Computers Math

Researchers have developed a mathematical model that can predict the optimum exercise regime for building muscle.
The researchers, from the University of Cambridge, used methods of theoretical biophysics to construct the model, which can tell how much a specific amount of exertion will cause a muscle to grow and how long it will take. The model could form the basis of a software product, where users could optimise their exercise regimes by entering a few details of their individual physiology.
The model is based on earlier work by the same team, which found that a component of muscle called titin is responsible for generating the chemical signals which affect muscle growth.
The results, reported in the Biophysical Journal, suggest that there is an optimal weight at which to do resistance training for each person and each muscle growth target. Muscles can only be near their maximal load for a very short time, and it is the load integrated over time which activates the cell signalling pathway that leads to synthesis of new muscle proteins. But below a certain value, the load is insufficient to cause much signalling, and exercise time would have to increase exponentially to compensate. The value of this critical load is likely to depend on the particular physiology of the individual.
We all know that exercise builds muscle. Or do we? “Surprisingly, not very much is known about why or how exercise builds muscles: there’s a lot of anecdotal knowledge and acquired wisdom, but very little in the way of hard or proven data,” said Professor Eugene Terentjev from Cambridge’s Cavendish Laboratory, one of the paper’s authors.
When exercising, the higher the load, the more repetitions or the greater the frequency, then the greater the increase in muscle size. However, even when looking at the whole muscle, why or how much this happens isn’t known. The answers to both questions get even trickier as the focus goes down to a single muscle or its individual fibres. More

The miniaturization of microelectronic sensor technology, microelectronic robots or intravascular implants is progressing rapidly. However, it also poses major challenges for research. One of the biggest is the development of tiny but efficient energy storage devices that enable the operation of autonomously working microsystems — in more and more smaller areas of the human body for example. In addition, these energy storage devices must be bio-compatible if they are to be used in the body at all. Now there is a prototype that combines these essential properties. The breakthrough was achieved by an international research team led by Prof. Dr. Oliver G. Schmidt, Professorship of Materials Systems for Nanoelectronics at Chemnitz University of Technology, initiator of the Center for Materials, Architectures and Integration of Nanomembranes (MAIN) at Chemnitz University of Technology and director at the Leibniz Institute for Solid State and Materials Research (IFW) Dresden. The Leibniz Institute of Polymer Research Dresden (IPF) was also involved in the study as a cooperation partner.
In the current issue of Nature Communication, the researchers report on the smallest microsupercapacitors to date, which already functions in (artificial) blood vessels and can be used as an energy source for a tiny sensor system to measure pH.
This storage system opens up possibilities for intravascular implants and microrobotic systems for next-generation biomedicine that could operate in hard-to-reach small spaces deep inside the human body. For example, real-time detection of blood pH can help predict early tumor growing. “It is extremely encouraging to see how new, extremely flexible, and adaptive microelectronics is making it into the miniaturized world of biological systems,” says research group leader Prof. Dr. Oliver G. Schmidt, who is extremely pleased with this research success.
The fabrication of the samples and the investigation of the biosupercapacitor were largely carried out at the Research Center MAIN at Chemnitz University of Technology.
“The architecture of our nano-bio supercapacitors offers the first potential solution to one of the biggest challenges — tiny integrated energy storage devices that enable the self-sufficient operation of multifunctional microsystems,” says Dr. Vineeth Kumar, researcher in Prof. Schmidt’s team and a research associate at the MAIN research center.
Smaller than a speck of dust — voltage comparable to a AAA battery
Ever smaller energy storage devices in the submillimeter range — so-called “nano-supercapacitors” (nBSC) — for even smaller microelectronic components are not only a major technical challenge, however. This is because, as a rule, these supercapacitors do not use biocompatible materials but, for example, corrosive electrolytes and quickly discharge themselves in the event of defects and contamination. Both aspects make them unsuitable for biomedical applications in the body. So-called “biosupercapacitors (BSCs)” offer a solution. They have two outstanding properties: they are fully biocompatible, which means that they can be used in body fluids such as blood and can be used for further medical studies. More

An undergraduate student from the University of Surrey has discovered a way to suppress hot-carrier effects that have plagued devices that use thin-film transistor architecture — such as smartwatches and solar panels.
Hot-carrier effects occur when unwanted electron energy builds up in certain regions of transistors, resulting in devices performing unreliably.
In her final-year project, Lea Motte studied a new device, the multimodal transistor, an alternative to conventional thin-film transistors, invented and developed by PhD candidate Eva Bestelink and supervisor Dr Radu Sporea at Surrey.
Lea used a defining feature of multimodal transistors, the separation of controls for introducing electrons into the device and allowing them to move across the transistor. Through computer simulations, Lea discovered that choosing the right voltage to apply to the transport control region can prevent unwanted hot-carrier effects. In addition, it ensures that the current through the transistor remains constant in a wide range of operating conditions.
In a paper published in the journal Advanced Electronic Materials, PhD student Eva Bestelink systematically studies Lea’s discovery of the unusual behaviour in multimodal transistors by confirming it with measurements in microcrystalline silicon transistors and performing extensive device simulations to understand the device physics that underpins its unique ability.
This discovery means that future technologies that use multimodal transistors could be more power-efficient, and it could lead to high-performance amplifiers, which are essential for measuring signals from environmental and biological sensors.
Eva Bestelink, lead author of the study from the University of Surrey, said:
“We now have a better understanding of what the multimodal transistor can offer when made with materials that cause numerous challenges to regular devices.
“For circuit designers, this work offers insight into how to operate the device for optimum performance. In the long term, the multimodal transistor offers an alternative for emerging high-performance materials, where traditional solutions are no longer applicable.”
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In a step toward a future of higher performance memory devices, researchers from National Taiwan Normal University and Kyushu University have developed a new device that needs only a single semiconductor known as perovskite to simultaneously store and visually transmit data.
By integrating a light-emitting electrochemical cell with a resistive random-access memory that are both based on perovskite, the team achieved parallel and synchronous reading of data both electrically and optically in a ‘light-emitting memory.’
At the most fundamental level, digital data is stored as a basic unit of information known as a bit, which is often represented as either a one or a zero. Thus, the pursuit of better data storage comes down to finding more efficient ways to store and read these ones and zeros.
While flash memory has become extremely popular, researchers have been searching for alternatives that could further improve speed and simplify fabrication.
One candidate is nonvolatile resistive random-access memory, or RRAM. Instead of storing charge in transistors like in flash memory, resistive memory uses materials that can switch between states of high and low resistance to represent ones and zeros.
“However, the electrical measurements needed to check the resistance and read zeros and ones from RRAM can limit the overall speed,” explains Chun-Chieh Chang, professor at National Taiwan Normal University and one of the corresponding authors of the study published in Nature Communications. More

The quantum computing market is projected to reach $65 billion by 2030, a hot topic for investors and scientists alike because of its potential to solve incomprehensibly complex problems.
Drug discovery is one example. To understand drug interactions, a pharmaceutical company might want to simulate the interaction of two molecules. The challenge is that each molecule is composed of a few hundred atoms, and scientists must model all the ways in which these atoms might array themselves when their respective molecules are introduced. The number of possible configurations is infinite — more than the number of atoms in the entire universe. Only a quantum computer can represent, much less solve, such an expansive, dynamic data problem.
Mainstream use of quantum computing remains decades away, while research teams in universities and private industry across the globe work on different dimensions of the technology.
A research team led by Xu Yi, assistant professor of electrical and computer engineering at the University of Virginia School of Engineering and Applied Science, has carved a niche in the physics and applications of photonic devices, which detect and shape light for a wide range of uses including communications and computing. His research group has created a scalable quantum computing platform, which drastically reduces the number of devices needed to achieve quantum speed, on a photonic chip the size of a penny.
Olivier Pfister, professor of quantum optics and quantum information at UVA, and Hansuek Lee, assistant professor at the Korean Advanced Institute of Science and Technology, contributed to this success.
Nature Communications recently published the team’s experimental results, A Squeezed Quantum Microcomb on a Chip. Two of Yi’s group members, Zijiao Yang, a Ph.D. student in physics, and Mandana Jahanbozorgi, a Ph.D. student of electrical and computer engineering, are the paper’s co-first authors. A grant from the National Science Foundation’s Engineering Quantum Integrated Platforms for Quantum Communication program supports this research. More

Artificial intelligence (AI) will fundamentally change medicine and healthcare: Diagnostic patient data, e.g. from ECG, EEG or X-ray images, can be analyzed with the help of machine learning, so that diseases can be detected at a very early stage based on subtle changes. However, implanting AI within the human body is still a major technical challenge. TU Dresden scientists at the Chair of Optoelectronics have now succeeded for the first time in developing a bio-compatible implantable AI platform that classifies in real time healthy and pathological patterns in biological signals such as heartbeats. It detects pathological changes even without medical supervision. The research results have now been published in the journal Science Advances.
In this work, the research team led by Prof. Karl Leo, Dr. Hans Kleemann and Matteo Cucchi demonstrates an approach for real-time classification of healthy and diseased bio-signals based on a biocompatible AI chip. They used polymer-based fiber networks that structurally resemble the human brain and enable the neuromorphic AI principle of reservoir computing. The random arrangement of polymer fibers forms a so-called “recurrent network,” which allows it to process data, analogous to the human brain. The nonlinearity of these networks enables to amplify even the smallest signal changes, which — in the case of the heartbeat, for example — are often difficult for doctors to evaluate. However, the nonlinear transformation using the polymer network makes this possible without any problems.
In trials, the AI was able to differentiate between healthy heartbeats from three common arrhythmias with an 88% accuracy rate. In the process, the polymer network consumed less energy than a pacemaker. The potential applications for implantable AI systems are manifold: For example, they could be used to monitor cardiac arrhythmias or complications after surgery and report them to both doctors and patients via smartphone, allowing for swift medical assistance.
“The vision of combining modern electronics with biology has come a long way in recent years with the development of so-called organic mixed conductors,” explains Matteo Cucchi, PhD student and first author of the paper. “So far, however, successes have been limited to simple electronic components such as individual synapses or sensors. Solving complex tasks has not been possible so far. In our research, we have now taken a crucial step toward realizing this vision. By harnessing the power of neuromorphic computing, such as reservoir computing used here, we have succeeded in not only solving complex classification tasks in real time but we will also potentially be able to do this within the human body. This approach will make it possible to develop further intelligent systems in the future that can help save human lives.”
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A new study published in Physical Review Letters describes a way to increase the cargo capacity of microscopic, self-propelled droplets known as “microswimmers.” Researchers from the University of Pennsylvania and the Max Planck Institutefor Dynamics and Self-Organisation found that when a school of microswimmers move in the same direction inside a narrow channel, they can increase the number of particles they can carry by 10-fold. Their findings have implications for applications ranging from drug -elivery systems to materials with active coatings.
Like many scientific endeavors, this one began with a simple observation. While attending a conference dinner at the Georgia Aquarium, physicist Arnold Mathijssen and his colleagues noticed that large schools of swimming fish seemed to be carrying small particles and debris in their wake. This happens because of hydrodynamic entrainment, a process where, as an object moves through liquid, it generates a flow and causes nearby objects to be dragged along with it.
“We were wondering, As the fish in the aquarium are swimming forward, does a particle also get dragged forwards, or is it pushed backwards by their tails?” says Mathijssen. “Our central question was if these guys move things forward or not, and the hypothesis was that, if we can see this happening in the aquarium, maybe this is applicable under a microscope as well.”
To answer the question, Max Planck Institute researchers Chenyu Jin, Yibo Chen, and Corinna Maass ran experiments using synthetic microswimmers, self-propelled droplets of oil and surfactant that are a model system for microscopic robots. Using their microswimmers, the researchers were able to measure the strength of the flows generated by an individual swimmer and the amount of material that an individual could carry with them as they travelled through a two-dimensional channel. Then, once the data were collected, Mathijssen and his group developed a theoretical model to help explain their findings.
One particular challenge for developing the model was devising a way to describe the effects of the walls of the microscopic channel because, unlike at the aquarium, this experiment was conducted in a confined space. “That confinement really affects the flows and, as a result, affects the total volume of stuff you can transport. There is quite a bit of literature in terms of modeling active particles, but it’s difficult to get it right in complex environments,” Mathijssen says.
Using their data and newly-developed model, the researchers found that the transport capacity of an individual microswimmer could be increased by 10-fold when they swam together inside a narrow channel. They also found that the entrainment velocity, or the speed at which particles move forwards, was much larger than initially anticipated. More

Be it nuclear power plants, patient monitoring equipment in hospitals, or self-driving cars — integrations of physical processes with computers and process control, or cyber-physical systems (CPS), are everywhere. However, the widespread application of CPS also makes them prime targets for hackers. A simple change in the value of a sensor can create havoc. Vulnerability to malicious attacks has created the need for systems that can withstand the corruption of sensors and still provide safe and efficient process control.
In a recent study published in IEEE Transactions on Automatic Control, Professor Yongsoon Eun from Daegu Gyeongbuk Institute of Science and Technology, and his colleague from Hyundai Motor Company, Yechan Jeong, have developed a method for resilient state estimation (RSE) for systems that are under attack. State estimation refers to the use of external variables, i.e., sensor readings, to determine the internal state of the system using mathematical models called “observers.” This is a critical step in process control. When the internal state of a system can be determined despite the corruption of sensors, it is called RSE.
“Would you ride an autonomous vehicle or live near a computer-controlled power plant if safety and security were not considered in their design? The importance of resiliency in control systems has been recognized for over a decade.” explains Prof. Eun.
All control systems are subject to variations or “disturbances” in the process, which cause errors in state estimation. However, as the disturbance increases, so does the error, leading to a breakdown in system resiliency. Making use of a kind of observer known as “Unknown Input Observer (UIO),” the new RSE method overcomes this limitation and provides a way for state estimation that can withstand both malicious attacks as well as external disturbances.
In this method, a UIO is designed for each sensor, the estimates from each UIO are combined, and the error is processed to provide the true value of the internal state of the system. The benefit of using a UIO is that its estimation error always converges to zero, regardless of external disturbances to the process. This is unlike other observers, which can only provide a range for estimation error. Another novelty of this method is that it deploys a ‘partial state UIO,’ a technique newly developed by Prof. Eun’s team, by which as much partial information on internal states as possible is extracted from each sensor when full state information extraction is not feasible. This greatly expands the applicability of the new RSE method based on UIO.
“The proposed method gives a system a level of tolerance for faults and attacks and, in cases where it is inevitable, allows graceful degradation of system functionality. This makes it crucial to the design of CPS,” concludes Prof. Eun.
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Materials provided by DGIST (Daegu Gyeongbuk Institute of Science and Technology). Note: Content may be edited for style and length. More