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Category: robotics/AI – Page 4

Scientists are racing against time to try and create revolutionary, sustainable energy sources (such as solid-state batteries) to combat climate change. However, this race is more like a marathon, as conventional approaches are trial-and-error in nature, typically focusing on testing individual materials and set pathways one by one.

To get us to the finish line faster, researchers at Tohoku University developed a data-driven AI framework that points out potential solid-state electrolyte (SSE) candidates that could be “the one” to create the ideal sustainable energy solution.

This model does not only select optimal candidates, but can also predict how the reaction will occur and why this candidate is a good choice—providing interesting insights into potential mechanisms and giving researchers a huge head start without even stepping foot into the lab.

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Researchers at Korea’s Daegu Gyeongbuk Institute of Science and Technology (DGIST) have developed a porous laser-induced graphene (LIG) sensor array that functions as a “next-generation AI electronic nose” capable of distinguishing scents like the human olfactory system does and analyzing them using artificial intelligence.

This technology converts scent molecules into electrical signals and trains AI models on their unique patterns. It holds great promise for applications in personalized health care, the cosmetics industry, and environmental monitoring.

While conventional electronic noses (e-noses) have already been developed and used in areas such as food safety and gas detection in industrial settings, they struggle to distinguish subtle differences between similar smells or analyze complex scent compositions. For instance, distinguishing among floral perfumes with similar notes or detecting the faint odor of fruit approaching spoilage remains challenging for current systems. This gap has driven demand for next-generation e-nose technologies with greater precision, sensitivity, and adaptability.

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Four children have gained life-changing improvements in sight following treatment with a pioneering new genetic medicine through Moorfields Eye Hospital and UCL Institute of Ophthalmology.

The work was funded by the NIHR Research Professorship, Meira GTx and Moorfields Eye Charity.

The 4 children were born with a severe impairment to their sight due to a rare genetic deficiency that affects the ‘AIPL1’ gene. The defect causes the retinal cells to malfunction and die. Children affected are only able to distinguish between light and darkness. They are legally certified as blind from birth.

The new treatment is designed to enable the retinal cells to work better and to survive longer. The procedure, developed by UCL scientists, consists of injecting healthy copies of the gene into the retina through keyhole surgery. These copies are contained inside a harmless virus, so they can penetrate the retinal cells and replace the defective gene.

The condition is very rare, and the first children identified were from overseas. To mitigate any potential safety issues, the first 4 children received this novel therapy in only one eye.

The eye gene therapy was delivered via keyhole surgery at Great Ormond Street Hospital. The children were assessed in the NIHR Moorfields Clinical Research Facility, and the NIHR Moorfields Biomedical Research Centre provided infrastructure support for the research.

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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.

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Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence (AI) model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data.

AI often struggles with analyzing complex information that unfolds over long periods of time, such as climate trends, biological signals, or financial data. One new type of AI model called “state-space models” has been designed specifically to understand these sequential patterns more effectively. However, existing state-space models often face challenges—they can become unstable or require a significant amount of computational resources when processing long data sequences.

To address these issues, CSAIL researchers T. Konstantin Rusch and Daniela Rus have developed what they call “linear oscillatory state-space models” (LinOSS), which leverage principles of forced harmonic oscillators—a concept deeply rooted in physics and observed in .

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Featuring the Electro-Mechanical Brake and by-wire technology on the rear brakes, the project will also include ZF’s Integrated Brake Control and traditional front calipers, creating a ‘hybrid’ braking system of by-wire and hydraulics that offers increased flexibility to the manufacturer. The agreement will also provide significant steering technology with ZF’s Electric Recirculating Ball Steering Gear. This cutting-edge braking technology combined with traditional braking systems and innovative steering tools further solidifies ZF’s position as the industry leader in providing complete chassis solutions to its customers while providing a major customer win.

“We are all proud to see ZF’s technology leadership in the Chassis segment providing tangible value for our customers. Our goal when combining our steering, braking, dampers and actuators as well as corresponding software businesses into a single division was to create the world’s most comprehensive Chassis Solutions product and system offering,” said Peter Holdmann, Board of Management member at ZF and head of Division Chassis Solutions. “This combined center of expertise allows us to offer comprehensive solutions that integrate advanced engineering, innovative design, and cutting-edge technology to deliver unparalleled performance and safety.”

The road to the software-defined vehicle With the Electro-Mechanical Brake (EMB) as a key component of the brake-by-wire technology, ZF lays the foundation for the software-defined vehicle that will lead to new functions and features, many that emphasize safety as much as driving comfort. One such feature being explored with by-wire technology is the ability for the vehicle to autonomously brake and steer in a crash situation.

ZF’s Electro-Mechanical Brake provides premium performance for automatic emergency braking, full energy recuperation and redundant fallback options up to full automated driving for passenger car and light truck segments.

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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 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.

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