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关键词： COVID-19   Elective procedures   Google Trends   Neurosurgery  

摘要： OBJECTIVE: In the wake of the COVID-19 pandemic, the Centers for Medicare & Medicaid Services (CMS) has recommended the temporary cessation of all elective surgeries. The effects on patients' interest of elective neurosurgical procedures are currently unexplored. METHODS: Using Google Trends, search terms of 7 different neurosurgical procedure categories (trauma, spine, tumor, movement disorder, epilepsy, endovascular, and miscellaneous) were assessed in terms of relative search volume (RSV) between January 2015 and September 2020. Analyses of search terms were performed for over the short term (February 18, 2020, to April 18, 2020), intermediate term (January 1, 2020, to May 31, 2020), and long term (January 2015 to September 2020). State-level interest during phase I reopening (April 28, 2020, to May 31, 2020) was also evaluated. RESULTS: In the short term, RSVs of 4 categories (epilepsy, movement disorder, spine, and tumor) were significantly lower in the post-CMS announcement period. In the intermediate term, RSVs of 5 categories (miscellaneous, epilepsy, movement disorder, spine, and tumor) were significantly lower in the post-CMS announcement period. In the long term, RSVs of nearly all categories (endovascular, epilepsy, miscellaneous, movement disorder, spine, and tumor) were significantly lower in the post-CMS announcement period. Only the movement disorder procedure category had significantly higher RSV in states that reopened early. CONCLUSIONS: With the recommendation for cessation of elective surgeries, patient interests in overall elective neurosurgical procedures have dropped significantly. With gradual reopening, there has been a resurgence in some procedure types. Google Trends has proven to be a useful tracker of patient interest and may be used by neurosurgical departments to facilitate outreach strategies.

关键词： Search engine   Biomedical images   Deep learning   Websites  

摘要： The development of efficient search engine queries for biomedical images, especially in case of query-mismatch is still defined as an ill-posed problem. Vector-space model is found to be useful for handling the query-mismatch issue. However, vector-space model does not consider the relational details among the keywords and biomedical image search space is not evaluated. Therefore, in this paper, we have proposed a deep learning based fusion vector-space based model. The proposed model enhances the biomedical image query similarity matching approach by fusing the vector space model and convolutional neural networks. Deep learning model is defined by converting the vector-space model to a classification model. Finally, deep learning model is trained to implement the search engine for biomedical images. Extensive experiments reveal that the proposed model achieves significant improvement over the existing models.

关键词： 大数据   智慧课堂   教学设计   信息资源  

摘要： 智慧课堂的教学设计是当今时代发展的必然要求,利用新时代大数据教育的信息化打造智能、高效的课堂,应用到教学的全过程,可以提高教学质量、促进学生的智慧发展。本文基于智慧课堂的内涵及特征,通过智慧课堂教学模式的设计,为解决当前课堂教学效率低的现实提供途径,促进智慧课堂教学模式的发展。

关键词： 图书馆   管理   人力资源   信息资源  

摘要： 创新是国家进步的精神,是国家繁荣的不懈动力,也是中国民族的民族力量,它在激烈的国际竞争中获胜。创新是一个国家发展的无穷引擎,必须加以遵循。图书馆需要沿着这条道路发展自己。当今社会的发展给图书馆带来了新的发展途径,图书馆的功能从过去的受益者和服务者都得到了极大的发挥。图书馆理解需要用户在新的社会环境中发现和理解;创新也必须是真正的创新,它教会了图书馆的一切工作。

关键词： 大数据   信息资源   开发利用  

摘要： 随着我国经济的不断发展,数据对于社会的进步来说是最基本的战略性资源,对于社会发展的变革也起着重要的促进作用。本文主要以大数据为背景,深入探讨大数据背景下的科技信息资源的概念以及特征,在此条件下,分析了在大数据时代下我国进一步推进信息资源开发与利用存在的障碍,针对所分析的问题分析我国科技信息资源开发与利用今后发展的规划路线,这对于日后的信息开发与利用工作打下坚实的理论基础。

关键词： 云平台   信息资源   集成管控   管理体系  

摘要： 随着信息资源的广泛应用,企业对信息资源管控的要求也相应提高。通过提升对信息资源的管控能力,能够有效压缩软硬件支出,推动信息资源共享,改善用户体验,提高工作效率。文章结合保变电气搭建云平台下企业信息资源集成管控体系的实践经验,简要分析云平台下的企业信息资源集成管控体系搭建过程以及取得的具体成效。