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

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 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.

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.

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.

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.

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 continuous development of the shipping market, the China Containerized Freight Index (CCFI) serves as an important indicator for measuring the state of the global container shipping market, and research on its fluctuation characteristics is conducted to help market participants better grasp market dynamics and formulate effective market strategies. To address the issue that CCFI fluctuations are influenced by multiple factors, this paper focused on the composition of the CCFI, selected five major representative routes. First, decomposed the freight index of each route using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm optimized by particle swarm adaptation. Second, reconstructed the components into high-frequency and low-frequency parts based on the variance contribution rate. Finally, the BEKK-GARCH model was used to analyze the volatility effects of the original series and the three groups of reconstructed series for the selected routes, and the volatility relationships between different route markets were obtained. The research results show that the impact of major events on CCFI fluctuations is profound, while the impact of short-term market behaviors and sudden events is smaller in magnitude, shorter in duration and higher in frequency; bidirectional spillover effects exist among the trend terms of the freight indices of all routes, and these effects are transmitted to one another.

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.

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.