In this paper the conditional location problem is discussed. conditional location problems have a wide range of applications in location science. A new meta-heuristic algorithm for solving conditional p-median problems is proposed and results are compared to those of the previous studies. This algorithm produces much better results than the previous formulations.

In this paper‎, ‎we consider a k-component coherent system while the system lifetimes are observed‎, ‎the system structure is known and the component lifetime follows the proportional hazard rate model‎. ‎We discuss the prediction problem based on Type-II censored coherent system lifetime data‎. ‎For predicting the future system failures‎, ‎we obtain the maximum likelihood predictor‎, ‎the best unbiased predictor‎, ‎the conditional median predictor and the Bayesian predictors‎. ‎As it seems that the integrals of the Bayes prediction do not possess closed forms‎, ‎the Metropolis-Hastings method is applied to approximating these integrals‎. ‎Different interval predictors based on classical and Bayesian approaches are derived‎. ‎A numerical example is presented to illustrate the prediction methods used in this paper‎. ‎A Monte Carlo simulation study is performed to evaluate and compare the performances of different prediction methods.

In this paper, we discuss the prediction problem based on censored coherent system lifetime data when the system structure is known and the component lifetime follows the proportional reversed hazard model. Different point and interval predictors based on classical and Bayesian approaches are derived. A numerical example is presented to illustrate the prediction methods used in this paper. Monte Carlo simulation study is performed to evaluate and compare the performances of different prediction methods.

In this paper, the problem of predicting times to failure of units censored in multiple stages of progressively hybrid censoring for the proportional hazards family is considered. We discuss different classical predictors. The best unbiased predictor (BUP), the maximum likelihood predictor (MLP) and conditional median predictor (CMP) are all derived. As an example, the obtained results are computed for exponential distribution. A numerical example is presented to illustrate the prediction methods discussed here. Using simulation studies, the predictors are compared in terms of bias and mean squared prediction error (MSPE).

In this paper, we discuss different predictors of times to failure of units censored in multiple stages in a progressively censored sample from proportional hazard rate models. The maximum likelihood predictors, best unbiased predictors and conditional median predictors are considered. We also consider Bayesian point predictors for the times to failure of units. A numerical example and a Monte Carlo simulation study are presented to illustrate all the prediction methods discussed in this paper.

median rhomboid glossists are the atrophic lesion of mid-dorsal surface of the tongue, with the incidence of 0-3.34%. It is usually with no symptoms and has an unknown etiology.In order to investigate the relationship between diabetes and MRG, 220 diabetic patients were clinically examined from those referred to Shariaty, Taleghani General Hospitals and the Institute of Metabolism and Endocrinology - Tehran during Oct 1998 to Jan 1999. A total of 220 patients were examined as control from those referred to Shahid Beheshti Dental School who had no history of any systemic disease, Smoking or the use of denture and matched by age and sex. In addition, two cytological smear samples were taken, one from MRG region and one from anterior, normal, part of the tongue of 15 proved MRG as well as 5 control cases. Results proved MRG in 10 diabetic patients and 2 control cases. This difference was statistically significant (P<0.025). Candida mycelia in MRG area was found to be 3.5 times more than normal anterior area of the tongue in diabetics (P<0.06).In conclusion: there seem to be a relationship between diabetes and the presence of MRG.