Herpes simplex virus (HSV) type 1 and 2 are common infectious agents worldwide. Data on prevalence of HSV-1 and HSV-2 infection are limited in Asia, especially in Iran. The aim of this study was to determine the seroprevalence of HSV type 1 and 2 based on age, gender, marital status, education, living area, job, symptoms and history of disease variables. The study population included 800 randomly selected persons from laboratories in Gilan Province, Iran, from 2010 to 2011. Demographic data gathered by a well-designed questionnaire and for serological studies, blood samples were collected and centrifuged. ELISA HSV-1, 2 and HSV-2 specific ELISA kits were used to determine IgG type specific antibodies in sera samples. Person’s chi-square test was applied to compare HSV-1 and HSV-2 seropositivities. HSV-1 and HSV-2 IgG antibodies were positive in 467 (58.4%) and 28 (3.5%) subjects, respectively. There was significant correlation between age, marital status, job, symptoms, history of disease and HSV seroprevalence (P<0.05). Our findings were in agreement with prior studies in which HSV-1 infections was more prevalent than HSV-2 and seropositivity increased with age.

Purpose: Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval performance measures in various real-world studies, the purpose of this study is to address the changes of interval inputs of two-stage processes for the perturbations of interval outputs of two-stage systems, given that the overall efficiency scores are maintained. Design/methodology/approach: Actually, an interval inverse two-stage data envelopment analysis (DEA) model is proposed to plan resources. To illustrate, an interval two-stage network DEA model with external interval inputs and outputs and also its inverse problem are suggested to estimate the upper and lower bounds of the entire efficiency and the stages efficiency along with the variations of interval inputs. Findings: An example from the literature and a real case study of the banking industry are applied to demonstrate the introduced approach. The results show the proposed approach is suitable to estimate the resources of two-stage systems when interval measures are presented. Originality/value: To the best of the authors’ knowledge, there is no study to estimate the fluctuation of imprecise inputs related to network structures for the changes of imprecise outputs while the interval efficiency of network processes is maintained. Accordingly, this paper considers the resource planning problem when there are imprecise and interval measures in two-stage networks.

In this study, ZnO nanoparticles were prepared by a sol-gel method for the first time. Taguchi method was used to identify the several factors that may affect degradation percentage of betamethasone sodium phosphate in wastewater in UV/K₂S₂O₈/nano-ZnO system. Our experimental design consisted of testing five factors, i.e., dosage of K₂S₂O₈, concentration of betamethasone sodium phosphate, amount of ZnO, irradiation time and initial pH. With four levels of each factor tested. It was found that, optimum parameters are irradiation time, 180 min; pH 9.0; betamethasone sodium phosphate, 30 mg/L; amount of ZnO, 13 mg; K₂S₂O₈, 1 mM. The percentage contribution of each factor was determined by the analysis of variance (ANOVA). The results showed that irradiation time; pH; amount of ZnO; drug concentration and dosage of K₂S₂O₈ contributed by 46.73, 28.56, 11.56, 6.70, and 6.44%, respectively. Finally, the kinetics process was studied and the photodegradation rate of betamethasone sodium phosphate was found to obey pseudo-first-order kinetics equation represented by the Langmuir-Hinshelwood model.

Data modeling is a very crucial stage for decision making in applied sectors. Probability distributions are considered important tools for decision making. So far, numerous probability distributions have been developed and implemented. Most of these distributions are developed by introducing from one to eight additional parameters. Sometimes, the addition of new parameters leads to re-parameterization problems. To avoid such issues, we introduce a novel probabilistic approach. The proposed approach may be termed as a new weighted sine-$G$ method. The beauty and key advantage of the new weighted sine-$G$ method are that it has no additional parameters. Through using the new weighted sine-$G$ method, a new weighted sine-Weibull distribution is introduced, which is a modification of the Weibull distribution. The estimators of the new model are also derived. Furthermore, a simulation study is carried out to evaluate the estimators of the new weighted sine-Weibull distribution. Finally, a practical application from the reliability sector is considered to evaluate the new weighted sine-Weibull distribution. Based on certain decision tools, it is observed that the proposed model is the best competing distribution for applying it in the reliability sector.

This work considers a multifactor linear mixed model under heteroscedasticity in random-effect factors and the skew-normal errors for modeling the correlated datasets. We implement an expectation–maximization (EM) algorithm to achieve the maximum likelihood estimates using conditional distributions of the skew-normal distribution. The EM algorithm is also implemented to extend the local influence approach under three model perturbation schemes in this model. Furthermore, a Monte Carlo simulation is conducted to evaluate the efficiency of the estimators. Finally, a real data set is used to make an illustrative comparison among the following four scenarios: normal/skew-normal errors and heteroscedasticity/homoscedasticity in random-effect factors. The empirical studies show our methodology can improve the estimates when the model errors follow from a skew-normal distribution. In addition, the local influence analysis indicates that our model can decrease the effects of anomalous observations in comparison to normal ones.