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Category: robotics/AI – Page 4

A new AI-powered method is changing how scientists measure the universe. Developed by researchers at the Flatiron Institute and their partners, this technique offers a far more accurate way to determine the cosmos’ key properties.

The approach, known as Simulation-Based Inference of Galaxies (SimBIG), pulls hidden clues from galaxy patterns. It goes beyond older techniques by uncovering information that was previously out of reach.

Using AI, the team cut uncertainty in critical parameters—like how clumpy matter is in the universe—to less than half. These results match closely with other cosmic measurements, including the light from the universe’s earliest moments.

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For the design of future materials, it is important to understand how the individual atoms inside a material interact with each other quantum mechanically. Previously inexplicable vibrational states between carbon chains (carbyne) and nanotubes have puzzled materials scientists.

Researchers from Austria, Italy, France, China and Japan led by the University of Vienna have now succeeded in getting to the bottom of this phenomenon with the help of Raman spectroscopy, innovative theoretical models and the use of machine learning. The results, published in Nature Communications, show the universal applicability of as a sensor due to its sensitivity to external influences.

For the design of future materials, it is important to understand how matter interacts on an atomic scale. These quantum mechanical effects determine all macroscopic properties of matter, such as electrical, magnetic, optical or . In experiments, scientists use Raman spectroscopy, in which light interacts with matter, to determine the vibrational eigenstates of the atomic nuclei of the samples.

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A University of Nebraska–Lincoln engineering team is another step closer to developing soft robotics and wearable systems that mimic the ability of human and plant skin to detect and self-heal injuries.

Engineer Eric Markvicka, along with graduate students Ethan Krings and Patrick McManigal, recently presented a paper at the IEEE International Conference on Robotics and Automation in Atlanta, Georgia, that sets forth a systems-level approach for a technology that can identify damage from a puncture or , pinpoint its location and autonomously initiate self-repair.

The paper was among the 39 of 1,606 submissions selected as an ICRA 2025 Best Paper Award finalist. It was also a finalist for the Best Student Paper Award and in the mechanism and design category.

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An AI scanning billions of particle collisions at CERN’s Large Hadron Collider has detected something extraordinary — a mysterious particle decay at 4.8 TeV that doesn’t match any known physics. While scientists aren’t calling it official yet, this anomaly could be our first glimpse of a fifth fundamental force of nature.

🔬 What We Cover:

The real AI discovery behind the viral headlines.

How machine learning found what human scientists missed.

Why this 2.9-sigma anomaly has top physicists watching closely.

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High manufacturing costs are limiting patient access to CAR T cell therapies, according to new research, which indicates that decentralization, vector-free modification technologies, and AI would help make production cheaper.

Making CAR T therapies is an expensive business. A recent study suggested that producing a single batch can cost anywhere between $170,000 and $220,000, depending on the logistical, processing, and distribution steps involved.

The fundamental problem is that CAR T production is not a good fit for centralized manufacturing, according to Martin Bonamino, PhD, leader of the experimental cancer immunotherapy group at Brazil’s National Cancer Institute (INCA).

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