基于MIDAS技术的多层分布式应用程序开发

MIDAS能有效地利用DCOM、TCP/IP、OLEnterprise和CORBA技术在Internet/Intranet上建立结构为“瘦客户端 应用程序服务器 数据库系统”的多层分布式应用程序。本文讨分析了MIDAS的原理和组成结构，着重研究了基于MIDAS技术的多层分布式应用程序的开发过程和一些具体应用。     

共和干热岩地热井GR2井区三维地应力场反演分析

GR2 地热井位于共和恰卜恰镇，干热岩地热井井深达到4000 米，大深度井区极大增加了反演的难度．井区附近20 千米内无大型构造，附近地应力场的主要影响因素为板块构造应力和自重应力．由于井中未进行三维地应力场测井工作，所以我们以地面地应力调查结果和震源机制解反演结果为依据，基于MIDAS GTS 和大比例尺高清卫星云图建立有限元数值模型，使用改进的边界法进行反演计算，最终预估了井中地应力场分布特征．对于后续的井网布置以及干热岩井场开采具有重要意义．     

考虑地震动多点激励与材料应变率效应的主跨300m级独塔斜拉桥弹塑性分析

建立主跨300m级独塔斜拉桥MIDAS/Civil空间有限元模型,开发Civil 2MARC可视化接口程序,进而将MIDAS/Civil模型转入MSC.MARC程序中并检验了模型转化前后的一致性。然后,开发了基于地表功率谱的地表多点地震动生成理论的多点地震动可视化软件MEMS_b(Multiple Earthquake Motions Simulation b)。进而说明了适用于多点位移-速度计算模型的相对精确性和容易实现的优势,并与MSC.MARC程序相结合应用于该桥梁的多点激励分析。最后分析了该桥梁地震作用下的薄弱环节与弹塑性反应,并研究多点输入和材料率效应对桥梁地震反应的影响规律。结果表明:(1)开发的可视化接口程序Civil2MARC界面便于操作,运行稳定,转化高效且结果可靠;(2)开发的可视化多点地震动生成程序MEMS_b界面友好,参数设置灵活且结果有效;(3)位移-速度多点计算模型理论完备,并可通过MSC.MARC程序对位移-速度多点计算模型加以实现;(4)解释并给出了应变率效应和多点地震动对该斜拉桥的塑性铰首次出现时间、位置以及计算末态塑性铰分布的影响规律。本文涉及到理论模型、程序开发及应用计算,可为相关工程提供参考。     

A Novel Cyclodextrin-containing Glass Thermoplastic System (GTS) for Formulating Poorly Water Soluble Drug Candidates: Preclinical and Clinical Results

We present a novel solid solution/dispersion technology with glass thermoplastic properties that provide good dissolution rates and oral bioavailabilities for poorly water-soluble weak bases. In this process, a thermoplastic gum was prepared by mixing a polyhydroxy acid such as citric acid or tartaric acid with a weakly basic drug, hydroxypropyl--cyclodextrin (HP--CD) and a cellulose polymer such as hydroxypropylmethylcellulose (HPMC) in a protic solvent. Removal of the solvent gave a material which could be loaded into hard gelatin capsules. Several model compounds were processed in this manner including methylene blue and itraconazole. The resulting data indicated that dissolution properties of GTS's based on methylene blue was pH independent and rapid with 80% dissolved within 30 min. Three GTS formulations of itraconazole containing 100 mg of the drug and 500 mg of citric acid as well as various concentrations of HP--CD and HMPC were found to dissolve rapidly (100% in 45 min). One of these formulations was selected for human pharmacokinetic evaluation and demonstrated significant oral bioavailability relative to unmanipulated drug.The studies suggest that the components of the GTS provide for solubilization through complexation and reduced pH and that the cellulose polymer acts to inhibit recrystallization of the supersaturated solution formed. The rationaldevelopment of the GTS dosage form can be useful for generating acceptable formulations for poorly water-soluble drug candidates.     

An approach for identifying and predicting economic recessions in real‐time using time–frequency functional models

Identifying periods of recession and expansion is a challenging topic of ongoing interest with important economic and monetary policy implications. Given the current state of the global economy, significant attention has recently been devoted to identifying and forecasting economic recessions. Consequently, we introduce a novel class of Bayesian hierarchical probit models that take advantage of dimension‐reduced time–frequency representations of various market indices. The approach we propose can be viewed as a Bayesian mixed frequency data regression model, as it relates high‐frequency daily data observed over several quarters to a binary quarterly response indicating recession or expansion. More specifically, our model directly incorporates time–frequency representations of the entire high‐dimensional non‐stationary time series of daily log returns, over several quarters, as a regressor in a predictive model, while quantifying various sources of uncertainty. The necessary dimension reduction is achieved by treating the time–frequency representation (spectrogram) as an “image” and finding its empirical orthogonal functions. Subsequently, further dimension reduction is accomplished through the use of stochastic search variable selection. Overall, our dimension reduction approach provides an extremely powerful tool for feature extraction, yielding an interpretable image of features that predict recessions. The effectiveness of our model is demonstrated through out‐of‐sample identification (nowcasting) and multistep‐ahead prediction (forecasting) of economic recessions. In fact, our results provide greater than 85% and 80% out‐of‐sample forecasting accuracy for recessions and expansions respectively, even three quarters ahead. Finally, we illustrate the utility and added value of including time–frequency information from the NASDAQ index when identifying and predicting recessions. Copyright © 2012 John Wiley & Sons, Ltd.