Toffee Crisp and Blue Riband can't be called chocolate any more Toffee Crisp and Blue Riband bars can no longer be called chocolate after maker Nestle changed their recipes. To be described as milk chocolate in the UK a product needs to have at least 20% cocoa solids and 20% milk solids, a level each product fell below once a higher amount of cheaper vegetable fat was used. Nestle said its reformulations were needed due to higher input costs but were carefully developed and sensory tested and there were no plans to alter the recipes of other chocolate products. As many ingredient costs, such as cocoa and butter, increased food companies have altered recipes to use less of the expensive ingredients, as well as shrinking serving sizes. Nestle now describes the treats as being encased in a smooth milk chocolate flavour coating rather than being covered in milk chocolate.

Ghislaine Maxwell's ultimate humiliation: Epstein's sex trafficker girlfriend poses in outrageous outfits and exposes herself in dozens of photos released from the billionaire paedophile's files Silent Trump flees growing storm over Epstein'cover-up' as he jets off for holidays without ANY comment How you can ease the agony of carpal tunnel syndrome. The'change of pace' sex move that sends ANY woman wild. Here's the precise moment to deploy it and what to do with your eyes. Corey Feldman walks back claim that Corey Haim'molested' him after late star's mother slammed his comments Emily in Paris cast left'aghast' and'walking on eggshells' as off-camera drama becomes overwhelming... and whispers swirl about a CURSE Truth about THIS photo of Karoline Leavitt's face... and why if she was non-binary and disabled, Vanity Fair would never have done this: KENNEDY After 27 years as a TV anchor I was suddenly pulled off screens. My boss's explanation was a brutal lesson in loyalty I was dead for 105 minutes and learned exactly how you get into heaven... then Jesus spoke six words into my mind and sent me back Jake Paul's jaw is broken in Anthony Joshua battering: YouTuber-turned-boxer rushes to hospital I was falsely accused of being the Brown University shooter... America's great divide laid bare as Wall Street splurges record bonuses on outrageously lavish homes while the rest of the country struggles Andrew's fury at anyone who doesn't bow and scrape.

From bonobos and rats to tickling robots, research is finally cracking the secrets of why we're ticklish, and what that reveals about our brains In a grey-walled room in the Dutch city of Nijmegen, a strange activity is underfoot. Wearing a cap covered in sensors and positioning themselves into a chair, a person places their bare feet over two holes in a platform. Beneath this lies a robot, which uses a metal probe to begin to tickle their soles. Here, at Radboud University's Touch and Tickle lab, volunteers are being mercilessly tickled in the name of science. "We can manipulate how strong the stimulation is, how fast and where it is going to be applied on your foot," says Konstantina Kilteni, who runs the lab, of the robot tickling experiment.

McDonald's pulls AI Christmas ad after backlash McDonald's has taken down a Christmas advert made with Artificial Intelligence (AI) following online backlash. The 45-second advert was produced with generative AI clips and released publicly on McDonald's Netherlands YouTube channel on 6 December. Viewers on social media denounced the use of AI in the film, with one commenter calling it the most god-awful ad I've seen this year . On 9 December McDonald's Netherlands removed the video, adding in a statement to BBC News that the moment served as an important learning as the company explored the effective use of AI. The advert was created for McDonald's by Dutch company TBWA\Neboko and US production company The Sweetshop.

We may earn revenue from the products available on this page and participate in affiliate programs. At, we've published our prestigious Best of What's New list since 1988. For 153 years, we've celebrated the science and technology that shapes our everyday lives and launches humanity forward. Innovation doesn't follow a straight path, and the detours, stumbles, and dead ends force great minds to pioneer change. Looking back at the early days of our Best of What's New lists, we see technologies that now seem quaint or have been completely forgotten, but we also see the roots of future greatness. Our list this year is the culmination of countless hours of debate, hands-on testing, and expert conversations. This is the Best of What's New 2025. From the most detailed movie of the night sky ever made to the first commercial soft landing on the moon, this year has been an inflection point for exploring and understanding the vast expanse above our heads. We also saw breakthroughs in small changes to commercial airliners that improve efficiency, as well as a new type of rocket engine that might be the future of extremely high speed air travel, plus the closest view of Mercury we've ever seen! Vera C. Rubin Observatory by U.S. National Science Foundation & Department of Energy: World's largest digital camera to conduct 10-year survey of the night sky Prepare to see space like never before. The Vera C. Rubin Observatory is a groundbreaking US-funded project that will capture the most detailed, dynamic map of the night sky ever made. Using the world's largest digital camera, it will capture a time-lapse of the entire sky every few nights to reveal billions of objects and catch fast-changing events like supernovae and near-Earth asteroids. Its massive dataset will help scientists better understand dark matter, dark energy, and the structure of the universe while also improving planetary defense. The 3,200-megapixel Legacy Survey of Space and Time (LSST) camera is the size of a small car and twice as heavy, tipping the scales at 6,000 pounds. The sensor's huge number of megapixels is equivalent to 260 modern cell phone sensors. The camera is so powerful, it could snap a clear image of a golf ball from 15 miles away. By making its data widely available, the observatory will also open new doors for discovery for researchers, students, and citizen scientists around the world. Deployed on Boeing 787-9 aircraft starting in January, the coating uses tiny, sharkskin-like grooves called riblets to guide airflow smoothly along the aircraft's surface.

Ghislaine Maxwell's ultimate humiliation: Epstein's sex trafficker girlfriend poses in outrageous outfits and exposes herself in dozens of photos released from the billionaire paedophile's files Silent Trump flees growing storm over Epstein'cover-up' as he jets off for holidays without ANY comment How you can ease the agony of carpal tunnel syndrome. The'change of pace' sex move that sends ANY woman wild. Here's the precise moment to deploy it and what to do with your eyes. Corey Feldman walks back claim that Corey Haim'molested' him after late star's mother slammed his comments Emily in Paris cast left'aghast' and'walking on eggshells' as off-camera drama becomes overwhelming... and whispers swirl about a CURSE Truth about THIS photo of Karoline Leavitt's face... and why if she was non-binary and disabled, Vanity Fair would never have done this: KENNEDY After 27 years as a TV anchor I was suddenly pulled off screens. My boss's explanation was a brutal lesson in loyalty I was dead for 105 minutes and learned exactly how you get into heaven... then Jesus spoke six words into my mind and sent me back Jake Paul's jaw is broken in Anthony Joshua battering: YouTuber-turned-boxer rushes to hospital I was falsely accused of being the Brown University shooter... America's great divide laid bare as Wall Street splurges record bonuses on outrageously lavish homes while the rest of the country struggles Andrew's fury at anyone who doesn't bow and scrape.

Bridging the sim-to-real gap remains a fundamental challenge in robotics, as accurate dynamic parameter estimation is essential for reliable model-based control, realistic simulation, and safe deployment of manipulators. Traditional analytical approaches often fall short when faced with complex robot structures and interactions. Data-driven methods offer a promising alternative, yet conventional neural networks such as recurrent models struggle to capture long-range dependencies critical for accurate estimation. In this study, we propose a Transformer-based approach for dynamic parameter estimation, supported by an automated pipeline that generates diverse robot models and enriched trajectory data using Jacobian-derived features. The dataset consists of 8,192 robots with varied inertial and frictional properties. Leveraging attention mechanisms, our model effectively captures both temporal and spatial dependencies. Experimental results highlight the influence of sequence length, sampling rate, and architecture, with the best configuration (sequence length 64, 64 Hz, four layers, 32 heads) achieving a validation R2 of 0.8633. Mass and inertia are estimated with near-perfect accuracy, Coulomb friction with moderate-to-high accuracy, while viscous friction and distal link center-of-mass remain more challenging. These results demonstrate that combining Transformers with automated dataset generation and kinematic enrichment enables scalable, accurate dynamic parameter estimation, contributing to improved sim-to-real transfer in robotic systems

Automatic Sign Language Recognition (ASLR) has emerged as a vital field for bridging the gap between deaf and hearing communities. However, the problem of sign-to-sign retrieval or detecting a specific sign within a sequence of continuous signs remains largely unexplored. We define this novel task as Sign Language Spotting. In this paper, we present a first step toward sign language retrieval by addressing the challenge of detecting the presence or absence of a query sign video within a sentence-level gloss or sign video. Unlike conventional approaches that rely on intermediate gloss recognition or text-based matching, we propose an end-to-end model that directly operates on pose keypoints extracted from sign videos. Our architecture employs an encoder-only backbone with a binary classification head to determine whether the query sign appears within the target sequence. By focusing on pose representations instead of raw RGB frames, our method significantly reduces computational cost and mitigates visual noise. We evaluate our approach on the Word Presence Prediction dataset from the WSLP 2025 shared task, achieving 61.88\% accuracy and 60.00\% F1-score. These results demonstrate the effectiveness of our pose-based framework for Sign Language Spotting, establishing a strong foundation for future research in automatic sign language retrieval and verification. Code is available at https://github.com/EbimoJohnny/Pose-Based-Sign-Language-Spotting

Predicting protein secondary structures such as alpha helices, beta sheets, and coils from amino acid sequences is essential for understanding protein function. This work presents a transformer-based model that applies attention mechanisms to protein sequence data to predict structural motifs. A sliding-window data augmentation technique is used on the CB513 dataset to expand the training samples. The transformer shows strong ability to generalize across variable-length sequences while effectively capturing both local and long-range residue interactions.

Stock market prediction is a long-standing challenge in finance, as accurate forecasts support informed investment decisions. Traditional models rely mainly on historical prices, but recent work shows that financial news can provide useful external signals. This paper investigates a multimodal approach that integrates companies' news articles with their historical stock data to improve prediction performance. We compare a Graph Neural Network (GNN) model with a baseline LSTM model. Historical data for each company is encoded using an LSTM, while news titles are embedded with a language model. These embeddings form nodes in a heterogeneous graph, and GraphSAGE is used to capture interactions between articles, companies, and industries. We evaluate two targets: a binary direction-of-change label and a significance-based label. Experiments on the US equities and Bloomberg datasets show that the GNN outperforms the LSTM baseline, achieving 53% accuracy on the first target and a 4% precision gain on the second. Results also indicate that companies with more associated news yield higher prediction accuracy. Moreover, headlines contain stronger predictive signals than full articles, suggesting that concise news summaries play an important role in short-term market reactions.