Computers Math

Cambridge researchers have developed a method for measuring overall fitness accurately on wearable devices — and more robustly than current consumer smartwatches and fitness monitors — without the wearer needing to exercise.
Normally, tests to accurately measure VO2max — a key measurement of overall fitness and an important predictor of heart disease and mortality risk — require expensive laboratory equipment and are mostly limited to elite athletes. The new method uses machine learning to predict VO2max — the capacity of the body to carry out aerobic work — during everyday activity, without the need for contextual information such as GPS measurements.
In what is by far the largest study of its kind, the researchers gathered activity data from more than 11,000 participants in the Fenland Study using wearable sensors, with a subset of participants tested again seven years later. The researchers used the data to develop a model to predict VO2max, which was then validated against a third group who carried out a standard lab-based exercise test. The model showed a high degree of accuracy compared to lab-based tests, and outperforms other approaches.
Some smartwatches and fitness monitors currently on the market claim to provide an estimate of VO2max, but since the algorithms powering these predictions aren’t published and are subject to change at any time, it’s unclear whether the predictions are accurate, or whether an exercise regime is having any effect on an individual’s VO2max over time.
The Cambridge-developed model is robust, transparent and provides accurate predictions based on heart rate and accelerometer data only. Since the model can also detect fitness changes over time, it could also be useful in estimating fitness levels for entire populations and identifying the effects of lifestyle trends. The results are reported in the journal npj Digital Medicine.
A measurement of VO2max is considered the ‘gold standard’ of fitness tests. Professional athletes, for example, test their VO2max by measuring their oxygen consumption while they exercise to the point of exhaustion. There are other ways of measuring fitness in the laboratory, like heart rate response to exercise tests, but these require equipment like a treadmill or exercise bike. Additionally, strenuous exercise can be a risk to some individuals. More

Scientists have, for the first time, developed a quantum experiment that allows them to study the dynamics, or behavior, of a special kind of theoretical wormhole. The experiment has not created an actual wormhole (a rupture in space and time), rather it allows researchers to probe connections between theoretical wormholes and quantum physics, a prediction of so-called quantum gravity. Quantum gravity refers to a set of theories that seek to connect gravity with quantum physics, two fundamental and well-studied descriptions of nature that appear inherently incompatible with each other.
“We found a quantum system that exhibits key properties of a gravitational wormhole yet is sufficiently small to implement on today’s quantum hardware,” says Maria Spiropulu, the principal investigator of the U.S. Department of Energy Office of Science research program Quantum Communication Channels for Fundamental Physics (QCCFP) and the Shang-Yi Ch’en Professor of Physics at Caltech. “This work constitutes a step toward a larger program of testing quantum gravity physics using a quantum computer. It does not substitute for direct probes of quantum gravity in the same way as other planned experiments that might probe quantum gravity effects in the future using quantum sensing, but it does offer a powerful testbed to exercise ideas of quantum gravity.”
The research will be published December 1 in the journal Nature. The study’s first authors are Daniel Jafferis of Harvard University and Alexander Zlokapa (BS ’21), a former undergraduate student at Caltech who started on this project for his bachelor’s thesis with Spiropulu and has since moved on to graduate school at MIT.
Wormholes are bridges between two remote regions in spacetime. They have not been observed experimentally, but scientists have theorized about their existence and properties for close to 100 years. In 1935, Albert Einstein and Nathan Rosen described wormholes as tunnels through the fabric of spacetime in accordance with Einstein’s general theory of relativity, which describes gravity as a curvature of spacetime. Researchers call wormholes Einstein-Rosen bridges after the two physicists who invoked them, while the term “wormhole” itself was coined by physicist John Wheeler in the 1950s.
The notion that wormholes and quantum physics, specifically entanglement (a phenomenon in which two particles can remain connected across vast distances), may have a connection was first proposed in theoretical research by Juan Maldacena and Leonard Susskind in 2013. The physicists speculated that wormholes (or “ER”) were equivalent to entanglement (also known as “EPR” after Albert Einstein, Boris Podolsky [PhD ’28], and Nathan Rosen, who first proposed the concept). In essence, this work established a new kind of theoretical link between the worlds of gravity and quantum physics. “It was a very daring and poetic idea,” says Spiropulu of the ER = EPR work.
Later, in 2017, Jafferis, along with his colleagues Ping Gao and Aron Wall, extended the ER = EPR idea to not just wormholes but traversable wormholes. The scientists concocted a scenario in which negative repulsive energy holds a wormhole open long enough for something to pass through from one end to the other. The researchers showed that this gravitational description of a traversable wormhole is equivalent to a process known as quantum teleportation. In quantum teleportation, a protocol that has been experimentally demonstrated over long distances via optical fiber and over the air, information is transported across space using the principles of quantum entanglement. More

It’s easy to control the trajectory of a basketball: all we have to do is apply mechanical force coupled with human skill. But controlling the movement of quantum systems such as atoms and electrons is much more challenging, as these minuscule scraps of matter often fall prey to perturbations that knock them off their path in unpredictable ways. Movement within the system degrades — a process called damping — and noise from environmental effects such as temperature also disturbs its trajectory.
One way to counteract the damping and the noise is to apply stabilizing pulses of light or voltage of fluctuating intensity to the quantum system. Now researchers from Okinawa Institute of Science and Technology (OIST) in Japan have shown that they can use artificial intelligence to discover these pulses in an optimized way to appropriately cool a micro-mechanical object to its quantum state and control its motion. Their research was published in November, 2022, in Physical Review Research as a Letter.
Micro-mechanical objects, which are large compared to an atom or electron, behave classically when kept at a high temperature, or even at room temperature. However, if such mechanical modes can be cooled down to their lowest energy state, which physicists call the ground state, quantum behaviour could be realised in such systems. These kinds of mechanical modes then can be used as ultra-sensitive sensors for force, displacement, gravitational acceleration etc. as well as for quantum information processing and computing.
“Technologies built from quantum systems offer immense possibilities,” said Dr. Bijita Sarma, the article’s lead author and a Postdoctoral Scholar at OIST Quantum Machines Unit in the lab of Professor Jason Twamley. “But to benefit from their promise for ultraprecise sensor design, high-speed quantum information processing, and quantum computing, we must learn to design ways to achieve fast cooling and control of these systems.”
The machine learning-based method that she and her colleagues designed demonstrates how artificial controllers can be used to discover non-intuitive, intelligent pulse sequences that can cool a mechanical object from high to ultracold temperatures faster than other standard methods. These control pulses are self-discovered by the machine learning agent. The work showcases the utility of artificial machine intelligence in the development of quantum technologies.
Quantum computing has the potential to revolutionise the world by enabling high computing speeds and reformatting cryptographic techniques. That is why, many research institutes and big-tech companies such as Google and IBM are investing a lot of resources in developing such technologies. But to enable this, researchers must achieve complete control over the operation of such quantum systems at very high speed, so that the effects of noise and damping can be eliminated.
“In order to stabilize a quantum system, control pulses must be fast — and our artificial intelligence controllers have shown the promise to achieve such feat,” Dr Sarma said. “Thus, our proposed method of quantum control using an AI controller could provide a breakthrough in the field of high-speed quantum computing, and it might be a first step to achieve quantum machines that are self-driving, similar to self-driving cars. We are hopeful that such methods will attract many quantum researchers for future technological developments.” More

Until now, it has been extremely tedious to perform measurements with high sensitivity and high spatial resolution using X-ray light in the tender energy range of 1.5 — 5.0 keV. Yet this X-ray light is ideal for investigating energy materials such as batteries or catalysts, but also biological systems. A team from HZB has now solved this problem: The newly developed monochromator optics increase the photon flux in the tender energy range by a factor of 100 and thus enable highly precise measurements of nanostructured systems. The method was successfully tested for the first time on catalytically active nanoparticles and microchips.
A climate-neutral energy supply requires a wide variety of materials for energy conversion processes, for example catalytically active materials and new electrodes for batteries. Many of these materials have nanostructures that increase their functionality. When investigating these samples, spectroscopic measurements to detect the chemical properties are ideally combined with X-ray imaging with high spatial resolution at the nanoscale. However, since key elements in these materials, such as molybdenum, silicon or sulphur, react predominantly to X-rays in the so-called tender photon energy range, there has been a major problem until now.
This is because in this “tender” energy range between soft and hard X-rays, conventional X-ray optics from plane grating or crystal monochromators deliver only very low efficiencies. A team from HZB has now solved this problem: “We have developed novel monochromator optics. These optics are based on an adapted, multilayer-coated sawtooth grating with a plane mirror,” says Frank Siewert from the HZB Optics and Beamlines Department. The new monochromator concept increases the photon flux in the tender X-ray range by a factor of 100 and thus enables highly sensitive spectromicroscopic measurements with high resolutions for the first time. “Within a short time we were able to collect data from NEXAFS spectromicroscopy on the nanoscale. We have demonstrated this on catalytically active nanoparticles and modern microchip structures,” says Stephan Werner, first author of the publication. “The new development now enables experiments that would otherwise have required months of data collection,” Werner emphasises.
“This monochromator will become the method of choice for imaging in this X-ray energy range, not only at synchrotrons worldwide, but also at free-electron lasers and laboratory sources,” says Gerd Schneider, who heads the X-ray Microscopy Department at HZB. He expects enormous effects on many areas of materials research: Studies in the tender X-ray range could significantly advance the development of energy materials and thus contribute to climate-neutral solutions for electricity and energy supply.
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Materials provided by Helmholtz-Zentrum Berlin für Materialien und Energie. Note: Content may be edited for style and length. More

Even the best water filters let some things through, but designing improved materials and then testing them is time consuming and difficult. Now, researchers in ACS Central Science report that artificial intelligence (AI) could speed up the development of promising materials. In a proof-of-concept study, they simulated different patterns of water-attracting and water-repelling groups lining a filter’s porous membrane and found optimal arrangements that should let water through easily and slow down some contaminants.
Filter systems, ranging from faucet attachments to room-sized industrial systems, clean up water for drinking and other uses. However, current filtration membranes have a hard time if the water is extremely dirty or has small, neutral molecules, such as boric acid — a common insecticide used on crop plants. This is because synthetic porous materials are generally limited to sorting compounds by either size or charge. But biological membranes have pores made of proteins, such as aquaporin, that can separate water from other molecules by both size and charge because of the different types of functional groups, or collections of atoms, lining the channels. Inspired to do the same with a synthetic porous material, M. Scott Shell and colleagues wanted to use computers to design the inside of a carbon nanotube pore to filter boric acid-containing water.
The researchers simulated a carbon nanotube channel with hydroxyl (water-attracting) and/or methyl (water-repelling) groups tethered to each atom on the inner wall. Then they designed and tested thousands of functional group patterns with optimization algorithms and machine learning, a type of AI, to assess how quickly water and boric acid would move through the pore. Here’s what they found: The optimal patterns had one or two rows of hydroxyl groups sandwiched between methyl groups, forming rings around the midsection of the pore. In these simulations, water went through the pore nearly twice as fast as boric acid. Another series of simulations showed that other neutral solutes, including phenol, benzene and isopropanol, could also become separated from water with the optimized carbon nanotube designs.This study demonstrates AI’s usefulness toward developing water purification membranes with novel properties, the researchers say, and could form the basis of a new type of filter system. They add that the approach could be adapted to design surfaces that could have unique interactions with water or other molecules, such as coatings that resist fouling.
The authors acknowledge funding from the U.S. Department of Energy (via the Center for Materials for Water and Energy Systems (M-WET), an Energy Frontier Research Center) with additional support from the U.S. National Science Foundation, the California NanoSystems Institute, the Materials Research Science and Engineering Center and a National Science Foundation Graduate Research Fellowship.
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Materials provided by American Chemical Society. Note: Content may be edited for style and length. More

Three-dimensional (3D) metal-plastic composite structures have widespread potential applicability in smart electronics, micro/nanosensing, internet-of-things (IoT) devices, and even quantum computing. Devices constructed using these structures have a higher degree of design freedom, and can have more complex features, complex geometry, and increasingly smaller sizes. But current methods to fabricate such parts are expensive and complicated.
Recently, a group of researchers from Japan and Singapore developed a new multimaterial digital light processing 3D printing (MM-DLP3DP) process to fabricate metal-plastic composite structures with arbitrarily complex shapes. Explaining the motivation behind the study, lead authors Professor Shinjiro Umezu, Mr. Kewei Song from Waseda University and Professor Hirotaka Sato from Nanyang Technological University, Singapore state, “Robots and IoT devices are evolving at a lightning pace. Thus, the technology to manufacture them must evolve as well. Although existing technology can manufacture 3D circuits, stacking flat circuits is still an active area of research. We wanted to address this issue to create highly functional devices to promote the progress and development of human society.” The study has been published in ACS Applied Materials & Interfaces.
The MM-DLP3DP process is a multi-step process that begins with the preparation of the active precursors — chemicals which can be converted into the desired chemical after 3D printing, as the desired chemical cannot be 3D printed itself. Here, palladium ions are added to light-cured resins to prepare the active precursors. This is done to promote electroless plating (ELP), a process that describes the auto-catalytic reduction of metal ions in an aqueous solution to form a metal coating. Next, the MM-DL3DP apparatus is used to fabricate microstructures containing nested regions of the resin or the active precursor. Finally, these materials are directly plated, and 3D metal patterns are added to them using ELP.
The research team manufactured a variety of parts with complex topologies to demonstrate the manufacturing capabilities of the proposed technique. These parts had complex structures with multimaterial nesting layers, including microporous and tiny hollow structures, the smallest of which was 40 μm in size. Moreover, the metal patterns on these parts were very specific and could be precisely controlled. The team also manufactured 3D circuit boards with complex metal topologies, like an LED stereo circuit with nickel and a double-sided 3D circuit with copper.
“Using the MM-DLP3DP process, arbitrarily complex metal-plastic 3D parts having specific metal patterns can be fabricated. Furthermore, selectively inducing metal deposition using active precursors can provide higher quality metal coatings. Together, these factors can contribute to the development of highly integrated and customizable 3D microelectronics,” Umezu, Song, and Sato state.
The new manufacturing process promises to be a breakthrough technology for the manufacturing of circuits, with applications in a diverse variety of technologies, including 3D electronics, metamaterials, flexible wearable devices, and metal hollow electrodes.
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Materials provided by Waseda University. Note: Content may be edited for style and length. More

Testing time perception in an unusually lifelike setting — a virtual reality ride on a New York City subway train — an interdisciplinary Cornell research team found that crowding makes time seem to pass more slowly.
As a result, rush-hour commutes on public transit may feel significantly longer than other rides that objectively take the same amount of time.
The research adds to evidence that social context and subjective feelings distort our sense of the passage of time, and may have practical implications for people’s willingness to use public transit, particularly after the pandemic.
“It’s a new way of thinking about social crowding, showing that it changes how we perceive time,” said Saeedeh Sadeghi, M.S. ’19, a doctoral student in the field of psychology. “Crowding creates stressful feelings, and that makes a trip feel longer.”
Sadeghi is the lead author of “Affective Experience in a Virtual Crowd Regulates Perceived Travel Time,” published Nov. 3 in the journal Virtual Reality. Co-authors are Ricardo Daziano, associate professor of civil and environmental engineering in the College of Engineering; So-Yeon Yoon, associate professor in the Department of Human Centered Design in the College of Human Ecology (CHE); and Adam K. Anderson, professor in the Department of Psychology and in CHE.
Prior research has identified subjective emotions, heart rate and a situation’s complexity, including the number of items requiring attention, among factors that can influence one’s experience of time. Experiments typically have been conducted in lab settings using simple tasks and stimuli, such as shapes or images on a computer screen, for short durations. More

Owing to their lightweight nature and flexibility, thermally conductive polymer composites are applied between a heat source and a sink in electronics to dissipate the generated heat to the surroundings. Efficient heat dissipation is achieved due to the use of fillers with certain orientations that facilitate heat flow. The conventional process of modifying the orientation of the filler material, however, is an energy-intensive process that requires the use of electric/magnetic fields and surface modifications that can compromise the filler’s quality and its thermal properties.
Now, in a new study, Professor Chae Bin Kim and his team at Pusan National University, Republic of Korea, have developed an energy-efficient process to change the orientation of the filler without the need for surface modifications.
This paper was made available online on 17 October 2022 and will be published in Volume 117 of the journal Polymer Testing on 1 January 2023.
The proposed method makes use of thermophoresis, a phenomenon in which a temperature gradient causes solid particles suspended in a fluid medium to move or rotate. To prepare the polymer composite, the researchers suspended thermally conductive hexagonal boron nitride (h-BN) filler particles in a UV-curable liquid and coated it between two glass plates. A temperature gradient was applied along the film thickness, causing the filler particles to rotate, and realign along the applied temperature gradient. On achieving the desired orientations, the composite was photocured, resulting in a solid composite with fixed filler orientations that form a heat transfer pathway.
“To our best knowledge, the current study is the first experimental demonstration of controlling anisotropic filler orientation using thermophoresis,” says Professor Kim.
Thermally conductive polymer composites such as thermal paste are used in phones, laptops, even in batteries. Considering the growing production of electronics and the expected transition to electric vehicles, the proposed method has the potential to lower the energy cost of manufacturing thermally conductive polymer composites. Furthermore, by avoiding the need for surface modifications, highly efficient thermally conductive polymer composites can be developed for improved heat dissipation and extend the life of electronics.
“Efficient heat dissipating materials can ensure best operating conditions for the device with improved reliability, lifespan, and user’s safety,” says Professor Kim.
Apart from improving thermal conductivity, fillers are also used to alter a composite’s optical, electrical, and mechanical properties. By offering a way to reorient the filler without any surface modifications, the proposed method can also be adopted to tune the properties of a wide range of polymer composites without deteriorating their quality.
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Materials provided by Pusan National University. Note: Content may be edited for style and length. More