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Blog – Page 13

In a demographically diverse sample of healthy people, Cornell researchers found dramatic changes over the human lifespan in the brain’s “blue spot”—a tiny region involved in cognition and believed to be the first affected by neurodegenerative conditions including Alzheimer’s disease.

Using specialized MRI scans to measure the intensity of neuromelanin, a pigment that gives the locus coeruleus (LC) its , the research team observed an inverted U-shaped curve that peaked in later middle age before dropping off sharply, a finding that helps characterize healthy aging patterns.

Maintaining a stronger blue signal after age 60 was associated with better cognitive performance, according to the study involving 134 participants aged 19 to 86. Because of the participants’ diversity, including about 40% who were non-white, the researchers also discovered higher peaks among Black participants and women, groups known to be more susceptible to Alzheimer’s.

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On this day, April 25, in 1929, the world learned how astronomer Edwin Hubble had discovered that the universe was much larger than we had believed. On this day in 2025, you can preorder a book and on the 29th learn about this and four other Astrophysics discoveries that changed how we see the universe — and ourselves — in The Story of Astrophysics in Five Revolutions.

By Ersilia Vaudo, translated by Vanessa Di Stefano. If you use this link we get a penny or something.

He had published his seminal paper in the March issue of Astrophysical Journal but on April 25th in Proceedings of the National Academy of Sciences he published the how, and people began to think about what it meant. He had discovered that the collection of gas and dust we call Andromeda was actually another galaxy.

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Researchers have created a light-powered soft robot that can carry loads through the air along established tracks, similar to cable cars or aerial trams. The soft robot operates autonomously, can climb slopes at angles of up to 80 degrees, and can carry loads up to 12 times its weight.

“We’ve previously created soft robots that can move quickly through the water and across solid ground, but wanted to explore a design that can carry objects through the air across open space,” says Jie Yin, associate professor of mechanical and aerospace engineering at North Carolina State University and corresponding author of a paper on the work published in Advanced Science.

“The simplest way to do this is to follow an established track—similar to the aerial trams you see in the mountains. And we’ve now demonstrated that this is possible.”

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The evolution of wireless communications and the miniaturization of electrical circuits have fundamentally reshaped our lives and the digital landscape. However, as we push toward higher-frequency communications in an increasingly connected world, engineers face growing challenges from multipath propagation—a phenomenon where the same radio signal reaches receiving antennas through multiple routes, usually with time delays and altered amplitudes.

Multipath interference leads to many reliability issues, ranging from “ghosting” in television broadcasts to signal fading in wireless communications.

Addressing multipath interference has long presented two fundamental physical challenges. First, multipath signals share the same frequency with the main (leading) signal, rendering conventional frequency-based filtering techniques ineffective. Second, the incident angles of these signals are variable and unpredictable. These limitations have made passive solutions particularly difficult to implement, as traditional materials bound by linear time-invariant (LTI) responses maintain the same scattering profile for a given frequency, regardless of when the signal arrives.

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A research team from the Skoltech AI Center proposed a new neural network architecture for generating structured curved coordinate grids, an important tool for calculations in physics, biology, and even finance. The study is published in the Scientific Reports journal.

“Building a coordinate grid is a key task for modeling. Breaking down a complex space into manageable pieces is necessary, as it allows you to accurately determine the changes in different quantities—temperature, speed, pressure, and so on,” commented the lead author of the paper, Bari Khairullin, a Ph.D. student from the Computational and Data Science and Engineering program at Skoltech.

“Without a good grid, calculations become either inaccurate or impossible. In physics, they help model the movement of liquids and gases, in biology, tissue growth and drug distribution, and in finance, they predict market fluctuations. The proposed approach opens up new possibilities in building grids using artificial intelligence.”

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Scientists and engineers are developing from eco-friendly sources like plant waste. A key component, lignocellulose—found in and many plants—can be easily collected and chemically modified to improve its properties.

By using these kinds of chemical changes, researchers are creating and new ways to design and build sustainably. With about 181.5 billion tons of wood produced globally each year, it’s one of the largest renewable material sources.

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At a time when we run ourselves ragged to meet society’s expectations of productivity, performance and time optimization, is it right that our robot vacuum cleaners and other smart appliances should sit idle for most of the day?

Computer scientists at the University of Bath in the UK think not. In a new paper, they propose over 100 ways to tap into the latent potential of our robotic devices. The researchers say these devices could be reprogrammed to perform helpful tasks around the home beyond their primary functions, keeping them physically active during their regular downtime.

New functions could include playing with the cat, watering plants, carrying groceries from car to kitchen, delivering breakfast in bed and closing windows when it rains.

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Combinatorial optimization problems (COPs) arise in various fields such as shift scheduling, traffic routing, and drug development. However, they are challenging to solve using traditional computers in a practical timeframe.

Alternatively, annealing processors (APs), which are specialized hardware for solving COPs, have gained significant attention. They are based on the Ising model, in which COP variables are presented as magnetic spins and constraints as interactions between spins. Solutions are obtained by finding the spin state that minimizes the energy of the system.

There are two types of Ising models, the sparsely-coupled model and the fully-coupled model. Sparsely-coupled models offer high scalability by allowing more spins, but require COPs to be transformed to fit the model. Fully-coupled models, on the other hand, allow any COP to be mapped directly without transformation, making them highly desirable.

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Using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), Chinese astronomers have discovered a new millisecond pulsar. The newfound pulsar, designated PSR J2129-1210O, was missed by previous searches as its spin period is close to the harmonics of the known pulsar PSR J2129+1210A.

The finding was reported in a paper published April 23 on the arXiv pre-print server.

Pulsars are highly magnetized, rotating emitting a beam of electromagnetic radiation. The most rapidly rotating pulsars, with rotation periods below 30 milliseconds, are known as (MSPs).

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Humans are known to make mental associations between various real-world stimuli and concepts, including colors. For example, red and orange are typically associated with words such as “hot” or “warm,” blue with “cool” or “cold,” and white with “clean.”

Interestingly, some past psychology studies have shown that even if some of these associations arise from people’s direct experience of seeing colors in the world around them, many people who were born blind still make similar color-adjective associations. The processes underpinning the formation of associations between colors and specific adjectives have not yet been fully elucidated.

Researchers at the University of Wisconsin-Madison recently carried out a study to further investigate how language contributes to how we learn about color, using mathematical and computational tools, including Open AI’s GPT-4 (LLM). Their findings, published in Communications Psychology, suggest that color-adjective associations are rooted in the structure of language itself and are thus not only learned through experience.

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