Lifeboat Foundation

A collaboration of nuclear theorists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Argonne National Laboratory, Temple University, Adam Mickiewicz University of Poland, and the University of Bonn, Germany, has used supercomputers to predict the spatial distributions of charges, momentum, and other properties of “up” and “down” quarks within protons. The results, just published in Physical Review D, revealed key differences in the characteristics of the up and down quarks.

“This work is the first to leverage a new theoretical approach to obtain a high-resolution map of quarks within a proton,” said Swagato Mukherjee of Brookhaven Lab’s nuclear theory group and a co-author on the paper. “Our calculations show that the up quark is more symmetrically distributed and spread over a smaller distance than the down quark. These differences imply that up and down quarks may make different contributions to the fundamental properties and structure of the proton, including its internal energy and spin.”

Co-author Martha Constantinou of Temple University noted, “Our calculations provide input for interpreting data from nuclear physics experiments exploring how quarks and the gluons that hold them together are distributed within the proton, giving rise to the proton’s overall properties.”

Rechargeable lithium-ion batteries power smartphones, electric vehicles and storage for solar and wind energy, among other technologies.

They descend from another technology, the lithium-metal battery, that hasn’t been developed or adopted as broadly. There’s a reason for that: While lithium-metal batteries have the potential to hold about double the energy that lithium-ion batteries can, they also present a far greater risk of catching fire or even exploding.

Now, a study by members of the California NanoSystems Institute at UCLA reveals a fundamental discovery that could lead to safer lithium-metal batteries that outperform today’s lithium-ion batteries. The research was published today in the journal Nature.

The video conferencing company benefitted hugely from the remote work boom. Now it’s asking some staff to come to the office regularly.

This video explains what are carbohydrates?

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It’s one of the mysteries of nature: How does the axolotl, a small salamander, boast a superhero-like ability to regrow nearly any part of its body? For years, scientists have studied the amazing regenerative properties of the axolotl to inform wound healing in humans.

Now, Stanford Medicine researchers have made a leap forward in understanding what sets the axolotl apart from other animals. Axolotls, they discovered, have an ultra-sensitive version of mTOR, a molecule that acts as an on-off switch for protein production. And, like survivalists who fill their basements with non-perishable food for hard times, axolotl cells stockpile messenger RNA molecules, which contain genetic instructions for producing proteins. The combination of an easily activated mTOR molecule and a repository of ready-to-use mRNAs means that after an injury, axolotl cells can quickly produce the proteins needed for tissue regeneration.

The new findings were published July 26 in Nature.

Introduction to george hotz and connor leahy.

The debate opens with introductions to the two featured guests — George Hotz and Connor Leahy. Hotz is described as a maverick hacker known for daring technical exploits like jailbreaking the iPhone. His hacker skills are likened to the technical finesse of Elon Musk combined with the wit of Tony Stark. Leahy is introduced as a steadfast defender of AI safety, determined to safeguard humanity from potential threats posed by artificial intelligence. His goal is to “break the damning prophecy and render us super saved.”

George hotz’s opening statement: intelligence and power.

A rising in the number of cases in Florida of Hansen’s disease, commonly known as leprosy, highlights the increase in tropical disease infections found in the US. The World’s Marco Werman speaks with professor Peter Hotez, co-director of the Texas Children’s Hospital Center for Vaccine Development and dean of the National School of Tropical Medicine at Baylor College of Medicine, about the conditions driving the uptick in tropical illnesses more commonly found in the global south.

A new artificial intelligence model finds that X-ray images collected during routine medical care can provide warning signs for diabetes, even in patients who don’t meet the guidelines for elevated risk. The model could help physicians detect the disease earlier and prevent complications, says a multi-institutional team which published the findings in Nature Communications.

Applying the computational method known as deep learning to images and electronic health record data, the researchers developed a model that successfully flagged elevated diabetes risk in a retrospective analysis, often years before patients were diagnosed with the disease. That’s significant, the researchers say, given the prevalence of diabetes in the U.S. has more than doubled over the past 35 years.

Current guidelines suggest screening patients for type 2 diabetes if they are between 35 and 70 years old and have a body mass index (BMI) in the overweight to obese range.

A research team consisting of the National Institute for Materials Science (NIMS) and the Tokyo University of Science has developed the fastest electric double layer transistor using a highly ion-conductive ceramic thin film and a diamond thin film.

This transistor may be used to develop energy-efficient, high-speed edge AI devices with a wide range of applications, including future event prediction and pattern recognition/determination in images (including facial recognition), voices and odors. This research was published in the June 16, 2023, issue of Materials Today Advances.

An electric double layer transistor works as a switch using electrical resistance changes caused by the charge and discharge of an electric double layer formed at the interface between the electrolyte and semiconductor. Because this transistor is able to mimic the electrical response of human cerebral neurons (i.e., acting as a neuromorphic transistor), its use in AI devices is potentially promising.