Soil Temperature (ST) is critical for environmental applications. While its measurement is often difficult, estimation from environmental parameters has shown promise. The purpose of this study was to model ST in cold season from soil properties and environmental parameters. This study was conducted as a pot experiment in Ardebil, Iran. Automatic thermal sensors were installed at 5 and 10 cm depths. Besides, soil properties and environmental parameters were determined based on field and laboratory works. Machine learning methods including Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Interface System (ANFIS) were used for modeling ST. The air Temperature was observed as the most effective factor in ST modeling. The relationship between soil and air Temperature was stronger at 5 cm depth compared to 10 cm. The R2 between soil and air Temperature was higher in the absence of sunlight than in its presence. The prediction of ANFIS (R2= 0. 96 and MAPE= 10. 5) was closer to the observed ST values compared to the ANN (R2= 0. 91 and MAPE= 35) and MLR (R2= 0. 57 and MAPE= 41). The results revealed the advantage of ANFIS method for ST modeling. This approach can be applied for soil depths and locations with data gap.

Statistical methods are efficient and useful tools to perceive and evaluate climate behavior. One of statistical applications in climatology is modeling of climatic elements behavior. An applied statistic models is ARIMA. In this statistical pattern values are modeled based on their historical behavior modeled. In this paper, the annual Temperature of Tabriz city was modeled during 1951-2005. To forecast Mean while adjusting method of ARIMA have been introduced step by step. The ARIMA (0, 1, 2)can was identified as final pattern based on common modeling. The first order of difference degree shows a linear trend what is approved by a constant term in co model. According to the ARIMA (0, 1, 2)canthe amount of this trend is 0.033 Ċ par year. The second order of moving average in Indicates that Temperature for each year is functioned by stochastic parameters of previous year. Finally, According to the adjusted model, the Temperature of 20 year with 95% confidence interval have been calculated.

Li-ion battery Temperature affects its performance, significantly, and keeping its Temperature in proper operational Temperature is obligatory; so, this guarantees the performance, safety, and life span of the battery. In this study, the performance of the planar Li-ion battery Temperature management system with 6 cells in cold weather (initial Temperature -20oC) is investigated. The maximum Temperature difference and the average Temperature of the battery cells are the two main performance criteria of the Temperature management system. Effects of mass flow rate, number of heating plates, and flow arrangement consisting of parallel, counter, and zig-zag flow arrangements on the performance criterion and also on the warm-up time are studied. Results show that by increasing the fluid mass flow rate batteries reach the proper Temperature (20 oC) faster and the Temperature difference decreases. At a constant mass rate, the addition of heating plates decreases the warm-up time. The zig-zag flow arrangement has better performance in terms of Temperature difference criteria up to 8 times and reaches 2.1 degrees at the maximum value, but parallel-flow warms up the batteries faster than the other flow arrangements.

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.

Soil Temperature is one of the main characteristics of soil that its changes have a great impact on many processes such as growth, plants flourishing and soil formation. Nevertheless, Temperatures throughout the soils profile are not measured continuously. As a result, we encounter the lack of statistics in soils Temperature data, while meteorological parameters are being measured regularly. Since presented relationships in the previous investigations do not provide the appropriate accuracy to predict soils Temperature, the objective of this paper is to introduce a high accuracy relationship based on comparison of regression methods and Artificial Neural Network (ANN) by using daily meteorological data of three stations located in Mashhad, Sabzevar and Shiraz. Solidarity coefficients indicated that ambient Temperature, evapotranspiration and evaporation have the most solidarity with the soil Temperature at a depth of 5 cm, respectively. According to solidarity coefficient and results of the 2 models, air Temperature, evapotranspiration, humidity and effective precipitation with daytime lag of one day were regarded as the best input parameters, respectively. The results showed that second order regression with single variable had the lowest accuracy while the highest accuracy was observed in the ANN method. In the meantime, multiple regressions had a reasonable accuracy. In calculation of freezing depth we concluded that Finish equation has an acceptable accuracy, whereas by considering an added parameter related to the precipitation depth in the cold days to the equations order, the results of the Finish equations will improve dramatically. Maximum error of 15% was observed for the recommended equation in Shiraz station and count of error in Mashhad and Sabzevar 0.6% and 2% observed respectively.

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.

Because of nonlinear dependence between the random variables, probability distributions of multivariate random variables are generally more complex compared to their univariate counterparts. One approach to solve this problem is the use of copulas, which have more considered by researchers over recent years. In this study, the dependence of rainfall and Temperature in Tabriz station was modeled using copulas. In this regard, monthly data of rainfall and Temperature of Tabriz for a period of 1971-2008 was used. At the first step, by application of Kolmogorov-Smirnov test, exponential and generalized extreme value (GEV) distributions are selected as appropriate univariate distributions for rainfall and Temperature, respectively. Then three type of copulas functions namely, Clayton, Gumbel and Frank are used for creating bivariate distribution of rainfall and Temperature in Tabriz. The results indicated that the performances of all types of copulas are close together. However, an only Frank copula has capability to modeling the negative dependence between variables. Therefore, Frank function is selected as appropriate copulas for modeling dependence between rainfall and Temperature in Tabriz station.

Thermal modeling of an automotive cabin was performed in this paper to predict the inside cabin air Temperature. To implement this task, thermal and ventilation loads were estimated and the mass and energy balance conservation equations for dry air and water vapor with considering a new panuneter (air circulation ratio) as well as the balance equations of internal components of a cabin were derived and solved simultaneously. The performance of the proposed thermal modeling of a cabin was compared with the data collected from hot room experimental tests. These tests were run for various design parameters such as evaporating cooling load and cabin size (air volume inside cabin). The comparison of experimental and numerical results showed a good agreement.Parametric analysis with three parameters namely, vehicle speed, number of passengers, and A/C air mass flow rate was performed to investigate the effects of these panuneters on cabin air Temperature.