Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog – Page 2332

The researchers are excited by the potential of how cells cooperate and communicate in the body and how they can be reprogrammed to create new structures and functions.

With the help of Simon Garnier at the New Jersey Institute of Technology, the team characterized the different types of Anthrobots that were produced.

They observed that bots fell into a few discrete categories of shape and movement, ranging in size from 30 to 500 micrometers (from the thickness of a human hair to the point of a sharpened pencil), filling an important niche between nanotechnology and larger engineered devices.

Some were spherical and fully covered in cilia, and some were irregular or football-shaped with more patchy coverage of cilia or just covered with cilia on one side. They traveled in straight lines, moved in tight circles, combined those movements, or just sat around and wiggled.

Continue reading “These ‘anthrobots’ created from human cells are healing neurons” | >

The accelerator, an advanced wakefield laser accelerator, is under 20 feet long, generating a 10 billion electron-volt (10 GeV) electron beam.

Bjorn “Manuel” Hegelich, associate professor of physics at UT and CEO of TAU Systems, alluding to the size of the chamber where the beam was produced stated: “We can now reach those energies in 10 centimeters.”

Scientists are aiming to use this technology for assessing the resilience of space-bound electronics against radiation, capturing the 3D internal configurations of emerging semiconductor chip designs, and potentially pioneering new cancer treatments and advanced medical imaging methodologies.

Furthermore, the statement noted that this accelerator could also be used to drive another device called an X-ray free electron laser, which could take slow-motion movies of processes on the atomic or molecular scale.

Continue reading “Scientists make a laser accelerator with 10 billion electron-volt beam” | >

Multifunctional computer chips have evolved to do more with integrated sensors, processors, memory and other specialized components. However, as chips have expanded, the time required to move information between functional components has also grown.

“Think of it like building a house,” said Sang-Hoon Bae, an assistant professor of mechanical engineering and at the McKelvey School of Engineering at Washington University in St. Louis. “You build out laterally and up vertically to get more function, more room to do more specialized activities, but then you have to spend more time moving or communicating between rooms.”

To address this challenge, Bae and a team of international collaborators, including researchers from the Massachusetts Institute of Technology, Yonsei University, Inha University, Georgia Institute of Technology and the University of Notre Dame, demonstrated monolithic 3D integration of layered 2D material into novel processing hardware for artificial intelligence (AI) computing.

Read more | >

By strategically straining materials that are as thin as a single layer of atoms, University of Rochester scientists have developed a new form of computing memory that is at once fast, dense, and low-power. The researchers outline their new hybrid resistive switches in a study published in Nature Electronics.

Developed in the lab of Stephen M. Wu, an assistant professor of electrical and and of physics, the approach marries the best qualities of two existing forms of resistive switches used for : memristors and . Both forms have been explored for their advantages over today’s most prevalent forms of memory, including dynamic random access memory (DRAM) and , but they have their drawbacks.

Wu says that memristors, which apply voltage to a thin filament between two electrodes, tend to suffer from a relative lack of reliability compared to other forms of memory. Meanwhile, phase-change materials, which involve selectively melting a material into either an amorphous state or a crystalline state, require too much power.

Read more | >

Summary: Researchers used AI to select and generate images for studying brain’s visual processing. Functional MRI (fMRI) recorded heightened brain activity in response to these images, surpassing control images.

The approach enabled tuning visual models to individual responses, enhancing the study of brain’s reaction to visual stimuli. This method, offering an unbiased, systematic view of visual processing, could revolutionize neuroscience and therapeutic approaches.

Read more | >