A Comparative Analysis of the Validity of US State- and County-Level Social Capital Measures and Their Associations with Population Health

The goals of this study were to validate a number of available collective social capital measures at the US state and county levels, and to examine the relative extent to which these social capital measures are associated with population health outcomes. Measures of social capital at the US state level included aggregate indices based on the Annenberg National Health Communication Survey and the Behavioral Risk Factor Surveillance System (BRFSS), Petris Social Capital Index (PSCI), Putnam’s index, and Kim et al.’s scales. County-level measures consisted of PSCI, Rupasingha et al.’s social capital index, and a BRFSS-derived measure. These measures, except for the PSCI, showed evidence of acceptable validity. Moreover, we observed differences across the social capital measures in their associations with population health outcomes. The implications of the findings for future research in this area were discussed.     

Fertility Decline in Prussia, 1875–1910: A Pooled Cross-Section Time Series Analysis

Marital fertility level and decline are examined in 407 small areas in Prussia using quinquennial data for the period 1875 to 1910 from an unusually rich and detailed data set, and pooled cross-section time-series methods. Religion, ethnicity, and prevalence of mineworkers are the only statistically significant factors associated with marital fertility level. However, none of these are important predictors of marital fertility decline. Marital fertility decline in nineteenth-century Prussia is better predicted by increased women's labour force participation in non-traditional occupations, the growth of financial institutions, the development of a transport-communications infrastructure, reduction in infant mortality and improvements in education.     

On the estimators and tests for the semiparametric hazards regression model

In the accelerated hazards regression model with censored data, estimation of the covariance matrices of the regression parameters is difficult, since it involves the unknown baseline hazard function and its derivative. This paper provides simple but reliable procedures that yield asymptotically normal estimators whose covariance matrices can be easily estimated. A class of weight functions are introduced to result in the estimators whose asymptotic covariance matrices do not involve the derivative of the unknown hazard function. Based on the estimators obtained from different weight functions, some goodness-of-fit tests are constructed to check the adequacy of the accelerated hazards regression model. Numerical simulations show that the estimators and tests perform well. The procedures are illustrated in the real world example of leukemia cancer. For the leukemia cancer data, the issue of interest is a comparison of two groups of patients that had two different kinds of bone marrow transplants. It is found that the difference of the two groups are well described by a time-scale change in hazard functions, i.e., the accelerated hazards model.     

An alternative to unrelated randomized response techniques with logistic regression analysis

The randomized response technique (RRT) is an important tool that is commonly used to protect a respondent’s privacy and avoid biased answers in surveys on sensitive issues. In this work, we consider the joint use of the unrelated-question RRT of Greenberg et al. (J Am Stat Assoc 64:520–539, 1969) and the related-question RRT of Warner (J Am Stat Assoc 60:63–69, 1965) dealing with the issue of an innocuous question from the unrelated-question RRT. Unlike the existing unrelated-question RRT of Greenberg et al. (1969), the approach can provide more information on the innocuous question by using the related-question RRT of Warner (1965) to effectively improve the efficiency of the maximum likelihood estimator of Scheers and Dayton (J Am Stat Assoc 83:969–974, 1988). We can then estimate the prevalence of the sensitive characteristic by using logistic regression. In this new design, we propose the transformation method and provide large-sample properties. From the case of two survey studies, an extramarital relationship study and a cable TV study, we develop the joint conditional likelihood method. As part of this research, we conduct a simulation study of the relative efficiencies of the proposed methods. Furthermore, we use the two survey studies to compare the analysis results under different scenarios.     

A joint optimization of data ferry trajectories and communication powers of ground sensors for long-term environmental monitoring

Recently, various hybrid wireless sensor networks which consist of several robotic vehicles and a number of static ground sensors have been investigated. In this kind of system, the main role of the mobile nodes is to deliver the messages produced by the sensor nodes, and naturally their trajectory control becomes a significant issue closely related to the performance of the entire system. Previously, several communication power control strategies such as topology control are investigated to improve energy-efficiency of wireless sensor networks. However, to the best of our knowledge, no communication power control strategy has been investigated in the context of the hybrid wireless sensor networks. This paper introduces a new strategy to utilize the communication power control in multiple data ferry assisted wireless sensor network for long-term environmental monitoring such that the lifetime of the sensor network is maximized. We formally define the problem of our interest and show it is NP-hard. We further prove there exists no approximation algorithm for the problem which can produce a feasible solution for every possible problem instance even though there is a feasible solution. Then, we propose heuristic algorithms along with rigorous theoretical performance analysis for both the single data ferry case and the multiple data ferry case under certain condition.     

Developing a supplier selection system through integrating fuzzy AHP and fuzzy DEA: a case study on an auto lighting system company in Taiwan

Supplier selection plays a very important role in supply chain management. This study intends to develop a novel performance evaluation method, which integrates both fuzzy analytical hierarchy process (AHP) method and fuzzy data envelopment analysis (DEA) for assisting organisations to make the supplier selection decision. Fuzzy AHP method is first applied to find the indicators’ weights through expert questionnaire survey. Then, these weights are integrated with fuzzy DEA. We use α -cut set and extension principle of fuzzy set theory to simplify the fuzzy DEA as a pair of traditional DEA model. Finally, fuzzy ranking using maximising and minimising set method is able to rank the evaluation samples. A case study on an internationally well-known auto lighting OEM company shows that the proposed method is very suitable for practical applications.     

Evaluation of safety stock methods in multilevel material requirements planning (MRP) systems

This paper evaluates alternative methods of establishing the safety stock level taking into consideration of historical measures of forecasting accuracy and the needs for master production scheduling and material requirement planning under a rolling time horizon. A computer model is used to simulate the forecasting, master production scheduling and material planning activities in a company that produces to stock and the production activities are managed by multilevel MRP systems. The simulation output is analysed to evaluate the impact of safety stock methods on MRP system performance. The result of the study shows that using safety stock can help to reduce total cost, schedule instability and improve service level in the MRP systems. Guidelines are developed to help managers select methods to determine safety stock in MRP system operations.     

Assembly block storage location assignment at a shipyard: a case of Hyundai Heavy Industries

It is not a trivial issue how to manage assembly blocks at a shipyard. Based on the project experience in Hyundai Heavy Industries, currently the largest shipbuilding company in the world and the leader in the Korean merchant shipbuilding industry, this study points out the difficulties on the block stockyard operations, formalises the assembly block storage location assignment problem, and develops the assembly block storage location assignment algorithm, the purpose of which is to reduce the number of unproductive block moves. Through simulation experiments for various situations, this study demonstrates the usefulness of the assembly block storage location assignment algorithm. In addition, this study examines the impacts of block move sequence rules and of block stockyard layouts on the block stockyard operations.     

Manufacturing bill-of-material planning

The bill-of-material BOM in the machine tool industry takes two different forms in design and manufacturing functions: Engineering BOM E BOM , which is used by the design engineer to represent designed product structure; and manufacturing BOM M BOM , which is used by MRPII system for MRP explosion. The designer constructs the E BOM after the product has been designed. Next, the E BOM is transformed into the M BOM by considering assembly sequence and constraints. Constructing a M BOM simply involves compressing the E BOM into a three-level M BOM. Planning of a M BOM still depends primarily on the experience input of a manufacturing engineer and is performed manually. This trial and error and time consuming approach creates an inconsistent method for planning the M BOM. Therefore, in this study, a three-stage M BOM planning method is developed. Stage one plans the initial M BOM, stage two improves the M BOM and stage three tunes the M BOM. Concepts and algorithms of each stage are highlighted in this study. Moreover, an illustration is presented to demonstrate the feasibility of M BOM planning.