The article engages with the recent studies on multilevel regulation. The starting point for the argument is that contemporary multilevel regulation—as most other studies of (postnational) rulemaking—is limited in its analysis. The limitation concerns its monocentric approach that, in turn, deepens the social illegitimacy of contemporary multilevel regulation. The monocentric approach means that the study of multilevel regulation originates in the discussions on the foundation of modern States instead of returning to the origins of rules before the nation State was even created, which is where the actual social capital underlying (contemporary) rules can be found, or so I wish to argue. My aim in this article is to reframe the debate. I argue that we have an enormous reservoir of history, practices, and ideas ready to help us think through contemporary (social) legitimacy problems in multilevel regulation: namely all those practices which preceded the capture of law by the modern State system, such as historical alternative dispute resolution (ADR) practices.
The prediction of mechanical elastic response of laminated hybrid polymer composites with basic carbon nanostructure, that is carbon nanotubes and graphene, inclusions has gained importance in many advanced industries like aerospace and automotive. For this purpose, in the current work, a hierarchical, four-stage, multilevel framework is established, starting from the nanoscale, up to the laminated hybrid composites. The proposed methodology starts with the evaluation of the mechanical properties of carbon nanostructure inclusions, at the nanoscale, using advanced 3D spring-based finite element models. The nanoinclusions are considered to be embedded randomly in the matrix material, and the Halpin-Tsai model is used in order to compute the average properties of the hybrid matrix at the lamina micromechanics level. Then, the standard Halpin-Tsai equations are employed to establish the orthotropic elastic properties of the unidirectional carbon fiber composite at the lamina macromechanics level. Finally, the lamination theory is implemented in order to establish the macroscopic force-strain and moment-curvature relations at the laminate level. The elastic mechanical properties of specific composite configurations and their performance in different mechanical tests are evaluated using finite element analysis and are found to considerably increase with the nanomaterial volume fraction increase for values up to 0.5. Further, the hybrid composite structures with graphene inclusions demonstrate better mechanical performance as compared to the identical structures with CNT inclusions. Comparisons with theoretical or other numerical techniques, where it is possible, demonstrate the accuracy of the proposed technique.
ObjectiveTo compare estimates of effect and variability resulting from standard linear regression analysis and hierarchical multilevel analysis with cross-classified multilevel analysis under various scenarios.Study design and settingWe performed a simulation study based on a data structure from an observational study in clinical mental health care. We used a Markov chain Monte Carlo approach to simulate 18 scenarios, varying sample sizes, cluster sizes, effect sizes and between group variances. For each scenario, we performed standard linear regression, multilevel regression with random intercept on patient level, multilevel regression with random intercept on nursing team level and cross-classified multilevel analysis.ResultsApplying cross-classified multilevel analyses had negligible influence on the effect estimates. However, ignoring cross-classification led to underestimation of the standard errors of the covariates at the two cross-classified levels and to invalidly narrow confidence intervals. This may lead to incorrect statistical inference. Varying sample size, cluster size, effect size and variance had no meaningful influence on these findings.ConclusionIn case of cross-classified data structures, the use of a cross-classified multilevel model helps estimating valid precision of effects, and thereby, support correct inferences.
MULTIFILE
