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Experimentos STEM de estudiantes ganan una oportunidad de vuelo mediante un concurso tecnológico de la NASA

La NASA ha elegido a 57 equipos ganadores en un primer desafío nacional diseñado para atraer, involucrar y preparar a los futuros profesionales de las ciencias, la tecnología, la ingeniería y las matemáticas.
Source: Experimentos STEM de estudiantes ganan una oportunidad de vuelo mediante un concurso tecnológico de la NASA
STEM Student Experiments Win Flight Opportunity in NASA Tech Contest

NASA selected 57 winning teams in an inaugural nationwide challenge designed to attract, engage, and prepare future science, technology, engineering, and mathematics professionals.
Source: STEM Student Experiments Win Flight Opportunity in NASA Tech Contest
California Students to Hear from NASA Astronauts Aboard Space Station

Preschool through sixth grade students from West Hollywood, California, will have an opportunity next week to hear from NASA astronauts aboard the International Space Station.
Source: California Students to Hear from NASA Astronauts Aboard Space Station
Wired - Science / What Happens If a Space Elevator Breaks
« Last post by feeds on Today at 12:48:56 PM »
What Happens If a Space Elevator Breaks

These structures are a sci-fi solution to the problem of getting objects into orbit without a rocket—but you don’t want to be under one if the cable snaps.
Source: What Happens If a Space Elevator Breaks
South Georgia: The museum at the end of the world reopens for business

On a British island at the edge of the Antarctic is one of the most remote tourist spots in the world.
Source: South Georgia: The museum at the end of the world reopens for business
Babies can tell who has close relationships based on one clue: saliva

Learning to navigate social relationships is a skill that is critical for surviving in human societies. For babies and young children, that means learning who they can count on to take care of them.

MIT neuroscientists have now identified a specific signal that young children and even babies use to determine whether two people have a strong relationship and a mutual obligation to help each other: whether those two people kiss, share food, or have other interactions that involve sharing saliva.

In a new study, the researchers showed that babies expect people who share saliva to come to one another’s aid when one person is in distress, much more so than when people share toys or interact in other ways that do not involve saliva exchange. The findings suggest that babies can use these cues to try to figure out who around them is most likely to offer help, the researchers say.

“Babies don’t know in advance which relationships are the close and morally obligating ones, so they have to have some way of learning this by looking at what happens around them,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of MIT’s McGovern Institute for Brain Research and the Center for Brains, Minds, and Machines (CBMM), and the senior author of the new study.

MIT postdoc Ashley Thomas, who is also affiliated with the CBMM, is the lead author of the study, which appears today in Science. Brandon Woo, a Harvard University graduate student; Daniel Nettle, a professor of behavioral science at Newcastle University; and Elizabeth Spelke, a professor of psychology at Harvard and CBMM member, are also authors of the paper.

Sharing saliva

In human societies, people typically distinguish between “thick” and “thin” relationships. Thick relationships, usually found between family members, feature strong levels of attachment, obligation, and mutual responsiveness. Anthropologists have also observed that people in thick relationships are more willing to share bodily fluids such as saliva.

“That inspired both the question of whether infants distinguish between those types of relationships, and whether saliva sharing might be a really good cue they could use to recognize them,” Thomas says.

To study those questions, the researchers observed toddlers (16.5 to 18.5 months) and babies (8.5 to 10 months) as they watched interactions between human actors and puppets. In the first set of experiments, a puppet shared an orange with one actor, then tossed a ball back and forth with a different actor.

After the children watched these initial interactions, the researchers observed the children’s reactions when the puppet showed distress while sitting between the two actors. Based on an earlier study of nonhuman primates, the researchers hypothesized that babies would look first at the person whom they expected to help. That study showed that when baby monkeys cry, other members of the troop look to the baby’s parents, as if expecting them to step in.

The MIT team found that the children were more likely to look toward the actor who had shared food with the puppet, not the one who had shared a toy, when the puppet was in distress.

In a second set of experiments, designed to focus more specifically on saliva, the actor either placed her finger in her mouth and then into the mouth of the puppet, or placed her finger on her forehead and then onto the forehead of the puppet. Later, when the actor expressed distress while standing between the two puppets, children watching the video were more likely to look toward the puppet with whom she had shared saliva.

Social cues

The findings suggest that saliva sharing is likely an important cue that helps infants to learn about their own social relationships and those of people around them, the researchers say.

“The general skill of learning about social relationships is very useful,” Thomas says. “One reason why this distinction between thick and thin might be important for infants in particular, especially human infants, who depend on adults for longer than many other species, is that it might be a good way to figure out who else can provide the support that they depend on to survive.”

The researchers did their first set of studies shortly before Covid-19 lockdowns began, with babies who came to the lab with their families. Later experiments were done over Zoom. The results that the researchers saw were similar before and after the pandemic, confirming that pandemic-related hygiene concerns did not affect the outcome.

“We actually know the results would have been similar if it hadn’t been for the pandemic,” Saxe says. “You might wonder, did kids start to think very differently about sharing saliva when suddenly everybody was talking about hygiene all the time? So, for that question, it’s very useful that we had an initial data set collected before the pandemic.”

Doing the second set of studies on Zoom also allowed the researchers to recruit a much more diverse group of children because the subjects were not limited to families who could come to the lab in Cambridge during normal working hours.

In future work, the researchers hope to perform similar studies with infants in cultures that have different types of family structures. In adult subjects, they plan to use functional magnetic resonance imaging (fMRI) to study what parts of the brain are involved in making saliva-based assessments about social relationships.

The research was funded by the National Institutes of Health; the Patrick J. McGovern Foundation; the Guggenheim Foundation; a Social Sciences and Humanities Research Council Doctoral Fellowship; MIT’s Center for Brains, Minds, and Machines; and the Siegel Foundation.

Source: Babies can tell who has close relationships based on one clue: saliva
Physicists discover “secret sauce” behind exotic properties of a new quantum material

MIT physicists and colleagues have discovered the “secret sauce” behind some of the exotic properties of a new quantum material that has transfixed physicists due to those properties, which include superconductivity. Although theorists had predicted the reason for the unusual properties of the material, known as a kagome metal, this is the first time that the phenomenon behind those properties has been observed in the laboratory.

“The hope is that our new understanding of the electronic structure of a kagome metal will help us build a rich platform for discovering other quantum materials,” says Riccardo Comin, the Class of 1947 Career Development Associate Professor of Physics at MIT, whose group led the study. That, in turn, could lead to a new class of superconductors, new approaches to quantum computing, and other quantum technologies.

The work is reported in the Jan. 13 online issue of the journal Nature Physics.

Classical physics can be used to explain any number of phenomena that underlie our world — until things get exquisitely small. Subatomic particles like electrons and quarks behave differently, in ways that are still not fully understood. Enter quantum mechanics, the field that tries to explain their behavior and resulting effects.

The kagome metal at the heart of the current work is a new quantum material, or one that manifests the exotic properties of quantum mechanics at a macroscopic scale. In 2018 Comin and Joseph Checkelsky, MIT’s Mitsui Career Development Associate Professor of Physics, led the first study on the electronic structure of kagome metals, spurring interest into this family of materials. Members of the kagome metal family are composed of layers of atoms arranged in repeating units similar to a Star of David or sheriff’s badge. The pattern is also common in Japanese culture, particularly as a basket-weaving motif.

“This new family of materials has attracted a lot of attention as a rich new playground for quantum matter that can exhibit exotic properties such as unconventional superconductivity, nematicity, and charge-density waves,” says Comin.

Unusual properties

Superconductivity and hints of charge density wave order in the new family of kagome metals studied by Comin and colleagues were discovered in the laboratory of Professor Stephen Wilson at the University of California at Santa Barbara, where single crystals were also synthesized (Wilson is a coauthor of the Nature Physics paper). The specific kagome material explored in the current work is made of only three elements (cesium, vanadium, and antimony) and has the chemical formula CsV3Sb5.

The researchers focused on two of the exotic properties that a kagome metal shows when cooled below room temperatures. At those temperatures, electrons in the material begin to exhibit collective behavior. “They talk to each other, as opposed to moving independently,” says Comin.

One of the resulting properties is superconductivity, which allows a material to conduct electricity extremely efficiently. In a regular metal, electrons behave much like people dancing individually in a room. In a kagome superconductor, when the material is cooled to 3 kelvins (about -454 degrees Fahrenheit) the electrons begin to move in pairs, like couples at a dance. “And all these pairs are moving in unison, as if they were part of a quantum choreography,” says Comin.

At 100 K, the kagome material studied by Comin and collaborators exhibits yet another strange kind of behavior known as charge density waves. In this case, the electrons arrange themselves in the shape of ripples, much like those in a sand dune. “They’re not going anywhere; they’re stuck in place,” Comin says. A peak in the ripple represents a region that is rich in electrons. A valley is electron-poor. “Charge density waves are very different from a superconductor, but they’re still a state of matter where the electrons have to arrange in a collective, highly organized fashion. They form, again, a choreography, but they’re not dancing anymore. Now they form a static pattern.”

Comin notes that kagome metals are of great interest to physicists in part because they can exhibit both superconductivity and charge density waves. “These two exotic phenomena are often in competition with one another, therefore it is unusual for a material to host both of them.”

The secret sauce?

But what is behind the emergence of these two properties? “What causes the electrons to start talking to each other, to start influencing each other? That is the key question,” says first author Mingu Kang, a graduate student in the MIT Department of Physics also affiliated with the Max Planck POSTECH Korea Research Initiative. That’s what the physicists report in Nature Physics. “By exploring the electronic structure of this new material, we discovered that the electrons exhibit an intriguing behavior known as an electronic singularity,” Kang says. This particular singularity is named for Léon van Hove, the Belgian physicist who first discovered it.

The van Hove singularity involves the relationship between the electrons’ energy and velocity. Normally, the energy of a particle in motion is proportional to its velocity squared. “It’s a fundamental pillar of classical physics that [essentially] means the greater the velocity, the greater the energy,” says Comin. Imagine a Red Sox pitcher hitting you with a fast ball. Then imagine a kid trying to do the same. The pitcher’s ball would hurt a lot more than the kid’s, which has less energy.

What the Comin team found is that in a kagome metal, this rule doesn’t hold anymore. Instead, electrons traveling with different velocities happen to all have the same energy. The result is that the pitcher’s fast ball would have the same physical effect as the kid’s. “It’s very counterintuitive,” Comin says. He noted that relating the energy to the velocity of electrons in a solid is challenging and requires special instruments at two international synchrotron research facilities: Beamline 4A1 of the Pohang Light Source and Beamline 7.0.2 (MAESTRO) of the Advanced Light Source at Lawrence Berkeley National Lab.

Comments Professor Ronny Thomale of the Universität Würzburg (Germany): "Theoretical physicists (including my group) have predicted the peculiar nature of van Hove singularities on the kagome lattice, a crystal structure made of corner-sharing triangles. Riccardo Comin has now provided the first experimental verification of these theoretical suggestions." Thomale was not involved in the work.

When many electrons exist at once with the same energy in a material, they are known to interact much more strongly. As a result of these interactions, the electrons can pair up and become superconducting, or otherwise form charge density waves. “The presence of a van Hove singularity in a material that has both makes perfect sense as the common source for these exotic phenomena” adds Kang. “Therefore, the presence of this singularity is the ‘secret sauce’ that enables the quantum behavior of kagome metals.”

The team’s new understanding of the relationship between energy and velocities in the kagome material “is also important because it will enable us to establish new design principles for the development of new quantum materials,” Comin says. Further, “we now know how to find this singularity in other systems.”

Direct feedback

When physicists are analyzing data, most of the time that data must be processed before a clear trend is seen. The kagome system, however, “gave us direct feedback on what’s happening,” says Comin. “The best part of this study was being able to see the singularity right there in the raw data.”

Additional authors of the Nature Physics paper are Shiang Fang of Rutgers University; Jeung-Kyu Kim, Jonggyu Yoo, and Jae-Hoon Park of Max Planck POSTECH/Korea Research Initiative and Pohang University of Science and Technology (Korea); Brenden Ortiz of the University of California, Santa Barbara; Jimin Kim of the Institute for Basic Science (Korea); Giorgio Sangiovanni of the Universität Würzburg (Germany); Domenico Di Sante of the University of Bologna (Italy) and the Flatiron Institute; Byeong-Gyu Park of Pohang Light Source (Korea); Sae Hee Ryu, Chris Jozwiak, Aaron Bostwick and Eli Rotenberg of Lawrence Berkeley National Laboratory; and Efthimios Kaxiras of Harvard University.

This work was funded by the Air Force Office of Scientific Research, the National Science Foundation, the National Research Foundation of Korea, a Samsung Scholarship, a Rutgers Center for Material Theory Distinguished Postdoctoral Fellowship, the California NanoSystems Institute, the European Union Horizon 2020 program, the German Research Foundation, and it used the resources of the Advanced Light Source, a Department of Energy Office of Science user facility.

Source: Physicists discover “secret sauce” behind exotic properties of a new quantum material
MIT Research / Computing for ocean environments
« Last post by feeds on Today at 12:48:55 PM »
Computing for ocean environments

There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery.

“The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures,” says Wim van Rees, the ABS Career Development Professor at MIT. “At the same time, the ocean holds countless opportunities — from aquaculture to energy harvesting and exploring the many ocean creatures we haven’t discovered yet.”

Ocean engineers and mechanical engineers, like van Rees, are using advances in scientific computing to address the ocean’s many challenges, and seize its opportunities. These researchers are developing technologies to better understand our oceans, and how both organisms and human-made vehicles can move within them, from the micro scale to the macro scale.

Bio-inspired underwater devices

An intricate dance takes place as fish dart through water. Flexible fins flap within currents of water, leaving a trail of eddies in their wake.

“Fish have intricate internal musculature to adapt the precise shape of their bodies and fins. This allows them to propel themselves in many different ways, well beyond what any man-made vehicle can do in terms of maneuverability, agility, or adaptivity,” explains van Rees.

According to van Rees, thanks to advances in additive manufacturing, optimization techniques, and machine learning, we are closer than ever to replicating flexible and morphing fish fins for use in underwater robotics. As such, there is a greater need to understand how these soft fins impact propulsion.

Van Rees and his team are developing and using numerical simulation approaches to explore the design space for underwater devices that have an increase in degrees of freedom, for instance due to fish-like, deformable fins.

These simulations help the team better understand the interplay between the fluid and structural mechanics of fish’s soft, flexible fins as they move through a fluid flow. As a result, they are able to better understand how fin shape deformations can harm or improve swimming performance. “By developing accurate numerical techniques and scalable parallel implementations, we can use supercomputers to resolve what exactly happens at this interface between the flow and the structure,” adds van Rees.

Through combining his simulation algorithms for flexible underwater structures with optimization and machine learning techniques, van Rees aims to develop an automated design tool for a new generation of autonomous underwater devices. This tool could help engineers and designers develop, for example, robotic fins and underwater vehicles that can smartly adapt their shape to better achieve their immediate operational goals — whether it’s swimming faster and more efficiently or performing maneuvering operations.

“We can use this optimization and AI to do inverse design inside the whole parameter space and create smart, adaptable devices from scratch, or use accurate individual simulations to identify the physical principles that determine why one shape performs better than another,” explains van Rees.

Swarming algorithms for robotic vehicles

Like van Rees, Principal Research Scientist Michael Benjamin wants to improve the way vehicles maneuver through the water. In 2006, then a postdoc at MIT, Benjamin launched an open-source software project for an autonomous helm technology he developed. The software, which has been used by companies like Sea Machines, BAE/Riptide, Thales UK, and Rolls Royce, as well as the United States Navy, uses a novel method of multi-objective optimization. This optimization method, developed by Benjamin during his PhD work, enables a vehicle to autonomously choose the heading, speed, depth, and direction it should go in to achieve multiple simultaneous objectives.

Now, Benjamin is taking this technology a step further by developing swarming and obstacle-avoidance algorithms. These algorithms would enable dozens of uncrewed vehicles to communicate with one another and explore a given part of the ocean.

To start, Benjamin is looking at how to best disperse autonomous vehicles in the ocean.

“Let’s suppose you want to launch 50 vehicles in a section of the Sea of Japan. We want to know: Does it make sense to drop all 50 vehicles at one spot, or have a mothership drop them off at certain points throughout a given area?” explains Benjamin.

He and his team have developed algorithms that answer this question. Using swarming technology, each vehicle periodically communicates its location to other vehicles nearby. Benjamin’s software enables these vehicles to disperse in an optimal distribution for the portion of the ocean in which they are operating.

Central to the success of the swarming vehicles is the ability to avoid collisions. Collision avoidance is complicated by international maritime rules known as COLREGS — or “Collision Regulations.” These rules determine which vehicles have the “right of way” when crossing paths, posing a unique challenge for Benjamin’s swarming algorithms.

The COLREGS are written from the perspective of avoiding another single contact, but Benjamin’s swarming algorithm had to account for multiple unpiloted vehicles trying to avoid colliding with one another.

To tackle this problem, Benjamin and his team created a multi-object optimization algorithm that ranked specific maneuvers on a scale from zero to 100. A zero would be a direct collision, while 100 would mean the vehicles completely avoid collision.

“Our software is the only marine software where multi-objective optimization is the core mathematical basis for decision-making,” says Benjamin.

While researchers like Benjamin and van Rees use machine learning and multi-objective optimization to address the complexity of vehicles moving through ocean environments, others like Pierre Lermusiaux, the Nam Pyo Suh Professor at MIT, use machine learning to better understand the ocean environment itself.

Improving ocean modeling and predictions

Oceans are perhaps the best example of what’s known as a complex dynamical system. Fluid dynamics, changing tides, weather patterns, and climate change make the ocean an unpredictable environment that is different from one moment to the next. The ever-changing nature of the ocean environment can make forecasting incredibly difficult.

Researchers have been using dynamical system models to make predictions for ocean environments, but as Lermusiaux explains, these models have their limitations.

“You can’t account for every molecule of water in the ocean when developing models. The resolution and accuracy of models, and the ocean measurements are limited. There could be a model data point every 100 meters, every kilometer, or, if you are looking at climate models of the global ocean, you may have a data point every 10 kilometers or so. That can have a large impact on the accuracy of your prediction,” explains Lermusiaux.

Graduate student Abhinav Gupta and Lermusiaux have developed a new machine-learning framework to help make up for the lack of resolution or accuracy in these models. Their algorithm takes a simple model with low resolution and can fill in the gaps, emulating a more accurate, complex model with a high degree of resolution.

For the first time, Gupta and Lermusiaux’s framework learns and introduces time delays in existing approximate models to improve their predictive capabilities.

“Things in the natural world don’t happen instantaneously; however, all the prevalent models assume things are happening in real time,” says Gupta. “To make an approximate model more accurate, the machine learning and data you are inputting into the equation need to represent the effects of past states on the future prediction.”

The team’s “neural closure model,” which accounts for these delays, could potentially lead to improved predictions for things such as a Loop Current eddy hitting an oil rig in the Gulf of Mexico, or the amount of phytoplankton in a given part of the ocean.

As computing technologies such as Gupta and Lermusiaux’s neural closure model continue to improve and advance, researchers can start unlocking more of the ocean’s mysteries and develop solutions to the many challenges our oceans face.

Source: Computing for ocean environments
Wired - Science / Europe Is in the Middle of a Messy Nuclear Slowdown
« Last post by feeds on Today at 05:38:37 AM »
Europe Is in the Middle of a Messy Nuclear Slowdown

Germany has almost finished phasing out nuclear plants, and aging infrastructure is leading neighbors down the same path. But will green energy goals suffer?
Source: Europe Is in the Middle of a Messy Nuclear Slowdown
False banana: Is Ethiopia's enset 'wondercrop' for climate change?

The banana-like crop has the potential to feed more than 100 million people, according to research.
Source: False banana: Is Ethiopia's enset 'wondercrop' for climate change?
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