A mushroom variety that is often been considered useless has now become the prime candidate for computer chip bases. Scientists say the mushroom's skins work nearly as well as traditional computer chip substrates. Researchers from the Johannes Kepler University in Austria stumbled upon the biodegradable alternative while analyzing the uses of fungi-derived materials. Their study, published in the journal Science Advances, shows Ganoderma lucidum mushroom skin works well as a substitute for the substrate used in electrical circuits. "There was a fair share of serendipity involved," Martin Kaltenbrunner, head of the university's Division of Soft Matter Physics and co-author of the paper, told CNN.

A squishy robot smaller than a postage stamp can run 70 of its body lengths every second – more than three times faster than a cheetah, relative to its body size. "It is really, really fast and, to be honest, that was a little bit of a surprise," says Martin Kaltenbrunner at Johannes Kepler University Linz in Austria. "We actually bought a better version of a high-speed camera during the experiment because the one we had wasn't good enough." He and his colleagues made the ultra-fast soft robot out of a rubbery material and controlled it with electric currents and a magnetic field. They hope it will eventually be used in medicine, for delivering drugs or performing procedures inside the human body. The robot is made of an elastic material curled into an upside-down U-shape with embedded metal wires running through it.

Depending on the robot's work, sustainability could look different from machine to machine. In general, making a more sustainable robot starts with ethically sourced recycled or sustainable materials, functioning as energy efficiently as possible. Then the robot has to be repairable if broken and recyclable when it's time to retire. While some sensors or computer chips might not currently be recyclable or reusable, those pieces wouldn't make up a large percentage of the machine. Some definitions of sustainability include the robot's function.

Soft, edible robots that mimic real organisms could be used to deliver drugs to animals. That is just one potential application of a new material made from biodegradable gel. "The question is, could we develop a material that is, at the same time, very reliable while you use it, but once triggered can completely degrade?" says Martin Kaltenbrunner at Johannes Kepler University Linz in Austria. Kaltenbrunner and his colleagues created a gel out of ingredients that are safe to eat, including gelatine – which can be fully degraded by the body – citric acid to stop bacterial growth and glycerol for softness and to prevent dehydration. The biogel is designed to be eaten by bacteria commonly found in waste water, meaning it will break down naturally if it ends up in landfill, for instance, but remain stable otherwise.

In the form of topic discussions, users interact with each other to share knowledge and exchange information in online forums. Modeling the evolution of topic discussion reveals how information propagates on Internet and can thus help understand sociological phenomena and improve the performance of applications such as recommendation systems. In this paper, we argue that a user’s participation in topic discussions is motivated by either her friends or her own preferences. Inspired by the theory of information flow, we propose dynamic topic discussion models by mining influential relationships between users and individual preferences. Reply relations of users are exploited to construct the fundamental influential social network. The property of discussed topics and time lapse factor are also considered in our modeling. Furthermore, we propose a novel measure called ParticipationRank to rank users according to how important they are in the social network and to what extent they prefer to participate in the discussion of a certain topic. The experiments show our model can simulate the evolution of topic discussions well and predict the tendency of user’s participation accurately.

Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. We use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We also compare network properties of our co-participation network to a previously defined reply-answer network on news forums.