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Furthermore, in this paper, according to the proposed method, we divided the stance phase into two fractions, namely the first stance phase (FSP) and the second stance phase (SSP), as shown in Fig. 2.
Based on the Qi et al. (2015) classification method, we divided daytime population into four types: (1) residential population; (2) working population; (3) students; and (4) random population (customers, tourists, and patients).
In this method, we divided the first 800ms of ERP activities, which was recorded after onset of test object set into separate time windows with 48ms length and 36ms overlap between each two adjacent windows.
Using the aforementioned method, we divided all PicTar targets into two groups.
Using this method, we divided the data set into 10 clusters, and three subsets (i.e., training, test, and validation sets) were selected from them.
To apply the PERR adjustment method, we divided the unadjusted hazard ratio of exposed versus unexposed groups during the study by the unadjusted hazard ratio of exposed versus unexposed "before" the study.
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In individually normalizing method, we divide the pixel number in a bin by the total pixel number of the SCD.
To implement the box-counting method, we divide the 3D PET/CT image of the human body in cubic boxes (3D pixels-voxels) of linear size s.
In the method, we divide the model Eq. (1) for a station by the same model but for another station, as follows: (5) (6) (7) (8) (9)where (10)is the latitude of the midpoint between the two stations, (11)is the distance between the two stations (x1 > x2 is assumed here), (12 is the dimensionless resonance latitude (offset to zero at x0), and (13)is the dimensionless resonance width.
In this method, we divide the limit cycle trajectory into N finite elements, and approximate each with a K degree Lagrange interpolating polynomial, x t).
Methods: We divided traditional 2 day USLS-BL course structure into two separate 1-day courses, conducted at 5 months apart.
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