Lifeboat Foundation

The A17 Bionic chip initially used in the iPhone 15 Pro and ‌iPhone 15 Pro‌ Max later this year will fundamentally differ from a version of the same chip set to be manufactured in 2024, a new rumor claims.

The A17 Bionic is expected to be Apple’s first chip manufactured with a 3nm fabrication process, resulting in major performance and efficiency improvements over the 5nm technique used for the A14, A15, and A16 chips. The initial version of the A17 Bionic chip will reportedly be manufactured using TSMC’s N3B process, but Apple is planning to switch the A17 over to N3E sometime next year. The move is said to be a cost-cutting measure that could come at the expense of reduced efficiency.

Many people love the Raspberry Pi (us included). Not only are the computing boards cheap, but you can do so much with them. Wild, wonderful projects spring up all the time with RPi boards as a central piece. The latest creation to catch our eye might just rank as one of the most astonishing: a truck transformed into a dot matrix printer.

As spotted by Tom’s Hardware, YouTuber Ryder Damen (who runs the channel Ryder Calm Down) uses a Raspberry Pi to control his homebrew “printer,” which involves a pickup truck, water, and a whole array of gear to spell out messages on the ground. Damen calls it “skywriting, but on the road.”

In the video, Damen explains how the idea came to be (watching trucks paint markers on the road), as well as the process of constructing the “printer” and the materials used. A plywood and a trailer hitch form the frame of the rig, with solenoids, valves, and hoses then mounted to the wood to serve as printer parts. The solenoids control the valves—when 12V current is applied to them, they open. Meanwhile, the hoses split the water flow from a central point (a pump and a bucket full of water) to each valve.

Intel – the world’s biggest computer-chip maker – has released its newest quantum chip and has begun shipping it to quantum scientists and engineers to use in their research. Dubbed Tunnel Falls, the chip contains a 12-qubit array and is based on silicon spin-qubit technology.

The distribution of the quantum chip to the quantum community is part of Intel’s plan to let researchers gain hands-on experience with the technology, while at the same time enabling new quantum research.

The first quantum labs to get access to the chip include the University of Maryland, Sandia National Laboratories, the University of Rochester and the University of Wisconsin-Madison.

Most of us don’t think of atoms as having their own unique vibrations, but they do. In fact, it’s a feature so fundamental to nature’s building blocks that a team of University of Washington researchers recently observed and used this phenomenon in their research study. By studying the light atoms emitted when stimulated by a laser, they were able to detect vibrations sometimes referred to as atomic “breathing.”

The result is a breakthrough that may one day allow us to build better tools for many kinds of quantum technologies.

Led by Mo Li, a professor of photonics and nano devices in both the UW Department of Electrical and Computer Engineering and the UW Physics Department, the researchers set out to build a better quantum emitter, or QE, one that could be incorporated into optical circuits.

“Learn to code.” That three-word pejorative is perpetually on the lips and at the fingertips of internet trolls and tech bros whenever media layoffs are announced. A useless sentiment in its own right, but with the recent advent of code generating AIs, knowing the ins and outs of a programming language like Python could soon be about as useful as knowing how to fluently speak a dead language like Sanskrit. In fact, these genAIs are already helping professional software developers code faster and more effectively by handling much of the programming grunt work.

Two of today’s most widely distributed and written coding languages are Java and Python. The former almost single handedly revolutionized cross-platform operation when it was released in the mid-’90s and now drives “everything from smartcards to space vehicles,” as Java Magazine put it in 2020 — not to mention Wikipedia’s search function and all of Minecraft. The latter actually predates Java by a few years and serves as the code basis for many modern apps like Dropbox, Spotify and Instagram.

They differ significantly in their operation in that Java needs to be compiled (having its human-readable code translated into computer-executable machine code) before it can run. Python, meanwhile, is an interpreted language, which means that its human code is converted into machine code line-by-line as the program executes, enabling it to run without first being compiled. The interpretation method allows code to be more easily written for multiple platforms while compiled code tends to be focused to a specific processor type. Regardless of how they run, the actual code-writing process is nearly identical between the two: Somebody has to sit down, crack open a text editor or Integrated Development Environment (IDE) and actually write out all those lines of instruction. And until recently, that somebody typically was a human.

To rapidly test for COVID-19 treatments without animal studies, researchers make a model human body out of “organ chips.”

The first lithography tools were fairly simple, but the technologies that produce today’s chips are among humankind’s most complex inventions.

When we talk about computing these days, we tend to talk about software and the engineers who write it. But we wouldn’t be anywhere without the hardware and the physical sciences that have enabled it to be created—disciplines like optics, materials science, and mechanical engineering. It’s thanks to advances in these areas that we can fabricate the chips on which all the 1

Semiconductor lithography, the manufacturing process responsible for producing computer… More.

Water and carbon make a quantum couple: the flow of water on a carbon surface is governed by an unusual phenomenon dubbed quantum friction. A new work published in Nature Nanotechnology experimentally demonstrates this phenomenon—which was predicted in a previous theoretical study—at the interface between liquid water and graphene, a single layer of carbon atoms. Advanced ultrafast techniques were used to perform this study. These results could lead to applications in water purification and desalination processes and maybe even to liquid-based computers.

For the last 20 years, scientists have been puzzled by how water behaves near carbon surfaces. It may flow much faster than expected from conventional flow theories or form strange arrangements such as square ice. Now, an international team of researchers from the Max Plank Institute for Polymer Research of Mainz (Germany), the Catalan Institute of Nanoscience and Nanotechnology (ICN2, Spain), and the University of Manchester (England), reports in the study published in Nature Nanotechnology on June 22, 2023, that water can interact directly with the carbon’s electrons—a quantum phenomenon that is very unusual in fluid dynamics.

A liquid, such as water, is made up of small molecules that randomly move and constantly collide with each other. A solid, in contrast, is made of neatly arranged atoms that bathe in a cloud of electrons. The solid and the liquid worlds are assumed to interact only through collisions of the liquid molecules with the solid’s atoms—the liquid molecules do not “see” the solid’s electrons. Nevertheless, just over a year ago, a paradigm-shifting theoretical study proposed that at the water-carbon interface, the liquid’s molecules and the solid’s electrons push and pull on each other, slowing down the liquid flow: this new effect was called quantum friction. However, the theoretical proposal lacked experimental verification.

Cotton is the primary source of natural fiber on Earth, yet only four of 50 known species are suitable for textile production. Computer scientists at DePaul University applied a bioinformatics workflow to reconstruct one of the most complete genomes of a top cotton species, African domesticated Gossypium herbaceum cultivar Wagad. Experts say the results give scientists a more complete picture of how wild cotton was domesticated over time and may help to strengthen and protect the crop for farmers in the U.S., Africa and beyond.

The findings are published in the journal G3 Genes|Genomes|Genetics. Thiru Ramaraj, assistant professor of computer science in DePaul’s Jarvis College of Computing and Digital Media, is lead author on the publication. Leaps in technological advancement in the past decade made it possible for Ramaraj to analyze the genome in his Chicago lab.

“The power of this technology is it allows us to create high-quality genomes that supply a level of detail that simply wasn’t possible before,” says Ramaraj, who specializes in bioinformatics. “This opens up the possibility for more researchers to sequence many crops that are important to the global economy and to feeding the population.”