WorldWideScience

Sample records for network model binding

  1. Results on a Binding Neuron Model and Their Implications for Modified Hourglass Model for Neuronal Network

    Directory of Open Access Journals (Sweden)

    Viswanathan Arunachalam

    2013-01-01

    Full Text Available The classical models of single neuron like Hodgkin-Huxley point neuron or leaky integrate and fire neuron assume the influence of postsynaptic potentials to last till the neuron fires. Vidybida (2008 in a refreshing departure has proposed models for binding neurons in which the trace of an input is remembered only for a finite fixed period of time after which it is forgotten. The binding neurons conform to the behaviour of real neurons and are applicable in constructing fast recurrent networks for computer modeling. This paper develops explicitly several useful results for a binding neuron like the firing time distribution and other statistical characteristics. We also discuss the applicability of the developed results in constructing a modified hourglass network model in which there are interconnected neurons with excitatory as well as inhibitory inputs. Limited simulation results of the hourglass network are presented.

  2. Analysis of functional importance of binding sites in the Drosophila gap gene network model.

    Science.gov (United States)

    Kozlov, Konstantin; Gursky, Vitaly V; Kulakovskiy, Ivan V; Dymova, Arina; Samsonova, Maria

    2015-01-01

    The statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. As the model may predict different levels of the functional importance of specific binding sites in different genomic and regulatory contexts, it is essential to formulate and study such models under different modeling assumptions. We elaborate a two-layer model for the Drosophila gap gene network and include in the model a combined set of transcription factor binding sites and concentration dependent regulatory interaction between gap genes hunchback and Kruppel. We show that the new variants of the model are more consistent in terms of gene expression predictions for various genetic constructs in comparison to previous work. We quantify the functional importance of binding sites by calculating their impact on gene expression in the model and calculate how these impacts correlate across all sites under different modeling assumptions. The assumption about the dual interaction between hb and Kr leads to the most consistent modeling results, but, on the other hand, may obscure existence of indirect interactions between binding sites in regulatory regions of distinct genes. The analysis confirms the previously formulated regulation concept of many weak binding sites working in concert. The model predicts a more or less uniform distribution of functionally important binding sites over the sets of experimentally characterized regulatory modules and other open chromatin domains.

  3. Classification of Beta-lactamases and penicillin binding proteins using ligand-centric network models.

    Directory of Open Access Journals (Sweden)

    Hakime Öztürk

    Full Text Available β-lactamase mediated antibiotic resistance is an important health issue and the discovery of new β-lactam type antibiotics or β-lactamase inhibitors is an area of intense research. Today, there are about a thousand β-lactamases due to the evolutionary pressure exerted by these ligands. While β-lactamases hydrolyse the β-lactam ring of antibiotics, rendering them ineffective, Penicillin-Binding Proteins (PBPs, which share high structural similarity with β-lactamases, also confer antibiotic resistance to their host organism by acquiring mutations that allow them to continue their participation in cell wall biosynthesis. In this paper, we propose a novel approach to include ligand sharing information for classifying and clustering β-lactamases and PBPs in an effort to elucidate the ligand induced evolution of these β-lactam binding proteins. We first present a detailed summary of the β-lactamase and PBP families in the Protein Data Bank, as well as the compounds they bind to. Then, we build two different types of networks in which the proteins are represented as nodes, and two proteins are connected by an edge with a weight that depends on the number of shared identical or similar ligands. These models are analyzed under three different edge weight settings, namely unweighted, weighted, and normalized weighted. A detailed comparison of these six networks showed that the use of ligand sharing information to cluster proteins resulted in modules comprising proteins with not only sequence similarity but also functional similarity. Consideration of ligand similarity highlighted some interactions that were not detected in the identical ligand network. Analysing the β-lactamases and PBPs using ligand-centric network models enabled the identification of novel relationships, suggesting that these models can be used to examine other protein families to obtain information on their ligand induced evolutionary paths.

  4. Comparison of Transcription Factor Binding Site Models

    KAUST Repository

    Bhuyan, Sharifulislam

    2012-05-01

    Modeling of transcription factor binding sites (TFBSs) and TFBS prediction on genomic sequences are important steps to elucidate transcription regulatory mechanism. Dependency of transcription regulation on a great number of factors such as chemical specificity, molecular structure, genomic and epigenetic characteristics, long distance interaction, makes this a challenging problem. Different experimental procedures generate evidence that DNA-binding domains of transcription factors show considerable DNA sequence specificity. Probabilistic modeling of TFBSs has been moderately successful in identifying patterns from a family of sequences. In this study, we compare performances of different probabilistic models and try to estimate their efficacy over experimental TFBSs data. We build a pipeline to calculate sensitivity and specificity from aligned TFBS sequences for several probabilistic models, such as Markov chains, hidden Markov models, Bayesian networks. Our work, containing relevant statistics and evaluation for the models, can help researchers to choose the most appropriate model for the problem at hand.

  5. Convolutional neural network architectures for predicting DNA–protein binding

    Science.gov (United States)

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  6. Collaborative networks: Reference modeling

    NARCIS (Netherlands)

    Camarinha-Matos, L.M.; Afsarmanesh, H.

    2008-01-01

    Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of

  7. Current approaches to gene regulatory network modelling

    Directory of Open Access Journals (Sweden)

    Brazma Alvis

    2007-09-01

    Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

  8. Dangerous connections : on binding site models of infectious disease dynamics

    NARCIS (Netherlands)

    Leung, Ka Yin; Diekmann, Odo

    2017-01-01

    We formulate models for the spread of infection on networks that are amenable to analysis in the large population limit. We distinguish three different levels: (1) binding sites, (2) individuals, and (3) the population. In the tradition of physiologically structured population models, the

  9. Modeling Network Interdiction Tasks

    Science.gov (United States)

    2015-09-17

    118 xiii Table Page 36 Computation times for weighted, 100-node random networks for GAND Approach testing in Python ...in Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 38 Accuracy measures for weighted, 100-node random networks for GAND...networks [15:p. 1]. A common approach to modeling network interdiction is to formulate the problem in terms of a two-stage strategic game between two

  10. Modelling computer networks

    International Nuclear Information System (INIS)

    Max, G

    2011-01-01

    Traffic models in computer networks can be described as a complicated system. These systems show non-linear features and to simulate behaviours of these systems are also difficult. Before implementing network equipments users wants to know capability of their computer network. They do not want the servers to be overloaded during temporary traffic peaks when more requests arrive than the server is designed for. As a starting point for our study a non-linear system model of network traffic is established to exam behaviour of the network planned. The paper presents setting up a non-linear simulation model that helps us to observe dataflow problems of the networks. This simple model captures the relationship between the competing traffic and the input and output dataflow. In this paper, we also focus on measuring the bottleneck of the network, which was defined as the difference between the link capacity and the competing traffic volume on the link that limits end-to-end throughput. We validate the model using measurements on a working network. The results show that the initial model estimates well main behaviours and critical parameters of the network. Based on this study, we propose to develop a new algorithm, which experimentally determines and predict the available parameters of the network modelled.

  11. Modeling the citation network by network cosmology.

    Science.gov (United States)

    Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

    2015-01-01

    Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.

  12. Brain Network Modelling

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther

    Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

  13. Modeling Epidemic Network Failures

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée; Fagertun, Anna Manolova

    2013-01-01

    This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....

  14. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  15. The necessity of connection structures in neural models of variable binding.

    Science.gov (United States)

    van der Velde, Frank; de Kamps, Marc

    2015-08-01

    In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1-11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other ('connectivity based') type uses dedicated connections structures to achieve novel variable binding. Feldman argued that label (synchrony) based models are the only possible candidates to handle novel variable binding, whereas connectivity based models lack the flexibility required for that. We argue and illustrate that Feldman's analysis is incorrect. Contrary to his conclusion, connectivity based models are the only viable candidates for models of novel variable binding because they are the only type of models that can produce behavior. We will show that the label (synchrony) based models analyzed by Feldman are in fact examples of connectivity based models. Feldman's analysis that novel variable binding can be achieved without existing connection structures seems to result from analyzing the binding problem in a wrong frame of reference, in particular in an outside instead of the required inside frame of reference. Connectivity based models can be models of novel variable binding when they possess a connection structure that resembles a small-world network, as found in the brain. We will illustrate binding with this type of model with episode binding and the binding of words, including novel words, in sentence structures.

  16. Peptide microarrays to probe for competition for binding sites in a protein interaction network

    NARCIS (Netherlands)

    Sinzinger, M.D.S.; Ruttekolk, I.R.R.; Gloerich, J.; Wessels, H.; Chung, Y.D.; Adjobo-Hermans, M.J.W.; Brock, R.E.

    2013-01-01

    Cellular protein interaction networks are a result of the binding preferences of a particular protein and the entirety of interactors that mutually compete for binding sites. Therefore, the reconstruction of interaction networks by the accumulation of interaction networks for individual proteins

  17. Statistical Models for Social Networks

    NARCIS (Netherlands)

    Snijders, Tom A. B.; Cook, KS; Massey, DS

    2011-01-01

    Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

  18. Mathematical model of highways network optimization

    Science.gov (United States)

    Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.

    2017-12-01

    The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.

  19. Coevolutionary modeling in network formation

    KAUST Repository

    Al-Shyoukh, Ibrahim

    2014-12-03

    Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

  20. Coevolutionary modeling in network formation

    KAUST Repository

    Al-Shyoukh, Ibrahim; Chasparis, Georgios; Shamma, Jeff S.

    2014-01-01

    Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

  1. Modeling online social signed networks

    Science.gov (United States)

    Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

    2018-04-01

    People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

  2. Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.

    Science.gov (United States)

    Han, Youngmahn; Kim, Dongsup

    2017-12-28

    Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding. Deep convolutional neural network (DCNN) is a deep learning method inspired by visual recognition process of animal brain and it is known to be able to capture meaningful local patterns from 2D images. Once the peptide-MHC interactions can be encoded into image-like array(ILA) data, DCNN can be employed to build a predictive model for peptide-MHC binding prediction. In this study, we demonstrated that DCNN is able to not only reliably predict peptide-MHC binding, but also sensitively detect locally-clustered interactions. Nonapeptide-HLA-A and -B binding data were encoded into ILA data. A DCNN, as a pan-specific prediction model, was trained on the ILA data. The DCNN showed higher performance than other prediction tools for the latest benchmark datasets, which consist of 43 datasets for 15 HLA-A alleles and 25 datasets for 10 HLA-B alleles. In particular, the DCNN outperformed other tools for alleles belonging to the HLA-A3 supertype. The F1 scores of the DCNN were 0.86, 0.94, and 0.67 for HLA-A*31:01, HLA-A*03:01, and HLA-A*68:01 alleles, respectively, which were significantly higher than those of other tools. We found that the DCNN was able to recognize locally-clustered interactions that could synergistically stabilize peptide binding. We developed ConvMHC, a web server to provide user-friendly web interfaces for peptide-MHC class I binding predictions using the DCNN. ConvMHC web server can be accessible via http://jumong.kaist.ac.kr:8080/convmhc

  3. A neighbourhood evolving network model

    International Nuclear Information System (INIS)

    Cao, Y.J.; Wang, G.Z.; Jiang, Q.Y.; Han, Z.X.

    2006-01-01

    Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development

  4. Dynamically Partitionable Autoassociative Networks as a Solution to the Neural Binding Problem

    Directory of Open Access Journals (Sweden)

    Kenneth Jeffrey Hayworth

    2012-09-01

    Full Text Available An outstanding question in theoretical neuroscience is how the brain solves the neural binding problem. In vision, binding can be summarized as the ability to represent that certain properties belong to one object while other properties belong to a different object. I review the binding problem in visual and other domains, and review its simplest proposed solution – the anatomical binding hypothesis. This hypothesis has traditionally been rejected as a true solution because it seems to require a type of one-to-one wiring of neurons that would be impossible in a biological system (as opposed to an engineered system like a computer. I show that this requirement for one-to-one wiring can be loosened by carefully considering how the neural representation is actually put to use by the rest of the brain. This leads to a solution where a symbol is represented not as a particular pattern of neural activation but instead as a piece of a global stable attractor state. I introduce the Dynamically Partitionable AutoAssociative Network (DPAAN as an implementation of this solution and show how DPANNs can be used in systems which perform perceptual binding and in systems that implement syntax-sensitive rules. Finally I show how the core parts of the cognitive architecture ACT-R can be neurally implemented using a DPAAN as ACT-R’s global workspace. Because the DPAAN solution to the binding problem requires only ‘flat’ neural representations (as opposed to the phase encoded representation hypothesized in neural synchrony solutions it is directly compatible with the most well developed neural models of learning, memory, and pattern recognition.

  5. Exact model reduction of combinatorial reaction networks

    Directory of Open Access Journals (Sweden)

    Fey Dirk

    2008-08-01

    Full Text Available Abstract Background Receptors and scaffold proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. Due to combinatorial reasons the number of distinguishable species grows exponentially with the number of binding domains and can easily reach several millions. Even by including only a limited number of components and binding domains the resulting models are very large and hardly manageable. A novel model reduction technique allows the significant reduction and modularization of these models. Results We introduce methods that extend and complete the already introduced approach. For instance, we provide techniques to handle the formation of multi-scaffold complexes as well as receptor dimerization. Furthermore, we discuss a new modeling approach that allows the direct generation of exactly reduced model structures. The developed methods are used to reduce a model of EGF and insulin receptor crosstalk comprising 5,182 ordinary differential equations (ODEs to a model with 87 ODEs. Conclusion The methods, presented in this contribution, significantly enhance the available methods to exactly reduce models of combinatorial reaction networks.

  6. Developing Personal Network Business Models

    DEFF Research Database (Denmark)

    Saugstrup, Dan; Henten, Anders

    2006-01-01

    The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...

  7. Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models*

    Science.gov (United States)

    Isakova, Alina; Berset, Yves; Hatzimanikatis, Vassily; Deplancke, Bart

    2016-01-01

    Many transcription factors (TFs) have the ability to cooperate on DNA elements as heterodimers. Despite the significance of TF heterodimerization for gene regulation, a quantitative understanding of cooperativity between various TF dimer partners and its impact on heterodimer DNA binding specificity models is still lacking. Here, we used a novel integrative approach, combining microfluidics-steered measurements of dimer-DNA assembly with mechanistic modeling of the implicated protein-protein-DNA interactions to quantitatively interrogate the cooperative DNA binding behavior of the adipogenic peroxisome proliferator-activated receptor γ (PPARγ):retinoid X receptor α (RXRα) heterodimer. Using the high throughput MITOMI (mechanically induced trapping of molecular interactions) platform, we derived equilibrium DNA binding data for PPARγ, RXRα, as well as the PPARγ:RXRα heterodimer to more than 300 target DNA sites and variants thereof. We then quantified cooperativity underlying heterodimer-DNA binding and derived an integrative heterodimer DNA binding constant. Using this cooperativity-inclusive constant, we were able to build a heterodimer-DNA binding specificity model that has superior predictive power than the one based on a regular one-site equilibrium. Our data further revealed that individual nucleotide substitutions within the target site affect the extent of cooperativity in PPARγ:RXRα-DNA binding. Our study therefore emphasizes the importance of assessing cooperativity when generating DNA binding specificity models for heterodimers. PMID:26912662

  8. Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models.

    Science.gov (United States)

    Isakova, Alina; Berset, Yves; Hatzimanikatis, Vassily; Deplancke, Bart

    2016-05-06

    Many transcription factors (TFs) have the ability to cooperate on DNA elements as heterodimers. Despite the significance of TF heterodimerization for gene regulation, a quantitative understanding of cooperativity between various TF dimer partners and its impact on heterodimer DNA binding specificity models is still lacking. Here, we used a novel integrative approach, combining microfluidics-steered measurements of dimer-DNA assembly with mechanistic modeling of the implicated protein-protein-DNA interactions to quantitatively interrogate the cooperative DNA binding behavior of the adipogenic peroxisome proliferator-activated receptor γ (PPARγ):retinoid X receptor α (RXRα) heterodimer. Using the high throughput MITOMI (mechanically induced trapping of molecular interactions) platform, we derived equilibrium DNA binding data for PPARγ, RXRα, as well as the PPARγ:RXRα heterodimer to more than 300 target DNA sites and variants thereof. We then quantified cooperativity underlying heterodimer-DNA binding and derived an integrative heterodimer DNA binding constant. Using this cooperativity-inclusive constant, we were able to build a heterodimer-DNA binding specificity model that has superior predictive power than the one based on a regular one-site equilibrium. Our data further revealed that individual nucleotide substitutions within the target site affect the extent of cooperativity in PPARγ:RXRα-DNA binding. Our study therefore emphasizes the importance of assessing cooperativity when generating DNA binding specificity models for heterodimers. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  9. Mathematical Modelling Plant Signalling Networks

    KAUST Repository

    Muraro, D.; Byrne, H.M.; King, J.R.; Bennett, M.J.

    2013-01-01

    methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more

  10. An insect-inspired model for visual binding I: learning objects and their characteristics.

    Science.gov (United States)

    Northcutt, Brandon D; Dyhr, Jonathan P; Higgins, Charles M

    2017-04-01

    Visual binding is the process of associating the responses of visual interneurons in different visual submodalities all of which are responding to the same object in the visual field. Recently identified neuropils in the insect brain termed optic glomeruli reside just downstream of the optic lobes and have an internal organization that could support visual binding. Working from anatomical similarities between optic and olfactory glomeruli, we have developed a model of visual binding based on common temporal fluctuations among signals of independent visual submodalities. Here we describe and demonstrate a neural network model capable both of refining selectivity of visual information in a given visual submodality, and of associating visual signals produced by different objects in the visual field by developing inhibitory neural synaptic weights representing the visual scene. We also show that this model is consistent with initial physiological data from optic glomeruli. Further, we discuss how this neural network model may be implemented in optic glomeruli at a neuronal level.

  11. Complex Networks in Psychological Models

    Science.gov (United States)

    Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

    We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

  12. Change in Allosteric Network Affects Binding Affinities of PDZ Domains: Analysis through Perturbation Response Scanning

    Science.gov (United States)

    Gerek, Z. Nevin; Ozkan, S. Banu

    2011-01-01

    The allosteric mechanism plays a key role in cellular functions of several PDZ domain proteins (PDZs) and is directly linked to pharmaceutical applications; however, it is a challenge to elaborate the nature and extent of these allosteric interactions. One solution to this problem is to explore the dynamics of PDZs, which may provide insights about how intramolecular communication occurs within a single domain. Here, we develop an advancement of perturbation response scanning (PRS) that couples elastic network models with linear response theory (LRT) to predict key residues in allosteric transitions of the two most studied PDZs (PSD-95 PDZ3 domain and hPTP1E PDZ2 domain). With PRS, we first identify the residues that give the highest mean square fluctuation response upon perturbing the binding sites. Strikingly, we observe that the residues with the highest mean square fluctuation response agree with experimentally determined residues involved in allosteric transitions. Second, we construct the allosteric pathways by linking the residues giving the same directional response upon perturbation of the binding sites. The predicted intramolecular communication pathways reveal that PSD-95 and hPTP1E have different pathways through the dynamic coupling of different residue pairs. Moreover, our analysis provides a molecular understanding of experimentally observed hidden allostery of PSD-95. We show that removing the distal third alpha helix from the binding site alters the allosteric pathway and decreases the binding affinity. Overall, these results indicate that (i) dynamics plays a key role in allosteric regulations of PDZs, (ii) the local changes in the residue interactions can lead to significant changes in the dynamics of allosteric regulations, and (iii) this might be the mechanism that each PDZ uses to tailor their binding specificities regulation. PMID:21998559

  13. Delay and Disruption Tolerant Networking MACHETE Model

    Science.gov (United States)

    Segui, John S.; Jennings, Esther H.; Gao, Jay L.

    2011-01-01

    To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity

  14. A model of coauthorship networks

    Science.gov (United States)

    Zhou, Guochang; Li, Jianping; Xie, Zonglin

    2017-10-01

    A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property

  15. Telecommunications network modelling, planning and design

    CERN Document Server

    Evans, Sharon

    2003-01-01

    Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.

  16. Campus network security model study

    Science.gov (United States)

    Zhang, Yong-ku; Song, Li-ren

    2011-12-01

    Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.

  17. Generalized Network Psychometrics : Combining Network and Latent Variable Models

    NARCIS (Netherlands)

    Epskamp, S.; Rhemtulla, M.; Borsboom, D.

    2017-01-01

    We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

  18. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  19. Modeling of fluctuating reaction networks

    International Nuclear Information System (INIS)

    Lipshtat, A.; Biham, O.

    2004-01-01

    Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press

  20. Modeling Shear Induced Von Willebrand Factor Binding to Collagen

    Science.gov (United States)

    Dong, Chuqiao; Wei, Wei; Morabito, Michael; Webb, Edmund; Oztekin, Alparslan; Zhang, Xiaohui; Cheng, Xuanhong

    2017-11-01

    Von Willebrand factor (vWF) is a blood glycoprotein that binds with platelets and collagen on injured vessel surfaces to form clots. VWF bioactivity is shear flow induced: at low shear, binding between VWF and other biological entities is suppressed; for high shear rate conditions - as are found near arterial injury sites - VWF elongates, activating its binding with platelets and collagen. Based on parameters derived from single molecule force spectroscopy experiments, we developed a coarse-grain molecular model to simulate bond formation probability as a function of shear rate. By introducing a binding criterion that depends on the conformation of a sub-monomer molecular feature of our model, the model predicts shear-induced binding, even for conditions where binding is highly energetically favorable. We further investigate the influence of various model parameters on the ability to predict shear-induced binding (vWF length, collagen site density and distribution, binding energy landscape, and slip/catch bond length) and demonstrate parameter ranges where the model provides good agreement with existing experimental data. Our results may be important for understanding vWF activity and also for achieving targeted drug therapy via biomimetic synthetic molecules. National Science Foundation (NSF),Division of Mathematical Sciences (DMS).

  1. PL/SQL and Bind Variable: the two ways to increase the efficiency of Network Databases

    Directory of Open Access Journals (Sweden)

    Hitesh KUMAR SHARMA

    2011-12-01

    Full Text Available Modern data analysis applications are driven by the Network databases. They are pushing traditional database and data warehousing technologies beyond their limits due to their massively increasing data volumes and demands for low latency. There are three major challenges in working with network databases: interoperability due to heterogeneous data repositories, proactively due to autonomy of data sources and high efficiency to meet the application demand. This paper provides the two ways to meet the third challenge of network databases. This goal can be achieved by network database administrator with the usage of PL/SQL blocks and bind variable. The paper will explain the effect of PL/SQL block and bind variable on Network database efficiency to meet the modern data analysis application demand.

  2. Network model of security system

    Directory of Open Access Journals (Sweden)

    Adamczyk Piotr

    2016-01-01

    Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.

  3. A dynamically coupled allosteric network underlies binding cooperativity in Src kinase.

    Science.gov (United States)

    Foda, Zachariah H; Shan, Yibing; Kim, Eric T; Shaw, David E; Seeliger, Markus A

    2015-01-20

    Protein tyrosine kinases are attractive drug targets because many human diseases are associated with the deregulation of kinase activity. However, how the catalytic kinase domain integrates different signals and switches from an active to an inactive conformation remains incompletely understood. Here we identify an allosteric network of dynamically coupled amino acids in Src kinase that connects regulatory sites to the ATP- and substrate-binding sites. Surprisingly, reactants (ATP and peptide substrates) bind with negative cooperativity to Src kinase while products (ADP and phosphopeptide) bind with positive cooperativity. We confirm the molecular details of the signal relay through the allosteric network by biochemical studies. Experiments on two additional protein tyrosine kinases indicate that the allosteric network may be largely conserved among these enzymes. Our work provides new insights into the regulation of protein tyrosine kinases and establishes a potential conduit by which resistance mutations to ATP-competitive kinase inhibitors can affect their activity.

  4. Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution.

    Directory of Open Access Journals (Sweden)

    Amanda Tse

    Full Text Available Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib and promiscuous (Bosutinib, Dasatinib kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations

  5. Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

    Science.gov (United States)

    Tse, Amanda; Verkhivker, Gennady M.

    2015-01-01

    Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating

  6. Predicting nucleic acid binding interfaces from structural models of proteins.

    Science.gov (United States)

    Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

    2012-02-01

    The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

  7. Target-Centric Network Modeling

    DEFF Research Database (Denmark)

    Mitchell, Dr. William L.; Clark, Dr. Robert M.

    In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...

  8. Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks

    Science.gov (United States)

    Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

    2016-05-01

    RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

  9. What binds Bronisław Malinowski with social networks?

    Directory of Open Access Journals (Sweden)

    Zbigniew Meger

    2012-12-01

    Full Text Available Bronislaw Malinowski, a Polish anthropologist, ethnologist and sociologist, is the father of phatic communication. He has defined and described for the first time a phatic function of language and its importance in societies with low levels of development. As it turns, the phatic communication, which does not transfer any content, is also present today, and even - due social networks portals - comes into new stage of development. Although an impression that the phatic communication has low importance, it plays an important role in society. Also in educational activities this communication has an impact on interpersonal relationships. Through it, people better cooperate with each other and create groups of collaborating individuals. In this way are also built relationships between students and the educational institutions. They are also generated positive emotions which help in all processes of learning. Generally, the phatic communication can have a positive influence on educational processes.

  10. Continuum Model for River Networks

    Science.gov (United States)

    Giacometti, Achille; Maritan, Amos; Banavar, Jayanth R.

    1995-07-01

    The effects of erosion, avalanching, and random precipitation are captured in a simple stochastic partial differential equation for modeling the evolution of river networks. Our model leads to a self-organized structured landscape and to abstraction and piracy of the smaller tributaries as the evolution proceeds. An algebraic distribution of the average basin areas and a power law relationship between the drainage basin area and the river length are found.

  11. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...

  12. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo

    2015-09-15

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

  13. Network modelling methods for FMRI.

    Science.gov (United States)

    Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

    2011-01-15

    There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.

  14. HOCOMOCO: a comprehensive collection of human transcription factor binding sites models

    Science.gov (United States)

    Kulakovskiy, Ivan V.; Medvedeva, Yulia A.; Schaefer, Ulf; Kasianov, Artem S.; Vorontsov, Ilya E.; Bajic, Vladimir B.; Makeev, Vsevolod J.

    2013-01-01

    Transcription factor (TF) binding site (TFBS) models are crucial for computational reconstruction of transcription regulatory networks. In existing repositories, a TF often has several models (also called binding profiles or motifs), obtained from different experimental data. Having a single TFBS model for a TF is more pragmatic for practical applications. We show that integration of TFBS data from various types of experiments into a single model typically results in the improved model quality probably due to partial correction of source specific technique bias. We present the Homo sapiens comprehensive model collection (HOCOMOCO, http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/hocomoco/) containing carefully hand-curated TFBS models constructed by integration of binding sequences obtained by both low- and high-throughput methods. To construct position weight matrices to represent these TFBS models, we used ChIPMunk software in four computational modes, including newly developed periodic positional prior mode associated with DNA helix pitch. We selected only one TFBS model per TF, unless there was a clear experimental evidence for two rather distinct TFBS models. We assigned a quality rating to each model. HOCOMOCO contains 426 systematically curated TFBS models for 401 human TFs, where 172 models are based on more than one data source. PMID:23175603

  15. HOCOMOCO: A comprehensive collection of human transcription factor binding sites models

    KAUST Repository

    Kulakovskiy, Ivan V.; Medvedeva, Yulia A.; Schaefer, Ulf; Kasianov, Artem S.; Vorontsov, Ilya E.; Bajic, Vladimir B.; Makeev, Vsevolod J.

    2012-01-01

    Transcription factor (TF) binding site (TFBS) models are crucial for computational reconstruction of transcription regulatory networks. In existing repositories, a TF often has several models (also called binding profiles or motifs), obtained from different experimental data. Having a single TFBS model for a TF is more pragmatic for practical applications. We show that integration of TFBS data from various types of experiments into a single model typically results in the improved model quality probably due to partial correction of source specific technique bias. We present the Homo sapiens comprehensive model collection (HOCOMOCO, http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/ hocomoco/) containing carefully hand-curated TFBS models constructed by integration of binding sequences obtained by both low- and high-throughput methods. To construct position weight matrices to represent these TFBS models, we used ChIPMunk software in four computational modes, including newly developed periodic positional prior mode associated with DNA helix pitch. We selected only one TFBS model per TF, unless there was a clear experimental evidence for two rather distinct TFBS models. We assigned a quality rating to each model. HOCOMOCO contains 426 systematically curated TFBS models for 401 human TFs, where 172 models are based on more than one data source. The Author(s) 2012.

  16. HOCOMOCO: A comprehensive collection of human transcription factor binding sites models

    KAUST Repository

    Kulakovskiy, Ivan V.

    2012-11-21

    Transcription factor (TF) binding site (TFBS) models are crucial for computational reconstruction of transcription regulatory networks. In existing repositories, a TF often has several models (also called binding profiles or motifs), obtained from different experimental data. Having a single TFBS model for a TF is more pragmatic for practical applications. We show that integration of TFBS data from various types of experiments into a single model typically results in the improved model quality probably due to partial correction of source specific technique bias. We present the Homo sapiens comprehensive model collection (HOCOMOCO, http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/ hocomoco/) containing carefully hand-curated TFBS models constructed by integration of binding sequences obtained by both low- and high-throughput methods. To construct position weight matrices to represent these TFBS models, we used ChIPMunk software in four computational modes, including newly developed periodic positional prior mode associated with DNA helix pitch. We selected only one TFBS model per TF, unless there was a clear experimental evidence for two rather distinct TFBS models. We assigned a quality rating to each model. HOCOMOCO contains 426 systematically curated TFBS models for 401 human TFs, where 172 models are based on more than one data source. The Author(s) 2012.

  17. Discrete persistent-chain model for protein binding on DNA.

    Science.gov (United States)

    Lam, Pui-Man; Zhen, Yi

    2011-04-01

    We describe and solve a discrete persistent-chain model of protein binding on DNA, involving an extra σ(i) at a site i of the DNA. This variable takes the value 1 or 0, depending on whether or not the site is occupied by a protein. In addition, if the site is occupied by a protein, there is an extra energy cost ɛ. For a small force, we obtain analytic expressions for the force-extension curve and the fraction of bound protein on the DNA. For higher forces, the model can be solved numerically to obtain force-extension curves and the average fraction of bound proteins as a function of applied force. Our model can be used to analyze experimental force-extension curves of protein binding on DNA, and hence deduce the number of bound proteins in the case of nonspecific binding. ©2011 American Physical Society

  18. Research on the model of home networking

    Science.gov (United States)

    Yun, Xiang; Feng, Xiancheng

    2007-11-01

    It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network

  19. Mathematical Modelling Plant Signalling Networks

    KAUST Repository

    Muraro, D.

    2013-01-01

    During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.

  20. A relay network of extracellular heme-binding proteins drives C. albicans iron acquisition from hemoglobin.

    Science.gov (United States)

    Kuznets, Galit; Vigonsky, Elena; Weissman, Ziva; Lalli, Daniela; Gildor, Tsvia; Kauffman, Sarah J; Turano, Paola; Becker, Jeffrey; Lewinson, Oded; Kornitzer, Daniel

    2014-10-01

    Iron scavenging constitutes a crucial challenge for survival of pathogenic microorganisms in the iron-poor host environment. Candida albicans, like many microbial pathogens, is able to utilize iron from hemoglobin, the largest iron pool in the host's body. Rbt5 is an extracellular glycosylphosphatidylinositol (GPI)-anchored heme-binding protein of the CFEM family that facilitates heme-iron uptake by an unknown mechanism. Here, we characterize an additional C. albicans CFEM protein gene, PGA7, deletion of which elicits a more severe heme-iron utilization phenotype than deletion of RBT5. The virulence of the pga7-/- mutant is reduced in a mouse model of systemic infection, consistent with a requirement for heme-iron utilization for C. albicans pathogenicity. The Pga7 and Rbt5 proteins exhibit distinct cell wall attachment, and discrete localization within the cell envelope, with Rbt5 being more exposed than Pga7. Both proteins are shown here to efficiently extract heme from hemoglobin. Surprisingly, while Pga7 has a higher affinity for heme in vitro, we find that heme transfer can occur bi-directionally between Pga7 and Rbt5, supporting a model in which they cooperate in a heme-acquisition relay. Together, our data delineate the roles of Pga7 and Rbt5 in a cell surface protein network that transfers heme from extracellular hemoglobin to the endocytic pathway, and provide a paradigm for how receptors embedded in the cell wall matrix can mediate nutrient uptake across the fungal cell envelope.

  1. Energy modelling in sensor networks

    Science.gov (United States)

    Schmidt, D.; Krämer, M.; Kuhn, T.; Wehn, N.

    2007-06-01

    Wireless sensor networks are one of the key enabling technologies for the vision of ambient intelligence. Energy resources for sensor nodes are very scarce. A key challenge is the design of energy efficient communication protocols. Models of the energy consumption are needed to accurately simulate the efficiency of a protocol or application design, and can also be used for automatic energy optimizations in a model driven design process. We propose a novel methodology to create models for sensor nodes based on few simple measurements. In a case study the methodology was used to create models for MICAz nodes. The models were integrated in a simulation environment as well as in a SDL runtime framework of a model driven design process. Measurements on a test application that was created automatically from an SDL specification showed an 80% reduction in energy consumption compared to an implementation without power saving strategies.

  2. Thermodynamic model of binding of flexible bivalent haptens to antibody

    Energy Technology Data Exchange (ETDEWEB)

    Dembo, M; Goldstein, B

    1978-01-01

    Studies by Wilder et al. of the binding of Fab' fragments to small haptens have shown that the cross-linking constant (the equilibrium constant for binding an additional Fab' fragment to a hapten-Fab' complex) is strongly dependent on the length of the hapten. We present a simple model for predicting the relationship between the intermolecular cross-linking constant and the monovalent hapten-antibody binding constant. In particular we used the model to obtain the dependence of the cross-linking constant on the length of th hapten, the depth to which the hapten fills th Fab' binding site, and the size of the Fab' fragment. To test the model, we devised expressions which allowed us to analyze the data of Wilder et al. From their data we determined the values of two parameters which we took to be unknown in the theory, the size of the Fab' fragment and the depth to which the hapten fills the Fab' binding site. The values arrived at in this way agreed well with published measurements of these parameters.

  3. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.

    2015-01-01

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation

  4. An evolving network model with community structure

    International Nuclear Information System (INIS)

    Li Chunguang; Maini, Philip K

    2005-01-01

    Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

  5. Brand Marketing Model on Social Networks

    Directory of Open Access Journals (Sweden)

    Jolita Jezukevičiūtė

    2014-04-01

    Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.

  6. A novel Direct Small World network model

    Directory of Open Access Journals (Sweden)

    LIN Tao

    2016-10-01

    Full Text Available There is a certain degree of redundancy and low efficiency of existing computer networks.This paper presents a novel Direct Small World network model in order to optimize networks.In this model,several nodes construct a regular network.Then,randomly choose and replot some nodes to generate Direct Small World network iteratively.There is no change in average distance and clustering coefficient.However,the network performance,such as hops,is improved.The experiments prove that compared to traditional small world network,the degree,average of degree centrality and average of closeness centrality are lower in Direct Small World network.This illustrates that the nodes in Direct Small World networks are closer than Watts-Strogatz small world network model.The Direct Small World can be used not only in the communication of the community information,but also in the research of epidemics.

  7. RMBNToolbox: random models for biochemical networks

    Directory of Open Access Journals (Sweden)

    Niemi Jari

    2007-05-01

    Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

  8. Binding Forces and Teachers' School Life: A Recursive Model.

    Science.gov (United States)

    Pang, Nicolas Sun-Keung

    2003-01-01

    Formulates theoretical model of four binding forces in school organizations--bureaucratic linkage, cultural linkage, tight coupling, and loose coupling--and examines their effects on teachers' feelings about school life. Finds, for example, that loose coupling had a positive effect on teachers' sense of community, job satisfaction, and commitment.…

  9. Non-equilibrium repressor binding kinetics link DNA damage dose to transcriptional timing within the SOS gene network.

    Science.gov (United States)

    Culyba, Matthew J; Kubiak, Jeffrey M; Mo, Charlie Y; Goulian, Mark; Kohli, Rahul M

    2018-06-01

    Biochemical pathways are often genetically encoded as simple transcription regulation networks, where one transcription factor regulates the expression of multiple genes in a pathway. The relative timing of each promoter's activation and shut-off within the network can impact physiology. In the DNA damage repair pathway (known as the SOS response) of Escherichia coli, approximately 40 genes are regulated by the LexA repressor. After a DNA damaging event, LexA degradation triggers SOS gene transcription, which is temporally separated into subsets of 'early', 'middle', and 'late' genes. Although this feature plays an important role in regulating the SOS response, both the range of this separation and its underlying mechanism are not experimentally defined. Here we show that, at low doses of DNA damage, the timing of promoter activities is not separated. Instead, timing differences only emerge at higher levels of DNA damage and increase as a function of DNA damage dose. To understand mechanism, we derived a series of synthetic SOS gene promoters which vary in LexA-operator binding kinetics, but are otherwise identical, and then studied their activity over a large dose-range of DNA damage. In distinction to established models based on rapid equilibrium assumptions, the data best fit a kinetic model of repressor occupancy at promoters, where the drop in cellular LexA levels associated with higher doses of DNA damage leads to non-equilibrium binding kinetics of LexA at operators. Operators with slow LexA binding kinetics achieve their minimal occupancy state at later times than operators with fast binding kinetics, resulting in a time separation of peak promoter activity between genes. These data provide insight into this remarkable feature of the SOS pathway by demonstrating how a single transcription factor can be employed to control the relative timing of each gene's transcription as a function of stimulus dose.

  10. Brand Marketing Model on Social Networks

    OpenAIRE

    Jolita Jezukevičiūtė; Vida Davidavičienė

    2014-01-01

    The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalys...

  11. Brand marketing model on social networks

    OpenAIRE

    Jezukevičiūtė, Jolita; Davidavičienė, Vida

    2014-01-01

    Paper analyzes the brand and its marketing solutions on social networks. This analysis led to the creation of improved brand marketing model on social networks, which will contribute to the rapid and cheap organization brand recognition, increase competitive advantage and enhance consumer loyalty. Therefore, the brand and a variety of social networks are becoming a hot research area for brand marketing model on social networks. The world‘s most successful brand marketing models exploratory an...

  12. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  13. Network bandwidth utilization forecast model on high bandwidth networks

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-03-30

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  14. Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

    KAUST Repository

    Wong, Ka-Chun; Peng, Chengbin; Li, Yue

    2016-01-01

    Protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.

  15. Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

    KAUST Repository

    Wong, Ka-Chun

    2016-02-02

    Protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.

  16. An acoustical model based monitoring network

    NARCIS (Netherlands)

    Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der

    2010-01-01

    In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the

  17. Differential Binding Models for Direct and Reverse Isothermal Titration Calorimetry.

    Science.gov (United States)

    Herrera, Isaac; Winnik, Mitchell A

    2016-03-10

    Isothermal titration calorimetry (ITC) is a technique to measure the stoichiometry and thermodynamics from binding experiments. Identifying an appropriate mathematical model to evaluate titration curves of receptors with multiple sites is challenging, particularly when the stoichiometry or binding mechanism is not available. In a recent theoretical study, we presented a differential binding model (DBM) to study calorimetry titrations independently of the interaction among the binding sites (Herrera, I.; Winnik, M. A. J. Phys. Chem. B 2013, 117, 8659-8672). Here, we build upon our DBM and show its practical application to evaluate calorimetry titrations of receptors with multiple sites independently of the titration direction. Specifically, we present a set of ordinary differential equations (ODEs) with the general form d[S]/dV that can be integrated numerically to calculate the equilibrium concentrations of free and bound species S at every injection step and, subsequently, to evaluate the volume-normalized heat signal (δQ(V) = δq/dV) of direct and reverse calorimetry titrations. Additionally, we identify factors that influence the shape of the titration curve and can be used to optimize the initial concentrations of titrant and analyte. We demonstrate the flexibility of our updated DBM by applying these differentials and a global regression analysis to direct and reverse calorimetric titrations of gadolinium ions with multidentate ligands of increasing denticity, namely, diglycolic acid (DGA), citric acid (CIT), and nitrilotriacetic acid (NTA), and use statistical tests to validate the stoichiometries for the metal-ligand pairs studied.

  18. Spinal Cord Injury Model System Information Network

    Science.gov (United States)

    ... the UAB-SCIMS More The UAB-SCIMS Information Network The University of Alabama at Birmingham Spinal Cord Injury Model System (UAB-SCIMS) maintains this Information Network as a resource to promote knowledge in the ...

  19. Eight challenges for network epidemic models

    Directory of Open Access Journals (Sweden)

    Lorenzo Pellis

    2015-03-01

    Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

  20. Entropy Characterization of Random Network Models

    Directory of Open Access Journals (Sweden)

    Pedro J. Zufiria

    2017-06-01

    Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.

  1. The model of social crypto-network

    Directory of Open Access Journals (Sweden)

    Марк Миколайович Орел

    2015-06-01

    Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks

  2. Introducing Synchronisation in Deterministic Network Models

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.

    2006-01-01

    The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...... to the suggestion of suitable network models. An existing model for flow control is presented and an inherent weakness is revealed and remedied. Examples are given and numerically analysed through deterministic network modelling. Results are presented to highlight the properties of the suggested models...

  3. Bayesian Network Webserver: a comprehensive tool for biological network modeling.

    Science.gov (United States)

    Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

    2013-11-01

    The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

  4. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S.; Lauemoller, S.L.; Worning, Peder

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict bind...... of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa....

  5. DeepSite: protein-binding site predictor using 3D-convolutional neural networks.

    Science.gov (United States)

    Jiménez, J; Doerr, S; Martínez-Rosell, G; Rose, A S; De Fabritiis, G

    2017-10-01

    An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. Here we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine-learning based method demonstrates superior performance to two other competitive algorithmic strategies. DeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface. gianni.defabritiis@upf.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  6. How to model wireless mesh networks topology

    International Nuclear Information System (INIS)

    Sanni, M L; Hashim, A A; Anwar, F; Ali, S; Ahmed, G S M

    2013-01-01

    The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches

  7. Model checking mobile ad hoc networks

    NARCIS (Netherlands)

    Ghassemi, Fatemeh; Fokkink, Wan

    2016-01-01

    Modeling arbitrary connectivity changes within mobile ad hoc networks (MANETs) makes application of automated formal verification challenging. We use constrained labeled transition systems as a semantic model to represent mobility. To model check MANET protocols with respect to the underlying

  8. Yeast ribonuclease III uses a network of multiple hydrogen bonds for RNA binding and cleavage.

    Science.gov (United States)

    Lavoie, Mathieu; Abou Elela, Sherif

    2008-08-19

    Members of the bacterial RNase III family recognize a variety of short structured RNAs with few common features. It is not clear how this group of enzymes supports high cleavage fidelity while maintaining a broad base of substrates. Here we show that the yeast orthologue of RNase III (Rnt1p) uses a network of 2'-OH-dependent interactions to recognize substrates with different structures. We designed a series of bipartite substrates permitting the distinction between binding and cleavage defects. Each substrate was engineered to carry a single or multiple 2'- O-methyl or 2'-fluoro ribonucleotide substitutions to prevent the formation of hydrogen bonds with a specific nucleotide or group of nucleotides. Interestingly, introduction of 2'- O-methyl ribonucleotides near the cleavage site increased the rate of catalysis, indicating that 2'-OH are not required for cleavage. Substitution of nucleotides in known Rnt1p binding site with 2'- O-methyl ribonucleotides inhibited cleavage while single 2'-fluoro ribonucleotide substitutions did not. This indicates that while no single 2'-OH is essential for Rnt1p cleavage, small changes in the substrate structure are not tolerated. Strikingly, several nucleotide substitutions greatly increased the substrate dissociation constant with little or no effect on the Michaelis-Menten constant or rate of catalysis. Together, the results indicate that Rnt1p uses a network of nucleotide interactions to identify its substrate and support two distinct modes of binding. One mode is primarily mediated by the dsRNA binding domain and leads to the formation of stable RNA/protein complex, while the other requires the presence of the nuclease and N-terminal domains and leads to RNA cleavage.

  9. Agent-based modeling and network dynamics

    CERN Document Server

    Namatame, Akira

    2016-01-01

    The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

  10. eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models.

    Directory of Open Access Journals (Sweden)

    Michal Brylinski

    2014-09-01

    Full Text Available Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4-9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite.

  11. Nonparametric Bayesian Modeling of Complex Networks

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Mørup, Morten

    2013-01-01

    an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...

  12. Network structure exploration via Bayesian nonparametric models

    International Nuclear Information System (INIS)

    Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z

    2015-01-01

    Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)

  13. Modelling the structure of complex networks

    DEFF Research Database (Denmark)

    Herlau, Tue

    networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

  14. Building functional networks of spiking model neurons.

    Science.gov (United States)

    Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

    2016-03-01

    Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

  15. Cellulase enzyme: Homology modeling, binding site identification and molecular docking

    Science.gov (United States)

    Selvam, K.; Senbagam, D.; Selvankumar, T.; Sudhakar, C.; Kamala-Kannan, S.; Senthilkumar, B.; Govarthanan, M.

    2017-12-01

    Cellulase is an enzyme that degrades the linear polysaccharide like cellulose into glucose by breaking the β-1,4- glycosidic bonds. These enzymes are the third largest enzymes with a great potential towards the ethanol production and play a vital role in degrading the biomass. The production of ethanol depends upon the ability of the cellulose to utilize the wide range of substrates. In this study, the 3D structure of cellulase from Acinetobacter sp. was modeled by using Modeler 9v9 and validated by Ramachandran plot. The accuracy of the predicted 3D structure was checked using Ramachandran plot analysis showed that 81.1% in the favored region, compatibility of an atomic model (3D) with amino acid sequence (1D) for the model was observed as 78.21% and 49.395% for Verify 3D and ERRAT at SAVES server. As the binding efficacy with the substrate might suggests the choice of the substrate as carbon and nitrogen sources, the cellobiose, cellotetraose, cellotetriose and laminaribiose were employed in the docking studies. The docking of cellobiose, cellotetraose, cellotetriose and laminaribiose with cellulase exhibited the binding energy of -6.1523 kJ/mol, -7.8759 kJ/mol,-6.1590 kJ/mol and -6.7185 kJ/mol, respectively. These docking studies revealed that cellulase has the greater potential towards the cellotetraose as a substrate for the high yield of ethanol.

  16. Modeling, Optimization & Control of Hydraulic Networks

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat

    2014-01-01

    . The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used......Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...

  17. Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

    Science.gov (United States)

    Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

    2018-02-20

    Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

  18. Port Hamiltonian modeling of Power Networks

    NARCIS (Netherlands)

    van Schaik, F.; van der Schaft, Abraham; Scherpen, Jacquelien M.A.; Zonetti, Daniele; Ortega, R

    2012-01-01

    In this talk a full nonlinear model for the power network in port–Hamiltonian framework is derived to study its stability properties. For this we use the modularity approach i.e., we first derive the models of individual components in power network as port-Hamiltonian systems and then we combine all

  19. Modelling traffic congestion using queuing networks

    Indian Academy of Sciences (India)

    Flow-density curves; uninterrupted traffic; Jackson networks. ... ness - also suffer from a big handicap vis-a-vis the Indian scenario: most of these models do .... more well-known queuing network models and onsite data, a more exact Road Cell ...

  20. Settings in Social Networks : a Measurement Model

    NARCIS (Netherlands)

    Schweinberger, Michael; Snijders, Tom A.B.

    2003-01-01

    A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive

  1. Network interconnections: an architectural reference model

    NARCIS (Netherlands)

    Butscher, B.; Lenzini, L.; Morling, R.; Vissers, C.A.; Popescu-Zeletin, R.; van Sinderen, Marten J.; Heger, D.; Krueger, G.; Spaniol, O.; Zorn, W.

    1985-01-01

    One of the major problems in understanding the different approaches in interconnecting networks of different technologies is the lack of reference to a general model. The paper develops the rationales for a reference model of network interconnection and focuses on the architectural implications for

  2. Performance modeling of network data services

    Energy Technology Data Exchange (ETDEWEB)

    Haynes, R.A.; Pierson, L.G.

    1997-01-01

    Networks at major computational organizations are becoming increasingly complex. The introduction of large massively parallel computers and supercomputers with gigabyte memories are requiring greater and greater bandwidth for network data transfers to widely dispersed clients. For networks to provide adequate data transfer services to high performance computers and remote users connected to them, the networking components must be optimized from a combination of internal and external performance criteria. This paper describes research done at Sandia National Laboratories to model network data services and to visualize the flow of data from source to sink when using the data services.

  3. HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

    KAUST Repository

    Kulakovskiy, Ivan V.

    2015-11-19

    Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.

  4. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo; Burger, Martin; Haskovec, Jan; Markowich, Peter A.; Schlottbom, Matthias

    2017-01-01

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes

  5. Network models in economics and finance

    CERN Document Server

    Pardalos, Panos; Rassias, Themistocles

    2014-01-01

    Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis  that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.

  6. An ODP computational model of a cooperative binding object

    Science.gov (United States)

    Logé, Christophe; Najm, Elie; Chen, Ken

    1997-12-01

    A next generation of systems that should appear will have to manage simultaneously several geographically distributed users. These systems belong to the class of computer-supported cooperative work systems (CSCW). The development of such complex systems requires rigorous development methods and flexible open architectures. Open distributed processing (ODP) is a standardization effort that aims at providing such architectures. ODP features appropriate abstraction levels and a clear articulation between requirements, programming and infrastructure support. ODP advocates the use of formal methods for the specification of systems and components. The computational model, an object-based model, one of the abstraction levels identified within ODP, plays a central role in the global architecture. In this model, basic objects can be composed with communication and distribution abstractions (called binding objects) to form a computational specification of distributed systems, or applications. Computational specifications can then be mapped (in a mechanism akin to compilation) onto an engineering solution. We use an ODP-inspired method to computationally specify a cooperative system. We start from a general purpose component that we progressively refine into a collection of basic and binding objects. We focus on two issues of a co-authoring application, namely, dynamic reconfiguration and multiview synchronization. We discuss solutions for these issues and formalize them using the MT-LOTOS specification language that is currently studied in the ISO standardization formal description techniques group.

  7. Model of Organizational Structure for University Institutes Binding with the Venezuelan Socioeconomic Reality

    Directory of Open Access Journals (Sweden)

    Rafael Pertuz Belloso

    2014-01-01

    Full Text Available The present study is aimed at proposing a model of organizational structure for university institutes binding with the Venezuelan socioeconomic reality. This is a descriptive non-experimental cross-sectional research study. The study population included 746 professors and administration from the Cabimas and Maracaibo Technological Universities. Data was collected using a questionnaire consisting of 54 items and analyzed using the percentage frequency distribution. Results obtained indicate the sub-systems not integrated in the studied institutions, coexisting bureaucratic structural typologies, and a clear decontextualized implementation of the nation’s plans, which shows low relevance and relationship to the Venezuelan socioeconomic reality. To remedy this situation, a mixed departmental/matrix organizational structure model was designed that integrates the department into a matrix network linking teaching, research, and social action projects. The implementation of this model was proposed in three stages or phases in order to achieve the operational characteristics of the departmental model.

  8. Synergistic effects in threshold models on networks

    Science.gov (United States)

    Juul, Jonas S.; Porter, Mason A.

    2018-01-01

    Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

  9. Gossip spread in social network Models

    Science.gov (United States)

    Johansson, Tobias

    2017-04-01

    Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

  10. Evaluation of EOR Processes Using Network Models

    DEFF Research Database (Denmark)

    Winter, Anatol; Larsen, Jens Kjell; Krogsbøll, Anette

    1998-01-01

    The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels......) including estimation of their "petrophysical" properties (e.g. absolute permeability). 3) Mathematical modelling and computer studies of multiphase transport through pore space using mathematical network models. 4) Investigation of link between pore-scale and macroscopic recovery mechanisms....

  11. Towards reproducible descriptions of neuronal network models.

    Directory of Open Access Journals (Sweden)

    Eilen Nordlie

    2009-08-01

    Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.

  12. Improved Maximum Parsimony Models for Phylogenetic Networks.

    Science.gov (United States)

    Van Iersel, Leo; Jones, Mark; Scornavacca, Celine

    2018-05-01

    Phylogenetic networks are well suited to represent evolutionary histories comprising reticulate evolution. Several methods aiming at reconstructing explicit phylogenetic networks have been developed in the last two decades. In this article, we propose a new definition of maximum parsimony for phylogenetic networks that permits to model biological scenarios that cannot be modeled by the definitions currently present in the literature (namely, the "hardwired" and "softwired" parsimony). Building on this new definition, we provide several algorithmic results that lay the foundations for new parsimony-based methods for phylogenetic network reconstruction.

  13. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  14. Integration of Genome-Wide TF Binding and Gene Expression Data to Characterize Gene Regulatory Networks in Plant Development.

    Science.gov (United States)

    Chen, Dijun; Kaufmann, Kerstin

    2017-01-01

    Key transcription factors (TFs) controlling the morphogenesis of flowers and leaves have been identified in the model plant Arabidopsis thaliana. Recent genome-wide approaches based on chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) enable systematic identification of genome-wide TF binding sites (TFBSs) of these regulators. Here, we describe a computational pipeline for analyzing ChIP-seq data to identify TFBSs and to characterize gene regulatory networks (GRNs) with applications to the regulatory studies of flower development. In particular, we provide step-by-step instructions on how to download, analyze, visualize, and integrate genome-wide data in order to construct GRNs for beginners of bioinformatics. The practical guide presented here is ready to apply to other similar ChIP-seq datasets to characterize GRNs of interest.

  15. Tool wear modeling using abductive networks

    Science.gov (United States)

    Masory, Oren

    1992-09-01

    A tool wear model based on Abductive Networks, which consists of a network of `polynomial' nodes, is described. The model relates the cutting parameters, components of the cutting force, and machining time to flank wear. Thus real time measurements of the cutting force can be used to monitor the machining process. The model is obtained by a training process in which the connectivity between the network's nodes and the polynomial coefficients of each node are determined by optimizing a performance criteria. Actual wear measurements of coated and uncoated carbide inserts were used for training and evaluating the established model.

  16. Modelling of virtual production networks

    Directory of Open Access Journals (Sweden)

    2011-03-01

    Full Text Available Nowadays many companies, especially small and medium-sized enterprises (SMEs, specialize in a limited field of production. It requires forming virtual production networks of cooperating enterprises to manufacture better, faster and cheaper. Apart from that, some production orders cannot be realized, because there is not a company of sufficient production potential. In this case the virtual production networks of cooperating companies can realize these production orders. These networks have larger production capacity and many different resources. Therefore it can realize many more production orders together than each of them separately. Such organization allows for executing high quality product. The maintenance costs of production capacity and used resources are not so high. In this paper a methodology of rapid prototyping of virtual production networks is proposed. It allows to execute production orders on time considered existing logistic constraints.

  17. A Network Disruption Modeling Tool

    National Research Council Canada - National Science Library

    Leinart, James

    1998-01-01

    Given that network disruption has been identified as a military objective and C2-attack has been identified as the mechanism to accomplish this objective, a target set must be acquired and priorities...

  18. Modeling Epidemics Spreading on Social Contact Networks.

    Science.gov (United States)

    Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; Fang, Hua

    2015-09-01

    Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.

  19. Spatial Epidemic Modelling in Social Networks

    Science.gov (United States)

    Simoes, Joana Margarida

    2005-06-01

    The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.

  20. Implementing network constraints in the EMPS model

    Energy Technology Data Exchange (ETDEWEB)

    Helseth, Arild; Warland, Geir; Mo, Birger; Fosso, Olav B.

    2010-02-15

    This report concerns the coupling of detailed market and network models for long-term hydro-thermal scheduling. Currently, the EPF model (Samlast) is the only tool available for this task for actors in the Nordic market. A new prototype for solving the coupled market and network problem has been developed. The prototype is based on the EMPS model (Samkjoeringsmodellen). Results from the market model are distributed to a detailed network model, where a DC load flow detects if there are overloads on monitored lines or intersections. In case of overloads, network constraints are generated and added to the market problem. Theoretical and implementation details for the new prototype are elaborated in this report. The performance of the prototype is tested against the EPF model on a 20-area Nordic dataset. (Author)

  1. Role models for complex networks

    Science.gov (United States)

    Reichardt, J.; White, D. R.

    2007-11-01

    We present a framework for automatically decomposing (“block-modeling”) the functional classes of agents within a complex network. These classes are represented by the nodes of an image graph (“block model”) depicting the main patterns of connectivity and thus functional roles in the network. Using a first principles approach, we derive a measure for the fit of a network to any given image graph allowing objective hypothesis testing. From the properties of an optimal fit, we derive how to find the best fitting image graph directly from the network and present a criterion to avoid overfitting. The method can handle both two-mode and one-mode data, directed and undirected as well as weighted networks and allows for different types of links to be dealt with simultaneously. It is non-parametric and computationally efficient. The concepts of structural equivalence and modularity are found as special cases of our approach. We apply our method to the world trade network and analyze the roles individual countries play in the global economy.

  2. Modeling the interdependent network based on two-mode networks

    Science.gov (United States)

    An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

    2017-10-01

    Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

  3. Latent variable models are network models.

    Science.gov (United States)

    Molenaar, Peter C M

    2010-06-01

    Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

  4. Homophyly/Kinship Model: Naturally Evolving Networks

    Science.gov (United States)

    Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

    2015-10-01

    It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

  5. Nanoscale capacitance: A quantum tight-binding model

    Science.gov (United States)

    Zhai, Feng; Wu, Jian; Li, Yang; Lu, Jun-Qiang

    2017-01-01

    Landauer-Buttiker formalism with the assumption of semi-infinite electrodes as reservoirs has been the standard approach in modeling steady electron transport through nanoscale devices. However, modeling dynamic electron transport properties, especially nanoscale capacitance, is a challenging problem because of dynamic contributions from electrodes, which is neglectable in modeling macroscopic capacitance and mesoscopic conductance. We implement a self-consistent quantum tight-binding model to calculate capacitance of a nano-gap system consisting of an electrode capacitance C‧ and an effective capacitance Cd of the middle device. From the calculations on a nano-gap made of carbon nanotube with a buckyball therein, we show that when the electrode length increases, the electrode capacitance C‧ moves up while the effective capacitance Cd converges to a value which is much smaller than the electrode capacitance C‧. Our results reveal the importance of electrodes in modeling nanoscale ac circuits, and indicate that the concepts of semi-infinite electrodes and reservoirs well-accepted in the steady electron transport theory may be not applicable in modeling dynamic transport properties.

  6. Neural network tagging in a toy model

    International Nuclear Information System (INIS)

    Milek, Marko; Patel, Popat

    1999-01-01

    The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed

  7. An endogenous model of the credit network

    Science.gov (United States)

    He, Jianmin; Sui, Xin; Li, Shouwei

    2016-01-01

    In this paper, an endogenous credit network model of firm-bank agents is constructed. The model describes the endogenous formation of firm-firm, firm-bank and bank-bank credit relationships. By means of simulations, the model is capable of showing some obvious similarities with empirical evidence found by other scholars: the upper-tail of firm size distribution can be well fitted with a power-law; the bank size distribution can be lognormally distributed with a power-law tail; the bank in-degrees of the interbank credit network as well as the firm-bank credit network fall into two-power-law distributions.

  8. Modelling and designing electric energy networks

    International Nuclear Information System (INIS)

    Retiere, N.

    2003-11-01

    The author gives an overview of his research works in the field of electric network modelling. After a brief overview of technological evolutions from the telegraph to the all-electric fly-by-wire aircraft, he reports and describes various works dealing with a simplified modelling of electric systems and with fractal simulation. Then, he outlines the challenges for the design of electric networks, proposes a design process, gives an overview of various design models, methods and tools, and reports an application in the design of electric networks for future jumbo jets

  9. Queueing Models for Mobile Ad Hoc Networks

    NARCIS (Netherlands)

    de Haan, Roland

    2009-01-01

    This thesis presents models for the performance analysis of a recent communication paradigm: \\emph{mobile ad hoc networking}. The objective of mobile ad hoc networking is to provide wireless connectivity between stations in a highly dynamic environment. These dynamics are driven by the mobility of

  10. Modeling GMPLS and Optical MPLS Networks

    DEFF Research Database (Denmark)

    Christiansen, Henrik Lehrmann; Wessing, Henrik

    2003-01-01

    . The MPLS concept is attractive because it can work as a unifying control structure. covering all technologies. This paper describes how a novel scheme for optical MPLS and circuit switched GMPLS based networks can incorporated in such multi-domain, MPLS-based scenarios and how it could be modeled. Network...

  11. Cyber threat model for tactical radio networks

    Science.gov (United States)

    Kurdziel, Michael T.

    2014-05-01

    The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.

  12. Modeling documents with Generative Adversarial Networks

    OpenAIRE

    Glover, John

    2016-01-01

    This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents. We propose a model that is based on the recently proposed Energy-Based GAN, but instead uses a Denoising Autoencoder as the discriminator network. Document representations are extracted from the hidden layer of the discriminator and evaluated both quantitatively and qualitatively.

  13. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

    Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz

    2015-01-01

    Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....

  14. A DNA-binding-site landscape and regulatory network analysis for NAC transcription factors in Arabidopsis thaliana

    DEFF Research Database (Denmark)

    Lindemose, Søren; Jensen, Michael Krogh; de Velde, Jan Van

    2014-01-01

    regulatory networks of 12 NAC transcription factors. Our data offer specific single-base resolution fingerprints for most TFs studied and indicate that NAC DNA-binding specificities might be predicted from their DNA-binding domain's sequence. The developed methodology, including the application......Target gene identification for transcription factors is a prerequisite for the systems wide understanding of organismal behaviour. NAM-ATAF1/2-CUC2 (NAC) transcription factors are amongst the largest transcription factor families in plants, yet limited data exist from unbiased approaches to resolve...... the DNA-binding preferences of individual members. Here, we present a TF-target gene identification workflow based on the integration of novel protein binding microarray data with gene expression and multi-species promoter sequence conservation to identify the DNA-binding specificities and the gene...

  15. Novel Strategy for Discrimination of Transcription Factor Binding Motifs Employing Mathematical Neural Network

    Science.gov (United States)

    Sugimoto, Asuka; Sumi, Takuya; Kang, Jiyoung; Tateno, Masaru

    2017-07-01

    Recognition in biological macromolecular systems, such as DNA-protein recognition, is one of the most crucial problems to solve toward understanding the fundamental mechanisms of various biological processes. Since specific base sequences of genome DNA are discriminated by proteins, such as transcription factors (TFs), finding TF binding motifs (TFBMs) in whole genome DNA sequences is currently a central issue in interdisciplinary biophysical and information sciences. In the present study, a novel strategy to create a discriminant function for discrimination of TFBMs by constituting mathematical neural networks (NNs) is proposed, together with a method to determine the boundary of signals (TFBMs) and noise in the NN-score (output) space. This analysis also leads to the mathematical limitation of discrimination in the recognition of features representing TFBMs, in an information geometrical manifold. Thus, the present strategy enables the identification of the whole space of TFBMs, right up to the noise boundary.

  16. Modeling trust context in networks

    CERN Document Server

    Adali, Sibel

    2013-01-01

    We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.? In this brief, 'trust context' is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout

  17. Graphical Model Theory for Wireless Sensor Networks

    International Nuclear Information System (INIS)

    Davis, William B.

    2002-01-01

    Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm

  18. Modeling Network Traffic in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Sheng Ma

    2004-12-01

    Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.

  19. Sparsity in Model Gene Regulatory Networks

    International Nuclear Information System (INIS)

    Zagorski, M.

    2011-01-01

    We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)

  20. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    Science.gov (United States)

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Elastic Network Model of a Nuclear Transport Complex

    Science.gov (United States)

    Ryan, Patrick; Liu, Wing K.; Lee, Dockjin; Seo, Sangjae; Kim, Young-Jin; Kim, Moon K.

    2010-05-01

    The structure of Kap95p was obtained from the Protein Data Bank (www.pdb.org) and analyzed RanGTP plays an important role in both nuclear protein import and export cycles. In the nucleus, RanGTP releases macromolecular cargoes from importins and conversely facilitates cargo binding to exportins. Although the crystal structure of the nuclear import complex formed by importin Kap95p and RanGTP was recently identified, its molecular mechanism still remains unclear. To understand the relationship between structure and function of a nuclear transport complex, a structure-based mechanical model of Kap95p:RanGTP complex is introduced. In this model, a protein structure is simply modeled as an elastic network in which a set of coarse-grained point masses are connected by linear springs representing biochemical interactions at atomic level. Harmonic normal mode analysis (NMA) and anharmonic elastic network interpolation (ENI) are performed to predict the modes of vibrations and a feasible pathway between locked and unlocked conformations of Kap95p, respectively. Simulation results imply that the binding of RanGTP to Kap95p induces the release of the cargo in the nucleus as well as prevents any new cargo from attaching to the Kap95p:RanGTP complex.

  2. The QKD network: model and routing scheme

    Science.gov (United States)

    Yang, Chao; Zhang, Hongqi; Su, Jinhai

    2017-11-01

    Quantum key distribution (QKD) technology can establish unconditional secure keys between two communicating parties. Although this technology has some inherent constraints, such as the distance and point-to-point mode limits, building a QKD network with multiple point-to-point QKD devices can overcome these constraints. Considering the development level of current technology, the trust relaying QKD network is the first choice to build a practical QKD network. However, the previous research didn't address a routing method on the trust relaying QKD network in detail. This paper focuses on the routing issues, builds a model of the trust relaying QKD network for easily analysing and understanding this network, and proposes a dynamical routing scheme for this network. From the viewpoint of designing a dynamical routing scheme in classical network, the proposed scheme consists of three components: a Hello protocol helping share the network topology information, a routing algorithm to select a set of suitable paths and establish the routing table and a link state update mechanism helping keep the routing table newly. Experiments and evaluation demonstrates the validity and effectiveness of the proposed routing scheme.

  3. A Model of Network Porosity

    Science.gov (United States)

    2016-11-09

    Figure 1. We generally express such networks in terms of the services running in each enclave as well as the routing and firewall rules between the...compromise a server, they can compromise other devices in the same subnet or protected enclave. They probe attached firewalls and routers for open ports and...spam and malware filter would prevent this content from reaching its destination. Content filtering provides another layer of defense to other controls

  4. Thermal conductivity model for nanofiber networks

    Science.gov (United States)

    Zhao, Xinpeng; Huang, Congliang; Liu, Qingkun; Smalyukh, Ivan I.; Yang, Ronggui

    2018-02-01

    Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

  5. Thermal conductivity model for nanofiber networks

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Xinpeng [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Huang, Congliang [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China; Liu, Qingkun [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Smalyukh, Ivan I. [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Yang, Ronggui [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Buildings and Thermal Systems Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA

    2018-02-28

    Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

  6. A quantum-implementable neural network model

    Science.gov (United States)

    Chen, Jialin; Wang, Lingli; Charbon, Edoardo

    2017-10-01

    A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

  7. Combinatorial explosion in model gene networks

    Science.gov (United States)

    Edwards, R.; Glass, L.

    2000-09-01

    The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

  8. The ties that bind: a network approach to creating a programme in faculty development.

    Science.gov (United States)

    Baker, Lindsay; Reeves, Scott; Egan-Lee, Eileen; Leslie, Karen; Silver, Ivan

    2010-02-01

    Current trends in medical education reflect the changing health care environment. An increasingly large and diverse student population, a move to more distributed models of education, greater community involvement and an emphasis on social accountability, interprofessional education and student-centred approaches to learning necessitate new approaches to faculty development to help faculty members respond effectively to this rapidly changing landscape. Drawing upon the tenets of network theory and the broader organisational literature, we propose a 'fishhook' model of faculty development programme formation. The model is based on seven key factors which supported the successful formation of a centralised programme for faculty development that addressed many of the contemporary issues in medical education. These factors include: environmental readiness; commitment and vision of a mobiliser; recruitment of key stakeholders and leaders to committees; formation of a collaborative network structure; accumulation of networking capital; legitimacy, and flexibility. Our aim in creating this model is to provide a guide for other medical schools to consider when developing similar programmes. The model can be adapted to reflect the local goals, settings and cultures of other medical education contexts.

  9. Complex networks under dynamic repair model

    Science.gov (United States)

    Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao

    2018-01-01

    Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

  10. Performance modeling, stochastic networks, and statistical multiplexing

    CERN Document Server

    Mazumdar, Ravi R

    2013-01-01

    This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan

  11. Network Modeling and Simulation A Practical Perspective

    CERN Document Server

    Guizani, Mohsen; Khan, Bilal

    2010-01-01

    Network Modeling and Simulation is a practical guide to using modeling and simulation to solve real-life problems. The authors give a comprehensive exposition of the core concepts in modeling and simulation, and then systematically address the many practical considerations faced by developers in modeling complex large-scale systems. The authors provide examples from computer and telecommunication networks and use these to illustrate the process of mapping generic simulation concepts to domain-specific problems in different industries and disciplines. Key features: Provides the tools and strate

  12. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S.; Lauemoller, S.L.; Worning, Peder

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict...

  13. Modeling acquaintance networks based on balance theory

    Directory of Open Access Journals (Sweden)

    Vukašinović Vida

    2014-09-01

    Full Text Available An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona network because the properties of the IB model more closely matched those of the e-mail URV network than the other models

  14. Optimal transportation networks models and theory

    CERN Document Server

    Bernot, Marc; Morel, Jean-Michel

    2009-01-01

    The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.

  15. Flood routing modelling with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    R. Peters

    2006-01-01

    Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

  16. Linear approximation model network and its formation via ...

    Indian Academy of Sciences (India)

    To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...

  17. Modeling Security Aspects of Network

    Science.gov (United States)

    Schoch, Elmar

    With more and more widespread usage of computer systems and networks, dependability becomes a paramount requirement. Dependability typically denotes tolerance or protection against all kinds of failures, errors and faults. Sources of failures can basically be accidental, e.g., in case of hardware errors or software bugs, or intentional due to some kind of malicious behavior. These intentional, malicious actions are subject of security. A more complete overview on the relations between dependability and security can be found in [31]. In parallel to the increased use of technology, misuse also has grown significantly, requiring measures to deal with it.

  18. Target-mediated drug disposition model for drugs with two binding sites that bind to a target with one binding site.

    Science.gov (United States)

    Gibiansky, Leonid; Gibiansky, Ekaterina

    2017-10-01

    The paper extended the TMDD model to drugs with two identical binding sites (2-1 TMDD). The quasi-steady-state (2-1 QSS), quasi-equilibrium (2-1 QE), irreversible binding (2-1 IB), and Michaelis-Menten (2-1 MM) approximations of the model were derived. Using simulations, the 2-1 QSS approximation was compared with the full 2-1 TMDD model. As expected and similarly to the standard TMDD for monoclonal antibodies (mAb), 2-1 QSS predictions were nearly identical to 2-1 TMDD predictions, except for times of fast changes following initiation of dosing, when equilibrium has not yet been reached. To illustrate properties of new equations and approximations, several variations of population PK data for mAbs with soluble (slow elimination of the complex) or membrane-bound (fast elimination of the complex) targets were simulated from a full 2-1 TMDD model and fitted to 2-1 TMDD models, to its approximations, and to the standard (1-1) QSS model. For a mAb with a soluble target, it was demonstrated that the 2-1 QSS model provided nearly identical description of the observed (simulated) free drug and total target concentrations, although there was some minor bias in predictions of unobserved free target concentrations. The standard QSS approximation also provided a good description of the observed data, but was not able to distinguish between free drug concentrations (with no target attached and both binding site free) and partially bound drug concentrations (with one of the binding sites occupied by the target). For a mAb with a membrane-bound target, the 2-1 MM approximation adequately described the data. The 2-1 QSS approximation converged 10 times faster than the full 2-1 TMDD, and its run time was comparable with the standard QSS model.

  19. Modeling and optimization of an electric power distribution network ...

    African Journals Online (AJOL)

    Modeling and optimization of an electric power distribution network planning system using ... of the network was modelled with non-linear mathematical expressions. ... given feasible locations, re-conductoring of existing feeders in the network, ...

  20. An evolving network model with modular growth

    International Nuclear Information System (INIS)

    Zou Zhi-Yun; Liu Peng; Lei Li; Gao Jian-Zhi

    2012-01-01

    In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing the ratio of the number of inner-module edges and the number of inter-module edges. In view of the mean-field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module. (interdisciplinary physics and related areas of science and technology)

  1. Modeling of contact tracing in social networks

    Science.gov (United States)

    Tsimring, Lev S.; Huerta, Ramón

    2003-07-01

    Spreading of certain infections in complex networks is effectively suppressed by using intelligent strategies for epidemic control. One such standard epidemiological strategy consists in tracing contacts of infected individuals. In this paper, we use a recently introduced generalization of the standard susceptible-infectious-removed stochastic model for epidemics in sparse random networks which incorporates an additional (traced) state. We describe a deterministic mean-field description which yields quantitative agreement with stochastic simulations on random graphs. We also discuss the role of contact tracing in epidemics control in small-world and scale-free networks. Effectiveness of contact tracing grows as the rewiring probability is reduced.

  2. A Network Model of Credit Risk Contagion

    Directory of Open Access Journals (Sweden)

    Ting-Qiang Chen

    2012-01-01

    Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.

  3. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    Directory of Open Access Journals (Sweden)

    Lund Ole

    2009-09-01

    Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

  4. The International Trade Network: weighted network analysis and modelling

    International Nuclear Information System (INIS)

    Bhattacharya, K; Mukherjee, G; Manna, S S; Saramäki, J; Kaski, K

    2008-01-01

    Tools of the theory of critical phenomena, namely the scaling analysis and universality, are argued to be applicable to large complex web-like network structures. Using a detailed analysis of the real data of the International Trade Network we argue that the scaled link weight distribution has an approximate log-normal distribution which remains robust over a period of 53 years. Another universal feature is observed in the power-law growth of the trade strength with gross domestic product, the exponent being similar for all countries. Using the 'rich-club' coefficient measure of the weighted networks it has been shown that the size of the rich-club controlling half of the world's trade is actually shrinking. While the gravity law is known to describe well the social interactions in the static networks of population migration, international trade, etc, here for the first time we studied a non-conservative dynamical model based on the gravity law which excellently reproduced many empirical features of the ITN

  5. Keystone Business Models for Network Security Processors

    OpenAIRE

    Arthur Low; Steven Muegge

    2013-01-01

    Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor...

  6. Stochastic modeling and analysis of telecoms networks

    CERN Document Server

    Decreusefond, Laurent

    2012-01-01

    This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an

  7. Decomposed Implicit Models of Piecewise - Linear Networks

    Directory of Open Access Journals (Sweden)

    J. Brzobohaty

    1992-05-01

    Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.

  8. Artificial Immune Networks: Models and Applications

    Directory of Open Access Journals (Sweden)

    Xian Shen

    2008-06-01

    Full Text Available Artificial Immune Systems (AIS, which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern rec- ognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also dis- cussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.

  9. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo

    2017-04-10

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.

  10. Adaptive-network models of collective dynamics

    Science.gov (United States)

    Zschaler, G.

    2012-09-01

    Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

  11. Network Design Models for Container Shipping

    DEFF Research Database (Denmark)

    Reinhardt, Line Blander; Kallehauge, Brian; Nielsen, Anders Nørrelund

    This paper presents a study of the network design problem in container shipping. The paper combines the network design and fleet assignment problem into a mixed integer linear programming model minimizing the overall cost. The major contributions of this paper is that the time of a vessel route...... is included in the calculation of the capacity and that a inhomogeneous fleet is modeled. The model also includes the cost of transshipment which is one of the major cost for the shipping companies. The concept of pseudo simple routes is introduced to expand the set of feasible routes. The linearization...

  12. Characterization and Modeling of Network Traffic

    DEFF Research Database (Denmark)

    Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur

    2011-01-01

    -arrival time, IP addresses, port numbers and transport protocol are the only necessary parameters to model network traffic behaviour. In order to recreate this behaviour, a complex model is needed which is able to recreate traffic behaviour based on a set of statistics calculated from the parameters values...

  13. Phenomenological network models: Lessons for epilepsy surgery.

    Science.gov (United States)

    Hebbink, Jurgen; Meijer, Hil; Huiskamp, Geertjan; van Gils, Stephan; Leijten, Frans

    2017-10-01

    The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies. Here we study, using such a model, the influence of connections on seizures and how this might change our traditional views of epilepsy surgery. We use a simple network model consisting of four interconnected neuronal populations. One of these populations can be made hyperexcitable, modeling a pathological region of cortex. Using model simulations, the effect of surgery on the seizure rate is studied. We find that removal of the hyperexcitable population is, in most cases, not the best approach to reduce the seizure rate. Removal of normal populations located at a crucial spot in the network, the "driver," is typically more effective in reducing seizure rate. This work strengthens the idea that network structure and connections may be more important than localizing the pathological node. This can explain why lesionectomy may not always be sufficient. © 2017 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

  14. Agent based modeling of energy networks

    International Nuclear Information System (INIS)

    Gonzalez de Durana, José María; Barambones, Oscar; Kremers, Enrique; Varga, Liz

    2014-01-01

    Highlights: • A new approach for energy network modeling is designed and tested. • The agent-based approach is general and no technology dependent. • The models can be easily extended. • The range of applications encompasses from small to large energy infrastructures. - Abstract: Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed

  15. A comprehensive Network Security Risk Model for process control networks.

    Science.gov (United States)

    Henry, Matthew H; Haimes, Yacov Y

    2009-02-01

    The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.

  16. Sequence-based model of gap gene regulatory network.

    Science.gov (United States)

    Kozlov, Konstantin; Gursky, Vitaly; Kulakovskiy, Ivan; Samsonova, Maria

    2014-01-01

    The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded. The reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3

  17. Discrete dynamic modeling of cellular signaling networks.

    Science.gov (United States)

    Albert, Réka; Wang, Rui-Sheng

    2009-01-01

    Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

  18. Neural network modeling of associative memory: Beyond the Hopfield model

    Science.gov (United States)

    Dasgupta, Chandan

    1992-07-01

    A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

  19. Constitutive modelling of composite biopolymer networks.

    Science.gov (United States)

    Fallqvist, B; Kroon, M

    2016-04-21

    The mechanical behaviour of biopolymer networks is to a large extent determined at a microstructural level where the characteristics of individual filaments and the interactions between them determine the response at a macroscopic level. Phenomena such as viscoelasticity and strain-hardening followed by strain-softening are observed experimentally in these networks, often due to microstructural changes (such as filament sliding, rupture and cross-link debonding). Further, composite structures can also be formed with vastly different mechanical properties as compared to the individual networks. In this present paper, we present a constitutive model presented in a continuum framework aimed at capturing these effects. Special care is taken to formulate thermodynamically consistent evolution laws for dissipative effects. This model, incorporating possible anisotropic network properties, is based on a strain energy function, split into an isochoric and a volumetric part. Generalisation to three dimensions is performed by numerical integration over the unit sphere. Model predictions indicate that the constitutive model is well able to predict the elastic and viscoelastic response of biological networks, and to an extent also composite structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Modelling students' knowledge organisation: Genealogical conceptual networks

    Science.gov (United States)

    Koponen, Ismo T.; Nousiainen, Maija

    2018-04-01

    Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.

  1. Modelling Users` Trust in Online Social Networks

    Directory of Open Access Journals (Sweden)

    Iacob Cătoiu

    2014-02-01

    Full Text Available Previous studies (McKnight, Lankton and Tripp, 2011; Liao, Lui and Chen, 2011 have shown the crucial role of trust when choosing to disclose sensitive information online. This is the case of online social networks users, who must disclose a certain amount of personal data in order to gain access to these online services. Taking into account privacy calculus model and the risk/benefit ratio, we propose a model of users’ trust in online social networks with four variables. We have adapted metrics for the purpose of our study and we have assessed their reliability and validity. We use a Partial Least Squares (PLS based structural equation modelling analysis, which validated all our initial assumptions, indicating that our three predictors (privacy concerns, perceived benefits and perceived risks explain 48% of the variation of users’ trust in online social networks, the resulting variable of our study. We also discuss the implications and further research opportunities of our study.

  2. Bayesian network modelling of upper gastrointestinal bleeding

    Science.gov (United States)

    Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri

    2013-09-01

    Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.

  3. Development of estrogen receptor beta binding prediction model using large sets of chemicals.

    Science.gov (United States)

    Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

    2017-11-03

    We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

  4. A Model of Network Porosity

    Science.gov (United States)

    2016-02-04

    of complex systems [1]. Although the ODD protocol was originally intended for individual-based or agent-based models ( ABM ), we adopt this protocol for...applies to information transfer between air-gapped systems . Trust relationships between devices (e.g. a trust relationship created by a domain controller...prevention systems , and data leakage protection systems . 2.2 ATTACKER The model specifies an attacker who gains access to internal enclaves by

  5. Modeling and optimization of potable water network

    Energy Technology Data Exchange (ETDEWEB)

    Djebedjian, B.; Rayan, M.A. [Mansoura Univ., El-Mansoura (Egypt); Herrick, A. [Suez Canal Authority, Ismailia (Egypt)

    2000-07-01

    Software was developed in order to optimize the design of water distribution systems and pipe networks. While satisfying all the constraints imposed such as pipe diameter and nodal pressure, it was based on a mathematical model treating looped networks. The optimum network configuration and cost are determined considering parameters like pipe diameter, flow rate, corresponding pressure and hydraulic losses. It must be understood that minimum cost is relative to the different objective functions selected. The determination of the proper objective function often depends on the operating policies of a particular company. The solution for the optimization technique was obtained by using a non-linear technique. To solve the optimal design of network, the model was derived using the sequential unconstrained minimization technique (SUMT) of Fiacco and McCormick, which decreased the number of iterations required. The pipe diameters initially assumed were successively adjusted to correspond to the existing commercial pipe diameters. The technique was then applied to a two-loop network without pumps or valves. Fed by gravity, it comprised eight pipes, 1000 m long each. The first evaluation of the method proved satisfactory. As with other methods, it failed to find the global optimum. In the future, research efforts will be directed to the optimization of networks with pumps and reservoirs. 24 refs., 3 tabs., 1 fig.

  6. Ties That Bind: A Social Network Approach to Understanding Student Integration and Persistence. ASHE Annual Meeting Paper.

    Science.gov (United States)

    Thomas, Scott L.

    This study examined the social networks of college students and how such networks affect student commitment and persistence. The study's theoretical framework was based on application of the social network paradigm to Tinto's Student Integration Model, in which a student's initial commitment is modified over time as a result of the student's…

  7. Modelling dendritic ecological networks in space: An integrated network perspective

    Science.gov (United States)

    Erin E. Peterson; Jay M. Ver Hoef; Dan J. Isaak; Jeffrey A. Falke; Marie-Josee Fortin; Chris E. Jordan; Kristina McNyset; Pascal Monestiez; Aaron S. Ruesch; Aritra Sengupta; Nicholas Som; E. Ashley Steel; David M. Theobald; Christian E. Torgersen; Seth J. Wenger

    2013-01-01

    Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of...

  8. PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. Hadapiningradja Kusumodestoni

    2015-11-01

    Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.

  9. Artificial neural network cardiopulmonary modeling and diagnosis

    Science.gov (United States)

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  10. Green Network Planning Model for Optical Backbones

    DEFF Research Database (Denmark)

    Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir; Jensen, Michael

    2010-01-01

    on the environment in general. In network planning there are existing planning models focused on QoS provisioning, investment minimization or combinations of both and other parameters. But there is a lack of a model for designing green optical backbones. This paper presents novel ideas to be able to define......Communication networks are becoming more essential for our daily lives and critically important for industry and governments. The intense growth in the backbone traffic implies an increment of the power demands of the transmission systems. This power usage might have a significant negative effect...

  11. A Model for Telestrok Network Evaluation

    DEFF Research Database (Denmark)

    Storm, Anna; Günzel, Franziska; Theiss, Stephan

    2011-01-01

    analysis lacking, current telestroke reimbursement by third-party payers is limited to special contracts and not included in the regular billing system. Based on a systematic literature review and expert interviews with health care economists, third-party payers and neurologists, a Markov model...... was developed from the third-party payer perspective. In principle, it enables telestroke networks to conduct cost-effectiveness studies, because the majority of the required data can be extracted from health insurance companies’ databases and the telestroke network itself. The model presents a basis...

  12. PROJECT ACTIVITY ANALYSIS WITHOUT THE NETWORK MODEL

    Directory of Open Access Journals (Sweden)

    S. Munapo

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: This paper presents a new procedure for analysing and managing activity sequences in projects. The new procedure determines critical activities, critical path, start times, free floats, crash limits, and other useful information without the use of the network model. Even though network models have been successfully used in project management so far, there are weaknesses associated with the use. A network is not easy to generate, and dummies that are usually associated with it make the network diagram complex – and dummy activities have no meaning in the original project management problem. The network model for projects can be avoided while still obtaining all the useful information that is required for project management. What are required are the activities, their accurate durations, and their predecessors.

    AFRIKAANSE OPSOMMING: Die navorsing beskryf ’n nuwerwetse metode vir die ontleding en bestuur van die sekwensiële aktiwiteite van projekte. Die voorgestelde metode bepaal kritiese aktiwiteite, die kritieke pad, aanvangstye, speling, verhasing, en ander groothede sonder die gebruik van ’n netwerkmodel. Die metode funksioneer bevredigend in die praktyk, en omseil die administratiewe rompslomp van die tradisionele netwerkmodelle.

  13. Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks

    CERN Document Server

    Santi, Paolo

    2012-01-01

    Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and

  14. Modeling Renewable Penertration Using a Network Economic Model

    Science.gov (United States)

    Lamont, A.

    2001-03-01

    This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.

  15. Security Modeling on the Supply Chain Networks

    Directory of Open Access Journals (Sweden)

    Marn-Ling Shing

    2007-10-01

    Full Text Available In order to keep the price down, a purchaser sends out the request for quotation to a group of suppliers in a supply chain network. The purchaser will then choose a supplier with the best combination of price and quality. A potential supplier will try to collect the related information about other suppliers so he/she can offer the best bid to the purchaser. Therefore, confidentiality becomes an important consideration for the design of a supply chain network. Chen et al. have proposed the application of the Bell-LaPadula model in the design of a secured supply chain network. In the Bell-LaPadula model, a subject can be in one of different security clearances and an object can be in one of various security classifications. All the possible combinations of (Security Clearance, Classification pair in the Bell-LaPadula model can be thought as different states in the Markov Chain model. This paper extends the work done by Chen et al., provides more details on the Markov Chain model and illustrates how to use it to monitor the security state transition in the supply chain network.

  16. An evolving model of online bipartite networks

    Science.gov (United States)

    Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang

    2013-12-01

    Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.

  17. An autocatalytic network model for stock markets

    Science.gov (United States)

    Caetano, Marco Antonio Leonel; Yoneyama, Takashi

    2015-02-01

    The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.

  18. Binding and segmentation via a neural mass model trained with Hebbian and anti-Hebbian mechanisms.

    Science.gov (United States)

    Cona, Filippo; Zavaglia, Melissa; Ursino, Mauro

    2012-04-01

    Synchronization of neural activity in the gamma band, modulated by a slower theta rhythm, is assumed to play a significant role in binding and segmentation of multiple objects. In the present work, a recent neural mass model of a single cortical column is used to analyze the synaptic mechanisms which can warrant synchronization and desynchronization of cortical columns, during an autoassociation memory task. The model considers two distinct layers communicating via feedforward connections. The first layer receives the external input and works as an autoassociative network in the theta band, to recover a previously memorized object from incomplete information. The second realizes segmentation of different objects in the gamma band. To this end, units within both layers are connected with synapses trained on the basis of previous experience to store objects. The main model assumptions are: (i) recovery of incomplete objects is realized by excitatory synapses from pyramidal to pyramidal neurons in the same object; (ii) binding in the gamma range is realized by excitatory synapses from pyramidal neurons to fast inhibitory interneurons in the same object. These synapses (both at points i and ii) have a few ms dynamics and are trained with a Hebbian mechanism. (iii) Segmentation is realized with faster AMPA synapses, with rise times smaller than 1 ms, trained with an anti-Hebbian mechanism. Results show that the model, with the previous assumptions, can correctly reconstruct and segment three simultaneous objects, starting from incomplete knowledge. Segmentation of more objects is possible but requires an increased ratio between the theta and gamma periods.

  19. Endocytosis of wheat germ agglutinin binding sites from the cell surface into a tubular endosomal network.

    Science.gov (United States)

    Raub, T J; Koroly, M J; Roberts, R M

    1990-04-01

    By using fluorescence and electron microscopy, the endocytic pathway encountered by cell surface components after they had bound wheat germ agglutinin (WGA) was visualized. The majority of these components are thought to consist of sialylated glycoproteins (HMWAG) that represent a subpopulation of the total cell surface proteins but most of the externally disposed plasma membrane proteins of the cell. Examination of semi-thin sections by medium- and high-voltage electron microscopy revealed the three-dimensional organization of vesicular and tubular endosomes. Binding of either fluorescein isothiocyanate-, horseradish peroxidase-, or ferritin-conjugated WGA to cells at 4 degrees C showed that the HMWAG were distributed uniformly over the cell surface. Warming of surface-labeled cells to 37 degrees C resulted in the endocytosis of WGA into peripheral endosomes via invagination of regions of both coated and uncoated membrane. The peripheral endosome appeared as isolated complexes comprising a vesicular element (300-400 nm diam.) surrounded by and continuous with tubular cisternae (45-60 nm diam.), which did not interconnect the endosomes. After 30 min or more label also became localized in a network of anastomosing tubules (45-60 nm diam.) that were located in the centrosomal region of the cell. Endocytosed WGA-HMWAG complexes did not become associated with cisternae of the Golgi apparatus, although tubular and vesicular endosomes were noted in the vicinity of the trans-Golgi region. The accumulation of WGA-HMWAG in the endosomes within the centrosomal region was inhibited when cells were incubated at 18 degrees C. None of these compartments contained acid phosphatase activity, a result that is consistent with other data that the HMWAG do not pass through lysosomes initially. The kinetics of labeling were consistent with the interpretation that recycling of most of the WGA binding surface glycoproteins occurred rapidly from early peripheral endosomes followed by the

  20. Transferable tight-binding model for strained group IV and III-V materials and heterostructures

    Science.gov (United States)

    Tan, Yaohua; Povolotskyi, Michael; Kubis, Tillmann; Boykin, Timothy B.; Klimeck, Gerhard

    2016-07-01

    It is critical to capture the effect due to strain and material interface for device level transistor modeling. We introduce a transferable s p3d5s* tight-binding model with nearest-neighbor interactions for arbitrarily strained group IV and III-V materials. The tight-binding model is parametrized with respect to hybrid functional (HSE06) calculations for varieties of strained systems. The tight-binding calculations of ultrasmall superlattices formed by group IV and group III-V materials show good agreement with the corresponding HSE06 calculations. The application of the tight-binding model to superlattices demonstrates that the transferable tight-binding model with nearest-neighbor interactions can be obtained for group IV and III-V materials.

  1. A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

    KAUST Repository

    Wong, Ka-Chun; Li, Yue; Peng, Chengbin; Wong, Hau-San

    2015-01-01

    Transcription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

  2. A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

    KAUST Repository

    Wong, Ka-Chun

    2015-06-11

    Transcription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

  3. Keystone Business Models for Network Security Processors

    Directory of Open Access Journals (Sweden)

    Arthur Low

    2013-07-01

    Full Text Available Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor” models nor the silicon intellectual-property licensing (“IP-licensing” models allow small technology companies to successfully compete. This article describes an alternative approach that produces an ongoing stream of novel network security processors for niche markets through continuous innovation by both large and small companies. This approach, referred to here as the "business ecosystem model for network security processors", includes a flexible and reconfigurable technology platform, a “keystone” business model for the company that maintains the platform architecture, and an extended ecosystem of companies that both contribute and share in the value created by innovation. New opportunities for business model innovation by participating companies are made possible by the ecosystem model. This ecosystem model builds on: i the lessons learned from the experience of the first author as a senior integrated circuit architect for providers of public-key cryptography solutions and as the owner of a semiconductor startup, and ii the latest scholarly research on technology entrepreneurship, business models, platforms, and business ecosystems. This article will be of interest to all technology entrepreneurs, but it will be of particular interest to owners of small companies that provide security solutions and to specialized security professionals seeking to launch their own companies.

  4. Modeling and Simulation Network Data Standards

    Science.gov (United States)

    2011-09-30

    approaches . 2.3. JNAT. JNAT is a Web application that provides connectivity and network analysis capability. JNAT uses propagation models and low-fidelity...COMBATXXI Movement Logger Data Output Dictionary. Field # Geocentric Coordinates (GCC) Heading Geodetic Coordinates (GDC) Heading Universal...B-8 Field # Geocentric Coordinates (GCC) Heading Geodetic Coordinates (GDC) Heading Universal Transverse Mercator (UTM) Heading

  5. The Kuramoto model in complex networks

    Science.gov (United States)

    Rodrigues, Francisco A.; Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen

    2016-01-01

    Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in networks, including the presence of noise and inertia. The rich potential for applications is discussed for special fields in engineering, neuroscience, physics and Earth science. Finally, we conclude by discussing problems that remain open after the last decade of intensive research on the Kuramoto model and point out some promising directions for future research.

  6. An architectural model for network interconnection

    NARCIS (Netherlands)

    van Sinderen, Marten J.; Vissers, C.A.; Kalin, T.

    1983-01-01

    This paper presents a technique of successive decomposition of a common users' activity to illustrate the problems of network interconnection. The criteria derived from this approach offer a structuring principle which is used to develop an architectural model that embeds heterogeneous subnetworks

  7. Computational Modeling of Complex Protein Activity Networks

    NARCIS (Netherlands)

    Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude

    2017-01-01

    Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a

  8. A Model of Mental State Transition Network

    Science.gov (United States)

    Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo

    Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.

  9. UAV Trajectory Modeling Using Neural Networks

    Science.gov (United States)

    Xue, Min

    2017-01-01

    Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

  10. Modeling Insurgent Network Structure and Dynamics

    Science.gov (United States)

    Gabbay, Michael; Thirkill-Mackelprang, Ashley

    2010-03-01

    We present a methodology for mapping insurgent network structure based on their public rhetoric. Indicators of cooperative links between insurgent groups at both the leadership and rank-and-file levels are used, such as joint policy statements or joint operations claims. In addition, a targeting policy measure is constructed on the basis of insurgent targeting claims. Network diagrams which integrate these measures of insurgent cooperation and ideology are generated for different periods of the Iraqi and Afghan insurgencies. The network diagrams exhibit meaningful changes which track the evolution of the strategic environment faced by insurgent groups. Correlations between targeting policy and network structure indicate that insurgent targeting claims are aimed at establishing a group identity among the spectrum of rank-and-file insurgency supporters. A dynamical systems model of insurgent alliance formation and factionalism is presented which evolves the relationship between insurgent group dyads as a function of their ideological differences and their current relationships. The ability of the model to qualitatively and quantitatively capture insurgent network dynamics observed in the data is discussed.

  11. Hybrid simulation models of production networks

    CERN Document Server

    Kouikoglou, Vassilis S

    2001-01-01

    This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.

  12. Propagating semantic information in biochemical network models

    Directory of Open Access Journals (Sweden)

    Schulz Marvin

    2012-01-01

    Full Text Available Abstract Background To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation. Results A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements. Conclusions Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.

  13. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  14. Modeling Multistandard Wireless Networks in OPNET

    DEFF Research Database (Denmark)

    Zakrzewska, Anna; Berger, Michael Stübert; Ruepp, Sarah Renée

    2011-01-01

    Future wireless communication is emerging towards one heterogeneous platform. In this new environment wireless access will be provided by multiple radio technologies that are cooperating and complementing one another. The paper investigates the possibilities of developing such a multistandard sys...... system using OPNET Modeler. A network model consisting of LTE interworking with WLAN and WiMAX is considered from the radio resource management perspective. In particular, implementing a joint packet scheduler across multiple systems is discussed more in detail....

  15. Modelling dendritic ecological networks in space: anintegrated network perspective

    Science.gov (United States)

    Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Dan J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordon, Chris E.; McNyset, Kristina; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas; Steel, E. Ashley; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J.

    2013-01-01

    Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within

  16. Unified Model for Generation Complex Networks with Utility Preferential Attachment

    International Nuclear Information System (INIS)

    Wu Jianjun; Gao Ziyou; Sun Huijun

    2006-01-01

    In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics of this new network are given.

  17. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  18. On traffic modelling in GPRS networks

    DEFF Research Database (Denmark)

    Madsen, Tatiana Kozlova; Schwefel, Hans-Peter; Prasad, Ramjee

    2005-01-01

    Optimal design and dimensioning of wireless data networks, such as GPRS, requires the knowledge of traffic characteristics of different data services. This paper presents an in-detail analysis of an IP-level traffic measurements taken in an operational GPRS network. The data measurements reported...... here are done at the Gi interface. The aim of this paper is to reveal some key statistics of GPRS data applications and to validate if the existing traffic models can adequately describe traffic volume and inter-arrival time distribution for different services. Additionally, we present a method of user...

  19. Systems pharmacology - Towards the modeling of network interactions.

    Science.gov (United States)

    Danhof, Meindert

    2016-10-30

    Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and

  20. A Networks Approach to Modeling Enzymatic Reactions.

    Science.gov (United States)

    Imhof, P

    2016-01-01

    Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. © 2016 Elsevier Inc. All rights reserved.

  1. A improved Network Security Situation Awareness Model

    Directory of Open Access Journals (Sweden)

    Li Fangwei

    2015-08-01

    Full Text Available In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.

  2. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...

  3. Fractional virus epidemic model on financial networks

    Directory of Open Access Journals (Sweden)

    Balci Mehmet Ali

    2016-01-01

    Full Text Available In this study, we present an epidemic model that characterizes the behavior of a financial network of globally operating stock markets. Since the long time series have a global memory effect, we represent our model by using the fractional calculus. This model operates on a network, where vertices are the stock markets and edges are constructed by the correlation distances. Thereafter, we find an analytical solution to commensurate system and use the well-known differential transform method to obtain the solution of incommensurate system of fractional differential equations. Our findings are confirmed and complemented by the data set of the relevant stock markets between 2006 and 2016. Rather than the hypothetical values, we use the Hurst Exponent of each time series to approximate the fraction size and graph theoretical concepts to obtain the variables.

  4. Entanglement effects in model polymer networks

    Science.gov (United States)

    Everaers, R.; Kremer, K.

    The influence of topological constraints on the local dynamics in cross-linked polymer melts and their contribution to the elastic properties of rubber elastic systems are a long standing problem in statistical mechanics. Polymer networks with diamond lattice connectivity (Everaers and Kremer 1995, Everaers and Kremer 1996a) are idealized model systems which isolate the effect of topology conservation from other sources of quenched disorder. We study their behavior in molecular dynamics simulations under elongational strain. In our analysis we compare the measured, purely entropic shear moduli G to the predictions of statistical mechanical models of rubber elasticity, making extensive use of the microscopic structural and topological information available in computer simulations. We find (Everaers and Kremer 1995) that the classical models of rubber elasticity underestimate the true change in entropy in a deformed network significantly, because they neglect the tension along the contour of the strands which cannot relax due to entanglements (Everaers and Kremer (in preparation)). This contribution and the fluctuations in strained systems seem to be well described by the constrained mode model (Everaers 1998) which allows to treat the crossover from classical rubber elasticity to the tube model for polymer networks with increasing strand length within one transparant formalism. While this is important for the description of the effects we try to do a first quantitative step towards their explanation by topological considerations. We show (Everaers and Kremer 1996a) that for the comparatively short strand lengths of our diamond networks the topology contribution to the shear modulus is proportional to the density of entangled mesh pairs with non-zero Gauss linking number. Moreover, the prefactor can be estimated consistently within a rather simple model developed by Vologodskii et al. and by Graessley and Pearson, which is based on the definition of an entropic

  5. Northern emporia and maritime networks. Modelling past communication using archaeological network analysis

    DEFF Research Database (Denmark)

    Sindbæk, Søren Michael

    2015-01-01

    preserve patterns of thisinteraction. Formal network analysis and modelling holds the potential to identify anddemonstrate such patterns, where traditional methods often prove inadequate. Thearchaeological study of communication networks in the past, however, calls for radically different analytical...... this is not a problem of network analysis, but network synthesis: theclassic problem of cracking codes or reconstructing black-box circuits. It is proposedthat archaeological approaches to network synthesis must involve a contextualreading of network data: observations arising from individual contexts, morphologies...

  6. Complementary three-dimensional quantitative structure-activity relationship modeling of binding affinity and functional potency

    DEFF Research Database (Denmark)

    Tosco, Paolo; Ahring, Philip K; Dyhring, Tino

    2009-01-01

    Complementary 3D-QSAR modeling of binding affinity and functional potency is proposed as a tool to pinpoint the molecular features of the ligands, and the corresponding amino acids in the receptor, responsible for high affinity binding vs those driving agonist behavior and receptor activation. Th...

  7. Performance modeling, loss networks, and statistical multiplexing

    CERN Document Server

    Mazumdar, Ravi

    2009-01-01

    This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I

  8. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  9. Specific binding of a dihydropyrimidinone derivative with DNA: Spectroscopic, calorimetric and modeling investigations

    International Nuclear Information System (INIS)

    Wang Gongke; Yan Changling; Wang Dongchao; Li Dan; Lu Yan

    2012-01-01

    One of the dihydropyrimidinone derivative 5-(ethoxycarbonyl)-6-methyl-4-(4-methoxyphenyl) -3,4-dihydropyrimidin-2(1H)-one (EMMD) was synthesized, and its binding properties with calf-thymus DNA (ctDNA) were investigated using spectroscopic, viscometric, isothermal titration calorimetric (ITC) and molecular modeling techniques. Fluorescence spectra suggested that the fluorescence enhancement of the binding interaction of EMMD to ctDNA was a static process with ground state complex formation. The binding constant determined with spectroscopic titration and ITC was found to be in the same order of 10 4 M −1 . According to the results of the viscosity analysis, fluorescence competitive binding experiment, fluorescence quenching studies, absorption spectral and ITC investigations, it can be concluded that EMMD is intercalative binding to ctDNA. Furthermore, the results of molecular modeling confirmed those obtained from spectroscopic, viscosimetric and ITC investigations. Additionally, ITC studies also indicated that the binding interaction is predominantly enthalpy driven. - Highlights: ► Medically important dihydropyrimidinones derivative EMMD is synthesized. ► EMMD is intercalative binding into ctDNA helix. ► Hydrogen bonding may play an essential role in the binding of EMCD with ctDNA. ► This binding interaction is predominantly enthalpy driven.

  10. UAV Trajectory Modeling Using Neural Networks

    Science.gov (United States)

    Xue, Min

    2017-01-01

    Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural

  11. Mapping and modeling of physician collaboration network.

    Science.gov (United States)

    Uddin, Shahadat; Hamra, Jafar; Hossain, Liaquat

    2013-09-10

    Effective provisioning of healthcare services during patient hospitalization requires collaboration involving a set of interdependent complex tasks, which needs to be carried out in a synergistic manner. Improved patients' outcome during and after hospitalization has been attributed to how effective different health services provisioning groups carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among physicians on the effective outcome in delivering health services for improved patient outcomes. However, there are very few systematic empirical studies with a focus on the effect of collaboration networks among healthcare professionals and patients' medical condition. On the basis of the fact that collaboration evolves among physicians when they visit a common hospitalized patient, in this study, we first propose an approach to map collaboration network among physicians from their visiting information to patients. We termed this network as physician collaboration network (PCN). Then, we use exponential random graph (ERG) models to explore the microlevel network structures of PCNs and their impact on hospitalization cost and hospital readmission rate. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks such as PCN. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the electronic health insurance claims dataset of a very large Australian health insurance organization, we construct and model PCNs. We notice that the 2-star (subset of 3 nodes in which 1 node is connected to each of the other 2 nodes) parameter of ERG has significant impact on hospitalization cost. Further, we identify that triangle (subset of 3 nodes in which each node is connected to

  12. Modeling In-Network Aggregation in VANETs

    NARCIS (Netherlands)

    Dietzel, Stefan; Kargl, Frank; Heijenk, Geert; Schaub, Florian

    2011-01-01

    The multitude of applications envisioned for vehicular ad hoc networks requires efficient communication and dissemination mechanisms to prevent network congestion. In-network data aggregation promises to reduce bandwidth requirements and enable scalability in large vehicular networks. However, most

  13. Different Epidemic Models on Complex Networks

    International Nuclear Information System (INIS)

    Zhang Haifeng; Small, Michael; Fu Xinchu

    2009-01-01

    Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals' relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemic models on complex networks, and obtain the epidemic threshold for each case. Finally, we present numerical simulations for each case to verify our results.

  14. Centralized Bayesian reliability modelling with sensor networks

    Czech Academy of Sciences Publication Activity Database

    Dedecius, Kamil; Sečkárová, Vladimíra

    2013-01-01

    Roč. 19, č. 5 (2013), s. 471-482 ISSN 1387-3954 R&D Projects: GA MŠk 7D12004 Grant - others:GA MŠk(CZ) SVV-265315 Keywords : Bayesian modelling * Sensor network * Reliability Subject RIV: BD - Theory of Information Impact factor: 0.984, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/dedecius-0392551.pdf

  15. Modelling Pollutant Dispersion in a Street Network

    Science.gov (United States)

    Salem, N. Ben; Garbero, V.; Salizzoni, P.; Lamaison, G.; Soulhac, L.

    2015-04-01

    This study constitutes a further step in the analysis of the performances of a street network model to simulate atmospheric pollutant dispersion in urban areas. The model, named SIRANE, is based on the decomposition of the urban atmosphere into two sub-domains: the urban boundary layer, whose dynamics is assumed to be well established, and the urban canopy, represented as a series of interconnected boxes. Parametric laws govern the mass exchanges between the boxes under the assumption that the pollutant dispersion within the canopy can be fully simulated by modelling three main bulk transfer phenomena: channelling along street axes, transfers at street intersections, and vertical exchange between street canyons and the overlying atmosphere. Here, we aim to evaluate the reliability of the parametrizations adopted to simulate these phenomena, by focusing on their possible dependence on the external wind direction. To this end, we test the model against concentration measurements within an idealized urban district whose geometrical layout closely matches the street network represented in SIRANE. The analysis is performed for an urban array with a fixed geometry and a varying wind incidence angle. The results show that the model provides generally good results with the reference parametrizations adopted in SIRANE and that its performances are quite robust for a wide range of the model parameters. This proves the reliability of the street network approach in simulating pollutant dispersion in densely built city districts. The results also show that the model performances may be improved by considering a dependence of the wind fluctuations at street intersections and of the vertical exchange velocity on the direction of the incident wind. This opens the way for further investigations to clarify the dependence of these parameters on wind direction and street aspect ratios.

  16. The Channel Network model and field applications

    International Nuclear Information System (INIS)

    Khademi, B.; Moreno, L.; Neretnieks, I.

    1999-01-01

    The Channel Network model describes the fluid flow and solute transport in fractured media. The model is based on field observations, which indicate that flow and transport take place in a three-dimensional network of connected channels. The channels are generated in the model from observed stochastic distributions and solute transport is modeled taking into account advection and rock interactions, such as matrix diffusion and sorption within the rock. The most important site-specific data for the Channel Network model are the conductance distribution of the channels and the flow-wetted surface. The latter is the surface area of the rock in contact with the flowing water. These parameters may be estimated from hydraulic measurements. For the Aespoe site, several borehole data sets are available, where a packer distance of 3 meters was used. Numerical experiments were performed in order to study the uncertainties in the determination of the flow-wetted surface and conductance distribution. Synthetic data were generated along a borehole and hydraulic tests with different packer distances were simulated. The model has previously been used to study the Long-term Pumping and Tracer Test (LPT2) carried out in the Aespoe Hard Rock Laboratory (HRL) in Sweden, where the distance travelled by the tracers was of the order hundreds of meters. Recently, the model has been used to simulate the tracer tests performed in the TRUE experiment at HRL, with travel distance of the order of tens of meters. Several tracer tests with non-sorbing and sorbing species have been performed

  17. Advances in dynamic network modeling in complex transportation systems

    CERN Document Server

    Ukkusuri, Satish V

    2013-01-01

    This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.

  18. Distributed Bayesian Networks for User Modeling

    DEFF Research Database (Denmark)

    Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang

    2006-01-01

    The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used by such ada......The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...

  19. A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

    Science.gov (United States)

    Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher

    2005-01-01

    This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

  20. A network model for Ebola spreading.

    Science.gov (United States)

    Rizzo, Alessandro; Pedalino, Biagio; Porfiri, Maurizio

    2016-04-07

    The availability of accurate models for the spreading of infectious diseases has opened a new era in management and containment of epidemics. Models are extensively used to plan for and execute vaccination campaigns, to evaluate the risk of international spreadings and the feasibility of travel bans, and to inform prophylaxis campaigns. Even when no specific therapeutical protocol is available, as for the Ebola Virus Disease (EVD), models of epidemic spreading can provide useful insight to steer interventions in the field and to forecast the trend of the epidemic. Here, we propose a novel mathematical model to describe EVD spreading based on activity driven networks (ADNs). Our approach overcomes the simplifying assumption of homogeneous mixing, which is central to most of the mathematically tractable models of EVD spreading. In our ADN-based model, each individual is not bound to contact every other, and its network of contacts varies in time as a function of an activity potential. Our model contemplates the possibility of non-ideal and time-varying intervention policies, which are critical to accurately describe EVD spreading in afflicted countries. The model is calibrated from field data of the 2014 April-to-December spreading in Liberia. We use the model as a predictive tool, to emulate the dynamics of EVD in Liberia and offer a one-year projection, until December 2015. Our predictions agree with the current vision expressed by professionals in the field, who consider EVD in Liberia at its final stage. The model is also used to perform a what-if analysis to assess the efficacy of timely intervention policies. In particular, we show that an earlier application of the same intervention policy would have greatly reduced the number of EVD cases, the duration of the outbreak, and the infrastructures needed for the implementation of the intervention. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Modeling Network Transition Constraints with Hypergraphs

    DEFF Research Database (Denmark)

    Harrod, Steven

    2011-01-01

    Discrete time dynamic graphs are frequently used to model multicommodity flows or activity paths through constrained resources, but simple graphs fail to capture the interaction effects of resource transitions. The resulting schedules are not operationally feasible, and return inflated objective...... values. A directed hypergraph formulation is derived to address railway network sequencing constraints, and an experimental problem sample solved to estimate the magnitude of objective inflation when interaction effects are ignored. The model is used to demonstrate the value of advance scheduling...... of train paths on a busy North American railway....

  2. Mathematical model for spreading dynamics of social network worms

    International Nuclear Information System (INIS)

    Sun, Xin; Liu, Yan-Heng; Han, Jia-Wei; Liu, Xue-Jie; Li, Bin; Li, Jin

    2012-01-01

    In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks

  3. Model parameter updating using Bayesian networks

    International Nuclear Information System (INIS)

    Treml, C.A.; Ross, Timothy J.

    2004-01-01

    This paper outlines a model parameter updating technique for a new method of model validation using a modified model reference adaptive control (MRAC) framework with Bayesian Networks (BNs). The model parameter updating within this method is generic in the sense that the model/simulation to be validated is treated as a black box. It must have updateable parameters to which its outputs are sensitive, and those outputs must have metrics that can be compared to that of the model reference, i.e., experimental data. Furthermore, no assumptions are made about the statistics of the model parameter uncertainty, only upper and lower bounds need to be specified. This method is designed for situations where a model is not intended to predict a complete point-by-point time domain description of the item/system behavior; rather, there are specific points, features, or events of interest that need to be predicted. These specific points are compared to the model reference derived from actual experimental data. The logic for updating the model parameters to match the model reference is formed via a BN. The nodes of this BN consist of updateable model input parameters and the specific output values or features of interest. Each time the model is executed, the input/output pairs are used to adapt the conditional probabilities of the BN. Each iteration further refines the inferred model parameters to produce the desired model output. After parameter updating is complete and model inputs are inferred, reliabilities for the model output are supplied. Finally, this method is applied to a simulation of a resonance control cooling system for a prototype coupled cavity linac. The results are compared to experimental data.

  4. RNA binding protein RNPC1 inhibits breast cancer cells metastasis via activating STARD13-correlated ceRNA network.

    Science.gov (United States)

    Zhang, Zhiting; Guo, Qianqian; Zhang, Shufang; Xiang, Chenxi; Guo, Xinwei; Zhang, Feng; Gao, Lanlan; Ni, Haiwei; Xi, Tao; Zheng, Lufeng

    2018-05-07

    RNA binding proteins (RBPs) are pivotal post-transcriptional regulators. RNPC1, an RBP, acts as a tumor suppressor through binding and regulating the expression of target genes in cancer cells. This study disclosed that RNPC1 expression was positively correlated with breast cancer patients' relapse free and overall survival, and RNPC1suppressed breast cancer cells metastasis. Mechanistically, RNPC1 promoting a competing endogenous network (ceRNA) crosstalk between STARD13, CDH5, HOXD10, and HOXD1 (STARD13-correlated ceRNA network) that we previously confirmed in breast cancer cells through stabilizing the transcripts and thus facilitating the expression of these four genes in breast cancer cells. Furthermore, RNPC1 overexpression restrained the promotion of STARD13, CDH5, HOXD10, and HOXD1 knockdown on cell metastasis. Notably, RNPC1 expression was positively correlated with CDH5, HOXD1 and HOXD10 expression in breast cancer tissues, and attenuated adriamycin resistance. Taken together, these results identified that RNPC1 could inhibit breast cancer cells metastasis via promoting STARD13-correlated ceRNA network.

  5. Complex networks-based energy-efficient evolution model for wireless sensor networks

    Energy Technology Data Exchange (ETDEWEB)

    Zhu Hailin [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China)], E-mail: zhuhailin19@gmail.com; Luo Hong [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China); Peng Haipeng; Li Lixiang; Luo Qun [Information Secure Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China)

    2009-08-30

    Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

  6. Complex networks-based energy-efficient evolution model for wireless sensor networks

    International Nuclear Information System (INIS)

    Zhu Hailin; Luo Hong; Peng Haipeng; Li Lixiang; Luo Qun

    2009-01-01

    Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

  7. Modeling online social networks based on preferential linking

    International Nuclear Information System (INIS)

    Hu Hai-Bo; Chen Jun; Guo Jin-Li

    2012-01-01

    We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro-mechanisms of network growth and the macrostructures of online social networks

  8. Modeling the Effect of Bandwidth Allocation on Network Performance

    African Journals Online (AJOL)

    ... The proposed model showed improved performance for CDMA networks, but further increase in the bandwidth did not benefit the network; (iii) A reliability measure such as the spectral efficiency is therefore useful to redeem the limitation in (ii). Keywords: Coverage Capacity, CDMA, Mobile Network, Network Throughput ...

  9. Aeronautical telecommunications network advances, challenges, and modeling

    CERN Document Server

    Musa, Sarhan M

    2015-01-01

    Addresses the Challenges of Modern-Day Air Traffic Air traffic control (ATC) directs aircraft in the sky and on the ground to safety, while the Aeronautical Telecommunications Network (ATN) comprises all systems and phases that assist in aircraft departure and landing. The Aeronautical Telecommunications Network: Advances, Challenges, and Modeling focuses on the development of ATN and examines the role of the various systems that link aircraft with the ground. The book places special emphasis on ATC-introducing the modern ATC system from the perspective of the user and the developer-and provides a thorough understanding of the operating mechanism of the ATC system. It discusses the evolution of ATC, explaining its structure and how it works; includes design examples; and describes all subsystems of the ATC system. In addition, the book covers relevant tools, techniques, protocols, and architectures in ATN, including MIPv6, air traffic control (ATC), security of air traffic management (ATM), very-high-frequenc...

  10. Modelling dependable systems using hybrid Bayesian networks

    International Nuclear Information System (INIS)

    Neil, Martin; Tailor, Manesh; Marquez, David; Fenton, Norman; Hearty, Peter

    2008-01-01

    A hybrid Bayesian network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment, the models are invariably hybrid and the need for efficient and accurate computation is paramount. We apply a new iterative algorithm that efficiently combines dynamic discretisation with robust propagation algorithms on junction tree structures to perform inference in hybrid BNs. We illustrate its use in the field of dependability with two example of reliability estimation. Firstly we estimate the reliability of a simple single system and next we implement a hierarchical Bayesian model. In the hierarchical model we compute the reliability of two unknown subsystems from data collected on historically similar subsystems and then input the result into a reliability block model to compute system level reliability. We conclude that dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems

  11. Logic integer programming models for signaling networks.

    Science.gov (United States)

    Haus, Utz-Uwe; Niermann, Kathrin; Truemper, Klaus; Weismantel, Robert

    2009-05-01

    We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.

  12. Vitamin K3 disrupts the microtubule networks by binding to tubulin: a novel mechanism of its antiproliferative activity.

    Science.gov (United States)

    Acharya, Bipul R; Choudhury, Diptiman; Das, Amlan; Chakrabarti, Gopal

    2009-07-28

    Vitamin K3 (2-methyl-1,4-naphthoquinone), also known as menadione, is the synthetic precursor of all the naturally occurring vitamin K in the body. Vitamin K is necessary for the production of prothrombin and five other blood-clotting factors in humans. We have examined the effects of menadione on cellular microtubules ex vivo as well as its binding with purified tubulin and microtubules in vitro. Cell viability experiments using human cervical epithelial cancer cells (HeLa) and human oral epithelial cancer cells (KB) indicated that the IC(50) values for menadione are 25.6 +/- 0.6 and 64.3 +/- 0.36 microM, respectively, in those cells. Mendione arrests HeLa cells in mitosis. Immunofluorescence studies using an anti-alpha-tubulin antibody showed a significant irreversible depolymeriztion of the interphase microtubule network and spindle microtubule in a dose-dependent manner. In vitro polymerization of purified tubulin into microtubules is inhibited by menadione with an IC(50) value of 47 +/- 0.65 microM. The binding of menadione with tubulin was studied using menadione fluorescence and intrinsic tryptophan fluorescence of tubulin. Binding of menadione to tubulin is slow, taking 35 min for equilibration at 25 degrees C. The association reaction kinetics is biphasic in nature, and the association rate constants for fast and slow phases are 189.12 +/- 17 and 32.44 +/- 21 M(-1) s(-1) at 25 degrees C, respectively. The stoichiometry of menadione binding to tubulin is 1:1 (molar ratio) with a dissociation constant from 2.44 +/- 0.34 to 3.65 +/- 0.25 microM at 25 degrees C. Menadione competes for the colchicine binding site with a K(i) of 2.5 muM as determined from a modified Dixon plot. The obtained data suggested that menadione binds at the colchicine binding site to tubulin. Thus, we can conclude one novel mechanism of inhibition of cancer cell proliferation by menadione is through tubulin binding.

  13. Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility.

    Science.gov (United States)

    Liu, Sheng; Zibetti, Cristina; Wan, Jun; Wang, Guohua; Blackshaw, Seth; Qian, Jiang

    2017-07-27

    Computational prediction of transcription factor (TF) binding sites in different cell types is challenging. Recent technology development allows us to determine the genome-wide chromatin accessibility in various cellular and developmental contexts. The chromatin accessibility profiles provide useful information in prediction of TF binding events in various physiological conditions. Furthermore, ChIP-Seq analysis was used to determine genome-wide binding sites for a range of different TFs in multiple cell types. Integration of these two types of genomic information can improve the prediction of TF binding events. We assessed to what extent a model built upon on other TFs and/or other cell types could be used to predict the binding sites of TFs of interest. A random forest model was built using a set of cell type-independent features such as specific sequences recognized by the TFs and evolutionary conservation, as well as cell type-specific features derived from chromatin accessibility data. Our analysis suggested that the models learned from other TFs and/or cell lines performed almost as well as the model learned from the target TF in the cell type of interest. Interestingly, models based on multiple TFs performed better than single-TF models. Finally, we proposed a universal model, BPAC, which was generated using ChIP-Seq data from multiple TFs in various cell types. Integrating chromatin accessibility information with sequence information improves prediction of TF binding.The prediction of TF binding is transferable across TFs and/or cell lines suggesting there are a set of universal "rules". A computational tool was developed to predict TF binding sites based on the universal "rules".

  14. Bayesian Recurrent Neural Network for Language Modeling.

    Science.gov (United States)

    Chien, Jen-Tzung; Ku, Yuan-Chu

    2016-02-01

    A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.

  15. Research on network information security model and system construction

    OpenAIRE

    Wang Haijun

    2016-01-01

    It briefly describes the impact of large data era on China’s network policy, but also brings more opportunities and challenges to the network information security. This paper reviews for the internationally accepted basic model and characteristics of network information security, and analyses the characteristics of network information security and their relationship. On the basis of the NIST security model, this paper describes three security control schemes in safety management model and the...

  16. A Complex Network Approach to Distributional Semantic Models.

    Directory of Open Access Journals (Sweden)

    Akira Utsumi

    Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.

  17. Two stage neural network modelling for robust model predictive control.

    Science.gov (United States)

    Patan, Krzysztof

    2018-01-01

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Neural Networks For Electrohydrodynamic Effect Modelling

    Directory of Open Access Journals (Sweden)

    Wiesław Wajs

    2004-01-01

    Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamiceffect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.

  19. Social network models predict movement and connectivity in ecological landscapes

    Science.gov (United States)

    Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.

    2011-01-01

    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, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  20. Social network models predict movement and connectivity in ecological landscapes.

    Science.gov (United States)

    Fletcher, Robert J; Acevedo, Miguel A; Reichert, Brian E; Pias, Kyle E; Kitchens, Wiley M

    2011-11-29

    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, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  1. Ties that Bind: A Social Network Approach To Understanding Student Integration and Persistence.

    Science.gov (United States)

    Thomas, Scott L.

    2000-01-01

    This study used a social network paradigm to examine college student integration of 329 college freshmen at a private liberal arts college. Analysis of the structural aspects of students' on-campus associations found differential effects of various social network characteristics on student commitment and persistence. (DB)

  2. The ties that bind: the networked performance of gender, sexuality, and friendship on Myspace

    NARCIS (Netherlands)

    van Doorn, N.

    2010-01-01

    Although the body of research on social network sites (SNSs) continues to increase, scholarship in this relatively new field has largely neglected the gendered dimensions of networked interaction on SNSs. Through an empirical analysis of users’ comment exchanges, this study demonstrates how a group

  3. Neural network models of categorical perception.

    Science.gov (United States)

    Damper, R I; Harnad, S R

    2000-05-01

    Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.

  4. Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Carson M Andorf

    Full Text Available Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH with one or two binding sites, or multiple-interface hubs (MIH with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations or party hubs (i.e., simultaneously interact with multiple partners.Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.

  5. Ripples Make Waves: Binding Structured Activity and Plasticity in Hippocampal Networks

    Directory of Open Access Journals (Sweden)

    Josef H. L. P. Sadowski

    2011-01-01

    Full Text Available Establishing novel episodic memories and stable spatial representations depends on an exquisitely choreographed, multistage process involving the online encoding and offline consolidation of sensory information, a process that is largely dependent on the hippocampus. Each step is influenced by distinct neural network states that influence the pattern of activation across cellular assemblies. In recent years, the occurrence of hippocampal sharp wave ripple (SWR oscillations has emerged as a potentially vital network phenomenon mediating the steps between encoding and consolidation, both at a cellular and network level by promoting the rapid replay and reactivation of recent activity patterns. Such events facilitate memory formation by optimising the conditions for synaptic plasticity to occur between contingent neural elements. In this paper, we explore the ways in which SWRs and other network events can bridge the gap between spatiomnemonic processing at cellular/synaptic and network levels in the hippocampus.

  6. Combination of Bayesian Network and Overlay Model in User Modeling

    Directory of Open Access Journals (Sweden)

    Loc Nguyen

    2009-12-01

    Full Text Available The core of adaptive system is user model containing personal information such as knowledge, learning styles, goals… which is requisite for learning personalized process. There are many modeling approaches, for example: stereotype, overlay, plan recognition… but they don’t bring out the solid method for reasoning from user model. This paper introduces the statistical method that combines Bayesian network and overlay modeling so that it is able to infer user’s knowledge from evidences collected during user’s learning process.

  7. Networks model of the East Turkistan terrorism

    Science.gov (United States)

    Li, Ben-xian; Zhu, Jun-fang; Wang, Shun-guo

    2015-02-01

    The presence of the East Turkistan terrorist network in China can be traced back to the rebellions on the BAREN region in Xinjiang in April 1990. This article intends to research the East Turkistan networks in China and offer a panoramic view. The events, terrorists and their relationship are described using matrices. Then social network analysis is adopted to reveal the network type and the network structure characteristics. We also find the crucial terrorist leader. Ultimately, some results show that the East Turkistan network has big hub nodes and small shortest path, and that the network follows a pattern of small world network with hierarchical structure.

  8. Pruning Boltzmann networks and hidden Markov models

    DEFF Research Database (Denmark)

    Pedersen, Morten With; Stork, D.

    1996-01-01

    We present sensitivity-based pruning algorithms for general Boltzmann networks. Central to our methods is the efficient calculation of a second-order approximation to the true weight saliencies in a cross-entropy error. Building upon previous work which shows a formal correspondence between linear...... Boltzmann chains and hidden Markov models (HMMs), we argue that our method can be applied to HMMs as well. We illustrate pruning on Boltzmann zippers, which are equivalent to two HMMs with cross-connection links. We verify that our second-order approximation preserves the rank ordering of weight saliencies...

  9. Compartmentalization analysis using discrete fracture network models

    Energy Technology Data Exchange (ETDEWEB)

    La Pointe, P.R.; Eiben, T.; Dershowitz, W. [Golder Associates, Redmond, VA (United States); Wadleigh, E. [Marathon Oil Co., Midland, TX (United States)

    1997-08-01

    This paper illustrates how Discrete Fracture Network (DFN) technology can serve as a basis for the calculation of reservoir engineering parameters for the development of fractured reservoirs. It describes the development of quantitative techniques for defining the geometry and volume of structurally controlled compartments. These techniques are based on a combination of stochastic geometry, computational geometry, and graph the theory. The parameters addressed are compartment size, matrix block size and tributary drainage volume. The concept of DFN models is explained and methodologies to compute these parameters are demonstrated.

  10. Analysis and Comparison of Typical Models within Distribution Network Design

    DEFF Research Database (Denmark)

    Jørgensen, Hans Jacob; Larsen, Allan; Madsen, Oli B.G.

    This paper investigates the characteristics of typical optimisation models within Distribution Network Design. During the paper fourteen models known from the literature will be thoroughly analysed. Through this analysis a schematic approach to categorisation of distribution network design models...... for educational purposes. Furthermore, the paper can be seen as a practical introduction to network design modelling as well as a being an art manual or recipe when constructing such a model....

  11. Fundamentals of complex networks models, structures and dynamics

    CERN Document Server

    Chen, Guanrong; Li, Xiang

    2014-01-01

    Complex networks such as the Internet, WWW, transportationnetworks, power grids, biological neural networks, and scientificcooperation networks of all kinds provide challenges for futuretechnological development. In particular, advanced societies havebecome dependent on large infrastructural networks to an extentbeyond our capability to plan (modeling) and to operate (control).The recent spate of collapses in power grids and ongoing virusattacks on the Internet illustrate the need for knowledge aboutmodeling, analysis of behaviors, optimized planning and performancecontrol in such networks. F

  12. A Search Model with a Quasi-Network

    DEFF Research Database (Denmark)

    Ejarque, Joao Miguel

    This paper adds a quasi-network to a search model of the labor market. Fitting the model to an average unemployment rate and to other moments in the data implies the presence of the network is not noticeable in the basic properties of the unemployment and job finding rates. However, the network...

  13. Joint Modelling of Structural and Functional Brain Networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten

    -parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...

  14. Artificial Neural Network Modeling of an Inverse Fluidized Bed ...

    African Journals Online (AJOL)

    A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data ...

  15. Degree distribution of a new model for evolving networks

    Indian Academy of Sciences (India)

    on intuitive but realistic consideration that nodes are added to the network with both preferential and random attachments. The degree distribution of the model is between a power-law and an exponential decay. Motivated by the features of network evolution, we introduce a new model of evolving networks, incorporating the ...

  16. Neural Network Based Models for Fusion Applications

    Science.gov (United States)

    Meneghini, Orso; Tema Biwole, Arsene; Luda, Teobaldo; Zywicki, Bailey; Rea, Cristina; Smith, Sterling; Snyder, Phil; Belli, Emily; Staebler, Gary; Canty, Jeff

    2017-10-01

    Whole device modeling, engineering design, experimental planning and control applications demand models that are simultaneously physically accurate and fast. This poster reports on the ongoing effort towards the development and validation of a series of models that leverage neural-­network (NN) multidimensional regression techniques to accelerate some of the most mission critical first principle models for the fusion community, such as: the EPED workflow for prediction of the H-Mode and Super H-Mode pedestal structure the TGLF and NEO models for the prediction of the turbulent and neoclassical particle, energy and momentum fluxes; and the NEO model for the drift-kinetic solution of the bootstrap current. We also applied NNs on DIII-D experimental data for disruption prediction and quantifying the effect of RMPs on the pedestal and ELMs. All of these projects were supported by the infrastructure provided by the OMFIT integrated modeling framework. Work supported by US DOE under DE-SC0012656, DE-FG02-95ER54309, DE-FC02-04ER54698.

  17. A network of networks model to study phase synchronization using structural connection matrix of human brain

    Science.gov (United States)

    Ferrari, F. A. S.; Viana, R. L.; Reis, A. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.

    2018-04-01

    The cerebral cortex plays a key role in complex cortical functions. It can be divided into areas according to their function (motor, sensory and association areas). In this paper, the cerebral cortex is described as a network of networks (cortex network), we consider that each cortical area is composed of a network with small-world property (cortical network). The neurons are assumed to have bursting properties with the dynamics described by the Rulkov model. We study the phase synchronization of the cortex network and the cortical networks. In our simulations, we verify that synchronization in cortex network is not homogeneous. Besides, we focus on the suppression of neural phase synchronization. Synchronization can be related to undesired and pathological abnormal rhythms in the brain. For this reason, we consider the delayed feedback control to suppress the synchronization. We show that delayed feedback control is efficient to suppress synchronous behavior in our network model when an appropriate signal intensity and time delay are defined.

  18. Hybrid incompatibility arises in a sequence-based bioenergetic model of transcription factor binding.

    Science.gov (United States)

    Tulchinsky, Alexander Y; Johnson, Norman A; Watt, Ward B; Porter, Adam H

    2014-11-01

    Postzygotic isolation between incipient species results from the accumulation of incompatibilities that arise as a consequence of genetic divergence. When phenotypes are determined by regulatory interactions, hybrid incompatibility can evolve even as a consequence of parallel adaptation in parental populations because interacting genes can produce the same phenotype through incompatible allelic combinations. We explore the evolutionary conditions that promote and constrain hybrid incompatibility in regulatory networks using a bioenergetic model (combining thermodynamics and kinetics) of transcriptional regulation, considering the bioenergetic basis of molecular interactions between transcription factors (TFs) and their binding sites. The bioenergetic parameters consider the free energy of formation of the bond between the TF and its binding site and the availability of TFs in the intracellular environment. Together these determine fractional occupancy of the TF on the promoter site, the degree of subsequent gene expression and in diploids, and the degree of dominance among allelic interactions. This results in a sigmoid genotype-phenotype map and fitness landscape, with the details of the shape determining the degree of bioenergetic evolutionary constraint on hybrid incompatibility. Using individual-based simulations, we subjected two allopatric populations to parallel directional or stabilizing selection. Misregulation of hybrid gene expression occurred under either type of selection, although it evolved faster under directional selection. Under directional selection, the extent of hybrid incompatibility increased with the slope of the genotype-phenotype map near the derived parental expression level. Under stabilizing selection, hybrid incompatibility arose from compensatory mutations and was greater when the bioenergetic properties of the interaction caused the space of nearly neutral genotypes around the stable expression level to be wide. F2's showed higher

  19. Simple molecular model for the binding of antibiotic molecules to bacterial ion channels

    Science.gov (United States)

    Mafé, Salvador; Ramírez, Patricio; Alcaraz, Antonio

    2003-10-01

    A molecular model aimed at explaining recent experimental data by Nestorovich et al. [Proc. Natl. Acad. Sci. USA 99, 9789 (2002)] on the interaction of ampicillin molecules with the constriction zone in a channel of the general bacterial porin, OmpF (outer membrane protein F), is presented. The model extends T. L. Hill's theory for intermolecular interactions in a pair of binding sites [J. Am. Chem. Soc. 78, 3330 (1956)] by incorporating two binding ions and two pairs of interacting sites. The results provide new physical insights on the role of the complementary pattern of the charge distributions in the ampicillin molecule and the narrowest part of the channel pore. Charge matching of interacting sites facilitates drug binding. The dependence of the number of ampicillin binding events per second with the solution pH and salt concentration is explained qualitatively using a reduced number of fundamental concepts.

  20. QSAR modelling using combined simple competitive learning networks and RBF neural networks.

    Science.gov (United States)

    Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

    2018-04-01

    The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

  1. Binding modes of dihydroquinoxalinones in a homology model of bradykinin receptor 1.

    Science.gov (United States)

    Ha, Sookhee N; Hey, Pat J; Ransom, Rick W; Harrell, C Meacham; Murphy, Kathryn L; Chang, Ray; Chen, Tsing-Bau; Su, Dai-Shi; Markowitz, M Kristine; Bock, Mark G; Freidinger, Roger M; Hess, Fred J

    2005-05-27

    We report the first homology model of human bradykinin receptor B1 generated from the crystal structure of bovine rhodopsin as a template. Using an automated docking procedure, two B1 receptor antagonists of the dihydroquinoxalinone structural class were docked into the receptor model. Site-directed mutagenesis data of the amino acid residues in TM1, TM3, TM6, and TM7 were incorporated to place the compounds in the binding site of the homology model of the human B1 bradykinin receptor. The best pose in agreement with the mutation data was selected for detailed study of the receptor-antagonist interaction. To test the model, the calculated antagonist-receptor binding energy was correlated with the experimentally measured binding affinity (K(i)) for nine dihydroquinoxalinone analogs. The model was used to gain insight into the molecular mechanism for receptor function and to optimize the dihydroquinoxalinone analogs.

  2. Linear approximation model network and its formation via ...

    Indian Academy of Sciences (India)

    niques, an alternative `linear approximation model' (LAM) network approach is .... network is LPV, existing LTI theory is difficult to apply (Kailath 1980). ..... Beck J V, Arnold K J 1977 Parameter estimation in engineering and science (New York: ...

  3. Equity venture capital platform model based on complex network

    Science.gov (United States)

    Guo, Dongwei; Zhang, Lanshu; Liu, Miao

    2018-05-01

    This paper uses the small-world network and the random-network to simulate the relationship among the investors, construct the network model of the equity venture capital platform to explore the impact of the fraud rate and the bankruptcy rate on the robustness of the network model while observing the impact of the average path length and the average agglomeration coefficient of the investor relationship network on the income of the network model. The study found that the fraud rate and bankruptcy rate exceeded a certain threshold will lead to network collapse; The bankruptcy rate has a great influence on the income of the platform; The risk premium exists, and the average return is better under a certain range of bankruptcy risk; The structure of the investor relationship network has no effect on the income of the investment model.

  4. The transcriptional regulatory network mediated by banana (Musa acuminata) dehydration-responsive element binding (MaDREB) transcription factors in fruit ripening.

    Science.gov (United States)

    Kuang, Jian-Fei; Chen, Jian-Ye; Liu, Xun-Cheng; Han, Yan-Chao; Xiao, Yun-Yi; Shan, Wei; Tang, Yang; Wu, Ke-Qiang; He, Jun-Xian; Lu, Wang-Jin

    2017-04-01

    Fruit ripening is a complex, genetically programmed process involving the action of critical transcription factors (TFs). Despite the established significance of dehydration-responsive element binding (DREB) TFs in plant abiotic stress responses, the involvement of DREBs in fruit ripening is yet to be determined. Here, we identified four genes encoding ripening-regulated DREB TFs in banana (Musa acuminata), MaDREB1, MaDREB2, MaDREB3, and MaDREB4, and demonstrated that they play regulatory roles in fruit ripening. We showed that MaDREB1-MaDREB4 are nucleus-localized, induced by ethylene and encompass transcriptional activation activities. We performed a genome-wide chromatin immunoprecipitation and high-throughput sequencing (ChIP-Seq) experiment for MaDREB2 and identified 697 genomic regions as potential targets of MaDREB2. MaDREB2 binds to hundreds of loci with diverse functions and its binding sites are distributed in the promoter regions proximal to the transcriptional start site (TSS). Most of the MaDREB2-binding targets contain the conserved (A/G)CC(G/C)AC motif and MaDREB2 appears to directly regulate the expression of a number of genes involved in fruit ripening. In combination with transcriptome profiling (RNA sequencing) data, our results indicate that MaDREB2 may serve as both transcriptional activator and repressor during banana fruit ripening. In conclusion, our study suggests a hierarchical regulatory model of fruit ripening in banana and that the MaDREB TFs may act as transcriptional regulators in the regulatory network. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

  5. Feature network models for proximity data : statistical inference, model selection, network representations and links with related models

    NARCIS (Netherlands)

    Frank, Laurence Emmanuelle

    2006-01-01

    Feature Network Models (FNM) are graphical structures that represent proximity data in a discrete space with the use of features. A statistical inference theory is introduced, based on the additivity properties of networks and the linear regression framework. Considering features as predictor

  6. Related work on reference modeling for collaborative networks

    NARCIS (Netherlands)

    Afsarmanesh, H.; Camarinha-Matos, L.M.; Camarinha-Matos, L.M.; Afsarmanesh, H.

    2008-01-01

    Several international research and development initiatives have led to development of models for organizations and organization interactions. These models and their approaches constitute a background for development of reference models for collaborative networks. A brief survey of work on modeling

  7. A random spatial network model based on elementary postulates

    Science.gov (United States)

    Karlinger, Michael R.; Troutman, Brent M.

    1989-01-01

    A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

  8. PageRank model of opinion formation on Ulam networks

    Science.gov (United States)

    Chakhmakhchyan, L.; Shepelyansky, D.

    2013-12-01

    We consider a PageRank model of opinion formation on Ulam networks, generated by the intermittency map and the typical Chirikov map. The Ulam networks generated by these maps have certain similarities with such scale-free networks as the World Wide Web (WWW), showing an algebraic decay of the PageRank probability. We find that the opinion formation process on Ulam networks has certain similarities but also distinct features comparing to the WWW. We attribute these distinctions to internal differences in network structure of the Ulam and WWW networks. We also analyze the process of opinion formation in the frame of generalized Sznajd model which protects opinion of small communities.

  9. An Improved Car-Following Model in Vehicle Networking Based on Network Control

    Directory of Open Access Journals (Sweden)

    D. Y. Kong

    2014-01-01

    Full Text Available Vehicle networking is a system to realize information interoperability between vehicles and people, vehicles and roads, vehicles and vehicles, and cars and transport facilities, through the network information exchange, in order to achieve the effective monitoring of the vehicle and traffic flow. Realizing information interoperability between vehicles and vehicles, which can affect the traffic flow, is an important application of network control system (NCS. In this paper, a car-following model using vehicle networking theory is established, based on network control principle. The car-following model, which is an improvement of the traditional traffic model, describes the traffic in vehicle networking condition. The impact that vehicle networking has on the traffic flow is quantitatively assessed in a particular scene of one-way, no lane changing highway. The examples show that the capacity of the road is effectively enhanced by using vehicle networking.

  10. Genome-Wide Mapping of Collier In Vivo Binding Sites Highlights Its Hierarchical Position in Different Transcription Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Mathilde de Taffin

    Full Text Available Collier, the single Drosophila COE (Collier/EBF/Olf-1 transcription factor, is required in several developmental processes, including head patterning and specification of muscle and neuron identity during embryogenesis. To identify direct Collier (Col targets in different cell types, we used ChIP-seq to map Col binding sites throughout the genome, at mid-embryogenesis. In vivo Col binding peaks were associated to 415 potential direct target genes. Gene Ontology analysis revealed a strong enrichment in proteins with DNA binding and/or transcription-regulatory properties. Characterization of a selection of candidates, using transgenic CRM-reporter assays, identified direct Col targets in dorso-lateral somatic muscles and specific neuron types in the central nervous system. These data brought new evidence that Col direct control of the expression of the transcription regulators apterous and eyes-absent (eya is critical to specifying neuronal identities. They also showed that cross-regulation between col and eya in muscle progenitor cells is required for specification of muscle identity, revealing a new parallel between the myogenic regulatory networks operating in Drosophila and vertebrates. Col regulation of eya, both in specific muscle and neuronal lineages, may illustrate one mechanism behind the evolutionary diversification of Col biological roles.

  11. Genome-Wide Mapping of Collier In Vivo Binding Sites Highlights Its Hierarchical Position in Different Transcription Regulatory Networks

    Science.gov (United States)

    Dubois, Laurence; Bataillé, Laetitia; Painset, Anaïs; Le Gras, Stéphanie; Jost, Bernard; Crozatier, Michèle; Vincent, Alain

    2015-01-01

    Collier, the single Drosophila COE (Collier/EBF/Olf-1) transcription factor, is required in several developmental processes, including head patterning and specification of muscle and neuron identity during embryogenesis. To identify direct Collier (Col) targets in different cell types, we used ChIP-seq to map Col binding sites throughout the genome, at mid-embryogenesis. In vivo Col binding peaks were associated to 415 potential direct target genes. Gene Ontology analysis revealed a strong enrichment in proteins with DNA binding and/or transcription-regulatory properties. Characterization of a selection of candidates, using transgenic CRM-reporter assays, identified direct Col targets in dorso-lateral somatic muscles and specific neuron types in the central nervous system. These data brought new evidence that Col direct control of the expression of the transcription regulators apterous and eyes-absent (eya) is critical to specifying neuronal identities. They also showed that cross-regulation between col and eya in muscle progenitor cells is required for specification of muscle identity, revealing a new parallel between the myogenic regulatory networks operating in Drosophila and vertebrates. Col regulation of eya, both in specific muscle and neuronal lineages, may illustrate one mechanism behind the evolutionary diversification of Col biological roles. PMID:26204530

  12. Modeling management of research and education networks

    NARCIS (Netherlands)

    Galagan, D.V.

    2004-01-01

    Computer networks and their services have become an essential part of research and education. Nowadays every modern R&E institution must have a computer network and provide network services to its students and staff. In addition to its internal computer network, every R&E institution must have a

  13. Marketing communications model for innovation networks

    Directory of Open Access Journals (Sweden)

    Tiago João Freitas Correia

    2015-10-01

    Full Text Available Innovation is an increasingly relevant concept for the success of any organization, but it also represents a set of internal and external considerations, barriers and challenges to overcome. Along the concept of innovation, new paradigms emerge such as open innovation and co-creation that are simultaneously innovation modifiers and intensifiers in organizations, promoting organizational openness and stakeholder integration within the value creation process. Innovation networks composed by a multiplicity of agents in co-creative work perform as innovation mechanisms to face the increasingly complexity of products, services and markets. Technology, especially the Internet, is an enabler of all process among organizations supported by co-creative platforms for innovation. The definition of marketing communication strategies that promote motivation and involvement of all stakeholders in synergic creation and external promotion is the central aspect of this research. The implementation of the projects is performed by participative workshops with stakeholders from Madan Parque through IDEAS(REVOLUTION methodology and the operational model LinkUp parameterized for the project. The project is divided into the first part, the theoretical framework, and the second part where a model is developed for the marketing communication strategies that appeal to the Madan Parque case study. Keywords: Marketing Communication; Open Innovation, Technology; Innovation Networks; Incubator; Co-Creation.

  14. A graph model for opportunistic network coding

    KAUST Repository

    Sorour, Sameh

    2015-08-12

    © 2015 IEEE. Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in complexity. In this paper, we design a simple IDNC-like graph model for a specific subclass of ONC, by introducing a more generalized definition of its vertices and the notion of vertex aggregation in order to represent the storage of non-instantly-decodable packets in ONC. Based on this representation, we determine the set of pairwise vertex adjacency conditions that can populate this graph with edges so as to guarantee decodability or aggregation for the vertices of each clique in this graph. We then develop the algorithmic procedures that can be applied on the designed graph model to optimize any performance metric for this ONC subclass. A case study on reducing the completion time shows that the proposed framework improves on the performance of IDNC and gets very close to the optimal performance.

  15. Efficient Bayesian network modeling of systems

    International Nuclear Information System (INIS)

    Bensi, Michelle; Kiureghian, Armen Der; Straub, Daniel

    2013-01-01

    The Bayesian network (BN) is a convenient tool for probabilistic modeling of system performance, particularly when it is of interest to update the reliability of the system or its components in light of observed information. In this paper, BN structures for modeling the performance of systems that are defined in terms of their minimum link or cut sets are investigated. Standard BN structures that define the system node as a child of its constituent components or its minimum link/cut sets lead to converging structures, which are computationally disadvantageous and could severely hamper application of the BN to real systems. A systematic approach to defining an alternative formulation is developed that creates chain-like BN structures that are orders of magnitude more efficient, particularly in terms of computational memory demand. The formulation uses an integer optimization algorithm to identify the most efficient BN structure. Example applications demonstrate the proposed methodology and quantify the gained computational advantage

  16. Modeling stochasticity in biochemical reaction networks

    International Nuclear Information System (INIS)

    Constantino, P H; Vlysidis, M; Smadbeck, P; Kaznessis, Y N

    2016-01-01

    Small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. The chemical master equation (CME) is the most detailed mathematical model that can describe stochastic behaviors. However, because of its complexity the CME has been solved for only few, very small reaction networks. As a result, the contribution of CME-based approaches to biology has been very limited. In this review we discuss the approach of solving CME by a set of differential equations of probability moments, called moment equations. We present different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme. We also provide information on the stability analysis of stochastic systems. Finally, we speculate on the utility of CME-based modeling formalisms, especially in the context of synthetic biology efforts. (topical review)

  17. SUSTAIN: a network model of category learning.

    Science.gov (United States)

    Love, Bradley C; Medin, Douglas L; Gureckis, Todd M

    2004-04-01

    SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

  18. Binding free energy analysis of protein-protein docking model structures by evERdock.

    Science.gov (United States)

    Takemura, Kazuhiro; Matubayasi, Nobuyuki; Kitao, Akio

    2018-03-14

    To aid the evaluation of protein-protein complex model structures generated by protein docking prediction (decoys), we previously developed a method to calculate the binding free energies for complexes. The method combines a short (2 ns) all-atom molecular dynamics simulation with explicit solvent and solution theory in the energy representation (ER). We showed that this method successfully selected structures similar to the native complex structure (near-native decoys) as the lowest binding free energy structures. In our current work, we applied this method (evERdock) to 100 or 300 model structures of four protein-protein complexes. The crystal structures and the near-native decoys showed the lowest binding free energy of all the examined structures, indicating that evERdock can successfully evaluate decoys. Several decoys that show low interface root-mean-square distance but relatively high binding free energy were also identified. Analysis of the fraction of native contacts, hydrogen bonds, and salt bridges at the protein-protein interface indicated that these decoys were insufficiently optimized at the interface. After optimizing the interactions around the interface by including interfacial water molecules, the binding free energies of these decoys were improved. We also investigated the effect of solute entropy on binding free energy and found that consideration of the entropy term does not necessarily improve the evaluations of decoys using the normal model analysis for entropy calculation.

  19. A statistical model for investigating binding probabilities of DNA nucleotide sequences using microarrays.

    Science.gov (United States)

    Lee, Mei-Ling Ting; Bulyk, Martha L; Whitmore, G A; Church, George M

    2002-12-01

    There is considerable scientific interest in knowing the probability that a site-specific transcription factor will bind to a given DNA sequence. Microarray methods provide an effective means for assessing the binding affinities of a large number of DNA sequences as demonstrated by Bulyk et al. (2001, Proceedings of the National Academy of Sciences, USA 98, 7158-7163) in their study of the DNA-binding specificities of Zif268 zinc fingers using microarray technology. In a follow-up investigation, Bulyk, Johnson, and Church (2002, Nucleic Acid Research 30, 1255-1261) studied the interdependence of nucleotides on the binding affinities of transcription proteins. Our article is motivated by this pair of studies. We present a general statistical methodology for analyzing microarray intensity measurements reflecting DNA-protein interactions. The log probability of a protein binding to a DNA sequence on an array is modeled using a linear ANOVA model. This model is convenient because it employs familiar statistical concepts and procedures and also because it is effective for investigating the probability structure of the binding mechanism.

  20. Molecular modeling studies of novel retro-binding tripeptide active-site inhibitors of thrombin.

    Science.gov (United States)

    Lau, W F; Tabernero, L; Sack, J S; Iwanowicz, E J

    1995-08-01

    A novel series of retro-binding tripeptide thrombin active-site inhibitors was recently developed (Iwanowicz, E. I. et al. J. Med. Chem. 1994, 37, 2111(1)). It was hypothesized that the binding mode for these inhibitors is similar to that of the first three N-terminal residues of hirudin. This binding hypothesis was subsequently verified when the crystal structure of a member of this series, BMS-183,507 (N-[N-[N-[4-(Aminoiminomethyl)amino[-1-oxobutyl]-L- phenylalanyl]-L-allo-threonyl]-L-phenylalanine, methyl ester), was determined (Taberno, L.J. Mol. Biol. 1995, 246, 14). The methodology for developing the binding models of these inhibitors, the structure-activity relationships (SAR) and modeling studies that led to the elucidation of the proposed binding mode is described. The crystal structure of BMS-183,507/human alpha-thrombin is compared with the crystal structure of hirudin/human alpha-thrombin (Rydel, T.J. et al. Science 1990, 249,227; Rydel, T.J. et al. J. Mol Biol. 1991, 221, 583; Grutter, M.G. et al. EMBO J. 1990, 9, 2361) and with the computational binding model of BMS-183,507.

  1. Multiplicative Attribute Graph Model of Real-World Networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Myunghwan [Stanford Univ., CA (United States); Leskovec, Jure [Stanford Univ., CA (United States)

    2010-10-20

    Large scale real-world network data, such as social networks, Internet andWeb graphs, is ubiquitous in a variety of scientific domains. The study of such social and information networks commonly finds patterns and explain their emergence through tractable models. In most networks, especially in social networks, nodes also have a rich set of attributes (e.g., age, gender) associatedwith them. However, most of the existing network models focus only on modeling the network structure while ignoring the features of nodes in the network. Here we present a class of network models that we refer to as the Multiplicative Attribute Graphs (MAG), which naturally captures the interactions between the network structure and node attributes. We consider a model where each node has a vector of categorical features associated with it. The probability of an edge between a pair of nodes then depends on the product of individual attributeattribute similarities. The model yields itself to mathematical analysis as well as fit to real data. We derive thresholds for the connectivity, the emergence of the giant connected component, and show that the model gives rise to graphs with a constant diameter. Moreover, we analyze the degree distribution to show that the model can produce networks with either lognormal or power-law degree distribution depending on certain conditions.

  2. Multilevel method for modeling large-scale networks.

    Energy Technology Data Exchange (ETDEWEB)

    Safro, I. M. (Mathematics and Computer Science)

    2012-02-24

    Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

  3. A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

    Science.gov (United States)

    Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

    2018-02-01

    Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

  4. A fusion networking model for smart grid power distribution backbone communication network based on PTN

    Directory of Open Access Journals (Sweden)

    Wang Hao

    2016-01-01

    Full Text Available In current communication network for distribution in Chinese power grid systems, the fiber communication backbone network for distribution and TD-LTE power private wireless backhaul network of power grid are both bearing by the SDH optical transmission network, which also carries the communication network of transformer substation and main electric. As the data traffic of the distribution communication and TD-LTE power private wireless network grow rapidly in recent years, it will have a big impact with the SDH network’s bearing capacity which is mainly used for main electric communication in high security level. This paper presents a fusion networking model which use a multiple-layer PTN network as the unified bearing of the TD-LTE power private wireless backhaul network and fiber communication backbone network for distribution. Network dataflow analysis shows that this model can greatly reduce the capacity pressure of the traditional SDH network as well as ensure the reliability of the transmission of the communication network for distribution and TD-LTE power private wireless network.

  5. Road network safety evaluation using Bayesian hierarchical joint model.

    Science.gov (United States)

    Wang, Jie; Huang, Helai

    2016-05-01

    Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model. A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Models as Tools of Analysis of a Network Organisation

    Directory of Open Access Journals (Sweden)

    Wojciech Pająk

    2013-06-01

    Full Text Available The paper presents models which may be applied as tools of analysis of a network organisation. The starting point of the discussion is defining the following terms: supply chain and network organisation. Further parts of the paper present basic assumptions analysis of a network organisation. Then the study characterises the best known models utilised in analysis of a network organisation. The purpose of the article is to define the notion and the essence of network organizations and to present the models used for their analysis.

  7. Histone and RNA-binding protein interaction creates crosstalk network for regulation of alternative splicing.

    Science.gov (United States)

    Kim, Yong-Eun; Park, Chungoo; Kim, Kyoon Eon; Kim, Kee K

    2018-04-30

    Alternative splicing is an essential process in eukaryotes, as it increases the complexity of gene expression by generating multiple proteins from a single pre-mRNA. However, information on the regulatory mechanisms for alternative splicing is lacking, because splicing occurs over a short period via the transient interactions of proteins within functional complexes of the spliceosome. Here, we investigated in detail the molecular mechanisms connecting alternative splicing with epigenetic mechanisms. We identified interactions between histone proteins and splicing factors such as Rbfox2, Rbfox3, and splicing factor proline and glutamine rich protein (SFPQ) by in vivo crosslinking and immunoprecipitation. Furthermore, we confirmed that splicing factors were bound to specific modified residues of histone proteins. Additionally, changes in histone methylation due to histone methyltransferase inhibitor treatment notably affected alternative splicing in selected genes. Therefore, we suggested that there may be crosstalk mechanisms connecting histone modifications and RNA-binding proteins that increase the local concentration of RNA-binding proteins in alternative exon loci of nucleosomes by binding specific modified histone proteins, leading to alternative splicing. This crosstalk mechanism may play a major role in epigenetic processes such as histone modification and the regulation of alternative splicing. Copyright © 2018 Elsevier Inc. All rights reserved.

  8. Resolving structural variability in network models and the brain.

    Directory of Open Access Journals (Sweden)

    Florian Klimm

    2014-03-01

    Full Text Available 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, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful

  9. Women’s Social Networks and Birth Attendant Decisions: Application of the Network-Episode Model

    OpenAIRE

    Edmonds, Joyce K.; Hruschka, Daniel; Bernard, H. Russell; Sibley, Lynn

    2011-01-01

    This paper examines the association of women's social networks with the use of skilled birth attendants in uncomplicated pregnancy and childbirth in Matlab, Bangladesh. The Network-Episode Model was applied to determine if network structure variables (density / kinship homogeneity / strength of ties) together with network content (endorsement for or against a particular type of birth attendant) explain the type of birth attendant used by women above and beyond the variance explained by women'...

  10. A last updating evolution model for online social networks

    Science.gov (United States)

    Bu, Zhan; Xia, Zhengyou; Wang, Jiandong; Zhang, Chengcui

    2013-05-01

    As information technology has advanced, people are turning to electronic media more frequently for communication, and social relationships are increasingly found on online channels. However, there is very limited knowledge about the actual evolution of the online social networks. In this paper, we propose and study a novel evolution network model with the new concept of “last updating time”, which exists in many real-life online social networks. The last updating evolution network model can maintain the robustness of scale-free networks and can improve the network reliance against intentional attacks. What is more, we also found that it has the “small-world effect”, which is the inherent property of most social networks. Simulation experiment based on this model show that the results and the real-life data are consistent, which means that our model is valid.

  11. Sequence2Vec: A novel embedding approach for modeling transcription factor binding affinity landscape

    KAUST Repository

    Dai, Hanjun

    2017-07-26

    Motivation: An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. Results: Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model (HMM) which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these HMMs into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA data sets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods.

  12. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.

    Science.gov (United States)

    Tsai, Zing Tsung-Yeh; Shiu, Shin-Han; Tsai, Huai-Kuang

    2015-08-01

    Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

  13. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.

    Directory of Open Access Journals (Sweden)

    Zing Tsung-Yeh Tsai

    2015-08-01

    Full Text Available Transcription factor (TF binding is determined by the presence of specific sequence motifs (SM and chromatin accessibility, where the latter is influenced by both chromatin state (CS and DNA structure (DS properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

  14. Statistical-mechanical lattice models for protein-DNA binding in chromatin

    International Nuclear Information System (INIS)

    Teif, Vladimir B; Rippe, Karsten

    2010-01-01

    Statistical-mechanical lattice models for protein-DNA binding are well established as a method to describe complex ligand binding equilibria measured in vitro with purified DNA and protein components. Recently, a new field of applications has opened up for this approach since it has become possible to experimentally quantify genome-wide protein occupancies in relation to the DNA sequence. In particular, the organization of the eukaryotic genome by histone proteins into a nucleoprotein complex termed chromatin has been recognized as a key parameter that controls the access of transcription factors to the DNA sequence. New approaches have to be developed to derive statistical-mechanical lattice descriptions of chromatin-associated protein-DNA interactions. Here, we present the theoretical framework for lattice models of histone-DNA interactions in chromatin and investigate the (competitive) DNA binding of other chromosomal proteins and transcription factors. The results have a number of applications for quantitative models for the regulation of gene expression.

  15. Adaptive Networks Theory, Models and Applications

    CERN Document Server

    Gross, Thilo

    2009-01-01

    With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often fundamentally entangled. Recent research has shown that such networks can exhibit a plethora of new phenomena which are ultimately required to describe many real-world networks. Some of those phenomena include robust self-organization towards dynamical criticality, formation of complex global topologies based on simple, local rules, and the spontaneous division of "labor" in which an initially homogenous population of network nodes self-organizes into functionally distinct classes. These are just a few. This book is a state-of-the-art survey of those unique networks. In it, leading researchers set out to define the future scope and direction of some of the most advanced developments in the vast field of complex network science and its applications.

  16. A graph model for opportunistic network coding

    KAUST Repository

    Sorour, Sameh; Aboutoraby, Neda; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

    2015-01-01

    © 2015 IEEE. Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase

  17. Modeling and control of magnetorheological fluid dampers using neural networks

    Science.gov (United States)

    Wang, D. H.; Liao, W. H.

    2005-02-01

    Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.

  18. Structural equation models from paths to networks

    CERN Document Server

    Westland, J Christopher

    2015-01-01

    This compact reference surveys the full range of available structural equation modeling (SEM) methodologies.  It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable.  This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method.  This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow in importance in the near future.  SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists.  Latent variable theory and application are comprehensively explained, and methods are presented for extending their power, including guidelines for data preparation, sample size calculation, and the special treatment of Likert scale data.  Tables of software, methodologies and fit st...

  19. Network formation under heterogeneous costs: The multiple group model

    NARCIS (Netherlands)

    Kamphorst, J.J.A.; van der Laan, G.

    2007-01-01

    It is widely recognized that the shape of networks influences both individual and aggregate behavior. This raises the question which types of networks are likely to arise. In this paper we investigate a model of network formation, where players are divided into groups and the costs of a link between

  20. Neural networks in economic modelling : An empirical study

    NARCIS (Netherlands)

    Verkooijen, W.J.H.

    1996-01-01

    This dissertation addresses the statistical aspects of neural networks and their usability for solving problems in economics and finance. Neural networks are discussed in a framework of modelling which is generally accepted in econometrics. Within this framework a neural network is regarded as a

  1. Multiple Social Networks, Data Models and Measures for

    DEFF Research Database (Denmark)

    Magnani, Matteo; Rossi, Luca

    2017-01-01

    Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one...

  2. Asn792 participates in the hydrogen bond network around the K+-binding pocket of gastric H,K-ATPase.

    NARCIS (Netherlands)

    Swarts, H.G.P.; Koenderink, J.B.; Willems, P.H.G.M.; Krieger, E.; Pont, J.J.H.H.M. de

    2005-01-01

    Asn792 present in M5 of gastric H,K-ATPase is highly conserved within the P-type ATPase family. A direct role in K+ binding was postulated for Na,K-ATPase but was not found in a recent model for gastric H,K-ATPase (Koenderink, J. B., Swarts, H. G. P., Willems, P. H. G. M., Krieger, E., and De Pont,

  3. Agent Based Modeling on Organizational Dynamics of Terrorist Network

    OpenAIRE

    Bo Li; Duoyong Sun; Renqi Zhu; Ze Li

    2015-01-01

    Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model ...

  4. Learning Analytics for Networked Learning Models

    Science.gov (United States)

    Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

    2014-01-01

    Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

  5. Network model for fine coal dewatering. Part I. The model

    Energy Technology Data Exchange (ETDEWEB)

    Qamar, I.; Tierney, J.W.; Chiang, S.H.

    1985-08-01

    There is a body of well established research in filtration and related subjects, but much of it has been empirical - based on correlations from experimental data. This approach has the disadvantage that it lacks generality, and it is difficult to predict the behavior of new or different systems. A more general method for studying dewatering is needed-one which will include the microscopic characteristics of the filter cake, which, like other porous media, contains a complicated network of interconnected pores through which the fluid must flow. These pores play an important role in dewatering because they give rise to capillary forces when one fluid is displacing another. In this report, we describe a network model which we believe satisfies these requirements. In the main body of this report, the model is described in detail. Background information is given where appropriate, and a brief description is given of the experimental work being done in our laboratories to verify the model. A detailed description of the experimental procedures and results is given in other DOE reports. The computer programs which are needed to solve the model are described in detail in the Appendices and are accompanied by flow charts, sample problems, and sample outputs. Sufficient detail is given in order to use the model programs on other computer systems. 32 refs., 7 figs., 5 tabs.

  6. Ising and Potts models: binding disorder-and dimension effects

    International Nuclear Information System (INIS)

    Curado, E.M.F.

    1983-01-01

    Within the real space renormalization group framework, some thermal equilibrium properties of pure and disordered insulating systems are calculated. In the pure hypercubic lattice system, the Ising model surface tension and the correlation length of the q-state Potts model, which generalizes the former are analyzed. Several asymptotic behaviors are obtained (for the first time as far as we know) for both functions and the influence of dimension over them can be observed. Accurate numerical proposals for the surface tension are made in several dimensions, and the effect of the number of states (q) on the correlation lenght is shown. In disordered systems, attention is focused essentiall on those which can be theoretically represented by pure sistem Hamiltonians where probability distributions are assumed for the coupling constants (disorder in the bonds). It is obtained with high precision several approximate critical surfaces for the quenched square-lattice Ising model, whose probability distribution can assume two positive values (hence there is no frustration). These aproximate surfaces contain all the exact known points. In the cases where the coupling constant probability distribution can also assume negative values (allowing disordered and frustrated systems), a theoretical treatment which distinguishes the frustration effect from the dilution one is proposed. This distinction can be seen by the different ways in which the bonds of any series-parallel topological array combine. (Author) [pt

  7. Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

    Directory of Open Access Journals (Sweden)

    Lan Liu

    2017-01-01

    Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when qmodel is effective, and the results may help to decide the SDN control strategy to defend against network malware and provide a theoretical basis to reduce and prevent network security incidents.

  8. Stochastic actor-oriented models for network change

    NARCIS (Netherlands)

    Snijders, T.A.B.

    1996-01-01

    A class of models is proposed for longitudinal network data. These models are along the lines of methodological individualism: actors use heuristics to try to achieve their individual goals, subject to constraints. The current network structure is among these constraints. The models are continuous

  9. Modeling the reemergence of information diffusion in social network

    Science.gov (United States)

    Yang, Dingda; Liao, Xiangwen; Shen, Huawei; Cheng, Xueqi; Chen, Guolong

    2018-01-01

    Information diffusion in networks is an important research topic in various fields. Existing studies either focus on modeling the process of information diffusion, e.g., independent cascade model and linear threshold model, or investigate information diffusion in networks with certain structural characteristics such as scale-free networks and small world networks. However, there are still several phenomena that have not been captured by existing information diffusion models. One of the prominent phenomena is the reemergence of information diffusion, i.e., a piece of information reemerges after the completion of its initial diffusion process. In this paper, we propose an optimized information diffusion model by introducing a new informed state into traditional susceptible-infected-removed model. We verify the proposed model via simulations in real-world social networks, and the results indicate that the model can reproduce the reemergence of information during the diffusion process.

  10. Sequential memory: Binding dynamics

    Science.gov (United States)

    Afraimovich, Valentin; Gong, Xue; Rabinovich, Mikhail

    2015-10-01

    Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories—episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L - 1, where L is the number of modalities.

  11. An Improved Walk Model for Train Movement on Railway Network

    International Nuclear Information System (INIS)

    Li Keping; Mao Bohua; Gao Ziyou

    2009-01-01

    In this paper, we propose an improved walk model for simulating the train movement on railway network. In the proposed method, walkers represent trains. The improved walk model is a kind of the network-based simulation analysis model. Using some management rules for walker movement, walker can dynamically determine its departure and arrival times at stations. In order to test the proposed method, we simulate the train movement on a part of railway network. The numerical simulation and analytical results demonstrate that the improved model is an effective tool for simulating the train movement on railway network. Moreover, it can well capture the characteristic behaviors of train scheduling in railway traffic. (general)

  12. Infinite Multiple Membership Relational Modeling for Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Schmidt, Mikkel Nørgaard; Hansen, Lars Kai

    Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiplemembership latent feature model for networks. Contrary to existing...... multiplemembership models that scale quadratically in the number of vertices the proposedmodel scales linearly in the number of links admittingmultiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show...

  13. Discriminating binding mechanisms of an intrinsically disordered protein via a multi-state coarse-grained model

    International Nuclear Information System (INIS)

    Knott, Michael; Best, Robert B.

    2014-01-01

    Many proteins undergo a conformational transition upon binding to their cognate binding partner, with intrinsically disordered proteins (IDPs) providing an extreme example in which a folding transition occurs. However, it is often not clear whether this occurs via an “induced fit” or “conformational selection” mechanism, or via some intermediate scenario. In the first case, transient encounters with the binding partner favour transitions to the bound structure before the two proteins dissociate, while in the second the bound structure must be selected from a subset of unbound structures which are in the correct state for binding, because transient encounters of the incorrect conformation with the binding partner are most likely to result in dissociation. A particularly interesting situation involves those intrinsically disordered proteins which can bind to different binding partners in different conformations. We have devised a multi-state coarse-grained simulation model which is able to capture the binding of IDPs in alternate conformations, and by applying it to the binding of nuclear coactivator binding domain (NCBD) to either ACTR or IRF-3 we are able to determine the binding mechanism. By all measures, the binding of NCBD to either binding partner appears to occur via an induced fit mechanism. Nonetheless, we also show how a scenario closer to conformational selection could arise by choosing an alternative non-binding structure for NCBD

  14. Discriminating binding mechanisms of an intrinsically disordered protein via a multi-state coarse-grained model

    Energy Technology Data Exchange (ETDEWEB)

    Knott, Michael [Department of Chemistry, Cambridge University, Lensfield Road, Cambridge CB2 1EW (United Kingdom); Best, Robert B., E-mail: robertbe@helix.nih.gov [Department of Chemistry, Cambridge University, Lensfield Road, Cambridge CB2 1EW (United Kingdom); Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-0520 (United States)

    2014-05-07

    Many proteins undergo a conformational transition upon binding to their cognate binding partner, with intrinsically disordered proteins (IDPs) providing an extreme example in which a folding transition occurs. However, it is often not clear whether this occurs via an “induced fit” or “conformational selection” mechanism, or via some intermediate scenario. In the first case, transient encounters with the binding partner favour transitions to the bound structure before the two proteins dissociate, while in the second the bound structure must be selected from a subset of unbound structures which are in the correct state for binding, because transient encounters of the incorrect conformation with the binding partner are most likely to result in dissociation. A particularly interesting situation involves those intrinsically disordered proteins which can bind to different binding partners in different conformations. We have devised a multi-state coarse-grained simulation model which is able to capture the binding of IDPs in alternate conformations, and by applying it to the binding of nuclear coactivator binding domain (NCBD) to either ACTR or IRF-3 we are able to determine the binding mechanism. By all measures, the binding of NCBD to either binding partner appears to occur via an induced fit mechanism. Nonetheless, we also show how a scenario closer to conformational selection could arise by choosing an alternative non-binding structure for NCBD.

  15. Stabilization of model-based networked control systems

    Energy Technology Data Exchange (ETDEWEB)

    Miranda, Francisco [CIDMA, Universidade de Aveiro, Aveiro (Portugal); Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); Abreu, Carlos [Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); CMEMS-UMINHO, Universidade do Minho, Braga (Portugal); Mendes, Paulo M. [CMEMS-UMINHO, Universidade do Minho, Braga (Portugal)

    2016-06-08

    A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.

  16. A Unified Model of the GABA(A) Receptor Comprising Agonist and Benzodiazepine Binding Sites

    DEFF Research Database (Denmark)

    Kongsbak, Kristine Grønning; Bergmann, Rikke; Sørensen, Pernille Louise

    2013-01-01

    We present a full-length a1b2c2 GABA receptor model optimized for agonists and benzodiazepine (BZD) allosteric modulators. We propose binding hypotheses for the agonists GABA, muscimol and THIP and for the allosteric modulator diazepam (DZP). The receptor model is primarily based on the glutamate......-gated chloride channel (GluCl) from C. elegans and includes additional structural information from the prokaryotic ligand-gated ion channel ELIC in a few regions. Available mutational data of the binding sites are well explained by the model and the proposed ligand binding poses. We suggest a GABA binding mode...... of the agonists in the orthosteric site. The carbonyl group of DZP is predicted to interact with two threonines a1T206 and c2T142, similar to the acidic moiety of GABA. The chlorine atom of DZP is placed near the important a1H101 and the N-methyl group near a1Y159, a1T206, and a1Y209. We present a binding mode...

  17. Memory binding in clinical and non-clinical psychotic experiences: how does the continuum model fare?

    Science.gov (United States)

    Chhabra, S; Badcock, J C; Maybery, M T

    2013-07-01

    Both clinical and non-clinical auditory hallucinations (AH) have been associated with source memory deficits, supporting a continuum of underlying cognitive mechanisms, though few studies have employed the same task in patient and nonpatient samples. Recent commentators have called for more debate on the continuum model of psychosis. Consequently, the current study investigated the continuity model of AH with reference to memory binding. We used an identical voice and word recognition memory task to assess binding in two separate studies of: (1) healthy hallucination-prone individuals and controls (30 high and 30 low scorers on the Launay-Slade Hallucination Scale-Revised) and (2) schizophrenia patient samples (32 with AH, 32 without AH) and 32 healthy controls. There was no evidence of impaired binding in high hallucination-prone, compared to low hallucination-prone individuals. In contrast, individuals with schizophrenia (both with and without AH) had difficulties binding (remembering "who said what"), alongside difficulties remembering individual words and voices. Binding ability and memory for voices were also negatively linked to the loudness of hallucinated voices reported by patients with AH. These findings suggest that different mechanisms may exist in clinical and non-clinical hallucinators, adding to the growing debate on the continuum model of psychotic symptoms.

  18. Mixture models with entropy regularization for community detection in networks

    Science.gov (United States)

    Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

    2018-04-01

    Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

  19. Conceptual and methodological biases in network models.

    Science.gov (United States)

    Lamm, Ehud

    2009-10-01

    Many natural and biological phenomena can be depicted as networks. Theoretical and empirical analyses of networks have become prevalent. I discuss theoretical biases involved in the delineation of biological networks. The network perspective is shown to dissolve the distinction between regulatory architecture and regulatory state, consistent with the theoretical impossibility of distinguishing a priori between "program" and "data." The evolutionary significance of the dynamics of trans-generational and interorganism regulatory networks is explored and implications are presented for understanding the evolution of the biological categories development-heredity, plasticity-evolvability, and epigenetic-genetic.

  20. Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

    Directory of Open Access Journals (Sweden)

    Mohammad Taghi Ameli

    2012-01-01

    Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.

  1. Switching performance of OBS network model under prefetched real traffic

    Science.gov (United States)

    Huang, Zhenhua; Xu, Du; Lei, Wen

    2005-11-01

    Optical Burst Switching (OBS) [1] is now widely considered as an efficient switching technique in building the next generation optical Internet .So it's very important to precisely evaluate the performance of the OBS network model. The performance of the OBS network model is variable in different condition, but the most important thing is that how it works under real traffic load. In the traditional simulation models, uniform traffics are usually generated by simulation software to imitate the data source of the edge node in the OBS network model, and through which the performance of the OBS network is evaluated. Unfortunately, without being simulated by real traffic, the traditional simulation models have several problems and their results are doubtable. To deal with this problem, we present a new simulation model for analysis and performance evaluation of the OBS network, which uses prefetched IP traffic to be data source of the OBS network model. The prefetched IP traffic can be considered as real IP source of the OBS edge node and the OBS network model has the same clock rate with a real OBS system. So it's easy to conclude that this model is closer to the real OBS system than the traditional ones. The simulation results also indicate that this model is more accurate to evaluate the performance of the OBS network system and the results of this model are closer to the actual situation.

  2. Modeling MAC layer for powerline communications networks

    Science.gov (United States)

    Hrasnica, Halid; Haidine, Abdelfatteh

    2001-02-01

    The usage of electrical power distribution networks for voice and data transmission, called Powerline Communications, becomes nowadays more and more attractive, particularly in the telecommunication access area. The most important reasons for that are the deregulation of the telecommunication market and a fact that the access networks are still property of former monopolistic companies. In this work, first we analyze a PLC network and system structure as well as a disturbance scenario in powerline networks. After that, we define a logical structure of the powerline MAC layer and propose the reservation MAC protocols for the usage in the PLC network which provides collision free data transmission. This makes possible better network utilization and realization of QoS guarantees which can make PLC networks competitive to other access technologies.

  3. Binding of carvedilol to serum albumins investigated by multi-spectroscopic and molecular modeling methods

    Energy Technology Data Exchange (ETDEWEB)

    Safarnejad, Azam; Shaghaghi, Masoomeh [Department of Chemistry, Payame Noor University, P.O. Box. 19395-3697, Tehran (Iran, Islamic Republic of); Dehghan, Golamreza [Department of Biology, Faculty of Natural Sciences, University of Tabriz, Tabriz (Iran, Islamic Republic of); Soltani, Somaieh, E-mail: soltanisomaieh@gmail.com [Drug applied research center and pharmacy faculty, Tabriz University of Medical Sciences, Tabriz (Iran, Islamic Republic of)

    2016-08-15

    Carvedilol (CAR) binding to human and bovine serum albumins (HSA and BSA) was studied using fluorescence, UV–vis absorption and Fourier transform infrared spectroscopy (FTIR) and molecular docking techniques at different temperatures (288, 298 and 308 K) under physiologic pH. Results obtained from fluorescence data indicated that values of binding sites (n), effective quenching constants (Ka) and binding constants (K{sub b}) decreased under higher temperature and that the quenching mechanism was static. The thermodynamic parameters including enthalpy (ΔH), entropy (ΔS) and Gibb's free energy (ΔG) changes were calculated by the van't Hoff equation and these data showed that hydrogen bonds and Van der Waals contacts were the main binding force in HSA–CAR and BSA–CAR systems. Binding distance (r) between HSA–CAR and BSA–CAR were calculated by the Förster (fluorescence resonance energy transfer (FRET)) method. FTIR absorption studies showed that the secondary structure was changed according to the interaction of HSA/BSA and CAR. Results determined by molecular docking were in agreement with thermodynamic and FRET data and confirmed that the binding mechanism of Carvedilol to HSA and BSA is different. - Highlights: • The quenching mechanism between Carvedilol and HSA /BSA is a static process. • Hydrogen bonds and Van der Waals contacts were stabilized the Carvedilol albumin complexes. • Molecular modeling simulations confirmed the fluorescence spectroscopy and FRET analysis. • According to the binding mechanism differences between HSA and BSA, the results of BSA experiments could not be applied for HSA binding.

  4. Modeling of flux, binding and substitution of urea molecules in the urea transporter dvUT.

    Science.gov (United States)

    Zhang, Hai-Tian; Wang, Zhe; Yu, Tao; Sang, Jian-Ping; Zou, Xian-Wu; Zou, Xiaoqin

    2017-09-01

    Urea transporters (UTs) are transmembrane proteins that transport urea molecules across cell membranes and play a crucial role in urea excretion and water balance. Modeling the functional characteristics of UTs helps us understand how their structures accomplish the functions at the atomic level, and facilitates future therapeutic design targeting the UTs. This study was based on the crystal structure of Desulfovibrio vulgaris urea transporter (dvUT). To model the binding behavior of urea molecules in dvUT, we constructed a cooperative binding model. To model the substitution of urea by the urea analogue N,N'-dimethylurea (DMU) in dvUT, we calculated the occupation probability of DMU along the urea pore and the ratio of the occupation probabilities of DMU at the external (S ext ) and internal (S int ) binding sites, and we established the mutual substitution rule for binding and substitution of urea and DMU. Based on these calculations and modelings, together with the use of the Monte Carlo (MC) method, we further modeled the urea flux in dvUT, equilibrium urea binding to dvUT, and the substitution of urea by DMU in the dvUT. Our modeling results are in good agreement with the existing experimental functional data. Furthermore, the modelings have discovered the microscopic process and mechanisms of those functional characteristics. The methods and the results would help our future understanding of the underlying mechanisms of the diseases associated with impaired UT functions and rational drug design for the treatment of these diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. A network-flow based valve-switching aware binding algorithm for flow-based microfluidic biochips

    DEFF Research Database (Denmark)

    Tseng, Kai-Han; You, Sheng-Chi; Minhass, Wajid Hassan

    2013-01-01

    -flow based resource binding algorithm based on breadth-first search (BFS) and minimum cost maximum flow (MCMF) in architectural-level synthesis. The experimental results show that our methodology not only makes significant reduction of valve-switching activities but also diminishes the application completion......Designs of flow-based microfluidic biochips are receiving much attention recently because they replace conventional biological automation paradigm and are able to integrate different biochemical analysis functions on a chip. However, as the design complexity increases, a flow-based microfluidic...... biochip needs more chip-integrated micro-valves, i.e., the basic unit of fluid-handling functionality, to manipulate the fluid flow for biochemical applications. Moreover, frequent switching of micro-valves results in decreased reliability. To minimize the valve-switching activities, we develop a network...

  6. Ripple-Spreading Network Model Optimization by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xiao-Bing Hu

    2013-01-01

    Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.

  7. Grain-Boundary Resistance in Copper Interconnects: From an Atomistic Model to a Neural Network

    Science.gov (United States)

    Valencia, Daniel; Wilson, Evan; Jiang, Zhengping; Valencia-Zapata, Gustavo A.; Wang, Kuang-Chung; Klimeck, Gerhard; Povolotskyi, Michael

    2018-04-01

    Orientation effects on the specific resistance of copper grain boundaries are studied systematically with two different atomistic tight-binding methods. A methodology is developed to model the specific resistance of grain boundaries in the ballistic limit using the embedded atom model, tight- binding methods, and nonequilibrium Green's functions. The methodology is validated against first-principles calculations for thin films with a single coincident grain boundary, with 6.4% deviation in the specific resistance. A statistical ensemble of 600 large, random structures with grains is studied. For structures with three grains, it is found that the distribution of specific resistances is close to normal. Finally, a compact model for grain-boundary-specific resistance is constructed based on a neural network.

  8. DRD2 genotype-based variation of default mode network activity and of its relationship with striatal DAT binding.

    Science.gov (United States)

    Sambataro, Fabio; Fazio, Leonardo; Taurisano, Paolo; Gelao, Barbara; Porcelli, Annamaria; Mancini, Marina; Sinibaldi, Lorenzo; Ursini, Gianluca; Masellis, Rita; Caforio, Grazia; Di Giorgio, Annabella; Niccoli-Asabella, Artor; Popolizio, Teresa; Blasi, Giuseppe; Bertolino, Alessandro

    2013-01-01

    The default mode network (DMN) comprises a set of brain regions with "increased" activity during rest relative to cognitive processing. Activity in the DMN is associated with functional connections with the striatum and dopamine (DA) levels in this brain region. A functional single-nucleotide polymorphism within the dopamine D2 receptor gene (DRD2, rs1076560 G > T) shifts splicing of the 2 D2 isoforms, D2 short and D2 long, and has been associated with striatal DA signaling as well as with cognitive processing. However, the effects of this polymorphism on DMN have not been explored. The aim of this study was to evaluate the effects of rs1076560 on DMN and striatal connectivity and on their relationship with striatal DA signaling. Twenty-eight subjects genotyped for rs1076560 underwent functional magnetic resonance imaging during a working memory task and 123 55 I-Fluoropropyl-2-beta-carbomethoxy-3-beta(4-iodophenyl) nortropan Single Photon Emission Computed Tomography ([(123)I]-FP-CIT SPECT) imaging (a measure of dopamine transporter [DAT] binding). Spatial group-independent component (IC) analysis was used to identify DMN and striatal ICs. Within the anterior DMN IC, GG subjects had relatively greater connectivity in medial prefrontal cortex (MPFC), which was directly correlated with striatal DAT binding. Within the posterior DMN IC, GG subjects had reduced connectivity in posterior cingulate relative to T carriers. Additionally, rs1076560 genotype predicted connectivity differences within a striatal network, and these changes were correlated with connectivity in MPFC and posterior cingulate within the DMN. These results suggest that genetically determined D2 receptor signaling is associated with DMN connectivity and that these changes are correlated with striatal function and presynaptic DA signaling.

  9. The protein network surrounding the human telomere repeat binding factors TRF1, TRF2, and POT1.

    Directory of Open Access Journals (Sweden)

    Richard J Giannone

    2010-08-01

    Full Text Available Telomere integrity (including telomere length and capping is critical in overall genomic stability. Telomere repeat binding factors and their associated proteins play vital roles in telomere length regulation and end protection. In this study, we explore the protein network surrounding telomere repeat binding factors, TRF1, TRF2, and POT1 using dual-tag affinity purification in combination with multidimensional protein identification technology liquid chromatography--tandem mass spectrometry (MudPIT LC-MS/MS. After control subtraction and data filtering, we found that TRF2 and POT1 co-purified all six members of the telomere protein complex, while TRF1 identified five of six components at frequencies that lend evidence towards the currently accepted telomere architecture. Many of the known TRF1 or TRF2 interacting proteins were also identified. Moreover, putative associating partners identified for each of the three core components fell into functional categories such as DNA damage repair, ubiquitination, chromosome cohesion, chromatin modification/remodeling, DNA replication, cell cycle and transcription regulation, nucleotide metabolism, RNA processing, and nuclear transport. These putative protein-protein associations may participate in different biological processes at telomeres or, intriguingly, outside telomeres.

  10. Comparison of the kinetics of different Markov models for ligand binding under varying conditions

    International Nuclear Information System (INIS)

    Martini, Johannes W. R.; Habeck, Michael

    2015-01-01

    We recently derived a Markov model for macromolecular ligand binding dynamics from few physical assumptions and showed that its stationary distribution is the grand canonical ensemble [J. W. R. Martini, M. Habeck, and M. Schlather, J. Math. Chem. 52, 665 (2014)]. The transition probabilities of the proposed Markov process define a particular Glauber dynamics and have some similarity to the Metropolis-Hastings algorithm. Here, we illustrate that this model is the stochastic analog of (pseudo) rate equations and the corresponding system of differential equations. Moreover, it can be viewed as a limiting case of general stochastic simulations of chemical kinetics. Thus, the model links stochastic and deterministic approaches as well as kinetics and equilibrium described by the grand canonical ensemble. We demonstrate that the family of transition matrices of our model, parameterized by temperature and ligand activity, generates ligand binding kinetics that respond to changes in these parameters in a qualitatively similar way as experimentally observed kinetics. In contrast, neither the Metropolis-Hastings algorithm nor the Glauber heat bath reflects changes in the external conditions correctly. Both converge rapidly to the stationary distribution, which is advantageous when the major interest is in the equilibrium state, but fail to describe the kinetics of ligand binding realistically. To simulate cellular processes that involve the reversible stochastic binding of multiple factors, our pseudo rate equation model should therefore be preferred to the Metropolis-Hastings algorithm and the Glauber heat bath, if the stationary distribution is not of only interest

  11. Comparison of the kinetics of different Markov models for ligand binding under varying conditions

    Energy Technology Data Exchange (ETDEWEB)

    Martini, Johannes W. R., E-mail: jmartin2@gwdg.de [Max Planck Institute for Developmental Biology, Tübingen (Germany); Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen, Göttingen (Germany); Habeck, Michael, E-mail: mhabeck@gwdg.de [Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen, Göttingen (Germany); Max Planck Institute for Biophysical Chemistry, Göttingen (Germany)

    2015-03-07

    We recently derived a Markov model for macromolecular ligand binding dynamics from few physical assumptions and showed that its stationary distribution is the grand canonical ensemble [J. W. R. Martini, M. Habeck, and M. Schlather, J. Math. Chem. 52, 665 (2014)]. The transition probabilities of the proposed Markov process define a particular Glauber dynamics and have some similarity to the Metropolis-Hastings algorithm. Here, we illustrate that this model is the stochastic analog of (pseudo) rate equations and the corresponding system of differential equations. Moreover, it can be viewed as a limiting case of general stochastic simulations of chemical kinetics. Thus, the model links stochastic and deterministic approaches as well as kinetics and equilibrium described by the grand canonical ensemble. We demonstrate that the family of transition matrices of our model, parameterized by temperature and ligand activity, generates ligand binding kinetics that respond to changes in these parameters in a qualitatively similar way as experimentally observed kinetics. In contrast, neither the Metropolis-Hastings algorithm nor the Glauber heat bath reflects changes in the external conditions correctly. Both converge rapidly to the stationary distribution, which is advantageous when the major interest is in the equilibrium state, but fail to describe the kinetics of ligand binding realistically. To simulate cellular processes that involve the reversible stochastic binding of multiple factors, our pseudo rate equation model should therefore be preferred to the Metropolis-Hastings algorithm and the Glauber heat bath, if the stationary distribution is not of only interest.

  12. Modelling the impact of social network on energy savings

    International Nuclear Information System (INIS)

    Du, Feng; Zhang, Jiangfeng; Li, Hailong; Yan, Jinyue; Galloway, Stuart; Lo, Kwok L.

    2016-01-01

    Highlights: • Energy saving propagation along a social network is modelled. • This model consists of a time evolving weighted directed network. • Network weights and information decay are applied in savings calculation. - Abstract: It is noted that human behaviour changes can have a significant impact on energy consumption, however, qualitative study on such an impact is still very limited, and it is necessary to develop the corresponding mathematical models to describe how much energy savings can be achieved through human engagement. In this paper a mathematical model of human behavioural dynamic interactions on a social network is derived to calculate energy savings. This model consists of a weighted directed network with time evolving information on each node. Energy savings from the whole network is expressed as mathematical expectation from probability theory. This expected energy savings model includes both direct and indirect energy savings of individuals in the network. The savings model is obtained by network weights and modified by the decay of information. Expected energy savings are calculated for cases where individuals in the social network are treated as a single information source or multiple sources. This model is tested on a social network consisting of 40 people. The results show that the strength of relations between individuals is more important to information diffusion than the number of connections individuals have. The expected energy savings of optimally chosen node can be 25.32% more than randomly chosen nodes at the end of the second month for the case of single information source in the network, and 16.96% more than random nodes for the case of multiple information sources. This illustrates that the model presented in this paper can be used to determine which individuals will have the most influence on the social network, which in turn provides a useful guide to identify targeted customers in energy efficiency technology rollout

  13. PipY, a Member of the Conserved COG0325 Family of PLP-Binding Proteins, Expands the Cyanobacterial Nitrogen Regulatory Network

    Directory of Open Access Journals (Sweden)

    José I. Labella

    2017-07-01

    Full Text Available Synechococcus elongatus PCC 7942 is a paradigmatic model organism for nitrogen regulation in cyanobacteria. Expression of genes involved in nitrogen assimilation is positively regulated by the 2-oxoglutarate receptor and global transcriptional regulator NtcA. Maximal activation requires the subsequent binding of the co-activator PipX. PII, a protein found in all three domains of life as an integrator of signals of the nitrogen and carbon balance, binds to PipX to counteract NtcA activity at low 2-oxoglutarate levels. PII-PipX complexes can also bind to the transcriptional regulator PlmA, whose regulon remains unknown. Here we expand the nitrogen regulatory network to PipY, encoded by the bicistronic operon pipXY in S. elongatus. Work with PipY, the cyanobacterial member of the widespread family of COG0325 proteins, confirms the conserved roles in vitamin B6 and amino/keto acid homeostasis and reveals new PLP-related phenotypes, including sensitivity to antibiotics targeting essential PLP-holoenzymes or synthetic lethality with cysK. In addition, the related phenotypes of pipY and pipX mutants are consistent with genetic interactions in the contexts of survival to PLP-targeting antibiotics and transcriptional regulation. We also showed that PipY overexpression increased the length of S. elongatus cells. Taken together, our results support a universal regulatory role for COG0325 proteins, paving the way to a better understanding of these proteins and of their connections with other biological processes.

  14. Hybrid neural network bushing model for vehicle dynamics simulation

    International Nuclear Information System (INIS)

    Sohn, Jeong Hyun; Lee, Seung Kyu; Yoo, Wan Suk

    2008-01-01

    Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers

  15. Model for the growth of the world airline network

    Science.gov (United States)

    Verma, T.; Araújo, N. A. M.; Nagler, J.; Andrade, J. S.; Herrmann, H. J.

    2016-06-01

    We propose a probabilistic growth model for transport networks which employs a balance between popularity of nodes and the physical distance between nodes. By comparing the degree of each node in the model network and the World Airline Network (WAN), we observe that the difference between the two is minimized for α≈2. Interestingly, this is the value obtained for the node-node correlation function in the WAN. This suggests that our model explains quite well the growth of airline networks.

  16. Modelling the dependability in Network Function Virtualisation

    OpenAIRE

    Lin, Wenqi

    2017-01-01

    Network Function Virtualization has been brought up to allow the TSPs to have more possibilities and flexibilities to provision services with better load optimizing, energy utilizing and dynamic scaling. Network functions will be decoupled from the underlying dedicated hardware into software instances that run on commercial off-the-shelf servers. However, the development is still at an early stage and the dependability concerns raise by the virtualization of the network functions are touched ...

  17. Mode Choice Modeling Using Artificial Neural Networks

    OpenAIRE

    Edara, Praveen Kumar

    2003-01-01

    Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

  18. Bayesian Networks for Modeling Dredging Decisions

    Science.gov (United States)

    2011-10-01

    years, that algorithms have been developed to solve these problems efficiently. Most modern Bayesian network software uses junction tree (a.k.a. join... software was used to develop the network . This is by no means an exhaustive list of Bayesian network applications, but it is representative of recent...characteristic node (SCN), state- defining node ( SDN ), effect node (EFN), or value node. The five types of nodes can be described as follows: ERDC/EL TR-11

  19. A genetic algorithm for solving supply chain network design model

    Science.gov (United States)

    Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

    2013-09-01

    Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

  20. Runoff Modelling in Urban Storm Drainage by Neural Networks

    DEFF Research Database (Denmark)

    Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld

    1995-01-01

    A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....

  1. A model for the study of electrostatic binding between a pair of ...

    African Journals Online (AJOL)

    A model for the study of electrostatic binding between a pair of molecules at large distances. A Umar, G Hussin. Abstract. No Abstract. Nigerian Journal of Chemical Research Vol 5 2000: 1-9. Full Text: EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT.

  2. Three-dimensional model of a selective theophylline-binding RNA molecule

    Energy Technology Data Exchange (ETDEWEB)

    Tung, Chang-Shung; Oprea, T.I.; Hummer, G.; Garcia, A.E.

    1995-07-01

    We propose a three-dimensional (3D) model for an RNA molecule that selectively binds theophylline but not caffeine. This RNA, which was found using SELEX [Jenison, R.D., et al., Science (1994) 263:1425] is 10,000 times more specific for theophylline (Kd=320 nM) than for caffeine (Kd=3.5 mM), although the two ligands are identical except for a methyl group substituted at N7 (present only in caffeine). The binding affinity for ten xanthine-based ligands was used to derive a Comparative Molecular Field Analysis (CoMFA) model (R{sup 2} = 0.93 for 3 components, with cross-validated R{sup 2} of 0.73), using the SYBYL and GOLPE programs. A pharmacophoric map was generated to locate steric and electrostatic interactions between theophylline and the RNA binding site. This information was used to identify putative functional groups of the binding pocket and to generate distance constraints. Based on a model for the secondary structure (Jenison et al., idem), the 3D structure of this RNA was then generated using the following method: each helical region of the RNA molecule was treated as a rigid body; single-stranded loops with specific end-to-end distances were generated. The structures of RNA-xanthine complexes were studied using a modified Monte Carlo algorithm. The detailed structure of an RNA-ligand complex model, as well as possible explanations for the theophylline selectivity will be discussed.

  3. Model of community emergence in weighted social networks

    Science.gov (United States)

    Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

    2009-04-01

    Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

  4. A single charge in the actin binding domain of fascin can independently tune the linear and non-linear response of an actin bundle network.

    Science.gov (United States)

    Maier, M; Müller, K W; Heussinger, C; Köhler, S; Wall, W A; Bausch, A R; Lieleg, O

    2015-05-01

    Actin binding proteins (ABPs) not only set the structure of actin filament assemblies but also mediate the frequency-dependent viscoelastic moduli of cross-linked and bundled actin networks. Point mutations in the actin binding domain of those ABPs can tune the association and dissociation dynamics of the actin/ABP bond and thus modulate the network mechanics both in the linear and non-linear response regime. We here demonstrate how the exchange of a single charged amino acid in the actin binding domain of the ABP fascin triggers such a modulation of the network rheology. Whereas the overall structure of the bundle networks is conserved, the transition point from strain-hardening to strain-weakening sensitively depends on the cross-linker off-rate and the applied shear rate. Our experimental results are consistent both with numerical simulations of a cross-linked bundle network and a theoretical description of the bundle network mechanics which is based on non-affine bending deformations and force-dependent cross-link dynamics.

  5. A control model for district heating networks with storage

    NARCIS (Netherlands)

    Scholten, Tjeert; De Persis, Claudio; Tesi, Pietro

    2014-01-01

    In [1] pressure control of hydraulic networks is investigated. We extend this work to district heating systems with storage capabilities and derive a model taking the topology of the network into account. The goal for the derived model is that it should allow for control of the storage level and

  6. Travel Time Reliability for Urban Networks : Modelling and Empirics

    NARCIS (Netherlands)

    Zheng, F.; Liu, Xiaobo; van Zuylen, H.J.; Li, Jie; Lu, Chao

    2017-01-01

    The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the past decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is applied to field travel time data

  7. Deterministic ripple-spreading model for complex networks.

    Science.gov (United States)

    Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Hines, Evor L; Di Paolo, Ezequiel

    2011-04-01

    This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

  8. Mathematical modelling of complex contagion on clustered networks

    Science.gov (United States)

    O'sullivan, David J.; O'Keeffe, Gary; Fennell, Peter; Gleeson, James

    2015-09-01

    The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010), adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010), to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

  9. Mathematical modelling of complex contagion on clustered networks

    Directory of Open Access Journals (Sweden)

    David J. P. O'Sullivan

    2015-09-01

    Full Text Available The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010, adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the complex contagion effects of social reinforcement are important in such diffusion, in contrast to simple contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010, to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

  10. A small-world network model of facial emotion recognition.

    Science.gov (United States)

    Takehara, Takuma; Ochiai, Fumio; Suzuki, Naoto

    2016-01-01

    Various models have been proposed to increase understanding of the cognitive basis of facial emotions. Despite those efforts, interactions between facial emotions have received minimal attention. If collective behaviours relating to each facial emotion in the comprehensive cognitive system could be assumed, specific facial emotion relationship patterns might emerge. In this study, we demonstrate that the frameworks of complex networks can effectively capture those patterns. We generate 81 facial emotion images (6 prototypes and 75 morphs) and then ask participants to rate degrees of similarity in 3240 facial emotion pairs in a paired comparison task. A facial emotion network constructed on the basis of similarity clearly forms a small-world network, which features an extremely short average network distance and close connectivity. Further, even if two facial emotions have opposing valences, they are connected within only two steps. In addition, we show that intermediary morphs are crucial for maintaining full network integration, whereas prototypes are not at all important. These results suggest the existence of collective behaviours in the cognitive systems of facial emotions and also describe why people can efficiently recognize facial emotions in terms of information transmission and propagation. For comparison, we construct three simulated networks--one based on the categorical model, one based on the dimensional model, and one random network. The results reveal that small-world connectivity in facial emotion networks is apparently different from those networks, suggesting that a small-world network is the most suitable model for capturing the cognitive basis of facial emotions.

  11. A general evolving model for growing bipartite networks

    International Nuclear Information System (INIS)

    Tian, Lixin; He, Yinghuan; Liu, Haijun; Du, Ruijin

    2012-01-01

    In this Letter, we propose and study an inner evolving bipartite network model. Significantly, we prove that the degree distribution of two different kinds of nodes both obey power-law form with adjustable exponents. Furthermore, the joint degree distribution of any two nodes for bipartite networks model is calculated analytically by the mean-field method. The result displays that such bipartite networks are nearly uncorrelated networks, which is different from one-mode networks. Numerical simulations and empirical results are given to verify the theoretical results. -- Highlights: ► We proposed a general evolving bipartite network model which was based on priority connection, reconnection and breaking edges. ► We prove that the degree distribution of two different kinds of nodes both obey power-law form with adjustable exponents. ► The joint degree distribution of any two nodes for bipartite networks model is calculated analytically by the mean-field method. ► The result displays that such bipartite networks are nearly uncorrelated networks, which is different from one-mode networks.

  12. Modeling geomagnetic induced currents in Australian power networks

    Science.gov (United States)

    Marshall, R. A.; Kelly, A.; Van Der Walt, T.; Honecker, A.; Ong, C.; Mikkelsen, D.; Spierings, A.; Ivanovich, G.; Yoshikawa, A.

    2017-07-01

    Geomagnetic induced currents (GICs) have been considered an issue for high-latitude power networks for some decades. More recently, GICs have been observed and studied in power networks located in lower latitude regions. This paper presents the results of a model aimed at predicting and understanding the impact of geomagnetic storms on power networks in Australia, with particular focus on the Queensland and Tasmanian networks. The model incorporates a "geoelectric field" determined using a plane wave magnetic field incident on a uniform conducting Earth, and the network model developed by Lehtinen and Pirjola (1985). Model results for two intense geomagnetic storms of solar cycle 24 are compared with transformer neutral monitors at three locations within the Queensland network and one location within the Tasmanian network. The model is then used to assess the impacts of the superintense geomagnetic storm of 29-31 October 2003 on the flow of GICs within these networks. The model results show good correlation with the observations with coefficients ranging from 0.73 to 0.96 across the observing sites. For Queensland, modeled GIC magnitudes during the superstorm of 29-31 October 2003 exceed 40 A with the larger GICs occurring in the south-east section of the network. Modeled GICs in Tasmania for the same storm do not exceed 30 A. The larger distance spans and general east-west alignment of the southern section of the Queensland network, in conjunction with some relatively low branch resistance values, result in larger modeled GICs despite Queensland being a lower latitude network than Tasmania.

  13. Systems and methods for modeling and analyzing networks

    Science.gov (United States)

    Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W

    2013-10-29

    The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

  14. Analysis of 3D models of octopus estrogen receptor with estradiol: evidence for steric clashes that prevent estrogen binding.

    Science.gov (United States)

    Baker, Michael E; Chandsawangbhuwana, Charlie

    2007-09-28

    Relatives of the vertebrate estrogen receptor (ER) are found in Aplysia californica, Octopus vulgaris, Thais clavigera, and Marisa cornuarietis. Unlike vertebrate ERs, invertebrate ERs are constitutively active and do not bind estradiol. To investigate the molecular basis of the absence of estrogen binding, we constructed a 3D model of the putative steroid-binding domain on octopus ER. Our 3D model indicates that binding of estradiol to octopus ER is prevented by steric clashes between estradiol and amino acids in the steroid-binding pocket. In this respect, octopus ER resembles vertebrate estrogen-related receptors (ERR), which have a ligand-binding pocket that cannot accommodate estradiol. Like ERR, octopus ER also may have the activation function 2 domain (AF2) in a configuration that can bind to coactivators in the absence of estrogens, which would explain constitutive activity of octopus ER.

  15. DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding.

    Science.gov (United States)

    Ma, Wenxiu; Yang, Lin; Rohs, Remo; Noble, William Stafford

    2017-10-01

    Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of adjacent base pairs, etc. Several methods have been developed to jointly account for DNA sequence and shape properties in predicting TF binding affinity. However, a limitation of these methods is that they typically require a training set of aligned TF binding sites. We describe a sequence + shape kernel that leverages DNA sequence and shape information to better understand protein-DNA binding preference and affinity. This kernel extends an existing class of k-mer based sequence kernels, based on the recently described di-mismatch kernel. Using three in vitro benchmark datasets, derived from universal protein binding microarrays (uPBMs), genomic context PBMs (gcPBMs) and SELEX-seq data, we demonstrate that incorporating DNA shape information improves our ability to predict protein-DNA binding affinity. In particular, we observe that (i) the k-spectrum + shape model performs better than the classical k-spectrum kernel, particularly for small k values; (ii) the di-mismatch kernel performs better than the k-mer kernel, for larger k; and (iii) the di-mismatch + shape kernel performs better than the di-mismatch kernel for intermediate k values. The software is available at https://bitbucket.org/wenxiu/sequence-shape.git. rohs@usc.edu or william-noble@uw.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

  16. Ocean wave prediction using numerical and neural network models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

  17. Dynamic thermo-hydraulic model of district cooling networks

    International Nuclear Information System (INIS)

    Oppelt, Thomas; Urbaneck, Thorsten; Gross, Ulrich; Platzer, Bernd

    2016-01-01

    Highlights: • A dynamic thermo-hydraulic model for district cooling networks is presented. • The thermal modelling is based on water segment tracking (Lagrangian approach). • Thus, numerical errors and balance inaccuracies are avoided. • Verification and validation studies proved the reliability of the model. - Abstract: In the present paper, the dynamic thermo-hydraulic model ISENA is presented which can be applied for answering different questions occurring in design and operation of district cooling networks—e.g. related to economic and energy efficiency. The network model consists of a quasistatic hydraulic model and a transient thermal model based on tracking water segments through the whole network (Lagrangian method). Applying this approach, numerical errors and balance inaccuracies can be avoided which leads to a higher quality of results compared to other network models. Verification and validation calculations are presented in order to show that ISENA provides reliable results and is suitable for practical application.

  18. Dynamic Pathloss Model for Future Mobile Communication Networks

    DEFF Research Database (Denmark)

    Kumar, Ambuj; Mihovska, Albena Dimitrova; Prasad, Ramjee

    2016-01-01

    that are essentially static. Therefore, once the signal level drops beyond the predicted values due to any variance in the environmental conditions, very crowded areas may not be catered well enough by the deployed network that had been designed with the static path loss model. This paper proposes an approach......— Future mobile communication networks (MCNs) are expected to be more intelligent and proactive based on new capabilities that increase agility and performance. However, for any successful mobile network service, the dexterity in network deployment is a key factor. The efficiency of the network...... planning depends on how congruent the chosen path loss model and real propagation are. Various path loss models have been developed that predict the signal propagation in various morphological and climatic environments; however they consider only those physical parameters of the network environment...

  19. Dynamic Pathloss Model for Place and Time Itinerant Networks

    DEFF Research Database (Denmark)

    Kumar, Ambuj; Mihovska, Albena; Prasad, Ramjee

    2018-01-01

    that are essentially static. Therefore, once the signal level drops beyond the predicted values due to any variance in the environmental conditions, very crowded areas may not be catered well enough by the deployed network that had been designed with the static path loss model. This paper proposes an approach......t Future mobile communication networks are expected to be more intelligent and proactive based on new capabilities that increase agility and performance. However, for any successful mobile network service, the dexterity in network deployment is a key factor. The efficiency of the network planning...... depends on how congruent the chosen path loss model and real propagation are. Various path loss models have been developed that predict the signal propagation in various morphological and climatic environments; however they consider only those physical parameters of the network environment...

  20. An information spreading model based on online social networks

    Science.gov (United States)

    Wang, Tao; He, Juanjuan; Wang, Xiaoxia

    2018-01-01

    Online social platforms are very popular in recent years. In addition to spreading information, users could review or collect information on online social platforms. According to the information spreading rules of online social network, a new information spreading model, namely IRCSS model, is proposed in this paper. It includes sharing mechanism, reviewing mechanism, collecting mechanism and stifling mechanism. Mean-field equations are derived to describe the dynamics of the IRCSS model. Moreover, the steady states of reviewers, collectors and stiflers and the effects of parameters on the peak values of reviewers, collectors and sharers are analyzed. Finally, numerical simulations are performed on different networks. Results show that collecting mechanism and reviewing mechanism, as well as the connectivity of the network, make information travel wider and faster, and compared to WS network and ER network, the speed of reviewing, sharing and collecting information is fastest on BA network.

  1. A Mathematical Model to Improve the Performance of Logistics Network

    Directory of Open Access Journals (Sweden)

    Muhammad Izman Herdiansyah

    2012-01-01

    Full Text Available The role of logistics nowadays is expanding from just providing transportation and warehousing to offering total integrated logistics. To remain competitive in the global market environment, business enterprises need to improve their logistics operations performance. The improvement will be achieved when we can provide a comprehensive analysis and optimize its network performances. In this paper, a mixed integer linier model for optimizing logistics network performance is developed. It provides a single-product multi-period multi-facilities model, as well as the multi-product concept. The problem is modeled in form of a network flow problem with the main objective to minimize total logistics cost. The problem can be solved using commercial linear programming package like CPLEX or LINDO. Even in small case, the solver in Excel may also be used to solve such model.Keywords: logistics network, integrated model, mathematical programming, network optimization

  2. A Network Contention Model for the Extreme-scale Simulator

    Energy Technology Data Exchange (ETDEWEB)

    Engelmann, Christian [ORNL; Naughton III, Thomas J [ORNL

    2015-01-01

    The Extreme-scale Simulator (xSim) is a performance investigation toolkit for high-performance computing (HPC) hardware/software co-design. It permits running a HPC application with millions of concurrent execution threads, while observing its performance in a simulated extreme-scale system. This paper details a newly developed network modeling feature for xSim, eliminating the shortcomings of the existing network modeling capabilities. The approach takes a different path for implementing network contention and bandwidth capacity modeling using a less synchronous and accurate enough model design. With the new network modeling feature, xSim is able to simulate on-chip and on-node networks with reasonable accuracy and overheads.

  3. Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

    Directory of Open Access Journals (Sweden)

    Ayman Hamdy Kassem

    2011-01-01

    Full Text Available This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.

  4. Model and simulation of Krause model in dynamic open network

    Science.gov (United States)

    Zhu, Meixia; Xie, Guangqiang

    2017-08-01

    The construction of the concept of evolution is an effective way to reveal the formation of group consensus. This study is based on the modeling paradigm of the HK model (Hegsekmann-Krause). This paper analyzes the evolution of multi - agent opinion in dynamic open networks with member mobility. The results of the simulation show that when the number of agents is constant, the interval distribution of the initial distribution will affect the number of the final view, The greater the distribution of opinions, the more the number of views formed eventually; The trust threshold has a decisive effect on the number of views, and there is a negative correlation between the trust threshold and the number of opinions clusters. The higher the connectivity of the initial activity group, the more easily the subjective opinion in the evolution of opinion to achieve rapid convergence. The more open the network is more conducive to the unity of view, increase and reduce the number of agents will not affect the consistency of the group effect, but not conducive to stability.

  5. New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.

    Science.gov (United States)

    Riera-Fernández, Pablo; Munteanu, Cristian R; Escobar, Manuel; Prado-Prado, Francisco; Martín-Romalde, Raquel; Pereira, David; Villalba, Karen; Duardo-Sánchez, Aliuska; González-Díaz, Humberto

    2012-01-21

    : Metabolic networks (72.3%), Parasite-Host networks (93.3%), CoCoMac brain cortex co-activation network (89.6%), NW Spain fasciolosis spreading network (97.2%), Spanish financial law network (89.9%) and World trade network for Intelligent & Active Food Packaging (92.8%). In order to seek these models, we studied an average of 55,388 pairs of nodes in each model and a total of 332,326 pairs of nodes in all models. Finally, this method was used to solve a more complicated problem. A model was developed to score the connectivity quality in the Drug-Target network of US FDA approved drugs. In this last model the θ(k) values were calculated for three types of molecular networks representing different levels of organization: drug molecular graphs (atom-atom bonds), protein residue networks (amino acid interactions), and drug-target network (compound-protein binding). The overall accuracy of this model was 76.3%. This work opens a new door to the computational reevaluation of network connectivity quality (collation) for complex systems in molecular, biomedical, technological, and legal-social sciences as well as in world trade and industry. Copyright © 2011 Elsevier Ltd. All rights reserved.

  6. Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models.

    Science.gov (United States)

    de Witte, Wilhelmus E A; Rottschäfer, Vivi; Danhof, Meindert; van der Graaf, Piet H; Peletier, Lambertus A; de Lange, Elizabeth C M

    2018-05-18

    Drug-target binding kinetics (as determined by association and dissociation rate constants, k on and k off ) can be an important determinant of the kinetics of drug action. However, the effect compartment model is used most frequently instead of a target binding model to describe hysteresis. Here we investigate when the drug-target binding model should be used in lieu of the effect compartment model. The utility of the effect compartment (EC), the target binding kinetics (TB) and the combined effect compartment-target binding kinetics (EC-TB) model were tested on either plasma (EC PL , TB PL and EC-TB PL ) or brain extracellular fluid (ECF) (EC ECF , TB ECF and EC-TB ECF ) morphine concentrations and EEG amplitude in rats. It was also analyzed when a significant shift in the time to maximal target occupancy (Tmax TO ) with increasing dose, the discriminating feature between the TB and EC model, occurs in the TB model. All TB models assumed a linear relationship between target occupancy and drug effect on the EEG amplitude. All three model types performed similarly in describing the morphine pharmacodynamics data, although the EC model provided the best statistical result. The analysis of the shift in Tmax TO (∆Tmax TO ) as a result of increasing dose revealed that ∆Tmax TO is decreasing towards zero if the k off is much smaller than the elimination rate constant or if the target concentration is larger than the initial morphine concentration. The results for the morphine PKPD modelling and the analysis of ∆Tmax TO indicate that the EC and TB models do not necessarily lead to different drug effect versus time curves for different doses if a delay between drug concentrations and drug effect (hysteresis) is described. Drawing mechanistic conclusions from successfully fitting one of these two models should therefore be avoided. Since the TB model can be informed by in vitro measurements of k on and k off , a target binding model should be considered more often

  7. A comprehensive multi-local-world model for complex networks

    International Nuclear Information System (INIS)

    Fan Zhengping; Chen Guanrong; Zhang Yunong

    2009-01-01

    The nodes in a community within a network are much more connected to each other than to the others outside the community in the same network. This phenomenon has been commonly observed from many real-world networks, ranging from social to biological even to technical networks. Meanwhile, the number of communities in some real-world networks, such as the Internet and most social networks, are evolving with time. To model this kind of networks, the present Letter proposes a multi-local-world (MLW) model to capture and describe their essential topological properties. Based on the mean-field theory, the degree distribution of this model is obtained analytically, showing that the generated network has a novel topological feature as being not completely random nor completely scale-free but behaving somewhere between them. As a typical application, the MLW model is applied to characterize the Internet against some other models such as the BA, GBA, Fitness and HOT models, demonstrating the superiority of the new model.

  8. Faradaic Impedance Spectroscopy for Detection of Small Molecules Binding using the Avidin-Biotin Model

    International Nuclear Information System (INIS)

    Yoetz-Kopelman, Tal; Ram, Yaron; Freeman, Amihay; Shacham-Diamand, Yosi

    2015-01-01

    The changes in the Faradaic impedance of gold/biomolecules system due to specific binding of small molecule to a significantly larger binding protein molecule were investigated. The biotin (244.31 Da) - avidin (66000 Da) couple was used as a model for small ligand - binding protein biorecognition. The study was carried out under open circuit potential in the presence of [Fe(CN) 6 ] −3/−4 redox couple. An equivalent electrical circuit was proposed and used for the interpretation of the recorded impedance spectra. Adsorption of thiolated avidin increased the electron transfer resistance, R ct , by a factor of about 7.5 while subsequent addition of biotin within the concentration range of 4.1-40.9 nM reduced the value of R ct by amount proportional to the biotin concentration. The addition of biotin did not affect, however, the equivalent double layer capacitance or other equivalent circuit parameters. A simple model based on effective surface coverage by the avidin molecules and the effect of the added biotin on electron transfer through the coated surface is proposed. A model for the minimum detection limit based on the random distribution of the binding protein and its dimensions is proposed

  9. Hydrometeorological network for flood monitoring and modeling

    Science.gov (United States)

    Efstratiadis, Andreas; Koussis, Antonis D.; Lykoudis, Spyros; Koukouvinos, Antonis; Christofides, Antonis; Karavokiros, George; Kappos, Nikos; Mamassis, Nikos; Koutsoyiannis, Demetris

    2013-08-01

    Due to its highly fragmented geomorphology, Greece comprises hundreds of small- to medium-size hydrological basins, in which often the terrain is fairly steep and the streamflow regime ephemeral. These are typically affected by flash floods, occasionally causing severe damages. Yet, the vast majority of them lack flow-gauging infrastructure providing systematic hydrometric data at fine time scales. This has obvious impacts on the quality and reliability of flood studies, which typically use simplistic approaches for ungauged basins that do not consider local peculiarities in sufficient detail. In order to provide a consistent framework for flood design and to ensure realistic predictions of the flood risk -a key issue of the 2007/60/EC Directive- it is essential to improve the monitoring infrastructures by taking advantage of modern technologies for remote control and data management. In this context and in the research project DEUCALION, we have recently installed and are operating, in four pilot river basins, a telemetry-based hydro-meteorological network that comprises automatic stations and is linked to and supported by relevant software. The hydrometric stations measure stage, using 50-kHz ultrasonic pulses or piezometric sensors, or both stage (piezometric) and velocity via acoustic Doppler radar; all measurements are being temperature-corrected. The meteorological stations record air temperature, pressure, relative humidity, wind speed and direction, and precipitation. Data transfer is made via GPRS or mobile telephony modems. The monitoring network is supported by a web-based application for storage, visualization and management of geographical and hydro-meteorological data (ENHYDRIS), a software tool for data analysis and processing (HYDROGNOMON), as well as an advanced model for flood simulation (HYDROGEIOS). The recorded hydro-meteorological observations are accessible over the Internet through the www-application. The system is operational and its

  10. Empirical tight-binding modeling of ordered and disordered semiconductor structures; Empirische Tight-Binding-Modellierung geordneter und ungeordneter Halbleiterstrukturen

    Energy Technology Data Exchange (ETDEWEB)

    Mourad, Daniel

    2010-11-30

    In this thesis, we investigate the electronic and optical properties of pure as well as of substitutionally alloyed II-VI and III-V bulk semiconductors and corresponding semiconductor quantum dots by means of an empirical tight-binding (TB) model. In the case of the alloyed systems of the type A{sub x}B{sub 1-x}, where A and B are the pure compound semiconductor materials, we study the influence of the disorder by means of several extensions of the TB model with different levels of sophistication. Our methods range from rather simple mean-field approaches (virtual crystal approximation, VCA) over a dynamical mean-field approach (coherent potential approximation, CPA) up to calculations where substitutional disorder is incorporated on a finite ensemble of microscopically distinct configurations. In the first part of this thesis, we cover the necessary fundamentals in order to properly introduce the TB model of our choice, the effective bond-orbital model (EBOM). In this model, one s- and three p-orbitals per spin direction are localized on the sites of the underlying Bravais lattice. The matrix elements between these orbitals are treated as free parameters in order to reproduce the properties of one conduction and three valence bands per spin direction and can then be used in supercell calculations in order to model mixed bulk materials or pure as well as mixed quantum dots. Part II of this thesis deals with unalloyed systems. Here, we use the EBOM in combination with configuration interaction calculations for the investigation of the electronic and optical properties of truncated pyramidal GaN quantum dots embedded in AlN with an underlying zincblende structure. Furthermore, we develop a parametrization of the EBOM for materials with a wurtzite structure, which allows for a fit of one conduction and three valence bands per spin direction throughout the whole Brillouin zone of the hexagonal system. In Part III, we focus on the influence of alloying on the electronic

  11. Hidden long evolutionary memory in a model biochemical network

    Science.gov (United States)

    Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

    2018-04-01

    We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

  12. Identification of biomolecule mass transport and binding rate parameters in living cells by inverse modeling

    Directory of Open Access Journals (Sweden)

    Shirmohammadi Adel

    2006-10-01

    Full Text Available Abstract Background Quantification of in-vivo biomolecule mass transport and reaction rate parameters from experimental data obtained by Fluorescence Recovery after Photobleaching (FRAP is becoming more important. Methods and results The Osborne-Moré extended version of the Levenberg-Marquardt optimization algorithm was coupled with the experimental data obtained by the Fluorescence Recovery after Photobleaching (FRAP protocol, and the numerical solution of a set of two partial differential equations governing macromolecule mass transport and reaction in living cells, to inversely estimate optimized values of the molecular diffusion coefficient and binding rate parameters of GFP-tagged glucocorticoid receptor. The results indicate that the FRAP protocol provides enough information to estimate one parameter uniquely using a nonlinear optimization technique. Coupling FRAP experimental data with the inverse modeling strategy, one can also uniquely estimate the individual values of the binding rate coefficients if the molecular diffusion coefficient is known. One can also simultaneously estimate the dissociation rate parameter and molecular diffusion coefficient given the pseudo-association rate parameter is known. However, the protocol provides insufficient information for unique simultaneous estimation of three parameters (diffusion coefficient and binding rate parameters owing to the high intercorrelation between the molecular diffusion coefficient and pseudo-association rate parameter. Attempts to estimate macromolecule mass transport and binding rate parameters simultaneously from FRAP data result in misleading conclusions regarding concentrations of free macromolecule and bound complex inside the cell, average binding time per vacant site, average time for diffusion of macromolecules from one site to the next, and slow or rapid mobility of biomolecules in cells. Conclusion To obtain unique values for molecular diffusion coefficient and

  13. Linear control theory for gene network modeling.

    Science.gov (United States)

    Shin, Yong-Jun; Bleris, Leonidas

    2010-09-16

    Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  14. Modeling Temporal Evolution and Multiscale Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2013-01-01

    Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change......-point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights...

  15. Growth of cortical neuronal network in vitro: Modeling and analysis

    International Nuclear Information System (INIS)

    Lai, P.-Y.; Jia, L. C.; Chan, C. K.

    2006-01-01

    We present a detailed analysis and theoretical growth models to account for recent experimental data on the growth of cortical neuronal networks in vitro [Phys. Rev. Lett. 93, 088101 (2004)]. The experimentally observed synchronized firing frequency of a well-connected neuronal network is shown to be proportional to the mean network connectivity. The growth of the network is consistent with the model of an early enhanced growth of connection, but followed by a retarded growth once the synchronized cluster is formed. Microscopic models with dominant excluded volume interactions are consistent with the observed exponential decay of the mean connection probability as a function of the mean network connectivity. The biological implications of the growth model are also discussed

  16. SPLAI: Computational Finite Element Model for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ruzana Ishak

    2006-01-01

    Full Text Available Wireless sensor network refers to a group of sensors, linked by a wireless medium to perform distributed sensing task. The primary interest is their capability in monitoring the physical environment through the deployment of numerous tiny, intelligent, wireless networked sensor nodes. Our interest consists of a sensor network, which includes a few specialized nodes called processing elements that can perform some limited computational capabilities. In this paper, we propose a model called SPLAI that allows the network to compute a finite element problem where the processing elements are modeled as the nodes in the linear triangular approximation problem. Our model also considers the case of some failures of the sensors. A simulation model to visualize this network has been developed using C++ on the Windows environment.

  17. Modeling the propagation of mobile malware on complex networks

    Science.gov (United States)

    Liu, Wanping; Liu, Chao; Yang, Zheng; Liu, Xiaoyang; Zhang, Yihao; Wei, Zuxue

    2016-08-01

    In this paper, the spreading behavior of malware across mobile devices is addressed. By introducing complex networks to model mobile networks, which follows the power-law degree distribution, a novel epidemic model for mobile malware propagation is proposed. The spreading threshold that guarantees the dynamics of the model is calculated. Theoretically, the asymptotic stability of the malware-free equilibrium is confirmed when the threshold is below the unity, and the global stability is further proved under some sufficient conditions. The influences of different model parameters as well as the network topology on malware propagation are also analyzed. Our theoretical studies and numerical simulations show that networks with higher heterogeneity conduce to the diffusion of malware, and complex networks with lower power-law exponents benefit malware spreading.

  18. Analysis and logical modeling of biological signaling transduction networks

    Science.gov (United States)

    Sun, Zhongyao

    The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science, linguistics, etc. It is the intrinsic similarities embedded in the entities and the way they interact with one another in these systems that link them together. In this dissertation, I present from both the aspect of theoretical analysis and the aspect of application three projects, which primarily focus on signal transduction networks in biology. In these projects, I assembled a network model through extensively perusing literature, performed model-based simulations and validation, analyzed network topology, and proposed a novel network measure. The application of network modeling to the system of stomatal opening in plants revealed a fundamental question about the process that has been left unanswered in decades. The novel measure of the redundancy of signal transduction networks with Boolean dynamics by calculating its maximum node-independent elementary signaling mode set accurately predicts the effect of single node knockout in such signaling processes. The three projects as an organic whole advance the understanding of a real system as well as the behavior of such network models, giving me an opportunity to take a glimpse at the dazzling facets of the immense world of network science.

  19. Kinetic modeling of receptor-ligand binding applied to positron emission tomographic studies with neuroleptic tracers

    Energy Technology Data Exchange (ETDEWEB)

    Logan, J; Wolf, A P; Shiue, C Y; Fowler, J S

    1987-01-01

    Positron emission tomography (PET) with labeled neuroleptics has made possible the study of neurotransmitter-receptor systems in vivo. In this study we investigate the kinetics of the 3,4-dihydroxyphenylethylamine (dopamine) receptor-ligand binding using PET data from a series of experiments in the baboon with the /sup 18/F-labeled drugs spiperone, haloperidol, and benperidol. Models used to describe these systems are based on first-order kinetics which applies at high specific activity (low receptor occupancy). The parameters governing the uptake and loss of drug from the brain were found by fitting PET data from regions with little or no receptor concentration (cerebellum) and from experiments in which specific binding was blocked by pretreatment with the drug (+)-butaclamol. Receptor constants were determined by fitting data from receptor-containing structures. Correcting the arterial plasma activities (the model driving function) for the presence of drug metabolites was found to be important in the modeling of these systems.

  20. Sliding mechanics of coated composite wires and the development of an engineering model for binding.

    Science.gov (United States)

    Zufall, S W; Kusy, R P

    2000-02-01

    A tribological (friction and wear) study, which was designed to simulate clinical sliding mechanics, was conducted as part of an effort to determine the suitability of poly(chloro-p-xylylene) coatings for composite orthodontic archwires. Prototype composite wires, having stiffnesses similar to those of current initial and intermediate alignment wires, were tested against stainless steel and ceramic brackets in the passive and active configurations (with and without angulation). Kinetic coefficient of friction values, which were determined to quantify sliding resistances as functions of the normal forces of ligation, had a mean that was 72% greater than uncoated wire couples at 0.43. To improve analysis of the active configuration, a mathematical model was developed that related bracket angulation, bracket width, interbracket distance, wire geometry, and wire elastic modulus to sliding resistance. From this model, kinetic coefficients of binding were determined to quantify sliding resistances as functions of the normal forces of binding. The mean binding coefficient was the same as that of uncoated wire couples at 0.42. Although penetrations through the coating were observed on many specimens, the glass-fiber reinforcement within the composite wires was undamaged for all conditions tested. This finding implies that the risk of glass fiber release during clinical use would be eliminated by the coating. In addition, the frictional and binding coefficients were still within the limits outlined by conventional orthodontic wire-bracket couples. Consequently, the coatings were regarded as an improvement to the clinical acceptability of composite orthodontic archwires.

  1. Third nearest neighbor parameterized tight binding model for graphene nano-ribbons

    Directory of Open Access Journals (Sweden)

    Van-Truong Tran

    2017-07-01

    Full Text Available The existing tight binding models can very well reproduce the ab initio band structure of a 2D graphene sheet. For graphene nano-ribbons (GNRs, the current sets of tight binding parameters can successfully describe the semi-conducting behavior of all armchair GNRs. However, they are still failing in reproducing accurately the slope of the bands that is directly associated with the group velocity and the effective mass of electrons. In this work, both density functional theory and tight binding calculations were performed and a new set of tight binding parameters up to the third nearest neighbors including overlap terms is introduced. The results obtained with this model offer excellent agreement with the predictions of the density functional theory in most cases of ribbon structures, even in the high-energy region. Moreover, this set can induce electron-hole asymmetry as manifested in results from density functional theory. Relevant outcomes are also achieved for armchair ribbons of various widths as well as for zigzag structures, thus opening a route for multi-scale atomistic simulation of large systems that cannot be considered using density functional theory.

  2. Abnormal binding and disruption in large scale networks involved in human partial seizures

    Directory of Open Access Journals (Sweden)

    Bartolomei Fabrice

    2013-12-01

    Full Text Available There is a marked increase in the amount of electrophysiological and neuroimaging works dealing with the study of large scale brain connectivity in the epileptic brain. Our view of the epileptogenic process in the brain has largely evolved over the last twenty years from the historical concept of “epileptic focus” to a more complex description of “Epileptogenic networks” involved in the genesis and “propagation” of epileptic activities. In particular, a large number of studies have been dedicated to the analysis of intracerebral EEG signals to characterize the dynamic of interactions between brain areas during temporal lobe seizures. These studies have reported that large scale functional connectivity is dramatically altered during seizures, particularly during temporal lobe seizure genesis and development. Dramatic changes in neural synchrony provoked by epileptic rhythms are also responsible for the production of ictal symptoms or changes in patient’s behaviour such as automatisms, emotional changes or consciousness alteration. Beside these studies dedicated to seizures, large-scale network connectivity during the interictal state has also been investigated not only to define biomarkers of epileptogenicity but also to better understand the cognitive impairments observed between seizures.

  3. UML modelling of network warfare examples

    CSIR Research Space (South Africa)

    Veerasamy, N

    2011-08-01

    Full Text Available ] Affects both civilian and military domains [8] [9] Related to the concepts of infowar, information operations, hacking, hackivism, cyberterrorism and cybotage depending on motivations and techniques [10] Encompasses both technological solutions...]. In addition, Williers as well as Qingbao and Anwar discuss more offensive aspects of information security and Network Warfare like Hacking, Vulnerability Injection, Network Attacks, Denial of Capability, Interception and Blockage [6] [18] [19]. Various...

  4. Neural Network Models for Time Series Forecasts

    OpenAIRE

    Tim Hill; Marcus O'Connor; William Remus

    1996-01-01

    Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...

  5. Stochastic model and method of zoning water networks

    OpenAIRE

    Тевяшев, Андрей Дмитриевич; Матвиенко, Ольга Ивановна

    2014-01-01

    Water consumption at different time of the day is uneven. The model of steady flow distribution in water-supply networks is calculated for maximum consumption and effectively used in the network design and reconstruction. Quasi-stationary modes, in which the parameters are random variables and vary relative to their mean values are more suitable for operational management and planning of rational network operation modes.Leaks, which sometimes exceed 50 % of the volume of water supplied, are o...

  6. A three-dimensional computational model of collagen network mechanics.

    Directory of Open Access Journals (Sweden)

    Byoungkoo Lee

    Full Text Available Extracellular matrix (ECM strongly influences cellular behaviors, including cell proliferation, adhesion, and particularly migration. In cancer, the rigidity of the stromal collagen environment is thought to control tumor aggressiveness, and collagen alignment has been linked to tumor cell invasion. While the mechanical properties of collagen at both the single fiber scale and the bulk gel scale are quite well studied, how the fiber network responds to local stress or deformation, both structurally and mechanically, is poorly understood. This intermediate scale knowledge is important to understanding cell-ECM interactions and is the focus of this study. We have developed a three-dimensional elastic collagen fiber network model (bead-and-spring model and studied fiber network behaviors for various biophysical conditions: collagen density, crosslinker strength, crosslinker density, and fiber orientation (random vs. prealigned. We found the best-fit crosslinker parameter values using shear simulation tests in a small strain region. Using this calibrated collagen model, we simulated both shear and tensile tests in a large linear strain region for different network geometry conditions. The results suggest that network geometry is a key determinant of the mechanical properties of the fiber network. We further demonstrated how the fiber network structure and mechanics evolves with a local formation, mimicking the effect of pulling by a pseudopod during cell migration. Our computational fiber network model is a step toward a full biomechanical model of cellular behaviors in various ECM conditions.

  7. Hybrid network defense model based on fuzzy evaluation.

    Science.gov (United States)

    Cho, Ying-Chiang; Pan, Jen-Yi

    2014-01-01

    With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.

  8. A Secure Network Coding-based Data Gathering Model and Its Protocol in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Qian Xiao

    2012-09-01

    Full Text Available To provide security for data gathering based on network coding in wireless sensor networks (WSNs, a secure network coding-based data gathering model is proposed, and a data-privacy preserving and pollution preventing (DPPaamp;PP protocol using network coding is designed. DPPaamp;PP makes use of a new proposed pollution symbol selection and pollution (PSSP scheme based on a new obfuscation idea to pollute existing symbols. Analyses of DPPaamp;PP show that it not only requires low overhead on computation and communication, but also provides high security on resisting brute-force attacks.

  9. Generalized Tavis-Cummings models and quantum networks

    Science.gov (United States)

    Gorokhov, A. V.

    2018-04-01

    The properties of quantum networks based on generalized Tavis-Cummings models are theoretically investigated. We have calculated the information transfer success rate from one node to another in a simple model of a quantum network realized with two-level atoms placed in the cavities and interacting with an external laser field and cavity photons. The method of dynamical group of the Hamiltonian and technique of corresponding coherent states were used for investigation of the temporal dynamics of the two nodes model.

  10. Analysis of organizational culture with social network models

    OpenAIRE

    Titov, S.

    2015-01-01

    Organizational culture is nowadays an object of numerous scientific papers. However, only marginal part of existing research attempts to use the formal models of organizational cultures. The lack of organizational culture models significantly limits the further research in this area and restricts the application of the theory to practice of organizational culture change projects. The article consists of general views on potential application of network models and social network analysis to th...

  11. Interpretation of Ocular Melanin Drug Binding Assays. Alternatives to the Model of Multiple Classes of Independent Sites.

    Science.gov (United States)

    Manzanares, José A; Rimpelä, Anna-Kaisa; Urtti, Arto

    2016-04-04

    Melanin has a high binding affinity for a wide range of drugs. The determination of the melanin binding capacity and its binding affinity are important, e.g., in the determination of the ocular drug distribution, the prediction of drug effects in the eye, and the trans-scleral drug delivery. The binding parameters estimated from a given data set vary significantly when using different isotherms or different nonlinear fitting methods. In this work, the commonly used bi-Langmuir isotherm, which assumes two classes of independent sites, is confronted with the Sips isotherm. Direct, log-log, and Scatchard plots are used, and the interpretation of the binding curves in the latter is critically analyzed. In addition to the goodness of fit, the emphasis is placed on the physical meaning of the binding parameters. The bi-Langmuir model imposes a bimodal distribution of binding energies for the sites on the melanin granules, but the actual distribution is most likely continuous and unimodal, as assumed by the Sips isotherm. Hence, the latter describes more accurately the distribution of binding energies and also the experimental results of melanin binding to drugs and metal ions. Simulations are used to show that the existence of two classes of sites cannot be confirmed on the sole basis of the shape of the binding curve in the Scatchard plot, and that serious doubts may appear on the meaning of the binding parameters of the bi-Langmuir model. Experimental results of melanin binding to chloroquine and metoprolol are used to illustrate the importance of the choice of the binding isotherm and of the method used to evaluate the binding parameters.

  12. Mathematics of epidemics on networks from exact to approximate models

    CERN Document Server

    Kiss, István Z; Simon, Péter L

    2017-01-01

    This textbook provides an exciting new addition to the area of network science featuring a stronger and more methodical link of models to their mathematical origin and explains how these relate to each other with special focus on epidemic spread on networks. The content of the book is at the interface of graph theory, stochastic processes and dynamical systems. The authors set out to make a significant contribution to closing the gap between model development and the supporting mathematics. This is done by: Summarising and presenting the state-of-the-art in modeling epidemics on networks with results and readily usable models signposted throughout the book; Presenting different mathematical approaches to formulate exact and solvable models; Identifying the concrete links between approximate models and their rigorous mathematical representation; Presenting a model hierarchy and clearly highlighting the links between model assumptions and model complexity; Providing a reference source for advanced undergraduate...

  13. Exponential random graph models for networks with community structure.

    Science.gov (United States)

    Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

    2013-09-01

    Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

  14. Dynamic Evolution Model Based on Social Network Services

    Science.gov (United States)

    Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen

    2013-11-01

    Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

  15. Nonlinear adaptive inverse control via the unified model neural network

    Science.gov (United States)

    Jeng, Jin-Tsong; Lee, Tsu-Tian

    1999-03-01

    In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

  16. Literature Review on Modeling Cyber Networks and Evaluating Cyber Risks.

    Energy Technology Data Exchange (ETDEWEB)

    Kelic, Andjelka; Campbell, Philip L

    2018-04-01

    The National Infrastructure Simulations and Analysis Center (NISAC) conducted a literature review on modeling cyber networks and evaluating cyber risks. The literature review explores where modeling is used in the cyber regime and ways that consequence and risk are evaluated. The relevant literature clusters in three different spaces: network security, cyber-physical, and mission assurance. In all approaches, some form of modeling is utilized at varying levels of detail, while the ability to understand consequence varies, as do interpretations of risk. This document summarizes the different literature viewpoints and explores their applicability to securing enterprise networks.

  17. Constraints and entropy in a model of network evolution

    Science.gov (United States)

    Tee, Philip; Wakeman, Ian; Parisis, George; Dawes, Jonathan; Kiss, István Z.

    2017-11-01

    Barabási-Albert's "Scale Free" model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the "Scale Free" model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the "Scale Free" and "constraints" model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.

  18. A large deformation viscoelastic model for double-network hydrogels

    Science.gov (United States)

    Mao, Yunwei; Lin, Shaoting; Zhao, Xuanhe; Anand, Lallit

    2017-03-01

    We present a large deformation viscoelasticity model for recently synthesized double network hydrogels which consist of a covalently-crosslinked polyacrylamide network with long chains, and an ionically-crosslinked alginate network with short chains. Such double-network gels are highly stretchable and at the same time tough, because when stretched the crosslinks in the ionically-crosslinked alginate network rupture which results in distributed internal microdamage which dissipates a substantial amount of energy, while the configurational entropy of the covalently-crosslinked polyacrylamide network allows the gel to return to its original configuration after deformation. In addition to the large hysteresis during loading and unloading, these double network hydrogels also exhibit a substantial rate-sensitive response during loading, but exhibit almost no rate-sensitivity during unloading. These features of large hysteresis and asymmetric rate-sensitivity are quite different from the response of conventional hydrogels. We limit our attention to modeling the complex viscoelastic response of such hydrogels under isothermal conditions. Our model is restricted in the sense that we have limited our attention to conditions under which one might neglect any diffusion of the water in the hydrogel - as might occur when the gel has a uniform initial value of the concentration of water, and the mobility of the water molecules in the gel is low relative to the time scale of the mechanical deformation. We also do not attempt to model the final fracture of such double-network hydrogels.

  19. Constructive Lower Bounds on Model Complexity of Shallow Perceptron Networks

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra

    2018-01-01

    Roč. 29, č. 7 (2018), s. 305-315 ISSN 0941-0643 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : shallow and deep networks * model complexity and sparsity * signum perceptron networks * finite mappings * variational norms * Hadamard matrices Subject RIV: IN - Informatics, Computer Science Impact factor: 2.505, year: 2016

  20. Line and lattice networks under deterministic interference models

    NARCIS (Netherlands)

    Goseling, Jasper; Gastpar, Michael; Weber, Jos H.

    Capacity bounds are compared for four different deterministic models of wireless networks, representing four different ways of handling broadcast and superposition in the physical layer. In particular, the transport capacity under a multiple unicast traffic pattern is studied for a 1-D network of

  1. Radio Channel Modelling for UAV Communication over Cellular Networks

    DEFF Research Database (Denmark)

    Amorim, Rafhael Medeiros de; Nguyen, Huan Cong; Mogensen, Preben Elgaard

    2017-01-01

    The main goal of this paper is to obtain models for path loss exponents and shadowing for the radio channel between airborne Unmanned Aerial Vehicles (UAVs) and cellular networks. In this pursuit, field measurements were conducted in live LTE networks at the 800 MHz frequency band, using a commer...

  2. Modelling aspects of distributed processing in telecommunication networks

    NARCIS (Netherlands)

    Tomasgard, A; Audestad, JA; Dye, S; Stougie, L; van der Vlerk, MH; Wallace, SW

    1998-01-01

    The purpose of this paper is to formally describe new optimization models for telecommunication networks with distributed processing. Modem distributed networks put more focus on the processing of information and less on the actual transportation of data than we are traditionally used to in

  3. Oxygen diffusion in a network model of the myocardial microcirculation

    NARCIS (Netherlands)

    Wieringa, P. A.; Stassen, H. G.; van Kan, J. J.; Spaan, J. A.

    1993-01-01

    Oxygen supply was studied in a three-dimensional capillary network model of the myocardial microcirculation. Capillary networks were generated using one common strategy to locate the capillary branchings and segments, arterioles and venules. Flow paths developed with different capillary flow

  4. Modelling flow dynamics in water distribution networks using ...

    African Journals Online (AJOL)

    One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics ...

  5. Modelling the permeability of polymers: a neural network approach

    NARCIS (Netherlands)

    Wessling, Matthias; Mulder, M.H.V.; Bos, A.; Bos, A.; van der Linden, M.K.T.; Bos, M.; van der Linden, W.E.

    1994-01-01

    In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a

  6. Towards a Social Networks Model for Online Learning & Performance

    Science.gov (United States)

    Chung, Kon Shing Kenneth; Paredes, Walter Christian

    2015-01-01

    In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

  7. Capacity allocation in wireless communication networks - models and analyses

    NARCIS (Netherlands)

    Litjens, Remco

    2003-01-01

    This monograph has concentrated on capacity allocation in cellular and Wireless Local Area Networks, primarily with a network operator’s perspective. In the introduc- tory chapter, a reference model has been proposed for the extensive suite of capacity allocation mechanisms that can be applied at

  8. DAILY RAINFALL-RUNOFF MODELLING BY NEURAL NETWORKS ...

    African Journals Online (AJOL)

    K. Benzineb, M. Remaoun

    2016-09-01

    Sep 1, 2016 ... The hydrologic behaviour modelling of w. Journal of ... i Ouahrane's basin from rainfall-runoff relation which is non-linea networks ... will allow checking efficiency of formal neural networks for flows simulation in semi-arid zone.

  9. Energy Model of Networks-on-Chip and a Bus

    NARCIS (Netherlands)

    Wolkotte, P.T.; Smit, Gerardus Johannes Maria; Kavaldjiev, N.K.; Becker, Jens E.; Becker, Jürgen; Nurmi, J.; Takala, J.; Hamalainen, T.D.

    2005-01-01

    A Network-on-Chip (NoC) is an energy-efficient onchip communication architecture for Multi-Processor Systemon-Chip (MPSoC) architectures. In earlier papers we proposed two Network-on-Chip architectures based on packet-switching and circuit-switching. In this paper we derive an energy model for both

  10. modeling and optimization of an electric power distribution network

    African Journals Online (AJOL)

    user

    EDNEPP) was solved by a mixed binary integer programming (MBIP) formulation of the network, where the steady-state operation of the network was modelled with non-linear mathematical expressions. The non-linear terms are linearized, using ...

  11. Agent Based Modeling on Organizational Dynamics of Terrorist Network

    Directory of Open Access Journals (Sweden)

    Bo Li

    2015-01-01

    Full Text Available Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model are developed for modeling the hybrid relational structure and complex operational processes, respectively. To intuitively elucidate this method, the agent based modeling is used to simulate the terrorist network and test the performance in diverse scenarios. Based on the experimental results, we show how the changes of operational environments affect the development of terrorist organization in terms of its recovery and capacity to perform future tasks. The potential strategies are also discussed, which can be used to restrain the activities of terrorists.

  12. System-level Modeling of Wireless Integrated Sensor Networks

    DEFF Research Database (Denmark)

    Virk, Kashif M.; Hansen, Knud; Madsen, Jan

    2005-01-01

    Wireless integrated sensor networks have emerged as a promising infrastructure for a new generation of monitoring and tracking applications. In order to efficiently utilize the extremely limited resources of wireless sensor nodes, accurate modeling of the key aspects of wireless sensor networks...... is necessary so that system-level design decisions can be made about the hardware and the software (applications and real-time operating system) architecture of sensor nodes. In this paper, we present a SystemC-based abstract modeling framework that enables system-level modeling of sensor network behavior...... by modeling the applications, real-time operating system, sensors, processor, and radio transceiver at the sensor node level and environmental phenomena, including radio signal propagation, at the sensor network level. We demonstrate the potential of our modeling framework by simulating and analyzing a small...

  13. A continuous-time control model on production planning network ...

    African Journals Online (AJOL)

    A continuous-time control model on production planning network. DEA Omorogbe, MIU Okunsebor. Abstract. In this paper, we give a slightly detailed review of Graves and Hollywood model on constant inventory tactical planning model for a job shop. The limitations of this model are pointed out and a continuous time ...

  14. Modeling the microscopic electrical properties of thrombin binding aptamer (TBA) for label-free biosensors

    Science.gov (United States)

    Alfinito, Eleonora; Reggiani, Lino; Cataldo, Rosella; De Nunzio, Giorgio; Giotta, Livia; Guascito, Maria Rachele

    2017-02-01

    Aptamers are chemically produced oligonucleotides, able to bind a variety of targets such as drugs, proteins and pathogens with high sensitivity and selectivity. Therefore, aptamers are largely employed for producing label-free biosensors (aptasensors), with significant applications in diagnostics and drug delivery. In particular, the anti-thrombin aptamers are biomolecules of high interest for clinical use, because of their ability to recognize and bind the thrombin enzyme. Among them, the DNA 15-mer aptamer (TBA), has been widely explored around the possibility of using it in aptasensors. This paper proposes a microscopic model of the electrical properties of TBA and of the aptamer-thrombin complex, combining information from both structure and function, following the issues addressed in an emerging branch of electronics known as proteotronics. The theoretical results are compared and validated with measurements reported in the literature. Finally, the model suggests resistance measurements as a novel tool for testing aptamer-target affinity.

  15. Non-consensus Opinion Models on Complex Networks

    Science.gov (United States)

    Li, Qian; Braunstein, Lidia A.; Wang, Huijuan; Shao, Jia; Stanley, H. Eugene; Havlin, Shlomo

    2013-04-01

    Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, 2009). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor W to each individual's original opinion when determining their future opinion (NCO W model). We find that as W increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, 2011), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not

  16. A tight binding model study of tunneling conductance spectra of spin and orbitally ordered CMR manganites

    Science.gov (United States)

    Panda, Saswati; Sahoo, D. D.; Rout, G. C.

    2018-04-01

    We report here a tight binding model for colossal magnetoresistive (CMR) manganites to study the pseudo gap (PG) behavior near Fermi level. In the Kubo-Ohata type DE model, we consider first and second nearest neighbor interactions for transverse spin fluctuations in core band and hopping integrals in conduction band, in the presence of static band Jahn-Teller distortion. The model Hamiltonian is solved using Zubarev's Green's function technique. The electron density of states (DOS) is found out from the Green's functions. We observe clear PG near Fermi level in the electron DOS.

  17. Linear control theory for gene network modeling.

    Directory of Open Access Journals (Sweden)

    Yong-Jun Shin

    Full Text Available Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain and linear state-space (time domain can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  18. Modelling, synthesis and analysis of biorefinery networks

    DEFF Research Database (Denmark)

    Bertran, Maria-Ona

    for the conversion of biomass into chemicals, fuels and energy, because they have the potential to maximize biomass value while reducing emissions. The design of biorefinery networks is a complex decisionmaking problem that involves the selection of feedstocks, processing technologies, products, geographical...... locations, and operating conditions, among others. Unlike petroleumbased processing networks, biorefineries rely on feedstocks that are nonhomogeneous across geographical areas in terms of their availability, type and properties. For this reason, the performance of biorefinery networks depends...... of reactions to convert available biomassbased feedstocks into desired products, the selection of processing routes and technologies from a large set of alternatives, or the generation of hybrid technologies through process intensification. Systematic process synthesis and design methods have been developed...

  19. Phase-synchronisation in continuous flow models of production networks

    Science.gov (United States)

    Scholz-Reiter, Bernd; Tervo, Jan Topi; Freitag, Michael

    2006-04-01

    To improve their position at the market, many companies concentrate on their core competences and hence cooperate with suppliers and distributors. Thus, between many independent companies strong linkages develop and production and logistics networks emerge. These networks are characterised by permanently increasing complexity, and are nowadays forced to adapt to dynamically changing markets. This factor complicates an enterprise-spreading production planning and control enormously. Therefore, a continuous flow model for production networks will be derived regarding these special logistic problems. Furthermore, phase-synchronisation effects will be presented and their dependencies to the set of network parameters will be investigated.

  20. Hybrid modeling and empirical analysis of automobile supply chain network

    Science.gov (United States)

    Sun, Jun-yan; Tang, Jian-ming; Fu, Wei-ping; Wu, Bing-ying

    2017-05-01

    Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.

  1. A model-based approach to identify binding sites in CLIP-Seq data.

    Directory of Open Access Journals (Sweden)

    Tao Wang

    Full Text Available Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present a novel model-based approach (MiClip to identify high-confidence protein-RNA binding sites from CLIP-seq datasets. This approach assigns a probability score for each potential binding site to help prioritize subsequent validation experiments. The MiClip algorithm has been tested in both HITS-CLIP and PAR-CLIP datasets. In the HITS-CLIP dataset, the signal/noise ratios of miRNA seed motif enrichment produced by the MiClip approach are between 17% and 301% higher than those by the ad hoc method for the top 10 most enriched miRNAs. In the PAR-CLIP dataset, the MiClip approach can identify ∼50% more validated binding targets than the original ad hoc method and two recently published methods. To facilitate the application of the algorithm, we have released an R package, MiClip (http://cran.r-project.org/web/packages/MiClip/index.html, and a public web-based graphical user interface software (http://galaxy.qbrc.org/tool_runner?tool_id=mi_clip for customized analysis.

  2. Rate concept and retarded master equations for dissipative tight-binding models

    International Nuclear Information System (INIS)

    Egger, R.; Mak, C.H.; Weiss, U.

    1994-01-01

    Employing a ''noninteracting-cluster approximation,'' the dynamics of multistate dissipative tight-binding models has been formulated in terms of a set of generalized retarded master equations. The rates for the various pathways are expressed as power series in the intersite couplings. We apply this to the superexchange mechanism, which is relevant for bacterial photosynthesis and bridged electron transfer systems. This approach provides a general and unified description of both incoherent and coherent transport

  3. Three-dimensional stochastic model of actin–myosin binding in the sarcomere lattice

    Energy Technology Data Exchange (ETDEWEB)

    Mijailovich, Srboljub M.; Kayser-Herold, Oliver; Stojanovic, Boban; Nedic, Djordje; Irving, Thomas C.; Geeves, MA (Harvard); (IIT); (U. Kent); (Kragujevac)

    2016-11-18

    The effect of molecule tethering in three-dimensional (3-D) space on bimolecular binding kinetics is rarely addressed and only occasionally incorporated into models of cell motility. The simplest system that can quantitatively determine this effect is the 3-D sarcomere lattice of the striated muscle, where tethered myosin in thick filaments can only bind to a relatively small number of available sites on the actin filament, positioned within a limited range of thermal movement of the myosin head. Here we implement spatially explicit actomyosin interactions into the multiscale Monte Carlo platform MUSICO, specifically defining how geometrical constraints on tethered myosins can modulate state transition rates in the actomyosin cycle. The simulations provide the distribution of myosin bound to sites on actin, ensure conservation of the number of interacting myosins and actin monomers, and most importantly, the departure in behavior of tethered myosin molecules from unconstrained myosin interactions with actin. In addition, MUSICO determines the number of cross-bridges in each actomyosin cycle state, the force and number of attached cross-bridges per myosin filament, the range of cross-bridge forces and accounts for energy consumption. At the macroscopic scale, MUSICO simulations show large differences in predicted force-velocity curves and in the response during early force recovery phase after a step change in length comparing to the two simplest mass action kinetic models. The origin of these differences is rooted in the different fluxes of myosin binding and corresponding instantaneous cross-bridge distributions and quantitatively reflects a major flaw of the mathematical description in all mass action kinetic models. Consequently, this new approach shows that accurate recapitulation of experimental data requires significantly different binding rates, number of actomyosin states, and cross-bridge elasticity than typically used in mass action kinetic models to

  4. A DUAL NETWORK MODEL OF INTERLOCKING DIRECTORATES

    Directory of Open Access Journals (Sweden)

    Humphry Hung

    2003-01-01

    Full Text Available The article proposes an integrative framework for the study of interlocking directorates by using an approach that encompasses the concepts of multiple networks and resource endowment. This serves to integrate the traditional views of interorganizational linkages and intra-class cohesion. Through appropriate strategic analysis of relevant resource endowment of internal environment and external networks of organizations and corporate elites, this article argues that the selection of directors, if used effectively, can be adopted as a strategic device to enhance the corporation's overall performance.

  5. Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

    Science.gov (United States)

    Gupta, Shikha; Basant, Nikita; Rai, Premanjali; Singh, Kunwar P

    2015-11-01

    Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The

  6. Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins

    DEFF Research Database (Denmark)

    Rognan, D; Lauemoller, S L; Holm, A

    1999-01-01

    A simple and fast free energy scoring function (Fresno) has been developed to predict the binding free energy of peptides to class I major histocompatibility (MHC) proteins. It differs from existing scoring functions mainly by the explicit treatment of ligand desolvation and of unfavorable protein...... coordinates of the MHC-bound peptide have first been determined with an accuracy of about 1-1.5 A. Furthermore, it may be easily recalibrated for any protein-ligand complex.......) and of a series of 16 peptides to H-2K(k). Predictions were more accurate for HLA-A2-binding peptides as the training set had been built from experimentally determined structures. The average error in predicting the binding free energy of the test peptides was 3.1 kJ/mol. For the homology model-derived equation...

  7. A Simplified Network Model for Travel Time Reliability Analysis in a Road Network

    Directory of Open Access Journals (Sweden)

    Kenetsu Uchida

    2017-01-01

    Full Text Available This paper proposes a simplified network model which analyzes travel time reliability in a road network. A risk-averse driver is assumed in the simplified model. The risk-averse driver chooses a path by taking into account both a path travel time variance and a mean path travel time. The uncertainty addressed in this model is that of traffic flows (i.e., stochastic demand flows. In the simplified network model, the path travel time variance is not calculated by considering all travel time covariance between two links in the network. The path travel time variance is calculated by considering all travel time covariance between two adjacent links in the network. Numerical experiments are carried out to illustrate the applicability and validity of the proposed model. The experiments introduce the path choice behavior of a risk-neutral driver and several types of risk-averse drivers. It is shown that the mean link flows calculated by introducing the risk-neutral driver differ as a whole from those calculated by introducing several types of risk-averse drivers. It is also shown that the mean link flows calculated by the simplified network model are almost the same as the flows calculated by using the exact path travel time variance.

  8. Copula-based modeling of degree-correlated networks

    International Nuclear Information System (INIS)

    Raschke, Mathias; Schläpfer, Markus; Trantopoulos, Konstantinos

    2014-01-01

    Dynamical processes on complex networks such as information exchange, innovation diffusion, cascades in financial networks or epidemic spreading are highly affected by their underlying topologies as characterized by, for instance, degree–degree correlations. Here, we introduce the concept of copulas in order to generate random networks with an arbitrary degree distribution and a rich a priori degree–degree correlation (or ‘association’) structure. The accuracy of the proposed formalism and corresponding algorithm is numerically confirmed, while the method is tested on a real-world network of yeast protein–protein interactions. The derived network ensembles can be systematically deployed as proper null models, in order to unfold the complex interplay between the topology of real-world networks and the dynamics on top of them. (paper)

  9. neural network based model o work based model of an industrial oil

    African Journals Online (AJOL)

    eobe

    technique. g, Neural Network Model, Regression, Mean Square Error, PID controller. ... during the training processes. An additio ... used to carry out simulation studies of the mode .... A two-layer feed-forward neural network with Matlab.

  10. A Gaussian graphical model approach to climate networks

    Energy Technology Data Exchange (ETDEWEB)

    Zerenner, Tanja, E-mail: tanjaz@uni-bonn.de [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Friederichs, Petra; Hense, Andreas [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany); Lehnertz, Klaus [Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn (Germany); Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany)

    2014-06-15

    Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.

  11. A Gaussian graphical model approach to climate networks

    International Nuclear Information System (INIS)

    Zerenner, Tanja; Friederichs, Petra; Hense, Andreas; Lehnertz, Klaus

    2014-01-01

    Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately

  12. Quebec mental health services networks: models and implementation

    Directory of Open Access Journals (Sweden)

    Marie-Josée Fleury

    2005-06-01

    Full Text Available Purpose: In the transformation of health care systems, the introduction of integrated service networks is considered to be one of the main solutions for enhancing efficiency. In the last few years, a wealth of literature has emerged on the topic of services integration. However, the question of how integrated service networks should be modelled to suit different implementation contexts has barely been touched. To fill that gap, this article presents four models for the organization of mental health integrated networks. Data sources: The proposed models are drawn from three recently published studies on mental health integrated services in the province of Quebec (Canada with the author as principal investigator. Description: Following an explanation of the concept of integrated service network and a description of the Quebec context for mental health networks, the models, applicable in all settings: rural, urban or semi-urban, and metropolitan, and summarized in four figures, are presented. Discussion and conclusion: To apply the models successfully, the necessity of rallying all the actors of a system, from the strategic, tactical and operational levels, according to the type of integration involved: functional/administrative, clinical and physician-system is highlighted. The importance of formalizing activities among organizations and actors in a network and reinforcing the governing mechanisms at the local level is also underlined. Finally, a number of integration strategies and key conditions of success to operationalize integrated service networks are suggested.

  13. Age and sex differences in oxytocin and vasopressin V1a receptor binding densities in the rat brain: focus on the social decision-making network.

    Science.gov (United States)

    Smith, Caroline J W; Poehlmann, Max L; Li, Sara; Ratnaseelan, Aarane M; Bredewold, Remco; Veenema, Alexa H

    2017-03-01

    Oxytocin (OT) and vasopressin (AVP) regulate various social behaviors via activation of the OT receptor (OTR) and the AVP V1a receptor (V1aR) in the brain. Social behavior often differs across development and between the sexes, yet our understanding of age and sex differences in brain OTR and V1aR binding remains incomplete. Here, we provide an extensive analysis of OTR and V1aR binding density throughout the brain in juvenile and adult male and female rats, with a focus on regions within the social decision-making network. OTR and V1aR binding density were higher in juveniles than in adults in regions associated with reward and socio-spatial memory and higher in adults than in juveniles in key regions of the social decision-making network and in cortical regions. We discuss possible implications of these shifts in OTR and V1aR binding density for the age-specific regulation of social behavior. Furthermore, sex differences in OTR and V1aR binding density were less numerous than age differences. The direction of these sex differences was region-specific for OTR but consistently higher in females than in males for V1aR. Finally, almost all sex differences in OTR and V1aR binding density were already present in juveniles and occurred in regions with denser binding in adults compared to juveniles. Possible implications of these sex differences for the sex-specific regulation of behavior, as well potential underlying mechanisms, are discussed. Overall, these findings provide an important framework for testing age- and sex-specific roles of OTR and V1aR in the regulation of social behavior.

  14. A ternary logic model for recurrent neuromime networks with delay.

    Science.gov (United States)

    Hangartner, R D; Cull, P

    1995-07-01

    In contrast to popular recurrent artificial neural network (RANN) models, biological neural networks have unsymmetric structures and incorporate significant delays as a result of axonal propagation. Consequently, biologically inspired neural network models are more accurately described by nonlinear differential-delay equations rather than nonlinear ordinary differential equations (ODEs), and the standard techniques for studying the dynamics of RANNs are wholly inadequate for these models. This paper develops a ternary-logic based method for analyzing these networks. Key to the technique is the realization that a nonzero delay produces a bounded stability region. This result significantly simplifies the construction of sufficient conditions for characterizing the network equilibria. If the network gain is large enough, each equilibrium can be classified as either asymptotically stable or unstable. To illustrate the analysis technique, the swim central pattern generator (CPG) of the sea slug Tritonia diomedea is examined. For wide range of reasonable parameter values, the ternary analysis shows that none of the network equilibria are stable, and thus the network must oscillate. The results show that complex synaptic dynamics are not necessary for pattern generation.

  15. Business Process Modeling Languages Supporting Collaborative Networks

    NARCIS (Netherlands)

    Soleimani Malekan, H.; Afsarmanesh, H.; Hammoudi, S.; Maciaszek, L.A.; Cordeiro, J.; Dietz, J.L.G.

    2013-01-01

    Formalizing the definition of Business Processes (BPs) performed within each enterprise is fundamental for effective deployment of their competencies and capabilities within Collaborative Networks (CN). In our approach, every enterprise in the CN is represented by its set of BPs, so that other

  16. Message Integrity Model for Wireless Sensor Networks

    Science.gov (United States)

    Qleibo, Haider W.

    2009-01-01

    WSNs are susceptible to a variety of attacks. These attacks vary in the way they are performed and executed; they include but not limited to node capture, physical tampering, denial of service, and message alteration. It is of paramount importance to protect gathered data by WSNs and defend the network against illegal access and malicious…

  17. Modeling urbanization patterns with generative adversarial networks

    OpenAIRE

    Albert, Adrian; Strano, Emanuele; Kaur, Jasleen; Gonzalez, Marta

    2018-01-01

    In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory. We generated a synthetic urban "universe" that qualitatively reproduces the complex spatial organization observed in global urban patterns, while being able to quantitatively recover certain key high-level urban spatial metrics.

  18. Stochastic network interdiction optimization via capacitated network reliability modeling and probabilistic solution discovery

    International Nuclear Information System (INIS)

    Ramirez-Marquez, Jose Emmanuel; Rocco S, Claudio M.

    2009-01-01

    This paper introduces an evolutionary optimization approach that can be readily applied to solve stochastic network interdiction problems (SNIP). The network interdiction problem solved considers the minimization of the cost associated with an interdiction strategy such that the maximum flow that can be transmitted between a source node and a sink node for a fixed network design is greater than or equal to a given reliability requirement. Furthermore, the model assumes that the nominal capacity of each network link and the cost associated with their interdiction can change from link to link and that such interdiction has a probability of being successful. This version of the SNIP is for the first time modeled as a capacitated network reliability problem allowing for the implementation of computation and solution techniques previously unavailable. The solution process is based on an evolutionary algorithm that implements: (1) Monte-Carlo simulation, to generate potential network interdiction strategies, (2) capacitated network reliability techniques to analyze strategies' source-sink flow reliability and, (3) an evolutionary optimization technique to define, in probabilistic terms, how likely a link is to appear in the final interdiction strategy. Examples for different sizes of networks are used throughout the paper to illustrate the approach

  19. SCYNet. Testing supersymmetric models at the LHC with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Bechtle, Philip; Belkner, Sebastian; Hamer, Matthias [Universitaet Bonn, Bonn (Germany); Dercks, Daniel [Universitaet Hamburg, Hamburg (Germany); Keller, Tim; Kraemer, Michael; Sarrazin, Bjoern; Schuette-Engel, Jan; Tattersall, Jamie [RWTH Aachen University, Institute for Theoretical Particle Physics and Cosmology, Aachen (Germany)

    2017-10-15

    SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model. (orig.)

  20. Numeral eddy current sensor modelling based on genetic neural network

    International Nuclear Information System (INIS)

    Yu Along

    2008-01-01

    This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

  1. Modelling and predicting biogeographical patterns in river networks

    Directory of Open Access Journals (Sweden)

    Sabela Lois

    2016-04-01

    Full Text Available Statistical analysis and interpretation of biogeographical phenomena in rivers is now possible using a spatially explicit modelling framework, which has seen significant developments in the past decade. I used this approach to identify a spatial extent (geostatistical range in which the abundance of the parasitic freshwater pearl mussel (Margaritifera margaritifera L. is spatially autocorrelated in river networks. I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. Although I used a variety of modelling approaches in my thesis, I focus here on the details of this relatively new spatial stream network model, thus advancing the study of biogeographical patterns in river networks.

  2. Particle swarm optimization of a neural network model in a ...

    Indian Academy of Sciences (India)

    . Since tool life is critically affected by the tool wear, accurate prediction of this wear ... In their work, they established an improvement in the quality ... objective optimization of hard turning using neural network modelling and swarm intelligence ...

  3. Modelling the self-organization and collapse of complex networks

    Indian Academy of Sciences (India)

    Modelling the self-organization and collapse of complex networks. Sanjay Jain Department of Physics and Astrophysics, University of Delhi Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore Santa Fe Institute, Santa Fe, New Mexico.

  4. Three-state combinatorial switch models as applied to the binding of oxygen by human hemoglobin.

    Science.gov (United States)

    Straume, M; Johnson, M L

    1988-02-23

    We have generated a series of all 6561 unique, discrete three-state combinatorial switch models to describe the partitioning of the cooperative oxygen-binding free change among the 10 variously ligated forms of human hemoglobin tetramers. These models were inspired by the experimental observation of Smith and Ackers that the cooperative free energy of the intersubunit contact regions of the 10 possible ligated forms of human hemoglobin tetramers can be represented by a particular distribution of three distinct energy levels [Smith, F. R., & Ackers, G. K. (1985) Proc. Natl. Acad. Sci. U.S.A. 82, 5347-5351]. A statistical thermodynamic formulation accounting for both dimer-tetramer equilibria and ligand binding properties of hemoglobin solutions as a function of oxygen and protein concentrations was utilized to exhaustively test these thermodynamic models. In this series of models each of the 10 ligated forms of the hemoglobin tetramer can exist in one, and only one, of three possible energy levels; i.e., each ligated form was assumed to be associated with a discrete energy state. This series of models includes all possible ways that the 10 ligation states of hemoglobin can be distributed into three distinct cooperative energy levels. The mathematical models, as presented here, do not permit equilibria between energy states to exist for any of the 10 unique ligated forms of hemoglobin tetramers. These models were analyzed by nonlinear least-squares estimation of the free energy parameters characteristic of this statistical thermodynamic development.(ABSTRACT TRUNCATED AT 250 WORDS)

  5. Numerical Analysis of Modeling Based on Improved Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Shao Jie

    2014-01-01

    Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.

  6. Feed forward neural networks modeling for K-P interactions

    International Nuclear Information System (INIS)

    El-Bakry, M.Y.

    2003-01-01

    Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K-P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental data. The proposed FFNN model results showed good fitting to the experimental data. The neural network model performance was also tested at non-trained space and was found to be in good agreement with the experimental data

  7. Modeling Evolution on Nearly Neutral Network Fitness Landscapes

    Science.gov (United States)

    Yakushkina, Tatiana; Saakian, David B.

    2017-08-01

    To describe virus evolution, it is necessary to define a fitness landscape. In this article, we consider the microscopic models with the advanced version of neutral network fitness landscapes. In this problem setting, we suppose a fitness difference between one-point mutation neighbors to be small. We construct a modification of the Wright-Fisher model, which is related to ordinary infinite population models with nearly neutral network fitness landscape at the large population limit. From the microscopic models in the realistic sequence space, we derive two versions of nearly neutral network models: with sinks and without sinks. We claim that the suggested model describes the evolutionary dynamics of RNA viruses better than the traditional Wright-Fisher model with few sequences.

  8. Infection dynamics on spatial small-world network models

    Science.gov (United States)

    Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario

    2017-11-01

    The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.

  9. Modelling, Synthesis, and Configuration of Networks-on-Chips

    DEFF Research Database (Denmark)

    Stuart, Matthias Bo

    This thesis presents three contributions in two different areas of network-on-chip and system-on-chip research: Application modelling and identifying and solving different optimization problems related to two specific network-on-chip architectures. The contribution related to application modelling...... is an analytical method for deriving the worst-case traffic pattern caused by an application and the cache-coherence protocol in a cache-coherent shared-memory system. The contributions related to network-on-chip optimization problems consist of two parts: The development and evaluation of six heuristics...... for solving the network synthesis problem in the MANGO network-on-chip, and the identification and formalization of the ReNoC configuration problem together with three heuristics for solving it....

  10. Statistical inference to advance network models in epidemiology.

    Science.gov (United States)

    Welch, David; Bansal, Shweta; Hunter, David R

    2011-03-01

    Contact networks are playing an increasingly important role in the study of epidemiology. Most of the existing work in this area has focused on considering the effect of underlying network structure on epidemic dynamics by using tools from probability theory and computer simulation. This work has provided much insight on the role that heterogeneity in host contact patterns plays on infectious disease dynamics. Despite the important understanding afforded by the probability and simulation paradigm, this approach does not directly address important questions about the structure of contact networks such as what is the best network model for a particular mode of disease transmission, how parameter values of a given model should be estimated, or how precisely the data allow us to estimate these parameter values. We argue that these questions are best answered within a statistical framework and discuss the role of statistical inference in estimating contact networks from epidemiological data. Copyright © 2011 Elsevier B.V. All rights reserved.

  11. Neural network modeling for near wall turbulent flow

    International Nuclear Information System (INIS)

    Milano, Michele; Koumoutsakos, Petros

    2002-01-01

    A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control

  12. Modeling and Analysis of New Products Diffusion on Heterogeneous Networks

    Directory of Open Access Journals (Sweden)

    Shuping Li

    2014-01-01

    Full Text Available We present a heterogeneous networks model with the awareness stage and the decision-making stage to explain the process of new products diffusion. If mass media is neglected in the decision-making stage, there is a threshold whether the innovation diffusion is successful or not, or else it is proved that the network model has at least one positive equilibrium. For networks with the power-law degree distribution, numerical simulations confirm analytical results, and also at the same time, by numerical analysis of the influence of the network structure and persuasive advertisements on the density of adopters, we give two different products propagation strategies for two classes of nodes in scale-free networks.

  13. Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

    Directory of Open Access Journals (Sweden)

    Natalie Berestovsky

    Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them

  14. Mutation analysis and molecular modeling for the investigation of ligand-binding modes of GPR84.

    Science.gov (United States)

    Nikaido, Yoshiaki; Koyama, Yuuta; Yoshikawa, Yasushi; Furuya, Toshio; Takeda, Shigeki

    2015-05-01

    GPR84 is a G protein-coupled receptor for medium-chain fatty acids. Capric acid and 3,3'-diindolylmethane are specific agonists for GPR84. We built a homology model of a GPR84-capric acid complex to investigate the ligand-binding mode using the crystal structure of human active-state β2-adrenergic receptor. We performed site-directed mutagenesis to subject ligand-binding sites to our model using GPR84-Giα fusion proteins and a [(35)S]GTPγS-binding assay. We compared the activity of the wild type and mutated forms of GPR84 by [(35)S]GTPγS binding to capric acid and diindolylmethane. The mutations L100D `Ballesteros-Weinstein numbering: 3.32), F101Y (3.33) and N104Q (3.36) in the transmembrane helix III and N357D (7.39) in the transmembrane helix VII resulted in reduced capric acid activity but maintained the diindolylmethane responses. Y186F (5.46) and Y186H (5.46) mutations had no characteristic effect on capric acid but with diindolylmethane they significantly affected the G protein activation efficiency. The L100D (3.32) mutant responded to decylamine, a fatty amine, instead of a natural agonist, the fatty acid capric acid, suggesting that we have identified a mutated G protein-coupled receptor-artificial ligand pairing. Our molecular model provides an explanation for these results and interactions between GPR84 and capric acid. Further, from the results of a double stimulation assay, we concluded that diindolylmethane was a positive allosteric modulator for GPR84. © The Authors 2014. Published by Oxford University Press on behalf of the Japanese Biochemical Society. All rights reserved.

  15. A mechanism-based binding model for the population pharmacokinetics and pharmacodynamics of omalizumab

    Science.gov (United States)

    Hayashi, Naoto; Tsukamoto, Yuko; Sallas, William M; Lowe, Philip J

    2007-01-01

    Aim Omalizumab, a humanized IgG monoclonal antibody that binds to human immunoglobulin E (IgE), interrupts the allergic cascade in asthmatic patients. The aim was to compare simultaneously drug exposure and IgE biomarker responses in Japanese and White patient populations. Methods An instantaneous equilibrium drug–ligand binding and turnover population model was built from 202 Japanese patients. A posterior predictive evaluation for the steady-state distributions of omalizumab and IgE was then carried out against 531 White patients. Results The mean parameters estimated from the Japanese patients were as follows: omalizumab clearance 7.32 ± 0.153 ml h−1, IgE clearance 71.0 ± 4.68 ml h−1 and the difference between that for omalizumab and the complex 5.86 ± 0.920 ml h−1, the volume of distribution for omalizumab and IgE 5900 ± 107 ml, and that for the complex 3630 ± 223 ml, the rate of IgE production 30.3 ± 2.04 µg h−1. Half-lives of IgG (23 days) and IgE (2.4 days) were close to previous reports. The dissociation constant for binding, 1.07 nM, was similar to in vitro values. Clearance and volume of distribution for omalizumab varied with bodyweight, whereas the clearance and rate of production of IgE were predicted accurately by baseline IgE. Overall, these covariates explained much of the interindividual variability. Conclusions The predictiveness of the Japanese model was confirmed by Monte-Carlo simulations for a White population, also providing evidence that the pharmacokinetics of omalizumab and IgE were similar in these two populations. Furthermore, the model enabled the estimation of not only omalizumab disposition parameters, but also the binding with and the rate of production, distribution and elimination of its target, IgE. PMID:17096680

  16. Bayesian network modeling of operator's state recognition process

    International Nuclear Information System (INIS)

    Hatakeyama, Naoki; Furuta, Kazuo

    2000-01-01

    Nowadays we are facing a difficult problem of establishing a good relation between humans and machines. To solve this problem, we suppose that machine system need to have a model of human behavior. In this study we model the state cognition process of a PWR plant operator as an example. We use a Bayesian network as an inference engine. We incorporate the knowledge hierarchy in the Bayesian network and confirm its validity using the example of PWR plant operator. (author)

  17. Attention-based Memory Selection Recurrent Network for Language Modeling

    OpenAIRE

    Liu, Da-Rong; Chuang, Shun-Po; Lee, Hung-yi

    2016-01-01

    Recurrent neural networks (RNNs) have achieved great success in language modeling. However, since the RNNs have fixed size of memory, their memory cannot store all the information about the words it have seen before in the sentence, and thus the useful long-term information may be ignored when predicting the next words. In this paper, we propose Attention-based Memory Selection Recurrent Network (AMSRN), in which the model can review the information stored in the memory at each previous time ...

  18. Design and realization of a network security model

    OpenAIRE

    WANG, Jiahai; HAN, Fangxi; Tang, Zheng; TAMURA, Hiroki; Ishii, Masahiro

    2002-01-01

    The security of information is a key problem in the development of network technology. The basic requirements of security of information clearly include confidentiality, integrity, authentication and non-repudiation. This paper proposes a network security model that is composed of security system, security connection and communication, and key management. The model carries out encrypting, decrypting, signature and ensures confidentiality, integrity, authentication and non-repudiation. Finally...

  19. Trojan detection model based on network behavior analysis

    International Nuclear Information System (INIS)

    Liu Junrong; Liu Baoxu; Wang Wenjin

    2012-01-01

    Based on the analysis of existing Trojan detection technology, this paper presents a Trojan detection model based on network behavior analysis. First of all, we abstract description of the Trojan network behavior, then according to certain rules to establish the characteristic behavior library, and then use the support vector machine algorithm to determine whether a Trojan invasion. Finally, through the intrusion detection experiments, shows that this model can effectively detect Trojans. (authors)

  20. Aplikasi Bidirectional Assosiatif Memori (BAM) Network pada Pengenalan Model

    OpenAIRE

    Iskandar, Iskhaq

    2001-01-01

    Penelitian ini bertujuan untuk menyusun suatu simulasi komputer yang dapat dipergunakan untuk menguji kemampuan memori komputer dalam mengenali suatu model tertentu berdasarkan algoritma Bidirectional Assosiatif Memori Neural Network. Model yang digunakan dalam penelitian dalam penelitian ini adalah huruf-huruf abjad yang dinyatakan dalam kode polar –1 dan +1 dalam bentuk matrik [5x3]. Hasil yang didapat dalam penelitian ini menunjukkan bahwa rancangan network yang disusun mampu mengenali mod...

  1. A network security situation prediction model based on wavelet neural network with optimized parameters

    Directory of Open Access Journals (Sweden)

    Haibo Zhang

    2016-08-01

    Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.

  2. Extending a configuration model to find communities in complex networks

    International Nuclear Information System (INIS)

    Jin, Di; Hu, Qinghua; He, Dongxiao; Yang, Bo; Baquero, Carlos

    2013-01-01

    Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities. (paper)

  3. Spatial-temporal modeling of malware propagation in networks.

    Science.gov (United States)

    Chen, Zesheng; Ji, Chuanyi

    2005-09-01

    Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation.

  4. Statistical mechanics of attractor neural network models with synaptic depression

    International Nuclear Information System (INIS)

    Igarashi, Yasuhiko; Oizumi, Masafumi; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato

    2009-01-01

    Synaptic depression is known to control gain for presynaptic inputs. Since cortical neurons receive thousands of presynaptic inputs, and their outputs are fed into thousands of other neurons, the synaptic depression should influence macroscopic properties of neural networks. We employ simple neural network models to explore the macroscopic effects of synaptic depression. Systems with the synaptic depression cannot be analyzed due to asymmetry of connections with the conventional equilibrium statistical-mechanical approach. Thus, we first propose a microscopic dynamical mean field theory. Next, we derive macroscopic steady state equations and discuss the stabilities of steady states for various types of neural network models.

  5. Reactor pressure vessel embrittlement: Insights from neural network modelling

    Science.gov (United States)

    Mathew, J.; Parfitt, D.; Wilford, K.; Riddle, N.; Alamaniotis, M.; Chroneos, A.; Fitzpatrick, M. E.

    2018-04-01

    Irradiation embrittlement of steel pressure vessels is an important consideration for the operation of current and future light water nuclear reactors. In this study we employ an ensemble of artificial neural networks in order to provide predictions of the embrittlement using two literature datasets, one based on US surveillance data and the second from the IVAR experiment. We use these networks to examine trends with input variables and to assess various literature models including compositional effects and the role of flux and temperature. Overall, the networks agree with the existing literature models and we comment on their more general use in predicting irradiation embrittlement.

  6. Randomizing growing networks with a time-respecting null model

    Science.gov (United States)

    Ren, Zhuo-Ming; Mariani, Manuel Sebastian; Zhang, Yi-Cheng; Medo, Matúš

    2018-05-01

    Complex networks are often used to represent systems that are not static but grow with time: People make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology—a time-respecting null model—that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two data sets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.

  7. Innovative research of AD HOC network mobility model

    Science.gov (United States)

    Chen, Xin

    2017-08-01

    It is difficult for researchers of AD HOC network to conduct actual deployment during experimental stage as the network topology is changeable and location of nodes is unfixed. Thus simulation still remains the main research method of the network. Mobility model is an important component of AD HOC network simulation. It is used to describe the movement pattern of nodes in AD HOC network (including location and velocity, etc.) and decides the movement trail of nodes, playing as the abstraction of the movement modes of nodes. Therefore, mobility model which simulates node movement is an important foundation for simulation research. In AD HOC network research, mobility model shall reflect the movement law of nodes as truly as possible. In this paper, node generally refers to the wireless equipment people carry. The main research contents include how nodes avoid obstacles during movement process and the impacts of obstacles on the mutual relation among nodes, based on which a Node Self Avoiding Obstacle, i.e. NASO model is established in AD HOC network.

  8. Modified network simulation model with token method of bus access

    Directory of Open Access Journals (Sweden)

    L.V. Stribulevich

    2013-08-01

    Full Text Available Purpose. To study the characteristics of the local network with the marker method of access to the bus its modified simulation model was developed. Methodology. Defining characteristics of the network is carried out on the developed simulation model, which is based on the state diagram-layer network station with the mechanism of processing priorities, both in steady state and in the performance of control procedures: the initiation of a logical ring, the entrance and exit of the station network with a logical ring. Findings. A simulation model, on the basis of which can be obtained the dependencies of the application the maximum waiting time in the queue for different classes of access, and the reaction time usable bandwidth on the data rate, the number of network stations, the generation rate applications, the number of frames transmitted per token holding time, frame length was developed. Originality. The technique of network simulation reflecting its work in the steady condition and during the control procedures, the mechanism of priority ranking and handling was proposed. Practical value. Defining network characteristics in the real-time systems on railway transport based on the developed simulation model.

  9. A model of mitochondrial creatine kinase binding to membranes: adsorption constants, essential amino acids and the effect of ionic strength

    DEFF Research Database (Denmark)

    Fedosov, Sergey; Belousova, Lubov; Plesner, Igor

    1993-01-01

    The quantitative aspects of mitochondrial creatinekinase (mitCK) binding to mitochondrial membranes were investigated. A simple adsorption and binding model was used for data fitting, taking into account the influence of protein concentration, pH, ionic strength and substrate concentration on the...

  10. Investigation on the protein-binding properties of icotinib by spectroscopic and molecular modeling method

    Science.gov (United States)

    Zhang, Hua-xin; Xiong, Hang-xing; Li, Li-wei

    2016-05-01

    Icotinib is a highly-selective epidermal growth factor receptor tyrosine kinase inhibitor with preclinical and clinical activity in non-small cell lung cancer, which has been developed as a new targeted anti-tumor drug in China. In this work, the interaction of icotinib and human serum albumin (HSA) were studied by three-dimensional fluorescence spectra, ultraviolet spectra, circular dichroism (CD) spectra, molecular probe and molecular modeling methods. The results showed that icotinib binds to Sudlow's site I in subdomain IIA of HSA molecule, resulting in icotinib-HSA complexes formed at ground state. The number of binding sites, equilibrium constants, and thermodynamic parameters of the reaction were calculated at different temperatures. The negative enthalpy change (ΔHθ) and entropy change (ΔSθ) indicated that the structure of new complexes was stabilized by hydrogen bonds and van der Waals power. The distance between donor and acceptor was calculated according to Förster's non-radiation resonance energy transfer theory. The structural changes of HSA caused by icotinib binding were detected by synchronous spectra and circular dichroism (CD) spectra. Molecular modeling method was employed to unfold full details of the interaction at molecular level, most of which could be supported by experimental results. The study analyzed the probability that serum albumins act as carriers for this new anticarcinogen and provided fundamental information on the process of delivering icotinib to its target tissues, which might be helpful in understanding the mechanism of icotinib in cancer therapy.

  11. Binding of carbendazim to bovine serum albumin: Insights from experimental and molecular modeling studies

    Science.gov (United States)

    Li, Jinhua; Zhang, Yulei; Hu, Lin; Kong, Yaling; Jin, Changqing; Xi, Zengzhe

    2017-07-01

    Carbendazim (CBZ) is a widely used benzimidazole fungicide in agriculture to control a wide range of fruit and vegetable pathogens, which may lead to potential health hazards. To evaluate the potential toxicity of CBZ, the binding mechanism of bovine serum albumin (BSA) with CBZ was investigated by the fluorescence quenching technology, UV absorbance spectra, circular dichroism (CD), and molecular modeling. The fluorescence titration and UV absorbance spectra revealed that the fluorescence quenching mechanism of BSA by CBZ was a combined quenching process. In addition, the studies of CD spectra suggested that the binding of CBZ to BSA changed the secondary structure of protein. Furthermore, the thermodynamic functions of enthalpy change (ΔH0) and entropy change (ΔS0) for the reaction were calculated to be 24.87 kJ mol-1 and 162.95 J mol-1 K-1 according to Van't Hoff equation. These data suggested that hydrophobic interaction play a major role in the binding of CBZ to BSA, which was in good agreement with the result of molecular modeling study.

  12. Novel thrombopoietin mimetic peptides bind c-Mpl receptor: Synthesis, biological evaluation and molecular modeling.

    Science.gov (United States)

    Liu, Yaquan; Tian, Fang; Zhi, Dejuan; Wang, Haiqing; Zhao, Chunyan; Li, Hongyu

    2017-02-01

    Thrombopoietin (TPO) acts in promoting the proliferation of hematopoietic stem cells and by initiating specific maturation events in megakaryocytes. Now, TPO-mimetic peptides with amino acid sequences unrelated to TPO are of considerable pharmaceutical interest. In the present paper, four new TPO mimetic peptides that bind and activate c-Mpl receptor have been identified, synthesized and tested by Dual-Luciferase reporter gene assay for biological activities. The molecular modeling research was also approached to understand key molecular mechanisms and structural features responsible for peptide binding with c-Mpl receptor. The results presented that three of four mimetic peptides showed significant activities. In addition, the molecular modeling approaches proved hydrophobic interactions were the driven positive forces for binding behavior between peptides and c-Mpl receptor. TPO peptide residues in P7, P13 and P7' positions were identified by the analysis of hydrogen bonds and energy decompositions as the key ones for benefiting better biological activities. Our data suggested the synthesized peptides have considerable potential for the future development of stable and highly active TPO mimetic peptides. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Molecular Modeling of the M3 Acetylcholine Muscarinic Receptor and Its Binding Site

    Directory of Open Access Journals (Sweden)

    Marlet Martinez-Archundia

    2012-01-01

    Full Text Available The present study reports the results of a combined computational and site mutagenesis study designed to provide new insights into the orthosteric binding site of the human M3 muscarinic acetylcholine receptor. For this purpose a three-dimensional structure of the receptor at atomic resolution was built by homology modeling, using the crystallographic structure of bovine rhodopsin as a template. Then, the antagonist N-methylscopolamine was docked in the model and subsequently embedded in a lipid bilayer for its refinement using molecular dynamics simulations. Two different lipid bilayer compositions were studied: one component palmitoyl-oleyl phosphatidylcholine (POPC and two-component palmitoyl-oleyl phosphatidylcholine/palmitoyl-oleyl phosphatidylserine (POPC-POPS. Analysis of the results suggested that residues F222 and T235 may contribute to the ligand-receptor recognition. Accordingly, alanine mutants at positions 222 and 235 were constructed, expressed, and their binding properties determined. The results confirmed the role of these residues in modulating the binding affinity of the ligand.

  14. Maritime piracy situation modelling with dynamic Bayesian networks

    CSIR Research Space (South Africa)

    Dabrowski, James M

    2015-05-01

    Full Text Available A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a...

  15. The communications model of using social network by sports clubs

    OpenAIRE

    S. Kowalski

    2012-01-01

    This article presents a model of marketing communications using social network by sports clubs. It presents the links between the sports club and its environment and uses of the Internet to promote it. The model of communication is composed of the elements responsible for the success of marketing in the web. Article also includes recommendations for using the model in practice.

  16. Use of computational modeling in preparation and evaluation of surface imprinted xerogels for binding tetracycline

    International Nuclear Information System (INIS)

    Pace, Samantha J.; Nguyen, Eric; Baria, Maximillian P.; Mojica, Elmer-Rico E.

    2015-01-01

    Linear alkyl alkoxysilanes (methoxy and ethoxy-based) of varying length were used in preparing tetracycline surface imprinted silica xerogels by the sol–gel process. The resulting xerogels were characterized in terms of binding tetracycline (TC) by using tritium-labeled TC. Results showed preferential binding in the ethoxysilane based xerogels in comparison to methoxysilane based xerogels. A computational approach using the interaction energy (IE) between TC and each alkyl alkoxysilane was deduced as a rational way of predicting the formulation that would provide the best analytical performance for a given molecularly imprinted xerogel (MIX). Hartree-Fock calculations revealed an increase in IE as the length of the carbon chain increases until an optimum value at C6 in both alkoxysilanes. This is consistent with the experimental results wherein the C6 xerogel formulation has the highest imprinting factor. These results show the potential of using computational modeling as a rational way of preparing surface imprinted materials. (author)

  17. Diffusion Retardation by Binding of Tobramycin in an Alginate Biofilm Model

    DEFF Research Database (Denmark)

    Cao, Bao; Christophersen, Lars; Kolpen, Mette

    2016-01-01

    Microbial cells embedded in a self-produced extracellular biofilm matrix cause chronic infections, e. g. by Pseudomonas aeruginosa in the lungs of cystic fibrosis patients. The antibiotic killing of bacteria in biofilms is generally known to be reduced by 100–1000 times relative to planktonic...... bacteria. This makes such infections difficult to treat. We have therefore proposed that biofilms can be regarded as an independent compartment with distinct pharmacokinetics. To elucidate this pharmacokinetics we have measured the penetration of the tobramycin into seaweed alginate beads which serve...... to be uniformly distributed throughout the volume of the alginate bead. The power-law appears to be a consequence of binding to a multitude of different binding sites. In a diffusion model these results are shown to produce pronounced retardation of the penetration of tobramycin into the biofilm. This filtering...

  18. Study on models of O2 binding to heme using density functional theory

    Directory of Open Access Journals (Sweden)

    Hovorun D. M.

    2009-08-01

    Full Text Available Aim. To study a mechanism of molecular oxygen binding to heme three models of geometry structure of the complex are considered: the axis of O2 molecule is situated perpendicularly to the porphin macrocycle, parallel, and angularly. Methods. The Fe(II porphin complexes with dioxygen are calculated by the quantum-chemical method of density functional theory with the UB3LYP/6-311G approximation. Results. The optimized geometry and electron structures as well as the absorption IR spectra of the complexes in the high-spin (septet state are described. Conclusions. It is shown that the main mechanism of spin-orbit coupling during the O2 binding to heme is connected with peculiarity of the O2 molecule electronic structure.

  19. A network growth model based on the evolutionary ultimatum game

    International Nuclear Information System (INIS)

    Deng, L L; Zhou, G G; Cai, J H; Wang, C; Tang, W S

    2012-01-01

    In this paper, we provide a network growth model with incorporation into the ultimatum game dynamics. The network grows on the basis of the payoff-oriented preferential attachment mechanism, where a new node is added into the network and attached preferentially to nodes with higher payoffs. The interplay between the network growth and the game dynamics gives rise to quite interesting dynamical behaviors. Simulation results show the emergence of altruistic behaviors in the ultimatum game, which is affected by the growing network structure. Compared with the static counterpart case, the levels of altruistic behaviors are promoted. The corresponding strategy distributions and wealth distributions are also presented to further demonstrate the strategy evolutionary dynamics. Subsequently, we turn to the topological properties of the evolved network, by virtue of some statistics. The most studied characteristic path length and the clustering coefficient of the network are shown to indicate their small-world effect. Then the degree distributions are analyzed to clarify the interplay of structure and evolutionary dynamics. In particular, the difference between our growth network and the static counterpart is revealed. To explain clearly the evolved networks, the rich-club ordering and the assortative mixing coefficient are exploited to reveal the degree correlation. (paper)

  20. Language Networks as Models of Cognition: Understanding Cognition through Language

    Science.gov (United States)

    Beckage, Nicole M.; Colunga, Eliana

    Language is inherently cognitive and distinctly human. Separating the object of language from the human mind that processes and creates language fails to capture the full language system. Linguistics traditionally has focused on the study of language as a static representation, removed from the human mind. Network analysis has traditionally been focused on the properties and structure that emerge from network representations. Both disciplines could gain from looking at language as a cognitive process. In contrast, psycholinguistic research has focused on the process of language without committing to a representation. However, by considering language networks as approximations of the cognitive system we can take the strength of each of these approaches to study human performance and cognition as related to language. This paper reviews research showcasing the contributions of network science to the study of language. Specifically, we focus on the interplay of cognition and language as captured by a network representation. To this end, we review different types of language network representations before considering the influence of global level network features. We continue by considering human performance in relation to network structure and conclude with theoretical network models that offer potential and testable explanations of cognitive and linguistic phenomena.