Detecting heavy metal pollution in soils and seaports is vital for regional environmental monitoring. The Pollution Load Index (PLI), an international standard, is commonly used to assess heavy metal containment. However, the conventional PLI assessment involves laborious procedures and data analysis of sediment samples. To address this challenge, we propose a deep-learning-based model that simplifies the heavy metal assessment process. Our model tackles the issue of data scarcity in the water-sediment domain, which is traditionally plagued by challenges in data collection and varying standards across nations. By leveraging transfer learning, we develop an accurate quantitative assessment method for predicting PLI. Our approach allows the transfer of learned features across domains with different sets of features. We evaluate our model using data from six major ports in New South Wales, Australia: Port Yamba, Port Newcastle, Port Jackson, Port Botany, Port Kembla, and Port Eden. The results demonstrate significantly lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of approximately 0.5 and 0.03, respectively, compared to other models. Our model performance is up to 2 orders of magnitude than other baseline models. Our proposed model offers an innovative, accessible, and cost-effective approach to predicting water quality, benefiting marine life conservation, aquaculture, and industrial pollution monitoring.

--Unmanned aerial vehicles (UA Vs) are increasingly employed in diverse applications such as land surveying, material transport, and environmental monitoring. Following missions like data collection or inspection, UA Vs must land safely at docking stations for storage or recharging, which is an essential requirement for ensuring operational continuity. However, accurate landing remains challenging due to factors like GPS signal interference. T o address this issue, we propose a deviation warning system for UA V landings, powered by a novel vision-based model called AeroLite-MDNet. This model integrates a multiscale fusion module for robust cross-scale object detection and incorporates a segmentation branch for efficient orientation estimation. We introduce a new evaluation metric, A verage Warning Delay (A WD), to quantify the system's sensitivity to landing deviations. Furthermore, we contribute a new dataset, UA VLand-Data, which captures real-world landing deviation scenarios to support training and evaluation. Experimental results show that our system achieves an A WD of 0.7 seconds with a deviation detection accuracy of 98.6%, demonstrating its effectiveness in enhancing UA V landing reliability. NMANNED aerial vehicles (UA Vs), also known as drones, have been widely used in fire detection, geological hazard monitoring, and dangerous behavior monitoring [1] for their agility, compactness, and cost-efficiency. To reduce the dependency of UA Vs on human labor and skills, UA V nests are widely used to minimize manual operations, allowing the UA Vs to perform autonomous monitoring. UA V nests also offer functionalities such as safe parking, charging, data transmission, routine maintenance, repairs, and communication relays [2].

Storm surge forecasting plays a crucial role in coastal disaster preparedness, yet existing machine learning approaches often suffer from limited spatial resolution, reliance on coastal station data, and poor generalization. Moreover, many prior models operate directly on unstructured spatial data, making them incompatible with modern deep learning architectures. In this work, we introduce a novel approach that projects unstructured water elevation fields onto structured Red Green Blue (RGB)-encoded image representations, enabling the application of Convolutional Long Short Term Memory (ConvLSTM) networks for end-to-end spatiotemporal surge forecasting. Our model further integrates ground-truth wind fields as dynamic conditioning signals and topo-bathymetry as a static input, capturing physically meaningful drivers of surge evolution. Evaluated on a large-scale dataset of synthetic storms in the Gulf of Mexico, our method demonstrates robust 48-hour forecasting performance across multiple regions along the Texas coast and exhibits strong spatial extensibility to other coastal areas. By combining structured representation, physically grounded forcings, and scalable deep learning, this study advances the frontier of storm surge forecasting in usability, adaptability, and interpretability.

Sen. Tim Kaine, D-Va., tells Fox News Digital he's frustrated by US officials not being forthcoming about the drone incursions over Langley Air Force Base. FIRST ON FOX: A group of House Republicans is demanding details on how government agencies are addressing the growing threat of unauthorized drone incursions on U.S. military installations. In letters sent Thursday, the Subcommittee on Military and Foreign Affairs requested a trove of documents and communications from the Departments of Defense (DoD), Transportation (DOT), and Justice (DOJ). The letters note that in 2024 alone, there were 350 drone incursions at over 100 U.S. military bases. Lawmakers believe many of the responses to the illegal incursions, including an instance where a group of drones traipsed over Langley Air Force Base for over two weeks in December 2023, have been insufficient and fragmented.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Summer just started, and the first significant heat wave of the season is almost over. Some 265 million people across the Midwest and the eastern United States have experienced a week of temperatures in the 90s and triple digits, with a slew of all-time records set on Tuesday. While extreme heat waves can be caused by any number of factors, this particular one is thanks to a phenomenon called a heat dome: a ridge of atmospheric pressure that settles over a region like, well, a dome. Or, as the National Weather Service's Alex Lamers so wonderfully described it to NPR, think of it as a lid placed over a grilled cheese, which, as we all know, makes the cheese melt much faster.

Breakthroughs, discoveries, and DIY tips sent every weekday. The lifeblood of global communication flows through more than 807,800 miles worth of garden hose-wide cables woven across the sea floor. These cables, which reportedly transmit over 10 trillion worth of financial data every day, are vulnerable to extreme weather, decay, and, if recent reports are to be believed, acts of sabotage. The Associated Press estimates that at least 11 cables have been damaged since October 2023 in the Baltic Sea alone. Finnish and German authorities traced several of those incidents back to dragged anchors, which they allege may have been intentionally deployed to cause damage for political ends.

Diplomacy is a complex multiplayer game that requires both cooperation and competition, posing significant challenges for AI systems. Traditional methods rely on equilibrium search to generate extensive game data for training, which demands substantial computational resources. Large Language Models (LLMs) offer a promising alternative, leveraging pre-trained knowledge to achieve strong performance with relatively small-scale fine-tuning. However, applying LLMs to Diplomacy remains challenging due to the exponential growth of possible action combinations and the intricate strategic interactions among players. To address this challenge, we propose DipLLM, a fine-tuned LLM-based agent that learns equilibrium policies for Diplomacy. DipLLM employs an autoregressive factorization framework to simplify the complex task of multi-unit action assignment into a sequence of unit-level decisions. By defining an equilibrium policy within this framework as the learning objective, we fine-tune the model using only 1.5% of the data required by the state-of-the-art Cicero model, surpassing its performance. Our results demonstrate the potential of fine-tuned LLMs for tackling complex strategic decision-making in multiplayer games.

At least 14 people were injured when Russian drones attacked the Ukrainian Black Sea city of Odesa overnight, damaging high-rise buildings and railway infrastructure, Ukrainian authorities said.

Russia's increased sense of vulnerability may be the most important result of a recent large-scale Ukrainian drone attack named Operation Spiderweb, experts tell Al Jazeera. The operation destroyed as much as a third of Russia's strategic bomber fleet on the tarmac of four airfields deep inside Russia on June 1. Days later, Russia started to build shelters for its bombers and relocate them. An open source intelligence (OSINT) researcher nicknamed Def Mon posted time-lapse satellite photographs on social media showing major excavations at the Kirovskoe airfield in annexed Crimea as well as in Sevastopol, Gvardiyskoye and Saki, where Russia was constructing shelters for military aircraft. They reported similar work at several airbases in Russia, including the Engels base, which was targeted in Ukraine's attacks on June 1.

Waves of Russian missile and drone strikes have killed at least 15 people and injured 116 others, with most of the casualties in Kyiv, Ukrainian officials have reported. The massive aerial assault overnight into Tuesday struck 27 locations in the Ukrainian capital, damaging residential buildings and critical infrastructure, according to Interior Minister Ihor Klymenko. Ukrainian officials were quick to call for international attention on the attacks as Kyiv pushes diplomatic efforts to raise pressure on Moscow to agree a ceasefire. "Today, the enemy spared neither drones nor missiles," Klymenko said, describing the attack as one of the largest against Kyiv since Russia launched its full-scale invasion of the country in February 2022. Thirty apartments were destroyed in a single residential block, and emergency services were searching through the rubble for possible survivors, Klymenko added.