UK Prime Minister Theresa May has said she is disappointed by the US's "unjustified decision" to slap tariffs on EU steel and aluminium. The tariffs of 25% on steel and 10% on aluminium, which affect the EU, Canada and Mexico, came into effect on Friday. All three are planning retaliatory moves. Mrs May said the EU and UK should be exempted and would work together to "protect and safeguard our workers and industries". UK Steel said the tariffs, which apply to a wide range of steel and aluminium products such as sheets, plates, bars, pipes and "semi-finished" products, will damage not only the UK steel sector but also the US economy.

A new robot created in Nova Scotia may mean farmers could get some help tackling troublesome weeds in their fields. This month, Nexus Robotics, a technology startup based in Dartmouth, N.S., won the weed-and-feed competition at the agBOT Challenge, an international showdown between agricultural robots in Rockville, Ind. Dubbed R2 Weed2 or Hal-Bot, the autonomous machine uses artificial intelligence to distinguish between weeds and crops and is designed to both pluck weeds and spray herbicide. "We want to get rid of the weed and keep the crop and even fertilize it. So one of the advancements … we made is vision systems can be better than humans at distinguishing them," said Thomas Trappenberg, part of the small team behind the battery-powered robot. VIDEO: Halifax startup takes on big agriculture corporations, and wins international robotics competition.

The demand of metals by modern technology has been shifting from common base metals to a variety of minor metals, such as cobalt or indium. The industrial importance and limited geological availability of some minor metals have led to them being considered more "critical," and there is a growing interest in such critical metals and their producing companies. In this research, we create a novel framework, Dynamic Advisor-Based Ensemble (dynABE), to predict the stock trend of major critical metal producers. Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors," each advisor dealing with a particular economic sector. Then through ensembles of weak classifiers, each advisor produces a prediction result, and all the advisors are combined again in a biased online update fashion to dynamically make the final prediction. Based on a misclassification error of 32% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296%, or an excess return of 130% within one year. In addition, the feature set selected by dynABE also suggests potentially influential factors to metal criticality, because stock prices of major producers influence metal production. Therefore, not only does this research propose a novel framework for specialized stock trend prediction, it also provides domain insights into dynamic features that potentially influence metal criticality.

Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation mechanisms. This paper presents a new data-driven framework for root-cause analysis, based on a spatiotemporal graphical modeling approach built on the concept of symbolic dynamics for discovering and representing causal interactions among sub-systems of complex CPSs. We formulate the root-cause analysis problem as a minimization problem via the proposed inference based metric and present two approximate approaches for root-cause analysis, namely the sequential state switching ($S^3$, based on free energy concept of a restricted Boltzmann machine, RBM) and artificial anomaly association ($A^3$, a classification framework using deep neural networks, DNN). Synthetic data from cases with failed pattern(s) and anomalous node(s) are simulated to validate the proposed approaches. Real dataset based on Tennessee Eastman process (TEP) is also used for comparison with other approaches. The results show that: (1) $S^3$ and $A^3$ approaches can obtain high accuracy in root-cause analysis under both pattern-based and node-based fault scenarios, in addition to successfully handling multiple nominal operating modes, (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy, and (3) the proposed framework is robust and adaptive in different fault conditions and performs better in comparison with the state-of-the-art methods.

Autonomous luggage maker ForwardX Robotics today announced it has raised $10 million to bring its suitcase Ovis to market. At $399, the luggage can move a maximum 6.2 miles per hour and will ship to its first customers in late 2018. ForwardX was founded in 2016, but its luggage initially grabbed the world's attention in January at the Consumer Electronics Show (CES) 2018 in Las Vegas. The 9.9 lb suitcase is made of polypropylene and carbon fiber and is able to follow you by deploying computer vision that tracks your body and face, even if you are momentarily out of sight. Though Ovis has been tested and found to be useful in environments outside airports, like city streets, its battery only lasts for four hours of use, and it must be switched to the old-fashioned manual mode on escalators since it cannot yet handle moving stairs.

Upon closer inspection, the satellite had noticed that an area that should have been shrouded in forest, was now barren. Within hours, a call had been made to a global conservation group, who mounted a legal case against the logging companies operating in the area. That process, historically, could have taken months of observing and recording changes. What's more, in remote areas such as the Ussuri Taiga in Russia's Far East, policing illegal logging operations have historically had little impact on the extraction of timber. But thanks to artificial intelligence (AI) and satellites, the ability to observe and respond to changes has become much faster.

Most conventional androids are fairly rigid, susceptible to damage and difficult to repair. However, scientists are determined to (literally) give them thicker skins. They've experimented with soft, deformable circuits that are flexible, and could reduce business expenses in the long term -- but are still prone to tearing and puncturing. The solution to these issues may lie in one recent advancement. A group of researchers from Carnegie Mellon University have found a way to counter surface damage and electrical failure commonly observed in soft materials used in engineering robotic electronics.

Researchers at the University of Illinois have developed a Roomba-like robot that can tend to crops autonomously. At Carnegie Mellon, they're building a suite of A.I. and drones to take on some of agriculture's most demanding tasks. And just last year, a team of automated machines farmed an acre and a half of barley, from planting to harvesting, without a single human setting foot on the field. A Swiss company called ecoRobotix recently unveiled its contribution to automated agriculture -- a robotic weed-killing machine. The four-wheeled robot doesn't look like much more than a mobile table top, but Reuters reports that the unassuming machine may reshape the way we approach agriculture.

Forget ruby-encrusted swords or diamond-tipped chainsaws. The scanning probe microscope is, quite literally, the sharpest object ever made. Hidden under its bulky silver exterior is a thin metal wire, as fine as a human hair. Scientists wield the wire not as a weapon, but as an intricate paintbrush--using its needlelike tip to position single atoms on a tiny semiconductor canvas. Ever since scientists at IBM invented the scanning probe microscope some 35 years ago, researchers have used it to create designs both goofy and groundbreaking.

In a field of sugar beet in Switzerland, a solar-powered robot that looks like a table on wheels scans the rows of crops with its camera, identifies weeds and zaps them with jets of blue liquid from its mechanical tentacles. Undergoing final tests before the liquid is replaced with weedkiller, the Swiss robot is one of new breed of AI weeders that investors say could disrupt the $100 billion pesticides and seeds industry by reducing the need for universal herbicides and the genetically modified (GM) crops that tolerate them. Dominated by companies such as Bayer, DowDuPont, BASF and Syngenta, the industry is bracing for the impact of digital agricultural technology and some firms are already adapting their business models. Herbicide sales are worth $26 billion a year and account for 46 percent of pesticides revenue overall while 90 percent of GM seeds have some herbicide tolerance built in, according to market researcher Phillips McDougall. The company said it is close to signing a financing round with investors and is due to go on the market by early 2019.