Inaugural editorial of Nitrogen Cycling
Peer-Reviewed Publication
Updates every hour. Last Updated: 23-Dec-2025 17:12 ET (23-Dec-2025 22:12 GMT/UTC)
Inverse lithography technology (ILT) is driving transformative innovations in semiconductor patterning processes. This paper reviews the evolution of ILT, providing an analysis of the applications in semiconductor manufacturing. In recent years, artificial intelligence (AI) has introduced breakthroughs for ILT, such as convolutional neural networks, generative adversarial networks, and model-driven deep learning, demonstrating potential in large-scale integrated circuit design and fabrication. This paper discusses future directions for ILT, which is expected to provide insights into semiconductor industry development.
The review, entitled “Design strategies for high entropy materials in water electrolysis: enhancing activity, stability, and reaction kinetics,” presents an integrated framework guiding the development of HEMs from atomic-level tuning to industrial-scale application. “The core advantage of HEMs lies in their multi-element composition, which brings synergistic effects that single-element catalysts cannot achieve,” said Dr. Jing Zhang, the first author and a PhD candidate at Shanghai University. “This allows us to simultaneously optimize activity, stability, and reaction kinetics.”
A recent study developed a highly accurate risk prediction framework for preterm birth (PTB) that could broaden the potential of AI-driven multi-omics applications in precision obstetrics and biomedical research.
The model, deeply integrating genomics, transcriptomics, and large language models (LLMs) for the first time for PTB risk prediction, has shown its effectiveness and clinical application prospects.
The research was conducted by a collaborative team led by BGI Genomics, together with Professor Huang Hefeng's team, Shenzhen Longgang Maternal and Child Health Hospital, Fujian Maternity and Child Health Hospital, and OxTium Technology. The research was published in npj Digital Medicine on August 20th.
A research team has developed 3D-NOD, a spatiotemporal deep learning framework that leverages 3D point cloud data to identify new plant organs with exceptional precision.
A research team has developed VRoot, an immersive virtual reality (VR) platform that enables users to manually reconstruct root systems from MRI scans with greater precision and usability than traditional desktop tools.
A research team has developed a hybrid evaluation method that integrates a radiative transfer model with multidimensional imaging data, offering more precise monitoring of rice canopy traits linked to water stress.
A research team has developed an advanced deep learning model, LKNet, to improve the accuracy of rice panicle counting in dense crop canopies.
A research team has developed a cost-effective method to measure wheat plant height in unprecedented detail using drone-based cross-circling oblique (CCO) imaging and 3D canopy modeling.