"model applied on" is grammatically correct and can be used in written English.
It is most commonly used in technical or scientific contexts to describe a process or experiment in which a particular model or theory is used as a tool or framework. Example: The regression model was applied on the data to determine the relationship between income and education level.
The second method is an econometric conditional demand model applied on data from the same survey.
The result shows the model applied on the recent data set gives higher predictive accuracy on both estimation and holdout sample.
First results based on a simple Corona model applied on a Helium Argon mixture confirm the values obtained by Langmuir probe measurements.
The ε can be calculated using (varepsilon = {text{RT}};ln ;left[ {1 + frac{1}{{C_{text{e}} }}} right])With the obtained E = 22.36 kJ mol−1 for the DRK model applied on the results (Table 1), it should be concluded that a chemisorption process has been occurred.
As a starting hypothesis, we will consider limitations corresponding to the nominal elongation when all the chambers are inflated (80%), thus limiting the possible lengths of the robot to L 0model applied on the previous.
For the automatic extraction of features and tumor detection a with an enhanced feature using Gaussian mixture model applied on MRI images with wavelet features and principal component analysis was proposed by Chaddad [11] with an accuracy of T1- weighted 95% and T2- weighted 92% for FLAIR MRI weighted images.
This model applied one-on-one learning principles whereby HCPs worked in pairs to learn how to use the devices and provide feedback to one another [ 20].
An optimization model applying on the recycling of waste plastics is developed and tested in the Tokyo Metropolitan Region TMRR) in Japan.
The PFR model applies on the high-resolution DEM, and its derivative such as slope has provided an improved quality of outcomes of landslide susceptibility mapping in conjunction with the ASTER DEM and Google Earth's images.
It is shown that recently developed analytical models, applied on different kinds of textile composites, could yield good results, while maintaining more flexibility, easy to apply and the less time consuming in comparing with numerical FE (Finite Element) models.
The association of these variables with climate, topography, landscape, human settlement, and geographic position was explored by means of stepwise regression models applied on the axes obtained from principal component analysis.
