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.

Container stowage planning (CSPP) is a critical component of maritime transportation and terminal operations, directly affecting supply chain efficiency. Owing to its complexity, CSPP has traditionally relied on human expertise. While reinforcement learning (RL) has recently been applied to CSPP, systematic benchmark comparisons across different algorithms remain limited. To address this gap, we develop a Gym environment that captures the fundamental features of CSPP and extend it to include crane scheduling in both multi-agent and single-agent formulations. Within this framework, we evaluate five RL algorithms: DQN, QR-DQN, A2C, PPO, and TRPO under multiple scenarios of varying complexity. The results reveal distinct performance gaps with increasing complexity, underscoring the importance of algorithm choice and problem formulation for CSPP.

With the growth of global maritime transportation, energy optimization has become crucial for reducing costs and ensuring operational efficiency. Shaft power is the mechanical power transmitted from the engine to the shaft and directly impacts fuel consumption, making its accurate prediction a paramount step in optimizing vessel performance. Power consumption is highly correlated with ship parameters such as speed and shaft rotation per minute, as well as weather and sea conditions. Frequent access to this operational data can improve prediction accuracy. However, obtaining high-quality sensor data is often infeasible and costly, making alternative sources such as noon reports a viable option. In this paper, we propose a transfer learning-based approach for predicting vessels' shaft power, where a model is initially trained on high-frequency data from a vessel and then fine-tuned with low-frequency daily noon reports from other vessels. We tested our approach on sister vessels (identical dimensions and configurations), a similar vessel (slightly larger with a different engine), and a different vessel (distinct dimensions and configurations). The experiments showed that the mean absolute percentage error decreased by 10.6% for sister vessels, 3.6% for a similar vessel, and 5.3% for a different vessel, compared to the model trained solely on noon report data. Keywords: transfer learning, shaft power prediction, noon reports, sensor data, maritime.

French troops board oil tanker linked to Russian'shadow fleet' French soldiers have boarded an oil tanker believed to be part of Russia's shadow fleet, used to evade sanctions imposed because of the war in Ukraine. The Boracay left Russia last month and was off the coast of Denmark when unidentified drones forced the temporary closure of several airports last week. It has been anchored off western France for a few days. French President Emmanuel Macron said at an EU leaders' summit in Copenhagen on Wednesday that the crew had committed serious offences, but did not elaborate. Kremlin spokesman Dmitry Peskov said Russia had no knowledge of the vessel.

Large Language Models (LLMs) are increasingly tasked with analyzing legal texts and citing relevant statutes, yet their reliability is often compromised by general pre-training that ingests legal texts without specialized focus, obscuring the true depth of their legal knowledge. This paper introduces CLaw, a novel benchmark specifically engineered to meticulously evaluate LLMs on Chinese legal knowledge and its application in reasoning. CLaw comprises two key components: (1) a comprehensive, fine-grained corpus of all 306 Chinese national statutes, segmented to the subparagraph level and incorporating precise historical revision timesteps for rigorous recall evaluation (64,849 entries), and (2) a challenging set of 254 case-based reasoning instances derived from China Supreme Court curated materials to assess the practical application of legal knowledge. Our empirical evaluation reveals that most contemporary LLMs significantly struggle to faithfully reproduce legal provisions. As accurate retrieval and citation of legal provisions form the basis of legal reasoning, this deficiency critically undermines the reliability of their responses. We contend that achieving trustworthy legal reasoning in LLMs requires a robust synergy of accurate knowledge retrieval--potentially enhanced through supervised fine-tuning (SFT) or retrieval-augmented generation (RAG)--and strong general reasoning capabilities. This work provides an essential benchmark and critical insights for advancing domain-specific LLM reasoning, particularly within the complex legal sphere.