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In the search for new particles and forces in nature, physicists are on the hunt for behaviors within atoms and molecules that are forbidden by the tried-and-true Standard Model of particle physics. Any deviations from this model could indicate what physicists affectionately refer to as “new physics.”

Caltech assistant professor of physics Nick Hutzler and his group are in pursuit of specific kinds of deviations that would help solve the mystery of why there is so much matter in our universe. When our universe was born about 14 billion years ago, matter and its partner, antimatter, are believed to have existed in equal measure.

Typically, matter and antimatter cancel each other out, but some kind of asymmetry existed between the different types of particles to cause matter to win out over antimatter. Hutzler’s group uses tabletop experiments to look for symmetry violations—the deviant particle behaviors that led to our lopsided matter-dominated universe.

Summary: A new machine learning model, AutMedAI, can predict autism in children under two with nearly 80% accuracy, offering a promising tool for early detection and intervention.

The model analyzes 28 parameters available before 24 months, such as age of first smile and eating difficulties, to identify children likely to have autism. Early diagnosis is crucial for optimal development, and further validation of the model is underway.

Information and Energy are related. The Second Law of Thermodynamics applies to changes in energy and heat, but it does not apply to information dynamics. Advances in Infodynamics have made it clear that Total Information contains Useful Information…

Scientists from the National University of Singapore (NUS) have shown that excitonic resonances and transitions between excitons can significantly increase the efficiency of generating entangled photon pairs. This could lead to the development of efficient ultrathin quantum light sources.

Quantum simulation enables scientists to simulate and study complex systems that are challenging or even impossible using classical computers across various fields, including financial modeling, cybersecurity, pharmaceutical discoveries, AI and machine learning. For instance, exploring molecular vibronic spectra is critical in understanding the molecular properties in molecular design and analysis.

The electron shell of atoms acts as an “electromagnetic shield,” preventing direct access to the nucleus and its properties. A team in the group of Klaus Blaum, director at the Max Planck Institute for Nuclear Physics in Heidelberg, has now succeeded in precisely measuring the effect of this shielding in beryllium atoms. The study is published in the journal Nature.

Imagine knowing your milk has gone bad without having to open your fridge. A technology called printed electronics could one day make innovations like this possible.

Could AI agents ever outdo the generalized smarts of human intelligence? That was one of the questions raised at the AGI-24 conference.

Engineers have designed a tiny battery, smaller than a grain of sand, to power microscopic robots for jobs such as drug delivery or locating leaks in gas pipelines.

A tiny battery designed by MIT engineers could enable the deployment of cell-sized, autonomous robots for drug delivery within in the human body, as well as other applications such as locating leaks in gas pipelines.

The new battery, which is 0.1 millimeters long and 0.002 millimeters thick — roughly the thickness of a human hair — can capture oxygen from air and use it to oxidize zinc, creating a current with a potential of up to 1 volt. That is enough to power a small circuit, sensor, or actuator, the researchers showed.

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