The Impact of E‐Replenishment Strategy on Make‐to‐Order Supply Chain Performance

This research investigates the impact of electronic replenishment strategy on the operational activities and performance of a two‐stage make‐to‐order supply chain. We develop simulation‐based rolling schedule procedures that link the replenishment processes of the channel members and apply them in an experimental analysis to study manual, semi‐automated, and fully automated e‐replenishment strategies in decentralized and coordinated decision‐making supply chain structures. The average operational cost reductions for moving from a manual‐based system to a fully automated system are 19.6, 29.5, and 12.5%, respectively, for traditional decentralized, decentralized with information sharing, and coordinated supply chain structures. The savings are neither equally distributed among participants, nor consistent across supply chain structures. As expected, for the fully coordinated system, total costs monotonically decrease with higher levels of automation. However, for the two decentralized structures, under which most firms operate today, counter‐intuitive findings reveal that the unilateral application of e‐procurement technology by the buyer may lower his purchasing costs, but increase the seller's and system's costs. The exact nature of the relationship is determined by the channel's operational flexibility. Broader results indicate that while the potential economic benefit of e‐replenishment in a decentralized system is substantial, greater operational improvements maybe possible through supply chain coordination.     

The U.S. mainstream expands – again

Building on neo-assimilation ideas, we conceptualise the ‘mainstream’ and then find compelling evidence of its expansion in the U.S. in the rising number of individuals who come from mixed minority–majority backgrounds. In terms of the contexts in which they are raised and their social identities and affiliations, on the whole they resemble whites more than they do minorities, and they marry whites at very high rates. Individuals who are partly black are exceptions to some of these patterns. We note an important similarity between this expansion and the one associated with post-World War II assimilation: both are connected with growing diversity of identity and ethnic expression in the mainstream. An equally important difference lies in the more modest magnitude of the current expansion. We close with a series of questions for future research raised by our argument.     

Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions

Joint modelling skewness and heterogeneity is challenging in data analysis, particularly in regression analysis which allows a random probability distribution to change flexibly with covariates. This paper, based on a skew Laplace normal (SLN) mixture of location, scale, and skewness, introduces a new regression model which provides a flexible modelling of location, scale and skewness parameters simultaneously. The maximum likelihood (ML) estimators of all parameters of the proposed model via the expectation-maximization (EM) algorithm as well as their asymptotic properties are derived. Numerical analyses via a simulation study and a real data example are used to illustrate the performance of the proposed model.     

Storytelling as a Way to Work Through Intractable Conflicts: The German-Jewish Experience and Its Relevance to the Palestinian-Israeli Context

The storytelling method can be used to work through intractable conflicts. Working-through enables people who have suffered traumatic social experiences to learn to live with these painful events while developing an ability to listen to the pain of the "other." The storytelling approach focuses on the way personal storytelling facilitates the working-through processes in intractable conflicts. The storytelling approach was used in To Reflect and Trust (TRT), a dialogue group that began in 1992 and involved descendants of Nazi perpetrators and Jewish descendants of Holocaust survivors. The storytelling method was applied to a year-long Jewish-Palestinian student workshop held at Ben Gurion University in 2000–2001.     

Helping Low-Conflict Divorced Parents Establish Appropriate Boundaries Using a Variation of the Miracle Question: An Integration of Solution-Focused Therapy and Structural Family Therapy

The authors discuss the implications of divorce and the effect that divorce has on the relationships between family members. Parents are encouraged to develop appropriate co-parental boundaries that direct and influence subsequent interactions between family members. Using an integration of solution-focused therapy and structural family therapy, a variation of the miracle question is suggested as a means to help parents gain confidence in making changes in behavior. The miracle question also is intended to help parents set new goals for themselves and increase the ability to cooperate and communicate as co-parents. A case study provides an example of this idea.     

Robust mixture regression modeling using the least trimmed squares (LTS)-estimation method

Mixture regression models are used to investigate the relationship between variables that come from unknown latent groups and to model heterogenous datasets. In general, the error terms are assumed to be normal in the mixture regression model. However, the estimators under normality assumption are sensitive to the outliers. In this article, we introduce a robust mixture regression procedure based on the LTS-estimation method to combat with the outliers in the data. We give a simulation study and a real data example to illustrate the performance of the proposed estimators over the counterparts in terms of dealing with outliers.     

On the generalization of the hazard rate twisting-based simulation approach

Estimating the probability that a sum of random variables (RVs) exceeds a given threshold is a well-known challenging problem. A naive Monte Carlo simulation is the standard technique for the estimation of this type of probability. However, this approach is computationally expensive, especially when dealing with rare events. An alternative approach is represented by the use of variance reduction techniques, known for their efficiency in requiring less computations for achieving the same accuracy requirement. Most of these methods have thus far been proposed to deal with specific settings under which the RVs belong to particular classes of distributions. In this paper, we propose a generalization of the well-known hazard rate twisting Importance Sampling-based approach that presents the advantage of being logarithmic efficient for arbitrary sums of RVs. The wide scope of applicability of the proposed method is mainly due to our particular way of selecting the twisting parameter. It is worth observing that this interesting feature is rarely satisfied by variance reduction algorithms whose performances were only proven under some restrictive assumptions. It comes along with a good efficiency, illustrated by some selected simulation results comparing the performance of the proposed method with some existing techniques.     

Myth of the Eternal State: Armenian Outlaws,American Outsiders,and the Ottoman Search for Order

Focusing on violent Ottoman contexts, this study explores state behavior, charts criminal taxonomy, and contributes to the state-outlaw paradigm an analytical conduit through which to explain state responses to the outlaws on discursive and institutional levels. At the center of this study are several cases that demonstrate the engagement of local outlaws with evangelical outsiders named George Knapp, George White, and Ellen Stone. It is my contention that, while mitigating specific activities involving these outlaws and outsiders, the state under study invents new traditions, empowers security networks, claims unaccountability, and executes double standards in pursuit of social order within its borders.     

Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling

In this article, we propose mixtures of skew Laplace normal (SLN) distributions to model both skewness and heavy-tailedness in the neous data set as an alternative to mixtures of skew Student-t-normal (STN) distributions. We give the expectation–maximization (EM) algorithm to obtain the maximum likelihood (ML) estimators for the parameters of interest. We also analyze the mixture regression model based on the SLN distribution and provide the ML estimators of the parameters using the EM algorithm. The performance of the proposed mixture model is illustrated by a simulation study and two real data examples.