Considering the role of universities in engineering education, it is necessary to study the status of the educational system and its strengths and weaknesses in order to improve the process of engineering education. In this research, the factors affecting the increase of academic years of engineering students and the status of students who do not complete their studies in the authorized years have been investigated. At first, the effective indicators on increasing the academic years of engineering students were identified and prioritized using the AHP technique. The ranking results show the average of basic courses, the average of main courses, the average of general courses, the number of conditional semesters, the average of optional courses and the number of units dropped have the most impact on increasing the academic years of engineering students. Then, a model was provided to predict the increase in academic years according to the educational status of engineering students using an artificial neural network. According to the results of the neural network, the factors of the number of units dropped, the average of the main courses, the average of the basic courses, the number of conditional terms, the duration of marriage and the average of the high school math and physics courses have the greatest impact on increasing academic years. Finally, by comparing the results of AHP method and neural network, the average factors of basic and main courses, the number of conditional terms and the number of units dropped in both methods were identified as the most effective factors that should be paid more attention while studying engineering students.

 In many statistical studies‎, ‎we are faced with variables such as duration of unemployment‎, ‎duration of illness to death‎, ‎the lifespan of a piece‎, ‎duration of approval of a bill in the parliament‎, ‎and other similar cases‎. ‎In such studies‎, ‎the aim is to find and model the sources that affect the duration of the study period to control the mentioned period by identifying and changing the sources of risk‎. ‎In this paper‎, ‎the concepts of survival analysis have been used to model the factors affecting the duration of unemployment‎. ‎To investigate the effects of explanatory variables on the duration of unemployment‎, ‎the Cox proportional hazard model and frailty have been used‎. ‎The data of this study were collected to investigate the effect of gender‎, ‎age‎, ‎education level‎, ‎and marital status as predictor variables affecting the duration of unemployment‎. ‎The sample includes 546 people with information about the start date and end date of the unemployment period for each person from the beginning of April 1397 to the beginning of April 1398‎. ‎The results show that the length of the unemployment period is affected by variables such as education level‎, ‎gender‎, ‎and marital status‎.

Recently, in most of scientific studies, the use of survival analysis is performed for examining duration time models.  One of the important applications of survival analysis is the study of recommitment crime in criminology which has not yet been considered in Iran.  So, with attention to the necessity and importance of predicting recommitment time and the analysis of duration model, in this study 209 individuals who are released from prison are followed and examined for 2 years. Chosen explanatory variables are, age, marriage status, job skill, education, kind of crime, duration of detention and the number of previous arrests. The dependent variable is the duration time till recommitment. The nonparametric methods of Kaplan-Meier and semiparametric model of Cox’s proportional hazard are applied for analysis of the data. Three different Cox’s models are used for predicting hazard of duration time till recommitment.  The results show that age, marriage status and the kind of crime are significant on the hazard of time to recommitment. In the third model we found a significant interaction effect of the kind of crime and marriage status.