984 resultados para Natural computing


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This paper presents the application of Networks of Evolutionary Processors to Decision Support Systems, precisely Knowledge-Driven DSS. Symbolic information and rule-based behavior in Networks of Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network. The non-deterministic and massive parallel way of operation results in NP-problem solving in linear time. A working NEP example is shown.

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* Work partially supported by contribution of EU commission Under The Fifth Framework Programme, project “MolCoNet” IST-2001-32008.

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Transition P systems are computational models based on basic features of biological membranes and the observation of biochemical processes. In these models, membrane contains objects multisets, which evolve according to given evolution rules. In the field of Transition P systems implementation, it has been detected the necessity to determine whichever time are going to take active evolution rules application in membranes. In addition, to have time estimations of rules application makes possible to take important decisions related to the hardware / software architectures design. In this paper we propose a new evolution rules application algorithm oriented towards the implementation of Transition P systems. The developed algorithm is sequential and, it has a linear order complexity in the number of evolution rules. Moreover, it obtains the smaller execution times, compared with the preceding algorithms. Therefore the algorithm is very appropriate for the implementation of Transition P systems in sequential devices.

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Membrane computing is a recent area that belongs to natural computing. This field works on computational models based on nature's behavior to process the information. Recently, numerous models have been developed and implemented with this purpose. P-systems are the structures which have been defined, developed and implemented to simulate the behavior and the evolution of membrane systems which we find in nature. What we show in this paper is an application capable to simulate the P-systems based on a multiagent systems (MAS) technology. The main goal we want to achieve is to take advantage of the inner qualities of the multiagent systems. This way we can analyse the proper functioning of any given p-system. When we observe a P-system from a different perspective, we can be assured that it is a particular case of the multiagent systems. This opens a new possibility, in the future, to always evaluate the P-systems in terms of the multiagent systems technology.

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The authors thank Professor Iber^e Luiz Caldas for the suggestions and encouragement. The authors F.F.G.d.S., R.M.R., J.C.S., and H.A.A. acknowledge the Brazilian agency CNPq and state agencies FAPEMIG, FAPESP, and FAPESC, and M.S.B. also acknowledges the EPSRC Grant Ref. No. EP/I032606/1.

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W.-X.W. was supported by NSFC under Grant No. 11105011, CNNSF under Grant No. 61074116 and the Fundamental Research Funds for the Central Universities. Y.-C.L. was supported by ARO under Grant W911NF-14-1-0504

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Funding: This work was supported by a Clinical PhD Fellowship to MRP (090665) and a Principal Research Fellowship to AHF (079838) from the Wellcome Trust (http://www.wellcome.ac.uk). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Acknowledgements We thank the EPSRC National Crystallography Service (University of Southampton) for the data collections and the EPSRC National Mass Spectrometry Service (University of Swansea) for the HRMS data.

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Acknowledgements KK, MJ, RE and HD are grateful for financial support through the Leverhulme Trust-Royal Society Africa award (AA090088). MJ, RE, HD, JT and KH thank EU FP7 for financial support (contract no. 312184). HD thanks the School of Natural and Computing Sciences, University of Aberdeen, for a PhD scholarship to XLW. PCD gratefully acknowledges grants from the National Institute of Health (GM097509 and GMS10RR029121). We thank the Bruker Therapeutic Discovery Mass Spectrometry Center for recording the MSn spectra.

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Peer reviewed

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F. Meneguzzi thanks Fundaç ao de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS, Brazil) for the financial support through the ACI program (Grant ref. 3541-2551/12-0) and the ARD program (Grant ref. 12/0808-5), as well as Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) through the Universal Call (Grant ref. 482156/2013-9) and PQ fellowship (Grant ref. 306864/2013-4). N. Oren and W.W. Vasconcelos acknowledge the support of the Engineering and Physical Sciences Research Council (EPSRC, UK) within the research project “Scrutable Autonomous Systems” (SAsSY11, Grant ref. EP/J012084/1).