Nippon Yusen and other companies aim to complete the world's first ammonia-fueled medium gas carrier for international shipping in November. Japan's public and private sectors are stepping up joint efforts to revive the nation's struggling shipbuilding industry. Last year, the transport ministry drew up a road map for revitalizing Japan's shipbuilding sector, setting a target of doubling shipbuilding volume by 2035 from the 2024 level. The plan calls for ¥1 trillion in combined public-and private-sector investment. In addition to capital spending, the road map aims to strengthen the industry as a whole by improving productivity through the use of robots and artificial intelligence technology, while also developing human resources. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

These experimental ship designs rely on smaller nd potentially safer molten salt nuclear reactors. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A graphic rendering of a containership that is powered by small modular nuclear reactors. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Just how likely are you to get sick on a cruise ship? Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . A medical team in head-to-toe hazmat suits boarding a cruise ship was one of the many harrowing images from the COVID-19 pandemic.

Is the war entering a new phase? Ukraine released video of strikes on 20 Russian vessels in the Black Sea, including 17 oil tankers, two gas tankers and a tugboat, in a large-scale drone operation. Kyiv says it has targeted 136 ships linked to Russia's shadow fleet across the Black and Azov seas in the past 10 days. Pezeshkian vows Iran will defend'every inch' of its territory The history of the US and Iraq's complicated relationship

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Could Israel sabotage the deal? The United States has renewed its attacks against Iran, in response to an incident a day earlier when a cargo vessel was struck by an Iranian drone. On Friday, the US Central Command, which oversees military operations in the Middle East, said it had issued a "powerful response to yesterday's attack". "Iran's dangerous behavior undermined freedom of navigation as commerce increasingly flows through the vital international trade corridor." US strikes were reported near the southern Iranian port of Sirik after the announcement.

Maritime object detection is essential for navigation safety, surveillance, and autonomous operations, yet constrained by two key challenges: the scarcity of annotated maritime data and poor generalization across various maritime attributes (e.g., object category, viewpoint, location, and imaging environment). To address these challenges, we propose Neptune-X, a data-centric generative-selection framework that enhances training effectiveness by leveraging synthetic data generation with task-aware sample selection. From the generation perspective, we develop X-to-Maritime, a multi-modality-conditioned generative model that synthesizes diverse and realistic maritime scenes. A key component is the Bidirectional ObjectWater Attention module, which captures boundary interactions between objects and their aquatic surroundings to improve visual fidelity. To further improve downstream tasking performance, we propose Attribute-correlated Active Sampling, which dynamically selects synthetic samples based on their task relevance. To support robust benchmarking, we construct the Maritime Generation Dataset, the first dataset tailored for generative maritime learning, encompassing a wide range of semantic conditions. Extensive experiments demonstrate that our approach sets a new benchmark in maritime scene synthesis, significantly improving detection accuracy, particularly in challenging and previously underrepresented settings.

Offline Learning from Observation (LfO) focuses on enabling agents to imitate expert behavior using datasets that contain only expert state trajectories and separate transition data with suboptimal actions. This setting is both practical and critical in real-world scenarios where direct environment interaction or access to expert action labels is costly, risky, or infeasible. Most existing LfO methods attempt to solve this problem through state or state-action occupancy matching. They typically rely on pretraining a discriminator to differentiate between expert and non-expert states, which could introduce errors and instability--especially when the discriminator is poorly trained. While recent discriminator-free methods have emerged, they generally require substantially more data, limiting their practicality in low-data regimes.

Accurate vessel traffic flow prediction is crucial for smart port operations and navigational safety. However, maritime traffic flow data are often highly sparse with intermittent bursts, making robust forecasting challenging. Under such conditions, conventional spatio-temporal graph neural networks (ST-GNNs) can degrade toward conservative near-zero predictions and fail to capture non-zero activity. Although zero-inflated negative binomial (ZINB) models partially address excess zeros, their two-part formulation can still remain conservative around abrupt transitions. To address these issues, we propose a model-agnostic learnable Tweedie head that can be attached as a plug-and-play output module to arbitrary ST-GNN backbones. Instead of likelihood-based Tweedie training, which typically requires surrogate objectives, our approach optimizes the closed-form Tweedie unit deviance and predicts the mean for point forecasting while learning a node-level variance power to capture heterogeneous variability across port areas. Experiments on a maritime traffic graph constructed from real-world AIS data in the Port of Los Angeles and Long Beach show that the proposed head consistently improves RMSE across multiple ST-GNN backbones, especially on non-zero events, leading to more reliable forecasts for practical maritime traffic control.

Understanding how decision makers balance operational efficiency with environmental and ecological risks is central to vessel navigation. We model vessel speed as a control variable in a constrained optimization framework in which vessel operators balance multiple competing objectives, including transit efficiency, ice related navigational risk, and whale related ecological risk. The underlying risk parameters are estimated using over 14 million Automatic Identification System (AIS) observations from the United States Arctic (2010-2019), together with environmental covariates and spatially explicit whale density estimates. The framework incorporates a nonlinear risk objective, vessel heterogeneity, and regularization to ensure stable and interpretable results.The inferred trade offs reveal distinct decision making patterns across vessel groups and navigational statuses. Vessel types such as Tug Tow and Cargo balance operational speed with environmental and ecological considerations. In contrast, several vessel groups, including Fishing, Passenger, and Unspecified vessels, are strongly influenced by ice related risk, while Pleasure Craft and Tankers exhibit higher sensitivity to whale related risk. Across navigational status categories, similar heterogeneity is observed. The dominant status, under way using engine, displays a clear trade off, whereas other statuses, such as aground and undefined, are strongly shaped by ice related constraints. Statuses including restricted maneuverability and engaged in fishing exhibit higher estimated sensitivity to whale related risk, though with substantial uncertainty.Sensitivity analysis indicates that increasing whale-related risk weighting produces limited changes in model-implied optimal speed, whereas increasing ice-related risk leads to more consistent reductions.