BIMLR: a method for constructing rooted phylogenetic networks from rooted phylogenetic trees.

PubMed

Wang, Juan; Guo, Maozu; Xing, Linlin; Che, Kai; Liu, Xiaoyan; Wang, Chunyu

2013-09-15

Rooted phylogenetic trees constructed from different datasets (e.g. from different genes) are often conflicting with one another, i.e. they cannot be integrated into a single phylogenetic tree. Phylogenetic networks have become an important tool in molecular evolution, and rooted phylogenetic networks are able to represent conflicting rooted phylogenetic trees. Hence, the development of appropriate methods to compute rooted phylogenetic networks from rooted phylogenetic trees has attracted considerable research interest of late. The CASS algorithm proposed by van Iersel et al. is able to construct much simpler networks than other available methods, but it is extremely slow, and the networks it constructs are dependent on the order of the input data. Here, we introduce an improved CASS algorithm, BIMLR. We show that BIMLR is faster than CASS and less dependent on the input data order. Moreover, BIMLR is able to construct much simpler networks than almost all other methods. BIMLR is available at http://nclab.hit.edu.cn/wangjuan/BIMLR/. © 2013 Elsevier B.V. All rights reserved.

The Course as Token: A Construction of/by Networks.

ERIC Educational Resources Information Center

Gaskell, Jim; Hepburn, Gary

1998-01-01

Describes the way in which a new applied-physics course introduced in British Columbia as part of a program in applied academics can be seen to construct different networks in different contexts. Employs actor network theory (ANT). Contains 20 references. (DDR)

Signalling Network Construction for Modelling Plant Defence Response

PubMed Central

Miljkovic, Dragana; Stare, Tjaša; Mozetič, Igor; Podpečan, Vid; Petek, Marko; Witek, Kamil; Dermastia, Marina; Lavrač, Nada; Gruden, Kristina

2012-01-01

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for

Construction of ontology augmented networks for protein complex prediction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

Constructing a Watts-Strogatz network from a small-world network with symmetric degree distribution.

PubMed

Menezes, Mozart B C; Kim, Seokjin; Huang, Rongbing

2017-01-01

Though the small-world phenomenon is widespread in many real networks, it is still challenging to replicate a large network at the full scale for further study on its structure and dynamics when sufficient data are not readily available. We propose a method to construct a Watts-Strogatz network using a sample from a small-world network with symmetric degree distribution. Our method yields an estimated degree distribution which fits closely with that of a Watts-Strogatz network and leads into accurate estimates of network metrics such as clustering coefficient and degree of separation. We observe that the accuracy of our method increases as network size increases.

A computational geometry approach to pore network construction for granular packings

NASA Astrophysics Data System (ADS)

van der Linden, Joost H.; Sufian, Adnan; Narsilio, Guillermo A.; Russell, Adrian R.; Tordesillas, Antoinette

2018-03-01

Pore network construction provides the ability to characterize and study the pore space of inhomogeneous and geometrically complex granular media in a range of scientific and engineering applications. Various approaches to the construction have been proposed, however subtle implementational details are frequently omitted, open access to source code is limited, and few studies compare multiple algorithms in the context of a specific application. This study presents, in detail, a new pore network construction algorithm, and provides a comprehensive comparison with two other, well-established Delaunay triangulation-based pore network construction methods. Source code is provided to encourage further development. The proposed algorithm avoids the expensive non-linear optimization procedure in existing Delaunay approaches, and is robust in the presence of polydispersity. Algorithms are compared in terms of structural, geometrical and advanced connectivity parameters, focusing on the application of fluid flow characteristics. Sensitivity of the various networks to permeability is assessed through network (Stokes) simulations and finite-element (Navier-Stokes) simulations. Results highlight strong dependencies of pore volume, pore connectivity, throat geometry and fluid conductance on the degree of tetrahedra merging and the specific characteristics of the throats targeted by the merging algorithm. The paper concludes with practical recommendations on the applicability of the three investigated algorithms.

Hydrogel Bioprinted Microchannel Networks for Vascularization of Tissue Engineering Constructs

PubMed Central

Bertassoni, Luiz E.; Cecconi, Martina; Manoharan, Vijayan; Nikkhah, Mehdi; Hjortnaes, Jesper; Cristino, Ana Luiza; Barabaschi, Giada; Demarchi, Danilo; Dokmeci, Mehmet R.; Yang, Yunzhi; Khademhosseini, Ali

2014-01-01

Vascularization remains a critical challenge in tissue engineering. The development of vascular networks within densely populated and metabolically functional tissues facilitate transport of nutrients and removal of waste products, thus preserving cellular viability over a long period of time. Despite tremendous progress in fabricating complex tissue constructs in the past few years, approaches for controlled vascularization within hydrogel based engineered tissue constructs have remained limited. Here, we report a three dimensional (3D) micromolding technique utilizing bioprinted agarose template fibers to fabricate microchannel networks with various architectural features within photo cross linkable hydrogel constructs. Using the proposed approach, we were able to successfully embed functional and perfusable microchannels inside methacrylated gelatin (GelMA), star poly (ethylene glycol-co-lactide) acrylate (SPELA), poly (ethylene glycol) dimethacrylate (PEGDMA) and poly (ethylene glycol) diacrylate (PEGDA) hydrogels at different concentrations. In particular, GelMA hydrogels were used as a model to demonstrate the functionality of the fabricated vascular networks in improving mass transport, cellular viability and differentiation within the cell-laden tissue constructs. In addition, successful formation of endothelial monolayers within the fabricated channels was confirmed. Overall, our proposed strategy represents an effective technique for vascularization of hydrogel constructs with useful applications in tissue engineering and organs on a chip. PMID:24860845

Automated Construction of Node Software Using Attributes in a Ubiquitous Sensor Network Environment

PubMed Central

Lee, Woojin; Kim, Juil; Kang, JangMook

2010-01-01

In sensor networks, nodes must often operate in a demanding environment facing restrictions such as restricted computing resources, unreliable wireless communication and power shortages. Such factors make the development of ubiquitous sensor network (USN) applications challenging. To help developers construct a large amount of node software for sensor network applications easily and rapidly, this paper proposes an approach to the automated construction of node software for USN applications using attributes. In the proposed technique, application construction proceeds by first developing a model for the sensor network and then designing node software by setting the values of the predefined attributes. After that, the sensor network model and the design of node software are verified. The final source codes of the node software are automatically generated from the sensor network model. We illustrate the efficiency of the proposed technique by using a gas/light monitoring application through a case study of a Gas and Light Monitoring System based on the Nano-Qplus operating system. We evaluate the technique using a quantitative metric—the memory size of execution code for node software. Using the proposed approach, developers are able to easily construct sensor network applications and rapidly generate a large number of node softwares at a time in a ubiquitous sensor network environment. PMID:22163678

Automated construction of node software using attributes in a ubiquitous sensor network environment.

PubMed

Lee, Woojin; Kim, Juil; Kang, JangMook

2010-01-01

In sensor networks, nodes must often operate in a demanding environment facing restrictions such as restricted computing resources, unreliable wireless communication and power shortages. Such factors make the development of ubiquitous sensor network (USN) applications challenging. To help developers construct a large amount of node software for sensor network applications easily and rapidly, this paper proposes an approach to the automated construction of node software for USN applications using attributes. In the proposed technique, application construction proceeds by first developing a model for the sensor network and then designing node software by setting the values of the predefined attributes. After that, the sensor network model and the design of node software are verified. The final source codes of the node software are automatically generated from the sensor network model. We illustrate the efficiency of the proposed technique by using a gas/light monitoring application through a case study of a Gas and Light Monitoring System based on the Nano-Qplus operating system. We evaluate the technique using a quantitative metric-the memory size of execution code for node software. Using the proposed approach, developers are able to easily construct sensor network applications and rapidly generate a large number of node softwares at a time in a ubiquitous sensor network environment.

[Strategic thinking of the construction of national schistosomiasis laboratory network in China].

PubMed

Qin, Zhi-Qiang; Xu, Jing; Feng, Ting; Zhu, Hong-Qing; Li, Shi-Zhu; Xiao, Ning; Zhou, Xiao-Nong

2013-08-01

A schistosomiasis laboratory network and its quality assurance system have been built and will be more and more perfect in China. This paper introduces the present situation of schistosomiasis diagnosis in China and expounds the basic ideas and the progress in the construction of schistosomiasis network platform. Furthermore, the face of schistosomiasis diagnosis network platform construction and operation of the challenge and the future work will be put forward in the latter part of this paper.

Construction of multi-scale consistent brain networks: methods and applications.

PubMed

Ge, Bao; Tian, Yin; Hu, Xintao; Chen, Hanbo; Zhu, Dajiang; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

Review of pore network modelling of porous media: Experimental characterisations, network constructions and applications to reactive transport.

PubMed

Xiong, Qingrong; Baychev, Todor G; Jivkov, Andrey P

2016-09-01

Pore network models have been applied widely for simulating a variety of different physical and chemical processes, including phase exchange, non-Newtonian displacement, non-Darcy flow, reactive transport and thermodynamically consistent oil layers. The realism of such modelling, i.e. the credibility of their predictions, depends to a large extent on the quality of the correspondence between the pore space of a given medium and the pore network constructed as its representation. The main experimental techniques for pore space characterisation, including direct imaging, mercury intrusion porosimetry and gas adsorption, are firstly summarised. A review of the main pore network construction techniques is then presented. Particular focus is given on how such constructions are adapted to the data from experimentally characterised pore systems. Current applications of pore network models are considered, with special emphasis on the effects of adsorption, dissolution and precipitation, as well as biomass growth, on transport coefficients. Pore network models are found to be a valuable tool for understanding and predicting meso-scale phenomena, linking single pore processes, where other techniques are more accurate, and the homogenised continuum porous media, used by engineering community. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks.

PubMed

Zhou, Xiaobo; Wang, Xiaodong; Pal, Ranadip; Ivanov, Ivan; Bittner, Michael; Dougherty, Edward R

2004-11-22

We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes. We propose a fully Bayesian approach to constructing probabilistic gene regulatory networks (PGRNs) that emphasizes network topology. The method computes the possible parent sets of each gene, the corresponding predictors and the associated probabilities based on a nonlinear perceptron model, using a reversible jump Markov chain Monte Carlo (MCMC) technique, and an MCMC method is employed to search the network configurations to find those with the highest Bayesian scores to construct the PGRN. The Bayesian method has been used to construct a PGRN based on the observed behavior of a set of genes whose expression patterns vary across a set of melanoma samples exhibiting two very different phenotypes with respect to cell motility and invasiveness. Key biological features have been faithfully reflected in the model. Its steady-state distribution contains attractors that are either identical or very similar to the states observed in the data, and many of the attractors are singletons, which mimics the biological propensity to stably occupy a given state. Most interestingly, the connectivity rules for the most optimal generated networks constituting the PGRN are remarkably similar, as would be expected for a network operating on a distributed basis, with strong interactions between the components.

Fast Construction of Near Parsimonious Hybridization Networks for Multiple Phylogenetic Trees.

PubMed

Mirzaei, Sajad; Wu, Yufeng

2016-01-01

Hybridization networks represent plausible evolutionary histories of species that are affected by reticulate evolutionary processes. An established computational problem on hybridization networks is constructing the most parsimonious hybridization network such that each of the given phylogenetic trees (called gene trees) is "displayed" in the network. There have been several previous approaches, including an exact method and several heuristics, for this NP-hard problem. However, the exact method is only applicable to a limited range of data, and heuristic methods can be less accurate and also slow sometimes. In this paper, we develop a new algorithm for constructing near parsimonious networks for multiple binary gene trees. This method is more efficient for large numbers of gene trees than previous heuristics. This new method also produces more parsimonious results on many simulated datasets as well as a real biological dataset than a previous method. We also show that our method produces topologically more accurate networks for many datasets.

Construction of road network vulnerability evaluation index based on general travel cost

NASA Astrophysics Data System (ADS)

Leng, Jun-qiang; Zhai, Jing; Li, Qian-wen; Zhao, Lin

2018-03-01

With the development of China's economy and the continuous improvement of her urban road network, the vulnerability of the urban road network has attracted increasing attention. Based on general travel cost, this work constructs the vulnerability evaluation index for the urban road network, and evaluates the vulnerability of the urban road network from the perspective of user generalised travel cost. Firstly, the generalised travel cost model is constructed based on vehicle cost, travel time, and traveller comfort. Then, the network efficiency index is selected as an evaluation index of vulnerability: the network efficiency index is composed of the traffic volume and the generalised travel cost, which are obtained from the equilibrium state of the network. In addition, the research analyses the influence of traffic capacity decrease, road section attribute value, and location of road section, on vulnerability. Finally, the vulnerability index is used to analyse the local area network of Harbin and verify its applicability.

Dynamic Construction Scheme for Virtualization Security Service in Software-Defined Networks

PubMed Central

Lin, Zhaowen; Tao, Dan; Wang, Zhenji

2017-01-01

For a Software Defined Network (SDN), security is an important factor affecting its large-scale deployment. The existing security solutions for SDN mainly focus on the controller itself, which has to handle all the security protection tasks by using the programmability of the network. This will undoubtedly involve a heavy burden for the controller. More devastatingly, once the controller itself is attacked, the entire network will be paralyzed. Motivated by this, this paper proposes a novel security protection architecture for SDN. We design a security service orchestration center in the control plane of SDN, and this center physically decouples from the SDN controller and constructs SDN security services. We adopt virtualization technology to construct a security meta-function library, and propose a dynamic security service composition construction algorithm based on web service composition technology. The rule-combining method is used to combine security meta-functions to construct security services which meet the requirements of users. Moreover, the RETE algorithm is introduced to improve the efficiency of the rule-combining method. We evaluate our solutions in a realistic scenario based on OpenStack. Substantial experimental results demonstrate the effectiveness of our solutions that contribute to achieve the effective security protection with a small burden of the SDN controller. PMID:28430155

Dynamic Construction Scheme for Virtualization Security Service in Software-Defined Networks.

PubMed

Lin, Zhaowen; Tao, Dan; Wang, Zhenji

2017-04-21

For a Software Defined Network (SDN), security is an important factor affecting its large-scale deployment. The existing security solutions for SDN mainly focus on the controller itself, which has to handle all the security protection tasks by using the programmability of the network. This will undoubtedly involve a heavy burden for the controller. More devastatingly, once the controller itself is attacked, the entire network will be paralyzed. Motivated by this, this paper proposes a novel security protection architecture for SDN. We design a security service orchestration center in the control plane of SDN, and this center physically decouples from the SDN controller and constructs SDN security services. We adopt virtualization technology to construct a security meta-function library, and propose a dynamic security service composition construction algorithm based on web service composition technology. The rule-combining method is used to combine security meta-functions to construct security services which meet the requirements of users. Moreover, the RETE algorithm is introduced to improve the efficiency of the rule-combining method. We evaluate our solutions in a realistic scenario based on OpenStack. Substantial experimental results demonstrate the effectiveness of our solutions that contribute to achieve the effective security protection with a small burden of the SDN controller.

A novel constructive-optimizer neural network for the traveling salesman problem.

PubMed

Saadatmand-Tarzjan, Mahdi; Khademi, Morteza; Akbarzadeh-T, Mohammad-R; Moghaddam, Hamid Abrishami

2007-08-01

In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.

Construction of regulatory networks using expression time-series data of a genotyped population.

PubMed

Yeung, Ka Yee; Dombek, Kenneth M; Lo, Kenneth; Mittler, John E; Zhu, Jun; Schadt, Eric E; Bumgarner, Roger E; Raftery, Adrian E

2011-11-29

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview.

PubMed

Skinner, Jeff; Kotliarov, Yuri; Varma, Sudhir; Mine, Karina L; Yambartsev, Anatoly; Simon, Richard; Huyen, Yentram; Morgun, Andrey

2011-07-14

DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes. Each significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools. DAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.

A Constructive Neural-Network Approach to Modeling Psychological Development

ERIC Educational Resources Information Center

Shultz, Thomas R.

2012-01-01

This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…

Construction of Injectable Double-Network Hydrogels for Cell Delivery.

PubMed

Yan, Yan; Li, Mengnan; Yang, Di; Wang, Qian; Liang, Fuxin; Qu, Xiaozhong; Qiu, Dong; Yang, Zhenzhong

2017-07-10

Herein we present a unique method of using dynamic cross-links, which are dynamic covalent bonding and ionic interaction, for the construction of injectable double-network (DN) hydrogels, with the objective of cell delivery for cartilage repair. Glycol chitosan and dibenzaldhyde capped poly(ethylene oxide) formed the first network, while calcium alginate formed the second one, and in the resultant DN hydrogel, either of the networks could be selectively removed. The moduli of the DN hydrogel were significantly improved compared to that of the parent single-network hydrogels and were tunable by changing the chemical components. In situ 3D cell encapsulation could be easily performed by mixing cell suspension to the polymer solutions and transferred through a syringe needle before sol-gel transition. Cell proliferation and mediated differentiation of mouse chondrogenic cells were achieved in the DN hydrogel extracellular matrix.

Final Environmental Assessment for Wide Area Coverage Construct Land Mobile Network Communications Infrastructure Malmstrom Air Force Base, Montana

DTIC Science & Technology

2008-02-01

FINAL ENVIRONMENTAL ASSESSMENT February 2008 Malmstrom ® AFB WIDE AREA COVERAGE CONSTRUCT LAND MOBILE NETWORK COMMUNICATIONS INFRASTRUCTURE...Wide Area Coverage Construct Land Mobile Network Communications Infrastructure Malmstrom Air Force Base, Montana 5a. CONTRACT NUMBER 5b. GRANT...SIGNIFICANT IMPACT WIDE AREA COVERAGE CONSTRUCT LAND MOBILE NETWORK COMMUNICATIONS INFRASTRUCTURE MALMSTROM AIR FORCE BASE, MONTANA The

Constructively determining the MBL spectrum using Tensor Networks

NASA Astrophysics Data System (ADS)

Clark, Bryan; Yu, Xiongjie; Pekker, David

All the eigenstates of a many-body localized phase can be compactly represented in the tensor-network language. Current algorithms to find these states often only target single states and/or require difficult optimization to find. In this talk we will show how to generate every eigenstate in the spectrum constructively and discuss its implication for the properties of the MBL phase.

Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

PubMed

Kaiser, Jacob L; Bland, Cassidy L; Klinke, David J

2016-03-01

Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016. © 2016 American Institute of Chemical Engineers.

Multilevel regularized regression for simultaneous taxa selection and network construction with metagenomic count data.

PubMed

Liu, Zhenqiu; Sun, Fengzhu; Braun, Jonathan; McGovern, Dermot P B; Piantadosi, Steven

2015-04-01

Identifying disease associated taxa and constructing networks for bacteria interactions are two important tasks usually studied separately. In reality, differentiation of disease associated taxa and correlation among taxa may affect each other. One genus can be differentiated because it is highly correlated with another highly differentiated one. In addition, network structures may vary under different clinical conditions. Permutation tests are commonly used to detect differences between networks in distinct phenotypes, and they are time-consuming. In this manuscript, we propose a multilevel regularized regression method to simultaneously identify taxa and construct networks. We also extend the framework to allow construction of a common network and differentiated network together. An efficient algorithm with dual formulation is developed to deal with the large-scale n ≪ m problem with a large number of taxa (m) and a small number of samples (n) efficiently. The proposed method is regularized with a general Lp (p ∈ [0, 2]) penalty and models the effects of taxa abundance differentiation and correlation jointly. We demonstrate that it can identify both true and biologically significant genera and network structures. Software MLRR in MATLAB is available at http://biostatistics.csmc.edu/mlrr/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Construction of a pulse-coupled dipole network capable of fear-like and relief-like responses

NASA Astrophysics Data System (ADS)

Lungsi Sharma, B.

2016-07-01

The challenge for neuroscience as an interdisciplinary programme is the integration of ideas among the disciplines to achieve a common goal. This paper deals with the problem of deriving a pulse-coupled neural network that is capable of demonstrating behavioural responses (fear-like and relief-like). Current pulse-coupled neural networks are designed mostly for engineering applications, particularly image processing. The discovered neural network was constructed using the method of minimal anatomies approach. The behavioural response of a level-coded activity-based model was used as a reference. Although the spiking-based model and the activity-based model are of different scales, the use of model-reference principle means that the characteristics that is referenced is its functional properties. It is demonstrated that this strategy of dissection and systematic construction is effective in the functional design of pulse-coupled neural network system with nonlinear signalling. The differential equations for the elastic weights in the reference model are replicated in the pulse-coupled network geometrically. The network reflects a possible solution to the problem of punishment and avoidance. The network developed in this work is a new network topology for pulse-coupled neural networks. Therefore, the model-reference principle is a powerful tool in connecting neuroscience disciplines. The continuity of concepts and phenomena is further maintained by systematic construction using methods like the method of minimal anatomies.

Paving the Way Towards Reactive Planar Spanner Construction in Wireless Networks

NASA Astrophysics Data System (ADS)

Frey, Hannes; Rührup, Stefan

A spanner is a subgraph of a given graph that supports the original graph's shortest path lengths up to a constant factor. Planar spanners and their distributed construction are of particular interest for geographic routing, which is an efficient localized routing scheme for wireless ad hoc and sensor networks. Planarity of the network graph is a key criterion for guaranteed delivery, while the spanner property supports efficiency in terms of path length. We consider the problem of reactive local spanner construction, where a node's local topology is determined on demand. Known message-efficient reactive planarization algorithms do not preserve the spanner property, while reactive spanner constructions with a low message overhead have not been described so far. We introduce the concept of direct planarization which may be an enabler of efficient reactive spanner construction. Given an edge, nodes check for all incident intersecting edges a certain geometric criterion and withdraw the edge if this criterion is not satisfied. We use this concept to derive a generic reactive topology control mechanism and consider two geometric criteria. Simulation results show that direct planarization increases the performance of localized geographic routing by providing shorter paths than existing reactive approaches.

Differential Engagement of Brain Regions within a "Core" Network during Scene Construction

ERIC Educational Resources Information Center

Summerfield, Jennifer J.; Hassabis, Demis; Maguire, Eleanor A.

2010-01-01

Reliving past events and imagining potential future events engages a well-established "core" network of brain areas. How the brain constructs, or reconstructs, these experiences or scenes has been debated extensively in the literature, but remains poorly understood. Here we designed a novel task to investigate this (re)constructive process by…

Constructing networks from a dynamical system perspective for multivariate nonlinear time series.

PubMed

Nakamura, Tomomichi; Tanizawa, Toshihiro; Small, Michael

2016-03-01

We describe a method for constructing networks for multivariate nonlinear time series. We approach the interaction between the various scalar time series from a deterministic dynamical system perspective and provide a generic and algorithmic test for whether the interaction between two measured time series is statistically significant. The method can be applied even when the data exhibit no obvious qualitative similarity: a situation in which the naive method utilizing the cross correlation function directly cannot correctly identify connectivity. To establish the connectivity between nodes we apply the previously proposed small-shuffle surrogate (SSS) method, which can investigate whether there are correlation structures in short-term variabilities (irregular fluctuations) between two data sets from the viewpoint of deterministic dynamical systems. The procedure to construct networks based on this idea is composed of three steps: (i) each time series is considered as a basic node of a network, (ii) the SSS method is applied to verify the connectivity between each pair of time series taken from the whole multivariate time series, and (iii) the pair of nodes is connected with an undirected edge when the null hypothesis cannot be rejected. The network constructed by the proposed method indicates the intrinsic (essential) connectivity of the elements included in the system or the underlying (assumed) system. The method is demonstrated for numerical data sets generated by known systems and applied to several experimental time series.

Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences.

PubMed

Zhong, Suyu; He, Yong; Gong, Gaolang

2015-05-01

Using diffusion MRI, a number of studies have investigated the properties of whole-brain white matter (WM) networks with differing network construction methods (node/edge definition). However, how the construction methods affect individual differences of WM networks and, particularly, if distinct methods can provide convergent or divergent patterns of individual differences remain largely unknown. Here, we applied 10 frequently used methods to construct whole-brain WM networks in a healthy young adult population (57 subjects), which involves two node definitions (low-resolution and high-resolution) and five edge definitions (binary, FA weighted, fiber-density weighted, length-corrected fiber-density weighted, and connectivity-probability weighted). For these WM networks, individual differences were systematically analyzed in three network aspects: (1) a spatial pattern of WM connections, (2) a spatial pattern of nodal efficiency, and (3) network global and local efficiencies. Intriguingly, we found that some of the network construction methods converged in terms of individual difference patterns, but diverged with other methods. Furthermore, the convergence/divergence between methods differed among network properties that were adopted to assess individual differences. Particularly, high-resolution WM networks with differing edge definitions showed convergent individual differences in the spatial pattern of both WM connections and nodal efficiency. For the network global and local efficiencies, low-resolution and high-resolution WM networks for most edge definitions consistently exhibited a highly convergent pattern in individual differences. Finally, the test-retest analysis revealed a decent temporal reproducibility for the patterns of between-method convergence/divergence. Together, the results of the present study demonstrated a measure-dependent effect of network construction methods on the individual difference of WM network properties. © 2015 Wiley

[Construction and optimization of ecological network for nature reserves in Fujian Province, China].

PubMed

Gu, Fan; Huang, Yi Xiong; Chen, Chuan Ming; Cheng, Dong Liang; Guo, Jia Lei

2017-03-18

The nature reserve is very important to biodiversity maintenance. However, due to the urbanization, the nature reserve has been fragmented with reduction in area, leading to the loss of species diversity. Establishing ecological network can effectively connect the fragmented habitats and plays an important role in species conversation. In this paper, based on deciding habitat patches and the landscape cost surface in ArcGIS, a minimum cumulative resistance model was used to simulate the potential ecological network of Fujian provincial nature reserves. The connectivity and importance of network were analyzed and evaluated based on comparison of connectivity indices (including the integral index of connectivity and probability of connectivity) and gravity model both before and after the potential ecological network construction. The optimum ecological network optimization measures were proposed. The result demonstrated that woodlands, grasslands and wetlands together made up the important part of the nature reserve ecological network. The habitats with large area had a higher degree of importance in the network. After constructing the network, the connectivity level was significantly improved. Although interaction strength between different patches va-ried greatly, the corridors between patches with large interaction were very important. The research could provide scientific reference and basis for nature protection and planning in Fujian Province.

Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

PubMed

Gibson, Scott M; Ficklin, Stephen P; Isaacson, Sven; Luo, Feng; Feltus, Frank A; Smith, Melissa C

2013-01-01

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.

Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory

PubMed Central

Isaacson, Sven; Luo, Feng; Feltus, Frank A.; Smith, Melissa C.

2013-01-01

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust. PMID:23409071

Constructing financial network based on PMFG and threshold method

NASA Astrophysics Data System (ADS)

Nie, Chun-Xiao; Song, Fu-Tie

2018-04-01

Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low-dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis.

Constructing an integrated gene similarity network for the identification of disease genes.

PubMed

Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

2017-09-20

Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

Simple Deterministically Constructed Recurrent Neural Networks

NASA Astrophysics Data System (ADS)

Rodan, Ali; Tiňo, Peter

A large number of models for time series processing, forecasting or modeling follows a state-space formulation. Models in the specific class of state-space approaches, referred to as Reservoir Computing, fix their state-transition function. The state space with the associated state transition structure forms a reservoir, which is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be potentially exploited by the reservoir-to-output readout mapping. The largely "black box" character of reservoirs prevents us from performing a deeper theoretical investigation of the dynamical properties of successful reservoirs. Reservoir construction is largely driven by a series of (more-or-less) ad-hoc randomized model building stages, with both the researchers and practitioners having to rely on a series of trials and errors. We show that a very simple deterministically constructed reservoir with simple cycle topology gives performances comparable to those of the Echo State Network (ESN) on a number of time series benchmarks. Moreover, we argue that the memory capacity of such a model can be made arbitrarily close to the proved theoretical limit.

Evolutionary Construction of Block-Based Neural Networks in Consideration of Failure

NASA Astrophysics Data System (ADS)

Takamori, Masahito; Koakutsu, Seiichi; Hamagami, Tomoki; Hirata, Hironori

In this paper we propose a modified gene coding and an evolutionary construction in consideration of failure in evolutionary construction of Block-Based Neural Networks. In the modified gene coding, we arrange the genes of weights on a chromosome in consideration of the position relation of the genes of weight and structure. By the modified gene coding, the efficiency of search by crossover is increased. Thereby, it is thought that improvement of the convergence rate of construction and shortening of construction time can be performed. In the evolutionary construction in consideration of failure, the structure which is adapted for failure is built in the state where failure occured. Thereby, it is thought that BBNN can be reconstructed in a short time at the time of failure. To evaluate the proposed method, we apply it to pattern classification and autonomous mobile robot control problems. The computational experiments indicate that the proposed method can improve convergence rate of construction and shorten of construction and reconstruction time.

Constructing regional climate networks in the Amazonia during recent drought events.

PubMed

Guo, Heng; Ramos, Antônio M T; Macau, Elbert E N; Zou, Yong; Guan, Shuguang

2017-01-01

Climate networks are powerful approaches to disclose tele-connections in climate systems and to predict severe climate events. Here we construct regional climate networks from precipitation data in the Amazonian region and focus on network properties under the recent drought events in 2005 and 2010. Both the networks of the entire Amazon region and the extreme networks resulted from locations severely affected by drought events suggest that network characteristics show slight difference between the two drought events. Based on network degrees of extreme drought events and that without drought conditions, we identify regions of interest that are correlated to longer expected drought period length. Moreover, we show that the spatial correlation length to the regions of interest decayed much faster in 2010 than in 2005, which is because of the dual roles played by both the Pacific and Atlantic oceans. The results suggest that hub nodes in the regional climate network of Amazonia have fewer long-range connections when more severe drought conditions appeared in 2010 than that in 2005.

Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients.

PubMed

Li, Lianshuo; Wang, Zicheng; He, Peng; Ma, Shining; Du, Jie; Jiang, Rui

2016-10-01

Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 diabetes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D. Copyright © 2016. Production and hosting by Elsevier Ltd.

Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China

NASA Astrophysics Data System (ADS)

Gao, Yu; Ma, Lei; Liu, Jiaxun; Zhuang, Zhuzhou; Huang, Qiuhao; Li, Manchun

2017-04-01

Fragmentation and reduced continuity of habitat patches threaten the environment and biodiversity. Recently, ecological networks are increasingly attracting the attention of researchers as they provide fundamental frameworks for environmental protection. This study suggests a set of procedures to construct an ecological network. First, we proposed a method to construct a landscape resistance surface based on the assessment of habitat quality. Second, to analyze the effect of the resistance surface on corridor simulations, we used three methods to construct resistance surfaces: (1) the method proposed in this paper, (2) the entropy coefficient method, and (3) the expert scoring method. Then, we integrated habitat patches and resistance surfaces to identify potential corridors using graph theory. These procedures were tested in Changzhou, China. Comparing the outputs of using different resistance surfaces demonstrated that: (1) different landscape resistance surfaces contribute to how corridors are identified, but only slightly affect the assessment of the importance of habitat patches and potential corridors; (2) the resistance surface, which is constructed based on habitat quality, is more applicable to corridor simulations; and (3) the assessment of the importance of habitat patches is fundamental for ecological network optimization in the conservation of critical habitat patches and corridors.

Genonets server-a web server for the construction, analysis and visualization of genotype networks.

PubMed

Khalid, Fahad; Aguilar-Rodríguez, José; Wagner, Andreas; Payne, Joshua L

2016-07-08

A genotype network is a graph in which vertices represent genotypes that have the same phenotype. Edges connect vertices if their corresponding genotypes differ in a single small mutation. Genotype networks are used to study the organization of genotype spaces. They have shed light on the relationship between robustness and evolvability in biological systems as different as RNA macromolecules and transcriptional regulatory circuits. Despite the importance of genotype networks, no tool exists for their automatic construction, analysis and visualization. Here we fill this gap by presenting the Genonets Server, a tool that provides the following features: (i) the construction of genotype networks for categorical and univariate phenotypes from DNA, RNA, amino acid or binary sequences; (ii) analyses of genotype network topology and how it relates to robustness and evolvability, as well as analyses of genotype network topography and how it relates to the navigability of a genotype network via mutation and natural selection; (iii) multiple interactive visualizations that facilitate exploratory research and education. The Genonets Server is freely available at http://ieu-genonets.uzh.ch. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Characterizing system dynamics with a weighted and directed network constructed from time series data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Xiaoran, E-mail: sxr0806@gmail.com; School of Mathematics and Statistics, The University of Western Australia, Crawley WA 6009; Small, Michael, E-mail: michael.small@uwa.edu.au

In this work, we propose a novel method to transform a time series into a weighted and directed network. For a given time series, we first generate a set of segments via a sliding window, and then use a doubly symbolic scheme to characterize every windowed segment by combining absolute amplitude information with an ordinal pattern characterization. Based on this construction, a network can be directly constructed from the given time series: segments corresponding to different symbol-pairs are mapped to network nodes and the temporal succession between nodes is represented by directed links. With this conversion, dynamics underlying the timemore » series has been encoded into the network structure. We illustrate the potential of our networks with a well-studied dynamical model as a benchmark example. Results show that network measures for characterizing global properties can detect the dynamical transitions in the underlying system. Moreover, we employ a random walk algorithm to sample loops in our networks, and find that time series with different dynamics exhibits distinct cycle structure. That is, the relative prevalence of loops with different lengths can be used to identify the underlying dynamics.« less

Individual Morphological Brain Network Construction Based on Multivariate Euclidean Distances Between Brain Regions.

PubMed

Yu, Kaixin; Wang, Xuetong; Li, Qiongling; Zhang, Xiaohui; Li, Xinwei; Li, Shuyu

2018-01-01

Morphological brain network plays a key role in investigating abnormalities in neurological diseases such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, most of the morphological brain network construction methods only considered a single morphological feature. Each type of morphological feature has specific neurological and genetic underpinnings. A combination of morphological features has been proven to have better diagnostic performance compared with a single feature, which suggests that an individual morphological brain network based on multiple morphological features would be beneficial in disease diagnosis. Here, we proposed a novel method to construct individual morphological brain networks for two datasets by calculating the exponential function of multivariate Euclidean distance as the evaluation of similarity between two regions. The first dataset included 24 healthy subjects who were scanned twice within a 3-month period. The topological properties of these brain networks were analyzed and compared with previous studies that used different methods and modalities. Small world property was observed in all of the subjects, and the high reproducibility indicated the robustness of our method. The second dataset included 170 patients with MCI (86 stable MCI and 84 progressive MCI cases) and 169 normal controls (NC). The edge features extracted from the individual morphological brain networks were used to distinguish MCI from NC and separate MCI subgroups (progressive vs. stable) through the support vector machine in order to validate our method. The results showed that our method achieved an accuracy of 79.65% (MCI vs. NC) and 70.59% (stable MCI vs. progressive MCI) in a one-dimension situation. In a multiple-dimension situation, our method improved the classification performance with an accuracy of 80.53% (MCI vs. NC) and 77.06% (stable MCI vs. progressive MCI) compared with the method using a single feature. The results indicated

Systematic construction and control of stereo nerve vision network in intelligent manufacturing

NASA Astrophysics Data System (ADS)

Liu, Hua; Wang, Helong; Guo, Chunjie; Ding, Quanxin; Zhou, Liwei

2017-10-01

A system method of constructing stereo vision by using neural network is proposed, and the operation and control mechanism in actual operation are proposed. This method makes effective use of the neural network in learning and memory function, by after training with samples. Moreover, the neural network can learn the nonlinear relationship in the stereoscopic vision system and the internal and external orientation elements. These considerations are Worthy of attention, which includes limited constraints, the scientific of critical group, the operating speed and the operability in technical aspects. The results support our theoretical forecast.

Analysis of Informationization Construction of Business Financial Management under the Network Economy

NASA Astrophysics Data System (ADS)

Dong, Yahui; Zhang, Pengwei; Li, Wei

To strengthen the informationization construction of the financial management has great significance to the achievement of business management informationization, and under the network economic environment, it is an important task of the financial management that how to conduct informationization construction of traditional financial management to provide true, reliable and complete financial information system for the business managers. This paper thoroughly researches the problem of financial information orientation management (FIOM) by taking the method of combining theory with practice. This paper puts forward the thinking method of financial information management, makes the new contents of E-finance. At last, this paper rebuilds the system of finance internal control from four aspects such as control of organization and management, system development control and safety control of network system.

Developmental Self-Construction and -Configuration of Functional Neocortical Neuronal Networks

PubMed Central

Bauer, Roman; Zubler, Frédéric; Pfister, Sabina; Hauri, Andreas; Pfeiffer, Michael; Muir, Dylan R.; Douglas, Rodney J.

2014-01-01

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative (‘winner-take-all’, WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data. PMID:25474693

Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China

PubMed Central

Gao, Yu; Ma, Lei; Liu, Jiaxun; Zhuang, Zhuzhou; Huang, Qiuhao; Li, Manchun

2017-01-01

Fragmentation and reduced continuity of habitat patches threaten the environment and biodiversity. Recently, ecological networks are increasingly attracting the attention of researchers as they provide fundamental frameworks for environmental protection. This study suggests a set of procedures to construct an ecological network. First, we proposed a method to construct a landscape resistance surface based on the assessment of habitat quality. Second, to analyze the effect of the resistance surface on corridor simulations, we used three methods to construct resistance surfaces: (1) the method proposed in this paper, (2) the entropy coefficient method, and (3) the expert scoring method. Then, we integrated habitat patches and resistance surfaces to identify potential corridors using graph theory. These procedures were tested in Changzhou, China. Comparing the outputs of using different resistance surfaces demonstrated that: (1) different landscape resistance surfaces contribute to how corridors are identified, but only slightly affect the assessment of the importance of habitat patches and potential corridors; (2) the resistance surface, which is constructed based on habitat quality, is more applicable to corridor simulations; and (3) the assessment of the importance of habitat patches is fundamental for ecological network optimization in the conservation of critical habitat patches and corridors. PMID:28393879

Connectivity Strength-Weighted Sparse Group Representation-Based Brain Network Construction for MCI Classification

PubMed Central

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-01-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. PMID:28150897

New PBO GPS Station Construction: Eastern Region Network Enhancements and Multiple-Monument Stability Comparisons

NASA Astrophysics Data System (ADS)

Dittmann, S. T.; Austin, K. E.; Berglund, H. T.; Blume, F.; Feaux, K.; Mann, D.; Mattioli, G. S.; Walls, C. P.

2013-12-01

The Plate Boundary Observatory (PBO) network consists of 1100 continuously operating, permanent GPS stations throughout the United States. The majority of this network was constructed using NSF-MREFC funding as part of the EarthScope Project during FY2003-FY2008. Since FY2009, UNAVCO has operated and maintained PBO through a Cooperative Agreement (CA) with NSF. Construction of new, permanent GPS monuments in the PBO network was the result of two change orders to the original PBO O&M CA. Change Order 33 (CO33) allocated funds to construct additional GPS stations at six locations in the Eastern Region of PBO. Three of these locations were designed to replace poorly performing existing GPS monuments in Georgia, Texas and New York. The remaining three new locations were selected to fill in gaps in network coverage in Pennsylvania, Wisconsin and North Dakota. Construction of all six new sites was completed in September 2013. Important scientific goals for CO33 include improvement of the stable North American reference frame, measurement of the vertical signal associated with the Glacial Isostatic Adjustment, and improved constraints on surface deformation and possible earthquakes, which occur in the low-strain tectonic setting of the eastern North American Plate. Change Order 35 (CO35) allocated funds to construct two additional geodetic monuments at five existing PBO stations in order to test and compare the long-term stability of various monument designs under near-identical geologic conditions. Sites were chosen to yield a variety of geographic, hydrologic and geologic conditions, including both fine-grained alluvium and crystalline bedrock. At each location, three different monuments (deep drill braced, short drill braced/driven-braced, mast/pillar) were built with 10 meter spacing, with shared power systems and data telemetry infrastructure. Construction of these multi-monument test locations began in October 2012 and finished in September 2013. See G010- Berglund

A KST framework for correlation network construction from time series signals

NASA Astrophysics Data System (ADS)

Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

2018-04-01

A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

Evidence reasoning method for constructing conditional probability tables in a Bayesian network of multimorbidity.

PubMed

Du, Yuanwei; Guo, Yubin

2015-01-01

The intrinsic mechanism of multimorbidity is difficult to recognize and prediction and diagnosis are difficult to carry out accordingly. Bayesian networks can help to diagnose multimorbidity in health care, but it is difficult to obtain the conditional probability table (CPT) because of the lack of clinically statistical data. Today, expert knowledge and experience are increasingly used in training Bayesian networks in order to help predict or diagnose diseases, but the CPT in Bayesian networks is usually irrational or ineffective for ignoring realistic constraints especially in multimorbidity. In order to solve these problems, an evidence reasoning (ER) approach is employed to extract and fuse inference data from experts using a belief distribution and recursive ER algorithm, based on which evidence reasoning method for constructing conditional probability tables in Bayesian network of multimorbidity is presented step by step. A multimorbidity numerical example is used to demonstrate the method and prove its feasibility and application. Bayesian network can be determined as long as the inference assessment is inferred by each expert according to his/her knowledge or experience. Our method is more effective than existing methods for extracting expert inference data accurately and is fused effectively for constructing CPTs in a Bayesian network of multimorbidity.

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

PubMed

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.

PubMed

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-05-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l 1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A Method of DTM Construction Based on Quadrangular Irregular Networks and Related Error Analysis

PubMed Central

Kang, Mengjun

2015-01-01

A new method of DTM construction based on quadrangular irregular networks (QINs) that considers all the original data points and has a topological matrix is presented. A numerical test and a real-world example are used to comparatively analyse the accuracy of QINs against classical interpolation methods and other DTM representation methods, including SPLINE, KRIGING and triangulated irregular networks (TINs). The numerical test finds that the QIN method is the second-most accurate of the four methods. In the real-world example, DTMs are constructed using QINs and the three classical interpolation methods. The results indicate that the QIN method is the most accurate method tested. The difference in accuracy rank seems to be caused by the locations of the data points sampled. Although the QIN method has drawbacks, it is an alternative method for DTM construction. PMID:25996691

Constructing general partial differential equations using polynomial and neural networks.

PubMed

Zjavka, Ladislav; Pedrycz, Witold

2016-01-01

Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Optimal CDS Construction Algorithm with Activity Scheduling in Ad Hoc Networks

PubMed Central

Penumalli, Chakradhar; Palanichamy, Yogesh

2015-01-01

A new energy efficient optimal Connected Dominating Set (CDS) algorithm with activity scheduling for mobile ad hoc networks (MANETs) is proposed. This algorithm achieves energy efficiency by minimizing the Broadcast Storm Problem [BSP] and at the same time considering the node's remaining energy. The Connected Dominating Set is widely used as a virtual backbone or spine in mobile ad hoc networks [MANETs] or Wireless Sensor Networks [WSN]. The CDS of a graph representing a network has a significant impact on an efficient design of routing protocol in wireless networks. Here the CDS is a distributed algorithm with activity scheduling based on unit disk graph [UDG]. The node's mobility and residual energy (RE) are considered as parameters in the construction of stable optimal energy efficient CDS. The performance is evaluated at various node densities, various transmission ranges, and mobility rates. The theoretical analysis and simulation results of this algorithm are also presented which yield better results. PMID:26221627

Causality in Psychiatry: A Hybrid Symptom Network Construct Model

PubMed Central

Young, Gerald

2015-01-01

Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments. PMID:26635639

Construction and analysis of circular RNA molecular regulatory networks in liver cancer.

PubMed

Ren, Shuangchun; Xin, Zhuoyuan; Xu, Yinyan; Xu, Jianting; Wang, Guoqing

2017-01-01

Liver cancer is the sixth most prevalent cancer, and the third most frequent cause of cancer-related deaths. Circular RNAs (circRNAs), a kind of special endogenous ncRNAs, have been coming back to the forefront of cancer genomics research. In this study, we used a systems biology approach to construct and analyze the circRNA molecular regulatory networks in the context of liver cancer. We detected a total of 127 differentially expressed circRNAs and 3,235 differentially expressed mRNAs. We selected the top-5 upregulated circRNAs to construct a circRNA-miRNA-mRNA network. We enriched the pathways and gene ontology items and determined their participation in cancer-related pathways such as p53 signaling pathway and pathways involved in angiogenesis and cell cycle. Quantitative real-time PCR was performed to verify the top-five circRNAs. ROC analysis showed circZFR, circFUT8, circIPO11 could significantly distinguish the cancer samples, with an AUC of 0.7069, 0.7575, and 0.7103, respectively. Our results suggest the circRNA-miRNA-mRNA network may help us further understand the molecular mechanisms of tumor progression in liver cancer, and reveal novel biomarkers and therapeutic targets.

Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel® Xeon Phi™ Coprocessor.

PubMed

Misra, Sanchit; Pamnany, Kiran; Aluru, Srinivas

2015-01-01

Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the whole-genome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel Xeon Phi coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel® Xeon® processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains.

Linear programming model to construct phylogenetic network for 16S rRNA sequences of photosynthetic organisms and influenza viruses.

PubMed

Mathur, Rinku; Adlakha, Neeru

2014-06-01

Phylogenetic trees give the information about the vertical relationships of ancestors and descendants but phylogenetic networks are used to visualize the horizontal relationships among the different organisms. In order to predict reticulate events there is a need to construct phylogenetic networks. Here, a Linear Programming (LP) model has been developed for the construction of phylogenetic network. The model is validated by using data sets of chloroplast of 16S rRNA sequences of photosynthetic organisms and Influenza A/H5N1 viruses. Results obtained are in agreement with those obtained by earlier researchers.

Construction and analysis of gene-gene dynamics influence networks based on a Boolean model.

PubMed

Mazaya, Maulida; Trinh, Hung-Cuong; Kwon, Yung-Keun

2017-12-21

Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though. To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene

Forecasting Construction Cost Index based on visibility graph: A network approach

NASA Astrophysics Data System (ADS)

Zhang, Rong; Ashuri, Baabak; Shyr, Yu; Deng, Yong

2018-03-01

Engineering News-Record (ENR), a professional magazine in the field of global construction engineering, publishes Construction Cost Index (CCI) every month. Cost estimators and contractors assess projects, arrange budgets and prepare bids by forecasting CCI. However, fluctuations and uncertainties of CCI cause irrational estimations now and then. This paper aims at achieving more accurate predictions of CCI based on a network approach in which time series is firstly converted into a visibility graph and future values are forecasted relied on link prediction. According to the experimental results, the proposed method shows satisfactory performance since the error measures are acceptable. Compared with other methods, the proposed method is easier to implement and is able to forecast CCI with less errors. It is convinced that the proposed method is efficient to provide considerably accurate CCI predictions, which will make contributions to the construction engineering by assisting individuals and organizations in reducing costs and making project schedules.

Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

PubMed Central

CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

2016-01-01

Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

Constructing a Bayesian network model for improving safety behavior of employees at workplaces.

PubMed

Mohammadfam, Iraj; Ghasemi, Fakhradin; Kalatpour, Omid; Moghimbeigi, Abbas

2017-01-01

Unsafe behavior increases the risk of accident at workplaces and needs to be managed properly. The aim of the present study was to provide a model for managing and improving safety behavior of employees using the Bayesian networks approach. The study was conducted in several power plant construction projects in Iran. The data were collected using a questionnaire composed of nine factors, including management commitment, supporting environment, safety management system, employees' participation, safety knowledge, safety attitude, motivation, resource allocation, and work pressure. In order for measuring the score of each factor assigned by a responder, a measurement model was constructed for each of them. The Bayesian network was constructed using experts' opinions and Dempster-Shafer theory. Using belief updating, the best intervention strategies for improving safety behavior also were selected. The result of the present study demonstrated that the majority of employees do not tend to consider safety rules, regulation, procedures and norms in their behavior at the workplace. Safety attitude, safety knowledge, and supporting environment were the best predictor of safety behavior. Moreover, it was determined that instantaneous improvement of supporting environment and employee participation is the best strategy to reach a high proportion of safety behavior at the workplace. The lack of a comprehensive model that can be used for explaining safety behavior was one of the most problematic issues of the study. Furthermore, it can be concluded that belief updating is a unique feature of Bayesian networks that is very useful in comparing various intervention strategies and selecting the best one form them. Copyright © 2016 Elsevier Ltd. All rights reserved.

Delineating the Construct Network of the Personnel Reaction Blank: Associations with Externalizing Tendencies and Normal Personality

PubMed Central

Blonigen, Daniel M.; Patrick, Christopher J.; Gasperi, Marianna; Steffen, Benjamin; Ones, Deniz S.; Arvey, Richard D.; de Oliveira Baumgartl, Viviane; do Nascimento, Elizabeth

2010-01-01

Integrity testing has long been utilized in personnel selection to screen for tendencies toward counterproductive workplace behaviors. The construct of externalizing from the psychopathology literature represents a coherent spectrum marked by disinhibitory traits and behaviors. The present study used a sample of male and female undergraduates to examine the construct network of the Personnel Reaction Blank (PRB; Gough, Arvey, & Bradley, 2004), a measure of integrity, in relation to externalizing as well as normal-range personality constructs assessed by the Multidimensional Personality Questionnaire (MPQ; Tellegen & Waller, 2008). Results revealed moderate to strong associations between several PRB scales and externalizing, which were largely accounted for by MPQ traits subsumed by Negative Emotionality and Constraint. After accounting for MPQ traits in the prediction of externalizing, a modest predictive increment was achieved when adding the PRB scales, particularly biographical indicators from the Prosocial Background subscale. The findings highlight externalizing as a focal criterion for scale development in the integrity testing literature, and help delineate the construct network of the PRB within the domains of personality and psychopathology. PMID:21171783

Use of limited data to construct Bayesian networks for probabilistic risk assessment.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groth, Katrina M.; Swiler, Laura Painton

2013-03-01

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was tomore » establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.« less

A Time-constrained Network Voronoi Construction and Accessibility Analysis in Location-based Service Technology

NASA Astrophysics Data System (ADS)

Yu, W.; Ai, T.

2014-11-01

Accessibility analysis usually requires special models of spatial location analysis based on some geometric constructions, such as Voronoi diagram (abbreviated to VD). There are many achievements in classic Voronoi model research, however suffering from the following limitations for location-based services (LBS) applications. (1) It is difficult to objectively reflect the actual service areas of facilities by using traditional planar VDs, because human activities in LBS are usually constrained only to the network portion of the planar space. (2) Although some researchers have adopted network distance to construct VDs, their approaches are used in a static environment, where unrealistic measures of shortest path distance based on assumptions about constant travel speeds through the network were often used. (3) Due to the computational complexity of the shortest-path distance calculating, previous researches tend to be very time consuming, especially for large datasets and if multiple runs are required. To solve the above problems, a novel algorithm is developed in this paper. We apply network-based quadrat system and 1-D sequential expansion to find the corresponding subnetwork for each focus. The idea is inspired by the natural phenomenon that water flow extends along certain linear channels until meets others or arrives at the end of route. In order to accommodate the changes in traffic conditions, the length of network-quadrat is set upon the traffic condition of the corresponding street. The method has the advantage over Dijkstra's algorithm in that the time cost is avoided, and replaced with a linear time operation.

Soft-Input Soft-Output Modules for the Construction and Distributed Iterative Decoding of Code Networks

NASA Technical Reports Server (NTRS)

Benedetto, S.; Divsalar, D.; Montorsi, G.; Pollara, F.

1998-01-01

Soft-input soft-output building blocks (modules) are presented to construct and iteratively decode in a distributed fashion code networks, a new concept that includes, and generalizes, various forms of concatenated coding schemes.

Extended evolution: A conceptual framework for integrating regulatory networks and niche construction

PubMed Central

Renn, Jürgen

2015-01-01

ABSTRACT This paper introduces a conceptual framework for the evolution of complex systems based on the integration of regulatory network and niche construction theories. It is designed to apply equally to cases of biological, social and cultural evolution. Within the conceptual framework we focus especially on the transformation of complex networks through the linked processes of externalization and internalization of causal factors between regulatory networks and their corresponding niches and argue that these are an important part of evolutionary explanations. This conceptual framework extends previous evolutionary models and focuses on several challenges, such as the path‐dependent nature of evolutionary change, the dynamics of evolutionary innovation and the expansion of inheritance systems. J. Exp. Zool. (Mol. Dev. Evol.) 324B: 565–577, 2015. © 2015 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution published by Wiley Periodicals, Inc. PMID:26097188

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

NASA Astrophysics Data System (ADS)

Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

2015-03-01

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

A Study on the Application of the Extended Matrices Based on TRIZ in Constructing a Collaborative Model of Enterprise Network

NASA Astrophysics Data System (ADS)

Yang, Yan; Shao, Yunfei; Tang, Xiaowo

Based on mass related literature on enterprise network, the key influence factors are reduced to Trust, Control, Relationship and Interaction. Meanwhile, the specific contradiction matrices, judgment matrices and strategy collections based on TRIZ are constructed which make the connotation of contradiction matrices in TRIZ extended. Finally they are applied to the construction of the collaborative model on enterprise network based on Multi Agent System (MAS).

Early-life exposure to caffeine affects the construction and activity of cortical networks in mice.

PubMed

Fazeli, Walid; Zappettini, Stefania; Marguet, Stephan Lawrence; Grendel, Jasper; Esclapez, Monique; Bernard, Christophe; Isbrandt, Dirk

2017-09-01

The consumption of psychoactive drugs during pregnancy can have deleterious effects on newborns. It remains unclear whether early-life exposure to caffeine, the most widely consumed psychoactive substance, alters brain development. We hypothesized that maternal caffeine ingestion during pregnancy and the early postnatal period in mice affects the construction and activity of cortical networks in offspring. To test this hypothesis, we focused on primary visual cortex (V1) as a model neocortical region. In a study design mimicking the daily consumption of approximately three cups of coffee during pregnancy in humans, caffeine was added to the drinking water of female mice and their offspring were compared to control offspring. Caffeine altered the construction of GABAergic neuronal networks in V1, as reflected by a reduced number of somatostatin-containing GABA neurons at postnatal days 6-7, with the remaining ones showing poorly developed dendritic arbors. These findings were accompanied by increased synaptic activity in vitro and elevated network activity in vivo in V1. Similarly, in vivo hippocampal network activity was altered from the neonatal period until adulthood. Finally, caffeine-exposed offspring showed increased seizure susceptibility in a hyperthermia-induced seizure model. In summary, our results indicate detrimental effects of developmental caffeine exposure on mouse brain development. Copyright © 2017 Elsevier Inc. All rights reserved.

Persistent homology of time-dependent functional networks constructed from coupled time series

NASA Astrophysics Data System (ADS)

Stolz, Bernadette J.; Harrington, Heather A.; Porter, Mason A.

2017-04-01

We use topological data analysis to study "functional networks" that we construct from time-series data from both experimental and synthetic sources. We use persistent homology with a weight rank clique filtration to gain insights into these functional networks, and we use persistence landscapes to interpret our results. Our first example uses time-series output from networks of coupled Kuramoto oscillators. Our second example consists of biological data in the form of functional magnetic resonance imaging data that were acquired from human subjects during a simple motor-learning task in which subjects were monitored for three days during a five-day period. With these examples, we demonstrate that (1) using persistent homology to study functional networks provides fascinating insights into their properties and (2) the position of the features in a filtration can sometimes play a more vital role than persistence in the interpretation of topological features, even though conventionally the latter is used to distinguish between signal and noise. We find that persistent homology can detect differences in synchronization patterns in our data sets over time, giving insight both on changes in community structure in the networks and on increased synchronization between brain regions that form loops in a functional network during motor learning. For the motor-learning data, persistence landscapes also reveal that on average the majority of changes in the network loops take place on the second of the three days of the learning process.

Research on Risk Manage of Power Construction Project Based on Bayesian Network

NASA Astrophysics Data System (ADS)

Jia, Zhengyuan; Fan, Zhou; Li, Yong

With China's changing economic structure and increasingly fierce competition in the market, the uncertainty and risk factors in the projects of electric power construction are increasingly complex, the projects will face huge risks or even fail if we don't consider or ignore these risk factors. Therefore, risk management in the projects of electric power construction plays an important role. The paper emphatically elaborated the influence of cost risk in electric power projects through study overall risk management and the behavior of individual in risk management, and introduced the Bayesian network to the project risk management. The paper obtained the order of key factors according to both scene analysis and causal analysis for effective risk management.

47 CFR 54.636 - Eligible participant-constructed and owned network facilities for consortium applicants.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Support for Health Care Providers Healthcare Connect Fund § 54.636 Eligible participant-constructed and... proposals. Requests for proposals must provide sufficient detail so that cost-effectiveness can be evaluated... its own network facilities is the most cost-effective option after competitive bidding, pursuant to...

47 CFR 54.636 - Eligible participant-constructed and owned network facilities for consortium applicants.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Support for Health Care Providers Healthcare Connect Fund § 54.636 Eligible participant-constructed and... proposals. Requests for proposals must provide sufficient detail so that cost-effectiveness can be evaluated... its own network facilities is the most cost-effective option after competitive bidding, pursuant to...

[Regional ecological planning and ecological network construction: a case study of "Ji Triangle" Region].

PubMed

Li, Bo; Han, Zeng-Lin; Tong, Lian-Jun

2009-05-01

By the methods of in situ investigation and regional ecological planning, the present ecological environment, ecosystem vulnerability, and ecological environment sensitivity in "Ji Triangle" Region were analyzed, and the ecological network of the study area was constructed. According to the ecological resources abundance degree, ecological recovery, farmland windbreak system, environmental carrying capacity, forestry foundation, and ecosystem integrity, the study area was classified into three regional ecological function ecosystems, i. e., east low hill ecosystem, middle plain ecosystem, and west plain wetland ecosystem. On the basis of marking regional ecological nodes, the regional ecological corridor (Haerbin-Dalian regional axis, Changchun-Jilin, Changchun-Songyuan, Jilin-Songyuan, Jilin-Siping, and Songyuan-Siping transportation corridor) and regional ecological network (one ring, three links, and three belts) were constructed. Taking the requests of regional ecological security into consideration, the ecological environment security system of "Ji Triangle" Region, including regional ecological conservation district, regional ecological restored district, and regional ecological management district, was built.

Perspectives of construction robots

NASA Astrophysics Data System (ADS)

Stepanov, M. A.; Gridchin, A. M.

2018-03-01

This article is an overview of construction robots features, based on formulating the list of requirements for different types of construction robots in relation to different types of construction works.. It describes a variety of construction works and ways to construct new or to adapt existing robot designs for a construction process. Also, it shows the prospects of AI-controlled machines, implementation of automated control systems and networks on construction sites. In the end, different ways to develop and improve, including ecological aspect, the construction process through the wide robotization, creating of data communication networks and, in perspective, establishing of fully AI-controlled construction complex are formulated.

Visual Representations of Microcosm in Textbooks of Chemistry: Constructing a Systemic Network for Their Main Conceptual Framework

ERIC Educational Resources Information Center

Papageorgiou, George; Amariotakis, Vasilios; Spiliotopoulou, Vasiliki

2017-01-01

The main objective of this work is to analyse the visual representations (VRs) of the microcosm depicted in nine Greek secondary chemistry school textbooks of the last three decades in order to construct a systemic network for their main conceptual framework and to evaluate the contribution of each one of the resulting categories to the network.…

A construct-network approach to bridging diagnostic and physiological domains: application to assessment of externalizing psychopathology.

PubMed

Patrick, Christopher J; Venables, Noah C; Yancey, James R; Hicks, Brian M; Nelson, Lindsay D; Kramer, Mark D

2013-08-01

A crucial challenge in efforts to link psychological disorders to neural systems, with the aim of developing biologically informed conceptions of such disorders, is the problem of method variance (Campbell & Fiske, 1959). Since even measures of the same construct in differing domains correlate only moderately, it is unsurprising that large sample studies of diagnostic biomarkers yield only modest associations. To address this challenge, a construct-network approach is proposed in which psychometric operationalizations of key neurobehavioral constructs serve as anchors for identifying neural indicators of psychopathology-relevant dispositions, and as vehicles for bridging between domains of clinical problems and neurophysiology. An empirical illustration is provided for the construct of inhibition-disinhibition, which is of central relevance to problems entailing deficient impulse control. Findings demonstrate that: (1) a well-designed psychometric index of trait disinhibition effectively predicts externalizing problems of multiple types, (2) this psychometric measure of disinhibition shows reliable brain response correlates, and (3) psychometric and brain-response indicators can be combined to form a joint psychoneurometric factor that predicts effectively across clinical and physiological domains. As a methodology for bridging between clinical problems and neural systems, the construct-network approach provides a concrete means by which existing conceptions of psychological disorders can accommodate and be reshaped by neurobiological insights. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Construction of pore network models for Berea and Fontainebleau sandstones using non-linear programing and optimization techniques

NASA Astrophysics Data System (ADS)

Sharqawy, Mostafa H.

2016-12-01

Pore network models (PNM) of Berea and Fontainebleau sandstones were constructed using nonlinear programming (NLP) and optimization methods. The constructed PNMs are considered as a digital representation of the rock samples which were based on matching the macroscopic properties of the porous media and used to conduct fluid transport simulations including single and two-phase flow. The PNMs consisted of cubic networks of randomly distributed pores and throats sizes and with various connectivity levels. The networks were optimized such that the upper and lower bounds of the pore sizes are determined using the capillary tube bundle model and the Nelder-Mead method instead of guessing them, which reduces the optimization computational time significantly. An open-source PNM framework was employed to conduct transport and percolation simulations such as invasion percolation and Darcian flow. The PNM model was subsequently used to compute the macroscopic properties; porosity, absolute permeability, specific surface area, breakthrough capillary pressure, and primary drainage curve. The pore networks were optimized to allow for the simulation results of the macroscopic properties to be in excellent agreement with the experimental measurements. This study demonstrates that non-linear programming and optimization methods provide a promising method for pore network modeling when computed tomography imaging may not be readily available.

The construction of a public key infrastructure for healthcare information networks in Japan.

PubMed

Sakamoto, N

2001-01-01

The digital signature is a key technology in the forthcoming Internet society for electronic healthcare as well as for electronic commerce. Efficient exchanges of authorized information with a digital signature in healthcare information networks require a construction of a public key infrastructure (PKI). In order to introduce a PKI to healthcare information networks in Japan, we proposed a development of a user authentication system based on a PKI for user management, user authentication and privilege management of healthcare information systems. In this paper, we describe the design of the user authentication system and its implementation. The user authentication system provides a certification authority service and a privilege management service while it is comprised of a user authentication client and user authentication serves. It is designed on a basis of an X.509 PKI and is implemented with using OpenSSL and OpenLDAP. It was incorporated into the financial information management system for the national university hospitals and has been successfully working for about one year. The hospitals plan to use it as a user authentication method for their whole healthcare information systems. One implementation of the system is free to the national university hospitals with permission of the Japanese Ministry of Education, Culture, Sports, Science and Technology. Another implementation is open to the other healthcare institutes by support of the Medical Information System Development Center (MEDIS-DC). We are moving forward to a nation-wide construction of a PKI for healthcare information networks based on it.

Design and Construction of a High-speed Network Connecting All the Protein Crystallography Beamlines at the Photon Factory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matsugaki, Naohiro; Yamada, Yusuke; Igarashi, Noriyuki

2007-01-19

A private network, physically separated from the facility network, was designed and constructed which covered all the four protein crystallography beamlines at the Photon Factory (PF) and Structural Biology Research Center (SBRC). Connecting all the beamlines in the same network allows for simple authentication and a common working environment for a user who uses multiple beamlines. Giga-bit Ethernet wire-speed was achieved for the communication among the beamlines and SBRC buildings.

A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.

PubMed

Choi, Yoonha; Coram, Marc; Peng, Jie; Tang, Hua

2017-07-01

Constructing expression networks using transcriptomic data is an effective approach for studying gene regulation. A popular approach for constructing such a network is based on the Gaussian graphical model (GGM), in which an edge between a pair of genes indicates that the expression levels of these two genes are conditionally dependent, given the expression levels of all other genes. However, GGMs are not appropriate for non-Gaussian data, such as those generated in RNA-seq experiments. We propose a novel statistical framework that maximizes a penalized likelihood, in which the observed count data follow a Poisson log-normal distribution. To overcome the computational challenges, we use Laplace's method to approximate the likelihood and its gradients, and apply the alternating directions method of multipliers to find the penalized maximum likelihood estimates. The proposed method is evaluated and compared with GGMs using both simulated and real RNA-seq data. The proposed method shows improved performance in detecting edges that represent covarying pairs of genes, particularly for edges connecting low-abundant genes and edges around regulatory hubs.

Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.

PubMed

Pan, Yue; Lu, Lingyun; Chen, Junquan; Zhong, Yong; Dai, Zhehao

2018-01-01

This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis. Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database. The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. The relationships among the DEGs and module in PPI network were analyzed by plug-in NetworkAnalyzer and MCODE seperately. Through the TargetScan and comparing target genes with DEGs, the miRNA-mRNA regulation network was established. Totally 346 DEGs and 90 DEMIs were found to be differentially expressed. These DEGs were enriched in biological processes and KEGG pathway of inflammatory immune response. 25 genes in the PPI network were selected as hub genes. Top 10 hub genes were TYROBP, HLA-DRA, VWF, PPBP, SERPING1, HLA-DPA1, SERPINA1, KIF20A, FERMT3, HLA-E. PPI network of DEGs followed a pattern of power law network and met the characteristics of small-world network. MCODE analysis identified 4 clusters and the most significant cluster consisted of 11 nodes and 55 edges. SEPP1, CKS2, TCAP, BPI were identified as the seed genes in their own clusters, respectively. The miRNA-mRNA regulation network which was composed of 89 pairs was established. MiR-210 had the highest connectivity with 12 target genes. Among the predicted target of MiR-96, HLA-DPA1 and TYROBP were the hub genes. Our study indicated possible differentially expressed genes and miRNA, and microRNA-mRNA negative regulatory networks in osteosarcoma by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of osteosarcoma.

Construction of an integrated gene regulatory network link to stress-related immune system in cattle.

PubMed

Behdani, Elham; Bakhtiarizadeh, Mohammad Reza

2017-10-01

The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

PubMed

Valdés, Julio J; Barton, Alan J

2007-05-01

A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

Dual Cross-Linked Biofunctional and Self-Healing Networks to Generate User-Defined Modular Gradient Hydrogel Constructs.

PubMed

Wei, Zhao; Lewis, Daniel M; Xu, Yu; Gerecht, Sharon

2017-08-01

Gradient hydrogels have been developed to mimic the spatiotemporal differences of multiple gradient cues in tissues. Current approaches used to generate such hydrogels are restricted to a single gradient shape and distribution. Here, a hydrogel is designed that includes two chemical cross-linking networks, biofunctional, and self-healing networks, enabling the customizable formation of modular gradient hydrogel construct with various gradient distributions and flexible shapes. The biofunctional networks are formed via Michael addition between the acrylates of oxidized acrylated hyaluronic acid (OAHA) and the dithiol of matrix metalloproteinase (MMP)-sensitive cross-linker and RGD peptides. The self-healing networks are formed via dynamic Schiff base reaction between N-carboxyethyl chitosan (CEC) and OAHA, which drives the modular gradient units to self-heal into an integral modular gradient hydrogel. The CEC-OAHA-MMP hydrogel exhibits excellent flowability at 37 °C under shear stress, enabling its injection to generate gradient distributions and shapes. Furthermore, encapsulated sarcoma cells respond to the gradient cues of RGD peptides and MMP-sensitive cross-linkers in the hydrogel. With these superior properties, the dual cross-linked CEC-OAHA-MMP hydrogel holds significant potential for generating customizable gradient hydrogel constructs, to study and guide cellular responses to their microenvironment such as in tumor mimicking, tissue engineering, and stem cell differentiation and morphogenesis. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Episodic specificity induction impacts activity in a core brain network during construction of imagined future experiences

PubMed Central

Madore, Kevin P.; Szpunar, Karl K.; Addis, Donna Rose; Schacter, Daniel L.

2016-01-01

Recent behavioral work suggests that an episodic specificity induction—brief training in recollecting the details of a past experience—enhances performance on subsequent tasks that rely on episodic retrieval, including imagining future experiences, solving open-ended problems, and thinking creatively. Despite these far-reaching behavioral effects, nothing is known about the neural processes impacted by an episodic specificity induction. Related neuroimaging work has linked episodic retrieval with a core network of brain regions that supports imagining future experiences. We tested the hypothesis that key structures in this network are influenced by the specificity induction. Participants received the specificity induction or one of two control inductions and then generated future events and semantic object comparisons during fMRI scanning. After receiving the specificity induction compared with the control, participants exhibited significantly more activity in several core network regions during the construction of imagined events over object comparisons, including the left anterior hippocampus, right inferior parietal lobule, right posterior cingulate cortex, and right ventral precuneus. Induction-related differences in the episodic detail of imagined events significantly modulated induction-related differences in the construction of imagined events in the left anterior hippocampus and right inferior parietal lobule. Resting-state functional connectivity analyses with hippocampal and inferior parietal lobule seed regions and the rest of the brain also revealed significantly stronger core network coupling following the specificity induction compared with the control. These findings provide evidence that an episodic specificity induction selectively targets episodic processes that are commonly linked to key core network regions, including the hippocampus. PMID:27601666

Bringing abundance into environmental politics: Constructing a Zionist network of water abundance, immigration, and colonization.

PubMed

Alatout, Samer

2009-06-01

For more than five decades, resource scarcity has been the lead story in debates over environmental politics. More importantly, and whenever environmental politics implies conflict, resource scarcity is constructed as the culprit. Abundance of resources, if at all visited in the literature, holds less importance. Resource abundance is seen, at best, as the other side of scarcity--maybe the successful conclusion of multiple interventions that may turn scarcity into abundance. This paper reinstates abundance as a politico-environmental category in its own right. Rather than relegating abundance to a second-order environmental actor that matters only on occasion, this paper foregrounds it as a crucial element in modern environmental politics. On the substantive level, and using insights from science and technology studies, especially a slightly modified actor-network framework, I describe the emergence and consolidation of a Zionist network of abundance, immigration, and colonization in Palestine between 1918 and 1948. The essential argument here is that water abundance was constructed as fact, and became a political rallying point around which a techno-political network emerged that included a great number of elements. To name just a few, the following were enrolled in the service of such a network: geologists, geophysicists, Zionist settlement experts, Zionist organizations, political and technical categories of all sorts, Palestinians as the negated others, Palestinian revolts in search of political rights, the British Mandate authorities, the hydrological system of Palestine, and the absorptive capacity of Palestine, among others. The point was to successfully articulate these disparate elements into a network that seeks opening Palestine for Jewish immigration, redefining Palestinian geography and history through Judeo-Christian Biblical narratives, and, in the process, de-legitimizing political Palestinian presence in historic Palestine.

Construction and development of IGP DMC of China National Seismological Network

NASA Astrophysics Data System (ADS)

Zheng, X.; Zheng, J.; Lin, P.; Yao, Z.; Liang, J.

2011-12-01

In 2003, CEA (China Earthquake Administration) commenced the construction of China Digital Seismological Observation Network. By the end of 2007, a new-generation digital seismological observation system had been established, which consists of 1 National Seismic Network, 32 regional seismic networks, 2 small-aperture seismic arrays, 6 volcano monitoring networks and 19 mobile seismic networks, as well as CENC (China Earthquake Network Center) DMC (Data Management Centre) and IGP (Institute of Geophysics) DMC. Since then, the seismological observation system of China has completely entered a digital time. For operational, data backup and data security considerations, the DMC at the Institute of Geophysics (IGP), CEA was established at the end of 2007. IGP DMC now receives and archives waveform data from more than 1000 permanent seismic stations around China in real-time. After the great Wenchuan and Yushu earthquakes, the real-time waveform data from 56 and 8 portable seismic stations deployed in the aftershock area are added to IGP DMC. The technical system of IGP DMC is designed to conduct data management, processing and service through the network of CEA. We developed and integrated a hardware system with high-performance servers, large-capacity disc arrays, tape library and other facilities, as well as software packages for real-time waveform data receiving, storage, quality control, processing and service. Considering the demands from researchers for large quantities of seismic event waveform data, IGP DMC adopts an innovative "user order" method to extract event waveform data. Users can specify seismic stations, epicenter distance and record length. In a short period of 3 years, IGP DMC has supplied about 350 Terabytes waveform data to over 200 researches of more than 40 academic institutions. According to incomplete statistics, over 40 papers have been published in professional journals, in which 30 papers were indexed by SCI. Now, IGP DMC has become an

S-net project: Construction of large scale seafloor observatory network for tsunamis and earthquakes in Japan

NASA Astrophysics Data System (ADS)

Mochizuki, M.; Kanazawa, T.; Uehira, K.; Shimbo, T.; Shiomi, K.; Kunugi, T.; Aoi, S.; Matsumoto, T.; Sekiguchi, S.; Yamamoto, N.; Takahashi, N.; Shinohara, M.; Yamada, T.

2016-12-01

National Research Institute for Earth Science and Disaster Resilience ( NIED ) has launched the project of constructing an observatory network for tsunamis and earthquakes on the seafloor. The observatory network was named "S-net, Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench". The S-net consists of 150 seafloor observatories which are connected in line with submarine optical cables. The total length of submarine optical cable is about 5,700 km. The S-net system extends along Kuril and Japan trenches around Japan islands from north to south covering the area between southeast off island of Hokkaido and off the Boso Peninsula, Chiba Prefecture. The project has been financially supported by MEXT Japan. An observatory package is 34cm in diameter and 226cm long. Each observatory equips two units of a high sensitive water-depth sensor as a tsunami meter and four sets of three-component seismometers. The water-depth sensor has measurement resolution of sub-centimeter level. Combination of multiple seismometers secures wide dynamic range and robustness of the observation that are needed for early earthquake warning. The S-net is composed of six segment networks that consists of about 25 observatories and 800-1,600km length submarine optical cable. Five of six segment networks except the one covering the outer rise area of the Japan Trench has been already installed. The data from the observatories on those five segment networks are being transferred to the data center at NIED on a real-time basis, and then verification of data integrity are being carried out at the present moment. Installation of the last segment network of the S-net, that is, the outer rise one is scheduled to be finished within FY2016. Full-scale operation of the S-net will start at FY2017. We will report construction and operation of the S-net submarine cable system as well as the outline of the obtained data in this presentation.

A Networked Learning Model for Construction of Personal Learning Environments in Seventh Grade Life Science

ERIC Educational Resources Information Center

Drexler, Wendy

2010-01-01

The purpose of this design-based research case study was to apply a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. API widgets were…

Network Metamodeling: The Effect of Correlation Metric Choice on Phylogenomic and Transcriptomic Network Topology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weighill, Deborah A; Jacobson, Daniel A

We explore the use of a network meta-modeling approach to compare the effects of similarity metrics used to construct biological networks on the topology of the resulting networks. This work reviews various similarity metrics for the construction of networks and various topology measures for the characterization of resulting network topology, demonstrating the use of these metrics in the construction and comparison of phylogenomic and transcriptomic networks.

The model of encryption algorithm based on non-positional polynomial notations and constructed on an SP-network

NASA Astrophysics Data System (ADS)

Kapalova, N.; Haumen, A.

2018-05-01

This paper addresses to structures and properties of the cryptographic information protection algorithm model based on NPNs and constructed on an SP-network. The main task of the research is to increase the cryptostrength of the algorithm. In the paper, the transformation resulting in the improvement of the cryptographic strength of the algorithm is described in detail. The proposed model is based on an SP-network. The reasons for using the SP-network in this model are the conversion properties used in these networks. In the encryption process, transformations based on S-boxes and P-boxes are used. It is known that these transformations can withstand cryptanalysis. In addition, in the proposed model, transformations that satisfy the requirements of the "avalanche effect" are used. As a result of this work, a computer program that implements an encryption algorithm model based on the SP-network has been developed.

Facile one-step construction of covalently networked, self-healable, and transparent superhydrophobic composite films

NASA Astrophysics Data System (ADS)

Lee, Yujin; You, Eun-Ah; Ha, Young-Geun

2018-07-01

Despite the considerable demand for bioinspired superhydrophobic surfaces with highly transparent, self-cleaning, and self-healable properties, a facile and scalable fabrication method for multifunctional superhydrophobic films with strong chemical networks has rarely been established. Here, we report a rationally designed facile one-step construction of covalently networked, transparent, self-cleaning, and self-healable superhydrophobic films via a one-step preparation and single-reaction process of multi-components. As coating materials for achieving the one-step fabrication of multifunctional superhydrophobic films, we included two different sizes of Al2O3 nanoparticles for hierarchical micro/nano dual-scale structures and transparent films, fluoroalkylsilane for both low surface energy and covalent binding functions, and aluminum nitrate for aluminum oxide networked films. On the basis of stability tests for the robust film composition, the optimized, covalently linked superhydrophobic composite films with a high water contact angle (>160°) and low sliding angle (<1°) showed excellent thermal stability (up to 400 °C), transparency (≈80%), self-healing, self-cleaning, and waterproof abilities. Therefore, the rationally designed, covalently networked superhydrophobic composite films, fabricated via a one-step solution-based process, can be further utilized for various optical and optoelectronic applications.

Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks

PubMed Central

Baetica, Ania-Ariadna; Singhal, Vipul; Murray, Richard M.

2017-01-01

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces. PMID:28566513

Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks.

PubMed

Meng, X Flora; Baetica, Ania-Ariadna; Singhal, Vipul; Murray, Richard M

2017-05-01

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces. © 2017 The Author(s).

Construction of reliable protein-protein interaction networks with a new interaction generality measure.

PubMed

Saito, Rintaro; Suzuki, Harukazu; Hayashizaki, Yoshihide

2003-04-12

Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed. We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.

The networked student: A design-based research case study of student constructed personal learning environments in a middle school science course

NASA Astrophysics Data System (ADS)

Drexler, Wendy

This design-based research case study applied a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted emerging Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. The personal learning environments constructed used Application Programming Interface (API) widgets to access, organize, and synthesize content from a number of educational Internet resources and social network connections. This study examined the nature of personal learning environments; the processes students go through during construction, and patterns that emerged. The project was documented from both an instructional and student-design perspective. Findings revealed that students applied the processes of: practicing digital responsibility; practicing digital literacy; organizing content; collaborating and socializing; and synthesizing and creating. These processes informed a model of the networked student that will serve as a framework for future instructional designs. A networked learning approach that incorporates these processes into future designs has implications for student learning, teacher roles, professional development, administrative policies, and delivery. This work is significant in that it shifts the focus from technology innovations based on tools to student empowerment based on the processes required to support learning. It affirms the need for greater attention to digital literacy and responsibility in K12 schools as well as consideration for those skills students will need to achieve success in the 21st century. The design-based research case study provides a set of design principles for teachers to follow when facilitating student construction of personal learning environments.

Statistical identification of gene association by CID in application of constructing ER regulatory network

PubMed Central

Liu, Li-Yu D; Chen, Chien-Yu; Chen, Mei-Ju M; Tsai, Ming-Shian; Lee, Cho-Han S; Phang, Tzu L; Chang, Li-Yun; Kuo, Wen-Hung; Hwa, Hsiao-Lin; Lien, Huang-Chun; Jung, Shih-Ming; Lin, Yi-Shing; Chang, King-Jen; Hsieh, Fon-Jou

2009-01-01

Background A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor α (ERα) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A). Results The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. Conclusion CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association

An AIEE fluorescent supramolecular cross-linked polymer network based on pillar[5]arene host-guest recognition: construction and application in explosive detection.

PubMed

Shao, Li; Sun, Jifu; Hua, Bin; Huang, Feihe

2018-05-08

Here a novel fluorescent supramolecular cross-linked polymer network with aggregation induced enhanced emission (AIEE) properties was constructed via pillar[5]arene-based host-guest recognition. Furthermore, the supramolecular polymer network can be used for explosive detection in both solution and thin films.

Use of a probabilistic neural network to reduce costs of selecting construction rock

USGS Publications Warehouse

Singer, Donald A.; Bliss, James D.

2003-01-01

Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

PubMed

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

Evaluating the Impacts of Health, Social Network and Capital on Craft Efficiency and Productivity: A Case Study of Construction Workers in China

PubMed Central

Yi, Wen; Miao, Mengyi; Zhang, Lei

2018-01-01

The construction industry has been recognized, for many years, as among those having a high likelihood of accidents, injuries and occupational illnesses. Such risks of construction workers can lead to low productivity and social problems. As a result, construction workers’ well-being should be highly addressed to improve construction workers’ efficiency and productivity. Meanwhile, the social support from a social network and capital (SNC) of construction workers has been considered as an effective approach to promote construction workers’ physical and mental health (P&M health), as well as their work efficiency and productivity. Based on a comprehensive literature review, a conceptual model, which aims to improve construction workers’ efficiency and productivity from the perspective of health and SNC, was proposed. A questionnaire survey was conducted to investigate the construction workers’ health, SNC and work efficiency and productivity in Nanjing, China. A structural equation model (SEM) was employed to test the three hypothetical relationships among construction workers’ P&M health, SNC and work efficiency and productivity. The results indicated that the direct impacts from construction workers’ P&M health on work efficiency and productivity were more significant than that from the SNC. In addition, the construction workers’ social capital and the network can indirectly influence the work efficiency and productivity by affecting the construction workers’ P&M health. Therefore, strategies for enhancing construction workers’ efficiency and productivity were proposed. Furthermore, many useable suggestions can be drawn from the research findings from the perspective of a government. The identified indicators and relationships would contribute to the construction work efficiency and productivity assessment and health management from the perspective of the construction workers. PMID:29462861

Evaluating the Impacts of Health, Social Network and Capital on Craft Efficiency and Productivity: A Case Study of Construction Workers in China.

PubMed

Yuan, Jingfeng; Yi, Wen; Miao, Mengyi; Zhang, Lei

2018-02-15

The construction industry has been recognized, for many years, as among those having a high likelihood of accidents, injuries and occupational illnesses. Such risks of construction workers can lead to low productivity and social problems. As a result, construction workers' well-being should be highly addressed to improve construction workers' efficiency and productivity. Meanwhile, the social support from a social network and capital (SNC) of construction workers has been considered as an effective approach to promote construction workers' physical and mental health (P&M health), as well as their work efficiency and productivity. Based on a comprehensive literature review, a conceptual model, which aims to improve construction workers' efficiency and productivity from the perspective of health and SNC, was proposed. A questionnaire survey was conducted to investigate the construction workers' health, SNC and work efficiency and productivity in Nanjing, China. A structural equation model (SEM) was employed to test the three hypothetical relationships among construction workers' P&M health, SNC and work efficiency and productivity. The results indicated that the direct impacts from construction workers' P&M health on work efficiency and productivity were more significant than that from the SNC. In addition, the construction workers' social capital and the network can indirectly influence the work efficiency and productivity by affecting the construction workers' P&M health. Therefore, strategies for enhancing construction workers' efficiency and productivity were proposed. Furthermore, many useable suggestions can be drawn from the research findings from the perspective of a government. The identified indicators and relationships would contribute to the construction work efficiency and productivity assessment and health management from the perspective of the construction workers.

Feasibility of Construction of the Continuously Operating Geodetic GPS Network of Sinaloa, Mexico

NASA Astrophysics Data System (ADS)

Vazquez, G. E.; Jacobo, C.

2011-12-01

This research is based on the study and analysis of feasibility for the construction of the geodetic network for GPS continuous operation for Sinaloa, hereafter called (RGOCSIN). A GPS network of continuous operation is defined as that materialized structure physically through permanent monuments where measurements to the systems of Global Positioning (GPS) is performed continuously throughout a region. The GPS measurements in this network are measurements of accuracy according to international standards to define its coordinates, thus constituting the basic structure of geodetic referencing for a country. In this context is that in the near future the RGOCSIN constitutes a system state only accurate and reliable georeferencing in real-time (continuous and permanent operation) and will be used for different purposes; i.e., in addition to being fundamental basis for any lifting topographic or geodetic survey, and other areas such as: (1) Different construction processes (control and monitoring of engineering works); (2) Studies of deformation of the Earth's crust (before and after a seismic event); (3) GPS meteorology (weather forecasting); (4) Demarcation projects (natural and political); (5) Establishment of bases to generate mapping (necessary for the economic and social development of the state); (6) Precision agriculture (optimization of economic resources to the various crops); (7) Geographic information systems (Organization and planning activities associated with the design and construction of public services); (8) Urban growth (possible settlements in the appropriate form and taking care of the environmental aspect), among others. However there are criteria and regulations according to the INEGI (Instituto Nacional de Estadística y Geografía, http://www.inegi.org.mx/) that must be met; even for this stage of feasibility of construction that sees this project as a first phase. The fundamental criterion to be taken into account according to INEGI is a

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

NASA Astrophysics Data System (ADS)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Construct Validation of Wenger's Support Network Typology.

PubMed

Szabo, Agnes; Stephens, Christine; Allen, Joanne; Alpass, Fiona

2016-10-07

The study aimed to validate Wenger's empirically derived support network typology of responses to the Practitioner Assessment of Network Type (PANT) in an older New Zealander population. The configuration of network types was tested across ethnic groups and in the total sample. Data (N = 872, Mage = 67 years, SDage = 1.56 years) from the 2006 wave of the New Zealand Health, Work and Retirement study were analyzed using latent profile analysis. In addition, demographic differences among the emerging profiles were tested. Competing models were evaluated based on a range of fit criteria, which supported a five-profile solution. The "locally integrated," "community-focused," "local self-contained," "private-restricted," and "friend- and family-dependent" network types were identified as latent profiles underlying the data. There were no differences between Māori and non-Māori in final profile configurations. However, Māori were more likely to report integrated network types. Findings confirm the validity of Wenger's network types. However, the level to which participants endorse accessibility of family, frequency of interactions, and community engagement can be influenced by sample and contextual characteristics. Future research using the PANT items should empirically verify and derive the social support network types, rather than use a predefined scoring system. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Construction of diabatic energy surfaces for LiFH with artificial neural networks

NASA Astrophysics Data System (ADS)

Guan, Yafu; Fu, Bina; Zhang, Dong H.

2017-12-01

A new set of diabatic potential energy surfaces (PESs) for LiFH is constructed with artificial neural networks (NNs). The adiabatic PESs of the ground state and the first excited state are directly fitted with NNs. Meanwhile, the adiabatic-to-diabatic transformation (ADT) angles (mixing angles) are obtained by simultaneously fitting energy difference and interstate coupling gradients. No prior assumptions of the functional form of ADT angles are used before fitting, and the ab initio data including energy difference and interstate coupling gradients are well reproduced. Converged dynamical results show remarkable differences between adiabatic and diabatic PESs, which suggests the significance of non-adiabatic processes.

Constructing a generalized network design model to study air distribution in ventilation networks in subway with a single-track tunnel

NASA Astrophysics Data System (ADS)

Lugin, IV

2018-03-01

In focus are the features of construction of the generalized design model for the network method to study air distribution in ventilation system in subway with the single-track tunnel. The generalizations, assumptions and simplifications included in the model are specified. The air distribution is calculated with regard to the influence of topology and air resistances of the ventilation network sections. The author studies two variants of the subway line: half-open and closed with dead end on the both sides. It is found that the total air exchange at a subway station depends on the station location within the line. The operating mode of fans remains unaltered in this case. The article shows that elimination of air leakage in the station ventilation room allows an increase in the air flow rate by 7–8% at the same energy consumption by fans. The influence of the stop of a train in the tunnel on the air distribution is illustrated.

Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

PubMed Central

Smallbone, Kieran; Klipp, Edda; Mendes, Pedro; Liebermeister, Wolfram

2013-01-01

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. PMID:24324546

A GIS analysis of suitability for construction aggregate recycling sites using regional transportation network and population density features

USGS Publications Warehouse

Robinson, G.R.; Kapo, K.E.

2004-01-01

Aggregate is used in road and building construction to provide bulk, strength, support, and wear resistance. Reclaimed asphalt pavement (RAP) and reclaimed Portland cement concrete (RPCC) are abundant and available sources of recycled aggregate. In this paper, current aggregate production operations in Virginia, Maryland, and the District of Columbia are used to develop spatial association models for the recycled aggregate industry with regional transportation network and population density features. The cost of construction aggregate to the end user is strongly influenced by the cost of transporting processed aggregate from the production site to the construction site. More than 60% of operations recycling aggregate in the mid-Atlantic study area are located within 4.8 km (3 miles) of an interstate highway. Transportation corridors provide both sites of likely road construction where aggregate is used and an efficient means to move both materials and on-site processing equipment back and forth from various work sites to the recycling operations. Urban and developing areas provide a high market demand for aggregate and a ready source of construction debris that may be processed into recycled aggregate. Most aggregate recycling operators in the study area are sited in counties with population densities exceeding 77 people/km2 (200 people/mile 2). No aggregate recycling operations are sited in counties with less than 19 people/km2 (50 people/mile2), reflecting the lack of sufficient long-term sources of construction debris to be used as an aggregate source, as well as the lack of a sufficient market demand for aggregate in most rural areas to locate a recycling operation there or justify the required investment in the equipment to process and produce recycled aggregate. Weights of evidence analyses (WofE), measuring correlation on an area-normalized basis, and weighted logistic regression (WLR), are used to model the distribution of RAP and RPCC operations relative

LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering.

PubMed

Specht, Alicia T; Li, Jun

2017-03-01

To construct gene co-expression networks based on single-cell RNA-Sequencing data, we present an algorithm called LEAP, which utilizes the estimated pseudotime of the cells to find gene co-expression that involves time delay. R package LEAP available on CRAN. jun.li@nd.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Taxonomies of networks from community structure

PubMed Central

Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

2014-01-01

The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: they can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi. PMID:23030977

Taxonomies of networks from community structure

NASA Astrophysics Data System (ADS)

Onnela, Jukka-Pekka; Fenn, Daniel J.; Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

2012-09-01

The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

Surrogate-assisted identification of influences of network construction on evolving weighted functional networks

NASA Astrophysics Data System (ADS)

Stahn, Kirsten; Lehnertz, Klaus

2017-12-01

We aim at identifying factors that may affect the characteristics of evolving weighted networks derived from empirical observations. To this end, we employ various chains of analysis that are often used in field studies for a data-driven derivation and characterization of such networks. As an example, we consider fully connected, weighted functional brain networks before, during, and after epileptic seizures that we derive from multichannel electroencephalographic data recorded from epilepsy patients. For these evolving networks, we estimate clustering coefficient and average shortest path length in a time-resolved manner. Lastly, we make use of surrogate concepts that we apply at various levels of the chain of analysis to assess to what extent network characteristics are dominated by properties of the electroencephalographic recordings and/or the evolving weighted networks, which may be accessible more easily. We observe that characteristics are differently affected by the unavoidable referencing of the electroencephalographic recording, by the time-series-analysis technique used to derive the properties of network links, and whether or not networks were normalized. Importantly, for the majority of analysis settings, we observe temporal evolutions of network characteristics to merely reflect the temporal evolutions of mean interaction strengths. Such a property of the data may be accessible more easily, which would render the weighted network approach—as used here—as an overly complicated description of simple aspects of the data.

An evolutionary algorithm that constructs recurrent neural networks.

PubMed

Angeline, P J; Saunders, G M; Pollack, J B

1994-01-01

Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods.

A template for constructing Bayesian networks in forensic biology cases when considering activity level propositions.

PubMed

Taylor, Duncan; Biedermann, Alex; Hicks, Tacha; Champod, Christophe

2018-03-01

The hierarchy of propositions has been accepted amongst the forensic science community for some time. It is also accepted that the higher up the hierarchy the propositions are, against which the scientist are competent to evaluate their results, the more directly useful the testimony will be to the court. Because each case represents a unique set of circumstances and findings, it is difficult to come up with a standard structure for evaluation. One common tool that assists in this task is Bayesian networks (BNs). There is much diversity in the way that BN can be constructed. In this work, we develop a template for BN construction that allows sufficient flexibility to address most cases, but enough commonality and structure that the flow of information in the BN is readily recognised at a glance. We provide seven steps that can be used to construct BNs within this structure and demonstrate how they can be applied, using a case example. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

A neural network construction method for surrogate modeling of physics-based analysis

NASA Astrophysics Data System (ADS)

Sung, Woong Je

In this thesis existing methodologies related to the developmental methods of neural networks have been surveyed and their approaches to network sizing and structuring are carefully observed. This literature review covers the constructive methods, the pruning methods, and the evolutionary methods and questions about the basic assumption intrinsic to the conventional neural network learning paradigm, which is primarily devoted to optimization of connection weights (or synaptic strengths) for the pre-determined connection structure of the network. The main research hypothesis governing this thesis is that, without breaking a prevailing dichotomy between weights and connectivity of the network during learning phase, the efficient design of a task-specific neural network is hard to achieve because, as long as connectivity and weights are searched by separate means, a structural optimization of the neural network requires either repetitive re-training procedures or computationally expensive topological meta-search cycles. The main contribution of this thesis is designing and testing a novel learning mechanism which efficiently learns not only weight parameters but also connection structure from a given training data set, and positioning this learning mechanism within the surrogate modeling practice. In this work, a simple and straightforward extension to the conventional error Back-Propagation (BP) algorithm has been formulated to enable a simultaneous learning for both connectivity and weights of the Generalized Multilayer Perceptron (GMLP) in supervised learning tasks. A particular objective is to achieve a task-specific network having reasonable generalization performance with a minimal training time. The dichotomy between architectural design and weight optimization is reconciled by a mechanism establishing a new connection for a neuron pair which has potentially higher error-gradient than one of the existing connections. Interpreting an instance of the absence of

A new method for constructing networks from binary data

NASA Astrophysics Data System (ADS)

van Borkulo, Claudia D.; Borsboom, Denny; Epskamp, Sacha; Blanken, Tessa F.; Boschloo, Lynn; Schoevers, Robert A.; Waldorp, Lourens J.

2014-08-01

Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.

Construction of a polyhedron decorated MOF with a unique network through the combination of two classic secondary building units.

PubMed

Ren, Guo-Jian; Chang, Ze; Xu, Jian; Hu, Zhenpeng; Liu, Yan-Qing; Xu, Yue-Ling; Bu, Xian-He

2016-02-04

A novel decorated metal-organic polyhedron (MOP) based metal-organic framework with a unique 4,9-connected network is successfully constructed, which displays a relatively strong interaction toward H2 and CO2 probably due to the existence of open metal sites in the secondary building units.

Construction and analysis of lncRNA-lncRNA synergistic networks to reveal clinically relevant lncRNAs in cancer.

PubMed

Li, Yongsheng; Chen, Juan; Zhang, Jinwen; Wang, Zishan; Shao, Tingting; Jiang, Chunjie; Xu, Juan; Li, Xia

2015-09-22

Long non-coding RNAs (lncRNAs) play key roles in diverse biological processes. Moreover, the development and progression of cancer often involves the combined actions of several lncRNAs. Here we propose a multi-step method for constructing lncRNA-lncRNA functional synergistic networks (LFSNs) through co-regulation of functional modules having three features: common coexpressed genes of lncRNA pairs, enrichment in the same functional category and close proximity within protein interaction networks. Applied to three cancers, we constructed cancer-specific LFSNs and found that they exhibit a scale free and modular architecture. In addition, cancer-associated lncRNAs tend to be hubs and are enriched within modules. Although there is little synergistic pairing of lncRNAs across cancers, lncRNA pairs involved in the same cancer hallmarks by regulating same or different biological processes. Finally, we identify prognostic biomarkers within cancer lncRNA expression datasets using modules derived from LFSNs. In summary, this proof-of-principle study indicates synergistic lncRNA pairs can be identified through integrative analysis of genome-wide expression data sets and functional information.

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

PubMed

Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

2016-01-01

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

Construction of Multimodal Transport Information Platform

NASA Astrophysics Data System (ADS)

Wang, Ya; Cheng, Yu; Zhao, Zhi

2018-06-01

With the rapid development of economy, the volume of transportation in China is increasing, the opening process of the market is accelerating, the scale of enterprises is expanding, the service quality is being improved, and the container multimodal transport is developing continuously.The hardware infrastructure of container multimodal transport is improved obviously, but the network platform construction of multimodal transport is still insufficient.Taking Shandong region of China as an example, the present situation of container multimodal transport in Shandong area can no longer meet the requirement of rapid development of container, and the construction of network platform needs to be solved urgently. Therefore, this paper will briefly describe the conception of construction of multimodal transport network platform in Shandong area.In order to achieve the rapid development of multimodal transport.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

PubMed

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Microfabrication of channel arrays promotes vessel-like network formation in cardiac cell construct and vascularization in vivo.

PubMed

Zieber, Liran; Or, Shira; Ruvinov, Emil; Cohen, Smadar

2014-06-01

Pre-vascularization is important for the reconstruction of dense and metabolically active myocardial tissue and its integration with the host myocardium after implantation. Herein, we demonstrate that the fabrication of micro-channels in alginate scaffold combined with the presentation of adhesion peptides and an angiogenic growth factor promote vessel-like networks in the construct, both in vitro and in vivo. Using a CO2 laser engraving system, 200 µm diameter channels were formed from top to bottom of the 2 mm thick alginate scaffold, with a channel-to-channel distance of 400 µm. Cells were seeded in a sequential manner onto the scaffolds: first, human umbilical vascular endothelial cells (HUVECs) were seeded and cultured for three days, then neonatal rat cardiomyocytes (CMs) and cardiofibroblasts were added at a final cell ratio of 50:35:15, respectively, and the constructs were cultivated for an additional seven days. A vessel-like network was formed within the cell constructs, wherein HUVECs were organized around the channels in a multilayer manner, while the CMs were located in-between the channels and exhibited the characteristic morphological features of a mature cardiac fiber. Acellular scaffolds with the affinity-bound basic fibroblast growth factor were implanted subcutaneously in mice. Increased cell penetration into the channeled scaffold and greater vessel density were found in comparison with the nonchanneled scaffolds. Our results thus point to the importance of micro-channels as a major structural promoter of vascularization in scaffolds, in conjunction with the sequential preculture of ECs and angiogenic factor presentation.

Scale-free effect of substitution networks

NASA Astrophysics Data System (ADS)

Li, Ziyu; Yu, Zhouyu; Xi, Lifeng

2018-02-01

In this paper, we construct the growing networks in terms of substitution rule. Roughly speaking, we replace edges of different colors with different initial graphs. Then the evolving networks are constructed. We obtained the free-scale effect of our substitution networks.

Maximization Network Throughput Based on Improved Genetic Algorithm and Network Coding for Optical Multicast Networks

NASA Astrophysics Data System (ADS)

Wei, Chengying; Xiong, Cuilian; Liu, Huanlin

2017-12-01

Maximal multicast stream algorithm based on network coding (NC) can improve the network's throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network's maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.

Highly sensitive piezo-resistive graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone composites with improved conductive network construction.

PubMed

Zhao, Hang; Bai, Jinbo

2015-05-13

The constructions of internal conductive network are dependent on microstructures of conductive fillers, determining various electrical performances of composites. Here, we present the advanced graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone (GCHs/PDMS) composites with high piezo-resistive performance. GCH particles were synthesized by the catalyst chemical vapor deposition approach. The synthesized GCHs can be well dispersed in the matrix through the mechanical blending process. Due to the exfoliated GNP and aligned CNTs coupling structure, the flexible composite shows an ultralow percolation threshold (0.64 vol %) and high piezo-resistive sensitivity (gauge factor ∼ 10(3) and pressure sensitivity ∼ 0.6 kPa(-1)). Slight motions of finger can be detected and distinguished accurately using the composite film as a typical wearable sensor. These results indicate that designing the internal conductive network could be a reasonable strategy to improve the piezo-resistive performance of composites.

Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

PubMed Central

Hosseini, S. M. Hadi; Kesler, Shelli R.

2013-01-01

In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672

Robot Competence Development by Constructive Learning

NASA Astrophysics Data System (ADS)

Meng, Q.; Lee, M. H.; Hinde, C. J.

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system’s adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.

Robot Competence Development by Constructive Learning

NASA Astrophysics Data System (ADS)

Meng, Q.; Lee, M. H.; Hinde, C. J.

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system's adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.

A Real-Time Construction Safety Monitoring System for Hazardous Gas Integrating Wireless Sensor Network and Building Information Modeling Technologies.

PubMed

Cheung, Weng-Fong; Lin, Tzu-Hsuan; Lin, Yu-Cheng

2018-02-02

In recent years, many studies have focused on the application of advanced technology as a way to improve management of construction safety management. A Wireless Sensor Network (WSN), one of the key technologies in Internet of Things (IoT) development, enables objects and devices to sense and communicate environmental conditions; Building Information Modeling (BIM), a revolutionary technology in construction, integrates database and geometry into a digital model which provides a visualized way in all construction lifecycle management. This paper integrates BIM and WSN into a unique system which enables the construction site to visually monitor the safety status via a spatial, colored interface and remove any hazardous gas automatically. Many wireless sensor nodes were placed on an underground construction site and to collect hazardous gas level and environmental condition (temperature and humidity) data, and in any region where an abnormal status is detected, the BIM model will alert the region and an alarm and ventilator on site will start automatically for warning and removing the hazard. The proposed system can greatly enhance the efficiency in construction safety management and provide an important reference information in rescue tasks. Finally, a case study demonstrates the applicability of the proposed system and the practical benefits, limitations, conclusions, and suggestions are summarized for further applications.

A Real-Time Construction Safety Monitoring System for Hazardous Gas Integrating Wireless Sensor Network and Building Information Modeling Technologies

PubMed Central

Cheung, Weng-Fong; Lin, Tzu-Hsuan; Lin, Yu-Cheng

2018-01-01

In recent years, many studies have focused on the application of advanced technology as a way to improve management of construction safety management. A Wireless Sensor Network (WSN), one of the key technologies in Internet of Things (IoT) development, enables objects and devices to sense and communicate environmental conditions; Building Information Modeling (BIM), a revolutionary technology in construction, integrates database and geometry into a digital model which provides a visualized way in all construction lifecycle management. This paper integrates BIM and WSN into a unique system which enables the construction site to visually monitor the safety status via a spatial, colored interface and remove any hazardous gas automatically. Many wireless sensor nodes were placed on an underground construction site and to collect hazardous gas level and environmental condition (temperature and humidity) data, and in any region where an abnormal status is detected, the BIM model will alert the region and an alarm and ventilator on site will start automatically for warning and removing the hazard. The proposed system can greatly enhance the efficiency in construction safety management and provide an important reference information in rescue tasks. Finally, a case study demonstrates the applicability of the proposed system and the practical benefits, limitations, conclusions, and suggestions are summarized for further applications. PMID:29393887

Structural and robustness properties of smart-city transportation networks

NASA Astrophysics Data System (ADS)

Zhang, Zhen-Gang; Ding, Zhuo; Fan, Jing-Fang; Meng, Jun; Ding, Yi-Min; Ye, Fang-Fu; Chen, Xiao-Song

2015-09-01

The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities. Project supported by the Major Projects of the China National Social Science Fund (Grant No. 11 & ZD154).

Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

PubMed

Park, Chihyun; Ahn, Jaegyoon; Kim, Hyunjin; Park, Sanghyun

2014-01-01

The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

Three-dimensional aromatic networks.

PubMed

Toyota, Shinji; Iwanaga, Tetsuo

2014-01-01

Three-dimensional (3D) networks consisting of aromatic units and linkers are reviewed from various aspects. To understand principles for the construction of such compounds, we generalize the roles of building units, the synthetic approaches, and the classification of networks. As fundamental compounds, cyclophanes with large aromatic units and aromatic macrocycles with linear acetylene linkers are highlighted in terms of transannular interactions between aromatic units, conformational preference, and resolution of chiral derivatives. Polycyclic cage compounds are constructed from building units by linkages via covalent bonds, metal-coordination bonds, or hydrogen bonds. Large cage networks often include a wide range of guest species in their cavity to afford novel inclusion compounds. Topological isomers consisting of two or more macrocycles are formed by cyclization of preorganized species. Some complicated topological networks are constructed by self-assembly of simple building units.

Composing Music with Complex Networks

NASA Astrophysics Data System (ADS)

Liu, Xiaofan; Tse, Chi K.; Small, Michael

In this paper we study the network structure in music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurrences. We analyze sample compositions from Bach, Mozart, Chopin, as well as other types of music including Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. Power-law exponents of degree distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be created by using a biased random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. The newly created music from complex networks will be played in the presentation.

Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network

NASA Astrophysics Data System (ADS)

Li, Huajiao; An, Haizhong; Wang, Yue; Huang, Jiachen; Gao, Xiangyun

2016-05-01

Keeping abreast of trends in the articles and rapidly grasping a body of article's key points and relationship from a holistic perspective is a new challenge in both literature research and text mining. As the important component, keywords can present the core idea of the academic article. Usually, articles on a single theme or area could share one or some same keywords, and we can analyze topological features and evolution of the articles co-keyword networks and keywords co-occurrence networks to realize the in-depth analysis of the articles. This paper seeks to integrate statistics, text mining, complex networks and visualization to analyze all of the academic articles on one given theme, complex network(s). All 5944 ;complex networks; articles that were published between 1990 and 2013 and are available on the Web of Science are extracted. Based on the two-mode affiliation network theory, a new frontier of complex networks, we constructed two different networks, one taking the articles as nodes, the co-keyword relationships as edges and the quantity of co-keywords as the weight to construct articles co-keyword network, and another taking the articles' keywords as nodes, the co-occurrence relationships as edges and the quantity of simultaneous co-occurrences as the weight to construct keyword co-occurrence network. An integrated method for analyzing the topological features and evolution of the articles co-keyword network and keywords co-occurrence networks is proposed, and we also defined a new function to measure the innovation coefficient of the articles in annual level. This paper provides a useful tool and process for successfully achieving in-depth analysis and rapid understanding of the trends and relationships of articles in a holistic perspective.

Recursive random forest algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways.

PubMed

Deng, Wenping; Zhang, Kui; Busov, Victor; Wei, Hairong

2017-01-01

Present knowledge indicates a multilayered hierarchical gene regulatory network (ML-hGRN) often operates above a biological pathway. Although the ML-hGRN is very important for understanding how a pathway is regulated, there is almost no computational algorithm for directly constructing ML-hGRNs. A backward elimination random forest (BWERF) algorithm was developed for constructing the ML-hGRN operating above a biological pathway. For each pathway gene, the BWERF used a random forest model to calculate the importance values of all transcription factors (TFs) to this pathway gene recursively with a portion (e.g. 1/10) of least important TFs being excluded in each round of modeling, during which, the importance values of all TFs to the pathway gene were updated and ranked until only one TF was remained in the list. The above procedure, termed BWERF. After that, the importance values of a TF to all pathway genes were aggregated and fitted to a Gaussian mixture model to determine the TF retention for the regulatory layer immediately above the pathway layer. The acquired TFs at the secondary layer were then set to be the new bottom layer to infer the next upper layer, and this process was repeated until a ML-hGRN with the expected layers was obtained. BWERF improved the accuracy for constructing ML-hGRNs because it used backward elimination to exclude the noise genes, and aggregated the individual importance values for determining the TFs retention. We validated the BWERF by using it for constructing ML-hGRNs operating above mouse pluripotency maintenance pathway and Arabidopsis lignocellulosic pathway. Compared to GENIE3, BWERF showed an improvement in recognizing authentic TFs regulating a pathway. Compared to the bottom-up Gaussian graphical model algorithm we developed for constructing ML-hGRNs, the BWERF can construct ML-hGRNs with significantly reduced edges that enable biologists to choose the implicit edges for experimental validation.

S-net : Construction of large scale seafloor observatory network for tsunamis and earthquakes along the Japan Trench

NASA Astrophysics Data System (ADS)

Mochizuki, M.; Uehira, K.; Kanazawa, T.; Shiomi, K.; Kunugi, T.; Aoi, S.; Matsumoto, T.; Sekiguchi, S.; Yamamoto, N.; Takahashi, N.; Nakamura, T.; Shinohara, M.; Yamada, T.

2017-12-01

NIED has launched the project of constructing a seafloor observatory network for tsunamis and earthquakes after the occurrence of the 2011 Tohoku Earthquake to enhance reliability of early warnings of tsunamis and earthquakes. The observatory network was named "S-net". The S-net project has been financially supported by MEXT.The S-net consists of 150 seafloor observatories which are connected in line with submarine optical cables. The total length of submarine optical cable is about 5,500 km. The S-net covers the focal region of the 2011 Tohoku Earthquake and its vicinity regions. Each observatory equips two units of a high sensitive pressure gauges as a tsunami meter and four sets of three-component seismometers. The S-net is composed of six segment networks. Five of six segment networks had been already installed. Installation of the last segment network covering the outer rise area have been finally finished by the end of FY2016. The outer rise segment has special features like no other five segments of the S-net. Those features are deep water and long distance. Most of 25 observatories on the outer rise segment are located at the depth of deeper than 6,000m WD. Especially, three observatories are set on the seafloor of deeper than about 7.000m WD, and then the pressure gauges capable of being used even at 8,000m WD are equipped on those three observatories. Total length of the submarine cables of the outer rise segment is about two times longer than those of the other segments. The longer the cable system is, the higher voltage supply is needed, and thus the observatories on the outer rise segment have high withstanding voltage characteristics. We employ a dispersion management line of a low loss formed by combining a plurality of optical fibers for the outer rise segment cable, in order to achieve long-distance, high-speed and large-capacity data transmission Installation of the outer rise segment was finished and then full-scale operation of S-net has started

Polymorphous Supercapacitors Constructed from Flexible Three-Dimensional Carbon Network/Polyaniline/MnO2 Composite Textiles.

PubMed

Wang, Jinjie; Dong, Liubing; Xu, Chengjun; Ren, Danyang; Ma, Xinpei; Kang, Feiyu

2018-04-04

Polymorphous supercapacitors were constructed from flexible three-dimensional carbon network/polyaniline (PANI)/MnO 2 composite textile electrodes. The flexible textile electrodes were fabricated through a layer-by-layer construction strategy: PANI, carbon nanotubes (CNTs), and MnO 2 were deposited on activated carbon fiber cloth (ACFC) in turn through an electropolymerization process, "dipping and drying" method, and in situ chemical reaction, respectively. In the fabricated ACFC/PANI/CNTs/MnO 2 textile electrodes, the ACFC/CNT hybrid framework serves as a porous and electrically conductive 3D network for the rapid transmission of electrons and electrolyte ions, where ACFC, PANI, and MnO 2 are high-performance supercapacitor electrode materials. In the electrolyte of H 2 SO 4 solution, the textile electrode-based symmetric supercapacitor delivers superior areal capacitance, energy density, and power density of 4615 mF cm -2 (for single electrode), 157 μW h cm -2 , and 10372 μW cm -2 , respectively, whereas asymmetric supercapacitor assembled with the prepared composite textile as the positive electrode and ACFC as the negative electrode exhibits an improved energy density of 413 μW h cm -2 and a power density of 16120 μW cm -2 . On the basis of the ACFC/PANI/CNTs/MnO 2 textile electrodes, symmetric and asymmetric solid-state textile supercapacitors with a PVA/H 2 SO 4 gel electrolyte were also produced. These solid-state textile supercapacitors exhibit good electrochemical performance and high flexibility. Furthermore, flexible solid-state fiber-like supercapacitors were prepared with fiber bundle electrodes dismantled from the above composite textiles. Overall, this work makes a meaningful exploration of the versatile applications of textile electrodes to produce polymorphous supercapacitors.

Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

NASA Astrophysics Data System (ADS)

Arabzadeh, Vida; Niaki, S. T. A.; Arabzadeh, Vahid

2017-10-01

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg-Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

Exploring network operations for data and information networks

NASA Astrophysics Data System (ADS)

Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming

2017-01-01

Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

PubMed

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with

Step-by-Step Construction of Gene Co-expression Networks from High-Throughput Arabidopsis RNA Sequencing Data.

PubMed

Contreras-López, Orlando; Moyano, Tomás C; Soto, Daniela C; Gutiérrez, Rodrigo A

2018-01-01

The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.

Complex Dynamical Networks Constructed with Fully Controllable Nonlinear Nanomechanical Oscillators.

PubMed

Fon, Warren; Matheny, Matthew H; Li, Jarvis; Krayzman, Lev; Cross, Michael C; D'Souza, Raissa M; Crutchfield, James P; Roukes, Michael L

2017-10-11

Control of the global parameters of complex networks has been explored experimentally in a variety of contexts. Yet, the more difficult prospect of realizing arbitrary network architectures, especially analog physical networks that provide dynamical control of individual nodes and edges, has remained elusive. Given the vast hierarchy of time scales involved, it also proves challenging to measure a complex network's full internal dynamics. These span from the fastest nodal dynamics to very slow epochs over which emergent global phenomena, including network synchronization and the manifestation of exotic steady states, eventually emerge. Here, we demonstrate an experimental system that satisfies these requirements. It is based upon modular, fully controllable, nonlinear radio frequency nanomechanical oscillators, designed to form the nodes of complex dynamical networks with edges of arbitrary topology. The dynamics of these oscillators and their surrounding network are analog and continuous-valued and can be fully interrogated in real time. They comprise a piezoelectric nanomechanical membrane resonator, which serves as the frequency-determining element within an electrical feedback circuit. This embodiment permits network interconnections entirely within the electrical domain and provides unprecedented node and edge control over a vast region of parameter space. Continuous measurement of the instantaneous amplitudes and phases of every constituent oscillator node are enabled, yielding full and detailed network data without reliance upon statistical quantities. We demonstrate the operation of this platform through the real-time capture of the dynamics of a three-node ring network as it evolves from the uncoupled state to full synchronization.

Structure and formation of ant transportation networks

PubMed Central

Latty, Tanya; Ramsch, Kai; Ito, Kentaro; Nakagaki, Toshiyuki; Sumpter, David J. T.; Middendorf, Martin; Beekman, Madeleine

2011-01-01

Many biological systems use extensive networks for the transport of resources and information. Ants are no exception. How do biological systems achieve efficient transportation networks in the absence of centralized control and without global knowledge of the environment? Here, we address this question by studying the formation and properties of inter-nest transportation networks in the Argentine ant (Linepithema humile). We find that the formation of inter-nest networks depends on the number of ants involved in the construction process. When the number of ants is sufficient and networks do form, they tend to have short total length but a low level of robustness. These networks are topologically similar to either minimum spanning trees or Steiner networks. The process of network formation involves an initial construction of multiple links followed by a pruning process that reduces the number of trails. Our study thus illuminates the conditions under and the process by which minimal biological transport networks can be constructed. PMID:21288958

Construction of a magnetostrictive hysteresis operator using a tripod-like primitive hopfield neural network

NASA Astrophysics Data System (ADS)

Adly, A. A.; Abd-El-Hafiz, S. K.

2018-05-01

It is well known that accurate modeling of magnetostrictive hysteresis is crucial to different industrial applications. Although several magnetostrictive models have been developed in the past, the accuracy-efficiency balance has always been crucial. Recently, the possibility of constructing a primitive vector hysteresis operator using a tri-node Hopfield Neural Network (HNN) was demonstrated. Based upon the fact that mechanical stress along a certain direction results in dimensional deformation, this paper introduces a novel extension to the aforementioned recently developed approach. More specifically, a stress-driven evolution of a tri-node HNN hysteresis operator pair is proposed, thus yielding a tripod-like HNN pair having different input offset values. Model identification, sample simulation results and comparison with experimental measurements are given in the paper.

Concept mapping and network analysis: an analytic approach to measure ties among constructs.

PubMed

Goldman, Alyssa W; Kane, Mary

2014-12-01

Group concept mapping is a mixed-methods approach that helps a group visually represent its ideas on a topic of interest through a series of related maps. The maps and additional graphics are useful for planning, evaluation and theory development. Group concept maps are typically described, interpreted and utilized through points, clusters and distances, and the implications of these features in understanding how constructs relate to one another. This paper focuses on the application of network analysis to group concept mapping to quantify the strength and directionality of relationships among clusters. The authors outline the steps of this analysis, and illustrate its practical use through an organizational strategic planning example. Additional benefits of this analysis to evaluation projects are also discussed, supporting the overall utility of this supplemental technique to the standard concept mapping methodology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Differential network as an indicator of osteoporosis with network entropy.

PubMed

Ma, Lili; Du, Hongmei; Chen, Guangdong

2018-07-01

Osteoporosis is a common skeletal disorder characterized by a decrease in bone mass and density. The peak bone mass (PBM) is a significant determinant of osteoporosis. To gain insights into the indicating effect of PBM to osteoporosis, this study focused on characterizing the PBM networks and identifying key genes. One biological data set with 12 monocyte low PBM samples and 11 high PBM samples was derived to construct protein-protein interaction networks (PPINs). Based on clique-merging, module-identification algorithm was used to identify modules from PPINs. The systematic calculation and comparison were performed to test whether the network entropy can discriminate the low PBM network from high PBM network. We constructed 32 destination networks with 66 modules divided from monocyte low and high PBM networks. Among them, network 11 was the only significantly differential one (P<0.05) with 8 nodes and 28 edges. All genes belonged to precursors of osteoclasts, which were related to calcium transport as well as blood monocytes. In conclusion, based on the entropy in PBM PPINs, the differential network appears to be a novel therapeutic indicator for osteoporosis during the bone monocyte progression; these findings are helpful in disclosing the pathogenetic mechanisms of osteoporosis.

Cross-linked carbon networks constructed from N-doped nanosheets with enhanced performance for supercapacitors

NASA Astrophysics Data System (ADS)

Liu, Qingqing; Zhong, Jialiang; Sun, Zhipeng; Mi, Hongyu

2017-02-01

Hierarchically porous carbons offer great benefits for constructing advanced electrodes for energy-related applications. Herein, we reported facile synthesis of cross-linked carbon networks (HPCNs) made from N-doped nanosheets. By using MgO as self-sacrificial templates, the polyethylene glycol and melamine precursors were first uniformly coated on the template, and then annealed at the elevated temperature in inert atmosphere before removing the templates by mild acid etching. Interestingly, the capacitance performance of HPCNs could be easily modulated by adjusting the mass ratio of the precursors and templates, as well as the carbonization temperature. The optimized HPCNs showed specific capacitances of 192.6 F g-1 at 1.0 A g-1 and 156.2 F g-1 even at 20 A g-1 in 6.0 M KOH solution, and long-term cyclability with 85.5% capacitance retention at high current load of 10 A g-1 after 8000 successive cycles, which were attributed to structural merits of these continuous networks including high surface area of 370.8 m2 g-1, high pore volume of 1.65 cm3 g-1, as well as high nitrogen content of 9.920 wt.%. Owing to simplicity of the synthesis method and superior performance, such HPCNs may promise great potential in energy storage fields.

Constructing of Research-Oriented Learning Mode Based on Network Environment

ERIC Educational Resources Information Center

Wang, Ying; Li, Bing; Xie, Bai-zhi

2007-01-01

Research-oriented learning mode that based on network is significant to cultivate comprehensive-developing innovative person with network teaching in education for all-around development. This paper establishes a research-oriented learning mode by aiming at the problems existing in research-oriented learning based on network environment, and…

On the Reliability of Individual Brain Activity Networks.

PubMed

Cassidy, Ben; Bowman, F DuBois; Rae, Caroline; Solo, Victor

2018-02-01

There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales. If the interpretations from functional networks are inconsistent across spatiotemporal scales, then the whole validity of the functional network paradigm is called into question. This paper investigates the consistency of resting state network structure when using different temporal sampling or spatial parcellation, or different methods for constructing the networks. To pursue this, we develop a novel network comparison framework based on persistent homology from a topological data analysis. We use the new network comparison tools to characterize the spatial and temporal scales under which consistent functional networks can be constructed. The methods are illustrated on Human Connectome Project data, showing that the DISCOH 2 network construction method outperforms other approaches at most data spatiotemporal resolutions.

University of California San Francisco (UCSF-1): Construction of Directional Regulatory Networks Using Orthogonal CRISPR/Cas Screens | Office of Cancer Genomics

Cancer.gov

UCSF investigators developed an orthogonal CRISPR/Cas system which can be used to quantify gene regulation and construct directional regulatory networks. They combined two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell. Read the abstract.

Construction of local gene network for revealing different liver function of rats fed deep-fried oil with or without resistant starch.

PubMed

Wang, Zhiwei; Liao, Tianqi; Zhou, Zhongkai; Wang, Yuyang; Diao, Yongjia; Strappe, Padraig; Prenzler, Paul; Ayton, Jamie; Blanchard, Chris

2016-09-06

To study the mechanism underlying the liver damage induced by deep-fried oil (DO) consumption and the beneficial effects from resistant starch (RS) supplement, differential gene expression and pathway network were analyzed based on RNA sequencing data from rats. The up/down regulated genes and corresponding signaling pathways were used to construct a novel local gene network (LGN). The topology of the network showed characteristics of small-world network, with some pathways demonstrating a high degree. Some changes in genes led to a larger probability occurrence of disease or infection with DO intake. More importantly, the main pathways were found to be almost the same between the two LGNs (30 pathways overlapped in total 48) with gene expression profile. This finding may indicate that RS supplement in DO-containing diet may mainly regulate the genes that related to DO damage, and RS in the diet may provide direct signals to the liver cells and modulate its effect through a network involving complex gene regulatory events. It is the first attempt to reveal the mechanism of the attenuation of liver dysfunction from RS supplement in the DO-containing diet using differential gene expression and pathway network. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Young Women Online: Collaboratively Constructing Identities

ERIC Educational Resources Information Center

Paechter, Carrie

2013-01-01

In this paper I examine how young women construct their identities with others in online communities. I argue that the proliferation of social networking and its popularity among young people means that performed identities are increasingly collaboratively constructed, with the individual having less control over their public image than was…

Social network models predict movement and connectivity in ecological landscapes

USGS Publications Warehouse

Fletcher, R.J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, W.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.

Construct mine environment monitoring system based on wireless mesh network

NASA Astrophysics Data System (ADS)

Chen, Xin; Ge, Gengyu; Liu, Yinmei; Cheng, Aimin; Wu, Jun; Fu, Jun

2018-04-01

The system uses wireless Mesh network as a network transmission medium, and strive to establish an effective and reliable underground environment monitoring system. The system combines wireless network technology and embedded technology to monitor the internal data collected in the mine and send it to the processing center for analysis and environmental assessment. The system can be divided into two parts: the main control network module and the data acquisition terminal, and the SPI bus technology is used for mutual communication between them. Multi-channel acquisition and control interface design Data acquisition and control terminal in the analog signal acquisition module, digital signal acquisition module, and digital signal output module. The main control network module running Linux operating system, in which the transplant SPI driver, USB card driver and AODV routing protocol. As a result, the internal data collection and reporting of the mine are realized.

Small diameter symmetric networks from linear groups

NASA Technical Reports Server (NTRS)

Campbell, Lowell; Carlsson, Gunnar E.; Dinneen, Michael J.; Faber, Vance; Fellows, Michael R.; Langston, Michael A.; Moore, James W.; Multihaupt, Andrew P.; Sexton, Harlan B.

1992-01-01

In this note is reported a collection of constructions of symmetric networks that provide the largest known values for the number of nodes that can be placed in a network of a given degree and diameter. Some of the constructions are in the range of current potential engineering significance. The constructions are Cayley graphs of linear groups obtained by experimental computation.

Bayesian-network-based safety risk assessment for steel construction projects.

PubMed

Leu, Sou-Sen; Chang, Ching-Miao

2013-05-01

There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites. Copyright © 2013 Elsevier Ltd. All rights reserved.

Biocomputional construction of a gene network under acid stress in Synechocystis sp. PCC 6803.

PubMed

Li, Yi; Rao, Nini; Yang, Feng; Zhang, Ying; Yang, Yang; Liu, Han-ming; Guo, Fengbiao; Huang, Jian

2014-01-01

Acid stress is one of the most serious threats that cyanobacteria have to face, and it has an impact at all levels from genome to phenotype. However, very little is known about the detailed response mechanism to acid stress in this species. We present here a general analysis of the gene regulatory network of Synechocystis sp. PCC 6803 in response to acid stress using comparative genome analysis and biocomputational prediction. In this study, we collected 85 genes and used them as an initial template to predict new genes through co-regulation, protein-protein interactions and the phylogenetic profile, and 179 new genes were obtained to form a complete template. In addition, we found that 11 enriched pathways such as glycolysis are closely related to the acid stress response. Finally, we constructed a regulatory network for the intricate relationship of these genes and summarize the key steps in response to acid stress. This is the first time a bioinformatic approach has been taken systematically to gene interactions in cyanobacteria and the elaboration of their cell metabolism and regulatory pathways under acid stress, which is more efficient than a traditional experimental study. The results also provide theoretical support for similar research into environmental stresses in cyanobacteria and possible industrial applications. Copyright © 2014 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Modeling the interdependent network based on two-mode networks

NASA Astrophysics Data System (ADS)

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.

THE CRITICAL-PATH METHOD OF CONSTRUCTION CONTROL.

ERIC Educational Resources Information Center

DOMBROW, RODGER T.; MAUCHLY, JOHN

THIS DISCUSSION PRESENTS A DEFINITION AND BRIEF DESCRIPTION OF THE CRITICAL-PATH METHOD AS APPLIED TO BUILDING CONSTRUCTION. INTRODUCING REMARKS CONSIDER THE MOST PERTINENT QUESTIONS PERTAINING TO CPM AND THE NEEDS ASSOCIATED WITH MINIMIZING TIME AND COST ON CONSTRUCTION PROJECTS. SPECIFIC DISCUSSION INCLUDES--(1) ADVANTAGES OF NETWORK TECHNIQUES,…

An American Construction of European Education Space

ERIC Educational Resources Information Center

Silova, Iveta; Brehm, William C.

2010-01-01

The construction of the European education space has typically been attributed to European education policy makers, institutions, and networks. Rarely do scholars consider the role of outside, non-European actors in shaping the terrain of European education thought and practice. This article considers the construction of the European education…

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

PubMed

Navlakha, Saket; Barth, Alison L; Bar-Joseph, Ziv

2015-07-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

PubMed Central

Navlakha, Saket; Barth, Alison L.; Bar-Joseph, Ziv

2015-01-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains. PMID:26217933

Molecular ecological network analyses.

PubMed

Deng, Ye; Jiang, Yi-Huei; Yang, Yunfeng; He, Zhili; Luo, Feng; Zhou, Jizhong

2012-05-30

Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open

Defeat and entrapment: more than meets the eye? Applying network analysis to estimate dimensions of highly correlated constructs.

PubMed

Forkmann, Thomas; Teismann, Tobias; Stenzel, Jana-Sophie; Glaesmer, Heide; de Beurs, Derek

2018-01-25

Defeat and entrapment have been shown to be of central relevance to the development of different disorders. However, it remains unclear whether they represent two distinct constructs or one overall latent variable. One reason for the unclarity is that traditional factor analytic techniques have trouble estimating the right number of clusters in highly correlated data. In this study, we applied a novel approach based on network analysis that can deal with correlated data to establish whether defeat and entrapment are best thought of as one or multiple constructs. Explanatory graph analysis was used to estimate the number of dimensions within the 32 items that make up the defeat and entrapment scales in two samples: an online community sample of 480 participants, and a clinical sample of 147 inpatients admitted to a psychiatric hospital after a suicidal attempt or severe suicidal crisis. Confirmatory Factor analysis (CFA) was used to test whether the proposed structure fits the data. In both samples, bootstrapped exploratory graph analysis suggested that the defeat and entrapment items belonged to different dimensions. Within the entrapment items, two separate dimensions were detected, labelled internal and external entrapment. Defeat appeared to be multifaceted only in the online sample. When comparing the CFA outcomes of the one, two, three and four factor models, the one factor model was preferred. Defeat and entrapment can be viewed as distinct, yet, highly associated constructs. Thus, although replication is needed, results are in line with theories differentiating between these two constructs.

Brain-Inspired Constructive Learning Algorithms with Evolutionally Additive Nonlinear Neurons

NASA Astrophysics Data System (ADS)

Fang, Le-Heng; Lin, Wei; Luo, Qiang

In this article, inspired partially by the physiological evidence of brain’s growth and development, we developed a new type of constructive learning algorithm with evolutionally additive nonlinear neurons. The new algorithms have remarkable ability in effective regression and accurate classification. In particular, the algorithms are able to sustain a certain reduction of the loss function when the dynamics of the trained network are bogged down in the vicinity of the local minima. The algorithm augments the neural network by adding only a few connections as well as neurons whose activation functions are nonlinear, nonmonotonic, and self-adapted to the dynamics of the loss functions. Indeed, we analytically demonstrate the reduction dynamics of the algorithm for different problems, and further modify the algorithms so as to obtain an improved generalization capability for the augmented neural networks. Finally, through comparing with the classical algorithm and architecture for neural network construction, we show that our constructive learning algorithms as well as their modified versions have better performances, such as faster training speed and smaller network size, on several representative benchmark datasets including the MNIST dataset for handwriting digits.

Regenerating time series from ordinal networks.

PubMed

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Regenerating time series from ordinal networks

NASA Astrophysics Data System (ADS)

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Integrated Sensor Architecture (ISA) for Live Virtual Constructive (LVC) Environments

DTIC Science & Technology

2014-03-01

connect, publish their needs and capabilities, and interact with other systems even on disadvantaged networks. Within the ISA project, three levels of...constructive, disadvantaged network, sensor 1. INTRODUCTION In 2003 the Networked Sensors for the Future Force (NSFF) Advanced Technology Demonstration...While this combination is less optimal over disadvantaged networks, and we do not recommend it there, TCP and TLS perform adequately over networks with

Research of ad hoc network based on SINCGARS network

NASA Astrophysics Data System (ADS)

Nie, Hao; Cai, Xiaoxia; Chen, Hong; Chen, Jian; Weng, Pengfei

2016-03-01

In today's world, science and technology make a spurt of progress, so society has entered the era of information technology, network. Only the comprehensive use of electronic warfare and network warfare means can we maximize their access to information and maintain the information superiority. Combined with the specific combat mission and operational requirements, the research design and construction in accordance with the actual military which are Suitable for the future of information technology needs of the tactical Adhoc network, tactical internet, will greatly improve the operational efficiency of the command of the army. Through the study of the network of the U.S. military SINCGARS network, it can explore the routing protocol and mobile model, to provide a reference for the research of our army network.

Managing biological networks by using text mining and computer-aided curation

NASA Astrophysics Data System (ADS)

Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo

2015-11-01

In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

MX Hierarchical Networking System.

DTIC Science & Technology

1982-02-01

criteria considered in this report are defined as follows: 1. Single Network Limitation. Will the scheme allow the maximum size network anticipated to be...AD-AI16 758 CONSTRUCTION ENGINEERING RESEARCH LAB (ARMY) CHAMPAIGN IL F/G 5/2 MX HIERARCHICAL NETWORKING SYSTEM.(U) FEB 82 M O-CONNOR, L M SOLISH, L...laboratory__ _ _ _ _ _ _ _ _ _ _ _ MX HIERARCHICAL NETWORKING SYSTEM by Michael O’Connor L. Michael Golish Lee Boyer IsI Approved for public

Constructing Social Networks from Unstructured Group Dialog in Virtual Worlds

NASA Astrophysics Data System (ADS)

Shah, Fahad; Sukthankar, Gita

Virtual worlds and massively multi-player online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. However these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. In this paper, we present techniques for inferring the existence of social links from unstructured conversational data collected from groups of participants in the Second Life virtual world. We present an algorithm for addressing this problem, Shallow Semantic Temporal Overlap (SSTO), that combines temporal and language information to create directional links between participants, and a second approach that relies on temporal overlap alone to create undirected links between participants. Relying on temporal overlap is noisy, resulting in a low precision and networks with many extraneous links. In this paper, we demonstrate that we can ameliorate this problem by using network modularity optimization to perform community detection in the noisy networks and severing cross-community links. Although using the content of the communications still results in the best performance, community detection is effective as a noise reduction technique for eliminating the extra links created by temporal overlap alone.

Holding-based network of nations based on listed energy companies: An empirical study on two-mode affiliation network of two sets of actors

NASA Astrophysics Data System (ADS)

Li, Huajiao; Fang, Wei; An, Haizhong; Gao, Xiangyun; Yan, Lili

2016-05-01

Economic networks in the real world are not homogeneous; therefore, it is important to study economic networks with heterogeneous nodes and edges to simulate a real network more precisely. In this paper, we present an empirical study of the one-mode derivative holding-based network constructed by the two-mode affiliation network of two sets of actors using the data of worldwide listed energy companies and their shareholders. First, we identify the primitive relationship in the two-mode affiliation network of the two sets of actors. Then, we present the method used to construct the derivative network based on the shareholding relationship between two sets of actors and the affiliation relationship between actors and events. After constructing the derivative network, we analyze different topological features on the node level, edge level and entire network level and explain the meanings of the different values of the topological features combining the empirical data. This study is helpful for expanding the usage of complex networks to heterogeneous economic networks. For empirical research on the worldwide listed energy stock market, this study is useful for discovering the inner relationships between the nations and regions from a new perspective.

The parametric modified limited penetrable visibility graph for constructing complex networks from time series

NASA Astrophysics Data System (ADS)

Li, Xiuming; Sun, Mei; Gao, Cuixia; Han, Dun; Wang, Minggang

2018-02-01

This paper presents the parametric modified limited penetrable visibility graph (PMLPVG) algorithm for constructing complex networks from time series. We modify the penetrable visibility criterion of limited penetrable visibility graph (LPVG) in order to improve the rationality of the original penetrable visibility and preserve the dynamic characteristics of the time series. The addition of view angle provides a new approach to characterize the dynamic structure of the time series that is invisible in the previous algorithm. The reliability of the PMLPVG algorithm is verified by applying it to three types of artificial data as well as the actual data of natural gas prices in different regions. The empirical results indicate that PMLPVG algorithm can distinguish the different time series from each other. Meanwhile, the analysis results of natural gas prices data using PMLPVG are consistent with the detrended fluctuation analysis (DFA). The results imply that the PMLPVG algorithm may be a reasonable and significant tool for identifying various time series in different fields.

Soft- to network hard-material for constructing both ion- and electron-conductive hierarchical porous structure to significantly boost energy density of a supercapacitor.

PubMed

Yang, Pingping; Xie, Jiale; Guo, Chunxian; Li, Chang Ming

2017-01-01

Soft-material PEDOT is used to network hard Co 3 O 4 nanowires for constructing both ion- and electron-conductive hierarchical porous structure Co 3 O 4 /PEDOT to greatly boost the capacitor energy density than sum of that of plain Co 3 O 4 nanowires and PEDOT film. Specifically, the networked hierarchical porous structure of Co 3 O 4 /PEDOT is synthesized and tailored through hydrothermal method and post-electrochemical polymerization method for the PEDOT coating onto Co 3 O 4 nanowires. Typically, Co 3 O 4 /PEDOT supercapacitor gets a highest areal capacitance of 160mFcm -2 at a current density of 0.2mAcm -2 , which is about 2.2 times larger than the sum of that of plain Co 3 O 4 NWs (0.92mFcm -2 ) and PEDOT film (69.88mFcm -2 ). Besides, if only PEDOT as active mass is counted, Co 3 O 4 /PEDOT cell can achieve a highest capacitance of 567.21Fg -1 , this is the highest capacitance value obtained by PEDOT-based supercapacitors. Furthermore, this soft-hard network porous structure also achieves a high cycling stability of 93% capacitance retention after the 20,000th cycle. This work demonstrates a new approach to constructing both ion and electron conductive hierarchical porous structure to significantly boost energy density of a supercapacitor. Copyright © 2016 Elsevier Inc. All rights reserved.

A method for the automated construction of the joint system of equations to solve the problem of the flow distribution in hydraulic networks

NASA Astrophysics Data System (ADS)

Novikov, A. E.

1993-10-01

There are several methods of solving the problem of the flow distribution in hydraulic networks. But all these methods have no mathematical tools for forming joint systems of equations to solve this problem. This paper suggests a method of constructing joint systems of equations to calculate hydraulic circuits of the arbitrary form. The graph concept, according to Kirchhoff, has been introduced.

Integrated cellular network of transcription regulations and protein-protein interactions.

PubMed

Wang, Yu-Chao; Chen, Bor-Sen

2010-03-08

With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

Computing preimages of Boolean networks.

PubMed

Klotz, Johannes; Bossert, Martin; Schober, Steffen

2013-01-01

In this paper we present an algorithm based on the sum-product algorithm that finds elements in the preimage of a feed-forward Boolean networks given an output of the network. Our probabilistic method runs in linear time with respect to the number of nodes in the network. We evaluate our algorithm for randomly constructed Boolean networks and a regulatory network of Escherichia coli and found that it gives a valid solution in most cases.

[Social networks in drinking behaviors among Japanese: support network, drinking network, and intervening network].

PubMed

Yoshihara, Chika; Shimizu, Shinji

2005-10-01

The national representative sample was analyzed to examine the relationship between respondents' drinking practice and the social network which was constructed of three different types of network: support network, drinking network, and intervening network. Non-parametric statistical analysis was conducted with chi square method and ANOVA analysis, due to the risk of small samples in some basic tabulation cells. The main results are as follows: (1) In the support network of workplace associates, moderate drinkers enjoyed much more sociable support care than both nondrinkers and hard drinkers, which might suggest a similar effect as the French paradox. Meanwhile in the familial and kinship network, the more intervening care support was provided, the harder respondents' drinking practice. (2) The drinking network among Japanese people for both sexes is likely to be convergent upon certain types of network categories and not decentralized in various categories. This might reflect of the drinking culture of Japan, which permits people to drink everyday as a practice, especially male drinkers. Subsequently, solitary drinking is not optional for female drinkers. (3) Intervening network analysis showed that the harder the respondents' drinking practices, the more frequently their drinking behaviors were checked in almost all the categories of network. A rather complicated gender double-standard was found in the network of hard drinkers with their friends, particularly for female drinkers. Medical professionals played a similar intervening role for men as family and kinship networks but to a less degree than friends for females. The social network is considerably associated with respondents' drinking, providing both sociability for moderate drinkers and intervention for hard drinkers, depending on network categories. To minimize the risk of hard drinking and advance self-healthy drinking there should be more research development on drinking practice and the social network.

MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features.

PubMed

Xu, Juan; Li, Chuan-Xing; Li, Yong-Sheng; Lv, Jun-Ying; Ma, Ye; Shao, Ting-Ting; Xu, Liang-De; Wang, Ying-Ying; Du, Lei; Zhang, Yun-Peng; Jiang, Wei; Li, Chun-Quan; Xiao, Yun; Li, Xia

2011-02-01

Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA-miRNA synergistic network from a new view. Here, we constructed a miRNA-miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.

BioNSi: A Discrete Biological Network Simulator Tool.

PubMed

Rubinstein, Amir; Bracha, Noga; Rudner, Liat; Zucker, Noga; Sloin, Hadas E; Chor, Benny

2016-08-05

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

Network marketing on a small-world network

NASA Astrophysics Data System (ADS)

Kim, Beom Jun; Jun, Tackseung; Kim, Jeong-Yoo; Choi, M. Y.

2006-02-01

We investigate a dynamic model of network marketing in a small-world network structure artificially constructed similarly to the Watts-Strogatz network model. Different from the traditional marketing, consumers can also play the role of the manufacturer's selling agents in network marketing, which is stimulated by the referral fee the manufacturer offers. As the wiring probability α is increased from zero to unity, the network changes from the one-dimensional regular directed network to the star network where all but one player are connected to one consumer. The price p of the product and the referral fee r are used as free parameters to maximize the profit of the manufacturer. It is observed that at α=0 the maximized profit is constant independent of the network size N while at α≠0, it increases linearly with N. This is in parallel to the small-world transition. It is also revealed that while the optimal value of p stays at an almost constant level in a broad range of α, that of r is sensitive to a change in the network structure. The consumer surplus is also studied and discussed.

"Time-dependent flow-networks"

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Molkentin, Nora; Lopez, Cristobal; Hernandez-Garcia, Emilio; Marwan, Norbert; Kurths, Jürgen

2015-04-01

Complex networks have been successfully applied to various systems such as society, technology, and recently climate. Links in a climate network are defined between two geographical locations if the correlation between the time series of some climate variable is higher than a threshold. Therefore, network links are considered to imply information or heat exchange. However, the relationship between the oceanic and atmospheric flows and the climate network's structure is still unclear. Recently, a theoretical approach verifying the correlation between ocean currents and surface air temperature networks has been introduced, where the Pearson correlation networks were constructed from advection-diffusion dynamics on an underlying flow. Since the continuous approach has its limitations, i.e. high computational complexity and fixed variety of the flows in the underlying system, we introduce a new, method of flow-networks for changing in time velocity fields including external forcing in the system, noise and temperature-decay. Method of the flow-network construction can be divided into several steps: first we obtain the linear recursive equation for the temperature time-series. Then we compute the correlation matrix for time-series averaging the tensor product over all realizations of the noise, which we interpret as a weighted adjacency matrix of the flow-network and analyze using network measures. We apply the method to different types of moving flows with geographical relevance such as meandering flow. Analyzing the flow-networks using network measures we find that our approach can highlight zones of high velocity by degree and transition zones by betweenness, while the combination of these network measures can uncover how the flow propagates within time. Flow-networks can be powerful tool to understand the connection between system's dynamics and network's topology analyzed using network measures in order to shed light on different climatic phenomena.

A statistical analysis of UK financial networks

NASA Astrophysics Data System (ADS)

Chu, J.; Nadarajah, S.

2017-04-01

In recent years, with a growing interest in big or large datasets, there has been a rise in the application of large graphs and networks to financial big data. Much of this research has focused on the construction and analysis of the network structure of stock markets, based on the relationships between stock prices. Motivated by Boginski et al. (2005), who studied the characteristics of a network structure of the US stock market, we construct network graphs of the UK stock market using same method. We fit four distributions to the degree density of the vertices from these graphs, the Pareto I, Fréchet, lognormal, and generalised Pareto distributions, and assess the goodness of fit. Our results show that the degree density of the complements of the market graphs, constructed using a negative threshold value close to zero, can be fitted well with the Fréchet and lognormal distributions.

Animal transportation networks

PubMed Central

Perna, Andrea; Latty, Tanya

2014-01-01

Many group-living animals construct transportation networks of trails, galleries and burrows by modifying the environment to facilitate faster, safer or more efficient movement. Animal transportation networks can have direct influences on the fitness of individuals, whereas the shape and structure of transportation networks can influence community dynamics by facilitating contacts between different individuals and species. In this review, we discuss three key areas in the study of animal transportation networks: the topological properties of networks, network morphogenesis and growth, and the behaviour of network users. We present a brief primer on elements of network theory, and then discuss the different ways in which animal groups deal with the fundamental trade-off between the competing network properties of travel efficiency, robustness and infrastructure cost. We consider how the behaviour of network users can impact network efficiency, and call for studies that integrate both network topology and user behaviour. We finish with a prospectus for future research. PMID:25165598

Exchanging expertise and constructing boundaries: The development of a transnational knowledge network around heroin-assisted treatment.

PubMed

Duke, Karen

2016-05-01

Over the last 20 years, supervised injectable and inhalable heroin prescribing has been developed, tested and in some cases introduced as a second line treatment for limited groups of entrenched heroin users in a number of European countries and Canada. Based on documentary analyses and eleven key informant interviews, this paper investigates the growth of 'expertise' and the sharing of knowledge between scientific stakeholders from different countries involved in researching and developing this area of treatment. Drawing on Stone's concept of the 'knowledge network' (Stone, 2013) and Gieryn's theory of 'boundary-work' (Gieryn, 1983), the analysis demonstrates the collective power of this group of scientists in producing a particular form of knowledge and expertise which has accrued and been exchanged over time. It also illustrates the ways in which this type of science has gained credibility and authority and become legitimised, reinforced and reproduced by those who employ it in both scientific and political debates. Boundaries were constructed by the knowledge network between different types of professions/disciplines, different forms of science and between the production of science and its consumption by non-scientists. The uniformity of the knowledge network in terms of their professional and disciplinary backgrounds, methodological expertise and ideological perspectives has meant that alternative forms of knowledge and perspectives have been neglected. This limits the nature and scope of the scientific evidence on which to base policy and practice decisions impacting on the work of policy makers and practitioners as well as the experiences of those in treatment who are most affected by this research and policy development. Copyright © 2015 Elsevier B.V. All rights reserved.

Simulation-based modeling of building complexes construction management

NASA Astrophysics Data System (ADS)

Shepelev, Aleksandr; Severova, Galina; Potashova, Irina

2018-03-01

The study reported here examines the experience in the development and implementation of business simulation games based on network planning and management of high-rise construction. Appropriate network models of different types and levels of detail have been developed; a simulation model including 51 blocks (11 stages combined in 4 units) is proposed.

On construction of stochastic genetic networks based on gene expression sequences.

PubMed

Ching, Wai-Ki; Ng, Michael M; Fung, Eric S; Akutsu, Tatsuya

2005-08-01

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

Construction of Large-Volume Tissue Mimics with 3D Functional Vascular Networks

PubMed Central

Kang, Tae-Yun; Hong, Jung Min; Jung, Jin Woo; Kang, Hyun-Wook; Cho, Dong-Woo

2016-01-01

We used indirect stereolithography (SL) to form inner-layered fluidic networks in a porous scaffold by introducing a hydrogel barrier on the luminal surface, then seeded the networks separately with human umbilical vein endothelial cells and human lung fibroblasts to form a tissue mimic containing vascular networks. The artificial vascular networks provided channels for oxygen transport, thus reducing the hypoxic volume and preventing cell death. The endothelium of the vascular networks significantly retarded the occlusion of channels during whole-blood circulation. The tissue mimics have the potential to be used as an in vitro platform to examine the physiologic and pathologic phenomena through vascular architecture. PMID:27228079

Universal method for constructing N-port non-blocking optical router based on 2 × 2 optical switch for photonic networks-on-chip.

PubMed

Chen, Qiaoshan; Zhang, Fanfan; Ji, Ruiqiang; Zhang, Lei; Yang, Lin

2014-05-19

We propose a universal method for constructing N-port non-blocking optical router for photonic networks-on-chip, in which all microring (MR) optical switches or Mach-Zehnder (M-Z) optical switches behave as 2 × 2 optical switches. The optical router constructed by the proposed method has minimum optical switches, in which the number of the optical switches is reduced about 50% compared to the reported optical routers based on MR optical switches and more than 30% compared to the reported optical routers based on M-Z optical switches, and therefore is more compact in footprint and more power-efficient. We also present a strict mathematical proof of the non-blocking routing of the proposed N-port optical router.

Microvascular Guidance: A Challenge to Support the Development of Vascularised Tissue Engineering Construct

PubMed Central

Sukmana, Irza

2012-01-01

The guidance of endothelial cell organization into a capillary network has been a long-standing challenge in tissue engineering. Some research efforts have been made to develop methods to promote capillary networks inside engineered tissue constructs. Capillary and vascular networks that would mimic blood microvessel function can be used to subsequently facilitate oxygen and nutrient transfer as well as waste removal. Vascularization of engineering tissue construct is one of the most favorable strategies to overpass nutrient and oxygen supply limitation, which is often the major hurdle in developing thick and complex tissue and artificial organ. This paper addresses recent advances and future challenges in developing three-dimensional culture systems to promote tissue construct vascularization allowing mimicking blood microvessel development and function encountered in vivo. Bioreactors systems that have been used to create fully vascularized functional tissue constructs will also be outlined. PMID:22623881

[Construction of automatic elucidation platform for mechanism of traditional Chinese medicine].

PubMed

Zhang, Bai-xia; Luo, Si-jun; Yan, Jing; Gu, Hao; Luo, Ji; Zhang, Yan-ling; Tao, Ou; Wang, Yun

2015-10-01

Aim at the two problems in the field of traditional Chinese medicine (TCM) mechanism elucidation, one is the lack of detailed biological processes information, next is the low efficient in constructing network models, we constructed an auxiliary elucidation system for the TCM mechanism and realize the automatic establishment of biological network model. This study used the Entity Grammar Systems (EGS) as the theoretical framework, integrated the data of formulae, herbs, chemical components, targets of component, biological reactions, signaling pathways and disease related proteins, established the formal models, wrote the reasoning engine, constructed the auxiliary elucidation system for the TCM mechanism elucidation. The platform provides an automatic modeling method for biological network model of TCM mechanism. It would be benefit to perform the in-depth research on TCM theory of natures and combination and provides the scientific references for R&D of TCM.

Preserved Network Metrics across Translated Texts

NASA Astrophysics Data System (ADS)

Cabatbat, Josephine Jill T.; Monsanto, Jica P.; Tapang, Giovanni A.

2014-09-01

Co-occurrence language networks based on Bible translations and the Universal Declaration of Human Rights (UDHR) translations in different languages were constructed and compared with random text networks. Among the considered network metrics, the network size, N, the normalized betweenness centrality (BC), and the average k-nearest neighbors, knn, were found to be the most preserved across translations. Moreover, similar frequency distributions of co-occurring network motifs were observed for translated texts networks.

Integrated cellular network of transcription regulations and protein-protein interactions

PubMed Central

2010-01-01

Background With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. Results In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. Conclusions We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology. PMID:20211003

Using Sphinx to Improve Onion Routing Circuit Construction

NASA Astrophysics Data System (ADS)

Kate, Aniket; Goldberg, Ian

This paper presents compact message formats for onion routing circuit construction using the Sphinx methodology developed for mixes. We significantly compress the circuit construction messages for three onion routing protocols that have emerged as enhancements to the Tor anonymizing network; namely, Tor with predistributed Diffie-Hellman values, pairing-based onion routing, and certificateless onion routing. Our new circuit constructions are also secure in the universal composability framework, a property that was missing from the original constructions. Further, we compare the performance of our schemes with their older counterparts as well as with each other.

Construction of Endo-Time and its Manipulation in Autopoietic Systems

NASA Astrophysics Data System (ADS)

Balaž, Igor

2005-10-01

Two main factors determine construction of internal temporal architecture in autopoietic systems: external pressure and network of internal interdependences. External influences are given for systems and they are only able to incorporate them into its own functional and temporal blueprint, with very small space for further manipulations. But, internal processes, or more precisely, irreversible reductions toward determined states are enclosed into mobile and alterative network of re-productive cycles. On that basis autopoietic systems are able to construct and manipulate with different temporal strategies as reversibility, delaying, circularity, spiral flows, different distribution of times and so on. Special case is construction of transient time fields, called here intersubjective times, that arise as fusions of two or more specific temporal architectures during their interactions. This paper describes construction of internal proliferation of time patterns and analyze their functional usefulness.

Applications of Coding in Network Communications

ERIC Educational Resources Information Center

Chang, Christopher SungWook

2012-01-01

This thesis uses the tool of network coding to investigate fast peer-to-peer file distribution, anonymous communication, robust network construction under uncertainty, and prioritized transmission. In a peer-to-peer file distribution system, we use a linear optimization approach to show that the network coding framework significantly simplifies…

[Introduction of computerized anesthesia-recording systems and construction of comprehensive medical information network for patients undergoing surgery in the University of Tokyo Hospital].

PubMed

Kitamura, Takayuki; Hoshimoto, Hiroyuki; Yamada, Yoshitsugu

2009-10-01

The computerized anesthesia-recording systems are expensive and the introduction of the systems takes time and requires huge effort. Generally speaking, the efficacy of the computerized anesthesia-recording systems on the anesthetic managements is focused on the ability to automatically input data from the monitors to the anesthetic records, and tends to be underestimated. However, once the computerized anesthesia-recording systems are integrated into the medical information network, several features, which definitely contribute to improve the quality of the anesthetic management, can be developed; for example, to prevent misidentification of patients, to prevent mistakes related to blood transfusion, and to protect patients' personal information. Here we describe our experiences of the introduction of the computerized anesthesia-recording systems and the construction of the comprehensive medical information network for patients undergoing surgery in The University of Tokyo Hospital. We also discuss possible efficacy of the comprehensive medical information network for patients during surgery under anesthetic managements.

Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design.

PubMed

Hu, Chelsea Y; Takahashi, Melissa K; Zhang, Yan; Lucks, Julius B

2018-05-22

RNA regulators are powerful components of the synthetic biology toolbox. Here, we expand the repertoire of synthetic gene networks built from these regulators by constructing a transcriptional negative autoregulation (NAR) network out of small RNAs (sRNAs). NAR network motifs are core motifs of natural genetic networks, and are known for reducing network response time and steady state signal. Here we use cell-free transcription-translation (TX-TL) reactions and a computational model to design and prototype sRNA NAR constructs. Using parameter sensitivity analysis, we design a simple set of experiments that allow us to accurately predict NAR function in TX-TL. We transfer successful network designs into Escherichia coli and show that our sRNA transcriptional network reduces both network response time and steady-state gene expression. This work broadens our ability to construct increasingly sophisticated RNA genetic networks with predictable function.

Network structure of production

PubMed Central

Atalay, Enghin; Hortaçsu, Ali; Roberts, James; Syverson, Chad

2011-01-01

Complex social networks have received increasing attention from researchers. Recent work has focused on mechanisms that produce scale-free networks. We theoretically and empirically characterize the buyer–supplier network of the US economy and find that purely scale-free models have trouble matching key attributes of the network. We construct an alternative model that incorporates realistic features of firms’ buyer–supplier relationships and estimate the model’s parameters using microdata on firms’ self-reported customers. This alternative framework is better able to match the attributes of the actual economic network and aids in further understanding several important economic phenomena. PMID:21402924

Intelligent Resource Management for Local Area Networks: Approach and Evolution

NASA Technical Reports Server (NTRS)

Meike, Roger

1988-01-01

The Data Management System network is a complex and important part of manned space platforms. Its efficient operation is vital to crew, subsystems and experiments. AI is being considered to aid in the initial design of the network and to augment the management of its operation. The Intelligent Resource Management for Local Area Networks (IRMA-LAN) project is concerned with the application of AI techniques to network configuration and management. A network simulation was constructed employing real time process scheduling for realistic loads, and utilizing the IEEE 802.4 token passing scheme. This simulation is an integral part of the construction of the IRMA-LAN system. From it, a causal model is being constructed for use in prediction and deep reasoning about the system configuration. An AI network design advisor is being added to help in the design of an efficient network. The AI portion of the system is planned to evolve into a dynamic network management aid. The approach, the integrated simulation, project evolution, and some initial results are described.

The network of concepts in written texts

NASA Astrophysics Data System (ADS)

Caldeira, S. M. G.; Petit Lobão, T. C.; Andrade, R. F. S.; Neme, A.; Miranda, J. G. V.

2006-02-01

Complex network theory is used to investigate the structure of meaningful concepts in written texts of individual authors. Networks have been constructed after a two phase filtering, where words with less meaning contents are eliminated and all remaining words are set to their canonical form, without any number, gender or time flexion. Each sentence in the text is added to the network as a clique. A large number of written texts have been scrutinised, and it is found that texts have small-world as well as scale-free structures. The growth process of these networks has also been investigated, and a universal evolution of network quantifiers have been found among the set of texts written by distinct authors. Further analyses, based on shuffling procedures taken either on the texts or on the constructed networks, provide hints on the role played by the word frequency and sentence length distributions to the network structure.

Self-constructed tree-shape high thermal conductivity nanosilver networks in epoxy.

PubMed

Pashayi, Kamyar; Fard, Hafez Raeisi; Lai, Fengyuan; Iruvanti, Sushumna; Plawsky, Joel; Borca-Tasciuc, Theodorian

2014-04-21

We report the formation of high aspect ratio nanoscale tree-shape silver networks in epoxy, at low temperatures (<150 °C) and atmospheric pressures, that are correlated to a ∼200 fold enhancement of thermal conductivity (κ) of the nanocomposite compared to the polymer matrix. The networks form through a three-step process comprising of self-assembly by diffusion limited aggregation of polyvinylpyrrolidone (PVP) coated nanoparticles, removal of PVP coating from the surface, and sintering of silver nanoparticles in high aspect ratio networked structures. Controlling self-assembly and sintering by carefully designed multistep temperature and time processing leads to κ of our silver nanocomposites that are up to 300% of the present state of the art polymer nanocomposites at similar volume fractions. Our investigation of the κ enhancements enabled by tree-shaped network nanocomposites provides a basis for the development of new polymer nanocomposites for thermal transport and storage applications.

Neural-like growing networks

NASA Astrophysics Data System (ADS)

Yashchenko, Vitaliy A.

2000-03-01

On the basis of the analysis of scientific ideas reflecting the law in the structure and functioning the biological structures of a brain, and analysis and synthesis of knowledge, developed by various directions in Computer Science, also there were developed the bases of the theory of a new class neural-like growing networks, not having the analogue in world practice. In a base of neural-like growing networks the synthesis of knowledge developed by classical theories - semantic and neural of networks is. The first of them enable to form sense, as objects and connections between them in accordance with construction of the network. With thus each sense gets a separate a component of a network as top, connected to other tops. In common it quite corresponds to structure reflected in a brain, where each obvious concept is presented by certain structure and has designating symbol. Secondly, this network gets increased semantic clearness at the expense owing to formation not only connections between neural by elements, but also themselves of elements as such, i.e. here has a place not simply construction of a network by accommodation sense structures in environment neural of elements, and purely creation of most this environment, as of an equivalent of environment of memory. Thus neural-like growing networks are represented by the convenient apparatus for modeling of mechanisms of teleological thinking, as a fulfillment of certain psychophysiological of functions.

Network connectivity value.

PubMed

Dragicevic, Arnaud; Boulanger, Vincent; Bruciamacchie, Max; Chauchard, Sandrine; Dupouey, Jean-Luc; Stenger, Anne

2017-04-21

In order to unveil the value of network connectivity, we formalize the construction of ecological networks in forest environments as an optimal control dynamic graph-theoretic problem. The network is based on a set of bioreserves and patches linked by ecological corridors. The node dynamics, built upon the consensus protocol, form a time evolutive Mahalanobis distance weighted by the opportunity costs of timber production. We consider a case of complete graph, where the ecological network is fully connected, and a case of incomplete graph, where the ecological network is partially connected. The results show that the network equilibrium depends on the size of the reception zone, while the network connectivity depends on the environmental compatibility between the ecological areas. Through shadow prices, we find that securing connectivity in partially connected networks is more expensive than in fully connected networks, but should be undertaken when the opportunity costs are significant. Copyright © 2017 Elsevier Ltd. All rights reserved.

Depth optimal sorting networks resistant to k passive faults

DOE Office of Scientific and Technical Information (OSTI.GOV)

Piotrow, M.

In this paper, we study the problem of constructing a sorting network that is tolerant to faults and whose running time (i.e. depth) is as small as possible. We consider the scenario of worst-case comparator faults and follow the model of passive comparator failure proposed by Yao and Yao, in which a faulty comparator outputs directly its inputs without comparison. Our main result is the first construction of an N-input, k-fault-tolerant sorting network that is of an asymptotically optimal depth {theta}(log N+k). That improves over the recent result of Leighton and Ma, whose network is of depth O(log N +more » k log log N/log k). Actually, we present a fault-tolerant correction network that can be added after any N-input sorting network to correct its output in the presence of at most k faulty comparators. Since the depth of the network is O(log N + k) and the constants hidden behind the {open_quotes}O{close_quotes} notation are not big, the construction can be of practical use. Developing the techniques necessary to show the main result, we construct a fault-tolerant network for the insertion problem. As a by-product, we get an N-input, O(log N)-depth INSERT-network that is tolerant to random faults, thereby answering a question posed by Ma in his PhD thesis. The results are based on a new notion of constant delay comparator networks, that is, networks in which each register is used (compared) only in a period of time of a constant length. Copies of such networks can be put one after another with only a constant increase in depth per copy.« less

Flow networks for Ocean currents

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Molkenthin, Nora; Marwan, Norbert; Kurths, Jürgen

2014-05-01

Complex networks have been successfully applied to various systems such as society, technology, and recently climate. Links in a climate network are defined between two geographical locations if the correlation between the time series of some climate variable is higher than a threshold. Therefore, network links are considered to imply heat exchange. However, the relationship between the oceanic and atmospheric flows and the climate network's structure is still unclear. Recently, a theoretical approach verifying the correlation between ocean currents and surface air temperature networks has been introduced, where the Pearson correlation networks were constructed from advection-diffusion dynamics on an underlying flow. Since the continuous approach has its limitations, i.e., by its high computational complexity, we here introduce a new, discrete construction of flow-networks, which is then applied to static and dynamic velocity fields. Analyzing the flow-networks of prototypical flows we find that our approach can highlight the zones of high velocity by degree and transition zones by betweenness, while the combination of these network measures can uncover how the flow propagates within time. We also apply the method to time series data of the Equatorial Pacific Ocean Current and the Gulf Stream ocean current for the changing velocity fields, which could not been done before, and analyse the properties of the dynamical system. Flow-networks can be powerful tools to theoretically understand the step from system's dynamics to network's topology that can be analyzed using network measures and is used for shading light on different climatic phenomena.

High speed all optical networks

NASA Technical Reports Server (NTRS)

Chlamtac, Imrich; Ganz, Aura

1990-01-01

An inherent problem of conventional point-to-point wide area network (WAN) architectures is that they cannot translate optical transmission bandwidth into comparable user available throughput due to the limiting electronic processing speed of the switching nodes. The first solution to wavelength division multiplexing (WDM) based WAN networks that overcomes this limitation is presented. The proposed Lightnet architecture takes into account the idiosyncrasies of WDM switching/transmission leading to an efficient and pragmatic solution. The Lightnet architecture trades the ample WDM bandwidth for a reduction in the number of processing stages and a simplification of each switching stage, leading to drastically increased effective network throughputs. The principle of the Lightnet architecture is the construction and use of virtual topology networks, embedded in the original network in the wavelength domain. For this construction Lightnets utilize the new concept of lightpaths which constitute the links of the virtual topology. Lightpaths are all-optical, multihop, paths in the network that allow data to be switched through intermediate nodes using high throughput passive optical switches. The use of the virtual topologies and the associated switching design introduce a number of new ideas, which are discussed in detail.

Network Analysis on Attitudes

PubMed Central

Borsboom, Denny; van Harreveld, Frenk; van der Maas, Han L. J.

2017-01-01

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs. PMID:28919944

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system.

PubMed

Tudor, Catalina O; Ross, Karen E; Li, Gang; Vijay-Shanker, K; Wu, Cathy H; Arighi, Cecilia N

2015-01-01

Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction

Understanding sexual harassment using aggregate construct models.

PubMed

Nye, Christopher D; Brummel, Bradley J; Drasgow, Fritz

2014-11-01

Sexual harassment has received a substantial amount of empirical attention over the past few decades, and this research has consistently shown that experiencing these behaviors has a detrimental effect on employees' well-being, job attitudes, and behaviors at work. However, these findings, and the conclusions that are drawn from them, make the implicit assumption that the empirical models used to examine sexual harassment are properly specified. This article presents evidence that properly specified aggregate construct models are more consistent with theoretical structures and definitions of sexual harassment and can result in different conclusions about the nomological network of harassment. Results from 3 large samples, 2 military and 1 from a civilian population, are used to illustrate the differences between aggregate construct and reflective indicator models of sexual harassment. These analyses suggested that the factor structure and the nomological network of sexual harassment differ when modeling harassment as an aggregate construct. The implications of these results for the continued study of sexual harassment are discussed. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

A compositional framework for reaction networks

NASA Astrophysics Data System (ADS)

Baez, John C.; Pollard, Blake S.

Reaction networks, or equivalently Petri nets, are a general framework for describing processes in which entities of various kinds interact and turn into other entities. In chemistry, where the reactions are assigned ‘rate constants’, any reaction network gives rise to a nonlinear dynamical system called its ‘rate equation’. Here we generalize these ideas to ‘open’ reaction networks, which allow entities to flow in and out at certain designated inputs and outputs. We treat open reaction networks as morphisms in a category. Composing two such morphisms connects the outputs of the first to the inputs of the second. We construct a functor sending any open reaction network to its corresponding ‘open dynamical system’. This provides a compositional framework for studying the dynamics of reaction networks. We then turn to statics: that is, steady state solutions of open dynamical systems. We construct a ‘black-boxing’ functor that sends any open dynamical system to the relation that it imposes between input and output variables in steady states. This extends our earlier work on black-boxing for Markov processes.

Network-assisted crop systems genetics: network inference and integrative analysis.

PubMed

Lee, Tak; Kim, Hyojin; Lee, Insuk

2015-04-01

Although next-generation sequencing (NGS) technology has enabled the decoding of many crop species genomes, most of the underlying genetic components for economically important crop traits remain to be determined. Network approaches have proven useful for the study of the reference plant, Arabidopsis thaliana, and the success of network-based crop genetics will also require the availability of a genome-scale functional networks for crop species. In this review, we discuss how to construct functional networks and elucidate the holistic view of a crop system. The crop gene network then can be used for gene prioritization and the analysis of resequencing-based genome-wide association study (GWAS) data, the amount of which will rapidly grow in the field of crop science in the coming years. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reverse logistics in the Brazilian construction industry.

PubMed

Nunes, K R A; Mahler, C F; Valle, R A

2009-09-01

In Brazil most Construction and Demolition Waste (C&D waste) is not recycled. This situation is expected to change significantly, since new federal regulations oblige municipalities to create and implement sustainable C&D waste management plans which assign an important role to recycling activities. The recycling organizational network and its flows and components are fundamental to C&D waste recycling feasibility. Organizational networks, flows and components involve reverse logistics. The aim of this work is to introduce the concepts of reverse logistics and reverse distribution channel networks and to study the Brazilian C&D waste case.

Discovering Implicit Entity Relation with the Gene-Citation-Gene Network

PubMed Central

Song, Min; Han, Nam-Gi; Kim, Yong-Hwan; Ding, Ying; Chambers, Tamy

2013-01-01

In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG) network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG) network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner. PMID:24358368

An improved network model for railway traffic

NASA Astrophysics Data System (ADS)

Li, Keping; Ma, Xin; Shao, Fubo

In railway traffic, safety analysis is a key issue for controlling train operation. Here, the identification and order of key factors are very important. In this paper, a new network model is constructed for analyzing the railway safety, in which nodes are regarded as causation factors and links represent possible relationships among those factors. Our aim is to give all these nodes an importance order, and to find the in-depth relationship among these nodes including how failures spread among them. Based on the constructed network model, we propose a control method to ensure the safe state by setting each node a threshold. As the results, by protecting the Hub node of the constructed network, the spreading of railway accident can be controlled well. The efficiency of such a method is further tested with the help of numerical example.

Hubless satellite communications networks

NASA Technical Reports Server (NTRS)

Robinson, Peter Alan

1994-01-01

Frequency Comb Multiple Access (FCMA) is a new combined modulation and multiple access method which will allow cheap hubless Very Small Aperture Terminal (VSAT) networks to be constructed. Theoretical results show bandwidth efficiency and power efficiency improvements over other modulation and multiple access methods. Costs of the VSAT network are reduced dramatically since a hub station is not required.

Low-rank network decomposition reveals structural characteristics of small-world networks

NASA Astrophysics Data System (ADS)

Barranca, Victor J.; Zhou, Douglas; Cai, David

2015-12-01

Small-world networks occur naturally throughout biological, technological, and social systems. With their prevalence, it is particularly important to prudently identify small-world networks and further characterize their unique connection structure with respect to network function. In this work we develop a formalism for classifying networks and identifying small-world structure using a decomposition of network connectivity matrices into low-rank and sparse components, corresponding to connections within clusters of highly connected nodes and sparse interconnections between clusters, respectively. We show that the network decomposition is independent of node indexing and define associated bounded measures of connectivity structure, which provide insight into the clustering and regularity of network connections. While many existing network characterizations rely on constructing benchmark networks for comparison or fail to describe the structural properties of relatively densely connected networks, our classification relies only on the intrinsic network structure and is quite robust with respect to changes in connection density, producing stable results across network realizations. Using this framework, we analyze several real-world networks and reveal new structural properties, which are often indiscernible by previously established characterizations of network connectivity.

Query construction, entropy, and generalization in neural-network models

NASA Astrophysics Data System (ADS)

Sollich, Peter

1994-05-01

We study query construction algorithms, which aim at improving the generalization ability of systems that learn from examples by choosing optimal, nonredundant training sets. We set up a general probabilistic framework for deriving such algorithms from the requirement of optimizing a suitable objective function; specifically, we consider the objective functions entropy (or information gain) and generalization error. For two learning scenarios, the high-low game and the linear perceptron, we evaluate the generalization performance obtained by applying the corresponding query construction algorithms and compare it to training on random examples. We find qualitative differences between the two scenarios due to the different structure of the underlying rules (nonlinear and ``noninvertible'' versus linear); in particular, for the linear perceptron, random examples lead to the same generalization ability as a sequence of queries in the limit of an infinite number of examples. We also investigate learning algorithms which are ill matched to the learning environment and find that, in this case, minimum entropy queries can in fact yield a lower generalization ability than random examples. Finally, we study the efficiency of single queries and its dependence on the learning history, i.e., on whether the previous training examples were generated randomly or by querying, and the difference between globally and locally optimal query construction.

Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

PubMed

Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

2017-10-01

This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

The guitar chord-generating algorithm based on complex network

NASA Astrophysics Data System (ADS)

Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais

2016-02-01

This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.

Common quandaries and their practical solutions in Bayesian network modeling

Treesearch

Bruce G. Marcot

2017-01-01

Use and popularity of Bayesian network (BN) modeling has greatly expanded in recent years, but many common problems remain. Here, I summarize key problems in BN model construction and interpretation,along with suggested practical solutions. Problems in BN model construction include parameterizing probability values, variable definition, complex network structures,...

The Orphan Disease Networks

PubMed Central

Zhang, Minlu; Zhu, Cheng; Jacomy, Alexis; Lu, Long J.; Jegga, Anil G.

2011-01-01

The low prevalence rate of orphan diseases (OD) requires special combined efforts to improve diagnosis, prevention, and discovery of novel therapeutic strategies. To identify and investigate relationships based on shared genes or shared functional features, we have conducted a bioinformatic-based global analysis of all orphan diseases with known disease-causing mutant genes. Starting with a bipartite network of known OD and OD-causing mutant genes and using the human protein interactome, we first construct and topologically analyze three networks: the orphan disease network, the orphan disease-causing mutant gene network, and the orphan disease-causing mutant gene interactome. Our results demonstrate that in contrast to the common disease-causing mutant genes that are predominantly nonessential, a majority of orphan disease-causing mutant genes are essential. In confirmation of this finding, we found that OD-causing mutant genes are topologically important in the protein interactome and are ubiquitously expressed. Additionally, functional enrichment analysis of those genes in which mutations cause ODs shows that a majority result in premature death or are lethal in the orthologous mouse gene knockout models. To address the limitations of traditional gene-based disease networks, we also construct and analyze OD networks on the basis of shared enriched features (biological processes, cellular components, pathways, phenotypes, and literature citations). Analyzing these functionally-linked OD networks, we identified several additional OD-OD relations that are both phenotypically similar and phenotypically diverse. Surprisingly, we observed that the wiring of the gene-based and other feature-based OD networks are largely different; this suggests that the relationship between ODs cannot be fully captured by the gene-based network alone. PMID:21664998

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method

PubMed Central

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-01-01

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs. PMID:29113310

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

PubMed

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Construction of Course Ubiquitous Learning Based on Network

ERIC Educational Resources Information Center

Wang, Xue; Zhang, Wei; Yang, Xinhui

2017-01-01

Ubiquitous learning has been more and more recognized, which describes a new generation of learning from a new point of view. Ubiquitous learning will bring the new teaching practice and teaching reform, which will become an essential way of learning in 21st century. Taking translation course as a case study, this research constructed a system of…

Tree tensor network approach to simulating Shor's algorithm

NASA Astrophysics Data System (ADS)

Dumitrescu, Eugene

2017-12-01

Constructively simulating quantum systems furthers our understanding of qualitative and quantitative features which may be analytically intractable. In this paper, we directly simulate and explore the entanglement structure present in the paradigmatic example for exponential quantum speedups: Shor's algorithm. To perform our simulation, we construct a dynamic tree tensor network which manifestly captures two salient circuit features for modular exponentiation. These are the natural two-register bipartition and the invariance of entanglement with respect to permutations of the top-register qubits. Our construction help identify the entanglement entropy properties, which we summarize by a scaling relation. Further, the tree network is efficiently projected onto a matrix product state from which we efficiently execute the quantum Fourier transform. Future simulation of quantum information states with tensor networks exploiting circuit symmetries is discussed.

Protein interaction networks from literature mining

NASA Astrophysics Data System (ADS)

Ihara, Sigeo

2005-03-01

The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

A character network study of two Sci-Fi TV series

NASA Astrophysics Data System (ADS)

Tan, M. S. A.; Ujum, E. A.; Ratnavelu, K.

2014-03-01

This work is an analysis of the character networks in two science fiction television series: Stargate and Star Trek. These networks are constructed on the basis of scene co-occurrence between characters to indicate the presence of a connection. Global network structure measures such as the average path length, graph density, network diameter, average degree, median degree, maximum degree, and average clustering coefficient are computed as well as individual node centrality scores. The two fictional networks constructed are found to be quite similar in structure which is astonishing given that Stargate only ran for 18 years in comparison to the 48 years for Star Trek.

Complex network structure of musical compositions: Algorithmic generation of appealing music

NASA Astrophysics Data System (ADS)

Liu, Xiao Fan; Tse, Chi K.; Small, Michael

2010-01-01

In this paper we construct networks for music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurring connections. We analyze classical music from Bach, Mozart, Chopin, as well as other types of music such as Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. We conjecture that preserving the universal network properties is a necessary step in artificial composition of music. Power-law exponents of node degree, node strength and/or edge weight distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be composed artificially using a controlled random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. By generating a large number of compositions, we find that this algorithm generates music which has the necessary qualities to be subjectively judged as appealing.

Migration of optical core network to next generation networks - Carrier Grade Ethernet Optical Transport Network

NASA Astrophysics Data System (ADS)

Glamočanin, D.

2017-05-01

In order to maintain the continuity of the telecom operators’ network construction, while monitoring development needs, increasing customers’ demands and application of technological improvements, it is necessary to migrate optical transport core network to the next generation networks - Carrier Grade Ethernet Optical Transport Network (OTN CE). The primary objective of OTN CE is to realize an environment that is based solely on the switching in the optical domain, i.e. the realization of transparent optical networks and optical switching to the second layer of ISO / OSI model. The realization of such a network provides opportunities for further development of existing, but also technologically more demanding, new services. It is also a prerequisite to provide higher scalability, reliability, security and quality of QoS service, as well as prerequisites for the establishment of SLA (Service Level Agreement) for existing services, especially traffic in real time. This study aims to clarify the proposed model, which has the potential to be eventually adjusted in accordance with new scientific knowledge in this field as well as market requirements.

Inferring Centrality from Network Snapshots

NASA Astrophysics Data System (ADS)

Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng

2017-01-01

The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data.

Constructing networks with correlation maximization methods.

PubMed

Mellor, Joseph C; Wu, Jie; Delisi, Charles

2004-01-01

Problems of inference in systems biology are ideally reduced to formulations which can efficiently represent the features of interest. In the case of predicting gene regulation and pathway networks, an important feature which describes connected genes and proteins is the relationship between active and inactive forms, i.e. between the "on" and "off" states of the components. While not optimal at the limits of resolution, these logical relationships between discrete states can often yield good approximations of the behavior in larger complex systems, where exact representation of measurement relationships may be intractable. We explore techniques for extracting binary state variables from measurement of gene expression, and go on to describe robust measures for statistical significance and information that can be applied to many such types of data. We show how statistical strength and information are equivalent criteria in limiting cases, and demonstrate the application of these measures to simple systems of gene regulation.

Spectra of English evolving word co-occurrence networks

NASA Astrophysics Data System (ADS)

Liang, Wei

2017-02-01

Spectral analysis is a powerful tool that provides global measures of the network properties. In this paper, 200 English articles are collected. A word co-occurrence network is constructed from each single article (denoted by single network). Furthermore, 5 large English word co-occurrence networks are constructed (denoted by large network). Spectra of their adjacency matrices are computed. The largest eigenvalue, λ1, depends on the network size N and the number of edges E as λ1 ∝N0.66 and λ1 ∝E0.54, respectively. The number of different eigenvalues, Nλ, increase in the manner of Nλ ∝N0.58 and Nλ ∝E0.47. The middle part of the spectral distribution can be fitted by a line with slope - 0.01 in each of the large networks, whereas two segments with the same slope - 0.03 for 0 ≪ N < 260 and - 0.02 for 260 < N < 2800 are needed for the single networks. An "M"-shape distribution appears in each of the spectral densities of the large networks. These and other results can provide useful insight into the structural properties of English linguistic networks.

Manganese porphyrin decorated on DNA networks as quencher and mimicking enzyme for construction of ultrasensitive photoelectrochemistry aptasensor.

PubMed

Huang, Liaojing; Zhang, Li; Yang, Liu; Yuan, Ruo; Yuan, Yali

2018-05-01

In this work, the manganese porphyrin (MnPP) decorated on DNA networks could serve as quencher and mimicking enzyme to efficiently reduce the photocurrent of photoactive material 3,4,9,10-perylene tetracarboxylic acid (PTCA), which was elaborately used to construct a novel label-free aptasensor for ultrasensitive detection of thrombin (TB) in a signal-off manner. The Au-doped PTCA (PTCA-PEI-Au) with outstanding membrane-forming and photoelectric property was modified on electrode to acquire a strong initial photoelectrochemistry (PEC) signal. Afterward, target binding aptamer Ι (TBAΙ) was modified on electrode to specially recognize target TB, which could further combine with TBAII and single-stranded DNA P1-modified platinum nanoparticles (TBAII-PtNPs-P1) for immobilizing DNA networks with abundant MnPP. Ingeniously, the MnPP could not only directly quench the photocurrent of PTCA, but also acted as hydrogen peroxide (HRP) mimicking enzyme to remarkably stimulate the deposition of benzo-4-chlorhexidine (4-CD) on electrode for further decreasing the photocurrent of PTCA, thereby obtaining a definitely low photocurrent for detection of TB. As a result, the proposed PEC aptasensor illustrated excellent sensitivity with a low detection limit down to 3 fM, exploiting a new avenue about intergrating two functions in one substance for ultrasensitive biological monitoring. Copyright © 2017 Elsevier B.V. All rights reserved.

Relationship between microscopic dynamics in traffic flow and complexity in networks.

PubMed

Li, Xin-Gang; Gao, Zi-You; Li, Ke-Ping; Zhao, Xiao-Mei

2007-07-01

Complex networks are constructed in the evolution process of traffic flow, and the states of traffic flow are represented by nodes in the network. The traffic dynamics can then be studied by investigating the statistical properties of those networks. According to Kerner's three-phase theory, there are two different phases in congested traffic, synchronized flow and wide moving jam. In the framework of this theory, we study different properties of synchronized flow and moving jam in relation to complex network. Scale-free network is constructed in stop-and-go traffic, i.e., a sequence of moving jams [Chin. Phys. Lett. 10, 2711 (2005)]. In this work, the networks generated in synchronized flow are investigated in detail. Simulation results show that the degree distribution of the networks constructed in synchronized flow has two power law regions, so the distinction in topological structure can really reflect the different dynamics in traffic flow. Furthermore, the real traffic data are investigated by this method, and the results are consistent with the simulations.

Enhancement of COPD biological networks using a web-based collaboration interface

PubMed Central

Boue, Stephanie; Fields, Brett; Hoeng, Julia; Park, Jennifer; Peitsch, Manuel C.; Schlage, Walter K.; Talikka, Marja; Binenbaum, Ilona; Bondarenko, Vladimir; Bulgakov, Oleg V.; Cherkasova, Vera; Diaz-Diaz, Norberto; Fedorova, Larisa; Guryanova, Svetlana; Guzova, Julia; Igorevna Koroleva, Galina; Kozhemyakina, Elena; Kumar, Rahul; Lavid, Noa; Lu, Qingxian; Menon, Swapna; Ouliel, Yael; Peterson, Samantha C.; Prokhorov, Alexander; Sanders, Edward; Schrier, Sarah; Schwaitzer Neta, Golan; Shvydchenko, Irina; Tallam, Aravind; Villa-Fombuena, Gema; Wu, John; Yudkevich, Ilya; Zelikman, Mariya

2015-01-01

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be

Enhancement of COPD biological networks using a web-based collaboration interface.

PubMed

Boue, Stephanie; Fields, Brett; Hoeng, Julia; Park, Jennifer; Peitsch, Manuel C; Schlage, Walter K; Talikka, Marja; Binenbaum, Ilona; Bondarenko, Vladimir; Bulgakov, Oleg V; Cherkasova, Vera; Diaz-Diaz, Norberto; Fedorova, Larisa; Guryanova, Svetlana; Guzova, Julia; Igorevna Koroleva, Galina; Kozhemyakina, Elena; Kumar, Rahul; Lavid, Noa; Lu, Qingxian; Menon, Swapna; Ouliel, Yael; Peterson, Samantha C; Prokhorov, Alexander; Sanders, Edward; Schrier, Sarah; Schwaitzer Neta, Golan; Shvydchenko, Irina; Tallam, Aravind; Villa-Fombuena, Gema; Wu, John; Yudkevich, Ilya; Zelikman, Mariya

2015-01-01

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be

GenCLiP 2.0: a web server for functional clustering of genes and construction of molecular networks based on free terms.

PubMed

Wang, Jia-Hong; Zhao, Ling-Feng; Lin, Pei; Su, Xiao-Rong; Chen, Shi-Jun; Huang, Li-Qiang; Wang, Hua-Feng; Zhang, Hai; Hu, Zhen-Fu; Yao, Kai-Tai; Huang, Zhong-Xi

2014-09-01

Identifying biological functions and molecular networks in a gene list and how the genes may relate to various topics is of considerable value to biomedical researchers. Here, we present a web-based text-mining server, GenCLiP 2.0, which can analyze human genes with enriched keywords and molecular interactions. Compared with other similar tools, GenCLiP 2.0 offers two unique features: (i) analysis of gene functions with free terms (i.e. any terms in the literature) generated by literature mining or provided by the user and (ii) accurate identification and integration of comprehensive molecular interactions from Medline abstracts, to construct molecular networks and subnetworks related to the free terms. http://ci.smu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

[Construction of DICOM-WWW gateway by open source, and application to PDAs using the high-speed mobile communications network].

PubMed

Yokohama, Noriya

2003-09-01

The author constructed a medical image network system using open source software that took security into consideration. This system was enabled for search and browse with a WWW browser, and images were stored in a DICOM server. In order to realize this function, software was developed to fill in the gap between the DICOM protocol and HTTP using PHP language. The transmission speed was evaluated by the difference in protocols between DICOM and HTTP. Furthermore, an attempt was made to evaluate the convenience of medical image access with a personal information terminal via the Internet through the high-speed mobile communication terminal. Results suggested the feasibility of remote diagnosis and application to emergency care.

A Network Coding Based Routing Protocol for Underwater Sensor Networks

PubMed Central

Wu, Huayang; Chen, Min; Guan, Xin

2012-01-01

Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime. PMID:22666045

A network coding based routing protocol for underwater sensor networks.

PubMed

Wu, Huayang; Chen, Min; Guan, Xin

2012-01-01

Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime.

Characteristics of real futures trading networks

NASA Astrophysics Data System (ADS)

Wang, Junjie; Zhou, Shuigeng; Guan, Jihong

2011-01-01

Futures trading is the core of futures business, and it is considered as one of the typical complex systems. To investigate the complexity of futures trading, we employ the analytical method of complex networks. First, we use real trading records from the Shanghai Futures Exchange to construct futures trading networks, in which nodes are trading participants, and two nodes have a common edge if the two corresponding investors appear simultaneously in at least one trading record as a purchaser and a seller, respectively. Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks. Empirical results show that the futures trading networks exhibit features such as scale-free behavior with interesting odd-even-degree divergence in low-degree regions, small-world effect, hierarchical organization, power-law betweenness distribution, disassortative mixing, and shrinkage of both the average path length and the diameter as network size increases. To the best of our knowledge, this is the first work that uses real data to study futures trading networks, and we argue that the research results can shed light on the nature of real futures business.

An Overview of Total Quality Management (TQM) practice in Construction Sector

NASA Astrophysics Data System (ADS)

Likita, A. J.; Zainun, N. Y.; Rahman, I. Abdul; Awal, A. S. M. Abdul; Alias, A. R.; Rahman, M. Q. Abdul; Ghazali, F. E. Mohamed

2018-04-01

In construction sector TQM can be termed as a philosophy which guides construction professionals on the proper execution of construction projects in terms of quality. The aim of this paper is to discuss on quality management practice in construction sector. This paper evaluated five previous researches and the findings were discussed to find a conclusion of TQM practise in construction sector. The study found that TQM had been successfully practice in construction sector at Saudi Arabia, India, US and South Africa. Application of Artificial Neural Network (ANN) help to improve the implementation of TQM in construction sector. In conclusion, quality management practices will give better control of processes in construction sector.

[Delineation of ecological security pattern based on ecological network].

PubMed

Fu, Qiang; Gu, Chao Lin

2017-03-18

Ecological network can be used to describe and assess the relationship between spatial organization of landscapes and species survival under the condition of the habitat fragmentation. Taking Qingdao City as the research area, woodland and wetland ecological networks in 2005 were simulated based on least cost path method, and the ecological networks were classified by their corridors' cumulative cost value. We made importance distinction of ecological network structure elements such as patches and corridors using betweenness centrality index and correlation length-percentage of importance of omitted patches index, and then created the structure system of ecological network. Considering the effects brought by the newly-added construction land from 2005 to 2013, we proposed the ecological security pattern for construction land change of Qingdao City. The results showed that based on ecological network framework, graph theory based methods could be used to quantify both attributes of specific ecological land (e.g., the area of an ecological network patch) and functional connection between ecological lands. Between 2005 and 2013, large area of wetlands had been destroyed by newly-added construction land, while the role of specific woodland and wetland played in the connection of the whole network had not been considered. The delineation of ecological security pattern based on ecological network could optimize regional ecological basis, provide accurate spatial explicit decision for ecological conservation and restoration, and meanwhile provide scientific and reasonable space guidance for urban spatial expansion.

Multiscale Embedded Gene Co-expression Network Analysis

PubMed Central

Song, Won-Min; Zhang, Bin

2015-01-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma. PMID:26618778

Multiscale Embedded Gene Co-expression Network Analysis.

PubMed

Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Multiple network alignment via multiMAGNA+.

PubMed

Vijayan, Vipin; Milenkovic, Tijana

2017-08-21

Network alignment (NA) aims to find a node mapping that identifies topologically or functionally similar network regions between molecular networks of different species. Analogous to genomic sequence alignment, NA can be used to transfer biological knowledge from well- to poorly-studied species between aligned network regions. Pairwise NA (PNA) finds similar regions between two networks while multiple NA (MNA) can align more than two networks. We focus on MNA. Existing MNA methods aim to maximize total similarity over all aligned nodes (node conservation). Then, they evaluate alignment quality by measuring the amount of conserved edges, but only after the alignment is constructed. Directly optimizing edge conservation during alignment construction in addition to node conservation may result in superior alignments. Thus, we present a novel MNA method called multiMAGNA++ that can achieve this. Indeed, multiMAGNA++ outperforms or is on par with existing MNA methods, while often completing faster than existing methods. That is, multiMAGNA++ scales well to larger network data and can be parallelized effectively. During method evaluation, we also introduce new MNA quality measures to allow for more fair MNA method comparison compared to the existing alignment quality measures. MultiMAGNA++ code is available on the method's web page at http://nd.edu/~cone/multiMAGNA++/.

On the topological structure of multinationals network

NASA Astrophysics Data System (ADS)

Joyez, Charlie

2017-05-01

This paper uses a weighted network analysis to examine the structure of multinationals' implantation countries network. Based on French firm-level dataset of multinational enterprises (MNEs) the network analysis provides information on each country position in the network and in internationalization strategies of French MNEs through connectivity preferences among the nodes. The paper also details network-wide features and their recent evolution toward a more decentralized structure. While much has been said on international trade network, this paper shows that multinational firms' studies would also benefit from network analysis, notably by investigating the sensitivity of the network construction to firm heterogeneity.

Inferring Centrality from Network Snapshots

PubMed Central

Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng

2017-01-01

The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data. PMID:28098166

Techtalk: Wikis and Collaborative Knowledge Construction

ERIC Educational Resources Information Center

Caverly, David C.; Ward, Anne

2008-01-01

In the last column, we began discussing Web 2.0 applications. In this column, we'll review participatory, social networking software called "wikis." We'll define wikis, discuss their use in college classrooms, explore benefits provided for collaborative knowledge construction, and explain five types of wikis with applications to developmental…

Designing Networks that are Capable of Self-Healing and Adapting

DTIC Science & Technology

2017-04-01

from statistical mechanics, combinatorics, boolean networks, and numerical simulations, and inspired by design principles from biological networks, we... principles for self-healing networks, and applications, and construct an all-possible-paths model for network adaptation. 2015-11-16 UNIT CONVERSION...combinatorics, boolean networks, and numerical simulations, and inspired by design principles from biological networks, we will undertake the fol

A network analysis of the Chinese stock market

NASA Astrophysics Data System (ADS)

Huang, Wei-Qiang; Zhuang, Xin-Tian; Yao, Shuang

2009-07-01

In many practical important cases, a massive dataset can be represented as a very large network with certain attributes associated with its vertices and edges. Stock markets generate huge amounts of data, which can be use for constructing the network reflecting the market’s behavior. In this paper, we use a threshold method to construct China’s stock correlation network and then study the network’s structural properties and topological stability. We conduct a statistical analysis of this network and show that it follows a power-law model. We also detect components, cliques and independent sets in this network. These analyses allows one to apply a new data mining technique of classifying financial instruments based on stock price data, which provides a deeper insight into the internal structure of the stock market. Moreover, we test the topological stability of this network and find that it displays a topological robustness against random vertex failures, but it is also fragile to intentional attacks. Such a network stability property would be also useful for portfolio investment and risk management.

Construction of multi-agent mobile robots control system in the problem of persecution with using a modified reinforcement learning method based on neural networks

NASA Astrophysics Data System (ADS)

Patkin, M. L.; Rogachev, G. N.

2018-02-01

A method for constructing a multi-agent control system for mobile robots based on training with reinforcement using deep neural networks is considered. Synthesis of the management system is proposed to be carried out with reinforcement training and the modified Actor-Critic method, in which the Actor module is divided into Action Actor and Communication Actor in order to simultaneously manage mobile robots and communicate with partners. Communication is carried out by sending partners at each step a vector of real numbers that are added to the observation vector and affect the behaviour. Functions of Actors and Critic are approximated by deep neural networks. The Critics value function is trained by using the TD-error method and the Actor’s function by using DDPG. The Communication Actor’s neural network is trained through gradients received from partner agents. An environment in which a cooperative multi-agent interaction is present was developed, computer simulation of the application of this method in the control problem of two robots pursuing two goals was carried out.

Comprehensive analysis of differentially expressed profiles of lncRNAs and construction of miR-133b mediated ceRNA network in colorectal cancer.

PubMed

Wu, Hao; Wu, Runliu; Chen, Miao; Li, Daojiang; Dai, Jing; Zhang, Yi; Gao, Kai; Yu, Jun; Hu, Gui; Guo, Yihang; Lin, Changwei; Li, Xiaorong

2017-03-28

Growing evidence suggests that long non-coding RNAs (lncRNAs) play a key role in tumorigenesis. However, the mechanism remains largely unknown. Thousands of significantly dysregulated lncRNAs and mRNAs were identified by microarray. Furthermore, a miR-133b-meditated lncRNA-mRNA ceRNA network was revealed, a subset of which was validated in 14 paired CRC patient tumor/non-tumor samples. Gene set enrichment analysis (GSEA) results demonstrated that lncRNAs ENST00000520055 and ENST00000535511 shared KEGG pathways with miR-133b target genes. We used microarrays to survey the lncRNA and mRNA expression profiles of colorectal cancer and para-cancer tissues. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed to explore the functions of the significantly dysregulated genes. An innovate method was employed that combined analyses of two microarray data sets to construct a miR-133b-mediated lncRNA-mRNA competing endogenous RNAs (ceRNA) network. Quantitative RT-PCR analysis was used to validate part of this network. GSEA was used to predict the potential functions of these lncRNAs. This study identifies and validates a new method to investigate the miR-133b-mediated lncRNA-mRNA ceRNA network and lays the foundation for future investigation into the role of lncRNAs in colorectal cancer.

Comprehensive analysis of aberrantly expressed lncRNAs and construction of ceRNA network in gastric cancer

PubMed Central

Bennet, Duraisamy; Chandramohan, Servarayan Murugesan; Murugan, Avaniyapuram Kannan; Munirajan, Arasambattu Kannan

2018-01-01

Gastric cancer remains fifth most common cancer often diagnosed at an advanced stage and is the second leading cause of cancer-related death worldwide. Long non-coding RNAs (lncRNAs) involved in various cellular pathways are essential for tumor occurrence and progression and they have high potential to promote or suppress the expression of many genes. In this study, we profiled 19 selected cancer-associated lncRNAs in thirty gastric adenocarcinomas and matching normal tissues by qRT-PCR. Our results showed that most of the lncRNAs were significantly upregulated (12/19). Further, we performed bioinformatic screening of miRNAs that share common miRNA response elements (MREs) with lncRNAs and their downstream mRNA targets. The prediction identified three microRNAs (miR-21, miR-145 and miR-148a) and five gastric cancer-specific target genes (EGFR, KLF4, DNMT1 and AGO4) which also showed strong correlation with lncRNAs in regression analysis. Finally, we constructed an integrated lncRNA-miRNA-mRNA interaction network of the candidate genes to understand the post-transcriptional gene regulation. The ceRNA network analysis revealed that the differentially regulated miR-21 and miR-148a were playing as central candidates coordinating sponging activity of the lncRNAs analyzed (H19, TUG1 and MALAT1) in this study and the overexpression of H19 and miR-21 could be a signature event of gastric tumorigenesis that could serve as prognostic indicators and therapeutic targets. PMID:29719612

[Sharing patient information using iPads in the introduction of IT for home medical care-construction of a network for home care].

PubMed

Morishima, Atsutomo; Kijima, Yasuaki

2012-12-01

In hospitals, information technology(IT)has been a natural part of care for some time. However, IT has not yet been widely introduced into home care. While performing home care, healthcare providers must carry numerous patients' medical records, and are unable to share the information included in those records with other providers. Thus, we introduced the system of sharing patient information using iPads. This system enables access to patient information regardless of time and place. In addition, we can share information such as X-ray images and computed tomography(CT)scans between different clinics. Thus, we are able to give clearer instructions to patients and other providers in a smoother way. This system could be used to construct a network for home care. In the future, we could aim to share patient information within a much wider network, including families and other kinds of organizations, for optimal care. This system will aid in the development of IT use in home care.

Inferring general relations between network characteristics from specific network ensembles.

PubMed

Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

2012-01-01

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

Latest Trends in Home Networking Technologies

NASA Astrophysics Data System (ADS)

Tsutsui, Akihiro

Broadband access service, including FTTH, is now in widespread use in Japan. More than half of the households that have broadband Internet access construct local area networks (home networks) in their homes. In addition, information appliances such as personal computers, networked audio, and visual devices and game machines are connected to home networks, and many novel service applications are provided via the Internet. However, it is still difficult to install and incorporate these devices and services because networked devices have been developed in different communities. I briefly explain the current status of information appliances and home networking technologies and services and discuss some of the problems in this and their solutions.

Stepwise construction of a metabolic network in Event-B: The heat shock response.

PubMed

Sanwal, Usman; Petre, Luigia; Petre, Ion

2017-12-01

There is a high interest in constructing large, detailed computational models for biological processes. This is often done by putting together existing submodels and adding to them extra details/knowledge. The result of such approaches is usually a model that can only answer questions on a very specific level of detail, and thus, ultimately, is of limited use. We focus instead on an approach to systematically add details to a model, with formal verification of its consistency at each step. In this way, one obtains a set of reusable models, at different levels of abstraction, to be used for different purposes depending on the question to address. We demonstrate this approach using Event-B, a computational framework introduced to develop formal specifications of distributed software systems. We first describe how to model generic metabolic networks in Event-B. Then, we apply this method for modeling the biological heat shock response in eukaryotic cells, using Event-B refinement techniques. The advantage of using Event-B consists in having refinement as an intrinsic feature; this provides as a final result not only a correct model, but a chain of models automatically linked by refinement, each of which is provably correct and reusable. This is a proof-of-concept that refinement in Event-B is suitable for biomodeling, serving for mastering biological complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

The network construction of CSELF for earthquake monitoring and its preliminary observation

NASA Astrophysics Data System (ADS)

Tang, J.; Zhao, G.; Chen, X.; Bing, H.; Wang, L.; Zhan, Y.; Xiao, Q.; Dong, Z.

2017-12-01

The Electromagnetic (EM) anomaly in short-term earthquake precursory is most sensitive physical phenomena. Scientists believe that EM monitoring for earthquake is one of the most promising means of forecasting. However, existing ground-base EM observation confronted with increasing impact cultural noises, and the lack of a frequency range of higher than 1Hz observations. Control source of extremely low frequency (CSELF) EM is a kind of good prospective new approach. It not only has many advantages with high S/N ratio, large coverage area, probing depth ect., thereby facilitating the identification and capture anomaly signal, and it also can be used to study the electromagnetic field variation and to study the crustal medium changes of the electric structure.The first CSELF EM network for earthquake precursory monitoring with 30 observatories in China has been constructed. The observatories distribute in Beijing surrounding area and in the southern part of North-South Seismic Zone. GMS-07 system made by Metronix is equipped at each station. The observation mixed CSELF and nature source, that is, if during the control source is off transmitted, the nature source EM signal will be recorded. In genernal, there are 3 5 frequencies signals in the 0.1-300Hz frequency band will be transmit in every morning and evening in a fixed time (length 2 hours). Besides time, natural field to extend the frequency band (0.001 1000 Hz) will be observed by using 3 sample frequencies, 4096Hz sampling rate for HF, 256Hz for MF and 16Hz for LF. The low frequency band records continuously all-day and the high and medium frequency band use a slices record, the data records by cycling acquisition in every 10 minutes with length of about 4 to 8 seconds and 64 to 128 seconds , respectively. All the data is automatically processed by server installed in the observatory. The EDI file including EM field spectrums and MT responses and time series files will be sent the data center by internet

Privacy Breach Analysis in Social Networks

NASA Astrophysics Data System (ADS)

Nagle, Frank

This chapter addresses various aspects of analyzing privacy breaches in social networks. We first review literature that defines three types of privacy breaches in social networks: interactive, active, and passive. We then survey the various network anonymization schemes that have been constructed to address these privacy breaches. After exploring these breaches and anonymization schemes, we evaluate a measure for determining the level of anonymity inherent in a network graph based on its topological structure. Finally, we close by emphasizing the difficulty of anonymizing social network data while maintaining usability for research purposes and offering areas for future work.

Multidimensional Analysis of Linguistic Networks

NASA Astrophysics Data System (ADS)

Araújo, Tanya; Banisch, Sven

Network-based approaches play an increasingly important role in the analysis of data even in systems in which a network representation is not immediately apparent. This is particularly true for linguistic networks, which use to be induced from a linguistic data set for which a network perspective is only one out of several options for representation. Here we introduce a multidimensional framework for network construction and analysis with special focus on linguistic networks. Such a framework is used to show that the higher is the abstraction level of network induction, the harder is the interpretation of the topological indicators used in network analysis. Several examples are provided allowing for the comparison of different linguistic networks as well as to networks in other fields of application of network theory. The computation and the intelligibility of some statistical indicators frequently used in linguistic networks are discussed. It suggests that the field of linguistic networks, by applying statistical tools inspired by network studies in other domains, may, in its current state, have only a limited contribution to the development of linguistic theory.

Sentient networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chapline, G.

1998-03-01

The engineering problems of constructing autonomous networks of sensors and data processors that can provide alerts for dangerous situations provide a new context for debating the question whether man-made systems can emulate the cognitive capabilities of the mammalian brain. In this paper we consider the question whether a distributed network of sensors and data processors can form ``perceptions`` based on sensory data. Because sensory data can have exponentially many explanations, the use of a central data processor to analyze the outputs from a large ensemble of sensors will in general introduce unacceptable latencies for responding to dangerous situations. A bettermore » idea is to use a distributed ``Helmholtz machine`` architecture in which the sensors are connected to a network of simple processors, and the collective state of the network as a whole provides an explanation for the sensory data. In general communication within such a network will require time division multiplexing, which opens the door to the possibility that with certain refinements to the Helmholtz machine architecture it may be possible to build sensor networks that exhibit a form of artificial consciousness.« less

Analytical network process based optimum cluster head selection in wireless sensor network.

PubMed

Farman, Haleem; Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

2017-01-01

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of

Analytical network process based optimum cluster head selection in wireless sensor network

PubMed Central

Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

2017-01-01

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of

Artificial Neural Network with Hardware Training and Hardware Refresh

NASA Technical Reports Server (NTRS)

Duong, Tuan A. (Inventor)

2003-01-01

A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

Well-Constructed Single-Layer Molybdenum Disulfide Nanorose Cross-Linked by Three Dimensional-Reduced Graphene Oxide Network for Superior Water Splitting and Lithium Storage Property

PubMed Central

Zhao, Yanyan; Kuai, Long; Liu, Yanguo; Wang, Pengpeng; Arandiyan, Hamidreza; Cao, Sufeng; Zhang, Jie; Li, Fengyun; Wang, Qing; Geng, Baoyou; Sun, Hongyu

2015-01-01

A facile one-step solution reaction route for growth of novel MoS2 nanorose cross-linked by 3D rGO network, in which the MoS2 nanorose is constructed by single-layered or few-layered MoS2 nanosheets, is presented. Due to the 3D assembled hierarchical architecture of the ultrathin MoS2 nanosheets and the interconnection of 3D rGO network, as well as the synergetic effects of MoS2 and rGO, the as-prepared MoS2-NR/rGO nanohybrids delivered high specific capacity, excellent cycling and good rate performance when evaluated as an anode material for lithium-ion batteries. Moreover, the nanohybrids also show excellent hydrogen-evolution catalytic activity and durability in an acidic medium, which is superior to MoS2 nanorose and their nanoparticles counterparts. PMID:25735416

Social Networking on the Semantic Web

ERIC Educational Resources Information Center

Finin, Tim; Ding, Li; Zhou, Lina; Joshi, Anupam

2005-01-01

Purpose: Aims to investigate the way that the semantic web is being used to represent and process social network information. Design/methodology/approach: The Swoogle semantic web search engine was used to construct several large data sets of Resource Description Framework (RDF) documents with social network information that were encoded using the…

Measuring and modeling correlations in multiplex networks.

PubMed

Nicosia, Vincenzo; Latora, Vito

2015-09-01

The interactions among the elementary components of many complex systems can be qualitatively different. Such systems are therefore naturally described in terms of multiplex or multilayer networks, i.e., networks where each layer stands for a different type of interaction between the same set of nodes. There is today a growing interest in understanding when and why a description in terms of a multiplex network is necessary and more informative than a single-layer projection. Here we contribute to this debate by presenting a comprehensive study of correlations in multiplex networks. Correlations in node properties, especially degree-degree correlations, have been thoroughly studied in single-layer networks. Here we extend this idea to investigate and characterize correlations between the different layers of a multiplex network. Such correlations are intrinsically multiplex, and we first study them empirically by constructing and analyzing several multiplex networks from the real world. In particular, we introduce various measures to characterize correlations in the activity of the nodes and in their degree at the different layers and between activities and degrees. We show that real-world networks exhibit indeed nontrivial multiplex correlations. For instance, we find cases where two layers of the same multiplex network are positively correlated in terms of node degrees, while other two layers are negatively correlated. We then focus on constructing synthetic multiplex networks, proposing a series of models to reproduce the correlations observed empirically and/or to assess their relevance.

Neural network approaches to capture temporal information

NASA Astrophysics Data System (ADS)

van Veelen, Martijn; Nijhuis, Jos; Spaanenburg, Ben

2000-05-01

The automated design and construction of neural networks receives growing attention of the neural networks community. Both the growing availability of computing power and development of mathematical and probabilistic theory have had severe impact on the design and modelling approaches of neural networks. This impact is most apparent in the use of neural networks to time series prediction. In this paper, we give our views on past, contemporary and future design and modelling approaches to neural forecasting.

Acting discursively: the development of UK organic food and farming policy networks.

PubMed

TOMLINSON, Isobel Jane

2010-01-01

This paper documents the early evolution of UK organic food and farming policy networks and locates this empirical focus in a theoretical context concerned with understanding the contemporary policy-making process. While policy networks have emerged as a widely acknowledged empirical manifestation of governance, debate continues as to the concept's explanatory utility and usefulness in situations of network and policy transformation since, historically, policy networks have been applied to "static" circumstances. Recognizing this criticism, and in drawing on an interpretivist perspective, this paper sees policy networks as enacted by individual actors whose beliefs and actions construct the nature of the network. It seeks to make links between the characteristics of the policy network and the policy outcomes through the identification of discursively constructed "storylines" that form a tool for consensus building in networks. This study analyses the functioning of the organic policy networks through the discursive actions of policy-network actors.

Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

PubMed

Yang, Yi; Hu, Xiao-Pan; Ma, Bin-Guang

2017-02-28

Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

Computationally Efficient Nonlinear Bell Inequalities for Quantum Networks

NASA Astrophysics Data System (ADS)

Luo, Ming-Xing

2018-04-01

The correlations in quantum networks have attracted strong interest with new types of violations of the locality. The standard Bell inequalities cannot characterize the multipartite correlations that are generated by multiple sources. The main problem is that no computationally efficient method is available for constructing useful Bell inequalities for general quantum networks. In this work, we show a significant improvement by presenting new, explicit Bell-type inequalities for general networks including cyclic networks. These nonlinear inequalities are related to the matching problem of an equivalent unweighted bipartite graph that allows constructing a polynomial-time algorithm. For the quantum resources consisting of bipartite entangled pure states and generalized Greenberger-Horne-Zeilinger (GHZ) states, we prove the generic nonmultilocality of quantum networks with multiple independent observers using new Bell inequalities. The violations are maximal with respect to the presented Tsirelson's bound for Einstein-Podolsky-Rosen states and GHZ states. Moreover, these violations hold for Werner states or some general noisy states. Our results suggest that the presented Bell inequalities can be used to characterize experimental quantum networks.

Study of Personalized Network Tutoring System Based on Emotional-cognitive Interaction

NASA Astrophysics Data System (ADS)

Qi, Manfei; Ma, Ding; Wang, Wansen

Aiming at emotion deficiency in present Network tutoring system, a lot of negative effects is analyzed and corresponding countermeasures are proposed. The model of Personalized Network tutoring system based on Emotional-cognitive interaction is constructed in the paper. The key techniques of realizing the system such as constructing emotional model and adjusting teaching strategies are also introduced.

Applying Sensor-Based Technology to Improve Construction Safety Management.

PubMed

Zhang, Mingyuan; Cao, Tianzhuo; Zhao, Xuefeng

2017-08-11

Construction sites are dynamic and complicated systems. The movement and interaction of people, goods and energy make construction safety management extremely difficult. Due to the ever-increasing amount of information, traditional construction safety management has operated under difficult circumstances. As an effective way to collect, identify and process information, sensor-based technology is deemed to provide new generation of methods for advancing construction safety management. It makes the real-time construction safety management with high efficiency and accuracy a reality and provides a solid foundation for facilitating its modernization, and informatization. Nowadays, various sensor-based technologies have been adopted for construction safety management, including locating sensor-based technology, vision-based sensing and wireless sensor networks. This paper provides a systematic and comprehensive review of previous studies in this field to acknowledge useful findings, identify the research gaps and point out future research directions.

Efficient Construction of Mesostate Networks from Molecular Dynamics Trajectories.

PubMed

Vitalis, Andreas; Caflisch, Amedeo

2012-03-13

The coarse-graining of data from molecular simulations yields conformational space networks that may be used for predicting the system's long time scale behavior, to discover structural pathways connecting free energy basins in the system, or simply to represent accessible phase space regions of interest and their connectivities in a two-dimensional plot. In this contribution, we present a tree-based algorithm to partition conformations of biomolecules into sets of similar microstates, i.e., to coarse-grain trajectory data into mesostates. On account of utilizing an architecture similar to that of established tree-based algorithms, the proposed scheme operates in near-linear time with data set size. We derive expressions needed for the fast evaluation of mesostate properties and distances when employing typical choices for measures of similarity between microstates. Using both a pedagogically useful and a real-word application, the algorithm is shown to be robust with respect to tree height, which in addition to mesostate threshold size is the main adjustable parameter. It is demonstrated that the derived mesostate networks can preserve information regarding the free energy basins and barriers by which the system is characterized.

Lagged correlation networks

NASA Astrophysics Data System (ADS)

Curme, Chester

Technological advances have provided scientists with large high-dimensional datasets that describe the behaviors of complex systems: from the statistics of energy levels in complex quantum systems, to the time-dependent transcription of genes, to price fluctuations among assets in a financial market. In this environment, where it may be difficult to infer the joint distribution of the data, network science has flourished as a way to gain insight into the structure and organization of such systems by focusing on pairwise interactions. This work focuses on a particular setting, in which a system is described by multivariate time series data. We consider time-lagged correlations among elements in this system, in such a way that the measured interactions among elements are asymmetric. Finally, we allow these interactions to be characteristically weak, so that statistical uncertainties may be important to consider when inferring the structure of the system. We introduce a methodology for constructing statistically validated networks to describe such a system, extend the methodology to accommodate interactions with a periodic component, and show how consideration of bipartite community structures in these networks can aid in the construction of robust statistical models. An example of such a system is a financial market, in which high frequency returns data may be used to describe contagion, or the spreading of shocks in price among assets. These data provide the experimental testing ground for our methodology. We study NYSE data from both the present day and one decade ago, examine the time scales over which the validated lagged correlation networks exist, and relate differences in the topological properties of the networks to an increasing economic efficiency. We uncover daily periodicities in the validated interactions, and relate our findings to explanations of the Epps Effect, an empirical phenomenon of financial time series. We also study bipartite community

Research of cost aspects of cement pavements construction

NASA Astrophysics Data System (ADS)

Bezuglyi, Artem; Illiash, Sergii; Tymoshchuk, Oleksandr

2017-09-01

The tendency to increasing traffic volume on public roads and to increased axle loads of vehicles makes the road scientists to develop scientifically justified methods for preserving the existing and developing the new transport network of Ukraine. One of the options for solving such issues is the construction of roads with rigid (cement concrete) pavement. However, any solution must be justified considering technical and economic components. This paper presents the results of the research of cost aspects of cement pavements construction.

Sexual network analysis of a gonorrhoea outbreak

PubMed Central

De, P; Singh, A; Wong, T; Yacoub, W; Jolly, A

2004-01-01

Objectives: Sexual partnerships can be viewed as networks in order to study disease transmission. We examined the transmission of Neisseria gonorrhoeae in a localised outbreak in Alberta, Canada, using measures of network centrality to determine the association between risk of infection of network members and their position within the sexual network. We also compared risk in smaller disconnected components with a large network centred on a social venue. Methods: During the investigation of the outbreak, epidemiological data were collected on gonorrhoea cases and their sexual contacts from STI surveillance records. In addition to traditional contact tracing information, subjects were interviewed about social venues they attended in the past year where casual sexual partnering may have occurred. Sexual networks were constructed by linking together named partners. Univariate comparisons of individual network member characteristics and algebraic measures of network centrality were completed. Results: The sexual networks consisted of 182 individuals, of whom 107 were index cases with laboratory confirmed gonorrhoea and 75 partners of index cases. People who had significantly higher information centrality within each of their local networks were found to have patronised a popular motel bar in the main town in the region (p = 0.05). When the social interaction through the bar was considered, a large network of 89 individuals was constructed that joined all eight of the largest local networks. Moreover, several networks from different communities were linked by individuals who served as bridge populations as a result of their sexual partnering. Conclusion: Asking clients about particular social venues emphasised the importance of location in disease transmission. Network measures of centrality, particularly information centrality, allowed the identification of key individuals through whom infection could be channelled into local networks. Such individuals would be ideal

FPGA implementation of motifs-based neuronal network and synchronization analysis

NASA Astrophysics Data System (ADS)

Deng, Bin; Zhu, Zechen; Yang, Shuangming; Wei, Xile; Wang, Jiang; Yu, Haitao

2016-06-01

Motifs in complex networks play a crucial role in determining the brain functions. In this paper, 13 kinds of motifs are implemented with Field Programmable Gate Array (FPGA) to investigate the relationships between the networks properties and motifs properties. We use discretization method and pipelined architecture to construct various motifs with Hindmarsh-Rose (HR) neuron as the node model. We also build a small-world network based on these motifs and conduct the synchronization analysis of motifs as well as the constructed network. We find that the synchronization properties of motif determine that of motif-based small-world network, which demonstrates effectiveness of our proposed hardware simulation platform. By imitation of some vital nuclei in the brain to generate normal discharges, our proposed FPGA-based artificial neuronal networks have the potential to replace the injured nuclei to complete the brain function in the treatment of Parkinson's disease and epilepsy.

Route Network Construction with Location-Direction-Enabled Photographs

NASA Astrophysics Data System (ADS)

Fujita, Hideyuki; Sagara, Shota; Ohmori, Tadashi; Shintani, Takahiko

2018-05-01

We propose a method for constructing a geometric graph for generating routes that summarize a geographical area and also have visual continuity by using a set of location-direction-enabled photographs. A location- direction-enabled photograph is a photograph that has information about the location (position of the camera at the time of shooting) and the direction (direction of the camera at the time of shooting). Each nodes of the graph corresponds to a location-direction-enabled photograph. The location of each node is the location of the corresponding photograph, and a route on the graph corresponds to a route in the geographic area and a sequence of photographs. The proposed graph is constructed to represent characteristic spots and paths linking the spots, and it is assumed to be a kind of a spatial summarization of the area with the photographs. Therefore, we call the routes on the graph as spatial summary route. Each route on the proposed graph also has a visual continuity, which means that we can understand the spatial relationship among the continuous photographs on the route such as moving forward, backward, turning right, etc. In this study, when the changes in the shooting position and shooting direction satisfied a given threshold, the route was defined to have visual continuity. By presenting the photographs in order along the generated route, information can be presented sequentially, while maintaining visual continuity to a great extent.

Effects of local and global network connectivity on synergistic epidemics

NASA Astrophysics Data System (ADS)

Broder-Rodgers, David; Pérez-Reche, Francisco J.; Taraskin, Sergei N.

2015-12-01

Epidemics in networks can be affected by cooperation in transmission of infection and also connectivity between nodes. An interplay between these two properties and their influence on epidemic spread are addressed in the paper. A particular type of cooperative effects (called synergy effects) is considered, where the transmission rate between a pair of nodes depends on the number of infected neighbors. The connectivity effects are studied by constructing networks of different topology, starting with lattices with only local connectivity and then with networks that have both local and global connectivity obtained by random bond-rewiring to nodes within a certain distance. The susceptible-infected-removed epidemics were found to exhibit several interesting effects: (i) for epidemics with strong constructive synergy spreading in networks with high local connectivity, the bond rewiring has a negative role in epidemic spread, i.e., it reduces invasion probability; (ii) in contrast, for epidemics with destructive or weak constructive synergy spreading on networks of arbitrary local connectivity, rewiring helps epidemics to spread; (iii) and, finally, rewiring always enhances the spread of epidemics, independent of synergy, if the local connectivity is low.

Effects of local and global network connectivity on synergistic epidemics.

PubMed

Broder-Rodgers, David; Pérez-Reche, Francisco J; Taraskin, Sergei N

2015-12-01

Epidemics in networks can be affected by cooperation in transmission of infection and also connectivity between nodes. An interplay between these two properties and their influence on epidemic spread are addressed in the paper. A particular type of cooperative effects (called synergy effects) is considered, where the transmission rate between a pair of nodes depends on the number of infected neighbors. The connectivity effects are studied by constructing networks of different topology, starting with lattices with only local connectivity and then with networks that have both local and global connectivity obtained by random bond-rewiring to nodes within a certain distance. The susceptible-infected-removed epidemics were found to exhibit several interesting effects: (i) for epidemics with strong constructive synergy spreading in networks with high local connectivity, the bond rewiring has a negative role in epidemic spread, i.e., it reduces invasion probability; (ii) in contrast, for epidemics with destructive or weak constructive synergy spreading on networks of arbitrary local connectivity, rewiring helps epidemics to spread; (iii) and, finally, rewiring always enhances the spread of epidemics, independent of synergy, if the local connectivity is low.

Seismic waveform inversion using neural networks

NASA Astrophysics Data System (ADS)

De Wit, R. W.; Trampert, J.

2012-12-01

Full waveform tomography aims to extract all available information on Earth structure and seismic sources from seismograms. The strongly non-linear nature of this inverse problem is often addressed through simplifying assumptions for the physical theory or data selection, thus potentially neglecting valuable information. Furthermore, the assessment of the quality of the inferred model is often lacking. This calls for the development of methods that fully appreciate the non-linear nature of the inverse problem, whilst providing a quantification of the uncertainties in the final model. We propose to invert seismic waveforms in a fully non-linear way by using artificial neural networks. Neural networks can be viewed as powerful and flexible non-linear filters. They are very common in speech, handwriting and pattern recognition. Mixture Density Networks (MDN) allow us to obtain marginal posterior probability density functions (pdfs) of all model parameters, conditioned on the data. An MDN can approximate an arbitrary conditional pdf as a linear combination of Gaussian kernels. Seismograms serve as input, Earth structure parameters are the so-called targets and network training aims to learn the relationship between input and targets. The network is trained on a large synthetic data set, which we construct by drawing many random Earth models from a prior model pdf and solving the forward problem for each of these models, thus generating synthetic seismograms. As a first step, we aim to construct a 1D Earth model. Training sets are constructed using the Mineos package, which computes synthetic seismograms in a spherically symmetric non-rotating Earth by summing normal modes. We train a network on the body waveforms present in these seismograms. Once the network has been trained, it can be presented with new unseen input data, in our case the body waves in real seismograms. We thus obtain the posterior pdf which represents our final state of knowledge given the

Similarity networks as a knowledge representation for space applications

NASA Technical Reports Server (NTRS)

Bailey, David; Thompson, Donna; Feinstein, Jerald

1987-01-01

Similarity networks are a powerful form of knowledge representation that are useful for many artificial intelligence applications. Similarity networks are used in applications ranging from information analysis and case based reasoning to machine learning and linking symbolic to neural processing. Strengths of similarity networks include simple construction, intuitive object storage, and flexible retrieval techniques that facilitate inferencing. Therefore, similarity networks provide great potential for space applications.

A network model of the interbank market

NASA Astrophysics Data System (ADS)

Li, Shouwei; He, Jianmin; Zhuang, Yaming

2010-12-01

This work introduces a network model of an interbank market based on interbank credit lending relationships. It generates some network features identified through empirical analysis. The critical issue to construct an interbank network is to decide the edges among banks, which is realized in this paper based on the interbank’s degree of trust. Through simulation analysis of the interbank network model, some typical structural features are identified in our interbank network, which are also proved to exist in real interbank networks. They are namely, a low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of out-degree and in-degree.

Social networks to biological networks: systems biology of Mycobacterium tuberculosis.

PubMed

Vashisht, Rohit; Bhardwaj, Anshu; Osdd Consortium; Brahmachari, Samir K

2013-07-01

Contextualizing relevant information to construct a network that represents a given biological process presents a fundamental challenge in the network science of biology. The quality of network for the organism of interest is critically dependent on the extent of functional annotation of its genome. Mostly the automated annotation pipelines do not account for unstructured information present in volumes of literature and hence large fraction of genome remains poorly annotated. However, if used, this information could substantially enhance the functional annotation of a genome, aiding the development of a more comprehensive network. Mining unstructured information buried in volumes of literature often requires manual intervention to a great extent and thus becomes a bottleneck for most of the automated pipelines. In this review, we discuss the potential of scientific social networking as a solution for systematic manual mining of data. Focusing on Mycobacterium tuberculosis, as a case study, we discuss our open innovative approach for the functional annotation of its genome. Furthermore, we highlight the strength of such collated structured data in the context of drug target prediction based on systems level analysis of pathogen.

Geometric Assortative Growth Model for Small-World Networks

PubMed Central

2014-01-01

It has been shown that both humanly constructed and natural networks are often characterized by small-world phenomenon and assortative mixing. In this paper, we propose a geometrically growing model for small-world networks. The model displays both tunable small-world phenomenon and tunable assortativity. We obtain analytical solutions of relevant topological properties such as order, size, degree distribution, degree correlation, clustering, transitivity, and diameter. It is also worth noting that the model can be viewed as a generalization for an iterative construction of Farey graphs. PMID:24578661

Cross over of recurrence networks to random graphs and random geometric graphs

NASA Astrophysics Data System (ADS)

Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.

2017-02-01

Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability density variations of the representative attractor from which it is constructed. Here we numerically investigate the properties of recurrence networks from standard low-dimensional chaotic attractors using some basic network measures and show how the recurrence networks are different from random and scale-free networks. In particular, we show that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to the time series and into the classical random graphs by increasing the range of interaction to the system size. We also highlight the effectiveness of a combined plot of characteristic path length and clustering coefficient in capturing the small changes in the network characteristics.

Thermodynamic characterization of synchronization-optimized oscillator networks

NASA Astrophysics Data System (ADS)

Yanagita, Tatsuo; Ichinomiya, Takashi

2014-12-01

We consider a canonical ensemble of synchronization-optimized networks of identical oscillators under external noise. By performing a Markov chain Monte Carlo simulation using the Kirchhoff index, i.e., the sum of the inverse eigenvalues of the Laplacian matrix (as a graph Hamiltonian of the network), we construct more than 1 000 different synchronization-optimized networks. We then show that the transition from star to core-periphery structure depends on the connectivity of the network, and is characterized by the node degree variance of the synchronization-optimized ensemble. We find that thermodynamic properties such as heat capacity show anomalies for sparse networks.

Endogenous network of firms and systemic risk

NASA Astrophysics Data System (ADS)

Ma, Qianting; He, Jianmin; Li, Shouwei

2018-02-01

We construct an endogenous network characterized by commercial credit relationships connecting the upstream and downstream firms. Simulation results indicate that the endogenous network model displays a scale-free property which exists in real-world firm systems. In terms of the network structure, with the expansion of the scale of network nodes, the systemic risk increases significantly, while the heterogeneities of network nodes have no effect on systemic risk. As for firm micro-behaviors, including the selection range of trading partners, actual output, labor requirement, price of intermediate products and employee salaries, increase of all these parameters will lead to higher systemic risk.

Applying Sensor-Based Technology to Improve Construction Safety Management

PubMed Central

Zhang, Mingyuan; Cao, Tianzhuo; Zhao, Xuefeng

2017-01-01

Construction sites are dynamic and complicated systems. The movement and interaction of people, goods and energy make construction safety management extremely difficult. Due to the ever-increasing amount of information, traditional construction safety management has operated under difficult circumstances. As an effective way to collect, identify and process information, sensor-based technology is deemed to provide new generation of methods for advancing construction safety management. It makes the real-time construction safety management with high efficiency and accuracy a reality and provides a solid foundation for facilitating its modernization, and informatization. Nowadays, various sensor-based technologies have been adopted for construction safety management, including locating sensor-based technology, vision-based sensing and wireless sensor networks. This paper provides a systematic and comprehensive review of previous studies in this field to acknowledge useful findings, identify the research gaps and point out future research directions. PMID:28800061

Entropy of network ensembles

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra

2009-03-01

In this paper we generalize the concept of random networks to describe network ensembles with nontrivial features by a statistical mechanics approach. This framework is able to describe undirected and directed network ensembles as well as weighted network ensembles. These networks might have nontrivial community structure or, in the case of networks embedded in a given space, they might have a link probability with a nontrivial dependence on the distance between the nodes. These ensembles are characterized by their entropy, which evaluates the cardinality of networks in the ensemble. In particular, in this paper we define and evaluate the structural entropy, i.e., the entropy of the ensembles of undirected uncorrelated simple networks with given degree sequence. We stress the apparent paradox that scale-free degree distributions are characterized by having small structural entropy while they are so widely encountered in natural, social, and technological complex systems. We propose a solution to the paradox by proving that scale-free degree distributions are the most likely degree distribution with the corresponding value of the structural entropy. Finally, the general framework we present in this paper is able to describe microcanonical ensembles of networks as well as canonical or hidden-variable network ensembles with significant implications for the formulation of network-constructing algorithms.

Network Analysis on Attitudes: A Brief Tutorial.

PubMed

Dalege, Jonas; Borsboom, Denny; van Harreveld, Frenk; van der Maas, Han L J

2017-07-01

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs.

Up the ANTe: Understanding Entrepreneurial Leadership Learning through Actor-Network Theory

ERIC Educational Resources Information Center

Smith, Sue; Kempster, Steve; Barnes, Stewart

2017-01-01

This article explores the role of educators in supporting the development of entrepreneurial leadership learning by creating peer learning networks of owner-managers of small businesses. Using actor-network theory, the authors think through the process of constructing and maintaining a peer learning network (conceived of as an actor-network) and…

Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

NASA Astrophysics Data System (ADS)

Kasiviswanathan, K.; Sudheer, K.

2013-05-01

Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

Deciphering microbial interactions and detecting keystone species with co-occurrence networks

PubMed Central

Berry, David; Widder, Stefanie

2014-01-01

Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets. PMID:24904535

Deciphering microbial interactions and detecting keystone species with co-occurrence networks.

PubMed

Berry, David; Widder, Stefanie

2014-01-01

Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

Networked differential GPS system

NASA Technical Reports Server (NTRS)

Sheynblat, Leonid (Inventor); Kalafus, Rudolph M. (Inventor); Loomis, Peter V. W. (Inventor); Mueller, K. Tysen (Inventor)

1994-01-01

An embodiment of the present invention relates to a worldwide network of differential GPS reference stations (NDGPS) that continually track the entire GPS satellite constellation and provide interpolations of reference station corrections tailored for particular user locations between the reference stations Each reference station takes real-time ionospheric measurements with codeless cross-correlating dual-frequency carrier GPS receivers and computes real-time orbit ephemerides independently. An absolute pseudorange correction (PRC) is defined for each satellite as a function of a particular user's location. A map of the function is constructed, with iso-PRC contours. The network measures the PRCs at a few points, so-called reference stations and constructs an iso-PRC map for each satellite. Corrections are interpolated for each user's site on a subscription basis. The data bandwidths are kept to a minimum by transmitting information that cannot be obtained directly by the user and by updating information by classes and according to how quickly each class of data goes stale given the realities of the GPS system. Sub-decimeter-level kinematic accuracy over a given area is accomplished by establishing a mini-fiducial network.

Directional virtual backbone based data aggregation scheme for Wireless Visual Sensor Networks.

PubMed

Zhang, Jing; Liu, Shi-Jian; Tsai, Pei-Wei; Zou, Fu-Min; Ji, Xiao-Rong

2018-01-01

Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods.

The New Generation Russian VLBI Network

NASA Technical Reports Server (NTRS)

Finkelstein, Andrey; Ipatov, Alexander; Smolentsev, Sergey; Mardyshkin, Vyacheslav; Fedotov, Leonid; Surkis, Igor; Ivanov, Dmitrij; Gayazov, Iskander

2010-01-01

This paper deals with a new project of the Russian VLBI Network dedicated for Universal Time determinations in quasi on-line mode. The basic principles of the network design and location of antennas are explained. Variants of constructing receiving devices, digital data acquisition system, and phase calibration system are specially considered. The frequency ranges and expected values of noise temperature are given.

DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDermott, Jason E.; Costa, Michelle N.; Stevens, S.L.

A difficult problem that is currently growing rapidly due to the sharp increase in the amount of high-throughput data available for many systems is that of determining useful and informative causative influence networks. These networks can be used to predict behavior given observation of a small number of components, predict behavior at a future time point, or identify components that are critical to the functioning of the system under particular conditions. In these endeavors incorporating observations of systems from a wide variety of viewpoints can be particularly beneficial, but has often been undertaken with the objective of inferring networks thatmore » are generally applicable. The focus of the current work is to integrate both general observations and measurements taken for a particular pathology, that of ischemic stroke, to provide improved ability to produce useful predictions of systems behavior. A number of hybrid approaches have recently been proposed for network generation in which the Gene Ontology is used to filter or enrich network links inferred from gene expression data through reverse engineering methods. These approaches have been shown to improve the biological plausibility of the inferred relationships determined, but still treat knowledge-based and machine-learning inferences as incommensurable inputs. In this paper, we explore how further improvements may be achieved through a full integration of network inference insights achieved through application of the Gene Ontology and reverse engineering methods with specific reference to the construction of dynamic models of transcriptional regulatory networks. We show that integrating two approaches to network construction, one based on reverse-engineering from conditional transcriptional data, one based on reverse-engineering from in situ hybridization data, and another based on functional associations derived from Gene Ontology, using probabilities can improve results of clustering as

Communication Network Design: West Ottawa School District.

ERIC Educational Resources Information Center

Couch, David deS.

This report describes the technical details and rationale behind the decisions in the design and development of the communications network installed as part of a 1991-1993 district-wide construction project in the West Ottawa Public Schools (Michigan). The project called for development of a communications network to carry voice, data, and video…

Dissipation, Voltage Profile and Levy Dragon in a Special Ladder Network

ERIC Educational Resources Information Center

Ucak, C.

2009-01-01

A ladder network constructed by an elementary two-terminal network consisting of a parallel resistor-inductor block in series with a parallel resistor-capacitor block sometimes is said to have a non-dispersive dissipative response. This special ladder network is created iteratively by replacing the elementary two-terminal network in place of the…

Visual analytics of brain networks.

PubMed

Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

2012-05-15

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.

A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

PubMed

Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

2013-01-01

One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

Features and perspectives of automatized construction crane-manipulators

NASA Astrophysics Data System (ADS)

Stepanov, Mikhail A.; Ilukhin, Peter A.

2018-03-01

Modern construction industry still has a high percentage of manual labor, and the greatest prospects of improving the construction process are lying in the field of automatization. In this article automatized construction manipulator-cranes are being studied in order to achieve the most rational design scheme. This is done through formulating a list of general conditions necessary for such cranes and a set of specialized kinematical conditions. A variety of kinematical schemes is evaluated via these conditions, and some are taken for further dynamical analisys. The comparative dynamical analisys of taken schemes was made and the most rational scheme was defined. Therefore a basis for a more complex and practical research of manipulator-cranes design is given and ways to implement them on practical level can now be calculated properly. Also, the perspectives of implementation of automated control systems and informational networks on construction sites in order to boost the quality of construction works, safety of labour and ecological safety are shown.

The interplay between cognitive risk and resilience factors in remitted depression: A network analysis.

PubMed

Hoorelbeke, Kristof; Marchetti, Igor; De Schryver, Maarten; Koster, Ernst H W

2016-05-01

Individuals in remission from depression are at increased risk for developing future depressive episodes. Several cognitive risk- and resilience factors have been suggested to account for this vulnerability. In the current study we explored how risk- and protective factors such as cognitive control, adaptive and maladaptive emotion regulation, residual symptomatology, and resilience relate to one another in a remitted depressed (RMD) sample. We examined the relationships between these constructs in a cross-sectional dataset of 69 RMD patients using network analyses in order to obtain a comprehensive, data-driven view on the interplay between these constructs. We subsequently present an association network, a concentration network, and a relative importance network. In all three networks resilience formed the central hub, connecting perceived cognitive control (i.e., working memory complaints), emotion regulation, and residual symptomatology. The contribution of the behavioral measure for cognitive control in the network was negligible. Moreover, the directed relative importance network indicates bidirectional influences between these constructs, with all indicators of centrality suggesting a key role of resilience in remission from depression. The presented findings are cross-sectional and networks are limited to a fixed set of key constructs in the literature pertaining cognitive vulnerability for depression. These findings indicate the importance of resilience to successfully cope with stressors following remission from depression. Further in-depth studies will be essential to identify the specific underlying resilience mechanisms that may be key to successful remission from depression. Copyright © 2016 Elsevier B.V. All rights reserved.

New Forms of Citizenship, European Construction and the Reconfiguration of the University

ERIC Educational Resources Information Center

Stoer, Stephen

2006-01-01

This article examines the impact of new forms of citizenship and European construction on the university. If one conceives new forms of citizenship as identity-driven, rather than based on territory, and European construction as the intertwining of four metaphors (the flag, association, the network and the bazaar), what are the implications for a…

Pan- and core- network analysis of co-expression genes in a model plant

DOE PAGES

He, Fei; Maslov, Sergei

2016-12-16

Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of ‘pan-network’ andmore » ‘core-network’ representing union and intersection between a sizeable fractions of individual networks, respectively. Here, we showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.« less

Pan- and core- network analysis of co-expression genes in a model plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Fei; Maslov, Sergei

Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of ‘pan-network’ andmore » ‘core-network’ representing union and intersection between a sizeable fractions of individual networks, respectively. Here, we showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.« less

Social Network Map: Some Further Refinements on Administration.

ERIC Educational Resources Information Center

Tracy, Elizabeth M.; Abell, Neil

1994-01-01

Notes that social network mapping techniques have been advanced as means of assessing social and environmental resources. Addresses issue of convergent construct validity, correlations among dimensions of perceived social support as measured by social network data with other standardized social support instruments. Findings confirm that structural…

Construction of In-house Databases in a Corporation

NASA Astrophysics Data System (ADS)

Okuda, Yasukazu; Yoshikawa, Ichirou; Sasano, Fumio

The authors describe the outline and the construction process of the in-house technical information system of Mitsui Petrochemical Industries Ltd., “MITOLIS”. This system was constructed in 1981 and has been improved since then to make better use of in-house technical reports. Bibliographic data and keywords of technical reports of R & D division are stored in the host computer system in Iwakuni and can be retrieved by the company members on the desk-side terminal connected to the local area network (LAN). The number of stored reports reaches 6100 from 1970 to 1987.

A Novel College Network Resource Management Method using Cloud Computing

NASA Astrophysics Data System (ADS)

Lin, Chen

At present information construction of college mainly has construction of college networks and management information system; there are many problems during the process of information. Cloud computing is development of distributed processing, parallel processing and grid computing, which make data stored on the cloud, make software and services placed in the cloud and build on top of various standards and protocols, you can get it through all kinds of equipments. This article introduces cloud computing and function of cloud computing, then analyzes the exiting problems of college network resource management, the cloud computing technology and methods are applied in the construction of college information sharing platform.

Constructing Game Agents from Video of Human Behavior

DTIC Science & Technology

2009-01-01

Future Work Constructing autonomous agents is an important task in video game development. Games such as Quake, Warcraft III, and Halo 2 (Damian 2005...Vision. Rio de Janeiro, Brazil: IEEE Press. Kelley, J. P.; Botea, A.; and Koenig, S. 2008. Offline planning with hierarchical task networks in video ...

Network analysis of Chinese provincial economies

NASA Astrophysics Data System (ADS)

Sun, Xiaoqi; An, Haizhong; Liu, Xiaojia

2018-02-01

Global economic system is a huge network formed by national subnetworks that contains the provincial networks. As the second largest world economy, China has "too big to fail" impact on the interconnected global economy. Detecting the critical sectors and vital linkages inside Chinese economic network is meaningful for understanding the origin of this Chinese impact. Different from tradition network research at national level, this paper focuses on the provincial networks and inter-provincial network. Using Chinese inter-regional input-output table to construct 30 provincial input-output networks and one inter-provincial input-output network, we identify central sectors and vital linkages, as well as analyze economic structure similarity. Results show that (1) Communication Devices sector in Guangdong and that in Jiangsu, Transportation and Storage sector in Shanghai play critical roles in Chinese economy. (2) Advanced manufactures and services industry occupy the central positions in eastern provincial economies, while Construction sector, Heavy industry, and Wholesale and Retail Trades sector are influential in middle and western provinces. (3) The critical monetary flow paths in Chinese economy are Communication Devices sector to Communication Devices sector in Guangdong, Metals Mining sector to Iron and Steel Smelting sector in Henan, Communication Devices sector to Communication Devices sector in Jiangsu, as well as Petroleum Mining sector in Heilongjiang to Petroleum Processing sector in Liaoning. (4) Collective influence results suggest that Finance sector, Transportation and Storage sector, Production of Electricity and Heat sector, and Rubber and Plastics sector in Hainan are strategic influencers, despite being weakly connected. These sectors and input-output relations are worthy of close attention for monitoring Chinese economy.

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

PubMed

Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

Theory, construction and operation of simple tensiometers.

USGS Publications Warehouse

Stannard, D.I.

1986-01-01

The tensiometer presented here in detail is suited to diverse on-site applications. Constructed from readily available, inexpensive parts, it can measure as much as 0.85 bar of tension. Design features include a flushing system for removal of entrapped air or mercury, and an easily maintained modular network of nylon manometers and water-supply tubes. -from Author

Networks of Memories

DTIC Science & Technology

2013-03-01

2000). The construction of  autobiographical   memories in the self­memory system. Psychological Review, 107(2), 261­288. Dennis, S., & Chapman, A. (2010...AFRL-OSR-VA-TR-2013-0131 Networks of Memories Simon Dennis, Mikhail Belkin Ohio State University March 2013 Final...Back (Rev. 8/98) 1 Networks of  Memories FA9550­09­1­0614 Professor Jay Myung PI: Simon Dennis Ohio State University February 15, 2013 2 Introduction

Preferential attachment in evolutionary earthquake networks

NASA Astrophysics Data System (ADS)

Rezaei, Soghra; Moghaddasi, Hanieh; Darooneh, Amir Hossein

2018-04-01

Earthquakes as spatio-temporal complex systems have been recently studied using complex network theory. Seismic networks are dynamical networks due to addition of new seismic events over time leading to establishing new nodes and links to the network. Here we have constructed Iran and Italy seismic networks based on Hybrid Model and testified the preferential attachment hypothesis for the connection of new nodes which states that it is more probable for newly added nodes to join the highly connected nodes comparing to the less connected ones. We showed that the preferential attachment is present in the case of earthquakes network and the attachment rate has a linear relationship with node degree. We have also found the seismic passive points, the most probable points to be influenced by other seismic places, using their preferential attachment values.

On Applications of Disruption Tolerant Networking to Optical Networking in Space

NASA Technical Reports Server (NTRS)

Hylton, Alan Guy; Raible, Daniel E.; Juergens, Jeffrey; Iannicca, Dennis

2012-01-01

The integration of optical communication links into space networks via Disruption Tolerant Networking (DTN) is a largely unexplored area of research. Building on successful foundational work accomplished at JPL, we discuss a multi-hop multi-path network featuring optical links. The experimental test bed is constructed at the NASA Glenn Research Center featuring multiple Ethernet-to-fiber converters coupled with free space optical (FSO) communication channels. The test bed architecture models communication paths from deployed Mars assets to the deep space network (DSN) and finally to the mission operations center (MOC). Reliable versus unreliable communication methods are investigated and discussed; including reliable transport protocols, custody transfer, and fragmentation. Potential commercial applications may include an optical communications infrastructure deployment to support developing nations and remote areas, which are unburdened with supporting an existing heritage means of telecommunications. Narrow laser beam widths and control of polarization states offer inherent physical layer security benefits with optical communications over RF solutions. This paper explores whether or not DTN is appropriate for space-based optical networks, optimal payload sizes, reliability, and a discussion on security.

Co-occurrence network analysis of Chinese and English poems

NASA Astrophysics Data System (ADS)

Liang, Wei; Wang, Yanli; Shi, Yuming; Chen, Guanrong

2015-02-01

A total of 572 co-occurrence networks of Chinese characters and words as well as English words are constructed from both Chinese and English poems. It is found that most of the networks have small-world features; more Chinese networks have scale-free properties and hierarchical structures as compared with the English networks; all the networks are disassortative, and the disassortativeness of the Chinese word networks is more prominent than those of the English networks; the spectral densities of the Chinese word networks and English networks are similar, but they are different from those of the ER, BA, and WS networks. For the above observed phenomena, analysis is provided with interpretation from a linguistic perspective.

Stochastic flux analysis of chemical reaction networks

PubMed Central

2013-01-01

Background Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. Results We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. Conclusions We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network. PMID:24314153

Stochastic flux analysis of chemical reaction networks.

PubMed

Kahramanoğulları, Ozan; Lynch, James F

2013-12-07

Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.

Complex Network Simulation of Forest Network Spatial Pattern in Pearl River Delta

NASA Astrophysics Data System (ADS)

Zeng, Y.

2017-09-01

Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network's power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network's degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network's main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc.) for networking a standard and base datum.

Predicting local field potentials with recurrent neural networks.

PubMed

Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

2016-08-01

We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

Complex root networks of Chinese characters

NASA Astrophysics Data System (ADS)

Lee, Po-Han; Chen, Jia-Ling; Wang, Po-Cheng; Chi, Ting-Ting; Xiao, Zhi-Ren; Jhang, Zih-Jian; Yeh, Yeong-Nan; Chen, Yih-Yuh; Hu, Chin-Kun

There are several sets of Chinese characters still available today, including Oracle Bone Inscriptions (OBI) in Shang Dynasty, Chu characters (CC) used in Chu of Warring State Period, Small Seal Script in dictionary Shuowen Jiezi (SJ) in Eastern Han Dynasty, and Kangxi Dictionary (KD) in Qing Dynasty. Such as Chinese characters were all constructed via combinations of meaningful patterns, called roots. Our studies for the complex networks of all roots indicate that the roots of the characters in OBI, CC, SJ and KD have characteristics of small world networks and scale-free networks.

Self-organized topology of recurrence-based complex networks

NASA Astrophysics Data System (ADS)

Yang, Hui; Liu, Gang

2013-12-01

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., "what is the self-organizing geometry of a recurrence network?" and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.

Self-organized topology of recurrence-based complex networks.

PubMed

Yang, Hui; Liu, Gang

2013-12-01

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., "what is the self-organizing geometry of a recurrence network?" and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.

Self-organized topology of recurrence-based complex networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Hui, E-mail: huiyang@usf.edu; Liu, Gang

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article ismore » to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., “what is the self-organizing geometry of a recurrence network?” and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.« less

Information transmission on hybrid networks

NASA Astrophysics Data System (ADS)

Chen, Rongbin; Cui, Wei; Pu, Cunlai; Li, Jie; Ji, Bo; Gakis, Konstantinos; Pardalos, Panos M.

2018-01-01

Many real-world communication networks often have hybrid nature with both fixed nodes and moving modes, such as the mobile phone networks mainly composed of fixed base stations and mobile phones. In this paper, we discuss the information transmission process on the hybrid networks with both fixed and mobile nodes. The fixed nodes (base stations) are connected as a spatial lattice on the plane forming the information-carrying backbone, while the mobile nodes (users), which are the sources and destinations of information packets, connect to their current nearest fixed nodes respectively to deliver and receive information packets. We observe the phase transition of traffic load in the hybrid network when the packet generation rate goes from below and then above a critical value, which measures the network capacity of packets delivery. We obtain the optimal speed of moving nodes leading to the maximum network capacity. We further improve the network capacity by rewiring the fixed nodes and by considering the current load of fixed nodes during packets transmission. Our purpose is to optimize the network capacity of hybrid networks from the perspective of network science, and provide some insights for the construction of future communication infrastructures.

A game-theoretic approach to optimize ad hoc networks inspired by small-world network topology

NASA Astrophysics Data System (ADS)

Tan, Mian; Yang, Tinghong; Chen, Xing; Yang, Gang; Zhu, Guoqing; Holme, Petter; Zhao, Jing

2018-03-01

Nodes in ad hoc networks are connected in a self-organized manner. Limited communication radius makes information transmit in multi-hop mode, and each forwarding needs to consume the energy of nodes. Insufficient communication radius or exhaustion of energy may cause the absence of some relay nodes and links, further breaking network connectivity. On the other hand, nodes in the network may refuse to cooperate due to objective faulty or personal selfish, hindering regular communication in the network. This paper proposes a model called Repeated Game in Small World Networks (RGSWN). In this model, we first construct ad hoc networks with small-world feature by forming "communication shortcuts" between multiple-radio nodes. Small characteristic path length reduces average forwarding times in networks; meanwhile high clustering coefficient enhances network robustness. Such networks still maintain relative low global power consumption, which is beneficial to extend the network survival time. Then we use MTTFT strategy (Mend-Tolerance Tit-for-Tat) for repeated game as a rule for the interactions between neighbors in the small-world networks. Compared with other five strategies of repeated game, this strategy not only punishes the nodes' selfishness more reasonably, but also has the best tolerance to the network failure. This work is insightful for designing an efficient and robust ad hoc network.

Protein complex prediction in large ontology attributed protein-protein interaction networks.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

2013-01-01

Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

Using Bayesian networks to support decision-focused information retrieval

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lehner, P.; Elsaesser, C.; Seligman, L.

This paper has described an approach to controlling the process of pulling data/information from distributed data bases in a way that is specific to a persons specific decision making context. Our prototype implementation of this approach uses a knowledge-based planner to generate a plan, an automatically constructed Bayesian network to evaluate the plan, specialized processing of the network to derive key information items that would substantially impact the evaluation of the plan (e.g., determine that replanning is needed), automated construction of Standing Requests for Information (SRIs) which are automated functions that monitor changes and trends in distributed data base thatmore » are relevant to the key information items. This emphasis of this paper is on how Bayesian networks are used.« less

A Key Pre-Distribution Scheme Based on µ-PBIBD for Enhancing Resilience in Wireless Sensor Networks.

PubMed

Yuan, Qi; Ma, Chunguang; Yu, Haitao; Bian, Xuefen

2018-05-12

Many key pre-distribution (KPD) schemes based on combinatorial design were proposed for secure communication of wireless sensor networks (WSNs). Due to complexity of constructing the combinatorial design, it is infeasible to generate key rings using the corresponding combinatorial design in large scale deployment of WSNs. In this paper, we present a definition of new combinatorial design, termed “µ-partially balanced incomplete block design (µ-PBIBD)”, which is a refinement of partially balanced incomplete block design (PBIBD), and then describe a 2-D construction of µ-PBIBD which is mapped to KPD in WSNs. Our approach is of simple construction which provides a strong key connectivity and a poor network resilience. To improve the network resilience of KPD based on 2-D µ-PBIBD, we propose a KPD scheme based on 3-D Ex-µ-PBIBD which is a construction of µ-PBIBD from 2-D space to 3-D space. Ex-µ-PBIBD KPD scheme improves network scalability and resilience while has better key connectivity. Theoretical analysis and comparison with the related schemes show that key pre-distribution scheme based on Ex-µ-PBIBD provides high network resilience and better key scalability, while it achieves a trade-off between network resilience and network connectivity.

A Key Pre-Distribution Scheme Based on µ-PBIBD for Enhancing Resilience in Wireless Sensor Networks

PubMed Central

Yuan, Qi; Ma, Chunguang; Yu, Haitao; Bian, Xuefen

2018-01-01

Many key pre-distribution (KPD) schemes based on combinatorial design were proposed for secure communication of wireless sensor networks (WSNs). Due to complexity of constructing the combinatorial design, it is infeasible to generate key rings using the corresponding combinatorial design in large scale deployment of WSNs. In this paper, we present a definition of new combinatorial design, termed “µ-partially balanced incomplete block design (µ-PBIBD)”, which is a refinement of partially balanced incomplete block design (PBIBD), and then describe a 2-D construction of µ-PBIBD which is mapped to KPD in WSNs. Our approach is of simple construction which provides a strong key connectivity and a poor network resilience. To improve the network resilience of KPD based on 2-D µ-PBIBD, we propose a KPD scheme based on 3-D Ex-µ-PBIBD which is a construction of µ-PBIBD from 2-D space to 3-D space. Ex-µ-PBIBD KPD scheme improves network scalability and resilience while has better key connectivity. Theoretical analysis and comparison with the related schemes show that key pre-distribution scheme based on Ex-µ-PBIBD provides high network resilience and better key scalability, while it achieves a trade-off between network resilience and network connectivity. PMID:29757244

A directed network of Greek and Roman mythology

NASA Astrophysics Data System (ADS)

Choi, Yeon-Mu; Kim, Hyun-Joo

2007-08-01

We construct a directed network using a dictionary of Greek and Roman mythology in which the nodes represent the entries listed in the dictionary and we make directional links from an entry to other entries that appear in its explanatory part. We find that this network is clearly not a random network but a directed scale-free network in which the distributions of out-degree and in-degree follow a power-law with exponents γout≈3.0 and γin≈2.5, respectively. Also we measure several quantities which describe the topological properties of the network and compare it to that of other real networks.

A quantum network of clocks

NASA Astrophysics Data System (ADS)

Kómár, P.; Kessler, E. M.; Bishof, M.; Jiang, L.; Sørensen, A. S.; Ye, J.; Lukin, M. D.

2014-08-01

The development of precise atomic clocks plays an increasingly important role in modern society. Shared timing information constitutes a key resource for navigation with a direct correspondence between timing accuracy and precision in applications such as the Global Positioning System. By combining precision metrology and quantum networks, we propose a quantum, cooperative protocol for operating a network of geographically remote optical atomic clocks. Using nonlocal entangled states, we demonstrate an optimal utilization of global resources, and show that such a network can be operated near the fundamental precision limit set by quantum theory. Furthermore, the internal structure of the network, combined with quantum communication techniques, guarantees security both from internal and external threats. Realization of such a global quantum network of clocks may allow construction of a real-time single international time scale (world clock) with unprecedented stability and accuracy.

Fault-Tolerant Algorithms for Connectivity Restoration in Wireless Sensor Networks.

PubMed

Zeng, Yali; Xu, Li; Chen, Zhide

2015-12-22

As wireless sensor network (WSN) is often deployed in a hostile environment, nodes in the networks are prone to large-scale failures, resulting in the network not working normally. In this case, an effective restoration scheme is needed to restore the faulty network timely. Most of existing restoration schemes consider more about the number of deployed nodes or fault tolerance alone, but fail to take into account the fact that network coverage and topology quality are also important to a network. To address this issue, we present two algorithms named Full 2-Connectivity Restoration Algorithm (F2CRA) and Partial 3-Connectivity Restoration Algorithm (P3CRA), which restore a faulty WSN in different aspects. F2CRA constructs the fan-shaped topology structure to reduce the number of deployed nodes, while P3CRA constructs the dual-ring topology structure to improve the fault tolerance of the network. F2CRA is suitable when the restoration cost is given the priority, and P3CRA is suitable when the network quality is considered first. Compared with other algorithms, these two algorithms ensure that the network has stronger fault-tolerant function, larger coverage area and better balanced load after the restoration.

English and Chinese languages as weighted complex networks

NASA Astrophysics Data System (ADS)

Sheng, Long; Li, Chunguang

2009-06-01

In this paper, we analyze statistical properties of English and Chinese written human language within the framework of weighted complex networks. The two language networks are based on an English novel and a Chinese biography, respectively, and both of the networks are constructed in the same way. By comparing the intensity and density of connections between the two networks, we find that high weight connections in Chinese language networks prevail more than those in English language networks. Furthermore, some of the topological and weighted quantities are compared. The results display some differences in the structural organizations between the two language networks. These observations indicate that the two languages may have different linguistic mechanisms and different combinatorial natures.

Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

PubMed

Li, Wan; Yang, Chunlan; Shi, Feng; Wang, Qun; Wu, Shuicai; Lu, Wangsheng; Li, Shaowu; Nie, Yingnan; Zhang, Xin

2018-04-16

Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.

Uni10: an open-source library for tensor network algorithms

NASA Astrophysics Data System (ADS)

Kao, Ying-Jer; Hsieh, Yun-Da; Chen, Pochung

2015-09-01

We present an object-oriented open-source library for developing tensor network algorithms written in C++ called Uni10. With Uni10, users can build a symmetric tensor from a collection of bonds, while the bonds are constructed from a list of quantum numbers associated with different quantum states. It is easy to label and permute the indices of the tensors and access a block associated with a particular quantum number. Furthermore a network class is used to describe arbitrary tensor network structure and to perform network contractions efficiently. We give an overview of the basic structure of the library and the hierarchy of the classes. We present examples of the construction of a spin-1 Heisenberg Hamiltonian and the implementation of the tensor renormalization group algorithm to illustrate the basic usage of the library. The library described here is particularly well suited to explore and fast prototype novel tensor network algorithms and to implement highly efficient codes for existing algorithms.

Constructing a Cooperative Distance Learning System: The CORAL Experience.

ERIC Educational Resources Information Center

Chou, Chien; Sun, Chuen-Tsai

1996-01-01

Describes the development of the Cooperative Remotely Accessible Learning (CORAL) system on the Internet at National Chiao Tung University (Taiwan) that promotes cooperative constructive distance learning and provides the first comprehensive and networked courseware written in Chinese. Results of formative evaluation are described and future…

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

A neural network approach to burst detection.

PubMed

Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

2002-01-01

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

A biologically inspired network design model.

PubMed

Zhang, Xiaoge; Adamatzky, Andrew; Chan, Felix T S; Deng, Yong; Yang, Hai; Yang, Xin-She; Tsompanas, Michail-Antisthenis I; Sirakoulis, Georgios Ch; Mahadevan, Sankaran

2015-06-04

A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould, and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach.

A Biologically Inspired Network Design Model

PubMed Central

Zhang, Xiaoge; Adamatzky, Andrew; Chan, Felix T.S.; Deng, Yong; Yang, Hai; Yang, Xin-She; Tsompanas, Michail-Antisthenis I.; Sirakoulis, Georgios Ch.; Mahadevan, Sankaran

2015-01-01

A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould, and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach. PMID:26041508

Structural factoring approach for analyzing stochastic networks

NASA Technical Reports Server (NTRS)

Hayhurst, Kelly J.; Shier, Douglas R.

1991-01-01

The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.

Navigable networks as Nash equilibria of navigation games.

PubMed

Gulyás, András; Bíró, József J; Kőrösi, Attila; Rétvári, Gábor; Krioukov, Dmitri

2015-07-03

Common sense suggests that networks are not random mazes of purposeless connections, but that these connections are organized so that networks can perform their functions well. One function common to many networks is targeted transport or navigation. Here, using game theory, we show that minimalistic networks designed to maximize the navigation efficiency at minimal cost share basic structural properties with real networks. These idealistic networks are Nash equilibria of a network construction game whose purpose is to find an optimal trade-off between the network cost and navigability. We show that these skeletons are present in the Internet, metabolic, English word, US airport, Hungarian road networks, and in a structural network of the human brain. The knowledge of these skeletons allows one to identify the minimal number of edges, by altering which one can efficiently improve or paralyse navigation in the network.

Phylogeny of metabolic networks: a spectral graph theoretical approach.

PubMed

Deyasi, Krishanu; Banerjee, Anirban; Deb, Bony

2015-10-01

Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms.

Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network.

PubMed

Yang, Liang; Jin, Di; He, Dongxiao; Fu, Huazhu; Cao, Xiaochun; Fogelman-Soulie, Francoise

2017-03-29

Due to the importance of community structure in understanding network and a surge of interest aroused on community detectability, how to improve the community identification performance with pairwise prior information becomes a hot topic. However, most existing semi-supervised community detection algorithms only focus on improving the accuracy but ignore the impacts of priors on speeding detection. Besides, they always require to tune additional parameters and cannot guarantee pairwise constraints. To address these drawbacks, we propose a general, high-speed, effective and parameter-free semi-supervised community detection framework. By constructing the indivisible super-nodes according to the connected subgraph of the must-link constraints and by forming the weighted super-edge based on network topology and cannot-link constraints, our new framework transforms the original network into an equivalent but much smaller Super-Network. Super-Network perfectly ensures the must-link constraints and effectively encodes cannot-link constraints. Furthermore, the time complexity of super-network construction process is linear in the original network size, which makes it efficient. Meanwhile, since the constructed super-network is much smaller than the original one, any existing community detection algorithm is much faster when using our framework. Besides, the overall process will not introduce any additional parameters, making it more practical.

A quantum network of clocks

NASA Astrophysics Data System (ADS)

Komar, Peter; Kessler, Eric; Bishof, Michael; Jiang, Liang; Sorensen, Anders; Ye, Jun; Lukin, Mikhail

2014-05-01

Shared timing information constitutes a key resource for positioning and navigation with a direct correspondence between timing accuracy and precision in applications such as the Global Positioning System (GPS). By combining precision metrology and quantum networks, we propose here a quantum, cooperative protocol for the operation of a network consisting of geographically remote optical atomic clocks. Using non-local entangled states, we demonstrate an optimal utilization of the global network resources, and show that such a network can be operated near the fundamental limit set by quantum theory yielding an ultra-precise clock signal. Furthermore, the internal structure of the network, combined with basic techniques from quantum communication, guarantees security both from internal and external threats. Realization of such a global quantum network of clocks may allow construction of a real-time single international time scale (world clock) with unprecedented stability and accuracy. See also: Komar et al. arXiv:1310.6045 (2013) and Kessler et al. arXiv:1310.6043 (2013).

Evolving phenotypic networks in silico.

PubMed

François, Paul

2014-11-01

Evolved gene networks are constrained by natural selection. Their structures and functions are consequently far from being random, as exemplified by the multiple instances of parallel/convergent evolution. One can thus ask if features of actual gene networks can be recovered from evolutionary first principles. I review a method for in silico evolution of small models of gene networks aiming at performing predefined biological functions. I summarize the current implementation of the algorithm, insisting on the construction of a proper "fitness" function. I illustrate the approach on three examples: biochemical adaptation, ligand discrimination and vertebrate segmentation (somitogenesis). While the structure of the evolved networks is variable, dynamics of our evolved networks are usually constrained and present many similar features to actual gene networks, including properties that were not explicitly selected for. In silico evolution can thus be used to predict biological behaviours without a detailed knowledge of the mapping between genotype and phenotype. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

The ASCI Network for SC 2000: Gigabyte Per Second Networking

DOE Office of Scientific and Technical Information (OSTI.GOV)

PRATT, THOMAS J.; NAEGLE, JOHN H.; MARTINEZ JR., LUIS G.

2001-11-01

This document highlights the Discom's Distance computing and communication team activities at the 2000 Supercomputing conference in Dallas Texas. This conference is sponsored by the IEEE and ACM. Sandia's participation in the conference has now spanned a decade, for the last five years Sandia National Laboratories, Los Alamos National Lab and Lawrence Livermore National Lab have come together at the conference under the DOE's ASCI, Accelerated Strategic Computing Initiatives, Program rubric to demonstrate ASCI's emerging capabilities in computational science and our combined expertise in high performance computer science and communication networking developments within the program. At SC 2000, DISCOM demonstratedmore » an infrastructure. DISCOM2 uses this forum to demonstrate and focus communication and pre-standard implementation of 10 Gigabit Ethernet, the first gigabyte per second data IP network transfer application, and VPN technology that enabled a remote Distributed Resource Management tools demonstration. Additionally a national OC48 POS network was constructed to support applications running between the show floor and home facilities. This network created the opportunity to test PSE's Parallel File Transfer Protocol (PFTP) across a network that had similar speed and distances as the then proposed DISCOM WAN. The SCINET SC2000 showcased wireless networking and the networking team had the opportunity to explore this emerging technology while on the booth. This paper documents those accomplishments, discusses the details of their convention exhibit floor. We also supported the production networking needs of the implementation, and describes how these demonstrations supports DISCOM overall strategies in high performance computing networking.« less

Stochastic fluctuations and the detectability limit of network communities.

PubMed

Floretta, Lucio; Liechti, Jonas; Flammini, Alessandro; De Los Rios, Paolo

2013-12-01

We have analyzed the detectability limits of network communities in the framework of the popular Girvan and Newman benchmark. By carefully taking into account the inevitable stochastic fluctuations that affect the construction of each and every instance of the benchmark, we come to the conclusion that the native, putative partition of the network is completely lost even before the in-degree/out-degree ratio becomes equal to that of a structureless Erdös-Rényi network. We develop a simple iterative scheme, analytically well described by an infinite branching process, to provide an estimate of the true detectability limit. Using various algorithms based on modularity optimization, we show that all of them behave (semiquantitatively) in the same way, with the same functional form of the detectability threshold as a function of the network parameters. Because the same behavior has also been found by further modularity-optimization methods and for methods based on different heuristics implementations, we conclude that indeed a correct definition of the detectability limit must take into account the stochastic fluctuations of the network construction.

A social network analysis of social cohesion in a constructed pride: implications for ex situ reintroduction of the African lion (Panthera leo).

PubMed

Abell, Jackie; Kirzinger, Morgan W B; Gordon, Yvonne; Kirk, Jacqui; Kokeŝ, Rae; Lynas, Kirsty; Mandinyenya, Bob; Youldon, David

2013-01-01

Animal conservation practices include the grouping of captive related and unrelated individuals to form a social structure which is characteristic of that species in the wild. In response to the rapid decline of wild African lion (Panthera leo) populations, an array of conservational strategies have been adopted. Ex situ reintroduction of the African lion requires the construction of socially cohesive pride structures prior to wild release. This pilot study adopted a social network theory approach to quantitatively assess a captive pride's social structure and the relationships between individuals within them. Group composition (who is present in a group) and social interaction data (social licking, greeting, play) was observed and recorded to assess social cohesion within a released semi-wild pride. UCINET and SOCPROG software was utilised to represent and analyse these social networks. Results indicate that the pride is socially cohesive, does not exhibit random associations, and the role of socially influential keystone individuals is important for maintaining social bondedness within a lion pride. These results are potentially informative for the structure of lion prides, in captivity and in the wild, and could have implications for captive and wild-founder reintroductions.

A Social Network Analysis of Social Cohesion in a Constructed Pride: Implications for Ex Situ Reintroduction of the African Lion (Panthera leo)

PubMed Central

Abell, Jackie; Kirzinger, Morgan W. B.; Gordon, Yvonne; Kirk, Jacqui; Kokeŝ, Rae; Lynas, Kirsty; Mandinyenya, Bob; Youldon, David

2013-01-01

Animal conservation practices include the grouping of captive related and unrelated individuals to form a social structure which is characteristic of that species in the wild. In response to the rapid decline of wild African lion (Panthera leo) populations, an array of conservational strategies have been adopted. Ex situ reintroduction of the African lion requires the construction of socially cohesive pride structures prior to wild release. This pilot study adopted a social network theory approach to quantitatively assess a captive pride’s social structure and the relationships between individuals within them. Group composition (who is present in a group) and social interaction data (social licking, greeting, play) was observed and recorded to assess social cohesion within a released semi-wild pride. UCINET and SOCPROG software was utilised to represent and analyse these social networks. Results indicate that the pride is socially cohesive, does not exhibit random associations, and the role of socially influential keystone individuals is important for maintaining social bondedness within a lion pride. These results are potentially informative for the structure of lion prides, in captivity and in the wild, and could have implications for captive and wild-founder reintroductions. PMID:24376544

Modular and hierarchical structure of social contact networks

NASA Astrophysics Data System (ADS)

Ge, Yuanzheng; Song, Zhichao; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

2013-10-01

Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.

Construction and analysis of protein-protein interaction network correlated with ankylosing spondylitis.

PubMed

Kanwal, Attiya; Fazal, Sahar

2018-01-05

Ankylosing spondylitis, a systemic illness is a foundation of progressing joint swelling that for the most part influences the spine. However, it frequently causes aggravation in different joints far from the spine, and in addition organs, for example, the eyes, heart, lungs, and kidneys. It's an immune system ailment that may be activated by specific sorts of bacterial or viral diseases that initiate an invulnerable reaction that don't close off after the contamination is recuperated. The particular reason for ankylosing spondylitis is obscure, yet hereditary qualities assume a huge part in this condition. The rising apparatuses of network medicine offer a stage to investigate an unpredictable illness at framework level. In this study, we meant to recognize the key proteins and the biological regulator pathways including in AS and further investigating the molecular connectivity between these pathways by the topological examination of the Protein-protein communication (PPI) system. The extended network including of 93 nodes and have 199 interactions respectively scanned from STRING database and some separated small networks. 24 proteins with high BC at the threshold of 0.01 and 55 proteins with large degree at the threshold of 1 have been identified. CD4 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for AS and the crosstalk between them. The finding of this research suggests that AS variation is orchestrated by an integrated PPI network centered on CD4 out of 93 nodes. Ankylosing spondylitis, a systemic disease is an establishment of advancing joint swelling that generally impacts the spine. Be that as it may, it as often as possible causes disturbance in various joints a long way from the spine, and what's more organs. It's a resistant framework affliction that might be actuated by particular sorts

Constructing, Perceiving, and Maintaining Scenes: Hippocampal Activity and Connectivity

PubMed Central

Zeidman, Peter; Mullally, Sinéad L.; Maguire, Eleanor A.

2015-01-01

In recent years, evidence has accumulated to suggest the hippocampus plays a role beyond memory. A strong hippocampal response to scenes has been noted, and patients with bilateral hippocampal damage cannot vividly recall scenes from their past or construct scenes in their imagination. There is debate about whether the hippocampus is involved in the online processing of scenes independent of memory. Here, we investigated the hippocampal response to visually perceiving scenes, constructing scenes in the imagination, and maintaining scenes in working memory. We found extensive hippocampal activation for perceiving scenes, and a circumscribed area of anterior medial hippocampus common to perception and construction. There was significantly less hippocampal activity for maintaining scenes in working memory. We also explored the functional connectivity of the anterior medial hippocampus and found significantly stronger connectivity with a distributed set of brain areas during scene construction compared with scene perception. These results increase our knowledge of the hippocampus by identifying a subregion commonly engaged by scenes, whether perceived or constructed, by separating scene construction from working memory, and by revealing the functional network underlying scene construction, offering new insights into why patients with hippocampal lesions cannot construct scenes. PMID:25405941

Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks

PubMed Central

Pei, Sen; Tang, Shaoting; Zheng, Zhiming

2015-01-01

Detecting spreading outbreaks in social networks with sensors is of great significance in applications. Inspired by the formation mechanism of humans’ physical sensations to external stimuli, we propose a new method to detect the influence of spreading by constructing excitable sensor networks. Exploiting the amplifying effect of excitable sensor networks, our method can better detect small-scale spreading processes. At the same time, it can also distinguish large-scale diffusion instances due to the self-inhibition effect of excitable elements. Through simulations of diverse spreading dynamics on typical real-world social networks (Facebook, coauthor, and email social networks), we find that the excitable sensor networks are capable of detecting and ranking spreading processes in a much wider range of influence than other commonly used sensor placement methods, such as random, targeted, acquaintance and distance strategies. In addition, we validate the efficacy of our method with diffusion data from a real-world online social system, Twitter. We find that our method can detect more spreading topics in practice. Our approach provides a new direction in spreading detection and should be useful for designing effective detection methods. PMID:25950181

47 CFR 22.946 - Service commencement and construction systems.

Code of Federal Regulations, 2011 CFR

2011-10-01

... telephone network (PSTN) and must be providing service to mobile stations operated by its subscribers and roamers. A cellular system is considered to be providing service only if mobile stations can originate... 47 Telecommunication 2 2011-10-01 2011-10-01 false Service commencement and construction systems...

Communications among elements of a space construction ensemble

NASA Technical Reports Server (NTRS)

Davis, Randal L.; Grasso, Christopher A.

1989-01-01

Space construction projects will require careful coordination between managers, designers, manufacturers, operators, astronauts, and robots with large volumes of information of varying resolution, timeliness, and accuracy flowing between the distributed participants over computer communications networks. Within the CSC Operations Branch, we are researching the requirements and options for such communications. Based on our work to date, we feel that communications standards being developed by the International Standards Organization, the CCITT, and other groups can be applied to space construction. We are currently studying in depth how such standards can be used to communicate with robots and automated construction equipment used in a space project. Specifically, we are looking at how the Manufacturing Automation Protocol (MAP) and the Manufacturing Message Specification (MMS), which tie together computers and machines in automated factories, might be applied to space construction projects. Together with our CSC industrial partner Computer Technology Associates, we are developing a MAP/MMS companion standard for space construction and we will produce software to allow the MAP/MMS protocol to be used in our CSC operations testbed.

Google matrix of the world network of economic activities

NASA Astrophysics Data System (ADS)

Kandiah, Vivek; Escaith, Hubert; Shepelyansky, Dima L.

2015-07-01

Using the new data from the OECD-WTO world network of economic activities we construct the Google matrix G of this directed network and perform its detailed analysis. The network contains 58 countries and 37 activity sectors for years 1995 and 2008. The construction of G, based on Markov chain transitions, treats all countries on equal democratic grounds while the contribution of activity sectors is proportional to their exchange monetary volume. The Google matrix analysis allows to obtain reliable ranking of countries and activity sectors and to determine the sensitivity of CheiRank-PageRank commercial balance of countries in respect to price variations and labor cost in various countries. We demonstrate that the developed approach takes into account multiplicity of network links with economy interactions between countries and activity sectors thus being more efficient compared to the usual export-import analysis. The spectrum and eigenstates of G are also analyzed being related to specific activity communities of countries.

Construction of diagnosis system and gene regulatory networks based on microarray analysis.

PubMed

Hong, Chun-Fu; Chen, Ying-Chen; Chen, Wei-Chun; Tu, Keng-Chang; Tsai, Meng-Hsiun; Chan, Yung-Kuan; Yu, Shyr Shen

2018-05-01

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online

Genome-wide network of regulatory genes for construction of a chordate embryo.

PubMed

Shoguchi, Eiichi; Hamaguchi, Makoto; Satoh, Nori

2008-04-15

Animal development is controlled by gene regulation networks that are composed of sequence-specific transcription factors (TF) and cell signaling molecules (ST). Although housekeeping genes have been reported to show clustering in the animal genomes, whether the genes comprising a given regulatory network are physically clustered on a chromosome is uncertain. We examined this question in the present study. Ascidians are the closest living relatives of vertebrates, and their tadpole-type larva represents the basic body plan of chordates. The Ciona intestinalis genome contains 390 core TF genes and 119 major ST genes. Previous gene disruption assays led to the formulation of a basic chordate embryonic blueprint, based on over 3000 genetic interactions among 79 zygotic regulatory genes. Here, we mapped the regulatory genes, including all 79 regulatory genes, on the 14 pairs of Ciona chromosomes by fluorescent in situ hybridization (FISH). Chromosomal localization of upstream and downstream regulatory genes demonstrates that the components of coherent developmental gene networks are evenly distributed over the 14 chromosomes. Thus, this study provides the first comprehensive evidence that the physical clustering of regulatory genes, or their target genes, is not relevant for the genome-wide control of gene expression during development.

Design and implementation of dynamic hybrid Honeypot network

NASA Astrophysics Data System (ADS)

Qiao, Peili; Hu, Shan-Shan; Zhai, Ji-Qiang

2013-05-01

The method of constructing a dynamic and self-adaptive virtual network is suggested to puzzle adversaries, delay and divert attacks, exhaust attacker resources and collect attacking information. The concepts of Honeypot and Honeyd, which is the frame of virtual Honeypot are introduced. The techniques of network scanning including active fingerprint recognition are analyzed. Dynamic virtual network system is designed and implemented. A virtual network similar to real network topology is built according to the collected messages from real environments in this system. By doing this, the system can perplex the attackers when Hackers attack and can further analyze and research the attacks. The tests to this system prove that this design can successfully simulate real network environment and can be used in network security analysis.

Characterizing air quality data from complex network perspective.

PubMed

Fan, Xinghua; Wang, Li; Xu, Huihui; Li, Shasha; Tian, Lixin

2016-02-01

Air quality depends mainly on changes in emission of pollutants and their precursors. Understanding its characteristics is the key to predicting and controlling air quality. In this study, complex networks were built to analyze topological characteristics of air quality data by correlation coefficient method. Firstly, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) indexes of eight monitoring sites in Beijing were selected as samples from January 2013 to December 2014. Secondly, the C-C method was applied to determine the structure of phase space. Points in the reconstructed phase space were considered to be nodes of the network mapped. Then, edges were determined by nodes having the correlation greater than a critical threshold. Three properties of the constructed networks, degree distribution, clustering coefficient, and modularity, were used to determine the optimal value of the critical threshold. Finally, by analyzing and comparing topological properties, we pointed out that similarities and difference in the constructed complex networks revealed influence factors and their different roles on real air quality system.

Navigable networks as Nash equilibria of navigation games

PubMed Central

Gulyás, András; Bíró, József J.; Kőrösi, Attila; Rétvári, Gábor; Krioukov, Dmitri

2015-01-01

Common sense suggests that networks are not random mazes of purposeless connections, but that these connections are organized so that networks can perform their functions well. One function common to many networks is targeted transport or navigation. Here, using game theory, we show that minimalistic networks designed to maximize the navigation efficiency at minimal cost share basic structural properties with real networks. These idealistic networks are Nash equilibria of a network construction game whose purpose is to find an optimal trade-off between the network cost and navigability. We show that these skeletons are present in the Internet, metabolic, English word, US airport, Hungarian road networks, and in a structural network of the human brain. The knowledge of these skeletons allows one to identify the minimal number of edges, by altering which one can efficiently improve or paralyse navigation in the network. PMID:26138277