Background: The public's understanding of radiation and protection of radiation harm is not high. Therefore, it is meaningful to know the radiation safety literacy level in terms of health care. This study developed a measurement tool that can help to identify the actual condition of radiation safety literacy and conducted the statistical assessment of the developed tool. Materials and Methods: We developed a radiation safety literacy measurement tool in four steps: preliminary term extraction, content validity, face validity, and response scale composition using expert groups such as radiologist, professor of radiological science, angiography nurse, and professor of Korean language. And we developed a questionnaire and conducted a survey on samples of n = 280 (male: 124, female: 156). For statistical assessment, descriptive analysis, Cronbach's coefficient, and correlation analysis were performed, and receiver operating characteristic (ROC) curve was obtained. Results: As a result of developing measurement tool of four steps, radiation safety literacy measurement tool consisting of a total of 46 items were developed. The result of the survey showed high reliability with the internal consistency reliability coefficient of 0. 963. The correlation coefficient of the developed measurement tool with the rapid estimate of adult literacy in medicine was found to be valid with 0. 448 (p<0. 05). Conclusion: The radiation safety literacy measurement tool developed in this study can be used as a useful tool for the process of patient evaluation for appropriate communication between the healthcare provider and the patient regarding radiation.

In data envelopment analysis in which the comparison between a unit in relation to the antecedent set is used to detect the proportional inefficiency and providing an optimal model, detecting outliers is critical for achieving accurate results. In these deterministic frontier models, statistical tests are now applied to detect such outliers. This paper deals with introducing two statistical tests for detecting outliers in data envelopment analysis. In the presented methods, each observation is deleted from the sample once and the resulting DEA model is solved, leading to a distribution of efficiency estimates before and after the elimination. Based on the achieved distribution, two statistical tests are then designed to identify the potential outlier (s).We illustrate the method through a real data set. The method could be used in a first step before using any frontier estimation.

Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD).Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests.Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable.Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables.

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.

One of the most reliable methods for assessing the effects of climate change is the use of climate variables simulated by general atmospheric circulation (GCM) models, but these simulations have uncertainties. Avoiding the uncertainties associated with climate change in research will reduce the credibility of the results and result in unrealistic and unrealistic results. In this study, the data of temperature, precipitation and radiation simulated by 5 models HADCM3, NCCCSM, NCCCSM, INCM3, GFCM21 and MPEH5 at the Synoptic Station of Rasht based on A1B, A2 and B1 emission scenarios were statistically analyzed. Also, the uncertainty of each of the general atmospheric circulation models and diffusion scenarios was investigated by weighting method. The results of the test of the calculated statistics for each model were presented individually. The INCM3 model in the A1B scenario for simulating rainfall, the NCCCSM model in the A1B scenario for radiation simulation and GFCM21 under B1 scenario for simulating minimum temperature and HADCM3 model in the A1B scenario for maximum temperature simulation. The results of ensemble performance of the models also showed that in the future planning, the group composition of the models can be simulated with less uncertainty and more accurately the climatic parameters. In order to select the best scenario based on the group implementation of the models, the best match is the simulated rainfall data with real data based on B1 scenario, for A1B scenario data and minimum and maximum temperature based on scenario B1.

Collection of appropriate quantitative data is necessary for proper management and planning in rangelands. One of the ways to access this information is choosing the appropriate methods of sampling and determining the appropriate length of transect for vegetation cover measurement. Therefore, this study was conducted in 2016 in Ariz rangelands located in the northwestern of Sanandaj city. For this purpose, transects with a length of 20, 50, 100, 150, 200, 300 and 400 meters, each with four replications were established perpendicular to slope and parallel to slope in the vegetation type of Bromus tomentellus-Festuca ovina. The 400-m transect was considered as a control for calculations. The obtained data from sampling were compared by ANOVA. The obtained results from different transect lengths were also compared by (E%) 2×T factor. The mean comparisons of vegetation cover percentage of transects were performed using t-student test. According to the results of this study, the transects of 20, 50, 100 and 150m length showed a significant difference with the control transect (400 m) in terms of accuracy index ((E%) 2 × T). However, there was no significant difference among the transects of 200, 300 and 400 meters length for the accuracy index. Therefore, the application of 200-meter transect is recommended regarding the cost and time.