In a paper published in Polymer Science & Technology, an international team of scientists

has, for the first time, introduced oligoethylene glycol side chains into A-A type polymers containing BNBP units, designing a polar side chain-functionalized organic boron polymer, PBN-OEG. The introduction of polar ethylene glycol side chains improves the miscibility between the host material and small molecule dopants, exhibiting a more efficient n-type small molecule doping level compared to the control material PBN-alkyl, which only contains alkyl side chains. Consequently, PBN-OEG possesses superior thermoelectric properties, with an optimal electrical conductivity of up to 1.95 S cm^-1 and a maximum power factor of up to 4.7 μW m^-1 K^-2. Furthermore, the oligoethylene glycol side chains promote the swelling of PBN-OEG films in aqueous electrolyte solutions, facilitating the ionic transport of hydrated cations. Therefore, PBN-OEG can be used as a channel material for organic field-effect transistors (OECTs), achieving a large volumetric capacitance (C*) of 97.7 F cm^-3 and a high figure of merit (μC*) of 2.6 F cm^-1 V^-1 s^-1. This study demonstrates the potential of n-type BNBP-based OMIEC materials in the fields of organic thermoelectric transistors (OTEs) and OECTs. This study is led by Jian Liu (Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China) and Jun Liu (Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China).

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.

The azuki bean beetle is a common pest of stored beans and peas. Researchers at Kyushu University have found that when beetles infected with Wolbachia bacteria are exposed to elevated temperature and carbon dioxide they tend to produce larger eggs to enhance the survivability of their offspring. Interestingly, these larger eggs gave rise only to male larvae.