Computers Math

Next-gen electronics is envisioned to be non-rigid, component-free, flexible, bendable, and easily integrable with different objects.
Direct-write printing techniques provide unique opportunity to enable this vision through use of nanomaterial so-called functional inks, that can be tailored to add desired functionalities on various flexible substrates, such as textiles or plastic.
The technology, known as Printed Electronics (PE), has been known for decades, but has recently gained considerable attention due to innovation in material inks, process technology and design revolution.
To keep the research community abreast with the latest technological advancements in the area of droplet-based PE techniques for next-gen devices, researchers from Aarhus University have now published a comprehensive review of the technology in the   scientific journal Advanced Materials.
“Through this paper, we have tried to fill the existing void in literature by discussing techniques, material inks, ink properties, post processing, substrates and application to provide a complete guide. PE is an industry relevant technology and the gateway to future portable electronics, where advanced printers can print complex circuits on any material,” says Assistant Professor Shweta Agarwala, an expert in PE at the Department of Electrical and Computer Engineering at Aarhus University.
PE is already being used for many different applications today. It is an attractive method to impart electrical functionality on any surface and the major advantage of PE is that it is inexpensive and readily scalable.
“PE offers a wide range of advantages over conventional lithography-based technologies. It provides much more production flexibility, it is cheaper and far simpler. More importantly, it opens up a plethora of new possibilities to print flexible electrical circuits directly onto a wide range of substrates such as plastics, papers, clothes, and quite literally any other planar and non-planar surfaces. The research area is moving forwards fast, and this publication provides an overview of how far we have progressed today,” says Hamed Abdolmaleki, a PhD student and first author of the paper.
Even though PE is being used in more and more industries, and is considered very important in the electronics of the future, the technology is still in its infancy.
For Shweta Agarwala, the sustainability aspect is very important for the future perspectives of electronics and PE technology:
“PE is the way towards biodegradable electronics, and with this technology, we can address the huge societal problem that electronics already present, and which will only get more pressing in the future. The world is not only suffering from a huge amount of plastic pollution; it is also burdened by enormous pollution from electronics in all the devices we discard rapidly. In the review article, we have also discussed the emerging field of biodegradable substrates that will have huge environmental impact,” she adds.
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Materials provided by Aarhus University. Original written by Jesper Bruun. Note: Content may be edited for style and length. More

A new type of universal computer memory — ULTRARAM™ — has taken a step closer towards development with a successful experiment by Lancaster physicists.
Professor Manus Hayne, who is leading the research, commented: “These new results confirm the astonishing properties of ULTRARAM™, allowing us to demonstrate its potential as a fast and efficient non-volatile memory with high-endurance.”
Currently, the two main types of memory, dynamic RAM (DRAM) and flash, have complementary characteristics and roles:- DRAM is fast, so used for active (working) memory but it is volatile, meaning that information is lost when power is removed. Indeed, DRAM continually ‘forgets’ and needs to be constantly refreshed. Flash is non-volatile, allowing you to carry data in your pocket, but is very slow and wears out. It is well-suited for data storage but can’t be used for active memory.”Universal memory” is a memory where the data is very robustly stored, but can also easily be changed; something that was widely considered to be unachievable until now.
The Lancaster team has solved the paradox of universal memory by exploiting a quantum mechanical effect called resonant tunnelling that allows a barrier to switch from opaque to transparent by applying a small voltage.
Their new non-volatile RAM, called ULTRARAM™, is a working implementation of so-called ‘universal memory’, promising all the advantages of DRAM and flash, with none of the drawbacks.
In their latest work, published in IEEE Transactions on Electron Devices, the researchers have integrated ULTRARAM™ devices into small (4-bit) arrays for the first time. This has allowed them to experimentally verify a novel, patent-pending, memory architecture that would form the basis of future ULTRARAM™ memory chips.
They have also modified the device design to take full advantage of the physics of resonant tunnelling, resulting in devices that are 2,000 times faster than the first prototypes, and with program/erase cycling endurance that is at least ten times better than flash, without any compromise in data retention.
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When you bump into someone in the workplace or at your local coffee shop, you might call that an “encounter.” That’s the scientific term for it, too. As part of urgent efforts to fight COVID-19, a science is rapidly developing for measuring the number of encounters and the different levels of interaction in a group.
At the National Institute of Standards and Technology (NIST), researchers are applying that science to a concept they have created called “encounter metrics.” They have developed an encrypted method that can be applied to a device such as your phone to help with the ultimate goal of slowing down or preventing future pandemics. The method is also applicable to the COVID-19 pandemic.
Their research is explained in a pilot study published in the Journal of Research of NIST.
Encounter metrics measure the levels of interactions between members of a population. A level of interaction could be the number of people in a bathroom who are talking to each other or a group of people walking down a hallway. There are numerous levels of interactions because there are so many different ways people can interact with one another in different environments.
In order to mitigate the spread of an infectious disease there is the assumption that less communication and interaction with people in a community is essential. Fewer interactions among people means there is less of a chance of the disease spreading from one person to another. “We need to measure that. It’s important to develop technology to measure that and then see how we can use that technology to shape our working environment to slow future pandemics,” said NIST researcher René Peralta, an author of the NIST study.
Picture two people walking from opposite ends of a hallway who meet in the middle. To record this encounter, each person could carry their own phone or a Bluetooth device that broadcasts a signal as soon as the encounter occurs. One way of labeling this encounter is through the exchange of device IDs or pseudonyms. Each device sends its own pseudonym that belongs to the device itself. The pseudonyms could be changed every 10 minutes as a way to promote the privacy of the person’s identity. More

A Japanese research group combined motion analysis that uses smartphone application and machine learning that uses an anomaly detection method, thereby developing a technique to easily screen for carpal tunnel syndrome. Carpal tunnel syndrome is common amongst middle-aged women. The disease causes compressed nerves in the wrist, causing numbness and difficulty with finger movements. While an accurate diagnosis can be reached with nerve conduction study, this is not widely used because it requires expensive devices and specialized skills. Thus, a simple screen tool that does not require any specialized knowledge or techniques is desired.
The research group of Dr. Koji Fujita of Tokyo Medical and Dental University and associate professor Yuta Sugiura of Keio University focused on increasingly poor movements of the thumb with the advancement of the disease, and analyzed its characteristics. They developed a game application for smartphones that is played using the thumbs and prepared a program that acquires the trajectory of the thumb during a game play and estimates the possibility of the disease with machine learning. The application can screen for possible carpal tunnel syndrome using a simple game that can be played in 30 sec — 1 minute. Even without gathering patient data, they were able to effectively construct an estimate mode from the data of 12 asymptomatic participants using the anomaly detection method. When this program was applied to 15 new asymptomatic subjects and 36 patients with carpal tunnel syndrome to verify its accuracy, the result was promising with 93% sensitivity, 69% specificity, and 0.86 Area Under the Curve (AUC)(1). This is equivalent or better than the results of physical examinations by expert orthopedic surgeons.
The developed tool can be used to screen for possible carpal tunnel syndrome at sites where no expert is present, such as at home or at a health center. In the future, the research group aims to develop a system that is able to encourage an examination by an expert when the disease is suspected in order to prevent exacerbation. It would prevent inconvenience and social loss associated with exacerbation of a disease, which is more common among women, and contribute to creating a society where women play an active role.
The research was conducted as part of JST’s Strategic Basic Research Program, Precursory Research for Embryonic Science and Technology (PRESTO).
(1) Area Under the Curve (AUC)
This assessment item is used for each test method, and a higher value indicates a better test. Sensitivity is the ratio of correct positive results for subjects with a disease. Specificity is the ratio of correct negative results for subjects without a disease. AUC is the comprehensive assessment indicator of accuracy that combines sensitivity and specificity and takes a value between 0 and 1.
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The use of artificial intelligence (AI) is becoming more common in many branches of industry and online retailing. Traditional lines of work, such as transport logistics and driving, are developing in a similar direction although mainly out of public view. Scientists at the University of Göttingen have now investigated how efficient the use of AI can be in the commercial management of trucks. Their answer: the best option is an intelligent combination of human decision-making and AI applications. The study was published in the International Journal of Logistics Management.
“As has happened in the private sector, digital applications — as well as machine learning, a kind of AI — are increasingly permeating operations and processes in the transport and logistics sector,” explains Professor Matthias Klumpp from the Faculty of Economics. “The question in the commercial sector, however, is whether or not this contributes to achieving goals and efficiency in companies.”
To answer this question, the researchers compared the work efficiency of truck drivers in relation to their use of AI applications such as dynamic real-time navigation systems, cruise control and automated gear-shifting based on speed and topography and others. Looking at retail trade delivery by truck, they studied three comparison groups: the first drove exclusively following human decision-making patterns; the second used a combination of human and machine; and the third relied exclusively on fully automated decisions.
The researchers from the Production and Logistics Research Group concluded that an intelligent combination of human work and decision-making capabilities with AI applications promises the highest transport and driving efficiency: “On average, the second group achieved the most efficient transport trips, with the fewest interventions and deviations from the optimal path,” the authors said. “Clearly, neither a purely human decision-making structure nor a fully automated driving system can promise to meet current logistics requirements.”
The scientists therefore deduce that despite the progress of AI in the field of transportation by truck, human experience and decision-making capabilities will still be necessary in the longer term. “However, extensive training and qualification needs will occur by working with AI applications, especially for simple logistics activities,” the authors conclude. “Technology and AI innovations are therefore not a question for management alone. In particular, efficiency and competitive advantages can be achieved through their application in operational transport.”
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International genomics research led by the University of Leicester has used artificial intelligence (AI) to study an aggressive form of cancer, which could improve patient outcomes.
Mesothelioma is caused by breathing asbestos particles and most commonly occurs in the linings of the lungs or abdomen. Currently, only seven per cent of people survive five years after diagnosis, with a prognosis averaging 12 to 18 months.
New research undertaken by the Leicester Mesothelioma Research Programme has now revealed, using AI analysis of DNA-sequenced mesotheliomas, that they evolve along similar or repeated paths between individuals. These paths predict the aggressiveness and possible therapy of this otherwise incurable cancer.
Professor Dean Fennell, Chair of Thoracic Medical Oncology at the University of Leicester and Director of the Leicester Mesothelioma Research Programme, said:
“It has long been appreciated that asbestos causes mesothelioma, however how this occurs remains a mystery.
“Using AI to interrogate genomic ‘big data’, this initial work shows us that mesotheliomas follow ordered paths of mutations during development, and that these so-called trajectories predict not only how long a patient may survive, but also how to better treat the cancer — something Leicester aims to lead on internationally through clinical trial initiatives.”
While use of asbestos is now outlawed — and stringent regulations in place on its removal — each year around 25 people are diagnosed with mesothelioma in Leicestershire and 190 are diagnosed in the East Midlands. Cases of mesothelioma in the UK have increased by 61% since the early 1990s.
Until very recently, chemotherapy was the only licenced choice for patients with mesothelioma. However, treatment options start to become limited once people stop responding to their treatment.
Professor Fennell in collaboration with the University of Southampton recently made a major breakthrough in treating the disease by demonstrating that use of an immunotherapy drug called nivolumab increased survival and stabilised the disease for patients. This was the first-ever trial to demonstrate improved survival in patients with relapsed mesothelioma.
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Inspired by the mastery of artificial intelligence (AI) over games like Go and Super Mario, scientists at the National Synchrotron Light Source II (NSLS-II) trained an AI agent — an autonomous computational program that observes and acts — how to conduct research experiments at superhuman levels by using the same approach. The Brookhaven team published their findings in the journal Machine Learning: Science and Technology and implemented the AI agent as part of the research capabilities at NSLS-II.
As a U.S. Department of Energy (DOE) Office of Science User Facility located at DOE’s Brookhaven National Laboratory, NSLS-II enables scientific studies by more than 2000 researchers each year, offering access to the facility’s ultrabright x-rays. Scientists from all over the world come to the facility to advance their research in areas such as batteries, microelectronics, and drug development. However, time at NSLS-II’s experimental stations — called beamlines — is hard to get because nearly three times as many researchers would like to use them as any one station can handle in a day — despite the facility’s 24/7 operations.
“Since time at our facility is a precious resource, it is our responsibility to be good stewards of that; this means we need to find ways to use this resource more efficiently so that we can enable more science,” said Daniel Olds, beamline scientist at NSLS-II and corresponding author of the study. “One bottleneck is us, the humans who are measuring the samples. We come up with an initial strategy, but adjust it on the fly during the measurement to ensure everything is running smoothly. But we can’t watch the measurement all the time because we also need to eat, sleep and do more than just run the experiment.”
“This is why we taught an AI agent to conduct scientific experiments as if they were video games. This allows a robot to run the experiment, while we — humans — are not there. It enables round-the-clock, fully remote, hands-off experimentation with roughly twice the efficiency that humans can achieve,” added Phillip Maffettone, research associate at NSLS-II and first author on the study.
According to the researchers, they didn’t even have to give the AI agent the rules of the ‘game’ to run the experiment. Instead, the team used a method called “reinforcement learning” to train an AI agent on how to run a successful scientific experiment, and then tested their agent on simulated research data from the Pair Distribution Function beamline at NSLS-II.
Beamline Experiments: A Boss Level Challenge
Reinforcement learning is one strategy of training an AI agent to master an ability. The idea of reinforcement learning is that the AI agent perceives an environment — a world — and can influence it by performing actions. Depending on how the AI agent interacts with the world, it may receive a reward or a penalty, reflecting if this specific interaction is a good choice or a poor one. The trick is that the AI agent retains the memory of its interactions with the world, so that it can learn from the experience for when it tries again. In this way, the AI agent figures out how to master a task by collecting the most rewards. More

Engineers at Duke University have developed an electronics-free, entirely soft robot shaped like a dragonfly that can skim across water and react to environmental conditions such as pH, temperature or the presence of oil. The proof-of-principle demonstration could be the precursor to more advanced, autonomous, long-range environmental sentinels for monitoring a wide range of potential telltale signs of problems.
The soft robot is described online March 25 in the journal Advanced Intelligent Systems.
Soft robots are a growing trend in the industry due to their versatility. Soft parts can handle delicate objects such as biological tissues that metal or ceramic components would damage. Soft bodies can help robots float or squeeze into tight spaces where rigid frames would get stuck.
The expanding field was on the mind of Shyni Varghese, professor of biomedical engineering, mechanical engineering and materials science, and orthopaedic surgery at Duke, when inspiration struck.
“I got an email from Shyni from the airport saying she had an idea for a soft robot that uses a self-healing hydrogel that her group has invented in the past to react and move autonomously,” said Vardhman Kumar, a PhD student in Varghese’s laboratory and first author of the paper. “But that was the extent of the email, and I didn’t hear from her again for days. So the idea sort of sat in limbo for a little while until I had enough free time to pursue it, and Shyni said to go for it.”
In 2012, Varghese and her laboratory created a self-healing hydrogel that reacts to changes in pH in a matter of seconds. Whether it be a crack in the hydrogel or two adjoining pieces “painted” with it, a change in acidity causes the hydrogel to form new bonds, which are completely reversible when the pH returns to its original levels. More