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.

A Philippine Navy band plays music to welcome the Royal New Zealand Navy frigate HMNZS Te Kaha upon arrival at the South Harbor, for a four-day goodwill visit in metro Manila in April 2017. New Zealand intends to spend about 1.6 billion New Zealand dollars ($936 million) on drones, ship maintenance and naval upgrades to bolster the island nation's maritime security at a time of increasing concern about supply routes. Defense Minister Chris Penk said Saturday that the government will invest in two types of drones: one for the southwest Pacific to provide long-duration intelligence, surveillance and reconnaissance; the other is a polar-capable vehicle that can operate from naval vessels in the Southern Ocean. "New Zealand's prosperity and security depend on the sea," Penk said in a statement. "Recent events have served as a reminder of how quickly disruptions to international shipping routes can affect economies and supply chains across the globe. The oceans are not a barrier to danger, but a vital national interest that must be actively secured."

How well do you know Iran? A fragile ceasefire in the US-Israel war on Iran is coming under growing strain as several Gulf countries have reported drone attacks. Qatar said on Sunday that a drone struck a cargo ship in Qatari waters, sparking a fire, while Kuwait and the United Arab Emirates said they repelled drone attacks. Qatar's Ministry of Defence said the freighter had been arriving in the country's waters from the UAE capital, Abu Dhabi, and was hit by a drone northeast of the port of Mesaieed. "The vessel continued its journey toward Mesaieed Port after the fire was brought under control," the ministry said. The United Kingdom Maritime Trade Operations (UKMTO) said a bulk carrier reported being struck by an "unknown projectile", and a small fire had been extinguished, but there were no casualties from the incident.

A member of NOPO, Iran's counter-terrorism special force, stands guard under a billboard of Iran's late supreme leader, Ayatollah Ali Khamenei, in Tehran, on April 23. Doha - A drone struck a commercial vessel in Qatari waters on Sunday, the country's defense ministry said, after Iran's Islamic Revolutionary Guards threatened to target U.S. vessels in the region. Arch-foes the United States and Iran have been clashing in the Gulf and trading accusations in recent days, as Washington waits for Tehran to respond to its latest negotiating position. A commercial cargo vessel in the country's territorial waters -- northeast of Mesaieed Port -- coming from Abu Dhabi, was targeted by a drone on Sunday morning. The incident resulted in a limited fire on board the vessel, with no reported injuries, the Qatari ministry said on X.

This section presents more comprehensive experimental results. A.1 Comparison with post-hoc methods We also compare the performance of our textual outlier method with post-hoc approaches, which are another prominent approach in OoD detection. We conducted comparisons with six widely used and recently proposed methods known for their detection performance (MSP [4], ODIN [8], Mahalanobis [7], Energy [10], ReAct [14], KNN [15]). All advanced baseline methods follow the original paper's settings. Among these methods, our textual outlier approach demonstrate the best performance, further emphasizing its effectiveness as demonstrated in Table 6.

China's People's Armed Forces Maritime Militia deployed thousands of fishing vessels in coordinated formations that could disrupt global shipping lanes, analysts warn.

Flexport CEO Ryan Petersen says the conflict is stranding cargo and threatening inflation. After years of chaos in the global supply chain, Ryan Petersen, CEO of the logistics company Flexport, felt 2026 might offer some modicum of order. The pandemic was firmly in the rearview mirror. Red Sea shipping channels--which had been closed due to the Gaza crisis--were finally opening. The Supreme Court struck down many of Donald Trump's tariffs, and some Flexport customers were hoping for refunds.

Description-level outliers include class-relevant information, but when the class label is omitted, they become very vague and difficult to interpret.

Tommy Flowers: nothing like the machine he proposed had ever been contemplated. Tommy Flowers: nothing like the machine he proposed had ever been contemplated. Move over, Alan Turing: meet the working-class hero of Bletchley Park you didn't see in the movies The Oxbridge-educated boffin is feted as the codebreaking genius who helped Britain win the war. But should a little-known Post Office engineer named Tommy Flowers be seen as the real father of computing? T his is a story you know, right? It's early in the war and western Europe has fallen. Only the Channel stands between Britain and the fascist yoke; only Atlantic shipping lanes offer hope of the population continuing to be fed, clothed and armed. But hunting "wolf packs" of Nazi U-boats pick off merchant shipping at will, coordinated by radio instructions the Brits can intercept but can't read, thanks to the fiendish Enigma encryption machine.

With increasing processing power, deploying AI models for remote sensing directly onboard satellites is becoming feasible. However, new constraints arise, mainly when using raw, unprocessed sensor data instead of preprocessed ground-based products. While current solutions primarily rely on preprocessed sensor images, few approaches directly leverage raw data. This study investigates the effects of utilising raw data on deep learning models for object detection and classification tasks. We introduce a simulation workflow to generate raw-like products from high-resolution L1 imagery, enabling systemic evaluation. Two object detection models (YOLOv11n and YOLOX-S) are trained on both raw and L1 datasets, and their performance is compared using standard detection metrics and explainability tools. Results indicate that while both models perform similarly at low to medium confidence thresholds, the model trained on raw data struggles with object boundary identification at high confidence levels. It suggests that adapting AI architectures with improved contouring methods can enhance object detection on raw images, improving onboard AI for remote sensing.