302 research outputs found

    An intelligent self-configurable mechanism for distributed energy storage systems

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    Next generation of smart grid technologies demand intel- ligent capabilities for communication, interaction, monitoring, storage, and energy transmission. Multiagent systems are envisioned to provide autonomic and adaptability features to these systems in order to gain advantage in their current environments. In this paper we present a mechanism for providing distributed energy storage systems (DESSs) with intelligent capabilities. In more detail, we propose a self-con gurable mechanism which allows a DESS to adapt itself according to the future environmental requirements. This mechanism is aimed at reducing the costs at which energy is purchased from the market.This work has been partially supported by projects TIN2012-36586-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). An intelligent self-configurable mechanism for distributed energy storage systems. Cybernetics and Systems. 45(3):292-305. https://doi.org/10.1080/01969722.2014.894859S292305453Abbey , C. and G. Joos . “Coordination of Distributed Storage with Wind Energy in a Rural Distribution System.” Paper presented at Industry Applications Conference, 42nd IAS Annual Meeting, September 23–27, 2007, New Orleans, USA .Alberola , J. M. , V. Julian , and A. Garcia-Fornes . “Multi-Dimensional Transition Deliberation for Organization Adaptation in Multiagent Systems.” Paper presented at the 11th International Conference on Aut. Agents and MAS (AAMAS12), June 4–8, 2012, Valencia, Spain .Chouhan , N. S. and M. Ferdowsi . “Review of Energy Storage Systems.” Paper presented at North American Power Symposium (NAPS), October 4–6, 2009, Mississippi, USA.Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B. (2005). Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models. IEEE Transactions on Power Systems, 20(2), 1035-1042. doi:10.1109/tpwrs.2005.846054Costa , L. , F. Bourry , J. Juban , and G. Kariniotakis . “Management of Energy Storage Coordinated with Wind Power under Electricity Market Conditions.” Paper presented at 10th International Conference on Probabilistic Methods Applied to Power Systems, May 25–29, 2008, Rincón, Puerto Rico .Eyer , J. and G. Corey . “Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide.” Sandia National Laboratories, 2010. Technical Report .Jiang , Z. “Agent-Based Control Framework for Distributed Energy Resources Microgrids.” Paper presented at International Conference on Intelligent Agent Technology, December 18–22, 2006, Hong Kong .Karnouskos , S. and T. N. De Holanda . “Simulation of a Smart Grid City with Software Agents.” Paper presented at Third UKSim European Symposium on Computer Modeling and Simulation, November 25–27, 2009, Athens, Greece .Ketter, W., Collins, J., & Reddy, P. (2013). Power TAC: A competitive economic simulation of the smart grid. Energy Economics, 39, 262-270. doi:10.1016/j.eneco.2013.04.015Lakshman, A., & Malik, P. (2010). Cassandra. ACM SIGOPS Operating Systems Review, 44(2), 35. doi:10.1145/1773912.1773922Logenthiran, T., Srinivasan, D., Khambadkone, A. M., & Aung, H. N. (2012). Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator. IEEE Transactions on Smart Grid, 3(2), 925-933. doi:10.1109/tsg.2012.2189028Maly, D. K., & Kwan, K. S. (1995). Optimal battery energy storage system (BESS) charge scheduling with dynamic programming. IEE Proceedings - Science, Measurement and Technology, 142(6), 453-458. doi:10.1049/ip-smt:19951929Mihailescu , R. C. , M. Vasirani , and S. Ossowski . “Dynamic Coalition Formation and Adaptation for Virtual Power Stations in Smart Grids.” Paper presented at 2nd International Workshop on Agent Technologies for Energy Systems, May 2, 2011, Taipei, Taiwan .Mohd , A. , E. Ortjohann , A. Schmelter , N. Hamsic , and D. Morton . “Challenges in Integrating Distributed Energy Storage Systems into Future Smart Grid.” Paper presented at IEEE International Symposium on Industrial Electronics, June 30–July 2, 2008, Cambridge, UK .Mohsenian-Rad, A.-H., & Leon-Garcia, A. (2010). Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. IEEE Transactions on Smart Grid, 1(2), 120-133. doi:10.1109/tsg.2010.2055903Momoh , J. A. “Smart Grid Design for Efficient and Flexible Power Networks Operation and Control.” Paper presented at IEEE PES Power Systems Conference and Exposition, March 15–18, 2009, Seattle, USA .Nguyen, C. P., & Flueck, A. J. (2012). Agent Based Restoration With Distributed Energy Storage Support in Smart Grids. IEEE Transactions on Smart Grid, 3(2), 1029-1038. doi:10.1109/tsg.2012.2186833Nourai , A. “Installation of the First Distributed Energy Storage System (DESS) At American Electric Power.” Sandia National Laboratories, 2007. Technical Report.Oyarzabal , J. , J. Jimeno , J. Ruela , A. Engler , and C. Hardt . “Agent Based Micro Grid Management System.” Paper presented at International Conference on Future Power Systems, November 16–18, 2005, Amsterdam, Netherlands .Pinson, P., Chevallier, C., & Kariniotakis, G. N. (2007). Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power. IEEE Transactions on Power Systems, 22(3), 1148-1156. doi:10.1109/tpwrs.2007.901117Pipattanasomporn , M. , H. Feroze , and S. Rahman . “Multi-agent Systems in a Distributed Smart Grid: Design and Implementation.” Paper presented at IEEE/PES Power Systems Conference and Exposition, March 15–18, 2009, Seattle, USA .Reddy , P. P. and M. M. Veloso . “Factored Models for Multiscale Decision Making in Smart Grid Customers.” Paper presented at the Twenty-sixth AAAI Conference on Artificial Intelligence, July 22–26, 2012, Toronto, Canada .Ribeiro, P. F., Johnson, B. K., Crow, M. L., Arsoy, A., & Liu, Y. (2001). Energy storage systems for advanced power applications. Proceedings of the IEEE, 89(12), 1744-1756. doi:10.1109/5.975900Schutte , S. and M. Sonnenschein . “Mosaik-Scalable Smart Grid Scenario Specification.” Paper presented at Proceedings of the 2012 Winter Simulation Conference (WSC), December 9–12, 2012, Berlin, Germany .Sioshansi, R., Denholm, P., Jenkin, T., & Weiss, J. (2009). Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects. Energy Economics, 31(2), 269-277. doi:10.1016/j.eneco.2008.10.005Szkuta, B. R., Sanabria, L. A., & Dillon, T. S. (1999). Electricity price short-term forecasting using artificial neural networks. IEEE Transactions on Power Systems, 14(3), 851-857. doi:10.1109/59.780895Van Dam, K. H., Houwing, M., Lukszo, Z., & Bouwmans, I. (2008). Agent-based control of distributed electricity generation with micro combined heat and power—Cross-sectoral learning for process and infrastructure engineers. Computers & Chemical Engineering, 32(1-2), 205-217. doi:10.1016/j.compchemeng.2007.07.012Vosen, S. (1999). Hybrid energy storage systems for stand-alone electric power systems: optimization of system performance and cost through control strategies. International Journal of Hydrogen Energy, 24(12), 1139-1156. doi:10.1016/s0360-3199(98)00175-xVytelingum , P. , T. D. Voice , S. Ramchurn , A. Rogers , and N. R. Jennings . “Agent-Based Micro-Storage Management for the Smart Grid.” Paper presented at Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, May 10–14, 2010a, Toronto, Canada .Vytelingum , P. , T. D. Voice , S. Ramchurn , A. Rogers , and N. R. Jennings . “Intelligent Agents for the Smart Grid.” Paper presented at the 9th International Conference on Autonomous Agents and Multiagent Systems, May 10–14, 2010b, Toronto, Canada

    Advances in infrastructures and tools for multiagent systems

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    In the last few years, information system technologies have focused on solving challenges in order to develop distributed applications. Distributed systems can be viewed as collections of service-provider and ser vice-consumer components interlinked by dynamically defined workflows (Luck and McBurney 2008).Alberola Oltra, JM.; Botti Navarro, VJ.; Such Aparicio, JM. (2014). Advances in infrastructures and tools for multiagent systems. Information Systems Frontiers. 16:163-167. doi:10.1007/s10796-014-9493-6S16316716Alberola, J. M., BĂșrdalo, L., JuliĂĄn, V., Terrasa, A., & GarcĂ­a-Fornes, A. (2014). An adaptive framework for monitoring agent organizations. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9478-x .Alfonso, B., Botti, V., Garrido, A., & Giret, A. (2014). A MAS-based infrastructure for negotiation and its application to a water-right market. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9443-8 .Andrighetto, G., Castelfranchi, C., Mayor, E., McBreen, J., LĂłpez-SĂĄnchez, M., & Parsons, S. (2013). (Social) norm dynamics. In G. Andrighetto, G. Governatori, P. Noriega, & L. W. van der Torre (Eds.), Normative multi-agent systems (pp. 135–170). Dagstuhl: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik.Baarslag, T., Fujita, K., Gerding, E. H., Hindriks, K., Ito, T., Jennings, N. R., et al. (2013). Evaluating practical negotiating agents: results and analysis of the 2011 international competition. Artificial Intelligence, 198, 73–103.Boissier, O., Bordini, R. H., HĂŒbner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747–761.Campos, J., Esteva, M., LĂłpez-SĂĄnchez, M., Morales, J., & SalamĂł, M. (2011). Organisational adaptation of multi-agent systems in a peer-to-peer scenario. Computing, 91(2), 169–215.Carrera, A., Iglesias, C. A., & Garijo, M. (2014). Beast methodology: an agile testing methodology for multi-agent systems based on behaviour driven development. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9438-5 .Criado, N., Such, J. M., & Botti, V. (2014). Norm reasoning services. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9444-7 .Del Val, E., Rebollo, M., & Botti, V. (2014). Enhancing decentralized service discovery in open service-oriented multi-agent systems. Journal of Autonomous Agents and Multi-Agent Systems, 28(1), 1–30.Denti, E., Omicini, A., & Ricci, A. (2002). Coordination tools for MAS development and deployment. Applied Artificial Intelligence, 16(9–10), 721–752.Dignum, V., & Dignum, F. (2012). A logic of agent organizations. Logic Journal of IGPL, 20(1), 283–316.Ferber, J., & Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-agent systems. In Multi agent systems. Proceedings. International Conference on (pp. 128–135). 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    Assessing gastro-intestinal related quality of life in cystic fibrosis: Validation of PedsQL GI in children and their parents

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    Background: Most patients with cystic fibrosis (CF) suffer from pancreatic insufficiency, leading to fat malabsorption, malnutrition and abdominal discomfort. Until recently, no specific tool was available for assessing gastro-intestinal related quality of life (GI QOL) in patients with CF. As the Horizon2020 project MyCyFAPP aims to improve GI QOL by using a newly designed mobile application, a sensitive and reliable outcome measure was needed. We aimed to study the applicability of the existing child-specific Pediatric Quality of Life Inventory, Gastrointestinal Symptoms Scales and Module (PedsQL GI) in children with CF. Methods: A multicenter, prospective observational study was performed in 6 European centers to validate the PedsQL GI in children with CF during 3 months. Results: In total, 248 children and their parents were included. Within-patient variability of PedsQL GI was low (24.11), and there was reasonable agreement between children and parents (ICC 0.681). Nine of 14 subscales were informative (no ceiling effect). The PedsQL GI and the median scores for 4 subscales were significantly lower in patients compared to healthy controls. Positive associations were found between PedsQL GI and age (OR = 1.044, p = 0.004) and between PedsQL GI and BMI z-score (OR = 1.127, p = 0.036). PedsQL GI correlated with most CFQ-R subscales (r 0.268 to 0.623) and with a Visual Analogue Scale (r = 0.20). Conclusions: PedsQL GI is a valid and applicable instrument to assess GI QOL in children with CF. Future research efforts should examine the responsiveness of the CF PedsQL GI to change in the context of clinical interventions and trials

    Clinical and Organizational Factors Related to the Reduction of Mechanical Restraint Application in an Acute Ward: An 8-Year Retrospective Analysis

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    Background: The purpose of this study was to describe the frequency of mechanical restraint use in an acute psychiatric ward and to analyze which variables may have significantly influenced the use of this procedure. Methods: This retrospective study was conducted in the Servizio Psichiatrico di Diagnosi e Cura (SPDC) of Modena Centro. The following variables of our sample, represented by all restrained patients admitted from 1-1-2005 to 31-12-2012, were analyzed: age, gender, nationality, psychiatric diagnoses, organic comorbidity, state and duration of admission, motivation and duration of restraints, nursing shift and hospitalization day of restraint, number of patients admitted at the time of restraint and institutional changes during the observation period. The above variables were statistically compared with those of all other non-restrained patients admitted to our ward in the same period. Results: Mechanical restraints were primarily used as a safety procedure to manage aggressive behavior of male patients, during the first days of hospitalization and night shifts. Neurocognitive disorders, organic comorbidity, compulsory state and long duration of admission were statistically significantly related to the increase of restraint use (p<.001, multivariate logistic regression). Institutional changes, especially more restricted guidelines concerning restraint application, were statistically significantly related to restraint use reduction (p<.001, chi2 test, multivariate logistic regression). Conclusion: The data obtained highlight that mechanical restraint use was influenced not only by clinical factors, but mainly by staff and policy factors, which have permitted a gradual but significant reduction in the use of this procedure through a multidimensional approach

    Human germline heterozygous gain-of-function STAT6 variants cause severe allergic disease

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    sharma et al. define a new primary atopic disorder caused by heterozygous gain-of-function variants in STAT6. this results in severe, early-onset allergies, and is seen in 16 patients from 10 families. Anti-IL-4R &amp; alpha; antibody and JAK inhibitor treatment were highly effective.STAT6 (signal transducer and activator of transcription 6) is a transcription factor that plays a central role in the pathophysiology of allergic inflammation. we have identified 16 patients from 10 families spanning three continents with a profound phenotype of early-life onset allergic immune dysregulation, widespread treatment-resistant atopic dermatitis, hypereosinophilia with esosinophilic gastrointestinal disease, asthma, elevated serum IgE, IgE-mediated food allergies, and anaphylaxis. the cases were either sporadic (seven kindreds) or followed an autosomal dominant inheritance pattern (three kindreds). all patients carried monoallelic rare variants in STAT6 and functional studies established their gain-of-function (GOF) phenotype with sustained STAT6 phosphorylation, increased STAT6 target gene expression, and T(H)2 skewing. Precision treatment with the anti-IL-4R &amp; alpha; antibody, dupilumab, was highly effective improving both clinical manifestations and immunological biomarkers. this study identifies heterozygous GOF variants in STAT6 as a novel autosomal dominant allergic disorder. We anticipate that our discovery of multiple kindreds with germline STAT6 GOF variants will facilitate the recognition of more affected individuals and the full definition of this new primary atopic disorder

    Human germline heterozygous gain-of-function STAT6 variants cause severe allergic disease

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    STAT6 (signal transducer and activator of transcription 6) is a transcription factor that plays a central role in the pathophysiology of allergic inflammation. We have identified 16 patients from 10 families spanning three continents with a profound phenotype of early-life onset allergic immune dysregulation, widespread treatment-resistant atopic dermatitis, hypereosinophilia with esosinophilic gastrointestinal disease, asthma, elevated serum IgE, IgE-mediated food allergies, and anaphylaxis. The cases were either sporadic (seven kindreds) or followed an autosomal dominant inheritance pattern (three kindreds). All patients carried monoallelic rare variants in STAT6 and functional studies established their gain-of-function (GOF) phenotype with sustained STAT6 phosphorylation, increased STAT6 target gene expression, and TH2 skewing. Precision treatment with the anti-IL-4Rα antibody, dupilumab, was highly effective improving both clinical manifestations and immunological biomarkers. This study identifies heterozygous GOF variants in STAT6 as a novel autosomal dominant allergic disorder. We anticipate that our discovery of multiple kindreds with germline STAT6 GOF variants will facilitate the recognition of more affected individuals and the full definition of this new primary atopic disorder
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