Recommendations for the quantitative analysis of landslide risk

This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts

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

A reliability index extrapolation method for separable limit states

When the limit state function (or performance function) of a structure can be written as the difference of a capacity function and a response function that are expressed in terms of independent sets of random variables (i.e., when the limit state function has a separable form), efficient simulation based techniques (e.g., Separable Monte Carlo Simulation method) can be used to predict the reliability of the structure. The accuracies of these simulation based techniques, on the other hand, diminishes as the structural reliability increases. This paper proposes a reliability index extrapolation method to predict reliability of a highly safe structure that has a separable limit state function. In this method, the standard deviations of the random variables that contribute to the capacity function are artificially inflated by using a scale parameter to obtain various (smaller) scaled reliability index values (that can be predicted accurately with small number of samples). The standard deviations of the random variables that contribute to the response function are kept unchanged in order to use the same response values in prediction of various scaled reliability indices. Then, least square regression is used to build a relationship between the standard deviation scale parameter and scaled reliability index values. Finally, an extrapolation is performed to estimate the actual (higher) reliability index. The accuracy of the proposed method is evaluated through reliability assessment of mathematical and structural mechanics example problems as well as a reliability based design optimization problem. It is found that the proposed method can provide reasonable accuracy for high reliability index estimations with only 1000 response function evaluations