A recent advancement in location-allocation modeling formulates a two-step approach to a new problem of minimizing disparity of spatial accessibility. Our field work in a health care planning project in a rural county in China indicated that residents valued distance or travel time from the nearest hospital foremost and then considered quality of care including less waiting time as a secondary desirability. Based on the case study, this paper further clarifies the sequential decision-making approach, termed “two-step optimization for spatial accessibility improvement (2SO4SAI).” The first step is to find the best locations to site new facilities by emphasizing accessibility as proximity to the nearest facilities with several alternative objectives under consideration. The second step adjusts the capacities of facilities for minimal inequality in accessibility, where the measure of accessibility accounts for the match ratio of supply and demand and complex spatial interaction between them. The case study illustrates how the two-step optimization method improves both aspects of spatial accessibility for health care access in rural China.

No abstract

The original minimal exposure path problem in wireless sensor networks did not consider path constraint conditions. To consider the actual demand, this article proposes a minimal exposure path problem that requires the passage of the path through the boundary of a certain region. In this situation, because a corresponding weighted graph model cannot be developed, the methods that are used to solve the original minimal exposure path problem (the grid method and the Voronoi diagram method) are ineffective. Thus, this article first converts the problem into an optimization problem with constraint conditions. Because of the difficulty in finding a solution due to the model's high nonlinearity and high dimensional complexity, as well as the special characteristics of the problem, a hybrid genetic algorithm is proposed to find the solutions. This article also provides a proof for the convergence of the designed algorithm. A series of simulation experiments demonstrates that the designed optimization model with constraints and the hybrid genetic algorithm can effectively solve the proposed minimal exposure path problem.

The purpose of this study was to examine the relationships among job satisfaction, organizational commitment, and turnover intention of workers in two casinos in Macau. The current study was a correlational study and used convenience sampling, and a total of 105 surveys were retrieved from employees working in two casinos in Macau. For our sample, we found that job satisfaction had a significant and positive correlation with organizational commitment. We also found that the association between job satisfaction and turnover intention was positive but not significant and the association between organizational commitment and turnover intention was negative but not significant. Finally, the regression model indicated that job satisfaction and organizational commitment were effective predictors of employees' turnover intention.

No abstract

No abstract

No abstract

Blur kernel (BK) estimation is the crucial technique to guarantee the success of blind image deblurring. In this paper, we propose a multi-regularization-constrained method to estimate an accurate BK from a single motion-blurred image. First, in order to generate sharp and reliable intermediate latent results, we propose a model which combines the spatial scale, L 0 norm, and the dark channel prior. Second, in order to preserve the continuity and the sparsity, and to remove the flaw in the BK, a dual-constrained regularization model, which combines the L 0 -regularized intensity prior and the L 2 -regularized gradient prior, is proposed for accurate BK estimation. The proposed model can not only preserve the continuity and the sparsity of the BK very well but also can remove the flaw thoroughly. Finally, we propose an efficient optimization strategy which can solve the proposed model efficiently. Extensive experiments compared with the state-of-the-art methods demonstrate that our method estimates more accurate BKs and obtains higher quality deblurring images in terms of both subjective vision and quantitative metrics.INDEX TERMS Blind image deblurring, blur kernel, spatial scale, L 0 -regularized intensity prior, L 2 -regularized gradient prior.