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Technology Aviation Airbus wants to build world's first hydrogen fuel jet engine Commercial hydrogen planes could take off by 2035. 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. Unlike jet fuel, hydrogen fuel's primary byproduct is water vapor. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Kornelius Raeth 1 Nicole Ludwig 1 2 Abstractscoring rules distribute the training gradient in proportion to Sample-based generative models are increasingly data density, with no awareness of the decision maker's cost structure. The model's limited capacity is allocated globused for probabilistic forecasting in high-stakes ally, leaving decision-critical regions of the output space decision settings, yet their training objectives are potentially underserved. These models are commonly trained with strictly proper Given a forecast, a decision maker with cost function c(a,y), scoring rules, such as the energy score, which al-of action aand outcome y, selects the action that minimises locate their training signal in proportion to dataexpected cost under the forecast distribution; a point forecast density, with no awareness of where forecast eris insufficient to evaluate this expectation. A good forecast rors are most costly for downstream decisions. Crucially, the energy score objective with a differentiable deci-observed cost of the optimal action is itself a proper scoring sion loss that directly penalises the cost incurredrule (Hartline et al., 2025; Kleinberg et al., 2023), placing by acting on the model's forecast. This combinedit in the same family as the energy score which licenses loss is theoretically grounded, as the decision losstheir combination as a theoretically well-founded training is itself a proper scoring rule. Introduction score acts as that anchor, preventing the model from collapsing outside cost-sensitive regions. Our method is theo-tion based on a temperature forecast, balancing asset loss against the cost of intervention. In the weather domain, retically grounded and leads to better downstream decisions state-of-the-art forecasting systems (Lang et al., 2024; Pricewhile retaining full probabilistic forecasts, as validated on et al., 2023) are trained with strictly proper scoring rulessynthetic and real-world forecasting tasks. A gradient analysis showing which regions benefitscore reduces to the continuous ranked probability score from the decision loss and why, based on the cost (CRPS), widely used in meteorological forecast verificafunction structure. Both model classes introduced above are commonly trained by minimising strictly proper sion calibration.

Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neural networks (STGNNs) have recently achieved strong performance in point forecasting by jointly modeling temporal dynamics and relational dependencies across interconnected energy nodes. However, in real-world energy systems, accurate point forecasts alone are insufficient, as operators also require reliable uncertainty estimates to support risk-aware decision-making, grid stability, and operational planning under uncertainty. Conformal prediction provides a principled and model-agnostic framework for uncertainty quantification with statistical coverage guarantees, making it particularly attractive for safety-critical energy applications. However, existing conformal prediction approaches often fail to fully capture the complex spatial-temporal structure of energy systems. To address these limitations, we propose STOIC (Spatial-Temporal Graph Conformal Prediction with In-Context Learning), a novel framework that integrates graph-based forecasting with the zero-shot calibration capabilities of tabular foundation models. STOIC first generates point forecasts using an STGNN and subsequently reformulates spatial-temporal residuals into a tabular representation suitable for in-context learning. Leveraging a tabular foundation model, STOIC calibrates prediction intervals without task-specific retraining, effectively capturing both sequential and relational dependencies. We evaluate STOIC on five diverse benchmarks, including synthetic simulations as well as real-world electricity and district heating networks. Across all datasets, STOIC consistently outperforms existing conformal prediction baselines, delivering more reliable and robust uncertainty estimates for complex graph-structured energy time series.

The pointy'trullo' is making a comeback thanks to its clever cooling attributes. 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. Tourists stand among the trulli of Alberobello, the whitewashed limestone houses with the typical conical roofs. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

When you purchase through links in our articles, we may earn a small commission. It's solar-powered, wire-free, and supports local storage so no subscription needed. The Tapo MagCam 2K+ (also known as the Tapo C425) is a standout security camera for three big reasons: it's wire-free with solar-powered battery, it has a magnetic mount for easy installation, and it supports local storage so you don't have to pay a subscription fee. Its solar-powered battery is the best thing about it. The panel is separate from the camera, so you can mount the camera wherever you need to capture exactly the right footage, and you can mount the solar panel up to 13 feet away so that it gets optimal sun exposure.

Plus: The US House has passed new youth online safety legislation. AI agents are not your "coworkers" Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool--one that your company nonetheless calls Alex, an "employee" with a title and defined responsibilities. How well do you think you would work with Alex? If you're anything like the managers studied by Boston University professor Emma Wiles, treating that AI as a coworker would lead you to do a worse job. They caught 18% fewer errors when the work was attributed to an agentic AI employee rather than a chatbot. This is an alarming glimpse of the future Silicon Valley is hurling us toward.

A PhD student spotted the wolves going after the so-called'king of the forest' on a trail camera. 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. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . While reviewing footage for her PhD research, Polish Academy of Sciences ecologist Robin Wijnands spotted something pretty wild .

'Adelphailurus kansensis' was about the size of a puma and lived in North America over five million years ago. 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. An artist's rendering of what the early saber-toothed cat, 'Adelphailurus kansensis,' might have looked like in its heyday 5 million years ago. About the size of today's mountain lions, the cats already had teeth optimized for slicing and shredding flesh, though the fangs were much smaller than those of later sabertooths, such as the iconic'Smilodon.' Breakthroughs, discoveries, and DIY tips sent six days a week.