13th International Conference on Membrane Computing - MTA Sztaki

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M.A. Martínez-del-Amor, I. Pérez-Hurtado, M. García-Quismondo, L.F. Macías-Ramos, L. Valencia-Cabrera, A. Romero-Jiménez, C. Graciani, A. Riscos-Núñez, M.A. Colomer, M.J. Pérez-Jiménez Fig. 3. Evolution of the female Red Deer Fig. 4. Evolution of the male Red Deer Fig. 5. Evolution of the Fallow Deer Fig. 6. Evolution of the Roe Deer Fig. 7. Evolution of the Sheep The main difference between the DNDP and the DCBA algorithms is the way the algorithms distribute the objects between different rule blocks that compete 306
DCBA: Simulating population dynamics P systems with proportional object distribution for the same objects. In the model, the behaviour of the ungulates are modelled by using rule blocks that do not compete for objects. So, the simulator provides similar results for both DCBA and DNDP algorithms. In the case of the Bearded Vulture, there is a set of rules r 22,i,j that compete for B objects because k 1,14 is not 0 (the Bearded Vulture needs to feed on bones to survive). The k i,14 constants are 0 for ungulates (2 ≤ i ≤ 7), because they do not need to feed on bones to survive. The initial amount of bones and the amount of bones generated during the simulation is enough to support the Bearded Vulture population regardless the way the simulation algorithm distributes the bones between vultures of different ages (rules r 22,1,j ). Since there is a small initial population of bearded vultures (20 pairs), some differences can be noticed between the results from DCBA, DNDP, C++ simulator and the actual ecosystem data for the Bearded Vulture (39 bearded vultures with DCBA for year 2008, 36 with DNDP, 38 with the C++ simulator and 37 on the actual ecosystem). In Figure 8 it is shown the comparison between the actual data for the year 2008 and the simulation results obtained by using the C++ ad hoc simulator, the DNDP algorithm and the DCBA algorithm implemented in pLinguaCore. In the case of the Pyrenean Chamois, there is a difference between the actual population data on the ecosystem (12000 animals) and the results provided by the other simulators (above 20000 animals), this is because the population of Pyrenean Chamois was restarted on year 2004 [4]. Taking this into account, one can notice that all the simulators behave in a similar way for the above model and they can reproduce the actual data after 14 simulated years. So, the DCBA algorithm is able to reproduce the semantics of PDP systems and it can be used to simulate the behaviour of actual ecosystems by means of PDP systems. 5 Conclusions and Future Work In this paper we have introduced a novel algorithm for Population Dynamics P systems (PDP systems), called Direct distribution based on Consistent Blocks Algorithm (DCBA). This new algorithm performs an object distribution along the rules that eventually compete for objects. The main procedure is divided into two stages: selection and execution. Selection stage is also divided into three micro-phases: phase 1 (distribution), where by using a table and the construction of rule blocks, the distribution process takes place; phase 2 (maximality), where a random order is applied to the remaining rule blocks in order to assure the maximality condition; and phase 3 (probability), where the number of application of rule blocks is translated to application of rules by using random numbers respecting the probabilities. The algorithm is validated towards a real ecosystem model, showing that they reproduce the same results as the original simulator written in C++. The accelerators in High Performance Computing offers new approaches to accelerate the simulation of P systems and Population Dynamics. An initial parallelization work of the DCBA by using multi-core processors is described in [9]. The analysis of the two parallel levels (simulations and environments), and 307
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Erzsébet Csuhaj-Varjú Marian Gheo
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Editors Erzsébet Csuhaj-Varjú Dep
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Preface In addition to the texts or
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Contents M.A. Martínez-del-Amor, I
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(Tissue) P systems with decaying ob
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Sorin Istrail, Solomon Marcus of pr
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Sorin Istrail, Solomon Marcus proba
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Sorin Istrail, Solomon Marcus stati
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Sorin Istrail, Solomon Marcus 4.
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Sorin Istrail, Solomon Marcus his f
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Turing’s three pioneering initiat
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Turing’s three pioneering initiat
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MP systems for systems biology tere
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Yu. Rogozhin this obstacle and to g
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Yu. Rogozhin 10. J. Cocke, M. Minsk
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M. Stannett The question arises, wh
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Regular Contributions
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T. Ahmed, G. DeLancy, A. Paun almos
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T. Ahmed, G. DeLancy, A. Paun purpo
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T. Ahmed, G. DeLancy, A. Paun Fig.
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T. Ahmed, G. DeLancy, A. Paun gener
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T. Ahmed, G. DeLancy, A. Paun
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T. Ahmed, G. DeLancy, A. Paun Table
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Asynchronuous and maximally paralle
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A. Alhazov, R. Freund, H. Heikenwä
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A. Alhazov, Yu. Rogozhin With coope
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A. Alhazov, Yu. Rogozhin 2.3 P Syst
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A. Alhazov, Yu. Rogozhin Such a sys
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A. Alhazov, Yu. Rogozhin applied, f
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A. Alhazov, Yu. Rogozhin - one extr
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B. Aman, G. Ciobanu formalisms is e
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B. Aman, G. Ciobanu membranes appea
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B. Aman, G. Ciobanu • [ ] recep r
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B. Aman, G. Ciobanu Definition 3. A
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B. Aman, G. Ciobanu { w i , for all
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B. Aman, G. Ciobanu The four transi
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B. Aman, G. Ciobanu A state space i
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B. Aman, G. Ciobanu to reiterate th
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On structures and behaviors of spik
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L. Cienciala, L. Ciencialová, M. P
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H. ElGindy, R. Nicolescu, H. Wu pat
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H. ElGindy, R. Nicolescu, H. Wu pro
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H. ElGindy, R. Nicolescu, H. Wu (b)
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H. ElGindy, R. Nicolescu, H. Wu 8 r
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H. ElGindy, R. Nicolescu, H. Wu Pse
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H. ElGindy, R. Nicolescu, H. Wu to
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H. ElGindy, R. Nicolescu, H. Wu ter
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H. ElGindy, R. Nicolescu, H. Wu 4.
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H. ElGindy, R. Nicolescu, H. Wu Tab
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H. ElGindy, R. Nicolescu, H. Wu (wh
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A formal framework for P systems wi
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A new approach for solving SAT by P
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Maintenance of chronobiological inf
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4 3.5 3 2.5 2 1.5 1 0.5 0 0 50 100
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Using a kernel P system to solve th
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Sublinear-space P systems with acti
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Modelling ecological systems with t
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D. Sburlan hence one can gather use
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D. Sburlan assuming that M is in a
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D. Sburlan q 1 {t−, T 1 ↓, T 2
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D. Sburlan In case of M, the states
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D. Sburlan We introduced a formal s
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P. Sosík [9, 10], several research
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P. Sosík The rules of a system lik
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P. Sosík 1. The family Π is polyn
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P. Sosík (a) subsequently and recu
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P. Sosík contentFinal = content(l,
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P. Sosík Hence, with the aid of th
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P. Sosík 8. Lakshmanan K. and Rama
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S. Verlan, J. Quiros more relevance
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S. Verlan, J. Quiros For a regular
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S. Verlan, J. Quiros digital FPGA c
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S. Verlan, J. Quiros where k j = N
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S. Verlan, J. Quiros Now let us con
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S. Verlan, J. Quiros We consider a
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S. Verlan, J. Quiros the present co
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S. Verlan, J. Quiros Using similar
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S. Verlan, J. Quiros 5. M. Maliţa,
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Simplifying Event-B models of P sys
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Author Index Adorna H., 143 Agrigor
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13th International Conference on Membrane Computing - MTA Sztaki

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