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).

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.

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.

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).

Abstract: In this paper we present the concept of modeling and solving uncertainly defined boundary value problems described by 2D Laplace's equation. We define uncertainty of input data (shape of boundary and boundary conditions) using interval numbers. Uncertainty can be considered separately for selected or simultaneously for all input data. We propose interval parametric integral equations system (IPIES) to solve so-define problems. We obtain IPIES in result of PIES modification, which was previously proposed for precisely (exactly) defined problems. For this purpose we have to include uncertainly defined input data into mathematical formalism of PIES. We use pseudo-spectral method for numerical solving of IPIES and propose modification of directed interval arithmetic to obtain interval solutions. We present the strategy on examples of potential problems. To verify correctness of the method, we compare obtained interval solutions with analytical ones. For this purpose, we obtain interval analytical solutions using classical and directed interval arithmetic.

Abstract: The Probabilistic Capacitated Vehicle Routing Problem (PCVRP) is a generalization of the classical Capacitated Vehicle Rout- ing Problem (CVRP). The main difference is the stochastic presence of the customers, that is, the number of them to be visited each time is a random variable, where each customer associates with a given probability of presence. We consider a special case of the PCVRP, in which a eet of identical vehicles must serve customers, each with a given demand consisting in a set of rectangular items. The vehicles have a two-dimensional loading surface and a maximum capacity. The resolution of problem consists in finding an a priori route visiting all customers which minimizes the expected length over all possibilities. We propose a hybrid heuristic, based on a branch-and-bound algorithm, for the resolution of the problem. The effectiveness of the approach is shown by means of computational results.

Abstract: One of the most evident and well-known limitations of the Semantic Web technology is its lack of capability to deal with uncertain knowledge. As uncertainty is often part of the knowledge itself or can be inducted by external factors, such a limitation may be a serious barrier for some practical applications. A number of approaches have been proposed to extend the capabilities in terms of uncertainty representation; some of them are just theoretical or not compatible with the current semantic technology; others focus exclusively on data spaces in which uncertainty is or can be quantified. Human-inspired models have been adopted in the context of different disciplines and domains (e.g. robotics and human-machine interaction) and could be a novel, still largely unexplored, pathway to represent uncertain knowledge in the Semantic Web. Human-inspired models are expected to address uncertainties in a way similar to the human one. Within this paper, we (i) briefly point out the limitations of the Semantic Web technology in terms of uncertainty representation, (ii) discuss the potentialities of human-inspired solutions to represent uncertain knowledge in the Semantic Web, (iii) present a human-inspired model and (iv) a reference architecture for implementations in the context of the legacy technology.

Abstract: A new Monte Carlo algorithm for solving singular linear sys- tems of equations is introduced. In fact, we consider the convergence of resolvent operator R and we construct an algorithm based on the map- ping of the spectral parameter . The approach is applied to systems with singular matrices. For such matrices we show that fairly high accuracy can be obtained.