土钉瞬态应力动力特性对比研究

基于探索性的洞室土钉支护试验,概述了洞室土钉支护抗动载试验的基本情况,并给出了洞室拱顶土钉的测试数据。依照实验现象,考察对比了毛洞段与土钉支护构措段的瞬态应变和炸药药量的关系,及其两者的瞬态应变和加载次数的关系,构措段具有较好的降低应变的能力。依照一维粘弹性理论对拱顶土钉进行了Maxwell模型的参数拟合;并用此模型确定了土钉用建筑钢的动力粘性因数,其与前拟合公式者相近。     

考虑断裂过程区的混凝土Ⅰ-Ⅱ复合型断裂判据研究

根据以前的混凝土Ⅰ-Ⅱ复合型断裂实验资料,同时采用数值模拟实验的方法,基于双K断裂准则,分别计算了混凝土Ⅰ-Ⅱ复合型断裂试件失稳断裂破坏时,断裂过程区影响的临界有效裂缝长度ac及应力强度因子KⅠ、KⅡ。根据分析计算结果,建立了考虑断裂过程区的新的Ⅰ-Ⅱ复合型断裂K判据,并与课题组以前的K判据作了分析比较。     

指数样条和有理插值在零息票债券定价中的应用

文章介绍了贴现函数的定义,对贴现函数性质进行了研究,综述了逼近它的几种方法;然后,在相关研究的基础上,针对贴现函数性质,引入指数样条和有理插值,提供了一种逼近和计算贴现函数的新方法,并给出一个实例说明方法的有效性。     

北羌塘拗陷巴贡组致密砂岩储层特征及控制因素

北羌塘拗陷的上三叠统巴贡组碎屑岩是羌塘盆地的储层重点层系,分析沉积、成岩作用对巴贡组碎屑岩储层物性的影响机制,可以为下步勘探工作提供地质依据.研究表明,巴贡组砂岩总体的成分成熟度和结构成熟度中等偏低,溶蚀孔隙是主要的孔隙类型,孔隙结构表现为分选差、喉道细和连通差.沉积因素是控制优质储层的主要因素,形成于分流河道微相的砂...     

The trefoil factor family – small peptides with multiple functionalities

The trefoil factor family (TFF) comprises a group of small peptides which are highly expressed in tissues containing mucus-producing cells – especially in the mucosa lining the gastrointestinal tract. The peptides seem crucial for epithelial restitution and may work via other pathways than the conventional factors involved in restitution. In vitro studies have shown that the TFFs promote restitution using multiple mechanisms. The peptides also have other functionalities including interactions with the immune system. Moreover, therapeutic effects of the TFFs have been shown in several animal models of gastrointestinal damage. Still it is not clear which of their in vitro properties are involved in the in vivo mode of action. This review describes the TFF family with emphasis on their biological properties and involvement in mucosal protection and repair. Received 10 October 2008; received after revision 07 November 2008; accepted 10 November 2008     

稻鸭共育体系多因素正交旋转回归试验研究

以水稻种植密度、纯N量、放鸭数3个因子,设置5个水平组成正交旋转回归试验,试验数据分析显示,3个因子对水稻产量影响显著,影响的大小顺序是放鸭数>纯N量>密度,而且密度与纯N量的交互作用明显,纯N量与放鸭数之间的交互作用明显,都达到显著水平.通过方程模拟选优得出,密度在18万~25.5万株/hm2之间、纯N量在72.9~131.85 kg/hm2之间、放鸭数取225~360只/hm2之间,水稻单产可达到较高水平.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

多元统计分析在河流污染状况综合评价中的应用

以湟水流域为例 ,针对流域内具有代表性的断面 ,运用因子分析方法和聚类分析方法就各断面水质污染程度和污染相似性进行定量化的综合评价 ,并以判别分析方法对相关结果加以校验 ,为河流治污工作的开展提供一定的科学依据 .     

改进型多路径分配模型及算法设计

针对现有的有效路径和有效路段两个概念存在的缺点 ,通过引入量化指标——临界相对差重新定义它们 ,对改进的多路径分配模型进行了深入探讨 ,并通过一个路网实例进行验证 ,取得了满意的结果 .