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.

With the advancement of global ports' green and low-carbon transformation, port microgrids, as key carriers for integrating high-penetration renewable energy, face the challenge of balancing heterogeneous optimization objectives in practical operation. Existing optimal scheduling methods based on the traditional Multi-Objective Particle Swarm Optimization (MOPSO) algorithm often rely on empirically determined conversion coefficients when coordinating economic and energy-consumption objectives. This approach suffers from strong subjectivity and lacks sufficient criteria for screening the Pareto solution set, making it difficult to consistently obtain globally optimal scheduling schemes. To address these issues, this paper proposes a method that introduces Grey Relational Analysis (GRA) into the traditional MOPSO algorithm to evaluate the Pareto solution set and thereby derive the optimal scheduling scheme. First, considering the high penetration of renewable energy and the source-load characteristics of port microgrids, a multi-objective optimization scheduling model is established, aiming to minimize comprehensive operational costs and maximize the local consumption rate of wind and solar power. Second, within the MOPSO framework, GRA is introduced as a decision-making tool to objectively evaluate the Pareto-optimal solution set generated during iterations, thereby accurately selecting the scheduling scheme with the best overall performance. The effectiveness of the proposed algorithm is verified using typical daily measured data from the Chuanshan Port microgrid demonstration project at Ningbo-Zhoushan Port. The results show that, compared to the scheduling algorithm based on traditional MOPSO, the proposed method significantly improves the consumption of renewable energy while maintaining system economic efficiency, achieving a 5.82% increase in the local consumption rate of wind and solar power and an approximately 9% reduction in the system's comprehensive operational costs, providing a feasible technical pathway for the effective utilization of high-density new energy in ports.

Considering the rudder features of a twin-propeller and twin-rudder ship, a series of numerical simulations of rudder-force tests with different rudder sectional parameters are carried out by using the Computational Fluid Dynamics method, from which the normal force coefficients of the rudder are obtained and the effects of rudder aspect ratio and thickness ratio on the hydrodynamic performances of the rudder are analyzed. On this basis, the standard turning circle and zigzag maneuvering motions are numerically simulated with the established mathematical model of ship maneuvering motion with four degrees of freedom. The maneuvering parameters are obtained from numerical simulations and the effects of rudder aspect ratio and thickness ratio on the turning ability, course-keeping ability and yaw-checking ability of the twin-propeller and twin-rudder ship are discussed. The research findings provide reference significance for optimizing rudder geometric parameter design and improving ship maneuverability.

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.

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.

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.

In response to recent adjustments in the fluvial shoal~channel pattern of the Tongzhou Shoal Reach in the lower Yangtze River caused by upstream reservoir operations and natural evolution, which threaten the stability of the 12.5-m deep-draft channel, this study investigates the characteristics of recent river regime evolution and the corresponding channel response mechanisms based on measured hydrological, sediment, and topographic data from 2018 to 2024. Spatiotemporal comparison, cross-section analysis, and erosion-deposition calculation were employed. The shoal-channel adjustments in the reach are pronounced and exhibit systematic spatial differences. The annual swing amplitude of the thalweg in Nantong Waterway reaches 0.4 km, and its navigation-obstructing shoal undergoes a three-stage dynamic evolution of "downstream incision-disconnection-aggregation," characterized by channel erosion and bar deposition, together with seasonal patterns of flood-season deposition and dry-season erosion. In contrast, the Tongzhou Shoal Waterway is mainly characterized by continuous retreat along the right margin of Xinkaisha and the development of chutes, which drive the entire Kuzigangsha to migrate southeastward and squeeze the navigation channel. The study further quantifies the key regulatory role of hydrodynamic forcing. During high-flow years, enhanced hydrodynamics induce approximately 30% reduction in the shoal area in the Nantong Waterway, improving channel conditions, but simultaneously intensify chute development and sandbody migration in the Tongzhou Shoal Waterway. During low-flow years, shoal deposition intrude into the navigation channel, deteriorating channel conditions, while the Tongzhou Shoal Waterway exhibits localized adjustments. These findings provide critical scientific basis for predicting the evolution of deep-draft channels and for optimizing the design of dredging and regulation projects, thereby establishing an important theoretical foundation for the long-term stability and sustainable management of the channel.

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 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