Analysis of sea water infiltration in a sewage treatment plant using Random Forests and variable importance measures

Treball de Fi de Màster Universitari en Matemàtica Computacional (Pla de 2013). Codi: SIQ527. Curs 2016/2017In ltration of salt water in water-treatment plants is a current challenge in Spanish coasts. This is a problem because when the sea water enters into the plant, it damages the lters and compromise the quality of the water ltration. In order to detect this water in ltration, the usual approach is to measure conductivity of the ows (ability to conduct electricity). The work subsequently described in this document is a project funded by a research internship of the Universitat Jaume I's C atedra FACSA de Innovaci on del Ciclo Integral del Agua. The goal of this project is to detect when a saltwater in ltration has occurred and to detect the most relevant variables which are related with the rising of water conductivity. The approach chosen to deal with this problem has been the technique of Random Forests, a family of algorithm based on decision trees. The reasons of this elections are mainly the exibility with respect missing data that Random Forests allow; the \black box"-like behaviour that does not need an a priori knowledge of the data structure; and the ability to explore the variables' importance through several measures. In this project the mentioned methodology will be explained in detail, as well as the results obtained and the conclusions that follows them

Analysis of blood flow in one dimensional elastic artery using Navier-Stokes conservation laws

En los últimos años, la simulación computacional en ámbitos médicos ha aumentado notablemente en múltiples ramas de la ciencia, desde modelización a métodos numéricos, pasando por informática. Los principales objetivos de esta disciplina incipiente son comprobar hipótesis antes de una intervención, o ver qué efecto podría tener un medicamento antes de tomarlo, entre otros. En este trabajo deduciremos desde los principios físicos más básicos un modelo unidimensional para la simulación del flujo sanguíneo en arterias elásticas. Proporcionaremos un marco histórico, así como una revisión de este tipo de modelos. Estudiaremos también desde el punto de vista del análisis matemático las ecuaciones del modelo obtenido, alcanzando un resultado original para la formación de ondas de choque en arterias deformables. A continuación, realizaremos algunas simulaciones numéricas usando el método de elementos finitos de Galerkin discontinuo. Puesto que esto es en realidad una familia de métodos, motivaremos y detallaremos las elecciones y las estrategias de implementación.In the last years, medical computer simulation has seen a great growth in several scientific branches, from modelling to numerical methods, going through computer science. The main goals of this incipient discipline are testing hypotheses before an intervention, or see what effect could have a drug in the system before actually taking it, among others. In this work we deduce from the most basic physical principles a one dimensional model for the simulation of blood flow in elastic arteries. We will provide some historical background, as well as a brief state of the art of these models. We will also study from a calculus point of view the equations of the model obtained, achieving an original result for the formation of shock waves in compliant vessels. Afterwards we will make some numerical simulations using Galerkin Discontinuous Finite Element Method. Since this is actually a family of methods, we will motivate and detail the elections and the implementation strategies

Position based constraint enforcement in game physics

La simulación de sistemas mecánicos para videojuegos y otras aplicaciones interactivas impone restricciones importantes en cuanto a estabilidad, flexibilidad en las escenas y complejidad computacional. En los últimos años han aparecido varias estrategias para la resolución de sistemas mecánicos con restricciones. Algunas de las más populares en el desarrollo de videojuegos usan solamente la posición de las partículas y un algoritmo de proyección sobre la variedad definida por las restricciones, evitando la manipulación de la primera derivada del sistema (las velocidades). De esta forma se consigue una gran estabilidad numérica. El principal defecto de estos métodos es su dependencia en parámetros sin significado físico, por lo que es difícil simular materiales concretos. En este trabajo explicamos los susodichos métodos y nos centramos en la simulación de materiales elásticos, tomando como referencia otro modelo ampliamente estudiado que depende de parámetros físicos reales. Proponemos un algoritmo para ajustar los parámetros no físicos del algoritmo basado en posiciones y probamos este procedimiento en un cubo elástico. La extrapolación a otros objetos más complejos no debería resultar muy difícil. Como última contribución relacionamos estos algoritmos con algunos métodos numéricos clásicos y resaltamos las principales hipótesis que se asumen en el proceso. Esta parte, aunque no es muy robusta porque no llegamos a alcanzar un resultado cerrado, puede ser útil como un primer paso para trabajos futuros que involucren el tema de la convergencia de este tipo de métodos.Mechanical systems simulation for video games and other interactive applications imposes important restrictions as regards to stability, flexibility in the scenes and computational complexity. In the last few years several resolution strategies for mechanical systems with constraints have appeared. Some of the most popular ones in the development of video games use only the positions of the particles and a projection algorithm over the manifold defi ned by the constraints, avoiding manipulation of the system's fi rst derivative (velocities). In this way, a great numerical stability is obtained. The main drawback of these methods is its dependence in non-physical parameters, so is hard to simulate a speci c material. In this work we explain all the aforementioned methods and focus in the simulation of elastic materials taking as reference another model deeply studied that depends on real, physical parameters. We propose an algorithm to fit the non-physical parameters of the position based algorithm and test this procedure in a elastic cube. An extrapolation to other, more complex objects should not be dificult. As a last contribution we relate these algorithms with some classical numerical methods and point out which are the main hypothesis assumed in the process. This part, although is not very robust since we have not been able to reach a closed result, can be useful as a fi rst step for future works dealing with the convergence topic of this kind of methods

Shock wave formation in compliant arteries

[EN] We focus on the problem of shock wave formation in a model of blood flow along an elastic artery. We analyze the conditions under which this phenomenon can appear and we provide an estimation of the instant of shock formation. Numerical simulations of the model have been conducted using the Discontinuous Galerkin Finite Element Method. The results are consistent with certain phenomena observed by practitioners in patients with arteriopathies, and they could predict the possible formation of a shock wave in the aorta.C. Rodero and I. Garcia-Fernandez are supported by Projects TIN2014-59932-JIN (MINECO/FEDER, EU) and CIB16-BM019 (IISCII). J. A. Conejero is supported by MEC, Project MTM2016-75963-P.Rodero, C.; Conejero, JA.; García-Fernández, I. (2019). Shock wave formation in compliant arteries. Evolution Equations and Control Theory (Online). 8(1):221-230. https://doi.org/10.3934/eect.2019012S2212308

Visibility graphs of fractional Wu-Baleanu time series

[EN] We study time series generated by the parametric family of fractional discrete maps introduced by Wu and Baleanu, presenting an alternative way of introducing these maps. For the values of the parameters that yield chaotic time series, we have studied the Shannon entropy of the degree distribution of the natural and horizontal visibility graphs associated to these series. In these cases, the degree distribution can be fitted with a power law. We have also compared the Shannon entropy and the exponent of the power law fitting for the different values of the fractionary exponent and the scaling factor of the model. Our results illustrate a connection between the fractionary exponent and the scaling factor of the maps, with the respect to the onset of the chaos.J.A. Conejero is supported Ministerio de Economia y Competitividad Grant Project MTM2016-75963-P. Carlos Lizama is supported by CONICYT, under Fondecyt Grant number 1180041. Cristobal Rodero-Gomez is funded by European Commission H2020 research and Innovation programme under the Marie Sklodowska-Curie grant agreement No. 764738.Conejero, JA.; Lizama, C.; Mira-Iglesias, A.; Rodero-Gómez, C. (2019). Visibility graphs of fractional Wu-Baleanu time series. The Journal of Difference Equations and Applications. 25(9-10):1321-1331. https://doi.org/10.1080/10236198.2019.1619714S13211331259-10Anand, K., & Bianconi, G. (2009). Entropy measures for networks: Toward an information theory of complex topologies. Physical Review E, 80(4). doi:10.1103/physreve.80.045102Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512. doi:10.1126/science.286.5439.509Brzeziński, D. W. (2017). Comparison of Fractional Order Derivatives Computational Accuracy - Right Hand vs Left Hand Definition. Applied Mathematics and Nonlinear Sciences, 2(1), 237-248. doi:10.21042/amns.2017.1.00020Brzeziński, D. W. (2018). Review of numerical methods for NumILPT with computational accuracy assessment for fractional calculus. Applied Mathematics and Nonlinear Sciences, 3(2), 487-502. doi:10.2478/amns.2018.2.00038DONNER, R. V., SMALL, M., DONGES, J. F., MARWAN, N., ZOU, Y., XIANG, R., & KURTHS, J. (2011). RECURRENCE-BASED TIME SERIES ANALYSIS BY MEANS OF COMPLEX NETWORK METHODS. International Journal of Bifurcation and Chaos, 21(04), 1019-1046. doi:10.1142/s0218127411029021Edelman, M. (2015). On the fractional Eulerian numbers and equivalence of maps with long term power-law memory (integral Volterra equations of the second kind) to Grünvald-Letnikov fractional difference (differential) equations. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(7), 073103. doi:10.1063/1.4922834Edelman, M. (2018). On stability of fixed points and chaos in fractional systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(2), 023112. doi:10.1063/1.5016437Gao, Z.-K., Small, M., & Kurths, J. (2016). Complex network analysis of time series. EPL (Europhysics Letters), 116(5), 50001. doi:10.1209/0295-5075/116/50001Iacovacci, J., & Lacasa, L. (2016). Sequential visibility-graph motifs. Physical Review E, 93(4). doi:10.1103/physreve.93.042309Indahl, U. G., Naes, T., & Liland, K. H. (2018). A similarity index for comparing coupled matrices. Journal of Chemometrics, 32(10), e3049. doi:10.1002/cem.3049Kantz, H., & Schreiber, T. (2003). Nonlinear Time Series Analysis. doi:10.1017/cbo9780511755798Lacasa, L., & Iacovacci, J. (2017). Visibility graphs of random scalar fields and spatial data. Physical Review E, 96(1). doi:10.1103/physreve.96.012318Lacasa, L., Luque, B., Ballesteros, F., Luque, J., & Nuño, J. C. (2008). From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences, 105(13), 4972-4975. doi:10.1073/pnas.0709247105Lizama, C. (2015). lp-maximal regularity for fractional difference equations on UMD spaces. Mathematische Nachrichten, 288(17-18), 2079-2092. doi:10.1002/mana.201400326Lizama, C. (2017). The Poisson distribution, abstract fractional difference equations, and stability. Proceedings of the American Mathematical Society, 145(9), 3809-3827. doi:10.1090/proc/12895Luque, B., Lacasa, L., Ballesteros, F., & Luque, J. (2009). Horizontal visibility graphs: Exact results for random time series. Physical Review E, 80(4). doi:10.1103/physreve.80.046103Luque, B., Lacasa, L., Ballesteros, F. J., & Robledo, A. (2011). Feigenbaum Graphs: A Complex Network Perspective of Chaos. PLoS ONE, 6(9), e22411. doi:10.1371/journal.pone.0022411Luque, B., Lacasa, L., & Robledo, A. (2012). Feigenbaum graphs at the onset of chaos. Physics Letters A, 376(47-48), 3625-3629. doi:10.1016/j.physleta.2012.10.050May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261(5560), 459-467. doi:10.1038/261459a0Núñez, Á. M., Luque, B., Lacasa, L., Gómez, J. P., & Robledo, A. (2013). Horizontal visibility graphs generated by type-I intermittency. Physical Review E, 87(5). doi:10.1103/physreve.87.052801Ravetti, M. G., Carpi, L. C., Gonçalves, B. A., Frery, A. C., & Rosso, O. A. (2014). Distinguishing Noise from Chaos: Objective versus Subjective Criteria Using Horizontal Visibility Graph. PLoS ONE, 9(9), e108004. doi:10.1371/journal.pone.0108004Robledo, A. (2013). Generalized Statistical Mechanics at the Onset of Chaos. Entropy, 15(12), 5178-5222. doi:10.3390/e15125178Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi:10.1002/j.1538-7305.1948.tb01338.xSong, C., Havlin, S., & Makse, H. A. (2006). Origins of fractality in the growth of complex networks. Nature Physics, 2(4), 275-281. doi:10.1038/nphys266West, J., Lacasa, L., Severini, S., & Teschendorff, A. (2012). Approximate entropy of network parameters. Physical Review E, 85(4). doi:10.1103/physreve.85.046111Wu, G.-C., & Baleanu, D. (2013). Discrete fractional logistic map and its chaos. Nonlinear Dynamics, 75(1-2), 283-287. doi:10.1007/s11071-013-1065-7Wu, G.-C., & Baleanu, D. (2014). Discrete chaos in fractional delayed logistic maps. Nonlinear Dynamics, 80(4), 1697-1703. doi:10.1007/s11071-014-1250-3Zhang, J., & Small, M. (2006). Complex Network from Pseudoperiodic Time Series: Topology versus Dynamics. Physical Review Letters, 96(23). doi:10.1103/physrevlett.96.23870