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Singular AR models are significantly different from regular AR models because many results for regular AR models are no longer true.
Best predictor models are significantly improved when the influences of elevation, coastal effects and urban structure are added.
While the study in [21] shows that the typical random-based mobility models are significantly different from the real-move pattern of the daily lives of human being.
We show that the spatial models are significantly different and that they considerably improve the likelihood of observing the original data.
In addition, the performances of the neural network models are significantly superior compared with those of second-order swelling kinetics, quadratic and cubic models of response surface methodology.
The entrainment coefficients found by the fitting of the analytical models are significantly lower than the measured spread rate of the plumes.
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Full-spectrum models were significantly affected by lactose particle size.
The RHL models were significantly affected by urban and forest areas, reflecting urban heat islands.
The performance of the MJS and ANN models was significantly improved by incorporating root-zone soil moisture.
To examine whether the filled test models were significantly different from the original inverted models, we adopted the F test.
The results indicated that the response-surface models were significantly (p < 0.05) fitted for all studied response variables.
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