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A team of scientists from King Abdullah University of Science and Technology (KAUST) has revealed their plan to bring a new type of solar cell to the market, one that could revolutionize the field of renewable energy. The solar cell, called a perovskite/silicon tandem, combines two different materials to capture more sunlight and convert it into electricity.

Perovskite is a material that can absorb light very efficiently, while silicon is a material that can maintain its performance for a long time. By stacking them together, the researchers have achieved record-breaking efficiency levels, surpassing the previous limits of single-material solar cells. In 2023, the KAUST team, led by Professor Stefaan De Wolf, reported two world records for power conversion efficiency and five other records achieved by other groups worldwide. This shows the rapid advancement of perovskite/silicon tandem technology and its potential to dominate the solar market.

Year 2014 Basically once the master qubit is found it could even lead to a sorta master algorithm. Also it could show who actually pulls the strings of reality.


Whatever the u-bit is, it rotates quickly (Image: Natalie Nicklin)

Our best theory of nature has imaginary numbers at its heart. Making quantum physics more real conjures up a monstrous entity pulling the universe’s strings

Leader:The u-bit may be omniscient, but it’s no God particle

The key to understanding our universe lies in two theories—one of the generally-very-big and one of the generally-very-small. Albert Einstein’s Theory of General Relativity explains things like gravity and time, while Quantum Field Theory explores the subatomic world. However, one celestial object frustrates astrophysicists and quantum theorists in equal measure: black holes.

Because black holes release Hawking radiation (named for famous physicist Stephen Hawking), they eventually evaporate, which seemingly destroys the information that fell into the black hole. However, quantum field theory states that information cannot be destroyed. Result? Paradox.

One approach to AI uses a process called machine learning. In machine learning, a computer model is built to predict what may happen in the real world. The model is taught to analyze and recognize patterns in a data set. This training enables the model to then make predictions about new data. Some AI programs can also teach themselves to ask new questions and make novel connections between pieces of information.

“Computer models and humans can really work well together to improve human health,” explains Dr. Grace C.Y. Peng, an NIH expert on AI in medicine. “Computers are very good at doing calculations at a large scale, but they don’t have the intuitive capability that we have. They’re powerful, but how helpful they’re going to be lies in our hands.”

Researchers are exploring ways to harness the power of AI to improve health care. These include assisting with diagnosing and treating medical conditions and delivering care.

Part 3: This is the last of a three-part series on how Stanford Medicine researchers are designing vaccines that protect people from not merely individual viral strains but broad ranges of them. The ultimate goal: a vaccine with coverage so broad it can protect against viruses never before encountered.

Until now, vaccine efforts have mainly focused on stimulating B cells, described and discussed in Part 1 and Part 2. These antibody-producing immune cells’ virtue of being highly specific in what they target is also a vice. An antibody against influenza is unlikely to ever bind to, say, a coronavirus or a rabies virus.

Even when a virus mutates in some small way that distorts or disguises one of its biochemical bull’s-eyes, antibodies that worked before (because they aimed at that particular bull’s-eye) are now unemployed.

As we turn our attention from the front of the eye to the back, we also look to the future. Many studies have combined oculomics with AI tools to predict biological age from retinal biomarkers, such as retinal vasculature [1, 6], and even linked this to chronic disease risk, such as cardiovascular disease and cancer [7]. High resolution imaging tools also enable direct visualisation of the neural layers within the retina, which can show signs of neurodegenerative diseases, such as Alzheimer’s disease [1, 6], Parkinson’s disease [8], multiple sclerosis [6, 9], and even rare conditions, such as Lafora disease [10]. In many cases, the oculomic signs are present before symptoms arise. For example, it has been shown that proteins related to Alzheimer’s disease (such as amyloid-beta) accumulate at least one decade prior to cognitive decline [11] and these proteins also accumulate in the retina [12]. This is particularly pertinent to clinical research and drug development, as it enables identification of those who may benefit from intervention before irreversible damage has taken place.

Advances in imaging technology mean that we can now detect biomarkers at cellular resolution. We are continually finding new applications for imaging techniques to detect disease before it takes hold, providing the opportunity to intervene and potentially avoid disease altogether. It’s definitely an exciting time for oculomics research!

Crystallomancy has come a long way since Ancient Roman times, and it makes one wonder whether the scryers of the past could have predicted the transformation of orb-gazing from a mystical art to a rigorous science. Not only does Oculomics enable us to look into your past and present, but also has the potential to look into your future, providing you the opportunity to change your “fate”. Although we cannot be sure what form the advancements in imaging and AI tools will take over the coming years, we can be sure of one thing – that oculomics has a promising future in the quest for longevity.