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.

With inland waterways transitioning from linear to networked operation, accurately identifying critical segments is essential for optimizing resource allocation and enhancing system resilience. Existing methods have limitations in effectively identifying segments that play a decisive role in maintaining global connectivity. To address this issue, a community bridge-based method is proposed. Firstly, a weighted topological network is constructed using waterway class and length. Then, the Louvain algorithm is applied to divide the inland waterway network into multiple communities with strong internal connectivity, and edges connecting different communities are identified as critical segments. Finally, attack simulation experiments are conducted to evaluate the effectiveness of the proposed method. Taking the Jiangsu inland waterway network as a case study, the results show a maximum modularity of 0. 901, indicating a pronounced community structure characteristics, and the network can be divided into 18 communities. Currently, 46 critical segments are identified in the network. If all critical segments fail simultaneously, both relative network efficiency and the relative size of the largest connected component decrease by nearly 80%, validating the effectiveness of the identification method. After implementing the 2017—2035 and 2023—2035 waterway network upgrades, the community structure becomes more compact, and the number of identified critical segments decreases while the results remain consistent. The identified critical segments provide theoretical support for routine maintenance and safety supervision of inland waterways, strengthening navigational assurance to enhance network resilience.

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.

Against the backdrop of increasing global supply chain uncertainties, how to enhance the ability of ports to cope with external shocks has become a hot topic in both academia and industry. To this end, this study is based on panel data of 16 listed Chinese port companies from 2004 to 2023. A web crawling technique was used to obtain the text of corporate annual reports. The term frequency-inverse document frequency method was applied to extract the frequency of digitalization-related keywords, so as to quantify the degree of digital technology application. Meanwhile, the sensitivity index method was used to measure the level of port resilience. On this basis, a fixed-effects model was further constructed to empirically examine the empowering effect of digital technology on port resilience and its underlying mechanism. The results show that the application of digital technology significantly improves the resilience of major Chinese ports. For each standard deviation increase in the digital technology level, port resilience increases by about 0. 2 standard deviations. This finding remains valid after a series of robustness tests. Digital technology exerts its effect by strengthening absorptive capacity and adaptive capacity, among which the enhancing effect on adaptive capacity is particularly prominent. However, the path of improving resilience through innovation capacity has not yet emerged. Under the impact of the 2020 global public health event, the empowering effect of digital technology on port resilience was significantly enhanced. In contrast, under the impact of climate change and the 2008 financial crisis, the empowering effect of digital technology on port resilience did not change significantly. These conclusions provide a new perspective for seeking to improve port resilience in the current context of sharply increasing global uncertainties.

Ship motion modeling is crucial for developing intelligent control technology. Traditional modeling methods, however, have drawbacks such as a large number of parameters and insufficient precision. To address these issues, this paper focuses on the latest intelligent research and training ship "Xin-Hong-Zhuan" of Dalian Maritime University. A ship motion characteristic model is constructed using the characteristic modeling method. First, the study begins with Kalman filtering to preprocess real-ship test data. Next, the nonlinear innovation recursive least squares method with a forgetting factor is used to identify the model's parameters. Finally, turning circle tests and zigzag maneuver tests are conducted to verify the model's effectiveness and accuracy. The results show that the model has an agreement of 89.7%, fewer parameters, and higher precision than the traditional Nomoto model. This research offers a theoretical reference for applying characteristic models in navigation and is significant for improving the precision of ship motion 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.

To address the insufficient real-time capability and long-horizon accuracy degradation of ship maneuvering motion prediction under environmental disturbances such as waves, an online prediction method based on an improved Long Short-Term Memory (LSTM) neural network is proposed. A multi-layer LSTM is adopted as the core predictor, and an embedded sliding-window structure is introduced to compute the error metrics within the window in real time. When the window-averaged error exceeds a preset threshold, model retraining and updating are triggered, thereby achieving timely online prediction. The results indicate that, compared with offline prediction, the proposed online method maintains stable prediction accuracy under long-horizon conditions with continuously switching wave states. With the same window length, the online method with a stricter threshold achieves a maximum RMSE improvement of 56.85%, while the cumulative update time is only 3.82 s. The proposed online prediction method delivers satisfactory long-horizon prediction performance for ship maneuvering motion and shows practical value for accurate long-horizon prediction under complex sea conditions. Key words:navigation safety; online prediction; long short-term memory neural network; ship maneuvering; wave influence; sliding time window

Suction pile can not only provide sufficient bearing capacity for deepwater oil and gas well construction, but also be used more and more widely in subsea production systems as the foundation of subsea structure. The stability of suction pile structure in offshore installation faces challenges due to its large span and harsh working environment and installation conditions. Taking a large suction pile with a diameter of 8 m and a total height of 19. 68 m applied to a gas field in the South China Sea as the research object, a 1∶1 finite element model was constructed. Based on the operating environment of the gas field in the South China Sea, the typical installation process of suction pile under transporting, lifting and installation during offshore construction is studied, and the worst conditions under each working condition are obtained through load calculation and analysis. The results show that the maximum stress under the transportation condition is negative transverse acceleration + vertical acceleration + Y negative wind load, and the high stress is concentrated at the fixed place between the suction pile and barge. In the lifting condition, the trapped water on the suction pile is considered for air and underwater lifting analysis. The high stress occurs at the welding point of the lifting point, which is the focus area of the field operation. The calculation of suction pile installation and inclination of manifold installation under the installation condition meets the standard requirements. Based on the above calculation, combined with the offshore installation practice, the whole offshore construction process of suction pile is safe and reliable, and the final installation precision is very high. The relevant research results can provide reference for the optimal design and offshore installation of deep-water suction piles.

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 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.