Will Gulf states join war? A drone attack on the Khor Mor gasfield in the semi-autonomous Kurdish region of northern Iraq has caused a fire at the facility and cut power to the region, according to Iraqi and Kurdish officials. The Sunday night attack left more than 70 percent of the Kurdish region without power. Al Jazeera's fact-checking agency Sanad has verified footage of smoke and flames rising from the vicinity of the gasfield in the Sulaimaniyah governorate. Omid Ahmed, spokesperson for the Kurdistan Regional Government's Ministry of Electricity, said the attack suspended gas supply to power plants in the cities of Erbil, Sulaimaniyah, Bazian, Chamchamal and Duhok, the official Iraqi News Agency (INA) reported.

Transformers are a crucial but nearly invisible part of our grid. The devices help efficiently move power across long distances, but they're complicated and in short supply. And given that their basic design hasn't changed much for about a century, they could use an upgrade. As the world races to meet rising electricity demand from EVs, data centers, and more, researchers and companies are looking to modernize transformers. New, solid-state versions could help relieve some of the supply concerns and make sure the grid can handle the growing pressure. Conventional transformers generally rely on huge amounts of specialized steel alloys and miles of copper wire to do their work of transferring electricity between circuits. Solid-state models, in comparison, combine magnetic components with high-power semiconductors, making them more compact and allowing for more control over the flow of electricity. These devices are still in development, and can't replace all traditional transformers today, especially the largest ones that work at the high voltages necessary for long-distance transmission. But a new generation of transformer technology could be a crucial part of our future grid.

Paris Marx, author of Hyperscale, has been tracking global data center development for years. The US has a lot to learn from what's happened everywhere from Ireland to New Zealand. Paris Marx's new book couldn't have landed at a better time. The longtime critic of Big Tech and host of the Tech Won't Save Us podcast has been thinking about data centers for years, and Hyperscale: The Ambition and Excess of Big Tech's Data Empires comes out right as opposition hits a fever pitch. "I certainly didn't predict that it was all going to kind of come to a head as my book was coming out," he says with a laugh.

Try to solve an exclusive'Popular Science' crossword, then make your own. 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. Crossword Generator lets users create 13x13 grid puzzles for free. Crossword puzzles range from those easy elementary school-level varieties up to nightmarish labyrinths of text . But crafting even the most simple versions remains its own artform.

From e-bikes to stoves, here's how companies are adding energy storage in surprising ways. If you want to install a big battery in New York City, you have to cut through a notoriously tough tangle of regulations. That's made it difficult to get large energy storage projects on the grid. Faced with those obstacles, some startups are getting creative, finding ways to use relatively small batteries in unexpected places, from induction stovetops to food carts. By deploying these more consumer-focused batteries, the companies are helping people directly, cutting pollution or power bills. And these units, while small on their own, could add up to a lot of capacity.

Moving power protection up the voltage stack, outside the building, and into the power path doesn't just solve outages; it changes density, permitting timelines and backup power economics. On July 22, 2026, a transmission line fault in Ashburn, Virginia--the heart of the world's largest data center cluster--knocked more than 3 gigawatts of load off the grid in seconds. Two years earlier, a single failed surge arrester dropped roughly 60 Virginia facilities and 1,500 megawatts at once. No one could anticipate so much uniform load responding to grid faults the same way, at the same time. The AI power debate is mostly about generation: more turbines, more solar, more transmission. The grid needs more electrons.

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.

Plus: Why the OpenAI hack is the scariest AI mishap yet. The power line that could reshape New York's grid is hitting snags During a heat wave on July 3, New York State's grid imported enough electricity from Canada to meet about 9% of its total demand that day. Some of that power shuttled in on a 339-mile power line stretching from Quebec to Queens. It opened in May and is officially the longest underground transmission line in North America. It could provide up to 20% of New York City's electricity demand, largely with abundant hydropower from Quebec. One wrinkle: The line has been down for most of this month, and some experts are concerned about how drought will affect the power supply feeding it.

Today's deep learning architectures are primarily based on perceptron models, which do not capture the oscillatory dynamics characteristic of biological neural activity. Although oscillatory systems have recently gained attention for their closer resemblance to neural behavior, they often lack a structured mechanism to represent rich spatio-temporal dynamics in a controllable and interpretable manner. In this paper, we propose a bio-inspired oscillatory state system (BioOSS), a 2D topographically organized oscillatory state-space model designed to generate diverse oscillation-driven spatio-temporal patterns. BioOSS comprises two coupled state components: punits that represent membrane-potential-like variables inspired by pyramidal-cell activity, and o units that act as velocity-like latent states controlling phase, time scales, and damping. The model incorporates trainable parameters for damping and effective oscillation rates, enabling flexible adaptation to task-specific temporal structures while remaining efficient for long-sequence learning via scanfriendly diagonal dynamics. We evaluate BioOSS on both synthetic and real-world tasks, demonstrating superior performance and enhanced interpretability compared to alternative architectures.

The very challenging task of learning solution operators of PDEs on arbitrary domains accurately and efficiently is of vital importance to engineering and industrial simulations. Despite the existence of many operator learning algorithms to approximate such PDEs, we find that accurate models are not necessarily computationally efficient and vice versa. We address this issue by proposing a geometry aware operator transformer (GAOT) for learning PDEs on arbitrary domains. GAOT combines novel multiscale attentional graph neural operator encoders and decoders, together with geometry embeddings and (vision) transformer processors to accurately map information about the domain and the inputs into a robust approximation of the PDE solution. Multiple innovations in the implementation of GAOT also ensure computational efficiency and scalability. We demonstrate this significant gain in both accuracy and efficiency of GAOT over several baselines on a large number of learning tasks from a diverse set of PDEs, including achieving state of the art performance on three large scale three-dimensional industrial CFD datasets. Our project page for accessing the source code is available at camlab-ethz.github.io/GAOT.