Nowadays, the health systems consume a signi, cant share of the budget in each country. Hospitals are considered as the largest organizations to provide the healthcare services. Nurses as one of the major portion of hospitals' human resources consume a considerable part of the hospitals' annual budget. From this point of view, the hospitals' managers must arrange the available nurses e, ciently. This problem is worsened by the shortage of the available nurses in many countries. In this setting, the Nurse Scheduling Problem (NSP) has received signi, cant attention in recent years. In this problem, the aim is to assign the shifts to the nurses by satisfying the requirements during the planning horizon. Several factors such as hospital managers' policies, labor laws, governmental regulation, and the status of the nurses at the end of the previous planning horizon should be considered for assigning the shifts to the nurses. Several mathematical models and heuristic and meta-heuristic algorithms have been proposed to solve the NSP by considering various assumptions and constraints. In the real-world problems, the good quality solutions can be obtained by considering the uncertainty concepts in the research problem. In this point of view, in the current study, the uncertainty concepts are considered in the nurse scheduling problem. It can be stated that the nurses' preferences for the working shifts cannot be deterministically speci, ed. For this reason, , rst, a mathematical programming model is developed to maximize the nurses' preferences to work in their favorable shifts. Then, a fuzzy mathematical Modeling approach based on the averaging fuzzy operator is proposed to investigate the uncertainty concepts on the nurses' preferences for the working shifts. Then, some random test problems are generated and solved to evaluate the performance of the developed fuzzy model. Regarding the obtained results, it can be stated that high quality schedules are generated by the proposed fuzzy model.

In this paper, a new method is presented to determine the optimal setting of distance relays using probabilistic Modeling of the affected uncertainties. Due to model of uncertainties, the corresponding density functions of each uncertainty are presented. Then, using Monte Carlo simulation, the probability distribution of the impedance seen by the distance relay is obtained. The three zones of distance relay need to be set in a way that the relay operates correctly for an internal fault of the protection zone (sensitivity), and it does not operate for an external fault of the protection zone (selectivity). According to this, the probabilistic indices of sensitivity and selectivity are defined independently for each zone of the distance relay. In the following, different scenarios are proposed to maximize the selectivity or sensitivity indices.Finally, due to importance of selectivity in compare to sensitivity, the scenario of maximized sensitivity with perfect selectivity is proposed.According to this scenario, the problem of determining optimum setting of distance relay for each zone is defined as an optimization problem with the objective of maximizing the probability of sensitivity and with the constraint of perfect selectivity.Since the proposed formulation is nonlinear, Genetic algorithm is used to solve this problem. The proposed method is applied to the IEEE 39 bus test system and the advantages of the proposed formulation for each zone of the distance relay are presented.

This research investigates the collapse responses of a concrete moment frame considering Modeling uncertainties. These Modeling uncertainties are considered for evaluating a collapse response related to the modified Ibarra-Krawinkler moment-rotation parameters for beam and column elements of a given structure. To analyze these uncertainties, the correlations between the model parameters in one component and between two structural components were considered. Latin Hypercube Sampling (LHS) method was employed to produce independent random variables. Moreover, Cholesky decomposition was adopted to produce correlated random variables. Performing 281 simulations for the uncertainties involved considering their inter-correlations, incremental dynamic analysis (IDA) was done using 44 far-field accelerograms to determine structural collapse responses. Collapse responses of each simulation, including mean collapse capacity, mean collapse drift, and mean annual frequency, were obtained. Then, the collapse responses were predicted using the response surface method and artificial neural network. The results show that the Correlation coefficients (R) between the target data resulted from incremental dynamic analysis (IDA), output data resulted from response surface method (RSM), and artificial neural network (ANN) were obtained for the collapse responses above 0. 98. The maximum prediction errors for mean collapse capacity and mean collapse drift are less than 5% and for mean annual frequency less than 10% under response surface method (RSM), and artificial neural network (ANN).

Collapse performance evaluation of structures has been a concern for researchers due to its complexity and uncertainty in Modeling and simulation. Concentrate plastic hinges are best candidates for Modeling collapse behavior of structures. Collapse fragility curves are affected by various sources of uncertainty. Existing uncertainties in modified Ibarra and Krawinkler moment-rotation model for concrete moment frame buildings were investigated in this paper. LHS simulation method was used to generate random variables considering the correlation among Modeling uncertainties in one component and two structural components. Collapse responses including mean collapse capacity and standard deviation were obtained for each simulation by generating random samples for uncertainties using incremental dynamic analysis (IDA). As much effort is needed for implementation of IDA, MLP artificial neural networks, GMDH artificial neural network and response surface method were used to estimate and anticipate the collapse behavior of the structure. Results show that using above methods will lead to high accuracy anticipations with an error of less than 10% for GMDH neural network and an error of less than 7% for MLP and response surface methods.

Background and Aim: In patients with complete denture, some clinicians have used modelling plastic impression compound (MPIC) along tissue conditioner (TC) materials simultaneously. Little information is available on the composition of these materials and the interaction between them. The purpose of this study was to evaluate the influence of two components of MPIC on the structure and chemical composition of TC.Materials and Methods: In this experimental study, MPIC specimens were provided in 25×2 mm discs. Specimens were randomly divided into three groups and were immersed in ethanol 70%, plasticizer (dibutyl phthalate) and a mixture of them (ethanol 70% and dibutyl phthalate). All of the discs were weighed with a digital balance before and 2, 4, 6 and 24 hours after immersion. Values were analyzed by non parametric Kruskal-Wallis (a= 0.05) and SPSS 16 for Windows (SPSS Inc., Chicago, IL) was used for statistical analysis.Results: Statistical analysis indicated significant differences among all groups (p>.05).Conclusion: Dibutyl phthalate (DBP) had high impact on the solubility of MP, while the mixture of dibutyl phthalate (DBP) and ethanol demonstrated the highest impact.

In this paper, a study was done of the Modeling and parameter identification of a rotary electrohydraulic servo system in presence of noise and structural uncertainties. The mathematical model accounted for all the system dynamics, apart from few trivial assumptions that were put together to alleviate the complexity of the expressions. The behavior of the spool dynamics in servo-valve is modeled with an appropriate 2nd-order transfer function. In fact, electro-hydraulic systems are known to be highly nonlinear and non differentiable due to many factors, such as leakage, friction, and especially, the fluid flow expression through the servo-valve. Then the system is written in linear parameters (LP) form and continuous-time least-squares estimation method is used to parameter identification of the system. Furthermore, the constant parameters of the valve can be identified using frequency response methods. In comparison with similar works, the experimental results present significant reduction in identification time. The method is validated with the nonlinear model of the system and substituting the procured parameters in the model.

In this paper, the long-term dynamics of an electricity market is modeled, considering the load uncertainty. Moreover, the generation side uncertainties, including the uncertainties of the generators availabilities, the hydro generations and the wind generations are observed. The problem is analyzed by the System Dynamics (SD) method. Also, the effects of capacity payment on power market dynamics, with and without uncertainties, are modeled and analyzed. The simulation results show how the uncertainties may affect the long-term behavior of a power market. Moreover, it is shown how the effect of uncertainties on market dynamics, may be improved through capacity payment.

The operation planning problem encounters several uncertainties in terms of the power system’ s parameters such as load, operating reserve and wind power generation. The Modeling of those uncertainties is an important issue in power system operation. The system operators can implement different approaches to manage these uncertainties such as stochastic and fuzzy methods. In this paper, new fuzzy based Modeling approach is implemented to develop the new formulation of power system problems under an uncertain environment with energy storage systems. Interval type-2 fuzzy membership function (MF) is implemented to model the uncertainty of available wind power generation and the type-1 fuzzy MF is used to model the other parameters in weekly unit commitment (UC) problem. The proposed approach is applied to two different test systems which have conventional generating units, wind farms and pumped storage plants to consider differences between the type-1 and type-2 fuzzy approaches for uncertainty Modeling. The results show that the total profit of UC problem using type-2 fuzzy MF is better than type-1 fuzzy MF.

Background and Aim: There are different methods of oral health education for child population. Theater show is a method that has not received enough attention until now. The aim of this study was to evaluate the effect of using theater show on dental health education in Yazd female elementary students.Materials and Methods: This was a quasi experimental study performed by using pre- and post-test method in Iran over the year 2004. One elementary school from each region of the city was selected. Twenty students from each grade of each school were included in the study (n=200). The information on knowledge, attitudes and practice of oral health were collected by using a valid and reliable questionnaire before and after theater show performance. Data were analyzed using SPSS and Wilcoxon signed rank test.Results: This study showed that the theater show could improve knowledge, attitude and proper oral health practice of children. The effect of the theater show on knowledge was more than attitude and was more on attitude than practice (P<0.001). Only literacy on the part of mothers was significantly correlated with practice (P=0.021). There were significant correlations between Knowledge (P=0.020), Attitude (P=0.0001) and oral health Practice (P=0.022) with students' grade level.Conclusion: This study showed that using theater show for oral health education, could improve the oral health related knowledge, attitude and proper oral health practice by elementary school students.