Researchers are always looking for new ways to improve the agility, performance, and efficiency of walking robots. Most of the time, this focus has centered on motor advancements. But a team at ETH Zurich and the Max Planck Institute for Intelligent Systems (MPI-IS) is focused on an alternative approach--artificial, electrostatically-powered musculature inspired by animal biology and human anatomy. Both two- and four-legged robots have become pretty agile over the past few years thanks to design advancements in motor technologies and artificial intelligence. For many of them, however, energy requirements and costs remain a major hurdle, especially when it comes to AI systems needed to interpret vast quantities of environmental sensor data.

Artificial muscles are a progressing technology that could one day enable robots to function like living organisms. Such muscles open up new possibilities for how robots can shape the world around us; from assistive wearable devices that can redefine our physical abilities at old age, to rescue robots that can navigate rubble in search of the missing. But just because artificial muscles can have a strong societal impact during use, doesn't mean they have to leave a strong environmental impact after use. The topic of sustainability in soft robotics has been brought into focus by an international team of researchers from the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart (Germany), the Johannes Kepler University (JKU) in Linz (Austria), and the University of Colorado (CU Boulder), Boulder (USA). The scientists collaborated to design a fully biodegradable, high performance artificial muscle – based on gelatin, oil, and bioplastics.

The worst flames in firefighting are the ones you don't see coming. In the midst of the chaos of a burning building, it's difficult to spot the warning signs of impending flashover -- a deadly fire phenomenon in which nearly all combustible items in a room spontaneously ignite. Flashover is one of the leading causes of firefighter deaths, but new research suggests that artificial intelligence (AI) could provide much-needed forewarning to first responders. Researchers at the National Institute of Standards and Technology (NIST), Hong Kong Polytechnic University and other institutions have created a Flashover Prediction Neural Network (FlashNet) model to predict deadly events seconds before they occur. In a recent study that was published in Engineering Applications of Artificial Intelligence, FlashNet outperformed existing AI-based flashover forecasting tools, boasting an accuracy of up to 92.1% across more than a dozen popular residential floorplans in the US.

With the machine-generated optimized conditions, researchers at the University of Illinois Urbana-Champaign and collaborators in Poland and Canada doubled the average yield of a special, hard-to-optimize type of reaction linking carbon atoms together in pharmaceutically important molecules. The researchers say their system provides a platform that also could be used to find general conditions for other classes of reactions and solutions for similarly complex problems. They reported their findings in the journal Science. "Generality is critical for automation, and thus making molecular innovation accessible even to nonchemists," said study co-leader Dr. Martin D. Burke, an Illinois professor of chemistry and of the Carle Illinois College of Medicine, as well as a medical doctor. "The challenge is the haystack of possible reaction conditions is astronomical, and the needle is hidden somewhere inside. By leveraging the power of artificial intelligence and building-block chemistry to create a feedback loop, we were able to shrink the haystack. And we found the needle."

Artificial intelligence, "building-block" chemistry and a molecule-making machine teamed up to find the best general reaction conditions for synthesizing chemicals important to biomedical and materials research--a finding that could speed innovation and drug discovery as well as make complex chemistry automated and accessible. With the machine-generated optimized conditions, researchers at the University of Illinois Urbana-Champaign and collaborators in Poland and Canada doubled the average yield of a special, hard-to-optimize type of reaction linking carbon atoms together in pharmaceutically important molecules. The researchers say their system provides a platform that also could be used to find general conditions for other classes of reactions and solutions for similarly complex problems. They reported their findings in the journal Science. "Generality is critical for automation, and thus making molecular innovation accessible even to nonchemists," said study co-leader Dr. Martin D. Burke, an Illinois professor of chemistry and of the Carle Illinois College of Medicine, as well as a medical doctor.

In firefighting, the worst flames are the ones you don't see coming. Amid the chaos of a burning building, it is difficult to notice the signs of impending flashover -- a deadly fire phenomenon wherein nearly all combustible items in a room ignite suddenly. Flashover is one of the leading causes of firefighter deaths, but new research suggests that artificial intelligence (AI) could provide first responders with a much-needed heads-up. Researchers at the National Institute of Standards and Technology (NIST), the Hong Kong Polytechnic University and other institutions have developed a Flashover Prediction Neural Network (FlashNet) model to forecast the lethal events precious seconds before they erupt. In a new study published in Engineering Applications of Artificial Intelligence, FlashNet boasted an accuracy of up to 92.1% across more than a dozen common residential floorplans in the U.S. and came out on top when going head-to-head with other AI-based flashover predicting programs. Flashovers tend to suddenly flare up at approximately 600 degrees Celsius (1,100 degrees Fahrenheit) and can then cause temperatures to shoot up further.

Researchers have taught a clever new tool how to carry out one of crop breeding's more challenging tasks. Growers need seeds that germinate effectively and uniformly within a given period to maximise crop productivity. Seed suppliers must therefore test seed samples to ensure a certain germination rate is met, a process that is difficult and time consuming. SeedGerm – based on machine learning-driven image analysis – performs this tricky process in a low-cost, high-throughput and semi-automated way. The product is the result of a collaboration between the Earlham Institute (EI), the John Innes Centre, Syngenta and NIAB. Details of the research step-forward is published, open-access in New Phytologist, along with the open-source software and data.

Researchers have used machine learning to predict the conditions needed for stem cells to develop a certain way, which could be used to grow 3D organ models. Researchers have used a computational model to learn how to manipulate stem cell arrangement, including those that may eventually be useful in generating personalised organs. According to the team, their discovery could be used to develop model organs grown from a patient's own cells, which could'revolutionise' how diseases are treated by increasing disease understanding or testing drugs. The study was conducted by a team from Gladstone Institutes, in collaboration with Boston University, both US. Induced pluripotent stem (iPS) cells, similar to the stem cells found in an embryo, have the potential to become nearly every type of cell in the body.

The platform, HRMAn ('Herman'), which stands for Host Response to Microbe Analysis, is open-source, easy-to-use and can be tailored for different pathogens including Salmonella enterica. Pioneered by scientists at the Francis Crick Institute and UCL, HRMAn uses deep neural networks to analyse complex patterns in images of pathogen and human ('host') cell interactions, pulling out the same detailed characteristics that scientists do by-hand. The research is published in the open access journal eLife, which includes a link to download the platform and access tutorial videos. "What used to be a manual, time-consuming task for biologists now takes us a matter of minutes on a computer, enabling us to learn more about infectious pathogens and how our bodies respond to them, more quickly and more precisely," says Eva Frickel, Group Leader at the Crick, who led the project. "HRMAn can actually see host-pathogen interactions like a biologist, but unlike us, it doesn't get tired and need to sleep!"