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A CLASS OF STATIONARY MODELS OF SINGULAR STOCHASTIC CONTROL 被引量:9
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作者 刘坤会 秦明达 陆传赉 《Acta Mathematica Scientia》 SCIE CSCD 2004年第1期139-150,共12页
A class of stationary models of singular stochastic control has been studied, in which the state is extended to solution of a class of S.D.E. from Wiener process. The existence of optimal control has been proved in al... A class of stationary models of singular stochastic control has been studied, in which the state is extended to solution of a class of S.D.E. from Wiener process. The existence of optimal control has been proved in all cases under some weaker conditions, and the structure of optimal control may be characterized. 展开更多
关键词 Singular stochastic control stationary model stochastic differential equation variational equation system
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Settlement Prediction for Buildings Surrounding Foundation Pits Based on a Stationary Auto-regression Model 被引量:3
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作者 TIAN Lin-ya HUA Xi-sheng 《Journal of China University of Mining and Technology》 EI 2007年第1期78-81,共4页
To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitori... To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits. 展开更多
关键词 foundation pit BUILDING settlement monitoring datum stability stationary auto-regression model settlement prediction
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Reversed-phase fused-core HPLC modeling of peptides 被引量:3
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作者 Matthias D'Hondt Bert Gevaert +5 位作者 Sofie Stalmans Sylvia Van Dorpe Evelien Wynendaele Kathelijne Peremans Christian Burvenich Bart De Spiegeleer 《Journal of Pharmaceutical Analysis》 SCIE CAS 2013年第2期93-101,共9页
Different fused-core stationary phase chemistries(C18,Amide,Phenyl-hexyl and Peptide ES-C18) were used for the analysis of 21 structurally representative model peptides.In addition,the effects of the mobile phase co... Different fused-core stationary phase chemistries(C18,Amide,Phenyl-hexyl and Peptide ES-C18) were used for the analysis of 21 structurally representative model peptides.In addition,the effects of the mobile phase composition(ACN or MeOH as organic modifier;formic acid or acetic acid,as acidifying component) on the column selectivity,peak shape and overall chromatographic performance were evaluated.The RP-amide column,combined with a formic acid-acetonitrile based gradient system,performed as best.A peptide reversed-phase retention model is proposed,consisting of 5 variables:log SumAA,log Sv,clog P,log nHDon and log nHAcc.Quantitative structure-retention relationship(QSRR) models were constructed for 16 different chromatographic systems.The accuracy of this peptide retention model was demonstrated by the comparison between predicted and experimentally obtained retention times,explaining on average 86% of the variability.Moreover,using an external set of 5 validation peptides,the predictive power of the model was also demonstrated.This peptide retention model includes the novel in-silico calculated amino acid descriptor,AA,which was calculated from log P,3D-MoRSE,RDF and WHIM descriptors. 展开更多
关键词 Peptides Fused-core(core-shell core-enhanced poro-shell HALO s) stationary phases RP-HPLC peptide retention model In-silico amino acid descriptor
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