Stochastic free vibration analyses of composite shallow doubly curved shells – A Kriging model approach

This paper presents the Kriging model approach for stochastic free vibration analysis of composite shallow doubly curved shells. The finite element formulation is carried out considering rotary inertia and transverse shear deformation based on Mindlin’s theory. The stochastic natural frequencies are expressed in terms of Kriging surrogate models. The influence of random variation of different input parameters on the output natural frequencies is addressed. The sampling size and computational cost is reduced by employing the present method compared to direct Monte Carlo simulation. The convergence studies and error analysis are carried out to ensure the accuracy of present approach. The stochastic mode shapes and frequency response function are also depicted for a typical laminate configuration. Statistical analysis is presented to illustrate the results using Kriging model and its performance.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

Quantification of the layer dispersion degree in polymer layered silicate nanocomposites by transmission electron microscopy

As the performance of polymer layered silicate nanocomposites strongly depends on their interior layer dispersion, quantification of the layer dispersion degree is needed. In this work, a new methodology was developed to determine the dispersion parameter D_0.1, based on the measurement of the free-path spacing distance between the single clay sheets from the transmission electron microscopy (TEM) images. Several examples of exfoliated, intercalated, and immiscible composites were studied. It was found that the exfoliated composites had D_0.1 over 8%, while that of intercalated composites were between 4 and 8%. In the case of intercalation, a high frequency peak appeared at a short spacing distance in the histogram, which was a characteristic of the intercalation, distinct from the exfoliation. The main utility of this TEM methodology is for the quantification of exfoliated or intercalated samples with small number of layers with stacks. The dispersion parameter D_0.1 below 4% was suggested to classify as immiscible. A unique advantage of the TEM measurement is that the dispersion degree of different fillers can be counted individually.     

黄河流域非点源污染负荷定量化与控制研究进展

通过文献分析,总结了黄河流域非点源污染负荷定量化与污染控制两方面的研究进展,分析了黄河流域非点源污染研究现阶段存在的问题,即农业、城市非点源污染研究总体不多,自主研发模型少见、控制管理措施单一、新型污染物研究不足等。未来黄河流域地区需加大基础数据的监测,加强部门之间数据共享,建立非点源污染数据库;进一步对非点源污染机理进行探究,研发适合该流域的水文-水质-水动力非点源污染和水库水动力-水质耦合模型;深入非点源污染防控研究,制定全过程综合控制模式与管理体系。     

海绵城市建设雨水基础设施优化配置研究进展

气候变化和快速城市化改变了城市生态水文循环过程,加剧了水资源短缺、水环境污染、水生态破坏、暴雨洪涝灾害等城市问题。为获取低影响开发措施（low impact development,LID）/绿色基础设施（green infrastructure,GI）与城市灰色基础设施耦合系统空间优化配置方案以及多目标成本-效益最优曲线,本文系统归纳了不同规划目标导向下新建、改建城区海绵城市建设核心指标与控制要点;针对现有研究中的科学和技术问题,构建了雨水基础设施区域优化配置模式,主要由雨水基础设施性能指标数据库、成本效益定量化及计算机辅助决策三部分构成。探讨了数值计算和模型模拟中典型LID设施的关键参数及其性能指标;基于环境经济学理论分析了海绵城市建设成本和效益指标定量化方法;探索有限成本投入下,改善城市水环境、水生态、水资源、水安全的多目标智能优化算法,结合模型模拟以实现城市生态水文良性循环和可持续发展。     

Effect of processing on sphingolipid content in soybean products

Soybeans are believed to be a rich source of sphingolipids, a class of polar lipids that has received attention for their possible cancer-inhibiting activities. The effect of processing on the sphingolipid content of various soybean products has not been determined. Glucosylceramide (GlcCer), the major sphingolipid type in soybeans, was measured in several processed soybean products to illustrate which product(s) GlcCer is partitioned into during processing and where it is lost. Whole soybeans were processed into full-fat flakes, from which crude oil was extracted. Crude oil was refined by conventional methods, and defatted soy flakes were further processed into alcohol-washed and acid-washed soy protein concentrates (SPC) and soy protein isolates (SPI) by laboratory-scale methods that simulated industrial practices. GlcCer was isolated from the samples by solvent extraction, solvent partition, and TLC and was quantified by HPLC. GlcCer remained mostly within the defatted soy flakes (91%) rather than in the oil (9%) after oil extraction. Only 52, 42, and 26% of GlcCer from defatted soy flakes was recovered in the acid-washed SPC, alcohol-washed SPC, and SPI products, respectively. All protein products had a similar GlcCer concentration of about 281 nmol/g (dry wt basis). The minor quantity of GlcCer in the crude oil was almost completely removed by water degumming.     

A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model

Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin near Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400–600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LP_τ (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones.     

二醇柱高效液相色谱法测定人参提取液中的人参皂苷

采用二醇柱,在等度洗脱条件下,建立了高效液相色谱(HPLC)定量分析人参提取液中人参皂苷的检测方法.优化的色谱条件:LiChrospher100 Diol柱(250 mm x2 mm i.d.,5μm),流动相为乙腈-水(体积比为82∶18),流速为1.0 L.min-1,检测波长为200 nm,柱温为15℃.研究结果表明:在较低温度下,采用二醇柱分析7种主要人参皂苷,具有分析速度快,不需要梯度洗脱程序,方法简单等优点.     

社交网络用户隐私泄露量化评估方法

社交网络用户隐私泄露的量化评估有利于帮助用户了解个人隐私泄露状况,提高公众隐私保护和防范意识,同时也能为个性化隐私保护方法的设计提供依据.针对目前隐私量化评估方法主要用于评估隐私保护方法的保护效果,无法有效评估社交网络用户的隐私泄露风险的问题,提出了一种社交网络用户隐私泄露量化评估方法.基于用户隐私偏好矩阵,利用皮尔逊相似度计算用户主观属性敏感性,然后取均值得到客观属性敏感性;采用属性识别方法推测用户隐私属性,并利用信息熵计算属性公开性;通过转移概率和用户重要性估计用户数据的可见范围,计算数据可见性;综合属性敏感性、属性公开性和数据可见性计算隐私评分,对隐私泄露风险进行细粒度的个性化评估,同时考虑时间因素,支持用户隐私泄露状况的动态评估,为社交网络用户了解隐私泄露状况、针对性地进行个性化隐私保护提供支持.在新浪微博数据上的实验结果表明,所提方法能够有效地对用户的隐私泄露状况进行量化评估.     

Pavement crack detection using hessian structure propagation

Pavement images are widely used in transportation agencies to detect cracks accurately so that the best proper plans of maintenance and rehabilitation could be made. Although crack in a pavement image is perceived because the intensity of crack pixels contrasts with that of the pavement background, there are still challenges in distinguishing cracks from complex textures, heavy noise, and interference. Unlike the intensity or the first-order edge feature of crack, this paper proposes the second-order directional derivative to characterize the directional valley-like structure of crack. The multi-scale Hessian structure is first proposed to analytically adapt to the direction and valley of cracking in the Gaussian scale space. The crack structure field is then proposed to mimic the curvilinear propagation of crack in the local area, which is iteratively applied at every point of the crack curve to infer the crack structure at the gaps and intersections. Finally, the most salient centerline of the crack within its curvilinear buffer is exactly located with non-maximum suppression along the perpendicular direction of crack. The experiments on large numbers of images of various crack types and with diverse conditions of noise, illumination and interference demonstrate the proposed method can detect pavement cracks well with an average Precision, Recall and F-measure of 92.4%, 88.4%, and 90.4% respectively. Also, the proposed method achieves the best performance of crack detection on the benchmark datasets among methods that also require no training and publicly offer the detection results for every image.