International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

Variational Bayesian Approximations for Nonlinear Inverse Problems

Bayesian formulations represent one of the prominent approaches for addressing problems of model calibration. Existing Bayesian methodologies are hampered by the high-dimensionality of unknown model parameters and the high computational cost for inference. The present paper advocates a Variational Bayesian inference engine which exploits derivative information available from deterministic adjoint formulations. Furthermore we propose sparsity-enforcing priors that are suited for spatially-varying model parameters and a greedy algorithm for learning the associated basis set

A COMPARATIVE STUDY OF RELIABILITY ESTIMATION PROCEDURES FOR HIGH DIMENSIONS

In this paper, available standard procedures for the evaluation of failure probabilities are compared and critically discussed in view of their applications in high dimensions, multiple failure domains and implicit performance function (FE-analyses). Approximate FORM/SORM and Importance Sampling are compared with Line Sampling. It is shown that FORM lacks robustness in high dimensions and SORM is simply not feasible for this class of problems. Importance Sampling also looses its robustnes and accuracy in case a surrogate limit state functions, determined by the response surface method, is used. The proposed Line Sampling procedure, however, does not require any approximating surrogate limit state surface and therefore combines the property of robustnes with accuracy. Moreover, multiple failure domain can be treated quite simple and straight forward