Lifeboat Foundation

At the turn of the 20th century, scientists discovered that atoms were composed of smaller particles. They found that inside each atom, negatively charged electrons orbit a nucleus made of positively charged protons and neutral particles called neutrons. This discovery led to research into atomic nuclei and subatomic particles.

An understanding of these particles’ structures provides crucial insights about the forces that hold matter together and enables researchers to apply this knowledge to other scientific problems. Although electrons have been relatively straightforward to study, protons and neutrons have proved more challenging. Protons are used in medical treatments, scattering experiments, and fusion energy, but nuclear scientists have struggled to precisely measure their underlying structure—until now.

In a recent paper, a team led by Constantia Alexandrou at the University of Cyprus modeled the location of one of the subatomic particles inside a proton, using only the basic theory of the strong interactions that hold matter together rather than assuming these particles would act as they had in experiments. The researchers employed the 27-petaflop Cray XK7 Titan supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and a method called lattice quantum chromodynamics (QCD). The combination allowed them to map subatomic particles on a grid and calculate interactions with high accuracy and precision.

Red blood cells are amazing. They pick up oxygen from our lungs and carry it all over our body to keep us alive. The hemoglobin molecule in red blood cells transports oxygen by changing its shape in an all-or-nothing fashion. Four copies of the same protein in hemoglobin open and close like flower petals, structurally coupled to respond to each other. Using supercomputers, scientists are just starting to design proteins that self-assemble to combine and resemble life-giving molecules like hemoglobin. The scientists say their methods could be applied to useful technologies such as pharmaceutical targeting, artificial energy harvesting, ‘smart’ sensing and building materials, and more.

Scientists have revealed the precise molecular mechanisms that cause drops of liquid to combine, in a discovery that could have a range of applications.

Insights into how droplets merge could help make 3D printing technologies more accurate and may help improve the forecasting of thunderstorms and other weather events, the study suggests.

The Kaikoura earthquake in New Zealand in 2016 caused widespread damage. LMU researchers have now dissected its mechanisms revealing surprising insights on earthquake physics with the aid of simulations carried out on the supercomputer SuperMUC.

The 2016 Kaikoura earthquake (magnitude 7.8) on the South Island of New Zealand is among the most intriguing and best-documented seismic events anywhere in the world – and one of the most complex. The earthquake exhibited a number of unusual features, and the underlying geophysical processes have since been the subject of controversy. LMU geophysicists Thomas Ulrich and Dr. Alice-Agnes Gabriel, in cooperation with researchers based at the Université Côte d’Azur in Valbonne and at Hong Kong Polytechnic University, have now simulated the course of the earthquake with an unprecedented degree of realism. Their model, which was run on the Bavarian Academy of Science’s supercomputer SuperMUC at the Leibniz Computing Center (LRZ) in Munich, elucidates dynamic reasons for such uncommon multi-segment earthquake. This is an important step towards improving the accuracy of earthquake hazard assessments in other parts of the world. Their findings appear in the online journal Nature Communications.

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Cognitive computing (CC) technology revolves around making computers adept at mimicking the processes of the human brain, which is basically making them more intelligent. Even though the phrase cognitive computing is used synonymously with AI, the term is closely associated with IBM’s cognitive computer system, Watson. IBM Watson is a supercomputer that leverages AI-based disruptive technologies like machine learning (ML), real-time analysis, natural language processing, etc. to augment decision making and deliver superior outcomes.

If you shrank yourself down and entered a proton, you’d experience among the most intense pressures found anywhere in the universe.

Finding the best light-harvesting chemicals for use in solar cells can feel like searching for a needle in a haystack. Over the years, researchers have developed and tested thousands of different dyes and pigments to see how they absorb sunlight and convert it to electricity. Sorting through all of them requires an innovative approach.

Now, thanks to a study that combines the power of supercomputing with data science and experimental methods, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Cambridge in England have developed a novel “design to device” approach to identify promising materials for dye-sensitized solar cells (DSSCs). DSSCs can be manufactured with low-cost, scalable techniques, allowing them to reach competitive performance-to-price ratios.

The team, led by Argonne materials scientist Jacqueline Cole, who is also head of the Molecular Engineering group at the University of Cambridge’s Cavendish Laboratory, used the Theta supercomputer at the Argonne Leadership Computing Facility (ALCF) to pinpoint five high-performing, low-cost dye materials from a pool of nearly 10,000 candidates for fabrication and device testing. The ALCF is a DOE Office of Science User Facility.

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The gravitational pull of a black hole is so strong that nothing, not even light, can escape once it gets too close. However, there is one way to escape a black hole — but only if you’re a subatomic particle.

As black holes gobble up the matter in their surroundings, they also spit out powerful jets of hot plasma containing electrons and positrons, the antimatter equivalent of electrons. Just before those lucky incoming particles reach the event horizon, or the point of no return, they begin to accelerate. Moving at close to the speed of light, these particles ricochet off the event horizon and get hurled outward along the black hole’s axis of rotation.

Known as relativistic jets, these enormous and powerful streams of particles emit light that we can see with telescopes. Although astronomers have observed the jets for decades, no one knows exactly how the escaping particles get all that energy. In a new study, researchers with Lawrence Berkeley National Laboratory (LBNL) in California shed new light on the process. [The Strangest Black Holes in the Universe].

Dr. Rico explained: “When we compare human genomes from different people, we see that they are way more different than we initially expected when the Human Genome Project was declared to be ”completed” in 2003. One of the main contributions to these differences are the so called Copy Number Variable (CNV) regions. CNV regions are in different copy number depending on each individual, and their variability can be greater in some human populations than others. The number of copies of CNV regions can contribute to both normal phenotypic variability in the populations and susceptibility to certain diseases.

Research has shown a direct relationship between mutations in introns and variability in human populations.

One of the greatest challenges of genomics is to reveal what role the ”dark side” of the human genome plays: those regions where it has not yet been possible to find specific functions. The role that introns play within that immense part of the genome is especially mysterious. The introns, which represent almost half the size of the human genome, are constitutive parts of genes that alternate with regions that code for proteins, called exons.

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