In this article, an attempt has been made to estimate the amount of sound transmission loss in a flat oval channel by applying the approach of statistical energy analysis. Correct estimation of sound transmission loss in an air conditioning channel is of great importance due to the harmful effects of noise pollution in the environment on human health. simulation with the statistical energy analysis method is a powerful approach to estimate sound and vibration in problems in which we deal with complex and multi-part systems; is considered. In this method, first, a system is divided into several subsystems, and then by writing a matrix equation that includes the energy exchanges between subsystems and energy loss coefficients; It is investigated from the perspective of vibration and sound estimation.On average, the model presented in this research is able to estimate the sound transmission loss in different dimensions of the air conditioning channels according to the experimental results in the accuracy range of ± 2.5 dB. Considering that it seems that the results obtained from modeling with this method are in good agreement with the experimental data; The results of this research can be used as an efficient approach to estimate noise in oval shaped channels stretched in different lengths.

Introduction and purpose: Despite recent efforts to identify microplastics in the aquatic environment worldwide, identifying the various sources of its release remains a challenging task. Understanding and identifying the different sources of aquatic pollution and the processes affecting them is essential for a comprehensive description of the quality of water resources. The aim of this study is therefore to introduce statistical methods to determine the sources of microplastics in aquatic environments. Methods: This review article first identifies the pathways of microplastic entry into the aquatic environment, followed by an examination of four commonly used multivariate statistical methods: Principal Component analysis (PCA), Cluster analysis (CA), Hierarchical Cluster analysis (HCA) and Positive Matrix Factorization (PMF). Results: Multivariate statistical analysis can be used to determine different variables such as size, shape, color, and density of microplastics. It can also determine the sources of microplastics (domestic wastewater, industrial effluents, agricultural activities, surface runoff, air currents, etc.). It also identifies which variables have the greatest impact on pollution and suggests the best solutions to reduce pollution. Conclusion: the study of pollution based on multivariate statistical analysis can provide important information on the main sources of microplastic pollution and the relative contribution of different sources in the aquatic environment, which can help to improve environmental management and reduce pollution.

Flood frequency analysis method is employed to predict the design floods needed for the implementation of hydraulic structures along a river. In this method, the magnitudes of floods are made related to their frequencies using statistical distribution functions. The accuracy of flood estimation depends on some factors including fitting the best probability distribution function along with an estimation of its parameters, period of available data, commonplace errors in measurement and an existence of outlier data. Calibrated hydrological models can also be applied to simulate large flood events. Uncertainties arising from the hydrological modeling, e.g. the modeling assumptions and the uncertainties in inputs and in parameters which are used in the models, are the factors which affect the simulated peak discharges. In the course of this paper, flood frequency analysis was performed using a hydrological modeling approach with the results compared with traditional flood frequency analysis as based on the observed data. The method was applied to Tangrah Watershed. The average rainfall values were initially calculated for different durations and for different return periods. For each rainfall value, a Monte Carlo scheme was employed to consider the uncertainties in rainfall spatial distribution and Antecedent soil Moisture Condition (AMC). Using different combinations of rainfall depths along with their spatial distributions over watershed and also AMC conditions as the inputs of hydrologic model (HEC-HMS), peak discharges of different return periods were found out. Besides the modeling approach, the best probability distribution was fitted as based on “Anderson-Darling” goodness-of-fit test with flood peak discharges being calculated while using the observed data. It was deduced that the outlier rainfall exerts a significant effect on rainfall – runoff simulation results and this effect increases with increase in return period. For instance, the simulated peak discharge of 50% probability reduces from 1297 to 338 cm2 following an omission of the outlier for the case of 200 year return period of 6hr duration rainfalls. The spatial rainfall distribution and antecedent soil moisture conditions largely influence the peak discharge of different return periods. Also, an observed peak discharge is seen as greater than the results obtained from rainfall simulation. As an example, for a return period of 200 years, hydrologic modeling results are found out as lower than the frequency analysis results by about 30.4%. An incorrect selection of statistical distribution plays a major role in increasing the difference between the results obtained from these two methods.

The occurrence of skin cancer is related to many parameters which are spatially distributed. GIS can be used for analysing such spatial relationships. On the other hand, the occurrence of skin cancer is a probabilistic phenomenon. In other words, even in presence of all causing factors, skin cancer does not necessarily occur. In this research, a geostatistical model is developed to simulate the spatial distribution of skin cancer. First, the data framework is designed and proper data are gathered. Then, the data are processed in GIS and prepared to enter the model. Finally, by creation of the geostatistical model, the relations between the disease and its factors are studied. As a result, the effecting factors and their importance degrees are extracted. The developed model can be used both as simulator tool and as a decision support tool. In the other words, by changing the values of one or more parameters, the probability of occurring changes in the occurrence of disease can be studied. This can help the managers in evaluating the result of any possible course of action, when challenging the disease.

Dear Editor, We are thankful to the referees Dr. Mohamad Amin Pourhoseingholi et al. and the Editor for pointing out some valuable suggestion and important notes needed in the manuscript. The major concern of reviewer was about the positive Hepatitis D patient. Due to high number of cases in our study, the prevalence of Hepatitis D is very low.1) The only positive Hepatitis D is a 31 year-old woman. We have mentioned that she was an Afghanian lady but we have considered her native because she has Iranian ID Card, although we have not assessed her ancestry.2) As the reviewer has mentioned, chi square test is impractical in the present study so fisher exact test is used. We have mentioned to this important note in the methods and conclusion sections.3) It has mentioned that repeated test is performed to assess association between Hepatitis D and Tatto. So P value of likelihood ratio is not reported and both of P values are related to fisher exact test.