Aurelia Butler

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  • 63 Shares169 Views

    in Computers Math

    Shifting colors for on-chip photonics

    The ability to precisely control and change properties of a photon, including polarization, position in space, and arrival time, gave rise to a wide range of communication technologies we use today, including the Internet. The next generation of photonic technologies, such as photonic quantum networks and computers, will require even more control over the properties of a photon.
    One of the hardest properties to change is a photon’s color, otherwise known as its frequency, because changing the frequency of a photon means changing its energy.
    Today, most frequency shifters are either too inefficient, losing a lot of light in the conversion process, or they can’t convert light in the gigahertz range, which is where the most important frequencies for communications, computing, and other applications are found.
    Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed highly efficient, on-chip frequency shifters that can convert light in the gigahertz frequency range. The frequency shifters are easily controlled, using continuous and single-tone microwaves.
    “Our frequency shifters could become a fundamental building block for high-speed, large-scale classical communication systems as well as emerging photonic quantum computers,” said Marko Lončar, the Tiantsai Lin Professor of Electrical Engineering and senior author of the paper.
    The paper outlines two types of on-chip frequency shifter — one that can covert one color to another, using a shift of a few dozen gigahertz, and another that can cascade multiple shifts, a shift of more than 100 gigahertz. More

  • 113 Shares109 Views

    in Heart

    50 years ago, corporate greenwashing was well under way

    Environmental advertising: A question of integrity— Science News, November 27, 1971

    A new report published by the Council on Economic Priorities clearly outlines facts showing that much corporate advertising on environmental themes is irrelevant or even deceptive.… A large percentage of the environmental advertising comes from companies that are the worst polluters.

    Update

    Concerns about “greenwashing,” a term coined in the 1980s to describe the practice of organizations marketing their products as environmentally friendly when they are not, have persisted into the current climate crisis. As more consumers have become environmentally conscious, corporations’ greenwashing tactics have evolved. For instance, some energy companies in the United States have claimed that natural gas is a “clean” energy source because the power plants emit less carbon dioxide than coal plants. But natural gas plants can emit large amounts of methane, a potent greenhouse gas. In 2022, the U.S. Federal Trade Commission plans to review its “Green Guides,” rules for companies that make environmental claims. More

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    in Heart

    Albatrosses divorce more often when ocean waters warm

    When it comes to fidelity, birds fit the bill: Over 90 percent of all bird species are monogamous and — mostly — stay faithful, perhaps none more famously than the majestic albatross. Albatross couples rarely separate, sticking with the same breeding partner year after year. But when ocean waters are warmer than average, more of the birds split up, a new study finds.

    In years when the water was warmer than usual, the divorce rate — typically less than 4 percent on average — rose to nearly 8 percent among albatrosses in part of the Falkland Islands, researchers report November 24 in Proceedings of the Royal Society B. It’s the first evidence that the environment, not just breeding failure, affects divorce in wild birds. In fact, the team found that during warmer years, even some females that had bred successfully ditched their partners.

    The result suggests that as the climate changes as a result of human activity, higher instances of divorce in albatrosses and perhaps other socially monogamous animals may be “an overlooked consequence,” the researchers write.

    Albatrosses can live for decades, sometimes spending years out on the ocean searching for food and returning to land only to breed. Pairs that stay together have the benefits of familiarity and improved coordination, which help when raising young. This stability is particularly important in dynamic, marine environments, says Francesco Ventura, a conservation biologist at the University of Lisbon in Portugal.

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    But if breeding doesn’t work out, many birds — mostly females — leave their partner and try to find better luck elsewhere (SN: 3/7/98). Breeding is more likely to fail in years with more difficult conditions, with knock-on effects on divorce rates the following years. Ventura wanted to find out whether the environment also has a direct impact: changing the rate of divorce regardless of whether the breeding had gone well.

    Ventura and his team analyzed data collected from 2004 to 2019 on a large colony of black-browed albatrosses (Thalassarche melanophris) living on New Island in the Falkland Islands. The team recorded nearly 2,900 breeding attempts in 424 females, and tracked bird breakups. Then, accounting for previous breeding success in individual pairs, the researchers checked to see if environmental conditions had any noticeable further impact on pairings.

    Breeding failure, especially early on, was still the main factor behind a divorce: Each female lays just a single egg, and those birds whose eggs didn’t hatch were over five times as likely to separate from their partners as those who succeeded, or those whose hatched chicks didn’t survive. In some years, the divorce rate was lower than 1 percent.

    Yet this rate increased in line with average water temperatures, reaching a maximum of 7.7 percent in 2017 when waters were the warmest. The team’s calculations revealed that the probability of divorce was correlated with rising temperatures. And surprisingly, females in successful breeding pairs were more likely to be affected by the harsher environment than males or females that either didn’t breed, or failed. When ocean temperatures dropped again in 2018 and 2019, so did divorce rates.

    Warmer water means fewer nutrients, so some birds may be fueling up out at sea for longer, delaying their return to the colony or turning up bedraggled and unappealing. If members of pairs return at different times, this can lead to breakups (SN: 10/6/04).

    What’s more, worse conditions one year might raise stress-related hormones in the birds too, which can affect mate choice. A bird may incorrectly attribute its stress to its partner, rather than the harsher environment, and separate even if hatching was successful, the researchers speculate.

    Such misreading between cues and reality could make separation a less-effective behavior, suggests Antica Culina, an evolutionary ecologist at the Netherlands Institute of Ecology in Wageningen who was not involved in the study. If animals divorce for the wrong reason and do worse the following season, that can lead to lower breeding success overall and possibly population decline.

    Similar patterns could be found in other socially monogamous animals, including mammals, the researchers suggest. “If you imagine a population with a very low number of breeding pairs … this might have much more serious repercussions,” Ventura says. More

  • 113 Shares179 Views

    in Computers Math

    AI used to optimize several flow battery properties simultaneously

    Scientists seek stable, high-energy batteries designed for the electric grid.
    Bringing new sources of renewable energy like wind and solar power onto the electric grid will require specially designed large batteries that can charge when the sun is shining and give energy at night. One type of battery is especially promising for this purpose: the flow battery. Flow batteries contain two tanks of electrically active chemicals that exchange charge and can have large volumes that hold a lot of energy.
    For researchers working on flow batteries, their chief concern involves finding target molecules that offer the ability to both store a lot of energy and remain stable for long periods of time.
    To find the right flow battery molecules, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have turned to the power of artificial intelligence (AI) to search through a vast chemical space of over a million molecules. Discovering the right molecules requires optimizing between several different characteristics. “In these batteries, we know that a majority of the molecules that we need will have to satisfy multiple properties,” said Argonne chemist Rajeev Assary. “By optimizing several properties simultaneously, we have a better shot of finding the best possible chemistry for our battery.”
    In a new study that follows on from work done last year, Assary and his colleagues in Argonne’s Joint Center for Energy Storage Research modeled anolyte redoxmers, or electrically active molecules in a flow battery. For each redoxmer, the researchers identified three properties that they wanted to optimize. The first two, reduction potential and solvation free energy, relate to how much energy the molecule can store. The third, fluorescence, serves as a kind of self-reporting marker that indicates the overall health of the battery.
    Because it is extraordinarily time consuming to calculate the properties of interest for all potential candidates, the researchers turned to a machine learning and AI technique called active learning, in which a model can actually train itself to identify increasingly plausible targets. “We’re essentially looking for needles in haystacks,” said Argonne postdoctoral researcher Hieu Doan. “When our model finds something that looks like a needle, it teaches itself how to find more.”
    For the most efficient use of active learning, the researchers started with a fairly small “haystack” — a dataset of 1400 redoxmer candidates whose properties they already knew from quantum mechanical simulations. By using this dataset as practice, they were able to see that the algorithm correctly identified the molecules with the best properties. More

  • 100 Shares149 Views

    in Computers Math

    New chip hides wireless messages in plain sight

    Emerging 5G wireless systems are designed to support high-bandwidth and low-latency networks connecting everything from autonomous robots to self-driving cars. But these large and complex communication networks could also pose new security concerns.
    Encryption methods now used to secure communications from eavesdroppers can be challenging to scale towards such high-speed and ultra-low latency systems for 5G and beyond. This is because the very nature of encryption requires exchange of information between sender and receiver to encrypt and decrypt a message. This exchange makes the link vulnerable to attacks; it also requires computing that increases latency. Latency, the amount of time between sending instructions on a network and the arrival of the data, is a key measure for tasks like autonomous driving and industrial automation. For networks that support latency-critical systems such as self-driving cars, robots and other cyber-physical systems, minimizing time-to-action is critical.
    Seeking to close this security gap, Princeton University researchers have developed a methodology that incorporates security in the physical nature of the signal. In a report published Nov. 22 in Nature Electronics, the researchers describe how they developed a new millimeter-wave wireless microchip that allows secure wireless transmissions to prevent interception without reducing latency, efficiency and speed of the 5G network. According to senior researcher Kaushik Sengupta, the technique should make it very challenging to eavesdrop on such high-frequency wireless transmissions, even with multiple colluding bad actors.
    “We are in a new era of wireless — the networks of the future are going to be increasingly complex while serving a large set of different applications that demand very different features,” Sengupta said. “Think low-power smart sensors in your home or in an industry, high-bandwidth augmented reality or virtual reality, and self-driving cars. To serve this and serve this well, we need to think about security holistically and at every level.”
    Instead of relying on encryption, the Princeton method shapes the transmission itself to foil would-be eavesdroppers. To explain this, it helps to picture wireless transmissions as they emerge from an array of antennas. With a single antenna, radio waves radiate from the antenna in a wave. When there are multiple antennas working as an array, these waves interfere with each other like waves of water in a pond. The interference increases the size of some wave crests and troughs and smooths out others.
    An array of antennas is able to use this interference to direct a transmission along a defined path. But besides the main transmission, there are secondary paths. These secondary paths are weaker than the main transmission, but in a typical system they contain the exact same signal as the main path. By tapping these paths, potential eavesdroppers can compromise the transmission. More

  • 163 Shares109 Views

    in Computers Math

    New method gives rapid, objective insight into how cells are changed by disease

    A new “image analysis pipeline” is giving scientists rapid new insight into how disease or injury have changed the body, down to the individual cell.
    It’s called TDAExplore, which takes the detailed imaging provided by microscopy, pairs it with a hot area of mathematics called topology, which provides insight on how things are arranged, and the analytical power of artificial intelligence to give, for example, a new perspective on changes in a cell resulting from ALS and where in the cell they happen, says Dr. Eric Vitriol, cell biologist and neuroscientist at the Medical College of Georgia.
    It is an “accessible, powerful option” for using a personal computer to generate quantitative — measurable and consequently objective — information from microscopic images that likely could be applied as well to other standard imaging techniques like X-rays and PET scans, they report in the journal Patterns.
    “We think this is exciting progress into using computers to give us new information about how image sets are different from each other,” Vitriol says. “What are the actual biological changes that are happening, including ones that I might not be able to see, because they are too minute, or because I have some kind of bias about where I should be looking.”
    At least in the analyzing data department, computers have our brains beat, the neuroscientist says, not just in their objectivity but in the amount of data they can assess. Computer vision, which enables computers to pull information from digital images, is a type of machine learning that has been around for decades, so he and his colleague and fellow corresponding author Dr. Peter Bubenik, a mathematician at the University of Florida and an expert on topological data analysis, decided to partner the detail of microscopy with the science of topology and the analytical might of AI. Topology and Bubenik were key, Vitriol says.
    Topology is “perfect” for image analysis because images consist of patterns, of objects arranged in space, he says, and topological data analysis (the TDA in TDAExplore) helps the computer also recognize the lay of the land, in this case where actin — a protein and essential building block of the fibers, or filaments, that help give cells shape and movement — has moved or changed density. It’s an efficient system, that instead of taking literally hundreds of images to train the computer how to recognize and classify them, it can learn on 20 to 25 images. More

  • 75 Shares189 Views

    in Computers Math

    Origami, kirigami inspire mechanical metamaterials designs

    The ancient arts of origami, the art of paper-folding, and kirigami, the art of paper-cutting, have gained popularity in recent years among researchers building mechanical metamaterials. Folding and cutting 2D thin-film materials transforms them into complex 3D structures and shapes with unique and programmable mechanical properties.
    In Applied Physics Reviews, by AIP Publishing, researchers in the United States and China categorize origami- and kirigami-based mechanical metamaterials, artificially engineered materials with unusual mechanical properties, into six groups based on two different criteria.
    “Origami and kirigami are, by nature, mechanical metamaterials, because their properties are mainly determined by how the crease patterns and/or cuts are made and just slightly depend on the material that folds the origami or kiragami,” said author Hanqing Jiang.
    The researchers divided the mechanical metamaterials into three categories that include origami-based metamaterials (folding only), kirigami-based metamaterials (cutting only), and hybrid origami-kirigami metamaterials (both folding and cutting). The hybrid origami-kirigami metamaterials, in particular, offer great potential in shape morphing.
    Each group was subdivided into a rigid or deformable category based on the elastic energy landscape. Metamaterials were classified as rigid if energy was stored in the creases or linkages only. Metamaterials were put in the deformable category if energy was stored in both creases or linkages and panels.
    The researchers want to discover new origami and kirigami designs, especially curved origami designs, hybrid origami-kirigami designs, modular designs, and hierarchical designs.
    They plan to focus on the selection of new materials for origami- and kirigami-based mechanical metamaterials. Traditionally paper is used to prototype metamaterials but there are limits based on the fragility and plasticity of paper. To design for real-world applications, it will be helpful to explore materials with different properties such as thin or thick, soft or hard, and elastic or plastic.
    They want to use the energy landscape and energy distribution as two powerful tools to analyze mechanical performances of origami and kirigami and will seek to carefully design the actuation method of origami- and kirigami-based mechanical metamaterials.
    “Origami- and kiragami-based mechanical metamaterials can be applied in many fields, including flexible electronics, medical devices, robotics, civil engineering and aerospace engineering,” said Jiang.
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  • 138 Shares199 Views

    in Computers Math

    Social stress key to population's rate of COVID-19 infection, study finds

    Mathematicians have analysed global COVID-19 data to identify two constants which can drastically change a country’s rate of infection.
    An international team of researchers led by Professor Alexander Gorban from the University of Leicester used available data from 13 countries to determine the rate of stress response, or ‘mobilisation’ and the rate of spontaneous exhaustion, or ‘demobilisation’.
    Their findings, published in Scientific Reports, show that social stress — which varied broadly across the countries studied — drives the multi-wave dynamics of COVID-19 outbreaks.
    The study analysed data from China, the USA, UK, Germany, Colombia, Italy, Spain, Israel, Russia, France, Brazil, India, and Iran — and contributed to the research team’s proposed new system of models, which combine the dynamics of the established concept of social stress with classical epidemic models.
    Alexander Gorban is a Professor of Applied Mathematics at the University of Leicester, and Director of the Centre for Artificial Intelligence, Data Analysis and Modelling. Professor Gorban said:
    “We tried to use the pandemic for research and quantify the social and cultural differences between countries. We measured how variable countries are in two processes: mobilisation of people for rational protective behaviour and exhaustion of this mobilisation with destroying of rational behaviour. More