An international research team led by Satoshi N. Omura and Osamu Nureki from the University of Tokyo has engineered a novel variant of the Cas9 protein, a commonly used protein in gene editing technology, called “eSaCas9-NNG”. This novel variant surpasses other variants at all four key performance criteria. As such, it is expected to make a significant contribution to a wide array of applications, such as more precise and safer gene therapies. The findings were published in the journal Nature Communications.

Researchers have introduced an Ethereum-based fully distributed authentication mechanism that could significantly enhance security and efficiency in vehicular ad-hoc networks (VANETs), addressing long-standing challenges in certificate management, single points of failure, and real-time authentication latency. Published in Blockchain, this work presents EBDA, a novel framework that replaces traditional PKI certificate systems with a blockchain-maintained Graph of Trust (GoT), enabling certificate-free, fully decentralized identity authentication for vehicles.)

High-order Landau modes offer immense potential for large-capacity information processing. However, exploiting the high-order Landau modes and controlling their spatial distributions have remained elusive. Now, a collaborative team from Beijing Institute of Technology, The University of Hong Kong, Tsinghua University, and Tongji University, writing in the Science Bulletin, proposes a physical mechanism to reshape high-order Landau modes. By constructing pseudo-magnetic fields, pseudo-electric fields, and imaginary momentum, the researchers provide a versatile framework to tailor the spatial profiles of Landau modes to meet diverse requirements, such as spectral selection, mode localization, broadening. These findings can advance the applications in large-capacity information processing, frequency multiplexing, and wave packet reshaping.

A research team led by Prof. Yoonseob KIM, Associate Professor of the Department of Chemical and Biological Engineering at The Hong Kong University of Science and Technology (HKUST) has reported a significant breakthrough in lithium metal battery (LMB) technology. The team has successfully synthesized a novel single-crystalline 3D borate covalent organic framework (B-COF), which demonstrates exceptional performance as a solid-state electrolyte, thereby enhancing the performance of solid-state lithium batteries. This advancement promises safer and higher energy density solutions for electric vehicles and large-scale energy storage. The research paper, titled “Single-Crystalline Borate Covalent Organic Frameworks for Solid-State Lithium Metal Batteries,” has been published in the prestigious journal Advanced Science. 

What if AI could discover breakthrough materials before we even test them? A new physics-informed AI model makes discovery faster and more reliable. This approach could reshape the future of electronics.

Metals such as titanium are prized for their strength, light weight, and resistance to corrosion, making them essential for aircraft, spacecraft, and medical implants. Now, a joint research team has developed a groundbreaking processing technique that dramatically improves the strength and toughness of titanium alloys—in just a few milliseconds.

A collaborative research group led by Professor Gyoujin Choand Professor Inki Kim of Department of Biophysics at Sungkyunkwan University, in partnership with Professor Junsuk Rho of the Department of Mechanical Engineering at POSTECH, has developed a fully automated roll-to-roll manufacturing platform capable of producing large-area visible metalenses at a rate of 300 units per second, marking a major breakthrough in translating metasurface technology from the laboratory to real-world industrial deployment.

“AI for Scientific Management" emphasizes prescriptive intelligence—using AI to autonomously optimize organizational systems and bridge the gap between AI theory and management practice. We invite high-quality research that develops new analytical frameworks, provides empirical evidence of AI effectiveness, or introduces innovative simulation models that push the boundaries of how businesses are scientifically managed in the age of big data. Journal of Management Analytics is an SSCI and ABS Grade 2 journal that focuses on the theory of data analytics and its applications in business and management.