Origami robots can be formed by tightly integrating multiple functions of actuation, sensing and communication. But the task is challenging as conventional materials including plastics and paper used for such robotic designs impose constraints to limit add-on functionalities. To install multifunctionalities to the system scientists must typically include external electronics that increase the weight of the robot. In a recent study now published on Science Robotics, Haitao Yang and colleagues at the interdisciplinary departments of Chemical and Biomolecular Engineering, Biomedical Engineering and Electrical and Computer Engineering in the U.S. and Singapore developed a graphene oxide (GO)-enabled templating synthesis process to produce reconfigurable, compliant and multifunctional metallic backbones. The backbones formed the basis for origami robots coupled with built-in strain sensing and wireless communication capabilities.

In the gold rush to bring artificial intelligence to the healthcare and biopharma industries, AI has long been pitched as a way to accelerate the pace of drug development and discovery. Sometimes vaguely and sometimes not, many companies have claimed their code can help early research get done quicker, deeper and cheaper. Now, Insilico Medicine may have hit pay dirt, demonstrating in a paper published in Nature Biotechnology that its computer networks could potentially shave years off of traditional hit-to-lead timelines. Over 21 days, the startup and its partners used its AI programs to conceptualize and generate 30,000 novel small molecules that may work against fibrosis. Within 25 more days, they had screened out and synthesized the six most promising compounds, tested them in vitro for selectivity and metabolic stability and had the lead candidate go on to show favorable activity in live mouse models.

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date Jezero delta or groundwater alteration of portions of the Jezero sedimentary sequence.

From biodegradable plastics to humanoid robots, a new wave of emerging technologies is on the horizon that have the potential to provide major benefits to societies and economies in the years to come, a new World Economic Forum (WEF) report said. An international Steering Committee of leading technology experts identified this year's "Top 10 Emerging Technologies" -- humanoid (and animaloid) robots designed to socialize with people; a system for pinpointing the source of a food-poisoning outbreak in seconds and minuscule lenses that will pave the way for diminutive cameras and other devices, among others. "Technologies that are emerging today will soon be shaping the world tomorrow and well into the future – with impacts to economies and to society at large," said Mariette DiChristina, Editor-in-Chief of Scientific American, and chair of the Emerging Technologies Steering Committee. Bioplastics are advanced solvents and enzymes that are transforming woody wastes into better biodegradable plastics. Like standard plastics derived from petrochemicals, biodegradable versions consist of polymers (long-chain molecules) that can be moulded while in their fluid state into a variety of forms.

Artificial intelligence is one of a series of technologies that are on the radar for implementation in Chile's mining industry. AI, which, simply put, is the ability of a computer program or machine to think and learn from observing large quantities of data, to identify trends and make recommendations to improve decision making, all in a matter of milliseconds. The impending tsunami of data that will be collected from sensors and internet of things (IoT) devices will be too overwhelming for humans to compute. Businesses that are able to compute and extract value from huge volumes of data are expected to have a key advantage over their competitors by being able to improve efficiency, productivity and lower costs as well as identify new business opportunities. A recent study by consultancy Accenture, showed 82% of executives in the global mining industry expecting to increase investment in digital technology over the next three years.

According to a release issued earlier this month by the Los Alamos National Laboratory (LANL), researchers have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds. The new approach can be applied to new molecules in milliseconds, enabling research into a far greater number of compounds over much longer timescales. The new technique, called ANI-1ccx potential, promises to advance the capabilities of researchers in many fields and improve the accuracy of machine learning-based potentials in future studies of metal alloys and detonation physics. "Our quantum mechanical calculations to create ANI-1ccx potential were conducted over two years with time split on the Comet supercomputer at the San Diego Supercomputer Center and the Badger supercomputer at LANL," said Olexandr Isayev, paper author and a pharmacy professor at the University of North Carolina at Chapel Hill. "We chose these two supercomputers to train our neural networks as there are few machines that can run these – due to the high memory and core requirements."

The article presents a solution approach for the Torpedo Scheduling Problem, an operational planning problem found in steel production. The problem consists of the integrated scheduling and routing of torpedo cars, i. e. steel transporting vehicles, from a blast furnace to steel converters. In the continuous metallurgic transformation of iron into steel, the discrete transportation step of molten iron must be planned with considerable care in order to ensure a continuous material flow. The problem is solved by a Simulated Annealing algorithm, coupled with an approach of reducing the set of feasible material assignments. The latter is based on logical reductions and lower bound calculations on the number of torpedo cars. Experimental investigations are performed on a larger number of problem instances, which stem from the 2016 implementation challenge of the Association of Constraint Programming (ACP). Our approach was ranked first (joint first place) in the 2016 ACP challenge and found optimal solutions for all used instances in this challenge.

Not every AI-loving company operates in logistics, marketing or healthcare. Artificial intelligence isn't picky about which industries it revolutionizes, and many niches have already embraced automation. Given the global value of AI, it's no surprise the tech is spreading. Statista estimates that worldwide AI revenue will hit $90 billion by 2025. With so much wealth to go around, no industry is safe from disruption.

Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science. Quantum-chemical simulations such as density functional theory (DFT) have been widely used for calculating the molecule properties, however, because of the heavy computational cost, it is difficult to search a huge number of potential chemical compounds. Machine learning methods for molecular modeling are attractive alternatives, however, the development of expressive, accurate, and scalable graph neural networks for learning molecular representations is still challenging. In this work, we propose a simple and powerful graph neural networks for molecular property prediction. We model a molecular as a directed complete graph in which each atom has a spatial position, and introduce a recursive neural network with simple gating function. We also feed input embeddings for every layers as skip connections to accelerate the training. Experimental results show that our model achieves the state-of-the-art performance on the standard benchmark dataset for molecular property prediction.

Automation and digitalization are increasingly used in almost every major industry to improve efficiency. Thanks to rapid advances in artificial intelligence and robotics, and innovations like X-ray diffraction and electric vehicles, the mining sector is also catching up with the technological revolution. According to a White Paper by the World Economic Forum and Accenture, digitalization could bring about over USD $425 billion of value for the mining industry, customers, society and environment by 2025. It could also lead to a reduction of 610 million t of CO2 emissions, as well as a significant improvement in safety, saving lives and preventing injuries. With the vision to create a sustainable and smart mining system, a team of experts have developed a 5G radio network under the EU-funded SIMS project.