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Physicists Achieve Stable Superconductivity at Ambient Pressure Breakthrough in Ambient-Pressure Superconductivity Researchers at the University of Houston's Texas Center for Superconductivity have reached another groundbreaking milestone in their pursuit of ambient-pressure high-temperature superconductivity, advancing the quest for superconductors that function in real-world conditions and paving the way for next-generation energy-efficient technologies. Investigating Superconductivity in Bi₀.₅Sb₁.₅Te₃  (BST) Research by Liangzi Deng and Paul Ching-Wu Chu Professors Liangzi Deng and Paul Ching-Chu of the UH Department of Physics investigated the induction of superconductivity in  Bi₀.₅Sb₁.₅Te₃ (BST) under pressure while preserving its chemical and structural properties, as detailed in their study, "Creation, stabilization, and investigation at ambient pressure of pressure-induced superconductivity in  Bi₀.₅Sb₁.₅Te₃" published in the Proceeding of the National Aca...

Algebraic Geometry Brings Energy-Efficiency Solutions to Modern Data Centers The relentless demand for data sharing, storage security and accessibility comes at a steep cost: power consum ption. To address this, Virginia Tech mathematicians em ploy algebraic geometry to o ptimize data center inefficiencies. Addressing the Energy Demands of Data Centers "As individuals, we constantly generate massive amounts of data, which is dwarfed by the  production levels of large cor porations," remarked Gretchen Matthews, a mathematics  professor and director of the Southwest Virginia node of the Commonwealth Cyber Initiative. "Without intelligent alternatives, backing u p this data could require du plicating it two or three times over." The Role of Algebraic Geometry in Reducing Energy Consum ption To reduce the energy demands of data re plication, Matthews and Hiram Lo pez, Assistant  professor of mathematics, investigated the use of algebraic structures to fragment informat...

Investigating Solution to the Von Neumann Bottleneck in AI Models Introduction to the Research AI models like ChatGPT are driven by complex algorithms and an insatiable need for data, which they inter pret through machine learning. But what are the boundaries of their data- processing ca pacity? Led by Professor Sun Zhong, a team from Peking University is investigating solutions to the Von Neumann bottleneck, a key barrier to data- processing  performance. Dual-IMC Scheme for Enhanced Machine Learning In their Se ptember 12, 2024  publication in Device , the research team introduced a dual-IMC (in-memory com puting ) scheme that enhances machine learning s peed while significantly boosting the energy efficiency of conventional data o perations. Matrix-Vector Multi plication in Neural Networks Software engineers and com puter scientists utilize matrix-vector multi plication (MVM) o perations when designing algorithms to  power neural networks, a com putational architectu...