Hexbyte Glen Cove Blue Origin delays William Shatner’s space flight

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William Shatner (pictured September 2017), who played Captain James T. Kirk in the cult classic TV series “Star Trek,” is set to become the first member of the iconic show’s cast to journey to the final frontier as a guest aboard a Blue Origin rocket.

Blue Origin announced Sunday it was delaying an upcoming flight set to carry actor William Shatner to space due to anticipated winds.

Shatner, who played Captain James T. Kirk in the cult classic TV series “Star Trek,” is due to become the first member of the iconic show’s cast to journey to the final frontier as a guest aboard a Blue Origin suborbital rocket.

His history-making flight was scheduled for October 12.

But “due to forecasted winds on Tuesday, October 12, Blue Origin’s mission operations team has made the decision to delay the launch of NS-18 and is now targeting Wednesday, October 13,” a spokeswoman said in a statement.

The new flight is scheduled for 8:30 am (1330 GMT).

Shatner, 90, will be the oldest person ever to go to space.

His trip will take him and the NS-18 rocket crew just beyond the Karman line, 62 miles (100 kilometers) high, where they will experience four minutes of weightlessness and gaze out at the curvature of the planet.

Blue Origin’s decision to invite one of the most recognizable galaxy-faring characters from science fiction for its second crewed flight has helped maintain excitement around the nascent space tourism sector.

For fans, the 10-minute hop from a West Texas base back to Earth will be a fitting coda for a pop culture phenomenon that inspired generations of astronauts.



© 2021 AFP

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Blue Origin delays William Shatner’s space flight (2021, October 10)
retrieved 11 October 2021
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Hexbyte Glen Cove Zen stones naturally placed atop pedestals of ice: A phenomenon finally understood thumbnail

Hexbyte Glen Cove Zen stones naturally placed atop pedestals of ice: A phenomenon finally understood

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A laboratory reproduction of the Zen stone phenomenon in a lyophilizer. Credit: © Nicolas Taberlet / Nicolas Plihon

Like a work of art enshrined in a museum, some stones end up on a pedestal of ice in nature, with no human intervention. This “Zen stone” phenomenon, named after the stacked stones in Japanese gardens, appears on the surface of frozen lakes, Lake Baikal (Russia) in particular. These structures result from the phenomenon of sublimation, which causes a body, in this case ice, to change from solid to gaseous form without the intermediary form of a liquid.

This was recently demonstrated by researchers from the CNRS and l’Université Claude Bernard Lyon 1, who reproduced the phenomenon in the laboratory.

They showed that the shade created by the stone hinders the that sublimates the ice, thereby sculpting the pedestal. This research has helped bring to light and understand a rare phenomenon of sublimation within a natural context on Earth.

It was published in the journal PNAS during the week of 27 September 2021.

A video of the phenomenon reproduced in the laboratory:







Credit: CNRS


More information:
Sublimation-driven morphogenesis of Zen stones on ice surfaces, PNAS (2021). DOI: 10.1073/pnas.2109107118

Citation:
Zen stones naturally placed atop pedestals of ice: A phenomenon finally understood (2021, September 27)
retrieved 28 September 2021
from https://phys.org/news/2021-09-zen-stones-naturally-atop-pedestals.html

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Hexbyte Glen Cove During epic migrations, great snipes fly at surprising heights by day and lower by night thumbnail

Hexbyte Glen Cove During epic migrations, great snipes fly at surprising heights by day and lower by night

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A great snipe in flight, with a mountain in the background. Credit: Åke Lindström

Don’t let the great snipe’s pudginess fool you. A stocky marsh bird with a 20-inch wingspan, great snipes are also speedy marathoners that can migrate from Sweden to Central Africa in just three days, without even stopping to eat, drink, or sleep. Now, researchers find that the snipes also rise nearly 2,500 meters in elevation at dawn and descend again at dusk each day, perhaps to avoid overheating from daytime solar radiation by climbing to higher, cooler altitudes. The findings appear June 30 in the journal Current Biology.

The also spent much more time in than previously thought: during its migration, one bird flew at nearly 8,700 meters (almost as high as Mount Everest) for five consecutive hours, which may be the highest altitude ever recorded for a tracked migrating bird.

“Anyone who studies animal behavior will often find that there is huge variation between individuals. But these birds almost do the exact same thing,” says lead author Åke Lindström, a professor in the biodiversity department at Lund University in Sweden. “In the great snipe’s migration pattern, we found a very, very strong diel cycle—higher at day, lower at night. They seem to have found a behavior that’s really optimal for them.”

The great snipes are no strangers to long flights, flying 6,000 kilometer nonstop from breeding grounds in Sweden to Africa’s Sahel region for a monthlong stopover in autumn, traveling 1,500-3,000 kilometers within Africa to their final wintering grounds, and migrating 5,200 kilometers back to Southeast Europe in the spring. But until now, scientists have only been able to get a sneak peek of the birds’ journeys.

A snipe sitting with nets being raised in the background. Credit: Åke Lindström

Previous research tracked the birds only when they passed in range of radar, so scientists commonly assumed that the snipes—and many other birds—maintained a steady, favorable cruising altitude to minimize energy loss. But thanks to new technology, scientists can now track birds throughout their entire migration.

In this study, Lindström and his team attached mini data-loggers, weighing only about 1% of the birds’ total body mass, to the legs of 14 snipes. The loggers recorded measurements on activity, air pressure, and temperature every hour during their flights.

The researchers found a distinct pattern in all three seasonal migrations. After a night at moderate to , the birds ascended to very high altitudes at dawn, stayed at those high altitudes during the day, and descended again in late afternoon or evening to heights similar to the previous night. The snipes typically flew at about 1,600-2,100 meters above sea level at night and 3,900-4,500 meters above sea level during the day.

But why the snipes change their elevation depending on time of day remains an unanswered question. Wind conditions or speed, which are usually the primary factors that influence migration, do not consistently change between day and night. The presence of the sun, however, does—leading to three possible explanations.

A great snipe sitting. Credit: Åke Lindström

First, although flying higher in daylight could help the snipes find landmarks, migratory birds are known to be excellent navigators that don’t need to rely on the physical landscape for directions. Higher elevation during the day could also help the snipes escape the range of birds of prey that hunt during the daytime.

But the most likely reason for this daily elevation change comes from the sun’s warmth. When flying, the great snipes flap their wings seven beats per second, generating large amounts of body heat. At night when temperatures are cooler, this isn’t a problem. But during the daytime, the sun’s rays most likely increase their body temperature even more. “When the sun comes up, there is also to consider: imagine the difference in temperature when you sit in the shade versus when you sit in the sun,” explains Lindström.

Flying more than 2,000 meters higher during the day, where the air is 13 degrees Celsius (55 degrees Fahrenheit) cooler, therefore, might help the birds keep from overheating. But the snipes also ascended farther than the researchers had ever expected. They repeatedly reached heights of over 6,000 meters, and one bird flew at nearly 8,700 meters—only about 150 meters lower than the summit of Mount Everest. Though conditions above 8,000 meters are brutal for humans, the great snipes seemed to have it figured out.

“They are already way ahead of humans in terms of lung capacity and supplying muscles with oxygen,” Lindström says. “If you asked the average great snipe how and why they can fly so high, and if they could answer, they’d probably just be surprised by the question. They wouldn’t understand that they have done anything special.”



More information:
Current Biology, Lindstrom et al.: “Extreme altitude changes between night and day during marathon flights of Great Snipes.” www.cell.com/current-biology/f … 0960-9822(21)00745-4 , DOI: 10.1016/j.cub.2021.05.047

Citation:
During epic migrations, great snipes fly at surprising heights by day and lower by night (2021, June 30)
retrieved 1 July 2021
from https://phys.org/news/2021-06-epic-migrations-great-snipes-heights.html

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Hexbyte Glen Cove New take on machine learning helps us 'scale up' phase transitions thumbnail

Hexbyte Glen Cove New take on machine learning helps us ‘scale up’ phase transitions

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A correlation configuration (top left) is reduced using a newly developed block-cluster transformation (top right). Both the original and reduced configurations have an improved estimator technique applied to give configuration pairs of different size (bottom row). Using these training pairs, a CNN can learn to convert small patterns to large ones, achieving a successful inverse RG transformation. Credit: Tokyo Metropolitan University

Researchers from Tokyo Metropolitan University have enhanced “super-resolution” machine learning techniques to study phase transitions. They identified key features of how large arrays of interacting particles behave at different temperatures by simulating tiny arrays before using a convolutional neural network to generate a good estimate of what a larger array would look like using correlation configurations. The massive saving in computational cost may realize unique ways of understanding how materials behave.

We are surrounded by different states or phases of matter, i.e. gases, liquids, and solids. The study of , how one phase transforms into another, lies at the heart of our understanding of matter in the universe, and remains a hot topic for physicists. In particular, the idea of universality, in which wildly different materials behave in similar ways thanks to a few shared features, is a powerful one. That’s why physicists study model systems, often simple grids of particles on an array that interact via simple rules. These models distill the essence of the common physics shared by materials and, amazingly, still exhibit many of the properties of real materials, like phase transitions. Due to their elegant simplicity, these rules can be encoded into simulations that tell us what materials look like under different conditions.

However, like all simulations, the trouble starts when we want to look at lots of particles at the same time. The computation time required becomes particularly prohibitive near phase transitions, where dynamics slows down, and the correlation length, a measure of how the state of one atom relates to the state of another some distance away, grows larger and larger. This is a real dilemma if we want to apply these findings to the real world: real materials generally always contain many more orders of magnitude of atoms and molecules than simulated matter.

That’s why a team led by Professors Yutaka Okabe and Hiroyuki Mori of Tokyo Metropolitan University, in collaboration with researchers in Shibaura Institute of Technology and Bioinformatics Institute of Singapore, have been studying how to reliably extrapolate smaller simulations to larger ones using a concept known as an inverse renormalization group (RG). The renormalization group is a fundamental concept in the understanding of phase transitions and led Wilson to be awarded the 1982 Nobel Prize in Physics. Recently, the field met a powerful ally in convolutional neural networks (CNN), the same machine learning tool helping computer vision identify objects and decipher handwriting. The idea would be to give an algorithm the state of a small array of particles and get it to estimate what a larger array would look like. There is a strong analogy to the idea of super-resolution images, where blocky, pixelated images are used to generate smoother images at a higher resolution.

Trends found from simulations of larger systems are faithfully reproduced by the trained CNNs for both Ising (left) and three-state Potts (right) models. (inset) Correct temperature rescaling is achieved using data at some arbitrary system size. Credit: Tokyo Metropolitan University

The team has been looking at how this is applied to spin models of matter, where particles interact with other nearby particles via the direction of their spins. Previous attempts have particularly struggled to apply this to systems at temperatures above a phase transition, where configurations tend to look more random. Now, instead of using spin configurations i.e. simple snapshots of which direction the particle spins are pointing, they considered correlation configurations, where each particle is characterized by how similar its own spin is to that of other particles, specifically those which are very far away. It turns out correlation configurations contain more subtle queues about how particles are arranged, particularly at higher temperatures.

Like all machine learning techniques, the key is to be able to generate a reliable training set. The team developed a new algorithm called the block-cluster transformation for correlation configurations to reduce these down to smaller patterns. Applying an improved estimator technique to both the original and reduced patterns, they had pairs of configurations of different size based on the same information. All that’s left is to train the CNN to convert the small patterns to larger ones.

The group considered two systems, the 2D Ising model and the three-state Potts model, both key benchmarks for studies of condensed matter. For both, they found that their CNN could use a simulation of a very small array of points to reproduce how a measure of the correlation g(T) changed across a phase transition point in much larger systems. Comparing with direct simulations of larger systems, the same trends were reproduced for both systems, combined with a simple temperature rescaling based on data at an arbitrary system size.

A successful implementation of inverse RG transformations promises to give scientists a glimpse of previously inaccessible system sizes, and help physicists understand the larger scale features of materials. The team now hopes to apply their method to other models which can map more complex features such as a continuous range of spins, as well as the study of quantum systems.



More information:
Kenta Shiina et al, Inverse renormalization group based on image super-resolution using deep convolutional networks, Scientific Reports (2021). DOI: 10.1038/s41598-021-88605-w

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Tokyo Metropolitan University

Citation:
New take on machine learning helps us ‘scale up’ phase transitions (2021, May 31)
retrieved 31 May 2021
from https://phys.org/news/2021-05-machine-scale-phase-transitions.html

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