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Computational damage predictions are compared with experimental data, showing the applicability for complex structures of the analysis tools here presented.
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Although the cross-sectional structure of the analysis and the existence of potential endogeneity bias warrant caution in developing policy recommendations, we offer three tentative policy implications.
Due to the structure of the analysis problems in computational settings, the complexity of the problem space manifests itself as a high-dimensional analysis problem, where dimensionality is most often associated with the number of measurements being considered simultaneously.
Figure 1 illustrates the basic structure of the analysis.
The structure of the analysis including clarifying examples is presented in Figure 1.
The structure of the analysis is automatically documented together with all execution parameters of the participating components, which enables reproduction of the results.
The central panel displays the input form for the different analysis steps, while at the bottom a graphical representation of the workflow allows easily monitoring the elements and structure of the Analysis.
After consensus was reached between the first and second authors about the scope of the analytic focus, an interpretive matrix was developed that outlined the structure of the analysis and identified distinct experiences that characterized the themes associated with transitioning.
To assist in the structuring of this analysis, we utilized a consultation at the end of the HRCS Global program.
A schematic example of the structure of a canned analysis digital object is illustrated in Fig. 2. Figure 2: Structure of the canned analysis digital object.
This approach reconstructs the data structures of the guest for analysis at the hypervisor level.
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