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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.

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.

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.

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