Analyzing Financial Statements for Effective Financial Management in 1920\u27s

末政芳信教授古稀記念特

基于引文内容分析的引用情感识别研究

［目的/意义］针对自动识别论文引用情感问题，提出一种基于引文内容分析的识别方法并进行可视化展示，克服基于简单引用频次计量无法区分不同引用情感的问题。［方法/过程］首先，利用正则表达式抽取出论文全文中的引文内容信息; 然后，利用 TF-IDF 算法筛选出引用情感特征词，结合情感词典，利用情感分析技术对引文内容进行引用情感识别; 最后，利用可视化工具展示出引用情感整体分布情况。［结果/结论］该方法能够有效识别出抗衰老领域论文数据集中引用情感情况。实验结果显示，该领域正面引用占总引用次数的21% ，中立引用占总引用次数的 78% ，负面引用仅占总引用次数的 1% 。与传统引文网络相比较，基于引用情感的可视化图谱可以有效识别出不同引用情感在整体数据集合上的分布情况。</p

室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future