HERRAMIENTAS INTELIGENTES PARA LA GESTIÓN DE PROYECTOS

[EN] The application of Artificial Intelligence (AI) in Project Management (PM) is a current research field with a growing demand from companies and organizations, interested in applying these technologies to get the most out of their data and expertise. Many organizations use software tools that allow automating the status of projects (situation analysis). These tools also provide predictions on the evolution of projects, using classic techniques such as Earned Value Management (EVM). However, the standard tools do not have many advanced functionalities, based on AI, which would allow to take much better advantage of the knowledge acquired by the organization. This fact is especially important in Risk Management (RM), which is one of the most complex aspects of PM. The objective of this work is to propose a methodology for research and development of tools based on AI technologies that allow organizations to analyze information from projects already developed (historical information) and to use it to improve RM in the planning of future projects.[ES] La aplicación de la Inteligencia Artificial (IA) en la Gestión de Proyectos (GP) es un campo de investigación actual y además existe una demanda creciente por parte de empresas y organizaciones para aplicar estas tecnologías. Muchas organizaciones utilizan herramientas de software que permiten automatizar el estado de los proyectos (análisis de situación). Estas herramientas también proporcionan predicciones sobre la evolución de los proyectos, utilizando técnicas clásicas como la Gestión del Valor Ganado (EVM). Sin embargo, estas herramientas, en general, no disponen de muchas funcionalidades avanzadas, basadas en la IA, que permitirían aprovechar mucho mejor los conocimientos adquiridos por la organización. Este hecho es especialmente importante en la Gestión de Riesgos (GR), que es uno de los aspectos más complejos de la GP. El objetivo de este trabajo es proponer una metodología para la investigación y el desarrollo de herramientas basadas en las tecnologías de la IA que permitan a las organizaciones analizar la información histórica de los proyectos ya ejecutados (información histórica) y utilizarla para poder mejorar la GR en la planificación de los proyectos futuros.Palomares Chust, A.; Heras, S.; Gil Pérez, A. (2020). Intelligent Project Management Tools. Asociación Española de Dirección e Ingeniería de Proyectos (AIEPRO). 1860-1870. http://hdl.handle.net/10251/177493S1860187

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

Transcriptional and metabolic response of CHO cells to different carbon dioxide concentrations

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Micromechanics of fully lamellar TiAl alloys

Fully lamellar gamma titanium aluminides are very promising materials for aerospace applications, due to their increased thrust-to-weight ratios and improved efficiency under aggressive environments at temperatures up to 750 ºC. For that reason, they are projected to replace the heavier Ni- base superalloys currently used for low pressure turbine (LPT) blades manufacturing. However, their ductility is limited due to their inherent anisotropy, associated to the lamellar microstructure. The objective of this work was to study the mechanical response of single colonies of polycrystalline γ-TiAl, as a function of layer thickness and layer orientation, and to relate this mechanical response with the operative deformation mechanisms. With this aim, micropillars with lamellae oriented at 0º, 45º and 90º with respect to the loading direction were compressed at room temperature and elevated temperature. The results revealed a large plastic anisotropy, that was rationalized, based on slip/twin trace analysis, according to the relative orientation of the main operative deformation modes with respect to the lamellar interfaces. Loading at 45º resulted in the activation of soft longitudinal deformation modes, where both the slip plane and the slip direction were parallel to the interfaces, and therefore, little interaction of dislocations with lamellar interfaces is expected. At 0º loading, deformation was mainly accommodated by harder mixed deformation modes (with an oblique slip plane but a slip direction parallel to the lamellar interfaces), although the lamellar interfaces seemed to be relatively transparent to slip transfer. On the contrary, 90º loading represented the hardest direction and deformation was accommodated by the activation of transverse deformation modes, confined to individual lamellae, together with longitudinal modes that were activated due to their softer nature, despite their very small Schmid factors. Finally, a thorough study of pillar size effects revealed that the results were insensitive to pillar size for dimensions above 5 mm. The results can therefore be successfully applied for developing mesoscale plasticity models that capture the micromechanics of fully lamellar TiAl microstructures at larger length scales Additionally, microtensile specimens were also milled out of single colonies and in-situ tested in the SEM, to study the role of interlamellar interfaces on the plastic deformation and fracture under tension. EBSD was used before and after the test to study the role of different type of interfaces (true twin, pseudo twin and order variant) on slip/twin transfer. This study emphasizes the complexity of the micromechanics of fully lamellar TiAl alloys, where the activation of different deformation modes is strongly affected, not only by the lamellar orientation, but also by the character of the interfaces between the different lamellae. References A.J. Palomares, M.T. Pérez-Prado, J.M. Molina-Aldareguia, Acta Mater. 123 (2017) 102-114 A.J. Palomares, I. Sabirov, M.T. Pérez-Prado, J.M. Molina-Aldareguia, Scripta Mater. 139 (2017) 17-2

NIC40 : valoración de las inversiones inmobiliarias en las SOCIMI (España)

Máster en Auditoría de Cuentas (M142

General logarithmic image processing convolution

The logarithmic image processing model (LIP) is a robust mathematical framework, which, among other benefits, behaves invariantly to illumination changes. This paper presents, for the first time, two general formulations of the 2-D convolution of separable kernels under the LIP paradigm. Although both formulations are mathematically equivalent, one of them has been designed avoiding the operations which are computationally expensive in current computers. Therefore, this fast LIP convolution method allows to obtain significant speedups and is more adequate for real-time processing. In order to support these statements, some experimental results are shown in Section V