinery during normal navigation. Most accidents on board are related to human factors which, at the same time, are associated with the workload of the crew members and the working environment. The number of alarms is so high that, most of the time, instead of helping to prevent accidents, it causes more stress for crew members, which can result in accidents. Convolutional Neural Networks (CNNs) are being employed in the recognition of images, which depends on the quality of the images, the image recognition algorithm, and the very complex configuration of the neural network. This research study aims to develop a user-friendly image recognition tool that may act as a visual sensor of alarms adjusted to the particular needs of the ship operator. To achieve this, a marine engineering simulator was employed to develop an image recognition tool that advises marine engineering officers when they are conducting their maintenance activities, with the aim to reduce their stress as a work risk prevention tool. Results showed adequate accuracy for three-layer Convolutional Neural Networks and balanced data, and the use of external cameras stands out for user-friendly applications. [ABSTRACT FROM AUTHOR]

Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency of ship target detection algorithms. Most existing target detection methods perform well in conditions of a general scenario but underperform in complex conditions. In this study, a collaborative network for target detection under foggy weather conditions is proposed, aiming to achieve improved accuracy while satisfying the need for real-time detection. First, a collaborative block was designed and SCConv and PCA modules were introduced to enhance the detection of low-quality images. Second, the PAN + FPN structure was adopted to take full advantage of its lightweight and efficient features. Finally, four detection heads were used to enhance the performance. In addition to this, a dataset for foggy ship detection was constructed based on ShipRSImageNet, and the mAP on the dataset reached 48.7%. The detection speed reached 33.3 frames per second (FPS), which is ultimately comparable to YOLOF. It shows that the model proposed has good detection effectiveness for remote sensing ship images during low-contrast foggy days. [ABSTRACT FROM AUTHOR]

In the field of target detection, a prominent area is represented by ship detection in SAR imagery based on deep learning, particularly for fine-grained ship detection, with dataset quality as a crucial factor influencing detection accuracy. Datasets constructed with commonly used slice-based annotation methods suffer from a lack of scalability and low efficiency in repeated editing and reuse. Existing SAR ship datasets mostly consist of medium to low resolution imagery, leading to coarse ship categories and limited background scenarios. We developed the "annotate entire image, then slice" workflow (AEISW) and constructed a sub-meter SAR fine-grained ship detection dataset (SDFSD) by using 846 sub-meter SAR images that include 96,921 ship instances of 15 ship types across 35,787 slices. The data cover major ports and shipping routes globally, with varied and complex backgrounds, offering diverse annotation information. Several State-of-the-Art rotational detection models were used to evaluate the dataset, providing a baseline for ship detection and fine-grained ship detection. The SDFSD is a high spatial resolution ship detection dataset that could drive advancements in research on ship detection and fine-grained detection in SAR imagery. [ABSTRACT FROM AUTHOR]

Introduction Surgical volume at Military Treatment Facilities (MTFs) has been gradually decreasing for roughly the past 2 decades. The Knowledge, Skills, and Abilities (KSA) Clinical Readiness Program linked surgical volume and readiness using a tool known as the KSA metric. However, the extent to which military medical missions contribute to the readiness of critical wartime specialties has not been evaluated using this metric. Methods In this study, a retrospective analysis was conducted using the surgical case logs from the US Naval Ship (USNS) Comfort missions in 2018 and 2019. The comprehensive case log data were categorized by year, surgeon, procedure, and location. The analysis focused on providing detailed descriptive statistics, including percentages pertaining to the types of procedures performed during these missions. The 2018 mission was 11 weeks in duration, and supported activities in Ecuador, Peru, Colombia, and Honduras. The USNS Comfort mission in 2019 lasted 6 months (June-November 2019), and visited 12 countries in Central America, South America, and the Caribbean. Results The 2019 mission case log, spanning 6 months, was evaluated using the KSA score in order to assess readiness and compare against 6 months of MTF KSA values within the same calendar year. In 2019, the orthopedic surgeon aboard the USNS Comfort had a total KSA score of 44,006, but the 6-month USNS Comfort mission only contributed 5,364 points (12% of the annual score). The general surgery practice aboard the USNS Comfort produced lower KSA scores compared to each surgeon's respective MTF practice (Table III). Analyzing the cases logged by general surgeons also highlights minimal surgical diversity during these missions, with more than 90% of cases being hernia repairs or laparoscopic cholecystectomies (Table I). In addition, 35% of total procedures performed in 2018 and 2019 were performed laparoscopically. Conclusions The analysis of operative data from the 2019 USNS Comfort mission, in comparison with the surgeons' work at their respective MTFs, reveals limited benefit in the ability of hospital-ship missions to bolster surgical readiness as measured by the KSA score. However, this is not a reflection on the value of Global Health Engagement (GHE) itself but a review of the way in which it is leveraged to support surgical readiness. Military surgeons participate in GHE as part of a larger strategy to strengthen relationships with partner nations, improve military medical force interoperability, and bolster partner nation medical capacity and capabilities. The KSA score offers an excellent tool to compare readiness metrics across significantly different GHE missions, and facilitates the opportunity for future prospective studies to improve case volume, diversity, and ultimately readiness. [ABSTRACT FROM AUTHOR]

The article "Exam exam on maritime law and law of the sea: Highway to Hell: Coastal fires out of control" deals with a case in which a fire breaks out on a transport ship carrying electric cars. Maritime law and law of the sea questions are addressed, in particular the liability for value replacement and the right of innocent passage in the coastal sea. The companies involved are Motzdo-AG, Wolfo-AG, Gapag-Floyd GmbH, and B-GmbH. It is discussed whether claims for value replacement and salvage claims exist. The proposed solutions are based on German law. [Extracted from the article]

A formation and containment control problem is discussed for the Nonlinear Multi-Autonomous Ship Systems (NM-ASSs) with uncertainties and disturbances based on the Interval Type-2 (IT-2) Takagi-Sugeno Fuzzy Model (T-SFM) in this paper. A different formation control scheme is provided by using the state feedback controller of leader ships. Because of this feature, information communication between leader ships, which are farthest from each other in formation and containment problems, isn't required. However, the analysis problem in the IT-2 fuzzy containment controller design method is caused by the leader's formation controller. A design concept for the unknown leader's input of linear multi-agent systems is successfully extended to solve the problem by the expression of IT-2 T-SFM. Nevertheless, the analysis process will become conservative while the agent number or fuzzy rule number is increased. Thus, a relaxed analysis method is also considered for the containment controller design. Additionally, the passive performance constraint is combined into the IT-2 fuzzy formation controller design method to dissipate the disturbance effect and improve the control performance. Finally, two examples are provided to illustrate the advantage of the proposed IT-2 fuzzy controller design method in the formation and containment control problem of NM-ASSs. [ABSTRACT FROM AUTHOR]

The study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self‐supervised vessel trajectory segmentation method (SS‐VTS), which segments VTs based on their inherent spatio‐temporal semantics. SS‐VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi‐dimensional feature sequence of the VTs using self‐supervised learning. Finally, spatio‐temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS‐VTS achieves state‐of‐the‐art segmentation results compared to seven baseline methods. [ABSTRACT FROM AUTHOR]

An evaluation was conducted to assess the surf-riding/broaching vulnerability of a 9.77-ton fishing boat by applying the regulations for stability assessment proposed by IMO (International Maritime Organization). Both Level 1 and 2 assessments were conducted and included a range of parameters along with the IMO second-generation intact stability criteria. In particular, it is considered three cases of wave forces acting on the hull for the surf-riding/broaching vulnerability Level 2 assessment calculations: (a) Froude-Krylov force (fFK) + 0.1M, (b) Froude-Krylov force (fFK) + added mass of the ship (Ma), and (c) Froude-Krylov force (fFK) + diffraction force (fD) + added mass of the ship (Ma). Previous results provided by IMO correspond to (b), and accurate calculation of wave forces helps to obtain more design margins. The design margins are high in the order (a) < (b) < (c), as described in the classification criteria. However, in certain cases, the assessment results may not differ significantly, so the hydrodynamic approximation assumption may be useful. [ABSTRACT FROM AUTHOR]

Berthing is one of the most dangerous phases in the process of ship navigation, and its risk assessment is crucial for both ship safety and port scheduling. To effectively enhance the safety and reliability of the berthing process, a berthing risk assessment method based on the synergy of "human-ship-environment" has been established. First, the impact of the human, ship, and environmental factors on berthing risk was analyzed, and a risk assessment index system for ship berthing was constructed. Then, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods were employed for a comprehensive risk assessment. AHP was used to determine the weight of each factor reasonably, while FCE was applied for the evaluation of the berthing risk. Finally, the proposed method was applied to evaluate the berthing operations of two ships, namely the KCS ship type and the S-175 large passenger ship type, at the Qingdao Intelligent Ship Testing Field, China. The experiment's results indicate that the evaluation results of the method proposed here have good consistency with the expert survey method. [ABSTRACT FROM AUTHOR]

This study investigates the problem of tracking the trajectory of a dynamic positioning (DP) ship under sudden surges of elevated sea states. First, the tracking problem is reformulated as an error calibration problem through the introduction of fully actuated system (FAS) approaches, thereby simplifying controller design. Second, a predefined-time control term is designed to maintain the convergence time of the trajectory tracking error within a specified range; however, the upper bound of the perturbation must be estimated in advance. The high sea state during operation can result in an abrupt change in the upper bound of disturbance, thereby affecting the control accuracy and stability of the system. Therefore, a linear control matrix is developed to eliminate the system's dependence on the estimation of the upper bound of disturbance following smooth switching, thereby achieving control decoupling and providing a conservative switching time. Additionally, a nonlinear reduced-order expansion observer (RESO) is constructed for feedforward compensation. The stability of the system is demonstrated using the Lyapunov function, indicating that the selection of appropriate poles can theoretically enhance the system's convergence with greater control accuracy and robustness after switching. Finally, the effectiveness of the proposed method is validated through simulations and comparative experiments. [ABSTRACT FROM AUTHOR]

Harbour tugs are usually used to moor ships if large ships do not have their own additional propulsion devices (thrusters). Alternatively, during ship loading operations, ships sometimes have to be transferred from one quay to another, and in some cases, port users (shipping companies or other companies) have to pay for port tug services. In such cases, it is very important to guarantee the safety of shipping during mooring operations and to use tugboats optimally and at the same time reduce the cost of tugboat services for ship operators and other companies. For the optimal use of tugboats, it is very important to accurately estimate the required traction force (bollard pull) of tugboats and their quantity, taking into account the parameters of moored ships, the locations of berths, hydro-meteorological and hydrological conditions, and clearance (the gap between the ship's hull and the bottom of the water area), in order to guarantee the safety of navigation and not to order an excess of tugboats in terms of their quantity and powers. This article presents a methodology developed for estimating the required bollard pull and the number of tugs, taking into account the parameters of the ship, hydro-meteorological and hydrological conditions, clearance, and the locations of berths. The developed methodology for estimating the number of tugboats and their traction force (bollard pull) was tested in real conditions (with real ships and tugboats) and using a calibrated simulator, and we found that it can be successfully applied in any port or other complex shipping area by adapting it to specific conditions. The developed methodology for calculating the traction power (bollard pull) of tugboats allows us to determine the required traction force of tugboats in advance with sufficient accuracy, achieved by assessing the specific parameters and environmental conditions of the vessel served by tugboats. In the most difficult areas of the port, in terms of the use of tugboats, this methodology allows us to make reasonable decisions regarding the number of tugboats and the traction force (bollard pull) required and at the same time reduces the risk of emergency situations. [ABSTRACT FROM AUTHOR]

Feature selection and fusion in ship radiated noise-based underwater target recognition have remained challenging tasks. This paper proposes a novel feature extraction method based on multi-dimensional feature selection and fusion. Redundant features are filtered through feature visualization techniques. The Sub-regional Feature Enhancement modules (SFE) and Multi-activated Channel Aggregation modules (MCA) within the neural network are utilized to achieve underwater target recognition. Experimental results indicate that our network, named Sub-Regional Channel Aggregation Net (SRCA-Net), utilizing 3-s sound segments for ship radiated noise recognition, surpasses existing models, achieving an accuracy of 78.52% on the public DeepShip dataset. [ABSTRACT FROM AUTHOR]

The response of a ship or other vessel to surface sea waves, including extreme waves, may compromise crew and vessel safety and long-term operational capability. Herein, a novel high-fidelity numerical time-dependent simulation approach is presented using Smoothed Particle Hydrodynamics (SPH) for modelling sea waves coupled with Finite Element Analysis for modelling vessel structural response under wave loading conditions. The results are compared with physical scale model wave tank test results. Good agreement was obtained for heave and pitch motions and vertical bending moments for various forward (head) speeds in regular head waves, heave and pitch motions, and vertical bending moments. High computational demands can be met by the increasing availability of computation power. Ongoing research is outlined. The implications for the design of vessels such as ships and for through-life assessment are discussed. [ABSTRACT FROM AUTHOR]

Ship grounding onto multiple rocks is one of the scenarios where a ship may suffer severe hull damage, thus leading to some serious consequences, such as casualties, oil spill pollution, and property damage. Ship bottom raking is the most common and severe damage type in grounding caused by sharp rocks moving against the bottom plate. This paper investigates the dynamic responses of ship grounding onto multiple sharp rocks, which has rarely been studied in the literature. Nine ship grounding in-tank model tests were conducted to provide experimental data for ship grounding onto a single rock or multiple rocks. A simplified scaled ship model with replaceable bottom plating was designed and used in the model test. Some artificial cone rock models with a 1 mm tip radius and a 15° semi-apex angle were assumed. The damage modes of the bottom plating and motions during ship grounding onto multiple rocks were obtained and recorded in the model tests, as well as the longitudinal grounding resistances. The effects of the initial relative height of each rock and the size of rock distribution on the structural damage mode and dynamic response of a ship model in multi-rock ship grounding were investigated. In addition, the results obtained from single-rock and multi-rock ship grounding model tests are compared. [ABSTRACT FROM AUTHOR]

BACKGROUND: Dockworkers are exposed to physical overloads that can contribute to the development of musculoskeletal disorders, leading to functional disability and absenteeism. OBJECTIVE: to map, critically appraise, and synthesize the available evidence on the prevalence of musculoskeletal diseases associated with port occupational activities. METHODS: A comprehensive search was conducted in structured and unstructured databases in August 2023, with no date or language restriction, to identify observational studies evaluating the prevalence of musculoskeletal disorders in dockworkers' occupational activity. The risk of bias was assessed using validated tools based on the included study designs. Data from studies were pooled in meta-analyses. The certainty of the evidence was assessed using the GRADE approach. RESULTS: We identified 12 analytical cross-sectional studies involving 7821 participants in ports of five countries. Most studies (75%) had a moderate methodological quality according to the Joanna Briggs Institute tool. Considering the overall worker categories and any musculoskeletal disorders, the meta-analysis showed a prevalence of 58% (95% Confidence Interval [95% CI] 37% to 78%), with degenerative spinal diseases 42% (95% CI –0.6% to 91%) and low back pain 36% (95% CI 21% to 50%) being the most prevalent conditions. Symptoms were predominantly in foremen and stevedores. The certainty of the evidence was very low. CONCLUSIONS: Musculoskeletal disorders seem prevalent among dockworkers, mainly degenerative spinal diseases and low back pain. Studies with greater methodological consistency are still needed to validate these hypotheses and assist in decision-making for implementing preventive and informational policies in maritime port management organizations. PROSPERO registry CRD42021257677. [ABSTRACT FROM AUTHOR]

As more and more ships sail on the sea, the probability of collision between ships also increases. At present, researches more on head-on ship collisions than oblique ship collisions. According to statistical data, the probability of oblique ship collision is higher than that of head-on ship collision, and oblique ship collision may cause a wider range of structural damage. Therefore, this paper studies the damage deformation of ship side structures in oblique collision scenarios. In this paper, a simplified analysis method is proposed to predict the structural response of the struck ship's side structures by bulbous bow in oblique collision scenarios and the analytical results match well with the numerical simulation results, which verifies the accuracy of the simplified analysis method. The simplified analysis method can be used to assess the crashworthiness of ship side structures by bulbous bow in oblique collision scenarios. [ABSTRACT FROM AUTHOR]

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This article explores the pivotal role played by convict transports in the expansion and redirection of British maritime trade to the East. Through an investigation into the business of transporting prisoners across the seas in the late eighteenth and early nineteenth century, it reveals that contractors, shipowners and captains connected to convict transportation were at the forefront of exploiting new trading opportunities in the Indian and Pacific Oceans. By analysing the voyages of convict ships within the prism of long-haul trading ventures, this work reveals the often overlooked business acumen of those who organized and executed convict transportation as they participated in inter- and intra-continental maritime trade. [ABSTRACT FROM AUTHOR]

When Portuguese sailors confirmed that there was an all-sea route to South Asia from Europe, they introduced new ship designs and building methods to the Indian Ocean World. They found sophisticated maritime skills and a long history of trade over water, both local and long distance. The meeting of the two successful technologies led to some borrowing of different methods and materials, and European use of vessels of Asian design. There were cases of borrowing specific design features, mostly by Asian shipbuilders, but there was surprisingly little melding of aspects of construction. There was some specialization, with Europeans concentrating on building and using larger seagoing cargo carriers. The arrival of steam propulsion for ships after 1800 changed all maritime technologies beyond recognition. [ABSTRACT FROM AUTHOR]

Gas emissions from ships are a major environmental concern of the international maritime community. Market-based measures with incentive have been developed to reduce gas emissions. One such measure is the differentiation of port dues through the provision of rebates to environmentally friendly vessels. Existing research reveals several limitations that hinder their actual impact on emissions, such as low rebate rates, the lack of transparency of the schemes, and the costs for the port authorities, among other factors. Our article reveals that differentiated schemes can be improved to elicit changes in the shipping industry. Based on data from the Port of Tallinn, we develop an illustrative air emission index to show how transparency in the scheme can be reconciled with environmental targets. The brackets and rates of the scheme should be determined transparently based on an ex ante target and on the distribution of the emission index of vessels that call at the port. From a policy perspective, we highlight how such a transparent process can increase compliance with the schemes and how surcharges are essential to the provision of attractive rebates by preserving the financial stability of the port authority. [ABSTRACT FROM AUTHOR]

A cost–benefit analysis of air pollution reduction measures in maritime transport systems can support the sustainability commitments of the industry and validate the related economic incentives. However, a systematic cost–benefit analysis approach has not yet been framed, as data availability and resources are limited. This study explores an alternative cost–benefit estimate approach to reduce air pollution in shipping based on a broad review and applies it to a case study regarding the domestic emission controls of Xiamen shipping. The results show that switching to a fuel with a maximum allowable fuel sulphur content of 1.5% to 0.5% for cargo ships and other vessels leads to more than nine times the external benefits to costs, while switching to a fuel with less than 0.5% sulphur content may lead to below-cost external benefits; the benefits/cost ratio based on shore power is 3.14. The proposed approach contributes to estimating not only the input costs but also their external benefits to fit the externality of sustainability actions. In the future, more site-specific factors and parameters and more case studies are recommended to improve the research reliability and accuracy as well as enrich the knowledge base for shipping sustainability development. [ABSTRACT FROM AUTHOR]

This Letter investigates the influence of source motion on the performance of the ray-based blind deconvolution algorithm (RBD). RBD is used to estimate channel impulse responses and source signals from opportunistic sources such as shipping vessels but was derived under a stationary source assumption. A theoretical correction for Doppler from a simplified moving source model is used to quantify the biases in estimated arrival angles and travel times from RBD. This correction is numerically validated using environmental data from the SBCeX16 experiment in the Santa Barbara Channel. Implications for source localization and potential passive acoustic tomography using RBD are discussed. [ABSTRACT FROM AUTHOR]

Sea haze reduces the visibility of ships and makes the optical imaging system blind, which increases the difficulty of ship remote sensing. In this paper, we propose a new method for polarization remote sensing of ships in sea haze. The method takes advantage of image haze removal and exposure correction to improve the visibility of ships in sea haze. Different from the existing polarization-based image haze removal methods, the proposed method does not depend on any assumptions about the polarization properties of incident light. The exposure correction method is proposed based on the Retinex model and Bayes theorem, which can improve the visibility of ships in haze removal images. To evaluate the effectiveness of the proposed method, we implemented an outdoor experiment on polarization remote sensing of ships in sea haze weather. The experimental results indicate that the method can improve the visibility of ships significantly. In addition, the method outperforms the Stokes parameter-based ship remote sensing method in terms of the image quality metric fog aware density evaluator and can be easily generalized to RGB image haze removal. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) have gained much attention as a safer and more efficient mode of transportation and a potential solution to reduce the workload of seafarers. Despite the highly sophisticated autonomous systems that enable MASS to make independent decisions, the presence of humans on board or in the loop of safety management highlights the need for effective human-machine interaction. However, a potentially systematic review of critical aspects of human-MASS interaction has not yet been conducted. In this paper, we aim to fill this gap by reviewing the literature related to human-MASS interaction from four crucial perspectives: the state of the art of human-MASS interaction, situational awareness for MASS, collision avoidance methods for MASS within a mixed waterborne transport system (MWTS), and human trust in MASS. Our review reveals that human-MASS interaction for safety and efficiency mainly focuses on four key aspects: (i) human factors, (ii) available technologies supporting the autonomy of MASS, (iii) system analysis and design for human-MASS interaction, and (iv) potential requirements regarding regulations. Moreover, we provide a detailed discussion of the three fundamental factors that influence human-MASS interaction, including situational awareness, decision-making for MASS in a mixed waterborne transport system, and human trust in the autonomous system of MASS. Finally, based on our analysis, we propose an integrated framework of human-MASS interaction in which these human factors are taken into account. We anticipate that these factors and their interaction will receive more attention to improve the safety and efficiency of MASS. [ABSTRACT FROM AUTHOR]

Remote sensing ship image detection methods have broad application prospects in areas such as maritime traffic and fisheries management. However, previous detection methods relied heavily on a large amount of accurately annotated training data. When the number of remote sensing ship targets is scarce, the detection performance of previous methods is unsatisfactory. To address this issue, this paper proposes a few-shot detection method based on global and local feature aggregation. Specifically, we introduce global and local feature aggregation. We aggregate query-image global and local features with support features. This encourages the model to learn invariant features under varying global feature conditions which enhances the model's performance in training and inference. Building upon this, we propose combined feature aggregation, where query features are aggregated with all support features in the same batch, further reducing the confusion of target features caused by the imbalance between base-class samples and novel-class samples, improving the model's learning effectiveness for novel classes. Additionally, we employ an adversarial autoencoder to reconstruct support features, enhancing the model's generalization performance. Finally, the model underwent extensive experiments on the publicly available remote sensing ship dataset HRSC-2016. The results indicate that compared to the baseline model, our model achieved new state-of-the-art performance under various dataset settings. This model presented in this paper will provide new insights for few-shot detection work based on meta -learning. [ABSTRACT FROM AUTHOR]

Hull optimization plays a crucial role in enhancing ship performance and efficiency. This study aimed to improve ship performance, particularly by reducing total resistance, through automated optimization methods. To this end, an integrated, fully automated optimization program was developed based on Python, incorporating Enhanced Free-Form Deformation (FFD) technology, scripted CFD numerical evaluation, and the Particle Swarm Optimization (PSO) algorithm. This program allowed precise control of hull form, improving efficiency while reducing costs. The KCS hull, known for its excellent resistance performance, was chosen as the optimization target, with the goal of minimizing total resistance by adjusting the bulbous bow line plan. The research results indicated that the optimized scheme exhibited lower resistance characteristics compared to the original design while satisfying design constraints. This study not only provides a new optimization strategy for ship design but also lays a foundation for future hull optimization research. [ABSTRACT FROM AUTHOR]

Extreme work contexts are characterized by highly demanding labor under intense, stressful, and risky conditions. The literature has to date been predominantly focused on operational, organizational, and institutional responses to these challenges. Consequently, scant attention has been paid to how individuals understand and respond to extreme work contexts when managing their careers. We conducted semi‐structured interviews with 33 Iranian merchant ship officers and developed a grounded theory model of career proactivity in extreme work contexts. Our model delineates two stages leading to proactive career behaviors: The first stage is sensemaking, where individuals wake up to the challenges of their extreme work context, engage in comparative elaboration, and arrive at a settled understanding of the extremity of their work context. The second stage is agency, where individuals engage in experimentation of when they can modify situations or adapt to them, which ultimately helps them choose one of the following proactive career behaviors: exit planning, job crafting, career drifting, and job embracing. To support the generalizability of our model, we interviewed nine firefighters, which confirmed the model's applicability to another extreme context. We discuss the theoretical and critical implications of our model for recent conversations in extreme context research and career research. [ABSTRACT FROM AUTHOR]

This study investigates the deflection and drag on flexible marine structures under steady-flow conditions and internal solitary waves (ISWs) using free-hanging risers as a representative example. We examine the relationship between the Cauchy number (Ca), buoyancy parameter (B), deflected height (hd), and effective length (le). Our findings reveal that flow fields influenced by ISWs closely resemble steady flow. This similarity enables the use of steady-flow analyses as a proxy in extreme motion studies of flexible marine structures. We also discover that an inclined configuration of flexible marine structures, such as free-hanging risers, diminishes the horizontal forces exerted by both steady currents and ISWs. Additionally, for both scenarios, increasing the weight of longer flexible marine structures is more effective than increasing stiffness in reducing deflection. The proposed method accurately predicts the deformation of flexible marine structures caused by ship motion in deep-sea mining and the movements of ocean risers with floating platforms. This finding is important for the design and optimization of these structures. [ABSTRACT FROM AUTHOR]

Very short-term ship motion forecasting aims to predict future movements using historical ship motion data. While ship motion features both temporal and frequency characteristics, the latter is often neglected. This paper proposes a fully adaptive time–frequency coupling forecasting model using self-attention mechanism based on the Deep Operator Network (DeepONet), abbreviated as TFD. The multi-head attention layers enable the trunk net to adaptively learn the relationships between different frequencies in the frequency domain and assign varying weights accordingly. Thus, compared to the Fourier transform and multilayer perceptron-net enhance model based on DeepONet (FMD), which relies on manually specified filter frequencies, the TFD model is capable of fully learning the motion patterns in both the time and frequency domains, establishing nonlinear mapping relationships between them. It exhibits greater interpretability and generalization. The TFD model is tested for accuracy and generalization using ship motion data from the Iowa University experimental tank. The results indicate that, compared to the DeepONet and FMD, the TFD model reduces the mean square error (MSE) by up to 64.72% and 52.45%, with an average reduction of 55.57% and 42.47%. In terms of generalization, the forecasting MSE is reduced by up to 65.04% and 46.08%. Compared to the DeepONet and FMD, the proposed TFD model demonstrates significant improvements in forecasting horizon and generalization, providing a notable advantage in very short-term ship motion prediction applications. [ABSTRACT FROM AUTHOR]

This article discusses the control problem of marine autonomous surface ships (MASS) under cross‐water scenarios, that is, from open water to restricted water, where several practical facts, such as uncertain dynamic, unknown disturbance and actuator wear suppression, are taken into account. To resolve such a control design challenge, the predefined performance control (PPC)‐based and barrier Lyapunov function (BLF)‐based ideas are employed, and a prespecified performance function (PPF) is designed to implement the transformation of cross‐water design. Under the adaptive backstepping design framework, with aid of PPC‐based and BLF‐based design ideas, an adaptive neural control solution is developed for MASS under cross‐water scenarios. Furthermore, to reduce the actuator wear and tear caused by high‐frequency corresponding control commands and hull vibration, a new multichannel concurrent event‐triggered protocol (ETP) is constructed in the controller‐actuator (C‐A) channel. Finally, a concurrent event‐triggered adaptive neural control scheme is proposed for MASS under cross‐water scenarios. The theoretical analysis indicates that all signals in the control system are ultimately bounded, and the Zeno behavior is avoided. The simulation and comparison results verify the effectiveness and superiority of the developed control scheme. [ABSTRACT FROM AUTHOR]

With the flourishing development of deep learning, synthetic aperture radar (SAR) ship detection based on this method has been widely applied across various domains. However, most deep-learning-based detection methods currently only use the amplitude information from SAR images. In fact, phase information and time-frequency features can also play a role in ship detection. Additionally, the background noise and the small size of ships also pose challenges to detection. Finally, satellite-based detection requires the model to be lightweight and capable of real-time processing. To address these difficulties, we propose a multi-domain joint SAR ship detection method that integrates complex information with deep learning. Based on the imaging mechanism of line-by-line scanning, we can first confirm the presence of ships within echo returns in the eigen-subspace domain, which can reduce detection time. Benefiting from the complex information of single-look complex (SLC) SAR images, we transform the echo returns containing ships into the time-frequency domain. In the time-frequency domain, ships exhibit distinctive features that are different from noise, without the limitation of size, which is highly advantageous for detection. Therefore, we constructed a time-frequency SAR image dataset (TFSID) using the images in the time-frequency domain, and utilizing the advantages of this dataset, we combined space-to-depth convolution (SPDConv) and Inception depthwise convolution (InceptionDWConv) to propose Efficient SPD-InceptionDWConv (ESIDConv). Using this module as the core, we proposed a lightweight SAR ship detector (LSDet) based on YOLOv5n. The detector achieves a detection accuracy of 99.5 with only 0.3 M parameters and 1.2 G operations on the dataset. Extensive experiments on different datasets demonstrated the superiority and effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]