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Scientists from NASA observed the bursting expansion of gas, stars, and dust from the glittering territory of the dual star clusters using Hubble and Webb space telescopes. NGC 460 and NGC 456 stay in the Small Magellanic Cloud, which are open clusters, with dwarf galaxies and orbit the Milky Way. These clusters are part of the extensive star complex clusters and nebulae that are most likely to be linked to each other. Stars are born upon the collapse of clouds.

Hubble and Webb Reveal Explosive Star Births in Small Magellanic Cloud

As per a report from NASA, the open clusters are from anywhere from a few dozen to many young stars, which are loosely bound by gravity. The images captured by Hubble capture the glowing and ionised gas, which comes from stellar radiation and blows bubbles in the form of gas and dust, which is blue in colour. The infrared of Webb shows the clumps and delicate filament-like structures and dust, which is red in colour.

NGC 460 and NGC 456: A Window into Early Universe Star Formation

Hubble shows the images of dust in the form of a silhouette against the blocking light; however, in the images of Webb, the dust is warmed by starlight and glows with infrared waves. The blend of gas and dust between the stars of the universe is called the interstellar medium. The region holding these clusters is known as the N83-84-85 complex and is home to multiple, rare O-type stars. These are hot and extremely massive stars that burn hydrogen like the Sun.

Such a state mimics the condition in the early universe; therefore, the Small Magellanic Cloud gives a nearby lab to find out the theories regarding star formation and the interstellar medium of the cosmos’s early stage.

With these observations, the researchers tend to study the gas flow from convergence to divergence, which helps in refining the difference between the Small Magellanic Cloud and its dwarf galaxy, and the Large Magellanic Cloud. Further, it helps in knowing the interstellar medium and gravitational interactions between the galaxies.

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AI Model Learns to Predict Human Gait for Smarter, Pre-Trained Exoskeleton Control

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Scientists at Georgia Tech have created an AI technique that pre-trains exoskeleton controllers using existing human motion datasets, removing the need for lengthy lab-based retraining. The system predicts joint behavior and assistance needs, enabling controllers that work as well as hand-tuned versions. This advance accelerates prototype development and could improve…

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Scientists Build One of the Most Detailed Digital Simulations of the Mouse Cortex Using Japan’s Fugaku Supercomputer

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Researchers from the Allen Institute and Japan’s University of Electro-Communications have built one of the most detailed mouse cortex simulations ever created. Using Japan’s Fugaku supercomputer, the team modeled around 10 million neurons and 26 billion synapses, recreating realistic structure and activity. The virtual cortex offers a new platform for studying br…

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UC San Diego Engineers Create Wearable Patch That Controls Robots Even in Chaotic Motion

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UC San Diego engineers have developed a soft, AI-enabled wearable patch that can interpret gestures with high accuracy even during vigorous or chaotic movement. The armband uses stretchable sensors, a custom deep-learning model, and on-chip processing to clean motion signals in real time. This breakthrough could enable intuitive robot control for rehabilitation, indus…

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