Sentence examples for model that subsequently from inspiring English sources

Picking a model that subsequently spends an inordinate of time in the repair shop can quickly siphon away any savings realized from buying a used car in the first place.

RBC defines the top choroidal boundary and is used as initialization for the statistical model that subsequently finds the CSI (choroidal sclera interface) boundary.

Random Forest modeling was used in view of the development of an accurate and reliable model that was subsequently validated.

Such a model can be configured to obtain a specific process model that is subsequently used to handle individual cases, for instance, to process customer orders.

This required the production of a finished clay model that was subsequently cast in plaster and then reproduced in stone or wood in a more or less mechanical way by means of a pointing machine (see Pointing below).

It is shown how to extract the global stiffness kernels from simulations with the 3D inhomogeneous continuum, and implement them in a 1D non-local Winkler-type model that can subsequently serve as a stand-alone, condensed substructure.

We present an adapted version of the SPEROS model that is subsequently used to study the interaction of water erosion, tillage erosion and rock fragment cover under various land use scenarios on semi-arid hillslopes in southeast Spain.

We performed forward selection of logistic regression models, selecting the best model that was subsequently assessed for discrimination and calibration, validated, and converted into a clinical diagnostic chart to benchmark the boundaries of detection from a cohort of 30 patients considered to have LVNC.

A 24 full factorial central composite design was used to develop the quadratic model that was subsequently optimized and the optimal conditions were as follows: temperature 40 °C, pH 7.72, enzyme concentration 5.28 mg/g oil, buffer concentration 1 g/g oil and there was 65.5% conversion in 6 h.

Segmentation for 3D Printing – This step begins with DICOM images from the medical imaging study and allows for segmentation to transform them into an unaltered surface-based 3D model that can subsequently be used for 3D printing.

Using training data to build a model that is subsequently used to predict on new data relies on the obvious assumption that the two samples are drawn from the same population.