Juan Ramón

The COVID-19 global health emergency has greatly impacted the educational field. Faced with unprecedented stress situations, professors, students, and families have employed various coping and resilience strategies throughout the confinement period. High and persistent stress levels are associated with other pathologies; hence, their detection and prevention are needed. Consequently, this study aimed to design a predictive model of stress in the educational field based on artificial intelligence that included certain sociodemographic variables, coping strategies, and resilience capacity, and to study the relationship between them. The non-probabilistic snowball sampling method was used, involving 337 people from the university education community in south-eastern Spain. The Perceived Stress Scale, Stress Management Questionnaire, and Brief Resilience Scale were administered. The Statistical Package for the Social Sciences was used to design the architecture of artificial neural networks. The results found that stress levels could be predicted by the synaptic weights of coping strategies and timing of the epidemic, with a predictive capacity of over 80% found in the neural network model. Additionally, direct and significant associations were identified between the use of certain coping strategies, stress levels, and resilience. The conclusions of this research are essential for effective stress detection, and therefore, early intervention in the field of educational psychology, by discussing the influence of resilience or lack thereof on the prediction of stress levels. Identifying the variables that maintain a greater predictive power in stress levels is an effective strategy to design more adjusted prevention programs and to anticipate the needs of the community.

Artificial intelligence is a useful predictive tool for a wide variety of fields of knowledge. Despite this, the educational field is still an environment that lacks a variety of studies that use this type of predictive tools. In parallel, it is postulated that the levels of self-esteem in the university environment may be related to the strategies implemented to solve problems. For these reasons, the aim of this study was to analyze the levels of self-esteem presented by teaching staff and students at university and to design an algorithm capable of predicting these levels on the basis of their coping strategies, resilience, and sociodemographic variables. For this purpose, the Rosenberg Self-Esteem Scale, the Perceived Stress Scale, and the Brief Resilience Scale were administered. The results showed a relevant role of resilience and stress perceived in predicting participants’ self-esteem levels. The findings highlight the usefulness of artificial neural networks for predicting psychological variables in education.

The main problem of the Spanish local finance system derives from the existence of two opposite models: LOFCA model versus Floral system. As a result of this opposition, the Spanish system has moved some distance away from the theoretical principles of sound fiscal federalism. The analysis carried out in the article points out clearly the need to improve the fiscal mechanisms of responsibility and financial autonomy and to introduce a system of interjurisdictional responsibility which should palliate the defects detected in the actual system in order to maximise the political and economic advantages of an authentic fiscal decentralisation.Le problème majeur du système espagnol de finances locales réside dans l’existence de deux modèles opposés: le modèle LOFCA et le système Floral. En raison de cette opposition, le système espagnol s’est considérablement écarté des principes affirmés par la théorie d’un authentique fédéralisme fiscal. L’analyse conduite dans l’article met en évidence la nécessité d’améliorer les mécanismes fiscaux de responsabilité et d’autonomie financière des collectivités locales espagnoles en introduisant un système de responsabilité inter-juridictionnelle capable d’éliminer les incohérences du système ; ce qui permettra de tirer pleinement partie des avantages politiques et économiques inhérents à la décentralisation fiscale.

The retina pigment epithelium (RPE) is a highly specialised epithelium that serves as a multifunctional and indispensable component of the vertebrate eye. Although a great deal of attention has been paid to its transdifferentiation capabilities and its ancillary functions in neural retina development, little is known about the molecular mechanisms that specify the RPE itself. Recent advances in our understanding of the genetic network that controls the progressive specification of the eye anlage in vertebrates have provided some of the initial cues to the mechanisms responsible for RPE patterning. Here, we have outlined many recent findings that suggest that a limited number of transcription factors, including Otx2, Mitf and Pax6 and a few signalling cascades, are the elements required for the onset of RPE specification in vertebrates. Furthermore, using this information and the data available on the specification of the pigmented cells of primitive chordates, we have ventured some hypotheses on the origin of RPE cells during evolution. BioEssays 26:766–777, 2004. © 2004 Wiley Periodicals, Inc.