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Data integrity refers to designing a DBMS that ensures the correctness and stability of its data across all applications that access the system.
Conformational correctness and stability of the peptide designs, as evaluated by molecular dynamics simulations, correctly predicted the immunogenicity of the peptides.
Using the structured-output learning framework, we provide theoretical analyses and carry out simulations to characterize the effect of growing experimental annotations on the correctness and stability of performance estimates corresponding to different types of methods.
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The statistical performance indices based on error from standard numerical solutions are used to validate the correctness, consistency, robustness and stability of the proposed stochastic solver.
Further, its conformational correctness and stability enhancement was assessed using molecular dynamics (MD) simulations.
The correctness of the POD process is assessed by, for example, checking convergence and stability of the iterative estimation process (i.e. formal errors of, and correlations between, estimated parameters), the observation fit and overlap analysis between consecutive orbit solutions.
A series of simulations demonstrate the correctness and effectiveness of the transient stability mechanism.
In the end, we provide two illustrated examples to affirm the correctness and reasonableness of acquired finite-time passivity and stability results.
Correctness and advantages of the method are shown.
The correctness and practicality of this design scheme are verified.
The correctness and efficiency of this method were demonstrated.
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