密度分布非均匀的反馈控制Logistic单种群模型平衡态的稳定性

本文对种群密度在非均匀分布情形下,考虑了具反馈控制的滞后 Logistic 生态模型平衡位置的稳定性;分别给出了在常时滞和弱连续时滞以及强连续时滞情况下的稳定性条件;其结果是对 Gopalsamy 在密度均匀分布情形下相应结果的推广.     

社会资本与我国家族企业的发展

改革开放以来,我国家族企业迅速崛起。此现象按新古典经济学的理论无法解释,因为家族企业创业时既缺少物质资本,又缺少人力资本,也没有技术支持,甚至连存在的合法性都成问题。其实,经济增长不仅需要物质资本和人力资本,还需要社会资本。家族企业之所以在夹缝中突起,很大程度依靠其深厚的社会资本。但也要看到,如果社会资本不能应经济发展而提高,就可能走向反面,成为家族企业发展的障碍。     

Working Time,Gender and Family: An East‐West European Comparison

This article provides a comparison of three West European countries with five Central East European countries in respect of working time and the integration of work and family life. The countries are the Netherlands, Sweden and the UK in West Europe and Bulgaria, the Czech Republic, Hungary, Romania and Slovenia in Central East Europe. As well as providing an East–West comparison, the article also takes into account the differing institutional and policy contexts in the selected countries and the different routes to flexibility. A further aim of the article is to extend our understanding of the culture and values which underpin the organization of family and work in each country. Whilst there is a clear East–West divide, all eight countries demonstrate diverse routes to flexibility and different mixes of social policies and gender cultures which have lead to considerable differences in the integration of work and family life.     

家族企业管理机制创新探析

改革开放以来 ,我国私营经济获得了长足的发展 ,并为我国经济和社会发展做出了重要贡献 ,成为社会主义市场经济的重要组成部分。但随着市场外在条件和社会经济发展格局的变化 ,作为私营资本形式之一的家族企业 ,其自身的不适应性逐步凸现出来。克服家族式管理的不足 ,转换管理机制和管理方式 ,通过资本市场使家族企业完成由家庭化向专业化、由宗法化向市场化的转变 ,是使其真正做大做强的必由之路     

Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.     

An individual measure of relative survival

Summary.  Relative survival techniques are used to compare survival experience in a study cohort with that expected if background population rates apply. The techniques are especially useful when cause-specific death information is not accurate or not available as they provide a measure of excess mortality in a group of patients with a certain disease. Whereas these methods are based on group comparisons, we present here a transformation approach which instead gives for each individual an outcome measure relative to the appropriate background population. The new outcome measure is easily interpreted and can be analysed by using standard survival analysis techniques. It provides additional information on relative survival and gives new options in regression analysis. For example, one can estimate the proportion of patients who survived longer than a given percentile of the respective general population or compare survival experience of individuals while accounting for the population differences. The regression models for the new outcome measure are different from existing models, thus providing new possibilities in analysing relative survival data. One distinctive feature of our approach is that we adjust for expected survival before modelling. The paper is motivated by a study into the survival of patients after acute myocardial infarction.     

现代家庭和谐心理教育探析

构建和谐社会需要相适应的现代家庭教育,特别是家庭和谐心理教育。个体自身的心理和谐、人与自然的心理和谐、人与社会的心理和谐、人与人的心理和谐都给家庭和谐心理教育提出了新要求。家庭和谐心理教育要探索新形势下开展和谐心理教育的方法途径,实现家庭与家庭、家庭与学校、家庭与社会教育机构的良性互动,为建设和谐社会发挥作用。     

On Block Updating in Markov Random Field Models for Disease Mapping

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely poor due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different applications: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. Implementation of such block algorithms is relatively easy using methods for fast sampling of Gaussian Markov random fields ( Rue, 2001 ). By comparison, Monte Carlo estimates based on single-site updating can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components.     

让素质教育走进中国家庭

在提倡素质教育的今天,家庭中的素质教育却不容乐观。文章针对家庭中素质教育的现状,指出应以培养健康人格为核心,转变家长观念,对孩子进行科学合理的思想道德素质教育、身体心理素质教育和科学文化素质教育。     

Nonparametric smoothing in the analysis of air pollution and respiratory illness

While most of epidemiology is observational, rather than experimental, the culture of epidemiology is still derived from agricultural experiments, rather than other observational fields, such as astronomy or economics. The mismatch is made greater as focus has turned to continue risk factors, multifactorial outcomes, and outcomes with large variation unexplainable by available risk factors. The analysis of such data is often viewed as hypothesis testing with statistical control replacing randomization. However, such approaches often test restricted forms of the hypothesis being investigated, such as the hypothesis of a linear association, when there is no prior empirical or theoretical reason to believe that if an association exists, it is linear. In combination with the large nonstochastic sources of error in such observational studies, this suggests the more flexible alternative of exploring the association. Conclusions on the possible causal nature of any discovered association will rest on the coherence and consistency of multiple studies. Nonparametric smoothing in general, and generalized additive models in particular, represent an attractive approach to such problems. This is illustrated using data examining the relationship between particulate air pollution and daily mortality in Birmingham, Alabama; between particulate air pollution, ozone, and SO₂ and daily hospital admissions for respiratory illness in Philadelphia; and between ozone and particulate air pollution and coughing episodes in children in six eastern U.S. cities. The results indicate that airborne particles and ozone are associated with adverse health outcomes at very low concentrations, and that there are likely no thresholds for these relationships.