Spatial Data Usage,Spatial Thinking and Spatial Knowledge Generation: The Case of Planning Practitioners in Arequipa,Peru

Spatial thinking and spatial knowledge generation in decision-making are still not mature fields of study in planning research, despite these being crucial elements in addressing the issues of the twenty-first-century city. This article contributes to their understanding by exploring their interrelationships with spatial data usage. Through storylines, it analyzes the arguments that planning practitioners offer in support of infrastructure-led decisions in Arequipa (Peru), before and after spatial data usage. The article concludes that spatial data usage improve spatial thinking to different extents, yet suggests aligning spatial data generation and the inclusion of GIS-based spatial analyses with the spatial knowledge needed by routine planning practice.     

扁平超宽连续箱梁桥空间受力性能分析与研究

扁平超宽箱梁桥由于具有较大的宽跨比，其受力性能呈现明显的空间效应，通常的单梁模型计算已不能满足设计的精度要求，主要体现在宽箱梁各腹板纵向受力的差异性以及明显的剪力滞效应，同时由于大宽跨比使得横向预应力的作用不仅增加了顶板的压应力，也使得整体箱梁截面下缘出现拉应力，造成边腹板底部纵向裂缝，这些都给宽箱梁的设计带来了不利。因此为研究宽箱梁的空间效应，本文运用ANSYS有限元软件建立了全桥实体模型，计算了各腹板纵向受力差异及剪力滞效应，以及横向预应力对箱梁横向受力的影响，得到了一些有益的结论，为设计人员提供一定的参考。     

试析“韩流”与韩国对华文化外交

文化外交是主权国家利用文化手段达到特定政治目的或对外战略意图的活动。韩国对华文化外交的实施对"韩流"在中国的兴起及风行创造了友好的政治氛围,奠定了广泛的受众基础,提供了坚实的保障。它的主要特色在于:以对外文化交流机构为依托,以韩国语为载体,以文化产业为主导。"韩流"不仅为韩国带来了为数可观的直接和间接经济效益,还提升了韩国的国家形象,改变了中国大众的对韩印象,对加强中韩民众的相互信任起到十分重要的作用。     

Trend smoothness achieved by penalized least squares with the smoothing parameter chosen by optimality criteria

This work presents a study about the smoothness attained by the methods more frequently used to choose the smoothing parameter in the context of splines: Cross Validation, Generalized Cross Validation, and corrected Akaike and Bayesian Information Criteria, implemented with Penalized Least Squares. It is concluded that the amount of smoothness strongly depends on the length of the series and on the type of underlying trend, while the presence of seasonality even though statistically significant is less relevant. The intrinsic variability of the series is not statistically significant and its effect is taken into account only through the smoothing parameter.     

Robustness to non-normality and autocorrelation of individuals control charts

This paper studies the effects of non-normality and autocorrelation on the performances of various individuals control charts for monitoring the process mean and/or variance. The traditional Shewhart X chart and moving range (MR) chart are investigated as well as several types of exponentially weighted moving average (EWMA) charts and combinations of control charts involving these EWMA charts. It is shown that the combination of the X and MR charts will not detect small and moderate parameter shifts as fast as combinations involving the EWMA charts, and that the performana of the X and MR charts is very sensitive to the normality assumption. It is also shown that certain combinations of EWMA charts can be designed to be robust to non-normality and very effective at detecting small and moderate shifts in the process mean and/or variance. Although autocorrelation can have a significant effect on the in-control performances of these combinations of EWMA charts, their relative out-of-control performances under independence are generally maintained for low to moderate levels of autocorrelation.     

超声波提取胡椒中有效成分条件优化

试验研究温度、时间、超声波频率对超声波-食用乙醇提取胡椒有效成分的影响,以胡椒粗提取物的提取率为指标,正交实验确定较适宜提取条件为,温度55℃、时间55 min,超声波频率60 KHz,胡椒粗提取物的提取率为4.29%.     

分数阶长短波方程的整体光滑解

本文研究了分数阶长短波方程组的周期初边值问题,运用一致先验估计和Galerkin方法证明了分数阶长短波方程整体光滑解的存在性和唯一性.     

Model-based clustering of multivariate skew data with circular components and missing values

Motivated by classification issues that arise in marine studies, we propose a latent-class mixture model for the unsupervised classification of incomplete quadrivariate data with two linear and two circular components. The model integrates bivariate circular densities and bivariate skew normal densities to capture the association between toroidal clusters of bivariate circular observations and planar clusters of bivariate linear observations. Maximum-likelihood estimation of the model is facilitated by an expectation maximization (EM) algorithm that treats unknown class membership and missing values as different sources of incomplete information. The model is exploited on hourly observations of wind speed and direction and wave height and direction to identify a number of sea regimes, which represent specific distributional shapes that the data take under environmental latent conditions.     

An Additive–Multiplicative Restricted Mean Residual Life Model

The mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an additive–multiplicative restricted mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline. For the suggested model, some covariate effects are allowed to be time‐varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial.     

Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location‐Specific Premiums in the Netherlands

As flood risks grow worldwide, a well‐designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood‐loss‐sharing program involving private insurance based on location‐specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS‐based flood model and a stochastic optimization procedure with respect to location‐specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile‐related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures.