Step growth in single crystal diamond grown by microwave plasma chemical vapor deposition

Single crystal diamond films of varying quality are deposited using microwave plasma chemical vapor deposition (MPCVD) apparatus. Unpolished natural diamond seeds are used as substrates in the temperature (T-s) range 850-1200 degrees C. The gas mixture of methane (CH4), hydrogen (H,) and oxygen (0,) is used for the deposition of diamond. The deposition pressure is varied in the range 90 to 150 Torr. The films are characterized using scanning electron microscopy (SEM), Atomic force microscopy (AFM) and Raman spectroscopy techniques. The growth morphology of the films is found to be a sensitive function of the deposition parameters. The crystalline nature of the films change from polycrystalline to single crystal as we increase T, and for a certain set of parameters the filamentary growth of the diamond crystals can be seen. The films are polycrystalline in the range Of substrate temperature 850-900 degrees C and oriented grains of diamond crystals arc evident as the T, increases. The single crystal diamond growth is observed to proceed via the step growth mechanism with the evidence of bunching of the steps. Our study explores evolution of the growth of single crystal diamond in a wide range of parameters. (c) 2005 Elsevier B.V. All rights reserved

An integrated fuzzy regression–analysis of variance algorithm for improvement of electricity consumption estimation in uncertain environments

This study presents an integrated fuzzy regression analysis of variance (ANOVA) algorithm to estimate andpredict electricity consumption in uncertain environment. The proposed algorithm is composed of 16 fuzzy regression models. This is because there is no clear cut as to which of the recent fuzzy regression model is suitable for a given set of actual data with respect to electricity consumption. Furthermore, it is difficult to model uncertain behavior of electricity consumption with conventional time series and proper fuzzy regression could be an ideal substitute for such cases. The algorithm selects the best model by mean absolute percentage error (MAPE), index of confidence (IC), distance measure, and ANOVA for electricity estimation and prediction. Monthly electricity consumption of Iran from 1992 to 2004 is considered to show the applicability and superiority of the proposed algorithm. The unique features of this study are threefold. The proposed algorithm selects the best fuzzy regression model for a given set of uncertain data by standard andproven methods. The selection process is based on MAPE, IC, distance to ideal point, and ANOVA. In contrast to previous studies, this study presents an integrated approach because it considers the most important fuzzy regression approaches, MAPE, IC, distance measure, and ANOVA for selection of the preferred model for the given data. Moreover, it always guarantees the preferred solution through its integrated mechanism