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Garrido-Atienza, M.J., Schmalfuß, B., Valero, J. (2022) "Random attractors for setvalued dynamical systems for stochastic evolution equations driven by a nontrivial fractional noise", Stochastics and Dynamics, 22 (3):2240018

María José Garrido (Departamento de Ecuaciones Diferenciales y Análisis Numérico, Universidad de Sevilla) Björn Schmalfuß (Institut für Stochastik, Friedrich-Schiller-Universität Jena) and José Valero (Centro de Investigación Operativa, Universidad Miguel Hernandez de Elche) 
Abstract: In this paper, we prove the existence of weak pullback mean random attractors for a non-local stochastic reaction-diffusion equation with a nonlinear multiplicative […]

By salonso, ago

Caballero, R., Marín-Rubio, P., Valero, J. (2022) "Weak mean random attractors for non-local random and stochastic reaction-diffusion equationsl",Stochastics and Dynamics, 22 (3):2240016

Rubén Caballero, José Valero (Centro de Investigación Operativa, Universidad Miguel Hernandez de Elche) and Pedro Marín (Dpto. Ecuaciones Diferenciales y Análisis Numérico, Universidad de Sevilla)
Abstract: In this paper, we prove the existence of weak pullback mean random attractors for a non-local stochastic reaction-diffusion equation with a nonlinear multiplicative noise. The existence and uniqueness of solutions […]

By salonso, ago

Beer, G., Cánovas, M.J., López, M.A., Parra, J., (2022) «Correction to: Lipschitz modulus of linear and convex inequality systems with the Hausdorff metric (Mathematical Programming, (2021), 189, 1-2, (75-98), 10.1007/s10107-020-01543-9)», Mathematical Programming, 194(1-2):1185–1186

Gerald Beer (Department of Mathematics, California State University Los Angeles), María Josefa Cánovas, Juan Parra (University Miguel Hernández of Elche) and Marco Antonio López (Department of Mathematics, University of Alicante)
Abstract: The article “Lipschitz modulus of linear and convex inequality systems with the Hausdorff metric”, written by Beer,G., Cánovas, M.J., López, M.A., Parra, J.was originally published […]

By salonso, ago

Valencia, K., Botella, F., Rusu, C. (2022) "A Property Checklist to Evaluate the User Experience for People with Autism Spectrum Disorder", th International Conference on Social Computing and Social Media, SCSM 2022 Held as Part of the 24th HCI International Conference, HCII 2022, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13315 LNCS:205 – 216

Katherine Valencia, Cristian Rusu (Pontificia Universidad Católica de Valparaíso) and Federico Botella (University Miguel Hernández of Elche) 
Abstract: Autism Spectrum Disorder (ASD) refers to a neurodevelopmental disorder which is characterized by repetitive patterns, and difficulties with social interaction and communication. People with ASD have, an affinity with technology; many studies designed and/or developed specific systems for […]

By salonso, ago

Camacho, J., Cánovas, M.J., Parra, J. (2022) “Lipschitz upper semicontinuity in linear optimization via local directional convexity”, Optimization,

Jesús Camacho, María Josefa Cánovas and Juan Parra (Operations Research Center, University Miguel Hernández of Elche) 
Abstract: This work is focussed on computing the Lipschitz upper semicontinuity modulus of the argmin mapping for canonically perturbed linear programs. The immediate antecedent can be traced out from Camacho J et al. [2022. From calmness to Hoffman constants for […]

By salonso, ago

Guerrero, N.M., Aparicio, J., Valero-Carreras, D. (2022) “Combining Data Envelopment Analysis and Machine Learning”, Mathematics, 10 (6):909

Nadia Guerrero, Juan Aparicio and Daniel Valero (Operations Research Center, University Miguel Hernández of Elche) 
Abstract: Data Envelopment Analysis (DEA) is one of the most used non-parametric techniques for technical efficiency assessment. DEA is exclusively concerned about the minimization of the empirical error, satisfying, at the same time, some shape constraints (convexity and free disposability). Unfortunately, […]

By salonso, ago