Technology Aviation The world's largest electric plane takes flight The 25,000-pound aircraft flew for 27 minutes. 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. Heart Aerospace's X1 demonstrator, the largest battery-electric aircraft ever flown, crosses the Champlain Valley near Plattsburgh, New York, at sunrise during its first flight in August 2026. 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 .

Security researchers found that in less than 60 seconds, they could open a hatch on a plane's exterior, plug in a tiny device, and redirect the aircraft's autopilot or sabotage its flight plan. Even as the digital components of so many life-critical systems have proven susceptible to cybersabotage-- cars, medical devices, even water utilities and power grids --the computer systems of airplanes have, thankfully, remained uniquely inaccessible to hackers. But one group of academic researchers has spent years testing a different, devious approach to aviation cybersecurity. Perhaps, they suggest, a plane could be hacked the same way that spies and saboteurs have targeted other high-value, offline computers: by surreptitiously gaining physical access to one and plugging in a device designed to silently run the attackers' malicious code. Tomorrow at the Usenix Cybersecurity Conference, researchers from the University of California at San Diego and Oberlin College will present a hacking technique capable of commandeering the autopilot of a Boeing 737 to redirect its navigation or silently altering key values in the plane's takeoff and fuel calculations while spoofing the results on the pilot's screen--subtle changes the researchers say could potentially cause anything from runway overruns on takeoff to diversions to a different country's airspace to catastrophic crashes.

Unmanned Aerial Vehicles (UAVs) are evolving into language-interactive platforms, enabling more intuitive forms of human-drone interaction. While prior works have primarily focused on high-level planning and long-horizon navigation, we shift attention to language-guided fine-grained trajectory control, where UAVs execute short-range, reactive flight behaviors in response to language instructions. We formalize this problem as the Flying-on-a-Word (Flow) task and introduce UAV imitation learning as an effective approach. In this framework, UAVs learn fine-grained control policies by mimicking eUAxpert pilotVtrajectoriesFlopaired withwatomic Fly around the tree ahead Land on the left side of carlanguage instructions. To support this paradigm, we present UAV-Flow, the firstreal-world benchmark for language-conditioned, fine-grained UAV control.

Aircraft manufacturing is the jewel in the crown of industry, in which generating high-fidelity airfoil geometries with controllable and editable representations remains a fundamental challenge. Existing deep learning methods, which typically rely on predefined parametric representations (e.g., Bézier curves) or discrete point sets, face an inherent trade-off between expressive power and resolution adaptability. To tackle this challenge, we introduce FuncGenFoil, a novel functionspace generative model that directly reconstructs airfoil geometries as function curves. Our method inherits the advantages of arbitrary-resolution sampling and smoothness from parametric functions, as well as the strong expressiveness of discrete point-based representations. Empirical evaluations demonstrate that FuncGenFoil improves upon state-of-the-art methods in airfoil generation, achieving a relative 74.4% reduction in label error and a 23.2% increase in diversity on the AF-200K dataset. Our results highlight the advantages of function-space modeling for aerodynamic shape optimization, offering a powerful and flexible framework for high-fidelity airfoil design.

While unmanned aerial vehicles (UAVs) offer wide-area, high-altitude coverage for anomaly detection, they face challenges such as dynamic viewpoints, scale variations, and complex scenes. Existing datasets and methods, mainly designed for fixed ground-level views, struggle to adapt to these conditions, leading to significant performance drops in drone-view scenarios.To bridge this gap, we introduce A2Seek (Aerial Anomaly Seek), a large-scale, reasoning-centric benchmark dataset for aerial anomaly understanding. This dataset covers various scenarios and environmental conditions, providing high-resolution real-world aerial videos with detailed annotations, including anomaly categories, frame-level timestamps, region-level bounding boxes, and natural language explanations for causal reasoning. Building on this dataset, we propose A2Seek-R1, a novel reasoning framework that generalizes R1-style strategies to aerial anomaly understanding, enabling a deeper understanding of "Where" anomalies occur and "Why" they happen in aerial frames.To this end, A2Seek-R1 first employs a graph-of-thought (GoT)-guided supervised fine-tuning approach to activate the model's latent reasoning capabilities on A2Seek. Then, we introduce Aerial Group Relative Policy Optimization (A-GRPO) to design rule-based reward functions tailored to aerial scenarios. Furthermore, we propose a novel "seeking" mechanism that simulates UAV flight behavior by directing the model's attention to informative regions.Extensive experiments demonstrate that A2Seek-R1 achieves up to a 22.04\% improvement in AP for prediction accuracy and a 13.9\% gain in mIoU for anomaly localization, exhibiting strong generalization across complex environments and out-of-distribution scenarios. Our dataset and code are released at https://2-mo.github.io/A2Seek/.

Technology Aviation The world's largest RC Boeing 777-9X takes flight Filmmaker Tyler Perry piloted the remote-controlled behemoth, which weighs 630 pounds with a 33-foot wingspan. 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. The remote-controlled aircraft is roughly the same size as a human-piloted Cessna 150. 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 .

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 .

A Ukrainian flying ace is leveraging his aerobatics skills to protect his countrymen from nightly attacks. The most challenging part of an international aerobatics contest is the Free Unknown. Pilots arrive at a competition after having polished sequences of loops, stall turns, and barrel rolls. But for the Free Unknown section they learn which assortment of tricks they must perform only a day in advance. Contestants plan out how they will string together the stipulated moves in the most pleasing fashion, but they cannot rehearse the routine, except in their minds. It's a test of imagination and airmanship that often decides the competition. In 2019, the World Intermediate Aerobatics Championship, which was held on an airfield in the Czech town of Břeclav, contained three Free Unknowns. The winner of the first was a twenty-five-year-old Ukrainian pilot named Timur Fatkullin. At the controls of his red-and-silver Extra 330LX--a nimble German sports plane--he made the unusual move of starting his sequence upside down. He then executed a complicated routine as if he'd practiced it for months. The Ukrainian team, boosted by Fatkullin's performance, won gold. Trevor Dugan, who served as a navigator with the R.A.F. in Afghanistan and Iraq, was on the British team, which took bronze. Fatkullin, he said, was "absolutely phenomenal." Not long after that championship, Fatkullin stopped entering aerobatics competitions: first came the pandemic, then the war with Russia. He moves through life impatiently. Now thirty-two, he has five children. He is tall, with a tight beard, pale-green eyes, and a square jaw. Even in casual situations, he stands ramrod straight, as though about to give or receive an order. He often wears a shirt with three buttons undone, a beige leather flying jacket with the collar turned up, combat pants, and Nike high-tops. He plays the guitar, a little piano. He often carries a thick fold of high-value bills. He speaks several languages, including English (almost perfectly) and Spanish (conversationally). He once spent thirty days in jail after breaking the ribs of a man who'd threatened his wife. He can dance the tango. When Fatkullin was in his mid-twenties, he started doing stunts with a group of other extreme athletes: parachutists, motorcyclists, a free diver.

The use of transfer learning within Bayesian optimization addresses the disadvantages of the so-called \textit{cold start} problem by using source data to aid in the optimization of a target problem. We present a method that leverages an ensemble of surrogate models using transfer learning and integrates it in a constrained Bayesian optimization framework. We identify challenges particular to aircraft design optimization related to heterogeneous design variables and constraints. We propose the use of a partial-least-squares dimension reduction algorithm to address design space heterogeneity, and a \textit{meta} data surrogate selection method to address constraint heterogeneity. Numerical benchmark problems and an aircraft conceptual design optimization problem are used to demonstrate the proposed methods. Results show significant improvement in convergence in early optimization iterations compared to standard Bayesian optimization, with improved prediction accuracy for both objective and constraint surrogate models.