Can Americans spell the National Spelling Bee's winning words? The BBC challenged Americans to spell words used in the last three Scripps National Spelling Bee competitions. Shrey Parikh, a 14-year-old, won the competition this year after correctly spelling 32 words in a 90-second lighting round tiebreaker. He defeated 12-year-old Ishaan Gupta, who spelled 25 words correctly. Parikh won out against 247 spellers competing in the annual contest, aged between nine and 15, taking home a $52,000 (£39,000) cash prize.

The world's first'hovertrain' could reach speeds of 270 mph in the 1960s But the futuristic Aérotrain never saw the light of day. 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. This cancelled Mongolian postage stamp shows the Aérotrain Orleans, circa 1979. 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 .

We analysed thousands of Trump's posts - here's what we found In 2026, Donald Trump's use of social media has escalated. The BBC sifted through thousands of posts on his platform Truth Social to analyse what the President has been saying and when. What was the busiest day? When are the busiest hours? What type of content does President Trump share?

Fans react to Stephen Colbert's final show Fans lined up outside the Ed Sullivan Theater one last time for "The Late Show" as Stephen Colbert prepared to host his final episode, marking the end of the franchise's 33-year run on CBS. The network announced last summer that the show would end, calling it "purely a financial decision against a challenging backdrop in late night. Colbert took on the show on September 2015 after David Letterman retired from the role he'd held for 22 years. Rescuers removed the vehicle from near the shoreline and police arrested the driver, who was attempting to use the vehicle's wade mode. As part of the ongoing investigation into the crash that killed 14 people, officials released footage showing the engine detaching during takeoff.

Air France and Airbus have been found guilty of manslaughter over a 2009 plane crash which killed 228 people. The Paris Appeals Court found the airline and aircraft manufacturer guilty of corporate manslaughter over the incident, in which flight AF447 between Rio de Janeiro and Paris crashed into the Atlantic Ocean. The passenger jet stalled during a storm and plunged into the water, killing all on board. A court had previously cleared the companies in April 2023 but they were found guilty after this appeal. The Airbus A330 vanished from radars during a storm, with its wreckage found after a long search of 10,000 sq km (3,860 sq miles) of sea floor.

The LinkedIn cofounder now has an AI drug discovery startup--and thinks not asking chatbots for medical advice is "bordering on committing malpractice." Following a three-decade career at the helm of some of Silicon Valley's most powerful companies--cofounding LinkedIn and sitting on the boards of PayPal and OpenAI-- Reid Hoffman recently turned his attention to health care. Hoffman's startup, Manas AI, is building an AI engine that aims to fast-track the traditionally slow process of drug discovery for various cancers. Inspired by a dinner with renowned cancer physician Siddhartha Mukherjee, the company's cofounder and CEO, its mission statement is to "shift drug discovery from a decade-long process to one that takes a few years." But Hoffman's enthusiasm for generative AI, in particular, stretches far beyond novel drug targets and small molecules.

Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditional federated models often assume homogeneity in the degradation processes across clients, an assumption that may not hold in many industrial settings. To overcome this, this paper proposes a personalized federated prognostic model designed to accommodate clients with heterogeneous degradation processes, allowing them to build tailored prognostic models. The prognostic model iteratively facilitates the underlying pairwise collaborations between clients with similar degradation patterns, which enhances the performance of personalized federated learning. To estimate parameters jointly using decentralized datasets, we develop a federated parameter estimation algorithm based on proximal gradient descent. The proposed approach addresses the limitations of existing federated prognostic models by simultaneously achieving model personalization, preserving data privacy, and providing comprehensive failure time distributions. The superiority of the proposed model is validated through extensive simulation studies and a case study using the turbofan engine degradation dataset from the NASA repository.

Preprocessing leakage arises when scaling, imputation, or other data-dependent transformations are estimated before resampling, inflating apparent performance while remaining hard to detect. We present fastml, an R package that provides a single-call interface for leakage-aware machine learning through guarded resampling, where preprocessing is re-estimated inside each resample and applied to the corresponding assessment data. The package supports grouped and time-ordered resampling, blocks high-risk configurations, audits recipes for external dependencies, and includes sandboxed execution and integrated model explanation. We evaluate fastml with a Monte Carlo simulation contrasting global and fold-local normalization, a usability comparison with tidymodels under matched specifications, and survival benchmarks across datasets of different sizes. The simulation demonstrates that global preprocessing substantially inflates apparent performance relative to guarded resampling. fastml matched held-out performance obtained with tidymodels while reducing workflow orchestration, and it supported consistent benchmarking of multiple survival model classes through a unified interface.

Background: Determining an adequate sample size is essential for developing reliable and generalisable clinical prediction models, yet practical guidance on selecting appropriate methods remains limited. Existing analytical and simulation-based approaches often rely on restrictive assumptions and focus on mean-based criteria. We present and validate pmsims, an R package that uses Gaussian process surrogate modelling to provide a flexible and computationally efficient simulation-based framework for sample size determination across diverse prediction settings. Methods: We conducted a comprehensive simulation study with two aims. First, we compared three search engines implemented in pmsims: a Gaussian process-based adaptive method, a deterministic bisection method, and a hybrid approach, across binary, continuous, and survival outcomes. Second, we benchmarked the best-performing pmsims engine against existing analytical (pmsampsize) and simulation-based (samplesizedev) methods, evaluating recommended sample sizes, computational time, and achieved performance on large independent validation datasets. Results: The Gaussian process-based method consistently produced the most stable sample size estimates, particularly in low-signal, high-dimensional settings. In benchmarking, pmsims achieved performance close to prespecified targets across all outcome types, matching simulation-based approaches and outperforming analytical methods in more challenging scenarios. Conclusions: pmsims provides an efficient and flexible framework for principled sample size planning in clinical prediction modelling, requiring fewer model evaluations than non-adaptive simulation approaches.

We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning. As a result, structured physics knowledge can be embedded into larger systems, allowing them, for example, to match observations by performing precise simulations, while achieves high sample efficiency. Specifically, in this paper we demonstrate how to perform backpropagation analytically through a physical simulator defined via a linear complementarity problem. Unlike traditional finite difference methods, such gradients can be computed analytically, which allows for greater flexibility of the engine. Through experiments in diverse domains, we highlight the system's ability to learn physical parameters from data, efficiently match and simulate observed visual behavior, and readily enable control via gradient-based planning methods. Code for the engine and experiments is included with the paper.