Mashable Selects Look Up Mashable Voices Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Switch Off Trending Now In My Bag All Series This is the only camera you'll need on vacation Stop dragging around a bulky rig to take photos and videos when you should be relaxing. The new Insta360 Luna Ultra is such a good travel camera, you can leave your bulky DSLR or mirrorless cameras behind. Packing your bags for a trip can be a daunting task. Unfortunately, I can't help you with clothes, toiletries, and other essentials you'll need to bring on your vacation. However, I can help you lighten your load when it comes to packing a camera.

You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Queen Elizabeth's in-flight requests revealed - from a favourite drink to her secret weapon to fight jet lag The down-to-earth Cotswolds pub - serving pizzas and Negronis - that has its much fancier rivals truly'Stumped' Here's how to book with confidence and make the most of your package holiday Austria's 007 heaven: As the unveiling of the next Bond edges closer, how the film franchise's coolest attraction lets fans relive Daniel Craig's thrilling Spectre chase at 3,000M Would YOU go on a first date abroad? Singletons reveal what happened when they agreed to holiday with someone they'd just met The tiny European island with incredible beaches set for a tourism boost from The Odyssey - and it's not in Greece Is this the five-bed'secret' retreat - boasting its own pool, terrace and 60 acres - that Tom Holland and Zendaya stayed in on the lavish Beaverbrook estate after tying the knot (again)? The secret UK getaways where you can escape Britain's summer crowds, from glorious Welsh mountains to sleepy Norfolk villages Forget the overcrowded Canary Islands, this is why GREECE is the best place to visit this summer: Delicious food, gorgeous beaches, the friendliest locals. I've stayed in countless Greek hotels over the past 40 years - here are my top ten... Is this the world's most luxurious cruise ship?

EasyJet agrees to £5.7bn takeover by US firm No-frills airline EasyJet has agreed to be taken over in a £5.7bn deal by US firm Apollo after a rival bidder dropped out. The takeover was agreed after US investment firm Castlelake, which had made a series of offers for the carrier, said it was withdrawing from the bid battle. Apollo said it wants to help the airline grow and that it does not intend to cut any jobs in the first 12 months after the takeover has been completed, implying that passengers can expect largely the same service. EasyJet is one of Europe's largest airlines. It employs more than 19,000 people, and flies around 1,200 routes across 35 European countries.

These ultra-long commercial flights are becoming more common thanks to design innovations and fuel capacity. 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. At 24 hours and 24 minutes of flight time, Project Sunrise broke the old record for longest-ever flight by a commercial-style aircraft. 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 .

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. Just how likely are you to get sick on a cruise ship? 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 medical team in head-to-toe hazmat suits boarding a cruise ship was one of the many harrowing images from the COVID-19 pandemic.

Lindt has partially U-turned on its decision to hike prices after Easter chocolate sales dropped. The Swiss chocolate maker said a necessary groupwide price surge of 11.8% was one of the reasons revenue shrank in the first half of this year, particularly in the UK, Germany, and Switzerland. It also blamed weaker Easter demand and a drop in tourism from Asia and the Middle East due to geopolitical uncertainties. In response, it said it has adjusted prices and boosted marketing in certain regions for the second half of the year. Around Easter, Lindt is known for its chocolate rabbits wrapped in gold-coloured foil and decorated with a red ribbon and bell on their necks.

More people are using generative artificial intelligence to make travel plans for their summer vacation and letting their children use the technology when doing their homework during summer holidays. Six out of 10 people who responded to a survey on this year's summer holidays said they are using generative artificial intelligence to make travel plans. The survey, conducted by Meiji Yasuda Life Insurance on 1,120 people in their 20s to 50s in June, showed that 61.2% of those planning to travel in Japan or abroad refer to generative AI to make travel itineraries, as well as obtain information on local food and transportation. "The main tool people use for planning trips and doing research when they get there is shifting from travel guidebooks to generative AI," the firm said. Asked how they plan to spend their summer holidays, 58.4% said they are going out, down by 6.3 percentage points from last year. The rate of those traveling in Japan was 57.6%, up by 1 percentage point, while the ratio of those traveling overseas halved from 13.5% last year to 6.4%.

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Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity. As algorithms increasingly determine access to economic and social opportunities, it has become widely recognized that these systems are deeply embedded with the structural inequalities and prejudices of their environments. The field of algorithmic fairness emerged in response to the growing recognition that models optimized for predictive accuracy can systematically disadvantage marginalized groups. Early mitigation strategies, however, rested on fragile simplifications that limited their effectiveness in complex sociotechnical environments. This thesis identifies and addresses two fundamental limitations of contemporary fairness paradigms: the reliance on deterministic point estimates for auditing and the treatment of individuals as isolated entities devoid of structural context. First, the diagnosis of algorithmic unfairness has traditionally depended on scalar metrics that fail to capture the nuances of real-world deployment. This deterministic approach ignores the high statistical variance inherent in small, intersectional groups, often leading to false alarms or missed detections of bias. Furthermore, standard auditing struggles with the opacity of black-box models, frequently conflating unjustifiable bias with the influence of legitimate features.

Online ride-hailing platforms aim to deliver efficient mobility-on-demand services, often facing challenges in balancing dynamic and spatially heterogeneous supply and demand. Existing methods typically fall into two categories: reinforcement learning (RL) approaches, which suffer from data inefficiency, oversimplified modeling of real-world dynamics, and difficulty enforcing operational constraints; or decomposed online optimization methods, which rely on manually designed highlevel objectives that lack awareness of low-level routing dynamics. To address this issue, we propose a novel hybrid framework that integrates large language model (LLM) with mathematical optimization in a dynamic hierarchical system: (1) it is training-free, removing the need for large-scale interaction data as in RL, and (2) it leverages LLM to bridge cognitive limitations caused by problem decomposition by adaptively generating high-level objectives. Within this framework, LLM serves as a meta-optimizer, producing semantic heuristics that guide a low-level optimizer responsible for constraint enforcement and real-time decision execution. These heuristics are refined through a closed-loop evolutionary process, driven by harmony search, which iteratively adapts the LLM prompts based on feasibility and performance feedback from the optimization layer. Extensive experiments based on scenarios derived from both the New York and Chicago taxi datasets demonstrate the effectiveness of our approach, achieving an average improvement of 16% compared to state-of-the-art baselines.