Structural empowerment,psychological empowerment,and work engagement: A cross-country study

In this cross-country study we drew on job demands-resources theory to investigate whether psychological empowerment mediates the positive association between structural empowerment and work engagement and, consequently, task performance and intention to quit. A total of 1033 employees working in the service sector in Spain (N = 515) and the United Kingdom (N = 518) participated in the study. Multi-group structural equation modeling analyses revealed that psychological empowerment partially mediated the positive relationship between structural empowerment and work engagement, and that work engagement associated positively with task performance and negatively with intention to quit. Invariance analyses suggested that the positive link between psychological empowerment and work engagement was stronger for employees working in the UK than in Spain, providing support for partial structural invariance of the hypothesized model. These findings suggest that psychological empowerment is an underlying mechanism that may explain why structural empowerment relates positively to work engagement with implications for theory (i.e., extend the nomological network of the investigated constructs) and management practice (e.g., emphasize the role of structural empowerment for work design).     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

几类全微分方程问题的求解定理

利用全微分方程的条件，给出一类微分方程的积分因子及通解公式，得出一类全微分方程中未知函数所满足的二阶线性微分方程，获得未知函数及全微分方程的通解。     

低浓度三分子反应模型双曲型反应－扩散方程的稳定性与化学振荡

考虑浓度扩散和扩散流随时间的变化，得到低浓度三分子反应模型的双曲型反应－扩散方程；讨论了方程的稳定性及存在行波解的条件，得到了化学振荡的色散关系和相速度．     

将分段函数表示为初等函数的方法

本文给出了一种化具有多个分段点，且在各区间段上的表达式为多项式函数的分段函数为初等函数的具体方法．     

二阶非线性微分方程解的振动性和渐近性

讨论了二阶非线性常微分方程(α(t)(?)(x)x′)′+q(t)f(x)=r(t)的解的振动性和渐近性.获得了有关该方程的五个新的定理.     

Some remarks on the physical models concerning two different approaches to inference in statistical process control

Summary For technological applications it can be useful to identify some simple physical mechanisms, which, on the basis of the available knowledge of the production process, may suggest the most appropriate approach to statistical control of the random quantities of interest. For this purpose the notion of rupture point is introduced firstly. A rupture point is characterized bym randomly arising out of control states, assumed to be mutually exclusive and stochastically independent. Shewhart's control charts seem to represent the natural statistical tool for controlling a rupture point; however it is shown that they are fully justified only when the hazard rates attached to the causes of failure are constant. Otherwise, typically in the presence of time increasing hazard rates, Shewhart's control charts should be completed by a preventive intervention rule (preventive maintenance). In the second place, the notion of dynamic instability point is introduced, which is specifically characterized by assuming that the random quantity of interest is ruled by a stochastic differential equation with constant coefficients. By discretization, developed according to a possibly new approach, it is shown that the former model reduces to an equation error model, which is among the simplest used in adaptive control, and thus particularly easy to deal with in regard to parameter estimation and the definition of the optimum control rule.     

变质量任意阶非线性非完整系统的相对论性广义Mac-Millan型方程

提出了变质量系统的相对论性万有D’Alembert原理,构造了相对论性广义动能函数,建立了变质量任意阶非线性非完整系统在准坐标下和广义坐标下的相对论性广义Mar-Millan型方程,并得到厂相应的型方程     

一类非线性波动方程的新精确解

利用李群对称方法,通过构造变换不变量,将一类1 1维非线性波动方程化为常微分方程,得到了这一类非线性波动方程的一些新的显式精确解,包括孤子解、三角函数解和椭圆函数周期解。     

Causal inference with generalized structural mean models

Summary.  We estimate cause–effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient non-compliance and self-selected treatment switches in randomized clinical trials. Additive and multiplicative structural mean models have proved useful for this but suffer from the classical limitations of linear and log-linear models when accommodating binary data. We propose the generalized structural mean model to overcome these limitations. This is a semiparametric two-stage model which extends the structural mean model to handle non-linear average exposure effects. The first-stage structural model describes the causal effect of received exposure by contrasting the means of observed and potential exposure-free outcomes in exposed subsets of the population. For identification of the structural parameters, a second stage 'nuisance' model is introduced. This takes the form of a classical association model for expected outcomes given observed exposure. Under the model, we derive estimating equations which yield consistent, asymptotically normal and efficient estimators of the structural effects. We examine their robustness to model misspecification and construct robust estimators in the absence of any exposure effect. The double-logistic structural mean model is developed in more detail to estimate the effect of observed exposure on the success of treatment in a randomized controlled blood pressure reduction trial with self-selected non-compliance.