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A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System 被引量:1
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作者 WU Huisheng LIU Zhaoli +1 位作者 ZHANG Shuwen ZUO Xiuling 《Chinese Geographical Science》 SCIE CSCD 2011年第3期346-353,共8页
The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation sy... The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network. 展开更多
关键词 spatio-temporal data model reverse map with overlay model road network incremental updating vehicle navigation system data center vehicle terminal
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AcMNPV As A Model for Baculovirus DNA Replication
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作者 Eric B. Carstens 《Virologica Sinica》 SCIE CAS CSCD 2009年第4期243-267,共25页
Baculoviruses were first identified as insect-specific pathogens, and it was this specificity that lead to their use as safe, target specific biological pesticides. For the past 30 years, AcMNPV has served as the subj... Baculoviruses were first identified as insect-specific pathogens, and it was this specificity that lead to their use as safe, target specific biological pesticides. For the past 30 years, AcMNPV has served as the subject of intense basic molecular research into the baculovirus infectious cycle including the interaction of the virus with a continuous insect cell line derived from Spodoptera frugiperda. The studies on baculoviruese have led to an in-depth understanding of the physical organization of the viral genomes including many complete genomic sequences, the time course of gene expression, and the application of this basic research to the use of baculoviruses not only as insecticides, but also as a universal eukaryotic protein expression system, and a potential vector in gene therapy. A great deal has also been discovered about the viral genes required for the replication of the baculovirus genome, while much remains to be learned about the mechanism of viral DNA replication. This report outlines the current knowledge of the factors involved in baculovirus DNA replication, using data on AcMNPV as a model for most members of the Baculoviridae. 展开更多
关键词 BACULOVIRUS DNA replication ACMNPV Molecular virology REVIEW
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基于动力系统自记忆原理的软土地基沉降预测 被引量:2
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作者 黄仁东 金浩 汪宏 《科技导报》 CAS CSCD 北大核心 2013年第9期31-35,共5页
基于软土地基沉降的过程是一个非线性的动力系统演变过程,为较为准确地对软土地基的沉降量进行预测,将"动力系统自记忆原理"引入软土地基沉降预测。采用双向差分原理反导出软土地基沉降的非线性常微分方程,将其作为微分动力核... 基于软土地基沉降的过程是一个非线性的动力系统演变过程,为较为准确地对软土地基的沉降量进行预测,将"动力系统自记忆原理"引入软土地基沉降预测。采用双向差分原理反导出软土地基沉降的非线性常微分方程,将其作为微分动力核,建立软土地基沉降自记忆模型,并将该模型用于汕汾高速公路软土地基沉降的预测。研究结果表明,将自记忆原理引入软土地基沉降的预测中,提高了预测的精度和适用范围,为软土地基沉降的预测提供了一种新的方法。 展开更多
关键词 软土地基沉降 预测 自记忆原理 数据机制模型 动力系统
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