小鼠趾叶炎与P物质的相关性研究

采用组织化学和免疫组化的染色方法,检测趾叶炎小鼠趾部组织中炎性细胞、肥大细胞及其脱颗粒数和P物质表达的动态变化.结果表明,炎症组小鼠趾部P物质在真皮网状层及血管和腺体周围随着二磷酸组织胺免疫时间的推移,表达逐渐增强,多呈网状走行,且与肥大细胞串联及相邻排列;炎症组小鼠趾部肥大细胞及其脱颗粒和炎性细胞随二磷酸组织胺免疫时间的延长急剧增多,均极显著高于正常组(P<0.01),在给药后14 h三者达到峰值,而后减少,给药后72 h又出现上升趋势;同时,在真皮出现了许多增生的动脉,管壁增厚,管腔内有血栓形成.由此推断,P物质参与小鼠趾叶炎的发病过程.     

财政政策背景下的家庭教育投资行为分析

概述了国内外对公共教育投资和家庭教育投资的研究,选择新古典经济学中的家长最优化框架来构建家庭教育投资的一般模型.在此基础上,引入了3类公共教育财政政策,即强制供给、收入补贴和专项补贴.重点考察了在这3类教育财政背景下家庭教育投资的行为,并对一般模型和这3类模型的结论做了进一步的比较分析,为教育财政政策的制定提供了一些可行建议.     

Sobolev方程的混合有限元法

对Sobolev方程,作者构造了一组简单的低阶四边形混合元.结合半离散有限元计算格式,通过分析,作者改进了郑和胡的收敛性结果.与已有文献中的有限元相比,该元素计算自由度少,精度较高.数值实验也验证了方法的有效性.     

整数规划的凸填充函数算法

改造文献［１］的求解连续总体优化问题的凸填充函数算法使之适于求解整数规划问题．首先,在求出整数规划问题的一个离散局部极小解的基础上构造了整数规划问题的凸填充函数;其次,通过寻找该填充函数的离散局部极小解,以期找到整数规划问题的比当前离散局部极小解好的解．本文的算法是直接法,数值试验表明算法是有效的．     

膨胀土-混凝土界面剪切特性研究及界面系数修正

为对不同含水率下与不同法向应力下的膨胀性土的界面抗剪强度-界面剪切位移曲线进行分析,通过设计室内膨胀土-混凝土界面直剪试验,研究各含水率对膨胀土-混凝土界面各力学性质的影响。结果表明：不同含水率条件下的应力应变曲线均无应变软化现象,基本符合应变硬化模型;含水率的增长,对界面抗剪强度有较为明显的劣化影响,应力应变曲线的峰值也随之愈发滞后;同一含水率的界面抗剪强度与法向应力较好地符合摩尔-库伦强度准则,随着含水率的逐渐增长,界面抗剪强度也呈现递减态势;界面黏聚力先呈增长态势,后呈逐渐减小态势;界面等效剪切模量呈现下降趋势,同时通过微观角度对界面黏聚力和界面内摩擦角引起的界面强度变化规律进行了机理性的分析。界面内摩擦角则随着含水率增加逐渐地呈现反比例函数降低态势,引入界面摩擦系数,进一步细致分析界面摩擦力学性质,同时修正了界面黏聚力和法向应力对界面摩擦带来的较大影响,针对于浅层边坡土体与桩基础的摩擦估算,考虑到法向荷载对于界面摩擦系数影响较为合理。     

PORLES: A parallel object-relational database system

We developed a parallel object-relational DBMS named PORLES. It uses BSP model as its parallel computing model, and monoid calculus as its basis of data model. In this paper, we introduce its data model, parallel query optimization, transaction processing system and parallel access method in detail.     

De Casteljau Algorithm and Degree Elevation of Toric Surface Patches

De Casteljau algorithm and degree elevation of Bézier and NURBS curves/surfaces are two important techniques in computer aided geometric design. This paper presents the de Casteljau algorithm and degree elevation of toric surface patches, which include tensor product and triangular rational Bézier surfaces as special cases. Some representative examples of toric surface patches with common shapes are illustrated to verify these two algorithms. Moreover, the authors also apply the degree elevation of toric surface patches to isogeometric analysis. And two more examples show the effectiveness of proposed method.     

Engineering of multiferroic BiFeO3 grain boundaries with head-to-head polarization configurations

Confined low dimensional charges with high density such as two-dimensional electron gas (2DEG) at interfaces and charged domain walls in ferroelectrics show gre...     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.