Non-holonomic vehicles are of immense practical value and increasingly subject to automation. However, controlling them accurately, e.g., when parking, is known to be challenging for automatic control methods, including model predictive control (MPC). Combining results from MPC theory and sub-Riemannian geometry in the form of homogeneous nilpotent system approximations, this paper proposes a comprehensive, ready-to-apply design procedure for MPC controllers to steer controllable, driftless non-holonomic vehicles into given setpoints. It can be ascertained that the resulting controllers nominally asymptotically stabilize the setpoint for a large-enough prediction horizon. The design procedure is exemplarily applied to four vehicles, including the kinematic car and a differentially driven mobile robot with up to two trailers. The controllers use a non-quadratic cost function tailored to the non-holonomic kinematics. Novelly, for the considered example vehicles, it is proven that a quadratic cost employed in an otherwise similar controller is insufficient to reliably asymptotically stabilize the closed loop. Since quadratic costs are the conventional choice in control, this highlights the relevance of the findings. To the knowledge of the authors, it is the first time that MPC controllers of the proposed structure are applied to non-holonomic vehicles beyond very simple ones, in particular (partly) on hardware.

"Quality is never an accident. It is always the result of intelligent effort" – John Ruskin The adoption of artificial intelligence (AI) is gathering pace. And with a significant level of adoption in emerging markets, the trend has seen an increase in almost every industry, encompassing a range of business sectors from production, through marketing and sales to HR and risk management. Alongside this trend, companies are broadening their focus to include stakeholders beyond their shareholders. This can be attributed to a variety of factors.

The invasion of Ukraine has put the U.S. and Europe on a wartime mission to abandon Russian fossil fuels. This series looks at speeding up zero-carbon alternatives by lowering political and financial barriers. Sign up here to get the next story sent to your inbox. Trying to attach a million-dollar, 60-ton wind turbine blade to its base is challenging in any circumstance -- getting the angle wrong by even a fraction of a degree could affect the machine's ability to generate power. Now imagine trying to do it in the middle of the North Sea, one of the world's windiest spots, with waves swelling around you. It's like tying a thread to a kite at the beach and then trying to put it through the eye of a needle.

All workers, and especially those on the frontline, deserve and need mentorships, training, and career guidance. If they receive that type of attention, their wages increase and they can have exciting career pathways with a higher loyalty. Frontline workers in the United States – truck drivers, manufacturing line workers, packers and shippers, grocery clerks, servers, healthcare assistants, housekeepers, janitors and so on – are frequently trapped in positions with low wages and little to no prospects for advancement. However, if these services gradually become more important, this could eventually change. What if we told you we could speed it up by investing now?

Climate change is here, and it's set to get much worse, experts say – and as a result, many industries have pledged to reduce their carbon footprints in the coming decades. Now, the recent jump in energy prices due mainly to the war in Ukraine, also emphasizes the need for development of cheap, renewable forms of energy from freely available sources, like the sun and wind – as opposed to reliance on fossil fuels controlled by nation-states. But going green is easier for some industries than for others,- and one area where it is likely to be a significant challenge is in data centers, which require huge amounts of electricity to cool off, in some cases, the millions of computers deployed. Growing consumer demand to reduce carbon output, along with rules that regulators are likely to impose in the near future, require companies that run data centers to take immediate steps to go green. And artificial intelligence, machine learning, neural networks, and other related technologies can help enterprises of all kinds achieve that goal, without having to spend huge sums to accomplish it.

Around the time computers were old enough to drink, software engineering guru Gerald Weinberg said: "If builders built buildings the way programmers wrote programs, then the first woodpecker that came along would destroy civilization." This is not a plotline science fiction authors have ever neglected. Actually, some titles are still worth a trip to the library: for example, Poul Anderson's Sam Hall from 1953, which shows how too much reliance on "infallible" computer surveillance can turn into an autoimmune collapse for a nation-state, or, for that matter, any large organization. At the more obscure end of the spectrum, there is Swedish Nobel Laureate Hannes Alfvén, publishing in Swedish under the pseudonym Oluf Johannesson, with Sagan om den stora Datamaskinen [Tale of the Big Computer] from 1966. As with almost all science fiction pieces, however, they miss the future by a wide margin.

Depending on climate conditions, the availability of renewables and other factors, immersion cooling can make a profound difference in both energy consumption and costs.

Unmanned surface vehicles (USVs) are of increasing importance to a growing number of sectors in the maritime industry, including offshore exploration, marine transportation and defence operations. A major factor in the growth in use and deployment of USVs is the increased operational flexibility that is offered through use of autonomous navigation systems that generate optimised trajectories. Unlike path planning in terrestrial environments, planning in the maritime environment is more demanding as there is need to assure mitigating action is taken against the significant, random and often unpredictable environmental influences from winds and ocean currents. With the focus of these necessary requirements as the main basis of motivation, this paper proposes a novel motion planner, denoted as GPMP2*, extending the application scope of the fundamental GP-based motion planner, GPMP2, into complex maritime environments. An interpolation strategy based on Monte-Carlo stochasticity has been innovatively added to GPMP2* to produce a new algorithm named GPMP2* with Monte-Carlo stochasticity (MC-GPMP2*), which can increase the diversity of the paths generated. In parallel with algorithm design, a ROS based fully-autonomous framework for an advanced unmanned surface vehicle, the WAM-V 20 USV, has been proposed. The practicability of the proposed motion planner as well as the fully-autonomous framework have been functionally validated in a simulated inspection missions for an offshore wind farm in ROS.

Project proposal, submitted under the MINE.THE.GAP 2nd open call, passed the evaluation phase and was selected for funding. The digital solution proposed will provide a 3D interactive imaging for smart exploration and exploitation of an industrial minerals deposit according to its final uses (e.g., insulation, construction, agriculture, filtration etc.). The main objective of this PoC is a digital Demo interface that will provide a view of a future Prototype and allow use to engage Bêta-Testers of the future Prototype. This innovation will allow users to define their own scenarios regarding final applications for the deposit and retrieve a 3D bloc model of the corresponding market value. The solution will advocate a better use of mineral resources and will democratize best practices.

Take the challenge of demographic shifts. A.I., in conjunction with hybrid cloud, is helping many companies automate certain routine business activities, and move people to higher-value work. In manufacturing, a factory floor operator can now rely on A.I. to detect defects that are invisible to the human eye. In health care, A.I.-enabled virtual agents can handle millions of calls at once. In the energy sector, autonomous robots can use cloud and A.I. to analyze data at the edge to improve equipment uptime and prevent power outages.