Computers Math

A team of researchers from Yale University and other institutions globally has developed an innovative patient triage platform powered by artificial intelligence (AI) that the researchers say is capable of predicting patient disease severity and length of hospitalization during a viral outbreak.
The platform, which leverages machine learning and metabolomics data, is intended to improve patient management and help health care providers allocate resources more efficiently during severe viral outbreaks that can quickly overwhelm local health care systems. Metabolomics is the study of small molecules related to cell metabolism.
“Being able to predict which patients can be sent home and those possibly needing intensive care unit admission is critical for health officials seeking to optimize patient health outcomes and use hospital resources most efficiently during an outbreak,” said senior author Vasilis Vasiliou, a professor of epidemiology at Yale School of Public Health (YSPH).The researchers developed the platform using COVID-19 as a disease model. The findings were published online in the journal Human Genomics.
The platform integrates routine clinical data, patient comorbidity information, and untargeted plasma metabolomics data to drive its predictions.
“Our AI-powered patient triage platform is distinct from typical COVID-19 AI prediction models,” said Georgia Charkoftaki, a lead author of the study and an associate research scientist in the Department of Environmental Health Sciences at YSPH. “It serves as the cornerstone for a proactive and methodical approach to addressing upcoming viral outbreaks.”
Using machine learning, the researchers built a model of COVID-19 severity and prediction of hospitalization based on clinical data and metabolic profiles collected from patients hospitalized with the disease. “The model led us to identify a panel of unique clinical and metabolic biomarkers that were highly indicative of disease progression and allows the prediction of patient management needs very soon after hospitalization,” the researchers wrote in the study.
For the study, the research team collected comprehensive data from 111 COVID-19 patients admitted to Yale New Haven Hospital during a two-month period in 2020 and 342 healthy individuals (health care workers) who served as controls. The patients were categorized into different classes based on their treatment needs, ranging from not requiring external oxygen to requiring positive airway pressure or intubation. More

Recently, a Korean company donated a wearable robot, designed to aid patients with limited mobility during their rehabilitation, to a hospital. These patients wear this robot to receive assistance for muscle and joint exercises while performing actions such as walking or sitting. Wearable devices including smartwatches or eyewear that people wear and attached to their skin have the potential to enhance our quality of life, offering a glimmer of hope to some people much like this robotic innovation.
The strain sensors used in these rehabilitative robots analyze data by translating specific physical changes in specific regions into electric signals. Notably flexible, these sensors are pliable and adept at gauging even the most subtle bodily changes as they are made from lightweight materials for ease of attachment to the skin. However, conventional soft strain sensors often exhibit inadequate durability due to susceptibility to external factors such as temperature and humidity. Furthermore, their complicated fabrication process poses challenges for widespread commercialization.
A research team led by Professor Sung-Min Park from the Department of Convergence IT Engineering and the Department of Mechanical Engineering and PhD candidate Sunguk Hong from the Department of Mechanical Engineering at Pohang University of Science and Technology (POSTECH) has successfully overcome the limitations of these soft strain sensors by integrating computer vision technology into optical sensors. Their research findings were featured in npj Flexible Electronics.
The research team developed a sensor technology known as computer vision-based optical strain (CVOS) during their study. Unlike conventional sensors reliant on electrical signals, CVOS sensors employ computer vision and optical sensors to analyze microscale optical patterns, extracting data regarding changes. This approach inherently enhances durability by eliminating elements that compromise sensor functionalities and streamlining fabrication processes, thereby facilitating sensor commercialization.
In contrast to conventional sensors that solely detect biaxial strain, CVOS sensors exhibit the exceptional ability to detect three-axial rotational movements through real-time multiaxial strain mapping. In essence, these sensors enable the precise recognition of intricate and various bodily motions through a single sensor. The research team substantiated this claim through experiments applying CVOS sensors to assistive devices in rehabilitative treatments.
Through integration of an AI-based response correction algorithm that corrects diverse error factors arising during signal detection, the experiment results showed a high level of confidence. Even after undergoing more than 10,000 iterations, these sensors consistently maintained their exceptional performance.
Professor Sung-Min Park who led the research explained, “The CVOS sensors excel in distinguishing body movements across diverse direction and angles, thereby optimizing effective rehabilitative interventions.” He further added, “By tailoring design indicators and algorithms to align with specific objectives, CVOS sensors have boundless potential for applications spanning industries.” More

The Empa spin-off BTRY wants to revolutionize rechargeable batteries: Their thin-film batteries are not only safer and longer-lasting than conventional lithium-ion batteries, they are also much more environmentally friendly to manufacture and can be charged and discharged in just one minute. For now, the battery is very small, but the founders have big plans for it.
Lithium-ion batteries are everywhere: from smartphones and laptops to cars and even satellites. It is currently our most mature battery technology. Yet it is not ideal for many applications. Lithium-ion batteries lose capacity with every charge and discharge cycle, charge relatively slowly and only work well in a narrow temperature range.
According to Empa researchers Abdessalem Aribia and Moritz Futscher from Empa’s Thin Films and Photovoltaics laboratory, it is time to rethink battery technology. Compared to other existing or developing technologies, their lithium metal-based solid-state battery brings some significant advantages: It can be charged and discharged within one minute, lasts about ten times as long as a lithium-ion battery, and is insensitive to temperature fluctuations.
In addition, unlike lithium-ion batteries, it is not flammable — a major advantage, because today’s rechargeable batteries are considered hazardous materials. Incorrect handling or damage to a conventional lithium-ion cell can lead to a fire that releases toxic gases and is extremely difficult to extinguish. “By contrast, if you cut our battery with scissors,” Aribia says, “you will simply get two batteries that are half as good.”
Aribia and Futscher now want to bring this promising technology to market. Together with lab head Yaroslav Romanyuk, they have founded a spin-off called BTRY (pronounced “battery”). Aribia, who takes on the role of CTO at BTRY, had never previously thought of starting his own company. CEO Moritz Futscher, on the other hand, has been interested in startups since he was a student. The two researchers have been working together on the battery project for years and are a well-established team. “We are convinced that our product can offer real added value,” says Futscher.
High-precision manufacturing
The new battery is a so-called thin-film solid-state battery. The technology itself is not new: Such batteries have been known since the 1980s. However, due to the very low mass of their thin-film components — the entire cell is only a few micrometers thick — they have been able to store very little energy so far. Futscher and Aribia have succeeded in stacking the thin-film cells on top of each other, increasing their capacity. More

Does hot water freeze faster than cold water? Aristotle may have been the first to tackle this question that later became known as the Mpemba effect.
This phenomenon originally referred to the non-monotonic initial temperature dependence of the freezing start time, but it has been observed in various systems — including colloids — and has also become known as a mysterious relaxation phenomenon that depends on initial conditions.
However, very few have previously investigated the effect in quantum systems.
Now, a team of researchers from Kyoto University and the Tokyo University of Agriculture and Technology has shown that the temperature quantum Mpemba effect can be realized over a wide range of initial conditions.
“The quantum Mpemba effect bears the memory of initial conditions that result in anomalous thermal relaxation at later times,” explains project leader and co-author Hisao Hayakawa at KyotoU’s Yukawa Institute for Theoretical Physics.
Hayakawa’s team prepared two systems with quantum dots connected to a heat bath, one with a current flowing and the other in an equilibrium state. Both were quenched to a low-temperature equilibrium state, allowing the team to follow their time evolution toward a steady state regarding the density matrix, energy, entropy, and — most critically — temperature.
“When the two copies crossed each other before reaching the same equilibrium state — so that the hotter part became colder and vice versa in an identity reversal — we knew we had achieved the thermal quantum Mpemba effect,” says co-author Satoshi Takada of TUAT.
“After analyzing the quantum master equation, we also discovered we had obtained the thermal quantum Mpemba effect in a wide range of parameters, including reservoir temperatures and chemical potentials,” adds first and corresponding author Amit Kumar Chatterjee, also of KyotoU.
“Our results encourage us to explore the potential use of the quantum Mpemba effect in future applications beyond thermal analyses,” reflects Hayakawa. More

Countries like Japan are experiencing declining birth rates and an aging population. The increased burden of care for this aging population may lead to a shortage of caregivers in a decade’s time. Thus, the recruitment and allocation of resources must be planned in advance. Technological interventions in the form of robots that provide home care services to the aged appear to be a promising solution to this problem.
Although care robots are being developed and improved at a rapid pace, their social acceptance has been limited. It is suspected that the ethical issues surrounding the use of such robots may be obstructing the implementation of this technology. Many acceptance models have demonstrated that the ethical perceptions of older people, their families, and professional caregivers regarding care robots can impact their willingness to adopt this technology. However, there is no universal model that can elucidate the relationship between ethical perceptions and the willingness to use care robots across countries and cultural contexts.
To fill this knowledge gap, a team of international researchers led by Professor Sayuri Suwa from Chiba University, including Dr. Hiroo Ide from the University of Tokyo, Dr. Yumi Akuta from Tokyo Healthcare University, Dr. Naonori Kodate from University College Dublin, Dr. Jaakko Hallila from Seinäjoki University of Applied Sciences, and Dr. Wenwei Yu from Chiba University, among others, conducted a cross-sectional study across Japan, Ireland, and Finland. The findings of their study were made available online on July 25, 2023, and will be published in January 2024 in Volume 116 of the journal Archives of Gerontology and Geriatrics.
Sharing the motivation behind the study, Prof. Suwa explains, “Today, in Japan’s super-aged society, various care robots, including monitoring cameras, have been developed and marketed to compensate for the shortage of care staff and to alleviate their stress. However, there are no discussions among users — older people, family caregivers, and care staff — and developers regarding the willingness to use care robots, the protection of privacy, and the appropriate use of personal information associated with the use of care robots. The desire to improve this situation and to promote appropriate utilization of care robots beyond Japan was the impetus for this research.”
The team developed a questionnaire that examined the ethical issues that could affect the willingness to use a care robot across the three countries. The survey was conducted between November 2018 and February 2019 among older people, their family caregivers, and professional caregivers. This study was also reviewed by multiple ethical committees in all three countries. The researchers analyzed a total of 1,132 responses, which comprised 664 responses from Japan, 208 from Ireland, and 260 from Finland. They found that the willingness to use care robots was highest in Japan (77.1%), followed by Ireland (70.3%), and was lowest in Finland (52.8%).
Next, the researchers developed a conceptual model and evaluated it using statistical methods. From the questionnaire, the researchers included responses to ten items in the model, categorized into four broad domains — acquisition of personal information, use of personal information for medical and long-term care, secondary use of personal information, and participation in research and development. They then improved the model using Akaike’s information criterion (AIC). The model underwent incremental improvements to attain better (smaller) AIC values. The final model was then applied to each country.
Thus, this study demonstrated the successful use of a single universal model that could explain the correlation between ethical perceptions and social implementation of care robots across three countries with different geographies, demographics, cultures, and systems.
Discussing the importance and long-term impact of their study, Prof. Suwa concludes, “From our results, we can infer that social implementation of care robots can be promoted if developers and researchers encourage potential users to participate in the development process, proposed in the form of a co-design and co-production concept. We hope that the process of developing care robots will be improved to contribute to human well-being in a global aging society.” More

National University of Singapore (NUS) physicists have developed a technique to precisely control the alignment of supermoiré lattices by using a set of golden rules, paving the way for the advancement of next generation moiré quantum matter.
Moiré patterns are formed when two identical periodic structures are overlaid with a relative twist angle between them or two different periodic structures but overlaid with or without twist angle. The twist angle is the angle between the crystallographic orientations of the two structures. For example, when graphene and hexagonal boron nitride (hBN) which are layered materials are overlaid on each other, the atoms in the two structures do not line up perfectly, creating a pattern of interference fringes, called a moiré pattern. This results in an electronic reconstruction. The moiré pattern in graphene and hBN has been used to create new structures with exotic properties, such as topological currents and Hofstadter butterfly states. When two moiré patterns are stacked together, a new structure called supermoiré lattice is created. Compared with the traditional single moiré materials, this supermoiré lattice expands the range of tunable material properties allowing for potential use in a much larger variety of applications.
A research team led by Professor Ariando from the NUS Department of Physics developed a technique and successfully realised the controlled alignment of the hBN/graphene/hBN supermoiré lattice. This technique allows for the precise arrangement of two moiré patterns, one on top of the other. Meanwhile, the researchers also formulated the “Golden Rule of Three” to guide the use of their technique for creating supermoiré lattices.
The findings were published in the journal Nature Communications.
There are three main challenges in creating a graphene supermoire — lattice. First, the traditional optical alignment strongly depends on the straight edges of graphene, but it is time-consuming and labour-intensive to find a suitable graphene flake; Second, even if the straight-edged graphene sample is used, there is a low probability of 1/8 to obtain a double-aligned supermoiré lattice, due to the uncertainty of its edge chirality and lattice symmetry. Third, although the edge chirality and lattice symmetry can be identified, the alignment errors are often found to be large (greater than 0.5 degrees), as it is physically challenging to align two different lattice materials.
Dr Junxiong Hu, the lead author for the research paper, said, “Our technique helps to solve a real-life problem. Many researchers have told me that they usually take almost one week to make a sample. With our technique, they can not only greatly shorten the fabrication time, but also greatly improve the accuracy of the sample.”
The researchers use a “30° rotation technique” at the start to control the alignment of the top hBN and graphene layers. Then they use a “flip-over technique” to control the alignment of the top hBN and bottom hBN layers. Based on these two methods, they can control the lattice symmetry and tune the band structure of the graphene supermoiré lattice. They have also shown that the neighbouring graphite edge can act as a guide for the stacking alignment. In this study, they have fabricated 20 moiré samples with accuracy better than 0.2 degrees.
Prof Ariando said, “We have established three golden rules for our technique which can help many researchers in the two-dimensional materials community. Many scientists working in other strongly correlated systems like magic-angle twisting bilayer graphene or ABC-stacking multilayer graphene are also expected to benefit from our work. Through this technical improvement, I hope that it will accelerate the development of the next generation of moiré quantum matter.”
Currently, the research team is using this technique to fabricate the single-layer graphene supermoiré lattice and explore the unique properties in this material system. Moreover, they are also extending the current technique to other material systems, to discover other novel quantum phenomena. More

A team of computer scientists at the University of Massachusetts Amherst, led by Emery Berger, recently unveiled a prize-winning Python profiler called Scalene. Programs written with Python are notoriously slow — up to 60,000 times slower than code written in other programming languages — and Scalene works to efficiently identify exactly where Python is lagging, allowing programmers to troubleshoot and streamline their code for higher performance.
There are many different programming languages — C++, Fortran and Java are some of the more well-known ones — but, in recent years, one language has become nearly ubiquitous: Python.
“Python is a ‘batteries-included’ language,” says Berger, who is a professor of computer science in the Manning College of Information and Computer Sciences at UMass Amherst, “and it has become very popular in the age of data science and machine learning because it is so user-friendly.” The language comes with libraries of easy-to-use tools and has an intuitive and readable syntax, allowing users to quickly begin writing Python code.
“But Python is crazy inefficient,” says Berger. “It easily runs between 100 to 1,000 times slower than other languages, and some tasks might take 60,000 times as long in Python.”
Programmers have long known this, and to help fight Python’s inefficiency, they can use tools called “profilers.” Profilers run programs and then pinpoint why and which parts are slow.
Unfortunately, existing profilers do surprisingly little to help Python programmers. At best, they indicate that a region of code is slow, and leave it to the programmer to figure out what, if anything, can be done.
Berger’s team, which included UMass computer science graduate students Sam Stern and Juan Altmayer Pizzorno, built Scalene to be the first profiler that not only precisely identifies inefficiencies in Python code, but also uses AI to suggest how the code can be improved. More

Researchers at Duke University have implemented a quantum-based method to observe a quantum effect in the way light-absorbing molecules interact with incoming photons. Known as a conical intersection, the effect puts limitations on the paths molecules can take to change between different configurations.
The observation method makes use of a quantum simulator, developed from research in quantum computing, and addresses a long-standing, fundamental question in chemistry critical to processes such as photosynthesis, vision and photocatalysis. It is also an example of how advances in quantum computing are being used to investigate fundamental science.
The results appear online August 28 in the journal Nature Chemistry.
“As soon as quantum chemists ran into these conical intersection phenomena, the mathematical theory said that there were certain molecular arrangements that could not be reached from one to the other,” said Kenneth Brown, the Michael J. Fitzpatrick Distinguished Professor of Engineering at Duke. “That constraint, called a geometric phase, isn’t impossible to measure, but nobody has been able to do it. Using a quantum simulator gave us a way to see it in its natural quantum existence.”
Conical intersections can be visualized as a mountain peak touching the tip of its reflection coming from above and govern the motion of electrons between energy states. The bottom half of the conical intersection represents the energy states and physical locations of an unexcited molecule in its ground state. The top half represents the same molecule but with its electrons excited, having absorbed energy from an incoming light particle.
The molecule can’t stay in the top state — its electrons are out of position relative to their host atoms. To return to the more favorable lower energy state, the molecule’s atoms begin rearranging themselves to meet the electrons. The point where the two mountains meet — the conical intersection — represents an inflection point. The atoms can either fail to get to the other side by readjusting to their original state, dumping excess energy in the molecules around them in the process, or they can successfully make the switch.
Because the atoms and electrons are moving so fast, however, they exhibit quantum effects. Rather than being in any one shape — at any one place on the mountain — at any given time, the molecule is actually in many shapes at once. One could think of all these possible locations as being represented by a blanket wrapped around a portion of the mountainous landscape. More