M. Àngela Nebot Castells

Social Aggravation Estimation to Seismic Hazard Using Classical Fuzzy Methods

In the last years, from a disasters perspective, risk has been dimensioned to allow a better management. However, this conceptualization turns out to be limited or constrained, by the generalized use of a fragmented risk scheme, which always consider first, the approach and applicability of each discipline involved. To be congruent with risk definition, it is necessary to consider an integral frame, and social factors must be included. Even those indicators that could tell something about the organizational and institutional capacity to withstand natural hazards, should be invited to the table. In this article, we analyze one of the most important elements in risk formation: the social aggravation, which can be regarded as the convolution of the resilience capacity and social fragility of an urban center. We performed a social aggravation estimation over Barcelona, Spain and Bogota, Colombia considering a particular hazard in the form of seismic activity. The Aggravation coefficient was achieved through a Mamdami fuzzy approach, supported by well-established fuzzy theory, which is characterized by a high expressive power and an intuitive human-like manner.

A prospective fuzzy approach for the development of integral seismic risk scenarios for Barcelona, Spain

We create a set of synthetic seismic risk scenarios by combining stochastic seismic simulations with social fragility indicators by mean of a fuzzy Mamdani type inference nested-model. The original values of the social economic variables were modified by arbitrary increments to simulate either constrains or improvement in their reported levels, and the Fuzzy Seismic Risk Model was applied again for each of these variations to produce a range of final integral seismic risk levels. Even if this experiment clearly needs to be further tuned, the use of fuzzy inference in the creation of risk scenarios becomes a simpler task once suitable membership functions have been defined, since the non-linear influence of each of the variables involved can be easily quantified. The final product is capable to facilitate the prospective view needed in decision-making planning while avoiding compensability issues, commonly reflected when composite indicators are used to represent social dimensions.

Small-particle pollution modeling using fuzzy approaches

Kinetic Analysis of the Coke Calcination Processes in Rotary Kilns.- Behavior of Elastomeric Seismic Isolators Varying Rubber Material and Pad Thickness: A Numerical Insights.- Numerical Simulation of Coastal Flows in Open Multiply-connected Irregular Domains.- System Dynamics and Agent-based Simulation for Prospective Health Technology Assessments.- Simple and Efficient Algorithms to get a Finer Resolution in a Stochastic Discrete Time Agent-based Simulation.- Numerical Study of Turbulent Boundary-layer Flow Induced by a Sphere above a Flat Plate.- Airflow and Particle Deposition in a Dry Powder Inhaler: An Integrated CFD Approach.Air pollution caused by small particles is a major public health problem in many cities of the world. One of the most contaminated cities is Mexico City. The fact that it is located in a volcanic crater surrounded by mountains helps thermal inversion and imply a huge pollution problem by trapping a thick layer of smog that float over the city. Modeling air pollution is a political and administrative important issue due to the fact that the prediction of critical events should guide decision making. The need for countermeasures against such episodes requires predicting with accuracy and in advance relevant indicators of air pollution, such are particles smaller than 2.5 microns (PM 2.5). In this work two different fuzzy approaches for modeling PM 2.5 concentrations in Mexico City metropolitan area are compared with respect the simple persistence method.

Using the Fuzzy Inductive Reasoning methodology to improve coherence in algorithmic musical beat patterns

In the present work, the Fuzzy Inductive Reasoning methodology (FIR) is used to improve coherence among beat patterns, structured in a musical A-B form. Patterns were generated based on a probability matrix, encoding a particular musical style, designed by experts. Then, all possible patterns were generated and the most probables were selected. A-B musical forms were created and the coherence of the sequence was evaluated by experts by using linguistic quantities. The output pairs (A-B pattern and its qualification) were used as inputs to train a FIR system, and the variables that produce “coherent” outputs and the relations among them where identified as rules. The extracted rules are discussed in the context of the musical form and from the psychological perception.