A Russian drone with a high-explosive warhead has hit the Chornobyl nuclear power plant in the Kyiv region, Ukraine said, amid warnings by the military that Russia launched 133 unmanned vehicles against the country. Ukrainian President Volodymyr Zelenskyy said on Friday that the drone strike significantly damaged the protective containment shelter and started a fire, which has been put out. The Kremlin responded saying Russia does not hit nuclear sites. Radiation levels at the site have not increased, according to Zelenskyy and the International Atomic Energy Agency (IAEA). The IAEA did not attribute blame but said the drone strike occurred at 01:50am local time (23:50 GMT) and that there was "no indication of a breach in the … inner containment" shell, a protective cover built around the fourth reactor of the plant.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An alleged drone struck the protective shell covering the Chernobyl Nuclear Power Plant in Ukraine early Friday, and Ukrainian President Volodymyr Zelenskyy is pointing the finger at Russia. The International Atomic Energy Agency reported on X that overnight Thursday, the IAEA team at the Chornobyl site heard an explosion coming from the New Safe Confinement. The site protects the remains of the nuclear reactor that exploded in Chernobyl in 1986 and was reportedly set ablaze after an unmanned aerial vehicle (UAV) struck the NSC roof.

Ukrainian President Volodymyr Zelenskyy said a Russian drone attack caused a fire at the site of the former Chernobyl nuclear power plant in Ukraine. The UN's nuclear watchdog said radiation levels were normal following the fire.

Ukrainian President Volodymyr Zelenskyy said Friday that a Russian drone had struck a cover built to contain radiation at the Chernobyl nuclear power plant, adding that "radiation levels have not increased." The Ukrainian air force said that Russia had launched more than 100 drones across the country overnight -- including attack drones -- targeting northern regions of the country where the Chernobyl power plant lies. "Last night, a Russian attack drone with a high-explosive warhead struck the cover protecting the world from radiation at the destroyed 4th power unit of the Chernobyl Nuclear Power Plant," Zelenskyy said in a social media post. The International Atomic Energy Agency also reported an "explosion" at the site, and said "radiation levels inside and outside remain normal and stable." The agency, which has had a team deployed on the site since the early stages of Russia's invasion of Ukraine, published images apparently showing the drone on fire after crashing into the covering.

The IAEA, which monitors nuclear safety the world, said radiation levels inside and outside Chernobyl remain normal and stable. The agency remains on "high alert" after the incident, with its director general Rafael Mariano Grossi saying there is "no room for complacency". Chernobyl is the site of the world's worst nuclear accident - a catastrophic explosion that sent a plume of radioactive material into the air in 1986, triggering a public health emergency across Europe. Zelensky posted footage on X appearing to show damage to the giant shield, made of concrete and steel, which covers the remains of the reactor that lost its roof in the explosion. The shield is designed to prevent further radioactive material leaking out over the next century.

The world's electricity use will grow every year by more than the amount consumed annually by Japan because of a surge in electric transport, air conditioning and datacentres, according to the world's energy watchdog. The International Energy Agency has raised its predictions for the world's rising demand for electricity, pegging the growth at almost 4% a year until 2027, up from its previous forecast of 3.4% year. The influential Paris-based agency said the "new age of electricity" was dawning as a result of the climate crisis as more people begin to use air conditioning to cope with extreme temperature rises and economies begin to turn away from using fossil fuels in favour of cleaner power. More governments are taking steps to rely on electricity for transport and heating systems as well as heavy industry, according to the report, and there is also expected to be a rapid expansion of energy-hungry datacentres used to train artificial intelligence (AI). The forecasts are likely to stoke fears that the race to build more datacentres to support the boom in AI could become a drain on energy supplies, causing costs to rocket and stalling efforts to cut fossil fuels from power generation.

Advancements in deep learning have improved the ability to model complex, nonlinear relationships, such as those encountered in complex material inverse problems. However, the effectiveness of these methods often depends on large datasets, which are not always available. In this study, the incorporation of domain-specific knowledge of mechanical behavior is investigated to evaluate the impact on the predictive performance of the models in data-scarce scenarios. To demonstrate this, stress-strain curves were used to predict key microstructural features of porous materials, and the performance of models trained with and without domain knowledge was compared using five deep learning models: Convolutional Neural Networks, Extreme Gradient Boosting, K-Nearest Neighbors, Long Short-Term Memory, and Random Forest. The results of the models with domain-specific characteristics consistently achieved higher $R^2$ values and improved learning efficiency compared to models without prior knowledge. When the models did not include domain knowledge, the model results revealed meaningful patterns were not recognized, while those enhanced with mechanical insights showed superior feature extraction and predictions. These findings underscore the critical role of domain knowledge in guiding deep learning models, highlighting the need to combine domain expertise with data-driven approaches to achieve reliable and accurate outcomes in materials science and related fields.

With the rapid development of energy storage technology, high-performance solid-state electrolytes (SSEs) have become critical for next-generation lithium-ion batteries. These materials require high ionic conductivity, excellent electrochemical stability, and good mechanical properties to meet the demands of electric vehicles and portable electronics. However, traditional methods like density functional theory (DFT) and empirical force fields face challenges such as high computational costs, poor scalability, and limited accuracy across material systems. Universal machine learning interatomic potentials (uMLIPs) offer a promising solution with their efficiency and near-DFT-level accuracy.This study systematically evaluates six advanced uMLIP models (MatterSim, MACE, SevenNet, CHGNet, M3GNet, and ORBFF) in terms of energy, forces, thermodynamic properties, elastic moduli, and lithium-ion diffusion behavior. The results show that MatterSim outperforms others in nearly all metrics, particularly in complex material systems, demonstrating superior accuracy and physical consistency. Other models exhibit significant deviations due to issues like energy inconsistency or insufficient training data coverage.Further analysis reveals that MatterSim achieves excellent agreement with reference values in lithium-ion diffusivity calculations, especially at room temperature. Studies on Li3YCl6 and Li6PS5Cl uncover how crystal structure, anion disorder levels, and Na/Li arrangements influence ionic conductivity. Appropriate S/Cl disorder levels and optimized Na/Li arrangements enhance diffusion pathway connectivity, improving overall ionic transport performance.

The increasing use and implementation of Autonomous Surface Vessels (ASVs) for various activities in maritime environments is expected to drive a rise in developments and research on their control. Particularly, the coordination of multiple ASVs presents novel challenges and opportunities, requiring interdisciplinary research efforts at the intersection of robotics, control theory, communication systems, and marine sciences. The wide variety of missions or objectives for which these vessels can be collectively used allows for the application and combination of different control techniques. This includes the exploration of machine learning to consider aspects previously deemed infeasible. This review provides a comprehensive exploration of coordinated ASV control while addressing critical gaps left by previous reviews. Unlike previous works, we adopt a systematic approach to ensure integrity and minimize bias in article selection. We delve into the complex world of sub-actuated ASVs with a focus on customized control strategies and the integration of machine learning techniques for increased autonomy. By synthesizing recent advances and identifying emerging trends, we offer insights that drive this field forward, providing both a comprehensive overview of state-of-the-art techniques and guidance for future research efforts.

We propose a novel model for the prediction of robot trajectories on rough offroad terrain from the onboard camera images. This model enforces the laws of classical mechanics through a physics-aware neural symbolic layer while preserving the ability to learn from large-scale data as it is end-to-end differentiable. The proposed hybrid model integrates a black-box component that predicts robot-terrain interaction forces with a neural-symbolic layer. This layer includes a differentiable physics engine that computes the robot's trajectory by querying these forces at the points of contact with the terrain. As the proposed architecture comprises substantial geometrical and physics priors, the resulting model can also be seen as a learnable physics engine conditioned on real images that delivers $10^4$ trajectories per second. We argue and empirically demonstrate that this architecture reduces the sim-to-real gap and mitigates out-of-distribution sensitivity. The differentiability, in conjunction with the rapid simulation speed, makes the model well-suited for various applications including model predictive control, trajectory shooting, supervised and reinforcement learning or SLAM. The codes and data are publicly available.