Departamento de Matemáticas
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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 ... -
La enseñanza de las matemáticas en las aulas de educación infantil: percepciones de los futuros maestros a través del prácticum
(Dykinson, 2021)La mayoría de los estudiantes se encuentran en el Prácticum con una situación desajustada con lo que han aprendido en la facultad. Por lo tanto, los estudiantes del Prácticum están expuestos a una clara incongruencia al ... -
Matemáticas manipulativas y alto rendimiento
(Dykinson, 2019)Se presenta un estudio de enseñanza-aprendizaje de las Matemáticas a través del uso de materiales manipulativos. Este ha consistido en el diseño e implementación de talleres matemáticos que se han desarrollado dentro del ... -
¿Conoce el profesorado de asignaturas STEM a mujeres científicas? Una experiencia de formación continua con profesorado de ESO y bachillerato
(Dykinson, 2020)La educación científica ciudadana se considera fundamental en la sociedad del conocimiento, actualmente basada en la tecnología y la ciencia más que en otras épocas. Esta educación científica puede darse tanto fuera de ... -
¿Afecta una intervención didáctica a la percepción del género en la ciencia de futuros docentes?
(Dykinson, 2020)La percepción del género en la ciencia que muestran los estudiantes para maestro no es distinta según el género. Esta muestra altos niveles de acuerdo con afirmaciones equitativas, y un desacuerdo moderado con afirmaciones ... -
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, ...
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