Search

The sensitivity of hurricane-driven storm surge in the Houston Ship Channel Industrial Corridor to variations in storm characteristics is studied. Hurricane surge from Hurricane Ike from 2008 was simulated using the Delft3D Flexible Mesh model. Use of the HURDAT2 wind parameter database allowed alteration of relevant hurricane characteristics to develop these alternate scenarios. Moving the actual landfall location of Ike toward the southwest at a distance of 50 km yielded a 123% increase at Manchester relative to observations during the storm. Relocation of the landfall location 200 km further southwest caused a 40% decrease in water level at the Galveston Bay entrance relative to observed values, but with a corresponding 50% water level increase at Manchester. Reduction of the storm forward speed to 25% of the actual speed over the nearshore led to a 25% increase in water level in Manchester over recorded values, while a 57% reduction of the storm forward speed over land increased water levels in Manchester by 30%. Incorporating river flow also identified locations where Galena Park's residual floodwater is likely to remain after flooding. The results of this work can be used to guide modeling efforts of storm impacts on areas with narrow waterways. [ABSTRACT FROM AUTHOR]

This paper discusses the implications of organizational control on the race for technological leadership in merchant empires. I provide an illustrative framework in which poor organizations have reduced incentives to invest, which in turn stifle technology improvements making leaders lag new entrants. In the late sixteenth century, Portugal's large ships carried more merchandise and were more fitting of the monarch's grandiose preferences, but they also were more prone to disaster. The merchant-controlled Dutch East India Company however, invested in smaller but more seaworthy vessels conducting more voyages at a much lower loss rate. The surviving historical evidence shows Portugal relying on large ships well into the seventeenth century suggesting her technological edge was gone by the time the Dutch dominated the Indian Ocean. [ABSTRACT FROM AUTHOR]

In order to reduce carbon emissions, which are currently a problem in the shipping and offshore plant sectors, the international community is strengthening regulations such as the Energy Efficiency Design Index (EEDI) and Energy Efficiency Existing Ship Index (EEXI). To cope with this, eco-friendly fuel propulsion technology is being developed, and the development of an ammonia fuel supply system is in progress. Among them, fuel valve train (FVT) technology was researched for the final supply and cutoff of fuel and purging through nitrogen for ammonia engines. In this paper, we analyzed the change in ammonia supply due to FVT opening and the change in nitrogen supply due to closure. In addition, a plan to minimize risk factors was presented by applying a control method to remove residual fuel in FVT. According to the presented FVT model, the difference in the flow rate of supplied fuel was as much as 17.8 kg/s. Additionally, by opening the gas bleed valve at intervals during the closing process and purging about 0.28 kg of nitrogen, the internal fuel could be completely discharged. This is expected to have an impact on improving the marine environment through the application of eco-friendly fuels and the development of fuel supply system technology. [ABSTRACT FROM AUTHOR]

Ship detection in synthetic aperture radar (SAR) images is widely applied in marine monitoring. In recent years, convolutional neural networks (CNNs) have made significant advances in SAR ship detection. However, the scene imbalance problem, i.e. much more offshore scenes than inshore scenes in SAR images, severely hampers the improvement of detection accuracy. In this article, we propose scene-aware data augmentation to address the problem. First, a novel concept, inshore rate, is introduced to estimate the land area percentage of various scenes in SAR images. We can divide image samples into inshore and offshore ones based on the inshore rate without manual category annotations. Second, to augment inshore samples, we synthesize dummy inshore images by cropping the ground truth bounding boxes (GT bboxes) from offshore images and pasting them onto inshore images. This offline data augmentation strategy can address the scene imbalance problem and improve detection accuracy. The experiments conducted on SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset (LS-SSDD) demonstrate the effectiveness of the proposed method in improving detection performance. [ABSTRACT FROM AUTHOR]

In addressing the challenge of ships being prone to occlusion in multi‐target situations during ship target detection, leading to missed and false detections, this paper proposes an enhanced ship detection algorithm for YOLOv7‐tiny. The proposed method incorporates several key modifications. Firstly, it introduces the Convolutional Block Attention Module in the Backbone section of the original model, emphasizing position information while attending to channel features to enhance the network's ability to extract crucial information. Secondly, it replaces standard convolution with GSConv convolution in the Neck section, preserving detailed information and reducing computational load. Subsequently, the lightweight operator Content‐Aware ReAssembly of Features is employed to replace the original nearest‐neighbour interpolation, mitigating the loss of feature information during the up‐sampling process. Finally, the localization loss function, SIOU Loss, is utilized to calculate loss, expedite training convergence, and enhance detection accuracy. The research results indicate that the precision of the improved model is 91.2%, mAP@0.5 is 94.5%, and the F1‐score is 90.7%. These values are 3.7%, 5.5%, and 4.2% higher than those of the original YOLOv7‐tiny model, respectively. The improved model effectively enhances detection accuracy. Additionally, the improved model achieves an FPS of 145.4, meeting real‐time requirements. [ABSTRACT FROM AUTHOR]

Ship design involves optimizing the hull in order to enhance safety, economic efficiency, and technical efficiency. Despite the long-term research on this problem and a number of significant conclusions, some of its content still needs to be improved. In this study, block and midship coefficients are incorporated to optimize the ship's hull. The considered ship was a patrol vessel. The seakeeping analysis was performed employing strip theory. The hull form was generated using a fuzzy model. Though the body lines generated by the midship coefficient (CM) and block coefficient (CB) varied indecently, the other geometric parameters remained the same. Multi-objective optimization was used to optimize CB and CM. According to the results of this study, these coefficients have a significant impact on the pitch motion of the patrol vessel as well as the motion sickness index. Heave and roll motions, as well as the added resistance, were not significantly influenced by the coefficients of CM and CB. However, increasing the hull form parameters increases the maximum Response Amplitude Operator (RAO) of heave and roll motions. The frequency of occurrence of the maximum roll RAO was in direct relation with CB and CM. These coefficients, however, had no meaningful impact on the occurrence frequency of other motion indices. In the end, the CB and CM coefficients were selected based on the vessel's seakeeping performance. These findings might be used by shipbuilders to construct the vessel with more efficient seakeeping performance. [ABSTRACT FROM AUTHOR]

Closed ro-ro spaces on maritime vehicle carriers are usually protected by a total-flooding carbon dioxide system. Such systems have many benefits, for example that there are no residues that can adversely affect the protected objects (in this case thousands of vehicles) and the agent is electrically non-conductive. However, there could be a considerable time delay from the start of a fire until the carbon dioxide system is discharged. Experience has shown that this delay time can cause significant fire damage and jeopardize the performance of the system. Within the EU funded LASH FIRE project, design and installation guidelines for supplementary automatic water-based fire sprinkler systems were developed. An important design feature is that the system automatically activates at an early stage of a fire. This would allow more time to fight the fire manually or to safely evacuate the space and discharge the CO2 system when the fire is controlled to one or a few vehicles instead of at a time when it has escalated in size. The work was partly based on a comprehensive literature review that identified relevant standards and information applicable to the design of automatic fire sprinkler and deluge water spray systems. Large-scale fire tests verified that the suggested system designs were able to provide control of realistic vehicle fires, including fires in passenger cars and a freight truck. [ABSTRACT FROM AUTHOR]

The gradual opening of the Arctic shipping route has made navigation possible. However, the harm caused by ship exhaust emissions is increasingly severe. Therefore, it is necessary to study the diffusion characteristics of ship exhaust plumes during Arctic navigation. The study focuses on a merchant vessel as the subject of investigation, employing computational fluid dynamics (CFD) simulation techniques to analyze the diffusion characteristics of particulate matter (PM) within ship exhaust plumes under Arctic environmental conditions. The diffusion law of ship exhaust plume PM is clarified, and the influence of three factors, synthetic wind speed, yaw angle and chimney angle, on the PM diffusion is analyzed. It was found that after the PM was discharged from the chimney, the majority of the PM dispersed directly backward along with the external flow field, while a minor fraction lingered at the stern of the ship for an extended period before eventually diffusing backward. Among them, 1235 particles were captured within a range of 200 m from the stern, with a capture rate of 0.6%. When the synthetic wind shows a yaw angle, the capture rate of PM in the interval increases rapidly with the increase of yaw angle, while other factors have less influence on the capture rate of PM. This study provides foundational guidance for predicting PM diffusion from ship exhaust plumes in Arctic environments, thereby enabling more effective strategies for managing such emissions. [ABSTRACT FROM AUTHOR]

Intelligent ship navigation scheduling and planning is of great significance for ensuring the safety of maritime production and life and promoting the development of the marine economy. In this paper, an intelligent ship scheduling and path planning method is proposed for a practical application scenario wherein the emergency rescue center receives rescue messages and dispatches emergency rescue ships to the incident area for rescue. Firstly, the large-scale sailing route of the task ship is pre-planned in the voyage planning stage by using the improved A* algorithm. Secondly, the full-coverage path planning algorithm is used to plan the ship's search route in the regional search stage by updating the ship's navigation route in real time. In order to verify the effectiveness of the proposed algorithm, comparative experiments were carried out with the conventional algorithm in the two operation stages of rushing to the incident sea area and regional search and rescue. The experimental results show that the proposed algorithm can adapt to emergency search and rescue tasks in the complex setting of the sea area and can effectively improve the efficiency of the operation, ensure the safety of the operation process, and provide a more intelligent and efficient solution for the planning of maritime emergency rescue tasks. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) has the advantage of continuous observation throughout the day and in all weather conditions, and is used in a wide range of military and civil applications. Among these, the detection of ships at sea is an important research topic. Ships in SAR images are characterized by dense alignment, an arbitrary orientation and multiple scales. The existing detection algorithms are unable to solve these problems effectively. To address these issues, A YOLOV8-based oriented ship detection and classification method using SAR imaging with lightweight receptor field feature convolution, bottleneck transformers and a probabilistic intersection-over-union network (R-LRBPNet) is proposed in this paper. First, a CSP bottleneck with two bottleneck transformer (C2fBT) modules based on bottleneck transformers is proposed; this is an improved feature fusion module that integrates the global spatial features of bottleneck transformers and the rich channel features of C2f. This effectively reduces the negative impact of densely arranged scenarios. Second, we propose an angle decoupling module. This module uses probabilistic intersection-over-union (ProbIoU) and distribution focal loss (DFL) methods to compute the rotated intersection-over-union (RIoU), which effectively alleviates the problem of angle regression and the imbalance between angle regression and other regression tasks. Third, the lightweight receptive field feature convolution (LRFConv) is designed to replace the conventional convolution in the neck. This module can dynamically adjust the receptive field according to the target scale and calculate the feature pixel weights based on the input feature map. Through this module, the network can efficiently extract details and important information about ships to improve the classification performance of the ship. We conducted extensive experiments on the complex scene SAR dataset SRSDD and SSDD+. The experimental results show that R-LRBPNet has only 6.8 MB of model memory, which can achieve 78.2% detection accuracy, 64.2% recall, a 70.51 F1-Score and 71.85% mAP on the SRSDD dataset. [ABSTRACT FROM AUTHOR]

Background: Seafarers' diets are often high in fat, sugar and calories, thus contributing to an increased risk of obesity, metabolic syndrome and cardiovascular disease. The multitude of obstacles to healthy eating in the on-board environment on merchant ships makes it essential to find new approaches for health promotion. This study explored seafarers' attitudes, the status quo of support measures and chances to improve nutrition on merchant ships from the perspective of crews and cooks. Methods: In the course of the EU-funded project "e-healthy ship", European and Southeast Asian seafarers (N = 810) and ship cooks (N = 62) were examined by using two questionnaires on 68 ships of two German shipping companies. Results: Almost all seafarers (98.8%) considered a healthy diet important for their well-being and the majority of seafarers reported being open-minded about changing their eating habits (88.4%). However, European seafarers were less likely to respond that they are willing to eat less meat [OR 0.11; 95%CI (0.07–0.17); p <.001], more vegetables [OR 0.10; 95%CI (0.02–0.49); p =.005] and more fruits [OR 0.11; 95%CI (0.02–0.61); p =.011] than their Southeast Asian colleagues. On the one hand, 82.3% of the ship cooks reported having taken part in at least one cooking course organized by their employer (1: 33.9%, 2: 25.8%, 3: 14.5%, 4 or more: 8.1%), on the other hand, slightly above half stated that the last of these courses had taken place more than 2 years ago. Furthermore, the ship cooks showed a positive attitude towards the use of a tablet-based digital platform that supports the ship cooks in daily and complex tasks (> 85% agreement). Conclusions: To improve nutrition on board merchant ships, various parameters need to be adjusted, such as ensuring a demand-oriented food supply on board or supporting seafarers' healthy food choices through target group-specific nutrition education. Ship cooks would be able to play a crucial role if they receive support. The development of a tablet-based digital platform that supports the ship cooks in their daily tasks, offers training and empowers them to implement health-promoting measures themselves seems to be an accepted and promising approach. [ABSTRACT FROM AUTHOR]

The Beirut port explosion on the 4th of August 2020 added to the struggles of the Lebanese people who were facing a financial crisis, a global pandemic, and political unrest. We hypothesized that these compounded challenges would have significant psychological implications on emerging adults, including university students, already vulnerable to life's transitions involving self-discovery and independence. This qualitative study sought to investigate university students' experience of the Beirut port explosion. An online survey with two open-ended questions was administered between December 11, 2020 and January 14, 2021 in a private university with two campuses. Thematic analysis of 1,044 data items revealed four themes: (1) Experiencing emotional upheaval, (2) Realizing the fragility of life, (3) Overcoming adversity, and (4) Changing one's outlook on life. Responses revealed a number of emotions with a keenly negative valence. The collective's role in fostering social support was evident through decreased fearful reactions. Many lost trust in the judicial system and favored emigration over standing for justice. The limited requests for retribution are likely due to a sense of impunity and adaptation to cumulative trauma. We suggest that this adaptation offers a clearer understanding of life's purpose despite its frailty. The role of impunity and social support in influencing healing warrants further research. [ABSTRACT FROM AUTHOR]

The action of wind and waves has a significant effect on the ship's roll, which can be a source of chaos and even capsize. The influence of random wave excitation is considered in order to investigate complex dynamic behavior by analytical and numerical methods. Chaotic rolling motions are theoretically studied in detail by means of the relevant Melnikov method with or without noise excitation. Numerical simulations are used to verify and analyze the appropriate parameter excitation and noise conditions. The results show that by changing the parameters of the excitation amplitude or the noise intensity, chaos can be induced or suppressed. [ABSTRACT FROM AUTHOR]

Ship voyage to Antarctica under stressful conditions impacts the health of expedition members. Measures can be taken to prevent adverse physical and psychological effects. Compared with minimally stressed control subjects in Delhi (India), these effects were investigated in a placebo-controlled study with multi-species probiotics intervention on selected anthropometric, body physiological and biochemical parameters. Total 39 participants volunteered for the study, of which 19 participated in the Antarctic expedition by ship voyage and 20 were the control subjects at Delhi. Participants were randomly divided into two groups and received daily either a probiotic or placebo for 24 days. The results suggest that probiotic intervention during the Antarctic ship voyage reduced sea sickness in probiotic group (10% cases) compared to the placebo group (44% cases). After completion of the ship voyage and intervention, body anthropometric parameters were maintained in the probiotic group. In contrast, significant alterations were observed in the placebo group with respect to their body composition and biochemical parameters as compared to the control group in Delhi. This allows the conclusion that multi-species probiotic intervention may benefit anthropometric and biochemical parameters and reduce the negative influence of stress during the ship voyage. These findings may be useful in improving expedition members' physiological health and body homeostasis during stressful conditions. [ABSTRACT FROM AUTHOR]

Deep learning methods have greatly promoted the ship detection performance in synthetic aperture radar (SAR) images. However, due to the different imaging mechanisms and target characteristics, detecting ships in SAR imagery precisely and efficiently is still challenging. To address this issue, a streamlined rotational ship detection network (SRSD-Net) is proposed in this letter. First, we construct a lightweight network using one-level feature and a receptive field block (RFB) to extract ship features with rich context. Second, a simple and effective representation for rotated ship bounding boxes using the minimum external horizontal box and two phase factors is proposed. Finally, to reduce the missed detections of small ships, k-nearest label assignment (KNLA) is designed to make all targets equally contribute to the network training. Extensive experiments are conducted on high-resolution SAR images dataset (HRSID) and the results show that the proposed method can achieve high detection accuracy with low computational cost. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence of numerous outliers. To address these issues, this paper proposes a SAR ship classification method based on text-generated images to tackle dataset imbalance. Firstly, an image generation module is introduced to augment SAR ship data. This method generates images from textual descriptions to overcome the problem of insufficient samples and the imbalance between ship categories. Secondly, given the limited information content in the black background of SAR ship images, the Tokens-to-Token Vision Transformer (T2T-ViT) is employed as the backbone network. This approach effectively combines local information on the basis of global modeling, facilitating the extraction of features from SAR images. Finally, a Squeeze-and-Excitation (SE) model is incorporated into the backbone network to enhance the network's focus on essential features, thereby improving the model's generalization ability. To assess the model's effectiveness, extensive experiments were conducted on the OpenSARShip2.0 and FUSAR-Ship datasets. The performance evaluation results indicate that the proposed method achieves higher classification accuracy in the context of imbalanced datasets compared to eight existing methods. [ABSTRACT FROM AUTHOR]

With the development of the intelligent vision industry, ship detection and identification technology has gradually become a research hotspot in the field of marine insurance and port logistics. However, due to the interference of rain, haze, waves, light, and other bad weather, the robustness and effectiveness of existing detection algorithms remain a continuous challenge. For this reason, an improved YOLOv8n algorithm is proposed for the detection of ship targets under unforeseen environmental conditions. In the proposed method, the efficient multi-scale attention module (C2f_EMAM) is introduced to integrate the context information of different scales so that the convolutional neural network can generate better pixel-level attention to high-level feature maps. In addition, a fully-concatenate bi-directional feature pyramid network (Concatenate_FBiFPN) is adopted to replace the simple superposition/addition of feature map, which can better solve the problem of feature propagation and information flow in target detection. An improved spatial pyramid pooling fast structure (SPPF2+1) is also designed to emphasize low-level pooling features and reduce the pooling depth to accommodate the information characteristics of the ship. A comparison experiment was conducted between other mainstream methods and our proposed algorithm. Results showed that our proposed algorithm outperformed other models by achieving 99.4% of accuracy, 98.2% of precision, 98.5% of recall, 99.1% of mAP@.5, and 85.4% of mAP@.5:.95 on the SeaShips dataset. [ABSTRACT FROM AUTHOR]

The complexities that attend global mobilities have shown us how migrants recreate home by drawing from their countries of emigration and immigration. In so many ways, any homemaking practices are embedded in home's mobile and sedentarist aspects. Amongst overseas Filipino workers (OFW), this means the performance of Filipino traditions like fiestas, and consumption of Filipino food whilst at the same time learning the language of their destination countries and partaking of their cultural and social practices. Filipino seafarers, however, present us with an interesting case: they perform homemaking practices within the constrained and limiting spaces of the ship where they both work and live. Filipino seafarers have a transportable home ready for unpacking and reconstruction on every ship that they board, drawing less on what the ship offers, but more on what reminds them of home back in the Philippines. Drawn from data gathered from more than a decade of engaging with seafarers on board ships and ashore, this article focuses on the homemaking practices of Filipino seafarers viewed as a means to meaning-making, where ships conceived as non-places could be turned into home, and seafarer wellbeing is specifically defined as self-preservation on board and continuing authority back home. [ABSTRACT FROM AUTHOR]

This paper presents a method for developing and testing a risk-based control system, as a first step towards including the human supervisor explicitly in the design of the system. The result is a control system with improved decision-making capabilities compared to existing control systems. The methodology presented in the paper uses the Systems Theoretic Process Analysis (STPA) to analyse the risks of an autonomous ship within its concept of operations (CONOPS), and a Human-STPA (H-STPA) is used to analyse human responsibilities and involvement. The STPA results are then used to construct a Bayesian belief network (BBN)-based risk model to assess the operational risk of the ship. This is represented as a risk cost, describing the expected cost of consequences caused by potential hazardous events. This cost is combined with fuel costs, operations costs, and the potential loss of income if new missions are not undertaken using a supervisory risk controller (SRC). The SRC is capable of making decisions about how the ship should be safely operated and notifies the human supervisor in due time when it is necessary for them to take control. The last part of the methodology presented in this paper is testing the control system using a set of verification objectives based on results from the STPA and H-STPA. A case study involving an autonomous cargo ship with a human supervisor located in a remote operation center (ROC) is included; it shows that the proposed control system can operate the ship safely in different conditions and situations. By designing the SRC to notify the human supervisor before it reaches its operational limit, the ship is able to operate in a wider range of conditions compared to when just the autonomous control system is in charge. Hence, the proposed methodology shows promising results and provides useful insights related to shared control for autonomous ships. [ABSTRACT FROM AUTHOR]

The integrated navigation system ensures maritime autonomous surface ships (MASSs) to safely, efficiently, and autonomously complete various operations in different complex navigation environments. Investigating robust algorithms for integrated navigation is crucial for enhancing the fault tolerance of the system and ensuring the stable and continuous output of the ship's motion state. However, existing research primarily focused on optimising particular filtering algorithms or examining the foundations of information allocation within a predetermined integrated navigation structure. As such, strategies for enhancing the robustness of the MASS integrated navigation system and the design of subsystems for federated filters in the event of navigation sensor failures have not been sufficiently investigated for complex maritime navigation scenarios. Consequently, this research introduces an observability sharing factor accounting for both system characteristics and state estimation performance in integrated navigation systems, employing nonlinear sampling filtering. Subsequently, a robust integrated navigation framework with distributed federal filter is developed. Within this framework, an adaptive federated filtering integrated navigation algorithm is proposed based on the observability sharing factor to allocate information in federated filtering. Finally, both the theoretical correctness and effectiveness of the algorithm were verified through simulations and real-ship experiments to assist with the development of accurate and fault-tolerant maritime navigation systems. [ABSTRACT FROM AUTHOR]

The maritime industry is following the trend of increased autonomy and digitalisation applied in aviation, automotive, military, and chemical industries. Maritime autonomous and unmanned vehicles have received significant attention recently, both from academia and industry. This paper investigates research into the progress of the development of autonomous and unmanned shipping by employing bibliometric analysis tools VOSviewer and CiteSpace, to present a comprehensive picture of this emerging field of research. Bibliometrics is applied to investigate the collected data sample from Scopus related to predefined keywords. Bibliometric tools assist review by network visualisation, clustering, and metrics. Therefore, this paper presents an analysis aiming at (1) increasing the understanding of the structure and contents of the academic field of autonomous and unmanned shipping; (2) determining and mapping scientific networks in this field; (3) analysing and visualising major divisions within the field; (4) identifying research needs and future research directions in the field. Through clusters generated by bibliometric tools, research divisions are identified and discussed. Furthermore, potential research directions are outlined. [ABSTRACT FROM AUTHOR]

The aims were to highlight noise levels on board and the health effects of noise on seafarers. Data was collected from multiple databases: PubMed, Web of Science, Scopus, and Ebsco Host. Initially, the search resulted in a total of 197 articles, 16 were chosen. Several ships were found which most sailors had noise-induced hearing loss (NIHL) (n = 6). The engine room has been defined as having the highest level of noise. In addition, noise exposure was associated with hearing loss, tinnitus, sleep disturbances, communication difficulties, poor concentration, dizziness, depression, anxiety, headache, fatigue, and stress. Noise exposure is not the only factor that causes health problems: the duration of exposure while working, years of career as a maritime worker, age, lifestyle habits (smoking, alcohol consumption), and even hobbies related to loud sound (such as concert/disco attendance, listen to loud music, etc.) were associated with the adverse health effects experienced by seafarers. [ABSTRACT FROM AUTHOR]

This study meta-synthesized qualitative studies on the parents' experiences of losing their adolescent children due to the human-made disaster, Ferry Sewol disaster in Korea, 2014. Five Korean and five international electronic databases were searched. Twenty-one studies were selected and critically appraised. Thematic analysis was employed. Four themes (with 10 subthemes) were derived: screaming in excruciating pain at the unbelievable deaths of children, family love evolving amidst pain and deepening into higher value, relationships collapsed and reformed while experiencing various social perspectives, and transitioning from a life of holding on to a life of progress. Bereaved parents experience psychological, physical, social pain for a long time, but gradually tried not to waste their children's death, forming new values and life goals. There is pressing need to devise sustained recovery strategies that account for distinct characteristics and needs of affected population groups. [ABSTRACT FROM AUTHOR]

Nearshore ship detection has significant applications in both the military and civilian domains. Compared to synthetic aperture radar (SAR), polarimetric synthetic aperture radar (PolSAR) provides richer information for analyzing the scattering mechanisms of ships and enables better detection of ship targets. However, ships in nearshore areas tend to be highly concentrated, and ship detection is often affected by adjacent strong scattering, resulting in false alarms or missed detections. While the GP-PNF detector performs well in PolSAR ship detection, it cannot obtain satisfactory results in these scenarios, and it also struggles in the presence of azimuthal ambiguity or strong clutter interference. To address these challenges, we propose a nearshore ship detection method named ECD-PNF by integrating superpixel-level GP-PNF and refined polarimetric decomposition. Firstly, polarimetric superpixel segmentation and sea–land segmentation are performed to reduce the influence of land on ship detection. To estimate the sea clutter more accurately, an automatic censoring (AC) mechanism combined with superpixels is used to select the sea clutter superpixels. By utilizing refined eight-component polarimetric decomposition to improve the scattering vector, the physical interpretability of the detector is enhanced. Additionally, the expression of polarimetric coherence is improved to enhance the target clutter ratio (TCR). Finally, this paper combines the third eigenvalue of eigenvalue–eigenvector decomposition to reduce the impact of azimuthal ambiguity. Three spaceborne PolSAR datasets from Radarsat-2 and GF-3 are adopted in the experiments for comparison. The proposed ECD-PNF method achieves the highest figure of merit (FoM) value of 0.980, 1.000, and 1.000 for three datasets, validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) enables precise object localization and imaging, which has propelled the rapid development of algorithms for maritime ship identification and detection. However, most current deep learning-based algorithms tend to increase network depth to improve detection accuracy, which may result in the loss of effective features of the target. In response to this challenge, this paper innovatively proposes an object-enhanced network, OE-YOLO, designed specifically for SAR ship detection. Firstly, we input the original image into an improved CFAR detector, which enhances the network's ability to localize and perform object extraction by providing more information through an additional channel. Additionally, the Coordinate Attention mechanism (CA) is introduced into the backbone of YOLOv7-tiny to improve the model's ability to capture spatial and positional information in the image, thereby alleviating the problem of losing the position of small objects. Furthermore, to enhance the model's detection capability for multi-scale objects, we optimize the neck part of the original model to integrate the Asymptotic Feature Fusion (AFF) network. Finally, the proposed network model is thoroughly tested and evaluated using publicly available SAR image datasets, including the SAR-Ship-Dataset and HRSID dataset. In comparison to the baseline method YOLOv7-tiny, OE-YOLO exhibits superior performance with a lower parameter count. When compared with other commonly used deep learning-based detection methods, OE-YOLO demonstrates optimal performance and more accurate detection results. [ABSTRACT FROM AUTHOR]

Deep learning-based ship-detection methods have recently achieved impressive results in the synthetic aperture radar (SAR) community. However, numerous challenging issues affecting ship detection, such as multi-scale characteristics of the ship, clutter interference, and densely arranged ships in complex inshore, have not been well solved so far. Therefore, this article puts forward a novel SAR ship-detection method called multi-level feature-refinement anchor-free framework with a consistent label-assignment mechanism, which is capable of boosting ship-detection performance in complex scenes. First, considering that SAR ship detection is susceptible to complex background interference, we develop a stepwise feature-refinement backbone network to refine the position and contour of the ship object. Next, we devise an adjacent feature-refined pyramid network following the backbone network. The adjacent feature-refined pyramid network consists of the sub-pixel sampling-based adjacent feature-fusion sub-module and adjacent feature-localization enhancement sub-module, which can improve the detection capability of multi-scale objects by mitigating multi-scale high-level semantic loss and enhancing low-level localization features. Finally, to solve the problems of unbalanced positive and negative samples and densely arranged ship detection, we propose a consistent label-assignment mechanism based on consistent feature scale constraints to assign more appropriate and consistent labels to samples. Extensive qualitative and quantitative experiments on three public datasets, i.e., SAR Ship-Detection Dataset (SSDD), High-Resolution SAR Image Dataset (HRSID), and SAR-Ship-Dataset illustrate that the proposed method is superior to many state-of-the-art SAR ship-detection methods. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Ship detection finds extensive applications in fisheries management, maritime rescue, and surveillance. However, detecting nearshore targets in SAR images is challenging due to land scattering interference and non-axisymmetric ship shapes. Existing SAR ship detection models struggle to adapt to oriented ship detection in complex nearshore environments. To address this, we propose an oriented-reppoints target detection scheme guided by scattering points in SAR images. Our method deeply integrates SAR image target scattering characteristics and designs an adaptive sample selection scheme guided by target scattering points. This incorporates scattering position features into the sample quality measurement scheme, providing the network with a higher-quality set of proposed reppoints. We also introduce a novel supervised guidance paradigm that uses target scattering points to guide the initialization of reppoints, mitigating the influence of land scattering interference on the initial reppoints quality. This achieves adaptive feature learning, enhancing the quality of the initial reppoints set and the performance of object detection. Our method has been extensively tested on the SSDD and HRSID datasets, where we achieved mAP scores of 89.8% and 80.8%, respectively. These scores represent significant improvements over the baseline methods, demonstrating the effectiveness and robustness of our approach. Additionally, our method exhibits strong anti-interference capabilities in nearshore detection and has achieved state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The prediction of fuel consumption and resulting transportation costs is a crucial stage in ship design, particularly for conditions involving motion in waves. This study investigates the real-time fuel consumption of a container ship when sailing in waves. The overall ship performance is evaluated using a novel non-linear coupled hull-engine-propeller interaction model. A series of towing tank experiments for hull resistance in waves and propeller performance are conducted. The ship engine is mathematically modelled by a quasi-steady-state model equipped with a linear Proportional-Integrator (PI) governor. Various scenarios of shipping transportation are studied, and the resulting instantaneous fuel consumptions and their correlation to other dynamic particulars are demonstrated. Additionally, daily fuel consumption and fuel cost per voyage distance are presented. It is also shown that the controller can effectively adjust the fuel rate, resulting in minimum fuel consumption. The study concludes that there is no correlation between fuel consumption and the frequency of fuel rates. The present framework and mathematical model can also be employed for ship design and existing ships to predict the total required energy per voyage. [ABSTRACT FROM AUTHOR]

Despite the fact that there is an existing body of literature addressing the computation of Coupling Loss Factors (CLFs) via the Finite Element Method (FEM), no publications have sufficiently taken into account real structural joints in their approach. Previous research has focused on academic cases of trivial connections, rarely involving more than two steel plates. To enable Statistical Energy Analysis (SEA) on a real ship, a methodology for determining CLFs for non-trivial systems is proposed, considering realistic boundary conditions and irregularities that can occur in marine structures. Based on the method, a library of CLFs is created by selecting the tested connections to enable modelling of about 90% of the acoustic paths on an existing jack-up vessel. Boundary conditions were set by introducing spring elements with a stiffness calibrated to the type of connection and taking the adjacent structure into account. In previous works, CLFs were determined for basic connections of rectangular plates. The lack of scantling variations, ignoring discontinuities and only defining parallel edges in the considered models, lead to the overestimation of energy transmission in real structures. To consider the influence of the above, random deviations from the initial stiffness of the springs at individual edges and point restraints at random points are introduced in this paper. [ABSTRACT FROM AUTHOR]

Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model's understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network's capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability. [ABSTRACT FROM AUTHOR]

The emission reductions mandated by International Maritime Regulations present an opportunity to implement full electric and hybrid vessels using large-scale battery energy storage systems (BESSs). lithium-ionion batteries (LIB), due to their high power and specific energy, which allows for scalability and adaptability to large transportation systems, are currently the most widely used electrochemical storage system. Hence, BESSs are the focus of this review proposing a comprehensive discussion on the commercial LIB chemistries that are currently available for marine applications and their potential role in ship services. This work outlines key elements that are necessary for designing a BESS for ships, including an overview of the regulatory framework for large-scale onboard LIB installations. The basic technical information about system integration has been summarized from various research projects, white papers, and test cases mentioned in available studies. The aim is to provide state-of-the-art information about the installation of BESSs on ships, in accordance with the latest applicable rules for ships. The goal of this study is to facilitate and promote the widespread use of batteries in the marine industry. [ABSTRACT FROM AUTHOR]

Northwestern Baffin Island, Nunavut, Canada, preserves the stratigraphic record in the northern margin of the Foxe Basin. The Ordovician succession exposed on an unnamed peninsula west of Steensby Inlet, northwestern Baffin Island includes the Lower and Middle Ordovician Ship Point Formation and Upper Ordovician Frobisher Bay and Amadjuak formations. Nearly 7000 conodont specimens recovered from six sections on this peninsula allow the establishment of seven conodont zones throughout this succession: Rossodus manitouensis Taxon-range Zone, Acodus deltatus–Oneotodus costatus Assemblage Zone, Oepikodus communis Interval Zone, and Cooperignathus aranda–Jumudontus gananda Assemblage Zone in units 2 and 3 of the Ship Point Formation, correlative to the upper Tremadocian and Floian, Lower Ordovician; Phragmodus polonicus Taxon-range Zone confined to unit 4 of the Ship Point Formation, correlative to the middle Darriwilian, Middle Ordovician; and Appalachignathus delicatuluss–Belodina confluens and Belodina confluens–Periodon grandis Assemblage zones limited to the Frobisher Bay and Amadjuak formations, respectively, correlative to the lower Katian, Upper Ordovician. The establishment of these conodont zones proves that (1) the Ship Point Formation in the Foxe Basin is not a continuous stratigraphic unit but has a stratigraphic interval missing that is assignable to the Dapingian and lower Darriwilian, Middle Ordovician; (2) the base of the Ship Point Formation is diachronous in the Foxe Basin, which is correlated to the upper Tremadocian on northwestern Baffin Island but to the upper Floian on Melville Peninsula; and (3) the strata younger than lower Amadjuak Formation have been eroded in this area. [ABSTRACT FROM AUTHOR]

The article focuses on the observation of Grey-faced Buzzards landing on a ship during their spring migration, a behavior not previously documented in this species or other raptors. Topics include the challenges of migrating over water bodies, the ecological and migratory patterns of Grey-faced Buzzards, and the details of the observed behavior and its potential significance for understanding migration strategies.

This study aims to develop a practical path following controller and examine its control effects for large-sized ships in shallow water. First, a new controller is designed and implemented in a ship manoeuvring simulator, and the controller's tracking capacity is evaluated via controlling a 6 DOF math model following a prescribed path at various speeds and water depths. Then, towing tank tests are conducted with the corresponding physical model to validate the simulation results. Based on experimental results, comparisons are executed between the proposed controller and the traditional controllers (e.g. fuzzy controller). Finally, the applicability of the controller is investigated through simulations of the ship transiting the Panama Canal, meanwhile, the bank effects on the controller's performance are discussed. The results show that the designed controller offers satisfactory tracking performance. Simulation results match well with the experimental results despite slight discrepancies. Additionally, satisfactory path following performance is obtained by the simulations in the canal. To conclude, the proposed controller is able to fulfill path following missions in shallow water with high precision and can be applied in the manoeuvring simulator. [ABSTRACT FROM AUTHOR]

The authors propose a new method named the RAO-based Translation Process (RTP) method for simply estimating the extreme value distribution of the nonlinear ship response in irregular waves based on the nonlinear response in regular waves. The proposed method combines the existing idea of the Nonlinear Correction (NLC) method, which is based on wave height-dependent RAO, with the theory of the translation process, and thus has both simplicity and a clear theoretical background. Furthermore, it is positioned as an extension of the Equivalent Design Wave (EDW) method used in conventional ship structural design. The validity of the proposed method is confirmed through a numerical study on the extreme value distribution of hull-girder sectional forces considering body nonlinear effects. The proposed method can be used for various purposes, and in particular, it can reduce the computational demand for strength evaluation by Direct Load and Structure Analysis (DLSA), which is a computationally extensive analysis method. Therefore, the proposed method is expected to make a significant contribution to structural design. [ABSTRACT FROM AUTHOR]

In order to describe and solve chaotic yawing in straight-line navigation, taking large tanker Davis Sea as an example, the yawing model and corresponding high-gain adaptive controller is established. Chaotic yawing is proved to exist. Mathematical analysis indicates the error of the controlled system is globally ultimately bounded. Simulation results show the proposed control law is effective and robust to model perturbation. Compared with sliding mode control, the accuracy of proposed method is 69.9% better and the control energy consumption is 32.6% smaller. [ABSTRACT FROM AUTHOR]

In this study, the berthing and unberthing performances of a single-propeller and single-rudder inland container ship with a flapped rudder and bow thruster were investigated using free-running model tests. A navigation method was used for the tests to adjust the bow thruster, so that the heading remained parallel to the pier and the ship berths or unberths while sailing in oblique conditions. As a result, the proposed method enables the target ship to berth or unberth in shallow water with h / d = 1.5 , where h is the water depth and d is the ship draft. Such a free-running test using the navigation method is useful for evaluating the berthing and unberthing performance of single-shaft ships with a bow thruster. In addition, a bow thruster impeller revolution controller was incorporated into the maneuvering simulation model proposed by the authors, and berthing calculations were performed. The calculation results were compared with free-running test results for validation. These results are consistent with the free-running test results with practical accuracy. [ABSTRACT FROM AUTHOR]

BOG re-liquefaction process is a necessary part for the safety of LNG cargo ships, and it is also the most common way to deal with evaporated gas (BOG) in LNG ships. Therefore, how to optimize the re-liquefaction process and save energy consumption is a problem that cannot be ignored in BOG re-liquefaction system. In this paper, based on the traditional BOG re-liquefaction process, a liquid nitrogen precooling circulation system is added and linked with the nitrogen purging system. Aspen HYSYS software was used to simulate and analyze the exergy efficiency, specific power consumption, exergy efficiency in the proposed liquefaction process. In addition, we introduced a bacterial foraging algorithm to optimize the process parameters. According to the operation results, the optimized process has a specific power consumption of 1.028 kWh/kg LNG , which is 21.53 % lower than that of the original NH 3 and CO 2 precooling system. Changing the traditional precooling system to liquid nitrogen tank reduces the complexity of the liquefaction system and improves the reliability of the system. Because liquid nitrogen has excellent cryogenic energy, small size, cheap price, safety in the actual use process and the linkage with the purge device on board, it shows excellent performance in the process of BOG pre-cooling liquefaction. [ABSTRACT FROM AUTHOR]

On 1 August 2022, the Grand Chamber of the European Court of Justice (ECJ) rendered its preliminary ruling in the joined cases C-14/21 and C-15/21, which concerned the detention of two German-flagged civilian rescue vessels operated by the German non-governmental organisation Sea Watch. The detention was executed by Italian port authorities following port State inspections carried out pursuant to Directive 2009/16 on Port State Control (PSC Directive). The ECJ's judgment is the first to comprehensively address and clarify the legal framework governing the European Union's system of port State control. The Court provided several clarifications concerning the interpretation of the PSC Directive as well as relevant rules of the international law of the sea. Overall, these clarifications may be considered positive for the rule of law in the context of port State inspections of civilian search and rescue vessels. [ABSTRACT FROM AUTHOR]

Driven primarily by economic considerations, the maritime transportation industry has continuously evolved over the years. In recent times, economic consideration has shifted the focus of technology developers to experiment and refine automation onboard ships. However, since the maritime industry is a slow mover in adopting disruptive technologies due to inherent economic considerations, the acceptance and policy making for full utilisation of the benefits of automated technologies is limited. It thus becomes essential to understand and study the possible potential of uncrewed and autonomous ships in the maritime transportation sector which would eventually help provide the correct and required impetus to studies and development of technologies to support uncrewed and autonomous ships. Though numerous critical questions need to be addressed before either 'uncrewed' or 'autonomous' ships can be successfully and/ or economically utilised, the article aims to address the question of 'the potential of uncrewed and autonomous ships' as a start point to assist and motivate the growth of these platforms. [ABSTRACT FROM AUTHOR]

The aim of system safety, as a sub-discipline of engineering, is to implement scientific, engineering and management knowledge to provide identification, evaluation, prevention, and control of identified hazards throughout the life cycle and within the defined boundaries of operational effectiveness, time, and cost. By utilizing risk analysis, the system safety function can assign expected values to certain hazards and/or failures to determine the likelihood of their occurrence. Autonomous and unmanned shipping are emerging topics, where technologies needed for their successful implementation in global fleet already exists and it is crucial to demonstrate that they are as safe as conventional ships. Through literature it is suggested that by eliminating human error as a cause of 53% of maritime accidents, autonomous and unmanned shipping will increase maritime safety, but it is important to consider that new types of accidents can appear. Considering that autonomous and unmanned ships need to operate with unattended ship machinery for extended time periods and that empirical data is not available, new framework for reliability assessment is needed. The aim of this paper is to provide overview of risk approaches that can be applied for reliability assessment of autonomous and unmanned ship. Within this paper, literature review is performed where reliability methods and their application to autonomous shipping are outlined. Furthermore, Bayesian network is selected as most promising one and further discussed. [ABSTRACT FROM AUTHOR]