Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 20

Self-driving cars will soon be able to “think” like human drivers under complex traffic environments, thanks to a cognitive encoding framework built by a multidisciplinary research team from the School of Engineering at the Hong Kong University of Science and Technology (HKUST).

This innovation significantly enhances the safety of autonomous vehicles (AVs), reducing overall traffic risk by 26.3% and cutting potential harm to high-risk such as pedestrians and cyclists by an impressive 51.7%. Even the AVs themselves benefited, with their risk levels lowered by 8.3%, paving the way for a new framework to advance the automation of vehicle safety.

Existing AVs have one common limitation: their decision-making systems can only make pairwise risk assessments, failing to holistically consider interactions among multiple road users. This contrasts with a proficient driver who, for example, can skillfully navigate an intersection by prioritizing pedestrian protection while slightly compromising the safety of nearby vehicles. Once pedestrians are confirmed to be safe, the driver can then shift focus to nearby vehicles. Such risk management ability exhibited by humans is known as “social sensitivity.”

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An international team of scientists has published a new report that moves toward a better understanding of the behavior of some of the heaviest particles in the universe under extreme conditions, which are similar to those just after the Big Bang.

The review article, published in the journal Physics Reports, is authored by physicists Juan M. Torres-Rincón, from the Institute of Cosmos Sciences at the University of Barcelona (ICCUB), Santosh K. Das, from the Indian Institute of Technology Goa (India), and Ralf Rapp, from Texas A&M University (United States).

The authors have published a comprehensive review that explores how particles containing (known as charm and bottom hadrons) interact in a hot, dense environment called hadronic matter. This environment is created in the last phase of high-energy collisions of atomic nuclei, such as those taking place at the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC).

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The discovery of two new genetic disorders comes from a study delivered through the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Center (BRC) and The University of Manchester and could provide answers for several thousands of people with neurodevelopmental conditions around the world.

Since the breakthrough, 18-year-old Rose Anderson from Stretford in Manchester has received a diagnosis of one of the newly discovered conditions.

Rose has been known to the team at the Manchester Center for Genomic Medicine at Manchester University NHS Foundation Trust (MFT) for nearly her whole life, although a precise diagnosis for her seizures and has proved difficult to find.

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How can the strange properties of quantum particles be exploited to perform extremely accurate measurements? This question is at the heart of the research field of quantum metrology. One example is the atomic clock, which uses the quantum properties of atoms to measure time much more accurately than would be possible with conventional clocks.

However, the fundamental laws of quantum physics always involve a certain degree of uncertainty. Some randomness or a certain amount of statistical noise has to be accepted. This results in fundamental limits to the accuracy that can be achieved. Until now, it seemed to be an immutable law that a clock twice as accurate requires at least twice as much energy.

Now a team of researchers from TU Wien, Chalmers University of Technology, Sweden, and the University of Malta has demonstrated that special tricks can be used to increase accuracy exponentially. The crucial point is using two different time scales—similar to how a clock has a second hand and a minute hand.

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Microglia are a specialized type of immune cell that accounts for about 10% of all cells within the brain and spinal cord. They function by eliminating infectious microbes, dead cells, and aggregated proteins, as well as soluble antigens that may endanger the brain and, during development, also help shape neural circuits enabling specific brain functions.

When microglia don’t function properly, they can trigger neuroinflammation and fail to clear away damaged cells and harmful protein clumps—such as the neurofibrillary tangles and amyloid plaques seen in Alzheimer’s disease. This contributes to numerous neurodegenerative diseases, including Alzheimer’s, Parkinson’s and Huntington’s disease, as well as amyotrophic lateral sclerosis (ALS), multiple sclerosis, and other disorders. In fact, neuroinflammation can occur even before proteins start to form pathogenic aggregates and, in turn, accelerates protein aggregation.

Researchers and drug developers aiming to better understand and target microglia functions in the brain are challenged by the fact that human microglia can only be obtained through biopsies, and rodents’ microglia differ from their human counterparts in many critical features. This supply issue prompted them to work on methods to create microglia in the culture dish using stem cells as a starting point. However, to date, this process has remained inefficient, and requires weeks to complete at significant costs.

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Partial differential equations (PDEs) are a class of mathematical problems that represent the interplay of multiple variables, and therefore have predictive power when it comes to complex physical systems. Solving these equations is a perpetual challenge, however, and current computational techniques for doing so are time-consuming and expensive.

Now, research from the University of Utah’s John and Marcia Price College of Engineering is showing a way to speed up this process: encoding those equations in light and feeding them into their newly designed “optical neural engine,” or ONE.

The researchers’ ONE combines diffractive optical neural networks and optical matrix multipliers. Rather than representing PDEs digitally, the researchers represented them optically, with variables represented by the various properties of a light wave, such as its intensity and phase. As a wave passes through the ONE’s series of optical components, those properties gradually shift and change, until they ultimately represent the solution to the given PDE.

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For the first time, scientists have used Earth-based telescopes to look back over 13 billion years to see how the first stars in the universe affect light emitted from the Big Bang.

Using telescopes high in the Andes mountains of northern Chile, astrophysicists have measured this polarized microwave light to create a clearer picture of one of the least understood epochs in the history of the universe, the Cosmic Dawn.

“People thought this couldn’t be done from the ground. Astronomy is a technology-limited field, and from the Cosmic Dawn are famously difficult to measure,” said Tobias Marriage, project leader and a Johns Hopkins professor of physics and astronomy. “Ground-based observations face additional challenges compared to space. Overcoming those obstacles makes this measurement a significant achievement.”

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Astronomers have discovered the largest known cloud of energetic particles surrounding a galaxy cluster—spanning nearly 20 million light-years. The finding challenges long-standing theories about how particles stay energized over time. Instead of being powered by nearby galaxies, this vast region seems to be energized by giant shockwaves and turbulence moving through the hot gas between galaxies.

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Coordinated behaviors like swarming—from ant colonies to schools of fish—are found everywhere in nature. Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have given a nod to nature with a next-generation robot system that’s capable of movement, exploration, transport and cooperation.

A study in Science Advances describing the new soft robotic system was co-led by L. Mahadevan, the Lola England de Valpine Professor of Applied Mathematics, Physics, and Organismic and Evolutionary Biology in SEAS and the Faculty of Arts and Sciences, in collaboration with Professor Ho-Young Kim at Seoul National University. Their work paves new directions for future, low-power swarm robotics.

The new robots, called link-bots, are comprised of centimeter-scale, 3D-printed particles strung into V-shaped chains via notched links and are capable of coordinated, life-like movements without any embedded power or control systems. Each particle’s legs are tilted to allow the bot to self-propel when placed on a uniformly vibrating surface.

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Researchers at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have made a breakthrough in laser technology by using machine learning (ML) to help stabilize a high-power laser.

This advancement, spearheaded by Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) and Engineering Divisions, promises to accelerate progress in physics, medicine, and energy. The researchers report their work in the journal High Power Laser Science and Engineering.

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