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.

To accurately predict the thermal fire hazard of these containers under different stowage methods on a ship, this paper establishes typical working conditions, such as the position of the fire container, stowage height and wind speed. It then simulates the fire scene of lithium battery containers on the deck using FLACS 10.9 and establishes a prediction model for the temperature and heat flow density of the fire flow field using the CatBoost algorithm. The results demonstrate that the air volume within the upper and lower spaces of the lithium battery container is directly proportional to the change in fire temperature. The maximum temperature occurs when the fire layer is in the 7th layer, and the range of damage caused by high temperatures and heat flux is minimised when the fire layer is between the 7th and 8th layers. Increasing the stowage height decreases airflow, resulting in higher maximum fire temperatures, a larger temperature influence range and a longer vertical diffusion distance of heat flux. When the wind speed is in the range of 1-4 m/s, it helps to dissipate heat and reduce the maximum temperature. However, when the wind speed reaches 5 m/s, the oxygen uptake rate of the flame increases, resulting in a higher maximum temperature. When the wind speed reaches 6 m/s, the heat dissipation effect dominates and the maximum fire temperature decreases again. The higher the wind speed, the smaller the area of damage caused by high temperatures and heat flux. Comparing the temperature and heat flux density values predicted by the CatBoost algorithm with the measured samples shows that the model is highly accurate and can identify overheating spots. These research results can inform the determination of lithium battery container accumulation modes and the corresponding fire monitoring.

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.

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.

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.

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.

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.

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.

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.