Angel Eduardo Muñoz Zavala

Background: The aortic arch (AA) influences catheter navigation during endovascular treatment (EVT) for acute ischemic stroke. Whether routine inclusion of the AA in stroke CT angiography (CTA) protocols is necessary remains debated, particularly in settings aiming to streamline imaging workflows. Purpose: To evaluate whether machine learning algorithms (MLAs) can use routinely available clinical and laboratory data to estimate AA configuration in patients with stroke risk factors. Materials and Methods: In this retrospective study, 108 patients who underwent CTA, including the AA, were analyzed. Eight MLAs were trained to classify AA anatomy as favorable or unfavorable using clinical and laboratory variables available at presentation. Class imbalance was addressed using the Synthetic Minority Oversampling Technique. Model performance was evaluated using five-fold cross-validation and summarized with seven performance metrics. Results: After class balancing, mean accuracy across classifiers ranged from 0.52 to 0.77. Gaussian copula and Naive Bayes classifiers showed the highest accuracy, while k-nearest neighbors and support vector machines demonstrated higher sensitivity. Specificity was highest for regularized discriminant analysis and the Gaussian copula. AUROC values ranged from 0.49 to 0.80 across models. Conclusion: Routinely available clinical and laboratory data contain a limited yet detectable signal, enabling several machine learning algorithms to estimate AA configuration with moderate accuracy. These findings are exploratory and do not support modification of CTA protocols. However, they suggest that pre-imaging clinical data may provide supportive information for future workflow-oriented decision-support tools. Further validation in larger, externally tested cohorts is required before clinical integration can be considered.

This study introduces an innovative approach for modeling infectious diseases through hybrid simulations, combining agent-based models and Markov chains. These simulations allow for the accurate evaluation of disease spread and intervention strategies in an accessible manner. The hybrid model incorporates key parameters such as infection and recovery rates, along with transitions between health states. By varying these parameters, the results can mirror those of classic models like SIS, SIR, SEAIR and VSEIQR. Through our simulations, we analyzed how changing these parameters impacts disease transmission, demonstrating the stochastic nature of epidemics. Infectious diseases remain a significant cause of global mortality, influenced by pathogen characteristics and public health interventions. The findings highlight that hybrid simulations are a powerful tool for understanding and predicting epidemiological behaviors, offering valuable insights into epidemic control and providing an accessible framework for educational and research purposes.

Implements an explicit exploration strategy for evolutionary algorithms in order to have a more effective search in solving optimization problems. Along with this exploration search strategy, a set of four different Estimation of Distribution Algorithms (EDAs) are also implemented for solving optimization problems in continuous domains. The implemented explicit exploration strategy in this package is described in Salinas-Gutiérrez and Muñoz Zavala (2023) .

In the realm of digital ecosystems, the integration of Lean UX principles into virtual environments for older adults presents a promising avenue for enhancing cognitive health and addressing neurocognitive disorders. This research builds upon the foundation that virtual reality (VR) and augmented reality (AR) technologies can significantly improve the quality of life for the elderly, particularly in fostering social interaction and cognitive stimulation. Previous studies have demonstrated the efficacy of VR and AR in engaging older adults, yet there remains a gap in the application of Lean UX methodologies to optimize these digital experiences. This study investigates the incorporation of Lean UX into digital ecosystems for older adults, aiming to tailor virtual environments to their unique needs and preferences, thereby aiding in the treatment and management of neurocognitive disorders. Employing a user-centered design approach, iterative prototyping, and multidisciplinary collaboration, the research analyzes user engagement, adaptability, and satisfaction. The findings reveal that Lean UX can lead to more intuitive, accessible, and personalized VR interfaces, resulting in increased user satisfaction and potentially mitigating the effects of neurocognitive decline. The implications of this research underscore the importance of empathetic design in creating inclusive digital solutions that support the well-being of older adults.

This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN architectures considered in the literature for time series forecasting purposes: feedforward neural networks, radial basis function networks, recurrent neural networks, and self-organizing maps. We analyze the strengths and weaknesses of these architectures in the context of time series modeling. We then summarize some recent time series ANN modeling applications found in the literature, focusing mainly on the previously outlined architectures. In our opinion, these summarized techniques constitute a representative sample of the research and development efforts made in this field. We aim to provide the general reader with a good perspective on how ANNs have been employed for time series modeling and forecasting tasks. Finally, we comment on possible new research directions in this area.

La educación inclusiva es compleja debido a la necesidad de adaptar procesos a necesidades individuales de aprendizaje. Además, los actuales modelos educativos nacionales para la educación inclusiva carecen de una metodología ágil, flexible y colaborativa en la que participen tanto, padres, educadores, e incluso desarrolladores de tecnología de información (IT). COVID-19 aceleró e-learning, pero generó nuevos retos. El método de investigación incluye la prueba de una propuesta de metodología de aprendizaje ágil en un instituto de educación primaria para tres alumnos con discalculia. Entre los beneficios de la metodología propuesta está la comprobación del diseño iterativo, la aplicación de un enfoque ágil, la rápida adaptación y la adaptación y formación sobre aplicaciones y contenido. En esta experiencia de investigación se midieron las mejoras en el aprendizaje de los estudiantes, demostrando que la metodología apoya la educación inclusiva, mejorando intervenciones colaborativas, currículo flexible, atención a necesidades de aprendizaje, integración de las TI y formación de instructores. Así, la metodología propuesta responde a retos de la evolución en la educación inclusiva y modelos nacionales, integrando TI rapidamente, implicando a los colaboradores, y adaptandose a actitudes y necesidades específicas de aprendizaje, pudiendo evolucionar hacia una "metodología de aprendizaje ágil e integradora".