Search

Assessing or predicting seismic damage in buildings is an essential and challenging component of seismic risk studies. Machine learning methods offer new perspectives for damage characterization, taking advantage of available data on the characteristics of built environments. In this study, we aim (1) to characterize seismic damage using a classification model trained and tested on damage survey data from earthquakes in Nepal, Haiti, Serbia and Italy and (2) to test how well a model trained on a given region (host) can predict damage in another region (target). The strategy adopted considers only simple data characterizing the building (number of stories and building age), seismic ground motion (macroseismic intensity) and a traffic-light-based damage classification model (green, yellow, red categories). The study confirms that the extreme gradient boosting classification model (XGBC) with oversampling predicts damage with 60% accuracy. However, the quality of the survey is a key issue for model performance. Furthermore, the host-to-target test suggests that the model's applicability may be limited to regions with similar contextual environments (e.g., socio-economic conditions). Our results show that a model from one region can only be applied to another region under certain conditions. We expect our model to serve as a starting point for further analysis in host-to-target region adjustment and confirm the need for additional post-earthquake surveys in other regions with different tectonic, urban fabric and socio-economic contexts. [ABSTRACT FROM AUTHOR]

Introduction: Deliberate self-harm (DSH) is one of the common psychiatric emergencies in medical practice. It has become a global health problem with rates increasing over time. Very few studies have been conducted on this important health issue in Nepal. We conducted a hospital based study to evaluate the cause, mode and psychiatric comorbidities present in patients of DSH., Methods: This cross sectional study was performed on 200 cases of deliberate self-harm in a tertiary referral centre in Eastern Nepal from April 2012 to July 2012 by the data collected from the medical records of these patients. Various sociodemographic data and psychiatric comorbidities prevalent in them were studied., Results: Most of the patients (77%) were below the age of 35. The female-to-male ratio was 1.35:1. 76% of the patients had received formal education. Majority (73.5%) were married. By occupation, 38% were housewives and 25.5% were students. 72.5% of cases had consumed organophosphates/-chlorides. Interpersonal conflict (72%) was the major cause for DSH. Psychiatric disorders according to ICD-10 criteria were found in 37% of cases and premorbid personality problems were found in 20% of cases. The most prevalent psychiatric disorder was adjustment disorder (13.5%) followed by mood disorder (11%)., Conclusion: Majority of DSH cases were of younger generation. Psychiatric disorders and comorbid personality problems were commonly seen in DSH patients. This has significance for proper evaluation and management.