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Efforts to understand the mechanics of tornado formation have taken an innovative turn with the exploration of cosmic rays. These high-energy particles, generated by interactions between cosmic rays and Earth’s atmosphere, are being proposed as a tool to remotely measure atmospheric pressure changes within supercell thunderstorms. This method could shed light on the low-pressure regions believed to play a critical role in the development of tornadoes.

Study Details and Proposed Methods

According to a study accepted by Physical Review D, muons, subatomic particles created by cosmic rays, could offer insight into the atmospheric conditions within tornadoes and supercell storms. Dr. William Luszczak, a physicist at Ohio State University, has told Science News that using these particles to monitor pressure changes from a safe distance. He explained that a detector placed up to five kilometres away could identify variations in muon intensity, which correlate with changes in air density and pressure.

Computer models have demonstrated that regions of lower pressure are instrumental in tornado development. By tracking muons as they pass through these areas, researchers aim to overcome the challenges of placing traditional pressure sensors directly in the path of destructive storms.

Practical Considerations and Challenges

The research team has proposed a detector spanning 1,000 square metres to track muons across tornado paths. While such a scale would demand waiting for storms to pass near fixed equipment, a portable 100-square-metre version could be deployed at predicted severe weather sites. Past experiments, like the GRAPES-3 project in India, have shown the feasibility of using muons to measure atmospheric phenomena, including thunderstorm voltages.

Despite these advances, Dr. Hiroyuki Tanaka from the University of Tokyo has raised concerns about the practicality of building sufficiently portable detectors while talking to Science News. Challenges in applying the technique to supercells, which are smaller than cyclones, have also been noted. Field testing of this concept has been planned for the upcoming summer, offering an opportunity to validate its potential.

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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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