陕西高校各类教育提升全人素质的预测模型

采用"高校全人素质量表"随机抽样省部属8所高校4 953名大学生,对调查数据做SPSS多元回归参数估计和AMOS结构方程模型构建。结果表明,德育教育和专业教育分别对大学生全人素质16项指数产生显著正效应;德育教育"立德效应"主提升德道品质、心身健康、人生意义等素质;专业教育"树人效应"主提升求知钻研、学术探索、技术能力、语言思维等素质。研究结果为陕西高校全人素质教育提供了实证参数和理论依据。     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

模具钢干铣削试验及参数优化研究

为了对铣削力做进一步的研究,以及预测铣削参数的改变对铣削力变化的影响,文章建立了铣削力预测模型,引 入了PSO优化算法。试验采用正交设计方法,干式铣削SKD61模具钢;KISTLER测力仪测量铣削力;HRsoft_DW数采软 件采集试验数据,并对数据进行极差分析。研究结果表明每齿进给量是铣削参数中影响铣削力最为主要的因素。研究 验证了PSO算法对铣削参数优化问题具有有效性。     

Statistical inference based on generalized Lindley record values

This paper addresses the problems of frequentist and Bayesian estimation for the unknown parameters of generalized Lindley distribution based on lower record values. We first derive the exact explicit expressions for the single and product moments of lower record values, and then use these results to compute the means, variances and covariance between two lower record values. We next obtain the maximum likelihood estimators and associated asymptotic confidence intervals. Furthermore, we obtain Bayes estimators under the assumption of gamma priors on both the shape and the scale parameters of the generalized Lindley distribution, and associated the highest posterior density interval estimates. The Bayesian estimation is studied with respect to both symmetric (squared error) and asymmetric (linear-exponential (LINEX)) loss functions. Finally, we compute Bayesian predictive estimates and predictive interval estimates for the future record values. To illustrate the findings, one real data set is analyzed, and Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and prediction.     

Conditional moving linear regression: Modeling the recruitment process for ALLHAT

Effective recruitment is a prerequisite for successful execution of a clinical trial. ALLHAT, a large hypertension treatment trial (N = 42,418), provided an opportunity to evaluate adaptive modeling of recruitment processes using conditional moving linear regression. Our statistical modeling of recruitment, comparing Brownian and fractional Brownian motion, indicates that fractional Brownian motion combined with moving linear regression is better than classic Brownian motion in terms of higher conditional probability of achieving a global recruitment goal in 4-week ahead projections. Further research is needed to evaluate how recruitment modeling can assist clinical trialists in planning and executing clinical trials.     

Prediction intervals in exponential families

A method for obtaining prediction intervals for an outcome of a future experiment is presented. The method uses hypothesis testing as a tool to derive prediction intervals and assumes that the probability distributions of informative and future experiments are one parameter exponential families. Asymptotic similar mean coverage prediction intervals are derived using the score test as a test statistics. Examples are presented and asymptotic prediction limits are compared with the prediction limits given in the literature.     

词语语义加工中N400影响因素研究述评

作为语义加工大脑指标的N400成分受多种因素的影响。研究者从词类、词频、拼写相似性、语义关联、词语具体性、语境以及词汇隐喻等方面对词语语义加工中影响N400成分的因素进行了大量的研究。然而，这些研究对词语的出现条件进行了严格的限制：对词语列表中的词语的考察脱离了语境的制约，对完形填空中词语出现概率的考察受到句法结构的制约。本文对这些影响N400成分的因素进行了评述，建议该领域的研究重点可以倾向于脱离句法结构的条件限制，考察不同结构中词语或词语组合的语义加工过程，倾向于词语语义加工的跨语言渗透，并提出在词汇学习或教学中遴选词语的策略和语境制约的作用。     

基于测井资料的低孔低渗储层产能预测研究

针对低孔低渗储层产能预测模型受敏感因素影响大，主次因素在低产情况下区别不明显，产能预测评价相对较困难等实际问题，以准噶尔盆地夏子街地区夏77井、夏79井为例，开展低孔低渗储层产能预测的前期试验研究，对多个产能预测模型及其适用条件进行了对比分析，从Darcy模型、Jones（1976）模型、Vogel/Harrison（1968）模型、裂缝模型中进行优选和调试。通过分析产能预测模型结果与测试结果在产量上的相关性，表明基于测井资料的预测结果与试油测试结果相当吻合，从而确认产能预测模型结果的可靠性。