Science

The field of machine learning and artificial intelligence is rapidly expanding, with applications ranging from computer vision to text generation. However, the increasing complexity of neural networks is raising concerns about their sustainability due to growing energy consumption and training times. This has led scientists to explore alternative approaches to make machine learning more energy-
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Recent research conducted by the HEFTY Topical Collaboration delved into the recombination of charm and bottom quarks into Bc mesons within the quark-gluon plasma (QGP). This investigation aimed to shed light on the kinetics of heavy-quark bound states within the expanding QGP fireball resulting from high-energy heavy-ion collisions. The researchers developed a transport model to
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Researchers at Finland’s Aalto University have recently made a groundbreaking discovery in the field of microbiology by utilizing magnets to manipulate the movement of bacteria. This innovative approach not only allows for the alignment of bacteria but also opens up new possibilities for a wide range of studies, including complex materials, phase transitions, and condensed
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The ability to manipulate magnetization orientation on ultrafast time scales is crucial for advancements in various technological fields, such as data storage technologies and spintronics. Traditional methods involve the use of intense laser pulses to induce thermal effects, leading to changes in the magnetic properties of materials. However, these methods suffer from limitations due to
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A recent breakthrough achieved by a research team from Japan has revolutionized the observation of magnetic fields at minuscule scales. With the collaboration of several prestigious institutions and the utilization of cutting-edge technology, the team has made significant strides in enhancing our understanding of magnetic phenomena at the atomic level. This groundbreaking discovery opens up
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Transport networks, such as river systems, play a crucial role in the functioning of various natural and human-made systems. Understanding how these networks form and evolve is essential for optimizing their stability and resilience. While tree-like structures are efficient for transport, networks with loops have shown to be more damage-resistant. Researchers from the Faculty of
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