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 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.

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.

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.

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.

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.

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.

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

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.