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Dartmouth researchers have developed a self-powered pump that uses natural light and chemistry to target and remove specific water pollutants, according to a new report in the journal Science (“A molecular anion pump”).

As water enters the pump, a wavelength of light activates a synthetic molecular receptor designed to bond to negatively charged ions, or anions, a class of pollutants linked to metabolic disruptions in plants and animals. A second wavelength deactivates the receptors as water exits the pump and causes them to release the pollutants, trapping them in a non-reactive substrate until they can be safely discarded.

“This is a proof of concept that you can use a synthetic receptor to convert light energy into chemical potential for removing a contaminant from a waste source,” says the study’s senior author, Ivan Aprahamian, professor and chair of the Department of Chemistry at Dartmouth.

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Neuromorphic computers are devices that try to achieve reasoning capability by emulating a human brain. They are a different type of computer architecture that copies the physical characteristics and design principles of biological nervous systems. Although neuromorphic computations can be emulated, it’s very inefficient for classical computers to simulate. Typically new hardware is required.

The first neuromorphic computer at the scale of a full human brain is about to come online. It’s called DeepSouth, and will be finished in April 2024 at Western Sydney University. This computer should enable new research into how our brain actually functions, potentially leading to breakthroughs in how AI is created.

One important characteristic of this neuromorphic computer is that it’s constructed out of commodity hardware. Specifically, it’s built on top of FPGAs. This means it will be much easier for other organizations to copy the design. It also means that once AI starts self-improving, it can probably build new iterations of hardware quite easily. Instead of having to build factories from the ground up, leveraging existing digital technology allows all the existing infrastructure to be reused. This might have implications for how quickly we develop AGI, and how quickly superintelligence arises.

#ai #neuromorphic #computing.

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The new groundbreaking Language Velocity Field (LVF) method is helping researchers trace dispersion patterns of languages, including Greek, across the world.

The spatial evolution of languages can help deepen our understanding of people diffusion and cultural spread. The language velocity field estimation is different from the frequently used phylogeographic approach which cannot fully explain the language evolution induced by the horizontal contact among languages, such as borrowing and areal diffusion.

The study of language evolution, particularly its spatial dispersion, offers valuable insights into our collective past. Traditional approaches, such as the phylogeographic approach, often miss the complexity of language evolution.

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New data on the rotation around both long and short axes of plastic strands may help researchers track and remove microplastics that pollute the ocean.

Pollution from tiny plastic particles (microplastics) increasingly threatens ocean and river ecosystems, and potentially human health, but researchers don’t have a good understanding of how and where these pollutants are transported by flowing waters. Now a research team has observed 1.2-mm-long, 10-µm-wide strands—similar to the most common type of microplastic particles—as they moved in turbulent flows mimicking those in natural environments [1]. The experiments reveal new aspects of their motion, including the rates at which fibers spin around their long axes. The researchers hope that their results will help engineers design structures that can concentrate plastics for easier removal.

Scientists currently have a limited understanding of where microplastics tend to accumulate in the environment, says fluid dynamics expert Alfredo Soldati of the Vienna University of Technology. Where plastics gather depends on natural fluid flows and on the nature of the plastic objects themselves.

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On cosmological scales, dark matter so dominates the gravitational behavior of the Universe that, to first approximation, researchers can ignore the gravitational pull of visible matter when simulating the large-scale distribution of galaxies. Still, determining subtle yet important properties of the Universe, such as variations in the amount of dark energy, requires knowing the exact locations of the subatomic particles (baryons) that make up the Universe’s visible matter, as well as what these particles are doing and how they are interacting with dark matter. Now Tassia Ferreira of the University of Oxford, UK, and her collaborators have identified a statistical correlation between two observable features of the Universe that has the potential to reveal the extent of astronomers’ understanding of how baryons shape the large-scale structure of the cosmos [1].

The uncovered correlation is between variations across the sky of the amount of “cosmic shear” and the intensity of the diffuse background of cosmic x rays. Cosmic shear is the apparent warping of the shapes and positions of distant galaxies by the gravitational pulls of intervening clusters of galaxies and other large concentrations of matter. The x-ray background emanates mostly from hot, thin plasma held in the gravitational potentials of those same intervening structures.

Ferreira and her collaborators found that the cosmic shear and the x-ray background are strongly correlated. This correlation is unsurprising given that both features are manifestations of the same dark-matter structures. But the researchers also found that the baryons’ locations influenced how well various physical models reproduced the correlation. One important factor is the amount of plasma (which is made of baryons) that supermassive black holes expel into intergalactic space.

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Our brain measures time by counting experiences, not by following a strict chronological order.

A new study by a team of UNLV researchers suggests that there’s a lot of truth to the trope “time flies when you’re having fun.”

In their study, recently published in the journal Current Biology, the researchers discovered that our perception of time is based on the number of experiences we have, not on an internal clock. Additionally, they found that increasing speed or output during an activity appears to affect how our brains perceive time.

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