Design of a Game Mechanic to Promote Collaborative Constructions in Virtual Environments

La característica de ubicuidad que internet dispone al servicio de las herramientas tecnológicas, ha sido aprovechada en innumerables áreas de aplicación. Dinámicas sociales de competencia, cooperación y colaboración emergen naturalmente en nuevos entornos virtuales donde los canales de comunicación abundan. En el ámbito educativo, saber aprovechar estas dinámicas para potenciar los procesos de aprendizaje, se convierte en un reto que requiere del desarrollo de nuevas estrategias que integren las herramientas tecnológicas con las herramientas propias del área. En este trabajo se presenta el diseño de una propuesta para la abstracción y representación de objetivos y su consecución a través de una mecánica de juego colaborativa que puede ser implementada en entornos virtuales. Algunas consideraciones técnicas son abordadas desde un punto de vista general, así como un esquema preliminar de su implementación.The ubiquitous feature that internet dispose to the service of the technological tools, has been exploited in innumerable areas of application. Social dynamics of competition, cooperation and collaboration naturally emerge in new virtual environments where communication channels abound. In the educational field, knowing how to take advantage of these dynamics to enhance learning processes, becomes a challenge that requires the development of new strategies that integrate technological tools with the tools of the area. This paper presents the design of a proposal for the abstraction and representation of objectives and their achievement through a collaborative game mechanic that can be implemented in virtual environments. Some technical considerations are approached from a general point of view, as well as a preliminary scheme of its implementation

Dietary Fat Patterns and Outcomes in Acute Pancreatitis in Spain

Background/Objective: Evidence from basic and clinical studies suggests that unsaturated fatty acids (UFAs) might be relevant mediators of the development of complications in acute pancreatitis (AP). Objective: The aim of this study was to analyze outcomes in patients with AP from regions in Spain with different patterns of dietary fat intake. Materials and Methods: A retrospective analysis was performed with data from 1,655 patients with AP from a Spanish prospective cohort study and regional nutritional data from a Spanish cross-sectional study. Nutritional data considered in the study concern the total lipid consumption, detailing total saturated fatty acids, UFAs and monounsaturated fatty acids (MUFAs) consumption derived from regional data and not from the patient prospective cohort. Two multivariable analysis models were used: (1) a model with the Charlson comorbidity index, sex, alcoholic etiology, and recurrent AP; (2) a model that included these variables plus obesity. Results: In multivariable analysis, patients from regions with high UFA intake had a significantly increased frequency of local complications, persistent organ failure (POF), mortality, and moderate-to-severe disease in the model without obesity and a higher frequency of POF in the model with obesity. Patients from regions with high MUFA intake had significantly more local complications and moderate-to-severe disease; this significance remained for moderate-to-severe disease when obesity was added to the model. Conclusions: Differences in dietary fat patterns could be associated with different outcomes in AP, and dietary fat patterns may be a pre-morbid factor that determines the severity of AP. UFAs, and particulary MUFAs, may influence the pathogenesis of the severity of AP

Bridging the Gap Between National and Ecosystem Accounting Application in Andalusian Forests, Spain

National accounting either ignores or fails to give due values to the ecosystem services, products, incomes and environmental assets of a country. To overcome these shortcomings, we apply spatially-explicit extended accounts that incorporate a novel environmental income indicator, which we test in the forests of Andalusia (Spain). Extended accounts incorporate nine farmer activities (timber, cork, firewood, nuts, livestock grazing, conservation forestry, hunting, residential services and private amenity) and seven government activities (fire services, free access recreation, free access mushroom, carbon, landscape conservation, threatened biodiversity and water yield). To make sure the valuation remains consistent with standard accounts, we simulate exchange values for non-market final forest product consumption in order to measure individual ecosystem services and environmental income indicators. Manufactured capital and environmental assets are also integrated. When comparing extended to standard accounts, our results are 3.6 times higher for gross value added. These differences are explained primarily by the omission in the standard accounts of carbon activities and undervaluation of private amenity, free access recreation, landscape and threatened biodiversity ecosystem services. Extended accounts measure a value of Andalusian forest ecosystem services 5.4 times higher than that measured using the valuation criteria of standard accounts

Tobacco and cognitive performance in schizophrenia patients: the design of the COGNICO study

Las personas con esquizofrenia constituyen una parte sustancial de las personas que todavía fuman. La hipótesis de la automedicación en relación al rendimiento cognitivo mantiene que los pacientes fuman para mejorar su déficit cognitivo basándose en los efectos estimulantes de la nicotina. El objetivo de este artículo es describir la metodología del estudio COGNICO. Estudio cuasiexperimental, observacional, prospectivo, multicéntrico y con seguimiento a 3, 6, 12 y 18 meses. Fue llevado a cabo en tres ciudades del norte de España (Oviedo, Ourense y Santiago de Compostela). Se reclutaron 81pacientes con esquizofrenia fumadores (edad media de 43,35 años (DT=8,83). 72,8% varones). Se asignaron a 3 grupos: a) control: pacientes fumadores; b) pacientes que dejan de fumar mediante parches de nicotina; c)pacientes que dejan de fumar mediante vareniclina. Como medida primaria se aplicó la batería neuropsicológica MATRICS. Además, se llevó a cabo una evaluación comprehensiva de los pacientes, que incluía el número de cigarrillos por día, la dependencia física y psicológica a la nicotina y el CO expirado. También se realizó una evaluación clínica general (PANSS, HDRS, ICG, C-SSRS) así como un seguimiento de las medidas antropométricas y los signos vitales. Se pretende identificar la relación entre el patrón de consumo de tabaco y el rendimiento cognitivo mediante la comparación de las puntuaciones en la batería neuropsicológica MATRICS durante los períodos de seguimiento.People with schizophrenia constitute a substantial part of the people who still smoke. Regarding cognitive performance, the self-medication hypothesis states that patients smoke to improve their cognitive deficits based on the stimulating effects of nicotine. The aim of this paper is to describe in detail the methodology used in the COGNICO study. A quasi-experimental, observational, prospective, multicenter study with follow-ups over 18 months was conducted in three cities in northern Spain (Oviedo, Ourense and Santiago de Compostela). A total of 81 outpatient smokers with schizophrenia were recruited with a mean age 43.35 years (SD = 8.83), 72.8% of them male. They were assigned to 3 groups: a) control group (smokers); b) patients who quit smoking using nicotine patches; c) patients who quit smoking with Varenicline. The MATRICS neuropsychological battery was applied as a primary measure. In addition, a comprehensive assessment of patients was performed, including the number of cigarettes per day, physical and psychological dependence on nicotine and CO expired. Clinical evaluation (PANSS, HDRS, CGI, C-SSRS), anthropometric measurements and vital signs assessment was also performed. The aim is to identify the relationship between the pattern of tobacco use and cognitive performance by comparing scores on the neuropsychological battery MATRICS during the follow-up periods (3, 6, 12 and 18months). The importance of this study lies in addressing a topical issue often ignored by clinicians: the unacceptably high rates of tobacco use in patients with severe mental disorder

Función del profesional en enfermería en la atención del acoso escolar en niños, niñas y adolescentes

Actualmente se ha incrementado y visualizado el fenómeno del acoso escolar o bullying(por su traducción al inglés); sin embargo, llama la atención que esta práctica ha  existido durante mucho tiempo, pero es hasta ahora donde se evidencian las consecuencias que genera en los niños, niñas y adolescentes que, según diversos estudios, puede generar suicidios en esta población. Diversas profesiones han profundizado en el tema; a pesar de ello, no se ha visto avance en el manejo y prevención del acoso y se ha incrementado el número de casos que llegan a instituciones de salud relacionados con los daños físicos y psicológicos que se ocasionan en el niño víctima de acoso escolar. Es un reto para enfermería poder abordar esta situación y plantear posibles estrategias de intervención para su manejo y prevención, no solo en la víctima y victimario sino también en la familia, escuelas e instituciones de salud en los diferentes niveles de atención, ya que cuenta con las herramientas para diseñar intervenciones en el manejo de la comunidad y del paciente institucionalizado. &nbsp

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Mapa de series, geoseries y geopermaseries de vegetación de España (Memoria del mapa de vegetación potencial de España). Parte II

Peer Reviewe

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications