Computers Math

Insect cyborgs may sound like science fiction, but it’s a relatively new phenomenon based on using electrical stimuli to control the movement of insects. These hybrid insect computer robots, as they are scientifically called, herald the future of small, high mobile and efficient devices.
Despite significant progress being made, however, further advances are complicated by the vast differences between different insects’ nervous and muscle systems.
In a recent study published in the journal eLife, an international research group has studied the relationship between electrical stimulation in stick insects’ leg muscles and the resultant torque (the twisting force that makes the leg move).
They focused on three leg muscles that play essential roles in insect movement: one for propulsion, one for joint stiffness, and one for transitioning between standing and swinging the leg. The experiments involved the researchers keeping the body of the stick insects fixed, and electrically stimulating one out of the three leg muscles to produce walking-like movements.
The research was led by Dai Owaki, associate professor at the Department of Robotics at Tohoku University’s Graduate School of Engineering. Experiments were conducted at Bielefeld University, Germany, in a lab run by Professors Volker Dürr and Josef Schmitz.
“Based on our measurements, we could generate a model that predicted the created torque when different patterns of electrical stimulation were applied to a leg muscle,” points out Owaki. “We also identified a nearly linear relationship between the duration of the electrical stimulation and the torque generated, meaning we could predict how much twisting force we would generate by just looking at the length of the applied electrical pulse.”
Using only a few measurements, Owaki and his collaborators could apply this to each individual insect. As a result of these findings, scientists will be able to refine the motor control of tuned biohybrid robots, making their movements more precise.
While the team knows their insights could lead to adaptable and highly mobile devices with various applications, they still cite some key challenges that need to be addressed. “First, model testing needs to be implemented in free-walking insects, and the electrical stimuli must be refined to mimic natural neuromuscular signals more closely,” adds Owaki. More

As the internet quickly fills with viral videos of futuristic robots darting and racing around like the animals they’re built to mimic, Duke researchers say that there’s an element of their movement’s programming that should not be overlooked: rhythm.
When analyzing legs, wings and fins for moving robots or animals in the real world, the mathematics looks fairly straightforward. Limbs with multiple sections of various lengths create different ratios for leverage, bodies with alternate shapes and sizes create drag coefficients and centers of mass, and feet, wings or fins of various shapes and sizes push on the world around them.
All of these options create more degrees of freedom in the final design. But until now, say the researchers, nobody was paying much attention to the timing of how they’re all working together.
“Minimizing the amount of work being done by varying the speed over the mover is an idea that’s been around a long time,” said Adrian Bejan, the J.A. Jones Distinguished Professor of Mechanical Engineering at Duke. “But varying the rhythm of that movement — the music of how the pieces move together over time — is a design aspect that has been overlooked, even though it can improve performance.”
The reasoning and mathematics exploring this thesis was published in a paper online August 28 in the journal Scientific Reports.
To illustrate his point in the paper, Bejan points to natural swimmers such as frogs or humans doing the breaststroke. Their swim gate is characterized by three time-intervals: a slow period of reaching forward, a fast period of pushing backward and a static period of coasting. For optimum performance, the lengths of time for those intervals typically go long, fast, long. But in certain situations — outracing or outmaneuvering a predator, for example — the ratios of those periods change drastically.
In the design of robots built to emulate dogs, fish or birds, incorporating different rhythms into their standard cruising movements can make their normal operations more efficient. And those optimal rhythms will, in turn, affect the choices made for all of the other pieces of the overall design.
The work builds on research Bejan published nearly 20 years ago, where he demonstrated that size and speed go hand-in-hand across the entire animal kingdom whether on land, in the air or under water. The physics underlying that work dealt with weight falling forward from a given animal’s height over and over again. In this paper, Bejan shows that his previous work was incomplete, and that all animals, robots and other moving things can further optimize their mechanics by adding an element of rhythm.
“You can — and indeed you should — teach rhythms of movements to competitive swimmers and runners looking for an edge,” Bejan said. “Rhythm increases the number of knobs you can turn when trying to move through the world. It is yet another example of how good design — whether made by humans or through natural evolution — is truly a form of art.” More

Imagine a device smaller than a toddler’s shoebox that can simulate any human disease in multiple organs or test new drugs without ever entering — or harming — the body.
Scientists at Northwestern University have developed this new technology — called Lattice — to study interactions between up to eight unique organ tissue cultures (cells from a human organ) for extended periods of time to replicate how actual organs will respond. It is a major advancement from current in vitro systems, which can only study two cell cultures simultaneously.
The goal is to simulate what happens inside the body to analyze, for example, how obesity might affect a particular disease; how women metabolize drugs differently than men; or what might be initially driving a disease that eventually impacts multiple organs.
“When something’s happening in the body, we don’t know exactly who’s talking to whom,” said lead scientist Julie Kim, professor of obstetrics and gynecology at Northwestern University Feinberg School of Medicine. “Currently, scientists use dishes that have one or two cell types, and then do in-depth research and analysis, but Lattice provides a huge advancement. This platform is much better suited to mimic what’s happening in the body, because it can simulate so many organs at once.”
A study detailing the new technology will be published Oct. 3 in the journal Lab on a Chip.
Choose-your-own-adventure disease simulator
The microfluidic device has a series of channels and pumps that cause media (simulated blood) to flow between the eight wells. A computer connected to Lattice precisely controls how much media flows through each well, where it flows and when. Depending on which disease or drug the scientist wants to test, they can fill each well with a different organ tissue, hormone, disease or medication. More

A team led by Northwestern University researchers has developed the first artificial intelligence (AI) to date that can intelligently design robots from scratch.
To test the new AI, the researchers gave the system a simple prompt: Design a robot that can walk across a flat surface. While it took nature billions of years to evolve the first walking species, the new algorithm compressed evolution to lightning speed — designing a successfully walking robot in mere seconds.
But the AI program is not just fast. It also runs on a lightweight personal computer and designs wholly novel structures from scratch. This stands in sharp contrast to other AI systems, which often require energy-hungry supercomputers and colossally large datasets. And even after crunching all that data, those systems are tethered to the constraints of human creativity — only mimicking humans’ past works without an ability to generate new ideas.
The study will be published on Oct. 3 in the Proceedings of the National Academy of Sciences.
“We discovered a very fast AI-driven design algorithm that bypasses the traffic jams of evolution, without falling back on the bias of human designers,” said Northwestern’s Sam Kriegman, who led the work. “We told the AI that we wanted a robot that could walk across land. Then we simply pressed a button and presto! It generated a blueprint for a robot in the blink of an eye that looks nothing like any animal that has ever walked the earth. I call this process ‘instant evolution.'”
Kriegman is an assistant professor of computer science, mechanical engineering and chemical and biological engineering at Northwestern’s McCormick School of Engineering, where he is a member of the Center for Robotics and Biosystems. David Matthews, a scientist in Kriegman’s laboratory, is the paper’s first author. Kriegman and Matthews worked closely with co-authors Andrew Spielberg and Daniela Rus (Massachusetts Institute of Technology) and Josh Bongard (University of Vermont) for several years before their breakthrough discovery.
From xenobots to new organisms
In early 2020, Kriegman garnered widespread media attention for developing xenobots, the first living robots made entirely from biological cells. Now, Kriegman and his team view their new AI as the next advance in their quest to explore the potential of artificial life. The robot itself is unassuming — small, squishy and misshapen. And, for now, it is made of inorganic materials. But Kriegman says it represents the first step in a new era of AI-designed tools that, like animals, can act directly on the world. More

Australian researchers have developed a molecular-sized, more efficient version of a widely used electronic sensor, in a breakthrough that could bring widespread benefits.
Piezoresistors are commonly used to detect vibrations in electronics and automobiles, such as in smart phones for counting steps, and for airbag deployment in cars. They are also used in medical devices such as implantable pressure sensors, as well as in aviation and space travel.
In a nationwide initiative, researchers led by Dr Nadim Darwish from Curtin University, Professor Jeffrey Reimers from the University of Technology Sydney, Associate Professor Daniel Kosov from James Cook University, and Dr Thomas Fallon from the University of Newcastle, have developed a piezoresistor that is about 500,000 times smaller than the width of a human hair.
Dr Darwish said they had developed a more sensitive, miniaturised type of this key electronic component, which transforms force or pressure to an electrical signal and is used in many everyday applications.
“Because of its size and chemical nature, this new type of piezoresistor will open up a whole new realm of opportunities for chemical and biosensors, human-machine interfaces, and health monitoring devices,” Dr Darwish said.
“As they are molecular-based, our new sensors can be used to detect other chemicals or biomolecules like proteins and enzymes, which could be game-changing for detecting diseases.”
Dr Fallon said the new piezoresistor was made from a single bullvalene molecule that when mechanically strained reacts to form a new molecule of different shape, altering electricity flow by changing resistance. More

A collaboration of FLEET researchers from the University of Wollongong and Monash University have reviewed the superconducting diode effect, one of the most fascinating phenomena recently discovered in quantum condensed-matter physics.
A superconducting diode enables dissipationless supercurrent to flow in only one direction, and provides new functionalities for superconducting circuits.
This non-dissipative circuit element is key to future ultra-low energy superconducting and semiconducting-superconducting hybrid quantum devices, with potential for quantum technologies in both classical and quantum computing.
SUPERCONDUCTORS AND DIODE EFFECTS
A superconductor is characterized by zero resistivity and perfect diamagnetic behavior, which leads to dissipationless transport and magnetic levitation.
‘Conventional’ superconductors and the underlying phenomenon of low-temperature superconductivity are explained well by microscopic Bardeen-Cooper-Schrieffer (BCS) theory proposed in 1957.
The prediction of Fulde-Ferrell-Larkin-Ovchinnikov ferromagnetic superconducting phase in 1964-65 and the discovery of ‘high-temperature’ superconductivity in antiferromagnetic structures in 1986-87, has set the stage for the field of unconventional superconductivity wherein superconducting order can be stabilized in functional materials such as magnetic superconductors, ferroelectric superconductors, and helical or chiral topological superconductors. More

Young people (24 years and younger) spend an average of six hours a day online, primarily using their smartphones, according to research from the University of Surrey. Older people (those 24 years and older) spend 4.6 hours online.
Surrey’s study, which involved 796 participants, introduces a new internet addiction spectrum, categorising internet users into five groups: Casual Users (14.86%): This group mainly goes online for specific tasks and logs off without lingering. They show no signs of addiction and are generally older, with an average age of 33.4 years. They are the least interested in exploring new apps. Initial Users (22.86%): These individuals often find themselves online longer than they initially planned and are somewhat neglectful of household chores but don’t consider themselves addicted. They are moderately interested in apps and have an average age of 26.1 years. Experimenters (21.98%): This group feels uneasy or anxious when not connected to the internet. Once they go online, they feel better. Experimenters are more willing to try out new apps and technology, and their average age is between 22.8 and 24.3 years. Addicts-in-Denial (17.96%): These users display addictive behaviours like forming new relationships online and neglecting real-world responsibilities to be online. However, they won’t admit to feeling uneasy when they’re not connected. They are also quite confident in using mobile technology. Addicts (22.36%): This group openly acknowledges their internet addiction and recognises its negative impact on their lives. They are the most confident in using new apps and technology. Their time online is significantly greater than that of the Casual Users. More

Identifying the underlying cause of extreme events such as floods, heavy downpours or tornados is immensely difficult and can take a concerted effort by scientists over several decades to arrive at feasible physical explanations.
Extreme events cause significant deviation from expected behavior and can dictate the overall outcome for a number of scientific problems and practical situations. For example, practical scenarios where a fundamental understanding of extreme events can be of vital importance include rogue waves in the ocean that could endanger ships and offshore structures or increasingly frequent “1,000-year rains,” such as the life-threatening deluge in April that deposited 20 inches of rainfall within a seven-hour period in the Fort Lauderdale area.
At the core of uncovering such extreme events is the physics of fluids — specifically turbulent flows, which exhibit a wide range of interesting behavior in time and space. In fluid dynamics, a turbulent flow refers to an irregular flow whereby eddies, swirls and flow instabilities occur. Because of the random nature and irregularity of turbulent streams, they are notoriously difficult to understand or to apply order through equations.
Researchers from Florida Atlantic University’s College of Engineering and Computer Science leveraged a computer-vision deep learning technique and adapted it for nonlinear analysis of extreme events in wall-bounded turbulent flows, which are pervasive in numerous physics and engineering applications and impact wind and hydrokinetic energy, among others.
The study focused on recognizing and regulating organized structures within wall-bounded turbulent flows using a variety of machine learning techniques to overcome the non-linear nature of this phenomenon.
Results, published in the journal Physical Review Fluids, demonstrate that the technique the researchers employed can be invaluable for accurately identifying the sources of extreme events in a completely data-driven manner. The framework they formulated is sufficiently general to be extendable to other scientific domains, where the underlying spatial dynamics governing the evolution of critical phenomena may not be known beforehand.
Using a neural network architecture called Convolutional Neural Network (CNN) that specializes in uncovering spatial relationships, researchers trained a network to estimate the relative intensity of ejection structures within turbulent flow simulation without any a-priori knowledge of the underlying flow dynamics. More