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The marine green fuel supply chain is a crucial component in the development of green ports. Focusing on the issue of cooperative contracts between the port and green marine fuel supplier within the marine green fuel supply chain, first, a two-echelon marine green fuel supply chain optimization model consisting of a green marine fuel supplier and a port was constructed. Second, the changes in profits and decisions of a green marine fuel supplier and a port were compared under four scenarios: no contract, cost-sharing contract, revenue-sharing contract, and combined revenue-sharing + cost-sharing contract. Finally, the propositions were validated using numerical simulation methods. The research findings show that, in terms of revenue, the combined profits of a port and green marine fuel supplier under the combined revenue-sharing + cost-sharing contract exceeded those under the revenue-sharing contract alone. Regarding the sales price of marine green fuel and bunkering service quality, these factors peaked under the cost-sharing contract and were at their lowest under the revenue-sharing contract. An increase in the port's investment proportion in marine green fuel bunkering service quality led to decreases in the bunkering volume, sales price, wholesale price, and bunkering service quality of marine green fuel under both the cost-sharing contract and the revenue-sharing + cost-sharing contract. Additionally, the higher proportion of revenue shared by the port with the green marine fuel supplier resulted in lower sales price, wholesale price, and bunkering service quality of green marine fuel under both the revenue-sharing contract and revenue-sharing + cost-sharing contract.

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Rapid urbanization and heavy industrialization generally result in serious aerosol pollution. Contrary to this conventional wisdom, Zhanjiang, one industrial city in the southernmost point of the Chinese mainland, is not accompanied by aerosol pollution and its air quality index always ranks high compared to other cities in China. To investigate this contradiction, 72-hour total suspended particles (TSPs) and water-soluble inorganic ions (WSIIs; including Mg2+, Ca2+, K+, Na+, NH4+, Cl-, NO3-, and SO42-) were collected in Zhanjiang, China, from November 2018 to November 2019. The relative humidity (RH) was higher than 80% throughout the whole year in Zhanjiang. However, the TSPs and WSIIs were not correlated with RH, indicating that RH can increase the particle size, but this had a minor impact on the dry deposition rate. The larger particles induced by RH were easily captured by wet precipitation, leading to a seasonal pattern with higher TSP and WSII mass concentrations during the dry and cool season and lower values during the hot and rainy season. This seasonal pattern and high aerosol acidity indicate that TSPs, WSIIs, and acidic gaseous precursors from the local sources were preferentially scavenged by the abundant rainfall and high precipitation frequency. Principal component analysis (PCA) results suggest that relatively clean marine emissions and secondary aerosols were the most important sources of TSPs and WSIIs. Our results indicate that the inconsistency between the heavy industrial activities and excellent air quality in Zhanjiang may be related to the high precipitation frequency (63%) and the marine dilution effect (27%).

Adverse weather conditions such as rain and haze often lead to a degradation in the quality of maritime images, which is crucial for activities like navigation, fishing, and search and rescue. Therefore, it is of great interest to develop an effective algorithm to recover high-quality maritime images under adverse weather conditions. This paper proposes a prompt-based learning method with degradation perception for maritime image restoration, which contains two key components: a restoration module and a prompting module. The former is employed for image restoration, whereas the latter encodes weather-related degradation-specific information to modulate the restoration module, enhancing the recovery process for improved results. Inspired by the recent trend of prompt learning in artificial intelligence, this paper adopts soft-prompt technology to generate learnable visual prompt parameters for better perceiving the degradation-conditioned cues. Extensive experimental results on several benchmarks show that our approach achieves superior restoration performance in maritime image dehazing and deraining tasks.

Compared to structured ocean environments, unstructured ocean environments are inherently more complex. In such unstructured environments, the presence of narrow waterways poses unique navigational hurdles for autonomous surface vehicles (ASVs) due to their restricted connectivity. Current path planning algorithms designed for unstructured environments, particularly those characterized by narrow spaces, often face difficulties in efficiently exploring the target area while producing high-quality paths. In this study, we tackle the aforementioned complexities by incorporating progressive sampling and point cloud clustering, which jointly expedite the detection of constrained waterways in unstructured marine environments. More specifically, we generate multiple random trees from these sampling points, thereby bolstering both navigational accuracy and overall computational efficiency. Building upon these core techniques, we introduce a novel extension of the traditional rapidly-exploring random trees (RRT) connect algorithm-referred to as multiple RRT-connect (multi-RRT-connect)-aimed at swiftly determining a viable path between prescribed start and goal coordinates. As the number of samples expands, the random trees gradually enlarge and interlink, mirroring the functionality of classic RRT-connect and ultimately forming a continuous corridor. Subsequently, the derived path undergoes iterative refinement and optimization, culminating in a significantly reduced trajectory length.We subjected the proposed algorithm to rigorous testing through comprehensive simulations alongside meticulous comparisons with established state-of-the-art solutions. The results highlight the algorithm's distinct advantages across multiple dimensions such as path construction success, computational efficiency, and trajectory refinement quality, thereby underscoring its potential to advance autonomous navigation in challenging maritime settings.

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