Introduction: Clinical performance of light cured resin composites is related to their degree of polymerization. The purpose of this study was to compare the degree of conversion of packable and hybrid composites by FTIR (Fourier Transform Infrared Spectroscopy).Materials & Methods: In this experimental study, 40 composite disks were prepared in two groups from Z250 and P60 composites. Each group was divided into four subgroups (2mm thickness cured with QTH unit, 2mm thickness cured with LED unit, 5mm thickness cured with QTH unit, and 5mm thickness cured with LED unit). Then samples were evaluated by FTIR to determine the degree of conversion (DC). Data were analyzed by Kolmogorov-Smirnov and three way ANOVA.Results: There was not a significant difference among the DC of the materials tested. LED significantly increased the degree of conversion of materials tested (P<0.001). DC values were significantly greater in 2mm diameter samples vs 5mm ones (P<0.001).Conclusion: The use of incremental technique in deep cavities restored with these composites is suggested. Also using LED is advised for curing composites because of a better result.

In this paper, we propose a new residual analysis method using Fourier series Transform into fuzzy time series Model for improving the forecast- ing performance. This hybrid Model takes advantage of the high predictable power of fuzzy time series Model and Fourier series Transform to t the esti- mated residuals into frequency spectra, select the low-frequency terms, lter out high-frequency terms, and then have well forecasting performance. Two numerical examples are given to show that our proposed Model can be applied with the best forecasting performance than the other Models.

In this paper a recognition system for Persian words is introduced which utilizes the local higher order of the log-polar image autocorrelation for feature extraction of Persian sub-words. This feature extraction technique brings up leads to system robustness in cases of writing variations alteration like scaled or rotated handwritings. Also using the log-polar Transform, the sub-word image sampling will be performed so that most of acquired samples will be centered in a certain area. The proposed method uses the discrete Hidden Markov’s Model (HMM) as a classifier. Furthermore a net of dictionaries were employed to increase the reliability and precision of the system output. Finally, the Iran-Shahr database is utilized to evaluate the system performance. Comparing the results of the proposed method and other previous methods, proves that a less sensitivity has been achieved by the proposed method about handwriting variations.

In this study, for the first time, groundwater level (GWL) variations of the Sarab-e Qanbar well located in the city of Kermanshah, are simulated over a 13-year period by a hybrid Model named WANFIS (wavelet-adaptive neuro fuzzy inference system). In order to develop the hybrid Model, the wavelet Transform and the adaptive neuro fuzzy inference system (ANFIS) Model are utilized. Furthermore, the 9 and 4 year data are used for training and testing the artificial intelligence Models, respectively. Moreover, the effective lags are detected by the autocorrelation function (ACF) and then eight different Models are developed for each of the ANFIS and WANFIS Models using them. After that, all mother wavelets are evaluated and Dmey mother wavelet is chosen as the most optimal. For this mother wavelet, the values of scatter index (SI), variance account for (VAF) and Root mean square error (RMSE) are obtained 0. 192, 94. 951 and 3. 117, respectively. Next, the superior Model is detected through the analysis of the results obtained by all ANFIS and WANFIS Models. The superior Model estimates the objective function values with reasonable accuracy. For example, the correlation coefficient (R), Scatter Index (SI) and variance account for (VAF) for this Model are obtained 0. 974, 0. 192 and 94. 951, respectively. The Modeling results indicate that the wavelet Transform noticeably enhances the ANFIS Model accuracy. Finally, the lags of the time series data for the Sarab-e Qanbar well including (t-1), (t-2), (t-3) and (t-4) are introduced as the most effective lags.

In contrast to the conventional aerodynamic Models, the Model of wavelet Transform and high order Taylor series of aerodynamic functions are proposed. Using different mother wavelet Transforms, different aerodynamic functions are constructed. Results show that wavelet aerodynamic Model and also high order aerodynamic Model of aircraft have more accuracy than the conventional aerodynamic Models. The suitable wavelet aerodynamics for identification and simulation of the aircraft response is proposed.