Computers Math

Taking inspiration from music streaming services, a team of engineers at the University of Michigan, Google and Georgia Tech has designed the simplest way for users to program their own exoskeleton assistance settings.
Of course, what’s simple for the users is more complex underneath, as a machine learning algorithm repeatedly offers pairs of assistance profiles that are most likely to be comfortable for the wearer. The user then selects one of these two, and the predictor offers another assistance profile that it believes might be better. This approach enables users to set the exoskeleton assistance based on their preferences using a very simple interface, conducive to implementing on a smartwatch or phone.
“It’s essentially like Pandora music,” said Elliott Rouse, U-M associate professor of robotics and mechanical engineering and corresponding author of the study in Science Robotics. “You give it feedback, a thumbs up or thumbs down, and it curates a radio station based on your feedback. This is a similar idea, but it’s with exoskeleton assistance settings. In both cases, we are creating a model of the user’s preferences and using this model to optimize the user’s experience.”
The team tested the approach with 14 participants, each wearing a pair of ankle exoskeletons as they walked at a steady pace of about 2.3 miles per hour. The volunteers could take as much time as they wanted between choices, although they were limited to 50 choices. Most participants were choosing the same assistance profile repeatedly by the 45th decision.
After 50 rounds, the experimental team began testing the users to see whether the final assistance profile was truly the best — pairing it against 10 randomly generated (but plausible) profiles. On average, participants chose the settings suggested by the algorithm about nine out of 10 times, which highlights the accuracy of the proposed approach.
“By using clever algorithms and a touch of AI, our system figures out what users want with easy yes-or-no questions,” said Ung Hee Lee, a recent U-M doctoral graduate from mechanical engineering and first author of the study, now at the robotics company Nuro. “I’m excited that this approach will make wearable robots comfortable and easy to use, bringing them closer to becoming a normal part of our day-to-day life.”
The control algorithm manages four exoskeleton settings: how much assistance to give (peak torque), how long to go between peaks (timing), and how the exoskeleton both ramps up and reduces the assistance on either side of each peak. This assistance approach is based on how our calf muscle adds force to propel us forward in each step. More

Ever since Antonie van Leeuwenhoek discovered the world of bacteria through a microscope in the late seventeenth century, humans have tried to look deeper into the world of the infinitesimally small.
There are, however, physical limits to how closely we can examine an object using traditional optical methods. This is known as the ‘diffraction limit’ and is determined by the fact that light manifests as a wave. It means a focused image can never be smaller than half the wavelength of light used to observe an object.
Attempts to break this limit with “super lenses” have all hit the hurdle of extreme visual losses, making the lenses opaque. Now physicists at the University of Sydney have shown a new pathway to achieve superlensing with minimal losses, breaking through the diffraction limit by a factor of nearly four times. The key to their success was to remove the super lens altogether.
The research is published today in Nature Communications.
The work should allow scientists to further improve super-resolution microscopy, the researchers say. It could advance imaging in fields as varied as cancer diagnostics, medical imaging, or archaeology and forensics.
Lead author of the research, Dr Alessandro Tuniz from the School of Physics and University of Sydney Nano Institute, said: “We have now developed a practical way to implement superlensing, without a super lens.
“To do this, we placed our light probe far away from the object and collected both high- and low-resolution information. By measuring further away, the probe doesn’t interfere with the high-resolution data, a feature of previous methods.”
Previous attempts have tried to make super lenses using novel materials. However, most materials absorb too much light to make the super lens useful. More

As the COVID-19 pandemic scattered and isolated people, researchers across Virginia Tech connected for a data-driven collaboration seeking improved drugs to fight the disease and potentially many other illnesses.
A multidisciplinary collaboration spanning several colleges at Virginia Tech resulted in a newly published study, “Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios,” in the Journal of Physical Chemistry Letters.
The study focuses on using computer algorithms to generate adaptations to molecules in compounds for existing and potential medications that can improve those molecules’ ability to bind to the main protease, a protein-based enzyme that breaks down complex proteins, in SARS-CoV-2, the virus that causes COVID-19.
This process allows exponentially more molecular adaptations to be considered than traditional trial-and-error methods of testing drugs one by one could allow. Candidate molecule adaptations can be identified among myriad possibilities, then narrowed to a few or one that can be created in a laboratory and tested for effectiveness.
“We present a novel transferable data-driven framework that can be used to accelerate the design of new small molecules and materials, with desired properties, by changing the combination of building blocks as well as decorating them with functional groups,” said Sanket A. Deshmukh, associate professor of chemical engineering in the College of Engineering. A “functional group” is a cluster of atoms that generally retains its characteristic properties, regardless of the other atoms in the molecule.
“Interestingly, the newly designed functionalized drug not only had a better half maximal effective concentration value than its parent drug, but also several of the proposed and used antivirals including Remdesivir,” Deshmukh said, referring to a measure of compound potency.
Moving through all the phases of the study would not have been possible without extensive cross-departmental collaboration. More

Artificial intelligence has exploded in popularity and is being harnessed by some scientists to predict which molecules could treat illnesses, or to quickly screen existing medicines for new applications. Researchers reporting in ACS Central Science have used one such deep learning algorithm, and found that dihydroartemisinin (DHA), an antimalarial drug and derivative of a traditional Chinese medicine, could treat osteoporosis as well. The team showed that in mice, DHA effectively reversed osteoporosis-related bone loss.
In healthy people, there is a balance between the osteoblasts that build new bone and osteoclasts that break it down. But when the “demolition crew” becomes overactive, it can result in bone loss and a disease called osteoporosis, which typically affects older adults. Current treatments for osteoporosis primarily focus on slowing the activity of osteoclasts. But osteoblasts — or more specifically, their precursors known as bone marrow mesenchymal stem cells (BMMSCs) — could be the basis for a different approach. During osteoporosis, these multipotent cells tend to turn into fat-creating cells instead, but they could be reprogrammed to help treat the disease. Previously, Zhengwei Xie and colleagues developed a deep learning algorithm that could predict how effectively certain small-molecule drugs reversed changes to gene expression associated with the disease. This time, joined by Yan Liu and Weiran Li, they wanted to use the algorithm to find a new treatment strategy for osteoporosis that focused on BMMSCs.
The team ran the program on a profile of differently expressed genes in newborn and adult mice. One of the top-ranked compounds identified was DHA, a derivative of artemisinin and a key component of malaria treatments. Administering DHA extract for six weeks to mice with induced osteoporosis significantly reduced bone loss in their femurs and nearly completely preserved bone structure. To improve delivery, the team designed a more robust system using injected, DHA-loaded nanoparticles. Bones of mice with osteoporosis that received the treatment were similar to those of the control group, and the treatment showed no evidence of toxicity. In further tests, the team determined that DHA interacted with BMMSCs to maintain their stemness and ultimately produce more osteoblasts. The researchers say that this work demonstrates that DHA is a promising therapeutic agent for osteoporosis.
The authors acknowledge funding from the National Natural Science Foundations of China, the Beijing International Science and Technology Cooperation, the Beijing Natural Science Foundation, Peking University Clinical Medicine Plus X — Young Scholars Project, the Ten-Thousand Talents Program, the Key R & D Plan of Ningxia Hui Autonomous Region, the Innovative Research Team of High-Level Local Universities in Shanghai, the Beijing Nova Program, the China National Postdoctoral Program for Innovative Talents, the China Postdoctoral Science Foundation, and the Peking University Medicine Sailing Program for Young Scholars’ Scientific & Technological Innovation. More

A collaborative research team, led by Professor Hyun-Kon Song in the School of Energy and Chemical Engineering at UNIST, Dr. Seo-Hyun Jung from Research Center for Advanced Specialty Chemicals at Korea Research Institute of Chemical Technology (KRICT), and Dr. Tae-Hee Kim from the Ulsan Advanced Energy Technology R&D Center at Korea Institute of Energy Research (KIER), has achieved a groundbreaking milestone in battery technology. Their remarkable achievement in developing a non-flammable gel polymer electrolyte (GPE) is set to revolutionize the safety of lithium-ion batteries (LIBs) by mitigating the risks of thermal runaway and fire incidents.
In the past, the potential flammability of LIBs has raised significant concerns, especially in electric vehicles, where fire hazards pose a serious threat to underground parking lots. Addressing this critical issue, the research team has successfully developed a groundbreaking non-flammable polymer semi-solid electrolyte, offering a promising solution to mitigate battery fires.
Conventionally, non-flammable electrolytes have heavily relied on the incorporation of flame retardant additives or solvents with exceptionally high boiling points. However, these methods often resulted in a considerable decrease in ion conductivity, compromising the overall performance of the electrolyte.
In their breakthrough research, the research team introduced a trace amount of polymer into the electrolyte, creating a semi-solid electrolyte. This novel approach dramatically increased the lithium ion conductivity by 33% compared to existing liquid electrolytes. Moreover, the pouch-type batteries incorporating this non-flammable semi-solid electrolyte exhibited a remarkable 110% improvement in life characteristics, effectively preventing unnecessary electrolyte reactions during the formation and operation of the solid-electrolyte interphase (SEI) layer.
The key advantage of this innovative electrolyte lies in its exceptional performance and non-combustibility. By suppressing radical chain reactions with fuel compounds during the combustion process, the polymer semi-solid electrolyte effectively inhibits the occurrence of battery fires. The research team demonstrated the excellence of the developed polymer by quantitatively analyzing its ability to stabilize and suppress radicals.
Jihong Jeong (School of Energy and Chemical Engineering, UNIST) emphasized, “The interaction between the polymerized material inside the battery and volatile solvents allows us to effectively suppress radical chain reactions. Through electrochemical quantification, this breakthrough will greatly contribute to understanding the mechanism of non-flammable electrolytes.”
Co-first author Mideum Kim, a master student in the School of Energy and Chemical Engineering at UNIST and the Korea Research Institute of Chemical Technology (KRICT), further confirmed the exceptional safety of the battery itself through various experiments. The team’s comprehensive approach included applying the non-flammable semi-solid electrolyte to pouch-type batteries, ensuring the evaluation of electrolyte non-combustibility extended to practical battery applications.
“The research team’s multidisciplinary composition, involving electrochemistry from UNIST, polymer synthesis from the KRICT Research Center for Advanced Specialty Chemicals, and battery safety testing by the Ulsan Advanced Energy Technology R&D Center at Korea Institute of Energy Research (KIER), has been instrumental in achieving this breakthrough,” stated Professor Song. “The use of non-flammable semi-solid electrolytes, which can be directly incorporated into existing battery assembly processes, will accelerate the future commercialization of safer batteries.”
The research study has applied for five patents in Korea and two overseas, further highlighting the significance of this achievement. Additionally, it has been selected as a supplementary cover for ACS Energy Letters, with publication online on October 13, 2023. This study has been made possible through the support of the National Research Foundation of Korea (NRF), the Ministry of Science and ICT (MSIT), the Korea Evaluation Institute of Industrial Technology (KEIT), the Korea Research Institute of Chemical Technology, and Samsung SDI Co., Ltd. More

Investigators from the Smidt Heart Institute at Cedars-Sinai found that an artificial intelligence (AI) algorithm can detect an abnormal heart rhythm in people not yet showing symptoms.
The algorithm, which identified hidden signals in common medical diagnostic testing, may help doctors better prevent strokes and other cardiovascular complications in people with atrial fibrillation — the most common type of heart rhythm disorder.
Previously developed algorithms have been primarily used in white populations. This algorithm works in diverse settings and patient populations, including U.S. veterans and underserved populations. The findings were published today in the peer-reviewed journal JAMA Cardiology.
“This research allows for better identification of a hidden heart condition and informs the best way to develop algorithms that are equitable and generalizable to all patients,” said David Ouyang, MD, a cardiologist in the Department of Cardiology in the Smidt Heart Institute at Cedars-Sinai, a researcher in the Division of Artificial Intelligence in Medicine, and senior author of the study.
Experts estimate that about 1 in 3 people with atrial fibrillation do not know they have the condition.
In atrial fibrillation, the electrical signals in the heart that regulate the pumping of blood from the upper chambers to the lower chambers are chaotic. This can cause blood in the upper chambers to pool and form blood clots that can travel to the brain and trigger an ischemic stroke.
To create the algorithm, investigators programmed an artificial intelligence tool to study patterns found in electrocardiogram readings. An electrocardiogram is a test that monitors electrical signals from the heart. People who undergo this test have electrodes placed on their body that detect the heart’s electrical activity. More

Colder is not always better for energy-hungry data centers, especially when it comes to their power bills. A new analysis says that keeping the centers at 41°C, or around 105°F, could save up to 56% in cooling costs worldwide. The study, publishing October 10 in the journal Cell Reports Physical Science, proposes new temperature guidelines that may help develop and manage more efficient data centers and IT servers in the future.
“The cooling system accounts for over one-third of the data center’s total energy consumption, so many studies talk about reducing the energy consumption of cooling systems,” says senior author Shengwei Wang of the Hong Kong Polytechnic University. “But rather than finding better ways to cool the data centers, why not redesign the servers to operate at higher temperatures?”
Data centers typically operate at temperatures between 20-25°C (68-77°F) today. The conventional cooling systems that maintain these centers work by pulling computer-generated hot air past water-chilled coils to cool down the air before it cycles back to the space. The heated water then enters either chillers or a process called free-cooling before circulating back to the coils. Unlike energy-intensive chillers that operate similarly to air conditioners, free-cooling uses ambient air to cool the water with much less energy use.
To save energy, data centers are often built in colder areas to leverage free-cooling. But thanks to advances in electronic technology, engineers and scientists know that it’s no longer necessary to blast the chiller-based air conditioning at data centers. Many IT servers already allow a higher temperature operation above 30°C (86°F). This means that in most climates, including those that are hotter, data centers can also benefit from free-cooling by raising the temperature of data centers.
“The question is, to what temperature?” says Wang. To find out, Wang and his team built a model based on the conventional cooling system and simulated the system’s operation under different climate conditions. The results showed that data centers in almost all regions across climate zones could rely nearly 100% on free-cooling throughout the year when operated at 41°C, which they coined “global free-cooling temperature.” These data centers could save 13%-56% of energy compared to those that run at 22°C (71.6°F).
Depending on an area’s temperature and humidity, the researchers say that data centers might not even need to raise the temperature that far to take full advantage of free-cooling. For example, the temperatures for Beijing, Kunming, and Hong Kong to entirely rely on free-cooling are 39°C (102.2°F), 38°C (100.4°F), and 40°C (104°F), respectively.
“But before we raise the temperature settings, we need to ensure three things,” says Wang. “First, we need to ensure the reliability of server operation. Second, the computational efficiency needs to remain the same. Third, we need to ensure the servers’ energy consumption is not increased by activating their built-in cooling protection, such as the fans.” That said, Wang is optimistic that it is possible for the next generation of servers to work at up to 40°C without performance degradation.
“For the first time we can provide cooling system engineers and server design engineers a concrete goal to work towards,” says Wang. “I think 41°C is achievable in the near future. We’re only 10°C (18°F) or less away.” More

The changing information technology industry, latest artificial intelligence applications, high demand for IT professionals, and evolving need for learning are leading to the search for innovations in education that will allow current and future employees to acquire knowledge in a contemporary and accessible way.
This is particularly relevant in the field of programming, where the complexity of the process often creates learning difficulties. Researchers from Kaunas University of Technology (KTU) and universities in Poland, Portugal, and Italy are proposing to gamify this process.
“Gamification is a learning method in which traditional game elements and principles such as levels, points or leader boards are used,” explains KTU researcher Rytis Maskeliūnas.
According to him, the main goal of this approach is to make learning as enjoyable and challenging as a game. This dynamic method should encourage learners to become more involved in learning activities and help them retain information more easily.
Creates a personalised learning process
KTU professor highlights that the possibility to personally adaptthe learning process based on each learner’s specific needs, abilities, and level of progress is one of the main advantages of gamification.
Maskeli?nas says that such personal adaptation is a complex process, which starts with the identification of the student’s initial knowledge, abilities, strengths, and weaknesses. Then, with the help of the AI or tutor, goals are selected and an individual learning plan is generated. More