The James Webb Space Telescope (JWST) has been giving us a fabulous new view on the Universe since its launch.

This new image of the protostar HH30 is in amazing new detail thanks to the JWST. It was first discovered using the Hubble Space Telescope, but this Herbig-Haro object, which is a dark molecular cloud, is a perfect object for JWST.

The image shows the protoplanetary disk seen edge on, with a conical outflow of gas and dust, with a narrow jet blasting out into space.

A machine-learning algorithm rapidly generates designs that can be simpler than those developed by humans.

Researchers in optics and photonics rely on devices that interact with light in order to transport it, amplify it, or change its frequency, and designing these devices can be painstaking work requiring human ingenuity. Now a research team has demonstrated that the discovery of the core design concepts can be automated using machine learning, which can rapidly provide efficient designs for a wide range of uses [1]. The team hopes the approach will streamline research and development for scientists and engineers who work with optical, mechanical, or electrical waves, or with combinations of these wave types.

When a researcher needs a transducer, an amplifier, or a similar element in their experimental setup, they draw on design concepts tested and proven in earlier experiments. “There are literally hundreds of articles that describe ideas for the design of devices,” says Florian Marquardt of the University of Erlangen-Nuremberg in Germany. Researchers often adapt an existing design to their specific needs. But there is no standard procedure to find the best design, and researchers could miss out on simpler designs that would be easier to implement.

Researchers have shown that they can distribute quantum keys under realistic conditions using commercial lasers.

Recent advances in robotics and machine learning have enabled the automation of many real-world tasks, including various manufacturing and industrial processes. Among other applications, robotic and artificial intelligence (AI) systems have been successfully used to automate some steps in manufacturing clothes.

Researchers at Laurentian University in Canada recently set out to explore the possibility of fully automating the knitting of clothes. To do this, they developed a model to convert fabric images into comprehensive instructions that knitting robots could read and follow. Their model, outlined in a paper published in Electronics, was found to successfully realize patterns for the creation of single-yarn and multi-yarn knitted items of clothing.

“Our paper addresses the challenge of automating knitting by converting fabric images into machine-readable instructions,” Xingyu Zheng and Mengcheng Lau, co-authors of the paper, told Tech Xplore.

Researchers have created the first laboratory analog of the ‘black hole bomb’, a theoretical concept developed by physicists in the 1970s.

TikTok fined €530M for illegally transferring EEA user data to China, violating GDPR Article 46.

Workflow slashes CVE ticketing time by 60% using Tines, CrowdStrike, and ServiceNow for faster action.

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An alternative periodic table of elements focusing on highly charged ions reveals new science that could support the quest for more accurate optical atomic clocks