Since models of transport demand have shifted from trip-based to Activity-based approaches, prediction of activities and their duration has gained further importance. Activity duration is an important component of Activity participation behavior of individuals, and therefore, an important determinant of individual travel behavior. Since duration data is non-negative and often censored, it follows non-normal distributions, hence, linear regression is not a suitable model. duration models offer vaster suitability for this kind of data and thus have been used for behavioral analysis of individual trips. Since for the past two decades, shopping has been under spotlight as indispensable diurnal activities for human, the current paper has tried to compare the results of several approaches with their different modeling assumptions, by modeling duration shopping Activity concurrent with recognition of influential factors on duration variable and proper distributions for such data. case study of this research, is the shopping trips of 2 and 3 home-based trip tours data obtained from inquiry a sample of Qazvin residents comprising personal and household information, the characteristics of transport networks and the features of susceptible sites for doing shopping Activity which constitute 99 percent of Qazvin's citizen's daily travels. The analysis and analogy of modeling results indicate estimation of parametric approach with regard to goodness of fit, is better than other approaches and suitable distributions has used for shopping Activity duration data is log-logistic distribution. Considering that scale parameter estimated of model is lower than one, the hazard curve is non-monotonic and an inverted U-shape. Worker's female than male and male than other, has associated lower duration to shop with significant difference (p=0.005). Non- motoric travel modes has positive influence to reduction of shopping duration (p<0.05). In addition, covariates such as distance and commercial destination travel attraction, are significant portion to increase of shopping Activity duration (p<0.05).

Equity concerns of urban planners and policy-makers cannot be addressed unless disability effects on daily activities are disentangled. The  ndings, however, strongly depend on how disability is incorporated into the model. Two Multiple Discrete-Continuous Extreme Value (MDCEV) models for analyzing disability effects on daily Activity type and duration are discussed and compared in this paper. In the \classic" approach, an independent dummy variable is used to distinguish disability. However, in the \separate" approach, the dataset is divided into disabled and non-disabled groups and, then, a separate model is calibrated for the disabled group. The two approaches achieve different coe cients and elasticity values, evidencing that model speci cation matters for policy assessments. Three transferability metrics are adopted to illustrate that the separate approach outperforms the classic approach in explaining travel patterns of persons with disabilities. Finally, three policies that have been practiced across the globe to prevent social exclusion of disabled people are discussed in terms of the effects of model speci cation on the policy assessment outcomes. This assessment offers managerial insights for policymakers to develop appropriate infrastructure and accessibility strategies for disabled people.

This paper explores the transferability of the Multiple Discrete-Continuous Extreme Value (MDCEV) model for Activity type and duration using various transfer methods and sample sizes. This study employs the data of travel demand studies in two major cities, Shiraz and Mashhad in Iran. The model is first developed for Shiraz and then transferred to Mashhad. The adopted transfer methods are transfer scaling, Bayesian updating, combined transfer estimation, and joint context estimation. Aggregate and disaggregate transfer measures are adopted to examine the transferred models' general prediction and policy predictability. The results indicate the joint context estimation method's superiority in terms of estimation and policy prediction powers. The available massive data to the authors enabled measuring the value of sample size in this study. The sample size sensitivity analysis revealed a decrease in the marginal gain of the transferred model's performance as the sample size increases. Remarkably, the transferred model outperforms even the locally estimated model when 1) advanced transfer techniques are applied (i. e., the combined transfer estimation and the joint context estimation), and 2) the application context sample size is large enough (i. e., more than 30 percent).

Background: Opiates and other related materials may inhibit the physiologic secretion of adrenocorticotropin. The aim of the present study is to determine the association between the cortisol level and adrenal insufficiency in addicted patients.Materials and methods: For this clinical trial, 100 patients who had elective abdominal surgery or inguinal hernial repairs were selected randomly. They were distributed in two groups: opium addicts (cases), and nonaddicts (controls). One day before surgery cortisol was measured by radio immunoassay (RIA).Results: Of 50 addicted patients, 17 revealed to have lower than normal cortisol levels whereas 5 had higher levels. There exists a non-significant association between the cortisol level and duration of addiction, and also cortisol level and the degree of dependency. Cortisol levels were within normal range in all controls. Post-anesthetic complications and withdrawal syndrome, especially pain and shivering in recovery room, were more obvious among addicted patients as compared to controls and the difference was statistically significant. (P<0.0001) The duration of recovery was much shorter among addicted patients as compared to the controls, (p<0.00001).Conclusion: Addicts should be considered as high risk patients for elective surgery. Adrenal insufficiency and post-anesthetic complications deserve special attention in this group.

This paper studies the constrained consumable resource allocation in an uncertain project network. The project network is a directed and acyclic graph in the fuzzy environment.It is supposed that the Activity duration is a positive trapezoidal fuzzy number (TFN).Parameters of the Activity duration depend on the amount of resource allocated to it. By ranking the paths and activities an algorithm is developed for allocating the constrained resource to activities in such a way that the completion time of project is decreased. The proposed algorithm is illustrated by an example.