Lifeboat Foundation

Scientists have made a freak discovery that’s potentially brought us closer to dinosaurs than we’ve ever been before.

Archaeologists uncovered one of the most well-preserved dinosaur fossils — so preserved that its very skin was still intact after all these years.

Talk about a good skincare routine, the discovery is now being hailed as a ‘one-in-a-billion’ find.

Sir Fred Hoyle was an English astronomer who formulated the theory of stellar nucleosynthesis. He also held controversial stances on other scientific matters — in particular his rejection of the “Big Bang” theory, a term coined by him on BBC radio, and his promotion of panspermia and the Steady-state theory of the universe.

Galaxies spotted by the James Webb Space Telescope seem far too massive to have formed so early on in the universe’s history, which could be a problem for our ideas of galaxy formation.

By Leah Crane and Alex Wilkins.

Experts consider glass nanoparticles kept inside extreme vacuum layers as potential platforms for examining the quantum world’s limits. However, a question in the field of quantum theory remains unanswered: at which size does an object start being described by quantum physics laws rather than classical physics laws?

Achieving Quantum-State Cooling in More Than One Direction Is Challenging

Continue reading “Quantum Physics: Scientists Cool Nanoparticles to Ground-State in 2D Motion” »

Computer models are an important tool for studying how the brain makes and stores memories and other types of complex information. But creating such models is a tricky business. Somehow, a symphony of signals—both biochemical and electrical—and a tangle of connections between neurons and other cell types creates the hardware for memories to take hold. Yet because neuroscientists don’t fully understand the underlying biology of the brain, encoding the process into a computer model in order to study it further has been a challenge.

Now, researchers at the Okinawa Institute of Science and Technology (OIST) have altered a commonly used computer model of memory called a Hopfield network in a way that improves performance by taking inspiration from biology. They found that not only does the new network better reflect how neurons and other cells wire up in the brain, it can also hold dramatically more memories.

The complexity added to the network is what makes it more realistic, says Thomas Burns, a Ph.D. student in the group of Professor Tomoki Fukai, who heads OIST’s Neural Coding and Brain Computing Unit. “Why would biology have all this complexity? Memory capacity might be a reason,” Mr. Burns says.

National University of Singapore (NUS) pharmaceutical scientists have developed synthetic peptide nanonets for treating infections by bacteria strains resistant to last-resort antibiotics.

In nature, trap-and-kill is a common immune defense mechanism employed by various species, including humans. In response to the presence of pathogens, peptides are released from host cells and they promptly self-assemble in solution to form cross-linked nanonets, which then entrap the bacteria and render them more vulnerable to antimicrobial components.

Several research groups have explored synthetic biomimetics of nanonets as an avenue for addressing the global healthcare challenge of widespread antibiotic resistance. However, most prominent studies in the field only yielded disjointed short nanofibrils restricted to the bacterial surfaces and are incapable of physically immobilizing the bacteria. Additionally, these designs were lacking in control over the initiation of the self-assembly process.

Optical computing has been gaining wide interest for machine learning applications because of the massive parallelism and bandwidth of optics. Diffractive networks provide one such computing paradigm based on the transformation of the input light as it diffracts through a set of spatially-engineered surfaces, performing computation at the speed of light propagation without requiring any external power apart from the input light beam. Among numerous other applications, diffractive networks have been demonstrated to perform all-optical classification of input objects.

Researchers at the University of California, Los Angeles (UCLA), led by Professor Aydogan Ozcan, have introduced a “time-lapse” scheme to significantly improve the image classification accuracy of diffractive optical networks on complex input objects. The findings are published in the journal Advanced Intelligent Systems.

In this scheme, the object and/or the diffractive network are moved relative to each other during the exposure of the output detectors. Such a “time-lapse” scheme has previously been used to achieve super-resolution imaging, for example, in security cameras, by capturing multiple images of a scene with lateral movements of the camera.

Wearable devices such as smartwatches, fitness trackers, and virtual reality headsets are becoming commonplace. They are powered by flexible electronics that consist of electrodes with plastic or metal foil as substrates. However, both of these come with their own drawbacks. Plastics suffer from poor adhesion and low durability, while metal foils make the devices bulky and less flexible.

In light of this, paper is a promising alternative. It is porous, light, thin, foldable, and flexible. Moreover, paper has randomly distributed fibers that provide a large surface area for depositing active electrode material, making for excellent electrochemical properties.

Accordingly, researchers have developed various paper-based supercapacitors, devices that store electric charge and energy, by stacking multiple sheets, acting as positive and negative electrodes and separators. However, such an arrangement increases device size and resistance. In addition, they tend to form creases, peel off, and slip over each other, which further deteriorate device performance.