Lifeboat Foundation

New tech aids stroke recovery:

A groundbreaking new robotic hand that can feel touch like a human one and pave the way for new prosthetics and robots.

Looking like a glittering cosmic geode, a trio of dazzling stars blaze from the hollowed-out cavity of a reflection nebula in this new image from NASA’s Hubble Space Telescope. The triple-star system is made up of the variable star HP Tau, HP Tau G2, and HP Tau G3.

HP Tau is known as a T Tauri star, a type of young variable star that hasn’t begun nuclear fusion yet but is beginning to evolve into a hydrogen-fueled star similar to our sun. T Tauri stars tend to be younger than 10 million years old―in comparison, our sun is around 4.6 billion years old―and are often found still swaddled in the clouds of dust and gas from which they formed.

As with all variable stars, HP Tau’s brightness changes over time. T Tauri stars are known to have both periodic and random fluctuations in brightness. The random variations may be due to the chaotic nature of a developing young star, such as instabilities in the accretion disk of dust and gas around the star, material from that disk falling onto the star and being consumed, and flares on the star’s surface. The periodic changes may be due to giant sunspots rotating in and out of view.

For the first time, scientists have seen the small ripples that result from black holes’ motion, which are gently stretching and squeezing everything in the universe.

They revealed that they could “hear” low-frequency gravitational waves, which are produced by massive objects colliding and moving around in space and causing changes in the universe’s fabric.

Jupiter’s moon, Europa, has long been hypothesized to contain a vast, liquid water ocean beneath its icy crust. But has this crust remained stationary, or has it moved over millions of years since it could be separated from the ocean below? This is what two recent studies published in The Planetary Science Journal and JGR Planets hope to address as high-resolution images from NASA’s Juno spacecraft revealed some unique surface features on the small moon. These images and studies hold the potential to help scientists better understand what they refer to as “true polar wander” on Europa, which is a hypothesis stating that Europa’s outer icy shell moves freely since it’s allegedly detached from the ocean underneath.

“True polar wander occurs if Europa’s icy shell is decoupled from its rocky interior, resulting in high stress levels on the shell, which lead to predictable fracture patterns,” said Dr. Candy Hansen, who is a co-investigator on Juno, along with being lead author of The Planetary Science Journal study and a co-author on the JGR Planets study. “This is the first time that these fracture patterns have been mapped in the southern hemisphere, suggesting that true polar wander’s effect on Europa’s surface geology is more extensive than previously identified.”

If you zoom in on a chemical reaction to the quantum level, you’ll notice that particles behave like waves that can ripple and collide. Scientists have long sought to understand quantum coherence, the ability of particles to maintain phase relationships and exist in multiple states simultaneously; this is akin to all parts of a wave being synchronized. It has been an open question whether quantum coherence can persist through a chemical reaction where bonds dynamically break and form.

Scientists from the University of Arizona have achieved far-field coupling of light to ultra-high quality factor microtoroids using a single objective lens. This could provide the foundation for a fully on-chip multiplexed microtoroid sensing platform.

In an amazing phenomenon of quantum physics known as tunneling, particles appear to move faster than the speed of light. However, physicists from Darmstadt believe that the time it takes for particles to tunnel has been measured incorrectly. They propose a new method to stop the speed of quantum particles.

“This is a really nice way of incorporating something you know about your physical system deep inside your machine-learning scheme. It goes far beyond just performing feature engineering on your data samples or simple inductive biases,” Schäfer says.

This generative classifier can determine what phase the system is in given some parameter, like temperature or pressure. And because the researchers directly approximate the probability distributions underlying measurements from the physical system, the classifier has system knowledge.

This enables their method to perform better than other machine-learning techniques. And because it can work automatically without the need for extensive training, their approach significantly enhances the computational efficiency of identifying phase transitions.

Named after an Italian theoretical physicist, Majoranas are complex quasiparticles that could be the key to building next-generation quantum computing systems.