In joint research with the University of Tokyo (UTokyo), the National Institute of Advanced Industrial Science and Technology (AIST), Tohoku University, and Kyoto Institute of Technology, the National Institute for Materials Science (NIMS) developed and published "elemental reactivity maps" for discovering new phases. The research team proposed maps that use machine learning to identify over 3,000 element combinations that could potentially form new phases from among a total of 85,320 combinations of up to three elements selected from the 80 elements easily handled in the laboratory. This research result was published in Chemistry of Materials on February 21, 2025.

This article presents a novel bi-directional converter based interconnection planning approach for hybrid AC/DC microgrids with high renewable penetration. The study proposes a tri-level planning framework incorporating dynamic converter efficiency and a data-correlated uncertainty set, demonstrating improved cost-efficiency and robustness.

This article delves into the critical issue of corrosion and material degradation in geological CO₂ storage, highlighting the challenges and potential solutions for maintaining the integrity of storage systems. It underscores the importance of understanding these processes to ensure safe and effective carbon capture and storage.

Discover how a novel thickened supercritical CO₂ flooding method can enhance oil recovery in high-water-cut mature reservoirs. This study explores the potential of a specially designed copolymer thickener to significantly boost crude oil extraction, offering a new approach to improve efficiency in challenging reservoir conditions.

The increase in precision agriculture has promoted the development of picking robot technology, and the visual recognition system at its core is crucial for improving the level of agricultural automation. This paper reviews the progress of visual recognition technology for picking robots, including image capture technology, target detection algorithms, spatial positioning strategies and scene understanding. This article begins with a description of the basic structure and function of the vision system of the picking robot and emphasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural environment. Subsequently, various image processing techniques and vision algorithms, including color image analysis, three-dimensional depth perception, and automatic object recognition technology that integrates machine learning and deep learning algorithms, were analyzed. At the same time, the paper also highlights the challenges of existing technologies in dynamic lighting, occlusion problems, fruit maturity diversity, and real-time processing capabilities. This paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of picking. At the same time, this paper also introduces innovative research, such as the application of convolutional neural networks (CNNs) for accurate fruit detection and the development of event-based vision systems to improve the response speed of the system. At the end of this paper, the future development of visual recognition technology for picking robots is predicted, and new research trends are proposed, including the refinement of algorithms, hardware innovation, and the adaptability of technology to different agricultural conditions. The purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricultural robotics, including current achievements, existing challenges and future development prospects.

A research team led by Dr. Young-Min Kim and Dr. Byeong-Chan Suh from Lightweight Materials Research Division at the Korea Institute of Materials Science(KIMS) has developed the world's first solid-state hydrogen storage material capable of storing and transporting hydrogen safely without the need for high-pressure tanks or cryogenic systems.

In recent years, the field of clinical laboratory medicine has witnessed remarkable advancements, driven by technological innovations, interdisciplinary research, and the growing demand for precision diagnostics. As Co-Editor-in-Chief, I am pleased to introduce LabMed Discovery (LMD), a new open-access, peer-reviewed journal dedicated to facilitating scholarly communication and fostering innovation in laboratory medicine, in vitro diagnostics, and emerging diagnostic technologies.

LabMed Discovery is proudly sponsored by Ruijin Hospital and Shanghai Jiao Tong University and serves as the official journal of the College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine. With a clear mission to “lead the innovation of laboratory medicine technology and promote international exchanges in medical technology,” LMD aspires to be a leading platform for global researchers and clinicians to disseminate cutting-edge research, novel diagnostic methodologies, and transformative clinical applications.

MicroRNAs (miRNAs) are endogenous single-stranded non-coding RNAs approximately 22 ​nt in length, which play a regulatory role in various biological processes by targeting and inhibiting protein-coding gene transcripts. Among the wide range of miRNAs, miR-668 exhibits abnormal expression patterns in different diseases, including head and neck squamous cell carcinoma (HNSC), hepatocellular carcinoma (HCC), breast cancer (BrC), ischemic acute kidney injury (ischemic AKI), myocardial infarction (MI), ischemic stroke (IS), Parkinson’s disease (PD), smoking-related interstitial fibrosis (SRIF), premature aging, and non-segmental vitiligo (NSV), among others. The expression of miR-668 is regulated by various upstream factors, including other non-coding RNAs and endogenous proteins. Functionally, miR-668 targets 6 protein-coding genes (SDF-1, Dvl1, BTG2, MTFP1, FHIT, and NFKBIA) and is involved in 3 signaling pathways: the Drp signaling pathway, NF-κB signaling pathway, and NLRP3 signaling pathway. Notably, miR-668 has contrasting effects depending on the context; it can promote the development and metastasis of HCC and induce radiation resistance in BrC cells, while also promoting aging and inhibiting HNSC progression in human keratinocytes. Furthermore, miR-668 exhibits anti-apoptotic effects, alleviates ischemia/reperfusion injury, improves intracerebral expansion, relieves hydrocephalus, and has prognostic value in gastric cancer (GC), HCC, and BrC, with high expression predicting poor outcomes. Notably, the cancer drugs melatonin and Pterostilbene (Ptero) exert their therapeutic effects by modulating miR-668. Future studies should focus on validating the reliability of miR-668 as a biomarker and elucidating the specific mechanisms through which miR-668 participates in cancer therapy.