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唐代尚书省“诸司符”初探——以俄藏Дx02160Vb文书为线索 被引量:4
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作者 管俊玮 《史林》 CSSCI 北大核心 2021年第3期1-10,219,共11页
俄藏Дx02160Vb可定名为《唐开元五年(717)前后尚书户部下沙州符为长流人事》。唐代"省符"根据其内容主导者的不同可被划分为"诸司符"与"奉制符"两种不同的类型。与附在制敕后起中转作用的"奉制符&q... 俄藏Дx02160Vb可定名为《唐开元五年(717)前后尚书户部下沙州符为长流人事》。唐代"省符"根据其内容主导者的不同可被划分为"诸司符"与"奉制符"两种不同的类型。与附在制敕后起中转作用的"奉制符"不同,以Дx02160Vb为代表的"诸司符"不附在制敕之后,而是由尚书二十四司主导并独立下行。《唐六典》所说"大事则听制敕,小事则俟省符"中之"省符"即指"诸司符",尚书省一般用其裁决日常政务中不需要上呈皇帝的"小事"。以《开元公式令》所载"符式"为依据,结合出土资料可对"诸司符"的格式进行复原。"制敕"与"诸司符"在唐前期国家日常政务处理体系中的这种不同分工,也为进一步追溯"堂帖"的制度起源提供了线索。 展开更多
关键词 唐代 Дx02160Vb 省符 诸司 堂帖
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Characters of Temperature Variation and Minimal Temperature Forecast inside of Solar Greenhouse in Winter in Shouguang City of Shandong Province 被引量:2
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作者 袁静 李树军 +2 位作者 崔建云 邱刚 李楠 《Agricultural Science & Technology》 CAS 2012年第9期2001-2005,共5页
[Objective] The aim was to improve meteorological service of protected agriculture and to reduce effects of meteorological disasters. [Method] Characters of temperature variation in solar greenhouse and minimal temper... [Objective] The aim was to improve meteorological service of protected agriculture and to reduce effects of meteorological disasters. [Method] Characters of temperature variation in solar greenhouse and minimal temperature forecast models in winter were analyzed based on meteorological data inside and outside of solar greenhouse in winter during 2008-2011, as per correlation and stepwise regression method. [Result] Temperature was of significant changes in solar greenhouse in sunny and cloudy days and the change was higher in sunny days. In overcast days, temperature in solar greenhouse was lower and plants were affected seriously. In addition, the minimal temperature was of good correlation with outside temperature and humidity, temperature and soil temperature in greenhouse. [Conclusion] The minimal temperature forecast model of solar greenhouse is established and the average absolute error of the forecasted minimums in different types of weather was less than 1 ℃ and the average relative error was lower than 10%. 展开更多
关键词 Solar greenhouse Temperature Variation characters Forecast model
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