Ship collisions pose significant risks to ship structures and the safety of lives on board. Rapid analysis of the extent of structural damage from collisions can provide a critical basis for emergency risk mitigation and damage control and rescue operations. While the Finite Element Method (FEM) can accurately calculate the degree of structural collision damage, it is time-consuming and requires numerous parameters, making it unsuitable for rapid damage assessment under limited input conditions. Using a dataset of 202 ship collision accidents that occurred between 2015 and 2019, this study applies statistical analysis to establish relationships between damage factors and damage levels. A Bayesian network model is developed to analyze the risk of collision damage grades under the combined influence of multiple factors. The results demonstrate that the proposed method agrees well with actual accident cases and can rapidly estimate the collision damage level of ship structures even when only limited parameters are available.

In the global energy transportation landscape, maritime transport accounts for over 70% of crude oil shipments, making the reliability of its channels paramount to national energy security. Escalating geopolitical tensions have significantly elevated the risk of disruption to critical maritime channels, posing severe challenges to the stability of energy supply chains. To accurately assess this risk, this study employs complex network theory and integrates Automatic Identification System (AIS) trajectory data with vessel capacity weights to construct a global directed weighted network for crude oil maritime transportation, in which critical channels are abstracted as network nodes. Based on this framework, three progressive attack simulation strategies are designed-single channel disruption, compound scenario failure, and optimal disruption sequence-to systematically investigate the multidimensional vulnerability of the network to channel blockages. The findings indicate: 1) Functional differentiation. critical channels exhibit significant functional differentiation. Some are vital for global efficiency; for instance, removing the Strait of Malacca reduces network efficiency by 1.99%. Conversely, others demonstrate structural suboptimality, as exemplified by the removal of the Panama Canal, which paradoxically increases topological efficiency by 2.57%, revealing quantifiable suboptimal paths within the network. 2) Asynchronous vulnerability. The network exhibits asynchronous responses across performance dimensions. While macro-connectivity remains highly robust against single-point or regional failures, the core structure is highly fragile. For example, the network's K-Core value plummets after attacks targeting only four optimal nodes. 3) Non-linear degradation. Under optimal sequence attacks, global network efficiency follows a "U-shaped" trajectory-initially declining before rebounding beyond its original value due to topological reconfiguration. Notably, the collapse of the core structure occurs significantly earlier than the deterioration of overall transmission functionality, with the latter even showing a paradoxical recovery in the later stages of the attack. This study elucidates the underlying failure mechanisms of the global crude oil maritime transportation network under channel blockages, offering a new analytical paradigm and decision-making basis for ensuring national energy transportation security and enhancing the resilience of global supply chains.

Collision accidents pose a serious threat to the navigation and operational safety of fishing vessels. To enhance the decision-making capability of fishing vessel operators, this paper proposes an intelligent collision avoidance decision-making method based on the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). This method determines the encounter situation of the vessel in accordance with the COLREGS requirements and calculates the degree of collision risk, represented by the Collision Risk Index (I_CR), based on the Distance to the Closest Point of Approach (d_CPA) and the Time to the Closest Point of Approach (t_CPA). By investigating the collision avoidance behaviors of multiple fishing vessel operators, the Goodwin marine observation results were revised to establish the Safe Distance Approach (S_DA) specific to fishing vessels. Furthermore, the traditional Artificial Potential Field (APF) method was improved and integrated with a Genetic Algorithm (GA) to ensure that fishing vessels can avoid collisions safely and effectively. Finally, simulation experiments were conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that the proposed method overcomes the limitations of the single artificial potential field approach in previous vessel collision avoidance decision-making systems. It achieves optimal steering amplitude and determines the appropriate timing for returning to the original course, thereby providing a safe and collision-free decision-making solution that complies with COLREGS. This study can serve as a valuable reference for practical collision avoidance operations by deck officers.

In response to increasingly stringent maritime emission regulations, vessels using alternative fuels—such as Liquefied Natural Gas (LNG) and methanol—are being gradually deployed. However, the number of ports offering bunkering for these new fuels remains significantly lower than those supplying conventional fuels, making route planning and operational management more challenging for alternatively fueled vessels. Shipping companies urgently need to rationally design routes and support the operation of alternative fuel bulk carriers within the context of scarce refueling infrastructure. To this end, this study develops a mixed-integer nonlinear programming (MINLP) model aiming to minimize total emissions, incorporating constraints such as total voyage duration and adjustable speed ranges. To address the nonlinear nature of the model, a heuristic algorithm based on variable neighborhood search is proposed to efficiently obtain near-optimal solutions. Finally, through case studies under both single-voyage and continuous-voyage scenarios, the collaborative optimization of refueling strategies and speed adjustments is thoroughly analyzed, demonstrating the effectiveness of the proposed model and algorithm.

Aiming to address the issues associated with traditional artificial cargo hold clearing operations, such as high risk, low efficiency and an insufficient level of automation, this paper introduces the Intelligent Cargo Hold Clearing Robot (ICHCR) system for bulk carriers. Based on dual-mode control, the ICHCR system achieves unmanned operation and robot-shore collaborative intelligent cargo hold clearing. The article presents an intelligent robot hardware system for cargo hold clearing and a cloud control platform for immersive operation. Intelligent control methods for the ICHCR are investigated, including perception and localization inside the cargo holds, and dual control modes of cloud control and autonomous navigation operation. Other core technologies include robot-shore cooperative cargo hold clearing. Finally, the system was applied and validated on a 70,000-tonne Panamax bulk carrier in a grain port. Experimental results demonstrate that the ICHCR system improves the safety of cargo hold clearing operations, optimizing the overall process and reduces time consumption, meeting the requirements of safety and efficiency.

In response to the global sulfur cap regulations established by the IMO MARPOL Convention and the management requirements of China's ship emission control areas, it is imperative to improve the efficiency and accuracy of maritime ship emission monitoring. This study proposes a three-dimensional "Terrestrial-Maritime-Aerial" monitoring technology for ship exhaust emissions, which integrates shore stations, bridges, ships, and mobile platforms into a unified network to address the practical monitoring demands of diverse and complex navigation environments. Additionally, an automatic peak signal recognition algorithm has been developed to enable intelligent remote monitoring of ship exhaust emissions and facilitate precise source tracing. Finally, an integrated intelligent control system was constructed, incorporating intelligent remote monitoring, precise source tracing, and enforcement verification, thereby promoting seamless information flow across multiple aspects of maritime supervision.

To enhance the cognitive capabilities of waterborne transportation systems for intelligent ship navigation, this study first reviews the development of electronic map systems designed for the new generation of shipping systems and summarizes the technical requirements for future map functionalities in waterborne transportation. Subsequently, the Pan-information-based Navigation Scenario Map (PNSM) is proposed as a core component of the "Navigation Brain System". The elements, characteristics, and conceptual foundations of the PNSM are analyzed, with emphasis placed on its capabilities in object-oriented modeling and computer-cognitive representation of navigational environment data. Key technologies underpinning the PNSM are further discussed, including spatiotemporal object modeling of scenario elements, hierarchical organization of all elements, dynamic association among objects, adaptive cross-domain computing for scenario data, cognitive modeling of scenario semantics, intelligent information services, and multi-dimensional dynamic visualization of the scenario map. The application process and practical effectiveness of the PNSM are illustrated using data from the Intelligent Shipping Project in the Water Network Areas of Zhejiang Province, China. Research results demonstrate that the PNSM can effectively integrate, organize, correlate, and visualize water traffic data, enabling comprehensive cognitive representation of all elements within the waterborne transportation system and their interrelationships. It provides essential technical support for the advancement of next-generation shipping systems, including intelligent navigation technologies and standardized frameworks.

To address challenges such as significant scale variations, high aspect ratios, dense arrangements, and complex backgrounds in ship target detection from remote sensing images, this paper proposes an improved YOLOv7-based algorithm. Using YOLOv7 as the baseline network, the prior anchor generation algorithm is optimized for the dataset. A long-edge representation method combined with circular smooth labeling is introduced to detect ship targets with uncertain rotation directions. The YOLOv7 network is enhanced by embedding both the GAM (Global Attention Mechanism) and SimAM (Simple Attention Mechanism) modules, which effectively suppress interference from complex background regions in remote sensing images and improve target detection accuracy. Additionally, the coordinate loss function is optimized to accelerate model convergence. Experimental results on the DOTA-ship and HRSC2016 datasets for both single-class and multi-class detection tasks show mAP values of 86.1%, 97.7%, and 87.1%, respectively-representing improvements of 7.8%, 4.6%, and 7.9% over the original YOLOv7 model. These results validate the effectiveness and superiority of the proposed method.

To further standardize the management of ship dismantling, it is essential to evaluate and analyze relevant policies as a basis for improving the full life cycle management system of ships. This paper reviews and analyzes 42 policy documents issued by governmental and industrial organizations over the past five years. Based on the content analysis of these policies, a Policy Consistency Index model (abbreviated as the "P Index Model") is established. The ship dismantling policies are categorized into three types: macro guidance, supplementary refinement, and guidance and encouragement. Representative policy documents are selected as evaluation targets, and their rationality is empirically analyzed. The results indicate that China's ship dismantling policies require improvements at three levels. At the national level, it is recommended to establish a coordinated ship dismantling management mechanism involving relevant regulatory departments and to develop unified planning and industry support policies. At the local level, local governments should promptly formulate and implement specific measures to ensure effective policy enforcement. At the industry level, associations need to enhance communication and collaboration with local governments, particularly in establishing consensus on the qualifications of compliant ship dismantling companies, to jointly promote the standardized development of the industry.

Addressing the requirements for ship’s autonomous navigation and high-precision path following control, this paper proposes a fuzzy control method with partitioned integral regions based on the Integral Line-of-Sight (ILOS) guidance law for path following control of autonomous navigation ships. Based on the established ship motion model, an extended Kalman filter is employed to estimate ship states such as speed and heading using GNSS measurement data. Fuzzy controllers are designed to dynamically adjust the look-ahead distance parameter according to different navigation conditions, thereby improving tracking performance. The 700 Twenty-foot Equivalent Unit (TEU) battery-powered container ship built by COSCO Shipping Group is taken as the plant for path following experiments on a simulation platform. Comparative analyses with results from different algorithms show that the proposed control algorithm achieves superior performance and smoother rudder angle manipulation. This method can provide reference for path following control of autonomous navigation ships.