On 3-Stage Clos Networks with Different Nonblocking Requirements on Two Types of Calls

Hwang and Lin introduced a new nonblocking requirement for 2-cast traffic which imposes different requirements on different types of coexisting calls. The requirement is strictly nonblocking for point-to-point calls among the 2-cast traffic, and is rearrangeable for genuine 2-cast calls. They conjectured that the 3-stage Clos network C(n,n,r,r,2n) satisfies the above requirement. We prove that C(n,n,4,r,2n) satisfies the above requirement.Supported in part by NSC91-2115-M009-002.Supported in part by the National Science Council under grant NSC91-2115-M009-010 and by the Li-Li-Tai-Yang Network Research Center of National Chiao Tung University.     

Educational needs of inpatients with severe & persistent mental illness: a partial replication

Although a great deal has been written about the importance of patient education, few replications of past research have been conducted to increase clinicians' confidence in the conclusions of previous studies. This is especially true of studies conducted in clinical settings where findings may or may not be generalizable to other institutions. This partial replication study tested whether previous findings about patient education in an outpatient setting were applicable to an inpatient setting using a modified version of the instrument. A 1998 study published in this journal suggested that differences may exist between the perceived health education needs of outpatients and their health care providers. In this partial replication, we examined the differences between the educational needs of inpatients and those perceived by their attending nurses. As in the original study, we found discrepancies between patients' actual needs and nurses' perceptions of those needs, supporting the importance of assessing patients' health educational needs in the treatment planning process.     

Knowledge Life Cycle,Knowledge Inventory,and Knowledge Acquisition Strategies*

For a knowledge‐ and skill‐centric organization, the process of knowledge management encompasses three important and closely related elements: (i) task assignments, (ii) knowledge acquisition through training, and (iii) maintaining a proper level of knowledge inventory among the existing workforce. Trade‐off on choices between profit maximization in the short run and agility and flexibility in the long term is a vexing problem in knowledge management. In this study, we examine the effects of different training strategies on short‐term operational efficiency and long‐term workforce flexibility. We address our research objective by developing a computational model for task and training assignment in a dynamic knowledge environment consisting of multiple distinct knowledge dimensions. Overall, we find that organizational slack is an important variable in determining the effectiveness of training strategies. Training strategies focused on the most recent skills are found to be the preferred option in most of the considered scenarios. Interestingly, increased efficiencies in training can actually create preference conflict between employees and the firm. Our findings indicate that firms facing longer knowledge life cycles, higher slack in workforce capacity, and better training efficiencies actually face more difficult challenges in knowledge management.     

Non-ergodic martingale estimating functions and related asymptotics

Well-known estimation methods such as conditional least squares, quasilikelihood and maximum likelihood (ML) can be unified via a single framework of martingale estimating functions (MEFs). Asymptotic distributions of estimates for ergodic processes use constant norm (e.g. square root of the sample size) for asymptotic normality. For certain non-ergodic-type applications, however, such as explosive autoregression and super-critical branching processes, one needs a random norm in order to get normal limit distributions. In this paper, we are concerned with non-ergodic processes and investigate limit distributions for a broad class of MEFs. Asymptotic optimality (within a certain class of non-ergodic MEFs) of the ML estimate is deduced via establishing a convolution theorem using a random norm. Applications to non-ergodic autoregressive processes, generalized autoregressive conditional heteroscedastic-type processes, and super-critical branching processes are discussed. Asymptotic optimality in terms of the maximum random limiting power regarding large sample tests is briefly discussed.     

Bayes sequential estimation for a Poisson process under a LINEX loss function

In this paper, within the framework of a Bayesian model, we consider the problem of sequentially estimating the intensity parameter of a homogeneous Poisson process with a linear exponential (LINEX) loss function and a fixed cost per unit time. An asymptotically pointwise optimal (APO) rule is proposed. It is shown to be asymptotically optimal for the arbitrary priors and asymptotically non-deficient for the conjugate priors in a similar sense of Bickel and Yahav [Asymptotically pointwise optimal procedures in sequential analysis, in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, University of California Press, Berkeley, CA, 1967, pp. 401–413; Asymptotically optimal Bayes and minimax procedures in sequential estimation, Ann. Math. Statist. 39 (1968), pp. 442–456] and Woodroofe [A.P.O. rules are asymptotically non-deficient for estimation with squared error loss, Z. Wahrsch. verw. Gebiete 58 (1981), pp. 331–341], respectively. The proposed APO rule is illustrated using a real data set.     

How to be Resilient? Local Philanthropy as a Collective Response to Natural Disasters

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - When communities are struck by natural disasters, human service organizations play an important role in supplementing...     

Joint modelling of longitudinal binary data and survival data

The medical costs in an ageing society substantially increase when the incidences of chronic diseases, disabilities and inability to live independently are high. Healthy lifestyles not only affect elderly individuals but also influence the entire community. When assessing treatment efficacy, survival and quality of life should be considered simultaneously. This paper proposes the joint likelihood approach for modelling survival and longitudinal binary covariates simultaneously. Because some unobservable information is present in the model, the Monte Carlo EM algorithm and Metropolis-Hastings algorithm are used to find the estimators. Monte Carlo simulations are performed to evaluate the performance of the proposed model based on the accuracy and precision of the estimates. Real data are used to demonstrate the feasibility of the proposed model.     

The Short-Term Impact of Involuntary Migration in China’s Three Gorges: A Prospective Study

The aim of this study is to measure the short-term impact of involuntary migration resulting from China’s Three Gorges Dam project on the 1.3 million persons being displaced. We focus on the social, economic, and mental and physical health impact using three sets of indicators. Using a prospective research design, we gathered information about these indicators from a sample of migrants first before they moved and then again after they moved. Changes in the migrants’ wellbeing during the period, when benchmarked to corresponding changes computed for a control group of non-migrants, are attributed to the impact of involuntary migration. Our results showed that although the displaced have enjoyed a relative gain in housing quality, most of the changes were in the negative direction and many of such negative changes were statistically significant.