Aurelia Butler

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    in Heart

    Collapse may not always be inevitable for marine ice cliffs

    When it comes to global warming and sea level rise, scientists have made some dire predictions. One of the most calamitous involves the widespread collapse of ice cliffs along the edges of Greenland and Antarctica, which could raise sea level as much as 4 meters by 2200 (SN: 2/6/19). Now, new simulations suggest that massive glaciers flowing into the sea may not be as vulnerable to such collapses as once believed.

    One hypothesis that projected calamitous sea level rise is called the marine ice cliff instability. It suggests that sea-facing bluffs of ice more than 100 meters tall will fail and then slough off to expose fresh ice. Those new cliffs will in turn disintegrate, fall into the sea and float away, setting off a relatively rapid retreat of the glacier that boosts sea level rise.

    Although discussed for years, the phenomenon hasn’t yet been seen in today’s glaciers, says Jeremy Bassis, a glaciologist at the University of Michigan in Ann Arbor. “But that may not be surprising, due to the relatively short record of observations in the field and by satellites,” he says.

    Because of the dearth of field data, Bassis and colleagues decided to use computer simulations to explore ice-cliff behavior. Unlike previous models, the researchers’ simulations considered how ice flows under pressure as well as how it fractures when highly stressed. This blended model is “a pioneering composite,” says Nicholas Golledge, a glaciologist at the Victoria University of Wellington in New Zealand, who wasn’t involved in the study.

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    First, the researchers simulated the collapse of a 135-meter-tall ice cliff on dry land. Over a virtual period of weeks, the face of the cliff shattered and then slumped down to the base, where the icy rubble helped buttress the cliff against further collapse. Researchers have often seen this result in the field, Bassis says.

    Then, the team simulated a 400-meter-tall glacier flowing into water that was 290 meters deep. These dimensions are typical of some of the massive glaciers in Greenland flowing into deep fjords, Bassis says. When the cybercliff collapsed, ice that fell into the water at the cliff’s base floated away, leading to repeated failures and rapid, runaway collapse of the glacier. But adding even a small amount of back pressure at the base of the cliff — as would happen if icebergs got stuck and couldn’t waft away, or if they froze in place — prevented a runaway collapse, Bassis and his team reports in the June 18 Science. “We didn’t expect this to be the case,” Bassis says. “But if small bergs got stuck in the shallows ahead of the ice cliff, it was enough to buttress the [cliff] face,” he says.

    Simulations of an 800-meter-tall glacier flowing into 690 meters of water, comparable to the dimensions of the Thwaites and Pine Island glaciers in Antarctica, yielded similar results. The researchers also found that in relatively warm ambient temperatures, ice flow upstream of the cliff thins the glacier and reduces the height of the cliff, thus reducing the likelihood of runaway collapses.

    The team’s simulations “capture what I think of as realistic behavior,” says Golledge, who coauthored a commentary on the study in the same issue of Science. Future fieldwork may help validate the group’s results. If the simulations hold, Golledge says, the less dire results may mean slower sea level rise in the short term than otherwise predicted.

    Bassis and his colleagues’ analysis “is an important piece of work,” says Ted Scambos, a glaciologist at the University of Colorado Boulder, who was not involved in the study. The results, he says, “provide a balance between the possibilities for extreme runaway collapse and some that are more realistic.” More

  • 113 Shares179 Views

    in Physics

    Physicists used LIGO’s mirrors to approach a quantum limit

    Quantum mechanics usually applies to very small objects: atoms, electrons and the like. But physicists have now brought the equivalent of a 10-kilogram object to the edge of the quantum realm.

    Scientists with the Advanced Laser Interferometer Gravitational-Wave Observatory, or LIGO, reduced vibrations in a combination of the facility’s mirrors to nearly the lowest level allowed by quantum mechanics, they report in the June 18 Science.

    The researchers quelled differences between the jiggling of LIGO’s four 40-kilogram mirrors, putting them in near-perfect sync. When the mirrors are combined in this way, they behave effectively like a single, 10-kilogram object.

    LIGO is designed to measure gravitational waves, using laser light that bounces between sets of mirrors in the detector’s two long arms (SN: 2/11/16). But physicist Vivishek Sudhir of MIT and colleagues instead used the laser light to monitor the mirrors’ movements to extreme precision and apply electric fields to resist the motion. “It’s almost like a noise-canceling headphone,” says Sudhir. But instead of measuring nearby sounds and canceling out that noise, the technique cancels out motion.

    The researchers reduced the mirrors’ relative motions to about 10.8 phonons, or quantum units of vibration, close to the zero-phonon quantum limit.

    The study’s purpose is not to better understand gravitational waves, but to get closer to revealing secrets of quantum mechanics. Scientists are still trying to understand why large objects don’t typically follow the laws of quantum mechanics. Such objects lose their quantum properties, or decohere. Studying quantum states of more massive objects could help scientists pin down how decoherence happens.

    Previous studies have observed much smaller objects in quantum states. In 2020, physicist Markus Aspelmeyer of the University of Vienna and colleagues brought vibrations of a nanoparticle to the quantum limit (SN: 1/30/20). LIGO’s mirrors are “a fantastic system to study decoherence effects on super-massive objects in the quantum regime,” says Aspelmeyer. More

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    in Computers Math

    New method could reveal what genes we might have inherited from Neanderthals

    Using neural networks, researchers from the University of Copenhagen have developed a new method to search the human genome for beneficial mutations from Neanderthals and other archaic humans. These humans are known to have interbred with modern humans, but the overall fate of the genetic material inherited from them is still largely unknown. Among others, the researchers found previously unreported mutations involved in core pathways in metabolism, blood-related diseases and immunity.
    Thousands of years ago, archaic humans such as Neanderthals and Denisovans went extinct. But before that, they interbred with the ancestors of present-day humans, who still to this day carry genetic mutations from the extinct species.
    Over 40 percent of the Neanderthal genome is thought to have survived in different present-day humans of non-African descent, but spread out so that any individual genome is only composed of up to two percent Neanderthal material. Some human populations also carry genetic material from Denisovans — a mysterious group of archaic humans that may have lived in Eastern Eurasia and Oceania thousands of years ago.
    The introduction of beneficial genetic material into our gene pool, a process known as adaptive introgression, often happened because it was advantageous to humans after they expanded across the globe. To name a few examples, scientists believe some of the mutations affected skin development and metabolism. But many mutations are yet still undiscovered.
    Now, researchers from GLOBE Institute at the University of Copenhagen have developed a new method using deep learning techniques to search the human genome for undiscovered mutations.
    “We developed a deep learning method called ‘genomatnn’ that jointly models introgression, which is the transfer of genetic information between species, and natural selection. The model was developed in order to identify regions in the human genome where this introgression could have happened,” says Associate Professor Fernando Racimo, GLOBE Institute, corresponding author of the new study. More

  • 150 Shares169 Views

    in Computers Math

    Highly sensitive test for SARS-CoV-2 may enable rapid point-of-care testing for COVID

    A team of scientists headed by SANKEN (The Institute of Scientific and Industrial Research) at Osaka University demonstrated that single virus particles passing through a nanopore could be accurately identified using machine learning. The test platform they created was so sensitive that the coronaviruses responsible for the common cold, SARS, MERS, and COVID could be distinguished from each other. This work may lead to rapid, portable, and accurate screening tests for COVID and other viral diseases.
    The global coronavirus pandemic has revealed the crucial need for rapid pathogen screening. However, the current gold-standard for detecting RNA viruses — including SARS-CoV-2, the virus that causes COVID — is reverse transcription-polymerase chain reaction (RT-PCR) testing. While accurate, this method is relatively slow, which hinders the timely interventions required to control an outbreak.
    Now, scientists led by Osaka University have developed an intelligent nanopore system that can be used for the detection of SARS-CoV-2 virus particles. Using machine-learning methods, the platform can accurately discriminate between similarly sized coronaviruses responsible for different respiratory diseases. “Our innovative technology has high sensitivity and can even electrically identify single virus particles,” first author Professor Masateru Taniguchi says. Using this platform, the researchers were able to achieve a sensitivity of 90% and a specificity of 96% for SARS-CoV-2 detection in just five minutes using clinical saliva samples.
    To fabricate the device, nanopores just 300 nanometers in diameter were bored into a silicon nitride membrane. When a virus was pulled through a nanopore by the electrophoretic force, the opening became partially blocked. This temporarily decreased the ionic flow inside the nanopore, which was detected as a change in the electrical current. The current as a function of time provided information on the volume, structure, and surface charge of the target being analyzed. However, to interpret the subtle signals, which could be as small as a few nanoamps, machine learning was needed. The team used 40 PCR-positive and 40 PCR-negative saliva samples to train the algorithm.
    “We expect that this research will enable rapid point-of-care and screening tests for SARS-CoV-2 without the need for RNA extraction,” Professor Masateru Taniguchi explains. “A user-friendly and non-invasive method such as this is more amenable to immediate diagnosis in hospitals and screening in places where large crowds are gathered.” The complete test platform consists of machine learning software on a server, a portable high-precision current measuring instrument, and cost-effective semiconducting nanopore modules. By using a machine-learning method, the researchers expect that this system can be adapted for use in the detection of emerging infectious diseases in the future. The team hopes that this approach will revolutionize public health and disease control.
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  • 100 Shares189 Views

    in Computers Math

    Defining the Hund physics landscape of two-orbital systems

    Electrons are ubiquitous among atoms, subatomic tokens of energy that can independently change how a system behaves — but they also can change each other. An international research collaboration found that collectively measuring electrons revealed unique and unanticipated findings. The researchers published their results on May 17 in Physical Review Letters.
    “It is not feasible to obtain the solution just by tracing the behavior of each individual electron,” said paper author Myung Joon Han, professor of physics at KAIST. “Instead, one should describe or track all the entangled electrons at once. This requires a clever way of treating this entanglement.”
    Professor Han and the researchers used a recently developed “many-particle” theory to account for the entangled nature of electrons in solids, which approximates how electrons locally interact with one another to predict their global activity.
    Through this approach, the researchers examined systems with two orbitals — the space in which electrons can inhabit. They found that the electrons locked into parallel arrangements within atom sites in solids. This phenomenon, known as Hund’s coupling, results in a Hund’s metal. This metallic phase, which can give rise to such properties as superconductivity, was thought only to exist in three-orbital systems.
    “Our finding overturns a conventional viewpoint that at least three orbitals are needed for Hund’s metallicity to emerge,” Professor Han said, noting that two-orbital systems have not been a focus of attention for many physicists. “In addition to this finding of a Hund’s metal, we identified various metallic regimes that can naturally occur in generic, correlated electron materials.”
    The researchers found four different correlated metals. One stems from the proximity to a Mott insulator, a state of a solid material that should be conductive but actually prevents conduction due to how the electrons interact. The other three metals form as electrons align their magnetic moments — or phases of producing a magnetic field — at various distances from the Mott insulator. Beyond identifying the metal phases, the researchers also suggested classification criteria to define each metal phase in other systems.
    “This research will help scientists better characterize and understand the deeper nature of so-called ‘strongly correlated materials,’ in which the standard theory of solids breaks down due to the presence of strong Coulomb interactions between electrons,” Professor Han said, referring to the force with which the electrons attract or repel each other. These interactions are not typically present in solid materials but appear in materials with metallic phases.
    The revelation of metals in two-orbital systems and the ability to determine whole system electron behavior could lead to even more discoveries, according to Professor Han.
    “This will ultimately enable us to manipulate and control a variety of electron correlation phenomena,” Professor Han said.
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  • 75 Shares169 Views

    in Computers Math

    AI system-on-chip runs on solar power

    AI is used in an array of extremely useful applications, such as predicting a machine’s lifetime through its vibrations, monitoring the cardiac activity of patients and incorporating facial recognition capabilities into video surveillance systems. The downside is that AI-based technology generally requires a lot of power and, in most cases, must be permanently connected to the cloud, raising issues related to data protection, IT security and energy use.
    CSEM engineers may have found a way to get around those issues, thanks to a new system-on-chip they have developed. It runs on a tiny battery or a small solar cell and executes AI operations at the edge — i.e., locally on the chip rather than in the cloud. What’s more, their system is fully modular and can be tailored to any application where real-time signal and image processing is required, especially when sensitive data are involved. The engineers will present their device at the prestigious 2021 VLSI Circuits Symposium in Kyoto this June.
    The CSEM system-on-chip works through an entirely new signal processing architecture that minimizes the amount of power needed. It consists of an ASIC chip with a RISC-V processor (also developed at CSEM) and two tightly coupled machine-learning accelerators: one for face detection, for example, and one for classification. The first is a binary decision tree (BDT) engine that can perform simple tasks but cannot carry out recognition operations.
    “When our system is used in facial recognition applications, for example, the first accelerator will answer preliminary questions like: Are there people in the images? And if so, are their faces visible?” says Stéphane Emery, head of system-on-chip research at CSEM. “If our system is used in voice recognition, the first accelerator will determine whether noise is present and if that noise corresponds to human voices. But it can’t make out specific voices or words — that’s where the second accelerator comes in.”
    The second accelerator is a convolutional neural network (CNN) engine that can perform these more complicated tasks — recognizing individual faces and detecting specific words — but it also consumes more energy. This two-tiered data processing approach drastically reduces the system’s power requirement, since most of the time only the first accelerator is running.
    As part of their research, the engineers enhanced the performance of the accelerators themselves, making them adaptable to any application where time-based signal and image processing is needed. “Our system works in basically the same way regardless of the application,” says Emery. “We just have to reconfigure the various layers of our CNN engine.”
    The CSEM innovation opens the door to an entirely new generation of devices with processors that can run independently for over a year. It also sharply reduces the installation and maintenance costs for such devices, and enables them to be used in places where it would be hard to change the battery.
    Video: https://www.youtube.com/watch?v=2wJi4BHdXGo&t=2s
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  • 125 Shares189 Views

    in Computers Math

    New invention keeps qubits of light stable at room temperature

    As almost all our private information is digitalized, it is increasingly important that we find ways to protect our data and ourselves from being hacked.
    Quantum Cryptography is the researchers’ answer to this problem, and more specifically a certain kind of qubit — consisting of single photons: particles of light.
    Single photons or qubits of light, as they are also called, are extremely difficult to hack.
    However, in order for these qubits of light to be stable and work properly they need to be stored at temperatures close to absolute zero — that is minus 270 C — something that requires huge amounts of power and resources.
    Yet in a recently published study, researchers from University of Copenhagen, demonstrate a new way to store these qubits at room temperature for a hundred times longer than ever shown before.
    “We have developed a special coating for our memory chips that helps the quantum bits of light to be identical and stable while being in room temperature. In addition, our new method enables us to store the qubits for a much longer time, which is milliseconds instead of microseconds — something that has not been possible before. We are really excited about it,” says Eugene Simon Polzik, professor in quantum optics at the Niels Bohr Institute. More

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    in Heart

    A new book uses stories from tsunami survivors to decode deadly waves

    TsunamiJames Goff and Walter DudleyOxford Univ., $34.95

    On March 27, 1964, Ted Pederson was helping load oil onto a tanker in Seward, Alaska, when a magnitude 9.2 quake struck. Within seconds, the waterfront began sliding into the bay. As Pederson ran up the dock toward shore, a tsunami lifted the tanker and rafts of debris onto the dock, knocking him unconscious.

    Pederson survived, but more than 100 others in Alaska did not. His story is just one of more than 400 harrowing eyewitness accounts that bring such disasters to life in Tsunami. Written by geologist James Goff and oceanographer Walter Dudley, the book also weaves in accounts from researchers examining the geologic record to shed light on prehistoric tsunamis.

    Chapter by chapter, Goff and Dudley offer readers a primer on tsunamis: Most are caused by undersea earthquakes, but some are triggered by landslides, the sudden collapse of volcanic islands or meteorites hitting the ocean (SN: 3/6/04, p. 152). Readers may be surprised to learn that tsunamis need not occur on the coast: Lake Tahoe (SN: 6/10/00, p. 378) and New Zealand’s Lake Tarawera are just two of many inland locales mentioned that have experienced freshwater tsunamis.

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    Copiously illustrated and peppered with maps, the book takes readers on a world-spanning tour of ancient and recent tsunamis, from a deep-ocean impact off the coast of South America about 2.5 million years ago to numerous tsunamis of the 21st century. The authors’ somber treatment of the Indian Ocean tsunami of December 2004 stands out (SN: 1/8/05, p. 19). Triggered by a magnitude 9.1 earthquake, the megawave killed more than 130,000 people in Indonesia alone.

    The authors — Goff is a professor at the University of New South Wales in Sydney and Dudley is a researcher at the University of Hawaii at Hilo — help readers understand tsunamis’ power via descriptions of the damage they’ve wrought. For instance, the account of a huge wave in Alaska that scoured mature trees from steep slopes along fjords up to a height of 524 meters — about 100 meters taller than the Empire State Building — may leave readers stunned. But it’s the heart-thumping stories of survivors who ran to high ground, clambered up tall trees or clung to debris after washing out to sea that linger with the reader. They remind us of the human cost of living on the shore when great waves strike.

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