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In the ever-evolving world of financial markets, understanding the unpredictable nature of stock market fluctuations is crucial. A new study has taken a leap in this field by developing an innovative quantum mechanics model to analyze the stock market.

This model not only encompasses economic uncertainty and investor behavior but also aims to unravel the mysteries behind stock market anomalies like fat tails, volatility clustering, and contrarian effects.

The core of this model is quantum mechanics, a pillar of physics known for explaining the behavior of subatomic particles.

Health Care.

As the U.S. Struggles With a Stillbirth Crisis, Australia Offers a Model for How to Do Better.

Australia has emerged as a global leader in the effort to lower the number of babies that die before taking their first breaths. It’s an approach that could benefit America, which lags behind other wealthy nations in reducing stillbirths.

Black holes are very important for galactic formation.


Astronomers have discovered that the supermassive black holes in the centers of early galaxies are much more massive than expected. These surprisingly hefty black holes offer new insights into the origins of all supermassive black holes, as well as the earliest stages of their host galaxy’s lives.

In nearby, mature like our Milky Way, the total mass of stars vastly outweighs the mass of the big black hole found at the galaxy’s center by about 1,000 to 1. In the newfound distant galaxies, however, that mass difference drops to 100 or 10 to 1, and even to 1 to 1, meaning the black hole can equal the combined mass of its host galaxy’s stars.

This picture of unexpectedly massive black holes in fledgling galaxies comes from the James Webb Space Telescope (JWST), NASA’s latest flagship observatory. Until JWST, which launched in late 2021, astronomers were generally limited in their studies of distant black holes to stupendously bright quasars, composed of monster, matter-devouring black holes that completely outshine the stars in their host galaxies.

And that, according to the researchers, is exactly what the AI did, identifying whether prints from different types of fingers came from the same person with 75 to 90 percent accuracy, the BBC reported.

“It is clear that it isn’t using traditional markers that forensics have been using for decades,” study co-author Hod Lipson, a roboticist at Columbia University, told the broadcaster.

The researchers trained their AI model on a database of 60,000 fingerprints. Lead author Gabe Guo, a senior undergrad at Columbia University, told CNN that the AI was able to look beyond finger features known as “minutiae” that detectives have relied on for centuries.