Thoracic aorta cardiac-cycle related dynamic changes assessed with a 256-slice CT scanner

Objective: The aim of our study was to demonstrate whether the dynamic changes previously documented at the ascending and abdominal aorta are replicated at the thoracic aorta. Methods and results: A consecutive series of thirty patients referred to our institution to undergo CT angiography of the thoracic aorta (CTA) constituted the study population. Patients with diffuse aortic atherosclerosis were excluded from the analysis. All studies were acquired with a 256-MDCT scanner and ECG-gating was performed in all cases. Two orthogonal imaging planes (maximal and minimal diameters) were obtained at three different levels of the descending thoracic aorta, using the distance from the left subclavian artery as proximal landmark: 10, 40, and 80 mm distance. The mean age was 58.9±15.7 years and 16 (53%) patients were male. Descending aorta measurements at 10, 40, and 80 mm distance from the left subclavian artery were all significantly larger within the systolic window (P<0.01 for all comparisons). Measurements of the maximal diameter were systematically larger than the minimal diameters among all aortic positions including ungated, systolic, and diastolic measurements (P<0.05 for all comparisons). Conclusions: The main finding of our pilot investigation was that the thoracic descending aorta undergoes significant conformational changes during the cardiac cycle, irrespective from the distance from the left subclavian artery.Fil: Carrascosa, Patricia. Diagnóstico Maipú; ArgentinaFil: Capuñay, Carlos. Diagnóstico Maipú; ArgentinaFil: Deviggiano, Alejandro. Diagnóstico Maipú; ArgentinaFil: Rodriguez Granillo, Gaston Alfredo. Diagnóstico Maipú; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sagarduy, María Inés. Diagnóstico Maipú; ArgentinaFil: Cortines, Patricio. Diagnóstico Maipú; ArgentinaFil: Carrascosa, Jorge. Diagnóstico Maipú; ArgentinaFil: Parodi, Juan C.. Sanatorio Trinidad; Argentin

Disseny, càlcul i projecte d’estructures d’un edifici d’habitatges a Pallejà

El Treball es desenvolupa en l’àmbit del disseny, càlcul i projecte d’estructures d’un edifici d’habitatges i locals comercials en Pallejà. El projecte enceta amb la introducció, seguit per la memòria descriptiva, fent referència a les característiques de l’edifici i al seu entorn. Continua amb la justificació de la solució adoptada de l’estructura, seguit de l’eina utilitzada per el càlcul. Els següents apartats a desenvolupar són els pressupostos i els costos energètics del projecte, les emissions de CO2 generades per l’estructura projectada, la valoració dels cost econòmic i l’impacte mediambiental de l’estructura. El Treball finalitza amb les conclusions, la bibliografia i els annexos, on trobem els plànols de estructura, informació de càlcul estreta pel software i els pressupostos. Durant la memòria del projecte es farà referencia a les normatives aplicades i els documents consultats per a l’adopció de cada solució a fi de justificar tant els processos de càlcul com els de les solucions adoptades

Meta-razonamiento en Agentes con Restricciones Temporales Críticas

El paradigma de agentes/sistemas multi-agente es uno de lo smodelos computacionales de mayor relevancia de los últimos tiempos, habiendo dado lugar a múltiples investigaciones y aplicaciones concretas. Este modelo computacional tiene como objetivo la construcción de sistemas que se enfrenten a situaciones mostrando ciertas características propias de un ser humano, tales como inteligencia, reactividad, pro-actividad,... De entre todas las variedades de tipos de agente que se pueden definir, el trabajo realizado se centra en aquellos agentes que deben trabajar en un entorno con restricciones temporales críticas, es decir, donde existen ciertos problemas a los que el agente debe dar respuesta antes de que pase un determinado tiempo o las consecuencias serán catastróficas. En un agente de este tipo es fundamental tratar de conseguir un uso óptimo del tiempo de procesador, recurso más importante en esta clase de sistemas. Es por esto que resulta relevante ocnseguir que dicho agente sea capaz de dedicar su timpo de procesador a aquello que sea necesario de acuerdo a la situación en la que se encuentre. Para conseguir esta adaptación es fundamental que el agente sea capaz de razonar sobre su propio proceso de razonamiento, es decir, meta-razonar, siempre teniendo en cuenta que este proceso de metarazonamiento va a consumir también tiempo de procesador. De esta manera, el objetivo de este trabajo es el estudio de las capacidades necesarias para poder incorporar la habilidad de meta-razonar a un agente con restricciones temporales críticas, así como la incorporación a una arquitectura de agente concreta, la de agente ARTIS. Después del estudio comentado, se llegó a la conclusión de que para poder incorporar la habilidad de meta-razonamiento a un agente con restricciones temporales críticas era necesario incluir al agente las siguientes capacidades: detección de situaciones significativas, adaptar su comportamiento, adaptar el proceso de razonamiento del agente teniendo en ..Carrascosa Casamayor, C. (2004). Meta-razonamiento en Agentes con Restricciones Temporales Críticas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2670Palanci

Beam hardening artifact reduction using dual energy computed tomography: implications for myocardial perfusion studies

Background: Myocardial perfusion computed tomography (CTP) using conventional single energy (SE) imaging is influenced by the presence of beam hardening artifacts (BHA), occasionally resembling perfusion defects and commonly observed at the left ventricular posterobasal wall (PB). We therefore sought to explore the ability of dual energy (DE) CTP to attenuate the presence of BHA. Methods: Consecutive patients without history of coronary artery disease who were referred for computed tomography coronary angiography due to atypical chest pain and a normal stress-rest SPECT and had absence or mild coronary atherosclerosis constituted the study population. The study group was acquired using DE and the control group using SE imaging. Results: Demographical characteristics were similar between groups, as well as the heart rate and the effective radiation dose. Myocardial signal density (SD) levels were evaluated in 280 basal segments among the DE group (140 PB segments for each energy level from 40 keV to 100 keV; and 140 reference segments), and in 40 basal segments (at the same locations) among the SE group. Among the DE group, myocardial SD levels and myocardial SD ratio evaluated at the reference segment were higher at low energy levels, with significantly lower SD levels at increasing energy levels. Myocardial signal-to-noise ratio was not significantly influenced by the energy level applied, although 70 keV was identified as the energy level with the best overall signal-to-noise ratio. Significant differences were identified between the PB segment and the reference segment among the lower energy levels, whereas at ≥ 70 keV myocardial SD levels were similar. Compared to DE reconstructions at the best energy level (70 keV), SE acquisitions showed no significant differences overall regarding myocardial SD levels among the reference segments. Conclusions: Beam hardening artifacts that influence the assessment of myocardial perfusion can be attenuated using DE at 70 keV or higher.Fil: Rodriguez Granillo, Gaston Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Cardiológicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiológicas; Argentina. Diagnóstico Maipú; ArgentinaFil: Carrascosa, Patricia. Diagnóstico Maipú; ArgentinaFil: Cipriano, Silvia. Diagnóstico Maipú; ArgentinaFil: De Zan, Macarena. Diagnóstico Maipú; ArgentinaFil: Deviggiano, Alejandro. Diagnóstico Maipú; ArgentinaFil: Capunay, Carlos. Diagnóstico Maipú; ArgentinaFil: Cury, Ricardo C.. Miami Cardiac and Vascular Institute and Baptist Health; Estados Unido

Supportive consensus

[EN] The paper is concerned with the consensus problem in a multi-agent system such that each agent has boundary constraints. Classical Olfati-Saber's consensus algorithm converges to the same value of the consensus variable, and all the agents reach the same value. These algorithms find an equality solution. However, what happens when this equality solution is out of the range of some of the agents? In this case, this solution is not adequate for the proposed problem. In this paper, we propose a new kind of algorithms called supportive consensus where some agents of the network can compensate for the lack of capacity of other agents to reach the average value, and so obtain an acceptable solution for the proposed problem. Supportive consensus finds an equity solution. In the rest of the paper, we define the supportive consensus, analyze and demonstrate the network's capacity to compensate out of boundaries agents, propose different supportive consensus algorithms, and finally, provide some simulations to show the performance of the proposed algorithms.The author(s) received specific funding for this work from the Valencian Research Institute for Artificial Intelligence (VRAIN) where the authors are currently working. This work is partially supported by the Spanish Government project RTI2018-095390-B-C31, GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Palomares Chust, A.; Rebollo Pedruelo, M.; Carrascosa Casamayor, C. (2020). Supportive consensus. PLoS ONE. 15(12):1-30. https://doi.org/10.1371/journal.pone.0243215S1301512Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and Cooperation in Networked Multi-Agent Systems. Proceedings of the IEEE, 95(1), 215-233. doi:10.1109/jproc.2006.887293Pérez, I. J., Cabrerizo, F. J., Alonso, S., Dong, Y. C., Chiclana, F., & Herrera-Viedma, E. (2018). On dynamic consensus processes in group decision making problems. Information Sciences, 459, 20-35. doi:10.1016/j.ins.2018.05.017Fischbacher, U., & Gächter, S. (2010). Social Preferences, Beliefs, and the Dynamics of Free Riding in Public Goods Experiments. American Economic Review, 100(1), 541-556. doi:10.1257/aer.100.1.541Du, S., Hu, L., & Song, M. (2016). Production optimization considering environmental performance and preference in the cap-and-trade system. Journal of Cleaner Production, 112, 1600-1607. doi:10.1016/j.jclepro.2014.08.086Alfonso, B., Botti, V., Garrido, A., & Giret, A. (2013). A MAS-based infrastructure for negotiation and its application to a water-right market. Information Systems Frontiers, 16(2), 183-199. doi:10.1007/s10796-013-9443-8Rebollo M, Carrascosa C, Palomares A. Consensus in Smart Grids for Decentralized Energy Management. In: Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection. Springer; 2014. p. 250–261.Zhao, T., & Ding, Z. (2018). Distributed Agent Consensus-Based Optimal Resource Management for Microgrids. IEEE Transactions on Sustainable Energy, 9(1), 443-452. doi:10.1109/tste.2017.2740833Qiu, Z., Liu, S., & Xie, L. (2018). Necessary and sufficient conditions for distributed constrained optimal consensus under bounded input. International Journal of Robust and Nonlinear Control, 28(6), 2619-2635. doi:10.1002/rnc.4040Wei Ren, & Beard, R. W. (2005). Consensus seeking in multiagent systems under dynamically changing interaction topologies. IEEE Transactions on Automatic Control, 50(5), 655-661. doi:10.1109/tac.2005.846556Ren, W., & Beard, R. W. (2008). Distributed Consensus in Multi-vehicle Cooperative Control. Communications and Control Engineering. doi:10.1007/978-1-84800-015-5Knorn F, Corless MJ, Shorten RN. A result on implicit consensus with application to emissions control. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference; 2011. p. 1299–1304.Roy, S. (2015). Scaled consensus. Automatica, 51, 259-262. doi:10.1016/j.automatica.2014.10.073Mo, L., & Lin, P. (2018). Distributed consensus of second-order multiagent systems with nonconvex input constraints. International Journal of Robust and Nonlinear Control, 28(11), 3657-3664. doi:10.1002/rnc.4076Wang, Q., Gao, H., Alsaadi, F., & Hayat, T. (2014). An overview of consensus problems in constrained multi-agent coordination. Systems Science & Control Engineering, 2(1), 275-284. doi:10.1080/21642583.2014.897658Xi, J., Yang, J., Liu, H., & Zheng, T. (2018). Adaptive guaranteed-performance consensus design for high-order multiagent systems. Information Sciences, 467, 1-14. doi:10.1016/j.ins.2018.07.069Fontan A, Shi G, Hu X, Altafini C. Interval consensus: A novel class of constrained consensus problems for multiagent networks. In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC); 2017. p. 4155–4160.Hou, W., Wu, Z., Fu, M., & Zhang, H. (2018). Constrained consensus of discrete-time multi-agent systems with time delay. International Journal of Systems Science, 49(5), 947-953. doi:10.1080/00207721.2018.1433899Elhage N, Beal J. Laplacian-based consensus on spatial computers. In: AAMAS; 2010. p. 907–914.Cavalcante R, Rogers A, Jennings N. Consensus acceleration in multiagent systems with the Chebyshev semi-iterative method. In: Proc. of AAMAS’11; 2011. p. 165–172.Hu, H., Yu, L., Zhang, W.-A., & Song, H. (2013). Group consensus in multi-agent systems with hybrid protocol. Journal of the Franklin Institute, 350(3), 575-597. doi:10.1016/j.jfranklin.2012.12.020Ji, Z., Lin, H., & Yu, H. (2012). Leaders in multi-agent controllability under consensus algorithm and tree topology. Systems & Control Letters, 61(9), 918-925. doi:10.1016/j.sysconle.2012.06.003Li, Y., & Tan, C. (2019). A survey of the consensus for multi-agent systems. Systems Science & Control Engineering, 7(1), 468-482. doi:10.1080/21642583.2019.1695689Salazar, N., Rodriguez-Aguilar, J. A., & Arcos, J. L. (2010). Robust coordination in large convention spaces. AI Communications, 23(4), 357-372. doi:10.3233/aic-2010-0479Pedroche F, Rebollo M, Carrascosa C, Palomares A. On the convergence of weighted-average consensus. CoRR. 2013;abs/1307.7562

FLaMAS: Federated Learning Based on a SPADE MAS

[EN] In recent years federated learning has emerged as a new paradigm for training machine learning models oriented to distributed systems. The main idea is that each node of a distributed system independently trains a model and shares only model parameters, such as weights, and does not share the training data set, which favors aspects such as security and privacy. Subsequently, and in a centralized way, a collective model is built that gathers all the information provided by all of the participating nodes. Several federated learning framework proposals have been developed that seek to optimize any aspect of the learning process. However, a lack of flexibility and dynamism is evident in many cases. In this regard, this study aims to provide flexibility and dynamism to the federated learning process. The methodology used consists of designing a multi-agent system that can form a federated learning framework where the agents act as nodes that can be easily added to the system dynamically. The proposal has been evaluated with different experiments on the SPADE platform; the results obtained demonstrate the benefits of the federated system while facilitating flexibility and scalability.This research was partially supported by the MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government.Rincón-Arango, JA.; Julian, V.; Carrascosa Casamayor, C. (2022). FLaMAS: Federated Learning Based on a SPADE MAS. Applied Sciences. 12(7):1-14. https://doi.org/10.3390/app1207370111412

Agent Bodies: An Interface Between Agent and Environment

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23850-0_2Interfacing the agents with their environment is a classical problem when designing multiagent systems. However, the models pertaining to this interface generally choose to either embed it in the agents, or in the environment. In this position paper, we propose to highlight the role of agent bodies as primary components of the multiagent system design. We propose a tentative definition of an agent body, and discuss its responsibilities in terms of MAS components. The agent body takes from both agent and environment: low-level agent mechanisms such as perception and influences are treated locally in the agent bodies. These mechanism participate in the cognitive process, but are not driven by symbol manipulation. Furthermore, it allows to define several bodies for one mind, either to simulate different capabilities, or to interact in the different environments - physical, social- the agent is immersed in. We also draw the main challenges to apply this concept effectively.Saunier, J.; Carrascosa Casamayor, C.; Galland, S.; Kanmeugne, PS. (2015). Agent Bodies: An Interface Between Agent and Environment. En Agent Environments for Multi-Agent Systems IV. 4th International Workshop, E4MAS 2014 - 10 Years Later, Paris, France, May 6, 2014. 25-40. doi:10.1007/978-3-319-23850-0_2S2540Barella, A., Ricci, A., Boissier, O., Carrascosa, C.: MAM5: Multi-agent model for intelligent virtual environments. In: 10th European Workshop on Multi-Agent Systems (EUMAS 2012), pp. 16–30 (2012)Behe, F., Galland, S., Gaud, N., Nicolle, C., Koukam, A.: An ontology-based metamodel for multiagent-based simulations. Int. J. Simul. Model. Pract. 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Autonomous Agents and Multiagent Systems (AAMAS 2015), Istanbul, Turkey, May 2015Galland, S., Gaud, N., Demange, J., Koukam, A.: Environment model for multiagent-based simulation of 3D urban systems. In: the 7th European Workshop on Multiagent Systems (EUMAS 2009), Ayia Napa, Cyprus, December 2009 (paper 36)Gechter, F., Contet, J.M., Lamotte, O., Galland, S., Koukam, A.: Virtual intelligent vehicle urban simulator: application to vehicle platoon evaluation. Simul. Model. Practice Theor. (SIMPAT) 24, 103–114 (2012)Gibson, J.J.: The Theory of Affordances. Hilldale, USA (1977)Gouaïch, A., Michel, F., Guiraud, Y.: MIC $$^{*}$$ : a deployment environment for autonomous agents. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 109–126. Springer, Heidelberg (2005)Gouaïch, A., Michel, F.: Towards a unified view of the environment (s) within multi-agent systems. Informatica (Slovenia) 29(4), 423–432 (2005)Helleboogh, A., Vizzari, G., Uhrmacher, A., Michel, F.: Modeling dynamic environments in multiagent simulation. Int. J. Auton. Agents Multiagent Syst. 14(1), 87–116 (2007)Ketenci, U.G., Bremond, R., Auberlet, J.M., Grislin, E.: Drivers with limited perception: models and applications to traffic simulation. Recherche transports sécurité, RTS (2013)Michel, F.: The IRM4S model: the influence/reaction principle for multiagent based simulation. ACM, May 2007Okuyama, F.Y., Bordini, R.H., da Rocha Costa, A.C.: ELMS: an environment description language for multi-agent simulation. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 67–83. Springer, Heidelberg (2005)Platon, E., Sabouret, N., Honiden, S.: Environmental support for tag interactions. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 106–123. Springer, Heidelberg (2007)Ribeiro, T., Vala, M., Paiva, A.: Censys: a model for distributed embodied cognition. In: Aylett, R., Krenn, B., Pelachaud, C., Shimodaira, H. (eds.) IVA 2013. LNCS, vol. 8108, pp. 58–67. Springer, Heidelberg (2013)Ricci, A., Viroli, M., Omicini, A.: Programming MAS with artifacts. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) PROMAS 2005. LNCS (LNAI), vol. 3862, pp. 206–221. Springer, Heidelberg (2006)Ricci, A., Omicini, A., Viroli, M., Gardelli, L., Oliva, E.: Cognitive stigmergy: towards a framework based on agents and artifacts. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 124–140. Springer, Heidelberg (2007)Ricci, A., Piunti, M., Viroli, M.: Environment programming in multi-agent systems: an artifact-based perspective. Auton. Agent. Multi-Agent Syst. 23(2), 158–192 (2011)Ricci, A., Viroli, M., Omicini, A.: Environment-based coordination through coordination artifacts. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 190–214. Springer, Heidelberg (2005)Ricci, A., Viroli, M., Omicini, A.: $${\sf CArtA gO}$$ : a framework for prototyping artifact-based environments in MAS. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 67–86. Springer, Heidelberg (2007)Rincon, J.A., Garcia, E., Julian, V., Carrascosa, C.: Developing adaptive agents situated in intelligent virtual environments. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS, vol. 8480, pp. 98–109. Springer, Heidelberg (2014)Saunier, J., Balbo, F., Pinson, S.: A formal model of communication and context awareness in multiagent systems. J. Logic Lang. Inform. 23(2), 219–247 (2014). http://dx.doi.org/10.1007/s10849-014-9198-8Saunier, J., Jones, H.: Mixed agent/social dynamics for emotion computation. In: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, pp. 645–652. International Foundation for Autonomous Agents and Multiagent Systems (2014)Simonin, O., Ferber, J.: Modeling self satisfaction and altruism to handle action selection and reactive cooperation. In: 6th International Conference on the Simulation of Adaptive Behavior (SAB 2000 volume 2), pp. 314–323 (2000)Thalmann, D., Musse, S.R.: Crowd Simulation. Springer, London (2007)Thiebaux, M., Marsella, S., Marshall, A., Kallmann, M.: Smartbody: Behavior realization for embodied conversational agents. In: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, vol. 1, pp. 151–158 (2008)Viroli, M., Holvoet, T., Ricci, A., Schelfthout, K., Zambonelli, F.: Infrastructures for the environment of multiagent system. Int. J. Auton. Agent. 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