论大历十才子诗歌中的“清”之美     

张菁菁 《安康学院学报》2014,(5):65-70

“清”是中国古代文论中一重要概念。中唐前期“大历十才子”的诗歌创作,在一定程度上是“清”理念的文笔实践。他们的山水诗体现了清静、清冷之美,田园诗显示了清淡、清纯之美,现实生活诗蕴含着清闲、清苦之美,心境感悟诗表达了清空、清虚之美。十才子有意识或无意识地运用多种创作手法,在其诗歌中塑造出这种美。这些诗作中的“清”之美,不仅继承了前人诗歌中“清”方面的精华,更重要的是启发了后世对“清”理念的进一步阐释,从这一意义上说,“大历十才子”诗歌中“清”之美在古代文论中占有不可忽视的地位。  相似文献   

明代钱塘状元茅瓒生卒年考     

周腊生 《孝感职业技术学院学报》2010,13(1):51-53

明代钱塘状元茅瓒的生年应为正德四年（1509年）,卒年应为嘉靖四十五年（1566年）。  相似文献   

The role of ingroup assortative sociality in the COVID-19 pandemic: A multilevel analysis of google trends data in the United States     

《Int J Intercult Relat》2021

This study tested how family ties and religiosity, two extended elements of ingroup assortative sociality, would predict group-level COVID-19 severity in the U.S. and how COVID-19 threat would predict ingroup assortative sociality at a weekly level. Multilevel models which analyzed the state-level archival (e.g., religious participation) and Google trends data (e.g., marriage for family ties; prayer for religiosity) on ingroup assortative sociality showed that religious search volume (from 2004 to 2019) significantly and negatively predicted COVID-19 severity (i.e., shorter time delay of first documented cases, shorter overall doubling times, higher reproductive ratio and higher case fatality ratio) across states (Study 1a) and counties (Study 1b) while search volume for family ties only significantly and negatively predicted county-level COVID-19 severity. Multilevel analyses also found that weekly COVID-19 severity weakly predicted weekly search volume of marriage and religion (Study 2a), but when COVID-19 threat was in the collective consciousness in a given week (i.e., Google search volume for coronavirus within 52 weeks), collective levels of ingroup assortative sociality increased from the previous week (Study 2b). Evidence across studies suggested that religiosity, compared with family ties, could serve a more important role for the U.S. people during the deadly pandemic.  相似文献   

基于GEPlugin的Google Earth二次开发     

尹训怡 《人天科学研究》2010,(9):112-114

针对Google Earth的广阔应用前景,从插件GEPlugin的特性谈起,介绍了一种基于GEPlugin的二次开发方式,并给予了实例验证。  相似文献   

Weg3D及Web三维可视化新发展——以WebGL和03D为例     

韩义 《科技广场》2010,(5):81-86

随着计算机和互联网的发展,人们的生活工作环境将越来越向互联网靠拢,当然三维可视化也不例外.在WebOS有一天最终取代单机版操作系统的时候,三维可视化必须为其在互联网的发展中找到突破口.本文简述全球走向网络化的大趋势,同时以WebGL和Google 03D为例介绍三维图形走向网络的最前沿技术动态及简单开发,并同时给出笔者对Web3D发展的一些思考和观点.  相似文献   

百度、Google、Sogou三种图像搜索引擎功能的比较   总被引：1，自引：0，他引：1  

高瑞 《中国科技信息》2010,(18)

本文介绍了图像搜索的几种途径,然后针对百度、Google、Sogou这三种常用的图像搜索引擎进行了用户界面设计、高级检索功能的比较,并通过实际测试分析了它们的检索数量、检索用时、查准率。  相似文献   

海外经历能否提升科研论文产出？——以长江学者为例          下载免费PDF全文

潘虹  唐莉 《复旦教育论坛》2023,21(4):105-113

随着科技全球化进程的深化，人才的国际流动日益频繁。那么海外经历能否提升科研论文产出？本文聚焦我国高层次人才“长江学者奖励”计划，以1998－2017年入选的1782名长江学者特聘教授为研究对象，构建长江学者特聘教授履历数据库与科研数据库，从学习、工作两个维度实证研究了海外经历对高层次人才科研论文产出的影响效应。研究发现，海外经历正向影响国际科研合作与国际科研生产力。具言之，不同类别的海外经历对科研论文产出的正向影响具有差异性，海外学习经历的正向效应远远大于海外工作经历的正向效应，海归学者在科研论文产出上的优势因学科领域与高校平台的不同而差异性显著。  相似文献   

Unifying telescope and microscope: A multi-lens framework with open data for modeling emerging events     

《Information processing & management》2022,59(2):102811

Open data is becoming ubiquitous as governments, companies, and even individuals have the option to offer more or less unrestricted access to their non-sensitive data. The benefits of open data, such as accessibility and transparency, have motivated and enabled a large number of research studies and applications in both academia and industry. However, each open data only offers a single perspective, and its potential inherent limitations (e.g., demographic biases) may lead to poor decisions and misjudgments. This paper discusses how to create and use multiple digital lenses empowered by open data, including census data (macro lens), search logs (meso lens), and social data (micro lens), to investigate general real-world events. To reveal the unique angles and perspectives brought by each open lens, we summarize and compare the underpinning open data from eleven dimensions, such as utility, data volume, dynamic variability, and demographic fairness. Then, we propose an easy-to-use and generalized open data driven framework, which automatically retrieves multi-source data, extracts features, and trains machine learning models for the event specified by answering what, when, and where questions. With low labor efforts, the framework’s generalization and automation capabilities guarantee an instant investigation of general events and phenomena, such as disasters, sports events, and political activities. We also conduct two case studies, i.e., the COVID-19 pandemic and Great American Eclipse (see Appendix), to demonstrate its feasibility and effectiveness at different time granularities.  相似文献