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Violation analysis.
Architectural verification and violation analysis.
4. Architectural verification and violation analysis: After the architecture formalization (step 3), the architects (group 1) execute DCL2Check to compare the planned architecture to the implemented code and to reveal the architectural violations.
Li et al. ([2009b]) developed a constraint-softened interval-fuzzy linear programming (CS-IFLP) method for violation analysis of waste management systems, which can deal with uncertainties presented in terms of fuzzy sets and intervals, allow fuzzy relaxation levels for system constraints, and help to analyze tradeoffs among economic objectives, satisfaction degrees, and constraint-violation risk.
For the first iteration, 50 structures were calculated and the 20 lowest-energy structures were used for calibration and violation analysis.
For the next seven iterations, 20 structures were calculated, with the seven lowest-energy structures used for calibration and violation analysis.
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Huang et al. ([2002]) proposed a violation analysis-based interval-parameter fuzzy integer programming (IPFIP) model for regional waste management, where the model's decision space can be expanded through introduction of violation variables to relax critical constraints.
Huang et al. ([2002]) developed a violation-analysis-based interval-parameter fuzzy integer programming (VA-IPFIP) model and applied it to planning of regional solid waste management systems.
In case of violation, the analysis was stratified for the variable.
To avoid this violation, the data analysis could be performed by allowing inclusion of correlated observations, e.g. including only one prosthesis per patient or by including a shared frailty variable in the Cox regression.
Violations were classified as: minor violations: inadvertent violations and significant (> 4 h) delays in TEG or aPTT analysis major violations: intentional violations by the medical staff for clinical reasons.
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