Explaining employment relationships with social exchange and job embeddedness.

The research reported in this article clarifies how employee–organization relationships (EORs) work. Specifically, the authors tested whether social exchange and job embeddedness mediate how mutual-investment (whereby employers offer high inducements to employees for their high contributions) and over-investment (high inducements without corresponding high expected contributions) EOR approaches, which are based on Tsui, Pearce, Porter, and Tripoli's (1997) framework, affect quit propensity and organizational commitment. Two studies evaluated these intervening mechanisms. Study 1 surveyed 953 Chinese managers attending part-time master of business administration (MBA) programs in China, whereas Study 2 collected cross-sectional and longitudinal data from 526 Chinese middle managers in 41 firms. Standard and multilevel causal modeling techniques affirmed that social exchange and job embeddedness translate EOR influence. A second multilevel test using lagged outcome measures further established that job embeddedness mediates long-term EOR effects over 18 months. These findings corroborate prevailing views that social exchange explains how mutual- and over-investment EORs motivate greater workforce commitment and loyalty. This study enriches EOR perspectives by identifying job embeddedness as another mediator that is more enduring than social exchange. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Correction to: The publication trajectory of graduate students,post-doctoral fellows,and new professors in psychology

Scientometrics - In the original publication, Fig. 6 does not properly label the values. The revised version of the Fig. 6 is given inthe correction article.     

The Construction of Smooth Models using Irregular Embeddings Determined by a Gamma Test Analysis

One of the key problems in forming a smooth model from input-output data is the determination of which input variables are relevant in predicting a given output. In this paper, we show how the Gamma test can be used to select that combination of input variables which can best be employed to form a smooth model of an output. For time series prediction this amounts to the selection of an appropriate irregular embedding. We give some simple zero noise examples of time series analysis, and illustrate how using these techniques a binary message encoded into a chaotic carrier can be retrieved without knowledge of the dynamics used to generate the carrier. Provided the underlying dynamics are such as to produce a smooth embedding model with bounded partial derivatives, the sampling distribution is dense in input space, and any associated distribution of measurement error has the first few moments bounded, so that the typical prerequisite conditions of the Gamma test are satisfied, we conclude that the Gamma test is an effective tool in the determination of irregular time series embeddings. These techniques can also be useful in practical applications which involve filtering seismic data to detect anomalous events.     

A new algorithm for the design of multifunctional observers

This paper presents a general algorithm for low-order multifunctional observer design with arbitrary eigenvalues. The feature of this algorithm is that it can generate a functional observer with different orders which are no larger but usually much less thanm(v - 1), wheremis the number of functionals andvis the observability index of(A, C). Since the order needed for the observer varies with the functionals besides other system parameters, this design approach should be practical. The resulting observer system matrix is in its Jordan form. The key step of this algorithm is the generation of the basis for the transformation matrix which relates the system and observer states. The computation of this algorithm is quite reliable. It is based on the block observable lower Hessenberg form of(A, C), and all its initial and major computation involves only the orthogonal operations.     

A simple and robust batch-ordering inventory policy under incomplete demand knowledge

Generally, the derivation of an inventory policy requires the knowledge of the underlying demand distribution. Unfortunately, in many settings demand is not completely observable in a direct way or inventory records may be inaccurate. A variety of factors, including the potential inaccuracy of inventory records, motivate managers to seek replenishment policies where the inventory is reviewed periodically and a fixed quantity Q is ordered once the inventory level is found to be under a certain point r. To apply such a policy, however, firms must derive the values r and Q without a clear understanding of the demand distribution. We develop estimators of the first two moments of the (periodic) demand by means of renewal theoretical concepts and a regression-based method, and use these estimators in conjunction with the Power Approximation (PA) method of Ehrhardt and Mosier (1984) to obtain an (r, Q) replenishment policy. The proposed methodology is robust and easy to code into a spreadsheet application. A series of numerical studies are carried out to evaluate the accuracy and precision of the estimators, and to investigate the impact of the estimation on the optimality of the inventory policies. Our experiments indicate that the proposed (r, Q) policy is very close, with regard to the expected total cost per period, to the (s, S) policy obtained via the PA method when the demand process is fully observable and inventory records are accurate.     

岩石边坡稳定性和Fuzzy 综合评判法

采用Ｆｕｚｚｙ数学方法,充分考虑工程实际经验,建立了Ｆｕｚｚｙ综合评判模型。根据不同的稳定状态,将边坡划分为五个等级;综合分析影响边坡稳定的各种作用因素,根据它们作用程度的不同,赋予不同权值;采用二级综合评判,使评判结果更符合实际。     

未知光源位置环境中物体形状恢复的神经网络方法研究

用神经网络方法解决未知光源位置环境中物体三维形状恢复的问题.对漫反射表面,用神经网络方法由已知表面形状物体及其对应图像的灰度值进行学习,所得权值可视为环境光源参数.由此可恢复同样光源环境中其它物体的三维形状.实验证明,神经网络方法可以解决未知光源位置环境(包括多个光源)中漫反射表面物体的三维形状恢复问题.