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## Comparison of logistic and neural network models to fit to the egg production curve of White Leghorn hens

Fonte: Poultry Science Assoc Inc
Publicador: Poultry Science Assoc Inc

Tipo: Artigo de Revista Científica
Formato: 705-711

Português

Relevância na Pesquisa

56.84183%

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); Neural networks are capable of modeling any complex function and can be used in the poultry and animal production areas. The aim of this study was to investigate the possibility of using neural networks on an egg production data set and fitting models to the egg production curve by applying 2 approaches, one using a nonlinear logistic model and the other using 2 artificial neural network models [multilayer perceptron (MLP) and radial basis function]. Two data sets from 2 generations of a White Leghorn strain that had been selected mainly for egg production were used. In the first data set, the mean weekly egg-laying rate was ascertained over a 54-wk egg production period. This data set was used to adjust and test the logistic model and to train and test the neural networks. The second data set, covering 52 wk of egg production, was used to validate the models. The mean absolute deviation, mean square error, and R(2) were used to evaluate the fit of the models. The MLP neural network had the best fit in the test and validation phases. The advantage of using neural networks is that they can be fitted to any kind of data set and do not require model assumptions such as those required in the nonlinear methodology. The results confirm that MLP neural networks can be used as an alternative tool to fit to egg production. The benefits of the MLP are the great flexibility and their lack of a priori assumptions when estimating a noisy nonlinear model.

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## Global asymptotic stability for neural network models with distributed delays

Fonte: Elsevier
Publicador: Elsevier

Tipo: Artigo de Revista Científica

Publicado em /07/2009
Português

Relevância na Pesquisa

66.67464%

#Delayed neural network models#Distributed delays#Time-varing delays#Global asymptotic stability#M-matrix

In this paper, we obtain the global asymptotic stability of the zero solution of
a general n-dimensional delayed differential system, by imposing a condition of
dominance of the nondelayed terms which cancels the delayed effect.
We consider several delayed differential systems in general settings, which allow
us to study, as subclasses, the well known neural network models of Hopfield, Cohn-Grossberg, bidirectional associative memory, and static with S-type distributed delays. For these systems, we establish sufficient conditions for the existence of a
unique equilibrium and its global asymptotic stability, without using the Lyapunov
functional technique. Our results improve and generalize some existing ones.; Fundação para a Ciência e a Tecnologia (FCT)

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## When individual behaviour matters: homogeneous and network models in epidemiology

Fonte: The Royal Society
Publicador: The Royal Society

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

46.953745%

Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to quantify the extent to which real populations depart from the homogeneous-mixing assumption, in terms of both the underlying network structure and the resulting epidemiological dynamics. We find that human contact patterns are indeed more heterogeneous than assumed by homogeneous-mixing models, but are not as variable as some have speculated. We then evaluate a variety of methodologies for incorporating contact heterogeneity, including network-based models and several modifications to the simple SIR compartmental model. We conclude that the homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks. In general, however, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.

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## Detail in Network Models of Epidemiology: are we there yet?

Fonte: PubMed
Publicador: PubMed

Tipo: Artigo de Revista Científica

Publicado em 01/09/2010
Português

Relevância na Pesquisa

46.980444%

Network models of infectious disease epidemiology can potentially provide insight into how to tailor control strategies for specific regions, but only if the network adequately reflects the structure of the region’s contact network. Typically, the network is produced by models that incorporate details about human interactions. Each detail added renders the models more complicated and more difficult to calibrate, but also more faithful to the actual contact network structure. We propose a statistical test to determine when sufficient detail has been added to the models and demonstrate its application to the models used to create a synthetic population and contact network for the U.S.

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## Social network models predict movement and connectivity in ecological landscapes

Fonte: National Academy of Sciences
Publicador: National Academy of Sciences

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.03395%

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently...

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## Resolving Structural Variability in Network Models and the Brain

Fonte: Public Library of Science
Publicador: Public Library of Science

Tipo: Artigo de Revista Científica

Publicado em 27/03/2014
Português

Relevância na Pesquisa

47.08992%

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks...

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## Network models provide insights into how oriens–lacunosum-moleculare and bistratified cell interactions influence the power of local hippocampal CA1 theta oscillations

Fonte: Frontiers Media S.A.
Publicador: Frontiers Media S.A.

Tipo: Artigo de Revista Científica

Publicado em 07/08/2015
Português

Relevância na Pesquisa

47.018135%

Hippocampal theta is a 4–12 Hz rhythm associated with episodic memory, and although it has been studied extensively, the cellular mechanisms underlying its generation are unclear. The complex interactions between different interneuron types, such as those between oriens–lacunosum-moleculare (OLM) interneurons and bistratified cells (BiCs), make their contribution to network rhythms difficult to determine experimentally. We created network models that are tied to experimental work at both cellular and network levels to explore how these interneuron interactions affect the power of local oscillations. Our cellular models were constrained with properties from patch clamp recordings in the CA1 region of an intact hippocampus preparation in vitro. Our network models are composed of three different types of interneurons: parvalbumin-positive (PV+) basket and axo-axonic cells (BC/AACs), PV+ BiCs, and somatostatin-positive OLM cells. Also included is a spatially extended pyramidal cell model to allow for a simplified local field potential representation, as well as experimentally-constrained, theta frequency synaptic inputs to the interneurons. The network size, connectivity, and synaptic properties were constrained with experimental data. To determine how the interactions between OLM cells and BiCs could affect local theta power...

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## Computerized Closed Queuing Network Models of Flexible Manufacturing Systems: A Comparative Evaluation

Fonte: University of Rochester
Publicador: University of Rochester

Tipo: Trabalho em Andamento

Português

Relevância na Pesquisa

56.70105%

Several closed queueing network models are available for stochastic performance evaluation of Flexible Manufacturing Systems (FMS). These models are particularly useful during the planning and design phases of FMS, since they can be applied to study gross tradeoffs between principal design parameters. This paper reviews and analyzes several computerized models for evaluating complex FMS facilities with respect to the desired allocation of the following key resources: manufacturing centers, transporters, pallets and tools. It focuses on such issues as the structure of the mathematical system models, the variety of performance measures (model outputs), model inputs, accuracy of results and computational effort. In addition, some alternatives to overcome part of the common limitations of the models are suggested and tested empirically.

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## A Simulation Study Comparing Epidemic Dynamics on Exponential Random Graph and Edge-Triangle Configuration Type Contact Network Models

Fonte: Public Library of Science
Publicador: Public Library of Science

Tipo: Artigo de Revista Científica

Publicado em 10/11/2015
Português

Relevância na Pesquisa

47.094004%

We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR) epidemic dynamics. The types of network models are exponential random graph models (ERGMs) and extensions of the configuration model. We use three kinds of empirical contact networks, chosen to provide both variety and realistic patterns of human contact: a highly clustered network, a bipartite network and a snowball sampled network of a “hidden population”. In the case of the snowball sampled network we present a novel method for fitting an edge-triangle model. In our results, ERGMs consistently capture clustering as well or better than configuration-type models, but the latter models better capture the node degree distribution. Despite the additional computational requirements to fit ERGMs to empirical networks, the use of ERGMs provides only a slight improvement in the ability of the models to recreate epidemic features of the empirical network in simulated SIR epidemics. Generally, SIR epidemic results from using configuration-type models fall between those from a random network model (i.e., an Erdős-Rényi model) and an ERGM. The addition of subgraphs of size four to edge-triangle type models does improve agreement with the empirical network for smaller densities in clustered networks. Additional subgraphs do not make a noticeable difference in our example...

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## Boolean network models of cellular regulation: prospects and limitations

Fonte: The Royal Society
Publicador: The Royal Society

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

46.968975%

Computer models are valuable tools towards an understanding of the cell's biochemical regulatory machinery. Possible levels of description of such models range from modelling the underlying biochemical details to top-down approaches, using tools from the theory of complex networks. The latter, coarse-grained approach is taken where regulatory circuits are classified in graph-theoretical terms, with the elements of the regulatory networks being reduced to simply nodes and links, in order to obtain architectural information about the network. Further, considering dynamics on networks at such an abstract level seems rather unlikely to match dynamical regulatory activity of biological cells. Therefore, it came as a surprise when recently examples of discrete dynamical network models based on very simplistic dynamical elements emerged which in fact do match sequences of regulatory patterns of their biological counterparts. Here I will review such discrete dynamical network models, or Boolean networks, of biological regulatory networks. Further, we will take a look at such models extended with stochastic noise, which allow studying the role of network topology in providing robustness against noise. In the end, we will discuss the interesting question of why at all such simple models can describe aspects of biology despite their simplicity. Finally...

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## Effect of network topology on two-phase imbibition relative permeability

Fonte: Kluwer Academic Publishers
Publicador: Kluwer Academic Publishers

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

56.890723%

#Keywords: Drainage#Mathematical models#Mechanical permeability#Topology#Drainage displacements#Network models#Network topology#Relative permeability#Two-phase imbibition#Two phase flow#Drainage

In a previous study Arns et al. (2004, Transport Porous Media 55, 21-46) we considered the ro le of topology on drainage relative permeability curves computed using network models derived from a suite of tomographic images of Fontainebleau sandstone. The

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## Performance Analysis Of Neural Network Models For Oxazolines And Oxazoles Derivatives Descriptor Dataset

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 10/12/2013
Português

Relevância na Pesquisa

46.99042%

#Computer Science - Computational Engineering, Finance, and Science#Computer Science - Neural and Evolutionary Computing

Neural networks have been used successfully to a broad range of areas such as
business, data mining, drug discovery and biology. In medicine, neural networks
have been applied widely in medical diagnosis, detection and evaluation of new
drugs and treatment cost estimation. In addition, neural networks have begin
practice in data mining strategies for the aim of prediction, knowledge
discovery. This paper will present the application of neural networks for the
prediction and analysis of antitubercular activity of Oxazolines and Oxazoles
derivatives. This study presents techniques based on the development of Single
hidden layer neural network (SHLFFNN), Gradient Descent Back propagation neural
network (GDBPNN), Gradient Descent Back propagation with momentum neural
network (GDBPMNN), Back propagation with Weight decay neural network (BPWDNN)
and Quantile regression neural network (QRNN) of artificial neural network
(ANN) models Here, we comparatively evaluate the performance of five neural
network techniques. The evaluation of the efficiency of each model by ways of
benchmark experiments is an accepted application. Cross-validation and
resampling techniques are commonly used to derive point estimates of the
performances which are compared to identify methods with good properties.
Predictive accuracy was evaluated using the root mean squared error (RMSE)...

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## Resolving structural variability in network models and the brain

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 12/06/2013
Português

Relevância na Pesquisa

47.082617%

#Quantitative Biology - Neurons and Cognition#Nonlinear Sciences - Adaptation and Self-Organizing Systems#Physics - Data Analysis, Statistics and Probability

Large-scale white matter pathways crisscrossing the cortex create a complex
pattern of connectivity that underlies human cognitive function. Generative
mechanisms for this architecture have been difficult to identify in part
because little is known about mechanistic drivers of structured networks. Here
we contrast network properties derived from diffusion spectrum imaging data of
the human brain with 13 synthetic network models chosen to probe the roles of
physical network embedding and temporal network growth. We characterize both
the empirical and synthetic networks using familiar diagnostics presented in
statistical form, as scatter plots and distributions, to reveal the full range
of variability of each measure across scales in the network. We focus on the
degree distribution, degree assortativity, hierarchy, topological Rentian
scaling, and topological fractal scaling---in addition to several summary
statistics, including the mean clustering coefficient, shortest path length,
and network diameter. The models are investigated in a progressive, branching
sequence, aimed at capturing different elements thought to be important in the
brain, and range from simple random and regular networks, to models that
incorporate specific growth rules and constraints. We find that synthetic
models that constrain the network nodes to be embedded in anatomical brain
regions tend to produce distributions that are similar to those extracted from
the brain. We also find that network models hardcoded to display one network
property do not in general also display a second...

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## Using topological characteristics to evaluate complex network models can be misleading

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 31/10/2010
Português

Relevância na Pesquisa

47.0178%

Graphical models are frequently used to represent topological structures of
various complex networks. Current criteria to assess different models of a
network mainly rely on how close a model matches the network in terms of
topological characteristics. Typical topological metrics are clustering
coefficient, distance distribution, the largest eigenvalue of the adjacency
matrix, and the gap between the first and the second largest eigenvalues, which
are widely used to evaluate and compare different models of a network. In this
paper, we show that evaluating complex network models based on the current
topological metrics can be quite misleading. Taking several models of the
AS-level Internet as examples, we show that although a model seems to be good
to describe the Internet in terms of the aforementioned topological
characteristics, it is far from being realistic to represent the real Internet
in performances such as robustness in resisting intentional attacks and traffic
load distributions. We further show that it is not useful to assess network
models by examining some topological characteristics such as clustering
coefficient and distance distribution, if robustness of the Internet against
random node removals is the only concern. Our findings shed new lights on how
to reasonably evaluate different models of a network...

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## Towards a Better Understanding of Large Scale Network Models

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

47.05331%

Connectivity and capacity are two fundamental properties of wireless
multi-hop networks. The scalability of these properties has been a primary
concern for which asymptotic analysis is a useful tool. Three related but
logically distinct network models are often considered in asymptotic analyses,
viz. the dense network model, the extended network model and the infinite
network model, which consider respectively a network deployed in a fixed finite
area with a sufficiently large node density, a network deployed in a
sufficiently large area with a fixed node density, and a network deployed in
$\Re^{2}$ with a sufficiently large node density. The infinite network model
originated from continuum percolation theory and asymptotic results obtained
from the infinite network model have often been applied to the dense and
extended networks. In this paper, through two case studies related to network
connectivity on the expected number of isolated nodes and on the vanishing of
components of finite order k>1 respectively, we demonstrate some subtle but
important differences between the infinite network model and the dense and
extended network models. Therefore extra scrutiny has to be used in order for
the results obtained from the infinite network model to be applicable to the
dense and extended network models. Asymptotic results are also obtained on the
expected number of isolated nodes...

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## A survey of statistical network models

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 29/12/2009
Português

Relevância na Pesquisa

46.952046%

#Statistics - Methodology#Computer Science - Learning#Physics - Physics and Society#Quantitative Biology - Molecular Networks#Statistics - Machine Learning

Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions...

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## Enhanced Gravity Model of trade: reconciling macroeconomic and network models

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 01/06/2015
Português

Relevância na Pesquisa

46.973623%

#Physics - Physics and Society#Condensed Matter - Disordered Systems and Neural Networks#Quantitative Finance - Economics

The bilateral trade relations between world countries form a complex network,
the International Trade Network (ITN), which is involved in an increasing
number of worldwide economic processes, including globalization, integration,
industrial production, and the propagation of shocks and instabilities.
Characterizing the ITN via a simple yet accurate model is an open problem. The
classical Gravity Model of trade successfully reproduces the volume of trade
between two connected countries using known macroeconomic properties such as
GDP and geographic distance. However, it generates a network with an
unrealistically homogeneous topology, thus failing to reproduce the highly
heterogeneous structure of the real ITN. On the other hand, network models
successfully reproduce the complex topology of the ITN, but provide no
information about trade volumes. Therefore macroeconomic and network models of
trade suffer from complementary limitations but are still largely incompatible.
Here, we make an important step forward in reconciling the two approaches, via
the introduction of what we denote as the Enhanced Gravity Model (EGM) of
trade. The EGM combines the maximum-entropy nature of network models with the
established econometric structure of the Gravity Model. Using a single...

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## Direct and Stochastic Generation of Network Models from Tomographic Images: Effect of Topology on Residual Saturations

Fonte: Kluwer Academic Publishers
Publicador: Kluwer Academic Publishers

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

66.8954%

#Keywords: Computer simulation#Flow patterns#Pore size#Stochastic control systems#Topology#Phase saturations#Tomography#geometry#hydrocarbon reservoir#multiphase flow#porous medium

We generate the network model equivalents of four samples of Fontainebleau sandstone obtained from the analysis of microtomographic images. We present the measured distributions of flow-relevant geometric and topological properties of the pore space. We generate via bond dilution from a regular lattice, stochastic network models with identical geometric (pore-size, throat-size) and topological (coordination number distribution) properties. We then simulate the two-phase flow properties directly on the network model and the stochastic equivalent for each sample. The simulations on the stochastic networks are found to provide a poor representation of the results on the direct network equivalents. We find that the description of the network topology is particularly crucial in accurately predicting the residual phase saturations. We also find it necessary to introduce into the stochastic network geometry both extended pore-pore correlations and local pore-throat correlations to obtain good agreement with flow properties on the rock-equivalent network.

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## Volume Conservation of the Intermediate Phase in Three-Phase Pore-Network Models

Fonte: Kluwer Academic Publishers
Publicador: Kluwer Academic Publishers

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

66.803457%

#Keywords: Capillarity#Floods#Phase composition#Phase equilibria#Porosity#Transport properties#Wetting#Multi-phase transport properties#Non-wetting fluids#Pore-network models#Volume conservation

Quasi-static rule-based network models used to calculate capillary dominated multi-phase transport properties in porous media employ equilibrium fluid saturation distributions which assume that pores are fully filled with a single bulk fluid with other fluids present only as wetting and/or spreading films. We show that for drainage dominated three-phase displacements in which a non-wetting fluid (gas) displaces a trapped intermediate fluid (residual oil) in the presence of a mobile wetting fluid (water) this assumption distorts the dynamics of three-phase displacements and results in significant volume errors for the intermediate fluid and erroneous calculations of intermediate fluid residual saturations, relative permeabilities and recoveries. The volume errors are associated with the double drainage mechanism which is responsible for the mobilization of waterflood residual oil. A simple modification of the double drainage mechanism is proposed which allows the presence of a relatively small number of partially filled pores and removes the oil volume errors.

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## Effect of Network Topology on Relative Permeability

Fonte: Kluwer Academic Publishers
Publicador: Kluwer Academic Publishers

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

56.85235%

#Keywords: Crystal lattices#Drainage#Mathematical models#Multiphase flow#Pore size#Random processes#Sandstone#Tomography#Topology#Coordination number#Topological bonds

We consider the role of topology on drainage relative permeabilities derived from network models. We describe the topological properties of rock networks derived from a suite of tomographic images of Fontainbleau sandstone (Lindquist et al., 2000). All rock networks display a broad distribution of coordination number and the presence of long-range topological bonds. We show the importance of accurately reproducing sample topology when deriving relative permeability curves from the model networks. Comparisons between the relative permeability curves for the rock networks and those computed on a regular cubic lattice with identical geometric characteristics (pore and throat size distributions) show poor agreement. Relative permeabilities computed on regular lattices and on diluted lattices with a similar average coordination number to the rock networks also display poor agreement. We find that relative permeability curves computed on stochastic networks which honour the full coordination number distribution of the rock networks produce reasonable agreement with the rock networks. We show that random and regular lattices with the same coordination number distribution produce similar relative permeabilities and that the introduction of longer-range topological bonds has only a small effect. We show that relative permeabilities for networks exhibiting pore-throat size correlations and sizes up to the core-scale still exhibit a significant dependence on network topology. The results show the importance of incorporating realistic 3D topologies in network models for predicting multiphase flow properties.

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