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The huge fuel consumption of shipping activities has a great impact on the ecological environment, port city environment, air quality, and residents' health. This paper uses Automatic Identification System (AIS) data records and ship-related data in 2021 coastal waters of the United States to calculate pollutant emissions from ships in 30 ports of the United States in 2021. After calculating the pollutant emissions from ships at each port, the multiscale geographically weighted regression (MGWR) model is used to analyze the factors affecting the ship pollutant emissions. Geographically weighted regression (GWR) model is used to investigate the spatial heterogeneity of various factors affecting the characteristics of ship pollutant emissions at different scales. This paper mainly compares the effect of models of GWR and MGWR. MGWR may truly reveal the scale difference between different variables. While controlling the social and economic attributes, the coastline length, container throughput, and population are used to describe the spatial effects of ship pollutant emissions in the United States. The results denote that the distribution trend of ship pollutant emissions has a gap based on various ship types and ports. NOx accounts for the highest proportion of pollutant emissions from port ships, followed by SO₂ and CO. The impact coefficients of coastline length and population on pollutant emissions in port areas are mostly positive, indicating that the growth of coastline length and population will increase pollutant emissions in port areas, while the effect of container throughput is opposite. Relevant departments should put forward effective measures to curb NOx emission. Port managers should reasonably plan the number of ship transactions according to the coastline length of the port.

As global aquaculture ventures further into offshore environments, the safe transport of large-scale aquaculture net cages across varied marine conditions has become a critical technical concern. This study conducts a detailed numerical simulation of the wet towing process for an octagonal aquaculture cage using AQWA software, systematically examining the effects of towline length, towing speed, towing configuration, and environmental factors on the cage's dynamic response and towline load characteristics. The findings reveal that towline length and towing speed are pivotal in determining the pitch amplitude of the cage and the resulting towline tension. Compared to a towline length of 200 m, using a towline length of 150 m combined with a towing speed of 0.5 m/s reduces pitch amplitude by 51.6% and towline tension by 24.8%. Additionally, an interval towing arrangement significantly enhances cage stability while minimizing towline stress. Under conditions of low wave height and longer wave period, the cage's motion response remains stable, contributing to enhanced transport safety. This research offers critical theoretical foundations and optimization strategies for the wet towing design considerations of offshore aquaculture cages, providing valuable insights to advance transport safety in challenging marine environments.

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Maritime high-speed over-the-horizon wireless communication is realizable through evaporation ducts. Detailed measurement, analysis, and modeling of duct channels are essential for application of this communication technique. In this paper, X-band electromagnetic (EM) wave propagation systems were developed and deployed for a 133-km over-the-horizon microwave link in coastal areas of the Yellow Sea. The propagation length was 7.7 times the line-of-sight length. Measurement results including the path loss (PL) and meteorological data were obtained during a 54-day period in autumn 2021. The long-term channel results were analyzed on the basis of statistical analysis and model simulations. Results showed that our measurement system, with a maximum measurable power loss of 200 dB, had connected with a probability of 56.2% during the measurement period. Model simulation showed that evaporation duct environments are not ideal in autumn, with an average evaporation duct height (EDH) of 10.6 m. The land breeze in autumn introduced dry and cold air to the link, which could promote evaporation of seawater and reduce PL by approximately 40 dB. Annual spatiotemporal characteristics of EDH showed that evaporation ducts are most suitable for over-the-horizon communication in spring, especially May.

Introduction: Various types of ships sail at sea, and identifying maritime ship types through shipradiated noise is one of the tasks of ocean observation. The ocean environment is complex and changeable, such rapid environmental changes underline the difficulties of obtaining a huge amount of samples. Meanwhile, the length of each sample has a decisive influence on the classification results, but there is no universal sampling length selection standard.

Vessel strikes are a substantial source of mortality for large whales worldwide and may pose conservation threats for small populations. Model-based estimates of mortality rates, which inform management strategies to reduce vessel strike mortality, typically assume a reduced likelihood that a whale-vessel collision will be lethal to the whale at slower vessel speeds. In this study, we reviewed and updated available data on observed whale-vessel interactions in U.S. waters and developed a new model characterizing the probability that an interaction will be lethal to the whale as a function of vessel speed, length (as a proxy for mass), and whale taxon. We found a significant effect of vessel size class on the probability of lethality. In addition, decreasing vessel speeds reduced the likelihood of a lethal outcome for all vessel size classes, but this effect was strongest for vessels less than 108m in length. The probability that a strike by a very large ocean-going vessel will be lethal exceeded 0.80 at all speeds above 5 knots. Whale taxon also affected both the likelihood of a lethal strike and the effect of vessel speed. Humpback whales (Megaptera novaeangliae) had significantly lower rates of lethal strikes compared to other large whales. This difference may be associated with data limitations, differing behavioral responses between species, varying vessel types between regions or differences in body composition and blubber thickness. The model is consistent with biophysical models that demonstrate a high rate of strike lethality for large vessels with high masses. Vessel speed restrictions are one of the primary approaches to reduce the risk of vessel strikes to whales in the face of continued industrialization of the oceans, and the model presented here will help better inform management efforts.

A whale shark, measuring around 4 meters in length, was killed after it was struck by a boat in Jayapura waters, Papua, on Thursday morning.

For decades, the shipping sector has been incorporated into the global decarbonization process. At present, global shipping - as a whole - aims to reduce its emission levels by 40 % by 2030 in relation to the 2008 level. In reducing greenhouse gas emissions, regulations such as the MARPOL 73/78 Convention and Energy Efficiency Design Index as well as other monitoring and managing schemes already in operation (e.g., Ship Energy Efficiency Management Plan and Energy Efficiency Operational Indicator) play a crucial role in measuring fuel consumption and ship engine emission output. Energy Efficiency Existing Ship Index (EEXI) is another measure, projected to be ratified in 2023, in-line with decarbonization targets in which the International Maritime Organization has planned a 70 % reduction in emissions level by 2050 using the same 2008 baseline. For this to happen, ship speed may need to be reduced, a decrease of fleet capacity may also need to be considered, and new ships may need to replace older ones already in service. The costs of implementing these types of reforms are obviously significant to the sector. Such change will augment the overall shipping overhead, effecting subsequent transportation and consumer costs. This paper aims to specify the scale of the expected costs of implementing EEXI globally. The current maritime fleet has been analyzed in terms of energy demand, deadweight tonnage, and expected CO2 emission reduction marginal abatement costs (MAC). Two pathways to achieve the desired EEXI values are presented, including the most common and available technologies to reduce demand. These technologies are subjected to MAC valuation and presented quantitatively for the world fleet. The research also investigates alternative fuel options in regard to lessening the CO2 impact, developing wind support systems, and avoiding conventional advancements to ships (e.g., upgrading the propeller or the propulsion system). At length, the target of the work is t

In dense anchorage areas, the challenge of navigation for Unmanned Surface Vehicles (USVs) is particularly pronounced, especially regarding path safety and economy. A Risk-Aware Path Optimization Algorithm (RAPO) is proposed to enhance the safety and efficiency of USV navigating in anchorage areas. The algorithm incorporates risk assessment based on the A* algorithm to generate an optimized path and employs a Dual-Phase Smoothing Strategy to ensure path smoothness. First, the anchorage area is spatially separated using a Voronoi polygon, the RAPO algorithm includes a grid risk function, derived from the ship domain and Gaussian influence function, in the path evaluation criteria, directing USV to successfully bypass high-risk areas and as a result. Then the DPSS is used to decrease path turning points and boost path continuity, which in turn improves path economy. Simulation results demonstrate that this method significantly reduces the path length and the number of turning points, enhancing USV navigation safety in anchorage areas.

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.

With the global wave of intelligence and automation, ship autopilot technology has become the key to improving the efficiency of marine transportation, reducing operating costs, and ensuring navigation safety. However, existing reinforcement learning (RL)-based autopilot methods still face challenges such as low learning efficiency, redundant invalid exploration, and limited obstacle avoidance capability. To this end, this research proposes a GEPA model that integrates prior knowledge and hierarchical reward and punishment mechanisms to optimize the autopilot strategy for unmanned vessels based on deep Q-network (DQN). The GEPA model introduces a priori knowledge to guide the decision-making of the intelligent agent, reduces invalid explorations, and accelerates the learning convergence, and combines with hierarchical composite reward and punishment mechanisms to improve the rationality and safety of autopilot by means of end-point incentives, path-guided rewards, and irregular obstacle avoidance penalties. The experimental results show that the GEPA model outperforms the existing methods in terms of navigating efficiency, training convergence speed, path smoothness, obstacle avoidance ability and safety, with the number of training rounds to complete the task reduced by 24.85%, the path length reduced by up to about 70 pixels, the safety distance improved by 70.6%, and the number of collisions decreased significantly. The research in this paper provides an effective reinforcement learning optimization strategy for efficient and safe autonomous navigating of unmanned ships in complex marine environments, and can provide important theoretical support and practical guidance for the development of future intelligent ship technology.