This paper reviews the historical development, research progress, and major challenges of nuclear-powered civilian vessels, and further explores their developmental trends and potential application pathways. First, it outlines the historical evolution of three typical applications, namely nuclear-powered merchant ships, nuclear-powered icebreakers, and offshore floating nuclear power plants, and summarizes the current status of relevant standards, regulations, and regulatory frameworks. Secondly, this study systematically reviews the current international research progress on various technical routes such as pressurized water reactors, fast reactors, molten salt reactors, small modular reactors, and micro modular reactors, and analyzes their potential advantages in civilian ship applications, as well as their limitations in terms of safety, economy, and regulation. Finally, the paper forecasts trends towards miniaturization, modularization, and inherent safety, and suggests steady progress through land-based testing, demonstration operations on fixed routes, segmented push-tug shipping modes, focused application scenarios, with the goal of improving economic efficiency and regulatory framework refinement.

To address the difficulty in quantitatively measuring and recognizing the similarity of multi-ship encounter scenarios, a similarity-based recognition method for multi-ship encounter scenarios based on topological graph sequences is proposed. First, multi-ship encounter scenarios are extracted from Automatic Identification System (AIS) data and represented using a topological graph sequence-based model that characterizes ship interaction relationships. Then, a two-stage similarity recognition algorithm is designed to calculate the similarity between graph sequences and identify similar encounter scenarios. Taking the waters of Ningbo-Zhoushan Port as a case study, 2, 898 multi-ship encounter scenarios are extracted from one month of AIS data, and two typical scenario types with higher proportions are selected for experimental validation. The recognition results are comparatively analyzed based on encounter feature parameters. Experimental results show that the identified similar scenarios exhibit dynamic evolution characteristics highly consistent with those of the original scenarios, and the proposed method can effectively recognize multi-ship encounter scenarios with similar encounter relationships. This demonstrates the feasibility and effectiveness of the proposed method for similarity measurement of multi-ship encounter scenarios. The findings can provide a reference for collision avoidance decision-making and encounter risk analysis in multi-ship encounter scenarios.

The Shipborne Helicopter Landing Platform (SHLP) provides an effective solution for ensuring the safe landing of shipborne helicopters by compensating for ship motions induced by wind, waves, and currents. To address the stabilization control problem of the SHLP subject to compound disturbances caused by ship motions, dynamic uncertainties, and load perturbations, a preset-time robust adaptive stabilization control method is proposed. First, a novel barrier Lyapunov function is designed, which allows the convergence time for regulating the supporting surface of the SHLP to the desired horizontal position to be preset in advance. Then, an adaptive compound disturbance estimator is constructed to achieve online estimation of the compound disturbances acting on the SHLP. Finally, by integrating the backstepping design approach with a projection algorithm, a preset-time robust adaptive stabilization control law for the SHLP is developed. Numerical simulation results demonstrate the effectiveness and robustness of the proposed control method under compound disturbance conditions.

To address the limitations of the conventional square grid in ship path planning—such as insufficient safety margins, low search efficiency, and poor adaptability to ship maneuvering characteristics—this study proposes a ship path planning method based on a regular hexagonal grid and an improved A^* algorithm. A hexagonal grid neighborhood model is constructed according to the geometric properties of the regular hexagonal grid and the required ship-obstacle safety clearance, together with an encoding scheme suitable for hexagonal cells. A ship motion cost model is developed by incorporating ship inertia and turning constraints. Based on this model, the traditional A^* algorithm is improved by optimizing the heuristic function and introducing a turning-penalty mechanism, thereby forming a hexagonal-grid-based search algorithm for ship path planning. Comparative experiments show that, compared with the square-grid 8-neighborhood method, the proposed method shortens the path by 5.51% and reduces the number of search nodes by 30. 7% ; compared with the 4-neighborhood method, the path length is reduced by 17. 0% and the number of turning points decreases by 38. 6%. The paths generated on the hexagonal grid are smoother and more consistent with ship maneuvering characteristics. Key words:intelligent navigation; ship path planning; hexagonal grid; improved A^* algorithm; ship maneuverability

Foggy weather significantly degrades ship visibility and image quality, posing serious risks to navigation safety. Enhancing the dehazing performance of ship navigation images is therefore of great importance. To address the insufficient fog removal and poor detail restoration of existing dehazing methods in maritime scenarios, this study proposes an end-to-end ship image dehazing method that integrates an improved CycleGAN with attention mechanisms. A Squeeze-and-Excitation （SE）channel-attention module is introduced to aggregate feature maps, compress spatial information, and strengthen the network's ability to learn global representations. Multi-scale channel fusion is achieved through skip connections, which not only reduces computational complexity but also enables the model to better capture fog characteristics under complex atmospheric conditions and to process ship targets of different sizes. Furthermore, a Channel Attention module is incorporated to enhance feature selection and improve the restoration of ship contours and fine structural details. Quantitative evaluations and real fog-navigation experiments confirm the robustness of the proposed method, demonstrating consistent improvements over existing dehazing approaches across all tested metrics and navigation scenarios.

In order to solve the complexity and uncertainty problems in the berthing process of large ships, this paper develops a decision support model based on Case-Based Reasoning (CBR). This model integrates CBR technology with a cloud model and BP neural networks. It comprehensively considers multi-dimensional attributes such as vessel characteristics, meteorological and hydrological conditions, and port factors to establish a case framework comprising a basic information domain, a characteristic attribute domain, and a decision support domain. By integrating expert scoring with the cloud model, the model processes the randomness and fuzziness in expert evaluations to optimize the case attribute weights. Furthermore, it utilizes BP neural network to achieve case reuse and decision prediction, thereby reducing subjective errors introduced by manual intervention. In this paper, we collect the berthing cases of Chiwan and Shekou container terminals at Shenzhen Port for model validation. The preliminary verification model can provide relevant decision support for pilots, expand new scenarios of artificial intelligence technology in maritime applications, and provide new ideas for intelligent berthing planning of unmanned ships.

This study develops a coverage path planning method for multi-UAV maritime search and rescue (MSAR) missions under dynamic ocean conditions and time-critical constraints, aiming to balance search efficiency and resource allocation. Firstly, a grid-based regional decomposition approach is adopted to discretize complex maritime environments into visual planning cells, while a Gaussian Mixture Model (GMM) is employed to construct a prior target-drift distribution and generate a probabilistic map for path guidance. Secondly, for multi-UAV coverage planning, an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed to jointly optimize task allocation, path safety, coverage of high-priority areas, and energy consumption control. Thirdly, to enhance global search capability and convergence performance, the algorithm incorporates a Sigmoid-based adaptive inertia weight strategy, a two-level elite-guided crossover strategy, and a constraint-penalty mechanism. Finally, three UAVs were deployed to conduct simulation tests over MSAR regions of various shapes. Results show that, compared with classical baseline algorithms, the proposed method achieves up to 30. 27% improvement in cumulative detection probability, 82. 5% improvement in workload balance, and 1. 28% reduction in total path length within the first 50 steps, demonstrating its effectiveness and practicality for improving MSAR efficiency and coordination.

Hydrodynamic wake is one of the important physical fields for the non-acoustic detection of submarines, and its accurate prediction is difficult due to the complexity of the marine environment. The Volume of Fluid (VOF) method is used to numerically simulate the stratified flow field of a submarine under direct navigation, and the influence laws of the speed, dive depth, and seawater stratification on the free-surface wake features and internal-wave wake features are obtained. Canny edge detection operator theory is applied to extract the wake profile features, and the wake angle and transverse wave tensor angle are measured. The results show that the submarine's wake does not present a typical "V" wave when the Fr number is low at a large dive depth, but gradually changes from a "spot" to a parallel wave with the increase of the Fr number, and eventually evolves into a distinct "V" wave. The strong stratification of seawater enhances free-surface perturbation, making the free-surface wake characteristics more pronounced. These results provide a basis for image recognition of hydrodynamic wake characteristics of submarines.

In the automated container terminal yard with a vertical layout, the collaborative scheduling and resource allocation of non-crossing Automated Stacking Cranes (ASC) are crucial for improving operation efficiency. The traditional fixed buffer block strategy is prone to causing path conflicts and resource competition bottlenecks due to the uneven distribution of tasks, resulting in limited operation efficiency. To address this issue, this paper proposes a joint optimization framework for the scheduling of two ASCs and the decision-making of flexible buffer blocks. The aim is to achieve efficient collaboration of yard resources through a dynamic task-block matching mechanism and a safety time interval constraint model. Firstly, a mixed-integer programming model is constructed. With the goal of minimizing the task completion time, it couples the path planning of ASCs, task sequences, and the dynamic allocation of buffer blocks. Secondly, a multi-chromosome encoding genetic algorithm is designed, and the sorting crossover and Gaussian mutation strategies are adopted to enhance the solution efficiency for large-scale instances. Numerical experiments show that compared with the single fixed buffer block strategy, the flexible buffer block mechanism can reduce the average task completion time by 13.9%, verifying the adaptability and accuracy of the dynamic allocation. This study provides theoretical support and decision-making basis for the resource allocation in automated terminal yards.

With the implementation of the "2024-2025 Energy Conservation and Carbon Reduction Action Plan", the port industry, as a core component of the transportation system, faces urgent tasks in energy conservation and emission reduction. To enhance the carbon emission efficiency and competitiveness of regional port clusters, and to promote more sustainable development of port clusters in the Bohai Rim region, this study measures the carbon emission efficiency and dynamic efficiency of 14 major ports in the Bohai Rim from 2007 to 2022 using the unexpected output super-efficiency SBM-Malmquist model. A Tobit regression model is further constructed to analyze the influencing factors of port carbon emission efficiency. The results reveal an overall upward trend in carbon emission efficiency among Bohai Rim ports. The regional ranking of carbon emission efficiency has shifted from Shandong > Liaodong > Jingjinji to Jingjinji > Shandong >Liaodong, with the efficiency gap between Jingjinji and the Shandong and Liaodong Peninsula ports widening. Scientific innovation and ecological compensation are identified as primary drivers of port carbon emission efficiency improvement, while the level of openness, transport capacity, and industrial structure exert negative effects. Based on these findings, this paper proposes pathways for improving carbon emission efficiency and optimizing management in Bohai Rim port clusters.

Efficient scheduling of large vessels entering and leaving ports is critical to improving port efficiency, particularly for ports affected by tidal constraints. This study investigates the ship scheduling problem in tide-influenced ports, incorporating tidal window constraints while accounting for berth size differences, vessel safety distances, and mooring／unmooring operations. A Mixed-Integer Linear Programming (MILP) model is developed with the objective of minimizing the total delay time of all vessels, and a Lagrangian relaxation heuristic algorithm is designed for its solution. A case study using real tidal data from the Ningbo-Zhoushan Wai Diao operation area was conducted. The results show that the proposed scheduling model and algorithm, which consider tidal window constraints, can reduce vessel delays by 28. 5% while meeting safety requirements. This approach provides valuable insights for scheduling in ports significantly affected by tides.

During the reconstruction of the ship lock, excavation of the rock masses on both sides is often required. In current practice, slope instability is commonly judged using indicators such as displacement, the extent of the plastic zone, and the number of numerical iterations. These criteria typically rely on manual interpretation, which can be subjective and may not clearly determine whether slope failure has occurred. Thus, this paper proposes a slope stability analysis method based on a kinetic-energy evolution criterion, and applies it to the reconstructed ship-lock slope at Baishi Hydropower Station. A representative excavation cross-section at the ship lock is selected and the strength reduction method is adopted to analyze the kinetic energy evolution of the slope soil under different reduction coefficients, thereby identifying the occurrence of instability and failure. The proposed criterion is further compared with the conventional static assessment method based on displacement. The results show that the dynamic analysis method based on the kinetic energy evolution can obtain an accurate slope safety factor. Compared with the traditional displacement judgment method, the method proposed in this paper provides a more objective and explicit identification of slope instability which has greater advantages. The findings of this paper provide practical value and guidance for the high rock slope engineering, which can serve as useful reference for professionals in related fields.

To accommodate the complex operating conditions of inland waterway vessels and address the needs for green and intelligent development in the shipping industry, this paper proposes a comprehensive evaluation method for selecting ship propulsion schemes based on the entropy weight method and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution)-Grey Relational Analysis （GRA). The power required for ship propulsion is estimated using the fundamental theory of ship-engine-propeller matching. Based on this, the power of the propulsion system and the selection and configuration of four power and propulsion schemes during actual ship navigation are determined. An evaluation index system is established for ship propusion options, in which the weight of each indicator is determined by the entropy weight method. The four power and propulsion schemes of an inland bulk carrier are then comprehensively evaluated using the TOPSIS and GRA. The results indicate that scheme 4 （diesel-gas-electric hybrid propulsion) performs best among the alternatives. The findings of this study can provide a reference for the selection of propulsion schemes for efficient and green ships.

Ship fuel consumption prediction plays a crucial role in navigation decision-making and the intelligent evaluation of energy efficiency, particularly for future Marine Autonomous Surface Ships (MASS). This study leverages an onboard measurement and data acquisition system installed on a 28, 000 DWT bulk carrier operating on global routes. With the system, navigation-related data from 2010 to 2016 across different sea areas, loading conditions, and meteorological and sea states were collected and analyzed, including ship speed, course, sway, main engine speed, and environmental parameters. Using real-time inputs such as wave height, wave direction, speed, wind speed, pitch angle, main engine power, and main engine speed, a fuel consumption prediction model was developed based on the lightGBM algorithm. The performance of this model was compared with other machine learning approaches, including Support Vector Regression（SVR）, Long Short-term Memory （LSTM）, Gated Recurrent Unit （GRU）, Artificial Neural Network （ANN）, and Extreme Gradient Boosting （XGBootst）. Results show that the proposed model achieves superior performance, with RMSE reduced by 7.26%, MAE reduced by at least 2.62%, R² increased by 0.23%, and runtime shortened by 73.76%. Furthermore, the navigation data was divided into four subsets based on actual loading conditions to further validate the generalization capability of the lightGBM model. The results indicate that the proposed LightGBM model provides an effective solution for predicting ship fuel consumption, striking a balance between accuracy and computation efficiency. This study also provides a valuable reference for selecting optimal fuel consumption prediction methods in comparable vessel types.

The application of green ship technologies, such as hull form optimization, has been widely studied for improving energy efficiency during ship operation. However, environmental evaluations of the shipbuilding and scrapping stages remain insufficient, making it difficult to assess the lifecycle environmental impacts of these technologies holistically. To address this gap, this study selects a 150, 000-ton shuttle tanker as the research object. Experimental measurements and statistical analyses were conducted to quantify differences in energy and material consumption over the ship's lifecycle resulting from hull form optimization. Using the Life Cycle Assessment （LCA）method implemented in SimaPro V9. 6 software, an environmental impact assessment was performed to analyze the effects of hull form optimization on Global Warming Potential （GWP）and its contribution to carbon emission reduction. The results indicate firstly that the carbon reduction contribution of hull form optimization is highest in the shipbuilding stage, followed by the operation stage, and lowest in the scrapping stage. This suggests that a comprehensive evaluation of green ship technologies should account for not only the operational phase but also the construction and dismantling phases. Furthermore, while both LCA and the Energy Efficiency Design Index （EEDI）methods show broadly consistent trends in assessing the carbon reduction effect of hull form optimization during operation, the LCA results are more conservative. This discrepancy arises partly from differences in operational condition assumptions and the fact that the carbon emission factors in the Ecoinvent-3 database, commonly used in LCA, do not fully account for fuel combustion processes.

With the continued advancement of China "dual-carbon" goals and the increasingly stringent emission reduction regulations of the International Maritime Organization (IMO), the shipping industry is facing more severe emission reduction challenges and urgently needs to clarify the green transition pathways. Most of the existing research focuses on the selection of alternative fuels, while there is relatively little research on emission reduction strategies from the perspective of the fleet. To address the shortcomings of the existing research, this study identifies the key factors influencing fleet green transition decisions. Based on this, a bi-objective linear programming model jointly considering economic and environmental objectives is established, and a genetic algorithm combined with the ε-constraint method is constructed to solve the model. Finally, taking the 10, 000-11, 000 TEU container fleet of COSCO Shipping Group as a case study, this research derives the optimal green transition strategy for the fleet during the planning horizon, encompassing fuel choices and operational configurations for individual vessels. Sensitivity analysis reveals that fluctuations in fuel prices significantly affect the selection of engine types during vessel retrofitting and renewal decisions, while the stringency of emission reduction targets directly influences fleet transition costs, thereby affecting corporate proactiveness in pursuing decarbonization initiatives. Consequently, policymakers should establish appropriately calibrated emission reduction targets and incentive mechanisms to accelerate the advancement of low-carbon technologies, reduce fleet transition costs, and expedite the achievement of decarbonization objectives in the shipping industry.

Improving ship energy efficiency and reducing greenhouse gas emissions are major research priorities in the maritime industry. Accurate prediction of main engine power is fundamental to enhancing vessel energy efficiency. Using historical operational data collected from a Very Large Crude Carrier (VLCC), this study integrated and cleaned meteorological data to construct training and test datasets. Three models for main-engine power estimation are investigated and compared:a mechanistic model （SNNM), a non-mechanistic model based on Random Forest （RF), and a semi-mechanistic RF-based model. Simulation results indicate that while the mechanistic SNNM model can meet application requirements under specific engineering conditions, but R² coefficient is relatively low. In contrast, both the non-mechanistic model based on RF and the semi-mechanistic RF-based model demonstrated excellent predictive accuracy for both main engine shaft rotational speed and power, with R² values exceeding 0. 98.

This paper presents a contrast-enhancement-based dehazing algorithm to address detail loss, dim brightness, and color distortion in dehazed images of offshore tugboat sailing scenes with large sea-sky regions. First, atmospheric light estimation is optimized using quadtree segmentation to locate the light source in regions with minimal local pixel variance. Then, mean squared error contrast preserves image details, and a contrast overall cost function combined with an information loss function is used to find optimal transmission, enhancing contrast and making the sky region clearer. A fast guided filter further refines the transmission map, reducing block artifacts and maintaining real-time performance while restoring image authenticity. Finally, adaptive histogram equalization preserves contrast information in the sky, avoiding over-bright or over-dark areas. Experiments show that the image obtained using the proposed algorithm improves structural similarity, peak signal-to-noise ratio, and mean squared error by 15. 94%, 11. 46%, and 25. 82%, respectively, compared with the OCE method, while preventing color cast and halo effects, enhancing sea-sky boundary clarity, and meeting real-time requirements for restoring a realistic maritime environment.

This paper aims to introduce the fundamental content, legislative purpose, and implementation pathways of Korea's "Act on Promoting the Development and Commercialization of Autonomous Ships." It delves into an in-depth analysis of the measures taken to promote the research, development, and commercialization of autonomous surface ships, the regulatory framework and foundational systems established, as well as the intrinsic logic and practical as well as long-term significance underlying these aspects. By sharing Korea's basic practices in promoting cutting-edge maritime technological innovation through legislation and driving the development of smart shipping via commercialization, this paper seeks to provide valuable references for China's related legislative efforts, the acceleration of smart shipping development, and the advancement of its transport sector.