An atmospheric pressure dielectric barrier plasma discharge has been used to study a thin film deposition process. The DBD device is enclosed in a vacuum chamber and one of the electrodes is a rotating cylinder. Thus, this device is able to simulate continuous processing in arbitrary deposition condition of pressure and atmosphere composition. A deposition process of thin organosilicon films has been studied reproducing a nitrogen atmosphere with small admixtures of hexamethyldisiloxane (HMDSO) vapours. The plasma discharge has been characterized with optical emission spectroscopy and voltagecurrent measurements. Thin films chemical composition and morphology have been characterized with FTIR spectroscopy, atomic force microscopy (AFM) and contact angle measurements. A strong dependency of deposit character from the HMDSO concentration has been found and then compared with the same dependency of a typical low pressure plasma enhanced chemical vapour deposition process.
The streamer regime of a dielectric barrier discharge device is studied by performing a detailed statistical analysis of current-voltage measurements in air. A wide bandwidth Rogowski coil, designed to work down to the nanoseconds time scale, is used to record the discharge current. The temporal structure of the latter is identified and characterized by its probability density distribution as a function of the applied voltage. The results suggest the existence of two discharge regimes, separated by a well defined voltage threshold, reflecting the different behaviors of the microdischarges. The autocorrelations of the discharge signal are evaluated as a function of the applied voltage, indicating the presence of strong correlations at short-time scales (up to the order of 102 ns) and residual correlations at longer times. The latter are shown to be due to the nonstationarity of the discharge process.
The accuracy of earnings predictions is hampered by the several predominantly unpredictable effects due to the complex evolution of economy. Finding out which are the dominant market features embracing uncertainty is therefore the key to get beyond present state-of-art earnings forecasts. The analysis of annual revenues and earnings data (1954-2008) from the 500 largest-revenue U.S. companies suggests a linear relation between company expected mean profit and revenue. Annual profit fluctuations are then obtained as difference between actual annual profits and expected mean values. It is found that the temporal evolution of profit fluctuations for a single company displays a slowly decaying autocorrelation, yielding Hurst exponents in the range H=0.75+/-0.17 . The study of profits cross correlations between companies suggests a way to distinguish typical earnings years from anomalous ones by looking at minimal information structures contained within the space defined by the associated covariance metric.
Abstract. -We analyze annual revenues and earnings data for the 500 largest-revenue U.S. companies during the period 1954-2007. We find that mean year profits are proportional to mean year revenues, exception made for few anomalous years, from which we postulate a linear relation between company expected mean profit and revenue. Mean annual revenues are used to scale both company profits and revenues. Annual profit fluctuations are obtained as difference between actual annual profit and its expected mean value, scaled by a power of the revenue to get a stationary behavior as a function of revenue. We find that profit fluctuations are broadly distributed having approximate power-law tails with a Lévy-type exponent α ≃ 1.7, from which we derive the associated break-even probability distribution. The predictions are compared with empirical data.
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