Existing link prediction methods for maritime shipping networks primarily rely on single-dimensional network structural information and fail to comprehensively account for multiple influencing factors. To address this limitation, a coupled similarity index integrating multi-level information is proposed. Specifically, to incorporate the effects of practical node attributes such as port operations, hinterland economic conditions, and maritime distances on link prediction, similarity indices based on node realistic attributes are constructed. The predictive performance of 22 individual similarity indices from four perspectives—local network structure, global network structure, quasi-local network structure, and node realistic attributes—is evaluated using the Area Under the Curve (AUC) metric. The index with the highest AUC value from each perspective is then selected. Subsequently, weighted averaging and grid search are employed to determine the optimal weight combination, thereby constructing the coupled similarity index with the highest predictive accuracy. Experimental results on the global container shipping network demonstrate that the Resource Allocation (RA), Random Walk with Restart (RWR), Superposed Random Walk (SRW), and Similarity based on Port Operations (SPO) indices achieve the best prediction performance within their respective perspectives. The proposed coupled similarity index significantly outperforms all individual indices. In particular, the relatively high weight assigned to the SPO index indicates that node realistic attributes play a dominant role in link prediction. Furthermore, comparison with newly added shipping routes in 2024 shows that the proposed method achieves a prediction accuracy of 45.48%, and the spatial distribution of the predicted routes is highly consistent with the global shipping network pattern, thereby validating the effectiveness of the proposed coupled similarity index.

Transshipment hub ports typically handle high-density water-to-water transshipment, while limited port resources pose significant challenges for efficient berth scheduling. To address this issue, a berth scheduling approach integrating multiple transshipment modes is proposed by incorporating a double-berthing operation, which allows vessels to berth side-by-side at the same berth and conduct direct transshipment via quay cranes. The double-berthing operation is further integrated with indirect transshipment and improved direct transshipment to form a coordinated transshipment framework. A mixed-integer programming model is formulated to jointly determine berth allocation and transshipment mode selection, with the objective of minimizing transshipment costs and vessel-related operational costs. The model incorporates practical constraints including transshipment time windows, vessel matching relationships, and quay crane width limitations. To solve the resulting complex optimization problem, an Adaptive Large Neighborhood Search (ALNS) algorithm is developed. Multiple destroy and repair operators together with an adaptive weight updating mechanism are designed to enhance the exploration capability and computational efficiency of the algorithm. Computational experiments of different scales are conducted, and the proposed algorithm is compared with the Gurobi solver, Genetic Algorithm (GA), and Simulated Annealing (SA). In addition, weight sensitivity analysis is performed to evaluate the robustness of the model under different cost weight settings. The applicability of the proposed approach is further verified using real arrival data from feeder berths at Shanghai Yangshan Port. The results show that, compared with traditional scheduling schemes, the proposed approach significantly reduces vessel waiting time and overall completion time while substantially decreasing truck transportation and yard storage costs. Specifically, the total vessel waiting time is reduced by 63.48%, the total operational time by 5.73%, and the overall operational cost by 65.09%. Furthermore, the improved ALNS achieves solutions within 3% of those obtained by Gurobi for small-and medium-sized instances, and obtains high-quality solutions within shorter computation times than GA and SA for large-scale instances, demonstrating strong robustness and computational efficiency.

Demand uncertainty and inland port congestion pose significant challenges to the efficient operation of port collection and distribution systems. This paper proposes a bi-level programming model that simultaneously account for demand uncertainty and inland port congestion. The upper-level model optimizes inland port location and capacity configuration by minimizing construction costs and congestion costs, while the lower-level model optimizes transport paths by minimizing transportation costs, transfer costs, carbon emission costs, and time costs. A Genetic Algorithm is employed to solve the model, with empirical analysis based on data from the Liaoning port cluster and its northeastern hinterland. The results show that higher-grade inland ports effectively absorb demand fluctuations and reduce congestion costs, while reducing carbon emissions by increasing the share of railway transport. A maximum regret value of θ=0.25 achieves the optimal balance between total cost and stability of the Liaoning network. Additionally, a carbon tax of ￥0.5/kg achieves the best emission reduction and congestion control outcomes.

This paper studies the routing design problem of inland river container liners. In accordance with the significant impact of loading volumes on the draft of inland ships, a strategy was proposed to optimize the last port of call on the ship's outbound route based on loading volumes. In view of the features that loaded containers can only be transported in one direction while empty containers can be transported in both directions on inland waterway, the node variables of empty containers were introduced. Then, two route design models considering cargo flow balance were established under two scenarios depending on whether the demand is divisible. Ten groups of real ships with different sizes on the Yangtze River were selected for example validation analysis. The results indicate that optimizing the last port of call for ships based on loading volumes and considering cargo flow balance are beneficial for improving the shipping company's voyage profits. Both the number of ships deployed on the route and the number of ports of call show a trend of first increasing and then remaining unchanged with the increase in the seasonal water depth of the Yangtze River channel or the decreases in the confidence level set by the shipping company. The shipping company's voyage profit also increases with water depth. The research conclusions can provide useful references for operation decision-making of shipping companies on inland waterway liner transportation.

This study analyzes the ultimate strength of an ultra-large container ship in accordance with the requirements of the International Association of Classification Societies (IACS) to assess the reliability of its hull structural ultimate strength.. A twin-island ultra-large container vessel is selected as the research object. Based on IACS UR S11A the ultimate strength of the ship is calculated, validated, and subjected to reliability analysis. Results indicate that ship may face a risk of insufficient strength under extreme operating conditions, and structural reinforcement is therefore required in accordance with UR S11A. By innovatively integrating the requirements of UR S11A with structural reliability theory, this study realizes a quantitative assessment of the ultimate strength for ultra-large container ships. The findings provide reference value for inspection and maintenance recommendations for aging vessels, as well as for route optimization.

With the vigorous growth of the shipping industry, ensuring navigational safety and improving operational efficiency have imposed higher demands on accurate wave simulations under extreme sea states. However, the applicability of the third-generation spectral wave model WAVEWATCH III (WW3) and the identification of optimal simulation configurations during the rapid intensification stage of super typhoons remain insufficient, limiting its ability to address two key challenges in typhoon-related maritime early warning: accurate simulation of typhoon-generated waves during the rapid intensification stage and precise classification of wave-induced disaster risk. To tackle these issues, a multi-dimensional sensitivity analysis framework is employed. First, typhoon characteristics and ship-accident information are compiled using the Northwest Pacific tropical cyclone best-track dataset provided by the China Meteorological Administration (CMA) and vessel accident investigation reports from the Guangdong Maritime Safety Administration. Second, high-resolution typhoon-wave simulation experiments are established using WW3. For source-term parameterizations, three widely used WW3 schemes (ST2, ST4, and ST6) are adopted. For atmospheric forcing, wind fields from the Weather Research and Forecasting (WRF) model, the fifth-generation ECMWF global atmospheric reanalysis (ERA5), and the NCEP Final Analysis (NCEP-FNL) are used to examine WW3 performance and to determine the optimal simulation configuration. Combined with observations from National Oceanic Administration buoys and standard statistical validation metrics, a complete technical workflow is developed. Under rapid intensification conditions of super typhoons, WW3 experiments with different source-term schemes and wind forcing fields are conducted and evaluated against buoy measurements. The results indicate that simulations driven by WRF winds and using the ST2 scheme achieve superior performance near the typhoon center, whereas the ST4 and ST6 schemes perform better in other regions.

As the world's largest Roll-on/Roll-off Passenger (Ro-Pax) terminal, Zhanjiang Xuwen Port faces severe vessel scheduling pressure during peak operation periods, placing higher requirements on scheduling efficiency. To address this issue, this paper, based on previous research, proposes an optimization method for the inbound and outbound sequence of Ro-Pax vessels by further considering constraints on dedicated berth for Ro-Ro vessels carrying dangerous goods and the characteristics of variable-speed vessel navigation. First, the inbound and outbound sequencing problem of Ro-Pax vessels is modeled as an Asymmetric Traveling Salesman Problem with Time Windows (ATSPTW). Then, constraints are formulated according to the navigation rules of the harbor basin and channel, and a weight matrix reflecting the minimum scheduling time intervals between vessels was constructed. Subsequently, a bi-objective dynamic programming model is established to minimize total scheduling time and total waiting time. Finally, a bi-objective Pareto optimization algorithm extended from the Held-Karp algorithm is developed to solve the model. The numerical experiment resulst based on actual operational data show that the optimized scheduling plan significantly outperforms the actual scheduling plan in terms of both total scheduling time and total waiting time, with optimization rates reaching 6.50% and 31.09%, respectively. The simulation experiments conducted using the FlexSim 3D simulation software further verify the safety and robustness of the optimized scheduling plan, with no violation of navigation rules observed throughout the entire operation process. The proposed optimization method can significantly improve scheduling efficiency and provide decision support for vessel scheduling at Xuwen Port and other similar ports during peak operation periods.

To address the challenge of simultaneously extracting local and global features in multi-task segmentation of Arctic sea ice, a hybrid architecture integrating residual networks and state space models was proposed in this paper. In this architecture, residual networks were employed as the encoder to extract multi-scale local features. Enhanced Visual State Space (EVSS) modules were adopted in the decoder to achieve global context modeling through the combination of 2D Selective Scan and dual attention mechanisms. In the skip connections, Cross-Scale Contextual Fusion (CSCF) modules were designed to integrate multi-level features from the encoder. Attention mechanisms were utilized to enhance the representation of key regions. In the training phase, a Decoder Stage-Wise Supervision (DSWS) was introduced to optimize the calculation of loss functions. Finally, sea ice concentration, development stages, and floe size predictions were generated simultaneously through parallel segmentation heads. Experiments on the AI4Arctic dataset demonstrate that the proposed model achieves a comprehensive score of 84.014. Its performance exceeds that of mainstream methods such as Swin Transformer and SegFormer in all three prediction tasks. Fine-grained segmentation of sea ice distribution is achieved in this paper. The results provide technical support for subsequent identification and delimitation of navigable water areas. The obtained segmentation results hold significant application value in Arctic route planning and ship navigation safety assessment.

Emergency anchoring operations pose a serious threat to the safety of subsea pipelines. Existing empirical formulas and theoretical models often show limited adaptability under complex geological conditions, particularly in characterizing the nonlinear evolution of soil strength during high-velocity penetration. To address this problem, this study takes the Hall anchor, which is widely used on merchant ships, as the research object and investigates the penetration behavior of ship emergency anchoring under static conditions using the Coupled Eulerian-Lagrangian (CEL) method. The Mohr-Coulomb constitutive model and the Einav strain-rate softening relationship are introduced to describe the dynamic degradation of soil strength. The anchor-soil contact interface is treated using the penalty function method, and an Eulerian domain with 586 000 elements is determined through a mesh convergence test. Taking the muddy clay in the waters of an offshore wind farm as the engineering background, the maximum penetration depths of emergency anchoring for a 20 000 GT LPG carrier and a 50 000 DWT oil tanker are calculated to be 2.908 m and 3.033 m, respectively. Compared with the DNV energy method and Young's formula, the proposed method can better reflect soil heterogeneity and large-deformation characteristics. The simulation results are further compared with experimental data, showing maximum errors of 13.1% in penetration depth and 16.1% in bottom-impact velocity, both of which are within the acceptable range for engineering applications. The results indicate that the proposed method has high reliability and is consistent with engineering practice, providing theoretical and technical support for subsea pipeline design, route planning, and anchoring risk control.

With its prominent advantages of adapting to high water heads, shortening dam-passing time, saving energy without water consumption, and enabling flexible layout, the shiplift has gradually become a key navigation facility for overcoming concentrated water level drops in modern inland waterway navigation and water conservancy hub projects. This paper reviews the development history and system architecture of shiplift technology, focusing on analyzing the technical principles and engineering applicability of three mainstream shiplift types systematically. It concentrates on the structural design, construction manufacturing, and safety assurance of counterweight vertical shiplift (including rack and pinion vertical and wire rope hoist types)—which possess broad applicability and potential for large-scale development. Combined with typical projects like Three Gorges, Goupitang, and Baise shiplift, it details China's breakthroughs in ultra-large shiplift technologies. Addressing industry demands for ultra-high capacity, intelligent operation and maintenance, and green low-carbon solutions, this section projects three major technological trends:series-matrix layout, friction driven models, and intelligent monitoring and diagnostics. Research indicates that China's shiplift technology has achieved leapfrog development, transitioning from "following and introducing" to "leading and innovating." It has established an independent system featuring multiple parallel technical routes. In the future, this technology will provide critical equipment support for the construction of the national comprehensive three-dimensional transportation network and the Belt and Road Initiative, driving the technological advancement of global inland waterway shipping.