曲面响应法预测砂浆性能的试验研究

本试验以砂浆性能为研究对象,采用Box-Behnken的中心组合实验设计及响应面分析方法对腻子粘结强度进行预测研究。试验选用三因素三水平的响应曲面分析法,建立了粘结强度的二次多项数学模型,并以粘结强度为响应值作响应面和等高线,考察了水粉比、灰粉比和胶粉比对粘结强度的影响,并根据实际选择响应曲面方程,利用最小二乘法估计相应的系数,剔除最不显著因素,建立最终的数学方程。结果表明:该预测模型能很好地描述粘结强度与水粉比、灰粉比和胶粉比之间的关系,可以对腻子粘结强度进行分析和预测,为砂浆性能研究提供一种新方法。     

Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape

For quantitative water management, fine analysis of spatial and temporal interactions between cropping systems and water resources helps identify time and site-specific withdrawal situations. However, it is a methodological challenge to provide fine-resolution analyses at the scale of large watersheds used for crises management. In this study, we present a methodology based on multiple methods and mixed sources of information to finely model an agricultural landscape (AL) that represents the spatial distribution of cropping systems. Our approach is based on progressively hybridizing databases and local actors' and experts' knowledge to produce a spatially explicit and dynamic model. The Land Parcel Identification System database, which resulted from the European Common Agricultural Policy, is crucial for our method since it provides the spatial and temporal basis of our model (i.e., geographic delineation of islets and information about crop sequences). Local knowledge is used to identify factors determining spatial distribution of cropping systems and to build a generic model that simulates farmers' crop-management strategies. The model was qualitatively and quantitatively evaluated using a multi-agent simulation platform (MAELIA). We asked local experts on quantitative water management to evaluate the ability of the platform to reproduce intra- and inter-annual dynamics at different levels when using our model of the AL as input. The experts were satisfied with the results; they validated the predicted dynamics of the variables, and some discussed the objectivity of the values. We discuss the method’s contribution to current challenges in modeling large agricultural areas and the associated tradeoffs. The approach is promising for policy makers who wish to develop integrated, locally adapted land-management strategies.     

加强施工机械设备管理

为了进一步提高劳动生产率、提高企业经济效益,根据施工单位机械设备数量不足、严重老化、技术性和配套性落后等问题,进行分析和探讨解决的方法及措施。     

材料试验检测技术在公路工程中的应用分析

试验检测技术作为当前对公路路面施工中工程质量保证当中的重要环节,对路面工程施工的质量有着重要的作用。本文以公路路面养护中的主要材料试验检测方法和指标作为基础,从材料试验检测的作用和实际应用对其进行阐述,并指出在养护中运用这些指标和方法的重要性。     

关于对高职学生现用教材使用情况问卷调查的分析报告

本文分别从问卷调查的基本情况论述了高职学生现用教材使用情况问卷调查的分析报告。     

探索性因子分析中统计方法的比对研究

本文以探索性因子分析中统计方法的选择为目标,针对在许多应用探索性因子分析的文献中存在的统计方法选择不当致使计算结果失真这一现象,对探索性因子分析中的15种统计方法进行了比对研究,并给出了建议。     

浅析大学物理微积分思想与矢量思想

本文探讨了微积分思想和矢量思想在大学物理教学中的应用,致力于总结出一个可靠的应用模式用于提高大学物理教学的质量。     

Exploring the use of content analysis methodology in consumer research

Content analysis is a viable way to thematise consumer experiences. The purpose of this review study is to examine the use of content analysis in consumer research (1977–2017). The authors explore how content analysis has been used. The reviewed studies address consumer experiences. The results show that qualitative content analysis is not used as a method in its own right; it is more often applied as a supplement to quantitative testing. There is also a lack of rigorous reporting of methodologies in many studies. The systematic review provides four propositions, content analysis studies: 1) vary in execution and reporting; 2) have a tendency towards methodological vagueness; 3) do not apply content analysis as a sole method; 4) are versatile. The study can also serve as point of departure for novice researchers wishing to engage with content analysis research. We suggest that further research is needed to explore the use of qualitative methodologies in consumer research.     

Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes

This paper contributes to the nascent literature on nowcasting and forecasting GDP in emerging market economies using big data methods. This is done by analyzing the usefulness of various dimension-reduction, machine learning and shrinkage methods, including sparse principal component analysis (SPCA), the elastic net, the least absolute shrinkage operator, and least angle regression when constructing predictions using latent global macroeconomic and financial factors (diffusion indexes) in a dynamic factor model (DFM). We also utilize a judgmental dimension-reduction method called the Bloomberg Relevance Index (BRI), which is an index that assigns a measure of importance to each variable in a dataset depending on the variable’s usage by market participants. Our empirical analysis shows that, when specified using dimension-reduction methods (particularly BRI and SPCA), DFMs yield superior predictions relative to both benchmark linear econometric models and simple DFMs. Moreover, global financial and macroeconomic (business cycle) diffusion indexes constructed using targeted predictors are found to be important in four of the five emerging market economies that we study (Brazil, Mexico, South Africa, and Turkey). These findings point to the importance of spillover effects across emerging market economies, and underscore the significance of characterizing such linkages parsimoniously when utilizing high-dimensional global datasets.