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Assessing post-seismic damage on an urban/regional scale remains relatively difficult owing to the significant amount of time and resources required to acquire information and conduct a building-by-building seismic damage assessment. However, the application of new methods based on artificial intelligence, combined with the increasingly systematic availability of field surveys of post-seismic damage, has provided new perspectives for urban/regional seismic damage assessment. This study analyzes the effectiveness and relevance of a number of machine learning techniques for analyzing spatially distributed seismic damage after an earthquake at the regional scale. The basic structural parameters of a portfolio of buildings and the post-earthquake damage surveyed after the Nepal 2015 earthquake are analyzed and combined with macro-seismic intensity values provided by the United States Geological Survey ShakeMap tool. Among the methods considered, the random forest regression model provides the best damage predictions for specified ground motion intensity values and structural parameters. For traffic-light-based damage classification (three classes: green-, amber-, and red-tagged buildings based on post-earthquake damage grade), a mean accuracy of 0.68 is obtained. This study shows that restricting learning to basic features of buildings (i.e. number of stories, height, plinth area, and age), which could be readily available from authoritative databases (e.g. national census) or field-surveyed databases, yields a reliable prediction of building damage (4 features/3 damage grade accuracy: 0.64).

Building-specific loss assessment methodologies use component fragility curves to compute expected losses in the aftermath of earthquakes. Such curves are not available for steel columns assuming they remain elastic because of capacity design considerations. Nonetheless, first-story steel columns in moment-resisting frames (MRFs) are expected to experience damage through flexural yielding and formation of geometric instabilities. This paper uses an experimental database that was recently assembled to develop two sets of univariate drift-based column fragility curves that consider the influence of loading history. Ordinal logistic regression is also employed to develop multivariate fragility curves that capture geometric and loading parameters that affect column performance. The implications of the proposed fragility curves for building-specific loss assessment are demonstrated using an eight-story office building with steel MRFs. It is shown that structural repair costs in this case may increase by 10%, regardless of seismic intensity, when column damage is considered. Similarly, the contribution of structural component repairs to expected annual losses may double over the building's life span.

The evidence to support implantable cardioverter defibrillator (ICD) in subjects with nonischemic cardiomyopathy (NICM) for primary prevention of sudden cardiac death (SCD) is not robust. This meta‐analysis intends to assess the impact of routine ICDimplantation for primary prevention of mortality due to SCDin NICMbased on all the published randomized clinical trials (RCTs). Six RCTs were selected using PubMed/Medline, EMBASE, and CENTRALfrom inception to December 2016. Outcomes were calculated as random‐effects relative risk (RR) and risk difference (RD) with 95% confidence interval (CI). Patients were randomized to ICDarm and control arm (usual care, medical treatment, and anti‐arrhythmic drugs). ICDsignificantly reduced all‐cause mortality in NICMpatients (RR, 0.74, 95% CI, 0.56‐0.97, P= .03, I2= 40). Mortality benefit was achieved due to a significant reduction in sudden cardiac death (SCD) (RR, 0.47, 95% CI, 0.30‐0.73, P< .001, I2= 0). There were no statistical differences between two groups with regard to risk of noncardiac mortality, non‐SCD, cardiac arrest, cardiac transplant, sustained ventricular tachycardia (VT), and VTrequiring medical treatment. Our results support efficacy of ICDs at reducing all‐cause mortality due to a reduction in SCD.

Pancreas divisum is the most common congenital malformation of the pancreas. Sometimes it is considered an etiology when patients present with recurrent episodes of pancreatitis. Endoscopic retrograde pancreatography (ERP) with selective cannulation of the minor papilla with sphincterotomy and stent placement are performed in these patients. Proximal migration of pancreatic stents in pancreas divisum is rare and challenging to manage. We describe a case of proximal migration of a pancreatic stent in a patient with pancreas divisum and perform a review of literature.

No relevant conflicts of interest to declare.

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