The middling mobile: finding place in the liquid city

The middling mobile, differentiated by their modest and unsure mobility, are a vast of bulk of people who intend or expect to move on. As they travel, they tread differing intensities of rhythm and social embeddedness felt to be chaotic, constraining, liberating, or comforting. Owing to their aspirations to ‘get out’ of a rhythmic life, or to move somewhere familiar, the middling mobile use movement and rootedness to navigate and manage everyday life in the liquid city. My analysis offers a holistic exposition of a life that flows between mobility/immobility and rhythmic/arrhythmic forms, and the transitions between them.     

To stay or to go? Postretirement housing choices of single Baby Boomer women

Single women of the Baby Boomer generation are often financially disadvantaged in the retirement planning process due to their lower accumulated savings compared to male retirees. This disadvantage impacts significant consumption decisions such as postretirement housing choices. This study uses the theory of planned behavior to examine how certainty in intentions influences preparing and planning for postretirement housing. A typology of single Baby Boomer women is developed based on their financial, demographic, and psychological circumstances. Each segment likely requires different informational strategies and financial services to foster proactive planning for retirement. Significant implications exist for social policy and the financial services sector.     

Multidimensional self-esteem as a mediator of the relationship between sports participation and victimization: a study of African American girls

The purpose of this study that focused on African American high school girls was threefold. First, the relationship of sports participation and victimization was explored. Second, the impact of sports participation on self-esteem was assessed. Third, the role of self-esteem and its disaggregated components (social acceptance, competence, and self-confidence) as mediators of the relationship between sports participation and victimization was examined. In accordance with the sport protection hypothesis, it was hypothesized that sports participation would be related to enhanced self-esteem and reduce victimization. Results suggest that sports participation appears to have some relationship to lower rates of victimization. There was also support for our assertion that sports participation was related to enhanced self-esteem. Finally, overall self-esteem and, specifically, the individual component competence mediated the relationship between sports participation and victimization.     

Innovation in user-centered skills and performance improvement for sustainable complex service systems

In order to leverage individual and organizational learning and to remain competitive in current turbulent markets it is important for employees, managers, planners and leaders to perform at high levels over time. Employee competence and skills are extremely important matters in view of the general shortage of talent and the mobility of employees with talent. Two factors emerged to have the greatest impact on the competitiveness of complex service systems: improving managerial and employee's knowledge attainment for skills, and improving the training and development of the workforce. This paper introduces the knowledge-based user-centered service design approach for sustainable skill and performance improvement in education, design and modeling of the next generation of complex service systems. The rest of the paper cover topics in human factors and sustainable business process modeling for the service industry, and illustrates the user-centered service system development cycle with the integration of systems engineering concepts in service systems. A roadmap for designing service systems of the future is discussed. The framework introduced in this paper is based on key user-centered design principles and systems engineering applications to support service competitiveness.     

Hierarchische Planung für industrielle Logistikdienstleister

In this paper a planning and control system for logistics service providers (called LPS system) is developed under consideration of their specific flexibility potentials. Due to the complexity of such planning decisions (different planning levels and data quality of these levels as well as time horizons etc.), a multi-stage, hierarchical planning system is proposed which minimizes the relevant costs under consideration of the flexibilities. The advantage of a hierarchical planning concept consists in a limited planning complexity because otherwise data collection and solution development would lead to unsolvable problems in practice. The results of using LPS systems for a real-life case show a significant pay-off for logistics service providers due to the remarkable cost savings by applying a hierarchical planning concept.     

Determination of recovery effort for a probabilistic recovery system under various inventory control policies

In this study we investigate the desired level of recovery under various inventory control policies when the success of recovery is probabilistic. All the used and returned items go into a recovery process that is modelled as a single stage operation. The recovery effort is represented by the expected time spent for it. The effect of increasing recovery effort on the success probability together with unit cost of the operation is included by assuming general forms of dependencies. Alternative to recovered items, demand is satisfied by brand-new items. Four inventory control policies that differ in timing of and information used in purchasing decision are proposed. The objective is to find the recovery level together with inventory control parameter that minimize the long-run average total cost. A numerical study covering a wide range of system parameters is carried out. Finally computational results are presented with their managerial implications.     

Computing Optimal Beams in Two and Three Dimensions

The problem of computing an optimal beam among weighted regions (called the optimal beam problem) arises in several applied areas such as radiation therapy, stereotactic brain surgery, medical surgery, geological exploration, manufacturing, and environmental engineering. In this paper, we present computational geometry techniques that enable us to develop efficient algorithms for solving various optimal beam problems among weighted regions in two and three dimensional spaces. In particular, we consider two types of problems: the covering problems (seeking an optimal beam to contain a specified target region), and the piercing problems (seeking an optimal beam of a fixed shape to pierce the target region). We investigate several versions of these problems, with a variety of beam shapes and target region shapes in 2-D and 3-D. Our algorithms are based on interesting combinations of computational geometry techniques and optimization methods, and transform the optimal beam problems to solving a collection of instances of certain special non-linear optimization problems. Our approach makes use of interesting geometric observations, such as utilizing some new features of Minkowski sums.     

Bayesian prediction for progressively censored data from the Burr model

Received: August 14, 2000; revised version: April 23, 2001     

Inference for the generalized Rayleigh distribution based on progressively censored data

In this paper, and based on a progressive type-II censored sample from the generalized Rayleigh (GR) distribution, we consider the problem of estimating the model parameters and predicting the unobserved removed data. Maximum likelihood and Bayesian approaches are used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are used to predict the life lengths of the removed units in multiple stages of the progressively censored sample. Artificial and real data analyses have been performed for illustrative purposes.     

Is regression adjustment supported by the Neyman model for causal inference?

This paper examines both theoretically and empirically whether the common practice of using OLS multivariate regression models to estimate average treatment effects (ATEs) under experimental designs is justified by the Neyman model for causal inference. Using data from eight large U.S. social policy experiments, the paper finds that estimated standard errors and significance levels for ATE estimators are similar under the OLS and Neyman models when baseline covariates are included in the models, even though theory suggests that this may not have been the case. This occurs primarily because treatment effects do not appear to vary substantially across study subjects.