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.

Proper modeling of rainfall-runoff is essential for water quantity and quality management. However, comprehensive evaluation of soft computing techniques for rainfall-runoff modeling in developing countries is still lacking. Towards this end, in the present study two new soft computing techniques of genetic programming (GP) and M5 model tree were formulated for daily streamflow prediction. Firstly, the daily meteorological and hygrometric data including rainfall, temperature, evapotranspiration, relative humidity and discharge were collected for the years 1970-2012 throughout Amameh Watershed in Tehran, Iran. Secondly, the input variables were determined using cross-correlation and then 62 different scenarios were developed. Thirdly, the data were standardized in the range of zero to one. Finally, performance of the scenarios was assessed using the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). Totally, 80 and 20 percent of instances were used for training and testing, respectively. The results showed that the scenario number 54 was the best using both GP and M5 model tree techniques. However, GP showed much better performance than M5 model tree with MSE, RMSE, and MAE values of 0. 001, 0. 031 and 0. 009 for training and 0. 001, 0. 032 and 0. 009 for testing, respectively. The scenario 54 had eight inputs including rainfall, discharge, and delay for two days, temperature, evapotranspiration and relative humidity.

The purpose of this study is to evaluate the application of the SARIMA time series statistical model in different climatic regions of Iran to forecast meteorological drought. For this putpose, the 12-month standardized precipitation index (SPI) time series was used. Precipitation data were used over 20-years (2000– 2019) at seven synoptic stations of Iran located in different climatic regions ranging from extra-arid to per-humid. The SARIMA time series model was developed after checking the stationary and seasonality of precipitation data in the MiniTab software. Akaike and Bayesian Information Criteria (AIC & BIC) were used to identify the optimal model. The results showed that due to the periodic and seasonal trend of precipitation, the use of the SARIMA model is appropriate in all the study stations. The precipitation residuals in the study stations were very close to normal and the model performance was satisfactory in forecasting precipitation and consequently drought. It is also expected that in the extra-arid moderate climates, arid and semi-arid (very cold and cold) climates, Mediterranean moderate climates, humid cold climates, and per-humid types A and B climates, severe drought, moderate drought, near-normal and moderately wet would occur, respectively.

Introduction: In recent years, remote sensing (RS) products have emerged as effective tools for monitoring air pollution. This study aims to predict the concentrations of particulate matter with a diameter smaller than 10μm (PM10) using a multivariate linear regression (MLR) model, incorporating both Aerosol Optical Depth (AOD) products and meteorological parameters. Material and Methods: In this study, data on PM10 concentrations, Aerosol Optical Depth (AOD), and meteorological parameters (wind speed, temperature, humidity, and horizontal visibility) were used. The study focused on the time 15:00 each day, as this time was identified as having significant data relevance. The methodology section also consisted of three steps: 1) pairwise correlation analysis: The relationship between meteorological parameters, AOD, and PM10 was assessed using the pairwise correlation method. 2) Model development: A MLR model was developed to predict PM10 concentrations. 3) Validation: The model was validated using a separate dataset, ensuring that 70% of the data was used for training, and 30% for testing and validation. Results: The pairwise correlation analysis revealed a strong correlation (0.86) between AOD remote sensing index and PM10. The highest correlation (0.9) was observed during the spring season. The five developed equations to estimate the PM10 index yielded correlation coefficients ranging from 0.86 to 0.90. Notably, the highest correlation was achieved when AOD data and all the meteorological parameters were utilized simultaneously. These results highlighted the utility of remote sensing products and meteorological data in air quality monitoring and prediction. Conclusion: This study demonstrates that a MLR model incorporating AOD and meteorological parameters can effectively predict PM10 concentrations in Ahvaz City, particularly during dust storms in hot seasons. These findings can aid policymakers and public health officials in developing strategies to mitigate the adverse effects of dust storms on air quality and public health.

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.

Introduction: The rapid increase in population, growth of urbanization and industrialization in recent years, which is generally associated with an increase in demand and energy consumption, and as a result, an increase in pollutant emission sources, has exacerbated air pollution as one of the biggest current crises of urban societies and consequently health risks and related social inequalities in terms of time and space. On the other hand, meteorological parameters directly affect the amount of pollutants as well as the duration of their presence in the atmosphere, and the present research was conducted in order to investigate this effect and discover the relationships between criteria air pollutants and atmospheric elements.Material and Methods: In addition to investigating the status of meteorological elements (temperature, precipitation, wind speed, relative humidity, radiation, sunshine hours and cloudiness) and air pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3) and particulate matters with aerodynamic diameters less than 10 microns and 2.5 microns (PM10 and PM2.5)) in Tabriz city during 2004-2021, the present study has explored the relationships between pollutants and meteorological parameters in monthly and seasonal time scales using Pearson's correlation test at the 95% confidence level and the effect of these elements on the concentration of pollutants using Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) in R 4.3.1 statistical software.Results and Discussion: Based on the results of Pearson correlation analysis, NO2 and PM2.5, SO2 and PM2.5 pollutants and PM2.5 and PM10 pollutants have shown a significant positive correlation in pairs, so it seems that these pollutants have similar emission sources. Also, the results of this research demonstrate that the concentration of air pollutants in Tabriz was affected by weather conditions during the entire statistical period in the monthly and seasonal time scales. NO2 and PM2.5 pollutants had the most negative monthly correlation with the parameters of temperature, wind speed and sunshine hours and the most positive correlation with relative humidity; PM2.5 had the most positive correlation with pressure; CO and SO2 had the most negative correlation with radiation; O3 had a strong positive correlation with temperature, wind speed and sunny hours and the most negative correlation with pressure, relative humidity and cloudiness; and NO2 and PM10 pollutants had the most positive correlation with cloudiness. The results of fitting Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) for each criteria in Tabriz city indicated the better performance of GAM in analyzing the relationships between all air pollutants and the set of independent variables except NO2.Conclusion: The results of this research indicate that the effect of atmospheric elements on the concentration of pollutants in Tabriz city is different depending on the type of pollutant and at different times, and it can be acknowledged that the effect of a specific meteorological parameter on air pollution is uncertain. However, wind speed, radiation, temperature and air pressure are the most important meteorological elements related to the concentration of pollutants in Tabriz city. Also, the results suggest that both MLR and GAM can describe the variability of the response variable by a set of predictor variables and explain the linear and non-linear relationships between them. However, considering the non-linear relationship between the concentration of atmospheric pollutants and meteorological elements, GAM is able to justify a higher percentage of changes in all criteria atmospheric pollutants except NO2.

Early blight disease caused by Alternaria solani spreads well in abundance of humidity and the presence of water on the leaves. The early symptoms of disease are brown to black spots as concentric embowed rings. The fungal spores are distributed by wind and rain to healthy leaves. High humidity and temperatures between 18 and 25oC, dew, rain and sprinkler irrigation increase its spread. The purpose of this study was to study the influence of meteorological variables (Temperature, Relative humidity and Precipitation) on spores’ population of early blight pathogen of potato and possible prognosis of disease in Kermanshah province during 2013-2014 crop seasons. To perform the study, potato plants were cultivated in Razi University farm and the symptoms of the disease were monitored continuously through the season. Besides, the number of fungi spores were also measured using a standard spore trap. On the other hand, two early blight prognosis empirical models were developed using on-site meteorological data and those obtained from nearby synoptic station. The correlation between the population of spores in the air and meteorological variables were worked out. The results showed that there exist a significant relation between the spore population and maximum-minimum air temperature (p=0.01) and relative humidity (p=0.05). According to results of this study, the proposed model can be satisfactorily used for early blight early warning in western region of Iran.

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.

In studying some phenomena, the researchers are usually encountered with data that are not Euclidean in nature. To investigate the properties of these data, it is required to use some new statistical tools. Statistical methods to analysis these typical data are called non-linear statistics. Circular statistics is an example of this field. After explaining some circular distributions that are able to model skew circular data, probabilistic modeling of wind direction data related to meteorological stations of Kurdistan province is studied in this paper.

With the proliferation of regional climate simulation software, it is possible to simulate various scenarios such as the impact of filling a lake with water or drying of water of a lake and its impact on the climate surrounding area, in the computer virtual world. This lake, as one of the major lakes in the North West of the country, sometimes in periods of drought, is facing the threat of loss of water. This factor can be effective on climate components of the surrounding areas including HDD and CDD indices changes as components for energy supply and demand. Therefore, in this study, using the software TAPM, two scenarios of a Lake full of water (the real condition of the lake) and drying lake were done to simulate the HDD and CDD indices changes for town of Marivan, which is located in the vicinity of the lake. But before that, on the basis of actual and daily data of minimum and maximum temperatures for 1990 to 2010, the HDD and CDD was calculated for Marivan. According to the long-term observations, it was determined that there is a significant process only for the HDD and CDD index for March. These changes, respectively for the HDD and CDD indices, with statistics (R =-0. 54; t =-2. 20) and (t = 0. 38; R = 0. 41) confirm reducing the need for heating energy and increased demand for cooling energy in this month. Also, the outputs show that, in the scenario of dry lake, the HDD has increased in most cold months compared with a lake filled with water, and on the other hand, the CDD also show an increase in warm months. On the other hand, based on an annual average, the findings showed that drying lake will reduce 30-degree day calorie of energy demand for heating and increase 111 degree-days calorie of energy demand for cooling.