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‘Democratizing chemical analysis’:Chemists use machine learning and robotics to identify chemical compositions from images

Chemists have created a machine learning tool that can identify the chemical composition of dried salt solutions from an image with 99% accuracy. By using robotics to prepare thousands of samples and artificial intelligence to analyze their data, they created a simple, inexpensive tool that could expand possibilities for performing chemical analysis.

New AI model analyzes full night of sleep with high accuracy in largest study of its kind

Researchers have developed a powerful AI tool, built on the same transformer architecture used by large language models like ChatGPT, to process an entire night's sleep. To date, it is one of the largest studies, analyzing 1,011,192 hours of sleep. The model, called patch foundational transformer for sleep (PFTSleep), analyzes brain waves, muscle activity, heart rate, and breathing patterns to classify sleep stages more effectively than traditional methods, streamlining sleep analysis, reducing variability, and supporting future clinical tools to detect sleep disorders and other health risks.

Magnetic microalgae on a mission to become robots

Scientists have developed a single-cell green microalgae coated with magnetic material. This miniature robot was put to the test: would the microalgae with its magnetic coating be able to swim through narrow spaces and, additionally, in a viscous fluid that mimics those found in the human body? Would the tiny robot be able to fight its way through these difficult conditions?

Muscles from the printer: Silicone that moves

Researchers are working on artificial muscles that can keep up with the real thing. They have now developed a method of producing the soft and elastic, yet powerful structures using 3D printing. One day, these could be used in medicine or robotics -- and anywhere else where things need to move at the touch of a button.

Smart, energy-efficient robot grippers cut production costs

Energy remains a significant factor in industrial production processes. High levels of energy consumption make production more expensive and exacerbate the climate crisis. A new type of robot technology needs 90% less electricity than conventional systems. The technology uses lightweight, shape memory materials to construct novel, non-pneumatic, industrial gripper systems that function without the need for additional sensors.

Artificial muscles for tremor suppression

Scientists have developed a biorobotic arm that can mirror human tremors, such as those experienced by individuals that live with Parkinson's disease. Artificial muscles on either side of the forearm contract and relax to suppress the involuntary shaking of the wrist and hand. The researchers see their biorobotic arm not only as a platform for other scientists in the field to test new ideas in exoskeleton technology. The arm also serves as a test bed to see how well artificial muscles known as HASELs can one day become the building blocks of wearable devices. The vision is to one day develop a sleeve that tremor patients can comfortably wear to be able to better cope with everyday tasks such as holding a cup.

Scientists develop open-source software for modeling soft materials

A team of researchers created Morpho, an open-source programmable environment that enables researchers and engineers to conduct shape optimization and design for soft materials. Applications can be for anything from artificial hearts to robot materials that mimic flesh and soft tissue.

A springtail-like jumping robot

Springtails, small bugs often found crawling through leaf litter and garden soil, are expert jumpers. Inspired by these hopping hexapods, roboticists have made a walking, jumping robot that pushes the boundaries of what small robots can do. The research glimpses a future where nimble microrobots can crawl through tiny spaces, skitter across dangerous ground, and sense their environments without human intervention.

A new model accurately predicts the movement of elite athletes to catch the ball in parabolic flight

How does a tennis player like Carlos Alcaraz decide where to run to return Novak Djokovic's ball by just looking at the ball's initial position? These behaviours, so common in elite athletes, are difficult to explain with current computational models, which assume that the players must continuously follow the ball with their eyes. Now, researchers have developed a model that, by combining optical variables with environmental factors such as gravity, accurately predicts how a person will move to catch a moving object just from an initial glance. These results could have potential applications in fields such as robotics, sports training or even space exploration.
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