Background and Aim: When using improper recording materials during the fabrication of dentures, the whole process could be compromised in spite of the precision administered during the rest of the procedure. The aim of this study was to compare the flow of Pars Wax, Azardent, Crystal and Asia Wax with the Dental Modelling Wax Detry (Manufactured in Dentsply) according to standard indices of ADA, specification no. 24.Materials and Methods: All flow tests were conducted in accordance with the corresponding ADA specifications. Two samples of each wax (10 mm wide x 6 mm heigh) were prepared. A force of 19.6N (2kg) was applied on each specimen at three different temperatures: 23, 37 and 45°C. The geometric changes were accurately measured with a micrometer and compared to those obtained prior to the tests. The ratios of the dimensions after force application to the initial dimensions were determined. Based on these ratios, wax flows were compared to standard indices. Results: The amount of flow at 23°C and 45°C, obtained for all studied waxes was in accordance with the corresponding standard values, proposed by ADA. However, at 37°C the flow of none of the studied waxes was in the range of the ADA standards.Conclusion: Considering that the tested waxes demonstrated conflicting results at different temperatures when compared with the standard values, further investigation using different batch numbers is suggested.

Due to the stochastic nature of wind energy, allocating an appropriate investment incentive for wind generation technology (WGT) is a complicated issue. We propose an improvement on the traditional incentive, known as capacity payment mechanism (CPM), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. In CPM, a part of investment cost of each generator is recovered through fixed payments. However, in our proposal, wind generators are rewarded according to dynamic forecasts of the wind energy potential of the wind farm where they are located. We use an auto-regressive moving average (ARMA) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. Using the system dynamics (SD) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. Performing a simulation analysis, we conclude that while a fixed CPM for wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

This paper presents a simple and practical method for measurement and compensation of odometry errors. For a typical differential drive mobile robot, the three dominating systematic error sources unequal wheel diameters, imprecise average wheel diameter and the uncertainty about the effective wheel base, are considered. Odometric accuracy of the mobile robot calibrated with this procedure is considerably improved. Calibration coefficients are obtained by accurate measurement of wheel diameter ratios and the effective wheel base, and applied to robot through drivers controller software. The new method is distinct from previous studies performed in this subject in three aspects: -The experimental text are easily performed. -The method is valid regardless of errors magnitude, which is direct consequence of mathematics with no approximation in the modeling procedure. -Accurate relations to determine average wheel diameters that are additional outcome of this method.

In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under investigation. However, they have been known as black-box models due to their problem in providing insight into the relationship learned. In this study, firstly we develop a rainfall-runoff model using an ANN approach, and secondly we describe different approaches including Neural Interpretation Diagram, Garson’s algorithm, and randomization approach to understand the relationship learned by the ANN model. The results indicate that ANNs are promising tools not only in accurate modelling of complex processes but also in providing insight from the learned relationship, which would assist the modeller in understanding of the process under investigation as well as in evaluation of the model.

The present study aimed to analyze the comprehensive relationships between suspended sediment yield, and rainfall and runoff characteristics on storm basis in Khamsan Representative Watershed with an area of 4193 ha in Kurdistan Province, Iran. In due course, 9 storm events occurred during study period were analyzed 4 of which generated runoff and consequently yielded sediment. Different bivariate regression models were then applied for estimating sediment yield. The results of the analysis verified better performance of linear regression developed based on maximum rainfall intensity with respective determination and efficiency coefficients, and estimation error of 0.843, 0.84 and 39%.

A model has been used to simulate potassium (K) uptake by wheat in a pot culture experiment. Three soils from India, namely Alfisol, Inceptisol and Vertisol, were differentially K exhausted by Sudan grass (Surghum vulgare var. Sudanensis) for a period of 280 days and were used to simulate potassium uptake by wheat (Triticum aestivum) and also to predict the amounts of K released or fixed during cropping. Except in Alfisol all the predicted values of K uptake closely agreed with that of measured values. When predicted K uptake values were plotted against the observed values, r2 values were found to be 0.927, 0.828 and 0.721 in Inceptisol, Alfisol, and Vertisol, respectively. There is a close relationship between observed and predicted values of K uptake as evident from the high r2 values, but in case of Alfisol and Vertisol the model has over-predicted K uptake, which perhaps was due to over prediction of K release from non-exchangeable form. The model has been validated and has been applied to simulate response towards fertilizer application at different available K. It was showing that maximum response occurs at a particular value of available K, which shifts towards higher value as release threshold level (RTL) increases. Predicted K uptake was most sensitive to changes in root parameters such as root length density (RLD) and maximum influx rate (Vmax), since changes in the time at which maximum root length density was attained (RLD-B) and the decay constant of Vmax (Vmax-B) gave the greatest changes in K uptake in almost all the soils except highly exhausted Alfisol in which predicted potassium uptake was more sensitive to changes in RLD-B and intercept (c) than to root uptake kinetics, as described by Michaelis-Menten constant (Km) and Vmax. The predicted potassium uptake was least sensitive to changes in rate constants of release and fixation and fixation threshold level in Inceptisol and Vertisol, but was sensitive to release threshold level and rate constant of release in Alfisol.