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New Technology of Library and Information Service  2010, Vol. 26 Issue (6): 83-87    DOI: 10.11925/infotech.1003-3513.2010.06.14
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Design and Implementation of E-mail Pushing Service System for Paper Indexed by Three Famous Indexes
Li Jing,Tan Ying,Shi Qiaomei
(Northwestern Polytechnical University Library, Xi’an 710072, China)
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Abstract  

Aiming at the status of passive check mode, poor real-time and low efficiency in the traditional methods of papers indexed by three famous indexes,the E-mail pushing service system is established. This paper describes the system design ideas and implementation in detail, including how to import data, read records, change English name to Chinese name, uniform data format and push E-mails.

Key wordsSCI      EI      ISTP      VBA      Mail merge      E-mail pushing     
Received: 07 May 2010      Published: 26 July 2010
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  G250.7

 
Corresponding Authors: Li jing     E-mail: lijing@nwpu.edu.cn

Cite this article:

Li Jing Tan Ying Shi Qiaomei. Design and Implementation of E-mail Pushing Service System for Paper Indexed by Three Famous Indexes. New Technology of Library and Information Service, 2010, 26(6): 83-87.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2010.06.14     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2010/V26/I6/83

[1] Excel Home. Excel VBA 实战技巧精粹[M]. 北京:人民邮电出版社,2008.
[2]  李政,梁海英,李昊.VBA应用基础与实例教程[M].北京:国防工业出版社,2005.
[3]   Liger F, McQueen C, Wilton P. VB.NET字符串和正则表达式参考手册[M]. 康博译. 北京:清华大学出版社, 2002.
[4]   邮件合并全攻略[EB/OL].[2010-03-20]. http://club.excelhome.net/viewthread.php?tid=41488.

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