The same pulling force that causes "tears" in a glass of wine also shapes embryos. It's another example of how genes exploit mechanical forces for growth and development. Sip a glass of wine, and you will notice liquid continuously weeping down the wetted side of the glass. In 1855, James Thomson, brother of Lord Kelvin, explained in the that these wine "tears" or "legs" result from the difference in surface tension between alcohol and water. "This fact affords an explanation of several very curious motions," Thomson wrote.

Health Fitness & Exercise Deep sighs are not only satisfying--they're healthy Those deep breaths can really help your lungs. Breakthroughs, discoveries, and DIY tips sent every weekday. There's something to be said about a good sigh . Sometimes that deep exhale doesn't just feel psychologically satisfying, but physically restorative. According to a study published in the journal, new evidence indicates sighing truly is a way to help reset your body--specifically, the fluid that coats your lungs .

The same phenomena that let beetles float across ponds and cause Cheerios to cluster together in your cereal bowl can be harnessed to make tiny floating robots. One of these, the Marangoni effect, arises when a fluid with a lower surface tension rapidly spreads out across the surface of a fluid with higher surface tension. This effect is exploited by Stenus beetles, which have evolved to zip across ponds by secreting a substance called stenusin, as well as soap-powered toy boats. To investigate how this could be used by engineers, Jackson Wilt at Harvard University and his colleagues 3D-printed round, plastic pucks around a centimetre in diameter. Inside each was an air chamber for buoyancy and a tiny fuel tank containing alcohol, which has a lower surface tension than water, in concentrations from 10 to 50 per cent.

This work aims to improve fuel chamber injectors' performance in turbofan engines, thus implying improved performance and reduction of pollutants. This requires the development of models that allow real-time prediction and improvement of the fuel/air mixture. However, the work carried out to date involves using experimental data (complicated to measure) or the numerical resolution of the complete problem (computationally prohibitive). The latter involves the resolution of a system of partial differential equations (PDE). These problems make difficult to develop a real-time prediction tool. Therefore, in this work, we propose using machine learning in conjunction with (complementarily cheaper) single-phase flow numerical simulations in the presence of tangential discontinuities to estimate the mixing process in two-phase flows. In this meaning we study the application of two proposed neural network (NN) models as PDE surrogate models. Where the future dynamics is predicted by the NN, given some preliminary information. We show the low computational cost required by these models, both in their training and inference phases. We also show how NN training can be improved by reducing data complexity through a modal decomposition technique called higher order dynamic mode decomposition (HODMD), which identifies the main structures inside flow dynamics and reconstructs the original flow using only these main structures. This reconstruction has the same number of samples and spatial dimension as the original flow, but with a less complex dynamics and preserving its main features. The core idea of this work is to test the limits of applicability of deep learning models to data forecasting in complex fluid dynamics problems. Generalization capabilities of the models are demonstrated by using the same NN architectures to forecast the future dynamics of four different two-phase flows.

This research spans academia, militaries (though it can be difficult to suss out the actual breakthroughs from government propaganda), and private enterprise. Perhaps the most well known privately-owned robotics developer is Boston Dynamics, makers of the Atlas. You may remember this bipedal robot from September when it showed off its uncanny parkour abilities, which the robot can pull off 80 percent of the time. The Atlas is able to move so fluidly thanks to a novel optimization algorithm that breaks down complex movements into smaller reference motions for its arms, torso, and legs. However, while Boston Dynamics' Big Dog was developed as a quadrupedal cargo carrier for military operations, the Atlas is strictly for use as an emergency first responder.

A new host of liquid metals that have applications towards soft robotics are making movies like'The Terminator' transcend make-believe. According to researchers, experimental liquid metals like gallium and other alloys, when supplemented with nickel or iron, are able to flex and mold into shapes with the use of magnets, much like the iconic movie villain, T-1000 from'The Terminator 2: Judgement Day.' While other such metals have been developed, they contended with two major drawbacks. A new host of liquid metals that have applications towards soft robotics are making movies like'The Terminator' transcend make-believe toward real life. Researchers say experimental liquid metals like gallium and other alloys, when supplemented with nickel or iron, are able to flex and mold into shapes with the use of magnets. A new material revealed by the American Chemical Society solves to major problems experienced by similar substances.

Networks, which represent agents and interactions between them, arise in myriad applications throughout the sciences, engineering, and even the humanities. To understand large-scale structure in a network, a common task is to cluster a network's nodes into sets called "communities" such that there are dense connections within communities but sparse connections between them. A popular and statistically principled method to perform such clustering is to use a family of generative models known as stochastic block models (SBMs). In this paper, we show that maximum likelihood estimation in an SBM is a network analog of a well-known continuum surface-tension problem that arises from an application in metallurgy. To illustrate the utility of this bridge, we implement network analogs of three surface-tension algorithms, with which we successfully recover planted community structure in synthetic networks and which yield fascinating insights on empirical networks from the field of hyperspectral video segmentation.

We've seen RoboBees that can fly, stick to walls, and dive into water. Now, get ready for a hybrid RoboBee that can fly, dive into water, swim, propel itself back out of water, and safely land. New floating devices allow this multipurpose air-water microrobot to stabilize on the water's surface before an internal combustion system ignites to propel it back into the air. This latest-generation RoboBee, which is 1,000 times lighter than any previous aerial-to-aquatic robot, could be used for numerous applications, from search-and-rescue operations to environmental monitoring and biological studies. The research is described in Science Robotics.

The RoboBee project that Harvard first unveiled in 2013 has gone through a plethora of changes and upgrades. The latest one is its most advanced yet. We have seen this tiny robot perform some crazy things. It can stick to surfaces and go swim underwater. Now, researchers have upgraded the robotic bee to fly, dive into water and hop right back up into the air.

Apparently, we haven't seen RoboBee's final form yet. Harvard researchers introduced the robot back in 2013 and developed a version that uses static to stick to walls in 2016. Now, the scientists have created an upgraded robotic bee that can fly, dive into water and hop right back up into the air. That's a lot tougher than it sounds, since the tiny machine is only two centimeters tall and is about one-fifteenth the weight of a penny. For such a small robot, swimming in water is like swimming in molasses and breaking through the water's surface is akin to breaking through a brick wall. To solve the issue, the researchers from Harvard Wyss Institute and John A. Paulson School of Engineering designed new mechanisms that make it possible for the RoboBee to transition seamlessly from water to air.