This article firstly reviews the relevant background and process of revising China Maritime Code which is the first comprehensive and systematic revision in over thirty years. After elucidating the new historical mission of the Law in the new era, from the perspective of providing a solid legal safeguard for high-quality development of the shipping industry, it focuses on analyzing seven key institutional developments as follows: harmonizing domestic and international carriage of goods by sea rules, establishing the legal status of electronic transport records, regarding the port operators as actual carriers, prudently increasing limitation of liability amounts, clarifying the non-typical security attribute of ship finance leasing, improving the marine insurance rule system, and introducing countermeasure clauses. Finally, it proposes directional considerations regarding the implementation of new law, formulation of supporting rules, and future legislative development.

This article focuses on the significant revisions to the time limit system in the new Maritime Code of China, systematically examining the institutional restructuring in four key areas: the one-year time limit for the carriage of goods by sea, the recourse time limitation, special causes for the interruption of maritime time limitations, and the commencement of the time limitation for marine insurance claims. The research shows that the new Code has made important progress in maintaining the internationally accepted one-year benchmark, constructing a balanced bilateral time limit structure, appropriately broadening the causes for interruption, and unifying the commencement standard for insurance claims, thereby significantly enhancing the legal system's certainty and international harmonization. However, the new Code might face challenges in local adaptation, including the absence of an agreement-based extension mechanism, limited recourse time limitation relief space, and unclear special rules for liability insurance and subrogation. By analyzing the new legal system and evaluating its effectiveness and potential limitations, this article aims to provide response strategies for the shipping industry and judicial practice, promoting the continuous optimization of China's maritime legal environment.

Autonomous berthing is a key element of intelligent navigation, yet its strong scenario dependence limits the application of theoretical research into actual implementations. Variations in ship type, propulsion configuration, and berth conditions impose distinct requirements on trajectory planning and control. At the same time, defining the completion criteria for autonomous berthing operation and establishing a comprehensive evaluation framework are essential for ensuring practicality and safety. This paper systematically reviews recent advances in trajectory planning and motion control for autonomous berthing. First, the key technical elements, including trajectory planning and motion control methods, are introduced. Second, different berthing strategies tailored to specific ship types and propulsion systems are analyzed in depth. Subsequently, berthing completion standards, performance evaluation metrics, and experimental validation approaches are discussed. Finally, the major challenges in the current research are summarized, and potential directions for future development are outlined.

The continuous increase in crisscross navigation between passenger and cargo ships poses a significant threat to navigation safety in restricted inland waters. Traditional row-by-row crossing operation methods are inadequate for ships navigating such complex environments, leading to a surge in navigational risks. This study proposes an enhanced row-by-row following ship crossing operation method, building upon traditional approaches to address the dual peak periods of passenger ship departures and tidal effects. Based on traffic conflict technology and dynamic ship domain theory, large-angle and small-angle row-by-row following ship crossing models were developed. The advantages of the proposed methods are validated using actual Automatic Identification System (AIS) data collected from the turnaround area of the busy Huangpu River. Results indicate that both the large-angle and small-angle row-by-row crossing methods effectively mitigate the safety limitations of traditional methods. Furthermore, the small-angle row-by-row crossing method improves passenger ship crossing efficiency by up to 50% compared to the large-angle method. The proposed row-by-row following vessel crossing operation method demonstrates significant potential for enhancing navigation efficiency and safety in restricted inland waterways, particularly in congested turnaround areas.

To address the challenges in dynamic modeling of Autonomous Underwater Vehicle (AUV), this paper proposes a black-box identification method for nonlinear systems based on deep convolutional neural networks, taking into account the nonlinear characteristics of the AUV's six-degree-of-freedom (6-DOF) motion. First, the frequency corresponding to the maximum amplitude of the rudder signal is extracted and used as a threshold for Variational Mode Decomposition (VMD) denoising. This reduces noise in the experimental data of the AUV model and resolves the issue of difficult parameter tuning in VMD decomposition. Then, a black-box model for the nonlinear system is constructed using Bidirectional Long Short-Term Memory (BiLSTM) and Attention mechanisms, with the Adam optimization algorithm employed to solve the AUV 6-DOF motion model. Finally, the AUV model data are used for model training and predictive validation, and the results are compared with modeling methods such as CNN-LSTM, CNN-BiLSTM, and CNN-LSTM-Attention to analyze the velocity, Euler angles, and trajectory of AUV motion. Experimental results show that, compared to the CNN-LSTM model, the proposed method improves the Root Mean Square Error (RMSE), the coefficient of determination (R²), and the Symmetric Mean Absolute Percentage Error (SMAPE) by 79.29%, 3.84%, and 74.41%, respectively, validating the feasibility and effectiveness of the proposed dynamic modeling approach. This method provides an alternative strategy for precise obstacle avoidance and autonomous navigation of underwater vehicles.

As an important component of the waterway transportation system, Ro-Ro passenger ship transportation plays a significant role in inland river, coastal, and even cross-strait transport services. In recent years, collisions involving Ro-Ro passenger ships have occurred from time to time. To mitigate the losses caused by such accidents, this paper proposes an emergency decision-making model for Ro-Ro passenger ship collisions based on a fuzzy Bayesian network. The identified emergency decision variables for RoPax ship collisions are fuzzified by introducing fuzzy logic. Combined with improved IF-THEN rules, confidence rule bases are established and then converted into a conditional probability table, thereby constructing a complete Bayesian network inference structure. Ultimately, the optimal emergency decision scheme is determined through utility value evaluation. The results demonstrate that the proposed emergency decision-making model is effective and feasible, aligning with practical application requirements. This study provides ship decision-makers with a reference basis for emergency response in the event of a RoPax ship collision.

To support the autonomous navigation of cargo-carrying vessels with specific time and position requirements, research on high-precision trajectory tracking control is necessary. In response to the limited existing studies on cargo vessels and the insufficient consideration of actuator characteristics-where thrust and torque are often treated as directly controllable inputs, leading to limited practical feasibility-a control method combining a virtual vessel leader and an Integral Line-of-Sight (ILOS) approach is proposed. This method uses a propeller speed prediction algorithm to synchronize the real vessel with the virtual vessel and employs speed feedback correction to compensate for disturbances. To improve tracking accuracy, the relative positions are utilized to determine the desired heading through the improved ILOS method, thereby reducing the problem to one of course keeping. Ultimately, vessel trajectory tracking is achieved. Simulation results show that the controlled vessel accomplishes trajectory tracking under disturbance, with a steady-state error of less than ±0.5 m, an error convergence time of less than 50 s, an 86% reduction in rudder jitter, and 71% reduction in propeller speed jitter. The proposed control method is straight forward, demonstrating high performance, can serve as a valuable reference for engineering applications.

Maritime fire incidents pose a significant threat to the safety of ships, with human factors being the primary cause of these accidents. Accurately identifying the emotional changes of crew members in maritime fire scenarios is of great significance for enhancing their firefighting capabilities. Virtual reality technology is employed to simulate maritime fire scenes and collect Electroencephalogram (EEG) signals from multiple subjects. The EEG signals are preprocessed and decomposed into sub-signals of different frequency bands using discrete wavelet transform. Three features, including mean absolute value, standard deviation, and root mean square, are extracted from each sub-frequency band to establish a feature set. Multiple machine learning models suitable for emotion recognition are constructed, and the models are evaluated using metrics such as precision, accuracy, and F₁ score. Experimental results show that the support vector machine classification model performs the best, with an accuracy of 87.97%, which significantly improves the three-class classification problem of crew members' fear emotions in maritime environments. Combining virtual reality technology with EEG emotion recognition techniques can effectively induce and identify crew members' fear emotions in fire scenarios. This method is beneficial for assessing and improving the emergency response capabilities of crew members in firefighting training.

Currently, ship navigators can assess flow patterns using basic instruments and adjust maneuvering strategies accordingly. Access to detailed flow field data of a waterway can provide valuable information and early warnings for ships transiting the area. This study analyzes surface flow in the waters near Jianghan Bridge, captured by video. By employing Large-Scale Particle Image Velocimetry (LSPIV), a method is developed to measure surface flow velocity in the navigation channel, enabling analysis of surface flow characteristics and acquisition of surface flow field data. The obtained flow field data are validated through comparison with optical flow methods and Acoustic Doppler Velocimetry. Results demonstrate that the proposed surface flow velocity measurement method can effectively capture detailed flow pattern characteristics of surface currents in the study area. This approach provides data support for navigation and path planning of both conventional ships and smart ships utilizing big data, contributing practical value to the enhancement of maritime safety and operational efficiency.

With the rapid development of global shipping, port cargo volumes are increasing significantly, leading to growing issues of vessel congestion and delays, which in turn severely constrain port operations. In response to the challenges posed by surging cargo volumes, ship congestion, and aggravated pollution, this paper proposes a Time-Berth & Pollution (TB&P) model. The model takes the difference between the actual and expected time in port as the objective function, aiming to minimize operating costs and pollution emissions, subject to constraints related to time, space, and machinery/equipment. To solve the TB&P model, an improved version of the basic Beluga Whale Optimization (BWO) algorithm is developed, termed the Opposition Learning Beluga Whale Optimization (OBWO) algorithm. The feasibility and superiority of the proposed model and improved algorithm are verified through case data from a port. Results demonstrate that, compared with traditional models, the established model significantly reduces the extent of ship delays and mitigates water pollution in the port area. Furthermore, the proposed OBWO algorithm exhibits enhanced stability and accuracy relative to other selected algorithms.

The arrival and handling times of vessels are subject to significant uncertainty. Triangular fuzzy numbers, characterized by upper and lower bounds and a most likely value, provide an effective means of representing such imprecise information. In this context, this paper first establishes a fuzzy integer programming model for berth allocation at container terminals, aiming to minimize the total departure delay time of vessels. An improved Multi-Verse Optimizer (MVO) algorithm is then proposed to solve the model, incorporating solution repair and breakout strategies. Comparative analysis shows that, in contrast to deterministic berth allocation schemes, the proposed fuzzy berth allocation approach demonstrates notable advantages in reducing total departure delay time and exhibits greater effectiveness in handling uncertainty. Moreover, the improved MVO algorithm achieves solution speed improvements of 59.9%, 44%, and 26.1% in small, medium, and large scale experiments, respectively, compared to the standard multi-verse optimizer. These results indicate that the proposed algorithm can efficiently solve the fuzzy integer programming model for berth allocation and offers valuable decision-making support for addressing berth allocation problems under fuzzy uncertainty.

In the context of carbon emission reduction, this study constructs a bi-level planning model for port microgrid investment and deployment, with the government as the upper-level decision-maker and the port area as the lower-level follower. The model incorporates the interests of the port area, including berthed vessels, and aims to maximize environmental benefits while minimizing the total cost of the port area over the planning period. Using the Column and Constraint Generation (CCG) algorithm, the optimal investment and operation strategy for the port area during the planning horizon is derived. The study analyzes the deployment of the port microgrid system under varying incentive budgets and evaluates the resulting environmental benefits, comparing the effectiveness of different incentive strategies. The results demonstrate that a hybrid incentive strategy can significantly enhance investment motivation in port microgrid systems, thereby effectively fostering innovation in the energy structure of the port region and accelerating the emission reduction process.

To foster a new development paradigm, the Yangtze River Economic Belt serves as a crucial hub connecting the domestic and international circulations. As the leading port and core node of the Yangtze River Economic Belt, Shanghai Port's strategic role as one of the most critical infrastructure has become increasingly prominent. The Shanghai International Shipping Center is facing a bottleneck of spatial resource constraints in upgrading its service capacity. This paper evaluates port operational efficiency by constructing models for port service intensity, waiting probability, and queuing theory. It quantifies the impact of resource constraints using a ship loss rate model under different system capacities, while comparing development strategies of typical domestic and foreign ports to analyze the core challenges confronting the Shanghai International Shipping Center. Model analysis indicates that the structural contradiction between port service demand and spatial resource supply has become a key constraint on its high-quality development. Looking ahead, the expansion and upgrading of port-shipping resources and optimizing spatial resource allocation will enable the Shanghai International Shipping Center to effectively unleash the advantages of direct river-sea transportation, significantly improve port service capacity and the navigation efficiency of the Yangtze River Golden Waterway, and reduce the comprehensive logistics costs of the whole society. This development path will not only promote the green transformation and digital-intelligent upgrading of the shipping industry but also strengthen the country's supply chain security and expand high-level opening-up, laying a solid foundation for the long-term and sustainable development of the Shanghai International Shipping Center.

Accurate forecasting of port container throughput is of great significance for port operators and government administrations in making scientific decisions. Existing forecasting methods, however, often pay insufficient attention to short-calendar-time PCT and exhibit limited accuracy in handling nonlinear and non-stationary fluctuation series. This paper takes the container throughput of Shanghai Port as the research object and proposes a novel deep learning model based on secondary decomposition using CCVMD and STL. Using the correlation coefficient as a reference, variational mode decomposition is first applied to the original time series. Subsequently, a secondary decomposition divides the data into seasonal, trend, and residual components. An algorithm-optimized long short-term memory neural network is then employed to predict each component separately, and the final prediction results are aggregated. Experimental results show that the combined decomposition model with data preprocessing significantly outperforms other models in PCT forecasting. The proposed model achieves a mean absolute percentage error of 0.021 703, a root mean square error percentage of 0.026 852, and a mean absolute error percentage of 0.022 14, indicating superior overall performance compared to 12 benchmark models and several models from prior studies. Furthermore, the secondary decomposition approach demonstrates enhanced reliability in tracking extreme values, removing and reducing noise, and improving interpretability.

To address the issue of low accuracy in ship trajectory prediction in complex navigable waters, this paper proposes a GRU-Attention-BiLSTM model for ship trajectory prediction. In the encoder part, the Gated Recurrent Unit (GRU) is employed to capture temporal features in trajectory sequences. The decoder adopts a Bidirectional Long Short-Term Memory Network (BiLSTM) integrated with an Attention mechanism to adjust the weights of data features. The model input is based on the longitude, latitude, speed and heading of the ship at the historical moment, and the ship density in the water area after median filtering smoothing is introduced as an additional feature. Using Automatic Identification System (AIS) data from the core port area of Ningbo-Zhoushan Port in March 2024, the model was trained and validated. Quantitative and qualitative comparisons with GRU, LSTM, Seq2Seq-LSTM, Attention-BiLSTM, and Transformer models demonstrate that the proposed model achieves superior prediction performance across different prediction durations and navigation scenarios.

To address the safety and economic requirements for ships navigating the complex ice environments of Arctic waters, this paper proposes a multi-objective improved Sparrow Search Algorithm (SSA) to optimize both wind resistance and ice resistance. The Risk Index Outcome (RIO), calculated by the Polar Operational Limit Assessment Risk Indexing System (POLARIS), and the safe water depth threshold are adopted as constraints to ensure navigation safety and mitigate the impact of resistance on navigation efficiency along Arctic routes. First, meteorological and ice data for the Arctic route are processed, and a grid environment map is constructed according to ship type. Second, safe navigable areas are identified, and a multi-objective function model is established. Finally, the improved sparrow search algorithm is applied to optimize the route and is compared with other typical path planning algorithms to verify the effectiveness and feasibility of the proposed method. The results indicate that the optimal path generated by the improved sparrow search algorithm, based on the multi-objective model of wind and ice resistance, can significantly reduce ship resistance during navigation-achieving a reduction of up to 10.9%. Moreover, there is no significant difference in path length or running time compared with other algorithms. This study provides an economical and reliable optimization solution for ship navigation in Arctic routes.

To study the pitching stabilization performance of super-large ships under severe sea conditions, this paper takes the tanker "KVLCC2" as the research object. A weighting matrix is utilized to stabilize its transfer function model in Mathematica, and the stability of the model is verified using the root trajectory shaping method. Subsequently, a simplified first-order closed-loop gain-shaping algorithm is applied to design the robust controller. In addition, a dual nonlinear feedback control algorithm is proposed to be incorporated into the control system to further enhance its pitching stabilization performance. To validate the effectiveness of the dual nonlinear feedback control system for pitching stabilization, wind scale of 7 and 8 wind and wave models along with perturbation links are introduced into the system for simulation experiments. The experimental results demonstrate that even with a time lag constant of 0.15, the dual nonlinear feedback control system effectively improves the ship's pitching stabilization performance under rough sea conditions. The proposed dual nonlinear feedback control system can provide technical support for the smooth and efficient navigation of super-large ships in varying sea conditions.

This study employs the MARIS model and a convective diffusion model to simulate the diffusion of nuclear wastewater released from Japan. Based on the simulation results, it proposes optimized methods for ballast water exchange to prevent the direct discharge of radioactive ballast water into ports, thereby mitigating potential threats to the ecological environment. The research focuses on the Fukushima nuclear incident and the subsequent continuous release of 1.3 million tons of nuclear wastewater into the ocean. Results indicate that radioactive substances are mainly concentrated in the surface layer of the ocean, with detectable enrichment of radioactive elements such as cesium in seawater and aquatic organisms near the Fukushima nuclear power plant. Consequently, ships operating near eastern Japanese ports are taking in ballast water contaminated with radioactive materials, including cesium-134 and cesium-137. Using a convective diffusion module, the study simulates the variation in radioactive substance concentrations during ballast water exchange at different distances, providing theoretical support for optimizing exchange strategies. The findings show that performing a secondary ballast water exchange more than 20 nautical miles from Japan's coast can reduce radioactive substance concentrations in ballast water to one ten-thousandth of the pre-exchange levels. The conclusions of this study can assist maritime regulatory authorities in formulating effective management measures, thereby contributing to the protection of marine ecosystems.

In the context of port shore power deployment, studying the impact of different policies on port and shipping enterprises is crucial for improving shore power utilization and achieving established emission reduction goals. To explore the policy effects on these enterprises, a Stackelberg game model was constructed with the port as the leader and shipping companies as the follower, incorporating innovation subsidies into the framework. This model aims to address innovation challenges in shore power equipment and examines the combined impact of subsidies and carbon trading policies on port and shipping enterprises. The model is solved using backward induction and numerically simulated via Matlab. The results indicate that in the early stages of emission reduction, subsidy policies help enhance innovation levels, while after the maturation of shore power technology, the implementation of dual policies involving both subsidies and carbon trading is more effective in motivating the industry to develop emission reduction technologies. Therefore, it is recommended that the government take measures to expand market scale, strengthen societal low-carbon awareness, and increase innovation subsidies in the early phase to promote shore power utilization. After the technology matures, the subsidy ratio and carbon price can be adjusted to sustain the utilization of shore power.

To accurately predict the fuel consumption of in service ships, analyze the complex and variable influencing factors of fuel consumption, and quantify their respective impacts, this study selects tankers and bulk carriers for operational data collection and preprocessing. A fuel consumption prediction model based on the Extreme Gradient Boosting (XGBoost) algorithm is established, and factor importance is evaluated using the Gain method. The results demonstrate that the proposed model achieves strong computational and predictive performance, with mean absolute percentage errors of 4.88% and 3.92% for the tanker and bulk carrier models, respectively. Among internal factors, ship speed shows the greatest influence, with weights of 0.671 and 0.429 for the two vessel types. Regarding external factors, navigation environment conditions such as wind and waves also exhibit significant impacts.

To effectively identify vessels using fuel with excessive sulfur content, a reverse calculation method for determining fuel sulfur content was developed based on emission and diffusion characteristics. A Gaussian puff compensation model was applied to estimate the emission source strength of vessels from monitored SO₂ concentrations at designated points. In addition, a computational model for vessel fuel consumption was established using key vessel parameters, including the power and fuel consumption rates of main and auxiliary engines. The proposed method demonstrated superior performance compared to the mainstream carbon balance method in detecting vessels with non-compliant sulfur content, achieving detection and false detection rates of 86.60% and 2.06%, respectively. Over a 30-day continuous monitoring period, the fuel sulfur content of 2,743 vessels was successfully determined, representing an effective detection rate of 82.72%. Among these, 131 vessels were identified as potentially exceeding sulfur limits. Subsequent verification confirmed that 111 vessels used non-compliant fuel, resulting in an assessment accuracy of 84.73%. These findings demonstrate the method's capability to enable real-time monitoring of fuel sulfur content without requiring CO₂ concentration data.

To achieve carbon neutrality, governments and enterprises are accelerating the decarbonization process in the shipping industry. This paper focuses on the Low-Carbon Maritime Supply Chain (LMSC), considering both government policies and consumers' green preferences. A two-stage Stackelberg game model between shipping companies and freight forwarders is developed to determine optimal pricing and carbon emission reduction strategies, while examining the impacts of carbon taxes, government subsidies, and consumers' green preferences on decision-making. The analysis yields three main findings: First, cooperation between freight forwarders and shipping companies maximizes overall profits for the LMSC; however, to achieve higher emission reduction levels, both parties should co-lead the supply chain. Second, government subsidies and enhanced low-carbon awareness produce dual effects: while they help reduce emissions and increase corporate profits, they may also raise TEU market prices for consumers under certain conditions. Third, although carbon taxes significantly improve the low-carbon performance of the LMSC, they reduce profits for all participants and increase TEU market prices.