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

New paradigmatic orientations and research agenda of human factors science in the intelligence era

Our recent research shows that the design philosophy of human factors science in the intelligence age is expanding from "user-centered design" to "human-centered AI". The human-machine relationship presents a trans-era evolution from "human-machine interaction" to "human-machine/AI teaming". These changes have raised new questions and challenges for human factors science. The interdisciplinary field of human factors science includes any work that adopts a human-centered approach, such as human factors, ergonomics, engineering psychology, and human-computer interaction. These changes compel us to re-examine current human factors science's paradigms and research agenda. Existing research paradigms are primarily based on non-intelligent technologies. In this context, this paper reviews the evolution of the paradigms of human factors science. It summarizes the new conceptual models and frameworks we recently proposed to enrich the research paradigms for human factors science, including a human-AI teaming model, a human-AI joint cognitive ecosystem framework, and an intelligent sociotechnical systems framework. This paper further enhances these concepts and looks forward to the application of these concepts. This paper also looks forward to the future research agenda of human factors science in the areas of "human-AI interaction", "intelligent human-machine interface", and "human-AI teaming". It analyzes the role of the research paradigms on the future research agenda. We believe that the research paradigms and agenda of human factors science influence and promote each other. Human factors science in the intelligence age needs diversified and innovative research paradigms, thereby further promoting the research and application of human factors science.Comment: 26 pages, in Chinese languag

Research On The Decision Support System Of Smart Government Based On Data Warehouse

在电子政务阶段，我国已经开始利用计算机技术建设决策支持系统以辅助政府决策，使政府从主观、经验式的传统决策向靠数据说话、用数据决策的电子政务决策发展。随着电子政务的不断发展，政府业务内容和业务能力的不断提升，电子政务系统中聚集了海量数据，除了以结构化数据为主体，还包括半结构化、非结构化的数据。在此基础上，电子政务决策支持系统尤其是其核心技术数据仓库已经无法满足政府的业务需求。近年来，随着“智慧城市”、“互联网+”、“云计算”、“大数据”等概念的不断推出，为电子政务决策支持系统迈向智慧政府决策支持系统提供了契机。2015年，国务院相继发布了《关于运用大数据加强对市场主体服务和监管的若干意见》、《关...In the stage of e-government,China has begun to use computer technology to build decision support systems for assisting government decision-making,in the way,the government has changed from traditional decision-making in the characteristic of subjective and empirical to e-government characterized in data speaking and decision-making.With the continuous development of e-government and improvement o...学位：管理学硕士院系专业：公共事务学院_行政管理学号：1392013115030