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Many marine megafauna taxa are tied to the sea surface for breathing which makes them vulnerable to vessel collisions. Sea turtles have developed efficient mechanisms to reduce surface time for breathing to a few seconds, but they can extend their surface periods to rest or to rewarm after diving into deep and colder waters. However, knowledge of collision occurrences is limited to data of turtles stranded along the coastline worldwide, whereas events occurring offshore go likely underestimated due to the sinking of carcasses. Here we performed a spatially explicit assessment to identify, for the first time, oceanic areas of higher exposure for sea turtles from maritime traffic in the Tyrrhenian Sea, Western Mediterranean. Satellite-tracking data were used to estimate utilization distributions of loggerhead turtles using Brownian bridge kernel density estimation. Maritime traffic density maps based on Automatic Identification System (AIS) data were extracted from open-access data layers, provided by the European Maritime Safety Agency, summarized, and used for the exposure analysis. Turtle occurrences were also investigated in response to vessel densities and seasonal patterns by fitting a generalized additive model to the data. Our results demonstrated that loggerhead turtles are potentially exposed to maritime traffic across the entire basin, especially in the easternmost part. The exposure varies among spring/summer and autumn/winter months. Highest turtle occurrences were found in regions primarily subjected to cargo, tanker, and passenger transportation. This study represents the first-ever effort to characterize the exposure of oceanic loggerhead turtles to maritime traffic and highlights oceanic areas of higher exposure where research and conservation efforts should be directed to understand the effective impact of this stressor on the species.

It is significant for port ship scheduling and traffic management to be able to obtain more precise location and shape information from ship instance segmentation in SAR pictures. Instance segmentation is more challenging than object identification and semantic segmentation in high-resolution RS images. Predicting class labels and pixel-wise instance masks is the goal of this technique, which is used to locate instances in images. Despite this, there are now just a few methods available for instance segmentation in high-resolution RS data, where a remote-sensing image's complex background makes the task more difficult. This research proposes a unique method for YOLOv7 to improve HR-RS image segmentation one-stage detection. First, we redesigned the structure of the one-stage fast detection network to adapt to the task of ship target segmentation and effectively improve the efficiency of instance segmentation. Secondly, we improve the backbone network structure by adding two feature optimization modules, so that the network can learn more features and have stronger robustness. In addition, we further modify the network feature fusion structure, improve the module acceptance domain to increase the prediction ability of multi-scale targets, and effectively reduce the amount of model calculation. Finally, we carried out extensive validation experiments on the sample segmentation datasets HRSID and SSDD. The experimental comparisons and analyses on the HRSID and SSDD datasets show that our model enhances the predicted instance mask accuracy, enhancing the instance segmentation efficiency of HR-RS images, and encouraging further enhancements in the projected instance mask accuracy. The suggested model is a more precise and efficient segmentation in HR-RS imaging as compared to existing approaches.

In general, a single scalar hydrophone cannot determine the orientation of an underwater acoustic target. However, through a study of sea trial experimental data, the authors found that the sound field interference structures of inbound and outbound ships differ owing to changes in the topography of the shallow continental shelf. Based on this difference, four different convolutional neural networks (CNNs), AlexNet, visual geometry group, residual network (ResNet), and dense convolutional network (DenseNet), are trained to classify inbound and outbound ships using only a single scalar hydrophone. Two datasets, a simulation and a sea trial, are used in the CNNs. Each dataset is divided into a training set and a test set according to the proportion of 40% to 60%. The simulation dataset is generated using underwater acoustic propagation software, with surface ships of different parameters (tonnage, speed, draft) modeled as various acoustic sources. The experimental dataset is obtained using submersible buoys placed near Qingdao Port, including 321 target ships. The ships in the dataset are labeled inbound or outbound using ship automatic identification system data. The results showed that the accuracy of the four CNNs based on the sea trial dataset in judging vessels' inbound and outbound situations is above 90%, among which the accuracy of DenseNet is as high as 99.2%. This study also explains the physical principle of classifying inbound and outbound ships by analyzing the low-frequency analysis and recording diagram of the broadband noise radiated by the ships. This method can monitor ships entering and leaving ports illegally and with abnormal courses in specific sea areas.

The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union's incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port-a region characterized by high shipping activity-this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under vari

Vessel strikes are one of the main threats to large whales globally and to endangered blue, fin, and humpback whales in California waters. For over 10 years, NOAA has established seasonal voluntary Vessel Speed Reduction (VSR) zones off of California and requested that all vessels 300 gross tons (GT) or larger decrease speeds to 10 knots or less to reduce the risk of vessel strikes on endangered whales. We offer a comprehensive analysis quantifying cooperation levels of all vessels ≥ 300 GT from 2010 to 2019 within designated VSR zones using Automatic Identification Systems (AIS) data. While average speeds of large vessels have decreased across the years studied, cooperation with voluntary 10-knot speed reduction requests has been lower than estimated to be needed to reduce vessel-strike related mortality to levels that do not inhibit reaching and maintaining optimal sustainable populations. A comparison of vessel speeds across inactive and active voluntary VSR time periods show a modest (+ 15%) increase in cooperation from 2017 to 2019. A complementary, incentive-based VSR program that was started in 2014 and scaled up in 2018 within the region likely improved voluntary VSR cooperation levels, as participating container and car carrier vessels traveled at lower speeds during the VSR season than vessels not enrolled in the incentive-based effort. Comparisons of vessel speeds in the incentive-based VSR program across inactive and active time periods showed a significant (+ 41%) increase in cooperation. With cooperation levels for the voluntary VSR hovering around 50%, and the challenge of funding and sustaining an incentive-based VSR program, voluntary VSR approaches may be insufficient to achieve cooperation levels needed to significantly reduce the risk of vessel strike-related mortality for these federally protected whales, suggesting that VSR regulations warrant consideration.

Entanglements and vessel strikes impact large whales worldwide. Post-event health status is often unknown because whales are seen once or over short spans that conceal long-term health declines. Well-studied populations with high site fidelity verified by photo-ID offer opportunity to confirm deaths, health declines and recoveries. We used known outcome entanglements and vessel strikes of right whales (Eubalaena glacialis) and humpback whales (Megaptera novaeangliae) to model probabilities of deaths, health declines and recoveries with Random Forest (RF) classification trees. Variables included presence or absence of phrases from case narratives ('deep laceration', 'cyamid', 'healing', 'superficial') and a categorical variable for vessel size. Health status post-entanglement was correctly classified in 95.7% of right whale and 93.6% of humpback whale cases (expected by chance=50%). Health status post-vessel strike was correctly classified in 91.4% of right whale and 88.6% of humpback whale cases. Important variables included cyamid presence, emaciation, discolored skin, constricting entanglements, gear-free resightings, superficial or healing lacerations, and vessel size. Cross-validated RF models were applied to unknown outcome cases to estimate the probability of deaths, health declines and recoveries. Total serious injuries (probability of death or health decline > 0.50) assigned by RF were nearly equal to current injury assessment methods applied by biologists for known outcomes. However, RF consistently predicted higher serious injury totals for unknown outcomes, suggesting that current assessment methods may underestimate risk for cases lacking details or long-term observations. Advantages of the RF method include: 1) risk models are based on known outcomes; 2) unknown outcomes are assigned post-event health status probabilities; and 3) identification of important predictor variables improves data collection standards.

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.

The accumulation of aquatic organisms on the wetted surfaces of vessels (i.e., vessel biofouling) negatively impacts world-wide shipping through reductions in vessel performance and fuel efficiency, and increases in emissions. Vessel biofouling is also a potent mechanism for the introduction and spread of marine non-indigenous species. Guidance and regulations from the International Maritime Organization, New Zealand, and California have recently been adopted to address biosecurity risks, primarily through preventive management. However, appropriate reactive management measures may be necessary for some vessels. Vessel in-water cleaning or treatment (VICT) has been identified as an important tool to improve operating efficiency and to reduce biosecurity risks. VICT can be applied proactively [i.e., to prevent the occurrence of, or to remove, microfouling (i.e., slime) or prevent the occurrence of macrofouling organisms - large, distinct multicellular organisms visible to the human eye], or reactively (i.e., to remove macrofouling organisms). However, unmanaged VICT includes its own set of biosecurity and water quality risks. Regulatory policies and technical advice from California and New Zealand have been developed to manage these risks, but there are still knowledge gaps related to the efficacy of available technologies. Research efforts are underway to address these gaps in order to inform the regulatory and non-regulatory application of VICT.

India Introduces Biometric Facial ID for Seafarers.