There has been much recent interest in hierarchies of progressively stronger convexifications of polynomial optimisation problems (POP). These often converge to the global optimum of the POP, asymptotically, but prove challenging to solve beyond the first level in the hierarchy for modest instances. We present a finer-grained variant of the Lasserre hierarchy, together with first-order methods for solving the convexifications, which allow for efficient warm-starting with solutions from lower levels in the hierarchy.

Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks.

In a commonly-used version of the Simple Assembly Line Balancing Problem (SALBP-1) tasks are assigned to stations along an assembly line with a fixed cycle time in order to minimize the required number of stations. It has traditionally been assumed that the total work needed for each product unit has been partitioned into economically indivisible tasks. However, in practice, it is sometimes possible to divide particular tasks in limited ways at additional time penalty cost. Despite the penalties, task division where possible, now and then leads to a reduction in the minimum number of stations. Deciding which allowable tasks to divide creates a new assembly line balancing problem, TDALBP (Task Division Assembly Line Balancing Problem). We propose a mathematical model of the TDALBP, an exact solution procedure for it and present promising computational results for the adaptation of some classical SALBP instances from the research literature. The results demonstrate that the TDALBP sometimes has the potential to significantly improve assembly line performance.

Computational simulations with different fidelity have been widely used in engineering design. A high-fidelity (HF) model is generally more accurate but also more time-consuming than an low-fidelity (LF) model. To take advantages of both HF and LF models, multi-fidelity surrogate models that aim to integrate information from both HF and LF models have gained increasing popularity. In this paper, a multi-fidelity surrogate model based on support vector regression named as Co_SVR is developed by combining HF and LF models. In Co_SVR, a kernel function is used to map the map the difference between the HF and LF models. Besides, a heuristic algorithm is used to obtain the optimal parameters of Co_SVR. The proposed Co_SVR is compared with two popular multi-fidelity surrogate models Co_Kriging model, Co_RBF model, and their single-fidelity surrogates through several numerical cases and a pressure vessel design problem. The results show that Co_SVR provides competitive prediction accuracy for numerical cases, and presents a better performance compared with the Co_Kriging and Co_RBF models and single-fidelity surrogate models.

How does the Earth system function? The Earth system is incredibly complex, and understanding how it works is important for the survival of our species. Earth system science is an area of knowledge that has advanced rapidly in recent years. So, too, has artificial intelligence. To learn how the latter helps the former, we spoke with Markus Reichstein, who heads the Max Planck Institute's Biogeochemical Integration Department.

However, some of the biggest arguments or worries about AI have already been answered without most people noticing. Don't worry about future artificial intelligence challenges. Humans develop AI and write policy. IBM's AI platform Watson is 90% accurate in making treatment decisions in early-stage lung cancer. Self-driving cars are safer than human drivers.

Take a look at the iPhone sitting in front of you. It arrived as a finished product to you on a container ship, but each element of its manufacture was also shipped in turn -- from crude oil deliveries to a refinery, to silicon for glassmakers and electronic components, and from ores to be processed into metals, to the wholesale transportation of wires, chips and plastics. The phone also needs to be charged: oil products or biomass are transported daily to power stations, generating the electricity through wires that run into your home -- themselves made of concrete, sand, wood, metal and ceramics. In fact, 90 percent of almost everything is transported by ship at some point. It is the backbone of the globalized, connected society we live in.

Once billionaire Jeff Bezos' Blue Origin lander makes it to the moon, the Amazon CEO says it won't have to go very far to re-fuel. In a space summit in Boston, Bezos told an audience that his somewhat mysterious moon lander will use ice harvested from the lunar surface to create fuel. 'We know things about the moon now we didn't know about during the Apollo days,' Bezos said at the conference as reported by CNBC. 'We can harvest that ice and use to make hydrogen and oxygen, which are rocket propellants.' Jeff Bezos says a recently discovered trove of water and ice in the moon's surface could fuel a lunar lander owned by his company Blue Origin.

As I write this, Futurama's Bender is on my TV expressing his opinions about the flaws of us humans. Although he may take it a little farther than I would, it's true that we don't have the best natural detection capabilities. And when you're talking about detecting structural flaws in something like a nuclear reactor, human error isn't something with which I'd want to take a chance. Luckily, technology is able to help us with this, and it's sure to be much more helpful than Bender the Robot. A system in development at Purdue University is poised to help operators detect cracks and their severity in nuclear reactors, according to a recent article by Chris Adam.

Robots can do all sorts of things that humans can: they can deliver packages, drive cars, make lattes, and now, rather disconcertingly, they can bleed. In a paper published in Nature, researchers from Cornell University and the University of Pennsylvania explore the use of'electrolytic vascular systems for energy-dense robots' which in this particular case means, a robotic fish that relies on a form of'blood.' As reported by Gizmodo, the specimen uses a type of human-engineered circulatory system to pump a synthetic'blood' -- an electrolyte solution used as hydraulic fuel -- to provide its propulsion and power. Using a'blood' as fuel, researchers say a robotic fish could is able to swim for 36 hours straight Researchers are employing the use of a'blood' like substance to power a robotic fish. The solution is infused with electrolytes to power the bot and is also used to hydraulicly propel it through the water.