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Superradiant atoms offer a groundbreaking method for measuring time with an unprecedented level of precision. In a recent study published by the scientific journal Nature Communications, researchers from the University of Copenhagen present a new method for measuring the time interval, seconds, that overcomes some of the limitations that even today’s most advanced atomic clocks encounter. This advancement could have broad implications in areas such as space exploration, volcanic monitoring, and GPS systems.

The second, which is the most precisely defined unit of measurement, is currently measured by atomic clocks in different places around the world that together tell us what time it is. Using radio waves, atomic clocks continuously send signals that synchronize our computers, phones, and watches.

Oscillations are the key to keeping time. In a grandfather clock, these oscillations are from a pendulum’s swinging from side to side every second, while in an atomic clock, it is a laser beam that corresponds to an energy transition in strontium and oscillates about a million billion times per second.

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Researchers at AMOLF, working alongside colleagues from Germany, Switzerland, and Austria, have realized a new type of metamaterial through which sound waves flow in an unprecedented fashion. It provides a novel form of amplification of mechanical vibrations, which has the potential to improve sensor technology and information processing devices.

This metamaterial is the first instance of a so-called ‘bosonic Kitaev chain’, which gets its special properties from its nature as a topological material. It was realized by making nanomechanical resonators interact with laser light through radiation pressure forces. The discovery, which is published on March 27 in the renowned scientific journal Nature, was achieved in an international collaboration between AMOLF, the Max Planck Institute for the Science of Light, the University of Basel, ETH Zurich, and the University of Vienna.

The ‘Kitaev chain’ is a theoretical model that describes the physics of electrons in a superconducting material, specifically a nanowire. The model is famous for predicting the existence of special excitations at the ends of such a nanowire: Majorana zero modes. These have gained intense interest because of their possible use in quantum computers.

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A new electrical power converter design developed by Kobe University offers significantly improved efficiency at a reduced cost and lower maintenance. This direct current voltage boost converter is set to make a substantial impact on the development of electric and electronic components in various sectors, including power generation, healthcare, mobility, and information technology.

Devices that harvest energy from sunlight or vibrations, or power medical devices or hydrogen-fueled cars have one key component in common. This so-called “boost converter” converts low-voltage direct current input into high-voltage direct current output. Because it is such a ubiquitous and key component, it is desirable that it uses as few parts as possible for reduced maintenance and cost and at the same time that it operates at the highest possible efficiency without generating electromagnetic noise or heat. The main working principle of boost converters is to quickly change between two states in a circuit, one that stores energy and another that releases it. The faster the switching is, the smaller the components can be and therefore the whole device can be downsized. However, this also increases the electromagnetic noise and heat production, which deteriorates the performance of the power converter.

The team of Kobe University power electronics researcher Mishima Tomokazu made significant progress in developing a new direct current power conversion circuit. They managed to combine high-frequency switching (about 10 times higher than before) with a technique that reduces electromagnetic noise and power losses due to heat dissipation, called “soft switching,” while also reducing the number of components and, therefore, keeping cost and complexity low.

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Argonne National Laboratory scientists have used anomaly detection in the ATLAS collaboration to search for new particles, identifying a promising anomaly that could indicate new physics beyond the Standard Model.

Scientists used a neural network, a type of brain-inspired machine learning algorithm, to sift through large volumes of particle collision data in a study that marks the first use of a neural network to analyze data from a collider experiment.

Particle physicists are tasked with mining this massive and growing store of collision data for evidence of undiscovered particles. In particular, they’re searching for particles not included in the Standard Model of particle physics, our current understanding of the universe’s makeup that scientists suspect is incomplete.

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Research in Science Immunology shows how tissue and myeloid cells react differently to allergens in the lungs of people with asthma versus the lungs of those without asthma.

Learn more on WorldAsthmaDay:

Segmental allergen challenge in allergic asthmatics reveals a role for monocyte-derived cells in the TH2-dependent inflammatory response.

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The problem of personal identity is a longstanding philosophical topic albeit without final consensus. In this article the somewhat similar problem of AI identity is discussed, which has not gained much traction yet, although this investigation is increasingly relevant for different fields, such as ownership issues, personhood of AI, AI welfare, brain–machine interfaces, the distinction between singletons and multi-agent systems as well as to potentially support finding a solution to the problem of personal identity. The AI identity problem analyses the criteria for two AIs to be considered the same at different points in time. Two approaches to tackle the problem are proposed: One is based on the personal identity problem and the concept of computational irreducibility, while the other one applies multi-factor authentication to the AI identity problem. Also, a range of scenarios is examined regarding AI identity, such as replication, fission, fusion, switch off, resurrection, change of hardware, transition from non-sentient to sentient, journey to the past, offspring and identity change.

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Now that’s Wonderful. It’s touching by how they were brought to tears in making progress in fighting neurogenitive disease.

Auckland scientists are celebrating an important breakthrough after zeroing in on a rare genetic mutation causing motor neuron disease. Their work is now being published in the journal Brain, and national correspondent Amanda Gillies spoke to the lead researcher. ➡️ SUBSCRIBE: https://bit.ly/NewshubYouTube.

You’re joining us on the official YouTube channel for Warner Bros. Discovery’s Newshub. Here you will find livestreams and news videos from our award-winning team of journalists working across our bulletins and shows — AM, Paddy Gower Has Issues, Newshub Nation, and The Hui.

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