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OBJETIVO: explorar la actitud y los sentimientos de estudiantes de Enfermería hacia los cuidados ante la muerte, así como las diferencias según las características demográficas, la experiencia previa con la muerte y la realización de prácticas clínicas. MÉTODO: estudio descriptivo transversal en estudiantes de Enfermería de universidades de Galicia a través de la escala validada FATCOD-S. Otras variables: edad, sexo, escuela/facultad, curso académico, entorno residencial en la infancia, vivencia de experiencia previa con el final de la vida y si ya habían realizado prácticas clínicas. Se incluyó una pregunta abierta acerca de sus sentimientos ante el fenómeno de estudio. Análisis bivariante mediante t de Student, ANOVA o U de Mann-Whitney y coeficiente de correlación de Spearman. RESULTADOS: se obtuvo respuesta de 463 estudiantes. La puntuación media global de la escala FATCOD fue de 98,1 puntos. Quienes tenían 20 años o menos del curso 1º tenían valores estadísticamente más bajos que los mayores, tanto en el factor I (p= 0,014) como en el II (p= 0,04); tenían puntuaciones más altas en ambos factores (p= 0,02) si declaraban haber tenido experiencias previas. Se encontró un gradiente creciente entre las puntuaciones de ambos factores y el curso académico (p< 0,001). En relación a los sentimientos se identificaron cuatro categorías: la tristeza y ansiedad ante la muerte; la falta de experiencia y/o formación; la satisfacción y el orgullo personal; y la empatía, respeto y alivio. CONCLUSIONES: los estudiantes de Enfermería gallegos presentaron una actitud positiva hacia los cuidados al final de la vida. La edad y la experiencia previa resultaron ser factores clave, así como la realización de prácticas clínicas

OBJECTIVE: to explore the attitude and feelings of Nursing students towards care at death, as well as differences according to demographical characteristics, previous experience with death, and clinical practices completed. METHOD: a descriptive cross-sectional study on Nursing students from Universities of Galicia, through the validated FATCOD-S scale. Other variables were: age, gender, school, academic course, residential environment at childhood, previous experience with end of life, and if they had already completed clinical practices. An open question was included regarding their feelings towards the subject of the study. Bivariate analysis was conducted through Student's t, ANOVA or Mann-Whitney's U tests, and Spearman Correlation Coefficient. RESULTS: there were answers by 463 students. The overall mean score in the FATCOD scale was 98.1. Students in their 1st year who were ≤20-year-old presented statistically lower values than older students, both in Factor I (p= 0.014) and in Factor II (p= 0.04); they had higher scores in both factors (p= 0.02) if they declared having had previous experiences. An increasing gradient was found between the scores in both factors and the academic year (p< 0.001). In terms of feelings, four categories were identified: sadness and anxiety when faced with death; lack of experience and/or training; satisfaction and personal pride; and empathy, respect and relief. CONCLUSIONS: nursing students in Galicia presented a positive attitude towards end-of-life care. Age and previous experience represented key factors, as well as having completed clinical practices

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As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

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The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.

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Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios.

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The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.