Physicists have discovered a new way to coat soft robots in materials that allow them to move and function in a more purposeful way. The research, led by the UK's University of Bath, is described today in Science Advances. 

The authors of the study believe their breakthrough modeling on ‘active matter’ could mark a turning point in the design of robots. With further development of the concept, it may be possible to determine the shape, movement, and behavior of a soft solid not by its natural elasticity but by human-controlled activity on its surface.

The surface of an ordinary soft material always shrinks into a sphere. Think of the way water beads into droplets: the beading occurs because the surface of liquids and other soft material naturally contracts into the smallest surface area possible – i.e. a sphere. But active matter can be designed to work against this tendency. An example of this in action would be a rubber ball that’s wrapped in a layer of nano-robots, where the robots are programmed to work in unison to distort the ball into a new, pre-determined shape (say, a star).

It is hoped that active matter will lead to a new generation of machines whose function will come from the bottom up. So, instead of being governed by a central controller (the way today’s robotic arms are controlled in factories), these new machines would be made from many individual active units that cooperate to determine the machine’s movement and function. This is akin to the workings of our own biological tissues, such as the fibers in heart muscle.

Using this idea, scientists could design soft machines with arms made of flexible materials powered by robots embedded in their surfaces. They could also tailor the size and shape of drug delivery capsules, by coating the surface of nanoparticles in a responsive, active material. This in turn could have a dramatic effect on how a drug interacts with cells in the body.

Work on active matter challenges the assumption that the energetic cost of the surface of a liquid or soft solid must always be positive because a certain amount of energy is always necessary to create a surface.

Dr. Jack Binysh, study first author, said: “Active matter makes us look at the familiar rules of nature – rules like the fact that surface tension has to be positive – in a new light. Seeing what happens if we break these rules, and how we can harness the results, is an exciting place to be doing research.”

Corresponding author Dr. Anton Souslov added: “This study is an important proof of concept and has many useful implications. For instance, future technology could produce soft robots that are far squishier and better at picking up and manipulating delicate materials.”

For the study, the researchers developed theory and simulations that described a 3D soft solid whose surface experiences active stresses. They found that these active stresses expand the surface of the material, pulling the solid underneath along with it, and causing a global shape change. The researchers found that the precise shape adopted by the solid could then be tailored by altering the elastic properties of the material.

In the next phase of this work – which has already begun – the researchers will apply this general principle to design specific robots, such as soft arms or self-swimming materials. They will also look at collective behavior – for example, what happens when you have many active solids, all packed together.

A Bocconi University, Milan, Italy study on couple dissolution shows that an ML approach can advance demographic research, detecting complex patterns in relatively small datasets

The life satisfaction of both partners and the woman’s percentage of housework turned out to be the most important predictors of union dissolution, when scholars affiliated to Bocconi’s Dondena Centre for Research on Social Dynamics and Public Policy used a machine learning (ML) technique to analyze data on 2,038 married or cohabiting couples who participated in the German Socio-Economic Panel Survey. 
 
The couples were observed, on average, for 12 years, leading to a total of 18,613 observations. During the observation period, 914 couples (45%) split up.

In their article, newly published online on Demography, Bruno Arpino (University of Florence), Marco Le Moglie (Catholic University, Milan), and Letizia Mencarini (Bocconi), used an ML technique called Random Survival Forests (RSF) to overcome the difficulty to manage a large number of independent variables in conventional models.
 
“A clear-cut example of the potential difficulties of considering all variables and their possible interactions concerns the ‘big five personality traits,” Professor Mencarini said. “To account for both partners’ traits (10 variables) and all their two-way interactions (25 variables), one would need to include 35 independent variables, which would be very problematic in a regression model.” ML tools are, on the contrary, capable of detecting complex patterns in relatively small datasets.
 
Another advantage of ML is supposed to be its superior predictive power compared to conventional models, more attuned to explaining how certain mechanisms work than to predict the future behavior of the variables. When the authors divided their sample into two parts and used the results of the first half to predict the outcomes of the second half, they found that the predictive accuracy of RSF was considerably superior to that of conventional models. Nonetheless, the predictive accuracy of RSF was limited despite the use, as input variables, of all the most important predictors of union dissolution identified in the literature.

Among the variables with the greatest predictive ability, the authors found the life satisfaction of both partners, woman’s percentage of housework, marital status (i.e., married vs. cohabiting), woman’s working hours, woman’s level of openness, and man’s level of extraversion.
 
The analysis also found that many variables interact in complex ways. For instance, when a man’s life satisfaction was high, a higher woman’s life satisfaction constantly increased the union’s chances of surviving. But when man’s life satisfaction was low, the association between woman’s life satisfaction and union survival was negative after a given threshold.
 
The authors, though, did not detect any interaction effect when considering personal traits: a woman’s openness and a man’s extraversion make union dissolution more likely, irrespective of their partner’s personality.