南京中山植物园游憩功能满意度评价研究

随着植物园"旅游经济"的发展,植物园在承担植物科学研究及科普功能的同时,也成为公众日常休闲的重要绿地。本文选取南京中山植物园作为研究对象,建立评价体系并运用模糊综合评价法对游客满意度进行研究,探寻影响植物园游憩功能的显著因子,为未来植物园规划设计提供参考。结果表明:游客对南京中山植物园的游憩功能达到基本满意水平。其中,植物物种多样性、景区分布、建筑景观、声景观的满意度较高,滨水景观的满意度评价值较低。通过Pearson相关系数发现植物色彩丰富度、科普场所、科普内容是影响游客满意度的显著因子。运用IPA图对16项评价因子进行重要度-满意度分析,并对各象限内的因子提出改进方向。最后基于游憩功能满意度评价结果,提出改进策略:加大对植物园休息设施、卫生设施、娱乐设施和商业服务设施的投入;更新科普方式,增添特色游憩主题园区;完善现有道路体系,增强对北部专类园的可达性;增加色叶植物的种类、提高色彩丰富度等。以期为中山植物园游憩功能的优化及其他植物园的规划设计提供借鉴。     

大众传播中的“国家教师”媒介形象分析

运用传播学内容分析法，以中央媒体对获得"全国教书育人楷模"和"最美教师"荣誉称号的"国家教师"相关报道为研究对象，从外在和内在两个方面对大众传播的"国家教师"媒介形象进行分析。外在形象包括教师性别、年龄、教龄、外表、场景以及任教的学校类别、区域、学科专业等方面，内在形象包括职业精神、职业知识和职业能力等方面。研究发现，大众媒体对"国家教师"形象的刻画和宣传符合社会公众认知教师职业的基本逻辑，起到了传递教师精神、端正教师形象、唤起社会尊重教师的功效，但也存在城乡教师形象传播刻板化以及教师普通人形象展现不充分等问题，可在后续的传播中对"国家教师"形象的价值倾向进行适当调整。     

Introduction: making the case for qualitative interviews

ABSTRACT

We introduce four papers comprising a Themed Section for this issue of the International Journal of Social Research Methodology, which together ‘Make the Case for Qualitative Interviews’. Here our aim is to show how this collection provides a timely contribution to key debates concerning the value of qualitative interviews, particularly as these are employed and analysed in much recent social scientific thinking. We explore ways to move beyond recent, sometimes constraining and occasionally dismissive, approaches to interviews in the social sciences through reframing and reconfiguring central questions germane to these debates. We also seek to challenge a broader neo-liberal trend towards valuing quantitative over depth qualitative research. Through this Introduction, and the collection of papers that follows, we seek to re-establish the value of qualitative interviews by shifting the focus from a preoccupation with what interviews can be said to do, towards questions centring on what can be done with interviews.     

FEPA-金融时间序列自适应组合预测模型

本文报告一种金融时间序列预测的信号分析、信息融合与智能计算组合模型，简称FEPA，由针对金融时间序列（FTS）信号分析的经验模态分解（EMD）、用于数据降维的主成分分析（PCA）和用于非线性建模的人工神经网络（ANN）三部分组成。该模型首先应用滑动窗口截取原始金融时间序列最近期数据集，应用EMD分解算法把数据集分解成不同尺度的本征模态函数（IMF），然后通过主成分分析将分解后的数据降维，提取最有信息量的特征；然后将这些特征输入到神经网络进行组合预测。本文提出的组合预测模型FEPA是基于分解-提优-合成的信息融合思想，有效提高了预测可靠性。其创新点在于：1）首次给出了EMD算法的结构化表达，提供了今后融合更多信息的算法接口；2）通过多步长预测输出深入研究EMD分解的有效信息结构；3）通过切换到更细时间框架来处理EMD的端点效应，并探索了两级时间框架下的预测效果；4）给出了金融时间序列组合预测模型的一般性架构，具有可升级性和可扩展性。并且通过滑动窗口EMD使得实证更能切近实际。通过在沪深300股指和澳大利亚股指上的实证，结果表明FEPA预测模型在沪深300股指日线和15分钟线上的预测命中率高达78%和82%，在澳大利亚股指日线上也达到了74%的命中率，经比较，明显高于文献中常见的5种模型。     

基于FESA/TRIZ的机械产品创新设计方法

机械产品的设计一直注重于市场需求和产品功能的开发，随着科学和技术的发展，环境因素变得越来越重要。 提出一种基于功能一环境一结构分析( FESA)与TRIZ的机械产品创新设计方法，通过分析机械产品结构之间的功能要素 以及功能要素与环境要素的相互作用关系，确定机械产品中存在的问题。建立根原因模型图，寻找结构设计上的不足和 缺陷，利用TRIZ创新理论来实现机械产品的改进创新。通过污水处理系统的创新设计实例验证了该方法的可行性。     

Heteroscedastic Global Tests that the Regression Parameters for Two or More Independent Groups are Identical

Simulation results are reported on methods that allow both within group and between group heteroscedasticity when testing the hypothesis that independent groups have identical regression parameters. The methods are based on a combination of extant techniques, but their finite-sample properties have not been studied. Included are results on the impact of removing all leverage points or just bad leverage points. The method used to identify leverage points can be important and can improve control over the Type I error probability. Results are illustrated using data from the Well Elderly II study.     

Finite mixture of regression models for a stratified sample

Despite the popularity and importance, there is limited work on modelling data which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modelling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation–maximisation algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the proposed modelling approach. The methodology was implemented on real data with good results.     

An evaluation of the bootstrap for model validation in mixture models

Bootstrapping has been used as a diagnostic tool for validating model results for a wide array of statistical models. Here we evaluate the use of the non-parametric bootstrap for model validation in mixture models. We show that the bootstrap is problematic for validating the results of class enumeration and demonstrating the stability of parameter estimates in both finite mixture and regression mixture models. In only 44% of simulations did bootstrapping detect the correct number of classes in at least 90% of the bootstrap samples for a finite mixture model without any model violations. For regression mixture models and cases with violated model assumptions, the performance was even worse. Consequently, we cannot recommend the non-parametric bootstrap for validating mixture models.

The cause of the problem is that when resampling is used influential individual observations have a high likelihood of being sampled many times. The presence of multiple replications of even moderately extreme observations is shown to lead to additional latent classes being extracted. To verify that these replications cause the problems we show that leave-k-out cross-validation where sub-samples taken without replacement does not suffer from the same problem.     

On approximations via convolution-defined mixture models

An often-cited fact regarding mixing or mixture distributions is that their density functions are able to approximate the density function of any unknown distribution to arbitrary degrees of accuracy, provided that the mixing or mixture distribution is sufficiently complex. This fact is often not made concrete. We investigate and review theorems that provide approximation bounds for mixing distributions. Connections between the approximation bounds of mixing distributions and estimation bounds for the maximum likelihood estimator of finite mixtures of location-scale distributions are reviewed.     

Bootstrap estimation and model selection for multivariate normal mixtures using parallel computing with graphics processing units

In applications of multivariate finite mixture models, estimating the number of unknown components is often difficult. We propose a bootstrap information criterion, whereby we calculate the expected log-likelihood at maximum a posteriori estimates for model selection. Accurate estimation using the bootstrap requires a large number of bootstrap replicates. We accelerate this computation by employing parallel processing with graphics processing units (GPUs) on the Compute Unified Device Architecture (CUDA) platform. We conducted a runtime comparison of CUDA algorithms between implementation on the GPU and that on a CPU. The results showed significant performance gains in the proposed CUDA algorithms over multithread CPUs.