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.

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.

Land Surface Temperature (LST) is one of the most important physical and climatological crucial yet variable parameter in environmental phenomena studies such as, soil moisture conditions, urban heat island, vegetation health, fire risk for forest areas and heats effects on human’ s health. These studies need to land surface temperature with high spatial and temporal resolution. Remote sensing satellite sensors due to their technical constraints cannot take the high spatial and temporal land surface temperature data simultaneously. For example combining Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) LST products have spatial resolution of 90 m with repeat cycle of 16 days, whereas Moderate Resolution Imaging Spectro-radiometer (MODIS) LST products have spatial resolution of 1 km with daily repeat cycle. To address this shortage, this work used the Spatial and Temporal Data Fusion Approach (STDFA) to estimate the high spatial and temporal resolution LST by ASTER LST and MODIS LST products. This method was tested and validated in study areas located in Tehran, Iran. The MODIS daily 1-km LST product and the 16-day repeat cycle ASTER 90-m LST product are used to produce a synthetic “ daily” LST product at ASTER spatial resolution. The actual ASTER LST products were used to evaluate the precision of the synthetic daily LST product. Here, the correlation coefficient was equal to 0. 88, Root Mean Square Error (RMSE) reached about 3. 38 K. The results showed that the algorithm can produce high-resolution temporal synthetic ASTER data that were similar to the actual observations.

Background and Objectives: Soil moisture is one of the key variables which by controlling evapotranspiration processes influences the water cycle and heat exchange between the earth and the atmosphere. The amount of soil moisture is also important for hydrological, biological and biochemical cycles. With the help of soil moisture information in regular intervals, the degree of drought development can be determined in regions with dry climates. Furthermore, continuous monitoring of soil moisture in agricultural areas can help to plan irrigation of crops effectively. Soil moisture is also used to identify areas susceptible to fire in forest areas. Therefore, monitoring of soil moisture is important in any regions and different time periods. Due to factors such as lack of uniformity in physical properties of soil, topography, land cover, evapotranspiration and rainfall, soil moisture is known as a variable factor in spatial and temporal intervals. Therefore, the use of conventional and traditional methods for soil moisture determination (such as gravimetric and neutron probe) is not appropriate to understand the spatial and temporal variation of this parameter in large scales. To resolve this problem in past two decades, remote sensing technology (especially in visible/infrared spectrum) widely used to estimate of soil moisture indirectly. The objective of this study was to estimate surface soil moisture using Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Materials and Methods: For this purpose, Landsat 8 satellite imagery was downloaded at the same time as ground sampling. The samples were transferred to the laboratory and soil moisture was measured by weighted method. Then, using the expert software such as ArcGIS, the indices were estimated and the values of these indicators were transferred to SPSS software for statistical regression. In this study, a PTF were obtained to predict soil moisture condition using LST and NDVI and NDMI derived from Landsat 8 data. Multiple linear regression method was used to derive the PTF. After derivation of the pedotransfer function, the accuracy of the derived PTF was evaluated. This research was carried out in the Dehzad area of Izeh city of Khuzestan province. Results: Comparison between measured and predicted soil moisture values indicated that the PTF had good prediction (R2=0. 78), Coefficient of Residual Mass (CRM), Mean Absolute Error (MAE), Modified Coefficient Efficiency (E), Modified Index of agreement (d) also showed that the model had good performance (CRM=0. 001, MAE=0. 0013, E=0. 9998 and d=0. 9999). Furthermore, a soil moisture map was obtained for the study area. The result indicated that Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) can be used to predict soil surface moisture content successfully. Conclusion: The result of this research has been presented by a PTF and in the form of soil moisture map. The soil moisture map simulated by this model can predict 78% of soil moisture variation in the region.

Abstract LST (land surface temperature) derived from thermal infrared remote sensing images is directly related to land use and land cover changes. Remote sensing, as an irreplaceable method to obtain LST at global and regional scales, enables effective monitoring of LST with Spatio-temporal continuity. LST helps in separating urban areas from bare areas and improves land use/cover generation through classification of remotely sensed imagery. In this study, a Python-based user interface was developed to make land surface temperature retrieval easier and faster. LST can be retrieved by inputting required parameters in different methods such as Single Channel Algorithm (SCA), Radiative Transfer Equation (RTE) method, Split Window Algorithm (SWA), and two Mono Window Algorithms (MWA), from Landsat missions (Landsat 5, 7, and 8). Comparing the results in this study showed that RTE and SCA with root mean square error (RMSE) equal to 3.76 and 8.97 degrees Celsius had the highest and lowest accuracy. LST is affected by atmospheric particulate matter, land cover and urban morphology. Various methods of LST retrieval consider surface temperature, water vapor and other atmospheric factors. The developed user interface helps researchers and managers in monitoring land surface temperature change through time as affected by land use/cover, especially urban land use

Different types of contact, including contact between node pairs, any-contact of nodes, and contacts of the entire network, are used to characterize social relations in mobile social networks. Different modes of routing, from the point of view of message delivery semantics, encompass unicasting, multicasting, any-casting, and broadcasting. Studies have shown that using probability distribution functions of contact data, which is mainly assumed to be homogeneous for nodes, improves the performance of these networks. However, there exists an important challenge in studies on distributions. A lot of works apply the distribution of one type of contact to other types. Hence in routing applications, it causes to use of the distribution of one type of contact for any mode of routing. This study provides a complete solution to model each type of homogeneous contact data distribution and to use them in different modes of routing. We propose a routing algorithm that uses this new model. Results show that our solution improves the average latency of comparing methods Epidemic, TCCB, and DR about 3.5-times, 30%, and 45%, respectively. It achieves a delivery rate of about 5% and 6%, and average latency about 6% and 8% better than that of DR and TCCB, respectively.

The current research focuses on the synthesis and characterization of sodium potassium niobate (KNN), a lead-free piezoelectric compound. The objective is to replace lead-based compounds like PZT using KNN-LST, which is sodium potassium niobate piezoceramic powder doped with lithium, antimony, and tantalum. To this end, solid-state method was employed to sinter the samples at 1110 °C with varying percentages of NiO sinter aid (0%, 0.75%, 1.5%, and 2.25%). X-ray diffraction analysis confirmed the formation of pure KNN-LST perovskite phase at 1110 °C with a composition containing 1.5% NiO. Scanning electron microscope images also demonstrated that cubic particles inherent in KNN were observed in samples with no NiO addition as well as those containing 0.75% and 1.5% NiO; however, these particles were transformed into cylindrical grains when incorporating 2.25% NiO. With an increase in the sintering percentage from zero NiO content to higher amounts, sample density also increases from approximately 85% up to its peak at around 94%, followed by a slight decrease by about 91%. Similarly, the dielectric coefficient increased from approximately850 without any contribution from NiO sinter addition up to1500 upon addition of 1.5%NiO sinter but then, it decreased again by approximately 1200 followed by addition of 2.25% NiO sinter. Conversely, dielectric loss initially reduced from roughly 0.14% during no NiO sinter addition by merely 0.04% during 1.