Examinando por Autor "Cano, Javier"

Mostrando 1 - 7 de 7

Bayesian Reliability Analysis for Hardware/Software Systems
(2010) Cano, Javier; Moguerza, Javier; Rios Insua, David
Bayesian Reliability, Availability and Maintainability Analysis for Hardware Systems Described Through Continuous Time Markov Chains
(2010) Cano, Javier; Moguerza, Javier; Rios Insua, David
Cost Efficient Equitable Water Distribution in Algeria: A Bi-criteria Fair Division Problem with Network Constraints
(2010) Udías, Angel Luis; Rios Insua, David; Cano, Javier; Fellag, Hocine
Evaluation of stress and anxiety levels on science and engineering undergraduate students in spain when facing written assessments guides for positive interventions
(Elsevier, 2024-11) Simón de Blas, Clara; Rojas, Karina; García Sipols, Ana E.; Hernández Alonso, Sonia; Castellanos, María Eugenia; Cano, Javier; Córdoba, Claudia
Anxiety and stress disorders are increasingly common, especially among undergraduate students, significantly affecting their family, social, and academic lives. The isolation from restrictive measures during the COVID-19 pandemic in Spain has further exacerbated mental health issues. The disruption of in-person teaching has also impacted traditional learning and evaluation processes, increasing stress and anxiety levels in students. Based on this background, this study aims to analyze the incidence of these disorders among undergraduates and their relationship with various academic, demographic, and family factors, considering the influence of COVID-19. Our results were obtained from a survey conducted among first- and second-year URJC students who are enrolled in an experimental degree program. The statistical analysis provides guidelines for positive interventions to increase student motivation, which further leads to academic success. Results show that women exhibit higher levels of stress and a greater prevalence of anxiety compared to men. The study highlights the influence of specific factors on anxiety levels among students, proposing direct lines of action that enhance positive feelings concerning academic tasks
Out of the Niche: Using Direct Search Methods to Find Multiple Global Optima
(MDPI, 2022-04-30) Cano, Javier; Alfaro, Cesar; Gomez, Javier; Duarte, Abraham
Multimodal optimization deals with problems where multiple feasible global solutions coexist. Despite sharing a common objective function value, some global optima may be preferred to others for various reasons. In such cases, it is paramount to devise methods that are able to find as many global optima as possible within an affordable computational budget. Niching strategies have received an overwhelming attention in recent years as the most suitable technique to tackle these kinds of problems. In this paper we explore a different approach, based on a systematic yet versatile use of traditional direct search methods. When tested over reference benchmark functions, our proposal, despite its apparent simplicity, noticeably resists the comparison with state-of-the-art niching methods in most cases, both in the number of global optima found and in the number of function evaluations required. However, rather than trying to outperform niching methods—far more elaborated—our aim is to enrich them with the knowledge gained from exploiting the distinctive features of direct search methods. To that end, we propose two new performance measures that can be used to evaluate, compare and monitor the progress of optimization algorithms of (possibly) very different nature in their effort to find as many global optima of a given multimodal objective function as possible. We believe that adopting these metrics as reference criteria could lead to more sophisticated and computationally-efficient algorithms, which could benefit from the brute force of derivative-free local search methods.
Reliability and Optimization of the Operational Cost of Water Distribution in Kabylia
(2010) Udias, Angel Luis; Rios Insua, David; Cano, Javier; Fellag, Hocine
Uncertainty in functional network representations of brain activity of alcoholic patients
(Springer, 2020-10-12) Zanin, Massimiliano; Belkoura, Sedik; Gomez, Javier; Alfaro, Cesar; Cano, Javier
In spite of the large attention received by brain activity analyses through functional networks, the effects of uncertainty on such representations have mostly been neglected. We here elaborate the hypothesis that such uncertainty is not just a nuisance, but that on the contrary is condition-dependent. We test this hypothesis by analysing a large set of EEG brain recordings corresponding to control subjects and patients suffering from alcoholism, through the reconstruction of the corresponding Maximum Spanning Trees (MSTs), the assessment of their topological differences, and the comparison of two frequentist and Bayesian reconstruction approaches. A machine learning model demonstrates that the Bayesian reconstruction encodes more information than the frequentist one, and that such additional information is related to the uncertainty of the topological structures. We finally show how the Bayesian approach is more effective in the validation of generative models, over and above the frequentist one, by proposing and disproving two models based on additive noise.