Computers Math

Materials consisting of a few atomic layers display properties determined by quantum physics. In a stack of such layers, vibrations of the atoms can be triggered by infrared light. New experimental and theoretical work shows that atomic vibrations within the layers of hexagonal boron nitride, the so-called transverse optical phonons, couple directly to motions of the layers against each other. For a period of some 20 ps, the coupling results in a frequency down-shift of the optical phonons and their optical resonance. This behavior is a genuine property of the quantum material and of interest for applications in high-frequency optoelectronics.
Hexagonal boron nitride consist of layers in which covalently bonded boron and nitrogen atoms form a regular array of six-rings. Neighboring layers are coupled via the much weaker van der Waals interaction. Vibrations of boron and nitrogen atoms in the layer, the so-called transverse optical (TO) phonons, show an oscillation frequency on the order of 40 Terahertz (THz, 4×1013 vibrations per second) which is ten to hundred times higher than that of shear and breathing motions of the layers relative to each other. So far, there was nearly no insight into the lifetime of such motions after optical excitation and into their coupling.
An international collaboration of scientists from Berlin, Montpellier, Nantes, Paris and Ithaca (USA) now presents detailed experimental and theoretical results on ultrafast dynamics of coupled phonons in few-layer hexagonal boron nitride. Transverse optical (TO) phonons in a stack of 8 to 9 boron nitride layers display a lifetime of 1.2 ps (1 ps = 10-12 s), while shear and breathing modes show a decay time of 22 ps. Such lifetimes were directly measured in femtosecond pump-probe experiments and are in very good agreement with values derived from a theoretical analysis of the phonon decay channels.
Excitations of shear and breathing modes induce a characteristic spectral down-shift of the TO phonon resonance in the optical spectra . Theoretical calculations give the coupling energy between the different modes of the layer stack and show that the corresponding coupling is negligibly small in a bulk boron nitride crystal consisting of many layers. Thus, the observed coupled vibrational dynamics represent a genuine property of the quantum material.
The spectral shift of the TO phonon resonance in the optical spectra is a nonlinear optical effect which can be induced by light of moderate power. This is of interest for applications in optoelectronics and holds potential for optical modulators and switches in the giga- to terahertz frequency range.
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Materials provided by Max Born Institute for Nonlinear Optics and Short Pulse Spectroscopy (MBI). Note: Content may be edited for style and length. More

A new study from UCLA finds a visit from human-controlled robot encourages a positive outlook and improves medical interactions for hospitalized children.
Robin is a social companion robot that stands at about 4 feet tall and has the capabilities to move, talk and play with others while being remotely controlled by humans. Specialists from UCLA Mattel Children’s Hospital’s Chase Child Life Program conducted hour-long video visits with young patients using Robin, comparing it to interactions using a standard tablet, from October 2020 to April 2021. At the conclusion of the study period, children and their parents were interviewed about their experiences and child life specialists provided feedback in a focus group. Researchers then used a transcript of the discussion to identify recurrent and salient themes.
Ninety percent of parents who had a visit with Robin indicated they were “extremely likely” to request another visit, compared to 60% of parents whose children interacted with the tablet. Children reported a 29% increase in positive affect — described as the tendency to experience the world in a positive way, including emotions, interactions with others and with life’s challenges — after a visit with Robin and a 33% decrease in negative affect. Children who had a tablet visit reported a 43% decrease in positive affect and a 33% decrease in negative affect.
Parents whose children had a visit from Robin reported their children had no change in positive affect and a 75% decrease in negative affect. Parents whose children had a tablet visit reported their children had a 16% increase in positive affect and no change in negative affect.
The study is being presented on October 11 at the American Academy of Pediatrics (AAP) National Conference.
Child life specialists who oversaw visits with Robin reported benefits that included a greater display of intimacy and interactivity during play, increased control over their hospital experience and the formation of a new, trusting friendship.
“Our team has demonstrated that a social companion robot can go beyond video chats on a tablet to give us a more imaginative and profound way to make the hospital less stressful,” said Justin Wagner, MD, a pediatric surgeon at UCLA Mattel Children’s Hospital and senior author of the study. “As the pandemic continues, our patients are still feeling anxious and vulnerable in a variety of ways, so it’s critical that we be as creative as possible to make their experiences easier when they need our help.”
“We saw the positive effect in children, their families and healthcare workers,” adds Wagner. The analysis also suggests benefits to staff, including an increased sense of intimacy with and focus on the patient, increased staff engagement in social care and relative ease in maintaining infection control practices.
In the study, child life specialists also reported the challenges of limited time for patient encounters and a learning curve for operating Robin.
The authors say the evidence illustrates benefits for young patients and supports the incorporation of a social robot like Robin in an inpatient pediatric multidisciplinary care setting.
The study’s other authors are Dr. Gabriel Oland, Joseph Wertz, W. Scott Comulada, Valentina Ogaryan, Megan Pike, and Dr. Shant Shekherdimian of UCLA. More

Researchers at the National Institute of Standards and Technology (NIST) and collaborators have devised and tested a new, highly sensitive method of detecting and counting defects in transistors — a matter of urgent concern to the semiconductor industry as it develops new materials for next-generation devices. These defects limit transistor and circuit performance and can affect product reliability.
A typical transistor is, for most uses, basically a switch. When it’s on, current flows from one side of a semiconductor to the other; switching it off stops the current. Those actions respectively create the binary 1s and 0s of digital information.
Transistor performance critically depends on how reliably a designated amount of current will flow. Defects in the transistor material, such as unwanted “impurity” regions or broken chemical bonds, interrupt and destabilize the flow. These defects can manifest themselves immediately or over a period of time while the device is operating.
Over many years, scientists have found numerous ways to classify and minimize those effects.
But defects become harder to identify as transistor dimensions become almost unimaginably small and switching speeds very high. For some promising semiconductor materials in development — such as silicon carbide (SiC) instead of silicon (Si) alone for novel high-energy, high-temperature devices — there has been no simple and straightforward way to characterize defects in detail.
“The method we developed works with both traditional Si and SiC, allowing us for the first time to identify not only the type of defect but the number of them in a given space with a simple DC measurement,” said NIST’s James Ashton, who conducted the research with colleagues at NIST and Pennsylvania State University. They published their results on October 6 in the Journal of Applied Physics. The research focuses on interactions between the two kinds of electrical charge carriers in a transistor: negatively charged electrons and positively charged “holes,” which are spaces where an electron is missing from the local atomic structure. More

Wearable sensors to monitor everything from step count to heart rate are nearly ubiquitous. But for scenarios such as measuring the onset of frailty in older adults, promptly diagnosing deadly diseases, testing the efficacy of new drugs or tracking the performance of professional athletes, medical-grade devices are needed.
University of Arizona engineers have developed a type of wearable they call a “biosymbiotic device,” which has several unprecedented benefits. Not only are the devices custom 3D-printed and based on body scans of wearers, but they can operate continuously using a combination of wireless power transfer and compact energy storage. The team, led by Philipp Gutruf, assistant professor of biomedical engineering and Craig M. Berge Faculty Fellow in the College of Engineering, published its findings today in the journal Science Advances.
“There’s nothing like this out there,” said Gutruf, a member of the university’s BIO5 Institute. “We introduce a completely new concept of tailoring a device directly to a person and using wireless power casting to allow the device to operate 24/7 without ever needing to recharge.”
Custom Fit Enables Precise Monitoring
Current wearable sensors face various limitations. Smartwatches, for example, need to be charged, and they can only gather limited amounts of data due to their placement on the wrist. By using 3D scans of a wearer’s body, which can be gathered via methods including MRIs, CT scans and even carefully combined smartphone images, Gutruf and his team can 3D-print custom-fitted devices that wrap around various body parts. Think a virtually unnoticeable, lightweight, breathable, mesh cuff designed specifically for your bicep, calf or torso. The ability to specialize sensor placement allows researchers to measure physiological parameters they otherwise couldn’t.
“If you want something close to core body temperature continuously, for example, you’d want to place the sensor in the armpit. Or, if you want to measure the way your bicep deforms during exercise, we can place a sensor in the devices that can accomplish that,” said Tucker Stuart, a doctoral student in biomedical engineering and first author on the paper. “Because of the way we fabricate the device and attach it to the body, we’re able to use it to gather data a traditional, wrist-mounted wearable device wouldn’t be able to collect.”
Because these biosymbiotic devices are custom fitted to the wearer, they’re also highly sensitive. Gutruf’s team tested the device’s ability to monitor parameters including temperature and strain while a person jumped, walked on a treadmill and used a rowing machine. In the rowing machine test, subjects wore multiple devices, tracking exercise intensity and the way muscles deformed with fine detail. The devices were accurate enough to detect body temperature changes induced by walking up a single flight of stairs. More

As the popularity of fitness trackers has increased, so have the opportunities to use such devices to not only track fitness goals but also increase the motivation to meet those goals. Researchers in the College of Engineering and the College of Health and Human Development at Penn State have teamed up to use control systems engineering tools to tailor motivational messages sent to individual device users.
The results of their study were published today (Oct. 7) in Health Psychology.
“One of the really exciting advances of the last 15 years has been the advent of wearable and portable consumer technology that can be used to help promote physical activity,” said David Conroy, professor of kinesiology and human development and family studies, and co-principal investigator on the paper. “You can get real-time feedback from these devices and monitor your goals, and you can even push people messages, depending on what their goals are and what their behavior is. We know that those messages work well for improving behavior on average. But nobody is average, and we don’t know how to make sure each individual consistently gets the greatest benefit from a limited number of messages.”
Conroy said that researchers have tried several strategies, including messages that are specific to certain population segments; messages based on recent behavior — for example, sending one of two different messages depending on if a user did or did not meet their goals the previous day; and customizing the messages by putting in a person’s name or something they might like. So far, none of these approaches has proven to be consistently effective in improving the messages’ effects.
The new messaging approach developed by Conroy and Constantino Lagoa, co-principal investigator and professor of electrical engineering, applies tools used regularly in controlled systems engineering to behavior science.
“Essentially, we’re using the same mathematical tools that people in control engineering usually use to model behaviors as differential equations,” Lagoa said. “We’re using those models to design feedback controllers that take into account the current state of the person and together with the model decide what is the best time to send the messages.”
Conroy emphasized that establishing the correct dosing — meaning the type of message and its timing, frequency and context — is a critical part to this approach. More

Researchers at the Technion’s Rappaport Faculty of Medicine have developed an innovative algorithm that detects an uninterrupted common denominator in multidimensional data gathered from tumors of different patients. The study, which was published in Cell Systems, was led by Professor Shai Shen-Orr, Dr. Yishai Ofran, and Dr. Ayelet Alpert, and conducted in collaboration between researchers at the Technion, the Rambam Health Care Campus, the Shaare Zedek Medical Center and the University of Texas.
In recent years, cancer research has undergone a series of significant revolutions, including the introduction of single-cell high-resolution characterization capabilities, or, more specifically, simultaneous high-throughput profiling of cancer samples using single-cell RNA sequencing and proteomics analysis. This has led to the generation of vast quantities of multidimensional data on a huge number of cells, allowing for the characterization of both the healthy tissue and malignant tissues. This high amount of data has revealed the great variability between tumors of different patients, where cellular characterization that is derived from the patient’s genetic background is unique to each patient.
Despite the substantial advantage that is derived from such an accurate characterization of the specific patient, this development hinders comparison of different patients: in the absence of a common denominator, the comparison, which is essential for identifying prognostic markers (e.g. mortality or severity of illness), becomes impossible.
The tuMap algorithm developed by the Technion researchers provides a solution to this complex challenge by means of a “variance-based comparison.” The innovative algorithm delivers the possibility to place numerous different tumors on a uniform scale that provides a benchmark for comparison. In this way, the tumors of different patients can be meaningfully compared, as well as tumors of the same patient over the disease course (for example, on diagnosis and after treatment). The resolution provided by the algorithm can be leveraged for clinical applications such as prediction of various clinical indices with a very high accuracy, outperforming traditional tools. Although the researchers tested the algorithm on leukemia tumors, they believe that it will also be relevant for other cancer types.
The research was sponsored by the Israel Science Foundation, the Rappaport Family Institute for Research in the Medical Sciences, and the National Institutes of Health (NIH).
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A study in the journal Physica A leverages concepts from physics to model how campaign strategies influence the opinions of an electorate in a two-party system.
Researchers created a numerical model that describes how external influences, modeled as a random field, shift the views of potential voters as they interact with each other in different political environments.
The model accounts for the behavior of conformists (people whose views align with the views of the majority in a social network); contrarians (people whose views oppose the views of the majority); and inflexibles (people who will not change their opinions).
“The interplay between these behaviors allows us to create electorates with diverse behaviors interacting in environments with different levels of dominance by political parties,” says first author Mukesh Tiwari, PhD, associate professor at the Dhirubhai Ambani Institute of Information and Communication Technology.
“We are able to model the behavior and conflicts of democracies, and capture different types of behavior that we see in elections,” says senior author Surajit Sen, PhD, professor of physics in the University at Buffalo College of Arts and Sciences.
Sen and Tiwari conducted the study with Xiguang Yang, a former UB physics student. Jacob Neiheisel, PhD, associate professor of political science at UB, provided feedback to the team, but was not an author of the research. The study was published online in Physica A in July and will appear in the journal’s Nov. 15 volume. More

Understanding structure-property relations is a key goal of materials research, according to Joshua Agar, a faculty member in Lehigh University’s Department of Materials Science and Engineering. And yet currently no metric exists to understand the structure of materials because of the complexity and multidimensional nature of structure.
Artificial neural networks, a type of machine learning, can be trained to identify similarities?and even correlate parameters such as structure and properties?but there are two major challenges, says Agar. One is that the majority of vast amounts of data generated by materials experiments are never analyzed. This is largely because such images, produced by scientists in laboratories all over the world, are rarely stored in a usable manner and not usually shared with other research teams. The second challenge is that neural networks are not very effective at learning symmetry and periodicity (how periodic a material’s structure is), two features of utmost importance to materials researchers.
Now, a team led by Lehigh University has developed a novel machine learning approach that can create similarity projections via machine learning, enabling researchers to search an unstructured image database for the first time and identify trends. Agar and his collaborators developed and trained a neural network model to include symmetry-aware features and then applied their method to a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years at the University of California, Berkeley. The results: they were able to group similar classes of material together and observe trends, forming a basis by which to start to understand structure-property relationships.
“One of the novelties of our work is that we built a special neural network to understand symmetry and we use that as a feature extractor to make it much better at understanding images,” says Agar, a lead author of the paper where the work is described: “Symmetry-Aware Recursive Image Similarity Exploration for Materials Microscopy,” published today in Nature Computational Materials Science. In addition to Agar, authors include, from Lehigh University: Tri N. M. Nguyen, Yichen Guo, Shuyu Qin and Kylie S. Frew and, from Stanford University: Ruijuan Xu. Nguyen, a lead author, was an undergraduate at Lehigh University and is now pursuing a Ph.D. at Stanford.
The team was able to arrive at projections by employing Uniform Manifold Approximation and Projection (UMAP), a non-linear dimensionality reduction technique. This approach, says Agar, allows researchers to learn .” ..in a fuzzy way, the topology and the higher-level structure of the data and compress it down into 2D.”
“If you train a neural network, the result is a vector, or a set of numbers that is a compact descriptor of the features. Those features help classify things so that some similarity is learned,” says Agar. “What’s produced is still rather large in space, though, because you might have 512 or more different features. So, then you want to compress it into a space that a human can comprehend such as 2D, or 3D?or, maybe, 4D.”
By doing this, Agar and his team were able to take the 25,000-plus images and group very similar classes of material together. More