Computers Math

Transparent materials have become an essential component in a wide variety of technological applications, ranging from everyday electronics like tablets and smartphones to more sophisticated uses in solar panels, medicine, and optics. Just as for any other product to be mass-produced, quality control is important for these materials, and several techniques have been developed to detect microscopic scratches or imperfections.
One attractive approach to scanning for damages on materials is using “Lamb waves.” Named after the British mathematician Sir Horace Lamb, these are elastic waves generated in solid plates following an appropriate mechanical excitation. Because the propagation of Lamb waves is affected by surface damage (such as scratches), they can be leveraged to ensure that the scanned material is free from imperfections. Unfortunately, the generation and subsequent measurement of Lamb waves on transparent materials are not straightforward.
While laser-based techniques exist for generating Lamb waves in a contactless manner, the laser parameters need to be carefully calibrated for each material to avoid causing damage. Moreover, existing approaches do not generate Lamb waves of sufficient amplitude; as such, repeated measurements have to be conducted and averaged to get reliable data, which is time-consuming. As for measuring the generated Lamb waves, no existing technique can quickly detect and use them to look for submillimeter-scale damage on transparent surfaces.
To address these issues, a research team led by Professor Naoki Hosoya from Shibaura Institute of Technology and Takashi Onuma from Photron Limited, Japan, developed a novel framework for the generation and detection of “S0 mode” (zero-order symmetrical mode) Lamb waves in transparent materials. Their approach is presented in a paper recently published online in the journal Optics and Lasers in Engineering.
First, the team had to find a convenient technique to generate Lamb waves without damaging the sample. To this end, they leveraged an approach that they had used successfully in other endeavors to generate mechanical oscillations in a contactless way: laser-induced plasma (LIP) shock waves. To put it simply, LIP can be generated by focusing a beam of high-energy laser on a tiny volume of gas. The energy of the laser energizes the gas molecules and causes them to ionize, creating an unstable “plasma bubble” close to the material’s surface. “The plasma bubble expands to its surroundings at super high speeds, generating a shock wave that is used as the excitation force to produce Lamb waves on the target structure,” explains Prof. Hosoya.
Next, the researchers needed to measure the generated waves. They achieved this by using a high-speed polarization camera, which, as the name implies, can capture the polarization of the light traveling through the transparent sample. This polarization contains information directly related to the material’s mechanical stress distribution, which, in turn, reflects the propagation of Lamb waves.
To put their strategy to the test, the team created microscopic scratches on a few flat, transparent polycarbonate plates and compared the propagation of Lamb waves on damaged and pristine samples. As expected, the scratches caused noticeable differences in the stress distribution of the plates as the waves propagated over the damaged areas, demonstrating the potential of this novel approach by detecting scratches measuring only several dozen micrometers.
While the findings are exciting, further studies are warranted to gain a more in-depth understanding of their strategy and its limits. Prof. Hosoya says, “The effects of the damage size or type, the camera lens magnification, and the properties of the transparent sample on the detectable defect size limit of our method needs to be verified as part of future works.”
Hopefully, this ingenious non-contact, non-destructive damage detection scheme will help reduce the production costs of high-quality transparent materials.
Story Source:
Materials provided by Shibaura Institute of Technology. Note: Content may be edited for style and length. More

In a negotiation, how tough should your first offer be? New research shows the first offer can have a significant impact on the eventual outcome, and if you try to drive too hard a bargain, it could backfire.
Whether you’re buying a house, a car, or second-hand furniture, it’s likely you will need to negotiate the price, so being able to negotiate effectively could save you significant cash.
Behavioural economist Professor Lionel Page from the University of Technology Sydney (UTS) said opening offers in real-world negotiations are sometimes intended to signal the “toughness” of the buyer — but whether this strategy actually works was not known.
“This experiment allowed us to study whether and how the level of the opening offer influences the beliefs of buyers and sellers, their actions and the final bargaining outcome,” said Professor Page.
The researchers conducted the experiment using a bargaining game where players exchanged offers for a split of $10. The aim was to mimic the start of a typical negotiation process.
They found that the success or failure of a negotiation depended not only on the final offer on the table but also on the emerging dynamics of the bargaining process. More

A team of researchers from Bristol’s Quantum Engineering and Technology Labs (QETLabs) has shown how to protect qubits from errors using photons in a silicon chip.
Quantum computers are gaining pace. They promise to provide exponentially more computing power for certain very tricky problems. They do this by exploiting the peculiar behaviour of quantum particles, such as photons of light.
However, quantum states of particles are very fragile. The quantum bits, or qubits, that underpin quantum computing pick up errors very easily and are damaged by the environment of the everyday world. Fortunately, we know in principle how to correct for these errors.
Quantum error correcting codes are a method to protect, or to nurture, qubits, by embedding them in a more robust entangled state of many particles. Now a team led by researchers at Bristol’s Quantum Engineering and Technology Laboratories (QETLabs) has demonstrated this using a quantum photonic chip.
The team showed how large states of entangled photons can contain individual logical qubits and protect them from the harmful effects of the classical world. The Bristol-led team included researchers from DTU in Copenhagen who fabricated the chip.
Dr Caterina Vigliar, first author on the work, said: “The chip is really versatile. It can be programmed to deliver different kinds of entangled states called graphs. Each graph protects logical quantum bits of information from different environmental effects.”
Anthony Laing, co-Director of QETLabs, and an author on the work said: “Finding ways to efficiently deliver large numbers of error protected qubits is key to one day delivering quantum computers.”
Story Source:
Materials provided by University of Bristol. Note: Content may be edited for style and length. More

One third of children with a microdeletion of chromosome 22 will later develop a psychotic illness such as schizophrenia. But how do we know which of these children might be affected? Today, various studies have contributed to the understanding of the neurobiological mechanisms that are associated with the development of psychotic illnesses. The problem is that the ability to identify those at risk and adapt their treatment accordingly remains limited. Indeed, many variables — other than neurobiological — contribute to their development.
This is why a team from the University of Geneva (UNIGE) has joined forces with a team from the EPFL to use in a longitudinal manner an artificial intelligence tool: the network analysis method. This algorithm correlates many variables from different backgrounds — neurobiological, psychological, cognitive, etc. — over a period of twenty years, in order to determine which current symptoms are predictive of a psychotic illness in the child’s future developmental trajectory. These results, to be read in the journal eLife, will enable early treatment of children deemed to be at risk of developing psychological disorders, with the aim of preventing or even avoiding them.
One in 4,000 people have a microdeletion of chromosome 22, which can lead to the development of psychotic illnesses, such as schizophrenia, in adolescence. However, only one third of them will eventually be affected by a psychotic disorder. How can we determine which ones? “For the time being, the analyses are looking at the neurobiological mechanisms involved in psychological disorders, as well as the presence of certain symptoms that are assimilated to a psychological illness, without knowing which are the most relevant,” explains Corrado Sandini, a researcher at the Department of Psychiatry of UNIGE Faculty of Medicine, to the Fondation Pôle Autisme and first author of the study.
Not being able to take into account the degree of importance of each symptom can be problematic in predicting the course of the disease and providing the most appropriate treatment for the patient. “This is why we thought of using the network analysis method,” he continues. This methodology, which is currently used on adults, makes it possible to combine variables from completely different worlds in the same analysis space, while considering them individually. “Since the development of psychotic illnesses depends on many variables other than purely neurobiological ones, this algorithm would make it possible to highlight the most important symptoms to alert about the potential risks of a child becoming schizophrenic, for example,” says Stéphan Eliez, professor in the Department of Psychiatry at the UNIGE Faculty of Medicine and to the Fondation Pôle Autisme.
Finding the predictive symptoms
The Geneva team has joined forces with researchers at EPFL to develop this methodology and apply it to a cohort of children and adolescents suffering from a microdeletion of chromosome 22, some of whom have been followed for more than twenty years. “The aim is to adapt network analysis by tailoring it to young patients in a longitudinal manner, in order to obtain insightful statistics on highly intertwined variables throughout the child’s developmental trajectory,” emphasises Dimitri Van De Ville, a professor in the Department of Radiology and Medical Informatics at UNIGE Faculty bof Medicine and at the EPFL Institute of Bioengineering. The aim is to find the variables in childhood that will foresay the development of psychotic illnesses. “We will therefore know which battle to fight, thanks to key factors that will enable us to act where and, above all, when it is necessary,” explains Stéphan Eliez. “If we can identify them, we can try to regulate the symptom to reduce the risk of developing a psychotic illness later on.”
To test the methodology, 40 variables were taken into account for 70 children suffering from a microdeletion of chromosome 22, observed every three years from childhood to adulthood. “These variables included hallucinations, general mood, feelings of guilt and the management of daily stress,” explains Corrado Sandini. Questionnaires completed by parents also provided valuable data. Visual representations then shed light/highlighted/determined the most important variables that predict the development of psychological problems three years later. “We found that an anxious 10-year-old whose anxiety turns into an inability to cope with stress in adolescence is likely to develop a psychological illness. The evolution of anxiety is therefore a significant warning signal,” continues the Geneva researcher. Similarly, sadness, which over time becomes a feeling of guilt, is also a very important symptom.
A personalised method for each child
In order to confirm the results of their algorithm, the researchers applied it to other cohorts vulnerable to psychotic illnesses that have been followed for many years, and were thus able to confirm that the computer tool works. The aim is now to use it as a predictive tool, but also to refine it by integrating other variables, such as weight, to contribute to the clinical assessment. Finally, the interest of this method is obviously the prediction, with the aim of avoiding the disease, but above all its fully personalised quality that studies the developmental trajectory specific to each child. More

Scientists have shown that orangutan call signals believed to be closest to the precursors to human language, travel through forest over long distances without losing their meaning. This throws into question the accepted mathematical model on the evolution of human speech according to researchers from the University of Warwick.
The currently accepted model, developed by mathematicians, predicts that human ancestors strung sounds together in their calls in order to increase their chances of carrying a signal’s content to a recipient over distance. Because signal quality degrades over larger distances, it is proposed that human ancestors started linking sounds together to effectively convey a package of information even if it is distorted.
Researchers from the University of Warwick’s Department of Psychology set out to collect empirical data to investigate the model. They selected a range of sounds from previously collected audio recordings of orangutan communications. Specific consonant-like and vowel-like signals were played out and re-recorded across the rainforest at set distances of 25, 50, 75 and 100 metres. The quality and content of the signals received were analysed. The results are revealed in the study: Orangutan information broadcast via consonant-like and vowel-like calls breaches mathematical models of linguistic evolution published today in Biology Letters.
The team found that although the quality of the signal may have degraded, the content of the signal was still intact — even at long distance. In fact the informational characteristics of calls remained uncompromised until the signal became inaudible. This calls into question the existing and accepted theory of language development.
Dr Adriano Lameira, an evolutionary psychologist from the University of Warwick, led the study. He said:
“We used our bank of audio data recordings from our studies of orangutan in Indonesia. We selected the clear vowel-like and consonant-like signals and played them out and re-recorded them over measured distances in a rainforest setting. The purpose of this study was to look at the signals themselves and understand how they behaved as a package of information. This study is neat because it is only across distance that you can hope to assess this error limit theory — it disregards other aspects of communication like gestures, postures, mannerisms and facial expressions. More

Medical sensing technology has taken great strides in recent years, with the development of wearable devices that can track pulse, brain function, biomarkers in sweat and more. However, there is one big problem with existing wearable pressure sensors: even the slightest amount of pressure, something as light as a tight long sleeve shirt over a sensor, can throw them off track.
Texas Engineers have solved this problem, which has been vexing the field for years now. And they did it by innovating a first-ever hybrid sensing approach that allows the device to possess properties of the two predominant types of sensors in use today.
“The field of flexible pressure sensors is extremely crowded, and after two decades we hit a bottleneck because no one could solve the tradeoff between pressure and sensitivity,” said Nanshu Lu, an associate professor in the Department of Aerospace Engineering and Engineering Mechanics and the corresponding author of the new research published today in Advanced Materials. “This is the first sensor able to leverage a new hybrid mode to withstand pressure without a significant decay in sensitivity.”
Soft pressure sensors today are generally made of three layers — a deformable sensing layer sandwiched in between a pair of electrodes. These sensors generally fall into one of two categories — piezo-capacitive and piezo-resistive.
Lu’s team utilized an electrically conductive and highly porous nanocomposite as the sensing layer and added an extra insulating layer to the sensor, which gave it capabilities of both types of sensors. This new hybrid sensing is what allows it to better withstand pressure.
Typical sensors experience a 10-fold decline in sensitivity when experiencing any pressure beyond a slight touch. This sensor, applied to a test subject’s forehead, was able to withstand the pressure of a tight-fitting virtual reality headset on top of it with only a minimal loss in sensitivity. Pressure can not only cause a loss of accuracy in many sensors, but it can blunt the ability to deliver a reading at all.
“As we apply external pressure, the sensitivity drops, but is still on par with other sensors at zero pressure,” said Lu, who also has appointments in the Department of Electrical and Computer Engineering, Walker Department of Mechanical Engineering, Department of Biomedical Engineering and UT Austin’s Texas Materials Institute.
Lu has long been a pioneer in this sensing field, primarily through her electronic tattoo technology — a series of devices that are so lightweight and stretchable that they can be placed over the heart, the brain, or the muscle for extended periods with little or no discomfort.
But, Lu has even grander visions for these sensors and e-tattoos. She is working on ways to allow the sensor material to be wrapped around almost any object and give it the sensitivity of human skin. The most obvious application is wrapping it around robotic hands and fingers to give them the ability to recognize objects by touching them. But there are many other things it could do.
“The applications could be unlimited,” Lu said. “Stretchable, e-skin could be wrapped around almost any object.”
Video of Soft Pressure Sensor Breakthrough: https://www.youtube.com/watch?v=AXcGxeOYLkY
Story Source:
Materials provided by University of Texas at Austin. Note: Content may be edited for style and length. More

When Kamlendra Singh flew back to Missouri from India in April, he developed a cough and fever on the plane, despite being vaccinated for COVID-19 and testing negative for the virus right before departure.
Still, Singh tested positive for COVID-19, most likely due to infection from the Delta variant, upon his arrival home in Boone County — a diagnosis other fully vaccinated people and those who have already tested positive for the contagious virus were experiencing. He wanted to know why.
Following his recovery at home, Singh, a professor in the MU College of Veterinary Medicine and Bond Life Sciences Center, teamed up with MU undergraduate student Austin Spratt, Saathvik Kannan, a freshman at Hickman High School, and Siddappa Byrareddy, a professor at the University of Nebraska Medical Center, to analyze protein sequences for more than 300,000 COVID-19 samples of two emerging variants around the world, known as Delta and Delta Plus.
Using bioinformatics tools and programming, the team identified five specific mutations that are far more prevalent in Delta Plus infections compared to Delta infections, including one mutation, K417N, that is present in all Delta Plus infections but not present in nearly any Delta infections. The findings provide important clues to researchers about the structural changes to the virus recently and highlight the need to expand the toolbox in the fight against COVID-19.
“Whether it is natural antibodies produced from previously having COVID-19 or the antibodies produced from the vaccine, we are showing structurally how dangerous and clever the virus is by being able to mutate in a way that the antibodies don’t seem to recognize and defend against these new variants,” Spratt said. “These findings help explain why there have been so many people testing positive for the Delta variants despite being vaccinated or having previously been infected with COVID-19.”
Singh explained that while COVID-19 vaccines have been effective, another possible tool in responding to the pandemic could be the development of antiviral drugs that target specific areas of the virus that remain unchanged by mutations.
“There has not yet been a vaccine for HIV due to the unpredictable variability that often comes with viruses that mutate frequently,” Singh said. “If we can develop small molecule drugs that target the part of the virus that does not mutate, that will be the ultimate solution for combatting the virus.”
“Evolutionary analysis of the Delta and Delta Plus variants of the SARS-CoV-2 viruses” was recently published in the Journal of Autoimmunity. Funding was provided by MU’s Bond Life Sciences Center and the National Strategic Research Institute at the University of Nebraska.
Story Source:
Materials provided by University of Missouri-Columbia. Note: Content may be edited for style and length. More

Most emerging infectious diseases of humans (like COVID-19) are zoonotic — caused by viruses originating from other animal species. Identifying high-risk viruses earlier can improve research and surveillance priorities. A study publishing in PLOS Biology on September 28th by Nardus Mollentze, Simon Babayan, and Daniel Streicker at University of Glasgow, United Kingdom suggests that machine learning (a type of artifical intelligence) using viral genomes may predict the likelihood that any animal-infecting virus will infect humans, given biologically relevant exposure.
Identifying zoonotic diseases prior to emergence is a major challenge because only a small minority of the estimated 1.67 million animal viruses are able to infect humans. To develop machine learning models using viral genome sequences, the researchers first compiled a dataset of 861 virus species from 36 families. They then built machine learning models, which assigned a probability of human infection based on patterns in virus genomes. The authors then applied the best-performing model to analyze patterns in the predicted zoonotic potential of additional virus genomes sampled from a range of species.
The researchers found that viral genomes may have generalizable features that are independent of virus taxonomic relationships and may preadapt viruses to infect humans. They were able to develop machine learning models capable of identifying candidate zoonoses using viral genomes. These models have limitations, as computer models are only a preliminary step of identifying zoonotic viruses with potential to infect humans. Viruses flagged by the models will require confirmatory laboratory testing before pursuing major additional research investments. Further, while these models predict whether viruses might be able to infect humans, the ability to infect is just one part of broader zoonotic risk, which is also influenced by the virus’ virulence in humans, ability to transmit between humans, and the ecological conditions at the time of human exposure.
According to the authors, “Our findings show that the zoonotic potential of viruses can be inferred to a surprisingly large extent from their genome sequence. By highlighting viruses with the greatest potential to become zoonotic, genome-based ranking allows further ecological and virological characterisation to be targeted more effectively.”
“These findings add a crucial piece to the already surprising amount of information that we can extract from the genetic sequence of viruses using AI techniques,” Babayan adds. “A genomic sequence is typically the first, and often only, information we have on newly-discovered viruses, and the more information we can extract from it, the sooner we might identify the virus’ origins and the zoonotic risk it may pose. As more viruses are characterized, the more effective our machine learning models will become at identifying the rare viruses that ought to be closely monitored and prioritized for preemptive vaccine development.”
Story Source:
Materials provided by PLOS. Note: Content may be edited for style and length. More