RESUMO
Simulation studies of the atomic shear stress in the local potential energy minima (inherent structures) are reported for binary liquid mixtures in 2D and 3D. These inherent structure stresses are fundamental to slow stress relaxation and high viscosity in supercooled liquids. We find that the atomic shear stress in the inherent structures (IS's) of both liquids at rest exhibits slowly decaying anisotropic correlations. We show that the stress correlations contribute significantly to the variance of the total shear stress of the IS configurations and consider the origins of the anisotropy and spatial extent of the stress correlations.
RESUMO
BACKGROUND: Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified. OBJECTIVE: The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets. DESIGN: Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits. Measurement Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated. RESULTS: The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution. CONCLUSIONS: For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.