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.

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.

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 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.

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.

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 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.

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.

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.