Degradation MODELing is an effective approach for reliability assessment and predicting of remaining useful life in order to investigate the relation between failure time and degradation of a system or unit. The possibility of an association between degradation of the unit during the time and the effects of covariates on degradation processes should be taken into account in the MODEL to improve the explanatory capabilities of degradation MODELs. Sometimes, the exact amount of degradation could not be observed because of time, cost, and measurement tools limitations. Therefore, approximate degradation values can be compared with a critical threshold. In this paper, the degradation processes MODELed with a GENERALIZED linear mixed-effect MODEL in order to take into account the correlation between times. Also, maximum likelihood estimation and Bayesian estimate of parameters are derived.

Question and answering service is one of the popular services in the World Wide Web. The main goal of these services is to find the best answer for user's input question as quick as possible. In order to achieve this aim, most of these use new techniques foe question matching. We have a lot of question and answering services in Persian web, so it seems that developing a question matching MODEL might be useful. This paper introduces a new question matching MODEL for Persian. This MODEL is based on statistical language MODEL and employs GENERALIZED bigram and trigram MODEL. We also describe some results regarding the employment of natural language processing in question matching MODEL. Most of the Q & A services have large number of questions and answers, hence we considered an optimized implementation for the MODEL. We evaluated our MODEL with Rasekhoon question and answering archive which contains about 18000 pairs of questions and answers. The results showed the improvement of precision and recall measures through using this MODEL.

Statistical process control methods for monitoring processes with univariate or multivariate measurements are used widely when the quality variables fit to known probability distributions. Some processes, however, are better characterized by a profile or a function of quality variables. For each profile, it is assumed that a collection of data on the response variable along with the values of the corresponding quality variables is measured. While the linear function is the simplest, it occurs frequently that many of the nonlinear functions may be transferred to linear functions easily. This paper proposes a control chart based on the GENERALIZED linear test (GLT) to monitor coefficients of the linear profiles and an R-chart to monitor the error variance, the combination of which is called GLT/R chart. While fixed values of the explanatory variables are cornerstones in other control charts proposed to monitor profiles, in GLT/R chart, it is not a necessary condition. In order to illustrate the robustness of the GLT/R chart a simulation study has been done in two different cases, i.e. fixed and non-fixed values of the explanatory variables. Then, the results obtained from GLT/R charts are compared to the ones from a multivariate T2 and Exponentially Weighted Moving Average/R (EWMA/R) control charts.