Under the Carbon Intensity Indicator (CⅡ) rules of the International Maritime Organization (IMO), most theoretical studies manage ship carbon intensity primarily by reducing carbon emissions. However, reducing carbon emissions at the expense of ship transport work no longer aligns with the goal of carbon peaking intensity. Therefore, considering sulfur emission limits, a model was developed to determine whether fuel switching or scrubber retrofitting should be adopted. Combining with carbon intensity management, a decision model for the ship deployment and scheduling problem is proposed, subject to the constraints on sailing speed, fleet deployment, and carbon intensity compliance. To solve the proposed mixed-integer nonlinear programming model, a hybrid algorithm combining linearization and CPLEX is designed. The model is validated using five routes operated by COSCO Shipping. The results show that, compared with the genetic algorithm, the proposed hybrid algorithm increases the solution time slightly by 7.6%, while reducing the operating cost significantly by 33.4%, and all solutions satisfy the engineering constraints. Without carbon intensity management, the carbon intensity of some routes deteriorates to a non-compliant level, which confirms that carbon intensity management can effectively reduce the risk of ship downgrade and service suspension. Based on the above results, two managerial insights are obtained. First, to reduce fleet fuel consumption, liner companies should reduce ship deadweight while still meeting cargo demand, and lower sailing speed within the allowable range. To reduce fleet carbon intensity, besides lowering speed within the allowable range, liner companies should also increase cargo demand to increase ship deadweight. Second, a higher reduction factor imposes stricter carbon intensity requirement. Limited by the minimum and maximum sailing speeds, carbon intensity management requires the deployment of ships with larger deadweight. To avoid carbon intensity non-compliance and excessively low ship loading rate, liner companies should focus on improving transport work by increasing cargo demand.

To enhance the safety and efficiency of maritime route planning, this study proposes a hybrid clustering method integrating multiple algorithms to evaluate the impact of tropical cyclones in the Western Pacific. Firstly, using tropical cyclone data from the National Oceanic and Atmospheric Administration (NOAA), we characterize genesis patterns, intensity variations, and track features of tropical cyclones from 1924 to 2023. Secondly, the K-means clustering algorithm is employed to analyze spatiotemporal distribution, movement speed and direction, while a Gaussian Mixture Model（GMM）is used to identify hotspot regions and high-frequency activity belts. Finally, to overcome the limitations of two-dimensional clustering, we propose a hybrid clustering approach combining 2D and 3D clustering analyses. By overlaying tropical cyclone tracks with major shipping routes, we reveal their potential impacts on maritime safety. Experimental results demonstrate that tropical cyclones primarily affect latitudes between 10°N and 25°N, with seasonal variations:cyclones are generally weaker and more localized in winter-spring, whereas typhoons are stronger and more frequent in summer-autumn. In terms of movement direction, low-latitude cyclones initially move westward from east and then recurve northward near 15°N, while high-latitude cyclones move westward before turning northeastward. The hybrid clustering method effectively identifies tropical cyclone risk zones, providing critical references for shipping route planning and risk management.

To address global climate change and achieve the greenhouse gas reduction targets set by the International Maritime Organization (IMO), the global fleet faces complex challenges in balancing emission reduction effectiveness and economic feasibility during energy transition and fuel pathway selection, necessitating more systematic assessment and optimization of fleet-level emission reduction pathways. Existing research still lacks comprehensive comparative analysis of multi-fuel pathways, particularly systematic comparisons that balance carbon reduction effects and cost-effectiveness, making it difficult to support scientific decision-making for fleet decarbonization routes. To address these issues, a technology-economic assessment method for evaluating and optimizing shipping greenhouse gas reduction pathways is proposed. First, taking the global fleet as the research object, quantitative modeling and feature extraction of carbon reduction amounts and costs are conducted for each of the 18 preset fuel pathways. Second, a comprehensive evaluation index is established to account for both carbon reduction effects and economic feasibility, enabling coupled comparisons of multiple fuel pathways in terms of emission reduction potential and cost constraints. Combined with scenario analysis and pathway optimization mechanisms, a complete technical assessment framework is formed. The results indicate that pathways primarily based on methanol have the lowest carbon reduction costs, followed by ammonia pathways, while green methanol pathways outperform Liquefied Natural Gas （LNG)-based pathways. Green methanol and ammonia fuel pathways demonstrate the best carbon reduction performance. Considering medium-to long-term perspectives, green methanol and green ammonia can serve as optimal fuel choices, providing a feasible technical pathway for global fleet greenhouse gas reduction route planning and fuel transition decision-making.

For the path-following control problem of the Underactuated Surface Vessel (USV) under the unknown marine disturbances, this note proposes a robust bounded compensating control algorithm based on the switching L1-VS (L1 Virtual Ship) guidance. The control strategy is divided into two modules:guidance and control. For the guidance module, the control difficulty caused by the time varying reference signal is alleviated by the L1-VS guidance technique with switching mechanism. For the control module, a robust bounded compensating technique is considered to approximate the model nonlinear terms, effectively reducing nonlinear approximation error while ensuring the low designed complexity of control law. Besides, the Event-Triggered Control (ETC) technique with dynamic/static mixed threshold is used to handle the problem of communication load and actuator wear. Then, the GUUB (Global Uniform and Ultimately Bounded) stable of the control system is proved based on Lyapunov theorem. Finally, an experiment simulating the narrow-channel crossing mission is conducted, where the robustness and superiority of the algorithm is verified.

To overcome the deficiencies of traditional ant colony optimization (ACO) in pheromone updating, local optima convergence, and path planning safety, this study proposes a global path planning algorithm based on improved ACO and turning-point refinement. The heuristic function is improved using the reciprocal of the Euclidean distance between current path nodes and the destination, along with balancing parameters for iteration number, search quality, and efficiency, thereby enhancing global and local search capabilities while avoiding local optima. An adaptive pheromone evaporation coefficient is designed by utilizing characteristics of cosine function to dynamically adjust the convergence of the proposed ant colony optimization in its early and late stages. Considering the complexity of maritime environments and practical navigation requirements, a grid-based navigation environment is constructed. An obstacle-adjacent node detection method and fixed-point approximation algorithm is proposed for turning point refinement to improve navigation safety and ensure optimized paths better conform to maritime practice. Simulation experiments demonstrate that, compared with traditional ACO and other improved algorithms, the proposed algorithm shortens the average path length by approximately 39% and reduces the average iteration number by 79%, significantly improving solution quality and convergence efficiency. It effectively alleviates issues of insufficient search directionality and susceptibility to local optima. These results verify the reliability of the proposed approach for global path planning of unmanned surface vehicles and its high efficiency in redundant waypoint optimization, thereby providing effective decision support in practical applications.

In order to solve the problems of low positioning accuracy, poor anti-interference performance and actuator wear due to the uncertainty of model parameters and unknown time-varying environmental interference in the control of Dynamic Positioning Vessel (DPV), an adaptive backstepping sliding mode control method based on a dynamic event triggering mechanism was proposed. Firstly, sliding mode control is combined with backstepping technology to ensure the robustness of the system to uncertainty and interference. At the same time, the adaptive law is used to estimate the unknown uncertainty term. Based on this, an adaptive backstepping sliding mode control controller is designed. Finally, a dynamic event-triggered mechanism is designed to reduce the frequency of actuator update, thereby reducing unnecessary wear and tear. In order to verify the effectiveness and stability of the proposed control method, the Lyapunov stability theory method is used to prove that all signals in the system are uniformly and ultimately bounded, and Zeno phenomenon can be effectively avoided. The simulation results further verify the superior performance and wide application prospect of the proposed control method in DPV control system.

The port, industry and city are significantly related, and their integration degree reflects the coordinated evolution relationship among the three in the spiral development. Against the backdrop of deepening reforms in China's port management system, there is a growing need to scientifically assess the state of port-industry-city integration and analyze its underlying mechanisms. To address the issues of multidimensional indicator overlap and the difficulty in quantifying systemic synergy in existing research, this study constructs a coupling coordination degree model based on principal component analysis. Based on the panel data of 75 port cities in China from 2004 to 2023, the model applies principal component analysis to reduce the dimensionality of high-dimensional indicators across the port, industry, and city subsystems, thereby addressing multicollinearity issues among the indicators. Subsequently, a coupling coordination degree model is employed to quantify the level of synergy among the three subsystems, while the criteria importance through intercriteria correlation weighting method and panel entropy weight method are integrated for comprehensive weighting and robustness testing. The research shows that the overall integration level of Chinese port cities showed an upward trend during the study period, with its evolution exhibiting phased fluctuations influenced by the port management system. Significant disparities in integration were observed both across and within regions, with a maximum range of 4.65. Institutional changes in port management, path dependence in industrial development, and differences in regional institutional flexibility were identified as the core drivers of this spatial-temporal differentiation. Accordingly, policy recommendations such as establishing a cross-regional collaborative governance system and implementing differentiated industrial development strategies are proposed to advance the coordinated development of the port-industry-city system and provide a decision-making reference.

With the rapid development of the maritime shipping industry, maritime emergencies show an increasing frequency and an expanding impact range. When only post-incident rescue dispatching is relied on, excessive response time and high dispatching cost are caused. To enhance maritime emergency capability, an optimization method for rescue-base location and scale configuration in high-risk areas was proposed. First, the impact of maritime risk factors on navigation safety was considered, and an accident analysis framework based on Geographic Information Systems (GIS) and random forest was established to determine high-risk areas; then, the Fuzzy Comprehensive Evaluation Method (FCEM) was introduced to calculate the comprehensive impact index of interference factors on candidate locations for rescue bases. Finally, considering the supportive role of islands, a rescue equipment location and configuration model was developed with the objective of maximizing area coverage while minimizing configuration cost, and an improved multi-objective particle swarm optimization (IMOPSO) algorithm incorporating a derivation strategy and a sharing mechanism was designed to solve the model. Numerical experiment results for the South China Sea show that, compared with NSGA-Ⅱ and the standard multi-objective particle swarm optimization (MPOSO) algorithm, the proposed algorithm performs better in the uniformity and diversity of the Pareto solution set, the number of non-dominated solutions, and the solution time, with an overall improvement of 28. 88%~84.82%. Sensitivity analysis shows that both the coverage objective and the cost objective are significantly sensitive to response time and the number of candidate sites, and a trade-off between rescue timeliness and construction investment is required. Compared with the existing configuration scheme in the South China Sea, the optimized scheme reduces configuration cost by 13. 22% and increases sea-area coverage by 11. 98%, and the effectiveness and engineering applicability of the proposed method is validated.

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.

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.