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Category: robotics/AI – Page 17

Researchers are investigating fluid-robot interactions at these scales, motivated by fish that use vortices to save energy. Onboard sensing, computation, and actuation are essential for effective navigation. Despite their potential, data-driven algorithms frequently lack practical validation.

Using inertial measurements to infer background flows is a new approach that was motivated by fish’s vestibular systems’ ability to sense acceleration. This method provides an affordable substitute for intricate flow sensors in self-driving cars.

In this regard, the Caltech team developed an underwater robot that uses these flows to reduce energy consumption by “surfing” vortices to reach its destination.

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The advancement can enable turbulent analysis of entire nuclear fusion reactors.

“By utilizing deep learning on GPUs, we have reduced computation time by a factor of 1,000 compared to traditional CPU-based codes,” said the joint research team.

“This advancement represents a cornerstone for digital twin technologies, enabling turbulent analysis of entire nuclear fusion reactors or replicating real Tokamaks in a virtual computing environment.”

Researchers underlined that the proposed FPL-net can solve the FPL equation in a single step, achieving results 1,000 times faster than previous methods with an error margin of just one-hundred-thousandth, demonstrating exceptional accuracy.

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A privately built spacecraft is hours from attempting to land on the moon, a feat that only one other company has accomplished in spaceflight history.

The robotic lander, dubbed Blue Ghost, has been in orbit around the moon for roughly two weeks, preparing for its daring descent. Texas-based company Firefly Aerospace developed the spacecraft, which aims to touch down on the lunar surface early Sunday at around 3:34 a.m. ET.

If all goes according to plan, Blue Ghost will become the second privately built vehicle to land on the moon successfully. In February 2024, another Texas-based company, Intuitive Machines, made history when its Odysseus lander pulled off a nail-biting touchdown near the moon’s south pole.

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Figure fast-tracks home testing for Figure 2 humanoid robot, powered by Helix AI.

Figure plans to start testing its humanoid robots in homes much sooner than expected.

The California-based startup’s CEO, Brett Adcock, revealed that it will begin alpha testing its Figure 2 robot in residential settings in late 2025.

This faster timeline is due to its advanced Vision-Language-Action (VLA) model, Helix, which enhances the robot’s adaptability and functionality.

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Researchers have discovered that incipient ferroelectricity can revolutionize computer memory, enabling ultra-low power devices.

These unique transistors shift behavior based on temperature, making them suitable for both traditional memory and neuromorphic computing, which mimics the brain’s energy efficiency. The use of strontium titanate thin films reveals unexpected ferroelectric-like properties, hinting at new possibilities in advanced electronics.

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A team of researchers at the George R. Brown School of Engineering and Computing at Rice University has developed an innovative artificial intelligence (AI)-enabled, low-cost device that will make flow cytometry—a technique used to analyze cells or particles in a fluid using a laser beam—affordable and accessible.

The prototype identifies and counts cells from unpurified blood samples with similar accuracy as the more expensive and bulky conventional flow cytometers, provides results within minutes and is significantly cheaper and compact, making it highly attractive for point-of-care clinical applications, particularly in low-resource and rural areas.

Peter Lillehoj, the Leonard and Mary Elizabeth Shankle Associate Professor of Bioengineering, and Kevin McHugh, assistant professor of bioengineering and chemistry, led the development of this new device. The study was published in Microsystems & Nanoengineering.

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A research team, led by Professor Jimin Lee and Professor Eisung Yoon in the Department of Nuclear Engineering at UNIST, has unveiled a deep learning–based approach that significantly accelerates the computation of a nonlinear Fokker–Planck–Landau (FPL) collision operator for fusion plasma.

The findings are published in the Journal of Computational Physics.

Nuclear fusion reactors, often referred to as artificial sun, rely on maintaining a high-temperature plasma environment similar to that of the sun. In this state, matter is composed of negatively charged electrons and positively charged ions. Accurately predicting the collisions between these particles is crucial for sustaining a stable fusion reaction.

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The National Synchrotron Light Source II (NSLS-II)—a U.S. Department of Energy (DOE) Office of Science user facility at DOE’s Brookhaven National Laboratory—is among the world’s most advanced synchrotron light sources, enabling and supporting science across various disciplines. Advances in automation, robotics, artificial intelligence (AI), and machine learning (ML) are transforming how research is done at NSLS-II, streamlining workflows, enhancing productivity, and alleviating workloads for both users and staff.

As synchrotron facilities rapidly advance—providing brighter beams, automation, and robotics to accelerate experiments and discovery—the quantity, quality, and speed of data generated during an experiment continues to increase. Visualizing, analyzing, and sorting these large volumes of data can require an impractical, if not impossible, amount of time and attention.

Presenting scientists with is as important as preparing samples for beam time, optimizing the experiment, performing error detection, and remedying anything that may go awry during a measurement.

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