Browsing by Issue Date, starting with "2025-03-04"
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- Negative affectivity and suicide risk: the buffering role of gratitude and optimism in spanish adolescentsPublication . Sánchez-Álvarez, Nicolás; Brás, Marta; Carmo, Cláudia; Neves de Jesus, Saúl; Extremera, NatalioThe role of optimism and gratitude in the link between negative affectivity and suicide risk (viz., depressive symptoms and suicidal ideation) was examined in a sample of 1401 Spanish adolescents. Overall, the results of a set of hierarchical regression analyses supported the prediction of optimism and gratitude as predictors of suicide risk. Moreover, we analyzed whether the negative affectivity optimism/gratitude interaction term explains the unique variance in depressive symptoms and suicidal ideation. The results show that optimism and gratitude buffered the association between negative affectivity and depressive symptoms/ suicidal ideation, indicating that among adolescents experiencing negative affectivity, those who presented a high level of optimism and gratitude reported a lower risk of suicide. Finally, the practical implications of these novel findings regarding the role of optimism and gratitude in preventing suicide risk among adolescents are discussed.
- Intelligent monitoring systems for electric vehicle chargingPublication . Martins, Jaime; Rodrigues, JoaoFeatured Application This paper reviews EV charging challenges and existing monitoring methods to pinpoint key gaps. From our review, we propose a practical monitoring framework that leverages IoT sensors, edge computing, and cloud services for real-time oversight, predictive maintenance, and responsive analysis of user behavior.Abstract The growing adoption of electric vehicles (EVs) presents new challenges for managing parking infrastructure, particularly concerning charging station utilization and user behavior patterns. This review examines the current state-of-the-art in intelligent monitoring systems for EV charging stations in parking facilities. We specifically focus on two key inefficiencies: vehicles occupying charging spots beyond the optimal fast-charging range (80% state-of-charge) and remaining connected even after reaching full capacity (100%). We analyze the theoretical and practical foundations of these systems, summarizing existing research on intelligent monitoring architectures and commercial implementations. Building on this analysis, we also propose a novel monitoring framework that integrates Internet of things (IoT) sensors, edge computing, and cloud services to enable real-time monitoring, predictive maintenance, and adaptive control. This framework addresses both the technical aspects of monitoring systems and the behavioral factors influencing charging station management. Based on a comparative analysis and simulation studies, we propose performance benchmarks and outline critical research directions requiring further experimental validation. The proposed architecture aims to offer a scalable, adaptable, and secure solution for optimizing EV charging infrastructure utilization while addressing key research gaps in the field.