Is the war entering a new phase? Ukraine launched its deepest-ever attack into Russia, targeting two Arctic gas condensate facilities more than 3,000km (1,865 miles) from its border, on Wednesday, signalling that Ukraine has further developed its military capabilities as the Russia-Ukraine war continues through its fifth year. Unconfirmed videos circulating on social media show large clouds of thick, dark smoke and fire at the Novy Urengoy plant in western Siberia, recorded from a distance. Dmitry Artyukhov, the governor of the Yamalo-Nenets Autonomous Okrug region, wrote on Telegram that the drone attacks had been intercepted and "debris from the drone caused a fire upon impact". There have been no reports of deaths or injuries.

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Improving energy performance can effectively extend the time a robot can operate and reduce battery load, enabling lighter, more flexible, and more durable robotic systems. Nature has evolved optimal energy-saving locomotion strategies through billions of years of natural selection, providing unparalleled blueprints for robotic optimization. Among diverse modes of aquatic locomotion, intermittent swimming, also called bout-and-glide swimming, is a widespread adaptive behavior in aquatic organisms of a wide range of sizes, including larval zebrafish, red-nose tetra, koi carp, and even whales. This natural bout-and-glide gait features alternating motion phases: short periods of active body and tail undulation for propulsion, followed by passive gliding with a streamlined, straight body posture. It is widely recognized that this intermittent swimming gait is closely associated with optimizing biological energy, making it of great research value to transplant and explore such natural motion mechanisms into robotic control systems.

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Estimates suggest AI will create close to £370bn in cumulative value for fossil fuel companies between 2026 and 2030. Estimates suggest AI will create close to £370bn in cumulative value for fossil fuel companies between 2026 and 2030. AI's potential climate benefits outweighed by role in boosting fossil fuels, study finds AI-driven productivity gains enable more planet-heating pollution from fossil fuels than they avoid from renewables, a study has found. Researchers modelled the technical potential for AI to boost clean power generation along with projections for how it can help produce coal, oil and gas. Across 64 scenarios, they found net yearly carbon pollution rose by 0.47-1.8

Chevron and Williams are big winners in the race to power artificial intelligence as they build out gas-fired power plants and pipelines. It's been a banner year for oil and gas companies. Some of the world's biggest oil giants have announced billions of dollars in quarterly profits over the past two weeks, boosted largely by the soaring price of oil thanks to the conflict in the Middle East. But the artificial intelligence boom is also giving fossil fuel companies a new industry to sell their gas, pipelines, and power plants to: data centers . Two American oil and gas companies, Williams and Chevron, are presenting that demand to investors as a huge win.

Researchers discovered a vibrant ecosystem that is'dark, ancient, and almost entirely hidden' from humans. 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 research team led by University of Michigan isolated and grew more than 200 kinds of fungi from the deep subsurface. The collection is now the first public collection of deep subsurface fungi, and is stored at the U-M Herbarium. Breakthroughs, discoveries, and DIY tips sent six days a week.

Predicting a complete spatially correlated field from sparse observations is a fundamental challenge in spatial statistics and environmental modelling. Classical interpolation methods such as Kriging rely on Gaussian process assumptions and variography, which can limit their effectiveness in non-stationary settings and require substantial domain expertise. In this work, we leverage an architecture based on convolutional neural networks (CNNs) for spatial interpolation that is trained and applied on a single partially observed field, without access to external data or prior fields. The model is supervised directly on the observed locations and learns to predict values at unobserved points on the user defined grid. Unlike Kriging, our method does not require explicit covariance modelling or variogram estimation, and it can flexibly capture local spatial patterns in a data-driven manner. This work demonstrates the potential of CNNs for single-instance spatial interpolation under sparse supervision, offering a practical alternative to classical geostatistical methods, and extending the use of CNNs to a new problem domain.

Numeric tabular datasets are the dominant data format in scientific practice, yet large language models lack native mechanisms for representing numeric datasets in a meaningful way across heterogeneous feature spaces. Existing approaches either target predictive modeling over individual datasets, which requires a shared set of variable definitions, or lack mechanisms for interpretable cross-dataset alignment. The proposed methodology characterizes numeric tabular datasets through structured exploratory data analysis descriptors, embeds those descriptors into a shared vector space using a pretrained sentence transformer, and quantifies cross-dataset similarity via Canonical Correlation Analysis (CCA). Furthermore, a penalized formulation of CCA is applied to recover sparse, interpretable variable-level correspondences between datasets, identifying which statistical descriptors or variable-level quantities drive cross-dataset alignment without requiring shared variable names or feature conventions. Differential privacy is optionally applied to the descriptor set prior to embedding, supporting deployment in sensitive data contexts without requiring access to raw observations at time of comparison. The methodology is evaluated across 15 datasets spanning general-purpose benchmarks, materials informatics, and nuclear-grade graphite characterization. Results demonstrate a total P@1 score of 0.9, with known nearest-neighbor retrieval and cluster structure remaining robust across embedding ablations and differential privacy budgets. The proposed framework provides a principled pathway for integrating heterogeneous numeric data into retrieval-augmented generation pipelines while preserving statistical context, with direct applications to data-driven algorithm selection and simulation model initialization for unknown datasets.