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The use of artificial intelligence (AI) is transforming the study of ancient manuscripts, unlocking texts that have remained unreadable for centuries. From deciphering burnt Roman scrolls to analysing damaged cuneiform tablets, AI-driven tools are revealing new information about the ancient world. This advancement allows researchers to examine vast archives, identify previously unknown writings, and reconstruct missing text with unprecedented accuracy. Scholars are now equipped with more data than ever before, opening up opportunities to answer historical questions that were once out of reach.

Breakthrough in Herculaneum Scroll Deciphering

As reported by nature.com, AI has successfully revealed significant portions of Greek text. Computer scientists Brent Seales from the University of Kentucky and participants of the competition called Vesuvius Challenge, utilised advanced neural networks to detect ink patterns on the fragile scrolls. As reported by Nature.com, the February 2024 winning entry employed a TimeSformer model, revealing text attributed to an ancient Greek philosophical work. Federica Nicolardi, a papyrologist at the University of Naples, described the discovery as a “historic moment for the field.”

Applications in Other Historical Archives

AI tools are also being applied to diverse historical collections. Researchers in South Korea are employing transformer-based networks to translate the extensive records of the Joseon dynasty, written in Hanja. This system has expedited translations of state records, offering insights into the political and cultural trends of the era. Similarly, initiatives such as the Fragmentarium project at Ludwig-Maximilians University are using neural networks to identify overlapping cuneiform fragments, uncovering lines from the Epic of Gilgamesh and a previously unknown hymn to Babylon.

Challenges and Future Prospects

Concerns regarding accuracy and accessibility remain as AI enables rapid decoding of large text corpora. Experts emphasise the need for interdisciplinary collaboration and open-source data to ensure transparency and replicability. Brent Seales has noted to nature.com that the success of AI in these applications underscores its role as a complement to human expertise, providing data that specialists can interpret and analyse. As AI continues to evolve, researchers anticipate breakthroughs in decoding lost languages and exploring underground libraries, potentially reshaping the understanding of ancient civilisations.

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Scientists Recreate Cosmic Ray Physics Using Cold Atom in New Laboratory Study

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Scientists Recreate Cosmic Ray Physics Using Cold Atom in New Laboratory Study

For the first time, researchers have managed to simulate a fundamental process of cosmic particle acceleration in a laboratory: the first series of discoveries that will transform our understanding of cosmic rays. Now, scientists from the Universities of Birmingham and Chicago have created a tiny, 100-micrometre Fermi accelerator, in which mobile optical potential barriers collide with trapped atoms, in a partial replica of how cosmic particles pick up energy in space. The technique not only replicates cosmic ray behaviour but also sets a new benchmark in quantum acceleration technology.

Lab-Built Fermi Accelerator Using Cold Atoms Validates Cosmic Ray Theory and Advances Quantum Tech

As per findings published in Physical Review Letters, this fully controllable setup demonstrated particle acceleration through the Fermi mechanism first proposed by physicist Enrico Fermi in 1949. Long theorised to underlie cosmic ray generation, the process had never been reliably replicated in a lab. By combining energy gains with particle losses, researchers created energy spectra similar to those observed in space, offering the first direct validation of Bell’s result, a cornerstone of cosmic ray physics.

In Fermi acceleration, ultracold atoms are accelerated to more than 0.5 metres per second using laser-controlled barriers. Dr Amita Deb, a coauthor and researcher at the University of Birmingham, mentioned, ‘Our chimney is more powerful than conventional quantum nano-measurements, which are the best acceleration tools in the world so far, and while its simplicity and small size can be compelling, its lack of a theoretical speed limit is the most attractive feature.’ The ultracold atomic jets could be readily controlled with high precision in the subsequent experiments.

This progress means that, for the first time, complicated astrophysical events like shocks and turbulence can be studied in a laboratory, lead author Dr Vera Guarrera stated. This opens new avenues for high-energy astrophysics and also for applications in quantum wavepacket control and quantum chemistry.

Researchers plan to find out how different behaviour affects energy cutoffs and acceleration rates. A compact Fermi accelerator of this type could be a cornerstone for studies of fundamental physics and also connect to emerging technologies such as atomtronics.

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Scientists Say Dark Matter Could Turn Failed Stars Into ‘Dark Dwarfs’

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Scientists Say Dark Matter Could Turn Failed Stars Into ‘Dark Dwarfs’

Astronomers now propose that “failed stars” known as brown dwarfs could be powered by dark matter. Dark matter makes up about 85 percent of the universe’s matter but does not shine; it interacts only via gravity. Brown dwarfs form like stars but lack enough mass to ignite fusion. The theory suggests brown dwarfs in galaxy centers might trap dark matter in their interiors. When that dark matter annihilates, it releases energy that heats the star, turning the dwarf into a brighter “dark dwarf.” If such objects exist, finding them would give scientists a new clue to the nature of dark matter.

Dark Matter in Failed Stars

According to the new model, dense brown dwarfs at the centers of galaxies act like gravity wells that accumulate dark matter. Because dark matter interacts only via gravity, it naturally drifts to galactic cores, where it can be captured by star. As University of Hawai‘i physicist Jeremy Sakstein explains, once inside a star dark matter can annihilate with itself, releasing energy that heats the dwarf. The more dark matter a brown dwarf collects, the more energy it outputs. Crucially, this effect only works if dark matter particles self-annihilate (as with heavy WIMPs); lighter or non-interacting candidates like axions would not create dark dwarfs.

They propose using a chemical signature: a dark dwarf should hold on to lithium-7 that normal brown dwarfs burn away. The researchers say powerful telescopes like NASA’s James Webb Space Telescope might already be sensitive enough to spot cool, dim dark dwarfs near the Milky Way’s center. Detecting even one would strongly suggest that dark matter is made of heavy, self-interacting particles (like WIMPs).

In related work, Colgate astrophysicist Jillian Paulin coauthored studies of ancient “dark stars” fueled by dark matter, while SLAC physicist Rebecca Leane and collaborators have shown that dark matter capture could heat brown dwarfs and exoplanets – a process called “dark kinetic heating”. Together, these ideas highlight how even dim, unusual stars could illuminate the nature of dark matter.

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New Gel-Based Robotic Skin Feels Touch, Heat, and Damage Like Human Flesh

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New Gel-Based Robotic Skin Feels Touch, Heat, and Damage Like Human Flesh

Researchers have created a novel electronic “skin” that could let robots experience a sense of touch. This low-cost, gelatin-based material is highly flexible and durable and can be molded over a robot hand. Equipped with electrodes, the skin detects pressure, temperature changes, and even sharp damage. In tests it responded to pokes, burns and cuts. Unlike conventional designs that use separate sensors for each stimulus, this single “multi-modal” material simplifies the hardware while providing rich tactile data. The findings, published in Science Robotics, suggest it could be used on humanoid robots or prosthetic limbs to give them a more human-like touch.

Multi-Modal Touch and Heat Sensing

According to the paper, unlike typical robotic skins, which require multiple specialized sensors, the new gel acts as a single multi-modal sensor. Its uniform conductive layer responds differently to a light touch, a temperature change or even a scratch by altering tiny electrical pathways. This design makes the skin simpler and more robust: researchers note it’s easier to fabricate and far less costly than conventional multi-sensor skins. In effect, one stretchy sheet of this material can replace many parts, cutting complexity while maintaining rich sensory feedback.

Testing the Skin and Future Applications

The research team tested the skin by casting the gel into a human-hand shape and outfitting it with electrodes. They put it through a gauntlet of trials: blasting it with a heat gun, pressing it with fingers and a robotic arm, and even slicing it open with a scalpel. Those harsh tests generated over 1.7 million data points from 860,000 tiny conductive channels, which fed into a machine-learning model so the skin could learn to distinguish different types of touch.

UCL’s Dr. Thomas George Thuruthel, a co-author of the study, said the robotic skin isn’t yet as sensitive as human skin but “may be better than anything else out there at the moment.” He noted that the material’s flexibility and ease of manufacture as key advantages. Moreover, the team believes this technology could ultimately help make robots and prosthetic devices with a more lifelike sense of touch.

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