The recent global outbreak of monkeypox (mpox) has underscored the urgent need for rapid, accurate, and accessible diagnostic tools. The unprecedented spread of mpox beyond its endemic regions has highlighted significant gaps in our preparedness for emerging infectious diseases, particularly in diagnostics. Traditional methods like polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), while accurate, often require sophisticated equipment and trained personnel, leading to potential delays in diagnosis and disease management. In response, the development of point-of-care (POC) biosensors has emerged as a critical area of research, offering the promise of rapid, on-site detection with minimal resource requirements. This article explores various biosensing technologies, including CRISPR-based systems, electrochemical sensors, optical biosensors, and microfluidic devices. We discuss the principles behind these technologies, their performance characteristics, advantages, limitations, and potential for real-world application. By leveraging the current state of knowledge, this article provides a comprehensive overview of the latest developments in POC biosensors for mpox detection to enhance cognizance among researchers, healthcare professionals, and policymakers about the latest advancements and opportunities in this critical area of public health, contributing to enhanced global health security and preparedness against mpox and other emerging infectious diseases.

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.