3D printed antenna arrays developed for flexible wireless systems
Peer-Reviewed Publication
Updates every hour. Last Updated: 21-Dec-2025 05:11 ET (21-Dec-2025 10:11 GMT/UTC)
Washington State University-led researchers have developed a chip-sized processor and 3D printed antenna arrays that could someday lead to flexible and wearable wireless systems and improved electronic communications in a wide variety of auto, aviation, and space industry applications. Reporting in the journal Nature Communications, the researchers used 3D printing, the processor, and an ink made from copper nanoparticles to create the flexible antenna arrays.
New research suggests that dark matter may once again hold the key to one of astronomy’s enduring mysteries: the excess of gamma rays shining from the Milky Way’s center. By modeling the galaxy’s early history and violent mergers, the team found that dark matter in the core may be shaped far differently than previously assumed, potentially matching the puzzling radiation pattern first detected by NASA’s Fermi telescope. The findings revive dark matter as a serious contender for explaining the Milky Way’s enigmatic central glow.
A research team from Sichuan University, in collaboration with Southeast University and the Hong Kong University of Science and Technology (Guangzhou), has proposed a novel dual three-phase four-level space vector pulse width modulation (DTP-FL SVPWM) strategy to improves the efficiency and precision of high-power motor drives. The proposed strategy is designed for the developed dual three-phase open-winding permanent magnet synchronous motors (DTP-OW-PMSM). This technology achieves high modulation precision and low switching frequency. On this basis, the current harmonics caused by the DC-link voltage deviations are reduced by compensating for the duty ratio.
Abstract
A team of scientists from School of Physics and Astronomy at Queen Mary University of London has developed a novel artificial intelligence method that could revolutionize our understanding of the universe's most mysterious shapes. Using advanced machine learning, researchers can now explore complex geometric spaces, like the fabric of spacetime itself, without relying on traditional symmetry assumptions.
This new algorithm, called AInstein, tackles one of the most complex puzzles in physics and mathematics: finding the precise shape of space under Einstein field equations. Remarkably, it can do so on spaces as intricate as higher-dimensional spheres, opening new avenues for discovery and shedding light on our understanding of the universe.