Aurelia Butler

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  • 125 Shares99 Views

    in Computers Math

    3D display could soon bring touch to the digital world

    Imagine an iPad that’s more than just an iPad — with a surface that can morph and deform, allowing you to draw 3D designs, create haiku that jump out from the screen and even hold your partner’s hand from an ocean away.
    That’s the vision of a team of engineers from the University of Colorado Boulder. In a new study, they’ve created a one-of-a-kind shape-shifting display that fits on a card table. The device is made from a 10-by-10 grid of soft robotic “muscles” that can sense outside pressure and pop up to create patterns. It’s precise enough to generate scrolling text and fast enough to shake a chemistry beaker filled with fluid.
    It may also deliver something even rarer: the sense of touch in a digital age.
    “As technology has progressed, we started with sending text over long distances, then audio and now video,” said Brian Johnson, one of two lead authors of the new study who earned his doctorate in mechanical engineering at CU Boulder in 2022. “But we’re still missing touch.”
    Johnson and his colleagues described their shape display July 31 in the journal Nature Communications.
    The group’s innovation builds off a class of soft robots pioneered by a team led by Christoph Keplinger, formerly an assistant professor of mechanical engineering at CU Boulder. They’re called Hydraulically Amplified Self-Healing ELectrostatic (HASEL) actuators. The prototype display isn’t ready for the market yet. But the researchers envision that, one day, similar technologies could lead to sensory gloves for virtual gaming or a smart conveyer belt that can undulate to sort apples from bananas.
    “You could imagine arranging these sensing and actuating cells into any number of different shapes and combinations,” said Mantas Naris, co-lead author of the paper and a doctoral student in the Paul M. Rady Department of Mechanical Engineering. “There’s really no limit to what these technologies could, ultimately, lead to.”
    Playing the accordion More

  • 63 Shares189 Views

    in Computers Math

    Way cool: ‘freeze ray’ technology

    You know that freeze-ray gun that “Batman” villain Mr. Freeze uses to “ice” his enemies? A University of Virginia professor thinks he may have figured out how to make one in real life.
    The discovery — which, unexpectedly, relies on heat-generating plasma — is not meant for weaponry, however. Mechanical and aerospace engineering professor Patrick Hopkins wants to create on-demand surface cooling for electronics inside spacecraft and high-altitude jets.
    “That’s the primary problem right now,” Hopkins said. “A lot of electronics on board heat up, but they have no way to cool down.”
    The U.S. Air Force likes the prospect of a freeze ray enough that it has granted the professor’s ExSiTE Lab (Experiments and Simulations in Thermal Engineering) $750,000 over three years to study how to maximize the technology.
    From there, the lab will partner with Hopkins’ UVA spinout company, Laser Thermal, for the fabrication of a prototype device.
    The professor explained that, on Earth — or in the air closer to it — the electronics in military craft can often be cooled by nature. The Navy, for example, uses ocean water as part of its liquid cooling systems. And closer to the ground, the air is dense enough to help keep aircraft components chilled.
    However, “With the Air Force and Space Force, you’re in space, which is a vacuum, or you’re in the upper atmosphere, where there’s very little air that can cool,” he said. “So what happens is your electronics keep getting hotter and hotter and hotter. And you can’t bring a payload of coolant onboard because that’s going to increase the weight, and you lose efficiency.”
    Hopkins believes he’s on track toward a lightweight solution. He and collaborators recently published a review article about this and other prospects for the technology in the journal ACS Nano. More

  • 175 Shares179 Views

    in Computers Math

    Researchers successfully train a machine learning model in outer space for the first time

    For the first time, a project led by the University of Oxford has trained a machine learning model in outer space, on board a satellite. This achievement could revolutionise the capabilities of remote-sensing satellites by enabling real-time monitoring and decision making for a range of applications.
    Data collected by remote-sensing satellites is fundamental for many key activities, including aerial mapping, weather prediction, and monitoring deforestation. Currently, most satellites can only passively collect data, since they are not equipped to make decisions or detect changes. Instead, data has to be relayed to Earth to be processed, which typically takes several hours or even days. This limits the ability to identify and respond to rapidly emerging events, such as a natural disaster.
    To overcome these restrictions, a group of researchers led by DPhil student Vít Růžička (Department of Computer Science, University of Oxford), took on the challenge of training the first machine learning program in outer space. During 2022, the team successfully pitched their idea to the Dashing through the Stars mission, which had issued an open call for project proposals to be carried out on board the ION SCV004 satellite, launched in January 2022. During the autumn of 2022, the team uplinked the code for the program to the satellite already in orbit.
    The researchers trained a simple model to detect changes in cloud cover from aerial images directly onboard the satellite, in contrast to training on the ground. The model was based on an approach called few-shot learning, which enables a model to learn the most important features to look for when it has only a few samples to train from. A key advantage is that the data can be compressed into smaller representations, making the model faster and more efficient.
    Vít Růžička explained: ‘The model we developed, called RaVAEn, first compresses the large image files into vectors of 128 numbers. During the training phase, the model learns to keep only the informative values in this vector; the ones that relate to the change it is trying to detect (in this case, whether there is a cloud present or not). This results in extremely fast training due to having only a very small classification model to train.’
    Whilst the first part of the model, to compress the newly-seen images, was trained on the ground, the second part (which decided whether the image contained clouds or not) was trained directly on the satellite.
    Normally, developing a machine learning model would require several rounds of training, using the power of a cluster of linked computers. In contrast, the team’s tiny model completed the training phase (using over 1300 images) in around one and a half seconds. More

  • 138 Shares169 Views

    in Computers Math

    Engineering team uses diamond microparticles to create high security anti-counterfeit labels

    Counterfeiting is a serious problem affecting a wide range of industries — from medicine to electronics, inflicting enormous economic losses, posing safety concerns and putting health at risk.
    Counterfeiters and anti-counterfeiters are now locked in a technological arms race. Despite anti-counterfeiting tools becoming more and more high-tech — including holograms, thermochromic ink and radio frequency identification tags, fake products are becoming harder and harder to tell apart from the genuine articles because counterfeiters are using increasingly advanced technology.
    Recently, a team of researchers led by Dr Zhiqin Chu of the Department of Electrical and Electronic Engineering of the University of Hong Kong (HKU), together with Professor Lei Shao of the School of Electronics and Information Technology of Sun Yat-sen University, and Professor Qi Wang from Dongguan Institute of Opto-Electronics of Peking University developed a pioneering technological solution that counterfeiters have no response to.
    Dr Chu’s team created diamond-based anti-counterfeiting labels that are unique and known in the industry as PUFs — Physically Unclonable Functions.
    The team made these labels by planting tiny artificial diamonds — known as diamond microparticles, on a silicon plate using a method called Chemical Vapour Deposition (CVD).
    The diamond microparticles, all different in shape and size, form a unique pattern when they scatter on the silicon substrate. Such pattern is impossible to replicate and therefore scatters light in a unique way. Put simply, it forms a unique “fingerprint” than can be scanned using a phone.
    The second level of uniqueness, and hence security, comes from the fact that these diamond microparticles have defects known as silicon-vacancy (SiV) centers. More

  • 138 Shares159 Views

    in Computers Math

    Reinforcement learning allows underwater robots to locate and track objects underwater

    A team led by the Institut de Ciències del Mar (ICM-CSIC) in Barcelona in collaboration with the Monterey Bay Aquarium Research Institute (MBARI) in Califòrnia, the Universitat Politècnica de Catalunya (UPC) and the Universitat de Girona (UdG), proves for the first time that reinforcement learning -i.e., a neural network that learns the best action to perform at each moment based on a series of rewards- allows autonomous vehicles and underwater robots to locate and carefully track marine objects and animals. The details are reported in a paper published in the  journal Science Robotics.
    Currently, underwater robotics is emerging as a key tool for improving knowledge of the oceans in the face of the many difficulties in exploring them, with vehicles capable of descending to depths of up to 4,000 meters. In addition, the in-situ data they provide help to complement other data, such as that obtained from satellites. This technology makes it possible to study small-scale phenomena, such as CO2 capture by marine organisms, which helps to regulate climate change.
    Specifically, this new work reveals that reinforcement learning, widely used in the field of control and robotics, as well as in the development of tools related to natural language processing such as ChatGPT, allows underwater robots to learn what actions to perform at any given time to achieve a specific goal. These action policies match, or even improve in certain circumstances, traditional methods based on analytical development.
    “This type of learning allows us to train a neural network to optimize a specific task, which would be very difficult to achieve otherwise. For example, we have been able to demonstrate that it is possible to optimize the trajectory of a vehicle to locate and track objects moving underwater,” explains Ivan Masmitjà, the lead author of the study, who has worked between ICM-CSIC and MBARI.
    This “will allow us to deepen the study of ecological phenomena such as migration or movement at small and large scales of a multitude of marine species using autonomous robots. In addition, these advances will make it possible to monitor other oceanographic instruments in real time through a network of robots, where some can be on the surface monitoring and transmitting by satellite the actions performed by other robotic platforms on the seabed,” points out the ICM-CSIC researcher Joan Navarro, who also participated in the study.
    To carry out this work, researchers used range acoustic techniques, which allow estimating the position of an object considering distance measurements taken at different points. However, this fact makes the accuracy in locating the object highly dependent on the place where the acoustic range measurements are taken. And this is where the application of artificial intelligence and, specifically, reinforcement learning, which allows the identification of the best points and, therefore, the optimal trajectory to be performed by the robot, becomes important.
    Neural networks were trained, in part, using the computer cluster at the Barcelona Supercomputing Center (BSC-CNS), where the most powerful supercomputer in Spain and one of the most powerful in Europe are located. “This made it possible to adjust the parameters of different algorithms much faster than using conventional computers,” indicates Prof. Mario Martin, from the Computer Science Department of the UPC and author of the study. More

  • 125 Shares139 Views

    in Heart

    Cow poop emits climate-warming methane. Adding red algae may help

    Earth has a cow problem. Cow agriculture is one of the largest emitters of climate-warming methane to the atmosphere.

    But adding a type of red algae known for its methane-inhibiting properties to cow feces might help. Doing so reduces the production of methane within feces by about 44 percent, researchers report July 13 in Frontiers in Sustainable Food Systems. That offers a promising new avenue to reduce overall methane emissions from cattle, the scientists say.

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    Cow agriculture is responsible for nearly a quarter of the world’s emissions of methane, a potent greenhouse gas (SN: 11/18/15; SN: 5/5/22). The cows make methane in their guts during digestion that is then released to the world, mostly via burps. A smaller — but not insignificant — amount of methane is also emitted directly from the cows’ feces during decomposition.

    Researchers have been actively seeking solutions to the gut-produced methane. Adding just a pinch — 0.5 percent of the dry feed — of the red algae Asparagopsis taxiformis to the cows’ food can prevent about 65 percent of that methane production.

    Ubiquitous in tropical ocean waters, A. taxiformis contains an organic compound called bromoform, which inactivates an enzyme that normally helps the methane reaction along. This research has raised concerns that the milk of dairy cows fed the algae may contain toxic levels of bromoform as well as iodine in their milk and meat. The U.S. Environmental Protection Agency has assessed bromoform as a probable human carcinogen, and too much iodine can cause thyroid malfunction.

    The red algae species Asparagopsis taxiformis (pictured) produces an organic compound that interferes with bacterial methane production, including in cows’ guts and feces.Jean-Pascal Quod/Wikimedia Commons (CC BY-SA 3.0)

    Mohammad Ramin, an animal scientist at the Swedish University of Agricultural Sciences in Umeå, and colleagues wondered whether it might be possible to cut out the middleman — by adding the algae directly to the cows’ poop. That wouldn’t reduce the gut-produced methane, but it might reduce overall cattle emissions without impacting meat or milk.

    Methane emitted from feces is primarily a problem when it comes to dairy cows, says Sara Place, an animal scientist at Colorado State University in Fort Collins who was not involved in the study. Dairy cows tend to be raised in environments with more oxygen-poor soils, and methane-producing bacteria thrive in such anaerobic environments. Cows raised for beef tend to live out their lives and defecate in open pasturelands or in an enclosed, but dry, feedlot, which is less conducive ground for methane production.

    In the new study, Ramin and colleagues added algae to four dairy cows’ feces. Two had been fed the algae, and two hadn’t. Each fecal sample was divided further, with one subsample given additional algae and the other left alone. Then, all the fecal samples were allowed to incubate, slowly decomposing in the laboratory. After nine weeks, the team analyzed the subsamples to see how much methane they contained.

    As expected, adding algae to the cows’ food did initially reduce methane in their poop. But once the poop began to decompose, the production of new methane wasn’t affected by whether the cows had eaten the algae or not. The team also examined the microbial communities living in the different types of poop, and they found that there wasn’t much difference between the algae-fed cows and the control cows. That suggests that algae food supplements aren’t that effective at inhibiting methane production outside the stomach.

    But adding the algae directly to the feces did make a noticeable difference to methane coming from decomposition. That, the team says, suggests that this would be an effective part of the solution to the larger cow-methane problem.

    The major strength of this new work is that it focuses on providing a solution to an understudied part of the cow-methane problem, says Christopher Glasson, a chemist at the University of Waikato in Tauranga, New Zealand, who studies agrichemicals derived from seaweed. But ultimately, he says, it may just not be cost-effective to produce A. taxiformis for this particular purpose. “I think [this strategy] is likely to be nonviable due to the cost of the production of the seaweed.”

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    A. taxiformis may still be most effective at suppressing fermentation in a cow’s guts rather than in its manure. The good news, Glasson says, is that state-of-the-art feed additive technologies that use specific extracts from the algae rather than the whole biomass greatly mitigate the risk of iodine or bromoform toxicity.

    And the study’s conclusion that algae in the cows’ feed doesn’t affect methane production in their feces might also be good news, in a way, Place says. One proposed avenue for mitigating emissions from cow feces is to harness the methane to make biogas. “If you feed [algae] to cattle for methane mitigation [and] if you don’t see any results [in the manure], that could be good for biogas production,” she adds — a possible two-fer for the industry.  More

  • 113 Shares179 Views

    in Computers Math

    This 3D printed gripper doesn’t need electronics to function

    This soft robotic gripper is not only 3D printed in one print, it also doesn’t need any electronics to work.
    The device was developed by a team of roboticists at the University of California San Diego, in collaboration with researchers at the BASF corporation, who detailed their work in a recent issue of Science Robotics.
    The researchers wanted to design a soft gripper that would be ready to use right as it comes off the 3D printer, equipped with built in gravity and touch sensors. As a result, the gripper can pick up, hold, and release objects. No such gripper existed before this work.
    “We designed functions so that a series of valves would allow the gripper to both grip on contact and release at the right time,” said Yichen Zhai, a postdoctoral researcher in the Bioinspired Robotics and Design Lab at the University of California San Diego and the leading author of the paper, which was published in the June 21 issue of Science Robotics. “It’s the first time such a gripper can both grip and release. All you have to do is turn the gripper horizontally. This triggers a change in the airflow in the valves, making the two fingers of the gripper release.”
    This fluidic logic allows the robot to remember when it has grasped an object and is holding on to it. When it detects the weight of the object pushing to the side, as it is rotating to the horizontal, it releases the object.
    Soft robotics holds the promise of allowing robots to interact safely with humans and delicate objects. This gripper can be mounted on a robotic arm for industrial manufacturing applications, food production and the handling of fruits and vegetables. It can also be mounted onto a robot for research and exploration tasks. In addition, it can function untethered, with a bottle of high-pressure gas as its only power source.
    Most 3D-printed soft robots often have a certain degree of stiffness; contain a large number of leaks when they come off the printer; and need a fair amount of processing and assembly after printing in order to be usable. More

  • 75 Shares109 Views

    in Computers Math

    Fusion model hot off the wall

    Humans may never be able to tame the Sun, but hydrogen plasma — making up most of the Sun’s interior — can be confined in a magnetic field as part of fusion power generation: with a caveat.
    The extremely high temperature plasmas, typically as high as 100 million degrees Celsius, confined in the tokamaks — donut-shaped fusion reactors — cause damage to the containment walls of these mega devices. Researchers inject hydrogen and inert gases near the device wall to cool the plasma by radiation and recombination, which is the reverse of ionization. Heat load mitigation is critical to extending the lifetime of future fusion device.
    Understanding and predicting the process of the vibrational and rotational temperatures of hydrogen molecules near the walls could enhance the recombination, but effective strategies have remained elusive.
    An international team of researchers led by Kyoto University has recently found a way to explain the rotational temperatures measured in three different experimental fusion devices in Japan and the United States. Their model evaluates the surface interactions and electron-proton collisions of hydrogen molecules.
    “In our model, we targeted the evaluation on the rotational temperatures in the low energy levels, enabling us to explain the measurements from several experimental devices,” adds corresponding author Nao Yoneda of KyotoU’s Graduate School of Engineering.
    By enabling the prediction and control of the rotational temperature near the wall surface, the team was able to dissipate plasma heat flux and optimize the devices’ operative conditions.
    “We still need to understand the mechanisms of rotational-vibrational hydrogen excitations,” Yoneda reflects, “but we were pleased that the versatility of our model also allowed us to reproduce the measured rotational temperatures reported in literature.” More