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Physical models with different length scales are created in order to directly resolve the very narrow escaping gaps on the pot shell in the physical model with smallest scale.
Since the AIC differences is relatively low, and the prediction RMSE not only have smaller differences but also it is unavailable in a realistic case, we will henceforth use the model with smallest fit RMSE that is ARIMA 2,1,2)–as a baseline univariate model for further comparison.
These models were tested with R package AICcmodavg with the model with smallest AIC score selected.
Among these, a model is chosen that has the greatest agreement to data, given its number of effective parameters, as measured by the model with smallest deviance information criterion (DIC) [ 13].
In each simulation, we calculated the usual 95% confidence interval for the regression parameter in the hypothesized model with the largest F test P value (coincidentally the model with smallest Akaike information criterion) when compared with the saturated model.
In some temperature-dependent cases, the data within the thermal range was better fitted by linear regression (the exponential and linear model having the same number of parameters, the model with smallest sum of residuals was considered to be the best).
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Figure 7 Fractional model with small α.
A model with smaller AIC value represents a better overall fit.
The Miocene Kahramanmaraş Peripheral Foreland Basin (KPFB) resemble to classic foreland basin model, with small differences.
However, with the rough-walled model with smaller fracture aperture (0.26 mm), only dispersed flow was observed.
We consider a channel model with small scale fading and large scale path loss for a practical scenario.
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