Complexity of Simulating R Systems by P Systems

We show multiple ways to simulate R systems by non-cooperative P systems with atomic control by promoters and/or inhibitors, or with matter-antimatter annihi- lation rules, with a slowdown by a factor of constant. The descriptional complexity is also linear with respect to that of simulated R system. All these constants depend on how general the model of R systems is, as well as on the chosen control ingredients of P systems. Special attention is paid to the di erences in the mode of rule application in these models

Multiple scattering in random mechanical systems and diffusion approximation

This paper is concerned with stochastic processes that model multiple (or iterated) scattering in classical mechanical systems of billiard type, defined below. From a given (deterministic) system of billiard type, a random process with transition probabilities operator P is introduced by assuming that some of the dynamical variables are random with prescribed probability distributions. Of particular interest are systems with weak scattering, which are associated to parametric families of operators P_h, depending on a geometric or mechanical parameter h, that approaches the identity as h goes to 0. It is shown that (P_h -I)/h converges for small h to a second order elliptic differential operator L on compactly supported functions and that the Markov chain process associated to P_h converges to a diffusion with infinitesimal generator L. Both P_h and L are selfadjoint (densely) defined on the space L2(H,{\eta}) of square-integrable functions over the (lower) half-space H in R^m, where {\eta} is a stationary measure. This measure's density is either (post-collision) Maxwell-Boltzmann distribution or Knudsen cosine law, and the random processes with infinitesimal generator L respectively correspond to what we call MB diffusion and (generalized) Legendre diffusion. Concrete examples of simple mechanical systems are given and illustrated by numerically simulating the random processes.Comment: 34 pages, 13 figure

Modeling of Decision Trees Through P systems

[EN] In this paper, we propose a decision-tree modeling in the framework of membrane computing. We propose an algorithm to obtain a P system that is equivalent to any decision tree taken as input. In our case, and unlike previous proposals, we formulate the concepts of decision trees endogenously, since there is no external agent involved in the modeling. The tree structure can be defined naturally by the topology of the regions in the P system and the decision rules are defined by communication rules of the P system.Sempere Luna, JM. (2019). Modeling of Decision Trees Through P systems. New Generation Computing. 37(3):325-337. https://doi.org/10.1007/s00354-019-00052-4325337373Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Chapman & Hall, Boca Raton (1984)Cardona, M., Colomer, M.A., Margalida, A., Palau, A., Pérez-Hurtado, I., Pérez-Jiménez, M.J., Sanuy, D.: A computational modeling for real ecosystems based on P systems. Nat. Comput. 10(1), 39–53 (2011)Cecilia, J.M., García, J.M., Guerrero, G.D., Martínez-del-Amor, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J.: Simulation of P systems with active membranes on CUDA. Brief. Bioinform. 11(3), 313–322 (2010)Díaz-Pernil, D., Peña-Cantillana, F., Gutiérrez-Naranjo, M.A.: Self-constructing Recognizer P Systems. In: Proceedings of the Thirteenth Brainstorming Week on Membrane Computing. Fénix Editora, pp. 137–154 (2014)Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8, 87–102 (1992)Kingsford, C., Salzberg, S.L.: What are decision trees ? Nat. Biotechnol. 26(9), 1011–1013 (2008)Martín-Vide, C., Păun, Gh, Pazos, J., Rodríguez-Patón, A.: Tissue P systems. Theor. Comput. Sci. 296, 295–326 (2003)Martínez-del-Amor, M.A., García-Quismondo, M., Macías-Ramos, L.F., Valencia-Cabrera, L., Riscos-Núñez, A., Pérez-Jiménez, M.J.: Simulating P systems on GPU devices: a survey. Fund. Inf. 136(3), 269–284 (2015)Mitchell, T.: Machine Learning. McGraw-Hill, New York City (1997)Păun, Gh: Membrane Computing, An Introduction. Springer, Berlin (2002)Păun, Gh, Rozenberg, G., Salomaa, A. (eds.): The Oxford Handbook of Membrane Computing. Oxford University Press, Oxford (2010)Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Burlington (1993)Sempere, J.M.: A View of P systems from information theory. In: Proceedings of the 17th international conference on membrane computing (CMC 2016) LNCS vol. 10105. Springer, pp. 352–362 (2017)Sammut, C., Webb, G.I. (eds.): Encyclopedia of Machine Learning. Springer, Berlin (2011)Wang, J., Hu, J., Peng, H., Pérez-Jiménez, M.J., Riscos-Núñez, A.: Decision tree models induced by membrane systems. Rom. J. Inf. Sci. Technol. 18(3), 228–239 (2015)Zhang, C., Ma, Y. (eds.): Ensemble Machine Learning, Methods and Applications. Springer, Berlin (2012)Zhang, X., Wang, B., Ding, Z., Tang, J., He, J.: Implementation of membrane algorithms on GPU. J. Appl. Math. 2014, 7 (2014

A Methodology for Modeling and Optimizing Social Systems

[EN] A system methodology for modeling and optimizing social systems is presented. It allows constructing dynamical models formulated stochastically, i.e., their results are given by confidence intervals. The models provide optimal intervention ways to reach the stated objectives. Two optimization methods are used: (1) to test strategies and scenarios and (2) to optimize with a genetic algorithm. The application case presented is a small nonformal education Spanish business. First, the model is validated in the 2008-2012 period, and subsequently, the optimal way to obtain a maximum profit in the 2013-2025 period is obtained using the two methods.Caselles, A.; Soler Fernández, D.; Sanz, MT.; Micó, JC. (2020). A Methodology for Modeling and Optimizing Social Systems. Cybernetics & Systems. 51(3):265-314. https://doi.org/10.1080/01969722.2019.1684042S265314513Caselles, A. 1993. System Decomposition and Coupling. Cybernetics and Systems: An International Journal 24 (4):305–323. doi:10.1080/01969729308961712.CASELLES, A. (1994). IMPROVEMENTS IN THE SYSTEMS-BASED MODELS GENERATOR SIGEM. Cybernetics and Systems, 25(1), 81-103. doi:10.1080/01969729408902317Caselles, A., Soler, D., Sanz, M. T., & Micó, J. C. (2014). SIMULATING DEMOGRAPHY AND HUMAN DEVELOPMENT DYNAMICS. Cybernetics and Systems, 45(6), 465-485. doi:10.1080/01969722.2014.929347Djidjeli, K., Price, W. G., Temarel, P., & Twizell, E. H. (1998). Partially implicit schemes for the numerical solutions of some non-linear differential equations. Applied Mathematics and Computation, 96(2-3), 177-207. doi:10.1016/s0096-3003(97)10133-3Gutiérrez, M. M. and H. P. Leone. 2012. DE2M: An environment for developing distributed and executable enterprise models. Advances in Engineering Software 47:80–103. doi:10.1016/j.advengsoft.2011.12.002.SANZ, M. T., MICÓ, J. C., CASELLES, A., & SOLER, D. (2014). A Stochastic Model for Population and Well-Being Dynamics. The Journal of Mathematical Sociology, 38(2), 75-94. doi:10.1080/0022250x.2011.629064Sanz, M. T., Caselles, A., Micó, J. C., & Soler, D. (2016). Including an environmental quality index in a demographic model. International Journal of Global Warming, 9(3), 362. doi:10.1504/ijgw.2016.075448Shannon, R., & Johannes, J. D. (1976). Systems Simulation: The Art and Science. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(10), 723-724. doi:10.1109/tsmc.1976.430943

Computer simulation in archaeology. Art, science or nightmare?

[EN] By simulating historical processes and not just the archaeological material, I intend to explain social causality at the micro and macro levels. The target is no more an archaeological artifact but a human society at large, although existing only in the virtual world. A new target, the artificial society, is created with its own structure and behavior. With the possibility of simulating virtual social systems, a new methodology of scientific inquiry becomes possible.[ES] Se propone ampliar el concepto mismo de Arqueología Virtual a la simulación de sociedades humanas y no sólo la simulación de objetos que existieron en el pasado. La idea es poder disponer de una herramienta para explicar las formas de causalidad social a un nivel tanto micro como macro. El objetivo, por tanto, ya no es el artefacto arqueológico, sino la sociedad en su sentido más amplio, si bien se trata de una sociedad que sólo existe en un mundo “virtual”. Esta sociedad artificial, nuevo objetivo del análisis se crea con su propia estructura y conducta simuladas. Con la posibilidad de simular sistemas sociales virtuales, se hace posible una nueva metodología para la investigación científica.This research is funded by the Spanish Ministry for Scienc and Innovation, under grant No. HAR2009-12258, and it is a part of the joint research team “Social and environmental transitions: Simulating the past to understand human behaviour (SimulPast)”(www.simulpast.es), funded by the same national agency under the program CONSOLIDER-INGENIO 2010, CSD2010-00034.Barceló, JA. (2012). Computer simulation in archaeology. Art, science or nightmare?. Virtual Archaeology Review. 3(5):8-12. https://doi.org/10.4995/var.2012.4489OJS81235ALTAWEEL, M. (2008): Investigating agricultural sustainability and strategies in northern Mesopotamia: results produced using a socio-ecological modeling approach, Journal of Archaeological Science nº 35, pp. 821-835. http://dx.doi.org/10.1016/j.jas.2007.06.012BARCELÓ, J.A. (2009): Computational Intelligence in Archaeology. Hershey (NY): The IGI Group. http://dx.doi.org/10.4018/978-1-59904-489-7BARCELÓ, J.A.; CUESTA, J.A:; DEL CASTILLO, F. GALÁN, J.M.; MAMELI, L., MIGUEL, F.; SANTOS, J. I.; VILÀ, X.. (2010): "Patagonian Ethnogenesis: towards a computational simulation approach", in Proceedings of the 3rd World Congress on Social Simulation WCSS2010 CDROM.. Kassel: Center for Environmental Systems Research, University of Kassel; pp. 1-9.CHRISTIANSEN J.H., ALTAWEEL M, (2006): "Simulation of natural and social process interactions: An example from Bronze Age Mesopotamia". Social Science Computer Review. 24(2), pp. 209-226. http://dx.doi.org/10.1177/0894439305281500COSTOPOULOS, A. LAKE, M.V. (2010): Simulating Change Archaeology Into the Twenty-first Century Salt Lake, The University of Utah Press.DORAN JM, PALMER M, GILBERT N, & MELLARS P, (1994): "The EOS project: Modeling Upper Paleolithic social change". In Gilbert N and Doran J, eds. Simulating Societies: The Computer Simulation of Social Phenomena, London: UCL Press, pp. 195-221.EDMONDS, B., MOSS, S. (2011): Simulating Social Complexity: A Handbook. Berlin, Springer.EPSTEIN, J. M. (2006): Generative Social Science: Studies in Agent-Based Computational Modeling). Princeton University Press.GUMERMAN, G. J., SWEDLUND, A. C., DEAN, J. S, EPSTEIN J. M. (2003): The Evolution of Social Behavior in the Prehistoric American Southwest. Artificial Life 9, pp. 435-444. http://dx.doi.org/10.1162/106454603322694861JANSSEN, M.A, (2009): "Understanding Artificial Anasazi", in Journal of Artificial Societies and Social Simulation 12 (4) 13 http://jasss.soc.surrey.ac.uk/12/4/13.htmlKOHLER, T.A., KRESL, J., WEST, C.V., CARR, E., WILSHUSEN, R. (2000): Be there then: A modeling approach to settlement determinants and spatial efficiency among late ancestral Pueblo populations of the Mesa Verde region, U. S. Southwest. In Kohler TA and Gumerman GJ, eds. Dynamics inHuman and Primate Societies: Agent-Based Modeling of Social and Spatial Processes, New York: Oxford University Press, pp. 145-178.KOHLER, T. A., LEEUW, S. v.d. (2007): The Model-Based Archaeology of Socionatural Systems. Santa Fe: School of Advanced Research Press.KOHLER, T A, VARIEN, M. D., WRIGHT, A., KUCKELMAN, K. A. (2008): Mesa Verde Migrations: New archaeological research and computer simulation suggest why ancestral Puebloans deserted the northern Southwest United States. American Scientist nº 96, pp. 146-153. http://dx.doi.org/10.1511/2008.70.3641MACY, M. W. WILLER, R. (2002): "From Factors to Actors: Computational Sociology and Agent-Based Models", en Annual Review of Sociology, nº 28, pp. 143-166.SAWYER, R. K. (2005): Social emergence: societies as complex systems. New York: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511734892SOKOLOWSKI, J.A., BANKS, C.M., (2009): Modeling and Simulation for Analyzing Global Events London, Wiley. http://dx.doi.org/10.1002/9780470486993ZACHARIAS, G.L., MACMILLAN, M., VAN HEMEL, S.B. (2008): Behavioral Modeling and Simulation: From Individuals to Societies, Washington, National Academies Press

A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning

Progress in biomechanical modelling of human soft tissue is the basis for the development of new clinical applications capable of improving the diagnosis and treatment of some diseases (e.g. cancer), as well as the surgical planning and guidance of some interventions. The finite element method (FEM) is one of the most popular techniques used to predict the deformation of the human soft tissue due to its high accuracy. However, FEM has an associated high computational cost, which makes it difficult its integration in real-time computer-aided surgery systems. An alternative for simulating the mechanical behaviour of human organs in real time comes from the use of machine learning (ML) techniques, which are much faster than FEM. This paper assesses the feasibility of ML methods for modelling the biomechanical behaviour of the human liver during the breathing process, which is crucial for guiding surgeons during interventions where it is critical to track this deformation (e.g. some specific kind of biopsies) or for the accurate application of radiotherapy dose to liver tumours. For this purpose, different ML regression models were investigated, including three tree-based methods (decision trees, random forests and extremely randomised trees) and other two simpler regression techniques (dummy model and linear regression). In order to build and validate the ML models, a labelled data set was constructed from modelling the deformation of eight ex-vivo human livers using FEM. The best prediction performance was obtained using extremely randomised trees, with a mean error of 0.07 mm and all the samples with an error under 1 mm. The achieved results lay the foundation for the future development of some real-time software capable of simulating the human liver deformation during the breathing process during clinical interventions.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects TIN2014-52033-R and DPI2013-40859-R, both also supported by European FEDER funds. The authors acknowledge the kind collaboration of the personnel from the hospital involved in the research.Lorente, D.; Martínez-Martínez, F.; Rupérez Moreno, MJ.; Lago, MA.; Martínez-Sober, M.; Escandell-Montero, P.; Martínez-Martínez, JM.... (2017). A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Systems with Applications. 71:342-357. doi:10.1016/j.eswa.2016.11.037S3423577

Modelling of Solar Radiation Interception and Biomass Production in an Intercropping System of Rubber with Banana and Pineapple

Simulation modelling is a powerful approach for studying complex intercropping systems in entirety and a complementary tool to conventional field experiments. This study aimed to: 1) construct a dynamic model to simulate the biological productivity of an immature rubber (R), banana (8) and pineapple (P) intercropping system based on the interception and utilisation of incident solar radiation (SR), 2) evaluate growth and yield of the intercrop components using the model, 3) compare production for various cropping scenarios and 4) investigate the likelihood and effects of water stress on crop growth using a simple water budget. A FORTRAN computer model, SURHIS (Sharing and Utilisation of Radiation intercepted in a Hedgerow-Intercropping System), was developed for simulating daily SR interception and growth of R-B-P intercropping system. SR interception was modelled using a modified Monsi-Saeki equation by including a clump factor to account for the loss in intercepted SR resulting from the wide row spacing between the crops. Crop growth was modelled based on the net biomass resulting from the difference between crop photosynthesis and respi ration. Simulation results showed that increments in the leaf area index (LAI) had a greater effect on SR interception by component crops compared to height increments. Changes in height affected only fractional interception, whereas LAI increments affected both fractional and total interception. The crop growth modules were suffiCiently accurate in estimating LAI and dry matter yield (OMY) but less precise for crop height. The girth of rubber was estimated with good accuracy. The general trend in overestimation for later part of the simulation period can be attributed to model assumptions for potential production conditions