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Category: robotics/AI – Page 136

AI tool may spot ADHD years before children are diagnosed

Attention-deficit/hyperactivity disorder (ADHD) affects millions of children, yet many go years without a diagnosis, missing the chance for early support that can change long-term outcomes even when early signs are present. In a new study, Duke Health researchers found that artificial intelligence tools can analyze routine electronic health records to accurately estimate a child’s risk of developing ADHD years before a typical diagnosis. By reviewing patterns in everyday medical data, the approach could help flag children who may benefit from earlier evaluation and follow-up.

The research, published in Nature Mental Health, highlights how powerful insights can come from information already collected during regular health care visits to help support early decision making by primary care providers.

“We have this incredibly rich source of information sitting in electronic health records,” said Elliot Hill, lead author of the study and data scientist in the Department of Biostatistics & Bioinformatics at Duke University School of Medicine.

An interplanetary shortcut can speed up trips to Mars

Whether it’s robotic rovers heading to Mars or, one day, a crew of astronauts, a round-trip journey is an incredibly long one. But there may be a way to find a shortcut. A new study published in the journal Acta Astronautica suggests that hundreds of days could be shaved off a return trip to the Red Planet by using the early orbital data of asteroids. This could bring the total mission time down to as low as 153 days.

To identify optimal routes and calculate fuel needs, planners of interplanetary missions use precise planetary data. Sending missions to other worlds rarely involves early orbital data from asteroids.

When it comes to Mars missions, a key planning consideration is a phenomenon known as Mars opposition. This occurs roughly every 26 months when Earth passes directly between the sun and Mars. During this alignment, the two planets are on the same side of the sun, bringing Mars to its closest point to Earth.

Bananas, cups and peelers: Robots learn how to handle curved objects like fruits and tools

It does not take much to confuse some robots. A machine might be great at handling a simple object like a box, yet when it tries to work with a more irregular shape like a banana, it often fails.

But help is at hand. Researchers from the Swiss Federal Technology Institute of Lausanne (EPFL) and Idiap Research Institute have developed a new approach that lets robots more reliably manipulate a variety of different shapes by teaching them to follow the unique geometry of any object they encounter.

Their work is detailed in a paper published in the journal Science Robotics.

Machine learning offers faster, more reliable analysis of Fermi surfaces in search of spintronic materials

The search for next-generation electronic materials often starts with studying the Fermi surface, which serves as a map of a material’s electronic structure. Its shape varies with crystal structure, composition, and electronic band arrangement, directly impacting properties such as carrier density, magnetic behavior, and spin polarization. This makes it a crucial tool for understanding and engineering new materials.

The Fermi surface of a material is determined experimentally using techniques such as angle-resolved photoemission spectroscopy (ARPES). However, interpreting ARPES data requires specialized expertise, and the measurements themselves are often susceptible to noise. As experiments produce larger amounts of data, carefully reviewing every image by hand becomes time-consuming and inefficient.

Better volcano eruption predictions on Earth—and Venus—thanks to Mauna Loa study

When Mauna Loa erupted in 2022, the largest lava flow headed on a path headed directly toward Daniel K. Inouye State Highway 200, also known as Saddle Road, a critical route that carries many residents from their homes on one side to their jobs on the other.

No one could accurately predict whether the lava would continue to flow and eventually block the highway, or stop short, sparing the road.

However, when the volcano next erupts scientists will be better able to monitor the eruption in real time and make more accurate predictions about where the lava will flow and when the volcano might erupt. These advances are thanks to the availability of satellite data from public and private sources as well as machine learning algorithms developed at Pitt with help from a colleague in Italy, as highlighted in a recent publication in the Journal of Volcanology and Geothermal Research.

Neural network speeds tuning of attosecond light pulses for physics experiments

Researchers from Skoltech and the Shanghai Institute of Optics and Fine Mechanics have developed an approach that helps optimize the parameters of a laser-plasma source of attosecond pulses—ultrashort flashes of light used in physics experiments. Instead of relying on a large number of time-consuming calculations, the team trained a neural network to quickly identify promising settings and thereby speed up the optimization of the sophisticated laboratory equipment.

The results were published in Communications in Nonlinear Science and Numerical Simulation.

Attosecond pulse sources are used as research tools. They are applied in ultrafast spectroscopy, studies of magnetic materials, chiral molecules, and electron dynamics in matter. The goal of this work is to make it faster to tune a light source with the required properties for such experiments.

DuctGPT demonstrates how AI can accelerate discovery of next-generation fusion materials

Scientists at Ames National Laboratory developed a new artificial intelligence (AI) tool that accelerates discovery of materials needed for next-generation fusion energy systems. The tool, DuctGPT, combines advanced AI with physics-based modeling to help researchers predict materials with the appropriate properties to function in the extreme conditions inside of fusion reactors.

This research is discussed in “DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys,” published in Acta Materialia.

The challenge is to rapidly explore a wide range of potential alloy compositions that can maintain high-temperature strength, while retaining the ductility necessary for manufacturing the materials.

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