Computers Math

Researchers have shown that the UK’s existing copper network cables can support faster internet speeds, but only to a limit. They say additional investment is urgently needed if the government is serious about its commitment to making high-speed internet available to all.
The researchers, from the University of Cambridge and BT, have established the maximum speed at which data can be transmitted through existing copper cables. This limit would allow for faster internet compared to the speeds currently achievable using standard infrastructure, however it will not be able to support high-speed internet in the longer term.
The team found that the ‘twisted pair’ copper cables that reach every house and business in the UK are physically limited in their ability to support higher frequencies, which in turn support higher data rates.
While full-fibre internet is currently available to around one in four households, it is expected to take at least two decades before it reaches every home in the UK. In the meantime, however, existing infrastructure can be improved to temporarily support high-speed internet.
The results, reported in the journal Nature Communications, both establish a physical limit on the UK’s ubiquitous copper cables, and emphasise the importance of immediate investment in future technologies.
The Cambridge-led team used a combination of computer modelling and experiments to determine whether it was possible to get higher speeds out of existing copper infrastructure and found that it can carry a maximum frequency of about 5 GHz, above the currently used spectrum, which is lower than 1 GHz. Above 5 GHz however, the copper cables start to behave like antennas. More

Semiconducting nanomaterials with 3D network structures have high surface areas and lots of pores that make them excellent for applications involving adsorbing, separating, and sensing. However, simultaneously controlling the electrical properties and creating useful micro- and macro-scale structures, while achieving excellent functionality and end-use versatility, remains challenging. Now, Osaka University researchers, in collaboration with The University of Tokyo, Kyushu University, and Okayama University, have developed a nanocellulose paper semiconductor that provides both nano-micro-macro trans-scale designability of the 3D structures and wide tunability of the electrical properties. Their findings are published in ACS Nano.
Cellulose is a natural and easy to source material derived from wood. Cellulose nanofibers (nanocellulose) can be made into sheets of flexible nanocellulose paper (nanopaper) with dimensions like those of standard A4. Nanopaper does not conduct an electric current; however, heating can introduce conducting properties. Unfortunately, this exposure to heat can also disrupt the nanostructure.
The researchers have therefore devised a treatment process that allows them to heat the nanopaper without damaging the structures of the paper from the nanoscale up to the macroscale.
“An important property for the nanopaper semiconductor is tunability because this allows devices to be designed for specific applications,” explains study author Hirotaka Koga. “We applied an iodine treatment that was very effective for protecting the nanostructure of the nanopaper. Combining this with spatially controlled drying meant that the pyrolysis treatment did not substantially alter the designed structures and the selected temperature could be used to control the electrical properties.”
The researchers used origami (paper folding) and kirigami (paper cutting) techniques to provide playful examples of the flexibility of the nanopaper at the macrolevel. A bird and box were folded, shapes including an apple and snowflake were punched out, and more intricate structures were produced by laser cutting. This demonstrated the level of detail possible, as well as the lack of damage caused by the heat treatment.
Examples of successful applications showed nanopaper semiconductor sensors incorporated into wearable devices to detect exhaled moisture breaking through facemasks and moisture on the skin. The nanopaper semiconductor was also used as an electrode in a glucose biofuel cell and the energy generated lit a small bulb.
“The structure maintenance and tunability that we have been able to show is very encouraging for the translation of nanomaterials into practical devices,” says Associate Professor Koga. “We believe that our approach will underpin the next steps in sustainable electronics made entirely from plant materials.”
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An international research team has achieved an important milestone in the quest for high density, low-power consuming nonvolatile magnetic memory.
“We established a new method to enable magnetization reversal on perpendicularly magnetized ferromagnets — without the need for an external magnetic field,” said Makoto Kohda, co-author of the study and professor at Tohoku University’s Graduate School of Engineering.
Spintronic devices optimize the intrinsic spin of electrons and their associated magnetic movement. With society needing better performing electronics with less power consumption, spintronics will play a large part in next-generation nanoelectronic devices.
A spin current converted from a charge current creates a spin-orbit torque (SOT) on ferromagnets, enabling electrical control of the magnetization. Currently, this is done unidirectionaly and external magnetic fields must be used to switch perpendicular magnetized ferromagnets. So-called field free switching, along with diminished current density for reduced energy consumption, is essential for commercial viability.
Kohda and his team comprised Professor Emeritus Junsaku Nitta from Tohoku University’s Graduate School of Engineering and colleagues from the Korea Advanced Institute of Science and Technology (KAIST), such as researcher Jeonchun Ryu, professor Byong-Guk Park and professor Kyung-Jin Lee.
They harnessed spin generated in all directions to create field free switching using polycrystalline CoFeB/Ti/CoFeB — crucial because this material is already employed in the mass production of spintronic devices. Furthermore, the new method brought about a 30% lower current density than existing spin current based magnetization reversal.
“International collaboration is the key for demonstrating next-generation technology in nonvolatile memory. The next step for us will be to apply this principle to spintronic devices’ mass production to help usher in the power-saving technology required for IoT and AI,” added Kohda.
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When you live in the driest State in the driest country in the world, bushfires are an unfortunate, and all-too-regular part of life. Learning how to survive such emergencies is important for all people, but especially for our youngest citizens
Now, a new virtual reality (VR) experience developed by the University of South Australia is educating children about bushfires and helping them learn how to be safer in a bushfire incident.
Focusing on children aged 10-12 years, the new VR experience presents a scenario where children are tasked to look after a friend’s dog just before a fire event begins to unfold. They participate in a series of problem-solving activities to help save and protect themselves and the dog.
Published in the Journal of Educational Computing, the research demonstrates how immersive VR experiences can deliver significant positive learning outcomes for primary children, independent of their gender, background knowledge or perceived ability to respond to bushfire hazards.
The findings showed that more than 80 per cent of children agreed or strongly agreed that they felt more confident to calmly evaluate the options and make wise decisions to protect themselves from a bushfire. This is especially significant considering that 91 per cent of participants originally lacked any knowledge of fires, and that 67 per cent had said that they were too young to make safety decisions in a fire.
The project was part of Safa Molan’s PhD project. Her supervisor and fellow researcher, UniSA’s Professor Delene Weber says immersive VR experiences have enormous potential to engage, educate and empower younger generations. More

Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS), Cornell University and Shanghai Jiao Tong University have developed collectives of microrobots which can move in any desired formation. The miniature particles are capable of reconfiguring their swarm behavior quickly and robustly. Floating on the surface of water, the versatile microrobotic discs can go round in circles, dance the boogie, bunch up into a clump, spread out like gas or form a straight line like beads on a string.
Each robot is slightly bigger than a hair’s width. They are 3D printed using a polymer and then coated with a thin top layer of cobalt. Thanks to the metal the microrobots become miniature magnets. Meanwhile, wire coils which create a magnetic field when electricity flows through them surround the setup. The magnetic field allows the particles to be precisely steered around a one-centimeter-wide pool of water. When they form a line, for instance, the researchers can move the robots in such a way that they “write” letters in the water. The research project of Gaurav Gardi and Prof. Metin Sitti from MPI-IS, Steven Ceron and Prof. Kirstin Petersen from Cornell University and Prof. Wendong Wang from Shanghai Jiao Tong University titled “Microrobot Collectives with Reconfigurable Morphologies, Behaviors, and Functions” was published in Nature Communications on April 26, 2022.
Collective behavior emerges from the interactions between the robots
Collective behavior and swarm patterns are found everywhere in nature. A flock of birds exhibits swarm behavior, as does a school of fish. Robots can also be programmed to act in swarms — and have been seen doing so quite prominently. A technology company recently presented a drone lightshow that won the company a Guinness World Record by programming several hundred drones and flying them side-by-side, creating amazing patterns in the night sky. Each drone in this swarm was equipped with computational power steering it in every possible direction. But what if the single particle is so tiny that computation isn’t an option? When a robot is just 300 micrometers wide, one cannot program it with an algorithm.
Three different forces are at play to compensate for the lack of computation. One is the magnetic force. Two magnets with opposite poles attract. Two identical poles repel each other. The second force is the fluid environment; the water around the discs. When particles swim in a swirl of water, they displace the water and affect the other surrounding particles in the system. The speed of the swirl and its magnitude determine how the particles interact. Thirdly, if two particles float next to each other, they tend to drift towards each other: they bend the water surface in such a way that they slowly come together. Scientists and cereal lovers call this the cheerio effect: if you let two cheerios float on milk, they will soon bump into each other. On the flip side, this effect can also cause two things to repel each other (try a hairpin and a cheerio).
Three forces allow for reconfigurability
The scientists use all three forces to create a coordinated, collective pattern of motion for several dozen microrobots as one system. A video (https://youtu.be/q91AWmTBzG8) shows how the scientists steer the robots through a parcour, displaying the formation that best suits the obstacle course, e.g. when they enter a narrow passage, the microrobots line up in single file and disperse again when they come out. The scientists can also make the robots dance, alone or as pairs. Additionally, they show how they put a tiny plastic ball into the water container and then aggregate the robots into a clump to push the floating ball along. They can place the tiny particles inside two gears and move the particles in a way that causes both gears to rotate. A more ordered pattern is also possible with each particle keeping an identical distance to its neighbor. All these different locomotion modes and formations are achieved through external computation: an algorithm is programmed to create a rotating or oscillating magnetic field which triggers the desired movement and reconfigurability.
“Depending on how we change the magnetic fields, the discs behave in a different way. We are tuning one force and then another until we get the movement we want. If we rotate the magnetic field within the coils too vigorously, the force which is causing the water to move around is too strong and the discs move away from each other. If we rotate too slow, then the cheerio effect which attracts the particles is too strong. We need to find the balance between the three,” Gaurav Gardi explains. He is a Ph.D. student in the Physical Intelligence department at MPI-IS and one of the two lead authors of the publication together with Steven Ceron from Cornell University.
A model for future biomedical and environmental applications
The future scenario for such microrobotic collectives is to go even smaller. “Our vision is to develop a system that is even tinier, made of particles only one micrometer small. These collectives could potentially go inside the human body and navigate through complex environments to deliver drugs, for instance, to block or unblock passages, or to stimulate a hard-to-reach area,” Gardi says.
“Robot collectives with robust transitions between locomotion behaviors are very rare. However, such versatile systems are advantageous to operate in complex environments. We are very happy we succeeded in developing such a robust and on-demand reconfigurable collective. We see our research as a blueprint for future biomedical applications, minimally invasive treatments, or environmental remediation,” adds Metin Sitti, who leads the Physical Intelligence Department and is a pioneer in the field of small-scale robotics and physical intelligence. More

A prognostic model developed by University of Pittsburgh School of Medicine data scientists and UPMC neurotrauma surgeons is the first to use automated brain scans and machine learning to inform outcomes in patients with severe traumatic brain injuries (TBI).
In a study reported today in the journal Radiology, the team showed that their advanced machine-learning algorithm can analyze brain scans and relevant clinical data from TBI patients to quickly and accurately predict survival and recovery at six-months after the injury.
“Every day, in hospitals across the United States, care is withdrawn from patients who would have otherwise returned to independent living,” said co-senior author David Okonkwo, M.D., Ph.D., professor of neurological surgery at Pitt and UPMC. “The majority of people who survive a critical period in an acute care setting make a meaningful recovery — which further underscores the need to identify patients who are more likely to recover.”
It often takes two weeks for TBI patients to emerge from their coma and begin their recoveries — yet severe TBI patients are often taken off life support within the first 72 hours after hospital admission. The new predictive algorithm, validated across two independent patient cohorts, could be used to screen patients shortly after admission and can improve clinicians’ ability to deliver the best care at the right time.
TBI is one of the most pressing public health issues in the U.S. — every year, nearly 3 million people seek TBI care across the nation, and TBI remains a leading cause of death in people under the age of 45.
Recognizing the need for better ways to assist clinicians, the team of data scientists at Pitt set out to leverage their expertise in advanced artificial intelligence to develop a sophisticated tool to understand the nature of each unique patient’s TBI.
“There is a great need for better quantitative tools to help intensive care neurologists and neurosurgeons make more informed decisions for patients in critical condition,” said corresponding author Shandong Wu, Ph.D., associate professor of radiology, bioengineering and biomedical informatics at Pitt. “This collaboration with Dr. Okonkwo’s team gave us an opportunity to use our expertise in machine learning and medical imaging to develop models that use both brain imaging and other clinically available data to address an unmet need.”
Led by the co-first authors Matthew Pease, M.D., and Dooman Arefan, Ph.D., the group developed a custom artificial intelligence model that processed multiple brain scans from each patient and combined it with an estimate of coma severity and information about the patient’s vital signs, blood tests and heart function. Importantly, because brain imaging techniques evolve over time and image quality can vary dramatically from patient to patient, the researchers accounted for data irregularity by training their model on different image-taking protocols.
The model proved itself by accurately predicting patients’ risk of death and unfavorable outcomes at six months following the traumatic incident. To validate the model, Pitt researchers tested it with two patient cohorts: one of over 500 severe TBI patients previously treated at UPMC and the other an external cohort of 220 patients from 18 institutions across the country, through the TRACK-TBI consortium. The external cohort was critical to test the model’s prediction ability.
“We hope this research shows that AI can provide a tool to improve clinical decision-making early when a TBI patient is admitted to the emergency room, towards yielding a better outcome for the patients,” said Wu and Okonkwo.
Additional authors of this paper include Ava Puccio, Ph.D., Kerri Hochberger, Enyinna Nwachuku, M.D., Souvik Roy, and Stephanie Casillo, all of UPMC; Jason Barber and Nancy Temkin, Ph.D., of the University of Washington; and Esther Yuh, M.D., of the University of California, San Francisco; and group-author investigators from the TRACK-TBI Consortium.
This research was supported by National Institutes of Health National Cancer Institute (grant R01CA218405), National Institute of Neurological Disorders and Stroke (grant U01 NS1365885 and U01 NS086090), Department of Defense (grants W911QY-14-C-0070, W81XWH-18- 2-0042, W81XWH-15-9-0001 and W81XWH-14-2-0176), Radiological Society of North America Research Scholar grant RSCH1530, Amazon Machine Learning Research Award and the Congress of Neurological Surgeons Data Science Fellowship grant. More

With e-commerce orders pouring in, a warehouse robot picks mugs off a shelf and places them into boxes for shipping. Everything is humming along, until the warehouse processes a change and the robot must now grasp taller, narrower mugs that are stored upside down.
Reprogramming that robot involves hand-labeling thousands of images that show it how to grasp these new mugs, then training the system all over again.
But a new technique developed by MIT researchers would require only a handful of human demonstrations to reprogram the robot. This machine-learning method enables a robot to pick up and place never-before-seen objects that are in random poses it has never encountered. Within 10 to 15 minutes, the robot would be ready to perform a new pick-and-place task.
The technique uses a neural network specifically designed to reconstruct the shapes of 3D objects. With just a few demonstrations, the system uses what the neural network has learned about 3D geometry to grasp new objects that are similar to those in the demos.
In simulations and using a real robotic arm, the researchers show that their system can effectively manipulate never-before-seen mugs, bowls, and bottles, arranged in random poses, using only 10 demonstrations to teach the robot.
“Our major contribution is the general ability to much more efficiently provide new skills to robots that need to operate in more unstructured environments where there could be a lot of variability. The concept of generalization by construction is a fascinating capability because this problem is typically so much harder,” says Anthony Simeonov, a graduate student in electrical engineering and computer science (EECS) and co-lead author of the paper. More

People could benefit from fully automated personal nutritional advice, as a new research paper shows that an app improved healthy diet in clinical trials.
A paper published in the Journal of Medical Internet Research today (Mon 25 April 22) shows how the eNutri app developed by researchers in human nutrition and biomedical engineering at the University of Reading helped people to eat more healthily. Participants who were given automated personalised nutrition advice improved their healthy diet score by 6% compared to a control group who were given general healthy eating guidance.
Dr Roz Fallaize, Dietitian and Research Fellow at the University of Reading’s Department of Food and Nutritional Science said:
“The research demonstrates that the eNutri technology is effective in helping users to improve their healthy food intake, with a significant improvement in diet quality for the group who had access to automated, personalised nutrition advice.”
“While having a registered nutritionist or dietitian giving personalised dietary advice is ideal, this is often only available to those with health concerns or with the financial resource to pay. There is also growing interest in nutrition apps and web services, but many commercial apps tend to focus on weight loss or calorie counting rather than healthy eating.”
“Nutritional advice should always be focused on healthy, balanced diets and positive relationships with food, and I’m pleased that our study helped people eat better. One exciting aspect of the eNutri system is the potential to offer it to lots of people at low-cost”
Dr Rodrigo Zenun Franco, a PhD graduate from the University of Reading and lead author of the paper said:
“The eNutri app prioritises healthy eating based on evidence and uniquely uses a diet scoring system to provide food-based advice that is tailored to the individual user.”
“We are continuing to develop eNutri to suit specific population groups including those with heart conditions and hope to make this available to the public in the future”
People were either assigned to receive personalised nutrition advice or given general healthy eating advice. Those in the personalised group then had their diets scored according to 11 criteria based on UK dietary guidance. The eNutri app gave an automated assessment of diet quality giving the user a ‘healthy diet score’ out of 100.
The ‘healthy diet score’ includes assessments of intake of fruit, vegetables, wholegrains, red and processed meat, with higher points awarded when users have the recommended intake of these. The personalised advice is then targeted to areas of their diet which they would benefit most from changing.
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