Solving Problems with Uncertainties (SPU) Session 1

Time and Date: 13:15 - 14:55 on 12th June 2018

Room: M8

Chair: Vassil Alexandrov

334 Statistical and Multivatiate Analysis Applied to a Database of Patients with Type-2 Diabetes [abstract]
Abstract: The prevalence of type 2 Diabetes Mellitus (T2DM) has reached critical proportions globally over the past few years. Diabetes can cause devastating personal suffering and its treatment represents a major economic burden for every country around the world. To property guide effective actions and measures, the present study aims to examine the profile of the diabetic population in Mexico. We used the Karhunen-Lo\`{e}ve transform which is a form of principal component analysis, to identify the factors that contribute to T2DM. The results revealed a unique profile of patients who cannot control this disease. Results also demonstrated that compared to young patients, old patients tend to have better glycemic control. Statistical analyses reveal patient profiles and their health results and identify the variables that measure overlapping health issues as reported in the database (i.e. collinearity).
Diana Canales, Neil Hernandez-Gress, Ram Akella and Ivan Perez
368 Bayesian based approach learning for outcome prediction of soccer matches [abstract]
Abstract: In the current world, sports produce considerable data such as players skills, game results, season matches, leagues management, etc. The big challenge in sports science is to analyze this data to gain a competitive advantage. The analysis can be done using several techniques and statistical methods in order to produce valuable information. The problem of modeling soccer data has become increasingly popular in the last few years, with the prediction of results being the most popular topic. In this paper, we propose a Bayesian Model based on rank position and shared history that predicts the outcome of future soccer matches. The model was tested using a data set containing the results of over 200,000 soccer matches from different soccer leagues around the world.
Laura Hervert-Escobar, Neil Hernandez-Gress and Timothy I. Matis
387 Reducing Data Uncertainty in Forest Fire Spread Prediction: a Matter of Error Function Assessment [abstract]
Abstract: Forest fires are a significant problem that every year causes important damages around the world. In order to efficiently tackle these hazards, one can rely on forest fire spread simulators. Any forest fire evolution model requires several input data parameters to describe the scenario where the fire spread is taking place, however, these data are usually subject to high levels of uncertainty. To reduce the impact of the input-data uncertainty, different strategies have been developed during the last years. One of these strategies consists of adjusting the input parameters according to the observed evolution of the fire. This strategy emphasizes how critical is the fact of counting on reliable and solid metrics to assess the error of the computational forecasts. The aim of this work is to assess eight different error functions applied to forest fires spread simulations in order to understand their respective advantages and drawbacks, as well as to determine in which cases they are beneficial or not.
Carlos Carrillo, Ana Cortés, Tomàs Margalef, Antonio Espinosa and Andrés Cencerrado
335 Analysis of the accuracy of OpenFOAM solvers for the problem of supersonic flow around a cone [abstract]
Abstract: The numerical results of comparing the accuracy for some OpenFOAM solvers are presented. The comparison was made for the problem of inviscid compressible flow around a cone at zero angle of attack. The results obtained with the help of various OpenFOAM solvers are compared with the known numerical solution of the problem with the variation of cone angle and flow velocity. This study is a part of a project aimed to create a reliable numerical technology for modelling the flows around elongated bodies of rotation (EBR).
Alexander Bondarev and Artem Kuvshinnikov