In radar signature analysis, the root mean square (RMS) surface slope is utilized to assess the relative contribution of multiple scattering effects. For an exponentially correlated surface, an effective RMS slope can be determined by truncating the high frequency tail of the roughness spectrum. The choice of the cutoff frequency and the effect on surface scattering simulations are discussed.

Abstract. The root-mean-squared error (RMSE) and mean absolute (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it the reader decide which more relevant. In recent reprise 200-year debate Willmott Matsuura (2005) Chai Draxler (2014) give arguments favoring one metric or other. How...

The topographical features of fractured tensile, flexural, K1C, and impact specimens of monolithic epoxy have been studied and correlated with mechanical properties and surface features of samples before fracture. The topographical features studied include waviness (Wa), roughness average (Ra), root mean square value (Rq), and maximum roughness height (Rmax or Rz). As surface notches generate t...

In this study, the performance of three different self organization feature map (SOFM) network models denoted as SOFM1, SOFM2, and SOFM3 having neighborhood shapes, namely, SquareKohonenful, LineKohonenful, and DiamondKohenenful, respectively, to predict the critical factor of safety (Fs) of a widely-used artificial slope subjected to earthquake forces was investigated and compared. For this pu...

[1] Inverse weighted distance and regression nonexact techniques were evaluated for interpolating methods snow water equivalent (SWE) across the entire Colorado River Basin of the western United States. A 1-km spacing was used for the gridding of snow telemetry (SNOTEL) measurements for the years 1993, 1998, and 1999, which on average, represented higher than average, average, and lower than av...

‎This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...