A robot crushed a man to death in South Korea after the machine apparently failed to differentiate him from the boxes of produce it was handling, the Yonhap news agency reported on Wednesday. The man, a robotics company worker in his 40s, was inspecting the robot's sensor operations at a distribution centre for agricultural produce in South Gyeongsang province. The industrial robot, which was lifting boxes filled with bell peppers and placing them on a pallet, appears to have malfunctioned and identified the man as a box, Yonhap reported, citing the police. The robotic arm pushed the man's upper body down against the conveyor belt, crushing his face and chest, according to Yonhap. He was transferred to the hospital but died later, the report said.

A robot crushed a man to death in South Korea after the machine apparently failed to differentiate him from the boxes of produce it was handling, the Yonhap news agency reported on Wednesday. The man, a robotics company worker in his 40s, was inspecting the robot's sensor operations at a distribution centre for agricultural produce in South Gyeongsang province. The industrial robot, which was lifting boxes filled with bell peppers and placing them on a pallet, appears to have malfunctioned and identified the man as a box, Yonhap reported, citing the police. The robotic arm pushed the man's upper body down against the conveyor belt, crushing his face and chest, according to Yonhap. He was transferred to the hospital but died later, the report said.

Contact matrices are an important ingredient in age-structured epidemic models to inform the simulated spread of the disease between sub-groups of the population. These matrices are generally derived using resource-intensive diary-based surveys and few exist in the Global South or tailored to vulnerable populations. In particular, no contact matrices exist for refugee settlements - locations under-served by epidemic models in general. In this paper we present a novel, mixed-method approach, for deriving contact matrices in populations which combines a lightweight, rapidly deployable, survey with an agent-based model of the population informed by census and behavioural data. We use this method to derive the first set of contact matrices for the Cox's Bazar refugee settlement in Bangladesh. The matrices from the refugee settlement show strong banding effects due to different age cut-offs in attendance at certain venues, such as distribution centres and religious sites, as well as the important contribution of the demographic profile of the settlement which was encoded in the model. These can have significant implications to the modelled disease dynamics. To validate our approach, we also apply our method to the population of the UK and compare our derived matrices against well-known contact matrices previously collected using traditional approaches. Overall, our findings demonstrate that our mixed-method approach can address some of the challenges of both the traditional and previously proposed agent-based approaches to deriving contact matrices, and has the potential to be rolled-out in other resource-constrained environments. This work therefore contributes to a broader aim of developing new methods and mechanisms of data collection for modelling disease spread in refugee and IDP settlements and better serving these vulnerable communities.

The worldwide need for clothing will propel the apparel industry from US$1.5tn in value in 2020 to US$2.25tn by 2025. The growth of the human population and its demand for clothing is a foregone conclusion, but the ability of manufacturers to meet expectations without overextending themselves is not. Like every other industry, apparel and textiles must learn to serve a growing population while remaining aware of the planet's finite resources. Leveraging artificial intelligence (AI) to meet demand without exceeding the available supply is nothing new. How does it apply to apparel manufacturing?

As the coronavirus pandemic enveloped the world last year, businesses increasingly turned to automation in order to address rapidly changing conditions. Floor-cleaning and microbe-zapping disinfecting robots were introduced in hospitals, supermarkets and other environments. Some enterprises found that, given the new emphasis on hygiene and social distancing, robotic operations offered a marketing advantage. The American fast food chain White Castle began using hamburger-cooking robots in an effort to create "an avenue for reduced human contact with food during the cooking process". With the worst days of the pandemic hopefully now behind us, the jobs story has turned out to be unexpectedly complicated. While overall unemployment rates remain elevated, both the US and the UK are experiencing widespread worker shortages, focused especially in those occupations that tend to offer gruelling work conditions and relatively low pay.

Yet, if they are working with flawed data and inaccurate forecasting techniques, they will end up with too few or, too many staff on the shop floor and in the warehouse, and not enough stock to meet sudden surges in demand. This typically results in unfulfilled sales opportunities and increased costs and abandoned shopping baskets. Conscious that this situation is unsustainable, retailers are increasingly focused on using artificial intelligence and machine learning to more accurately forecast demand. With computing and data processing power escalating all the time, the more-forward thinking are already assessing the benefits that more accurate forecasting could have on their sales and profitability. Key to the success of any forecasting system is the data fed into it.

The Dutch engineer behind the idea for circular runways at airports has revealed plans to build a test runway for unmanned delivery drones. Henk Hesselink is collaborating with Valkenburg airport, a disused naval base near The Hague, which has ambitions to become a drone innovation centre. His design for circular runways at passenger airports captured global attention last year, in aviation circles and on social media. The point, he explained, was to make more efficient use of space, reduce tricky crosswind landings and cut down on noise pollution. Mr Hesselink, a senior research and development manager at the Netherlands Aerospace Centre, says growing demand for drone delivery services will call for "a network of drones, surfing distribution centres".

Profits and gross margins are under pressure for fashion retailers. But could Artificial Intelligence hold the key to optimising markdowns and unlocking value in the supply chain? Fashion retailing is a dynamic, complex and highly competitive business – and it's not getting any easier. Margins are coming under increasing pressure as a number of factors play-out: Consumers are becoming more demanding, rising inflation coupled with stagnant pay-growth is impacting buying power, costs are going up and online competition continues to gather pace. A particular challenge for bricks & mortar retailers is the incessant growth of online sales. According to the IMRG Capgemini Online Retail Sales Index for August 2017, clothing sales grew by 17.9%, year on year, and figures from the UK's Office for National Statistics (ONS) indicate that 15.3% of all retail spending is now conducted online.

Next to the M56, on the outskirts of Manchester, the future has landed. A cluster of huge distribution centres sits at the heart of Airport City, a new development part-funded by the Beijing Construction Engineering Group (two years ago, it was visited by president Xi Jinping of China). Among the biggest buildings is one of Amazon's self-styled "fulfilment centres". Known within the company as MAN1, it opened in September last year, but everything inside, from the chairs to the wall-mounted screens, looks as if it has just come out of a box. Deeper within the centre, beyond the reception area and meeting rooms, there is something else just as new: a great expanse of space behind a metal cage, where dozens of robots, finished in Amazon orange and each emblazoned with its own number, glide across the floor, gracefully avoiding collisions and sprinting to their next task. Amazon employees call them "drives", but to all intents and purposes these are droids, summoned from the dreams of science fiction and put to work. In some Amazon warehouses, workers – or, in the company's parlance, "associates" – still pace up and down huge aisles, picking out goods and preparing them for shipment; these shifts are said sometimes to involve hikes of 11 miles. The humans in charge of the process known as "picking" now remain in closed workstations, built around a screen that tells them what they need to get next, while the robots bring the shelves – reinvented as four-sided fabric towers, full of pouches that contain everything from DVDs to dolls – to them. There are tasks only a human can do, such as the careful packing of boxes.

Amazon has set up a secretive think tank dedicated to advancing driverless technology. The e-commerce firm won't build its own delivery vehicles but will invest in existing robotics, it is rumoured. The rumours come after the company recently patented a roadway control system for driverless vehicles. Amazon has set up a secretive think tank dedicated to advancing driverless technology. Amazon now has 45,000 robots shuffling products around 20 distributions centres.