In a new study, scientists from Singapore and Spain have presented a new avenue for exploring exotic physics in graphene. They focus on electronic interactions in graphene when it is sandwiched in a three-layer structure which provides a platform to exploit unique electronic band configurations.
DESI Survey announces the most precise measurements of our expanding #universe using the BAO signal in 6.1 Million #galaxies and #Quasars from Year 1, tracing dark energy through cosmic time.
With 5,000 tiny robots in a mountaintop telescope, researchers can look 11 billion years into the past. The light from far-flung objects in space is just now reaching the Dark Energy Spectroscopic Instrument (DESI), enabling us to map our cosmos as it was in its youth and trace its growth to what we see today. Understanding how our universe has evolved is tied to how it ends, and to one of the biggest mysteries in physics: dark energy, the unknown ingredient causing our universe to expand faster and faster.
“Gravity pulls matter together, so that when we throw a ball in the air, the Earth’s gravity pulls it down toward the planet,” Mustapha Ishak-Boushaki, a professor of physics in the School of Natural Sciences and Mathematics (NSM) at UT Dallas, and member of the DESI collaboration, said in a statement. “But at the largest scales, the universe acts differently. It’s acting like there is something repulsive pushing the universe apart and accelerating its expansion. This is a big mystery, and we are investigating it on several fronts. Is it an unknown dark energy in the universe, or is it a modification of Albert Einstein’s theory of gravity at cosmological scales?”
DESI’s data, however, shows that the universe may have evolved in a way that isn’t quite consistent with the Lambda CDM model, indicating that the effects of dark energy on the universe may have changed since the early days of the cosmos.
“Our results show some interesting deviations from the standard model of the universe that could indicate that dark energy is evolving over time,” Ishak-Boushaki said. “The more data we collect, the better equipped we will be to determine whether this finding holds. With more data, we might identify different explanations for the result we observe or confirm it. If it persists, such a result will shed some light on what is causing cosmic acceleration and provide a huge step in understanding the evolution of our universe.”
For the first time, scientists have built a fusion experiment using permanent magnets, a technique that could show a simple way to build future devices for less cost and allow researchers to test new concepts for future fusion power plants.
Watch more videos on complexity and emergence: https://bit.ly/3TxyQBmThe world works at different levels—fundamental physics, physics, chemistry, biology, ps…
The recent development of AI presents challenges, but also great opportunities. In this clip I will discuss the topiv in general.
… whar hasn’t worked perfectly is AI sound post-production, I apologize smile
Mind also my backup channel:
https://odysee.com/@TheMachian: c.
My books: www.amazon.com/Alexander-Unzicker/e/B00DQCRYYY/
The total solar eclipse isn’t the only reason to keep your eyes to the sky this year. For the first time in 80 years, a star system 3,000 light years away will be visible to the naked eye thanks to a once-in-a-lifetime nova outburst.
NASA announced that the nova, which will create a “new” star in the night sky, will light up the night sky some time between now and September and be as bright as the North Star. One of only five recurring novae in our galaxy, it will be visible for a week before it fades back down.
Jonathan Blazek, an assistant professor of physics at Northeastern University, says this is an exciting moment for amateur astronomers and astrophysicists alike. It’s not technically a new star, just a star that is now bright enough for people to see more clearly, Blazek says, but it provides an opportunity to see and understand the cosmos in a new way.
After three years of collecting scores of data on hundreds of stars, the ULLYSES (Ultraviolet Legacy Library of Young Stars as Essential Standards) survey conducted by NASA’s Hubble Space Telescope officially ended in December 2023, culminating in 220 total stars examined during the survey on data regarding their size, distance from Earth, temperature, chemical characteristics, and rotational speed. Additionally, ULYYSES also contains another 275 stars from the Hubble archive, providing researchers with several decades of new stellar data and holds the potential to help astronomers gain new insights into stellar formation and evolution throughout the universe.
Hubble image of a star-forming region known as the Tarantula Nebula, which contains massive, young blue stars, which was observed during the ULYYSES survey (top panel). Artist’s illustration of a cooler, redder, young star smaller than our Sun that is still gathering material from its planet-forming disk (bottom panel). (Credit: NASA, ESA, STScI, Francesco Paresce (INAF-IASF Bologna), Robert O’Connell (UVA), SOC-WFC3, ESO)
“I believe the ULLYSES project will be transformative, impacting overall astrophysics – from exoplanets, to the effects of massive stars on galaxy evolution, to understanding the earliest stages of the evolving universe,” said Dr. Julia Roman-Duval, who is Implementation Team Lead for ULLYSES and an Associate Astronomer at the Space Telescope Science Institute (STScI). “Aside from the specific goals of the program, the stellar data can also be used in fields of astrophysics in ways we can’t yet imagine.”
A new theoretical framework for plastic neural networks predicts dynamical regimes where synapses rather than neurons primarily drive the network’s behavior, leading to an alternative candidate mechanism for working memory in the brain.
The brain is an immense network of neurons, whose dynamics underlie its complex information processing capabilities. A neuronal network is often classed as a complex system, as it is composed of many constituents, neurons, that interact in a nonlinear fashion (Fig. 1). Yet, there is a striking difference between a neural network and the more traditional complex systems in physics, such as spin glasses: the strength of the interactions between neurons can change over time. This so-called synaptic plasticity is believed to play a pivotal role in learning. Now David Clark and Larry Abbott of Columbia University have derived a formalism that puts neurons and the connections that transmit their signals (synapses) on equal footing [1]. By studying the interacting dynamics of the two objects, the researchers take a step toward answering the question: Are neurons or synapses in control?
Clark and Abbott are the latest in a long line of researchers to use theoretical tools to study neuronal networks with and without plasticity [2, 3]. Past studies—without plasticity—have yielded important insights into the general principles governing the dynamics of these systems and their functions, such as classification capabilities [4], memory capacities [5, 6], and network trainability [7, 8]. These works studied how temporally fixed synaptic connectivity in a network shapes the collective activity of neurons. Adding plasticity to the system complicates the problem because then the activity of neurons can dynamically shape the synaptic connectivity [9, 10].
