Lifeboat Foundation

There has been plenty of empirical evidence which shows that the single-particle picture holds to a good approximation in atomic nuclei. In this picture, protons and neutrons move independently inside a mean-field potential generated by an interaction among the nucleons. This leads to the concept of nuclear shells, similar to the electronic shells in atoms. In particular, the magic numbers due to closures of the nucleonic shells, corresponding to noble gases in elements, have been known to play an important role in nuclear physics. Here we propose a periodic table for atomic nuclei, in which the elements are arranged according to the known nucleonic shells. The nuclear periodic table clearly indicates that nuclei in the vicinity of the magic numbers can be understood in terms of a shell closure with one or two additional nucleons or nucleon holes, while nuclei far from the magic numbers are characterized by nuclear deformation.

We report terahertz (THz) light-induced second harmonic generation, in superconductors with inversion symmetry that forbid even-order nonlinearities. The THz second harmonic emission vanishes above the superconductor critical temperature and arises from precession of twisted Anderson pseudospins at a multicycle, THz driving frequency that is not allowed by equilibrium symmetry. We explain the microscopic physics by a dynamical symmetry breaking principle at sub-THz-cycle by using quantum kinetic modeling of the interplay between strong THz-lightwave nonlinearity and pulse propagation. The resulting nonzero integrated pulse area inside the superconductor leads to light-induced nonlinear supercurrents due to subcycle Cooper pair acceleration, in contrast to dc-biased superconductors, which can be controlled by the band structure and THz driving field below the superconducting gap.

Still, commercial crew has managed to move faster and stay on budget compared to other NASA projects which explicitly eschew private capital and insist that NASA is the only suitable customer for their wares. The agency estimates it saved as much as $30 billion by building its new spacecraft this way.

What if THIS is the most important thing that happens in 2020?

Subtle patterns can be seen in people’s reaction times as their memories are recalled, and boosting these brainwaves could help treat Alzheimer’s disease.

An international team of scientists has identified 29 independent genetic risk variants — 19 of them novel — linked to problematic alcohol use and revealed genetic relationships with numerous other traits.

A study of 73 COVID-19 patients found that they only remain infectious for 11 days.

On May 30th, SpaceX is slated to launch its first human passengers to space on the company’s new Crew Dragon spacecraft. The mission is a critical test flight for NASA as part of the agency’s Commercial Crew Program, aimed at getting private companies to create vehicles that can take astronauts to orbit. Here’s what to expect as the mission unfolds.

More than 500 strains of bacteria have been found living in seven types of tumour. Understanding their behaviour may lead to new kinds of treatments.

When you think of the words “data” and “mine”, no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining — that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction.

Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need. It should come as little surprise then that mining and resource extraction companies are looking to robotics, autonomous systems, and AI applications of all sorts to minimize risk, maximize return, and also lessen the environmental impact that mining has on our ecosystem.

On a recent AI Today podcast episode, Antoine Desmet of mining technology and equipment company Komatsu shared how they’re using advanced forms of AI, automation, and robotics to make an impact on the organization’s operations. Antoine has an interesting background, starting his career as a telecom engineer and receiving a Ph.D in neural network engineering. After getting his Ph.D, he returned to Komatsu and started working in surface analytics. He states that at the time there was a lot of data to work with, but very few analytics in place. He decided to start implementing machine learning and in the last few years his company has seen significant growth through this approach, with his data science team growing from just one person to ten people.