A pop-up urban port for delivery drones -- and one day, potentially flying taxis -- launched Monday in Britain, lifting a box of prosecco for a brief celebratory test flight hailed as groundbreaking. Air-One, a so-called "vertiport" for drones and future electric vehicles taking off and landing vertically, was proclaimed as the first of its kind by proponents and heralding a new era of low-emission futuristic air transport. Based in Coventry, a former car manufacturing powerhouse in central England, the site will be used for a month-long showcase of the burgeoning industry. The inaugural flight symbolically lifted the six-bottle box of sparkling wine, weighing around 12 kilograms, from the launch pad. The commercial drone used -- Malloy Aeronautics' T150, on loan from its day job doing logistics for the British military -- is the largest ever to fly in such an urban environment, according to Ricky Sandhu, founder and executive chairman of Urban-Air Port, the British firm behind the vertiport venture.

New additive manufacturing methods are needed to realize more complex soft robots. One example is soft fluidic robotics, which exploits fluidic power and stiffness gradients. Porous structures are an interesting type for this approach, as they are flexible and allow for fluid transport. Within this work, the Infill-Foam (InFoam) is proposed to print structures with graded porosity by liquid rope coiling (LRC). By exploiting LRC, the InFoam method could exploit the repeatable coiling patterns to print structures. To this end, only the characterization of the relation between nozzle height and coil radius and the extruded length were necessary (at a fixed temperature). Then by adjusting the nozzle height and/or extrusion speed the porosity of the printed structure could be set. The InFoam method was demonstrated by printing porous structures using styrene-ethylene-butylene-styrene (SEBS) with porosities ranging from 46\% to 89\%. In compression tests, the cubes showed large changes in modulus (more than 200 times), density (-89\% compared to bulk), and energy dissipation. The InFoam method combined coiling and normal plotting to realize a large range of porosity gradients. This grading was exploited to realize rectangular structures with varying deformation patterns, which included twisting, contraction, and bending. Furthermore, the InFoam method was shown to be capable of programming the behavior of bending actuators by varying the porosity. Both the output force and stroke showed correlations similar to those of the cubes. Thus, the InFoam method can fabricate and program the mechanical behavior of a soft fluidic (porous) actuator by grading porosity.

Many governments around the world have rightly put ethical development and deployment at the heart of their AI thinking. Core to this complex issue is a set of interconnected problems - AI systems that may automate societal problems, either due to a systemic lack of diversity in development teams, perhaps, or the use of training data that contains historic or structural biases. The design of systems may also be a factor. The result may be the algorithmic exclusion of individuals or groups because of their ethnicity, gender, sexuality, religion, or socioeconomic background. For example, facial recognition systems that misidentify black or Asian people because of a lack of relevant data; or CV-scanning applications that reject applicants from some postcodes/zip codes because, historically, human employers have actively excluded those jobseekers.

Most technological advances spark incremental progress. And then a few come along that are truly game-changing. AI is one of those truly transformative technologies. It's set to revolutionise our lives and workplaces at rapid speed in the coming decade. It will potentially reshape almost every job, industry and life.

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25, 256 MRI scans from 6, 314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing.

Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. Although it aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure. On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.

Abstract--Artificial Intelligence (AI) has found its applications in a variety of environments ranging from data science to cybersecurity. AI helps break through the limitations of traditional algorithms and provides more efficient and flexible methods for solving problems. In this paper, we focus on the applications of artificial intelligence in authentication, which is used in a wide range of scenarios including facial recognition to access buildings, keystroke dynamics to unlock smartphones. With the emerging AI-assisted authentication schemes, our comprehensive survey provides an overall understanding on a high level, which paves the way for future research in this area. In contrast to other relevant surveys, our research is the first of its kind to focus on the roles of AI in authentication. Learning and neural networks are The traditional password-based authentication method has two main mechanisms used in AI. Learning is the process of slowly faded out due to its inadequate ...

It's a collision course: We'll need to feed another 2 billion people by mid-century, even as climate change threatens our ability to produce food. Georgia, Florida and other Southeastern states must play a central role if we're to feed the world and simultaneously protect the planet. If we fail to rise to this challenge, we risk a multitude of problems driven by hungry people. And a new report released from the UN Intergovernmental Panel on Climate Change only heightens the concern. Our best chance to get off this collision course is through innovation.

From Harry Potter to The Jetsons, flying cars have been staple features of science fiction blockbusters for years. And with the futuristic vehicles rapidly getting closer to becoming a reality, the world's first vertiport has opened this week. The Air-One vertiport is located in Coventry and will be a hub for drones and electric vertical take-off and landing (eVTOL) aircrafts, including flying cars and taxis. The site will serve as a blueprint for more than 200 vertiports planned worldwide over the next five years, according to Urban-Air Port, the UK-based developer of Air-One. 'The opening of Air-One is a momentous moment – the starting gun for a new age of transport, an age of zero-emission, congestion-free travel between and within cities that will make people healthier, happier and more connected than ever before,' said Ricky Sandhu, Founder and Executive Chairman of Urban-Air Port.

This is an exciting conversation on cutting-edge Digital Human technology and its applications. Very cool stuff! Sramana Mitra: Let's start by introducing our audience to yourself as well as to the company. Danny Tomsett: I'm the CEO and Founder of a fantastic Digital Human company called UneeQ Digital Humans. Digital Humans hasn't been a common part of our lives until 2016. Even then, it's been an emerging technology. We've led the way in the global market. The company started in 2010 not with Digital Humans but with a focus on how we create better emotional connection value through digital channels. It became very clear to us that over the last 10 years, we were getting increasingly good at taking the human out of the process. The problem was that we were taking the human out of the process. You have all these organizations with fantastic people and human skills creating amazing brand experience and customer experience, and replacing that with self-service forms and