Área de Matemática Aplicada
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A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors
(PLoS computational biology. 2021, 17(2), e1008266., 2021-02)Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are ... -
Discrete choice modeling using Kernel Logistic Regression
(Elsevier, 2020-04-25)The Kernel Logistic Regression is a popular technique in machine learning. In this work this technique is applied to the field of discrete choice modeling. This approach is equivalent to specifying non-parametric utilities ... -
PITS: An Intelligent Transportation System in pandemic times
(Elsevier, 2022)The control of the pandemic caused by SARS-CoV-2 is a challenge for governments all around the globe. To manage this situation, countries have adopted a bundle of measures, including restrictions to population mobility. ... -
GreenITS: a proposal to compute low-pollution routes
(Elsevier, 2022)A way to reduce carbon emissions in cities is through movement by bicycle or on foot. However, it sometimes means to pass through high-pollution zones and consequently breath low quality air. We then propose a green ... -
Obtención de un número difuso utilizable en aplicaciones a partir de datos ordenados
(Universidad de Huelva, 2010)Los datos imprecisos son tratados mediante números difusos en aplicaciones reales. El problema principal radica en la obtención de la función de pertenencia del número difuso a partir de un conjunto de datos. En este ... -
Revisiting kernel logistic regression under the random utility models perspective. An interpretable machine-learning approach
(Taylor & Francis, 2021-01)The success of machine-learning methods is spreading their use to many different fields. This paper analyses one of these methods, the Kernel Logistic Regression (KLR), from the point of view of Random Utility Model (RUM) ... -
Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm
(Springer, 2020-07)The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including ... -
A methodology for automatic parameter-tuning and center selection in density-peak clustering methods
(Springer, 2020-08)The density-peak clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach. The method has the advantage of allowing non-convex clusters, and clusters of variable size and ... -
A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems
(Elsevier, 2019-06)Metaheuristic optimization algorithms address two main tasks in the process of problem solving: i) exploration (also called diversification) and ii) exploitation (also called intensification). Guaranteeing a trade-off ... -
IoT based monitoring of air quality and traffic using regression analysis
(Elsevier, 2022-01)Dynamic traffic management (DTM) systems are used to reduce the negative externalities of traffic congestion, such as air pollution in urban areas. They require traffic and environmental monitoring infrastructures. In this ... -
A bilevel approach to enhance prefixed traffic signal optimization
(Elsevier, 2019-09)The segmentation of multivariate temporal series has been studied in a wide range of applications. This study investigates a challenging segmentation problem on traffic engineering, namely, identification of time-of-day ... -
Universal scaling laws rule explosive growth in human cancers
(Nature, 2020-12)Most physical and other natural systems are complex entities that are composed of a large number of interacting individual elements. It is a surprising fact that they often obey the so-called scaling laws that relate an ... -
The Galerkin–Fourier method for the study of nonlocal parabolic equations
(Springer Nature Switzerland AG, 2019-06-04)The aim of this paper is the study of a type of nonlocal parabolic equation. The formulation includes a convolution kernel k in the diffusion term and a design function h that plays the role of the diffusion coefficient. ... -
Optimal design problems governed by the nonlocal p -Laplacian equation
(AIMS, 2021-03)In the present work, a nonlocal optimal design model has been considered as an approximation of the corresponding classical or local optimal design problem. The new model is driven by the nonlocal p-Laplacian equation, the ... -
Interplay of Darwinian Selection, Lamarckian Induction and Microvesicle Transfer on Drug Resistance in Cancer
(Springer, 2019-06)Development of drug resistance in cancer has major implications for patients’ outcome. It is related to processes involved in the decrease of drug efficacy, which are strongly influenced by intratumor heterogeneity and ... -
Identification of a transient state during the acquisition of temozolomide resistance in glioblastoma
(Springer, 2020-01)Drug resistance limits the therapeutic efficacy in cancers and leads to tumor recurrence through ill-defined mechanisms. Glioblastoma (GBM) are the deadliest brain tumors in adults. GBM, at diagnosis or after treatment, ... -
The interplay of blood flow and temperature in regional hyperthermia: a mathematical approach
(The Royal Society, 2021-01)In recent decades, hyperthermia has been used to raise oxygenation levels in tumours undergoing other therapeutic modalities, of which radiotherapy is the most prominent one. It has been hypothesized that oxygenation ... -
A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors
(PLOS, 2021-02)Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are ... -
A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs
(Elsevier, 2019-10-10)Several common general purpose optimization algorithms are compared for finding A- and D-optimal designs for different types of statistical models of varying complexity, including high dimensional models with five and ... -
Stochastic modelling of slow-progressing tumors
(Elsevier, 2017-02)Tumor-normal cell interplay defines the course of a neoplastic malignancy. The outcome of this dual relation is the ultimate prevailing of one of the cells and the death or retreat of the other. In this paper we study the ...
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