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Blog – Page 9

Composite adhesives like epoxy resins are excellent tools for joining and filling materials including wood, metal, and concrete. But there’s one problem: once a composite sets, it’s there forever. Now there’s a better way. Researchers have developed a simple polymer that serves as a strong and stable filler that can later be dissolved. It works like a tangled ball of yarn that, when pulled, unravels into separate fibers.

A new study led by researchers at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) outlines a way to engineer pseudo-bonds in materials. Instead of forming chemical bonds, which is what makes epoxies and other composites so tough, the chains of molecules entangle in a way that is fully reversible. The research is published in the journal Advanced Materials.

“This is a brand new way of solidifying materials. We opened a new path to composites that doesn’t go with the traditional ways,” said Ting Xu, a faculty senior scientist at Berkeley Lab and one of the lead authors for the study.

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Imagine that a robot is helping you clean the dishes. You ask it to grab a soapy bowl out of the sink, but its gripper slightly misses the mark.

Using a new framework developed by MIT and NVIDIA researchers, you could correct that robot’s behavior with simple interactions. The method would allow you to point to the bowl or trace a trajectory to it on a screen, or simply give the robot’s arm a nudge in the right direction.

The work has been published on the pre-print server arXiv.

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Optoelectronics are promising devices that combine optical components, which operate leveraging light, with electronics, which leverage electrical current. Optoelectronic systems could transmit data faster than conventional electronics, thus opening new possibilities for the development of high-speed communication technology.

Despite their potential, the deployment of optoelectronics has so far been limited, in part due to reported difficulties in synchronizing optically generated signals with those of traditional electronic clocks. These signals are difficult to synchronize as optical and electronic components typically operate at different frequencies.

The frequencies of optical signals (i.e., generally hundreds of gigahertz) are generally significantly higher than those of , which range from megahertz to a few gigahertz. This mismatch in frequencies makes aligning the frequencies of the two types of components challenging, which in turn adversely impacts the reliability and efficiency of optoelectronics.

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It is estimated that about 80 million people worldwide live with a tremor. For example, those who live with Parkinson’s disease. The involuntary periodic movements sometimes strongly affect how patients are able to perform daily activities, such as drinking from a glass or writing.

Wearable soft robotic devices offer a potential solution to suppress such tremors. However, existing prototypes are not yet sophisticated enough to provide a real remedy.

Scientists at the Max Planck Institute for Intelligent Systems (MPI-IS), the University of Tübingen, and the University of Stuttgart under the Bionic Intelligence Tübingen Stuttgart (BITS) collaboration want to change this. The team equipped a biorobotic arm with two strands of strapped along the forearm.

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Johns Hopkins University engineers have developed a pioneering prosthetic hand that can grip plush toys, water bottles, and other everyday objects like a human, carefully conforming and adjusting its grasp to avoid damaging or mishandling whatever it holds.

The system’s hybrid design is a first for robotic hands, which have typically been too rigid or too soft to replicate a human’s touch when handling objects of varying textures and materials. The innovation offers a promising solution for people with hand loss and could improve how robotic arms interact with their environment.

Details about the device appear in Science Advances.

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An international team of scientists developed augmented reality glasses with technology to receive images beamed from a projector, to resolve some of the existing limitations of such glasses, such as their weight and bulk. The team’s research is being presented at the IEEE VR conference in Saint-Malo, France, in March 2025.

Augmented reality (AR) technology, which overlays and virtual objects on an image of the real world viewed through a device’s viewfinder or , has gained traction in recent years with popular gaming apps like Pokémon Go, and real-world applications in areas including education, manufacturing, retail and health care. But the adoption of wearable AR devices has lagged over time due to their heft associated with batteries and electronic components.

AR glasses, in particular, have the potential to transform a user’s physical environment by integrating virtual elements. Despite many advances in hardware technology over the years, AR glasses remain heavy and awkward and still lack adequate computational power, battery life and brightness for optimal user experience.

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Neural networks, a type of artificial intelligence modeled on the connectivity of the human brain, are driving critical breakthroughs across a wide range of scientific domains. But these models face significant threat from adversarial attacks, which can derail predictions and produce incorrect information.

Los Alamos National Laboratory researchers have now pioneered a novel purification strategy that counteracts adversarial assaults and preserves the robust performance of . Their research is published on the arXiv preprint server.

“Adversarial attacks to AI systems can take the form of tiny, near-invisible tweaks to input images, subtle modifications that can steer the model toward the outcome an attacker wants,” said Manish Bhattarai, Los Alamos computer scientist. “Such vulnerabilities allow malicious actors to flood digital channels with deceptive or harmful content under the guise of genuine outputs, posing a direct threat to trust and reliability in AI-driven technologies.”

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You can talk to an AI chatbot about pretty much anything, from help with daily tasks to the problems you may need to solve. Its answers reflect the human data that taught it how to act like a person; but how human-like are the latest chatbots, really?

As people turn to AI chatbots for more of their internet needs, and the bots get incorporated into more applications from shopping to health care, a team of researchers sought to understand how AI bots replicate human , which is the ability to understand and share another person’s feelings.

A study posted to the arXiv preprint server and led by UC Santa Cruz Professor of Computational Media Magy Seif El-Nasr and Stanford University Researcher and UCSC Visiting Scholar Mahnaz Roshanaei, explores how GPT-4o, the latest model from OpenAI, evaluates and performs empathy. In investigating the main differences between humans and AI, they find that major gaps exist.

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A team of AI researchers at Palisade Research has found that several leading AI models will resort to cheating at chess to win when playing against a superior opponent. They have published a paper on the arXiv preprint server describing experiments they conducted with several well-known AI models playing against an open-source chess engine.

As AI models continue to mature, researchers and users have begun considering risks. For example, chatbots not only accept wrong answers as fact, but fabricate false responses when they are incapable of finding a reasonable reply. Also, as AI models have been put to use in real-world business applications such as filtering resumes and estimating stock trends, users have begun to wonder what sorts of actions they will take when they become uncertain, or confused.

In this new study, the team in California found that many of the most recognized AI models will intentionally cheat to give themselves an advantage if they determine they are not winning.

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Einstein’s theory of general relativity suggests that the “memory” of ancient events, such as black hole mergers, may be etched into the fabric of space-time by gravitational waves. New research shows how this theory of gravitational memory could finally be proven.

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