Evaluación holística del riesgo sísmico en zonas urbanas

Risk has been defined, for management purposes, as the potential economic, social and environmental consequences of hazardous events that may occur in a specified period of time. In the past, the concept of risk has been defined in many cases in a fragmentary way, according to each scientific discipline involved in its appraisal. A framework and a multidisciplinary risk evaluation model is proposed in this article that take into account not only the expected physical damage, the number and type of casualties or the economic losses, but also the conditions related to soial fragility and lack of resilience which favor second order effects (or indirect effects) when an earthquake strikes an urban centre. Thus, the mentioned evaluation is holistic, that is based on an integrated and comprehensive approach, made by using indicatos andoriented towards guiding decision-making. Th conceptual framework and the model proposed are also valid for their applicationto multihazard risk evaluation, although this article has been focused on the evaluation of the seismic riskk. The first step in obtaining the Urban Seismic Risk index (USRi) consists of calculating a Physical Risk index for each unit of analysis starting form existing physical risk scenarios. An impact factor, associated with a set of socio-economic and lack of resilience conditions of the community, is applied in a second step to the physical risk index in order to obtain the USRi. It has been demostrated that the proponed holistic evaluationmodel of the seismic risk in robust, provideing stable and reliable values of the USRi. Finally, numerical simulation results of the seismic risk obtained with the proposed model are given in the article for the cities of Bogota (Colombia), Barcelona (Spain) and Manila (The Phillipines)

Predicting outcomes of pelvic exenteration using machine learning

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods