302 research outputs found

    Sugerencias acerca del mando en "Blanquerna"

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    Reorganization in Dynamic Agent Societies

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    En la nueva era de tecnologías de la información, los sistemas tienden a ser cada vez más dinámicos, compuestos por entidades heterogéneas capaces de entrar y salir del sistema, interaccionar entre ellas, y adaptarse a las necesidades del entorno. Los sistemas multiagente han contribuído en los ultimos años, a modelar, diseñar e implementar sistemas autónomos con capacidad de interacción y comunicación. Estos sistemas se han modelado principalmente, a través de sociedades de agentes, las cuales facilitan la interación, organización y cooperación de agentes heterogéneos para conseguir diferentes objetivos. Para que estos paradigmas puedan ser utilizados para el desarrollo de nuevas generaciones de sistemas, características como dinamicidad y capacidad de reorganización deben estar incorporadas en el modelado, gestión y ejecución de estas sociedades de agentes. Concretamente, la reorganización en sociedades de agentes ofrece un paradigma para diseñar aplicaciones abiertas, dinámicas y adaptativas. Este proceso requiere determinar las consecuencias de cambiar el sistema, no sólo en términos de los beneficios conseguidos sinó además, midiendo los costes de adaptación así como el impacto que estos cambios tienen en todos los componentes del sistema. Las propuestas actuales de reorganización, básicamente abordan este proceso como respuestas de la sociedad cuando ocurre un cambio, o bien como un mecanismo para mejorar la utilidad del sistema. Sin embargo, no se pueden definir procesos complejos de decisión que obtengan la mejor configuración de los componentes organizacionales en cada momento, basándose en una evaluación de los beneficios que se podrían obtener así como de los costes asociados al proceso. Teniendo en cuenta este objetivo, esta tesis explora el área de reorganización en sociedades de agentes y se centra principalmente, en una propuesta novedosa para reorganización. Nuestra propuesta ofrece un soporte de toma de decisiones que considera cambios en múltiplesAlberola Oltra, JM. (2013). Reorganization in Dynamic Agent Societies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19243Palanci

    Myxomycetes ibéricos. IV

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    Iberian Myxomycetes IV. Hundred twenty (120) taxa of Myxomycetes from the Iberian Peninsula are recorded here. Data on their ecology, chorology, phenology and habitat are also added.Myxomycetes Ibéricos IV. Citamos ciento veinte (120) taxones de Myxomycetes procedentes de la Península Ibérica, aportando datos sobre su ecología, localización geográfica, fecha y hábitat

    Sistema sanitario y cambio soscial : un modelo de path analysis para el caso de España

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    Los sistemas sanitarios deben analizarse como procesos de mantenimiento y mejora del nivel (y distribución) sanitari0 en una población dada. Aquí definimos los sistemas sanitarios como "sistemas-abiertos-relacionados", siguiendo la metodología del análisis de sistemas y las aportaciones concretas de Caudill. Los problemas fundamentales de estos estudios derivan de inferencias generalizadas sobre sociedades de tradición anglosajona, post industriales, que no entran en el análisis de las diferencias regionales

    Myxomycetes ibéricos. IV

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    Myxomycetes Ibéricos IV. Citamos ciento veinte (120) taxones de Myxomycetes procedentes de la Península Ibérica, aportando datos sobre su ecología, localización geográfica, fecha y hábitat.Iberian Myxomycetes IV. Hundred twenty (120) taxa of Myxomycetes from the Iberian Peninsula are recorded here. Data on their ecology, chorology, phenology and habitat are also added

    Myxomycetes Ibéricos. II

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    Fifty-one (5 1 ) taxa of Myxomycetes from the Iberian Peninsula are recorded here. Data on their ecology, chorology and habitat are also added.Citamos cincuenta y un (5 1 ) taxones de Myxomycetes procedentes de la Península Ibérica, aportando datos sobre su ecología, localización geognífica, fecha y habitat

    A Bibliometric Diagnosis and Analysis about Smart Cities

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    [EN] This article aims to present a bibliometric analysis of Smart Cities. The study analyzes the most important journals during the period between 1991 and 2019. It provides helpful insights into the document types, the distribution of countries/territories, the distribution of institutions, the authors' geographical distribution, the most active authors and their research interests or fields, the relationships between principal authors and more relevant publications, and the most cited articles. This paper also provides important information about the core and historical references and the most cited papers. The analysis used the keywords and thematic noun-phrases in the titles and abstracts of the sample papers to explore the hot research topics in the top journals (e.g., 'Smart Cities', 'Intelligent Cities', 'Sustainable Cities', 'e-Government', 'Digital Transformation', 'Knowledge-Based City', etc.). The main objective is to have a quantitative description of the published literature about Smart Cities; this description will be the basis for the development of a methodology for the diagnosis of the maturity of a Smart City. The results presented here help to define the scientific concept of Smart Cities and to measure the importance that the term has gained through the years. The study has allowed us to know the main indicators of the published literature in depth, from the date of publication of the first articles and the evolution of these indicators to the present day. From the main indicators in the literature, some were selected to be applied: The most influential journals on Smart Cities according to the general citation structure in Smart Cities, Global Impact Factor of Smart Cities, number of publications, publications on Smart Cities around the world, and their correlation.Pérez, LM.; Oltra Badenes, RF.; Oltra Gutiérrez, JV.; Gil Gómez, H. (2020). A Bibliometric Diagnosis and Analysis about Smart Cities. Sustainability. 12(16):1-43. https://doi.org/10.3390/su12166357S1431216Guo, Y.-M., Huang, Z.-L., Guo, J., Li, H., Guo, X.-R., & Nkeli, M. J. (2019). Bibliometric Analysis on Smart Cities Research. Sustainability, 11(13), 3606. doi:10.3390/su11133606Mora, L., Bolici, R., & Deakin, M. (2017). The First Two Decades of Smart-City Research: A Bibliometric Analysis. Journal of Urban Technology, 24(1), 3-27. doi:10.1080/10630732.2017.1285123Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Li, C., Liu, X., Dai, Z., & Zhao, Z. (2019). Smart City: A Shareable Framework and Its Applications in China. Sustainability, 11(16), 4346. doi:10.3390/su11164346Merigó, J. M., & Yang, J.-B. (2016). Accounting Research: A Bibliometric Analysis. Australian Accounting Review, 27(1), 71-100. doi:10.1111/auar.12109Garg, K. C., & Sharma, C. (2017). Bibliometrics of Library and Information Science research in India during 2004-2015. DESIDOC Journal of Library & Information Technology, 37(3), 221-227. doi:10.14429/djlit.37.3.11188Metse, A. P., Wiggers, J. H., Wye, P. M., Wolfenden, L., Prochaska, J. J., Stockings, E. A., … Bowman, J. A. (2016). Smoking and Mental Illness: A Bibliometric Analysis of Research Output Over Time. Nicotine & Tobacco Research, 19(1), 24-31. doi:10.1093/ntr/ntw249Broadus, R. N. (1987). Toward a definition of «bibliometrics». Scientometrics, 12(5-6), 373-379. doi:10.1007/bf02016680Hood, W. W., & Wilson, C. S. (2001). Scientometrics, 52(2), 291-314. doi:10.1023/a:1017919924342Thelwall, M. (2008). Bibliometrics to webometrics. Journal of Information Science, 34(4), 605-621. doi:10.1177/0165551507087238Bar-Ilan, J. (2008). Informetrics at the beginning of the 21st century—A review. 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Journal of the American Society for Information Science and Technology, 55(14), 1216-1227. doi:10.1002/asi.20077Gupta, B. . M., & Dhawan, S. (2019). Electronic books A scientometric assessment of global literature during 1993 2018. DESIDOC Journal of Library & Information Technology, 39(5), 251-258. doi:10.14429/djlit.39.5.14573Kokol, P., Blažun Vošner, H., & Završnik, J. (2020). Application of bibliometrics in medicine: a historical bibliometrics analysis. Health Information & Libraries Journal, 38(2), 125-138. doi:10.1111/hir.12295Michalopoulos, A., & Falagas, M. E. (2005). A Bibliometric Analysis of Global Research Production in Respiratory Medicine. Chest, 128(6), 3993-3998. doi:10.1378/chest.128.6.3993Lefaivre, K. A., Shadgan, B., & O’Brien, P. J. (2011). 100 Most Cited Articles in Orthopaedic Surgery. Clinical Orthopaedics & Related Research, 469(5), 1487-1497. doi:10.1007/s11999-010-1604-1Kelly, J. C., Glynn, R. W., O’Briain, D. E., Felle, P., & McCabe, J. P. (2010). The 100 classic papers of orthopaedic surgery. The Journal of Bone and Joint Surgery. British volume, 92-B(10), 1338-1343. doi:10.1302/0301-620x.92b10.24867Zhang, M., Zhou, Y., Lu, Y., He, S., & Liu, M. (2019). The 100 most-cited articles on prenatal diagnosis. Medicine, 98(38), e17236. doi:10.1097/md.0000000000017236Zou, Y., Luo, Y., Zhang, J., Xia, N., Tan, G., & Huang, C. (2019). Bibliometric analysis of oncolytic virus research, 2000 to 2018. Medicine, 98(35), e16817. doi:10.1097/md.0000000000016817Svider, P. F., Choudhry, Z. A., Choudhry, O. J., Baredes, S., Liu, J. K., & Eloy, J. A. (2012). The use of theh-indexin academic otolaryngology. The Laryngoscope, 123(1), 103-106. doi:10.1002/lary.23569Poskevicius, L., De la Flor-Martínez, M., Galindo-Moreno, P., & Juodzbalys, G. (2019). Scientific Publications in Dentistry in Lithuania, Latvia, and Estonia Between 1996 and 2018: A Bibliometric Analysis. Medical Science Monitor, 25, 4414-4422. doi:10.12659/msm.914223Ahmad, P., Asif, J. A., Alam, M. K., & Slots, J. (2019). A bibliometric analysis of Periodontology 2000. Periodontology 2000, 82(1), 286-297. doi:10.1111/prd.12328Kostoff, R. N., Toothman, D. R., Eberhart, H. J., & Humenik, J. A. (2001). Text mining using database tomography and bibliometrics: A review. Technological Forecasting and Social Change, 68(3), 223-253. doi:10.1016/s0040-1625(01)00133-0Grant, J. (2000). Evaluating «payback» on biomedical research from papers cited in clinical guidelines: applied bibliometric study. BMJ, 320(7242), 1107-1111. doi:10.1136/bmj.320.7242.1107Vergidis, P. I., Karavasiou, A. I., Paraschakis, K., Bliziotis, I. A., & Falagas, M. E. (2005). Bibliometric analysis of global trends for research productivity in microbiology. European Journal of Clinical Microbiology & Infectious Diseases, 24(5), 342-346. doi:10.1007/s10096-005-1306-xSuárez Roldan, C., Chaparro, N., & Rojas-Galeano, S. (2019). Análisis Bibliométrico de la Revista Ingeniería (2010-2017). Ingeniería, 24(2). doi:10.14483/23448393.14678Ratten, V., Pellegrini, M. M., Fakhar Manesh, M., & Dabić, M. (2020). Trends and changes in Thunderbird International Business Review journal: A bibliometric review. Thunderbird International Business Review, 62(6), 721-732. doi:10.1002/tie.22124Baker, H. K., Kumar, S., & Pattnaik, D. (2020). Fifty years of The Financial Review  : A bibliometric overview. Financial Review, 55(1), 7-24. doi:10.1111/fire.12228Charlesworth, M., Klein, A. A., & White, S. M. (2019). A bibliometric analysis of the conversion and reporting of pilot studies published in six anaesthesia journals. Anaesthesia, 75(2), 247-253. doi:10.1111/anae.14817Van Noorden, R., Maher, B., & Nuzzo, R. (2014). The top 100 papers. Nature, 514(7524), 550-553. doi:10.1038/514550aNicoll, L. H., Oermann, M. H., Carter‐Templeton, H., Owens, J. K., & Edie, A. H. (2020). A bibliometric analysis of articles identified by editors as representing excellence in nursing publication: Replication and extension. Journal of Advanced Nursing, 76(5), 1247-1254. doi:10.1111/jan.14316Liu, W., Wang, Z., & Zhao, H. (2020). Comparative study of customer relationship management research from East Asia, North America and Europe: A bibliometric overview. Electronic Markets, 30(4), 735-757. doi:10.1007/s12525-020-00395-7Cronin, B. (2001). Bibliometrics and beyond: some thoughts on web-based citation analysis. Journal of Information Science, 27(1), 1-7. doi:10.1177/016555150102700101Durieux, V., & Gevenois, P. A. (2010). Bibliometric Indicators: Quality Measurements of Scientific Publication. Radiology, 255(2), 342-351. doi:10.1148/radiol.09090626Guerola Navarro, V., Oltra Badenes, R. F., Gil Gomez, H., & Gil Gomez, J. A. (2020). Customer Relationship Management (CRM): A Bibliometric Analysis. International Journal of Services Operations and Informatics, 10(3), 1. doi:10.1504/ijsoi.2020.10030517Vicedo, P., Gil-Gómez, H., Oltra-Badenes, R., & Guerola-Navarro, V. (2020). A bibliometric overview of how critical success factors influence on enterprise resource planning implementations. Journal of Intelligent & Fuzzy Systems, 38(5), 5475-5487. doi:10.3233/jifs-179639Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981-1012. doi:10.1016/j.techfore.2006.04.004Fersht, A. (2009). The most influential journals: Impact Factor and Eigenfactor. Proceedings of the National Academy of Sciences, 106(17), 6883-6884. doi:10.1073/pnas.0903307106Fu, H.-Z., Wang, M.-H., & Ho, Y.-S. (2013). Mapping of drinking water research: A bibliometric analysis of research output during 1992–2011. Science of The Total Environment, 443, 757-765. doi:10.1016/j.scitotenv.2012.11.061Fu, H., Ho, Y., Sui, Y., & Li, Z. (2010). A bibliometric analysis of solid waste research during the period 1993–2008. Waste Management, 30(12), 2410-2417. doi:10.1016/j.wasman.2010.06.008Wang, H., He, Q., Liu, X., Zhuang, Y., & Hong, S. (2012). Global urbanization research from 1991 to 2009: A systematic research review. Landscape and Urban Planning, 104(3-4), 299-309. doi:10.1016/j.landurbplan.2011.11.006Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809-1831. doi:10.1007/s11192-015-1645-

    Using a Case-Based Reasoning Approach for Trading in Sports Betting Markets

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    The sports betting market has emerged as one of the most lucrative markets in recent years. Trading in sports betting markets entails predicting odd movements in order to bet on an outcome, whilst also betting on the opposite outcome, at different odds in order to make a profit, regardless of the final result. These markets are mainly composed by humans, which take decisions according to their past experience in these markets. However, human rational reasoning is limited when taking quick decisions, being influenced by emotional factors and offering limited calibration capabilities for estimating probabilities. In this paper, we show how artificial techniques could be applied to this field and demonstrate that they can outperform even the bevahior of high-experienced humans. To achieve this goal, we propose a case-based reasoning model for trading in sports betting markets, which is integrated in an agent to provide it with the capabilities to take trading decisions based on future odd predictions. In order to test the performance of the system, we compare trading decisions taken by the agent with trading decisions taken by human traders when they compete in real sports betting markets.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and project TIN2011-27652-C03-01. Juan M. Alberola has received a grant from Ministerio de Ciencia e Innovacion de Espana (AP2007-00289).Alberola Oltra, JM.; García Fornes, AM. (2013). Using a Case-Based Reasoning Approach for Trading in Sports Betting Markets. Applied Intelligence. 38(3):465-477. https://doi.org/10.1007/s10489-012-0381-9S465477383Aamodt A (1990) Knowledge-intensive case-based reasoning and sustained learning. In: Topics in case-based reasoning. Springer, Berlin, pp 274–288Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Ahn JJ, Byun HW, Oh KJ, Kim TY (2012) Bayesian forecaster using class-based optimization. Appl Intell 36(3):553–563Alberola JM, Garcia-Fornes A, Espinosa A (2010) Price prediction in sports betting markets. In: Proceedings of the 8th German conference on multiagent system technologies, pp 197–208Arias-Aranda D, Castro JL, Navarro M, Zurita JM (2009) A cbr system for knowing the relationship between flexibility and operations strategy. In: Proceedings of the 18th international symposium on foundations of intelligent systems, ISMIS’09, pp 463–472Ates C (2004) Prediction markets are only human: subadditivity in probability judgments. In: MSC in finance and international businessBerlemann M, Schmidt C (2001) Predictive accuracy of political stock markets—empirical evidence from a European perspective. Technical report 2001-57Betfair (2009) http://www.betfaircorporate.comChen Y, Goel S, Pennock D (2008) Pricing combinatorial markets for tournaments. In: STOC’08: proceedings of the 40th annual ACM symposium on theory of computing. ACM Press, New York, pp 305–314Debnath S, Pennock DM, Giles CL, Lawrence S (2003) Information incorporation in online in-game sports betting markets. In: Proceedings of the 4th ACM conference on electronic commerce, EC ’03. ACM Press, New York, pp 258–259. doi: 10.1145/779928.779987Fischoff B, Slovic P, Lichtenstein S (1977) Knowing with certainty: the appropriateness of extreme confidence. J Exp Psychol Human Percept Perform 3:552–564Forsythe R, Rietz T, Ross T (1999) Wishes, expectations and actions: a survey on price formation in election stock markets. J Econ Behav Organ 39(1):83–110Fortnow L, Kilian J, Pennock DM, Wellman MP (2005) Betting Boolean-style: a framework for trading in securities based on logical formulas. Decis Support Syst 39(1):87–104. doi: 10.1016/j.dss.2004.08.010Gayer G (2010) Perception of probabilities in situations of risk: a case based approach. Games Econ Behav 68(1):130–143Guo M, Pennock D (2009) Combinatorial prediction markets for event hierarchies. In: Proc of the 8th AAMAS’09. Int foundation for autonomous agents and multiagent systems, pp 201–208Huang W, Lai K, Nakamori Y, Wang S (2004) Forecasting foreign exchange rates with artificial neural networks: a review. Int J Inf Technol Decis Mak 3(1):145–165Hüllermeier E (2007) Case-based approximate reasoning. Theory and decision library, vol 44. Springer, BerlinKim K-J, Ahn H (2012) Simultaneous optimization of artificial neural networks for financial forecasting. Appl Intell 36(4):887–898LeBaron B (1998) Agent based computational finance: suggested readings and early research. J Econ Dyn ControlLiu Y, Yang C, Yang Y, Lin F, Du X, Ito T (2012) Case learning for cbr-based collision avoidance systems. Appl Intell 36(2):308–319Love BC (2008) Behavioural finance and sports betting markets. In: MSC in finance and international businessLuque C, Valls JM, Isasi P (2011) Time series prediction evolving Voronoi regions. Appl Intell 34(1):116–126Mantaras RLD, McSherry D, Bridge D, Leake D, Smyth B, Craw S, Faltings B, Maher M, Lou C, Forbus MCK, Keane M, Aamodt A, Watson I (2005) Retrieval, reuse, revision and retention in case-based reasoning. Knowl Eng Rev 20(3):215–240Moody J (1995) Economic forecasting: challenges and neural network solutions. In: Proceedings of the international symposium on artificial neural networksOntañón S, Plaza E (2009) Argumentation-based information exchange in prediction markets. Argument Multi-Agent Syst 5384:181–196Ontañón S, Plaza E (2011) An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems. 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    Comparative study between manual injection intraosseous anesthesia and conventional oral anesthesia

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    Objective: To compare intraosseous anesthesia (IA) with the conventional oral anesthesia techniques. Materials and methods: A simple-blind, prospective clinical study was carried out. Each patient underwent two anesthetic techniques: conventional (local infiltration and locoregional anesthetic block) and intraosseous, for res-pective dental operations. In order to allow comparison of IA versus conventional anesthesia, the two operations were similar and affected the same two teeth in opposite quadrants. Results: A total of 200 oral anesthetic procedures were carried out in 100 patients. The mean patient age was 28.6±9.92 years. Fifty-five vestibular infiltrations and 45 mandibular blocks were performed. All patients were also subjected to IA. The type of intervention (conservative or endodontic) exerted no significant influence (p=0.58 and p=0.62, respectively). The latency period was 8.52±2.44 minutes for the conventional techniques and 0.89±0.73 minutes for IA - the difference being statistically significant (p<0.05). Regarding patient anesthesia sensation, the infiltrative techniques lasted a maximum of one hour, the inferior alveolar nerve blocks lasted between 1-3 hours, and IA lasted only 2.5 minutes - the differences being statistically significant (p?0.0000, ?=0.29). Anesthetic success was recorded in 89% of the conventional procedures and in 78% of the IA. Most patients preferred IA (61%) (p=0.0032). Conclusions: The two anesthetic procedures have been compared for latency, duration of anesthetic effect, anesthetic success rate and patient preference. Intraosseous anesthesia has been shown to be a technique to be taken into account when planning conservative and endodontic treatments. © Medicina Oral S. L

    Side effects and complications of intraosseous anesthesia and conventional oral anesthesia

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    Objective: To analyze the side effects and complications following intraosseous anesthesia (IA), comparing them with those of the conventional oral anesthesia techniques. Material and method: A simple-blind, prospective clinical study was carried out. Each patient underwent two anesthetic techniques: conventional (local infiltration and locoregional anesthetic block) and intraosseous, for respective dental operations. In order to allow comparison of IA versus conventional anesthesia, the two operations were similar and affected the same two teeth in opposite quadrants. Heart rate was recorded in all cases before injection of the anesthetic solution and again 30 seconds after injection. The complications observed after anesthetic administration were recorded. Results: A total of 200 oral anesthetic procedures were carried out in 100 patients. Both IA and conventional anesthesia resulted in a significant increase in heart rate, though the increase was greater with the latter technique. Incidents were infrequent with either anesthetic technique, with no significant differences between them. Regarding the complications, there were significant differences in pain at the injection site, with more intense pain in the case of IA (x2=3.532, p=0.030, ?2=0.02), while the limitation of oral aperture was more pronounced with conventional anesthesia (x2=5.128, p<0.05, ?2=0.014). Post-anesthetic biting showed no significant differences (x2=4.082, p=0.121, ?2=0.009). Conclusions: Both anesthetic techniques significantly increased heart rate, and IA caused comparatively more pain at the injection site, while limited oral aperture was more frequent with conventional anesthesia. Post-anesthetic biting showed no significant differences between the two techniques
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