Computers Math

A bird landing on a branch makes the maneuver look like the easiest thing in the world, but in fact, the act of perching involves an extremely delicate balance of timing, high-impact forces, speed, and precision. It’s a move so complex that no flapping-wing robot (ornithopter) has been able to master it, until now.
Raphael Zufferey, a postdoctoral fellow in the Laboratory of Intelligent Systems (LIS) and Biorobotics ab (BioRob) in the School of Engineering, is the first author on a recent Nature Communications paper describing the unique landing gear that makes such perching possible. He built and tested it in collaboration with colleagues at the University of Seville, Spain, where the 700-gram ornithopter itself was developed as part of the European project GRIFFIN.
“This is the first phase of a larger project. Once an ornithopter can master landing autonomously on a tree branch, then it has the potential to carry out specific tasks, such as unobtrusively collecting biological samples or measurements from a tree. Eventually, it could even land on artificial structures, which could open up further areas of application,” Zufferey says.
He adds that the ability to land on a perch could provide a more efficient way for ornithopters – which, like many unmanned aerial vehicles (UAVs) have limited battery life – to recharge using solar energy, potentially making them ideal for long-range missions.
“This is a big step toward using flapping-wing robots, which as of now can really only do free flights, for manipulation tasks and other real-world applications,” he says.
Maximizing strength and precision; minimizing weight and speed
The engineering problems involved in landing an ornithopter on a perch without any external commands required managing many factors that nature has already so perfectly balanced. The ornithopter had to be able to slow down significantly as it perched, while still maintaining flight. The claw needed to be strong enough to grasp the perch and support the weight of the robot, without being so heavy that it could not be held aloft. “That’s one reason we went with a single claw rather than two,” Zufferey notes. Finally, the robot needed to be able to perceive its environment and the perch in front of it in relation to its own position, speed, and trajectory. More

In new research published in Nature Communications, University of Sussex scientists demonstrate how a highly conductive paint coating that they have developed mimics the network spread of a virus through a process called ‘explosive percolation’ — a mathematical process which can also be applied to population growth, financial systems and computer networks, but which has not been seen before in materials systems. The finding was a serendipitous development as well as a scientific first for the researchers.
The process of percolation — the statistical connectivity in a system, such as when water flows through soil or through coffee grounds — is an important component in the development of liquid technology. And it was that process which researchers in the University of Sussex Material Physics group were expecting to see when they added graphene oxide to polymer latex spheres, such as those used in emulsion paint, to make a polymer composite.
But when they gently heated the graphene oxide to make it electrically conductive, the scientists kick-started a process that saw this conductive system grow exponentially, to the extent that the new material created consumed the network, similar to the way a new strain of a virus can become dominant. This emergent material behaviour led to a new highly-conductive paint solution that, because graphene oxide is a cheap and easy to mass produce nanomaterial, is both one of the most affordable and most conductive low-loading composites reported. Before, now, it was accepted that such paints or coatings were necessarily one or the other.
Electrically conductive paints and inks have a range of useful applications in new printed technologies, for example by imparting coatings with properties such as anti-static or making coatings that block electromagnetic interference (EMI), as well as being vital in the development of wearable health monitors.
Alan Dalton, Professor of Experimental Physics, who heads up the Materials Physics Group at the University of Sussex explains the potential of this serendipitous finding: “My research team and I have been working on developing conductive paints and inks for the last ten years and it was to both my surprise and delight that we have discovered the key to revolutionising this work is a mathematical process that we normally associate with population growth and virus transmission.
“By enabling us to create highly-conductive polymer composites that are also affordable, thanks to the cheap and scalable nature of graphene oxide, this development opens up the doors to a range of applications that we’ve not even been able to fully consider yet, but which could greatly enhance the sustainability of Electric Vehicle materials — including batteries — as well as having the potential to add conductive coatings to materials, such as ceramics, that aren’t inherently so. We can’t wait to get going on exploring the possibilities.”
Dr Sean Ogilvie, a research fellow in Professor Dalton’s Materials Physics Group at the University of Sussex, who worked on this development adds: “The most exciting aspect of these nanocomposites is that we are using a very simple process, similar to applying emulsion paint and drying with a heat gun, which then kickstarts a process creating chemical bridges between the graphene sheets, producing electrical paths which are more conductive than if they were made entirely from graphene. More

A recent study by York University researchers suggests an innovative artificial intelligence (AI) technique they developed is considerably more effective than the human eye when it comes to predicting therapy outcomes in patients with brain metastases. The team hopes the new research and technology could eventually lead to more tailored treatment plans and better health outcomes for cancer patients.
“This is a sophisticated and comprehensive analysis of MRIs to find features and patterns that are not usually captured by the human eye,” says York Research Chair Ali Sadeghi-Naini, associate professor of biomedical engineering and computer science in the Lassonde School of Engineering, and lead on the study.
“We hope our technique, which is a novel AI-based predictive method of detecting radiotherapy failure in brain metastasis, will be able to help oncologists and patients make better informed decisions and adjust treatment in a situation where time is of the essence.”
Previous studies have shown that using standard practices, such as MRI imaging — assessing the size, location — and number of brain metastases — as well as the primary cancer type and overall condition of the patient, oncologists are able to predict treatment failure (defined as continued growth of the tumour) about 65 per cent of the time. The researchers created and tested several AI models and their best one had an 83 per cent accuracy.
Brain metastases are a type of cancerous tumour that develops when primary cancers in the lungs, breasts, colon or other parts of the body are spread to the brain via the bloodstream or lymphatic system. While there are various treatment options, stereotactic radiotherapy is one of the more common, with treatment consisting of concentrated doses of radiation targeted at the area with the tumour.
“Not all of the tumours respond to radiation — up to 30 per cent of these patients have continued growth of their tumour, even after treatment,” Sadeghi-Naini says. “This is often not discovered until months after treatment via follow-up MRI.”
This delay is time patients with brain metastases cannot afford, as it is a particularly debilitating condition with most people succumbing to the disease between three months to five years after diagnosis. “It’s very important to predict therapy response even before that therapy begins,” Sadeghi-Naini continues. More

Looking at the future of battery materials.
Designing a battery is a three-part process. You need a positive electrode, you need a negative electrode, and — importantly — you need an electrolyte that works with both electrodes.
An electrolyte is the battery component that transfers ions — charge-carrying particles — back and forth between the battery’s two electrodes, causing the battery to charge and discharge. For today’s lithium-ion batteries, electrolyte chemistry is relatively well-defined. For future generations of batteries being developed around the world and at the U.S. Department of Energy’s (DOE) Argonne National Laboratory, however, the question of electrolyte design is wide open.
“While we are locked into a particular concept for electrolytes that will work with today’s commercial batteries, for beyond-lithium-ion batteries the design and development of different electrolytes will be crucial,” said Shirley Meng, chief scientist at the Argonne Collaborative Center for Energy Storage Science (ACCESS) and professor of molecular engineering at the Pritzker School of Molecular Engineering of The University of Chicago. “Electrolyte development is one key to the progress we will achieve in making these cheaper, longer-lasting and more powerful batteries a reality, and taking one major step towards continuing to decarbonize our economy.”
In a new paper published in Science, Meng and colleagues laid out their vision for electrolyte design in future generations of batteries.
Even relatively small departures from today’s batteries will require a rethinking of electrolyte design, according to Meng. Switching from a nickel-containing oxide to a sulfur-based material as the main constituent of a lithium-ion battery’s positive electrode could yield significant performance benefits and reduce costs if scientists can figure out how to rejigger the electrolyte, she said. More

When it comes to predicting disasters brought on by extreme events (think earthquakes, pandemics or “rogue waves” that could destroy coastal structures), computational modeling faces an almost insurmountable challenge: Statistically speaking, these events are so rare that there’s just not enough data on them to use predictive models to accurately forecast when they’ll happen next.
But a team of researchers from Brown University and Massachusetts Institute of Technology say it doesn’t have to be that way.
In a new study in Nature Computational Science, the scientists describe how they combined statistical algorithms — which need less data to make accurate, efficient predictions — with a powerful machine learning technique developed at Brown and trained it to predict scenarios, probabilities and sometimes even the timeline of rare events despite the lack of historical record on them.
Doing so, the research team found that this new framework can provide a way to circumvent the need for massive amounts of data that are traditionally needed for these kinds of computations, instead essentially boiling down the grand challenge of predicting rare events to a matter of quality over quantity.
“You have to realize that these are stochastic events,” said George Karniadakis, a professor of applied mathematics and engineering at Brown and a study author. “An outburst of pandemic like COVID-19, environmental disaster in the Gulf of Mexico, an earthquake, huge wildfires in California, a 30-meter wave that capsizes a ship — these are rare events and because they are rare, we don’t have a lot of historical data. We don’t have enough samples from the past to predict them further into the future. The question that we tackle in the paper is: What is the best possible data that we can use to minimize the number of data points we need?”
The researchers found the answer in a sequential sampling technique called active learning. These types of statistical algorithms are not only able to analyze data input into them, but more importantly, they can learn from the information to label new relevant data points that are equally or even more important to the outcome that’s being calculated. At the most basic level, they allow more to be done with less. More

A new study by UCLA psychologists reveals that when VR is used to teach language, context and realism matter.
The research is published in the journal npj Science of Learning.
“The context in which we learn things can help us remember them better,” said Jesse Rissman, the paper’s corresponding author and a UCLA associate professor of psychology. “We wanted to know if learning foreign languages in virtual reality environments could improve recall, especially when there was the potential for two sets of words to interfere with each other.”
Researchers asked 48 English-speaking participants to try to learn 80 words in two phonetically similar African languages, Swahili and Chinyanja, as they navigated virtual reality settings.
Wearing VR headsets, participants explored one of two environments — a fantasy fairyland or a science fiction landscape — where they could click to learn the Swahili or Chinyanja names for the objects they encountered. Some participants learned both languages in the same VR environment; others learned one language in each environment.
Participants navigated through the virtual worlds four times over the course of two days, saying the translations aloud each time. One week later, the researchers followed up with a pop quiz to see how well the participants remembered what they had learned. More

Researchers from the University of Jyväskylä and the Central Finland Health Care District have developed an AI based neural network to detect an early knee osteoarthritis from x-ray images. AI was able to match a doctors’ diagnosis in 87% of cases. The result is important because x-rays are the primary diagnostic method for early knee osteoarthritis. An early diagnosis can save the patient from unnecessary examinations, treatments and even knee joint replacement surgery.
Osteoarthritis is the most common joint-related ailment globally. In Finland alone, it causes as many as 600,000 medical visits every year. It has been estimated to cost the national economy up to €1 billion every year.
The new AI based method was trained to detect a radiological feature predictive of osteoarthritis from x-rays. The finding is not at the moment included in the diagnostic criteria, but orthopaedic specialists consider it as an early sign of osteoarthritis. The method was developed in Digital Health Intelligence Lab at the University of Jyväskylä as a part of the AI Hub Central Finland project. It utilises neural network technologies that are widely used globally.
“The aim of the project was to train the AI to recognise an early feature of osteoarthritis from an x-ray. Something that experienced doctors can visually distinguish from the image, but cannot be done automatically,” explains Anri Patron, the researcher responsible for the development of the method.
In practice, the AI tries to detect whether there is spiking on the tibial tubercles in the knee joint or not. Tibial spiking can be a sign of osteoarthritis.
The reliability of the method was evaluated together with specialists from the Central Finland Healthcare District. More

The nature and properties of materials depend strongly on dimension. Imagine how different life in a one-dimensional or two-dimensional world would be from the three dimensions we’re commonly accustomed to. With this in mind, it is perhaps not surprising that fractals – objects with fractional dimension — have garnered significant attention since their discovery. Despite their apparent strangeness, fractals arise in surprising places — from snowflakes and lightning strikes to natural coastlines.
Researchers at the University of Cambridge, the Max Planck Institute for the Physics of Complex Systems in Dresden, the University of Tennessee, and the Universidad Nacional de La Plata have uncovered an altogether new type of fractal appearing in a class of magnets called spin ices. The discovery was surprising because the fractals were seen in a clean three-dimensional crystal, where they conventionally would not be expected. Even more remarkably, the fractals are visible in dynamical properties of the crystal, and hidden in static ones. These features motivated the appellation of “emergent dynamical fractal.”
The fractals were discovered in crystals of the material dysprosium titanate, where the electron spins behave like tiny bar magnets. These spins cooperate through ice rules that mimic the constraints that protons experience in water ice. For dysprosium titanate, this leads to very special properties.
Jonathan Hallén of the University of Cambridge is a PhD student and the lead author on the study. He explains that “at temperatures just slightly above absolute zero the crystal spins form a magnetic fluid.” This is no ordinary fluid, however.
“With tiny amounts of heat the ice rules get broken in a small number of sites and their north and south poles, making up the flipped spin, separate from each other traveling as independent magnetic monopoles.”
The motion of these magnetic monopoles led to the discovery here. As Professor Claudio Castelnovo, also from the University of Cambridge, points out: “We knew there was something really strange going on. Results from 30 years of experiments didn’t add up.”
Referring to a new study on the magnetic noise from the monopoles published earlier this year, Castelnovo continued, “After several failed attempts to explain the noise results, we finally had a eureka moment, realizing that the monopoles must be living in a fractal world and not moving freely in three dimensions, as had always been assumed.” More