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NASA’s Juno spacecraft has delivered breathtaking images of Jupiter, highlighting the planet’s swirling, multicoloured storms and unique moons. During Juno’s 66th close flyby on October 23, the spacecraft approached the planet’s polar regions and captured close-up views of its fifth-largest moon, Amalthea. The raw images collected by JunoCam have since been processed by citizen scientists, who enhanced colours and contrasts to reveal Jupiter’s atmospheric details in a new light.

Spectacular Details of Jupiter’s Storms Revealed

Citizen scientist Jackie Branc processed one of Juno’s most striking images, showcasing a region on Jupiter called a Folded Filamentary Region (FFR), located near the planet’s subpolar areas. FFRs are known for their complex cloud patterns, which include white billows and fine, thread-like filaments. This recent image captures Jupiter’s stormy atmosphere with an emphasis on these fine details, giving scientists and the public alike a vivid view of the planet’s dynamic weather systems.

Juno’s data, available to the public online, allows enthusiasts and researchers to adjust image features such as contrast and colour balance. This collaborative effort has enabled a range of perspectives on Jupiter’s atmospheric bands, turbulent clouds, and powerful vortices.

Amalthea: A Close-Up of Jupiter’s Unique Moon

Juno also captured images of Amalthea, a small, potato-shaped moon only 84 kilometres in radius. In images processed by Gerald Eichstädt, the white balance was adjusted to distinguish Amalthea from the blackness of space, presenting the moon in stark relief. This view of Amalthea, with its rugged, irregular shape, adds to our understanding of Jupiter’s complex satellite system.

Launched in 2016, the Juno mission was originally planned to conclude in 2021, but its mission has been extended, with plans to end in September 2025. When its mission concludes, Juno will plunge into Jupiter’s atmosphere, marking the end of its successful exploration journey.

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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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