A time crystal, a long-life quantum system approaching perpetual motion, has been hooked up to its environment for the first time, unlocking an intriguing way to increase quantum computational and sensing power.

Researchers have forged a new path to vibration isolation that could help absorb shocks in a variety of applications, including vehicles, buildings and more.

Quantum computing is one of the frontiers of research and innovation due to its potential to outperform classical computers in specific tasks. The EQUALITY (Efficient QUantum ALgorithms for IndusTrY) project, funded by the European Union under the Horizon Europe programme, had the mission of developing innovative quantum computing algorithms to solve strategic industrial problems while dealing with the limitations of this nascent technology.

This paper proposes GAN-Solar, a novel quality optimization model for short-term solar radiation forecasting. Based on Generative Adversarial Networks (GANs), the model addresses spatial texture degradation and intensity distortion in predictions, significantly improving forecast quality and reliability for high-precision applications.

In a comprehensive analysis that offers a global view of carbon emission trends, researchers are exploring the factors driving CO2 emission peaks in countries worldwide. The study, titled "Carbon Emission Peaks in Countries Worldwide and Their National Drivers," is led by Prof. Chao He from the Collaborative Innovation Center for Emissions Trading System Co-Constructed by the Province and Ministry in Wuhan, China, and the National Science Library (Wuhan) at the Chinese Academy of Sciences. This research provides critical insights into the national drivers behind carbon emission peaks, offering a detailed understanding of global emission trends.

Researchers at Lehigh University are developing a faster, more accurate way to predict how metals solidify during 3D printing and other additive manufacturing processes. Supported by a three-year, $350,000 grant from the National Science Foundation, assistant professor Parisa Khodabakhshi is creating a physics-based, data-driven model that connects manufacturing process parameters with the resulting material microstructure. The approach aims to replace costly trial-and-error methods with efficient simulation tools that can guide the design of high-performance metal components. The project’s outcomes could accelerate innovation across industries that rely on advanced manufacturing—such as aerospace, automotive, and healthcare—while helping train the next generation of engineers and scientists.