Lifeboat Foundation

Researchers have made unparalleled ultrawide-bandgap semiconductors through temperature and timing, just like baking bread.

Alloying, the process of mixing metals in different ratios, has been a known method for creating materials with enhanced properties for thousands of years, ever since copper and tin were combined to form the much harder bronze. Despite its age, this technology remains at the heart of modern electronics and optics industries. Semiconducting alloys, for instance, can be engineered to optimize a device’s electrical, mechanical and optical properties.

Alloys of oxygen with group III elements, such as aluminum, gallium, and indium, are important semiconductor materials with vast applications in high-power electronics, solar-blind photodetectors and transparent devices. The defining property of a semiconductor is its bandgap, a barrier over which only electrons with the required energy can pass. Beta-phase aluminum gallium oxides are notable because of their relatively large bandgap, but most III-O alloys are expensive to make and of unsatisfactory quality.

The Tibet ASγ experiment, a China-Japan joint research project on cosmic-ray observation, has discovered ultra-high-energy diffuse gamma rays from the Milky Way galaxy. The highest energy detected is estimated to be unprecedentedly high, nearly 1 Peta electronvolts (PeV, or one million billion eV).

Surprisingly, these gamma rays do not point back to known high-energy gamma-ray sources, but are spread out across the Milky Way (see Figure 1).

Scientists believe these gamma rays are produced by the nuclear interaction between cosmic rays escaping from the most powerful galactic sources (“PeVatrons”) and interstellar gas in the Milky Way galaxy. This observational evidence marks an important milestone in revealing the origin of cosmic rays, which has puzzled mankind for more than a century.

NASA’s Juno spacecraft captured a new aurora feature on Jupiter that is characterized by faint ring-shaped emissions that expand rapidly over time. These auroral emissions are believed to be triggered by charged particles coming from the edge of the planet’s magnetosphere.

NASA’s Juno mission has detected new auroral emissions on Jupiter which appear to ripple over the planet’s poles.

The Ultraviolet Spectrograph (UVS) on the Juno spacecraft captured this glowing phenomenon, which is characterized by faint ring-shaped emissions that expand rapidly over time at speeds between 2 and 4.8 miles per second (3.3 and 7.7 kilometers per second). Researchers from the Southwest Research Institute (SwRI), where Juno’s UVS instrument was built, suggest these auroral emissions are triggered by charged particles coming from the edge of Jupiter’s massive magnetosphere, according to a statement from the institute.

A $2 million federal grant will enable Houston-based PolyVascular to launch human trials of what it hails as the first polymer-based heart valve for children.

In conjunction with the grant, Dr. Will Clifton has joined the medical device company as chief operating officer. He will oversee the grant as principal investigator, and will manage the company’s operations and R&D. Clifton is president and co-founder of Houston-based Enventure, a medical innovation incubator and education hub. He previously was senior director of medical affairs at Houston-based Procyrion, a clinical-stage medical device company.

PolyVascular’s Phase II grant came from the Small Business Innovation Research (SBIR) program, which promotes technological projects.

Next year, the Ford Thunderbird returns as a vertical take-off and landing vehicle in a surprise move designed to compete with GM’s future Cadillac VTOL.

Colonel Mark M. Zais, chief data scientist at United States Special Operations Command (USSOCOM) stresses the importance of AI-related education in the DOD. In his 2020 CJCS Strategic Essay Competition first place Strategy Article, he says, “Without that education, we face a world where senior leaders use AI-enabled technologies to make decisions related to national security without a full grasp of the tools that they—and our adversaries—possess.”

With the release of its first artificial intelligence (AI) strategy in 2019, the Department of Defense (DOD) formalized the increased use of AI technology throughout the military, challenging senior leaders to create “organizational AI strategies” and “make related resource allocation decisions.”1 Unfortunately, most senior leaders currently have limited familiarity with AI, having developed their skills in tactical counterinsurgency environments, which reward strength (physical and mental), perseverance, and diligence. Some defense scholars have advocated a smarter military, emphasizing intellectual human capital and arguing that cognitive ability will determine success in strategy development, statesmanship, and decisionmaking.2 AI might complement that ability but cannot be a substitute for it. Military leaders must leverage AI to help them adapt and be curious. As innovative technologies with AI applications increasingly become integral to DOD modernization and near-peer competition, senior leaders’ knowledge of AI is critical for shaping and applying our AI strategy and creating properly calibrated expectations.

War is about decisionmaking, and AI enables the technology that will transform how humans and machines make those decisions.3 Successful use of this general-purpose technology will require senior leaders who truly understand its capabilities and can demystify the hyperbole.4 Within current AI strategy development and application, many practitioners have a palpable sense of dread as we crest the waves of a second AI hype cycle, seemingly captained by novices of the AI seas.5 In-house technical experts find it difficult to manage expectations and influence priorities, clouded by buzzwords and stifled by ambitions for “quick wins.” The importance of AI-related education increases with AI aspirations and the illusion of progress. Without that education, we face a world where senior leaders use AI-enabled technologies to make decisions related to national security without a full grasp of the tools that they—and our adversaries—possess. This would be equivalent to a combat arms officer making strategic military landpower decisions without the foundations of military education in maneuver warfare and practical experience.

Continue reading “Artificial Intelligence: A Decisionmaking Technology” »

Dr Joan Mannick, Head of Research and Development at Life Biosciences, discusses the #geroscience approach in disease treatment and the exciting work being done at Life Biosciences.

#Ageing is the greatest risk factor for almost every chronic disease. Multiple studies have shown that ageing is a modifiable risk factor that can be targeted therapeutically.

Continue reading “Targeting the biology of ageing to prevent, treat and reverse age-related diseases | Dr Joan Mannick” »

The European Organization for Nuclear Research (CERN) involves 23 countries, 15000 researchers, billions of dollars a year, and the biggest machine in the world: the Large Hadron Collider. Even with so much organizational and mechanical firepower behind it, though, CERN and the LHC are outgrowing their current computing infrastructure, demanding big shifts in how the world’s biggest physics experiment collects, stores and analyzes its data. At the 2021 EuroHPC Summit Week, Maria Girone, CTO of the CERN openlab, discussed how those shifts will be made.

The answer, of course: HPC.

The Large Hadron Collider – a massive particle accelerator – is capable of collecting data 40 million times per second from each of its 150 million sensors, adding up to a total possible data load of around a petabyte per second. This data describes whether a detector was hit by a particle, and if so, what kind and when.

Pugs, Ferraris, mountains, brunches, beaches, and babies — Instagram is full of them. In fact, it’s become one of the largest image databases on the planet over the last decade and the company’s owner, Facebook, is using this treasure trove to teach machines what’s in a photo.

Facebook announced on Thursday that it had built an artificial intelligence program that can “see” what it is looking at. It did this by feeding it over 1 billion public images from Instagram.

The “computer vision” program, nicknamed SEER, outperformed existing AI models in an object recognition test, Facebook said.