DBELA is a Displacement-Based Earthquake Loss Assessment methodology for urban areas which relates the displacement capacity of the building stock to the displacement demand from earthquake scenarios. The building stock is modeled as a random population of building classes with varying geometrical and material properties. The period of vibration of each building in the random population is calculated using a simplified equation based on the height of the building and building type, whilst the displacement capacity at different limit states is predicted using simple equations which are a function of the randomly simulated geometrical and material properties. The displacement capacity of each building is then compared to the displacement demand obtained, from an over-damped displacement spectrum, using its period of vibration; the proportion of buildings which exceed each damage state can thus be estimated. DBELA has been calibrated to the Turkish building stock following the collection of a large database of structural characteristics of buildings from the northern Marmara region. The probabilistic distributions for each of the structural characteristics (e.g. story height, steel properties etc.) have been defined using the aforementioned database. The methodology has then been applied to predict preliminary damage distributions and social losses for the Istanbul Metropolitan Municipality for a Mw 7.5 scenario earthquake.
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Background: The diagnosis of sarcopenia is essential for early treatment of sarcopenia in older adults, for which assessment of appendicular lean mass (ALM) is needed. Multi-frequency bio-electrical impedance analysis (MF-BIA) may be a valid assessment tool to assess ALM in older adults, but the evidences are limited. Therefore, we validated the BIA to diagnose low ALM in older adults.Methods: ALM was assessed by a standing-posture 8 electrode MF-BIA (Tanita MC-780) in 202 community-dwelling older adults (age ≥ 55 years), and compared with dual-energy X-ray absorptiometry (DXA) (Hologic Inc., Marlborough, MA, United States; DXA). The validity for assessing the absolute values of ALM was evaluated by: (1) bias (mean difference), (2) percentage of accurate predictions (within 5% of DXA values), (3) the mean absolute error (MAE), and (4) limits of agreement (Bland-Altman analysis). The lowest quintile of ALM by DXA was used as proxy for low ALM (< 22.8 kg for men, < 16.1 kg for women). Sensitivity and specificity of diagnosing low ALM by BIA were assessed.Results: The mean age of the subjects was 72.1 ± 6.4 years, with a BMI of 25.4 ± 3.6 kg/m2, and 71% were women. BIA slightly underestimated ALM compared to DXA with a mean bias of -0.6 ± 1.2 kg. The percentage of accurate predictions was 54% with a MAE of 1.1 kg, and limits of agreement were -3.0 to + 1.8 kg. The sensitivity for ALM was 80%, indicating that 80% of subjects who were diagnosed as low ALM according to DXA were also diagnosed low ALM by BIA. The specificity was 90%, indicating that 90% of subjects who were diagnosed as normal ALM by DXA were also diagnosed as normal ALM by the BIA.Conclusion: This comparison showed a poor validity of MF-BIA to assess the absolute values of ALM, but a reasonable sensitivity and specificity to recognize the community-dwelling older adults with the lowest muscle mass.
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Bergen city centre is prone to both subsidence and flooding. With a predicted increase in precipitation due to climate change, a higher proportion of rainfall becomes surface runoff, which results in increased peak flood discharges. In addition, it has been predicted that sea-level rise and increasing storm surges will result in coastal flooding. In this study, the dual hazards of flooding and subsidence are analysed to exemplify possible risk assessment maps for areas most prone to the combination of both. Risk assessment maps are a support tool to identify areas where mitigation of subsidence and adaptation for surface water management will be most efficient and measures can be implemented. The results show that dual hazard assessment, like that described in this paper, can be a useful tool for decision-makers when prioritizing areas to implement measures such as Sustainable Urban Drainage Systems.
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