Un modelo para representar el estado anímico de los estudiantes en el aula

[EN] Students’ affective state has influence in their learning process. Specifically, a positive state has positive consequences in aspects such as motivation and achievements. Even though the affective state is represented in the long-term, this can be modified through the emotions that emerge during the classes. In this paper, we present a model to represent the students’ affective state in order to identify those factors that are related to positive affective states. Considering this model, we collected students’ emotions during different lessons in different subjects. The results show that lessons that are more related to positive affective states are those oriented to working on projects and those that revise the previous content.[ES] El estado anímico de los estudiantes tiene una infuencia directa en el proceso de enseñanza-aprendizaje. En concreto, un estado positivo permite mejorar aspectos como la motivación y los logros conseguidos. A pesar de que el estado anímico representa un estado a largo plazo, éste puede ser modicado a través de las emociones que tienen los estudiantes en función de las clases. En este artículo, presentamos un modelo para representar el estado anímico de los estudiantes con la finanalidad de identificar qué factores infuyen para conseguir estados positivos que faciliten su aprendizaje. Considerando este modelo, hemos recogido resultados midiendo las emociones que tienen los alumnos durante las distintas sesiones de varias asignaturas. Los resultados demuestran que las clases que generan más emociones positivas son aquellas en donde se trabaja por proyectos y en las que se repasan conceptos anteriores.Proyecto financiado por la Universitat Politècnica de ValènciaDel Val Noguera, E.; Alberola, JM.; Alfonso, B.; Alberola (2017). Un modelo para representar el estado anímico de los estudiantes en el aula. En In-Red 2017. III Congreso Nacional de innovación educativa y de docencia en red. Editorial Universitat Politècnica de València. 361-371. https://doi.org/10.4995/INRED2017.2017.6793OCS36137

Supporting Dynamicity in Emergency Response Applications

Multiagent Systems are a promising paradigm for software development. It is feasible to model such systems with many components where each one can solve a specific problem. This division of responsibilities allows multiagent systems to work in dynamically changing environments. An example of an environment that is very changeable is related with emergencies management. Emergency management systems depend on the cooperation of all their components due to their specialization. In order to obtain this cooperation, the components need to interact with each other and adapt their interactions depending on their purpose and the system components they are interacting with. Also, new components may arrive on the scene, which must be informed about the interaction policies that original components are using. Although Multiagent Systems are suited to managing scenarios of this kind, their effectiveness depends on their capacity to dynamically modify and adapt the protocols that control the interactions among agents in the system. In this paper, an infrastructure to support dynamically changing interaction protocols is presented

An intelligent self-configurable mechanism for distributed energy storage systems

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

A computer-based support system for cooperative tasks in nursing homes

Different studies have shown the benefits of a cooperative activities programme for the elderly. Members of a group with similar abilities or disabilities are often encouraged by having the opportunity to share their experiences, knowledge, or opinions. Nevertheless, when caregivers try to plan specific cooperative activities, different aspects, as the individual needs of each person, should be taken into account, which notably increases the complexity of that planification. This paper proposes a computer-based support tool for recreational therapists which facilitates the management task of grouping elderly people into cooperative groups for existing activities. To do this, an iterative learning process is proposed allowing the formation of proper distributions of elderly people into activities. (c) 2019 The Authors. Published by Atlantis Press SARL.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013. A. Costa thanks the Fundacao para a Ciencia e a Tecnologia (FCT) the Post-Doc scholarship with the Ref. SFRH/BPD/102696/2014. This work is also partially supported by the MINECO/FEDER TIN2015-655-15C4-1-R

Repurposing bioenergetic modulators against protozoan parasites responsible for tropical diseases

Malaria, leishmaniasis and trypanosomiasis are arthropod-borne, parasitic diseases that constitute a major global health problem. They are generally found in developing countries, where lack of access to preventive tools and treatment hinders their management. Because these parasites share an increased demand on glucose consumption with most cancer cells, six compounds used in anti-tumoral research were selected to be tested as antiparasitic agents in in vitro models of Leishmania infantum, Trypanosoma brucei, T. cruzi, and Plasmodium falciparum: dichloroacetic acid (DCA), 3-bromopyruvic acid (3BP), 2-deoxy-D-glucose (2DG), lonidamine (LND), metformin (MET), and sirolimus (SIR). No parasite-killing activity was found in L. infantum promastigotes, whereas DCA and 3BP reduced the burden of intra-macrophagic amastigotes. For T. brucei all selected compounds, but 2DG, decreased parasite survival. DCA, 2DG, LND and MET showed parasite-killing activity in T. cruzi. Finally, anti-plasmodial activity was found for DCA, 2DG, LND, MET and SIR. These results reinforce the hypothesis that drugs with proven efficacy in the treatment of cancer by interfering with ATP production, proliferation, and survival cell strategies might be useful in treating threatening parasitic diseases and provide new opportunities for their repurposing.info:eu-repo/semantics/publishedVersio

Infant gut microbiota modulation by human milk disaccharides in humanized microbiome mice

Human milk glycans present a unique diversity of structures that suggest different mechanisms by which they may affect the infant microbiome development. A humanized mouse model generated by infant fecal transplantation was utilized here to evaluate the impact of fucosyl-α1,3-GlcNAc (3FN), fucosyl-α1,6-GlcNAc, lacto-N-biose (LNB) and galacto-N-biose on the fecal microbiota and host-microbiota interactions. 16S rRNA amplicon sequencing showed that certain bacterial genera significantly increased (Ruminococcus and Oscillospira) or decreased (Eubacterium and Clostridium) in all disaccharide-supplemented groups. Interestingly, cluster analysis differentiates the consumption of fucosyl-oligosaccharides from galactosyl-oligosaccharides, highlighting the disappearance of Akkermansia genus in both fucosyl-oligosaccharides. An increment of the relative abundance of Coprococcus genus was only observed with 3FN. As well, LNB significantly increased the relative abundance of Bifidobacterium, whereas the absolute levels of this genus, as measured by quantitative real-time PCR, did not significantly increase. OTUs corresponding to the species Bifidobacterium longum, Bifidobacterium adolescentis and Ruminococcus gnavus were not present in the control after the 3-week intervention, but were shared among the donor and specific disaccharide groups, indicating that their survival is dependent on disaccharide supplementation. The 3FN-feeding group showed increased levels of butyrate and acetate in the colon, and decreased levels of serum HDL-cholesterol. 3FN also down-regulated the pro-inflammatory cytokine TNF-α and up-regulated the anti-inflammatory cytokines IL-10 and IL-13, and the Toll-like receptor 2 in the large intestine tissue. The present study revealed that the four disaccharides show efficacy in producing beneficial compositional shifts of the gut microbiota and in addition, the 3FN demonstrated physiological and immunomodulatory roles

Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation

Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.This research was funded by projects AIM, ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE), e-DIVITA, ref.20509/PDC/18 (Proof of Concept, 2018) and it is the result of the activity performed under the program Groups of Excellence of the Region of Murcia (Spain), the Fundación Séneca, Science and Technology Agency of the region of Murcia project under grant 19884/GERM/15 and ATENTO, ref. 20889/PI/18. All remaining errors are our responsibility

Advances in infrastructures and tools for multiagent systems

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., Búrdalo, L., Julián, V., Terrasa, A., & García-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., López-Sánchez, 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., Hübner, 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., López-Sánchez, M., Morales, J., & Salamó, 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). IEEE.Fogués, R. L., Such, J. M., Espinosa, A., & Garcia-Fornes, A. (2014). BFF: a tool for eliciting tie strength and user communities in social networking services. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9453-6 .Garcia, E., Giret, A., & Botti, V. (2011). Evaluating software engineering techniques for developing complex systems with multiagent approaches. Information and Software Technology, 53(5), 494–506.Garcia-Fornes, A., Hübner, J., Omicini, A., Rodriguez-Aguilar, J., & Botti, V. (2011). Infrastructures and tools for multiagent systems for the new generation of distributed systems. Engineering Applications of Articial Intelligence, 24(7), 1095–1097.Jennings, N., Faratin, P., Lomuscio, A., Parsons, S., Sierra, C., & Wooldridge, M. (2001). Automated negotiation: prospects, methods and challenges. International Journal of Group Decision and Negotiation, 10(2), 199–215.Jung, Y., Kim, M., Masoumzadeh, A., & Joshi, J. B. (2012). A survey of security issue in multi-agent systems. Artificial Intelligence Review, 37(3), 239–260.Kota, R., Gibbins, N., & Jennings, N. R. (2012). Decentralized approaches for self-adaptation in agent organizations. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 7(1), 1.Kraus, S. (1997). Negotiation and cooperation in multi-agent environments. Artificial Intelligence, 94(1), 79–97.Lin, Y. I., Chou, Y. W., Shiau, J. Y., & Chu, C. H. (2013). Multi-agent negotiation based on price schedules algorithm for distributed collaborative design. Journal of Intelligent Manufacturing, 24(3), 545–557.Luck, M., & McBurney, P. (2008). Computing as interaction: agent and agreement technologies.Luck, M., McBurney, P., Shehory, O., & Willmott, S. (2005). Agent technology: Computing as interaction (A roadmap for agent based computing). AgentLink.Ossowski, S., & Menezes, R. (2006). On coordination and its significance to distributed and multiagent systems. 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Distributed norm management for multi-agent systems. Expert Systems with Applications, 39(5), 5990–5999.Wooldridge, M. (2002). An introduction to multiagent systems. New York: Wiley.Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: theory and practice. Knowledge Engineering Review, 10(2), 115–152

Extracellular Signal–Regulated Kinase (Erk) Activation by the Pre-T Cell Receptor in Developing Thymocytes in Vivo

The first checkpoint in T cell development occurs between the CD4−CD8− and CD4+CD8+ stages and is associated with formation of the pre-T cell receptor (TCR). The signaling mechanisms that drive this progression remain largely unknown. Here, we show that extracellular signal–regulated kinases (ERKs)-1/2 are activated upon engagement of the pre-TCR. Using a novel experimental system, we demonstrate that expression of the pre-TCR by developing thymocytes induces ERK-1/2 activation within the thymus. In addition, the activation of this pre-TCR signaling cascade is mediated through Lck. These findings directly link pre-TCR complex formation with specific downstream signaling components in vivo