906 resultados para Complex systems prediction


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Plants are necessarily complex systems that require monitoring of multiple environmental signals and, in response to those signals, coordination of differentiation and development of an extensive array of cell types at multiple locations. This coordination must rely on integration of long-distance signals that provide a means of communication among different plant parts. We propose that the relatively well-characterized classical phytohormones must act with several other long-distance signals to achieve this level of organization with dynamic yet measured responses. This is supported by observations that classical phytohormones: (i) operate in complex yet experimentally unresolved networks involving cross-talk and feedback, (ii) are generally multifunctional and nonspecific and hence must rely on other long-distance cues or pre-set conditions to achieve specificity and (iii) are likely to mask roles of other long-distance signals in several experimental contexts. We present evidence for involvement of novel long-distance signals in three developmental processes-branching, flowering and nodulation, and discuss the possible identities of novel signalling molecules.

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Networks exhibiting accelerating growth have total link numbers growing faster than linearly with network size and either reach a limit or exhibit graduated transitions from nonstationary-to-stationary statistics and from random to scale-free to regular statistics as the network size grows. However, if for any reason the network cannot tolerate such gross structural changes then accelerating networks are constrained to have sizes below some critical value. This is of interest as the regulatory gene networks of single-celled prokaryotes are characterized by an accelerating quadratic growth and are size constrained to be less than about 10,000 genes encoded in DNA sequence of less than about 10 megabases. This paper presents a probabilistic accelerating network model for prokaryotic gene regulation which closely matches observed statistics by employing two classes of network nodes (regulatory and non-regulatory) and directed links whose inbound heads are exponentially distributed over all nodes and whose outbound tails are preferentially attached to regulatory nodes and described by a scale-free distribution. This model explains the observed quadratic growth in regulator number with gene number and predicts an upper prokaryote size limit closely approximating the observed value. (c) 2005 Elsevier GmbH. All rights reserved.

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Ecosystems and the species and communities within them are highly complex systems that defy predictions with any degree of certainty. Managing and conserving these systems in the face of uncertainty remains a daunting challenge, particularly with respect to developing networks of marine reserves. Here we review several modelling frameworks that explicitly acknowledge and incorporate uncertainty, and then use these methods to evaluate reserve spacing rules given increasing levels of uncertainty about larval dispersal distances. Our approach finds similar spacing rules as have been proposed elsewhere - roughly 20-200 km - but highlights several advantages provided by uncertainty modelling over more traditional approaches to developing these estimates. In particular, we argue that uncertainty modelling can allow for (1) an evaluation of the risk associated with any decision based on the assumed uncertainty; (2) a method for quantifying the costs and benefits of reducing uncertainty; and (3) a useful tool for communicating to stakeholders the challenges in managing highly uncertain systems. We also argue that incorporating rather than avoiding uncertainty will increase the chances of successfully achieving conservation and management goals.

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Computer-based, socio-technical systems projects are frequently failures. In particular, computer-based information systems often fail to live up to their promise. Part of the problem lies in the uncertainty of the effect of combining the subsystems that comprise the complete system; i.e. the system's emergent behaviour cannot be predicted from a knowledge of the subsystems. This paper suggests uncertainty management is a fundamental unifying concept in analysis and design of complex systems and goes on to indicate that this is due to the co-evolutionary nature of the requirements and implementation of socio-technical systems. The paper shows a model of the propagation of a system change that indicates that the introduction of two or more changes over time can cause chaotic emergent behaviour.

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In this paper we consider the co-evolutionary dynamics of IS engagement where episodic change of implementation increasingly occurs within the context of linkages and interdependencies between systems and processes within and across organisations. Although there are many theories that interpret the various motors of change be it lifecycle, teleological, dialectic or evolutionary, our paper attempts to move towards a unifying view of change by studying co-evolutionary dynamics from a complex systems perspective. To understand how systems and organisations co-evolve in practice and how order emerges, or fails to emerge, we adopt complex adaptive systems theory to incorporate evolutionary and teleological motors, and actor-network theory to incorporate dialectic motors. We illustrate this through the analysis of the implementation of a novel academic scheduling system at a large research-intensive Australian university.

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Operationalising and measuring the concept of globalisation is important, as the extent to which the international economy is integrated has a direct impact on industrial dynamics, national trade policies and firm strategies. Using complex systems network analysis with longitudinal trade data from 1938 to 2003, this paper presents a new way to measure globalisation. It demonstrates that some important aspects of the international trade network have been remarkably stable over this period. However, several network measures have changed substantially over the same time frame. Taken together, these analyses provide a novel measure of globalisation.

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The XSophe computer simulation software suite consisting of a daemon, the XSophe interface and the computational program Sophe is a state of the art package for the simulation of electron paramagnetic resonance spectra. The Sophe program performs the computer simulation and includes a number of new technologies including; the SOPHE partition and interpolation schemes, a field segmentation algorithm, homotopy, parallelisation and spectral optimisation. The SOPHE partition and interpolation scheme along with a field segmentation algorithm greatly increases the speed of simulations for most systems. Multidimensional homotopy provides an efficient method for accurately tracing energy levels and hence tracing transitions in the presence of energy level anticrossings and looping transitions and allowing computer simulations in frequency space. Recent enhancements to Sophe include the generalised treatment of distributions of orientational parameters, termed the mosaic misorientation linewidth model and a faster more efficient algorithm for the calculation of resonant field positions and transition probabilities. For complex systems the parallelisation enables the simulation of these systems on a parallel computer and the optimisation algorithms in the suite provide the experimentalist with the possibility of finding the spin Hamiltonian parameters in a systematic manner rather than a trial-and-error process. The XSophe software suite has been used to simulate multifrequency EPR spectra (200 MHz to 6 00 GHz) from isolated spin systems (S > ~½) and coupled centres (Si, Sj _> I/2). Griffin, M.; Muys, A.; Noble, C.; Wang, D.; Eldershaw, C.; Gates, K.E.; Burrage, K.; Hanson, G.R."XSophe, a Computer Simulation Software Suite for the Analysis of Electron Paramagnetic Resonance Spectra", 1999, Mol. Phys. Rep., 26, 60-84.

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Complex systems techniques provide a powerful tool to study the emergent properties of networks of interacting genes. In this study we extract models of genetic regulatory networks from an artificial genome, represented by a sequence of nucleotides, and analyse how variations in the connectivity and degree of inhibition of the extracted networks affects the resulting classes of behaviours. For low connectivity systems were found to be very stable. Only with higher connectivity was a significant occurrence of chaos found. Most interestingly, the peak in occurrence of chaos occurs perched on the edge of a phase transition in the occurrence of attractors.

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A complexidade do ambiente em que vivem as organizações nos dias de hoje exige Sistemas de Informações (SIs) eficientes, que sirvam como apoio à tomada de decisão por parte dos gestores, em especial no que diz respeito às questões ligadas à Tecnologia da Informação (TI) diante dos novos paradigmas de gestão enfrentados pelas Pequenas e Médias Empresas (PMEs). Este trabalho tem como objetivo geral a avaliação da percepção dos gestores das PMEs brasileiras, quanto ao impacto do uso da TI como elemento de melhoria dos processos financeiros das organizações. Para que tal objetivo fosse alcançado, foram desenvolvidos e estudados os seguintes objetivos específicos: verificar de que forma a TI é percebida como útil nas pequenas e médias empresas, em especial na automação de rotinas financeiras de tesouraria e fluxo de caixa; verificar se a automação de rotinas financeiras com a utilização da TI subsidia os processos de tomada de decisão em tais empresas e, ainda, verificar se sua utilização é vista como uma vantagem estratégica. Em seu levantamento bibliográfico, este trabalho apresenta conceitos relacionados a Competição, Pequena e Média Empresa, Estratégia e Gestão de Competência. Esses conceitos foram encontrados na literatura e em artigos na imprensa especializada. Na pesquisa empírica realizada, este trabalho procurou, através de estudos de casos múltiplos em 5 empresas, aprofundar as questões relativas à percepção dos gestores em relação à TI utilizada nos procedimentos financeiros implantados nas organizações que dirigem. O planejamento da amostra foi feito de maneira a permitir não só a análise individual de cada respondente como também a análise das organizações onde atuavam. As conclusões a que este estudo chegou são: a) os gestores têm a percepção de que a utilização de TI nas rotinas financeiras e de fluxo de caixa é imprescindível, pois a automação é necessária para a manutenção e a agilização dos processos de relacionamento com clientes e fornecedores; b) a TI pode subsidiar processos de tomada de decisão estruturada, especificamente as decisões que tratam de rotinas financeiras. Outro aspecto que fica claro na percepção dos gestores é que a dependência da empresa aos SIs aumenta com o crescimento da empresa e, conseqüentemente, com o volume de informações com que os gestores têm de lidar; c) na percepção dos gestores, a utilização de TI nos processos financeiros das PMEs é importante, mas eles não são os determinantes da vantagem estratégica da organização. Os estudos permitiram duas outras observações importantes: d) as estruturas organizacionais das empresas estudadas eram pertinentes aos itens de TI que possuíam; e) há diferença de visão entre gestores e subordinados. A conclusão deste estudo, que responde ao objetivo geral, é que, na percepção dos gestores, a TI tem um impacto positivo como elemento de melhoria dos processos financeiros das organizações, porém não é o fator que define a vantagem estratégica das organizações: há outros aspectos que devem ser avaliados, e a construção de sistemas complexos que abrangem outras áreas da organização é necessária.(AU)

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In this thesis work we develop a new generative model of social networks belonging to the family of Time Varying Networks. The importance of correctly modelling the mechanisms shaping the growth of a network and the dynamics of the edges activation and inactivation are of central importance in network science. Indeed, by means of generative models that mimic the real-world dynamics of contacts in social networks it is possible to forecast the outcome of an epidemic process, optimize the immunization campaign or optimally spread an information among individuals. This task can now be tackled taking advantage of the recent availability of large-scale, high-quality and time-resolved datasets. This wealth of digital data has allowed to deepen our understanding of the structure and properties of many real-world networks. Moreover, the empirical evidence of a temporal dimension in networks prompted the switch of paradigm from a static representation of graphs to a time varying one. In this work we exploit the Activity-Driven paradigm (a modeling tool belonging to the family of Time-Varying-Networks) to develop a general dynamical model that encodes fundamental mechanism shaping the social networks' topology and its temporal structure: social capital allocation and burstiness. The former accounts for the fact that individuals does not randomly invest their time and social interactions but they rather allocate it toward already known nodes of the network. The latter accounts for the heavy-tailed distributions of the inter-event time in social networks. We then empirically measure the properties of these two mechanisms from seven real-world datasets and develop a data-driven model, analytically solving it. We then check the results against numerical simulations and test our predictions with real-world datasets, finding a good agreement between the two. Moreover, we find and characterize a non-trivial interplay between burstiness and social capital allocation in the parameters phase space. Finally, we present a novel approach to the development of a complete generative model of Time-Varying-Networks. This model is inspired by the Kaufman's adjacent possible theory and is based on a generalized version of the Polya's urn. Remarkably, most of the complex and heterogeneous feature of real-world social networks are naturally reproduced by this dynamical model, together with many high-order topological properties (clustering coefficient, community structure etc.).

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A formalism recently introduced by Prugel-Bennett and Shapiro uses the methods of statistical mechanics to model the dynamics of genetic algorithms. To be of more general interest than the test cases they consider. In this paper, the technique is applied to the subset sum problem, which is a combinatorial optimization problem with a strongly non-linear energy (fitness) function and many local minima under single spin flip dynamics. It is a problem which exhibits an interesting dynamics, reminiscent of stabilizing selection in population biology. The dynamics are solved under certain simplifying assumptions and are reduced to a set of difference equations for a small number of relevant quantities. The quantities used are the population's cumulants, which describe its shape, and the mean correlation within the population, which measures the microscopic similarity of population members. Including the mean correlation allows a better description of the population than the cumulants alone would provide and represents a new and important extension of the technique. The formalism includes finite population effects and describes problems of realistic size. The theory is shown to agree closely to simulations of a real genetic algorithm and the mean best energy is accurately predicted.

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We employ the methods of statistical physics to study the performance of Gallager type error-correcting codes. In this approach, the transmitted codeword comprises Boolean sums of the original message bits selected by two randomly-constructed sparse matrices. We show that a broad range of these codes potentially saturate Shannon's bound but are limited due to the decoding dynamics used. Other codes show sub-optimal performance but are not restricted by the decoding dynamics. We show how these codes may also be employed as a practical public-key cryptosystem and are of competitive performance to modern cyptographical methods.

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Resource allocation in sparsely connected networks, a representative problem of systems with real variables, is studied using the replica and Bethe approximation methods. An efficient distributed algorithm is devised on the basis of insights gained from the analysis and is examined using numerical simulations,showing excellent performance and full agreement with the theoretical results. The physical properties of the resource allocation model are discussed.

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The first and main contribution of this article is its access to the decision-making processes which drive innovation in policy-making within central government. The article will present a detailed case history of how the innovation came about and conclude by highlighting analytic possibilities for future research. The policy in focus is the UK’s Traffic Management Act 2004, which passed responsibility for managing incidents on major roads from the police to the Highways Agency (HA), and has been interpreted as a world first in traffic management. The article tracks the Traffic Management Act 2004 from problem identification to a preliminary evaluation. It is then suggested that future research could explain organizational change more theoretically. By taking a longitudinal and multi-level approach, the research falls into a processual account of organizational change. The second contribution of the article is to highlight two novel ways in which this approach is being applied to policy-making, through an institutional processualist research programme on public management reform and empirical investigations using complex systems to explain policy change.