This study investigates the subjective experiences of users in two robotic object delivery methods: direct handover and table placement, when users are occupied with another task. A user study involving 15 participants engaged in a typing game revealed that table placement significantly enhances user experience compared to direct handovers, particularly in terms of satisfaction, perceived safety and intuitiveness. Additionally, handovers negatively impacted typing performance, while all participants expressed a clear preference for table placement as the delivery method. These findings highlight the advantages of table placement in scenarios requiring minimal user disruption.

A new study has found that using delivery drones in dense urban environments might actually consume more energy than a conventional delivery van. Thomas Kirschstein, an economist at Martin Luther University in Halle-Wittenberg, Germany, developed a simulation to compare how energy efficient different delivery methods would be in a large and crowded city. He compared a delivery drone, electric van, and diesel van as they traveled through a digital recreation of Berlin to see which required the least amount of fossil fuel to complete equivalent delivery routes. The clear winner were electric vans, which consumed more than 50 percent less energy than diesel vans. The biggest surprise, however, came from drones, which turned out to be the most energy hungry of all the delivery methods, consuming as much as 10 times the amount of energy that the electric vans did.

For nearly 50 years, FedEx's local package delivery method has largely gone unchanged, but it may soon evolve. The multinational corporation is currently working with the city of Manchester to begin testing a new last-mile delivery method. It involves a highly automated robot, resembling a mini fridge on wheels, that will transport products from local hubs to their final destinations. Thanks to state-of-the-art cameras and sensors, the FedEx Sameday Bot can efficiently cover the last leg of deliveries without a human operator. And because it can travel on sidewalks, this technology could increase shipping speed while reducing roadway congestion – greatly benefiting New Hampshirites.

Almost a third (32%) of consumers surveyed globally by PwC plan to buy an AI device including robots or automated assistants, with retailers watching closely as'voice commerce' develops in the home. The findings are published today in PwC's Global Consumer Insights survey, which assesses the shopping behaviour, habits and expectations of over 22,000 consumers in 27 countries. The study reports that 10% of respondents already own artificial intelligence (AI) devices, such as robots and automated personal assistants like Amazon Echo or Google Home, and 32% said they plan to buy one. Both consumer and retailer habits and offerings still need time to adapt however, to make the most of the new voice commerce channel. Interest in the devices is strongest amongst consumers in emerging economies including China, Vietnam, Indonesia and Thailand.

Domino's is taking a page out of Santa's book on efficient winter transportation. The global pizza chain, which has already invested in high-tech delivery methods like autonomous robots, drones, "zero-click" ordering apps and satellite tracking to deliver food, is now training reindeer to transport pies. Reindeer delivery may be part of a contingency plan for Domino's Japan ahead of what is expected to be a particularly cold and snowy winter, RocketNews24 reports. The chain is attempting a trial period for performing training exercises in the city of Ishikari-- a particularly ice-prone area in Hokkaido-- to figure out if its reindeer delivery initiative is feasible. The technique will involve insulated pizza containers strapped to the animals' backs.

Clinical research of new recreational drugs and trends requires mining current information from non-traditional text sources. In this work we support such research through the use of multi-dimensional latent text models, such as factorial LDA, that capture orthogonal factors of corpora, creating structured output for researchers to better understand the contents of a corpus. Since a purely unsupervised model is unlikely to discover specific factors of interests to clinical researchers, we modify the structure of factorial LDA to incorporate prior knowledge, including the use of of observed variables, informative priors and background components. The resulting model learns factors that correspond to drug type, delivery method (smoking, injection, etc.), and aspect (chemistry, culture, effects, health, usage). We demonstrate that the improved model yields better quantitative and more interpretable results.